docs/source/tutorials/barren_plateaus_cn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f44a230e-bd91-40a1-b63c-32905ff369b6",
   "metadata": {},
   "source": [
    "# 贫瘠高原"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e551d9-a7dc-4fe7-84c5-294b1ae267d6",
   "metadata": {},
   "source": [
    "## 概述"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f45c29-9f8f-4723-a96e-2ce283926c75",
   "metadata": {},
   "source": [
    "贫瘠高原是一大类随机参数化量子电路（PQC）的基于梯度的优化中最大的困难。梯度消失几乎无处不在。 在此示例中，我们将展示量子神经网络 (QNN) 中的贫瘠高原。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99265d2-3fac-4fb3-998c-463eddf18f65",
   "metadata": {},
   "source": [
    "## 设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c67b2f15-03be-4be2-85c6-6c3fa8ce5a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorcircuit as tc\n",
    "\n",
    "tc.set_backend(\"tensorflow\")\n",
    "tc.set_dtype(\"complex64\")\n",
    "\n",
    "Rx = tc.gates.rx\n",
    "Ry = tc.gates.ry\n",
    "Rz = tc.gates.rz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9616f63-f4dc-4272-8dc0-fc676a059651",
   "metadata": {},
   "source": [
    "## 参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbe10483-6182-47ae-96ee-ee5cab773278",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 4  # 量子比特的数量\n",
    "nlayers = 1  # 电路的层数\n",
    "ncircuits = 3  # 有不同初始参数的电路数量\n",
    "ntrials = 2  # 有不同结构的随机电路数量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8f0080-416c-48fa-9273-cda153c035d0",
   "metadata": {},
   "source": [
    "## 产生 QNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9652bb49-9d1c-453c-bd8f-66afd88d2b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def op_expectation(params, seed, n, nlayers):\n",
    "    paramsc = tc.backend.cast(params, dtype=\"float32\")  # 门的参数\n",
    "    seedc = tc.backend.cast(seed, dtype=\"float32\")  # 电路结构的参数\n",
    "\n",
    "    c = tc.Circuit(n)\n",
    "    for i in range(n):\n",
    "        c.ry(i, theta=np.pi / 4)\n",
    "    for l in range(nlayers):\n",
    "        for i in range(n):\n",
    "            # 从 Rx、Ry 和 Rz 门中选择一个具有相等概率 = 1/3 的门； 状态是随机数种子。\n",
    "            c.unitary_kraus(\n",
    "                [Rx(paramsc[i, l]), Ry(paramsc[i, l]), Rz(paramsc[i, l])],\n",
    "                i,\n",
    "                prob=[1 / 3, 1 / 3, 1 / 3],\n",
    "                status=seedc[i, l],\n",
    "            )\n",
    "        for i in range(n - 1):\n",
    "            c.cz(i, i + 1)\n",
    "\n",
    "    return tc.backend.real(\n",
    "        c.expectation((tc.gates.z(), [0]), (tc.gates.z(), [1]))\n",
    "    )  # <Z_0Z_1> 的期望"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8c49368-a0bd-4021-964d-942ec922bc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 vmap 和 vvag 获得 ZZ 可观察的期望值和不同随机电路实例的梯度\n",
    "op_expectation_vmap_vvag = tc.backend.jit(\n",
    "    tc.backend.vmap(\n",
    "        tc.backend.vvag(op_expectation, argnums=0, vectorized_argnums=0),\n",
    "        vectorized_argnums=1,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e4d667-e6d5-41f2-9ac1-39e151889bce",
   "metadata": {},
   "source": [
    "## 批量方差计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c61124e8-0769-4118-a208-0a8984aa8e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variance of the gradients is: 0.19805922\n"
     ]
    }
   ],
   "source": [
    "seed = tc.array_to_tensor(\n",
    "    np.random.uniform(low=0.0, high=1.0, size=[ntrials, n, nlayers]), dtype=\"float32\"\n",
    ")\n",
    "params = tc.array_to_tensor(\n",
    "    np.random.uniform(low=0.0, high=2 * np.pi, size=[ncircuits, n, nlayers]),\n",
    "    dtype=\"float32\",\n",
    ")\n",
    "\n",
    "e, grad = op_expectation_vmap_vvag(params, seed, n, nlayers)  # 不同随机电路的 ZZ 可观测量和梯度的期望\n",
    "\n",
    "grad_var = tf.math.reduce_std(tf.math.reduce_std(grad, axis=0), axis=0)[\n",
    "    0, 0\n",
    "]  # 第一个参数的梯度方差\n",
    "print(\"The variance of the gradients is:\", grad_var.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a47801a-0ff8-422e-807c-2382e06971ce",
   "metadata": {},
   "source": [
    "## 结果"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "QNN 中的梯度方差（$nlayers=50$，$ntrials=20$，$ncircuits=20$）。 随着量子比特数量的增加，能量曲面变得指数级贫瘠。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "attachments": {
    "028e2e26-1f06-452d-93f9-62f73c77e0f8.jpg": {
     "image/jpeg": "/9j/4AAQSkZJRgABAQAASABIAAD/4QBARXhpZgAATU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAAqACAAQAAAABAAADd6ADAAQAAAABAAACLwAAAAD/4gJASUNDX1BST0ZJTEUAAQEAAAIwQURCRQIQAABtbnRyUkdCIFhZWiAH0AAIAAsAEwAzADthY3NwQVBQTAAAAABub25lAAAAAAAAAAAAAAAAAAAAAAAA9tYAAQAAAADTLUFEQkUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAApjcHJ0AAAA/AAAADJkZXNjAAABMAAAAGt3dHB0AAABnAAAABRia3B0AAABsAAAABRyVFJDAAABxAAAAA5nVFJDAAAB1AAAAA5iVFJDAAAB5AAAAA5yWFlaAAAB9AAAABRnWFlaAAACCAAAABRiWFlaAAACHAAAABR0ZXh0AAAAAENvcHlyaWdodCAyMDAwIEFkb2JlIFN5c3RlbXMgSW5jb3Jwb3JhdGVkAAAAZGVzYwAAAAAAAAARQWRvYmUgUkdCICgxOTk4KQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWFlaIAAAAAAAAPNRAAEAAAABFsxYWVogAAAAAAAAAAAAAAAAAAAAAGN1cnYAAAAAAAAAAQIzAABjdXJ2AAAAAAAAAAECMwAAY3VydgAAAAAAAAABAjMAAFhZWiAAAAAAAACcGAAAT6UAAAT8WFlaIAAAAAAAADSNAACgLAAAD5VYWVogAAAAAAAAJjEAABAvAAC+nP/AABEIAi8DdwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2wBDAAICAgICAgMCAgMFAwMDBQYFBQUFBggGBgYGBggKCAgICAgICgoKCgoKCgoMDAwMDAwODg4ODg8PDw8PDw8PDw//2wBDAQICAgQEBAcEBAcQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/3QAEADj/2gAMAwEAAhEDEQA/AP38ooooAKKKKACiiigAoor4u8af8FDv2NPAGr3Gg+I/iZZ/brSRoZo7O2vL8JIh2srPaQSoCpBB5oA+0aK8W+D37RfwR+P1lcX3wg8X2XiQWgDTwwlormFW4VpLeZUmRSeAWQAngV7TQAUUUUAFFFFABRXH+NPiH4B+G+mLrXxC8Sab4Z092Krcandw2cTMBnarTMoLY7DmvLPht+1Z+zx8YPF03gT4Y+ObDxHrkEMly9vaeY37mIqruHKBGALKMhj1oA+g6KK+fPjR+1X+z3+z1NbWnxf8a2mgXl2nmRWmya6u2jyQH+z2qSyhCQQGKhSQQDwaAPoOivGvg5+0J8F/2gNKuNY+D/iy08SQWZUXCQ74riDd90y28yxzRhsHaWQA4OM4Ney0AFFFY/iGx1LVNA1LTNG1BtI1C7tpobe9WNZWtZpEKpMI2+VzGxDBTwcYPFAGxRX5T/DjQ/ix8I/2+fDfwt8UfFvxJ8QdM1vwVe6xcxarcbLQXS3TQqYrSLEKBVTI4JBJ5xX6sUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//0P38ooooAKKKKACiiigArwjSf2bv2b/CzXM9j8OvDlvPfSSTTzzadbSzzPIxZ2kmlRpGySTyxr3evlf9pH9jn4L/ALVU+g3PxZgvppPDiXKWZs7o220XZjMm4BTu/wBUuM9OaAPzR17wp8OvDf8AwVP+Gtr+yhBZ2j/YJZfF1toewadbxYmWbzkh/dRs0WzcnA8zyjjzGyf3Wr8L7Twfqf8AwTN/ar+Gngj4d6s+ufC743X4064sb+GB7+1ulmgt/MF1FGjssTXUTr2K70Klgr1+6FAHy5+1v8VPjN8F/hcvxF+DvhG38ZNo95DPrVpJJIs6aTHlrh7dIxlnwAC3Plrl9jgHHb/A39oL4ZftB/DK1+Kvw/1NJNKkQ/a45mWOawmjXdLDdLkiN4xyTnaVw6kqQT7NcXFvaW8t1dyLDBCrPI7kKiIoyzMTwABySa/lo+PGi3vxI+Jfxh8V/sL6Lrv/AAqGG2hXxsdHnFvp2pSRzeZcfYYtpzHty+1VfC+Y4QQOFYA/Zn4SftjeKP2iP2kr7wZ8D/DcOr/CDwrDPBrXim4d4hNqBAMK2BAKyKCMbSMurGTcihPM/QSvlX9jDxL+z/4k/Z/8OP8As3RR2fhSzj8lrM4F3bXYAMyXoySbgsdzuSd+QykoVNfVVAHiHxr/AGc/g9+0TZ6Npnxi0L/hILLQrh7q1gNzPboJnXYWb7PJGXGP4WJHqK/K34CfD7wV8Lf+CuHjvwP8PdHg0HQdO8Jx/Z7O2XbFH5lrp0jkAknLOxYknJJJNfuDX44eAv8AlM38SP8AsU4P/SLTaAP2D1G/tdK0+61S9fZb2cTzSt6JGpZj+AFfi9/wTT+HmgftGat8Tf2xfjHpVv4j8SeIvEE9jpy6hEt1HYW8MUcrCBZAyrhZkhQ4yqRbVIDNn9gvHml3Wt+B/EWi2QJuL/Tru3iA6l5YWRf1NfmH/wAEatTtbv8AZR1TTYyBcab4nv45kPDAvb20ikjryGx+B9KAPNf2pPC2gfsg/tt/BD46fCuxh8OaV8Q72TQvEVlZIILSZGlgiklaFMJuZJxJgAAyQK5+Ylj+o/xo+FXij4qafpll4Y+Imt/DySwlkkkm0Uwh7lXUAJJ5yONq4yMY5Nfmv/wVc/4nXjb9m3wPYfPqmseK28hF+9/rbOH8MtKuP/rV+yVAHwl/wyD8Wf8Ao5nx5/31Y/8AxivqL4W+CdX+GvgyPw94l8Y6j40ubeSaZ9U1cxfaWRzuCsYlRNqDgcdOtel1VvbK11KzuNOvolntrqNopY25V0cFWU+xBwaAPzJ8VeN/Bb/8FN/BOsJr+ntYR/Dq9ia4F1EYVkN/KQhfdtDEc4zmv05trm2vbaK8s5Unt50WSOSNgyOjDKsrDggg5BHBFfKH/DB37Hf/AESXQv8AwHP+NfT+gaDo/hbQdN8MeHbSOw0rSLaGztLaIYjgt7dBHFGg7KiKFHsKANeivIvi18NPEnxJs9OtfDnxA1vwC9lJI8kui/Zd9yHAAWX7VBMMLjI2gHJ5r4D8GfATxT4q/aa+JXw18bfG34iapY+GtH8P31pLbeIJdLbzNQN2soaKxWGHAEC4xGDnJJJNAH6s0V8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVR/wxZoH/AEVv4of+FnqP/wAVQB9mUV8Z/wDDFmgf9Fb+KH/hZ6j/APFUf8MWaB/0Vv4of+FnqP8A8VQB9mUV8Z/8MWaB/wBFb+KH/hZ6j/8AFUf8MWaB/wBFb+KH/hZ6j/8AFUAfZlFfGf8AwxZoH/RW/ih/4Weo/wDxVH/DFmgf9Fb+KH/hZ6j/APFUAfZlFfGf/DFmgf8ARW/ih/4Weo//ABVH/DFmgf8ARW/ih/4Weo//ABVAH2ZRXxn/AMMWaB/0Vv4of+FnqP8A8VR/wxZoH/RW/ih/4Weo/wDxVAH2ZRXxn/wxZoH/AEVv4of+FnqP/wAVWh+wbrOv+IP2T/AeseKNXvde1O4TUDNe6jcyXd1Lt1C5VfMmlLO21QFXJ4UADgCgD68ooooA/9H9/KKKKACiiigAooooArXqXUlnOli4juWjYRM3RXI+Ung8A+xr8sbb4m/8FYvBkC6HrPwh8K+PZoRsXVLHUoLNZiP45I5buHk9flijHsK/VeigD8lfhv8AssftPfHX9ofwx+0l+2fcaXo0HgRhNoPhjSH85IbhG8yN5HVpUAWQLIWEsjuyKp2ooFfrVRRQB8xftX/APxP+0h8NofhnoXja58F6deX0DawbWJZGv9NBPnWu4kMhYYYHJUkbXVlJFetfCv4V+BPgt4F0v4cfDjS49J0PSY9kUScs7Hl5ZXPLyOfmd25Jr0OigD4g8FfsYaZ8J/2nbn46fB/xHN4T8M+IbaceIPC9vErWV9eH/Uyxg/LCoZmkIVcqwKoQkjKPt+iigD5//aO1r9o7QvBNjd/sw6Bo3iLxQ+oxpc2+uO0dslgYZTJIhWe3PmCURADeflLfL3H5K6R8Hf8Agpxov7Tet/tW2vw+8Iv4s17Tk0ye1e+B05YUigiDJGL0Sh9tupyZSMk8dAP3qooA8Z+Auq/HLWfh1bX37RGjaXoXjNp5xNa6O7PaLAG/cspaac7ivLfOeewr87pv2ev2rP2Q/jV4y+IP7I2hab4/8BfEKf7bfeGr27jsZrG63M5aCSaSJAqs7BCrElDsaMlEev13ooA/LD4R/s2ftEfGT9pPSv2qf2wbXT/Dz+EIDD4b8LafOt0lrJ82Jp5Y3kjJUsXyJGZ5NpOxI1Q/qfRRQAUUUUAFFFFABXxn8N/+T4PjV/2LPhD/ANC1CvsyvjP4b/8AJ8Hxq/7Fnwh/6FqFAH2ZRRRQBWvLy00+2e8v50toIhl5JGCIo9ycAVi6R4v8L6/M1to2qW95MuSUjkBfA6kL1I96+YP2j9Z1Bte0/QN7LZR24uNvQPI7suT64C4Hpk+tfO9jfXem3kN/YStBcW7B43U4KsOhFfgXFfjVLL80lgqdBShB2k27N97dFbzvfyP0vJvD9YrBrESqWlJXWmnzP1KorO0e7l1DSLG/nTy5bmCKVl/us6hiPwJrRr95pVFOKnHZ6n5tODjJxfQKKKK0JCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z/4J8/8mgfD7/c1L/05XVfZlfGf/BPn/k0D4ff7mpf+nK6oA+zKKKKAP//S/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP4b/wDJ8Hxq/wCxZ8If+hahX2ZXxn8N/wDk+D41f9iz4Q/9C1CgD7MooooA83+Inw20v4g2cS3EptL21z5M6ruwG6qy5G5e/UEHoeufK/DX7OlvZalHeeI9RW9t4WDCCJCokx0DsT09QBz619OUV8bmnh/lGNxax2JoKVTTW7s7bXV7P5r1uj3sHxPjsPQeHpVLR+Wno90IAAAAMAUtFFfZHghRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGf/BPn/k0D4ff7mpf+nK6r7Mr4z/4J8/8mgfD7/c1L/05XVAH2ZRRRQB//9P9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/hv/AMnwfGr/ALFnwh/6FqFfZlfGfw3/AOT4PjV/2LPhD/0LUKAPsyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/+CfP/JoHw+/3NS/9OV1X2ZXxn/wT5/5NA+H3+5qX/pyuqAPsyiiigD//1P38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z+G/8AyfB8av8AsWfCH/oWoV9mV8Z/Df8A5Pg+NX/Ys+EP/QtQoA+zKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z/4J8/8mgfD7/c1L/05XVfZlfGf/BPn/k0D4ff7mpf+nK6oA+zKKKKAP//V/fyiiigAooooAKKKKACvF/Gf7RvwA+HepNo3jr4j+HtC1FDhrW71O2iuFP8AtRM+9fqRXsNzALm2ltixUSoyZHUbhjIr4H+HX/BMf9jvwBoyafd+Cx4rviuJr/Wp5LmeU9zsUpCn/AI1+poA+2fCXjXwd4+0ePxF4F12x8RaVKSqXenXMV3AzDqBJEzLkdxniumr8LvDvgPSv2Pf+Cn/AIO+FnwIuJ7Xwd8TNIa51XQvOeeG3+S8II3lmxG1sJkZiXVWdQdjYr90aACivmP9q39oHXf2avhxb/EzTfBV54y0y2v7ePV/scio2n6ex/fXRU5Z9vCqMBQSC7qoyfXvhj8TvA/xj8D6X8Rfh1qker6Fq8YkhmjPIPRo5FPKSIcq6MAVIIIoA76ivibwh+2fonxP/aeuPgF8JfD0/izQtAtZ28QeJraVRY6fdr/qolJ+WVSytGSrZLn5Ayo5r7ZoAgubm2sreS6vJUggiG55JGCooHck8AVw/h74sfCzxbrcvhnwp4y0bWtYgRpJLKy1G2ublEQgMzRRuzgKSASRgZFeTftL/sp/Db9qzR9B8P8AxOutSh07QLt7yOHTrhLfz5HTy9sxeOQlQORt2nPevy7/AGYvhD4F+BP/AAVZ8afC/wCG9m9h4f0jwmDBFLNJO4ae30+aRjJIWYlndj1wM4AAwKAP3crz7x/8Wfhf8KbSC++Jni3SvCsF0SIW1O9htPNK9RGJWUuR3C5rurq6gsraa8unEcMCNI7HoqqMkn6AV+HX7Enwl8K/t0+OviT+11+0Ppi+K7ebWJNI0DSr4s9nZW0CLNt8nO1hHHNGiA5Xd5jkF23AA/Z3wV8QPAnxI0ceIPh74i0/xNphYp9p026iu4Q46qXiZgGHcHkV19fiD8UvBWhfsCftt/Cbxp8Hom0H4f8AxfuDomuaPE7GySYSxRecqMTsCG5jlQD7pSQLhHK1+qnxo8I/Gzxbp+mQfBbx/a+Abu3lka7mutIj1YXEbKAiKkkkYjKnJyM5zigD2qsfxDFrc2galD4ZmhttYktplspbhS8MdyUIiaRV5KB8FgOSM18Vf8KZ/bw/6ON0n/wjLX/5Jr6l+FOgfEnw14Ph0r4r+K4PGfiBJZWk1G3sE02N42bMaC3jd1BQcE55oA+C/hp4t/am8EftsaD8FPjP8TbfxrpOu+ErzXWtbLR7XTba3mjuDAiI6q08m3YTuZxndgrxmv08r85/Fv8AylI8Df8AZNr7/wBL5a/RigAr4z+G/wDyfB8av+xZ8If+hahX2ZXxn8N/+T4PjV/2LPhD/wBC1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP/gnz/wAmgfD7/c1L/wBOV1X2ZXxn/wAE+f8Ak0D4ff7mpf8ApyuqAPsyiiigD//W/fyiiigAooooAKKKKACvlz9q39qrwJ+yr4APibxHnU9f1MtBomiwt/pOo3XACgAErEhI8yTBCggAM7KrfS2pT3Nrp11c2UP2m4hid44s48x1UlVz/tHiv50vBNl+2vD+0NqH7S/xr/Z01X4k+Kl2jRYZ7xLOw0VVJKi2t/LnyYwf3RJG1i0h3StvAB+hP7Ff7MnxEt/GutftgftOP5/xW8bRkW1gVwmiWEgAWEKSdkpjCptyTHGNhJdpK/TGvzr+Fv7Uv7XfjH4haF4X8cfs0X3hLQdSuVivNWk1QTJZxEEmUx+Qu4DHTIr9FKAK91a2t/azWN9ClxbXCNHLFIodHRxhlZTkFSDgg8EV/LZ8dvEGo/CD4l/F/wAC/sOeItbPwkuLeF/GY0m3+1WGjyyzeVcfYp9wwu35NytHld8e9o4wy/vx+1v8M/jd8X/hcvw9+Cfiu18HzazeQ2+sXkySGf8AsmTK3AtnjOVkwQSvG9cqHTOT1/wJ/Z1+F/7PXwvt/hT4E0tP7L2H7dLcKsk2ozSLtlmujjDs44xjaq4RQFAFAHD/ALF/gz4A+DvgD4dj/Zyni1DwvqEQuH1Dg3V7dEBZZLs4DCcMNrRkDy8bAAoAr6sr89/g9+x74y/Zx/aQvvE/wR8Rwab8GfFcM9zrHhm6EkpttQAAiNgAQqAk53E/KimMq48sp+hFABX44eAv+UzfxI/7FOD/ANItNr9D/wBo74m/Fb4VeCbHxB8H/hzN8TdYuNRjtZtNgufsrQ2zwyu1wX2SZCuiJjH8ec8V+Nuhax+21on7YviL9rhf2bNUmn8QaUmlnSDeqqRBIbaHzBceWS2fs+ceWPvYzxyAfvF8Q7O61HwB4m0+xBa5utMvYogOpd4HVce+TX5nf8Ea7y1uf2Sr6CAgyWvifUI5QOocwWrjP/AWFfffwF8e/EX4k/Dq28U/FPwPL8PNflnnjk0maf7S8ccbYSTzNiZ3jnG3ivzF8N+CP2i/+Ce/xf8AHM3ws+G178V/g348vDqVvaaO/wDp+lXJJOwQqsjkKreWfkKuixtvVlZCAXv+Csg+3+Jf2c/D1n82pah4rb7Oo+8cSWiHH/ApEr9kq/If4f8Awq+P37XP7U/hf9pT4/8AguX4beBvhqm7w7oF7IJL64vQ29Z5VwrJiQLIzMif6uNFVhuev14oAKp6jYW+qafdaZdhjBeRPDJtYo2yRSrYZcEHB4I5FXKKAPgVv+CaP7KT6gmrPpWtNfRoYlnPiDUzKsZOSgf7RuC55xnFfbvhbw5png7wzpHhHRBIunaJZ29jbCWR5pBDbRrFHvkkJd22qMsxJJ5JJrdooA8j+LXhX4v+KLPTovhH48tfAtxbyO11LdaMmsC4RgAiqr3Fv5ZU5OQWznGBivk79mrQviH4d/a5+NGm/E/xXB4y1seH/Cjm/ttNXSozEzahsj+zrNOMrzlt/Oegr9Da+M/hv/yfB8av+xZ8If8AoWoUAfZlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8ABPn/AJNA+H3+5qX/AKcrqvsyvjP/AIJ8/wDJoHw+/wBzUv8A05XVAH2ZRRRQB//X/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP4b/wDJ8Hxq/wCxZ8If+hahX2ZXxn8N/wDk+D41f9iz4Q/9C1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP/gnz/yaB8Pv9zUv/TldV9mV8Z/8E+f+TQPh9/ual/6crqgD7MooooA//9D9/KKKKACiviL9sT9p3xl8F5PB/wALvgv4fj8VfFX4kXEtto1nOT9mgjgAM1zPhk+VdwwCyLgO7NtQg/PX7Ovjj9s3w1+2dH8EP2nPHWn+I4NR8FzeI47LSrWCO1t2a9W2jTzFtYJWdNkmeWUhhyx5AB+sVFfEtt8cfiJoP7eFz+zz4omtrjwf4j8KjXtCKQBLiG5hl8qaGSQH94D5UzjjIBQepr7aoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/hv/wAnwfGr/sWfCH/oWoV9mV8Z/Df/AJPg+NX/AGLPhD/0LUKAPsyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/wDgnz/yaB8Pv9zUv/TldV9mV8Z/8E+f+TQPh9/ual/6crqgD7MooooA/9H9/KKKKAPyr/bh1a6+A37S/wADv2vNY0+fUPBXhsahoOuSW8ZlaxTUY3jhuCo5x++c+5QJ951FcC/7Tn7PWpf8FGdK+LVr8Q9E/wCEQHwta0bUpLyOKFbo6rLL9mbzCrLPsIbySBJg521+k3xW+K3wg8La74X+EfxMeO7vfibLNp2naXJZPfR32zYJllRY3RYlEi72lwmMknAOPAb/AP4JpfsQ6lq51u4+GFuk7NvKQ3+oQW+c54giuViA/wBkIB7UAfO3wU8fWP7Xv/BQO7+O3w5hml+HPwr8NyaFb6pJE8Ud/qF3I7Hyw4DY2SucEAhUVmA8xRX65189/Dzxp8BvA3j+b9lX4eW1v4c1zw/pq6quj21i9rbmykZFM8UmxYpSXcByrM5bdu5DEfQlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXxn8N/+T4PjV/2LPhD/wBC1CvsyvjP4b/8nwfGr/sWfCH/AKFqFAH2ZRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGf/AAT5/wCTQPh9/ual/wCnK6r7Mr4z/wCCfP8AyaB8Pv8Ac1L/ANOV1QB9mUUUUAf/0v38ooooA/Kj9rbxJpHwo/by/Zw+LnxAlWz8HG11nSDezcW9ne3MLxI8jH5UBM8eWYgBQzdENfqlDNDcwx3FvIssUqh0dCGVlYZBBHBBHQ15d8Z/gp8N/j/4EvPhz8UtJXVtHumEijcY5oJ0BCTQSL80ci5OCOoJVgVJB/M8/wDBIjQbMNomgfGzxfp3hZiR/ZgkRh5Z6ruQxxHjv5P4UAddF4r0P4rf8FWNJuPh5cx6pa/DvwTdWWu3luweFJ5Z5dtsZFypZGuEyM5Db16o2P1Yr52/Zx/Zc+EP7LPhKbwp8K9OeJr5lkvr+6cTX17IgIVppAqjCgnaiKqLkkKCzE/RNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXxn8N/wDk+D41f9iz4Q/9C1CvsyvjP4b/APJ8Hxq/7Fnwh/6FqFAH2ZRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGf/BPn/k0D4ff7mpf+nK6r7Mr4z/4J8/8AJoHw+/3NS/8ATldUAfZlFFFAH//T/fyiiigD8sv26fEHxf8AiR8c/hN+x78L/FVx4Fs/H8N9qGsaraFkuDa2aO5iRkZHwEikJQMN7MgYhc55tP8Agjn8B5kEuqfELxxdXbDMko1CzUO3c4aycjPux+tb/wDwUb/4W9pvij4VeNPgR8NNX8VeN/ClzeX1jrmlpJPHp6t5Uc1neWyQSefDeRswI82JhtJVsFgeT0z9vr9tKKxhj1n9kDxBdXiqBLJby3sETN3KxvYSso9i7fWgD6M/Zv8A+Ce/wm/Zi+Ib/EnwV4n8TavqEllNYGHVry2ntvLnZGZtsVrC28FBg7scnivvKvg39m/9qj4+/GT4hv4R+Jf7Pms/DHSFsprkatfzzyQmaNkCwYks4F3OGJHz5+U8Ht95UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGfw3/wCT4PjV/wBiz4Q/9C1CvsyvjP4b/wDJ8Hxq/wCxZ8If+hahQB9mUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFNd0jRpJGCqoySTgADuTQ2A6is2x1nSNTd49NvoLto/vCKVZCv1Ck4rSrOlVjNc0HdeRU4Si7SVmFFFFaEhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8E+f+TQPh9/ual/6crqvsyvjP/gnz/wAmgfD7/c1L/wBOV1QB9mUUUUAf/9T9/KKKKAPDvj9+0T8Kv2aPBB8d/FbVDY2cknkW0EKeddXc5BbyoIgRuOASSSFUcsw4r867j/gsR8N/tl3baf8ACfxhOtnGs0m+CBJI4XGUkkRZH2Kw5BJwexru/wBpjRdO8Vf8FFf2afD/AI1hW78OxWGu3lnBOA0D6lbwvMpIPBZWigYf7SqK3fh3qNt4f/4KifFnQ9VkW2uPFfg/R7zT1c7ftCWYihkEefvEEMcDnCsegOADpP2Vf+ChPhL9q7xzL4M8KeBdc0aGG0nuW1G8WJrPdAYwYfMjZh5hEgIHpX6E1+XWmS+H/hf/AMFQV8E/DLy7Kw+IHg+S/wDE2m2mFtk1K3lkeC8eJMKk7xoATgEiQsRmTJ/UWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z+G/wDyfB8av+xZ8If+hahX2ZXxn8N/+T4PjV/2LPhD/wBC1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+Tv2jfEmpRX1h4WgkaKzeD7TKFOBKzOyqG9Qu3OPU57CvrGvLfid8M7X4g2UDxzC01GzyIpSMqyt1RwOcZ5BHT3ya+G8R8oxmOyithsC/fdtL2uk9VfzX37dT6LhTHUMPjoVcT8Kv8n0Z8GaXql/ot/Dqelztb3NuwZHU4II/mD3B4I4NfphoWoPq+iafqsieW17bwzFf7pkQMR+Ga+W/Dv7OWqjUo5PE99b/Yo2BZLYuzyAfw5ZV2g+vJ/nX1rFFHBEkMKhI4wFVRwABwAPpXwXgrwtmeXRryx0XCErWi+6vd26dvP5I+l4/wA5weKdNYd80le7Xbt5/oSUUUV+7H5wFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXxn/wT5/5NA+H3+5qX/pyuq+zK+M/+CfP/ACaB8Pv9zUv/AE5XVAH2ZRRRQB//1f38oor5z/aa/Zw0L9p7wNp/gTxB4i1bw1b6fqUepLcaPMkM7vHDNCI2Z0cFCJiSMZyBzQB23xA+Cfw4+J/ifwd4y8ZaY91rXgG9a/0a5iuJrd7adyhf/UugdH8tQyPuVgMEEZrxj9qD9i74WftTS6Prfie61Dw74o8PKyWGtaRMsF3HEx3GJyysHjDEso4ZSTtYbmB8R/ab+AHwH+LPjH4T/Avxt8UPEHhjxZY6XPbaLZaXIVn1KCKNfMmnfyJI9yLbMxJZB94153/w6I+Gf/RWfHP/AIHW/wD8YoA+of2Xf2HfhR+y3qur+LdAvtR8TeLtdj8m61jV5VlufJLB2ijCKoVWZVZydzMVGWwAB9nV8G/s3/sD+Df2bPiG/wARNB8d+JvEdy9lNY/ZdWuoprYLMyMX2pEh3jYMHPc195UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGfw3/5Pg+NX/Ys+EP/AELUK+zK+M/hv/yfB8av+xZ8If8AoWoUAfZlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8ABPn/AJNA+H3+5qX/AKcrqvsyvjP/AIJ8/wDJoHw+/wBzUv8A05XVAH2ZRRRQB//W/fyiiigD8gf+CkHxO8Kfs9fGn4H/ALQsFx5vjHwvJqUaaU8cnl6ppNwiwXaLcKrLDNEJ2Me8bTvJzlArdxo//BX79jzUdPhu7+71vS55FBe3n01nkjPcFoXkQ49Qxr9CviBH8MrHRZfFPxRj0iHStJQtJe6usAgtkYgEmWcbUBOB1GTivi26/bA/4JvWc7203ibwszoSCY9KaVePR0tmU/UGgD0P4D/t5/s7/tIeOH+Hnwu1K+u9ZS0lvSlxYy26eTCyK53vxnLjivsuvkf4JftC/se/E/xm/hr4Gaxo194kW1lnaOw097Wb7MjKJDvaCMbQWXI3c8cV9cUAFFFee+Mfi58Kfh2QPiB4z0XwyTjA1PUbazJz0/10iUAehUVi+HfEvh3xholp4l8Japa61pF+u+3vLKdLm2mQErujljLIwyCMgnkVtUAFFFFABRRRQAUVR1LVNN0ezk1HV7uGxtIRl5p5FijUepZiAPxNef8AhX42/Bnx1r8vhTwT480DxDrcEbyyWOnapa3d0kcZAdmhhkZwqlgCSMAkZ60AenUUV5h8SfjX8I/g9bW918UvGGl+F1u8+Qt/dRwSTbevlxsd7477Qcd6APT6K4XwB8Tvh18VdGPiD4a+JdO8T6cjeW8+nXMdyiP12P5ZOxsc7Wwfau6oAKKKx/EMGtXWgalbeG7mOy1aW2mSznmTzIorlkIikdP4lV8Er3AxQBsUV+Xvw11z9pn4f/tv6B8Gfi98WG+IGl674QvNckhi0m10q2gnS5MCIiQhnYIEJ3MwJzyOK/UKgAr4z+G//J8Hxq/7Fnwh/wChahX2ZXxn8N/+T4PjV/2LPhD/ANC1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP/AIJ8/wDJoHw+/wBzUv8A05XVfZlfGf8AwT5/5NA+H3+5qX/pyuqAPsyiiigD/9f9/KKKKAPyi/bT8LwfHn9sL4A/sz+Mpph4Dv4dU17UrOORolvpbKGRo43KkHgQlMghlWVyCDgjg/hv+zL+zrqH7ffxj+FOsfD3RZvDtl4Y0W703TntI/Kg3RxJPLCMZVmd/mdTuJPJzX6BfF/9nOD4n/F/4V/GjTdffw/rnwxu7qRdlsJ1vrS+VI57aQmRCgZFZVcbtu9vlNeJ/tQ/so/Frxr8U9F/aK/Zh8b2/gT4l6Zp7aRdG+j8yx1CwLmRUmAinGVYn70MgYBD8pjU0AeIfBX4TfDv9kr/AIKAy/CHwLo9odA+InhifWdMeaJJdR0m5hmZZ7WO8YGdrSVIS2x3bDBcdGLfrlX5y/swfsifGTwn8ZtU/aV/an8dW3jj4h3OnnSrFNPQrZWNqzAtszFbjJGVCpEirudjvZ8j9GqACvxn/bK/4J/fs4eC/gz8ZPj1b6bf6l4yukvtajuru+k8u3u7y581zHDD5UZUGRgokV8DGSTzX7MV8f8A7fv/ACZr8WP+wM//AKMSgCh/wTx/5Mu+FX/YMf8A9KZa+z6+MP8Agnj/AMmXfCr/ALBj/wDpTLX2fQB8w/tY/Hnxd+zl8NIfib4b8ET+NdPsb6AaytvKI3sdMJJnuQuCzlQAo42rnc5Cg16x8KPix4D+NngPTPiT8ONUj1XQ9Vj3RyLw8bj78UqdY5Yzw6HkH2wT6DPDBcwSW91GssMqlHRwGVlYYIYHggjqDX8svx51O88C/Er4w+G/2FdW13/hUEttC3jgaNCJ9NsHebZcfYZNwxHtyuVZMr5qBjbrlQD9u/A37aFj8Xf2nbr4I/Bzw5J4r8JeHbaf/hIPFMMoWzs7wf6mOEkbZlLK0ZKtl2JZAUjZm+5K+Vf2MNA/Z+0H9n7w4n7Nskd14Tuo/Na6ODd3F4QBO96cAi5BGHUgbMBVAQKK+qqAPm79o79lb4WftTaXoOifFX7dJYeHrp7yGGyuBbiSSRPLIlbYzFcdNpU571+XX7NXwm8BfBD/AIKv+Nvhn8M9NOk+HtJ8Jqbe3M0twVae30+aQmSZ3clpHZuWwM4GBgD91q/HDwF/ymb+JH/Ypwf+kWm0AfsPeXdvYWk99duI4LdGkkc9FRBlifoBX4g/sI/Cjwn+2r4z+Jf7X/7Qejw+LWvtak0rQ9N1JRcWdlbQRpNtED7kcJHNHGm4EAq7cuxYfsv8QrG71TwD4l0ywBa5u9MvIYgOpkkhdVA98mvzL/4I131rdfsl39tAwMtn4n1COUDqGaC1kGf+AsKAPJfjJ4K8P/sIftu/CH4i/B62Hh3wT8WbhtD17SLclbEP5sURlWLOEC/aI5UVeFaJsYDlT+qfxo8EfGTxtp+mW/wd+Ji/DW6tZZHu520W21r7VGygImy5dBHtOTlck5wa/Nf/AIKx/wDEx8Ufs4+GbL5tT1LxW32dR944ktIzgf70iV+yVAHwl/woT9t7/o6qP/whNK/+P19S/Cnw18Q/CXg+HRvij40Hj3Xklld9VGmw6UHjZson2aBnQbBxuBy3U16RVPUdPtdW0+60u+QyW15E8MqhipZJFKsNykEZB6ggjtQB+efi3/lKR4G/7Jtff+l8tfoxXwWf+CZn7F5uBdnwPdGdV2CT+3tZ3heuA32zOPavtfwt4a0bwZ4Z0jwf4dha20rQrO3sLOJpHlaO3tY1iiUySMzuQigFmYsepJPNAG7Xxn8N/wDk+D41f9iz4Q/9C1Cvb/i14P8Aix4us9Oh+FXxCX4f3FtJI1zK2kW+r/aUYAKm2d0Ee0gnIznOK+Tv2avDvj7wv+1z8aNL+JPi8eN9ZHh/wo51FdPi0wGJm1DZH5ELOny8/NnJzQB+h1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8E+f+TQPh9/ual/6crqvsyvjP/gnz/yaB8Pv9zUv/TldUAfZlFFFAH//0P38ooooA+Mv2rP2VvFHx9vvDnjD4dfEvVfhn4v8Jx3Mdnd2BYwzJcmNmSdY3ikIzGB98rgnKN0r5LHxl/4KVfsvfuvjJ4Bs/jh4TteG1fw8SmoLGP4njhjDfKvLbrQAnOZe9fZH7U37ZXwz/ZRg0S18X6fqeua74mE39l6bpduJZbhoCitl3ZUUbpFHVmOeFNfG4+I3/BTj9qP5Ph14SsPgD4RuumoazmXVWjPXaksbSAleVItYuTxL3AB9a/s1ft2/An9qHVJfCngue+0nxZawPcXGjanatDcxxxMqSMJE3wsFZgMCTdzyor7Mr4B/Zf8A2DdJ+AXxBvvjX4x8eaz8Q/iNq9rJaXmo37BIWSYozYjYyylv3agM8zAKMBVr7+oAK/I39r/xx+2x8StC+I/wH8I/ANtQ8Lav52nWeux6lCHmtg4KTiF2XBYD7pIr9cqKAPyC/Y+8aftt/Cvw58OfgL4p+ATWfhfSZIrC81yTUod8NrLOzyXBhRmyUDk7QTnFfr7RRQB8u/tb/Cb4wfG34Xr8OPhH4xg8FjV7yGHWrp4naeTSXytxHA6HKuQclePMAKF0BOe7+CHwC+Gn7P3w0s/hZ8PdMSDSYUP2l5QrzX0zqFlnuWwPMeTGDxtC4VQEAUez0UAfn/8ACj9jfxF+z3+0pfeO/gf4lh0X4S+KoJ5tc8KzI8qJfgYhaxAIWJcktuJyiqYwrIy+X+gFFFAHz/8AtHfED4z/AA48E2Ot/A3wAPiNrs+ox28+nm6W08q0aGV3uN7cHbIkabf9vPavx20OD9vrRP2vPEP7Wqfs8Sy6h4g0tNMbSzqduIY1SG3h3ibfuJItwcbf4j6V/QTRQB4z8BfGfxS8e/Dq28RfGLwYPAXiWWeeOTSxcLc7IkbEb+YvB3rzjtX5n6L8N/2l/wBgX4xeONW+Cnw8k+LHwg8fXZ1FNNsLhYL7SroljsVCsjYUMUyI3V0WPcyMMH9lqKAPyQ+HHwc/aF/ao/aj8N/tOftI+Eh8OvCPw7jz4b8NzTrc3cl3u3rPMVClSsmJGZljJMcSqhUMx/W+iigAooooAKKKKACvjP4b/wDJ8Hxq/wCxZ8If+hahX2ZXxn8N/wDk+D41f9iz4Q/9C1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP/gnz/yaB8Pv9zUv/TldV9mV8Z/8E+f+TQPh9/ual/6crqgD7MooooA//9H9/KKK5rxj4y8K/D7wzqHjLxvqtvomh6VH5t1eXUgjiiTIAyx7kkBQMliQACSBQBozaJotzqttr1xp9vLqdnG8UF08SNPFHLguiSEblVsDcAQDgZ6VqV+ZWo/8FcP2MrHVH0+31rVb6FW2/aoNLmEB56gS7JMf8AzX0b8Jv22f2WvjbeQaV8P/AIhafcapckLHY3hk0+7kc8bI4rtYmkb2j3UAfVFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFPUbQ6hp9zYLcS2huYniE0DBZY96ld8bEEBlzlSQcHtXwd+z74Rl8E/th/GnQ5tf1TxIw8PeE5PterzJPdHe2ofIXjjiXauOBt/Gvv2vjP4b/APJ8Hxq/7Fnwh/6FqFAH2ZRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfGf/BPn/k0D4ff7mpf+nK6r7Mr4z/4J8/8AJoHw+/3NS/8ATldUAfZlFFFAH//S/fyvy/8A+Cq2lpf/AAc8AXXiQTt4DsfG+jyeKRblgw0t/Njd2Kc7QXCj/bZMc4x+oFYHirwr4c8ceG9S8IeLtOh1bRtXge2u7W4XfFNFIMMrD+RHIPIIIBoA8B8T/CT4D23wM1yTwP4P8PDRf7Au5rGSzsLVoWiNqzRyRuiENkYYOCc9c96/M39iT9hr9m79o/8AYr8I698QvDXleJLqXVUOs6fM9rfr5d/OiEspMchRQFXzY3AAAxwK9G8W/sI/tQ/Baz1fTf2Mfiw8fg3VY545PCXiJvOghjuARJHazSRzIM7jg7Yn/vSMea8j+Amqf8FNP2UvhppvwS0L4D6br+l6RLcvbXZvIpXc3U7zsXeC92YDOcfKhC4zzk0AfWn7Lvif4qfAX4+6n+xR8XvEUvjPTDo417wdrlzn7W+nxyGGSyuSSxZoyrbMk7Qjc7GjVP0xr8yP2YvgR+0z4r/aCvP2tf2uPsGj6/BpDaNofh/TXWSOxt5X3uzsjyqMbnAHmyMxkYsVCqtfpvQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/Df8A5Pg+NX/Ys+EP/QtQr7A1H+0Dp9yNJMQvvKfyDOGMXm7Ts8wLhtu7G7HOOlfB37Pq/EBf2w/jSPiXJpcmtf8ACPeE8nSEnS18rdqGzAuGZ93XPOPSgD79ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP8A4J8/8mgfD7/c1L/05XVfZlfGf/BPn/k0D4ff7mpf+nK6oA+zKKKKAP/T/fyiiigD8WPD3iX9sb/goH4o8VeJfhN8Rf8AhTfwg0DU59K06ezhMmo6hJb4LTEo0chyrKzYmRF3BFDsrtXlvwe8Mf8ABRPxD4z8Xj9nz45y+M/Cngu9l0x9W8WQiO01TUbUgXFtaRS/2hK0aHC+fvjUn7rd6/XP9mr9njQv2ZvAuofD7wzq11q2mXmq3eqQ/a1RXt/tYTdApjABRSuQTzycnpX5/wCk6Z+2Z+wt4g8S+Gfhl8OIvjP8Jta1a71fTUs7j7Pqmntevve3cBZHIB7iGRSfmDKWKAA+m/2XP2q/G/xC8aax8Av2iPCP/CCfFvw7ai9e3jJaw1Sy3BDdWTlnBAYgModxjlWOHVPuuvzO+B+kftDftAftK6P+0r8Zfh2PhPoPgrRb3S9I0ye5FzqV7PqDDzZJjsiZYkQHAaNMMRtDZYj9MaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP4b/8AJ8Hxq/7Fnwh/6FqFfZlfGfw3/wCT4PjV/wBiz4Q/9C1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPMfiZ8SbT4fafCVh+16hebhDETtUBerueuBkcdSfxI8Q0H9o7WRqKL4ksLdrJ2AZrYOkkYPcBmYNj04+tan7RvhrUriXT/ABPbRtLaQRGCYqM+UdxZWPoGyRn1A9RXzDp2nX2rXsOnabA1xc3DBUjQZJJ/zyeg71/K/iLx5nuDz2WHw03GMbcsUrqV0tdveu9PLZan7Lwrw3l1fLlVqxTbvd32/wArH6hW88N1BHdW7iSKZQ6MOjKwyCPqKlrF8N6ZJovh7TNHmffJY20MLMOhMaBSR7ZHFbVf1DhZzlSjKorSaV12fVH49WjFTai7q+gUUUVuZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXxn/wT5/5NA+H3+5qX/pyuq+zK+M/+CfP/ACaB8Pv9zUv/AE5XVAH2ZRRRQB//1P38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z+G/8AyfB8av8AsWfCH/oWoV9gajYWuq6fc6XfKXtryJ4ZVVmQlJFKsAyEMpweqkEdQQa+Dv2ffAPhf4b/ALYfxp8NeELea209fD3hOUJNdXF4++RtQ3HzLmSWTHHTdgdhQB9+0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACMqupRwGVhgg8gg1nWWj6Rpsjy6dYwWryfeaKJULfUqBmtKis5UYSkpSSutvItTkk0nowooorQgKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP8A4J8/8mgfD7/c1L/05XVfZlfGf/BPn/k0D4ff7mpf+nK6oA+zKKKKAP/V/fyiiigAooooAKKKKACiiuW8YeOPBfw+0Z/EXjzXrDw5pcZCtdajcxWkAY9F8yVlXJxwM5NAHU0V454F/aH+A/xO1IaL8PfiFoPiHUjki0stRt5rkhRkkQq5cgAE5C4r2OgAooooAKKKKACioLq6trK3ku72ZLeCIbnkkYIigdyx4A+tcN4e+LPwr8Xa5L4Z8KeMtF1rWIEaSSystRtrm5REIDM0UcjOApIBJGBkUAegUUV578QPi18LvhTaQX3xN8W6V4VguiRC2p3kNr5pXqIxKylyO4UHFAHoVFcf4J+IPgP4k6OPEHw88Raf4m0zcU+06bdRXcQcdVLxMwDDuDyK7CgAoorjviDZeNtS8F6vYfDjUrbR/E1xAUsLy8i8+C3mYgCR4v49oyQvQnAPFAHY0V+V3xz8Oftdfsx/C7VP2gbb4+S+OLnwt5N1qejavotja6bf27SpHJFELfElu3z5XY2T93IzX6W+DvEUfjDwhofi2K3e0TW7G2vlhk+/ELmJZQjdOV3YPvQB0lfGfw3/AOT4PjV/2LPhD/0LUK+zK+M/hv8A8nwfGr/sWfCH/oWoUAfZlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8E+f+TQPh9/ual/6crqvsyvjP/gnz/wAmgfD7/c1L/wBOV1QB9mUUUUAf/9b9/KKKKACiiigAooooARmVFLMQABkk9AK/Bf4TaP8AD/8Ab0+K/j/9qP8Aaj1qKH4SeDNTbQ/C+l39/wDYNNG0CQvM5eMbjGYpHAYeY8mCdkapX7leK7S7v/C2sWNhn7TcWdxHFjr5jxsFx+Jr+c3/AIJY/sqeA/2iPD/iTxL8aY5vEPhrwhfi30vQ5J5YrJL66iV7q6ljjZSzlEhQZOCB8wO1cAH6/fCz9mD9iefxXonxf+B2h6G+q+GZna11Dw/febCrSxPCyyLBM8L5R2++pI6g19p1+Dv7T3wp8M/8E/v2g/hH8dP2dlm8N6J4r1T+yNe0SOeSS0uIN8ZcKsjMcSRu/wApJVJER0ANfvFQB8x/tXftAa9+zX8OLf4maZ4KvPGWmWt/BHq/2SRUbT9OY/vroqcs+3hVGAoJy7qoyfXvhh8T/A/xk8D6X8Rfh1qker6Fq8YkhmjPIPRo5FPKSIcq6MAVIwRXb3Vra31rNZXsKXFvcI0csUih0dHGGVlOQQQcEHgiv5a/jvr+ofCL4l/F/wADfsOeIdcPwkuLeF/GY0m3+1WGjyyzeXcfYp9wwu35NytHld8e8xoGUA/cHwf+2fovxQ/aeuPgH8JPD0/izQdAtZ28QeJraVRY6fdr/qolJ+WZSytGSrZLnKBljc19tV8p/sX+DvgD4P8AgB4dj/Zxni1DwvqEQuH1Dg3V5dEBZpLw4DCcMNrIQPLwEACgCvqygD5n/aW/ZT+Gv7Vmj6D4f+J11qUWnaBdveRw6dcJbiaR08vbMWjkJUDptKnPevy7/Zi+EXgX4Ff8FWvGnww+G1k9h4f0jwmDBFJNJcOGnt9PmkYySlmJaR2PXAzgADAr93K/HDwF/wApm/iR/wBinB/6RabQB+xF1dQWVrNeXTiOGBGkdj0VUGST9AK/Dr9iP4TeFP25/HXxJ/a6/aH0xfFcE2sSaRoGl3xL2dlbQIs23yc7WEcc0aIDld3mOQXbcP2c+Idldaj4A8TafYAtc3WmXsUQHUu8DquPfJr8zv8AgjXeWtz+yVfW8DAyWnifUI5QOoZoLVxn/gLCgDyL4p+CtC/YF/bb+E3jb4PRNoPgD4v3B0TXNGidjZJMJYojKqMTsCm4jlQD7pSQLhHK1+qnxo8I/Gvxbp+mQfBb4gW3gC7t5ZGu5rnSItXFzGygIipLJGIypycjOc4r81/+Csn+n+Jf2c/D1n82pah4rb7Oo+8cSWiHH/ApEr9kqAPhL/hTH7d//Rx+lf8AhF2n/wAk19M/Dmw8c+A/AD/8Lr8aWnijU7A3FxdawLKLSLdbZcuN8SuyIsSA7nLdOTivVK4n4j/Dzwr8WPA2sfDnxvbPeaFr0Bt7uFJXhZ4yQxAkjKsvIHQigD8+nTWP+Ch/i6FjHNpv7Nnha/WUF1aKbxpqFo/GFOGXTYXHcZkYf3h+5/TiONIkWKJQiIAFUDAAHQADtXwba/8ABNj9laxt47Oy0zW7eCFQqRx+IdUREUdAqi5AAHoK+3fD2haf4X0DTPDOkh1sdItYbO3EkjSuIrdBGgZ3JZ22qMsxJJ5JzQBsV8Z/Df8A5Pg+NX/Ys+EP/QtQr2/4teDPil4ys9Og+F/xFf4eT20kjXMqaVaar9pRgAqFbrhNpBOV5Oea+Tv2avDfjrwr+1z8aNJ+InjFvHOrjw/4Uc6i9hb6aTGzahsj8i2/d/Jz83U55oA/Q6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/+CfP/JoHw+/3NS/9OV1X2ZXxn/wT5/5NA+H3+5qX/pyuqAPsyiiigD//1/38ooooAKKKKACiiigAr8cdP8AftDfsC/Grx34j+D3w6ufit8IfiLdf2idP0qQLqOk3hZm2JEFdmQb2UYRgyBMujKQ37HUUAfjLffDz9o/9vb48eAfGHxd+Htx8J/hN8Nrr+0I7DVJN2o6ldB0cq0RVHAcxIp3IqpHv2s7tiv2aoooA+XP2t/hl8bfjB8Ll+HvwU8WWvg+XWbyG31i8mSQz/wBkyZW4W2eM5WTBBK8b1yodM5PYfAr9nb4Yfs9fDC3+FPgPS0/svYftstwqyTajNIu2Wa6OMOzjjGNqrhFAUAV7nRQB+e/wf/Y98Y/s5ftIX3ij4I+I4NN+DXiuGe51jwxdCSX7NqAAERsAMKiknO4n5UUxlXHllP0IoooA+f8A9o74mfFf4V+CbHxB8Hvh1N8TdZuNRjtZtNhuRatDbPDK7XBcpJkK6ImMfx5zxX43aFq37bmiftieIv2uF/Zt1Oa48QaUmlnSDfIqRBIbaHzBceWS2fs+ceWPvYzxz/QjRQB4z8BfHvxG+JHw6tvFPxU8Dy/D3X5Z545NJmn+0vHHG2I5PMCJneOcY4r8xvDngf8AaM/4J8fF/wAcz/Cv4b3nxX+Dnj28OpW9npD/AOn6Vckk7BCqyOQqt5Z+Qq6LGd6srKf2fooA/Ij4ffCn4/8A7XH7U/hf9pX9oDwXJ8NvA3w2Td4d0C8lEl9cXobes8q4VkxIFkZmRP8AVxoqsNz1+u9FFABRRRQAUUUUAFfGfw3/AOT4PjV/2LPhD/0LUK+zK+M/hv8A8nwfGr/sWfCH/oWoUAfZlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Z/8E+f+TQPh9/ual/6crqvsyvjP/gnz/wAmgfD7/c1L/wBOV1QB9mUUUUAf/9D9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/hv/AMnwfGr/ALFnwh/6FqFfZlfGfw3/AOT4PjV/2LPhD/0LUKAPsyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+M/+CfP/JoHw+/3NS/9OV1X2ZXxn/wT5/5NA+H3+5qX/pyuqAPsyiiigD//0f38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCnqMF1dafc21jcmyuZonSKdVVzE7KQrhWyrFTzg8HGDxXwd+z7oXinw7+2H8adO8YeJpPFmoDw94Tc30trBZsUZtQ2p5Vsqp8vrjJ719+18Z/Df8A5Pg+NX/Ys+EP/QtQoA+zKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKYssbllRwxXggHOPrXzr+0F4x1XRbKw0DSpmtv7QEjzyIdrFEwAgI5AJJ3fQDoTXyVpWsanod9Hqek3L2tzEcq6HB+h9Qe4PB71+McX+MlDKsxeAVBz5bczva11fRWd9H3XbzPvsj4DqYzC/WfaKN72Vr+Wuun4n6hUVzvhLWn8ReGdM1yRBHJeQJI6joHI+bHtnOPauir9gwuJhWpRrU37skmvRq6Phq1KVOcqct07fcFfGf/BPn/k0D4ff7mpf+nK6r7Mr4z/4J8/8mgfD7/c1L/05XVbmR9mUUUUAf//S/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvjP4b/wDJ8Hxq/wCxZ8If+hahX2ZXxn8N/wDk+D41f9iz4Q/9C1CgD7MooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvH/i38SpfAWn29vpkay6nf7vL38pGi4y5A6nJwo6dSemD7BXzn8fPA2r6/b2XiDRYWunsVeOaJBufy2O4Mqjk4Ocgc8g9jXxfiFi8dQyivVy5P2iS21aV1drzSv6bnv8L0cPUx1OGK+Dz2vbS/zPJNE+Pfjuw1FJ9WuE1K1LfPC0UcZ299jIqkH0zke1fbOmahbatp1rqlk263u4kljPQlXG4Z98Gvza0Twp4h8Q6immaVYyyzs20/KQqe7seFA7k1+jPhzSF0DQNP0RX8z7DBHEW/vFFAJ/E81+aeCOcZtivbrGzlOkrWcrt83VJvV6brpptc+t8QsBgqPs/q8VGb3S007tL+mbVFFFfv5+ZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5b8UvhynxA0qFbeVbfUbIs0DvnYQ+NyNjkA4Bzg4I6da+eNH/Z78ZXWoLFq7wWVoG+eRXEjFe+xR3/3sV9s0V+e8Q+GOU5njFjcTB8+l7Oylba/y00s7H1GV8X43CUPq9Frl6XV7en/AAblHTNOtdI0620uxXZb2kaxRjrhUGBn39avUUV9/TpxhFQirJaI+ZnNyblLdhXxn/wT5/5NA+H3+5qX/pyuq+zK+M/+CfP/ACaB8Pv9zUv/AE5XVWSfZlFFFAH/0/38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4z+G/8AyfB8av8AsWfCH/oWoV9mV8DeI/D37TfgX9p3x78T/hh8PtK8W6D4r0jQ7GOW911dMdH00XBf5BbzsctORyAMAEE5IAB980V8Z/8ACzP23f8Aoh/h3/wsf/vdR/wsz9t3/oh/h3/wsf8A73UAfZlFfGf/AAsz9t3/AKIf4d/8LH/73V598PP2mf2tfidba9deFvgloTx+HNa1DQbvzvFpjIvdMlMNwF/4l53IHHyt3FAH6G0V8Z/8LM/bd/6If4d/8LH/AO91H/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV8Z/8LM/bd/6If4d/8LH/AO91H/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV8Z/8LM/bd/6If4d/8LH/AO91H/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV+eXw/wD2mf2tfiWPEh8MfBLQn/4RXWrzQL3zfFpjxe2O3zgn/EvO5PnGG716D/wsz9t3/oh/h3/wsf8A73UAfZlFfGf/AAsz9t3/AKIf4d/8LH/73Uf8LM/bd/6If4d/8LH/AO91AH2ZRXxn/wALM/bd/wCiH+Hf/Cx/+91H/CzP23f+iH+Hf/Cx/wDvdQB9mUV8Z/8ACzP23f8Aoh/h3/wsf/vdR/wsz9t3/oh/h3/wsf8A73UAfZlFfGf/AAsz9t3/AKIf4d/8LH/73Uf8LM/bd/6If4d/8LH/AO91AH2ZRXxn/wALM/bd/wCiH+Hf/Cx/+91H/CzP23f+iH+Hf/Cx/wDvdQB9mUV8Z/8ACzP23f8Aoh/h3/wsf/vdR/wsz9t3/oh/h3/wsf8A73UAfZlFfGf/AAsz9t3/AKIf4d/8LH/73Uf8LM/bd/6If4d/8LH/AO91AH2ZRXxn/wALM/bd/wCiH+Hf/Cx/+91H/CzP23f+iH+Hf/Cx/wDvdQB9mUV8Z/8ACzP23f8Aoh/h3/wsf/vdXn3gL9pn9rX4kT+Kbbw18EtCZ/B+tXGg3/m+LTGBe2scUsgT/iXncm2ZMN3OfSgD9DaK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK+M/+Fmftu/8ARD/Dv/hY/wD3uo/4WZ+27/0Q/wAO/wDhY/8A3uoA+zKK/PLwR+0z+1r8QdU8WaR4d+CWhNceC9VbRtQ83xaUAu0hiuCEP9nncuyZPm9cjtXoP/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV8Z/8LM/bd/6If4d/8LH/AO91H/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV8Z/8LM/bd/6If4d/8LH/AO91H/CzP23f+iH+Hf8Awsf/AL3UAfZlFfGf/CzP23f+iH+Hf/Cx/wDvdR/wsz9t3/oh/h3/AMLH/wC91AH2ZRXxn/wsz9t3/oh/h3/wsf8A73Uf8LM/bd/6If4d/wDCx/8AvdQB9mUV8Z/8LM/bd/6If4d/8LH/AO91efeD/wBpn9rXxz4i8XeF9C+CWhNfeCL+PTdREni0oouJbeO5URn+z/mXy5V59cigD9DaK+M/+Fmftu/9EP8ADv8A4WP/AN7qP+Fmftu/9EP8O/8AhY//AHuoA+zK+M/+CfP/ACaB8Pv9zUv/AE5XVH/CzP23f+iH+Hf/AAsf/vdXZfsc/Dvxp8J/2b/B3w/+IdlFp/iHSVvRdQQzLcRoZr2eZMSp8rZjdScdCcdqAPpqiiigD//U/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8i+D/wltPhFZ+K7Oz1F9SHirxLrHiRzJGI/Jk1ecztCME7ljJwGOCfQV8n/G79q39o7SPi3rXwX/Zy+Bt344vtBW1+1a1eXItdKWS6t47hYw7COMsFkGd1whyD8uOa8fj/AOCgXx3+B3jTQ/Dn7bfwgTwNoniKYQW+vaTdLd2cLnH+tCSTowXO5wJhIqgsI2oA/W2imRyRzRrNCwdHAZWU5BB5BBHUGn0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5D8I/hLafCYeMxaai+o/8Jj4l1LxJJvjEfkSaiULQrgncqbOGOCc9K9er8lvGn/BToWP7UugfArwB4Qi13wvqniSy8LyeIZLl0je+e4ghvRbKqFJBbfaEH3vmPP3WUn9aaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8i+E/wltPhVd+O7q01F9QPjnxLeeJJA8Yj+zyXcMEJhXBO5VEAIY4Jz04rg/2pP2pvAH7KvgSDxZ4vim1TU9VmNrpGkWmDdahc4B2pnO1FyPMcg7cgAMzKrfHNt+0x/wAFML21Xxhb/szWCeHpB5q2Uuqxx6oIjzhke4WUPjjBtg2f4KAP1formvBmt6j4l8H6F4j1jS5dDv8AVbC1u7jT5yTLZzTxLI9u5ZUJaJiUbKqcjoOldLQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFfDv7W/7Ztt+znrHhn4a+C/C1x49+JfjU/wDEr0a3fylEZfy1lmcK7YZwQiqvzbXJZAuaAPoX4YfCW0+Geu+P9ctdRe/bx7rz67KjxhBbO9tBbeUpBO4AQBtxwecY4r12vyO8X/tm/txfATSoviF+0V8BNPi8DLJEl7c6HqaTXNksrBFaQLNcL1IALBEZiF3qWFfqB8PvHnhn4oeB9D+Ifg26+2aJ4htIr20lI2sYpV3AMp5Vl+6ynkMCDyKAOxooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK/Mz4+ft6/FH4a+N/Ffgf4Z/APX/GcXhEM15rbGWDS0SOETySeZHbyqURSc7pEPBoA/TOvIvh18JbT4eeMfiH4vt9Re9k+IOrQ6rLE8YQWzQ2kNoI1IJ3AiENkgcnFea/sb/tEal+1F8DrD4t6rosWgXF5d3dsbWGZp0UW0mwNvZVPzdele0fFP4m+EPg38Pdd+J3jy7+xaF4ftzcXDgbnPIVI4143SSOVRFyMswGRQB39FfkXov7af7cvxF8LyfGX4V/s72158OMPPbLd6kF1a9tIicywxh0ZtwBKhIHz0QydT90/sv/tLeBv2qfhfb/EjwWklk8crWmoafOQZ7G8jAZonIwGUqwZHAAZSMhW3KoB9F0UUUAFFFFAH/9X9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr8df+CrfjKy+I2j+CP2QPANuuv8AxG8X61aXsdpHhmsreJJUWWUjPl7y5OT0iWR2wuCfp39sn9sSL4CW2n/DL4Y2H/CW/GLxhtg0TRYV80wmUlFurlF5CA52ISDIQeQiuy1/2Nv2PZfgkNR+L3xfv/8AhLPjP4yzPrGrTN5v2US4Y2ts2MBRgB3UANgKoEaqKAPtLwdoB8KeEdD8LtObk6PY21mZT1k+zxLHvP8Avbc10dFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8FftxfHLxX4W0bQv2evgq/nfFn4tSHTtMEbENp1i2VutQkK5MYjTcEfjBDSDPlEV961+Nfij4Fft8+F/2tPiB+0L8L9I8KeIG17/AIl+k3Ov3MkkljpUeNkVvFFJEItwUb+pPJzln3AHj37T/wAEfCn7O/xY/Ya+E/hBN1ro/iZfPuCoWS7u5NT0lp7mT/akfJxk7Vwo+VRX761/NJ+2Dqf7ctz8eP2epfjVpHhS08WQ68D4Tj0tpTay332yxO293TORH5ogHylTtLc9Mfvx8CLn433fw6tJ/wBoa00qy8aGacXEWjFzZiEOfJKl3c7imN3zdaAPY6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD8AP2vPjf4b8Lf8FMvDur+P8ATbvxJp3wy0a3OjaJZR+dNfa1eRmW1WNMYEhmnifdyR5K4DMAp+mfE37ff7TPwdt7Xxx+0H+zde+Gvh/dyxpJf2WqRXtzZJKwCGeNVwGJIULL5GWIGc4B8n+PvhbR/h7/AMFcPhF8S/HCpD4b8X2gWC5nwIRqcFpcWUMe5uN6TNbMOeC6mv0e/bV1jwpon7J3xXufGUkUdhP4d1G2jEpAD3dxC0VoiZ/jadk2dw2D2oA948FeM/DXxE8I6R468HXyalomu20V3aXCZAkhlXcpwcFT2ZSAVOQQCCK6ivz/AP8Agl7puvaX+xJ8P4teV4zN/aM9skmQwtZr6d4jz/C4O9f9lhjiv0AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvyF/b00DU/gf+0h8Lv28bY2eqaR4Ot20bVdKnvILS6kgcXQElktwyieXZdyHykJcMikKVLsv69V+KHxoh0Tx1/wAFa/AXgj40iO78K6doazaBYX2DZTXkkM0ikxv8jO9wjAZ++8UaHOAtAGB+0f8A8FJPhH+0d8JdS+AXwW0u9fxL8RIk01J9fa00nT7OOZx5jy3E9wULgAhADgtyGJAVv1Z/Zl+Ec3wH+Angr4S3V6mo3Ph2wWK4uI8+XJcSu00xjzz5YkdgmQDtxkDpXmH7d3hT4Xa3+yf8RZPiTaWYs9I0W7n06aZED22oxxH7F9nY8rI0+xAFI3Z2HhiK5H/gmdr3i3xH+xb8Pr/xjLLcXESXttbSzEmR7K2u5orfJPOERQif7Cr9aAPvKiiigAooooAKKKKACiiigAooooAKKKKACiiigAryP9oD/kg3xI/7FrWP/SKWvXK8j/aA/wCSDfEj/sWtY/8ASKWgD4o/4JH/APJmGh/9hXVf/R5rzn/gstr2o2/7PHhLwdp8piXxL4otYp8Hh4oLedwhHceaUb6qK9G/4JH/APJmGh/9hXVf/R5rlP8AgsL4H1fxF+y/p3jDRYmkk8Fa/Z6hcFRnZbTRy2xfHtLLFk9AM5oA/UTw3oGmeFPD2l+F9EhFvp2j2sFnbRqMBIbdBHGoA7BVAr8h/wBhTb4E/bx/ah+FOlDydHubv+144F4jiYXTMAi9AAt3t47ADsK/VL4b/Ejw18TfhroPxR0G8ifRtd0+K/WXeNkaum51duimI7lcHlWUg4INflZ/wTqP/C1f2pf2kv2ktLBk8Oarqh0rS7nB2XMfntLlT6iGOBiO3mCgD9k6KKKACiiigD//1v38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPzs+Nf/BM74H/HT4v618bfEnibxVpPiHXPs/mjSr20t4Y/s9tHar5QezkkXMcY3ZkOST0BwPP/APh0f8DP+ijfEL/wc2n/AMg1+qdFAHPeEvDlp4O8KaL4RsJ5rm10OytrGKW5YPPJHbRrErysAoZ2CgsQACcnA6V0NFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfNXxr/ZZ+Hvx3+IXw1+JXi+/1O01T4W6gNS0uOxlhjgmmE9vcbblZYZWZN1sgwjIcFuckEfStFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeHfH79nX4V/tL+CD4E+KumNeWkcnn2txA/k3dnPjb5sEoB2tg4IIZWHDKa+M7H/glr8MdQ1LTf+FpfEjxt8RNA0aRZLPRtZ1XzLJdvAVgqBtuOP3Zj446cV+ntFAFLTNN07RtOtdH0i2jsrGxiSC3ghQRxRRRKFRERcBVVQAABgDirtFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8u/tK/sgfBr9qjTtPh+I9pc2uraOW+wavpsot9QtQxDFVkZXRkLAHa6MAeVwSTX1FRQB+XY/4JafD7xDeWKfFz4qeOviFoumSLJBpWq6sWtfl4Ct8pcDHH7toz6EV+lnh7w9ofhLQtP8ADHhmxi0zSdKgjtrW1gUJFDDEoVERRwAoGBWxRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfAXx2/4J1/Cb9oL4j6v8S/FvjHxfpl7rUcEc1npeo28FiqwQJbgJFJaykblQFssckk8A4r79ooA/LLwv/wSS+AXg/UNPv8AQ/HPjmFdOuI7mOAapZrAzxuHwyJYrkEj5sYyO9fpr4g8P6J4r0PUPDPiWxi1LStVgktrq2nUPFNDKpV0dTwQwODWxRQB+Wt1/wAEpfhTF9u0Dwt8SPG/hzwRqkrSXXhuy1Uf2e4c/MgV423KRx+8EjY6sa/QP4T/AAl8AfBDwJp3w4+GmlJpGhaYp8uJSWd3c5eWWRstJI55ZmOe3AAA9HooAKKKKACiiigD/9f9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK/Eb9rr/hcHiP/AIKYfDX4bfCbxXceFrvxD4QEEl2jM6WlvJJqf2u5ji3BDOtureUx5DhMEEAh37UH/BPzw38CvhHr/wC0P8EvHfiqx+IvguIarJqV5qhnkvkhYGfzmCI2/Zlhg7SRtZSGyAD9t6K8N/Zn+J9/8Z/gB4C+KGrIqaj4h0m2nuwg2p9qC7Jyo7KZFYqOwwK9yoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//9D9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/JD4mf8pifhL/2I1z/6Dq9fZn7bf/Jovxd/7FrUf/RRr0rU/gZ8LNY+MGk/HvUdDE3jzQ7FtNs9S+0XCmK0cTBo/JWQQNnz5PmaMt83XgY7Hxr4M8NfETwlq/gXxlZ/2joeu20lpe2xkki82CUbXTfEyOuR3VgfQ0AfLn/BPX/ky/4U/wDYKb/0olr7Lrjvh98P/CPws8G6V8PvAenjS9A0SLybO2Ekk3lRli2N8zPI3LE5Zia7GgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/0f38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q=="
    }
   },
   "cell_type": "markdown",
   "id": "7d1c8f77-42aa-4d5f-8862-c6d0e8dd0efa",
   "metadata": {},
   "source": [
    "![barren.jpg](attachment:028e2e26-1f06-452d-93f9-62f73c77e0f8.jpg)"
   ]
  }
 ],
 "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}