307_课程1_第2周_测验题.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***课程1_第2周_测验题***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.神经元计算什么？\n",
    "\n",
    "A. 【  】神经元计算激活函数后，再计算线性函数（z=Wx+b）\n",
    "\n",
    "B. 【  】神经元计算一个线性函数（z=Wx+b），然后接一个激活函数\n",
    "\n",
    "C. 【  】神经元计算一个函数g，它线性地缩放输入x（Wx+b）\n",
    "\n",
    "D. 【  】神经元先计算所有特征的平均值，然后将激活函数应用于输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "B.【 √ 】神经元计算一个线性函数（z=Wx+b），然后接一个激活函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.以下哪一个是逻辑回归的损失函数？\n",
    "\n",
    "A. 【  】$L^{(i)}(\\hat{y}^{(i)},y^{(i)})=|y^{(i)} - \\hat{y}^{(i)}|$\n",
    "\n",
    "B. 【  】$L^{(i)}(\\hat{y}^{(i)},y^{(i)})=max(0,y^{(i)} - \\hat{y}^{(i)})$\n",
    "\n",
    "C. 【  】$L^{(i)}(\\hat{y}^{(i)},y^{(i)})=|y^{(i)} - \\hat{y}^{(i)}|^2$\n",
    "\n",
    "D. 【  】$L^{(i)}(\\hat{y}^{(i)},y^{(i)})=-(y^{(i)}log(\\hat{y}^{(i)})+(1-y^{(i)})log(1-\\hat{y}^{(i)}))$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "D.【 √ 】$L^{(i)}(\\hat{y}^{(i)},y^{(i)})=-(y^{(i)}log(\\hat{y}^{(i)})+(1-y^{(i)})log(1-\\hat{y}^{(i)}))$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.假设img是一个（32, 32, 3）数组，表示一个32x32图像，它有三个颜色通道：红色、绿色和蓝色。如何将其重塑为列向量？\n",
    "\n",
    "A. 【  】x = img.reshape((1, 32 * 32, 3))\n",
    "\n",
    "B. 【  】x = img.reshape((32 * 32 * 3, 1))\n",
    "\n",
    "C. 【  】x = img.reshape((3, 32 * 32))\n",
    "\n",
    "D. 【  】x = img.reshape((32 * 32, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "B.【 √ 】x = img.reshape((32 * 32 * 3, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.考虑以下两个随机数组a和b：\n",
    "\n",
    "a = np.random.randn(2, 3) # a.shape = (2, 3)  \n",
    "b = np.random.randn(2, 1) # b.shape = (2, 1)  \n",
    "c = a + b\n",
    "\n",
    "c的维度是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. 【  】c.shape = (3, 2)\n",
    "\n",
    "B. 【  】c.shape = (2, 1)\n",
    "\n",
    "C. 【  】c.shape = (2, 3)\n",
    "\n",
    "D. 【  】计算不成立因为这两个矩阵维度不匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "C.【 √ 】c.shape = (2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第五题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.考虑以下两个随机数组a和b：\n",
    "\n",
    "a = np.random.randn(4, 3) # a.shape = (4, 3)  \n",
    "b = np.random.randn(3, 2) # b.shape = (3, 2)  \n",
    "c = a * b\n",
    "\n",
    "c的维度是什么？\n",
    "\n",
    "A. 【  】c.shape = (4, 3)\n",
    "\n",
    "B. 【  】c.shape = (3, 3)\n",
    "\n",
    "C. 【  】c.shape = (4, 2)\n",
    "\n",
    "D. 【  】计算不成立因为这两个矩阵维度不匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "D.【 √ 】计算不成立因为这两个矩阵维度不匹配\n",
    "\n",
    "Note：运算符 “*” 说明了按元素乘法来相乘，但是元素乘法需要两个矩阵之间的维数相同，所以这将报错，无法计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第六题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.假设每个示例有$n_x$个输入特性，$X=[X^{(1)}，X^{(2)}…,X^{(m)}]$。$X$的维数是多少？\n",
    "\n",
    "A. 【  】(m, 1)\n",
    "\n",
    "B. 【  】(1, m)\n",
    "\n",
    "C. 【  】($n_x$, m)\n",
    "\n",
    "D. 【  】(m, $n_x$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "C.【 √ 】($n_x$, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第七题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.np.dot(a，b)对a和b的进行矩阵乘法，而a * b执行元素的乘法，考虑以下两个随机数组a和b：\n",
    "\n",
    "a = np.random.randn(12288, 150) # a.shape = (12288, 150)  \n",
    "b = np.random.randn(150, 45) # b.shape = (150, 45)  \n",
    "c = np.dot(a, b)\n",
    "\n",
    "c的维度是什么？\n",
    "\n",
    "A. 【  】c.shape = (12288, 150)\n",
    "\n",
    "B. 【  】c.shape = (150, 150)\n",
    "\n",
    "C. 【  】c.shape = (12288, 45)\n",
    "\n",
    "D. 【  】计算不成立因为这两个矩阵维度不匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "C.【 √ 】c.shape = (12288, 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第八题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8.请考虑以下代码段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a.shape = (3,4)  \n",
    "#b.shape = (4,1)  \n",
    "for i in range(3):  \n",
    "  for j in range(4):  \n",
    "    c[i][j] = a[i][j] + b[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何将之矢量化？\n",
    "\n",
    "A. 【  】c = a + d\n",
    "\n",
    "B. 【  】c = a +b.T\n",
    "\n",
    "C. 【  】c = a.T + b.T\n",
    "\n",
    "D. 【  】c = a.T + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "B.【 √ 】c = a +b.T\n",
    "\n",
    "Note：a的每一行元素，逐行相加b的每一行元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第九题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9.请考虑以下代码段：\n",
    "\n",
    "a = np.random.randn(3, 3)  \n",
    "b = np.random.randn(3, 1)  \n",
    "c = a * b\n",
    "\n",
    "c的维度是什么？\n",
    "\n",
    "A. 【  】这会触发广播机制，b会被复制3次变成(3, 3)，而 * 操作是元素乘法，所以c.shape = (3, 3)\n",
    "\n",
    "B. 【  】这会触发广播机制，b会被复制3次变成(3, 3)，而 * 操作是矩阵乘法，所以c.shape = (3, 3)\n",
    "\n",
    "C. 【  】这个操作将一个3x3矩阵乘以一个3x1的向量，所以c.shape = (3, 1)\n",
    "\n",
    "D. 【  】这个操作会报错，因为你不能用 * 对这两个矩阵进行操作，你应该用np.dot(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "A.【 √ 】这会触发广播机制，b会被复制3次变成(3,3)，而 * 操作是元素乘法，所以c.shape = (3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第十题"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo8AAADhCAIAAAAmk8gvAAAgAElEQVR4nOydeXAU55n/35me+x7dxwhJSEIXEgKBuQwYMHYA2/hIfMS73jixE8eV9Sa12a3dqlQqValfOdndSm2yW+WKnazteO3ER2LjGIPDaU5jbiR0ADrQja7RXD093T3dvz++0bvtAWELS4xGej9/UMNMq6e7p/t97ufRqapKGAwGg8FgzGD0yT4ABoPBYDAYnwOT1gwGg8FgzHSYtGYwGAwGY6bDpDWDwWAwGDMdJq0ZDAaDwZjpMGnNYDAYDMZMh0lrBoPBYDBmOoZkHwBjJkKr8FVVVRRFURSTaboVu4luRXmS20/ERPuZiMkez2T3MxHTe76SpBqNRvysHMcRQuLxOF4wGIyZDJPWjM8Qj8cVRdHr9VjBdTqdXg85rSTpiKZKS5hp+5nu/V9/PwaDjhCi0+l0Op2qqvTn1ul0U/S9DAZjWtCxXmaMiVBVVRAEGGEm03QrdhNJqYm0hMlKtclqG5M9nsnuZyKm93zjcVWv1zPxzGCkHExaMz5DPB6Px+N6vd5gMBBCFEWBHZbs42JMMaqqSpKEF7FYzOVyJfuIGAzGjWDSmnEj4vE4nOHsPpkd6HS6aDTKcZzJZFJVlelhDEaqwOLWjM+AKCYhRBTFeDxuNpvH49bTvaxPpA1M9L2T1R4me/yTPZ7J7mcipv18DQYDx3GqqobDYYvFYjQaJ7lnBoORBJhtzbgOo6OjTU1N/f39SB6ORCIWi22av3Oy8drUjh9PzPSerygKgiCkpaXZbDZBEHw+38KFC61WKwIfDAZjxsIeUcZniEajVquV4zibzZafn5+bm+vxeGRZVpVpLvLRTVC5pE5wi060/URMtJ+pOp7J7mcipvl8TWY9TOpQKNTe3m4wGIxGIxPVDMbMhz2ljM9gtVoJIYqiiKLo8XiKiopYaHM2oaqqx+NRVTUtLS0YDIZCISaqGYyUgPUyYzAYDAZjpsOkNYPBYDAYMx0mrRkMBoPBmOkwac1gMBgMxkyHSWsGg8FgMGY6TFozGAwGgzHTYdKawWAwGIyZDpPWDMZ1UBRlojZ/iqLE43H6X1mWZVmOxWL4L/0Ic8Gn+zgZDMYcgUlrBuM6aGdKqqqKDqyQvvhIkiQIZoPBoNPpzGYzNsZmkPTjLdYZDAbjy8JWEwbjRkD0Ujv7umJYp/trv30MGNXr9cywZjAYUwuT1gzGhNCZkpDBEMP4yGg0chxHN8NccFmW8SbHcVpvOYPBYHxJWItgBmNCqKim73AcB2sbQydlWdbpdLTVNj6ifxWPx6lEZzAYjC8Dk9YMxnWAT/u6E020bnAErel/jUajJEnYwGAwsLg1g8GYKthqwmBch4SEcEVRJEmKxWK9vb2yLJPPmtFjY2Pd3d1wkiMBTVVVJqoZDMYUwmxrBuM6aC1meLyRBN7U1GQymTIzM0VRJIRYLBae59va2vx+v9VqzcjIMBgMEOcMBoMxhSRTWouiaDKZYrGY2WweHh7+6KOPRkZGZFk2m82lpaUrVqxwu92EELgWJ4r/KYpCjRiYOyiwmahYdqJpzRNtPxFTtZ/JMtnvxfaiKHIcl3ANJUkyGo14EY/HLRbLVB/sNEJDwnghCILFYolGoxaLRXuJIDgNBgOStHU6Hf5K+19RFHH6+EPsCjdVLBYzGo16vV4UxdbW1t27dx8/fvxv/uZv7r33XpplZjAYBgYGPvnkk40bNxJCxsbGPB5POBx2OBzam3OGI8sy0ujIeNIcGff5w4VAX2N7NvWcwbjFJFNaC4KAQtUzZ8786le/MplMNpstGo2azeZDhw4dPXr0Bz/4gdFotNvtNxCBer0+Ho+rqqoNE97EKjlVq0+yVrEbf6/JZMKLaDRqMBggpI1GI2qTjEYj3olGo1ar9RYc7ZQQj8c7OztNJlNBQQEEZ0NDQ3l5udPpxKdGo5FaujSjG3lher2epobh4oTD4fPnz/f29jqdzrKysuzsbIfDYTKZcGEtFksoFLp06ZLD4RAEIRwOO51OKq3j8bjdbj958qQoiuFwODc3t6Kigky/6jaF4GpQ5YZenASYkGYwkkXSpLUgCC6XixBy9erVf/u3fzMYDF//+tdzcnK8Xu/g4OD27dvfe++9Bx98cOHChYQQURRp94lr4ThOlmVRFLXGwQ0aUV33/clK96naz2SZ7PcKgmC1WtFsy2azQRirqioIgl6vh+FIN77BRZ5p6HQ6URTPnz/v8Xjy8vJ0Op2iKIcOHcrLy3M6nfF4XBRFmgIWj8fD4TDHcSaTyWg00jclSZIkyWazhUKho0ePtre3u91uWZZffvnlb3zjGyUlJQhCQ+rPmzevtrY2LS2toKDAYrFA10EmmiiKZ8+etdvt2dnZgiA0NDQMDQ1VVFTk5+cn+zp9USRJ0gppRVGi0WgsFvN4PLhDeJ6nWh38B8k8XAZj7pE0aQ2/aywW8/v9sVjs61//+oIFCwoLCwkhPp+vrKzsgw8+OHXqVE1NTSQSsdvtE+1HlmW6ykBI31heTlVFTbIqcyb7vRDPkFs6nU6W5WAw6PV68T6V/YjCUhN85gMndldXVzQahU9bkqTW1taurq7c3FwqVwgh586dGxgYcLvdEM+EEI/H4/P5MjMzLRYL7sPBwcGhoaF58+bdfvvtqqpeunSpvb09Ozvb6XRCs7FYLPPnz//qV7/qdrsFQbDZbGTce4xSrpGRkczMzNtuu02W5U8//fTTTz91OBwpJK2pZoMUOZPJZLfb8dzBH4NTHh0ddTgcTFQzGLeeZNrWJpPJbDbPnz//qaeeuuOOO6iUvXr1aiQSicVi7e3tMJchkq+7H1g2NLQGgRSPxyeS2XMtbm00GgOBgNVqRYrAkSNHLl++vHTp0vLycqzFEEUmkym1unkoimI0GkVRjMViEJm4T6xWKxQaURQFQYhGo21tbQMDA+hbgr8qKCjwer0ZGRmEkFgspqrqvHnzrFaroijd3d19fX0nTpwoLi7med7pdJpMJlVVERHPzc3FfQUth+aEGwyGysrKZcuWFRUVEUIyMzN/9rOfhcPhZF6gSYITwXkhD8Dv94+MjFRWVmqDI2lpaYQQnuchvBkMxi0jybZ1OBzW6/Vbtmy5dOlSIBA4cOBAJBLp7e3t7+9vbW2NRCJk3DqcCI7juru7m5qahoaGOI4zm83xeFwQBGpaJTDXpDXHcUiVQqSgoaHhgw8+sFgs69ate+KJJ5YsWYLAP3LQUqibh6Io8GzTxDEcPGQkTtnlcrlcrrVr16L4ymw2I27tcDiys7MJIUhpJIR0dnbu2bMnHA7H4/Hc3Ny0tDSTyUQVPiiCtE8ZzWGEKwIJazk5OWlpaVAr3W53OBwWRdHv93u93iRdoclB/VI462AweOLEiU8++cTtdq9du3bx4sVGo5FerolUZwaDMX0k7akLBAJut9vhcBBCXnvttf/+7/8uKCjIy8szmUy1tbWrVq3S6XR5eXl+vx8+zImkbywWCwQCAwMDXV1d8XjcbDZD/Ewk1eaatIaX2GAwjIyMOJ1Ov9/f1dUVi8WKi4vHxsaIxvtNU8RTAggVpCzgHPv7+4eHh2EdIh4vy/LY2Ng777wzPDys0+mo/6C4uPi2226bP3++wWCQJEmn03V2dvb09Cxfvry8vNztdre2tup0OqPRqCiKKIoWi0Ub0bfZbOhThv9CDRocHBwZGYFs7unpMRgMLpcrVUQ1IYQGSpB16PF4lixZkp6e3tHR8dZbb/3+97//yle+cscddyBdPIUiJgzGrCFp0hrVWcFg8M033/zd73739NNP19XVZWdnI0ImSdKrr77K87zX64WrdqL9GI3G8vLygoKCWCwGIa2dnnQtk63smu79TJbJfi/HcaOjo+np6UajMRQKffDBB6FQaNOmTStWrCgqKoKkQVYzz/P4UVIC2gEUzvB4PN7d3d3T0wOxitCywWDIyMioqqpSFIXnecQCYrFYVlaWx+OBL8FoNA4MDHR2djqdzrVr19rt9s7OzvPnz+fk5NCyJVpiEI/H4UsnhCBrjxCi0+msVmtjY+OFCxcKCgoIIa2trQg9JPMCTRI4tw0Gg6qq0Nt8Ph8ySFatWnXu3Lk9e/a8++679fX1d955Z2lpabKPl8GYc0xYl3wLgH/ye9/7XjQa/cUvfuFwOBwOB8dxwWCQ5/k1a9Y888wzTzzxRGZm5o0FNuMG0N8XTmBJkhDXT09Pv8Ff+f3+1tZWl8tVWVk5k4t2Lly48Pbbb3d0dGRmZiLL7Fe/+lVOTg7KDch46bAgCNSJDWUO/9LeoufPn3///ffb2to8Ho/Vag0Gg4IgPP744+vXryeE8DxPK7BpsADdAgghAwMDLS0tr732Wm5urizLvb29Ho/nrrvuWrNmzQzMxkKsHX7vhoaGUCi0YsWKG3u2eZ7X6/Wqqp4+ffrDDz/s7+/PyMj45je/6fP5HA4HFKaEagJkA6AkHb1l4KjQNnNFcslMvrsYjBlF0mxr6O9Go9Fmsw0ODur1erfbHYvFOI5zuVx79uzp6OiIRqOZmZkkpepWZxp0NURkGrHbWTDMEQOnfT7fN77xDcSkUVLldDoRXlEUhZZcOxwOlFmTcYFN94P7cP78+Y888oggCG63G/X9g4ODPp+PjJcgAwTL8ZruJDMzU6fT/fznP1dVNRgMBgIBm80GSZZC3VFugMVigddq+fLllZWVly9f7ujo+P73v19SUrJp06ZNmzYhXRFXEq1maK4JtCV4I/CT0e40qZIhwWDMEJImrY1GYzQa5Tiuqqrq4sWLb7311gMPPJCZmdnR0fHCCy+8+OKLt9122+DgYCQSMZlMKdSyY6ah7VEFEv6bokDout1ueO+pS59uAD85/e9E5iMEicPhKCsrI5r2IMgYR2YZrddSFIX6t1HhjXR05KyJomi1WouLi79Ml56ZBnwJer0enXPS0tIQ8n/ooYd27Njxxz/+8eWXX16/fv0DDzxQWFiIQg9cIkEQEOGmyRBUPNOR4cywZjC+OElbSpDPYjKZHn/88dWrV//Xf/3XkiVLCgsLly5dOjg4ePr06Q0bNvz617/evHlzCqU+zUBoZQ7WR/jDZ0Eja2oroxEKJArKqQHHcfTOSThfVQMZF8P4CDOqsQ3cuVSiIE8NVw+mIfIZkW6GkDbNJI/FYtFodDZJI4PBEAqFeJ4nhGRkZKiqum3btt/97nfPP/98V1fXI4888o//+I8dHR00XqDX6202G+2Rh+uDXUGvwt2YxDNiMFKLpMWtsQ4i+BePx1GvJctyVlaWz+fDstjW1ub1erOzs2eBjTJz+CI9ZGZ+3JrGj7W9VCc6NTTqov/V3vNf5L6i2Yv4rzb+Ck8vrSKjzNg7drJxa2TFa1UfZNHLsmyxWGiHeUEQ/vCHP7z00ku5ubmPPPLI/fffj+GhZNx7Ae2cEAItB7tKoYpBBiPpJFlah8Nh6joTRVGWZXgdFUUZHBzMyckh4+sFyzK7lcx8aQ1ophJSmdAdhYyL7YkGVE+0K1jMSIqm95s2GQrSBd+FgAK+GnonjchSwTYDnb2Tldba7amUpRl2QJblUCjEcZzD4dizZ8+bb7555cqV++6772tf+5rH44nFYhgBoN0hXs9YnYbBmIEkLW4djUbhKMNjj0QhmNSYz5GTkzM6Our1emOxGItb3zTw09IEq5kmPL4MMPLoqWm7jdLT/Fx5CcsYxjG181Aepp3ZhV1hHBz2D1c8nSVDpReV2aIoSpKEESOpC1V6YrEYAvN4ZvG+oiiRSMRqtaKyXBTFlStX3nXXXW1tbS+//PJ3vvOdJUuWbN26tbS0VJIku92O/qb0R5lNdyODMd0ks4ILqKqKTuA6nS4UCqGDh9frhdhmvrKkkCq2NRkfzkF7xSfUBcEBq/Vja+PQdA+qquLPEeDX5kOheAkzQjBekwpmbR02Gc9zThi1eUuuwSSYrG2NU7vuMwh9BR8JgkDGGxSGQiGDwWC1Wvv7+995552zZ89yHPfggw8WFBQUFBTgSZ+BV4bBmOEkX1pPFbFYjIxPkUqQ8drleC5z7ZTrhMgix3GQYSkkrRmT4ibqrW9i/2T8GYzH4wMDAz09Pa+//jrHccXFxStWrFiwYAHS8SwWi9b5gfQ97ehbohm2zZ5fxhwn5R8AOtRB258BbUDgHcWnhJDrtnGY9UBCS5KEQAMhJBKJcBwHY5S6jpHbzBZExpeEFlLTnLL8/Hyv1/uf//mfhw8fPnz48FtvvZWZmVlWVlZdXV1cXEwHkGvndaqqGo1GMdEV9yreT4iXMxhzipRfneGfxGtRFK/t6qyqqiAIqqqazea5JqrJeDwV9UgWiwVdpdCDE1U0ENIs3MCYWqAc4/ZDitnatWvXrFnT3Nx89OjRkydPXrhwITc3F93I0eeVjJvXqP6i+8HzS9NRGYy5ScpLaww57uvrc7vdJSUl6GOFzgwQS2jjnOzDTBrwNFLDBYFGjD8h404IfMQENuPLQ21rGM1I4kMqAHIIqqqqqqqqRkZGjhw5cvLkyba2tsLCwsrKysLCQrfbbbPZcGeirzuy0uby88tgUFJeWhNChoaGzpw5IwjCvHnzysrKfD6f2+1Gwgv85NouGXPN2avT6QKBgNPp1Ov1IyMjDofDbDaHw2Ek+6AxJ7Zk5TSMLw96niDRT1vJRh89dERJT0+/7777Nm/efOHChYaGhp07dxoMhrq6urq6OrReQZJ5LBZDUxrsB5UjSTozBiPJpLzoUhSlrKzMZrM1NzdfunSpqampoKBg/vz5CxcutNlskNmovYGJmezjTQI2mw1dtw4dOtTe3u71ejmOq6urKy4udjqdtFiZZeoyvjwo7oIZTTPktY8ebkW4fIxGY11dXXV1dV9f3+nTp8+fP3/ixInCwsKSkpK6ujq73Q6dEkkVCbkpDMZcg/vJT36S7GP4slgslszMzAULFhQXFzscjt7e3hMnTjQ3Nw8PD3Mc53Q6EZelCc9zCpTWcBzX19e3a9euX/7yl++8805XV9fGjRudTieig2h8ofWEC4IwMjJiNpsxsiJpR8+YBugPOjg4KIqiz+ebWi0N+h8hRNEgSRLtAYeiOPwXfWa8Xm9FRcWiRYvQZeH8+fMXLlwYGhqCGxwucaZKMuY4KW9r0qVHr9djIm9tbe3Vq1dhR7a2tubm5tbU1JSXl9MpinMKi8XS0dFx8uTJkZGRaDTqdDqdTufDDz9cUVHh8Xh0Oh1GJ2HjORgpYEwtaCCj1fyoGU0bgyO3EdozfR8yGy6fcDi8f//+np6eixcvZmZm4vn1er3M/cOYy8yeeusEJEnq7OxsbW29cuXK8PCw0Wisqampq6vLzc2l8TMUd9LtaX8lutYkV3pp2z3SJhUJjRvJeFDQbDajdavVasUGfX19fX19J06cGB0dVRSloqLC4XCcO3fO6XQ++uijE823xv5HR0cvXboUi8Wys7MdDgfP80mbraJOcP11KT+YZHqZ4LpxBjUSidhsNp7nOzs7vV7vokWLZmBb33g8Pjg42NLS0tDQcPXqVZfLVVVVVVZWlp+f73A4Evw9EPa0EoyM148xtxBjNjFrpTXged7v93d2dl66dGl4eFiW5cLCwtLSUpjaKDKBKZDQkIH2rkrWkWNYljZVB0zUW4pKcVmWW1tbL1y40NXVFQqFsrOz58+f7/P5CgoK3G43hpAWFhZOZKNgP8PDw52dnUhG0+v1yZTWE46JY+Obbsz1rxvH6SKRiMPh0Ov1Y2NjGRkZZWVlZrN5ZtqsqqoODw9fuXKlvb29t7c3GAwuWLBg/vz5paWlbrcbOefoOEsFs3Z2KpokJvUMGIwpY9ZK64TWj4ODg52dnQMDA2fPnlUUJT09fdGiRUuXLqWjiw0GA8q1kZOV1GP/PyRJwg8EIYroOypQMT3CYDAgKGi1Wru6us6dO9fc3Dw2NubxeEpKSvLz84uLi+n0ZeTWfpHcb1VVA4EAMnKx/+T5GJi0vjmuf92MRi4YDKInQSwWc7vdM1OeSZKk1aFHRkZ6enqGhoZOnjwpiqLL5aqurkatNhl/fgOBgNFoxBMdCoV0Oh3qORmM2cGsldYQbwmWaDQajcViTU1NZ86c6enpoa0Qq6urtTIM05zorOKkMJFMFQQhwW8piuLw8PD777/P87wsyx6Pp7i4uKSkJCsry2q10nFStO+6dsDURF+tnQhJmEdxdkFDyzTQMzMro6CV6vV67bEFg8GWlhY8vzqdrqioaNmyZTU1NXQDbfQqGo2yWm3GrGHWSmuKIAh0Oq8gCEhUkSSpr68PlqggCE6n87777svIyPB4PHQSIknq/N1oNGqxWKiYxCBhVVWtVqvf7+c4zuVy8Ty/e/fuEydORCKR7OzswsLC6urq+fPnU5MCHRxhkWsj9Dd2a2ubh9PXiJEzUh3Ed/EU0MLolMguFEURDjMkpg0MDJw7d66xsTEcDttstg0bNpSXl3s8HjoWbOafEYMxKWattMaDrZ1sSMalL9zLRqNRluXe3t6Wlpbu7u5Dhw4tXrx49erVpaWlkNkk2VlmAEdLK1h4nrfZbAMDA6+99trZs2dLS0sXL17s8XhqamqsVqvZbFYUBQNOUKtK9xONRhVFsdlskzKUZ4BtPZGWMFOiFTOV6183RSEzJ9BzA2j//2uPFk8Epuv29fW1trZ2d3c3NDRkZmYuXrx4xYoVdHwnDWAzGLOAWSuttWhduxRqN8uyLAiC3+//y1/+8vHHH1ssltWrV69evbq4uDi5zThR6EJz1GVZHhsbO3jw4LFjx4aGhurq6pYtW5aZmZmRkZGWlnbtOSIMj1ZuMJRpJtoNzGukyhNCYHXR8ca34oSvD5PWN8f1r5skxeEpwWhR/Nxo0HuLj++LgL5G0FYTpurh+UUbXVEUr1y5cu7cuWPHjgWDwfr6+rvvvru0tDSJR85gTDmzVlpry58AHUJMfbzU5kagWhTFaDTa1ta2Z8+e1tbW2trae++9t6qqKinHj+AiRKYgCJcuXdq3b98nn3xSXl6+cOHChQsXZmZmOp1OJI5pM8gIIdeKbWqaYyQXSaWu4BNVajGb6cZc/7rF4zo6Iws3yczsOJswpxw12Zg+jltdO10b938gEOB5vq+vb+/evWfPnq2oqNi2bduiRYuSeRoMxtQxa6X1TSPLMs/z7e3tH3/88bFjx7KysjZv3rxp0yaDwRCNRrW1LmiveK2RisISbX9yMkExCWxcrEQJrcSQCDY2Nnbs2LEdO3b09fWtXLnynnvuyc/PRxieVq1cq5cwGHMN6ioTBCEajfb39x86dOjgwYNGo3Hbtm333HOP0WhEDofJZKKzt6nurgU6AX0er308GYykwKT1X4GpoRXAgiAMDQ0dOXLkwIEDnZ2dW7du/fa3v202m5GnSsaHUQJkm3s8nmv3jCGA2BjiWVEUVDAjzIy90boss9lsMBgaGhp27dp17NixzMzMzZs3r1692uv1YsxlQjyPrSYMBi15QKcgMt5f5eTJk3/5y19aW1s3bNjw1FNPZWRkIFE8IYGU53kM1dXGuZkezJhRMGmdCIQrzVAzGAySJF26dOn111/ft2/fihUrvv3tb1dWVkajUVVVUdwZDoeRhq0oClLQ6UIAbzYhJBAIuFwuag1TF582fG4wGHie37Nnz/bt20dGRlatWrV58+bq6moksU80lYStKQwGBQo3Rm3SN1tbW1977bW9e/cuX7786aefrqioEAQBvi7Iaer3wvOLTuZ02mxyzoTB+CxMWv8VGvol464wQggSWDiOUxTFbrcPDg6+8sorBw4cyMrK+t73vldfX4+2X5hqJQiC2WzWtgWF3UzlaDwej0ajkOuyLEejUUKI0+kMhUKyLA8MDLz//vt79+6dN2/e/fffv27dOhR8Q5wnr5UYg5ECJETfRVEkhKBlCixmk8kUi8VefPHFHTt2uFyuZ555prKyMjc3V7uToaGhzMzMa/eMjmm34CwYjBvApPVfoTYuzYi+dptgMOhyuQKBwMGDB3/9619Ho9H77rvv6aefttls1D2uNawB+pgqioJGDaIowueG7+J5fu/evTt27PD7/cuWLduyZUtlZWVC+qskSQmtWqjiT1jrEgaDEPLZePN1U+eQOxKJRA4cOPDqq68qirJ48eKHHnpowYIF4XDYaDSimxDdw9yc2seYsTBp/RmoVU1zUNHmEGKVursJIXq9vr29/Te/+c2xY8fWrFnzta99raioCJUwNE+baGLbCYktkUjk7NmzJ0+e3LVrV1FR0bp169avX5+dnY1qaY7j0Mw5ofKKfjuFOcAZDEArHgkheNDQN9dqtcqyHA6HkVZCn6bGxsZ33nnnzJkz1dXVjz76aHl5eTAYdLvdNOecjM8IYTBmAkxa/xVtQQgFGjrP8xhshVZoNIclHA47nc7R0dHXX3/9wIEDxcXFa9eura+vT09Pt9lsGI2ljZ+NjY1hIdi7d+8f/vAHSZKWL19+5513VldXo913JBJxOp0JB6BNT0UtFhm3p5niz2AAbZkiBSoy7T8ai8UgvCHLkU969erV7du37969Oycn5+67766trUXLXuwBGgD68CfhrBgMDUxafwZqWEMBp9na8XicatmQwdTVhrUgFArt27dvz549oijW19evWrVq3rx5FosFMW9RFMPhcHd39/79+0+cOJGdnX3//fevXLkSSkBCJRhtW6Gd5nsD/zzLMmMwKGjkRyNNeC7oC+rconkqkOiCIBw4cGDXrl2RSGTx4sW33Xabz+dzOp2T7f3HYEwfTFp/DgmzgAAKRRB+xsMcjUYxfuDIkSO7du0aGhoqLCxcv359UVHR5cuX/X7/hx9+GAgEli1b9vDDD+fn52vHjWgd3UhqY+VYDEZSOHXq1N69exsbGwsLCzds2FBdXe10OpErSsNSVDmGyKfPLKulZEwrTFp/USRJEkVRp9PBIMab2gYLsixHIhG3260oyqVLl1566WUt/psAACAASURBVKUjR444HA7ko373u9+94447LBaL3+9HH2Ot750KbK3kZjAYt55YLNbe3n7q1KmTJ0/yPL906dJ169ZlZ2ejDz/5bAobPGEIe5HxPBX2FDOmAyatPx+ozAl9EMfGxtxud4LNHYlEhoaGOjs7L1++fPHiRUVR3G53IBAIhUK1tbUlJSVLlizJysoSRVGSpITWZnR+BsrGWJyMwbj1RCIR6v0OhUInTpw4fvx4R0dHfX19QUFBRUVFfn6+0WhEY2A0IkwQzLIsa+s2GYypgknrzwFZpgnVUyipIuMF2fF4HA2Ke3t729rarl69mpaWtmrVqrq6Or1ePzg4KAjCjh07BgYGvF5vRUVFcXHxwoUL9Xr92NiY3W6npjlhOagMRrJRVZXneYPBAEva7/f39fXt2rUrGAyaTKYFCxbU1tbm5eVRUQ0fOO2nFI/HZ+CwcMYsgEnrz0Fbhy0IQkJ7QkEQRkZG2tvbW1pa+vv7RVGsq6srKSkpLCz0er3asiu9Xt/T0/PBBx+0trZmZmZmZ2fPmzdvyZIlbreb7u3apBgGg3ErodlnkLva6WStra1Hjx69fPmyLMs+n2/JkiVVVVUWiwVuMOSyJMwKYzCmECatPx+M56OV06qqRiIRTP5oaWnp7e2NRqNpaWmLFy9etGgRx3Fut5sQgqFeZrOZ4zh0RDEajXq9PhgMnj9//ujRo2NjY1lZWQsWLIDkzsjIoOtCLBZj6jmDkSy0Y93j8Tim7EAM8zx/6tSpc+fO+f1+juMqKysXLlyYk5MzMDDA83xlZaXJZAqFQgmlmAzGl4dJ689HkiRIa8jaK1eu9Pb2tre3h0IhnU43f/78uro6DMOGck3/UFGUcDjscrmIxkZXVXVgYMDtdo+MjKCFmcViycrKKi4uhszG9C0Gg3HrSYg6Uy8XFgH0PtLpdOFwuLW19fLly42NjR6Px+FwXLlyZWxsbOvWrVu3bmVZZozpgEnrz4EK4OHh4fPnz7e2tvr9flVVPR7PokWLFi1a5HQ6tYNAaEMVDPwA9L/aBmeRSMRqtXZ0dLS2tra0tESjUa/XW1RUVFZW5vP5WJYZg5EUtC2JqPCm2rZ28HYsFguHwx9//PGJEyf27dt35syZLVu2/PjHP16yZEmSz4ExG5n90lp7grRKSlsWCVmr1+upWUxVY7zf0tLy6aeftrW1EUIKCgpKSkpycnIqKyun5PBQxxUIBFpbWxsaGgYHB81ms9frvf3227OysrTBb6Ix0OPxOAYN0Z0YDIYEjX6i8b03Bv1WiWZ6GGFx9NkOswW/JO+9994LL7zQ1dVVWlp63333PfDAAxkZGck+KMb0ggUW3Sq1/TOmjzkkrbEeYTIHGS+PttvtkHmYPC3LsizL8EV3d3c3NTXt27fP4XBkZGTk5+f7fL6cnByv1zuFhi9taKzX62OxWE9Pz4ULFzo6OkZGRgoKChYuXFhaWur1eiEssQ2+XVuuHQqFtP5znU53bRfVLwNbzRmM64IQdXd395kzZwRBcDgceXl5FRUVLJ41p7g1FT2zVlpfa1JTwuGwJEloURKNRiVJgvymNDY2HjhwYGBgwOl05uXl5ebmlpSU5Ofn00SzKUfbGTEYDIZCoZ6enoaGhvb2dq/XW1tbW1VV5fP5tB1PJ2qchAF/kNY3IWKVcQgher0eO0H/pqk4UUaS0d482t+UlQ7eHGg8rKpqNBql0S6TyZQwfYcx+4BRhNfXHTMx9d84W1dhPC3aCwpgQ5PP2otwLPf19X3yyScnT56UJKmioqK8vNzlclVUVCCvG3+CX2UKux+Ew2GLxYK1UqugjY2NxePx0dHRxsbGM2fOhEKh8vLyurq6FStWSJKk0+mwGc/zaK92rfmrKIosy9OnYTAYDCBJkqqqtPSLqT6zHogMbSuOW+B9nLXSGvYlldZ0XAe9uIIgcBxnNBr9fn9HR8fJkyfb29v1ev2yZcuqq6stFovdbk9PT4doxJ9gb1MbwaUTRLTF2VTKSpIUDAbHxsZgal+8eNFisXzlK19ZsWIF7Gw43HieR8dy2Ex08BdVTSZ1PNrbjhphLG49OxBFES4TVhY8hciyDC+UJEmKopjNZmZbz26wHmJaBF1vpztiOGulNYLBWhFIJRkdqNXX13f8+PGGhga/319UVFRXV4eAk06nczgcN5BPU551JYqioigoEiOfTSKjdwB6KrW0tBw8eDASiWzatGndunV2u91sNmsNaDQzh5CmWWlfHCgx1w4yYcxWbjDejfG5JFw9luEx19AKmunOxp210vq6CdJQewVBaG5u3rt3b0tLS1paWn19fXl5eXFxsV6vt9vtdLoOGS+1QiSYEEIjwVNoa4bDYZPJpBW3eP7j8TgkrqIokiQRQtAmaXBwkBBy4cKFjz76aHh4uL6+/vbbb8/NzbXZbLRmjPribkJaUzAnFKUsqGD5cifKmBHQxnz4QbXz1xk3BzWjqRkQjUZZd6PZDbJ9qZC+NuQ6HcxaaZ1AfJxdu3bt379/cHBw9erVd955Z3Z2ttlsttvtcBontAiOxWKQVQmyeco1aGgSCV+kDYYRTekn/ASCIFy4cOHjjz9ub29XVfXJJ59EyjrHcdpOLDdxnMiWDwQCwWAQzVbhO52qk2UkEVSbID/RarW63W63263tDcCYLNrmxIqi+P3+7u5u5quY3Xi93vT0dIfDQQiBNTXZmONNMBukNfp6Qr7S6bPafr+SJPX29u7YsWPHjh1r1qypr6+vra11Op1GoxHr1E3Ed5MLjXZHIpGenp6enp7//d//NZlMS5Ysueeee3w+XzwelySJlpFAFRgbG/N4PGTctDIajbRtC1JbaWZjKBT69NNPI5FITk4OrifuSEaqg/lRsVjM4XAMDQ2ZzeZ169bdXPkAg2ieRDKeZ7R3715FURIm7DFSFIxD1aYWQSdraGh45plnbvFTk/K5i7SldiQSIYTY7XadTheNRiGo/H7/oUOHtm/ffvXq1fXr1//617/W6XQ5OTkJ1ZApJKppaAR3j8PhKC0tzcvLu+OOOz799NPt27c/++yz1dXVX/3qV6urqyORCLw0EMkQ1cgzgiPBZrOhOQydTAAzGsuN1+stKyvD17E019kBrEBkJ7S2tsZiMfbLfhmQ2ql9B6pPfX19sg6JMYXQ+tVrpfWtP5iUf1DNZjMKq6DMYoSloihNTU1vvPHGwYMHy8vLH3300RUrVjidTm0WAGbsoIkgRm4k9TwmB03/Rgs2l8ulKMqSJUuWL18+ODj49ttv/+hHP5o/f/4jjzyydu1a/Ek4HDabzThN2ks1MzPTYDBEo1Fsg4tJxnUCp9PJhhPMMpDT4HA4cP/wPM/zvNFoTK37f0aRkA/McRwNpTFmH3hwkvLVKS+tYVsbjcZoNArX7u7du999993BwcGtW7e++eabeXl52Eybe4XkqVS0KrSt2RRFQTYc9D6LxaKqqtvtfu6555577rnt27f/+7//+09+8pONGzc+8cQTBQUFZLz1EmYK0WglyrV5nodngoyP/0PgXFVVGlZgpDqiKML4Qy0iihiZqJ5CUFLBKrhmDdfa1slKuU09cZWA2WwWRVEUxfb29nfeeefjjz8uKyt77LHH1q1bhxSAWCyGzQghgUDA7XbTpvypCFK14c/UVs1CuNJIfCgUuvPOO7dt29be3v7SSy9961vfuvPOO7du3Zqenu50Ok+dOlVTU+NyuQRBkCTJ6XTSdDbsnLocELRmbRRnDdqEMjhmWP7gl0Tb0QH/ncLuSYyZRhLFR8pL61gs9t57723fvl2W5TvvvPP111/3+Xz0U3QtoDa02+3W6rzIEk8tIxtNUSRJSqi9od3QJEniOI56sF0u1/PPP9/X1/eHP/zhX//1X4uLi2tqal544YWtW7d+//vfT0tLo5XZeIF9Go1G7F9bsH6Lz5QxHUCKxGIxVVUlScINgzyGZB9aagPtloax2fMyO7g2bo2SiqQcTMpIqYno6urKysr67ne/u3DhQq/XK8tyKBSy2+2ISVMZhgpI2n6EEJK6znBaxEXGS/0w7QN2Es3vjcfjPM9nZGTE43G32/2DH/zgiSeeOHHixAsvvHD69Om+vj6v1/ud73wHiWbUMtD2kYbPB9O9mK0wO8C6A1XMaDSaTCYWYf0yXLfjL5tZN5tI+H1hLyXlSFL+liorK1u3bt2KFSswmcNgMDidTqxEkGGQN06n02QyCYJAL7T2MYO3PCWAJ9xgMFCHDM4InUepAog6aYSo0fVFluWMjIw1a9a43e7a2lqr1frRRx81NjZiD9QNjm+RJAmtW1NRm2HcAPSix82DvAcUQCb7uFKVhBF/VM1N6kExpgz8oLCn8RpzGpNyMElbi5HzBaeu2WwOh8NGo/Hm1Pxro0S0/DFhh9r4q/ZPUsi80H120ov2tfaM6JmaTCbq3JZlORaL/ehHPwoGg+np6TabDXnyJpMpYcoWLil17qVumJ+RQMIQd/pDJ/eoUhd66bQ54cywnjVQUaItqU9IC79l7WaTbDlRZzXqr1iX3SkHJhQZv9Tp6ekejwe9U+AIpZuxK89gMBgzlqRJa21rTBrmQRQ2WYc0K4EYpvl0ZDyTnFVkMRgMRgqRNI8NRAheoKslclOTdTyzG3jnEHQRRREpwRSas8ZgMBiMmUkyPeEwrOPxOMLGKdesOyWg/dpoQwwIZq0Pg/kzGAwGY4aTTFsWlnR7eztS7FiXhulAO7CByunrimc0cL2lB8dgMBiML0bSpDUM6+Hh4ffff//SpUtE06SaMR3ADS5JkraGjVYmMH84g8FgzGSSGbdWFKWnp+fDDz9saGjgeT5ZRzK7wYwTQgj6VXEcR8sPtNMIUBjGIhEMBoMxM0m+J3xsbMxisdB5G4QQNETENnjBbL6bRtu+jRCCQQ70HW2n8ZQD9wYGiOF1OBwmhNAeOHS2GKapkvE+Uwn9K2grN8wyIeP3Gyam0LtxZGSEELJ//35CyNWrV+mfj42NkXFHRcKxpVDXHZzstQespAjqJJnsfgghPM/ThYgqwTSoREslVVXFxUwWgiCQ8R90dHT0yJEjAwMD5LOrqKqq1M2GHx13L73n6cbYBhML8Sc4QbpEa3erjD9W6M6E18FgEO8nfKpt4amMdx2hDxGSYYnmnsTTnXCoc4qkZZmNjY15PB6U/EajUUVR6KRqtNCKxWLhcDg9PZ0QIopiCjUwYdwCVFWFhofJ3HiNnjBUQVFVNRqNWq1WVPMTTQMZZXyQDpwNtNAfWXjRaNRms2Gf9IuGh4fPnTv3zjvv2O12n88HGW80GnEAyOBDj1tMSSEplYphMpnQKz4cDtNxOGazebaWaUw2sxJLkE6ng0TBcqSqaiwW0+l06EGEm0dVVe3olFsPngKDwSBJ0sWLF8+cOaPX69PS0miVLFovYMI9JikoisJxXDweR2Gn0WikqzG6V+Gupvcz7hYyfgVgA2hrcWlvxEgkgi6Tfr/f6/WOjY25XC4cIfYGqY/mjNg5ak2NRiN+I8xt4jgOtyXmAeJQ6UHOEZImrT0eDyEkGAyii3VnZ+fx48c7Ozvxo95+++0bN240m81ogclmQDESwJOMzrImk8lgMKAhK3Lg0TYViyaWV7j66XKjFUKYg0II4TguHA5zHGe1WlGhgLUMPd2i0eiBAwcuXrx44sSJefPm4VvIuKIAgwZeIkmSUI6YWn1b1c92zpdlGe17k3tUX5DJut8mktYT7Yf2J6ByJRKJmEwmLE2KplGEmtQWTzzPm81m3LQQeJFIRK/X0+PXZpImtBbG3S4IAp3PHQwGoena7XZYw5IkQT2le0hoEAktFooL/TQYDGLOkMvlwhUOh8M2mw3PiNZjQXsvgr6+vry8PFmWTSYTDDw8qlCb5pSoJsmt4IpGoz6fj+O4Y8eO7dq1KxaLKYqSl5fX3d39s5/9rKmp6e///u8JIRaLhRV3Ma4lEon09/d3dXWVlZX5fL54PO73+3fs2LFhwwYM8yaEiKIoSRK1rePxONpiI4SPZx5C/cSJE4cPH/b7/Q6HQ6/XP/DAAyUlJdSw1ul0GRkZOp1u06ZNJpPJbrejZ6coitAD+vr6du7c2dPTw3FcbW3txo0bbTYb1pdkXZ9JwfM8RDWWTlmWLRYLavSTfWhfiOmW1hzHRSIRVVWtVivHcbFY7PLly4WFhQ6HQxlPz8RYHaiMkz3+qcJkMnEcRw1Wq9UqCEIkEgkGgxhrpG09C50SHS84joPwg3xVVbW3t/fw4cNbt251Op16vf7UqVPDw8OrVq3Cn8OkpiMuTp8+nZGRUVBQABWBXl6TyXT58uWDBw9+85vfhCnc3Nzc0dGxaNEivV6PRy/ht4AyJElSJBJ54403fvjDH1qt1qGhoTfeeOPuu+/Oy8sTBKGnp4cQsnjx4jlVfZo0aQ0bSFGUYDD4xz/+cdWqVdu2baupqTGbzQMDA2+//fZLL73kdrufeOIJwkqxGdcDHrzjx49zHFdQUKDX6w8dOhQIBOCNhDpvMplw54TDYRi+WsuA2hl9fX3nzp0zGo3btm2TJGnXrl0NDQ35+fnau87n861cubKmpqa7u5sOJDUajcp4sqTVar333nv9fv+hQ4cEQXj44YdTRVST8bnXQ0NDmZmZZNxhwPO89nLNZCbrA5hIKt9gPzD7OI4TRfHUqVO//OUv7Xb7U089VVVVpf2hZVmGoyUpYP4CzSSlwXi4owEcRYhCYvYuvEpGo3F4eNjlcuFMR0ZGDh06tG3bNuyzpaVleHh4+fLl2AkseLrPo0ePVldX+3w+egFDoRDmCQ0PD7e0tDQ3NxcXFxNCWltbGxoa1q9fj1sLeSEY0YvBS1Rd0Ol0x44du3LlSmFhYVtb28GDBwVB+Id/+If+/v69e/euXLkyuW6MW08yO49yHNfb24t14cknn1y6dKnRaAwEAosWLVqwYEFra+uuXbseeeQRk8mU3DgQYwYiSZLFYsnPz4/H41euXCGEhMPhEydOrFu3Dkawy+XS6XShUOiNN97YuXNnfn6+JEnRaFSn0xUXF69evXr58uVutxurttlsXrJkicvlKi0tHRgYKCgoaG9vD4fDaWlpWOwQlVy5cqXb7YalHo1GEfzjOO7ChQsjIyNbt25dunQpIQRv8jzP83xaWlpyL9QXp6en51/+5V+Gh4dHR0cJIQUFBeFwOFVs68mu2hNJ64n2gxFBMK89Ho9er//jH/+Yk5PT0tLy2GOPPfTQQ2lpabIsOxwO+GYmfQJTBJ0sYjQakcxltVrT0tKQeomFtLu7+y9/+cunn346OjpaWFjY0dFhsVgyMzN//vOfZ2RkEEKQu2AwGILBIKJChBBFURC5R1wZojoej4dCIXw1AkCIp5hMJqrRVlRUVFdX7969+7nnnhsZGRkbGysoKIjFYtjMaDQi5ISNRVG0WCxwiZvN5nnz5p05cyY7O7ujo6OsrOzixYso9L169WpNTU2qhGmmiqRJ61Ao5HQ6PR5PZmZmRUXF/PnzEfBwu91Iedi6desvfvGL9vZ2eFfmWoiCcWOwKtnt9qVLlzY2NnZ3d3d3d8PIhm6OAJ7NZrv33ntXr17t9/ttNhsCsXa7PT09HVNEFUUxmUzp6elYwl5++eVAIDA4OLho0SIsUsi9kmVZr9e73W5CCJ3NihVfVdXR0VGz2VxdXY1VZu3atRDYybxAkycvL+873/kOlGOe551O58jISKo8d9MtrYPBYGZmJgRSLBZrb28/ePBgVlbW448/vm7dury8PLqlNnX81oNcn0AgAJ1VFMVIJBKJRKjBqiiKw+FYvXp1fX29yWSKRCJGo9Fut/v9fovFEgqF7Ha7w+FAi2JCSCgUoqFi5GxCYCMifvHixba2tmg0evDgwaampt7eXovFUlRUdNtttxFCrFarLMsej6eqquq11167cuVKa2vr2NjY+vXrqTciEAg0NTWdPn26q6srFov9+Mc/JoTQfLd77733z3/+86pVq86ePbt27dp9+/adPn26r6/P5/PhYZxTJE1aQ/PieX5gYOCpp55CToTBYMB6FwqFtmzZ8txzz50/f76ysjJVFHzGLcNgMCAdZtmyZcePH29oaOjt7S0uLs7JycEGFosFq4zJZLJarcjxjsfjKAhBFiu2VBSloaHh8OHDBoPh0UcfNRgMx44dGxkZQWSaFuoEg0GXy4V8Iohw/C2yhbu7u61WK0obLBZLIBAwGo0p5BNCsGnNmjUILiJ3F+l1yT60L8QtiFsTQkKhkM1m4ziurq5uaGjovvvuy8zMxFImyzLNZExi3FoQhBMnTvj9/rvuugv5krhRo9GoxWLBEdrt9oyMjEgkgpQOZJbhLJxOJ8xcLMhut1sUxfT0dFVVHQ5HR0eHKIoQpXq9HpEmlFe43W54SY1GY3p6Op4FLOaxWMzr9WZmZl66dKmnpycej5eVldGSCpfLBVO7oKDAbDZr+1pKkrRixYqf/vSngiD09/dXV1ePjIycOHEiGo0uX76c5p/PHZKZZQaPitVqhe9IFEWELuLxeHp6elNTk9vtxvKaKgo+45aB+Bacez6fr6mpqaWl5YknnoBZQPOxBwcHd+zYsXv3br1ej7UgHo/Pnz9/w4YNy5Ytg09bkqSRkRFBEOrr62tqaqLR6L59+6jpDOcelhUyHtdEIg/EOc/z6enp6enpra2t5eXl0Wh0165dwWBw0aJFtbW1Sb1Ik4BG6PGsUdNnTsUFPxfU5qmq6nQ6f/jDHxJNH1/a4ldRlOQm2ej1+nffffeOO+5wOBxICLdYLBaLRRAEhIp7e3v//Oc/nzt3DgnesVhMluW0tLSf//znVKlFJp3D4QgGgxaLxW63t7S0BINB8tlboqqqCqK3q6tr8eLF9fX1mZmZSChBOyYUgKWnp1dWVr766qvl5eUbN240GAy0PFJV1ZycnKysLFUzvwDZoJAFNTU1b7/9dkZGRnZ2dllZ2Y9//OOlS5dWVlbq9fq5ls+UTGkNrS0YDLa3t2/cuBFuE0EQHA4HNMF4PO5wOBAuomm9DAYhxGw28zxvs9msVuuSJUuef/55n883f/58pKSikNRgMMBXuXnzZhTbqKoKi1zrRqP+8OHh4bNnzzY2Nu7evTstLe22227LycmhaVbIx8GaojXNXS7XvHnzVFV98cUX7733Xrfb3dDQUFVVVVtbi3DPrb84jDmLxWLheV5RlGPHjsmyvH//fofDUV5eTjRFaIWFhY8//vijjz6KjZ1Op6IoIyMjNpsNrS/sdrsgCEVFRRzH7dq1a+PGjWfPnt2+ffv69euRkkaNWvog0DCTtggQMWw4w30+Xzgc5nk+JyeHHgltckC9ONgzTHMUJqxfv/7HP/7x888/jxy03NzcvLw8fOmcEtWEEO4nP/lJUr54cHDQ4/F8+umn58+fVxSlpqYGPhnUa0UikZ07dzY0NDzzzDN2uz2FPIqzA0EQELPMzMycmdYVWjEgdOLz+YaGhjZs2ABpjU9jsRjtXAHnDVJazGYztoH+Ho/HYZ2PjY0dP368sbExLy/v0UcfjUajlZWVHo8HxaNkfOlBki0twkb2mcPhqK2ttVgs77333vHjx9evX491bWb2CaA/6ODgIK7eXPMofhlw9ehQO6K5nnjR3d2t1+t9Pl9SDi8Wiy1YsMDpdL799ttnzpxZtGjRk08+ifufdkeBW9vhcFgsFnizzWaz1+slhEDW0tKsefPmtbW1bd++XVGUv/u7v1u4cCGKL+gNQyf7wWWVm5urlaDURodrCsWNVVVV2iJvugeA6Dh0br1eHwwGa2tru7u7v/rVrzqdTkmSMjMz6+vrfT7fLV6XtPX0qqqeOnVq2bJlCT/9tB9DcuNSbW1t27Zta21t3bdv37JlyywWC7qjdHV1PfbYYz6f76WXXqLtohi3DL/f39ra6nK5KisrZ6a0Bij/gCYOz5jW6qXACieaLhb0pLCgaHeIXfE873K5JEkSBIH2NQOiKGI1od3QCCFYYrRWxcy8bjgwrDgNDQ2hUGjFihWp1cVl5qB13tJf/MiRIwaDgVY6Jfeorl69mpWVBeUVMQ5lfGqAqqpoURKPxwVBwN1OszFoexPsEx/dIFSMBDR6AAk3v/Ypowdwg+OXJAn2N7qjkM9WptF09FuTyKm9nmS8DeKLL7747LPPJvz0003SdGpBECRJGhwcTE9Pf+aZZ/75n//55Zdf7uzsFEXx4MGDDz74YCQS+eEPf+hyuaxWK0pKGAwtCBAigKLX61Ewigc4QQelDafIuC4PMQzpDiNbEARBEFCog/wampJDY2w0e0i74uB9q9Xq9/vxvX6/H7lstLMxY1YyMxWyWCyGqqp4PA7fGBIh6Q2M+1an06GZqMlkQk4GbRgO6a7T6QKBANrgG43G0dFRrYil7dMBFdVo900/opvRJurUWXVdJEmiVjUhJDc3l3YMhFCEfwu9WabyqqUCSdOpsYAihrFly5YlS5b87ne/e/755+12u6IoaWlpv/nNb+rq6qLRKJrcJus4GTMWnueRU0Yrqci4WQDDl1Z/Ik1XqwKjyhOvsYH2U8S2tZ1KteIZqwleUOUAPjq0TYZTEYd3C68Hg0HIePtbMh67IeMRa0hKvIPGkTCXIQjprY4XuOGR3tHf35+bm4uibVoJho3pU4OUeNownGhMUrhLCSHRaFSWZTi0aYvAhINHtaTBYHC5XLDXUZmNFFHsH1km0LnnVOg6adIad0NxcfHu3btxNzz55JMNDQ3Dw8NOp3Px4sXonZKfn5+sI2TMcKhzG08sVgFBEBCHm2i8GNYpmimD7uJ0wAAU/4R4M/R96i6GNkDGNQOi6beMsUJYX5AQO6dWE0bSQdAHed10wAxUVXJN5j8FnUehXCKwbbVao9Ho4OBgfn5+bm4uGW+Zgu2vlbIJwRTaTE2bkma1WtXxaWY3qJaOxWLYG/4QHXDxYOI5pd9FkV/vPwAAIABJREFUCynnCEmT1vgl0J0Yd8Pg4GBNTY3WxMnPz6faHIORgF6vR9I15vdhkUIONvraUq0fzjSsUNp1is52gzpPLRL6KcQwXRHgOaerD4pMsJTgNTJ0tNOEbsmVYDD+is1mQ1YXx3FOpxNRnoRuu1oNFfctjUlD3cQMLqvVWlhYSOsazGbzDeLW6jh09aZGPATzyMhIeno6yqlvIKrj8Tjcq8gXwR7grqd+LFEUUR4210RDkrNLtMneWVlZ5Bqt7XN/jxmbzsO4BWAdMZlM8PUlrBSAiuFr0Q5Wom9qBxIkbJ+gyNObU/vn2uUs5dqZMWYBWn0UDS20nyY8DvQmp40EcNPSnWhb4t/gS7WTPK57MJh9/LmmML6dDvwgmog4/aI5234j5XNBUclDE4ioRcVgMBgMxqwh5aU1IUTrewS3LLmfwWAwGIxbwOxMgmeimsFgMBizidlgWwNJkpBbhM4VyT4cBoPBYDCmjJSX1jQRUVtBy1LPGAwGgzGbSHlprSjK6Oio3+/X6/VerxeD21CikOxDYzAYDAZjakh5kcZxXH9//yeffDI6OlpQUFBbW1tcXMx6SDEYDAZjNpHy0poQsmDBApvNdu7cuebm5tbW1nnz5hUXFy9btgzNb3me1+l0KNpDdwvtAFqU3muHMcCLnnKOdMyroNNhVVWlJ4UpFJFIxOFwIFsezUDIeLN+/Lksyyi+RE8PNPbD1biJEeMTTYuZ7IWdafuZ7v1P934wLwG3Cp2RcIOuFwyK9hHD9Bf0GEEHJ1mW8SiZzWY8ONfdyUy7H+ba/X8T56ttpAogMuhaajQa8Xq6a5FSXlpDkJSWlpaWlg4ODl68ePHChQsHDhzYvXt3bW3typUri4uLsSU62dKm0JBYaNBDm+rRpxELmVbmzXD0er0sy1iLaR9NTJsnhOh0OofD0dzcPDw87HK5PB6Pw+FIT0+nWgttwItyODo4D/doqlwExucChQwpmZjfwJI8vgi0V9e1k5VpG1psAMU3aQfKmAa0fdHxAmMIaFN0yOnpVnlTfhU2m82iKKJ/XlZWVmZmZllZ2dDQUFNT06VLl86ePVtaWrps2bL58+ej3V0kErFarUgdRw455hNrFedUJBAIIGZPxrsJomkRVfl5nt+3b19jY6Pf7zebzc8++6zJZELbTm4c9M3HFB1M8mlvb4eRPVmdcabpyMy2ABhdDIdTIBCgxsGkdj4HoRNcoOWQ8dlrNpsNazT6MkmSJEkSz/MXL16caD+T+t6Zdl9N9/5n2n60e0uYOQaRQQfx3fTOvzgpL60JISaTCTNHoeBkZ2dnZ2cvXLiwt7e3ubm5qanp1VdfNRqNt99++/r16zHGlXqxCCGCICiKAk+vdrp4ahVtu91unufj8TjGy4dCIb1eb7fbYTxFIpHe3t6mpqZXXnlFEIS7774bEQHy2cl39JThdRBFMRgMxmKxm+ieP9OeOrZaAdz5kiRZLBaLxQJhk7pK6i2DBo8SpsWIoogX+NRoNLpcLp1ON9GQ35l2P8y1+/8mzvdaw5oQUlBQgCURb1KZPa2Pkm6is0oVEi6QKIqI2ppMJnilotHowMBAc3PzhQsXent7Fy9evGnTpuLiYp7nA4FAVlbWtVL5c+elz0BgIhNCtN77aDQaj8c/+OCDw4cPO53OnJycDz/80O12/9M//VNVVZXdbtfugU7CwCUdHR1FY31BEOj0my/OTHvq2GoF8CurqkqnJd5gxAKDAksA9rRWlUcqjDYXZGxs7Aae8Jl2P8y1+3+qpLUgCAUFBWRcjVPHx/VOq42X8tKaaHxTCLXiTYiceDwO01AUxeHh4atXr548efLSpUvp6ekPP/xwbW0tIQRTVBN2iAFtqSWwQTQaxYzYkydPHjhwoKWlZeHChZWVlfn5+VVVVfv379fpdFu2bMHGVNfBZBtE8WFJ3xrfDoORQmjFcEIyB8JPhLU9nhvQxEyqpdGczen70tkgreHL1cpppDonSBpFUYLBoCiKfX19x44dO3XqlNPp3LBhw/Lly71eL6J39LrTkHYSzuemkGUZo50jkciePXt27txpMpmWL19eUFBQX1/v9Xox+BbTl3GmuFDatDv4dqC+0EA+8vgmO/xxpunIzLagwINCVxaME2YC5nMRRZHaA8jtMJvNgUDA4/FgsBDSRAghNMx0LTPtfphr9/9Nn2+ChU0jg7IsI9fsFhRWpLy01nrCEzzYCdeXvi8IAiEkFAodOXLkwIEDqqpWVFQ8+OCDVqvVbren6LIVi8XOnTu3a9eulpaWoqKiu+66q6KiQqfTZWdn0+RwmvqeoN+AiYwD+j5jNkGL99jv+wVBDqZerw8Gg42NjV1dXZIkFRQUFBUVeTwet9tN3VTXPlyM2QeeIJgxt6YMMuWl9c0BU1JRlLGxsebm5ubm5g8//HD58uWbN2+uq6vjOC4ajWIEOranv4QoitFo1O12wypN0AxuQnGTZRkKh9aUp1EQGkhGeIya/jqdjhakDQ4O7t+//5VXXikpKVm6dGldXV1hYaHL5UpRtYPBuMUkPLy0uwDtW6DVX8fGxjwez759+775zW8iWeSnP/3pww8/bLFY6BLBtB/GdDDnpDVEHfVcEUJ4nh8eHrbZbL///e///Oc/p6enP/bYY1u2bEGhi81mCwaDaLRCI9zohzDdh6o9SEokEkF2mCiK586d+5//+Z/Lly8vXbr0iSeecLlcXq+XTnFHASiT2QzGFwfPFy2voO/IsszzvMvlGhoa2rNnz5tvvrl9+3a32/3ss89+/etfX7hw4bXpZgzG1DJHpTUhJME4hmCOx+MfffTR22+/PTo6eueddz7wwAM+n09RFBrfFQRBVVVIRHW8g4rWAzbZSieYyESj4EO+Us2AKviyLIdCIa/X29fXt3379p07dxqNxvvvv3/jxo1Op9NisRgMBpYUxmB8EWKxmH4c2tZNW0+BDA88g4FAwOVynTt37u23396+ffvatWu/9a1vnT179k9/+tN//Md/VFZWYodGo1Gv17MsM8Y0MXeldTwep/7teDxuMpkEQdDpdGazWZbl06dPv/vuux9//HF9ff13v/vdqqoqiHPsBCKWQnd+Ew+qJEnXlbJUWvM8j/FisViss7Pz//2//9fe3l5fX//kk0+Wl5fTzmVa5QNGOY6Nxc8YjC8IFgc8eqqqRqNRm802NjZ24MCBV155ZXBw8Kmnnvrbv/1bmow5NDSUk5MDvzd1gNMQVZJPhjHrmHPSmlYV03cURRFF8VrPdjQajUQif/rTn/70pz95PJ5vf/vbtbW18Xjcbrej9xk20zYr/ZLZJVAg8IugwwkhxOl09vb2vvXWW4cPH45Go08//fTatWvT09OpOA+HwxzHJRShIa1dURQWQmMwJgImdYJSqyhKJBJRVbWjo+O11147ffp0UVHRU089VVtb63A4wuGw3W6XJCkSiXi9XqwDSAymzc7U1OlYzEgh5py0JhM8oqFQCHYq9Gvtw6YoysGDB3/7299evXr1gQceuO+++1wuF2JagBZ863S6m/CEUzGv/dKhoSGTyXT+/Pk333zz0qVLy5Yte+ihhxYvXkyD6DSnlxASi8XU8WEk1L93U9eGwZgTwP9ECKGtnvFmIBAIh8P79+9///33FUW5++6777333ry8vOvuhNrTCfnA093TijE3mYvSmoxnYOGJuoE3G08jxn5Yrdb+/v7f/va3p06dqqmpeeCBBzweT2ZmptvtnkLRyPP86OhoIBBobGzcv39/IBC45557tm3b5nA40KbNarXSRDOe5/V6vcViuYkZWQwGA8Tjcb/fPzQ0NDY2tnPnzlOnTi1YsOAb3/jGwoULsRT4/X6r1WqxWHieV1XVbrerqioIQoJDi8psJq0Z08Gck9a0aJKMu4sRZKKmqiRJRFP8rtWaIcivXr26Z8+e7du3l5aWLl26tKqqKicnx2KxIGx8E/EqHIYgCFevXm1qajp9+nRbW5vP59u8efOaNWsIIcFg0GAwIGqOjFN4uVHth57PMBS0msct6K3DYKQoeDrQCR8t9BsaGrq6urZs2bJp06acnBw09UPUDCuAdomIx+OIncGvlmBha/1eDMZUMeek9VQRDAYPHjx46NAhnufLy8uXLFlSUlLi8XiojasNkIuiSH3UCSFzSZJGR0e7u7svXLjQ2NgoSdKyZcvWrVs3b9685JwYg5EiaPsQaOum4OXG46bN4kR7XcS5AoHAyMhIS0vLmTNn+vr68vLyNm7cuHLlSmYTM2YsTFp/KeLx+IkTJ/bv39/d3V1QUFBdXV1eXu50OjMyMqBuo2s37WoiCILBYIDePTQ0NDQ01N7efvbs2StXrhQWFq5fv37JkiUJ7jUGg6EFqR54iLRNBWAuaxVi2twQM6fxSPb29l66dOnMmTN+vz8ej5eWlq5ataq8vJwQQpv9MRgzECatbxKIXr1eD2O6t7f3wIEDp0+f5jhu+fLllZWVRqPRZrPl5+cTQiRJUlWVFnh0dnY2Nzdfvnx5ZGREFMVFixatXr26qKiIECKKIlYilibGYFyX647I09rWCC0RTfcCRVGuXr3a3d3d2dnZ1tbW29tbXV1dWVlZW1ublpZGR9rc8lNhMCYBk9ZfCohhomlbePTo0WPHjvX19VVVVS1dujQjI8NisTidTp1OFwgErly50tjY2NbWptfry8vLKyoqamtrzWazTqfTSnQGg3ET0CYKNF0jFot1dHRcvny5p6env7+fEFJbW3vbbbfl5uYmVEXLshyLxRLGyDIYMwcmrW+Sa3u403ZIgiB0dHQcP368tbXVZDJVV1cvWLBgYGCgra1tYGAgLS1t0aJFlZWVXq8XveCvXSBYq2EG47rQkg1qQNMSrISE0M7Ozt7e3qtXr7a2tgYCgQULFtTX18+bNw/PHc/zZrOZdhZiijJj5sOk9U0SiUSsVivt46+tlqaT9Y4cOfLqq69inHZTU9Ntt912//3333HHHR6Ph3y2zAMTuGlIm1VkMRjXJcHjnZAUFo/Hh4eHu7q6Ojs7BwcHw+HwyMjIihUrbr/99qysLEKIKIrotYDnK0HnZk1DGTMZJq2/FBjCQ5/w0dFRk8kkSVJPT09jY2N7e7uqqhkZGWjuHQ6HjUbj/PnzKysr8/PzXS6XwWDQCmZt+gyDwfgioPWYwWAYGRlpa2traWkZGBiwWCzFxcUFBQUrV64khMRiMXQOTlCC0XcM9RosG5wxw2HS+iahs7xQsa3X60VRjEQiCFr39fVxHFdeXl5bW5ufn28ymYxGY3Nz85kzZ7q6ukRRzMzMrKmpqaioyMrKQh2XNrOM1WsyGBOBBG/aN3B0dHRwcPCTTz4ZGRkJh8NpaWlVVVWVlZWZmZmo7NI+SpjKYzAYtBN9wE0PvWUwbg1MWv8VKiBRxAlXNv1UO9+afkTfDIVCTU1N58+f7+3tNZlMeXl55eXlxcXFXq/XYDCg+pPjOHjw+vv7GxoaLl++HAwGdTpdTU1NSUlJcXGxNmx23V5I2ipSMt6iwWQysexxRuqi9S3RYXd4aqAH46lB+pi2JXAsFkNXk0uXLo2OjmZnZ+fn5y9YsKCgoMDhcOj1etZQjDHLYNL6/4jFYkgKpZ5tNCqiXU1ojwVJkjCzCzneLS0tPM8XFBSUlJQsWLDAbre7XK5rk1awAGG5GR4evnz5cldX19mzZ61Wa15e3sKFCysrK91uNxlXHbA9yknpIdGxH9oAG53wwWCkItFoVKfT0ck618aPeZ43mUwGg0EUxeHhYaR59/b2EkJ8Pl9RUdH8+fMxbofWYSd0FGYwUh0mrf8Kta1lWRYEwWw247/a9GwsIvF4PBKJHD9+vKurq7e312g0FhcXl5WV5ebm2u12iFtCiKqqsizDqkbaakKdqCzLwWAwEok0Nzc3NzcHAgGbzVZYWLh48eLS0lJsk5AFQ1NsEpYzlh3DSFHGxsZsNpv2EYNmTJvhj4yMmM1mTL27ePHi2bNnBwcH/X6/0+ksKSkpKyvLz893Op0JyWIYk8PcTozZBJPWf4WuDhTauF+SJEEQjEajyWQaHBw8ePDgsWPHcnJyXC6Xz+crKysrLCykpq0270wbCdPO1UYOOXUASpI0NDTU2dnZ2tra39/PcZzH47n77rs9Hg+yx2OxGJqBQ1RTuwEBvASnPYORcqiqijlyKJWGoOV53mAwQJB/8sknhw8f7uzsLCgoyM3NLSsrKykpycjI0KZ64AVNGcPTxx4NxqyBSev/A4FhzMwQRRECOBqNYvbOmTNn9u7d293dXVxcvHjx4tzc3JycnNzcXPxhLBbjOM5kMn1xMxeyNhQKud1uiGGe569cudLU1NTe3t7W1lZeXr5y5cra2lrM8yCEiKJICEH0bqqGajMYSUTbmlcLtNvOzs6dO3eePXvW6XQuWrSouLg4Pz8/LS2NerDosFo6fmOieXoMRqrDpPVf0c7PobKwv79fp9Pt2bPn6NGjJpPp/7d3rrFNnfcff3w/tmM7sXMlzSCBJMRJA0JtICxcBwUVirKijTK6tuuGpmrVpKmaKk3aNKnSpA1pb/ZiezF1XaupgtEOsSZNgVIuY6wsVQKh4ZpALiTBContOMfn5uP/i+8/z//gEP7c7cS/zwtkG/vk+Pg5z/f5/Z7fZdmyZfX19UVFRU6nE1ZvSs4VZofkFMxgXt9x1jD2x8XOHJ6Gw+GBgYHTp0+3t7cLgrB69epVq1YVFRWltC4wOtWnz3cEMSvgzTZsNlsymYzH4/F4vKur69///vfg4GBVVdWSJUvy8/MDgcC8efN4mxwEe/PI0DtuUVOgGTGXILX+XyYmJuAJR/X/eDx++vTpo0ePXrp0KRgMrly5MhgM5ubmWiwWp9NpMpkkSeJBMRzj7JAyUxifqqrKo1uNiwNEkCUSCSwCXC5XOBw+efLk559/Lori8uXLV61alZeXl5eXZ7PZNE3TNA1VS2lWImYvvP1rMpm8cuXKsWPH2tvbPR5PMBh8+umn582b5/f7uXspGo16vV48Rrdpl8tlMpk0TTMek1KoibkHqfX/gVnj1q1bBw8ebG1tLSgoeP755+vq6goKCnJzc43too3pXpqm8dxNRVEw6aTsWM9UgQGvIxgNVrXxg9Bjk8kkiuLFixePHz/e0dFRX1+/du3aFStWWCwWzFBIdyFnODEbQdESVVVPnDjR2tp68+bNurq65cuXV1RU+P1+j8fD7rTdo6qqrus88sOYVwkoeZqYe8xZteZ5nAgQhSGLF403P1pYYq+6ra3t4MGD4+PjS5cuXbNmTWVlpSAIfCH/hEkx0yVJkiRJVdWPPvrozJkzkiQ1Nzc3Nzc7HA5RFAVB4FOVUbnxGPa6MUT2juqOy4Kde3Z7f27a/5tL8F+fj7FHVY2HN9XAyEESBI+IxE3HoyNRrLe/v7+tre3IkSM2m23Tpk0bNmwoLCyMx+PQaYIgOHNKrTEj4Bul7OMiqNtisSA1Wdd1hHcxxi5duvTpp59+8sknNTU1TU1NDQ0N8+fPT7upOt2RzgtESJLU2dn54YcfXr16dcuWLbt27crPz9c0bXJyEqkskiQlEgk49mG74CDY7YMnn8+kbAYTBMrNptLEp7v9idmIqqrY8UWdr4cMd+CDBK6j6W9AXKTdbufjmSdffPHFF/v27RscHFy+fPnGjRuffvppl8vFG3U8zFkRxJxk1qv1TGUQkPVkMpmwrcUY435jbDnrun748OG9e/dGIpGamhpoXkFBAWNMURRZlm02W9olarpDT9d1RVESiYTL5ert7f3Tn/703//+t7Ky8gc/+MHKlSsZY3xD3ehCgLMd3kLuvTf+IT7VYv6dafIlHjcz3Y+Pw6nLpfEB4h4wSNjUstL4cVVVRVHUdd3j8WA1gGVBPB63WCzj4+P79u37+9//XlJSsmPHjqamJrfbzZMnsfVDW84EMZ25oNZsyts203uMzvBkMtnf3//JJ5+0trYKgrB9+/bNmzf7/f57Oc6TB94C0xR4kTv5MQlGo9HDhw+///77ZrN5y5YtL774ImwUzIDhcNjhcMxU6YzvqTODHmDGRDdPZMHytFditmMsWItF24MNeFmWUW075fWUZq8oUmY2m3VdP3369P79+y9evPjss8+++uqrNTU1RmfYA58JQWQJs16tgVFrYR2KooitL+55u3nzZnt7+/79+xVFmT9//nPPPbdq1SpMN7xyJ6qKYsvW2GbjyZPyu/CnKESKOmh+v58xFolEPB5PIpFob2//61//OjQ0tG3btvXr13u9XqfTie+OCswIRL+vL8W1HFeYeNzMpFiP6j6946//ALZ1SlwFahVAeu12u6Zpsizjnrpw4cKZM2c++OCDxYsXr1+/vqmpqaSkBHnSXNfh74Gtj0UAKTdBpDAX1NoYwGJ8PRwOM8Zyc3N7e3v/8Y9/fPXVV7quv/TSS9XV1TU1NXhPPB53OBxms/mOVRrS2Geat/ZKeR0e/pycHGawY7C8uHXrltvtHhkZee+99zo6OhobG5977rni4mK32+3xeHh5tbvoAbfjUVtKVVWsWjLN5TCHedxqbTKZZFnGT4wUBoRxPNgG9h1Dr8fHx6HZ//nPf/bv3x8OhxcvXvz666/n5uY6nU6MVa7TsiyzKYV+6C9HEHOZWa/W3FfMhQ2VyEKhUG5u7rlz5957772enp4NGza89NJLvP52IpHgxUTZlP2dTCaRwYki4Vjvp6tz5d3bEoTDYbvdjvAxY81U+K6dTufIyMiBAwdOnjyZn5+/dOnSb37zmwsXLrRYLDzqR5ZlXuURnzWmqMVisevXr4+NjeE92PZ+El8763ncao1DIYlA13W/3/+Nb3zjweIzcOtxlUWB3lgsJknSqVOnWlpaTCZTc3Pzxo0b8/LypjeeSSaTsViMx36rqhqNRrHbnfZ4EYLIQGa9WhvRNE0UxXA4HIvFTp06dezYsUQi8fLLL2/dupUxFovFMAsYc5nQoQ8zVwbma/KyaHxrGXvVvKKTruv4UpOTk4inS061CGOMxWKxEydOdHZ29vb2lpWVbdq0qa6uLhaLXbt2rba21mKxoBqUMS8cgj06OtrT0xONRn0+HxoqzKVxksk87igzHlkWj8dRaWTRokWBQOABDsXvEV3Xw+Hw0NDQ6OhoW1vbjRs3SktLv/vd7y5btowZwh6NBctEUXQ4HMicTGmFyWbIMCSILGfWqzUSN5GRNTw8fOnSpa+++ury5csbNmxYsWJFfX09YwxdNLKziggyuLq7u1taWjo6OoLBYGVl5W9+85vXXnvt29/+9oIFC3glKWbwk4+Pj1+6dMnr9dbU1KR9yUI8QvATY2XW1dU1MTGxYsWKmdzgWMvyD7LbM7VQdC8SiVy9erWzs/Py5cvj4+OvvvpqdXV1aWmp0TVF+foE8fDM+uLSVqt1YGCgu7v7/Pnz169fFwShsbHxpz/9qdVqRTq1JEm6rsNpnIXCg/Kl+fn5P//5zyORyMGDB999993Lly+/8847siy/8cYbPp/PYrFgi5qm1CwH0YhsaiOZF+YzijR2WyRJGhgY6OrqunjxYiQSKSsr+973vtfQ0HDr1i1Y6ll4rxHEY2XWq3V/f/+hQ4cuXLiwaNGiH//4xxUVFbwYCGMM2UfcdOAu4uxBVVU0IFFV1efzPf/888ePH6+urk4kErFYbGRkBGsasoGyHEmSLBaL1WpFWCUve8IMifuapoXD4eHh4dHR0a6uLoQ1LFmypKmpqbi4mDE2OTnJneqIJsPjbHNoEcTjYNZ7wkVRHBsbs1qtmC8URZEkyeVywUNu3JRNqb6ZbQwPD+fn51sslqNHjwqCYDKZ7Hb7U089VVBQkJxqI8aDzMkTPle5R084oh80TUPuPja5R0ZGent7r169OjIyEgwGg8HgsmXLsDiG2NtsNrjHsT9tjIfI2vuOIB4Vs962drlcdrtdkiSEX/GmkylBK2h1lZ1TBjQYrbgVRWlsbOTdxngfMOSqpSsAnsgEEokENkTsdjsWc3a7/ebNm7FYrLe39+zZs319fX6/f+XKlc888wy3obHTZLVaMXgQs8lXeChbm533HUE8Wma9WicSCavVivxjbLlhpjC2pjCbzbyZR7ZZisbOnmgGarfbI5EIHOAAZcuyMASP4IiiaLVa+VAZHR1FjDdixzRNmz9//tatW6uqqlCIPhaLYXlnzLbCEILtzqbc6TyFgSCIh2HWqzVvUMGmNl8BSncZU5ZRFyJd1U7ShSAImqZJkpSTk2MymZCcjSRXTLuwpRwOB3J7iOwEDm1d10dHR/v7+3t7e2/cuBEOh4uLi5ctWxYMBouKirB1naLQaF7JC/9hFGHxh43wdH0jgphjzPp7CWrNY1aNdc0wUyCEFZ66bJNqNlWOLScnB4Us4AM3mUxwjwuCwM2g6a0+iKxidHT0/Pnz58+fD4VCXq+3urq6vLy8qqrKYrEY87j4LYY7DnHjWBNz30xKp5DppVEIgrhf5oJaGzWGzxd8UW82m7N5puALFKM3EruSxtdNJlMWLmXYVNSVMek8a5FlWVGU/Pz82traxYsXI9AhhZTbjb94x3fyx9l8AxLEo2LWqzVBPAyIe+Dm40zl2bOB4uJin8+n67rb7eZXg9wtBJEhkFoTWY2xwh2vep2FAXfo8IFoTTaV8aiqKpnFBJEhkFoT2Y6mabz9edaqtbEpFiIYBEGgGDGCyBzobiSyGgRMoaHybK8U9EhwOBzZGcFAEBlOdhkQBJGCJEk8IZg39s5Cm1LTNEVRNE3DU14MPL1nRRAEJ+tmJYIwAjsyGo3KslxQUJC1FjavJoSnKCiU1jMiCOI2yLYmshqz2SzLcmdn59mzZ6FV2anWuq6jXTqeovBfek+JIAgj6VRrRVEYY8lkMhqNMsZGR0cPHDjw0Ucfffzxx6Ojo2yqkuj/C5Jlja+kPM1mcJHZVD1nxtjk5CQ2OmQBAAAMrUlEQVQzWFHG/8pAZFk2PlUUhZdzMb4ei8Xi8Th/ilpakByUqtY0Da/gX+NnVVUdGBjo6elBZJnT6TReHFmWE4lEMpnkbmFJkvAGnFtKDbixsTE8wKgG3MMMT/sDXYnHC0ra8YxzYxFAgiAygXR6wu12uyiKgiB4vd49e/bs27dPlmWXyyWKoiRJzc3Nv/vd7xhjsiyrqspzS1LgbaPYlEhnc6Ot6fCq4IIgoK6Z2+1G/VFomMPhQCFJTdMSiUSm+T+5hxbR2vitJUnCeSqKIoqiz+fD8IjFYqhYZzabuSRPHwzQS5vNhmPKsjwxMWGz2eLxeCQSSSaTxcXFoigi+gwtLpihzowgCIqiWCwWlGvldbvQJcXv9yuKEg6HLRZLIpGQJMntdlutVnSmwqeeyJUjCGJOkTa1jsVigiC4XK7Jyck//vGP77///ssvv7x7926fzxcOh3/729+2tLQsXLhw586dXq/3LhMct2xUVYVlEI/HMc8+oW+S2ZjNZp/Ph8gpi8XywQcffPnlly+++OLSpUv9fr/FYoHypQhS5oDfEf9KkiSKYigUOnXq1OrVqysrKzs7Ow8cOPDCCy80NjZKkvSHP/xh+/bt8+bNQ1F0HAHpWLIsw+A2lrlG0xekKnV1dQ0NDZ05c8btdldXV7/55puBQIArvaqqaOOmqurg4OC//vWvXbt2JZNJpCObTCZY3larVRTFgwcPfvHFF+FwuK+v7/XXX1+/fv2iRYuMkWu8+xlBEMQ9krYpIycnJx6PW63WUCjU2tr62muv7d6922QyTUxMBAKBt99+e3x8fN++fT/60Y/Y7QZ0Ck6nMxqNHjlypL293el0OhwOdBOiXTeAspoonG42m7u6ug4dOvTnP/958+bNP/vZz9asWcNlAwFWmeb/xGoMai0IAlwFY2Njvb29lZWVfX19hw8frqurq6uri0Qi165dw1eA99tms6Gjid1u58qqKMrExATqxqPRi9vt7u/vP3369K9+9atf/vKXw8PDe/bsOXbs2KpVq4qLi3VdR+8yrAXtdnsikfj8889/+MMf8p0FXt1WFMUjR46EQqFXXnklGAwmEolf/OIXFRUVFRUVRhM/0y4yQRCZT9rUGs7YZDK5d+/ekZGRtWvXer1e/FcikQgEArt3725ra2P3UP7QNg1Saw76JnG15pco3ed1r8DPjEpbyI0uLS1duHDh9evXJyYmwuFwY2OjoigDAwNXrlz51re+5fF40LnEbDaPjo5++umnH3/8cW9vb15e3tjYmKqqdXV1O3bs2LJli9VqVRRFEIRoNJpMJrdt27Zp0ybGmM/n+/73v/+3v/2ttra2uLjYZDLxy9Xf35+TkxOJRERRjMViqFeKPxeNRnNycmRZvnDhQm5ubmNjI7Z+v/Od7wSDQfwKWDbByZHGS0oQxGwkbWrNm0H19fWtWbOmrKwsEomYTCav14utvoaGhoaGBljVd2nmqCiK0+l84YUXtmzZAvMFvTLJ02iEF+f68MMP/X7/9u3ba2tr/X4/Y4y33spAwxpgnYFQL/i0nU5nKBS6evVqNBrduHFjd3d3KBRqb29ft25dXl4eY0xRFLPZnJ+fv2vXrg0bNvh8PqxRdF2PxWJsyudvt9vR/7uwsBALGsaYy+UKBAJYH7ApO1jTtIGBgX/+85+hUCgej589e/b3v/99cXGxIAg7duyw2WxYa2JJ4fV6zWYz1qMbN27Et4Ctbzab0TIuTdeSIIjZSjol7datWw6HIx6PC4IwOTlZWFiI1yHkmOzC4XBhYeFdZjcYjpgEEXkLDyfZ1sBkMo2Pj0PD4vH4zp07d+7cOTEx4fF4EGVm7FWcgf2/MQyMay+Px1NVVXXlypXjx497PJ7Fixd3d3cPDg4iRZi7u2HF3rhxo6+vr7CwsK+vz+VyoZN3YWEhxBUubnjLHQ4HspgcDockSdhBQDAEimZ7vd6ysrKKioqxsbHz588vWLAA0W24YoiFjMfjOTk5yGXAMD537lxJSYnP5+MXFkcj85ogiPsibWqtqmogEIjFYg6HA/E7yLGx2+2wpx0Ox/DwcFFR0d2P43K50DfJOKFnYZ3nmUgkEtzchJJJkgSp5o2nsLNrtVoz0CGBxUQymUSUA2PMYrEEAgGPx/Puu+/+5Cc/KSgoCAQCJ06cCAaDdXV1GDw8g6Cjo+Ozzz5DXLeqqpqm5efnL1++vKmpKTc3Fzsmdrt9fHx8aGjo2rVr5eXlExMThw4dKi4uzsvLg3mNCLVAILB161bGWHd395EjR1555RUMMzjqUbATMeTXr1/v7e2tqKi4cePG3r17161b19DQgMALYy92giCIeydtszN3M86fP//o0aPRaLSgoADRQFarVZKkUCi0Z8+et956q6CgAGbKTEw3U2g25PCLw8P0oH/Gi2Y0rzMNnKfJZHK5XPzF3NzcwsLCsrKynJwct9tdW1t78uTJ8vLyFCucMbZt27Zt27bd/U9omlZeXn7x4sXW1tYVK1Z0d3d//fXXb775Jr9iVqsVIwqD1u12FxUVcfsYWXB4p9PpfPbZZ1taWlpaWioqKgYHB4uKikpKSrxer67rkiThW2SgD4MgiAzH8utf/zpdfxtOTsbYX/7yF5/Pt2bNGhhAUOt33nnn66+/3r17N4LRaKvvSSJJEvYpUIwz3adzB9xu94IFC+rr671er8/nmzdvXl1dXW5u7v0eBx7vgoKCNWvWBAKBL7/8UpblXbt2FRUVYfsZb8MDxE8gB2zRokVsqtEkLlEoFHK73SUlJSUlJeFwGMnf69atKy8vRylyDGPY1mm8qvxPh0IhRVGeeuopWt0SROaTNtta0zS32x2Px5uampqbmz/77DNBEOrr60tLSyORSEdHR1dX1/r162GLqKo6UwYXkZ2UlJSUlpYyxhRF8Xq9q1evRpGy+1VBmLkLFixAcOIzzzwD3zh/AwK58RjF0bxe79q1a/n/8jd7vV4Y3AsXLiwoKOCb6BxklJEnnCCIByBtap1IJKxWayQScTqdb7311p49e9ra2oaGhpLJpCiKPT09mzZteuONN1BHIgP3U4n0oqqqw+HA8IBIx+Pxu++Y3BHukcYYSxF75G3zMpxorMkYs9lskHnosaZpPF5PFEW73Q4r31iYRRAE1DJ72G9OEERWkuZ9a0xq+fn5b7/99rlz53p6esbHx5csWbJ69erNmzd7PB6UuUZebLpOlchAEOKANV88HkeS/YMdin9cVVUEh/PcbgSW83dCqlVV5WkI+NdisSAfIZlMItZM13VUTXG73cYtakSlUUw4QRD3S9rU2mw2i6Locrk0TVNVFbt9o6Ojbreb+w+xsU0hOcR0ULAMmqfrOhwwD+AJV1UVVi+E3+Px4EUu0vBaI5YbB0f+AjevsZTEahINZqDlOBSHry3IE04QxAOQzlkDG34mkwn5NpOTk/n5+U6nEy2hJicnEdQDV2Qaz5PITHisFldW3ibr3uFDC004xsbG0OFj+pBLTsHlFrKNvw4nkNVqhdHPGEskEtFoVDeAFQajyqMEQdw/6VRrTHOY2nRddzgc6EsIMwW9uVBogkqdEClgYEBiMXLi8XhKVNe9AGMX6VXooMVtYt5nk02tDLjcGquXw4Vut9vRW5NNubstFguiyhH+hux2JGc/ustAEES2kP7oLWg2b3MJqeauwtlV1Jp4YvBRwROyH0CqOejElfJiytYyDzRjhuR1jF6cjHG/xmg9G3e+eUUagiCI+4L2zwiCIAgi0yG1JgiCIIhMh9SaIAiCIDIdUmuCIAiCyHRIrQmCIAgi0yG1JgiCIIhMh9SaIAiCIDKd9OdbExmFoihIJrbZbJIkRaNRn8+HkiD3xUwFbWYq43W/73/cZNr5PEJkWRYEQVXVRCKRk5MjiqLT6aSqBgSR4ZBaE7cBqRZFcWRkZHh4+OzZs6qqjo+P+3y+dJ8a8QgwmUyRSAStRxRFCQaDVVVVJNUEkfmYqKgnYQSFr3VdF0WRMYYS7owxWZbv6ziPyoZO1/jMtPN5VKANCdZkk5OTFotFEIRYLMZ/aIIgMhNSa+JuJJNJ9HjmtTaJWQ0qojudTuNy5AF6lxEE8YQhtSZuQ1VV3oLiYY5D+9YZi67rqqqiLRjq8PPeowRBZCyk1kQqj8TSIrUmCIJ4hJBaE7eBNsxsqp0zmqHxB8TcQJZlTdNsNhs6gZJhTRCZD6k1cRu0hTm3mf77KopiMpkoLJwgMhxSa4IgCILIdMi9SRAEQRCZDqk1QRAEQWQ6pNYEQRAEkemQWhMEQRBEpkNqTRAEQRCZDqk1QRAEQWQ6pNYEQRAEkemQWhMEQRBEpvM/SlaXyC/deBQAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10.请考虑以下计算图：\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "输出J是？\n",
    "\n",
    "A. 【  】J = (c - 1) * (b + a)\n",
    "\n",
    "B. 【  】J = (a - 1) * (b + c)\n",
    "\n",
    "C. 【  】J = a * b + b * c + a * c\n",
    "\n",
    "D. 【  】J = (b - 1) * (c + a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：  \n",
    "\n",
    "B.【 √ 】J = (a - 1) * (b + c)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.3",
   "language": "python",
   "name": "python3.6.3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "357.344px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}