Week-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <b> Week 1 </b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [20, 30, 40, 50, 60, 70]\n",
    "for i in l:\n",
    "    print(i)\n",
    "\n",
    "b1 = True\n",
    "b2 = False\n",
    "print(\"The type of l is a \", type(l))\n",
    "print(\n",
    "    \"The type of l is a \", type(l[2])\n",
    ")  # Datatype of numbers (integers) inside a list is always int type\n",
    "\n",
    "print(\n",
    "    \"Somtimes\",\n",
    "    b1,\n",
    "    \"is\",\n",
    "    b2,\n",
    "    \"but also sometimes\",\n",
    "    b2,\n",
    "    \"is\",\n",
    "    b1,\n",
    "    \"but what is \",\n",
    "    b1,\n",
    "    \"and what is\",\n",
    "    b2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <u> Type conversion and type casting </u>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a =  552.545 The type of a is  <class 'float'>\n",
      "The value of a when converted to integer is  552 and now it is <class 'int'>\n",
      "b1 =  True and type of b1 is  <class 'bool'>\n",
      "b2 =  False and type of b2 is  <class 'bool'>\n",
      "b3 =  True and type of b3 is  <class 'bool'>\n",
      "\n",
      "\n",
      "\n",
      "b1 =  True and type of b1 is  <class 'bool'>\n",
      "b2 =  True and type of b2 is  <class 'bool'>\n",
      "b3 =  True and type of b3 is  <class 'bool'>\n",
      "b4 =  True and type of b4 is  <class 'bool'>\n",
      "b5 =  False and type of b5 is  <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "a = 552.545\n",
    "print(\"a = \", a, \"The type of a is \", type(a))\n",
    "print(\n",
    "    \"The value of a when converted to integer is \",\n",
    "    int(a),\n",
    "    \"and now it is\",\n",
    "    type(int(a)),\n",
    ")\n",
    "\n",
    "b1 = bool(10)\n",
    "b2 = bool(\n",
    "    0\n",
    ")  # Boolean value of 0 is always false and for any other value float or int it is always true.\n",
    "b3 = bool(-30.52)\n",
    "\n",
    "\n",
    "print(\"b1 = \", b1, \"and type of b1 is \", type(b1))\n",
    "print(\"b2 = \", b2, \"and type of b2 is \", type(b2))\n",
    "print(\"b3 = \", b3, \"and type of b3 is \", type(b3))\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "\n",
    "b1 = bool(\"10\")\n",
    "b2 = bool(\"0\")  # Boolean value of strings are always true except for empty strings.\n",
    "b3 = bool(\"-30.52\")\n",
    "b4 = bool(\"Aniket Pandey\")\n",
    "b5 = bool(\"\")  # Boolean value of an empty string is always false.\n",
    "\n",
    "print(\"b1 = \", b1, \"and type of b1 is \", type(b1))\n",
    "print(\"b2 = \", b2, \"and type of b2 is \", type(b2))\n",
    "print(\"b3 = \", b3, \"and type of b3 is \", type(b3))\n",
    "print(\"b4 = \", b4, \"and type of b4 is \", type(b4))\n",
    "print(\"b5 = \", b5, \"and type of b5 is \", type(b5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <U>Operators and Expressions </u>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######\n",
    "\n",
    "    1. Parentheses: ()\n",
    "    2. Exponentiation: **\n",
    "    3. Unary positive and negative: +x, -x\n",
    "    4. Multiplication, division, and floor division: *, /, //\n",
    "    5. Addition and subtraction: +, -\n",
    "    6. Bitwise shift operators: <<, >>\n",
    "    7. Bitwise AND: &\n",
    "    8. Bitwise XOR: ^\n",
    "    9. Bitwise OR: |\n",
    "    10. Comparison operators: <, <=, >, >=, ==, !=\n",
    "    11. Membership operators: in, not in\n",
    "    12. Identity operators: is, is not\n",
    "    13. Logical NOT: not\n",
    "    14. Logical AND: and\n",
    "    15. Logical OR: or\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AniketPandey\n",
      "['Aniket', 'Pandey']\n",
      "\n",
      " This value of number is  58\n",
      "\n",
      " This value of number is  70\n",
      "2.3333333333333335\n",
      "\n",
      " 2\n",
      "\n",
      " 1\n",
      "<class 'float'>\n",
      "\n",
      " <class 'int'>\n",
      "\n",
      " <class 'int'>\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "\n",
      "\n",
      "\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = \"Aniket\"\n",
    "b = \"Pandey\"\n",
    "print(a + b)\n",
    "# print(a*b)                # This will not work as you cannot multiply two strings.\n",
    "\n",
    "l = [\"Aniket\"]\n",
    "m = [\"Pandey\"]\n",
    "print(l + m)  # This will also work you can concatinate two list.\n",
    "\n",
    "number = 12 + 23 * 2\n",
    "print(\n",
    "    \"\\n This value of number is \", number\n",
    ")  # Bodmas rule is followed here () has the highest precedence\n",
    "\n",
    "number = (12 + 23) * 2  # () parenthesis prevales as its precedence is higher.\n",
    "print(\"\\n This value of number is \", number)\n",
    "\n",
    "\n",
    "print(\n",
    "    7 / 3\n",
    ")  # This, / is a division operator it returns the quotient after division and type is always a flot.\n",
    "print(\n",
    "    \"\\n\", 7 // 3\n",
    ")  # This, // is a floor division operator it only returns the integer part of the division.\n",
    "print(\n",
    "    \"\\n\", 7 % 3\n",
    ")  # This, % is a  modulo operator it returns remainder of the division and its type is int.\n",
    "\n",
    "print(\n",
    "    type(7 / 3)\n",
    ")  # This, / is a division operator it returns the quotient after division and type is always a flot.\n",
    "print(\n",
    "    \"\\n\", type(7 // 3)\n",
    ")  # This, // is a floor division operator it only returns the integer part of the division nd type is int.\n",
    "print(\n",
    "    \"\\n\", type(7 % 3)\n",
    ")  # This, % is a  modulo operator it returns remainder of the division and its type is int.\n",
    "\n",
    "print(5 < 5)\n",
    "print(5 == 5)  # Relational operators always return boolean value.\n",
    "print(5 != 5)\n",
    "print(5 > 5)\n",
    "print(5 <= 5)\n",
    "print(5 >= 5)\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "\n",
    "print(True and False)\n",
    "print(False and True)\n",
    "print(False or True)\n",
    "print(\n",
    "    True and False or True\n",
    ")  # and operator has higer precedence its like (True and False) or True.\n",
    "print(False or False)\n",
    "print(False and False)\n",
    "print(True and True)\n",
    "print(\n",
    "    not (True and False)\n",
    ")  # not operator invertes whatever succeedes it, (True and False) returns False and not(False) is True.\n",
    "print(\n",
    "    not True and False\n",
    ")  # Here True and False where not eveluated first as whole as above as there is absence of () not True eveluates to False and False and False evaluates to False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <u> Introduction to Strings </u>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f\n",
      "Coff\n",
      "ffee\n",
      "\n",
      " Coffeeandbread\n",
      "34\n",
      "\n",
      " <class 'str'>\n",
      "\n",
      " 7\n",
      "\n",
      " <class 'int'>\n",
      "\n",
      " <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# indexing and slicing\n",
    "\n",
    "s = \"Coffee\"\n",
    "t = \"bread\"\n",
    "And = \"and\"\n",
    "\n",
    "\n",
    "print(\n",
    "    s[2:3]\n",
    ")  # will print f, will start from f ( index value 2) and will go to n-1 that is in this case 3-1 which is 2\n",
    "print(\n",
    "    s[0:4]\n",
    ")  # will print Coff, will start from f ( index value 0) and will go to n-1 that is in this case 4-1 which is 3\n",
    "print(s[2:])  # will print ffee, will start from f ( index value 2) and will go end.\n",
    "\n",
    "print(\"\\n\", s + And + t)  # Will concatinate i.e. join what two string.\n",
    "# print('/n',s-t)     # will throw an error as strings cannot be subtracted.\n",
    "\n",
    "\n",
    "number = \"1234567890\"\n",
    "a = number[2]\n",
    "b = number[3]\n",
    "print(a + b)  # REMEMBER a and b still hold string type.\n",
    "print(\"\\n\", type(a))  # str type\n",
    "\n",
    "a = int(number[2])  # Now a is int type\n",
    "b = int(number[3])  # Now b is int type\n",
    "print(\"\\n\", a + b)  # Sum is also ivaluated keeping a and b as integer type.\n",
    "print(\"\\n\", type(a + b))\n",
    "\n",
    "a = number[2]\n",
    "b = number[3]\n",
    "n = a + b\n",
    "print(\"\\n\", type(str(int(n))))  # String type.\n",
    "\"15 % 3 * 2 + 2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accept a sequence of five single digit numbers separated by commas as input. Print the product of all five numbers.\n",
    "\n",
    "##### Input 1,2,3,4,5\n",
    "##### Output 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '2', '3', '4', '5']\n",
      "120"
     ]
    }
   ],
   "source": [
    "numbers = input()\n",
    "numberlist = []\n",
    "numberlist = numbers.split(\",\")\n",
    "print(numberlist)\n",
    "result = 1\n",
    "numbers = [int(i) for i in numberlist]\n",
    "\n",
    "for i in numbers:\n",
    "    result = result * i\n",
    "print(result, end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For example, if the email-id is CS_BT_21_7412@student.onlinedegree.iitm.ac.in, then this student is from the computer science branch, pursuing a BTech degree from IITM, starting from the year 2021, with 7412 as the roll number. branch, degree and year are codes of length two, while roll and institute are codes of length four. Accept a student's email-id as input and print the following details, one item on each line:\n",
    "\n",
    "##### input CS_BT_21_7412@student.onlinedegree.iitm.ac.in\n",
    "\n",
    "##### Output \n",
    "##### BT\n",
    "##### 21\n",
    "##### 7412\n",
    "##### iitm\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CS\n",
      "BT\n",
      "21\n",
      "7412\n",
      "iitm\n"
     ]
    }
   ],
   "source": [
    "email_id = input()\n",
    "info = email_id.split(\"_\")\n",
    "\n",
    "print(info[0])\n",
    "print(info[1])\n",
    "print(info[2])\n",
    "print(info[3][0:4])\n",
    "print(info[3][26:30])"
   ]
  },
  {
   "attachments": {
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"
    },
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5ipX6fPMWnTp1SgHXXqsO7dvprnbt5O/vV1xl4zLktqAcn5Cgp8eOk/78Id+wcZNWLl/qruYAAHCR815UUPs77XVTRShJiQ6HIp5+Rl9+9bUVurNs2rxFY/5vvJrc2UhvvfE6gRlGjFG+BM1atVXd0NtVN/R2DXl0aGmXAwBAsbgc7m9x8Qlq36mbvvr6G5eQnN32L3aqeZu7FBef4JY6Fny4yHov72jU1G3tXKrL4XvuDoxRvgSHDx22ein2/XyglKsBAOTHx8dHf7vxhjyPufnmv5dQNZ6rrN/fEh0O9ezTT+np56zXvL291bJ5M91009+068uvtCcuXhcuXJAknT51SgMeHqJN66OLvWc5OTlZaWm/S5LS0n5XampqsV6/uJT177m7EJQBAFeMv914A8MArwBTp72ms2fPWSNualz/V63+dIUtBDudqercrYcO//KLvLy85HQ6NWXqNL30wqRSqhqeqNSCcqLDoTVR0ZKkv1SvrvvuvUdOZ6pmzn5XO3fukiTVCgzUyKeeNM50zX5+aINQNWvaWLHbduid2e/q1KlTqlixojp2aK+BDz1gbD922w7FxcfZ2s9p6cfLrQkgWW1kbzdDUtYvc5xOp96e+Y4qV66s7t26MdYJAMq47P/eSxfvA3kd0zmsk3XPyrqHZN0XJBX4Hmeq5dXpM3QwOVmSVLVKFQ0ZPMilntzOnTtvvvUr/6pVqqhb184u971Lub/FxSfojbf+o2PHjkmSAq69VsMefyzfSfxOZ6rmzV+g7Tu+0NmzZ63zLkWiw6Gotettw9IXzp/rUre/v59eemGiHho4yHpt5arVGjN6lHWs05mqlZ+u0ukzpyXZv7/Z28t637K/R1nf/9174mzHR66OUsK339p+nsg0nssrIyMjwx0X3hoTm/nD9+dPaoP69Wyf4l+aMlXvzplrbd97dw8tX7FSpnKejhilwQ8PtL2W8/waNa7XoUOHXcYh+fn6at7777r8ZW3bvpMOJDqs7f0/uk7cqPO3W6z6Q4KD9dn6KJd2TebNma0WzZvleQwAwP3yuxflxelMVcMmzZSefl6SVL58ee3aHmMLUdn3e3t7a8Pa1VaQyn4Puf22W7V7T5zxHte9WxdNn/ZKrjUMHzlKW7ZuM85HrFmjhiJXLjcGmfzOrVTpKi3+8APr/liU+1uiw6HwQY9o/wGHsY0bb7xBSz9aaKwvcnWURkaMsd6/7Pz8/JTqdFrv36DwAQVe9eLtmbM0bfoMq56mTRprwbw5uR7fqm17JR1MtrZnTJ+mrl3CJGX+/Dw4cJCVLUx15Hzfst6j7N9/k6xcYboGmcZzeMxkvmXLPzH+AyJJL78yTVtjYvM8//DhX4yD9VNPnNB9vfvK6fTMMUEAAM/k7++nV1+ZIv15b0pPT9eIkRdD0msz3tS5c+nW9iODH861d/ib3XtyvcetXLVaz02cbNzXIayrtsaYg64kHTp8WHd17Gy8x+V37h9//K57et1f5MlliQ6HOoR104FEc0iWpH37flaXf/dwqS8uPkFPjBhpDMmSMsfxFnKlkixf7NylzP7RTHc2apjn8R3a32X73nz73XdFarc4kWk8h8cEZS8vL7Vp1VLz587R/LlzFBISYvvBnTtvfp7nlytXTv373q9NG6L1zNgx8vOtau1LT0/X62+8WSx1jh0zWvt/3Jv5aS1bfSHBwdbrV/InLwDwZL/++qvenvlOrv/lDI1du4QpJCTY2t4SE6u4+AQlOhyav/BDK8z8pXqARo54Ms+269QJ0ZQXJ2nThmi1btXSdg/5aPFSl/Dz7POT9Wu29Z/r1AnRpg3R2rQhWnc2amidfuz4cY179rk8z/3rdddp/tw5+mbXDt3fu5fV9oXz59XvoXBJhb+/Pf7EUzp79qy1Xfcft2jThmit+HiJagUGWpc4/Mv/NGXqNFt9jz853LZ9bbVqenHSRK34eIlaNG+a64eKgjh58qQtZNavVzfP4wMDA23bO3d9WeS2s9v/0/fa/+NeDQofYHt93pzZ2v/jXqs32YRM4zk8ZjLfA/3u13MTxlnby5d8pNv/dae1nf3XIibvv/uO9c0MH/Cg2rZppbs6dtb585kzWj9assx2fQDAled/R37Vq6+9ket+05r/c959x3Y/eWrUGEkZtjA2a+bbebYbEhysjWvXXLzm7Jl6/InhWhO9Tl5eXkpPT9eHixZr6KNDJGUOm1iQLYjnPP+jBfNsv25fv3GTrb2Pliy1zr366qsVFbnSGv4weeKz8vb2toL+6VOnFBefUKiHgkWujtLevXutNur+4xZFrlxu7f/0k2W6s1lLK0ivXLXamiQXF5+g5OSLwwoqVaqkDWvXWPXNm/Ouhg4b7jLOuKDi4hPyXA4up6DatVS0vmv3IdN4Do/pUW7TupVt29/fz/Yp/uChQ3men/MTT3BQkJo2vjjJIf3cOY9duxAA4LmCg4J0f+/eVi/ngcRE7T9wcTxoWKcO+YbMdm1bu7w2auQI2/bGzy6G3cg1UcrIFt8eerC/y/kPPfiA1QmYfu6cNURxa0ys0s9dXBatb59eLmOE7+7xb11b7Rr5+Fylatdck2ftJmvXrbfV1/O+e237/f391LZ1K6u+P/74w6ovKnqt7dju3bq41BcYWFPZh09cacg0nsNjepTzk/0vfUH96193aEtMrPXJ0lPXLgQAlIz81lG+5+4exteff3acPln5qU6fzlz9IKvDsnz58pr8/HPGc/ITHBSk8hUq6Hx65jjnEydOWvuSk5NtvanzPpiv5Z+ssJ1/8uRJmSR8ax9j27SJ68oYoQ3q66ud24tUt5Q5Njq/+n755RfjuXu//8G23aRx/it3gExTWspMUC6KnOOStm3fccWPtQGAK9mlrKP84qTn9eSIkbZJZuOfGXtJS2fVDqxpDZ84kJhovZ69dzlzn0Mm2cNq1j3uxIkThRp6UBQ5hzcUpr6cY4j9/HyLtbbQBvUVn/BtgY9P+PY7ZcjL44Zf5ESmKR0eM/SiJPj6Fu9fRgDAleOrr79xGQywadPnxXb98hUqFNu1rmRVq1a1TQbM2cOeU3JysryyfWcbNfyX22orTmSaknFZ9yhv276jUDNfAQAwiYtP0PyFH7n01G6JidXWmNgi9+wdOJBodbvWqlnTer1d2za29W1XfLykwJPt6tWtm7mCgfUr+hNFqi0vOXttC1PfLTf/XfHxCVZ9jqSDxVrbnY0aKnbbDmt77br11iRJk3UbPrN1fder65lZgUxTOi7rHuUvcizxUrt2rVKqBABQlj01KsLKUuW8vVWu3MXb59AnnyrSNePiE2wT4mrXurhMWWC25dUkacXKTwt83dq1a9muu3nLFmPbdzRqorqht+uORk0LPTGsZo0atvpyTtDLS2BgoK2+L77YWai289O3T29lnwj43d7vc30Ww9aYWDl/+83arlSpkvWwEU9Dpikdl01QfvW1GbbtuPgExcdf/LRbuUoV20Lw7dq2sR0fudq+nuG06TNsf5EBAFemadNn2Fa56Nenl54d/4wVFE+fOpXrA0OyfLhoics6yROen2Qbw9umzcWVMbp2DlOFCt7W9sJFS4xhtm//Abq53q22faEN6qtKlcrW9qrVUUp02McQv/veHB07/pvS0n7X8d9+K/Q44V4977UNV5g3f6FLfU5nqpq2aKP6tze07esc1tF2XPS69bb9TmeqVq+JVlH5+/upd897besCD3pkqEt9cfEJenjIUFtv8gP97rcd06J5M1sS+HRVpG2/05mqhR8tLnKtuSHTeI7LZujF2zNnacOGz9SmTSv998eftHnLVts/QBE5luHJ+aupEaMitH7DRlWrdo1it23P9XGc2Xlnm618IDFR3e/pqapVqmjSxGdzfToTAKD0JHz7neqG3p7nMTVr1tT6qMxA5HSmavZ771n3g6uvvlrDnxgmf38/vfX2TB1NSZEkzV/4kR56sH+u//afOXNGdzZrqbvatlGtWoFa/skK/Xo0xfpVeuUqVdS/bx/reH9/Pw1++GG9PXOWvLwyHwxy9329ddutoWrdqoV++OFHbdi4SWfPZa5TfF/vvrbHa2efeHj+/Hm169BZnTq0V61agYqKXidHUpLVdmiD+i5153d/a9G8mRo2bKidu76Ul5d07tw53dPrfjW6459q0uRObdv+hXZ9+ZUuXMhc97d33we0I2az/P39FBwUpLCO7W3rJGede32N67Umaq1+//33S5qQOGb0KG2N2abDv2Q+4e7cuXPqcW8v1axRQ9deW03Hjh3ToUOHbSE5MLCm8THZ1atXt77Pvx5NUat2HRTWsYPiE77VV199Y30PcuPr66uMjIvrbo8e83+6/vq/qlWrlho+bKjxHDKN57hsgnJGRoZ+3LdPP+7b57IvtEF92z9AUubTlia/+LL1w3/hwgWtyfarIy8v2X6wTZre2UhbY7dZ21njtZKSDl7RP1QA4KkyMjKUlvZ7nsfs2/ez9ecHwwfZHrP80uSJVhidOuVFPThwkHWfCB/0aK5PW8uQdPbs2Rz3mYv3l4Xz5ricM+qpJ7V79x5t/+ILeXl5KSMjQ9/s3qNvdu8xHDvctvpG1y5hWrxkmXWu6z0us+1rq1XT3Pdmu1yvIPe3d95+Q5279bDC6IXz57Vj5y7t2LnL5Xr/93SErb4XJj2vPXHxLudmry+/e3Be/P39tGbVClt9UuaydocOH85qxDr+xhtv0NKPFhqvNe7/nratdpKUdFDvzH7P2p8h5dlX2zmso6ZNf93aPpqSoqMpKTp58lSuQZlM4zkum6EX3bt1Nf4A3NW2tVZ8vMR4zrqoSNWocb3LozK9vLzUvVuXfH9J8fr0acbzAQBl39aY2MxJZ38KCQ62jV9t0byZWmabxHcgMdHlV+ZZenTrouoB17rcLypVuirPiXAfLpirlyZP0tU+PsZ7TfWAAL014zUNfnhgrudWrFDRZZ+Xl5c6dWhveyJedgW5v/n7+2nb1k16oF9fVaxQ0XhszRo1NG/ObJdglxVkb7zxBuU8zdvbW/f06J5ruwVVkPp8fHz0dMQorY+KzHWZv65dwjQmYrQxY1QPCFD3rl3yrCM4KEhP53J+bsg0nsMro4y+Iy9NmWqbEbz/x72SpKUfL9fRo0f1l+rVdd+99xToWk5nqlZ+ukqnz5wu1HlZEh0OrYnKHE/VOazTFf3JCwCQqc7fbrF6IQeFD9DYMaMv6X6R/dzKlSure7duBV7DOfu5hbnPFabeuPgExW7bZrVxV7t2BarP6UzV+o0bdfToUYUEhyisU4cC1VZYiQ6HNm/eqtNnTqty5cpq1bJFoe/Xsdt2KC4+TlLR7ve5ZRQyjee67IIyAACewBSUARMyjee6bIZeAAAAAMWJoAwAAAAYEJQBAAAAgzK7PFzTJo2188+n1NSsUaOUqwEAwK5jx/Y6fPgXSZn3LCA3ZBrPVWYn8wEAAADuxNALAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAA4IyAAAAYEBQBgAAAAwIygAAAIABQRkAAAAwICgDAAAABgRlAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBQviQaSXQ4lJR0UJLk5+en0Ab1S6JZAAAsTmeq4hMSrO0WzZuVYjUAygK3BuW4+AQ9+/wkxSd8a3s9ICBA06a8yD9SAAC3czpT9cz4ZxW9br3tdR8fHz32yGANfXRIKVUGwNN5ZWRkZLjjwnHxCbq//0NKS0tTSHCQ2rVtI0nauetLKzjPmzObsAwAcBunM1X39OytA4kOBQlemeQAAAcjSURBVAQEqG3rlvL19VVy8iErOA8KH6CxY0aXcqUAPJHbgnLDJi2UkpKiTh3a6+03X7fte2nKVL07Z65CgoP02fpodzQPAICGDhuu6HXrFRIcpOVLF8vf38/atzUmVg+FD5YkfbNrh20fAEhumswXF5+glJQUSdILk5532d+nd09J0oFEh5zOVHeUAACANm+NkSQNf2KYSxBu0byZAgICJEkxsdtKvDYAns8tY5T9/Hw1KHyAJBk/oQcHBVl/jk9IYPgFAMAt+t3fW5JUv35d4/4a1/9VKSkp+va779S1S1hJlgagDHDb0Iu8JDocanNXJ0nS/h/3lnTzAABIujhMkDkzAExKfB1lpzNV4YMekST1uu+ekm4eAABJmeOXU1JSFBIcREgGYFQi6yi/NGWqJGnv9z/om917lJaWpk4d2uulFyaVRPMAAGjBh4uUnJys5ORD+vLrb6yQvHzp4tIuDYCHKpGg/O6cubbtgIAAdezQviSaBgBAkrT8kxUu6/rf3aM7q10AyFWJjFHeGhNr/Xnb9h1a+NFipaWl6flnx6t/3z7ubh4AAMXFJyg1NXOlJUfSQb359sxclzEFAKmUJvNlX7ty04Zo2yoYAACUBKczVU1btqHjBkCuSnwyn5S5dmVIcGY4jondXholAACucP7+fur255JwX3yxs5SrAeCJ3DJGeWtMrLZt36HAwMBcP6FXrVpVkpScnOyOEgAAV7hEh0OLFi+VpFwfUe3r6ytJOnT4cInVBaDscNtkvnfnzJWPj0+uQfmnfT9Lkpo2aeyuEgAAV7DgoCBrMnlYp44KbVDf5Zi93/8gSWrU8F8lWhuAssEtQy8a1K8vHx8fpaWlaeiw4S773545S2lpafLx8WHtSgCA2zSoX0+S9NSoCDmdqbZ9cfEJ2rZ9h6TMIA0AObltMl/k6ig9+dQoSVJIcJDatW0jSdr42SYdSHRIkmZMn8YjQwEAbpPocKhzt7uVlpamgIAAtW3dUr6+vtr7/Q9WSO513z2s6w/AyK2rXmyNidX0199wWbeyQf16emr4E/QmAwDcLtHh0NRpryl63Xrb6wEBARo29FFWuwCQqxJZHs7pTFV8QoKkzGEZLO4OACgNWev6165di6VJAeSrVNZRBgAAADxdqayjDAAAAHg6gjIAAABgQFAGAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAA4IyAAAAYEBQBgAAAAzKu/PiW2Ni9VD44DyPGRQ+QGPHjHZnGQAAAEChubVH2ZF00J2XBwAAANzGrT3KycnJkug1BgAAQNnDGGUAAADAwK1BeeeuLyVJ9erWdWczAAAAQLErkR5lPz9fSVKiw6FEh6MkmgQAAAAuiVdGRkaGuy7esEkLpaSkqEH9evpp389KS0uTJPn4+Kjf/b0ZtwwAAACP5dagXOemf1h/DggIUI3r/2oLzA3q19PK5Uvd1TwAAABQZG4detHrvnsUEhykeXNma9f2rVq5fKm+i/taI0c8KUmKT/hWCz5c5M4SAAAAgCJxa49yXoYOG67odevpVQYAAIBHKrXl4Xr1vFdSZq8yAAAA4GlYRxkAAAAwcNuT+RZ8uEjJyclq2qSxWjRv5q5mAAAAALdwW49ycnKy3p0zV0uWfmzcv237DklSSHCQu0oAAAAAisxtQblP756SpOh16xUXn2DbFxefoIUfLZYk3d2ju7tKAAAAAIrMratejH1mvJYsWy5Jatqksf5xy81KTj6kzVtjlJaWppDgIH22PtpdzQMAAABF5vbl4RZ8uEhvvj1TKSkp1ms+Pj7q1iVMY0aPkr+/nzubBwAAAIqkxNZRjotPUGpqqiQxuQ8AAAAer9QeOAIAAAB4MtZRBgAAAAwIygAAAIABQRkAAAAwICgDAAAABgRlAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAA4IyAAAAYEBQBgAAAAwIygAAAIABQRkAAAAwICgDAAAABgRlAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADMoX5uDTp0+7qw4AAADAo3hlZGRklHYRAAAAgKdh6AUAAABgQFAGAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAA4IyAAAAYPD/Ht7DRSj9fb8AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accept two positive integers x and y as input. Print the number of digits in xy.\n",
    "  ![image.png](attachment:image.png) \n",
    "  ![image-2.png](attachment:image-2.png)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4"
     ]
    }
   ],
   "source": [
    "a = int(input())\n",
    "b = int(input())\n",
    "\n",
    "print(len(str(a**b)),end=\"\")"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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VJm1vNjF5Ju1iovpbXlaqlSuWx0ymc7Luzl+4KOLaaefz+fTv901IeCPKjXOpkMaTE9V/ez1IZ0wp03PDzok+8vM/X5fw2v3dqZOT3qRMJNt2wpRoLE9KfG1I1n47Gb8vWfpU3Lpqfqfm/R6vj4fFa6NN4XCT7p8yLeGkGql+xniyGTeOHnda//OauGUdMmigHp47J2F9y9X4VyZ9mWT1Q4qMa1KNaZwct+/Tu5cmfnty3PYqk/H46GtlvD5Govtb2ZYz3TjEjVjT/uaRRG32/rp6Db9rpKTY95ud7MtlOs6QSKb3nrKJX/Jx39Dk9BhXIoniCbOMyWKSaPbzLdl4+9jKKqt93varTRHHIpu+sJT4WpbquZhKW+NEXBeLPR7OZSyczTXeyePqxfqTz/HqTOqDk7kI8UQnuknSlb+epvOKWmYcjzXO+WlNfpecG+NNVTjcpIFDh1n1KLq+NQaDGnDzEEkt59DO322Lew46GRtmmm+UiJP1LdMY4Oqe/yrDMOT3+7V31/a4ZbX3G2P1MZ0eg0m3v2AvR6bfeTplyqa/luo4nZvjApke32jp3ruxr//4o4u0ecvWjPMf3IhlEsV40ftOFDdmE6smyklwul1yM5dIyrw+OhkLeG0sN9t7QVLmebRucm3m99odO63lG264Pu56qQaGEdu7/rq46xUXF6lbSUCHG4P6/R/+J6Vtp6O8rOXm7fF33rUunN1KAurQoYOklif6U/FJ84cR//7oyAl90mScDWpr33SqyHEdnVGjdlde0moWda/7OHRK769/tVWQ6zR7cqwU+T2/+ce3ZBiGDjcGdc/YSr30y+dcnwntnrGVVuDXudOlEeUwDEOTp1WrqKijFQAUFRXFPF/tf9+lS5eY+0rUUEmtb8J0KwnophsH6OjRY9r3379XKBRSKBRKeGxibcM8vmZ56+obdMttd1rbyOYzpSL0k52OnFeZthOJAhBTouNmng/mcUs2AP78Cxsi3r4htbxZwwxKQ6GQplXP0HXXXmMNitr3ZwZyhmHonrGVCTt3sdiTYyTpg32NVoLMyc2Jg9RsNbz2mrV81VU9Uv67RB3G/XX1Vj2VWs7LG7/2FUnS3n2v6nBjUIZhaPacuSouKsrJE2/Z1Bn7+Wh+7/bz43BjUPdPmRb3ITP7TQSp9TlmHgtJaQetsZKIorc/e85c69V06bAfM7NO2bcvnT1m6axrSlSHzTq17tkanTzZnLBDlaydlpz7Diqrxltli3U+bdqyVUuWPhXzRlEmbb1d9Hft8/n0hX++IuLvzetNujf98tkGOcm8ptjrp72Op6OQjonRcEzvzt+iyx6+Qxd2vTjfxUlL87ZD+ujICVfKXejtfnNzs2657U4ZhhFR3+1t7Ow5c1VeVpp2p7kxGGx1nTaPiXmszJhakuvX6VzG9sliTCdi+1hxkFku8xwIhUKqrJqgxx9dZB3fWDGo/btPtX8di1OxfabtbF19Q8S5G0ui42a/zlVWTUiaOHb8nXetAbl4Mfu8BYt08uRJ60ZBrPNPaqlnga6XpzUDiJeuIU7G5Y8u/g/rGKbbNroZs9rFa3+ls9/rumdrtHffq/r11k0Jy+jz+fTV/v3Upctl2rN3X0R8KimrpO90ZNM2ZHMdcSpWjyd6+9F1sK6+QQ/OnJX2dqX02qp0xyycismzbRftYx4VI0YlvOnqZN21f17zcxiGoR8+9riOHj0as164dS55YTw5Wxte3BhxbB6cUZ1y4nu254ZTfWSzHPHaGfM6m0kCvFPtRKJrg/0c3Lvv1ayS/7KN36MT32N93j5fGZB2ufLZL0okXtti3/7yFU/r5MmTGV1znRw3to87xRsLOtncHPc45Gr8K9O+TJcuXWKO6dnLWVSUXr1w8vjv2bvPSjiwn7f22H78fZO0deOGnL5l1IlyutkHTNXIEXdZbeRzz/8ibnu9dt3ZewEDb46cpMDJvlwuZHvvKZV6m6/7hsk+r9NtefQ1tk/vXrqqR3cdOHhIv//D/0TEJNH9r0R6lpdZ+Q2/3V4bd71wuMlKiupWEmiV+O61e1SxOBXze4nb1/hUeLX+5Csuy7Q+uJ2LIEmfNH3Y6mcf7H7LGkdr3nYwq+3nwidNho58e40ufWBQq7fcOs2pMd5UFRcX6f5JE6369Oji/4gYI7XP+v7v901I+PYwp2LDXMimvmUTA9x261Cte7ZGoVBI++vq4x6H557/hbU87JbI65cbYzCZ9Bdy+Z1n21+TUhunS0Um4wJO9cfsfQd7Erp9DCSeVI5hvPwHt2KZEXdVWHHeSxs3p9RPGTxoYMTv3IpVneZmLpGUXX3MJAcoHi+N5TpxL8hrebQm15Lf7YmFqTQsyax4uuXpR5/PpzGjRyVct/sXv2hVhkQXyEyYiY/2p11mz5qZ9auNJOnkltd00Yhr9EmToY+OnIj43d8/+FvW27c7OqNGzdsOqXnbIX34xnutZp/0ulwEueaNBJ/Pp6U/ejziO7Y/DWMYRtxXHjktehaK6Kdyqh/4P9YTkT3Ly2Ker8NvH5b0oRPDMNStJKAp37lf/fr2UXFxkcLhJkktiUP22Zmij4109kJlGIZmzf5+q6CqMRjUvAWLrG3EmmHTvo0p06brFzXrs/pMqXDqwYpM2wkzIHxwRrX69e2tkkDAOu5SyxNm9mMffdyiz8tp1TMSBiyhUEjdSgJasfzJuANkhxuDOtwYjPldNwaD1hOchmFo9Zq1Wc1sdWrnW9byB/saW/3+783ZBd52Bw4espbLSq92ZJvj75tkBT6xZoyxB1gPzpyVk4FFJ9qB6PNje+0Oazafnbt2qzEYbBW02F9fnmwb8xYsSvu1RTNnzbaCsmTbT5f9mNmf/o314EM660pnnwCWYref9jq1actWba/dEbfdSNROS85+B3X1DTGfELffpP7pqtWtzvlM23q7+6dMs77rRGUwO0DpzGgQLZdtkJPMjof9KWSnrotePSZGwzEFJ67WGeO0/jTmxwosHeP6AKvT/vyj37gSfxd6u2/W92Qx77LlK9KOvWfN/n7C67S9TZv7g3k5uU7nMraPF2M6EdtL0rTqGdbxjTXjQXQcZF67Yr0N6Qv/fEVW7bnJqdg+m3bWMAyNrxpnvULRfq0Oh5siBkhjHTf7eTlvwSLr+4slFArF7buaA1pmsqaU/PxbvuLprMY58nkNcTIur6tvyLhtdDNmNW14cWNE+xs9o4b9+z/cGNQj8xdGnGf2MsaKtex9w3XP1mjUyBE5uRmXTdtgGEZG1xEnY/VY7O2tlLwOpiudtirdMQsnY/Jk7WKi+ms/Rocbg1r61LK4bXE2dXf+wkXW5+3Tu5eeWPxownoxZPCgiGPn9rkULZfjydnaX1cfkbidajKgE+eGk33kVNqZHz72eNqvVneqnXhk/sKE1wb7OXi4Maj5CxdlnISVTfzeGAxGJL472S7mq1+UyJKlTyVsS+3bX/dsje6d8K20tu/0uLFhGEnHgnbu2q1Vq9e0qse5Gv/Kpi8zdsxoq9z2Mb1M7/U5ffzr6huSHrtQKKQNL23Ma1J1JuV0sw+Yqv79+srv9ysUCulwYzBuff71b87O0GdPsnK6L5cLye49ZVtv83nfMN7ndaMtX7V6TcJ4Irr/FS9hKp47h99hxdUbXtwYs6+64aWN1nLlN/4t4ndevEcVzcmY3yvcvsanwsv1J19xWab1we1chHhObnvdSn7/6x/ebvV7e6JcvhkNx3S0+uf6OHRKwYmrXb8/k6uJMe3GjhmtlT/9Lx1uDKquvsG67jUGg9Z1wO/3x23jnY4NcyHT+pZtDDBq5Agr0XLtuvUxxx0bg8GIdjZ6jMaNMZh0+wu5/s6z6a9FbyfeOF2qMhkXcKo/Zh/LsL+9JtUx5EzyH9yMZYbdOtRKyk/0kK7ZT/H7/RHnmduxqlPcziXKtj6mmwOUrnyN5TpxL8iLebSSdK5bG96zd5+1nO2NsSVLn7JOpq/fMyrpSd2ly9ngqqkp/cY51z57bYkk6YNXW06yk1taHhw4399e7Xp0kqRWJ3w2PmmKvLHavO2Q3rpzaaufe9nHoVMRN9Odtr12h3Wx+mr/fq0usMXFRXp47hxrhrZMb06m48EZ1a0uPMXFRVryxGLrAZNQKKQNL26M9edp8fv9qlm/VsNuPXsRMf//0sbNEWWKFXwseWKx/H6/JMV81c7CRY9Zx/ff75sQc4DF/rnq6hvUGIz9ihynhTfU5WQ/8TyzaqXGjhltXdzt7d2ji//DWo41cGGeD316t7wK2AxY4jG/5+gBgZ7lZXpwRnXEz2J1VEoCLTceTK/s2ZvKR2zFbAM/Dp2y2jqzfncY0N1azzj4bkbbj6W5udmxbUktgYD5FOiQQQNjBoljx4zWkH88eWkYhrbX7nC0DE6Ld37079dXt9nOvdoduyJ+Hw436T+fXCapJeiJt41/v6/l5ob54ESq7AMLibb/zKqVsf48r+w3sePVqUcXLbD+neh1X4naaae/gz69e2nJE4tbxV+TJt6rbiUt2w2FQq3a6Wzb+u21O6xrSHlZadwymG1eXX2D9tfVx/0c8eSjDfI6rx8T+8woZ4zTaqxcqffXv5qXsmTq1O63PBd7e6Xdj3Uzwoy9Tfv++/dpfLIWZnsSb1B60sR7I2Jqt6/TuYztpfgxphOx/arVayKSBGMN5I0dM1rjq8ZJajkH7Ddr3ZTv2P67UyfroQemW2Mi9uvY0qeWWdfJkXdXxDxukybeq+9OnSxJ1s2BRJ5ZtTJm33XF8icjfja+alzSepbpm/TyfQ1xOi7PtG3MVcw69wfzrOVYr5IsLi5Szfq11tjF8y9siPi9OaOg3++PGWtF9w03btosr8v0OuJkrB6LfWaweHXQ3p/3Cqdj8mTtoll/E7VT5jXpZ8/EjyeyqbvmTVe/398q8V1qqRdLf3Q2YXfd+p9H/N7tc8mU6/HkbEXPijXy7oqUEzazPTdyFa9G1+Gnlv04pc9ncqKdCIebrM/v8/m0YvmTra4NJYGW19Wbx2vdszUZ9aVNmba7Xm0XM20/kvnVy7+2lqdXT23VtvTv11dfv+fspE9r1qZ3U9XpceNEY0FmfCpJK3/6X63+NlfjX070ZZzi9PGXYsf2rcbjX9kT/Wc5VyjljGbO/itFnkumxmDQuvb06d3L9b5cLiS695RtvfXafUO32vKtv3rZWo5O0pFa97/tbX8qbhk62FreHOe+s70M9ocyCuUelZMxf7o+OnJCh/ot0MFrHtbBax52LDfC7Wt8Kgqh/mQi07IUSn2QzvbrjLqjklrOU7MfFzGO1nA894WL4dTOtxScuFofh05ZP/vwwDuu7zcfY7yzZ820ls1Yz96HuX/SxLh/60Zs6LZM61u2MUDP8jLrd/YHD+PtI3qW61yNwSST6+88m/6aXaJxunS4dV/PTZnmP7gdy3y1fz9Jsh7SjWa/xtn7NJL7saoT3M4lkrzbBud7LDfbe0FezKM1uZb87lRi4f66euupmm4lAU28d0LSvym9+uwsvm4OaDml49daXsVxanfLDeGTv3m95eeDnJmNONp5RT51WVChf7rvbEP40ZETahy3UkbDsYR/a3YIU/nvvcdeTritTJiNgST95cnYwU+uFBcX6cEZ1RpfNc5K5HBTohtCVeMqreV4AyLpGH77sLiBzS1DB2vlimVauWJZq1cK2cV6tbbJ3qgmepLM/rnc7ES369FJ5/gukCR9ePAdVx+sSCTeK4CllsFW+yu7E83IMHfO96xl+yugonXudGnc77lf397Wst/vj/uEpb0cmbb77a68ROf720uSTm49IKPhmD4OndI5vgvUcYD7rzl1QqBrV6teTK+eGnc9+8NZXpeoHejd6+yNx6NHj0b8bsNLG62gJ9EDa/a3uKQTWNs7uLfdGv8pz57lZY68ecYp9jpcXlYat06ZrzeVEscwib4fp7+DRNe46649O9PCkSORs1Fk29bbk0imTflO3L8fcdfZ2bMzSchqC22Q07x+TNr3uUKfX/0tXdj1Yutn7y7YkjT2fO+xl9OKZd1gxrJnjNMK/WSnK/vIlFfa/XjtRXFxUcSAkxumTfmOFdt37Xq5K/sw5TK2TxRjOhHb2wfU7HFoNHtf3s3kB6/E9lL881mKTEJ+YHp13PUmTbw34uZAotlX4n3PJYGAtQ1JGj1qRMz1iovPvs4x09nI830NcTouz7RtzEXMGn0zN973X1xcZN2kih6wX/qjx7VyxTItf3JJ3P0EXG4PnZbJdcTpWD0WMzaWlHBsMxfjS+lwOiZPpV30+Xxxj1FxcZF1YydRgoITdff+SRPjbqN/v77WuWC/eZeLc8mU6/HkbDQ3N0e8JUZquamd6oxi2Z4bTserid4gaI+F4t24j8eJdsL+WW+7dWjc2TiLi4sikjXsr8xOV6bxu1fbxUzbDyeMHjXC6o+kk/TvxrjxVT26x/2dPT6NviGfy/EvJ/oyTnDj+Cfqw/Xr28daPnY8v4lwhVLOWO6d8C1rOdZ3YY8JBt58U8Tv3OjLuS3Ve0+Z1luv3Td0qy2vnjbF+pzxrrHZzOhdEghY7b/9OmlnHvvohzIK5R6VkzF/Oj5pMvT2lHU6Y5yWJJ3ju0Cdv39bzmbSzvQan6pCqD+ZyLQshVIfpLP9uo9Dp2Q0HNPJrQcktYxvXtApsyRUt7y//lW9PXmtVY/O97dXYOkY12Zkz/cYb/9+fSMeGLG/Yau8rDTu+L4bsWEuZFrfnIgBvjF2jKSWfmOsB7PN/rnP54s4nrkcg0kkH995pv21WOs6IZ/39TKVaf6D27HMhPFV1nKsh3Tt466jRkbe53E7VnWC27lEXm6D8z2Wm4t7QbnOozW5lvxuzvyWTeJZYzCoe8a2BOE+n0+PLlqQ0pNGRUUdM95nrnz4xnvWsnkinzFOy2g4ZiWgX1TxZVfL4P9WX132/263gsaPjpxQcOLqvCYHJHPR7T1zEuSWl5VFPI2yavWamOuNHTNaDz0wPe+vXLM32Idef93VfZUEAurfr6/69+ub0ZN/++vqrYvZtf+a+Bzv17ePdXG2J2O74aI7z5Yl3w9WxGIPbO4cfkfCdUsCAavtPdwYzGhQ1R70uHGDIPpBm/Z9rpAkffDfQX3wyuGWn/W6Qud28MYr3JIpLi6y6kW+A8ZcSHSdtSezDR0yOO569uSqN/+Yelt+4MBBa3nI4EEp/12+2QcSb77pxoTr3nTjAGs5k9nX3P4O7Dp2jH0uONHWm0kkPp8v4SvO7NfAAwcPpVTuttYGOaHQjsmFXS9WydOVEQ9Gnli9R0dn1HhuRnW7//X1G6zl95/7vafLapfPdt+uQ4cOGf2dpIjZ1B+aOStmfNS/X18rts/n9byQYnvp7Azhfr8/4XErLi6y2nv7AJ4bvB7b25OWo29YxzL89mHWcu2OzB6cscf0Tp/fXrqG5DIuT9Q25iJm3bHzbHx5ww3XJ1z33gnfsupfcVGx9XPzWGX7xsZCEe864nas3hgMWrFxeVlpxu1tPrgZk9vZZ1f98pf+JeExstep+obX0t5XwrjG9ka7ZONQjy5aoJUrlmnR/B9YP3P7XPLCeHImzNcpS7LGW0OhkCqrxif9WyfOjVzGqyWBQEY3dp1qJ+wPJUbfBI1mT9bYu8+dN2nFa3cLtV3M5r7T9ddday1PmTY9ZgJCSSBg9UfSedV7rseNpcgZ5uzJALkc/3KiL+OEXB//QqkvXi+nvb2OlRRkJj/4fL64s2vnqi/nNifqbSHdN8ymLe9ZXmZ9TreYD1sYhtHqbYD2f9sfPpUK4x5VLmP+aEcffM6aSfMc3wUKLB0jX6kzic9uXuNTUSj1x2mJyuL1+vDhwbMzpV9w2UXWJD8fvHJYH/x3y/lTPKw8L2WL5+iMGr27YIv17wu7XqzPr5vgWD2KJ99jvHNmz7KW7TOGJ5ocIB+xudsS1TcnYnL7m0+iH8wOh5usRNbbopJYc9n3SMSL33m8/lo+ZHNfL1/i5T/kIpaxvw0hVkL2y7/eJqklsTt6TD8XsWq23B6j81p99NJYbrb3grycR3t+zvaUpnC4SVXj77OC9QdnVLepm3F/P/U3SS0drPOKfLqw68X66MgJhZ//g84Yp3W+v70u7Hqxzm3/GVfL0XFwqS7ocpGOf++X+ujICZ0xTuvtyWt16YxBrj2lmY1zO/jUvtcVat7WciMt/ML/WDfWnWQ+jWK+Jmf2nLmat2CRvtq/n2644Xr169vbc50lv9+vUChk3UzKhe21O7Ru/c917PhxvfnHt1KaHbCp6ewFI9kT1WbnNBcuqviyTqxuudB+ePAdfXTkRMSMsvl28uRJaznRDFOmHt2/aHUE6urrPRfgfNL0obV8Xod28vW4VOEX9uujw2dvDH7umoDOK2qXj+JlpTEY1FPLfqyDh17X8Xfe9dxTrG6zz+Rjf9VYLGagms7Movbte+28TsT+JPxzz/8i4ROqx99511q2t5mpcvs7SIUTbb297txRkfimvSnVN1C05TYoU4V4TM4r8imwdIzee+xl6xrevO2Q/vbHP6vk6cqczdyTjvZ9rtD5/vb6OHRKZ4zTOrnlNU/G3OnwQpuTijmzZ+mesZUyDEPrnq3Rumdr1Kd3L91w/XXq26e35/qahRLbS2e/z1QemCS2b2G/TiaapcVkf7tdw2uvJZytIh+8eg3JZ1yei5jV3kdMNiNHSSCQcAwjHG7S6jVr9auXf63m5uactj1e4Hasbr/JZE+MKARuxuR29mP0+htvJtyXffv2euAE+7aTjfvFih3cPpe8Mp6cqSGDBurhuXPU5ysDZBIgV+8AACAASURBVBiGNXteopshTpwbuY5XO3fubLWj22t3pHQdcKqdsB+DVOLbbiWBiIcTcsX+eXt0z//bxXJh4r0T9PKvt+lwY8ssZANuHqJuJQHddOMA9endK6t4IR/jxl26dLGWd+7abW0jl+Nfdpn2ZZzQ1sbtP03uHH6H9ebxlzZutmasbAyebRe/2r9fRFJLW+vLSc7X27Z03zCRVavXaOuvXlZzc7NVp50w7JahmrdgkQzD0OYtWyPOGfPtgNEz30bz6j2qfMX878x9UX/d12j9u8u8CkcTdt28xqeiEOtPLnmxPnzSbBtHK2qnz37pcn105IQ+fOM9KxHuczd8Xp/8Yxb4fPqkydDRB5+LqEMdBnRXlwUVCf7KOfke4+1ZXqYhgwZaM75LSlqvP82xYaYxuZmAWlff0OoNahteOvvgV/QD3vnqe0Tz4ncer7+G7OQqlhl++zAtX/G09ZCuOUZp76ckS+x2K1bNlttjdF6rj14cy830XpCX82g9mfweDjepYsQo6wDPmT0r4WvhC9lnSlqe2DGD2qZ/BLFmQne7Ky+xgslPmgxXknh8pZep5OlKHfn2GutJ03cXbNGHr7+rTrNujVi3x6szHd9/uv73t79mJb83bztkPanttLFjRqu4qEhzfzBPoVBIhmFo05atVnDbrSSgym/8m2fOzc6dLs1Zhy0cblJl1XhPXSSzdWHXi9VhQHfr3Przj36Ts45bKtINiOI9jehF7Xp0kq+0s6SW17p98kFLANBx0NURbd7fT30Y8+8z4dYTpg/NnKV1z9a4su1C1JbaCCfl8qZyW/gOzKQIt+SjDfK6Qjsml0y9SRdeVqz3ntimM8ZpfXTkhP40cpm6LLor4kbGJVNv0iVTb0qwpdzwf7OPNUtK6Cc7Cz753c7LbU7P8jI9s2qlZs+Za5Vz567d2rlrt3742OPy+/0afvswTbx3gidmqCO2z47XY/umpvRiey/NrJWMV64hxOWpW7V6jZVcgdzG6oXE7ZjcLhQKeSIhIVtunkteGE9O15BBA7XkicWSWl6zW1nV8mro5SueTjkpyIlzIxfn8VU9urvyKvVUpPvwiRdmYCukMcxsFBcXqWb9Ws2cNdu6v3C4MajlK57W8hVPy+fz6av9+2l69dS0bxbmY9w4lZvIubimeqEv05bH7du6MaNHWcnvzz3/Cyv53T6L6OBBAyP+pi335aTs6q0X6mMu7K+r1/j7JrkWrxYXF+mr/ftp05at+u322ojfmf/+av9+cf++UPrCuYr531//qsIv7Lf+femMQY5PqufmNR7ZKYT64Cu9TKePvq/wC/t1avdbOmOc1oVdL9aFXS9Wux6drPX+3nx2zObgNQ9Lki5/fJQrk0SaPjpyQm9PWReRi5PrCTS9MMY7vXpqRPL73DnfS7j+pzE2dCIGqLhzuOrqGxQKhbS/rt56mNt8u5nf70/4gHc+x/O8+J2n0l9DdtyMZUaPGmG9bcL+kK59VnP7GxPs3I5VneRGv8GL9VHKbCzXnPyvXY9O6rbqm46UI9t7QV7No/Vk8vv9U6ZZF6eRd1d4JrnYTR0G9FD4hf06Y5yWJBUP/1KrdYyG464FsOcV+dRt1Tf15uDH9XHolCQp/MJ+tfvipZ5LyIkV5Bbf/i+u7GvYrUM17Nah2vDiRu3avVt7971qnZuHG4OaPWeuVv70v1Szfq0nkmRyIfrhlCGDBmrwoIEqK7s6otN+R8WIghvkytWDFZloCx2fRM4r8qldj0768OA7OmOc1mevLWl1c/bDN/7s2P7sNyLrG15z5Am+SfdPsS7q5WWlqrhzuMrLSiM6Yo/MXxjxWrS2bnzVuHwXwZOGDBqYdAYOU9cks3gm0xa+AzMhNRX2J9nTkWkbdGrnW/rLk7+zHh70wkOCTim0Y2LGq2ZS+cehUzpa/XN9YfPknOw/HReNuEahn+zUx6FT+jh0Su+vfzXfRXKM19ucnuVl+kXNeu2vq9fGTZu1Z+8+K14NhUJavuJp/eyZtXpm1UrPzQTvFmL7/Ci0BIh05fsaQlyeulWr11gzdfj9fn1j7BiVlV4d0T/aXrvDSlL9NMhlrF5IchGTm8rLSlOe+bpP715Z7ctNuTiX0h1PPrm5Qe+/sN+6mXK+v7185V3UaeZQVxPk/X6/lfgutbwRY3zVOKsdnvjtydr5u21Jx1adODdyEa86/UaCdHghmR3xFRcXackTi9UYDOqljZv1yp69+v0f/keGYUTcMEx3Mqh8jBunkgDsdjvolb5MWx+3b8uKi4vUp3cv7dy1O2JWRTPJKtbs2m29L5dpvfVKfXTb/rp6662CPp9Pt906VEMGD1J5WVlEHPP5K6/Kaj+D/zHLsGEY2vDiRus+tZmkMnLEXTH/rpD6wrmI+T/+S7M1Viy5m7Tr1jUemSuk+tBxcKmO/d8XrH5d0dDW49LGwXfVcXBpTssVnLDKyhuSpIvHXJ+XvKF8j/FGP7SS7CGWT1ts6FQMMOyWodYY5dp169Xz/7d37/FR1Pf++F9W21/3HCFbz55fFZENWq1gEs7XeuNqCxogEBWiAUxTQ1NASikXAaF8c/LIyYMilyKU5qDQ/Mg5KXLRgBoIlyqnJgEU7SVZBLWX7FLU9nv2pxtof9vLafP7Y51PPrs7Mzu7c9lJeD0fDx9O2NnZz3zmM5/LzHs+U5CPSKRbxHOkuiaVzet5bjzm6T6wSemzsy+T6/eLN/XJD+nuP/Ci+G21usipvqpV7LhG58bzUZbN2GCr7gW5MY7WtuB35TXtymsIjFqwcLFowCZPLMTaNfqvOegvrh59E67wfBo90b/iCs+nLX3dlhHKK4vkDqz3wRGuC3xXeB/8l7hO7sDx9r4eVTl5gVgHrvlQC7bWbUM4HEZXMISKyjl4sWmfrWlIReksejz2zua07dntouJ6Yski0dD2B8pT1Eqwxf/7HydTfMO9zp57J9tJSNuAe28Ref+PX7J39gP5daNnz54z/D15YNZQv110BFrb2sVFlNGjRqKxod7C1PZdeq8ut0Ik0t0nHzyaUfqwY68Xs/sYOGHQddc6sh/p1EGJwXn9VV/Kk4tHzuD3W4+Lv6/yXY3BG9VvBLnBwIm3idd1frTnTVw99uYsp8gafaXOGVGQH3ejo/lgC+p3NqAzcAbRaBSPllcYCsCyE/v25vWnvn3gzNvZTkLastWGuLFf7tY+ayTSjafWbwQQu4Z3rKXZlel0mt199WwGxZrhVJ8cAO6+684+06fQ48S4L53rycpsQbL/Cf8Bl46/gz//6v/gpv3zbUvnoOuuTfq3VU8uFw8iRqNRQ9dWrSgbTpStc++8K5YzuanuZD2R7r0bO1y48H62k+C4XL8fC+bPE33vjs4Atu+oF32Y6ppa+IfcYFsdYsV14zNv9/ZPtR56srse7Ktjmb543b4/K7z/PnFP+lDLEZTNmin+1ptd26i+NpbL9Lztq+djuqprakUAup0TJxRPLRKzKR45egzFU4tw5JM2wufzqR4jN46F9TjR51feRKf407u/s/X3gOy38RTT184HAPiHO3PFQ8r/eM/QLKcmxr+9PG7m9492vYErB3wWvm84W3770zVeNX29b2hVH8DrzcHkTx7+evW/XgMANB9qEZ/Pmlmq+30n772b5cQxNzJeI3Ps7stUPPY1VNfUiod0gd43HJRMn6b6Haf6qlZxw/Vfp+vgbMUG23EvyE1xtJ+ya8PKRe10pspftbpKdETlV6GmS76Y4NaK/M/B5FdMKIX66pH2vZ5ITfTM+wjObhAdaiD29PN1VVOT1j13xxrD//3+6VdsS/PVo2+Ke9XTx9Iry+zm9eagvGwWjrU0w+eLvZqiM3BGNDiJnLhhIf/2zV+wt/y8cfpNsZxJ51W+6ZOqIYlEutHa1o7WtnbN/LXaPz9+r1hWXjXiBnKw9uEjR3XWjHn3vV+KZTd29NUCW+TB/MBCe582HDtmtFj+SWsbIpFuQ9+Tn0iW81V+nbXbZ7212/WDBonl1rZ2y7c/7Nbeh53a2k9Yvn27yP0Ru19/bvcxMMKKul5uY61mtg66sLIJfzr3Ia7wfBqfGXKN5enLhr6cJ79/+pW4mVE+M+Qa3Lh3btKA8fdPv5JWX9ZOvq+PxhWeTwOIvT70T+/93tbfs5Mb6hyziqcW4cWmfWL2hWg0GndhVXbp0iXb09OX+vZAb31tJHhKqe87OgMZ/Va63Nq3L8jvvej4yqvHddaMkR/WdOMsx25qQ9zSL3eizyr3L1MF1QRDIXH+KWOfzkBAXLOb9mDxZR34bndfXT7nT7/Zt974YmefXCaPH+S2yWlyvyZVW9XRGRDnlcLusmTmerLv66PxmSHX4NoVE3HLq0txy6tLMWD8rQBi/dGLR5yfhbWhfod40K8zcAZr121IWseKsuF0f1XuE6WaFVBhVT0htz/NB9X7s4pgKCTagYJ8Z2eRlK+pvfPuuzprXh5GFOSjbutmPLGk981le/e9YPj72bhuLF/r8UvnqZPXv6wYy1ihr16352yQMcVTikRbtP/Ai3F9d7XZte0YyzlxnUGPFedtf79vqFD6xImzR9thwldi1zN+0toW93/l3xM5PRY2ek9N5nSf///K9eGasrvF35GXOhx/A2e6bbzZfHXz+eMkt1wb0qJ2L0CZOOIq39WOT5Kp5TNDrkHuzgr8w5254t/++5nXcGFFk+Npces1XjV29A3dPJGClX3ySRMLAcTe0tvRGRBv4xma61cdWzs59tDjxvGA1niNzHGyL1M8pfcNVIdajuBQyxHVz2RO9lUzrZfsvkbntvPRLbHBdt8LSjeO1mq2Bb/LBdbIzjTu2o29z8c6SgX5eRkHvgPxJ5lbK3IlOEfm31aGYW+txuD1JY6l4+KRMwjN3yWe2LzC82ncsGWma2d8l/1T2V1iWQ7ct8JDJaW48ZbhuPGW4ZoDTa83J+4iw/nzvxXLcqMnz/aTKJ0TXW/Au3tP79MyRl+tYjetC3a5fr+o7E6cPKW7X23tJ1BRORcVlXPR1u7MU7xXj75JBFz0RP/qmll95WBt5WlXLa1t7QiHY42oG4NjAOBvl/6U9G+evOsx7K3VGPbWatsDJ5Wnh4FYgNu2Z7en/E7jrt1iOdN8dfPg1Cr33NN7MdHIjbp0L+aNGtmb98psJ1qyfeNANnbMKLF84KXmlOtncpFTYfcxMMKKuv7OL90uluXzT026+2C2DsopHI5ryu7GzQcX9psZu/tinvytO4rQ/F1xM1gOGH8rcndW4Moce2fLNuvKHA9ypGBQq/uyTnJDnZPK2nUbRN9eLwio9OHecdiFCxfiPlPqNGVmBS0ffGhsFqn+0rcHeutr5XXcWiKRblHfb9/hzIxLbu3be705GJobu2Avz9yhJhLpFje5PR6PKx9s7YttiN39cif6rHL/UnnlqZZnt/9QnH+R7khav3M5BEbZ3VdP55x3W37b2SeXyeMHIxfG7epPyP2aPXv1Z6ZZumwFKirnYtmT3xH/ZndZMnM9+cocD27aPx+fK70DV+Z4cGWOB9et7r1ZFj1n/0yYibzeHDzX2CD+3lG/M6kvYUXZcLK/2trWLm4mpXPtyKp6Ip32R+5z3n/fBKNJtUxfrRcz0drWLsYjq1ZXaa5XNmumWH7/gw8Mb9/p68aRSDd+9vNfAEjunzp5/csou68P9qXr9nKAkDwbZKKQdK+rv/N6c8QM713BEOp3xtolrdm1rRzLWX2dIVNOnrd99b5hOqyq12bOiM1wG41GsWDhYtG/UP49U2bGwnLsR2dA+0FRrbe6ZKPP//kl98VNpvf7rccRPWPdW2esaOOtzNe+fv44LVv3bP/+hz8n/ZvvG2Mw7K3VuPnIIpVvpHZhRZOY1MfKhzyuzPHAv61MPDgNAJeOv4NfT9+Gv3UbnwTVLLde41VjVd/QjnijbDLSJy+e2vtQ4vYd9SKgffq0h1TXd8vYw23jAb3xGpnjZF/G680RZWT/gRfF9f/Ro0aaClw2kyYr6iW7r9G57Xy0OjY4euZ9vDN2Pc7dscaWtljvGpzZOFo72Rb8Pnz4MLEcCOjPQNW4azeqa2oBxC56NtTvMPXb8hM2/aki//slawtt+IftSbNk+reV4erRzs48n6mBk/Jwle/qlOtlMrugPEuOXlCsPBOPPONDrt8fN3OR1olfVf1vhtO0cPFS1X8PhkL40XN7xN9qr/yxq6LWuvG5YOFi3Qt2Cxf0vkp5dVW16jqRSDc2f38rgFinLPHpNTsvDl8z0x1BRjI5WDscDmPBwsWq60Ui3aip7Z2t1o1PtGfq739MvhhgZtbQ5cuWiPN0R/1O3Rv5HZ0B8RoYQD9ftTpp8kNe2eDUDZXyslmi03/46DHdfF27bgNKSmemdRyLpxbFbV/ridC6bc+mvHHgpFy/39A5DMTardH3jk85S5sWu4+BUWbr+uXLlojlp9Zv1ExjJNKNhYuXoryi0tabpnIddF3VVHx+yX1pBVgHQ6GsBP3ayWyemPGX8x8lvbnomrK7MXh9iesD3xX/9NiolOuozdRrVH+p960gP+mv3MhWc/JU7+wg8ncA4Iu39AbkatXPddueFRdKUulPfXu5vt78/a2aZVVuCxJn0LNz3O7Gvj0Qe22lonLO47r5ptzk/uqjM1XX6YucaEOy2S93os8q9y+7giHNc7ijM4CXP6m3CvLzVGdK0rpB1NEZwEqdG/h2c+qanhN9dfmcX7xU/ZWukUg3ar/7VFrbtZuTfXJ5/KBXLwZDIRQWFavOEm6W3K95+WCL5v427totzt1pDxaLf3dy3GeU0evJVw74bNK/RSLdtt9IH1GQHzcT5srVVUm/abZsWN1f3VG/U/XfE6/JFd5/n+Y21FhRTyS2P3ptg9Ln9Hg8cQFZTknsC6mxol50w8Qg8n2Dlw+2aJZhecZp+f5EKnZcNz7wUrOh/qkSNKzIVj2Y6VgGMP/AcV+6bi+/5UFr1vJIpBtb67bZnhY33btVZhkFemdL1JpdG7BuLGf1dYZMWX3eZvO+oVO0gp0ikW6UlFrTpo4oyI9r04FYDIWRWTztGgvLsR9aM2l2dAZEetVko88/5AezxBs4e6J/RWj+LtVAoUyuw1rRxludr24+f7LRLzNzPhhJr1Oz6f/9D8kTTyjCP2zHpeOxWZ6vXTHRlkkuB68vwbUrJoq//3L+I/xy6lZLHyZJJZ1rvE7ff5BZ1Te0I97Ibmb65AplfCHXeVrjVafGHqnGC9kYD2Q6XnMjqyaAcuqNdk72ZZTrSl3BkDiH5Em8tNjVV7WiXrL7nnJfGp9r0bqW+7fuKC4sewE90b+Kt7Nkck8t03tBZuNo7WRb8PuUokliWQ4iSNTRGRCB7x6PB4u/vRCdgd7Xxmr9p1e4lVeLOv26TrtZNQPP37qjuLCiCf/9TO9TLsqJkeo1SsrMbUb++/yS9C7wZ8L39dEp11GeYFbKmRHz5n4jLih2VcKNl47OAMorKkUDU5Cfl/R0ldyRKCmdKTpSwVAIzQdb8FBJaVqv3zlx8hQeKilF8yeD52AodnN7ygPTRedlxiMlqjeyc3J603bgpWZ0dAYyvoFVMn2aWH5q/UasXbdBnJeNu3bjoZLSpAF44vlaXjZLzI5x+OgxlFdUxq3TfLAFJaUzRf5+9dGZSflr5T4l+lzpHYYerHDamtqauI5AqnybPLHQVReRzVLeUAHEOiO3jfgSpj08AxMKJ2e0vVy/HytXLBN/V9fUYsHCxXEXtlrb2rF23QY8Wl4Rd54l5qscmHb46DGxnda2djQfbMGChYuT6qDAGf0Hw6xm5zmTaOO674rl6ppazTp0R/1OdAVDKWfUS1T1nZViuaJyLtau2yDOhda2dixYuBjfe3qLyb2w3praGtG2HD56DA+VlMadw8FQCGvXbUBF5VxEo1FxITITdh8DI8zW9bl+vxhoRKNRPFpegbptz8YNpJRtnDh5CidOnoq7iGw1uQ5K19p1GzD+/sm4/a6Rtge3OMlMnpjxhxO/xm/Kfhj3+9eumJiy7/n5Jfel1Ze122eGXBM3Y4qa3Xv2ib6s3sw/avpTvW9W8dQiUR91Bs5o1r/KDQ+PxxM3OwAQH7y0cnUVGnftRiTSLW5wpNv29Ke+fa7fjxmPxC64dQVDcWMfZf3yikqxnYL8PNU+qnzRTPm+FTcq3Nq3Ly+bJW6iGcm3obl+zJ83NytptYNdbYib+uVO9FnlB3qra2qxdt0GURdEIt1o3LU7biyzdPG3xXfHjR0jxpfhcFjUSUp+rVpdFfddIPUr1O1gV92QyO6+utw37gycwYTCyUltQGFRse3BTelysk9eXjZLXMvtCsZuEMn1olKmpzwwHeFwGD96bo8t/Rrl3FX2V2nzgd5yIF/LTqybnRz3GaF3Pfnj538qlgdKbyUCYvXU7XeNxPj7J+vOYmmFBfPniTYxGo0mBUJbUTas7K+eOHkqaXzb2tYeN74dmutHedmstPLBqnpixzN1cfuaqm1YuWKZ5a87NkLe365gyLZ60clxkRavN0f0l6PRqGYZlm8yTp40MWk7eqy+bhwOh5P6p8FQKK5/6vF44h6SktPiRD1oxVgGAAYOHCiWd9TvRDAUSnsCg75y3V4Opu0KhuLS2dEZcLw/4lQ/LxX5wSGF3uzaVo3lrL7OYIbZ89Yt9w3tNll6UKJ0Vjkad+0W+1m37VkUFhXHBfcp8QqZeqy8LO5vrZlvAWfGwnLsx97nm+L6GPK9ND3Z6PNfmePB4Kd6A8V6on9FcHby5BiZXIe1oo23Kl/dfv4AzvXLrDofjKS3MxAQ5UZ+s5LV/vTe/1H994tHzoiYnwHjb7Ul8F3xudI7cMOWmUkPk1g503yq3091jdeK+AErWNU3tDreyA5W9ckVc+dUxv2dapZrJ8YeRsYLTo8HzIzX3MaK8RgADBgwQCyvXbcBkUi3LWMLJ/sy5WWzRPkGPnl4bar2w2tO9FWtqJfsvqfcV8bnWrSu5QZnN+B/wn/AFZ5PY9C/PZBW4LsV94KsiKO1y1V2bVh53UM4HI6L6k8kdyyj0SgWLV2mua6sID8PLzYlF/COzoA4GNl4XafVPnO91/JtJp4AA8bfmtHrFNzgc6V34Pdbj6u+KsKMXL8fzzU2iJN77/NNmk//ar2tYPmyJfhJaxui0Si6giEsWrosqXwPzfUbespRvvGhdY4MzfXjyeXqnykXNcPhMMLhMKY9PANA7OmlVU+qzySkpbxsFl5//Q0cPnoM0WgUO+p3Js145PF4cPMXbhKzZJw//9uk2Qia9u0RDYpyg1TN5ImFqmm0cp/UfO7hL8U9IOIGXm8OdjxThzmPL0A4HE6Zb3VbNzucQntc5bsa/xP+Q9y/dQZ66/quYAgdnQFDM14kUm5EPrV+I6LRKA4fPaY7e8KMR0qwdk3ygzS5fj9qqqtEm6a1ndGjRopjduHChbTTa4bd54xs3NgxcfmhV4dOnliomqd6iqcW4YiUx1r10D/+4z+6KnhEeaW70rZ0Bs6I45BoaK4fTfv2qH5mhN3HwCizdf2qJ5fjwoX3RZvzvae3aN70qamu0h3wZUqtDkqX/FaifS802ZJOJ1mRJ2ZcmdM7K+UVnk/Dv60s5QOcbvVPX7tHzMpitf5U71tBro/06l+Px4PnGhuSBuTlZbPQ8B//ia5gCNFoFNU1tUk3SYy2Pf2xb792TS0uXryEw0ePaY59lP3SetPbl8eNFW278n2tcX+63Ni3B4CtmzehonIOOgNnUuZb0749WbkxaTW72xA39cud6LMmXrtQ+w1FTXVV0kXcHc/UxfVN1cqfnFcfJLye3Ql21g0yJ/rqmzauF9tXznm1bbvpDVaAs33yhvodor0Oh8Oa9aLSXqs9JGZW8dQiRLq7UV1Tq9nmy2lIrJudHPdpMXI9OXrmfYT/4ySA2LVh5RXyCnns9vLBFtv7b1s3bxIBl13BEFatror7TbNlw8r+6tBcv+741uPxYNPG9Sn3WY0V9cSIgnzUVFeJa12p2oZ0g/St5ES96OS4SI/cX9Yrw4B6m52K1deNlXzXS+fKFctU62Gn6kGrxjJTiiaJdu3EyVMYf38sUKqhfrvh49CXrttXfWelOKZa6XSqP+JUP8+ICV+5V7QLPp8v5b0GK8ZyVl5nMMvseeuW+4Z2W1Nbg3fefVf0R9T6iENz/bj0hz8iHA4jGo0iEunOeCwv10/K31qcGAsrkx8o54pWHyNVHZKNPv/Vo2/CNWV346NdbwCIPZB/YUWTJfERZtt4q/LV7ecP4Fy/zKrzIdv9yM/c8Dndz/907sO4MZ0T8T5Xj74J/m1luLDsBXF977PDr7P9dxWprvFaFT9gllV9QyvjjexiVR9AIZ93QOo3qjkx9jAyXnB6PGBmvOY2VozHgFhZUfJcLoe/ee+sxSl2ti8jj5lSzeTvRF/VinrJ7nvKfWl8rkh1LffCiib85fxHpuIizN4LsiKO1i62zfwO9L72tSsYcmwmDfmJj2y8rtMI+fU//3D7EN11P329fqc2U4PXl2DA+Fvxz4/f22cD3xWfm3675mfy02DXDxqU1nZHFOTj0Mv7456Oknk8HsypnK154Uo58dXeQODxeDDjkRLDnasBAwagad8ezHikJO7JLoWyLb1GacczdUmzV2SqbutmzKmcrZqWyRMLceK143EPn6i9/cHrzUHTvj2a2xma68eWTRt1Gxor9ynR5x75knhy2U1GFOTjWEuzZlnw+Xyoqa5yRQOt50/v/V4sXz36Jt11r/rnAUn/VpCfL/bf6KsetZSXzdI914FYI99Qv123Y1VeNgtbNm1ULZMF+Xk48MJeLFva+1odvQfD7GLnOZOovGwWDrywV/O1gGbLat3WzXhiySLV/Rk9aiQOvbxf9/W02TKiIB8nXjuOyRMLVc/hVG1LOuw+BkZYUdfXbd2MLZs2imDRRAX5eWio357WaEnJ0gAAIABJREFUDXuzdVC65Fe2GXkdWTY4nSdmePKuh39bGT477DrcuOsbfTbwHYjty2eHaV8cvnjxolgeMuSGtLffn+p9s1LVR0CsH3vo5f2a/YqmfXs0+wtK2zPoumtTpqU/9u2V7dRUV6mmy+fzYU7lbLx67LDmfq2prbHt7W1u7dt7vTl4sWmfZp/Gyn6BndzWhripX+5EnzXVtYvRo0biwAt7VftKIwryNa9b+Hw+bNm0EY0N9aJ+6AqmP+uOWXbWDYns7qsrx0qtrfT5fHhiySJbAqGtYEefXI3Xm4NXjx3GnMrZmu1cqvbaCuVls9BQv13zmp7SPurdrLW6LFl5PTnxNbnXrU5+YEEupw848PCucmNKsff5prhXH1tRNqzqr1ZXrdbsOxXk55kqn1bVE8q1Lq19teqcNcvI/lZXmX8rl5PjIj16/WXA/HGx8rrxfRPGa/anfD5fynQ6df3LirGMEiCnNU40qq9cty+eWqR7bK0674xwsp+XijzTu5H+uVVjOauuM1jB7HnrlvuGdlLSp3bMlPx59dhhfPGWm8W/m3lTaa7fL86Rgvy8lIFTToyF166p1T0+DfXbcd+E8brbyFaf//NL7ou7Dnvp+DtxM1abuQ5rto23Kl/dfP4onOqXWXU+pEpvd3dvuRk8eLChtP05GAvq1bsvACDpAeVE//3Ma+iJ/hVX+a52NN7Hk3c9btw7F58ddp3jExOlusZrZfyAWVb0Da2MN7KTVfcXFEqcocfjQfGU1Ncj7B57GB0vODkeMDtecxOrxmPlZbPE21js5mRfZkbpw6rLWumyu69qVb1k9z1lN4zPrbqW+6dzH4qJ9D6/cHzG7a4V94LMxtHa5Yqenp4euzYeiXTj9rtiBVVrtlyr3TbiS4hGo655OoOyq7WtHRWVsdcJPrFkERbMn2dqW4qcnJy0GqlgKITz538r/jb7ygw5LQX5+WlVGvJ3rXh1h5m0yDo6A+jujlWcQ4bckNbTb1bvU18i73u6+dYftLa1W37M5TwFMitTZsqzE7JxzthZVuX8NlMPZYOc9nTblnS5ob4we25EIt1xrxx1w/H+/dOviBljhr2lfXNQeRDUbfWBHYzmCaX2UEkpOgNn4PF48HbHTzPeTn+r960g10dAevlidV3UH/v28vgn3fatL7frZjnZL3Arq9sQN/XLnSrbmZ7HZs5bJ2SjbrDznJTbEjfmtx4n++RWX0/LROL+mr1GkO3j/bfuKIKzG/CX8x/hM0OuQe7OCs3X5EYi3QidP+/K8mlF2Uinv6r0y4H42cfsrJusqifcOI5Wo7W/8vV9s7NsuukacmIZtuO4pDsmW7tug5gdT85rOa2Z9qecqAfNjmWsbpvdPiYG3NM+uWEM2HywRcx8d/zHh9M+Xmbz0o11tZl9csN9Q7tZ0Ue0kxN5Z1U954Y+P2DddVizbbxV+erm8wdwtl9mRV5opVfuPx14Ya8jbem5O9YAALwPjkDkpQ4AwD8/fi9833BXPZRNdsQPmGX23HZLXZmKVX0AM+zq46Y7XrB6PGDneM0NrBqPZSM/3Hh+OtFXtXK/7R4/94XxeSLlXtlnh12Hv//xz/jL+Y9wle9q3Lh3rua1XKOsuhdkJo7WSrYGvwO9FbDH48GJ147b2rg17totXouQycUJ6n9Wra4Sr1lgmSAiIiKrMdA7GfPEGvKDxHywl4guF2xDiIjsk07gO8XTCn4n+8nX97ds2ohiB95EcLnSCqYguhyUV1TixMlTGJrrx6vHDmc7OUTkMF6HpUxNKJyMrmAIPp8Pp0+2OvKbSvD7DVtm4tLxcyIA/oYtM1O+kZGI+i6O14guL3Lw+/VrHsJvyn6Inuhf8dlh12Fo49eznTxX+ZTdPzB/3lx4PB5Eo1Gs27DRtt+JRLqxtW4bgNgs8wxypkikGy8fbAHAMkFEREREfcu2Z7cDiL0ibPmyJVlODRERERH1ZQx8Jzdau24D1q7boPm5fH0fAMaOGe1EsojoMhMMhXDi5CkAwPRpD2U5NUSUDbwOS5lobWtHVzD2xt+q76zMShquq5qKzwy5BgBwYWUTomfez0o6iIiIyD6fGXINBq2OTQbxp3Mf4sKKpiynyF2usvsHvN4cPLWmFouWLsPe55swedJEW2ZGWbdhI8LhMHw+H55cvszy7VPfo7zCY2iun2WCiIiILNNV/v/gT+c+TPp3ZcaNy/GJW+aJ9c6eewcAsHLFMj7ESUT9GtsQIiL7XVi5H385/xEA4C/nP8J7EzYlrcM3bpCTWtvaxax1Z8+9g2VLF8e9Hrm1rR01tWsQjUYBxGa2s/OtwkR0eQqGQqic8ziAWNBr2ayZWU4REWUDr8NSJgJn3gYQe1tANt9OdMPmGWI22A/+9WU+6ExERNQPDZyUhz++GUTkpQ5cOv4OPt73Fj5Xeke2k+UKtge/A0Dx1CKcPHUKe59vwvxvLcJzjQ1xFzLNaty1G3ufb4LH48GOZ+p4EZQAAOPGjsGhl/fDm+NlmSAiIiKiPqWxoR4dnQFLx01EREREdHn6+x/+nO0kEMUZN3YMJk8sxOGjx3Di5CmcOHkKHo8HN3/hJnQGzsStW5Cfx1e6E5GlJhROxqU//BHhcFj82zcfn8t7iUSXKV6HpUwsmD8PY0aPynq5+cyQa/D5hePxu/VH8ZfzH+HCyv3wbyvLapqIiIjIev/3tyfg//v5b/GX8x/hd+uP4rPDr4Mn7/psJyvrHAl+B4C1a2px8eIlHD56DHMeX4DTJ1st2W5rWzuqa2rh8Xiw7Qdbst65JHfh09lERERkNc4+m4x5Yg+ObYjocsA2hIjIfqxryY3qtm5G467d2Fq3DeFwGNFoNC7w3ePx4KuPzmTgOxFZrisYivt7xiMlWDB/XpZSQ0RuwOuwlIlslBu1N3Z9rvQOzv5KRETUj3x+yX34/JL74v7tyhwPbto/P0spcq8renp6epz8wbXrNuDChfdRt3WzJduLRLpRWFSMHc/UcVBCRERERERERERERESW6egMoLu7GwBQkJ/PmYFt0NEZQGfgDC5cuICBAwciP+82jBs7JtvJuqwEQyGcP/9bAMCQITdwYiHq19au2yCWiyZP4v1lIiIiInI1jteIiNQ5HvxORERERERERERERERERERERERERERERJSuT2U7AUREREREREREREREREREREREREREREREqTD4nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIhcj8HvREREREREREREREREREREREREREREROR6DH4nIiIiIiIiIiIiIiIiIiIiIiIiIiIiItdj8DsRERERERERERERERERERERERERERERuR6D34mIiIiIiIiIiIiIiIiIiIiIiIiIiIjI9Rj8TkRERERERERERERERERERERERERERESux+B3IiIiIiIiIiIiIiIiIiIiIiIiIiIiInI9Br8TERERERERERERERERERERERERERERkesx+J2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIXI/B70RERERERERERERERERERERERERERETkegx+JyIiIiIiIiIiIiIiIiIiIiIiIiIiIiLXY/A7EREREREREREREREREREREREREREREbkeg9+JiIiIiIiIiIiIiIiIiIiIiIiIiIiIyPUY/E5ERERERERERERERERERERERERERERErsfgdyIiIiIiIiIiIiIiIiIiIiIiIiIiIiJyPQa/ExEREREREREREREREREREREREREREZHrMfidiIiIiIiIiIiIiIiIiIiIiIiIiIiIiFyPwe9ERERERERERERERERERERERERERERE5HoMficiIiIiIiIiIiIiIiIiIiIiIiIiIiIi12PwOxERERERERERERERERERERERERERERG5HoPfiYiIiIiIiIiIiIiIiIiIiIiIiIiIiMj1GPxORERERERERERERERERERERERERERERK7H4HciIiIiIiIiIiIiIiIiIiIiIiIiIiIicj0GvxMRERERERERERERERERERERERERERGR6zH4nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIhcj8HvREREREREREREREREREREREREREREROR6DH4nIiIiIiIiIiIiIiIiIiIiIiIiIiIiItdj8DsRERERERERERERERERERERERERERERuR6D34mIiIiIiIiIiIiIiIiIiIiIiIiIiIjI9Rj8TkRERERERERERERERERERERERERERESux+B3IiIiIiIiIiIiIiIiIiIiIiIiIiIiInK9q7KdAKK+IhLpRmcgAADIycnBiIL8LKfIHKv2JxgK4fz53wIACvLz4fXmGP5uR2cA3d3dGX23L7Ejr4cMuQG5fr9laaTM9PcyzDKXHVaVq9a2drE8buwYS9JGBPS/PhERuYOZcQXZT+lXsN4nIiIiIiIiIiIiIiIiomy7oqenpyfbiSDqC1rb2lFRORcAUJCfhxeb9mU5ReZYtT9r123AjvqdAICG+u1pBVg+VFKKzsCZjL7bl9iR13MqZ2PVk8stSyNlpr+XYavKnBLMxgB6Y6wqVzfeMlws/+a9s5akjbRdTuW8v/WJiMgdzIwrlIdyGJhtH6Vfka16PxgK9fv2lYiIiIiIiIiIiIiIiIiM4czvl6FIpBuj7x2PaDQKAKiprkJ52SxD35UD8hh8S0RERkx5YDqi0Sg8Hg9OvHacM7lSv8RyTkSUPQsXL8WJk6cA9M8HEi9njbt249iPX8GJk6f4MB8RERERERERERERERERAbhMgt+VWeCAvvOKbvmV71bPIOr15mDlimWorqkFAGyt22Yo+L21rV0Evns8HsyfN9eyNBFR39Da1i6WGVTUt9nZziRSHraKRqMInT8Pr9f97TBRuvpLOXeybqDLU0dnAN3d3QCAgvx8PihClrh06ZJYDpx5m/3UPq6jM4DtO+rxk9Y20b4SERERERERERERERERESkui+D3zkAAFZWxQO1svaI7Xbv37BOvfLdjhvXyslnYWrcN4XAY4XAYjbt2pwyA37T5+2L5m4/PZaAK0WVIqUsBcObFPs7udkY245ESnHvnXQy79Yt94gE0okz0l3LuZN1Al6fqmlrxQC1n6CarlEyfBgAYMGAAymbNzHJqKBPBUAi79+zDgZeaEQ6Hs50cIiIiIiIiIiIiIiIiInKxyyL4ndRVfWclFi1dBiD17O/yrO8+nw8L5s9zJI1ERNT3rV1Tm+0kENmO5ZyIKHvKy2YZepsZuZf88BUQe9vcl8eNxZs//RmD4YmIiIiIiIiIiIiIiIgozqeynQDKnuKpRSjIzwMAMfu7lmVPfkcsL1ww3/a0EREREREREdHlZfSokaiprsLbHT9F3dbNGHTdtdlOEhERERERERERERERERG5jGMzvzfu2o2zZ8/i3DvvAgDuvutOFE2ehBEF+ZrfaW1rx4mTpwAAs2aWItfvV11PeT02ELtROm7sGADA2nUbAAAXLrwv1v3gw9+Jfx88eLCYHU5tG8FQCIdajuD1N07j0qVLGHbrFzFq5EgUTy3S3c8LFy4AAFY9uTytfZPT8MbpN8W6b5x+U6RZ3j8rLF38bVRUzgWgPft7467dYqa1gvy8lDPqKfum7IORfNM6hmrUjl+6IpFuNB9qSSqTebfdpptOre28/vobeP+DD3D9oEEYPnwYymbNhNebY+j7u3bvwdmz5/D+Bx9gwIABuOfuuzClaJJmeQfMnxvp6ugMoOXwEXFMjZy/mWo+2IKTp07h3DvvYsCAARg+7FbdfUyUmKfDbv0ihg8fjuIpRYaOSSpKvaBs//pBgzB48PVppVHejly/DB8+3LIZK63MB7lekynnIqBd32VaxtOhlM+z597BpUuXxHmYzm9kUm/pUfJm4MCBKd+UYWRdOX3KeZHJOWhFO5NJ2TVSVgBrjqXWbytthlq9fc89d+ueG2p1buOu3WIbSxd/WzXPlPyW9yfVb2kxWzemkm4Z0+s3yfWj2v4mliEz+aKVFi1G+hCZ9hH0yrla/1CtbTXaVqcqx8rvGe0rWVE3mO0TAfHjBSvOf730ZXIeZdpWqJU7s+e0/H0gvfIDJNdPqcq42bGSXI9+8OHvxL/v3fdCyj5tYt1iph3UYnV+pqrX5PxIZ0ystW4m/b7EeknZxutvnAYA1Nb8q+bxSKfsJLJrXCHvT2JZUiu/VtRZar+t189R6gK9c93Itqzo51vVt0xUt+1ZXLx4EQDSHlsXTZ6E+fPmWjJmIyIiIiIiIiIiIiIiIqL+7Yqenp4eO3+g+WALVq6uQjQaVf28ID8PmzdtUL35u3bdBvHa64b67ZrBB61t7SKAe07lbHGT+MZbhuumrSA/Dy827VPdBoC4V27Lhub6Ub/jGdU0P1RSis7AGQDAb947q/nbavsmp0GLvH9WKa+oFAEVatu/a9Q4Efyudxw6OgNYumwFuoIh1c99Ph82rvuu6ve1jqEa5bjKxy8dqcrk0Fw/Nm1cn3TjX05jQX4eli7+NuZ/a5HqdjweD55rbNANHpDLgJoZj5TgyeXLVG/+mz031PZHKy8XLFyMw0ePaaZx4MCBhtKiRj5ftmzaiPqdDeLvREbK/tp1G/Cj5/ZoHpOVK5aZemBidVW1Zl4AsfxYu6Y26d8T87pk+jQ8tX6jajq1yh8Qf9z18sPqfJCPkxa1+s5MGTciEunGwsVLRf2lJlW5MVNvAfF5I5d/uV49/uPDmgFOHZ0BTHt4BgBg8sRC1G3dnPR5dU2tZv4X5OehoX5HWoFO6bQziWVu4MCB+N7TW1S/NzTXj6Z9e1TTIrfHamXFimOpRW4zKmdXaNb/Ho8H236wRfVYy/mgVlclpi3V/ng8Hnzz8bmaDztYVTemyncg8zKW2L4MHDgQ//7MdtW89fl82PFMHUYU5OvWC0baTjVW9iEy7SPI2waS81s+pj87fUq3PVGrC2Stbe2a/Q8lr5VjarSvlG7dYHWfqPlgC2q/+5TobybSal+Nqtv2rGb5VLav1x6ZbSvkcrd50wZUznlcc1s11VW6bXRrWzuWPfkdzbzSG1sBqfsz8vma+Ltmxkqp+gSAej8y1fdGjxqJrZs3ZdyXsCI/M6nvU7X/slR9ikz7fXK9dOCFvZjz+IK4fEg8HpmWHZlT44rE7yaW39GjRpqqs/T268ALe1W/23ywBYuWLhNp0GqrbhvxJUSjUQzN9ePVY4eTPjfbzzfTt0zVhsr5oNcvTJfRayxEREREREREREREREREdPmwdeb3xl27UV3TG6gyNNePAQMGAAB++atfIxqNojNwBlMemJ5RsFMqBfl5AIBLly6JABOPx4Obv3ATgNhsjWrkm8lKmuVtdAVDKJ1VjmMtzZbOSpaTkyPS/MGHvxPBBz6fT7zqe/DgwZb9nqK25l8x/v7JAGL7Ls+2Js/6rjf7YUdnAI+WV4h8k9Os7Es4HEZF5dyUQT12koMOAPUy2RUM4dHyChx6eb9moMsvf/VrEUAh76tyUz4ajWLO4ws0y0hi4Idy3OVt7H2+CafffMuyoIFMJKZTzq/OwBnsfb4JHo/Hkt9Sgg3lc1Q+D5RAGK1AEa08Vc7daDQq6qN0y18k0o2S0plx9ciXx43F4MHX443Tb8YdMwC6AXq//NWvRToS8xOAKH8nXjue0XG3Ix/kulIOlJHLrdF0yNswW8YTj4lSbuT6ekf9Tly8eFH1mNhZb5U+XCIC1Q61HNEMct6ztzdwaNLEQsPpk9vQktKZhvPQTDtjpG1cuHgpGhvqU6YjkdljacQvf/VrUf9r1dsVlXM1g9YUeoHRgHp9kbg/0WgU33t6C86ePacb8Cj/XqZ1ox6rytiBl5rj3hAj7ysAhMNhLF22AnfdeYeoJ9Xqv2g0aqr+M8uqPkIqFZVzxD6rnY+Hjx5D3bZnVeuNjs5AXJC6WrmQj6lRZuoGs30ivfGC3F5cvHgp5fmiRq89Uo6r0h6pBZpa2VZcunQJUx6YnnROK+kAgOqaWhTk56nWQ3p5paRFGVuplVG9+knel0fLK3THZpmMlQYPHizyXt5feR9ycuLLx6rVVaLOANT7EidOnkJJ6UzVY5eKnfmZqr4fUZCPobl+dAVD+Elrm2YaI5Fu0Z8YmutPSoNV/b5U9YYVZcfJcYWeN06/KdrOTOosNZMmFop9az9xUnX/j0j7/sqrx1Xb7Y7OgDgOd915R9LnZo+3HX1LRd22Z20JfCciIiIiIiIiIiIiIiIiUtVjk9da23qG3jysZ+jNw3qGF9ze83LzobjPP/440vPNby0S64y/f1LSNr771Hrx+WutbYZ+67tPrdf9/MHpj6TchpKermAwaZ3hBbeLdb75rUVJ23lw+iPicz2p9k3+XG2frLbyO/9b9ffuHDlW/Htifig+/jgSly9q6f3Bvz8TVx7U8tbo/qY6lnqUdA4vuD0p3xPLZOLxTSwjatvoCgZ7xt8/Sazznz96LikN8rEdf/+knl90dOpuY+V3/rfuNjI9N1KdF//5o+fS2tdUaVEjny/Kvn78cSRuHbnsaJVDeZ1U5+7wgtuTfiOVxHKR+P1fdHTGnQOJxzSx7Nw5cmzK/FSrX1LVC3bnQ09PT9x+aLGijKci76vaMUmsrxPzwop6q6cnvgwnHlNl+2rtm0KpY+8cOTYpfXL9+4N/fybpc7lcZtJOGGln5HWMto1q+aRXbswey1TSrbcTj4VWPrzcfEikVU6zXCa++tjXU+5PYv8ocRtm6ka9fDdbxoz0mxLrRqPHIDEtqVjVhzDTR5C3rZbficdUrazLx1StHPb0xPfLUm1Daz9TMVI3WNUn+kVHZ1rbSLef8XLzId326OOPI3HbT9xfq9oKOa+MnG9q5asrGIwro2p1h7wNtWP/1ce+brj8JH7fqrFST49+26mQy4babxkpX3qsyE+z9b2c32q/39MT3x9P3Eez/b7Eeumrj3097njI65spO4n74cS4IvG7VtVZeuR8VJPYHqYa0yTWV2aPtxV9S61jnHh80+2rpWL0GgsRERERERERERERERERXT4+ZVdQ/abN3xfLT62pRfHUorjPvd4c1G3djNGjRgKIzRBYt+1Zu5KTFp/Ph6Z9e5Jm1hs3dgyea2wQfx8+egyRSLfTybPFk8uXiZn2fvTcHkQi3XGzvk+eWKg5w+m2Z7eL2eNmPFKiOovdgvnz8MSSRQBis+lVVf+bHbuhq7WtXaTzy+PGJs1i7/XmYE1tjcgHeVY9Nc81NiRtI9fvx+JvLxR/v/76G3GfRyLd+NFzewDEZkys3/FM0syAuf7YTHk+nw9AbLbTjs6A0d20zNa6bWJ52w+2qO6rnE6zJk8sxNo1tUkzBC6YPw9zKmeLv5/d/sO4zyORbvz7M9sBxPJU69z95uOxWWmj0Sh27d6TVtqUGTl9Ph/qtm5OSuOIgnysXNE7W3DL4SOa2/J4PNjxTJ1mfsrlL536xYl8MJoOJ8r4j195VSwvX7Yk6ZiMGzsGX310pvh79559cZ87UW99edxYALH2LRgKJX3e0RkQdeyEr9yblD7lszmVs5NmgFbqKyUPlTy3k17b+IDUxre1n0xru2aPZTq06m25PIbDYTQfbNHchpIPxVOLRFqV/7e2tYuZY4fm+tHYUK+6P3JfYuXqKt00Z1o3pmJlGdMqG4l1I6DdnsS1nW+cTmtfrGB1H0HP6FEjVduSBfPnYWhuLA/D4XBSvSH3ywry8zS3odRbTsqkTwQgbtZtrbKxaeN68bcyW7NRtd99Sixv2rg+qT3yenPi2t4DLzXHfW5HW/HEkkWa55vizZ/+LOl7GzY+LdLyzcfnJo2tAKBu62YxE3Rn4ExcGWptaxcziOuVH2Vs1hk4o9k2OzFWkvtSFY99Lem3cv1+VFetFn837T+Q1vatyE+z9f2Uokli+YhGnXLsx6+I5eIpvWm0ut83etRINDbUx52Dcttmtuw4Pa5IJdM6S4+y/z/7+S+SPpNndFfqm0MtyeMFpf3zeDxx9ZUVx9uuvqX8BgWPx4PnGhsyfjMKEREREREREREREREREZFRtgS/B0OhuGAAtWACRW3Nv4rl/QdetCM5aRt03bWar+geUZCPyRMLxd/Nh7QD5PoSrzdHBBdGo1Gs27ART63fCCB2E1sOyEkkBwo9uXyZ5noL5s8TN9NPnDzlygcHvN4crFyxDHMqZ8cFFSYqyM9TfZ09AIwdM1osv//BB3GfNR9qEYEPD0wt0gwM8HpzsHDBfPH3nr2ZB3tmQg7KHZrrTwoOUXi9OZj2YLElvzmj9GHNz+bPmyuWX/2v1+I+k/P0q4/O1Dx3y2b1Bs/KwbZGbPvBFjTUb8eOZ+o01/EPucHQtm7+wk2aZcfrzYkLIk6nfnEiH9JNR7bL+KyZpeJcVgKSFE7UW3PnVIpltQAnObBv5oxS1fR5PJ648i/zenNE0Hw0GkVrW7vhtGVi2oPFmuVq1Mje/L1w4YLlv613LNOhd+7J5VErCBHQz4e9+14Qy3IAnVo6lL5ENBrVDbbPtG5MxcoyptdvGjtmlFj2+Xya7YncV7x06VLqHcgCo32EVPS+e9edd4jl8+d/G/eZHARbObtCcxuJAY12y7RPJI8XCvLzNMvGiIJ88VCAEoBrhNyXmTyx0FDbm/jQgR1thdbx8Xpz4h5+SCQ/iKd3jOWyIT+MJNdPSxd/W/P7pQ+XiGWth/ncMlYaN3aMOB9Lpk9L67tW5mem9X2u3y/aNCU9iZQyP3rUyLg8t7rfp1cvmS072RhX6Mm0zkrlnrvvAhA7zonB/0o/d2iuXzwcqXY8lMB5ZR2FFcfbjr6lWuC7Vt4SEREREREREREREREREVnJluB3Ochv+rSHdNfN9fvFjHpdwZArA6ITTZICOuwI8suW+fPmxs3EbOQGuxzMkBiUoUYOaGhrP2FFsg0ryM+Pm7G1cddu1fXKy2Zh1ZM5vU4vAAAXLUlEQVTLVWf3NEIvD+TAtcRgV7V0KE6/+VZGacmUEowGAPdNGO/ob6vxenNEPREOh+PqCXlWxqLJk5K+q7aNX/7q12n9/rixYzBu7BhHgjkmT5ools+ePWv4e07kgxFOlfG777pTLC9eulx1ZvVcv1+cy3KglVP11oiCfFGnqj3c9cqrxwHEAqHkshUMhUT6bv9f/6KbPrm8BM68bThtVsvJGZjxd80cSyvJM+qmG/CmUIIXPR6P7oN/QHxQ+8lTxgN6ZXp1ox4ny5j8AMyg665N+/tOcaqPkMrAgdrn0rvv/VIspypfbqFXtuRA4vvvm6C7HbkvYvRNIe0nerd/zz136647b+430FC/HQ312+HN8YrfcbqPO2DAANV/l2eNvvNLt+tuY+yY0WJf5IdPlNnkPR6Pbj0ql62z594xnHaZFWMl+UGnrXXbNINwlfNR7lOkYkV+WlXfF95/HwD1B6Hkv+XAcsDZfp/ZsuO2cYWeVOe5Hnkm/8QHR5QH1KZPe0jUR52BM3HttlwuE+sss8fbjna/ozNgOPB97boNmv/Z/fAmEREREREREREREREREfVPV9mx0YsXL4rl/LzbUq4/7NYvipvinYGAbYFtVhkizfD8xuk3s5gSaykz3yo3sYHYbIhaM8MBQHd37w374cNuTfkbebf1loczb7/taPCWMmOrsn/VNbV4av1GfHncWNxzz90YO2aU7a9ol2ezNRJIPTTXj65gCF3B5GBQO8mBSmZmWbbS9YMGqdYTcpCqXHbVKEEgSmBJuiKRbuzavQc/fuVVXLp0yZbjItcv59551/D3nMwHPU6V8fnz5uKVV4+jKxibOXj8/ZMxNNeP+yaMx+hRI3XbESfrrWkPFmNH/U50BUMIhkKijgmGevc58SExebbnd9/7JR4q0X6IQM5vue3tS8wcSyt5vTnweDyIRqNxgXrpUM6pm79wU8p1C/J7z490zvVEWnWjnsutjBnhhj5CKvLMyf2B3NfYf+BF3VmpP/jwd2JZrsP1yOU11dtZcv3+pOPrpj6unJbBg6/XXdfrzVGtB+TZ5PXOeVmmb2GwYqw0buwYTJ5YiMNHjyEcDqOiMvaQ7oSv3ItRI0di7JjRGQcqW5GfVtX3xVOK8NT6jYhGozhy9FhcuVHeQqIWYO9kv89s2XHjuMIOuX4/fD4fwuFwXLmXH6SZUjQJ3hyvOGbNh1rEgxvyAzvyA3mA+eNtdbv/wYe/w6PlvW9FeGBqkW6/f0f9Tt00u/36DxERERERERERERERERG5j+3B70bozXLpRv35Vd7lZbOwtW6buEG/cMF83cCS7u70jrWZ2YGtUF42C96cHNR+9ymEw2FEo1EcPnoMhz8JLhma60fFY19La/bIdKQbRKQ1A+jlKFVwEoCMA1aNaNy1WwQn2cmK4Eo78yEVp8q415uDpn17sLqqWpy/XcEQdtTvxI76nfB4PPjyuLFYvmyJSkCjc/XWrJmlIuDnUMsRLJg/Tywr5JlCE4XD4bigs/7IzLG02s1fuCnj8yfdN9eYmV1WZqRu1HM5lDGjst1HMKo/9g2cfsjPiL7WxzXKzAM+Rlk1Vqrbuhlr123Aj57bg2g0inA4jL3PN2Hv800AYoHUcypnOx44a2V97/Xm4MvjxuLw0WNiNnmF8veXx43V3b5T/T4nyk5fN+Er92Lv801x+aQEtQ/N7X3IZvSokThx8hSO/fgV0aa8/sZpsZ5emTF7DKxo9xO///LBFsyb+w3NfpoyG72awYMHm0oLEREREREREREREREREV2ebAl+72vB7OlKN+Chrxl03bXihnaqmTL7SqCPrHhqEYqnFqH5YAtOnjqF02++JYKuuoIhVNfUouE//hNN+/ZYFpyo6I8Ba24yp3K2Ldtt3LVbzLLo8/nwWHkZ8vNuiwu2am1rR0Wl9lsSnGRXPhjhZBn3enNQt3UzgqEQDrUcwetvnMbPfv4LRKPRuKDVmuqquGBVJ+utXL9fzG6//8CLIvh9/4EXAcSCgfQCugvy83D3XXca+q2+PJtqpsfSapnOcgxYF8zutMuljBmVzT7C5WzyxELDD3IMSdE3tUpf7OMa4fP5MO3BYkPrZhqUauVYadWTyzF/3lw0H2rB66+/gTd/+jMxTjlx8hROnDyFyRMLUbd1s2W/mYrV5/6kT2a4j0ajaD7YIuog5YHLGaUP637fqX6fE2Wnrxs1cqR4OKO1rR3jxo4Rfb677rxDrFd4/32i/Cp+9vNfAEh+I1Ais8fbyna/ID8PnYEziEajqJzzOF49dlh1vReb9qWdTiIiIiIiIiIiIiIiIiIiPbYEv6fr7Ll3sp2EtHQGAmL5+kGDspiSvidw5u1sJ0FQAtyAWJBO86EWMet9VzCEiso5Wb9Rr7y2PpvSnfnULm+cflMs5+SoBx2tenK55b8biXTjqfUbAcSCfo61NNsa8Nja1i6WMw0ktyMf7GJFGc/1+7Fg/jwRWN7RGcD2HfVitubqmlr4h9yQ8cywZuutise+huqaWnQFQwiGeoNoAaBk+jTd795915196niaZfexTEU5Lj6fz5btyzo6A6lXMsBI3ajncitjRrm5j/DBh7/Lyu/aaUbpw47P3m01N/Vx9Qy67lrbz3mrx0pebw7Ky2aJh5+CoRB279knZoQ/fPQY1q7b4Nq6LFV9Xzy1SLxx4sjRYyieWoQjn7R7Pp8v5bnh1H5bUXbcMq6wS/HUIqxcXRUrl0eOYsiQG0TfYuaM0t71phSJB2ubD7ZgyJAbxMMOY0aP0v0Ns8fAqnZfeSDxoZJSdAbOoCsYwqrVVVi7ptb0tomIiIiIiIiIiIiIiIiIUvmUHRvNu+02sXz4yNGU67/73i/FstbNfTfdKJeDW/RmqezvM8QDQEF+vlh+5dXjKdc/e/acWNaaSe7iReePtRJUc6ylWQQ9dgbOiEBVqwy79Ytiuflgi+66wVBIBEHovSrejnNDPodPnjqls6Zz5GC/EQW95U4OqpIDx63SGQiI4zDtwWLbZ/oNnf+tWB4+7FbD37M7H4yyo4yna0RBPuq2bsYTSxaJf9u77wWxbEe9pad4SpFYPtRyBIdajqh+ppBnNZYDmy9HqY6lleTgxEHXXZvRNobmxmbx7wycSdkH6AycEctGZ2BVo1U36unPZcyOPoRTfYRUlHoyHA73iz6mPDO0PPuxHdtPFZgeDIXQ2taO1rZ2kbdOtxV65HM21UPDkUi32Be5jMpl125Gx0qZyvX7serJ5XiusUH824GXmg1/34r8tLq+n/CVewEAP2lti/u/8u+JnOz3mS07bhxX2On2//UvAIDTb74l+nw+ny+ujfZ6c0Q9ceToMbSfOKm6nsLs8ba63S/IzxMPozTU74DH4wEA7H2+KWX/n4iIiIiIiIiIiIiIiIjICrYEv48dM1osv/pfr+mu29rWLl5dnxgoIgetnHlbO2hFDhZ1wo9feVUsF02eFPeZfGNanvUw0YUL71ufsCzwenNE8Ic8q7GaSKRbBHJ4PJ64Bx3kG/Ln3nlXcxtmAs0eKinFjbcMx423DNcMUvF6c+KCTM5bXLZGjewt48qMjlp27+mdUfb++ybEfWb3uZHOOWxVoKFeEL9cTyQGSd9zz91i2UhQrF0Bg0YfQrh06ZLu5037D4jldILn3JIPVpVxPa1t7eJcXrW6SnO9slkzxfL7H3wglq2qt4ySA5z2H3gR+w+8CCB2fNUepsj1+9MKsO3LQbBmj6WVWg73PpSQTnmU3XXnHWK5+ZB+8Jd8rif2JWSZ1o16+lsZs6IP4YY+QipxDxfplC+3Hy/F2DG9sxsbCVxOd7/k7Sv1rpZnt/8QFZVzUVE5F5HuCADn2wo98jl74uQp3bxoaz8h9qWt/aT49zu/dLtYbty1W/f3zJYhvbGSUcr5OKFwsuY6IwryxTFS6kIjrMhPq+t7ZVbwaDSKBQsXi4cD5dnCZU72+8yWnWyMK7Kp8P77AMTqDeVcUHuIQVnvJ61teP2N0wDi81pm9njb2e57vTnY9oMt4u+Vq6scfziMiIiIiIiIiIiIiIiIiC4/tgS/e705mDyxEEAsEGHBwsWq60Ui3aipXSP+nlM5O+5zOZBLa8bFSKQbW+u26aYn3QCUzsAZzdfTN+7aLWa9U5uZbfjwYWJZa9b7js4ADqcICk0n6FUrrU6peOxrYrlyzuOaN8tXV1WLQI6vPjoz7rNcv1/MGKc3g2JV9b9lnE45aGzbs9s11zv95ltiWZ710wrFU4tE4MHho8c0A0g6OgP40XN7AMSCqOTAz1i6rDk3tCSew2vXbdBM597nmzL6jUS1331K87jL9UTJ9Glxn5WXzTKUpwCwdt0GlJTOzPic0QrO6+gMYKVO4K6sKxhC3bZnVT9rPtgSV7+kU3c5lQ+pAmytKuP6aeg9L18+2KJZbtraT4hl+fwHrKm30iEHQnUFYwFBpQ+XaK6/cMF8Q+kLhkIoLCrWPEf1WD0zcSasOJbp0Dr35PIIAFOKMgvWnDf3G2L5qfUbDfUlCvLzdGdsz7RuTMWJMuYUK/oQbugjpJJYvrSCCxcuXmrqd5yqG3L9fkPjBSD2oMfoe8enNaOwvP2uYEi3PXr5k+0W5Och1+8XnzndVuiRz9nVVdWq60Qi3dj8/a0AYm2r/HaR5cuWiGW9+ikS6cbCxUtRXlGpub9mxkqAsbddyA8eaM10HYl0izZVWd8os/lpdX0/oiA/rv8ExPZJa30n+79my042xhXZJD94oxx7+eHQxPWi0Sh+9vNfAAAmfZJPiaw43na2++PGjhHXcqLRKCrnPG74u3bJ9jUSIiIiIiIiIiIiIiIiIrKXLcHvALCmtibuBm15RWXcDcjmgy0oKZ0pAhYmTyxMCvSUgwC6gqG4bXR0BtC4azcKi4oNzfQnB0UpgTN6N0QfLa9A467d4qZwJNKNtes2oLqmVqxT9Z2VSd+TA+b2Pt+Etes2iOCk1rZ2rF23AY+WV6RMb05O72zAB15qRkdnAJFId1ygUyTSjdtGfAnTHp6hOyui3crLZolAqa5gCCWlM+OCkzo6AyivqIwL5Jg/b27Sdr48bqxYlrcRDIXQfLAFD5WU4sTJUxmnc97cb4hysKN+J1YlzEqnpFMpkwX5eaqzMpu145k6sVxdUxtXRiKRbjTu2o1HyytEENXKFcuS0mHluaFl+bIlcflVXlEpgo86OgOGy7JR4XAYhUXFaNy1G8FQCJFIN5oPtmBC4eS4wKbysllJ39247rtiubqmVvPY7qjfia5gCHv27kvahpZxY8eIvA6Hw3iopBTNB1vQ2taO1rZ2rFpdFXe8AODsuXd0t/m9p7dgwcLFSfm5aOkysY5a/ZKKnfmgGDBggFheu24DIpHupLrUijKux+vNwYxHYoHj0WgUhUXFcXWO8hvyAwmTJ02M24ZV9ZZR5WWzxPkEfBJEN7VId33lQYOuYEhzH6c8MB3hcBg/em5P2rNsGmln7GbFsUzH957eElceASSVxxmPlMQFv6Yj1+/HE0sWAYjtT2JfIhgKxfUlPB4Paqr1H5wxUzfqcaKMOclsH8ItfQQ9uX5/3Pky5YHpSeXCbF8JcLZuWFNbI/L98NFjeKikNK5NUc6Zisq5iEajIhDZKLkvY6Q9Wrr423Hfd7qt0FNeNksEeBsZW3310ZlxZTTX748LTn20vAJ1256NC4BVtnHi5CmcOHkq7sGjRJmOlQBg4MCBYnlH/U4EQyFRjhWLv71QLM//1iLNtCrumzBeM61qrMhPq+v7x8rL4v6ePu0h3fWd6PcB1pQdp8cV2ZTr98c9jKHV55PXU+ogeZb8RGaPt93t/qonl8dtX++NPnZyyzUSIiIiIiIiIiIiIiIiIrLXFT09PT12bbyjM4A5jy9IGYA7eWIh6rZuVv2s+WBLXEComqG5fhGYMKdyNlY9uTxpnQULFyfNtl6Qn4cXm2I3hVvb2lFRGQtW8fl8+OMf/xgXzJpOmletrko5a52c5ob67aozPN81alxS3sn7J6cZAA68sFd39lijHiopFbPUaaUtUSTSjYrKOeJ7Wobm+tG0b49qwFgwFMKUB6br5rucb/LxM6qjM5AUqGw0nXJ+p/rtG28Zrrte467deGr9xpTpqKmu0gxoNHtuGNmfxl2744KYUv2G0fKikMva5ImFum9E8Hg8eK6xQbOMG0mr8jta564WI+Vm9KiRIuBwaK4frx47LD6T83r0qJH42c9/kVH9snbdBuyo3wlAu66zMx/0tv+b984mrWe2jKeiVq+n8xtW1Fvp1Jdyeo3kfyTSHRd4pyXVuaEnVTtjpMwB8WVcbT2lTgSSywpg/ljqkX9brq/UaB1ro/mgMNIP0DtuVtWNqfLdTBmzsk1Mdz01VvQhzPQR5PQDyflttK6Qy5raekaOmdm+EpBeH9TscTWb76kY3b6dbUWqc1EhlxMz56xeG5NpfWvlWCkYCmH8/clBqYll3u4+jRX5aba+lyXmy/EfH075MJaZPEp3zGe2rXZyXJH4XTvaLD1yudArP3XbnsX3nt4CIDZGaGyo192u2XPCbN8yVd5EIt0Yfe94UTds2bRR92FPo1LVjTK7rpEQERERERERERERERERkbvYNvM7EJud+lhLM2Y8UhI3463C5/OhprpKN1iheGoRtmzaKGZeTvz+E0sWobpqdcq0rKmtETORpTLoumvxXGOD6voejwdPLFmkm+a1a2oxp3K26j4PzfWjoX67odkJdzxTp7rfioL8fPEbQ3P9Wb2p6/Xm4MWmfXhiySLVNHs8HsypnK0btJTr9+vm+4xHStC0b4+pdI4oyMehl/djssYr5Y2k0wrlZbNw6OX9YjbRRAX5eWio364b5GnVuZEqnQ312+NmL1QoZVmemdOMuq2bNctPQX4eDr28X7eMl5fNwoEX9mrmqZH6RsuIgnzNsunz+bBl00Y0NtSL87ErGD97qWz4sFs1j72R+iUVO/NB2b4y83Cq9cyW8VTqtm5GTXWVZj2Z6jesqLfSMaP0YdVlLV5vDl49dhhzKmdr7uPkiYUpzw09qdoZp5g9lkY17dujWf8rbYwVx3rtmlrNuhNI77iZrRv1OFHGnGJFH8ItfQQ9Xm8OmvbtUe1bW9VXApytG0YU5OPEa8cxeWKhat/ZbL6nOq6jR43EgRf2uqat0KMcf71xxpZNG3Xb+Lqtm7Fl00bN+slIfWt2rJTr96Omukp1H2RKP1RrDGe2T2NFflpZ3+f6/WJfC/LzDL2FxO5+n8xs2XFyXJFt8ltqJmnUPUD8W+PuufuulNs1e7ztbve93hxs+8EW8ffK1VW6b9uzg5uukRARERERERERERERERGRfWyd+T2R8mpzABgy5AZDN/RlHZ0BdHfHAkpzcnIyupEpb6MgP18EqWjNBheJdKMzEMj4N83uc+I21Ga/a21rT2tWPCeYPVbBUAjnz/9W/G3X/sl5m2mZMksuY0B8uTTKinMjFfmYZFqWjZL3x4rzxsr0yvlgNq8Tj70d5dyJfEi1XSvKeDrpyfQ3nDiPzLCzXkzVzjjJimMp05px2al9tvK4WVE36nGq7bWbVfvhhj5CKlrl2IqZi/W2bye762J5n/p6W2G2TkinfbZjrJTOdxPTakcdaDY/3VCH2tXvS2S2b+fkuKI/M3u83VBm7eLGayREREREREREREREREREZB1Hg9/dLJ1XoRMREVHfoBX8TtRfNR9swaKlywDE3miwdk1tllNE/QHHSkREREREREREREREREREROQWn8p2AoiIiIiIKLXWtnaUV1QiEunWXKd+Z4NYHjVypBPJIiIiIiIiIiIiIiIiIiIiIiJyDIPfiYiIiIhcLhLpxvxvLcKJk6dQUjoTzQdb4j4PhkIor6hEZ+AMgNjs3MVTi7KRVCIiIiIiIiIiIiIiIiIiIiIi2/z/MvtCeThOfaMAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "8\n",
      "4\n",
      "2\n",
      "31\n"
     ]
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}