Python_First_Glance - Extended.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/schwaben-github/Python_ExpertCall/blob/master/Python_First_Glance%20-%20Extended.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYFz_ekMnR2N"
      },
      "source": [
        "# \"Python at first glance\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5u5CKO4m7Pp"
      },
      "source": [
        "There's one issue that seems to confuse beginners as well as some experienced developers: Python objects. Specifically, the difference between mutable and immutable objects."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1yokwYcoAYN"
      },
      "source": [
        "### In Python, everything is an object\n",
        "(*introduction*)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1PVroHejmxzv"
      },
      "outputs": [],
      "source": [
        "isinstance(1, object)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mb63PRftoGS_"
      },
      "outputs": [],
      "source": [
        "isinstance(False, object)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7p804qNcoI1P"
      },
      "outputs": [],
      "source": [
        "def my_func():\n",
        "   return \"hello\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MKgdCWpYoMl3"
      },
      "outputs": [],
      "source": [
        "isinstance(my_func, object)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIGv5gLRo4U7"
      },
      "source": [
        "Python has a built-in function, `id`, which returns the address of an object in memory. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Q4fphQVo8Yu"
      },
      "outputs": [],
      "source": [
        "a = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "312QxOZeo_IH"
      },
      "outputs": [],
      "source": [
        "id(a)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXhy2TOdpG_4"
      },
      "source": [
        "Above, we created an object by the name of `x`, and assigned it the value of `1`. We then used `id(x)` and discovered that this object is found at the address `1470416816` in memory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hL29dhYkqSen"
      },
      "source": [
        "This allows us to check interesting things about Python. Let's say we create two variables in Python – one by the name of `x`, and one by the name of `y` – and assign them the same value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tkpc3E7YpDG3"
      },
      "outputs": [],
      "source": [
        "x = \"I love Python!\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_k0eQCYYqcTq"
      },
      "outputs": [],
      "source": [
        "y = \"I love Python!\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCPaudxKvxR-"
      },
      "source": [
        "We can use the equality operator (`==`) to verify that they indeed have the same value in Python's eyes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s8vY1b3Tv6KO"
      },
      "outputs": [],
      "source": [
        "x == y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e58aBISLwEnF"
      },
      "source": [
        "But are these the same object in memory? In theory, there can be two very different scenarios here.\n",
        "\n",
        "According to scenario (1), we really have two different objects, one by the name of `x`, and another by the name of `y`, that just happen to have the same value.\n",
        "\n",
        "Yet, it could also be the case that Python actually stores here only one object, which has two names that reference it – as shown in scenario (2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB7XKtA1ADWF"
      },
      "source": [
        "![image-19.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4kAAAIlCAIAAAD35SojAACCvklEQVR42uzdeXxMV+PH8UwiSCRiq7U11K6t0Ba1tFG62GPXKoZSPNqKoqVVhqqlHoSnVJWKXaslpWjVMlG1FQkidglC7BlJJtts+b2a++sxJjOTSWYy2T7v1/yR1507N2funJn5zjn3nOOWAQAAABQMbpwCAAAAkE0BAAAAsikAAADIpgAAAADZFAAAAGRTAAAAgGwKAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAkE0BAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAIBTqFSq0NBQ5eMCHucGoFDx8/MLsC4wMFClUsXExJBNAQD5Sa1Wq1Sq4ODgoKCggIAAPz8/a19sZt9kSgCFivQet8H0za5QKIKDg1UqlVqtJpsCAPJWTExMSEhIYGCgXC43S58KhUKpVKpUqoiICE4UUGx/ppp+Mvj7+ysUitDQULIpAMDJpEhq2sGnVCpDQ0MLTkcegAIiIiIiJCQkKCjI399f+tCQy+VKpdI1LalkUwAo+qlU+oLx8/NTKBQqlYpzAsBOMTExQUFB0jU/crncBW2oZFMAKLLUarVIpSEhIZwQALn+MFEqlVJCDQwMJJsCAHJD6sQPDg7mVABwCoVCkdefKmRTACiy5HJ5QEAA5wGAM7Ojm1tQUBDZFACQY9LsMJwHAM7NpkqlkmwKACCbAiCbAgAKLWl0rf37X/9XbGxsenq6tFGr1cbGxoq7EhMTC8vTT0lJuW7drVu3jEYjlQQWiTr/4MGDQlf4uLg4qfA3btzQ6XRmz+j69etJSUnSRqPReOvWLbH9/v37ZFMAQB5SKpVubvZ+zqekpHj/q3z58sePH5e2nzlzpmLFiuKuJUuWFJanv23bNm/rmjVrptFoqCSwqHLlylI9GTZsmF6vL1yFf/bZZ6XC16hR49KlS9JG03fxN998I21MSEh44YUXxPZ33nnHnidLNgUAuCKbJicny/7l5eV19uxZafvly5fLlCkj7lq5cmVhefqhoaEy6xo1aiRaj1BM3M1kz54+Pj5SPRk0aFChy6Z16tSRCl+hQoWLFy9KGy2+i1NSUpo1aya2DxkyxGAwkE0BAAUim+r1evEV5e3tfe3aNWn7vXv3fH19xV2bNm2Stvfr1691TowaNUqr1bry6W/durVkFmTTvGMwGKy9+m3btu3bt++MGTO2bt0aGxtrTwByotu3b3/zzTfdu3evX7/+4sWLi3w2ffbZZ6XCV6xY8cqVK9JG03fxTz/9JG00Go2tWrUS299//327siPZFAAKLF3UUV3U0aKRTTMyMjw9PUVzy61bt6SNDx8+rFy5srS9RIkS27dvl7bXrVtXlhNt2rRJS0tz5dNPTEyMetyhQ4fq169PNs0jGo0m22pQokQJuVz+4Ycfnjt3zmXX+27fvl0UwM6JOQt1NvX395cKX6VKlZs3b0obn3jiCfES7NixQ+z8+uuvi5MzceJEsikAFGKpa2YlDGj0z214i7SfFxs1CQWthCEhIW5ubjExMXbuL3r96tatK4aAJCYmihjq4+Nz8ODB3GXTtm3bujibZhUfH9+kSROyaT5mU0Eul//444+uiafFLZs2bdo0azYVHf2m72KpA0Ta7u7uvmDBArIpkM8Md2/or0Q+ul09Z0xNtueBRs1DfXTUowfGRBlTGVRR7KTtXP3/2fTfW/L899P3byk4JVSpVG5ubiqVys79K1SoIH1LPffccw8fPpQ2JicnP//889J2Pz+/EydOSNu///77+Y/r2LGjSABvv/222b0//PBDvn/Hk01dlk07d+5s+urPnj172LBhDRs2FG3zMpmsfPny27ZtI5s6neimr1atmri+VlxXWq5cufDwcLHzyJEjpe0eHh7fffcd2RTIZ+l//pI44qUEhf//34a3SN+7Kdvf8UadNnXjvIShz4sHJk3ubbh7g/NZDCXPf98snkrNqOn7txSEZtScZtMqVapI31KtWrUSY9hTU1Pbtm0rba9UqdL58+etPXz69Onu7u7SnmvWrCmArxfZ1GXZdObMmWb36vX6hw8frly50rTFvU6dOtevXyebOlfr1q2lwteoUUOtVksbrb2Lx48fL7Lpjz/+SDYF8pkxNTl50UemqSJpfGfD7Wu2H6U7+3fiqLaPHvVeS+2x3UyUWHxrkSYh7efFCcNbZE2oKUs/1V89W4iyqVwul76l2rdvn5qaKm3UarVdu3aVtletWvXq1atkU+Qimwrh4eE1atQQ/cgzZszI68/P4pZNX3311azZtEuXLqIxVYx0zMjImDVrlsXrUMmmQL7RXzufNL7TozzxzjMpITOM2nQbQST5q5Gm+6eumWVjfxQf2mN7UpZ+mrUZNWXpp/k1Xiqn2VRckda5c2cx975Op+vTp4/4qhNjpIpbNtXpdHFxcQcOHNicaceOHVFRUSkpKTZylVarTc8k5j/PpgrZt7/RaExNTY2IiPj99983b978yy+/HDt2TK1Wu3jwe66zqdFo/Prrrz08PKQ9W7RokZKSYnYG7JzSwdr+6Y/75ZdfRMHmzZuXboVWqxWvpsVsqtPpLl++vGvXrs2bN+/cufPMmTPibZJHlce0SNJGswJYvIb7tddekwpfs2ZN8SNTvIuffPJJ03fxwoULRTbdt28f2RQoAI1eBkN62JaEd18QSSJxRCtd5EGrO+/9MUHRVOysmfoWvfkw+/XyT0LN0oyaOKZD2s7VLu7oj4iIcHNzCw0NtXP/Bg0aSN9SvXr1Et/HBoNh8ODBYvyKaIYpPtk0JSVl586dvXv3lsvlIrJ4enpWrlz5ueee+/TTT6Ojoy2GjGbNmjXK1KNHj2zn+b9x44bYX6FQWAs9Dx48WLJkyYsvvlixYkVpSix3d/fy5cvXq1cvKCgoMjIyHxOqndk0IyPj2rVr4gKSqlWrRkdHS9vbtWsnnYE2bdrcuXPH9r9Tq9Wvv/66tH+7du0SEh69uRo97qmnnhIFq1KlSiMr/P39IyIizLLp4MGD9Xq9wWA4ceJEv379qlevXqpUKZlMVrJkySeeeKJ79+5hYWE2znmuK88LL7zQqFGjJk2aHD58WHobHjx4sFu3bmYFePnll/ft22fWsiuyqVwuFxVp0KBBFt/Fy5cvF6U6etSuX9FkUyDvw0Rqcsq3nyW80/hR4pw51JAYn3VPfVxM0vjOj9LGqDbaiP2cQFhMqOn7tyRN6mmxGdWVHf05+hb5+uuvP88kpj+UbN26Vdq+YMECG016RTKbXr9+vWfPnt7e3jaGnD/99NNr1qzJ2tT30ksvSTv4+vqK0GPNkiVLRFPi7NmzLe5z7NixNm3amA4nMlO1atWlS5fa2ZiXj9k0MTGxZcuWYoDdsWPHpO19+/YVOemHH36w/e/+/PPP0qVLS/ubLWgky5VSpUodOnTILJuOHj06PT19xYoVIkybKV++/Jo1ayzmS0cqj1QAT0/P7du3p6enf/vtt2ISKDPlypVbsWKFaT7u1q1b1mxq7V18+vRpabtSqcz29wDZFHCdf0LnxMBHAULhn/bbauPjP4WN2vSUVV8mvPPM/+8z8NnUHxYYdfTmw5b0/Vs0MwZnTahJk3q6ZrxUXn+LFO1sevbsWRGhpKBQvXr1OpmqV69umhHLlCnz3//+16z5aubMmWKHyZMn2/hHOp1OXNRbqVIlsSiXqb1799aqVUsc0NfXVy6X16lTRy6Xmy75U7JkyWnTptl5FUF+ZdOUlJQOHTpIe5YtW1Y0123YsKFEiRLS9sDAQNvXoX7++efidfnll19M73JiNv3Pf/7zxRdfSE2V0mz2tWvXrlWrluk5r169+rlz55xbeUQ2Xb9+/YwZM2wXoHLlymICjYyMjO7du0vbn3322bxY8IJsCriI9vDOhGEvPmoTHfuGPvbiY18eJ/9MeO+lfy8zbaz5UmFIeMB5gz0Md2/8MxmqlfFSZNOCmU3j4+PFmBKZTNawYcPly5dfu3YtNdO1a9dCQkLE7FpSxvrjjz9Mj3Dq1KmyZctK97Zs2VJc+ZfV+fPnRatYt27dsuaJS5cu1atXT1wX2Ldv3z///DM+Pj41NTU+Pn7Xrl19+vQRccfLy+u3334ryNk0KSlJzHNUsWLFM2fOSNvv3btXu3ZtcXGz6ZAdMw8fPhSzeDZo0ODevXum96573IQJE0TBBg4cuM6KjRs3iuOIbOrv7+/l5SUlyFGjRp06dUqj0SQmJu7YsUPUJZlMNmbMGOdWHqkAHh4eHTp0kIKpr69vUFCQtQIMGzZMRPlevXpJG5s3b042BQoxY3rqP82ig557NChq2WRj+v9fZm5IjNd8qTDpzW+rO/s3Jw05Zbmjf3iLtJ2r8+jCZbJp7rKp0WicM2eO+OJ/6aWXLly4kHW369evt2vXTuzWoUMH02PqdLpXXnlFLAN76tQpa/9u6dKlIneuWrXK7F69Xv/ee++JiX4+++yzrDE3NTV1/Pjx4qqAV155RQwwKoDZNDY2VgzVN13owWg0jho1ShwkJCTE2hEOHjwomhJHjBhhu4XVkXH6Yr76r7/+2iznHT58uGLFitIOzzzzjJgS2CmVx6wA5cuXX7t2rVlzuGkBGjZsGB///5eiidpCNgUKf+PWvZtJn/c1TQzaE/uMRqPRYPhnlnWF/6Me/63fGXVazhhyR3/1rIXxUpltqE4f0U82zV02vXr1as2aNcXomdOnT1vb8/Lly08//bRosNy1a5fpvd98843Ii9OmTbN4BNMO/apVq2ad7/Po0aOi/bVbt27WQmdSUlKnTp1EFN67d2+Bzabr1q0TfffDhg0zvVZSpVKJ0NmjRw9r0Up06JcsWdLsnDs9m3p7ey9cuDDrZRIGg6Fz584ivEZGRjqx8pgWwNfXNyQkJGv+tlYAkU3bt2+fF1d3kE0B1zEajdqjux513A9opFG+bYi/q4+9mDimw6O1f+aNNiSqOV1wtL5ljpfK2tefOKZD+v4tzmpGDchENs1pNl20aJHIlBMmTLDRLGc0Gk2f+OjRo013vnLlSqVKlaS7WrdubbFb/+LFi6L1a8CAAVln05w4caK17Gvmp59+Epnv888/L5jZ9Pr166I73tvbOywszOwgYgUjPz8/iyvuJiUlNW/eXKxhZjpC3+nZtGTJkpMnT7Y2vOyLL74QR969e7cTK49pAWbMmGEtYloswAcffCBteeONN8imQOGPC9r0lJAZj8bsD26S+tP/MkfxPyNyg+7SSU4UnCvt58Wmv39Mx0uRTfMlmwYGBtrTFy85c+aMr6+vtH+TJk2Skx8tfazT6Xr27CmGvEjzAZlZvny5FGXc3d3NxvRIRJLz9/cXHccWRUdHV6tWTdq5d+/eLp5PKttsqtfrjx8/bnohZr9+/bLGvmnTpokLGJYuXZr1OAcPHhQjgSZOnJjt1P2OZNNXX33Vxvxf4mIMmUy2detWJ1YeRwogfsy8+eabebFwANkUcDXDg9tJn/czHbOfMKjJ//89pFn6rnXG3H7cp+/fkvbzYm7crN2yxlOxAirZ1MXZtHr16tJutWrVynY4f0pKihjXUrZsWbPlCTZu3CjaMmfMmGH2WJ1OJ6JM3bp1xernpqpWrSrt0KdPH9sluXfvXqNGjUTQsTH6Kq+z6bhx4y5lunz58qVLl8LDwzdu3Dh48GDRiiyTyV588UWLy4ydPn26XLly0j4dO3bM2q0/ffp06d7SpUtbjPtOzKa214UKCQkRRxYLfjql8jhSgE8//VTa0rdv37z4fUI2BVzedGo06k4dSBzRyjwivNM4OXiMMTmXixxqj+2xEDu4cbPvlrZzNdnUldlUJINOnTplO5rEtHHU29vb7PrCGzduiLmf2rRpY9qqKrV0ihw2duxYi8cXA/AbNGjwtk29evXy8/OTdq5Xr54YY+T6bFqiRInSJkqVKiUCutQg+vLLL5uu6v7Yp6VWK9pWK1asGBUV9VgnQ1pamzZtpHtfeOGFbBc1cH02dUrlcaQAs2fPJpsCRS6e6nWpG+Y9mspU6mAd30l/7Xyuj6m/epaAxS3Xt9Q1s8imrsym0rRBMplsyJAh2X67GwyGoUOHipa8gwcPmt07evRoET7EPPOSFStWSB36pUuXNrvyUgS13M3WWadOHYutsK7Jpta4u7vXqlVrxowZYlC5ReI6B5lMNn/+fNO7jh8/Lmaz/+KLL+wpmIuzqVMqD9kUgHnTqfbQjoShLzzWaLr4Y2OqxpHDGu7eoNuam42bxVn6E8d0SFn6aa5n6Q8KCvLz8yOb5jqbDh8+PNtvd6PRKEZGZ82mGRkZv//+uzT23N3dfdasWaZtZh07dpQe2LRpU7MmVQezaYFqNy1XrlzTpk179uw5f/78mJiYbK8QjY2NFRO+tm3b1nRqghkzZoiR6dlezZm/2dSRyuNIAebPny9tGTp0KNkUKCIMd64nfZplEsphL6bvD+XkIE+q3N0byfPftzjvqYNHViqVbm4u+qgvktl08ODB9jR9DR48WAylP378uNkODx8+bNiwobTD888/Ly4DvXz5cuXKlaXMam2GKdNs2rRp0zF2mzlzposXLzXNpuPHj79sIiYm5sGDB/aPy9Hr9e+8845Yk1Os+JqamhoQECAmSLLzCeZXNnWk8jhSADFAavjw4dn+DCCbAoWh0TRVk7J8SsLAZy2sMzmuo/76RU4R8jqVamYMdtY0UmTT3GVTkQwsrtKUNT6K5k8fH58rV65k3WfSpEliB7G85KpVq8RkSeHh4dYOLs7qiBEjCnJltn9+UzsDpXjiou/+9OnT0gh9d3d3s77+gpNNnVJ5HCnAqlWrqmYaN24c2RQo/MHUYEjf++Oj3vx3GmumD0xQNH3Usz//fUNiPCcKTkml5jPwZ06/79wFosimucum5cuXl3Zr3LhxtkOtExMTxUqbtWvXtrj/kSNHRHOa1K2fnp4upspv166djWWcKlSoIO3WpUsXF08LlY/Z9P79+/Xr1xfrG0nnZ/bs2VIdq1ixosXFlgpCNnVK5XGkADqdTpMpLS0tT7Ij2RRwJX1MVOL7AY8aSj/vq4+9+E8zqhgXNei5tNClRoOecwVHU2mW0U55sWxpfmXT1atXF8Azb382NV1r9O+/s1mg+NChQyJ39u3b1+Js56mpqWKqIClpXb16VZpy393dfcmSJTaOX7duXTG8yWyCqiKcTTMyMiZMmCBehRMnTqSnp4vx+507d7Z/VnkXZ1OnVB5HCpDXyKaACxNDwoPk//7n0cT7iqbawzv/WbL01tXED9s/Gpsy4iVtRFhedJSgyNNFHc064Omfxvg8SKXiq8vNzc3i4jp5mk0Le7up6IKXyWRBQUE2Wiv1ev3w4cPFACAbS8CLNr8KFSqcP39+7dq10rRKVapUsXgZgNCjRw8xyH3VqlXFJ5seOHBADMmfPXv2iRMnpBmyPDw8Vq5caf9xdu3aJQo2adKkvM6mTqk8DmZTQyayad6eCCCvGXXa1E2LHk2zP6BR8uIJ0sD8fzr6f1/7qGd/QKOkyX3yLkygqKbSrG2lTu/Bz0qlUrm5ualUKrJpjrLpoUOHxBL2VapUsdH6FRYWJvpwGzVqdPv2bWt7njlzRkw+umLFCjHWx1pTq7Bq1SoxxWnDhg0vXbpUTLJpSkpKq1atpAO+8sorYr2oypUr37iRgzdOZGSkOPPPP/+8Wp39otOOREOnVB5HCnDw4MGOHTu++uqr27ZtK9bj9NPS0iZkyvbSinyxefPmadOm5agqo9hFh1MHEke1fdQ4+n6APubRhM/G5CTN7OEmU0o988+UUumpnDdkS3tsj3lb6fAWedSDTzZ1VjZNS0vr06eP+NZv3LhxeHi4WW+JwWD466+/6tWrJ+aTX758uY3/npqa+sYbb4iFKKXFRUuUKLFu3TrbxX7w4IFYtlQmk7388svHjx+3kVf0en0eXWjo4mxq2tjs4+Mj1sd66623sh1jZCopKUm87iVKlPjiiy8srpil1WpFknMkGjql8jhSAHEBa8WKFbPOaFZcsmlaWtqYMWOk7oaOHTtaXIIsH+3fv1/6wVS7du3169fTuIus/pk0anIfk3VKm6b9usLsolL95VOPLSmp8E//8xcuPIWtHzyWevBT18zK9WSlBSqbLliw4PPHicl9ZDJZ9+7dze797rvv8mJp7xyVcPz48dKcTTKZrFKlSp988onpvVOmTDGdL/PUqVM1a9YUz+jJJ5/87LPPjh49Gpfp77//njRpkjiau7t7//79LU5Qauqbb76RkpaYWL5GjRo2mlqFbdu2iVXXpcwxbNiwn3/++cqVK1J5rl27Fh4e/tNPP82YMePdd98dOnRojtJbgc2m58+fF0PBRIzbtGlTTo8zbdo08cOpVKlS/fv33759+7Vr1+Li4qKjo/fu3fvVV1/1799fTKHgYJe645XHkQKIZyqTyT755JPimE21Wu27774r3mZSBPz9998LyNV4J06ceOqpp0TZvLy8fvjhB74yYcqYnpby3eePLjMd0Cj5q5HGJPNOH6NBn7blG9PFohLHvqG7dJITCAu/drLODDW8RdrPi12ZSvM6m4oBOnZq27atixvzclrCEiVKbNiwwfQI27dvl1o3RYbw8vLyyeTl5SUSgLu7e6dOnezpmouOjq5Ro4bpP7VzdnS9Xr9kyRLTeCqTyUqWLFmmTBmpPGXKlPHy8ipZsqR012uvvebiyU3zKJvqdLquXbuaPuvatWvfuXMnp8e5cePGSy+9ZPHslSlTplSpUu7u7qVKlTp06JBTsqnjlceRApg+zVGjRhW7bKrT6cQwOrOX/L///a/r3xhZf29Vr17drGzlypWztoYvimMwNRjSwzYnDH3+UeL8z8u688ctB474u5pp7zw2D+XMoYaH9zmNME2l5teVZk6h7/pUKomIiHBzcwsNdf6yEcUhmxqNRpVK1bp1a9OGKDNPPPHERx99ZHsFTtOI+fbbb4vHenp67ty5086no9frQ0NDmzRpYqMwQpHJphkZGatXrzZt/xo+fHjujnPhwoXWrVvbOGnOzaYOVh5HCiBNAStV6fXr1xevbGo0GpcuXSp+pZnx9PT87LPP8vEbIjY2tnnz5hbL1qtXr3zPzSgg9DFnk8Z1fBQjBj6bun6uUWe1euhOH0x4r6Vpz37qpkU29kcx+p2jSUj7efFj85UOaJQvbaWu+SIZPnx4l5z4+OOPXdzLnNMSdu/e3eKK9mq1etWqVd27d2/cuLGvr69npmrVqrVq1WrixImRkZE5ulRs9+7d3bt3l/7j8OHDHz58mKMndefOnY0bN/bt2/e5556rUqVKyZIlS5Qo4enpWaFChSeffLJ58+adOnUaP378oUOHXN97qdFoxMl04nxG0dHR4kpTb2/vffv25fpQ8fHxK1eu7Ny5c506dcqUKSO9lJUqVWrUqNEbb7wxZ84c8XL06dNHeiLz58+38fru3r1b2q1r165//vmnEyuPIwWYNWtWhQoVypYtO2bMmGyvMylq2fSvv/4y618wU7Nmzfz6LFar1W3btrVWsJIlS+7atYuvUhg1CcnBHz628tOkHob7cbYeoktPXT/XtGc/4b2W2kM7OJnFXPr+LWap1AVj8AvIF0kxYTAY7t69e/bs2VOZoqOj83Hsr8FguH///pUrV06fPi2V5+LFi9euXUtISCh609v98ccfYgbQNm3aOB62dDrdzZs3xUt56dKlO3fu5OnF0C6uPAaD4eLFi+fOncujX4MFN5veunWrQYMGtvsU3nnnnXypxxqNRiy2YU379u2ZnxJp25YnKPwf5Yl3X/hneFN2FcNwJzbp426PJdqPuxluXeV8Fk+6qKOPDZLLXHG0QE0xRjZF4ZWamiqW9HR3d1+0aBHnpMh/pOQym6anpw8YMMB2+CtdurTF9m0XmDx5craX47i7u585c4YaVqwZjSnffZ48b7S4pa7/rzShaXaPM2qP/Ja84INHj13wQfr+UCNTQBQ/2mN7Hm9376mLOlrcvkiAvLNx40Zx6WDDhg1zMQoKxSWb/vrrr2IeYGv69Olj/3pizvX333+LqRlsmDJlCjUMgFOyaeKYDun7txTPLxIgj1y4cKFhw4ZiWM+3337LOSGbWmY0Glu2bGk79nl5eUVERDhSMoPBcPLkyYULF06fPn3u3Lk//fTTuXPnLE6Wa9Hu3btLlSplu5BNmjRhRBQAB+mijuqijub7gCcbAjLxSqFwOXXqlOmsTy+99FJOx42hGGXT8PDwbBtNe/To4WDJDAbDnj17hgwZ0qNHj/79+7///vuzZs1auXJlaGjonj17Dh8+HJFJpVLt37///v37WR8+adIk01knsvLx8Tl5kskpARRxZFMUFtsy/fzzzx988IHpFJA1atRwsMELRTybzp8/P9sp4g4cOOBgydRq9fTp08XQPDG+vnLlyg0bNmzduvVrmZo0adK1a1eLi9UmJSW1a9fOdlGXLVtGJQNANgXyXXp6usVv6vLly7NoDtk0G0OHDrUd+Dp16uTgtAUGg+HAgQMdOnSwZ7bkV1555ehRy4MPTp06ZZZuzQwcOJBKBoBsChTMbFqnTp2ffvqJxcbJptmw3Rjp7u4eEhLiSJm0Wm1YWFivXr38/PzszKZHjhyxdrTx48fbvuTU/mtYAYBsCuR1NvXw8PD19a1du/ZHH3107do1zgzZNBupqalNmjSxkfbKlSsXExOT6wIlJydv2bKlbdu2tts7TXXt2tXGZaOXL19+4oknbCxQ9uDBA+oZgCIsKCjIz8+P84ACTqvVhmTasGHDgQMH4uPjaS4lm9rl4cOH9evXt5EUW7RokevSxMfHL1u2rGHDhtmOtRI8PDw++eSTuDhbC/n079/fxhFOnDhBPQNQhCmVSjc3N84DgKKZTW/duvXUU0/ZiHqjR4/OXVFu3bo1ffr0J554Ittp880EBwfbXsFs+fLlNh6+ZcsW6hkAsikAFMpseu3aNdvT2q9evTqnxzQYDFFRUUOGDLG/udTU5MmTbS8Ucfr0abGqRFZLliyhngEo8tlUpVJxKgAUwWx66dIl20OUTp06laMDpqWlhYWFvf7667LcGj58+JUrV2z8i5SUFBtXr86YMYN6BqAIi4iIcHNz8/PzI54CcJBCoXBzc3Nw1LtLs6mvr2+Olm1ISEj48ccfmzVrJnNAz549IyMjbf+jcuXKWXv4hx9+SFUDULSFhIS4ZQoICAgNDeWEAMjFx4hcLndzc1MoFHn6j5ycTRs1apSSkmLnodRq9dKlS2vXri1zTPv27Y8dO2b7f9WrV8/aw99//30qHIAiT6VSBQQESAnV399fqVQSUgHYk9ZCQkL8/PykT488bTHNk2z6xhtv6PV6e45z8eLFn3/+2fFgKpPJXnzxxWyXobKRTYcNG0bNA1BMREREKBQK8TUj5VSFQqFSqRyZ/g9AEfugCAkJCQoKkhpKJUFBQa75lHByNh06dKidx3n//fe7d+8uc4aGDRvu3bvX9r9r3bq1tYf36NGDWgiguImJiZG+e0RjqnRNqjRRvzJTSEiIyhLOHlCUqNVq6a2tVCoVCoW/v7/pb9egoKCQkBC1Wu2y8jg5m3722Wd2Hqd169aNGze2HTp9fHzkcnmlSpVs7/bUU0/t3LnT9r8LCAggmwKAtZwqvpakbOqGYkkulwegODFtFjX9gRocHJyPv0KdnE3nzZtn53HGjRvXp08fG4mzatWqH3744YIFC1q0aGE7mz7xxBO//vprrrPpgAED+GYCAGuZNSsliijx4wTFhEKhUCqVUhJVqVSubBx1Zja9fPmyjWy6du1aO4+zfPnycePGWTyIu7v7M888s3jx4ps3bx44cKBDhw7ZZtNt27blOpsyFgoAAKCAyHE2vXnzZrVq1azlvK1bt9p5nE2bNk2ZMsXiAvcdO3bcs2ePRqPJyMg4fvx4ly5dyKYAAABkUwtu375tY83SsLAwO4+jUqnmzZtn9vBy5cqNHj366tWrYrB/ZGSk7a5/x7PpJ598Qj0AAAAolNn01q1b1rKpl5fX8ePH7TxOeHj40qVLTfvx5XL53Llz09PTTXe7cOHCgAEDHM+mXbt2tfbwmTNnUg8AAACKWjYtU6aM/QuWXrt2bf369dIDPT09n3322e+++06r1ZrtFhsbO2LECMez6dtvv002BQAAKEbZtFKlStHR0TnNpl5eXq1bt/7hhx+MRmPW3ezJptWqVdu+fXuus+m3335LPQAAAChq2fSJJ564evWqnceJjY3duHGjn59fz5499+zZYzGY2plN69evv3v37lxnUxesvgUAAIB8yKb2L2aVmJgYHh7+5Zdfnj592naEzTabNmrUKNt1ocimAAAARTCb2hinn6NsaieyKQAAANnUKmf16TsxmzZv3vyvv/6yfZx33nnH2sN/+ukn6gEAAEChzKY22k2ffPLJ+/fvuz6bvvLKK0ePHrV9nKCgIGsP37VrF/UAAACgqGXTypUrX79+3fXZtH379seOHbN9nMGDB5NNAQAAilE2za8+/R49etgeUGX7elOyKQAAQNHMpvkyFqpXr16RkZFkUwAAgGKXTWNjY6tUqeKydtP79+9/9NFHtrPpkCFDLl26lOtsunnzZuoBAABAocymly5d8vPzc1m7aUJCwsSJE21n05EjR2b7f9966y3mkAIAACCb5nk2/eCDD7Idg/Xaa6+RTQEAAIpRNq1YseLly5ddn00nT5589+5d28dp3bo12RQAAKCoZdMzZ86ULFnSYsgrU6bMyZMnXZ9Np06dmu28qvXr1yebAgAAFLVsGhERYS3klSlTJiIiwvXZdO7cuQkJCbaPU6lSJWsPX7FiBfUAAACAbJq99PT0adOm2c6mwcHBycnJNg6SnJzs5eVl7eFz5syhHgAAABTKbBoeHu7KbJqRkTF79mzb2XTJkiVpaWk2jnD9+vVSpUpZe/jMmTOpBwAAAIUym/7+++8FLZuuWrXKaDTaOMLRo0c9PT3JpgAAAEUtm+7YsaOgZdO1a9faPsKvv/7q7u5ONgUAAChq2fTYsWM2YuL+/fudXsT//e9/vr6+jmTT5cuX23g42RQAAKCwZlMbY6FkMplKpXJ6EVevXv3UU0/Z+KeLFy9OTU21cQTbo6nIpgAAAEUzm27ZssXpRdy6dWuTJk1s/NOFCxfaHqf/7rvvkk0BAACKYDa9cOGCj4+PK+exP3jwoI0VR2Uy2bx585KSkqw93Gg0vvzyy2RTAACAIphNr169amMe+88//9zpRYyMjOzdu7eNcDlt2jQb60I9ePCgTp06Nh6+YMEC6gEAAEChzKb37t2rXbu2tZzXpUsXvV7v3CJevnx54MCBNsLlhAkT4uLirD387NmzZcqUsfFw1iwFAAAorNk0PT29WbNm1nJehQoVHjx44NwiRkdHDx482Ea4/M9//nP16lVrD9+0aZPtKag2btxIPQAAACiU2TQjI6N79+62Z8J3bhFPnz7dq1cvG//xvffei46OtvbwkSNH2s6me/bsoR4AAAAU1mw6efJkG1GvWbNmGo3GiUUMCwt75ZVXbPzH/v37nz171uJj9Xp9/fr1bWfTqKgo6gEAAEBhzaY//vijjWWWPDw8vv/+e2eVT6PRzJ07t0KFCjbCZa9evc6cOWPx4QcPHrRRVJlM5unpaWMcFQAAAAp6No2Li/Py8rIR+J588kkbF4DmyM8//9ygQQPbDZ/WsqnBYOjTp4/tx9arV8/23KgAAAAo0Nk0IyOjadOmtjNf3759dTqdg4VLSkr6/PPPZdmxlk3Dw8PLli1r+7FDhw41Go3UAwAAgEKcTcePH28785UoUWLx4sUOFi4tLW3GjBm5y6YGg8H2zFNSIbdt20YlAAAAKNzZ9NChQx4eHraTn6+v765duxwp3L179z766KPcZdPjx497enrafmDLli1TU1OpBAAAAIU7myYmJtpe414il8sTEhJyXbjk5OSFCxdWr169ZMmStrNpZGSk6QN1Ol2XLl1sl83T0/P333+nBgAAABT6bJqRkTF79mzb4a9kyZKOD9i/dOnS1KlT69ata+MfvfXWW+fOnTN91IEDB0qXLm27eOPGjePlBwAAKCLZ9NatW9WqVbPRKjlr1iyDweB4EbOd33T48OFXrlwxfcjHH39sO5h26tQpKSmJlx8AAKCIZNOMjIz58+dbG2P06aefOmv8+++//968eXMbQXPEiBExMTGmD9m8ebOvr6+1/Rs0aBAXF8drDwAAUKSyaVJSUqtWrbKGP4VCkZaW5qwi/vzzzw0bNrSRTUeOHGmWTTMyMlatWuXj45N156pVqx49epQXHgAAoKhlU2mx+ypVqpiNTHLumqUbNmx4+umnc5pNMzIydu7caVa2smXLhoWF8aoDAAAUzWyakZGxZ88eceHp66+/7sjAfItCQ0OfeeaZHPXpC+fOnXvttdek6a7Kli27fv16XnIAAICinE0zMjLOnj370Ucfvfrqq9YyoiP27t3bpk2bXLSbSjQazVdffdWwYcNff/2VJaAAAACKfjaVOPEaU1P37t2bNGlSpUqVKlasWK9evTfeeGPEiBFffvnlunXrDh06FBcXp9Vqsz1IYmIiLzYAAEAxyqYAAAAA2RQAAABkUwAAAIBsCgAAALIpAAAAQDYFAAAA2RQAAAAgmwIAAIBsCgAAAJBNAQAAQDYFAAAAyKYAAAAA2RQAAABkUwAAAIBsCgAAALIpAAAAQDYFAAAA2RQAAAAgmwIAAIBsCgAAAJBNAQAAQDYFAAAAyKYAAAAgmwIAAABkUwAAAJBNAQAAALIpAAAAyKYAAAAA2RQAAABkUwAAAIBsCgAAAJBNAQAAQDYFAAAAyKYAADup1WrV45SPCwBQqAQGBiqtCw4OJpsCAAqKiIiI0NBQpVIZGBgYEBDgZge+6YHCxd/fP9v3tVwuDwgIUCqVISEhERERZFMAgIvExMQEBwcrFAqzryvxzRQcHGzWbspJA4owtVot/To1+0zw9/cPCgoKCQlRq9VkUwBAnkRSuVxu2vwpffGQPgEIKpUq689XhULhsg8KsikAFP1UGhQUJBpHlUpl/nbYASgs1Gp1SEiICKmBgYExMTFkUwBA7olUqlAoiKQAckelUknXo/v5+eX12CmyKQAU8WAaGBhIKgXguIiICKkNNTQ0lGwKAMgxuVzu7+/PeQDgLGq12s3NLSgoiGwKAMgxafoYzgMAZ2ZHNzelUkk2BQCQTQGQTQEAhZZCoZDL5XbubDAY9CbEdqPRaHF7wWf2jMwYDAZqCKwR9cRoNBbewptWcmvvYtPtdj5ZsikAIJeUSqWbm72f859//nnTf3Xt2vXhw4fS9i1btojtLVq0OH36dGF5+nPnzm1q3ZQpUwpj7IALGI1GUU82b95c6Mr/0ksvSYUfOHBgampqRkaGTqczfRdHRkZKe166dEns3LRp03Xr1pFNAQAFJZsOHz5c9q9GjRolJSVJ29euXSu2ly5d+syZM4Xl6QcFBcmsUygUNJ0WN+np6fa86FqtVtSTZcuWFbqn6e3tLRW+RYsWycnJGRkZGo3G4rv4woULYmeZTLZixQqyKQCgoGTTCRMmiK+oJk2apKenS9u3b98utnt5eV25ckXaPnXq1M9yYufOnWTTom3z5s3WXn2lUrls2bKjR4/euXNHp9O5uGAPHz7ct2/flClTunTpEh4eXuSzqY+Pj1T4li1bpqSkmD0jLy+v6Ohoac/bt2+LnWUy2YYNG8imAFC4RUarI6PVBbZ4OcqmU6dOFV9RzZs3F7lt79697u7u0nZfX9+bN29K20uXLi3LiQkTJrj46R8+fHj546ZMmSKKTTZ1unfffddGBShRokTp0qVr1KgxePDgsLAw8eMnrxmNxsmTJ5cqVUpqMjxy5EiRz6a+vr5S4du3by/Os8V3sVqtLl++vLTd3d39l19+IZsCQCG2YselHlNUPaaoBsw8sHFfjCZFV9BKGBwc7ObmplbblZ4XLVokvr26desmrsX8+++/y5QpI22vVavWvXv3Cks2zSo8PLxs2bJk03zJpqZ8fHxGjRp1584d12TTTz75RHRnF9tsavFdnJiY+PTTT0vbvb299+/fTzYF8pPRaExN16ek6cTNYMjBwAi93mD62DStnlNa3Gw7FCtlU3GbtT5yb/itglNClUrl5uamUqns2TkkJKREiRLSt9SgQYNENj19+rRoWXnmmWdE0u3du3e3xz3xxBOiAaZ9+/Zm965cuZJsWkyyqYeHR+vWrU1f/ddff71hw4YlS5YUmc/d3b1z584PHjwgmzpduXLlpMK//vrrWq1W2ijexc8++6x4F2s0Gn9/f2m7n5/f8ePHyaZAfkrQpH+x5tSI+Yel2/uLjl6ITbD/4UfP3RsVfER67MgFh7/feUlvYMxvsTNrfaRZPJWaUfeG3yoIzag5yqabNm0S2fT9998X2fTChQuVK1cWQyvEGKksv9b0r7/+urRbqVKlLl26VABfL7Kpa7JpqVKl9u3bZ5b27ty5s3v37k6dOnl6eooIO3bs2LyemKwYZlMRQ7t16yZOr/jp2LJlS/EuTklJadWqlbS9YsWKZ8+eJZsC+UlvMC4OPd9z6v/niV5TVWv+uGLnnDIGg3HJL48e22daWIFqLYMraVJ0G/fFDJh5IGtCXbT5XHRcUmHJpnv27BHNWp988onYfuPGjaeeekra/sorr0jDfsmmyFE2ffR+0WgmTpworh6pUKFCREQE2dS5KlSokDWbindxQECANEBKmrigY8eO0vYqVaqIkY5kUyDfnLh4v8+0MBEmJnx7XJ1k1+X58YlpQYv/Fg8cteDwHXUq57OYO3L23qLN57I2oy7afC6/xkvlKJseOnRIGi8ik8mmTZsmtt+7d09ckfbmm2+mpaUV22yanJx8+/bt2Ey3b9+2FtNd8dNar79///7NmzdjY2Pj4uISEhIKwlyt9mTTf/qsEhLatWsnwt+UKVMKSzY1Go0PHz6Mi4uTKoBIeC6rPHYWoFKlSlLhe/fuLSpG7dq1s76LdTpdz549pe3Vq1cXY6TIpkC+iU9MG7/0mMgQfaaFRVyy6+Knk5cfvDXjT+lRPaeqlvxyXqujAQYZUjPqos3nsjajvjf/8LZDsS7u6M9RNj19+rQY3jR//nyxPSkpqUGDBtL2nj17WpsAqAhnU61We/LkyYkTJ7766qs1atTwzvTkk0926NBh3LhxR44csXhOjEbjsH+tX78+2+x48uRJsf+GDRus7a/RaEJDQwcOHNioUaMKFSp4e3tXqlSpZcuWI0eOVKlU1n45FKhsmpGRsXr1ag8PD2nnjh07SsWOjIx87733pDOwcOHCbF+d69evjxw5Utp//vz5Yn+9Xj/MxLvvvtukSRNxFUG3bt2GWTFp0qTExESL2dRoNEZHR48fP/7FF1+sVKmSt7d3jRo13nzzzVWrViUkJDi98mRkZEhF+uSTT6RVMIxG49mzZ4OCgkwL8PrrrwcHB2ctgMimb731ltho+i4WjakGg2HQoEHSdrlcHh8fTzYF8pnRaFy7+0qvf7vme05VrfztUrYjovQG47fbLojM0W/6/qPn7nEyYZZQ94bfGrvkmMVmVFd29Nv/LXLjxo327du3zWQ6lYxWqx00aJC0XalUWstMRTWb3rx5c8SIEaKTNCtfX98PPvgga4OTVqsV/6tVq1bS8jw2PovGjh0r5loKDQ21ll8DAwO9vLwslsTb2/vtt9++evVqwc+mp06dEqe0SZMm0tCcmJiYatWqSRufeuqpbNvwli9fLgLu9OnTxfbz58/LcqVOnTp37941y6br1683GAxr1659+umnxaUIQsmSJbt162atqI5UHtGWGRMTo9Pp1qxZU6NGjaxH8PT07NSp0/Xr100fLi4QN82m4l08bdo003fxV199JW3v16+f7VpKNgVc5GJsgmgB7TFFNebrvx8kZtPwoE5K/+B/Rx916AcfeZCQxpmERXvDb03+PiJrQh275Jhrxkvl9bdI0c6mUVFRbdq0MR1a7unpWTKTGDcmeemll86fP2/28ObNm4spkw4fPmzjH8XHxz/77LPSzg0aNLA4uZJKpRIXV0hNgFJhxNAiEfXEipQFNpvGxMRUrVpV2rlx48bSaH2DwdCvXz/x7FavXm27vvXu3VvauVy5cqYXrTo3m65bt+7LL78UEzCVKFEi6zlXKBRZp2t1pPKIAlStWvXs2bPTp08XqzdZLEDfvn1N+/erVKkibR8yZEhh/FQhm4L2Le3UkJMiMfT/Yn/4xWy69Y9fuG8aZ7/bflGvp0MfttyJT12x45K18VJk04KZTW/dutW6dWvx9V+zZs3Jkyf/9ddfUVFRp0+fXr9+fY8ePUybMDt06GDWuzpnzhzR0mbasJfVvn37xAUV48ePz9o4ffLkSXG9YIkSJV599dU1a9ZERERERUWpVKrRo0eLOCJlHTsntc2vbHrlyhVR4KZNm4rSbtmyRaSufv362VhB6urVq6KR9bXXXjO9mOH8+fPDHyemSfLw8AgMDBxuxaeffpq1T79fv37SddheXl49e/bcvn37mTNnDhw4MGTIEPHq+/j47N2714mVRxSgQoUK7777rihA//79LRbAy8vLdN01kfvHjh1LNgUKpV8PxfZWPhoR9c3W8zZmg9Ib/rkMQOz81ow/j52/zzmEnSx29A+YeWDbodg78XkynI5smrtsqtfrg4KCRLJs2bLl6dOnzSJjamrqsmXLTFvUlixZYrrD+fPnxYKQrVq1snYxqNFo/OCDD0S/fFhYmNkOqampPXr0EP9l0qRJZiFYr9f//fffolXVw8Pjf//7X0HOpn/++ac4M126dBGNjvHx8XXr1pW2V65cOSYmxtoRvv/+e+nVcXd3nzdvno3/5eBYKKmN09fXNzg42LRtMi0tbdy4caKGjB07VlQPxyuPKICHh4dUgHLlyq1YscK0CpkVYNSoUaI+N27cmGwKFG63HqQMmv2XCArvLzpqo49enZRumi3GfXNMk6LlHCJHouOSso6XktpQnT6in2yau2x67NgxcZlg/fr1L1y4YHE3g8GwaNEi0dTn7+9///6jH6umE/R4e3v//fffFg+i0WgaNmwoeuQ1Go3ZDlu3bhX/YsiQIWI2dTNhYWEVK1aUdnv++edd33RqfzadPn26CH+mydJoNAYFBYm4Zm29BqPRKEaXly1b9vLly3mXTWUyWZkyZb7++uus87Beu3ZNzCT6wgsviIs1Ha88ZgUoW7bshg0bslZXawUQ14d8+umnZFOgUErT6v/7wxnTptCDZ6yupHf8wv0BXz7q0F+3+wpT7iN3pPFSWfv635t/eG/4LWc1o/r7+wcGBpJNc5pNP/zwQ5EMvv76axvHjI+Pb9GihXjiv/32m+m9y5YtE8eZNWuWxSPs2bNHdM5m7frXarUih1WqVOnUqVM2XgIx5rpkyZIHDhwomNn0ypUroom3cuXKZvO9Hz58WFze8MYbb1h8jWJjY2vWrCnt07Vr16zXejoxm5YqVWr69OkWfw+kpaWJybAqVaokkqXjlcesAPPnz7e4QoG1AohrGGxfSUI2BQq0/adu95u+X4SDr0PP6SxdQqo3GFfvuiym3B8068CZGDVnD47buC/mvfmHLY6XcvDIAZnIpjnNpo0aNRIT69y4ccP2YefNmyeSxPjx403viomJERdWvvjiixZntfzoo49E22rW6Hn9+nWxok/v3r1tXIKZkZGxbt06qQvYw8PD9ZPG25NNz58/L+qJTCb74IMPzF6FlJSU559/XiRXi22ia9asEX3Z2a6F62A2HThwoLVpRPV6ff/+/cWFoaKeOF55zApgbfi8tQK0bdtW2vjFF1+QTYHCSp2UPsIkGYxeeOTeQwufBcmpuo+/PS52G7/0mCY1B+Os94bf2rgvhhs3a7es8VSsgEo2dXE2FVdDduzYMdupSY8fP+7r6yvt3759e9PDmqYHLy+v8PBws8cmJiaK2TdffvnlrE2Av/32m1iva/bs2bZLcvjwYXEJ45gxY/Ixm/7xxx/af6WlpT148ODw4cMzZ86Uy+UidbVs2TIuLi7rcebOnSv2MbuEVzqlb731lnRvtWrVzKZPcno2tRHxDQbD0KFDxWL0Fy9edFblcbAAL7/8srQxODiYbAoUVjq9Ydmvj6Ys7Tt9/74IC2ngwvUE0w79n/dfNdjdoX/k7L2ssYMbNztv2w7Fkk1dlk0NBoNIBpMnT872sHFxcbVq1RJXDUpjvR81im/cKK4pzNqtv2/fPuled3f3b775JuvBFy5cKLURenh4DB8+fJlNn376qVjca8CAAXm9VL21bOru7i6Xyxs1atQ4U6NGjerUqePn52c6oVLz5s3PnbM8ScXZs2fFy5R1KbK7d++K5Td79uyZ7URgLs6mTqk8DmbTTp06SRvzaEgc2RRw1ZfWpQeml/3N3xRlttST3mAM+f1Rh75izl8XYhPsP350XBIBi1uubyt25DLqkU1zkU01Go1IBt9++222h01ISBAjo+vVqyfNkWmaperVqyfd27x5c7Nu/fHjx4vrBS2uZi52yKlevXrZvgAg77KpbeXLl//www9v375tIxp27txZTM9kNvfnpk2bpOsWPD09N27cmG2pXJxNnVJ5HMymXbp0IZsCRUFyqm7cN48G4A/56mDcg8euMUpM1n687FGH/ucrI9K1OWuTuBOfSrc1Nxu3RZvPZU2l780/vGjzuVzP0q9QKORyOdk0R9lUrVaLZLBmzZrsPz2Sk0W/fN26dc1mzjcYDKNHjxYB4sSJE6b/SAyp7tOnj8VmTnE1ak716dOn4GTTUqVKVapUqU6dOh9++OHx48ezbdBdvXq1mCLKtLHZYDAMGDBArB117172a/K5OJs6pfI4mE27desmbVy+fDnZFCjEDAbjj6oY0Szad/p+s4v8LpisINVrqmrnkRvZXkgE2P+7ZcWOSxbnPXXwyEql0s3NFR/1RTWbhoSEZHtYjUYjImb9+vXN2k2lJZ3ENaPTpk0z3S51wXt4eGzYsMHiwU2zae3atZvZberUqfYsypoX2dTT0/Ozzz5bZ+LXX3+NiorSaDR2Fik2NvbJJ5+UjhYQECAGA929e1dcsTpixAh7jpaP2TTXlcfBbPr222/bH47JpkCBdvlm4tCvDopkMGv96ZS0R60OP6piev07Rf+w/x66fkfDGYPjNCm6rM2lTpxGimyai2z68OFDkQzmz5+f7WFNLxls2rRpUlJS1gOKaX1atmwpzWBqNBonTZokbaxVq5a1Ad0ff/yxCHzr16/X283FwTRH85va0VhgEEcrW7asmBpWTPXq6em5Y8cOew7l4mzqlMrjYDZ955133DORTYFCL12rN12/dPDsv+Lu//+VYQnJ2k+Xh4u7vlx7Oi1dzxmDI+7Ep2adgX/R5nPRcUlO/C9k01xkU9NkMHr06GwPGxkZKaZAf/PNNy32qEyePFlM5C5NFJWQkNCgQQNp47vvvmutMMuWLfPw8JB6txctWlSQq7QTs2lGRsb27dtFY/OMGTOkKCYmcG3cuLHpMgcFJ5s6pfI4mE2vXLnydyY7TxHZFCi4jEbjrmM3e/3brd9bGfbrvz2q5649FCP0eyvD9pyI43TBwVRq1lbq9FRKNs11Ns3IyBDr+rz44otm4+6zWrt2rUgS1maUPHr0qLe3txQxpRnRVSqVNOV+1hn7TYl+f5lMNnToUBcPvc/HbKpWq5977jkxhkyj0dy5c0esaDpmzBg7r6py/RxSjlceBwuQ18imgEvF3U8eueCwafuo1K2/Keyq6NB/b/7hm/eTOVfIhcho9eTvI8xS6az1kXmRSiXBwcFubm4uWL6yiGXTpk2bimbOrAvcm0pLS3vzzTfF5Pkqlcribqmpqa1bt5Z2a9euXXp6umhJbdKkSUKC1Uk/rl27Jmbvr1Gjxs2bN4tJNjW9nsHHxyciIkJ06JcuXXr//v32NzpMnDgxRwVzMBo6XnnIpgBM3+pG0/VLh/33UOxdjSZV9/nKR3liwU/m00sB9qRSi22lzlqb1BqVSuXm5mYtMJFNre352WefiZWH+vTpY21loIyMjG3btol2zZdfftniyk+SuXPnSscsX778+fPnmzdvLhY9t9EEmJ6e3qtXLzEt6IQJE1w8+j4fs+nx48elpQTc3d3nzJkzePBgcV2mjfOc1ddffy1G/U+ePDnbBlcHo6HjlcfBApw6derjjz9esGABffpAEXE46m7ff9cvlbrvo66qxQj9vtP3H4q6y1mC/Y6cvWfWVjpg5oEVOy7ldSolmzqSTaOiokRrpaen59SpUy2uG3nkyBHRy1y6dOmtW7fa+O9RUVHSlYUeHh7jx4+XUlfp0qUPHz5su9g7duwQS8z7+Pj873//sxF3ilI2TU9Pb9WqlXTMZ599tnLlytLfn332Wc4+1Q8fFiewVq1akZGReZpNHa88DhagZs2aUjXr0aOHNPCObAoUbg8S0kwnOp2zIXLt7itibqlRwUfuJ6RxlmAPiz34K3ZcyvVkpQUqm+7bt2+PiV27dr3wwgvi+3j16tV7HueyDkdrJdyzZ8/SpUuliz5lMtkbb7zxxx9/mN4bFhYmGq4MBsPUqVOlOd6lZzRkyJBDhw4lJSUZDAa9Xn/t2rUlS5ZIIUBqkBsyZIhWq7WdeDp27CjtL5apbNmyZbZBMy0trV+/fqZzhQYGBm7btu3GjRs6nc6QKTU19e7du6dPn969e/f8+fN/+OGHIpBNpYtSRBuk6Cg/fvx4jg6SlJQkKqcUc3/66ac7d+5IsxloNJorV67s3r173rx5Dx8+dDwaOl55nDVRQMmSJfPivU82BVzNaDQu33HRdP0nMbFUz6mqb7aep0Mf2boTnzprfaRZW+nGfTGuTKV5nU1FQ5SdJkyY4OLnntMSli9f/sKFC+LhiYmJAwcOlMbIi0D53HPPvfjiiy+88ELNmjXFXe7u7m+++aaNhY6E77//3vSAMplszpw59jyX2NhYsUi6xMPDo1atWs8///yLmZo2bVqvXr2KFStK906dOrVoZNPo6GjxpETft8VmSNvWrl0rNVRLSpQoUbduXenUPffcczVq1PDw8KhTp45Tphd1vPI4UoCTJ0+aVpLVq1eTTYGiICpG/fa/o/JNb/2+2H/8wn3OD2ynUrPrSqUp9F2fSsmmDmbTjIyM+/fv/+c///Hx8bHxqDJlygwbNsyeYCpFTNFaJpPJypUrd/r0aTufTmxsbN++fcXlibYVmWyq0+l69Oghnpe7u/vcuXNzd5w5c+aYxtOsnJhNHaw8jhTg4sWL4rFeXl72zEtANgUKgaQU7ZSVEVmz6djFfz/UpHN+YJEmRbdxX4zZfKX50lbqmi+SmjVrVskJ0/WQXCOnJaxfv/7ly5ezpoTff/+9W7dulStX9vb2Fv3L3t7elStX7t+//+7du9PS7L3OR6/Xjxw5UvzHfv362b4MwExqampoaGjfvn2rVavm4+MjDVoXvbfe3t4VKlSoXr16165d9+zZ4/qaNnbsWOl5PfXUU3/99ZezDrthwwbRP165cmWz3w85iqc7d+7s0qVLpUqVSpcuLUZHlSpVys/Pr0aNGuPGjZMu6tBqteI1Wrt2rbUDGo3GoKAgabd69epduXLFWZXHkQLodLqmTZtKz2v06NH2V06yKVDQ/bT/0aRR4rbyt0t6A+uUwoK94beyzqLvmtFO+fVFosmh9HRX/67T5Jy1Edxarfb27dt79+797rvvlixZsmzZsj/++CMuLi4XQ+bT09PFv8tdbtDpdHfu3Pnzzz83bty4JNO33367adOmPXv2XLlyJSkpKUd514nS0tLEU3PiqlRi9QGZTPb22287OE1Benr6tWvXtm/fvmzZMuml/OWXXyIiIhITE8XcsUajUTwR2//O9Ck7sfI4WIC4uLgVK1Zs3bo1Fxc/kE2Bguv6Hc2g2X+Z9cxGRqs5MzATGa1+b/5h06oy+fuIApJKXfNFAuSdhIQEMV2op6fn9u3bOSdF/iOFbApY+TGq1c9cd9o0cEwJiUhO1XFmYOrI2XuPXfWx5FgB/AFDNkXhtXDhQtGh37p167yYEQlkU6BwSEl7bL79PtPCth+ONdChDyvZ9L35h/eG3yqeXyRAHjlz5owYOlaqVKlff/2Vc0I2BYqvExfvmw7VH7ng8O34FE4LsoqMVkdGq/N9wJMN/v7+gYGBvFIoXC5cuPDSSy+JwV6dO3fOi2E9IJsChYMmRfvFmlOmfbXLfr2g1zOtKQqlgEycBxRwCQkJtzKdP39+4cKFYs0kmUz29NNPnz17llNENgWKKaPRuDc8rt+/y5b2mKIaOvfg5ZuJnBmQTYG88+GHH/pk8vb2Np21vkKFCr/88gvnh2wKFF/X72jGLv5bBNOeU1Uhv1+m0RRkUyBPjRgxIuvs9NWrV1+3bp0TZ6QC2RQoZG7Hp3yx5pRYsLTHFNWoBYdj7zIyFGRTwHXZ1MPDw8/Pr0+fPpGRkdbmDQXZFCjKjEZjWro+6qp6SkiEaTDtN33/9sOxfDKiUFMoFHK5nPOAAi4kJESRacyYMStWrDhz5kx+LSIAsimQP+IepPxx/KZ023Yods6GyEGz/zINpr2VYd9tv5iarudcoVBTKpVubnzaAyCbAgXb/lO3zZYkNb31mqr6Ys2p+wlMWQKyKQCQTYF8zaZ9poV9sebUzfvJnCUUmWwaExPDqQBANgUKWTbtpQwb+tXBTWFXH2rSOUUoGkJDQ93c3Pz9/dVqNWcDgCNCQkLc3NyCg4PJpkDeZtN+0/e/O/fgxO9OrPzt0pW4RB0zRqFoUSgUbm5ufn5+SqWSBlQAuRATEyN9kuT1D12yKYqvew9TD0fdlW7hFx9cvpmYmKxlSD6KKqVS6efnJyXUoKAglUpFMyoAO1NpYGCgWyaFQpHXHx1kUwAoLtRqdXBwsFwud/tXQEBAUFBQSEgIJweAWR4NDQ1VKBT+/v7Sx4W/v79rPivIpgBQ7EREREhzSYpvHTc3N7lcHhgYqMykykTvP1CsqFSqkJCQoKAgqY9FRFKFQhEaGuqyYpBNAaC4CwkJUSqVAQEBpk2qNgSgiFIoFEoUJwqFIiAgwPQ3qkT6mZpfV/6QTQEAFppPpBYUi99nZLiiys4fJygy5HJ5QECA6DAJDQ2NiIjI988fsikAAAAKCrIpAAAAyKYAAAAA2RQAAABkUwAAAIBsCgAAALIpAAAAQDYFAAAA2RQAAAAgmwIAAIBsCgAAAJBNAQAAQDYFAAAAyKYAAAAgmwIAAABkUwAAAJBNAQAAALIpAAAAyKYAAAAA2RQAAAAgmwIAAIBsCgAAAJBNAQAAQDYFAAAAyKYAAAAgmwIAAABkUwAAAJBNAQAAALIpAAAAyKYAAAAA2RQAAABkUwAAAIBsigImJiZGZROnCAAAkE2R5yIiIgIDA92cQS6XBxQSSthBoVAEAIWBn5+fG1AUOf2LVaFQZPvhHxwcHBERQTZF/lCr1f7+/rz5AQCAGT8/P6VSqVaryaZwHYVCwXsPAABYExAQQDaF6xpNecsBAADbgoODyaZwBZVKxfsNAADY5ufnl+uefbIpyKYAAMDJQkNDyaZwxQh93mwAACBbSqWSbApXYOIVAABANkVBoVQqeb8BAADb6NOH6zC/KQAAsIGxUHA1ZjkFAADWgqkjS5eTTZH7Mfs21qiUy+W8OQEAKG78/f0dXL+UbIp8DriFQmhoqBJ2CAkJUQGFgYNrKgIFVkREhHPfLPZ8+AcGBkotU8HBwY6/ucimAAAAKCjIpgAAACCbAgAAAGRTAAAAkE0BAAAAsikAAADIpgAAAADZFAAAAGRTAAAAgGwKAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAgGwKAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAkE0BAAAAsikAAADIpgAAAADZFAAAAGRTAAAAgGwKAAAAsikAAABANgUAAADIpgAAACCbAgAAAGRTAAAAkE0BAAAAsikAAADIpgAAAADZFAAAAGRTAAAAgGwKAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAgGwKAAAAsikAAABANgUAAADZFAAAACCbAgAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAkE0BAAAAsikAAADIpgAAAADZFAAAAGRTAAAAgGwKAAAAkE0BAABANgUAAADIpgAAACCbAgAAAGRTAAAAkE0BAEWXWq1WqVSrVq2aZmLIkCGvmmjWrJksV9xdRQbXnnPOc8FXu3btV60bO3asSqVSq9VkUwBAfoqJiVGpVCJ9WkseZt9qPXv2nAagUDH7hWmmWbNm0pvdz88vICAgKCgoJCREpVKRTQEAeS4iIiI4ODggIMDPz88tk0wmK1++vNR2snDhQpVKFRMTw4kCiuHPVIVC4e/v72ZCiqoRERFkUwCAM6nV6uDgYPGtI5fLAwMDlUplXnfkASiMVCpVSEiIQqGQy+XSh4a/v39ISAjZFADgnFQqtZLK5XKlUul4EwiAYpVTFQqFaEbN3QcI2RQAkCH14Eup1N/f37lXjwEoVmJiYkRCVSqVZFMAQG74ZSKVAnAKtVodEBDg5uaW09ZTsikA4J92jty1cACANSqVys3NLafXnpJNAQCZ3wdkUwB5kE1z2htDNgUAkE0BkE0BAAWJv79/YGCg7X0ePHhw/V9xcXEGg0HartFoxPbY2Nj09PTC8qzVavV16x48eEDFgDWinmg0mkJX+NjYWKnwt2/fNhqNZs/I9F2s1WrFztevX09MTCSbAgBcISCT7X2GDx/u/a/nnnsuPj5e2h4SEiK2ly9f/vjx44XlWX/88cfe1r333nsifwOmdDqdqCfff/99oSt/xYoVpcK/8sorKSkpGRkZKSkpFt/FZ86cETt7e3svWbKEbAoAKCjZdNSoUWLR7caNG4vmoo0bN4rtpUuXjoqKKizPOigoyMbC4gqFgmxarBgMBjvbQbVaragny5YtK3TP1NvbWyp8ixYtkpOTMzIykpOTxTPy8vI6e/astOfly5fLlCkj7lq5ciXZFABQULLpxIkTxVeUv7+/TqeTtv/222+m32rXrl2Ttrdr1651TixevNjFz3rcuHElH+fp6Uk2zTuzZs2y9uq3a9du0KBBixYt2rNnj1qtFh3NrhEZGTlz5sxXX321bt26R48eLfLZ1MfHRyp869atU1NTMzIy9Hq9eEbe3t7iXXzv3j1fX19x16b/a+9OwKIq+zaA44CAQgKK5i6iWShqLhkmhZql6ZeoKRYuuJaaGSaZpgKWgTuSmmbuuwSSKPaaIpKyKO4ooCAgIIvIMsMww2yH74pTD8dh5sw+bPfvmuu9eseZM2cOZ+bc8yz/JyQE2RQAAOpLNl23bh25RA0bNozktpiYGBLp7Ozs8vLy6PstLS2bacLHx8fI77qgoODhy0JDQ0krEbKp3s2ZM0flaWBpadm7d++NGzc+f/7cOHtFUdTy5cvJqyckJDT6bEri5siRI8nQUvIpbt26dX5+Pn1nWVlZu3bt6PvNzMzOnTuHbAoAUF9Is5IrQ3eIYk43yndHL4fN/pjt27ebmprSV6lJkyaRlq3ExETSDNOzZ88XL140lGxa2+3bt1u1aoVsWofZlGZqauri4nLr1i1kU6NlU/KrrGfPnmQiII/H69mzJ32/tbV1bGwssikAQH3BnTeE6+nE9XTiLXm/8SVUPz8/ExMVF4UjR46YmZmR3Eay6YMHD1q3bk3f37dv37KyMvr+4ODgLQybN29+/fXXSQPMypUrt7wsJiYG2bSJZFMzM7OFCxcy//r+/v4eHh5dunThcDgk9jk5OaWmpiKb6p2dnR2982PGjCGDcxR+iisqKgYOHEjfb2Njo9GvBWRTAGOQPLpdscOnImjJv7fgpeLEi+qMi5LmZQr2rK554s/fSJJicTwblsrQHXQ2JTfBrpWy57lNJ5uePn2a9Pp99dVX5MxPT09/9dVX6ftdXFzKy8sVfwqk0g8++IB+mIWFRVpaWj08DsimxsmmFhYWly9flguIEokkNzfXz8/P1taWJL/x48fTAyKRTQ2RTT/++GOpVErfST7FQ4cOJRPChEKhq6srfb+9vb1GPxWQTQGMgeKXVWxawEwn5SsnyYqeqXiWWCTYv5Y7rTd5Ft/fU1ZSiOPZ4Mie5wp2rXwpoc4bIjwc0AgSqjrZNDo62tzcnL5Kff/99+T+/Pz8bt26kS5CuiQNsilolE1rPmUy2eHDh1u0aEFm1/3111/IpvrVpk2b2tmU+SkmvwfEYvH//d//0fe3b98+KysL2RSg3pGkJPK+dKuJJtOdhSe3UlIJ21Pu/s374h3yFN6id9Fo2tATamXoDt6S9+XaUKVZyY07m16/ft3CwoK+Sq1bt47cX1xc3KNHD/r+jz76SFnt/UafTUUiUXp6+sWLF8OqXbp0KSMjg30lAtF/SD5gj1BqPl4mk5WVlcXHx589ezYsLCwyMvLBgwcVFRVGnvyuXTal85CnpycJf6SRnqIosVhMHwHSE60yRCp8vIihsrJy2bJlJJtevXpVpIRYLGbPpkKh8N69e5GRkWFhYRcvXszKylLnL6vdyUO/NbJLynZA4YGyt7end97Dw4PcST7FY8eOJS8tkUgmT55M39+pUycyRwrZFKAeoWTSynP7ubMG1GTNBa6SVKXFxmW8Uv4PM2pCzIx+laE72LMsNBSV5w+RQagkoUoeXm+I7yUoKMjExKS0tJTlMUlJSWR6U1BQELm/vLz8jTfeIHOklF2MG3E2LS4u3r9//3vvvdexY0dyiFq0aNGpU6ehQ4du375d4YEVCoW9e/d2qrZmzRqVwfH8+fP0g3v37h0UFKTw8RRFZWRkrFix4o033mjVqhU9Prh58+Zt27Z98803169fT6oo1OdsWlVVFRERQRrphw8fTtfgTEhI6Nu3L30QZs2apXIFsrt37/br149+/KJFi8hfs6SkxOllpB2Rw+E4ODg4KfHhhx/Sk4SY2XTPnj10uIyMjHR1dbW3t6eHvlhaWnbp0mXBggWPHj3S+8kjk8noXRo5cmRhYSG9AydPnnz77beZO9CpU6epU6fW3gGSTT/99FNyJxkOzvwUy2SymTNn0vd369aN/SuizrIpnejr/LeXXpSUlISGhjbE1cagjuPpPz37C5mJpGLzIqqCpzjIRh7gzuxX05sfOE/GxUKIjYoo5rRcGyr/x5kNbrKUOleRkpISf3//1dUSExOZl8nt27fT9//+++/Knt4osylFUbGxsYMHD2YWRq095Xzo0KHXrl2Tu3TKZDISEXr37s3lclleSCqVkqZES0tLhfPGpFLpiRMnXnvtNeZ0Irk9cXZ2joqKqquLuPrZNDk5mRwcZ2dnehGy58+f9+rVi76zTZs2jx8/Zn+59evX04eCw+Hs2rWL3J+amtpMKz169KArWzGz6cmTJ8vLy5cuXUqqVch57bXXmJ8XvZw8ZAc6duyYmZlZUlLi7e1NBkLI6d69e3x8PPPpZGgpM5sq+xSfOXOGvn/r1q1qNlcbL5vKZLIDBw40b97c3Nx81apVykYUNRR8Pn/ChAlmZmZ9+/Y9d+5c40jbYDTSrBTe16Nq4sisAaLLv1O1LmPS7Mc87w9qWlgXD5c8vouj1yiJEy/JJVR6Oj/F5zaabKrrp6bRZVOKos6ePdu5c2fm0gNdu3bt0aOHo6Ojvb09MyN27NjxwoULclv46KOP6H81NzePjIxkea38/HwHBwcy4az2suZSqXTHjh3MMult2rTp3r17jx49OnfuTNog6eqV4eHh9TybPn36tEOHDiS4k5JGixYtIg2cmzZtYvvRKBKNHDmSfnCHDh3S09MNlE337t37ySef0OXVTE1NO3To4Ojo2LVrV2biHD58OJn5rpeTh+xAu3bt4uPjVe7AW2+9RYq7MbPp559/Xt++VTTLptu3byeLXHE4nHHjxj158qShtnOIRAsXLiR/MzMzs4MHD+LiCho0ncpkoqhTzJ79cp9xsvyXBolTlQLB7pU1U6C8+leeP4Te/MZN8vA6/8eZcpOlKkN31P/JUsimWmTTe/fukekjpqamI0eOjIiIKCoqEgqFAoHgwYMHgYGBnTp1IteaXr16ZWdnM7ewd+9eUjJ2yZIlLK916tQpUsArMDCw9gMiIiJsbGzoB7Rq1crHx+f+/fvl5eVCoTA/P//IkSMuLi5kTxwcHDIzM+tzNk1NTW3bti3JVaRROSYmhkSRUaNGVVZWKtvC3bt3yXx/Dw8P5lCT9PT0owxHjhwh032aN2++du3ao0qcOXOGfkVmNh0yZAidI9u3bx8YGJiZmSkUCouKin755RdStd7c3PzUqVN6PHnIDtja2rq6utI70Llz561btyrcATMzM+Zyo2TL3t7eDTibHjt2rHZbsaOjo0G/yAzVvCEWL1++nHwdkN+X6kzNA6iJnhXlFTt8aqLndGfhoQBKImI0pF2sGYw4rXdF0FcNpQkNdKRgOr+n0z/jjOvxCYBsqmk2raysnDBhAskWs2fPJm17zLax2NhYEkE4HM53333H7KZ79uwZ6bnu06ePXNMa89BNmzaNrLx17949uQcUFxe/9dZbJJiGhITU3vOCggJy/Js1a/btt98av8NQ/Wx6/vx5MvjSw8ODdCULBIIBAwaQcpu1DwWxadMmEssOHTrE9mWu2zx9Mhbz0qVLzENKUdTu3btJ4+XMmTNJPtb95Km9A05OTvHx8Sw74OnpSXbA2dm5wWfT6OhoZrExJhsbm5MnTzasy0ZwcDD59ck0dOhQlQOrAV66YOQ8Lv9ufE3+mPuWJCnu33RS8py/ZmpN9673h9KMhzhiTS2h/lMSlTFZirfk/XqbUJFNNc2mkZGRpMnG1dW1drZgPpKsuOPg4EBWKqePyfTp00mL3cWLFxVu4cWLF6Tzd/jw4XJTs6uqqnbu3Em3nJmamv7www/K9uTx48eOjo5kDKLx50Wpn01J3yaHwzlw4ADznwICAsgo0g0bNih8ukQiGTFiBKl8VFBQYNBs2qlTp0uXLin8w5EWyjfeeIMMhtT95JHbAQcHhxs3bqi/AySb+vr6NshsmpGRQca4KOTo6NhgGroo6sCBA3Itpkz0bDsA9c8o0ZUwrtebjKlOc2W8kn+mQJ3+hTvjvylQsweJLp2kUCixaZ4kfK4o5vRLHf3zhtTPov0mJiZ+fn7IpmpmUw8PD9LuxT58UyAQjBs3jjz4+PHjzH89ffo0GVn4zTffKGzLPHXqFN36xeFwduzYUXv7JIc5ODjIDRuQ+8ry9vYmUfjPP/+sn9n0ypUrZOJ8z549nz17qYb0o0ePyKyjIUOGKOzWf/jwIdnC7Nmz2Qs56ZhNW7duHRISovAPJxKJyJDiNm3a0BPq9XLyyO2AsmXule3AoEGD6DvXrl3b8LKpSCQaO3Ys+9DgkSNHNpSLRFhYGHOceG19+/ZVtqIJgOIvNQFf8Ouql0pE/XlImn6Pt3h4TW/+Dh9KiHIQCKlc4eGA2tP5608zKrKpRtmUNEA6OzurLKxz8OBBEkDnzZvH/KfCwkJSV9LR0bF2tz5FUaSIj62tbe3omZqaSi5tc+fOZc9hZ86coedFcTgcZiGwepJNKysrw8PDyQLupqamW7durZ1MRo8eTT/glVdeuXlTQQm/bdu2kS0oy236yqYrVqxQdp4wqyvY2dmRwvW6nzw67sC7775L3/njjz82sGxKUZSfnx9LKyN9cjeUPv0bN26QX1EsxRrCwsJwEQXNrrjPMsq/GVPTb/v1KL7fZ8w5UtK8TBwlIAlVrqOfTqj1oRkV2VSjbEpmvk+ePFnlZh8+fEgWK3/nnXfkOuW//PJLMmMmKipK7rmFhYXdu3cnhSdrF/Eha8lyOJyff/5Z5aWQ9BF/8cUXRh5ySrKpubn50aNH06qlp6c/fvz4+vXr27dv//jjj0lnN4fDmTFjBl3ZVM6hQ4dIXKudrkQi0ZgxY0iza1FRkUGzKcu6UDKZbPbs2WQMJCl6pfvJo+MOuLm50XcGBwc3sGx68+ZN8hFVxtXVVeF5o/U3l1Ao5PP5XC6Xx+Nxq/F4PIFAoFFJLYVSUlI6duyoskLEpEmTsEgdaJY2KEp09YxcDXbSmy++dpb925//40zcmtqtfMVE+VNl3pA6L9qPbKp+NmUmA3VanpiNo7Wbyi5evEjmntfu1g8NDSWtJ6GhobU3HhAQQMKcq6vrZ6xGjx5NZlxMnTpV92urdtmUjqeWDBYWFsy2sBYtWsyZM4cua1obs8LU4MGD5To8s7KySFPUggUL1PkON2Y21cvJo2M2JeMEVP6YqXfZdMqUKewxzszM7MyZM3p5AwUFBYmJiaGhoZs3b/bz81u2bNk31ZYuXerj4xMYGLh///6LFy8mJSWVlpZq/TsvKiqKlNhQpk2bNnT1MgAN4qlYJPhtTc2c/f9m7gv2raVEQrardVaygkSLW9O8zRuCbNpQsml5eTm5auzbt0/lZsvLy8nsk549e5Jhf+RfBw8eTP/r66+/zgxkMpls1qxZZPaSwhUjly5dql21ToWtsEbLpizRYuDAgUeOHGGZnUxRlJeXFzmX4uLimP+6Y8cOulW1RYsWf/31V33Lpno5eZpoNn369Cn5GafM4MGD1Vwolj2VhoaGzps3z9nZmWUwKIfDad269bBhw7y9vUNCQuQ+2Oq3b5Hflyz++OMPhC3Q+Lr7LEOu9Hr5d+PV6c2nJ8rg1tRucmcLXWSqbs/h/v37u7u7I5uqk01LS0vJJePw4cMqN1tRUdGvXz9l2ZQeQUfiFHMgZl5eXpcuXdi74LXOpvWq3fTVV191cXH57LPPjh8/rrIXvqqq6uzZs6Rn3MfHhxwZiURCOvSdnJzUmUNi5Gyql5NHx2w6ceJE+k65Ggj1PZvu2bOH/ZzmcDgsa9Opo6SkJDw8fO7cucx1EdTRqVMnLy+vS5cuaVHySSaTjR8/nn37Bl0mARpnu6lMJooO5c4e9NKyQAuGiW9FY8kxkP8WqlX9tGLLl/VhRpRbNWRTTbPpb7/9pnKzXC63d+/epJQPc4Ue2p07d8g4y4ULF5L7yVhSCwuL2stKyWVTDoczduzYJWo7evRo3Y43TWfIzs7mcrnq709paamTkxO9tf79+5NhEpmZmWTRo9WrV6uzwTrMplqfPDpmUzJBSp1wXI+yKVkWTJkBAwboMqX98ePHHh4eDg4OFhYWWvzaa968ee/evQMDA9X5dSUnNTW1ffv2LBt3dnZGngDNLrpPU5nToWqaTldMkBUX4PjAv5dAeiLUy8WkxImX6snuIZtq16evcJUmObm5uV27dqUfP2jQoNrzNAQCwfDhw0kRfrpbXyaTzZs3j76zX79+yiZ0r1q1ivSGHzlypD5/BNSvb6pOoPzuu+9IoCSlYffu3UtaoGNjY9XcVF316Wt98uiYTRcuXNi+WkhISEPKpqRJXJn169drvbvFxcXvvfcec6VX7VhbW8+fP7/2D1CVNm7cyFypVo6tra1cNTUAtmYwXmnF1sXcaX1IGSnudOeaIad7fSlhBY4SMKfn85a8X6+qRyGbappNxWIxuYjMnDlTZXPGjRs3yDA5ZVOzg4ODSZtiTEwMPeaNFE5fvny5slc5cuQIPb2Jw+HocmluWNm0qqoqISGBHNXFixfTHfpk9VEXFxc1W9CMPxdK95NHx2xaWVnJr2bQQR16zqZisXjgwIHsoTApKUm7fS0sLHR3d2eJhpr6+OOP+XzNikcKBAJSXUzhQOz//e9/uJSCWl9qMll1mf2+/wXTvsLjmyuCvqppG5s1QHTxBArvN2WimNPMVFp5/lD93E9kU41qSJF54m+88YbKHrytW7eSS4yyqj0pKSmkVNBXX31FUVR4eDjdiGNtbZ2YmKhs4/Hx8WR5zwkTJhh5CGkdZlOBQEDqdDo5ORUXFzPn7wcEBKj7NW70GlK6nzw67oBx6Dmb8ng8MoxDIScnJ+1mQXG53JUrV7IXwNfC7t27Nd0Tf39/lg3+8ssvuKCCOiRJcbwv3qmpVbl2uqzkuTQzmffVyJqBp4uHS58k4Vg1QeLES8xyUfV2wVKal5dXt27dkE3VzKbvvfceGWPGXue7tLSUNPe0bt367t27yo4PafMbOHCgUCj84osv6P/77rvvktUmFbb4kNYWOzs7liXmG1k2raqq2rJlC9lgTEzMnj176EJUtra2Gh2H77//njRaR0REGDqb6n7y6J5NpVKpoStm6jmbFhQUkMENCk2bNk2LvZTJZH/88Qcp06VHLi4uyqqgKXPjxg2WDS5fvhyXVVB9Sj/P5f8wo6aJdO5b4ptR9K/wyvBdNWuWTuvND5gjK0FtsqaVSpkz8QW7VtbnVErz8/MzMTEx3PYbWTYNDg4mhUJdXFyUrdgulUq3bdtGKnd6eHjIFd5nOnjwIP1Ia2vrq1evkkaibdu2sR/YBQsWMCtDcbn19GTTezZNS0tr27YtvU1vb28S7t3c3DRqP96/fz/5a86fP19l65uO0VD3k0fHHTh69OjQoUM/+eQTg/6S0XM2zc3NJQ3OemxWfPbs2YwZM9gXmtJO+/btz58/r9HOFBcXs+Tv6dOn4+IK7ChRpWDf2pphptN6C/aspkT/ruwsK3vB95/GWMu0r/DkVkoiwnFr9CQPr/N/nMmcgy/NSm4Qe45sqlE2ffbsWa9evcgEeXd399rFR8Vi8f79+0ldbVtb21u3brG8ek5ODj3AlMPhTJ48me7Qt7W1TU5WcQolJiba2dmRYWmzZ8/Ozs5mGcgortYIsilFUWRl9Y4dO9LjMk1NTdnTfG3JycnW1tbkz/THH38o/NOLRCL6qOoYDXU/eXTcAdJ93a9fP8PVdNdzNk1LS2OvUS9X51atFqbqRlN7e/tmBmBlZeXr66tZq4ZYTMap1PbBBx/gEgtsX4gy2T9rQc1/u2ZK/tIx0uyXhvJIkmJ5C4a9tEbUzSgcukZMmpUsl0rrw0qkdZhN/f39VzN8//33pOvZzMxs0aJFq1/2559/Gvkty+3h6tWr582bR6rH9OvXb9WqVcx/XbduHXN04LFjx8j6nxwOZ8CAAdu2bUtOTs7Ly8vNzb1w4cKMGTPILBYLC4uNGzey96JKpdJp06aRQ0T/x+jRo1U2AVIUtXr1avIUDofTo0eP5cuXR0VFZWdn51V78uRJfHz8oUOHVq9e/cknn9Rep74hZtOqqqrjx4/LTa1u3bq1pr98hEIhybj00IilS5fGxsY+e/YsLy8vJSUlIiJizZo106ZN00sJJ91PHl12ID8/nznBRp0xDA0gm1pYWGhRuamsrIzUwlBWMPXNN9/89ddfk5OTc3JyHj16tHPnThcXlxYtWqgzcWry5Mma/gpknohyhg4dqkXxVGhCKSTnMc/7g5rcOWeQ6EqY3IQnSiIWngyqmbPv6VS+arL02RMcvUb4W4XPFR4OqBl2/OPMOl+AtD5kUzJBR00+Pj5Gfsua7qGdnd2jR49qfn9KJOvXryd1SekWu5YtW1pbW1tZWTErJFpaWn7zzTcsY0YJZkl5+sqoztJBdGWiL774gsRTcr22srKyrmZlZWVpaUkeoGmDTr3NpkVFRXJV0t3d3bWYEHb16lW5hc0tLS3po9eyZUv6j9KjRw+6lVH3bKrjyaPLDty9e5f5oseOHWsM2dTR0VHTefFVVVUPHjywtbVlCaZubm6pqam1f0ReuXJl0qRJ9vb27An1nXfeSUlJ0WiXWLLy4MGDhUIhrrigLIhUbF70Uu30YG9KoOBDIXueW75yUs0jp/WuCF5KVfBwDBsTZnGo8hUTG2IqpQUFBZmYmCgroolsWjub0rV49u3bxz6PokePHrt27aqsrFRnl168eMGci9yhQ4fs7Gw1305FRcXGjRtJ2Sl2jSabymSyzz//nBm2Dh3SshTG2bNnWWr46Deb6njy6LIDOTk5ZHSlnZ2dptmpnmZTNzc3LcapkMl0CrVr146lVVksFu/Zs2fAgAEs8bRHjx7nzp3TaJfmz5+vbGsDBw6sXRsZ4J9gKpVURh7ger1ZMw3/Szdp+n3FD6YoccL/uLMHvlRS6sJRSibFkWwEXprwNG9IvS0OZdALCYuJEyeO04SaDYR6pOkeTp06NTdXwTiNrKyswMDAUaNGOTo6WlpaNm/e3MLCwsHB4f333w8ODlY/XNJfGj///DN5xXXr1mk0mZqiqPT09G3bto0ZM6ZXr16tW7duXs3c3Nze3t7R0fGdd94ZP358QEBAenq68c+xbdu20e9rwoQJ9+/f19dmIyIiSLe+o6OjLhXKMzMzf/rpJzc3t86dO9PNzJaWlu3bt3/zzTfd3d2PHz9O5x+xWEz+RixjUSiKIm/Zw8ND4Y5pd/LouAMzZsxo0aJFhw4dfv31V8PN1jdqNvX09NRiFz/88EOWbOrp6cmed8Vi8cmTJ1lmL7Vq1WrHDs1Wo/bx8VG2tZ49e2o68R+aCEnqLd4CV+Ykp8rTv7BkTUoiEuxZXTNl6p+SUiMkD+Kx9liDJje0VHg4oP5Pwzd+Nm1yv1XE4tzc3KSkpHv37iUlJeXk5NTh2DCxWJyfn//48eN71e7fv5+Wlpabm6tFt2d9by+gqB9//JE0XS1dulT3b1eBQJCVlUX+lBkZGSUlJQb90jbyySMUCh88eJCZmWnQN2XUbLps2TJN96+yspKUFFZY655eA4Ndbm7ulClTlG3E1NR01apVGg0x8fPzY2mFLS4uxlctyJGVFPLXeb20KulqD1lJoYock5fJ+3qU/LO4OMEa5oWw1tDShjXhCdkUGpmcnBwy3tTa2jo+Ph7HpEF/pWiZTTdt2qTp/j1+/JildJSzs3NZWZnqhgqpdMWKFXIDvZnmzp2r0fqlLNn0tdde0+OgK2g0hCHB3FkDuDP70Tfe50PFNy6qTjMymejCMe7cweSJ3FkDhMc3k4JT0FCIEy+RoaXceUPEiZdwIQGoy/YCmWzJkiXk2j1lypT6vCwWsqkBs6kWQ4LKy8u7du1q/h965mCvXr0mTJgwc+bM/fv3q3kyBQQEMCe1yVE2EkiLbDpo0CB1JlRCUyPNfixJu0tu0syHlFitbhdKyJdmPHjpudmPKSm+QxsYMrq0QdTSRzaFxo2iqLNnz5K4YmNjc/PmTRyWRptNs7Oz27Vrpyy3hYSEaLGL6enpv/322+5qkZGRWgxVpihqzZo1cmXMmBYvXszjaTADmiWbYp4+ANQmTrxUGbqj4c7EV31VMDHx8/PDHxoaQEuBVHrmzBlmUYLFixdrt5o6NIxsmp+f36VLF2W5ra5+VZeXlzPrRNSuuhoYGKjRBlmy6dtvv61mvQ8AAGRTACO4d+9eRLXDhw97eHiQxY3oCkIaDeqDRpVNzc3Nr1+vmzaDhw8fjhgxQlmafPXVVzUtaRYQEIB1oQAAkE2hQVDWPvX6668bdF14qBfZtKCgQFk2tbKyunv3rvHfIUVRoaGhbdu2VZYmX3/99b/++kujbf7666/IpgAAyKbQQLOpqampq6src6F5aLTZlKXdVItlavWipKRkyZIlLLX3XVxckpKS9JVNJ02ahLMKAJBNAephNm3evLmdnV3//v23bNnC5XJxZJp6Nm3btm1mZqbx3+GNGzfkFsyV8+GHHxYWFuorm3p5eeGsAoCmpn///u7u7jgOUD/FxsYeqPb777/fvn2bz+djERNk0zrLptnZ2aNHj2YJpqamprNmzdJ0s8imAABMbtVwHAAA2ZTN06dPp0+f3ozVK6+8smHDBmRTAABkUwBohNmUZS6UkbNpdnb2woULLS0t2bNp165dr169qsdsOn/+fJxVANAEs6mNjQ1WxQOAhpRNO3bsqOmwTq0VFBR8++23LCtUMVcrraio0HT7v//+u7INrlixAmcVADQ1QUFBJiYm/fv3r5N5BQDQaJSWlrq5uZmYmGj6ZaJNn36HDh3y8vKM8K64XO66devs7OxUBtN27dplZGRo8RJHjx5Vts2VK1fixAKAJsjLy8ukmpeX1507d3BAAEDTVOrn52djY2NiYnLgwAFNn15/+/SFQuHOnTtbtWqlMpi2bNlSi3dOY+nTRzYFgCbrwIED3bp1oxOqu7u7n58fQioAqJSZmenn50d/dXTr1k27ZUS1zKZZWVkGfW8ikWjv3r0qx5g2a9bMxsZm3bp1fD4f2RQAQL/Cw8Pd3d1NGIYPH+7v719Xy1YDQD0UHR0dFBTk5eVFN5SamJjY2NgEBQVpPWxdaTbNy8vr1KlTnbSbSiSS0NDQjh07qgym1tbWixYt0mWAAUs2XbZsGU44AICqqqo7d+7Q154BAwZw/tO9e/cRI0ZMnDjRv1q0ImhtBWhkMjMzo6Ojw8PD/fz83NzcSAeLiYmJm5ubn59feHi4ji+hNJumpaUpm4Fk0GwqkUgiIyP79OmjMpi2aNHis88+S01N1eXlUEMKAEDTK1N4eLi/v//EiRNHjBjBTKvQpIyAJsbOzo55AtC99m5ubgcOHNDvr9D6lU0pirp69eqwYcPMzMzYgymHw5k4cWJKSoqOr7hhwwZkUwAA3SlsN6VTLDRKyGpNjbe3t7+//8GDB+lPt+G+TLTJpvb29tpNilcpJSXl/fffNzc3V9lo+t577z19+lT3V/T19UU2BQDtZOSVJ2WUMm84JgAAhsqmKSkpLVq0UDbK8/79+3rflby8vFGjRpmamrKnUjMzs1GjRulrMtbChQuRTQFACxFxORPWRKu8ee9MXLXvDvMWHJZy4nIm8yYXcBXe+AIJjjkANOlseufOHWWhzcrKSu/D2/Pz86dOnaqyudTS0nLcuHG6d+UTnp6eyKYAoIW9kWnqZFM93gKOJSGhAgCyqTGyaV5e3sKFC5s3b84eTC0sLMaOHXvr1i19vS5FUaNHj1b2cuPGjcP5AQDK8AWSE5cz5dpEDR1Po27n48gDALKpYbPpixcv1qxZY2trq3Lyk6ura3x8vB7fvFAoHDJkiLJXHDVqFM4PANBR7TGpUbfz5fr0menWe2eismDq+dNVtJsCQNPNplFRUUbIpjweb+vWre3bt1fZm9+nT5+4uDiZTKbHN19WVtarVy9kUwCoc3yBJOp2Pksw9d6ZWFgixIECgKabTSMjIw2dTaVS6enTp5VV+Gdq3779lStXKIrS75svLCxs06YNsikA1KGE5KKAY0nsXfmeP13NyCvHsQKAJp1Nr127xjJTPjY2VscXpijqwoUL7dq1U9mV7+joeO7cOalUqvc3n5mZyeFwlL30Bx98gPMDAAwkI698b2Ta/C3xCpNocFiK509XEUwBANm0Bst402bNmulec/X+/fvdu3dXGUx79ux56tQpicQgQ6wSEhJYXh3tpgCgd4UlwhOXM1kiaUZeOV8gIZ37CKYAgGyqVjaNjIzU5VWTk5PHjBnD0mZJ69at2+7duysqKgz05kNCQpBNAcAI6OGkyibyBxxLSkguIo9EMAUAZFPF8dHS0lJZbjt8+LDWL5mdnT1nzhxlhf0JGxubHTt28Pl8w735devWIZsCgEElZZTujUwjHfRy05si4nKYM5wKS4QIpgCAbKrYkydP7OzslOW2wMBA7V6vuLjY19eXZcuklGlAQEB5uWG/l6dPn86yDx999BHODwDQDt13r3De/fwt8ScuZ9aedI8WUwAAtmxaUFDQpUsXZbnt008/1WLWvFgsPnLkiDrzn9asWVNaavCVqfv378+yGzNmzMD5AQCairqdzzKcNClD8TcbM5jO3xKPYAoAyKbyBAJBv379lOU2BwcHHo+n6YvFxcWprBjVsmXLpUuXFhUVGfqdFxUVWVtbs+zJnDlzcH4AgJqSMkqVDSddte9ORFwOS9l8tJgCAKjOplVVVaNGjWKJbpcuXdLolbhc7rBhw9iDqZWV1aJFi/Ly8pRtRCaTVVbj8XilpaUlJSXPnz/Pr5ZZrbCwUM1J/ZGRkaampiw7s2LFCpwfAMCOXrlUYUMp3XevMmgimAIAqJtNv/76a5boNnbsWI269U+ePMmeBZs1a+bk5LRv377o6Ojz58+fPXv2zJkzJ0+ePHHixJEjR3777bc9e/Zs3749sNr333/v4+OzdOnSzz//fNasWbNnz/7444/Hjx+/evXqnJwcdfZn+fLl7DuzdetWnB8AoCxQsizjFByWouYaTgimAAAaZNP9+/ezRDdzc/Pz58+r/0pr1qxhLxrF4XDs7e2dnZ379OnTvXv3rl27durUyc7OzsbGxsrKqpl6Bg4cmJiYqHJnuFyus7Mz+6ZCQkJwfgCAnITkImZhfLm+e2XDSZUhK0IhmAIAqM6mKSkpFhYWLOnNxcWFy+XqK5vqRdeuXaOiolTuTHh4OHsjroWFhe5rXwFAo8FeM1/hvHuVgsNSEEwBADTIphRFOTg4sLd0+vn51atsamlpGRYWpnJnXF1d2bfToUMHQ1ewAoD6jy+QRMTlKFzvnizjpN2W90amkU2RwvsAAGDC/s/z589XmQUvXLhQf7Jps2bNduzYIRaLWfbk/PnzKjfi5eWFkwOgKaP77pUt4xR1O59l3r1KUbfzydaibufjaAMAqJtNz5w5ozLG9ezZMy0trf5k02+//ZZlpIFAIFDZaGpubp6QkICTA6AJYum7V1YzX5dgGhGXg2MOAKBBNi0sLGSpwE8MGjRI5cDTn376yTjZdOrUqYWFhcp2IyQkxMzMjH0LU6ZMUbMQFQA0GlG385X13e+NTNNXt3tGXjlzOj8OOwCAZtlUZSWpZs2amZqafv311yq3s2fPHuNk01GjRuXm5ircB6FQ2KdPH/an9+jRQ9nTAaDxYVnvnu671+NrZeSVkxcKOJaEgw8AoE02ffDgQatWrdjbKSsqKlRu5+LFiyrrm+qFu7t7fr7iy0lsbKzK4v+XL1/GaQHQ6GXklSuLpPrqu2cJpt47E3UZrgoA0KSzKfuMqA8//FDNMlI8Hm/jxo3r16/f8J/11Zj/l/kf5H9ZHrNhw4Zdu3YdZThx4sT9+/eV9cjHxcWxVMUyMzPbtm0bzgmARkxlzXwDTZnnCyQkmHr+dBXBFABAp2xaUFCgsJjU0KFDS0tLG9YbPnjw4CuvvKKwHpa3t7dUKsU5AdAoFZYI90amKZzkpHK9e92DKRZ/AgDQZzatqqo6ffp0y5YtmWGub9++T548aYjvOTIysnXr1nLZdNq0aSKRCCcEQCNDFyhV2FDqvTPRoJG0djBFKVMAAL1lU5lMtnnzZjJg1NHRMTU1teG+7djY2LfeeosE05EjRxYXF+NsAGhMEpKLDFEzX1PMfUApUwAAvWVTepmosLCwwYMHd+7c+dq1aw39nfP5fB8fHysrq0mTJpWVleFUAGgc2Cc5RcTl6H2SEwtm9f69kWn46wAA6DOb0qRSaUZGRqN5//fv32dfRAoAGoqo2/nK1rvfG5lm/FGehSVClDIFADB4NgUAqFeSMkqDw1KUFSit2/Gd9F6hYhQAALIpADRydN+9woZS40xyAgAAZFMAAKWLi9Jd58YcTgoAAMimANBEZeSVK+u7X7XvTtTtfDSUAgAgmwIAGJbKAqVoKAUAQDYFADC4hOQiZQ2lhltcFAAAkE0BAGqwFCj13pmIvnsAAGRTAACD4wskUbfzV+27U38KlAIAALIpADQ5LPPuA44loaEUAADZFADA4ApLhChQCgAAyKYAUJdY5t17/nQ1OCwlKaMURwkAANkUAMCwkZS9Zj7m3QMAALIpABhcYYnwxOVMhX3387fEo0ApAAAgmwKAwdENpcr67jHvHgAAkE0BwBgSkosU9t3Tw0nRdw8AAMimAGBwLDXz52+JP3E5E333AACAbAoAhlVYIoyIy1E4nJRuKEXfPQAAIJsCgMGx18xH3z0AACCbAoDBZeSVB4elKFvvHjXzAQAA2RQADI6lZv78LfF7I9MwnBQAAJBNAcDgkTTqdv6qfXdQMx8AAJBNAaDOJGWUBoelKIyk3jsTo27no+8eAACQTQHAsOi+e2Xz7lEzHwAAkE0BwBiRlGUZp+CwlKSMUhwlAABANgUAw2KZd79q352o2/k4RAAAgGwKAIZVWCJUtowT3XePefcAAIBsCgAGj6QRcTkK591jGScAAEA2BQBjoIeTYt49AAAgmwJAXUrKKFXWdz9/SzyWcQIAAGRTADA4vkBy4nImyzJOmHcPAADIpgBgcMr67unhpJh3DwAAyKYAYHCFJcITlzMV1sxfte8O+u4BAADZFKARuvX4RWRCLrllP+er/1xBpeTirTzy3Mt38itFUt0jaURcjrKa+ScuZ2LePQAAIJsCNFoHL6RP9K3Jf7vPPpJKZWo+9/6TEs91f5Pn+h64K9QhmyYkFymrmY++ewAAQDYFaBIeZpUy4+CXwdeLeZXqPFEmo45dypj0X66d5Bt9Lj6HoihNd4BlGaf5W+JPXM5EzXwAAEA2BWgqBJUSn903SRz0WBsTfVetFsoKoWTVvtvkiXM3xWXma9DbXlgijLqdr3A4KT3vHn33AACAbArQ5MhkVNjfTycxuvV3hKdK1OjWf5zDZTZ2bjr1QCRW3aFP18wPOJaksGZ+wLEk1MwHAABkU4AmLaugfM7G2Jpmy81xhSUC9qdQFHWIMVB1km/0pVt57B36CclFAceSFPbdYxknAABANgWAf4klsh8O3yNJccramJh7BexP4VWImUvYf74lPreoQtmDM/LKlQ0nDQ5LwXBSAABANgWAGhRFRd3On+x/haTGoN+T2Tvo05/xPmPM0N9w4oFUprTRVK5yPl0zPyG5CEceAACQTQFAgcJS4aJtCSQ+eq2/llcsYMmyp/9++onfv1l2sv+VuAfPWTYedTufDCdNSC5C3z0AACCbAgAbqYwKCk0m2XSy/5XoO0pn6/MqxN/vrZmhvzj4emEp+uUBAACQTQH05+ajFx4/xDCnzCsrpP8ohzs94N/xoxN9o3dHPNKirCkAAACyKQAoxeWLlu2qKXQ6b3OcsulNf1zLJjWnJvtfuZGCkaMAAADIpgD6tv/PNBI6P/G7ci4+p/ZjRGLp2kP3mOWf1FxHCgAAANkUADSQml1GOusnrIn2P3i3stZs/Sd5PK/110iH/r7zaSwz9AEAAJBNAUBLFUKJ/8G7JJvODLyW/ozHfABFUWdis8kDpv4Q8zCzFMcNAAAA2RTAIMKv1hSHmugb/ce1bOY8J5FYuuZATcn9b3ff5AvEOGgAAADIpgAGkfOcP5uxfqnvgbt8YU050qyC8rmb4v5dp9TvSsiVLBk69AEAAJBNAQxEJJZu/f0hyaYzAq6mZpfR/0RR1PnruWSylOdPV1OeluGIAQAAIJsCGFD03ZfWLw37+yndOFopkvofustsUhVUYoUnAAAAZFMAQyrhVS4Iqlm/dPmvt+hBpU8La7r7J/pGh8ZkoeQ+AAAAsimAYUll1N7zaczZ+slZZRRFXUh8RtpTZ2+MzSoox7ECAABANgUwuJuPXny27u9/5zz5Rp/++6lQ9FLJ/XVH7tcufQoAAADIpgD6xxeIv91ds36p/6G76c94pCz/JN/oi7fy0KEPAACAbApgDDIZFRqTxZytHxSaTDr052+Oe1rIx1ECAABANgUwktTsMrI26STfaI+1MSSqbgl5KJbIcIgAAACQTQGMpFIs/eFwzQBTcvvE70r03XwcHwAAAGRTAOOhKOrSrbyJvvLZ9Mvg689LhTg+AAAAyKYARpXznD9/cxwzmE70jQ4KTcY6pQAAAMimAMYmlcq2hSYzs+nUH2JuPnqBIwMAAIBsClAHzsRmM7v1v9tzq7RchMMCAACAbApgbJUiacCxJOYsqPCrT1HWFAAAANkUoA7cSC2a+kNN6Siv9ddynqOsKQAAALIpgNGVC8TMRUon+kbv/CMVZU0BAACQTQGMTSqjIhNymfX252yMzSoox5EBAABANgUwKoqikjJKP98ST4LpJL8rBy+kS6VoNAUAAEA2BTAiiVR270nJ4p+vM0tHLQxKyC8W4OAAAAAgmwIYVqVIKqy+8YWS1OyyY5cy5myMZQbTGYHX4h4+x/R8AAAAZFMAw6KoqmW7bi4Ovr44+PqibQmzNsRO8rvCDKZT1saEXMnCFCgAAABkUwCDk8moT3/8mxlG5YLpr2cf8YUSHCgAAABkU4C6zKazN8Seis4sF4hxlAAAAJBNAeogm070jZ7kGz094GrAsaSHmaXoygcAAEA2BTBqNl2177bP7ps+u2+u3Ht7W2jy+YTcRzlcoUiKgwMAAIBsCmBsYomM3CRSmUyG+fgAAADIpgAAAACAbAoAAAAAgGwKAAAAAMimAAAAAADIpgAAAACAbAoAAAAAgGwKAAAAAMimAAAAAADIpgAAAAAAyKYAAAAAgGwKAAAAAIBsCgAAAADIpgAAAAAAyKYAAAAAgGwKAAAAAIBsCgAAAADIpgAAAAAAyKYAAAAAgGwKAAAAAIBsCgAAAADIpgAAAAAABvT/IOEdobOZOFcAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XQ_ek8aAjQX"
      },
      "source": [
        "We can use the id function introduced above to check this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SE8m-QpQAVyQ"
      },
      "outputs": [],
      "source": [
        "id(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kTWdqLfZAoZv"
      },
      "outputs": [],
      "source": [
        "id(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8nPTdmLBKcS"
      },
      "source": [
        "So as we can see, Python's behavior matches scenario (1) described above. Even though `y == z` in this example (that is, `x` and `y` have the same values), they are different objects in memory. This is because `id(x) != id(y)`, as we can verify explicitly:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QPdrnQQVAqpe"
      },
      "outputs": [],
      "source": [
        "id(x) == id(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HTENxJ2B7jq"
      },
      "source": [
        "There is a shorter way to make the comparison above, and that is to use Python's `is` operator. Checking whether `x is y` is the same as checking `id(x) == id(y)`, which means whether `x` and y are the same object in memory:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o8wuiMJZGpwU"
      },
      "outputs": [],
      "source": [
        "x == y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8J2hNbhGqp7"
      },
      "outputs": [],
      "source": [
        "id(x) == id(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_ruZg9MCGFr"
      },
      "outputs": [],
      "source": [
        "x is y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Els9iljUCPBk"
      },
      "source": [
        "This sheds light on the important difference between the equality operator `==` and the identity operator `is`.\n",
        "\n",
        "As you can see in the example above, it is completely possible for two names in Python (`x` and `y`) to be bound to two different objects (and thus, `x is y` is `False`), where these two objects have the same value (so `x == y` is `True`).\n",
        "\n",
        "How can we create another variable that points to the same object that `x` is pointing to? We can simply use the assignment operator =, like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wAfgcbhOGRf_"
      },
      "outputs": [],
      "source": [
        "x = \"I love Python!\"\n",
        "z = x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFCfnMFCG5ur"
      },
      "source": [
        "To verify that they indeed point to the same object, we can use the is operator:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TtqQE47-G8T-"
      },
      "outputs": [],
      "source": [
        "x is z"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5CaV_ehHB9W"
      },
      "source": [
        "Of course, this means they have the *same address in memory*, as we can verify explicitly by using `id`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pi45fJfNHIn-"
      },
      "outputs": [],
      "source": [
        "id(x)\n",
        "id(z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ku36gREjHTF7"
      },
      "source": [
        "And, of course, they have the same value, so we expect `x == z` to return True as well:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rI7SSPzeHV2g"
      },
      "outputs": [],
      "source": [
        "x == z"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HWuqVLSWFXB"
      },
      "source": [
        "## Mutable and immutable objects in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIyy5Vq3W9Cc"
      },
      "source": [
        "We have said that everything in Python is an object, yet there is an important distinction between objects. Some objects are *mutable* while some are *immutable*. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpD-LWmnXFqq"
      },
      "source": [
        "## Immutable objects in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpXEmdz1zcvg"
      },
      "source": [
        "For some types in Python, once we have created instances of those types, they never change. They are immutable.\n",
        "\n",
        "For example, `int` objects are immutable in Python. What will happen if we try to change the value of an `int` object?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqWguo8VWHFV"
      },
      "outputs": [],
      "source": [
        "x = 24601\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lID8Bo3vzm2C"
      },
      "outputs": [],
      "source": [
        "x = 24602\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqSka2BRzv6T"
      },
      "source": [
        "Well, it seems that we changed `x` successfully. This is exactly where many people get confused. What exactly happened under the hood here? Let's use `id` to further investigate:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wDfHYawZzsxP"
      },
      "outputs": [],
      "source": [
        "x = 24601\n",
        "print(x)\n",
        "\n",
        "id(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kLsKxbKuz-tv"
      },
      "outputs": [],
      "source": [
        "x = 24602\n",
        "print(x)\n",
        "\n",
        "id(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLL2SK6S0Nnv"
      },
      "source": [
        "So we can see that by assigning `x = 24602`, we didn't change the value of the object that `x` had been bound to before. Rather, we created a new object, and bound the name `x` to it!\n",
        "\n",
        "So after assigning `24601` to `x` by using `x = 24601`, we had the following state:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPhr8ZOe0ara"
      },
      "source": [
        "![image-46.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A19p-M7_0n9Q"
      },
      "source": [
        "And after using `x = 24602`, we created a *new object*, and bound the name `x` to this new object. The other object with the value of 24601 is no longer reachable by `x` (or any other name in this case):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHJ65bvV0wsT"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ym70lqPA0_8X"
      },
      "source": [
        "Whenever we assign a new value to a name (in the above example - `x`) that is bound to an int object, we actually change the binding of that name to another object!\n",
        "\n",
        "The same applies for `tuple`s, `string`s (`str` objects), and `bool`eans as well. In other words, `int` (and other number types such as `float`), `tuple`, `bool`, and `str` objects are **immutable**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MsSm0DO1qWr"
      },
      "source": [
        "## Want to test the hypothesis?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRaavfr91v8c"
      },
      "source": [
        "What happens if we create a `tuple` object (you will learn about tuples in the following week of the course!), and then give it a different value?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7YAXp2lB00mn"
      },
      "outputs": [],
      "source": [
        "my_tuple = (1, 2, 3)\n",
        "id(my_tuple)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JzV483xa2I-S"
      },
      "outputs": [],
      "source": [
        "my_tuple = (3, 4, 5)\n",
        "id(my_tuple)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4sdztB12bxP"
      },
      "source": [
        "Just like an `int` object, we can see that our assignment actually changed the object that the name `my_tuple` is bound to.\n",
        "\n",
        "What happens if we try to change one of the tuple's elements?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4-9qjkUA2gS_"
      },
      "outputs": [],
      "source": [
        "my_tuple[0] = 'a new value'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqMkygjG2qsW"
      },
      "source": [
        "As we can see, Python doesn't allow us to modify `my_tuple`'s contents, as it is immutable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k5ZUKE372tpj"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKgpaNUV8bZd"
      },
      "source": [
        "## Mutable objects in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_oNBWysAa3_"
      },
      "source": [
        "Some types in Python can be modified after creation, and they are called *mutable*. For example, we know that we can modify the contents of a list object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-nKllyPW8cf2"
      },
      "outputs": [],
      "source": [
        "this_list = [1, 2, 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-CNuR7dgcZCL"
      },
      "outputs": [],
      "source": [
        "this_list[0] = 'a new value'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLgTVaV5cbaJ",
        "outputId": "fcd6db4d-6305-49fd-b1eb-f45307c196a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['a new value', 2, 3]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "this_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKV8nYq6ch7E"
      },
      "source": [
        "Does that mean we actually created a new object when assigning a new value to the first element of `this_list`? Again, we can use id to check:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iVCEDZ2rceN4"
      },
      "outputs": [],
      "source": [
        "my_list = [1, 2, 3]\n",
        "id(my_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C3OaAMPtc-nB"
      },
      "outputs": [],
      "source": [
        "my_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k1bKlTCGdBmQ"
      },
      "outputs": [],
      "source": [
        "my_list[0] = 'a new value'\n",
        "id(my_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t2a0Ms5wdETp"
      },
      "outputs": [],
      "source": [
        "my_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDcn3gD0dM79"
      },
      "source": [
        "So our first assignment `my_list = [1, 2, 3]` created an object in the address `123456789`, with the values of `1`, `2`, and `3`:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAFAs4BCdtXN"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lD43Cfd0eFpP"
      },
      "source": [
        "We then modified the first element of this `list` object using `my_list[0] = 'a new value'`, that is - without creating a new `list` object:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWpUts6jeThH"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YqIKIh-ef2X"
      },
      "source": [
        "Now, let us create two names – `x` and `y`, both bound to the same `list` object. We can verify that either by using `is`, or by explicitly checking their `id`s:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lYWOXcEgdWvb"
      },
      "outputs": [],
      "source": [
        "x = y = [1, 2]\n",
        "x is y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "713sBRQTevfk"
      },
      "outputs": [],
      "source": [
        "id(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xoIv7PhexlR"
      },
      "outputs": [],
      "source": [
        "id(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mnnowpIuez4p"
      },
      "outputs": [],
      "source": [
        "id(x) == id(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0s_znAGe3xK"
      },
      "source": [
        "What happens now if we use `x.append(3)`? That is, if we add a new element (`3`) to the object by the name of `x`?\n",
        "\n",
        "\n",
        "Will `x` by changed? Will `y`?\n",
        "\n",
        "\n",
        "Well, as we already know, they are basically two names of the same *object*:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEewgfYCfPQZ"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4l0pDnpkfYkt"
      },
      "source": [
        "Since this object is changed, when we check its names we can see the new value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTgdQ626fEhk"
      },
      "outputs": [],
      "source": [
        "x.append(3)\n",
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NKZXYrxJfgwp"
      },
      "outputs": [],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9M0oh14fk8L"
      },
      "source": [
        "Note that `x` and `y` have the same `id` as before – as they are still bound to the same `list` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wZLLQDSnfpJj"
      },
      "outputs": [],
      "source": [
        "id(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fdmEeprrfu2_"
      },
      "outputs": [],
      "source": [
        "id(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kMvZghFfwgy"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eG4ltrbFhTdV"
      },
      "source": [
        "In addition to `list`s, other Python types that are mutable include `set`s and `dict`s."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyavuZGyvut8"
      },
      "source": [
        "## Implications for dictionary keys in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8oHL7VUvut9"
      },
      "source": [
        "Dictionaries (`dict` objects) are commonly used in Python. As a quick reminder, we define them like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L44hPc2nvut_"
      },
      "outputs": [],
      "source": [
        "my_dict = {\"name\": \"Tibor\", \"number_of_pets\": 2}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_kRy5RJvuuA"
      },
      "source": [
        "We can then access a specific element by its key name:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EC2GkQvQvuuB"
      },
      "outputs": [],
      "source": [
        "my_dict[\"name\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VllsygqZvuuC"
      },
      "source": [
        "Dictionaries are **mutable**, so we can change their content after creation. At any given moment, a key in the dictionary can point to one element only:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i-giRBrsvuuD"
      },
      "outputs": [],
      "source": [
        "my_dict[\"name\"] = \"Tiberius\"\n",
        "my_dict[\"name\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2APSCkMyvuuE"
      },
      "source": [
        "It is interesting to note that a dictionary's keys must be immutable:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CTYbfMqhvuuF"
      },
      "outputs": [],
      "source": [
        "my_dict = {[1,2]: \"Hello\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mcjd-hvvuuG"
      },
      "source": [
        "Why is that so?\n",
        "\n",
        "Let's consider the following hypothetical scenario (note: the snippet below can't really be run in Python):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-I4Yg0NivuuH"
      },
      "source": [
        "`>>> x = [1, 2]`\n",
        "\n",
        "`>>> y = [1, 2, 3]`\n",
        "\n",
        "`>>> my_dict = {x: 'a', y: 'b'}`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oepE9WFxvuuK"
      },
      "source": [
        "So far, things don't seem that bad. We'd assume that if we access `my_dict` with the key of `[1, 2]`, we will get the corresponding value of `'a'`, and if we access the key `[1, 2, 3]`, we will get the value `'b'`.\n",
        "\n",
        "Now, what would happen if we attempted to use:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FSly9-vGvuuV"
      },
      "outputs": [],
      "source": [
        "x.append(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFnNo4xOvuuX"
      },
      "source": [
        "In this case, `x` would have the value of `[1, 2, 3]`, and `y` would also have the value of `[1, 2, 3]`. What should we get when we ask for `my_dict[[1, 2, 3]]`? Will it be `'a'` or `'b'`? To avoid such cases, Python simply doesn't allow dictionary keys to be mutable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOMPrSnCvuua"
      },
      "source": [
        "## Taking things a bit further"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxgOs4S5vuud"
      },
      "source": [
        "Let's try to apply our knowledge to a case that is a bit more interesting.\n",
        "\n",
        "Below, we define a `list` (a mutable object) and a `tuple` (an immutable object). The `list` includes a `tuple`, and the `tuple` includes a `list`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Uj2zdY2vuue",
        "outputId": "21c9a01d-599b-4506-c5bf-83dbe1d0cd16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "my_list = [(1, 1), 2, 3]\n",
        "my_tuple = ([1, 1], 2, 3)\n",
        "type(my_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mN7xt9npvuuh",
        "outputId": "c72fb801-6b9f-415d-ae34-74a78f5d10db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(my_list[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fVS-RB9ivuui",
        "outputId": "8616396f-3b4f-489e-d7b8-62a5be2272dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(my_tuple)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VyaoR-K1vuuj",
        "outputId": "213b84da-12a0-4419-bf7e-b638ce7d2050"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(my_tuple[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMuk8OLuvuul"
      },
      "source": [
        "So far so good. Now, try to think for yourself – what will happen when we try to execute each of the following statements?\n",
        "\n",
        "(1) `>>> my_list[0][0] = 'Changed!'`\n",
        "\n",
        "(2) `>>> my_tuple[0][0] = 'Changed!'`\n",
        "\n",
        "In statement (1), what we are trying to do is change `my_list`'s first element, that is, a `tuple`. Since a `tuple` is immutable, this attempt is destined to fail:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YpjbpD3svuum",
        "outputId": "f263b88f-cf58-4c1f-8838-db8b2c3952ee"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "'tuple' object does not support item assignment",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-5-7ffb8039932b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Changed!'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
      ],
      "source": [
        "my_list[0][0] = 'Changed!'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRfHRCn-vuun"
      },
      "source": [
        "Note that what we were trying to do is not change the list, but rather – change the contents of its first element.\n",
        "\n",
        "Let's consider statement (2). In this case, we are accessing `my_tuple`'s first element, which happens to be a `list`, and modify it. Let's further investigate this case and look at the addresses of these elements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8r5JQKravuuo"
      },
      "outputs": [],
      "source": [
        "my_tuple = ([1, 1], 2, 3)\n",
        "id(my_tuple)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j7g-tNWevuup"
      },
      "outputs": [],
      "source": [
        "type(my_tuple[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rg0242lOvuuq"
      },
      "outputs": [],
      "source": [
        "id(my_tuple[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQpzwSA5vuur"
      },
      "source": [
        "![image-29.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we change `my_tuple[0][0]`, we do not really change `my_tuple` at all! Indeed, after the change, `my_tuple`'s first element will still be the object whose address in memory is `20446248`. We do, however, change the value of that object:"
      ],
      "metadata": {
        "id": "pWvazj3YwVXz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_tuple[0][0] = 'Changed!'\n",
        "id(my_tuple)"
      ],
      "metadata": {
        "id": "Mhnqq9qCwlwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id(my_tuple[0])"
      ],
      "metadata": {
        "id": "Z3YQ2SeDwpDX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_tuple"
      ],
      "metadata": {
        "id": "zYXOx4FxwrTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both `id(my_tuple)` and `id(my_tuple[0])` remain the same after the change!"
      ],
      "metadata": {
        "id": "AL6SJwGKwt5i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image-48.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "HVsmp9Mxw3gS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we only modified the value of `my_tuple[0]`, which is a *mutable list object*, this operation was indeed allowed by Python."
      ],
      "metadata": {
        "id": "_YbQW4m8xAuz"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "-MsSm0DO1qWr"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3.9.4 64-bit (system)",
      "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": "3.9.4"
    },
    "vscode": {
      "interpreter": {
        "hash": "7ec39dd714c508a86c0394efec1593fa149f37d72b598b21c30e2f9438986bb9"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}