{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The data block API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastai.gen_doc.nbdoc import *\n",
    "from fastai.tabular import *\n",
    "from fastai.text import *\n",
    "from fastai.vision import * \n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data block API lets you customize how to create a [`DataBunch`](/basic_data.html#DataBunch) by isolating the underlying parts of that process in separate blocks, mainly:\n",
    "  1. Where are the inputs and how to create them?\n",
    "  1. How to split the data into a training and validation set?\n",
    "  1. How to label the inputs?\n",
    "  1. What transforms to apply?\n",
    "  1. How to add a test set?\n",
    "  1. How to wrap in dataloaders and create the [`DataBunch`](/basic_data.html#DataBunch)?\n",
    "  \n",
    "For each of those questions, you can have multiple possible blocks: your inputs might be in a folder, a csv file, a dataframe. You may want to split them randomly, by certain indexes or depending on the folder they are in. You can have your labels in your csv file or your dataframe, but it may come from folders or a specific function of the input. You may or may not have data augmentation to deal with. Or a test set. Finally you have to set the arguments to put the data together in a [`DataBunch`](/basic_data.html#DataBunch) (batch size, collate function...)\n",
    "\n",
    "The data block API is called as such because you can mix and match each one of those blocks with the others, allowing you total flexibility to create your customized [`DataBunch`](/basic_data.html#DataBunch) for training. The factory methods of the various [`DataBunch`](/basic_data.html#DataBunch) are great for beginners but you can't always make your data fit in the tracks they require.\n",
    "\n",
    "<img src=\"imgs/mix_match.png\" alt=\"Mix and match\" width=\"200\">\n",
    "\n",
    "As usual, we'll begin with end-to-end examples, then switch to the details of each of those parts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by our traditional MNIST example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.MNIST_TINY)\n",
    "tfms = get_transforms(do_flip=False)\n",
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/7'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/3')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path/'train').ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [`vision.data`](/vision.data.html#vision.data), we create an easy [`DataBunch`](/basic_data.html#DataBunch) suitable for classification by simply typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is aimed at data that is in folders following an ImageNet style, with a train and valid directory containing each one subdirectory per class, where all the pictures are. There is also a test set containing unlabelled pictures. With the data block API, we can group everything together like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ImageItemList.from_folder(path) #Where to find the data? -> in path and its subfolders\n",
    "        .split_by_folder()              #How to split in train/valid? -> use the folders\n",
    "        .label_from_folder()            #How to label? -> depending on the folder of the filenames\n",
    "        .add_test_folder()              #Optionally add a test set (here default name is test)\n",
    "        .transform(tfms, size=64)       #Data augmentation? -> use tfms with a size of 64\n",
    "        .databunch())                   #Finally? -> use the defaults for conversion to ImageDataBunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y7W4vSim+Lz6fj7sSHD161JZpRI8D9s4EJLmsra3xniA5o2KxuO1kYcq4DoeD9/hI5gMsB0iTWDab7SjH08o2Y54UrLW2dRGgezA3N4e///u/BwC89dZb/PhOyx/oNbu6umwlHvSYmar9uPNQq7GrPR6l1GEA5wC8CWBgy2AAYBnAwAN+5hUArzz+EIV2RexF2C1iMweDHTsepVQQwF8C+G2tdaruOACtlNo27tNavwrg1a3XaGpsqLXmVejm5iZv/C0tLfGqYmBggPtmjY2NcZhL8sducbvdvBE5MTHBuf+Dg4O8sjXDZlOmMw+Fq1ar7SK1AWhfe3E6nXzPzWMszp49i+eeew6A1U+Nsg0dDgdLJevr69yLLR6Ps62RdGsmutDPAvYjNzweD8t4Xq+X778ZFdVqNbYRs61+O0svQGvaTDqdxpUrV2h8HOn6/X6uybp//z7eeustAPYjKx4G/W5mVD08PGyLpOn9KXIys9ranR05HqWUG5ZBfE1r/Y2th1eUUkNa6yWl1BCA1f0a5H5BIW4+n7dp6XRzU6kUTx4k0dVDIbHH4+HrQCDAe0PhcJj7th06dMgmtZGUUigUbJIeher5fJ6NzpTdWp12tBeaCJRStiPRqUPBuXPn8DM/8zMArAnC3Hsz+3FRCvXi4iLfx+327BwOB9uL2+3mvQNzIjIbjZqSsXkyabvYxKNoVZvJ5XKcHn3p0iXb/puZGk+Lj532zaPOBUePHsXFixcBACdPnuQFDdlOPB5nqU8p1TGdC3aS1aYA/DGAG1rrf2381zcBfHnr+ssA/mrvhye0G2Ivwm4Rmzl47CTi+QiALwF4Xyn17tZjvwfg9wH8mVLqNwDMAvjV/Rni/mEeykUrllQqxRKc1+vl5zyoTQ2tWn0+H0tng4ODnAkzODjIbdL7+vo4EvJ6vSyvrK6u8qb04uIibt68CQC4e/cut+9JJBLtspHcdvbicrk4+vT5fHzvjh49yhHP8PAwJx3UajXb4X5kL7dv38bly5cBWBvOlCn5oLb2ZDter9cW8VDmo9vtttWDmZFTh51A2rI2U6vVODlEKWUr9KZ78DhqBEU8x44dwwsvvADAsjGyCYqi3333XU58elBySjuyk6y21wH8dHWjxct7O5zmsZ1+TuEuYR5vAFgTA+nxfX19fD00NMTVxiMjI1wUFovF+GdNvTYej2N6ehqAVeFMjufmzZvskMzzeFqZdrQXt9vNk300GuV9nRMnTuCZZ54BYO330MLCTKUtFApcIDwzM4MbN24AsDLcSCLZ7r45nU5ehMRiMXZ2PT09PLmUSiWWeNfW1mxOyHQ87T4ZtYvNaK2fqF+ieVw6yaknTpzAs88+C8CyA7qXJPG/++67PB90Uo9G6dUmCIIgNJSOb5mzl5AcQpvPoVCIO0+fOHGC2+N3d3dzPn53d7dNXqOVTDqdZinm7t27fFjY1NQUZ8skEomfirqEvceMeIaHh/menjlzhjPZTAqFAheFzs3N2Q53o6ikXC5vG4lQEoPL5eJsxxMnTnCN0NDQED/HjKbM1zalnXaPdg4SdF9jsRgmJycBWF2oKeGI5gkAPH88/fTTPE+Uy2VbsalpA+1mB+J46ujq6mIt3+12cwWxw+FgmYza4EciET5H5/z58/jIRz4CwOrfRdkvTqfTlqVG+wFzc3N8vs/t27dZopmammLjIoMT9hen08mLicHBQXYIdL/rKZVKfETxpUuX8O671rbE8vKyLQuxXmJTStmy58iOTp48yQ7u8OHDvI+QyWRY619ZWeEsKrOHoNBaOBwOvn/RaJSdirmgmZiY4JKN06dPs8Nxu9224nXAmldoPlpfX+fOK/X7jLQoKZVKbbEoEalNEARBaCgS8dQRDod5tdHd3c2FpYFAgAsLaUUcjUa5c+zhw4dZrikUCrwCyWQy3H12fX2dr1dWVrjYcG5ujg+Joiw2oXE4HA5edQ4MDNiKBM1WNkStVmM59L333sP7778PwF5zsV2Wk9mHLRQKcc3G6OgoJ6CEw2F+r2q1yhHP8vIyZ1eVy+WOKRrtFOh+eDwenj9efPFFvPTSSwCAp556ik8v9fv9LNubNVkbGxuscpjyGtnj5z//eXzsYx/j51LLr3v37rFisrS0xIpJK59Y3LGOhz68Zqqqy+WySWem7EEf4P7+fp4Q+vr62IjME0MpY210dNTWHp8mr1QqxdlIS0tLLMtMT0+z44nH4/ycTCbDez9C41FK2VLh6T77fD6bnEFOYGlpibMQFxcX+d4VCoWHZh76/X5+7ZGRET6NdmhoiCeXQCBgO2OHJqD19XV+/1KpZHNOQnMxZfjR0VGcPXsWAPDSSy/hxRdfBGAtaGh+cDqdPPeYJx7Pzc3xNS1cx8bGWKJ78cUXeT5aWFhgqf7GjRucFXnv3j0+3+f+/ftsvzstbG0UIrUJgiAIDaVjIx6Kcrq7u1kCC4fDLJ0Fg0HeUA4GgzZJzXycVsLm86leJxaLceLA9PQ0b/7G43HeBFxZWeEVyNTUFGdDbWxsPLANj9BYzI7gZn+8QqHAEcfy8jJHrrdv3+ZMtng8butG/DDpy+v1cjR95swZHD9+HIC1qjV7v5k1ZfT+GxsbtohHaD4UdUYiEZZKX3rpJfzCL/wCAGBycpKleDNRyawDW1lZwaVLlwAAV69e5YiHZNX+/n6Ocnp6ejjbra+vj+vNLly4wAfUJRIJfOtb3wIAvPbaaxyNZzKZloqOO8bxKKVshZ2kpcdiMdueDEkaZtHe8PAwy2dut5uzUkxjcTqdtmvAku6mpqYAWMcb0/XGxganwW5ubvKEEY/HOYQmHVZoPtVq1SZJmN0qyKksLy9zH7bLly+z1La2tsbO4UFOh2QVt9vNE8fo6CjbXyAQ4Eksm83y68Xj8W3tiPaRhNYgEAiwczh16hTOnTsHwH4seqVSsdkSndlz6dIlvP322wCssgpyOOb8QAsRszvKz/7sz+KXfumXAFiZcbRwGR4e5gVwV1cXv8/Nmzd50VssFpsuvYnUJgiCIDSUjol4urq6uKZmdHSUVwDRaJTrJcLhMG/wBQIBm2RGzzc7Bbvdblvrelp5mMcWUASzurrKEU8ikeDVRTKZ5NVLPp+39duSzKTWoFqt2k6MJZnUTCi4ffs2F/nev3+fM4oe1j+LbIe+ulwutr++vj6OxM3suUKhwO+fSCRsRyvQdbu0T+p0zPnA7EROUY7H4+HPeLlc5vt67do1vPbaawCAt99+mzMkNzc3H3pfS6USR7tvv/0228Pk5CRLbWfPnuWklbNnz3LG5Te/+U288847AKxIutkRT8c4Hr/fz9kf586d4y4CZpaaz+djQ3A6nSypeb1eluYcDodtwjDPBKnHlPd6enr4+INYLMaa7+bmpq3fFunz5XKZr0ulUse1uW8nzEaf77//Pt8vs5h4bW2NFxZmMefD+meRHZmZlGRn0WiU7ZL2FwHL8ZgZkSS15fN523vJYqV1KBaL7ASuX7/Oxyj09vbyInZlZYXP9bly5Qr3X1taWmKn9ajFhHlWVy6X46y2q1evsvNKpVKcVTc6OsoOKZlMsu1duXIF169f39F77hcitQmCIAgNpWMiHq/Xyxu3ZnuK/v5+zizx+/28UnycFaNZ90OYK1jzVEiz8zRltWmtWXYzD49SStlOGhUaS6VSYemsWq3yStLlcnFNTTab5UgkkUhwhPqwhAKKqEle6+npYXk3FApxpON2u/l1MpkMr15v3rzJ72lmskm001pkMhn+7P/N3/wNS1r9/f2sgmQyGfzkJz8BYGXAPmlbm/rCU2rbtLKywlFyd3c3R9XPP/88z4PlcpmTY/L5fFOiHol4BEEQhIbSUREPrSB7e3s5vTEQCNjy5/ci4jFfjzaIq9UqJzGYZ6Qkk0leaUxOTtrO8zCPt6UV98bGBuvFm5ubvDKSxpD7h9aa01gLhYKt0wVdV6tVvl+VSuWR90JrzZEOpeqPj49zCnUoFOLUWLM5pNlI1qz7KhQKcv9blEqlwoqFufditkVyOBx8L/fqs2zODQsLCwCsDhdmF4Xz588DsPYR6WypK1eu8Fy1srLSlEMFO8bxmG0olFJ8UyqViu08dPM5dG0mETwsocBsaU/vSZNKT08Ph76m1FYsFvn9c7kch+SVSoUdzNzcHBvO7Owsb2KXSiV2VKVSqV1OIG1LzAJSM1uJ/v5m0d9OP6BmGx7AOm2Skk7M4zLM00WLxSIvQkzZpJMOAetktNb8OU0mk7YaL3p8PxYQ5mvT/PGDH/yA7ebixYuc9Ts5OckO0cyka6TjEalNEARBaCgdE/FUq1XOk5+ZmWHv3d3dzWGlGRUB2Db6Ma9dLpftjHVKrTW/Ulj7oLNbHgZtHJsdkT0eD0dI6+vrvGIyq+uFvceMcrb7O+9UpjVT8SmiocagR44csaX203vmcjmW8ZLJJNcOJZNJTtsulUpS99Um0P0xWzE16j0rlQrPKzdu3GCp+MiRI/zcw4cPc3eFYrHIagvNn42gYxyPeT799PQ0h5j9/f08kdPeDGB3MPR9/eNer5flEr/fz3tI5v8/CZQRNzg4yA4ul8uxvJJMJlnvN2U3Ye95kr0/wul08gfd6/Vy53JyPEePHmXZzcywzOfzLLumUinebzKvZY9P2Ckk+cfjcS56Xlxc5HlwaGgIFy5cAGDNNzSvNNLxiNQmCIIgNJQdRzxKKSeAdwAsaK1/USk1AeDrAKIALgH4kta6aW1zC4UCRwdKKd6gDYfDXN9T35XgUdc+n8/WYodWsNTtOhgM2qrSt2so6vP5OLLxer22Fjz02sFgkDvNplIp25k9tBqhjJh2odXtZT+or92hbuZmxEPZll1dXRzBpFIpljvm5+d55VkoFJre2qSRHESb2Q9I3svlcrZDKKm9l9/v53qy7u5u3iYwzyXbb3Yjtf0WgBsAwlvf/wGAf6O1/rpS6j8A+A0Af7TH49sxmUyGW4rH43H+Y5r7MPUOxnQaD3I820lt5DB8Pt8DnRe99ujoKBezjo6O8lh8Ph/LMiZmZokp+7WhzNLS9rKXmIcO0gd6aGiI7zul05v7gOl0mhcVt27dwnvvvQfAatlD6bgH8PiDA2Mz+wnNFWZ2bSaTYaktHA7b9qlpseRyuRq20NmR1KaUGgXwOQD/aet7BeCTAP5i6ylfBfDL+zFAof0QexF2i9jMwWKnEc+/BfAvAYS2vo8C2NRaU/rPPICRPR7brigWi7yCpM35J8WMeOqjHwC8UgCs6MRsCknXL7zwAkctoVCIVyMPSkwwM2HMFUubdSNueXvZK8xu5pFIhCXTp556Ck8//TQAcLdgr9drW4FS9trMzAw++OADAFZxn9kY9ABxYGxmvzETZczCZLKnaDRqi3joupERzyMdj1LqFwGsaq0vKaU+sds3UEq9AuCVxxhb06lWq+zMarWa7WRK4MNCUsByPOYJpbSvZJ562tXVxZMUOSZiuz5vWmu+bpcebgfNXlwuF0tooVCI5bUXX3wRFy9eBABO5w8Gg5w2nc1msby8DMAqIL5//z4AYGFhwVZkfBA4aDbTKJRSPN9UKhWey9xuNzsbswO7OZ/tNzt5p48A+LxS6rMAumDpr38IoFsp5dpakYwCWNjuh7XWrwJ4FQCUUm23USHsGrEXYbeIzRwwHul4tNa/C+B3AWBrNfIvtNb/WCn15wD+Eaysky8D+Kt9HKcN8swej8fWvoY29JVSHJU8SWaQWUzocDh4tfqg83ooey0UCnFrFPPM9EgkwisQs6YIgK07tRnxmBuF7UAr2st+YNZyUUQzMjLCstrhw4c5qcDMdqSC4OnpaT6ka2ZmhmU3s31OGyaUPBYHxWYajdvt5kxcSogCrPmT5jDzUMH6OWk/eZLY6ncAfF0p9b8CuALgj/dmSI+GJK1YLMZZRKa8FYlEMDMzA8BqtHj37t3Hep/6avX6EyUDgQC/ZygUYn3/yJEjXCl86NAhdjx+v99WqGo2DKVCwXQ6zZNTPp/vpAPimmYve4XZz48+pLFYDCdOnAAAPPPMMzh58iQAS14zsyYJuueLi4t8GNjs7KztdNGD5ngeQtvbTKMxD7gcHBzkJqHPPvssl4G4XC52Ntlslm3yYYcMqsW4AAAgAElEQVRe7jW7cjxa6+8D+P7W9RSAF/Z+SEKnIPYi7BaxmYNBW7bMoc36wcFBjIxYiS6HDh3iQ5eGhob40KV0Ov3YEY+Jw+HgTTiz1Q1JK2NjY/z+4+PjGBsbA2AVaFFUVt+yh1a5q6urXLtx584dbnMxMzPDGU4HZaO5lTGPxSDJtLe3l6OcT3/609ytvK+vj2VgcyVJ93F1dZUTCpaWlmw9+STSaS/M+9vse+dyuXh+HB0dxbPPPgsAOHv2LCtFlUqFtw3W1taaMse0peOhdObe3l7eSzl06BBP9oODgyxjmCnPu8XcN/L7/dzIkzT9sbExbjV+/PhxHsvY2BhnN9VjGibd8Lm5Ody7dw+A5XjoPPT79++33R5PJ0MTjNvtZs08FovxvT59+rTtjB26d3SfE4kEL4JWVla4AWg2m33kiaZCa6GU4rnB5XKxbZRKpYbI4mb5ht/vZ6cyNDTEMv+5c+c4pX94eJhtKx6PY2lpCYAl81J2Je2LNwLp1SYIgiA0lLaOeKLRKGcODQ0NsczR3d3NstjjbJjRz5hHIQwMDOD06dMAwFHO6Ogov+fIyIitdme7lWu1WuWVbaVSYanlvffew7Vr1wBYUY650Sy0DmQLfX19HN1OTEzwKbTmKthsd0QRz8zMDEe2q6urnFCSzWb39ZAwYe/YLrGoq6uLo5z19XWOZPfzXppybzQa5aj7zJkzfOTB2bNn+fHe3l6ObKanp1nOX15e5vHKCaSPgBxPJBLhNvOxWIwlsK6uLn5OIBBgicw8otYMVR0Ox7Zt8c3iqsHBQZw6dQoA8NGPfhSA5XjI+Hp7e21ZT9tRqVRsad7keN5991289dZbAKxMNjIEobWgD3pfXx9nsp08eZIXH26327avQ/ZA93NhYYGzLVdXV3lfp1gsisNpE2heefnll3keSCQS3OT1nXfewe3btwHYSyMexnZHstACpr7Yk95/YGCAF90TExOcUTs+Ps773tFolN8/Ho/j8uXLAIBvf/vbePPNNwFYjtIs32gUIrUJgiAIDaUtIx6zRQlFHMFg0Nb/jB4/fPgwF5CaJz2aB3UFg0FeqbpcLluBqimvkMRmSnq0yVzfaZoim83NTZZUNjc3WXbZ2NjAjRs3AFgSDK2YhNaF7CIajWJychKAJbvSsQcul4tXjWbvQJI4TKktHo9LQkEbQhHJiRMn8KlPfQqAlRlGh1D29vay9FooFPgogmw2a5PgTHVkuw73NO/09/fbMiXpBNsTJ05wNmVfXx/PQ16vl+cipRTPNwsLCxzxfPe738XU1BQAq0i+GfbXlo6HbkogEOCiqEAgwI9Xq1WW3SYmJnjCMCf+cDjMN7G/v992hLUZ2prdCMigyBC8Xu8Dq32pId/y8jKnSs/NzWF+fh6Ade4KHeNAZ2YIrYVSiu+/3+9nWXdoaIiv+/v7OaPI4XDwIieTyfCCg85Smp+fZ6ktHo83NItI2BvIkdy6dYsXjuPj4zhz5gwA4NSpU/jsZz8LwMpcNMskbt26BcCyDbObBc03Ho+Hs3DJpo4cOcIlG2YXFHPRW6vVuPNFoVBge1teXsbs7CwAK3uNOmWsra3ZekM2A5HaBEEQhIbSlhEPRRPr6+uYnp4GYIWMtDoNBoMsf5TLZV4lZLNZ3tD1+/0sx3V3d/NKwzwYyYx+/H4/rzBoNVIqlXgFZIbVuVyOT0Cdm5vjyGZubo5XIDMzMxx602pFaC2UUryZ29/fz5lso6OjvIE7PDzMz3G5XBzF5HI5zk4kW1heXub6iWw2e6BOF+0UKHvt7t27+N73vgcA+Lmf+zlOYOrq6mJlxOv18twzPj6O559/HoAltW0339RfA9bcRFsCPp+P/z+TybA8v7a2xrJuOp3mQwWvXr3KEdfa2hrPT+Vyuel1gW3neJRS/AdcW1vjzDCfz8fyh5nu2tvby5NBqVTiD7vZ08jj8TzyCGu3283PJ302m83a9mzoJq+srLCuv7KywpNNIpHgSWhjY4PHJRNQa6K15sVMX18fp6aOjY2xrdHEQs8n27x//z4XMZPEEo/Hbc1gZW+nfZmZmWEpdWFhgeehaDTKsn1PTw/L+cePH+eFbiAQsKXd0+e/XC7zvp/5lZxKJpPh6/n5eT7DaXZ2lh/P5/O8uDU7YhQKhZZK2RepTRAEQWgobRfxOJ1OXmksLS1xFOLz+VgC01rzqsPn83ECgsfjsWV8PAgKQ826n3K5zJKY2W+LsllWV1dZ9puenuYweGVlha+LxaJtdSO0HmZhnsfjYQllfHycC4gPHTrE/bAA2E6JNY89ePfddwF8GPGsra3ZCohbYeUpPB6ZTIYzZAuFAsuqsViMo+RwOMx9Gs3kJ/PYlnw+b6vtM6/pK0Uz5v9nMhl+z1wux3NSpVKx2VgzanR2Qts5Hq217Whr+iM7nU52Jslk0nZcAmmkoVCIndDDHA+9pnlEwdraGmefUdZIPB5HIpHga5LR1tfXeYypVIqNZacFZULzcDqd2+4VHjlyBM899xwAa7+H7EspxR9q88TaxcVFdjhUJZ5Op20n2grtDd33RCLBi4z6UzzNXou0P2QuaA+qHYjUJgiCIDSUtot4arUaRxmpVIrDXbN4L51O80rVzH0vlUq2xAGzcMt8nCKeVCrFUczU1BS3wqAOw2tra1hZWQFgrXrMDUFzE9nc1Gu1kFewY0Y8kUiE7WhycpIjHrNYWGvN2Ylra2sc3czPz9ukEEDktU6lUqmwMiLsjLZzPFpr2x4MOZ7FxUWe4FdWVni/JxgMcrqr3+/nScU8ttrs26aU4tcxNdX19XV2MpSllkwm2Qmm02lbKC0TTPtg9sjy+Xy8rzM5OcndKvr7+/n+mo5HKcUZjNevX+dMo/v37/NeJNmQ2IUgWIjUJgiCIDSUtot4ANg6TFOmWT6f5+wxs0uw2XvNvDYjnvprolarcXRVLBZtmSiAPcfe7Hkkq9r2wOwETHYRDAa5OPSZZ57BU089BcAqFH1QQgrJsXfu3OHiPVNqk9Y4gmCnLR0PobW2pRGSli4Iu8FMofb7/Vx8/Nxzz+H8+fMArIxIek6tVuN0+EKhwNmOCwsL3KViY2OD9/wEQbAjUpsgCILQUNo64hGEJ4GkM7fbzQko5lEbkUiE+/OZdWLZbJYLh9fW1mxdxkley2QyUiQsCA9gR45HKdUN4D8BOAtAA/gfANwC8F8AHAYwA+BXtdYbD3gJ4QDRbvbi9XrZ2fT09HBXArfbzfKaefxFsVjkIuLp6WlbI0YqHDaPsxYeTbvZjPBk7FRq+0MA39JanwTwDIAbAL4C4Dta62MAvrP1vSAAYi/C7hGbOUA8MuJRSkUA/ByAXwcArXUJQEkp9QUAn9h62lcBfB/A7+zHIIX2oR3txev1ciulWCzGLZYe1NvPjHimpqZYakskErYsR2FntKPNCE/GTqS2CQBrAP4fpdQzAC4B+C0AA1rrpa3nLAMY2O6HlVKvAHhlD8YqtAdtYy+U9m52rnA4HPy4KZWVSiXOUtvY2OCTRG/cuMHNYTc3N7nI1Hx94ZG0jc0Ie8NOpDYXgPMA/khrfQ5AFnUhr7Y+Ydt+yrTWr2qtL2itLzzpYIW2QOxF2C1iMweMnUQ88wDmtdZvbn3/F7CMYkUpNaS1XlJKDQFY3a9BCm1F29gLRSTlctl2gqPZ7p6eY542m0wmOaHg3r17HP2Y7ekl2tkVbWMzwt7wyIhHa70MYE4pdWLroZcBXAfwTQBf3nrsywD+al9GKLQV7WgvlUqF+/LROSvZbNbWcaBSqSCfzyOfzyOZTGJpaQlLS0tYXl7G+vo61tfXkc1mUa1WbXKb8Gja0WaEJ2OndTz/M4CvKaU8AKYA/PewnNafKaV+A8AsgF/dnyEKbYjYi7BbxGYOEKqRkoBSag2WfrvesDfdGTG03piAvR/XuNa6bw9fb19pYXsBWtNm9mNM7WYzaVj1P61GK9oL0KQ5pqGOBwCUUu+02iZgK44JaN1xNZJW/Ru04rhacUyNplX/BjIuO9KrTRAEQWgo4ngEQRCEhtIMx/NqE97zUbTimIDWHVcjadW/QSuOqxXH1Gha9W8g4zJo+B6PIAiCcLARqU0QBEFoKOJ4BEEQhIbSMMejlPqMUuqWUuquUqpp7c2VUoeUUt9TSl1XSl1TSv3W1uP/Sim1oJR6d+vfZ5swthml1Ptb7//O1mO9SqlvK6XubH3tafS4moHYy47GJvZi0Ao2I/ayw7E0Yo9HKeUEcBvAp2D1ZXobwBe11tf3/c1/eixDAIa01peVUiFYnXB/GVZVdEZr/X80ekzG2GYAXNBarxuP/e8AElrr39/6MPVorTu6NbzYy47HNgOxFwCtYzNiLzujURHPCwDuaq2nts7a+DqALzTovW1orZe01pe3rtOwDpwaacZYdsgXYJ1Fgq2vv9zEsTQKsZfH5yDaC9AiNiP2sjMa5XhGAMwZ38+jBW6GUuowgHMAqCvubyql3lNK/UmTJAoN4P9TSl3aOmME2OGZJB2G2MvOEHv5kJazGbGXB3NgkwuUUkEAfwngt7XWKQB/BOAogGcBLAH4P5swrI9qrc8D+AUA/5NS6ufM/3zYmSTC/iL2IuwGsZeH0yjHswDgkPH96NZjTUEp5YZlFF/TWn8DALTWK1rrqta6BuA/wgrdG4rWemHr6yqA/7o1hpUt3Zj044NwJonYyw4Qe7HRMjYj9vJoGuV43gZwTCk1oay2578G66yNhqOUUgD+GMANrfW/Nh4fMp72KwA+aPC4AlubkVBKBQB8emsMB/FMErGXR49L7MVOS9iM2MvO2Ol5PE+E1rqilPpNAH8LwAngT7TW1xrx3tvwEQBfAvC+Uurdrcd+D8AXlVLPwgo1ZwD8jw0e1wCA/2rZLVwA/l+t9beUUm/jgJ1JIvayI8ReDFrIZsRedoC0zBEEQRAayoFNLhAEQRCagzgeQRAEoaGI4xEEQRAaijgeQRAEoaGI4xEEQRAaijgeQRAEoaGI4xEEQRAaijgeQRAEoaGI4xEEQRAaijgeQRAEoaGI4xEEQRAaijgeQRAEoaGI4xEEQRAaijieHaCU+s9KqSWlVEopdVsp9U+bPSahdVFKZer+VZVS/67Z4xJak4NoL3Iswg5QSp0BcFdrXVRKnQTwfQCf01pfau7IhFZn6wjkZQCf1Vr/sNnjEVqbg2IvEvHsAK31Na11kb7d+ne0iUMS2od/COs44R81eyBCW3Ag7EUczw5RSv17pVQOwE0ASwD+W5OHJLQHXwbwp1qkBWFnHAh7EaltFyilnABeBPAJAH+gtS43d0RCK6OUGgcwBWBSaz3d7PEIrc1BsheJeHaB1rqqtX4dwCiAf9bs8Qgtz5cAvN7pk4iwZxwYexHH83i4IHs8wqP5JwC+2uxBCG3DgbEXcTyPQCnVr5T6NaVUUCnlVEr9AwBfBPCdZo9NaF2UUj8LYATAnzd7LELrc9DsxdXsAbQBGpas9h9gOepZAL+ttf5mU0cltDpfBvANrXW62QMR2oIDZS+SXCAIgiA0FJHaBEEQhIbyRI5HKfUZpdQtpdRdpdRX9mpQQuciNiPsBrGXzuSxpbatmpbbAD4FYB7A2wC+qLW+vnfDEzoJsRlhN4i9dC5PEvG8AKt/2ZTWugTg6wC+sDfDEjoUsRlhN4i9dChPktU2AmDO+H4ewMWH/YBSSjIZmozWWjXx7XdlM2IvLcG61rqvSe8tc0wbspM5Zt/TqZVSrwB4Zb/fR+gMxF5ajtlmD+BRiM20H0/ieBYAHDK+H916zIbW+lUArwKyGhEebTNiL4KBzDEdypPs8bwN4JhSakIp5QHwawCkqFJ4GGIzwm4Qe+lQHjvi0VpXlFK/CeBvATgB/InW+tqejUzoOMRmhN0g9tK5NLRzgYTBzafJyQW7QuylJbiktb7Q7EHsFLGZ5rOTOUY6FwiCIAgNRRyPIAiC0FDE8QiCIAgNRRyPIAiC0FDkPJ49wuVywe1287XDYfl0pRSUsvbaarUaarUaAKBSqdiuq9VqE0YtCILQeDrG8TgcDjidTgCA0+nka4fDwU7A7XbD4/EAALq6uthReDwevgYAM9OPnu/1evna5XLx69G10+nka601SqUSAKBUKiGfzwMA0uk00mnrnKdMJsOP53I5vhYEQeh0RGoTBEEQGkpbRzw+nw9dXV0ArAjG5/MBAPx+P0cwSimOeILBILq7uwEAPT09CAQCAIBAIMA/WygUUCgUAFgSWDAY5J+l53u9Xv5qRkEU8ZTLZWxsbAAANjY2EI/HAQDLy8tYWVkBAKytrXFUVi6XJeIRBOHA0NaOx+v1IhQKAQDC4TA7le7ubnZIpuOJxWIYHR0FABw+fBh9fVbT3YGBAb5eXl7G6uoqAEsO6+np4Z+l1zedHTkek0Qigfv37/PrLS8v8xgjkQgAIBqNIpVKAQCSySQymQwAuzRXKBRYsisWi+wQBUEQ2hmR2gRBEISG0pYRD2WJdXV1ccQTi8UwMDAAABgaGuLHzYgnGo1iaGgIADAyMmKLkOj5+Xyes82CwaBNaiOJjWQ8h8OB7VoOeb1e9Pb2ArCSDkii6+np4YgrmUxic3Pzp643NjaQSCQAWJFTNpsFAKRSKYl4BEHoCNrS8RBdXV0sXfX19eHw4cMAgMnJScRiMQD2bLdgMMjPN+U4n8/HziwcDrN8Vq1W+drMfDNTpR/keEi66+3tRaVSAWDJZeVyGYAl45GkF4/HWY67f/8+5ufnAdgz5crlMmfEAR9m3jWy154gCI8HzS90Td+bC+P6zFzAvndMjxE0r5RKJZbky+UyP97KiNQmCIIgNJS2jni8Xi/C4TAAK0HgyJEjAIAXXngBx48ff6zXDAQCLK8B9ohip9GFw+FgWQ4AS3der5dXI263mx93u90cTTmdTs6wC4fDmJuzTv6tVqucjFCtVrngtB1WN4JwUKHIxoxczPo/j8cDv98PwFJeSP4nqb63t9d2bbK2tgYAmJ6e5mSm1dVVfryV54a2djz1Yap53UqYxkfhsjn2UCjEEmAwGLQZGjmnVCqFpaUlAHaDamXjEgTBwuVy2bJh6ToYDHLmbE9PD8bGxgAA4+PjAKzs24mJCQDg/yPu3r0LAHjjjTfw5ptvArAWx1TK0cpzQ2vN0IIgCELH05YRTztsqJtjpIjH6XTytcfj4Ww3rTVnrJkRTzAYtBWfUlRUKBSQy+UAWBuL7fD3EJ6c7aJ7MwGlp6eHbSccDnOmptPp5M3nVCqF2dlZAMDS0pIkqTwmDoeD5XGzFZdZVO52uzmyicVinHAUDAZZXjOl/VAoxM/p7+8HYG0hULYuRUbEyMgIAOD06dO2BCZKVKrVavw4KSetQls6HhP6wNRqNd73KJfLKBaLAOwf0vrMEroplUqFs80KhQIXcObzeU5nzmazttekr2RkZgFrMBjcdmKovzah78kZAZYR0URiposnk0n+vbPZrEwaBwSyEXNP0Ov1ss2cOHECJ0+eBGDJMpS673K5OCPy3r17+Lu/+zsA1h4BfQbEhnaHuRcbCARs2bLkSAKBADuLI0eO8B60+Ryfz2fb+zHlOPpK71MP7W8fO3aM95RXV1dx8+ZNANb8QXMZLTxaBZHaBEEQhIbS1hGPGeVUKhWOSNLpNJLJJABL0qLVg8PhsOXPk7yVz+d5ZZBIJLiYc3Nzk6WujY0NlrfMEJtWLoODgzh06BAAK+vM7Hxt1gLVRzqEGfHQeGu1Gtcj9fX18aYhAB67GcUJnYsZXfv9fo5ygsEgotEoAODpp5/GhQsXAADHjx/nFbZSiu04EAjgypUrACybk+M4Ho6pmASDQVuLLvPaTAiiSCQUCvHjR48exdGjR/n5JLW5XC6et0zZ3PxK/1+pVGxHr1AkNDg4yPPHwMAAj2tjY4N/ttVoa8ejtd5WXstms5x6HAgEbMci0ETtcDi2fX59I0/SS1dXV1l2M5uEUoidyWRsjo8cUjgcZkPUWnNYTV8JGqPZ+83Uf80+b5lMxnaMg7AzHlTw2yzonpuptj6fzybhmPef9P/BwUG2ha6uLn7O4cOHOfMpFouxjdRqNV7MeDwe2+JLFi7bQ/fG3LN5+umnce7cOQDA8PAw3yefz2eT1+hv7fV6+bq3t5fvmdfr5dcvFAo8xywvL7PkT/Oay+WyLTLMDDhzLjEXt/S4udBuNURqEwRBEBrKIyMepdQhAH8KYACABvCq1voPlVK9AP4LgMMAZgD8qtZ640Gvsx/k83mWn8yCzM3NTQ5xQ6EQRxwul8u22iP5IR6P8+uk02nuFJ3JZDgSSqfTHNGYKw1a9aytrWF6ehqAJYvRxu7ExARnn9RqNVv2y6MwC1EDgQD/HvF4vGUjnmbaS33yCH01HzeTUZod/ZhRLkkvPT09LJ3FYjEMDg4CsKIcquc4cuQIS7BmooHf72d7NB+vVCr8uBlRt0q9WyvOMea9oWjmmWeewa/8yq8AsBI5zLY2pvz+oL8r2aHWmueqfD6PxcVFAMD169dZ8qe5xmy/RV8Ba16je2nKsPXF6K0a8exEaqsA+Oda68tKqRCAS0qpbwP4dQDf0Vr/vlLqKwC+AuB39m+oP006neYbmE6nOWS9desWT9LRaJQ/yKbM4HA4WFIzj0IolUoc7j6oB5I5qdFN7urqYicxMDDAzstsZOp2u/l6J9Q7HvpZMxOmBQ2rKfZS3//KvM9mVqO5p9Ho/Y36NGhyBt3d3SyhDA0NcSPboaEhDA8PA7CkHSoqHB8fZ9lmJ+TzeV5kLSwscBPacrncKmm2LTfH0Oe6p6eHnX9/fz//3U0JXyllk+1Jkk+n03xdqVRs8wf9bDKZxAcffAAA+OCDD3i+oefGYjF2cENDQzanZvZqoz3tVCrFc0+xWGzZPbxHLnm01kta68tb12kANwCMAPgCgK9uPe2rAH55vwYptA9iL8JuEZs5eOwquUApdRjAOQBvAhjQWi9t/dcyrDB5u595BcArjz/EB5NMJtnT12OuIKn+xev18urB4XBwhLS4uIiFhYVHvp+5iqavFOKaK8dqtWoL1U2Zw2yZ8yhqtRqvpDKZDP+u2WyWo7Jmy0UPo9H2Ym7Wm9KHeU33qVar2e6ZeT/o8Wq1yqvKarVqyzSi+x6JRNjWurq6bN2F6zsN13cfpk3j/v5+LhiMRqO2DWRaYYfDYX58JzKtSSaT4Z5/s7OzHP20UMTDtMocY0Y8lLDR39/Pspt5D7TWLJGZneYXFhZYSTEzZ832OZlMBu+//z4A4P333+cIhe7LkSNHWKp3u922+YNsM5PJ8FaBeahkoVBo2Yhnx45HKRUE8JcAfltrnarTzbVSatsZUGv9KoBXt16jYbMkTdibm5t8E10uly08pomc0qQfBf3O26VTm5Le6OgoSyTm6aZ+v9+W2m1+6LdzILVajceWSqVYIjGLWVvV8eyXvSilbJM3SY7mxNzb28vFvGZVucfjsTkVMxXfLCamVPVMJsMf4lwuZ3sOOY2JiQlOk+3p6dnW8ZhFw+b/k+2MjIzw5GKm2pqSan2fvwfd9+0WNKlUCjMzMwAsx0P7li3odFpmjjH3AmmRZ54CnE6n+TqXy/FRJvfv3+dFbL3joeebjiefz+PevXsAgPn5eVuXE8CaP8yejub8QY5sfX2dFxbr6+ss77XiwoLY0e6iUsoNyyC+prX+xtbDK0qpoa3/HwKwuj9DFNoNsRdht4jNHCx2ktWmAPwxgBta639t/Nc3AXwZwO9vff2rfRnhY0IRQSKR4HYh9atBWsnQ10dh1gMB9hqKWCzGq9bDhw9zVpsZ/Tidzm3b99RHPrTaKpfLHPEkk0kOpzOZTMu1wCAaYS8UBXi9XpY8ent7bR19qZjXrMPwer38tzXbJJVKJV6NFgoFtpf19XVuMZ9IJPhvXiwW+b4fOXKEaztGRkZs97e+W7r5mFKKJdi+vj7OUjPlOlNOMRNdzHtv/g1MidFkc3PTtqqmFXGr0IpzDP2NV1dXOTKemJjgaKZQKHA0s7S0hDt37gAA7ty5g/X1dQBWVESfXzOqdjgcbMOVSoU/11prvpdm4gkpKX19fRxpO51OjsZnZ2dx7do1ANa2AUVClUqlZRWRnUhtHwHwJQDvK6Xe3Xrs92AZw58ppX4DwCyAX92fIT4eZCzVavWxj4x2uVycSWZWLZu6O6Vtmzr98PAwO5tgMLhtGqVSiq+11jwJmlkxS0tLPPGZk2AymeTfqQUNa1/tRWu9bRpyOBxmZ3/ixAmcOXOGn2P2wqK/ealUsjVWNLtY0GSxubnJcmw6nebnl8tlniDMDgH9/f0PdTxmQZ/ZZNI8Vt1cnJh7fPXZUvScSCTCGZxdXV3bOp5EIsEt9BcXF1vxCPWWm2PI8cTjcZYmzXNvNjY2+G96584d3LhxAwBw48YNdgiPg5kaD1j31+yKYMq2ZKfz8/O4desWAGvOIMfTqvs7wA4cj9b6dQAP2gl/eW+HI7Q7Yi/CbhGbOXi0dcuc/cblcvFqY2BggPP5KUtuaGiIo5zu7m7e0O7p6eHrUCi0bVRiSmpmxJNOpzn0Xl1d5bB9fX2dQ/tCodDKEc++Q6vCcDjMEtXQ0BAncfT29tpqnnZyQKCZOGBGQmYdF90j84CtwcFBtoFIJLJt/dB2xxmYSRJm9FupVPg9c7kcJ5TE43G+TiQSvMl8+PBhW8ICPZ7NZlkynJ2d5aMQEolESx8Q1mqYUcO9e/fwne98B4AVXZICsba2xjWBO5XtH0R9coHZTqk+OYpsxmwX1spRjsmBdTz1Fe1m8SHdXJ/Px/rq2NgYSyqHDx8GAExOTvJegtm4sT7d9UHOYTvHs7m5yUa8uLhoa1JKDsnMyDqI0IcyGAzypD8yMsILArMvlnnMhNbadhqsWe1N1O/DmJM6SRvZbJblDHOPxZTyqtUq39PtUrjNe2im2iaTSXYw65/NtBAAACAASURBVOvrNmdDzWuTySQvgpRSnM3n8/nYplKpFO9HzM7OclbbxsaGOJ4dQH/HarXK9+zevXt8CrDD4bDtEW+3//Y41JdsmJmbZucVc4xmg+RW3tcxaY2eGYIgCMKB4UBFPPU1Heaqgla25nnokUiEazQmJye5VxatrKPRKG8CmsWpjwNJZ6urq9zzbXp6muW1TCZjO7SrHVY1+wX97mZEakY8kUiEoxiHw2HLWKNVqsfj4UiF7nc9ZoRSq9X4dYrFIq8wzRWpKX+YR3aYUc52xambm5sczS4uLnJ0cv/+fVvEQ8kFWmucP38eAHD27Fker9kSaHV1lTOdpqeneYNcop3dYx66uN8ZgWRLZJM+n89my9uNy7TTdpkXDpTjMZt6mjfUrG43s4Si0SiOHTsGADh16hQmJycBwHZULTkesyr9caBJLR6PczHa7Ows68iZTIYnlYPueEiW2tzc5AaLDoeDpajZ2VlbNwF6fqFQYIcRiURse3LbnRJrHrthHiNcLpe3vRdm1qIppZmSjHlNY4nH47Yjzul3Wl1dtTlN2m8cGRnBM888A8BK8aV9Lr/fz2Ocn5/Hm2++CQCYmZnh12nVgkLBwsx0BCypmB7rpM+8SG2CIAhCQ2nriGe7zCGCog+zgDAYDHI0Ew6HbbIbyS7mSjgajdpa0dO1ueG8k9by5iYgrYLNQsVMJrNty43l5WWuIykUCrJa3cIsDqbN1mw2y5GCz+djqcLpdNqiBop++vv7uQv08PCwLTHE7L+3XbRiZp7VRzFmUap5vd1j5sGBVPuRSqVYFstms7ZsO0pqOX36NJ5++mkAVsRDcqPWmqO+xcVFri0xiyCF1obmIXOequ8LR2xXXGz2FGxl2trxmHs29Sd6kgRmNl3s7e3lD2k0GmXZzdzX8fl8/LNm1lRvb6+t2SOw8yMJzOwlmgQTiQQ7m+XlZXY2MzMznDljdl1o1SNsm4GZuWUW+pEtmHKZ0+m0Nfqke7G+vs7y1srKCtuCuZgxM9PMzKX6LKadXNNX85hj83o7lFI86YRCIe7M8NRTT7EEHIlE+HcqFovseMz06532IhSaDy2AzHOYzMxMs7+geWoyFbZubm62xSJDpDZBEAShobR1xGN2ea2vnaE6jpGREa61GRkZ4bYq4+Pj27bDqe8g/KDuwLvBlNdIUllfX+fspampKe4uu7q6ypls8Xj8kavig0y5XOZ7YUYn9YWa22WalctljiYXFhb43poZQjuJbPYzEnU4HCz7Dg0N4cSJEwCAp59+mrMtQ6GQ7TRLs/iYIh6SF4XWh5IKKENzYGCAFRgz2aVUKtkiHlJPSqVSW0jybeN4yLGYx/sODAywFEZyGkE3a2BggG9ifT81CmEDgQBfP+io5Prr+sfM/ZtsNssfejNV1iwCXF9f53M7VlZWuEOBqfGb6b/tED43mlqtZtO2H+Sct0s7TafTXKjbqjgcDs5YO3nyJE6fPg3AfgJprVZjW3v//ffx4x//GADw3nvvsWOVRUv7UN+I2DyDB7DbMknIZieTVjjSfSeI1CYIgiA0lLaJeEhS6+np4ejm+PHjOHXqFADg6NGjNk9v5sObkppZg7Ndy5Tdsp0sk0gkuAh0fn6es61WVlZYRkskErYWLHRtdkouFou2uhDBjik9dNLfh1a4fr+fo5zPfOYznMkWiUTY1ovFIkfOr7/+Ov76r/8agJUd+SRdkoXWxKwZM6Mfs66sHZCIRxAEQWgobRPxmHU5tB8zPDzMh3B9+tOf5ufuZWW/eZbOdpiJAxS1rK+vc3rjnTt3uDPw/Pw8P04dCQTBxGxM2tXVhZMnTwIAfv7nf567bzscDra1eDzOh7zduHGD7avVDns7qND8YZZ+1O8jm5j1O4AV9dY3sQUsGyAVqLe3l/exy+Uy7/2Y+z31NWnNpm0cD2UPbW5u8h92Y2ODM3YcDseeOBvTKB52tn39uMxstNnZWZbaZmZmuEYnkUhIPY6wLbSpHAgE+CTbU6dOsePx+/1sl7VajRNQ7t27hw8++AAAMDc3xwkF0pOtNaCs2Fgsxs7B7XY/sBaQpNXjx48DAA4dOsRbBeaixOPxcAH0xYsX+X0SiQQnmxQKBbaDfD5vO2Kl2ZKcSG2CIAhCQ2mbiIcim3K5zG1kNjc3bRHPXuSv15/H8qjXNBs9Ui79zMwMy2vT09O8+WsmDggCYdaL9fX14fnnnwcAfPzjH+eIp75tCnVduH79Ot5//30A1rHHrSCjCB9CEUpfXx93mwgEAj/VBYWoj3hGR0dt9978OYp4XnjhBe50MDMzw/WBmUyG5yfzHKZEItH0ZJy2cTzkAMzivY2NDf4j/+hHP+J2OLFYjDXSekeyX9S/9nYnSlYqlbYo7hIai6nXDw0NcfbahQsXuO7M6XRyPdjc3BzefvttAMCPf/xjvPfeewDAzkjYf1wuF9cK+v1+LvTt7u627dNQBu7Q0BCGh4cBWIuIB81J1I+P6re8Xq+tHZi5DUDv39/fb+uuT++Tz+d5IZJKpdjxzc3N8V7g/fv32a7MOrf9RqQ2QRAEoaG0TcSzHYlEArdu3QJgrUCojcipU6ds3Yl3W6dDXt9MLnhQphy9T39/vy0DjrKOcrkcr26SySSvLmTz92DjcDhs8ho1AH322WfZjoeGhnhVq5TiTMgrV67g9ddfBwC88cYb3G5JaBwej4ejnL6+Phw5cgSAVU84NjYGwEoMoISC+o7pRH3EY3ZTofcxoxwTM6uNEhAGBgZsx2CbDYopIl5fX2f7KZVKnAHZyHY7O3Y8SikngHcALGitf1EpNQHg6wCiAC4B+JLWuqG9ORKJBKeSlstldgy9vb18aJapj5p6qsPheKT0Vu9stnM81L5nZGTEFh7Tzcxms3zzlVK2otFOphXtpRUw02vJNgcGBrgQ+plnnuFJLBqNskybTCbZ1t955x1cv36dH+8U2slmnE4nf/Z7enr4np07d44XDqOjozwnAB/OH/l83jbZ08LY7D1Jjzmdzm0Xv1pr3i9Op9PbzidmYam5p2P2nTTnxP3ciqhnN1LbbwG4YXz/BwD+jdZ6EsAGgN/Yy4EJbY/Yi7BbxGYOCDuKeJRSowA+B+B/A/C/KMs1fhLAf7f1lK8C+FcA/mgfxvhAstks56YDsJ2pQjU1oVCIN/sKhQKvEKkOArAnIJjn8fj9/m0bidJKtaury3YGDP1/OBzm9+zu7uaGjplMhl87l8vZViOdlHTQqvbSCtCqMhKJcKf0c+fOcSbb2bNnuVAUADeVnZ+f53qdN998kyOeTqkLazebqa+pIYVlfHycs83Mc3TMYs7V1VXcuXMHgKXakEwWCoVshxPWvwYA25xB2bIffPABR8MmZiPReqmNkguWlpY4qm7kHLRTqe3fAviXAEJb30cBbGqtaaNiHsDIdj+olHoFwCtPMsgHkclk+I+WzWbZECqVChdRmYe/pVIp7pu2vLzMhmAaUTgcZu02EonYMuXIuMhQIpEIG4bb7bYdoUAhcyAQ4OcHAgGbUzNb63eS40GL2ksrQAucsbExvPjiiwCs7DXKZDt69CjbjtaaF1BXr17Fu+++C8ByQp3icAzazmZoEeF0OnnOGBkZsS0cTEdBTmBpaQlXrlwBYGWVUeZiX18fzwP0ejRfAD99LAIdGPnWW2/hjTfe+Knxme+Zy+XYltbW1lq/gFQp9YsAVrXWlx7nDbTWr2qtL2itLzzOzwvthdiLsFvEZg4eO4l4PgLg80qpzwLoAhAG8IcAupVSrq0VySiAhd28MXny3t5elqUA2Dw6tf9Ip9PbrvDMmp5yucwrAK01/2x3dzdHKul0miOepaUl26Y/rV66u7s5976np4dXLxsbG3xNUVChUODXDgQC3Pna7Xbz7xcKhWzdsSlCMo8jrlQqnVT4ty/20inQSnN8fJz7DJ49e5ZlN5/Px7aezWZZXnvttdd4ldxJCQVbtJ3NlEollq6cTiffG7/fz8WcXq+X55V4PM7bAnfv3sXNmzcBWNGHOT/QPEBzYv1hl2bvNUouSCQSPK+Z1B982EpHoD/S8WitfxfA7wKAUuoTAP6F1vofK6X+HMA/gpV18mUAf7WbN6YJeHR0lIumgA8b2GWzWe4EUC6XHyktVKtV/kBqrXkPx+fz8d6P6cxSqZQtbZoMpFAo8M+ura3xDQ0Gg7xXQ6HxwMAAG9nIyAj/Hn6/n9Moq9UqG04kEmHntbS0xI5yaWmJpUFzv6cd5bf9spd2w9w3pPsfi8VYu3/qqac4E2pwcJBttFar8QSRSCS4OPRHP/oRT1zNlkn2mna0mVKpZDvhle5fsVjkOSESibCEf/fuXd6HWVlZYaeVy+X4OS6Xi+dFSq83F8L13Suoa0s8Ht/W8dDzgNY7OuFJCkh/B9Ym4F1Yeuwf782QhA5F7EXYLWIzHcquCki11t8H8P2t6ykALzzuG29Xw6CU4vAxmUzaOj8/CjOhYHNzk1ebDofjgUfHmlDEY+a4u1wuThhwu90cElP34JGREV6ZaK1ZghsdHeWVbTgcth23TavW2dlZzojL5/N8aFd9W/N2Zi/tpd1wOBxci0EbxZOTk5xEcPbsWUxMTACwJF2yy2QyiWvXrgEAfvKTn+Dy5csArJVxK6xU95t2sRnz2PVKpcLqTK1W42704XCY55KZmRnOJEun0zzP1ReSUyREqovZYRqwHwRHEUyxWOTop11oWucC+lCaKYTmOSNm2Gn2KtoJT9Jz6GGSHu3hmIVW5Dz6+/vZmLxeL0tqfX19HHpvbGywQQUCAXaU8/Pz/PcwDUpoX5xOJy+uSK8/evQoXnjBmkcnJyd5IWN2ulheXsabb74JAPja177Gk1W7TSwHCa01y/yVSoUXymbXgVQqxTL/wzoE1J84XK1WO3LBIb3aBEEQhIbStIiHQtBQKMQZPU6nkyUnh8Px2BHPfkGrETPriMZbHxKb0PgDgQCvgEKhkK09htmttpGtK4T9wel08v0lqe3IkSO4cMHK+B0aGuL/z+fz3MNvamqKM56WlpbY1jpx1dspaK15HqCvj4vZ1Z6+trvkvh0NndGp/XtXVxd/GIPBIEsRxWKRddNcLsd//Fb50O2F46HJJhQKsRRT3ztJHE/743Q6WZql/b6JiQmWYMkOAMvW6cTay5cv81EfqVSqk9LshR1AcwtJd8lkkks2OgmR2gRBEISG0vCIhw5Notz0UCjEOfDpdJqzOpaXl1vu/HiKSqg4NBKJcG2Pz+fjyKY+YqHxFwoFW5scM3utVaI64fExMygHBwe5/9rHPvYxAJbUZsrGdM9zuRzu3r0LwDrygDKkJMnk4FEf8aRSKZ4zOomGb57QOSRmXzX6I2cyGVu3ApIZzNTUSqXStEmaxkx7T9FolKWTSCRik09MyHA2Nze56ePa2hrLdKYT6rSGoQcJM5Otv7+fOxN8/OMfB2DZCzmeTCbD5+hcuXKFM9muXr3KKfdiBwcPsyiUvtJjZlGyy+ViWzO7ppTLZZ5vWnkxK1KbIAiC0FAaGvFQOxClFNe/3Ljx4fEb4XCYZayTJ09y0sHg4CCfvrixsWE7I9wsCH3QgUm7vTZb6dAK1el0ct0FHe7U39/PrXG6u7t5BVIvtVHklkwm+fdYX1+3JSbQczo1b/8gEAwGubj4zJkztgPdAGtlSlHzxsYGt8P5/ve/z4WiVLcjHEyonoui3vX1dVsNl1ncTnNld3c321gymbSpJ61Kwx1PNptFPp+3ZevQBHz8+HGcPn0agKWH0xGym5ubXA08NTXFH07z1NEndTbbXTudTt6L6u7uth11C1hdF6j4NRaL8V5VveMwi2XNJoBmOjUZVCsbi/BwBgcHcf78eQDA888/zydR0kKlVqvxJLK4uMjHtt+5c4eLiYWDzcOkNgA2qc3sAUnzVLlcfuKU7kYgUpsgCILQUBqeXEAtYUzPTtJWT08Pt50JBoO8YTYyMsKhZH23aYoQzDY5D7s2n/+oiMfj8XDUNT4+zvUYFOKaNUiRSMR2aJMJyYqmHLe+vs6/n9frZUmvWq3azkEXWh+SVg8dOoRnn30WAHD69Gm2WZI+0uk0S61TU1Mc8dy8ebMtVqnC/rNdVpvZxotszeFw8Lzi9/t5GyCZTLZFHWDTWgKY+x6UhRGPxznrK5PJ2E76pLCyq6uLm26Wy2VbbyPTqWx3rHS943mQ06LHXS4XO5tDhw7xNd1wr9drGxc5lXqpjRyJ3++3hcfkQD0eDzueSqXSFoZz0CEbGB0dxYkTJwAAH/3oRzmT7dixY5z9SPaQSCS4Aehbb72F27dvA7AKkaVQVAA+dDzU09FslgzYTz0lGzRPOfZ6vW2xcG39EQqCIAgdRdMiHpIfMpkMywyJRIIjnmQyyVJUJBLhFvLHjh3jFUClUrH1NKJVo3m0gHnueP01RTaVSmXbSAj4sLNwd3c3Xz/o2ATz+AUTiuhSqRSy2SwAq46HMljMyE0y2toDWm1OTk7ipZdeAgBcvHgRk5OTAKyTdcmuqVvx1atX8dprrwEAfvjDH/LhXdJ5WiBo7qEth2w2a6ttpEQlj8fD0v7IyAiOHTsGwJpPya7qk69aidbovrlFLpfDysoKAOvEProJXq9329NCPR4Pyxkej2dP/sj1TTrN19ru9en6YfKY6XgoeymRSNgcz3Z7TEJrQfc4HA7zQuj8+fN46qmnAFiyGy2WzOPXaSK4evUqF4rSaZSCYEILY7MfJMlum5ubLKmZx8aMjo7yc5aWlljCN/tHttqcIlKbIAiC0FBaNuKZnp5mOaOnp4dDTDOyMI8ZoOc2AnMM20U6hUKBV7u5XI5z8hcWFlh2mZub4yKxfD4vfdtaFJJP3W43FwufPn2ajze4ePEizpw5A8Cq46HIPJvNcv+1/7+9s41tM7vu/P/wnSIlUZQlmpb17rexx/Z4xpNMMuMgTdCmTYskxS6CBIts2mYxxaIJUmAXm7Sf+mE/pEW3u9kFNrvTJkUWmyJN0wbJhyCvyHQ2TTsZe2Zs2bIly5ZkWe+i9UJRFF/Eux8e3uP7aCSLsqWHD6nzAww9oiTyms/hPff87znn/vznPwcAvP7665zVJghbYZ4qClhRjq5bvHXrFjo7OwFYUY55irNWVaampjAzMwMAmJ2d5XnIrJ0sFApVP2jOVY4nk8nwB9Pr9fIbUigUuHGiz+djJxSPxznDra2tzZYlpieMamSIZbNZvvlzc3Nc/Do6Osr9uWZnZ9khLS4u2o7RlR5d7oCI2NbC4TCfJHvhwgVu/Nnd3c2NYjc2NviDnkqlMDAwAAD44Q9/CAAYGBiQ/RzhkWx2PLOzs7h8+TIAaxH74osvArCK0bXdxWIxnu/y+TwfozA+Ps5Oa2ZmhvfPl5aWeK95bW2tKhmVIrUJgiAIjuK6iEdLbWtra9yTbWxszHYaqc4ka29v595YHR0d3L7m6NGjXLxnRkhPGv3oCEyvSnQxrPkYYCUR6MhtYmKCJZdbt25x7cbc3BxHNoVCgVcdklzgLnSyQGdnJ7dzOnHiBCcXtLe3c6RdKBQ4eeTWrVvch1DLq+vr69ISSXgkm+eTubk5jk5GR0c5ueD06dMc2ZjHzLS0tOCpp54CAAwPD3PUPTQ0xKqRWeeTy+WqEvFU5HiIKAbgrwA8DUAB+D0AQwD+FkAPgDEAH1dKLT7JYDKZDL/J2gFp9B6O3+9nR9LT08P9sPr6+vhvg8EgO6pgMMhh6HbpzpWijUHLJevr65xNkkqluNo4lUrZpDad1ZRKpWyFYfWKU/ay3yil+MN97NgxPP300wCslH4tuzU0NNgkYS2l/uIXv8D169cBPGz4WI/nquwV9WIze02pVLIVlQ4ODgIAfvSjH/HeYk9PD9ujUornvo6ODl7chkIhttP5+fmqbkUAlUttXwbwA6XUKQDnAdwE8EUAP1VKHQfw0/L3ggCIvQi7R2zmALFjxENEzQDeB+B3AEAplQeQJ6KPAnh/+de+DuBVAF/Yj0ECD1eL5qpxdnbWtqmm62LS6TSvPBsaGngFYLa1CQaDtmstlzQ0NHBEtRm9etARTzqdZhllfHwc09PTAKwVhZYJFxcXOYlgaWnJ1v6iHnGLvewVukjvyJEj6OnpAQDe1AXeWdM1Pj4OwEok0DaoiwGFrak3m9lLNjY2eM7Y2NjgiGdtbY0j6WKxyJJwOBzm+S6ZTLI0F4lEeK4aHBxkua1aEU8lUlsvgHkAf01E5wFcAfB5AAml1HT5d2YAJLb6YyJ6GcDLezDWd5BOp1nGWl1d5Tf2zp07tt5FW8l0fr/f1nXAfHy7a32zzP5wZnW6fv3FxUVbtbF5fQDkFtfay+Og739rays7nlgsxraQz+d5Arhz5w5PDHfu3LFVkAuPpK5sZq/R800+n7ftF2r8fj9/H4lEbD0Cte3Nzs7yvnMmk+F5qFoZtJVIbT4AzwL4ilLqAoAMNoW8ylr2bbkjrpR6RSl1USl18UkHK9QEYi/CbhGbOWBUEvHcB3BfKfV6+ftvwzKKWSJKKqWmiSgJYG6/BrkdZp83LXNpdJQTCAS2jXi2ujaz4DZf6whJh6dEZEs40FJfNpvl8DiXy/FqxGxhUce41l4eB31/zdNozczDXC7H8trly5c54llYWJBIp3Lqymb2Ey3zl0olzpBNp9N8mq05l5mnKWcyGa4nnJ6etrXrqgY7Oh6l1AwRTRDRSaXUEIAPAhgs//s0gC+Vv353X0e6S7baE9oPtFPzer02h6Qxb36pVKp7x1Or9rId5mJCf+hzuRynyI+OjuIXv/gFAOCtt97iM3bkfJ3KqTeb2U/MeU1L+HrhU0tUWsfzOQDfIKIAgLsAfheWTPctIvoMgHEAH9+fIQo1iNiLsFvEZg4Q5GSxIhHNw9JvFxx70co4BPeNCdj7cXUrpdr28Pn2FRfbC+BOm9mPMdWazaRh1f+4DTfaC1ClOcZRxwMARHTZbZuAbhwT4N5xOYlb3wM3jsuNY3Iat74HMi470qtNEARBcBRxPIIgCIKjVMPxvFKF19wJN44JcO+4nMSt74Ebx+XGMTmNW98DGZeB43s8giAIwsFGpDZBEATBUcTxCIIgCI7imOMhol8noiEiGiGiqrU3J6JOIvoZEQ0S0Q0i+nz58T8hokkierv878NVGNsYEQ2UX/9y+bE4Ef2YiG6Xv7Y4Pa5qIPZS0djEXgzcYDNiLxWOxYk9HiLyAhgG8Kuw+jK9AeCTSqnBfX/xd44lCSCplHqTiBphdcL9GKyq6FWl1J87PSZjbGMALiqlFozH/gzAA6XUl8ofphalVF23hhd7qXhsYxB7AeAemxF7qQynIp53ARhRSt0tn7XxTQAfdei1bSilppVSb5av07AOnOqoxlgq5KOwziJB+evHqjgWpxB7eXwOor0ALrEZsZfKcMrxdACYML6/DxfcDCLqAXABgO6K+1kiukZEX6uSRKEA/IiIrpTPGAEqPJOkzhB7qQyxl4e4zmbEXrbnwCYXEFEUwN8D+EOl1AqArwDoB/AMgGkA/6UKw3pJKfUsgN8A8AdE9D7zh486k0TYX8RehN0g9vJonHI8kwA6je+Plh+rCkTkh2UU31BK/QMAKKVmlVIbSqkSgL+EFbo7ilJqsvx1DsB3ymOYLevGWj8+CGeSiL1UgNiLDdfYjNjLzjjleN4AcJyIeslqe/4JAN9z6LVtEBEB+CqAm0qpvzAeTxq/9tsArjs8rkh5MxJEFAHwa+UxfA/WWSTAwTmTROxl53GJvdhxhc2IvVRGpefxPBFKqSIRfRbADwF4AXxNKXXDidfeghcBfArAABG9XX7sjwF8koiegRVqjgH4fYfHlQDwHctu4QPwN0qpHxDRGzhgZ5KIvVSE2IuBi2xG7KUCpGWOIAiC4CgHNrlAEARBqA7ieARBEARHEccjCIIgOIo4HkEQBMFRxPEIgiAIjiKORxAEQXAUcTyCIAiCo4jjEQRBEBxFHI8gCILgKOJ4BEEQBEcRxyMIgiA4ijgeQRAEwVHE8QiCIAiOIo6nAojo/xLRNBGtENEwEf27ao9JcDdiM0KlENHqpn8bRPQ/qj2u/USORagAIjoDYEQplSOiUwBeBfCbSqkr1R2Z4FbEZoTHoXxk9gyADyulXqv2ePYLiXgqQCl1QymV09+W//VXcUiCyxGbER6TfwXr+On/V+2B7CfieCqEiP4nEa0BuAVgGsD3qzwkweWIzQiPwacB/B9V51KUSG27gIi8AN4D4P0A/lQpVajuiAS3IzYjVAoRdQO4C+CYUmq02uPZTyTi2QVKqQ2l1M8BHAXw76s9HsH9iM0Iu+BTAH5e704HEMfzuPgger2wO8RmhJ34twC+Xu1BOIE4nh0gonYi+gQRRYnIS0QfAvBJAD+t9tgEdyI2I+wWInovgA4Af1ftsTiB7PHsABG1Afg2gPOwHPU4gP+ulPrLqg5McC1iM8JuIaL/DaBBKfWpao/FCcTxCIIgCI4iUpsgCILgKOJ4BEEQBEd5IsdDRL9ORENENEJEX9yrQQn1i9iMsBvEXuqTx97jKRfGDQP4VQD3AbwB4JNKqcG9G55QT4jNCLtB7KV+8T3B374LVhPEuwBARN8E8FEA2xoFEUkmQ5VRSlEVX35XNiP24goWlFJtVXptmWNqkErmmCeR2joATBjf3y8/ZoOIXiaiy0R0+QleS6gPdrQZsRfXMV7F15Y5pk55koinIpRSrwB4BZDViLAzYi/CbhGbqT2eJOKZBNBpfH+0/JggbIfYjLAbxF7qlCdxPG8AOE5EvUQUAPAJAN/bm2EJdYrYjLAbxF7qlMeW2pRSRSL6LIAfAvAC+JpS6saejUyoO8RmhN0g9lK/ONoyR/TX6lPlrLZdIfbiCq4opS5WexCVIjZTffY7q00QBEEQdo04HkEQBMFRxPEIwj5DRCCqGYVTEPYdcTyCIAiCo+x7AWk9oVetoiEJfgAAIABJREFUm78CQKlUgpxtJJg0NDQAACKRCAAgHA4jHA4DAILBIILBIP9eNBoFAPj9frarQqGAXC4HAJibm8PkpFXCkslkkM1mnfuPCMIeI46nQogIgUAAAPhrMBhEKBQCAIRCIZ4wSqUSNjY2AAA+nw9erxcA+CsAKKVQLBYBAOvr6zzB5HI5vi4UCvw8Qu2hHUssFgMAtLS0oLm5GQDQ1NTEzqa1tRXt7e0ALCfk81kfy3w+j9XVVQDAyMgISqUSAGBmZkYcj1DTiNQmCIIgOIpEPI/AlNK8Xi+vYLVc0tTUhHg8DgCIx+O8UjUjnsbGRtsqV8svOlICgMXFRSwtLQEA5ufnMTs7CwBIpVJYXl4GAKyursoq1yUQEd9rv9+PtjareXM8Hkdraytf6/ve2NgIAIhGoxzlmLJbJBLhqCgcDnNkXCwWsb6+DgBIJpNIJBIAgBs3buDyZasf5srKCtbW1vb3PywIe4w4ni3QDsfv98Pv9wOwJo8jR44AsCYBwJJI9OTS0tICj8cKIDc2NlhGi8ViPDEdPnwYhw8fBgB0dHSw9j81NcXOZmBgAAMDAwCA4eFhTE1NAbAmIXE87kHLraFQCB0dVsPk48eP49ixYwCA/v5+HDp0CMDDhUo4HObFSyAQYAfj8/nYzrxer23Boxcw/f39eOqppwAAiUSCFyp3794VxyPUHCK1CYIgCI4iEc8W6MglEAjwarWtrY1XnBcuXAAAHD16lKWTpqYmzmorFosoFAoArBWxltei0SjLLuFwmF+nsbGRN46PHz/OMk4sFsPt27f59/Uq28xqkuQD5yEiTgbo6Ohguzh16hRHPH19fRzpmkkpOrLR97hSTBlvdXUVQ0NDAIC1tTWOlsUWhFpBHE8ZPRH4fD52FIcPH2ZdvbOzE2fOnAEAPPvsswCArq4um+Mxs9Z2wpRTmpub+TVNSe/UqVMs1zQ2NvLvTE5OYmZmBgBEfqsCpVIJPT09AIB3v/vdOHnyJACgt7eXpdRYLMZSqpnVqBcbu8Xr9drsUr/m3NwchoeHAYAz4ATB7YjUJgiCIDiKRDywpDUtgQSDQZY0Tp48iXPnzgGwNnd1JNLd3Q3AilT031W6kt2q+NSs9QkEApywcPjwYc5qMn8/l8shlUoBsGqApHDVeVpaWgBYdnHq1CkAQE9PD8tr5j3REtjGxgbfz0KhwAkoxWIR+XwegFW7o2VXIkJTUxMAK+LViQmtra3o7+8HAExMTPDvSMRTHTweD3/+TdUjn8/LZ3MbDqzjMSf7UCjEzqalpYWzlM6ePYv3vOc9AIBjx46xrKb3aXar01cCEdmynbR0UygUeNKanZ3FvXv3AFhOSE9govE7x/T0NADg5s2bvA9o7uEBYAeis87W1tZ4wZBKpZBOpwEA6XQa8/PzACzpTP+dz+fDxYvWiQTnz59npxaNRtHZaR3M2dvbyyn9OgNScJZQKMSyant7O44ePQrAWiDozzIRIZPJALDut3nv9fXKygovHvQecb0iUpsgCILgKAcu4tEhsd/vZ+kiHo/j+PHjACx5ra+vD4AlqZmyms5O2i6JQK84FxYWuPBzc9sb/Rx+v5+LBltbW1m6MTegA4EA1ww1NTVxIsHQ0BAXoAYCAQ7nJeJxDp1JduvWLd70TyaTLMcCDyOelZUVAFah8OjoKACr/kY/x+zsLEZGRgBYxaH6fnq9Xnzuc58DYEU2WlIze7vF43F+XNh/tOTt8/n4urm5mT/LZ86cwfPPPw8AOHHiBMvm0WiUP7+ZTIajn9XVVY5ylpeX2Va0nYyPj2Nubg4AsLS0xH9XLBZr+vN+IByPdhQej8fWP0sbRTKZxNNPPw0AeOGFFzhjKBaLsYyhHQaALXXbUqnE8svw8DDu378PwDIs01jM7Lmuri4AwOnTp3kvyWwe6fV6ba+rZZxDhw6xQwKsCQ0A7xMI+49eWNy/f9/Wr29hYQGAda+1BKrvz4MHD2ySmi4CXV5e5sdLpRLvGzY0NNicmpZzgIfObGVl5bEz5YTdY+6z6bnh9OnTnEbf29vL+28dHR08x+ivgF1O93g83JtxdXWVZbcrV64AAC5fvoxbt24BAMbGxniOyWQyLOHW4j6SWKwgCILgKHUf8ZgZa36/nyWtvr4+Xpn09PSgt7cXgH2zNhwOPzKBYGFhgVezi4uLGBwcBADcvn2bI56VlRVexeTzeV6dEhGvmqPRKEt9Zh8wn89na7GiV02JRII3MAuFgrRMqQI6ip2ZmeEV6/LyMm7cuAHASvrQySAPHjwAYNmL/rv19XWOUM3oaGNjgyMos3YrFovxtZkdJ5ls+4P5OdX3w+PxcNSZTCb5M3vp0iU899xzAKz7pKXPQCDAcms6nbZ1nTc73OvraDTKz6+LkpVSLKs2NDRwpHT//v2a/twfCMejDaehoYErzk+ePIlLly4BsEJlbSzxeNxmaI+SMWZnZ3Hnzh0AlhZrXmv93gyJi8WizaB1Jl06neYslo2NDVvobKZfm/qyzqQKBAIitVQB7TSWlpb4/q6urvKCo1Ao8D3VziGdTrOD2YyZYaknnZdeeollXz2Bmb+r2Y/syoMMEdkWq2fPngVgdRXRWaam3N3f38+LWKUUf05zuRzbhnn0yerqKjuYaDRqS5nXTkgXrvt8Ps6yPXPmDCYmJgBYstv4+DgAax7SEu+DBw9YhnVzOveOMxYRdRLRz4hokIhuENHny4/HiejHRHS7/LVl/4cruB2xF2G3iM0cPCpZKhUB/Ael1JtE1AjgChH9GMDvAPipUupLRPRFAF8E8IX9G+rjEQgEWF5rb2/nVic9PT28kojH47bjCvQK0owy5ufnWTLREtnNmzc5G2lsbIzb2ExPT/PvZrPZLfuqKaW4FmNtbc0mtWy3SjELEfV1qVSyrYZdQE3bS6Xo93xjY8MmtekVrnk0ho58HnWfdBQTDAZZRr106RInnZhJJuZpt/q1axxX2Yzf7+eIpKmpibPUPvjBD9qyTM05Q6skU1NTnIU2PT3Nma5LS0u2gmEt57e2tnIUlUwmuVZLv353dzcnLhSLRX7usbEx3L17FwBw584dzoIbHh7mx5eXl23zhJvY0fEopaYBTJev00R0E0AHgI8CeH/5174O4FW4cCIJBAJ8k3t6enDixAkA1l6OnvjNQi8zTdJ0PJOTk7yHoyW10dFRLuScnZ3lScds4mmmPTY3N7PUd+jQIZw/fx6AZVw6Y810fKVSyeaQ9H5SKpVix2Y6LTdQ6/ayW8xFwG6L/szsNT35nDx5knsBJpNJtguPx8OSXSqVYrsbGhriotRaxW0209TUxHu+x48fZ6ntxIkTLItpqRuwZDSdlTg+Ps77fNevX+dr/XPAupd6MdzS0sJzgpmOr7+2t7fzHk8kEuG5IZFI8BhisRjL9n6/n+ehfD7Pe4puY1fiMBH1ALgA4HUAibLBAMAMgMQ2f/MygJcff4hCrSL2IuwWsZmDQcWOh4iiAP4ewB8qpVbM3mFKKUVEW+pDSqlXALxSfg7Hd7qKxSLXS0xNTdkSDfTqZWlpiVcPwWCQ5Yv19XWOJq5evcqrF90NeHJyEpOTkwBQ0aozHo/zxvEzzzzDhaqnTp3iAjSv12s7gVKvXrLZLK9yb9++zbn9ZnTlJmrVXpxERzwtLS28wn7ppZdY2jly5Aivdj0eD2eyzczM8Cbz4OAg13bUOtW0Ga/Xy/JlPB7npI7nnnuOe/F1dHRwUkkul2MZbXh4mDf6Jycn+XM6PT3N10tLS7YEIjN5wZyTtJSnO+H39/fbTrjVKo0ZHTU3N/N1R0cHj/3q1at8qKSZBeeGer+KHA8R+WEZxDeUUv9QfniWiJJKqWkiSgKY269BPglmGGxWDJt7L21tbSxpNDU1sUGZxw+kUil+Hu1kVlZWdjXpmz22Ll68yJkryWSSjc/8sK2srNheUxv32NgYxsbGdvM2OEot28t+o6WSSCTCE4op5zz11FO8D2me07S0tMST2NWrV3Hz5k0Au7dBt1Itm9GLvI6ODpY7z507x+nRx44d40VhPp+37fPqSf0nP/kJF3xmMhnOKlNK8SK2WCxu2TjWpFQq8eddLywXFxdZlmttbeX5y+fz8Vxy9OhRtpljx47xeWEtLS1sG9lslm3JDY6nkqw2AvBVADeVUn9h/Oh7AD5dvv40gO/u/fCEWkPsRdgtYjMHj0oinhcBfArAABG9XX7sjwF8CcC3iOgzAMYBfHx/hvhkFItF3pTNZDLs9c3VSDwe5+LMlpYWzlQbHh7mRIJ8Ps+ymw6ZlVK7yhaJRqMcSp8+fZrlPbMVirkqWlhYsBWl6tNItXToUmraXvYTj8fDmVD9/f1cgHjhwgVu2dTX18fJMMFgkG3OjHivXLmCy5cvA7BkN7duIO+CqtmMVhr6+vo4g/C5557jxJ9IJMLzx82bNzkSGR4eZtVhaGiI62jW1tYqikD1vGEmEK2vr7OcarZkMg+J1PPRtWvXOLJ5+umnWWoLBAKcaHD8+HGWYbPZLEttOiKrJpVktf0cAG3z4w/u7XD2Bz2ZK6VsFefaQMLhMBtgOBzmcDqVStlSoTcfP/Co4qytKpPb2trYQLq6umx/v9VzpVIpdjZvvPEG6/puMJztqAd72Wu0fGp20ejs7GRn88wzz7DUFo1GeUGSzWY5k3FkZATXr18HYEmtWpKph/b5TtuMfn8bGxtZrnr22Wf5CJRkMskn/2azWS4Gv3nzJl577TUAwKuvvmqTripJmd8Oc37Sjkd/1fdfc+3aNQDW3tBHPvIRANYco51Ta2urLRX7xRdfBGA5RL1wccOeoJS8C4IgCI5yIHptmNGEjngymQwnDng8Ht5kNLNbzDDYfJ5K2lDoFUhbWxuvnnp7e/m6EvL5PK98crkcbwrWcjv0esPsNBwKhThTMhKJ2E6VBazVqE4oMeWczs5Ozl7zer18nxcWFjhr8tq1a/inf/onAFYdmZZb6yHicRqdSNTR0cHy2vPPP8/1U6urqxzl3LlzB2+++SYAK4NQy/DpdNomlzmFfq1isWiTXnXSypkzZ7i/Y0tLC0fVIyMjrOq4gQPheLbDPMdmp8ncdE5m5pn5c33zvV4vZyx1dXXZGpBu5Xi2c2T5fJ73oXK5nC0dU3AHZiPXWCzGXQfa29vZXkx5TUs7Fy5c4IlOL1IAS2LRi6OpqSne4xscHORMtuXlZVdkJtU6Zh/HSCTCCwRTlnr99dfxs5/9DIAlcZqLv2r0QTNlft2twBxLOBxmezS3EAKBADvclpaWqp90KlKbIAiC4CgHOuKpBB3d+P1+Xrlu1Q06EonYDn0ykwh0oah55MJm9MrD7GI7NzfH2TKLi4u2LtdCddF20djYyFHskSNHuK9WZ2cn24teSSeTSVt7FP1zk5WVFa4ju379On7yk58AsLKotLxWJ/3Zqob+rG1+r/VG/ujoqK0tlu44bspr1e76XCqVOAkqk8mwXR07dozl3MOHD7OdmjJwS0sLR27VinjE8WyBKZmZZ2VoScTspaYNsbm5mW94R0cHSyo9PT3seOLxOBeDbcZsNKmz1mZmZrgpYCqVYglGdH3nMfv29fT0cAeK3t5evu+JRIIl1tbWVtsx54BlQ3ovJxqNbnmEejab5b3H0dFRTpuenZ1lG6n2pFfr6Ek3nU5zpqjP58PVq1cBWKUL2vGUSiXObDWbs7oBLbvlcjmWzsw5o7W1lRepSimet0Kh0JbbBU4iUpsgCILgKBLxbMLr9XJk09DQwNJYd3c3t6XQIatZQNrQ0MBSm9kttq2tjVfBZo1GoVDglVQul+NVitlWfXR0lLNrVlZWJKutihARS6yJRAIXL14EYGVD6YSCzs5OrqHYSkarhEgkwjJtX18f9wkDHtZfSGLBk2Ee0Kc/a+YG/czMDBdwuhnzmBQ93snJSW7ptfn4FI3H46l65CaOZxOm42lpaWFn88ILL+BXfuVXAIAnmu3CVTMDzu/3s+Ti8/k43F1fX2fNfnFxkc/QGBkZ4YyakZERLhTMZrOuPVvjIGAWf4bDYdbUu7u7eXHS0NDwxKfBRiIR7hlmHuORzWZtJ0sKj49+//L5PC/+UqkUf55rTco25xiPx8P/DzM1v1Ao2Oarai9eRWoTBEEQHEUink14vV7bAUs6Y6mlpYVXtvqrGcFUslm3ufO0jnLGx8f52jxcbm5ujjNtJJOpupiJJmZUfOjQIVvSyaPswFxhm9mLADjpwDzNsre3Fx/60If4dXQW0+Y2KsLjo1f+tdzh28y49fv9XMcTCARsx6qYJ6BWO+IRx7MJn89nk9r0Xk1LSwvv7egba2Y6PYqtTjSdmZnhiug33niDtebFxUWeWNbW1rhzgVBdTKmtWCyyHFMoFFjOCIfDtv5dOgvRlFR1evzy8jI7nnA4zGeodHd3s30dPXqUZbfm5mZuvT8+Ps52UWuykLD3BINBXriYR3L7/X62k82Op9olGSK1CYIgCI4iEc8miIhXrWami9mTa7u/q/T5AasP19DQEACrLYfeON4swQjuwMxCnJqa4lNoE4kEurq6AFgrT20ja2trnF2kCxDv3bvHkur09DTLHbFYjO0iEAhwFmRbWxvbYltbG2fSra6ucisdXfMjHFwaGhr4sLr29naOfnw+H88rDx48sM0x1c5qk4hHEARBcBSJeDahlGLdPJPJ8OZcLpfjFar+udfr5eSCSlcQZiq01vIbGxtt+qtEPO5jY2ODI57Z2VmOeBoaGniPx+fzsR3Mz89z3Y0+MGxiYoJbtCwsLLA9hcNhW6NQ3VG4ra2N07NbWlpw+vRpAFbqr450JOI5mHi9XraNeDzOe4GJRIJtiYg4yllYWODuBm7YNxbHswlzglleXma5ZGZmhutrdHaRmYFkdoX1+Xy2TtZaRjGdExHZDqTSxlAHp0nWJUopvn/pdJptAQAnDNy4cYOdSSqV4hos7YDm5ua40G91dZUXMKFQiOuCurq6tkwY8Pv9bJfz8/OSVHDACQQCPH90d3dzC6f+/n4uZC+VSmyD9+/f53nLDY5HpDZBEATBUSTi2UShUODwtFAocDibz+c55dls/qgjno6ODu5ocPjwYd7si8ViWx7AFA6HuUaoq6uLpTY3H2stWGxsbHBzyYWFBQwMDACwoiItk+bzeZbgzMc0Ho+HG8YeOnSIOxQ899xz3HRUPydgRdE64pmenmYJWDhYaPUkHA5zZNPT08OtlTo7OznqTqfTHG2Pjo5yxOOGzhfieDZRKBRYxkin0/wBn5+f5461WkYza35OnTrF2nyxWOS9nEgksqXjaWxs5J5cS0tLXOuh9wAEd2NKZnqfzywgNWslzGI9s6ZL14Ulk0luzfT000+zhAKA7W9xcZEnDnE8Bxdta2YfyUQiwdeBQIAXKIuLiza51002I1KbIAiC4CgVRzxE5AVwGcCkUuq3iKgXwDcBtAK4AuBTSqnqx3B7jJnhpleuWn4zEwRCoZDtnB4tkcTjcY54vF6vrXmoeRStfh6zyV+1c+2fhHq3F/NAMG0X+Xye75lpA9pewuEwS7BtbW0cIZ89exbHjx/n5zPRkfDdu3c5g+3evXuu2CDea+rdZvYCPcdEo1GOctrb21m29Xg8HGkvLi5yZO62pKXdSG2fB3ATQFP5+z8F8F+VUt8kov8F4DMAvrLH46s62vGY2W6mXKKdit/vt00a2qn09fXxdSAQsBWhbtfJWD/utoOndkld24u+L5XcH+1sWltbWVI7duwYLly4AAC4ePEiP27Ksvl8njPmhoaGMDk5CcB+AGGdUdc2sxfoYzc6Ojpw5swZANbJt7poNJfLsSQ7OTnJ19VukbOZiqQ2IjoK4DcB/FX5ewLwAQDfLv/K1wF8bD8GKNQeYi/CbhGbOVhUGvH8NwD/CUBj+ftWAEtKKe1G7wPo2OoPiehlAC8/ySCriV5ZbrfC1I+nUim+DgQCnJk0PT3N4XEsFuNGkz6fj1e3oVCIHzeLUt3QRfYxObD2shV6ldrW1sbNQM+dO8cr1p6eHs5QAsCFfrOzsxgZGQEAvP3229xup4aj4EchNlMB2k66u7u5oDiRSHAN4eTkJK5fvw4AuHLliq3ezE3s6HiI6LcAzCmlrhDR+3f7AkqpVwC8Un6uuvvEaMeQyWQ4nA2Hw1ytfuPGDdbj+/r6OJMpFovxBLK2tsb9uZqbm1mPzWazrspEqQS32Us4HLYd8LUfhZfmHo6WPJqbm1li06muZ86cwbFjxwBYUkkymQTwsIOFRtvLzMwMO54bN25wCneNLka2xW024za0fXk8Hi7B6OnpQXd3NwCrI7W28ampKe5i/tZbb7k2S7aSiOdFAB8hog8DCMHSX78MIEZEvvKK5CiAyf0bplBDiL0Iu0Vs5oCxo+NRSv0RgD8CgPJq5D8qpf4NEf0dgH8NK+vk0wC+u4/jdC1aXsvlcryaXl5e5mLTe/fu2Q4N06uXQ4cO8Vk/uVyOI55YLMZFpBsbGzUX8ThtLzpZo6GhgZM4otGo7XqrzdZK2NzuyJS49OuGQiGW0trb2zmKaW9v5zY4WhJ59tlnOYnAXKUWi0Wk02kAlu2Mjo4CAH75y1/y6nV6epoluHpLLJA55tGYmbBawu/q6uI5g4h4vhkdHcXNmzcBWL0B3VqQ/iQFpF8A8E0i+s8A3gLw1b0ZUm2huxg0NTWxzNLf38+pss8//zwXiiYSiS2PTzCPXyiVShUfsVBj7Iu96L2xeDzOH8pDhw5xqmlraytP5Gtra/xBbGpq4r/1eDwsX5nZP2bD2PX1ddvPTHlNy6cdHR2cFt3V1cVOqLe3F4CVfaQdIhHx862urnJPwHv37rFG/9prr+G1114DYE/nP0Ac+DnG5/NxqnRTUxPbUldXF9tdPp/njMfbt2/jxo0bAKyekm7oUrAVu3I8SqlXAbxavr4L4F17PyShXhB7EXaL2MzBwNGWOcFgEJ2dnfD7/Zy5BdiL8Xa6NmtbzOuNjQ3bqtVsV6KvTalks3SyVV3G5t/ZCp2ZFo/HeYV76tQprtH4wAc+wCvrYDBoq9HRbB7vbmpE6h2v1/vIlb5+P80WRF1dXRz9JBIJdHRYyVCnTp1iSSubzXL0k06nuVBzeXmZX8+UOovFIkum8XicZdKuri5+/t7eXnR2dgKwMtjM+h3Akv3M4mNtAysrK7h37x4AK4ng2rVrAKwjrnXtWL3Ja0JlhMNhlmePHz+Os2fPArCORde2tLS0xO28JiYmWHYrFAqunUMcdTzRaBTvfe970dzczGmBXq/X5lS2czY7OZ5sNssfUlNSWV5e5upd05Fs5XQ2/47H42EH6ff7OftIa/pmo77W1lbOOOnr60N/fz8Au5ZPRLYqdx0GLy8vb3nuz0GfbIjI1nvqUSQSCc4eO3nypO0Uz63e89nZWT4ZdGpqivd+1tbWbGnu5vEWWlZtbGxkmePIkSPseBKJhC07cXO2WqlU4tfPZrP8mtevX+e9nKGhIdy6dQuA1cp+q0WTcHBobm7mBrLvfve7OR0/kUhwcbHZR3JmZsa18pqJ9GoTBEEQHMXxiOell15CMpnkIwT0KlKz3cpupxWfefBWKpXiwqmJiQneeKtUXtN4vV6ObiKRCG9Ya0mno6OD5ZRIJMLJBbFYjOWVzc+pV7CZTIazlB48eMAS0NraGq9YDuBmsg2v14toNPrIiEe/n/F4nDdez549yxllOgoFrIhHP5cpl/X39/N7rpTiSCUQCNjaGunoJxqNsuzW0tLCNhAKhdiezdZIOnI1X39xcZF7r127dg3f//73AVjRl5wqKmja2to4K/LChQucaJDP5zliv3HjBiek1IrtOOp4QqEQnnrqKTQ1NfEb6PV6d+VsNp/iqTGPKIjH4yyFJBIJ1khNac50Qts97vV6+TnNNuRaWuns7OQspWAwaDuB1DwS2xynPpvlwYMHtpbli4uLAKwMJ3E8Fl6vF7FYjN+nrdDvFRHZ9vv0+1woFFg6M3vldXR0sFMiIt6Ha2ho4GvTUWSzWc5wM0+YjUQifN/9fv+2/fcAa1GhJ4axsTGWRwYHBzmrrdbS54W9RduetuVkMsn7OidOnGD7WF5eZkn2X/7lX/j6UZ8VNyFSmyAIguAojkY8DQ0NOH/+PIjokStD4J3RznZRkY4motEoSyeFQoHbSaytrfGqtZKsOTPiISLbIV965ayjnMbGRluPNb0K9ng82/7/dDucyclJ7r11584dzM7OArBWMlo+cltHWafxer0cue7E4uIiv4czMzN8L2KxGMulXq+X7SUYDPL15nu1Vb1OKBSynQZqJp3o19osqep7rZMI7t69yzUWIyMjvEo17389HncgPBptY36/n1sq6ezIS5cuoauri39PZ19OTEywLb399tt80mitzBmOOp5cLofR0VFEo1HeDzEnabNS3JSnNj9uTvD62twrepIMIPO1zOfaSQ7cvH9kZqaZRytoAxkbG+OJZ3x8nENkve8jWFRaTLu5W4ReSGxsbLDz8ng8vHgIBoPskLScqtE25fV6WUYzFycbGxu2fRuzl5bZF07Lp7pf1tWrV/HjH/8YgOWE9D7kfvSPE2oDc8Ha1NTEhecvvPACAKu/n86UzGQyLM9evnyZ54/FxUWeb2ol+1GkNkEQBMFRHI141tfXMTQ0ZOtp5fV6ecVoSiGmHGdGRR6Ph+WtzQerVQMd2ubzeVtkYxYH6pXt/fv3uVBwbGyMr+fm5nh1LDwe8/PzGBoaAmBFP7qTs3kefSwW49VjPB5nWwuFQhxpmnVUpg2a12Zdmcfj4Wjb7/fz4w8ePMDt27cBgMc1MDDAyQUrKys1szoV9o9IJMJbBH19fXjmmWcAWK22ACsrU59OPDs7y1HOL3/5S55XMplMzdX8Oep41tbW8Oabb6KtrY0ry/1+PzsSUy/ffEy0qalrma6xsZHlks37KmbBqZ5K5UaxAAAJJklEQVRgTG1+pz0moLLOBdrZZLNZ1ucLhQI7pKmpKbz11lsArEJBPSHOzc2xNGQ2GBUeUiqVKioeBawUep3hdvv2bU6nNhc5nZ2dbHeNjY22bDeNKa81NDSwrfn9fv69fD7PWvva2ppt8aEd2MrKCk8Sg4ODACx5TUuttThZCHtPc3MzZ92eP38e586dAwAuGgUeymczMzMYHh4GYNmStsFatCOR2gRBEARHcTTiSafT+Md//EeEw2He0DVXkqZ0ZtZKBAIBXoUGg0FetSaTSRw+fPgdv+P1erkgM51O84ohkUjw6lfXET2K9fV1zkzKZDIsh+mVxtLSErfjWV5etsk1ZjscXcBq1uuk02lbm/taXLXsNxsbGxVLkCsrK7YW8LqOJ5fLsS3Mz8+z3QWDQY5+zULh5uZmto1oNMo2lcvlOKJdXl7mcWUyGVv9kNmqSR/ipiURPSbhYGL2FdR2ePbsWZbXzp07x622tDxstgKbmpriIw/u3btX01Kto44nk8ngn//5nwE8vAlm4aU5GZiyWDAYZFkkEolwqnRfXx9PKpFIxCa7aRlL9zMCLClEZzhtdjxbZU+tr69zKuzmbgiAZQj6+RcWFtghmbJbLpezTYJmqvTmFvyCnUKhwNLUbtEOIJ/Pc1aZx+PhBYHZZ8/sStHU1MS2Zh6XYDYSXVhY4HHl83leCDU1NdkkXj0GWVQIwENJt7W1lW3m4sWLuHTpEgCr4awuajb7RZrp+nreDAQCNdGTbTtEahMEQRAcxdGIx0SvAs1Vf7FYtGWvmbKbjn5CoZCt7YzOazcjJ+Bhoebq6io/5+joKLec39w12iwm1NfZbNYmtZnRDWBFQVpqW1paYulsfX2dI56D3vbmSXncSFC3FjFb0Jh2VCqV+N6srKxwyxoi4pVkOp3e8QRHn89ni3S1PRcKBX5tSRwRgIf1Yslkkjupnz59GmfOnAFgqTDmcTHAO5UY/fNIJGKrK6s1quZ4NMVikd/ARxWNminXWl+fmJiwnSJpplabZ/OYsp6WUXw+347FqubkVCwWeUIypTP92Hbp1IJ7MIt5C4UCLyqe5INbLBbZ8YhcKjwKLfN3dXVx40/zVFqznMTcI9bbBjMzMzZbq2V7E6lNEARBcJSqRzxKqZoMFYXaY7+i0FpeeQrOoRNY+vr6uE6nvb2dC0RN5UVnsi0sLPB2wujoKGfImvVjtUjVHY8gCMJBQGeynThxAn19fQDsHTRM9N7x6OgoBgYG+LpeOpyI1CYIgiA4SkURDxHFAPwVgKcBKAC/B2AIwN8C6AEwBuDjSqn6cMfCEyH2IuyWg2Azutj91KlTfOyBTo7ajO5Wf/XqVVy+fBnAw/rBeqDSiOfLAH6glDoF4DyAmwC+COCnSqnjAH5a/l4QALEXYffUvc3o/pOFQoELy/Ue98bGBtLpNFKpFFKpFO7cuYM7d+7gypUrGBgYsDWYrQd2dDxE1AzgfQC+CgBKqbxSagnARwF8vfxrXwfwsf0apFA7iL0Iu0Vs5uBRidTWC2AewF8T0XkAVwB8HkBCKaX7mcwASOzPEIUaQ+xF2C11bzOhUIiTCHK5HBcXh8NhzmCbmJhgOe3tt98GYJ1Oq2t36qnXXyWOxwfgWQCfU0q9TkRfxqaQVymliGjLnFIiehnAy088UqFWEHsRdkvd24zH48H9+/cBWPs2ustJZ2cnp/nfvn2bM9iuXr0KwGowqwud66kovZI9nvsA7iulXi9//21YRjJLREkAKH+d2+qPlVKvKKUuKqUu7sWABdcj9iLsFrGZA8aOEY9SaoaIJojopFJqCMAHAQyW/30awJfKX7+7ryMVagKxF2G3HASbKRQKuH79OgCr76PuQt3e3s4S3IMHDzA7OwsAHB1lMpm6LLCnSqquiegZWKmOAQB3AfwurGjpWwC6AIzDSnV8sMPzSIl3lVFKvbNabY8Re6krrjgRSdS7zQSDQe7VFggEuInw2toaX4dCIe4rqbsS1OK+TiVzTEWOZ69wq1EcJJxwPHuF2IsrcMTx7BVutRlxPHacdjzzADIAFnb6XYc5BPeNCdj7cXUrpdr28Pn2FRfbC+BOm9mPMdWazaRhFZ66DTfaC1ClOcZRxwMARHTZbSsoN44JcO+4nMSt74Ebx+XGMTmNW98DGZcd6dUmCIIgOIo4HkEQBMFRquF4XqnCa+6EG8cEuHdcTuLW98CN43LjmJzGre+BjMvA8T0eQRAE4WAjUpsgCILgKOJ4BEEQBEdxzPEQ0a8T0RARjRBR1c7VIKJOIvoZEQ0S0Q0i+nz58T8hokkierv878NVGNsYEQ2UX/9y+bE4Ef2YiG6Xv7Y4Pa5qIPZS0djEXgzcYDNiLxWOxYk9HiLyAhgG8KuwGgK+AeCTSqnBfX/xd44lCSCplHqTiBphtWD/GICPA1hVSv2502MyxjYG4KJSasF47M8APFBKfan8YWpRSn2hWmN0ArGXisc2BrEXAO6xGbGXynAq4nkXgBGl1F2lVB7AN2Ed8uQ4SqlppdSb5es0rJMOO6oxlgo5iIdhib08PgfRXgCX2IzYS2U45Xg6AJgHht+HC24GEfUAuABAt2P/LBFdI6KvVUmiUAB+RERXymeMAHV0GNYuEHupDLGXh7jOZsRetufAJhcQURTA3wP4Q6XUCoCvAOgH8AyAaQD/pQrDekkp9SyA3wDwB0T0PvOHytJFJf+9Coi9CLtB7OXROOV4JgF0Gt8fLT9WFYjID8sovqGU+gcAUErNKqU2lFIlAH8JK3R3FKXUZPnrHIDvlMdQ0WFYdYbYSwWIvdhwjc2IveyMU47nDQDHiaiXiAIAPgHgew69tg0iIgBfBXBTKfUXxuNJ49d+G8B1h8cVKW9GgogiAH6tPIbvwToEC6jxw7B2gdjLzuMSe7HjCpsRe6mMHU8g3QuUUkUi+iyAHwLwAviaUuqGE6+9BS8C+BSAASJ6u/zYHwP4JFmHUSkAYwB+3+FxJQB8x7Jb+AD8jVLqB0T0BoBvEdFnUD4My+FxOY7YS0WIvRi4yGbEXipAWuYIgiAIjnJgkwsEQRCE6iCORxAEQXAUcTyCIAiCo4jjEQRBEBxFHI8gCILgKOJ4BEEQBEcRxyMIgiA4yv8HYbbaNMMkmwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(3, figsize=(6,6), hide_axis=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((Image (3, 64, 64), Category 7), ['7', '3'])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train_ds[0], data.test_ds.classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at another example from [`vision.data`](/vision.data.html#vision.data) with the planet dataset. This time, it's a multiclassification problem with the labels in a csv file and no given split between valid and train data, so we use a random split. The factory method is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = untar_data(URLs.PLANET_TINY)\n",
    "planet_tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg', sep = ' ', ds_tfms=planet_tfms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data block API we can rewrite this like that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ImageItemList.from_csv(planet, 'labels.csv', folder='train', suffix='.jpg')\n",
    "        #Where to find the data? -> in planet 'train' folder\n",
    "        .random_split_by_pct()\n",
    "        #How to split in train/valid? -> randomly with the default 20% in valid\n",
    "        .label_from_df(sep=' ')\n",
    "        #How to label? -> use the csv file\n",
    "        .transform(planet_tfms, size=128)\n",
    "        #Data augmentation? -> use tfms with a size of 128\n",
    "        .databunch())                          \n",
    "        #Finally -> use the defaults for conversion to databunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=2, figsize=(9,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data block API also allows you to get your data together in problems for which there is no direct [`ImageDataBunch`](/vision.data.html#ImageDataBunch) factory method. For a segmentation task, for instance, we can use it to quickly get a [`DataBunch`](/basic_data.html#DataBunch). Let's take the example of the [camvid dataset](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/). The images are in an 'images' folder and their corresponding mask is in a 'labels' folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "camvid = untar_data(URLs.CAMVID_TINY)\n",
    "path_lbl = camvid/'labels'\n",
    "path_img = camvid/'images'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a file that gives us the names of the classes (what each code inside the masks corresponds to: a pedestrian, a tree, a road...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car', 'CartLuggagePram', 'Child', 'Column_Pole',\n",
       "       'Fence', 'LaneMkgsDriv', 'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', 'ParkingBlock',\n",
       "       'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', 'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',\n",
       "       'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel', 'VegetationMisc', 'Void', 'Wall'], dtype='<U17')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes = np.loadtxt(camvid/'codes.txt', dtype=str); codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we define the following function that infers the mask filename from the image filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can easily define a [`DataBunch`](/basic_data.html#DataBunch) using the data block API. Here we need to use `tfm_y=True` in the transform call because we need the same transforms to be applied to the target mask as were applied to the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (SegmentationItemList.from_folder(path_img)\n",
    "        .random_split_by_pct()\n",
    "        .label_from_func(get_y_fn, classes=codes)\n",
    "        .transform(get_transforms(), tfm_y=True, size=128)\n",
    "        .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=2, figsize=(7,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example for object detection. We use our tiny sample of the [COCO dataset](http://cocodataset.org/#home) here. There is a helper function in the library that reads the annotation file and returns the list of images names with the list of labelled bboxes associated to it. We convert it to a dictionary that maps image names with their bboxes and then write the function that will give us the target for each image filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coco = untar_data(URLs.COCO_TINY)\n",
    "images, lbl_bbox = get_annotations(coco/'train.json')\n",
    "img2bbox = dict(zip(images, lbl_bbox))\n",
    "get_y_func = lambda o:img2bbox[o.name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code is very similar to what we saw before. The only new addition is the use of special function to collate the samples in batches. This comes from the fact that our images may have multiple bounding boxes, so we need to pad them to the largest number of bounding boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ObjectItemList.from_folder(coco)\n",
    "        #Where are the images? -> in coco\n",
    "        .random_split_by_pct()                          \n",
    "        #How to split in train/valid? -> randomly with the default 20% in valid\n",
    "        .label_from_func(get_y_func)\n",
    "        #How to find the labels? -> use get_y_func\n",
    "        .transform(get_transforms(), tfm_y=True)\n",
    "        #Data augmentation? -> Standard transforms with tfm_y=True\n",
    "        .databunch(bs=16, collate_fn=bb_pad_collate))   \n",
    "        #Finally we convert to a DataBunch and we use bb_pad_collate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=2, ds_type=DatasetType.Valid, figsize=(6,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But vision isn't the only application where the data block API works, it can also be used for text or tabular data. With ouy sample of the IMDB dataset (labelled texts in a csv file), here is how to get the data together for a language model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb = untar_data(URLs.IMDB_SAMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_lm = (TextList.from_csv(imdb, 'texts.csv', cols='text')\n",
    "           #Where are the inputs? Column 'text' of this csv\n",
    "                   .random_split_by_pct()\n",
    "           #How to split it? Randomly with the default 20%\n",
    "                   .label_for_lm()\n",
    "           #Label it for a language model\n",
    "                   .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>  <col width='5%'>  <col width='95%'>  <tr>\n",
       "    <th>idx</th>\n",
       "    <th>text</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>0</th>\n",
       "    <th>xxbos xxmaj old xxmaj jane 's mannered tale seems very popular these days . i have lost count of the number of versions going around . xxmaj probably the reason is that her \" xxunk \" are our \" xxunk \" even at this late date . xxmaj this xxup tv mini - series gives it a mannered telling suitable to the novel . xxmaj xxunk , xxunk xxmaj emma</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>directed the chilling and disturbing xxmaj capote 's book about the reasons that xxunk these kids to the crime ( xxmaj are they xxmaj natural xxmaj born xxmaj killers ? ) . xxmaj the crime scenes are very brutal and haunting because of the lack of senses and reasons for what we witnessed . xxmaj stunning black & white cinematography from xxmaj xxunk xxmaj hall , excellent country - road</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>sisters get the idea of pushing xxmaj precious into the path of a drunken xxmaj hungarian count , xxunk the two gold - xxunk women into thinking he is one of the xxunk men in xxmaj europe . xxmaj but a case of mistaken identity makes the girls think the count is good - looking xxmaj ray xxmaj xxunk , who goes along with the scheme xxunk he has a</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>no xxunk the first xxmaj azumi film was a commercial product ; it was an adaptation of a popular manga and had cast of young , attractive actors and certainly was n't lacking in the budget department . xxmaj yet it more than entertained for what it was , and i ca n't xxunk i enjoyed it immensely . \\n\\n \" xxmaj azumi 2 \" lacks just about everything that</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>long flashback . xxmaj the xxunk of the brother and the sister , from a family of rich xxmaj xxunk oil owners , is brought to the xxunk by xxunk clothes , and xxunk cars that go at top speed in a xxunk landscape . xxmaj malone 's xxunk at the end of the movie is stunning : suit and xxunk , xxunk with a small xxunk : she 's</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_lm.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a classification problem, we just have to change the way labelling is done. Here we use the column 'label' of our csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clas = (TextList.from_csv(imdb, 'texts.csv', cols='text')\n",
    "                   .split_from_df(col='is_valid')\n",
    "                   .label_from_df(cols='label')\n",
    "                   .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>  <col width='90%'>  <col width='10%'>  <tr>\n",
       "    <th>text</th>\n",
       "    <th>target</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxbos xxmaj raising xxmaj victor xxmaj vargas : a xxmaj review \\n\\n xxmaj you know , xxmaj raising xxmaj victor xxmaj vargas is like sticking your hands into a big , xxunk bowl of xxunk . xxmaj it 's warm and gooey , but you 're not sure if it feels right . xxmaj try as i might , no matter how warm and gooey xxmaj raising xxmaj victor xxmaj</th>\n",
       "    <th>negative</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxbos xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . xxmaj there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words . xxmaj it 's not one of those films that tries too hard , nor does it come up with</th>\n",
       "    <th>positive</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxbos xxmaj now that xxmaj che(2008 ) has finished its relatively short xxmaj australian cinema run ( extremely limited xxunk screen in xxmaj xxunk , after xxunk ) , i can xxunk join both xxunk of \" xxmaj at xxmaj the xxmaj movies \" in taking xxmaj steven xxmaj soderbergh to task . \\n\\n xxmaj it 's usually satisfying to watch a film director change his style / subject ,</th>\n",
       "    <th>negative</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxbos xxmaj this film sat on my xxmaj xxunk for weeks before i watched it . i xxunk a self - indulgent xxunk flick about relationships gone bad . i was wrong ; this was an xxunk xxunk into the screwed - up xxunk of xxmaj new xxmaj xxunk . \\n\\n xxmaj the format is the same as xxmaj max xxmaj xxunk ' \" xxmaj la xxmaj xxunk , \"</th>\n",
       "    <th>positive</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxbos xxmaj many neglect that this is n't just a classic due to the fact that it 's the first xxup 3d game , or even the first xxunk - up . xxmaj it 's also one of the first xxunk games , one of the xxunk definitely the first ) truly claustrophobic games , and just a pretty well - xxunk gaming experience in general . xxmaj with graphics</th>\n",
       "    <th>positive</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_clas.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, for tabular data, we just have to pass the name of our categorical and continuous variables as an extra argument. We also add some [`PreProcessor`](/data_block.html#PreProcessor)s that are going to be applied to our data once the splitting and the labelling is done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult = untar_data(URLs.ADULT_SAMPLE)\n",
    "df = pd.read_csv(adult/'adult.csv')\n",
    "dep_var = '>=50k'\n",
    "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']\n",
    "cont_names = ['education-num', 'hours-per-week', 'age', 'capital-loss', 'fnlwgt', 'capital-gain']\n",
    "procs = [FillMissing, Categorify, Normalize]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (TabularList.from_df(df, path=adult, cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
    "                           .split_by_idx(valid_idx=range(800,1000))\n",
    "                           .label_from_df(cols=dep_var)\n",
    "                           .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <col width='10%'>  <tr>\n",
       "    <th>workclass</th>\n",
       "    <th>education</th>\n",
       "    <th>marital-status</th>\n",
       "    <th>occupation</th>\n",
       "    <th>relationship</th>\n",
       "    <th>race</th>\n",
       "    <th>sex</th>\n",
       "    <th>native-country</th>\n",
       "    <th>education-num_na</th>\n",
       "    <th>education-num</th>\n",
       "    <th>hours-per-week</th>\n",
       "    <th>age</th>\n",
       "    <th>capital-loss</th>\n",
       "    <th>fnlwgt</th>\n",
       "    <th>capital-gain</th>\n",
       "    <th>target</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Local-gov</th>\n",
       "    <th> HS-grad</th>\n",
       "    <th> Divorced</th>\n",
       "    <th> Craft-repair</th>\n",
       "    <th> Not-in-family</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-0.4224</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>0.0303</th>\n",
       "    <th>4.4430</th>\n",
       "    <th>-0.9781</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Self-emp-not-inc</th>\n",
       "    <th> 10th</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Craft-repair</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-1.5958</th>\n",
       "    <th>-2.6276</th>\n",
       "    <th>2.3758</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>0.4623</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> HS-grad</th>\n",
       "    <th> Divorced</th>\n",
       "    <th> Transport-moving</th>\n",
       "    <th> Not-in-family</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-0.4224</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>0.6899</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>-0.4378</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> Bachelors</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Prof-specialty</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>1.1422</th>\n",
       "    <th>0.2884</th>\n",
       "    <th>-1.0692</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>1.6128</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>1</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Self-emp-not-inc</th>\n",
       "    <th> Some-college</th>\n",
       "    <th> Never-married</th>\n",
       "    <th> Other-service</th>\n",
       "    <th> Own-child</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-0.0312</th>\n",
       "    <th>-0.8456</th>\n",
       "    <th>-1.2891</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>1.2244</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Provide inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic class to get your inputs into is the following one. It's also the same class that will contain all of your labels (hence the name [`ItemList`](/data_block.html#ItemList))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"ItemList\"><code>class</code> <code>ItemList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L37\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>ItemList</code>(`items`:`Iterator`, `path`:`PathOrStr`=`'.'`, `label_cls`:`Callable`=`None`, `xtra`:`Any`=`None`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `x`:`ItemList`=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList, title_level=3, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "This class regroups the inputs for our model in `items` and saves a `path` attribute which is where it will look for any files (image files, csv file with labels...) `create_func` is applied to `items` to get the final output. `label_cls` will be called to create the labels from the result of the label function, `xtra` contains additional information (usually an underlying dataframe) and `processor` is to be applied to the inputs after the splitting and labelling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has multiple subclasses depending on the type of data you're handling. Here is a quick list:\n",
    "  - [`CategoryList`](/data_block.html#CategoryList) for labels in classification\n",
    "  - [`MultiCategoryList`](/data_block.html#MultiCategoryList) for labels in a multi classification problem\n",
    "  - [`FloatList`](/data_block.html#FloatList) for float labels in a regression problem\n",
    "  - [`ImageItemList`](/vision.data.html#ImageItemList) for data that are images\n",
    "  - [`SegmentationItemList`](/vision.data.html#SegmentationItemList) like [`ImageItemList`](/vision.data.html#ImageItemList) but will default labels to [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList)\n",
    "  - [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList) for segmentation masks\n",
    "  - [`ObjectItemList`](/vision.data.html#ObjectItemList) like [`ImageItemList`](/vision.data.html#ImageItemList) but will default labels to `ObjectLabelList`\n",
    "  - `ObjectLabelList` for object detection\n",
    "  - [`PointsItemList`](/vision.data.html#PointsItemList) for points (of the type [`ImagePoints`](/vision.image.html#ImagePoints))\n",
    "  - [`TextList`](/text.data.html#TextList) for text data\n",
    "  - [`TextFilesList`](/text.data.html#TextFilesList) for text data stored in files\n",
    "  - [`TabularList`](/tabular.data.html#TabularList) for tabular data\n",
    "  - [`CollabList`](/collab.html#CollabList) for collaborative filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have selected the class that is suitable, you can instantiate it with one of the following factory methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_folder\"><code>from_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L85\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_folder</code>(`path`:`PathOrStr`, `extensions`:`StrList`=`None`, `recurse`=`True`, `kwargs`) → `ItemList`\n",
       "\n",
       "Get the list of files in `path` that have a suffix in `extensions`. `recurse` determines if we search subfolders.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_df\"><code>from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L91\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_df</code>(`df`:`DataFrame`, `path`:`PathOrStr`=`'.'`, `cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`0`, `kwargs`) → `ItemList`\n",
       "\n",
       "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `df`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_csv\"><code>from_csv</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L98\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_csv</code>(`path`:`PathOrStr`, `csv_name`:`str`, `cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`0`, `header`:`str`=`'infer'`, `kwargs`) → `ItemList`\n",
       "\n",
       "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `path/csv_name` opened with `header`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional step: filter your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The factory method may have grabbed too many items. For instance, if you were searching sub folders with the `from_folder` method, you may have gotten files you don't want. To remove those, you can use one of the following methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.filter_by_func\"><code>filter_by_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L111\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_by_func</code>(`func`:`Callable`) → `ItemList`\n",
       "\n",
       "Only keeps elements for which `func` returns `True`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.filter_by_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.filter_by_folder\"><code>filter_by_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L116\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_by_folder</code>(`include`=`None`, `exclude`=`None`)\n",
       "\n",
       "Only keep filenames in `include` folder or reject the ones in `exclude`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.filter_by_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.filter_by_rand\"><code>filter_by_rand</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L126\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_by_rand</code>(`p`:`float`, `seed`:`int`=`None`)\n",
       "\n",
       "Keep random sample of `items` with probability `p`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.filter_by_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.to_text\"><code>to_text</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L107\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>to_text</code>(`fn`:`str`)\n",
       "\n",
       "Save `self.items` to `fn` in `self.path`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.to_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing your own [`ItemList`](/data_block.html#ItemList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First check if you can't easily customize one of the existing subclass by:\n",
    "- subclassing an existing one and replacing the `get` method (or the `open` method if you're dealing with images)\n",
    "- applying a custom `processor` (see step 4)\n",
    "- changing the default `label_cls` for the label creation\n",
    "- adding a default [`PreProcessor`](/data_block.html#PreProcessor) with the `_processor` class variable\n",
    "\n",
    "If this isn't the case and you really need to write your own class, there is a [full tutorial](/tutorial.itemlist) that explains how to proceed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.analyze_pred\"><code>analyze_pred</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L69\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>analyze_pred</code>(`pred`:`Tensor`)\n",
       "\n",
       "Called on `pred` before `reconstruct` for additional preprocessing.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.analyze_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.reconstruct\"><code>reconstruct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L73\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>reconstruct</code>(`t`:`Tensor`, `x`:`Tensor`=`None`)\n",
       "\n",
       "Reconstuct one of the underlying item for its data `t`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.reconstruct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Split the data between the training and the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step is normally straightforward, you just have to pick oe of the following functions depending on what you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.random_split_by_pct\"><code>random_split_by_pct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L152\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>random_split_by_pct</code>(`valid_pct`:`float`=`0.2`, `seed`:`int`=`None`) → `ItemLists`\n",
       "\n",
       "Split the items randomly by putting `valid_pct` in the validation set. Set the `seed` in numpy if passed.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.random_split_by_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_files\"><code>split_by_files</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L164\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_files</code>(`valid_names`:`ItemList`) → `ItemLists`\n",
       "\n",
       "Split the data by using the names in `valid_names` for validation.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_fname_file\"><code>split_by_fname_file</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L169\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_fname_file</code>(`fname`:`PathOrStr`, `path`:`PathOrStr`=`None`) → `ItemLists`\n",
       "\n",
       "Split the data by using the file names in `fname` for the validation set. `path` will override `self.path`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_fname_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_folder\"><code>split_by_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L148\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_folder</code>(`train`:`str`=`'train'`, `valid`:`str`=`'valid'`) → `ItemLists`\n",
       "\n",
       "Split the data depending on the folder (`train` or `valid`) in which the filenames are.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>This method looks at the folder immediately after `self.path` for `valid` and `train`.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"This method looks at the folder immediately after `self.path` for `valid` and `train`.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_idx\"><code>split_by_idx</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L139\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_idx</code>(`valid_idx`:`Collection`\\[`int`\\]) → `ItemLists`\n",
       "\n",
       "Split the data according to the indexes in `valid_idx`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_idxs\"><code>split_by_idxs</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L135\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_idxs</code>(`train_idx`, `valid_idx`)\n",
       "\n",
       "Split the data between `train_idx` and `valid_idx`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_list\"><code>split_by_list</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L131\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_list</code>(`train`, `valid`)\n",
       "\n",
       "Split the data between `train` and `valid`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_valid_func\"><code>split_by_valid_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L159\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_valid_func</code>(`func`:`Callable`) → `ItemLists`\n",
       "\n",
       "Split the data by result of `func` (which returns `True` for validation set)  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_valid_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_from_df\"><code>split_from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L175\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_from_df</code>(`col`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`2`)\n",
       "\n",
       "Split the data from the `col` in the dataframe in `self.xtra`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_from_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-danger\" role=\"alert\"><i class=\"fa fa-danger-circle\"></i> <b>Warning: </b>This method assumes the data has been created from a csv file or a dataframe.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_warn(\"This method assumes the data has been created from a csv file or a dataframe.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Label the inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To label your inputs, use one of the following functions. Note that even if it's not in the documented arguments, you can always pass a `label_cls` that will be used to create those labels (the default is the one from your input [`ItemList`](/data_block.html#ItemList), and if there is none, it will go to [`CategoryList`](/data_block.html#CategoryList),  [`MultiCategoryList`](/data_block.html#MultiCategoryList) or [`FloatList`](/data_block.html#FloatList) depending on the type of the labels).\n",
    "\n",
    "The first example in these docs created labels as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.MNIST_TINY)\n",
    "ll = ImageItemList.from_folder(path).split_by_folder().label_from_folder().train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to save the data necessary to recreate your [`LabelList`](/data_block.html#LabelList) (not including saving the actual image/text/etc files), you can use `to_df` or `to_csv`:\n",
    "\n",
    "```python\n",
    "ll.train.to_csv('tmp.csv')\n",
    "```\n",
    "\n",
    "Or just grab a `pd.DataFrame` directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train/7/7994.png</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train/7/8437.png</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train/7/9767.png</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train/7/7236.png</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train/7/9445.png</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x  y\n",
       "0  train/7/7994.png  7\n",
       "1  train/7/8437.png  7\n",
       "2  train/7/9767.png  7\n",
       "3  train/7/7236.png  7\n",
       "4  train/7/9445.png  7"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.to_df().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_empty\"><code>label_empty</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L207\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_empty</code>()\n",
       "\n",
       "Label every item with an [`EmptyLabel`](/core.html#EmptyLabel).  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_empty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_list\"><code>label_from_list</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L190\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_list</code>(`labels`:`Iterator`, `kwargs`) → `LabelList`\n",
       "\n",
       "Label `self.items` with `labels` using `label_cls`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_df\"><code>label_from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L198\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_df</code>(`cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`1`, `kwargs`)\n",
       "\n",
       "Label `self.items` from the values in `cols` in `self.xtra`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-danger\" role=\"alert\"><i class=\"fa fa-danger-circle\"></i> <b>Warning: </b>This method assumes the data has been created from a csv file or a dataframe.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_warn(\"This method assumes the data has been created from a csv file or a dataframe.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_const\"><code>label_const</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L203\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_const</code>(`const`:`Any`=`0`, `kwargs`) → `LabelList`\n",
       "\n",
       "Label every item with `const`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_const)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_folder\"><code>label_from_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L215\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_folder</code>(`kwargs`) → `LabelList`\n",
       "\n",
       "Give a label to each filename depending on its folder.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>This method looks at the last subfolder in the path to determine the classes.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"This method looks at the last subfolder in the path to determine the classes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_func\"><code>label_from_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L211\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_func</code>(`func`:`Callable`, `kwargs`) → `LabelList`\n",
       "\n",
       "Apply `func` to every input to get its label.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_re\"><code>label_from_re</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L219\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_re</code>(`pat`:`str`, `full_path`:`bool`=`False`, `kwargs`) → `LabelList`\n",
       "\n",
       "Apply the re in `pat` to determine the label of every filename.  If `full_path`, search in the full name.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"CategoryList\"><code>class</code> <code>CategoryList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L268\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>CategoryList</code>(`items`:`Iterator`, `classes`:`Collection`=`None`, `kwargs`) :: [`CategoryListBase`](/data_block.html#CategoryListBase)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ItemList`](/data_block.html#ItemList) suitable for storing labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `processor` will default to [`CategoryProcessor`](/data_block.html#CategoryProcessor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"MultiCategoryList\"><code>class</code> <code>MultiCategoryList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L295\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>MultiCategoryList</code>(`items`:`Iterator`, `classes`:`Collection`=`None`, `sep`:`str`=`None`, `kwargs`) :: [`CategoryListBase`](/data_block.html#CategoryListBase)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ItemList`](/data_block.html#ItemList) suitable for storing list of labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `sep` is used to split the content of `items` in a list of labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"FloatList\"><code>class</code> <code>FloatList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L314\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>FloatList</code>(`items`:`Iterator`, `log`:`bool`=`False`, `kwargs`) :: [`ItemList`](/data_block.html#ItemList)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ItemList`](/data_block.html#ItemList) suitable for storing the floats in items for regression. Will add a `log` if this flag is `True`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invisible step: preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This isn't seen here in the API, but if you passed a `processor` (or a list of them) in your initial [`ItemList`](/data_block.html#ItemList) during step 1, it will be applied here. If you didn't pass any processor, a list of them might still be created depending on what is in the `_processor` variable of your class of items (this can be a list of [`PreProcessor`](/data_block.html#PreProcessor) classes).\n",
    "\n",
    "A processor is a transformation that is applied to all the inputs once at initialization, with a state computed on the training set that is then applied without modification on the validation set (and maybe the test set). For instance, it can be processing texts to tokenize then numericalize them. In that case we want the validation set to be numericalized with exactly the same vocabulary as the training set.\n",
    "\n",
    "Another example is in tabular data, where we fill missing values with (for instance) the median computed on the training set. That statistic is stored in the inner state of the [`PreProcessor`](/data_block.html#PreProcessor) and applied on the validation set.\n",
    "\n",
    "This is the generic class for all processors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"PreProcessor\"><code>class</code> <code>PreProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L32\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>PreProcessor</code>(`ds`:`Collection`=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(PreProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"PreProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L34\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`:`Any`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(PreProcessor.process_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process one `item`. This method needs to be written in any subclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"PreProcessor.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L35\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`ds`:`Collection`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(PreProcessor.process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process a dataset. This default to apply `process_one` on every `item` of `ds`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"CategoryProcessor\"><code>class</code> <code>CategoryProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L235\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>CategoryProcessor</code>(`ds`:[`ItemList`](/data_block.html#ItemList)) :: [`PreProcessor`](/data_block.html#PreProcessor)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`PreProcessor`](/data_block.html#PreProcessor) that will convert labels to codes usings `classes` (if passed) in a single classificatio problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.generate_classes\"><code>generate_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L242\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>generate_classes</code>(`items`)\n",
       "\n",
       "Generate classes from `items` by taking the sorted unique values.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.generate_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"MultiCategoryProcessor\"><code>class</code> <code>MultiCategoryProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L284\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>MultiCategoryProcessor</code>(`ds`:[`ItemList`](/data_block.html#ItemList)) :: [`CategoryProcessor`](/data_block.html#CategoryProcessor)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`PreProcessor`](/data_block.html#PreProcessor) that will convert labels to codes usings `classes` (if passed) in a single multi-classificatio problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryProcessor.generate_classes\"><code>generate_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L287\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>generate_classes</code>(`items`)\n",
       "\n",
       "Generate classes from `items` by taking the sorted unique values.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor.generate_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforms differ from processors in the sense they are applied on the fly when we grab one item. They also may change each time we ask for the same item in the case of random transforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L368\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=`(None, None)`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the xs of the train and validation set.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is primary for the vision application. The `kwargs` are the one expected by the type of transforms you pass. `tfm_y` is among them and if set to `True`, the transforms will be applied to input and target."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add a test set, you can use one of the two following methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.add_test\"><code>add_test</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L401\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>add_test</code>(`items`:`Iterator`, `label`:`Any`=`None`)\n",
       "\n",
       "Add test set containing items from `items` and an arbitrary `label`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.add_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>Here `items` can be an `ItemList` or a collection.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"Here `items` can be an `ItemList` or a collection.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.add_test_folder\"><code>add_test_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L410\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>add_test_folder</code>(`test_folder`:`str`=`'test'`, `label`:`Any`=`None`)\n",
       "\n",
       "Add test set containing items from folder `test_folder` and an arbitrary `label`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.add_test_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: convert to a [`DataBunch`](/basic_data.html#DataBunch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This last step is usually pretty straightforward. You just have to include all the arguments we pass to [`DataBunch.create`](/basic_data.html#DataBunch.create) (`bs`, `num_workers`,  `collate_fn`). The class called to create a [`DataBunch`](/basic_data.html#DataBunch) is set in the `_bunch` attribute of the inputs of the training set if you need to modify it. Normally, the various subclasses we showed before handle that for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.databunch\"><code>databunch</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L396\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>databunch</code>(`path`:`PathOrStr`=`None`, `kwargs`) → `ImageDataBunch`\n",
       "\n",
       "Create an [`DataBunch`](/basic_data.html#DataBunch) from self, `path` will override `self.path`, `kwargs` are passed to [`DataBunch.create`](/basic_data.html#DataBunch.create).  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.databunch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"LabelList\"><code>class</code> <code>LabelList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L415\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>LabelList</code>(`x`:[`ItemList`](/data_block.html#ItemList), `y`:[`ItemList`](/data_block.html#ItemList), `tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=`None`, `tfm_y`:`bool`=`False`, `kwargs`) :: [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList, title_level=3, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic dataset in fastai. Inputs are in `x`, targets in `y`. Optionally apply `tfms` to `x` and also `y` if `tfm_y` is `True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.export\"><code>export</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L463\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>export</code>(`fn`:`PathOrStr`)\n",
       "\n",
       "Export the minimal state and save it in `fn` to load an empty version for inference.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.transform_y\"><code>transform_y</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L499\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform_y</code>(`tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=`None`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the targets only.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.transform_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.load_empty\"><code>load_empty</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L470\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>load_empty</code>(`fn`:`PathOrStr`, `tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=`None`, `tfm_y`:`bool`=`False`, `kwargs`)\n",
       "\n",
       "Load the sate in `fn` to create an empty [`LabelList`](/data_block.html#LabelList) for inference.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.load_empty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.from_lists\"><code>from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L487\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_lists</code>(`path`:`PathOrStr`, `inputs`, `labels`) → `LabelList`\n",
       "\n",
       "Create a [`LabelList`](/data_block.html#LabelList) in `path` with `inputs` and `labels`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.to_df\"><code>to_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L455\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>to_df</code>()\n",
       "\n",
       "Create `pd.DataFrame` containing `items` from `self.x` and `self.y`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.to_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.to_csv\"><code>to_csv</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L459\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>to_csv</code>(`dest`:`str`)\n",
       "\n",
       "Save `self.to_df()` to a CSV file in `self.path`/`dest`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.to_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"ItemLists\"><code>class</code> <code>ItemLists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L330\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>ItemLists</code>(`path`:`PathOrStr`, `train`:[`ItemList`](/data_block.html#ItemList), `valid`:[`ItemList`](/data_block.html#ItemList), `test`:[`ItemList`](/data_block.html#ItemList)=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists, doc_string=False, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in `path` split between several streams of inputs, [`train`](/train.html#train), `valid` and maybe `test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.label_from_lists\"><code>label_from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L359\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_lists</code>(`train_labels`:`Iterator`, `valid_labels`:`Iterator`, `label_cls`:`Callable`=`None`, `kwargs`) → `LabelList`\n",
       "\n",
       "Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists.label_from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"LabelLists\"><code>class</code> <code>LabelLists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L384\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>LabelLists</code>(`path`:`PathOrStr`, `train`:[`ItemList`](/data_block.html#ItemList), `valid`:[`ItemList`](/data_block.html#ItemList), `test`:[`ItemList`](/data_block.html#ItemList)=`None`) :: [`ItemLists`](/data_block.html#ItemLists)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists, title_level=3, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"get_files\"><code>get_files</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L19\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get_files</code>(`path`:`PathOrStr`, `extensions`:`StrList`=`None`, `recurse`:`bool`=`False`) → `FilePathList`\n",
       "\n",
       "Return list of files in `c` that have a suffix in `extensions`. `recurse` determines if we search subfolders.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(get_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undocumented Methods - Methods moved below this line will intentionally be hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L52\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`) → `Any`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryListBase.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L265\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`, `classes`=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.get_processors\"><code>get_processors</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L385\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get_processors</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.get_processors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.from_lists\"><code>from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L487\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_lists</code>(`path`:`PathOrStr`, `inputs`, `labels`) → `LabelList`\n",
       "\n",
       "Create a [`LabelList`](/data_block.html#LabelList) in `path` with `inputs` and `labels`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.set_item\"><code>set_item</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L424\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>set_item</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.set_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L434\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`x`, `y`, `kwargs`) → `LabelList`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L274\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.predict\"><code>predict</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L429\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>predict</code>(`res`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L77\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`:`Iterator`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `kwargs`) → `ItemList`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.clear_item\"><code>clear_item</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L425\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>clear_item</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.clear_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L63\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`, `processor`=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.process_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L57\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`processor`=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L391\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L368\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=`(None, None)`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the xs of the train and validation set.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists.transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L478\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`xp`=`None`, `yp`=`None`, `filter_missing_y`:`bool`=`False`)\n",
       "\n",
       "Launch the preprocessing on `xp` and `yp`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L493\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `tfm_y`:`bool`=`None`, `kwargs`)\n",
       "\n",
       "Set the `tfms` and `` tfm_y` value to be applied to the inputs and targets.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L285\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor.process_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"FloatList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L324\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L246\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.process_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.create_classes\"><code>create_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L238\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>create_classes</code>(`classes`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.create_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L248\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`ds`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L302\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"FloatList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L321\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.get_label_cls\"><code>get_label_cls</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L180\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get_label_cls</code>(`labels`, `label_cls`:`Callable`=`None`, `sep`:`str`=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.get_label_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"FloatList.reconstruct\"><code>reconstruct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L328\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>reconstruct</code>(`t`)\n",
       "\n",
       "Reconstuct one of the underlying item for its data `t`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList.reconstruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryList.analyze_pred\"><code>analyze_pred</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L307\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>analyze_pred</code>(`pred`, `thresh`:`float`=`0.5`)\n",
       "\n",
       "Called on `pred` before `reconstruct` for additional preprocessing.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList.analyze_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryList.reconstruct\"><code>reconstruct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L310\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>reconstruct</code>(`t`)\n",
       "\n",
       "Reconstuct one of the underlying item for its data `t`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList.reconstruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryList.reconstruct\"><code>reconstruct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L281\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>reconstruct</code>(`t`)\n",
       "\n",
       "Reconstuct one of the underlying item for its data `t`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.reconstruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.transform_y\"><code>transform_y</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L499\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform_y</code>(`tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=`None`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the targets only.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.transform_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryList.analyze_pred\"><code>analyze_pred</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L279\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>analyze_pred</code>(`pred`, `thresh`:`float`=`0.5`)\n",
       "\n",
       "Called on `pred` before `reconstruct` for additional preprocessing.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.analyze_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Methods - Please document or move to the undocumented section"
   ]
  }
 ],
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