{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a1cab6c",
   "metadata": {},
   "source": [
    "# Comparison Between TreeValue and Jax LibTree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9426f1e",
   "metadata": {},
   "source": [
    "In this section, we will take a look at the feature and performance of the [jax-libtree](https://jax.readthedocs.io/en/latest/pytrees.html) library, which is developed by Google."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f4b6d16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:49.014960Z",
     "iopub.status.busy": "2023-10-22T14:52:49.014670Z",
     "iopub.status.idle": "2023-10-22T14:52:49.023987Z",
     "shell.execute_reply": "2023-10-22T14:52:49.023095Z"
    }
   },
   "outputs": [],
   "source": [
    "_TREE_DATA_1 = {'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1085059",
   "metadata": {},
   "source": [
    "## Mapping Operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ded33f7",
   "metadata": {},
   "source": [
    "### TreeValue's Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa9abbed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:49.028183Z",
     "iopub.status.busy": "2023-10-22T14:52:49.027599Z",
     "iopub.status.idle": "2023-10-22T14:52:51.508447Z",
     "shell.execute_reply": "2023-10-22T14:52:51.507446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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DtccN9sZDeigfvfZYp9Qx2JRLkaWAQksBOmX8dhgUhGMQyVlOXWqqHaW4Aq7otm5rqiUJqzHpQE11Wly3OT1eHbXHjW2N2N32LmWJHY2E2j+6dm/rXHucaxwkxuyFRCGSc7zoqKmucFW2r2foqe1SU93RqClRaqpD4XD0Vumj1R7r0WPE2HXdPoWKHEM2g825DLUUicb1QqISyTledW7UVOosx9+pUVN3NdVqlZoUYxI2i41USyoqZfwMf3TXSEiFstNknh5Fpx8wovZYGIBEcu4PguEg1Qf6VO9rK8EecHTZ3rmm2k+nVpYSmPXtNdWpFhvGGNdURyLg8Diitcd2t7PLdu1RGgnBIbXHhqwBOxkqDFgiOfdHnRs1VbqqulyBHqumOrmPGzUFQwGana00O1qOWnvsdnlIVadg0Rzs6qdWqMjrmMwz52NSm450eEEYKERy7u861k48Wk21O+KhwlGJyWI6pFFTp5pqsw3NCTRqOnQtvc7fUoc2EiICn5R8htPvZOGQBUzIGC9qjwXhcCI5J5LONdUVrkpq3fV8Xb+br+t2MX/oPPQ63VFXHe9JTXU4HKbN3Uazo4UGeyNunye6rXPtsQE9mk6NhBSSRKY+kzzTIJbsXcY3zXv59ak3MHPQ6X3zhghC/yWScyJbXv4xT2x8hml5U0gyJB2xptqDp/tGTaZklEoV/mCAZsfBZZqC4ROrPQ5Hwvx9x0u8X7KMH474PpeMvDgG74gg9BsiOSeqvS3F3L76HhYWzOOqMT/BH/JT5qygxF5GqaPssD7Vhy5+20EpSeg0etx+93EvYpqmS6XQkk+RpZBMfQaKbq7CPyz9iD9v+xtz8mZxwynXoErgm2sEoQdEck5E9e4Gbvz0doqSCrl36uIjTvx1NGo6Vk21l/Yx7K61xwZUndYG7qg9LrTkM9RahEXds+W7ttRt49EvnmJY8hAWT74Zo2hEJAgiOScaT9DLravuJBQO88SshzCqj10+5ww4KbGXtXfVc1Z26VNt99vZ76xheMqwLje3mNUmCs35FFoKGGzOQ3WCk3mlbeXct+5hDCoD905dTIZR9MIQBjSRnBNJOBLmoQ2P823LPp6e9ShphtQeH6OjprrcUcleezHb63ayvnIDPxj9/T5fxLTJ08z96x+l2dvMPVMWMzS5qFePLwj9iEjOieT5bX9nRfknPDL9PoanDO2VYy4p+ZBnN/+R/13wBgaVvleO2R1P0MuTm55lS912fnPaz5kxaFqfn1MQ4tCboktMglheupIPSpfzq1Ov77XEDO2NhXRKbUwSM4BepePOybeysGAuT2x6ltd2vxGT8wpCvBGL2SWALXXb+NO2v/Ljk36YEDXDCknBNWOvINOYyQtfvUyjp4nrx10tKjmEAUUk536u0lHFYxufYVbeDC4efqHc4fSq84YsYpApm0c3PkWDu4HbJ918XBOcgpAIxLBGP2b3O7h/3aMMtuTy83HXyB1Onzg18xQem/EglY5qbl11J/XuBrlDEoSYEMm5nwqEAzyw/lHCkTB3Tr4VtVJ97Bf1U4VJ+Tw16xGUChU3f3YHe1uK5Q5JEPqcSM79UCQS4Xeb/0x5WwX3TluMVWuRO6Q+Z9On8NiMBxiSXMjiNfeyoWaT3CEJQp8Sybkfem3PG6yt/pw7Jt9CnjlX7nBiRq/Scdfk25g7eDYPbXhcVHIICU1MCPYza6rW8fqet7h+3FWMSx8jdzgxp5AUXDv2CrIOVHI0eZq4TlRyCAlIJOd+ZG9LMc9sfo4Lhp7DmQXz5Q5HVucNWUS6IZUnN/2eek8jt0+8SVRyCAlFDGv0E3Wueu5b9zBj0k7mJ6MulTucuDAlexJPznqICnslt626iwZ3o9whCUKvEcm5H/AEvTyw/lFS9CncNvHGPlleqr8qsObz9KxHkCQFN322mH2ikkNIEOJfeZwLR8I8sekZ7H4H90y5Hb1KJ3dIccemt/H4zAcpSirk9jX38kXNl3KHJAgnTCTnOPfXHS+yrf4r7ppyG6l6m9zhxC29SsfdU27jjLxZPLThcd769l25QxKEEyImBOPYe/s+YGnJchZPuolhyUPkDifuKSQF1427kmxTFi989TK1rjquG3cVSjEMJPRDIjnHqc21W/nHVy9z+agfMSV7ktzh9CvnDVlEmt7GU1/+gXp3A7dPuilmXfUEobeIS4o4VGGv5PFNzzA7byYXDTtP7nD6pak5k3lkxn2UtJZy62d3ikoOod8RyTnOtHhb+e26h8m3DuaGcVfLHU6/Nix5CL+b8wSSpODmVXeISg6hXxHJOY4EwgEe2vA4CkmR8M2MYsWmT+HxmQ+SbxnMYlHJIfQjIjnHifZmRn+iyrGf+6fdhUXTsxWshaPTq3TcM3Uxs/Nm8tCGx3m/eJncIQnCMYkJwTjxz12vs7Z6HfdNu4tsU5bc4SQcpaTg+nFXkWPK5m87XqTcXiEqOYS4JpJzHFhd9Tlvfvs2N4y7mrFpo+UOJ6GdN2QRaQYbT276PQ2eRm6beKOo5BDikrhskNmupj08s/k5Lhp6HgsL5skdzoAwNXsyj0y/j+KWEm5bdReNnia5QxKEw4jkLKM6dz0Pb3iCCZmnctmoS+QOp1vPr/870s1GpJuNpN7T/3tID08ZylOzHyEYDnHTZ4spbi2ROyRB6EIkZ5m4gx7uX/cINr2NG0/9uWhmJIMMQzpPzHyQQaYcbl99DxtrNssdkiBEiYwgg1AkzCMbnsAZcHHPlNvRiWZGsjFpTNx/+t3Myp3BgxseY0nJh3KHJAiAmBCUxV+2v8Cupm94dMb92PQpcocz4CklBTeccjWDzDn8dfs/2O/Yz5VjfiJ+mxFkJZJzjL27730+LP2IOybfwtDkIgA+3PMRZ/79/C77aZQa0k1pTBo8gZ9Pu5ZZRdPlCPeoguEgD658jBc3/pM6Zz1DUgu5Yeo1XDf1KrlD+87OG7KIVL2Np75sX13llgm/QqvU9ugY2/d/xZQ/zMYT8ADw+qUv83/jvgfA3sZ9jH9mGk6fE4C/ff+PXDnpJ736NQiJQ1waxNDmuq3846t/8tOTL2Vy1oRu9/WH/FS1VfPfHe8y5/kz+cfGV2IU5fG55NWfct+Kh6lorcQX9PF17W6uf/vX3PT+YrlDOyHTcibz8PTfsqfpG25ZdSdNnuYevX5s9mieu+Dp6ONr//tLKlor8Yf8/PBfP4km5p9MuFQkZqFbIjnHSIWjkse+eJoz8mZxwdBzu2xbOGIekSdd0T/Bx+1U3b2XW2f/Bmi/e/CWJXfgD/nlCP0wTe5mauy17L19Bw33VXDDtGui255e9Xs2VvTvW6RHpAzjyVkP4w8FuPGzxZS0lvbo9T+beBk/nfBjAFo9bVz66hXctuRuNldtBdoT+J8ufLbX4xYSi0jOMdDibeWetQ9SmFTADaccu5mRUqEkx5rNb+ffGX2u2d3C1urtfRlmj/z5ot8xJLWIVKONp899lHRTWnTbv7e+KWNkvSPTmMGTMx8i25jJbavvZlNtzyo5/njhM4zNbr+haE3p5zy75jkArDoLb132Knq1uPFF6J5Izn3MH/Lz4IbH0Cq13DX5VlSKw4f5/SE/z6z+AzP/tID03w5Gc1sS0s1GDItTu+xX66iLVdjd0qq0jMoYGX2sUWoYlXnw8TcN38oRVq8zaUw8cPrdTMmexAPrH2NJD3py6NV63rrsVUxaU5fn//F/zzMktai3QxUSkJgQ7EORSIRnN/+R/c5anpz1MCaN6Yj7XfjSD/lg97FLuAKhQG+HKByDSqHixtN+QVFSIX/d8SL7nTXHXclR3lKJ2+/u8tzmqq1cOFr06BaOTVw596FXdr3Guv0bWDzpJnKO0syosrWqS2K+Z95imh+oJvKki8b7K2MVao/4gj521e2JPvaH/Hxduzv6eFjaUDnC6lPnDVnErRN/w4dlK3nki6fwhXzd7l/dtp8fvno54UgYIPob0yOfPMnS3cv7PF6h/xPJuY98XP4Zb37zDteNvYoxaScfdb9DezabtWb0ah1lzeVc/ebP+zrM7+z6t39NSVMpja4mbvzf7dQ7G6LbfnjK92WMrO+cnjOFh06/l11Nu1m8+l5afW1H3C8QCnDxP39Mg7N99ZXLT/sRf7jgKaD9t6kf//sKylsqYha30D+J5NwHvm7czXPb/sLFwy9iQcHcbvfNNGewYPjBfW5Zcgf6220UPHwSNkN83qCSYkgm3ZRG0SMnk3ZvHn/8/C/Rbb+afgOT8rovE+zPRtqG8+Ssh3EHPfzqk1sobSs7bJ9bltzJurINQPtvEc9d8DTXTrmSi8deBLRP7n7/lUvjpvpGiE8iOfeyWlcdD3/xBBMyTuXSk/7vuF7znx//k5tm/pL8lMFoVVqKbIU8tuhBnrvw6WO/WAYSEq/96EXumbeYvKRcNEoNIzOG89wFT/PseY/LHV6fyzJm8uSsh8gyZnLrqrv4snZLdNub29/md2v+CLRPnL5+6cvRScG/ff+PFNkKAdhUuZnfvHdb7IMX+g0pEolE5A4iUTj9Tm767A70aj2PTr8vIXpmrKlax2Mbn2bJhW/JHUrcCYaD/G7Ln1hduZZrxl7BWYUL5A5JSBxvimqNXhIMh3h049N4Q14enn5vQiRmoXsqhYobT/0Fgy15/Hn736lyVIueHEKvEcm5l/xl+wt80/wtj814EJveJnc4QoxIksT3hp1PhiGdp7/8A42eZm6a8Ise9+QQhEOJH/G94L/fvsfyspXcPOHXFCblyx2OIIPpg6by8Izf8nXTLu5Y89ujVnIIwvESyfkEbardzMtfv8oVoy9nUtZpcocjyGhkSnslh9Pv4qZPF1Nhj886daF/EMn5BJS0lvLYxmeYO3g25w1ZJHc4QhzIMmby1OyHSTOkctNni9lct1XukIR+SiTn76jZ28L96x9lZMowbjjlmmO/QBgwTGoTD0y7m8nZE7l/3SMsK10hd0hCPyQmBL8Df8jPg+sfQ6fScfukm1CK2XnhEGqlmhtP/QVZxkz+tO1vVNqrRCWH0CMiOfdQOBLmiU2/o8ZVx1OzHsGoNsodkhCnJEnikpEXk25I47mtf6HJ28xNp/0SjVIjd2hCPyB+jPfQy1+/yqbazdwx+WayTZlyhyP0A3MHz+aR6b/lq4avRSWHcNxEcu6Bj8o+4e29/+NX469ndOooucMR+pGRthE8OesR7H4HN392B5WOKrlDEuKcSM7HaWfjLv647a/8YMT3mJ03Q+5whH4o25TJU7MexqZP4ZZVd7GjYafcIQlxTCTn41DjquXhDU8wKes0LhlxsdzhCP2YWWPmwWn3cFrGKdzz+YN8XP6Z3CEJcUpMCB6DM+Dk3s8fIsOYzo2n/QJJkuQOSejn1Eo1N532S7JNWTyz+TmKW0t6VMnh9DuPuqqOkDjElXM3guEQj2x4imA4yL1T7xD9EoRe01HJ8avx17O0dAWPb3zmuPs7r6/Z2MfRCfFAJOduPL/973zbspe7p9xOktYqdzhCApqXP4f7pt3J1vod3Ln2vuOq5Chvq6Rc3Bqe8ERyPoo3v3mHFWUfc/OEX1NgHSx3OEICG5s2mqdnP4rdZ+fmz+6gylHd7f4ZxnTe+vadGEUnyEUk5yNYV72Bf+76N1eP/ZloZiTERI4piydnPUyKLpmbP7uTrxq/Puq+mcZ0VlWsoc5VH8MIhVgTyfkQxa0lPL35Oeblz+HswoVyhyMMIGaNmYdOv5dTM8Zx99oH+KRi1RH3yzCmY/c7eGfv/2IcoRBLIjl30uRp5v71j3KSbQTXj7ta7nCEAUitVHPzhF9x8fALeWbzc7y0818cupJcpiEDJHh77/u0+ewyRSr0NZGcD/CFfDy44TEMKgO3TfyNaGYkyKajkuOX46/j3X3v8/imZwiEA9HtGqWGZG0S9e563i9eKmOkQl8SGYgDzYw2Pkudq557pt4umhkJcWHe4DncO/VOqp37qbBX4vA7ottyLYPwhXz855v/4gl6ZYxS6CsiOSnd8mIAACAASURBVAP/+OqfbK7bxt1TbifLKJoZCfHjlPQxPDv7cV75+t/c/NkdVDtrAMg1D0Kv0lPnqmdZiegXnYgGZHJ2+B2UtJbhCXr5qPwT3itewq9OvY6RtuFyhxZXPq/ewL/3vEGECI988ST7DyQGIbYUkoKbJvwSq9bKTZ8uZmfjLjIMGWgUahQoeHXPf7oMewiJQYocOtuQwEKRMH/c+lfe3vsevqAPvVpPIBTgunFXcslI0TOjs88qV3PX2gfwh/yoFWoCYT/ZpmxeWPAnbPoUucMbkHwhH09t+gOb6jYzJu1klpWswKZLocnbzAOn38OC/DPkDlHoPW8OqCvnt755h1d3/ZsIESxaM4GQH2/IR4FF3GRyqFd3v0EwHCTdkEayLok0fRr7nTWsLP9U7tAGBKffidPvxO53UOuqo9ZVR4O7ke8Pv4CTU09ic+1WJCSQ2icQX931OuFIWO6whV40oBoffVq5GkmhwKxubxpj1VrxuhtYV/MFU3ImyRxdfKmyV3VZsUOS2hNBratOxqgSlzPgZG3VejbUbGJHw058QR/uoIdgONgp6UYIH/hFN0IEb8hHo7uJJH0S37bsY/3+jUzLmSzfFyH0qgGVnL1BL4pDhtmlA88LXQ1JLuKLmi+xaMxA+5CQFJEYbM2VObLEZFKbWFgwj4UF8/CFfGyv38mm2s2srvqcKkc1nqCHcCSMUW3EoDKgUigxqHyEiaCRNIQjYV7d/R+RnBPIgErOkzJPY1fTHvzhABqFGm/Qi4TEKelj5Q4t7lwx+nJ2Nu6i3lUPCgkiEiNShrAgf57coSU8rVLLxKxTmZh1KjeccjUlrWV8WbeFddUb2NGwE3vATiQCWqUavdqAJIFVa2Fr3Xa2NXzFuLTR3R7fE/TgDnrwh/34w358IT/+kJ9AOIA/HMAX8gHgPfDfDv6wv8sNMSqFEqV0MIUoJEX7JKWkQKPUoFNqUSvUqBVqNAo1GoUWvUqHQaVHrVD34juWmI44IbiXEhppkiOePuUNeHl+1d/5tm4fRCIoFAom5k/g0ik/6NGqyMMowkbfTYrFy/u/v3U/6/dtpNXTRq5tENOHTEWv0csdVp+///HM7newuXYrG2u/ZG31BhrdjUCEMOAJuJmTN4NfnHY99oCdVp8dd9CNO+jBFXThCXlxB9yECBEkRIQw4QN/t38WOfA4BECYrmPYYSLAwXQhHfg49HH7hwIligOftf+tOPCcEiUahRqDynDgjx69So9FbcasNmPVmLFoLJjVJpSSMgbvalx684jJeT2bqCYxy6YikQiVjdXYXW3YLDayUnpe1zyZ0xhEdh9E1y6R3//e0NfvfzwLRUI0+1po8rbQ6G1iR8NX7Gz4mj3N39LgakKhUHDGsFmY9CaCBAkSIkSIcPTvMCqlErVKjVqpRqlQolKqUCqVKCQFKoUSlar9qlat7PqLtVKh7LLYRDgc7jIJGQ6HCUXCRMJhQuEQgVCQcDhEKBwiGA4TCgcIBIP4g34i4QgKlKhQHkja7Z+rUXX5MKmNJGmSSNElk6JJwqazkaJNxqI2J/rCF28OqGENaJ/YyksbBGmD5A5FELrlDXmpcddR56mn1l1Po7eRtkAbvkiAjg8/AbSpekaljsIb9OJwO2mMNJNqS0Wr0aFVa1ErVdH/alSauEhqoXCIQDCAL+gnEAwQCPrxBnx4/V7cfi9evxdfwI8yoEAdUKF2qdGgpv1vFXqFjhRtCun6VDL0GWTq00nTpybUlfaAS86CEI/CkQgN3gYqndXUuGup9dTR4m/Fjx/fgT9+/ASlIDqtDqPOiFlrwagzYtTo0Wp0aFT9ZxxXqVCi1CjRaXTd7uf1e/H4Pbh9Hlw+Fy6vhyZfC8FAAI2nEo1HgxYtWjToJT1pehsZunRyjNnkmXIx9eNWDCI5C4IMIpEIDb5GKp1VVDirqHbtxx5y4sWL90AqDivCmPQmzHoTGfoMzAYzBo0+Lq58Y0Wn0aHT6Eg2JXd5PhAK4va6cXgd2N0OWtxt7PfVUumuRuvWoGvWoUNLqsbGIFMOuaZBDDYO6ld9c0RyFoQYCYaDVLqq2GcvpdheQkugFc+BZOzFi1qjJsmYRJoxDYvBjEFrZADl4R5RK1VYjRasRgvY2p8LhUM4PO3JutXVSrWzlhp/HfuaS9A16zBIejJ1GQyxFFJkKSBdnybvF3EMIjkLQh8KhoMUO0rZ0/otZY4KnGEXbty4cKNQK0gxJ5NuTCfJlIROLRYQPhFKhZIkYxJJxiTy0nKJRMDpddDibKXV1Uqls4oaTx3fePZhqNOTprFRZCngpKQR7T2y44xIzoLQB2rd9exq3c3u1m9pDrbgxIkbDzqtjlSLjUJLARaDZUANUcSaJIFZb8asN5OXlks4HKbN3UajvYn6tgbq/Q2UNVbwReNmMrRpjEgaxqjkkVg1FrlDB0RyFoReEwgH2Nm8my1N26nz1ePChRMXOr2OrOQs0qypaMXVsWwUCgXJpmSSTckMySrC6XFS11ZPXUs99b4GSuvKWVu3niJzIRPSTiHPJO/dsCI5C8IJcgXdbG/6ii2N22kKNdOGnYg6Qro1jaHJQzDpTXKHKBxCkiTMBjNmg5mirEJana3UttRR1VZDk6OFbxx7ydFlcUrqGE5KGoFKEftUKZKzIHxH3pCPjfVf8mXTNlrDrbRhR2/QUWgrIDM5/sYwhSOTkKJX1EOzQ9S01FDZUEWzt4WKqirW121kSsZETk4ehSKGw1AiOQtCD4UiIbY2bmdD/Zc0hBppoRWbNYWxaaOxGOJjvFL4blRKJbmpg8ixZVPXUk9lYxUt3lYaqhrZ0byTGZnTYjbcIZKzIPRArbuOpZUrqPJV00wrJpOR8VnjxNBFglFICrJSMslMyaC2uY7SujLsbgeVJfsZmzSKOTmz0Cn7dv5AJGdBOA4RImyo28Tn9RuojzSCDkZmjcBmHpgNmAYKCYmslEwykjOobKikvL4SV6ubSlc1Z+bO69OraJGcBeEYvCEf75YtYa+rmEaayErLojAzv0edDIX+TSFJDE7PI82axu7K3ex1F9Na2sbMjNOZnD6hT84Zl8nZ0Wpn06pNfPHJBnZs3EEwEOTO39/FuKmnyB3agBOJRLjzp4vZu3MvE2ZO5NanbpM7pJhyBpy8VfoeJd5SnGo3J+eOItmUJHdYgkwMWj3ji8ZT3lBBZV0Fn9SuwhVwMydnRpf2qb0hLpPz7+/+HdvWb5M7DAH4+J2VlH5TKncYsnAEnPy7+C3K/RUEdEFOLThF1CkfYuNnX/DEzY8DcNXia5h/0XyZI+p7kgT56XkYtQZ2V37DuqYv8Id9LBw0r1dvKorL38ssyVbmnDuHxc/ewRnnixWF5eJoc/Dqc//i3B+fJ3coMReOhHm/fBll/grChjDji8aKxCx0kWZNZUz+yTQqmtnSsp0tTb17QRmXyfkX9/+S6+65gfGnn4pa03/aICaaf/3uFQpGFDJl7lS5Q4m51bWfs89djFftZUz+aFRK+b4Pq8uqefmZl3jl2Zdli+G7Kt9bzoM/f4ANn6wnFAzJHU6vSzJZGZk3nHqpkY/3r6bKVd1rx47LYQ1Bft/u+IbVy1bz5GtPEwgE5A4npmrddXzRuJkGqZlxeWNQy9AnOeD3s+HjDax85yN2bdkFwKyzZ0e3b17zJY/+5hEArrj1SlQqFUv+/T711fUUjSzi2ruuJ6cgBwCPy83bL77Nho/X01jbiFavY8TYEVz4s4sYNnpYl/Me776XTP0BAX/X74u/PfIX/vbIXwDQ6nX8a82rRCIRtm/YxvYN27CmJDH7nNnMvWAeGYNO7CadYCDIXVfcQfGuYgDueu5uxk4eRzgc5pYf3kRFcQWSQuKu5+5hzMQxJ3SuY0m12MhOy6a+voHlVZ/ws+GX9sr4c1xeOQvyCofD/O3Rv3LWDxdF/4EPJOvrN9ISaSE3Lae9JWUMVZVU8dLTL3L1wqv4/d2/Y9eWXUiSxEnjRzF1/pF/g/nkvY/5y8PPU11aTcAfYM/2Pbz+538D7cn2rivu5N2X3qG2spZgIIjL7mTzmi+558q7+OKTDdHj9GTf45Wamcq8C+dhtJhoa27l3Zff4RcX3MD919/Huo8+JxgIfqf3SaVW8ZuHb0JvNADw4lP/IBQKseKtFVQUVwBw/uUX9Hli7lCYkQ8aqPHVsKf12145prhyFg7z4X+W0dZi5/tXXix3KDHnDnrYZy/BJXkYmxqbpcyCgSCfr1jLyndWsmfb7ujzOQU5zDhzJjPOmkFq5tF7D5d+U8qcc+fwvSsvxpxkZsVby9n79V4A3vjLf6jY156sLvjphZz34/OoLKnkyVufoK25jT8/+GfGTBqD3mjo0b6vrXsdOPaEoMli4uo7ruVnt1zJlrWbWbV0FVvWbuarjTv4auMOLMlWZp0zi/kXLSAjp2dX0xmDMrj2zmt55o6nqS6t5q2/vcmHbywDYNiY4fzftT/o0fFOhCRJ5KXnUllVxdctuxmZNPyEjymSs9BFS2MLrz//OtfceQ06Q/dLCCWiatd+PHixGs0xG86oKq3iuXv/AIA1xcq0Bacz86xZFI4sPK7XF44o5Nq7r49WCpx72XnRaqc1H64FID07nR9edwmSQmLEuJGcf/kFvPzMS7jsTrZ+vpWp86f1aN+eUqlVTJw9iYmzJ+GyO/n8o3WsWbqKPdv38L9X3sPebOeG3/68x8edOn8aX23awcp3VvLW398EwGAy8OsHf4NSGdv1BNOtqeyt3ke1az/hSPiE6+DFsIbQxSvPvEzB8AKmzT9d7lBk0epvI0BAttuxVWo1Go0Gleb4r5vGn37qYSVc46aMw+1009bcCkDBiEIkxcF9CkcWRT/fX76/R/ueKIVKiUajRq3VdDnPd/XTm6/oMoZ9xa1XkJYd+1VOVEo1apUaT9iLI+A88eP1QkxCgvB5faxdvgaA75920WHbN63ayPdPu4jzL7+AH/3i0liHFxOBcIAwYZQxbBGZk5/DdffcwMq3V7B3517effkd3n35HfKH5TP9zBlMXzid5LSj3yZuTvou4+KRg58eMz/2ZN8jC4VCbF+/jdVLV7Np1Ub8Pj8AZquZmYtmMv97C7/bgYHK4koaaxujj7eu28aMs2Z95+OdCKVCSZgIwch3G0vvTCRnISoSjhx7pwSnV+lQosQf8MXsnGqNmjnnzmHOuXOo2FfBR2+vYM2y1ZR9W0bZt2X86w//ZPRpozn3svMYO3ncYa9XHOXq02AyYE2x0tbcRtk3pUQikegVdsmegzcWZeVl9WjfzrpcsUcO//5x2Z28/vzrrPtoHfaWtuhrRp06irkXzmPS7MknVC7rcbl55o6nCAVDmK1mHG0O1n64hrGTx3apbomFSCSCP+hHiQKd8sSHBEVyFqJ0Bh1vfvnfw54v+7aMWy65aUDcvp2mS0WLlja3XZbz5w3J44pbr+THv7qM9SvXRScJd2zcQUq67YjJuTunL5zOB68toa66jv88/zrnXHouVaVVvPvSOwAYzUbGTxvf4307GM0Hh392bdnFzEUz0eoPJqaG2sboJJ01xcrMs2cx9/x5hyX5I7nx4l9TWVIJwG//cj+jTh112D5/ffiv1FXVISkk7vj9Xbzy7Evs3rqbFx7/O8PHjDiu8/QWt99DJARWtRWjynDCx4vL5PzGX//Dm399o8tzD/3ywejnz771e3LyB16Jl9D3sgyZJKms1HsbsLsdWAxmWeLQaDXMXDSLmYtmUV1azcp3ViApej5FdPHV/8f2DduoKqnivy+8xX9feCu6TalUcu1d10XL0Xqyb4fCEYUYTAbcTjefr1jL5yvaJxU76pwlSWLMpLHMvWAuE2dNQqnqvUm6T977ODoMN++CeQwZNYQrb7+aWy+5Ga/by7N3PM1DLz6CSh2bNFfTXIMJA4WW/F45npgQFIROlJKS0cknYcVCaV37r/dyyynI4fIbf8plv768x681mAw89I9HOO+y88kYlIFSpYxeAd/3tweYfMaU77RvB51Bx21PL+bk007GaDYeNsE3eOhg7v7jPUyZO7VXE3N1WTX/eOIFoH3M/ZIbfgRAXlEeZ/7gLABK9pTw6h/+2Wvn7I4v4GN/Uy1WycrYlJN75ZhS5AjffevZRDU1vXKCRDSZ0xhEdp8dX7z/3evr998b8vK3PS9TGionNyuH3DR5F/oU4lskEmFryTaULiWTkk7l7Lwze+Owb4orZ0E4hE6p46zc+WRIaZTWlNPqbJM7JCGO7a0pxu8KkKcZxNyc3puEFMlZEI6gyFLA1LRJpJHKjrKdNDma5Q5JiDORSIR9NcU0NDaQJWVw7uCzeqVKo4NIzoJwFNMypjAhZTwZ4TR2le2mrrVe7pCEOBGJRNhduYf6hgYGSdmcm3cWGfr0Xj1HXFZrCEI8UEgSC3LOwKI281ndWr6p2Euzo5lhOUNRKmJ7a7AQP1xeN7urdhN2h8lTDOKC/HMY3AdrCYrkLAjdkCSJqRmT0Cl1fFqzmoaWRja5t3BS7nAshth2rBPkFSFCZUM1ZXXlWMNmBmtyOTf/LNJ1fXOruEjOgnAcxqeOJc80iKUVKyj1lrNt3w5s1hSKsgrRaQZeg6iBpsXZwr79JQS9AbLJYGzyyczNmY1a0XfNsURyFoTjlKqz8aOhF7OubiMbG76kpa2NjfYvyUnLIS8tF7VS/HNKNE6vi+KaEhwOB8kkka0p4IzsWb12o0l3xHeTIPSAUlIyPXMK42yjWV+3ka3NO2ipb6a6cT/pSWkMTstFr9XLHaZwgtpcbZQ3VNLqaCMpYiFfkcfEtFOZmH4aKik28w0iOQvCd2BWm5g/aA5jUkaxtm49xY5S7M0ONrV8SYrFxqDUbKwGa6+uxiz0rVA4RF1rA1WNVQS8fixYKFQMZlzqaCanT+jVMrnjIZKzIJyATEMG3ys4nwZPI1ubdrCzZRetbW3sbNuFQq0gIymdLFsWBo24mo5HESLYXXZqWupoaGtAE1JjwUKqKoWxttGMTx0b86TcQSRnQegFafpU5g+aw7SMSWxv3snO5l00Bpppa2ijunE/JoORNGsaNrMNgxj2kF2rs41GRxP1rfWEA2FMmMiRssgz5jLGdjLDrUNQxmj44mhEchaEXmRUG5maMYkpGROpdFazq3UP37bto9XVRp2rjlLK0Oq0pJptpFpsWI1WuUMeEAKhIM2OFpocTTTZm1GEJAzoScVGuiaVk5JHMCp5JFZN/JRHiuQsCH1AQiLPNIg80yDOyJ5Fsb2UEkcpJfYy2rx27F47tQ11hJQhrAYrSaYkko1WUTvdS4KhEK2uVlqcrbS5W3F53GgjWgzoySKDdG0qQyyFFFkKyTZkxuXcgEjOgtDH1AoVI5KGMiJpKBEiVLn2s6+thBJHKU2+ZnwOP02ORqqoiiZrs96ExWDGrDejVWvl/hLintPrwu524PQ4sLsduLxO1BENOrQYMZIupZFjzKLIUkCRtZBkTZLcIR+TSM6CEEMSErnGHHKNOcxmOs6Ak0pXNZXOaipd1TT5mvA6fLQ52qinAT9+lColZoMJk86EUWfEqDNg0BpRxOHVXl/zBwO4fW5cPjdOjxOnx4HT40QZUaFFgxYNJoykS6lkGTPJMw0i15hDliELdQzXhewN/StaQUgwJrWJkUnDGZk0HABnwEmNu45aTz11njpq3fU4g078dj9t9jYaaSKAnwBBtBptNFHr1Fp0Gh16jQ6dRhvTBWp7U8c6fN6AD6/fizfgw+PztCdkr4twKIwaNRrUqFFjxECKlEKKNokMfToZ+nQy9RlkGjL6XTI+VP+OXhASjEltYqjVxFBrUfS5Nr+dWk8dDZ5Gmn0tNHmbafW34fX7CPj9uHDSSivBTh9KpRKtWotOo0Wt0qBRqlGrNKhVajQqNVq1FpVCiVKpQqVQ9umYaygcJhQOEQwFCYQCBIIB/KEA/oCfQNDf/jjox+v34Q34UEQkVNEPJSpUGNBhxYxBqcems2HTJpOiTSFDn0a6Ph2dMvGGfkRyFoQ4Z9VYsGosDLcOjT4XjoRp89tp9DbR4m/F7rfT5ndg99uxB+z4Qn6CoSBBb5AgAXx4CREmFP07RJjIgb/DSJICpUKBSqlCqVCiUEhIKLp031MoFCilg12Gg+FQl2W8guEgEGlPxqFQe0IOB5Ei7cdSIKFEiQIFyuhH++datBgxoEKFUWXArDZj1Vgwq80kaSyk6JKxaVMwqQ8uKJvojrhMVRPNePDKEU+/YCMZPX1Xqyre/+719fufCNxBD46AA2fAhTvowR104w668YS8uAIu3CEP/pAfX8iHL+THHfRQ3lpGQUoBkkIiQvsNGhEOpodDH0sHPjooDrSHl5Bw+9zUOuoYkToMtUKFWqFGo9BgUBvQK3XolXqMaiMGlR69So9RZcCkNmLVWGN2e3Sce/OIV842UmIdiNCJeP+FE2VQ6TGo9GQc58+w13a/wVfVO7lqyk8ACEcihCJBgpFgdJ9AOEgoEoo+1ig0XSYltUotRCTUShU76nbyZOXvuXfC7eSaB/XK1zTQiGENQRjgQpEwy8tWcmbBPNL1vdObeFbudP69502WliznmrFX9MoxBxqxTJUgDHDr939Bi7eVswoX9NoxJUnizIJ5rCz/FHfQ02vHHUhEchaEAW5J8TImZp5KhqF318Cbn38GEeCTilW9etyBQiRnQRjAyu2VfN20m7OHnNXrxzaqjczKnc77+z7gCHUHwjGI5CwIA9j/ij8g25jJ2NST++T45w45i/2uWrY1fNUnx09kIjkLwgDlCrhYVbmWc4rO6rObUPLMuYyyjWRJ8dI+OX4iE8lZEAao5aUfIwFzBs/q0/OcXXQmm2q3UOeu79PzJBqRnAVhAApHwiwtXc7cwbMxqPr2hp4p2ZNI1iWxtGR5n54n0YjkLAgD0Je1W6lz17OocGGfn0spKTirYAHLyz7GF/L1+fkShUjOgjAALSlZxrj0MQwy58TkfAsL5uEL+lhVuTY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       "</svg>\n"
      ],
      "text/plain": [
       "<FastTreeValue 0x7efef8f6fa00>\n",
       "├── 'a' --> 1\n",
       "├── 'b' --> 4\n",
       "└── 'x' --> <FastTreeValue 0x7efef8f9c9a0>\n",
       "    ├── 'c' --> 9\n",
       "    └── 'd' --> 16"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import mapping, FastTreeValue\n",
    "\n",
    "t = FastTreeValue(_TREE_DATA_1)\n",
    "mapping(t, lambda x: x ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dc3420b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:51.512944Z",
     "iopub.status.busy": "2023-10-22T14:52:51.512125Z",
     "iopub.status.idle": "2023-10-22T14:52:54.428359Z",
     "shell.execute_reply": "2023-10-22T14:52:54.427485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.59 µs ± 57.1 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit mapping(t, lambda x: x ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "289e14c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:54.432414Z",
     "iopub.status.busy": "2023-10-22T14:52:54.431856Z",
     "iopub.status.idle": "2023-10-22T14:52:54.568507Z",
     "shell.execute_reply": "2023-10-22T14:52:54.567440Z"
    }
   },
   "outputs": [
    {
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",
      "image/png": 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       "<!-- node_7efef8faee80&#45;&gt;value__node_7efef8faee80__c -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>node_7efef8faee80&#45;&gt;value__node_7efef8faee80__c</title>\n",
       "<path fill=\"none\" stroke=\"#1f9929\" stroke-opacity=\"0.800000\" d=\"M287.69,-87.61C274.34,-73.77 255.17,-53.9 240.89,-39.1\"/>\n",
       "<ellipse fill=\"#1f9929\" fill-opacity=\"0.800000\" stroke=\"#1f9929\" stroke-opacity=\"0.800000\" cx=\"239.49\" cy=\"-37.65\" rx=\"2\" ry=\"2\"/>\n",
       "<text text-anchor=\"middle\" x=\"272\" y=\"-57.8\" font-family=\"Times-Roman bold\" font-size=\"14.00\" fill=\"#00730a\">c</text>\n",
       "</g>\n",
       "<!-- value__node_7efef8faee80__d -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>value__node_7efef8faee80__d</title>\n",
       "<polygon fill=\"#73ff7e\" fill-opacity=\"0.600000\" stroke=\"transparent\" stroke-width=\"1.5\" points=\"459,-36 310,-36 310,0 459,0 459,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-14.3\" font-family=\"monospace\" font-size=\"14.00\" fill=\"#003304\">(16, (&#39;x&#39;, &#39;d&#39;))</text>\n",
       "</g>\n",
       "<!-- node_7efef8faee80&#45;&gt;value__node_7efef8faee80__d -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>node_7efef8faee80&#45;&gt;value__node_7efef8faee80__d</title>\n",
       "<path fill=\"none\" stroke=\"#1f9929\" stroke-opacity=\"0.800000\" d=\"M319.12,-87.61C332.2,-73.89 350.93,-54.23 364.99,-39.47\"/>\n",
       "<ellipse fill=\"#1f9929\" fill-opacity=\"0.800000\" stroke=\"#1f9929\" stroke-opacity=\"0.800000\" cx=\"366.72\" cy=\"-37.66\" rx=\"2\" ry=\"2\"/>\n",
       "<text text-anchor=\"middle\" x=\"354.5\" y=\"-57.8\" font-family=\"Times-Roman bold\" font-size=\"14.00\" fill=\"#00730a\">d</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<FastTreeValue 0x7efef8fae880>\n",
       "├── 'a' --> (1, ('a',))\n",
       "├── 'b' --> (4, ('b',))\n",
       "└── 'x' --> <FastTreeValue 0x7efef8faee80>\n",
       "    ├── 'c' --> (9, ('x', 'c'))\n",
       "    └── 'd' --> (16, ('x', 'd'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping(t, lambda x, p: (x ** 2, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fce10189",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:54.572874Z",
     "iopub.status.busy": "2023-10-22T14:52:54.572248Z",
     "iopub.status.idle": "2023-10-22T14:52:57.638041Z",
     "shell.execute_reply": "2023-10-22T14:52:57.637042Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.73 µs ± 23.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit mapping(t, lambda x, p: (x ** 2, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d4346d",
   "metadata": {},
   "source": [
    "### pytree's tree_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91b8e706",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:57.642250Z",
     "iopub.status.busy": "2023-10-22T14:52:57.641564Z",
     "iopub.status.idle": "2023-10-22T14:52:57.648474Z",
     "shell.execute_reply": "2023-10-22T14:52:57.647658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 'b': 4, 'x': {'c': 9, 'd': 16}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jax.tree_util import tree_map\n",
    "\n",
    "tree_map(lambda x: x ** 2, _TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41fa094b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:52:57.652218Z",
     "iopub.status.busy": "2023-10-22T14:52:57.651462Z",
     "iopub.status.idle": "2023-10-22T14:53:02.991523Z",
     "shell.execute_reply": "2023-10-22T14:53:02.990326Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.56 µs ± 50.9 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_map(lambda x: x ** 2, _TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36191201",
   "metadata": {},
   "source": [
    "## Flatten and Unflatten Operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a733f250",
   "metadata": {},
   "source": [
    "### TreeValue's Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "676f03c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:02.995662Z",
     "iopub.status.busy": "2023-10-22T14:53:02.995033Z",
     "iopub.status.idle": "2023-10-22T14:53:03.003518Z",
     "shell.execute_reply": "2023-10-22T14:53:03.002846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('a',), 1), (('b',), 2), (('x', 'c'), 3), (('x', 'd'), 4)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import flatten, flatten_keys, flatten_values\n",
    "\n",
    "t_flatted = flatten(t)\n",
    "t_flatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ca0363d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:03.007463Z",
     "iopub.status.busy": "2023-10-22T14:53:03.006934Z",
     "iopub.status.idle": "2023-10-22T14:53:10.194555Z",
     "shell.execute_reply": "2023-10-22T14:53:10.193493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "885 ns ± 7.13 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit flatten(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62d3bd2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:10.199314Z",
     "iopub.status.busy": "2023-10-22T14:53:10.198519Z",
     "iopub.status.idle": "2023-10-22T14:53:10.206512Z",
     "shell.execute_reply": "2023-10-22T14:53:10.205639Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a',), ('b',), ('x', 'c'), ('x', 'd')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import flatten_keys\n",
    "\n",
    "flatten_keys(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58fbf8c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:10.211494Z",
     "iopub.status.busy": "2023-10-22T14:53:10.209839Z",
     "iopub.status.idle": "2023-10-22T14:53:16.395140Z",
     "shell.execute_reply": "2023-10-22T14:53:16.394053Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "753 ns ± 18.7 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit flatten_keys(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1448e341",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:16.400359Z",
     "iopub.status.busy": "2023-10-22T14:53:16.399507Z",
     "iopub.status.idle": "2023-10-22T14:53:16.406280Z",
     "shell.execute_reply": "2023-10-22T14:53:16.405318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import flatten_values\n",
    "\n",
    "flatten_values(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "956cf1c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:16.410310Z",
     "iopub.status.busy": "2023-10-22T14:53:16.409772Z",
     "iopub.status.idle": "2023-10-22T14:53:20.910792Z",
     "shell.execute_reply": "2023-10-22T14:53:20.909439Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "553 ns ± 5.12 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit flatten_values(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f35e3a6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:20.917433Z",
     "iopub.status.busy": "2023-10-22T14:53:20.915810Z",
     "iopub.status.idle": "2023-10-22T14:53:21.052254Z",
     "shell.execute_reply": "2023-10-22T14:53:21.051331Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<TreeValue 0x7efe1869abe0>\n",
       "├── 'a' --> 1\n",
       "├── 'b' --> 2\n",
       "└── 'x' --> <TreeValue 0x7efe1869aca0>\n",
       "    ├── 'c' --> 3\n",
       "    └── 'd' --> 4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import unflatten\n",
    "\n",
    "unflatten(t_flatted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52980ab8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:21.057011Z",
     "iopub.status.busy": "2023-10-22T14:53:21.056397Z",
     "iopub.status.idle": "2023-10-22T14:53:29.029432Z",
     "shell.execute_reply": "2023-10-22T14:53:29.028498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "982 ns ± 7.66 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit unflatten(t_flatted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff80a6c",
   "metadata": {},
   "source": [
    "### pytree's Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e89fc565",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:29.033757Z",
     "iopub.status.busy": "2023-10-22T14:53:29.033046Z",
     "iopub.status.idle": "2023-10-22T14:53:29.039303Z",
     "shell.execute_reply": "2023-10-22T14:53:29.038624Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leaves: [1, 2, 3, 4]\n",
      "Treedef: PyTreeDef({'a': *, 'b': *, 'x': {'c': *, 'd': *}})\n"
     ]
    }
   ],
   "source": [
    "from jax.tree_util import tree_flatten\n",
    "\n",
    "leaves, treedef = tree_flatten(_TREE_DATA_1)\n",
    "print('Leaves:', leaves)\n",
    "print('Treedef:', treedef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d59c4d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:29.042860Z",
     "iopub.status.busy": "2023-10-22T14:53:29.042306Z",
     "iopub.status.idle": "2023-10-22T14:53:30.879003Z",
     "shell.execute_reply": "2023-10-22T14:53:30.877702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.25 µs ± 13.9 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_flatten(_TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bcb1318c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:30.883413Z",
     "iopub.status.busy": "2023-10-22T14:53:30.882767Z",
     "iopub.status.idle": "2023-10-22T14:53:30.888711Z",
     "shell.execute_reply": "2023-10-22T14:53:30.888058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jax.tree_util import tree_unflatten\n",
    "\n",
    "tree_unflatten(treedef, leaves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "638b5144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:30.892733Z",
     "iopub.status.busy": "2023-10-22T14:53:30.891952Z",
     "iopub.status.idle": "2023-10-22T14:53:39.370667Z",
     "shell.execute_reply": "2023-10-22T14:53:39.369457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.04 µs ± 6.45 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_unflatten(treedef, leaves)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd1522b",
   "metadata": {},
   "source": [
    "## All Operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112c91a1",
   "metadata": {},
   "source": [
    "### TreeValue's Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1b9c1c51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:39.375612Z",
     "iopub.status.busy": "2023-10-22T14:53:39.374953Z",
     "iopub.status.idle": "2023-10-22T14:53:39.384152Z",
     "shell.execute_reply": "2023-10-22T14:53:39.383326Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(flatten_values(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb8b0b2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:39.388124Z",
     "iopub.status.busy": "2023-10-22T14:53:39.387847Z",
     "iopub.status.idle": "2023-10-22T14:53:45.134901Z",
     "shell.execute_reply": "2023-10-22T14:53:45.134051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "707 ns ± 6.23 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit all(flatten_values(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86462abd",
   "metadata": {},
   "source": [
    "### pytree.tree_all's performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "64d39484",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:45.140906Z",
     "iopub.status.busy": "2023-10-22T14:53:45.140289Z",
     "iopub.status.idle": "2023-10-22T14:53:45.146465Z",
     "shell.execute_reply": "2023-10-22T14:53:45.145689Z"
    }
   },
   "outputs": [],
   "source": [
    "from jax.tree_util import tree_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a8427db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:45.150181Z",
     "iopub.status.busy": "2023-10-22T14:53:45.149624Z",
     "iopub.status.idle": "2023-10-22T14:53:45.155162Z",
     "shell.execute_reply": "2023-10-22T14:53:45.154281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_all(_TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e4a4cd59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:45.160203Z",
     "iopub.status.busy": "2023-10-22T14:53:45.159613Z",
     "iopub.status.idle": "2023-10-22T14:53:47.260977Z",
     "shell.execute_reply": "2023-10-22T14:53:47.259805Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.53 µs ± 6.82 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_all(_TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0221182",
   "metadata": {},
   "source": [
    "## Reduce Operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646b4a2e",
   "metadata": {},
   "source": [
    "### TreeValue's Reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "105e9001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:47.265630Z",
     "iopub.status.busy": "2023-10-22T14:53:47.265036Z",
     "iopub.status.idle": "2023-10-22T14:53:47.274629Z",
     "shell.execute_reply": "2023-10-22T14:53:47.273858Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "\n",
    "def _flatten_reduce(tree):\n",
    "    values = flatten_values(tree)\n",
    "    return reduce(lambda x, y: x + y, values)\n",
    "\n",
    "_flatten_reduce(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71c145c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:47.278592Z",
     "iopub.status.busy": "2023-10-22T14:53:47.278042Z",
     "iopub.status.idle": "2023-10-22T14:53:58.037829Z",
     "shell.execute_reply": "2023-10-22T14:53:58.036836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.31 µs ± 28 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit _flatten_reduce(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20b34964",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:58.041955Z",
     "iopub.status.busy": "2023-10-22T14:53:58.041394Z",
     "iopub.status.idle": "2023-10-22T14:53:58.050650Z",
     "shell.execute_reply": "2023-10-22T14:53:58.049710Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _flatten_reduce_with_init(tree):\n",
    "    values = flatten_values(tree)\n",
    "    return reduce(lambda x, y: x + y, values, 0)\n",
    "\n",
    "_flatten_reduce_with_init(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca8562c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:53:58.054745Z",
     "iopub.status.busy": "2023-10-22T14:53:58.054058Z",
     "iopub.status.idle": "2023-10-22T14:54:09.457771Z",
     "shell.execute_reply": "2023-10-22T14:54:09.456745Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4 µs ± 23.8 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit _flatten_reduce_with_init(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b2a28a",
   "metadata": {},
   "source": [
    "### pytree.tree_reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "14e9237e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:09.462163Z",
     "iopub.status.busy": "2023-10-22T14:54:09.461625Z",
     "iopub.status.idle": "2023-10-22T14:54:09.469414Z",
     "shell.execute_reply": "2023-10-22T14:54:09.468701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jax.tree_util import tree_reduce\n",
    "\n",
    "tree_reduce(lambda x, y: x + y, _TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6c5cfda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:09.472975Z",
     "iopub.status.busy": "2023-10-22T14:54:09.472446Z",
     "iopub.status.idle": "2023-10-22T14:54:12.137657Z",
     "shell.execute_reply": "2023-10-22T14:54:12.136548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.29 µs ± 66.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_reduce(lambda x, y: x + y, _TREE_DATA_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e79fb73b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:12.142274Z",
     "iopub.status.busy": "2023-10-22T14:54:12.141993Z",
     "iopub.status.idle": "2023-10-22T14:54:12.150504Z",
     "shell.execute_reply": "2023-10-22T14:54:12.149680Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reduce(lambda x, y: x + y, _TREE_DATA_1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af47dd74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:12.154719Z",
     "iopub.status.busy": "2023-10-22T14:54:12.153945Z",
     "iopub.status.idle": "2023-10-22T14:54:14.975855Z",
     "shell.execute_reply": "2023-10-22T14:54:14.974566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.46 µs ± 60.8 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_reduce(lambda x, y: x + y, _TREE_DATA_1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e58b70f",
   "metadata": {},
   "source": [
    "## Structure Transpose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a31f9e",
   "metadata": {},
   "source": [
    "### Subside and Rise in TreeValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b6af689f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:14.980929Z",
     "iopub.status.busy": "2023-10-22T14:54:14.980609Z",
     "iopub.status.idle": "2023-10-22T14:54:15.132156Z",
     "shell.execute_reply": "2023-10-22T14:54:15.131221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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JtkONy5UDGMDg81uVR0vR7DRYJodPg8mOaZp3G9my7YyeSfQVeoY3uZWt63HpEUUaRNc39wdlraRn5pW/oo6ljwBTPD+jSaXBcT3cqzajey+fdSIMLuxgKv8AsqOB+fema54Q0LxHPFPq1j9okiXYjec6YGc/wsKk0Pwxo/hpZ10iz+zCcqZP3rvuxnH3icdTT0sPSxoQ3trcXE9vBcwyzW5AmjSQM0ZPTcByM4PWp6x9F0GPSLvVLwzefdajcmaSTbtwvRE6nhR375NbFIlgRkYNVjbwWmmvb20McMKRkJHGoVVGOgA4FWaRlDoVYZBGCKAPF7bStNt/hhbeJoL+ZdatkXyZhcH5GD4EQTOMY7Y5617E8EV7aJHeW8cisFZo5EDAMMEcH0I/SsODwF4Xtru3uotHhE1uAI2LM2MdCQTgkepya6Om3cqUrhWH4X03StLsruLSb37XFJdySyt5qybZDjcuVAxjA4PNblUdL0ew0WCaHT4PJjmmadxvZsu2Mnkn0FIXQvVla3rcekRRRpE1zf3B2WtpGfmlb+ijqWPAFatYmueENC8RzxT6tY/aJIl2I3nOmBnP8LChW6grX1Od8QaDcWPw78QyTSCfU71ftN3IgwuQR8q/7KqMD8+9V9eka6h8PmPU0u473VbSaxgSNVMEagljkcsPUnp0rrdF8LaL4eiuI9LshAlzjzVMjuHxnH3ifU1Hpfg3w9o2oNf6fpcMFy2fnBY7c9doJIX8MVVyuZGrcXtraNCtzcwwtM4jiEkgUyOeirnqfYVPWPqugx6vq2k3k837nTpGmWDb9+TA2sTnjbycY61sVJIVELaAXTXQhjFwyCMy7BvKg5C564yTx71LRQI4XxBa2+vePrfQ9XuJE01bD7RFbrKY1uJS5ByQRnAHSum0LStP0WykstNkdrdZSdjTGTyyQPlBJJA6HHv70us+HtJ8QwJDqtjHcqhyhbIZfXDAgj86l0nR9P0OxFnptqlvbhi2xcnJPck8k8DrTvoU3pYswW0FsrrbwxxK7mRhGgXcxOSxx1JPU1kXum6VP4u0zUJ73y9Tt4pFt7bzVHmKQQx2kbjgZ6VuVRn0ewudXtdVlg3XtqrJDLvYbQwweM4PXuKQky9UN39p+xz/AGPyvtXlt5PnZ2b8fLuxzjOM4qaigRxvgb7UdR8RHVdg1j7WguhF/qseWNmzvjb6811s1tBcNE00McjRP5kZdAdjYxuGehwTz71zNvZ61pfjnUbiHT47nS9UaJnuBOqNblI9pyp5bOO1dXTZUt7nO+NYrQaA99POba5s2EtpcIMusvRQB/Fu+6V7g1Q8FM2o3F/quqDGvb/IuLdlx9kjHKoo/ukfNnuT7V0t3pdnf3NpcXUPmyWj+ZDljtV8Y3bc4JHYkHHaj+y7Mat/agh23hi8kyKxG5M5wQDg+2RxRfSwX0sTi2gF010IYxcMgjMuwbyoOQueuMk8e9ZHizTdK1bRDa6ze/Y7TzUYy+asfzA8DLAjmtyqOraPYa5Ymz1GDz7csH2b2XkdOVINJCT1L1FFFAjlNVnfxVPPoOnsRYIdmpXi9AO8KHux7noo96o6xC1t8QdNhguVsfP0mW1s5igYRyBgeFPBO3tVyT4ZeD5pXlk0jLuxZj9pm5J/4HWvf+GtH1PSoNMvbFJrSBVWJGZsoAMDDZz096q6Lukee6vFNeQeP3gm+07LW0ilmRQBJJGuZDgcDAzkdq09eka6h8PmPU0u473VbSaxgSNVMEagljkcsPUnp0rt9M0bTtH0/wCwWFnHBa85jAyGz1znk/jVHS/Bvh7RtQa/0/S4YLls/OCx2567QSQv4Yo5g5katxe2to0K3NzDC0ziOISSBTI56Kuep9hU9Y+q6DHq+raTeTzfudOkaZYNv35MDaxOeNvJxjrWxUkhUQtoBdNdCGMXDIIzLsG8qDkLnrjJPHvUtFAjkdXni8M+Lotamby7DUYDb3bdlljBeNj7lQy/lVvwZaSpo76ldLtvNVla8mB6qG+4v4LtH51U8W6bq3iK5t9EXToho7SRy3N88y5IU5KKnUHgc+9daAFAAAAHAApvYpvQjgtoLZXW3hjiV3MjCNAu5icljjqSeprIvdN0qfxdpmoT3vl6nbxSLb23mqPMUghjtI3HAz0rcqjPo9hc6va6rLBuvbVWSGXew2hhg8ZwevcUhJl6qGt20N7od9Z3FwtvFcQNC0rEYTcNuefrV+oLyzttQs5bS7hSa3lXa8bjIYUCRwUlrdeDtb0GKy8Q31/De3K2slldyiT92R99MAbQuB09qm0JpINd8SIdUSxa21U3d0rxq3m25QEDJ+6OOoroNI8GeHdCvDd6bpcUNxggSFmcrnrjcTj8Kfq/hDQNeu0utT02KedAAHLMpI9DgjP41V0XzI8+0gG10bwNdzN5Fsb26Cs44Qyb/LJ7e9dHpqT/APCy4opr4X1zaaMY7q5WMJlmmyoKjgHHOK6y90fTtQ0w6bdWcUlltCiErhQB0xjpjtiodF8PaT4egeHSrJLZJDl8EsWPbJJJNDkDlc5rUv7THxMj/sP7OZjp6fbvtefLEXmHG3bzv+97dK0PiFs/4Qy7LeZ5geIw7MZ83zF2deMZxn2zTPDtnrNx4k1DXNY0+PTjLbx20Nus4lOFLEksOOpo8XWes6xPaaTZ6fGdPeWGa4vnnA2bZNxUJ1JwoOenNHUOqNvRf7W/syP+2/sn27J3fZN2zHb73OfWtCiipIOiooor1j6AK52uirna5MV0PPx/2fn+hj+JTrg0dzoAtDeA/MLnONm0524/izjGeOtV/Awth4I0j7IZDD9nGPMxuzk56e+a3yMgg965bwPaa1pGmjRdT0+OOCyUrBeRzqwnBYn7nVeD3rl6HD0OlFtALproQxi4ZBGZdg3lQchc9cZJ496848X6fP4o8XT6fa2tnKbC1jMp1C7ljiG4swKpGQSfVvYDjv6ZWLq/hLQdeuorrU9NiuJ4xhXJZTj0OCMj2NCdgi7MxfhjcXEvhq5huLhJ/st9LBG8bs6bBggKzcleTjPbFdfBbQWyutvDHEruZGEaBdzE5LHHUk9TUGnaVY6THNHYW6wJNM0zqpJBdsZIz06DgcVcobuxN3Zh3um6VP4u0zUJ73y9Tt4pFt7bzVHmKQQx2kbjgZ6VuVRn0ewudXtdVlg3XtqrJDLvYbQwweM4PXuKvUgYyaaK3iMs0qRxjGXdgAMnA5NefapDfaZ4u0S81fV01e2nvvKtbURiFrdnyFcBfvhRxk+td7eWdtqFpLaXcKTW8o2vG4yGFY+leCfDei3ovNP0qKK4H3ZCzOV+m4nH4U07DTSNi4vbW0aFbm5hhaZxHEJJApkc9FXPU+wqesfVdBj1fVtJvJ5v3OnSNMsG378mBtYnPG3k4x1rYpCCohbQC6a6EMYuGQRmXYN5UHIXPXGSePepaKBHnuq6Rp2ufE64sNTlfyG0uN/s6ymMSkSN1IIJxnOPx7Vp+AHKWerWMdw9xZWOoyQWsjvuPlgKdue4BJrV1XwloWuTyT6lp6XErosZZnYHCkkYweOp5HPNaGnabZ6RYx2Vhbpb28f3Y0HH/wBc+9U3oW5aWJYLaC2V1t4Y4ldzIwjQLuYnJY46knqayL3TdKn8XaZqE975ep28Ui29t5qjzFIIY7SNxwM9K3Koz6PYXOr2uqywbr21Vkhl3sNoYYPGcHr3FSSmXqZNNFbxGWaVI4xjLuwAGTgcmn1BeWdtqFpLaXcKTW8o2vG4yGFAjgtUhvtM8XaJeavq6avbT33lWtqIxC1uz5CuAv3wo4yfWptCaSDXfEiHVEsWttVN3dK8at5tuUBAyfujjqK39K8E+G9FvReafpUUVwPuyFmcr9NxOPwqTV/CGga9dpdanpsU86AAOWZSR6HBGfxqrovmRnfDgGLwFYvIPLjYzSqG42oZGI/Tmumtbu2vrZLm0uIriB87ZYXDq2Dg4I4PIIqK7svN0iewtHW13QNDEypkRZXAIXjp6VHouk2+haNaaZaj91bptBPVj1LH3JJP40nrqS3fUv1FcW0F3CYbmGOaIkEpIgYZByDg+hANS0UhHOePL6607wZqFzZytFKAi+anWNWdVZh9ATUGh+EvDukXlpqGmzym4dTiU3bP9pypySCcN68DtXTzQx3ELwzRrJE6lXRxkMD1BHesTS/Bfh3RdQN/p+lxQXOCBIGZtueuASQPwp30KT0sbL20ElxHcPDG08QIjkKAsgPUA9RnAz9Kpa/aWV/oN5a6jcfZrOWMrLNvVNi+uW4H41pVWv7C21SwmsbyLzbaZdsibiu4fUYNISE0yC3ttJs7e0l822igRIpNwbegUAHI4ORjkVaqK1tobO0htbdNkMKLHGuScKBgDJ56CpaBHD+I/wC0z400Iah9nGifbVNqYM+b5/lnHmZ4xndjb2613BGRg1znjDTtRvrPT7jS4Y57qwvo7tYHcIJQoYFcngH5q27Ca5uLCGW8tfslw6gyQeYJPLPpuHBpvYp7IdFZ20FoLSK3ijtguwQogCBfTaOMV5pa+TNr8XhuW5d/C0d2y27MvySzKAwti+eUU5I9cbecV6dNEs8EkL7tjqVbaxU4PoRyPqKz38PaU+iJoxs0FggASJWI24OQQwOQc85zmhOwJ2L09tb3UPk3EEU0WQdkiBlyDkHB9CAfwqWkVQqhRnAGOTk/nS0iTD8L6bpWl2V3FpN79riku5JZW81ZNshxuXKgYxgcHmtyqOl6PYaLBNDp8HkxzTNO43s2XbGTyT6Cr1DG9zN1hbi7tnstO1ePTr9gHWQxrKwXPPyMehwRmvN5I5f+EQ8b6Y0i3mo27JLc30bEi4BAbp/CVVSCo4Feha14W0TxE0barp8dw8Y2q5ZlYD0ypBxVnTNE0zRrE2Wn2UUFu2SyKM7s8fMTyfxpp2Gmkjideka6h8PmPU0u473VbSaxgSNVMEagljkcsPUnp0qfQmkg13xIh1RLFrbVTd3SvGrebblAQMn7o46iui0vwb4e0bUGv9P0uGC5bPzgsdueu0EkL+GKXV/CGga9dpdanpsU86AAOWZSR6HBGfxp3Q+ZbGd8OAYvAVi8g8uNjNKobjahkYj9Oa6a1u7a+tkubS4iuIHztlhcOrYODgjg8gioruy83SJ7C0dbXdA0MTKmRFlcAheOnpUei6Tb6Fo1pplqP3Vum0E9WPUsfckk/jSeupLd9S/UVxbQXcJhuYY5oiQSkiBhkHIOD6EA1LRSEYnizS5dW8PzR23F7AVubRu6zIdy/njH41iabqEXjLxLp17EM2WmWq3DL6XMowFPuihvxNdRq9zf2mnPNpmni/ugQFgMyxZyeTuPHHWs3wdocuh6Ky3axC+up3urryvuh3OcD2AwKa2KT0Nt7aCS4juHhjaeIERyFAWQHqAeozgZ+lUtftLK/wBBvLXUbj7NZyxlZZt6psX1y3A/GtKq1/YW2qWE1jeRebbTLtkTcV3D6jBpCQmmQW9tpNnb2kvm20UCJFJuDb0CgA5HByMcirVRWttDZ2kNrbpshhRY41yThQMAZPPQVLQI8y0/R59R8QeKYZPEF3pfkXzThbWQRsQygh3J5KYAwOB1qKDVrrUbLwPq+pTr/wAfdxA9yVAVnIZI3x0525rttY8H6Br90l1qemxzzqMCTcykj0O0jP41dudE0y80kaVPYwvYBQog24VQOmMdMe1VzF8yOImt7ufxfd2hvft97b+HZYbi4SMJud3JRSq8A45xWXeymb4RRMmopJbz2UFnDYCJd32kOuSG6k8Hj2zXo+i+HtJ8PQPDpVklskhy+CWLHtkkkmqkXgvw5BrP9rR6TAt7v3iTnAb1C52g++KOZBzIp+Pgq+CLkSGQzK0PlFMZMvmLt68Y3Yz7Zra0X+1v7Mj/ALb+yfbsnd9k3bMdvvc59axPF1nrOsT2mk2enxnT3lhmuL55wNm2TcVCdScKDnpzW/qlxe2umzTafY/brpceXb+aIt+SAfmPAwMn8KXQXSxyfgv+0/7X1gW32c6ANQuNplz5/m7vm2442Zz15ruKwPB+l3ul6GU1FUS8uLiW5ljjbcELsTjPet+h7ilua+m/8ev/AAI1bqppv/Hr/wACNW69Gl8CPaofw4hRRRWhqFFFFABRRRQAUUUUAFFFFAFTUv8Aj1/4EKyK2ryFp4NiYznPNUP7Nn/2PzrjrwlKd0jzcVSnKpeKKlFW/wCzZ/8AY/Oj+zZ/9j86w9lPsc3sKn8pUoq3/Zs/+x+dH9mz/wCx+dHsp9g9hU/lKlFW/wCzZ/8AY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP8A7H50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/ANj86PZT7B7Cp/KVKKt/2bP/ALH50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/wBj86PZT7B7Cp/KVKKt/wBmz/7H50f2bP8A7H50eyn2D2FT+UqUVb/s2f8A2Pzo/s2f/Y/Oj2U+wewqfylSirf9mz/7H50f2bP/ALH50eyn2D2FT+UqUVb/ALNn/wBj86P7Nn/2Pzo9lPsHsKn8pUoq3/Zs/wDsfnR/Zs/+x+dHsp9g9hU/lKlFW/7Nn/2Pzo/s2f8A2Pzo9lPsHsKn8pUoq3/Zs/8AsfnR/Zs/+x+dHsp9g9hU/lKlFW/7Nn/2Pzo/s2f/AGPzo9lPsHsKn8pUoq3/AGbP/sfnR/Zs/wDsfnR7KfYPYVP5SpRVv+zZ/wDY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP/sfnR/Zs/8AsfnR7KfYPYVP5SpRVv8As2f/AGPzo/s2f/Y/Oj2U+wewqfylSirf9mz/AOx+dH9mz/7H50eyn2D2FT+UqUVb/s2f/Y/Oj+zZ/wDY/Oj2U+wewqfylSirf9mz/wCx+dH9mz/7H50eyn2D2FT+UqUVb/s2f/Y/Oj+zZ/8AY/Oj2U+wewqfylSirf8AZs/+x+dH9mz/AOx+dHsp9g9hU/lKlFW/7Nn/ANj86P7Nn/2Pzo9lPsHsKn8pUoq3/Zs/+x+dH9mz/wCx+dHsp9g9hU/lKlFW/wCzZ/8AY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP8A7H50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/ANj86PZT7B7Cp/KVKKt/2bP/ALH50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/wBj86PZT7B7Cp/Ka9FFFeme2Fc7XRVkf2bP/sfnXNiIylayOLGU5T5eVX3KlFW/7Nn/ANj86P7Nn/2Pzrl9lPscPsKn8pUoq3/Zs/8AsfnR/Zs/+x+dHsp9g9hU/lKlFW/7Nn/2Pzo/s2f/AGPzo9lPsHsKn8pUoq3/AGbP/sfnR/Zs/wDsfnR7KfYPYVP5SpRVv+zZ/wDY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP/sfnR/Zs/8AsfnR7KfYPYVP5SpRVv8As2f/AGPzo/s2f/Y/Oj2U+wewqfylSirf9mz/AOx+dH9mz/7H50eyn2D2FT+UqUVb/s2f/Y/Oj+zZ/wDY/Oj2U+wewqfylSirf9mz/wCx+dH9mz/7H50eyn2D2FT+UqUVb/s2f/Y/Oj+zZ/8AY/Oj2U+wewqfylSirf8AZs/+x+dH9mz/AOx+dHsp9g9hU/lKlFW/7Nn/ANj86P7Nn/2Pzo9lPsHsKn8pUoq3/Zs/+x+dH9mz/wCx+dHsp9g9hU/lKlFW/wCzZ/8AY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP8A7H50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/ANj86PZT7B7Cp/KVKKt/2bP/ALH50f2bP/sfnR7KfYPYVP5SpRVv+zZ/9j86P7Nn/wBj86PZT7B7Cp/KVKKt/wBmz/7H50f2bP8A7H50eyn2D2FT+UqUVb/s2f8A2Pzo/s2f/Y/Oj2U+wewqfylSirf9mz/7H50f2bP/ALH50eyn2D2FT+UqUVb/ALNn/wBj86P7Nn/2Pzo9lPsHsKn8pUoq3/Zs/wDsfnR/Zs/+x+dHsp9g9hU/lKlFW/7Nn/2Pzo/s2f8A2Pzo9lPsHsKn8pUoq3/Zs/8AsfnR/Zs/+x+dHsp9g9hU/lKlFW/7Nn/2Pzo/s2f/AGPzo9lPsHsKn8pUoq3/AGbP/sfnR/Zs/wDsfnR7KfYPYVP5SpRVv+zZ/wDY/Oj+zZ/9j86PZT7B7Cp/KVKKt/2bP/sfnR/Zs/8AsfnR7KfYPYVP5SpRVv8As2f/AGPzo/s2f/Y/Oj2U+wewqfylzTf+PX/gRq3VezhaCDY+M5zxVivQppqCTPXopqmkwoooqzQKKKKACiiigAooooAKKKKAPP8A/hJNW/5+/wDyGn+FH/CSat/z9/8AkNP8KKKAD/hJNW/5+/8AyGn+FH/CSat/z9/+Q0/woooAP+Ek1b/n7/8AIaf4Uf8ACSat/wA/f/kNP8KKKAD/AISTVv8An7/8hp/hR/wkmrf8/f8A5DT/AAoooAP+Ek1b/n7/APIaf4Uf8JJq3/P3/wCQ0/woooAP+Ek1b/n7/wDIaf4Uf8JJq3/P3/5DT/CiigA/4STVv+fv/wAhp/hR/wAJJq3/AD9/+Q0/woooAP8AhJNW/wCfv/yGn+FH/CSat/z9/wDkNP8ACiigA/4STVv+fv8A8hp/hR/wkmrf8/f/AJDT/CiigA/4STVv+fv/AMhp/hR/wkmrf8/f/kNP8KKKAD/hJNW/5+//ACGn+FH/AAkmrf8AP3/5DT/CiigA/wCEk1b/AJ+//Iaf4Uf8JJq3/P3/AOQ0/wAKKKAD/hJNW/5+/wDyGn+FH/CSat/z9/8AkNP8KKKAD/hJNW/5+/8AyGn+FH/CSat/z9/+Q0/woooAP+Ek1b/n7/8AIaf4Uf8ACSat/wA/f/kNP8KKKAD/AISTVv8An7/8hp/hR/wkmrf8/f8A5DT/AAoooAP+Ek1b/n7/APIaf4Uf8JJq3/P3/wCQ0/woooAP+Ek1b/n7/wDIaf4Uf8JJq3/P3/5DT/CiigA/4STVv+fv/wAhp/hR/wAJJq3/AD9/+Q0/woooAP8AhJNW/wCfv/yGn+FH/CSat/z9/wDkNP8ACiigA/4STVv+fv8A8hp/hR/wkmrf8/f/AJDT/CiigA/4STVv+fv/AMhp/hR/wkmrf8/f/kNP8KKKAD/hJNW/5+//ACGn+FH/AAkmrf8AP3/5DT/CiigA/wCEk1b/AJ+//Iaf4Uf8JJq3/P3/AOQ0/wAKKKAD/hJNW/5+/wDyGn+FH/CSat/z9/8AkNP8KKKAD/hJNW/5+/8AyGn+FH/CSat/z9/+Q0/woooAP+Ek1b/n7/8AIaf4Uf8ACSat/wA/f/kNP8KKKAD/AISTVv8An7/8hp/hR/wkmrf8/f8A5DT/AAoooAP+Ek1b/n7/APIaf4Uf8JJq3/P3/wCQ0/woooAP+Ek1b/n7/wDIaf4Uf8JJq3/P3/5DT/CiigA/4STVv+fv/wAhp/hR/wAJJq3/AD9/+Q0/woooAP8AhJNW/wCfv/yGn+FH/CSat/z9/wDkNP8ACiigA/4STVv+fv8A8hp/hR/wkmrf8/f/AJDT/CiigA/4STVv+fv/AMhp/hR/wkmrf8/f/kNP8KKKAD/hJNW/5+//ACGn+FH/AAkmrf8AP3/5DT/CiigA/wCEk1b/AJ+//Iaf4Uf8JJq3/P3/AOQ0/wAKKKAD/hJNW/5+/wDyGn+FH/CSat/z9/8AkNP8KKKAD/hJNW/5+/8AyGn+FH/CSat/z9/+Q0/woooAP+Ek1b/n7/8AIaf4Uf8ACSat/wA/f/kNP8KKKAD/AISTVv8An7/8hp/hR/wkmrf8/f8A5DT/AAoooAP+Ek1b/n7/APIaf4Uf8JJq3/P3/wCQ0/woooAP+Ek1b/n7/wDIaf4Uf8JJq3/P3/5DT/CiigA/4STVv+fv/wAhp/hR/wAJJq3/AD9/+Q0/woooAP8AhJN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      ],
      "text/plain": [
       "<FastTreeValue 0x7efe1869adf0>\n",
       "├── 'a' --> {'a': 1, 'b': 10, 'c': {'x': 100, 'y': 400}}\n",
       "└── 'b' --> <FastTreeValue 0x7efe1869aa90>\n",
       "    ├── 'x' --> {'a': 2, 'b': 20, 'c': {'x': 200, 'y': 500}}\n",
       "    └── 'y' --> {'a': 3, 'b': 30, 'c': {'x': 300, 'y': 600}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import subside\n",
    "\n",
    "value = {\n",
    "    'a': FastTreeValue({'a': 1, 'b': {'x': 2, 'y': 3}}),\n",
    "    'b': FastTreeValue({'a': 10, 'b': {'x': 20, 'y': 30}}),\n",
    "    'c': {\n",
    "        'x': FastTreeValue({'a': 100, 'b': {'x': 200, 'y': 300}}),\n",
    "        'y': FastTreeValue({'a': 400, 'b': {'x': 500, 'y': 600}}),\n",
    "    },\n",
    "}\n",
    "subside(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b85caa84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:15.136007Z",
     "iopub.status.busy": "2023-10-22T14:54:15.135752Z",
     "iopub.status.idle": "2023-10-22T14:54:29.085749Z",
     "shell.execute_reply": "2023-10-22T14:54:29.084494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.2 µs ± 253 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit subside(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f304d286",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:29.090649Z",
     "iopub.status.busy": "2023-10-22T14:54:29.089870Z",
     "iopub.status.idle": "2023-10-22T14:54:29.100046Z",
     "shell.execute_reply": "2023-10-22T14:54:29.099259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': {'x': <FastTreeValue 0x7efe186b8490>\n",
       "  ├── 'a' --> 2\n",
       "  ├── 'b' --> 20\n",
       "  └── 'c' --> <FastTreeValue 0x7efe186b8250>\n",
       "      ├── 'x' --> 200\n",
       "      └── 'y' --> 500,\n",
       "  'y': <FastTreeValue 0x7efe1869a760>\n",
       "  ├── 'a' --> 3\n",
       "  ├── 'b' --> 30\n",
       "  └── 'c' --> <FastTreeValue 0x7efe186b8130>\n",
       "      ├── 'x' --> 300\n",
       "      └── 'y' --> 600},\n",
       " 'a': <FastTreeValue 0x7efe1869a8e0>\n",
       " ├── 'a' --> 1\n",
       " ├── 'b' --> 10\n",
       " └── 'c' --> <FastTreeValue 0x7efe1869ac10>\n",
       "     ├── 'x' --> 100\n",
       "     └── 'y' --> 400}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treevalue import raw, rise\n",
    "\n",
    "value = FastTreeValue({\n",
    "    'a': raw({'a': 1, 'b': {'x': 2, 'y': 3}}),\n",
    "    'b': raw({'a': 10, 'b': {'x': 20, 'y': 30}}),\n",
    "    'c': {\n",
    "        'x': raw({'a': 100, 'b': {'x': 200, 'y': 300}}),\n",
    "        'y': raw({'a': 400, 'b': {'x': 500, 'y': 600}}),\n",
    "    },\n",
    "})\n",
    "rise(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "62d34321",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:29.104034Z",
     "iopub.status.busy": "2023-10-22T14:54:29.103281Z",
     "iopub.status.idle": "2023-10-22T14:54:44.117617Z",
     "shell.execute_reply": "2023-10-22T14:54:44.116316Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.4 µs ± 187 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit rise(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f0ed4793",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-10-22T14:54:44.121799Z",
     "iopub.status.idle": "2023-10-22T14:54:44.131735Z",
     "shell.execute_reply": "2023-10-22T14:54:44.131009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': <FastTreeValue 0x7efe186b0280>\n",
       " ├── 'a' --> 1\n",
       " ├── 'b' --> 10\n",
       " └── 'c' --> <FastTreeValue 0x7efe186b0c70>\n",
       "     ├── 'x' --> 100\n",
       "     └── 'y' --> 400,\n",
       " 'b': {'x': <FastTreeValue 0x7efe186b0460>\n",
       "  ├── 'a' --> 2\n",
       "  ├── 'b' --> 20\n",
       "  └── 'c' --> <FastTreeValue 0x7efe186b02e0>\n",
       "      ├── 'x' --> 200\n",
       "      └── 'y' --> 500,\n",
       "  'y': <FastTreeValue 0x7efe186b06d0>\n",
       "  ├── 'a' --> 3\n",
       "  ├── 'b' --> 30\n",
       "  └── 'c' --> <FastTreeValue 0x7efe186b0b80>\n",
       "      ├── 'x' --> 300\n",
       "      └── 'y' --> 600}}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt = {'a': None, 'b': {'x': None, 'y': None}}\n",
    "rise(value, template=vt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a6ad3639",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:44.135642Z",
     "iopub.status.busy": "2023-10-22T14:54:44.135024Z",
     "iopub.status.idle": "2023-10-22T14:54:55.992225Z",
     "shell.execute_reply": "2023-10-22T14:54:55.991178Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.6 µs ± 129 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit rise(value, template=vt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ee26c6",
   "metadata": {},
   "source": [
    "### pytree.tree_transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f24f0f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:55.996671Z",
     "iopub.status.busy": "2023-10-22T14:54:55.995910Z",
     "iopub.status.idle": "2023-10-22T14:54:56.007986Z",
     "shell.execute_reply": "2023-10-22T14:54:56.007286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': {'a': 1, 'b': 10, 'c': {'x': 100, 'y': 400}},\n",
       " 'b': {'x': {'a': 2, 'b': 20, 'c': {'x': 200, 'y': 500}},\n",
       "  'y': {'a': 3, 'b': 30, 'c': {'x': 300, 'y': 600}}}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jax.tree_util import tree_structure, tree_transpose\n",
    "\n",
    "sto = tree_structure({'a': 1, 'b': 2, 'c': {'x': 3, 'y': 4}})\n",
    "sti = tree_structure({'a': 1, 'b': {'x': 2, 'y': 3}})\n",
    "\n",
    "value = (\n",
    "    {'a': 1, 'b': {'x': 2, 'y': 3}},\n",
    "    {\n",
    "        'a': {'a': 10, 'b': {'x': 20, 'y': 30}},\n",
    "        'b': [\n",
    "            {'a': 100, 'b': {'x': 200, 'y': 300}},\n",
    "            {'a': 400, 'b': {'x': 500, 'y': 600}},\n",
    "        ],\n",
    "    }\n",
    ")\n",
    "tree_transpose(sto, sti, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d04072f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-22T14:54:56.011586Z",
     "iopub.status.busy": "2023-10-22T14:54:56.011053Z",
     "iopub.status.idle": "2023-10-22T14:55:09.230903Z",
     "shell.execute_reply": "2023-10-22T14:55:09.229579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.3 µs ± 77.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit tree_transpose(sto, sti, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2a580a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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