test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-07-11 21:16:05,536] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
      "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n",
      "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3\n",
      "\u001b[93m [WARNING] \u001b[0m please install triton==1.0.0 if you want to use sparse attention\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "import numpy as np \n",
    "import VirusDataset \n",
    "import ioutils \n",
    "import random \n",
    "import pandas as pd\n",
    "import pytorch_lightning as L\n",
    "import importlib \n",
    "importlib.reload(VirusDataset)\n",
    "strategy = L.strategies.DeepSpeedStrategy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## hello"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7fe00407ccb0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "random.seed(1509)\n",
    "torch.manual_seed(1509)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "a = torch.tensor((1,23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import esm \n",
    "import VirusDataset  \n",
    "import importlib \n",
    "importlib.reload(VirusDataset)\n",
    "model, alphabet = esm.pretrained.esm2_t12_35M_UR50D()\n",
    "batch_converter = alphabet.get_batch_converter()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read positive samples\n",
      "read negative samples\n",
      "read virus sequences\n"
     ]
    }
   ],
   "source": [
    "X1, y, X2 = VirusDataset.readVirusSequences(trunc=998)\n",
    "newX1 = [(y, x[1]) for x, y in zip(X1, y)] \n",
    "_, _, X1 = batch_converter(X1)\n",
    "X2_labels, _, X2 = batch_converter(X2)\n",
    "trainset = VirusDataset.SeqDataset2(X1, y, X2[random.sample(range(X2.shape[0]), X1.shape[0])])\n",
    "testset = VirusDataset.TestDataset(X2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirusDataset.TestDataset at 0x7fddf05dccb0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = VirusDataset.SeqdataModule(trainset = None, testset = testset, batch_size=16)  \n",
    "ds.test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m ds \u001b[38;5;241m=\u001b[39m VirusDataset\u001b[38;5;241m.\u001b[39mSeqdataModule(batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m) \n\u001b[0;32m----> 2\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/ionChannel/VirusDataset.py:185\u001b[0m, in \u001b[0;36mSeqdataModule.setup\u001b[0;34m(self, stage)\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stage \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 185\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ds = VirusDataset.SeqdataModule(batch_size=20) \n",
    "ds.setup(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import models\n",
    "import esm\n",
    "importlib.reload(models)\n",
    "esm35m, alphabet = esm.pretrained.esm2_t12_35M_UR50D()\n",
    "clsmodel = models.ionclf(esm35m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clsmodel.load_state_dict(torch.load(\"./train624.pt\"), strict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import json\n",
    "path = \"/home/tyfei/ionChannel/ckpts/Lora32unFix3/\"\n",
    "# strategy = L.strategies.DeepSpeedStrategy()\n",
    "k = 2\n",
    "with open(os.path.join(path, \"config.json\"), \"r\") as f:\n",
    "    configs = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, alphabet = esm.pretrained.esm2_t12_35M_UR50D()\n",
    "model = models.fixParameters(model, unfix=configs[\"pretrain_model\"][\"unfix_layers\"])\n",
    "model = models.addlora(\n",
    "    model,\n",
    "    layers=configs[\"pretrain_model\"][\"add_lora\"],\n",
    "    ranks=configs[\"pretrain_model\"][\"rank\"],\n",
    "    alphas=configs[\"pretrain_model\"][\"alpha\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "clsmodel = models.ionclf(\n",
    "    model,\n",
    "    step_lambda=configs[\"model\"][\"lambda_adapt\"],\n",
    "    lamb=configs[\"model\"][\"lambda_ini\"],\n",
    "    max_lambda=configs[\"model\"][\"max_lambda\"],\n",
    "    step=configs[\"model\"][\"lambda_step\"],\n",
    "    p=configs[\"model\"][\"dropout\"],\n",
    "    thres=configs[\"model\"][\"lambda_thres\"],\n",
    "    lr=configs[\"model\"][\"lr\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clsmodel.load_state_dict(torch.load(\"./ckpts/Lora32unFix3/epoch=13-validate_acc=0.94.ckpt\", map_location=\"cuda:1\")[\"state_dict\"])\n",
    "clsmodel.load_state_dict(torch.load(\"./ckpts/Lora32unFix3/last-v1.ckpt\", map_location=\"cuda:1\")[\"state_dict\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA GeForce RTX 4090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5]\n",
      "/home/tyfei/anaconda3/envs/esm2/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=127` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34be89a1697247b48f353997dd7c33a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer = L.Trainer(devices=[1]) \n",
    "res = trainer.predict(clsmodel, ds.predict_dataloader())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0.0194, 0.0190, 0.0298, 0.0326, 0.0401, 0.0257, 0.0303, 0.0182, 0.0219,\n",
       "         0.0266, 0.0229, 0.0310, 0.0244, 0.0280, 0.7715, 0.0247]),\n",
       " tensor([0.0628, 0.0200, 0.0173, 0.0227, 0.0246, 0.0208, 0.0281, 0.0300, 0.0273,\n",
       "         0.0169, 0.0183, 0.0213, 0.0236, 0.9002, 0.5222, 0.0266]),\n",
       " tensor([0.0198, 0.1326, 0.9762, 0.0374, 0.0225, 0.0196, 0.9727, 0.0217, 0.8978,\n",
       "         0.0216, 0.0261, 0.0198, 0.0184, 0.0267, 0.0605, 0.0181]),\n",
       " tensor([0.0433, 0.0237, 0.0271, 0.8868, 0.0719, 0.0247, 0.0167, 0.0194, 0.0338,\n",
       "         0.0203, 0.0266, 0.0178, 0.0234, 0.0172, 0.0361, 0.0216]),\n",
       " tensor([0.0235, 0.0202, 0.0217, 0.0364, 0.0275, 0.6174, 0.0287, 0.0169, 0.9285,\n",
       "         0.0261, 0.0225, 0.0273, 0.0429, 0.0229, 0.0168, 0.0244]),\n",
       " tensor([0.0230, 0.0200, 0.0364, 0.0235, 0.6549, 0.0267, 0.0258, 0.0195, 0.0276,\n",
       "         0.0236, 0.0300, 0.0275, 0.0200, 0.0408, 0.0254, 0.0419]),\n",
       " tensor([0.0193, 0.0258, 0.0345, 0.0204, 0.0282, 0.0607, 0.0218, 0.0386, 0.8068,\n",
       "         0.8754, 0.0203, 0.0182, 0.0225, 0.0197, 0.0186, 0.0222]),\n",
       " tensor([0.0236, 0.0234, 0.9183, 0.2187, 0.0212, 0.0349, 0.0294, 0.0247, 0.0350,\n",
       "         0.0212, 0.0179, 0.0156, 0.0192, 0.0245, 0.0192, 0.0295]),\n",
       " tensor([0.0331, 0.0290, 0.0224, 0.0217, 0.0224, 0.0255, 0.0193, 0.0231, 0.0543,\n",
       "         0.0301, 0.0238, 0.0206, 0.0216, 0.0260, 0.0332, 0.0785]),\n",
       " tensor([0.0318, 0.0173, 0.0217, 0.0195, 0.0169, 0.0234, 0.0384, 0.0203, 0.0239,\n",
       "         0.0274, 0.0240, 0.0234, 0.0266, 0.0273, 0.0310, 0.0210]),\n",
       " tensor([0.0329, 0.0758, 0.0330, 0.0213, 0.0218, 0.0332, 0.0356, 0.0225, 0.2733,\n",
       "         0.0255, 0.0279, 0.8684, 0.0181, 0.0218, 0.3673, 0.0241]),\n",
       " tensor([0.4640, 0.0182, 0.0305, 0.5159, 0.0306, 0.0295, 0.0185, 0.0205, 0.1453,\n",
       "         0.0156, 0.0227, 0.0222, 0.2821, 0.0361, 0.0237, 0.0252]),\n",
       " tensor([0.0256, 0.0260, 0.0186, 0.4514, 0.0283, 0.0244, 0.0190, 0.1095, 0.1133,\n",
       "         0.0228, 0.0444, 0.0195, 0.0176, 0.0262, 0.0210, 0.0460]),\n",
       " tensor([0.0213, 0.0187, 0.0244, 0.0570, 0.0310, 0.0191, 0.0221, 0.0316, 0.0191,\n",
       "         0.0254, 0.0638, 0.0324, 0.0202, 0.0202, 0.0243, 0.0582]),\n",
       " tensor([0.0203, 0.0256, 0.0276, 0.0256, 0.0259, 0.0257, 0.0190, 0.9750, 0.0247,\n",
       "         0.0205, 0.0315, 0.0260, 0.0233, 0.0274, 0.0347, 0.0183]),\n",
       " tensor([0.0317, 0.0176, 0.0196, 0.0202, 0.0310, 0.0297, 0.0218, 0.5091, 0.0223,\n",
       "         0.0279, 0.0342, 0.0352, 0.0271, 0.0200, 0.0311, 0.0299]),\n",
       " tensor([0.0171, 0.0211, 0.0359, 0.0276, 0.0167, 0.0228, 0.0229, 0.0271, 0.0249,\n",
       "         0.0247, 0.0232, 0.0318, 0.0346, 0.0238, 0.0437, 0.4039]),\n",
       " tensor([0.0201, 0.0183, 0.0265, 0.0206, 0.0173, 0.0398, 0.0262, 0.0318, 0.0307,\n",
       "         0.0271, 0.0159, 0.0199, 0.0231, 0.0371, 0.0255, 0.0191]),\n",
       " tensor([0.0206, 0.0212, 0.0190, 0.0245, 0.0281, 0.0313, 0.0288, 0.0680, 0.0244,\n",
       "         0.0222, 0.0194, 0.0265, 0.0202, 0.0390, 0.1093, 0.0171]),\n",
       " tensor([0.0290, 0.0225, 0.0196, 0.9281, 0.0248, 0.0238, 0.0224, 0.0317, 0.0216,\n",
       "         0.0296, 0.0201, 0.0200, 0.0422, 0.9531, 0.0204, 0.0285]),\n",
       " tensor([0.0195, 0.0175, 0.0189, 0.0181, 0.0423, 0.0227, 0.0207, 0.0252, 0.5909,\n",
       "         0.2225, 0.0262, 0.4251, 0.5149, 0.9566, 0.0534, 0.0397]),\n",
       " tensor([0.0294, 0.0391, 0.0244, 0.0174, 0.0220, 0.0215, 0.0239, 0.0450, 0.0238,\n",
       "         0.0227, 0.0286, 0.0222, 0.0188, 0.0187, 0.0240, 0.0215]),\n",
       " tensor([0.0184, 0.0265, 0.0190, 0.0175, 0.0198, 0.0372, 0.0223, 0.0328, 0.0241,\n",
       "         0.0182, 0.5266, 0.0463, 0.0239, 0.2351, 0.2297, 0.0164]),\n",
       " tensor([0.0202, 0.0183, 0.0183, 0.0206, 0.0233, 0.0228, 0.0244, 0.0186, 0.4514,\n",
       "         0.0397, 0.0203, 0.0223, 0.0207, 0.0177, 0.0347, 0.0221]),\n",
       " tensor([0.0181, 0.0163, 0.0231, 0.0196, 0.0269, 0.0187, 0.0201, 0.0311, 0.0443,\n",
       "         0.3019, 0.0178, 0.8647, 0.6711, 0.0365, 0.0289, 0.0213]),\n",
       " tensor([0.0206, 0.0189, 0.5467, 0.0258, 0.0512, 0.0184, 0.0257, 0.0210, 0.0226,\n",
       "         0.0171, 0.0252, 0.0258, 0.0264, 0.0325, 0.0277, 0.0241]),\n",
       " tensor([0.0446, 0.0685, 0.3705, 0.0310, 0.0191, 0.0207, 0.0246, 0.6740, 0.0209,\n",
       "         0.0371, 0.0300, 0.0483, 0.0217, 0.0171, 0.0418, 0.0275]),\n",
       " tensor([0.0276, 0.0272, 0.0446, 0.0226, 0.0282, 0.0287, 0.0298, 0.0182, 0.9542,\n",
       "         0.0771, 0.0358, 0.0168, 0.0239, 0.0246, 0.0312, 0.0163]),\n",
       " tensor([0.0186, 0.0223, 0.0226, 0.7890, 0.0269, 0.0165, 0.0197, 0.0216, 0.0199,\n",
       "         0.4758, 0.0179, 0.0610, 0.1644, 0.0707, 0.4659, 0.0189]),\n",
       " tensor([0.2457, 0.1133, 0.0218, 0.0356, 0.0263, 0.0246, 0.4413, 0.0368, 0.0262,\n",
       "         0.0226, 0.0174, 0.8771, 0.0542, 0.0175, 0.0230, 0.0189]),\n",
       " tensor([0.0333, 0.0233, 0.4990, 0.0209, 0.0184, 0.6859, 0.0169, 0.0275, 0.0271,\n",
       "         0.0407, 0.0292, 0.0172, 0.2619, 0.0289, 0.0685, 0.0176]),\n",
       " tensor([0.0283, 0.3916, 0.0199, 0.0194, 0.0216, 0.0207, 0.0232, 0.0243, 0.0271,\n",
       "         0.0361, 0.0393, 0.0482, 0.0343, 0.0221, 0.0197, 0.0285]),\n",
       " tensor([0.9893, 0.0288, 0.0206, 0.0214, 0.0226, 0.0180, 0.0227, 0.0192, 0.0205,\n",
       "         0.0204, 0.0216, 0.0187, 0.0192, 0.0243, 0.0191, 0.0208]),\n",
       " tensor([0.0332, 0.0262, 0.0224, 0.0236, 0.0353, 0.4792, 0.8773, 0.0243, 0.0216,\n",
       "         0.0232, 0.0185, 0.0204, 0.0175, 0.0230, 0.0302, 0.0375]),\n",
       " tensor([0.0424, 0.0286, 0.0251, 0.0394, 0.0278, 0.0260, 0.0581, 0.0370, 0.0246,\n",
       "         0.0392, 0.0178, 0.0180, 0.0188, 0.0220, 0.0254, 0.0235]),\n",
       " tensor([0.0181, 0.5281, 0.0217, 0.0287, 0.0315, 0.0191, 0.0207, 0.0246, 0.6740,\n",
       "         0.0209, 0.0371, 0.0300, 0.0483, 0.0217, 0.0171, 0.0418]),\n",
       " tensor([0.0206, 0.0256, 0.0360, 0.0222, 0.0190, 0.0226, 0.9293, 0.0303, 0.0403,\n",
       "         0.0250, 0.0268, 0.0179, 0.0181, 0.0349, 0.0178, 0.0386]),\n",
       " tensor([0.0262, 0.0252, 0.0309, 0.0167, 0.0274, 0.0214, 0.0373, 0.0291, 0.0227,\n",
       "         0.0196, 0.0248, 0.0190, 0.0225, 0.0186, 0.0458, 0.0249]),\n",
       " tensor([0.0270, 0.0310, 0.0198, 0.0194, 0.0375, 0.0307, 0.0488, 0.0199, 0.0533,\n",
       "         0.0265, 0.0184, 0.0260, 0.0198, 0.0290, 0.0155, 0.0232]),\n",
       " tensor([0.9880, 0.0793, 0.0408, 0.0405, 0.0561, 0.0232, 0.0336, 0.9793, 0.2875,\n",
       "         0.1056, 0.0483, 0.0465, 0.0205, 0.0190, 0.0329, 0.0194]),\n",
       " tensor([0.0181, 0.0301, 0.0215, 0.0193, 0.0200, 0.0167, 0.0276, 0.0232, 0.0179,\n",
       "         0.0259, 0.0248, 0.0284, 0.0224, 0.0300, 0.0277, 0.0387]),\n",
       " tensor([0.1630, 0.0261, 0.0672, 0.0233, 0.0346, 0.0209, 0.0225, 0.0281, 0.0294,\n",
       "         0.0215, 0.0240, 0.0244, 0.6830, 0.0196, 0.4535, 0.0203]),\n",
       " tensor([0.0587, 0.1251, 0.7258, 0.6733, 0.0188, 0.3218, 0.0284, 0.1731, 0.9851,\n",
       "         0.0269, 0.0244, 0.0252, 0.0288, 0.0255, 0.0276, 0.0382]),\n",
       " tensor([0.0214, 0.0353, 0.0180, 0.0231, 0.0322, 0.2511, 0.0325, 0.0192, 0.0290,\n",
       "         0.0271, 0.0765, 0.0257, 0.0252, 0.0606, 0.0240, 0.0297]),\n",
       " tensor([0.0306, 0.0321, 0.0275, 0.0230, 0.0246, 0.0335, 0.0340, 0.0273, 0.0413,\n",
       "         0.0203, 0.0365, 0.0234, 0.0158, 0.0216, 0.0231, 0.1811]),\n",
       " tensor([0.0369, 0.0281, 0.0223, 0.6799, 0.0218, 0.0193, 0.0384, 0.0278, 0.4058,\n",
       "         0.0292, 0.0602, 0.0317, 0.0181, 0.9897, 0.0819, 0.0186]),\n",
       " tensor([0.0234, 0.0359, 0.0233, 0.9775, 0.0863, 0.0271, 0.0216, 0.0214, 0.0314,\n",
       "         0.3620, 0.8832, 0.5900, 0.0217, 0.6451, 0.0735, 0.0276]),\n",
       " tensor([0.0403, 0.0320, 0.4296, 0.0259, 0.6895, 0.8887, 0.0257, 0.9516, 0.9883,\n",
       "         0.9875, 0.0183, 0.0404, 0.9382, 0.0208, 0.4733, 0.0180]),\n",
       " tensor([0.0202, 0.0314, 0.0267, 0.0273, 0.0272, 0.9808, 0.0631, 0.0301, 0.0200,\n",
       "         0.0351, 0.0238, 0.0345, 0.0335, 0.0228, 0.0179, 0.0281]),\n",
       " tensor([0.1698, 0.0425, 0.0246, 0.5152, 0.1073, 0.0225, 0.0236, 0.0397, 0.0171,\n",
       "         0.8159, 0.9828, 0.9182, 0.0171, 0.6087, 0.8506, 0.9895]),\n",
       " tensor([0.9375, 0.9740, 0.9690, 0.1020, 0.0215, 0.0267, 0.0262, 0.0211, 0.0391,\n",
       "         0.0162, 0.0177, 0.1388, 0.0747, 0.0579, 0.0334, 0.0242]),\n",
       " tensor([0.0237, 0.0242, 0.0311, 0.0240, 0.0210, 0.0229, 0.0186, 0.0214, 0.0256,\n",
       "         0.0230, 0.3339, 0.0176, 0.0213, 0.0305, 0.0276, 0.0223]),\n",
       " tensor([0.0296, 0.0173, 0.0388, 0.0254, 0.0190, 0.0226, 0.0208, 0.0246, 0.0476,\n",
       "         0.0341, 0.0182, 0.0208, 0.4549, 0.0169, 0.9501, 0.9384]),\n",
       " tensor([0.0257, 0.9516, 0.0260, 0.5553, 0.5572, 0.0622, 0.0846, 0.0250, 0.0279,\n",
       "         0.0167, 0.0181, 0.0247, 0.0193, 0.0192, 0.0500, 0.0345]),\n",
       " tensor([0.0364, 0.0201, 0.9788, 0.0301, 0.0200, 0.0239, 0.0276, 0.0744, 0.0238,\n",
       "         0.0232, 0.0308, 0.0235, 0.0208, 0.0210, 0.0192, 0.0220]),\n",
       " tensor([0.0419, 0.0309, 0.0244, 0.4717, 0.0626, 0.0213, 0.0257, 0.0314, 0.0651,\n",
       "         0.6105, 0.0177, 0.0221, 0.5290, 0.0241, 0.9155, 0.0199]),\n",
       " tensor([0.0346, 0.0199, 0.0281, 0.0368, 0.0314, 0.0197, 0.6984, 0.0200, 0.5098,\n",
       "         0.0445, 0.3179, 0.0199, 0.4149, 0.0277, 0.0314, 0.9482]),\n",
       " tensor([0.5476, 0.0182, 0.8243, 0.0317, 0.0497, 0.0221, 0.0218, 0.0254, 0.8654,\n",
       "         0.0311, 0.0207, 0.0361, 0.0216, 0.0185, 0.0226, 0.0252]),\n",
       " tensor([0.0292, 0.0177, 0.0640, 0.0271, 0.0232, 0.0194, 0.0293, 0.0212, 0.0165,\n",
       "         0.0571, 0.0217, 0.2419, 0.0272, 0.0241, 0.0540, 0.9295]),\n",
       " tensor([0.2342, 0.0381, 0.0468, 0.0250, 0.0215, 0.0235, 0.0295, 0.0330, 0.9781,\n",
       "         0.0267, 0.0173, 0.0199, 0.0171, 0.0192, 0.0430, 0.0260]),\n",
       " tensor([0.0194, 0.0202, 0.0346, 0.0352, 0.0280, 0.0942, 0.0199, 0.0293, 0.0290,\n",
       "         0.0286, 0.0191, 0.0166, 0.0181, 0.0282, 0.0230, 0.0232]),\n",
       " tensor([0.0195, 0.0246, 0.0336, 0.0273, 0.0218, 0.0189, 0.9221, 0.1684, 0.8286,\n",
       "         0.0193, 0.0301, 0.9341, 0.9862, 0.9265, 0.5765, 0.0230]),\n",
       " tensor([0.0251, 0.0321, 0.1533, 0.0284, 0.0171, 0.0241, 0.9654, 0.6591, 0.0215,\n",
       "         0.0321, 0.0224, 0.0256, 0.0199, 0.0226, 0.0196, 0.0207]),\n",
       " tensor([0.6556, 0.0184, 0.0256, 0.0240, 0.0268, 0.0216, 0.0161, 0.0196, 0.0859,\n",
       "         0.9660, 0.0206, 0.0297, 0.3933, 0.0192, 0.0228, 0.0250]),\n",
       " tensor([0.0208, 0.0201, 0.0347, 0.0262, 0.0221, 0.0241, 0.1199, 0.0241, 0.0162,\n",
       "         0.0184, 0.0198, 0.0194, 0.0203, 0.0280, 0.0883, 0.0233]),\n",
       " tensor([0.5139, 0.0225, 0.0252, 0.0218, 0.0278, 0.1269, 0.0259, 0.0187, 0.0179,\n",
       "         0.0216, 0.0197, 0.0231, 0.0347, 0.0190, 0.0340, 0.0187]),\n",
       " tensor([0.0216, 0.0395, 0.0312, 0.0176, 0.0272, 0.0501, 0.0228, 0.0197, 0.0256,\n",
       "         0.0379, 0.0216, 0.0406, 0.0216, 0.0569, 0.0228, 0.0277]),\n",
       " tensor([0.9555, 0.0301, 0.0379, 0.0201, 0.0226, 0.0238, 0.0220, 0.0806, 0.0314,\n",
       "         0.0200, 0.0890, 0.0185, 0.0234, 0.0276, 0.0446, 0.0776]),\n",
       " tensor([0.0227, 0.0213, 0.4154, 0.0189, 0.0196, 0.3656, 0.0346, 0.0277, 0.3149,\n",
       "         0.9606, 0.0441, 0.0285, 0.0261, 0.0463, 0.0186, 0.0556]),\n",
       " tensor([0.0303, 0.0417, 0.0355, 0.0278, 0.0211, 0.0590, 0.0196, 0.0166, 0.1888,\n",
       "         0.0346, 0.0202, 0.0206, 0.0234, 0.0244, 0.8942, 0.0173]),\n",
       " tensor([0.8302, 0.0235, 0.0425, 0.8497, 0.4542, 0.0191, 0.0166, 0.6883, 0.0270,\n",
       "         0.0185, 0.0286, 0.0413, 0.0211, 0.0662, 0.0197, 0.7007]),\n",
       " tensor([0.0260, 0.0203, 0.0208, 0.0166, 0.0361, 0.0288, 0.0193, 0.0234, 0.0239,\n",
       "         0.0183, 0.8683, 0.0228, 0.0266, 0.9784, 0.0220, 0.0266]),\n",
       " tensor([0.0281, 0.0194, 0.9714, 0.0210, 0.0203, 0.0218, 0.0236, 0.0252, 0.3305,\n",
       "         0.0187, 0.0247, 0.0348, 0.0211, 0.0215, 0.8450, 0.9265]),\n",
       " tensor([0.5765, 0.0230, 0.0251, 0.0321, 0.1533, 0.0284, 0.0204, 0.0241, 0.9654,\n",
       "         0.9396, 0.0215, 0.0321, 0.0224, 0.0256, 0.0199, 0.0183]),\n",
       " tensor([0.0204, 0.0241, 0.0277, 0.0185, 0.0250, 0.0195, 0.0265, 0.0264, 0.5819,\n",
       "         0.0209, 0.0183, 0.0282, 0.0475, 0.3871, 0.0245, 0.0172]),\n",
       " tensor([0.0192, 0.7508, 0.0247, 0.0218, 0.0253, 0.0166, 0.0307, 0.0736, 0.6280,\n",
       "         0.0345, 0.5030, 0.0218, 0.9676, 0.0290, 0.0274, 0.0363]),\n",
       " tensor([0.0224, 0.0270, 0.0230, 0.0215, 0.0178, 0.0278, 0.0314, 0.1471, 0.0305,\n",
       "         0.0218, 0.0173, 0.0232, 0.0213, 0.0185, 0.0215, 0.0207]),\n",
       " tensor([0.0232, 0.0211, 0.0307, 0.0229, 0.0356, 0.0279, 0.0207, 0.0448, 0.0251,\n",
       "         0.0493, 0.0336, 0.0294, 0.0246, 0.0221, 0.0228, 0.0209]),\n",
       " tensor([0.0204, 0.0227, 0.1640, 0.0229, 0.0253, 0.9826, 0.0347, 0.0224, 0.0559,\n",
       "         0.0196, 0.0220, 0.2336, 0.0165, 0.0269, 0.0489, 0.0195]),\n",
       " tensor([0.0206, 0.1488, 0.0425, 0.0603, 0.0267, 0.3490, 0.0236, 0.0722, 0.0185,\n",
       "         0.0257, 0.0249, 0.3284, 0.0372, 0.0176, 0.0175, 0.0194]),\n",
       " tensor([0.0243, 0.1151, 0.0179, 0.0335, 0.0220, 0.0188, 0.0244, 0.0201, 0.0194,\n",
       "         0.0173, 0.0348, 0.0193, 0.0258, 0.2659, 0.0320, 0.0350]),\n",
       " tensor([0.0188, 0.0243, 0.0272, 0.0297, 0.0230, 0.0295, 0.0751, 0.9902, 0.0214,\n",
       "         0.2491, 0.0216, 0.0229, 0.0207, 0.0402, 0.0294, 0.0941]),\n",
       " tensor([0.0433, 0.0262, 0.0276, 0.9332, 0.0217, 0.2703, 0.0294, 0.0165, 0.0255,\n",
       "         0.0180, 0.0231, 0.0213, 0.8408, 0.0385, 0.0348, 0.0197]),\n",
       " tensor([0.7450, 0.0294, 0.0265, 0.9568, 0.0609, 0.0239, 0.0165, 0.0216, 0.0392,\n",
       "         0.9874, 0.0691, 0.0213, 0.0484, 0.0206, 0.0199, 0.6515]),\n",
       " tensor([0.8959, 0.2224, 0.0251, 0.0235, 0.0312, 0.0809, 0.5823, 0.0216, 0.1100,\n",
       "         0.9494, 0.0195, 0.0228, 0.0360, 0.0195, 0.9622, 0.0329]),\n",
       " tensor([0.0197, 0.9557, 0.0290, 0.0253, 0.0372, 0.0180, 0.7657, 0.0296, 0.0183,\n",
       "         0.0161, 0.0275, 0.9437, 0.0288, 0.0220, 0.0232, 0.0777]),\n",
       " tensor([0.0213, 0.0214, 0.0250, 0.0172, 0.0310, 0.0287, 0.0343, 0.0217, 0.0627,\n",
       "         0.0228, 0.0220, 0.0211, 0.0253, 0.0216, 0.0401, 0.0243]),\n",
       " tensor([0.0341, 0.0488, 0.0384, 0.0192, 0.0324, 0.0172, 0.0248, 0.0244, 0.0448,\n",
       "         0.0239, 0.0213, 0.0234, 0.0171, 0.0353, 0.0180, 0.1006]),\n",
       " tensor([0.0324, 0.0300, 0.0190, 0.0315, 0.0356, 0.0900, 0.9845, 0.1590, 0.0263,\n",
       "         0.3287, 0.9663, 0.0192, 0.0325, 0.0244, 0.9570, 0.9862]),\n",
       " tensor([0.0302, 0.5387, 0.0144, 0.0258, 0.7983, 0.1613, 0.8134, 0.0231, 0.0449,\n",
       "         0.0335, 0.0208, 0.0176, 0.9890, 0.0285, 0.0221, 0.0190]),\n",
       " tensor([0.0183, 0.0210, 0.0254, 0.0957, 0.0473, 0.0218, 0.0232, 0.0214, 0.0333,\n",
       "         0.0543, 0.1080, 0.0225, 0.0199, 0.0230, 0.0246, 0.0179]),\n",
       " tensor([0.0192, 0.0190, 0.3037, 0.0228, 0.0374, 0.0229, 0.0261, 0.0245, 0.0304,\n",
       "         0.0247, 0.0261, 0.0199, 0.0212, 0.0223, 0.2902, 0.0341]),\n",
       " tensor([0.0171, 0.0273, 0.0221, 0.0237, 0.7986, 0.0363, 0.2795, 0.0276, 0.0198,\n",
       "         0.0602, 0.0874, 0.0516, 0.0317, 0.0262, 0.0407, 0.0202]),\n",
       " tensor([0.0268, 0.0282, 0.1410, 0.0198, 0.0180, 0.0308, 0.0268, 0.0191, 0.3441,\n",
       "         0.0209, 0.0670, 0.0223, 0.0181, 0.0363, 0.0384, 0.0213]),\n",
       " tensor([0.0266, 0.0338, 0.0247, 0.0226, 0.0230, 0.9202, 0.0313, 0.0162, 0.0361,\n",
       "         0.0400, 0.0265, 0.0257, 0.0220, 0.0255, 0.0266, 0.3832]),\n",
       " tensor([0.0188, 0.0205, 0.0255, 0.0192, 0.0178, 0.0217, 0.0193, 0.0238, 0.0332,\n",
       "         0.0251, 0.0184, 0.0222, 0.0392, 0.0256, 0.0579, 0.0174]),\n",
       " tensor([0.2777, 0.0178, 0.4718, 0.0286, 0.0251, 0.0210, 0.0180, 0.1260, 0.0264,\n",
       "         0.0250, 0.0213, 0.0257, 0.0821, 0.0346, 0.0300, 0.0255]),\n",
       " tensor([0.0156, 0.0268, 0.0203, 0.0383, 0.0256, 0.0313, 0.0224, 0.0238, 0.0337,\n",
       "         0.4766, 0.0252, 0.0232, 0.9446, 0.6780, 0.0242, 0.0196]),\n",
       " tensor([0.0344, 0.0246, 0.0201, 0.0234, 0.0218, 0.0308, 0.0316, 0.0269, 0.0169,\n",
       "         0.0221, 0.0212, 0.0194, 0.0191, 0.4058, 0.0222, 0.0226]),\n",
       " tensor([0.0190, 0.0211, 0.0198, 0.0209, 0.0194, 0.0282, 0.0326, 0.0220, 0.0231,\n",
       "         0.0321, 0.0349, 0.0220, 0.1790, 0.4884, 0.9537, 0.0329]),\n",
       " tensor([0.0228, 0.0187, 0.0267, 0.0222, 0.0296, 0.0248, 0.0196, 0.0386, 0.0428,\n",
       "         0.0325, 0.0213, 0.4944, 0.9894, 0.0197, 0.5226, 0.0204]),\n",
       " tensor([0.0231, 0.0224, 0.1161, 0.0256, 0.0179, 0.0822, 0.0189, 0.0266, 0.0229,\n",
       "         0.0209, 0.0254, 0.0290, 0.0207, 0.0217, 0.0188, 0.0201]),\n",
       " tensor([0.0327, 0.0229, 0.0207, 0.0187, 0.0168, 0.0205, 0.0201, 0.0256, 0.0186,\n",
       "         0.0204, 0.0181, 0.0304, 0.2580, 0.9413, 0.0235, 0.0227]),\n",
       " tensor([0.0236, 0.0194, 0.0288, 0.0227, 0.0237, 0.0245, 0.0236, 0.0236, 0.9691,\n",
       "         0.0291, 0.0490, 0.0310, 0.9012, 0.0248, 0.0202, 0.0245]),\n",
       " tensor([0.0302, 0.0198, 0.0195, 0.0234, 0.0304, 0.0279, 0.0207, 0.0346, 0.0304,\n",
       "         0.0187, 0.0221, 0.0257, 0.0219, 0.1090, 0.0350, 0.0223]),\n",
       " tensor([0.0530, 0.0186, 0.0182, 0.0184, 0.0299, 0.0339, 0.0368, 0.0210, 0.0307,\n",
       "         0.0169, 0.0265, 0.0276, 0.0270, 0.0275, 0.0245, 0.0268]),\n",
       " tensor([0.0319, 0.0297, 0.0215, 0.0228, 0.0259, 0.0175, 0.0343, 0.0274, 0.0246,\n",
       "         0.0274, 0.0187, 0.0324, 0.0219, 0.0236, 0.7248, 0.5164]),\n",
       " tensor([0.0162, 0.0349, 0.0178, 0.1069, 0.9735, 0.0588, 0.0561, 0.9473, 0.0167,\n",
       "         0.9727, 0.0182, 0.0191, 0.8661, 0.0333, 0.0198, 0.0177]),\n",
       " tensor([0.0203, 0.0244, 0.3193, 0.7627, 0.0182, 0.9332, 0.0351, 0.0296, 0.0221,\n",
       "         0.0217, 0.9833, 0.0262, 0.0165, 0.0244, 0.0196, 0.0382]),\n",
       " tensor([0.0214, 0.0255, 0.0232, 0.9891, 0.0229, 0.0449, 0.0210, 0.0211, 0.0200,\n",
       "         0.0636, 0.0219, 0.0189, 0.0158, 0.0195, 0.0254, 0.0282]),\n",
       " tensor([0.0209, 0.0286, 0.0231, 0.0493, 0.0268, 0.0204, 0.0362, 0.0210, 0.0204,\n",
       "         0.0219, 0.0190, 0.0275, 0.0168, 0.0204, 0.3649, 0.0248]),\n",
       " tensor([0.0686, 0.9210, 0.0198, 0.0423, 0.0369, 0.0275, 0.0228, 0.0321, 0.0312,\n",
       "         0.2128, 0.0228, 0.0222, 0.0339, 0.0197, 0.0942, 0.0254]),\n",
       " tensor([0.0568, 0.8067, 0.0187, 0.0204, 0.3234, 0.0232, 0.0211, 0.0182, 0.0221,\n",
       "         0.0191, 0.0235, 0.0658, 0.8059, 0.0305, 0.0207, 0.0218]),\n",
       " tensor([0.0377, 0.0206, 0.0937, 0.0297, 0.0195, 0.0425, 0.3605, 0.0176, 0.0275,\n",
       "         0.0180, 0.3239, 0.0192, 0.9757, 0.1260, 0.0253, 0.0232]),\n",
       " tensor([0.0559, 0.0416, 0.0245, 0.0253, 0.0267, 0.0178, 0.0177, 0.0202, 0.0179,\n",
       "         0.4184, 0.0281, 0.0846, 0.0199, 0.0176, 0.0365, 0.0189]),\n",
       " tensor([0.0282, 0.0154, 0.0250, 0.0259, 0.0332, 0.0184, 0.0172, 0.5322, 0.0187,\n",
       "         0.0192, 0.0211, 0.0243, 0.0234, 0.0202, 0.0499, 0.0233]),\n",
       " tensor([0.0194, 0.0440, 0.9627, 0.0398, 0.2320, 0.0916, 0.0230, 0.0258, 0.0291,\n",
       "         0.9487, 0.0249, 0.5816, 0.0224, 0.0193, 0.1636, 0.0199]),\n",
       " tensor([0.7073, 0.0513, 0.0254, 0.0265, 0.0388, 0.0948, 0.0339, 0.0341, 0.2508,\n",
       "         0.8876, 0.0273, 0.0474, 0.0236, 0.0230, 0.0165, 0.0374]),\n",
       " tensor([0.0333, 0.0208, 0.0252, 0.0211, 0.0252, 0.0197, 0.0185, 0.0171, 0.0328,\n",
       "         0.0363, 0.0426, 0.0205, 0.0206, 0.0308, 0.5616, 0.7008]),\n",
       " tensor([0.0518, 0.1118, 0.0207, 0.0218, 0.0263, 0.0191, 0.0180, 0.0222, 0.0307,\n",
       "         0.0203, 0.0191, 0.6760, 0.0259, 0.0316, 0.0287, 0.0302]),\n",
       " tensor([0.0242, 0.0516, 0.0272, 0.0163, 0.0221, 0.0196, 0.0218, 0.0302, 0.0401,\n",
       "         0.1420, 0.5845, 0.0614, 0.0187, 0.0538, 0.0375, 0.0185]),\n",
       " tensor([0.0256, 0.0205, 0.0258, 0.0299, 0.0225, 0.0255, 0.0441, 0.0301, 0.0249,\n",
       "         0.0215, 0.0585, 0.0299, 0.0252, 0.0303, 0.0218, 0.0331]),\n",
       " tensor([0.1632, 0.0227, 0.5470, 0.0687, 0.0199, 0.0235, 0.0265, 0.0518, 0.3795,\n",
       "         0.7005, 0.0433, 0.0164, 0.0208, 0.0248, 0.1211, 0.0690]),\n",
       " tensor([0.0186, 0.0347, 0.0279, 0.0202, 0.0212, 0.0235, 0.0181, 0.0286, 0.5863,\n",
       "         0.0204, 0.0292, 0.0220, 0.0189, 0.0218, 0.0202, 0.9682]),\n",
       " tensor([0.0210, 0.0278, 0.0236, 0.0185, 0.0179, 0.0226, 0.0254, 0.0201, 0.0260,\n",
       "         0.0253, 0.0356, 0.0188, 0.0351, 0.0206, 0.0274, 0.0201]),\n",
       " tensor([0.0263, 0.8945, 0.0188, 0.0167, 0.0270, 0.0231, 0.0208, 0.0181, 0.0395,\n",
       "         0.0738, 0.0360, 0.0235, 0.2260, 0.0551, 0.0259, 0.0324]),\n",
       " tensor([0.0240, 0.0433, 0.0261, 0.0366, 0.0397, 0.0204, 0.0357, 0.0345, 0.4525,\n",
       "         0.0371, 0.0221, 0.0292, 0.1022, 0.7368, 0.0461, 0.0214]),\n",
       " tensor([0.0465, 0.0362, 0.0234, 0.0249, 0.0232, 0.2957, 0.0239, 0.0217, 0.0237,\n",
       "         0.0263, 0.0284, 0.0234, 0.5222, 0.0287, 0.0210, 0.0240]),\n",
       " tensor([0.0272, 0.0192, 0.0167, 0.0219, 0.0240, 0.0312, 0.0294, 0.0156, 0.0197,\n",
       "         0.0237, 0.0191, 0.0228, 0.0194, 0.0293, 0.0243, 0.0205]),\n",
       " tensor([0.0255, 0.0196, 0.0235, 0.0856, 0.0273, 0.0337, 0.4429, 0.0366, 0.0262,\n",
       "         0.0209, 0.0211, 0.0222, 0.0288, 0.0190, 0.0163, 0.0180]),\n",
       " tensor([0.0180, 0.0246, 0.7269, 0.0198, 0.0350, 0.0978, 0.0266, 0.0730, 0.0194,\n",
       "         0.0250, 0.0410, 0.0208, 0.0222, 0.0190, 0.0292, 0.0218]),\n",
       " tensor([0.4414, 0.0265, 0.0209, 0.0189, 0.5579, 0.0206, 0.0220, 0.0228, 0.0302,\n",
       "         0.0240, 0.0176, 0.0183, 0.0522, 0.0328, 0.0844, 0.0239]),\n",
       " tensor([0.0257, 0.0204, 0.0284, 0.0516, 0.0216, 0.3182, 0.0279, 0.0240, 0.0701,\n",
       "         0.0303, 0.0183, 0.0205, 0.0197, 0.4407, 0.0350, 0.6558]),\n",
       " tensor([0.5342, 0.0292, 0.6087, 0.0314, 0.0181, 0.0217, 0.0173, 0.0218, 0.2000,\n",
       "         0.0222, 0.0255, 0.0193, 0.0254, 0.0181, 0.0270, 0.0410]),\n",
       " tensor([0.0200, 0.0177, 0.0239, 0.8783, 0.0182, 0.0224, 0.0228, 0.0336, 0.0256,\n",
       "         0.0247, 0.0493, 0.5235, 0.1136, 0.0186, 0.1911, 0.0297]),\n",
       " tensor([0.0210, 0.0314, 0.0205, 0.0240, 0.0238, 0.0195, 0.0256, 0.0212, 0.0218,\n",
       "         0.3412, 0.0289, 0.0233, 0.0308, 0.0277, 0.0185, 0.0253]),\n",
       " tensor([0.0348, 0.3678, 0.0179, 0.0314, 0.0432, 0.0191, 0.0259, 0.0185, 0.0245,\n",
       "         0.3225, 0.0201, 0.0200, 0.0227, 0.5308, 0.0218, 0.5829]),\n",
       " tensor([0.0211, 0.0228, 0.0235, 0.0259, 0.0216, 0.8433, 0.0532, 0.0199, 0.0188,\n",
       "         0.0378, 0.0228, 0.0174, 0.0333, 0.0438, 0.0261, 0.0190]),\n",
       " tensor([0.0340, 0.0310, 0.7465, 0.8540, 0.9436, 0.0232, 0.0303, 0.9636, 0.0225,\n",
       "         0.0277, 0.0150, 0.0366, 0.0187, 0.0229, 0.0177, 0.0262]),\n",
       " tensor([0.0331, 0.0458, 0.0205, 0.0213, 0.7998, 0.1371, 0.0270, 0.0233, 0.0230,\n",
       "         0.0283, 0.0322, 0.0182, 0.0269, 0.0187, 0.0349, 0.0349]),\n",
       " tensor([0.0299, 0.0290, 0.0174, 0.0173, 0.0253, 0.0367, 0.0216, 0.0262, 0.7363,\n",
       "         0.0213, 0.1774, 0.0272, 0.0243, 0.0229, 0.0616, 0.0293]),\n",
       " tensor([0.0348, 0.0257, 0.9503, 0.0217, 0.0262, 0.0175, 0.0181, 0.0167, 0.0316,\n",
       "         0.0217, 0.0207, 0.0255, 0.0196, 0.0311, 0.0236, 0.0234]),\n",
       " tensor([0.0162, 0.0163, 0.0303, 0.0223, 0.0186, 0.0248, 0.4937, 0.3379, 0.4412,\n",
       "         0.0184, 0.0246, 0.0278, 0.0270, 0.0214, 0.0171, 0.0363]),\n",
       " tensor([0.0229, 0.0196, 0.0210, 0.0160, 0.7642, 0.5263, 0.0216, 0.0199, 0.0352,\n",
       "         0.0196, 0.0224, 0.0193, 0.0303, 0.0171, 0.0261, 0.0288]),\n",
       " tensor([0.0175, 0.0234, 0.0298, 0.0221, 0.0184, 0.0223, 0.0318, 0.0202, 0.0253,\n",
       "         0.0169, 0.0220, 0.9748, 0.1250, 0.6660, 0.0282, 0.0242]),\n",
       " tensor([0.0173, 0.0197, 0.0182, 0.0530, 0.4291, 0.0226, 0.0199, 0.0200, 0.0176,\n",
       "         0.0516, 0.0195, 0.0182, 0.0176, 0.0192, 0.0860, 0.0195]),\n",
       " tensor([0.0231, 0.0427, 0.0352, 0.0191, 0.0185, 0.0384, 0.0202, 0.4236, 0.0198,\n",
       "         0.0271, 0.0202, 0.9705, 0.0204, 0.0319, 0.0239, 0.0243]),\n",
       " tensor([0.0327, 0.0279, 0.0352, 0.0200, 0.0229, 0.0730, 0.0179, 0.0177, 0.0300,\n",
       "         0.1690, 0.0226, 0.0222, 0.3145, 0.0323, 0.0164, 0.0198]),\n",
       " tensor([0.0218, 0.0211, 0.0268, 0.0216, 0.5548, 0.0303, 0.8221, 0.0306, 0.0351,\n",
       "         0.0763, 0.3784, 0.9685, 0.0457, 0.0310, 0.0315, 0.0200]),\n",
       " tensor([0.7282, 0.0333, 0.0177, 0.0273, 0.0247, 0.0508, 0.0379, 0.0208, 0.0292,\n",
       "         0.0458, 0.8852, 0.0395, 0.0275, 0.0191, 0.0166, 0.3962]),\n",
       " tensor([0.0201, 0.0233, 0.0249, 0.0227, 0.0205, 0.4673, 0.0232, 0.0171, 0.0178,\n",
       "         0.0266, 0.0180, 0.9312, 0.0171, 0.0176, 0.0185, 0.5249]),\n",
       " tensor([0.0234, 0.0210, 0.0210, 0.0347, 0.0232, 0.2947, 0.4614, 0.0264, 0.9799,\n",
       "         0.0234, 0.7853, 0.0248, 0.0280, 0.0208, 0.0204, 0.6450]),\n",
       " tensor([0.1741, 0.0198, 0.0325, 0.0294, 0.0227, 0.0217, 0.0233, 0.0206, 0.0269,\n",
       "         0.0201, 0.1042, 0.0356, 0.0172, 0.0255, 0.0562, 0.0196]),\n",
       " tensor([0.0405, 0.0214, 0.0217, 0.2729, 0.0323, 0.0230, 0.6141, 0.0210, 0.0217,\n",
       "         0.0223, 0.0180, 0.0398, 0.0227, 0.0258, 0.0330, 0.0210]),\n",
       " tensor([0.6790, 0.9033, 0.0208, 0.0215, 0.0207, 0.0263, 0.0294, 0.0171, 0.0245,\n",
       "         0.0232, 0.0258, 0.0249, 0.0291, 0.0220, 0.0221, 0.0260]),\n",
       " tensor([0.0268, 0.0343, 0.0191, 0.0415, 0.0197, 0.0323, 0.0229, 0.0248, 0.0324,\n",
       "         0.0255, 0.0195, 0.0237, 0.0247, 0.0199, 0.0355, 0.9896]),\n",
       " tensor([0.6528, 0.0206, 0.0225, 0.9732, 0.0357, 0.0169, 0.0330, 0.0568, 0.0191,\n",
       "         0.0168, 0.0208, 0.0236, 0.0275, 0.0348, 0.0256, 0.1646]),\n",
       " tensor([0.0247, 0.0521, 0.0214, 0.9900, 0.0236, 0.0170, 0.0220, 0.0261, 0.0339,\n",
       "         0.0205, 0.9815, 0.3653, 0.0239, 0.0278, 0.0595, 0.0182]),\n",
       " tensor([0.0464, 0.0222, 0.0349, 0.0391, 0.0311, 0.0195, 0.0227, 0.0278, 0.0309,\n",
       "         0.0180, 0.0206, 0.0331, 0.0768, 0.0421, 0.0253, 0.0232]),\n",
       " tensor([0.0200, 0.0164, 0.0226, 0.0256, 0.0179, 0.6211, 0.0253, 0.0233, 0.1763,\n",
       "         0.0240, 0.8486, 0.9165, 0.0275, 0.0205, 0.0240, 0.0220]),\n",
       " tensor([0.0377, 0.0402, 0.0232, 0.0247, 0.0361, 0.0204, 0.0294, 0.0184, 0.3147,\n",
       "         0.0309, 0.0198, 0.0229, 0.0271, 0.0223, 0.1120, 0.0238]),\n",
       " tensor([0.0246, 0.0206, 0.0278, 0.0161, 0.0567, 0.0223, 0.0250, 0.2043, 0.0286,\n",
       "         0.0203, 0.0182, 0.0179, 0.0214, 0.0211, 0.0203, 0.0251]),\n",
       " tensor([0.0648, 0.0374, 0.5681, 0.0287, 0.0556, 0.0556, 0.0218, 0.0238, 0.0680,\n",
       "         0.0241, 0.0275, 0.0418, 0.4559, 0.3983, 0.0343, 0.2480]),\n",
       " tensor([0.0363, 0.0499, 0.0922, 0.0215, 0.0202, 0.0231, 0.0286, 0.0218, 0.0241,\n",
       "         0.8704, 0.3641, 0.0868, 0.8443, 0.0291, 0.0201, 0.0350]),\n",
       " tensor([0.0202, 0.0187, 0.0180, 0.0178, 0.0214, 0.1701, 0.0433, 0.0220, 0.0182,\n",
       "         0.0474, 0.8771, 0.0355, 0.0196, 0.0252, 0.0217, 0.5997]),\n",
       " tensor([0.9749, 0.0226, 0.0369, 0.0259, 0.0387, 0.0238, 0.0231, 0.0296, 0.0214,\n",
       "         0.0230, 0.0212, 0.0178, 0.0477, 0.9958, 0.0203, 0.0308]),\n",
       " tensor([0.3629, 0.5487, 0.0185, 0.0258, 0.0763, 0.0201, 0.0252, 0.0179, 0.0248,\n",
       "         0.0328, 0.0228, 0.0162, 0.2009, 0.0239, 0.0205, 0.0238]),\n",
       " tensor([0.0323, 0.0266, 0.0765, 0.0377, 0.0222, 0.0232, 0.0492, 0.0218, 0.0187,\n",
       "         0.8988, 0.0154, 0.0217, 0.0202, 0.0216, 0.0173, 0.0247]),\n",
       " tensor([0.0276, 0.0311, 0.0366, 0.0202, 0.0213, 0.0239, 0.0243, 0.0309, 0.0287,\n",
       "         0.0276, 0.0179, 0.0215, 0.0677, 0.0194, 0.0374, 0.0922]),\n",
       " tensor([0.0215, 0.3283, 0.0346, 0.0185, 0.2724, 0.0278, 0.0290, 0.0432, 0.0698,\n",
       "         0.3282, 0.0241, 0.0224, 0.0489, 0.0299, 0.0283, 0.0414]),\n",
       " tensor([0.0237, 0.0266, 0.0172, 0.0226, 0.0185, 0.1980, 0.0186, 0.0208, 0.0943,\n",
       "         0.0207, 0.9591, 0.0432, 0.0294, 0.5545, 0.0305, 0.0191]),\n",
       " tensor([0.0197, 0.0193, 0.0191, 0.0289, 0.3034, 0.0964, 0.0156, 0.0205, 0.0182,\n",
       "         0.0292, 0.0486, 0.0184, 0.0215, 0.0238, 0.2519, 0.0193]),\n",
       " tensor([0.0198, 0.0233, 0.0295, 0.8952, 0.5048, 0.1476, 0.0451, 0.0171, 0.0176,\n",
       "         0.0295, 0.0229, 0.0245, 0.2002, 0.0222, 0.0282, 0.0221]),\n",
       " tensor([0.0247, 0.0249, 0.0278, 0.0331, 0.0403, 0.0294, 0.0175, 0.0378, 0.0231,\n",
       "         0.0203, 0.0202, 0.1699, 0.1093, 0.0280, 0.3997, 0.0347]),\n",
       " tensor([0.0196, 0.0241, 0.0290, 0.0272, 0.0214, 0.0217, 0.1533, 0.0197, 0.8860,\n",
       "         0.0244, 0.0235, 0.9731, 0.0591, 0.0207, 0.0306, 0.0198]),\n",
       " tensor([0.0206, 0.0192, 0.0167, 0.0176, 0.0217, 0.0235, 0.0231, 0.0416, 0.0365,\n",
       "         0.0239, 0.0214, 0.0230, 0.3848, 0.0191, 0.3280, 0.9696]),\n",
       " tensor([0.9696, 0.9965, 0.6404, 0.0329, 0.0282, 0.0196, 0.0201, 0.0252, 0.0184,\n",
       "         0.0254, 0.0195, 0.0179, 0.0250, 0.0222, 0.0183, 0.0281]),\n",
       " tensor([0.0373, 0.0203, 0.9870, 0.4543, 0.0366, 0.0282, 0.0341, 0.0284, 0.0210,\n",
       "         0.0210, 0.0200, 0.0196, 0.0312, 0.0241, 0.0288, 0.0183]),\n",
       " tensor([0.8962, 0.0196, 0.0187, 0.0202, 0.1234, 0.0297, 0.6025, 0.0427, 0.0278,\n",
       "         0.9510, 0.0282, 0.0235, 0.8453, 0.9767, 0.0279, 0.0236]),\n",
       " tensor([0.0236, 0.0229, 0.0187, 0.0499, 0.0237, 0.0185, 0.0608, 0.8949, 0.0695,\n",
       "         0.0985, 0.0426, 0.0230, 0.0252, 0.0357, 0.9274, 0.0225]),\n",
       " tensor([0.0228, 0.0230, 0.0210, 0.1777, 0.0210, 0.0191, 0.0235, 0.0239, 0.0696,\n",
       "         0.0209, 0.0276, 0.0250, 0.0186, 0.0219, 0.0180, 0.0238]),\n",
       " tensor([0.0206, 0.1120, 0.0206, 0.0908, 0.0256, 0.0184, 0.0246, 0.2441, 0.0195,\n",
       "         0.0193, 0.0234, 0.0522, 0.0194, 0.9643, 0.1041, 0.0280]),\n",
       " tensor([0.0234, 0.0817, 0.0333, 0.0325, 0.0246, 0.0244, 0.0171, 0.0177, 0.0202,\n",
       "         0.0183, 0.1715, 0.0281, 0.0646, 0.0227, 0.0180, 0.0422]),\n",
       " tensor([0.0196, 0.0184, 0.0196, 0.0300, 0.6575, 0.0246, 0.0277, 0.0311, 0.0192,\n",
       "         0.0170, 0.5445, 0.0255, 0.0447, 0.0292, 0.0188, 0.0280]),\n",
       " tensor([0.0358, 0.0358, 0.0217, 0.0194, 0.9901, 0.0170, 0.0172, 0.0212, 0.0509,\n",
       "         0.0234, 0.0183, 0.0179, 0.3508, 0.0261, 0.1022, 0.0176]),\n",
       " tensor([0.0275, 0.0265, 0.0540, 0.0209, 0.0278, 0.8036, 0.0222, 0.0770, 0.5114,\n",
       "         0.7052, 0.0288, 0.0232, 0.1587, 0.0194, 0.3279, 0.0193]),\n",
       " tensor([0.7678, 0.9280, 0.0291, 0.0232, 0.0505, 0.0276, 0.3508, 0.0353, 0.0293,\n",
       "         0.0200, 0.0237, 0.0192, 0.0202, 0.0214, 0.0252, 0.0243]),\n",
       " tensor([0.0525, 0.0170, 0.0954, 0.0433, 0.5915, 0.0228, 0.0286, 0.6688, 0.1380,\n",
       "         0.0339, 0.0213, 0.0229, 0.0198, 0.0273, 0.0293, 0.9474]),\n",
       " tensor([0.0199, 0.0227, 0.0176, 0.0184, 0.9683, 0.0201, 0.0232, 0.0212, 0.5535,\n",
       "         0.2160, 0.0367, 0.8087, 0.0233, 0.1912, 0.0242, 0.0162]),\n",
       " tensor([0.0296, 0.0342, 0.0273, 0.0268, 0.0319, 0.0217, 0.9082, 0.0438, 0.2929,\n",
       "         0.0515, 0.0474, 0.0204, 0.0280, 0.0381, 0.0306, 0.0221]),\n",
       " tensor([0.5813, 0.0200, 0.0224, 0.0346, 0.0241, 0.2241, 0.0292, 0.5313, 0.0368,\n",
       "         0.0228, 0.0184, 0.0162, 0.7762, 0.0703, 0.9935, 0.8537]),\n",
       " tensor([0.0199, 0.9848, 0.2540, 0.0422, 0.0234, 0.0175, 0.0653, 0.0236, 0.0262,\n",
       "         0.0342, 0.9699, 0.9134, 0.0249, 0.9554, 0.2994, 0.0163]),\n",
       " tensor([0.0188, 0.0200, 0.6406, 0.0359, 0.0895, 0.2415, 0.0163, 0.4076, 0.0170,\n",
       "         0.0448, 0.0341, 0.2402, 0.0230, 0.0270, 0.0263, 0.0220]),\n",
       " tensor([0.9855, 0.0205, 0.0432, 0.9811, 0.0472, 0.6501, 0.0208, 0.9854, 0.0504,\n",
       "         0.9055, 0.0320, 0.0270, 0.3005, 0.9806, 0.0350, 0.0281]),\n",
       " tensor([0.1310, 0.0196, 0.8403, 0.0294, 0.0207, 0.1738, 0.7540, 0.0282, 0.0330,\n",
       "         0.7241, 0.0302, 0.0227, 0.5160, 0.0502, 0.0290, 0.0277]),\n",
       " tensor([0.0183, 0.0253, 0.2041, 0.0184, 0.8996, 0.0296, 0.6032, 0.0210, 0.0495,\n",
       "         0.0232, 0.0357, 0.0196, 0.9730, 0.0548, 0.4005, 0.0228]),\n",
       " tensor([0.0303, 0.7949, 0.5232, 0.0253, 0.3951, 0.0151, 0.0424, 0.0381, 0.9916,\n",
       "         0.7911, 0.0207, 0.0174, 0.0344, 0.0203, 0.0522, 0.8481]),\n",
       " tensor([0.0168, 0.0227, 0.0305, 0.0283, 0.0265, 0.4711, 0.9473, 0.0218, 0.6321,\n",
       "         0.0213, 0.0228, 0.0484, 0.0379, 0.0324, 0.0348, 0.9324]),\n",
       " tensor([0.4274, 0.8780, 0.4018, 0.0286, 0.0312, 0.0327, 0.0246, 0.0432, 0.0485,\n",
       "         0.0340, 0.0552, 0.3114, 0.0220, 0.0266, 0.0245, 0.0444]),\n",
       " tensor([0.0817, 0.0231, 0.0950, 0.0285, 0.9025, 0.0204, 0.0415, 0.0288, 0.0192,\n",
       "         0.0213, 0.0307, 0.4241, 0.0240, 0.0285, 0.0238, 0.0307]),\n",
       " tensor([0.0334, 0.0185, 0.0259, 0.2714, 0.0238, 0.0199, 0.0187, 0.0294, 0.0180,\n",
       "         0.0186, 0.0212, 0.0288, 0.0367, 0.0195, 0.0353, 0.0226]),\n",
       " tensor([0.0233, 0.0202, 0.0195, 0.0214, 0.0225, 0.2543, 0.0227, 0.0169, 0.0298,\n",
       "         0.0227, 0.9114, 0.0201, 0.0321, 0.0185, 0.0215, 0.0177]),\n",
       " tensor([0.0202, 0.0219, 0.0254, 0.0363, 0.0331, 0.0221, 0.0293, 0.3219, 0.0388,\n",
       "         0.0415, 0.0287, 0.0191, 0.0224, 0.0229, 0.0164, 0.0248]),\n",
       " tensor([0.0192, 0.7636, 0.0242, 0.0229, 0.0654, 0.0207, 0.4491, 0.0292, 0.0211,\n",
       "         0.0468, 0.0290, 0.0236, 0.0260, 0.0179, 0.0196, 0.0240]),\n",
       " tensor([0.0931, 0.0224, 0.0236, 0.0498, 0.0216, 0.0892, 0.0182, 0.0300, 0.0646,\n",
       "         0.0368, 0.0457, 0.0735, 0.0307, 0.0400, 0.0309, 0.0280]),\n",
       " tensor([0.0314, 0.0293, 0.0525, 0.2236, 0.8049, 0.0269, 0.0226, 0.0249, 0.0266,\n",
       "         0.0415, 0.0470, 0.9857, 0.0202, 0.0346, 0.7998, 0.0207]),\n",
       " tensor([0.7570, 0.3728, 0.9484, 0.0848, 0.6737, 0.4531, 0.0419, 0.0283, 0.9056,\n",
       "         0.0174, 0.0257, 0.0171, 0.0277, 0.4589, 0.0189, 0.0260]),\n",
       " tensor([0.0319, 0.0170, 0.0205, 0.0174, 0.0359, 0.0532, 0.0320, 0.0221, 0.0224,\n",
       "         0.0210, 0.5452, 0.5452, 0.1737, 0.0245, 0.9283, 0.9868]),\n",
       " tensor([0.9702, 0.0360, 0.0230, 0.0807, 0.3834, 0.0330, 0.7916, 0.0186, 0.0324,\n",
       "         0.0181, 0.0300, 0.0304, 0.0162, 0.0258, 0.0226, 0.0348]),\n",
       " tensor([0.0263, 0.0291, 0.0438, 0.2581, 0.0207, 0.0477, 0.0312, 0.0221, 0.0175,\n",
       "         0.0226, 0.0222, 0.0259, 0.0246, 0.0285, 0.0179, 0.0276]),\n",
       " tensor([0.0192, 0.0253, 0.0221, 0.0202, 0.0291, 0.0214, 0.0229, 0.0261, 0.0203,\n",
       "         0.0201, 0.0229, 0.0234, 0.0436, 0.0184, 0.0256, 0.0275]),\n",
       " tensor([0.0275, 0.0356, 0.0226, 0.6413, 0.0376, 0.0221, 0.0324, 0.0194, 0.0195,\n",
       "         0.9467, 0.0271, 0.0263, 0.8641, 0.0173, 0.7865, 0.0240]),\n",
       " tensor([0.0231, 0.0286, 0.0438, 0.0210, 0.0230, 0.0358, 0.0675, 0.0190, 0.0456,\n",
       "         0.0244, 0.0336, 0.0171, 0.0436, 0.0227, 0.0226, 0.0397]),\n",
       " tensor([0.0198, 0.0248, 0.0259, 0.0367, 0.9850, 0.0314, 0.0221, 0.0210, 0.4281,\n",
       "         0.0395, 0.0265, 0.0202, 0.0646, 0.0180, 0.0192, 0.0213]),\n",
       " tensor([0.0257, 0.0302, 0.0212, 0.0235, 0.0187, 0.0430, 0.0167, 0.0187, 0.0668,\n",
       "         0.0300, 0.0279, 0.0204, 0.0306, 0.0296, 0.0257, 0.0351]),\n",
       " tensor([0.0247, 0.0288, 0.0161, 0.0190, 0.6632, 0.8390, 0.9891, 0.0197, 0.0273,\n",
       "         0.0319, 0.8593, 0.0230, 0.0229, 0.0235, 0.0205, 0.0243]),\n",
       " tensor([0.0168, 0.0176, 0.0730, 0.0233, 0.0244, 0.0422, 0.0235, 0.0328, 0.0353,\n",
       "         0.0302, 0.0297, 0.0367, 0.0311, 0.0194, 0.0362, 0.0178]),\n",
       " tensor([0.0275, 0.0286, 0.0617, 0.0201, 0.0268, 0.0328, 0.0248, 0.0258, 0.1449,\n",
       "         0.0244, 0.0299, 0.0230, 0.0330, 0.0247, 0.2560, 0.0200]),\n",
       " tensor([0.0377, 0.0207, 0.0184, 0.0281, 0.0160, 0.0225, 0.2082, 0.1117, 0.0327,\n",
       "         0.3934, 0.2484, 0.0191, 0.0275, 0.0218, 0.0222, 0.1073]),\n",
       " tensor([0.0274, 0.0296, 0.8438, 0.2910, 0.0337, 0.0227, 0.9580, 0.0196, 0.0216,\n",
       "         0.0169, 0.0181, 0.0222, 0.0184, 0.0260, 0.0326, 0.0317]),\n",
       " tensor([0.0344, 0.9966, 0.0277, 0.0226, 0.0521, 0.3734, 0.0186, 0.0230, 0.0194,\n",
       "         0.4130, 0.0206, 0.0209, 0.0249, 0.0218, 0.0174, 0.0216]),\n",
       " tensor([0.9651, 0.9637, 0.6477, 0.9593, 0.0277, 0.0245, 0.0762, 0.0418, 0.0170,\n",
       "         0.0284, 0.0180, 0.5049, 0.0209, 0.0324, 0.8910, 0.9679]),\n",
       " tensor([0.9849, 0.0549, 0.0651, 0.3814, 0.6385, 0.9491, 0.7394, 0.0224, 0.8261,\n",
       "         0.1268, 0.2131, 0.0266, 0.0683, 0.9128, 0.9695, 0.9696]),\n",
       " tensor([0.9454, 0.8703, 0.6238, 0.8054, 0.0233, 0.9607, 0.3956, 0.8470, 0.0245,\n",
       "         0.0319, 0.0279, 0.0230, 0.0162, 0.0549, 0.4130, 0.0284]),\n",
       " tensor([0.0217, 0.0517, 0.5499, 0.5916, 0.9633, 0.0199, 0.7917, 0.8307, 0.5702,\n",
       "         0.9636, 0.2575, 0.2594, 0.0274, 0.0440, 0.0255, 0.6880]),\n",
       " tensor([0.0202, 0.0222, 0.1453, 0.0226, 0.9091, 0.0189, 0.0219, 0.9916, 0.0271,\n",
       "         0.0252, 0.0203, 0.0179, 0.0788, 0.0286, 0.3813, 0.0242]),\n",
       " tensor([0.0263, 0.2646, 0.2951, 0.7378, 0.2456, 0.0175, 0.0304, 0.0242, 0.0284,\n",
       "         0.0236, 0.9348, 0.0362, 0.0216, 0.0220, 0.9964, 0.0262]),\n",
       " tensor([0.0236, 0.4099, 0.0176, 0.0261, 0.0286, 0.0299, 0.0209, 0.0265, 0.0196,\n",
       "         0.0173, 0.0432, 0.0517, 0.0251, 0.0274, 0.0335, 0.0180]),\n",
       " tensor([0.0236, 0.0287, 0.0425, 0.0230, 0.0373, 0.2626, 0.0399, 0.0295, 0.0241,\n",
       "         0.0198, 0.0252, 0.4790, 0.0235, 0.0183, 0.0287, 0.0272]),\n",
       " tensor([0.0287, 0.0198, 0.0323, 0.0217, 0.0245, 0.0623, 0.0194, 0.0336, 0.0301,\n",
       "         0.0189, 0.0251, 0.0251, 0.0205, 0.0186, 0.0218, 0.0634]),\n",
       " tensor([0.0220, 0.0266, 0.0237, 0.4590, 0.2264, 0.6582, 0.0261, 0.0263, 0.0192,\n",
       "         0.0174, 0.0352, 0.0224, 0.5924, 0.0195, 0.7919, 0.4607]),\n",
       " tensor([0.0189, 0.0190, 0.2409, 0.2013, 0.0311, 0.0195, 0.0279, 0.0212, 0.0248,\n",
       "         0.0260, 0.0254, 0.0179, 0.0185, 0.0182, 0.0216, 0.0346]),\n",
       " tensor([0.0449, 0.0202, 0.0278, 0.8998, 0.9820, 0.0248, 0.0233, 0.9384, 0.9269,\n",
       "         0.9848, 0.0174, 0.0255, 0.0207, 0.0555, 0.0185, 0.0196]),\n",
       " tensor([0.0253, 0.0895, 0.0284, 0.0956, 0.0439, 0.2215, 0.0355, 0.0206, 0.0244,\n",
       "         0.0277, 0.0378, 0.0279, 0.0237, 0.0193, 0.1288, 0.0215]),\n",
       " tensor([0.8081, 0.0309, 0.0259, 0.0378, 0.0259, 0.4425, 0.0188, 0.0246, 0.0220,\n",
       "         0.0264, 0.0268, 0.0220, 0.0191, 0.0219, 0.0202, 0.0207]),\n",
       " tensor([0.4224, 0.0253, 0.0246, 0.0192, 0.0220, 0.0291, 0.0289, 0.0533, 0.0225,\n",
       "         0.0173, 0.0283, 0.1803, 0.0210, 0.0317, 0.0261, 0.0259]),\n",
       " tensor([0.0170, 0.0206, 0.0344, 0.0174, 0.0578, 0.7544, 0.0235, 0.0220, 0.1258,\n",
       "         0.0234, 0.0258, 0.1125, 0.0380, 0.2632, 0.0195, 0.0286]),\n",
       " tensor([0.3977, 0.0199, 0.0261, 0.0187, 0.0194, 0.0260, 0.2432, 0.0211, 0.0247,\n",
       "         0.7978, 0.4367, 0.0225, 0.0262, 0.0184, 0.0224, 0.0353]),\n",
       " tensor([0.0220, 0.0556, 0.0216, 0.0196, 0.0549, 0.0200, 0.0359, 0.0192, 0.0181,\n",
       "         0.0233, 0.1032, 0.0276, 0.0223, 0.0459, 0.0198, 0.0173]),\n",
       " tensor([0.1025, 0.0277, 0.0333, 0.0253, 0.0176, 0.7025, 0.0252, 0.9445, 0.1034,\n",
       "         0.0219, 0.0181, 0.5033, 0.1495, 0.5006, 0.0183, 0.3949]),\n",
       " tensor([0.3326, 0.0249, 0.0178, 0.0202, 0.0228, 0.0302, 0.0204, 0.0237, 0.7115,\n",
       "         0.0193, 0.0177, 0.0319, 0.7983, 0.0232, 0.0308, 0.0219]),\n",
       " tensor([0.0488, 0.0208, 0.0318, 0.0257, 0.5684, 0.0223, 0.0237, 0.0257, 0.0189,\n",
       "         0.0278, 0.0306, 0.0177, 0.0287, 0.0231, 0.0226, 0.0616]),\n",
       " tensor([0.0216, 0.0268, 0.0301, 0.0201, 0.5139, 0.0166, 0.0325, 0.0196, 0.0245,\n",
       "         0.0283, 0.0278, 0.0224, 0.0232, 0.1068, 0.1041, 0.0247]),\n",
       " tensor([0.0272, 0.0360, 0.0311, 0.0478, 0.0389, 0.0179, 0.8039, 0.0437, 0.0257,\n",
       "         0.0241, 0.0200, 0.0188, 0.0202, 0.0246, 0.0188, 0.0180]),\n",
       " tensor([0.0268, 0.9828, 0.9719, 0.9079, 0.9882, 0.0221, 0.6652, 0.2131, 0.0231,\n",
       "         0.0200, 0.0249, 0.0469, 0.0308, 0.0195, 0.0348, 0.0271]),\n",
       " tensor([0.0199, 0.0281, 0.0231, 0.0231, 0.0283, 0.3931, 0.0287, 0.0157, 0.0292,\n",
       "         0.0187, 0.9685, 0.0348, 0.0815, 0.0234, 0.0181, 0.0275]),\n",
       " tensor([0.0354, 0.0364, 0.0308, 0.0189, 0.1155, 0.0268, 0.0317, 0.0214, 0.0271,\n",
       "         0.0226, 0.0289, 0.0667, 0.0234, 0.0217, 0.0254, 0.0402]),\n",
       " tensor([0.0226, 0.0200, 0.6990, 0.3572, 0.0220, 0.0363, 0.0271, 0.0172, 0.0195,\n",
       "         0.0204, 0.0334, 0.0181, 0.0180, 0.0317, 0.0255, 0.0211]),\n",
       " tensor([0.8919, 0.1050, 0.9778, 0.0176, 0.6262, 0.0171, 0.0286, 0.6497, 0.0289,\n",
       "         0.0223, 0.7345, 0.0464, 0.5420, 0.0271, 0.0200, 0.0205]),\n",
       " tensor([0.0223, 0.0343, 0.0567, 0.0248, 0.0638, 0.0284, 0.0205, 0.0204, 0.0244,\n",
       "         0.0229, 0.0216, 0.0199, 0.0351, 0.0206, 0.0307, 0.0266]),\n",
       " tensor([0.0179, 0.0188, 0.0566, 0.0199, 0.0259, 0.0247, 0.0227, 0.9704, 0.0190,\n",
       "         0.9366, 0.0183, 0.0211, 0.0201, 0.0279, 0.0240, 0.0177]),\n",
       " tensor([0.0321, 0.0247, 0.0194, 0.0181, 0.8707, 0.0238, 0.0247, 0.0169, 0.0249,\n",
       "         0.0241, 0.0212, 0.0210, 0.0240, 0.3511, 0.0194, 0.0191]),\n",
       " tensor([0.0206, 0.0390, 0.9395, 0.0329, 0.0264, 0.0231, 0.8543, 0.0226, 0.6262,\n",
       "         0.0267, 0.0332, 0.0268, 0.0174, 0.0363, 0.0369, 0.0215]),\n",
       " tensor([0.0201, 0.0247, 0.0201, 0.0190, 0.0255, 0.0244, 0.4192, 0.0202, 0.0190,\n",
       "         0.0222, 0.3945, 0.0427, 0.0870, 0.0208, 0.0223, 0.0224]),\n",
       " tensor([0.0216, 0.0200, 0.0184, 0.2080, 0.0289, 0.0436, 0.1289, 0.0530, 0.9259,\n",
       "         0.0735, 0.0187, 0.0174, 0.7165, 0.0463, 0.0568, 0.8264]),\n",
       " tensor([0.0234, 0.0314, 0.0238, 0.0299, 0.0190, 0.0189, 0.1217, 0.0476, 0.0240,\n",
       "         0.1057, 0.9715, 0.0204, 0.0314, 0.0396, 0.0239, 0.6581]),\n",
       " tensor([0.0196, 0.0161, 0.0412, 0.0407, 0.0205, 0.0239, 0.0178, 0.0396, 0.0263,\n",
       "         0.0407, 0.2185, 0.0173, 0.7782, 0.0267, 0.0255, 0.0205]),\n",
       " tensor([0.0248, 0.0181, 0.0257, 0.0382, 0.0378, 0.0385, 0.0221, 0.0228, 0.0352,\n",
       "         0.0298, 0.0184, 0.0184, 0.0591, 0.0577, 0.0187, 0.0230]),\n",
       " tensor([0.0277, 0.0229, 0.0250, 0.0452, 0.0334, 0.1079, 0.0309, 0.0445, 0.0246,\n",
       "         0.0198, 0.0354, 0.0198, 0.0343, 0.0255, 0.0209, 0.0194]),\n",
       " tensor([0.0261, 0.0303, 0.0215, 0.0208, 0.0465, 0.0306, 0.0194, 0.0304, 0.0180,\n",
       "         0.0232, 0.0206, 0.8439, 0.0193, 0.0170, 0.0248, 0.5876]),\n",
       " tensor([0.0302, 0.9934, 0.0245, 0.4874, 0.1183, 0.0287, 0.8259, 0.0229, 0.0265,\n",
       "         0.0167, 0.0318, 0.0191, 0.0746, 0.0313, 0.0357, 0.0341]),\n",
       " tensor([0.0320, 0.0268, 0.0211, 0.0955, 0.0264, 0.0209, 0.0426, 0.0172, 0.0188,\n",
       "         0.2947, 0.0198, 0.0165, 0.0226, 0.0731, 0.0455, 0.0190]),\n",
       " tensor([0.0211, 0.8823, 0.0193, 0.0196, 0.0202, 0.0570, 0.2933, 0.0221, 0.2019,\n",
       "         0.0262, 0.0222, 0.0808, 0.0358, 0.0260, 0.0284, 0.0194]),\n",
       " tensor([0.0259, 0.0172, 0.0207, 0.0183, 0.2011, 0.0166, 0.0216, 0.0286, 0.0274,\n",
       "         0.9749, 0.0425, 0.0254, 0.6712, 0.0218, 0.0194, 0.0314]),\n",
       " tensor([0.0227, 0.0205, 0.0256, 0.4356, 0.0968, 0.0177, 0.0208, 0.0212, 0.0259,\n",
       "         0.0207, 0.0172, 0.0209, 0.0219, 0.0368, 0.0304, 0.0237]),\n",
       " tensor([0.0198, 0.0254, 0.8191, 0.5048, 0.0373, 0.0453, 0.0236, 0.0299, 0.0399,\n",
       "         0.0208, 0.0309, 0.9655, 0.0199, 0.9518, 0.0589, 0.7644]),\n",
       " tensor([0.0171, 0.0310, 0.5177, 0.0276, 0.0179, 0.0189, 0.0222, 0.0191, 0.0394,\n",
       "         0.0274, 0.0239, 0.2975, 0.9257, 0.0163, 0.0233, 0.0195]),\n",
       " tensor([0.0180, 0.0221, 0.0254, 0.1809, 0.0169, 0.0212, 0.0230, 0.0171, 0.0258,\n",
       "         0.0204, 0.0377, 0.1172, 0.0301, 0.0196, 0.0229, 0.0219]),\n",
       " tensor([0.0279, 0.0295, 0.0250, 0.0585, 0.0260, 0.0211, 0.0174, 0.0424, 0.0260,\n",
       "         0.8497, 0.9645, 0.0198, 0.0174, 0.0223, 0.0231, 0.0311]),\n",
       " tensor([0.0175, 0.0251, 0.0306, 0.0343, 0.0262, 0.0203, 0.0173, 0.0179, 0.0279,\n",
       "         0.0464, 0.0286, 0.0326, 0.0313, 0.0309, 0.0220, 0.0612]),\n",
       " tensor([0.0256, 0.0202, 0.0188, 0.7971, 0.3837, 0.9736, 0.2937, 0.0293, 0.0269,\n",
       "         0.0499, 0.0391, 0.3224, 0.0175, 0.0194, 0.0274, 0.0173]),\n",
       " tensor([0.0278, 0.0245, 0.0293, 0.0336, 0.0284, 0.0251, 0.0459, 0.0291, 0.5397,\n",
       "         0.3950, 0.9385, 0.1011, 0.0204, 0.0237, 0.0265, 0.0267]),\n",
       " tensor([0.0198, 0.0207, 0.0268, 0.0258, 0.0261, 0.0270, 0.0343, 0.0210, 0.0349,\n",
       "         0.0262, 0.7771, 0.0212, 0.0414, 0.0213, 0.0545, 0.0191]),\n",
       " tensor([0.4288, 0.0291, 0.0346, 0.1199, 0.0200, 0.0405, 0.0365, 0.0248, 0.0238,\n",
       "         0.0227, 0.0306, 0.0206, 0.9747, 0.0319, 0.0245, 0.0160]),\n",
       " tensor([0.0221, 0.0186, 0.0186, 0.0427, 0.0159, 0.0183, 0.0288, 0.0213, 0.0600,\n",
       "         0.0197, 0.0309, 0.0304, 0.0203, 0.0192, 0.0194, 0.0222]),\n",
       " tensor([0.0314, 0.0292, 0.0247, 0.0190, 0.0254, 0.0304, 0.0368, 0.0343, 0.0215,\n",
       "         0.0204, 0.0242, 0.0289, 0.2783, 0.0273, 0.2351, 0.0334]),\n",
       " tensor([0.0305, 0.0188, 0.0260, 0.0233, 0.0189, 0.0599, 0.0216, 0.0194, 0.0173,\n",
       "         0.0293, 0.0271, 0.0220, 0.0185, 0.0245, 0.9778, 0.0211]),\n",
       " tensor([0.9108, 0.0204, 0.0207, 0.0246, 0.0182, 0.9036, 0.3135, 0.0362, 0.4826,\n",
       "         0.0342, 0.0343, 0.1579, 0.0231, 0.1471, 0.0308, 0.0286]),\n",
       " tensor([0.0185, 0.0221, 0.0422, 0.0238, 0.0299, 0.0238, 0.0317, 0.0313, 0.0336,\n",
       "         0.0244, 0.0418, 0.7110, 0.0202, 0.0157, 0.0220, 0.3863]),\n",
       " tensor([0.0230, 0.0263, 0.0334, 0.0282, 0.0371, 0.0273, 0.0286, 0.0183, 0.0170,\n",
       "         0.0211, 0.0213, 0.0252, 0.0193, 0.0244, 0.0226, 0.9401]),\n",
       " tensor([0.0270, 0.0313, 0.0171, 0.0397, 0.0254, 0.0404, 0.0235, 0.0253, 0.0351,\n",
       "         0.0229, 0.6796, 0.0372, 0.0355, 0.0330, 0.0203, 0.0290]),\n",
       " tensor([0.0224, 0.0426, 0.0209, 0.0207, 0.0168, 0.0183, 0.0254, 0.0231, 0.0976,\n",
       "         0.0232, 0.0225, 0.0300, 0.0237, 0.0197, 0.0356, 0.1470]),\n",
       " tensor([0.4584, 0.0174, 0.0324, 0.0235, 0.0464, 0.2264, 0.9635, 0.0208, 0.4033,\n",
       "         0.0253, 0.0291, 0.0378, 0.0233, 0.0407, 0.1119, 0.0646]),\n",
       " tensor([0.0212, 0.0229, 0.0341, 0.0189, 0.0326, 0.0236, 0.0525, 0.0343, 0.0216,\n",
       "         0.0341, 0.0213, 0.0249, 0.0393, 0.0254, 0.0192, 0.0244]),\n",
       " tensor([0.0366, 0.0220, 0.0198, 0.0203, 0.0248, 0.0205, 0.0588, 0.9911, 0.0201,\n",
       "         0.1134, 0.9623, 0.0260, 0.6166, 0.9494, 0.0219, 0.0297]),\n",
       " tensor([0.0300, 0.0198, 0.1976, 0.0423, 0.0219, 0.2722, 0.0206, 0.0325, 0.0191,\n",
       "         0.0184, 0.0196, 0.0393, 0.2697, 0.0292, 0.0163, 0.5764]),\n",
       " tensor([0.0260, 0.0761, 0.0242, 0.0216, 0.0195, 0.0228, 0.0277, 0.0205, 0.3121,\n",
       "         0.0157, 0.0213, 0.9401, 0.0285, 0.0299, 0.0269, 0.0295]),\n",
       " tensor([0.0209, 0.0215, 0.0214, 0.0306, 0.0241, 0.8769, 0.0300, 0.0310, 0.0312,\n",
       "         0.0224, 0.0352, 0.5722, 0.0228, 0.5669, 0.1315, 0.0254]),\n",
       " tensor([0.3827, 0.0207, 0.0239, 0.0237, 0.0383, 0.0199, 0.9084, 0.0237, 0.0300,\n",
       "         0.0375, 0.0233, 0.0360, 0.0357, 0.0176, 0.0201, 0.0235]),\n",
       " tensor([0.0261, 0.0216, 0.0180, 0.0208, 0.0426, 0.3849, 0.0189, 0.0668, 0.5978,\n",
       "         0.8531, 0.2515, 0.0209, 0.0254, 0.0374, 0.0426, 0.0207]),\n",
       " tensor([0.9750, 0.0338, 0.0368, 0.8443, 0.9961, 0.0274, 0.0209, 0.0166, 0.2169,\n",
       "         0.0341, 0.0282, 0.0334, 0.0236, 0.0359, 0.9708, 0.8537]),\n",
       " tensor([0.0188, 0.0253, 0.0417, 0.0296, 0.0277, 0.0258, 0.0242, 0.0331, 0.9395,\n",
       "         0.0220, 0.9715, 0.8891, 0.0168, 0.0409, 0.0224, 0.1829]),\n",
       " tensor([0.0214, 0.0198, 0.4992, 0.0392, 0.0218, 0.0210, 0.0240, 0.0202, 0.0209,\n",
       "         0.0242, 0.0232, 0.0300, 0.0204, 0.1376, 0.0180, 0.0226]),\n",
       " tensor([0.0177, 0.0186, 0.6995, 0.0239, 0.7793, 0.0247, 0.0254, 0.0199, 0.0277,\n",
       "         0.0417, 0.0419, 0.0249, 0.1120, 0.0452, 0.0810, 0.0239]),\n",
       " tensor([0.3556, 0.0207, 0.0251, 0.0229, 0.0270, 0.0214, 0.0953, 0.0297, 0.0250,\n",
       "         0.0210, 0.0330, 0.0191, 0.9917, 0.0210, 0.0190, 0.0300]),\n",
       " tensor([0.0187, 0.0261, 0.0247, 0.0253, 0.0229, 0.0215, 0.0188, 0.0266, 0.0264,\n",
       "         0.0279, 0.0202, 0.0906, 0.9660, 0.0215, 0.0257, 0.0328]),\n",
       " tensor([0.0294, 0.0225, 0.3023, 0.0282, 0.0224, 0.0212, 0.0447, 0.0235, 0.0225,\n",
       "         0.0293, 0.0881, 0.0613, 0.2520, 0.0217, 0.8662, 0.9671]),\n",
       " tensor([0.0208, 0.0266, 0.0952, 0.9403, 0.0167, 0.5121, 0.0221, 0.0183, 0.1826,\n",
       "         0.0304, 0.6466, 0.0265, 0.0226, 0.0227, 0.0233, 0.0232]),\n",
       " tensor([0.0249, 0.0185, 0.0245, 0.4792, 0.0205, 0.0333, 0.0400, 0.0170, 0.0175,\n",
       "         0.0207, 0.0194, 0.0183, 0.0437, 0.0220, 0.0269, 0.0197]),\n",
       " tensor([0.0314, 0.6188, 0.0208, 0.0201, 0.0247, 0.0219, 0.0524, 0.0195, 0.0217,\n",
       "         0.0209, 0.6359, 0.0550, 0.1711, 0.0524, 0.0189, 0.1222]),\n",
       " tensor([0.0395, 0.0246, 0.0199, 0.0288, 0.0350, 0.0392, 0.0306, 0.0223, 0.0197,\n",
       "         0.0229, 0.7060, 0.0186, 0.0221, 0.0981, 0.0238, 0.0307]),\n",
       " tensor([0.0399, 0.0263, 0.0325, 0.3955, 0.0267, 0.0226, 0.0165, 0.0211, 0.0186,\n",
       "         0.0281, 0.0231, 0.0323, 0.0181, 0.0300, 0.0231, 0.0627]),\n",
       " tensor([0.0270, 0.0336, 0.0500, 0.9798, 0.9741, 0.0754, 0.6936, 0.0216, 0.0221,\n",
       "         0.0245, 0.0261, 0.0198, 0.6538, 0.0191, 0.0229, 0.0394]),\n",
       " tensor([0.0225, 0.2710, 0.0217, 0.0198, 0.0177, 0.0200, 0.0220, 0.0170, 0.0181,\n",
       "         0.0583, 0.9573, 0.8799, 0.0264, 0.0230, 0.0187, 0.9638]),\n",
       " tensor([0.0207, 0.0433, 0.0356, 0.0205, 0.0241, 0.0179, 0.0263, 0.0182, 0.0196,\n",
       "         0.0185, 0.0304, 0.0208, 0.0250, 0.0226, 0.0316, 0.0173]),\n",
       " tensor([0.0187, 0.0278, 0.0166, 0.7010, 0.1732, 0.0271, 0.0212, 0.0256, 0.0196,\n",
       "         0.0299, 0.0235, 0.0237, 0.0254, 0.0262, 0.0273, 0.0184]),\n",
       " tensor([0.0200, 0.1115, 0.0217, 0.0356, 0.0233, 0.0254, 0.4596, 0.0413, 0.0237,\n",
       "         0.0224, 0.0286, 0.0261, 0.0208, 0.0290, 0.0282, 0.0165]),\n",
       " tensor([0.9742, 0.0214, 0.0210, 0.0173, 0.0253, 0.0197, 0.0180, 0.0245, 0.1756,\n",
       "         0.9820, 0.0278, 0.0696, 0.0372, 0.0310, 0.0239, 0.0291]),\n",
       " tensor([0.0455, 0.0298, 0.0279, 0.0214, 0.0292, 0.0170, 0.0206, 0.0208, 0.3437,\n",
       "         0.0258, 0.0252, 0.0237, 0.0192, 0.0232, 0.4824, 0.0208]),\n",
       " tensor([0.0190, 0.5406, 0.0532, 0.7849, 0.7645, 0.1472, 0.9330, 0.4985, 0.0224,\n",
       "         0.0231, 0.5268, 0.0220, 0.0222, 0.0260, 0.0193, 0.0172]),\n",
       " tensor([0.0245, 0.0349, 0.7243, 0.5820, 0.0291, 0.0207, 0.0348, 0.6757, 0.0186,\n",
       "         0.0196, 0.0334, 0.0204, 0.0223, 0.0178, 0.0183, 0.0200]),\n",
       " tensor([0.0225, 0.0339, 0.0298, 0.0314, 0.0213, 0.0291, 0.0214, 0.0245, 0.0408,\n",
       "         0.0240, 0.0182, 0.0310, 0.0200, 0.0271, 0.0180, 0.0215]),\n",
       " tensor([0.0227, 0.0383, 0.0294, 0.0220, 0.0192, 0.0360, 0.6658, 0.0241, 0.0328,\n",
       "         0.0200, 0.0213, 0.0364, 0.0262, 0.0269, 0.0204, 0.0303]),\n",
       " tensor([0.0220, 0.0560, 0.0374, 0.0196, 0.0181, 0.0279, 0.0258, 0.3499, 0.0202,\n",
       "         0.0175, 0.0273, 0.0219, 0.0199, 0.0292, 0.0650, 0.0242]),\n",
       " tensor([0.4387, 0.0630, 0.3696, 0.0321, 0.3988, 0.8128, 0.4391, 0.9848, 0.0279,\n",
       "         0.0295, 0.0203, 0.6117, 0.0449, 0.0181, 0.0199, 0.0268]),\n",
       " tensor([0.0733, 0.0229, 0.0270, 0.0224, 0.4061, 0.7032, 0.0361, 0.0280, 0.0221,\n",
       "         0.0304, 0.0240, 0.0188, 0.0336, 0.0267, 0.0282, 0.6793]),\n",
       " tensor([0.0262, 0.0227, 0.0222, 0.9007, 0.0258, 0.0298, 0.0162, 0.0180, 0.0357,\n",
       "         0.0233, 0.0175, 0.0184, 0.0412, 0.0241, 0.0345, 0.0219]),\n",
       " tensor([0.0528, 0.5601, 0.0230, 0.0232, 0.0374, 0.0246, 0.0184, 0.0191, 0.0193,\n",
       "         0.1131, 0.0245, 0.0174, 0.7562, 0.0179, 0.0229, 0.0221]),\n",
       " tensor([0.0242, 0.1010, 0.6606, 0.7859, 0.0233, 0.1149, 0.2157, 0.0436, 0.0237,\n",
       "         0.0609, 0.0403, 0.0367, 0.4715, 0.5030, 0.0193, 0.0178]),\n",
       " tensor([0.0225, 0.6261, 0.0270, 0.0283, 0.0838, 0.0195, 0.0214, 0.7295, 0.3601,\n",
       "         0.1469, 0.0241, 0.0202, 0.0196, 0.4787, 0.9801, 0.0424]),\n",
       " tensor([0.7417, 0.6437, 0.0220, 0.0240, 0.0290, 0.0166, 0.0327, 0.0172, 0.4643,\n",
       "         0.9853, 0.0197, 0.0590, 0.0261, 0.0278, 0.0367, 0.0235]),\n",
       " tensor([0.4770, 0.0204, 0.0255, 0.0348, 0.8862, 0.0234, 0.9628, 0.0265, 0.0672,\n",
       "         0.0215, 0.0397, 0.0179, 0.0275, 0.0231, 0.0188, 0.1213]),\n",
       " tensor([0.0236, 0.0246, 0.0257, 0.0218, 0.0233, 0.0226, 0.0709, 0.0191, 0.2187,\n",
       "         0.0264, 0.0258, 0.0204, 0.0551, 0.0159, 0.0244, 0.0334]),\n",
       " tensor([0.9665, 0.0353, 0.5548, 0.0349, 0.0182, 0.0203, 0.0179, 0.0193, 0.0330,\n",
       "         0.1451, 0.0210, 0.0188, 0.0257, 0.0232, 0.0179, 0.0229]),\n",
       " tensor([0.0194, 0.0175, 0.0223, 0.0252, 0.0189, 0.0233, 0.0194, 0.0318, 0.0309,\n",
       "         0.0403, 0.9899, 0.0228, 0.0419, 0.0210, 0.0187, 0.0181]),\n",
       " tensor([0.0213, 0.0218, 0.0234, 0.0509, 0.9960, 0.0211, 0.0521, 0.0205, 0.0172,\n",
       "         0.0164, 0.0179, 0.0369, 0.0266, 0.0212, 0.0224, 0.0207]),\n",
       " tensor([0.0199, 0.0300, 0.0211, 0.7905, 0.0244, 0.0679, 0.0402, 0.0231, 0.0219,\n",
       "         0.0181, 0.0195, 0.0164, 0.4672, 0.0361, 0.4033, 0.3356]),\n",
       " tensor([0.1009, 0.7595, 0.0442, 0.0341, 0.0224, 0.0371, 0.0213, 0.0212, 0.0262,\n",
       "         0.0236, 0.0183, 0.0179, 0.0225, 0.0177, 0.0274, 0.0251]),\n",
       " tensor([0.0226, 0.9807, 0.0269, 0.0211, 0.0190, 0.0240, 0.7428, 0.0287, 0.0193,\n",
       "         0.0382, 0.0244, 0.0215, 0.0213, 0.0203, 0.0992, 0.0737]),\n",
       " tensor([0.1874, 0.0174, 0.0241, 0.4283, 0.2697, 0.0334, 0.0209, 0.0300, 0.0217,\n",
       "         0.0403, 0.0216, 0.0198, 0.1227, 0.0705, 0.0198, 0.0188]),\n",
       " tensor([0.0199, 0.9672, 0.0182, 0.0261, 0.0201, 0.0296, 0.0594, 0.1374, 0.0190,\n",
       "         0.0206, 0.7189, 0.0455, 0.0659, 0.0458, 0.0195, 0.0278]),\n",
       " tensor([0.1316, 0.0191, 0.0171, 0.0261, 0.6260, 0.9495, 0.0275, 0.2032, 0.2007,\n",
       "         0.0191, 0.0190, 0.0465, 0.0243, 0.0287, 0.0227, 0.0173]),\n",
       " tensor([0.0840, 0.0174, 0.9844, 0.0208, 0.0239, 0.0273, 0.0317, 0.0269, 0.3372,\n",
       "         0.0173, 0.0205, 0.0225, 0.0695, 0.0192, 0.0275, 0.0222]),\n",
       " tensor([0.0265, 0.0301, 0.0296, 0.0263, 0.3545, 0.7889, 0.9673, 0.0245, 0.0266,\n",
       "         0.0278, 0.0271, 0.0271, 0.0737, 0.0246, 0.0261, 0.0291]),\n",
       " tensor([0.0385, 0.0251, 0.0227, 0.0209, 0.9743, 0.7185, 0.0190, 0.0189, 0.0376,\n",
       "         0.0213, 0.0182, 0.0255, 0.0216, 0.7898, 0.0431, 0.0270]),\n",
       " tensor([0.0182, 0.9947, 0.0229, 0.0184, 0.0322, 0.0177, 0.0682, 0.0176, 0.0197,\n",
       "         0.0328, 0.0288, 0.0177, 0.0224, 0.0290, 0.0438, 0.9024]),\n",
       " tensor([0.0205, 0.0259, 0.0455, 0.0410, 0.0684, 0.0175, 0.2357, 0.0454, 0.0293,\n",
       "         0.0191, 0.0232, 0.1573, 0.1494, 0.0316, 0.0676, 0.1729]),\n",
       " tensor([0.0319, 0.9413, 0.0179, 0.0430, 0.0369, 0.9953, 0.0245, 0.4581, 0.0234,\n",
       "         0.0213, 0.0323, 0.0212, 0.4977, 0.8378, 0.0178, 0.0280]),\n",
       " tensor([0.0276, 0.0238, 0.7978, 0.0219, 0.5018, 0.0245, 0.0825, 0.0235, 0.0322,\n",
       "         0.0153, 0.0198, 0.0187, 0.0236, 0.0191, 0.0251, 0.0397]),\n",
       " tensor([0.1155, 0.0688, 0.0278, 0.4010, 0.0424, 0.5178, 0.0413, 0.2176, 0.0255,\n",
       "         0.0182, 0.0178, 0.0261, 0.2574, 0.0487, 0.0171, 0.0208]),\n",
       " tensor([0.0207, 0.0251, 0.0261, 0.1513, 0.1705, 0.0250, 0.0221, 0.2769, 0.0189,\n",
       "         0.0196, 0.0214, 0.0247, 0.0224, 0.0293, 0.0270, 0.9380]),\n",
       " tensor([0.9310, 0.0367, 0.0327, 0.0221, 0.0281, 0.0628, 0.1615, 0.7779, 0.0210,\n",
       "         0.9831, 0.0299, 0.4933, 0.3492, 0.0973, 0.0310, 0.0214]),\n",
       " tensor([0.0182, 0.0250, 0.0264, 0.0237, 0.0290, 0.0218, 0.0265, 0.0198, 0.0303,\n",
       "         0.0312, 0.0300, 0.0180, 0.2618, 0.1899, 0.0290, 0.5619]),\n",
       " tensor([0.0353, 0.0301, 0.9476, 0.0301, 0.0320, 0.0469, 0.0164, 0.2314, 0.0568,\n",
       "         0.0816, 0.0273, 0.2429, 0.0191, 0.0494, 0.0290, 0.0615]),\n",
       " tensor([0.0236, 0.4565, 0.0502, 0.0391, 0.0216, 0.9312, 0.0543, 0.0187, 0.1481,\n",
       "         0.0504, 0.0246, 0.1109, 0.0284, 0.4017, 0.0270, 0.1635]),\n",
       " tensor([0.3142, 0.5897, 0.0397, 0.0454, 0.0241, 0.0294, 0.4551, 0.0301, 0.3862,\n",
       "         0.5976, 0.0621, 0.4978, 0.0199, 0.0407, 0.0198, 0.0271]),\n",
       " tensor([0.0186, 0.0305, 0.0280, 0.0210, 0.0176, 0.0179, 0.0276, 0.0640, 0.0251,\n",
       "         0.0293, 0.0222, 0.0321, 0.0282, 0.0300, 0.0220, 0.0820]),\n",
       " tensor([0.0250, 0.0181, 0.0248, 0.0834, 0.0283, 0.0238, 0.0767, 0.0645, 0.1271,\n",
       "         0.0398, 0.0205, 0.0251, 0.0225, 0.0217, 0.0763, 0.0382]),\n",
       " tensor([0.3268, 0.0264, 0.0193, 0.0209, 0.0234, 0.0235, 0.0309, 0.0472, 0.0253,\n",
       "         0.0609, 0.3545, 0.2209, 0.0350, 0.4247, 0.0265, 0.0227]),\n",
       " tensor([0.0195, 0.0280, 0.0171, 0.0329, 0.0292, 0.0260, 0.7405, 0.0222, 0.0298,\n",
       "         0.7301, 0.0225, 0.0277, 0.0344, 0.0288, 0.0252, 0.1284]),\n",
       " tensor([0.0601, 0.8746, 0.7901, 0.0269, 0.0277, 0.0619, 0.9208, 0.0293, 0.0275,\n",
       "         0.0244, 0.1585, 0.3310, 0.0358, 0.0241, 0.9539, 0.0193]),\n",
       " tensor([0.0243, 0.0326, 0.1006, 0.1406, 0.0246, 0.1641, 0.0292, 0.8816, 0.0301,\n",
       "         0.0214, 0.0174, 0.0189, 0.0211, 0.0181, 0.1193, 0.0235]),\n",
       " tensor([0.0626, 0.9295, 0.0608, 0.0237, 0.0502, 0.0383, 0.1567, 0.0249, 0.0340,\n",
       "         0.0243, 0.0348, 0.0319, 0.4178, 0.0272, 0.0295, 0.0192]),\n",
       " tensor([0.0962, 0.0244, 0.0372, 0.0177, 0.0181, 0.0250, 0.0254, 0.0236, 0.1397,\n",
       "         0.2328, 0.0278, 0.1321, 0.0283, 0.0284, 0.4503, 0.0272]),\n",
       " tensor([0.0292, 0.0251, 0.4102, 0.0312, 0.0542, 0.0213, 0.9931, 0.0247, 0.5797,\n",
       "         0.8628, 0.0235, 0.0406, 0.0285, 0.0228, 0.0268, 0.0304]),\n",
       " tensor([0.0391, 0.0272, 0.0293, 0.4121, 0.0232, 0.4075, 0.0349, 0.0192, 0.0171,\n",
       "         0.0533, 0.8084, 0.0552, 0.0713, 0.8921, 0.0197, 0.2047]),\n",
       " tensor([0.6603, 0.2808, 0.0396, 0.0216, 0.0333, 0.0447, 0.0262, 0.0314, 0.3090,\n",
       "         0.0306, 0.0339, 0.8234, 0.0228, 0.8090, 0.0559, 0.0237]),\n",
       " tensor([0.0473, 0.0273, 0.0935, 0.0281, 0.1834, 0.5016, 0.6615, 0.9683, 0.6115,\n",
       "         0.0991, 0.6021, 0.0278, 0.0276, 0.5910, 0.6930, 0.0164]),\n",
       " tensor([0.4716, 0.9472, 0.0186, 0.0299, 0.0268, 0.0747, 0.0253, 0.4761, 0.0178,\n",
       "         0.0232, 0.0315, 0.0182, 0.4434, 0.0256, 0.8324, 0.6239]),\n",
       " tensor([0.1847, 0.0536, 0.0267, 0.0210, 0.0212, 0.0223, 0.0170, 0.0310, 0.0284,\n",
       "         0.0912, 0.0319, 0.0542, 0.0245, 0.3597, 0.0447, 0.0261]),\n",
       " tensor([0.0227, 0.0174, 0.6682, 0.0221, 0.6327, 0.5393, 0.0239, 0.0168, 0.0376,\n",
       "         0.0525, 0.0683, 0.1631, 0.0277, 0.0189, 0.0238, 0.8982]),\n",
       " tensor([0.0222, 0.0389, 0.0293, 0.0197, 0.0193, 0.0284, 0.0211, 0.0419, 0.3711,\n",
       "         0.0250, 0.0402, 0.5134, 0.0286, 0.0354, 0.0202, 0.0200]),\n",
       " tensor([0.7783, 0.0187, 0.0234, 0.0284, 0.0251, 0.0189, 0.0177, 0.0523, 0.0230,\n",
       "         0.0211, 0.0496, 0.0511, 0.0220, 0.0239, 0.0202, 0.0486]),\n",
       " tensor([0.0362, 0.3974, 0.2170, 0.0294, 0.0264, 0.0242, 0.4029, 0.0229, 0.0327,\n",
       "         0.0276, 0.0234, 0.0211, 0.0215, 0.0693, 0.0365, 0.7938]),\n",
       " tensor([0.0244, 0.0280, 0.0805, 0.9890, 0.0280, 0.9368, 0.0378, 0.0334, 0.6944,\n",
       "         0.0341, 0.1728, 0.0250, 0.2832, 0.7167, 0.0354, 0.3982]),\n",
       " tensor([0.0260, 0.9269, 0.1786, 0.0351, 0.0349, 0.0474, 0.8830, 0.0276, 0.0361,\n",
       "         0.0225, 0.0182, 0.0263, 0.0302, 0.0311, 0.0237, 0.0249]),\n",
       " tensor([0.0197, 0.0401, 0.0184, 0.0151, 0.0449, 0.0213, 0.0261, 0.0202, 0.0206,\n",
       "         0.0341, 0.9820, 0.0179, 0.0185, 0.0227, 0.0169, 0.0426]),\n",
       " tensor([0.0389, 0.0242, 0.9735, 0.9738, 0.0316, 0.0740, 0.2626, 0.0242, 0.0185,\n",
       "         0.0273, 0.5936, 0.9487, 0.0238, 0.7822, 0.0204, 0.0274]),\n",
       " tensor([0.0352, 0.0183, 0.0497, 0.0213, 0.0231, 0.0235, 0.0242, 0.0214, 0.0250,\n",
       "         0.0184, 0.0231, 0.0189, 0.0227, 0.0238, 0.0181, 0.0205]),\n",
       " tensor([0.0368, 0.0195, 0.0192, 0.0278, 0.0286, 0.0183, 0.0271, 0.0240, 0.0249,\n",
       "         0.0254, 0.1287, 0.0260, 0.0304, 0.0188, 0.0184, 0.0184]),\n",
       " tensor([0.3893, 0.0452, 0.0191, 0.0513, 0.9004, 0.2446, 0.0201, 0.9845, 0.0347,\n",
       "         0.0199, 0.0197, 0.0409, 0.0160, 0.0185, 0.0201, 0.1957]),\n",
       " tensor([0.0306, 0.0432, 0.0492, 0.1025, 0.9407, 0.8410, 0.0265, 0.0248, 0.0169,\n",
       "         0.0187, 0.0257, 0.0210, 0.0231, 0.9732, 0.6412, 0.0211]),\n",
       " tensor([0.0215, 0.0339, 0.0220, 0.0220, 0.0184, 0.0293, 0.0696, 0.0256, 0.7890,\n",
       "         0.0302, 0.1020, 0.0222, 0.0338, 0.0236, 0.0177, 0.0177]),\n",
       " tensor([0.0196, 0.0204, 0.0220, 0.0168, 0.0285, 0.0200, 0.0231, 0.0208, 0.0374,\n",
       "         0.0278, 0.0231, 0.1074, 0.0189, 0.0174, 0.0468, 0.0266]),\n",
       " tensor([0.0198, 0.0196, 0.0189, 0.0277, 0.0225, 0.0368, 0.0278, 0.0519, 0.5766,\n",
       "         0.0329, 0.0446, 0.3818, 0.0719, 0.1675, 0.2908, 0.9825]),\n",
       " tensor([0.8083, 0.0549, 0.9785, 0.0266, 0.0208, 0.0264, 0.0240, 0.0429, 0.0214,\n",
       "         0.0210, 0.0199, 0.0160, 0.6839, 0.0203, 0.0677, 0.0739]),\n",
       " tensor([0.0224, 0.0291, 0.0170, 0.0255, 0.0524, 0.0304, 0.0276, 0.2103, 0.0241,\n",
       "         0.0324, 0.0659, 0.0851, 0.0184, 0.0189, 0.0319, 0.0250]),\n",
       " tensor([0.0452, 0.0223, 0.0166, 0.4748, 0.0292, 0.0185, 0.0259, 0.9691, 0.0237,\n",
       "         0.3871, 0.0280, 0.0332, 0.0209, 0.0317, 0.0396, 0.0708]),\n",
       " tensor([0.0209, 0.0256, 0.0196, 0.0215, 0.0256, 0.1258, 0.0406, 0.0222, 0.3416,\n",
       "         0.0306, 0.0186, 0.0327, 0.0237, 0.0310, 0.0335, 0.0278]),\n",
       " tensor([0.0475, 0.0311, 0.1668, 0.0211, 0.0214, 0.0190, 0.0236, 0.0404, 0.0314,\n",
       "         0.0384, 0.0209, 0.3319, 0.0250, 0.9427, 0.0231, 0.6116]),\n",
       " tensor([0.0265, 0.0217, 0.0224, 0.9783, 0.0190, 0.0465, 0.9953, 0.0656, 0.0278,\n",
       "         0.9608, 0.0259, 0.0226, 0.0241, 0.0199, 0.9753, 0.0185]),\n",
       " tensor([0.2142, 0.0158, 0.0169, 0.0428, 0.0324, 0.0307, 0.0246, 0.0465, 0.3856,\n",
       "         0.0197, 0.0479, 0.0295, 0.0961, 0.0332, 0.0392, 0.0213]),\n",
       " tensor([0.0209, 0.0210, 0.0206, 0.0447, 0.0197, 0.0598, 0.0501, 0.9731, 0.2797,\n",
       "         0.0250, 0.0209, 0.0521, 0.0225, 0.5100, 0.9455, 0.9740]),\n",
       " tensor([0.9220, 0.5837, 0.9660, 0.9311, 0.0317, 0.0184, 0.0218, 0.7785, 0.3124,\n",
       "         0.0239, 0.0416, 0.4039, 0.0270, 0.0329, 0.2108, 0.0220]),\n",
       " tensor([0.0639, 0.0196, 0.0237, 0.0179, 0.0190, 0.0515, 0.5036, 0.0269, 0.0503,\n",
       "         0.0288, 0.0192, 0.0194, 0.0213, 0.4294, 0.0532, 0.2772]),\n",
       " tensor([0.0295, 0.0222, 0.1683, 0.0713, 0.0247, 0.0221, 0.0238, 0.2282, 0.0487,\n",
       "         0.0749, 0.9502, 0.0320, 0.0263, 0.0240, 0.0360, 0.0202]),\n",
       " tensor([0.0250, 0.0201, 0.0431, 0.0205, 0.0420, 0.0241, 0.0306, 0.0196, 0.0212,\n",
       "         0.0225, 0.0177, 0.0195, 0.0213, 0.0205, 0.0246, 0.0246]),\n",
       " tensor([0.0329, 0.0836, 0.0270, 0.0305, 0.2531, 0.1123, 0.9494, 0.0185, 0.0233,\n",
       "         0.0193, 0.0237, 0.0180, 0.1076, 0.0185, 0.1179, 0.0310]),\n",
       " tensor([0.0210, 0.0219, 0.0238, 0.0215, 0.0492, 0.0253, 0.0183, 0.0291, 0.0174,\n",
       "         0.9461, 0.0229, 0.0221, 0.0248, 0.0198, 0.0269, 0.0219]),\n",
       " tensor([0.0401, 0.0195, 0.0227, 0.0231, 0.0211, 0.6572, 0.0214, 0.0212, 0.0444,\n",
       "         0.0303, 0.9608, 0.0285, 0.0250, 0.0910, 0.0216, 0.9174]),\n",
       " tensor([0.0185, 0.1795, 0.0211, 0.0199, 0.0364, 0.0270, 0.0729, 0.0219, 0.0414,\n",
       "         0.0626, 0.0209, 0.0210, 0.0270, 0.0209, 0.0384, 0.0226]),\n",
       " tensor([0.0286, 0.0256, 0.0219, 0.0665, 0.0201, 0.1643, 0.3403, 0.0277, 0.0388,\n",
       "         0.3654, 0.0384, 0.7845, 0.0606, 0.8836, 0.8556, 0.8772]),\n",
       " tensor([0.6354, 0.9610, 0.8630, 0.0267, 0.0790, 0.7703, 0.2405, 0.0198, 0.0327,\n",
       "         0.4039, 0.0214, 0.0326, 0.3942, 0.0189, 0.0441, 0.0234]),\n",
       " tensor([0.0218, 0.0181, 0.0192, 0.0523, 0.0301, 0.2725, 0.0237, 0.0291, 0.0273,\n",
       "         0.0214, 0.0229, 0.0260, 0.7402, 0.2820, 0.4968, 0.0225]),\n",
       " tensor([0.0244, 0.0333, 0.0281, 0.0220, 0.0256, 0.0214, 0.0431, 0.1961, 0.0221,\n",
       "         0.0249, 0.0344, 0.9940, 0.0203, 0.0258, 0.0211, 0.0315]),\n",
       " tensor([0.0369, 0.1825, 0.0281, 0.0275, 0.6045, 0.0297, 0.0216, 0.0179, 0.0402,\n",
       "         0.0761, 0.0263, 0.0243, 0.0188, 0.0269, 0.0223, 0.0255]),\n",
       " tensor([0.0183, 0.0242, 0.0231, 0.0166, 0.5604, 0.9235, 0.9890, 0.0229, 0.3381,\n",
       "         0.0285, 0.0195, 0.0250, 0.0250, 0.0229, 0.0453, 0.4642]),\n",
       " tensor([0.0189, 0.7142, 0.0247, 0.0209, 0.0498, 0.0244, 0.0218, 0.0202, 0.0162,\n",
       "         0.0271, 0.0423, 0.0697, 0.0202, 0.0203, 0.0265, 0.0870]),\n",
       " tensor([0.0242, 0.0179, 0.0283, 0.8751, 0.0272, 0.0225, 0.0304, 0.0227, 0.0183,\n",
       "         0.0203, 0.0296, 0.0369, 0.0918, 0.0239, 0.0220, 0.0308]),\n",
       " tensor([0.0203, 0.1622, 0.0194, 0.0247, 0.0193, 0.0233, 0.0256, 0.0201, 0.0243,\n",
       "         0.0185, 0.0218, 0.0351, 0.0666, 0.0252, 0.0365, 0.0177]),\n",
       " tensor([0.0171, 0.0168, 0.0264, 0.0170, 0.8353, 0.0181, 0.0255, 0.0271, 0.0213,\n",
       "         0.0244, 0.8846, 0.0252, 0.0241, 0.0302, 0.2041, 0.0208]),\n",
       " tensor([0.0224, 0.0177, 0.0213, 0.0234, 0.0827, 0.0203, 0.0246, 0.0252, 0.0176,\n",
       "         0.8111, 0.0358, 0.1142, 0.4751, 0.0201, 0.7931, 0.0247]),\n",
       " tensor([0.0282, 0.0216, 0.0194, 0.2573, 0.0186, 0.0232, 0.4730, 0.0186, 0.4895,\n",
       "         0.9870, 0.0631, 0.0241, 0.0218, 0.0311, 0.0159, 0.0308]),\n",
       " tensor([0.0217, 0.0203, 0.0204, 0.0305, 0.0195, 0.9725, 0.0265, 0.0224, 0.5746,\n",
       "         0.0256, 0.0913, 0.0227, 0.0318, 0.0191, 0.0433, 0.0376]),\n",
       " tensor([0.2877, 0.0223, 0.0244, 0.0186, 0.0186, 0.0221, 0.0256, 0.0277, 0.0245,\n",
       "         0.0173, 0.0167, 0.0265, 0.0197, 0.0218, 0.0187, 0.0268]),\n",
       " tensor([0.5993, 0.0240, 0.0192, 0.0207, 0.0177, 0.0252, 0.0464, 0.0244, 0.0208,\n",
       "         0.0158, 0.1049, 0.0384, 0.0183, 0.4126, 0.5594, 0.0174]),\n",
       " tensor([0.0291, 0.5305, 0.0464, 0.0256, 0.0204, 0.0293, 0.0251, 0.9846, 0.0171,\n",
       "         0.0234, 0.4111, 0.0299, 0.0255, 0.0236, 0.1167, 0.0188]),\n",
       " tensor([0.0386, 0.0292, 0.0532, 0.0210, 0.0235, 0.0244, 0.0177, 0.0217, 0.0390,\n",
       "         0.0348, 0.0243, 0.0184, 0.0428, 0.0177, 0.0224, 0.0201]),\n",
       " tensor([0.0202, 0.0409, 0.0335, 0.0223, 0.0200, 0.0329, 0.0192, 0.0237, 0.0288,\n",
       "         0.0222, 0.0276, 0.0286, 0.0206, 0.0211, 0.0238, 0.0348]),\n",
       " tensor([0.0254, 0.0215, 0.0196, 0.0226, 0.0354, 0.0212, 0.0385, 0.1338, 0.4471,\n",
       "         0.0251, 0.0226, 0.0416, 0.0222, 0.0367, 0.0314, 0.0249]),\n",
       " tensor([0.0187, 0.0225, 0.8622, 0.0206, 0.0435, 0.0230, 0.0199, 0.0369, 0.0321,\n",
       "         0.0261, 0.0272, 0.0238, 0.0245, 0.0464, 0.0199, 0.0282]),\n",
       " tensor([0.0309, 0.0249, 0.0293, 0.0178, 0.0317, 0.0185, 0.0251, 0.3277, 0.0275,\n",
       "         0.0211, 0.0177, 0.0313, 0.3087, 0.0268, 0.0256, 0.0180]),\n",
       " tensor([0.0229, 0.0231, 0.0243, 0.0275, 0.0168, 0.0229, 0.0407, 0.0182, 0.5418,\n",
       "         0.0254, 0.4951, 0.0321, 0.4225, 0.0201, 0.0367, 0.0228]),\n",
       " tensor([0.0294, 0.0229, 0.0293, 0.0308, 0.0232, 0.5804, 0.0888, 0.2539, 0.0270,\n",
       "         0.0291, 0.0231, 0.0210, 0.0248, 0.0267, 0.0227, 0.0310]),\n",
       " tensor([0.0180, 0.0177, 0.0216, 0.0161, 0.0504, 0.1115, 0.0241, 0.0587, 0.0202,\n",
       "         0.0260, 0.0455, 0.0220, 0.0238, 0.0194, 0.0226, 0.0491]),\n",
       " tensor([0.0224, 0.0297, 0.0185, 0.0198, 0.0196, 0.0427, 0.0188, 0.0325, 0.0375,\n",
       "         0.0344, 0.0223, 0.0237, 0.0184, 0.0260, 0.0199, 0.0191]),\n",
       " tensor([0.0354, 0.0227, 0.0253, 0.0246, 0.0182, 0.0719, 0.8519, 0.8243, 0.0465,\n",
       "         0.0291, 0.0225, 0.1210, 0.0216, 0.0268, 0.0162, 0.0223]),\n",
       " tensor([0.0487, 0.0409, 0.0223, 0.0206, 0.4268, 0.0213, 0.0193, 0.0280, 0.8782,\n",
       "         0.4748, 0.1104, 0.9452, 0.0286, 0.4054, 0.0342, 0.0173]),\n",
       " tensor([0.0235, 0.0205, 0.0262, 0.0190, 0.0192, 0.0178, 0.0203, 0.0312, 0.0322,\n",
       "         0.0200, 0.0174, 0.0259, 0.0533, 0.0211, 0.0251, 0.0251]),\n",
       " tensor([0.0246, 0.0251, 0.0405, 0.0200, 0.0237, 0.0187, 0.0212, 0.0217, 0.0195,\n",
       "         0.0310, 0.0225, 0.0782, 0.0227, 0.0259, 0.0208, 0.0372]),\n",
       " tensor([0.0176, 0.0277, 0.0185, 0.0235, 0.0710, 0.0230, 0.0944, 0.0304, 0.0196,\n",
       "         0.0198, 0.0203, 0.0218, 0.0199, 0.8708, 0.2999, 0.0191]),\n",
       " tensor([0.0192, 0.9223, 0.0236, 0.3882, 0.8937, 0.0204, 0.0273, 0.0208, 0.0377,\n",
       "         0.0440, 0.0393, 0.0195, 0.0211, 0.7400, 0.9285, 0.0216]),\n",
       " tensor([0.0229, 0.0239, 0.0240, 0.4323, 0.0210, 0.0198, 0.7845, 0.0289, 0.3396,\n",
       "         0.6284, 0.0228, 0.0191, 0.5339, 0.0168, 0.1759, 0.0208]),\n",
       " tensor([0.0235, 0.0165, 0.0737, 0.0213, 0.0294, 0.0219, 0.0223, 0.0201, 0.0191,\n",
       "         0.0390, 0.0366, 0.0692, 0.0190, 0.0603, 0.0308, 0.0919]),\n",
       " tensor([0.0241, 0.0235, 0.6239, 0.0266, 0.0236, 0.5518, 0.0213, 0.0192, 0.0178,\n",
       "         0.0231, 0.2604, 0.8900, 0.5836, 0.0214, 0.8457, 0.0727]),\n",
       " tensor([0.0237, 0.0214, 0.4617, 0.0224, 0.0225, 0.0235, 0.0988, 0.0305, 0.0222,\n",
       "         0.0343, 0.0218, 0.0197, 0.7008, 0.5663, 0.0281, 0.0237]),\n",
       " tensor([0.0257, 0.0227, 0.0328, 0.9839, 0.9101, 0.0319, 0.0347, 0.2782, 0.3030,\n",
       "         0.0347, 0.3356, 0.0309, 0.0294, 0.0266, 0.0223, 0.0376]),\n",
       " tensor([0.7576, 0.0259, 0.0253, 0.0231, 0.0392, 0.0207, 0.0214, 0.0541, 0.7681,\n",
       "         0.2498, 0.4068, 0.7920, 0.3446, 0.1291, 0.2785, 0.0276]),\n",
       " tensor([0.0320, 0.0254, 0.0409, 0.0279, 0.4289, 0.9538, 0.7915, 0.6144, 0.4810,\n",
       "         0.3225, 0.5583, 0.0193, 0.0767, 0.5849, 0.0177, 0.7397]),\n",
       " tensor([0.0269, 0.8907, 0.7013, 0.8150, 0.9910, 0.0540, 0.8808, 0.0277, 0.0314,\n",
       "         0.0266, 0.6848, 0.0537, 0.0202, 0.0306, 0.0598, 0.9606]),\n",
       " tensor([0.1644, 0.1067, 0.0308, 0.0871, 0.0243, 0.0206, 0.0276, 0.4283, 0.0262,\n",
       "         0.9903, 0.0250, 0.0260, 0.6035, 0.0234, 0.4375, 0.2786]),\n",
       " tensor([0.0345, 0.3457, 0.0206, 0.0866, 0.9784, 0.0344, 0.0352, 0.0235, 0.0467,\n",
       "         0.0286, 0.0180, 0.0598, 0.0368, 0.0439, 0.0903, 0.0275]),\n",
       " tensor([0.0202, 0.9100, 0.0399, 0.0310, 0.0346, 0.1770, 0.0181, 0.0381, 0.9513,\n",
       "         0.0497, 0.0395, 0.0232, 0.5335, 0.3351, 0.0305, 0.2876]),\n",
       " tensor([0.0328, 0.0248, 0.0479, 0.0203, 0.0422, 0.0298, 0.0467, 0.6611, 0.8417,\n",
       "         0.0530, 0.0227, 0.0236, 0.8285, 0.0338, 0.9147, 0.0249]),\n",
       " tensor([0.4815, 0.2375, 0.0300, 0.1096, 0.0186, 0.0280, 0.1292, 0.0263, 0.0489,\n",
       "         0.9391, 0.0158, 0.8659, 0.0494, 0.4042, 0.0407, 0.0181]),\n",
       " tensor([0.0393, 0.0188, 0.1105, 0.0162, 0.1259, 0.0230, 0.0283, 0.0224, 0.0253,\n",
       "         0.8401, 0.3933, 0.0254, 0.9878, 0.0396, 0.9689, 0.4123]),\n",
       " tensor([0.0252, 0.0388, 0.0415, 0.4619, 0.0441, 0.1047, 0.0298, 0.0302, 0.2687,\n",
       "         0.0348, 0.0515, 0.4228, 0.0356, 0.0729, 0.0219, 0.8575]),\n",
       " tensor([0.9321, 0.0218, 0.5025, 0.6384, 0.9855, 0.0433, 0.0284, 0.0227, 0.0200,\n",
       "         0.0176, 0.9114, 0.0198, 0.1306, 0.9650, 0.0292, 0.1910]),\n",
       " tensor([0.4500, 0.2942, 0.6589, 0.0299, 0.2239, 0.1324, 0.0233, 0.0298, 0.0237,\n",
       "         0.0301, 0.0651, 0.0182, 0.0262, 0.0241, 0.0276, 0.0282]),\n",
       " tensor([0.0232, 0.0187, 0.0250, 0.0390, 0.0203, 0.9762, 0.0353, 0.6013, 0.0171,\n",
       "         0.0171, 0.5133, 0.0305, 0.8198, 0.0424, 0.0205, 0.0184]),\n",
       " tensor([0.0220, 0.0535, 0.0198, 0.0202, 0.7247, 0.0276, 0.0362, 0.0240, 0.0223,\n",
       "         0.0274, 0.0537, 0.0190, 0.0265, 0.0238, 0.0247, 0.0168]),\n",
       " tensor([0.0190, 0.0216, 0.0188, 0.0245, 0.0340, 0.0153, 0.0248, 0.0291, 0.0167,\n",
       "         0.0250, 0.0213, 0.0415, 0.0420, 0.3341, 0.0198, 0.2830]),\n",
       " tensor([0.0187, 0.9882, 0.1153, 0.0214, 0.0332, 0.0281, 0.0183, 0.0248, 0.5135,\n",
       "         0.0367, 0.0226, 0.0199, 0.7837, 0.0198, 0.0188, 0.0224]),\n",
       " tensor([0.0215, 0.0433, 0.0172, 0.0234, 0.8258, 0.0171, 0.0464, 0.0250, 0.0364,\n",
       "         0.0265, 0.0211, 0.0209, 0.0218, 0.0244, 0.0265, 0.0146]),\n",
       " tensor([0.3047, 0.0358, 0.9043, 0.0179, 0.6874, 0.0176, 0.0406, 0.0235, 0.7697,\n",
       "         0.0195, 0.0312, 0.1720, 0.3256, 0.0719, 0.0247, 0.3610]),\n",
       " tensor([0.0446, 0.0529, 0.4227, 0.0193, 0.0233, 0.0366, 0.0403, 0.0323, 0.0205,\n",
       "         0.5983, 0.0222, 0.6479, 0.8665, 0.0417, 0.1631, 0.0347]),\n",
       " tensor([0.0282, 0.2143, 0.0175, 0.0244, 0.0248, 0.0232, 0.0292, 0.0251, 0.0253,\n",
       "         0.0294, 0.0185, 0.0358, 0.0183, 0.0185, 0.0315, 0.0304]),\n",
       " tensor([0.0239, 0.0203, 0.0161, 0.0166, 0.0325, 0.0232, 0.2094, 0.0197, 0.9397,\n",
       "         0.0222, 0.0167, 0.0166, 0.0256, 0.0309, 0.0165, 0.9337]),\n",
       " tensor([0.0235, 0.0322, 0.0269, 0.0368, 0.0489, 0.6072, 0.0327, 0.0353, 0.3541,\n",
       "         0.0238, 0.0256, 0.0347, 0.0256, 0.0222, 0.0198, 0.0165]),\n",
       " tensor([0.0197, 0.0189, 0.8238, 0.0228, 0.0162, 0.5237, 0.0503, 0.0217, 0.0195,\n",
       "         0.0266, 0.7979, 0.8449, 0.0172, 0.0456, 0.0305, 0.0243]),\n",
       " tensor([0.0222, 0.0182, 0.0211, 0.0578, 0.0221, 0.0201, 0.0239, 0.0197, 0.0212,\n",
       "         0.0209, 0.0411, 0.0215, 0.8265, 0.0166, 0.1573, 0.0387]),\n",
       " tensor([0.0212, 0.0220, 0.0183, 0.0249, 0.0242, 0.0387, 0.0235, 0.0195, 0.6450,\n",
       "         0.1286, 0.0209, 0.0200, 0.0271, 0.0171, 0.0189, 0.0313]),\n",
       " tensor([0.0252, 0.6885, 0.0356, 0.0316, 0.0254, 0.0206, 0.0222, 0.8802, 0.1489,\n",
       "         0.0342, 0.0227, 0.0245, 0.3580, 0.0254, 0.0195, 0.0205]),\n",
       " tensor([0.0217, 0.9632, 0.5179, 0.0199, 0.0203, 0.0192, 0.0201, 0.0305, 0.0209,\n",
       "         0.0345, 0.1800, 0.0225, 0.0180, 0.0218, 0.0292, 0.0200]),\n",
       " tensor([0.0222, 0.9699, 0.9895, 0.7398, 0.0367, 0.0479, 0.0393, 0.0326, 0.2829,\n",
       "         0.0241, 0.0227, 0.9623, 0.6097, 0.0222, 0.0212, 0.0278]),\n",
       " tensor([0.0335, 0.0263, 0.0500, 0.0231, 0.0196, 0.0279, 0.9966, 0.0268, 0.0276,\n",
       "         0.0220, 0.0212, 0.5293, 0.0181, 0.6136, 0.0418, 0.0283]),\n",
       " tensor([0.0338, 0.0234, 0.0239, 0.0261, 0.9841, 0.7793, 0.0193, 0.0261, 0.5051,\n",
       "         0.0235, 0.0248, 0.0263, 0.0364, 0.0235, 0.0178, 0.0272]),\n",
       " tensor([0.0944, 0.0206, 0.0227, 0.0265, 0.0288, 0.0247, 0.9605, 0.4539, 0.0208,\n",
       "         0.2674, 0.0232, 0.0778, 0.0202, 0.0212, 0.0257, 0.0359]),\n",
       " tensor([0.0232, 0.0392, 0.0280, 0.0265, 0.0241, 0.0155, 0.0190, 0.0307, 0.0263,\n",
       "         0.0160, 0.0644, 0.0266, 0.0213, 0.0278, 0.0173, 0.0206]),\n",
       " tensor([0.0182, 0.9770, 0.0224, 0.1274, 0.0239, 0.0168, 0.0208, 0.0232, 0.0190,\n",
       "         0.0184, 0.0224, 0.0235, 0.1415, 0.0229, 0.7703, 0.0189]),\n",
       " tensor([0.0322, 0.0282, 0.3913, 0.9687, 0.0200, 0.1143, 0.0291, 0.0311, 0.6693,\n",
       "         0.0185, 0.0210, 0.0199, 0.0226, 0.0838, 0.0184, 0.0177]),\n",
       " tensor([0.0368, 0.6999, 0.0216, 0.9577, 0.4560, 0.5319, 0.0247, 0.0187, 0.0956,\n",
       "         0.0167, 0.9922, 0.0183, 0.0221, 0.0239, 0.0239, 0.0162]),\n",
       " tensor([0.0219, 0.0214, 0.0179, 0.0207, 0.0242, 0.0203, 0.0190, 0.0248, 0.9886,\n",
       "         0.0376, 0.9959, 0.0490, 0.0201, 0.4434, 0.4530, 0.0268]),\n",
       " tensor([0.0228, 0.0178, 0.0196, 0.3899, 0.6356, 0.0373, 0.0250, 0.0329, 0.3355,\n",
       "         0.0240, 0.0369, 0.0237, 0.0287, 0.2074, 0.6960, 0.0210]),\n",
       " tensor([0.0260, 0.0420, 0.3614, 0.1607, 0.0204, 0.0198, 0.0274, 0.0191, 0.0210,\n",
       "         0.1980, 0.0170, 0.0169, 0.0281, 0.0411, 0.0206, 0.0244]),\n",
       " tensor([0.0346, 0.0267, 0.0351, 0.5402, 0.0220, 0.0196, 0.3910, 0.8580, 0.0251,\n",
       "         0.0386, 0.0222, 0.0241, 0.0346, 0.8329, 0.0343, 0.1446]),\n",
       " tensor([0.0254, 0.0439, 0.0285, 0.0194, 0.0217, 0.0223, 0.9869, 0.9782, 0.0193,\n",
       "         0.5568, 0.0260, 0.0322, 0.0283, 0.0380, 0.0320, 0.6672]),\n",
       " tensor([0.0260, 0.0374, 0.0310, 0.0244, 0.0174, 0.0286, 0.0226, 0.0839, 0.0324,\n",
       "         0.0449, 0.0238, 0.9879, 0.0418, 0.2502, 0.0255, 0.0212]),\n",
       " tensor([0.0179, 0.0235, 0.0237, 0.0605, 0.0187, 0.0174, 0.4968, 0.0264, 0.0216,\n",
       "         0.0813, 0.0221, 0.0222, 0.0256, 0.0183, 0.0316, 0.0287]),\n",
       " tensor([0.0277, 0.0203, 0.0404, 0.9036, 0.0264, 0.0239, 0.0754, 0.0245, 0.0263,\n",
       "         0.0300, 0.0251, 0.0217, 0.0287, 0.0179, 0.0234, 0.0193]),\n",
       " tensor([0.0323, 0.1789, 0.0276, 0.8583, 0.0508, 0.0207, 0.0200, 0.4007, 0.0262,\n",
       "         0.0389, 0.0245, 0.0200, 0.0229, 0.4370, 0.0568, 0.0160]),\n",
       " tensor([0.0236, 0.6962, 0.9889, 0.9824, 0.7511, 0.0314, 0.0210, 0.0197, 0.0188,\n",
       "         0.0198, 0.8514, 0.0202, 0.0210, 0.0298, 0.0223, 0.0202]),\n",
       " tensor([0.0245, 0.8837, 0.0292, 0.9038, 0.0377, 0.0221, 0.0896, 0.2954, 0.0240,\n",
       "         0.0327, 0.0223, 0.1267, 0.9772, 0.0211, 0.0406, 0.0223]),\n",
       " tensor([0.0189, 0.0450, 0.0189, 0.0195, 0.0185, 0.0248, 0.9050, 0.0180, 0.0205,\n",
       "         0.2930, 0.0304, 0.0355, 0.0554, 0.0256, 0.9893, 0.9929]),\n",
       " tensor([0.0371, 0.4114, 0.7046, 0.0317, 0.0329, 0.0498, 0.0284, 0.0250, 0.4529,\n",
       "         0.0354, 0.0711, 0.1277, 0.9599, 0.0231, 0.0467, 0.7472]),\n",
       " tensor([0.0256, 0.3887, 0.6229, 0.0286, 0.4523, 0.0251, 0.4467, 0.0203, 0.7383,\n",
       "         0.0233, 0.3123, 0.9896, 0.0312, 0.0223, 0.5189, 0.1372]),\n",
       " tensor([0.0221, 0.0379, 0.0256, 0.0177, 0.4459, 0.0303, 0.0274, 0.0320, 0.3871,\n",
       "         0.0346, 0.3869, 0.0225, 0.9664, 0.0238, 0.0262, 0.0359]),\n",
       " tensor([0.0343, 0.8682, 0.5235, 0.0177, 0.0822, 0.0626, 0.3939, 0.0565, 0.0257,\n",
       "         0.0279, 0.1602, 0.0318, 0.4698, 0.0270, 0.0249, 0.0181]),\n",
       " tensor([0.0241, 0.0211, 0.0223, 0.0272, 0.9803, 0.0202, 0.0263, 0.0245, 0.0200,\n",
       "         0.0210, 0.7285, 0.0311, 0.9399, 0.0959, 0.4057, 0.0978]),\n",
       " tensor([0.4197, 0.0243, 0.1045, 0.4421, 0.0281, 0.0156, 0.3549, 0.7013, 0.0251,\n",
       "         0.3975, 0.0244, 0.0355, 0.0301, 0.0217, 0.0196, 0.0192]),\n",
       " tensor([0.0214, 0.9267, 0.0174, 0.9909, 0.0978, 0.0312, 0.9370, 0.5747, 0.4017,\n",
       "         0.0290, 0.2599, 0.4563, 0.0352, 0.9900, 0.0234, 0.0177]),\n",
       " tensor([0.9746, 0.1043, 0.0246, 0.0239, 0.4407, 0.0196, 0.0381, 0.0249, 0.0326,\n",
       "         0.1781, 0.8198, 0.0438, 0.0344, 0.1050, 0.9800, 0.0333]),\n",
       " tensor([0.9946, 0.2116, 0.0429, 0.0188, 0.9644, 0.1809, 0.0984, 0.0172, 0.0863,\n",
       "         0.0201, 0.8567, 0.5166, 0.4526, 0.0178, 0.4000, 0.0340]),\n",
       " tensor([0.0230, 0.0210, 0.0221, 0.9524, 0.5426, 0.0246, 0.0238, 0.0214, 0.8336,\n",
       "         0.0199, 0.0325, 0.0173, 0.0280, 0.0258, 0.8963, 0.1290]),\n",
       " tensor([0.0281, 0.0242, 0.0260, 0.0204, 0.0435, 0.0269, 0.0232, 0.0258, 0.0278,\n",
       "         0.0199, 0.0191, 0.0759, 0.0183, 0.0224, 0.0283, 0.0193]),\n",
       " tensor([0.4225, 0.0238, 0.5374, 0.0195, 0.0254, 0.4310, 0.0217, 0.0282, 0.0212,\n",
       "         0.6125, 0.0188, 0.0790, 0.0186, 0.3010, 0.0257, 0.0298]),\n",
       " tensor([0.0196, 0.0519, 0.1500, 0.0269, 0.3343, 0.0900, 0.0396, 0.0177, 0.0180,\n",
       "         0.0353, 0.2703, 0.0178, 0.0222, 0.0399, 0.0487, 0.0204]),\n",
       " tensor([0.0261, 0.0214, 0.1699, 0.0195, 0.0522, 0.0200, 0.0233, 0.0210, 0.0411,\n",
       "         0.0239, 0.0832, 0.0251, 0.0474, 0.0294, 0.0262, 0.0191]),\n",
       " tensor([0.0306, 0.0226, 0.0222, 0.0215, 0.0458, 0.0251, 0.0189, 0.0286, 0.0208,\n",
       "         0.9374, 0.0249, 0.0231, 0.0226, 0.9707, 0.5576, 0.0236]),\n",
       " tensor([0.9752, 0.0266, 0.0950, 0.9675, 0.0820, 0.0388, 0.7040, 0.0206, 0.9685,\n",
       "         0.0364, 0.0415, 0.0184, 0.0184, 0.0248, 0.0221, 0.0605]),\n",
       " tensor([0.0175, 0.0203, 0.0336, 0.3193, 0.0445, 0.8754, 0.0366, 0.0536, 0.0164,\n",
       "         0.0359, 0.9833, 0.0262, 0.0179, 0.0258, 0.0177, 0.0282]),\n",
       " tensor([0.0214, 0.0191, 0.0260, 0.0217, 0.1061, 0.0191, 0.9749, 0.0613, 0.0169,\n",
       "         0.0210, 0.0361, 0.0289, 0.0240, 0.0260, 0.0214, 0.0209]),\n",
       " tensor([0.0205, 0.0396, 0.0181, 0.0242, 0.0199, 0.0236, 0.0238, 0.0333, 0.0909,\n",
       "         0.0237, 0.0221, 0.0318, 0.0248, 0.0246, 0.3068, 0.0253]),\n",
       " tensor([0.4748, 0.0196, 0.0242, 0.0193, 0.0244, 0.3601, 0.0207, 0.0232, 0.0225,\n",
       "         0.8965, 0.0280, 0.0215, 0.0407, 0.0209, 0.0177, 0.4694]),\n",
       " tensor([0.0211, 0.5505, 0.9181, 0.6111, 0.0223, 0.0403, 0.0170, 0.0522, 0.0186,\n",
       "         0.9368, 0.0242, 0.0233, 0.0228, 0.0174, 0.0213, 0.0248]),\n",
       " tensor([0.0220, 0.0203, 0.0230, 0.0775, 0.0241, 0.0229, 0.0224, 0.0222, 0.0258,\n",
       "         0.0186, 0.0183, 0.0151, 0.0354, 0.0328, 0.0248, 0.4542]),\n",
       " tensor([0.9398, 0.0447, 0.0201, 0.0255, 0.0209, 0.0184, 0.0269, 0.0287, 0.0195,\n",
       "         0.0238, 0.0266, 0.9923, 0.0279, 0.0287, 0.0176, 0.0426]),\n",
       " tensor([0.0297, 0.0244, 0.0362, 0.7648, 0.0203, 0.0296, 0.0176, 0.0195, 0.0218,\n",
       "         0.0212, 0.0234, 0.2992, 0.9902, 0.0204, 0.0267, 0.1148]),\n",
       " tensor([0.0243, 0.5258, 0.0211, 0.0189, 0.0272, 0.0196, 0.0184, 0.0226, 0.0172,\n",
       "         0.5917, 0.0351, 0.0241, 0.0296, 0.0333, 0.0254, 0.0255]),\n",
       " tensor([0.0452, 0.0285, 0.0467, 0.0190, 0.0247, 0.0222, 0.0170, 0.0239, 0.0386,\n",
       "         0.0220, 0.7466, 0.0280, 0.0193, 0.0182, 0.0248, 0.0717]),\n",
       " tensor([0.0371, 0.0180, 0.0210, 0.0218, 0.0301, 0.0191, 0.0214, 0.0257, 0.7532,\n",
       "         0.0671, 0.0241, 0.0244, 0.8156, 0.0162, 0.0289, 0.0292]),\n",
       " tensor([0.0186, 0.0189, 0.4646, 0.0270, 0.0310, 0.0243, 0.0989, 0.0214, 0.0196,\n",
       "         0.0406, 0.0206, 0.0298, 0.0667, 0.0229, 0.0304, 0.0179]),\n",
       " tensor([0.3635, 0.0225, 0.0212, 0.0244, 0.0292, 0.0222, 0.8259, 0.0202, 0.2474,\n",
       "         0.0303, 0.0228, 0.9368, 0.9768, 0.0225, 0.0224, 0.0249]),\n",
       " tensor([0.0217, 0.0796, 0.0204, 0.0298, 0.0192, 0.0203, 0.0197, 0.0251, 0.0227,\n",
       "         0.0248, 0.0247, 0.0211, 0.0189, 0.0228, 0.0333, 0.0201]),\n",
       " tensor([0.0196, 0.5007, 0.0238, 0.0556, 0.2598, 0.9784, 0.0252, 0.0184, 0.0416,\n",
       "         0.0210, 0.0166, 0.0175, 0.0203, 0.0245, 0.0402, 0.0169]),\n",
       " tensor([0.0195, 0.0249, 0.0429, 0.0251, 0.0203, 0.0243, 0.1850, 0.0253, 0.0741,\n",
       "         0.0196, 0.0206, 0.0189, 0.0312, 0.0224, 0.0237, 0.0174]),\n",
       " tensor([0.0186, 0.0175, 0.0172, 0.0225, 0.0377, 0.4677, 0.1747, 0.9629, 0.9796,\n",
       "         0.6953, 0.0235, 0.0257, 0.0239, 0.0277, 0.0215, 0.0309]),\n",
       " tensor([0.0207, 0.0210, 0.0221, 0.1212, 0.0267, 0.0271, 0.0205, 0.0199, 0.0226,\n",
       "         0.0183, 0.0161, 0.0225, 0.0277, 0.0479, 0.0551, 0.0301]),\n",
       " tensor([0.4438, 0.0255, 0.0232, 0.0400, 0.0248, 0.0233, 0.0451, 0.0210, 0.0334,\n",
       "         0.0229, 0.0275, 0.0255, 0.0189, 0.0208, 0.0214, 0.0261]),\n",
       " tensor([0.0359, 0.9646, 0.0590, 0.0270, 0.5997, 0.0316, 0.0201, 0.0222, 0.0197,\n",
       "         0.0226, 0.1624, 0.0195, 0.0260, 0.0158, 0.0214, 0.0188]),\n",
       " tensor([0.0252, 0.0249, 0.0181, 0.0211, 0.0214, 0.9828, 0.1165, 0.0229, 0.0234,\n",
       "         0.0271, 0.0287, 0.9789, 0.1651, 0.0284, 0.0221, 0.0257]),\n",
       " tensor([0.0447, 0.1291, 0.0341, 0.0254, 0.4675, 0.0250, 0.0272, 0.0276, 0.0205,\n",
       "         0.0246, 0.0252, 0.0571, 0.9070, 0.0182, 0.8546, 0.0221]),\n",
       " tensor([0.1445, 0.5033, 0.0243, 0.6397, 0.0195, 0.0239, 0.0174, 0.0184, 0.0175,\n",
       "         0.0215, 0.0237, 0.0230, 0.0332, 0.0218, 0.0178, 0.0212]),\n",
       " tensor([0.0402, 0.0251, 0.0190, 0.0254, 0.0231, 0.0257, 0.0203, 0.0239, 0.0210,\n",
       "         0.0253, 0.0305, 0.0174, 0.0601, 0.0439, 0.0307, 0.0281]),\n",
       " tensor([0.8451, 0.0261, 0.1339, 0.2983, 0.0206, 0.1357, 0.0235, 0.0177, 0.0356,\n",
       "         0.0178, 0.0269, 0.0169, 0.0378, 0.0255, 0.0266, 0.0269]),\n",
       " tensor([0.0282, 0.0271, 0.0329, 0.0260, 0.0375, 0.0212, 0.0201, 0.0328, 0.0278,\n",
       "         0.6411, 0.0223, 0.0200, 0.0407, 0.0189, 0.0177, 0.0210]),\n",
       " tensor([0.0207, 0.0182, 0.0252, 0.0204, 0.0246, 0.0202, 0.0296, 0.0209, 0.0462,\n",
       "         0.4236, 0.0234, 0.0255, 0.0173, 0.0212, 0.0429, 0.0193]),\n",
       " tensor([0.0208, 0.0199, 0.0378, 0.0254, 0.0236, 0.0251, 0.0207, 0.0246, 0.0178,\n",
       "         0.0173, 0.0423, 0.0308, 0.0252, 0.0253, 0.0207, 0.0590]),\n",
       " tensor([0.0235, 0.0219, 0.0288, 0.0234, 0.0747, 0.0284, 0.0366, 0.0199, 0.0461,\n",
       "         0.0252, 0.0218, 0.0395, 0.0201, 0.0247, 0.0280, 0.0304]),\n",
       " tensor([0.8402, 0.0204, 0.0218, 0.0264, 0.0376, 0.0323, 0.0221, 0.0194, 0.1150,\n",
       "         0.0806, 0.0280, 0.4967, 0.0183, 0.8262, 0.7094, 0.9816]),\n",
       " tensor([0.0226, 0.0221, 0.0230, 0.0214, 0.0180, 0.0182, 0.0221, 0.0678, 0.0295,\n",
       "         0.0274, 0.0308, 0.0227, 0.0208, 0.0196, 0.0181, 0.0293]),\n",
       " tensor([0.0201, 0.0301, 0.0366, 0.0475, 0.0236, 0.0342, 0.0280, 0.5454, 0.0265,\n",
       "         0.0188, 0.0270, 0.0298, 0.0329, 0.0264, 0.3962, 0.3036]),\n",
       " tensor([0.0300, 0.0246, 0.0229, 0.0168, 0.0403, 0.2601, 0.0224, 0.6750, 0.0575,\n",
       "         0.3667, 0.0556, 0.0308, 0.0968, 0.0399, 0.0238, 0.9585]),\n",
       " tensor([0.0240, 0.0303, 0.0949, 0.0618, 0.0376, 0.0684, 0.0243, 0.0232, 0.0734,\n",
       "         0.8521, 0.0349, 0.4166, 0.9476, 0.0218, 0.0347, 0.0303]),\n",
       " tensor([0.0263, 0.0243, 0.0350, 0.0275, 0.0211, 0.0294, 0.0272, 0.0214, 0.0176,\n",
       "         0.4154, 0.3618, 0.0486, 0.0235, 0.0535, 0.0198, 0.0195]),\n",
       " tensor([0.7356, 0.1740, 0.6955, 0.0293, 0.0252, 0.0283, 0.0366, 0.8160, 0.0304,\n",
       "         0.0319, 0.0225, 0.9821, 0.0214, 0.5504, 0.8375, 0.0417]),\n",
       " tensor([0.0343, 0.0179, 0.3476, 0.8042, 0.0643, 0.7982, 0.2418, 0.3769, 0.0602,\n",
       "         0.9179, 0.5613, 0.4130, 0.7026, 0.1510, 0.0318, 0.0297]),\n",
       " tensor([0.6357, 0.0206, 0.0205, 0.0198, 0.7656, 0.0223, 0.0220, 0.0402, 0.0314,\n",
       "         0.0285, 0.0304, 0.5244, 0.0222, 0.0297, 0.0266, 0.0233]),\n",
       " tensor([0.0199, 0.9811, 0.0259, 0.3981, 0.0239, 0.0249, 0.6799, 0.0266, 0.0285,\n",
       "         0.0232, 0.0219, 0.0171, 0.0269, 0.4479, 0.0169, 0.0227]),\n",
       " tensor([0.6105, 0.0221, 0.0258, 0.1497, 0.1180, 0.0192, 0.0224, 0.0205, 0.0422,\n",
       "         0.5530, 0.0289, 0.0231, 0.0234, 0.0186, 0.0262, 0.0248]),\n",
       " tensor([0.0213, 0.7238, 0.0230, 0.0159, 0.0289, 0.0218, 0.0188, 0.0326, 0.0298,\n",
       "         0.0188, 0.0218, 0.0200, 0.0345, 0.0201, 0.0192, 0.0195]),\n",
       " tensor([0.0297, 0.0407, 0.9453, 0.0213, 0.0299, 0.0421, 0.0196, 0.0336, 0.0311,\n",
       "         0.0316, 0.0351, 0.5849, 0.0240, 0.0262, 0.0188, 0.0226]),\n",
       " tensor([0.0426, 0.0604, 0.0207, 0.0225, 0.0307, 0.0472, 0.0308, 0.0891, 0.2246,\n",
       "         0.3479, 0.0336, 0.0195, 0.0166, 0.0193, 0.0251, 0.0233]),\n",
       " tensor([0.0425, 0.0471, 0.0314, 0.6356, 0.0215, 0.0212, 0.6226, 0.6474, 0.4083,\n",
       "         0.7286, 0.0257, 0.3000, 0.9507, 0.0322, 0.2485, 0.0330]),\n",
       " tensor([0.9197, 0.0322, 0.0376, 0.0479, 0.5536, 0.9247, 0.0203, 0.0253, 0.0212,\n",
       "         0.4212, 0.0216, 0.0372, 0.0551, 0.0262, 0.0210, 0.0162]),\n",
       " tensor([0.3830, 0.0196, 0.0243, 0.2537, 0.0187, 0.0242, 0.0379, 0.0275, 0.9721,\n",
       "         0.0869, 0.2007, 0.9253, 0.0341, 0.0169, 0.0325, 0.0259]),\n",
       " tensor([0.0895, 0.0295, 0.0588, 0.0318, 0.0204, 0.9054, 0.0189, 0.0211, 0.0170,\n",
       "         0.0187, 0.0407, 0.6101, 0.0379, 0.0480, 0.0201, 0.4481]),\n",
       " tensor([0.0294, 0.9221, 0.8781, 0.0417, 0.9043, 0.4061, 0.5815, 0.5464, 0.3573,\n",
       "         0.0337, 0.4373, 0.1429, 0.5777, 0.3669, 0.9355, 0.9862]),\n",
       " tensor([0.0362, 0.5494, 0.9889, 0.2009, 0.9866, 0.0199, 0.7508, 0.8134, 0.0461,\n",
       "         0.4612, 0.0316, 0.0256, 0.9955, 0.5651, 0.0736, 0.0196]),\n",
       " tensor([0.0331, 0.0199, 0.0241, 0.0314, 0.0169, 0.0262, 0.0272, 0.0205, 0.0441,\n",
       "         0.0225, 0.0300, 0.0211, 0.0189, 0.0408, 0.0273, 0.0187]),\n",
       " tensor([0.0236, 0.0440, 0.9024, 0.0586, 0.0216, 0.0656, 0.0220, 0.0290, 0.0362,\n",
       "         0.5352, 0.0239, 0.0220, 0.0258, 0.0258, 0.0281, 0.0885]),\n",
       " tensor([0.0162, 0.0219, 0.9617, 0.0242, 0.0211, 0.0309, 0.0298, 0.0293, 0.0215,\n",
       "         0.0558, 0.0358, 0.0199, 0.0183, 0.0173, 0.0381, 0.0217]),\n",
       " tensor([0.0222, 0.6547, 0.0653, 0.8939, 0.0187, 0.1519, 0.0172, 0.0180, 0.4991,\n",
       "         0.0240, 0.0301, 0.0383, 0.0243, 0.0472, 0.0184, 0.0194]),\n",
       " tensor([0.0193, 0.0299, 0.0164, 0.0265, 0.0248, 0.0181, 0.0569, 0.2393, 0.0200,\n",
       "         0.0204, 0.0367, 0.0308, 0.0230, 0.9875, 0.0262, 0.1148]),\n",
       " tensor([0.0208, 0.9762, 0.2102, 0.0258, 0.2496, 0.0234, 0.0184, 0.0250, 0.0210,\n",
       "         0.0246, 0.0218, 0.0252, 0.0282, 0.0269, 0.0354, 0.0230]),\n",
       " tensor([0.0189, 0.0303, 0.0254, 0.0242, 0.0346, 0.0236, 0.0943, 0.0406, 0.0363,\n",
       "         0.4907, 0.0313, 0.0241, 0.0408, 0.0287, 0.0246, 0.0203]),\n",
       " tensor([0.0212, 0.0191, 0.0293, 0.0472, 0.1763, 0.9134, 0.6558, 0.0290, 0.0282,\n",
       "         0.0277, 0.0397, 0.0213, 0.0276, 0.0202, 0.0189, 0.0189]),\n",
       " tensor([0.0161, 0.0205, 0.0218, 0.0259, 0.0199, 0.0318, 0.0196, 0.0314, 0.0247,\n",
       "         0.0493, 0.0300, 0.0187, 0.0235, 0.8868, 0.0215, 0.0179]),\n",
       " tensor([0.0190, 0.0286, 0.4197, 0.0228, 0.0188, 0.4385, 0.0300, 0.0233, 0.0202,\n",
       "         0.0180, 0.0209, 0.0162, 0.0269, 0.0524, 0.0255, 0.9303]),\n",
       " tensor([0.0286, 0.0222, 0.0180, 0.0180, 0.0367, 0.0212, 0.0176, 0.0185, 0.0229,\n",
       "         0.0205, 0.0300, 0.7986, 0.9823, 0.1389, 0.0191, 0.0392]),\n",
       " tensor([0.1555, 0.0310, 0.0233, 0.0216, 0.0289, 0.0331, 0.0329, 0.0182, 0.0241,\n",
       "         0.0969, 0.0652, 0.0196, 0.9295, 0.8944, 0.3765, 0.0196]),\n",
       " tensor([0.0164, 0.0262, 0.0364, 0.0319, 0.0220, 0.0349, 0.0206, 0.0354, 0.0226,\n",
       "         0.7766, 0.0240, 0.0370, 0.7487, 0.0197, 0.0357, 0.0235]),\n",
       " tensor([0.0475, 0.9852, 0.0231, 0.0183, 0.4154, 0.0261, 0.0224, 0.0210, 0.0220,\n",
       "         0.0203, 0.0229, 0.0210, 0.0213, 0.0189, 0.9054, 0.0246]),\n",
       " tensor([0.0191, 0.0257, 0.0214, 0.0334, 0.0290, 0.0330, 0.0621, 0.0191, 0.0201,\n",
       "         0.0221, 0.0222, 0.0263, 0.0246, 0.0218, 0.0193, 0.0187]),\n",
       " tensor([0.0519, 0.0307, 0.0234, 0.0313, 0.0325, 0.0213, 0.0318, 0.0242, 0.4454,\n",
       "         0.0528, 0.0214, 0.3524, 0.0703, 0.0270, 0.5617, 0.8899]),\n",
       " tensor([0.4704, 0.0292, 0.7594, 0.0280, 0.0256, 0.0186, 0.0173, 0.0180, 0.0197,\n",
       "         0.0220, 0.0233, 0.1527, 0.0257, 0.0355, 0.0229, 0.0233]),\n",
       " tensor([0.0184, 0.0248, 0.5422, 0.0220, 0.0285, 0.0223, 0.0225, 0.0314, 0.0253,\n",
       "         0.0329, 0.0174, 0.0245, 0.0202, 0.0183, 0.0353, 0.0220]),\n",
       " tensor([0.0233, 0.0238, 0.1555, 0.0196, 0.0200, 0.0748, 0.0240, 0.0507, 0.0414,\n",
       "         0.2690, 0.1566, 0.0297, 0.0257, 0.0299, 0.0254, 0.3902]),\n",
       " tensor([0.0151, 0.0524, 0.0239, 0.0432, 0.0270, 0.3867, 0.0275, 0.9266, 0.0228,\n",
       "         0.6798, 0.0221, 0.3708, 0.0215, 0.0201, 0.0233, 0.0206]),\n",
       " tensor([0.0253, 0.4002, 0.0211, 0.0230, 0.0239, 0.0343, 0.0176, 0.0567, 0.0207,\n",
       "         0.0455, 0.0176, 0.0171, 0.0172, 0.0199, 0.0222, 0.0602]),\n",
       " tensor([0.3032, 0.0254, 0.0254, 0.0169, 0.0232, 0.0252, 0.0188, 0.0209, 0.0679,\n",
       "         0.0204, 0.0200, 0.0401, 0.0812, 0.0239, 0.0255, 0.0168]),\n",
       " tensor([0.0257, 0.0219, 0.9303, 0.6701, 0.0373, 0.0180, 0.0506, 0.0279, 0.0295,\n",
       "         0.0314, 0.0284, 0.0249, 0.0353, 0.0262, 0.0204, 0.0255]),\n",
       " tensor([0.0240, 0.0286, 0.0422, 0.0236, 0.0229, 0.0172, 0.0672, 0.0186, 0.4756,\n",
       "         0.0282, 0.0218, 0.0603, 0.0284, 0.0561, 0.1170, 0.9846]),\n",
       " tensor([0.4689, 0.1678, 0.0221, 0.0198, 0.0273, 0.9867, 0.0233, 0.0220, 0.0304,\n",
       "         0.0306, 0.0562, 0.9671, 0.0253, 0.9848, 0.9522, 0.0207]),\n",
       " tensor([0.9816, 0.0209, 0.0513, 0.0253, 0.0259, 0.0296, 0.0272, 0.0191, 0.0805,\n",
       "         0.0229, 0.0257, 0.0299, 0.0172, 0.0217, 0.0195, 0.0413]),\n",
       " tensor([0.0453, 0.0410, 0.0234, 0.0408, 0.0190, 0.9628, 0.0236, 0.0216, 0.0257,\n",
       "         0.0204, 0.9728, 0.0190, 0.0268, 0.8533, 0.1698, 0.4542]),\n",
       " tensor([0.8623, 0.8253, 0.0215, 0.0169, 0.1240, 0.0172, 0.0250, 0.0169, 0.0945,\n",
       "         0.0218, 0.0383, 0.0169, 0.0205, 0.3774, 0.0190, 0.0382]),\n",
       " tensor([0.0178, 0.0228, 0.0318, 0.0250, 0.0230, 0.0181, 0.0174, 0.0258, 0.0159,\n",
       "         0.0193, 0.0217, 0.0298, 0.9817, 0.0241, 0.0350, 0.1905]),\n",
       " tensor([0.0274, 0.2606, 0.0369, 0.0216, 0.0175, 0.0199, 0.0354, 0.0181, 0.0256,\n",
       "         0.1744, 0.0226, 0.0369, 0.0279, 0.0626, 0.0411, 0.0268]),\n",
       " tensor([0.0169, 0.0298, 0.0219, 0.9867, 0.0209, 0.4422, 0.0219, 0.0216, 0.0205,\n",
       "         0.0409, 0.0358, 0.9781, 0.0174, 0.0242, 0.0335, 0.7472]),\n",
       " tensor([0.0677, 0.0218, 0.0188, 0.0184, 0.0199, 0.0167, 0.0255, 0.0394, 0.0283,\n",
       "         0.0194, 0.0249, 0.0373, 0.0335, 0.0208, 0.0263, 0.0243]),\n",
       " tensor([0.0206, 0.0563, 0.0322, 0.0421, 0.0181, 0.0297, 0.0205, 0.0214, 0.0202,\n",
       "         0.0206, 0.0184, 0.0237, 0.0209, 0.0163, 0.0226, 0.0442]),\n",
       " tensor([0.0290, 0.0257, 0.0404, 0.0157, 0.0261, 0.0321, 0.0218, 0.0290, 0.0221,\n",
       "         0.0215, 0.3706, 0.0216, 0.0389, 0.0181, 0.0326, 0.0250]),\n",
       " tensor([0.0376, 0.0252, 0.0197, 0.0342, 0.0216, 0.0376, 0.0260, 0.0245, 0.0451,\n",
       "         0.0212, 0.0175, 0.0277, 0.0184, 0.0223, 0.0293, 0.0386]),\n",
       " tensor([0.0289, 0.0198, 0.0264, 0.0200, 0.3569, 0.0293, 0.0571, 0.7110, 0.9768,\n",
       "         0.0359, 0.0321, 0.1508, 0.1407, 0.0322, 0.0546, 0.0299]),\n",
       " tensor([0.0223, 0.0315, 0.0165, 0.0262, 0.0258, 0.0242, 0.0225, 0.0209, 0.0168,\n",
       "         0.5780, 0.0186, 0.7420, 0.0167, 0.0360, 0.0236, 0.0206]),\n",
       " tensor([0.8651, 0.9536, 0.5405, 0.0232, 0.0281, 0.0264, 0.8272, 0.0192, 0.8588,\n",
       "         0.0241, 0.2058, 0.0248, 0.0201, 0.0178, 0.0287, 0.0300]),\n",
       " tensor([0.0262, 0.0285, 0.0273, 0.0199, 0.0187, 0.2410, 0.0204, 0.0226, 0.0400,\n",
       "         0.0222, 0.0555, 0.9354, 0.0180, 0.0264, 0.0266, 0.0301]),\n",
       " tensor([0.0229, 0.2684, 0.0247, 0.0344, 0.0228, 0.0230, 0.0386, 0.0260, 0.0309,\n",
       "         0.0191, 0.3455, 0.0259, 0.0232, 0.0233, 0.0356, 0.0249]),\n",
       " tensor([0.0202, 0.0262, 0.0170, 0.0204, 0.0178, 0.5375, 0.9910, 0.8032, 0.0297,\n",
       "         0.0227, 0.0272, 0.0366, 0.0240, 0.0196, 0.2759, 0.4486]),\n",
       " tensor([0.3864, 0.0285, 0.0643, 0.1044, 0.0252, 0.2114, 0.4505, 0.8354, 0.2941,\n",
       "         0.2941, 0.0330, 0.0612, 0.0431, 0.6618, 0.0212, 0.0275]),\n",
       " tensor([0.0180, 0.0312, 0.0257, 0.5683, 0.0269, 0.3627, 0.2048, 0.0182, 0.0265,\n",
       "         0.0312, 0.0165, 0.0204, 0.0289, 0.0249, 0.0251, 0.0285]),\n",
       " tensor([0.0280, 0.0340, 0.0206, 0.0313, 0.0334, 0.0236, 0.0213, 0.0319, 0.0290,\n",
       "         0.0287, 0.0175, 0.0260, 0.0238, 0.0194, 0.0253, 0.0197]),\n",
       " tensor([0.5722, 0.7895, 0.8339, 0.0263, 0.0248, 0.0212, 0.5313, 0.0196, 0.8616,\n",
       "         0.1225, 0.0184, 0.7578, 0.0220, 0.2024, 0.0323, 0.0218]),\n",
       " tensor([0.0581, 0.8557, 0.0286, 0.0824, 0.0386, 0.0863, 0.4099, 0.0278, 0.0253,\n",
       "         0.0222, 0.0361, 0.0203, 0.0307, 0.0274, 0.0219, 0.0259]),\n",
       " tensor([0.0186, 0.0239, 0.0228, 0.0276, 0.0605, 0.1934, 0.0170, 0.0224, 0.0269,\n",
       "         0.0197, 0.0280, 0.0281, 0.0203, 0.0181, 0.0637, 0.1079]),\n",
       " tensor([0.5531, 0.9910, 0.7479, 0.0386, 0.0269, 0.4637, 0.1713, 0.0238, 0.0220,\n",
       "         0.0565, 0.0233, 0.0588, 0.0225, 0.0438, 0.0311, 0.0248]),\n",
       " tensor([0.0390, 0.0228, 0.0997, 0.0197, 0.0202, 0.9890, 0.0235, 0.2012, 0.0227,\n",
       "         0.8218, 0.0271, 0.0980, 0.0184, 0.0453, 0.0592, 0.3170]),\n",
       " tensor([0.0298, 0.0187, 0.0230, 0.0321, 0.6385, 0.0211, 0.0350, 0.0188, 0.0347,\n",
       "         0.0240, 0.0225, 0.0245, 0.1715, 0.0175, 0.0256, 0.0221]),\n",
       " tensor([0.0495, 0.3394, 0.0594, 0.0305, 0.0222, 0.0264, 0.0197, 0.0252, 0.0240,\n",
       "         0.0270, 0.5930, 0.0192, 0.0298, 0.0285, 0.0259, 0.0286]),\n",
       " tensor([0.9887, 0.1541, 0.0216, 0.0204, 0.0213, 0.0185, 0.0212, 0.2296, 0.0206,\n",
       "         0.9273, 0.0208, 0.0258, 0.2953, 0.0219, 0.0253, 0.0224]),\n",
       " tensor([0.0214, 0.0226, 0.0315, 0.0213, 0.0360, 0.0226, 0.0281, 0.0193, 0.0193,\n",
       "         0.0392, 0.0210, 0.0303, 0.0237, 0.0298, 0.0330, 0.0309]),\n",
       " tensor([0.0224, 0.0209, 0.0269, 0.3486, 0.0481, 0.0345, 0.0253, 0.0162, 0.8670,\n",
       "         0.0204, 0.0167, 0.0205, 0.0263, 0.0436, 0.8673, 0.0921]),\n",
       " tensor([0.0450, 0.0953, 0.0248, 0.0187, 0.0211, 0.0229, 0.0246, 0.0507, 0.0394,\n",
       "         0.0426, 0.0310, 0.0212, 0.0171, 0.0226, 0.0215, 0.0244]),\n",
       " tensor([0.0186, 0.0176, 0.0231, 0.0197, 0.0439, 0.0181, 0.0276, 0.0283, 0.0205,\n",
       "         0.4279, 0.0196, 0.7608, 0.0251, 0.0275, 0.0255, 0.9608]),\n",
       " tensor([0.0264, 0.0296, 0.0294, 0.0235, 0.0358, 0.8861, 0.9654, 0.0191, 0.0532,\n",
       "         0.0253, 0.0321, 0.0238, 0.0193, 0.0303, 0.0185, 0.1176]),\n",
       " tensor([0.0269, 0.0630, 0.0216, 0.8779, 0.0340, 0.0255, 0.0202, 0.0197, 0.0176,\n",
       "         0.0206, 0.0230, 0.0211, 0.0243, 0.9352, 0.0183, 0.0298]),\n",
       " tensor([0.0163, 0.4941, 0.0236, 0.0202, 0.7408, 0.0300, 0.0247, 0.0386, 0.0198,\n",
       "         0.0186, 0.0230, 0.0275, 0.0282, 0.0193, 0.0201, 0.0329]),\n",
       " tensor([0.0236, 0.0189, 0.0211, 0.0210, 0.0298, 0.0879, 0.0216, 0.0193, 0.0270,\n",
       "         0.0286, 0.0207, 0.0244, 0.9964, 0.3897, 0.0205, 0.0181]),\n",
       " tensor([0.0333, 0.2385, 0.0207, 0.0225, 0.0423, 0.0332, 0.9891, 0.0187, 0.0222,\n",
       "         0.0204, 0.0184, 0.0169, 0.0237, 0.0236, 0.0294, 0.0268]),\n",
       " tensor([0.0384, 0.0229, 0.1064, 0.0187, 0.0183, 0.0241, 0.0475, 0.0255, 0.0190,\n",
       "         0.0271, 0.0252, 0.0306, 0.0250, 0.0215, 0.0228, 0.0209]),\n",
       " tensor([0.0232, 0.0257, 0.0194, 0.0260, 0.0371, 0.0242, 0.0392, 0.9008, 0.0434,\n",
       "         0.0214, 0.0244, 0.0233, 0.4374, 0.4775, 0.0206, 0.0193]),\n",
       " tensor([0.0279, 0.1364, 0.0640, 0.0233, 0.0564, 0.4340, 0.0372, 0.0193, 0.0310,\n",
       "         0.0470, 0.0229, 0.0544, 0.0396, 0.0217, 0.0217, 0.0266]),\n",
       " tensor([0.0467, 0.0170, 0.5162, 0.0159, 0.0243, 0.0394, 0.0263, 0.0196, 0.0256,\n",
       "         0.8229, 0.0189, 0.6500, 0.0213, 0.0189, 0.0212, 0.0261]),\n",
       " tensor([0.0294, 0.0246, 0.9632, 0.1590, 0.4488, 0.0252, 0.0348, 0.0827, 0.2192,\n",
       "         0.0242, 0.0557, 0.0248, 0.0271, 0.0167, 0.0249, 0.3658]),\n",
       " tensor([0.5290, 0.0315, 0.0309, 0.0543, 0.1231, 0.0201, 0.0258, 0.0175, 0.0330,\n",
       "         0.9761, 0.0186, 0.0448, 0.0257, 0.0274, 0.0290, 0.0232]),\n",
       " tensor([0.0390, 0.8775, 0.0265, 0.0229, 0.0211, 0.0222, 0.0475, 0.0238, 0.0188,\n",
       "         0.0271, 0.0247, 0.0299, 0.0223, 0.0420, 0.0173, 0.2309]),\n",
       " tensor([0.0273, 0.0164, 0.0258, 0.0259, 0.0172, 0.0366, 0.0569, 0.0226, 0.9663,\n",
       "         0.0241, 0.0201, 0.0179, 0.0244, 0.0257, 0.0252, 0.0221]),\n",
       " tensor([0.9738, 0.0188, 0.0202, 0.4288, 0.0247, 0.0178, 0.0242, 0.0196, 0.0173,\n",
       "         0.0231, 0.0182, 0.0261, 0.0205, 0.0185, 0.0277, 0.0491]),\n",
       " tensor([0.0256, 0.0256, 0.0386, 0.4931, 0.0941, 0.0188, 0.1652, 0.1283, 0.0322,\n",
       "         0.0333, 0.0233, 0.0233, 0.0257, 0.0191, 0.0185, 0.0220]),\n",
       " tensor([0.0321, 0.4957, 0.0314, 0.0272, 0.0277, 0.0185, 0.0253, 0.0338, 0.3307,\n",
       "         0.0179, 0.0235, 0.0179, 0.0218, 0.2000, 0.0528, 0.0255]),\n",
       " tensor([0.0193, 0.0224, 0.0209, 0.0270, 0.0513, 0.0251, 0.9912, 0.0161, 0.0193,\n",
       "         0.0438, 0.7839, 0.0188, 0.0198, 0.9253, 0.0678, 0.0190]),\n",
       " tensor([0.0285, 0.9627, 0.3918, 0.0220, 0.6548, 0.1555, 0.0298, 0.0205, 0.0364,\n",
       "         0.0499, 0.0362, 0.0327, 0.0221, 0.0282, 0.0382, 0.8064]),\n",
       " tensor([0.0186, 0.9460, 0.9656, 0.8097, 0.0189, 0.0201, 0.9938, 0.0647, 0.9358,\n",
       "         0.0233, 0.0232, 0.0228, 0.9500, 0.0430, 0.0223, 0.0169]),\n",
       " tensor([0.0273, 0.9964, 0.0227, 0.9891, 0.0206, 0.0369, 0.0170, 0.0187, 0.0196,\n",
       "         0.0249, 0.0200, 0.0202, 0.0243, 0.0280, 0.0243, 0.0261]),\n",
       " tensor([0.2878, 0.0255, 0.5969, 0.0242, 0.0222, 0.9859, 0.9815, 0.0233, 0.0211,\n",
       "         0.0301, 0.0206, 0.0798, 0.0311, 0.0375, 0.0177, 0.0324]),\n",
       " tensor([0.9876, 0.0336, 0.0192, 0.0528, 0.0242, 0.0771, 0.0204, 0.0221, 0.0256,\n",
       "         0.0193, 0.0197, 0.9873, 0.0176, 0.9966, 0.0532, 0.7131]),\n",
       " tensor([0.0297, 0.0277, 0.0238, 0.0247, 0.0270, 0.7464, 0.1990, 0.0181, 0.0737,\n",
       "         0.0224, 0.0213, 0.0185, 0.0230, 0.0204, 0.0164, 0.0198]),\n",
       " tensor([0.0230, 0.0273, 0.0286, 0.0190, 0.0310, 0.0188, 0.0406, 0.0178, 0.0230,\n",
       "         0.0196, 0.0532, 0.0292, 0.6612, 0.0263, 0.0298, 0.0184]),\n",
       " tensor([0.0344, 0.0204, 0.0455, 0.9946, 0.8445, 0.0193, 0.0253, 0.9163, 0.0189,\n",
       "         0.0229, 0.0250, 0.0204, 0.0192, 0.0246, 0.0706, 0.0311]),\n",
       " tensor([0.0222, 0.0201, 0.0183, 0.0245, 0.0348, 0.0567, 0.0200, 0.0185, 0.0352,\n",
       "         0.0171, 0.2740, 0.0223, 0.0179, 0.0193, 0.0211, 0.0347]),\n",
       " tensor([0.0276, 0.0296, 0.0218, 0.0269, 0.0301, 0.0251, 0.0207, 0.0278, 0.9720,\n",
       "         0.0260, 0.0249, 0.4895, 0.9967, 0.0186, 0.0252, 0.0215]),\n",
       " tensor([0.0230, 0.0221, 0.0273, 0.0192, 0.0339, 0.0418, 0.3147, 0.0226, 0.8193,\n",
       "         0.0193, 0.0181, 0.8046, 0.4928, 0.7872, 0.8062, 0.0327]),\n",
       " tensor([0.0191, 0.0231, 0.8364, 0.5122, 0.2736, 0.0278, 0.0214, 0.1029, 0.0174,\n",
       "         0.0189, 0.0201, 0.0299, 0.0238, 0.0283, 0.0268, 0.7378]),\n",
       " tensor([0.0201, 0.0228, 0.7284, 0.0228, 0.0191, 0.0461, 0.0254, 0.0228, 0.9871,\n",
       "         0.0275, 0.0305, 0.0215, 0.0179, 0.1721, 0.0175, 0.0196]),\n",
       " tensor([0.9777, 0.0284, 0.0371, 0.0236, 0.0181, 0.0234, 0.0260, 0.0239, 0.0233,\n",
       "         0.0201, 0.0197, 0.0199, 0.0190, 0.0172, 0.1573, 0.1823]),\n",
       " tensor([0.9965, 0.9047, 0.0235, 0.5560, 0.0288, 0.0260, 0.0287, 0.0242, 0.0227,\n",
       "         0.8816, 0.0245, 0.4719, 0.4729, 0.0326, 0.0288, 0.0163]),\n",
       " tensor([0.0220, 0.0159, 0.0211, 0.0237, 0.0204, 0.0216, 0.0237, 0.0210, 0.0212,\n",
       "         0.0328, 0.0182, 0.0237, 0.0202, 0.0310, 0.0297, 0.0403]),\n",
       " tensor([0.0188, 0.0668, 0.0210, 0.0207, 0.0317, 0.0231, 0.0503, 0.0192, 0.6745,\n",
       "         0.0280, 0.4273, 0.0232, 0.0228, 0.0243, 0.0211, 0.0256]),\n",
       " tensor([0.0222, 0.0250, 0.0190, 0.0170, 0.0203, 0.0229, 0.0189, 0.0333, 0.0513,\n",
       "         0.0214, 0.3571, 0.0356, 0.0195, 0.0243, 0.0188, 0.0282]),\n",
       " tensor([0.0217, 0.0226, 0.0189, 0.1818, 0.0277, 0.0183, 0.0205, 0.0308, 0.7467,\n",
       "         0.0160, 0.0238, 0.0187, 0.0239, 0.1769, 0.1830, 0.0218]),\n",
       " tensor([0.0191, 0.0305, 0.1612, 0.0276, 0.0224, 0.0207, 0.0175, 0.0268, 0.0250,\n",
       "         0.0214, 0.0291, 0.0230, 0.0270, 0.9362, 0.0497, 0.0174]),\n",
       " tensor([0.0213, 0.0416, 0.0183, 0.0240, 0.0392, 0.4199, 0.0603, 0.0314, 0.0447,\n",
       "         0.0387, 0.0288, 0.0429, 0.0189, 0.0230, 0.0208, 0.0185]),\n",
       " tensor([0.0212, 0.0249, 0.0470, 0.8259, 0.0183, 0.0230, 0.6990, 0.9370, 0.0262,\n",
       "         0.0197, 0.0186, 0.0237, 0.0406, 0.0334, 0.0284, 0.8843]),\n",
       " tensor([0.0236, 0.0192, 0.0317, 0.0199, 0.7338, 0.0196, 0.0248, 0.0260, 0.0248,\n",
       "         0.0219, 0.0249, 0.0176, 0.0158, 0.0199, 0.0185, 0.0182]),\n",
       " tensor([0.0232, 0.0297, 0.3245, 0.7160, 0.0223, 0.0491, 0.0203, 0.9570, 0.0280,\n",
       "         0.0181, 0.6686, 0.0305, 0.0197, 0.0177, 0.0251, 0.0196]),\n",
       " tensor([0.4796, 0.0409, 0.0324, 0.0185, 0.0230, 0.0178, 0.0292, 0.0195, 0.0245,\n",
       "         0.0331, 0.0206, 0.0207, 0.0190, 0.0221, 0.0208, 0.0337]),\n",
       " tensor([0.0202, 0.0238, 0.0261, 0.0288, 0.0248, 0.4325, 0.0266, 0.0236, 0.0232,\n",
       "         0.0285, 0.0216, 0.0255, 0.0343, 0.0217, 0.0204, 0.0269]),\n",
       " tensor([0.0233, 0.0347, 0.0188, 0.0293, 0.0246, 0.0185, 0.1510, 0.0463, 0.0243,\n",
       "         0.0243, 0.0242, 0.0361, 0.0786, 0.0190, 0.9829, 0.0183]),\n",
       " tensor([0.1129, 0.0209, 0.1000, 0.0204, 0.1527, 0.0494, 0.0198, 0.0244, 0.2469,\n",
       "         0.0277, 0.0283, 0.0178, 0.0417, 0.0215, 0.0256, 0.0222]),\n",
       " tensor([0.0664, 0.0170, 0.0219, 0.0284, 0.0255, 0.0204, 0.0203, 0.0631, 0.2713,\n",
       "         0.9812, 0.0168, 0.6007, 0.0177, 0.0212, 0.0320, 0.0194]),\n",
       " tensor([0.5401, 0.0262, 0.6434, 0.0182, 0.0195, 0.0308, 0.0291, 0.0197, 0.0209,\n",
       "         0.0181, 0.0230, 0.9373, 0.2087, 0.0183, 0.2508, 0.0281]),\n",
       " tensor([0.0267, 0.0323, 0.9851, 0.2695, 0.0222, 0.0274, 0.0240, 0.0207, 0.0173,\n",
       "         0.0246, 0.9736, 0.8196, 0.0189, 0.1248, 0.0202, 0.0230]),\n",
       " tensor([0.0304, 0.0234, 0.0203, 0.0189, 0.0266, 0.2957, 0.1694, 0.0223, 0.0297,\n",
       "         0.0196, 0.0512, 0.0230, 0.0243, 0.0220, 0.0202, 0.0211]),\n",
       " tensor([0.0273, 0.0255, 0.0199, 0.0209, 0.0210, 0.0836, 0.0284, 0.0260, 0.0210,\n",
       "         0.0246, 0.4161, 0.0190, 0.9826, 0.9546, 0.0171, 0.0197]),\n",
       " tensor([0.0249, 0.0248, 0.0271, 0.0316, 0.0264, 0.0304, 0.0196, 0.0211, 0.0178,\n",
       "         0.0214, 0.0240, 0.1753, 0.0188, 0.0177, 0.0314, 0.0200]),\n",
       " tensor([0.0179, 0.0303, 0.0283, 0.0234, 0.0271, 0.2572, 0.0194, 0.0226, 0.0223,\n",
       "         0.8681, 0.0236, 0.0212, 0.1047, 0.0195, 0.0321, 0.2647]),\n",
       " tensor([0.0449, 0.3622, 0.2050, 0.9826, 0.0263, 0.0249, 0.0201, 0.0305, 0.0536,\n",
       "         0.7226, 0.9378, 0.0243, 0.0223, 0.0260, 0.0266, 0.0236]),\n",
       " tensor([0.0200, 0.0196, 0.9754, 0.5938, 0.0227, 0.0393, 0.0187, 0.0210, 0.0215,\n",
       "         0.0231, 0.1962, 0.0169, 0.3885, 0.0355, 0.0198, 0.2333]),\n",
       " tensor([0.0242, 0.9022, 0.0311, 0.8667, 0.0216, 0.0321, 0.0251, 0.0224, 0.0272,\n",
       "         0.0327, 0.0364, 0.9114, 0.0192, 0.0186, 0.9495, 0.0410]),\n",
       " tensor([0.0230, 0.0298, 0.0229, 0.0366, 0.0199, 0.0196, 0.0271, 0.0200, 0.0236,\n",
       "         0.0458, 0.0213, 0.0216, 0.0174, 0.1012, 0.0186, 0.2061]),\n",
       " tensor([0.0292, 0.1096, 0.1161, 0.0223, 0.0435, 0.0246, 0.0254, 0.0364, 0.0264,\n",
       "         0.0464, 0.0202, 0.0235, 0.0248, 0.0287, 0.0358, 0.0179]),\n",
       " tensor([0.0181, 0.0299, 0.2249, 0.0178, 0.0264, 0.0269, 0.0235, 0.0265, 0.9779,\n",
       "         0.0204, 0.0229, 0.0240, 0.0176, 0.0265, 0.0248, 0.0188]),\n",
       " tensor([0.9554, 0.0255, 0.0444, 0.0210, 0.0263, 0.0177, 0.0167, 0.0871, 0.0248,\n",
       "         0.0407, 0.0276, 0.0462, 0.0191, 0.0221, 0.0198, 0.0201]),\n",
       " tensor([0.0351, 0.0313, 0.0577, 0.0209, 0.0614, 0.0225, 0.0241, 0.0294, 0.0205,\n",
       "         0.0194, 0.0226, 0.0227, 0.0266, 0.0171, 0.0178, 0.0263]),\n",
       " tensor([0.0233, 0.0357, 0.0218, 0.0184, 0.0176, 0.0912, 0.0186, 0.0256, 0.0200,\n",
       "         0.1160, 0.0203, 0.0265, 0.0297, 0.0190, 0.7100, 0.0214]),\n",
       " tensor([0.0248, 0.0183, 0.0310, 0.0327, 0.0245, 0.0251, 0.0455, 0.0341, 0.0368,\n",
       "         0.0205, 0.0556, 0.0221, 0.0354, 0.0307, 0.0366, 0.0283]),\n",
       " tensor([0.0189, 0.0186, 0.0232, 0.0540, 0.0234, 0.0242, 0.0272, 0.4364, 0.0803,\n",
       "         0.0219, 0.0252, 0.1107, 0.0212, 0.1471, 0.0185, 0.0197]),\n",
       " tensor([0.1088, 0.0388, 0.0306, 0.0193, 0.0199, 0.0335, 0.0443, 0.0257, 0.0283,\n",
       "         0.1197, 0.0539, 0.0274, 0.5778, 0.2051, 0.4221, 0.0279]),\n",
       " tensor([0.0456, 0.1066, 0.0676, 0.0320, 0.0864, 0.0285, 0.7299, 0.0699, 0.0523,\n",
       "         0.0200, 0.3686, 0.0270, 0.6408, 0.0303, 0.2874, 0.6084]),\n",
       " tensor([0.8499, 0.3439, 0.0868, 0.0199, 0.0215, 0.6258, 0.3382, 0.0316, 0.0258,\n",
       "         0.0878, 0.0299, 0.0210, 0.0400, 0.0186, 0.2363, 0.0229]),\n",
       " tensor([0.8994, 0.0272, 0.0199, 0.1798, 0.0755, 0.5815, 0.4967, 0.0288, 0.5583,\n",
       "         0.0329, 0.0311, 0.0196, 0.0196, 0.0341, 0.8150, 0.0314]),\n",
       " tensor([0.0210, 0.0281, 0.0819, 0.0268, 0.0384, 0.0284, 0.0272, 0.0261, 0.0191,\n",
       "         0.0618, 0.5540, 0.0398, 0.9185, 0.0212, 0.0597, 0.0221]),\n",
       " tensor([0.1201, 0.7351, 0.0214, 0.0191, 0.0273, 0.0220, 0.0206, 0.1006, 0.0401,\n",
       "         0.0206, 0.0383, 0.0262, 0.3399, 0.0470, 0.0293, 0.0439]),\n",
       " tensor([0.0206, 0.0272, 0.0633, 0.0229, 0.0537, 0.7694, 0.0938, 0.0240, 0.0321,\n",
       "         0.0232, 0.1591, 0.9878, 0.8810, 0.0271, 0.7847, 0.5484]),\n",
       " tensor([0.3829, 0.0294, 0.0193, 0.5692, 0.0228, 0.0203, 0.9389, 0.0196, 0.0807,\n",
       "         0.0212, 0.0286, 0.0221, 0.0445, 0.9644, 0.0444, 0.3494]),\n",
       " tensor([0.3402, 0.9956, 0.9897, 0.0294, 0.0180, 0.0250, 0.5392, 0.0286, 0.3100,\n",
       "         0.0352, 0.0388, 0.6630, 0.0195, 0.0295, 0.0414, 0.0237]),\n",
       " tensor([0.4485, 0.0378, 0.0203, 0.0324, 0.0341, 0.0309, 0.0523, 0.0226, 0.0557,\n",
       "         0.1854, 0.0313, 0.4600, 0.0193, 0.0227, 0.5928, 0.0296]),\n",
       " tensor([0.5660, 0.0428, 0.0185, 0.6193, 0.1998, 0.0449, 0.0331, 0.0327, 0.4955,\n",
       "         0.0893, 0.0183, 0.0186, 0.0476, 0.0222, 0.0193, 0.3019]),\n",
       " tensor([0.0600, 0.2219, 0.4231, 0.0287, 0.0543, 0.0217, 0.0271, 0.3915, 0.0248,\n",
       "         0.0186, 0.0325, 0.0203, 0.9698, 0.0284, 0.0739, 0.0221]),\n",
       " tensor([0.0303, 0.0199, 0.2933, 0.0206, 0.0203, 0.0339, 0.0309, 0.0312, 0.8250,\n",
       "         0.0339, 0.0246, 0.0194, 0.0209, 0.0257, 0.1848, 0.0209]),\n",
       " tensor([0.0174, 0.6281, 0.0273, 0.0181, 0.0189, 0.1873, 0.0454, 0.1864, 0.0202,\n",
       "         0.0272, 0.4128, 0.0310, 0.8270, 0.0262, 0.0381, 0.0262]),\n",
       " tensor([0.0222, 0.0179, 0.0201, 0.0209, 0.0271, 0.9847, 0.0326, 0.0210, 0.2117,\n",
       "         0.0224, 0.0231, 0.0194, 0.0189, 0.2978, 0.1734, 0.0667]),\n",
       " tensor([0.0251, 0.0322, 0.0391, 0.0236, 0.9612, 0.0185, 0.1578, 0.0184, 0.0167,\n",
       "         0.0198, 0.0188, 0.0216, 0.9226, 0.0161, 0.9375, 0.0184]),\n",
       " tensor([0.4988, 0.0201, 0.0199, 0.0211, 0.0199, 0.9800, 0.4096, 0.0248, 0.0189,\n",
       "         0.0266, 0.0228, 0.0188, 0.0200, 0.0358, 0.0180, 0.0447]),\n",
       " tensor([0.0278, 0.0262, 0.0187, 0.2283, 0.5031, 0.0189, 0.1045, 0.0214, 0.0228,\n",
       "         0.0185, 0.0392, 0.0177, 0.3854, 0.0316, 0.0234, 0.0187]),\n",
       " tensor([0.0230, 0.0331, 0.0284, 0.0308, 0.9213, 0.0295, 0.0403, 0.0202, 0.5762,\n",
       "         0.0197, 0.5151, 0.0247, 0.1102, 0.0593, 0.0253, 0.0279]),\n",
       " tensor([0.0216, 0.0194, 0.0237, 0.0374, 0.8718, 0.0178, 0.0327, 0.0231, 0.0258,\n",
       "         0.0159, 0.0287, 0.0321, 0.0284, 0.4795, 0.0234, 0.0189]),\n",
       " tensor([0.0219, 0.0334, 0.0237, 0.0303, 0.0321, 0.0179, 0.0228, 0.0324, 0.0441,\n",
       "         0.0262, 0.0917, 0.0284, 0.0170, 0.0188, 0.0519, 0.0525]),\n",
       " tensor([0.0214, 0.0207, 0.3845, 0.5640, 0.0249, 0.2273, 0.0512, 0.0164, 0.0246,\n",
       "         0.1907, 0.0202, 0.1781, 0.0390, 0.0856, 0.2758, 0.0253]),\n",
       " tensor([0.9551, 0.0228, 0.0199, 0.0165, 0.0207, 0.0236, 0.0237, 0.9214, 0.0200,\n",
       "         0.0182, 0.0378, 0.0192, 0.0344, 0.0371, 0.0422, 0.0360]),\n",
       " tensor([0.0204, 0.0208, 0.0264, 0.0209, 0.2072, 0.0189, 0.0218, 0.8927, 0.2642,\n",
       "         0.0274, 0.0324, 0.0251, 0.0244, 0.5847, 0.0220, 0.0215]),\n",
       " tensor([0.0239, 0.9509, 0.0303, 0.0185, 0.0196, 0.0223, 0.0181, 0.0246, 0.0179,\n",
       "         0.0295, 0.9761, 0.0277, 0.0289, 0.0203, 0.0212, 0.9703]),\n",
       " tensor([0.0541, 0.0290, 0.9893, 0.7428, 0.0283, 0.0419, 0.0491, 0.3147, 0.0494,\n",
       "         0.0185, 0.0858, 0.0221, 0.0202, 0.0215, 0.0200, 0.5277]),\n",
       " tensor([0.0425, 0.0209, 0.0159, 0.0185, 0.0357, 0.0234, 0.0288, 0.0183, 0.0171,\n",
       "         0.0730, 0.0237, 0.0187, 0.3587, 0.0194, 0.1231, 0.0178]),\n",
       " tensor([0.0303, 0.4811, 0.0275, 0.0260, 0.0259, 0.0274, 0.6449, 0.7248, 0.5759,\n",
       "         0.7333, 0.0186, 0.0237, 0.0258, 0.0812, 0.9532, 0.8035]),\n",
       " tensor([0.5076, 0.5416, 0.0181, 0.0239, 0.0211, 0.0477, 0.0218, 0.1417, 0.0185,\n",
       "         0.0260, 0.0251, 0.0205, 0.0223, 0.0203, 0.0166, 0.0284]),\n",
       " tensor([0.0307, 0.0198, 0.0181, 0.0190, 0.0166, 0.0195, 0.0251, 0.0486, 0.0242,\n",
       "         0.0250, 0.0231, 0.0206, 0.0239, 0.0224, 0.0451, 0.0375]),\n",
       " tensor([0.0200, 0.0326, 0.0242, 0.0336, 0.0171, 0.0219, 0.0315, 0.0222, 0.0269,\n",
       "         0.0294, 0.0371, 0.0404, 0.0832, 0.0163, 0.0595, 0.0372]),\n",
       " tensor([0.0253, 0.0215, 0.0228, 0.0241, 0.0366, 0.0411, 0.0235, 0.0234, 0.0296,\n",
       "         0.0306, 0.0287, 0.0301, 0.0154, 0.0232, 0.2562, 0.0329]),\n",
       " tensor([0.0307, 0.5651, 0.2427, 0.0309, 0.0172, 0.0189, 0.0238, 0.0267, 0.0211,\n",
       "         0.2261, 0.0231, 0.0281, 0.0303, 0.1013, 0.0206, 0.0270]),\n",
       " tensor([0.0201, 0.0161, 0.0188, 0.9501, 0.0249, 0.0210, 0.0227, 0.0304, 0.0186,\n",
       "         0.0198, 0.0226, 0.0173, 0.0281, 0.0544, 0.0180, 0.0236]),\n",
       " tensor([0.0184, 0.0197, 0.7968, 0.0218, 0.0263, 0.0270, 0.0192, 0.0178, 0.0178,\n",
       "         0.0201, 0.0189, 0.0254, 0.0317, 0.0207, 0.0173, 0.0310]),\n",
       " tensor([0.1042, 0.0230, 0.0215, 0.0281, 0.0218, 0.0220, 0.7455, 0.6168, 0.0523,\n",
       "         0.0192, 0.0173, 0.4555, 0.0197, 0.0343, 0.0193, 0.0991]),\n",
       " tensor([0.0261, 0.0218, 0.0228, 0.0174, 0.0249, 0.0244, 0.0286, 0.2845, 0.0333,\n",
       "         0.0237, 0.0287, 0.0186, 0.0282, 0.0250, 0.0268, 0.0265]),\n",
       " tensor([0.0391, 0.0205, 0.0245, 0.3334, 0.2822, 0.0301, 0.0248, 0.0227, 0.0279,\n",
       "         0.0350, 0.0369, 0.0376, 0.0293, 0.0237, 0.0256, 0.0261]),\n",
       " tensor([0.0165, 0.0228, 0.9271, 0.0700, 0.0320, 0.0270, 0.0311, 0.0243, 0.0196,\n",
       "         0.0181, 0.0175, 0.6069, 0.0279, 0.0222, 0.0249, 0.0219]),\n",
       " tensor([0.0235, 0.0161, 0.3327, 0.0387, 0.0238, 0.0196, 0.0189, 0.0202, 0.0267,\n",
       "         0.0666, 0.0178, 0.0246, 0.0196, 0.0251, 0.0235, 0.0319]),\n",
       " tensor([0.9391, 0.9614, 0.3058, 0.9722, 0.9912, 0.0286, 0.0238, 0.0235, 0.1171,\n",
       "         0.0180, 0.0272, 0.0382, 0.0213, 0.0227, 0.0281, 0.0314]),\n",
       " tensor([0.7984, 0.0228, 0.0219, 0.0202, 0.0256, 0.0238, 0.0298, 0.0226, 0.0315,\n",
       "         0.0245, 0.0255, 0.0343, 0.0292, 0.0216, 0.0198, 0.0204]),\n",
       " tensor([0.0300, 0.0307, 0.0369, 0.0263, 0.0216, 0.0247, 0.0321, 0.0260, 0.0208,\n",
       "         0.0163, 0.0183, 0.0172, 0.0258, 0.0231, 0.0185, 0.0413]),\n",
       " tensor([0.0286, 0.0200, 0.0189, 0.0192, 0.0199, 0.0206, 0.0238, 0.0338, 0.0283,\n",
       "         0.0206, 0.0239, 0.0296, 0.0329, 0.2161, 0.0235, 0.0219]),\n",
       " tensor([0.0181, 0.0187, 0.2284, 0.0295, 0.0270, 0.0212, 0.0457, 0.0850, 0.0238,\n",
       "         0.7411, 0.0274, 0.0183, 0.0299, 0.0281, 0.5952, 0.0181]),\n",
       " tensor([0.0253, 0.0437, 0.6902, 0.5572, 0.0284, 0.0259, 0.0259, 0.0285, 0.0680,\n",
       "         0.1923, 0.0464, 0.0243, 0.0991, 0.0311, 0.0330, 0.0288]),\n",
       " tensor([0.0312, 0.9590, 0.2754, 0.0203, 0.0279, 0.0176, 0.0205, 0.0521, 0.0239,\n",
       "         0.0200, 0.0210, 0.9719, 0.0198, 0.0194, 0.9713, 0.0187]),\n",
       " tensor([0.0363, 0.0383, 0.0176, 0.0650, 0.2046, 0.0226, 0.9825, 0.0171, 0.0412,\n",
       "         0.5555, 0.0177, 0.0344, 0.0185, 0.0175, 0.0194, 0.5767]),\n",
       " tensor([0.0210, 0.0250, 0.0451, 0.0211, 0.0232, 0.0429, 0.0236, 0.0256, 0.5187,\n",
       "         0.0193, 0.0316, 0.0219, 0.0232, 0.0177, 0.0308, 0.0709]),\n",
       " tensor([0.0257, 0.5304, 0.8962, 0.0285, 0.0318, 0.0268, 0.0203, 0.0334, 0.0273,\n",
       "         0.0292, 0.0193, 0.0320, 0.0214, 0.6718, 0.0390, 0.6057]),\n",
       " tensor([0.6006, 0.0408, 0.8231, 0.6246, 0.8142, 0.0200, 0.0197, 0.0297, 0.0566,\n",
       "         0.7158, 0.0381, 0.0323, 0.1242, 0.0374, 0.5666, 0.0194]),\n",
       " tensor([0.9861, 0.0341, 0.0357, 0.0450, 0.1812, 0.9967, 0.9052, 0.7287, 0.2113,\n",
       "         0.0290, 0.0310, 0.4753, 0.9953, 0.0231, 0.0187, 0.7246]),\n",
       " tensor([0.0361, 0.9444, 0.0290, 0.0195, 0.0200, 0.8760, 0.0575, 0.0677, 0.2418,\n",
       "         0.0302, 0.9840, 0.9231, 0.9960, 0.7570, 0.8262, 0.0389]),\n",
       " tensor([0.0280, 0.0218, 0.0283, 0.2525, 0.9128, 0.0710, 0.0432, 0.0192, 0.0310,\n",
       "         0.2963, 0.3617, 0.4656, 0.5598, 0.2069, 0.5604, 0.0208]),\n",
       " tensor([0.0600, 0.7699, 0.0308, 0.0225, 0.0212, 0.0249, 0.2926, 0.0278, 0.1474,\n",
       "         0.0785, 0.0206, 0.0267, 0.0249, 0.7291, 0.0184, 0.0286]),\n",
       " tensor([0.7447, 0.0296, 0.1114, 0.0338, 0.0350, 0.0224, 0.0278, 0.0203, 0.0277,\n",
       "         0.0280, 0.5172, 0.9931, 0.5542, 0.0219, 0.6379, 0.0452]),\n",
       " tensor([0.0642, 0.7810, 0.1739, 0.0262, 0.0615, 0.0275, 0.0221, 0.9610, 0.6222,\n",
       "         0.0276, 0.0226, 0.9921, 0.0162, 0.0228, 0.1105, 0.0291]),\n",
       " tensor([0.0271, 0.0183, 0.9949, 0.0381, 0.0354, 0.1224, 0.0269, 0.9930, 0.0259,\n",
       "         0.7309, 0.9275, 0.8726, 0.0233, 0.0192, 0.0221, 0.8823]),\n",
       " tensor([0.5409, 0.1678, 0.0996, 0.0374, 0.2070, 0.0302, 0.6357, 0.1230, 0.0216,\n",
       "         0.0249, 0.0982, 0.5754, 0.0295, 0.1018, 0.0324, 0.2942]),\n",
       " tensor([0.0204, 0.0265, 0.4519, 0.2758, 0.1746, 0.0714, 0.0223, 0.0233, 0.0176,\n",
       "         0.0184, 0.0206, 0.7208, 0.0426, 0.0884, 0.0321, 0.0255]),\n",
       " tensor([0.0176, 0.2399, 0.0240, 0.5491, 0.4211, 0.0197, 0.8521, 0.0349, 0.9345,\n",
       "         0.0399, 0.6120, 0.3400, 0.0434, 0.0206, 0.1745, 0.9121]),\n",
       " tensor([0.0217, 0.0399, 0.2778, 0.1185, 0.0521, 0.0222, 0.0261, 0.0237, 0.3885,\n",
       "         0.8399, 0.7959, 0.7613, 0.9824, 0.0219, 0.9357, 0.1998]),\n",
       " tensor([0.8169, 0.0289, 0.9904, 0.0194, 0.0681, 0.0215, 0.1917, 0.9888, 0.9891,\n",
       "         0.4860, 0.6856, 0.0781, 0.0315, 0.0266, 0.0169, 0.6515]),\n",
       " tensor([0.0314, 0.5420, 0.0221, 0.6176, 0.0475, 0.0272, 0.8473, 0.0246, 0.0242,\n",
       "         0.8963, 0.7682, 0.1364, 0.9754, 0.2067, 0.0404, 0.4998]),\n",
       " tensor([0.0254, 0.4153, 0.0316, 0.0211, 0.0168, 0.7501, 0.2636, 0.6769, 0.5827,\n",
       "         0.7092, 0.9949, 0.0324, 0.0381, 0.9946, 0.9660, 0.0224]),\n",
       " tensor([0.9870, 0.9209, 0.0478, 0.1048, 0.0759, 0.0704, 0.0269, 0.0678, 0.0204,\n",
       "         0.8600, 0.4856, 0.0224, 0.0165, 0.0477, 0.0372, 0.0270]),\n",
       " tensor([0.6694, 0.0179, 0.7830, 0.4738, 0.2221, 0.1425, 0.0212, 0.0347, 0.9910,\n",
       "         0.0231, 0.9925, 0.3938, 0.0655, 0.9048, 0.1758, 0.3570]),\n",
       " tensor([0.0196, 0.0291, 0.9414, 0.1733, 0.0216, 0.0558, 0.9767, 0.9341, 0.0263,\n",
       "         0.0194, 0.9829, 0.6176, 0.0273, 0.5667, 0.4602, 0.2753]),\n",
       " tensor([0.5697, 0.1158, 0.0423, 0.0351, 0.0211, 0.0214, 0.0320, 0.0193, 0.0292,\n",
       "         0.0273, 0.0281, 0.0565, 0.0271, 0.0303, 0.9000, 0.0234]),\n",
       " tensor([0.0429, 0.0291, 0.1426, 0.0289, 0.0207, 0.0190, 0.0338, 0.0378, 0.0215,\n",
       "         0.0220, 0.0245, 0.0266, 0.0402, 0.0271, 0.0171, 0.0243]),\n",
       " tensor([0.0244, 0.0331, 0.0182, 0.0523, 0.0314, 0.1341, 0.1658, 0.0231, 0.0181,\n",
       "         0.0934, 0.0179, 0.0218, 0.1743, 0.0220, 0.0311, 0.3375]),\n",
       " tensor([0.4610, 0.0399, 0.0347, 0.0301, 0.0203, 0.0323, 0.0223, 0.0283, 0.0222,\n",
       "         0.0205, 0.0202, 0.0275, 0.0215, 0.0258, 0.0244, 0.0225]),\n",
       " tensor([0.0182, 0.6974, 0.1254, 0.0220, 0.0167, 0.0188, 0.0257, 0.0159, 0.0213,\n",
       "         0.7682, 0.0237, 0.0184, 0.0274, 0.0175, 0.0237, 0.0315]),\n",
       " tensor([0.0400, 0.0181, 0.0354, 0.0237, 0.0243, 0.0226, 0.0223, 0.0189, 0.0214,\n",
       "         0.0259, 0.0221, 0.0357, 0.0291, 0.5230, 0.0275, 0.0172]),\n",
       " tensor([0.0249, 0.0185, 0.0737, 0.0371, 0.0234, 0.0299, 0.0391, 0.0256, 0.0259,\n",
       "         0.0252, 0.0318, 0.0280, 0.0246, 0.0312, 0.5766, 0.3345]),\n",
       " tensor([0.9281, 0.5855, 0.0896, 0.0278, 0.0458, 0.0381, 0.2525, 0.1350, 0.0322,\n",
       "         0.7196, 0.0204, 0.0221, 0.0272, 0.0426, 0.0212, 0.0207]),\n",
       " tensor([0.0242, 0.0244, 0.0232, 0.0221, 0.0204, 0.0293, 0.0177, 0.0191, 0.0282,\n",
       "         0.0280, 0.0195, 0.0273, 0.0216, 0.0277, 0.0315, 0.0178]),\n",
       " tensor([0.0212, 0.0256, 0.4909, 0.0386, 0.0183, 0.0249, 0.0197, 0.0217, 0.0226,\n",
       "         0.0331, 0.4165, 0.0180, 0.0334, 0.0255, 0.0225, 0.4261]),\n",
       " tensor([0.0174, 0.0291, 0.0242, 0.1892, 0.0166, 0.0228, 0.0196, 0.0166, 0.0375,\n",
       "         0.0239, 0.9464, 0.2900, 0.6967, 0.0315, 0.0229, 0.0291]),\n",
       " tensor([0.0233, 0.9679, 0.1659, 0.0320, 0.3851, 0.0269, 0.1512, 0.0183, 0.0573,\n",
       "         0.0235, 0.9566, 0.0235, 0.0195, 0.0263, 0.0236, 0.0205]),\n",
       " tensor([0.0195, 0.0202, 0.0173, 0.8730, 0.0205, 0.0169, 0.8451, 0.0490, 0.0265,\n",
       "         0.8099, 0.0234, 0.0193, 0.0225, 0.0192, 0.0251, 0.0302]),\n",
       " tensor([0.0170, 0.0278, 0.0422, 0.0241, 0.0343, 0.0357, 0.0539, 0.0520, 0.0269,\n",
       "         0.0329, 0.0306, 0.0191, 0.0158, 0.0166, 0.0227, 0.0276]),\n",
       " tensor([0.0276, 0.0216, 0.0957, 0.0345, 0.0194, 0.0348, 0.0320, 0.0211, 0.0187,\n",
       "         0.0249, 0.0312, 0.0178, 0.0268, 0.0255, 0.9624, 0.0243]),\n",
       " tensor([0.0381, 0.0317, 0.0171, 0.0380, 0.0238, 0.0374, 0.0208, 0.0354, 0.0241,\n",
       "         0.0317, 0.0535, 0.0197, 0.0525, 0.2287, 0.0166, 0.0249]),\n",
       " tensor([0.0214, 0.0197, 0.0237, 0.0500, 0.0247, 0.0296, 0.0340, 0.0200, 0.0233,\n",
       "         0.3721, 0.1589, 0.0307, 0.6934, 0.0246, 0.7942, 0.0607]),\n",
       " tensor([0.0256, 0.0342, 0.0212, 0.0192, 0.0266, 0.0230, 0.0324, 0.0223, 0.0211,\n",
       "         0.0234, 0.9164, 0.0593, 0.0259, 0.0293, 0.0205, 0.0170]),\n",
       " tensor([0.4912, 0.0182, 0.0322, 0.0273, 0.0254, 0.0161, 0.0374, 0.0215, 0.0228,\n",
       "         0.0352, 0.0267, 0.0495, 0.0792, 0.9597, 0.0255, 0.0235]),\n",
       " tensor([0.0177, 0.0181, 0.0217, 0.0206, 0.0176, 0.0180, 0.0206, 0.9849, 0.0341,\n",
       "         0.1396, 0.0225, 0.0278, 0.0325, 0.0242, 0.0740, 0.0160]),\n",
       " tensor([0.0250, 0.0271, 0.0174, 0.0263, 0.0190, 0.0267, 0.0164, 0.0497, 0.0195,\n",
       "         0.0332, 0.0408, 0.0196, 0.7963, 0.0164, 0.0189, 0.6672]),\n",
       " tensor([0.0210, 0.0218, 0.0203, 0.0318, 0.0243, 0.0376, 0.0356, 0.0204, 0.7590,\n",
       "         0.0198, 0.0273, 0.0226, 0.0247, 0.0218, 0.0336, 0.0200]),\n",
       " tensor([0.0927, 0.0190, 0.1032, 0.9678, 0.0200, 0.0267, 0.7945, 0.1282, 0.0999,\n",
       "         0.9310, 0.0234, 0.0180, 0.1785, 0.0322, 0.0573, 0.0202]),\n",
       " tensor([0.1511, 0.0164, 0.0206, 0.0218, 0.9933, 0.0172, 0.0488, 0.0237, 0.0184,\n",
       "         0.0178, 0.0229, 0.0250, 0.0395, 0.0206, 0.0289, 0.0179]),\n",
       " tensor([0.0189, 0.9713, 0.0209, 0.0222, 0.0564, 0.0324, 0.0207, 0.0612, 0.0800,\n",
       "         0.0190, 0.2397, 0.0195, 0.0230, 0.0164, 0.0180, 0.0280]),\n",
       " tensor([0.0303, 0.0168, 0.0323, 0.0182, 0.0271, 0.0234, 0.0239, 0.2688, 0.0188,\n",
       "         0.0270, 0.5986, 0.1018, 0.0228, 0.0241, 0.0249, 0.0205]),\n",
       " tensor([0.0412, 0.0274, 0.0773, 0.0211, 0.0271, 0.0226, 0.0166, 0.0202, 0.0232,\n",
       "         0.0351, 0.0200, 0.3663, 0.0248, 0.0203, 0.0240, 0.0189]),\n",
       " tensor([0.0264, 0.0202, 0.0202, 0.0201, 0.0182, 0.0443, 0.0190, 0.0284, 0.0182,\n",
       "         0.0189, 0.8590, 0.0197, 0.0240, 0.6811, 0.9475, 0.0208]),\n",
       " tensor([0.0165, 0.0198, 0.0284, 0.0188, 0.0243, 0.0322, 0.0178, 0.0164, 0.0256,\n",
       "         0.0189, 0.0271, 0.0659, 0.0166, 0.3151, 0.1738, 0.0177]),\n",
       " tensor([0.0180, 0.0231, 0.0178, 0.0191, 0.9457, 0.0225, 0.3187, 0.0312, 0.0176,\n",
       "         0.0548, 0.0223, 0.0275, 0.0300, 0.0346, 0.0191, 0.0272]),\n",
       " tensor([0.0302, 0.0216, 0.0213, 0.0310, 0.0232, 0.0228, 0.0251, 0.0282, 0.0231,\n",
       "         0.0329, 0.0194, 0.0185, 0.0525, 0.0225, 0.0243, 0.0241]),\n",
       " tensor([0.0325, 0.0174, 0.0214, 0.0236, 0.0226, 0.0420, 0.0304, 0.0246, 0.1728,\n",
       "         0.0310, 0.0192, 0.0205, 0.0298, 0.0225, 0.0536, 0.0509]),\n",
       " tensor([0.0225, 0.0349, 0.8560, 0.0176, 0.5527, 0.0222, 0.0213, 0.9649, 0.2899,\n",
       "         0.0308, 0.0168, 0.8757, 0.0215, 0.0199, 0.0300, 0.0214]),\n",
       " tensor([0.0246, 0.0397, 0.0323, 0.0202, 0.0772, 0.0243, 0.0251, 0.0221, 0.0221,\n",
       "         0.0286, 0.0197, 0.0246, 0.0189, 0.0231, 0.0278, 0.0693]),\n",
       " tensor([0.0271, 0.0219, 0.0805, 0.0208, 0.7199, 0.0202, 0.0341, 0.0276, 0.0203,\n",
       "         0.0419, 0.5912, 0.0222, 0.0275, 0.0232, 0.0539, 0.0217]),\n",
       " tensor([0.0201, 0.0574, 0.0570, 0.0199, 0.0224, 0.0203, 0.0216, 0.0248, 0.9045,\n",
       "         0.0252, 0.0315, 0.0335, 0.0236, 0.0368, 0.0272, 0.0426]),\n",
       " tensor([0.0212, 0.0207, 0.0242, 0.0244, 0.0232, 0.0221, 0.0204, 0.0293, 0.0177,\n",
       "         0.0191, 0.0282, 0.0366, 0.0273, 0.0216, 0.0277, 0.0315]),\n",
       " tensor([0.0178, 0.0212, 0.0256, 0.4909, 0.0386, 0.0183, 0.0249, 0.0197, 0.0217,\n",
       "         0.0226, 0.0331, 0.4165, 0.0180, 0.0334, 0.0255, 0.0225]),\n",
       " tensor([0.4261, 0.5723, 0.4810, 0.7946, 0.0340, 0.5749, 0.7652, 0.2866, 0.2489,\n",
       "         0.9750, 0.0213, 0.0215, 0.0304, 0.0250, 0.2109, 0.0273]),\n",
       " tensor([0.0334, 0.0240, 0.0246, 0.0284, 0.0179, 0.0168, 0.0391, 0.0221, 0.0328,\n",
       "         0.0325, 0.0246, 0.0345, 0.0276, 0.0340, 0.0315, 0.0575]),\n",
       " tensor([0.0182, 0.0318, 0.0430, 0.0225, 0.0260, 0.0269, 0.3262, 0.1079, 0.0183,\n",
       "         0.0196, 0.0220, 0.0193, 0.3449, 0.0238, 0.0181, 0.0290]),\n",
       " tensor([0.3198, 0.0208, 0.0295, 0.0252, 0.0184, 0.0202, 0.1661, 0.0215, 0.0236,\n",
       "         0.9401, 0.9497, 0.0189, 0.0198, 0.6351, 0.0279, 0.0243]),\n",
       " tensor([0.0327, 0.0270, 0.0260, 0.0481, 0.3163, 0.1421, 0.0176, 0.0235, 0.0276,\n",
       "         0.0605, 0.0229, 0.0264, 0.0261, 0.0243, 0.0291, 0.0247]),\n",
       " tensor([0.0365, 0.0293, 0.0319, 0.0566, 0.0385, 0.0349, 0.0223, 0.0200, 0.0224,\n",
       "         0.0304, 0.0379, 0.0359, 0.2964, 0.0476, 0.0248, 0.0317]),\n",
       " tensor([0.0181, 0.0274, 0.0248, 0.0330, 0.0591, 0.9931, 0.4923, 0.7723, 0.1919,\n",
       "         0.2522, 0.8517, 0.9314, 0.0286, 0.1372, 0.0357, 0.1023]),\n",
       " tensor([0.0242, 0.3594, 0.7859, 0.0214, 0.0819, 0.4811, 0.0269, 0.0233, 0.3919,\n",
       "         0.1071, 0.0328, 0.4776, 0.2428, 0.4824, 0.1562, 0.6323]),\n",
       " tensor([0.0222, 0.0329, 0.0225, 0.1121, 0.0510, 0.9736, 0.0425, 0.0401, 0.0242,\n",
       "         0.0305, 0.0246, 0.0228, 0.8635, 0.5555, 0.0276, 0.0658]),\n",
       " tensor([0.0163, 0.0195, 0.7395, 0.9601, 0.0245, 0.0205, 0.0396, 0.0223, 0.0558,\n",
       "         0.0243, 0.0195, 0.2720, 0.0222, 0.5797, 0.8560, 0.0302]),\n",
       " tensor([0.0517, 0.0309, 0.0274, 0.0408, 0.0263, 0.0290, 0.0300, 0.0261, 0.0277,\n",
       "         0.0327, 0.3938, 0.3302, 0.0558, 0.9701, 0.0319, 0.0468]),\n",
       " tensor([0.0214, 0.0421, 0.0666, 0.5819, 0.0166, 0.0214, 0.0204, 0.0373, 0.0241,\n",
       "         0.0191, 0.2646, 0.0194, 0.5400, 0.0202, 0.0247, 0.0275]),\n",
       " tensor([0.0185, 0.0232, 0.0223, 0.0333, 0.0383, 0.0238, 0.0321, 0.0331, 0.4964,\n",
       "         0.0207, 0.0233, 0.6585, 0.0326, 0.3984, 0.0228, 0.0260]),\n",
       " tensor([0.0336, 0.0274, 0.0215, 0.7904, 0.0199, 0.0317, 0.0388, 0.0262, 0.3445,\n",
       "         0.0207, 0.0342, 0.0330, 0.0419, 0.0294, 0.1906, 0.9835]),\n",
       " tensor([0.0248, 0.0244, 0.1494, 0.9691, 0.0293, 0.0611, 0.0941, 0.0309, 0.0207,\n",
       "         0.2936, 0.0252, 0.0193, 0.6193, 0.9388, 0.0253, 0.9020]),\n",
       " tensor([0.0205, 0.0303, 0.0241, 0.0322, 0.0228, 0.1177, 0.9181, 0.0644, 0.7886,\n",
       "         0.0212, 0.0285, 0.0530, 0.8813, 0.0190, 0.0410, 0.0536]),\n",
       " tensor([0.0178, 0.9485, 0.1648, 0.0601, 0.0366, 0.0284, 0.0864, 0.0206, 0.0244,\n",
       "         0.2882, 0.8497, 0.8655, 0.0556, 0.9626, 0.0264, 0.4343]),\n",
       " tensor([0.0260, 0.3587, 0.0381, 0.0383, 0.0305, 0.0278, 0.0221, 0.2951, 0.5925,\n",
       "         0.2445, 0.0253, 0.0366, 0.0340, 0.0214, 0.0261, 0.0229]),\n",
       " tensor([0.9770, 0.0249, 0.9856, 0.0201, 0.0262, 0.2317, 0.0398, 0.0561, 0.2690,\n",
       "         0.2739, 0.0421, 0.0187, 0.0950, 0.0218, 0.0303, 0.0260]),\n",
       " tensor([0.3695, 0.0325, 0.0354, 0.7388, 0.6149, 0.0249, 0.9608, 0.0461, 0.5538,\n",
       "         0.0184, 0.0289, 0.2323, 0.0345, 0.6401, 0.0268, 0.0258]),\n",
       " tensor([0.0190, 0.4186, 0.0675, 0.3152, 0.0953, 0.0799, 0.5406, 0.0288, 0.0302,\n",
       "         0.0188, 0.0331, 0.0223, 0.0270, 0.0219, 0.0192, 0.5470]),\n",
       " tensor([0.0233, 0.0226, 0.9837, 0.0174, 0.0467, 0.7518, 0.0166, 0.0190, 0.0331,\n",
       "         0.0198, 0.0177, 0.0227, 0.0311, 0.0312, 0.7970, 0.0232]),\n",
       " tensor([0.0316, 0.4446, 0.0435, 0.0188, 0.0367, 0.0208, 0.3134, 0.0298, 0.5398,\n",
       "         0.0304, 0.4594, 0.0171, 0.0247, 0.4262, 0.0234, 0.0231]),\n",
       " tensor([0.0818, 0.0508, 0.2684, 0.0305, 0.0251, 0.0220, 0.0228, 0.0245, 0.0198,\n",
       "         0.0284, 0.0238, 0.0252, 0.0228, 0.7648, 0.0197, 0.0224]),\n",
       " tensor([0.0192, 0.0205, 0.0219, 0.0191, 0.6914, 0.0165, 0.0244, 0.0284, 0.8654,\n",
       "         0.0430, 0.0177, 0.0198, 0.1998, 0.0186, 0.0184, 0.9589]),\n",
       " tensor([0.0303, 0.0208, 0.0297, 0.0200, 0.0213, 0.0177, 0.0183, 0.0181, 0.0182,\n",
       "         0.0179, 0.0187, 0.0205, 0.0249, 0.0247, 0.8752, 0.0207]),\n",
       " tensor([0.0192, 0.0274, 0.0237, 0.0905, 0.0248, 0.0416, 0.3096, 0.0276, 0.2389,\n",
       "         0.0365, 0.0283, 0.0192, 0.0210, 0.1372, 0.0748, 0.0197]),\n",
       " tensor([0.0275, 0.0200, 0.0219, 0.0220, 0.0236, 0.0189, 0.0180, 0.0228, 0.0227,\n",
       "         0.0169, 0.0192, 0.0256, 0.0209, 0.9193, 0.9228, 0.0188]),\n",
       " tensor([0.0188, 0.0273, 0.0231, 0.0283, 0.0626, 0.0324, 0.0672, 0.0271, 0.0919,\n",
       "         0.0733, 0.0351, 0.0204, 0.4701, 0.0294, 0.0181, 0.0228]),\n",
       " tensor([0.0227, 0.0353, 0.0184, 0.0211, 0.0176, 0.0275, 0.0265, 0.0180, 0.0187,\n",
       "         0.0257, 0.0314, 0.7427, 0.0360, 0.0256, 0.0244, 0.0214]),\n",
       " tensor([0.0189, 0.0272, 0.0226, 0.0352, 0.0195, 0.0193, 0.0174, 0.0259, 0.0171,\n",
       "         0.0176, 0.0179, 0.9807, 0.0223, 0.0168, 0.0190, 0.8955]),\n",
       " tensor([0.0283, 0.0226, 0.0281, 0.0251, 0.0378, 0.0735, 0.0209, 0.0224, 0.0215,\n",
       "         0.0258, 0.0193, 0.0205, 0.0267, 0.0216, 0.0292, 0.0198]),\n",
       " tensor([0.0271, 0.0270, 0.0268, 0.0268, 0.0279, 0.0189, 0.0540, 0.0235, 0.0271,\n",
       "         0.0263, 0.0515, 0.0429, 0.0209, 0.0319, 0.0219, 0.0191]),\n",
       " tensor([0.0294, 0.0225, 0.0462, 0.0244, 0.0716, 0.0170, 0.0262, 0.0308, 0.0259,\n",
       "         0.0222, 0.0228, 0.0381, 0.0227, 0.0727, 0.0391, 0.0331]),\n",
       " tensor([0.0266, 0.0204, 0.0171, 0.0229, 0.0293, 0.0298, 0.0350, 0.5469, 0.0302,\n",
       "         0.0294, 0.0295, 0.0425, 0.0190, 0.0276, 0.0207, 0.0187]),\n",
       " tensor([0.0192, 0.0328, 0.0218, 0.0187, 0.0233, 0.5464, 0.0246, 0.1249, 0.0366,\n",
       "         0.1266, 0.0163, 0.0278, 0.0471, 0.0191, 0.7199, 0.9843]),\n",
       " tensor([0.5179, 0.0693, 0.0395, 0.0171, 0.0208, 0.0341, 0.0317, 0.0419, 0.0243,\n",
       "         0.0232, 0.3372, 0.0299, 0.0179, 0.0740, 0.0229, 0.0171]),\n",
       " tensor([0.0227, 0.0202, 0.6201, 0.0263, 0.0369, 0.0269, 0.0184, 0.0183, 0.0284,\n",
       "         0.0247, 0.0182, 0.5462, 0.0314, 0.7662, 0.0450, 0.0427]),\n",
       " tensor([0.0210, 0.2007, 0.0365, 0.0211, 0.9755, 0.0361, 0.0179, 0.0194, 0.0230,\n",
       "         0.0298, 0.6968, 0.0298, 0.0273, 0.0423, 0.0455, 0.0298]),\n",
       " tensor([0.0265, 0.2728, 0.6359, 0.0232, 0.0375, 0.0225, 0.0262, 0.0218, 0.0230,\n",
       "         0.0252, 0.8405, 0.0188, 0.0506, 0.4855, 0.0265, 0.9491]),\n",
       " tensor([0.0274, 0.0243, 0.0215, 0.0194, 0.6947, 0.0232, 0.0528, 0.4267, 0.0226,\n",
       "         0.0725, 0.0191, 0.0254, 0.0197, 0.0266, 0.0194, 0.0359]),\n",
       " tensor([0.0205, 0.7744, 0.7426, 0.0408, 0.0186, 0.0264, 0.0278, 0.0257, 0.0284,\n",
       "         0.0330, 0.0344, 0.0655, 0.0349, 0.0187, 0.0172, 0.0205]),\n",
       " tensor([0.0239, 0.0213, 0.0297, 0.0423, 0.0290, 0.0250, 0.0278, 0.0243, 0.3648,\n",
       "         0.0214, 0.0177, 0.0659, 0.0207, 0.0476, 0.0251, 0.0216]),\n",
       " tensor([0.0232, 0.0306, 0.0172, 0.0166, 0.0172, 0.0281, 0.0201, 0.0298, 0.0203,\n",
       "         0.0220, 0.0227, 0.9633, 0.0250, 0.0192, 0.0208, 0.0228]),\n",
       " tensor([0.0723, 0.0174, 0.0190, 0.0176, 0.0276, 0.0251, 0.0210, 0.0172, 0.0231,\n",
       "         0.0290, 0.0317, 0.0248, 0.0260, 0.0261, 0.0239, 0.0319]),\n",
       " tensor([0.0400, 0.3460, 0.0186, 0.0190, 0.0200, 0.0168, 0.0179, 0.0196, 0.0213,\n",
       "         0.0255, 0.0174, 0.2295, 0.0262, 0.0192, 0.0205, 0.0368]),\n",
       " tensor([0.0218, 0.0229, 0.0289, 0.7213, 0.9279, 0.0345, 0.0283, 0.0429, 0.0201,\n",
       "         0.9619, 0.0242, 0.0314, 0.0209, 0.0237, 0.7954, 0.0185]),\n",
       " tensor([0.8966, 0.0267, 0.0231, 0.0243, 0.0286, 0.0217, 0.0369, 0.0220, 0.1268,\n",
       "         0.0254, 0.0321, 0.0178, 0.0340, 0.0187, 0.0196, 0.0200]),\n",
       " tensor([0.0192, 0.0573, 0.0229, 0.0293, 0.9853, 0.0903, 0.0393, 0.1921, 0.0427,\n",
       "         0.0186, 0.0233, 0.0236, 0.0180, 0.0338, 0.0287, 0.0311]),\n",
       " tensor([0.0328, 0.0186, 0.0224, 0.0433, 0.0288, 0.0686, 0.0245, 0.0231, 0.1239,\n",
       "         0.0289, 0.3033, 0.0406, 0.0218, 0.0191, 0.0250, 0.0203]),\n",
       " tensor([0.0206, 0.0520, 0.0289, 0.0198, 0.0390, 0.7600, 0.0942, 0.9727, 0.0758,\n",
       "         0.0208, 0.0191, 0.0225, 0.0205, 0.0219, 0.0371, 0.0215]),\n",
       " tensor([0.0207, 0.0314, 0.0310, 0.0613, 0.0203, 0.0185, 0.0305, 0.0219, 0.0196,\n",
       "         0.0207, 0.0336, 0.0331, 0.0326, 0.0224, 0.0188, 0.7140]),\n",
       " tensor([0.0280, 0.0179, 0.0183, 0.0620, 0.0189, 0.0191, 0.0216, 0.8471, 0.1839,\n",
       "         0.0262, 0.0333, 0.0231, 0.5715, 0.0262, 0.0243, 0.0877]),\n",
       " tensor([0.2915, 0.3958, 0.0367, 0.0318, 0.5206, 0.0356, 0.7475, 0.0242, 0.2067,\n",
       "         0.8672, 0.9930, 0.6446, 0.9672, 0.5886, 0.2459, 0.9897]),\n",
       " tensor([0.2287, 0.2697, 0.1558, 0.0410, 0.0213, 0.9813, 0.0314, 0.2351, 0.0334,\n",
       "         0.1963, 0.3044, 0.9376, 0.0387, 0.9306, 0.1792, 0.9365]),\n",
       " tensor([0.6301, 0.6200, 0.0334, 0.0594, 0.6511, 0.3917, 0.2821, 0.0275, 0.0182,\n",
       "         0.2279, 0.7892, 0.0213, 0.0507, 0.4713, 0.1708, 0.8345]),\n",
       " tensor([0.9865, 0.4685, 0.3205, 0.0235, 0.0334, 0.9813, 0.0239, 0.0365, 0.0222,\n",
       "         0.0268, 0.0210, 0.9551, 0.9294, 0.0235, 0.4216, 0.1437]),\n",
       " tensor([0.0277, 0.0294, 0.0511, 0.0185, 0.4372, 0.0353, 0.0241, 0.3551, 0.0300,\n",
       "         0.0320, 0.5351, 0.5344, 0.4714, 0.0259, 0.0245, 0.0328]),\n",
       " tensor([0.2378, 0.0217, 0.4026, 0.0342, 0.9701, 0.1172, 0.9005, 0.0194, 0.0262,\n",
       "         0.4909, 0.0451, 0.0206, 0.1310, 0.0218, 0.9851, 0.8611]),\n",
       " tensor([0.0231, 0.0540, 0.9479, 0.0216, 0.2528, 0.0232, 0.0259, 0.9615, 0.0263,\n",
       "         0.0850, 0.0258, 0.0308, 0.0324, 0.3008, 0.0584, 0.8773]),\n",
       " tensor([0.9892, 0.0331, 0.0215, 0.9886, 0.0255, 0.7410, 0.0194, 0.0296, 0.0314,\n",
       "         0.0623, 0.0432, 0.0216, 0.0203, 0.0210, 0.0284, 0.7188]),\n",
       " tensor([0.0223, 0.3689, 0.0482, 0.0243, 0.9685, 0.0262, 0.8930, 0.0200, 0.0215,\n",
       "         0.5602, 0.0349, 0.0265, 0.0219, 0.0730, 0.3910, 0.0308]),\n",
       " tensor([0.9236, 0.0259, 0.9248, 0.8871, 0.4909, 0.8287, 0.4009, 0.5455, 0.5503,\n",
       "         0.0191, 0.0221, 0.9123, 0.0481, 0.4143, 0.6451, 0.4539]),\n",
       " tensor([0.6331, 0.0204, 0.0245, 0.0670, 0.4646, 0.2819, 0.3610, 0.2947, 0.7141,\n",
       "         0.0579, 0.0543, 0.0923, 0.6855, 0.1748, 0.0276, 0.9588]),\n",
       " tensor([0.3810, 0.9302, 0.1706, 0.7911, 0.0337, 0.9352, 0.5333, 0.3273, 0.7788,\n",
       "         0.4638, 0.5122, 0.6823, 0.5263, 0.5308, 0.0291, 0.8677]),\n",
       " tensor([0.0790, 0.0497, 0.0450, 0.0193, 0.9751, 0.9712, 0.9262, 0.0206, 0.0217,\n",
       "         0.3093, 0.0183, 0.9861, 0.3244, 0.9670, 0.9581, 0.9659]),\n",
       " tensor([0.9769, 0.9717, 0.9485, 0.6664, 0.8250, 0.6672, 0.7422, 0.4969, 0.3600,\n",
       "         0.0662, 0.6641, 0.4186, 0.0329, 0.0871, 0.0186, 0.0227]),\n",
       " tensor([0.0219, 0.8708, 0.8396, 0.0187, 0.0234, 0.0270, 0.7228, 0.9884, 0.0744,\n",
       "         0.9676, 0.0433, 0.0244, 0.0318, 0.0541, 0.0338, 0.1506]),\n",
       " tensor([0.0574, 0.0351, 0.0336, 0.0322, 0.0246, 0.0205, 0.0414, 0.9225, 0.0212,\n",
       "         0.0216, 0.0218, 0.0215, 0.0182, 0.0235, 0.0251, 0.0236]),\n",
       " tensor([0.3593, 0.0271, 0.0404, 0.0161, 0.3493, 0.0196, 0.0228, 0.0225, 0.0186,\n",
       "         0.0668, 0.0311, 0.0227, 0.0194, 0.0214, 0.0243, 0.0208]),\n",
       " tensor([0.9825, 0.0273, 0.4088, 0.0373, 0.0248, 0.0350, 0.0323, 0.0191, 0.0243,\n",
       "         0.0207, 0.0373, 0.0203, 0.9373, 0.0241, 0.0164, 0.0265]),\n",
       " tensor([0.0258, 0.0241, 0.9171, 0.0163, 0.0170, 0.0233, 0.0169, 0.0291, 0.0190,\n",
       "         0.0210, 0.0226, 0.9157, 0.0215, 0.0254, 0.0221, 0.1637]),\n",
       " tensor([0.0251, 0.0340, 0.0219, 0.0348, 0.0549, 0.4680, 0.0191, 0.0240, 0.3158,\n",
       "         0.0271, 0.5643, 0.0205, 0.0171, 0.0257, 0.0226, 0.0315]),\n",
       " tensor([0.9288, 0.0299, 0.2407, 0.0267, 0.0401, 0.0241, 0.0252, 0.5356, 0.0296,\n",
       "         0.0198, 0.0515, 0.0213, 0.8263, 0.0219, 0.0196, 0.0260]),\n",
       " tensor([0.0208, 0.3981, 0.0221, 0.0264, 0.0575, 0.0205, 0.0459, 0.9776, 0.0288,\n",
       "         0.0313, 0.0200, 0.6415, 0.0255, 0.0280, 0.0375, 0.0232]),\n",
       " tensor([0.0198, 0.0243, 0.7653, 0.0187, 0.0198, 0.0244, 0.0173, 0.0349, 0.0290,\n",
       "         0.0188, 0.0196, 0.0204, 0.0302, 0.5131, 0.0246, 0.0315]),\n",
       " tensor([0.0231, 0.0374, 0.0261, 0.0220, 0.0334, 0.0252, 0.0276, 0.0315, 0.0368,\n",
       "         0.0345, 0.0227, 0.0264, 0.0206, 0.0196, 0.0285, 0.0219]),\n",
       " tensor([0.0207, 0.0240, 0.0540, 0.2930, 0.0859, 0.0207, 0.0296, 0.0370, 0.0282,\n",
       "         0.0329, 0.0184, 0.0225, 0.0222, 0.0235, 0.0317, 0.0590]),\n",
       " tensor([0.8701, 0.0250, 0.4512, 0.6054, 0.0173, 0.0292, 0.0195, 0.0200, 0.0285,\n",
       "         0.0311, 0.0201, 0.0285, 0.0196, 0.0190, 0.0239, 0.0185]),\n",
       " tensor([0.0238, 0.0211, 0.0261, 0.0263, 0.0259, 0.0236, 0.0195, 0.0594, 0.8701,\n",
       "         0.0630, 0.0236, 0.0277, 0.0209, 0.0318, 0.0476, 0.5342]),\n",
       " tensor([0.0294, 0.0377, 0.0543, 0.2722, 0.0198, 0.0282, 0.0230, 0.0279, 0.0186,\n",
       "         0.0260, 0.0214, 0.0198, 0.0184, 0.0235, 0.0187, 0.0360]),\n",
       " tensor([0.0240, 0.0327, 0.0219, 0.0289, 0.0240, 0.0320, 0.1591, 0.0902, 0.0184,\n",
       "         0.0656, 0.0197, 0.0307, 0.0192, 0.0194, 0.0525, 0.0443]),\n",
       " tensor([0.0263, 0.0233, 0.0165, 0.0417, 0.0218, 0.0218, 0.0295, 0.0354, 0.0184,\n",
       "         0.0302, 0.0379, 0.0233, 0.0274, 0.0310, 0.0191, 0.0206]),\n",
       " tensor([0.0316, 0.1425, 0.0399, 0.0256, 0.0327, 0.0254, 0.0276, 0.3777, 0.0551,\n",
       "         0.0383, 0.0282, 0.0302, 0.0448, 0.0291, 0.0245, 0.0304]),\n",
       " tensor([0.0271, 0.0221, 0.0375, 0.0501, 0.0261, 0.0289, 0.0264, 0.0239, 0.0190,\n",
       "         0.6526, 0.0499, 0.0244, 0.6115, 0.0232, 0.0293, 0.0222]),\n",
       " tensor([0.0239, 0.5412, 0.0171, 0.0215, 0.0240, 0.0249, 0.1286, 0.0214, 0.0194,\n",
       "         0.0209, 0.1328, 0.7619, 0.0182, 0.0711, 0.9492, 0.0171]),\n",
       " tensor([0.0274, 0.0167, 0.6245, 0.0619, 0.0238, 0.0240, 0.8592, 0.0210, 0.0296,\n",
       "         0.0258, 0.3071, 0.0198, 0.0249, 0.0210, 0.0929, 0.9410]),\n",
       " tensor([0.0197, 0.0239, 0.0258, 0.0161, 0.0252, 0.0253, 0.0324, 0.0281, 0.0174,\n",
       "         0.0437, 0.0174, 0.0246, 0.0209, 0.0259, 0.0366, 0.0204]),\n",
       " tensor([0.0194, 0.0291, 0.9368, 0.0197, 0.0210, 0.0436, 0.9665, 0.3384, 0.0233,\n",
       "         0.0227, 0.0361, 0.0490, 0.0343, 0.0186, 0.2537, 0.0346]),\n",
       " tensor([0.0189, 0.0219, 0.7963, 0.0363, 0.2894, 0.0206, 0.0241, 0.0189, 0.0223,\n",
       "         0.0298, 0.0244, 0.0172, 0.0243, 0.8607, 0.0260, 0.0210]),\n",
       " tensor([0.0219, 0.0206, 0.0299, 0.0282, 0.9498, 0.0192, 0.0209, 0.0276, 0.0209,\n",
       "         0.0179, 0.0243, 0.0293, 0.0178, 0.0342, 0.0178, 0.0414]),\n",
       " tensor([0.0596, 0.0283, 0.0195, 0.7718, 0.0453, 0.0171, 0.0226, 0.0223, 0.0219,\n",
       "         0.0231, 0.9681, 0.4243, 0.0284, 0.0168, 0.0165, 0.1600]),\n",
       " tensor([0.0265, 0.0291, 0.0221, 0.0187, 0.0542, 0.0209, 0.0217, 0.0178, 0.2277,\n",
       "         0.0291, 0.0209, 0.0171, 0.0277, 0.0208, 0.0341, 0.0170]),\n",
       " tensor([0.0236, 0.0309, 0.0202, 0.0187, 0.0190, 0.0216, 0.0209, 0.0206, 0.0165,\n",
       "         0.3644, 0.4822, 0.0279, 0.0180, 0.1917, 0.0293, 0.0194]),\n",
       " tensor([0.0233, 0.0195, 0.0285, 0.1799, 0.0177, 0.0233, 0.0223, 0.0359, 0.0226,\n",
       "         0.0284, 0.0416, 0.9915, 0.0199, 0.0302, 0.0249, 0.2222]),\n",
       " tensor([0.1118, 0.0271, 0.0211, 0.0197, 0.0267, 0.0238, 0.0478, 0.0526, 0.0270,\n",
       "         0.0174, 0.0346, 0.0301, 0.0291, 0.0175, 0.0213, 0.0259]),\n",
       " tensor([0.0353, 0.0263, 0.0205, 0.0348, 0.0298, 0.0231, 0.0349, 0.0204, 0.3189,\n",
       "         0.0401, 0.0913, 0.0351, 0.6393, 0.0210, 0.0477, 0.4477]),\n",
       " tensor([0.0316, 0.0208, 0.0318, 0.0210, 0.0184, 0.0206, 0.0421, 0.0220, 0.0221,\n",
       "         0.9351, 0.0182, 0.0178, 0.0546, 0.0282, 0.1416, 0.0288]),\n",
       " tensor([0.0235, 0.0259, 0.9196, 0.0302, 0.0309, 0.0332, 0.0163, 0.0244, 0.0253,\n",
       "         0.0210, 0.0967, 0.0222, 0.0268, 0.8925, 0.0240, 0.0452]),\n",
       " tensor([0.0186, 0.0194, 0.0194, 0.0355, 0.1244, 0.0219, 0.0164, 0.0295, 0.1014,\n",
       "         0.0221, 0.0338, 0.0163, 0.0215, 0.1223, 0.5137, 0.0208]),\n",
       " tensor([0.8355, 0.0159, 0.0341, 0.0271, 0.3782, 0.0474, 0.0390, 0.0251, 0.0668,\n",
       "         0.0211, 0.0565, 0.0365, 0.0160, 0.0194, 0.0176, 0.0283]),\n",
       " tensor([0.0208, 0.0475, 0.0190, 0.4243, 0.0218, 0.0275, 0.0192, 0.0504, 0.0205,\n",
       "         0.0193, 0.0595, 0.0217, 0.0313, 0.0199, 0.0263, 0.8607]),\n",
       " tensor([0.1573, 0.0225, 0.7725, 0.0340, 0.0212, 0.0300, 0.0278, 0.0347, 0.0336,\n",
       "         0.0278, 0.0178, 0.0678, 0.4775, 0.0259, 0.0380, 0.0220]),\n",
       " tensor([0.0282, 0.0191, 0.0582, 0.2925, 0.0279, 0.0282, 0.0188, 0.0318, 0.6390,\n",
       "         0.0365, 0.0297, 0.4038, 0.0280, 0.0201, 0.0432, 0.9308]),\n",
       " tensor([0.1553, 0.4332, 0.3569, 0.4506, 0.0500, 0.0978, 0.0201, 0.0280, 0.0602,\n",
       "         0.0326, 0.0320, 0.3357, 0.0243, 0.0247, 0.0538, 0.9652]),\n",
       " tensor([0.0175, 0.0287, 0.0259, 0.0211, 0.0256, 0.0187, 0.0169, 0.1564, 0.0202,\n",
       "         0.0318, 0.0184, 0.0204, 0.0205, 0.0195, 0.0398, 0.0188]),\n",
       " tensor([0.0375, 0.0229, 0.0327, 0.4665, 0.3554, 0.0343, 0.0200, 0.0232, 0.0204,\n",
       "         0.0191, 0.9674, 0.0733, 0.0320, 0.0197, 0.0194, 0.0270]),\n",
       " tensor([0.0392, 0.0227, 0.0275, 0.0273, 0.6290, 0.6180, 0.0299, 0.0210, 0.0174,\n",
       "         0.0182, 0.0241, 0.0311, 0.3502, 0.0383, 0.0177, 0.0316]),\n",
       " tensor([0.1410, 0.0181, 0.0261, 0.0179, 0.0207, 0.0239, 0.0243, 0.0313, 0.0184,\n",
       "         0.0442, 0.8448, 0.0206, 0.0248, 0.0210, 0.9772, 0.9632]),\n",
       " tensor([0.0259, 0.0183, 0.0405, 0.0166, 0.0190, 0.0421, 0.3269, 0.0655, 0.0223,\n",
       "         0.0178, 0.9009, 0.0284, 0.0234, 0.5031, 0.0258, 0.0316]),\n",
       " tensor([0.0606, 0.0186, 0.0340, 0.0247, 0.0218, 0.0251, 0.0236, 0.0173, 0.0213,\n",
       "         0.0225, 0.0217, 0.0410, 0.0162, 0.0340, 0.0442, 0.0555]),\n",
       " tensor([0.9899, 0.9731, 0.0289, 0.8674, 0.0246, 0.0196, 0.0189, 0.0193, 0.0205,\n",
       "         0.0472, 0.0315, 0.0731, 0.0310, 0.0690, 0.0219, 0.0514]),\n",
       " tensor([0.1319, 0.0188, 0.0665, 0.0267, 0.0607, 0.0209, 0.9946, 0.9915, 0.6932,\n",
       "         0.0387, 0.0278, 0.0317, 0.0410, 0.0296, 0.0248, 0.5157]),\n",
       " tensor([0.0539, 0.0543, 0.9613, 0.4561, 0.0241, 0.0203, 0.0230, 0.5247, 0.0179,\n",
       "         0.4301, 0.0271, 0.0302, 0.0509, 0.0230, 0.0249, 0.1240]),\n",
       " tensor([0.3502, 0.0183, 0.3312, 0.0239, 0.0237, 0.0215, 0.0177, 0.0399, 0.0293,\n",
       "         0.0193, 0.0161, 0.9927, 0.0197, 0.0332, 0.0304, 0.0182]),\n",
       " tensor([0.0270, 0.5717, 0.0321, 0.0168, 0.0232, 0.0181, 0.0251, 0.9186, 0.0167,\n",
       "         0.8938, 0.0180, 0.7708, 0.0293, 0.0484, 0.0286, 0.0525]),\n",
       " tensor([0.1702, 0.0382, 0.0576, 0.5730, 0.0698, 0.0213, 0.0232, 0.0248, 0.0277,\n",
       "         0.0174, 0.0195, 0.0330, 0.0234, 0.0287, 0.0238, 0.0282]),\n",
       " tensor([0.0279, 0.0198, 0.0203, 0.7207, 0.0172, 0.0232, 0.0272, 0.2347, 0.0297,\n",
       "         0.0547, 0.0181, 0.0306, 0.0263, 0.0162, 0.1383, 0.0505]),\n",
       " tensor([0.0228, 0.0292, 0.0185, 0.0272, 0.0190, 0.0199, 0.0204, 0.0254, 0.0180,\n",
       "         0.0175, 0.0211, 0.0837, 0.0168, 0.2832, 0.0196, 0.0379]),\n",
       " tensor([0.0360, 0.9104, 0.6972, 0.5672, 0.8338, 0.0183, 0.1056, 0.0275, 0.0166,\n",
       "         0.0211, 0.0189, 0.1275, 0.0289, 0.0238, 0.0179, 0.2434]),\n",
       " tensor([0.0307, 0.0253, 0.0313, 0.0191, 0.0195, 0.0295, 0.0172, 0.0251, 0.0246,\n",
       "         0.0244, 0.0187, 0.0208, 0.0343, 0.0350, 0.2892, 0.9897]),\n",
       " tensor([0.6459, 0.0196, 0.4104, 0.0301, 0.0258, 0.8997, 0.0204, 0.0203, 0.0172,\n",
       "         0.0320, 0.0397, 0.0279, 0.0441, 0.0326, 0.0311, 0.0366]),\n",
       " tensor([0.0357, 0.9254, 0.0192, 0.0573, 0.0189, 0.9333, 0.0201, 0.0434, 0.0340,\n",
       "         0.9863, 0.0215, 0.2578, 0.1549, 0.0234, 0.0257, 0.8903]),\n",
       " tensor([0.0249, 0.0365, 0.0208, 0.0232, 0.0245, 0.0224, 0.0173, 0.0221, 0.3073,\n",
       "         0.0258, 0.0239, 0.0484, 0.0227, 0.0160, 0.0187, 0.0465]),\n",
       " tensor([0.0355, 0.0378, 0.0169, 0.0254, 0.0207, 0.0182, 0.0170, 0.2814, 0.0310,\n",
       "         0.0235, 0.0273, 0.0247, 0.0221, 0.0220, 0.0274, 0.0215]),\n",
       " tensor([0.0174, 0.0179, 0.0182, 0.0322, 0.0354, 0.0283, 0.0345, 0.0250, 0.0275,\n",
       "         0.0335, 0.0363, 0.7402, 0.0194, 0.0202, 0.0252, 0.0237]),\n",
       " tensor([0.0366, 0.8337, 0.0229, 0.0192, 0.0215, 0.0238, 0.0297, 0.0181, 0.0227,\n",
       "         0.1253, 0.3520, 0.0184, 0.0263, 0.0274, 0.0201, 0.8956]),\n",
       " tensor([0.7655, 0.0391, 0.0265, 0.0484, 0.0233, 0.0204, 0.0242, 0.0213, 0.0229,\n",
       "         0.0164, 0.0257, 0.0267, 0.0211, 0.0221, 0.0265, 0.0199]),\n",
       " tensor([0.0508, 0.0321, 0.0257, 0.0254, 0.0314, 0.0363, 0.0318, 0.0187, 0.0589,\n",
       "         0.0214, 0.2799, 0.0222, 0.2502, 0.0208, 0.0326, 0.2106]),\n",
       " tensor([0.0328, 0.0173, 0.0237, 0.3650, 0.6682, 0.9473, 0.0473, 0.0209, 0.4726,\n",
       "         0.7529, 0.0227, 0.0626, 0.2818, 0.0300, 0.0182, 0.0957]),\n",
       " tensor([0.0188, 0.5795, 0.0212, 0.0190, 0.0214, 0.0237, 0.0242, 0.0281, 0.0344,\n",
       "         0.0229, 0.0193, 0.0260, 0.0201, 0.4648, 0.0299, 0.0206]),\n",
       " tensor([0.0200, 0.0168, 0.0201, 0.1696, 0.0201, 0.0206, 0.9948, 0.8838, 0.0216,\n",
       "         0.0190, 0.0218, 0.0295, 0.0562, 0.0180, 0.0288, 0.0186]),\n",
       " tensor([0.0282, 0.0431, 0.0183, 0.0211, 0.0166, 0.9368, 0.2682, 0.0217, 0.0313,\n",
       "         0.0199, 0.0252, 0.0189, 0.0466, 0.0242, 0.0202, 0.1451]),\n",
       " tensor([0.9798, 0.0167, 0.9923, 0.0302, 0.0281, 0.0254, 0.0240, 0.3012, 0.0349,\n",
       "         0.0288, 0.0189, 0.0280, 0.2726, 0.0229, 0.0217, 0.0658]),\n",
       " tensor([0.0283, 0.0539, 0.0348, 0.0264, 0.0346, 0.5198, 0.0196, 0.5882, 0.0195,\n",
       "         0.0297, 0.0289, 0.0237, 0.0253, 0.0259, 0.5392, 0.0203]),\n",
       " tensor([0.9841, 0.0190, 0.0200, 0.0183, 0.2959, 0.0306, 0.0191, 0.2540, 0.0241,\n",
       "         0.0200, 0.0443, 0.9175, 0.0412, 0.0228, 0.0221, 0.0264]),\n",
       " tensor([0.0199, 0.1247, 0.0182, 0.3644, 0.9477, 0.0259, 0.0188, 0.0185, 0.0336,\n",
       "         0.0186, 0.0216, 0.0209, 0.0277, 0.0248, 0.0212, 0.0812]),\n",
       " tensor([0.0214, 0.1829, 0.0235, 0.0181, 0.0236, 0.0421, 0.0251, 0.0211, 0.0308,\n",
       "         0.0245, 0.0212, 0.0193, 0.0214, 0.0232, 0.0247, 0.0206]),\n",
       " tensor([0.0218, 0.0188, 0.0241, 0.0283, 0.0246, 0.0229, 0.0226, 0.0213, 0.0244,\n",
       "         0.0418, 0.0236, 0.0416, 0.0251, 0.0262, 0.0287, 0.0209]),\n",
       " tensor([0.0969, 0.6866, 0.6295, 0.0331, 0.0215, 0.0194, 0.0368, 0.0173, 0.0232,\n",
       "         0.0339, 0.0255, 0.0229, 0.0458, 0.7157, 0.0258, 0.0587]),\n",
       " tensor([0.0226, 0.0201, 0.4414, 0.4650, 0.0278, 0.0154, 0.0269, 0.0216, 0.0185,\n",
       "         0.0184, 0.0184, 0.0180, 0.0186, 0.0223, 0.0254, 0.0178]),\n",
       " tensor([0.0183, 0.0215, 0.0185, 0.0188, 0.0311, 0.0199, 0.0175, 0.7443, 0.0346,\n",
       "         0.0257, 0.0176, 0.0374, 0.0699, 0.0331, 0.0257, 0.0184]),\n",
       " tensor([0.4281, 0.0192, 0.0239, 0.0234, 0.4300, 0.0351, 0.0266, 0.0217, 0.0258,\n",
       "         0.0707, 0.0209, 0.0260, 0.0569, 0.0201, 0.0246, 0.0237]),\n",
       " tensor([0.0208, 0.0190, 0.1213, 0.0233, 0.8189, 0.0268, 0.0397, 0.0257, 0.0203,\n",
       "         0.0326, 0.0200, 0.0216, 0.0416, 0.5070, 0.9783, 0.0215]),\n",
       " tensor([0.0244, 0.0168, 0.0249, 0.0272, 0.0172, 0.0278, 0.0148, 0.0355, 0.0441,\n",
       "         0.0260, 0.0234, 0.0261, 0.0172, 0.9681, 0.0500, 0.2146]),\n",
       " tensor([0.0265, 0.0296, 0.3515, 0.0217, 0.0219, 0.7651, 0.0187, 0.0265, 0.0198,\n",
       "         0.0211, 0.0888, 0.0306, 0.0202, 0.0196, 0.0341, 0.0191]),\n",
       " tensor([0.0319, 0.0216, 0.0299, 0.0356, 0.0224, 0.0262, 0.0227, 0.2183, 0.0193,\n",
       "         0.0213, 0.9385, 0.1221, 0.0236, 0.4643, 0.9204, 0.0191]),\n",
       " tensor([0.9866, 0.8977, 0.0245, 0.0437, 0.0310, 0.0228, 0.0445, 0.0218, 0.0192,\n",
       "         0.0202, 0.0231, 0.0186, 0.0211, 0.0247, 0.0213, 0.9441]),\n",
       " tensor([0.0271, 0.0187, 0.9761, 0.0276, 0.0261, 0.0203, 0.0241, 0.0192, 0.9579,\n",
       "         0.0212, 0.0223, 0.0221, 0.0201, 0.0256, 0.0288, 0.0241]),\n",
       " tensor([0.8037, 0.0215, 0.0237, 0.0248, 0.0287, 0.0218, 0.0164, 0.0217, 0.0588,\n",
       "         0.0244, 0.0287, 0.0265, 0.5562, 0.0165, 0.0232, 0.0282]),\n",
       " tensor([0.0237, 0.0165, 0.0169, 0.0569, 0.0215, 0.1012, 0.0615, 0.0230, 0.0255,\n",
       "         0.0314, 0.0251, 0.1573, 0.1146, 0.0238, 0.0203, 0.0199]),\n",
       " tensor([0.6641, 0.0238, 0.0183, 0.0397, 0.0214, 0.0251, 0.0187, 0.0452, 0.1115,\n",
       "         0.0265, 0.0217, 0.0390, 0.0456, 0.0248, 0.0182, 0.0192]),\n",
       " tensor([0.0231, 0.9753, 0.0224, 0.0177, 0.4775, 0.0358, 0.0207, 0.0270, 0.0248,\n",
       "         0.0250, 0.0344, 0.0537, 0.0256, 0.9205, 0.0230, 0.0258]),\n",
       " tensor([0.0213, 0.0212, 0.0252, 0.0207, 0.0230, 0.0281, 0.0242, 0.0356, 0.0168,\n",
       "         0.0291, 0.0691, 0.0235, 0.0194, 0.0258, 0.0208, 0.0471]),\n",
       " tensor([0.0365, 0.0227, 0.3582, 0.0223, 0.0513, 0.0235, 0.0184, 0.0186, 0.1069,\n",
       "         0.0232, 0.0277, 0.0188, 0.0209, 0.0232, 0.0253, 0.0266]),\n",
       " tensor([0.0227, 0.0314, 0.0167, 0.0215, 0.0245, 0.0349, 0.6945, 0.0536, 0.9464,\n",
       "         0.0193, 0.0201, 0.0339, 0.0242, 0.0164, 0.0358, 0.0292]),\n",
       " tensor([0.0222, 0.0227, 0.0229, 0.0173, 0.0183, 0.8932, 0.0228, 0.0164, 0.6231,\n",
       "         0.0179, 0.0311, 0.0786, 0.0656, 0.0236, 0.0217, 0.0677]),\n",
       " tensor([0.2271, 0.9855, 0.9768, 0.0373, 0.4234, 0.0196, 0.0234, 0.0251, 0.0374,\n",
       "         0.0230, 0.0180, 0.7362, 0.0283, 0.0290, 0.0257, 0.0195]),\n",
       " tensor([0.0295, 0.0223, 0.0194, 0.0442, 0.0174, 0.0343, 0.2224, 0.4604, 0.0187,\n",
       "         0.0207, 0.0999, 0.0892, 0.0313, 0.0340, 0.0255, 0.0194]),\n",
       " tensor([0.0219, 0.0558, 0.0221, 0.2583, 0.0246, 0.0222, 0.0281, 0.5971, 0.0217,\n",
       "         0.0358, 0.0225, 0.0241, 0.0275, 0.0304, 0.0253, 0.0313]),\n",
       " tensor([0.0312, 0.9855, 0.0249, 0.0182, 0.0213, 0.0236, 0.0253, 0.0217, 0.0360,\n",
       "         0.0352, 0.0229, 0.0775, 0.1228, 0.0332, 0.0274, 0.0962]),\n",
       " tensor([0.0243, 0.9364, 0.0194, 0.0258, 0.0239, 0.0205, 0.0288, 0.2221, 0.0374,\n",
       "         0.0214, 0.0208, 0.0194, 0.0171, 0.0345, 0.0195, 0.2432]),\n",
       " tensor([0.0340, 0.0193, 0.0209, 0.0200, 0.0238, 0.0207, 0.0169, 0.0304, 0.7999,\n",
       "         0.3816, 0.0272, 0.7966, 0.0178, 0.7932, 0.9844, 0.1657]),\n",
       " tensor([0.2590, 0.0466, 0.0245, 0.0255, 0.0345, 0.0269, 0.0225, 0.0174, 0.0185,\n",
       "         0.0195, 0.0202, 0.0296, 0.0229, 0.0212, 0.1688, 0.0176]),\n",
       " tensor([0.0236, 0.0310, 0.4424, 0.0205, 0.4025, 0.0334, 0.0318, 0.0231, 0.0202,\n",
       "         0.0189, 0.0890, 0.0235, 0.0241, 0.0336, 0.0237, 0.0193]),\n",
       " tensor([0.0249, 0.0175, 0.0225, 0.0208, 0.0230, 0.0345, 0.0323, 0.0193, 0.0404,\n",
       "         0.0281, 0.1257, 0.9644, 0.6457, 0.0172, 0.0419, 0.0200]),\n",
       " tensor([0.0212, 0.0276, 0.0308, 0.0354, 0.0217, 0.0369, 0.0629, 0.0289, 0.0674,\n",
       "         0.0337, 0.0186, 0.6732, 0.0213, 0.0200, 0.0320, 0.0218]),\n",
       " tensor([0.3838, 0.0557, 0.0250, 0.5377, 0.0266, 0.0285, 0.0385, 0.0275, 0.2009,\n",
       "         0.7606, 0.0310, 0.0227, 0.0214, 0.0195, 0.0276, 0.0177]),\n",
       " tensor([0.0299, 0.0326, 0.0428, 0.0210, 0.0298, 0.0195, 0.0260, 0.0276, 0.0247,\n",
       "         0.0321, 0.0316, 0.0249, 0.0287, 0.1456, 0.0215, 0.0214]),\n",
       " tensor([0.0233, 0.0182, 0.2621, 0.0347, 0.0274, 0.0246, 0.0207, 0.0191, 0.0235,\n",
       "         0.0213, 0.9002, 0.3512, 0.0208, 0.0246, 0.1456, 0.9774]),\n",
       " tensor([0.1262, 0.0231, 0.0174, 0.9024, 0.0224, 0.0232, 0.0201, 0.5744, 0.0508,\n",
       "         0.0193, 0.0377, 0.0170, 0.0192, 0.1533, 0.0476, 0.0400]),\n",
       " tensor([0.0190, 0.0187, 0.0170, 0.0350, 0.0871, 0.0176, 0.0206, 0.0182, 0.0366,\n",
       "         0.0227, 0.0194, 0.0200, 0.0197, 0.0229, 0.0198, 0.0277]),\n",
       " tensor([0.0190, 0.7970, 0.0384, 0.0322, 0.0205, 0.0196, 0.0309, 0.0255, 0.0191,\n",
       "         0.4180, 0.0220, 0.0178, 0.0208, 0.0226, 0.0241, 0.0193]),\n",
       " tensor([0.0217, 0.0192, 0.0308, 0.0188, 0.0168, 0.0169, 0.0284, 0.0229, 0.0205,\n",
       "         0.0333, 0.2809, 0.0232, 0.0204, 0.0885, 0.0226, 0.0475]),\n",
       " tensor([0.0607, 0.0412, 0.0193, 0.0194, 0.0178, 0.0165, 0.0185, 0.0537, 0.8778,\n",
       "         0.0274, 0.0191, 0.1198, 0.0499, 0.3497, 0.0201, 0.0254]),\n",
       " tensor([0.0317, 0.0178, 0.0239, 0.0561, 0.0192, 0.0189, 0.0505, 0.0227, 0.0338,\n",
       "         0.0233, 0.0316, 0.3623, 0.0361, 0.0391, 0.0212, 0.1367]),\n",
       " tensor([0.6017, 0.0352, 0.3677, 0.0441, 0.0259, 0.0223, 0.0186, 0.0388, 0.0285,\n",
       "         0.0397, 0.0223, 0.0209, 0.0289, 0.3054, 0.9705, 0.0168]),\n",
       " tensor([0.1513, 0.0498, 0.1361, 0.0454, 0.0211, 0.0327, 0.0342, 0.5021, 0.0171,\n",
       "         0.0401, 0.9869, 0.0176, 0.5799, 0.0225, 0.0306, 0.0166]),\n",
       " tensor([0.0193, 0.0267, 0.0578, 0.0170, 0.3850, 0.0313, 0.0189, 0.0169, 0.0364,\n",
       "         0.0392, 0.0593, 0.0217, 0.0209, 0.0185, 0.0191, 0.0227]),\n",
       " tensor([0.0186, 0.0193, 0.0208, 0.0216, 0.0339, 0.0253, 0.0177, 0.0185, 0.0183,\n",
       "         0.0262, 0.0314, 0.0175, 0.0217, 0.0266, 0.1447, 0.1651]),\n",
       " tensor([0.9413, 0.0230, 0.0168, 0.0197, 0.0258, 0.0250, 0.0268, 0.4931, 0.0218,\n",
       "         0.0185, 0.0303, 0.0192, 0.0198, 0.0215, 0.0251, 0.0270]),\n",
       " tensor([0.0345, 0.0231, 0.0213, 0.0203, 0.0199, 0.9483, 0.0153, 0.0237, 0.0200,\n",
       "         0.0284, 0.3174, 0.0188, 0.4661, 0.0171, 0.0270, 0.0669]),\n",
       " tensor([0.0295, 0.0190, 0.0264, 0.0166, 0.0331, 0.0267, 0.4659, 0.0230, 0.0284,\n",
       "         0.3819, 0.0335, 0.0206, 0.0170, 0.0212, 0.0160, 0.1473]),\n",
       " tensor([0.0201, 0.0184, 0.0232, 0.0216, 0.0158, 0.0169, 0.0251, 0.0176, 0.0239,\n",
       "         0.0259, 0.0214, 0.0401, 0.0189, 0.0206, 0.2347, 0.0272]),\n",
       " tensor([0.0189, 0.0193, 0.5777, 0.1577, 0.0218, 0.6104, 0.0189, 0.0286, 0.0826,\n",
       "         0.1082, 0.0241, 0.0251, 0.0220, 0.0599, 0.0234, 0.0534]),\n",
       " tensor([0.0290, 0.0555, 0.0241, 0.0307, 0.0279, 0.0199, 0.0163, 0.0222, 0.0189,\n",
       "         0.0208, 0.0315, 0.4695, 0.9724, 0.9799, 0.9466, 0.0187]),\n",
       " tensor([0.0275, 0.0168, 0.0204, 0.0256, 0.0257, 0.0232, 0.0203, 0.0219, 0.0426,\n",
       "         0.0170, 0.1257, 0.0305, 0.0195, 0.0332, 0.0179, 0.0254]),\n",
       " tensor([0.2984, 0.0210, 0.0338, 0.7526, 0.0170, 0.0262, 0.0197, 0.0281, 0.0247,\n",
       "         0.0231, 0.0189, 0.2236, 0.0211, 0.0353, 0.0209, 0.0235]),\n",
       " tensor([0.0281, 0.0176, 0.0253, 0.0192, 0.0330, 0.0209, 0.0394, 0.0355, 0.5682,\n",
       "         0.4432, 0.0311, 0.0254, 0.0457, 0.3223, 0.0214, 0.0295]),\n",
       " tensor([0.0274, 0.0345, 0.3731, 0.1483, 0.0570, 0.9578, 0.6541, 0.4404, 0.0181,\n",
       "         0.4558, 0.0255, 0.0248, 0.1304, 0.0256, 0.8907, 0.0283]),\n",
       " tensor([0.0206, 0.0266, 0.0209, 0.2070, 0.3823, 0.9773, 0.0263, 0.0306, 0.0213,\n",
       "         0.0305, 0.0182, 0.0238, 0.0221, 0.0214, 0.4439, 0.0520]),\n",
       " tensor([0.0283, 0.0242, 0.4531, 0.0282, 0.0537, 0.6392, 0.0294, 0.0172, 0.0247,\n",
       "         0.0229, 0.0289, 0.0190, 0.8806, 0.0274, 0.2554, 0.0173]),\n",
       " tensor([0.0335, 0.0295, 0.4875, 0.0202, 0.0235, 0.0180, 0.0191, 0.0984, 0.9001,\n",
       "         0.0453, 0.0403, 0.0238, 0.0277, 0.0251, 0.0183, 0.0324]),\n",
       " tensor([0.0264, 0.0322, 0.0244, 0.0228, 0.0209, 0.0299, 0.0191, 0.6168, 0.0363,\n",
       "         0.0225, 0.0202, 0.0337, 0.0215, 0.0272, 0.0350, 0.0236]),\n",
       " tensor([0.0259, 0.0191, 0.0205, 0.0198, 0.0174, 0.6674, 0.0172, 0.0276, 0.0415,\n",
       "         0.0179, 0.0256, 0.0166, 0.0188, 0.0193, 0.0282, 0.0287]),\n",
       " tensor([0.0220, 0.0220, 0.0189, 0.0349, 0.0193, 0.0191, 0.0252, 0.1850, 0.0274,\n",
       "         0.0315, 0.0389, 0.0186, 0.0655, 0.0319, 0.0199, 0.0248]),\n",
       " tensor([0.0210, 0.0233, 0.0962, 0.5297, 0.0286, 0.0256, 0.6935, 0.0412, 0.0313,\n",
       "         0.0266, 0.0243, 0.0232, 0.0292, 0.0290, 0.9903, 0.0923]),\n",
       " tensor([0.0273, 0.0497, 0.0207, 0.0428, 0.9551, 0.0401, 0.0221, 0.0198, 0.0256,\n",
       "         0.0189, 0.0204, 0.0340, 0.0674, 0.1240, 0.0421, 0.0210]),\n",
       " tensor([0.0221, 0.8368, 0.0208, 0.0324, 0.0184, 0.0184, 0.0220, 0.0222, 0.0291,\n",
       "         0.0345, 0.9890, 0.0351, 0.0416, 0.0288, 0.0180, 0.0276]),\n",
       " tensor([0.0193, 0.0195, 0.0323, 0.0377, 0.6743, 0.0209, 0.0378, 0.0221, 0.6672,\n",
       "         0.0196, 0.0312, 0.0620, 0.0286, 0.0398, 0.0217, 0.8950]),\n",
       " tensor([0.0198, 0.9546, 0.5835, 0.0219, 0.0222, 0.0178, 0.0227, 0.0549, 0.2367,\n",
       "         0.0256, 0.0235, 0.0308, 0.0194, 0.0318, 0.0229, 0.9859]),\n",
       " tensor([0.0214, 0.0226, 0.0529, 0.0212, 0.0300, 0.0226, 0.0287, 0.0197, 0.0232,\n",
       "         0.0225, 0.0228, 0.0238, 0.0172, 0.7750, 0.0268, 0.0559]),\n",
       " tensor([0.0177, 0.0259, 0.0216, 0.0237, 0.0207, 0.0256, 0.4596, 0.0237, 0.0250,\n",
       "         0.0775, 0.0184, 0.0262, 0.3324, 0.9705, 0.0250, 0.5012]),\n",
       " tensor([0.0387, 0.7586, 0.0201, 0.1327, 0.0837, 0.0342, 0.0341, 0.0177, 0.0400,\n",
       "         0.0228, 0.0219, 0.0203, 0.0179, 0.0576, 0.8764, 0.0202]),\n",
       " tensor([0.0278, 0.0398, 0.0164, 0.6870, 0.1340, 0.0360, 0.0219, 0.0198, 0.0225,\n",
       "         0.0247, 0.0295, 0.1040, 0.0217, 0.0350, 0.0244, 0.0219]),\n",
       " tensor([0.0189, 0.0246, 0.0172, 0.0280, 0.0188, 0.0294, 0.0391, 0.0276, 0.0177,\n",
       "         0.0333, 0.0328, 0.0339, 0.0170, 0.0395, 0.0490, 0.4813]),\n",
       " tensor([0.9920, 0.5046, 0.0205, 0.0517, 0.0265, 0.0163, 0.0225, 0.0267, 0.0215,\n",
       "         0.0269, 0.7879, 0.0292, 0.0207, 0.0170, 0.0361, 0.0177]),\n",
       " tensor([0.7533, 0.0211, 0.0201, 0.5998, 0.0256, 0.0850, 0.0546, 0.0238, 0.0301,\n",
       "         0.7710, 0.0747, 0.0306, 0.0307, 0.0196, 0.0278, 0.0234]),\n",
       " tensor([0.0262, 0.5358, 0.0330, 0.0516, 0.0212, 0.0397, 0.0282, 0.0199, 0.0232,\n",
       "         0.0187, 0.0249, 0.0238, 0.0424, 0.0280, 0.0346, 0.0180]),\n",
       " tensor([0.0203, 0.0457, 0.0234, 0.0330, 0.0275, 0.0522, 0.0266, 0.0282, 0.0310,\n",
       "         0.0314, 0.0200, 0.0398, 0.0297, 0.0370, 0.0211, 0.0222]),\n",
       " tensor([0.0251, 0.0156, 0.0250, 0.1233, 0.0280, 0.0382, 0.0281, 0.0245, 0.0195,\n",
       "         0.0665, 0.0200, 0.0609, 0.0359, 0.0219, 0.0202, 0.0370]),\n",
       " tensor([0.0227, 0.0275, 0.0213, 0.2910, 0.0177, 0.0275, 0.0196, 0.0171, 0.0222,\n",
       "         0.0191, 0.0265, 0.0222, 0.5101, 0.0201, 0.0237, 0.0247]),\n",
       " tensor([0.0260, 0.3701, 0.0286, 0.0246, 0.5567, 0.0233, 0.0230, 0.1490, 0.0246,\n",
       "         0.0280, 0.0197, 0.0546, 0.0253, 0.0437, 0.0383, 0.0328]),\n",
       " tensor([0.0265, 0.7041, 0.0426, 0.0225, 0.0169, 0.0358, 0.0745, 0.0211, 0.0210,\n",
       "         0.0184, 0.1085, 0.0279, 0.0573, 0.0229, 0.0560, 0.4042]),\n",
       " tensor([0.0191, 0.0228, 0.0203, 0.0200, 0.9451, 0.0342, 0.0277, 0.0389, 0.9684,\n",
       "         0.8476, 0.9527, 0.0191, 0.0198, 0.0192, 0.0899, 0.5655]),\n",
       " tensor([0.0610, 0.0198, 0.9918, 0.0691, 0.0378, 0.8048, 0.0302, 0.0213, 0.0239,\n",
       "         0.2547, 0.0323, 0.0243, 0.0209, 0.0172, 0.0196, 0.0225]),\n",
       " tensor([0.4168, 0.0211, 0.0212, 0.0688, 0.0242, 0.0201, 0.0218, 0.0252, 0.0234,\n",
       "         0.1008, 0.0233, 0.0678, 0.0255, 0.0199, 0.0350, 0.0240]),\n",
       " tensor([0.0235, 0.0261, 0.0300, 0.0456, 0.0170, 0.0208, 0.0206, 0.1537, 0.1548,\n",
       "         0.7021, 0.0254, 0.6568, 0.0224, 0.0184, 0.0373, 0.7311]),\n",
       " tensor([0.2074, 0.0339, 0.0178, 0.0444, 0.0354, 0.0377, 0.0165, 0.0248, 0.0256,\n",
       "         0.2720, 0.0224, 0.0163, 0.0229, 0.0196, 0.8961, 0.0250]),\n",
       " tensor([0.0236, 0.0284, 0.0213, 0.0194, 0.0329, 0.0215, 0.6639, 0.0213, 0.5700,\n",
       "         0.2782, 0.0205, 0.0187, 0.0599, 0.0191, 0.0184, 0.0236]),\n",
       " tensor([0.0190, 0.0218, 0.0195, 0.0208, 0.0679, 0.0261, 0.0252, 0.4001, 0.0246,\n",
       "         0.2672, 0.0689, 0.0173, 0.0448, 0.0270, 0.0347, 0.0210]),\n",
       " tensor([0.0185, 0.2349, 0.0170, 0.0156, 0.0212, 0.0264, 0.0204, 0.0354, 0.0239,\n",
       "         0.9303, 0.6580, 0.0556, 0.0295, 0.0316, 0.0227, 0.0186]),\n",
       " tensor([0.0195, 0.4641, 0.0270, 0.0309, 0.0211, 0.0178, 0.0247, 0.0371, 0.0250,\n",
       "         0.0569, 0.3114, 0.0545, 0.0188, 0.0193, 0.0221, 0.0252]),\n",
       " tensor([0.0226, 0.0360, 0.0257, 0.0209, 0.0180, 0.0216, 0.0497, 0.0265, 0.0305,\n",
       "         0.0334, 0.0296, 0.0285, 0.0227, 0.0196, 0.0292, 0.0239]),\n",
       " tensor([0.0219, 0.8655, 0.3784, 0.0248, 0.0241, 0.0378, 0.0265, 0.0171, 0.0357,\n",
       "         0.0406, 0.0172, 0.0188, 0.0330, 0.0324, 0.0442, 0.0275]),\n",
       " tensor([0.0253, 0.1471, 0.0367, 0.0199, 0.0204, 0.9542, 0.0168, 0.0601, 0.0185,\n",
       "         0.1267, 0.0394, 0.0213, 0.0245, 0.0237, 0.0227, 0.0289]),\n",
       " tensor([0.0202, 0.0226, 0.0202, 0.0266, 0.0265, 0.0241, 0.0357, 0.0236, 0.0218,\n",
       "         0.5974, 0.0288, 0.0380, 0.0413, 0.0310, 0.0420, 0.0236]),\n",
       " tensor([0.0202, 0.0180, 0.0480, 0.0292, 0.0277, 0.0489, 0.0281, 0.0325, 0.0342,\n",
       "         0.0391, 0.0684, 0.0239, 0.0267, 0.0264, 0.0262, 0.7046]),\n",
       " tensor([0.0801, 0.0197, 0.0226, 0.0235, 0.0235, 0.0248, 0.0210, 0.0481, 0.0254,\n",
       "         0.0216, 0.0293, 0.0245, 0.0225, 0.0221, 0.0418, 0.0319]),\n",
       " tensor([0.0166, 0.0548, 0.7389, 0.0311, 0.0191, 0.0268, 0.0336, 0.0204, 0.0314,\n",
       "         0.0203, 0.0273, 0.0217, 0.0378, 0.0356, 0.0291, 0.0217]),\n",
       " tensor([0.0213, 0.0397, 0.0324, 0.0988, 0.0485, 0.0258, 0.0238, 0.0261, 0.0260,\n",
       "         0.0219, 0.2869, 0.0264, 0.0252, 0.0287, 0.0232, 0.0162]),\n",
       " tensor([0.0255, 0.0506, 0.0185, 0.0237, 0.0266, 0.0190, 0.0220, 0.0356, 0.0197,\n",
       "         0.0212, 0.0209, 0.0232, 0.0533, 0.0211, 0.0239, 0.0203]),\n",
       " tensor([0.0380, 0.0262, 0.0508, 0.0361, 0.0326, 0.0222, 0.0233, 0.0245, 0.0337,\n",
       "         0.0194, 0.0305, 0.0171, 0.0630, 0.0244, 0.0332, 0.0351]),\n",
       " tensor([0.0275, 0.0228, 0.0326, 0.0193, 0.0247, 0.0227, 0.0214, 0.0199, 0.0258,\n",
       "         0.0232, 0.0235, 0.0182, 0.0340, 0.3782, 0.4651, 0.5261]),\n",
       " tensor([0.0205, 0.0301, 0.0198, 0.0400, 0.0182, 0.4899, 0.0210, 0.0168, 0.2924,\n",
       "         0.0216, 0.0260, 0.0316, 0.0215, 0.5199, 0.0216, 0.0323]),\n",
       " tensor([0.0286, 0.0181, 0.0277, 0.0281, 0.7318, 0.0190, 0.0310, 0.0324, 0.0205,\n",
       "         0.3019, 0.0205, 0.0373, 0.0402, 0.0203, 0.0273, 0.0251]),\n",
       " ...]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "678063"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre = torch.concatenate(res).numpy()\n",
    "len(pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>origanism</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YP_009026398.1</td>\n",
       "      <td>Arhar cryptic virus-I segment RNA-3, strain Hy...</td>\n",
       "      <td>ORGANISM  Arhar cryptic virus-I Viruses. .</td>\n",
       "      <td>0.019432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YP_009956140.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.018958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YP_009956141.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.029809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YP_009956142.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.032612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YP_009956143.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.040137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id                                        description  \\\n",
       "0  YP_009026398.1  Arhar cryptic virus-I segment RNA-3, strain Hy...   \n",
       "1  YP_009956140.1          Mycobacterium phage Doug, complete genome   \n",
       "2  YP_009956141.1          Mycobacterium phage Doug, complete genome   \n",
       "3  YP_009956142.1          Mycobacterium phage Doug, complete genome   \n",
       "4  YP_009956143.1          Mycobacterium phage Doug, complete genome   \n",
       "\n",
       "                                           origanism   predict  \n",
       "0         ORGANISM  Arhar cryptic virus-I Viruses. .  0.019432  \n",
       "1  ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.018958  \n",
       "2  ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.029809  \n",
       "3  ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.032612  \n",
       "4  ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.040137  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = [] \n",
    "dess = [] \n",
    "oris = []\n",
    "for (id, des, ori) in X2_labels:\n",
    "    ids.append(id)\n",
    "    dess.append(des)\n",
    "    oris.append(ori)\n",
    "df = pd.DataFrame({\"id\":ids, \"description\":dess, \"origanism\":oris, \"predict\":pre})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./temp/test2_res.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([False,  True,  True, ..., False, False, False]), 541006)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phage = np.array([\"phage\" in name for name in df[\"origanism\"]])\n",
    "phage, np.sum(phage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "virusdf = df[~phage]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137057, 12992)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(virusdf), sum(virusdf[\"is\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>origanism</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>YP_009091851.1</td>\n",
       "      <td>Erinnyis ello granulovirus, complete genome</td>\n",
       "      <td>ORGANISM  Erinnyis ello granulovirus Viruses; ...</td>\n",
       "      <td>0.993528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3088</th>\n",
       "      <td>YP_009091885.1</td>\n",
       "      <td>Erinnyis ello granulovirus, complete genome</td>\n",
       "      <td>ORGANISM  Erinnyis ello granulovirus Viruses; ...</td>\n",
       "      <td>0.985456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3095</th>\n",
       "      <td>YP_009091892.1</td>\n",
       "      <td>Erinnyis ello granulovirus, complete genome</td>\n",
       "      <td>ORGANISM  Erinnyis ello granulovirus Viruses; ...</td>\n",
       "      <td>0.985354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>YP_009091941.1</td>\n",
       "      <td>Erinnyis ello granulovirus, complete genome</td>\n",
       "      <td>ORGANISM  Erinnyis ello granulovirus Viruses; ...</td>\n",
       "      <td>0.991594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>YP_004598986.1</td>\n",
       "      <td>Cavally virus, complete genome</td>\n",
       "      <td>ORGANISM  Cavally virus Viruses; Riboviria; Or...</td>\n",
       "      <td>0.986788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677592</th>\n",
       "      <td>YP_308941.1</td>\n",
       "      <td>Trichoplusia ni SNPV, complete genome</td>\n",
       "      <td>ORGANISM  Trichoplusia ni single nucleopolyhed...</td>\n",
       "      <td>0.995656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677633</th>\n",
       "      <td>YP_308982.1</td>\n",
       "      <td>Trichoplusia ni SNPV, complete genome</td>\n",
       "      <td>ORGANISM  Trichoplusia ni single nucleopolyhed...</td>\n",
       "      <td>0.985031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677737</th>\n",
       "      <td>YP_007378947.1</td>\n",
       "      <td>Halovirus HVTV-1, complete genome</td>\n",
       "      <td>ORGANISM  Haloarcula vallismortis tailed virus...</td>\n",
       "      <td>0.990092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677743</th>\n",
       "      <td>YP_007378953.1</td>\n",
       "      <td>Halovirus HVTV-1, complete genome</td>\n",
       "      <td>ORGANISM  Haloarcula vallismortis tailed virus...</td>\n",
       "      <td>0.989788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677834</th>\n",
       "      <td>YP_007379044.1</td>\n",
       "      <td>Halovirus HVTV-1, complete genome</td>\n",
       "      <td>ORGANISM  Haloarcula vallismortis tailed virus...</td>\n",
       "      <td>0.988976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3035 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id                                  description  \\\n",
       "3054    YP_009091851.1  Erinnyis ello granulovirus, complete genome   \n",
       "3088    YP_009091885.1  Erinnyis ello granulovirus, complete genome   \n",
       "3095    YP_009091892.1  Erinnyis ello granulovirus, complete genome   \n",
       "3144    YP_009091941.1  Erinnyis ello granulovirus, complete genome   \n",
       "3327    YP_004598986.1               Cavally virus, complete genome   \n",
       "...                ...                                          ...   \n",
       "677592     YP_308941.1        Trichoplusia ni SNPV, complete genome   \n",
       "677633     YP_308982.1        Trichoplusia ni SNPV, complete genome   \n",
       "677737  YP_007378947.1            Halovirus HVTV-1, complete genome   \n",
       "677743  YP_007378953.1            Halovirus HVTV-1, complete genome   \n",
       "677834  YP_007379044.1            Halovirus HVTV-1, complete genome   \n",
       "\n",
       "                                                origanism   predict  \n",
       "3054    ORGANISM  Erinnyis ello granulovirus Viruses; ...  0.993528  \n",
       "3088    ORGANISM  Erinnyis ello granulovirus Viruses; ...  0.985456  \n",
       "3095    ORGANISM  Erinnyis ello granulovirus Viruses; ...  0.985354  \n",
       "3144    ORGANISM  Erinnyis ello granulovirus Viruses; ...  0.991594  \n",
       "3327    ORGANISM  Cavally virus Viruses; Riboviria; Or...  0.986788  \n",
       "...                                                   ...       ...  \n",
       "677592  ORGANISM  Trichoplusia ni single nucleopolyhed...  0.995656  \n",
       "677633  ORGANISM  Trichoplusia ni single nucleopolyhed...  0.985031  \n",
       "677737  ORGANISM  Haloarcula vallismortis tailed virus...  0.990092  \n",
       "677743  ORGANISM  Haloarcula vallismortis tailed virus...  0.989788  \n",
       "677834  ORGANISM  Haloarcula vallismortis tailed virus...  0.988976  \n",
       "\n",
       "[3035 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virusdf[virusdf[\"predict\"] > 0.985]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33929, 678063)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is\"] = df[\"predict\"] > 0.90\n",
    "subdf = df[df[\"is\"]]\n",
    "len(subdf), len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phage = [\"phage\" in name for name in subdf[\"origanism\"]]\n",
    "phage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>origanism</th>\n",
       "      <th>predict</th>\n",
       "      <th>is</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>YP_009956168.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.900197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>YP_009956173.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.976232</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>YP_009956177.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.972736</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>YP_009956211.1</td>\n",
       "      <td>Mycobacterium phage Doug, complete genome</td>\n",
       "      <td>ORGANISM  Mycobacterium phage Doug Viruses; Du...</td>\n",
       "      <td>0.928504</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>NP_932364.1</td>\n",
       "      <td>Aeromonas phage 44RR2.8t, complete genome</td>\n",
       "      <td>ORGANISM  Aeromonas phage 44RR2.8t Viruses; Du...</td>\n",
       "      <td>0.918274</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677842</th>\n",
       "      <td>YP_007379052.1</td>\n",
       "      <td>Halovirus HVTV-1, complete genome</td>\n",
       "      <td>ORGANISM  Haloarcula vallismortis tailed virus...</td>\n",
       "      <td>0.960302</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677870</th>\n",
       "      <td>YP_009664766.1</td>\n",
       "      <td>Passion fruit yellow mosaic virus polymerase p...</td>\n",
       "      <td>ORGANISM  Passion fruit yellow mosaic virus Vi...</td>\n",
       "      <td>0.983582</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677875</th>\n",
       "      <td>YP_009801309.1</td>\n",
       "      <td>Klebsiella phage KP32_isolate 192, complete ge...</td>\n",
       "      <td>ORGANISM  Klebsiella phage KP32_isolate 192 Vi...</td>\n",
       "      <td>0.926805</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677910</th>\n",
       "      <td>YP_009801344.1</td>\n",
       "      <td>Klebsiella phage KP32_isolate 192, complete ge...</td>\n",
       "      <td>ORGANISM  Klebsiella phage KP32_isolate 192 Vi...</td>\n",
       "      <td>0.942260</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677928</th>\n",
       "      <td>YP_009147526.1</td>\n",
       "      <td>Erwinia phage phiEa2809, complete genome</td>\n",
       "      <td>ORGANISM  Erwinia phage phiEa2809 Viruses; Dup...</td>\n",
       "      <td>0.912912</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33929 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id                                        description  \\\n",
       "29      YP_009956168.1          Mycobacterium phage Doug, complete genome   \n",
       "34      YP_009956173.1          Mycobacterium phage Doug, complete genome   \n",
       "38      YP_009956177.1          Mycobacterium phage Doug, complete genome   \n",
       "72      YP_009956211.1          Mycobacterium phage Doug, complete genome   \n",
       "114        NP_932364.1          Aeromonas phage 44RR2.8t, complete genome   \n",
       "...                ...                                                ...   \n",
       "677842  YP_007379052.1                  Halovirus HVTV-1, complete genome   \n",
       "677870  YP_009664766.1  Passion fruit yellow mosaic virus polymerase p...   \n",
       "677875  YP_009801309.1  Klebsiella phage KP32_isolate 192, complete ge...   \n",
       "677910  YP_009801344.1  Klebsiella phage KP32_isolate 192, complete ge...   \n",
       "677928  YP_009147526.1           Erwinia phage phiEa2809, complete genome   \n",
       "\n",
       "                                                origanism   predict    is  \n",
       "29      ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.900197  True  \n",
       "34      ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.976232  True  \n",
       "38      ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.972736  True  \n",
       "72      ORGANISM  Mycobacterium phage Doug Viruses; Du...  0.928504  True  \n",
       "114     ORGANISM  Aeromonas phage 44RR2.8t Viruses; Du...  0.918274  True  \n",
       "...                                                   ...       ...   ...  \n",
       "677842  ORGANISM  Haloarcula vallismortis tailed virus...  0.960302  True  \n",
       "677870  ORGANISM  Passion fruit yellow mosaic virus Vi...  0.983582  True  \n",
       "677875  ORGANISM  Klebsiella phage KP32_isolate 192 Vi...  0.926805  True  \n",
       "677910  ORGANISM  Klebsiella phage KP32_isolate 192 Vi...  0.942260  True  \n",
       "677928  ORGANISM  Erwinia phage phiEa2809 Viruses; Dup...  0.912912  True  \n",
       "\n",
       "[33929 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdf[subdf[\"description\"] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     non-ion       0.98      0.95      0.96     10600\n",
      "         ion       0.95      0.98      0.97     10738\n",
      "\n",
      "    accuracy                           0.96     21338\n",
      "   macro avg       0.97      0.96      0.96     21338\n",
      "weighted avg       0.97      0.96      0.96     21338\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "pre = [i[0] for i in res] \n",
    "y = [i[1] for i in res]\n",
    "pre = torch.concatenate(pre).numpy() \n",
    "y = torch.concatenate(y).numpy() \n",
    "predict_label = (pre>0.5).astype(int) \n",
    "print(classification_report(y, predict_label, target_names=[\"non-ion\", \"ion\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "seqs = {}\n",
    "seqs[\"virus_seq\"] = utils.readDataset(\"/home/tyfei/datasets/ion_channel/Known_Virus_Ion_pro/all_Known_Virus_Ion_pro.rename_0.99.fasta\", trunc=1200)\n",
    "filtered = []\n",
    "labels = [] \n",
    "\n",
    "for i, tt in seqs.items():\n",
    "    labels.extend([i]*len(tt))\n",
    "    for name, seq in tt:\n",
    "        if \"*\" in seq:\n",
    "            filtered.append((name, seq[:-1].upper()))\n",
    "        else:\n",
    "            filtered.append((name, seq.upper()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 37/37 [00:06<00:00,  5.45it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm \n",
    "batch_labels, _, batch_tokens = batch_converter(filtered) \n",
    "allres = []\n",
    "clsmodel = clsmodel.cuda(1)\n",
    "batch_tokens = batch_tokens.to(\"cuda:1\")\n",
    "for i in tqdm(range(0,len(batch_tokens), 10)):\n",
    "    with torch.no_grad():\n",
    "        pre, _ = clsmodel(batch_tokens[i:i+10])\n",
    "        # token_representations = results[\"representations\"][12].cpu().numpy()\n",
    "        allres.append(pre.squeeze().cpu().numpy())\n",
    "allres = np.concatenate(allres, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 73.,  24.,  14.,   5.,   6.,   6.,   9.,   5.,  15., 212.]),\n",
       " array([0.00142959, 0.10092105, 0.20041251, 0.29990396, 0.39939541,\n",
       "        0.49888688, 0.5983783 , 0.69786978, 0.79736125, 0.89685267,\n",
       "        0.99634415]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allres\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(allres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([100.,  21.,  28.,  20.,  11.,  10.,   4.,   7.,   3., 165.]),\n",
       " array([5.68135176e-04, 1.00103624e-01, 1.99639112e-01, 2.99174607e-01,\n",
       "        3.98710102e-01, 4.98245597e-01, 5.97781062e-01, 6.97316587e-01,\n",
       "        7.96852052e-01, 8.96387577e-01, 9.95923042e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allres\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(allres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 93.,  10.,   9.,   9.,   8.,  19.,  18.,  34.,  45., 124.]),\n",
       " array([0.00586221, 0.10454156, 0.2032209 , 0.30190024, 0.40057957,\n",
       "        0.49925894, 0.59793824, 0.6966176 , 0.79529697, 0.89397627,\n",
       "        0.99265563]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allres\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(allres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 38.,  38.,  11.,   5.,   7.,  10.,  15.,  16.,  24., 205.]),\n",
       " array([0.00870654, 0.10700988, 0.20531322, 0.30361658, 0.40191993,\n",
       "        0.50022328, 0.5985266 , 0.69682997, 0.79513329, 0.89343667,\n",
       "        0.99173999]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allres\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(allres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 51.,  11.,   5.,  13.,   8.,  12.,   7.,   8.,  12., 242.]),\n",
       " array([0.00578006, 0.10388525, 0.20199043, 0.30009562, 0.39820081,\n",
       "        0.49630597, 0.59441119, 0.69251633, 0.79062152, 0.88872671,\n",
       "        0.9868319 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allres\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(allres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}