qiskit_tsp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m# useful additional packages\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnetworkx\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnx\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "# useful additional packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit_optimization.applications import Maxcut, Tsp\n",
    "from qiskit_algorithms import SamplingVQE, NumPyMinimumEigensolver\n",
    "from qiskit_algorithms.optimizers import SPSA\n",
    "from qiskit_algorithms.utils import algorithm_globals\n",
    "from qiskit.primitives import Sampler\n",
    "from qiskit_optimization.algorithms import MinimumEigenOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graph(G, colors, pos):\n",
    "    default_axes = plt.axes(frameon=True)\n",
    "    nx.draw_networkx(G, node_color=colors, node_size=600, alpha=0.8, ax=default_axes, pos=pos)\n",
    "    edge_labels = nx.get_edge_attributes(G, \"weight\")\n",
    "    nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distance\n",
      " [[  0.  48.  91.  33.  85.  26.  21.  58.  30.  48.  46.  56.  50.  71.\n",
      "   62.  84.  94.  23.  82.  61.]\n",
      " [ 48.   0.  63.  71.  93.  30.  60.  69.  71.  67.   2.  49.  17.  60.\n",
      "   83.  97.  68.  52.  89.  35.]\n",
      " [ 91.  63.   0.  92.  65.  66.  88.  59.  95.  69.  65.  40.  80.  31.\n",
      "   77.  74.   7.  77.  62.  31.]\n",
      " [ 33.  71.  92.   0.  62.  41.  12.  40.   4.  27.  70.  52.  78.  64.\n",
      "   35.  59.  92.  19.  60.  67.]\n",
      " [ 85.  93.  65.  62.   0.  71.  68.  27.  67.  37.  93.  44. 108.  38.\n",
      "   28.  10.  60.  62.   3.  63.]\n",
      " [ 26.  30.  66.  41.  71.   0.  31.  45.  41.  40.  29.  34.  40.  49.\n",
      "   56.  74.  69.  22.  68.  35.]\n",
      " [ 21.  60.  88.  12.  68.  31.   0.  42.  10.  31.  59.  49.  67.  62.\n",
      "   42.  66.  89.  11.  65.  61.]\n",
      " [ 58.  69.  59.  40.  27.  45.  42.   0.  44.  13.  69.  25.  83.  29.\n",
      "   18.  29.  57.  35.  24.  45.]\n",
      " [ 30.  71.  95.   4.  67.  41.  10.  44.   0.  31.  70.  55.  77.  67.\n",
      "   39.  63.  95.  20.  65.  69.]\n",
      " [ 48.  67.  69.  27.  37.  40.  31.  13.  31.   0.  67.  31.  79.  39.\n",
      "   16.  37.  68.  25.  35.  50.]\n",
      " [ 46.   2.  65.  70.  93.  29.  59.  69.  70.  67.   0.  49.  16.  61.\n",
      "   83.  98.  70.  51.  90.  37.]\n",
      " [ 56.  49.  40.  52.  44.  34.  49.  25.  55.  31.  49.   0.  65.  15.\n",
      "   42.  51.  40.  38.  41.  21.]\n",
      " [ 50.  17.  80.  78. 108.  40.  67.  83.  77.  79.  16.  65.   0.  77.\n",
      "   95. 112.  85.  61. 105.  52.]\n",
      " [ 71.  60.  31.  64.  38.  49.  62.  29.  67.  39.  61.  15.  77.   0.\n",
      "   46.  47.  29.  52.  35.  26.]\n",
      " [ 62.  83.  77.  35.  28.  56.  42.  18.  39.  16.  83.  42.  95.  46.\n",
      "    0.  24.  74.  40.  27.  63.]\n",
      " [ 84.  97.  74.  59.  10.  74.  66.  29.  63.  37.  98.  51. 112.  47.\n",
      "   24.   0.  70.  62.  12.  70.]\n",
      " [ 94.  68.   7.  92.  60.  69.  89.  57.  95.  68.  70.  40.  85.  29.\n",
      "   74.  70.   0.  78.  58.  35.]\n",
      " [ 23.  52.  77.  19.  62.  22.  11.  35.  20.  25.  51.  38.  61.  52.\n",
      "   40.  62.  78.   0.  59.  50.]\n",
      " [ 82.  89.  62.  60.   3.  68.  65.  24.  65.  35.  90.  41. 105.  35.\n",
      "   27.  12.  58.  59.   0.  60.]\n",
      " [ 61.  35.  31.  67.  63.  35.  61.  45.  69.  50.  37.  21.  52.  26.\n",
      "   63.  70.  35.  50.  60.   0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a graph of 20 nodes\n",
    "n = 20\n",
    "num_qubits = n**2\n",
    "tsp = Tsp.create_random_instance(n, seed=123)\n",
    "adj_matrix = nx.to_numpy_array(tsp.graph)\n",
    "print(\"distance\\n\", adj_matrix)\n",
    "\n",
    "colors = [\"r\" for node in tsp.graph.nodes]\n",
    "pos = [tsp.graph.nodes[node][\"pos\"] for node in tsp.graph.nodes]\n",
    "draw_graph(tsp.graph, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_tsp_solution(G, order, colors, pos):\n",
    "    G2 = nx.DiGraph()\n",
    "    G2.add_nodes_from(G)\n",
    "    n = len(order)\n",
    "    for i in range(n):\n",
    "        j = (i + 1) % n\n",
    "        node_i = order[i]\n",
    "        node_j = order[j]\n",
    "        if G.has_edge(node_i, node_j):\n",
    "            G2.add_edge(node_i, node_j, weight=G[node_i][node_j][\"weight\"])\n",
    "    default_axes = plt.axes(frameon=True)\n",
    "    nx.draw_networkx(\n",
    "        G2, node_color=colors, edge_color=\"b\", node_size=600, alpha=0.8, ax=default_axes, pos=pos\n",
    "    )\n",
    "    edge_labels = nx.get_edge_attributes(G2, \"weight\")\n",
    "    nx.draw_networkx_edge_labels(G2, pos, font_color=\"b\", edge_labels=edge_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem name: TSP\n",
      "\n",
      "Minimize\n",
      "  46*x_0_0*x_10_1 + 46*x_0_0*x_10_19 + 56*x_0_0*x_11_1 + 56*x_0_0*x_11_19\n",
      "  + 50*x_0_0*x_12_1 + 50*x_0_0*x_12_19 + 71*x_0_0*x_13_1 + 71*x_0_0*x_13_19\n",
      "  + 62*x_0_0*x_14_1 + 62*x_0_0*x_14_19 + 84*x_0_0*x_15_1 + 84*x_0_0*x_15_19\n",
      "  + 94*x_0_0*x_16_1 + 94*x_0_0*x_16_19 + 23*x_0_0*x_17_1 + 23*x_0_0*x_17_19\n",
      "  + 82*x_0_0*x_18_1 + 82*x_0_0*x_18_19 + 61*x_0_0*x_19_1 + 61*x_0_0*x_19_19\n",
      "  + 48*x_0_0*x_1_1 + 48*x_0_0*x_1_19 + 91*x_0_0*x_2_1 + 91*x_0_0*x_2_19\n",
      "  + 33*x_0_0*x_3_1 + 33*x_0_0*x_3_19 + 85*x_0_0*x_4_1 + 85*x_0_0*x_4_19\n",
      "  + 26*x_0_0*x_5_1 + 26*x_0_0*x_5_19 + 21*x_0_0*x_6_1 + 21*x_0_0*x_6_19\n",
      "  + 58*x_0_0*x_7_1 + 58*x_0_0*x_7_19 + 30*x_0_0*x_8_1 + 30*x_0_0*x_8_19\n",
      "  + 48*x_0_0*x_9_1 + 48*x_0_0*x_9_19 + 46*x_0_1*x_10_0 + 46*x_0_1*x_10_2\n",
      "  + 56*x_0_1*x_11_0 + 56*x_0_1*x_11_2 + 50*x_0_1*x_12_0 + 50*x_0_1*x_12_2\n",
      "  + 71*x_0_1*x_13_0 + 71*x_0_1*x_13_2 + 62*x_0_1*x_14_0 + 62*x_0_1*x_14_2\n",
      "  + 84*x_0_1*x_15_0 + 84*x_0_1*x_15_2 + 94*x_0_1*x_16_0 + 94*x_0_1*x_16_2\n",
      "  + 23*x_0_1*x_17_0 + 23*x_0_1*x_17_2 + 82*x_0_1*x_18_0 + 82*x_0_1*x_18_2\n",
      "  + 61*x_0_1*x_19_0 + 61*x_0_1*x_19_2 + 48*x_0_1*x_1_0 + 48*x_0_1*x_1_2\n",
      "  + 91*x_0_1*x_2_0 + 91*x_0_1*x_2_2 + 33*x_0_1*x_3_0 + 33*x_0_1*x_3_2\n",
      "  + 85*x_0_1*x_4_0 + 85*x_0_1*x_4_2 + 26*x_0_1*x_5_0 + 26*x_0_1*x_5_2\n",
      "  + 21*x_0_1*x_6_0 + 21*x_0_1*x_6_2 + 58*x_0_1*x_7_0 + 58*x_0_1*x_7_2\n",
      "  + 30*x_0_1*x_8_0 + 30*x_0_1*x_8_2 + 48*x_0_1*x_9_0 + 48*x_0_1*x_9_2\n",
      "  + 46*x_0_10*x_10_11 + 46*x_0_10*x_10_9 + 56*x_0_10*x_11_11 + 56*x_0_10*x_11_9\n",
      "  + 50*x_0_10*x_12_11 + 50*x_0_10*x_12_9 + 71*x_0_10*x_13_11 + 71*x_0_10*x_13_9\n",
      "  + 62*x_0_10*x_14_11 + 62*x_0_10*x_14_9 + 84*x_0_10*x_15_11 + 84*x_0_10*x_15_9\n",
      "  + 94*x_0_10*x_16_11 + 94*x_0_10*x_16_9 + 23*x_0_10*x_17_11 + 23*x_0_10*x_17_9\n",
      "  + 82*x_0_10*x_18_11 + 82*x_0_10*x_18_9 + 61*x_0_10*x_19_11 + 61*x_0_10*x_19_9\n",
      "  + 48*x_0_10*x_1_11 + 48*x_0_10*x_1_9 + 91*x_0_10*x_2_11 + 91*x_0_10*x_2_9\n",
      "  + 33*x_0_10*x_3_11 + 33*x_0_10*x_3_9 + 85*x_0_10*x_4_11 + 85*x_0_10*x_4_9\n",
      "  + 26*x_0_10*x_5_11 + 26*x_0_10*x_5_9 + 21*x_0_10*x_6_11 + 21*x_0_10*x_6_9\n",
      "  + 58*x_0_10*x_7_11 + 58*x_0_10*x_7_9 + 30*x_0_10*x_8_11 + 30*x_0_10*x_8_9\n",
      "  + 48*x_0_10*x_9_11 + 48*x_0_10*x_9_9 + 46*x_0_11*x_10_10 + 46*x_0_11*x_10_12\n",
      "  + 56*x_0_11*x_11_10 + 56*x_0_11*x_11_12 + 50*x_0_11*x_12_10\n",
      "  + 50*x_0_11*x_12_12 + 71*x_0_11*x_13_10 + 71*x_0_11*x_13_12\n",
      "  + 62*x_0_11*x_14_10 + 62*x_0_11*x_14_12 + 84*x_0_11*x_15_10\n",
      "  + 84*x_0_11*x_15_12 + 94*x_0_11*x_16_10 + 94*x_0_11*x_16_12\n",
      "  + 23*x_0_11*x_17_10 + 23*x_0_11*x_17_12 + 82*x_0_11*x_18_10\n",
      "  + 82*x_0_11*x_18_12 + 61*x_0_11*x_19_10 + 61*x_0_11*x_19_12 + 48*x_0_11*x_1_10\n",
      "  + 48*x_0_11*x_1_12 + 91*x_0_11*x_2_10 + 91*x_0_11*x_2_12 + 33*x_0_11*x_3_10\n",
      "  + 33*x_0_11*x_3_12 + 85*x_0_11*x_4_10 + 85*x_0_11*x_4_12 + 26*x_0_11*x_5_10\n",
      "  + 26*x_0_11*x_5_12 + 21*x_0_11*x_6_10 + 21*x_0_11*x_6_12 + 58*x_0_11*x_7_10\n",
      "  + 58*x_0_11*x_7_12 + 30*x_0_11*x_8_10 + 30*x_0_11*x_8_12 + 48*x_0_11*x_9_10\n",
      "  + 48*x_0_11*x_9_12 + 46*x_0_12*x_10_11 + 46*x_0_12*x_10_13 + 56*x_0_12*x_11_11\n",
      "  + 56*x_0_12*x_11_13 + 50*x_0_12*x_12_11 + 50*x_0_12*x_12_13\n",
      "  + 71*x_0_12*x_13_11 + 71*x_0_12*x_13_13 + 62*x_0_12*x_14_11\n",
      "  + 62*x_0_12*x_14_13 + 84*x_0_12*x_15_11 + 84*x_0_12*x_15_13\n",
      "  + 94*x_0_12*x_16_11 + 94*x_0_12*x_16_13 + 23*x_0_12*x_17_11\n",
      "  + 23*x_0_12*x_17_13 + 82*x_0_12*x_18_11 + 82*x_0_12*x_18_13\n",
      "  + 61*x_0_12*x_19_11 + 61*x_0_12*x_19_13 + 48*x_0_12*x_1_11 + 48*x_0_12*x_1_13\n",
      "  + 91*x_0_12*x_2_11 + 91*x_0_12*x_2_13 + 33*x_0_12*x_3_11 + 33*x_0_12*x_3_13\n",
      "  + 85*x_0_12*x_4_11 + 85*x_0_12*x_4_13 + 26*x_0_12*x_5_11 + 26*x_0_12*x_5_13\n",
      "  + 21*x_0_12*x_6_11 + 21*x_0_12*x_6_13 + 58*x_0_12*x_7_11 + 58*x_0_12*x_7_13\n",
      "  + 30*x_0_12*x_8_11 + 30*x_0_12*x_8_13 + 48*x_0_12*x_9_11 + 48*x_0_12*x_9_13\n",
      "  + 46*x_0_13*x_10_12 + 46*x_0_13*x_10_14 + 56*x_0_13*x_11_12\n",
      "  + 56*x_0_13*x_11_14 + 50*x_0_13*x_12_12 + 50*x_0_13*x_12_14\n",
      "  + 71*x_0_13*x_13_12 + 71*x_0_13*x_13_14 + 62*x_0_13*x_14_12\n",
      "  + 62*x_0_13*x_14_14 + 84*x_0_13*x_15_12 + 84*x_0_13*x_15_14\n",
      "  + 94*x_0_13*x_16_12 + 94*x_0_13*x_16_14 + 23*x_0_13*x_17_12\n",
      "  + 23*x_0_13*x_17_14 + 82*x_0_13*x_18_12 + 82*x_0_13*x_18_14\n",
      "  + 61*x_0_13*x_19_12 + 61*x_0_13*x_19_14 + 48*x_0_13*x_1_12 + 48*x_0_13*x_1_14\n",
      "  + 91*x_0_13*x_2_12 + 91*x_0_13*x_2_14 + 33*x_0_13*x_3_12 + 33*x_0_13*x_3_14\n",
      "  + 85*x_0_13*x_4_12 + 85*x_0_13*x_4_14 + 26*x_0_13*x_5_12 + 26*x_0_13*x_5_14\n",
      "  + 21*x_0_13*x_6_12 + 21*x_0_13*x_6_14 + 58*x_0_13*x_7_12 + 58*x_0_13*x_7_14\n",
      "  + 30*x_0_13*x_8_12 + 30*x_0_13*x_8_14 + 48*x_0_13*x_9_12 + 48*x_0_13*x_9_14\n",
      "  + 46*x_0_14*x_10_13 + 46*x_0_14*x_10_15 + 56*x_0_14*x_11_13\n",
      "  + 56*x_0_14*x_11_15 + 50*x_0_14*x_12_13 + 50*x_0_14*x_12_15\n",
      "  + 71*x_0_14*x_13_13 + 71*x_0_14*x_13_15 + 62*x_0_14*x_14_13\n",
      "  + 62*x_0_14*x_14_15 + 84*x_0_14*x_15_13 + 84*x_0_14*x_15_15\n",
      "  + 94*x_0_14*x_16_13 + 94*x_0_14*x_16_15 + 23*x_0_14*x_17_13\n",
      "  + 23*x_0_14*x_17_15 + 82*x_0_14*x_18_13 + 82*x_0_14*x_18_15\n",
      "  + 61*x_0_14*x_19_13 + 61*x_0_14*x_19_15 + 48*x_0_14*x_1_13 + 48*x_0_14*x_1_15\n",
      "  + 91*x_0_14*x_2_13 + 91*x_0_14*x_2_15 + 33*x_0_14*x_3_13 + 33*x_0_14*x_3_15\n",
      "  + 85*x_0_14*x_4_13 + 85*x_0_14*x_4_15 + 26*x_0_14*x_5_13 + 26*x_0_14*x_5_15\n",
      "  + 21*x_0_14*x_6_13 + 21*x_0_14*x_6_15 + 58*x_0_14*x_7_13 + 58*x_0_14*x_7_15\n",
      "  + 30*x_0_14*x_8_13 + 30*x_0_14*x_8_15 + 48*x_0_14*x_9_13 + 48*x_0_14*x_9_15\n",
      "  + 46*x_0_15*x_10_14 + 46*x_0_15*x_10_16 + 56*x_0_15*x_11_14\n",
      "  + 56*x_0_15*x_11_16 + 50*x_0_15*x_12_14 + 50*x_0_15*x_12_16\n",
      "  + 71*x_0_15*x_13_14 + 71*x_0_15*x_13_16 + 62*x_0_15*x_14_14\n",
      "  + 62*x_0_15*x_14_16 + 84*x_0_15*x_15_14 + 84*x_0_15*x_15_16\n",
      "  + 94*x_0_15*x_16_14 + 94*x_0_15*x_16_16 + 23*x_0_15*x_17_14\n",
      "  + 23*x_0_15*x_17_16 + 82*x_0_15*x_18_14 + 82*x_0_15*x_18_16\n",
      "  + 61*x_0_15*x_19_14 + 61*x_0_15*x_19_16 + 48*x_0_15*x_1_14 + 48*x_0_15*x_1_16\n",
      "  + 91*x_0_15*x_2_14 + 91*x_0_15*x_2_16 + 33*x_0_15*x_3_14 + 33*x_0_15*x_3_16\n",
      "  + 85*x_0_15*x_4_14 + 85*x_0_15*x_4_16 + 26*x_0_15*x_5_14 + 26*x_0_15*x_5_16\n",
      "  + 21*x_0_15*x_6_14 + 21*x_0_15*x_6_16 + 58*x_0_15*x_7_14 + 58*x_0_15*x_7_16\n",
      "  + 30*x_0_15*x_8_14 + 30*x_0_15*x_8_16 + 48*x_0_15*x_9_14 + 48*x_0_15*x_9_16\n",
      "  + 46*x_0_16*x_10_15 + 46*x_0_16*x_10_17 + 56*x_0_16*x_11_15\n",
      "  + 56*x_0_16*x_11_17 + 50*x_0_16*x_12_15 + 50*x_0_16*x_12_17\n",
      "  + 71*x_0_16*x_13_15 + 71*x_0_16*x_13_17 + 62*x_0_16*x_14_15\n",
      "  + 62*x_0_16*x_14_17 + 84*x_0_16*x_15_15 + 84*x_0_16*x_15_17\n",
      "  + 94*x_0_16*x_16_15 + 94*x_0_16*x_16_17 + 23*x_0_16*x_17_15\n",
      "  + 23*x_0_16*x_17_17 + 82*x_0_16*x_18_15 + 82*x_0_16*x_18_17\n",
      "  + 61*x_0_16*x_19_15 + 61*x_0_16*x_19_17 + 48*x_0_16*x_1_15 + 48*x_0_16*x_1_17\n",
      "  + 91*x_0_16*x_2_15 + 91*x_0_16*x_2_17 + 33*x_0_16*x_3_15 + 33*x_0_16*x_3_17\n",
      "  + 85*x_0_16*x_4_15 + 85*x_0_16*x_4_17 + 26*x_0_16*x_5_15 + 26*x_0_16*x_5_17\n",
      "  + 21*x_0_16*x_6_15 + 21*x_0_16*x_6_17 + 58*x_0_16*x_7_15 + 58*x_0_16*x_7_17\n",
      "  + 30*x_0_16*x_8_15 + 30*x_0_16*x_8_17 + 48*x_0_16*x_9_15 + 48*x_0_16*x_9_17\n",
      "  + 46*x_0_17*x_10_16 + 46*x_0_17*x_10_18 + 56*x_0_17*x_11_16\n",
      "  + 56*x_0_17*x_11_18 + 50*x_0_17*x_12_16 + 50*x_0_17*x_12_18\n",
      "  + 71*x_0_17*x_13_16 + 71*x_0_17*x_13_18 + 62*x_0_17*x_14_16\n",
      "  + 62*x_0_17*x_14_18 + 84*x_0_17*x_15_16 + 84*x_0_17*x_15_18\n",
      "  + 94*x_0_17*x_16_16 + 94*x_0_17*x_16_18 + 23*x_0_17*x_17_16\n",
      "  + 23*x_0_17*x_17_18 + 82*x_0_17*x_18_16 + 82*x_0_17*x_18_18\n",
      "  + 61*x_0_17*x_19_16 + 61*x_0_17*x_19_18 + 48*x_0_17*x_1_16 + 48*x_0_17*x_1_18\n",
      "  + 91*x_0_17*x_2_16 + 91*x_0_17*x_2_18 + 33*x_0_17*x_3_16 + 33*x_0_17*x_3_18\n",
      "  + 85*x_0_17*x_4_16 + 85*x_0_17*x_4_18 + 26*x_0_17*x_5_16 + 26*x_0_17*x_5_18\n",
      "  + 21*x_0_17*x_6_16 + 21*x_0_17*x_6_18 + 58*x_0_17*x_7_16 + 58*x_0_17*x_7_18\n",
      "  + 30*x_0_17*x_8_16 + 30*x_0_17*x_8_18 + 48*x_0_17*x_9_16 + 48*x_0_17*x_9_18\n",
      "  + 46*x_0_18*x_10_17 + 46*x_0_18*x_10_19 + 56*x_0_18*x_11_17\n",
      "  + 56*x_0_18*x_11_19 + 50*x_0_18*x_12_17 + 50*x_0_18*x_12_19\n",
      "  + 71*x_0_18*x_13_17 + 71*x_0_18*x_13_19 + 62*x_0_18*x_14_17\n",
      "  + 62*x_0_18*x_14_19 + 84*x_0_18*x_15_17 + 84*x_0_18*x_15_19\n",
      "  + 94*x_0_18*x_16_17 + 94*x_0_18*x_16_19 + 23*x_0_18*x_17_17\n",
      "  + 23*x_0_18*x_17_19 + 82*x_0_18*x_18_17 + 82*x_0_18*x_18_19\n",
      "  + 61*x_0_18*x_19_17 + 61*x_0_18*x_19_19 + 48*x_0_18*x_1_17 + 48*x_0_18*x_1_19\n",
      "  + 91*x_0_18*x_2_17 + 91*x_0_18*x_2_19 + 33*x_0_18*x_3_17 + 33*x_0_18*x_3_19\n",
      "  + 85*x_0_18*x_4_17 + 85*x_0_18*x_4_19 + 26*x_0_18*x_5_17 + 26*x_0_18*x_5_19\n",
      "  + 21*x_0_18*x_6_17 + 21*x_0_18*x_6_19 + 58*x_0_18*x_7_17 + 58*x_0_18*x_7_19\n",
      "  + 30*x_0_18*x_8_17 + 30*x_0_18*x_8_19 + 48*x_0_18*x_9_17 + 48*x_0_18*x_9_19\n",
      "  + 46*x_0_19*x_10_0 + 46*x_0_19*x_10_18 + 56*x_0_19*x_11_0 + 56*x_0_19*x_11_18\n",
      "  + 50*x_0_19*x_12_0 + 50*x_0_19*x_12_18 + 71*x_0_19*x_13_0 + 71*x_0_19*x_13_18\n",
      "  + 62*x_0_19*x_14_0 + 62*x_0_19*x_14_18 + 84*x_0_19*x_15_0 + 84*x_0_19*x_15_18\n",
      "  + 94*x_0_19*x_16_0 + 94*x_0_19*x_16_18 + 23*x_0_19*x_17_0 + 23*x_0_19*x_17_18\n",
      "  + 82*x_0_19*x_18_0 + 82*x_0_19*x_18_18 + 61*x_0_19*x_19_0 + 61*x_0_19*x_19_18\n",
      "  + 48*x_0_19*x_1_0 + 48*x_0_19*x_1_18 + 91*x_0_19*x_2_0 + 91*x_0_19*x_2_18\n",
      "  + 33*x_0_19*x_3_0 + 33*x_0_19*x_3_18 + 85*x_0_19*x_4_0 + 85*x_0_19*x_4_18\n",
      "  + 26*x_0_19*x_5_0 + 26*x_0_19*x_5_18 + 21*x_0_19*x_6_0 + 21*x_0_19*x_6_18\n",
      "  + 58*x_0_19*x_7_0 + 58*x_0_19*x_7_18 + 30*x_0_19*x_8_0 + 30*x_0_19*x_8_18\n",
      "  + 48*x_0_19*x_9_0 + 48*x_0_19*x_9_18 + 46*x_0_2*x_10_1 + 46*x_0_2*x_10_3\n",
      "  + 56*x_0_2*x_11_1 + 56*x_0_2*x_11_3 + 50*x_0_2*x_12_1 + 50*x_0_2*x_12_3\n",
      "  + 71*x_0_2*x_13_1 + 71*x_0_2*x_13_3 + 62*x_0_2*x_14_1 + 62*x_0_2*x_14_3\n",
      "  + 84*x_0_2*x_15_1 + 84*x_0_2*x_15_3 + 94*x_0_2*x_16_1 + 94*x_0_2*x_16_3\n",
      "  + 23*x_0_2*x_17_1 + 23*x_0_2*x_17_3 + 82*x_0_2*x_18_1 + 82*x_0_2*x_18_3\n",
      "  + 61*x_0_2*x_19_1 + 61*x_0_2*x_19_3 + 48*x_0_2*x_1_1 + 48*x_0_2*x_1_3\n",
      "  + 91*x_0_2*x_2_1 + 91*x_0_2*x_2_3 + 33*x_0_2*x_3_1 + 33*x_0_2*x_3_3\n",
      "  + 85*x_0_2*x_4_1 + 85*x_0_2*x_4_3 + 26*x_0_2*x_5_1 + 26*x_0_2*x_5_3\n",
      "  + 21*x_0_2*x_6_1 + 21*x_0_2*x_6_3 + 58*x_0_2*x_7_1 + 58*x_0_2*x_7_3\n",
      "  + 30*x_0_2*x_8_1 + 30*x_0_2*x_8_3 + 48*x_0_2*x_9_1 + 48*x_0_2*x_9_3\n",
      "  + 46*x_0_3*x_10_2 + 46*x_0_3*x_10_4 + 56*x_0_3*x_11_2 + 56*x_0_3*x_11_4\n",
      "  + 50*x_0_3*x_12_2 + 50*x_0_3*x_12_4 + 71*x_0_3*x_13_2 + 71*x_0_3*x_13_4\n",
      "  + 62*x_0_3*x_14_2 + 62*x_0_3*x_14_4 + 84*x_0_3*x_15_2 + 84*x_0_3*x_15_4\n",
      "  + 94*x_0_3*x_16_2 + 94*x_0_3*x_16_4 + 23*x_0_3*x_17_2 + 23*x_0_3*x_17_4\n",
      "  + 82*x_0_3*x_18_2 + 82*x_0_3*x_18_4 + 61*x_0_3*x_19_2 + 61*x_0_3*x_19_4\n",
      "  + 48*x_0_3*x_1_2 + 48*x_0_3*x_1_4 + 91*x_0_3*x_2_2 + 91*x_0_3*x_2_4\n",
      "  + 33*x_0_3*x_3_2 + 33*x_0_3*x_3_4 + 85*x_0_3*x_4_2 + 85*x_0_3*x_4_4\n",
      "  + 26*x_0_3*x_5_2 + 26*x_0_3*x_5_4 + 21*x_0_3*x_6_2 + 21*x_0_3*x_6_4\n",
      "  + 58*x_0_3*x_7_2 + 58*x_0_3*x_7_4 + 30*x_0_3*x_8_2 + 30*x_0_3*x_8_4\n",
      "  + 48*x_0_3*x_9_2 + 48*x_0_3*x_9_4 + 46*x_0_4*x_10_3 + 46*x_0_4*x_10_5\n",
      "  + 56*x_0_4*x_11_3 + 56*x_0_4*x_11_5 + 50*x_0_4*x_12_3 + 50*x_0_4*x_12_5\n",
      "  + 71*x_0_4*x_13_3 + 71*x_0_4*x_13_5 + 62*x_0_4*x_14_3 + 62*x_0_4*x_14_5\n",
      "  + 84*x_0_4*x_15_3 + 84*x_0_4*x_15_5 + 94*x_0_4*x_16_3 + 94*x_0_4*x_16_5\n",
      "  + 23*x_0_4*x_17_3 + 23*x_0_4*x_17_5 + 82*x_0_4*x_18_3 + 82*x_0_4*x_18_5\n",
      "  + 61*x_0_4*x_19_3 + 61*x_0_4*x_19_5 + 48*x_0_4*x_1_3 + 48*x_0_4*x_1_5\n",
      "  + 91*x_0_4*x_2_3 + 91*x_0_4*x_2_5 + 33*x_0_4*x_3_3 + 33*x_0_4*x_3_5\n",
      "  + 85*x_0_4*x_4_3 + 85*x_0_4*x_4_5 + 26*x_0_4*x_5_3 + 26*x_0_4*x_5_5\n",
      "  + 21*x_0_4*x_6_3 + 21*x_0_4*x_6_5 + 58*x_0_4*x_7_3 + 58*x_0_4*x_7_5\n",
      "  + 30*x_0_4*x_8_3 + 30*x_0_4*x_8_5 + 48*x_0_4*x_9_3 + 48*x_0_4*x_9_5\n",
      "  + 46*x_0_5*x_10_4 + 46*x_0_5*x_10_6 + 56*x_0_5*x_11_4 + 56*x_0_5*x_11_6\n",
      "  + 50*x_0_5*x_12_4 + 50*x_0_5*x_12_6 + 71*x_0_5*x_13_4 + 71*x_0_5*x_13_6\n",
      "  + 62*x_0_5*x_14_4 + 62*x_0_5*x_14_6 + 84*x_0_5*x_15_4 + 84*x_0_5*x_15_6\n",
      "  + 94*x_0_5*x_16_4 + 94*x_0_5*x_16_6 + 23*x_0_5*x_17_4 + 23*x_0_5*x_17_6\n",
      "  + 82*x_0_5*x_18_4 + 82*x_0_5*x_18_6 + 61*x_0_5*x_19_4 + 61*x_0_5*x_19_6\n",
      "  + 48*x_0_5*x_1_4 + 48*x_0_5*x_1_6 + 91*x_0_5*x_2_4 + 91*x_0_5*x_2_6\n",
      "  + 33*x_0_5*x_3_4 + 33*x_0_5*x_3_6 + 85*x_0_5*x_4_4 + 85*x_0_5*x_4_6\n",
      "  + 26*x_0_5*x_5_4 + 26*x_0_5*x_5_6 + 21*x_0_5*x_6_4 + 21*x_0_5*x_6_6\n",
      "  + 58*x_0_5*x_7_4 + 58*x_0_5*x_7_6 + 30*x_0_5*x_8_4 + 30*x_0_5*x_8_6\n",
      "  + 48*x_0_5*x_9_4 + 48*x_0_5*x_9_6 + 46*x_0_6*x_10_5 + 46*x_0_6*x_10_7\n",
      "  + 56*x_0_6*x_11_5 + 56*x_0_6*x_11_7 + 50*x_0_6*x_12_5 + 50*x_0_6*x_12_7\n",
      "  + 71*x_0_6*x_13_5 + 71*x_0_6*x_13_7 + 62*x_0_6*x_14_5 + 62*x_0_6*x_14_7\n",
      "  + 84*x_0_6*x_15_5 + 84*x_0_6*x_15_7 + 94*x_0_6*x_16_5 + 94*x_0_6*x_16_7\n",
      "  + 23*x_0_6*x_17_5 + 23*x_0_6*x_17_7 + 82*x_0_6*x_18_5 + 82*x_0_6*x_18_7\n",
      "  + 61*x_0_6*x_19_5 + 61*x_0_6*x_19_7 + 48*x_0_6*x_1_5 + 48*x_0_6*x_1_7\n",
      "  + 91*x_0_6*x_2_5 + 91*x_0_6*x_2_7 + 33*x_0_6*x_3_5 + 33*x_0_6*x_3_7\n",
      "  + 85*x_0_6*x_4_5 + 85*x_0_6*x_4_7 + 26*x_0_6*x_5_5 + 26*x_0_6*x_5_7\n",
      "  + 21*x_0_6*x_6_5 + 21*x_0_6*x_6_7 + 58*x_0_6*x_7_5 + 58*x_0_6*x_7_7\n",
      "  + 30*x_0_6*x_8_5 + 30*x_0_6*x_8_7 + 48*x_0_6*x_9_5 + 48*x_0_6*x_9_7\n",
      "  + 46*x_0_7*x_10_6 + 46*x_0_7*x_10_8 + 56*x_0_7*x_11_6 + 56*x_0_7*x_11_8\n",
      "  + 50*x_0_7*x_12_6 + 50*x_0_7*x_12_8 + 71*x_0_7*x_13_6 + 71*x_0_7*x_13_8\n",
      "  + 62*x_0_7*x_14_6 + 62*x_0_7*x_14_8 + 84*x_0_7*x_15_6 + 84*x_0_7*x_15_8\n",
      "  + 94*x_0_7*x_16_6 + 94*x_0_7*x_16_8 + 23*x_0_7*x_17_6 + 23*x_0_7*x_17_8\n",
      "  + 82*x_0_7*x_18_6 + 82*x_0_7*x_18_8 + 61*x_0_7*x_19_6 + 61*x_0_7*x_19_8\n",
      "  + 48*x_0_7*x_1_6 + 48*x_0_7*x_1_8 + 91*x_0_7*x_2_6 + 91*x_0_7*x_2_8\n",
      "  + 33*x_0_7*x_3_6 + 33*x_0_7*x_3_8 + 85*x_0_7*x_4_6 + 85*x_0_7*x_4_8\n",
      "  + 26*x_0_7*x_5_6 + 26*x_0_7*x_5_8 + 21*x_0_7*x_6_6 + 21*x_0_7*x_6_8\n",
      "  + 58*x_0_7*x_7_6 + 58*x_0_7*x_7_8 + 30*x_0_7*x_8_6 + 30*x_0_7*x_8_8\n",
      "  + 48*x_0_7*x_9_6 + 48*x_0_7*x_9_8 + 46*x_0_8*x_10_7 + 46*x_0_8*x_10_9\n",
      "  + 56*x_0_8*x_11_7 + 56*x_0_8*x_11_9 + 50*x_0_8*x_12_7 + 50*x_0_8*x_12_9\n",
      "  + 71*x_0_8*x_13_7 + 71*x_0_8*x_13_9 + 62*x_0_8*x_14_7 + 62*x_0_8*x_14_9\n",
      "  + 84*x_0_8*x_15_7 + 84*x_0_8*x_15_9 + 94*x_0_8*x_16_7 + 94*x_0_8*x_16_9\n",
      "  + 23*x_0_8*x_17_7 + 23*x_0_8*x_17_9 + 82*x_0_8*x_18_7 + 82*x_0_8*x_18_9\n",
      "  + 61*x_0_8*x_19_7 + 61*x_0_8*x_19_9 + 48*x_0_8*x_1_7 + 48*x_0_8*x_1_9\n",
      "  + 91*x_0_8*x_2_7 + 91*x_0_8*x_2_9 + 33*x_0_8*x_3_7 + 33*x_0_8*x_3_9\n",
      "  + 85*x_0_8*x_4_7 + 85*x_0_8*x_4_9 + 26*x_0_8*x_5_7 + 26*x_0_8*x_5_9\n",
      "  + 21*x_0_8*x_6_7 + 21*x_0_8*x_6_9 + 58*x_0_8*x_7_7 + 58*x_0_8*x_7_9\n",
      "  + 30*x_0_8*x_8_7 + 30*x_0_8*x_8_9 + 48*x_0_8*x_9_7 + 48*x_0_8*x_9_9\n",
      "  + 46*x_0_9*x_10_10 + 46*x_0_9*x_10_8 + 56*x_0_9*x_11_10 + 56*x_0_9*x_11_8\n",
      "  + 50*x_0_9*x_12_10 + 50*x_0_9*x_12_8 + 71*x_0_9*x_13_10 + 71*x_0_9*x_13_8\n",
      "  + 62*x_0_9*x_14_10 + 62*x_0_9*x_14_8 + 84*x_0_9*x_15_10 + 84*x_0_9*x_15_8\n",
      "  + 94*x_0_9*x_16_10 + 94*x_0_9*x_16_8 + 23*x_0_9*x_17_10 + 23*x_0_9*x_17_8\n",
      "  + 82*x_0_9*x_18_10 + 82*x_0_9*x_18_8 + 61*x_0_9*x_19_10 + 61*x_0_9*x_19_8\n",
      "  + 48*x_0_9*x_1_10 + 48*x_0_9*x_1_8 + 91*x_0_9*x_2_10 + 91*x_0_9*x_2_8\n",
      "  + 33*x_0_9*x_3_10 + 33*x_0_9*x_3_8 + 85*x_0_9*x_4_10 + 85*x_0_9*x_4_8\n",
      "  + 26*x_0_9*x_5_10 + 26*x_0_9*x_5_8 + 21*x_0_9*x_6_10 + 21*x_0_9*x_6_8\n",
      "  + 58*x_0_9*x_7_10 + 58*x_0_9*x_7_8 + 30*x_0_9*x_8_10 + 30*x_0_9*x_8_8\n",
      "  + 48*x_0_9*x_9_10 + 48*x_0_9*x_9_8 + 49*x_10_0*x_11_1 + 49*x_10_0*x_11_19\n",
      "  + 16*x_10_0*x_12_1 + 16*x_10_0*x_12_19 + 61*x_10_0*x_13_1 + 61*x_10_0*x_13_19\n",
      "  + 83*x_10_0*x_14_1 + 83*x_10_0*x_14_19 + 98*x_10_0*x_15_1 + 98*x_10_0*x_15_19\n",
      "  + 70*x_10_0*x_16_1 + 70*x_10_0*x_16_19 + 51*x_10_0*x_17_1 + 51*x_10_0*x_17_19\n",
      "  + 90*x_10_0*x_18_1 + 90*x_10_0*x_18_19 + 37*x_10_0*x_19_1 + 37*x_10_0*x_19_19\n",
      "  + 49*x_10_1*x_11_0 + 49*x_10_1*x_11_2 + 16*x_10_1*x_12_0 + 16*x_10_1*x_12_2\n",
      "  + 61*x_10_1*x_13_0 + 61*x_10_1*x_13_2 + 83*x_10_1*x_14_0 + 83*x_10_1*x_14_2\n",
      "  + 98*x_10_1*x_15_0 + 98*x_10_1*x_15_2 + 70*x_10_1*x_16_0 + 70*x_10_1*x_16_2\n",
      "  + 51*x_10_1*x_17_0 + 51*x_10_1*x_17_2 + 90*x_10_1*x_18_0 + 90*x_10_1*x_18_2\n",
      "  + 37*x_10_1*x_19_0 + 37*x_10_1*x_19_2 + 49*x_10_10*x_11_11 + 49*x_10_10*x_11_9\n",
      "  + 16*x_10_10*x_12_11 + 16*x_10_10*x_12_9 + 61*x_10_10*x_13_11\n",
      "  + 61*x_10_10*x_13_9 + 83*x_10_10*x_14_11 + 83*x_10_10*x_14_9\n",
      "  + 98*x_10_10*x_15_11 + 98*x_10_10*x_15_9 + 70*x_10_10*x_16_11\n",
      "  + 70*x_10_10*x_16_9 + 51*x_10_10*x_17_11 + 51*x_10_10*x_17_9\n",
      "  + 90*x_10_10*x_18_11 + 90*x_10_10*x_18_9 + 37*x_10_10*x_19_11\n",
      "  + 37*x_10_10*x_19_9 + 49*x_10_11*x_11_10 + 49*x_10_11*x_11_12\n",
      "  + 16*x_10_11*x_12_10 + 16*x_10_11*x_12_12 + 61*x_10_11*x_13_10\n",
      "  + 61*x_10_11*x_13_12 + 83*x_10_11*x_14_10 + 83*x_10_11*x_14_12\n",
      "  + 98*x_10_11*x_15_10 + 98*x_10_11*x_15_12 + 70*x_10_11*x_16_10\n",
      "  + 70*x_10_11*x_16_12 + 51*x_10_11*x_17_10 + 51*x_10_11*x_17_12\n",
      "  + 90*x_10_11*x_18_10 + 90*x_10_11*x_18_12 + 37*x_10_11*x_19_10\n",
      "  + 37*x_10_11*x_19_12 + 49*x_10_12*x_11_11 + 49*x_10_12*x_11_13\n",
      "  + 16*x_10_12*x_12_11 + 16*x_10_12*x_12_13 + 61*x_10_12*x_13_11\n",
      "  + 61*x_10_12*x_13_13 + 83*x_10_12*x_14_11 + 83*x_10_12*x_14_13\n",
      "  + 98*x_10_12*x_15_11 + 98*x_10_12*x_15_13 + 70*x_10_12*x_16_11\n",
      "  + 70*x_10_12*x_16_13 + 51*x_10_12*x_17_11 + 51*x_10_12*x_17_13\n",
      "  + 90*x_10_12*x_18_11 + 90*x_10_12*x_18_13 + 37*x_10_12*x_19_11\n",
      "  + 37*x_10_12*x_19_13 + 49*x_10_13*x_11_12 + 49*x_10_13*x_11_14\n",
      "  + 16*x_10_13*x_12_12 + 16*x_10_13*x_12_14 + 61*x_10_13*x_13_12\n",
      "  + 61*x_10_13*x_13_14 + 83*x_10_13*x_14_12 + 83*x_10_13*x_14_14\n",
      "  + 98*x_10_13*x_15_12 + 98*x_10_13*x_15_14 + 70*x_10_13*x_16_12\n",
      "  + 70*x_10_13*x_16_14 + 51*x_10_13*x_17_12 + 51*x_10_13*x_17_14\n",
      "  + 90*x_10_13*x_18_12 + 90*x_10_13*x_18_14 + 37*x_10_13*x_19_12\n",
      "  + 37*x_10_13*x_19_14 + 49*x_10_14*x_11_13 + 49*x_10_14*x_11_15\n",
      "  + 16*x_10_14*x_12_13 + 16*x_10_14*x_12_15 + 61*x_10_14*x_13_13\n",
      "  + 61*x_10_14*x_13_15 + 83*x_10_14*x_14_13 + 83*x_10_14*x_14_15\n",
      "  + 98*x_10_14*x_15_13 + 98*x_10_14*x_15_15 + 70*x_10_14*x_16_13\n",
      "  + 70*x_10_14*x_16_15 + 51*x_10_14*x_17_13 + 51*x_10_14*x_17_15\n",
      "  + 90*x_10_14*x_18_13 + 90*x_10_14*x_18_15 + 37*x_10_14*x_19_13\n",
      "  + 37*x_10_14*x_19_15 + 49*x_10_15*x_11_14 + 49*x_10_15*x_11_16\n",
      "  + 16*x_10_15*x_12_14 + 16*x_10_15*x_12_16 + 61*x_10_15*x_13_14\n",
      "  + 61*x_10_15*x_13_16 + 83*x_10_15*x_14_14 + 83*x_10_15*x_14_16\n",
      "  + 98*x_10_15*x_15_14 + 98*x_10_15*x_15_16 + 70*x_10_15*x_16_14\n",
      "  + 70*x_10_15*x_16_16 + 51*x_10_15*x_17_14 + 51*x_10_15*x_17_16\n",
      "  + 90*x_10_15*x_18_14 + 90*x_10_15*x_18_16 + 37*x_10_15*x_19_14\n",
      "  + 37*x_10_15*x_19_16 + 49*x_10_16*x_11_15 + 49*x_10_16*x_11_17\n",
      "  + 16*x_10_16*x_12_15 + 16*x_10_16*x_12_17 + 61*x_10_16*x_13_15\n",
      "  + 61*x_10_16*x_13_17 + 83*x_10_16*x_14_15 + 83*x_10_16*x_14_17\n",
      "  + 98*x_10_16*x_15_15 + 98*x_10_16*x_15_17 + 70*x_10_16*x_16_15\n",
      "  + 70*x_10_16*x_16_17 + 51*x_10_16*x_17_15 + 51*x_10_16*x_17_17\n",
      "  + 90*x_10_16*x_18_15 + 90*x_10_16*x_18_17 + 37*x_10_16*x_19_15\n",
      "  + 37*x_10_16*x_19_17 + 49*x_10_17*x_11_16 + 49*x_10_17*x_11_18\n",
      "  + 16*x_10_17*x_12_16 + 16*x_10_17*x_12_18 + 61*x_10_17*x_13_16\n",
      "  + 61*x_10_17*x_13_18 + 83*x_10_17*x_14_16 + 83*x_10_17*x_14_18\n",
      "  + 98*x_10_17*x_15_16 + 98*x_10_17*x_15_18 + 70*x_10_17*x_16_16\n",
      "  + 70*x_10_17*x_16_18 + 51*x_10_17*x_17_16 + 51*x_10_17*x_17_18\n",
      "  + 90*x_10_17*x_18_16 + 90*x_10_17*x_18_18 + 37*x_10_17*x_19_16\n",
      "  + 37*x_10_17*x_19_18 + 49*x_10_18*x_11_17 + 49*x_10_18*x_11_19\n",
      "  + 16*x_10_18*x_12_17 + 16*x_10_18*x_12_19 + 61*x_10_18*x_13_17\n",
      "  + 61*x_10_18*x_13_19 + 83*x_10_18*x_14_17 + 83*x_10_18*x_14_19\n",
      "  + 98*x_10_18*x_15_17 + 98*x_10_18*x_15_19 + 70*x_10_18*x_16_17\n",
      "  + 70*x_10_18*x_16_19 + 51*x_10_18*x_17_17 + 51*x_10_18*x_17_19\n",
      "  + 90*x_10_18*x_18_17 + 90*x_10_18*x_18_19 + 37*x_10_18*x_19_17\n",
      "  + 37*x_10_18*x_19_19 + 49*x_10_19*x_11_0 + 49*x_10_19*x_11_18\n",
      "  + 16*x_10_19*x_12_0 + 16*x_10_19*x_12_18 + 61*x_10_19*x_13_0\n",
      "  + 61*x_10_19*x_13_18 + 83*x_10_19*x_14_0 + 83*x_10_19*x_14_18\n",
      "  + 98*x_10_19*x_15_0 + 98*x_10_19*x_15_18 + 70*x_10_19*x_16_0\n",
      "  + 70*x_10_19*x_16_18 + 51*x_10_19*x_17_0 + 51*x_10_19*x_17_18\n",
      "  + 90*x_10_19*x_18_0 + 90*x_10_19*x_18_18 + 37*x_10_19*x_19_0\n",
      "  + 37*x_10_19*x_19_18 + 49*x_10_2*x_11_1 + 49*x_10_2*x_11_3 + 16*x_10_2*x_12_1\n",
      "  + 16*x_10_2*x_12_3 + 61*x_10_2*x_13_1 + 61*x_10_2*x_13_3 + 83*x_10_2*x_14_1\n",
      "  + 83*x_10_2*x_14_3 + 98*x_10_2*x_15_1 + 98*x_10_2*x_15_3 + 70*x_10_2*x_16_1\n",
      "  + 70*x_10_2*x_16_3 + 51*x_10_2*x_17_1 + 51*x_10_2*x_17_3 + 90*x_10_2*x_18_1\n",
      "  + 90*x_10_2*x_18_3 + 37*x_10_2*x_19_1 + 37*x_10_2*x_19_3 + 49*x_10_3*x_11_2\n",
      "  + 49*x_10_3*x_11_4 + 16*x_10_3*x_12_2 + 16*x_10_3*x_12_4 + 61*x_10_3*x_13_2\n",
      "  + 61*x_10_3*x_13_4 + 83*x_10_3*x_14_2 + 83*x_10_3*x_14_4 + 98*x_10_3*x_15_2\n",
      "  + 98*x_10_3*x_15_4 + 70*x_10_3*x_16_2 + 70*x_10_3*x_16_4 + 51*x_10_3*x_17_2\n",
      "  + 51*x_10_3*x_17_4 + 90*x_10_3*x_18_2 + 90*x_10_3*x_18_4 + 37*x_10_3*x_19_2\n",
      "  + 37*x_10_3*x_19_4 + 49*x_10_4*x_11_3 + 49*x_10_4*x_11_5 + 16*x_10_4*x_12_3\n",
      "  + 16*x_10_4*x_12_5 + 61*x_10_4*x_13_3 + 61*x_10_4*x_13_5 + 83*x_10_4*x_14_3\n",
      "  + 83*x_10_4*x_14_5 + 98*x_10_4*x_15_3 + 98*x_10_4*x_15_5 + 70*x_10_4*x_16_3\n",
      "  + 70*x_10_4*x_16_5 + 51*x_10_4*x_17_3 + 51*x_10_4*x_17_5 + 90*x_10_4*x_18_3\n",
      "  + 90*x_10_4*x_18_5 + 37*x_10_4*x_19_3 + 37*x_10_4*x_19_5 + 49*x_10_5*x_11_4\n",
      "  + 49*x_10_5*x_11_6 + 16*x_10_5*x_12_4 + 16*x_10_5*x_12_6 + 61*x_10_5*x_13_4\n",
      "  + 61*x_10_5*x_13_6 + 83*x_10_5*x_14_4 + 83*x_10_5*x_14_6 + 98*x_10_5*x_15_4\n",
      "  + 98*x_10_5*x_15_6 + 70*x_10_5*x_16_4 + 70*x_10_5*x_16_6 + 51*x_10_5*x_17_4\n",
      "  + 51*x_10_5*x_17_6 + 90*x_10_5*x_18_4 + 90*x_10_5*x_18_6 + 37*x_10_5*x_19_4\n",
      "  + 37*x_10_5*x_19_6 + 49*x_10_6*x_11_5 + 49*x_10_6*x_11_7 + 16*x_10_6*x_12_5\n",
      "  + 16*x_10_6*x_12_7 + 61*x_10_6*x_13_5 + 61*x_10_6*x_13_7 + 83*x_10_6*x_14_5\n",
      "  + 83*x_10_6*x_14_7 + 98*x_10_6*x_15_5 + 98*x_10_6*x_15_7 + 70*x_10_6*x_16_5\n",
      "  + 70*x_10_6*x_16_7 + 51*x_10_6*x_17_5 + 51*x_10_6*x_17_7 + 90*x_10_6*x_18_5\n",
      "  + 90*x_10_6*x_18_7 + 37*x_10_6*x_19_5 + 37*x_10_6*x_19_7 + 49*x_10_7*x_11_6\n",
      "  + 49*x_10_7*x_11_8 + 16*x_10_7*x_12_6 + 16*x_10_7*x_12_8 + 61*x_10_7*x_13_6\n",
      "  + 61*x_10_7*x_13_8 + 83*x_10_7*x_14_6 + 83*x_10_7*x_14_8 + 98*x_10_7*x_15_6\n",
      "  + 98*x_10_7*x_15_8 + 70*x_10_7*x_16_6 + 70*x_10_7*x_16_8 + 51*x_10_7*x_17_6\n",
      "  + 51*x_10_7*x_17_8 + 90*x_10_7*x_18_6 + 90*x_10_7*x_18_8 + 37*x_10_7*x_19_6\n",
      "  + 37*x_10_7*x_19_8 + 49*x_10_8*x_11_7 + 49*x_10_8*x_11_9 + 16*x_10_8*x_12_7\n",
      "  + 16*x_10_8*x_12_9 + 61*x_10_8*x_13_7 + 61*x_10_8*x_13_9 + 83*x_10_8*x_14_7\n",
      "  + 83*x_10_8*x_14_9 + 98*x_10_8*x_15_7 + 98*x_10_8*x_15_9 + 70*x_10_8*x_16_7\n",
      "  + 70*x_10_8*x_16_9 + 51*x_10_8*x_17_7 + 51*x_10_8*x_17_9 + 90*x_10_8*x_18_7\n",
      "  + 90*x_10_8*x_18_9 + 37*x_10_8*x_19_7 + 37*x_10_8*x_19_9 + 49*x_10_9*x_11_10\n",
      "  + 49*x_10_9*x_11_8 + 16*x_10_9*x_12_10 + 16*x_10_9*x_12_8 + 61*x_10_9*x_13_10\n",
      "  + 61*x_10_9*x_13_8 + 83*x_10_9*x_14_10 + 83*x_10_9*x_14_8 + 98*x_10_9*x_15_10\n",
      "  + 98*x_10_9*x_15_8 + 70*x_10_9*x_16_10 + 70*x_10_9*x_16_8 + 51*x_10_9*x_17_10\n",
      "  + 51*x_10_9*x_17_8 + 90*x_10_9*x_18_10 + 90*x_10_9*x_18_8 + 37*x_10_9*x_19_10\n",
      "  + 37*x_10_9*x_19_8 + 65*x_11_0*x_12_1 + 65*x_11_0*x_12_19 + 15*x_11_0*x_13_1\n",
      "  + 15*x_11_0*x_13_19 + 42*x_11_0*x_14_1 + 42*x_11_0*x_14_19 + 51*x_11_0*x_15_1\n",
      "  + 51*x_11_0*x_15_19 + 40*x_11_0*x_16_1 + 40*x_11_0*x_16_19 + 38*x_11_0*x_17_1\n",
      "  + 38*x_11_0*x_17_19 + 41*x_11_0*x_18_1 + 41*x_11_0*x_18_19 + 21*x_11_0*x_19_1\n",
      "  + 21*x_11_0*x_19_19 + 65*x_11_1*x_12_0 + 65*x_11_1*x_12_2 + 15*x_11_1*x_13_0\n",
      "  + 15*x_11_1*x_13_2 + 42*x_11_1*x_14_0 + 42*x_11_1*x_14_2 + 51*x_11_1*x_15_0\n",
      "  + 51*x_11_1*x_15_2 + 40*x_11_1*x_16_0 + 40*x_11_1*x_16_2 + 38*x_11_1*x_17_0\n",
      "  + 38*x_11_1*x_17_2 + 41*x_11_1*x_18_0 + 41*x_11_1*x_18_2 + 21*x_11_1*x_19_0\n",
      "  + 21*x_11_1*x_19_2 + 65*x_11_10*x_12_11 + 65*x_11_10*x_12_9\n",
      "  + 15*x_11_10*x_13_11 + 15*x_11_10*x_13_9 + 42*x_11_10*x_14_11\n",
      "  + 42*x_11_10*x_14_9 + 51*x_11_10*x_15_11 + 51*x_11_10*x_15_9\n",
      "  + 40*x_11_10*x_16_11 + 40*x_11_10*x_16_9 + 38*x_11_10*x_17_11\n",
      "  + 38*x_11_10*x_17_9 + 41*x_11_10*x_18_11 + 41*x_11_10*x_18_9\n",
      "  + 21*x_11_10*x_19_11 + 21*x_11_10*x_19_9 + 65*x_11_11*x_12_10\n",
      "  + 65*x_11_11*x_12_12 + 15*x_11_11*x_13_10 + 15*x_11_11*x_13_12\n",
      "  + 42*x_11_11*x_14_10 + 42*x_11_11*x_14_12 + 51*x_11_11*x_15_10\n",
      "  + 51*x_11_11*x_15_12 + 40*x_11_11*x_16_10 + 40*x_11_11*x_16_12\n",
      "  + 38*x_11_11*x_17_10 + 38*x_11_11*x_17_12 + 41*x_11_11*x_18_10\n",
      "  + 41*x_11_11*x_18_12 + 21*x_11_11*x_19_10 + 21*x_11_11*x_19_12\n",
      "  + 65*x_11_12*x_12_11 + 65*x_11_12*x_12_13 + 15*x_11_12*x_13_11\n",
      "  + 15*x_11_12*x_13_13 + 42*x_11_12*x_14_11 + 42*x_11_12*x_14_13\n",
      "  + 51*x_11_12*x_15_11 + 51*x_11_12*x_15_13 + 40*x_11_12*x_16_11\n",
      "  + 40*x_11_12*x_16_13 + 38*x_11_12*x_17_11 + 38*x_11_12*x_17_13\n",
      "  + 41*x_11_12*x_18_11 + 41*x_11_12*x_18_13 + 21*x_11_12*x_19_11\n",
      "  + 21*x_11_12*x_19_13 + 65*x_11_13*x_12_12 + 65*x_11_13*x_12_14\n",
      "  + 15*x_11_13*x_13_12 + 15*x_11_13*x_13_14 + 42*x_11_13*x_14_12\n",
      "  + 42*x_11_13*x_14_14 + 51*x_11_13*x_15_12 + 51*x_11_13*x_15_14\n",
      "  + 40*x_11_13*x_16_12 + 40*x_11_13*x_16_14 + 38*x_11_13*x_17_12\n",
      "  + 38*x_11_13*x_17_14 + 41*x_11_13*x_18_12 + 41*x_11_13*x_18_14\n",
      "  + 21*x_11_13*x_19_12 + 21*x_11_13*x_19_14 + 65*x_11_14*x_12_13\n",
      "  + 65*x_11_14*x_12_15 + 15*x_11_14*x_13_13 + 15*x_11_14*x_13_15\n",
      "  + 42*x_11_14*x_14_13 + 42*x_11_14*x_14_15 + 51*x_11_14*x_15_13\n",
      "  + 51*x_11_14*x_15_15 + 40*x_11_14*x_16_13 + 40*x_11_14*x_16_15\n",
      "  + 38*x_11_14*x_17_13 + 38*x_11_14*x_17_15 + 41*x_11_14*x_18_13\n",
      "  + 41*x_11_14*x_18_15 + 21*x_11_14*x_19_13 + 21*x_11_14*x_19_15\n",
      "  + 65*x_11_15*x_12_14 + 65*x_11_15*x_12_16 + 15*x_11_15*x_13_14\n",
      "  + 15*x_11_15*x_13_16 + 42*x_11_15*x_14_14 + 42*x_11_15*x_14_16\n",
      "  + 51*x_11_15*x_15_14 + 51*x_11_15*x_15_16 + 40*x_11_15*x_16_14\n",
      "  + 40*x_11_15*x_16_16 + 38*x_11_15*x_17_14 + 38*x_11_15*x_17_16\n",
      "  + 41*x_11_15*x_18_14 + 41*x_11_15*x_18_16 + 21*x_11_15*x_19_14\n",
      "  + 21*x_11_15*x_19_16 + 65*x_11_16*x_12_15 + 65*x_11_16*x_12_17\n",
      "  + 15*x_11_16*x_13_15 + 15*x_11_16*x_13_17 + 42*x_11_16*x_14_15\n",
      "  + 42*x_11_16*x_14_17 + 51*x_11_16*x_15_15 + 51*x_11_16*x_15_17\n",
      "  + 40*x_11_16*x_16_15 + 40*x_11_16*x_16_17 + 38*x_11_16*x_17_15\n",
      "  + 38*x_11_16*x_17_17 + 41*x_11_16*x_18_15 + 41*x_11_16*x_18_17\n",
      "  + 21*x_11_16*x_19_15 + 21*x_11_16*x_19_17 + 65*x_11_17*x_12_16\n",
      "  + 65*x_11_17*x_12_18 + 15*x_11_17*x_13_16 + 15*x_11_17*x_13_18\n",
      "  + 42*x_11_17*x_14_16 + 42*x_11_17*x_14_18 + 51*x_11_17*x_15_16\n",
      "  + 51*x_11_17*x_15_18 + 40*x_11_17*x_16_16 + 40*x_11_17*x_16_18\n",
      "  + 38*x_11_17*x_17_16 + 38*x_11_17*x_17_18 + 41*x_11_17*x_18_16\n",
      "  + 41*x_11_17*x_18_18 + 21*x_11_17*x_19_16 + 21*x_11_17*x_19_18\n",
      "  + 65*x_11_18*x_12_17 + 65*x_11_18*x_12_19 + 15*x_11_18*x_13_17\n",
      "  + 15*x_11_18*x_13_19 + 42*x_11_18*x_14_17 + 42*x_11_18*x_14_19\n",
      "  + 51*x_11_18*x_15_17 + 51*x_11_18*x_15_19 + 40*x_11_18*x_16_17\n",
      "  + 40*x_11_18*x_16_19 + 38*x_11_18*x_17_17 + 38*x_11_18*x_17_19\n",
      "  + 41*x_11_18*x_18_17 + 41*x_11_18*x_18_19 + 21*x_11_18*x_19_17\n",
      "  + 21*x_11_18*x_19_19 + 65*x_11_19*x_12_0 + 65*x_11_19*x_12_18\n",
      "  + 15*x_11_19*x_13_0 + 15*x_11_19*x_13_18 + 42*x_11_19*x_14_0\n",
      "  + 42*x_11_19*x_14_18 + 51*x_11_19*x_15_0 + 51*x_11_19*x_15_18\n",
      "  + 40*x_11_19*x_16_0 + 40*x_11_19*x_16_18 + 38*x_11_19*x_17_0\n",
      "  + 38*x_11_19*x_17_18 + 41*x_11_19*x_18_0 + 41*x_11_19*x_18_18\n",
      "  + 21*x_11_19*x_19_0 + 21*x_11_19*x_19_18 + 65*x_11_2*x_12_1 + 65*x_11_2*x_12_3\n",
      "  + 15*x_11_2*x_13_1 + 15*x_11_2*x_13_3 + 42*x_11_2*x_14_1 + 42*x_11_2*x_14_3\n",
      "  + 51*x_11_2*x_15_1 + 51*x_11_2*x_15_3 + 40*x_11_2*x_16_1 + 40*x_11_2*x_16_3\n",
      "  + 38*x_11_2*x_17_1 + 38*x_11_2*x_17_3 + 41*x_11_2*x_18_1 + 41*x_11_2*x_18_3\n",
      "  + 21*x_11_2*x_19_1 + 21*x_11_2*x_19_3 + 65*x_11_3*x_12_2 + 65*x_11_3*x_12_4\n",
      "  + 15*x_11_3*x_13_2 + 15*x_11_3*x_13_4 + 42*x_11_3*x_14_2 + 42*x_11_3*x_14_4\n",
      "  + 51*x_11_3*x_15_2 + 51*x_11_3*x_15_4 + 40*x_11_3*x_16_2 + 40*x_11_3*x_16_4\n",
      "  + 38*x_11_3*x_17_2 + 38*x_11_3*x_17_4 + 41*x_11_3*x_18_2 + 41*x_11_3*x_18_4\n",
      "  + 21*x_11_3*x_19_2 + 21*x_11_3*x_19_4 + 65*x_11_4*x_12_3 + 65*x_11_4*x_12_5\n",
      "  + 15*x_11_4*x_13_3 + 15*x_11_4*x_13_5 + 42*x_11_4*x_14_3 + 42*x_11_4*x_14_5\n",
      "  + 51*x_11_4*x_15_3 + 51*x_11_4*x_15_5 + 40*x_11_4*x_16_3 + 40*x_11_4*x_16_5\n",
      "  + 38*x_11_4*x_17_3 + 38*x_11_4*x_17_5 + 41*x_11_4*x_18_3 + 41*x_11_4*x_18_5\n",
      "  + 21*x_11_4*x_19_3 + 21*x_11_4*x_19_5 + 65*x_11_5*x_12_4 + 65*x_11_5*x_12_6\n",
      "  + 15*x_11_5*x_13_4 + 15*x_11_5*x_13_6 + 42*x_11_5*x_14_4 + 42*x_11_5*x_14_6\n",
      "  + 51*x_11_5*x_15_4 + 51*x_11_5*x_15_6 + 40*x_11_5*x_16_4 + 40*x_11_5*x_16_6\n",
      "  + 38*x_11_5*x_17_4 + 38*x_11_5*x_17_6 + 41*x_11_5*x_18_4 + 41*x_11_5*x_18_6\n",
      "  + 21*x_11_5*x_19_4 + 21*x_11_5*x_19_6 + 65*x_11_6*x_12_5 + 65*x_11_6*x_12_7\n",
      "  + 15*x_11_6*x_13_5 + 15*x_11_6*x_13_7 + 42*x_11_6*x_14_5 + 42*x_11_6*x_14_7\n",
      "  + 51*x_11_6*x_15_5 + 51*x_11_6*x_15_7 + 40*x_11_6*x_16_5 + 40*x_11_6*x_16_7\n",
      "  + 38*x_11_6*x_17_5 + 38*x_11_6*x_17_7 + 41*x_11_6*x_18_5 + 41*x_11_6*x_18_7\n",
      "  + 21*x_11_6*x_19_5 + 21*x_11_6*x_19_7 + 65*x_11_7*x_12_6 + 65*x_11_7*x_12_8\n",
      "  + 15*x_11_7*x_13_6 + 15*x_11_7*x_13_8 + 42*x_11_7*x_14_6 + 42*x_11_7*x_14_8\n",
      "  + 51*x_11_7*x_15_6 + 51*x_11_7*x_15_8 + 40*x_11_7*x_16_6 + 40*x_11_7*x_16_8\n",
      "  + 38*x_11_7*x_17_6 + 38*x_11_7*x_17_8 + 41*x_11_7*x_18_6 + 41*x_11_7*x_18_8\n",
      "  + 21*x_11_7*x_19_6 + 21*x_11_7*x_19_8 + 65*x_11_8*x_12_7 + 65*x_11_8*x_12_9\n",
      "  + 15*x_11_8*x_13_7 + 15*x_11_8*x_13_9 + 42*x_11_8*x_14_7 + 42*x_11_8*x_14_9\n",
      "  + 51*x_11_8*x_15_7 + 51*x_11_8*x_15_9 + 40*x_11_8*x_16_7 + 40*x_11_8*x_16_9\n",
      "  + 38*x_11_8*x_17_7 + 38*x_11_8*x_17_9 + 41*x_11_8*x_18_7 + 41*x_11_8*x_18_9\n",
      "  + 21*x_11_8*x_19_7 + 21*x_11_8*x_19_9 + 65*x_11_9*x_12_10 + 65*x_11_9*x_12_8\n",
      "  + 15*x_11_9*x_13_10 + 15*x_11_9*x_13_8 + 42*x_11_9*x_14_10 + 42*x_11_9*x_14_8\n",
      "  + 51*x_11_9*x_15_10 + 51*x_11_9*x_15_8 + 40*x_11_9*x_16_10 + 40*x_11_9*x_16_8\n",
      "  + 38*x_11_9*x_17_10 + 38*x_11_9*x_17_8 + 41*x_11_9*x_18_10 + 41*x_11_9*x_18_8\n",
      "  + 21*x_11_9*x_19_10 + 21*x_11_9*x_19_8 + 77*x_12_0*x_13_1 + 77*x_12_0*x_13_19\n",
      "  + 95*x_12_0*x_14_1 + 95*x_12_0*x_14_19 + 112*x_12_0*x_15_1\n",
      "  + 112*x_12_0*x_15_19 + 85*x_12_0*x_16_1 + 85*x_12_0*x_16_19 + 61*x_12_0*x_17_1\n",
      "  + 61*x_12_0*x_17_19 + 105*x_12_0*x_18_1 + 105*x_12_0*x_18_19\n",
      "  + 52*x_12_0*x_19_1 + 52*x_12_0*x_19_19 + 77*x_12_1*x_13_0 + 77*x_12_1*x_13_2\n",
      "  + 95*x_12_1*x_14_0 + 95*x_12_1*x_14_2 + 112*x_12_1*x_15_0 + 112*x_12_1*x_15_2\n",
      "  + 85*x_12_1*x_16_0 + 85*x_12_1*x_16_2 + 61*x_12_1*x_17_0 + 61*x_12_1*x_17_2\n",
      "  + 105*x_12_1*x_18_0 + 105*x_12_1*x_18_2 + 52*x_12_1*x_19_0 + 52*x_12_1*x_19_2\n",
      "  + 77*x_12_10*x_13_11 + 77*x_12_10*x_13_9 + 95*x_12_10*x_14_11\n",
      "  + 95*x_12_10*x_14_9 + 112*x_12_10*x_15_11 + 112*x_12_10*x_15_9\n",
      "  + 85*x_12_10*x_16_11 + 85*x_12_10*x_16_9 + 61*x_12_10*x_17_11\n",
      "  + 61*x_12_10*x_17_9 + 105*x_12_10*x_18_11 + 105*x_12_10*x_18_9\n",
      "  + 52*x_12_10*x_19_11 + 52*x_12_10*x_19_9 + 77*x_12_11*x_13_10\n",
      "  + 77*x_12_11*x_13_12 + 95*x_12_11*x_14_10 + 95*x_12_11*x_14_12\n",
      "  + 112*x_12_11*x_15_10 + 112*x_12_11*x_15_12 + 85*x_12_11*x_16_10\n",
      "  + 85*x_12_11*x_16_12 + 61*x_12_11*x_17_10 + 61*x_12_11*x_17_12\n",
      "  + 105*x_12_11*x_18_10 + 105*x_12_11*x_18_12 + 52*x_12_11*x_19_10\n",
      "  + 52*x_12_11*x_19_12 + 77*x_12_12*x_13_11 + 77*x_12_12*x_13_13\n",
      "  + 95*x_12_12*x_14_11 + 95*x_12_12*x_14_13 + 112*x_12_12*x_15_11\n",
      "  + 112*x_12_12*x_15_13 + 85*x_12_12*x_16_11 + 85*x_12_12*x_16_13\n",
      "  + 61*x_12_12*x_17_11 + 61*x_12_12*x_17_13 + 105*x_12_12*x_18_11\n",
      "  + 105*x_12_12*x_18_13 + 52*x_12_12*x_19_11 + 52*x_12_12*x_19_13\n",
      "  + 77*x_12_13*x_13_12 + 77*x_12_13*x_13_14 + 95*x_12_13*x_14_12\n",
      "  + 95*x_12_13*x_14_14 + 112*x_12_13*x_15_12 + 112*x_12_13*x_15_14\n",
      "  + 85*x_12_13*x_16_12 + 85*x_12_13*x_16_14 + 61*x_12_13*x_17_12\n",
      "  + 61*x_12_13*x_17_14 + 105*x_12_13*x_18_12 + 105*x_12_13*x_18_14\n",
      "  + 52*x_12_13*x_19_12 + 52*x_12_13*x_19_14 + 77*x_12_14*x_13_13\n",
      "  + 77*x_12_14*x_13_15 + 95*x_12_14*x_14_13 + 95*x_12_14*x_14_15\n",
      "  + 112*x_12_14*x_15_13 + 112*x_12_14*x_15_15 + 85*x_12_14*x_16_13\n",
      "  + 85*x_12_14*x_16_15 + 61*x_12_14*x_17_13 + 61*x_12_14*x_17_15\n",
      "  + 105*x_12_14*x_18_13 + 105*x_12_14*x_18_15 + 52*x_12_14*x_19_13\n",
      "  + 52*x_12_14*x_19_15 + 77*x_12_15*x_13_14 + 77*x_12_15*x_13_16\n",
      "  + 95*x_12_15*x_14_14 + 95*x_12_15*x_14_16 + 112*x_12_15*x_15_14\n",
      "  + 112*x_12_15*x_15_16 + 85*x_12_15*x_16_14 + 85*x_12_15*x_16_16\n",
      "  + 61*x_12_15*x_17_14 + 61*x_12_15*x_17_16 + 105*x_12_15*x_18_14\n",
      "  + 105*x_12_15*x_18_16 + 52*x_12_15*x_19_14 + 52*x_12_15*x_19_16\n",
      "  + 77*x_12_16*x_13_15 + 77*x_12_16*x_13_17 + 95*x_12_16*x_14_15\n",
      "  + 95*x_12_16*x_14_17 + 112*x_12_16*x_15_15 + 112*x_12_16*x_15_17\n",
      "  + 85*x_12_16*x_16_15 + 85*x_12_16*x_16_17 + 61*x_12_16*x_17_15\n",
      "  + 61*x_12_16*x_17_17 + 105*x_12_16*x_18_15 + 105*x_12_16*x_18_17\n",
      "  + 52*x_12_16*x_19_15 + 52*x_12_16*x_19_17 + 77*x_12_17*x_13_16\n",
      "  + 77*x_12_17*x_13_18 + 95*x_12_17*x_14_16 + 95*x_12_17*x_14_18\n",
      "  + 112*x_12_17*x_15_16 + 112*x_12_17*x_15_18 + 85*x_12_17*x_16_16\n",
      "  + 85*x_12_17*x_16_18 + 61*x_12_17*x_17_16 + 61*x_12_17*x_17_18\n",
      "  + 105*x_12_17*x_18_16 + 105*x_12_17*x_18_18 + 52*x_12_17*x_19_16\n",
      "  + 52*x_12_17*x_19_18 + 77*x_12_18*x_13_17 + 77*x_12_18*x_13_19\n",
      "  + 95*x_12_18*x_14_17 + 95*x_12_18*x_14_19 + 112*x_12_18*x_15_17\n",
      "  + 112*x_12_18*x_15_19 + 85*x_12_18*x_16_17 + 85*x_12_18*x_16_19\n",
      "  + 61*x_12_18*x_17_17 + 61*x_12_18*x_17_19 + 105*x_12_18*x_18_17\n",
      "  + 105*x_12_18*x_18_19 + 52*x_12_18*x_19_17 + 52*x_12_18*x_19_19\n",
      "  + 77*x_12_19*x_13_0 + 77*x_12_19*x_13_18 + 95*x_12_19*x_14_0\n",
      "  + 95*x_12_19*x_14_18 + 112*x_12_19*x_15_0 + 112*x_12_19*x_15_18\n",
      "  + 85*x_12_19*x_16_0 + 85*x_12_19*x_16_18 + 61*x_12_19*x_17_0\n",
      "  + 61*x_12_19*x_17_18 + 105*x_12_19*x_18_0 + 105*x_12_19*x_18_18\n",
      "  + 52*x_12_19*x_19_0 + 52*x_12_19*x_19_18 + 77*x_12_2*x_13_1 + 77*x_12_2*x_13_3\n",
      "  + 95*x_12_2*x_14_1 + 95*x_12_2*x_14_3 + 112*x_12_2*x_15_1 + 112*x_12_2*x_15_3\n",
      "  + 85*x_12_2*x_16_1 + 85*x_12_2*x_16_3 + 61*x_12_2*x_17_1 + 61*x_12_2*x_17_3\n",
      "  + 105*x_12_2*x_18_1 + 105*x_12_2*x_18_3 + 52*x_12_2*x_19_1 + 52*x_12_2*x_19_3\n",
      "  + 77*x_12_3*x_13_2 + 77*x_12_3*x_13_4 + 95*x_12_3*x_14_2 + 95*x_12_3*x_14_4\n",
      "  + 112*x_12_3*x_15_2 + 112*x_12_3*x_15_4 + 85*x_12_3*x_16_2 + 85*x_12_3*x_16_4\n",
      "  + 61*x_12_3*x_17_2 + 61*x_12_3*x_17_4 + 105*x_12_3*x_18_2 + 105*x_12_3*x_18_4\n",
      "  + 52*x_12_3*x_19_2 + 52*x_12_3*x_19_4 + 77*x_12_4*x_13_3 + 77*x_12_4*x_13_5\n",
      "  + 95*x_12_4*x_14_3 + 95*x_12_4*x_14_5 + 112*x_12_4*x_15_3 + 112*x_12_4*x_15_5\n",
      "  + 85*x_12_4*x_16_3 + 85*x_12_4*x_16_5 + 61*x_12_4*x_17_3 + 61*x_12_4*x_17_5\n",
      "  + 105*x_12_4*x_18_3 + 105*x_12_4*x_18_5 + 52*x_12_4*x_19_3 + 52*x_12_4*x_19_5\n",
      "  + 77*x_12_5*x_13_4 + 77*x_12_5*x_13_6 + 95*x_12_5*x_14_4 + 95*x_12_5*x_14_6\n",
      "  + 112*x_12_5*x_15_4 + 112*x_12_5*x_15_6 + 85*x_12_5*x_16_4 + 85*x_12_5*x_16_6\n",
      "  + 61*x_12_5*x_17_4 + 61*x_12_5*x_17_6 + 105*x_12_5*x_18_4 + 105*x_12_5*x_18_6\n",
      "  + 52*x_12_5*x_19_4 + 52*x_12_5*x_19_6 + 77*x_12_6*x_13_5 + 77*x_12_6*x_13_7\n",
      "  + 95*x_12_6*x_14_5 + 95*x_12_6*x_14_7 + 112*x_12_6*x_15_5 + 112*x_12_6*x_15_7\n",
      "  + 85*x_12_6*x_16_5 + 85*x_12_6*x_16_7 + 61*x_12_6*x_17_5 + 61*x_12_6*x_17_7\n",
      "  + 105*x_12_6*x_18_5 + 105*x_12_6*x_18_7 + 52*x_12_6*x_19_5 + 52*x_12_6*x_19_7\n",
      "  + 77*x_12_7*x_13_6 + 77*x_12_7*x_13_8 + 95*x_12_7*x_14_6 + 95*x_12_7*x_14_8\n",
      "  + 112*x_12_7*x_15_6 + 112*x_12_7*x_15_8 + 85*x_12_7*x_16_6 + 85*x_12_7*x_16_8\n",
      "  + 61*x_12_7*x_17_6 + 61*x_12_7*x_17_8 + 105*x_12_7*x_18_6 + 105*x_12_7*x_18_8\n",
      "  + 52*x_12_7*x_19_6 + 52*x_12_7*x_19_8 + 77*x_12_8*x_13_7 + 77*x_12_8*x_13_9\n",
      "  + 95*x_12_8*x_14_7 + 95*x_12_8*x_14_9 + 112*x_12_8*x_15_7 + 112*x_12_8*x_15_9\n",
      "  + 85*x_12_8*x_16_7 + 85*x_12_8*x_16_9 + 61*x_12_8*x_17_7 + 61*x_12_8*x_17_9\n",
      "  + 105*x_12_8*x_18_7 + 105*x_12_8*x_18_9 + 52*x_12_8*x_19_7 + 52*x_12_8*x_19_9\n",
      "  + 77*x_12_9*x_13_10 + 77*x_12_9*x_13_8 + 95*x_12_9*x_14_10 + 95*x_12_9*x_14_8\n",
      "  + 112*x_12_9*x_15_10 + 112*x_12_9*x_15_8 + 85*x_12_9*x_16_10\n",
      "  + 85*x_12_9*x_16_8 + 61*x_12_9*x_17_10 + 61*x_12_9*x_17_8 + 105*x_12_9*x_18_10\n",
      "  + 105*x_12_9*x_18_8 + 52*x_12_9*x_19_10 + 52*x_12_9*x_19_8 + 46*x_13_0*x_14_1\n",
      "  + 46*x_13_0*x_14_19 + 47*x_13_0*x_15_1 + 47*x_13_0*x_15_19 + 29*x_13_0*x_16_1\n",
      "  + 29*x_13_0*x_16_19 + 52*x_13_0*x_17_1 + 52*x_13_0*x_17_19 + 35*x_13_0*x_18_1\n",
      "  + 35*x_13_0*x_18_19 + 26*x_13_0*x_19_1 + 26*x_13_0*x_19_19 + 46*x_13_1*x_14_0\n",
      "  + 46*x_13_1*x_14_2 + 47*x_13_1*x_15_0 + 47*x_13_1*x_15_2 + 29*x_13_1*x_16_0\n",
      "  + 29*x_13_1*x_16_2 + 52*x_13_1*x_17_0 + 52*x_13_1*x_17_2 + 35*x_13_1*x_18_0\n",
      "  + 35*x_13_1*x_18_2 + 26*x_13_1*x_19_0 + 26*x_13_1*x_19_2 + 46*x_13_10*x_14_11\n",
      "  + 46*x_13_10*x_14_9 + 47*x_13_10*x_15_11 + 47*x_13_10*x_15_9\n",
      "  + 29*x_13_10*x_16_11 + 29*x_13_10*x_16_9 + 52*x_13_10*x_17_11\n",
      "  + 52*x_13_10*x_17_9 + 35*x_13_10*x_18_11 + 35*x_13_10*x_18_9\n",
      "  + 26*x_13_10*x_19_11 + 26*x_13_10*x_19_9 + 46*x_13_11*x_14_10\n",
      "  + 46*x_13_11*x_14_12 + 47*x_13_11*x_15_10 + 47*x_13_11*x_15_12\n",
      "  + 29*x_13_11*x_16_10 + 29*x_13_11*x_16_12 + 52*x_13_11*x_17_10\n",
      "  + 52*x_13_11*x_17_12 + 35*x_13_11*x_18_10 + 35*x_13_11*x_18_12\n",
      "  + 26*x_13_11*x_19_10 + 26*x_13_11*x_19_12 + 46*x_13_12*x_14_11\n",
      "  + 46*x_13_12*x_14_13 + 47*x_13_12*x_15_11 + 47*x_13_12*x_15_13\n",
      "  + 29*x_13_12*x_16_11 + 29*x_13_12*x_16_13 + 52*x_13_12*x_17_11\n",
      "  + 52*x_13_12*x_17_13 + 35*x_13_12*x_18_11 + 35*x_13_12*x_18_13\n",
      "  + 26*x_13_12*x_19_11 + 26*x_13_12*x_19_13 + 46*x_13_13*x_14_12\n",
      "  + 46*x_13_13*x_14_14 + 47*x_13_13*x_15_12 + 47*x_13_13*x_15_14\n",
      "  + 29*x_13_13*x_16_12 + 29*x_13_13*x_16_14 + 52*x_13_13*x_17_12\n",
      "  + 52*x_13_13*x_17_14 + 35*x_13_13*x_18_12 + 35*x_13_13*x_18_14\n",
      "  + 26*x_13_13*x_19_12 + 26*x_13_13*x_19_14 + 46*x_13_14*x_14_13\n",
      "  + 46*x_13_14*x_14_15 + 47*x_13_14*x_15_13 + 47*x_13_14*x_15_15\n",
      "  + 29*x_13_14*x_16_13 + 29*x_13_14*x_16_15 + 52*x_13_14*x_17_13\n",
      "  + 52*x_13_14*x_17_15 + 35*x_13_14*x_18_13 + 35*x_13_14*x_18_15\n",
      "  + 26*x_13_14*x_19_13 + 26*x_13_14*x_19_15 + 46*x_13_15*x_14_14\n",
      "  + 46*x_13_15*x_14_16 + 47*x_13_15*x_15_14 + 47*x_13_15*x_15_16\n",
      "  + 29*x_13_15*x_16_14 + 29*x_13_15*x_16_16 + 52*x_13_15*x_17_14\n",
      "  + 52*x_13_15*x_17_16 + 35*x_13_15*x_18_14 + 35*x_13_15*x_18_16\n",
      "  + 26*x_13_15*x_19_14 + 26*x_13_15*x_19_16 + 46*x_13_16*x_14_15\n",
      "  + 46*x_13_16*x_14_17 + 47*x_13_16*x_15_15 + 47*x_13_16*x_15_17\n",
      "  + 29*x_13_16*x_16_15 + 29*x_13_16*x_16_17 + 52*x_13_16*x_17_15\n",
      "  + 52*x_13_16*x_17_17 + 35*x_13_16*x_18_15 + 35*x_13_16*x_18_17\n",
      "  + 26*x_13_16*x_19_15 + 26*x_13_16*x_19_17 + 46*x_13_17*x_14_16\n",
      "  + 46*x_13_17*x_14_18 + 47*x_13_17*x_15_16 + 47*x_13_17*x_15_18\n",
      "  + 29*x_13_17*x_16_16 + 29*x_13_17*x_16_18 + 52*x_13_17*x_17_16\n",
      "  + 52*x_13_17*x_17_18 + 35*x_13_17*x_18_16 + 35*x_13_17*x_18_18\n",
      "  + 26*x_13_17*x_19_16 + 26*x_13_17*x_19_18 + 46*x_13_18*x_14_17\n",
      "  + 46*x_13_18*x_14_19 + 47*x_13_18*x_15_17 + 47*x_13_18*x_15_19\n",
      "  + 29*x_13_18*x_16_17 + 29*x_13_18*x_16_19 + 52*x_13_18*x_17_17\n",
      "  + 52*x_13_18*x_17_19 + 35*x_13_18*x_18_17 + 35*x_13_18*x_18_19\n",
      "  + 26*x_13_18*x_19_17 + 26*x_13_18*x_19_19 + 46*x_13_19*x_14_0\n",
      "  + 46*x_13_19*x_14_18 + 47*x_13_19*x_15_0 + 47*x_13_19*x_15_18\n",
      "  + 29*x_13_19*x_16_0 + 29*x_13_19*x_16_18 + 52*x_13_19*x_17_0\n",
      "  + 52*x_13_19*x_17_18 + 35*x_13_19*x_18_0 + 35*x_13_19*x_18_18\n",
      "  + 26*x_13_19*x_19_0 + 26*x_13_19*x_19_18 + 46*x_13_2*x_14_1 + 46*x_13_2*x_14_3\n",
      "  + 47*x_13_2*x_15_1 + 47*x_13_2*x_15_3 + 29*x_13_2*x_16_1 + 29*x_13_2*x_16_3\n",
      "  + 52*x_13_2*x_17_1 + 52*x_13_2*x_17_3 + 35*x_13_2*x_18_1 + 35*x_13_2*x_18_3\n",
      "  + 26*x_13_2*x_19_1 + 26*x_13_2*x_19_3 + 46*x_13_3*x_14_2 + 46*x_13_3*x_14_4\n",
      "  + 47*x_13_3*x_15_2 + 47*x_13_3*x_15_4 + 29*x_13_3*x_16_2 + 29*x_13_3*x_16_4\n",
      "  + 52*x_13_3*x_17_2 + 52*x_13_3*x_17_4 + 35*x_13_3*x_18_2 + 35*x_13_3*x_18_4\n",
      "  + 26*x_13_3*x_19_2 + 26*x_13_3*x_19_4 + 46*x_13_4*x_14_3 + 46*x_13_4*x_14_5\n",
      "  + 47*x_13_4*x_15_3 + 47*x_13_4*x_15_5 + 29*x_13_4*x_16_3 + 29*x_13_4*x_16_5\n",
      "  + 52*x_13_4*x_17_3 + 52*x_13_4*x_17_5 + 35*x_13_4*x_18_3 + 35*x_13_4*x_18_5\n",
      "  + 26*x_13_4*x_19_3 + 26*x_13_4*x_19_5 + 46*x_13_5*x_14_4 + 46*x_13_5*x_14_6\n",
      "  + 47*x_13_5*x_15_4 + 47*x_13_5*x_15_6 + 29*x_13_5*x_16_4 + 29*x_13_5*x_16_6\n",
      "  + 52*x_13_5*x_17_4 + 52*x_13_5*x_17_6 + 35*x_13_5*x_18_4 + 35*x_13_5*x_18_6\n",
      "  + 26*x_13_5*x_19_4 + 26*x_13_5*x_19_6 + 46*x_13_6*x_14_5 + 46*x_13_6*x_14_7\n",
      "  + 47*x_13_6*x_15_5 + 47*x_13_6*x_15_7 + 29*x_13_6*x_16_5 + 29*x_13_6*x_16_7\n",
      "  + 52*x_13_6*x_17_5 + 52*x_13_6*x_17_7 + 35*x_13_6*x_18_5 + 35*x_13_6*x_18_7\n",
      "  + 26*x_13_6*x_19_5 + 26*x_13_6*x_19_7 + 46*x_13_7*x_14_6 + 46*x_13_7*x_14_8\n",
      "  + 47*x_13_7*x_15_6 + 47*x_13_7*x_15_8 + 29*x_13_7*x_16_6 + 29*x_13_7*x_16_8\n",
      "  + 52*x_13_7*x_17_6 + 52*x_13_7*x_17_8 + 35*x_13_7*x_18_6 + 35*x_13_7*x_18_8\n",
      "  + 26*x_13_7*x_19_6 + 26*x_13_7*x_19_8 + 46*x_13_8*x_14_7 + 46*x_13_8*x_14_9\n",
      "  + 47*x_13_8*x_15_7 + 47*x_13_8*x_15_9 + 29*x_13_8*x_16_7 + 29*x_13_8*x_16_9\n",
      "  + 52*x_13_8*x_17_7 + 52*x_13_8*x_17_9 + 35*x_13_8*x_18_7 + 35*x_13_8*x_18_9\n",
      "  + 26*x_13_8*x_19_7 + 26*x_13_8*x_19_9 + 46*x_13_9*x_14_10 + 46*x_13_9*x_14_8\n",
      "  + 47*x_13_9*x_15_10 + 47*x_13_9*x_15_8 + 29*x_13_9*x_16_10 + 29*x_13_9*x_16_8\n",
      "  + 52*x_13_9*x_17_10 + 52*x_13_9*x_17_8 + 35*x_13_9*x_18_10 + 35*x_13_9*x_18_8\n",
      "  + 26*x_13_9*x_19_10 + 26*x_13_9*x_19_8 + 24*x_14_0*x_15_1 + 24*x_14_0*x_15_19\n",
      "  + 74*x_14_0*x_16_1 + 74*x_14_0*x_16_19 + 40*x_14_0*x_17_1 + 40*x_14_0*x_17_19\n",
      "  + 27*x_14_0*x_18_1 + 27*x_14_0*x_18_19 + 63*x_14_0*x_19_1 + 63*x_14_0*x_19_19\n",
      "  + 24*x_14_1*x_15_0 + 24*x_14_1*x_15_2 + 74*x_14_1*x_16_0 + 74*x_14_1*x_16_2\n",
      "  + 40*x_14_1*x_17_0 + 40*x_14_1*x_17_2 + 27*x_14_1*x_18_0 + 27*x_14_1*x_18_2\n",
      "  + 63*x_14_1*x_19_0 + 63*x_14_1*x_19_2 + 24*x_14_10*x_15_11 + 24*x_14_10*x_15_9\n",
      "  + 74*x_14_10*x_16_11 + 74*x_14_10*x_16_9 + 40*x_14_10*x_17_11\n",
      "  + 40*x_14_10*x_17_9 + 27*x_14_10*x_18_11 + 27*x_14_10*x_18_9\n",
      "  + 63*x_14_10*x_19_11 + 63*x_14_10*x_19_9 + 24*x_14_11*x_15_10\n",
      "  + 24*x_14_11*x_15_12 + 74*x_14_11*x_16_10 + 74*x_14_11*x_16_12\n",
      "  + 40*x_14_11*x_17_10 + 40*x_14_11*x_17_12 + 27*x_14_11*x_18_10\n",
      "  + 27*x_14_11*x_18_12 + 63*x_14_11*x_19_10 + 63*x_14_11*x_19_12\n",
      "  + 24*x_14_12*x_15_11 + 24*x_14_12*x_15_13 + 74*x_14_12*x_16_11\n",
      "  + 74*x_14_12*x_16_13 + 40*x_14_12*x_17_11 + 40*x_14_12*x_17_13\n",
      "  + 27*x_14_12*x_18_11 + 27*x_14_12*x_18_13 + 63*x_14_12*x_19_11\n",
      "  + 63*x_14_12*x_19_13 + 24*x_14_13*x_15_12 + 24*x_14_13*x_15_14\n",
      "  + 74*x_14_13*x_16_12 + 74*x_14_13*x_16_14 + 40*x_14_13*x_17_12\n",
      "  + 40*x_14_13*x_17_14 + 27*x_14_13*x_18_12 + 27*x_14_13*x_18_14\n",
      "  + 63*x_14_13*x_19_12 + 63*x_14_13*x_19_14 + 24*x_14_14*x_15_13\n",
      "  + 24*x_14_14*x_15_15 + 74*x_14_14*x_16_13 + 74*x_14_14*x_16_15\n",
      "  + 40*x_14_14*x_17_13 + 40*x_14_14*x_17_15 + 27*x_14_14*x_18_13\n",
      "  + 27*x_14_14*x_18_15 + 63*x_14_14*x_19_13 + 63*x_14_14*x_19_15\n",
      "  + 24*x_14_15*x_15_14 + 24*x_14_15*x_15_16 + 74*x_14_15*x_16_14\n",
      "  + 74*x_14_15*x_16_16 + 40*x_14_15*x_17_14 + 40*x_14_15*x_17_16\n",
      "  + 27*x_14_15*x_18_14 + 27*x_14_15*x_18_16 + 63*x_14_15*x_19_14\n",
      "  + 63*x_14_15*x_19_16 + 24*x_14_16*x_15_15 + 24*x_14_16*x_15_17\n",
      "  + 74*x_14_16*x_16_15 + 74*x_14_16*x_16_17 + 40*x_14_16*x_17_15\n",
      "  + 40*x_14_16*x_17_17 + 27*x_14_16*x_18_15 + 27*x_14_16*x_18_17\n",
      "  + 63*x_14_16*x_19_15 + 63*x_14_16*x_19_17 + 24*x_14_17*x_15_16\n",
      "  + 24*x_14_17*x_15_18 + 74*x_14_17*x_16_16 + 74*x_14_17*x_16_18\n",
      "  + 40*x_14_17*x_17_16 + 40*x_14_17*x_17_18 + 27*x_14_17*x_18_16\n",
      "  + 27*x_14_17*x_18_18 + 63*x_14_17*x_19_16 + 63*x_14_17*x_19_18\n",
      "  + 24*x_14_18*x_15_17 + 24*x_14_18*x_15_19 + 74*x_14_18*x_16_17\n",
      "  + 74*x_14_18*x_16_19 + 40*x_14_18*x_17_17 + 40*x_14_18*x_17_19\n",
      "  + 27*x_14_18*x_18_17 + 27*x_14_18*x_18_19 + 63*x_14_18*x_19_17\n",
      "  + 63*x_14_18*x_19_19 + 24*x_14_19*x_15_0 + 24*x_14_19*x_15_18\n",
      "  + 74*x_14_19*x_16_0 + 74*x_14_19*x_16_18 + 40*x_14_19*x_17_0\n",
      "  + 40*x_14_19*x_17_18 + 27*x_14_19*x_18_0 + 27*x_14_19*x_18_18\n",
      "  + 63*x_14_19*x_19_0 + 63*x_14_19*x_19_18 + 24*x_14_2*x_15_1 + 24*x_14_2*x_15_3\n",
      "  + 74*x_14_2*x_16_1 + 74*x_14_2*x_16_3 + 40*x_14_2*x_17_1 + 40*x_14_2*x_17_3\n",
      "  + 27*x_14_2*x_18_1 + 27*x_14_2*x_18_3 + 63*x_14_2*x_19_1 + 63*x_14_2*x_19_3\n",
      "  + 24*x_14_3*x_15_2 + 24*x_14_3*x_15_4 + 74*x_14_3*x_16_2 + 74*x_14_3*x_16_4\n",
      "  + 40*x_14_3*x_17_2 + 40*x_14_3*x_17_4 + 27*x_14_3*x_18_2 + 27*x_14_3*x_18_4\n",
      "  + 63*x_14_3*x_19_2 + 63*x_14_3*x_19_4 + 24*x_14_4*x_15_3 + 24*x_14_4*x_15_5\n",
      "  + 74*x_14_4*x_16_3 + 74*x_14_4*x_16_5 + 40*x_14_4*x_17_3 + 40*x_14_4*x_17_5\n",
      "  + 27*x_14_4*x_18_3 + 27*x_14_4*x_18_5 + 63*x_14_4*x_19_3 + 63*x_14_4*x_19_5\n",
      "  + 24*x_14_5*x_15_4 + 24*x_14_5*x_15_6 + 74*x_14_5*x_16_4 + 74*x_14_5*x_16_6\n",
      "  + 40*x_14_5*x_17_4 + 40*x_14_5*x_17_6 + 27*x_14_5*x_18_4 + 27*x_14_5*x_18_6\n",
      "  + 63*x_14_5*x_19_4 + 63*x_14_5*x_19_6 + 24*x_14_6*x_15_5 + 24*x_14_6*x_15_7\n",
      "  + 74*x_14_6*x_16_5 + 74*x_14_6*x_16_7 + 40*x_14_6*x_17_5 + 40*x_14_6*x_17_7\n",
      "  + 27*x_14_6*x_18_5 + 27*x_14_6*x_18_7 + 63*x_14_6*x_19_5 + 63*x_14_6*x_19_7\n",
      "  + 24*x_14_7*x_15_6 + 24*x_14_7*x_15_8 + 74*x_14_7*x_16_6 + 74*x_14_7*x_16_8\n",
      "  + 40*x_14_7*x_17_6 + 40*x_14_7*x_17_8 + 27*x_14_7*x_18_6 + 27*x_14_7*x_18_8\n",
      "  + 63*x_14_7*x_19_6 + 63*x_14_7*x_19_8 + 24*x_14_8*x_15_7 + 24*x_14_8*x_15_9\n",
      "  + 74*x_14_8*x_16_7 + 74*x_14_8*x_16_9 + 40*x_14_8*x_17_7 + 40*x_14_8*x_17_9\n",
      "  + 27*x_14_8*x_18_7 + 27*x_14_8*x_18_9 + 63*x_14_8*x_19_7 + 63*x_14_8*x_19_9\n",
      "  + 24*x_14_9*x_15_10 + 24*x_14_9*x_15_8 + 74*x_14_9*x_16_10 + 74*x_14_9*x_16_8\n",
      "  + 40*x_14_9*x_17_10 + 40*x_14_9*x_17_8 + 27*x_14_9*x_18_10 + 27*x_14_9*x_18_8\n",
      "  + 63*x_14_9*x_19_10 + 63*x_14_9*x_19_8 + 70*x_15_0*x_16_1 + 70*x_15_0*x_16_19\n",
      "  + 62*x_15_0*x_17_1 + 62*x_15_0*x_17_19 + 12*x_15_0*x_18_1 + 12*x_15_0*x_18_19\n",
      "  + 70*x_15_0*x_19_1 + 70*x_15_0*x_19_19 + 70*x_15_1*x_16_0 + 70*x_15_1*x_16_2\n",
      "  + 62*x_15_1*x_17_0 + 62*x_15_1*x_17_2 + 12*x_15_1*x_18_0 + 12*x_15_1*x_18_2\n",
      "  + 70*x_15_1*x_19_0 + 70*x_15_1*x_19_2 + 70*x_15_10*x_16_11 + 70*x_15_10*x_16_9\n",
      "  + 62*x_15_10*x_17_11 + 62*x_15_10*x_17_9 + 12*x_15_10*x_18_11\n",
      "  + 12*x_15_10*x_18_9 + 70*x_15_10*x_19_11 + 70*x_15_10*x_19_9\n",
      "  + 70*x_15_11*x_16_10 + 70*x_15_11*x_16_12 + 62*x_15_11*x_17_10\n",
      "  + 62*x_15_11*x_17_12 + 12*x_15_11*x_18_10 + 12*x_15_11*x_18_12\n",
      "  + 70*x_15_11*x_19_10 + 70*x_15_11*x_19_12 + 70*x_15_12*x_16_11\n",
      "  + 70*x_15_12*x_16_13 + 62*x_15_12*x_17_11 + 62*x_15_12*x_17_13\n",
      "  + 12*x_15_12*x_18_11 + 12*x_15_12*x_18_13 + 70*x_15_12*x_19_11\n",
      "  + 70*x_15_12*x_19_13 + 70*x_15_13*x_16_12 + 70*x_15_13*x_16_14\n",
      "  + 62*x_15_13*x_17_12 + 62*x_15_13*x_17_14 + 12*x_15_13*x_18_12\n",
      "  + 12*x_15_13*x_18_14 + 70*x_15_13*x_19_12 + 70*x_15_13*x_19_14\n",
      "  + 70*x_15_14*x_16_13 + 70*x_15_14*x_16_15 + 62*x_15_14*x_17_13\n",
      "  + 62*x_15_14*x_17_15 + 12*x_15_14*x_18_13 + 12*x_15_14*x_18_15\n",
      "  + 70*x_15_14*x_19_13 + 70*x_15_14*x_19_15 + 70*x_15_15*x_16_14\n",
      "  + 70*x_15_15*x_16_16 + 62*x_15_15*x_17_14 + 62*x_15_15*x_17_16\n",
      "  + 12*x_15_15*x_18_14 + 12*x_15_15*x_18_16 + 70*x_15_15*x_19_14\n",
      "  + 70*x_15_15*x_19_16 + 70*x_15_16*x_16_15 + 70*x_15_16*x_16_17\n",
      "  + 62*x_15_16*x_17_15 + 62*x_15_16*x_17_17 + 12*x_15_16*x_18_15\n",
      "  + 12*x_15_16*x_18_17 + 70*x_15_16*x_19_15 + 70*x_15_16*x_19_17\n",
      "  + 70*x_15_17*x_16_16 + 70*x_15_17*x_16_18 + 62*x_15_17*x_17_16\n",
      "  + 62*x_15_17*x_17_18 + 12*x_15_17*x_18_16 + 12*x_15_17*x_18_18\n",
      "  + 70*x_15_17*x_19_16 + 70*x_15_17*x_19_18 + 70*x_15_18*x_16_17\n",
      "  + 70*x_15_18*x_16_19 + 62*x_15_18*x_17_17 + 62*x_15_18*x_17_19\n",
      "  + 12*x_15_18*x_18_17 + 12*x_15_18*x_18_19 + 70*x_15_18*x_19_17\n",
      "  + 70*x_15_18*x_19_19 + 70*x_15_19*x_16_0 + 70*x_15_19*x_16_18\n",
      "  + 62*x_15_19*x_17_0 + 62*x_15_19*x_17_18 + 12*x_15_19*x_18_0\n",
      "  + 12*x_15_19*x_18_18 + 70*x_15_19*x_19_0 + 70*x_15_19*x_19_18\n",
      "  + 70*x_15_2*x_16_1 + 70*x_15_2*x_16_3 + 62*x_15_2*x_17_1 + 62*x_15_2*x_17_3\n",
      "  + 12*x_15_2*x_18_1 + 12*x_15_2*x_18_3 + 70*x_15_2*x_19_1 + 70*x_15_2*x_19_3\n",
      "  + 70*x_15_3*x_16_2 + 70*x_15_3*x_16_4 + 62*x_15_3*x_17_2 + 62*x_15_3*x_17_4\n",
      "  + 12*x_15_3*x_18_2 + 12*x_15_3*x_18_4 + 70*x_15_3*x_19_2 + 70*x_15_3*x_19_4\n",
      "  + 70*x_15_4*x_16_3 + 70*x_15_4*x_16_5 + 62*x_15_4*x_17_3 + 62*x_15_4*x_17_5\n",
      "  + 12*x_15_4*x_18_3 + 12*x_15_4*x_18_5 + 70*x_15_4*x_19_3 + 70*x_15_4*x_19_5\n",
      "  + 70*x_15_5*x_16_4 + 70*x_15_5*x_16_6 + 62*x_15_5*x_17_4 + 62*x_15_5*x_17_6\n",
      "  + 12*x_15_5*x_18_4 + 12*x_15_5*x_18_6 + 70*x_15_5*x_19_4 + 70*x_15_5*x_19_6\n",
      "  + 70*x_15_6*x_16_5 + 70*x_15_6*x_16_7 + 62*x_15_6*x_17_5 + 62*x_15_6*x_17_7\n",
      "  + 12*x_15_6*x_18_5 + 12*x_15_6*x_18_7 + 70*x_15_6*x_19_5 + 70*x_15_6*x_19_7\n",
      "  + 70*x_15_7*x_16_6 + 70*x_15_7*x_16_8 + 62*x_15_7*x_17_6 + 62*x_15_7*x_17_8\n",
      "  + 12*x_15_7*x_18_6 + 12*x_15_7*x_18_8 + 70*x_15_7*x_19_6 + 70*x_15_7*x_19_8\n",
      "  + 70*x_15_8*x_16_7 + 70*x_15_8*x_16_9 + 62*x_15_8*x_17_7 + 62*x_15_8*x_17_9\n",
      "  + 12*x_15_8*x_18_7 + 12*x_15_8*x_18_9 + 70*x_15_8*x_19_7 + 70*x_15_8*x_19_9\n",
      "  + 70*x_15_9*x_16_10 + 70*x_15_9*x_16_8 + 62*x_15_9*x_17_10 + 62*x_15_9*x_17_8\n",
      "  + 12*x_15_9*x_18_10 + 12*x_15_9*x_18_8 + 70*x_15_9*x_19_10 + 70*x_15_9*x_19_8\n",
      "  + 78*x_16_0*x_17_1 + 78*x_16_0*x_17_19 + 58*x_16_0*x_18_1 + 58*x_16_0*x_18_19\n",
      "  + 35*x_16_0*x_19_1 + 35*x_16_0*x_19_19 + 78*x_16_1*x_17_0 + 78*x_16_1*x_17_2\n",
      "  + 58*x_16_1*x_18_0 + 58*x_16_1*x_18_2 + 35*x_16_1*x_19_0 + 35*x_16_1*x_19_2\n",
      "  + 78*x_16_10*x_17_11 + 78*x_16_10*x_17_9 + 58*x_16_10*x_18_11\n",
      "  + 58*x_16_10*x_18_9 + 35*x_16_10*x_19_11 + 35*x_16_10*x_19_9\n",
      "  + 78*x_16_11*x_17_10 + 78*x_16_11*x_17_12 + 58*x_16_11*x_18_10\n",
      "  + 58*x_16_11*x_18_12 + 35*x_16_11*x_19_10 + 35*x_16_11*x_19_12\n",
      "  + 78*x_16_12*x_17_11 + 78*x_16_12*x_17_13 + 58*x_16_12*x_18_11\n",
      "  + 58*x_16_12*x_18_13 + 35*x_16_12*x_19_11 + 35*x_16_12*x_19_13\n",
      "  + 78*x_16_13*x_17_12 + 78*x_16_13*x_17_14 + 58*x_16_13*x_18_12\n",
      "  + 58*x_16_13*x_18_14 + 35*x_16_13*x_19_12 + 35*x_16_13*x_19_14\n",
      "  + 78*x_16_14*x_17_13 + 78*x_16_14*x_17_15 + 58*x_16_14*x_18_13\n",
      "  + 58*x_16_14*x_18_15 + 35*x_16_14*x_19_13 + 35*x_16_14*x_19_15\n",
      "  + 78*x_16_15*x_17_14 + 78*x_16_15*x_17_16 + 58*x_16_15*x_18_14\n",
      "  + 58*x_16_15*x_18_16 + 35*x_16_15*x_19_14 + 35*x_16_15*x_19_16\n",
      "  + 78*x_16_16*x_17_15 + 78*x_16_16*x_17_17 + 58*x_16_16*x_18_15\n",
      "  + 58*x_16_16*x_18_17 + 35*x_16_16*x_19_15 + 35*x_16_16*x_19_17\n",
      "  + 78*x_16_17*x_17_16 + 78*x_16_17*x_17_18 + 58*x_16_17*x_18_16\n",
      "  + 58*x_16_17*x_18_18 + 35*x_16_17*x_19_16 + 35*x_16_17*x_19_18\n",
      "  + 78*x_16_18*x_17_17 + 78*x_16_18*x_17_19 + 58*x_16_18*x_18_17\n",
      "  + 58*x_16_18*x_18_19 + 35*x_16_18*x_19_17 + 35*x_16_18*x_19_19\n",
      "  + 78*x_16_19*x_17_0 + 78*x_16_19*x_17_18 + 58*x_16_19*x_18_0\n",
      "  + 58*x_16_19*x_18_18 + 35*x_16_19*x_19_0 + 35*x_16_19*x_19_18\n",
      "  + 78*x_16_2*x_17_1 + 78*x_16_2*x_17_3 + 58*x_16_2*x_18_1 + 58*x_16_2*x_18_3\n",
      "  + 35*x_16_2*x_19_1 + 35*x_16_2*x_19_3 + 78*x_16_3*x_17_2 + 78*x_16_3*x_17_4\n",
      "  + 58*x_16_3*x_18_2 + 58*x_16_3*x_18_4 + 35*x_16_3*x_19_2 + 35*x_16_3*x_19_4\n",
      "  + 78*x_16_4*x_17_3 + 78*x_16_4*x_17_5 + 58*x_16_4*x_18_3 + 58*x_16_4*x_18_5\n",
      "  + 35*x_16_4*x_19_3 + 35*x_16_4*x_19_5 + 78*x_16_5*x_17_4 + 78*x_16_5*x_17_6\n",
      "  + 58*x_16_5*x_18_4 + 58*x_16_5*x_18_6 + 35*x_16_5*x_19_4 + 35*x_16_5*x_19_6\n",
      "  + 78*x_16_6*x_17_5 + 78*x_16_6*x_17_7 + 58*x_16_6*x_18_5 + 58*x_16_6*x_18_7\n",
      "  + 35*x_16_6*x_19_5 + 35*x_16_6*x_19_7 + 78*x_16_7*x_17_6 + 78*x_16_7*x_17_8\n",
      "  + 58*x_16_7*x_18_6 + 58*x_16_7*x_18_8 + 35*x_16_7*x_19_6 + 35*x_16_7*x_19_8\n",
      "  + 78*x_16_8*x_17_7 + 78*x_16_8*x_17_9 + 58*x_16_8*x_18_7 + 58*x_16_8*x_18_9\n",
      "  + 35*x_16_8*x_19_7 + 35*x_16_8*x_19_9 + 78*x_16_9*x_17_10 + 78*x_16_9*x_17_8\n",
      "  + 58*x_16_9*x_18_10 + 58*x_16_9*x_18_8 + 35*x_16_9*x_19_10 + 35*x_16_9*x_19_8\n",
      "  + 59*x_17_0*x_18_1 + 59*x_17_0*x_18_19 + 50*x_17_0*x_19_1 + 50*x_17_0*x_19_19\n",
      "  + 59*x_17_1*x_18_0 + 59*x_17_1*x_18_2 + 50*x_17_1*x_19_0 + 50*x_17_1*x_19_2\n",
      "  + 59*x_17_10*x_18_11 + 59*x_17_10*x_18_9 + 50*x_17_10*x_19_11\n",
      "  + 50*x_17_10*x_19_9 + 59*x_17_11*x_18_10 + 59*x_17_11*x_18_12\n",
      "  + 50*x_17_11*x_19_10 + 50*x_17_11*x_19_12 + 59*x_17_12*x_18_11\n",
      "  + 59*x_17_12*x_18_13 + 50*x_17_12*x_19_11 + 50*x_17_12*x_19_13\n",
      "  + 59*x_17_13*x_18_12 + 59*x_17_13*x_18_14 + 50*x_17_13*x_19_12\n",
      "  + 50*x_17_13*x_19_14 + 59*x_17_14*x_18_13 + 59*x_17_14*x_18_15\n",
      "  + 50*x_17_14*x_19_13 + 50*x_17_14*x_19_15 + 59*x_17_15*x_18_14\n",
      "  + 59*x_17_15*x_18_16 + 50*x_17_15*x_19_14 + 50*x_17_15*x_19_16\n",
      "  + 59*x_17_16*x_18_15 + 59*x_17_16*x_18_17 + 50*x_17_16*x_19_15\n",
      "  + 50*x_17_16*x_19_17 + 59*x_17_17*x_18_16 + 59*x_17_17*x_18_18\n",
      "  + 50*x_17_17*x_19_16 + 50*x_17_17*x_19_18 + 59*x_17_18*x_18_17\n",
      "  + 59*x_17_18*x_18_19 + 50*x_17_18*x_19_17 + 50*x_17_18*x_19_19\n",
      "  + 59*x_17_19*x_18_0 + 59*x_17_19*x_18_18 + 50*x_17_19*x_19_0\n",
      "  + 50*x_17_19*x_19_18 + 59*x_17_2*x_18_1 + 59*x_17_2*x_18_3 + 50*x_17_2*x_19_1\n",
      "  + 50*x_17_2*x_19_3 + 59*x_17_3*x_18_2 + 59*x_17_3*x_18_4 + 50*x_17_3*x_19_2\n",
      "  + 50*x_17_3*x_19_4 + 59*x_17_4*x_18_3 + 59*x_17_4*x_18_5 + 50*x_17_4*x_19_3\n",
      "  + 50*x_17_4*x_19_5 + 59*x_17_5*x_18_4 + 59*x_17_5*x_18_6 + 50*x_17_5*x_19_4\n",
      "  + 50*x_17_5*x_19_6 + 59*x_17_6*x_18_5 + 59*x_17_6*x_18_7 + 50*x_17_6*x_19_5\n",
      "  + 50*x_17_6*x_19_7 + 59*x_17_7*x_18_6 + 59*x_17_7*x_18_8 + 50*x_17_7*x_19_6\n",
      "  + 50*x_17_7*x_19_8 + 59*x_17_8*x_18_7 + 59*x_17_8*x_18_9 + 50*x_17_8*x_19_7\n",
      "  + 50*x_17_8*x_19_9 + 59*x_17_9*x_18_10 + 59*x_17_9*x_18_8 + 50*x_17_9*x_19_10\n",
      "  + 50*x_17_9*x_19_8 + 60*x_18_0*x_19_1 + 60*x_18_0*x_19_19 + 60*x_18_1*x_19_0\n",
      "  + 60*x_18_1*x_19_2 + 60*x_18_10*x_19_11 + 60*x_18_10*x_19_9\n",
      "  + 60*x_18_11*x_19_10 + 60*x_18_11*x_19_12 + 60*x_18_12*x_19_11\n",
      "  + 60*x_18_12*x_19_13 + 60*x_18_13*x_19_12 + 60*x_18_13*x_19_14\n",
      "  + 60*x_18_14*x_19_13 + 60*x_18_14*x_19_15 + 60*x_18_15*x_19_14\n",
      "  + 60*x_18_15*x_19_16 + 60*x_18_16*x_19_15 + 60*x_18_16*x_19_17\n",
      "  + 60*x_18_17*x_19_16 + 60*x_18_17*x_19_18 + 60*x_18_18*x_19_17\n",
      "  + 60*x_18_18*x_19_19 + 60*x_18_19*x_19_0 + 60*x_18_19*x_19_18\n",
      "  + 60*x_18_2*x_19_1 + 60*x_18_2*x_19_3 + 60*x_18_3*x_19_2 + 60*x_18_3*x_19_4\n",
      "  + 60*x_18_4*x_19_3 + 60*x_18_4*x_19_5 + 60*x_18_5*x_19_4 + 60*x_18_5*x_19_6\n",
      "  + 60*x_18_6*x_19_5 + 60*x_18_6*x_19_7 + 60*x_18_7*x_19_6 + 60*x_18_7*x_19_8\n",
      "  + 60*x_18_8*x_19_7 + 60*x_18_8*x_19_9 + 60*x_18_9*x_19_10 + 60*x_18_9*x_19_8\n",
      "  + 2*x_1_0*x_10_1 + 2*x_1_0*x_10_19 + 49*x_1_0*x_11_1 + 49*x_1_0*x_11_19\n",
      "  + 17*x_1_0*x_12_1 + 17*x_1_0*x_12_19 + 60*x_1_0*x_13_1 + 60*x_1_0*x_13_19\n",
      "  + 83*x_1_0*x_14_1 + 83*x_1_0*x_14_19 + 97*x_1_0*x_15_1 + 97*x_1_0*x_15_19\n",
      "  + 68*x_1_0*x_16_1 + 68*x_1_0*x_16_19 + 52*x_1_0*x_17_1 + 52*x_1_0*x_17_19\n",
      "  + 89*x_1_0*x_18_1 + 89*x_1_0*x_18_19 + 35*x_1_0*x_19_1 + 35*x_1_0*x_19_19\n",
      "  + 63*x_1_0*x_2_1 + 63*x_1_0*x_2_19 + 71*x_1_0*x_3_1 + 71*x_1_0*x_3_19\n",
      "  + 93*x_1_0*x_4_1 + 93*x_1_0*x_4_19 + 30*x_1_0*x_5_1 + 30*x_1_0*x_5_19\n",
      "  + 60*x_1_0*x_6_1 + 60*x_1_0*x_6_19 + 69*x_1_0*x_7_1 + 69*x_1_0*x_7_19\n",
      "  + 71*x_1_0*x_8_1 + 71*x_1_0*x_8_19 + 67*x_1_0*x_9_1 + 67*x_1_0*x_9_19\n",
      "  + 2*x_1_1*x_10_0 + 2*x_1_1*x_10_2 + 49*x_1_1*x_11_0 + 49*x_1_1*x_11_2\n",
      "  + 17*x_1_1*x_12_0 + 17*x_1_1*x_12_2 + 60*x_1_1*x_13_0 + 60*x_1_1*x_13_2\n",
      "  + 83*x_1_1*x_14_0 + 83*x_1_1*x_14_2 + 97*x_1_1*x_15_0 + 97*x_1_1*x_15_2\n",
      "  + 68*x_1_1*x_16_0 + 68*x_1_1*x_16_2 + 52*x_1_1*x_17_0 + 52*x_1_1*x_17_2\n",
      "  + 89*x_1_1*x_18_0 + 89*x_1_1*x_18_2 + 35*x_1_1*x_19_0 + 35*x_1_1*x_19_2\n",
      "  + 63*x_1_1*x_2_0 + 63*x_1_1*x_2_2 + 71*x_1_1*x_3_0 + 71*x_1_1*x_3_2\n",
      "  + 93*x_1_1*x_4_0 + 93*x_1_1*x_4_2 + 30*x_1_1*x_5_0 + 30*x_1_1*x_5_2\n",
      "  + 60*x_1_1*x_6_0 + 60*x_1_1*x_6_2 + 69*x_1_1*x_7_0 + 69*x_1_1*x_7_2\n",
      "  + 71*x_1_1*x_8_0 + 71*x_1_1*x_8_2 + 67*x_1_1*x_9_0 + 67*x_1_1*x_9_2\n",
      "  + 2*x_1_10*x_10_11 + 2*x_1_10*x_10_9 + 49*x_1_10*x_11_11 + 49*x_1_10*x_11_9\n",
      "  + 17*x_1_10*x_12_11 + 17*x_1_10*x_12_9 + 60*x_1_10*x_13_11 + 60*x_1_10*x_13_9\n",
      "  + 83*x_1_10*x_14_11 + 83*x_1_10*x_14_9 + 97*x_1_10*x_15_11 + 97*x_1_10*x_15_9\n",
      "  + 68*x_1_10*x_16_11 + 68*x_1_10*x_16_9 + 52*x_1_10*x_17_11 + 52*x_1_10*x_17_9\n",
      "  + 89*x_1_10*x_18_11 + 89*x_1_10*x_18_9 + 35*x_1_10*x_19_11 + 35*x_1_10*x_19_9\n",
      "  + 63*x_1_10*x_2_11 + 63*x_1_10*x_2_9 + 71*x_1_10*x_3_11 + 71*x_1_10*x_3_9\n",
      "  + 93*x_1_10*x_4_11 + 93*x_1_10*x_4_9 + 30*x_1_10*x_5_11 + 30*x_1_10*x_5_9\n",
      "  + 60*x_1_10*x_6_11 + 60*x_1_10*x_6_9 + 69*x_1_10*x_7_11 + 69*x_1_10*x_7_9\n",
      "  + 71*x_1_10*x_8_11 + 71*x_1_10*x_8_9 + 67*x_1_10*x_9_11 + 67*x_1_10*x_9_9\n",
      "  + 2*x_1_11*x_10_10 + 2*x_1_11*x_10_12 + 49*x_1_11*x_11_10 + 49*x_1_11*x_11_12\n",
      "  + 17*x_1_11*x_12_10 + 17*x_1_11*x_12_12 + 60*x_1_11*x_13_10\n",
      "  + 60*x_1_11*x_13_12 + 83*x_1_11*x_14_10 + 83*x_1_11*x_14_12\n",
      "  + 97*x_1_11*x_15_10 + 97*x_1_11*x_15_12 + 68*x_1_11*x_16_10\n",
      "  + 68*x_1_11*x_16_12 + 52*x_1_11*x_17_10 + 52*x_1_11*x_17_12\n",
      "  + 89*x_1_11*x_18_10 + 89*x_1_11*x_18_12 + 35*x_1_11*x_19_10\n",
      "  + 35*x_1_11*x_19_12 + 63*x_1_11*x_2_10 + 63*x_1_11*x_2_12 + 71*x_1_11*x_3_10\n",
      "  + 71*x_1_11*x_3_12 + 93*x_1_11*x_4_10 + 93*x_1_11*x_4_12 + 30*x_1_11*x_5_10\n",
      "  + 30*x_1_11*x_5_12 + 60*x_1_11*x_6_10 + 60*x_1_11*x_6_12 + 69*x_1_11*x_7_10\n",
      "  + 69*x_1_11*x_7_12 + 71*x_1_11*x_8_10 + 71*x_1_11*x_8_12 + 67*x_1_11*x_9_10\n",
      "  + 67*x_1_11*x_9_12 + 2*x_1_12*x_10_11 + 2*x_1_12*x_10_13 + 49*x_1_12*x_11_11\n",
      "  + 49*x_1_12*x_11_13 + 17*x_1_12*x_12_11 + 17*x_1_12*x_12_13\n",
      "  + 60*x_1_12*x_13_11 + 60*x_1_12*x_13_13 + 83*x_1_12*x_14_11\n",
      "  + 83*x_1_12*x_14_13 + 97*x_1_12*x_15_11 + 97*x_1_12*x_15_13\n",
      "  + 68*x_1_12*x_16_11 + 68*x_1_12*x_16_13 + 52*x_1_12*x_17_11\n",
      "  + 52*x_1_12*x_17_13 + 89*x_1_12*x_18_11 + 89*x_1_12*x_18_13\n",
      "  + 35*x_1_12*x_19_11 + 35*x_1_12*x_19_13 + 63*x_1_12*x_2_11 + 63*x_1_12*x_2_13\n",
      "  + 71*x_1_12*x_3_11 + 71*x_1_12*x_3_13 + 93*x_1_12*x_4_11 + 93*x_1_12*x_4_13\n",
      "  + 30*x_1_12*x_5_11 + 30*x_1_12*x_5_13 + 60*x_1_12*x_6_11 + 60*x_1_12*x_6_13\n",
      "  + 69*x_1_12*x_7_11 + 69*x_1_12*x_7_13 + 71*x_1_12*x_8_11 + 71*x_1_12*x_8_13\n",
      "  + 67*x_1_12*x_9_11 + 67*x_1_12*x_9_13 + 2*x_1_13*x_10_12 + 2*x_1_13*x_10_14\n",
      "  + 49*x_1_13*x_11_12 + 49*x_1_13*x_11_14 + 17*x_1_13*x_12_12\n",
      "  + 17*x_1_13*x_12_14 + 60*x_1_13*x_13_12 + 60*x_1_13*x_13_14\n",
      "  + 83*x_1_13*x_14_12 + 83*x_1_13*x_14_14 + 97*x_1_13*x_15_12\n",
      "  + 97*x_1_13*x_15_14 + 68*x_1_13*x_16_12 + 68*x_1_13*x_16_14\n",
      "  + 52*x_1_13*x_17_12 + 52*x_1_13*x_17_14 + 89*x_1_13*x_18_12\n",
      "  + 89*x_1_13*x_18_14 + 35*x_1_13*x_19_12 + 35*x_1_13*x_19_14 + 63*x_1_13*x_2_12\n",
      "  + 63*x_1_13*x_2_14 + 71*x_1_13*x_3_12 + 71*x_1_13*x_3_14 + 93*x_1_13*x_4_12\n",
      "  + 93*x_1_13*x_4_14 + 30*x_1_13*x_5_12 + 30*x_1_13*x_5_14 + 60*x_1_13*x_6_12\n",
      "  + 60*x_1_13*x_6_14 + 69*x_1_13*x_7_12 + 69*x_1_13*x_7_14 + 71*x_1_13*x_8_12\n",
      "  + 71*x_1_13*x_8_14 + 67*x_1_13*x_9_12 + 67*x_1_13*x_9_14 + 2*x_1_14*x_10_13\n",
      "  + 2*x_1_14*x_10_15 + 49*x_1_14*x_11_13 + 49*x_1_14*x_11_15 + 17*x_1_14*x_12_13\n",
      "  + 17*x_1_14*x_12_15 + 60*x_1_14*x_13_13 + 60*x_1_14*x_13_15\n",
      "  + 83*x_1_14*x_14_13 + 83*x_1_14*x_14_15 + 97*x_1_14*x_15_13\n",
      "  + 97*x_1_14*x_15_15 + 68*x_1_14*x_16_13 + 68*x_1_14*x_16_15\n",
      "  + 52*x_1_14*x_17_13 + 52*x_1_14*x_17_15 + 89*x_1_14*x_18_13\n",
      "  + 89*x_1_14*x_18_15 + 35*x_1_14*x_19_13 + 35*x_1_14*x_19_15 + 63*x_1_14*x_2_13\n",
      "  + 63*x_1_14*x_2_15 + 71*x_1_14*x_3_13 + 71*x_1_14*x_3_15 + 93*x_1_14*x_4_13\n",
      "  + 93*x_1_14*x_4_15 + 30*x_1_14*x_5_13 + 30*x_1_14*x_5_15 + 60*x_1_14*x_6_13\n",
      "  + 60*x_1_14*x_6_15 + 69*x_1_14*x_7_13 + 69*x_1_14*x_7_15 + 71*x_1_14*x_8_13\n",
      "  + 71*x_1_14*x_8_15 + 67*x_1_14*x_9_13 + 67*x_1_14*x_9_15 + 2*x_1_15*x_10_14\n",
      "  + 2*x_1_15*x_10_16 + 49*x_1_15*x_11_14 + 49*x_1_15*x_11_16 + 17*x_1_15*x_12_14\n",
      "  + 17*x_1_15*x_12_16 + 60*x_1_15*x_13_14 + 60*x_1_15*x_13_16\n",
      "  + 83*x_1_15*x_14_14 + 83*x_1_15*x_14_16 + 97*x_1_15*x_15_14\n",
      "  + 97*x_1_15*x_15_16 + 68*x_1_15*x_16_14 + 68*x_1_15*x_16_16\n",
      "  + 52*x_1_15*x_17_14 + 52*x_1_15*x_17_16 + 89*x_1_15*x_18_14\n",
      "  + 89*x_1_15*x_18_16 + 35*x_1_15*x_19_14 + 35*x_1_15*x_19_16 + 63*x_1_15*x_2_14\n",
      "  + 63*x_1_15*x_2_16 + 71*x_1_15*x_3_14 + 71*x_1_15*x_3_16 + 93*x_1_15*x_4_14\n",
      "  + 93*x_1_15*x_4_16 + 30*x_1_15*x_5_14 + 30*x_1_15*x_5_16 + 60*x_1_15*x_6_14\n",
      "  + 60*x_1_15*x_6_16 + 69*x_1_15*x_7_14 + 69*x_1_15*x_7_16 + 71*x_1_15*x_8_14\n",
      "  + 71*x_1_15*x_8_16 + 67*x_1_15*x_9_14 + 67*x_1_15*x_9_16 + 2*x_1_16*x_10_15\n",
      "  + 2*x_1_16*x_10_17 + 49*x_1_16*x_11_15 + 49*x_1_16*x_11_17 + 17*x_1_16*x_12_15\n",
      "  + 17*x_1_16*x_12_17 + 60*x_1_16*x_13_15 + 60*x_1_16*x_13_17\n",
      "  + 83*x_1_16*x_14_15 + 83*x_1_16*x_14_17 + 97*x_1_16*x_15_15\n",
      "  + 97*x_1_16*x_15_17 + 68*x_1_16*x_16_15 + 68*x_1_16*x_16_17\n",
      "  + 52*x_1_16*x_17_15 + 52*x_1_16*x_17_17 + 89*x_1_16*x_18_15\n",
      "  + 89*x_1_16*x_18_17 + 35*x_1_16*x_19_15 + 35*x_1_16*x_19_17 + 63*x_1_16*x_2_15\n",
      "  + 63*x_1_16*x_2_17 + 71*x_1_16*x_3_15 + 71*x_1_16*x_3_17 + 93*x_1_16*x_4_15\n",
      "  + 93*x_1_16*x_4_17 + 30*x_1_16*x_5_15 + 30*x_1_16*x_5_17 + 60*x_1_16*x_6_15\n",
      "  + 60*x_1_16*x_6_17 + 69*x_1_16*x_7_15 + 69*x_1_16*x_7_17 + 71*x_1_16*x_8_15\n",
      "  + 71*x_1_16*x_8_17 + 67*x_1_16*x_9_15 + 67*x_1_16*x_9_17 + 2*x_1_17*x_10_16\n",
      "  + 2*x_1_17*x_10_18 + 49*x_1_17*x_11_16 + 49*x_1_17*x_11_18 + 17*x_1_17*x_12_16\n",
      "  + 17*x_1_17*x_12_18 + 60*x_1_17*x_13_16 + 60*x_1_17*x_13_18\n",
      "  + 83*x_1_17*x_14_16 + 83*x_1_17*x_14_18 + 97*x_1_17*x_15_16\n",
      "  + 97*x_1_17*x_15_18 + 68*x_1_17*x_16_16 + 68*x_1_17*x_16_18\n",
      "  + 52*x_1_17*x_17_16 + 52*x_1_17*x_17_18 + 89*x_1_17*x_18_16\n",
      "  + 89*x_1_17*x_18_18 + 35*x_1_17*x_19_16 + 35*x_1_17*x_19_18 + 63*x_1_17*x_2_16\n",
      "  + 63*x_1_17*x_2_18 + 71*x_1_17*x_3_16 + 71*x_1_17*x_3_18 + 93*x_1_17*x_4_16\n",
      "  + 93*x_1_17*x_4_18 + 30*x_1_17*x_5_16 + 30*x_1_17*x_5_18 + 60*x_1_17*x_6_16\n",
      "  + 60*x_1_17*x_6_18 + 69*x_1_17*x_7_16 + 69*x_1_17*x_7_18 + 71*x_1_17*x_8_16\n",
      "  + 71*x_1_17*x_8_18 + 67*x_1_17*x_9_16 + 67*x_1_17*x_9_18 + 2*x_1_18*x_10_17\n",
      "  + 2*x_1_18*x_10_19 + 49*x_1_18*x_11_17 + 49*x_1_18*x_11_19 + 17*x_1_18*x_12_17\n",
      "  + 17*x_1_18*x_12_19 + 60*x_1_18*x_13_17 + 60*x_1_18*x_13_19\n",
      "  + 83*x_1_18*x_14_17 + 83*x_1_18*x_14_19 + 97*x_1_18*x_15_17\n",
      "  + 97*x_1_18*x_15_19 + 68*x_1_18*x_16_17 + 68*x_1_18*x_16_19\n",
      "  + 52*x_1_18*x_17_17 + 52*x_1_18*x_17_19 + 89*x_1_18*x_18_17\n",
      "  + 89*x_1_18*x_18_19 + 35*x_1_18*x_19_17 + 35*x_1_18*x_19_19 + 63*x_1_18*x_2_17\n",
      "  + 63*x_1_18*x_2_19 + 71*x_1_18*x_3_17 + 71*x_1_18*x_3_19 + 93*x_1_18*x_4_17\n",
      "  + 93*x_1_18*x_4_19 + 30*x_1_18*x_5_17 + 30*x_1_18*x_5_19 + 60*x_1_18*x_6_17\n",
      "  + 60*x_1_18*x_6_19 + 69*x_1_18*x_7_17 + 69*x_1_18*x_7_19 + 71*x_1_18*x_8_17\n",
      "  + 71*x_1_18*x_8_19 + 67*x_1_18*x_9_17 + 67*x_1_18*x_9_19 + 2*x_1_19*x_10_0\n",
      "  + 2*x_1_19*x_10_18 + 49*x_1_19*x_11_0 + 49*x_1_19*x_11_18 + 17*x_1_19*x_12_0\n",
      "  + 17*x_1_19*x_12_18 + 60*x_1_19*x_13_0 + 60*x_1_19*x_13_18 + 83*x_1_19*x_14_0\n",
      "  + 83*x_1_19*x_14_18 + 97*x_1_19*x_15_0 + 97*x_1_19*x_15_18 + 68*x_1_19*x_16_0\n",
      "  + 68*x_1_19*x_16_18 + 52*x_1_19*x_17_0 + 52*x_1_19*x_17_18 + 89*x_1_19*x_18_0\n",
      "  + 89*x_1_19*x_18_18 + 35*x_1_19*x_19_0 + 35*x_1_19*x_19_18 + 63*x_1_19*x_2_0\n",
      "  + 63*x_1_19*x_2_18 + 71*x_1_19*x_3_0 + 71*x_1_19*x_3_18 + 93*x_1_19*x_4_0\n",
      "  + 93*x_1_19*x_4_18 + 30*x_1_19*x_5_0 + 30*x_1_19*x_5_18 + 60*x_1_19*x_6_0\n",
      "  + 60*x_1_19*x_6_18 + 69*x_1_19*x_7_0 + 69*x_1_19*x_7_18 + 71*x_1_19*x_8_0\n",
      "  + 71*x_1_19*x_8_18 + 67*x_1_19*x_9_0 + 67*x_1_19*x_9_18 + 2*x_1_2*x_10_1\n",
      "  + 2*x_1_2*x_10_3 + 49*x_1_2*x_11_1 + 49*x_1_2*x_11_3 + 17*x_1_2*x_12_1\n",
      "  + 17*x_1_2*x_12_3 + 60*x_1_2*x_13_1 + 60*x_1_2*x_13_3 + 83*x_1_2*x_14_1\n",
      "  + 83*x_1_2*x_14_3 + 97*x_1_2*x_15_1 + 97*x_1_2*x_15_3 + 68*x_1_2*x_16_1\n",
      "  + 68*x_1_2*x_16_3 + 52*x_1_2*x_17_1 + 52*x_1_2*x_17_3 + 89*x_1_2*x_18_1\n",
      "  + 89*x_1_2*x_18_3 + 35*x_1_2*x_19_1 + 35*x_1_2*x_19_3 + 63*x_1_2*x_2_1\n",
      "  + 63*x_1_2*x_2_3 + 71*x_1_2*x_3_1 + 71*x_1_2*x_3_3 + 93*x_1_2*x_4_1\n",
      "  + 93*x_1_2*x_4_3 + 30*x_1_2*x_5_1 + 30*x_1_2*x_5_3 + 60*x_1_2*x_6_1\n",
      "  + 60*x_1_2*x_6_3 + 69*x_1_2*x_7_1 + 69*x_1_2*x_7_3 + 71*x_1_2*x_8_1\n",
      "  + 71*x_1_2*x_8_3 + 67*x_1_2*x_9_1 + 67*x_1_2*x_9_3 + 2*x_1_3*x_10_2\n",
      "  + 2*x_1_3*x_10_4 + 49*x_1_3*x_11_2 + 49*x_1_3*x_11_4 + 17*x_1_3*x_12_2\n",
      "  + 17*x_1_3*x_12_4 + 60*x_1_3*x_13_2 + 60*x_1_3*x_13_4 + 83*x_1_3*x_14_2\n",
      "  + 83*x_1_3*x_14_4 + 97*x_1_3*x_15_2 + 97*x_1_3*x_15_4 + 68*x_1_3*x_16_2\n",
      "  + 68*x_1_3*x_16_4 + 52*x_1_3*x_17_2 + 52*x_1_3*x_17_4 + 89*x_1_3*x_18_2\n",
      "  + 89*x_1_3*x_18_4 + 35*x_1_3*x_19_2 + 35*x_1_3*x_19_4 + 63*x_1_3*x_2_2\n",
      "  + 63*x_1_3*x_2_4 + 71*x_1_3*x_3_2 + 71*x_1_3*x_3_4 + 93*x_1_3*x_4_2\n",
      "  + 93*x_1_3*x_4_4 + 30*x_1_3*x_5_2 + 30*x_1_3*x_5_4 + 60*x_1_3*x_6_2\n",
      "  + 60*x_1_3*x_6_4 + 69*x_1_3*x_7_2 + 69*x_1_3*x_7_4 + 71*x_1_3*x_8_2\n",
      "  + 71*x_1_3*x_8_4 + 67*x_1_3*x_9_2 + 67*x_1_3*x_9_4 + 2*x_1_4*x_10_3\n",
      "  + 2*x_1_4*x_10_5 + 49*x_1_4*x_11_3 + 49*x_1_4*x_11_5 + 17*x_1_4*x_12_3\n",
      "  + 17*x_1_4*x_12_5 + 60*x_1_4*x_13_3 + 60*x_1_4*x_13_5 + 83*x_1_4*x_14_3\n",
      "  + 83*x_1_4*x_14_5 + 97*x_1_4*x_15_3 + 97*x_1_4*x_15_5 + 68*x_1_4*x_16_3\n",
      "  + 68*x_1_4*x_16_5 + 52*x_1_4*x_17_3 + 52*x_1_4*x_17_5 + 89*x_1_4*x_18_3\n",
      "  + 89*x_1_4*x_18_5 + 35*x_1_4*x_19_3 + 35*x_1_4*x_19_5 + 63*x_1_4*x_2_3\n",
      "  + 63*x_1_4*x_2_5 + 71*x_1_4*x_3_3 + 71*x_1_4*x_3_5 + 93*x_1_4*x_4_3\n",
      "  + 93*x_1_4*x_4_5 + 30*x_1_4*x_5_3 + 30*x_1_4*x_5_5 + 60*x_1_4*x_6_3\n",
      "  + 60*x_1_4*x_6_5 + 69*x_1_4*x_7_3 + 69*x_1_4*x_7_5 + 71*x_1_4*x_8_3\n",
      "  + 71*x_1_4*x_8_5 + 67*x_1_4*x_9_3 + 67*x_1_4*x_9_5 + 2*x_1_5*x_10_4\n",
      "  + 2*x_1_5*x_10_6 + 49*x_1_5*x_11_4 + 49*x_1_5*x_11_6 + 17*x_1_5*x_12_4\n",
      "  + 17*x_1_5*x_12_6 + 60*x_1_5*x_13_4 + 60*x_1_5*x_13_6 + 83*x_1_5*x_14_4\n",
      "  + 83*x_1_5*x_14_6 + 97*x_1_5*x_15_4 + 97*x_1_5*x_15_6 + 68*x_1_5*x_16_4\n",
      "  + 68*x_1_5*x_16_6 + 52*x_1_5*x_17_4 + 52*x_1_5*x_17_6 + 89*x_1_5*x_18_4\n",
      "  + 89*x_1_5*x_18_6 + 35*x_1_5*x_19_4 + 35*x_1_5*x_19_6 + 63*x_1_5*x_2_4\n",
      "  + 63*x_1_5*x_2_6 + 71*x_1_5*x_3_4 + 71*x_1_5*x_3_6 + 93*x_1_5*x_4_4\n",
      "  + 93*x_1_5*x_4_6 + 30*x_1_5*x_5_4 + 30*x_1_5*x_5_6 + 60*x_1_5*x_6_4\n",
      "  + 60*x_1_5*x_6_6 + 69*x_1_5*x_7_4 + 69*x_1_5*x_7_6 + 71*x_1_5*x_8_4\n",
      "  + 71*x_1_5*x_8_6 + 67*x_1_5*x_9_4 + 67*x_1_5*x_9_6 + 2*x_1_6*x_10_5\n",
      "  + 2*x_1_6*x_10_7 + 49*x_1_6*x_11_5 + 49*x_1_6*x_11_7 + 17*x_1_6*x_12_5\n",
      "  + 17*x_1_6*x_12_7 + 60*x_1_6*x_13_5 + 60*x_1_6*x_13_7 + 83*x_1_6*x_14_5\n",
      "  + 83*x_1_6*x_14_7 + 97*x_1_6*x_15_5 + 97*x_1_6*x_15_7 + 68*x_1_6*x_16_5\n",
      "  + 68*x_1_6*x_16_7 + 52*x_1_6*x_17_5 + 52*x_1_6*x_17_7 + 89*x_1_6*x_18_5\n",
      "  + 89*x_1_6*x_18_7 + 35*x_1_6*x_19_5 + 35*x_1_6*x_19_7 + 63*x_1_6*x_2_5\n",
      "  + 63*x_1_6*x_2_7 + 71*x_1_6*x_3_5 + 71*x_1_6*x_3_7 + 93*x_1_6*x_4_5\n",
      "  + 93*x_1_6*x_4_7 + 30*x_1_6*x_5_5 + 30*x_1_6*x_5_7 + 60*x_1_6*x_6_5\n",
      "  + 60*x_1_6*x_6_7 + 69*x_1_6*x_7_5 + 69*x_1_6*x_7_7 + 71*x_1_6*x_8_5\n",
      "  + 71*x_1_6*x_8_7 + 67*x_1_6*x_9_5 + 67*x_1_6*x_9_7 + 2*x_1_7*x_10_6\n",
      "  + 2*x_1_7*x_10_8 + 49*x_1_7*x_11_6 + 49*x_1_7*x_11_8 + 17*x_1_7*x_12_6\n",
      "  + 17*x_1_7*x_12_8 + 60*x_1_7*x_13_6 + 60*x_1_7*x_13_8 + 83*x_1_7*x_14_6\n",
      "  + 83*x_1_7*x_14_8 + 97*x_1_7*x_15_6 + 97*x_1_7*x_15_8 + 68*x_1_7*x_16_6\n",
      "  + 68*x_1_7*x_16_8 + 52*x_1_7*x_17_6 + 52*x_1_7*x_17_8 + 89*x_1_7*x_18_6\n",
      "  + 89*x_1_7*x_18_8 + 35*x_1_7*x_19_6 + 35*x_1_7*x_19_8 + 63*x_1_7*x_2_6\n",
      "  + 63*x_1_7*x_2_8 + 71*x_1_7*x_3_6 + 71*x_1_7*x_3_8 + 93*x_1_7*x_4_6\n",
      "  + 93*x_1_7*x_4_8 + 30*x_1_7*x_5_6 + 30*x_1_7*x_5_8 + 60*x_1_7*x_6_6\n",
      "  + 60*x_1_7*x_6_8 + 69*x_1_7*x_7_6 + 69*x_1_7*x_7_8 + 71*x_1_7*x_8_6\n",
      "  + 71*x_1_7*x_8_8 + 67*x_1_7*x_9_6 + 67*x_1_7*x_9_8 + 2*x_1_8*x_10_7\n",
      "  + 2*x_1_8*x_10_9 + 49*x_1_8*x_11_7 + 49*x_1_8*x_11_9 + 17*x_1_8*x_12_7\n",
      "  + 17*x_1_8*x_12_9 + 60*x_1_8*x_13_7 + 60*x_1_8*x_13_9 + 83*x_1_8*x_14_7\n",
      "  + 83*x_1_8*x_14_9 + 97*x_1_8*x_15_7 + 97*x_1_8*x_15_9 + 68*x_1_8*x_16_7\n",
      "  + 68*x_1_8*x_16_9 + 52*x_1_8*x_17_7 + 52*x_1_8*x_17_9 + 89*x_1_8*x_18_7\n",
      "  + 89*x_1_8*x_18_9 + 35*x_1_8*x_19_7 + 35*x_1_8*x_19_9 + 63*x_1_8*x_2_7\n",
      "  + 63*x_1_8*x_2_9 + 71*x_1_8*x_3_7 + 71*x_1_8*x_3_9 + 93*x_1_8*x_4_7\n",
      "  + 93*x_1_8*x_4_9 + 30*x_1_8*x_5_7 + 30*x_1_8*x_5_9 + 60*x_1_8*x_6_7\n",
      "  + 60*x_1_8*x_6_9 + 69*x_1_8*x_7_7 + 69*x_1_8*x_7_9 + 71*x_1_8*x_8_7\n",
      "  + 71*x_1_8*x_8_9 + 67*x_1_8*x_9_7 + 67*x_1_8*x_9_9 + 2*x_1_9*x_10_10\n",
      "  + 2*x_1_9*x_10_8 + 49*x_1_9*x_11_10 + 49*x_1_9*x_11_8 + 17*x_1_9*x_12_10\n",
      "  + 17*x_1_9*x_12_8 + 60*x_1_9*x_13_10 + 60*x_1_9*x_13_8 + 83*x_1_9*x_14_10\n",
      "  + 83*x_1_9*x_14_8 + 97*x_1_9*x_15_10 + 97*x_1_9*x_15_8 + 68*x_1_9*x_16_10\n",
      "  + 68*x_1_9*x_16_8 + 52*x_1_9*x_17_10 + 52*x_1_9*x_17_8 + 89*x_1_9*x_18_10\n",
      "  + 89*x_1_9*x_18_8 + 35*x_1_9*x_19_10 + 35*x_1_9*x_19_8 + 63*x_1_9*x_2_10\n",
      "  + 63*x_1_9*x_2_8 + 71*x_1_9*x_3_10 + 71*x_1_9*x_3_8 + 93*x_1_9*x_4_10\n",
      "  + 93*x_1_9*x_4_8 + 30*x_1_9*x_5_10 + 30*x_1_9*x_5_8 + 60*x_1_9*x_6_10\n",
      "  + 60*x_1_9*x_6_8 + 69*x_1_9*x_7_10 + 69*x_1_9*x_7_8 + 71*x_1_9*x_8_10\n",
      "  + 71*x_1_9*x_8_8 + 67*x_1_9*x_9_10 + 67*x_1_9*x_9_8 + 65*x_2_0*x_10_1\n",
      "  + 65*x_2_0*x_10_19 + 40*x_2_0*x_11_1 + 40*x_2_0*x_11_19 + 80*x_2_0*x_12_1\n",
      "  + 80*x_2_0*x_12_19 + 31*x_2_0*x_13_1 + 31*x_2_0*x_13_19 + 77*x_2_0*x_14_1\n",
      "  + 77*x_2_0*x_14_19 + 74*x_2_0*x_15_1 + 74*x_2_0*x_15_19 + 7*x_2_0*x_16_1\n",
      "  + 7*x_2_0*x_16_19 + 77*x_2_0*x_17_1 + 77*x_2_0*x_17_19 + 62*x_2_0*x_18_1\n",
      "  + 62*x_2_0*x_18_19 + 31*x_2_0*x_19_1 + 31*x_2_0*x_19_19 + 92*x_2_0*x_3_1\n",
      "  + 92*x_2_0*x_3_19 + 65*x_2_0*x_4_1 + 65*x_2_0*x_4_19 + 66*x_2_0*x_5_1\n",
      "  + 66*x_2_0*x_5_19 + 88*x_2_0*x_6_1 + 88*x_2_0*x_6_19 + 59*x_2_0*x_7_1\n",
      "  + 59*x_2_0*x_7_19 + 95*x_2_0*x_8_1 + 95*x_2_0*x_8_19 + 69*x_2_0*x_9_1\n",
      "  + 69*x_2_0*x_9_19 + 65*x_2_1*x_10_0 + 65*x_2_1*x_10_2 + 40*x_2_1*x_11_0\n",
      "  + 40*x_2_1*x_11_2 + 80*x_2_1*x_12_0 + 80*x_2_1*x_12_2 + 31*x_2_1*x_13_0\n",
      "  + 31*x_2_1*x_13_2 + 77*x_2_1*x_14_0 + 77*x_2_1*x_14_2 + 74*x_2_1*x_15_0\n",
      "  + 74*x_2_1*x_15_2 + 7*x_2_1*x_16_0 + 7*x_2_1*x_16_2 + 77*x_2_1*x_17_0\n",
      "  + 77*x_2_1*x_17_2 + 62*x_2_1*x_18_0 + 62*x_2_1*x_18_2 + 31*x_2_1*x_19_0\n",
      "  + 31*x_2_1*x_19_2 + 92*x_2_1*x_3_0 + 92*x_2_1*x_3_2 + 65*x_2_1*x_4_0\n",
      "  + 65*x_2_1*x_4_2 + 66*x_2_1*x_5_0 + 66*x_2_1*x_5_2 + 88*x_2_1*x_6_0\n",
      "  + 88*x_2_1*x_6_2 + 59*x_2_1*x_7_0 + 59*x_2_1*x_7_2 + 95*x_2_1*x_8_0\n",
      "  + 95*x_2_1*x_8_2 + 69*x_2_1*x_9_0 + 69*x_2_1*x_9_2 + 65*x_2_10*x_10_11\n",
      "  + 65*x_2_10*x_10_9 + 40*x_2_10*x_11_11 + 40*x_2_10*x_11_9 + 80*x_2_10*x_12_11\n",
      "  + 80*x_2_10*x_12_9 + 31*x_2_10*x_13_11 + 31*x_2_10*x_13_9 + 77*x_2_10*x_14_11\n",
      "  + 77*x_2_10*x_14_9 + 74*x_2_10*x_15_11 + 74*x_2_10*x_15_9 + 7*x_2_10*x_16_11\n",
      "  + 7*x_2_10*x_16_9 + 77*x_2_10*x_17_11 + 77*x_2_10*x_17_9 + 62*x_2_10*x_18_11\n",
      "  + 62*x_2_10*x_18_9 + 31*x_2_10*x_19_11 + 31*x_2_10*x_19_9 + 92*x_2_10*x_3_11\n",
      "  + 92*x_2_10*x_3_9 + 65*x_2_10*x_4_11 + 65*x_2_10*x_4_9 + 66*x_2_10*x_5_11\n",
      "  + 66*x_2_10*x_5_9 + 88*x_2_10*x_6_11 + 88*x_2_10*x_6_9 + 59*x_2_10*x_7_11\n",
      "  + 59*x_2_10*x_7_9 + 95*x_2_10*x_8_11 + 95*x_2_10*x_8_9 + 69*x_2_10*x_9_11\n",
      "  + 69*x_2_10*x_9_9 + 65*x_2_11*x_10_10 + 65*x_2_11*x_10_12 + 40*x_2_11*x_11_10\n",
      "  + 40*x_2_11*x_11_12 + 80*x_2_11*x_12_10 + 80*x_2_11*x_12_12\n",
      "  + 31*x_2_11*x_13_10 + 31*x_2_11*x_13_12 + 77*x_2_11*x_14_10\n",
      "  + 77*x_2_11*x_14_12 + 74*x_2_11*x_15_10 + 74*x_2_11*x_15_12 + 7*x_2_11*x_16_10\n",
      "  + 7*x_2_11*x_16_12 + 77*x_2_11*x_17_10 + 77*x_2_11*x_17_12 + 62*x_2_11*x_18_10\n",
      "  + 62*x_2_11*x_18_12 + 31*x_2_11*x_19_10 + 31*x_2_11*x_19_12 + 92*x_2_11*x_3_10\n",
      "  + 92*x_2_11*x_3_12 + 65*x_2_11*x_4_10 + 65*x_2_11*x_4_12 + 66*x_2_11*x_5_10\n",
      "  + 66*x_2_11*x_5_12 + 88*x_2_11*x_6_10 + 88*x_2_11*x_6_12 + 59*x_2_11*x_7_10\n",
      "  + 59*x_2_11*x_7_12 + 95*x_2_11*x_8_10 + 95*x_2_11*x_8_12 + 69*x_2_11*x_9_10\n",
      "  + 69*x_2_11*x_9_12 + 65*x_2_12*x_10_11 + 65*x_2_12*x_10_13 + 40*x_2_12*x_11_11\n",
      "  + 40*x_2_12*x_11_13 + 80*x_2_12*x_12_11 + 80*x_2_12*x_12_13\n",
      "  + 31*x_2_12*x_13_11 + 31*x_2_12*x_13_13 + 77*x_2_12*x_14_11\n",
      "  + 77*x_2_12*x_14_13 + 74*x_2_12*x_15_11 + 74*x_2_12*x_15_13 + 7*x_2_12*x_16_11\n",
      "  + 7*x_2_12*x_16_13 + 77*x_2_12*x_17_11 + 77*x_2_12*x_17_13 + 62*x_2_12*x_18_11\n",
      "  + 62*x_2_12*x_18_13 + 31*x_2_12*x_19_11 + 31*x_2_12*x_19_13 + 92*x_2_12*x_3_11\n",
      "  + 92*x_2_12*x_3_13 + 65*x_2_12*x_4_11 + 65*x_2_12*x_4_13 + 66*x_2_12*x_5_11\n",
      "  + 66*x_2_12*x_5_13 + 88*x_2_12*x_6_11 + 88*x_2_12*x_6_13 + 59*x_2_12*x_7_11\n",
      "  + 59*x_2_12*x_7_13 + 95*x_2_12*x_8_11 + 95*x_2_12*x_8_13 + 69*x_2_12*x_9_11\n",
      "  + 69*x_2_12*x_9_13 + 65*x_2_13*x_10_12 + 65*x_2_13*x_10_14 + 40*x_2_13*x_11_12\n",
      "  + 40*x_2_13*x_11_14 + 80*x_2_13*x_12_12 + 80*x_2_13*x_12_14\n",
      "  + 31*x_2_13*x_13_12 + 31*x_2_13*x_13_14 + 77*x_2_13*x_14_12\n",
      "  + 77*x_2_13*x_14_14 + 74*x_2_13*x_15_12 + 74*x_2_13*x_15_14 + 7*x_2_13*x_16_12\n",
      "  + 7*x_2_13*x_16_14 + 77*x_2_13*x_17_12 + 77*x_2_13*x_17_14 + 62*x_2_13*x_18_12\n",
      "  + 62*x_2_13*x_18_14 + 31*x_2_13*x_19_12 + 31*x_2_13*x_19_14 + 92*x_2_13*x_3_12\n",
      "  + 92*x_2_13*x_3_14 + 65*x_2_13*x_4_12 + 65*x_2_13*x_4_14 + 66*x_2_13*x_5_12\n",
      "  + 66*x_2_13*x_5_14 + 88*x_2_13*x_6_12 + 88*x_2_13*x_6_14 + 59*x_2_13*x_7_12\n",
      "  + 59*x_2_13*x_7_14 + 95*x_2_13*x_8_12 + 95*x_2_13*x_8_14 + 69*x_2_13*x_9_12\n",
      "  + 69*x_2_13*x_9_14 + 65*x_2_14*x_10_13 + 65*x_2_14*x_10_15 + 40*x_2_14*x_11_13\n",
      "  + 40*x_2_14*x_11_15 + 80*x_2_14*x_12_13 + 80*x_2_14*x_12_15\n",
      "  + 31*x_2_14*x_13_13 + 31*x_2_14*x_13_15 + 77*x_2_14*x_14_13\n",
      "  + 77*x_2_14*x_14_15 + 74*x_2_14*x_15_13 + 74*x_2_14*x_15_15 + 7*x_2_14*x_16_13\n",
      "  + 7*x_2_14*x_16_15 + 77*x_2_14*x_17_13 + 77*x_2_14*x_17_15 + 62*x_2_14*x_18_13\n",
      "  + 62*x_2_14*x_18_15 + 31*x_2_14*x_19_13 + 31*x_2_14*x_19_15 + 92*x_2_14*x_3_13\n",
      "  + 92*x_2_14*x_3_15 + 65*x_2_14*x_4_13 + 65*x_2_14*x_4_15 + 66*x_2_14*x_5_13\n",
      "  + 66*x_2_14*x_5_15 + 88*x_2_14*x_6_13 + 88*x_2_14*x_6_15 + 59*x_2_14*x_7_13\n",
      "  + 59*x_2_14*x_7_15 + 95*x_2_14*x_8_13 + 95*x_2_14*x_8_15 + 69*x_2_14*x_9_13\n",
      "  + 69*x_2_14*x_9_15 + 65*x_2_15*x_10_14 + 65*x_2_15*x_10_16 + 40*x_2_15*x_11_14\n",
      "  + 40*x_2_15*x_11_16 + 80*x_2_15*x_12_14 + 80*x_2_15*x_12_16\n",
      "  + 31*x_2_15*x_13_14 + 31*x_2_15*x_13_16 + 77*x_2_15*x_14_14\n",
      "  + 77*x_2_15*x_14_16 + 74*x_2_15*x_15_14 + 74*x_2_15*x_15_16 + 7*x_2_15*x_16_14\n",
      "  + 7*x_2_15*x_16_16 + 77*x_2_15*x_17_14 + 77*x_2_15*x_17_16 + 62*x_2_15*x_18_14\n",
      "  + 62*x_2_15*x_18_16 + 31*x_2_15*x_19_14 + 31*x_2_15*x_19_16 + 92*x_2_15*x_3_14\n",
      "  + 92*x_2_15*x_3_16 + 65*x_2_15*x_4_14 + 65*x_2_15*x_4_16 + 66*x_2_15*x_5_14\n",
      "  + 66*x_2_15*x_5_16 + 88*x_2_15*x_6_14 + 88*x_2_15*x_6_16 + 59*x_2_15*x_7_14\n",
      "  + 59*x_2_15*x_7_16 + 95*x_2_15*x_8_14 + 95*x_2_15*x_8_16 + 69*x_2_15*x_9_14\n",
      "  + 69*x_2_15*x_9_16 + 65*x_2_16*x_10_15 + 65*x_2_16*x_10_17 + 40*x_2_16*x_11_15\n",
      "  + 40*x_2_16*x_11_17 + 80*x_2_16*x_12_15 + 80*x_2_16*x_12_17\n",
      "  + 31*x_2_16*x_13_15 + 31*x_2_16*x_13_17 + 77*x_2_16*x_14_15\n",
      "  + 77*x_2_16*x_14_17 + 74*x_2_16*x_15_15 + 74*x_2_16*x_15_17 + 7*x_2_16*x_16_15\n",
      "  + 7*x_2_16*x_16_17 + 77*x_2_16*x_17_15 + 77*x_2_16*x_17_17 + 62*x_2_16*x_18_15\n",
      "  + 62*x_2_16*x_18_17 + 31*x_2_16*x_19_15 + 31*x_2_16*x_19_17 + 92*x_2_16*x_3_15\n",
      "  + 92*x_2_16*x_3_17 + 65*x_2_16*x_4_15 + 65*x_2_16*x_4_17 + 66*x_2_16*x_5_15\n",
      "  + 66*x_2_16*x_5_17 + 88*x_2_16*x_6_15 + 88*x_2_16*x_6_17 + 59*x_2_16*x_7_15\n",
      "  + 59*x_2_16*x_7_17 + 95*x_2_16*x_8_15 + 95*x_2_16*x_8_17 + 69*x_2_16*x_9_15\n",
      "  + 69*x_2_16*x_9_17 + 65*x_2_17*x_10_16 + 65*x_2_17*x_10_18 + 40*x_2_17*x_11_16\n",
      "  + 40*x_2_17*x_11_18 + 80*x_2_17*x_12_16 + 80*x_2_17*x_12_18\n",
      "  + 31*x_2_17*x_13_16 + 31*x_2_17*x_13_18 + 77*x_2_17*x_14_16\n",
      "  + 77*x_2_17*x_14_18 + 74*x_2_17*x_15_16 + 74*x_2_17*x_15_18 + 7*x_2_17*x_16_16\n",
      "  + 7*x_2_17*x_16_18 + 77*x_2_17*x_17_16 + 77*x_2_17*x_17_18 + 62*x_2_17*x_18_16\n",
      "  + 62*x_2_17*x_18_18 + 31*x_2_17*x_19_16 + 31*x_2_17*x_19_18 + 92*x_2_17*x_3_16\n",
      "  + 92*x_2_17*x_3_18 + 65*x_2_17*x_4_16 + 65*x_2_17*x_4_18 + 66*x_2_17*x_5_16\n",
      "  + 66*x_2_17*x_5_18 + 88*x_2_17*x_6_16 + 88*x_2_17*x_6_18 + 59*x_2_17*x_7_16\n",
      "  + 59*x_2_17*x_7_18 + 95*x_2_17*x_8_16 + 95*x_2_17*x_8_18 + 69*x_2_17*x_9_16\n",
      "  + 69*x_2_17*x_9_18 + 65*x_2_18*x_10_17 + 65*x_2_18*x_10_19 + 40*x_2_18*x_11_17\n",
      "  + 40*x_2_18*x_11_19 + 80*x_2_18*x_12_17 + 80*x_2_18*x_12_19\n",
      "  + 31*x_2_18*x_13_17 + 31*x_2_18*x_13_19 + 77*x_2_18*x_14_17\n",
      "  + 77*x_2_18*x_14_19 + 74*x_2_18*x_15_17 + 74*x_2_18*x_15_19 + 7*x_2_18*x_16_17\n",
      "  + 7*x_2_18*x_16_19 + 77*x_2_18*x_17_17 + 77*x_2_18*x_17_19 + 62*x_2_18*x_18_17\n",
      "  + 62*x_2_18*x_18_19 + 31*x_2_18*x_19_17 + 31*x_2_18*x_19_19 + 92*x_2_18*x_3_17\n",
      "  + 92*x_2_18*x_3_19 + 65*x_2_18*x_4_17 + 65*x_2_18*x_4_19 + 66*x_2_18*x_5_17\n",
      "  + 66*x_2_18*x_5_19 + 88*x_2_18*x_6_17 + 88*x_2_18*x_6_19 + 59*x_2_18*x_7_17\n",
      "  + 59*x_2_18*x_7_19 + 95*x_2_18*x_8_17 + 95*x_2_18*x_8_19 + 69*x_2_18*x_9_17\n",
      "  + 69*x_2_18*x_9_19 + 65*x_2_19*x_10_0 + 65*x_2_19*x_10_18 + 40*x_2_19*x_11_0\n",
      "  + 40*x_2_19*x_11_18 + 80*x_2_19*x_12_0 + 80*x_2_19*x_12_18 + 31*x_2_19*x_13_0\n",
      "  + 31*x_2_19*x_13_18 + 77*x_2_19*x_14_0 + 77*x_2_19*x_14_18 + 74*x_2_19*x_15_0\n",
      "  + 74*x_2_19*x_15_18 + 7*x_2_19*x_16_0 + 7*x_2_19*x_16_18 + 77*x_2_19*x_17_0\n",
      "  + 77*x_2_19*x_17_18 + 62*x_2_19*x_18_0 + 62*x_2_19*x_18_18 + 31*x_2_19*x_19_0\n",
      "  + 31*x_2_19*x_19_18 + 92*x_2_19*x_3_0 + 92*x_2_19*x_3_18 + 65*x_2_19*x_4_0\n",
      "  + 65*x_2_19*x_4_18 + 66*x_2_19*x_5_0 + 66*x_2_19*x_5_18 + 88*x_2_19*x_6_0\n",
      "  + 88*x_2_19*x_6_18 + 59*x_2_19*x_7_0 + 59*x_2_19*x_7_18 + 95*x_2_19*x_8_0\n",
      "  + 95*x_2_19*x_8_18 + 69*x_2_19*x_9_0 + 69*x_2_19*x_9_18 + 65*x_2_2*x_10_1\n",
      "  + 65*x_2_2*x_10_3 + 40*x_2_2*x_11_1 + 40*x_2_2*x_11_3 + 80*x_2_2*x_12_1\n",
      "  + 80*x_2_2*x_12_3 + 31*x_2_2*x_13_1 + 31*x_2_2*x_13_3 + 77*x_2_2*x_14_1\n",
      "  + 77*x_2_2*x_14_3 + 74*x_2_2*x_15_1 + 74*x_2_2*x_15_3 + 7*x_2_2*x_16_1\n",
      "  + 7*x_2_2*x_16_3 + 77*x_2_2*x_17_1 + 77*x_2_2*x_17_3 + 62*x_2_2*x_18_1\n",
      "  + 62*x_2_2*x_18_3 + 31*x_2_2*x_19_1 + 31*x_2_2*x_19_3 + 92*x_2_2*x_3_1\n",
      "  + 92*x_2_2*x_3_3 + 65*x_2_2*x_4_1 + 65*x_2_2*x_4_3 + 66*x_2_2*x_5_1\n",
      "  + 66*x_2_2*x_5_3 + 88*x_2_2*x_6_1 + 88*x_2_2*x_6_3 + 59*x_2_2*x_7_1\n",
      "  + 59*x_2_2*x_7_3 + 95*x_2_2*x_8_1 + 95*x_2_2*x_8_3 + 69*x_2_2*x_9_1\n",
      "  + 69*x_2_2*x_9_3 + 65*x_2_3*x_10_2 + 65*x_2_3*x_10_4 + 40*x_2_3*x_11_2\n",
      "  + 40*x_2_3*x_11_4 + 80*x_2_3*x_12_2 + 80*x_2_3*x_12_4 + 31*x_2_3*x_13_2\n",
      "  + 31*x_2_3*x_13_4 + 77*x_2_3*x_14_2 + 77*x_2_3*x_14_4 + 74*x_2_3*x_15_2\n",
      "  + 74*x_2_3*x_15_4 + 7*x_2_3*x_16_2 + 7*x_2_3*x_16_4 + 77*x_2_3*x_17_2\n",
      "  + 77*x_2_3*x_17_4 + 62*x_2_3*x_18_2 + 62*x_2_3*x_18_4 + 31*x_2_3*x_19_2\n",
      "  + 31*x_2_3*x_19_4 + 92*x_2_3*x_3_2 + 92*x_2_3*x_3_4 + 65*x_2_3*x_4_2\n",
      "  + 65*x_2_3*x_4_4 + 66*x_2_3*x_5_2 + 66*x_2_3*x_5_4 + 88*x_2_3*x_6_2\n",
      "  + 88*x_2_3*x_6_4 + 59*x_2_3*x_7_2 + 59*x_2_3*x_7_4 + 95*x_2_3*x_8_2\n",
      "  + 95*x_2_3*x_8_4 + 69*x_2_3*x_9_2 + 69*x_2_3*x_9_4 + 65*x_2_4*x_10_3\n",
      "  + 65*x_2_4*x_10_5 + 40*x_2_4*x_11_3 + 40*x_2_4*x_11_5 + 80*x_2_4*x_12_3\n",
      "  + 80*x_2_4*x_12_5 + 31*x_2_4*x_13_3 + 31*x_2_4*x_13_5 + 77*x_2_4*x_14_3\n",
      "  + 77*x_2_4*x_14_5 + 74*x_2_4*x_15_3 + 74*x_2_4*x_15_5 + 7*x_2_4*x_16_3\n",
      "  + 7*x_2_4*x_16_5 + 77*x_2_4*x_17_3 + 77*x_2_4*x_17_5 + 62*x_2_4*x_18_3\n",
      "  + 62*x_2_4*x_18_5 + 31*x_2_4*x_19_3 + 31*x_2_4*x_19_5 + 92*x_2_4*x_3_3\n",
      "  + 92*x_2_4*x_3_5 + 65*x_2_4*x_4_3 + 65*x_2_4*x_4_5 + 66*x_2_4*x_5_3\n",
      "  + 66*x_2_4*x_5_5 + 88*x_2_4*x_6_3 + 88*x_2_4*x_6_5 + 59*x_2_4*x_7_3\n",
      "  + 59*x_2_4*x_7_5 + 95*x_2_4*x_8_3 + 95*x_2_4*x_8_5 + 69*x_2_4*x_9_3\n",
      "  + 69*x_2_4*x_9_5 + 65*x_2_5*x_10_4 + 65*x_2_5*x_10_6 + 40*x_2_5*x_11_4\n",
      "  + 40*x_2_5*x_11_6 + 80*x_2_5*x_12_4 + 80*x_2_5*x_12_6 + 31*x_2_5*x_13_4\n",
      "  + 31*x_2_5*x_13_6 + 77*x_2_5*x_14_4 + 77*x_2_5*x_14_6 + 74*x_2_5*x_15_4\n",
      "  + 74*x_2_5*x_15_6 + 7*x_2_5*x_16_4 + 7*x_2_5*x_16_6 + 77*x_2_5*x_17_4\n",
      "  + 77*x_2_5*x_17_6 + 62*x_2_5*x_18_4 + 62*x_2_5*x_18_6 + 31*x_2_5*x_19_4\n",
      "  + 31*x_2_5*x_19_6 + 92*x_2_5*x_3_4 + 92*x_2_5*x_3_6 + 65*x_2_5*x_4_4\n",
      "  + 65*x_2_5*x_4_6 + 66*x_2_5*x_5_4 + 66*x_2_5*x_5_6 + 88*x_2_5*x_6_4\n",
      "  + 88*x_2_5*x_6_6 + 59*x_2_5*x_7_4 + 59*x_2_5*x_7_6 + 95*x_2_5*x_8_4\n",
      "  + 95*x_2_5*x_8_6 + 69*x_2_5*x_9_4 + 69*x_2_5*x_9_6 + 65*x_2_6*x_10_5\n",
      "  + 65*x_2_6*x_10_7 + 40*x_2_6*x_11_5 + 40*x_2_6*x_11_7 + 80*x_2_6*x_12_5\n",
      "  + 80*x_2_6*x_12_7 + 31*x_2_6*x_13_5 + 31*x_2_6*x_13_7 + 77*x_2_6*x_14_5\n",
      "  + 77*x_2_6*x_14_7 + 74*x_2_6*x_15_5 + 74*x_2_6*x_15_7 + 7*x_2_6*x_16_5\n",
      "  + 7*x_2_6*x_16_7 + 77*x_2_6*x_17_5 + 77*x_2_6*x_17_7 + 62*x_2_6*x_18_5\n",
      "  + 62*x_2_6*x_18_7 + 31*x_2_6*x_19_5 + 31*x_2_6*x_19_7 + 92*x_2_6*x_3_5\n",
      "  + 92*x_2_6*x_3_7 + 65*x_2_6*x_4_5 + 65*x_2_6*x_4_7 + 66*x_2_6*x_5_5\n",
      "  + 66*x_2_6*x_5_7 + 88*x_2_6*x_6_5 + 88*x_2_6*x_6_7 + 59*x_2_6*x_7_5\n",
      "  + 59*x_2_6*x_7_7 + 95*x_2_6*x_8_5 + 95*x_2_6*x_8_7 + 69*x_2_6*x_9_5\n",
      "  + 69*x_2_6*x_9_7 + 65*x_2_7*x_10_6 + 65*x_2_7*x_10_8 + 40*x_2_7*x_11_6\n",
      "  + 40*x_2_7*x_11_8 + 80*x_2_7*x_12_6 + 80*x_2_7*x_12_8 + 31*x_2_7*x_13_6\n",
      "  + 31*x_2_7*x_13_8 + 77*x_2_7*x_14_6 + 77*x_2_7*x_14_8 + 74*x_2_7*x_15_6\n",
      "  + 74*x_2_7*x_15_8 + 7*x_2_7*x_16_6 + 7*x_2_7*x_16_8 + 77*x_2_7*x_17_6\n",
      "  + 77*x_2_7*x_17_8 + 62*x_2_7*x_18_6 + 62*x_2_7*x_18_8 + 31*x_2_7*x_19_6\n",
      "  + 31*x_2_7*x_19_8 + 92*x_2_7*x_3_6 + 92*x_2_7*x_3_8 + 65*x_2_7*x_4_6\n",
      "  + 65*x_2_7*x_4_8 + 66*x_2_7*x_5_6 + 66*x_2_7*x_5_8 + 88*x_2_7*x_6_6\n",
      "  + 88*x_2_7*x_6_8 + 59*x_2_7*x_7_6 + 59*x_2_7*x_7_8 + 95*x_2_7*x_8_6\n",
      "  + 95*x_2_7*x_8_8 + 69*x_2_7*x_9_6 + 69*x_2_7*x_9_8 + 65*x_2_8*x_10_7\n",
      "  + 65*x_2_8*x_10_9 + 40*x_2_8*x_11_7 + 40*x_2_8*x_11_9 + 80*x_2_8*x_12_7\n",
      "  + 80*x_2_8*x_12_9 + 31*x_2_8*x_13_7 + 31*x_2_8*x_13_9 + 77*x_2_8*x_14_7\n",
      "  + 77*x_2_8*x_14_9 + 74*x_2_8*x_15_7 + 74*x_2_8*x_15_9 + 7*x_2_8*x_16_7\n",
      "  + 7*x_2_8*x_16_9 + 77*x_2_8*x_17_7 + 77*x_2_8*x_17_9 + 62*x_2_8*x_18_7\n",
      "  + 62*x_2_8*x_18_9 + 31*x_2_8*x_19_7 + 31*x_2_8*x_19_9 + 92*x_2_8*x_3_7\n",
      "  + 92*x_2_8*x_3_9 + 65*x_2_8*x_4_7 + 65*x_2_8*x_4_9 + 66*x_2_8*x_5_7\n",
      "  + 66*x_2_8*x_5_9 + 88*x_2_8*x_6_7 + 88*x_2_8*x_6_9 + 59*x_2_8*x_7_7\n",
      "  + 59*x_2_8*x_7_9 + 95*x_2_8*x_8_7 + 95*x_2_8*x_8_9 + 69*x_2_8*x_9_7\n",
      "  + 69*x_2_8*x_9_9 + 65*x_2_9*x_10_10 + 65*x_2_9*x_10_8 + 40*x_2_9*x_11_10\n",
      "  + 40*x_2_9*x_11_8 + 80*x_2_9*x_12_10 + 80*x_2_9*x_12_8 + 31*x_2_9*x_13_10\n",
      "  + 31*x_2_9*x_13_8 + 77*x_2_9*x_14_10 + 77*x_2_9*x_14_8 + 74*x_2_9*x_15_10\n",
      "  + 74*x_2_9*x_15_8 + 7*x_2_9*x_16_10 + 7*x_2_9*x_16_8 + 77*x_2_9*x_17_10\n",
      "  + 77*x_2_9*x_17_8 + 62*x_2_9*x_18_10 + 62*x_2_9*x_18_8 + 31*x_2_9*x_19_10\n",
      "  + 31*x_2_9*x_19_8 + 92*x_2_9*x_3_10 + 92*x_2_9*x_3_8 + 65*x_2_9*x_4_10\n",
      "  + 65*x_2_9*x_4_8 + 66*x_2_9*x_5_10 + 66*x_2_9*x_5_8 + 88*x_2_9*x_6_10\n",
      "  + 88*x_2_9*x_6_8 + 59*x_2_9*x_7_10 + 59*x_2_9*x_7_8 + 95*x_2_9*x_8_10\n",
      "  + 95*x_2_9*x_8_8 + 69*x_2_9*x_9_10 + 69*x_2_9*x_9_8 + 70*x_3_0*x_10_1\n",
      "  + 70*x_3_0*x_10_19 + 52*x_3_0*x_11_1 + 52*x_3_0*x_11_19 + 78*x_3_0*x_12_1\n",
      "  + 78*x_3_0*x_12_19 + 64*x_3_0*x_13_1 + 64*x_3_0*x_13_19 + 35*x_3_0*x_14_1\n",
      "  + 35*x_3_0*x_14_19 + 59*x_3_0*x_15_1 + 59*x_3_0*x_15_19 + 92*x_3_0*x_16_1\n",
      "  + 92*x_3_0*x_16_19 + 19*x_3_0*x_17_1 + 19*x_3_0*x_17_19 + 60*x_3_0*x_18_1\n",
      "  + 60*x_3_0*x_18_19 + 67*x_3_0*x_19_1 + 67*x_3_0*x_19_19 + 62*x_3_0*x_4_1\n",
      "  + 62*x_3_0*x_4_19 + 41*x_3_0*x_5_1 + 41*x_3_0*x_5_19 + 12*x_3_0*x_6_1\n",
      "  + 12*x_3_0*x_6_19 + 40*x_3_0*x_7_1 + 40*x_3_0*x_7_19 + 4*x_3_0*x_8_1\n",
      "  + 4*x_3_0*x_8_19 + 27*x_3_0*x_9_1 + 27*x_3_0*x_9_19 + 70*x_3_1*x_10_0\n",
      "  + 70*x_3_1*x_10_2 + 52*x_3_1*x_11_0 + 52*x_3_1*x_11_2 + 78*x_3_1*x_12_0\n",
      "  + 78*x_3_1*x_12_2 + 64*x_3_1*x_13_0 + 64*x_3_1*x_13_2 + 35*x_3_1*x_14_0\n",
      "  + 35*x_3_1*x_14_2 + 59*x_3_1*x_15_0 + 59*x_3_1*x_15_2 + 92*x_3_1*x_16_0\n",
      "  + 92*x_3_1*x_16_2 + 19*x_3_1*x_17_0 + 19*x_3_1*x_17_2 + 60*x_3_1*x_18_0\n",
      "  + 60*x_3_1*x_18_2 + 67*x_3_1*x_19_0 + 67*x_3_1*x_19_2 + 62*x_3_1*x_4_0\n",
      "  + 62*x_3_1*x_4_2 + 41*x_3_1*x_5_0 + 41*x_3_1*x_5_2 + 12*x_3_1*x_6_0\n",
      "  + 12*x_3_1*x_6_2 + 40*x_3_1*x_7_0 + 40*x_3_1*x_7_2 + 4*x_3_1*x_8_0\n",
      "  + 4*x_3_1*x_8_2 + 27*x_3_1*x_9_0 + 27*x_3_1*x_9_2 + 70*x_3_10*x_10_11\n",
      "  + 70*x_3_10*x_10_9 + 52*x_3_10*x_11_11 + 52*x_3_10*x_11_9 + 78*x_3_10*x_12_11\n",
      "  + 78*x_3_10*x_12_9 + 64*x_3_10*x_13_11 + 64*x_3_10*x_13_9 + 35*x_3_10*x_14_11\n",
      "  + 35*x_3_10*x_14_9 + 59*x_3_10*x_15_11 + 59*x_3_10*x_15_9 + 92*x_3_10*x_16_11\n",
      "  + 92*x_3_10*x_16_9 + 19*x_3_10*x_17_11 + 19*x_3_10*x_17_9 + 60*x_3_10*x_18_11\n",
      "  + 60*x_3_10*x_18_9 + 67*x_3_10*x_19_11 + 67*x_3_10*x_19_9 + 62*x_3_10*x_4_11\n",
      "  + 62*x_3_10*x_4_9 + 41*x_3_10*x_5_11 + 41*x_3_10*x_5_9 + 12*x_3_10*x_6_11\n",
      "  + 12*x_3_10*x_6_9 + 40*x_3_10*x_7_11 + 40*x_3_10*x_7_9 + 4*x_3_10*x_8_11\n",
      "  + 4*x_3_10*x_8_9 + 27*x_3_10*x_9_11 + 27*x_3_10*x_9_9 + 70*x_3_11*x_10_10\n",
      "  + 70*x_3_11*x_10_12 + 52*x_3_11*x_11_10 + 52*x_3_11*x_11_12\n",
      "  + 78*x_3_11*x_12_10 + 78*x_3_11*x_12_12 + 64*x_3_11*x_13_10\n",
      "  + 64*x_3_11*x_13_12 + 35*x_3_11*x_14_10 + 35*x_3_11*x_14_12\n",
      "  + 59*x_3_11*x_15_10 + 59*x_3_11*x_15_12 + 92*x_3_11*x_16_10\n",
      "  + 92*x_3_11*x_16_12 + 19*x_3_11*x_17_10 + 19*x_3_11*x_17_12\n",
      "  + 60*x_3_11*x_18_10 + 60*x_3_11*x_18_12 + 67*x_3_11*x_19_10\n",
      "  + 67*x_3_11*x_19_12 + 62*x_3_11*x_4_10 + 62*x_3_11*x_4_12 + 41*x_3_11*x_5_10\n",
      "  + 41*x_3_11*x_5_12 + 12*x_3_11*x_6_10 + 12*x_3_11*x_6_12 + 40*x_3_11*x_7_10\n",
      "  + 40*x_3_11*x_7_12 + 4*x_3_11*x_8_10 + 4*x_3_11*x_8_12 + 27*x_3_11*x_9_10\n",
      "  + 27*x_3_11*x_9_12 + 70*x_3_12*x_10_11 + 70*x_3_12*x_10_13 + 52*x_3_12*x_11_11\n",
      "  + 52*x_3_12*x_11_13 + 78*x_3_12*x_12_11 + 78*x_3_12*x_12_13\n",
      "  + 64*x_3_12*x_13_11 + 64*x_3_12*x_13_13 + 35*x_3_12*x_14_11\n",
      "  + 35*x_3_12*x_14_13 + 59*x_3_12*x_15_11 + 59*x_3_12*x_15_13\n",
      "  + 92*x_3_12*x_16_11 + 92*x_3_12*x_16_13 + 19*x_3_12*x_17_11\n",
      "  + 19*x_3_12*x_17_13 + 60*x_3_12*x_18_11 + 60*x_3_12*x_18_13\n",
      "  + 67*x_3_12*x_19_11 + 67*x_3_12*x_19_13 + 62*x_3_12*x_4_11 + 62*x_3_12*x_4_13\n",
      "  + 41*x_3_12*x_5_11 + 41*x_3_12*x_5_13 + 12*x_3_12*x_6_11 + 12*x_3_12*x_6_13\n",
      "  + 40*x_3_12*x_7_11 + 40*x_3_12*x_7_13 + 4*x_3_12*x_8_11 + 4*x_3_12*x_8_13\n",
      "  + 27*x_3_12*x_9_11 + 27*x_3_12*x_9_13 + 70*x_3_13*x_10_12 + 70*x_3_13*x_10_14\n",
      "  + 52*x_3_13*x_11_12 + 52*x_3_13*x_11_14 + 78*x_3_13*x_12_12\n",
      "  + 78*x_3_13*x_12_14 + 64*x_3_13*x_13_12 + 64*x_3_13*x_13_14\n",
      "  + 35*x_3_13*x_14_12 + 35*x_3_13*x_14_14 + 59*x_3_13*x_15_12\n",
      "  + 59*x_3_13*x_15_14 + 92*x_3_13*x_16_12 + 92*x_3_13*x_16_14\n",
      "  + 19*x_3_13*x_17_12 + 19*x_3_13*x_17_14 + 60*x_3_13*x_18_12\n",
      "  + 60*x_3_13*x_18_14 + 67*x_3_13*x_19_12 + 67*x_3_13*x_19_14 + 62*x_3_13*x_4_12\n",
      "  + 62*x_3_13*x_4_14 + 41*x_3_13*x_5_12 + 41*x_3_13*x_5_14 + 12*x_3_13*x_6_12\n",
      "  + 12*x_3_13*x_6_14 + 40*x_3_13*x_7_12 + 40*x_3_13*x_7_14 + 4*x_3_13*x_8_12\n",
      "  + 4*x_3_13*x_8_14 + 27*x_3_13*x_9_12 + 27*x_3_13*x_9_14 + 70*x_3_14*x_10_13\n",
      "  + 70*x_3_14*x_10_15 + 52*x_3_14*x_11_13 + 52*x_3_14*x_11_15\n",
      "  + 78*x_3_14*x_12_13 + 78*x_3_14*x_12_15 + 64*x_3_14*x_13_13\n",
      "  + 64*x_3_14*x_13_15 + 35*x_3_14*x_14_13 + 35*x_3_14*x_14_15\n",
      "  + 59*x_3_14*x_15_13 + 59*x_3_14*x_15_15 + 92*x_3_14*x_16_13\n",
      "  + 92*x_3_14*x_16_15 + 19*x_3_14*x_17_13 + 19*x_3_14*x_17_15\n",
      "  + 60*x_3_14*x_18_13 + 60*x_3_14*x_18_15 + 67*x_3_14*x_19_13\n",
      "  + 67*x_3_14*x_19_15 + 62*x_3_14*x_4_13 + 62*x_3_14*x_4_15 + 41*x_3_14*x_5_13\n",
      "  + 41*x_3_14*x_5_15 + 12*x_3_14*x_6_13 + 12*x_3_14*x_6_15 + 40*x_3_14*x_7_13\n",
      "  + 40*x_3_14*x_7_15 + 4*x_3_14*x_8_13 + 4*x_3_14*x_8_15 + 27*x_3_14*x_9_13\n",
      "  + 27*x_3_14*x_9_15 + 70*x_3_15*x_10_14 + 70*x_3_15*x_10_16 + 52*x_3_15*x_11_14\n",
      "  + 52*x_3_15*x_11_16 + 78*x_3_15*x_12_14 + 78*x_3_15*x_12_16\n",
      "  + 64*x_3_15*x_13_14 + 64*x_3_15*x_13_16 + 35*x_3_15*x_14_14\n",
      "  + 35*x_3_15*x_14_16 + 59*x_3_15*x_15_14 + 59*x_3_15*x_15_16\n",
      "  + 92*x_3_15*x_16_14 + 92*x_3_15*x_16_16 + 19*x_3_15*x_17_14\n",
      "  + 19*x_3_15*x_17_16 + 60*x_3_15*x_18_14 + 60*x_3_15*x_18_16\n",
      "  + 67*x_3_15*x_19_14 + 67*x_3_15*x_19_16 + 62*x_3_15*x_4_14 + 62*x_3_15*x_4_16\n",
      "  + 41*x_3_15*x_5_14 + 41*x_3_15*x_5_16 + 12*x_3_15*x_6_14 + 12*x_3_15*x_6_16\n",
      "  + 40*x_3_15*x_7_14 + 40*x_3_15*x_7_16 + 4*x_3_15*x_8_14 + 4*x_3_15*x_8_16\n",
      "  + 27*x_3_15*x_9_14 + 27*x_3_15*x_9_16 + 70*x_3_16*x_10_15 + 70*x_3_16*x_10_17\n",
      "  + 52*x_3_16*x_11_15 + 52*x_3_16*x_11_17 + 78*x_3_16*x_12_15\n",
      "  + 78*x_3_16*x_12_17 + 64*x_3_16*x_13_15 + 64*x_3_16*x_13_17\n",
      "  + 35*x_3_16*x_14_15 + 35*x_3_16*x_14_17 + 59*x_3_16*x_15_15\n",
      "  + 59*x_3_16*x_15_17 + 92*x_3_16*x_16_15 + 92*x_3_16*x_16_17\n",
      "  + 19*x_3_16*x_17_15 + 19*x_3_16*x_17_17 + 60*x_3_16*x_18_15\n",
      "  + 60*x_3_16*x_18_17 + 67*x_3_16*x_19_15 + 67*x_3_16*x_19_17 + 62*x_3_16*x_4_15\n",
      "  + 62*x_3_16*x_4_17 + 41*x_3_16*x_5_15 + 41*x_3_16*x_5_17 + 12*x_3_16*x_6_15\n",
      "  + 12*x_3_16*x_6_17 + 40*x_3_16*x_7_15 + 40*x_3_16*x_7_17 + 4*x_3_16*x_8_15\n",
      "  + 4*x_3_16*x_8_17 + 27*x_3_16*x_9_15 + 27*x_3_16*x_9_17 + 70*x_3_17*x_10_16\n",
      "  + 70*x_3_17*x_10_18 + 52*x_3_17*x_11_16 + 52*x_3_17*x_11_18\n",
      "  + 78*x_3_17*x_12_16 + 78*x_3_17*x_12_18 + 64*x_3_17*x_13_16\n",
      "  + 64*x_3_17*x_13_18 + 35*x_3_17*x_14_16 + 35*x_3_17*x_14_18\n",
      "  + 59*x_3_17*x_15_16 + 59*x_3_17*x_15_18 + 92*x_3_17*x_16_16\n",
      "  + 92*x_3_17*x_16_18 + 19*x_3_17*x_17_16 + 19*x_3_17*x_17_18\n",
      "  + 60*x_3_17*x_18_16 + 60*x_3_17*x_18_18 + 67*x_3_17*x_19_16\n",
      "  + 67*x_3_17*x_19_18 + 62*x_3_17*x_4_16 + 62*x_3_17*x_4_18 + 41*x_3_17*x_5_16\n",
      "  + 41*x_3_17*x_5_18 + 12*x_3_17*x_6_16 + 12*x_3_17*x_6_18 + 40*x_3_17*x_7_16\n",
      "  + 40*x_3_17*x_7_18 + 4*x_3_17*x_8_16 + 4*x_3_17*x_8_18 + 27*x_3_17*x_9_16\n",
      "  + 27*x_3_17*x_9_18 + 70*x_3_18*x_10_17 + 70*x_3_18*x_10_19 + 52*x_3_18*x_11_17\n",
      "  + 52*x_3_18*x_11_19 + 78*x_3_18*x_12_17 + 78*x_3_18*x_12_19\n",
      "  + 64*x_3_18*x_13_17 + 64*x_3_18*x_13_19 + 35*x_3_18*x_14_17\n",
      "  + 35*x_3_18*x_14_19 + 59*x_3_18*x_15_17 + 59*x_3_18*x_15_19\n",
      "  + 92*x_3_18*x_16_17 + 92*x_3_18*x_16_19 + 19*x_3_18*x_17_17\n",
      "  + 19*x_3_18*x_17_19 + 60*x_3_18*x_18_17 + 60*x_3_18*x_18_19\n",
      "  + 67*x_3_18*x_19_17 + 67*x_3_18*x_19_19 + 62*x_3_18*x_4_17 + 62*x_3_18*x_4_19\n",
      "  + 41*x_3_18*x_5_17 + 41*x_3_18*x_5_19 + 12*x_3_18*x_6_17 + 12*x_3_18*x_6_19\n",
      "  + 40*x_3_18*x_7_17 + 40*x_3_18*x_7_19 + 4*x_3_18*x_8_17 + 4*x_3_18*x_8_19\n",
      "  + 27*x_3_18*x_9_17 + 27*x_3_18*x_9_19 + 70*x_3_19*x_10_0 + 70*x_3_19*x_10_18\n",
      "  + 52*x_3_19*x_11_0 + 52*x_3_19*x_11_18 + 78*x_3_19*x_12_0 + 78*x_3_19*x_12_18\n",
      "  + 64*x_3_19*x_13_0 + 64*x_3_19*x_13_18 + 35*x_3_19*x_14_0 + 35*x_3_19*x_14_18\n",
      "  + 59*x_3_19*x_15_0 + 59*x_3_19*x_15_18 + 92*x_3_19*x_16_0 + 92*x_3_19*x_16_18\n",
      "  + 19*x_3_19*x_17_0 + 19*x_3_19*x_17_18 + 60*x_3_19*x_18_0 + 60*x_3_19*x_18_18\n",
      "  + 67*x_3_19*x_19_0 + 67*x_3_19*x_19_18 + 62*x_3_19*x_4_0 + 62*x_3_19*x_4_18\n",
      "  + 41*x_3_19*x_5_0 + 41*x_3_19*x_5_18 + 12*x_3_19*x_6_0 + 12*x_3_19*x_6_18\n",
      "  + 40*x_3_19*x_7_0 + 40*x_3_19*x_7_18 + 4*x_3_19*x_8_0 + 4*x_3_19*x_8_18\n",
      "  + 27*x_3_19*x_9_0 + 27*x_3_19*x_9_18 + 70*x_3_2*x_10_1 + 70*x_3_2*x_10_3\n",
      "  + 52*x_3_2*x_11_1 + 52*x_3_2*x_11_3 + 78*x_3_2*x_12_1 + 78*x_3_2*x_12_3\n",
      "  + 64*x_3_2*x_13_1 + 64*x_3_2*x_13_3 + 35*x_3_2*x_14_1 + 35*x_3_2*x_14_3\n",
      "  + 59*x_3_2*x_15_1 + 59*x_3_2*x_15_3 + 92*x_3_2*x_16_1 + 92*x_3_2*x_16_3\n",
      "  + 19*x_3_2*x_17_1 + 19*x_3_2*x_17_3 + 60*x_3_2*x_18_1 + 60*x_3_2*x_18_3\n",
      "  + 67*x_3_2*x_19_1 + 67*x_3_2*x_19_3 + 62*x_3_2*x_4_1 + 62*x_3_2*x_4_3\n",
      "  + 41*x_3_2*x_5_1 + 41*x_3_2*x_5_3 + 12*x_3_2*x_6_1 + 12*x_3_2*x_6_3\n",
      "  + 40*x_3_2*x_7_1 + 40*x_3_2*x_7_3 + 4*x_3_2*x_8_1 + 4*x_3_2*x_8_3\n",
      "  + 27*x_3_2*x_9_1 + 27*x_3_2*x_9_3 + 70*x_3_3*x_10_2 + 70*x_3_3*x_10_4\n",
      "  + 52*x_3_3*x_11_2 + 52*x_3_3*x_11_4 + 78*x_3_3*x_12_2 + 78*x_3_3*x_12_4\n",
      "  + 64*x_3_3*x_13_2 + 64*x_3_3*x_13_4 + 35*x_3_3*x_14_2 + 35*x_3_3*x_14_4\n",
      "  + 59*x_3_3*x_15_2 + 59*x_3_3*x_15_4 + 92*x_3_3*x_16_2 + 92*x_3_3*x_16_4\n",
      "  + 19*x_3_3*x_17_2 + 19*x_3_3*x_17_4 + 60*x_3_3*x_18_2 + 60*x_3_3*x_18_4\n",
      "  + 67*x_3_3*x_19_2 + 67*x_3_3*x_19_4 + 62*x_3_3*x_4_2 + 62*x_3_3*x_4_4\n",
      "  + 41*x_3_3*x_5_2 + 41*x_3_3*x_5_4 + 12*x_3_3*x_6_2 + 12*x_3_3*x_6_4\n",
      "  + 40*x_3_3*x_7_2 + 40*x_3_3*x_7_4 + 4*x_3_3*x_8_2 + 4*x_3_3*x_8_4\n",
      "  + 27*x_3_3*x_9_2 + 27*x_3_3*x_9_4 + 70*x_3_4*x_10_3 + 70*x_3_4*x_10_5\n",
      "  + 52*x_3_4*x_11_3 + 52*x_3_4*x_11_5 + 78*x_3_4*x_12_3 + 78*x_3_4*x_12_5\n",
      "  + 64*x_3_4*x_13_3 + 64*x_3_4*x_13_5 + 35*x_3_4*x_14_3 + 35*x_3_4*x_14_5\n",
      "  + 59*x_3_4*x_15_3 + 59*x_3_4*x_15_5 + 92*x_3_4*x_16_3 + 92*x_3_4*x_16_5\n",
      "  + 19*x_3_4*x_17_3 + 19*x_3_4*x_17_5 + 60*x_3_4*x_18_3 + 60*x_3_4*x_18_5\n",
      "  + 67*x_3_4*x_19_3 + 67*x_3_4*x_19_5 + 62*x_3_4*x_4_3 + 62*x_3_4*x_4_5\n",
      "  + 41*x_3_4*x_5_3 + 41*x_3_4*x_5_5 + 12*x_3_4*x_6_3 + 12*x_3_4*x_6_5\n",
      "  + 40*x_3_4*x_7_3 + 40*x_3_4*x_7_5 + 4*x_3_4*x_8_3 + 4*x_3_4*x_8_5\n",
      "  + 27*x_3_4*x_9_3 + 27*x_3_4*x_9_5 + 70*x_3_5*x_10_4 + 70*x_3_5*x_10_6\n",
      "  + 52*x_3_5*x_11_4 + 52*x_3_5*x_11_6 + 78*x_3_5*x_12_4 + 78*x_3_5*x_12_6\n",
      "  + 64*x_3_5*x_13_4 + 64*x_3_5*x_13_6 + 35*x_3_5*x_14_4 + 35*x_3_5*x_14_6\n",
      "  + 59*x_3_5*x_15_4 + 59*x_3_5*x_15_6 + 92*x_3_5*x_16_4 + 92*x_3_5*x_16_6\n",
      "  + 19*x_3_5*x_17_4 + 19*x_3_5*x_17_6 + 60*x_3_5*x_18_4 + 60*x_3_5*x_18_6\n",
      "  + 67*x_3_5*x_19_4 + 67*x_3_5*x_19_6 + 62*x_3_5*x_4_4 + 62*x_3_5*x_4_6\n",
      "  + 41*x_3_5*x_5_4 + 41*x_3_5*x_5_6 + 12*x_3_5*x_6_4 + 12*x_3_5*x_6_6\n",
      "  + 40*x_3_5*x_7_4 + 40*x_3_5*x_7_6 + 4*x_3_5*x_8_4 + 4*x_3_5*x_8_6\n",
      "  + 27*x_3_5*x_9_4 + 27*x_3_5*x_9_6 + 70*x_3_6*x_10_5 + 70*x_3_6*x_10_7\n",
      "  + 52*x_3_6*x_11_5 + 52*x_3_6*x_11_7 + 78*x_3_6*x_12_5 + 78*x_3_6*x_12_7\n",
      "  + 64*x_3_6*x_13_5 + 64*x_3_6*x_13_7 + 35*x_3_6*x_14_5 + 35*x_3_6*x_14_7\n",
      "  + 59*x_3_6*x_15_5 + 59*x_3_6*x_15_7 + 92*x_3_6*x_16_5 + 92*x_3_6*x_16_7\n",
      "  + 19*x_3_6*x_17_5 + 19*x_3_6*x_17_7 + 60*x_3_6*x_18_5 + 60*x_3_6*x_18_7\n",
      "  + 67*x_3_6*x_19_5 + 67*x_3_6*x_19_7 + 62*x_3_6*x_4_5 + 62*x_3_6*x_4_7\n",
      "  + 41*x_3_6*x_5_5 + 41*x_3_6*x_5_7 + 12*x_3_6*x_6_5 + 12*x_3_6*x_6_7\n",
      "  + 40*x_3_6*x_7_5 + 40*x_3_6*x_7_7 + 4*x_3_6*x_8_5 + 4*x_3_6*x_8_7\n",
      "  + 27*x_3_6*x_9_5 + 27*x_3_6*x_9_7 + 70*x_3_7*x_10_6 + 70*x_3_7*x_10_8\n",
      "  + 52*x_3_7*x_11_6 + 52*x_3_7*x_11_8 + 78*x_3_7*x_12_6 + 78*x_3_7*x_12_8\n",
      "  + 64*x_3_7*x_13_6 + 64*x_3_7*x_13_8 + 35*x_3_7*x_14_6 + 35*x_3_7*x_14_8\n",
      "  + 59*x_3_7*x_15_6 + 59*x_3_7*x_15_8 + 92*x_3_7*x_16_6 + 92*x_3_7*x_16_8\n",
      "  + 19*x_3_7*x_17_6 + 19*x_3_7*x_17_8 + 60*x_3_7*x_18_6 + 60*x_3_7*x_18_8\n",
      "  + 67*x_3_7*x_19_6 + 67*x_3_7*x_19_8 + 62*x_3_7*x_4_6 + 62*x_3_7*x_4_8\n",
      "  + 41*x_3_7*x_5_6 + 41*x_3_7*x_5_8 + 12*x_3_7*x_6_6 + 12*x_3_7*x_6_8\n",
      "  + 40*x_3_7*x_7_6 + 40*x_3_7*x_7_8 + 4*x_3_7*x_8_6 + 4*x_3_7*x_8_8\n",
      "  + 27*x_3_7*x_9_6 + 27*x_3_7*x_9_8 + 70*x_3_8*x_10_7 + 70*x_3_8*x_10_9\n",
      "  + 52*x_3_8*x_11_7 + 52*x_3_8*x_11_9 + 78*x_3_8*x_12_7 + 78*x_3_8*x_12_9\n",
      "  + 64*x_3_8*x_13_7 + 64*x_3_8*x_13_9 + 35*x_3_8*x_14_7 + 35*x_3_8*x_14_9\n",
      "  + 59*x_3_8*x_15_7 + 59*x_3_8*x_15_9 + 92*x_3_8*x_16_7 + 92*x_3_8*x_16_9\n",
      "  + 19*x_3_8*x_17_7 + 19*x_3_8*x_17_9 + 60*x_3_8*x_18_7 + 60*x_3_8*x_18_9\n",
      "  + 67*x_3_8*x_19_7 + 67*x_3_8*x_19_9 + 62*x_3_8*x_4_7 + 62*x_3_8*x_4_9\n",
      "  + 41*x_3_8*x_5_7 + 41*x_3_8*x_5_9 + 12*x_3_8*x_6_7 + 12*x_3_8*x_6_9\n",
      "  + 40*x_3_8*x_7_7 + 40*x_3_8*x_7_9 + 4*x_3_8*x_8_7 + 4*x_3_8*x_8_9\n",
      "  + 27*x_3_8*x_9_7 + 27*x_3_8*x_9_9 + 70*x_3_9*x_10_10 + 70*x_3_9*x_10_8\n",
      "  + 52*x_3_9*x_11_10 + 52*x_3_9*x_11_8 + 78*x_3_9*x_12_10 + 78*x_3_9*x_12_8\n",
      "  + 64*x_3_9*x_13_10 + 64*x_3_9*x_13_8 + 35*x_3_9*x_14_10 + 35*x_3_9*x_14_8\n",
      "  + 59*x_3_9*x_15_10 + 59*x_3_9*x_15_8 + 92*x_3_9*x_16_10 + 92*x_3_9*x_16_8\n",
      "  + 19*x_3_9*x_17_10 + 19*x_3_9*x_17_8 + 60*x_3_9*x_18_10 + 60*x_3_9*x_18_8\n",
      "  + 67*x_3_9*x_19_10 + 67*x_3_9*x_19_8 + 62*x_3_9*x_4_10 + 62*x_3_9*x_4_8\n",
      "  + 41*x_3_9*x_5_10 + 41*x_3_9*x_5_8 + 12*x_3_9*x_6_10 + 12*x_3_9*x_6_8\n",
      "  + 40*x_3_9*x_7_10 + 40*x_3_9*x_7_8 + 4*x_3_9*x_8_10 + 4*x_3_9*x_8_8\n",
      "  + 27*x_3_9*x_9_10 + 27*x_3_9*x_9_8 + 93*x_4_0*x_10_1 + 93*x_4_0*x_10_19\n",
      "  + 44*x_4_0*x_11_1 + 44*x_4_0*x_11_19 + 108*x_4_0*x_12_1 + 108*x_4_0*x_12_19\n",
      "  + 38*x_4_0*x_13_1 + 38*x_4_0*x_13_19 + 28*x_4_0*x_14_1 + 28*x_4_0*x_14_19\n",
      "  + 10*x_4_0*x_15_1 + 10*x_4_0*x_15_19 + 60*x_4_0*x_16_1 + 60*x_4_0*x_16_19\n",
      "  + 62*x_4_0*x_17_1 + 62*x_4_0*x_17_19 + 3*x_4_0*x_18_1 + 3*x_4_0*x_18_19\n",
      "  + 63*x_4_0*x_19_1 + 63*x_4_0*x_19_19 + 71*x_4_0*x_5_1 + 71*x_4_0*x_5_19\n",
      "  + 68*x_4_0*x_6_1 + 68*x_4_0*x_6_19 + 27*x_4_0*x_7_1 + 27*x_4_0*x_7_19\n",
      "  + 67*x_4_0*x_8_1 + 67*x_4_0*x_8_19 + 37*x_4_0*x_9_1 + 37*x_4_0*x_9_19\n",
      "  + 93*x_4_1*x_10_0 + 93*x_4_1*x_10_2 + 44*x_4_1*x_11_0 + 44*x_4_1*x_11_2\n",
      "  + 108*x_4_1*x_12_0 + 108*x_4_1*x_12_2 + 38*x_4_1*x_13_0 + 38*x_4_1*x_13_2\n",
      "  + 28*x_4_1*x_14_0 + 28*x_4_1*x_14_2 + 10*x_4_1*x_15_0 + 10*x_4_1*x_15_2\n",
      "  + 60*x_4_1*x_16_0 + 60*x_4_1*x_16_2 + 62*x_4_1*x_17_0 + 62*x_4_1*x_17_2\n",
      "  + 3*x_4_1*x_18_0 + 3*x_4_1*x_18_2 + 63*x_4_1*x_19_0 + 63*x_4_1*x_19_2\n",
      "  + 71*x_4_1*x_5_0 + 71*x_4_1*x_5_2 + 68*x_4_1*x_6_0 + 68*x_4_1*x_6_2\n",
      "  + 27*x_4_1*x_7_0 + 27*x_4_1*x_7_2 + 67*x_4_1*x_8_0 + 67*x_4_1*x_8_2\n",
      "  + 37*x_4_1*x_9_0 + 37*x_4_1*x_9_2 + 93*x_4_10*x_10_11 + 93*x_4_10*x_10_9\n",
      "  + 44*x_4_10*x_11_11 + 44*x_4_10*x_11_9 + 108*x_4_10*x_12_11\n",
      "  + 108*x_4_10*x_12_9 + 38*x_4_10*x_13_11 + 38*x_4_10*x_13_9 + 28*x_4_10*x_14_11\n",
      "  + 28*x_4_10*x_14_9 + 10*x_4_10*x_15_11 + 10*x_4_10*x_15_9 + 60*x_4_10*x_16_11\n",
      "  + 60*x_4_10*x_16_9 + 62*x_4_10*x_17_11 + 62*x_4_10*x_17_9 + 3*x_4_10*x_18_11\n",
      "  + 3*x_4_10*x_18_9 + 63*x_4_10*x_19_11 + 63*x_4_10*x_19_9 + 71*x_4_10*x_5_11\n",
      "  + 71*x_4_10*x_5_9 + 68*x_4_10*x_6_11 + 68*x_4_10*x_6_9 + 27*x_4_10*x_7_11\n",
      "  + 27*x_4_10*x_7_9 + 67*x_4_10*x_8_11 + 67*x_4_10*x_8_9 + 37*x_4_10*x_9_11\n",
      "  + 37*x_4_10*x_9_9 + 93*x_4_11*x_10_10 + 93*x_4_11*x_10_12 + 44*x_4_11*x_11_10\n",
      "  + 44*x_4_11*x_11_12 + 108*x_4_11*x_12_10 + 108*x_4_11*x_12_12\n",
      "  + 38*x_4_11*x_13_10 + 38*x_4_11*x_13_12 + 28*x_4_11*x_14_10\n",
      "  + 28*x_4_11*x_14_12 + 10*x_4_11*x_15_10 + 10*x_4_11*x_15_12\n",
      "  + 60*x_4_11*x_16_10 + 60*x_4_11*x_16_12 + 62*x_4_11*x_17_10\n",
      "  + 62*x_4_11*x_17_12 + 3*x_4_11*x_18_10 + 3*x_4_11*x_18_12 + 63*x_4_11*x_19_10\n",
      "  + 63*x_4_11*x_19_12 + 71*x_4_11*x_5_10 + 71*x_4_11*x_5_12 + 68*x_4_11*x_6_10\n",
      "  + 68*x_4_11*x_6_12 + 27*x_4_11*x_7_10 + 27*x_4_11*x_7_12 + 67*x_4_11*x_8_10\n",
      "  + 67*x_4_11*x_8_12 + 37*x_4_11*x_9_10 + 37*x_4_11*x_9_12 + 93*x_4_12*x_10_11\n",
      "  + 93*x_4_12*x_10_13 + 44*x_4_12*x_11_11 + 44*x_4_12*x_11_13\n",
      "  + 108*x_4_12*x_12_11 + 108*x_4_12*x_12_13 + 38*x_4_12*x_13_11\n",
      "  + 38*x_4_12*x_13_13 + 28*x_4_12*x_14_11 + 28*x_4_12*x_14_13\n",
      "  + 10*x_4_12*x_15_11 + 10*x_4_12*x_15_13 + 60*x_4_12*x_16_11\n",
      "  + 60*x_4_12*x_16_13 + 62*x_4_12*x_17_11 + 62*x_4_12*x_17_13 + 3*x_4_12*x_18_11\n",
      "  + 3*x_4_12*x_18_13 + 63*x_4_12*x_19_11 + 63*x_4_12*x_19_13 + 71*x_4_12*x_5_11\n",
      "  + 71*x_4_12*x_5_13 + 68*x_4_12*x_6_11 + 68*x_4_12*x_6_13 + 27*x_4_12*x_7_11\n",
      "  + 27*x_4_12*x_7_13 + 67*x_4_12*x_8_11 + 67*x_4_12*x_8_13 + 37*x_4_12*x_9_11\n",
      "  + 37*x_4_12*x_9_13 + 93*x_4_13*x_10_12 + 93*x_4_13*x_10_14 + 44*x_4_13*x_11_12\n",
      "  + 44*x_4_13*x_11_14 + 108*x_4_13*x_12_12 + 108*x_4_13*x_12_14\n",
      "  + 38*x_4_13*x_13_12 + 38*x_4_13*x_13_14 + 28*x_4_13*x_14_12\n",
      "  + 28*x_4_13*x_14_14 + 10*x_4_13*x_15_12 + 10*x_4_13*x_15_14\n",
      "  + 60*x_4_13*x_16_12 + 60*x_4_13*x_16_14 + 62*x_4_13*x_17_12\n",
      "  + 62*x_4_13*x_17_14 + 3*x_4_13*x_18_12 + 3*x_4_13*x_18_14 + 63*x_4_13*x_19_12\n",
      "  + 63*x_4_13*x_19_14 + 71*x_4_13*x_5_12 + 71*x_4_13*x_5_14 + 68*x_4_13*x_6_12\n",
      "  + 68*x_4_13*x_6_14 + 27*x_4_13*x_7_12 + 27*x_4_13*x_7_14 + 67*x_4_13*x_8_12\n",
      "  + 67*x_4_13*x_8_14 + 37*x_4_13*x_9_12 + 37*x_4_13*x_9_14 + 93*x_4_14*x_10_13\n",
      "  + 93*x_4_14*x_10_15 + 44*x_4_14*x_11_13 + 44*x_4_14*x_11_15\n",
      "  + 108*x_4_14*x_12_13 + 108*x_4_14*x_12_15 + 38*x_4_14*x_13_13\n",
      "  + 38*x_4_14*x_13_15 + 28*x_4_14*x_14_13 + 28*x_4_14*x_14_15\n",
      "  + 10*x_4_14*x_15_13 + 10*x_4_14*x_15_15 + 60*x_4_14*x_16_13\n",
      "  + 60*x_4_14*x_16_15 + 62*x_4_14*x_17_13 + 62*x_4_14*x_17_15 + 3*x_4_14*x_18_13\n",
      "  + 3*x_4_14*x_18_15 + 63*x_4_14*x_19_13 + 63*x_4_14*x_19_15 + 71*x_4_14*x_5_13\n",
      "  + 71*x_4_14*x_5_15 + 68*x_4_14*x_6_13 + 68*x_4_14*x_6_15 + 27*x_4_14*x_7_13\n",
      "  + 27*x_4_14*x_7_15 + 67*x_4_14*x_8_13 + 67*x_4_14*x_8_15 + 37*x_4_14*x_9_13\n",
      "  + 37*x_4_14*x_9_15 + 93*x_4_15*x_10_14 + 93*x_4_15*x_10_16 + 44*x_4_15*x_11_14\n",
      "  + 44*x_4_15*x_11_16 + 108*x_4_15*x_12_14 + 108*x_4_15*x_12_16\n",
      "  + 38*x_4_15*x_13_14 + 38*x_4_15*x_13_16 + 28*x_4_15*x_14_14\n",
      "  + 28*x_4_15*x_14_16 + 10*x_4_15*x_15_14 + 10*x_4_15*x_15_16\n",
      "  + 60*x_4_15*x_16_14 + 60*x_4_15*x_16_16 + 62*x_4_15*x_17_14\n",
      "  + 62*x_4_15*x_17_16 + 3*x_4_15*x_18_14 + 3*x_4_15*x_18_16 + 63*x_4_15*x_19_14\n",
      "  + 63*x_4_15*x_19_16 + 71*x_4_15*x_5_14 + 71*x_4_15*x_5_16 + 68*x_4_15*x_6_14\n",
      "  + 68*x_4_15*x_6_16 + 27*x_4_15*x_7_14 + 27*x_4_15*x_7_16 + 67*x_4_15*x_8_14\n",
      "  + 67*x_4_15*x_8_16 + 37*x_4_15*x_9_14 + 37*x_4_15*x_9_16 + 93*x_4_16*x_10_15\n",
      "  + 93*x_4_16*x_10_17 + 44*x_4_16*x_11_15 + 44*x_4_16*x_11_17\n",
      "  + 108*x_4_16*x_12_15 + 108*x_4_16*x_12_17 + 38*x_4_16*x_13_15\n",
      "  + 38*x_4_16*x_13_17 + 28*x_4_16*x_14_15 + 28*x_4_16*x_14_17\n",
      "  + 10*x_4_16*x_15_15 + 10*x_4_16*x_15_17 + 60*x_4_16*x_16_15\n",
      "  + 60*x_4_16*x_16_17 + 62*x_4_16*x_17_15 + 62*x_4_16*x_17_17 + 3*x_4_16*x_18_15\n",
      "  + 3*x_4_16*x_18_17 + 63*x_4_16*x_19_15 + 63*x_4_16*x_19_17 + 71*x_4_16*x_5_15\n",
      "  + 71*x_4_16*x_5_17 + 68*x_4_16*x_6_15 + 68*x_4_16*x_6_17 + 27*x_4_16*x_7_15\n",
      "  + 27*x_4_16*x_7_17 + 67*x_4_16*x_8_15 + 67*x_4_16*x_8_17 + 37*x_4_16*x_9_15\n",
      "  + 37*x_4_16*x_9_17 + 93*x_4_17*x_10_16 + 93*x_4_17*x_10_18 + 44*x_4_17*x_11_16\n",
      "  + 44*x_4_17*x_11_18 + 108*x_4_17*x_12_16 + 108*x_4_17*x_12_18\n",
      "  + 38*x_4_17*x_13_16 + 38*x_4_17*x_13_18 + 28*x_4_17*x_14_16\n",
      "  + 28*x_4_17*x_14_18 + 10*x_4_17*x_15_16 + 10*x_4_17*x_15_18\n",
      "  + 60*x_4_17*x_16_16 + 60*x_4_17*x_16_18 + 62*x_4_17*x_17_16\n",
      "  + 62*x_4_17*x_17_18 + 3*x_4_17*x_18_16 + 3*x_4_17*x_18_18 + 63*x_4_17*x_19_16\n",
      "  + 63*x_4_17*x_19_18 + 71*x_4_17*x_5_16 + 71*x_4_17*x_5_18 + 68*x_4_17*x_6_16\n",
      "  + 68*x_4_17*x_6_18 + 27*x_4_17*x_7_16 + 27*x_4_17*x_7_18 + 67*x_4_17*x_8_16\n",
      "  + 67*x_4_17*x_8_18 + 37*x_4_17*x_9_16 + 37*x_4_17*x_9_18 + 93*x_4_18*x_10_17\n",
      "  + 93*x_4_18*x_10_19 + 44*x_4_18*x_11_17 + 44*x_4_18*x_11_19\n",
      "  + 108*x_4_18*x_12_17 + 108*x_4_18*x_12_19 + 38*x_4_18*x_13_17\n",
      "  + 38*x_4_18*x_13_19 + 28*x_4_18*x_14_17 + 28*x_4_18*x_14_19\n",
      "  + 10*x_4_18*x_15_17 + 10*x_4_18*x_15_19 + 60*x_4_18*x_16_17\n",
      "  + 60*x_4_18*x_16_19 + 62*x_4_18*x_17_17 + 62*x_4_18*x_17_19 + 3*x_4_18*x_18_17\n",
      "  + 3*x_4_18*x_18_19 + 63*x_4_18*x_19_17 + 63*x_4_18*x_19_19 + 71*x_4_18*x_5_17\n",
      "  + 71*x_4_18*x_5_19 + 68*x_4_18*x_6_17 + 68*x_4_18*x_6_19 + 27*x_4_18*x_7_17\n",
      "  + 27*x_4_18*x_7_19 + 67*x_4_18*x_8_17 + 67*x_4_18*x_8_19 + 37*x_4_18*x_9_17\n",
      "  + 37*x_4_18*x_9_19 + 93*x_4_19*x_10_0 + 93*x_4_19*x_10_18 + 44*x_4_19*x_11_0\n",
      "  + 44*x_4_19*x_11_18 + 108*x_4_19*x_12_0 + 108*x_4_19*x_12_18\n",
      "  + 38*x_4_19*x_13_0 + 38*x_4_19*x_13_18 + 28*x_4_19*x_14_0 + 28*x_4_19*x_14_18\n",
      "  + 10*x_4_19*x_15_0 + 10*x_4_19*x_15_18 + 60*x_4_19*x_16_0 + 60*x_4_19*x_16_18\n",
      "  + 62*x_4_19*x_17_0 + 62*x_4_19*x_17_18 + 3*x_4_19*x_18_0 + 3*x_4_19*x_18_18\n",
      "  + 63*x_4_19*x_19_0 + 63*x_4_19*x_19_18 + 71*x_4_19*x_5_0 + 71*x_4_19*x_5_18\n",
      "  + 68*x_4_19*x_6_0 + 68*x_4_19*x_6_18 + 27*x_4_19*x_7_0 + 27*x_4_19*x_7_18\n",
      "  + 67*x_4_19*x_8_0 + 67*x_4_19*x_8_18 + 37*x_4_19*x_9_0 + 37*x_4_19*x_9_18\n",
      "  + 93*x_4_2*x_10_1 + 93*x_4_2*x_10_3 + 44*x_4_2*x_11_1 + 44*x_4_2*x_11_3\n",
      "  + 108*x_4_2*x_12_1 + 108*x_4_2*x_12_3 + 38*x_4_2*x_13_1 + 38*x_4_2*x_13_3\n",
      "  + 28*x_4_2*x_14_1 + 28*x_4_2*x_14_3 + 10*x_4_2*x_15_1 + 10*x_4_2*x_15_3\n",
      "  + 60*x_4_2*x_16_1 + 60*x_4_2*x_16_3 + 62*x_4_2*x_17_1 + 62*x_4_2*x_17_3\n",
      "  + 3*x_4_2*x_18_1 + 3*x_4_2*x_18_3 + 63*x_4_2*x_19_1 + 63*x_4_2*x_19_3\n",
      "  + 71*x_4_2*x_5_1 + 71*x_4_2*x_5_3 + 68*x_4_2*x_6_1 + 68*x_4_2*x_6_3\n",
      "  + 27*x_4_2*x_7_1 + 27*x_4_2*x_7_3 + 67*x_4_2*x_8_1 + 67*x_4_2*x_8_3\n",
      "  + 37*x_4_2*x_9_1 + 37*x_4_2*x_9_3 + 93*x_4_3*x_10_2 + 93*x_4_3*x_10_4\n",
      "  + 44*x_4_3*x_11_2 + 44*x_4_3*x_11_4 + 108*x_4_3*x_12_2 + 108*x_4_3*x_12_4\n",
      "  + 38*x_4_3*x_13_2 + 38*x_4_3*x_13_4 + 28*x_4_3*x_14_2 + 28*x_4_3*x_14_4\n",
      "  + 10*x_4_3*x_15_2 + 10*x_4_3*x_15_4 + 60*x_4_3*x_16_2 + 60*x_4_3*x_16_4\n",
      "  + 62*x_4_3*x_17_2 + 62*x_4_3*x_17_4 + 3*x_4_3*x_18_2 + 3*x_4_3*x_18_4\n",
      "  + 63*x_4_3*x_19_2 + 63*x_4_3*x_19_4 + 71*x_4_3*x_5_2 + 71*x_4_3*x_5_4\n",
      "  + 68*x_4_3*x_6_2 + 68*x_4_3*x_6_4 + 27*x_4_3*x_7_2 + 27*x_4_3*x_7_4\n",
      "  + 67*x_4_3*x_8_2 + 67*x_4_3*x_8_4 + 37*x_4_3*x_9_2 + 37*x_4_3*x_9_4\n",
      "  + 93*x_4_4*x_10_3 + 93*x_4_4*x_10_5 + 44*x_4_4*x_11_3 + 44*x_4_4*x_11_5\n",
      "  + 108*x_4_4*x_12_3 + 108*x_4_4*x_12_5 + 38*x_4_4*x_13_3 + 38*x_4_4*x_13_5\n",
      "  + 28*x_4_4*x_14_3 + 28*x_4_4*x_14_5 + 10*x_4_4*x_15_3 + 10*x_4_4*x_15_5\n",
      "  + 60*x_4_4*x_16_3 + 60*x_4_4*x_16_5 + 62*x_4_4*x_17_3 + 62*x_4_4*x_17_5\n",
      "  + 3*x_4_4*x_18_3 + 3*x_4_4*x_18_5 + 63*x_4_4*x_19_3 + 63*x_4_4*x_19_5\n",
      "  + 71*x_4_4*x_5_3 + 71*x_4_4*x_5_5 + 68*x_4_4*x_6_3 + 68*x_4_4*x_6_5\n",
      "  + 27*x_4_4*x_7_3 + 27*x_4_4*x_7_5 + 67*x_4_4*x_8_3 + 67*x_4_4*x_8_5\n",
      "  + 37*x_4_4*x_9_3 + 37*x_4_4*x_9_5 + 93*x_4_5*x_10_4 + 93*x_4_5*x_10_6\n",
      "  + 44*x_4_5*x_11_4 + 44*x_4_5*x_11_6 + 108*x_4_5*x_12_4 + 108*x_4_5*x_12_6\n",
      "  + 38*x_4_5*x_13_4 + 38*x_4_5*x_13_6 + 28*x_4_5*x_14_4 + 28*x_4_5*x_14_6\n",
      "  + 10*x_4_5*x_15_4 + 10*x_4_5*x_15_6 + 60*x_4_5*x_16_4 + 60*x_4_5*x_16_6\n",
      "  + 62*x_4_5*x_17_4 + 62*x_4_5*x_17_6 + 3*x_4_5*x_18_4 + 3*x_4_5*x_18_6\n",
      "  + 63*x_4_5*x_19_4 + 63*x_4_5*x_19_6 + 71*x_4_5*x_5_4 + 71*x_4_5*x_5_6\n",
      "  + 68*x_4_5*x_6_4 + 68*x_4_5*x_6_6 + 27*x_4_5*x_7_4 + 27*x_4_5*x_7_6\n",
      "  + 67*x_4_5*x_8_4 + 67*x_4_5*x_8_6 + 37*x_4_5*x_9_4 + 37*x_4_5*x_9_6\n",
      "  + 93*x_4_6*x_10_5 + 93*x_4_6*x_10_7 + 44*x_4_6*x_11_5 + 44*x_4_6*x_11_7\n",
      "  + 108*x_4_6*x_12_5 + 108*x_4_6*x_12_7 + 38*x_4_6*x_13_5 + 38*x_4_6*x_13_7\n",
      "  + 28*x_4_6*x_14_5 + 28*x_4_6*x_14_7 + 10*x_4_6*x_15_5 + 10*x_4_6*x_15_7\n",
      "  + 60*x_4_6*x_16_5 + 60*x_4_6*x_16_7 + 62*x_4_6*x_17_5 + 62*x_4_6*x_17_7\n",
      "  + 3*x_4_6*x_18_5 + 3*x_4_6*x_18_7 + 63*x_4_6*x_19_5 + 63*x_4_6*x_19_7\n",
      "  + 71*x_4_6*x_5_5 + 71*x_4_6*x_5_7 + 68*x_4_6*x_6_5 + 68*x_4_6*x_6_7\n",
      "  + 27*x_4_6*x_7_5 + 27*x_4_6*x_7_7 + 67*x_4_6*x_8_5 + 67*x_4_6*x_8_7\n",
      "  + 37*x_4_6*x_9_5 + 37*x_4_6*x_9_7 + 93*x_4_7*x_10_6 + 93*x_4_7*x_10_8\n",
      "  + 44*x_4_7*x_11_6 + 44*x_4_7*x_11_8 + 108*x_4_7*x_12_6 + 108*x_4_7*x_12_8\n",
      "  + 38*x_4_7*x_13_6 + 38*x_4_7*x_13_8 + 28*x_4_7*x_14_6 + 28*x_4_7*x_14_8\n",
      "  + 10*x_4_7*x_15_6 + 10*x_4_7*x_15_8 + 60*x_4_7*x_16_6 + 60*x_4_7*x_16_8\n",
      "  + 62*x_4_7*x_17_6 + 62*x_4_7*x_17_8 + 3*x_4_7*x_18_6 + 3*x_4_7*x_18_8\n",
      "  + 63*x_4_7*x_19_6 + 63*x_4_7*x_19_8 + 71*x_4_7*x_5_6 + 71*x_4_7*x_5_8\n",
      "  + 68*x_4_7*x_6_6 + 68*x_4_7*x_6_8 + 27*x_4_7*x_7_6 + 27*x_4_7*x_7_8\n",
      "  + 67*x_4_7*x_8_6 + 67*x_4_7*x_8_8 + 37*x_4_7*x_9_6 + 37*x_4_7*x_9_8\n",
      "  + 93*x_4_8*x_10_7 + 93*x_4_8*x_10_9 + 44*x_4_8*x_11_7 + 44*x_4_8*x_11_9\n",
      "  + 108*x_4_8*x_12_7 + 108*x_4_8*x_12_9 + 38*x_4_8*x_13_7 + 38*x_4_8*x_13_9\n",
      "  + 28*x_4_8*x_14_7 + 28*x_4_8*x_14_9 + 10*x_4_8*x_15_7 + 10*x_4_8*x_15_9\n",
      "  + 60*x_4_8*x_16_7 + 60*x_4_8*x_16_9 + 62*x_4_8*x_17_7 + 62*x_4_8*x_17_9\n",
      "  + 3*x_4_8*x_18_7 + 3*x_4_8*x_18_9 + 63*x_4_8*x_19_7 + 63*x_4_8*x_19_9\n",
      "  + 71*x_4_8*x_5_7 + 71*x_4_8*x_5_9 + 68*x_4_8*x_6_7 + 68*x_4_8*x_6_9\n",
      "  + 27*x_4_8*x_7_7 + 27*x_4_8*x_7_9 + 67*x_4_8*x_8_7 + 67*x_4_8*x_8_9\n",
      "  + 37*x_4_8*x_9_7 + 37*x_4_8*x_9_9 + 93*x_4_9*x_10_10 + 93*x_4_9*x_10_8\n",
      "  + 44*x_4_9*x_11_10 + 44*x_4_9*x_11_8 + 108*x_4_9*x_12_10 + 108*x_4_9*x_12_8\n",
      "  + 38*x_4_9*x_13_10 + 38*x_4_9*x_13_8 + 28*x_4_9*x_14_10 + 28*x_4_9*x_14_8\n",
      "  + 10*x_4_9*x_15_10 + 10*x_4_9*x_15_8 + 60*x_4_9*x_16_10 + 60*x_4_9*x_16_8\n",
      "  + 62*x_4_9*x_17_10 + 62*x_4_9*x_17_8 + 3*x_4_9*x_18_10 + 3*x_4_9*x_18_8\n",
      "  + 63*x_4_9*x_19_10 + 63*x_4_9*x_19_8 + 71*x_4_9*x_5_10 + 71*x_4_9*x_5_8\n",
      "  + 68*x_4_9*x_6_10 + 68*x_4_9*x_6_8 + 27*x_4_9*x_7_10 + 27*x_4_9*x_7_8\n",
      "  + 67*x_4_9*x_8_10 + 67*x_4_9*x_8_8 + 37*x_4_9*x_9_10 + 37*x_4_9*x_9_8\n",
      "  + 29*x_5_0*x_10_1 + 29*x_5_0*x_10_19 + 34*x_5_0*x_11_1 + 34*x_5_0*x_11_19\n",
      "  + 40*x_5_0*x_12_1 + 40*x_5_0*x_12_19 + 49*x_5_0*x_13_1 + 49*x_5_0*x_13_19\n",
      "  + 56*x_5_0*x_14_1 + 56*x_5_0*x_14_19 + 74*x_5_0*x_15_1 + 74*x_5_0*x_15_19\n",
      "  + 69*x_5_0*x_16_1 + 69*x_5_0*x_16_19 + 22*x_5_0*x_17_1 + 22*x_5_0*x_17_19\n",
      "  + 68*x_5_0*x_18_1 + 68*x_5_0*x_18_19 + 35*x_5_0*x_19_1 + 35*x_5_0*x_19_19\n",
      "  + 31*x_5_0*x_6_1 + 31*x_5_0*x_6_19 + 45*x_5_0*x_7_1 + 45*x_5_0*x_7_19\n",
      "  + 41*x_5_0*x_8_1 + 41*x_5_0*x_8_19 + 40*x_5_0*x_9_1 + 40*x_5_0*x_9_19\n",
      "  + 29*x_5_1*x_10_0 + 29*x_5_1*x_10_2 + 34*x_5_1*x_11_0 + 34*x_5_1*x_11_2\n",
      "  + 40*x_5_1*x_12_0 + 40*x_5_1*x_12_2 + 49*x_5_1*x_13_0 + 49*x_5_1*x_13_2\n",
      "  + 56*x_5_1*x_14_0 + 56*x_5_1*x_14_2 + 74*x_5_1*x_15_0 + 74*x_5_1*x_15_2\n",
      "  + 69*x_5_1*x_16_0 + 69*x_5_1*x_16_2 + 22*x_5_1*x_17_0 + 22*x_5_1*x_17_2\n",
      "  + 68*x_5_1*x_18_0 + 68*x_5_1*x_18_2 + 35*x_5_1*x_19_0 + 35*x_5_1*x_19_2\n",
      "  + 31*x_5_1*x_6_0 + 31*x_5_1*x_6_2 + 45*x_5_1*x_7_0 + 45*x_5_1*x_7_2\n",
      "  + 41*x_5_1*x_8_0 + 41*x_5_1*x_8_2 + 40*x_5_1*x_9_0 + 40*x_5_1*x_9_2\n",
      "  + 29*x_5_10*x_10_11 + 29*x_5_10*x_10_9 + 34*x_5_10*x_11_11 + 34*x_5_10*x_11_9\n",
      "  + 40*x_5_10*x_12_11 + 40*x_5_10*x_12_9 + 49*x_5_10*x_13_11 + 49*x_5_10*x_13_9\n",
      "  + 56*x_5_10*x_14_11 + 56*x_5_10*x_14_9 + 74*x_5_10*x_15_11 + 74*x_5_10*x_15_9\n",
      "  + 69*x_5_10*x_16_11 + 69*x_5_10*x_16_9 + 22*x_5_10*x_17_11 + 22*x_5_10*x_17_9\n",
      "  + 68*x_5_10*x_18_11 + 68*x_5_10*x_18_9 + 35*x_5_10*x_19_11 + 35*x_5_10*x_19_9\n",
      "  + 31*x_5_10*x_6_11 + 31*x_5_10*x_6_9 + 45*x_5_10*x_7_11 + 45*x_5_10*x_7_9\n",
      "  + 41*x_5_10*x_8_11 + 41*x_5_10*x_8_9 + 40*x_5_10*x_9_11 + 40*x_5_10*x_9_9\n",
      "  + 29*x_5_11*x_10_10 + 29*x_5_11*x_10_12 + 34*x_5_11*x_11_10\n",
      "  + 34*x_5_11*x_11_12 + 40*x_5_11*x_12_10 + 40*x_5_11*x_12_12\n",
      "  + 49*x_5_11*x_13_10 + 49*x_5_11*x_13_12 + 56*x_5_11*x_14_10\n",
      "  + 56*x_5_11*x_14_12 + 74*x_5_11*x_15_10 + 74*x_5_11*x_15_12\n",
      "  + 69*x_5_11*x_16_10 + 69*x_5_11*x_16_12 + 22*x_5_11*x_17_10\n",
      "  + 22*x_5_11*x_17_12 + 68*x_5_11*x_18_10 + 68*x_5_11*x_18_12\n",
      "  + 35*x_5_11*x_19_10 + 35*x_5_11*x_19_12 + 31*x_5_11*x_6_10 + 31*x_5_11*x_6_12\n",
      "  + 45*x_5_11*x_7_10 + 45*x_5_11*x_7_12 + 41*x_5_11*x_8_10 + 41*x_5_11*x_8_12\n",
      "  + 40*x_5_11*x_9_10 + 40*x_5_11*x_9_12 + 29*x_5_12*x_10_11 + 29*x_5_12*x_10_13\n",
      "  + 34*x_5_12*x_11_11 + 34*x_5_12*x_11_13 + 40*x_5_12*x_12_11\n",
      "  + 40*x_5_12*x_12_13 + 49*x_5_12*x_13_11 + 49*x_5_12*x_13_13\n",
      "  + 56*x_5_12*x_14_11 + 56*x_5_12*x_14_13 + 74*x_5_12*x_15_11\n",
      "  + 74*x_5_12*x_15_13 + 69*x_5_12*x_16_11 + 69*x_5_12*x_16_13\n",
      "  + 22*x_5_12*x_17_11 + 22*x_5_12*x_17_13 + 68*x_5_12*x_18_11\n",
      "  + 68*x_5_12*x_18_13 + 35*x_5_12*x_19_11 + 35*x_5_12*x_19_13 + 31*x_5_12*x_6_11\n",
      "  + 31*x_5_12*x_6_13 + 45*x_5_12*x_7_11 + 45*x_5_12*x_7_13 + 41*x_5_12*x_8_11\n",
      "  + 41*x_5_12*x_8_13 + 40*x_5_12*x_9_11 + 40*x_5_12*x_9_13 + 29*x_5_13*x_10_12\n",
      "  + 29*x_5_13*x_10_14 + 34*x_5_13*x_11_12 + 34*x_5_13*x_11_14\n",
      "  + 40*x_5_13*x_12_12 + 40*x_5_13*x_12_14 + 49*x_5_13*x_13_12\n",
      "  + 49*x_5_13*x_13_14 + 56*x_5_13*x_14_12 + 56*x_5_13*x_14_14\n",
      "  + 74*x_5_13*x_15_12 + 74*x_5_13*x_15_14 + 69*x_5_13*x_16_12\n",
      "  + 69*x_5_13*x_16_14 + 22*x_5_13*x_17_12 + 22*x_5_13*x_17_14\n",
      "  + 68*x_5_13*x_18_12 + 68*x_5_13*x_18_14 + 35*x_5_13*x_19_12\n",
      "  + 35*x_5_13*x_19_14 + 31*x_5_13*x_6_12 + 31*x_5_13*x_6_14 + 45*x_5_13*x_7_12\n",
      "  + 45*x_5_13*x_7_14 + 41*x_5_13*x_8_12 + 41*x_5_13*x_8_14 + 40*x_5_13*x_9_12\n",
      "  + 40*x_5_13*x_9_14 + 29*x_5_14*x_10_13 + 29*x_5_14*x_10_15 + 34*x_5_14*x_11_13\n",
      "  + 34*x_5_14*x_11_15 + 40*x_5_14*x_12_13 + 40*x_5_14*x_12_15\n",
      "  + 49*x_5_14*x_13_13 + 49*x_5_14*x_13_15 + 56*x_5_14*x_14_13\n",
      "  + 56*x_5_14*x_14_15 + 74*x_5_14*x_15_13 + 74*x_5_14*x_15_15\n",
      "  + 69*x_5_14*x_16_13 + 69*x_5_14*x_16_15 + 22*x_5_14*x_17_13\n",
      "  + 22*x_5_14*x_17_15 + 68*x_5_14*x_18_13 + 68*x_5_14*x_18_15\n",
      "  + 35*x_5_14*x_19_13 + 35*x_5_14*x_19_15 + 31*x_5_14*x_6_13 + 31*x_5_14*x_6_15\n",
      "  + 45*x_5_14*x_7_13 + 45*x_5_14*x_7_15 + 41*x_5_14*x_8_13 + 41*x_5_14*x_8_15\n",
      "  + 40*x_5_14*x_9_13 + 40*x_5_14*x_9_15 + 29*x_5_15*x_10_14 + 29*x_5_15*x_10_16\n",
      "  + 34*x_5_15*x_11_14 + 34*x_5_15*x_11_16 + 40*x_5_15*x_12_14\n",
      "  + 40*x_5_15*x_12_16 + 49*x_5_15*x_13_14 + 49*x_5_15*x_13_16\n",
      "  + 56*x_5_15*x_14_14 + 56*x_5_15*x_14_16 + 74*x_5_15*x_15_14\n",
      "  + 74*x_5_15*x_15_16 + 69*x_5_15*x_16_14 + 69*x_5_15*x_16_16\n",
      "  + 22*x_5_15*x_17_14 + 22*x_5_15*x_17_16 + 68*x_5_15*x_18_14\n",
      "  + 68*x_5_15*x_18_16 + 35*x_5_15*x_19_14 + 35*x_5_15*x_19_16 + 31*x_5_15*x_6_14\n",
      "  + 31*x_5_15*x_6_16 + 45*x_5_15*x_7_14 + 45*x_5_15*x_7_16 + 41*x_5_15*x_8_14\n",
      "  + 41*x_5_15*x_8_16 + 40*x_5_15*x_9_14 + 40*x_5_15*x_9_16 + 29*x_5_16*x_10_15\n",
      "  + 29*x_5_16*x_10_17 + 34*x_5_16*x_11_15 + 34*x_5_16*x_11_17\n",
      "  + 40*x_5_16*x_12_15 + 40*x_5_16*x_12_17 + 49*x_5_16*x_13_15\n",
      "  + 49*x_5_16*x_13_17 + 56*x_5_16*x_14_15 + 56*x_5_16*x_14_17\n",
      "  + 74*x_5_16*x_15_15 + 74*x_5_16*x_15_17 + 69*x_5_16*x_16_15\n",
      "  + 69*x_5_16*x_16_17 + 22*x_5_16*x_17_15 + 22*x_5_16*x_17_17\n",
      "  + 68*x_5_16*x_18_15 + 68*x_5_16*x_18_17 + 35*x_5_16*x_19_15\n",
      "  + 35*x_5_16*x_19_17 + 31*x_5_16*x_6_15 + 31*x_5_16*x_6_17 + 45*x_5_16*x_7_15\n",
      "  + 45*x_5_16*x_7_17 + 41*x_5_16*x_8_15 + 41*x_5_16*x_8_17 + 40*x_5_16*x_9_15\n",
      "  + 40*x_5_16*x_9_17 + 29*x_5_17*x_10_16 + 29*x_5_17*x_10_18 + 34*x_5_17*x_11_16\n",
      "  + 34*x_5_17*x_11_18 + 40*x_5_17*x_12_16 + 40*x_5_17*x_12_18\n",
      "  + 49*x_5_17*x_13_16 + 49*x_5_17*x_13_18 + 56*x_5_17*x_14_16\n",
      "  + 56*x_5_17*x_14_18 + 74*x_5_17*x_15_16 + 74*x_5_17*x_15_18\n",
      "  + 69*x_5_17*x_16_16 + 69*x_5_17*x_16_18 + 22*x_5_17*x_17_16\n",
      "  + 22*x_5_17*x_17_18 + 68*x_5_17*x_18_16 + 68*x_5_17*x_18_18\n",
      "  + 35*x_5_17*x_19_16 + 35*x_5_17*x_19_18 + 31*x_5_17*x_6_16 + 31*x_5_17*x_6_18\n",
      "  + 45*x_5_17*x_7_16 + 45*x_5_17*x_7_18 + 41*x_5_17*x_8_16 + 41*x_5_17*x_8_18\n",
      "  + 40*x_5_17*x_9_16 + 40*x_5_17*x_9_18 + 29*x_5_18*x_10_17 + 29*x_5_18*x_10_19\n",
      "  + 34*x_5_18*x_11_17 + 34*x_5_18*x_11_19 + 40*x_5_18*x_12_17\n",
      "  + 40*x_5_18*x_12_19 + 49*x_5_18*x_13_17 + 49*x_5_18*x_13_19\n",
      "  + 56*x_5_18*x_14_17 + 56*x_5_18*x_14_19 + 74*x_5_18*x_15_17\n",
      "  + 74*x_5_18*x_15_19 + 69*x_5_18*x_16_17 + 69*x_5_18*x_16_19\n",
      "  + 22*x_5_18*x_17_17 + 22*x_5_18*x_17_19 + 68*x_5_18*x_18_17\n",
      "  + 68*x_5_18*x_18_19 + 35*x_5_18*x_19_17 + 35*x_5_18*x_19_19 + 31*x_5_18*x_6_17\n",
      "  + 31*x_5_18*x_6_19 + 45*x_5_18*x_7_17 + 45*x_5_18*x_7_19 + 41*x_5_18*x_8_17\n",
      "  + 41*x_5_18*x_8_19 + 40*x_5_18*x_9_17 + 40*x_5_18*x_9_19 + 29*x_5_19*x_10_0\n",
      "  + 29*x_5_19*x_10_18 + 34*x_5_19*x_11_0 + 34*x_5_19*x_11_18 + 40*x_5_19*x_12_0\n",
      "  + 40*x_5_19*x_12_18 + 49*x_5_19*x_13_0 + 49*x_5_19*x_13_18 + 56*x_5_19*x_14_0\n",
      "  + 56*x_5_19*x_14_18 + 74*x_5_19*x_15_0 + 74*x_5_19*x_15_18 + 69*x_5_19*x_16_0\n",
      "  + 69*x_5_19*x_16_18 + 22*x_5_19*x_17_0 + 22*x_5_19*x_17_18 + 68*x_5_19*x_18_0\n",
      "  + 68*x_5_19*x_18_18 + 35*x_5_19*x_19_0 + 35*x_5_19*x_19_18 + 31*x_5_19*x_6_0\n",
      "  + 31*x_5_19*x_6_18 + 45*x_5_19*x_7_0 + 45*x_5_19*x_7_18 + 41*x_5_19*x_8_0\n",
      "  + 41*x_5_19*x_8_18 + 40*x_5_19*x_9_0 + 40*x_5_19*x_9_18 + 29*x_5_2*x_10_1\n",
      "  + 29*x_5_2*x_10_3 + 34*x_5_2*x_11_1 + 34*x_5_2*x_11_3 + 40*x_5_2*x_12_1\n",
      "  + 40*x_5_2*x_12_3 + 49*x_5_2*x_13_1 + 49*x_5_2*x_13_3 + 56*x_5_2*x_14_1\n",
      "  + 56*x_5_2*x_14_3 + 74*x_5_2*x_15_1 + 74*x_5_2*x_15_3 + 69*x_5_2*x_16_1\n",
      "  + 69*x_5_2*x_16_3 + 22*x_5_2*x_17_1 + 22*x_5_2*x_17_3 + 68*x_5_2*x_18_1\n",
      "  + 68*x_5_2*x_18_3 + 35*x_5_2*x_19_1 + 35*x_5_2*x_19_3 + 31*x_5_2*x_6_1\n",
      "  + 31*x_5_2*x_6_3 + 45*x_5_2*x_7_1 + 45*x_5_2*x_7_3 + 41*x_5_2*x_8_1\n",
      "  + 41*x_5_2*x_8_3 + 40*x_5_2*x_9_1 + 40*x_5_2*x_9_3 + 29*x_5_3*x_10_2\n",
      "  + 29*x_5_3*x_10_4 + 34*x_5_3*x_11_2 + 34*x_5_3*x_11_4 + 40*x_5_3*x_12_2\n",
      "  + 40*x_5_3*x_12_4 + 49*x_5_3*x_13_2 + 49*x_5_3*x_13_4 + 56*x_5_3*x_14_2\n",
      "  + 56*x_5_3*x_14_4 + 74*x_5_3*x_15_2 + 74*x_5_3*x_15_4 + 69*x_5_3*x_16_2\n",
      "  + 69*x_5_3*x_16_4 + 22*x_5_3*x_17_2 + 22*x_5_3*x_17_4 + 68*x_5_3*x_18_2\n",
      "  + 68*x_5_3*x_18_4 + 35*x_5_3*x_19_2 + 35*x_5_3*x_19_4 + 31*x_5_3*x_6_2\n",
      "  + 31*x_5_3*x_6_4 + 45*x_5_3*x_7_2 + 45*x_5_3*x_7_4 + 41*x_5_3*x_8_2\n",
      "  + 41*x_5_3*x_8_4 + 40*x_5_3*x_9_2 + 40*x_5_3*x_9_4 + 29*x_5_4*x_10_3\n",
      "  + 29*x_5_4*x_10_5 + 34*x_5_4*x_11_3 + 34*x_5_4*x_11_5 + 40*x_5_4*x_12_3\n",
      "  + 40*x_5_4*x_12_5 + 49*x_5_4*x_13_3 + 49*x_5_4*x_13_5 + 56*x_5_4*x_14_3\n",
      "  + 56*x_5_4*x_14_5 + 74*x_5_4*x_15_3 + 74*x_5_4*x_15_5 + 69*x_5_4*x_16_3\n",
      "  + 69*x_5_4*x_16_5 + 22*x_5_4*x_17_3 + 22*x_5_4*x_17_5 + 68*x_5_4*x_18_3\n",
      "  + 68*x_5_4*x_18_5 + 35*x_5_4*x_19_3 + 35*x_5_4*x_19_5 + 31*x_5_4*x_6_3\n",
      "  + 31*x_5_4*x_6_5 + 45*x_5_4*x_7_3 + 45*x_5_4*x_7_5 + 41*x_5_4*x_8_3\n",
      "  + 41*x_5_4*x_8_5 + 40*x_5_4*x_9_3 + 40*x_5_4*x_9_5 + 29*x_5_5*x_10_4\n",
      "  + 29*x_5_5*x_10_6 + 34*x_5_5*x_11_4 + 34*x_5_5*x_11_6 + 40*x_5_5*x_12_4\n",
      "  + 40*x_5_5*x_12_6 + 49*x_5_5*x_13_4 + 49*x_5_5*x_13_6 + 56*x_5_5*x_14_4\n",
      "  + 56*x_5_5*x_14_6 + 74*x_5_5*x_15_4 + 74*x_5_5*x_15_6 + 69*x_5_5*x_16_4\n",
      "  + 69*x_5_5*x_16_6 + 22*x_5_5*x_17_4 + 22*x_5_5*x_17_6 + 68*x_5_5*x_18_4\n",
      "  + 68*x_5_5*x_18_6 + 35*x_5_5*x_19_4 + 35*x_5_5*x_19_6 + 31*x_5_5*x_6_4\n",
      "  + 31*x_5_5*x_6_6 + 45*x_5_5*x_7_4 + 45*x_5_5*x_7_6 + 41*x_5_5*x_8_4\n",
      "  + 41*x_5_5*x_8_6 + 40*x_5_5*x_9_4 + 40*x_5_5*x_9_6 + 29*x_5_6*x_10_5\n",
      "  + 29*x_5_6*x_10_7 + 34*x_5_6*x_11_5 + 34*x_5_6*x_11_7 + 40*x_5_6*x_12_5\n",
      "  + 40*x_5_6*x_12_7 + 49*x_5_6*x_13_5 + 49*x_5_6*x_13_7 + 56*x_5_6*x_14_5\n",
      "  + 56*x_5_6*x_14_7 + 74*x_5_6*x_15_5 + 74*x_5_6*x_15_7 + 69*x_5_6*x_16_5\n",
      "  + 69*x_5_6*x_16_7 + 22*x_5_6*x_17_5 + 22*x_5_6*x_17_7 + 68*x_5_6*x_18_5\n",
      "  + 68*x_5_6*x_18_7 + 35*x_5_6*x_19_5 + 35*x_5_6*x_19_7 + 31*x_5_6*x_6_5\n",
      "  + 31*x_5_6*x_6_7 + 45*x_5_6*x_7_5 + 45*x_5_6*x_7_7 + 41*x_5_6*x_8_5\n",
      "  + 41*x_5_6*x_8_7 + 40*x_5_6*x_9_5 + 40*x_5_6*x_9_7 + 29*x_5_7*x_10_6\n",
      "  + 29*x_5_7*x_10_8 + 34*x_5_7*x_11_6 + 34*x_5_7*x_11_8 + 40*x_5_7*x_12_6\n",
      "  + 40*x_5_7*x_12_8 + 49*x_5_7*x_13_6 + 49*x_5_7*x_13_8 + 56*x_5_7*x_14_6\n",
      "  + 56*x_5_7*x_14_8 + 74*x_5_7*x_15_6 + 74*x_5_7*x_15_8 + 69*x_5_7*x_16_6\n",
      "  + 69*x_5_7*x_16_8 + 22*x_5_7*x_17_6 + 22*x_5_7*x_17_8 + 68*x_5_7*x_18_6\n",
      "  + 68*x_5_7*x_18_8 + 35*x_5_7*x_19_6 + 35*x_5_7*x_19_8 + 31*x_5_7*x_6_6\n",
      "  + 31*x_5_7*x_6_8 + 45*x_5_7*x_7_6 + 45*x_5_7*x_7_8 + 41*x_5_7*x_8_6\n",
      "  + 41*x_5_7*x_8_8 + 40*x_5_7*x_9_6 + 40*x_5_7*x_9_8 + 29*x_5_8*x_10_7\n",
      "  + 29*x_5_8*x_10_9 + 34*x_5_8*x_11_7 + 34*x_5_8*x_11_9 + 40*x_5_8*x_12_7\n",
      "  + 40*x_5_8*x_12_9 + 49*x_5_8*x_13_7 + 49*x_5_8*x_13_9 + 56*x_5_8*x_14_7\n",
      "  + 56*x_5_8*x_14_9 + 74*x_5_8*x_15_7 + 74*x_5_8*x_15_9 + 69*x_5_8*x_16_7\n",
      "  + 69*x_5_8*x_16_9 + 22*x_5_8*x_17_7 + 22*x_5_8*x_17_9 + 68*x_5_8*x_18_7\n",
      "  + 68*x_5_8*x_18_9 + 35*x_5_8*x_19_7 + 35*x_5_8*x_19_9 + 31*x_5_8*x_6_7\n",
      "  + 31*x_5_8*x_6_9 + 45*x_5_8*x_7_7 + 45*x_5_8*x_7_9 + 41*x_5_8*x_8_7\n",
      "  + 41*x_5_8*x_8_9 + 40*x_5_8*x_9_7 + 40*x_5_8*x_9_9 + 29*x_5_9*x_10_10\n",
      "  + 29*x_5_9*x_10_8 + 34*x_5_9*x_11_10 + 34*x_5_9*x_11_8 + 40*x_5_9*x_12_10\n",
      "  + 40*x_5_9*x_12_8 + 49*x_5_9*x_13_10 + 49*x_5_9*x_13_8 + 56*x_5_9*x_14_10\n",
      "  + 56*x_5_9*x_14_8 + 74*x_5_9*x_15_10 + 74*x_5_9*x_15_8 + 69*x_5_9*x_16_10\n",
      "  + 69*x_5_9*x_16_8 + 22*x_5_9*x_17_10 + 22*x_5_9*x_17_8 + 68*x_5_9*x_18_10\n",
      "  + 68*x_5_9*x_18_8 + 35*x_5_9*x_19_10 + 35*x_5_9*x_19_8 + 31*x_5_9*x_6_10\n",
      "  + 31*x_5_9*x_6_8 + 45*x_5_9*x_7_10 + 45*x_5_9*x_7_8 + 41*x_5_9*x_8_10\n",
      "  + 41*x_5_9*x_8_8 + 40*x_5_9*x_9_10 + 40*x_5_9*x_9_8 + 59*x_6_0*x_10_1\n",
      "  + 59*x_6_0*x_10_19 + 49*x_6_0*x_11_1 + 49*x_6_0*x_11_19 + 67*x_6_0*x_12_1\n",
      "  + 67*x_6_0*x_12_19 + 62*x_6_0*x_13_1 + 62*x_6_0*x_13_19 + 42*x_6_0*x_14_1\n",
      "  + 42*x_6_0*x_14_19 + 66*x_6_0*x_15_1 + 66*x_6_0*x_15_19 + 89*x_6_0*x_16_1\n",
      "  + 89*x_6_0*x_16_19 + 11*x_6_0*x_17_1 + 11*x_6_0*x_17_19 + 65*x_6_0*x_18_1\n",
      "  + 65*x_6_0*x_18_19 + 61*x_6_0*x_19_1 + 61*x_6_0*x_19_19 + 42*x_6_0*x_7_1\n",
      "  + 42*x_6_0*x_7_19 + 10*x_6_0*x_8_1 + 10*x_6_0*x_8_19 + 31*x_6_0*x_9_1\n",
      "  + 31*x_6_0*x_9_19 + 59*x_6_1*x_10_0 + 59*x_6_1*x_10_2 + 49*x_6_1*x_11_0\n",
      "  + 49*x_6_1*x_11_2 + 67*x_6_1*x_12_0 + 67*x_6_1*x_12_2 + 62*x_6_1*x_13_0\n",
      "  + 62*x_6_1*x_13_2 + 42*x_6_1*x_14_0 + 42*x_6_1*x_14_2 + 66*x_6_1*x_15_0\n",
      "  + 66*x_6_1*x_15_2 + 89*x_6_1*x_16_0 + 89*x_6_1*x_16_2 + 11*x_6_1*x_17_0\n",
      "  + 11*x_6_1*x_17_2 + 65*x_6_1*x_18_0 + 65*x_6_1*x_18_2 + 61*x_6_1*x_19_0\n",
      "  + 61*x_6_1*x_19_2 + 42*x_6_1*x_7_0 + 42*x_6_1*x_7_2 + 10*x_6_1*x_8_0\n",
      "  + 10*x_6_1*x_8_2 + 31*x_6_1*x_9_0 + 31*x_6_1*x_9_2 + 59*x_6_10*x_10_11\n",
      "  + 59*x_6_10*x_10_9 + 49*x_6_10*x_11_11 + 49*x_6_10*x_11_9 + 67*x_6_10*x_12_11\n",
      "  + 67*x_6_10*x_12_9 + 62*x_6_10*x_13_11 + 62*x_6_10*x_13_9 + 42*x_6_10*x_14_11\n",
      "  + 42*x_6_10*x_14_9 + 66*x_6_10*x_15_11 + 66*x_6_10*x_15_9 + 89*x_6_10*x_16_11\n",
      "  + 89*x_6_10*x_16_9 + 11*x_6_10*x_17_11 + 11*x_6_10*x_17_9 + 65*x_6_10*x_18_11\n",
      "  + 65*x_6_10*x_18_9 + 61*x_6_10*x_19_11 + 61*x_6_10*x_19_9 + 42*x_6_10*x_7_11\n",
      "  + 42*x_6_10*x_7_9 + 10*x_6_10*x_8_11 + 10*x_6_10*x_8_9 + 31*x_6_10*x_9_11\n",
      "  + 31*x_6_10*x_9_9 + 59*x_6_11*x_10_10 + 59*x_6_11*x_10_12 + 49*x_6_11*x_11_10\n",
      "  + 49*x_6_11*x_11_12 + 67*x_6_11*x_12_10 + 67*x_6_11*x_12_12\n",
      "  + 62*x_6_11*x_13_10 + 62*x_6_11*x_13_12 + 42*x_6_11*x_14_10\n",
      "  + 42*x_6_11*x_14_12 + 66*x_6_11*x_15_10 + 66*x_6_11*x_15_12\n",
      "  + 89*x_6_11*x_16_10 + 89*x_6_11*x_16_12 + 11*x_6_11*x_17_10\n",
      "  + 11*x_6_11*x_17_12 + 65*x_6_11*x_18_10 + 65*x_6_11*x_18_12\n",
      "  + 61*x_6_11*x_19_10 + 61*x_6_11*x_19_12 + 42*x_6_11*x_7_10 + 42*x_6_11*x_7_12\n",
      "  + 10*x_6_11*x_8_10 + 10*x_6_11*x_8_12 + 31*x_6_11*x_9_10 + 31*x_6_11*x_9_12\n",
      "  + 59*x_6_12*x_10_11 + 59*x_6_12*x_10_13 + 49*x_6_12*x_11_11\n",
      "  + 49*x_6_12*x_11_13 + 67*x_6_12*x_12_11 + 67*x_6_12*x_12_13\n",
      "  + 62*x_6_12*x_13_11 + 62*x_6_12*x_13_13 + 42*x_6_12*x_14_11\n",
      "  + 42*x_6_12*x_14_13 + 66*x_6_12*x_15_11 + 66*x_6_12*x_15_13\n",
      "  + 89*x_6_12*x_16_11 + 89*x_6_12*x_16_13 + 11*x_6_12*x_17_11\n",
      "  + 11*x_6_12*x_17_13 + 65*x_6_12*x_18_11 + 65*x_6_12*x_18_13\n",
      "  + 61*x_6_12*x_19_11 + 61*x_6_12*x_19_13 + 42*x_6_12*x_7_11 + 42*x_6_12*x_7_13\n",
      "  + 10*x_6_12*x_8_11 + 10*x_6_12*x_8_13 + 31*x_6_12*x_9_11 + 31*x_6_12*x_9_13\n",
      "  + 59*x_6_13*x_10_12 + 59*x_6_13*x_10_14 + 49*x_6_13*x_11_12\n",
      "  + 49*x_6_13*x_11_14 + 67*x_6_13*x_12_12 + 67*x_6_13*x_12_14\n",
      "  + 62*x_6_13*x_13_12 + 62*x_6_13*x_13_14 + 42*x_6_13*x_14_12\n",
      "  + 42*x_6_13*x_14_14 + 66*x_6_13*x_15_12 + 66*x_6_13*x_15_14\n",
      "  + 89*x_6_13*x_16_12 + 89*x_6_13*x_16_14 + 11*x_6_13*x_17_12\n",
      "  + 11*x_6_13*x_17_14 + 65*x_6_13*x_18_12 + 65*x_6_13*x_18_14\n",
      "  + 61*x_6_13*x_19_12 + 61*x_6_13*x_19_14 + 42*x_6_13*x_7_12 + 42*x_6_13*x_7_14\n",
      "  + 10*x_6_13*x_8_12 + 10*x_6_13*x_8_14 + 31*x_6_13*x_9_12 + 31*x_6_13*x_9_14\n",
      "  + 59*x_6_14*x_10_13 + 59*x_6_14*x_10_15 + 49*x_6_14*x_11_13\n",
      "  + 49*x_6_14*x_11_15 + 67*x_6_14*x_12_13 + 67*x_6_14*x_12_15\n",
      "  + 62*x_6_14*x_13_13 + 62*x_6_14*x_13_15 + 42*x_6_14*x_14_13\n",
      "  + 42*x_6_14*x_14_15 + 66*x_6_14*x_15_13 + 66*x_6_14*x_15_15\n",
      "  + 89*x_6_14*x_16_13 + 89*x_6_14*x_16_15 + 11*x_6_14*x_17_13\n",
      "  + 11*x_6_14*x_17_15 + 65*x_6_14*x_18_13 + 65*x_6_14*x_18_15\n",
      "  + 61*x_6_14*x_19_13 + 61*x_6_14*x_19_15 + 42*x_6_14*x_7_13 + 42*x_6_14*x_7_15\n",
      "  + 10*x_6_14*x_8_13 + 10*x_6_14*x_8_15 + 31*x_6_14*x_9_13 + 31*x_6_14*x_9_15\n",
      "  + 59*x_6_15*x_10_14 + 59*x_6_15*x_10_16 + 49*x_6_15*x_11_14\n",
      "  + 49*x_6_15*x_11_16 + 67*x_6_15*x_12_14 + 67*x_6_15*x_12_16\n",
      "  + 62*x_6_15*x_13_14 + 62*x_6_15*x_13_16 + 42*x_6_15*x_14_14\n",
      "  + 42*x_6_15*x_14_16 + 66*x_6_15*x_15_14 + 66*x_6_15*x_15_16\n",
      "  + 89*x_6_15*x_16_14 + 89*x_6_15*x_16_16 + 11*x_6_15*x_17_14\n",
      "  + 11*x_6_15*x_17_16 + 65*x_6_15*x_18_14 + 65*x_6_15*x_18_16\n",
      "  + 61*x_6_15*x_19_14 + 61*x_6_15*x_19_16 + 42*x_6_15*x_7_14 + 42*x_6_15*x_7_16\n",
      "  + 10*x_6_15*x_8_14 + 10*x_6_15*x_8_16 + 31*x_6_15*x_9_14 + 31*x_6_15*x_9_16\n",
      "  + 59*x_6_16*x_10_15 + 59*x_6_16*x_10_17 + 49*x_6_16*x_11_15\n",
      "  + 49*x_6_16*x_11_17 + 67*x_6_16*x_12_15 + 67*x_6_16*x_12_17\n",
      "  + 62*x_6_16*x_13_15 + 62*x_6_16*x_13_17 + 42*x_6_16*x_14_15\n",
      "  + 42*x_6_16*x_14_17 + 66*x_6_16*x_15_15 + 66*x_6_16*x_15_17\n",
      "  + 89*x_6_16*x_16_15 + 89*x_6_16*x_16_17 + 11*x_6_16*x_17_15\n",
      "  + 11*x_6_16*x_17_17 + 65*x_6_16*x_18_15 + 65*x_6_16*x_18_17\n",
      "  + 61*x_6_16*x_19_15 + 61*x_6_16*x_19_17 + 42*x_6_16*x_7_15 + 42*x_6_16*x_7_17\n",
      "  + 10*x_6_16*x_8_15 + 10*x_6_16*x_8_17 + 31*x_6_16*x_9_15 + 31*x_6_16*x_9_17\n",
      "  + 59*x_6_17*x_10_16 + 59*x_6_17*x_10_18 + 49*x_6_17*x_11_16\n",
      "  + 49*x_6_17*x_11_18 + 67*x_6_17*x_12_16 + 67*x_6_17*x_12_18\n",
      "  + 62*x_6_17*x_13_16 + 62*x_6_17*x_13_18 + 42*x_6_17*x_14_16\n",
      "  + 42*x_6_17*x_14_18 + 66*x_6_17*x_15_16 + 66*x_6_17*x_15_18\n",
      "  + 89*x_6_17*x_16_16 + 89*x_6_17*x_16_18 + 11*x_6_17*x_17_16\n",
      "  + 11*x_6_17*x_17_18 + 65*x_6_17*x_18_16 + 65*x_6_17*x_18_18\n",
      "  + 61*x_6_17*x_19_16 + 61*x_6_17*x_19_18 + 42*x_6_17*x_7_16 + 42*x_6_17*x_7_18\n",
      "  + 10*x_6_17*x_8_16 + 10*x_6_17*x_8_18 + 31*x_6_17*x_9_16 + 31*x_6_17*x_9_18\n",
      "  + 59*x_6_18*x_10_17 + 59*x_6_18*x_10_19 + 49*x_6_18*x_11_17\n",
      "  + 49*x_6_18*x_11_19 + 67*x_6_18*x_12_17 + 67*x_6_18*x_12_19\n",
      "  + 62*x_6_18*x_13_17 + 62*x_6_18*x_13_19 + 42*x_6_18*x_14_17\n",
      "  + 42*x_6_18*x_14_19 + 66*x_6_18*x_15_17 + 66*x_6_18*x_15_19\n",
      "  + 89*x_6_18*x_16_17 + 89*x_6_18*x_16_19 + 11*x_6_18*x_17_17\n",
      "  + 11*x_6_18*x_17_19 + 65*x_6_18*x_18_17 + 65*x_6_18*x_18_19\n",
      "  + 61*x_6_18*x_19_17 + 61*x_6_18*x_19_19 + 42*x_6_18*x_7_17 + 42*x_6_18*x_7_19\n",
      "  + 10*x_6_18*x_8_17 + 10*x_6_18*x_8_19 + 31*x_6_18*x_9_17 + 31*x_6_18*x_9_19\n",
      "  + 59*x_6_19*x_10_0 + 59*x_6_19*x_10_18 + 49*x_6_19*x_11_0 + 49*x_6_19*x_11_18\n",
      "  + 67*x_6_19*x_12_0 + 67*x_6_19*x_12_18 + 62*x_6_19*x_13_0 + 62*x_6_19*x_13_18\n",
      "  + 42*x_6_19*x_14_0 + 42*x_6_19*x_14_18 + 66*x_6_19*x_15_0 + 66*x_6_19*x_15_18\n",
      "  + 89*x_6_19*x_16_0 + 89*x_6_19*x_16_18 + 11*x_6_19*x_17_0 + 11*x_6_19*x_17_18\n",
      "  + 65*x_6_19*x_18_0 + 65*x_6_19*x_18_18 + 61*x_6_19*x_19_0 + 61*x_6_19*x_19_18\n",
      "  + 42*x_6_19*x_7_0 + 42*x_6_19*x_7_18 + 10*x_6_19*x_8_0 + 10*x_6_19*x_8_18\n",
      "  + 31*x_6_19*x_9_0 + 31*x_6_19*x_9_18 + 59*x_6_2*x_10_1 + 59*x_6_2*x_10_3\n",
      "  + 49*x_6_2*x_11_1 + 49*x_6_2*x_11_3 + 67*x_6_2*x_12_1 + 67*x_6_2*x_12_3\n",
      "  + 62*x_6_2*x_13_1 + 62*x_6_2*x_13_3 + 42*x_6_2*x_14_1 + 42*x_6_2*x_14_3\n",
      "  + 66*x_6_2*x_15_1 + 66*x_6_2*x_15_3 + 89*x_6_2*x_16_1 + 89*x_6_2*x_16_3\n",
      "  + 11*x_6_2*x_17_1 + 11*x_6_2*x_17_3 + 65*x_6_2*x_18_1 + 65*x_6_2*x_18_3\n",
      "  + 61*x_6_2*x_19_1 + 61*x_6_2*x_19_3 + 42*x_6_2*x_7_1 + 42*x_6_2*x_7_3\n",
      "  + 10*x_6_2*x_8_1 + 10*x_6_2*x_8_3 + 31*x_6_2*x_9_1 + 31*x_6_2*x_9_3\n",
      "  + 59*x_6_3*x_10_2 + 59*x_6_3*x_10_4 + 49*x_6_3*x_11_2 + 49*x_6_3*x_11_4\n",
      "  + 67*x_6_3*x_12_2 + 67*x_6_3*x_12_4 + 62*x_6_3*x_13_2 + 62*x_6_3*x_13_4\n",
      "  + 42*x_6_3*x_14_2 + 42*x_6_3*x_14_4 + 66*x_6_3*x_15_2 + 66*x_6_3*x_15_4\n",
      "  + 89*x_6_3*x_16_2 + 89*x_6_3*x_16_4 + 11*x_6_3*x_17_2 + 11*x_6_3*x_17_4\n",
      "  + 65*x_6_3*x_18_2 + 65*x_6_3*x_18_4 + 61*x_6_3*x_19_2 + 61*x_6_3*x_19_4\n",
      "  + 42*x_6_3*x_7_2 + 42*x_6_3*x_7_4 + 10*x_6_3*x_8_2 + 10*x_6_3*x_8_4\n",
      "  + 31*x_6_3*x_9_2 + 31*x_6_3*x_9_4 + 59*x_6_4*x_10_3 + 59*x_6_4*x_10_5\n",
      "  + 49*x_6_4*x_11_3 + 49*x_6_4*x_11_5 + 67*x_6_4*x_12_3 + 67*x_6_4*x_12_5\n",
      "  + 62*x_6_4*x_13_3 + 62*x_6_4*x_13_5 + 42*x_6_4*x_14_3 + 42*x_6_4*x_14_5\n",
      "  + 66*x_6_4*x_15_3 + 66*x_6_4*x_15_5 + 89*x_6_4*x_16_3 + 89*x_6_4*x_16_5\n",
      "  + 11*x_6_4*x_17_3 + 11*x_6_4*x_17_5 + 65*x_6_4*x_18_3 + 65*x_6_4*x_18_5\n",
      "  + 61*x_6_4*x_19_3 + 61*x_6_4*x_19_5 + 42*x_6_4*x_7_3 + 42*x_6_4*x_7_5\n",
      "  + 10*x_6_4*x_8_3 + 10*x_6_4*x_8_5 + 31*x_6_4*x_9_3 + 31*x_6_4*x_9_5\n",
      "  + 59*x_6_5*x_10_4 + 59*x_6_5*x_10_6 + 49*x_6_5*x_11_4 + 49*x_6_5*x_11_6\n",
      "  + 67*x_6_5*x_12_4 + 67*x_6_5*x_12_6 + 62*x_6_5*x_13_4 + 62*x_6_5*x_13_6\n",
      "  + 42*x_6_5*x_14_4 + 42*x_6_5*x_14_6 + 66*x_6_5*x_15_4 + 66*x_6_5*x_15_6\n",
      "  + 89*x_6_5*x_16_4 + 89*x_6_5*x_16_6 + 11*x_6_5*x_17_4 + 11*x_6_5*x_17_6\n",
      "  + 65*x_6_5*x_18_4 + 65*x_6_5*x_18_6 + 61*x_6_5*x_19_4 + 61*x_6_5*x_19_6\n",
      "  + 42*x_6_5*x_7_4 + 42*x_6_5*x_7_6 + 10*x_6_5*x_8_4 + 10*x_6_5*x_8_6\n",
      "  + 31*x_6_5*x_9_4 + 31*x_6_5*x_9_6 + 59*x_6_6*x_10_5 + 59*x_6_6*x_10_7\n",
      "  + 49*x_6_6*x_11_5 + 49*x_6_6*x_11_7 + 67*x_6_6*x_12_5 + 67*x_6_6*x_12_7\n",
      "  + 62*x_6_6*x_13_5 + 62*x_6_6*x_13_7 + 42*x_6_6*x_14_5 + 42*x_6_6*x_14_7\n",
      "  + 66*x_6_6*x_15_5 + 66*x_6_6*x_15_7 + 89*x_6_6*x_16_5 + 89*x_6_6*x_16_7\n",
      "  + 11*x_6_6*x_17_5 + 11*x_6_6*x_17_7 + 65*x_6_6*x_18_5 + 65*x_6_6*x_18_7\n",
      "  + 61*x_6_6*x_19_5 + 61*x_6_6*x_19_7 + 42*x_6_6*x_7_5 + 42*x_6_6*x_7_7\n",
      "  + 10*x_6_6*x_8_5 + 10*x_6_6*x_8_7 + 31*x_6_6*x_9_5 + 31*x_6_6*x_9_7\n",
      "  + 59*x_6_7*x_10_6 + 59*x_6_7*x_10_8 + 49*x_6_7*x_11_6 + 49*x_6_7*x_11_8\n",
      "  + 67*x_6_7*x_12_6 + 67*x_6_7*x_12_8 + 62*x_6_7*x_13_6 + 62*x_6_7*x_13_8\n",
      "  + 42*x_6_7*x_14_6 + 42*x_6_7*x_14_8 + 66*x_6_7*x_15_6 + 66*x_6_7*x_15_8\n",
      "  + 89*x_6_7*x_16_6 + 89*x_6_7*x_16_8 + 11*x_6_7*x_17_6 + 11*x_6_7*x_17_8\n",
      "  + 65*x_6_7*x_18_6 + 65*x_6_7*x_18_8 + 61*x_6_7*x_19_6 + 61*x_6_7*x_19_8\n",
      "  + 42*x_6_7*x_7_6 + 42*x_6_7*x_7_8 + 10*x_6_7*x_8_6 + 10*x_6_7*x_8_8\n",
      "  + 31*x_6_7*x_9_6 + 31*x_6_7*x_9_8 + 59*x_6_8*x_10_7 + 59*x_6_8*x_10_9\n",
      "  + 49*x_6_8*x_11_7 + 49*x_6_8*x_11_9 + 67*x_6_8*x_12_7 + 67*x_6_8*x_12_9\n",
      "  + 62*x_6_8*x_13_7 + 62*x_6_8*x_13_9 + 42*x_6_8*x_14_7 + 42*x_6_8*x_14_9\n",
      "  + 66*x_6_8*x_15_7 + 66*x_6_8*x_15_9 + 89*x_6_8*x_16_7 + 89*x_6_8*x_16_9\n",
      "  + 11*x_6_8*x_17_7 + 11*x_6_8*x_17_9 + 65*x_6_8*x_18_7 + 65*x_6_8*x_18_9\n",
      "  + 61*x_6_8*x_19_7 + 61*x_6_8*x_19_9 + 42*x_6_8*x_7_7 + 42*x_6_8*x_7_9\n",
      "  + 10*x_6_8*x_8_7 + 10*x_6_8*x_8_9 + 31*x_6_8*x_9_7 + 31*x_6_8*x_9_9\n",
      "  + 59*x_6_9*x_10_10 + 59*x_6_9*x_10_8 + 49*x_6_9*x_11_10 + 49*x_6_9*x_11_8\n",
      "  + 67*x_6_9*x_12_10 + 67*x_6_9*x_12_8 + 62*x_6_9*x_13_10 + 62*x_6_9*x_13_8\n",
      "  + 42*x_6_9*x_14_10 + 42*x_6_9*x_14_8 + 66*x_6_9*x_15_10 + 66*x_6_9*x_15_8\n",
      "  + 89*x_6_9*x_16_10 + 89*x_6_9*x_16_8 + 11*x_6_9*x_17_10 + 11*x_6_9*x_17_8\n",
      "  + 65*x_6_9*x_18_10 + 65*x_6_9*x_18_8 + 61*x_6_9*x_19_10 + 61*x_6_9*x_19_8\n",
      "  + 42*x_6_9*x_7_10 + 42*x_6_9*x_7_8 + 10*x_6_9*x_8_10 + 10*x_6_9*x_8_8\n",
      "  + 31*x_6_9*x_9_10 + 31*x_6_9*x_9_8 + 69*x_7_0*x_10_1 + 69*x_7_0*x_10_19\n",
      "  + 25*x_7_0*x_11_1 + 25*x_7_0*x_11_19 + 83*x_7_0*x_12_1 + 83*x_7_0*x_12_19\n",
      "  + 29*x_7_0*x_13_1 + 29*x_7_0*x_13_19 + 18*x_7_0*x_14_1 + 18*x_7_0*x_14_19\n",
      "  + 29*x_7_0*x_15_1 + 29*x_7_0*x_15_19 + 57*x_7_0*x_16_1 + 57*x_7_0*x_16_19\n",
      "  + 35*x_7_0*x_17_1 + 35*x_7_0*x_17_19 + 24*x_7_0*x_18_1 + 24*x_7_0*x_18_19\n",
      "  + 45*x_7_0*x_19_1 + 45*x_7_0*x_19_19 + 44*x_7_0*x_8_1 + 44*x_7_0*x_8_19\n",
      "  + 13*x_7_0*x_9_1 + 13*x_7_0*x_9_19 + 69*x_7_1*x_10_0 + 69*x_7_1*x_10_2\n",
      "  + 25*x_7_1*x_11_0 + 25*x_7_1*x_11_2 + 83*x_7_1*x_12_0 + 83*x_7_1*x_12_2\n",
      "  + 29*x_7_1*x_13_0 + 29*x_7_1*x_13_2 + 18*x_7_1*x_14_0 + 18*x_7_1*x_14_2\n",
      "  + 29*x_7_1*x_15_0 + 29*x_7_1*x_15_2 + 57*x_7_1*x_16_0 + 57*x_7_1*x_16_2\n",
      "  + 35*x_7_1*x_17_0 + 35*x_7_1*x_17_2 + 24*x_7_1*x_18_0 + 24*x_7_1*x_18_2\n",
      "  + 45*x_7_1*x_19_0 + 45*x_7_1*x_19_2 + 44*x_7_1*x_8_0 + 44*x_7_1*x_8_2\n",
      "  + 13*x_7_1*x_9_0 + 13*x_7_1*x_9_2 + 69*x_7_10*x_10_11 + 69*x_7_10*x_10_9\n",
      "  + 25*x_7_10*x_11_11 + 25*x_7_10*x_11_9 + 83*x_7_10*x_12_11 + 83*x_7_10*x_12_9\n",
      "  + 29*x_7_10*x_13_11 + 29*x_7_10*x_13_9 + 18*x_7_10*x_14_11 + 18*x_7_10*x_14_9\n",
      "  + 29*x_7_10*x_15_11 + 29*x_7_10*x_15_9 + 57*x_7_10*x_16_11 + 57*x_7_10*x_16_9\n",
      "  + 35*x_7_10*x_17_11 + 35*x_7_10*x_17_9 + 24*x_7_10*x_18_11 + 24*x_7_10*x_18_9\n",
      "  + 45*x_7_10*x_19_11 + 45*x_7_10*x_19_9 + 44*x_7_10*x_8_11 + 44*x_7_10*x_8_9\n",
      "  + 13*x_7_10*x_9_11 + 13*x_7_10*x_9_9 + 69*x_7_11*x_10_10 + 69*x_7_11*x_10_12\n",
      "  + 25*x_7_11*x_11_10 + 25*x_7_11*x_11_12 + 83*x_7_11*x_12_10\n",
      "  + 83*x_7_11*x_12_12 + 29*x_7_11*x_13_10 + 29*x_7_11*x_13_12\n",
      "  + 18*x_7_11*x_14_10 + 18*x_7_11*x_14_12 + 29*x_7_11*x_15_10\n",
      "  + 29*x_7_11*x_15_12 + 57*x_7_11*x_16_10 + 57*x_7_11*x_16_12\n",
      "  + 35*x_7_11*x_17_10 + 35*x_7_11*x_17_12 + 24*x_7_11*x_18_10\n",
      "  + 24*x_7_11*x_18_12 + 45*x_7_11*x_19_10 + 45*x_7_11*x_19_12 + 44*x_7_11*x_8_10\n",
      "  + 44*x_7_11*x_8_12 + 13*x_7_11*x_9_10 + 13*x_7_11*x_9_12 + 69*x_7_12*x_10_11\n",
      "  + 69*x_7_12*x_10_13 + 25*x_7_12*x_11_11 + 25*x_7_12*x_11_13\n",
      "  + 83*x_7_12*x_12_11 + 83*x_7_12*x_12_13 + 29*x_7_12*x_13_11\n",
      "  + 29*x_7_12*x_13_13 + 18*x_7_12*x_14_11 + 18*x_7_12*x_14_13\n",
      "  + 29*x_7_12*x_15_11 + 29*x_7_12*x_15_13 + 57*x_7_12*x_16_11\n",
      "  + 57*x_7_12*x_16_13 + 35*x_7_12*x_17_11 + 35*x_7_12*x_17_13\n",
      "  + 24*x_7_12*x_18_11 + 24*x_7_12*x_18_13 + 45*x_7_12*x_19_11\n",
      "  + 45*x_7_12*x_19_13 + 44*x_7_12*x_8_11 + 44*x_7_12*x_8_13 + 13*x_7_12*x_9_11\n",
      "  + 13*x_7_12*x_9_13 + 69*x_7_13*x_10_12 + 69*x_7_13*x_10_14 + 25*x_7_13*x_11_12\n",
      "  + 25*x_7_13*x_11_14 + 83*x_7_13*x_12_12 + 83*x_7_13*x_12_14\n",
      "  + 29*x_7_13*x_13_12 + 29*x_7_13*x_13_14 + 18*x_7_13*x_14_12\n",
      "  + 18*x_7_13*x_14_14 + 29*x_7_13*x_15_12 + 29*x_7_13*x_15_14\n",
      "  + 57*x_7_13*x_16_12 + 57*x_7_13*x_16_14 + 35*x_7_13*x_17_12\n",
      "  + 35*x_7_13*x_17_14 + 24*x_7_13*x_18_12 + 24*x_7_13*x_18_14\n",
      "  + 45*x_7_13*x_19_12 + 45*x_7_13*x_19_14 + 44*x_7_13*x_8_12 + 44*x_7_13*x_8_14\n",
      "  + 13*x_7_13*x_9_12 + 13*x_7_13*x_9_14 + 69*x_7_14*x_10_13 + 69*x_7_14*x_10_15\n",
      "  + 25*x_7_14*x_11_13 + 25*x_7_14*x_11_15 + 83*x_7_14*x_12_13\n",
      "  + 83*x_7_14*x_12_15 + 29*x_7_14*x_13_13 + 29*x_7_14*x_13_15\n",
      "  + 18*x_7_14*x_14_13 + 18*x_7_14*x_14_15 + 29*x_7_14*x_15_13\n",
      "  + 29*x_7_14*x_15_15 + 57*x_7_14*x_16_13 + 57*x_7_14*x_16_15\n",
      "  + 35*x_7_14*x_17_13 + 35*x_7_14*x_17_15 + 24*x_7_14*x_18_13\n",
      "  + 24*x_7_14*x_18_15 + 45*x_7_14*x_19_13 + 45*x_7_14*x_19_15 + 44*x_7_14*x_8_13\n",
      "  + 44*x_7_14*x_8_15 + 13*x_7_14*x_9_13 + 13*x_7_14*x_9_15 + 69*x_7_15*x_10_14\n",
      "  + 69*x_7_15*x_10_16 + 25*x_7_15*x_11_14 + 25*x_7_15*x_11_16\n",
      "  + 83*x_7_15*x_12_14 + 83*x_7_15*x_12_16 + 29*x_7_15*x_13_14\n",
      "  + 29*x_7_15*x_13_16 + 18*x_7_15*x_14_14 + 18*x_7_15*x_14_16\n",
      "  + 29*x_7_15*x_15_14 + 29*x_7_15*x_15_16 + 57*x_7_15*x_16_14\n",
      "  + 57*x_7_15*x_16_16 + 35*x_7_15*x_17_14 + 35*x_7_15*x_17_16\n",
      "  + 24*x_7_15*x_18_14 + 24*x_7_15*x_18_16 + 45*x_7_15*x_19_14\n",
      "  + 45*x_7_15*x_19_16 + 44*x_7_15*x_8_14 + 44*x_7_15*x_8_16 + 13*x_7_15*x_9_14\n",
      "  + 13*x_7_15*x_9_16 + 69*x_7_16*x_10_15 + 69*x_7_16*x_10_17 + 25*x_7_16*x_11_15\n",
      "  + 25*x_7_16*x_11_17 + 83*x_7_16*x_12_15 + 83*x_7_16*x_12_17\n",
      "  + 29*x_7_16*x_13_15 + 29*x_7_16*x_13_17 + 18*x_7_16*x_14_15\n",
      "  + 18*x_7_16*x_14_17 + 29*x_7_16*x_15_15 + 29*x_7_16*x_15_17\n",
      "  + 57*x_7_16*x_16_15 + 57*x_7_16*x_16_17 + 35*x_7_16*x_17_15\n",
      "  + 35*x_7_16*x_17_17 + 24*x_7_16*x_18_15 + 24*x_7_16*x_18_17\n",
      "  + 45*x_7_16*x_19_15 + 45*x_7_16*x_19_17 + 44*x_7_16*x_8_15 + 44*x_7_16*x_8_17\n",
      "  + 13*x_7_16*x_9_15 + 13*x_7_16*x_9_17 + 69*x_7_17*x_10_16 + 69*x_7_17*x_10_18\n",
      "  + 25*x_7_17*x_11_16 + 25*x_7_17*x_11_18 + 83*x_7_17*x_12_16\n",
      "  + 83*x_7_17*x_12_18 + 29*x_7_17*x_13_16 + 29*x_7_17*x_13_18\n",
      "  + 18*x_7_17*x_14_16 + 18*x_7_17*x_14_18 + 29*x_7_17*x_15_16\n",
      "  + 29*x_7_17*x_15_18 + 57*x_7_17*x_16_16 + 57*x_7_17*x_16_18\n",
      "  + 35*x_7_17*x_17_16 + 35*x_7_17*x_17_18 + 24*x_7_17*x_18_16\n",
      "  + 24*x_7_17*x_18_18 + 45*x_7_17*x_19_16 + 45*x_7_17*x_19_18 + 44*x_7_17*x_8_16\n",
      "  + 44*x_7_17*x_8_18 + 13*x_7_17*x_9_16 + 13*x_7_17*x_9_18 + 69*x_7_18*x_10_17\n",
      "  + 69*x_7_18*x_10_19 + 25*x_7_18*x_11_17 + 25*x_7_18*x_11_19\n",
      "  + 83*x_7_18*x_12_17 + 83*x_7_18*x_12_19 + 29*x_7_18*x_13_17\n",
      "  + 29*x_7_18*x_13_19 + 18*x_7_18*x_14_17 + 18*x_7_18*x_14_19\n",
      "  + 29*x_7_18*x_15_17 + 29*x_7_18*x_15_19 + 57*x_7_18*x_16_17\n",
      "  + 57*x_7_18*x_16_19 + 35*x_7_18*x_17_17 + 35*x_7_18*x_17_19\n",
      "  + 24*x_7_18*x_18_17 + 24*x_7_18*x_18_19 + 45*x_7_18*x_19_17\n",
      "  + 45*x_7_18*x_19_19 + 44*x_7_18*x_8_17 + 44*x_7_18*x_8_19 + 13*x_7_18*x_9_17\n",
      "  + 13*x_7_18*x_9_19 + 69*x_7_19*x_10_0 + 69*x_7_19*x_10_18 + 25*x_7_19*x_11_0\n",
      "  + 25*x_7_19*x_11_18 + 83*x_7_19*x_12_0 + 83*x_7_19*x_12_18 + 29*x_7_19*x_13_0\n",
      "  + 29*x_7_19*x_13_18 + 18*x_7_19*x_14_0 + 18*x_7_19*x_14_18 + 29*x_7_19*x_15_0\n",
      "  + 29*x_7_19*x_15_18 + 57*x_7_19*x_16_0 + 57*x_7_19*x_16_18 + 35*x_7_19*x_17_0\n",
      "  + 35*x_7_19*x_17_18 + 24*x_7_19*x_18_0 + 24*x_7_19*x_18_18 + 45*x_7_19*x_19_0\n",
      "  + 45*x_7_19*x_19_18 + 44*x_7_19*x_8_0 + 44*x_7_19*x_8_18 + 13*x_7_19*x_9_0\n",
      "  + 13*x_7_19*x_9_18 + 69*x_7_2*x_10_1 + 69*x_7_2*x_10_3 + 25*x_7_2*x_11_1\n",
      "  + 25*x_7_2*x_11_3 + 83*x_7_2*x_12_1 + 83*x_7_2*x_12_3 + 29*x_7_2*x_13_1\n",
      "  + 29*x_7_2*x_13_3 + 18*x_7_2*x_14_1 + 18*x_7_2*x_14_3 + 29*x_7_2*x_15_1\n",
      "  + 29*x_7_2*x_15_3 + 57*x_7_2*x_16_1 + 57*x_7_2*x_16_3 + 35*x_7_2*x_17_1\n",
      "  + 35*x_7_2*x_17_3 + 24*x_7_2*x_18_1 + 24*x_7_2*x_18_3 + 45*x_7_2*x_19_1\n",
      "  + 45*x_7_2*x_19_3 + 44*x_7_2*x_8_1 + 44*x_7_2*x_8_3 + 13*x_7_2*x_9_1\n",
      "  + 13*x_7_2*x_9_3 + 69*x_7_3*x_10_2 + 69*x_7_3*x_10_4 + 25*x_7_3*x_11_2\n",
      "  + 25*x_7_3*x_11_4 + 83*x_7_3*x_12_2 + 83*x_7_3*x_12_4 + 29*x_7_3*x_13_2\n",
      "  + 29*x_7_3*x_13_4 + 18*x_7_3*x_14_2 + 18*x_7_3*x_14_4 + 29*x_7_3*x_15_2\n",
      "  + 29*x_7_3*x_15_4 + 57*x_7_3*x_16_2 + 57*x_7_3*x_16_4 + 35*x_7_3*x_17_2\n",
      "  + 35*x_7_3*x_17_4 + 24*x_7_3*x_18_2 + 24*x_7_3*x_18_4 + 45*x_7_3*x_19_2\n",
      "  + 45*x_7_3*x_19_4 + 44*x_7_3*x_8_2 + 44*x_7_3*x_8_4 + 13*x_7_3*x_9_2\n",
      "  + 13*x_7_3*x_9_4 + 69*x_7_4*x_10_3 + 69*x_7_4*x_10_5 + 25*x_7_4*x_11_3\n",
      "  + 25*x_7_4*x_11_5 + 83*x_7_4*x_12_3 + 83*x_7_4*x_12_5 + 29*x_7_4*x_13_3\n",
      "  + 29*x_7_4*x_13_5 + 18*x_7_4*x_14_3 + 18*x_7_4*x_14_5 + 29*x_7_4*x_15_3\n",
      "  + 29*x_7_4*x_15_5 + 57*x_7_4*x_16_3 + 57*x_7_4*x_16_5 + 35*x_7_4*x_17_3\n",
      "  + 35*x_7_4*x_17_5 + 24*x_7_4*x_18_3 + 24*x_7_4*x_18_5 + 45*x_7_4*x_19_3\n",
      "  + 45*x_7_4*x_19_5 + 44*x_7_4*x_8_3 + 44*x_7_4*x_8_5 + 13*x_7_4*x_9_3\n",
      "  + 13*x_7_4*x_9_5 + 69*x_7_5*x_10_4 + 69*x_7_5*x_10_6 + 25*x_7_5*x_11_4\n",
      "  + 25*x_7_5*x_11_6 + 83*x_7_5*x_12_4 + 83*x_7_5*x_12_6 + 29*x_7_5*x_13_4\n",
      "  + 29*x_7_5*x_13_6 + 18*x_7_5*x_14_4 + 18*x_7_5*x_14_6 + 29*x_7_5*x_15_4\n",
      "  + 29*x_7_5*x_15_6 + 57*x_7_5*x_16_4 + 57*x_7_5*x_16_6 + 35*x_7_5*x_17_4\n",
      "  + 35*x_7_5*x_17_6 + 24*x_7_5*x_18_4 + 24*x_7_5*x_18_6 + 45*x_7_5*x_19_4\n",
      "  + 45*x_7_5*x_19_6 + 44*x_7_5*x_8_4 + 44*x_7_5*x_8_6 + 13*x_7_5*x_9_4\n",
      "  + 13*x_7_5*x_9_6 + 69*x_7_6*x_10_5 + 69*x_7_6*x_10_7 + 25*x_7_6*x_11_5\n",
      "  + 25*x_7_6*x_11_7 + 83*x_7_6*x_12_5 + 83*x_7_6*x_12_7 + 29*x_7_6*x_13_5\n",
      "  + 29*x_7_6*x_13_7 + 18*x_7_6*x_14_5 + 18*x_7_6*x_14_7 + 29*x_7_6*x_15_5\n",
      "  + 29*x_7_6*x_15_7 + 57*x_7_6*x_16_5 + 57*x_7_6*x_16_7 + 35*x_7_6*x_17_5\n",
      "  + 35*x_7_6*x_17_7 + 24*x_7_6*x_18_5 + 24*x_7_6*x_18_7 + 45*x_7_6*x_19_5\n",
      "  + 45*x_7_6*x_19_7 + 44*x_7_6*x_8_5 + 44*x_7_6*x_8_7 + 13*x_7_6*x_9_5\n",
      "  + 13*x_7_6*x_9_7 + 69*x_7_7*x_10_6 + 69*x_7_7*x_10_8 + 25*x_7_7*x_11_6\n",
      "  + 25*x_7_7*x_11_8 + 83*x_7_7*x_12_6 + 83*x_7_7*x_12_8 + 29*x_7_7*x_13_6\n",
      "  + 29*x_7_7*x_13_8 + 18*x_7_7*x_14_6 + 18*x_7_7*x_14_8 + 29*x_7_7*x_15_6\n",
      "  + 29*x_7_7*x_15_8 + 57*x_7_7*x_16_6 + 57*x_7_7*x_16_8 + 35*x_7_7*x_17_6\n",
      "  + 35*x_7_7*x_17_8 + 24*x_7_7*x_18_6 + 24*x_7_7*x_18_8 + 45*x_7_7*x_19_6\n",
      "  + 45*x_7_7*x_19_8 + 44*x_7_7*x_8_6 + 44*x_7_7*x_8_8 + 13*x_7_7*x_9_6\n",
      "  + 13*x_7_7*x_9_8 + 69*x_7_8*x_10_7 + 69*x_7_8*x_10_9 + 25*x_7_8*x_11_7\n",
      "  + 25*x_7_8*x_11_9 + 83*x_7_8*x_12_7 + 83*x_7_8*x_12_9 + 29*x_7_8*x_13_7\n",
      "  + 29*x_7_8*x_13_9 + 18*x_7_8*x_14_7 + 18*x_7_8*x_14_9 + 29*x_7_8*x_15_7\n",
      "  + 29*x_7_8*x_15_9 + 57*x_7_8*x_16_7 + 57*x_7_8*x_16_9 + 35*x_7_8*x_17_7\n",
      "  + 35*x_7_8*x_17_9 + 24*x_7_8*x_18_7 + 24*x_7_8*x_18_9 + 45*x_7_8*x_19_7\n",
      "  + 45*x_7_8*x_19_9 + 44*x_7_8*x_8_7 + 44*x_7_8*x_8_9 + 13*x_7_8*x_9_7\n",
      "  + 13*x_7_8*x_9_9 + 69*x_7_9*x_10_10 + 69*x_7_9*x_10_8 + 25*x_7_9*x_11_10\n",
      "  + 25*x_7_9*x_11_8 + 83*x_7_9*x_12_10 + 83*x_7_9*x_12_8 + 29*x_7_9*x_13_10\n",
      "  + 29*x_7_9*x_13_8 + 18*x_7_9*x_14_10 + 18*x_7_9*x_14_8 + 29*x_7_9*x_15_10\n",
      "  + 29*x_7_9*x_15_8 + 57*x_7_9*x_16_10 + 57*x_7_9*x_16_8 + 35*x_7_9*x_17_10\n",
      "  + 35*x_7_9*x_17_8 + 24*x_7_9*x_18_10 + 24*x_7_9*x_18_8 + 45*x_7_9*x_19_10\n",
      "  + 45*x_7_9*x_19_8 + 44*x_7_9*x_8_10 + 44*x_7_9*x_8_8 + 13*x_7_9*x_9_10\n",
      "  + 13*x_7_9*x_9_8 + 70*x_8_0*x_10_1 + 70*x_8_0*x_10_19 + 55*x_8_0*x_11_1\n",
      "  + 55*x_8_0*x_11_19 + 77*x_8_0*x_12_1 + 77*x_8_0*x_12_19 + 67*x_8_0*x_13_1\n",
      "  + 67*x_8_0*x_13_19 + 39*x_8_0*x_14_1 + 39*x_8_0*x_14_19 + 63*x_8_0*x_15_1\n",
      "  + 63*x_8_0*x_15_19 + 95*x_8_0*x_16_1 + 95*x_8_0*x_16_19 + 20*x_8_0*x_17_1\n",
      "  + 20*x_8_0*x_17_19 + 65*x_8_0*x_18_1 + 65*x_8_0*x_18_19 + 69*x_8_0*x_19_1\n",
      "  + 69*x_8_0*x_19_19 + 31*x_8_0*x_9_1 + 31*x_8_0*x_9_19 + 70*x_8_1*x_10_0\n",
      "  + 70*x_8_1*x_10_2 + 55*x_8_1*x_11_0 + 55*x_8_1*x_11_2 + 77*x_8_1*x_12_0\n",
      "  + 77*x_8_1*x_12_2 + 67*x_8_1*x_13_0 + 67*x_8_1*x_13_2 + 39*x_8_1*x_14_0\n",
      "  + 39*x_8_1*x_14_2 + 63*x_8_1*x_15_0 + 63*x_8_1*x_15_2 + 95*x_8_1*x_16_0\n",
      "  + 95*x_8_1*x_16_2 + 20*x_8_1*x_17_0 + 20*x_8_1*x_17_2 + 65*x_8_1*x_18_0\n",
      "  + 65*x_8_1*x_18_2 + 69*x_8_1*x_19_0 + 69*x_8_1*x_19_2 + 31*x_8_1*x_9_0\n",
      "  + 31*x_8_1*x_9_2 + 70*x_8_10*x_10_11 + 70*x_8_10*x_10_9 + 55*x_8_10*x_11_11\n",
      "  + 55*x_8_10*x_11_9 + 77*x_8_10*x_12_11 + 77*x_8_10*x_12_9 + 67*x_8_10*x_13_11\n",
      "  + 67*x_8_10*x_13_9 + 39*x_8_10*x_14_11 + 39*x_8_10*x_14_9 + 63*x_8_10*x_15_11\n",
      "  + 63*x_8_10*x_15_9 + 95*x_8_10*x_16_11 + 95*x_8_10*x_16_9 + 20*x_8_10*x_17_11\n",
      "  + 20*x_8_10*x_17_9 + 65*x_8_10*x_18_11 + 65*x_8_10*x_18_9 + 69*x_8_10*x_19_11\n",
      "  + 69*x_8_10*x_19_9 + 31*x_8_10*x_9_11 + 31*x_8_10*x_9_9 + 70*x_8_11*x_10_10\n",
      "  + 70*x_8_11*x_10_12 + 55*x_8_11*x_11_10 + 55*x_8_11*x_11_12\n",
      "  + 77*x_8_11*x_12_10 + 77*x_8_11*x_12_12 + 67*x_8_11*x_13_10\n",
      "  + 67*x_8_11*x_13_12 + 39*x_8_11*x_14_10 + 39*x_8_11*x_14_12\n",
      "  + 63*x_8_11*x_15_10 + 63*x_8_11*x_15_12 + 95*x_8_11*x_16_10\n",
      "  + 95*x_8_11*x_16_12 + 20*x_8_11*x_17_10 + 20*x_8_11*x_17_12\n",
      "  + 65*x_8_11*x_18_10 + 65*x_8_11*x_18_12 + 69*x_8_11*x_19_10\n",
      "  + 69*x_8_11*x_19_12 + 31*x_8_11*x_9_10 + 31*x_8_11*x_9_12 + 70*x_8_12*x_10_11\n",
      "  + 70*x_8_12*x_10_13 + 55*x_8_12*x_11_11 + 55*x_8_12*x_11_13\n",
      "  + 77*x_8_12*x_12_11 + 77*x_8_12*x_12_13 + 67*x_8_12*x_13_11\n",
      "  + 67*x_8_12*x_13_13 + 39*x_8_12*x_14_11 + 39*x_8_12*x_14_13\n",
      "  + 63*x_8_12*x_15_11 + 63*x_8_12*x_15_13 + 95*x_8_12*x_16_11\n",
      "  + 95*x_8_12*x_16_13 + 20*x_8_12*x_17_11 + 20*x_8_12*x_17_13\n",
      "  + 65*x_8_12*x_18_11 + 65*x_8_12*x_18_13 + 69*x_8_12*x_19_11\n",
      "  + 69*x_8_12*x_19_13 + 31*x_8_12*x_9_11 + 31*x_8_12*x_9_13 + 70*x_8_13*x_10_12\n",
      "  + 70*x_8_13*x_10_14 + 55*x_8_13*x_11_12 + 55*x_8_13*x_11_14\n",
      "  + 77*x_8_13*x_12_12 + 77*x_8_13*x_12_14 + 67*x_8_13*x_13_12\n",
      "  + 67*x_8_13*x_13_14 + 39*x_8_13*x_14_12 + 39*x_8_13*x_14_14\n",
      "  + 63*x_8_13*x_15_12 + 63*x_8_13*x_15_14 + 95*x_8_13*x_16_12\n",
      "  + 95*x_8_13*x_16_14 + 20*x_8_13*x_17_12 + 20*x_8_13*x_17_14\n",
      "  + 65*x_8_13*x_18_12 + 65*x_8_13*x_18_14 + 69*x_8_13*x_19_12\n",
      "  + 69*x_8_13*x_19_14 + 31*x_8_13*x_9_12 + 31*x_8_13*x_9_14 + 70*x_8_14*x_10_13\n",
      "  + 70*x_8_14*x_10_15 + 55*x_8_14*x_11_13 + 55*x_8_14*x_11_15\n",
      "  + 77*x_8_14*x_12_13 + 77*x_8_14*x_12_15 + 67*x_8_14*x_13_13\n",
      "  + 67*x_8_14*x_13_15 + 39*x_8_14*x_14_13 + 39*x_8_14*x_14_15\n",
      "  + 63*x_8_14*x_15_13 + 63*x_8_14*x_15_15 + 95*x_8_14*x_16_13\n",
      "  + 95*x_8_14*x_16_15 + 20*x_8_14*x_17_13 + 20*x_8_14*x_17_15\n",
      "  + 65*x_8_14*x_18_13 + 65*x_8_14*x_18_15 + 69*x_8_14*x_19_13\n",
      "  + 69*x_8_14*x_19_15 + 31*x_8_14*x_9_13 + 31*x_8_14*x_9_15 + 70*x_8_15*x_10_14\n",
      "  + 70*x_8_15*x_10_16 + 55*x_8_15*x_11_14 + 55*x_8_15*x_11_16\n",
      "  + 77*x_8_15*x_12_14 + 77*x_8_15*x_12_16 + 67*x_8_15*x_13_14\n",
      "  + 67*x_8_15*x_13_16 + 39*x_8_15*x_14_14 + 39*x_8_15*x_14_16\n",
      "  + 63*x_8_15*x_15_14 + 63*x_8_15*x_15_16 + 95*x_8_15*x_16_14\n",
      "  + 95*x_8_15*x_16_16 + 20*x_8_15*x_17_14 + 20*x_8_15*x_17_16\n",
      "  + 65*x_8_15*x_18_14 + 65*x_8_15*x_18_16 + 69*x_8_15*x_19_14\n",
      "  + 69*x_8_15*x_19_16 + 31*x_8_15*x_9_14 + 31*x_8_15*x_9_16 + 70*x_8_16*x_10_15\n",
      "  + 70*x_8_16*x_10_17 + 55*x_8_16*x_11_15 + 55*x_8_16*x_11_17\n",
      "  + 77*x_8_16*x_12_15 + 77*x_8_16*x_12_17 + 67*x_8_16*x_13_15\n",
      "  + 67*x_8_16*x_13_17 + 39*x_8_16*x_14_15 + 39*x_8_16*x_14_17\n",
      "  + 63*x_8_16*x_15_15 + 63*x_8_16*x_15_17 + 95*x_8_16*x_16_15\n",
      "  + 95*x_8_16*x_16_17 + 20*x_8_16*x_17_15 + 20*x_8_16*x_17_17\n",
      "  + 65*x_8_16*x_18_15 + 65*x_8_16*x_18_17 + 69*x_8_16*x_19_15\n",
      "  + 69*x_8_16*x_19_17 + 31*x_8_16*x_9_15 + 31*x_8_16*x_9_17 + 70*x_8_17*x_10_16\n",
      "  + 70*x_8_17*x_10_18 + 55*x_8_17*x_11_16 + 55*x_8_17*x_11_18\n",
      "  + 77*x_8_17*x_12_16 + 77*x_8_17*x_12_18 + 67*x_8_17*x_13_16\n",
      "  + 67*x_8_17*x_13_18 + 39*x_8_17*x_14_16 + 39*x_8_17*x_14_18\n",
      "  + 63*x_8_17*x_15_16 + 63*x_8_17*x_15_18 + 95*x_8_17*x_16_16\n",
      "  + 95*x_8_17*x_16_18 + 20*x_8_17*x_17_16 + 20*x_8_17*x_17_18\n",
      "  + 65*x_8_17*x_18_16 + 65*x_8_17*x_18_18 + 69*x_8_17*x_19_16\n",
      "  + 69*x_8_17*x_19_18 + 31*x_8_17*x_9_16 + 31*x_8_17*x_9_18 + 70*x_8_18*x_10_17\n",
      "  + 70*x_8_18*x_10_19 + 55*x_8_18*x_11_17 + 55*x_8_18*x_11_19\n",
      "  + 77*x_8_18*x_12_17 + 77*x_8_18*x_12_19 + 67*x_8_18*x_13_17\n",
      "  + 67*x_8_18*x_13_19 + 39*x_8_18*x_14_17 + 39*x_8_18*x_14_19\n",
      "  + 63*x_8_18*x_15_17 + 63*x_8_18*x_15_19 + 95*x_8_18*x_16_17\n",
      "  + 95*x_8_18*x_16_19 + 20*x_8_18*x_17_17 + 20*x_8_18*x_17_19\n",
      "  + 65*x_8_18*x_18_17 + 65*x_8_18*x_18_19 + 69*x_8_18*x_19_17\n",
      "  + 69*x_8_18*x_19_19 + 31*x_8_18*x_9_17 + 31*x_8_18*x_9_19 + 70*x_8_19*x_10_0\n",
      "  + 70*x_8_19*x_10_18 + 55*x_8_19*x_11_0 + 55*x_8_19*x_11_18 + 77*x_8_19*x_12_0\n",
      "  + 77*x_8_19*x_12_18 + 67*x_8_19*x_13_0 + 67*x_8_19*x_13_18 + 39*x_8_19*x_14_0\n",
      "  + 39*x_8_19*x_14_18 + 63*x_8_19*x_15_0 + 63*x_8_19*x_15_18 + 95*x_8_19*x_16_0\n",
      "  + 95*x_8_19*x_16_18 + 20*x_8_19*x_17_0 + 20*x_8_19*x_17_18 + 65*x_8_19*x_18_0\n",
      "  + 65*x_8_19*x_18_18 + 69*x_8_19*x_19_0 + 69*x_8_19*x_19_18 + 31*x_8_19*x_9_0\n",
      "  + 31*x_8_19*x_9_18 + 70*x_8_2*x_10_1 + 70*x_8_2*x_10_3 + 55*x_8_2*x_11_1\n",
      "  + 55*x_8_2*x_11_3 + 77*x_8_2*x_12_1 + 77*x_8_2*x_12_3 + 67*x_8_2*x_13_1\n",
      "  + 67*x_8_2*x_13_3 + 39*x_8_2*x_14_1 + 39*x_8_2*x_14_3 + 63*x_8_2*x_15_1\n",
      "  + 63*x_8_2*x_15_3 + 95*x_8_2*x_16_1 + 95*x_8_2*x_16_3 + 20*x_8_2*x_17_1\n",
      "  + 20*x_8_2*x_17_3 + 65*x_8_2*x_18_1 + 65*x_8_2*x_18_3 + 69*x_8_2*x_19_1\n",
      "  + 69*x_8_2*x_19_3 + 31*x_8_2*x_9_1 + 31*x_8_2*x_9_3 + 70*x_8_3*x_10_2\n",
      "  + 70*x_8_3*x_10_4 + 55*x_8_3*x_11_2 + 55*x_8_3*x_11_4 + 77*x_8_3*x_12_2\n",
      "  + 77*x_8_3*x_12_4 + 67*x_8_3*x_13_2 + 67*x_8_3*x_13_4 + 39*x_8_3*x_14_2\n",
      "  + 39*x_8_3*x_14_4 + 63*x_8_3*x_15_2 + 63*x_8_3*x_15_4 + 95*x_8_3*x_16_2\n",
      "  + 95*x_8_3*x_16_4 + 20*x_8_3*x_17_2 + 20*x_8_3*x_17_4 + 65*x_8_3*x_18_2\n",
      "  + 65*x_8_3*x_18_4 + 69*x_8_3*x_19_2 + 69*x_8_3*x_19_4 + 31*x_8_3*x_9_2\n",
      "  + 31*x_8_3*x_9_4 + 70*x_8_4*x_10_3 + 70*x_8_4*x_10_5 + 55*x_8_4*x_11_3\n",
      "  + 55*x_8_4*x_11_5 + 77*x_8_4*x_12_3 + 77*x_8_4*x_12_5 + 67*x_8_4*x_13_3\n",
      "  + 67*x_8_4*x_13_5 + 39*x_8_4*x_14_3 + 39*x_8_4*x_14_5 + 63*x_8_4*x_15_3\n",
      "  + 63*x_8_4*x_15_5 + 95*x_8_4*x_16_3 + 95*x_8_4*x_16_5 + 20*x_8_4*x_17_3\n",
      "  + 20*x_8_4*x_17_5 + 65*x_8_4*x_18_3 + 65*x_8_4*x_18_5 + 69*x_8_4*x_19_3\n",
      "  + 69*x_8_4*x_19_5 + 31*x_8_4*x_9_3 + 31*x_8_4*x_9_5 + 70*x_8_5*x_10_4\n",
      "  + 70*x_8_5*x_10_6 + 55*x_8_5*x_11_4 + 55*x_8_5*x_11_6 + 77*x_8_5*x_12_4\n",
      "  + 77*x_8_5*x_12_6 + 67*x_8_5*x_13_4 + 67*x_8_5*x_13_6 + 39*x_8_5*x_14_4\n",
      "  + 39*x_8_5*x_14_6 + 63*x_8_5*x_15_4 + 63*x_8_5*x_15_6 + 95*x_8_5*x_16_4\n",
      "  + 95*x_8_5*x_16_6 + 20*x_8_5*x_17_4 + 20*x_8_5*x_17_6 + 65*x_8_5*x_18_4\n",
      "  + 65*x_8_5*x_18_6 + 69*x_8_5*x_19_4 + 69*x_8_5*x_19_6 + 31*x_8_5*x_9_4\n",
      "  + 31*x_8_5*x_9_6 + 70*x_8_6*x_10_5 + 70*x_8_6*x_10_7 + 55*x_8_6*x_11_5\n",
      "  + 55*x_8_6*x_11_7 + 77*x_8_6*x_12_5 + 77*x_8_6*x_12_7 + 67*x_8_6*x_13_5\n",
      "  + 67*x_8_6*x_13_7 + 39*x_8_6*x_14_5 + 39*x_8_6*x_14_7 + 63*x_8_6*x_15_5\n",
      "  + 63*x_8_6*x_15_7 + 95*x_8_6*x_16_5 + 95*x_8_6*x_16_7 + 20*x_8_6*x_17_5\n",
      "  + 20*x_8_6*x_17_7 + 65*x_8_6*x_18_5 + 65*x_8_6*x_18_7 + 69*x_8_6*x_19_5\n",
      "  + 69*x_8_6*x_19_7 + 31*x_8_6*x_9_5 + 31*x_8_6*x_9_7 + 70*x_8_7*x_10_6\n",
      "  + 70*x_8_7*x_10_8 + 55*x_8_7*x_11_6 + 55*x_8_7*x_11_8 + 77*x_8_7*x_12_6\n",
      "  + 77*x_8_7*x_12_8 + 67*x_8_7*x_13_6 + 67*x_8_7*x_13_8 + 39*x_8_7*x_14_6\n",
      "  + 39*x_8_7*x_14_8 + 63*x_8_7*x_15_6 + 63*x_8_7*x_15_8 + 95*x_8_7*x_16_6\n",
      "  + 95*x_8_7*x_16_8 + 20*x_8_7*x_17_6 + 20*x_8_7*x_17_8 + 65*x_8_7*x_18_6\n",
      "  + 65*x_8_7*x_18_8 + 69*x_8_7*x_19_6 + 69*x_8_7*x_19_8 + 31*x_8_7*x_9_6\n",
      "  + 31*x_8_7*x_9_8 + 70*x_8_8*x_10_7 + 70*x_8_8*x_10_9 + 55*x_8_8*x_11_7\n",
      "  + 55*x_8_8*x_11_9 + 77*x_8_8*x_12_7 + 77*x_8_8*x_12_9 + 67*x_8_8*x_13_7\n",
      "  + 67*x_8_8*x_13_9 + 39*x_8_8*x_14_7 + 39*x_8_8*x_14_9 + 63*x_8_8*x_15_7\n",
      "  + 63*x_8_8*x_15_9 + 95*x_8_8*x_16_7 + 95*x_8_8*x_16_9 + 20*x_8_8*x_17_7\n",
      "  + 20*x_8_8*x_17_9 + 65*x_8_8*x_18_7 + 65*x_8_8*x_18_9 + 69*x_8_8*x_19_7\n",
      "  + 69*x_8_8*x_19_9 + 31*x_8_8*x_9_7 + 31*x_8_8*x_9_9 + 70*x_8_9*x_10_10\n",
      "  + 70*x_8_9*x_10_8 + 55*x_8_9*x_11_10 + 55*x_8_9*x_11_8 + 77*x_8_9*x_12_10\n",
      "  + 77*x_8_9*x_12_8 + 67*x_8_9*x_13_10 + 67*x_8_9*x_13_8 + 39*x_8_9*x_14_10\n",
      "  + 39*x_8_9*x_14_8 + 63*x_8_9*x_15_10 + 63*x_8_9*x_15_8 + 95*x_8_9*x_16_10\n",
      "  + 95*x_8_9*x_16_8 + 20*x_8_9*x_17_10 + 20*x_8_9*x_17_8 + 65*x_8_9*x_18_10\n",
      "  + 65*x_8_9*x_18_8 + 69*x_8_9*x_19_10 + 69*x_8_9*x_19_8 + 31*x_8_9*x_9_10\n",
      "  + 31*x_8_9*x_9_8 + 67*x_9_0*x_10_1 + 67*x_9_0*x_10_19 + 31*x_9_0*x_11_1\n",
      "  + 31*x_9_0*x_11_19 + 79*x_9_0*x_12_1 + 79*x_9_0*x_12_19 + 39*x_9_0*x_13_1\n",
      "  + 39*x_9_0*x_13_19 + 16*x_9_0*x_14_1 + 16*x_9_0*x_14_19 + 37*x_9_0*x_15_1\n",
      "  + 37*x_9_0*x_15_19 + 68*x_9_0*x_16_1 + 68*x_9_0*x_16_19 + 25*x_9_0*x_17_1\n",
      "  + 25*x_9_0*x_17_19 + 35*x_9_0*x_18_1 + 35*x_9_0*x_18_19 + 50*x_9_0*x_19_1\n",
      "  + 50*x_9_0*x_19_19 + 67*x_9_1*x_10_0 + 67*x_9_1*x_10_2 + 31*x_9_1*x_11_0\n",
      "  + 31*x_9_1*x_11_2 + 79*x_9_1*x_12_0 + 79*x_9_1*x_12_2 + 39*x_9_1*x_13_0\n",
      "  + 39*x_9_1*x_13_2 + 16*x_9_1*x_14_0 + 16*x_9_1*x_14_2 + 37*x_9_1*x_15_0\n",
      "  + 37*x_9_1*x_15_2 + 68*x_9_1*x_16_0 + 68*x_9_1*x_16_2 + 25*x_9_1*x_17_0\n",
      "  + 25*x_9_1*x_17_2 + 35*x_9_1*x_18_0 + 35*x_9_1*x_18_2 + 50*x_9_1*x_19_0\n",
      "  + 50*x_9_1*x_19_2 + 67*x_9_10*x_10_11 + 67*x_9_10*x_10_9 + 31*x_9_10*x_11_11\n",
      "  + 31*x_9_10*x_11_9 + 79*x_9_10*x_12_11 + 79*x_9_10*x_12_9 + 39*x_9_10*x_13_11\n",
      "  + 39*x_9_10*x_13_9 + 16*x_9_10*x_14_11 + 16*x_9_10*x_14_9 + 37*x_9_10*x_15_11\n",
      "  + 37*x_9_10*x_15_9 + 68*x_9_10*x_16_11 + 68*x_9_10*x_16_9 + 25*x_9_10*x_17_11\n",
      "  + 25*x_9_10*x_17_9 + 35*x_9_10*x_18_11 + 35*x_9_10*x_18_9 + 50*x_9_10*x_19_11\n",
      "  + 50*x_9_10*x_19_9 + 67*x_9_11*x_10_10 + 67*x_9_11*x_10_12 + 31*x_9_11*x_11_10\n",
      "  + 31*x_9_11*x_11_12 + 79*x_9_11*x_12_10 + 79*x_9_11*x_12_12\n",
      "  + 39*x_9_11*x_13_10 + 39*x_9_11*x_13_12 + 16*x_9_11*x_14_10\n",
      "  + 16*x_9_11*x_14_12 + 37*x_9_11*x_15_10 + 37*x_9_11*x_15_12\n",
      "  + 68*x_9_11*x_16_10 + 68*x_9_11*x_16_12 + 25*x_9_11*x_17_10\n",
      "  + 25*x_9_11*x_17_12 + 35*x_9_11*x_18_10 + 35*x_9_11*x_18_12\n",
      "  + 50*x_9_11*x_19_10 + 50*x_9_11*x_19_12 + 67*x_9_12*x_10_11\n",
      "  + 67*x_9_12*x_10_13 + 31*x_9_12*x_11_11 + 31*x_9_12*x_11_13\n",
      "  + 79*x_9_12*x_12_11 + 79*x_9_12*x_12_13 + 39*x_9_12*x_13_11\n",
      "  + 39*x_9_12*x_13_13 + 16*x_9_12*x_14_11 + 16*x_9_12*x_14_13\n",
      "  + 37*x_9_12*x_15_11 + 37*x_9_12*x_15_13 + 68*x_9_12*x_16_11\n",
      "  + 68*x_9_12*x_16_13 + 25*x_9_12*x_17_11 + 25*x_9_12*x_17_13\n",
      "  + 35*x_9_12*x_18_11 + 35*x_9_12*x_18_13 + 50*x_9_12*x_19_11\n",
      "  + 50*x_9_12*x_19_13 + 67*x_9_13*x_10_12 + 67*x_9_13*x_10_14\n",
      "  + 31*x_9_13*x_11_12 + 31*x_9_13*x_11_14 + 79*x_9_13*x_12_12\n",
      "  + 79*x_9_13*x_12_14 + 39*x_9_13*x_13_12 + 39*x_9_13*x_13_14\n",
      "  + 16*x_9_13*x_14_12 + 16*x_9_13*x_14_14 + 37*x_9_13*x_15_12\n",
      "  + 37*x_9_13*x_15_14 + 68*x_9_13*x_16_12 + 68*x_9_13*x_16_14\n",
      "  + 25*x_9_13*x_17_12 + 25*x_9_13*x_17_14 + 35*x_9_13*x_18_12\n",
      "  + 35*x_9_13*x_18_14 + 50*x_9_13*x_19_12 + 50*x_9_13*x_19_14\n",
      "  + 67*x_9_14*x_10_13 + 67*x_9_14*x_10_15 + 31*x_9_14*x_11_13\n",
      "  + 31*x_9_14*x_11_15 + 79*x_9_14*x_12_13 + 79*x_9_14*x_12_15\n",
      "  + 39*x_9_14*x_13_13 + 39*x_9_14*x_13_15 + 16*x_9_14*x_14_13\n",
      "  + 16*x_9_14*x_14_15 + 37*x_9_14*x_15_13 + 37*x_9_14*x_15_15\n",
      "  + 68*x_9_14*x_16_13 + 68*x_9_14*x_16_15 + 25*x_9_14*x_17_13\n",
      "  + 25*x_9_14*x_17_15 + 35*x_9_14*x_18_13 + 35*x_9_14*x_18_15\n",
      "  + 50*x_9_14*x_19_13 + 50*x_9_14*x_19_15 + 67*x_9_15*x_10_14\n",
      "  + 67*x_9_15*x_10_16 + 31*x_9_15*x_11_14 + 31*x_9_15*x_11_16\n",
      "  + 79*x_9_15*x_12_14 + 79*x_9_15*x_12_16 + 39*x_9_15*x_13_14\n",
      "  + 39*x_9_15*x_13_16 + 16*x_9_15*x_14_14 + 16*x_9_15*x_14_16\n",
      "  + 37*x_9_15*x_15_14 + 37*x_9_15*x_15_16 + 68*x_9_15*x_16_14\n",
      "  + 68*x_9_15*x_16_16 + 25*x_9_15*x_17_14 + 25*x_9_15*x_17_16\n",
      "  + 35*x_9_15*x_18_14 + 35*x_9_15*x_18_16 + 50*x_9_15*x_19_14\n",
      "  + 50*x_9_15*x_19_16 + 67*x_9_16*x_10_15 + 67*x_9_16*x_10_17\n",
      "  + 31*x_9_16*x_11_15 + 31*x_9_16*x_11_17 + 79*x_9_16*x_12_15\n",
      "  + 79*x_9_16*x_12_17 + 39*x_9_16*x_13_15 + 39*x_9_16*x_13_17\n",
      "  + 16*x_9_16*x_14_15 + 16*x_9_16*x_14_17 + 37*x_9_16*x_15_15\n",
      "  + 37*x_9_16*x_15_17 + 68*x_9_16*x_16_15 + 68*x_9_16*x_16_17\n",
      "  + 25*x_9_16*x_17_15 + 25*x_9_16*x_17_17 + 35*x_9_16*x_18_15\n",
      "  + 35*x_9_16*x_18_17 + 50*x_9_16*x_19_15 + 50*x_9_16*x_19_17\n",
      "  + 67*x_9_17*x_10_16 + 67*x_9_17*x_10_18 + 31*x_9_17*x_11_16\n",
      "  + 31*x_9_17*x_11_18 + 79*x_9_17*x_12_16 + 79*x_9_17*x_12_18\n",
      "  + 39*x_9_17*x_13_16 + 39*x_9_17*x_13_18 + 16*x_9_17*x_14_16\n",
      "  + 16*x_9_17*x_14_18 + 37*x_9_17*x_15_16 + 37*x_9_17*x_15_18\n",
      "  + 68*x_9_17*x_16_16 + 68*x_9_17*x_16_18 + 25*x_9_17*x_17_16\n",
      "  + 25*x_9_17*x_17_18 + 35*x_9_17*x_18_16 + 35*x_9_17*x_18_18\n",
      "  + 50*x_9_17*x_19_16 + 50*x_9_17*x_19_18 + 67*x_9_18*x_10_17\n",
      "  + 67*x_9_18*x_10_19 + 31*x_9_18*x_11_17 + 31*x_9_18*x_11_19\n",
      "  + 79*x_9_18*x_12_17 + 79*x_9_18*x_12_19 + 39*x_9_18*x_13_17\n",
      "  + 39*x_9_18*x_13_19 + 16*x_9_18*x_14_17 + 16*x_9_18*x_14_19\n",
      "  + 37*x_9_18*x_15_17 + 37*x_9_18*x_15_19 + 68*x_9_18*x_16_17\n",
      "  + 68*x_9_18*x_16_19 + 25*x_9_18*x_17_17 + 25*x_9_18*x_17_19\n",
      "  + 35*x_9_18*x_18_17 + 35*x_9_18*x_18_19 + 50*x_9_18*x_19_17\n",
      "  + 50*x_9_18*x_19_19 + 67*x_9_19*x_10_0 + 67*x_9_19*x_10_18 + 31*x_9_19*x_11_0\n",
      "  + 31*x_9_19*x_11_18 + 79*x_9_19*x_12_0 + 79*x_9_19*x_12_18 + 39*x_9_19*x_13_0\n",
      "  + 39*x_9_19*x_13_18 + 16*x_9_19*x_14_0 + 16*x_9_19*x_14_18 + 37*x_9_19*x_15_0\n",
      "  + 37*x_9_19*x_15_18 + 68*x_9_19*x_16_0 + 68*x_9_19*x_16_18 + 25*x_9_19*x_17_0\n",
      "  + 25*x_9_19*x_17_18 + 35*x_9_19*x_18_0 + 35*x_9_19*x_18_18 + 50*x_9_19*x_19_0\n",
      "  + 50*x_9_19*x_19_18 + 67*x_9_2*x_10_1 + 67*x_9_2*x_10_3 + 31*x_9_2*x_11_1\n",
      "  + 31*x_9_2*x_11_3 + 79*x_9_2*x_12_1 + 79*x_9_2*x_12_3 + 39*x_9_2*x_13_1\n",
      "  + 39*x_9_2*x_13_3 + 16*x_9_2*x_14_1 + 16*x_9_2*x_14_3 + 37*x_9_2*x_15_1\n",
      "  + 37*x_9_2*x_15_3 + 68*x_9_2*x_16_1 + 68*x_9_2*x_16_3 + 25*x_9_2*x_17_1\n",
      "  + 25*x_9_2*x_17_3 + 35*x_9_2*x_18_1 + 35*x_9_2*x_18_3 + 50*x_9_2*x_19_1\n",
      "  + 50*x_9_2*x_19_3 + 67*x_9_3*x_10_2 + 67*x_9_3*x_10_4 + 31*x_9_3*x_11_2\n",
      "  + 31*x_9_3*x_11_4 + 79*x_9_3*x_12_2 + 79*x_9_3*x_12_4 + 39*x_9_3*x_13_2\n",
      "  + 39*x_9_3*x_13_4 + 16*x_9_3*x_14_2 + 16*x_9_3*x_14_4 + 37*x_9_3*x_15_2\n",
      "  + 37*x_9_3*x_15_4 + 68*x_9_3*x_16_2 + 68*x_9_3*x_16_4 + 25*x_9_3*x_17_2\n",
      "  + 25*x_9_3*x_17_4 + 35*x_9_3*x_18_2 + 35*x_9_3*x_18_4 + 50*x_9_3*x_19_2\n",
      "  + 50*x_9_3*x_19_4 + 67*x_9_4*x_10_3 + 67*x_9_4*x_10_5 + 31*x_9_4*x_11_3\n",
      "  + 31*x_9_4*x_11_5 + 79*x_9_4*x_12_3 + 79*x_9_4*x_12_5 + 39*x_9_4*x_13_3\n",
      "  + 39*x_9_4*x_13_5 + 16*x_9_4*x_14_3 + 16*x_9_4*x_14_5 + 37*x_9_4*x_15_3\n",
      "  + 37*x_9_4*x_15_5 + 68*x_9_4*x_16_3 + 68*x_9_4*x_16_5 + 25*x_9_4*x_17_3\n",
      "  + 25*x_9_4*x_17_5 + 35*x_9_4*x_18_3 + 35*x_9_4*x_18_5 + 50*x_9_4*x_19_3\n",
      "  + 50*x_9_4*x_19_5 + 67*x_9_5*x_10_4 + 67*x_9_5*x_10_6 + 31*x_9_5*x_11_4\n",
      "  + 31*x_9_5*x_11_6 + 79*x_9_5*x_12_4 + 79*x_9_5*x_12_6 + 39*x_9_5*x_13_4\n",
      "  + 39*x_9_5*x_13_6 + 16*x_9_5*x_14_4 + 16*x_9_5*x_14_6 + 37*x_9_5*x_15_4\n",
      "  + 37*x_9_5*x_15_6 + 68*x_9_5*x_16_4 + 68*x_9_5*x_16_6 + 25*x_9_5*x_17_4\n",
      "  + 25*x_9_5*x_17_6 + 35*x_9_5*x_18_4 + 35*x_9_5*x_18_6 + 50*x_9_5*x_19_4\n",
      "  + 50*x_9_5*x_19_6 + 67*x_9_6*x_10_5 + 67*x_9_6*x_10_7 + 31*x_9_6*x_11_5\n",
      "  + 31*x_9_6*x_11_7 + 79*x_9_6*x_12_5 + 79*x_9_6*x_12_7 + 39*x_9_6*x_13_5\n",
      "  + 39*x_9_6*x_13_7 + 16*x_9_6*x_14_5 + 16*x_9_6*x_14_7 + 37*x_9_6*x_15_5\n",
      "  + 37*x_9_6*x_15_7 + 68*x_9_6*x_16_5 + 68*x_9_6*x_16_7 + 25*x_9_6*x_17_5\n",
      "  + 25*x_9_6*x_17_7 + 35*x_9_6*x_18_5 + 35*x_9_6*x_18_7 + 50*x_9_6*x_19_5\n",
      "  + 50*x_9_6*x_19_7 + 67*x_9_7*x_10_6 + 67*x_9_7*x_10_8 + 31*x_9_7*x_11_6\n",
      "  + 31*x_9_7*x_11_8 + 79*x_9_7*x_12_6 + 79*x_9_7*x_12_8 + 39*x_9_7*x_13_6\n",
      "  + 39*x_9_7*x_13_8 + 16*x_9_7*x_14_6 + 16*x_9_7*x_14_8 + 37*x_9_7*x_15_6\n",
      "  + 37*x_9_7*x_15_8 + 68*x_9_7*x_16_6 + 68*x_9_7*x_16_8 + 25*x_9_7*x_17_6\n",
      "  + 25*x_9_7*x_17_8 + 35*x_9_7*x_18_6 + 35*x_9_7*x_18_8 + 50*x_9_7*x_19_6\n",
      "  + 50*x_9_7*x_19_8 + 67*x_9_8*x_10_7 + 67*x_9_8*x_10_9 + 31*x_9_8*x_11_7\n",
      "  + 31*x_9_8*x_11_9 + 79*x_9_8*x_12_7 + 79*x_9_8*x_12_9 + 39*x_9_8*x_13_7\n",
      "  + 39*x_9_8*x_13_9 + 16*x_9_8*x_14_7 + 16*x_9_8*x_14_9 + 37*x_9_8*x_15_7\n",
      "  + 37*x_9_8*x_15_9 + 68*x_9_8*x_16_7 + 68*x_9_8*x_16_9 + 25*x_9_8*x_17_7\n",
      "  + 25*x_9_8*x_17_9 + 35*x_9_8*x_18_7 + 35*x_9_8*x_18_9 + 50*x_9_8*x_19_7\n",
      "  + 50*x_9_8*x_19_9 + 67*x_9_9*x_10_10 + 67*x_9_9*x_10_8 + 31*x_9_9*x_11_10\n",
      "  + 31*x_9_9*x_11_8 + 79*x_9_9*x_12_10 + 79*x_9_9*x_12_8 + 39*x_9_9*x_13_10\n",
      "  + 39*x_9_9*x_13_8 + 16*x_9_9*x_14_10 + 16*x_9_9*x_14_8 + 37*x_9_9*x_15_10\n",
      "  + 37*x_9_9*x_15_8 + 68*x_9_9*x_16_10 + 68*x_9_9*x_16_8 + 25*x_9_9*x_17_10\n",
      "  + 25*x_9_9*x_17_8 + 35*x_9_9*x_18_10 + 35*x_9_9*x_18_8 + 50*x_9_9*x_19_10\n",
      "  + 50*x_9_9*x_19_8\n",
      "\n",
      "Subject to\n",
      "  Linear constraints (40)\n",
      "    x_0_0 + x_0_1 + x_0_10 + x_0_11 + x_0_12 + x_0_13 + x_0_14 + x_0_15 + x_0_16\n",
      "    + x_0_17 + x_0_18 + x_0_19 + x_0_2 + x_0_3 + x_0_4 + x_0_5 + x_0_6 + x_0_7\n",
      "    + x_0_8 + x_0_9 == 1  'c0'\n",
      "    x_1_0 + x_1_1 + x_1_10 + x_1_11 + x_1_12 + x_1_13 + x_1_14 + x_1_15 + x_1_16\n",
      "    + x_1_17 + x_1_18 + x_1_19 + x_1_2 + x_1_3 + x_1_4 + x_1_5 + x_1_6 + x_1_7\n",
      "    + x_1_8 + x_1_9 == 1  'c1'\n",
      "    x_2_0 + x_2_1 + x_2_10 + x_2_11 + x_2_12 + x_2_13 + x_2_14 + x_2_15 + x_2_16\n",
      "    + x_2_17 + x_2_18 + x_2_19 + x_2_2 + x_2_3 + x_2_4 + x_2_5 + x_2_6 + x_2_7\n",
      "    + x_2_8 + x_2_9 == 1  'c2'\n",
      "    x_3_0 + x_3_1 + x_3_10 + x_3_11 + x_3_12 + x_3_13 + x_3_14 + x_3_15 + x_3_16\n",
      "    + x_3_17 + x_3_18 + x_3_19 + x_3_2 + x_3_3 + x_3_4 + x_3_5 + x_3_6 + x_3_7\n",
      "    + x_3_8 + x_3_9 == 1  'c3'\n",
      "    x_4_0 + x_4_1 + x_4_10 + x_4_11 + x_4_12 + x_4_13 + x_4_14 + x_4_15 + x_4_16\n",
      "    + x_4_17 + x_4_18 + x_4_19 + x_4_2 + x_4_3 + x_4_4 + x_4_5 + x_4_6 + x_4_7\n",
      "    + x_4_8 + x_4_9 == 1  'c4'\n",
      "    x_5_0 + x_5_1 + x_5_10 + x_5_11 + x_5_12 + x_5_13 + x_5_14 + x_5_15 + x_5_16\n",
      "    + x_5_17 + x_5_18 + x_5_19 + x_5_2 + x_5_3 + x_5_4 + x_5_5 + x_5_6 + x_5_7\n",
      "    + x_5_8 + x_5_9 == 1  'c5'\n",
      "    x_6_0 + x_6_1 + x_6_10 + x_6_11 + x_6_12 + x_6_13 + x_6_14 + x_6_15 + x_6_16\n",
      "    + x_6_17 + x_6_18 + x_6_19 + x_6_2 + x_6_3 + x_6_4 + x_6_5 + x_6_6 + x_6_7\n",
      "    + x_6_8 + x_6_9 == 1  'c6'\n",
      "    x_7_0 + x_7_1 + x_7_10 + x_7_11 + x_7_12 + x_7_13 + x_7_14 + x_7_15 + x_7_16\n",
      "    + x_7_17 + x_7_18 + x_7_19 + x_7_2 + x_7_3 + x_7_4 + x_7_5 + x_7_6 + x_7_7\n",
      "    + x_7_8 + x_7_9 == 1  'c7'\n",
      "    x_8_0 + x_8_1 + x_8_10 + x_8_11 + x_8_12 + x_8_13 + x_8_14 + x_8_15 + x_8_16\n",
      "    + x_8_17 + x_8_18 + x_8_19 + x_8_2 + x_8_3 + x_8_4 + x_8_5 + x_8_6 + x_8_7\n",
      "    + x_8_8 + x_8_9 == 1  'c8'\n",
      "    x_9_0 + x_9_1 + x_9_10 + x_9_11 + x_9_12 + x_9_13 + x_9_14 + x_9_15 + x_9_16\n",
      "    + x_9_17 + x_9_18 + x_9_19 + x_9_2 + x_9_3 + x_9_4 + x_9_5 + x_9_6 + x_9_7\n",
      "    + x_9_8 + x_9_9 == 1  'c9'\n",
      "    x_10_0 + x_10_1 + x_10_10 + x_10_11 + x_10_12 + x_10_13 + x_10_14 + x_10_15\n",
      "    + x_10_16 + x_10_17 + x_10_18 + x_10_19 + x_10_2 + x_10_3 + x_10_4 + x_10_5\n",
      "    + x_10_6 + x_10_7 + x_10_8 + x_10_9 == 1  'c10'\n",
      "    x_11_0 + x_11_1 + x_11_10 + x_11_11 + x_11_12 + x_11_13 + x_11_14 + x_11_15\n",
      "    + x_11_16 + x_11_17 + x_11_18 + x_11_19 + x_11_2 + x_11_3 + x_11_4 + x_11_5\n",
      "    + x_11_6 + x_11_7 + x_11_8 + x_11_9 == 1  'c11'\n",
      "    x_12_0 + x_12_1 + x_12_10 + x_12_11 + x_12_12 + x_12_13 + x_12_14 + x_12_15\n",
      "    + x_12_16 + x_12_17 + x_12_18 + x_12_19 + x_12_2 + x_12_3 + x_12_4 + x_12_5\n",
      "    + x_12_6 + x_12_7 + x_12_8 + x_12_9 == 1  'c12'\n",
      "    x_13_0 + x_13_1 + x_13_10 + x_13_11 + x_13_12 + x_13_13 + x_13_14 + x_13_15\n",
      "    + x_13_16 + x_13_17 + x_13_18 + x_13_19 + x_13_2 + x_13_3 + x_13_4 + x_13_5\n",
      "    + x_13_6 + x_13_7 + x_13_8 + x_13_9 == 1  'c13'\n",
      "    x_14_0 + x_14_1 + x_14_10 + x_14_11 + x_14_12 + x_14_13 + x_14_14 + x_14_15\n",
      "    + x_14_16 + x_14_17 + x_14_18 + x_14_19 + x_14_2 + x_14_3 + x_14_4 + x_14_5\n",
      "    + x_14_6 + x_14_7 + x_14_8 + x_14_9 == 1  'c14'\n",
      "    x_15_0 + x_15_1 + x_15_10 + x_15_11 + x_15_12 + x_15_13 + x_15_14 + x_15_15\n",
      "    + x_15_16 + x_15_17 + x_15_18 + x_15_19 + x_15_2 + x_15_3 + x_15_4 + x_15_5\n",
      "    + x_15_6 + x_15_7 + x_15_8 + x_15_9 == 1  'c15'\n",
      "    x_16_0 + x_16_1 + x_16_10 + x_16_11 + x_16_12 + x_16_13 + x_16_14 + x_16_15\n",
      "    + x_16_16 + x_16_17 + x_16_18 + x_16_19 + x_16_2 + x_16_3 + x_16_4 + x_16_5\n",
      "    + x_16_6 + x_16_7 + x_16_8 + x_16_9 == 1  'c16'\n",
      "    x_17_0 + x_17_1 + x_17_10 + x_17_11 + x_17_12 + x_17_13 + x_17_14 + x_17_15\n",
      "    + x_17_16 + x_17_17 + x_17_18 + x_17_19 + x_17_2 + x_17_3 + x_17_4 + x_17_5\n",
      "    + x_17_6 + x_17_7 + x_17_8 + x_17_9 == 1  'c17'\n",
      "    x_18_0 + x_18_1 + x_18_10 + x_18_11 + x_18_12 + x_18_13 + x_18_14 + x_18_15\n",
      "    + x_18_16 + x_18_17 + x_18_18 + x_18_19 + x_18_2 + x_18_3 + x_18_4 + x_18_5\n",
      "    + x_18_6 + x_18_7 + x_18_8 + x_18_9 == 1  'c18'\n",
      "    x_19_0 + x_19_1 + x_19_10 + x_19_11 + x_19_12 + x_19_13 + x_19_14 + x_19_15\n",
      "    + x_19_16 + x_19_17 + x_19_18 + x_19_19 + x_19_2 + x_19_3 + x_19_4 + x_19_5\n",
      "    + x_19_6 + x_19_7 + x_19_8 + x_19_9 == 1  'c19'\n",
      "    x_0_0 + x_10_0 + x_11_0 + x_12_0 + x_13_0 + x_14_0 + x_15_0 + x_16_0\n",
      "    + x_17_0 + x_18_0 + x_19_0 + x_1_0 + x_2_0 + x_3_0 + x_4_0 + x_5_0 + x_6_0\n",
      "    + x_7_0 + x_8_0 + x_9_0 == 1  'c20'\n",
      "    x_0_1 + x_10_1 + x_11_1 + x_12_1 + x_13_1 + x_14_1 + x_15_1 + x_16_1\n",
      "    + x_17_1 + x_18_1 + x_19_1 + x_1_1 + x_2_1 + x_3_1 + x_4_1 + x_5_1 + x_6_1\n",
      "    + x_7_1 + x_8_1 + x_9_1 == 1  'c21'\n",
      "    x_0_2 + x_10_2 + x_11_2 + x_12_2 + x_13_2 + x_14_2 + x_15_2 + x_16_2\n",
      "    + x_17_2 + x_18_2 + x_19_2 + x_1_2 + x_2_2 + x_3_2 + x_4_2 + x_5_2 + x_6_2\n",
      "    + x_7_2 + x_8_2 + x_9_2 == 1  'c22'\n",
      "    x_0_3 + x_10_3 + x_11_3 + x_12_3 + x_13_3 + x_14_3 + x_15_3 + x_16_3\n",
      "    + x_17_3 + x_18_3 + x_19_3 + x_1_3 + x_2_3 + x_3_3 + x_4_3 + x_5_3 + x_6_3\n",
      "    + x_7_3 + x_8_3 + x_9_3 == 1  'c23'\n",
      "    x_0_4 + x_10_4 + x_11_4 + x_12_4 + x_13_4 + x_14_4 + x_15_4 + x_16_4\n",
      "    + x_17_4 + x_18_4 + x_19_4 + x_1_4 + x_2_4 + x_3_4 + x_4_4 + x_5_4 + x_6_4\n",
      "    + x_7_4 + x_8_4 + x_9_4 == 1  'c24'\n",
      "    x_0_5 + x_10_5 + x_11_5 + x_12_5 + x_13_5 + x_14_5 + x_15_5 + x_16_5\n",
      "    + x_17_5 + x_18_5 + x_19_5 + x_1_5 + x_2_5 + x_3_5 + x_4_5 + x_5_5 + x_6_5\n",
      "    + x_7_5 + x_8_5 + x_9_5 == 1  'c25'\n",
      "    x_0_6 + x_10_6 + x_11_6 + x_12_6 + x_13_6 + x_14_6 + x_15_6 + x_16_6\n",
      "    + x_17_6 + x_18_6 + x_19_6 + x_1_6 + x_2_6 + x_3_6 + x_4_6 + x_5_6 + x_6_6\n",
      "    + x_7_6 + x_8_6 + x_9_6 == 1  'c26'\n",
      "    x_0_7 + x_10_7 + x_11_7 + x_12_7 + x_13_7 + x_14_7 + x_15_7 + x_16_7\n",
      "    + x_17_7 + x_18_7 + x_19_7 + x_1_7 + x_2_7 + x_3_7 + x_4_7 + x_5_7 + x_6_7\n",
      "    + x_7_7 + x_8_7 + x_9_7 == 1  'c27'\n",
      "    x_0_8 + x_10_8 + x_11_8 + x_12_8 + x_13_8 + x_14_8 + x_15_8 + x_16_8\n",
      "    + x_17_8 + x_18_8 + x_19_8 + x_1_8 + x_2_8 + x_3_8 + x_4_8 + x_5_8 + x_6_8\n",
      "    + x_7_8 + x_8_8 + x_9_8 == 1  'c28'\n",
      "    x_0_9 + x_10_9 + x_11_9 + x_12_9 + x_13_9 + x_14_9 + x_15_9 + x_16_9\n",
      "    + x_17_9 + x_18_9 + x_19_9 + x_1_9 + x_2_9 + x_3_9 + x_4_9 + x_5_9 + x_6_9\n",
      "    + x_7_9 + x_8_9 + x_9_9 == 1  'c29'\n",
      "    x_0_10 + x_10_10 + x_11_10 + x_12_10 + x_13_10 + x_14_10 + x_15_10 + x_16_10\n",
      "    + x_17_10 + x_18_10 + x_19_10 + x_1_10 + x_2_10 + x_3_10 + x_4_10 + x_5_10\n",
      "    + x_6_10 + x_7_10 + x_8_10 + x_9_10 == 1  'c30'\n",
      "    x_0_11 + x_10_11 + x_11_11 + x_12_11 + x_13_11 + x_14_11 + x_15_11 + x_16_11\n",
      "    + x_17_11 + x_18_11 + x_19_11 + x_1_11 + x_2_11 + x_3_11 + x_4_11 + x_5_11\n",
      "    + x_6_11 + x_7_11 + x_8_11 + x_9_11 == 1  'c31'\n",
      "    x_0_12 + x_10_12 + x_11_12 + x_12_12 + x_13_12 + x_14_12 + x_15_12 + x_16_12\n",
      "    + x_17_12 + x_18_12 + x_19_12 + x_1_12 + x_2_12 + x_3_12 + x_4_12 + x_5_12\n",
      "    + x_6_12 + x_7_12 + x_8_12 + x_9_12 == 1  'c32'\n",
      "    x_0_13 + x_10_13 + x_11_13 + x_12_13 + x_13_13 + x_14_13 + x_15_13 + x_16_13\n",
      "    + x_17_13 + x_18_13 + x_19_13 + x_1_13 + x_2_13 + x_3_13 + x_4_13 + x_5_13\n",
      "    + x_6_13 + x_7_13 + x_8_13 + x_9_13 == 1  'c33'\n",
      "    x_0_14 + x_10_14 + x_11_14 + x_12_14 + x_13_14 + x_14_14 + x_15_14 + x_16_14\n",
      "    + x_17_14 + x_18_14 + x_19_14 + x_1_14 + x_2_14 + x_3_14 + x_4_14 + x_5_14\n",
      "    + x_6_14 + x_7_14 + x_8_14 + x_9_14 == 1  'c34'\n",
      "    x_0_15 + x_10_15 + x_11_15 + x_12_15 + x_13_15 + x_14_15 + x_15_15 + x_16_15\n",
      "    + x_17_15 + x_18_15 + x_19_15 + x_1_15 + x_2_15 + x_3_15 + x_4_15 + x_5_15\n",
      "    + x_6_15 + x_7_15 + x_8_15 + x_9_15 == 1  'c35'\n",
      "    x_0_16 + x_10_16 + x_11_16 + x_12_16 + x_13_16 + x_14_16 + x_15_16 + x_16_16\n",
      "    + x_17_16 + x_18_16 + x_19_16 + x_1_16 + x_2_16 + x_3_16 + x_4_16 + x_5_16\n",
      "    + x_6_16 + x_7_16 + x_8_16 + x_9_16 == 1  'c36'\n",
      "    x_0_17 + x_10_17 + x_11_17 + x_12_17 + x_13_17 + x_14_17 + x_15_17 + x_16_17\n",
      "    + x_17_17 + x_18_17 + x_19_17 + x_1_17 + x_2_17 + x_3_17 + x_4_17 + x_5_17\n",
      "    + x_6_17 + x_7_17 + x_8_17 + x_9_17 == 1  'c37'\n",
      "    x_0_18 + x_10_18 + x_11_18 + x_12_18 + x_13_18 + x_14_18 + x_15_18 + x_16_18\n",
      "    + x_17_18 + x_18_18 + x_19_18 + x_1_18 + x_2_18 + x_3_18 + x_4_18 + x_5_18\n",
      "    + x_6_18 + x_7_18 + x_8_18 + x_9_18 == 1  'c38'\n",
      "    x_0_19 + x_10_19 + x_11_19 + x_12_19 + x_13_19 + x_14_19 + x_15_19 + x_16_19\n",
      "    + x_17_19 + x_18_19 + x_19_19 + x_1_19 + x_2_19 + x_3_19 + x_4_19 + x_5_19\n",
      "    + x_6_19 + x_7_19 + x_8_19 + x_9_19 == 1  'c39'\n",
      "\n",
      "  Binary variables (400)\n",
      "    x_0_0 x_0_1 x_0_2 x_0_3 x_0_4 x_0_5 x_0_6 x_0_7 x_0_8 x_0_9 x_0_10 x_0_11\n",
      "    x_0_12 x_0_13 x_0_14 x_0_15 x_0_16 x_0_17 x_0_18 x_0_19 x_1_0 x_1_1 x_1_2\n",
      "    x_1_3 x_1_4 x_1_5 x_1_6 x_1_7 x_1_8 x_1_9 x_1_10 x_1_11 x_1_12 x_1_13 x_1_14\n",
      "    x_1_15 x_1_16 x_1_17 x_1_18 x_1_19 x_2_0 x_2_1 x_2_2 x_2_3 x_2_4 x_2_5 x_2_6\n",
      "    x_2_7 x_2_8 x_2_9 x_2_10 x_2_11 x_2_12 x_2_13 x_2_14 x_2_15 x_2_16 x_2_17\n",
      "    x_2_18 x_2_19 x_3_0 x_3_1 x_3_2 x_3_3 x_3_4 x_3_5 x_3_6 x_3_7 x_3_8 x_3_9\n",
      "    x_3_10 x_3_11 x_3_12 x_3_13 x_3_14 x_3_15 x_3_16 x_3_17 x_3_18 x_3_19 x_4_0\n",
      "    x_4_1 x_4_2 x_4_3 x_4_4 x_4_5 x_4_6 x_4_7 x_4_8 x_4_9 x_4_10 x_4_11 x_4_12\n",
      "    x_4_13 x_4_14 x_4_15 x_4_16 x_4_17 x_4_18 x_4_19 x_5_0 x_5_1 x_5_2 x_5_3\n",
      "    x_5_4 x_5_5 x_5_6 x_5_7 x_5_8 x_5_9 x_5_10 x_5_11 x_5_12 x_5_13 x_5_14\n",
      "    x_5_15 x_5_16 x_5_17 x_5_18 x_5_19 x_6_0 x_6_1 x_6_2 x_6_3 x_6_4 x_6_5 x_6_6\n",
      "    x_6_7 x_6_8 x_6_9 x_6_10 x_6_11 x_6_12 x_6_13 x_6_14 x_6_15 x_6_16 x_6_17\n",
      "    x_6_18 x_6_19 x_7_0 x_7_1 x_7_2 x_7_3 x_7_4 x_7_5 x_7_6 x_7_7 x_7_8 x_7_9\n",
      "    x_7_10 x_7_11 x_7_12 x_7_13 x_7_14 x_7_15 x_7_16 x_7_17 x_7_18 x_7_19 x_8_0\n",
      "    x_8_1 x_8_2 x_8_3 x_8_4 x_8_5 x_8_6 x_8_7 x_8_8 x_8_9 x_8_10 x_8_11 x_8_12\n",
      "    x_8_13 x_8_14 x_8_15 x_8_16 x_8_17 x_8_18 x_8_19 x_9_0 x_9_1 x_9_2 x_9_3\n",
      "    x_9_4 x_9_5 x_9_6 x_9_7 x_9_8 x_9_9 x_9_10 x_9_11 x_9_12 x_9_13 x_9_14\n",
      "    x_9_15 x_9_16 x_9_17 x_9_18 x_9_19 x_10_0 x_10_1 x_10_2 x_10_3 x_10_4 x_10_5\n",
      "    x_10_6 x_10_7 x_10_8 x_10_9 x_10_10 x_10_11 x_10_12 x_10_13 x_10_14 x_10_15\n",
      "    x_10_16 x_10_17 x_10_18 x_10_19 x_11_0 x_11_1 x_11_2 x_11_3 x_11_4 x_11_5\n",
      "    x_11_6 x_11_7 x_11_8 x_11_9 x_11_10 x_11_11 x_11_12 x_11_13 x_11_14 x_11_15\n",
      "    x_11_16 x_11_17 x_11_18 x_11_19 x_12_0 x_12_1 x_12_2 x_12_3 x_12_4 x_12_5\n",
      "    x_12_6 x_12_7 x_12_8 x_12_9 x_12_10 x_12_11 x_12_12 x_12_13 x_12_14 x_12_15\n",
      "    x_12_16 x_12_17 x_12_18 x_12_19 x_13_0 x_13_1 x_13_2 x_13_3 x_13_4 x_13_5\n",
      "    x_13_6 x_13_7 x_13_8 x_13_9 x_13_10 x_13_11 x_13_12 x_13_13 x_13_14 x_13_15\n",
      "    x_13_16 x_13_17 x_13_18 x_13_19 x_14_0 x_14_1 x_14_2 x_14_3 x_14_4 x_14_5\n",
      "    x_14_6 x_14_7 x_14_8 x_14_9 x_14_10 x_14_11 x_14_12 x_14_13 x_14_14 x_14_15\n",
      "    x_14_16 x_14_17 x_14_18 x_14_19 x_15_0 x_15_1 x_15_2 x_15_3 x_15_4 x_15_5\n",
      "    x_15_6 x_15_7 x_15_8 x_15_9 x_15_10 x_15_11 x_15_12 x_15_13 x_15_14 x_15_15\n",
      "    x_15_16 x_15_17 x_15_18 x_15_19 x_16_0 x_16_1 x_16_2 x_16_3 x_16_4 x_16_5\n",
      "    x_16_6 x_16_7 x_16_8 x_16_9 x_16_10 x_16_11 x_16_12 x_16_13 x_16_14 x_16_15\n",
      "    x_16_16 x_16_17 x_16_18 x_16_19 x_17_0 x_17_1 x_17_2 x_17_3 x_17_4 x_17_5\n",
      "    x_17_6 x_17_7 x_17_8 x_17_9 x_17_10 x_17_11 x_17_12 x_17_13 x_17_14 x_17_15\n",
      "    x_17_16 x_17_17 x_17_18 x_17_19 x_18_0 x_18_1 x_18_2 x_18_3 x_18_4 x_18_5\n",
      "    x_18_6 x_18_7 x_18_8 x_18_9 x_18_10 x_18_11 x_18_12 x_18_13 x_18_14 x_18_15\n",
      "    x_18_16 x_18_17 x_18_18 x_18_19 x_19_0 x_19_1 x_19_2 x_19_3 x_19_4 x_19_5\n",
      "    x_19_6 x_19_7 x_19_8 x_19_9 x_19_10 x_19_11 x_19_12 x_19_13 x_19_14 x_19_15\n",
      "    x_19_16 x_19_17 x_19_18 x_19_19\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qp = tsp.to_quadratic_program()\n",
    "print(qp.prettyprint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_optimization.converters import QuadraticProgramToQubo\n",
    "\n",
    "qp2qubo = QuadraticProgramToQubo()\n",
    "qubo = qp2qubo.convert(qp)\n",
    "qubitOp, offset = qubo.to_ising()\n",
    "# print(\"Offset:\", offset)\n",
    "# print(\"Ising Hamiltonian:\")\n",
    "# print(str(qubitOp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "no embedding found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[49], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdwave\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msystem\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DWaveSampler, EmbeddingComposite\n\u001b[0;32m      4\u001b[0m sampler \u001b[38;5;241m=\u001b[39m EmbeddingComposite(DWaveSampler())\n\u001b[1;32m----> 5\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbqm_binary\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m20_nodes\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1024\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n",
      "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\dwave\\system\\composites\\embedding.py:239\u001b[0m, in \u001b[0;36mEmbeddingComposite.sample\u001b[1;34m(self, bqm, chain_strength, chain_break_method, chain_break_fraction, embedding_parameters, return_embedding, warnings, **parameters)\u001b[0m\n\u001b[0;32m    235\u001b[0m embedding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfind_embedding(source_edgelist, target_edgelist,\n\u001b[0;32m    236\u001b[0m                                 \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39membedding_parameters)\n\u001b[0;32m    238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bqm \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m embedding:\n\u001b[1;32m--> 239\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno embedding found\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(embedding, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124membed_bqm\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m    242\u001b[0m     embedding \u001b[38;5;241m=\u001b[39m EmbeddedStructure(target_edgelist, embedding)\n",
      "\u001b[1;31mValueError\u001b[0m: no embedding found"
     ]
    }
   ],
   "source": [
    "import dimod\n",
    "bqm_binary = dimod.as_bqm(qubo.objective.linear.to_array(), qubo.objective.quadratic.to_array(), dimod.BINARY)\n",
    "from dwave.system import DWaveSampler, EmbeddingComposite\n",
    "sampler = EmbeddingComposite(DWaveSampler())\n",
    "result = sampler.sample(bqm_binary, label=\"20_nodes\", num_reads=1024)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample(sample={0: 0, 1: 0, 2: 1, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 1, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 0, 52: 0, 53: 0, 54: 1, 55: 0, 56: 0, 57: 0, 58: 0, 59: 0, 60: 0, 61: 0, 62: 0, 63: 0, 64: 0, 65: 1, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 1, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 1, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0, 101: 0, 102: 0, 103: 0, 104: 0, 105: 0, 106: 1, 107: 0, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 0, 116: 0, 117: 0, 118: 0, 119: 1, 120: 0, 121: 1, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 1, 142: 0, 143: 0}, energy=-1440831.0, num_occurrences=1, chain_break_fraction=0.06944444444444445)\n",
      "{0: 0, 1: 0, 2: 1, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 1, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 0, 52: 0, 53: 0, 54: 1, 55: 0, 56: 0, 57: 0, 58: 0, 59: 0, 60: 0, 61: 0, 62: 0, 63: 0, 64: 0, 65: 1, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 1, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 1, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0, 101: 0, 102: 0, 103: 0, 104: 0, 105: 0, 106: 1, 107: 0, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 0, 116: 0, 117: 0, 118: 0, 119: 1, 120: 0, 121: 1, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 1, 142: 0, 143: 0}\n"
     ]
    }
   ],
   "source": [
    "print(result.first)\n",
    "print(result.first.sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Sample' object has no attribute 'is_feasible'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m result  \u001b[38;5;66;03m# SampleSet object\u001b[39;00m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Filter the SampleSet\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m feasible_sampleset \u001b[38;5;241m=\u001b[39m \u001b[43msampleset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_feasible\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Print the feasible samples\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(feasible_sampleset)\n",
      "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\dimod\\sampleset.py:1418\u001b[0m, in \u001b[0;36mSampleSet.filter\u001b[1;34m(self, pred)\u001b[0m\n\u001b[0;32m   1392\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter\u001b[39m(\u001b[38;5;28mself\u001b[39m, pred: Callable[[Any], \u001b[38;5;28mbool\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSampleSet\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m   1393\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new sampleset with rows filtered by the given predicate.\u001b[39;00m\n\u001b[0;32m   1394\u001b[0m \n\u001b[0;32m   1395\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \n\u001b[0;32m   1416\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1418\u001b[0m     keep \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromiter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1419\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatum\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdatum\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorted_by\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_dict_cast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcount\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1422\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrecord[keep], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvartype)\n",
      "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\dimod\\sampleset.py:1419\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   1392\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter\u001b[39m(\u001b[38;5;28mself\u001b[39m, pred: Callable[[Any], \u001b[38;5;28mbool\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSampleSet\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m   1393\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new sampleset with rows filtered by the given predicate.\u001b[39;00m\n\u001b[0;32m   1394\u001b[0m \n\u001b[0;32m   1395\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \n\u001b[0;32m   1416\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m   1418\u001b[0m     keep \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfromiter(\n\u001b[1;32m-> 1419\u001b[0m         (\u001b[43mpred\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatum\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m datum \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata(sorted_by\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_dict_cast\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)),\n\u001b[0;32m   1420\u001b[0m         count\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m)\n\u001b[0;32m   1422\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrecord[keep], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvartype)\n",
      "Cell \u001b[1;32mIn[41], line 25\u001b[0m, in \u001b[0;36m<lambda>\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m     22\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m result  \u001b[38;5;66;03m# SampleSet object\u001b[39;00m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Filter the SampleSet\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m feasible_sampleset \u001b[38;5;241m=\u001b[39m sampleset\u001b[38;5;241m.\u001b[39mfilter(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_feasible\u001b[49m)\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Print the feasible samples\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(feasible_sampleset)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Sample' object has no attribute 'is_feasible'"
     ]
    }
   ],
   "source": [
    "# def is_feasible(sample_record):\n",
    "#     try:\n",
    "#         sample = sample_record.sample\n",
    "#     except AttributeError:\n",
    "#         return False  # If sample_record does not have a 'sample' attribute, return False\n",
    "\n",
    "#     if sample.shape != (num_qubits,):\n",
    "#         return False  # If the shape of the sample is not as expected, return False\n",
    "\n",
    "#     # Check if the solution has exactly n edges (excluding self-loops)\n",
    "#     valid_edges = np.sum(sample[np.arange(num_qubits) // n != np.arange(num_qubits) % n])\n",
    "#     if valid_edges != n:\n",
    "#         return False\n",
    "\n",
    "#     # Check if there are any duplicate edges\n",
    "#     edges = [(idx // n, idx % n) for idx in np.flatnonzero(sample) if idx // n != idx % n]\n",
    "#     if len(edges) != len(set(edges)):\n",
    "#         return False\n",
    "\n",
    "#     return True\n",
    "\n",
    "sampleset = result  # SampleSet object\n",
    "\n",
    "# Filter the SampleSet\n",
    "feasible_sampleset = sampleset.filter(lambda row: row.is_feasible)\n",
    "\n",
    "# Print the feasible samples\n",
    "print(feasible_sampleset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best solution energy: -1440831.0\n",
      "Best solution tour:\n",
      "1. City 0 -> City 2\n",
      "2. City 1 -> City 4\n",
      "3. City 4 -> City 6\n",
      "4. City 6 -> City 7\n",
      "5. City 7 -> City 10\n",
      "6. City 8 -> City 10\n",
      "7. City 9 -> City 11\n",
      "8. City 10 -> City 1\n",
      "9. City 11 -> City 9\n"
     ]
    }
   ],
   "source": [
    "# n = 10  # Number of cities\n",
    "# n = 12\n",
    "best_solution = result.first.sample\n",
    "\n",
    "print(f\"Best solution energy: {result.first.energy}\")\n",
    "print(\"Best solution tour:\")\n",
    "\n",
    "tour = [(idx // n, idx % n) for idx, val in best_solution.items() if val == 1 and idx // n != idx % n]\n",
    "\n",
    "for i, (city1, city2) in enumerate(tour, start=1):\n",
    "    print(f\"{i}. City {city1} -> City {city2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping invalid solution with energy: -1440831.0\n",
      "Skipping invalid solution with energy: -1440693.0\n",
      "Valid solution found with energy: -1440615.0\n",
      "Tour:\n",
      "1. City 0 -> City 3\n",
      "2. City 2 -> City 0\n",
      "3. City 3 -> City 2\n",
      "4. City 3 -> City 11\n",
      "5. City 4 -> City 6\n",
      "6. City 5 -> City 4\n",
      "7. City 6 -> City 7\n",
      "8. City 7 -> City 5\n",
      "9. City 9 -> City 5\n",
      "10. City 10 -> City 1\n",
      "11. City 11 -> City 9\n",
      "12. City 11 -> City 10\n"
     ]
    }
   ],
   "source": [
    "# n = 10  # Number of cities\n",
    "num_qubits = n**2\n",
    "\n",
    "def is_valid_solution(sample):\n",
    "    # Check if the solution has exactly n edges (excluding self-loops)\n",
    "    valid_edges = np.sum(sample[np.arange(num_qubits) // n != np.arange(num_qubits) % n])\n",
    "    if valid_edges != n:\n",
    "        return False\n",
    "\n",
    "    # Check if there are any duplicate edges\n",
    "    edges = [(idx // n, idx % n) for idx in np.flatnonzero(sample) if idx // n != idx % n]\n",
    "    if len(edges) != len(set(edges)):\n",
    "        return False\n",
    "\n",
    "    return True\n",
    "\n",
    "# Sort the records in ascending order of energy\n",
    "sorted_records = sorted(result.record, key=lambda rec: rec.energy)\n",
    "\n",
    "# Iterate through the sorted records\n",
    "for record in sorted_records:\n",
    "    sample = record.sample\n",
    "    energy = record.energy\n",
    "\n",
    "    if is_valid_solution(sample):\n",
    "        print(f\"Valid solution found with energy: {energy}\")\n",
    "        print(\"Tour:\")\n",
    "\n",
    "        tour = [(idx // n, idx % n) for idx in np.flatnonzero(sample) if idx // n != idx % n]\n",
    "        for i, (city1, city2) in enumerate(tour, start=1):\n",
    "            print(f\"{i}. City {city1} -> City {city2}\")\n",
    "\n",
    "        break\n",
    "    else:\n",
    "        print(f\"Skipping invalid solution with energy: {energy}\")"
   ]
  }
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