Hardcoded Neural Network does not work as expected
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If anyone wants to start learning about RTneural, here's a snip that contains a model of the ProCo Rat pedal:
HiseSnippet 20992.3oc6810imccdcmUKp1Rh1dlDj.ja4vqjAnXN6226XXXY8VFgQThPjQSBBLLJ0cQwBpYWDcWTxLF494qPta9ZL24OR4avLqeO+qlc2h7+dQqoRPvfoSfEqpN0oN+2m89400yZ89O6lGc0ye9MO6hG7c9vO+Su5hG7m8vO3ye5se7O7iu75mdwO8GcwCdyG9K+ve9Ue1yt7IW7C97O8xm+7qd7EO3Auw+V94O367MuH92+0+5evkO4xm9nqd425hK9U2b8it5mc8mb8su7699e++2t9IO4mb4iu5Cu9Sdkqt98+oO5lm9Cu4I27Y5Y4Md3wEe5kO52d4u4pe9kbYeiGdwC9S9wO95au4YevsWd6UO+hG7M+A273O+C93a98O8z0+qt94W+qexU7EoK9.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.26qCYo7XuWnCsk93HkKSYbrk1+JjMGU8pqvowg7Ds8VuzVzVQO0itN3l1usaVkIwp7VUzqHsBd9KN+tyRWaPJxH8bjkEuc2XsyWGsWyoV7ZGyoYmTopGDYXT2X8aV2e0st1fpKWqwEVr2ejUN1lM8bu5krwA2w.WlxBoBhPQTr+PXQVZjke4Ibsxa8vom4VtkOFUtT8Xu2Bl1ymlI8.mzle44e+MVGmj8bsQMszF5lwhTIul53hr9NKGi49McJPhCYBSFSGKsqd+sFK4Irun210xbqKq9Xpm2rLqO3HvduJE44NujIukdrMNNmyhNKUVxR97vsXnWeZy7XHic5xy62Yn6ltNEo4p14IZ+FZcPUduayTRwOdrx6eRvpqr6mzakjr6sOhTE0QgfZT3OGiQYsOvQEKSi.TNZxgQqXVQjcCYKPFSakb4HuOZ2YuWkwVs0P1kl6uyJl.EIn7Wn.i0YKS.rccma8ZUGTjivwdCoJ1NcxhfoUH54R1b.Pa90sOqjDj66TwDpYQqEXuSwtISz68rncQy4pMV3pifv2uCIqcdx2sdv0Yq59GjDGEU3IxlfL9m16fqKiRGGJIjgdxMAgUjo.cjUWormJqoFGsZ2TVVNVkDAyt2A.d.0Qrd3NrsO5XEKphCKIqB5s9XYhTRQ8HmEJ+iFI2nykekW7e6W56929N2KIaJaEJWL8mszORKWHREEbohKtqPHFaMdE1PaEk6He3jmyQs6dOqTZhbJTrOlnMzQUYVVwp0Tn70ism+zYzAwOSN0J3bW9XEseSIFlvekhMoXhQJcTwnn9m7rr8wfCRxMgxoubbrO7RkQuBjRtcx5wQ24lIaZ8zpHXjOYEcRs497oTtIYLY7Wa62Glq96qTeZXTTFy6ysWsN+qjYk8YxtWgqseqjhdnIm2Jz.E75gyfgBfI0zaQsZq8flMGJEBclV4XkWojoHGU9noWfJgod9nt2daRIeTvDvP6UU986C3HQTnJPhiAeDKi8gTor+6gsSYVpISi6OrjUzI5dqWl4CSHw5TxQVI5IicoxZ+yblzZ5IEEn100WtXjjaD4+VwyL6TAJi414XzKJ1jFgw2bWdQISqDjKDLwPaP1GEQknRT1RZW0vZESGCOV5TqVMpKyqbtw0tLbrj2yp4NKCiy7fsQTFLyYb4f+.yRxSgRFJMc9tWopN3JKoILBuOKnYUm.00qHR0B99BLn0V8zpz5U9aK8tbercGj.jrQO5ZIwX8fJbjx3SSFUMQarHyJ8JWVQIyPi8.Y4.W7knND6WLjUWEKfBNQqwXVZWgYTdwJ6CEFpBxsZLyHCRxrAqE5YHObamaxZgxDanDUZZO8buQIEnlLvz4bHQ5ZR4UgFoPNHEV8bu8NO5xPfhNQwjPov1ahVGN5J8iEATQob1uMR+kkGKk4aD1Zxj+gNrJO.0UTdHsdahjPIzqjjo3gqtLlse0S2yjBKVwuJaiqCSlr5dRIt0hcGCH6epKZmgbNqjlNvwgo1jYYetKKBUYPmJNt+XnLKlkmELSqHdM0LfJwoHz0t+j1qLcwznGkCYjQICwal8KeSk8u1mTZxQvZePwEkozwjG7l1R6JM4j24wqasDV2dnsp6pdZyr3sTJBlp.K2DZChhjXp0u0dKMxxemxQ2pzi.EhkYoSYHqUXEfU5zK+sqGZOsLipsQjae17fH6yT4B1gbLwT19c0KV0Hn2jNdkLmt3Ht7fJy4soRsX6ctwqQckUp7X1EJlNKp7BSxl2DOnlCLQo.z00p7bTMN4TFCjG9ZonB06njw0rdUKaXJX8V8Xs2JoRAnxGRLNPxd6MkM04uDkwTq0r8a+igdOt3PtL+REibdMnjfccDSOyq88I6PgSnzEzQQRwuadyjivITxbxed2UEpTjYwj5WWoHIku5jNem+omPo1DsTp8JCmbSd51aGKyCgBSWlQzaRSUnzAJYmgZnqOoJojsUNSgpOzAf0hpFzcUQgpfcPUtNZ819PuIPZkhwAkDojM9laDHs16ezHCijoTXxUThfCzehdsruNvZKDNvoCs3uauwTE6H9Wz0qT8Sl7PNzI7dUwaRFWlZyqqRKXZ0ntnTJFGhTiktVIh7szx3Xe8nUJYKNFRMdObYKvQPYuixynbXLowUUPdxHCa9qygqLQ57gx0ZrhTPUFZlfllc8BmLqa1Z1lj2Ppx1nePKqG6SymBYpDnTl6cWxVIRUoSqvIjkwXe4pj66nQ35UYQlab0Ydo7C408fCBl9BknBPKZipBSXauMhBGGOy4Zli3Glx4qzfV530rTUxElZ8I+Dc4jqF8sPQraJf8fd6slzpmw1acDa0jddoM02YIcysNwF0LVapGJn2owAGghQsD0ybZHiBllp0RMsnrHH7oyvK6kU5ExZpVFkCizdadYZxdOqDth9vY5yhhcSwHToYY48QtoSV.IkFqh5Lf4TKHXPVbnYVJgNiYL4yTu8T3aJJujocVTMbYZTAP1kOiZ20WAxNSuHGxxmRBNu2p9XQSbV3fKk22p.kXepq8PDPQqatuGTEwgNSoUttoPdYJ4QzhcE.oqCzsoxXmCVGipMHLEs6rSIlTFTJQfhs+k5QQoiH+97K12uUZLkwFEdfBQXPBAlxQ0oEX5AR1ZxtBYjT.0T6+N4pZaGXWlOzYqAsUPAfLLsYuRg+JzyNdR1uaRgMtFzYx5HRVb+mQ5VgrQlS.2jVeY5xWE7WP+pWGqrAgSfJkD8DQoAmxMiawDw3giiIKMi86nzknDbR5Mjx2JUSlh6Lq5nH.DSaDStdsqTEGxhdT29byzvrLUrmbn3nKfJaO.fzFZYnVldoEdlH6OTBnx1woZnsn78aiIiHazgfhLSpCYlvDTBc8njdqRQYxzbMXltbKOF5kY1rwNqblk+hlh1Rokj2+Pqc15n6nGkFXn.V150P1ZTTpYYEVYwX5XFH5QVH0x2jFtaBpfJwJ2gfKvkrEeX7wfkTZZGgIjLkgLpllV4T7aJ5y8EVTdKnojjM.Mqs41dDomEQmpzAL95XQtpiJMEXcu1sYQ0ItFRbP1Jc8tFKvSJ6KIaa5qKURSeD0Agi9Wi9zPoiJIveFs4pc+jTIcWNpbEwD1butUN3EhpgX12V9pDmqNHiqAU32UwMBfrh07FFUMQxp7aTHaKL.SraCa7GCpOHs+TmYml7nhmCf9BO3t3uU.JGjGmhnfnOL6M5zFXvclB1L6LyrJ4bQ9aI6gsdD0sRwLMoCWxMTdeMzTB1fZUY6Uqeq995YqMDJfIEiPZNTnxiU2jaaBO3.hE.WpYmTl5BHycf7EJ+895BT.3fkRsB1J7GAkEoC4bSmuR6KmsBFTOpS.VshYeeMBkc79Qk.NqSGDthZNpT3zZRgpLsOlFRSPeBa5QPwM02CGpNlP0qCBTwDXp9w.XCE7SbDee82oYRk.+h.mLCHrkmslh8Qoqj8c8WGMjmdsNC.OjMWSl3DVPKBRJf529npnRQZEICb9StG5wT65p.zk5p6NUoDw0IOclpRWoa6M0ovVIL8ohxzAAB7P0UHLoJ.hctG2FJsW.UDvrMYwsQI1DGKZyzvzw.EBZgVHloI9tF.qXuTXAJMtnWKEmc.siXFgUAfg12.wJf6n1K5tqmaClyxAxE0FZvnYeeoCHhDspoX9aJw819HSTJxJjj.OmYZFroawfejdoBTnnQQ6eLJMxApfKqzzTdvCYskMQzCelLDSUJO5TQIsGMqS.ltdNoX.x1I.ItVMcVPKWx7LuOFwdu8kRboczJ0I8Xqys6yBRgtzTNYM5ZbcKNLXiDERL.DeTug01Z+HahxyMspUI7s0F5IeDxaF.dDjn1G95nKerijbqQiDMk2hBhTAlGJ5jkaGcixF.fGU1oJ3ts1jZfLxL.zTaSJNjvB1Lo77M4Ij.e1WjxFnhD.zuXLi1tuqFEtSeHkisjqwqZKGkj.nkK+wi8mUNXbSHlwF1RMgDzwHPYIG7J5jwtTSSuqNlP80J5IG7et8imdOz.qPKp0lou7oIXJW4p2oB4lDdkwb42QAWpP8SDi49ifKhgADvImwKGNqvXQmYUHScQLglTxbBQl45ATHb8e6XLo7jo.CP66PafjTskVgyI6iFj6Jibr+LKC50gxQvLtP.jRkxc3s2hdjhreEy1VBj83K.fRqQ1Dz9+xXucTlXuor9qM9G66p7ozZj+OEy3g9EVyj4XkxjgqkCVSiciZfjYllqDPKZXlXtB0krCdWT5Gy6qDG0o0QA3.NlLsDl59efGM8RLCTTGJ108nMVwvbbvj.ModsIyMmy2UlQxCdsWMsQ5ftjon909jQLkVcSkYn6M3bgQFJs0lN8YQgXRyT4jvzLybU84rOiX1nnvtb25TCyTTxf49W5.3+9hjH0RxdCoLZjIldIhvLcXl9R.2mrLtNMslU25bEXxwrsIeg.i1817NXXOZfyUFgOWsIilXRJmZ0n6h8+fyW3CeICZtdP.BOxju2hdBtG5wT.tJNrnGvl1tpyeDRbixPPGtcvZaI2wqAQq.BZ2uIU9MYaJCrPIkZtHoST5+CJnE0ZvDSiV6XvgXTSWGMyPFUi4HIw3FpTA12B+HUVkkJ9tjEASixvzKotovzq19HwD.MIOOkZs1vlbFmRfC1FsObnjIc8zUly01iB.ly0lEFb0BnoWGCF6m0oLcWoEilaK0JlYWklfyXksZU4Kce3+D5i1gvXmDows+hKiwhvUTRmyUeeW0nTGEsmCr7NRNKBCZdNyIYSlQalouWWDivNn5T9QM4rHWs0zf5MhY8jYZrU3+xKqheSlyylCsKlryn94Z6+vflxkbornXi5YW+B6GdOkgmhPoRmFWMeu90GtJcjzEHMMDEPkNYVHvrt6jhhOHAZ.0yQd+P+ps6MFlKJ09X3Z.BMzf9U20xVFLHaFnxJkNjBH1sFG0xf9zAfJpLlVtQqQVzIOHJxdYu+vACENlAnoFUSc9ThE5EGENWgUqfNcwktX1PlTDEZ1jAeZMPJcIZHtbrzMmuo98EEVfbJa5VcO2.y1xIaK6liq5TQFPRY5AXteqQocB+53mntcGZjLTXyO0acRzwU4bYuWwXJGDLoSlw2PIvNntqxQKS1fqwh5eJB1Tfc7r4AoC170SPMSUAMYgnkYvllLSC9I2aI3f4seRaEi1O35+gb.obHi5aiEOS8TAvkx0irp6JcGnbQqxZwaE3evTfMR0KAcODyj218FAUOzjuXYs0fg3DY3QgZUvTZUouegSFhnOF8vV99xXRVxJ8.J3XotJlA+BxdfLTYzsKVnC.fohyILK688EPgt6JynLNdEfKa2hubvqRB13.H1tusof3BPBddF3AeqktFCiGvEQOMq8AeQ5Dz66DnzVuIuuxgLCFeJPPEAv9RNvAMFJEYkERoBNGMQO.CiHCiQIeFlXMhvaI.BPJ6QdXhoih2yctoGGSwFab56TG3gWTbM4sGsZSum0mxrqWWZmOlMj+dZhbY5nQBc4LWtx8c2fzrDNHVT0roLPOLnaQthitncp.5loSozYTZB.QtRNPQTTVMxkVT1+koCjLlnrH2F5guXVoasRLIYAafXPgNUhaxvO.jRYxNK1o0A3bnWkckmhYdlYxRSLAsin7+FXQLX.WWY4i63v0w4L4gANXxT.EGbAUbCMnoEZ0TpZ3nHhVToyWobDokgba.KlDWGwoQEg2e1Jc.MHP87oFM66daggjrAXH2GlKMSfVMKahc.2xzEwAU1lh8DDSxXt8Qlp82XNOz41Z0P7KYFIbYYhziqVXs.nFoG7GgW.SsjVv7QJrR.J6z31j5BoXuzaQvpWYOR6kQ5JnLiR4q216y8.xjgZEKiFohoEjJ7uXFLZLQ3CSPArIhtllGTuQCcBnjL6JCyocb.qLBu.lV.54bqGyXZFVAv4W4dv2OICFlzFtB9eNJlsmoblJW1BfjxfBa.oBSP.KGoSEKaerkLVX33l586ZlGUVTusiRxbTLr1fBAdvPJKyR4wxAtxFEfPtR3WvDOUh2IDvC8ez.giAtSxELKWMSiEAGQM4WyX90WlksfN+fVJVrE0E.3fSTxfAUnJaxpRgbob.ovaUNor2lObOBTjFCQSYenkGzaDYg6KFtOyjrAa5AzyqwzZrOdfC5vkLLulw7+ru.DSbPoqES3i8e9n4bv0ccY1nQgeMcemFjn.ifV0BdKzMr1JP.Jjp76XbmjWM53MrICC9ichvJrwWmpzuV2r8nvFHxPmdoIanlYWexz0dnbS6ksi0TBCRyUghUL.9GFbFovJUHALXZz6CC8VoXnRLqp.Y95djhAll6c.RXNXqCSb4GmJ1KXlyTVClNUJhbAJxvLYg5O+AdpBPzh698mtoEdfEZEThxk2P1DLuJz5fdLSQK2VTlC7N.OuPTtl4PDlTR14Z.bTlz2gicVht0zHiWEU79XKYbhBlN4X0N5lhZy.mxy7hsnirI545X.+VAVWqtRfqrGAs7cPg4w59J2Qf7qNTQh+YldL2fGQ5tJ5kDYRerbnfNSi8SfoDFpHSsHY5nGsX.C2lQSjOuNLUHePZLP2h4Ix4GXfNCxuX4HssnR+LywU1cah3moTB3DNXnYsT5DU7gptBH4SikCKSJGhUNZoMAPObSmLSt9QCXF.8gXXH0ghCTonrfWF1eu4Ve.Aop+uPnE.lBSCrljmP39xFo4AXHGnv.f1cPaLA0pAIslXbQllxZImVU5uITckqWWAcLNNf9KSmJhtgCGpm.3xRYhLbCyrNI.oHdvfZ1bkuPNwGiSkYS1dxFFBtfExQTLZGfZ6SL0.IEkgDSc8BRFFKzDMLRYLtqfzyT+W4Q.93zLBCGPIP7VuDC.mqaWLxaAyigoACkvA5YzBsrlcX1cHO9ZOwDxAhWglB4zo6l5bRgLec8BphILchpR2JMiTMwcOfEBHqnggIbl0iQ6.eWf8IGrivMaz0YJImClySJhItVjQohoSu4LznJ4LPFWFFaJvgr1fnyg0HaG2fNxPtynmV.3CFKdkfNWOHSmwb43rLlbbsCU1c0AVyr7ePsSzZgBlV+NCCodC7Xw3n9XhCOSkOKAikMho11fDffQWfAeYHFoLdtVwPB3ZKkxbQ1mZN1WUN7kSKlwbPZv9WiC.8FbuRUQ7MLLydp1NHNAfra0DNVhY97.N8dQuYWF3NbnnjILccXeZnTUJzdA+4G7733xBlflF37D5A1MI3LBaUlAJRYsalMG3vhgdajYHPlq8nvEjIoyq3CGNIu3KPeAKHEhGxgIYcBb.2xjoIR08jyKEfRAd.EfWA6oNmKL13v4ovLNNXb1xf5Nl53AXnzToFP2tVRnKKPXqFloJnLVnnHk1pAKv8f0UZMP.UZ4x3BPz0w7O8CzX8.Nm3DBJmTDlsl0UzozJT4RlheX5+8QLvHJUtAb3+d2xoXx0k8fYLjUK6fR2G3VFFmF1tL6RDV96YN.mkijglQhwpMSgfOBkGXeUlY6l7MulC9m408PIwQEt50bMa1abPzO.SD4OuZRaNGP9l4MCPPYF3FfW.6hHPBkUogygn4XvsM5t2gjpMgVoyoZKM8axHHFAW+VpPW3Pr168YoXHRASjyHjA5uu2HXGT8AFrhIHXqaPuILC..YUFTsvJOSt8nJGwrLL2Oa4sns1MDcgRtLc.qMJF.vXBU4vDqzhfGkKVnpdavfcpfMzQR3J2zifns1vE4ClrBSO.mXFRd4YxsX5hLHTMwLi.HoNFlzNgzIA5bkjN15D3BXxfEKFvNENtJi9oCjj0SA4MahaDNZpMQqCTNjcSDXJSc.nJoltboXkSwFiAL4nRgxQWq5SVE3pbP1PtLg.+p.ZyT.Kyoq6hInUAYN5fZ.sG6EvoTP2P5+Kj1TwzmEFeelOU.sUStCSF1pYAwXojQR3t2PfgvlAf4NslCtsbtuSjWeTgbmFdTBVbGdESVnU3ltgGdPJNDmNbthIbWF7MDxfXR2c8aUgvPlVsf890yuYCUARPCEaAuFCybofX5PJskI7QhKnM4IdQtYfai0vAnUHmZxFmAhpXncwbm4yDPERQhalTytqaK85QLXVlAXTuEA0tTogQ2L5MTg0dPjVLY21wmu.5wTNkn+Ti847w1hX8fLRlN1wk93TigCSgU2ptguFsLPohzBUaXY.zcANdUuqyxNh4nXFBPMPUKwIXYS5AM7QFpaQOOLrgCGRVcFsNXWroiCrWSlMF41BZP8nZbHAdW3jH0.BaqaixaAKALXN0HbOmutnp5A25wjPXxaQO1JVVV+PiS1e5RFFKT36AyEhLVt8pgo2BkIPN0gyZLyvBCIFPdYkc78VllJOhtbyrhYjxfYvhVSZQB3OyLtD.C2dPB7yb2Plz.LMbMuVv9tsjY+QNWBhgiQxHsubCmXsL3TLny7AosZr+x.X.MmAe+Xh0qH+bYF7EN8VZNhILiB.UomQsso8Ey3Az0GiSFPFxLEKifG.6HlSTMO2f8.kSvvUfGCljbWPYLtIL1zYp.Sw4gFrqCOcIydEqpioyfL9rLJz52XXvFbmp0jPzi56krmTHlGAW6ehbALmBpALjnfkEhC2X+MgZkzXFAVl1WHeJmpe1hA8qV6N.oivjAbZvWiYJBGETVvH.enfNCcWkhoiDpm.vS3XpwFsjl1nmQ8TbyjJTzBv0F5TrmbZ5z.gaT9kPJGRtAmPlGUbdyfd9cE6BJ2R6S0acHFDkEy8FNWwBCykEpPPos2aDBNSPMmXFyIjdMDPhBy0OvzbYkYN38H.0hRGXkcZTYCYcBZlBSqlPkQDKnnyybBAPp5X3rDLdBmdcBMWgDofvmUh9xb1vMIWLamJABF1alxh8oCiElCbAzspoDMGAw.EJCfIRxodhGSBgiNYbfao8JyHGsAsCLI2Td38nHjDmYCMvRPKNNLixgWD4CFEggijofXKpn9dJDt84KBqyAM+Q0tZoCW2.jwN8Tz6D0Bhmv90ZJJ0Au4g0CbjpHQIvjxC5cZEC9Sfxl5.mxBvxwza+Bi3bJjhMPEgAE40fB7T7oT2lV0QrDCZ.0nFDX7wduhxMDnNedPZiSWUHIZ1XbD.AHFTlBgBsv5Nrc49TzJPc7nFIDKoi3aWAUDGZ2Sd3XMiIjs.gvdRyZ2utEX9P2RTJIL0rOKJrIsBtRep+m8mqTjOX1sAMEYFdODSIHXInEAj4MiLBMAOkPsRvvYtPSAWHfoK8eLLEOJUiBfFDHmdo3RyAhqGw0.BdolmtFzncmAzBn8cllNRdYZurbY.hmLgKgrtQwrgJTIAImcW1d1fq9X1cL7BB5YQv74Jti1dKXP0sTqDtVkkcxzHcftceAhSHGQCs+i7NzCTPX.kJA8.8p1AH1JP1rYbaAGKAQomLbUAibIDdKCIBkSaZIWoNEvcFRMminCoAoDfxL3hOm29BCdYm20c1T6vOWEYnMna1iPALLDACZiUuEONKq9UAbu6f37YHUFtrUyP..vMnGIq3egHLw.2lNA2n8o80YoH5hNM+2DaEPVlgV9qQ+bhDaBcrNpZuYL.BdwMCmjoPNbi8THF5.SdFbf8ZPV5cQitwxDVz2y7C3ChhQo3i4.iB8XO9XgONQitkQuQ0oMPYHEDFiC4jStEsF7Tn+MfHHzmgiW7gwtwZJQtMllBElwqBHohcI48.VfgKEbGRwqzy8dKBvBSqPD4iAKt53DbpeKMh8zrqseqDC9gBPtyt08xf.Bd+DIaZAnVL8EDhPXTgrQnh1IaenH9HPWWfYXW7ZJfwLMfB1nr4TGRzh5vcKhEV1pUACxrlxo.Fzb5f.puph9OXklZ5di0cNhAA.DWWBRDwnQ8qPPCnJg0poPCXRhOgsriGGBtTg.GOIVHtyIAUYP42Y1MKldWGzPfhGFtqrX3PpVP7J7DP5aMC40rXeOLELCIQ03ERmWgfwnaN0pis0ZQ6BlcJb.rwggc8XrvYvCKnrAFvqRFjsJRSMfxvoA4mpSJBLHiU+xPuqLSGPnivcMFVC.M9kQCeDdhLMqRw0AWzRZY5HqSFdh.yancJNFhnGy9AOFS3MCCHIAQ6TGmFZeWwIDIxKUEHskzqbfAjozpnPAny5582b5t2H.HrEMPrVyMJ4LfAvmJztijQpIBoYov3NbXnU9P1smmDdiAbLoYtyIWH4SC3aP4NLHrC9SKJtTqD7.79I1phn3BJpzY2joHrAzbQz1Tj2to1j1ARMenrDjkpq23jrWYFDBkqKof9FHoK4weA52GFjuP4H.LYMhlNa.USsFJ58HTRTCGRQFKfrg.24rSc+5mrGn2hvWoPkOlys.nXTP8JfdxjgyjoQ.rEnbVLa7zSg1ssnQ9PsoUKKwizDA4aUbDNHwowX.HeVZky06oA0xADhkwVpSplXLGfnRQcMalJkhJWyvicRX5bcC4HpiChSZYTbCBID+yJj.jQ0zCGniR3cQ89V46YfFBE2cgPxnfdQ+s2WXjNwzkvv6XczVVH3Py7kOeXJTCTmPyJ.K6PMA08NvkArLHN+HGLrpwyYnPkjiPnM.FgPGRrlJnqn.gGObTxQLS3GHt2L4vNdrENcrPgRUT5zYg8Y2fnfBNVfq8bp4gNfPF3T.pR2vnIgjeQxP7V2VzXY5B8TfAJ+vPUiSTuKL2BFwNpN8CsCL1gj+ykkSDjoXzAuSCMz2cXwlZtvnHhNYiLSXFnbv3ZDK1dlMJJYbTnVFHLXs9ioC2FgpeMgNaMH0rGjzabiaN49hTZ5vCwLnJGlKtG4UEphALPjA.UPB+5Uhdz2OlfgEIHozLcPp.poMI8xgDvTQdzcrcfRdDsAoSvwSpfm64fgcC1+Idku+4nfZthnPxBhwXNjsXNxeDvwuxNSRg9Nm0d4kipOZPmVx1QKgfg6.+.++Pf5AMR5Y59CiqxarhI4ELciiEJo2N0.9w.RFSETlTg4Q.xOvkkYJ9TzIbiAaXzAZmHITPrfB010zlhRjLQ8DWN5zAzSBWCnFG4kn0cU9T26NVQiwJ7vvXQYkDKCV1D4OH6qpZ3HpfV5IaelNpUPBPIRsP4frv0D44So8hSK4.0MNlSzZ2F3Iah2q8mtPqcqH1EGfNECAJOYljOBlOe3hnWlBzdCZEOAnOM0mDNpqybRUgSoc5bDMLIxLrx7.3jVVTIsgdRXDHsLZPv9AT4ZZ7zzznyV4DIdPwQLCPJHPBDLh4lhIKAH0kkLfEzPmY5aavMyUDXYTQzpCpKfxpYve+UCGbw.9K6h.krZfGZya6JUHchS7Q3XzzDIvqGjZBP3d4HxPzmRHvyFM4vTxpNg4x.Zvc1wlGJblLlYNw6nFgEjdT2qnmeqokLvzFznBJP9u1g.NeBOf0.I4EmHmmCosEzy1C4Ry8JOQ005f7RTZQiNc2nonjaHogu2RcB4rBT2fBFUMpa3ASiIEVhp0Zh7ngtWNlQkCbrgL7Rdvi.LNNl4+pfbkTIuWPUee+bnwmeJHRkr6aFicP3FDwQzO249RggVHoSI5cWlbKJlI.FU+lAM5fgD2XhYA7W0qD5Aw9abCd8FxSBBvp5H.Hkk9fAnRKCybyoHXL9YP70GL7saKYVBZl8..1hdaLcrOh1+F5TBEpZeCPNwMtHPALH9xfpgiDOXp9QmBnI+tZj1.eMnuCcHNr1dbKpnzHbd4cSQHOMRm2fvuVmDQ0lo5qL4uCjdNciYXLMxH3hAyhZj1sDY3AgXRv4kkqo8Q+DnY1czggf6JLuTvrUlQlYBgot+fBJKMv84DpDLr3jbTxnk9E5YiI4sAC1o7fCS663KHkdiLGF4J.SKmrDSKb0MrIcCQ3bZ.jdmQmKliDsBtO5DZNF8tm1OyP2sOcuwLnyZTVpiTx0xDvD+pgoQlysrw8M58B4tQBCN4KBpKuFc5WgHObX.jXFolRwbEaPYdMHY4IYblnfUtASp.htCAXt3lyBbHSlMzu2ilC5hXgFPlQydBf2XV9.jACv0Zv29MiXqjg5hCIlCYt8dKYRpSKMZQVq47hAdxLYY813zv2tWQTOAS5fMcgtpsyTHz5FTeKiRRuYBYCdRIzF6AfM2P+g.PevdGMaU6vMMrHlzIlbektUo3T3pNQVUok0Mi1.bZROF.ubHuEyMtBm9N5wyx3qA77YNtOfrFPJq6VgbIlr.XQ.5OpatIGyN7L.PiukcZwCGWaXNiRisbjhAfhmIRCpQ1.0EnFcvfJp9UwUpgLyEBJJPA8N0AoHpQCvtohHblrrbAB3NPtjViZ9.hxofoClDWGPUKYFqLkdZBjTZNHJyy.5M4I.BxqaR6j5vByUrhQLyo3WHNF55JfOYGvvXpxh5RCsI1bzcLQLETwakQC0pABKX6.Neo2JFNepQSHfVVQiJ6F9JjRZBqbUxZmc027W5Xc.IKXsv8gxpLEvT8AYmYfEODDckQHMEpqPwUsCfh3hFOUbpNOvEDJzGEzZTMwLgnlC9zpLUskCGFSRDPOhfvxvqmvXFqVkNXdLYxBLBmB7+4XMF4fQybe.Q9HfWG34wT6ezwCD4AJC1dDJ78xuanA0vy70XZO1ycFT7N.H2QfHd2DySciQzboKsGYm.xufEcvWKx1tozALHszaOPeW2ILp0v.lr0L51tfq0AhKsQlKcSkR6Xs.t8B.XzM0fBpmHC7MWvBn6aDXeFTOHUtHaF7Fj9P3t6.6eYCJsWL5fJJSfpTu3nl7UL+aPuuSbXYPHD06OFyIFMQCcgFSWRvMaLs7FIjT1a0FSF2.F9Qiw49o19B00nsc6KitrrnK+HDM6wV1sSV4x4AxFZ8NzRYdLfcATtjYlNxlSn.CX4zY9pgT5MP.IGLzFcNcQONL8OEx9S16YBpnjel4hMT8CR+svKRi5kpsyjuNy.GvVr6xpf0sxfYDywKGJk1ZH01mPUmKO7YnTB4fp1x6G.BznVrYPShYWpQ23zBbJz6QXPCSgcg4GxPQyHDUYC+0vL2rB3REBn3V5xE79KOxw7rSygbjEcLvYMX7ugi4MIGxHMkixWC9biPs0VCzyrfMobZj7f1tRGg.a4lQbEYJjJufin8QPyv2M01zfqfV6GSAFm3BEFAd+.iG2a.ZE3lBihRGVRtlfSKQAFcnbhJdFS1G5zcGMPrnysfsi8A1QYqgG7PfcZFnHBObDScYi3dVNmazkQlbpf7LFFnnfF7Ps1XFYZNldhQHfPni1a3vshxNXvrlSU+Ln8OyNeXHzJhDdwjVCjpAD8WEV8zkAIgQsfRDfB+RFFMm5CQ89ALXIPpv9n9AfF74Dz8YK3B9dzJQhfSxNJ.f19FSN9jj6SF5rhmTzZHFQLpnoQ+TxP6+Po3b0tAMAkwfsed5cI3IkAD6L8qCI3zwYuLDrCjJCJdfgXc0QqRzqGYkt3HaX8R4T5RvsEJ9NimY4qJivIxPqlFl3kPtWBicLqUcaqEFAeGWOk.tgrDAE358dL7dU3OXGaMuz8GCBAFCMb7KkrkrNY96MmcoBrcDBw0jQ4xTQG3MrFsDIvcYwQkNDeEC+k93QPNlFqQqEv.R35na4AK5SUHuZIC506Hg6T0aPr5x0EZzucvxChipi5pYjekYZpsuh6crepWXFq.SqPaQf838PNHH+mLPnmpqVM5BDyVAN4TdNGaSaIQYokoQDxEZjoCz3KDcrNs.FNow.aPJY1fdNgT9tLUCKR6TajVLncH.E66svgbvP47Y3NSlrTkYIv7bMfvnhsv7RLwJbHI50XLG1WjlHfZnT1CHPImNXfBcyNCpOR1Y+.tBfRlx.WYZFG86eQiHfi0Mpn.8Ul1xr.UVGNwjjZRThB3Av+yUSvaA6qQvGxbvQw7LGdUnGl.iQmPbRMGw8BR43zpHzUFSsECZdjW9vnYSQMDC5BbOW2Q8OKrOMCvoVl2JxdKcU6PQRNYRnMaSglW0ZV8HJkgCnPof0iWA0X6TPmAXCFRAFrRXxcmt.xr1tf6LxkpooVoXxZ.zWGtwKEc2isQEfR+vf6xLL9JJxo7Asp1YjD79FzjUfh9CSetSQyIkSYferBsMYUIHlPlZGDuzbpXM3akFXy1ilQPUY33GYBauMJqtiRfqXqAhBTwbl2GPVjGhx3BRPeZxOCFGAyXAio2coFEsLtbhdz3bqovzJ4SfdyLXqsCyruW4sNrncFFV2H3gnulXQsTMCd0IgqkvpyHTOzcs6urJQ3ggFthQz0XDSuji3umHLZ8tKdof5RgiQg6qLAtUBxRjHTgfFLXjg51lH1mUn7a6yCXNH0cRGVI5jb8gSNgHVI8xFD6zrqG7+KXo0w9BNbTBEQPIBvr3H6MlkiBg9PkooMQFpXmn0ZyPauTh7lgWBtCgoNLTfamrc.c7jwpAYY2byuALKVPDR.8LyJGJ3vhI9.LEWcRULfiufZzFL6rgs+fxMgBF6.gcWt9MPJwQLr7Jph1zIn8ZuWFIYWuGMd.NxQVYArdXZPccOm.kV5kXO3x97drKE.C.NgrcTc9kOhlRBUttNZFRQXw4ouVJ+K0YnfFenUOsqauaV4QApPU2SGkudZN1ksQX5dnE08NfFTspB.t5vB1QF.Zc65z0hZ0.HJP9VDYBr5oUrhZAgpqTZmvVXFuDGHcsfEDp2wgY9ajEQZEubroGjoatu.aXvnWHZx6WLlAoeBf8PjTJVdGZgKdFS+kA45L6V5UBpJdlrKbCAmdNICqf5TyMig.HNgNC6pd8kWl9OSX4f0QHlJWmsIGYlEJzrbXmGCn8ffI6ckn.5Db01GBZ..C9KcKyQJlo.5+jdOCt5xM8YfyvBvtYTlFYngICV9cHwhx5vhKQZuG5iHnzwQHHUNtB8eQ+6Kld.P4jHvXFYFj0z8O1PGqJH2Rv9DUmSVvfG7Y6Dofce0DQFqWjujxTtc3HARFD0JJAFn01X6.RoWm+TT2TUroQNKj2JrFTONBkkyPRc.M.BOutbzkd3HjmAze9tYzFB09oP5rGwnU2brnbL78ZaJndzs6.oVqdD3FYJu.FLlTHJd8pQqFGGolknRqAuqPiIWVsgHiZvRAj0lI2TRRJm.upYjxrY4SVkV46FUee8iosBLpBTZPsCzQ8CA6.gDfwXOser2QKnVLKR.BupgVuP5dBIWGtlesmvQXLTaIPeWMTYu8lZXbTgY56ouFfLL52O5C+XYlmwP+DX5DQ3TA0pFpAOHi64H31qkcXtzVelicxBA4fz0UWhpih1B8KZl+9f0gPIvCVkX+5rdqg7XIa6zKLyTBGYRWo7c0ho8ngR0S2glL2n2aL1Jz59PInDLqwxhzWY3pSbUjEzvoLViX36kKH5l6V5P96cBA6v2x4X7nZlJFbPgHXPRzhWYMr.pKXjGsXCRhaVv6RtwM.SLcq0jAoRLrCy6xic2vz8LlXZwf2jEGBJkeM.UTFu9c27MPqUfmJNJg5EOM34eBQGBsVTLjkD0MgVxf56Ry72GNCiVTCVDZEx7poQOxgEEp5TOmLXRochzs5nMWPlaFjpRFMqPnjFkpqfAJHS3BMXbGLc3X2LHkj.R56EY7SjsihaT2cEnoRFxARTlEJkkNz.OsneueB43b1HBI5OpahqPdM4PKQJE30aukwE8ujonJl.TS+GFfsHFCAvQy9vplLaET3I3wvxvnSRzY.xSWuyg4+MM+k8aknWctNUzwCeBAbiQraeDGGg5tQAiw9uQxEUbLZqIHWoAcVYXXVDmHEaGFORltl2BQlSm.lfp0gorqAAKCkP64Pw9INT+.McmdgXjNBj7hIcSNse5mOITYyS18Yxg.ugF.TpjDP+35AaR6DLvv28.hXhXG2uYldMAecCAR6BQBxzFdQoFfBvji.mRYjCTzknd4lx1Qix6YFDnNfqyvIOGLL8uPSkLFcAW9TNBvQLwP6B3Po6B2xTf6gbtMWAy8nksXTr22imDR02AJzEzYjQp9fSTfX6olDfdcWKQKmjkNBlOcXLQqf5nZ6A0T0MTzUEZtB3PIadNa+502.g+Mnv1rYVBhwzQl4xsVrydeLw.vwVfhX.htSU.Jz8kJHCnZ55PBxum9KArkL3rGVWIDV46XA+pi7WRzaSxvJcXTQ8DECePLzHfJYmNVWH8eZ4KLgA7tkC35893jlQvvNY7TrB9BWIfS8ZKlJ9S0sUbDS5VQwX7Pg+Aor24z8xTAEbwBVbQJYT3GlZ5NgKg34Fz9t0HstonP2TlEFBIWGNQ2Olc8ZAP2avdDb2VLfIo.flltcc27hR.Dsdyk68A1PYJqBxHyPalTTjCxMENcwrxgx6DkErQ5o6SONgLGUCDfLKFF1rBe5CcUBaRTM.LDBZggCKTFwrASBHJXgFho..2GwQTRqCxRAkQTFy2u+DVoHdcnc+Lpv2aSF4AkXAHMA6VdjlV.TxXXSelPJOLSZauQtzJJIF4ktSsYBUK7.gJClG1xItn9TEXp90gi6bPvb04T4Oo.m33zGa5ZTvhDLBIUKe0bDvdNEr8ooyCc1tI2gxeXae0kOUCXlVATzOpjhSCEkMFD4SNIheHSrAsDz0WB090z24ShURCcYW9gHiRGzmaPA3QswoHQcSiHKvdOGrMYkbPG.kvhlJBkHC81sOxJ4kqtBEBtasOlyAgHjfxj0Boo.hJCxB1lVgybGGlvHrLYryF9AWRlj53Em3zCkIxD4ARB.SI4hW4FzV1fHsxUXmVzUUGs0njj60XxSV6Ilv6JdWdDiBjVH0yiChgMJnICCDsLyAelZzQ1.K3NiHmDaI.vKCH09JoT.Mec5FCBIWywX2vRPnyswrjXfbrhuLBjNDAMSigRA64bPtB5Ptczi6HDKH7FEnyDCfKQiJgHgdgAaiMUEY0jOekV10PFXMifpCBBdwnKnoTn2LfFW8JLuOd5D.+XVhYCYrryNQnDWj2IL7heNuqzA7NvQoLLHLrhhPOARvwD93lUepjYNpfGXTtapePvz94CuXVGRmBsnBUbdYfhBlMlHbh3WZKXsSAyZGkph.BMb1JvmD+DGnqf4halLZn71SFS.Rzww65Lo9HvlCf8oiM4fDCUTabd0OCZjI6LjdxSRvS01o3L.CdxzUa5SFR+Tuy3hw3Olb9xAe4CFPqrVYRlbnxAszxrJnfbVtAOTGUI72Uvv7cWQAH.DDV9I0eznOzYN.v7n1o3bt.kwYeYgsFGuldRAuf3honlkXrMcoxgZqimYJnc0pH3CjgAJimrpZJDD875fXxnzsSCHqBDG2GgzrPwRLleCwBlReSklpoCmxwRybRnYIKzVuriNLB++DYc0n6EGTbNH8MnCltgRO4rNZ7cMTJu8CPDjhVcw5L3fJYUxbNEnyVfQktiA+.hiQGqkgJGIGkkMZ5nDE+KiWcqnVqqEV6hdyXp0kRFtDo4y4QWbjIH6aksOpUYrMwQQyTYGZOJ4i5l7D37oTKbFrrMPuGMepEU4YOw6DXBYFEyXh8IBQ7dSQIGLcMy.xDy4zEiEzNEGYqLN0NZ1FknErIR2hbI0w7vQph.UgpUJDZvlZKRVrZXHpPvslQgdUH3oQ0pufDcpLqWkgxILWgIpBDVXc2Kv6lykqPqvM3c8+OA+RXXLBHC6CpEitsMSG7Pv.QndgRQ5MS8PoVFn6KYnj2toCkn1hA43DjZtyj.K1gzq.9TFla8AkalQeAwVqrLEpCsbGGtAWrm6l1ovAFn2cP1+gsuiJtZLTNNNw0bl7A.tNUHB2IvI1.2VEHDhvTvoYZyc1T9zAcoOQ8PlVq0LnrGLJt.RPCI.EwIyDlkPqj5l.qGLvc.mrScgYeWcZLhpY.0ucRwQ3NQYRYZbY4aXH48VDGIiwgrvu1qFHs.+AKXQ3tiXmQK5AoSCxAc+0J+DHFfv+IoU2IMPQW7KjsXFsHy33Rt7qD5ag2fI2xLPBjRe0fIapNPxPbaJrCk879XYYf8o8qZc.fLlMu+nV1KjUFlinkSYTzGwFMuNPSukP3CE2sCusLMgKAe9f3OBwcMxF89AZMjn0GJsR4HvA9LZyPv4Z8PuVqNkQg9rES03xkkM08qVXNvxfREKfqfksg0dowXohCvNqBvHoFLj+p3nhNHoPkqnR.b+vveBxsmFALPwZYO8xoOhnVlkYTUHCVtRITjbftsd1KtfOfA6U7+A.0WTIDCO.gVOPDHgXBk2knMyGKv7f8F4h6sRtCt2YV2PjMLsE.AmCYXVoxofDllQ4lj9PMUz9ZsX6J0ek5pyjKyHL6RuEbqBAqvvZsLM1gyfz2wIVTatQBBR0L5F6fnjaNkgFgLBU.fpJWbZsSJ3pbfTbPUSNfSSiawdmbrPkkMs5ZFgbTyySPzvz2nvyBMIzHtpQESv9zB15uB2FXT.o95HXSChnsYlo7B8bfQySK4FswUtsjII.2L6VsCX4.EnYDXgArt4nQLriQAd.e5FuhLxqCjgrAQ7suAjTHYnstlbd0W1abJmhzxXv+FNIRAMhDB4JEvzxzPkAt3VgxMoXO1ZySwPLw4LPCDz7s2yEYwAj3nZ0CCyTJGsIlVXFC8zdOygnmBN4oL7Fw+MloEJpMTnQ69iBdNNMamiDjelgiBY9NYX44fJjPU2rrAOuvb6CZmpUqp0fIN4IRuNlCCCaEvHtE67Uf8caANwADDdvcTxksRFK7zBg7hPV4TW.nscphAbdmShRymFS3X7Olly2MLiCkio8+8dwTwsDrLMnrfirVNEGAOr.Uni7IXZ7RvrCUj+zwI0p17QrS4kF.AKcN20+LZRzHnuTF8aSjrMHJjiXV..dbl1zrHA.k5IEjeXF5hFrNKZDWnkgFwH.zdj.uQz2aW+lKn05znnET4kQOqOIrpAidqsraeIB38nOuZQI21mZXCY5CVzlV0OVNt9JnFGXhzDs9duwNPPnNxlfU5L7dOj1tRJaEZhqb0ZFjIDmKFInPZk1emgvuGzz.YeTlmxlBovTcASZp68b+AknmKji.T71vUo4AEfsA4sx1YydHFNkVH2tTtHGBqoT8npqDm9dmULB4bttx+iq8BCZqLDh.D4TaOHBykP9MPmHYGhAvZPgS.yhSjJ8zndBGon1wsthH.BBaOMJb.ZCYQtPjUlBuSCZwNdFhxsazrl5oVPjZHPVkpSS8voBV4XjBMCY8BDAchMaASP6qZPLX3qCXeJjMCSwLo7ELv.inON6esP+GnJengIyrIsvN0OOsHANqVqSOb3D3DngObrn.5EkrFv4Pro6nM6AC00KEUdm0YFW+EZo3n3XYdLKWh5Rz5GNZFHvfwjRWPkDMAQv3VvQf4jpJ0LhYM0PTuIgQYqi99PWozHGoEB7g1iZnbD3jUsgiY3qlMz4LCwA0QQGzQQNrTNxXdZP.RJRoihi35HhG34CT5C24PnN3JIAoLgJgHluu9EmXRgIgaHu2qsz.ZGlirEgcWcvmivdPSAlfyslUojz6iCnoJDxAyD.CJwPXyP9UJFmrxjQCZqg4M0fByLxsdIHiWJLmoTsTswRvtRXq1D4JBFhdOCjPIdaCveI7AZy8ji4NVze.NwNn+MHdTGFOKkXnAfEVTTz5biICRszU6LnyPz7cmSKnw7HD5AEBcOkgNGnulTxrB3tcOpWPjryA7MlJvFSeWW0PjhGPccN.VDjNNR6cVg9rxGlLNkIWpaGSxsdpu25HIopbppOvvcF0egIZAMEqBOOs1mVOvdf9stTvqorozSUT1OlDydOFlOiQWTZWn4O.i2gov+AgpqadGIFzcXg15AKQmB3HmcZPF35L3TKZTvvgyZEGJElAzxOcRPYnMjElcmSLr698nT3DnxsD3Qx.1azfIPJLLahoN.T0DDh4Jpna0wekTqc.JGS0X1AQRXNMjrIsFuP7zLLtFCVlhPIwxxzPtiY5vtV0Bx+eezcrimn96.3k8TTBJXZGN3kqNahSJA3b..iInenxzUSDZQvjQTLynwYJpKSfYhA1GH.Uy1nY.fII5+WSuV12+sJ5EK57F5W9QZ5fsGzjRBQ8kVKaZEoxSYhVNFJW1dWrzzW3GAHuf79f4CAbIQ+ll.VXC+5cRtUjmP1MumQZjgwEcKryC89JRByQokKp5DL5uaLZmnQk5sRj3axv0fCxqAhCtB7YctiYuCU8sPK6KlNQBEoW95ftjSTJArW0ZAzDxlJolngrE3ksE7.libWlmTDW5X9.xi7cLLDWfpIjVfQduNOm.eAL4IDGZYO5fSPHfTMP.SIBx496L91.CC8RLUUFISF16HF1NH6CCOuxfL1Y1syfxDyLyD3bf2JHfgYmayhxCCIatJCXZSkCZ0DqAUslYynjcceVlixTbuCJrz9B4bDylJJrLjXukaNgJXoqam3icC850Km3MInwaWsnw8st3B8MGS0l4mf9TfvBfDpXl26LRtHhmaCVVxPpPMvMSLJ6XPvjXChrNSVbf1KGADgxpgGqBT82dUD86EJfb.tO8Nj4Ss5.eArVrxvoCI3YRXAQP.7+y+jkLyrVHyFcT5pIvN6vb0Ltxglj.oYe33FJziMRFBdcH7yYZwY7ZGbSKeRl9PF.TFqXjXQ1b5ZDuEgiYfedNL3CB7F2QAtAZ+6qGNx5mrHAWXvr.aVo6kXzgzoKxrvAPH1DACvw.jZFb4L3ASAORfDi5JatyrSslCTjYlT8BLti7dmCcXM4GCAnBuiPnLlkxxIjcoP4qUTr75wQ++xyUGjRAxbmlrHWGA+6JaXECEySV2nsS5bXKzH28CBI.j.p2cPgOMhNDL0xAzOZfJg6OPsxfBK2EwPebXxrAP6VYj5SYPfQ2z1HkVSJZ4qWO3P83pfxrxcI2ajVvjL5qMbPDL8gSfH6g7rbnnqVyokDVfyVUzXLn.IyfKWXljThEJ7CkcQ0b5dfFSAcKu.7JlhjNgKegoyX.eMrNRAdigCUPls5ffgaIzUefL1MQDjLppwogTQ4OsN.AMtravXGi5uNa2qty2nU3wvhUfalMdgNo1PHP6cXOOShx5uNCPBJoap6lbtRO536frk2+FjJrQIk5zLFmdLSPcPs1Pqo5U9gy4lrNB.k.Zlk8412vgbRaOKdVXYAYNPM9X5dSNgMirwNnZHGMCE92GknYofpvpcZWyn1EncCHE308c.gItoDZ0TTR7zdayXOT6LOPX.Nbj+.86d.ZU3Ovd30.m3h6AniL7JuuZY0.xxLar5yoY79IHdE0EipY1Pzfk9AwjS8hOb0NIfr.yD.3+BN.yIs3JZtEeJQ.qL5P9oAHP15j0fUxPOCzJzJzBMX6Z4nSqXvvSsvLdd+RGSMHHrjgTaXPaJTxI4nDEkYb3pa9.Fdd.4uerLjLHQdtZz4MXpaOCktBJVCwPu.UAYo.LDdOfn8fAZ2oWmDKvLHet1zgxgEX1Gdvi3bylJ9zYPugQbJzkXiWPDyIsapwDvuLUW63HjDaH9JEQU5vNlDAukWhCWIijCiadFt4.DyCGoin8oUpdwHT7JyPR.7u3rcHFtFDFSxqHQ8HtlbrwMXyo.pjKHjx19TlhonQuRTjqn+S58ui22XBWNNwUD5sigtr4vchYvGxY1HAC3FrDfhSl1qKWv4sI0xbR.PKmN0CuuAP3gQqgOmc7+.0wO5rM4.XJeALlmLmxfRffqXHeWXXbDS6nzAltmRg3.FSz8xikaJxm.qKn6iTALXs8pqfXkIBMMO9FNyag9sOn97KO2UBT7I9bEKsR8ranHzIiqGjURF0dy33pOQrXGAJZZNgFJzlTF1vE8YzonMfG.fjL.XASI64csLYtgTJ1vefQRCZM.qN.PBxA2TMyPLGOPKzgT0MFTUL+JSY7hqP21u1ovLkYrjL+NPn7btLhYBGqAAhV26TrDB9Kh3aGL6d+Q8NKFLGfhUHpMlDPH1iLnboS3al5qkinQA6e60OWpAVI3JxTBntb3BSQGPfJDRgnikrchIXSZXXF3Qp887pDDn4LXTWjyt8GXGxAqdCFVdMrnFU9ZBR5BtiwTRvCHUaf7WBtpxDKA0tDXjyhsREyN9.vocUh+f1q6ZaR4NRlLpmZcOzYZARUjAWHb48OzDIHnyfxPSJCNd9iw+Ke.lLqF91AAHDAFJit3ZRJDc7.9rV9AYWm63JbmlBPIAM8.D92mv4A5g6LqTafkUchqL0QZwneBleFlXMq.wLYDf9WVn7R62kp0iFxSOxnwxpDJEhhczBJKXr+VSJrxkUfob3A.yPOnL8HlzfKZNbwgEv2jJXzidfaTlBHcM3dQhFqYDvNV6H1jIDLSypL0vo7sPwp1C6zHvJPrKCCXGNowjR6JHh0FSuxQtsbBoGH4C.rALNmtDJkw4.Uzw06xJZBXNB97rNcJ2LSRDTWIcGczMOEZGGeznijGtRagVyLgMvALVtJtjfxS3..oJrbQRipFCYpLltfv3EdPrQZ4XwDPubhCoBwEFdri7Ys5tHGWHYn.qii.Zeto+jjrRHC8xSmiiO3YEl3kVQGtRMbyObSx7twOxTMEh73DymvXPa1TC8xPMiA9oECHNY.vgGAg7M0tEST5k.XB5PnLA2RslSg.PyDYL4FlY6NEiTp1fPsAJYGst.e1xfi.98aK2n8Ekabg.UzAqPFOLfGYJd1DtSn3v7I8heh5RovAU.9SGp2Rftcs+lLybXJBZ6ErBxHSWcCzCgwhPOqOscSSnKfPOPECDlkw.4ALWC1YfOeN12y0LhVd.WpQxM0rPkVU5zeGo+a3nGSF.azvNNFVbQzGTYnBdrMcDGBCaYEHrP3a6MOdJOKvvOhmhg07NgqIEyQHJzDc+9Pw.d6y.CFxrmolsJP2A5rEhpTfZbyXg0BWrv0HolQsSyf2.zQLDI9dwMIAMnxh9f4edsbytEQRirh.dFN5F34PSSFjJ9Wigwh7gFgHWo21YSLdj045fzYQEUaNKoYpLGGqA+7FjtP68oiuPy1vjDF5MfIOkTTCQT43LoR9leMRt7qNwR4ynB7sP66jWoyxEAeuz6RebAiggrSb15BiBuEi3HRW9LeVdSHB+mZ9f2Gpn94rNGaNCxKT13Hm814.7idFYh.RKFvczrs0YuiM3LqR+D19OqjG.hmTbsjQihpjzpN6MDDOpM3LlVHYEm+IbfZwhNDzCF4ndtUmBcopUhoIABQ5rqhkVzqmwKXOmyegIp6XiPUFsypEa2c7FHOzfEIFmsvfeuLbbRPRSC5K5YgMOsfeET93hIc9rVDCYqqRfC.YYF7myccTeyDj3HVXOKMMvryEzPzodIrNWaOhYkIPgZE9FqbV.TgqqPeaynseLocm40W9cYbIf41ox6mkaSSXjVICUOQpA0yc3K8tvFsPT0HyCswY+7Nwq.0rFPne1XRRuKUIlTzo0Cy95rWGRGhdkUN8mue1GO.QLdQoEums3J51AXlohJv1Gmkx0RHhCLXQbpaVJaLKzSznEbOOlmCh6gke3kPXztFExXd9kuI43gJfdzVm+sAjOLge1C0e9bmjzwiBzfDR0S5rU9JXi.DvvCc9HDCoyuom7IQhFAfa41F6kLziUvOEShync1yQPfP3AGDJP0UN61TbCgklZBilm89wHAD+KC8kbVr1DSiB58WiwoKyrqe1Ozxlu9yhTKAUpe1EG.qObSsBYHSxim2RchJTEg8k1QycALpfP4gYl1fxzu2oRSEFpavlZmK1onmlfm.XtZ33ty16XhCBnnyP8qC8Dhy4MXxbyJapob.Xzy5SlgqZA0XvnJrN+iHMWDbjlkG77liJIBEEVuAuimcdYi44aEXAkGyy2NineuPQI4f3qym8rLX5oDLJeP.E4ytuQgzLh.xJq5Y6DVTCgDn5g5khZSc16GLuKtsoK2iy4A6NdRGzSB6CTYRyO6J3DP1qrMKniRm89AmZhZlAcY1Wm6Cr7PzCAFF4SEJW+7VafrUA7prvbVRXIZILyykxGPwntFmUdnAsZ.wnZnlIkyJK555zlZfwBBOz3r34j8pDoPFNP.s.47u5BLjxjFBk7b1I++DySC7NxbYkylIjVq0YSvVEgdTNqrRECnPHgwToaHC4448Qw.p1NI0DnjFme+eElaRAfCPS6k5Fudv47X7nbZxIO6FLjzTH1wfYhOR4y+9KjZGc.f7Rxm+8B0yIPBQGiMm8HZgkvP383s2YykLBwsBKvC00hPH+l6yq4ke0+4WdG+GdsK4su8y+zqd6+Mu0a+3qd5yu5se8+vu8kO51q+cWd6027Ttj+ve5y+3Kie2ubdRO8ydxSdmule2zatIMr292e00+lO91m+U9W4+3eLJwAEMSYaTQxeXrhum5zXG+tibjpJyhZ6pu2Q6diTdzSsNNfUJpR+w8nxVBqPPdx5nNhvx8WeWyHpMCkeECPBCG68ysNgj0.xQjuLlWmw79i4ilAN9OP2.WxJ981hLIiCD6SLb0G2aHjN5dYTzclYtYZcuciY7cYpfy.9TBv3d6NOwTGqwAVdl2eRrSOKmtSEXRnUSiw8H.BXBqPLHzBACHy81ctx7jJG1cFrUE9+80hLzvaAcUKzgs6sC0Aj2PgCmvAlLHi2aqD.BkL8zSgHCXhx2a2YXqWEI3Dny2XKx82YDFv8fp5GK8ne+cDYLfMalHSSAt.tuqeYT8UXWGpY.AHqPm95GGwad5q+O+W9lu4+5+0+u7Ve3O9m+A+he4O4m8K9e+sduewO5G+ydq+p6q+8lu4it4oO+1252c4ydqa+n25u5s9wO82b8Su5cezyt5xau5me0m8rKexO+pa+827re628s+PE7xMO6m7ja9828sd6+h+R8Dd6G8tO4lKe7K+ou2MO9pm7ceue1mv+6egthO5yd5iH7l25lm9yu41q9EO869W7l+Cu424M0my+vezG8Qek+re3MO81mcySdxUO6q7G+gW+Im4mb2u328oe1m7qu5Yui9f9jO6pu3Bu3Aeye7iu91ad1Gbq9397KdvC+A273O+C93a98O8hG7FOLcwu55me8u9IWc5K9fadxM7ecbgVzt8pmd6qbg27ze5Su91ewmd0ce8E+vO95m732+Y27nqdtVVd9E56b5W5BcA28To+KBL7hG7+zC+fG8rq+za+fmb8iu5YWb8iu3AeqG9Se5m9Y2dQ7.ewC9NeyKdw+9Z7K+se3u3yt8+W7a+ie5k7o9O329+5e8E+69o+nKu8R8Q3A28oQeB+zqd1sWyh2C9QW86t9QW8gw8867vezUO+2d6MeZ7w8S9zadJe1evel8C7e+K+C9e36+4u7Kt96+IW+3G+jqd+ad907x8k+j7+3yu8pO8Ct9+zKdV++7+x+k+u9q+jqekK4+v+3mb4e+qtN77O6i9nq024gO7sd7O3B1od5Q9QW+qu5IW7ou381OUOX+Y28r9V+j+8oK9zKe1kexU2d0c+j3Q9mc0uS+NW705S4W7l4kOMe+O6+A+i4e9cOye4OmeK+dnW4w4ouxmy+wm88+C9zDav9Z9D8l2c2+ke3EW7Emwzg4Sa89m8PYA5ydxk5b8O7iuTqO+zeDey+s5+7te.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.7ZOe+o546YW8twO3q5A4teGyetuwq73oe6u3qzu+6c8S+UmbQ+se32Kcv6h26x+969VOj2NW7AegQ1+kO7DbX3egRGk5Z22u8pe+om4Kdv+hG1d2ZP2xn8yUPk+Em9ngkyK9a9rau4SzF5GeW.Ne0OHm9q9xmhu8C4u4+zdP3Bn6xEcAvVnqwQJdR9Se3G7I2byserNRbwc2++jGl4tew+8eY4O6g+xqd9U2d519Z27Kdkfht3hWYSxe9Ce9Mezs+c+5XuvquW4e0qrW9Uunup8Mu9swsa8UeXzloSekbh7M3I5+4WthRLzu3yw2TqpewGiWbsJZmm+q3N8nKexKtPc38EmFdvq7A8a+vmFoN75eF+yen1+7wu6c+nupOYewum8Lwq9g5A+AOneqGFYf7hmw+EO7Kk3xqreI788pewq7w3aEGPS+S6T8K9k9++X8+e5i0m+02cmUdYlA+29G7W6dE4l8zqhfUd9Eu1WpscrsjPnuy4y6+pgUe2VhK9C+z75I.7+P9w4Em5bed9l744URe37awe3CS5qu6KHTmUe907g40MP+9udlKegMiK1ZD6LVa+iIA7+6RXjexkO5Y2728nSQFyGnuc7czYimFlE+NO783qeqzWtlDJg2q+6dzid8a0W5WL+G6uX4O1ew5er+hs+X+E6+w9KN9i8Wb5+EeO8024kfs4Wbw689+3XS3CdvoiQQVXW7+CvggePC
I'm still working out the details of the training scripts for my own models. It's mostly working, but seems to stop training before my desired threshold of tolerance is reached.
I'll post more when the script is working. -
@Dan-Korneff I'm not sure if the tolerance refers to an absolute loss/ESR value, or whether it actually refers to a threshold of progress between Epochs.
Also, depending on the size of the model, there's basically a hard limit to how close the model can be to the original. The Rat is quite harmonically rich, so it will be on the harder end to model.
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@Dan-Korneff Would it be CPU greedy to run several small models at the same time?
I'm thinking about modelling key component of a circuit. Not for reconstructing it though, just for having those models here and there in a DSP...In fact my question is, since a single component/sub-circuit has an easier behaviour than a full circuit, are the resultant models lighter to run, at least if you run only one of those?
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@ustk I think it should be doable. My plan is to do a Grey Box approach. Use ML for some items, and then analog modeling for others.
Test out the snippet above and see how much CPU it uses.
I'm still in the "trying to make this work" phase so I haven't gotten into measuring or optimizing yet. -
@ustk so basically every NN model uses a number of weights. This results in a certain number of parameters being able to be tweaked by the AI essentially. The more parameters the more CPU is required for processing.
With simpler circuits, or even say individual components, you could probably use very small models. I am actually planning to use the same approach as you, using NNs just for the non linnear stuff. I'm hoping to get it all working at higher sample rates though to reduce aliasing which would stack up quickly if you are chaining NNs together.
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@Dan-Korneff are there any flags I need to enable when compiling HISE to get the RT_Neural stuff compiling into a plugin properly?
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@ccbl I didn't add any flags and it exported correctly.
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@Dan-Korneff Thanks for sharing this.
What is the method to train your models?
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@Dan-Korneff I followed all the steps in your Gitlab to get the Aida-X trainer up and running, but when I get to the actual training part, it reads all the configs and starts the training process but then fails with
"RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
I tried with both 24bit and 32bit float input files.
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@ccbl I'm still working on the scripts, so they aren't 100% yet. Feel free to dig around and see if you can trace the issue.
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This post is deleted! -
@Dan-Korneff I'm also getting the
CUDNN_STATUS_NOT_SUPPORTED
error when training with CUDA. With CUDA disabled, the traning goes as expected but is very slow (as expected ;-))
Are you using CUDA to train the model? If so, how are you setting the enviorment?
Like:
conda env config vars set CUBLAS_WORKSPACE_CONFIG=:16:8 conda activate base
...or some other way?
Thank you!
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@tomekslesicki same is happening here. Looks like there's an incompatibility with the latest CUDA driver. I'll have to tweak that
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@Dan-Korneff the solution is to install CUDA Toolkit 11.8 and install pytorch 2.3.0 instead of the current version. Here's the install prompt:
conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia