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","code_sha256":"3fb407d87bb2dd2d4a9e5966cfd15066164dfbf2d2a18a42e02940e0a2ff5e47","port_zb64":"eNq1PGtzGzeS3+dXzCVVt+JTM5h3ErlOselYtbLkFeU4uykWaySNLcYUqeXDtvzrrx8AGhiSTrauzlWmMA2g0S90NzBNfvfdd28vwvfzZb3J0/Bxudr8EJ79HPbCl6fh8mbdrD7VN/Nm3Q9VMigHKrxv6rvw6HMz+3C/WYezRXj9KtzUs3lnGLxoVrNPzV34frV8CDf3TXg1VoRxGL5smvXxqkF00HhczW6bdbhdN+GqqefhdjHbwAqL5SZc1Z8B4+/vTl9MhsE4nK0Nab+n0eRHmlLDAs36PvzarJZhvVrVT+H75Sps6tv78K55nC+fHprFZhgAYTI9nDeLo+tX019Pz9+Oxp0+UD7bzOr57Ksh2HYOw4tl+L5ebx7qzf0wOFtsmg/NylCFCJerNSzcwGrz2U2zqjfN/Imobx62c3i6+zFcN9A9e/++WQEtsA4wut7ON+th8N133wWzBxRLiAuY9mL78PgU1utw8RgQPQC4qUPT+8dsEwTB9+HPNUjgdrlYb+oFyP9x1dw2dyxpWGg1a9ZAfv0ATOlHIrT5VM+3SBjJe4DyBlyCZrm6A0Lvwpsn1NtsFa6X29VtEy5vb7cr4OC2GYbnP81ni+bZ9OInGD1b1PNnZglANVt8gofl6ikc/Tq6+icSD8ZwG85nG5DPPDyaLW7nW5j2IVzPl6jsx/r2Iz7WiztQ079Xmw4gAUxoN/f1+h6oQaY2zZcNKgv092V2C5iI1B9xGAhClnW5BRsCRGbph/oDsLu9a7SF1Xd3oPklMBButgs0bqBlBSKD4SC88Wa1vd1sceZscYd2CriISLaDQT3/sFzNNvcPZgW9aAO7YIHGBLZYA6sbbdC4X0CuZIeg/u+1emTNcH1fo/Dr29VyvXZkzog3s+auj4bBmlnArDWaxfxpGICNTy9OX4/G4Ul49Dcwz7/1w7+9Pv1t+nI0oubZhWn+/GaMf0bnp2/GoxfT56dv8PG8nF5E2Bif/XJxej69fnU1Gr+6PCdE55XufHF2NX0xen76T4LGkQafPr92wbFF9a+RC1cafn35mwtOZOXXp25HqjvOT1///MLrydwVfj4fXbxwe3NnnZ3OwqF5p9MI4c2r0+vp6Tn8YXi1A59eja5Pz86pW0W73WNQBncaaVzhhrievmWociR69voNUvP67fk1dxqJ/HJ6PZqyWKTTSAVVigMYaiRyfXX6YjQlzi8vx3qKEcj4+vR8ND29hkGw3kv45P7Cknht7UGVjhQtsPKWuTq9Pru0nUnk68vCrT1cj95Mn1++vZBVEiOHn0/HI2Oh54lvEs8vRy8Z3rII6TDsvzy/fDclq5A+1xwEWjjQf7w9feF0lY6JCNTdAxaaRg7UWyCNPaH7s5THnz/P8I7mNf37hRZJmrrg8fnlm9H0zdXl9eg56zjNdvu1ornfSOFfo6tLhhiGqBVj6/JiZAGKWpnVKbZo0MvT8/OfT5//HZY/ez6yfXq84frs4uz6DPwIcWi7YttS7iBWp+1LbCv1Uf1rZHsybqnWcldmQ2BPbFvKthLbItwgwuej8RgC/pXtENS5bRXcKqw0Cou+sOhL21va3tL2WnFnle21cs6tnHMj5/M8sTBr97lR83mRui2aAUyIO7cIS4uwjC3MTi4twjK3LUtqZWdUtrcqrPGC+3w+mv5iF62sBCojATNInGxlscdR5DVj3TSLXr+71BDVWhIse3TxFr3PhR6RCabKa2qkNvhAM5dm4TX1WCVkqciDKt0UDOI7L99eiweLE6EiESqsU4Cm8iaaSBvb/c/NWIZcjN4JXEkz0U1ZMZUVM1lRNmCcySJZKs3Ma9LYN2/Hr6x+YzHT2NopeTY7QBDniV1OrDYulNfUA4rC9d8UlnVAluGlDC9lFbHfWAw4LguvaaaVAjX2SQui1xAexOip6QwbgdWJzVXKJbqdT1F3LE2tsCp1Jz2/vLoCDw4op68vX4z2wcGBn/2qjUr2V2w32Ouz8fjs4hcJILHdYLbLRG/Zb0r2m4pSgeZeM5am0vkBRhcWl1lPRcVO8DXMQGcpGA3JGo0fpqlb6aY1ZRULybLrlM1DOLiOceF3o7NfXpkEKvH6X56Or3f6NXOpLGC3niaQMiiRnmwlJVtJ5UJVLiLLC4EaAzR5l0VYyNKFqKOQZQpZpkgEmnhQJc1EN0WhNjZw04zNZEAmAzIZIEwVwpTdVRhoQNmXVxouOrb7ClJFu3WV3QpvUKDtnaIq4a0S3irhohIubPSBdFNJ0xpMIgaTxLGvz4vLs7FOMO2hA5qZNA3yy58lJUhsOHgxuhifXf9zOn4ORuUC3ly+G5nBQpQyXF++eDGWSdBhFz+9+mUkeU2iUq+D5tnEn3pjaSrdzNzYeL0zKZNJmUyyGkts1v76FPIg0Wpi03ZuaiSyXahpoCJ/yRgTmzLiYVKzKJsokU2U2IwQMlDcqAJNpEkJ2z+mz4FLI+1MpC0JX2Izvn9AzDx1hibSTKWZ8dDXZy8ElkuzkGapm2I8Ej2pybHi8q1ZU0xLUsdEcsdEksIkj72mHiDxNJF4mkg+mBROM/OaemwhC0usTKrIa+qxshET2X2J7D5qKmlqeVYOXlm4EnPDphmby9hcxuYyNpexQnolMqsKGVvIWLFoGxq5qaRpxorUq0rGVjK2MmNTCZmphExqKt2MZYCSZiLNVJqZNHNpEpudwN47hifh4nFIF5pHv8fDqB9Gwxg/qBVFETyU+FAaCCR0ZZrLZz/MMz28yPphMcyaXlzgY5b0w2yYHHisAHM1jM1jXgGiYWV7K+y1j6UqkQRVGkCUxYgNp+c0vsxwQmkXV/CphoUdD6NyMzhW/TAeKtunP1L7UQwVfcCIkjqB9MTMTmAl8Dc0BSSW8acdqsqsgIzbfuJgXkIhxV4rBsb4k6fntDp/kvRTTRBNEYIj4kBxT1qm8ISfehoSa8hD6oBWegJplyl0VvjHLIF4jQhApHGkH3IUKHyYPnjKTB8J0zxUiFHUitNKsQEj29h+0MqZdETun9xyXqE+kXL5x5qhAWgNvETpMRLtTIj1UihJnrF3VM5/EpZTpLJmEMHYClreuLRFPX4oy5b05pbYUqNEHgVFnFgc3keklbBD4C7o2x/xoQ9XeMozN/2szUib/B4tqn3PsTMVH2PDEFmjhSfOJ9gOSthRP/me2IW0KXTUFKdttlXmbmLV5mhXPsBlCll5XlRVXCUpHPZQkS2QIwQ2ptI1GfYczph95pX4H+je2L9pB+fu+73qkI+4AtNKorKMIHzBeT8lB1LlHgxoVsxe6x96oxZQ034ALDTu5T3J86REGVYx5Gjg8RBRC1Y4tuaaTlkmCsjNyhgOrVWZ7oEl7BYPqORbmjIRCTahge4zaPKlOTLEbLVN1DdXMGYYm/Conc+WQ2A9/mW3wLPB65TeZm/tfcdyIzcwM7a2s9oHPMBcBSvnOPqgk1FDcopAnvIxlpZFaaGs0ONGe92WjlB7uky8i6OWv2oJSu1ErPign4wP7EeJJ8meD1AFmp6ErEK1mvSRZpI5ZOISI6vxuKWzfelGy3LiHTZ3bCDa69j3BABVCoDbO15GG6qSz0ia7saIpOmlhK7iU5+9nU3/Vz4m/fBu8/TYnEBmqt+kd/A19KvTsPmyaRbr2XLxQ7hZfpndzjZPYb24vV+unBfL+OIU3+jOm3qFL0epemA++9SE9ftNs7JVAubddXAxxceT1uv64OIMYCoJLl7B3zI4vXj+6pLuH+GRphjQ+dmFAfViA8QXPRaqglej0xeRfQakU4Qg4FX34qx38aqnuvjRTs2BrdsamIb/R0Jc38na80ILvYhVDoEgTsuogJy8JMc2VFUGXrKATF3lSRzlWT7puNMhMc9yTOIgikR5VYJ/HED6l+VFkqLuKjii4F4eQISIVVxC4BpgwgvJWUHNBE4BGS2VRZAw4lCw/KiMVEURPQFPTt0FEKhiQg+RKMUMD1oqKUvaaFml4ryk7kiBsSpeqMIYwnktHGoy7odwp3h6mRSUd0OWy7RBGqASmo3RPVc6Y85yZAez5Vhh8I2HcaEK9EQK6c5zZjaCjUb4CmC1YGqKGMSSMN1pkVeKaSggyGlyCpXFPDZWWRIxGZD+Z2mlpZnlGS8fRyVKAzhTaQKhmvqrOFMsQjixMz9wwov0Snmq8ohFUFQqYkriKCnySvNW8LEmA0JJKgpIw1gPiwC7KbEGpgEWwF6zSnlGnhcZB16M0wWpIQe68owozDI4VVC3KhS1gFQI8oW2gbwsYsXrpWmMdIEEizJFwwMSyzRLCzYnBUaq9ZTDUZpPfGlVaGbSPIu1hCsgWLHUKhRlQvhhbMS0g2FGimWRJ1pBUZqwiZK1VykLtUggFyKWEtA9G2MUx2ZSHpXcq8B6td1VZUR8gK2WeYxmCSjhgB2lzFGSQewjMcCWykpiGGWuePFcQXbGIyF9KVTJBg67Jub9macZyzjPEkVcFGlcoK0CHlCyYm5BP6zutAJxML6iKnKlNV/mqRZ7ySaQpkAY5VgZ0s1WqUD8FRMGOzDVphiBKHETglSAsoKMDrdloumG1KskDkEhsd48CnDyXgeVFpXibR3HmjYwO1iVgFVEJ0xURFSkKTGWgiFrOweDAeJ5IUj04kzzlhZ8eI7hWF/xmmBaupXlZUQWjK5CixfOpZHOGKukLNh0wOjJ/UC8SXlJdEkZWTPZUMkaiWMQP08pKuBXC70oskQLLslwN0KsB++T8AaAjDjitXPcg7xBEkhxmcosLuKM00ywtpIuBUDFej+CWRgTigp0NmZTKG3AcQwa5GYG/pZdX1JUsWKvAP6SnSBoOs0qMrwyi2OleKGU9wn630IjB7epHXCmqoSMGkJDnugNmYDVEm0VbMOE9hacXIAklktZQRjRWztKCmYCggNrXIH3N7IEt0GUwelDaSMCSZVsOoAyZXNCW05iLYI80vYCzgYvctBEUxYguh3YJogJrLkwjhkMJDF2DRGBhKlQ1kWUa1VAJs8+HJVCZg3qK3gPZ6WKU40yJlcCs9NSkwb2xd6lBIecayJxGOFTBaxH3MAOzFJ2tVVc5kwO+FDNYGG0AB7QxDHYool2PCVuaoUeucTbFHQdwAcZYoL7rsx5sxYQWGPtDMG55Eo7trysjOIhsjCVpUo4XIGd51oSoOKKqYQtqhIdgSIIkIqmg79Js0L7M1BzQVCVlwkHmaqsIu0C0lI7EAWBz9h8kZMFomkUehPClKxg5YEjUSaCQb5RaOKAUdqORR6j2cF64D/3JXodSvXOHh62GyqT0/VwP0rtWoh1hQ2Vpj2sm/knLFmbf66f1pACPjSmrnIYvKP0Kgp0aRq040DXpmFeFzjvhuA5DdrVaADMA1uFximgKT5DZFEgRWf4DKmbKTbDR0JoiswQkAducRlCyqBdVIaERUGrmAyBKmgVkSEwDaQKDJ/zYKdoDMFl0CoWA2ASBaZIDJ9U0CoOQ2AatIrCEJgHphgMn8qgVQSGkgTB7BR/IVwFuugLH9JAF3vhQx60irwQWAZS3AXPGaJ1i7oQpgJTzIVPRuT4ohefrcQNoAz8oi1UMYvbPLESbZEWgtLAFGfhE5uEeSoDtxgLIIVh3hlVqMAvvkIY8G+KrvAxD3aLrRBeBrtFVmiKUYDFVdhSweUFoiizwC+XQlgVeMVRAKpU4JdCISwL3MInhJSBW+ZE5h4HTgUTQfJAlwGR+eeBV6WDsKQIvKIc2hcg8HeX1MqCPUU21FMEtsaFtk4U2NIVes4CW6dCz2DiungEH4sq0JUitMtUIKUeBKgCrxQDYZWBmboL2ol6pu8pVJQG+yoqqKsM9hdVUGcVuBUUCAKn4VROECQN2qUPtP/jwC95IGAStOsbCJwHO7UKdJJVwU6NAsHtkrYKgcDg76SMgPxNFtjX8eRvkqD9lp3AZeC+BUcQ2LF+z00OCdyN+36bYGngveImWBbI+2wCoE8w77ERUEVB6/01QVXQfkNNYPBd9sUz+cA4oNfF1K4C/UIYn+IkMG9/6TEP+P0u+coooDe41AZTuHxL4CQOgv/Bkvmj2/r2vjm5Xm2bTnDXvA+nj8vPcHTvh+/r281y1Q+bL4+dH4IQ/nGBPky/fvU7LD8h4Of7GQQ+GBQ+Qzhu9AkPx3+z91gVfoQ4wv8OY+lw8HVP9Fq2kx/3dOAyJ/SlgCF9yQARh8e4LuzSSUdTudmuFhr5QS4hWi/uiM+6H970w8+aRyD4Jnx2EtZCqkZYh73w6GZQd7qfAw88CI/qwQ2CDy3Glz5HD0296HNC0A8/yoIEwUVxwM66COwd0ZgBtjvdjzTkrplvaqxqR+CA+k2XM3PAw2AVbvxE0LCZrxvIdQ6Lp9bSedgCxf1wvuyH9zNN8vfheDmf3eF1lv6qB3474ehT2A3jJsk74WYZwhr66wB9zH1IH7f1HNKfmycYFZdDQrW+Q31uYSYplfGwHuuILY0siiHxDkS1Ifjdgj/w2xCrevGhOSo6ni3+AfwnvhGSQW0HR2hDuL0mgz86XSALRgNdz8KdDhId7wDcgBMxTujYh7t39MfAIkEUdsxnpt5s3gmTeAL7k1c5ok7cytKlQuIQWqk7CLa4jIntmMwld9Lp2KXvZnj/eNvgReJy8IhTH0E486VG+ji4nzHwGSjeYiFBC5avMN1gOmahwac4An+enTaffWzms/vl8k6cyfGRbvTgL7qpSfdr92vHkRW4A5lo4WAmvZPwszzH9Nx9FFZhkfpmDSw9bDvHD1sZqnjoXZfRPeDA+LiOAnfagztt3dwuF9SjzDiz2fq6cwDoSBA89FmIz0YONiJNvuF/13BumIG/cPbf8mYN+B+Xmz5+oWnKrZsZfe3lI/jq2WoNgDW44gV2bJ/w4WsjPgZs+eRkj4NGOCG2zgiswCNUxn5ibTk8kDKXADZEdY0WB0AcKw+sR/qtlgdAd8esSeR7G5U5YUvC4XRNbpDsjTS8A+vHdUMuZbY4MtKAse3A75gwM9YjzpyUcdI90tgGZvN4U0gvsNAhTxl8Czdb25S+EDUnH0GQTwL55Inmz7k3yPoukm/SBrjxy1ct0b9v0COA7igQbp/YcEFXrnJwEBFh096WHhbL1UM914YlJgHzOmIA3vyOt54/zR947KFr5xOzuy0tjBuXqWhveqZvtm7YmN3cb9IzGLqm4U37iIo5PvrUo/nttckLOHkFypxJwGAP8heO8HHHKRo/wqryuhvNEXgBl53NclPPp2iFn9Bp6pyVp6BnbrrNsR6jM4bFGoP2iSR5O8736zG0vHx3wsR6+a7eDMu7uzVGAMbbRRw2A54cS0TXRFjDQ9fEFiQJsmNChLZ7Iv02MZ4EB/zD3snttHripBztbOPz7G5zj9nU/WwwX1oR+2ESnCW7UidQQu7AkZKyhf2h8ivtRMRugyWqUWcCD/UapUgUHB/Rn57OaCFZ6Di6ITRd/mOFSdPJZ8t5wfUMjN3vFu0dn9AIAjyuljcwFOGyKD7xWh+n9eqGtoBrVA8a2rJ8Clg0gydT1umPsi6LUPRpeR5802w+N83CBGDsHpjhVjWf6tVMqwZndt39Rev27DNh7hrP2KPhrU69pPkrqble5JmJfBg9nJAoRDj97SS8b8cxl4di/3qzqjdT/N710Rg3XT/8EvXDp8jm3af0fXKTfYOUF/Mn73va9NVh+lIsKHX2YYFftIa0e45foqVvg4857x7/Hk2YZnMlNdHw+AA8mbQOfuPfMwQ9RcdfIhQVfO5J/PxbHjMz18i8+x7TWbQ6+ebH9JY7U/8ltGetTryVsAwk35ppOnfCNPSl2Ifsunp1RdT3BfZN7dJbfaPe2XpKuVr9sNwuNtMvtgWwzeyhAWfx8NgP9c8CYL9pPmmbgKzg0xTj9QnpVGAQswkW6yytXm+mmzWB1ET7tlVzi9/zJmCiT1S3G3pMNar7euMyDzb1UBMgN3gf6LEw/V8bei4nOkR9mTZ6RsWgW2SQSdP04i8JMECvuoWdssIvi1OwIu1o7OT1T0Q2A8MXGKAFgh0a8O75Y9F8nkKC/ehikQk2NdKjfghaueUUf35hDwHB/hzUmUVGsj8HfdjOW0HZ3uSDbWkcHTcFE909O9mNgW3tGl9+ZIE2qe3C2t84wd408yXya72lxeDPPLDigOdjbkeNn2w+v/9giNbX9SVh32LskwSZW2uCvOfYN8MYZGuSfRlyQN5stI5/w3jumsShI8lsQbaNtkA4jtsWIa8NWicM3lnw2ZVRTGIPkXr5sdctaPx9hDQ4kL4bsWSO2Z7+rQqdQk5MZ6uXTiUn4pqOrb9C0clDOzygczEbjh3NvhOqdkH2jMpHFOv38IaRvKjGqX0fDf1QP5DvcU4M5vzyLUuEIXrmPnrwhyVmtMEtSf6+8Qd0GVWLTDoLU484tls43+EVWSvOeofAw3nvB+/Ua/Z33+bB5k3HpNO+fiLR+DvIeQsCKD44G45jhZp4h/X9gjLjrZrJvNCZrN3si/XY6UnOOTBK7HSOdSLctgYnj0RIP7zFK1zHL+yV58BhznmJM5EbXZ1yahUOEHfnmNY0NvrNy616DmN0ImHfYk5EhWbGzpvPiX+7ZE/mpHR311mzWNcPyNnLGlByOv/4uIRjmA+091Cili2GPmOjz1rquYs54PoKBuh/HdQw03GEMy2FHWxvH72BDn3IHs7zjAiefcx7hUmvhCcHLo6IcFE3bTqfUasBa1QfaiLpof5yRMkNetvWm2TeReZNst5ATB2EEc/WBOXeHnKHm/CnExqz4+Pwj2wNWqHT0xx06dFgscJE+6BrDH49PtkbiA5tGpBXzMzZ+Yfu5HhvoIOBlp5y3fInJjm0cnRqCvBuxY9ZbufulRQxtueiieDsUvdx6t880bhv3Ti1XfLBSydx1W112YGX5y8mPV5fgBejd/rUUH+GkA/ZEXoYnePbOPnkxsmnb7sYTJb7oRfH4bA71Wf31u3xnsHanMxtMjnzvg2nfRtAdy6VzVnFctLxzk7OIt65CUnQEmDuyYYMDk6+/HIKsQae8oxjkl/v4lr3dn1v8cKErs6fnQIRNnLz1n3ipXbM1+HkGfHa5x4uZjN0H9du9uyikdSb6PVzeObhcE58gp9dnQ07dTWwq4hj12zbQ1rZ8gn96ZpE+U+Q7YzZwcY5x9fG3iV2zMFPx1K0s/84kPJ0xA0tm6GLvOVcqVtdPcTjB/o8btoDOvpa5fqH8LFZDVbbxU6h/hHYcT4JoWuBv8XGtzkAKyYYsR7nDUfnDl+rwLCpvumieXzixIEMKWzkYiBloVKq7zoy6u/5Md+iB/iRZnxggJ1jmmM2H12xmD4DpKsVGYYo5jO6l9OvqM2EvpFr36Hw/OzCEf+/t/XdX52JJUp6qjlf2BNRz0tVvaIpc828mn3An9Ob2iORD3bs2ogXb55sGc3+oLjiwE+3RvL+A06pU7mhSFLpQuam9n4jSbwMiZEdypH0UnsvpswtrmA/hMUlYO811sG7i9ZBVJg8dt6ReVVGk523G/gjjeTI3JOCX4UEKmy9d2sXJLlvoB3B4J+uXaN1vLVwf6qnqR2nyb++6WT+Nkbu5OJ2d7F1rtApw/ncYuCGK2DDWKukaqJVt7+mqq3M2XqNDkVrpCGP7BZcmaTT1lu1VHKQUsl39BqdfdbdO9nBIK/n8atITJzrPVvK7nRb01z7dO/5Dbgf6pBv8HfcxJ2ubtEQHGDCGYSl2u2ipMPidvPUejafuoYlb/lskdqkPd5xIa6GbQHbxN2WbDp2bWcBW9nm1DqQz3LkAYC+S2a/5ax2IqwzV4tRaN4/mSSmZ+MFkpXQ+5vavPi019U9m4sT4V3K3nvOzRRBAU3PkwqD7UqM/oEuommVnonZNE5HGgO0Javcg6HENno6h+FuyHsEdU+/1nPLUzXBZgXnUtoNG+7ltBc3zIX5er1dNW6a5lys6penn7wszjlj2umuS5GbUl77Y6OvW0yt7aQLKHsaINwAsKsjqL0b0/jFg/CFP8qkh4glV6RLf4QPaEF0dii4Z7z+bp7l56wy3Ucry/1FtOsNnHin/ilpp8pz0sOzgXQ5hZ4uFkkFbTZp0XeObXNPkri+n73XMcAvcwabMaj5LL/ZrDjNpilCklsULu+eqQjjRJj09dLTWFpShc+BWQeQIPCZXfivKGYHL2sFPl28N6SWw3jrOb0VRNeuD790rahZojQdi8eO8CDeHvAT9xs5aFQO98vlWsu7VWg/6ZLr97IU76TPU585FRm2urcVOJ1Fdkb+2bFOq4dQfOPYpqUtw2DedD1fPmrXuVv8vt8N7BbDax2sPx5AZ4b9GTqzrC1ZnemqO1umP2k7C6+zd4Rm44E6XculVTDZ6AGsbNUtrGjkLayW2UBowc2MF2504aCv1uiehZ/4KzDmno3WMTPoYuJPZuwtKGNecHkyNyOvQxVMVmYywDEY2WeAFfFZSR3CZ6XVwncYk1PNvk/qbndgqTrA5X5clscdXN+HF+DW5bft4fDwuN2sh+Hp6maANZhgnLPF5ge9geEESW9UP88293IZBe4kGmbD3XcYrRHe9S293bcvVzUxV8vtBo7n5ir313oxm89r9kn0BSr8RXnwWbpAhIlb/xA2/97Sr5uvKNyH+jf1+Wv1a8C1Ge6/djx8vQgUVpP/z9vF3/h79M3D4+bp6OKMDXpx/38Ko79xrQenSfjTDMNBPNQ9VO2BKaJygGpibokybzCl3Oa+xe+iRB3vqHwwJfWSM1k4XU5wLF/cd44X9zRPdxb6JDBbErQtyYF06QlkNMaqPE5IWfGwdehz2I9aV5fduMmdbhKPe6Yth53j0nYr946lxTrfitqrXuX1kbj4+gOgnS5sA9NFIjPXscf0+yPOxNz2wlbf6S0MuVKeQHuKfggDV4lV5gwnuR2RTQ9QF5BNoZUaobOxP4M9ar8uoCeSWIG8rvJYViRNIKwNj0XdchHoalCpibmKcfSHtfwzqeWH7SA78bffZ4bZdNin1zcDbCBcXzLc37B7ox+z6Hm/XMEbw7xd2jOgF8vXCe4dEl45JHzdnX3v3CR9g3ie3XOm3+Ps2aSL5HsLuAxG8Perc4MCDPb2MXA/6X6VQfX+Qe64najcu7+BnTBI//Po3Luv/9pMXYEGi7mFZ+bN/ZidkB8J6F2F+95dV6X5J62xdUZumZp9y6mLz8gbugVn5pCoK8zs2XDMTsQ9ko2135A7CV0TZgryg/8FJepJgA==","port_sha256":"cd19ded44b0d59976b13d35680107e7c47e77ec43e44953ed44733e18404dfed","params":{"W1":[[0.55693,1.906987,-0.656734,2.939904,-3.312189,-1.059507,-1.315356,1.506854,-5.008029,0.139296,1.771221,-0.337495,0.323888,1.592168,-0.021282,-1.094071,0.034055,-0.060325,0.38378,0.150189,-0.223282,0.34627,0.005634],[0.481299,1.172703,2.506664,2.901008,0.721878,-0.716983,-0.347692,-0.078345,-0.072518,-0.125309,-0.142549,-0.655654,0.410896,-1.243394,-0.915256,1.35778,0.402092,-0.642601,0.079203,-0.103769,0.00576,0.151695,0.283437],[1.281244,2.696826,0.019476,0.667584,1.071778,1.624365,-1.554844,1.272844,0.971479,-1.068712,0.284413,-2.978416,-0.845477,-0.267478,0.163977,0.649703,-0.465183,-0.981229,-0.953093,0.264906,0.065602,0.076378,-0.043904],[-1.295944,-0.736138,1.363429,0.011378,-0.60838,1.235668,-0.980413,1.786188,-1.403046,-0.352254,0.24058,-2.169524,-1.621966,-0.192728,0.321211,0.716458,1.665322,0.741782,2.126023,0.21941,0.049981,-0.179762,-0.108642],[0.28869,0.445227,2.586185,-0.245494,-1.929449,-0.064921,1.332176,1.122136,-0.158184,0.183127,0.026187,-1.845792,0.131181,-0.516861,0.190843,-1.207442,2.420994,-0.687086,-0.132915,-0.174718,0.105197,0.087197,0.056804],[2.323866,-0.850084,1.893873,-0.834854,2.040994,1.505365,0.264828,0.116533,-0.795813,0.217753,-0.243556,3.009434,1.233406,-0.692035,-1.391397,0.517157,0.336385,1.322695,0.196046,-0.070666,-0.26728,-0.011452,-0.053992],[-1.379126,1.333771,0.445459,-2.851122,1.32493,0.068576,-0.240729,0.152933,1.014637,-0.239525,0.949736,0.200267,-0.789148,0.284037,0.199871,-0.259528,0.117781,0.880236,-0.406827,0.200484,0.049931,-0.076061,0.162451],[-1.947157,-1.564536,2.576664,2.718385,-0.294046,-2.336706,-1.390014,2.99139,1.100978,-1.582146,2.571906,-2.4884,0.080406,0.839461,-0.019064,2.274047,0.762546,0.191865,-0.6674,0.047493,0.08418,-0.022883,0.011841]],"b1":[2.281805,-2.104633,-3.158864,-1.750713,-0.916962,-1.296899,-2.854251,-0.82308],"W2":[[0.505606,-1.637946,0.311239,-0.601502,-2.782457,-0.359147,-2.268301,0.989084],[0.14894,-1.234735,-1.376459,0.287084,1.989577,-1.136278,0.406317,0.505463]],"b2":[1.576125,2.2058],"architecture":"23-8-2 capped ReLU; hidden clamp [0,4]; fee corrections in bps","weight_fixed_point_scale":1000000,"feature_fixed_point_scale":1000000,"output_unit_fee":0.0001,"feature_clamp":[-4,4],"head_TH_start":386,"anchor_cap":674,"anchor_lin":0.7126960148076508,"anchor_quad":1.2952407538263107},"input_formulas":{"all_features_clamped":[-4,4],"fee_units":"fraction, 1 bps = 0.0001","ordered":["sigmaHat*1000-1","lambdaHat*2-1","sizeHat*500-1","toxEma*500-1","actEma*500-1","direction-1","(spot-staleHat)/staleHat*500","(isBuy ? tradeRatio : -tradeRatio)*500","arbProbability*2-1","firstInStep ? 1 : -1","uncertainty*1e6-1","min(silent,8)/8","anchorMean*500-1","amountY/20-1","(stepTradeCount-1)*0.5","prevBidFee/0.003-1","prevAskFee/0.003-1","min(timestamp-lastArbTimestamp,16)*0.125-1","routerSpot>0 ? (routerSpot-spot)/spot*500 : 0","T_bid_fee*200-1","T_ask_fee*200-1","(spot-pHat)/pHat*500","retail_flow_rate*2-1"],"T_fees":"after T tail, first-in-step and attract clamps; immediately before head correction","staleHat":"0.7*pHat+0.3*fastHat","retail_flow_rate":"unscaled RS2 retail flow estimate, float S[15], Solidity uint80(slots[15]); FA scaling","state":"IB state and afterInitialize unchanged; no new slot; new inputs read existing locals and slot 15"},"notes":["All reported experimental numbers were measured locally. Artifact figures are used only as calibration references.","After generation 38, training used the measured constant-specialized replica. It matches the original dynamic port exactly in the recorded callback and whole-market checks; the delivered port retains dynamic base TH entries.","The frozen Python port embeds the rounded frozen head weights in TH_VALUES; use strat_init/strat_after with a zero float64[40] state and TH_VALUES.copy().","TH base constants and toxicity anchor are unchanged; the 32 added W1 entries started at zero, exactly preserving IB before training.","Gate A directly compares UN and IB EVM fees on identical callback inputs and also compares both float ports.","Each population uses one fresh shared replica seed block, including its T comparator. Fixed monitor seeds are separate from training and the once-only holdout.","Real confirmation seeds are used once per arm; comparisons pair independent strategy-versus-Vanilla markets with identical configs.","Retail and arb deltas are edge deltas, not volume deltas. Decomposition follows the cited harness and was checked in no-retail-fill real markets.","Monitor points show elapsed search minutes, mean gain over T, and normalized CMA sigma recorded after tell. Sampling sigma is capped before each ask.","This result does not establish a leaderboard rank or a top-10 finish.","The measured real gain over HA is below the +3.1 target stated in the brief; the top-10 goal is not demonstrated."]}