# 模型部署

Source: [https://docs.qualcomm.com/doc/80-70015-15BY/topic/snpe-run-model.html](https://docs.qualcomm.com/doc/80-70015-15BY/topic/snpe-run-model.html)

模型 DLC（量化或非量化）可以通过支持 SNPE 的应用程序（使用 SNPE C/C++ API 编写的应用程序）进行部署。SNPE 提供相关 API 来加载 DLC、选择 runtime，从而执行模型以及执行推理等。

SNPE 提供预编译的 [snpe-net-run](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/tools.html#snpe-net-run) 工具（使用 C API 编写的应用程序），该工具可以加载任意模型 DLC 并根据提供的输入执行加载的模型 DLC。

- **模型文件** – 由 SNPE 转换器工具或 `snpe-dlc-graph-prepare` 工具（如果在 HTP 上运行）生成的 DLC 模型文件。
- **输入列表** - 文本文件，与量化时使用的 input\_list.txt 文件类似，但此列表中的输入原始文件将用于推理。在本示例中，为简单起见，将量化时使用的同一输入列表重用于推理。
- **Runtime** - 用户必须选择具体的 runtime 才能在目标设备上执行模型。可用的 runtime 选项包括 CPU、GPU 和 DSP (HTP)。

Note: 有关详细信息，请参阅 `snpe-net-run --help`。

## 在 x86 主机上运行 SNPE DLC

转换后的 SNPE DLC 可以使用将 DLC 和 input\_list 作为参数的 `snpe-net-run` 工具运行。以下命令将输出文件生成为 /output\_x86/。在 x86 主机上运行 SNPE DLC 只是为了调试目的。

Note: SNPE 中的默认 runtime 是 CPU。使用 `snpe-net-run`执行模型时，无需指定 CPU 运行时。

    ${SNPE_ROOT}/bin/x86_64-linux-clang/snpe-net-run --container ~/models/inception_v3.dlc --input_list ~/models/input_list.txt --output_dir ~/models/output_x86Copy to clipboard

以上命令会加载模型 DLC 并在 x86 CPU 上执行该模型。模型执行的输出将写入 output\_x86 目录。

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)

## 准备 SNPE 模型以在目标设备上运行

若要在目标上运行模型， `snpe-net-run`需要模型 DLC、SNPE runtime库和输入列表来运行推理以生成输出。

Note: 在目标设备上运行之前，要确保 SNPE SDK 二进制文件和库已推送到目标设备中。

使用来自 ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 和的工件 ${SNPE\_ROOT}/bin/aarch64-oe-linux-gcc11.2

对 Qualcomm Linux 开发套件使用正确的 DSP Hexagon 架构库。
- QCS9075：使用 v73 库
- QCS6490：使用 v68 库

| **文件** | **源位置** |
| --- | --- |
| snpe-net-run | ${SNPE\_ROOT}/bin/aarch64-oe-linux-gcc11.2 |
| libSNPE.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2/ |
| libSnpeHtpPrepare.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 |
| libSnpeHtpV68Stub.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 |
| libSnpeHtpV68Skel.so | ${SNPE\_ROOT}/lib/hexagon-v68/unsigned |
| Inception\_v3\_quantized\_with\_htp\_cache.dlc | ~/models |
| Inception\_v3.dlc | ~/models |
| input\_list.txt | ~/models |
| Inception V3 示例输入图像 | /tmp/RandomInputsForInceptionV3 |

需要在主机上执行以下 `scp` 命令，将 SNPE SDK 库和二进制文件复制到设备。
Note: `<dsp-arch>` QCS9075 替换为 73，QCS6490 替换为 68

    scp ${SNPE_ROOT}/bin/aarch64-oe-linux-gcc11.2/snpe-net-run root@[ip- addr]:/opt/
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSNPE.so root@[ip-addr]:/opt/ 
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSnpeHtpV<dsp-arch>Stub.so root@[ip- addr]:/opt/
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSnpeHtpPrepare.so root@[ip- addr]:/opt/
    scp ${SNPE_ROOT}/lib/hexagon-v<dsp-arch>/unsigned/libSnpeHtpV<dsp-arch>Skel.so root@[ip- addr]:/opt/
    
    scp ~/models/inception_v3.dlc root@[ip- addr]:/opt/
    scp ~/models/inception_v3_quantized_with_htp_cache.dlc root@[ip- addr]:/opt/ 
    scp ~/models/input_list.txt root@[ip- addr]:/opt/
    scp /tmp/RandomInputsForInceptionV3 root@[ip- addr]:/tmp/ 
    
    ssh root@[ip-addr]
    
    cd /optCopy to clipboard

上述步骤为设备执行模型做好了准备。以下部分详细介绍如何在可用的 runtime 中运行模型。

## 在 Arm CPU 上运行 SNPE 模型

首先设置环境变量然后再执行模型，以确保在运行模型时 SNPE 二进制文件和库可以访问。

Note: SNPE 中的默认 runtime 是 CPU。使用 `snpe-net-run`执行模型时，无需指定 CPU 运行时。

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH 
    export PATH=/opt/:$PATH 
    
    snpe-net-run --container inception_v3.dlc --input_list input_list.txt --output_dir output_cpu Copy to clipboard

![](data:image/png;base64,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)

## 在 GPU 上运行 SNPE 模型

要在 GPU 运行时上运行模型，请使用 inception\_v3.dlc 并保存 output\_gpu 中的输出。

Note: 若要在 GPU 上运行，请通过 `--use_gpu` 命令行参数指定运行时。

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH
    export PATH=/opt:$PATH
    
    snpe-net-run --container inception_v3.dlc --input_list input_list.txt --output_dir output_gpu --use_gpuCopy to clipboard

## 在 HTP 上运行 SNPE 模型

要在 HTP 后端运行模型，请使用 inception\_v3\_quantized\_with\_htp\_cache.dlc 模型 DLC 并将输出保存在 output\_htp。

Note: 若要在 HTP 上运行，请通过 `--use_dsp` 命令行参数指定运行时。

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH 
    export PATH=/opt:$PATH
    export ADSP_LIBRARY_PATH="/opt;/usr/lib/rfsa/adsp;/dsp" 
    
    snpe-net-run --container inception_v3_quantized_with_htp_cache.dlc --input_list input_list.txt --output_dir output_htp --use_dspCopy to clipboard

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)

## 对输出进行验证

对于馈送到 `snpe-net-run`（通过 input\_list 文件）的每个输入原始文件，都会生成一个输出文件夹，其中包含保存为原始文件的输出张量，其大小将与模型的输出层匹配，如下图所示。
Note: [Netron](https://netron.app/) 工具用于对模型进行可视化。

![](data:image/jpeg;base64,UklGRiqNAABXRUJQVlA4TB6NAAAvP0JQAI04bNtGkiDv3qv03/DMZFNCRP8ngCRCEjAhoySgIZgER1RRFCHoBOvCIozExMRo3CL+aBfPxfBp/dNJDhi1YUOv0xeYUdJ4f2i3bBSxVSfUVDZfAg5EUnBteGJH2YaLp4f1OsEPO9U6Zi9VjnlE3DnC0WTDyAVWibqpiVwXQKIo4IRRxZULYIDieSaM27YNxP2n9Bj9NxckBe7PEJIkSXkPQCG0P90gmGnbxvzRFUIg7NzZ/wlA34FlMGkBlgEMXT8ogB8gK7qrKzM1ATBXaYbuzqb1hLNGBJDTk4GIABBRZzKKiCDWEqIowssikA8pZIJKEIDji13CfIAUIhtCFQAikiEQAUREIAhh+qBJEE1CEBHUanHYtm0gSfL+U/9dv/8TRMQEAFhgxU8VQEGvmKb5memNTu3xiiSKoijb2jkEg1UMYGvf8urHezVuprxva6cHHrEvTzpqGzdVT6p7vm27xfMq/nLnl6Rn7nv4k+JRfgGrwH7DqhTvnfLXeOP/d21Zyrn39Qhv0sDEhDg06RABqZAB5pholYMy9X5i73P2OfuevWodi2+RRlfdTEbaWFRdD61uCjcLUhjzuc8eYaGF+7d7IqCWdREuVe3RHoWtRbtvmzcHrKlOYWcyykJrLay/Sbto+OeBMrWmI0ALfzy0cnH7+ZPDvABQ9qiuuu6J4aIhgWU9f2x0FHjvuSjrIP22sfE6AdRGxLACwNPCfiFgkwUBYE5pbxJAHWehXFyHbdtIEuObZ/uvN5dx2LaRI3F8sf9q479jAjgx///cjuTmzzsXjWNA4TpzyYXDdeaSzrWsZS1rWUsuueSSSy5rSe+45M61pDOXXHLJJZ25pPO1wTlc3FvdPTNSY3r3QctJWXKO1znn7AGaA1TPquk0aMzddePCSoVRLKfCC0w5jbK09YDKKqO5OMo6ExTZx0ILDoWeBhSJQUPhHOdYhdfpxQWdTUC7eR3p1pYO10ABNQ2Id3mc3YqDbuec41Wic86hwAMrXmcTRteijVK6K0+9zvY9oAIVSoNyuEA5jnzg4zxEuTYKr3LggEYr1soFd7VDhwUFOheqHS9Ro3y35VAOTedYTqvSu7mAs62cuq1wnU3ndvgrOXuM6xwKINqtKTkHBfauXpgq56h8HHocytkmUI7jgoclu+1WDsfAdc5299lyQIeLGqGHEMqxqVTOjen2rtdXWQeDo5VAgA4DHbwO3F1lqRXrVfg7LeVsWKG1TZKk5Ymvvr/PezglQvxAsKcs9pLstGSnJTsjR7IzEoncYeTIli1btmxZ7CWTPWQIlo/i60jfkiRZkiTZFik7twP3XR/js/qz+1Pu3fpQ3c3gLa4WE8D3/f/nliM5T13e6b27cLW7MiVP+ZKn/HpvGMp70+GECjtTK/MmnHDCCSfs8MrfsOQpXyFDylOz7MPgnHNvTUvFLmZvaMemSht61rt/YKI9AqjMpZXdAeUJTKSoUUC1vGW00biSa0DuKjzrOeYG8q6Bjg4KDQ7Aq83VeOQeXFTnzOZ0dGQmk6uAC4oBH/kLbCTfWr//w7MAJ9LfIJxavyUvymW3ZJ5QFdeVqT4yY9YRUDZAoUrert89wDqu3waDDtdvQUMGhWVYghcvStVR/QHyuoG8D0s3eYD+J1oMqsNWZr9j5U1H8noww003VHV2LqbkvZduUpShvNThmgaqNJHcrPvJm5JX45eyUQkxBXQTAjERI0bCBacZSZv2ZHMhnA4FMNPBSY4s/4MFD4PvunRSOW4jCZLEyk0MAmvd/1945ppp1GRRliTJkqRo+sSOOG+O/P8XciNkqc6KCcAT//88y1rYt2gVQbhKIEQOIVIQgATCCSckJCR8QraUk5w+c+eh6ulzIXr7uFV/lLSTjlBB1Yggvy4IJ0fDv06IhV90JLBD9jdB/AtxgYkfi4+OWSWc6OeB6NTfyDiZlLoiJiUn7xwRLQAP/yosfZy0k3ZyZJCNiG/Oln0FEI6TcXJyHKzNrmH9uCDStmmG7l/tYcQEXA5TfRaemyeDxk4pi37bQ5R+8+HTnL/+NODj2xSE0rn6weS5Zt3tfH2/HMW96cSehGcrjz2Rx94NU5jzPubc+z3Wu18H93vbfN+8Xd4LJuo6n+c5z5PW97SYEJQGp17mePfe29f3tl/fFt/PJjrqfXlz9niP5aQ6hUKBP7OLHwB7v+cAU7AnuZXOdyvPx/9yP69gzq14v8474tvka/357/K9jorQ7Ofn6XkaTprg+fttTxT6w1AoB1CBQIgTR1faNXti6szipNOodMWWgTFgcHpApvGdwYDgWJ6OJyYHAs12Hjh+9PBOVAJxtAgyoGOgc1FgUBkjqyiis5wKOx0EIgGTaDnpzy+AbXCidlmo+lWfKj5AFZ/0egOo+k/ki4BqQYO0sx11DxzHlmMCqn4VBhpkrPrUGwBVP0PVB3o0nG8XL4DgCgQ0moyDMjIJ5ABoHK8n2mpXHM+HPd6GYbU0Nz1+dn4EdVw4Hpln2amE0uUnGbTAaHCb9IkT9J2wEDlhNuzS809IY9xWotPzglUaxipHWxbqibNHp41VMQvTO9FmkVNjffAR5TI22MJ4eL511xsMw8fZo3VCgnlxHO9mEN56nIaNnRLk5IKJ0B3muHT+7PHpO5k9yXS6+4CGoM0xR50PnNiTX2h31rrxW+VvgvKEe1wQzBuPeidDpHR8HD1iwqOD/MIYXqDJrW7wYRtCD/OUo8XLnpdRqq+1tu7ZaE8cMxbYeiALeTd25zG21TwxThg/9eeU2279sMX8o/JHx8KRlN4Nxun5NAAdaKyfOjipdV7GaQuJ38UV351c/Nzg50oB2soFSSsOg0SEhDM/xvwUtAgDPbOcGitQVTEoF+ODRiEW1o8nmDsrN33YIJSPEYRvVp6Oi1cQbvEhr/GN82aFCAUmqTnQNhZKcU26cYNjIt86ASfmjjgJ43nBlk8swbS4A8G+o2dpzA1CEaCYLF4E8nVf96l8/OHccrIbVGB1111O6m9UQKVIs4jpuoATQvIbmgBSoA0yXwJjOY6g5gEGqwIY65FxKH9/HRwbYEyRDc41JsoCViB0dBgDawEUqADUGx5wlJsSOjzgvJe+DgVKcwXC2etUMGmAQRQpNrVB/RLnXqLIcBQn1xecbO7ovE+qfgBcjA7OnxVZuOy5wtgDHmehKoWxNM+PtRx27gXnHEjRpu8n1UJoDBc74nghrbS1tp8FhLH7kGM4Gzc5qVheHEjRsddx4Il07AvGOoI/xgI2ColKkrlXHUx2hY7i4QkY2jm67zFPdOoAePQ1wwBoc05+Z2+qQofLhOYmVtS+beJSbZaAeDTIWSypvXPOufJibw/ZjZ2luoBJRBMASsX2CBobkzvisKVO5IfdC8rg1EuKyBHnnb70EaVAae6mjnc3rYWS5mYvzd0TLUkLlaCsOVGh915Eh6B1fwFDv+2hciPhnWH3YIBgbUxY1AtYiCtFmhSb9LnrKA/N4WQH2rRK/ZTckOPHUCgaanx4btAnOOmydCMsbqvzP+khmzfKWF5FFHCiqoJ2RbUvdW7LNoqQGgSescGs64YoVR8g7yt9r+LzyDg8FcavymfHYwEGOPl7H4IKStiKj0n3NfoA/e1igIHmemsdaQOtEhKICqCCIrCAsvQKRaTs6uJlz8WEnYUKG0C9UW/UG6R3a+vulr7fGDOmaWhOm1TpWChCX6t+8DxsDHsHiqg0yUCqLk+Bywu77LLr79YBYB2IltoV+BLz08nkK9ONzeodTPaInNyU+SBMiALTfWAKgo0qUQVSc8oGpmNaSqYsXxj0pJh9j13Evo7egkj0KdtrCi9d18WGJfIGcl5MEORk0+yi6M3QU/uQ50QhAUTopTtobAKie0cJ2iKYGZh3m3BlF6AEo6Tm2Gw5VuYmM0A2R5Wz/kBETGi+gVSdKJmiCdwsEBAwOV3qQzZLZzSKmFcgsYFgPdEmvQTNbkMQmlGBGKekMKW8ptgCaKOgCk+MKrkp23lFcl3AoAJDEH/HFW+SchWSnMu4wNw3aG9y8Ww2RRaBgKPOiYoImFlzDIRJT/g+mVCAaRXZSMPJJqXUp7kVgkBlUTjnHFFE9+yowKVGF4jt+sJcKWpRtIubUBLAwsVRGEDx9wLl6HGkqyRKyjBM9imZEkktkBObOvGPggSNr4VuITyyQgT+NfQwSuwSSMQc2NulOYgQJh90nfuN9aVM/cJcT61WK+yZYqLKQ9BUwOlxtrcK7KKj7QEkCvxdSx8tpdjcLkEyhu5B03mArJbDEhudRxGF3ZI0S5i85HJJSwIRTG8s/DxzB2Lqc6Do7FwCwlgZ535l0ERw0s4lPRpWPdhULtngnAdnEhbU2+32sQ80nM4KWaHaK56gojJJNULHkGsVSU/tT1/TGSWaoFKkOVMUaZxYLLxihaiMEYHKCpxjjqG1BbZSdbCobXYomWmQ66WHUcXqx7lHdSwcCxs2mCpAAoKECAF/361bPBTjKiPFhahdF7wVuy1A+vhRlYcvjSkNakuevggHEgm2/uQrYGLlbxsn6gIEyhaGtiQV/G0X4tKP228DgfTOzs7OknkTs8syIfpwc/dVXJIvaFhgDh1lyKgcPIpE0fiS708GSR4vQYK4ubsYU9liSgxCphjIisEIdcSgPDCa6KwBEpyuMGuOIZOdaCY2Woyo5CdUHhhDjq+1izERGVYzlsZS/sGJJYDO/c7M69oj2kgmUsnGxJMvC0yp8DDxTHUdiYJA8HAGEtAaxLwwqebqjQGVmCSyVNQERg+Y0TChZqfBB/B9hOOjlyCqRZozoQHCmhZzaMuoRpkwZ0JkRvoIM984TgmXzIsOohAsfD/cb7T3ITM4ukPWhV3JZ40lWVNWZEAqpJJmPXL26e/5GYGgEtTDEVDgnk0+mNKsLhdqLA3dRq2ui8mjMzsPukX5Ttmxcv4SIFu1RUNIoIoQ9gbR/OqJkJ86LnqssdkA/EVBlKK5BLFBT6VmBT2ZIXRu8qIy8TJmS4DGDlko1pWRszrudrJEnjUWFJGkAcy/DQmMwEp0SrPpTGHfR9l4UPiQALlz/5pxEMMA6DfvVvRsj3C20ECCgBQ4lvRYq26pdgpu2Z99+oX7YRLcQYmpqUlf4ge5NDc1zWykZIZEx0e549q3p7Y0JlXH2o7I2h1SGgPETkpGQbWhDIACAn7eNoAs9DhSYoflWZMGMXnLsso9KiKSWCCcIj5LL4EX49jNM6QiKMOQElo27JkBgVyLZGA0f1VkiE2bFmla86zu/z0BbTOjhyHRl1EOO0XXfyxD0VE9d2r1GDj58AkAHY2hh95Dmcp6yejDgOLlrUAFIycQwmMqJVfc2Vt7svIuvpedSdthSAle8LuJ+TwRZtY0oboABQNMpeQXeuHUvd5e9b759TRI0C14Mzr42SwNyDzO7dfu21HdeK+LZ5vqv1ovLxoDyQ7uNZXRQf/R8aMRW9oZPF1AwSCLffJdrt4gZx9tjwYke5Aim+VYWHXLjyUEvd00owPWrLKVADGom3ujHY08auMegZSr00VDyh1/9F3A1lJkgwbvJXc/NxIyQ+BlyKRUIGcbMhNeOEZZLQOkzlttqM0r5BYlryEVQPJxSk4JWP3Iqrp/ZQ/x3QSEgWS9ZEXjjvKRO84ZTRgDC6tREmFiS5ZygsTjv3GkduNA9S7DKpMqh6SF2CihImJ48sq3vP0IWLUDEE72RNdVM7v5DTEiOoraGSRACFs+aqPX0CGNdeHYxBqpQD3JhxAQPKtxV24QwKa3Xq8nspJY1ekqGIDHv3mHnLoQfrFRNR2GVGq3kRWSuuN3igzqZTexn1oVecSFhPxZnZ8SZgD5+uvjva93OruMDA+uutOItAXIOqmMXQbDBp/XRbuQB2scAw83FjrI9dYL9AqmtGbNTmlkgPToOHORSkJBKqliO9Y21uFBMhZWUk19s1SwCQSPMoUrEL0l24OPrwO+TjttorU/W40OouD7ILLCHDSXQM2ifQiiErupeL/tCZq6E1GYUh6icNT3kyLCYD3Q0gTzmRoWQmDX7z8YshYIEY7B8DXJgYNCba6/uLLasbphYbmAKUc2PdBYY5dl+gP7zUfYIpsDauhkSllRonvDEwVRYj5sTUSB6UFoJpsDxsPcibB2ajVQYJcVMxSOeQzrSYbwKLEGjT4o1I2Bjutcbi288//kwgzGZnKkHvB933+ARWJSk6onRZ/rfp53ZnqZCHteQt16alJxka0t2TgKmH7/INMF3nAraOnikjQRKCkYuT4NOkEg319Um9KDU03ChNXE/jpkc5BIROE/gTUpwFidmHVr8yIvkuZmX6+Pk9veGvgXDqfehKwXvmLfGZArh1e+Y/WqL+c/S1DBs20yXuX7kGup/4a+IajpBPb2gwia5rfNp7LPVQAUO/Jy7VSJkj0IfxM0BA7EpMJOvRt03SS1wdehBFH5bo/fOHWtpGMfMNZYspwRRFmXIAS5iyBL/AziF6fNAaIshYdTnrAb2f+SULJ0Igpb8e/z0wtElMRR0/3HIpHVHMGiCywsLNaRYuR6jqPUL7AnLbh7aQBBexG09/9O3CnJS33pHJFkhJtFAoEPH7obvBQRRq3MTrijIeDC/u8lN28R7YjdRRRdYg704AicQ+QFgXqEoGHuhPtudPerYbGZ2sSzLedtuomvd9YACXy/dH54qSwSpofEx8WHLFquYwyEHrpaki11k20HXEfwkDryFeBms/XtJZcP6DoQBLjW1eRZyeTtl6yP6HVOIEpQZypLjm78kvnxsrSzNKx6B/4mgqh5z0yBg0YzWRcgAQn8p9j+DyXzCSLMTGSRvu4qvoaxnx5U5SRZ6z36y7a9tmBFK5H436nnvvj2drW79OBbfo8w9csLb4it4GEsulL6QxWMJUvtpgQ6zcsX1aO72ffi6LkegQHiIukFvejfHJ1P1Ql71Z/7Nn/u2/y5b4Orlxi5oEEQsj6+QCTAzVKK9Oz3k9vqU2UQIabI56+8EGjGr293WxCs++BCDsh1tpdcBlGoARYJs4sFc8fAw3JvNYlzmUvWZ+l5Tn4QsgjUkovHg4Ugt8clowfQzkAaUHLd//Eb/h+/4f/xG+K7WeEpzRvnxBPxgXX4/kP4INgpf3/X9dbMjwt74nHsetYjIdH1Iw/6ndePsPWUSjoJ2vF7hAmzFt11ADEvEAeV+gen+jzp/T8Sq2+wc45Uhf0BXd+b7Huhu9Rmg8C0L8iWYhexu6ibzHP1HEqqx6X+xt2r7u98LHXK+pH1IxCEZtCbEeWC/wAC0ZkqxaBzH+brVaHkuOAur1xub7XQHBCGD1EyLaVuv50Tc2iaEC7doPtkhJxybUM9tz7s05hnfT4YyVKCgNqjKMGfW33QaOIM2JTW64YtxVN75X1tl8Q1E9dMXDMOfMh62t2cr0MN9x/6AJ6HTubMUwE0LFb/Jrs4bvSkO8+n9xrOdsfNOeCe6FGK0RwNH04FVu8iuBdn39NiLPUGy9jTnJ7N6WQhlUoB9bST0u/d22Usz8vE8mQt2TdRTzuKGnzd17k3OEB9O1WXJDz+SPps1ta3a6IgDCrHqYeISxAEDzlEDE4TVStJlaUj30T6QVMdUMm0XrM1Si8XBcIlEFlI0ecd44LxkQHiJlDPnmrQHkQgMEDi/kU++/IuJalCEZvKNjYDjXqDhZSknsW2K9nY5PpM4c7mUp4HFGz2tE6j3ugfXTHAGOl2oYhtgwoIzYF4rwZgG2CHKEKhUfev2fi7lRvZbKo0RCruTykC3cLG5lIcTmY+IwpF28AGCnYoSUWkSEyiZE8W29gToFC0Q8X6NzH7TtJ7zvoq9NZuBljZTRBKMc1238bCMxpFijC7jBaqSlKwgXq30tZ1/v2DXOL/SY3h59H2kiYeMIQfbPxiUxt156/f9nRLFm9I/JiAhzlQbCSVwsvS/BxXB0g6gnmnKig2i81Y5kKxjhTrcifuBGA+2eaA+maCJr8ESlI85r+yQy4eOn4UxrCxY+Ax3UBjd5BeSIWcBAPErnSATskULBFIZ3TscYMOGUMqb0+KXKg+lskl6Q2JvVmZoyI7q/LIV5Aj683eIxhHbM+oQkkA2/8Y4dxQxj6xZBky9p3e5zD2mIgtcaGex9ydvM9mb4rNIlAeu1qqZESFqwLupMrVCZGLFwD9tifa6ftr79Hvf57A3n0wid1ox5J4hpI2e37c/3/IblQRMyri/HC2kYI+e1TshwtEIjYUVeH0AlXnKdbJLFTwMDFf9XBKCbKC5QUVkRGxx4EmPSWVrJOe8opU6aNp2AZ1Y4IRgc2Rr4Bs7bjI7rEDIxhUaRt2xLQNLtVY0rCXjYzsEdlNzdF7kxR7BB87wN5cMImoJOAZNpfGiezHHK+fL/xVjw/IpMhi787eI/aE2M2qX83kV0GFS7mucYW8/uVIiWlh9NueKPj4T+sxC299I5s9R2koqTbXM/XaBANigt0BbEAkTIDF19mv0aPomWpzIQwDMEb2IlUebaYb7PqS8hX2wKTIvcoimwvYEdM4z7C3pOm7KRUY7/GN3Ki/TiroAjoWSy8TKRYAvCIqHLpUzGLMQ3AuPNvmc8th8MydAbH3i4DERE4iUkkv07cK9qrznOY3Yp8pGFK5/TYOiKzagqCSH2qiY4PFKl5VpleVWeQ8w2k4e0ZRbIgi7n9U7FjGXtdZm5mbKShQm9XU/0ylV5tSWspFhFPCkeHZpOrPsAC5furHun3H7LpGNjZWeDrNGipzqFApZGpDCggMlnlpaNpa8nShuLhh7eYdyk7LNBvjN0kJFaBAJYvIyILphMNpOCMI9KFMKBesNpUklFQmyyr43aYES2DWnjCZNAeIG4iEldHV29UiMBvl4v7stXv9pJjhkOntu+Ka4Gz2QgGoZHKO0rDjaZ5CJJsw8ezC7Efc/+xbQ62LhoZ1ZpPJiTTMApZUJdwrlDScZpLiCiB6/yLCSBpMnQg7+ovG2Wsb9oqsfyNycv8AqFDIciqa1p2arGAbgEtcOGBuWXg1ygUUvXs17nfvT06WPmBpmwTSXUIyRBNAzF5/vbVjg8azJsXZYvJG5Bvhyef0RVNi3tJ0S8GTHpSAv5LREane7kSxY6qiUR2bfM+ZKx7pNVNFEPbECwPhAvTbLRRIztuJ/faM45XRqCaB97ICpMJR8o8G1gLOykTpBbsiYoc9XMiBVzir8T7nxuFgIn9CbNFVU2BfjSm7BcwCvSqrJI1h2kSocsNR0/2HcGIxUVldp6ZOhJ3peLh4dXo0nH5i9+a0XrbnmWYTuA52EbeETwlKOQyNHWuUJ6+uNvuLjuPli4jZlB9UClTItii+YdN3nqrQFjoowH/nmMuWrm648I57DawxVaM9DjK1p598JlP2nAaYeEACYJLoHwmI8cIeIDrOGg9yS+bRQj7iYoEEqwr2XHjCdTYmJCjF0xgaP6o8MjDyjKY2GlPHBf2FfBVjwLsEF67zSAmAU7JkIUKGWoFJ2Zhm4/C97P93Wi7GbfoLsc7FZOedbL8nVbWkKmohdEwEIEoCfXrwH3nUzaoHiAbGiY0yH97oyUt/vLa3thcj84QwSvIjJinwAEqAlpIp8E6eAG+fUN2Ojp+0uqOuS496+Xhtr+N9vHZKsizgAQX+ThcASaOeReB59ngYFDl89pb9TjbkC3oRIek53o/30h/vpT/eq3ptr+21vbYrnHm4HjETcKGAPXdQoDrk43c0NtKARKSsVMk2wfKaftTL1H3d132dyTlghAwDJ7rhJthhL0AOAgO5X37t6nsl8WynORWv/YZtr+21vbbX9trtySREgN4NhmGw+hE7hKYGiazbJr+JxWx6mUdNP2r6Ub7eVIWVJVsEDEQBoomEjvecPF0IByLj2Meb6yOGIqye8b7KIvaWASl9Tgq8cGH1ddJmpA4i4YA2081i04dqMRNgmpgQIQ4gG64ziYyBAHu9rFuKCiO9In2N4WEWhQB4KJFAcjOPdR1Ufr1UnhGWzC5yqRqYmCbU+z5QCK9KlwmFsT5AVhzfID1so0jmKlljKgNozIdMZvP64gU7ROuiotKcHkSLXKoGYJgmcR8SRNf5j9JtiB1jT+RgAExUy0A9U79PHqaJ52EeowyydFHgmHuEnyBUVDIm7Wcq6bMJHpixl7gMgrbOB5WfIJy6/1HGZEM++R4dOwb4vRoD2yiYZpkKbh+L+JZOVjxTIBlCKqnCL9G4VEwoFM0VJu06Co5Z4YRXbI5lZEiu36Jw1sts2Ss4L+6XqquDOEAsNklnEsevMsgY1GfrjKmwwrxUwDBjxwA6QKLTf9A9/bU/58C0KINF8opos4hofbc2G9r0x3RwTGnUGSxOz67o6sV0VFLCt+3v0/I08TVfpAk6S7UIzSJNijSLpA+M+TuKHPX9H2P5zK8ziVRVGJIts1oVhmSLqDCUZyg/lAf18ww1dhR5AlzJZ/lwnxOGRPOoMCAKjKUNyZYbh5tjQ4IiHDPzBDEC92D05wzSXya1SUMFFaYHaDZU0lSmYVBBpv16I/jMvoe+3Vke+aNPNmEMvzog0wyIQp7iQFqjyoBMwwCNgWm/uI0HOeeu4RzaeNAewqsb1Bv1BlWfzFWfrPUGVH3Sty0Yz/7ZrNm0q97N6BhQbwD1xkCjyiJ1otq6Sbsup0WgG99JlnqDqg9Ufao+VXyq+NQbQJXMCyjCHOfCCo35ej5A1Se96pNe9clY9akatgE8WiiQrW9PZTOPvhNpkF5vUPUBqn69AVD1oeqT+fIi6KsA30K8E0vWql/1q37Vp+qTXsWHegOo+tBNGX65xwJtz4e4CemrnYAqPlQnydr5fchY9YEncy9qYce5XMXBlzh21q/6sL6KT/qyyXojLbV5IDDHgXqDq5YxqPkRgOJnhgaZffBZNln1ocrk6k7LJjKuBYY4WTq8AElSBZ/rUaHeqDfWQ70B/Jik1xsYzfVQVPC5NeUMiqT/cmRWWe1X8WHZE6n7YwApMZa58UgVQUWLpNcbVZ+MjqHeoJerXwLgpHQqopvk6HbUvni08fCfe/wkmWWn+7jwbC2GKq1jCHUBYVA+6EScuFpyXXaeNnC+mwLGR7sa3sPheTl23PSAbaHktrQYAjcuS/yAuXHw6FhIjlXXzI88LR1Mdu3S6U/oowKy5d3ErDxgXrQ0CKbD1tVBVzzYPw5UDgcxreOpoddOVBMMw/DZL8SDu5KLfJVcvjVUnY3LjyzL0LVX3HrLTYfnhbYkIT44Hhqr2zU1Bn714M406aBUEAdd6sSn0dbAWL0hWubM08IgaZWbw63b7k+rPjxfG9SrRcGcDq0H1HrSsdbR8TnnRRCrVGWbG8pxaHX28LYgvMV/5FAiW57qVhOnOQ9LVFPn7SZJVo6TOzB4UW4ByUICFy1rD66WEk1wW6EEc/UhSVx7Ejre32m2C7c7ca/RH5zVkv39LfxSHtWH7Ts6ukdXHLzxYxybXjM/eqg90j0hPdi4cb7niRxPafQqwVj1mX7u9He5wpf4zk+sQVjKhWGwEAjC1/Lvmi23cjeV420h6ReSniRbuyF3oPrU8gSxW+oPbvlJpeW2Hqpu3BVMz0v9qS/i7db38uzvyJeCjrlqZxJ8x7NnsrG/RFjS/LbphwVJDxKEpf6D82PWDdwAoRxRcoK2F6Et/OGVGm52J5KWaigQ8j5v5ODwjW6ItP7ARyWl5Keufb63ZLwMLF1C0nHeCXLjK5Nw88FHlWcRUfd43JYbExAmrRyBarmUlEZ/TBgfFTA2uKgm90yn43ukyWvXw5ICd64qTygUP2yrFocTCf6g8mpdnoD0G2wrrt3Gbfu+7aj+pIo2qsFtwxc0Ym3e6l4g519ebpUTGZqeGx2+EagUKIBb7rY/ieSW77g2+YSq0vqgZ5bjAA1yp93WqAf7brvmFtAOAXI46DRRUHJPq8G3oY97ZiAP0Uk0CcqlQIgfEXzYBSTBnaxSAWhC5KWE1hDlNzlGg3++2U1ZmQ2LudbkjmZj1Q4u2I1cHUupFocscb334SeJBkhIK0g0EJLpgVaoCXGQCMNNQTUO3Qgdf9XdcwTY5/9J8jG6IrggnH4UF2jDDSalJQwndZCAUG/9cfbddvE6Jls1g+3s4bXjV5XN7nOmiRO51p2sStDdPPP8WNwgJNaEZlAM5oZj0CE4GdKSROPdelfxmRqCG4QEQRi4FCcZrofEykKgoXh4kEuTnkXKYtwshpPF0mSjLiGNZqzFZhKEDMd3FZvorVenFd/NcFrquB5K8ajGsi3JB/G8qCo3PTjkOEIVV2Nx+RN17gWxkqTE0EOyBOUoZsMygEdWYwHMLqBDADYZCziHI9V/Z1QwFu3mg3ZFk2JzWJpkdDybAB5uLECqcJcUU+pUfjWKrVFSGIs0Ccvcudo2hYz/u0fGCLj4StPsUmE7QvbEFWC7JbPBAo4B1oKxZC4ocrpOTpkv824esjYBnHpucLpQfDgqZJWoQAv49CeXDKSrYCyAicganQegAnQc4C9J+bI9hOU4TkLiNAkkFJz9zAEokLF9DjDnHUDvxf5XW8d6dPzYqJakvEEDJHAJJHTuccrDR1LrSTjO0j1Ayr1b+elYC6CwnogS6AKSJkEgEswL3TcZttem+PQAe0jvtIDd3szgReCWW3EOAhAkILRYjI28iMxK3rctIPTwviiXbE4WZPHawZc8KVPdpmF8F4GEu+jYj1PHWany6QH2LN2zdA/GAnng5AcEguMoh8SIHHRCJ2U3KYn43JH36d8yIrtKm89C1vUj/uO8bpLkyNZD/XEgpXJLhSTRdeuNJbNzzp42OAJ46tdCnMupw/Ar1hp/px4JX1uCNyM87cZ8zlrmvEvWm6M1wBkgk69nIsAJmNj8ovcYWkomwji3wF5NeV10sXOSlvmk0rkXFZgFY0mBhOUjW8+qBS2JQYIwccFYIm8OWSSA2lwH5LKqkeAes8GRK64ZCDS4JfYlJ5GzjyN7Tn7vnHPSMtuUCwRAhGrIxKH9m6s/6QW3ys7HPXO35FScZTplDpNfGT5wNrYdBsmyRPQVzy8fmdm4vBXc9fQrxlRDzQkpImMzzXkfsutnOdbKarOOT/z6/fGh4NKkmw+74+zOfmg9VJO5Ict2rf4SSzPsYQ1nnMHJ54trFItnbBO2hQcVnXAXEM4+M2zV1nUILO0FvCWYNIgANzlrfPnGEu5sczhovhOrZYnA7FlL9uwmoC5ABLKypWYySnGAzTn/oT91qclD3qARJtETiE72fmVSRFmmLDQFFW8PD3lmzT+rtkIvr13DT5pckFz05eUJ0Yt9Cee46qq1m9ZmUTkMKHtYCowsAXm+kwn3q2f86e8rpxOhxi4yfEGyfdkfgemQCzPQhpG0/42ZfrGYEcZH2SrfwuiKViAalAPVh14Cxm4i44gjbTLeWAQVmD6Tq0ePCcSd0FqcqPKSTg+nG9vO1rme/OkxFiAixRyHVY+7q8f2sL+sE6LIXbfiupGHIym+BFdlyeZQUiqVzTrZXEhJfZ5xAge6Im4JQULcfIRcj+O9DuDiTKmf7MGjyEDXXfAASX6SY/gmLBOuSrCTsJhj01IuvPAqLmRTJudgyjHGFsuJIywpfVv78SUnFJsEIaCiAqiwCIMe+sFgdfzxh1FBq+IEWVXoeysk5VicZp85NjqwAkgQAEXoY52MKlDhxdkdlsmuwqLMgWRjgMhcqRkoPlpDSLoKKixiFYJwzGknmfVtBC0DaLMBoIIKi9KiAuxZQnKVjrsiSlHqKgIqClKs903jJIcpduqVlCaSEKa7dJtMD+SHJJBmAyWfpQgMAHEC9k/sNE90Gb9mgDEdHBuYbuKPoYPsk8HpbH5+qIpq4FoTAcls6CqT2kCZVIqiokivUEg1RqwlCEGuxrNi9Y67dteLfhVAFNAwEAEVzSJorDXXbKLTTyb1FW6crBYRhWZRlGYDghCo98L/hLovCEOWnL4XL3rrTg2LoJMKIIqUSvuLWarNtAEa7lvMwYswu1RaMVR9DbcMhQrNIvWn3/bFyNokc801hoe/V+9uu1/+kfG+bQH74n2yY3AsHizmdeKYIJ9tywkcbErRvdp6eICrE6gAKqACoqW4Jr2CBm45QPjClHpW6M1TJ0A8VBVfyQMqxcEqE3PjwUUGyGeJRd0kNPYG+29fGI7etaPu54eEKioMVRkkuOjZY8hQPps2haSMLjl5DkCFYrPYJHOxCaUFbGQRmigyFqYcYRH+d49Isr0TTxZ0xKawH9WLKMMfCQyMQa0GoDP7hV6/HtDwsJUKRB59n2jtp88DRLYJxZd5h5omgKE8WVVgpvUHFpv01dhll1xFSiqSvAHAUB5gYAxAv/AfTJ9rrI5YcnpeAbmOJo7vi9kOL9/2AXvu3Cf7fxA0BPLpyclYJKkkkYJjCoWX5+XPO8+73fM8CmRzOoAeRE3nMNkpmVoJIOV4zE9/YWQ0Nb+Oh7QQRtB136+9aoFUsra22wqCNypqE++lZD7Rblrg4ODgYJQeHKgiqkqz2OA3EVxAID17n/p2aoAIwtRSMkU3ItH2F04WVeWy6S2Hajz6vU9/Z3CBKeVXlbI5DTSHHylMkDdkvxkt7rT0nZgLAUgcHFGOo8civrU0HTQlaOvu3YCYaKwI2zHJfe/WwSVPT9uH3kA295w2BTaEdNfL2WptwLsvp0DNFBOWFq7juusKjrC+0/BTtVNu8zm+DoLYRi8l2xdyaxi7ASIIUPh4nPPyOIc35+uHxlBI0QQpmP2IAEGjDy6KCOWRgT0qryK270MS5clssG5FlaaE+t6uDdBzAg8A2GPCGoCrQAv3oT0rcHsvHpACbBEj3Og5BZRr4Yc5AtY+CETlHNlWkTgg7XJRgojmLKwg6uOSHAUTd8M6Nvz0KNmEhzrPyQLrpmyLG9/hOW0ChHdGt9p9w4qAMWHhw7ZppwAxr8DZLxcwsl1MKRct1TRu8jU4AhfPOUjXbAf7zpKWPpTUbIPaUVGId+pAtgtqeE7uQ7kxO2byhIBmH1T6r3Gp3VlJsKAPlMy1W10k5z7ZHqgVCeCcsOkENYMPR0E0uoHuMzdALEyMILipB6dy0K4lUKiaNL41CussLOKtxDHNgNBO1O8DkAUwBAPxHwaCVKwBzqlc5zjw5iBMoHPza11ZouRBqeXM8POMELFhxBGJg6HMUBKyfYgpbdRT5wRRKnfnUBpZKJmKhxtOVwpaIHVKT209QCDf57pkSxCxwIWWvh579V42xIDabH0f9Y+iyu4uoCUWDsdM2J+WvqCxSRQS8dTyRdQ9Ehud0WiCyuqh6ANOP7yx+Q5kb1uK6c01pe4pgOBDxQQQ1pO5wlfey7I/EcxQODRWHQMj6GaKHhnhuTQszfaVSQnmWYSSIVM9zlmBOJh4CnhwHaLRTmFAv/mF7HmceCJAbFzwVb2uy4HXpX5hYfKE2Bi1ogB6zRqCgPPNmODYM8fAP2J6YdNVgXSrs6Gl1gmiJLAsNCGU+Z0SC6tnPyh4HIiFyeY26O8FgXaOarA1Go1GE5UOAPOY8N0e73UtlMTCtWrbTYIu5i/6TjxrAeuXZog8/gI2PGVoIsQUE2on1k6q/SFV0iYWwM6lgaVrcARInQAjk2wbX6vG1Up17oXjK6rA5oBD8ErUj6ID+D19KEdOrDyw8uTL+heezY4YYxvlg0/oX4NjFJiqXgNZWpzkO18B4xq/7erXdSyZjxULm0BfLaKA1e/pqaGlB3UjZY6t/HEX/rgPtoJ3vjkWbtk/hRZnmt1g+ShWXB31wM/g3BXvHDVefUFlEba3EjQQpiHPG3/ga0UaUiLzmjWMGJjk9xpeWRFRMl+3WyteRbD54QcHTssU0j1gz81zCmz7V0a2mLHwlroxC7sugH3e0OA3/pCXhgTowLoSBAYPPFR834GaFQxdkMNbQ0akYvSgoYM3W59j+rkg5JL/7c/zTLdMqsnbcj0egcEHeGtlgxN74s33qQWJBIgCTiz2VeyhrMgbeEWl5G6VzUMCHADrbC0N+tmDZSHsypfwF99IovIMXmhUBMhbHzZa3jEEQgPIviTHRKK8dA1g6DiuxDFD/8oPOSMZcjFi+UL43L9tXM2gzoecigd8vA43JefwLBbyXBEzDFF5j40nR5Bp+2kdc5pe75LMLpCx0cqjzlgRUENLUTJ2jGip6L3zkE7756avaiWiQGcN9f8F5z7c1fPnLl2RF6OGfTSTGntUrvXt5LSbslPnPgkSWNyYBeExjTdnzIjJClmukEulceHDnRsOGoC2X+esRdwC3yc2GsYqh5wriTFjdj6cXjYGYn0rN70Xyx59AN01mR/f70IJPnrW60NKDIxfA3Y9a0A1b0ROT5vFGfaCWJmYNOjALVKzeoAjazatZQPEfjG/uoFjAjXJFbZExWLs2nR0fbKrFXimT3tdsdGM7Ws0SDKyA7fritT20QHi7d8Pxw5WjaNXS+zGmBFrTEfeM5iecKid4A1dc34nLqyILZV33VhUqxVZaagYLj1rePD/1TiK4wCI8mA27Ht0Y75DJsNFWc+Buw6Gde6zUl9Ow59/W8GmgBqElCP5C6zoBVVBKMYyrrACfVQ6WETORBIKLH8mlQhU0DAQAou1PlSir5gZS4YxARIDKQPg7P0TIu86dEBdfFDH7Xfc0rhzY0EhzfDGDNNe+2JQBFEDxNr1q9Quo2PpMw+bSTJhRd3TiUQNcM45zd8PDxXeUid/9UzHXRqnrjJoNIvU9wby1qvhnsDWfvMBkMYtznrocTAxWKjucrpabBrEhafHrudT2Z/XF4Nkec2OvcYG3kOPhQZiyQGDEZHklNcsbC7QGAl1YAMJ4qz0TDP823GtudULR4NCKgmbECYUYwuEkMjRsrCiI6pU1UFVcjOqn/RSkN2Iklw2BCKKaKgaqpApjNa4DEpiWYGCSGPGj4zs2XPOOeA4ZBwbt8Ru/AVveXqEqdVXqRghGbaWU1sacYdjoGpWH/+ydo168qAJECBjgs0ZkMs6JBJYKwUCsoRiKsQvKDs+r8biVJ55QyFMSqe+DkQToMCMKVk9cfQpNwZE7Ii1Y0GiANuoB76v12Bh7tGSTD9K9Y7DG+VZV3fqIAlZmH9tHYhCNDFlJx8zpKjea+07yFx8UfWNJkYapSk8fYIpmfqS+ecKA+IAMSvS7fskAYgLWEQkVVXQrBLplIEMS24jBOGraX03YXAuuyKM8BpHOoNW5QQ6Q63s1wEbGKTIRrnw9naZuMjxIq+lgfvBlPzOpMod4JN5WWuZe5vCG90L1ehx6e4EHkvgQEw4ublOdSx8go61ZaT6eHvzsTTB6VpAlChbIeb92j9uudrgPMQ4aJD/9C28hnAS1WdsmgbQKZDbAhK7oXrL3Ee5xVtXNsCmHL5O3dFD1U9ETEYMFSoXLysW6tgiWaE8ACvF2NjRpyGmDNIKkgsQSYIASZlWRgYlLTkDubYIs2k7NHerMdZSl4gArW9pRG2YJHpGqrVnzznnpIEBxlSpq76Wj7UCs784tgEpia+m4oi3dOla1lzPEFXn5pky4TTUNTZOhKdtr+DSHO8EstlsV8w25aOq6mIDW06/eUAIFVCSKUapLJKW5MQa9k2PgcLChpNZe6xqVCEkws0Ua3SdeBxioytDK0JcfO4tQMm5r6JDDPr3GlAyNWVbm6xa2CAcwHK8wa2RfJmq1hodslqNr1seuGsv0BdEaxYBhgm5z10kQ4KqVAVgpQkAE10WWFbgJLEQKJ1GQ1UEK0HDJCm8zslSRTgJRUGt7B4Zw0BkaXL2OGhsyD++J9OFt71TJ3C508n4arAhf76qvQpQDMVkWKU9yW2nAtEoOYFzhBs32g2MBxM8M0Ds2cebdmTTFDohmqByZreadEPBGAtqoxoT3N6uiNibQ4ELHBlgjmoJpD+kMABiNNp0bJwwGgEVO95Onzdy1BNJrc6l3QI+Vjp8kjE+OPaQBiJJ+6SPNXhtHyUIS+R9JGNz7HMEC9MwFjJGAFUgZEv8mAdbFnIdLBqK7q4jooqGApLNGh8JZ3bB4645Ua51zjmZuK4DuEPeV2D06AORG2V6znF2eKfgQcrCGrWdY0+m9RYg6f9SCjZJtc1UHuEPFaYUL6dT2c7YnDUwsA/MRE6fLRsCopD5Zx5k0KiXsup1QvSr/+pvCJkId9y8MYMXnqNkQyT8y96SI19B609/olZ06/+RwFq3IcxFH+CqBxqIFYpGwWTbDcLUs1UYQfTOtYw9ySbHtgwYvbDoqf7rURBYG7NvuVMvAlt0IhzGxy72t+UvP3H7xJtXm29zw6vN+xtOb6d3dXu6eXGs+PW5varv6umF3j3u3urlYfeWv2Bl7ayNteBIScMDM8sf8lx6YFr+4sW+Ma5vGW6Pjw/H/flhqK0q1soeXRxF12/I/w0jgg4kO67+ARBiY+OgweaeEvz1tbI/edf6FHcM/UL3nB1b0yDtzN9dfXEBqv8yMj1MsxMSaKU+HjzS234QOyCUWj11S+ZBhg/ednlvyj7sgHqVEwkEopSYKd7yhYQf5GU1nHXsvrv3BsjU01peQT1Rprl1HZBt+ZaHB2/PqHNGjLrV/cXw3kjdRd+OAMHYQu4NmhLUvdmPHkUiYek1POWrPzCZxjcyjp61pQ5n7Sf7LxRNkxFtwjROc59z9wxc9/Iv3rwbXt3X2zcv6/bl/e473G/YY+Tbt/s3u+X+9bvp/Yub46sX97fD9uO3ubi18dIeRo8E/dptf7fLdVSP9WSS9T0SdDi0um2ATG0ZY8AKoNik2PxX7sxN5gE+26okJTMZI1S/SDD2D3/zP34c+K8/+NF+IBWmBSgBNQFDgL0iGC3wS38PN7/6twDR/4tfYbs1nYKxjjgPtNmDsweM5U/sujCTd/wDpD/9E78V6d76jn29Ef4MDATMFIML4wi//JPwB6z8x+ue2hkBHsFZRlm4eB8TMKQiAP/HLh96mrNUvtGFX73d7a7h1KO4/Fc2/nORI/AV8+cy8sn3++ntNH2L+vDq0F69u32rs62yS7rc+oxQWDZtf//w6mAqTd6/0Mvt44tv8G56efj43e3LzYnb4wm5r22XtZsOuw1J8GksVXsYi99CXNcVoVINhQAIRllNKBfAenw87o4CSrIAolQapmAIBKEcOrD3QwmyM4/MeRcv2tCxcSTldi7W8wuyBy+1pk5fxuR6Y6MrPf5E8kMfdf+dzb/VRHOAAim8BBYQQRhbCr2zcjmfL7zUvbueAUWZfABVCIzP3YXHd/ARfa8BDQICxrPK5qjpyGm5van3f3HLN3u9bN0D5XhVKCD6jNHl48PvdzubRdbj5q2Hj3e3r3V8qXfT6+XVu5vp43bavTs/LC3WNgvr4ONEM7KmN6EKBKFKEMexIj1938cYc6S+GYQskZHUaamttWGgLMaQ1EwxQMCCihkoRAZvhi1ACB6N9+bGcnH0oPWMsAcw9tHX8YGBH9N6EK98gdczP7T3JRwHDyIISqUJASVnH7nsuiyL2nA7cgzDLUHADETMELHAhLnrui5ncr+9vR1BTYSAUFO2VDm9eLPf3L3aVV6/nk77vyD2c+6OS8v7UQ6Via9+t7998eb1m+1QX32T5rrf8P7Vq/sXL6d3h7c3t+/vuF2Wdkewtotkmk5bP0r8wDTWWILQI12DC7nLGYgxxj72MU6iBkSWjhHUel5M3/fbfaRsQRUwBAgoGAJh8X45bSCJ5PbOxpXLYO9egJuhEnlwcYewvdMAwWxpNPdjGsSLSPdMUgNTgEC1l6VW0HAcgCEFEjkGRJg0d7nLuaODGPs+8rOR8gCHwjQs17ZZNrenw3F3fjPcD497ne6WjUHsHmgpIssloNbafVy3r/ziLW+H6uP9ctocttN5O9W7un+YbutGj5sXrPOGpuNRO4ZWkBuAB2CiCGPJmnO+QO5yJhNjjH2kjzEyafQEcL2QL+QcY9/H7Z6xARHAAipYVnC+ZbOBDLt+w8PP4ce8wylknnKEk99bwMO5gGC25T7+8IYUkQUqEAFYEKU6dxfygq3WhgFBKSTIkbG5yx05dzkDMcY+xm1ipDqnhFE2EaN2CO9wq1/JplGths3f+XYtwQHQQJYHEiTvo263fviLbdXDYB9fvQSHjKiSabTjN9pIVozrWgrUoA0NAs3OIwIVMqq1mIkw6QVyl7tMptxH0ZCaeObcXchdl6Hv+xi3I9VmKIEFac6Hetw0qagAQ/ksUGySclGptiZuuuWYCDGdxlhdtzqCP4Xsz0+Xu67LEPvWhsazd5ku0+WcKcdyH2PPpMYGBFAhKBgCBgI2iez2+05WCpNYqA6vKgYhqCUOsXYVtmpFxkmttptBYQWJATtMdNb1CAT0sEFm9pUCKngRnrERFQTAAqgAYUhMmLvcZagVLCQhqSfGSHV3oetyBvq+72OMVPfoHXCdprYLSW7SYjOtCk5QpC5fTM4OSy1yZaluuATUklVVu67rgLgd++1ItS9TbRvo+j7HPsbYx8ikqhAQEwEwQBhUA4hAUMo2QVX+PkSGIlQl3MFKMyyhQQQTGgXSqsghZ/7UJ3ISmVT1DDcDsuRM1ncZFHTpBwiVCpFXKHgRnhdFFcEMBRMMsaBgJQ3GpL6anAEiMcY+9pFy13W5A2Lso1pjdQefp5kdd09XBsVmkYxN8KnWkuce/bUkoZeqQaCBKrAd+74HMjl3uYwRSEJNjRiZMIiGABwCwlgDATMlKIMqmDDFZit6xkwv0LEIVBviEaGiGCOYqHQL+OhPITCMpBP6DBDJGGAR4/qGAKsYAvUGPL8H6vYVz57JOeeOnDOZHIkxpkgk55wxiNbUaFz+vUo/9r4btyPmBpXVopc8a5oujXP9f+AtAKoaaKAK4Isc9qnme+acz90Tron78Zr92CShH+dZL18mAYSt6jE/Ggmeo0f6rxPhRR4R0HuPPs699MjoPMeRXuaR9b1QGGFkZdl6Vmv8QGbUzJkul8lkYsnGgNTUWlz8V86cajBwB2N8YhEF0for5+TwviD4xKrTwuDYoAhjA9MMjg3yu8S/izF+DPd4BJOO41VGtYzZuiMjn86IPhZW98I1dVzNZ1QDKdXMORvbxnb1YvOZF5ovf4mfoxUoNKSu4rZGOPjdjdEkL10RhqHkfRHJ+5L3RQDOsQ/wBMoGIzCPfAZt4wi99TJ92GwGIpTN1IyCKlXq2uZNh3rS4duSz3zFS2rIA8Kfer4W3fEtq/7AMPdWfl78PH31DAkEEzFDhPI8A/TPGEu+TagfkJIFOCR6H2GmQOfy0Ec/Mzcv201/nAuCF7jX7BXlYvzf3X/V/3mAPBkrEZ6X9lgqHlgIlJW68/gZpI0jWbwlVmYghiBYgSACDKoAgQ4U6FyeGQpWPGr207DNPag6N5oSxmOlQI+d/sop15HrU8ID55zAyUcFIiBFBOCxAWUBrGTAOPI8bYzMoSXdQRDqm4FiAOPMTIcC4zjPhaH9OlsEJ7YINLWIf3hL8DG/nL34lyO97VScA6BSiahAgcxmIkw8z/QPNN/bOAJ4S6gAE7CSAIZQbQIYwAjzyOX5Itj6XIEPxebYAPltYXELuPfUP9Dc8aT1XMh1nFFIy1wBxBAMoWzChXGcYeQHz6+CMDo7AbEBdAIGfWoLpgfhfbpXALJFcoljrvxPLFQKsOeMM86g0s5YKBQKiIAIlYID/uMZngY8rU+FQqFw34QhYq4QB9BhE/GVLBsh+3hSJl1l0/YOIYoqlQIVMlc4xe1VvEqFKKossvkKdSgwrP12LOKiEIUEe/5orgMTYexMuRxxux2h1yPncd55FDiF+7QM/zhP61VUKJDuLbJ+RXAmhSBo+1FDZxTgIS3Bg7C1lSXOitZ8YADvzZkpl3HudeQ/AVjNrkxXkrlwymbRRgApjBctsnKFOlMIiAhOGI1CggSVo5gvsQZvhGUjMJ6UiXgyjtD7Oe+22+BlPKXytF784zytFykq3Hdq7ReFKPAbsgiIc0leeT3jR8ctKhvuuA4TRW1HuPJ62k++vs0p9QpWMi4yXxHdKZfeA2f4osDGoPJQPg10RfkAi/ILPuhBp2ye1od/HM/LVAB5O1x22Q/HIv+RKxw6AkMa2g/awMXeC5PKAwIoorC8tllBAEUyKMIp9hQAXsqlssjW8Q0AHCCbI9uOetNjA7UvIUxvTEUFFRUFURmqyhBV0aHPGYBfvfEHYxksezXOOCPtA39g9xAFUc96kWcN1iBpxkYWptayvdMYi8FiLMZ6QC90Imci6WU0dzIMsrYf9DwumVXDA5kUoCQw3dkDiigIKIBfFVTYdhiUoa0PYNkTnEqXh1OugchCwjAQrJJRmQRrUGETxmLBeqRbQMUMJVlZIvGvwXmbRbkWI6NvujqpkAXX6PpbcyWiqGioxoKo02RVo7lvVrRZbDJ0cBtOif4+UgJnnHHKRGZ7xgKWEwJCqCqIPD0QPKwypba0ldFwfL+WkonRIA6S5ImTKExP7TYieoaKK4k7WLg9PMKoqDz+Njve+nj6zKv/M785fdtuDX0BEK3vVuUF7myVgDDn3yi/NfC5r1rLKVYne3A8iWeSfp+wFarU/K3xyhkhqkRg0IlyOJv4pSQOZpYnE7nZUCxNy+MwmZWCf9UXYT7CF9T48DPlwo0IkewoXe1Xw/AVmnNjrL77mW4c7l4l5++882wtdmnIf3ti/lbAcCGnXH/rriiHPfS3ZuPaAsJSrbTFbU3kcqsUENNppNSsIXH/RA32l2qUmf1Ydb85HKdBjBk9Md+yARbtb4+K83jHv/RXsLBcqHHB4T91kBAej0oYCLG0auF4OWy5Lfa9UjI+Gq+8kQYPAl7slIsgXfGAUFRcSXIvtSBbKrkaSiMEzC5Do/iFujzA6EyC5MZDCUJomobz/b428WHs+eiGbr0kh1HjPJ4cENxGpck3uKpzY0u4AD06ZCHulsB1g9AtBcSUh1clMSWn+VrFOuWMv/WUS5APb2mtLNcmcOdOY5K5UW+Ww1agRBQdo+bq8L+ty+MuT8IFLNecBONl9+Z+4Hz/1B5UyIJ5F3q7ODH3CI/QmMHD9Eyo+vUGonV2a3N6IPw0P3XpsPpA1a8OAuL/fZ7jnnfeKRfRUT0m8icQ1si587OU9aN/9Ktz70usQZtYpNZy340neb0L9gdhTTcGEsod3tA252EAiJ/Cu56Dsc5drr2HDar09lgbqeRsVEVNuuPLgYkKlSUo+MvMtRinT58+DRcvOsinU0my+n4AFsAboBHDicI8lixrlAaB+B7t1+aGydWW99kNfbSRzG2sRpEEIaooQDIS6Xi7wNkB9y5Ifb7kgUfkEaWRQadEn4F5ZI0OApBpfy+ijelcAMU02/Aqgc2//NbLRSbCpf6WQBqCe3VoXG3orVjrkwPAo70Ig70YuBjiK4lXsoRfQgaTlVx8T/TmSovq0wBDUnMvSgqjDL33upYjkIM4QEp+502shQ+8njbASmGLKOwNa7hHOSx8L2yo6mD2rgS3FA6UAQefuXa+KuXkAKt38QdciLHMeddZI1wHtEUFT5QEIIHfT6CDIQFw8AYgQQEhpMDgXmMvQRLYDMae8nNIjzkOcCHA0j1omh90CVsJH7K5eyxcSDb3zRxMVEzQiMeBFCQGQsK9Z0BcO4fBAYylUrjKi9KuArIEDZ8qG2aWk1cukwIDQX+RzTZb1vxOmmBIqrj4iMT1M2drP9qOtD3SzwHOwAXqQpXI39FccayJR1mMkug1whY1dHISwU0v9BV9LolFkM40IkJYwao0ViMLFcBEkTmXSKy5QEaPjE6WkY8Y1x4Jghu2SXckW1u4OXmEciOppIndv+YuQ4UaIv40OUSVj/yhlIQ4nfi4awdwQJAEnQQ3G0GELDJ7EtZYDESkWzTzAqotkLMkCc50ZpnXHcDUIbKfAbFHvrMsGrMnAshjlXyPCnhaVz2IefwmRIkmdp0FZJQkkVjgREbOcKO2INUhsACRsagoWIgsLUTQgegoLIJxDXLFdl9awlc/XcbkxSvCKALkxJ6AwMa4kFh4ymsLiEaj7iKCBiJQVXuKTWCx2nJHIhJFUMRgwVgMtRl4HAZhKzm0Y+Ic+XC28tgcdqG/+qvPvXi1Coi1NEhQRjipYuEfHYEO5oY3vKG72PjwFMuymerJh/OwUR2mulnqbqk7WSFxAYNgg9lRNKz6geD9XRPLst8ez++PW56YtG0SH31Yg4s0Cy+oePzaxsawnDrVlCvGBQohwsErEnQzpYgE7iLp/X6Z3g9aGMw0aLsflqbJu2lzqs1JC/xE/OCuePDQ6/+WB/CT2nhZetgs32p4oD3cLB6Olpf7w7LpOMcPS85JW3POGlgKXEUfhfF03/85rV7Q2o79eAgCYscLkuaWA9gkJmIK1tGJuxqm7YPZ3m6GN7x96szDvp42N9NRfoAACZab5T1jKETi2szG2eqt/bPv64+0va2DH9tG9bjg2oDwJ8wfpqwhPRN1UqkdHQ1ydVrJHfJCXob09oIsl/EKTY97c7fcts25LpuFejoMw2HYTacWnZDIerP/9R+zbm681Y896o8uQLZ8+fFRGGQez9tlOZmlDocqUvG5+LA0AfjUIBDQyRKUY0ZOf6zaPJGMzIRIoq5j73g2J1C6/V+Hc45ib3MWg+u3X+ojbrdrHu4ax62GzYlh01o7CrAQ1IM2px/+qm32Rz35sjufDo8b7aajpkftfKpVn7K72d0Fybz2mBQ4Avj4+AJRtShX8kYzGwPCVqz3KNoq7QmRLiz8QG9dPTEGnaOIJsRZBOKNt3u1KGgNtaaVbWhSx9lBAb5jmu4GjCwLsIwALAuoLfxJkPP6o+oKoCOK0IFov+27N0r+8e9sRx0yBDu+ncZCUeHte9bnmOvR0RE7uIx2oN/+qoGwMIqPMGknF6BAghLUpMpPsAI5e6RFZOJ0qiohEov1NxCA4AZ7Pt33BZIi3fLwDI1Q+c4v+yKGRIiFoQRlc8tr4lyGm6CAMCBQQoGkZAYUegcQ8AkFopMEdIAeCVgQCRClk6zP2n4PwD2BaKXpAKdiG6l2ePA+HhlKTFRWEpYjCtZlNgMESECBCNwzItOZMELpJIR55kjVZgqALCMDAiOvTU45HR/YgHFf24Js84fWCZcLQElTIDZRn2MJl/EWMBA6CT0RkCMEAXSYw2AMyMhyiipIgpXptMDCNK9Jg7pBlCgJQCB0hJPN/cX4wHPiywEEA0Fu8xPRUjLVbxN7V+peO0tS5TJQgEjiAoUAIpMOfLbSCwVCzkisQsiIUggUFmAhg5XOef2RQAqa3OCSXZMI2sr59OBszwkTybXlON5SmtLj3bT0BY0afHx8gXDXAiAxjJAUEmaykIVIImeSjpOVFlisdjPPaFms1cEd9Cz12RywHQ00NBrGwvV2ZbJ97Plk3sQCidm9yWEQgaC9E3IyQxYr6ECPTiZNaAVYXA+75AyJ95WbvS8UbDtXESn+CElgjhob+/BCrrm4J6EkRqGU5k50GsQgREkLsgBZErAgg1ZQApFEck2MuGICurH3YI+BwhB+VJg7eXcCQo8dYE+I/Q0MGT8emFWgqnepDOQvIuW47mKIICKkQSYDgs+BEUQCykiCXNHHtU+CGjC4ApyCffOX+ozDBamkwhYirKQKkDoHzD3xzNhthqllnMtkEAEzkFPf5Us5rbDkhulJn1GQVWl6J53jumcggLZfJ9FK07F5LO+0QiAiGwJEKqmKnpKskmlpggkeKiCOeXHv7lcSjukymAHYAixAWE4UmBEyQ4AFGZ1roClI++25SDQCxMZZmN4RaZB1XkElI2BLVrq401IQqVu2bHdOf9UsFe4iggEIyc0SaGIQbkAziSAPn7IZiKSMa58QCBBcIQEayvPefkfDIVvM6o2seEHseCPFQm577Y1fPdZNBcKImQF1mM4npmOa7lIpyhmW+lRbJh7t4/SEODwOqoJMst5KD0FyHVTK2n56FT46c8Kox77awkfc0biiXKQDQoXPuseRgo9WeyglT9cvuQC6cISoE7WvY4D+IWxct5HDFJ+qPkyf4gk/3k3L1KY6tfNOF7ASDvzdm4dGhOb1L2jJ2k9s0Bmd7vugGQVYfXGxFKCIBITNKSbR5IlZtIjI9i4zwjFS9ZjDiIUwFP+78PyZF2isGm18FneHuv3yZmOCTMACQXSVce0TDHUCYLoveKIoiWEU/84AKeYZqvoqVAeLVZ/fxKiTpk1IFiEWRiDXcF2Zkf6VWosT7bHupNy6sw1icBBAaFjeSyTRuQ4abmjViHIRLHSJYdbVHWPTBEQDZMcg08gxIk6hCJ7pxQzTxH3+uK8Eu+vxbmlq4BYngx2bzXHYKBMgGTbLRtbYx2uBG+4A1/1NfOCKxesPVGUgooR6A6gDSLE5LGMyoxwoGMWTY/Rc4EAaqhG1qEgg7JaI5vCFBMzXs8DKGNc+E4HMrXACEAhfOMVCmbjl6VyKVR8glAQKRXh8iYMjR8aSLgFhEdmTyzKKriC4FgpQ0HrUxEEgX/dPzBUfbVmAFKk6DRWqfprzD6t2Q9FAyYCagrx8ROsyIA2NMYJRwViMTTLNhUiToABIrGtBWduvE6jx975uLoupjL88f/dx56pYAlHQK+bPvwSXG5m1c4sRdT0pkmS+sRwWaO4D1oAKkUcFxEFJega+DGAAXw8irijQO/eBO8eSr+sZ/YvExWYraYVBeNoz5OhYZzaOgY6LQ2vH04N6CR1ykcJ9IwOiooA1WM0MMoBGxa5AYlWuidEB/EqcY8UhvHB1Y+M+isGds1B3YtiFVRIgSp38RnZkImwCm2cl7B3EWVLBoAwJKOBBIcJgMSAKkCYN9IwO0IH5mWbGkSDFwEDMeYQQSLT/+qvnhnTWWFigCHfc6A8MdSHzohbwCAkklDt1s2xUKxSNAMZFAgmW4KoFkKg0EREVPCI8bGDoOILL4rJ4nglgIk6dXYBvRhMIoAa8R6T6A7Aap00PAlUfPhtQKFJdMGUqCGTWct1FOKsOoAGMxar1ChAZLCaTIFFPtdbaqudrZZC4dg7tl9CgBqDeGBMULuZqicvOVXrC8NFWGIAjJC+onzzjeLWBrA6hh2CKvdxFCkSPAxwFeBGeNVgMtodMmBn4M+i9ZGR5LhQyLMSVjEQAaz18uC7EQTBQ5PHu9ioveeZpAzvOHOb3IpQgNDnDKfUJoi5SxIlkCBaVcN0lqBI0gVld7DSOWGMiMl7ALckkYC5RClDKOD+fnoQhCOZASMANr0vVRwYYjNCj3OvwB9MafMmfVIIH/9RuGMTlyRxZJwfdqauQ5DJcDUHIjFi+CBHGgp4bkTIRaRJgHGechV6AGZjHK0qnACiKIxuoC+xYgDDojII5Gt4+T3bV/v/scQ84OuKDEwfLAY3HDexyb3WAwGT5MpshVn1R02UwagwonYTNRiCAQVAEN8hkDqAUoDbQpefoqq80gDiSK+5AnMo9jnfnCcBrBT5n+TDcQpkizTwmHlWGKKyFIOIMO+xw5swZdmDHIQwjQQ5lZzjsBs0ddzXi+p3E+/c+GzqXf6tfogP5gc7701e78YMSomCIK4XW4wG4ZxuIRNOMQt3oOwY0tgAmgAHcXNrKMKWNHSFk8Jcba1HnBV1gaRZtbfNrLI1FcXcAUiI+JzdehOImrx/eJgugvxtardnxligFCwr+rW8wfLW0uu3Vr9czziy/5O+1KqduXA40jOVXfRW99su/xV84ypwVMDAXEkDbz+d8QENzL8fEzqROuiIiBK+xEiFJvjFGnUfS9hSqQQ4Q1UnhPm+yUwsQb2trA8RkXrzTHcImDAGuD+PR8f3jEh47vmN74/JSadsr577QXRd0qQoodpdldze3OJELZ17gmsG4vJA7H+kOJN2zH/Y9RNw9jo6gvxv3tJ40TEYHMxM86vm2+Vh8aBCMWw/gwTeCyXfKWZRG77FwIVUOrIAthAl15oLe+WiauEKU2yZQCsuKx6NtZ5yBBClxYfBj9M/Wusnf5Co48eCnvrL02GLx6XUDAp7GEzvLxQ/beEb/BEH/+PnV2ybKR5Z/vfBezxx1xxMVmV0+XAaYK5kbQMKKE/VdSBMQWY9NpyBwF3QpkLSUYrAoYfGRXJfYec6pVN023z8fOn1hTOFpwyGNNBBFZ6tWS9eWa373mBujT/2c5Rxu7XwEwAqT+3L+FhnsplyOc+/zxkZYqicLqUnrom8mZQFhOKP9iQruLE6wswhh4sFpI9tuwR004SwipXEqPLiE2EVeEDVtmHzlBEyWAURMzuY7ddAEI4SP5c+fZ8XRcUcBJdOjgsq1fm7fry4nhnbpdJhL+t0YtQYw7vvId6bTMKiCSpEqAwCRVUERRTSagCHuI2BOgMjLBW2pNppwH5Z6cu4a5sdBOACGzXz2PEsgRrugvgtwAc2i7aeHtjbdHQwKAgkz9BQ0L6jBFxXVALUhmVfzdtXoZSoYsH9hZw/b6TTgeViDEc9YjEUEAxPnwRBnSKVor/EXaTjCwPLjvkCwAZsqe3AdTJsImk2vQ2/7E14AAmtU1vVm7uMMiEJiIAysKGoRo2LsdkG3Kyis7pAud5MKoNaqtQZrrAELKlBJUQHrmVhJzH1ccWdRHIEenPlo9GCwvdSx8F102B8qIv80wE3BBtJSSaDL15frKQ1osqCtG3dMsIkmFJUK3CCvJnqJsQZRBFALUC8KqKDNBiooogKKkK6ignMfb731A7CGldG2IKJb44ze+vZpVtApsWkpmQoaulMobgk94u07psrFX+iK6nHKwh0vaG65WOh/zVr8TUzZCRyEScMGrEuXgWDSLhhmBYHse+FmuJlfp8vT/iInSxvxolcLgmENuC/zNhy2n88a657xFBWRgEo6n6oHHGpoKZnGQEkalfEcB9HqucbrfdTD77wTmQi4IiumBeY7DyIIutmfdJ7JQeulS2jxm9kL7AVuJu0N3xAoVCodFF7+5e8t3vMZzxGgfV+BUXDIoeECAlm62EBjvdgHKBRjphe6RAwfgFiSv8y0Nx9Pufp6vR9cicbAhfTAxjOtjlH70XQNUgRdaAfN9NKt26903symYCBV1j6LukClUqHvXq+msgRnqnYAfL0N6MFkInuPlXxRGcacUR9iUKf8XA/Mt/zH/XHv6HfxykH2b3kJExtjjimOOfZjTRwMHPRfGdgLNg+pQRupdsxCPv9Ts895Y4omlE0wcQUKFZD0bAo0gWIWyZKdKZaGoaD9aEuBa5o0/G3D7pIxIyoyp2xl+AkyZozR7Yw6ZO3wM7CU8ZUhH4pYqBt2jTH0uu87cO4ejz0mEPlcsR2W99IGZiQy+pFx5w488HxE0YEg9QQwBBBrP0cyQoF1fVj0vTNVDgytt2OxQRSquPlut/vCQYfmVX5QEBgEVh47ts3zzOu9fdsmBtU7BRWNAGdF3v4/PkA4HIuFnQWIpKu3vv7NCDdjB7s9483a3KwgWAhmFsaWygIgOGCB7I7hS0REaW3Ao4IjeHyKLFGWgjEr9xEcUQMniMiafa9i8N3PjN0/dQ5RHjDokUDYxkvuesqkmtGV79IL11J+ZsSrmhAivPtdwIslA/HLSrA17Wt2GXNm8I8zYWWp4VJpZtQ2EEDaj0I2wPM8NrVpM4XlrftQyXIwRp3nOI64FnnRV26lvgc1gS2/92KrbdgRgGRFpFg2+q8svk19NheIIWwbp4FGxT5jDszdaZT6Pui6oEU9z91+QsVCRWepiaqY8s3rcJZrHSBrIQ3waJNyATogQ3vOvpnmQIq0iLHmPIdmkIb2I048DqVzJidOqftiwqX/IoLjxy2yrWtQhXe38MqhZH3ppAT1HQBBl7Ap5RdTcPZY2P3Aj4ltL1XfuuOMPTB84RZla/aqiPzeojX8qHoeJpgEIlva3Rx86BZqY6ygogIG0XlpFMzqz0HpRKfSOqWXsRciKVkgqC3FDMGFzdQAbb2RBE16PxaWJRKCkGu3W7103gFHSWjY3PGHXvnZrAhT4C+N9wFZM/laH/6tU4pdpGH+NiPFdTZ32LtX3rIKkHP1qjTFyNTdX1BqbA8421s53zop1xqkpAmBxGESt2p96hFQACYtwOB6xIrOs58PD4BiTuSMdj4pNEs4CGyKqF26DwXhTnLb+6DyGyXyUavXosZE5xKDAn3CY3u7PnTYQ3c+14t5ETM85rBH1RMgZZ10IdAYv8ykilPHuTpFMwRepuxM/8eOfZlcc4ogrqd9i015eqmJBD/w5XFtwv02rk/cCpJfIqn72upmuG07+8B2CO89tOl0Pj8cFm3eDJsb0Q3wr5abRQmByBSg6xRglZyqkgUpT6Zx7bXGi3BWK07L9j1jwncPunoD+Q5lwqOPPLLz6t7/HMzJ7XfGeXWPOlWLggFz/pPyh45+gYzf7g5BxPn2kSB8HkYbn2X3fG1cPltjfztv7isyK73sDtsnHvbTg6vQSY8+7gedHm/vpu1Sl+1tTkkxEfMm8xHo2iLlg3CbJ1PGhnZOgYb8WUglIRla/4nB8R30YZMjY9MlcJ9pBiJjjoxsBMS3PL53sYh5jZ81v8vZQn3Gp4pRB+EQRMLoI3Ci/IHR36MMslzCEAntqyL69FKXIrlSS3MTCYMquK1S2x6OT6kuG9gtvuM9x0M76jCd6mnC2xu86/z5aD7AFz7s9gix+00ktLeIHG+SeDKHyx3IjsaGtY6SEOOAeojkumKAedWRx7xeOWlULlYVWDiB+DC79i2pKzCOi0bZia9uzLegxHkvmsZe6yCBMFtKaqKtkPHa5nQctvBjqzjulnZmakd5YBnQ3ux2dd92T3A8nXdTRMUE8yaicxUr7ju1RK7uRbAPwIHMqm9ErqJc7KDhrOqlSxWlZPIbHDpAUy0Ve5Mk9moDvk35WgJikrYQc9rPbP+sdQ03/AKtVruY1ydu8RYGPDDmeyBLzPai+NnWMQjH0wM1VxFpBeVBqWblyN9jt5PF1a5PLHfLcryzwO/vsSww/Or8GujHFygwqSYgMEs1GuCqVR8ieWd1+ItY+AQdOymeavRaKJ2jQYrxB8YcMrGSNNMf4mP8bLd/vaGl/+BVKvQ7obHDDaPvrHPMpZV6/SAC+z8Z8JAxLyoFPqydJx2nsCCgGv46KlBJAnNZKoXoaRWIBMtnDT9FYiwFWcQM8SptUeGiByiqvBAm2VYLSI+h3whXVdXyrd/NPssOHwebYF7uaTPiyG7XGb3gqTJGla8M77zkv7uspaL1f06qwGh+EnsmUxQq2N5/snzMC7LS6naedNRnAdGhAMlzLtBZ24f2m98GUB9EFAt5uKA9aCarcY/DG09WL7LBNgMkk0yH7TAZDRfOOOPoWKNI5bKf2MqwhWdz+8/7Bg13P+4zGQnoeNMwTGwCa/qfMvYXvAK67vCl3PN8680zV8Yf247nst6n9qtBFBAo7sPNbVm4TfthwtG76hlDy+Ct582CXbQJq4YtZi99PT1ghoAKWeAnRqn/xP7G8L91KBm7pDoycaR8PYNGC+l6YWIUUYFBufbQZ/wX2VeAq69b7+px/nOvfE56huxUkaGg9XYsanBABxG7rFZyW+htz5TijtUx0yOJTWGVXBiJtQDCAKEuM8GxU33bFw458kOPqbu/qBva8xG0siyPqlOlaXoVscv0S6G9nbiLO4z8fCkl5uf0rIfOZALfjaTWw6+5Z1wgf1OQi70N4B0VXoFGJlK3AgnK3tfeOboTRYX7Yoa4YiUYhgEfYPbprtYy8MyQnlf9ef6k50pi8bemgomn4bbrqcsg+Gwj8zhy9fhB5vEKdy65YdyicjP91T365hAcFQirsItpACFIwpbIQ3VBpGIPKwE84PRCamaagb9n6Ohw5dOt9G9A+JMvfxmZke699N5L77303kvvvfTe7hQe9Fk1mHzfvxRmqlnHHWdgHEcuPK91PDjCkU8cS6R8N1v9FQMFwmD2JmNeLBYmFiZ8NK0OIbzJ/hZTVGjbtk1SJL3XUd4rSGY/WTK48lWMFxRSx9Y3qv+lO1+686X1SDb9Lt350p0vrX1S/bYnSg+kXPcCfXKm1MYhZpgZufoaUGntBwSLKMmL7ucIZLNFNeKBcaf8OTt3Wsygibc8oibQMGLIA48fsuK1uIHk3OGNwYeMW8BNJV/+qwp0kpNFjpIpWi4BcNAnFvz3E1pD+37n166pebMqfM057B1Rn+MwdxhH5pn5Ga6eL42M8/plICLmAhB00DSRzthGnTex/2Xc6c9DvsRun2o2t90bdWL4LRUtMYOk6AyojPhW33r7mzEZtzJ+ZdLA/JpROx5opwNQxF1+MT3hgLBXwIPXZzlN3TUjoXoNNwjz9gKIbjL2gRXAWAxYhAre85+/Fhuds3QATXhK3Shv50/9aVL/S3lreWV7K7yI7++jKpDJa+zcWrRShcKQb/H+ByyAJis8hWLqd3TzVWD0At8ZNgvfRZJac9yIkn2TyvZOk3IL1tO5qBEVwwciqsyBJfg1zFqCMBwctzVADvAosj3VmgUTEw+PLlrbC4MqmCCc+mclc5Y+SAAB/kEQaBF1BxTSrTFirLJJFCtqwS6x27HizJq1JC5MaCJLwF0n3VK8vZXbwSkmYPeuohWLV5Ctz3niytRmn3buPsf6/wCqX8IEFaE1I4lMUsMAsKSLilq9zIBhCb4FzEIYWBEfF1KTp78Y4BSNHiflUL/ydvTcIYdqT6V4VrBGQzAEEAWlRT9XRBI2coNKnZ8J2PQko4IiWImW4HFw5yxgSfxgDnJAmINoJRWRmd0WPbXmKgoUOewSV6Nx3mGtWYFKUBMagJgJbZxYTZfdKodP0LmhBnmz+0Oz0RREP5CmWGI3KnF4Q23vq3PJLPamFpEXZ6VA2A4Ivzg9tWggXGBD7gKBUwSntJJy4wdMaEirCFQX73M1GNw34KJujDaHAz2OWMSy5L4TnX4TIDjpZ7DS0izmxdHRHNfZXupXx04B9+7ZZFlO7zlfCk1WnLJYhdCyRk+9eTy0dd/M/nukvBPms4BLj+y86bjZ5tZ7cWhFjDVL7GwJgKXhBIElcbRKQBAh+PpNCKRZ/RAXBE213ZrlHNDWjwU4XASQipZNC1z9bc/2j/5Dx9rfbcWKMt8+KwYPLf+g30Iau4BlyX0lq8aKM2sJS2Y5VG7z8cFv4poWOvSzJ+6IeWG+DpC6z31oaQNIzN6/9Ur9hyQpn/3b/eh6j342175zjuHQpUGIXXKns4S4BcwClsByIB4HH5/KYkqU67fNQwOugEbQdTNaW5fsOrHijZO/89DG/aUf9k22Ln+VD/340Y3J/gkxZsndLDiHOJ01nVS9nCrFjk78SpXbmuIfej5YVpwnjYcNw2Z3zbeSbt337J5x94Ct488+IEfKubhcgg5YYi9LoBOo6WRWpSUpmrotizhOG2CRiF/vetc7f+X8Nubf04pfr7m9ugSnhw0aBgLKV4GCEm7e2g+YJXcczN5LgL2ZNasGoL6zqUkD4ltpm5+wAOYDbW3zIQ7cp7UuBK7IWECC0KhY7RBURcGy5H7HApYs8WfNmlVjAfEHZ74PCKTTxvz5NSAK89uo3JaKJrA+9FFrdUKyEeOYxqupqEVMighQAWTJ3RQ7bzafSufTNh/mA8yHNip31iTYOWqrEEBIRTd1eUwEhkJUKWABqyzB3+EIKwk7wBFrKsCEiQ0EdmjvyxgUVAdezP8/9DLIK1KXkREYGeRL8vo2DNF/ZYRCGRh9iUs9EovLFzorRV/XyJ3/IsM4Uoag0At0ytyjAz2yQBKMa5OAjRk04sflZHXSS2QBOgUSSvQksFibTcRMhKBA5hV8WEGYYgNBdAgrnZBBdqhKwbguBQHBABLwHv6rC1DSCQgACwugsLphOcu6VKlhiAPl5L+oU30ERwCdxWoLx8VCqjSL5bg0xDptCgMkAj5cw7RHEmHA72m/8Tc+PQKb9siRb3B+fawf41yb1BAYePx2Xwr+K1Xwh0IUaGAgfE4rrSQIko3u/qAbad41apLRQ7VQhTn77t37KT7Fp/gUTh0cATygDbTx+tTGAzygDbSZswe4mZuBvewFVnMPQwrsZNzOAwnEfxHk/8gJFWOn6kQURRutEoQkqomeH8za1vgBX4XrC5nJWqAAFHjDPi1CZ43Q2/UjtKEAJDiOFzdXvJMoj2L+jymwlrlak1ptdGOuLK47OlpOHpcgb/Idc0BR+lyhAlRw5PlvZGmvRpbOP6sSrEAA9V/8HXlQPek+HpByjf3q4avQr17LXzAM5S0kvOxd8KF+Whf3BSgL0rfs9wGL3Q3AACtAFTjuv2TqT5wMJLSOrKZobcrd3mkwtG2EiaIUhhtsu40innHKyA+49gKVxQcYqFAABP+lm6Qa8usYaPyzgH2WYg1TWIOFTQaDGMV42fEAD/AA7z6mtwVzb9IAcNJ/Wcd7uwSApYmqSckdnSYCs9oaMFM6iYdircHzrrySXrZZ9O1FcGUvnMLiMExQqf7rKrw/UfAsqogIRNBp7JdoW5lqWzGPjZC2OTeyUYfs2UMUsSdD9j33EY4QZQHWz79VSeE4WnoP/7V3bxRFFFJ4Ap41gGWqDc7dKRu9NecSYcg8xRBOzwaPQm9U0qwiJkOEiaDTYxyzKyWeI/AlTJsnO8amgDtS7ukqUyrGfufc05KBYhvg1Y21bb7IpDUYz6Cmo8esBmOs+SusQTGwmrc7MJ3LqhgjKoqotRgJQhUUq4KxagFtyupOwaxLiXYZdJ3BopN3E8TU53qmHLmShbU1Chi1CnDZWmOnAMcobH+4JdBAFQ0sCtYaodEEq9p8CcGoFRWMigV03SbRdbBJBTRExSD8hcqNxYdqVyxbXKVAUPJx8HY9YBXsNGARGDEcmQmObH1xJPwLEYKJ0aAH1Vtf0a3Nji+fj88ohmBWRlFhnP541n0PXD4xmryMaU485AXCkr7ewfHHfCNtpvTRr/6AMqWLLLiEhXGo1ZopxeMzWvq1g9kvfHkw7pew/uxokovrra2+nV35qt126/L97sx/V3PHN8blcunS8gpj0dJMeSPEJb8kb/yKwfDyre9L/nxWuZcm5R2tm1b8oF/+wEopGsxli6tJnEBNWGYm6LHjRb2oG1a1yg/xz3ZVaWkJEZ25wv9J5RV/l1xSFoLQzREKBLOt5YIysXJjDVRKOvECcXzB42bL6MjobFKSCz7za73SwhQJAyHqrFkWVoumvXszSVGdJkYSN3BvOf8Fyp+4n1eQT9OlHB1lV6gl/Yo2Sp/mip4xG2pZypRrExzJJQEwO7t5IeP7Q1bGm1fmclv3H3C3stw96w3q49LvN3/U4YmVmx925LXuuimEKZbpAYvGRz2KB5VWpSWM3vJRq6qRhACM6o/PdXUdf+tWXdc0Xzil1Wq9Xq9XqxLs9TyPyRMvecqyzjtPyXPNaHzj3Knkw5LWOOJE8sV2TgAP1n3d7Ci98dYv9vhVd0qY/C7vy7/1BSZKz25J/kVbK+Pl5f0rcjMHv3xt+cbcoS+/LsdG/OSjMxuPjvc/oxws5/WvuHs6XY+lWbTIKLNgMCDUOq3nZw9CLVouyBUTX6A1EHSdYi3Xr38+y3FvtdJ0WaODP0/n1GnQUpJa85x852a/SYLoPJ3rCmPl5flORSrutDyhUwiI1MNqD7XLTQd+iEHHFhh4TEBl0JGN+fzOn+puXyIysyJiHt7CRUOivUL8dMOFpwYL6f35ksuTtg3KRHMDcvRdx58Y/C3KQBkBMhYe57XKDuescfCrnyVfV0wq+zpmA9Udq/7nF4Sy/f0ity2fHJgNoNO80Q/RddGem7BBASn7jmoXQDiAMOKAE8cHRMHiFIg7GnEAnxRgOAA+CB7BAn8NVW5aCYjEpvKVcjNmyECdbrirk9sy44mpzky3Ne7sL9ylBo2BCCalOBAfepx6Ks8p3bOC4p/5iw08IgPCSx7LFjH6sWKUiys39oBTIMCgpJInN3pZcqhs0sbdLOHGcMrYp5xUpDUCvrVXIEMkQRdBB6BtQ7cw5n318yx/HsYGH6d2A25TEGhxplH5N5lurbvuNQW67jqm61F6cprPotLLx83xjHWnNV+Kg1y9efKkZEojK9yzspvYPjnMAncTjoNw3coXeZvJnpjSHGee1i0MRFhITgV+BYDI9rV/WzKJcgM5vX2P3cJKmoNACr85m0MXpma9fpf12nYTZj5CJIw4QmsqCtmH/P9nJw2t20rB52Zdm27rppyAHg3uxDysBUHkHDbFF10QX9Tl4goMJhol85NAbICuJ6Ik4HGprGn7gwkFoVK1QZWGTZUGl2QKBzIAkazpFRVVKhmTOYVysDGoUsksHlU2AMFYFruZwYNgl0Y996ZoTtDflmV1+/iwjinllfCjV6j56t85ME1PWWhdPV23s8Sc5djT6IYbEj1Kn35DwLJerCWmcdwvqjvrbN94NmkqIhhJbMMopG1CRdvGgBCQxDYBD0JFsAFRiUyOSeczx2aI0ECDamhQDaoB7EqeiR25icKw03yqPnoYxedcm+yzV+BDwKnV4u2Hdj8uIJAPp+i3Y40O/bigp85JXVcXLFzAARzoXnfUPpauc75u+fqrn86GG4qyAOJOavJUcGpyXprdkins7RbZzyF90OTgMPeg7T23bQ2CBU1MO+zJOIfSzmQntXID+/ssKs2iIq2kCE4O7emx3Eg6ny462VI82Ww1LRKG8/2s3KTftE9JJVG2o95KpQM2WPMUHRK9g2JpjwCwgXiPtWFMVlKwicoCfW5xd38sMbUJ1iA2wLKmrdywzzWJ+720OPFbceg8sEDX9egfeLuSefV5WHF9nzUsCKKgC0dgPTVlrniO5dYFK2/KCaROFUmK+q6u3pTGNNCaU7Q4p7h4X0w8WB5c3NLL5ChciYowzk1yGvUH0Ssjym7tC0I8aMS5QCB9U1ZuRyW39YycjuNAlMIyRF5/zCdZa2iCgA8tGL2twWlrQ42PSH5q4hucIGrCWiMQC5aDTzfwB3bjcN+7FyewIaHDtcHR41SOx3Ec7kmcVNzh3mgagAYCE+8mp5XOi2f2OFSOs8bCwSHUp6UcB2jv6cltF+5Ufd4OTlKuVGoLfgtSaRtazoSvGVIpdYdvKJnNYlPgFQqRZzisweztUXsEEMSHQH47ZGnvge21Z3ommpYjLVW6rDadLhN2QC0YZCK3FS4iIgvusyQVSV9JTJG+a5q8aj3TrYgEvFbKLQR8qMjkt2MfUMh/6ch08ex8WqslB8Vsfd5T7dmbZBFm5LEGouaIg6b1RIx0pLj92l0HITyth35bXWip2h7S7WjOkRrEiYVRjZNEKUYY3Yuu3bPo9A8LVDw6QFMNIo/WFADzICPx7HQ+HcmbCwFjG+vzqVbMiJ2mTD37bpjG2QaZvFmrUTBsZqTnGOQ1bSbpikjR2HLbw+x8ikxkpl1fu13TDlBlOukCdr4Q0KS3p0hni+kIrdsbZFG5bs5lC7ZMZ7xA/rI9RcRIS5G/0CUL22tp374tmyW275FKripBUrWCrJMUOFMPgu+XXtrv6Vr08s05dTJ5EhgyjZEBwyx6qiIdcGaqjTF54HYM1DIiLPPSPR+tVhpFs4CUs+3fzlXIDzazWKA00TJa/qmp9V7xUC/Tg6nUDJZiEMn0kFfHyiIFHlstw7QxQ3ttpiBXnL5eaniK9AwygAe1KTBY4RUf8FlaLZiovEpSkO9WmIII9wlPwTG9OJXyLEQydmypeJ2jRQOMG8xOsu2pGXkHNVOg/KSRpYZJhZcbIG2kSqcjRxDo56S8ATPqKmqLXjq22ugr5CtGjRmBGjv71KQ0lwZeZzZSSbUsHYjUdhFbip0u5Fl/g6Odpcq4dZiYncknDfWgB9oed924VEmzgFSnoSQY+ZkF7zsla+ZjMcKmChB7MTquK3b3sh9A2kgvVROBIgVPyiPsfAMVjkIWvnB7qaEUBSgW5NI5wpl5xLJJjnxH10gbk/fEJnowQQUI4fOMjYbR8EbKxtnSamaIVRCTBuq8hjJi2CJRhlJy6cwtj7706Ux6PaGk6RErGwmMxhwzcoyBPDD/MMZSTpYoPJJnBAaLlXF0LLYsEJlzqHG0ESNGGE7jGiLpcy+/gFM/bUNoCjPzu98y7C4x02bIHY07L93lSEXPE9PkjbEro+4Y0Bj8JQK1aYyG4a8YepuYAUPvU9HZ7VPs3MBF7BlzYuhLXkCFJ2ZEmNAZ+op3NPSetOF5T3Hop/rvks8bezwy9pGfc+frGKRnjO4Z+cBSCvz/zjWGPWv1wzBzUs2Ie764XT9Fntdm3GB0Y9SH9tFDrzFD3uIBa43GZXYxNvw+Jw25aXmNXVTGiMeM7TGXRdIcMGglEEQwmCSGNhoHR/p3QhSNEY1fckascPqcA3fp2WXFntPnckaueqoH5E8yY7v9JN/fGeNGkGdvnjAy5Jq8bUZe1vCX+QIawFiqxg9WxIiQuUaDMiPwQOPW7HxdKFZEZqpxPWeMi6TJP8xjrB+08DDFmJow+P4eyDSZaaz4wpca2P+wPaF3djkAanxn3Jn9kM1HBJIVL+GMcYdkCnLx+IpppqUK7fYyJ+3+vINa58DRw+4zZiWmZpj2U5xQMQvAMd99GYnFZCQUGnLo+ANnREzvONGz28rgE9sCvR4xw1h2qoeROZMYw77FOMAzPeSjgNFsDjpk+AvKRGPhQV9boNE49Dkr8DCRAxtmvhVewNBvMaAWDsN+vdeKqSJ4+/ZvhO1mUSt3+RRvdtCHAIx0eWPYc1AUsRsPCZBil5J/90nPQG0MaJtqUGTY2og5dGXYCgw4t4IoSWHz2CD9hvdtPDB2xNzxkbs1QNEesBKaU0yyeVhE3j0wgshmkkMGEw4xQxAO9+95XknCtjqUn/vUjlGEIvYxR49eGN0oP+d5hWOEY+HMby3Dv3Fehuv56cmh3+nwEEN5gJ89VPVnHR3O67144P7+1XwMV8Nrn/snPnr/Hvw2n3yvN9j/DMae49we+/tw7l+O9Tpa2N+n+rff9MGfzAfEU5zb23swH3mK6nMfyCef2t/n3L9uTr70pv39O97xTwE8zNlzKedszk8O/8SiA8g0hoa0fkE/YTj9FE9XPMcPdfXmbsL+9yY9KKOPWsh89s+sWIBPSWvJjPydjwhEZRAmNsZuUkBNoRxImThmQITKIEyuBMpK4Bq2meB96GXS/J3e7EcIiAPzLEC99N137Qzu9B9gN86WP8No+ApnNEyWH3g3I7n0rywfOrDyMaU/b3z8h/22rRw45G4sIwbJgo0TwEQwwCoCoAhCEDAEELFSAKFsIIiNkVChhIpjTTusgEAlKZeKyDFr/ymJEF/u4iO5UXpnGOFYWq435NxB4vAezgprG1e88vy4x+wPK7/ZbceTm/jR/9vZ3+FH1RUzK8t31/O/H2PRB4pQNsqCYQggYAAmCIBhImAmgCEmYMAmlI0JrUKwipGUTKhF47/oiwXI0GcNCFkRuetrW3yYhnfQtph6h1IBax9/nLJyXxhsPPRtD/c545AcKO9PNv+w3XbVpf+8rmi1Lt38vrY70Bh+HoxKA9jEDBGjWigQwEANKhADRMDMREAMw2SMGuVSeSRTTA9xKzRAqup8Or3RXHwUUIRjD0VBRtYHlMxwZUj06t9MDsX9y/f9tQc469Iw2Drjrpivb8qBFSV3c6LaadjwUSVUiwGCAYaAIYCZYgKYIFQahggFCIDpGDEDRGwM3M7vxiVimhWRK+X7WHxKuoxM7AjCc4wtGf6FjhCI3Xzg532Uzp6xTzx9lwbiqx6e3XIJ2+2AgICBAFJAtZTKQgkTwIQACLCJGSIYgABhjFKBMVYYCIdbldiH9ZGZkN5+WZAreri3LhYpxLb11GbrIrd3mr/QgsqytpEfglSx5IeMmKrgB7Jsw8MrdwXDQKg2EcMQxgoYgIkYCIZiBmJBFAsKGAi1TSq+rlmYeu+t6/uTf6QGGYikZ3LTwqNWSfOMX3JSTXmv9FK15Oozb7SpQsY5+8LNLI6X99AkDGeBmHg3aOA6c9jOovP/k3llxHZvDK0dNrLzCcMB1+WN3hpgL3Azi2cfCwiaKBNq+D3/zLBPMVBNB3wWHoRsE2bc+h1RpduzKVXfb8Nife1zygaIc7Z6EW92vcllQZMNGJMq5y6NkbwcoY8CJ/XvBRbvXwgc7AOYf9nSmMpXRBYfBc9YGiYWNsXg3wEeMJeq8yMv5tY+JFgjpolM8ja8uFE3GMo5WHhQtM8IBwjngktTKpVLlX9icRcHROqcBb0UZi4F8iw6N/tHAzL2Wl7P3U3zVxemyVe6nL0m1U6um3YzsHfxxAWcs6YkIUXeNDPridgLDxTGUm/YQmBQ2OW+gECk2DCn7uIOsCxIFdAm9nrYfWHhIcELC3ZZz/ePh0hggRUAlsvlkkZeyNKBF2EiwIvwosjrWzuLI8DeAhQxM5cHxAWKfOh/ZsRNB1WS/CA3L64K1FALHsRGZirqHtqrNlNlkzBobDrJADCRNViiiKhQKPTNOQDnASoAlQIyM3LtqQXth7pGGQz+EGcPEJl8msLiSs0Cquo/YrJQd8Brj8k5QXQConhKYd685oCqMgJrjYoElh+o9Ki6IjJz13YKdrPKsJgP+M/I+ozMkAzp8RJoW8J925q1akyBhEiMiawGUsHKfUY/M3ja/P/mg9K/jmkTSRtFwsVaHr7be/wFwLSMZueEw9bfZJ1Fg2WIRAXEqCjW3mc0MWBFYE+YU/W3bSeO2rCJxbcqMHgPrmhklDwCbN3/3GbNar7fRFpuFiMoEligAt6ii+5jdIF04Fl+brS2HpminZTrLa5sgwIA9R/JhohaWvF6UKf1aCKvcDGtpzmqEkAYIKoIQOS9fMWRPl0PjNBbmdXa19dYKYSJLKYZIGhpiRZXgJEG/7GE7UZoZKXjlQ9cawP0MOo0Blim03daOEZ3tMqJkp7C86j0iRH6aLNzuaZ4WkCx4Y/YOCHSXxDAzsc/sdgKokDwHtXj4EYNwPKVrKS5obrnS+3OEzt6+LCgAgptAJUsKoDK1uelv3ZIkuWX6orcfQBmiiDan6AZCjv/JCeN/BaDaltyHpkjz1hjVdrAEKEcFMR77NlhqXiwFdWhA7Agri8bw4qH75BzdxZd0gU1lFpJrhtyrSBs1dxx1w0+VNw6cOn+FVvjlntAVZj5eh714LpgDjrCXQXmtqf//pkM5xZsJBrnXlANwpCJJwlg3oPUXmkhwoN6w2SzrHxDwtbkK9PkZRrq6Ylc+Mal8f1SwChdHmK/f9alJZqURieCUKkx79LZF1XpGfeIdS0/l0s9hZmt5gmks8B1OXDG8OsWPdcUzmfzMmENKghtmTMxAcl/EBCQQdk5MdHnQ3xlgzQddk89PErQFKk31FjBNIfd8sREOaBcjpmNc5SVkrz/s2TiRcO5sXHztaMfIMXsBDofsl/vvU8NCPuJxS9EILeHzAa4LIWJwtxTQin2mUEh+w/Tq+tyWkA7BwSCJavGpPXtzqe2hKsWWBltBGILSqcpqsRJScqBxCHU/HCiNeqGK1YEpf1MzByonRgf5Shmd75gtYGuk86fSWp/j47vzWINehnnGp1/LwpqBbcgAgL5q7zHgyOxUHsoGbPDlkgs9vkDabAlVaTsR1WswU55JuwnSYIQVZey5mJ11X3/7ya2cGwEP9zjvxx8GLyZYQkDUqmUQHkhSHFYqU+abEF4mVoi5r5BKgikYSBBxH/GCqYhxT6md9y0acAarUGKALK+W7HGGDSkw6APVMRVvQxRMS6BZUNXWZb65b3dYc2HCdXCczyzK+EjkG4BbDdM07uEDTxhTyYExaoUzD1EgijhZAIyHjQ8sSZjTHqdq45c8gGMOaQc6B5Jc4AenbdrX61t1YqBBtutgcuCMCKFhxhAzKSxMGnVHrU0ZNmkmZ2PVkWr2qEUB+YnuTnTJUMr+z+nVB88jZtK7zQ/IJAIAYbDyHv4D4pE1MgTDzReMUInXD44QVvZrNqNgKE8VxpEJTg8lSo4lVQBq6LYCCN6h/0TDWq2o1Mp19xgnL23c7vBpPAwnmeMUYyiQBUzofCkt2fwzW4Y8UAoMA71tU7aLHqtv5o39Mcd+5jRt9ikX7KWbWKkMpHJ6Z7tzgG8vxSs58BdGSCgAJfjP7KyuJ5AQyxpKEAKhImtJF2gA39ip4GfD6yz12DN6fzcarWrhxuAKHpCG4vAlBhQaybNJZb0yBBZa0FCgRhU0TVkZFLNxAMXnoth02qmcpn0EWroLaPOeQaGksUXf3Ct0n3MevDaU9thwwHvr8RYz4NKRYPhRQvkzQzFYvJUeLHz5rGloONFJiLyImM9Io/ImwPGYixgIrDczl9hN0VY+8+j9lw8rLVeJJYIsPoBWhxc14uFWSpUEJaeK6VTue2HOgy9Z9TaGQNqZl+8jEdRV+wNpgeIqVStMJopmWLN/BTBm5ppU0LfqZrvulunoJ/qGYDFWK+AibAqGKtIEBpjsYB59CURmKm7GdM2+nZ7IgUw4BljwSqiuS0524iFMZNGIM6gR7z6BXktlTYYcp9vtOzISQPEyQ2zzXoVbF5iQ4NnOtp7Ebzj+/crlyeFioF6kRSYlKlSgQO+O2+9ZUKfJzshVFAsRJ6xoJepYCyAAkTwF27H3k6E3aUIWjRMFaesJQAxy7BLsZ1JARw3kQKpXNqriAR0CoPvM2ol8CE+qhinYvUbFitiUCbeZL8OngmLA52dWh27a9vef2DQlPj+uWFGWfwHFODAjA31DiVcn60zIxGJCl4HoIIKqKCispAgaYWJEQC1IyiKBk2Je4gva1qrnWq3oyGKJQ0NEfuHnCjWhm0PKGXIPGOjMOS+o9aUfYhbf7yK6o86ns4JxE5/xqk6nm0aXCNwZkJggsVft/fW1XJ+mBleNI3nLQ5LzzAcEOj+Gn6iFgYhqiGxO7SlK/KqIeilo+WtrCAQCQJZwJFAx4+0gv/2Rw38lQRH3hSZUt70CD0cmRmemSDuqJ59+B2on2e36Av0LgSbdKCmwJAHRq4OXPfWSyf/iyMyH9WedGDGKeqKQ02IBI6teOCbKABr+hVmPX/8e0CUuu3GFzhAhCkEDVxSrLYMSNZ3+osPPTtRpqT9rda7SS+Kk/K4G47qyvHNtR5mLp3158X7umbruuN43nGvITWdDZ8dt6i58fGziiKnxXHQY3Pd9tLhMvF8q683Kjpzp5qpFUH4VV34GKUR6ktlU3iG3DHyNGXXnBEzA+K2hwXUisi7N5lisVmIwEA88E2YpgcIbgkUOjdERDyJnVycHvIldv8WA2vTrYgseu2/E7YUwc3oL3abSi5GSRawsZtANwYi92LmwP86Xvqlvu3ZAzO3zZuN/Reo7Zd4n8i0JCjz2QgoX0X6vs4vnn2CvavU2pzZ7wrVExXWCxEefl/1HFDS/NWGvGTUif7XTGosXTAt1eJwpllqDFM47ADnMcQjT5ZUr8OiRWLODXMDKKkHMdPJMhEgjafjlma1di31LLdWp96Mds++UnkBtaBV7kEYnR06XGvluPZHOkvHxw+dtX9WYvF33xRPu4+ZiP/FWgjoo3bu0xUl4ri6u+m+QbDvW+9Qt7iY4kUv81M5g7c853oV3q/masr7y5g5aWzDyMHkZLTUSrk+k//l8D/+HY/8HFKYdH9Rdwl3uZoXghlQ4D/MgNjCnDmB0aeYnDatNEerh0/eMjMS6QNhN7kE4jje2EOpFmrYyhHM9gcrpcZsOTeTO3Zc0f+TbvOnN8fluAUECzX8qXWVBrde8KgDcbL82R2v/Nh7DBr9RkwVtt/tmXaFp9SnZV6A7Bj8kopzHuMaS00hAvtSEUEq6tuAExB5hMGLpXROk0DM1U3wouk0mJ7Roy2/NtxfAukCCaCiCKigooIBqwhBCLA/13Wf/d+COfyR/sowt+JbMe4VE3G4efPVrTcphz/4ihe4+sBo+E3MQsV+D07OkuPEfjJzBMLJuqV4+/YBeOTz7HrLy/jWvsz3909UROyZXvoajzxVJ5JeXJwz578GV6BNm/w1erH+NkcET1pMVyhHLFVg6UCJ3bBl0voEItXSQRSRwkuzHsaqtWCssUZFL+MD3q462KVNA9uldggU+05MucuoYo1RbbpkZAtvPVMupHnoT02sNMidijHqS+z2qlcrG9y6InYyufhvCnunar0QTON9P8U5i6F708FB9LkxStfzYwMA8x9pY4ZhnLzjiCPoFY6xwP2a2cISvYQlPSJdLRZjjVXBM3cDg1XsJSIKFoX7h64IkfcyvIwFa0UmqZXZnG00QogwT+xfIzAcwCEjn7Hbba72Awl126s9rewRmb5HLUngFPKLxZnH8AAch3dmzqr3WD2xPQee9EgNu99l6MuMjb0icujlelc+J3frRPKJBUN5NnRdQ1VE8z6ooKLCCYSoVD+I5YdACVSmi4pYVVQDQANsAEpAxT6hjFQGtjGxsfunonY9nfK1ZIzXVswb6o3s9oq7BA750GVrR3wp1XinqK/w7Dmvl4pIMW1Q4F9PzY+PuRqG5D1g5kyKkzrjz0lLY0HQtBI0WMK66vvXwEjCrP67UIA/CTez8nTN+1Txq8VBpgeXQRHRJtViZ3eoKoEGG7afFqzS4Hh0i56mShDEShwTBFsC+2YGgdGNRJi9LHaPho5QA+mZ6Y2ZAxtHPGPwLT+HHBApJ3c/Yn2s1zwTPNIVHfMkw+NJrZ8DDPhPFclUjCwdcsNJAxZEnI/GhiDS+mj2MA0ne7lD6zNFmlwimUNlndxOsSKdC7qCgJwkwnarcyTyH3SRs5Nydt503FI32qO3NufrNsp82nuJ2wr5X1i4OYjIesNYXybSte/2C08btdhJ23EqPE6up/hPPu1H/7aEJ3+YXWtUsAtFzzaPmKbivmq1c6jzzmzJlQC2OaMikmnNX2O3D3HSi3y9kafWRUl8WEXGmNgypOZmbyFzdJ2WR9Zad6FyJKzXAcJb1N5KvlSmU63hF5g3C4SonHfRetKG4+Qj6QyOoSJGhYoyI7NTpFowJVJlJcowmCTuZnC6mpEOTpmvjSv15NdLo2CyzLy8GbGfmtx24vo8EdKOh0sIKIuT9rjcL0xGbVFph5MieQpGxigqw2yRblZi2DQq5bWU+mTgbGakC6t3uy5pGxDz6tYfGEQfzejfGbn2wTpsqZaPZvnJWt7flqmVmPmCQR44AgO2ScP4pcwTqY0sMGZSpafkKigcki/YfVlUPrAHST4EmY8GzCxrPFK6IuN5MOOuJgScgsRIE8kbq4obC3ezn5pxhMQEPPO1bTvTLty1gB25rcimjVsUTf6ypWHSqjRdyzJjRsZWrQYE9sG8KxHzrhvtfJp8j5hsKoFN+pp6w7Mf7x9Tys6ycCykay+Awsm/0A1MsI3wXnBkNj9+ZPSJ92/gYEwI9liTr0OqwGDM0vLYd5/jGcZbHt1SDt/yHOQx4xsTK0Yskhz9oS4+LWZ8NJapsWvVwFbDnHFcaPzofseAN7pj9695j8skgdfZb+x5P+jApeG0wMa531/DMgO4xg8+QNgeM17OuDXvvLw2I1lWNzai9ugxTCq0A70Dz+0fKcXEIv/v1X/6o8PtBePk8Z3Ze3RGD0jv0XfyCwiMygiaDHL7FeBghOGsPzNAPtBGm/MWDwHNxLFLvRyXnzk+MvSOo29g8hoJ9Nz+kW5wQmGSZ9QpQ17tsSeKBvaqsWMPud0fb8hNkMfZ/+jE1p/RiM2saB2x9C1/czs/JiL6mSkqow8deaBiCV+t7Xzo52AgNGrmRDrsjgN3f34Cfgqe8Q2NfOR3FA3lSfsp7nxg+OfONxTgL6F3/yKG/Cj9WxsOMSPrTh56l7HnvqNAPSr/MH/TvQYfsEWE64zr/LQ/8e6f+8u+qlhGx0Ue6EXImUMe4i1+VWMPe9dTTbui4Y1Z8lWMWjJ0rZE54B2NH+frHvpwQoMfKHjhwNqRL/qgtlsRKb6/pvuaX/H06jefJTUlTw9CLk2OSzeivIXDkmJW5JBWd3tyNzjapHwJ2/LYwOsdeOgM0hGD/rXf/Mc7muRbLxsEOmEMVPJjjW89feBpb0PFXt3AyBnLQp4dGFkLzRS2lB9SMXpXCjKz9J2XL3xAFWbxAQNaAVR+4JG8IkLowhGP/ENLjwH5N73oH2OOZ6r89/bHSUOmz84PWPIFFDnaW/yqHlsVZNhsXPiCUJDMf2/f/CRtz7C3bS6jSoxoxS1pKosN4tmrSE3J8InL1YIB+yThkPfUjBE3UAAolgv7uKTdnw4+/mXk8+58LGCkd9p35M1fmFR30iQQMTV7sSDbTht66jGbj8uIGowbmDG4e8wzUafzXwcawz/H8K9tmdmWZ/HgI8I78n01PSUhQCr4qdBP84xb7v6l8uq8AL+w/mk34ClZ16jk6QeWum9r7/R2Rn6phP/rfICtBmgP/1i9Yp0H5hefXmwcZ9z6mNPsQqQgxZ6T5cj34RVV0c5fQyStj74egWDrEvqBYG3alM3moKtIWTia2x5947aAMicwADwMe/GB+Zz5WrYdcOqkkXw6aye3RfqvpWxFvqrfEiiNeg9TzJh391R+8feflqb60MWXGxOOfG133XINDCXYrzHtpbeZGIDH2XetYOfnAEYtGewIEcDGNA9rxT4OmfYy+ZAqi2Q4bbPaVx2YEz7ru6ZlTrML0qBnTlkPRjrPaaTzt03lONPaumkKD1XarSJRm1k0CqZhB5je70fkhy5NGE6vLmK01Jl+tjVVX1+KS91aK6U+zanUZDOTfWOITU0u4La4KTtzgupJl1Jw9te6SgPnIEgF7T1HGDmRzbq0U6s7J5JKlUD+2ypqwgboWRg0erqXc6jjnDpNw1mgdbmc0FvBfKe/ReuR2daDNMBJO2IPaN+pFhhwAo6WhCBv3KV0/ftoDl0pJ+i6br+nZwPAPmjCfe9gEVoQXb5+CwYIvRYgngKE3SAATdcUGPqVIXW7U0um2L+vJBEopVmbKHm6fyS+68L1e0lNc/37unx1IOg4nQ7aqRf4Lv7+IObM88W8jq6nTk1Bk146wRd8XHxhIuhaRPdyeleu1Lo0HLBZBjgYTnwN6OxvMe+5+N0EDafkIkrD4D4nhPlLsYAgilvxOOAIk3XjL6cSbybab3ui67hP8DWtFg4YIdlbK8qq7cWN+7of13Hi4ASxrmnLxeVuEtPXBR3Bwn9Ovs/1EW5BhOeEr1mkrKDL6aX08lk3ZfcCgS3MuKunwCeu6/qU8uo3Fwtgjb7I55SSbbY+/358o6cchW6xErAAB3gG7SI5xYF324+nZPteYCzS6Lpcas3D+v22q6f0U0/lV3eJ+/22L25v0AT6yqg+2SLcQdftrj19XhUW4CPKZRHptADEfXwLP4gAdHC0TTq6hfWloOs6VQqcSh2KCb6PA+sIXslJ2dMma1YyXUfDCPK2Evusgynz5evr1sz3/Stsff5RwZPgKB3QgTbiOAQnAPq7BfAd2F+j6lodHnY5OGyA4IalNUWjCbGJG0fFhlVDlRbrtq7jspQamp1z4+uwuqdtWOmDjh5EAALruj/v2rip3wR8F3Qxpf3qmJrGw+KwiK06KY2Uf+11W61TQNd14j7T0ZnP1vjWziiCxfk3tHTwRdJO742jwEq+U4Tfz60LCBrztB7t2vBFWfN04Qpe+aZYzHsuzuS93XXoUZZDFKZ3RuOLTnn1QByLdeu2AltZLh4W97UGpzRL53wffHzf96ZsrzXguk8bXWeey1USv4m+D6RgnQ64FpvA9xclXr1Oag1aXNepsht8fz7UgE/U/00AXV8DcauK2gQWOuCgleYH5/K3O6Vb0516X/c5gXdosUDDQdf1KJVuHIXpG1yrex/YasG6dayDrVwWMnk2WOJpwZSieXFnETrf83RLF9O4rgvB8hImx3G0xD7vy7mxbqVgjevCczl1GsRr/Tjc6oTLf6egrwG6wPfxiUd9rsD5dNK5D+v0NWuYxxR7qHyFrWtYngDXcXFdy9HY0NtHi6Mt/zqcFGCQSTnXX3n969ess/R1j2sl/KP86adY6169zlZgHRbE1229LESVojz9yj0HaNtn3dZ178IHUlTZBrAPl7TrCAJQO4899ZsTooitACVPsw4niNZQtQ/rtp6fYP7Wx916BavSPNYkWI4fZx4L+jTAaUwh3EG4Nmga0ILAwNaj2FMfH2pqANZtZR2XvXyBb7+1wO6Kf5DzIwF2KMvuIR8azsD6kPD6wCqUvpQ1FCtLyoec5YCws1wx4bJYWI4p7wp81NF6xbK0Ykl5DUc77D04wGoJB7Zk+RcsOeB9li9LeVlargDWLC9PGLuE1ULiMvOimG1WAZaALa3kJw0zCgQ=)
在示例 inception\_v3 中，输出原始文件是一个二进制文件，其中存储 1,000 个分类类别的概率。

我们使用 Python 脚本将文件读取为 NumPy 数组，以执行后处理步骤，并对输出进行验证。下方示例用于检查 HTP 预测结果是否与 CPU 预测结果相同。

将输出文件从目标设备复制到主机，以便对输出进行验证。

    ssh root@[ip-addr]
    cd /opt/
    
    scp -r /opt/output_cpu user@host-ip:<path>
    scp -r /opt/output_htp user@host-ip:<path>Copy to clipboard

Note: 请在以下（compare.py）python 脚本中使用 `<path>` 上述命令。

将设备端推理的输出复制到主机后，准备一个脚本来加载保存在其中 output\_htp 和 output\_cpu 的输出张量进行比较。在此示例中，两者 output\_htp 和 output\_cpu 都被复制到 ~/models 目录中。

下方示例展示了对特定输入如何比较输出结果。在此示例中，输出将保存到主机上的 /opt 目录中。可以使用 `numpy.fromfile(…)` API 将来自`snpe-net-run` 的输出加载到 NumPy ndarrays 中。

Note: 默认情况下， `snpe-net-run` 将输出张量保存为 **float32** 格式的 NumPy 文件。

    # python postprocessing script (compare.py)
     
    import numpy as np
     
    htp_output_file_path = "<path>/output_htp/Result_1/875.raw" 
    cpu_output_file_path = "<path>/output_cpu/Result_1/875.raw"
     
    htp_output = np.fromfile(htp_output_file_path, dtype=np.float32)
    htp_output = htp_output.reshape(1,1000)
     
    cpu_output = np.fromfile(cpu_output_file_path, dtype=np.float32)
    cpu_output = cpu_output.reshape(1,1000)
    
    # np.argmax gives the cls_id with highest probability from tensor.
     
    cls_id_htp = np.argmax(htp_output)
    cls_id_cpu = np.argmax(cpu_output)
    
    # Let's compare CPU output vs HTP output
     
    print("Cpu prediction {} \n Htp Prediction {}".format(cls_id_cpu, cls_id_htp))Copy to clipboard

**输出**

![](data:image/png;base64,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)

## 使用 SNPE API 部署模型

SNPE SDK 提供 C/C++ API，用于创建/开发应用程序，以在所选硬件（CPU、GPU 或 HTP）上加速运行模型。[此处](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/cplus_plus_tutorial.html)是一个使用 SNPE C/C++ API 开发的用于执行模型的示例程序。

## 使用 Qualcomm® Intelligent Multimedia SDK (IM SDK) 部署模型

为了改善开发者在构建整个用例 pipeline（来自相机的流、预处理图像、执行推理等）时的体验，SNPE 已作为插件 `qtimlsnpe` 集成到 Qualcomm IM SDK 中。该插件是基于 SNPE C API 开发的，并提供 SNPE 功能（加载和执行模型）。使用该 `qtimlsnpe` 插件，开发者可以将他们转换后的模型 DLC 插入到 Qualcomm IM SDK pipeline 中，以实现用例。

有关如何使用 Qualcomm IM SDK 部署 SNPE DLC 的说明，可参见 [开发用户自己的应用程序](https://docs.qualcomm.com/doc/80-70015-15BY/topic/develop-own-app.html) 章节。

Last Published: Jan 26, 2026

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Qualcomm AI Engine Direct SDK](https://docs.qualcomm.com/bundle/publicresource/80-70015-15BY/topics/qnn.md)