# Integrate AI Hub models in an application

This section describes, how a developer can use a custom model in a reference
            application. This use case is explained by using image classification models in AI Hub.
            Note that pre-/postprocessing for most of the publicly available image classification
            models are very similar and is likely to be supported by the qtimlvclassification
            plugin.

Developers can download the AI Hub model from this [link](https://aihub.qualcomm.com/iot/models). Select the chipset for which you want to download the model.
- For RB3Gen2 select *Qualcomm QCS6490*
- For QCS9075 select *Qualcomm QCS8550*

You need to push the label file that matches the model to the device. Label files
                    are available for download at the provided [links](https://github.com/quic/ai-hub-models/tree/main/qai_hub_models/labels).

In this example, we explain how to use a mobilenet-v2-quantized model from AI Hub and
            integrate the model into the gst-ai-classification reference application.

| S.No | Reference Application | Verified AI Hub Models | Label file |
| --- | --- | --- | --- |
| 1 | gst-ai-classification | googlenet\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 2 | gst-ai-classification | inception\_v3\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 3 | gst-ai-classification | mobilenet-v2-quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 4 | gst-ai-classification | MobileNet-v3-Large-Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 5 | gst-ai-classification | ResNet101Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 6 | gst-ai-classification | SqueezeNet-1\_1Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 7 | gst-ai-classification | ResNet18Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 8 | gst-ai-classification | resnext50\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 9 | gst-ai-classification | wideresnet50\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 10 | gst-ai-classification | shufflenet\_v2\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 11 | gst-ai-classification | resnext101\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) |
| 12 | gst-ai-segmentation | deeplabv3\_plus\_mobilenet\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/voc_labels.txt) |
| 13 | gst-ai-segmentation | fcn\_resnet50\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/voc_labels.txt) |
| 14 | gst-ai-segmentation | ffnet\_40s\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/voc_labels.txt) |
| 15 | gst-ai-segmentation | ffnet\_54s\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/voc_labels.txt) |
| 16 | gst-ai-segmentation | ffnet\_78s\_quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/voc_labels.txt) |
| 17 | gst-ai-object-detection | Yolo-NAS-Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/coco_labels.txt) |
| 18 | gst-ai-object-detection | Yolo-v7-Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/coco_labels.txt) |
| 19 | gst-ai-object-detection | YOLOv8-Detection-Quantized | [Labels](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/coco_labels.txt) |
| 20 | gst-ai-superresolution | QuickSRNetLarge-Quantized | Not applicable |
| 21 | gst-ai-superresolution | QuickSRNetMedium-Quantized | Not applicable |
| 22 | gst-ai-superresolution | QuickSRNetSmall-Quantized | Not applicable |
| 23 | gst-ai-superresolution | XLSR-Quantized | Not applicable |
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)
1. Open the downloaded TFLite model with a graph viewer tool like [Netron](https://netron.app/) and open
                model. Check post-processing requirements for the specific model, which needs to be
                updated into reference application.
2. Here is a screenshot of above-mentioned model. Click on input node of the model to see
                model properties. Copy the values from output node of the model. 
    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EN7BRpBbapYlSDuJ21XHMIcs1abQ1GNuCqi7/ezdFfb/GmY3TzHpYZK1Fxk/8dM5vzcrpFgvdyORuK94LLSWXyXSVtHoe+gurE2NTGii6x+qrebGpidfX6A6YeAEVMGIvQG+3NGGOOw5jDkjsOY4xdeWumReU/dSFx1Y3RRABQsXIsAtAEAND4n1GEY83FDFcZs+26tsy2mf2gdnWNXdeW2XZd+ocLKxsFU2PYtMtO6TY9f1rTBIgGAiACQI3rr0DjfwLl/S+ABcIEF4RBJRXhijluCwCEFoTlCS0IGh0xM8irf/OMvjVCNtIgIAEtqHH9BVqbA5ZRXW6RjU5MonMBXuqx1GJDVrCjnUhRaKqBUvmDl1EitbyMYmW0Xq5R+iz6h2Z9oeTaNApkNrxjAKMdahliBn3GYSyqO5YAbXtnYK06Di3ssew4AZYhMTsykdIu6jumYbsYC4y1CepczkrDsi11hhN6uOhTDCLLdgm2gXyBlpsYKhTVcFi0HUJUlQavpIoqdi/Neq++lGoN83GlWzLXB96jCodAXTymGintnXVLmglLBw9TwUxlvs21TP1YMrPJkKhq9G6SsQfvykTpJTd4FuVSb2kYK69cmcNXRFOtzNkdiEyemaVyZ+SSnb+AwETVFRxXuW09f00CdufNNCoFaoow4wsEV7EClw6JulxU8ERzaFwxlfcbGMEDMSl10KR0bcG/tEkoxVjLxWL+o5OwnUanrWWaU+X9jSOc/REXDDWbV3KD06wpdjYJJNr2JHFVg8ahc0JVy75CrqlVAswKFeFOVAwPVoAd4RW23FdYaqzEM7zajX2NbxAOuXBbeTbiNno2MvcHnSKqCCeyPTowy++fCwjBmGR59V8caew/jyAKtdGZhWpjXw6jTBHoJLghzSqqcp5bqpgupQ6alAb/JiGVw/Q2h4wROUvbJZEZQSVzeZ+MTvNO69HhZlQVL3ND1QxPZKnDeisocwWpmBpDXE/nQNBvsVCgBgJwamv82VQ62JCsptykOSDOKluRnlXQZs4emZJOkRy172cDtBlheUwxUhGxKAFqRSylk1Vyo1n0GUHI+42vHCj2WyyAHgO0CVfFEgGKWVZdy3Bvc6QQQsPQqueNq+MP1KP/l1Cl6+F80kM5R1eZb8/ZfMHwUAlCVK/pfYC2Pelv64bGEN9nd94yglWLWwqV7hY/ltUwEW4mIgpcdQuW0PtuPpa6Qps5uwORyf1GObzKxSTeVS+0heCtRZjjzg7C7o/uJhFEVz7mU1VV41xChciUiq5aAPz4p00ZhI4BCvXA9PXXX08/zmtWy3Yi0qNabp99JUXKKNxdMkZbTHosIICaFS+LxN5P80qFZEZd6v8cBNemocV8T4hlLuMvnzhiqQW11HLG7hY3jkgPPegdyCodwye3HOmIVtyQeFfB0EDXbIB0ctvr8kzEVLOjNBcE3sAeN/LYWzK2QJgUDJRwkOENj3T1iA1Uac3DzKxWTlMCwqSXoG1XyE01kRE+jImpglAyMMk5nqdp+gmmtlIcxuoZa2hg+RazpA3jWD2rbwiTlvgrUTCNWK/UkYJN201IW3e8pdLZTDHT0HtFQKoy84NZN0a5CqYBvTEFwyO9YnFTQO+oKXdzBUqDsHkE2eGGNVEDmkFlIlHcz5o77E+PQITyu0xbau/hpVIsAqUGjF8ybrOmqWx1Pp8ng69WSlLGnCZlThM2wgRASE5ISgBAAAEgAggAiAACQERITkhORASAAICmHTMVG68bfrCCpvlGpW6ZzsTiD74aWS0fJVBflkwCmnEA2C0hYwhsrlhIvMSZtt5I7oPAl85lCQddEosuDlDbJc0XQdTKAP1T4hAZrdo6Q5C8FxTwrBagA0wAagAgES0XUAcxCPCdQIVIJgtmMAMwYa0h8F4KfGgC1NB8B6hYgJWjYDVYNakAmicgasQoFs0339BD6OsNT15pCLkDVAToqqKIyR19GwDY4oBaestxFoKdGuBE2xFQExOIggVYEuBLPjYKRhqaALAGaKocZ+ra6A4g+ZGP1UBjNG7bIK3mIAlwHmzRG1QJps86GADeIiUkxyLerjc27w0G7DDMRqT1IAvsMNyCGG5BDAGOMRylYa2B5LwIBrfU4GZLZfJdBZvHgfvIvksBGgPQCBAAESk+myVCE3zIX5EPJBZft2kUgkJCXxBARGjRF8DyyQBgGkBBlFICBh6Y7tqSJkSz/QwZHgN+iAg+a7cy0M3v4S0Xhsy4RGTVlNg2kVVtx19oW9URIKK+aFvV0Z76LNvOTz0wmx6PEi3G8A9qj1vP2ZKEDSBuA7BtALa9CwA2osfLUA4Xt23ADqMEsAEt6ssvqBcI6nyBVXf5rJDjouxmV7jB18sHVzhKGtTXUmgOLL3kswCAFkgkgAACQAAqBGoMCACRUjvY87lFi17xUi9xzz7KD0VVawq120hWs2Hrh0JZXR3hnp0z1VAqC4FkHUuxthOxN7zXY8cMkc6tcKpoRUpiZn+hs9cLbW4iIlcM9ghHhgktH699/S7ahY/9UvfI8WdyUca1tVsfOpzjLsjV6lsgyGPBNE2UMRgdpRCN3LJxn/KZ9rCq0mjZEUs99CHRdtWVo3CA/C7dMEwpNQMmniummgGLFoDMDbu6aT25rWfBc7eoeAeytZuyva5e+lYfaMWOSwVoaTvzsSTJgANYeZsJnK0v+hUeUed8tBWFnq1rVA4nbhkc/5h6jvv6AQ050ZcsHYPoeH1BmW0An+Oi7IAv+UNzaSy4vrEFmG+/2llAQJQ7SgTCjQdbMNDYWYRD5sYco6oxQJJe64xihUOP/GDBVCSUYX+Tj3p0esWbCamuWu+MXeIOqbquIVwc7sHsBW5PCYLIY+/33QpuBTcYztxUoOXt6poRe2gq62Pt0mdIDztKh9NGuXSWob+NFjyvN1pX/qVUk+euZX0sLj0eLTlx+PRoWW0JjGqcP6Y/kmm3b/DhziACL8YFjvBpTw1CuzCjbsN8u1RL9tK3cHr1ljwjqOMviMzee2jbEMbqVWSG30YFT2240zbGFnj6j18kbwy19jTWuYWL0z8pRv8JqhNsRqxBgzbO+8//Pmao8fZFzBaWbXB7Jik1LHwrXr3UkA318RA9CXfOMXb0Js+e565lSTM7a/tMRvtAWi1f1UHI5qdxglVKZa/VQIj1rLQhCKNDqNgKFSe+ZQ71GiDObPH5w6i5n/p2QZgLS8NBdBWZjahReWlD/9RnLnPy0nCoCgL2wsrn551jKCklVlT98I3uTu7JaTWXxqKkB8f6h5XODC9FUH2uZaJGNDjm0VezOknSdv7eLeYOd1YZcXRFpdGP3uO+eUsLTie4zrrb9pY0l3Np43IskfNwqnvBGTeMnBV8gAqOfskfmktj8xDUHbDewdIusO6z2B47zXQ3HBST8Boi25B6tjnbgqW9YFnSRPJB5auM7R873eQYWO0OuDeVFysNdW5IoYJQNzEMSqZU1K1yCTtEqOo9vlS0NrbV9vr3OINSu2J1tDdq9ll/v6VyKtNUZg50N0rSykSe3gavtltLubNp9WuVS2HScnhXmwlUdx99sg3lM4FwLjHUDb79kj/0Znm/AXF882nAfG/4YKbl+KIhatzSfludJv6aE8YkiLyRLUT7+qpjFlTuJv3WKd63pANLPOYsw/pWpULxSeNG5/1d5g5punzodNqXVkIFsYxt24zZdjXL2DYrrrFtVsMamD2pdEOrmo3L2OxB61nGtllxDWuoMYESLdPsBjsGYmxDuuXcg+6yWwypqevtIv6KXiwBoSKKDFo8jf6D/h+UGaCNPsWo6+gS9M6gTVFfDLQ8lmlC6wxaEWh9rF1MuzISFSwcxhhzHMaYwxhzHMaYwxyHMb3drwdjjsOYwxyHMeY4zHEYIw2mAq0BQoGJABAREgkAASCAACICQAARgC4AiFT+pmAx9bdtKHam0UZwXE7lMKco1jQtaEnug4Dl2Cv6gjsKguS8oIIGmDZ9xjbHuwqyeW/gwvQNfoRvgTsLnhxo3I/y3EZJvcXrtMeLjdfZ3EhHXw7X+/JyuN5P8LTGgfcaqTzub/jzTGsbJO+FceLL/sY/mnXNlsrkuwq2Yeb9CtyubeA+0jibVXCkMdZpjWLTWjw+32GwGjbtwinnzdBr5juM+qexIpumAKTQNu08qaEr7gxYosMYcxhjDkuMId9ne5tNM4i/9vz6FlJnxuz3+5N6flYfI0bJ8S5gHNORaGAEHRvOeuWL12vIXfZmVQ1RYPEas1aAn/3cUxi0qHwJAMomjERiauzo5Gmf2/Nzukh6Orv79Y81fa9BcKtDhHxOwZWPK0s/fLrpuCojEvsuJHQOsUQ9x+HMxQIlTQIKpmalOU8gpTo8gVg1ElpbAJ50hoqrS3ee++mdKBYhJbZ/kXSc4vOzvYSy39t8B9B/E3Ubna75FAFmZqvtlSvYQOds3PH7cg8dbGlmiLs0JFpwh7W4psdvvdHQJCRd6mYl9cLnvZvTvf369TaUzg1Y0WBHQe22SInt9+dW4n5B7Z/b+Q7gOD172Oo4VOdUPXVVtcwtjSTBzuXFnlXsePmfUM31olvWxzr807R8RlTjy3JuaLNz5P1GrPZwYSSk7rDIhvLcITjCRt2RCtvcP+Y4pfszXYw75HS4AzDae/SCPBOsc6qevUaHmtFtcLQi8eOlcYRQcZab1VaWNMO7xqPFbUu+BXX2iTFJqN6uNCrp9D2edZEN36usQptNUmOzeHnaK+3lI7bi5bS/AzARqX3h9iFuIO+puGyv+CsCzVkSkttZhT8TlCLF3aTVrXHc6JbVaT5NN4rcgkrfvBsd8zga5h6hnAmqzrqvDeW5/wY2D4GpsRvOJhSuvKpb+u0ydnH9TcNn+duF6iKPOdTfLjSeuAe5HWblEWHSkONbWeFlimlX44poxjPLCImgurXDdf9WqoQ2PFhEp1e8xFv+sN0C31EFEKSMxrJs54xSEgTYQFyD2fAOm8baZNkY21AC28bYErJsUJEcDPDbn247uwsAEQGAmz29GIhARCAth2lBlFe/idK0BrA8rUF9iQhUNIdqAaDXa6iRYaCWNDeC4/Iof1ABQY2R6z3d3/ytsUth25Lb0Ta6G99y52A1KN4LMo7DjUcxq1HvALyXDJRuns1uRDbfJV7HkRV5PYIIiDx+speXkx3ShtT2CkgYP5knOZ0GnCImVkS+j57qk9+EAR2B6i0Q9fjpcH+vCAArBpoRBvk+djKn7h87AIQKAoqDDRg7xefnx4JF9pyU0kTejZ4otYThKmQtYATjJeS28MbUsJj4/XEjHCVvdQNrxuimbdJNc+c4voGZQuo32JkpYISD5PP2OvN5pOJCzaVhlANQv05WxXahMr7HF+Vdc9mfCmULjzGHMcdxGHNYosNuynFY/j/5//2FMTZtVXUoV9+hob5Db8bartpQ36GqqoG1rWpg9Q0NDVVVVVUdGauq6lhc5Ij1CcyE9nQInMmxZneH5o45vTGMb+YLZgM5qed5wT9C2mPHegOzIb8iv2zEU3zKeX1bmhAeeyr36s37fJlw1N83N3BPB9+Z7+zbNpdH8IzD+xqYvy2gVmtzae+2XOvpFe+xN/eNA3qNrIIEe7JDyZrnPV5cktSK0DLMIkhnC+zbwT/jNecV7rtuzjisP40ezyynPyHMl1VHquQg0m17cz99NctaAr8c0dgCd35NVXr0jFXvCEmrhO4RZjwuxfGOZ2qztM8t1no6Pr0KtuU12Qv8G1cTN8p4Xo/1G1QPO8pUQE74YIyJScF76v1nckoeZKY23plW3iTgNqTpPb3iHpjv9XOSsZJytZ3bzpaiW3UQj9LGXPwhP1jXk7SZKuL0Gj2e2bDe2gfNGG0vx7evYeb4XsJBozP7u6PtzYrlWj8cWfEkNrmjBKRDXnXxKdP+2KYRf0Gcbx+tmdr8bV82w1jND8/BXnzhaXXdDpWZsSsWOLOsWEnILRaeWaNHCSQYUdzDbDg+l8XNgPU575Q+9zWFOU5x9ZWrhWO9KxbGwHfbNE7Vvv0Nq0DCEapiqKpjDrMZyzDHcZziGluilggSBCkrQeIRAQTmOA5zHOYkZQzkDyICQCJS0WmfCtyfZlksVwCmWAAuLmFf/jJw3jMA45bYUkc+9liWcIy+cY0lAXla0mrwZKMIkO7jENlY4rgnVQESozGAGgBIRPxlPLiBEwD4DlAh1rOFc3AdgDHjHjQBohjNQFcNcKp34zTYG20BGE2ABNQkDclXn5CU8AgIITPg5gg01fAZxZ8TeBsAGM8g0RTPci4EOzWArdQRUAxNiKUDThNQQzk71i1YRAPARMCZcPaUdNOOgKZHQI7gqE1FxToVZQGnwQ7koLLyblbKEyANncGdiipp5EOEYUqtdUqtdYAYw721Rr21RgAThl3rGPncUvv1X8gG3svlbCjOK8HlpDL5rpI2j0NfStjoVavrCZsWq58ul1NaR8Yx1naaAQIE0CIVB9nvC9YQxmTuLsWMMYcxRgSACACIAPhh8ZEwEUAEAEQAEQFEBOgK1Q0l0xSAVBTdH3gdcZjauTyrq7FZjc1sSAk7XCVlLB6OSpQQxaN5fhpIGwGkjANVMlYCKWFDStQCEjrqAunc7NJLxjQD853RI5GS6TJvrzOfLnl7nbPuoZoOHK/PhOvZYUWV2OEfho/5m0t6OGJrWl+umOumK3PZU3o4iNDuwmnCXR6Pz/o11xpPu7o1vcbTxhjQd5nShqMvdJHHlcVmc8BsUgYNKpEw9RI6l7ZGvoTEvkgkACCiLhDP53WbSyQSEvsCwBoAQABQSIFSynht87y/3ufn/fU+n1qGZKxanBX0xnB9gODi6a+xziJh1xiKNT0um73SHku5vjOa72b6Sq36kDvOKV3oGXvLyMfdNfB7XcUV3Gkfd5eudz8mXe6xcHnXlz654i1s3iWtdqVXy5qSDn/3dhpUnp2Is1w40FyI9s7IhZpUMfGxekquZxIGjh3PY03c5NiZzq82A6jcC8fECB0OFznXvyGN9z3EjYwPuW9RSltf4JCcGepIbmjtQO3Vqm6XCCrt4V7crzJUbQKI31TU9sF5USj7SrI6/sJr9TdOqm5ZEveQfkvdRmRm56OLt+y8+l0YQbVtYypG65v9jT+529l4UadNsLij+XZgoG23222tumeyLx8qKnLc2fGhX9+NvdvZ47U8V9xuyuoznYUw7zeecyg9UuOEomj2j5ulIsw84OsW1fE8Fe9BULsVO8oqYebRXZAG1/QEpJgZORNOZaqqvtVnNp3g+JoMxH8i7unANVYbJqwyMi25Z5VLaUB8Jiu1FjdfIykc5sJZ0c65XI/bWEW5iwWxWaD6KXUmiwiJod59NGfa1cv9ZIFeD/oRQdB6c2c9Ia+6Lx8dFIO9HpQu+r63UTUXeNtevV/Lw9zRKFUSj0fuHGh7XPqKe+7v+FMqq5IWhRIT8SJ1t5NGC883b/+HuLWFq1r1Dw17la+niWJbcB14TtUIk2/UPg5nHDgA3G7pRnfVlLGaj8QXm72UgDXjJlz1gcNdrf661y1vx+Awc1ktOOUWTauFiSSJXNnFSIvb1v5hcOYcrHUEpKe/jmEsMosl9m4PkXme/Jiz2i8yXS1AkM78v78BN9eriusRv+f/NS+V58uuWutTnikHaFT6I0+fu03x112a0xvpb7I3zEGyQvXTaFzoAv75/seuynyTvcGav4V85f9e/z0l79WTE3W84xxLlmJZHyd4q2Qnz0T6af7cwa8BVDiFUso02na/As+3M3Gyc5sPFBNj8J/wRsvXU4Nw5cg7+clnH54oKigzAZfhPIfV0HIcY6xG5Vxb3m8M+K9rrWKtfcLxf93rXiIDaWa+0528dvhuqvQJpo4PiDahLq3yFR5x0QdKONz2EJkVhFnBUgpjpyC/XCmuR3jsjV6DfL6cSDG8Z4N7Jno6wp7n59WhnZYe9WdkVUekCYP+ogTu+bzquPC7ZJWkVY9BRuD7YwOfTkvznfaN9hc4//Tr87UD2yWxr3lf0k/z5/sfxxRWCmikzSp4Bd3KJO7Oba47Jq75HN5o+Xqa9Xw/4fn/nLU673WgM/3AMer47KV8aEiw7RKjHcYK6qpFbnfJB9RhDhpvIhwhmy+ECzdYof++brf02TUVBgdj0HJfaRttgl1zlxqmPJNbbRjrGVvWIuo2BEbZjvIOMY0O3TTuxdKAeOaZrhWeL2crvuC/xxM9vcjzZUeykHPWr3pJUzWFz1F4sxonCWEbNUftmsMNrEoFdwbUF5H5GoEhdbTinfWvSp9BPJv7vb7PSYvn87j3u8ceHix3j8TJhoKhr9erMpbh5+Zsz1LkwGGrdowBVWZQXzWuvmpcn/ua0hAL7A5T1DYXxKxqHRk0ResVgf3NpZjNfiotKDZrV7673vVVxfVVDVc+SCsx1udYWsjYqr0ZacmzE5WwuQTFDYUulcIz91awM1hqpReUxqWsiUP4kmFptBRlAJV74R5yD28O+IW50vVGx8v0kGf/EjFY/2yIcIOAmPxM3L7oHETOtUSJZdtY4NcPumbR6dfiA7awhbMLZVQOl4rWvutzCZJpWoP9wJ+mb0HmHGNkyuPx/nG/3z/fPw486pf1x2HMYY7DftJ/gcO6s8Q7PuzT2ORJGWOs6az3HylGCmpYzJjDGGMOcxyW6DiMMeYw5mgxFQgFJICAvkhOhCY3kN+76qVmh+otJE4sLS3VJORLKOAaAEAAKNXxTTDv9/stNgOHL/iYNaiJmdbbr9+91dQ7jtOW2bZdV+/sEzZC55j0vrKTnDCynQYRkc0Ys6kwb7wT/WkO+8dIm9P9/un02NawBmbbdfVOUsYYc55KRXbbBgZQIgBQSueY2na/ae1c1T7XtcxxGGPOPrlBAIBShQHgc34n5jH4ejZ5vzVr2hVDg6QUA0A7RRVPcWG6Q5Hc12gb9zhDPduz+L2Mpk728andP9fTKRbrH+tdiTL5rjLc/nm/f97vn/f7x5fn5/3zfhx1s/9jRgBMY0B+ABF1AUBIJAD5ASBaA7iif/w/EQFI1gUAEQAiApAMAJYnACi6AaM3gsr7DRBANBAAEQAiIK9+EwFAgtVnnJtP/AXmErCqEcTzoIKf+BBeGgBr1W5IhrLVmwHWqh0BQp+OJUmAPh1doKUO1oBQVlWMJGNXXbsbMOkyP1BTuYW2aatufHVTMJ3VOYT+4C5YMSyMbSgBqhtqgPpxLkoslrFBBPCPOXctEhv/818ROQ5Xu6iQxjlIq9Bqhkrvg9xIstJG+11fNPw5tUiA3hugHzPX2r6MH1/UE4pbaYzwjnNyYisQESo6rdmK2+qqcR42C/C5dAmQ5jbq/CluCyLCiHucnJOKuN2WPOyB5G0U1ugzvgQ3HsVMh+H8TG5RFK+385Czq6q8//w3Dxz1V+NWcT0xmGdC0LEXfq9LriS44obC7Lk/qRNOdwXKnsbhVbPE0pAqpRONFr4v34m3OhLsbWJygbdoYLxN5Z5K73fJOoDRK4GhnvOkk+ncYxhJcBy6p1rG7n3fXteOVuvcxBfNkuxoqkvlZLO319t3bdT6JmG9JUZfX8Nbzqe6oUPw2J8eACtwKKsLcenm2WxHGMqjag16ztkqv6/uBLHKHuv7zP6cd0bdpI1ms17KCWTekLo21omWSgDzbf+kERLaqq0MzLypHMQTqcvQbBbwEU7FbVcL40D4AUMq01zU+60LgAD+3HwIQOg4fI8LKOf6VK8hOhMovV0fFVslEACd1wutBuTVf/wnwr55//mf8Hhrbn6JV0kIKv3OckpbAIsGhkU5due6az+0P+x37hevBgeNSZB4I1vbPfABRsq04AP8C89AhiU1ABAB3HP/strK7YP3vJ5Aj/B5Ef7ev7grrE77/k8DqpiVpC9e6nlqQUALWgORublVjwi0ySUie8x3gtzH1nh94CO6AIhgvu2f/TFpWMJMwDJFZSYQxczw+SXcigcAQNB6p/N3glszFPGYNIercTbSA6PQ24RPGBMhzuwfZdLsdQ9/BsDLFNwTL5Af/3whlVsZ7JH+PBXHF38OuGf+/zTO561on86rf3keinorX9QFQMBbC31eF9A60cjta0XG/lIvQG/7if1Dn/HQ7+qWJvu2x1j3pUfA5sFZYctFhTj+08aAO7/eugd4zEZVx0CsoxaaAUD0fDnWaqgs0tEyRD77n+Mp/41oIWqa5as2JAgzkkovn0PmHmD1f1eW1Mp0hthqZ0THXHoA0PIy2chkv0ozAhzhz/f38LeFP48rz73oC/4xRq+f9oa+ircqqq58UREdQKeffN6s0FfHcIE/43EfEPLMVIzHutGQdWM5z05VOulJH/C+1IJAe1oPlo6/7DvjV8X1tPM9fn+5YW7xBNKZqTo5Zi4QVs09aLRvbkD1/yA6kw1In3Z1jh+5Qy9wg6aZEmCmJe4KdAeYfldCltp7kK7me2/mqP/BTahgMwzTw2H9nnrMxAL7XUe3oDW0CNPfdRqI9Z7xqAJwkY1VrCYtfBvbumVK74wP6E5/xl3eARa9AdEng/5C46ibvdpYZEfO9aRWw7Bv3m/UQaqkxlz9hGBTYLgMplEdOpObNsV2EnGGfltposps1m9UxKoFoj3vANZQLy1+jju9WNnqrtOIv7CAf1joiy+PL2bIR/5l8mtg/hR3JoQCEpISABBAAIhQUAIAQmJl7nemAv+muB4kXdEDLpRGgRsXCID59iG8kaJyUWAiAF/WMwiqYiKSR3lzerNcyu3CnrmPm9PAn6aAQIXg3Z3JFVPrdx20eAklQkemANTEKJu7FajJEUPmSYOSwsSqwN2ZQWHvipfpVBfIWyCoBlhPd1oEgJIDy9PUCICmTgCULwBQ4e+4ZQCsGOhWM8jz3VYq0HjmvqnHDgDVgIHiEA3GTvHwfBAsku8p5wD2/Q7ETvXCaMTMGI6anANCwB0IUjymNAyyXXhzh8VO70+HYslulNpVBpLydu//HMuVcMNZ0w2TYog3YJW9XGsIcq0+zxEl4AAt+OvU56P21xkagnsok1e/CUBBQACaDElTNNddZtxmQxiUIAbfVjwP8oON4UplJJoEKvcgzV6rbGWQksp3HkhOAAFogaTTi6jaI+wvrgcAGhcAYMQOeX9j9H7HAMDyBAAEUOP6C7jn86jHB76vd3swEqkLAAIAAoAiLzCRlY2Q2fUZmEAxylee4sZfMIv952lmKXQQpZkZO/7K1c0zw8b4qeaPhVDpbZ5FproEscbKUzC2ps/Kh0T9OHQBfz6vesx7VcyMATCz1gjSQJ+106jXAb1Yg5YdWceSIi6YfrGtzhSwzAV/ElXhFz5GxcS5ZjzbiAC1zm7z3ZrI8ZX+2Pvi0bd2KDF4i8I9300Elrs2w/wffMBbmlVITP3o7VHThbHKVvzl4HAXAQ6lRkpjXgeN/+kvGPzWbjP0bOCe6/iAudbXzwqFPa489vbeUlAdM8WZbPCWfGkjUuZ6gDvMrdcUUcTlf3gwGL/uZx3Pn20OhSu4XfuYAjesNsqB1pErwlxtO/Zlvtt97nMsi504cIxVrzTe9RY7zVV7m3zQiqd/KezEhT/7A6+AR1yFjUOXhMAf67vnnL4e320t8J/54Fr5Z/K7hJpGkLSn+rW+pCwg8jkqvyr+dkeXirjwbE2F0aWx5Y03x0c6Rrz8ZiTxdB9dr/1Sy1ZwA3Xb6Lm9ZZ/77S1bwXGVW4jSh5GDAz5YrKdfXE/sy4LzQ5/VGFT8E6rBt7pDmAwBGheSBL/tu+85L8N9yuWez7dHhj29OeEdeouS9U0P6S1MFn+OMjcbpW2BirrwI3ULxq8JQKcXjqNbiJwV/Gp9BRNPc76vxHXF9VRwk5mNnttzn/sceb+hcAQXcPCZIc1E1IcWgmVjX/5BMeMRV8Mr3PP34qgx+IclH9tO9pe/hnNeJnzLDT/Xt+eZeHp/TF2HaG/WgfBprSe7s2vnT7190Zcod6UdhJ5/ccMofclLBAlebwwOIwfbLbSd8LqPUh4ayOIP/2dmzWX+tkhFta7nJj9DoDdye/hukdWsJ+t+wdId+MbZFgizxuDgDxaheR/8pWiVo77NwLdz77t6bk/othv6vt49psAfz3uvfpUe+B9/BH8yWfi8ytysquitIi+EpdpHsVxgbCHaeRVbGiVohFyc0psZBde6UDfMuvc4A4wh51UK7aNR9dPYu2oc9M6LKa5Ko3N7hjIdTGPVbgaiRyBsN0/myhevnnsKOi/uWt9qrBahjwJuGW0YVp/5mxV9IWCnbXZEIiEpEUBoQkIiASAABABEhHwJwBoACPlS0RfC8EghZoFSsJyPAFDRq1Rtnxf6Mme+AGimA2C3hGolcI0LiZc4k+6ogIIA+zRAIcydyxIOujgWXTqgtkvaH0QJolYG6J8Sh8h41dYhAMgHABEAEAAQAWhBAEAEgJBIBAAEAAQAhEQCAAIAor4ACAAIiUQACACSAXYayAc2w9QFgAgAiJDIXBS+rBaAASYCaiBAIlouIAYYBPhOoEKQyYIJxABMWIMwbLMHdJEEP3wzJeUi1ithxapmGHbGdXoAwzaTUy6WOuP3HQlMf/8jUaZ05louaroYIVUi5kgMWy5iNcPwz3BUzcL0+53rYDNgqeVuOG2dZzkpmAGwPtZRoym3/DWKgS6is9iY6yDdgGh7s69bMuyMkhkzkYCWUfCVqau5M0iCdkKn+dSFdjKwdwk2l4B1NgWizDe/hOkfXLnoSqPQhSRADc13AD4CVBKJ1eiqhQqgeQKiRoxi0XzyDT2Evv5Aa5eqwSlfsZsAlbvnoDCjuMtPqYgil0+l+59Xxl7YFKM0Dw1aQVbdhMRsK3AFla123uW+9Vsw315JtubfEhr3xNtqQXrSVoPewULrilh5eRvBprEaEHbN3yO9+1Z7LyK6F95ukNTmrLieMpE52/xX1+4dRVJfa5vYKnLmuufvUWMdSkhFg41hIOeNYBcajITqiT03uliyvtb/ILlKkDQfIBmM9hkDFWnLtRRGDg8VSg+edNBBYs9vmy7jubLaLXz9aQRAyB2gIgCXiiI1d/RtANCUA6LqabZzAOxkAad1W4PeyOyInC/gOjWIULFjdA/EmZCOghFVOJ+vpKmByjkNUaDV29BmFuoKXC4YfvC1twvVPhpGl6pWMSrewZNWpSHP8Gqoc6/1u13hr6gMNfStAdda5WKw5Ebsaz6QKQI7lXbvzFVN6YadgmDs/lH5MMbWL8JVb+lC5+6pi70BIK/+FYIMKaFv/IWP1XMjxVVCiWi5wWCg/Iylx23tJvTFD//lgSTb9RbGktoloPJPWG3A+ypRznCpuIFDc2FMQqEsyXcwRCECLAkoantsFIJOSEKADUCb1XGmLkY6gObXIAGtWTI3Ug4HOyaY7z2lbiq1+73JfSkqDY4Ez0tRWxP3EN2uyLs1UzjclZkfIDLzortip2f0aCiTsaPCtausGxxckgcu3Np87/q/zEpvFAr3IXr+0u5S/fjahQzXL8K7ii/4e5rL+GufINSJuBGZCZvLXUFeuT/8qS0juV2NefkzhqtmUMdvTgw4rCSlyybuMdZtQUQwEaWpOLJ03gFIotCC20W7W3rS1cOwuo0Em4ljLl1sJxDwHqRtlwjnCxbHDOfGH+0mdQmo+N4zF76sBmyH5bb9Dq9pV2QTEuA82KJbKG9Mn3UwALxFSmiOJfjvsgbxrOv+uguccgwAtHt5gqGJI+5Rgs+uGJlFtre0znmnW6v8e5/8/J+uK75sbtdvgCQALnelN/YwuhrSjKheqt+z5bgRFZM6khWBF2oNQBiL29V/3QbfPFQ9OP7K3F98z9av7bFxtHBgmP/RJ41gQ+jxAPjjIT+cU7uLHvQc1h2rlX7nABERUJcGKEmEs38Xhw53x/VYQo+qToAO85UA9v85+VpuQuP6SxLEYD8pb7u7cy0lOaXkzqDiHa3iFrrYYZiNSOtBJAGcMGxFAluRQEDqGL5CLNagH8bpQJ0JpbuivqNrcWfEeyCf4d0q58XX7nRCQwVAmHkwwJK4S7gaHWhqagB59a9n9Fck3GAojDk8rW9BGkuqMaIO9H+/IdeBg2DefwCh8RYAeM7+AcjjY6eBij9gNWVGUONo4eiLzxfXgxD3lwIsjruF1r5k7aIHXQ0ANzt7EiKAkmPYb1uLzz9nfPEKg53oVgIB0vETl4ASgTGRbRflwUPJx4Uws7Srzw26BqPye6+GQtctNSSY/ufjA66oSbD4GZ9PXA+WmnOrMKRbFd98plLZ5b7WzYREqBrZ//mPkkzTP8P94xua60SttN71cis/NuqqlttCUBq10JkLFjxGTNLbB/0um8ju6vEn54EwqlWf5aYTzcHDpc5iF2lVobT7TRex0sOf1SsONy+x7r/MWYcRhBk+0mbTXdupy0T6FS/5X2U+2GYLHrtZPgXEiAdujTKRu8y+bj0ZYVg0KqFEbH4zb/EkspkR5nqrxOCo237KR/8uR1dWJRjKmYOLDBDKo4VuIKvDxi4hhsw/EiiLAnoEy0DmXzzvN9By5YMDZT9UA1bWgkyfO117Qqv4Czoy/wwgbDd/M6DP/FOAsjue56LoM//iLqrvuIeLsVc+v1YC0HLl/wcom3ueNPqsrBVdoGzu87rWheZf3AU1ceP6C14vr2qGsqv7oeup28kIoGR1V/e5J0yxV9Urc8/TDX3mn+Ki/EuoKlCRAQJARAARASACQAQABGB5QpMSEQAiAMsTABCAvPpNAEAEYHkiAEQAQABaEAFoQQBAUyPKvvkNA6h4wrXyKSAKSgBaINqOuGqLEjxVHrCJCDZomrcBKiq09PRmKF70F5r33Y2d8u3rBFBqVwWuTn6GG1vZPGd1xdgK5+fHE42u0vM3iLjbABKAJgGIACA1FfaKusJOp09o1/4Gm+lQx0CEugwKApDNStCnI8ugCQAg9WPv767JpNBfxVd+/1X4/rXftTrCcjLvY2N5wkt5sIKgfzSCzimN61kz3IiEX6J5E6Dde9hqb/AeeyQHk+w4PQHxIA5L0wjD1RUDwEv5bChodHytblkDZM5Pi7ZmEQUllHEzWS0PcFp9S3GeGb/HGenYhX91cf8VADfa8RvTrJbsFu2NStDbpxTCf1S+Da1dusvg4VLrCTaRVety49xIslb/AbHawOwNyZbcilnWt+opVDeL9UqQCLTnodaAxskiIvdcl2c2vH92Gpzr2CMepOOEu3j4DIgTdvpPRwfpwG4R3sitEetvu92vLX/O0Jli4pe9NgPOJRW1gQkBxurLhtPdJt1n3JA4r//WwiNdpdIHKI7MypLR4ys9C7P2iQwLkPllR49W6RsH9F5PcZ3zx221p6dHVVV1DaiqOj1cbMIdHUYNu7ltKVfKsZBy8VKUW185ZClBpP0ra9k7j+uxuIm6rXN4tzv09mrv5J1WXHpTfoZzjX3CTzZsIQLQuIAfkZ06YOVRAjopnse1otq2bauro9q2rU4nbsFyylioA7yUXzsyDrvat49GF73zuB5rP4qO3lMA3RQ4vKp9e13SuFw6728kUWeyGurnLOodc/d1W6R+VLsqqVQqtqqqak+PantSe+89U50+WrDbfeVPtfmv36jR62Mi9x3KQTHLRLoZ7QeK67HCE2W7TbjKNLTxfdbe98NWvMpeH0lwDWhbPGmzZds9/x7AiNzZXFUQzkavhsaFlI9q7/3Q51xmGztQVe3KQzuqU5WZatvKwIeFyu35tedqDrVd+m5fRXkE6mpFiG0rXsq16ch7iueJtr/kTUTIHlaE25M3W4+4WHr4n9ALhDaBzv96BpdSQd6E9vkcXaNyhUrFtisrDN6dremq2G0rpgIhkZCcMLUTEQC0SA117DAu2X7bz+2ww3Ns+zLXaOza5MNtLhzXaJXbrdr0nG0+t8lDG7o6apIbZxVSR84/7K2+1F47HLbBTzq2XyF56AYKqY4pXXZlr46/vbPjoh0fHp889BttKZLn0U8rlY03qO16aPeT3nq7yrG621KgOK8EGejGk7oqtn2NY954n4du/NbD3upvbHyXmW0lROC9ZJgedaZt26p9l732vot9F3sv27Znqj1tJjxZbz2qJ//fUXtU27bto70w8zv2d76jqj0KqNZVVVV71B61ZzoVbYKmSFPVsIOoQPOp31+hnzCaMdmu0G8jo5mCFarHM2qV+P/QHzV6m8bjIJorgFKLvJPqCU7TAPkcEWTsQzRw8QA2XioAWkI0QMoB+qcgEEDseunk3WiHpull4s3LkdvHnJ5CerpcREk8bl5aejodg7zcu4UqQPOeWFtpAJWOqMc7l8k8BZ1f9GYyTzKdzb2ciznIZZ6mcPCbJ08ASCWwdCG4zzAA6wqQd6Odk6f7PKlDuQjf+5Oni56KOSFvTpruvc0vEQCqgWzUsVKIkjg4AElotHO8Pxzr4fSi1f7yGPz+ctDnYi40nf3j6eC3h4NdIO84xZyIwVwnkQ5puP0wxpjjMOY4Diu4w5jjMOawpAQARSsnHztiSx01pY6YUk+uN6TUizqYVhMGrQfQMdiNMWw9bj+sxoXNGmos1sDsuhIAqKsBUP1zxYP4C/UNNWNZCaBUDFNRKlZSNBKHjoJ3rg+PivCGlvGWzb1JMbcTVh3lHv/WD/AFqpX2cVdkS51SSb5PsLjSXsHgBDJ3ryBwWcVVLUxEqVuNyjnQscLcK12puji33COT5V2cBq9qABSJuIkBgOndce94X2lKtxKWkWc8SK4VQN58UGWawny/5Vp2lPN3v4Hv40MaIW8qEJ4g/NzfImB4e6e85DvL/ZzxJeL55bEU4yX92Y2Az17c3aTFdkgXT6Hq94gWjWAuC4FRZ7qdTO8d+/aY+sFXbtT8bOK5ntS29gWzB68KBsfTK2yjInyuVx3Xc4ONhvwt/7ODW5W8thxL28L5/lXpNm4waxy8p9ZatdiZdqRn9QV8/UR8/a5sHW4jrEadyeqItzM1Rk/OOORX6i/BwzxsyXE8vcKtmurWXpUu3kkjULzyP+u7J2GtEo3X0/n2mRi8/VSgz3rATP20JCDJ22vP05S3154145aizOQj9T3tb2BrnPy4mcn3evGAz10EnvHfblUine85N0+oyfQRaC82y5Ru1bBpSQByqbBdnly6bkf43l1YTG97Zv33dId0/Kk/mzz34keBPJMFrZKsdY5LNqOBaZaFz5ejngXcud5VL/vnbPntM6sW42gejv/WDNOevI9lxm22kBzWVJrJRgZ8/TcfVJmi0b7H7KpgMjSwNd6ePquX2zbrpzp0Po+OIVmMqbSHm2te5l6P2Cyz1JsXbtdOH2h/UT/2XsWtmmmV8i3OYfYdP7X3dW3FjBnwrxYoHMMIGdtu/QdkLEIf6C9iDst8pBBy65LUjDG9d8lLZPqv976uRpaKmVNEyGE1gM2cYuZiLHMcVlLXdlxmLHMch9UANnPaMreugZUA1cwpMuQwxpjjOA5jzHEchzHHYcxJZIwxx3EcxhyHMcacIkTTmvbrv/+4CnbLmrZA1AlkdFzCohldXkNoWrslTduRT9uUJWxMaRxMcUmZtqQFrckaH6C16UN0hSl25BMEcARHAGoAEGzzDJADOQDwHSDP3gLoHZ0BmDDuQRMgSvQMsGpAV67PWyEuUSKA6AkwApNzQvPWJzgvPAJCyAywMoBTEQBUAm8DgFg6YDwDiB6wUwO6kTgCsiEKsTigS8eiTyRRGmwAECsWm4fTUTsAyY+AEiNRm7S3ddK+WVWEOIET1Oy1y8VL1141uA7nyPvS1htwXjsYEGOYbK2JY62Oa8RSV2vkVGukVh2Wtpqw1kDyXgSBW2pws6Uy+a6CzePAfWTfz0BgKgHiBaoFav16xQsAGAh4G2AIwJiDMcYcx6z/cxxWo/VGis2Yw9hP+i9wOjmOw0zkB6iupsxxWH2kvdcBYiYMg+oaFAx1NUhA/drjUHRjHIsha3djDQ2MNTB5HA5XLGYd9GIxaxjH2haXhwlpu9hxWFOZO9+W70FSVUGDSiQAICQnAkBAAgAQkppvBxpHQVJCUqPn62u3E2C0fXAj4LRRF4pqMPYC118pFL3RLyuURo74xlrwfXKLokrFnl80DvEGTOM/etJ0lJZEtdfX8acuwRxWfqbZEeWEzQ9pUIvfUkHksBn63/9pRlqr68j9G2L/t783YPQ+gWbC6CzuEqG/XLm3GTDrpdLlT2tLid/ksEoPZ707DYmAzxd3rJbS/QdVcWcuzqtWBKEicCZ3++PnjgPbXVF4glKKJ635wKug0+Hva0/TPyNCUK+ldumqzgSCc5TxPf+VwES2XZXz6dg7ktXVzGF9zrSWrdNDlq4lO56AeJowLbgXvp5bPt1jNw3NCKunF2iI0hNsZKjalN7bYlFpd96VBlr62tnhvL4tw+Ke8bhiK+3c31WwApzJWVX3eLNZ5kk/1UvVxdBcr05cj3K+7JIk5HIneyx5G40LONrPxO9xheeXJ6d14QltPaTOK0nFi1hN53A4vqbl+k+fQ516RKilS0vx4amH8eZIu9PJzxhRu7snNM1Ho3lj61qyYwkqGL8mBD1CtYrd1x91FxxX2wY3q+yByKgrkbN1Rhm/5VpgDYub6/E5ssHxcMVb/LHXMpf6XA8g79H5ZTe3wHv0uI2rH5NNiKowJ9cnrkc595VrnzqJpxS9hcYFgs6bPRH0YkB2nUcsPJEtpTT9A1hdXbn6krguTdrZkKC3N3i7W3Xd6b/WVbsLZtfdjzvS+ut+aZF5xUiycFuHmQjf3gK7X1uPspa5vFO0mXPLfa3zm4XQD7LILJaEjW6htSrxs9c6qlRK1YJQEZzr05uu8H6Hu8WNva5HMH59CUsgfG6u3/Tv5O3lpdD4kGn1fK9c+/RJPKVfLeFoPxN/j8s/X45PTCpEFTCA+cDWnOB0bOuRw1oKDO9SK3wn4l7zoHPcpre71+YvVHV9pBcXsSZ+Ql3afSM1eHlXkRFnj6uHjDFbob/BLS92CZCWh0Wbqz4PF9Yav64fZRiLGFuWiH03EIjZZc6DAXbLju4aEM7Njp7ne/193ql45nOfgB9P1XWPBu/vc8Ru79FjPU/xOAt6njyvx6WaYd4s168WnS+rKW53A6EyMPM/jvbS4dvplJIqTwD7GWuSw3Y91CjFbMZU7nRy+NAbt5wYkZTeUZHav1qzmtZHOrvc8qWabXfGF6MXJkw4XlOZGbUCi2tpI1+hTRjqLXBPfv5Anbv4JIbKOlx0CyKjbpvwYU6+1Rvdfb6uQKz3g2YHk4/YtY131hMGKx5wi7LQTKOSqPpmb8qHTOa2aqPjnN6cKFjbO7o93D/OiuR7AGDo15/s3zYsUEpJ3uKxr00Os93q+nE1tl3NxmUw7Nq6WqxtsW3bdW0Z+sxYGgZEq5u9cB0jLZUZ2MwtW7ubbbMSi2VQl0G1BhzPrWYuEawODS5QNw5ta1DdwLj5cnXGN7hA/TirbQZ1DdHO099hh6+Luo5pUKrkgLuasS6fdddLUe/lro7JAg5jzHEYY8xxWE20LYWmwZjjMMaY49SzGttmiY4WU4H6ggCA0IIAAghAC4CICEBe/SZCIgFW6FyP38EAdUEiAUZHBsY+Wy1yAVCqZFdHTu7hrKHPZv/p/76fzqojT0AyQMEZY8wpMGOMOQVvhL6t89x13AAVFIlUaDfCaxxylzlTxx1gK8rKGWOf0BAEgFKHBrA3lJLzQ93mnwD7oQonHOEgqUUDPCm5K3VMq7vP3wHW+lCzPTCXKfmuqb9JnftD+Vj+a86yLOsSVr76ctaoT5hL8l7o7jX5WFa373+6zFlh+DgiIimJiCRJaVllFRVLtMoqKirKPmfpq5y5lMl3ldTdsQKIfQRr2To2OoFDZiZiZiYmYqwmtUp0AkG3Rvlu0vV9hEl5hG7Jb2fKkzQAXg4QARxAQAZsrNayq5ZDmpUpleu1PhIhwYiwfVkJvR/nuj5SiBXQrdPWM7Fa4vaHMBquIWY6MVFb8tumM1mdGDKURgz1bEuUCwzc6G6uatBYo9/tXtWttYQHEkkoZ0zHtSZqmsp+GyHUUjW5BtgSjQbKPm7FnxdDOPtdz8fFptp9I2GznIlPUQ9ZUTPkt7O4aRZ9vELLtDYQ4IoJAOwTAEDqIZZwfYTNPivkJK1YS1XU9NtaMavlnXS8FIJ81sdBqKWqoabtAy2P505jAPIBQIRE61vJhbuL5wGAfACACABaIJGQGENuQwm7gTOQj3MtDcxQYaT/E8V4wUqaca2GtbKPCpUzn/BwTt5vm05mzEW6BO/Q6durRZMSABCSEvW1xLHgygADAcILjJRqAY0bZHzFJdKVuU9+x9BW15YyNoj8SjYS0FLiBoCxJTDa7/pqZ3fbCbP7/m274sp/qUBKU0HjfLasULmdcmBIlvPFZfXMaDwTjyatpOm3NXOJ5eOiJ9aKmUKpJaq4aXNVFLitA+2EGV9JMrua7/3FjhqTKtSCAiwmq9yKiRx4V275R9zq3qA1HvqJnmkklQaheuLnScNG/AUXY0Pf66vXNF3zbX9vyPpC3lL0duUNSD4l0Oh/87qCn1Ob7Mt+wH816D9dE+Pj3tBfdbfacrw34oNrFLa13j/ia4TaPusREX85BL/FVIMkhvY6NhxFiIZdaO8NYbXioutiaOi74XDajYa/y8AkecdYhd5IF3bdjyJF4Yuc1d9Z6MChRL96k0JKK97Pmqe8fBThkG2/csClGyqXr44bwGjfyS1dU6YdR4eCP8KvepDg2SzgXu91aPm/vnQUEyjqW5U38JughR1/PcJ8oc9JJyG7InQ25NeVz+eTTFGZmfdr/Nnqp68rd/yxOfgL2MIzF4/sQeW7Tj1IYgTwkRV/XmwAMhoRQiREihgAgAgAyCTbOgg6zaNqtbmBUtQ8cBBSjVJDBXHmwRTuB9JNKJwLu1A5b1/uEdQAQCi7L/9MULVsZWAmR+c3z8WKn3PHZ8utloODQ5nr6Uz+VLWL3oDkA1oD/NyENUAEK3iyiSudXwEn1PEXz+UL/TAX+IBgxacKlUPLABMIcyjBB+1C2BSEJGImImIKlESSSBIxMxEzE8k2EI17e7XRmc/6fsXCmU/BQTEJEm9kC/Lkgc8Q4T468GUPIGMvkIQA4flCKrcy2CP+vFawLf4ccM8G6nUR67mMvK1hPqa8p/JFXQAEXC78c1yQXQICXnkof0I+X1V4u2mYExI/W65KVynz+aW3kVUS95T7GGlPKiLoMtvQB4rrGXHi2dB0gas2JIi80la9+0dudKwNxlQI3OcZmRV9YHS+bhoo/2v9nPd2hrMGOMLPW+mn54Xw3JyiaEat83mxvEoue6jHTkD5X/tv5G5drBmEmYBrDPoPz2Vy0811+A1+9EVyoGf/Qf2ut+tlfozazmlKUpjOtR4xBcFBl9naCfQbuN4o/RXCxXXMWqEVs/BMVFHtCQczqS6o3CFDDpZWGNld0jmc7goM9fV6Qy/QSTP5duTT1Vw7crvEoINcD8LfsmU6F3nBW5v1s+iL+jrAUO/gbr2It05ryOdmVat7sJSXe7ffLSCa+lnqYYtllTsiRD8lpzq19Uz+CJ8rqgKsMJxaGF/tUYKwo0d9wmQEi5WgmpUAIGAsy6ALqmsAgAAQoaCEAi7qfVAsSnEDSa/nYQMqU0RBWyCxLxJ1bridk6QUjphdG6q1gm5103I2o74gAihfgIgASro8EdEVFQYOHEhdlqcr+j8sT/TNBi5PNHAgoUKYBBBBGoCBAwfm/T/wwQ4UM0E0cODAgUQDBxJRl4FERAOvqP6foLf/9VCouVyOcM5zDgbhbPAsRrtiikRKCoCmdgAgniCrqAVXSQkaQa+iU4R2MKAA1COJXk//4/j+VKwpgzD3QdZWqQelHq3/tIOetGaWTD2q3t4I+b5LBwMgWsa0jN+AgS/406wLiCY/OeFEwgLjjzyKk2PKDLLKMJ9zUT9FLB6J3yIpviIr7t8m2yGb85cMmVx4gEz2nBSvDSlHJyxKWGtohp/P0MsdOvFUIROfL0bclN1FAEdg5gEfwZDI3At1QkeHCeiETh0S0YFODG9h6hqRsLreXq5ZDWbpfDmu2cthxuxZ9bfqbK0QSr7BYznHPOf9S5wOr3DT/XmheUL3wkDyiGIMsdYOUUygXoRhTKhwPjC4dHYYOifU6Xr67k+xZI9U54tDKN5kd6l5ThejD+rFHye3IAFgKxbJJ0AUg2wwgMwMjmJBxRM4SgNCZIjsSVHo24YyCxegggOgJgYIoCsRKp8RSmHyuNTPmOlS/aH4tAlYdJ4AREmBgBhtIMAVxBqFgCiothoCTABrZgvSdSEBqG4oKQCA+AsNJU2EeqNYYiJm4KZi6HJFnun4GDeHY9/IAEyFL8rJtmgmgBDKbEEAkS4qgQBiX5SDMeATiHXUkqtzZorrQT4wsfPXxr7MVQwVmqAL+Od2iomh5+bqDEbKpTMc9Q5OoOSo8xAX52wHW9sIi90mDDvbMZxsh1O8/ql8CSAU8Lnf57JlzEX+BmfUqS1mo4AtAOoC/nxe1fgPJlhdkUhoUq7x7l3He9od1j41s4lhYLklTEPm6ha03oMtrLTX503dZb5XbP3LYeTBqgXeG/95b0xG7SrSMx3i9deLTE/JYiInFxC21lYS0z9lkc4mpYbUTmp+hqOLLreTpC75/cGf762Nl2YF1a48/Pd8n6tVVRjxJU66/5LXuu8fJrgzre+1+XReY2gpHnNDWe3fS3dT5r6g2JI72xzf6jMtXGUgH+zCBpxLqeqL7vWIL/qEX0Cjn9cA1j/EqNvrGoGOcB11y+jhK+M//29jihJH3Pq5VA4vnfPJ5e3+x9dWL/ASJX3Bn++tmYvA9+Tes3oux6MqzWb8e87pn72jMOyJFPWfPF2wNwtStluPgvnZsc8iuuykOUYvE1vyJ83xCx/DimIX24Pb0mu5rry/YHGTmY327dU+99tbFhHO1m55pP1q72GLEf3iemJf/kFG8ByMLVSceNv76w5hdgjQuNAl4Zfw3fecvp5P5Ya/r29bxg71lBaYCvwxq/P0d5ss/pzWSXDeGEqZgkuWj3MkCOPV8JFEHb2HC2c7mmBpyJ3H9VTsZ+Y2CbWbb7ShJc5KkivPyKqWSb7ELKg88bb3Z4oQJk0QvuWGn+vbM9GDPeKEY0Vrc06uYuKRxJ/TOomVl6VM/kK0jOguRJrwLqNwJ3dkLt+r4K4Kn+X1HmemKpET3P5KDxTXUxE6OMrnD1GGOYlDv54gV9puKP4EkvtTBsbSPSN66tKhucAUP5aoqlK4j30dYSomgT8b2VY7ByvpC+75POrb9P9h731X93JCn3JDz+fd80N9Z99bqWAey/PkI4exjSrCz1HmyyrNnNog0wEy7i249D+NjmB5DXrhO5CyQ5X+YvPN0u4d3OMT/fLxF/Y9U8S4fKxWhtKW3LmU9cGWuDEFmjXc4IPSiGo7oSIEn0EzLZkrWHpBScz8lB1qA6FqZLvbGgmls/dXXqKEII+51BeQZn/begGOSHZkvHNXgxvmnuvwmW8VAMXwGyvbWnuZTVMb7iUCvLVvEQKimVMKfxADY+4+CMDolXZEYl8kJ4DQhISCEwAQEfIlNDGlNi5luoJ0knNml89poSsGcUhA9+DS7z6IsFOpoQJdyT606p/wLV5xb09VHw9lej67BTFAUsJgH4EqdyEUf4GVUArN3Tc8hY3x/rK9Ytq2BRMAwBjWFkA0dyKEFim1w/F0SSd/f3yZ5PgN9AJ5RzbpqINzYM93I1fyD63Y68mSb36WIOdCC7Ca9Gxg1KwRDMZH7zo4LGXhuxAA+QEgAkBEAIgIABFARH0BEPUFCEBe/SYi5E99ASIARF0AEAFIAIgIyAeJ+QFTGwAQASAAhauby2oo0wDTQDJ00IoZxF9YfUI3EGLIhGYg9PncxUAdcwmWUXu8DMiqz6BerxzPRRf0+dzNynRg7bWrMrAabELZ1XUDwUA+920A5htX1w1EaFsDQh1zk1g1Zq0mVWeQDNF2VCqZmqz6ta8uFrjo87njeTBWhQ0LhQ9ZRS3YDOVCpzQcXQoJ0LhL9gynZyiTGL6yUXNovUvOePJNofYuuefeVrTzLrlVV5SNOdlFYiZwkhxQuQ2tfe5NtugKtc0Hr/hS5W/ku5z7XNFb/U/3YbQtlIudk63UHK/1tyk94Qwm/PyXDId7A4fOmSwNWKGZlxnXg8YFor6WzIjZN9BvfDLsfpOGBAKAfIBkqPwSYXPU5d1hW/UcMhLqDGcNRqFD47dXrjoFo0nj14/IMyF1VyT0uSjCJ/I2AOVM2O/C3CS6B8LMZ9PpPbsDsV45lHpusStkWnC7AOIvc9xguAqARR9mxxe9Z1wPEmNfPm4rlasUDhFVIzIjpKHNfOYOR/8l8Q/M/LH2j92P27XiNQQAyeVC5QSRC5v+SSCseZgA9KQuJaLds7o8CNu1fxVGhCQoP56bbMFwV2wFmO8NM7oa/CSidnE5SeUWNuS4/X8rN51srZzNXmWvDy3QQXOWBAIqQrNbwEQ05HsfFjKHN1XjxD0is5/53HzYGlDnu8VlKzhCMDlAsprhtYqSjF0F8RdWn3iR9Fc+Id/hrR4gCOOlgQVDnctDnHn9uM2rNgWJ5qqV2gN9AYHzFqGPb3LZKK8gRbjPzh1d+jPcX/cEWDIhVpYWgZiIJDrvcbsCUW7pDqG2uO22ICJAm7tnGkkq15vv2nrtdPzYCtJZ0e3iCBxoiihkvB4fbR/Vj+6VO8IoJhoINhPQ+3uiU8yYrAsi46DLNKH38gQXvBhXbx/9bw/XGSkcc9u9ByzUFZ1vcdkFg8xnaxDo/a950Lie+1Jvo2x4qWiZhjBzlt7Xw7m64vPvMQtgvvf4UTWW4pB+v34QRS8zBB5i665ab2k/ob1Q75f/X+weSUGa3WL0/eX9hQQiu/yNWjYlGSC6XFRwB4oa8lxplYDNb+YKvpIQRK0SIiJUN6h0sDpmyu3S/YWI23YxmsP2KnR8fH78f9xX7mYTZ3bDZjcRyusavyYEdQcAgjL2dbcGgNjX/a5nXWE18Me/tDQr+cVOnzAmQZ3LrWorQ6Ne2J13CeN/UC5c4dtvCoCfXUWakdUw3/cTzgbxjKhyW6Ay1NC2gfIJCs2ntzvoX1HxarbArhMmnihCX1n73Z6xGvuaknQ6f1O24gYSAKxByZQZIeWi8t/V1Oj02ElaTpB6/rSosTESCHiFe+lbMJAJv77sdnGbM8akwsd/+bF35TZntY/EmdHgXUrbLp5zqHoAorRnJKkmbFw9Qauc7tiMYx77bnZ7nn022FSkxVXmQqf/yI+UicweKFrg4VVmbd/+tYRVuvUp51axJzNWL7o0GnxTQZ1u4ycPCOwahX2Ne9y4+kJa7Yk1qby5LH38EKO6q3XkPNP12q59+xD3zanBK2svtt/yzMkKCoPeaAwscbzz0wj0insSCNA569SCEsde+TxSW7AGxJAJEy/SDPg5+XBAkQVTqb2jFMuFNfHYabxXIRAL/VRp15uZ7obTKP+Ioy7vItqe7mJdKyJMv34nMixr4laq5QLQEcOF13ru33lFINo+t2ymobflkgvMK1b69bu3llLnqLoFGL0istW9ubFesSOaPQBg+ikA4q9Vm64yNVlPubSLdiIjhSJ8j1ieEsfaoESUTfwuN5zGsHMfdgDQ7lmoW6DCjhBtNiYAEAEgFJCQSGjCFkQEEAAQAQABIEL+RABAAEBEBBAR0MLUjtEvaXYAkOZu7bZIQqDkSCQA6Gt9ZdFVW1gRgQFi3MPyF9Fm81Rj9F/RJcKn3Bg0DY49RRpUKBFi/KHq4DkcoAshxLiL7+jK4x9ELifEOiMEEBEBNI0CVCgRBy32dzWlJg/5O96DPu5K1/9Uv7Rf15+E0G8l1hl5Km9IpS8+eqNustylMVm1ox9evfjZn+oWu3vO4zk6XLxksRJqv9rFUd413JQsnjX84/pJ3eTHP5vamLDRL98wGPc8QoAKxRuHfJvJg+bOn6o3b26YmqWgmuHO0ptSzV2zjcZUa89sY5FjAczYqUnboRNPnrbcS5AlvjFz/uRPTZqa8cyDdFG35O9auu76f3pBG4symVE9uO3Qh4gpM8iCbsod6ydXqVlskH3DjNmTBl/evcuYNXHqAwf+Sck0OeqcIpcTByytehDiZ4vvvmDB4q6l4vDqTfXW0sUiZ6Ry0eU3ATmhi6O+IvSDDtL1cVdCF0LX9YOmrwZCdHncCklATuRyOdFqTuRyQgiRm55+3kJ02baq6XwiF2SbJ+55qxewmUICUCCAkG8TdQGIALQgAEi5DB4xLqWLILufn5crJlO5fmSXSwSlN6pCAVDNYLRFcECZyD3IzegSmgCorwGsVbsB1at2TCPlorKBkyRO7F+fSXNK96/PpEiy4RKK0Tx2vi4AnD5yCwBaIKlOC26MaF+rjt8+U7A9AICQlAhl4tgDSToo5+ZmI9mReNv9yvNODm7/RUi5qF20+NWzdDlSl+BZ+nSLiGK1eDHKFGTvfcm3YL7z135dt6zjrO17VEicK1Mt9nPVh8fn08KGDNb8g6MpmEhVFJlnQjPA4o+zG1OhpD2Q58uqUlXC3Oy0sRpmCPJMo8bPhOVTLrot358KZUOC+z5s2RNe5Om9OPlzzPe+S8nYryKZGN/oRJ/kGceMt3ZlL2bYHGfdNqSKBv9EL6O95A4njrra+CHJfBr/9QFB1tUptIcRddA5p2sVcW7APRj179VbUrdBqapnVfMOt613Gp3hw1pws/L2Dz9L+gihaq0y52zguNHbR3hiDJ2jVuFVblncSLdeg6h2d+dVw0LmI8AuISLoc7OZnTHcf9wIyArFe6DTXnoqMLB96iI8ypV8Vky7kHpOII0D1qjSZ6DXct1xPdZ+3t9xbr526O3V3sk7rbi0bvK8335Uz8jVMC6d9zcAJGhzsxlRL4Rzve6X+l494LtBhz/Uw/0j3RZFdt5p53B1rbX9W1Q5F3DhtuSguB7rlt7fcW4eOj1HAjfctFVkG7A0+vDLjjXADPLhni9HpYvyOKulfKRCHbWebC5320VRHbfvd1r2fLlwuUBFvqedwOXN6Q675p3H9Vj7UdTlYWYXDcujEnhSV8V6v8SorTRLpw+/7OhAS9tO0GY8zA6Y3neSBWSHnE7T4OOqyX71pqCiOjGGiqubwmpQdnVascZirsVq0H9Khrl1NRbLwES0ypR0WbE7tiGTUG0UagDzjQt1dFFfZQRpUFEdU4GQSADQF0QAAQABBBCSEgFERACIAOTVbwIAKrrDhn1HJbkPeBxBMmSTtkOfedoy2IIKytHIZkd4jLZDn9lHYAdbTseOGCA/CuJBheid0JvQAo146E/xmmX0hzAA+++CrrZxWSsdiIXIZMVLTPZMOmsa7Vxx9svMJCCZDDpz6UNOTQ5fkLdEDjUMUYcYdtBxtMNNgNgoMLgFfEXRAEDpLNQiiMSBWDOAKGgVJgEcLAAZ72DBiY8E2ACgGoB0ANUiDoB3AHsGknLgUAKjZd2BWvgWxexsQ6IS3oR0ANCU0Bs17wDUuQJAFVTPuC09utP1mL3T8VnFaqBaDgxEAWAMghBTF471Tbg4YjRDIVHiGlphplvSute818ydmD1z5090thajpAoQQASkCMBahJxnRwAIgOkAG+rznDUIAFHJmlrOOd6SEtYaOmGB4YdebsJiNrmlrpJJjjwfxh+5bdEL4awZOzJ2Yvwt7tW+d5FLSbTT9Ri/0zi+P0FFLgQOSEUwvL9f/4UKUOXJwW8orVo+a8ZD9ywqrWTTay8f8oSySitVlwC2Kw4qgyoj1ql0G/nNq+ffe5qlbbMKlmzkthdMkfSbIjtlj+st75l2vVFvbGg6i1LlWQ7ZWbPLnHruMm80pmUvO96d7BZTcpDkdlnquFPHTH2fAZoXoZUSU6qdeX2o2/wTwBfd5ERSRIn/peJz1iUukc8lCk4kieQataxLFDRArnkikrIVubq0v+FzRUSUyCv8rM/5WwUiZkKk1bOsijKrAMTMTLRmiJmJOJDWyIm9iIhyfD/rc12OmTLulLwf/vAyy19KIiIGwGpSwzLRJaxLXMKyun3/87n0nO8qs6xLXMIKZDAdE/cnIimJiCRJSQyOabBjOg4gKWIqMnNkd13mMt/4XNnnPrdEJuJIhLFvEgOa32Zk63OW9TmrLDVvj+zq8fDPLdGyPheJMDoFLaPVshiJMBFHIkxMhBzLaLUKxjKZRvVELURIAcOONtJfp43vNIEAIEQQEMHJ+21HyyIAAhuAhuG6BqARATQAi/FYWKtjHQMBERABAdHpLSabyuXeeDwaa3E1ykaElDCZ5jCb1R9piYqFfrOpxrGSli/2Wy3hhhptql3aaVJNLOPENuX6Wk0rpDf9zW9CsSULw8lE0gSA/nyp1B7Oa/nio0rMWsw0exMFv/0b06xnR8KGMqbcjrBw0502DS0rZrr7vzuS0nbizLzvpqwp6kR64WCytCTeWdoJ+2OJZKFc2gmQITyScuPaowZjhZFiItmb6HfMgbFqpbmqBOhaow6TUia/MPGbRx0vf804whIts8TR4pBOjMZH/bY1kI0dbzSWGDC0RKxzxFoCyE5nmc1YOWW5OPaVeDGVRRiwChAPGEuBQiZkutaoNdBXcDW/DcayjJY3tawRHQnfzyoVx+LpeKfn45LHW5Yv9/VFOxMncvATo2U0U+XUmItjo6Pl8qjz73Law3G85Jj9iaRBUgGjJaLJZflQPt9eiGbmFSqJZKbs3i8O0Xn9o4OpGmVwp+ePFfuGUonUWOFEMXdJPDk0+iirMxofjSVcTAy4q1mJUKG7z027Zb/tFqwirPjzUsJAX14rpRB6Ne1+J7qmpZUKZnrTgdQA9mrRE5WS7aUCFgudvyn1OY/aVBvAUKakaaHkplqnE864mClAsdNM5jVNK3SWtFIfREgqX4gNBwCQ0QFwNL8NDjA6BgCjg+ggOgaDAQDAAACMDgCg4QAwOsiOwQ4wOgCA4AAAklTASCJmZiJiZsjkGZmIiZmIiZiIiZiYmYmZmYiZmYiZiZiIiZiJmJmJA0kqYqQkIpJSEhG2u0wkJUkiKUlKkpKkJElEJImIpCQikpKIpCQpSUoiKYmIJAVKRcyaRaTguT8qVhZxMFeoLN4TVVXtKQf19qqoDiJK5LV3ampqCgXxU5/1pnVCSm2oezC4D4kpNaFscOs+hnk0ZopuILW3ap7APETf4TU8xMHQUJcIVGH2bnxDWzOQNwCsfhNvYTdxyInXQ01UGLKiLIvXiDKSYe2aJ+NaFkhD8dLKRmt3OhibPS00D1gf0StFB0qBUqTKIGtgG1cCmQigMHwax9CnBPVoN3I5fUTYSHn5Js/iX46PcjmoTzh8mwUqDYCJoKoMQVQDi4pEpSR0XRq68h0wAlhPoxiU7XyelT/4SZ/1KeAk5YxjkC3mSfGCXwgRXL1hgEpn4yMBzlMP3gKImmogIMlIJu5VeJT4stm6o5zv8yGoDZ2CHHS57BsAOE+AVHSL6FJqhV1D5OSiR3McubkQou+doBPGMZyjmxLC8ey8dVN+EaPhrTurvNEYlArYhMGW54TBmFXHYJg92Twrthojmbc0p6eLw9t3IbynkkY5VpWOVTmiWan/AbuUQAsE2D5G8gWAUm9BKqWcE4UsZPQYKW+ZYQx6J1DngSrosJo6aQudRkjqcv+SlEpPh207HdQCFwfSPgHwCdbT+Gg+tXPOmX2+94etXbAFQAwGrA0A70ZIx75ROctm+xLldCIApB0ALhwagCY0PlL3Ly8p53DYXPzT83YBTaOWOabJlwSdMD7q7Dp6p8S9p4ZhHXtsoBZjjxojpHfcE5anPppaiaJKptWmBJXpanw+4gq/W67YuZDS+1X4eP1vK31HyaSDkPIq/Z//stLv4HAElY/rf/mxvsKTHtM82SKs/P8VvqG+HhKBrQmhlBL6n/70J10EiMMPEMoocdDS+snTJ068m4NE4KzqLV3khFBA/el6G9QsJg9275o7V58rdl9wFV3caq64pPKpYfAGXeznPew4PLxAfH/BnNqluT87sEsooHYXc77/s93rJ0unNk3YzFqvMQld5QJ3fIkuFE+TBhuEjvvMORvMWbDBH5tS6w0PX2XOuosVULP9XV2Xn32B/eonX56zo7+z3vDwcvdZoIiaQbfyHu544O4L9PXvvvv6dYvdu4Y/q4DSNxw+QP/j8AXONjzcNcO3JaYM3e1uw123UjD5oICcOEgXQj9IP0joQhdCF7ouRC44DOUVIortbTqvyOVyQuRyQgixwhsiJ3I5cVchcsGx+aKWFhFR5Kt+FBC0m48wUkMhoqgjxqWEEFcM0s2bN0+MlCshpftM/T/SRZBe1Ag/zxBRMWVwsPNVW77jwdPsw4Ya6mFn0u1Tl7bzbCGVqKJUZCD9dvhGuugwkH7db+N7bdGarsv3p0JZYeFZ1f36r1//9blC2gWgDoA7UAO/gSMAjuCCXZZCA1pw4530LcqydtZA3Bgk8ZqG2G86eD4TGVFtiKqoxkGKG+1YEaATOiFKAEVtQIB31EEdvRYHZzwDSBrMYAZgAoBQRztoAtTQfAdaAFANgFSYlZUKQDuABnRF8803OAkdoNLGPQiZAdhlTSWgbwMAeAewZ5BoAuzUACfagovBuKeJCfQmThggMdLehEwE0HXVHUDSLAkjHquBxmgMRAFgDLwS6dzAb0JuEqlwDsmxFt/iNkgb73DHcu4AekcywXQsdQSQA8UQIoZjaGATQhrv3FL79V+/HTly/j1nbLcUrebPmNG0qXA1eKbtWhUr4ar719e2xaEjJzfdcxq9hSf0v12V6/Q1BxRdut9UZXvUHs206tUsrEKv597FjCG23HN8tY8sCsIVZTMWWuaf2Fi0Iq8gnPr+p8u8dzEXkeWI/ReRDMLWh5w16hOR5V1V2tYocM0REUlqddpz5viGyHKmVohW/HkhYoSISEoikpKkJELECEUQMcI0zVVUCy/EiL01SjXhQCJmJmIixglc2kWcQNyhNAOT8shH3NJCrVzPeDkAYAQAREAGN68125NrHK2slfx2FJgURoQhv51nLxd1fSR88ihUK9FwwXWjjOlwrWIWEu6m/rLjaJZrKI0YYi1RLjBgyW8XrcLAvKZydqdHjUSx3FBjerloh1taiBCyJtUAksLIy10zHkKG2GpWuurjVyxrFqdHHjXVTn3XSpuheN7ZdCH6eGH8mrDiz0txVLTafYSd4ljInKix8n4bexMFLauVPPxqtfH7tZv5hQihlqpHASuMyMfVKB0NGtPEVKL3NMWy1lzG9Eghmhjp750ajp3GNPN507ym5SJJhRF7uX5L6/TbYa25qhgPF+OYHunDMQtT84oDxWjJ6swv+66PkFQYSWLoLZw8zNh/8jT0RjmUdLFGGUI3XDh5Id0bPnkhBKw8ksQ+AgCIAAYjMAKSYTACACACADJJ5ZEkYmYiZiJiIiZJRMTMTMTMRFJxFJTFFw7KH3LWJbaM13iJlw3KL3b0EFteWh1106C8qRQhtkwJdQfpHJABhJbG0GCQbt9PpSy/Ty4Fvx8Mfjizpzm7EuDepilGrUD8jLCkhWW1DpHOgWJWnBSCHJZ/KKNv8ilB+sbHkwMYIKIBb2Enj6YBMMBIWQEMMOzkAQYxdSjn2ATUFsEN3QRjjAmWPpDp/cFv3VYd9xu3OczpeO8XmgfYCkzQDtXUAHaaKEQTwTaAJUpD85WAYMDPigS3pob1hzJ3yfHSJi/3dn7KT/Kk3IKuAJrAa1MYXhHQPYGUALAB6FsDpCAd0TPQokVwYym/l9/6rVIaPpBhI+7UJm2PNB2NiSeHxaQJsILgoFONVQPOg6QFM8CSfAQgNhoCaI6RoPYVnC+lFIMfgDt/sNXHCBkA)

    - For example, from above figure, q\_scale is the value mentioned in
                        quantization: linear, and q\_offset is ‘-59’ at output. Developer needs to
                        change the sign of q\_offset from the model graph, this is needed based on
                        the implementation of the plugin.
    - Push the model to the device.

            scp mobilenet_v2_quantized.tflite root@<IP addr of the target device>:/opt/Copy to clipboard
    - Push the labels to the device:

            wget https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txtCopy to clipboard

            scp imagenet_labels.txt root@<IP addr of the target device>:/opt/Copy to clipboard
    - Execute the reference application by giving the constants values in the
                        command line parameters:

            gst-ai-classification -s /opt/video.mp4 --ml-framework=2 --model=/opt/mobilenet_v2_quantized.tflite --labels=/opt/imagenet_labels.txt -k "Mobilenet,q-offsets=<69.0>,q-scales=<0.2386164367198944>;"Copy to clipboard
3. You can utilize sample applications to execute various AI Hub models on your
                devices. To execute different AI Hub models:
    1. Download the model and label files that you want to execute (similar to Step 2).
    2. Copy the model and label files to the device.
    3. Inspect the model file with the [Netron](https://netron.app/) application, fill in the
                        constant parameters accordingly.
    4. Follow the steps [here](https://docs.qualcomm.com/doc/80-70015-15B/topic/reference-app.html#concept_q4t_dqx_4bc__section_dl_model_labels_tflite) for the YOLOv8 and Yolo-NAS Quantized TFlite model file
                        (used for object detection) as an example to execute:

            gst-ai-object-detection --file-path=/opt/video.mp4 --ml-framework=2 --yolo-model-type=2 --model=/opt/YOLOv8-Detection-Quantized.tflite -k "YOLOv8,q-offsets=<21.0, 0.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>;" --labels=/opt/coco_labels.txtCopy to clipboard
    5. Take the deeplabv3\_plus\_mobilenet\_quantized.tflite
                        model file (used for segmentation) as an example to execute:

            gst-ai-segmentation --file-path=/opt/video.mp4 --ml-framework=2 --model=/opt/deeplabv3_plus_mobilenet_quantized.tflite --labels=/opt/voc_labels.txt - k "deeplab,q-offsets=<92.0>,q- scales=<0.04518842324614525>;"Copy to clipboard
    6. Take the quicksrnetsmall\_quantized.tflite model file
                        (used for superresolution) as an example to execute:
Note:  Ensure that the video used has a 128x128
                            resolution, which is the input to the
                        model.

            gst-ai-superresolution --input-file=/opt/video.mp4 --model=/opt/quicksrnetsmall_quantized.tflite --constants="srnet,q-offsets=<0.0>,q-scales=<1.0>;"Copy to clipboard
    7. For additional details regarding the input parameters, refer to the help section of the application.

Note:  In cases of segmentation, objects that are not recognized may appear against a white background. This issue stems from the label file and will be addressed in future updates.
4. Recompile the reference application if you want to make any changes and execute it.
                See [compile reference
                    apps using platform eSDK](https://docs.qualcomm.com/doc/80-70015-15B/topic/compile-app-esdk.html) for instructions on how to compile reference
                applications with the eSDK.

Last Published: Jan 21, 2026

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Source: [https://docs.qualcomm.com/doc/80-70015-15B/topic/integrate-aihub-model.html](https://docs.qualcomm.com/doc/80-70015-15B/topic/integrate-aihub-model.html)