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It’s also helpful to play with the various sorts of enter you’ll find a way to embrace in a prompt. Even though most tools restrict the quantity of input, it is possible to provide instructions in a single round that apply to subsequent prompts. Prompt engineering also can play a job in figuring out and mitigating various types of immediate injection attacks.
Text-to-image fashions typically do not perceive grammar and sentence structure in the identical means as massive language models,[62] and require a unique set of prompting methods. Prompt engineering performs a job in software program improvement by using AI fashions to generate code snippets or provide options to programming challenges. Using prompt engineering in software program growth can save time and help builders in coding tasks.
Few-shot Prompting
These jobs could presumably be for prompt engineers with general experience, or for folk with extra domain-specific expertise, similar to within the fields of finance, healthcare or law. Prompt engineering presents a revolutionary approach to enhancing the standard of AI textual content generation. The structured methodology of prompt development provides a method to develop queries that help the models generate outputs which are more prone to be high-quality and contextually relevant. Chain-of-thought prompting is an AI method that allows complex questions or problems to be broken down into smaller components.
In lesson 1, you’ll discover AI’s significance, understand key phrases like Machine Learning, Deep Learning, and Generative AI, discover AI’s influence on design, and grasp the artwork of making effective text prompts for design. As Ioana defined, the primary objective of perception turbines is to supply concise and informative summaries of person research sessions. They’re a form of narrow AI and may analyze the transcripts of a analysis session however don’t take any further data into consideration, like context, past research, or background details in regards to the product or users. So, insight turbines can’t interpret the entire picture of person interactions and experiences. AI may help to create designs that adapt to consumer interactions or environmental changes, based on prompts that specify the specified interplay patterns or adaptive behaviors. Designers can incorporate data analytics into prompts to create designs that are not only aesthetically pleasing but in addition optimized for performance metrics like user engagement or conversion charges.
Researchers and practitioners leverage generative AI to simulate cyberattacks and design better defense methods. Additionally, crafting prompts for AI models can aid in discovering vulnerabilities in software. Recognized by the World Economic Forum as one of many high jobs of the longer term, a career in AI prompt engineering can be fruitful. As AI evolves and LLMs turn into increasingly succesful, prompt engineering will turn out to be indispensable for harnessing the full potential of LLMs.
Directional-stimulus Prompting
Self-consistency is an advanced form of chain-of-thought prompting developed by Wang et al. (2002). It entails giving the AI multiple examples of the totally different sorts of reasoning that can lead it to the right reply after which choosing essentially the most consistent reply it provides. Least-to-most prompting is much like chain-of-thought prompting, nevertheless it involves breaking a problem down into smaller subproblems and prompting the AI to solve each sequentially.
Prompt engineering is all about creating a suitable foundation for the design of AI-driven merchandise, bearing in mind a customer’s needs, tastes, and targeted group. That is the great aspect of flexibility, as it facilitates modifying content material to suit the person’s specific objectives and targets. In today’s digital period, expertise has reached one other milestone, and the way we engage with it keeps evolving. One of the newest innovations has occurred in artificial intelligence (AI), whereby machines are taught to assume, learn, and even communicate like humans. For example, if you write advertising copy for product descriptions, discover different ways of asking for various variations, types and levels of detail. On the opposite hand, if you are attempting to understand a tough concept, it may be useful to ask the method it compares and contrasts with a related idea as a method to help perceive the variations.
Research has shown that in sufficiently large fashions, it might be very effective at getting the right solutions to math, reasoning, and different logic problems. In machine learning, a “zero-shot” prompt is the place you give no examples in any way, whereas a “few-shot immediate” is the place you give the mannequin a few examples of what you expect it to do. It can be an incredibly highly effective approach to steer an LLM in addition to reveal how you need knowledge formatted.
How Prompt Engineering Enhances The Design Process
Prompt engineering is a useful software for accurate and contextually relevant language translation between completely different languages. Translators can direct AI models to provide translations that seize the finer points and intricacies of the unique text, resulting in excellent-quality translations by giving particular directions. For example, the chatbot will check in actual time if a specific prompt generated a useful reply primarily based on the consumer’s following reply. If this prompt inexplicably confuses or aggravates the person, the chatbot can adapt the ask-it-this-way technique in dynamic actual time to add extra rationalization, for example, or propose another answer.
Context provides the AI mannequin with important background data, enabling it to provide related content. While prompt engineering should be most fitted for interacting with small models, it’s also the most tough task to realize. In the instance of “Negative Prompting”, GPT 4 manages to comply with the instruction, while Mistral failed to complete the duty of describing “Foundation Models without mentioning NLP”.
This refers to a clear outline of what specific action or response the AI is anticipated to generate. Using the term “technical assist specialist” permits the AI to create a response in a technical tone applicable for buyer assist. In different instances, researchers have found methods to craft explicit https://www.globalcloudteam.com/what-is-prompt-engineering/ prompts for the purpose of interpreting sensitive data from the underlying generative AI engine. For instance, experimenters have found that the secret name of Microsoft Bing’s chatbot is Sydney and that ChatGPT has a special DAN — aka “Do Anything Now” — mode that can break regular guidelines.
Yes, being exact with language is necessary, however somewhat experimentation additionally needs to be thrown in. The bigger the mannequin, the larger the complexity, and in flip, the higher the potential for unexpected, but doubtlessly wonderful results. However, by breaking down the problem into two discrete steps and asking the model to solve every one separately, it could possibly attain the best (if weird) answer. Anna Bernstein, for example, was a contract author and historic analysis assistant before she turned a immediate engineer at Copy.ai. In lesson four, you’ll explore the designer’s role in AI-driven solutions, how to address challenges, analyze concerns, and deliver moral options for real-world design functions. In this course, you’ll explore the way to work with AI in concord and incorporate it into your design course of to elevate your career to new heights.
Data-driven Design Choices
Examples can also be fed into an AI mannequin to receive a selected output about the examples offered. This prompt supplies the AI model with some related details about Einstein and then instructs it to create a short biography of him. Generative AI is nice at synthesizing huge amounts of knowledge, however it might possibly hallucinate (that’s an actual technical term). AI hallucinations occur when a chatbot was educated or designed with poor quality or inadequate data. When a chatbot hallucinates, it simply spews out false info (in a quite authoritative, convincing way). The researchers used related prompts to enhance performance on different logic, reasoning, and mathematical benchmarks.
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AWPyVslqY2pOd6uDljv140zdY97sFykQJ8VYWy+wsoWk/EfZXTPwmeKBe2PS71o1G4hvU9lQn2rHASWjwS8kfHgodDjuKIpQXnUFvs8N24XGazFjMJK3XnlhCEJHMkngBUctaeP/YFpSW5CjXq5X91olKvvRD8xGfRxxSEK+KVEVFnx17er3q3Wr2y+03Bxmx2XdExttWBJkkZO/jmE5AA5Z41FeHCmXCSiHAiPSX3ThDTKCtaj2AHE0RdOtOfdFtg97kojz16hse+cBy4QElA+JZW4R8SK2bqDxRbGdNaej6num0G1GBLSVRlR3fPW9jnuIbyo+vDh1rj7dLNd7JI9kvNrlwH8Z8uSyppWO+FAGsTPSiLplM+6T7EI0v2di0avltZx57UFgI+OFvJV+atmbMvGFsS2ny2rZYtXJi3J04RCuLSozqj2SVe6s+iVGuSUHTeornDcuNusNwlRGc+Y+zGWttGOeVAYFYCFuMOJcbWptxBBSpJwUkdR2oi72R57biQoKBFUSri20kqUsADjkmoeeCDxAXXaHoWZpbVM5Um8aZU2357isrfirB8tRPVSShSSf4PetTeMjxcX243iZsr2dXVyHAiEs3SfHXhyQ585pChyQORI5nPSiKVm0vxi7EdmUly33rV6JlwaOFQra2ZLqT2UU+4k+ilCtYM/dLtizkkMuac1m02TjzVQoxSPUgSCfzVzU/CPOfOWtZ+JJNLMnRGs4cH76S9JXlmHje9ocgupbx33inFEXXvZh4ndkO1dSWNH6yivTCMmFICmJA//AK1gE/FOR61tdM9Cm871cGIcyXbpTU2DJdjSGFBbbrSyhaFDkQRxBroV4N/Ffc9fsnZtr+d519iM78Gas4VMaTzSv8dPPPUetEUj9rW1/QOzRmMvW2potpE4qTH87eJcKQN7ASDyyPy1yU20Xy26l2sat1BZpaZUG4XaTIjvJzhxtSyQoZ7it5fdAtXS9S7W4Omo6HXI+nragLASSA+/+EV/qeVUWlJUlRSpJBHAgjiKIpHeCXabozZjrO/3LWmoI9qjy7c2y0t4KIWsOgkDAPSuj+yna/oHahHlPaF1LHu7cBSESVMpUA2pQJAO8B2NcVG2nXSQ02tZHMJBNdO/ufukF6X2LM3J9gtSb/NdnLyMHcGEI/1UA/XRFL1tW8nNV1YjfJFX6IiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiivFDIIzjNESdcrkGP73Y955Xb5tJaUYypZyo8SaqfiuW+SrzcrS4eDh/TQeIrciaALhR873F1irS+X11aVwOQaur5fXVpdeivLVn224qaV5ajlJ5j9IpdQtK0hSSCDyppMsrddTu5GDzFOWEwtlvC1HJ447VglAtfdbELiDyjT3LJIB5ivaK8PEVgW0sSdNRHQQFDexz7U3XZCn3Cok49aULvBeIyFEjOR60lNgjgakadjQ24UNVSPe+zsgr6f01kJAIx3rHT+mslHIVkcsDV7HlPW1zOCuOo+8O1L7Lzb7YcbUFJVxBFN595LaNzd31L4BPelKzQXobBLqzlfHc6JrVnaCObf3rfpHuvyaj3JQUhKvlAHBzxFe8hXteKGQRxrUUim1qzVaLOj2GFh2c6PdSOO4O5pjJZc3lSZThckOHeWsnPGsu+WeXpy7uS5RVJjSlEpkK4qSex9atOKCk7ySCDyI61N0cTGs5m5lUfGaqeSbw5BYDRYj3I1hOkg5BwRWa9yPxrBe61nctGJOLSuqXID3kOnebJ95BPP1HrWyo0lmUyl9hYUhQyCK0jDgvzJIDWQAR7w5/V61tnTVrkW6JmQ4recwdzPBP9tR9ZGzl5zkferBg88rZPAbmzX+3/nollSEOAb6QcHIyK9xQKDUarMk283hq2sq95PmEcM8kjua1xPujlwkKXvEpJzk81etOTV1llOoUtDqlAq3gSefoaZrSVIVuKBBTwINWDD4Y2x87TcqnYtUTSTcjxYDZZ7PMfGs9oBQwRkGsBnmPjSgz0rZetFquQp8zTcj2lhKnYThHmtZ+T6in5BnxrjGRKiOBbaxkEHl6GmHLmtxGfeR5i1+6lsfO/spf0XY5lrjuSZa1IMj3gwOSP7ajK2NjmeIcne//AI6qcwieUP8ABAu33f8APRORSEODC0gjOcEV7yr2vCM1Eqxr2ikX9jj/AO+K6f8Amj9VFfbDqvNz0WVc/wBzV8KgN90h/wCS+l/9KOf0RqfNz/c1fCoDfdIf+S+l/wDSjn9Ea+L0oFV7g9q8rqbsh0Dp+ZobTsldgt6nF26OsrMVBUSUDjnHOiLm3orZfr/aHMRD0fpW4XHeVuqeQyQw3/DdOEJ+s1O3w4+GVjY/Dcv99camakmteW44gZbjNniW2yeZOBlXXHapOWzSrbLaUoZSlKRgADAAq7cLUmO0cJ5elEXI/wARch2Ttt1c46oki4KSM9AEpArZ/gSsUG57SbrcpMdDj9ut4LBUM7hWvBI7HAxn1Nau8RA3dt2sR2ua/sFbp+57o39ealBH/NzX9IaIpG+KbUkvSGw3UMqE4pt+Y0mAhaTxAdVuq/1d4fXXMSuoPi70vK1FsJ1A1CaUt2ChucEgZJS2oFX5ElR+quX1EXQ7wm7C7HpTQtr1bMgNSL9e4yJq5DiAVMNODeQ2jPyfdIyeZJNb8umlIs2C7DnRGpEd5BQ406gKStJ6EHnWpfB7tf09rzZ1a7AZjTd80/FbhS4qlYWpDYCUOpHVJSBkjkcjtmQdxmQ2Yi3nnEIQhJUpSjgJA5knoKIuWnir2TW7ZVtEQ1Y2vKtV5YMyOz0ZUFFK0D0BwR6KFJHhm1ZM0jtr0zLivKQifKFtkJB4Lbf9zB9AopV8UinN4wtqdm2l7S22dNyESLZYY5htyEcUvOlRU4pJ6p5Afwc9aZ3h40/K1Ftk0xGjNqUIk1M91QHBKGfwmT8SkD4kURIu1iY7P2maolvqKlrusnJPo4QPzCt9+BPT8KffdSXh1hC5MWOyyysjJQFE72O2cCo+bSP8IOpP9Kyv6VVSi+57NhyVq0HomN9qqIl3xv6dhftcQL07HT7VEuKGm3Me8ErSreGe3AVByuhHjwZDex1kgf8AO0f+auue9EXUnZnpaDZdntgtlvjIajtW1jCUpxklAKlHuSSST3Nc9tvtlhaf2xaptdvZSzHbnKWhtIwlO+AsgDoMqNdOdBx0nRNkOOdujf0aa5teKJO7t51entLR/RIoiW/Cvq6Vo2863u8dwgR9IzJOM/PbdaKT9WT+WtKyZL8yS7LkuKcefWpxxajxUonJJ+utibGCRH2geuirgP8AXZrW1EU1/BXsSsb2nk7TL1Bal3CW8tEEOpChHbScb6QfnE9elS6d08FMELbCkqBBBGQRUYPAntbsE3S6dmdymNR7vb3VuRW3FBJksqO97meZTxyOeONTGdkMFjhjlRFzh8Z+xizaEu8HWem4aIcW7uralRm07qEPAZ3kgcACOnetE7N9TS9Ha8sOpYTqm3IE9lwkHGUbwCx9aSR9dSM8dW1qw6ovFv0Bp2Y1MFoeW/OeaUFIS8RuhsEcCRxz2PCo3aB0/K1VraxadhNKcdnz2WQEjJCSsbx+pOT9VEXW6TEjzoqZK47SlOJCslAJ4iuVu3ZtLW2XWbaUgBN5lAADgPwhrrQxD8uAhrHyEBP5BXJ7xBtKZ23a3QoYP36lH6iskURbl8AENqZrzUiXWkrCbY0QFAH/ABorpLpuChlpCUICUgDAAwBXNPwA6htNo2o3a13CY2w/dLYERA4oDzVocSSgd1YJOPQ102sT6C2niKInGynCRV2rbSgUjFXKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKT7jcgx+AZOXT/AKtfQC42C+OcGi5VyTcmYzqWj7xPysdBWUhaXEhSFAgjIIprqyTvKOSeZPesy3T1R1eWskoJ5dqzGLLJazZ/Oz0S0+w3IbLTqcpNIMmM7Bc8tfFs/JVTgQtKwFJOQRkGqXmG32y04nKTWNjywrLJGJAm0rl9dUpaU4sJArJlQnYzgbxvJUfdI60p2+3iOA66AVn81bDpABdarY3E8q9gQEx0hax7/wBlZuOle1bddCBgHjWsSXlbgAjavHHko4VWlQUMisMHeOTVSXFMnJ4oP5q9FmWSwtnzz0WS42h1BQsZBpBuNvUyvfA4Hke9L6VBQyDkGvHWkOoKFpyDX2OQxlfZoWzt7prJ7etXS5ugJSMqPAD1q9MhORneAJSo+6R19Kz7dbQzh9/i4eQ+j/bW26VoHMo6One5/IvLdbfKPtMkBTp5D6NKXAUcqa2sNZNWJv2KGQ7PdHup5hsdz+qtQB877DVSRMdLHc5ALKv+sLfYn2ozgLrqyCtKOaE9z+qlmJLjzmESorqXGnBlKhyNaNkOPvOLkyXVOPOEqWpR4k0u6R1Y9ZZHs7pK46z7yO34wrfkw+0Y5PS96iYcXvKeceZ7ltSbBjXCMuLKaDjbgwQa1pe7HL03I3DvOwnFfg3Po+h9a2bFlMTWESY7gW24MpUOtUzYcefHXGlNBbaxhQNacE7oHdlu11DHXx99itROkEEg5BrHRHXJdDaATk4pbv2m5dmk7iEqdjuqw0sDPH6J9adOltLIt6EzpyAZB4oSfmf21JyVMbWc+qq9Ph075jDa1tT0+tlXpfTDdtaRKlNjzsZSn6H9tOTh3r0cKZ2vtfxtLMexQ91+5vj8G1zDefnK/VUX/mVUlhmSrWxkGHQ5ZNH6n91lar17atKusxn8vvuKBU2g8UIzxUf0DrS9b7jEukNqdAfS8y6neSpP+/A1GqY9LlvuTZ76npDx3lrUckmnFoTXEjTcv2d1SnIjp/CNZ/1k+tSkuFWiHIfOHtULDj3NMfEH+WfZ3+a3y8y2+2pp1IUlQwRTF1Jp5UV32hpOUHkr9Bp6wJ8a5RW5kJ4OsujKVD/fnV19hqQ0pl1AUlQwQajaed9M/wB4UxWUkddHca7H62Wq2hg4PDBrKL4ZSMDeWeCUjmTShfbE9bXw42grZWcIUB17GljTumhHKZ9wTvPc0IPJHr8al5KqMR+Je91W4aCaSYw2tbUqjTmmVNLF1uiQqQeKEHk2P106DwFHKmLtL2mwdEwxFi7si7SU/gI+c7ufnq9PTrUOTLWy2GZKs7RBhsBJyaNT1/dZeuNpNm0SWGZGZMp5QPkNkbyW88VHt1x3pxWe82++29m52ySl5h5O8lQ6dwR0I6iokXKXcLlLdud1kqflyFb7i1H83pTl2cbQ5mkbiGVKLkN5X4ZjPP8AGT+NU1LgloRyHzx7eyrUHExdUkyD/KP6jv8ANSe3j60Uy/22tF/9Pc/8pVFRHkNV/TP6Kwfa1B/Wb+qc9xGW1VAj7pIgp0rpY97o7/RGp+y299BrTG2vYPovbRBh27WkWW8zb3lSGBHkFohZTunJHPga1FIrjjXZHYXBCtnGmFFPO1RT/wDbTWnWvAJsKUvCrXecZ/ykupO6N01D03Z4Nkt6FJjQGER2QtW8QhIAGT1OBRE440RCW+XSka/MgNKAHSnIhOEYxSXdIvmpPCiLjD4jRu7ctZjtdF/YK3d9zsaLuv8AUwHS2tf0hqS2t/BTsb1lqe46qvNuuqp9zeL8hTc9aElZ7JHLlTq2M+GXZzsbu0y76Mhz2pM9lLDxkSlOgpByMA8uNET+uVmanQXIslhDrLzam3G1pylSSMEEdQRXL7xL+G697H9RSbtZoT0nSkt0rjPpBUYuT+5OHpjkFHmPWuuAtwU1gjpTV1HpOHdIzsSdDZkx3klDjTrYWhaTzBB4EURcUbVd7rYp7V0slzlW+Ywd5qRFeU04g9wpJBFOjUO2bavqy2mz6j2h3+fBUMLjvTlltwfjpBwr681OnXngR2T6jmOz7M1cNPPuEqUiC4FMEnr5awd34JKR6UzoH3O/SqJAVcNcXh9nPFDUdtpRH8I732URQct9vnXWazbrZEdlSpCw20y0gqWtR5AAc6n94XfDxK2YWF/UWpGEjUN3aCHEc/ZWMghrPckAq+A7VuHZT4atmuy8B3S2mm0zVJ3Vz5J86Srv76vkg9kgD0ra408A3jc/NRFxn2mp3Noup0fRu0of/dVUp/udjZXK1eQOSY32qreGoPAtsWvt6nXufAvBlXCQ5JeKbgpKd9aio4GOAyafux3w5aD2MqnuaLizmjcwgP8AtMku53c4xkcOZoi0t4+GlI2MMqI/53j/AM1dc7q7J7WNjGltrunE6Y1exJcgpfRJAjvFpW+kEDiOnE1ptHgA2FFeDb73j/SSv1URbX0Ewo6HsRxztsb+jTXNDxUJ3dv2sEnpMR/RIrrLaNNR7Tao1riIUGIjKGGwo5O6kADJ68BWjtong02Sa61RcdY32FdVXG5OB18tTlIQVBIHBIHDgBRFz82MDMfaB6aKuB/12a1vU/Ne+GnZ5sh2ca51HpKLPblu6cmRFmRKLifLICjwI55QnjUA6Iq2H3ozyJEZ5bTrSgtC0KKVJUOIII4gjvTznbbdr9ytRsk/abqV+EpHlrZXcnSFp+io72VD0JxUiNjfhe2e7VdlNmvtyZnwbm+lwOSob+C5hRA3kLCk/kAPrTpi/c89KLeC3tb3hTOfkJYbCsfwuP2URQaQhbq0ttoUtayEpSBkknoKnV4LfDPdNPTG9p+uLeqPOcbKbZDdThbKVDi6sH5KiOAHMA1uLZZ4S9lezaU3cbTp9U64tn3Z1yX57qT3SMBCD6pSDUgbRZEsge6KIsNduIj43elc0fHNsruektqb+uY8VarPqRKHPNSn3WpSUhK0KPQq3Qsd8ntXVZ2CnysbtMHXegLFrG1SbHqK0R7jAlJ3XWH0byT6+h7EcRRFxUQ4tpaXGlqQtJylSTgg9wanP9zna1XqLUOo9Z6i1FdrhDtkZu2Qmpcx11pLrigtxSUqUQFBKEDPZZpd1H9zy2c3Gat+wagvVnQtWfIJRIbR6J3gFY+KjW/vDzsOtexDSA0pa5708uSVynpTrYbU4tWByBPIADn0oi3fDWVIFZdYsRBSgCsqiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKpUN5JAOMjnREn3K5Bj8Ax7zyuH8GkpKCMqWSVHiSaqdjuW+QoPgrDh4OV6eXCtyJoAyUfO9xdY6K0v9NWlEg5Bq6v9NWV869FeWpRttxLZ8tZ908x29aXEKSsBSTkHkaabDK3XRuZHHmKcsFhxlvDiiSeOO1YJWjVbMLjfl29yyCAeYBr2iisC2VQ4sISVE4ArAceLqz2zWRMZW4nKSeHToawkZzg96zxNFrrQqZHc3LsshH6KuAApwRmraP0VdRyo7VfI1QCuIrPEtK/NWWhYWApJyDWO+6lDZSQFFXJNew2VtI98njx3e1eHC4uVnjJa/kGnuWQQDzANHKvapWneSUgkEjmOYrGtlNXWetWrEgQIGHri8MIQOIb/ABj+qtdJYe33Jc10uyXiVOLUcnJrMvlnm6Wvjsm5b0piYoluWriQT0PY1bdUlSd5JBB4girBQwxsZzNzJVRxapmfL4bhZqT3+tYLyik7yTgg8DWc/wBawJFbDlrQJ26K1ku3veyvqy2o+8j/ANQ9a2rGkMy2UPsOBaFjKSDUe7fb5M6WlLBUndUPeTzB9PWt2aTtMu2Q/wC+nlEu4IbPJH9tRldGzl8Q5O96msOmkbL4Dc2/7f8AnolxSEK4KSDg54jrXuB2r2qVjeSQCRwxkcxUUp1MvaFtDjaUYFvgpTIu0gYaZHEN5+cr9XWtQhiU685cbo+p+bIO844o54npSjq3T1z0ZqZ66XYrnxLgsluavipBJ+SegP6OVY7y0uJC0KCkq4gjrVpwyniZFzsNyVQ8erKh8/guFmj2pLk8jSXIUUK3knBHEHtSrJ60kyetbb1pU+ifGzraC9Z5XsclZU0s++3nn+Mnsa3pCmR7hHRLiuhxpwZSoGonW22S7nNQiMVI3VDKwOI+HrUjdBWC4WaAXJ0hzL4BDJ5J9T6n0qHxOGMs8U5O9/8Ax1Vhwioljn8mZ5zNT+H9j0+CdZSlXBSQfiKPqorxYKhgHGetQatCYW1DalB0LEEKIEyr1KGI8Ycd3PJa/T061oQsz5cp69XySqTcZRK3FqOd30HalvaJpK9aE1i/qK7qeutvubh8ue5xWyT81XQdvhyrAfcbeQHWlhSFDII61csHpYY4vEYbk7rmvE9fVPqPJ3t5WD2pGmdaR5ZUghaCQRxBHQ0sTKRpfWpB6jKXNUffq5/9J/1RRWFRWK5Uh4LOincpO8KxHoaXOYFZtFUJdNSYm2tg5CRWW1HSjkBV/Ar2iLzlVt1oLGCKu0URJ67ehR+TVTUJDfJNZuBXtEVtLYAxirL0RDnMVlUURIr1naWfkirSbE0DncpewKMCiJNYtjbeMJFZRio3cborJooiTl25CjndFVIgoT80VnV7RFgrhIV0FWxbWwc7opSrzA7URYYhoAwAKx5duQtBwkUq1Q4neTiiKPfietZRsS1ytKCSLHLPAf8AVmuRnkPDmyv+Ka7sXm0sz2XGJDCHW3AUqQtIUlQPQg8xTSVs502XM/sbtn8kb/VRFpXwXWjz9hWnXFowSHeY/wCsNSSiWNvdHuCvLHYolsZRHiRWmGkfJbbQEpHwA4U42WwlPKiJPYtTbfzRWc1HSjkBV7Fe0RUlIIxisV+GhzgRWZRREjm0NE53RWSxBQ0eArOwO1GMUReISEiqqKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiiiiiIooooiKKKKIiivKwLjcUx/wLJy6f9WvoBcbBfHODRcq7IuLEd0NKOSeePmislKgtIUk5BpsEqOVKUSTzNZtuuCmD5Thyg/lFZnRWGWq12z3OeiV5EdqS2W3U5B/N60gyI7sFzyl5KD8lVOFKkrAUkgg8aokR25LZbdTkH81eGPLCsskYkCba/01QltTqwlIzxxWTJhOxnfLIKgo+6e9Klvt4YSHHR756dqzueALrUbG4nlRAgJYSFuD3/srO5UcqQ9Q6hRbUeyxiFyljgPoDua1wHSusFtEshbc6LJuWoIVtfbjuKK1KPvBPzB3NKLTrbzaXW1hSVDIIrWTinFqLjqypajlSieZpX0/f1wHBHeUVMk8R9H1FbT6SzfN1WjHX3f5+ifBwRisR9ghW+Bx+2slt1t1tLjawpKhkEdaqUkKGDWo0lpW/IwShYKP0VWV7owBknkK9dbUhWQOdXGWN076xk/ZWQuGq1WRuvyqliOQrzXeKz+asjlzNHKmvrHWDNiZ9jiEOz3R7qOe4PpH9VeWtdM7lathzmU7OZ2iy77q+22N9qM8ouOrI3kI5oT3P6qWIslmWwiRHdSttYylQ5GtGvuPvuLkSnVOPOHeUpRySaX9H6tes74ivqK4qz7yOZT+Mn9VSEmH8sfmel71Ew4vzSnnFmn2LZ1ztsO7Q3IM5lLrTowUn7R2Nalvlgm6VlezvFTsJwnyXe3ofWtwRpDMplL7DgW2sZSodRVq426JdIrkKayHWnBgg9PUdjWpT1Dqd3bdb9XSMrI++xWkHuIJHEVjIjOS3g0hJOSBwHGl7UOl51hmez4W9HdVhlwDOfxT6089G6PTbmkXC4tj2lXFDZ/xY9fX7Kl5KmNjPEve+ir8FFM6TwrWtqen1srmjtIN2plEuY0A+RlCOe56n1p18hRypoa+19G0rG9ki7r9zfH4JocdwfSV+gdahiZKuTqSrE1sNBDfQDXusvVGu7PpZ5mNKUp191Qy22eKEfSPb4daXoM6NcYrcyI8l1l0byVJPAio0zH5ct9ybPfU9IeO8tajk5py6B13I07KESQouQ3D+Ea+j+Mn19OtScuFcsQ5Dd3vUJBj3NOfEFmHTt3PxW7braoF5gvW65RkPx3klK0K+30PrWiNUaTuGhppZXvv2t5R8h4/M/FV61vuFMjTo6JUR1LjTgBSoHnVu6WuFeIbsC4R0vMOp3VJV9voa0aSqfSP7bhStfQxYjFbfYqNkggpyk5BrCbhuzXw02DxOOAzTr1hoifpWd5TYW/BfVhhzGSPxT6/bT92d7PxbGkXe8MgylDeaaUP3Idz+N9lT0tbEyPxb3vp3VUpsOqHTGACxGp6fWyubPNn7VmYbuFxYAfIy00f8X6n8b7Kf/LgKMAVYlSUsIJyM1XJppKl/M5XGnp4qCLlZpudz3K8kzG42AriT0FXmnUOoC0KBB603lvqfdKicjPM9ausSXYC98AqZUfeHb1r2aezctVqMxG8lyPN9yU7tarffLe9a7pFRIjSEFDjaxkEfrqNet9CXPZvcFBJck2OSv8AAPEZLRPzFev21Jxl5t5AcbVvJUMgise62uBeoL1tuUZEiM+koWhY4Efr9ayUNa+ikuNNwvWJ4bFikPK7XYqIcpSVjeSQQRnNJ6ITs98Mtg8SASBmn1tB2b3HRM8+zhci1yF/3u7jJQT8xXr9tbE2UbLBbGmtQX+MPaVe/HjrH7n+MofS9OlWebEIGQ+Pe4Og6qj0uE1Tqg0oFiNTsB1+XVal/auu/wDka4/xD+qipY+Un/c0VD/brv6Y/Uqw/wCGP/zO/QKuiivCQKgla17RVO+nvRvp70RVUVTvp716CDRF7RRRREUUUURFFFFERRRRREUV4SBXhWmiKqiqQsGqqIiiiiiK2tlKuYq37IjOd2siiiK0hkJ6Vc5V7RREUUUURFFFFERRRRREUUUURFFFeEgc6IvaKo8xNVBQNEXtFFFERRRRREUUUURFFFFERRRRREUUUURFFFUrSVJIBwcc+1EWBcrkGPwDB3nlf6tJKUEZWs5UeJNVOMOwZKkyPe3zlLnevTyrbiaALhR873F3KVaPKrSiQcg8aunlVlfOvZ1XlqU7bctw+U4Tu9fT1paSsKAUk5BppstLcdBQSMHn+inLCZcZaCVqJJ447VglaPSWzC435dlkFKVEEgHHKjlXtUuJKkKSFFJI5jpWBbKQ9R6jTbU+xxMLluDgkfMHc00ktLClPPrLjqzlSj3q7OgybLc1/fDLqXySiQevx7V65g8QalaeNrWXCgK6Z7pOQiwWI5zrFWopXvJOCORrKc51iO8zWYrFGnFpzURjqEd45R1T1T6ins26h1CXG1BSVDII61qeJFekyE+USndPyh9grZFkhvw4u6+s5Vx3Po1o1TG259/epGjkcH+EMx7kpEA0cq9qlYKkFIJGRjI6VpKTTW1nrRqwtiBBw9cXh7iBx3AfnH9ArXIZfLi5c10uyXTvLWo541lXq0ztMX1127FUlqWolqYeOfQ9jVLxB4g5GOdWCihYxnM03JVRxaqmfL4ThYBYDvWsB1SkK3knBHI1nu9aT5FbDlrw5p4aK1muC4IklW80flI6j8ZP6q2mxIakNJeZWFoWMhQ5Go9W+DJmy0ezlSN0glY6eg9a3ZpS1y7bBxLdVlz3g2fmf21GV0TOXxND71M4dNI2XwG5t/2/89EtLZbd3fMQlW6d4ZGcHvVeMCihQykgHHDnUUp1MvaFtCjaTjiDBAkXaSMMsjjuA/PV6dh1rT4YlOPO3K6PqfmyDvuLUc4Pas/VVgumjdUvT74tc6PcFktT1cSD9FXYgfm5VbfUlSd5JBB4gjtVpwyniji52m5KoePVtRJP4LhZo9qSpPWkt9SkKCkkgg5BFKknrSTKrcetKnT92c7Q3LW+IMpRWyo++3n/AFk+vcVvGJMjzI6JMZ0ONODKVDkaibbLbLuM5CYpUjcUCXB0+HrUj9B2O4Wm3Znvry8ApLJ+Z6n1NQuJQR8njHJ3v/4Viwioljm8mb5zLX/t/Y7D4JzOsMvgB5pKwCFDeAOCORquvaKhFZrK088lpBWTjA68qbMy5GY6Utk+WDjP0qVr5Dflxt1lwgg53eivSm2yClW6pJBBwQelSFJG0t591XMYqZWyCECzevVZ7FZ7SQoBJAIPCsCPSgx82vcq8UourO+/ZnfNQlS4qz74HzaW2H2pDYdZUFIUMgik6bMYiRz5yQsrGEo+lVNggSIrS3XiUh05S19GtaQB7Od2R96kaZ7oZxBHm05n8P7HolN+MxJSEPtIcSFBQC0gjIOQauDgKAMUEZFaqlbboyO9FNz9jF4/fhcv4rf9WivVh1Xm7uicLi90Zpm672p6G2cRo8zW+qbfZWJbhaYclu7gcWBkpHrjjTqmL3UE5qB/3S+SXtH6Tbz8m6un/wCya8r2pGr8WOwMfJ2s6e/lYqj+6x2C/wCdfT38rFccqKIuyCPFjsE67WNPfysU8dB7Ytne0lyWzofWFsva4AQqSmG8FloLzulXbO6r8hrh9U3fuYzhbvevgDzj23+dIoi6PNr3hVdYMR8bgyav+1I70RX6KspkIV1q4FA0RVUV4VAc6tqkIT1oiu14eFWfakd69EhChzoityZAbBNRk2y+O3Zlsk1S9o5MCffrlDUEzBDUlLcdXPdKlc1DqBy71Iq7OENqwelcaPEVoLVGhNrWpGdRxH0puFykzYspaTuSGnHFLCkq5EgKwR0Ioi6w7Ftuui9t+mBqbR0twobc8mVGfTuvRnMZ3Vj15gjgfy1s1pe+KgZ9zk0JqrTllv8Aqy8w5EOBelMohNvJKS8EZJdAPTjgHrU6oroCASaIs2irBlI716mQhXWiK9RVKVhXKvSoDnRF7RVpT6E8zVPtSO9EV+iraXkK5GqwQeRoi9orwkDnVtT6B1oiu0VY9qR3qpMhCutEV2ivAoHlXhUBzoi9UcDNJN5vMOzwZFyuMpuPFitKefecVhLbaRlSiegAGaz3n0BPOtUeICwXTWuynVOlbE6Uz7jb3Go/vY3l8CE59cY+uiLSk77pPshi6nNoj2S9ybYh3yzc0ISEkZxvpbJ3inr0OOnSpU6W1XaNW2WDqKwz25lvuLCJEZ9s5S42oZBFcM5WnL9Cva9Ny7PMbuqHvIVDUyrzvMzjd3MZzXW/wlaSv+gdhuldLamSpu4xmHXXmlnJa815bqWz6pCwPqoi34lW8KqrGbfSEZJqr2pHeiK/RVpL6FdauBQNEXtFUqcSnmatmSgdRRFeoqx7UjvVaXkK5GiK5RXgINe0RFFFWJEgNghPP7K+gE5BfHODRcr1yShtQSr6/SroIIyOVJe8VEkmrrEgsncV8g/mrIY8slhbNc5rKkxmpTRbdTkHke1IL7D0JzyXeKT8lXenGCCMg5FWpMZuS2W3BkHl6GvkchYvUkYkCbh5VQltTit0CsiREdjOeSUkgn3T3pVt9vDADroyvp6Vnc8AXWqyNxdZECAlhIWsDfx+Ss7kKOlIeodRItqfZouFyVjgPoeprXAdK6wW0S2Blzosq43+FbZDcd5RUpR97d47g7mlBtxDqEuNqCkqGQQeBFaxdU6tSnXllbizlSieJNLWndQKhLEV8ksk/wAX1HpW0+ls27dVpR1/M/z9E7bhbo1yjKiyWwpKuvUHuKYk+3yrJI9mk++yo/gnOhHb41sJtxDqQtCgpKhkEdRVmfAjXGOqNJQFJV+UHuKwwzGI56LZqKdtQ3utcOHqKsojqkOBCQeJxwpRudnlWyQIy0laFn8GoD5Xp8ac2ntPphJTLloBeIylJ+Z/bW8+ZrW8yioqeRz+TRVafsCIDaX5CB5uOA+j/bS50oApOul1RBbKUEFwjh2HqajiXzv7qXAjpY+gCrnXeNBWhtw5KjxA+aO9ZjbiHkJcQoKSoZBB5imIt5UhwuOKKio5JPWs+13Zy2OBt7Ko6jx/FPett9HZnm6qPixPmkIeLN9ycV2tMK8QnIM9oONuflSe49a1LerHP0tL9kk5ciOE+Q9jhjsexrcbTqH20uNqCkqGQR2rHulrh3aGuDNaC21j6we47GsVNUup3dls1lGysZ32K0m73rFbiuS3g0gZBIHDj9VLl+01PsEwRVIU6w6cMOAfK9Pj6U9NG6ORbkIuNxbBkkZQgj9z9fj9lS0tTGxnPe99FAQUcz5DEBYjU9Fc0fpBq1MomTGh5+MoQR8j1PrTt4UAVakPpZQTkZ+yoSSR877uVliijpI+VunvXj8tDBAVxJ7VcStLiQpJBB5UiqcLqyonOausSVwyCrJaUePoa9mHLLVazK67zzeism82W3323O2y5x0usOjBB5g9CD0IrRGpdMXLQ00RJJU/bXlH2aRjl+KexqQaHEOJCkEEHiCKxLvZ4N7gO264MB1l0YIPMHoR2NZKOrdSu7bhMQw+Ovjsddio3SCCCQcgisFER2Y+GW0nieJxTo1Zoy46Vm+zqSp6G8rEd4Dn+KexrYGzvZ6i2obvF4ZBkkBTLSh+5/jH8b7Kn5qyJkXi3vfTuqnTYdUPmMAFiNT07/JXNnmz9mzx27jcWB5+N5tpQ+R6n8b7K2ByGKMBPGmPtK2lwNEQvZ2N2TdZA/vePn5OfnK9PTrVec6WultqT7Fb444MLgOwGp3J+aztZbRbFotcdietTr76h+BawVJRnis+n204bfcod1htT4EhD0d9IW2tJyCKiJc5txukx26XaSp+XIVvLUo8vQdhTv2Y7SZOlZnsEpSnIDqvfa6oP0k/pFS02CFsIMZu8a9/Uq9T8TB1QfFFozoenc/HopKKTkYNIt0tuV+e2Pe796U4M+LcojU2E+h1l5IUhaTkEVecQlxJSoZBqEY90TlZaiCOrjtr0KbLHA4NZRfSw2CeJ6DvVc2IphzeAyCeBrIgW73xJkjKvmp7VtvkaRzFQ8NPK1/hN1VFvtq3XfbpwyvmhJ5JpW5Ucq15td2vWnZrbA0jdl3mWnESIDx48N9fZP21rMZJVyBjBcnQKWJgw6EvebAZknf91nbQ9q+m9nSYzdzUp+TJWndjskFYbz7yz2A/OadFnvNtv9sj3e0y25MWSgLbcQeBH6D3HSoIXu6Xi/3F++X+WuTPlK3lqUfkjokDoB2p67Hdrk3Qty9gkrW9bH1AvR8/JP00evcdasc/DhbTgxm8g16HsFTqbjEPqz4otCcgdx3PUdeimNviimN+3Rs6/wAvp/8ALV+qioH7Pq/6Tv0KtX21h39dn+oJ13I4bUBUB/ukSidLaWH/AGo5/RGp8XP9zV8KgN90h/5L6X/0o5/RGtNSagVXRnZvsI2T3fR9kmztntkffkQGHHHFxUlSlFAJJNc5q2raPFJt3sMGPbbTrxTEaK2lllH3uiK3UJGAMqaJPDvRF0Dg+GfYs5gr2X6fV8Yaa2HoLZboHZwuS/ozSNssq5oQmQqGwGy6EZ3d7HPG8r8prSngc22an2u6Uv8AG11eBcb1ZpzZ87yGmSYzqPc91tKU8FNuccdq3Ttj10jZzs11FrMBKl2q3uvtJPJTuMNp+tZSProiZW2/xh7OdiTyrJJW9eL6E5NvhkZazy8xZ4J+HOo03P7plrx6UVWnZ7aGGM8EvynHFEfEBIFQ+vF4uWoLrLvd4mOSps11T77zhypa1HJNPzQnh82m7RbKdQ6dtDXsJJS25IfS15pHPdzz+PKiKXezv7pZZZ85mDtD0a9aW3FBKpkF/wA9tPqpBAUB8M1NDSutLFqyyxNQafujE+3zWw6xIZXvIWk/78RzBrh1fbHddNXeVYr3DcizoThaeZWOKVD9HXNS5+587V7lbLzd9mU2YtcCSybhCbUrIadTgOBPYKTgn1SKIuid+1TarFbZF2u09iHDioLjz7ywhCEjmSTUPNqH3R/R1gmvWzZ7p2RqFxolJlvO+zxyR9HgVKH1CtF+NjxEXTXmrpGzTT9wW3p6xOlqX5asCZLHyt7ulB90DlkKPHhiNNmsl21DcmbRY7e/NmSFbrTLKCpSj8BRFLZP3S3aYH99WhbEWs/IDzoOPj/ZW1tl33R7RmoZzVr2haff0446QkS2nfaI4J+kcBSR64xUR3PCXtxatv3zOlm1AJ3vJTLbLuO27nOfStT3O2XGzTnrZdYT0SXHUUOsvIKVII6EGiLt2zqK23y2s3O1zmZUSS2HGXmlhSFpPIgjnURdfeOLZtYtU3TS910Jd5j1nmuxVrwwpCltqKSpO8c8xWsvAttrucG7Sdkt5mrdhSWly7UHFZ8pxAy42PQpyrHTdPeo57ZF+ZtY1e59K9Sz/wDdVRF0l8PXiX0ztuk3OJYbBcbabShtazKLZCwokDd3Se1Z223xhbOtibhs8xx67X3cCvvdDI3mwRwLijwRntz9Khh4RtcftdaX2kauASpdutbbrSVclO5IQD6FRTUdbxd7nf7rLvd5muy5055b8h91WVOOKOSon4miKYV0+6Za8elFVo2e2iOxngl+U44sj4gJAp6bPfullnnTWYW0PRbtrbcUEmZBf89CfVSCAoD4ZqImgfD/ALTNpFlVqHTloaMDeUht2Q+loOqScHczzwcjPLIpkX6w3bTF4lWG+Q1xZ0JwtPMr5pV/vxzRF3C0lrexavskTUOnrmxPt81sOMvtKylQ/X0IrKvup7XY7dIut2nsw4cVsuvPvLCENpHMknlXOX7n1taudp1Fd9l8+Wtdumx1XKChSshl9BAcSnsFpVk+rfqabnjT8RF32gasf2c2K4uNacsju5IS2rAmShzUvHNKeQHLOT2wRb62ofdH9G2Ca9bNn2npGonGiUmW877PHJ/F4FRH1Vq5j7phtGRJ35GgbI4xni2mQ6lWP4XH7KiTp/Tt71VdGrLp+2vzpj3yGmk5OOpPYeprZczwr7Y4duNw+8Ud4pTvFhqUhTv1JzxPpRFOHY74/wDZtr+axZNUMO6WuT5CEGS4FxlqPQO8Mf8AiAqVkK5tPtpcSsKSoAgg5BFcG5cSXb5TsKbHcjyGFFDjbiSlSFDmCDyqdHgR8Rt2ntr2QatuK5K4jRes77ysrDSflMEnmE809hkcsYIp9zrozHaU644lCEAqUpRwAB1JqKe2L7oBs20DOfselY72qbkwShxUZYRGQodC4c73/hBrWHju8R11twRsf0lcVx3ZTKX7y+0rCktK+QwCOW8PeV6FPeoMQ4cu4y2oUGO5IkPrCG220lSlqPIAdaIpgvfdL9oypXmMaBsiGM/uapDqlY/hcPsraWy/7o9orUE1m27QdPSNOuOkJEppz2iMD+McBSR64qIUPwr7Y5luFwFjjtbyd4MuykJcx8M8D6VrO/6evWl7m9Zr/bnoUxg4W06nB+PqPWiLuXYdUWu+26PdbVPYmQ5LYcZeZWFoWk8iCKxdW64sGj7JL1FqO6sW+3QWy4/IeVupQkfaew5muZ/gq8RN10Fq6Ns21BcVuaevroZi+arIhy1cEbvZKz7pHLJSeHHLh+6C7VrlddQWnZpDlrRAhsCfLbSrAddWSEZ7gJBx6k0RP7aH90ttMOY9B2d6KdubSFFImT3/ACEL9UoSCrHxxVexrxq6k2jDVU3V+mYMaHp20queICleY4EqAKffOOVQM09p+76qvMTT9hhLlz5rgaZaRzUf0DqTUndA7CdoWyTSO0G66thxWo8/TLzLamZCXCF5B3SB6URbAPj+2XqlCavZ7e1PpGA4Qxvgds72ak9sc2t2fads+gbQrdEft0Kd5+G5Sk77YadW2oqIOMZQT8K451KGbtOuOjfBbpfTdnlLYlahmToq1oVhSY4lPKcwfX3U/wDioikRtZ+6HaE0XPfseibQ9qeZHUUOPpdDMUKHMBeCVfEDFafT90w2iiX5i9n9kMfP7kJLoVj+Fj9FQ4Qhbi0toSVKWQABzJNbUk+GPa/F0yrVDmnkFhDPtCmEvpL4bxnO5zzjpzoinHsZ+6BbPdoFzj6f1bBd0vcZKg20t90ORVrPJPmcN3/xAVLBi6NLbCgoEEZyDXBfJScgkEV0o8KG3g3TYK5dNYXMlWkEOsS5Lisq9naRvoUT1IRw+oURSP2m7YNFbLLE5qHWd8ZgRU8EJUcuPK+ihA4qPwqG2t/umS0y3I+gtAedHSSEyblJKCr1DaAeHxNRT24bZtSbbNby9T3qQ6iGlam7dCKvcisA+6kDlvEcVHqT2xSDojZxrPaLPVb9IWJ+e43jzFpGG2/4SjwFEUpbf90w2hMvhVy2f2WQ1nilqS42rHxIV9lSL2I+OTZntVmsWG4l3Tl7fIS3GmLBaeV2Q4OBPYHBrn9qvwy7Y9HW1d2uely9GaTvOKiPJfKB1JCTnFaubcdYdS42tTbjaspUk4KSOvpRF3siTUupBBrOBBGaih4HtuE/ansy+9+oJZfvemnUwZTij7zzRTll0+pAUknqUE9alPGc8xA486Iqn3w2nnxpPU4XCSauzGnM5yTjp3rHTWzG0AXWlI8uNiq08quYCuB5VbTyq6nnX1y8L1h9UZW4vi2TwPalAEKAIOQaTXFDdKMbxPIVlw2nGm91xWc8cdqxSAarYhcTkrxQlRBKQSDkVVRVLiVKQpKVFJIIBHSsS2Eh6i1Gi2J9kiYcluDASOO56mmiGl7yn5Cyt5ZypRNXJUSRZ7m4i45cU8cokH51VOEHlUtTxtay4UBXTyOk5CLBYjvKsZSilW8k4IrJd5ViudaylYo05dNaj8g+yvk+X1H0fUelPRtaXEhaFApPEEVqaHGfkSUlkqTg8x9lbJskOREihL7hO9xCPo1o1Ubbc+h96kqORwf4QzHu7JQW224QVoCt05GRyNe4r2qVZxgHGa0lJLCudzbgtHChv469PU0ypc5cx1SiokZzk9TSpqO3zDxDilAEqI+lSA1wyDUzRxMazmGZVZr6iSSTlcLAbLNa5CspKUrSUqGQaxWuQrLbrM9arVettyesjwbeKlw3D8Sg07WXUPtpcbWFJUMgg86Z0t9ppkoWgOKXwSjvS1pe3TIEQ+1Oqw4d5LR4hAqPqo2lviaH3qYw6eQu8K1x7ksOMsu48xpK90hQ3hnBHWqsCvaK0FMK286GkFSiABSBKnKlOFKCdz7aULzGfkNDyln3Tko+lSI0MKwRgit6mY23NuoLEqiQSCK1m+9ZrXOsxtKVo3VDINYbXOs1jlR6804urSHHbW5k5VHWf4ppWbcS6gLQoFKuIIrAlyGWWCl1AXvjAT3ryzRZEdpSnlEBfFLf0awyAObznX3rep3ujl8Fubdf7f2PRZ7sdh8APMocCVBQChnBHI/Gq8AV7VKwSkgKKSRgEdK11IW3TE2nbUYGhIQixwmVeJQxGijjjPJauw+2tBqbuE2Y9fL7JVIuEola1KPyc9B2pS1vp29aI1vIueqFuXKPclkxrkoZ3c/NI5AgcMduVWJKkrTvoUCCMgg5zVzwikhii8RpuTuua8TYjUyVHk7m8rB7UjTOdI0ha2176FFKk8QRSzM50iy+tb8ijqTPJbM2TbVHrLJ+901ZVHcP4Rrsfpp/SKkZCmx7hGblxHUuNOp3kqScgioP2u2TrncG0QVKbUhQJdT83+2pY7L9PXmy2suXOS4ESAlSI6vm8PlHsT2FQGL00Rj8oJs7/d+4VqwKrmiqPI2DmZa/9n7HYa9Mk9ilKvlDPxr3Fe1SsEpICik9x0quK5LXG2PbNZ9l9rDLe7MvkxO7ChJOSSeG+vHJI/PyFRYdTeLxcn9UaqlrlXWYrfUVHg2DySkdAOWOlLu1TSGpNnm0qTqTWLj16gXdZ9kurgyWSTwQQOCSBwwOnEdawZTiHUBxtYUhXFJB4EV0HAKGnig8Zh5nHdcg4yxaskqvJHNLWD2/X13b8/5RpvTFrbWHG1FKknII6U4Z/wAs03Z/WpWVQtCL5Fe/slvH/SR/EFFJdFYed3VSnksH3B+gXSa5/uavhUBvukP/ACX0v/pRz+iNT5uf7mr4VAb7pD/yX0v/AKUc/ojXL12VQKqSWl/BFrHVFng3iNrK0MtzmEPpQtl0lIUAQDgetRtrols18S2w6x6RstvumuozEmLBZadbLDxKVpQARwR3oizPCx4XtbbCdZztR3DWFruFtuVvVEfix23EqKwtKkL94Y4YUPgs06/GzJWjw56uCFEEiCn6jOYB/NWUx4v/AA8NNhP7Y8QH/uz/APUrWniV2zbO9qnh91wzoXUrN1VAFtVJDbbiPLC5zO78pIzndVy7URc966deH+0NJ2N6UDTYSk21tWAOpyT+c1zFrqx4emgdi2kFY52tr7KIoL+MiG3D23TUtoCS5BjLXjqcEZ/IBSX4Xrq9ZNqQukc4XGtU50eu60T+inB4207u3SUMf83Rv/VSD4WIbdw2txYD2fLkwZbS8fRU3g/bRFqifJdmTpEt9aluPOrcWpRySokkk1NvwHbPLSdI3DXbrCHbnOnLhNuEZLTDaUndHYqUpWe4CahnqywztLanumnbk0W5Nvlux3EkY4pURkehGCPQ1IfwbeIey7MZkrQ2tZIi2W5yBJjTFfIjSCAlQX2QoJTx6EdiSCLoAqwp8ne3elQr8fOgLVb4li13FYQ1NdlqtskpABeSW1LQT6p3FDP4w7VMp7adoRmyG9O6stCYO5v+0GY3ubvfOa55+L7b9atrmoYen9JOl2w2Ra1pkkECU+oYK0g/NA4DvknrRFrTYPcXrXtk0fKYUUqVdWWCR9F0+WofkWaT9rR3tp+qld7vK/pFVc2Of4WNHf6chf0yatbVv8Jmqf8AS0r+kNETn2cuqb2R7SUpURvxoQPqPPFatrZ2z3/BPtG/7vC/pxWsaIunHh7tDSNiujvJbCUrtLLhwPnKG8T9ZJNQ58ZkNqHtwm+WgJL0CI4vHVW5jP5Eipx+HRoHYfoo4/5ljfzBUKPG+nd27SgP8mRPsVRE1/DHcHrXtdgzWFEKbgXFXD0iOkfnArWE2W9PmPzpCip2Q6p1ZPVSjk/bW0fC5FTO20WeEs4S/GnNn4GI6KYet9NTNH6tuum5zKm3YEpxrBHNOfdP1jFEVmyPaot6lTdPG5MKWNwuxAsZHbKaWf2R7Vv8q6l/jvVuDwqbbtM6MU9ojXDrUW3ynfOhznU5Qy4eaFn5qTzCuQOc4qYzutNmNutZvM/VFhZhJRv+cqS1ukenHjRFzAm2/VNzlLm3C33OTIdOVuuMOKUo+pI409NhjGptP7W9K3Vi03BvcuLTa1ezrA3FndVk45YNdB9kW0jZxtkauT2jVJd+9cksOIeYDa1p+a6lPPcVxxnB4cQK2S5pxhprLcZtOOPBAFEXJzbdeJV92v6wuUxalLVeZTScniG23ChA+pKUj6qa1p+/TUkTbGmYH2DkOxkq3kE+qeXWtm+KbQ0zQ22vULTzKkxbvIN1iOY4LQ8d5ePg5vp+r1o8OG1u3bLdXPDULRXZbuhDMpYRvFlSSdxzHUDeUCB0J7URNP8AZHtV/wAq6l/jvUl3RnWV7fTJu8a7zXUp3Qt9pxagO2SK6b2jWezO4WpN4i6nsTkMp3/O9obCQPXJ4Un7PtsGybaTrS5aH0rcGJU23NB5LnlJDUpOcL8lXz9zhnh1yMjjRFzTg2bUkaYxKj2e4ocZdQ4haY68pUCCDnHetj+KW4SbrtXdnywQ6/bYTigRgpKmgSMfEmum7mnIrbZKYrQ+CBXNfxhteTt0u7eMYYj/AMyiLL8FsRqXtwiBxAUW7fJcTkcj7oz+Qmpu7cYfl7INWLxytT/82oWeBwb23WOP+y5X/oqc23xAGxzVpA/5pkfzTRFyYraOvnVHYbsqZz7u9fDj19rR+s1q6tm69/wJ7LPjfP8Aa0URNXZww3K19p6O8gKQ5co6VA9RviuqMm0pVBcBQCC2oEemK5abLBnaRpkf9qxv6QV1rksD2Fzh8w/ZRFx/1Q0iPqa7sNpCUNzpCEgdAHFACtn6Jv0y1eG7XkKM4pKbjdYUZeD80jeP5dzHwrWesP8Albe/9Iyf6VVbd2UaUlau8P8AtEhQGVOyYj7E9pCRkq8pO8oAdTuBeB3xRFouupfh32bWnR2zGwW63x0B2TCZly3Uji8+4gKWonqMnA9AK5aV0D8I/id0jdNJ23Qes7vHtl7tDKIbDklYQ3MZQN1spUeG+EgAg88ZHM4IpKzrCgMkFAIIwQRwNcy/Floa16D2yT4dmaQzEucdq5JZQMJaU4VJWAOgKkKVjpvV0a2gbadm+hrG7d9RaqgMtIQVJbQ8lbrp6JQgHKia5cbZtpcva1tDumtJDBYZkqS1EYJyWY6BhCT644n1Joi3/wDc5r85bto+pbUVkMTLS2+sdN5t5KUn8jqvy101tTocaSoHPCuV/gFONqd7P/Ya/wDaGa6Y6WuJabS08fc6HtWQM5m3CxGTldYp3rQHBg0nvMltXKlJJBSCDkGqXG0uJIVXxjy0pJGHhJaeVVlRHIZJ5CqnGlIXukc+XrWTGjbh8xzir7KzOcALrXbGXGy8ixij8K7xWfzVk8qOVJF7vaLenyGMLkLHAD5o7msIDpHWWw5zIGcx0WRNvEOE+hh1WVKPHHzR3NZyFpcSFoUCCMgiteuKcWouuKKlqOSTSvY72YqhGkElongfof2VnfTWbduqjocSDpCH5NOnZOK5W2LdIyo0lGQeRHNJ7imHNgyrNIMSXlSD+5udCK2KhaVpCkkEEZBFY1xt0a5x1R5KMg8iOaT3FeIZjEbHRblTTtqG91rl3lVlDC5DnlpB4nHClC42mXbZHsroKwo/g1AfKFOfT2nxDQmVMR+GIylP0P7a33zNY3mUXDTvc/kCq0/YEQGkvyEDzSPdT9H+2l0cKBwps6w1gxYWfZIpS7PdHuIHzB9JVR3n1D7alSwEdJHc5ALNvOq7VZZLMWU4VOOkZCOO4PpGldl1t9pLrSwtCxlKgeBFaKkOyH3VyZTqnHnDvLUTnJpzaM1iu2OCBNUVRVH62z3HpW9Lh5ay7MyFGQ4sHSkSCzTp29a2bIjtyWy24nIP5qZ13tDkR4qCeB5ED5X9tPNp1t5tLrSwtKhkEHIIqiVFaltFp1OQfzVqQTugd2W9V0japtxqmG1V8uhocBvKPId6vXC3vQJG4pJUlXyVAc/7aWLLZPJxMmJy6eKEn5v9tSMk7Gt51Cw0skknh2tbVU2OxqbWLhcBvPK+Qk8kD9dLx9K8+ymjr3XsTSkURY5S9cnx+BZHHcH01en21Gf5lVJYalToEVDFc5ALO1FreyaZfYjT3Sp59QBQjiUJ+krsKXI0liWwiTHdS404ApKknIIqM86TMmyHJ099Tsh5W8tSj1p27PdfO2N4W6csuQlq4p6tH6Q9O4qTmwotjBYbuGvdQtPjwfMRILMOh6ev6yW8FAKGDSRcIGFF1se919aU48hmUymQw6lxtwbyVJOQRVa0JcG6oVFMeY3KcnhbUst+iQG+BrI84MozjJ6DvXsmOphzOOB5HvWRDhYUH3x73QHpWw57bcyjIYZA7kGqphQVrc9rl8Vn5KTyTSieAo5CkjUF+ZtEc8UqfUPcTn859KwAPneGjVSJ8KiiLnGwGp6qq76hgWYtpkqJW4R7qeJCe59KUYz7MllL7DiVoWMpUDzrT8qc7cZS333CtSzkk9aVdP6lkaffDUjecgOH3hzLZ7j9VScuFFsQLDd3v9Sg6fiAPnIkFmHQ9PX9ZJ/ag0/atS2t+0XiIh+M+nCkqHEHoQehHQ1GjVuj7ts5uRt0xS5FqfJMSVjkPoq7Edfy1KSNJZlsIkR3UuNrGUqSeBFYV+sNs1JbXrVdoqX47wwQRxB6EHoR3rWoa59DJ23CkMUwuLFIbfzbFRKlqCveB4GkxMJ2c+GWhzOCccqeWuNAXXRNy9jc334Lyv71kY+UPonsoVsvZPss+97TWodQxsSFe/HjrHyB0Uod+w6VaJq6GOETXuDp3VHpMLqn1BpWixGp2A6/Lqr2yjZYzZo7V4u8bddIC2GFDin8dXr6dK2tgYxQMAcKY+03abbdAW3AKZF0kgiLGB4k/SV2SPz1VJJZsRm6k6Dor1HHSYHSFzjZozJOpPxPT9Fnaz2i6e0P7Oi6uqW9IWAllrBWE54rI6AfnpwW24w7rEauEB9D0d5AWhaTkEVDW7XK6Xqc9eLzKW/LkHeWpR+SOgHYDtTy2VbUpWkZot0xanba6r8I1nJR+Oj9I61MTYCWwAxm7xr39SpNHx62SuLZ22gOQO7e56g79FI/UmnLNquzyLDfYTcqHKQUuNrH5CD0I6Ecqh7r/Z3e9kV3MGSXZmn5Sz7FMI4o/EX2I/PzFTNt8+Jcojc6E+h1h5IUhaTkEVi6i07aNU2mRZL3CRKiSU7q0KHLsQehHQio7DcSlw2X8O4+t1bsYwiDG6e2V9Wn62UEJqkqIUk5B4gikcQHrjIEdkcyMqxyrYu07ZTeNm91MfDkq0SVH2OTjJ/gK7KH562vsM2JCE0xq3VcTDqsORIjieKey1jv2FXSoxKnjpxUk3B0G5PT59FzaiwWsdVmia2zhqToB1+XVaJ/akvH+S7p/wCQf1UVPHcT9AUVA/4mP9IfqrV/gyT/ALl3+kLDuKctnhUCfukzRRpTSqiOd0d/ojU/ZLe+kjHOmPrDQuntUtIZ1DYLddG2lFbaZkVt5KFYxkBYOD8Kqyva4g0V2Ck7DNnZUd3Z9pwfC1Mf1asftG7Pf3gad/8ApbH9WiLkJW1tnGf2jdr+OXk2LP8AL66Vt7D9nn+b7TnD/spj+rShH2QaKixJMCPouxtRpoQJLKLcylt8IO8nfSE4Vg8RnkeNEXG6usnh1jKVsS0crHO1M/ZTujbD9m4OVbO9NH0Npj/1Ke9s03DtkJqBb4bMWMwkIaZZbCEIT0CUjgBRFy38ciC3t5lpP+TYv/qpM8HLBk7c7W0BziyT/qV071Bsr0Xf5puF70dZbhKKQgvyre064UjkN5SScCrNo2UaJsMxNxs+jbJAlIBCX41vZacAPMBSUg0RRL8Vnhin65QdfaIhhy+x2gmXESMGa2kcCn/rAOHqAB0FQWmQplvkuQp8V2NIZUUONOoKFoUOYIPEGu1V5jQoER2VMeaYZZSVuOuKCUoSOZJPACos3bUXht28bSHdn6bTCvdwbjOO/fRtAbS4tBGW23BhThA3jninCeGaIuetFdDv7j7YrFe9oTYJTmDncclrKPyVFTxTWXSmmtozWntJW6LCjwIDSHmmBgBwkq4nmTgjnREztjQ3trWjk975C/pk1Y2sAjabqkHpdpX9Iaz9hUdyVtl0Uw0kqJvkNRA7B1JP5ga6fMbGtA3R1cyboaxSJDyitx123NKUtR4kklOST3oi5s7OmyrZFtJX0RHhZ/8APFatrsfG2N6IhxX4UTRdkZjywkPtIgNBDoByN5O7hWDx41VG2HbN0qG9s700fjaY/wDUoiaHhwjKVsL0QoA8bJG/mCoQ+Ohst7e5SSP+a4f2KrqLa9NQ7XCat9uhMRYrCA20wy2EIbSOSUpHAD0FIGoNlmjNQTTcb3o+y3GUpISX5VvaecKRyG8pJOBRFy98HrBk+IDTrIGd5qb/ALK7UnPE74XH9pkb9luj0NtakiN7q2VEJRObHJJVySsdCeB5HHMSismyXRFknIuVn0XY4EtrIRIjW5lpxOQQcKSkEZBI+unK7p9KkfIFEXEO92C96auL1o1BapVvmsKKHGJDRQtJ+B+2sCuyWtNkOkNZt+RqjTFvuaQMJMhgKUkeiuY+o1r+L4Q9iDEoSE7ObeVA5AUpwpH1FVEUFfCbcNp9p2uWuZs3sMu6+Y4lm5R0e6yqKT75cWfdRgcQT1HXOK60CAHWeKeOKQNG6FsWlIiLfp6yQ7bGT/iorCW0k9zgcT6mn5Fi4b3SKIoyeJzw7W/bLpoMNLbiXu37zlvlqTwBPNteOO6rA+HOuZ+t9n+sNnV6esOsLHJt8pokAuI/BuJ+khY91aT3Brt7crUl4H3c5ph6s2cWHU8RVvv9kh3GMrJ8uSylxIPcZHA+ooi4uU6tl7+uYWu7PcNnMKbKv8aSlyI3EbK1qV1SQPmkZBzwwTmuk7nhC2HuyjIXs6gZJyQFuBJPw3q2boXZXpHQ7XkaW0xb7WlQwoxmEpUofjK5n6zREoWH77XDTcCXf7amBcnoyFy4qXA4GXSkbyQocDg9q5j+NVvytv8AeUY//bx/5ldZ0W78FjHSmZqLZVom/Tl3G8aMsc+WvAU/JtzLrigOWVKSSaIubfgUbLm3lhIH/NUr/wBFTq2/RlDYzq9WOVokfzafmnNlmjNPTRcLJo6y26UElAfi29plwJPMbyUg4NOK56ciXKC7Anw2pMZ9BQ606gLQtJ5gg8CKIuGNbS2gtlOw3ZO5jgs3780tFdPJWwjZmpWUbOtOD4Wtj+rVTmx3RD8ONbn9F2R2JC3/AGZhy3tKbZ3zle4kpwnePE45miLkzspG9tL0uO91jf0grrzJiK9hc4H5B+yse2bGNnsGQ3KjaA06y80oLbcRa2EqQociCE5Bp7GzhTW4Ug8McqIuI2shjV98Ha5Sf6VVS68AkD2zTuqspyBNZB4f9XUyZWxbZzIfW+7s902txxRUtSrUwSpROSSd3iaU7JoDTemG3W9P6et1rQ8d5xMOK2yFkciQgDNEXOLxN+F696FusvWmjLY7L07JWp59lhBUqAo8TlI4+X2PTkelRvIIOCMV2P2g3/SWh7I/fdX3mHbIDQwt2QsAKP0UjmpR+iASe1Rr03orw6eJFi73+x6RRGXCmKjOOtD2Z1wboKXShJwAr3sZGeHGiKAdFT7Z8L2yPTElTz+nPaPL99KpT6lowOuOA/LUHNYOQHtV3h21IQiGuc+Y6UDCQ3vndAHbFei2wuvAfc2Ug/AN/hRvZ/7EV/tDNdHrQSGUkHjXOXwCMrXtMvzwT7iLNuk9iX2yP5pro1ahhlI9KzxeisEvpp12y5AANOH3fs/spZBChkGmjHbcW6CgkYPP9FOaE0400EuHj27V5laPSX2FxvybLIKQSCQDjlRyr2qHApSClCt0kEA9qwLZSTfb6i3pEaPhclY4D6I7mmyELJLr6it1ZypR71U/GkWy4OIuOVrdOUvHkoVUupKCNrW3CrNfUSSScjhYBYznWrBUpJ3knBFX3OtY6+tZSsDE4LBfdzDDyiW+Xqn1+FOpK0rSFJOQeRFa1iMuvPJLRIwef6Kftpjvx4wS+s5PEJ+jWnUMbbm3Uth8z+bwtQPZ2+Sy1sNOqSpxtKig5SSM4NV8q9qh1K1tqShe6oggKxnB71qKWTY1nrJnT7Qhw8PXB4fg2xx3B9I1rjyX1urmTnS7KdO8tROeJ6VeuVun6e1A+L8S+uUoqalniFj9Hw6VU8etWCigZHHzDMlVLFauWSXwiLAJPf5GsFxam1BxBwocqzn+RrAf5VsOWvCLiyfGiNa+zH2KYrLPIg82/UelbNbdbdbS42sKSoZBHIio722JKlTEKjEo3VcVjp6etbv0tAmwbfuzXD7/ABS2fmCoquhYG+JofeprDp3tk8nGbf8Ab2/PbdLKmkLIK0hRScjI5GquQr2qXApSFJSrdJGAe1Rim7JnbQdoMXSUYQ4mJF1kj8AwOO6Ppq9O3etOlqY++5c7q+p+bIO+tSjnHpWXqG0XXSurJDup1Ll+3KKmJ6uKVDt6EcsdPhRIIIyDkGrVh1NHFHztNyVQ8drp5J/BcLNHtSVJ5UlvLU0sONqKVA5BFKknlSTJraetSn6LY+zfaKqCsW6csqj595PVv8ZPcdxW6mJDMhlL7LiVoWN5KknIIqJVsgzJs9HsZU2UKyXB09PX4VJHQlpulstYFweVhzCkMq5oH9vaobEoI+XxtHe9WLCKmVk3ko85lr/29vUdt/yTmUhKvlAHtXte14eRqEVmSXe70xaY6lFSS4QSkHkPU+lamu18eu0ta98qSTxUeav7Kcmu7PdlBSkvKWCoq/hjt9XamNHGFYIwRVowymjZH4gNyVQsarZ5p/CeLNGyVI3OlJptDiShacg8waTY3OlSPW3IoxiyrNfpekpKW5G+9a3lcccS0e4/V1rZcWVHmMIkxnkuNuDeSpJ4EVq+4zIkWGoSUBzzBupb6qNObZ3ZrvbLct24OqQ3IO+1GV/ix3+vtUNiMLHM8bR3v/dWbA6ubn8mtzN1/t/Yp0SoMWaEplx23koWHEhaQQFDkePWr4GBRQrJBxULc6K02AN0w9qe1K2bPLYEI3ZV3lgpiRAckn6auyR+eo4vi63Wc9qHUkpUi4yjvK3jkNjokdsdqVtomndQaM2gP3nWLzlzjXJZ9kuSh7qOyCOSSkcMduI61jSlJWkqSQQRkEdRV4wWihih8VpuTuuGccY3W1FZ5HI0sY3Qde/1+XUokzrSLJWppXmNqKVJOQRS1M60hzPkmpGRVykF8ltTY/tbeskgWuesuRVn8Iznin8dH6RUmIM6LcIrcyI+h1l1IUhSTkEVAi126fcrk2i3qU2tCgouj5nr/ZUwNkmn77ZrMXbrIWG3wFNsLHFP4x7Z7VXcZpYjH5To7/d+4XR+DsUqoqr7MaC+KxP/AKfrP3TsNQdMk9p1vg3NpLM6I1IbStLgS4gKAUk5B49QayRhIxQBiggkcKrFzoun2AN90bw/3FFYXsUv/pyv4gor1YdV453fdPs+azSAedWHIyVcxWRRXlZFgqtzR+bVP3ta+iPyUoUURYCbc0DwSKuCA0B8gfkrLooixBCQOQ/NVwRkgcqv0URYyoiD0rGlRkIQSAKUqxpSN5BAoi5y/dFEbV4V8hSBepi9BzWkNoisZQ0zKHykvY+XvYCk73DmMcOMMrLe7rpy6xr3ZJzsOdDcDrD7SsKQoda7S6/0VZdZWWZp/UVsZn2+YgoeYeTlKh+gjoRxFQd2i/c+nkznpmzzU6Wo6yVJh3BBJR6BxPP6xRFq1/xr7Yn7Mbbm1JkFG4ZiYx8z44zjP1fVWirpdLherhIu11mOypkpwuvPOqypajzJNSCHgU2yl7cL9mCM/L9pP2YrZuzn7n2w3MZmbQ9SqltJIUqHAQUJX6KcVxx8B9YoiZ3gS2P3DUmvBtMuMRabXYwtuItSeD0paSnh3CUqUc9yO1dL7NbUJQnKRTY0Noaz6UtMSyWK2MwYENAbZYZTupQkf78+ZrYUNjy0gYoi9EFnHyR+Sj2Jsck/mrLooisCMkdKpVEQelZNFEWMiIhJ5VcLCSMYq7RRFhOQG180irQtbQOd0UpUURYjUNCOQrJSgJqqiiKhTaVCsdyChefdrLooiTvvW19EVcbgoRxCRWbRRFb8kYxirS4qFHiKyaKIsZEVKTwFVqYSRjFXqKIsQwmz80fko9ha+gPyVl0URYohoHIVX7MnGMVfooixTDQeOKT7g0ltskDpSyeVJtzb3myAKL4TZcdvFQnatD2r3S17Ub1MuLjLy1251eUsKjEncU0ge6nhgHHHI45pk7ONqOstld6N70fc/Z3HE7jzK077Tyey09ftrqZtt2HaP2v2j716rtxW4ySYstr3X46j1SrseoPA1DPVngC1vb5a/wBi+p4E+Nk7gkoUy4B64yK+ltl8DwVrraB4r9qO0CzLsMlyFbYryd18w2ilxwHmN4kkA+laZ5mpIW/wIbXpL6W5k+zRWyeLnnKXj6gK33sg8CukNJzWL1q6avUFwZIW02pvcjNrHI7vNeDyzw9KL6sfwSbJJ2itISNUXuKpm46kW24ltYwpqMgHywR0KipSiO272qX1sjFSUoApJtdgcgKQyWuWAnA4H4U/LPaxGQHXUjfI4DtWzdrGrUs6Ryv2+3hhIWsDf6DtWdyo5Ui6g1C3bEezx8LkrHAdE+prAA6V1hqs55YWXOiyp19g2+Q3Gfc95Z44+aO5rPSoLSFJIIIyCK1i6t1xanXllbizlSj1NLunNQmMRDlKy1yST8z+ytmSl5W3bqtOKuDn2dpsnRcbdHuUcsPp9Qrqk9xTNlRZFsfMWSCR8xfRQp9pUlYCkkEEZBFY1wgMXGOph8Z6pV1Se9YYZjGc9Fkq6RtS241TGcq0llTywhIPHhwrMm2+TBf9mdSTn5CgPlCnDY7KmKEyZKcunilJ+b/bW6+VrW8yhoKaSR/hgWtr2VVksqIaEvPJHmcwk/N/tpZxijHemzq/WDFhZ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