From 90f1e66748383abd4923742a4478de16b1551af4 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Mon, 27 Jan 2025 16:31:43 -0500 Subject: [PATCH] docs: functional api (#3125) * Concepts page for the functional API * How-to guides that show functional API implementations * API reference for entrypoint, task, entrypoint.final * Add functional API version to the workflows --------- Co-authored-by: Vadym Barda Co-authored-by: ccurme --- docs/_scripts/generate_api_reference_links.py | 17 +- ...3-a466-46ad-aafe-2b870831057e.msgpack.zlib | 1 + ...b-d730-48bd-9652-983812fd7811.msgpack.zlib | 1 + ...0-1f8a-4057-81c4-b7bf073dc4c1.msgpack.zlib | 1 + ...1-d13a-4026-8f89-bdab67d1ad4d.msgpack.zlib | 1 + ...7-ad05-4f10-83bf-c3ff6ff8eb93.msgpack.zlib | 1 + ...6-11d5-40e1-8567-361e5bef8917.msgpack.zlib | 1 + ...8-f40f-4a51-a27f-7c6bb2bda0ba.msgpack.zlib | 1 + ...0-a5a6-4697-8115-322242f197b5.msgpack.zlib | 1 + ...nt-from-scratch-functional_11.msgpack.zlib | 1 + ...nt-from-scratch-functional_18.msgpack.zlib | 1 + ...nt-from-scratch-functional_20.msgpack.zlib | 1 + ...view-tool-calls-functional_16.msgpack.zlib | 1 + ...view-tool-calls-functional_17.msgpack.zlib | 1 + ...view-tool-calls-functional_20.msgpack.zlib | 1 + ...view-tool-calls-functional_21.msgpack.zlib | 1 + ...view-tool-calls-functional_25.msgpack.zlib | 1 + ...view-tool-calls-functional_26.msgpack.zlib | 1 + ...view-tool-calls-functional_28.msgpack.zlib | 1 + ...wait-user-input-functional_25.msgpack.zlib | 1 + ...wait-user-input-functional_29.msgpack.zlib | 1 + docs/codespell_notebooks.sh | 4 +- docs/docs/concepts/functional_api.md | 910 ++++++++++++ docs/docs/concepts/index.md | 1 + .../cross-thread-persistence-functional.ipynb | 362 +++++ .../how-tos/cross-thread-persistence.ipynb | 8 +- docs/docs/how-tos/index.md | 23 + ...ti-agent-multi-turn-convo-functional.ipynb | 449 ++++++ .../multi-agent-network-functional.ipynb | 500 +++++++ .../docs/how-tos/persistence-functional.ipynb | 349 +++++ docs/docs/how-tos/persistence.ipynb | 10 +- .../react-agent-from-scratch-functional.ipynb | 463 ++++++ .../review-tool-calls-functional.ipynb | 627 ++++++++ .../how-tos/wait-user-input-functional.ipynb | 561 ++++++++ docs/docs/tutorials/index.md | 2 +- docs/docs/tutorials/workflows.ipynb | 1240 ---------------- docs/docs/tutorials/workflows/img/agent.png | Bin 0 -> 81607 bytes .../tutorials/workflows/img/augmented_llm.png | Bin 0 -> 97799 bytes .../workflows/img/evaluator_optimizer.png | Bin 0 -> 35321 bytes .../workflows/img/parallelization.png | Bin 0 -> 48070 bytes .../tutorials/workflows/img/prompt_chain.png | Bin 0 -> 43553 bytes docs/docs/tutorials/workflows/img/routing.png | Bin 0 -> 57901 bytes docs/docs/tutorials/workflows/img/worker.png | Bin 0 -> 56995 bytes docs/docs/tutorials/workflows/index.md | 1273 +++++++++++++++++ docs/mkdocs.yml | 10 +- 45 files changed, 5565 insertions(+), 1264 deletions(-) create mode 100644 docs/cassettes/cross-thread-persistence-functional_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib create mode 100644 docs/cassettes/cross-thread-persistence-functional_d362350b-d730-48bd-9652-983812fd7811.msgpack.zlib create mode 100644 docs/cassettes/cross-thread-persistence-functional_d862be40-1f8a-4057-81c4-b7bf073dc4c1.msgpack.zlib create mode 100644 docs/cassettes/multi-agent-multi-turn-convo-functional_161e0cf1-d13a-4026-8f89-bdab67d1ad4d.msgpack.zlib create mode 100644 docs/cassettes/multi-agent-network-functional_29b47c57-ad05-4f10-83bf-c3ff6ff8eb93.msgpack.zlib create mode 100644 docs/cassettes/persistence-functional_08ae8246-11d5-40e1-8567-361e5bef8917.msgpack.zlib create mode 100644 docs/cassettes/persistence-functional_273d56a8-f40f-4a51-a27f-7c6bb2bda0ba.msgpack.zlib create mode 100644 docs/cassettes/persistence-functional_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib create mode 100644 docs/cassettes/react-agent-from-scratch-functional_11.msgpack.zlib create mode 100644 docs/cassettes/react-agent-from-scratch-functional_18.msgpack.zlib create mode 100644 docs/cassettes/react-agent-from-scratch-functional_20.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_16.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_17.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_20.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_21.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_25.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_26.msgpack.zlib create mode 100644 docs/cassettes/review-tool-calls-functional_28.msgpack.zlib create mode 100644 docs/cassettes/wait-user-input-functional_25.msgpack.zlib create mode 100644 docs/cassettes/wait-user-input-functional_29.msgpack.zlib create mode 100644 docs/docs/concepts/functional_api.md create mode 100644 docs/docs/how-tos/cross-thread-persistence-functional.ipynb create mode 100644 docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb create mode 100644 docs/docs/how-tos/multi-agent-network-functional.ipynb create mode 100644 docs/docs/how-tos/persistence-functional.ipynb create mode 100644 docs/docs/how-tos/react-agent-from-scratch-functional.ipynb create mode 100644 docs/docs/how-tos/review-tool-calls-functional.ipynb create mode 100644 docs/docs/how-tos/wait-user-input-functional.ipynb delete mode 100644 docs/docs/tutorials/workflows.ipynb create mode 100644 docs/docs/tutorials/workflows/img/agent.png create mode 100644 docs/docs/tutorials/workflows/img/augmented_llm.png create mode 100644 docs/docs/tutorials/workflows/img/evaluator_optimizer.png create mode 100644 docs/docs/tutorials/workflows/img/parallelization.png create mode 100644 docs/docs/tutorials/workflows/img/prompt_chain.png create mode 100644 docs/docs/tutorials/workflows/img/routing.png create mode 100644 docs/docs/tutorials/workflows/img/worker.png create mode 100644 docs/docs/tutorials/workflows/index.md diff --git a/docs/_scripts/generate_api_reference_links.py b/docs/_scripts/generate_api_reference_links.py index 8bc1f5e16..18b6bd332 100644 --- a/docs/_scripts/generate_api_reference_links.py +++ b/docs/_scripts/generate_api_reference_links.py @@ -1,17 +1,11 @@ import importlib import inspect import logging -import os import re -from typing import List, Literal, Optional -from typing_extensions import TypedDict - - from functools import lru_cache +from typing import List, Literal, Optional -import nbformat -from nbconvert.preprocessors import Preprocessor - +from typing_extensions import TypedDict logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @@ -52,6 +46,8 @@ MANUAL_API_REFERENCES_LANGGRAPH = [ (["langgraph.constants"], "langgraph.types", "Interrupt", "types"), (["langgraph.constants"], "langgraph.types", "interrupt", "types"), (["langgraph.constants"], "langgraph.types", "Command", "types"), + (["langgraph.func"], "langgraph.func", "entrypoint", "func"), + (["langgraph.func"], "langgraph.func", "task", "func"), ([], "langgraph.types", "RetryPolicy", "types"), ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"), ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"), @@ -88,8 +84,6 @@ _IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain") _IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph") - - @lru_cache(maxsize=10_000) def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]: """Get full module name using inspect, with LRU cache to memoize results.""" @@ -109,6 +103,7 @@ def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]: logger.warning(f"API Reference: Failed to load for class {class_name}, {e}") return None + def _get_doc_title(data: str, file_name: str) -> str: try: return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0] @@ -287,4 +282,4 @@ def update_markdown_with_imports(markdown: str) -> str: # Apply the replace_code_block function to all matches in the markdown updated_markdown = code_block_pattern.sub(replace_code_block, markdown) - return updated_markdown \ No newline at end of file + return updated_markdown diff --git a/docs/cassettes/cross-thread-persistence-functional_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib b/docs/cassettes/cross-thread-persistence-functional_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib new file mode 100644 index 000000000..6528a965f --- /dev/null +++ b/docs/cassettes/cross-thread-persistence-functional_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib @@ -0,0 +1 @@ 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FS8cYu+RljyUswmEL2UF2RY3lgillt2hlb1Nuxo9uUB6Je72NGNmEyNLMvL66qvXxCuXMHddbjA0NGnVdVazpHN9C3s08ChEW3mp2DVOcNtPwCQ2/W8gRZE4d1P6KtayI2hOPobyWRR28en3SXa3xJbVftPR3Cze5zVyD88BWcyZmLuB2hcuDG8zf+bRf0xey6JY94KPjw80/TFGa3bhWUgdiSw5c7NLHRcZmiVfkMLlFRcvXsLHuKr84cnrIrjuj0teuotUYyQF9GjonEvqA8dKDbzVohxMX6U61KcpMFwsnm7bmqnhV3w2CuBJqK15IyWzbyG0IfGy/Pdern3pSaOvW5Opt3NSCLNvJz+EnCkLZu08Ocii84HjjEyU9TXF7NJe2OS8y+yIjAHEq/wjKaPjcGMfGzJkmK/pOJ+JyZMnlV+rwxWqB/uc990ebX9x3WEIlYEbG3q4FViqnNTA4vgzVuOvCUIlSpvJ5m5AxXQZvCUvc0aEdjTuysrcnTkm9ezQ8ccjj0ryTgpwE7NE8+7OUt49MxTyU5isefc9JnXnnJOtVnhhQlXFZz47uradnQBL9jOW5HnCvzyMZxU/3zkhQlTbH0U4u4JcBff1nQ3pEU6aovV5iTZaUTfr0bE5zPZ9a8Q/ltyrh2u3bmdErluENiJIq9hpOuBo8/dilVAboTiVRU3zq2wWbFqUzb5fXb198VSpWtf2G6/XcFc4u24TCdDCFn0SR6hobL8BC0XmlovkTaBSauDRl3d6PxQQ4ebnxZXJruRmoysuWbUkpBa3qU3x2M7/tFZ6yW9cCoomtgXILPryciOK9ouU/iZEz6Gyhv3WTLdDoApd86hNHoSuCPTr13O+Wjl7rnmHwrW5zPVUruwNZdUm7f7LktHvjJVNt92CqlJefo4PbsudqVZMGKvnCv8WWTTQeuAqC2q5WPf+WPbDtUcq4PPFWxFPI9UEkE1DpIzOVO/hl60T3HA3JklXAERvR2ZFHK5gtO8jE5lZb/+Szpi5qbOgY0nCJoDTQcMW/Y8/9TQ4/1M318fvL7nQVmxOm53WtaGmptST6tgS22t5oazGpMblWFWSIeRsISISv8zqTXt2ouUCphttaLqMeGk0dw1tfYrjW3xVjuwpcNDHkNDUOYkyUQVR5S+GrW+1zXfQWnhqd0st2VFyetyg3LYVJf37Mza96oLg8NvomClo4wh0WnbUm9CS3MsGQ70iGRWzMhbpHft3xaJ38Cdb0qAjmiLMHJzi0BXcXzLjXfYUvNDqG0iVTSnd2iEqeSDljrxGbQ1J5FMuHxY5d2DA/T3nzwFB8lqk/pCWIVd2rnD7Sfn4aNiH+Sk184154P6NcTdFJcJ8y5lMMZWJZ32ekk0Y1y3lUV+ujQC0jEQSvDz03QDKYnYaNybyaSzbzdHzQdMvw7ppt9jjXV/52hiOZ+VMdc4uNvNulWveMis4WhCl9rsu1eHlSM0tYzj3aqCn/uRqDLPGiywxXEwu6qOn1GRoOedaSqBDxU57OQQaDuluNF9yoc7VjdFVLHrdxlS4aDp6fM3RMj9jOv8cye2IohKshuowTExe9eHQwa9PgoJq5sIs51DWQ9dx1MzEptWh9vl7+q8G0S0rkf0wXqcIgov5apqkPMydelt9eegeGdUvE2XGUW87GmsqGVav4GD+/PntlKsXKG8o93xKp5qT+ovORXMrrzs+XuzJN+rXLwtIr7ILkxVrcdbSHktAK7gPHjNn1Tjt4ljS3upcv9czvnHiRUqNV0VdRq+ml19dxXXVW9cvy4P26MGY/CYrgT1VSbTrydewSAY8OeFElTh+5dmdy0lrh68snUusDQt5tsbsLdxlKS7oRH1y/bldSznZXDG37pNrZfCl7dNBw3dKGiatxFKWzq+X4Sv6j6pHPkvz8j9FpYEcUuyCTUNBWodtgO2tEw5bR8FbSMZOW7sjRSm4RiTU1n0UGpeHmbGZ/po/vOxsSFyuFw/bMX2bzqsV6aoyx+M1DCJILI533gLmEwrMx3JDRJ8rpH2SVtnqf2Kxod3+XLc46mVng5L+OxGx4rX2s4jPGeE8+XdqcE/TRdG1fKT3TNClZ8/krfSqk/L6rP3X26IeykLKbqicaBUoKsidu1kyySPvb5LcF54V8fxPKfaR92/t1fEVWm67b/Szvct899ijze6Nbm5710sIKeE1fXibJc/N3Tl0oGE5PiLkGDXGolLgiVuTjpDkkEu/Nn8L8id7N16CLAOJipHnOpUe85WvzMEW7wjPaQMztrogCaynjWffkgE11jcCHpuxRsfZqbEPXQYEP2r03Bnokv8c4i5kzQt+ENbKtyOG8wd9f148oaQh/OTdIHfuV51Oc0VFIl7vzVO6O1OXlUpk0SZmEW1rvQ7Wp22a/jg3vrDqgWR5kRZ3ju39K9XjpxMlSHxYdpjkoPDv+nco0C54KOjh0LyNFfLl3mcmEZDD69CyGvVCgXdTR3CYlOVpeEmuQcSkoNtlMQLMFQ7FtpnN0zNQst4ANl0J39qMtDr915kgLhZpjogVrmQfI48Zz7s6y9S8Kw241a7lZ193ZadMyEB8um3ZbB/UkshCJS3ZhV6d7WpZOp3DVPXSY670+Ixu8/yIcIXt/PK26LrsDWdBIL/wqnkL+PcUoyubMJvO4j+/zkoDiWuL+qN2jZpfE23OQo/cehqLyLww0PJ7topm9deoLW+nA1AMhF2rrWnBU207+hjrskkfNznrctt7Sc+E876lkfSO/hvWJavXZ744unGGXtaqfD6jdS404oW8e+aB0P++tSngjtQILU06Bif1OquuZC9JmFrcWbtKeir+T57UtdneX+Hk2efx+xNj4F/Ck9MTXtU+jvcFP5IW337PFbrS3lG8Ndtodn1RQ8WVq7ywbMLnxXQvTT1UBfwqrTnRdW7WT363mXMxXEx+KpJ+lkk11mM+p3OSSVZLfe4XSlLmwcfcpxVgRlbJrSdJaaAxIKFD5/nJFkY3bewab76M3Q0OnOD99lNkgWaQ2xlpbsLXnRFojwlm8077uGGvWe0AreEMPJirpVmtyHgS1Q1MdWJmFxKpk6STW+U8nBj8gfFtbeJ3Q5Fy5c+OFy44GFXNbZv6eFJrT+853tL9tnu0MyX7B1PBODID/jAX0vJCZ3EFXv+Pgw/Jr0vZn6mJ9tm0Mt7wFsqa4YKzeZwopxXmWv+qzlx/dnUB8CM08Z/FoUbrR/kpHfAHEFUbznXa8smcvNVbeeUaanemlk7bouDkFbUnplzGvjE1W+WkbSTDLcAqEXe+85nKEfNvcuzKTQRfKkrDuY+uT1rQ96+8etT2ta+U6ePoGEiDvaiwToV0R3Vx4pdGvN/aa5XOCnLeaBA8VX31q7XKuazX4mYYvSmi9g2797J0Zc83Y8a9FDbvORxquT2t+SxJLAdBKV7GcSHm/FVl/+xLTojWxjX6ao22cmSXb3yDI3jraV1xcgHakFKH7tvMOoNzXg3ozpimn60M3YT5a9wHmHt2XvuKp4hW8us+EuuGKUIlOxfCm6rH1N57CWwLsg+MqtbHumM02TbjpN/SnxUU1y7mdjO2SGek1z65Tsk87i7mCOzsCuLg8Z3i/AJ3aA6r8HsklbG2nuXUtuTHrEvp04FFiSjxZN33wRg+9JiNqVenanlP6roSfhAd11NEtCn2AQDDsgU6EBZaRtQq34vHQPZ07mpR3o53fqN+bozPC1OkUiWgnzxTbx0PLGFjLN63L9tcwJaoCeyu8x6oGAoy0j+bkLkvsJFDSqFNQQ5nL0E0l+71Xhj/hUwOb02Nku7EjiEZuykBNCmR0PmPTt8iRxH1bk6THCajMBXzbM1C0U/Jn3y4FrrJrAZgKf5FLR5a0VgM2Z84UgfYnjPQxVc9z4zDnxrUlLuIiYnS/n3LkvrOhtPvt6dwMwRB1bDmgdYSaQrV5o3+BwRC9um4yU75jAtZ4KASnmwmJfBnTNcpN8Qaksd+HbNQvdBzm2NvIujl3jz3MudLsdkyaZg/3ENs6JIx5OsplmhpxoTCd4etZQ0HeuOa9TBXNshsKs5VR+99YCuQ+XF4rGK2OlbhAZ1kWdoLhqZOPlrVh/MrRGirgXD8GnX8iSdRAf5ltQ3pd/uYUs2m00KeMPoiWnOdpB4X3l403cj/lrRlxRPEOf+jiFK/dDm9O71JKvKDvUfk2Jza2s0tJ01y7ZdXu4VbPUCLUXCP+snTx7u0lj63r2w5t0O2nsS8YXeUbHmbnf1FeFc7qO6IS3HK9CHJ5lThcwTrcs1U8zbpN+FCDrYemODbNmw9kRFOZfs2utSoUKcS2+Z70kEBONhS7Bk0mHmSfiOkSiQnsl5PAURdMva6q84hjX/0qwZ3LSzqvukeVJDUrI/lhh6/g7LVXA3TW5eu+WcnRM7eoSWbGyaunlkTX1CJtOA/mI0Z38l+FcB8PphNnDXw620P0vv1toaKIpXnL9Qkwe/kZfTlyu3Lm3KdXHOwIutMUgulO33Ji7Sbg1d2rGjn9390Atfap3rUHOh9oQnK+HLBXqRVbQIyYTjH77TNqbBZUlz1o8S6V0zu25CDLXFZk/LFx0pxilSP/Et37qBZS71Y/L+ZNAfT8jDInmq4zNH1bkCFD33yWWWj/uLxThSgbwMr/1mht3L5TReny2PuQYfYvQ6qYiN3dgpkHZ2Vj+qXOv73iHg8rPe/+fEC6Yta/6V77S74hbWh5rIT3btvSFvZ4Jh3UDUFfZ2FSdCz2/YMc9tZtLFRP71owmgVI8YVj4uGVY1CnbQZLrBZ1eFqDu9BOnRQhNz4QeE3PyXiNHzXJt9xtaX9DCcZx9M5Wj+osn0ix4LPrV/he6cHPcd3mP3a06bgIh+H3o598w1KW3w83eno2vpdgKQnpFoaV6FEKmyVdxKiZBjWIPPRb9rQbzcs1sPUc5o2dI6qP8M+uy2/T53f2UhYn2W1ls7tVNBV2jmK8es5M3a2p7vhI7XVwi3Z3qnz/+5zX3i22kn7VfVNGuuBtsf2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\ No newline at end of file diff --git a/docs/codespell_notebooks.sh b/docs/codespell_notebooks.sh index 82a62414e..e58c4be78 100755 --- a/docs/codespell_notebooks.sh +++ b/docs/codespell_notebooks.sh @@ -1,5 +1,5 @@ ERROR_FOUND=0 -for file in $(find $1 -name "*.ipynb"); do +for file in $(find $1 -name "*.ipynb" | grep -v ".ipynb_checkpoints"); do OUTPUT=$(cat "$file" | jupytext --from ipynb --to py:percent | codespell -) if [ -n "$OUTPUT" ]; then echo "Errors found in $file" @@ -10,4 +10,4 @@ done if [ "$ERROR_FOUND" -ne 0 ]; then exit 1 -fi \ No newline at end of file +fi diff --git a/docs/docs/concepts/functional_api.md b/docs/docs/concepts/functional_api.md new file mode 100644 index 000000000..fff0f0f6f --- /dev/null +++ b/docs/docs/concepts/functional_api.md @@ -0,0 +1,910 @@ +# Functional API + +!!! warning "Beta" + The Functional API is currently in **beta** and is subject to change. Please [report any issues](https://github.com/langchain-ai/langgraph/issues) or feedback to the LangGraph team. + +## Overview + +The Functional API is an alternative to [Graph API (StateGraph)](low_level.md#stategraph) for development in LangGraph. + +It allows you to take advantage of LangGraph's key features for [persistence](persistence.md), [human-in-the-loop](human_in_the_loop.md) workflows, and [streaming](streaming.md) without explicitly specifying state, or control flow in terms of nodes and edges. + +The **Functional API** and the **[Graph API](./low_level.md)** can be used together in the same application, allowing you to intermix the two paradigms if needed. + +## Example + +Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review. + +```python +from langgraph.func import entrypoint, task +from langgraph.types import interrupt + +@task +def write_essay(topic: str) -> str: + """Write an essay about the given topic.""" + time.sleep(1) # A placeholder for a long-running task. + return f"An essay about topic: {topic}" + +@entrypoint(checkpointer=MemorySaver()) +def workflow(topic: str) -> dict: + """A simple workflow that writes an essay and asks for a review.""" + essay = write_essay("cat").result() + is_approved = interrupt({ + # Any json-serializable payload provided to interrupt as argument. + # It will be surfaced on the client side as an Interrupt when streaming data + # from the workflow. + "essay": essay, # The essay we want reviewed. + # We can add any additional information that we need. + # For example, introduce a key called "action" with some instructions. + "action": "Please approve/reject the essay", + }) + + return { + "essay": essay, # The essay that was generated + "is_approved": is_approved, # Response from HIL + } +``` + +??? example "Detailed Explanation" + + This workflow will write an essay about the topic "cat" and then pause to get a review from a human. The workflow can be interrupted for an indefinite amount of time until a review is provided. + + When the workflow is resumed, it executes from the very start, but because the result of the `write_essay` task was already saved, the task result will be loaded from the checkpoint instead of being recomputed. + + ```python + import time + import uuid + + from langgraph.func import entrypoint, task + from langgraph.types import interrupt + from langgraph.checkpoint.memory import MemorySaver + + @task + def write_essay(topic: str) -> str: + """Write an essay about the given topic.""" + time.sleep(1) # This is a placeholder for a long-running task. + return f"An essay about topic: {topic}" + + @entrypoint(checkpointer=MemorySaver()) + def workflow(topic: str) -> dict: + """A simple workflow that writes an essay and asks for a review.""" + essay = write_essay("cat").result() + is_approved = interrupt({ + # Any json-serializable payload provided to interrupt as argument. + # It will be surfaced on the client side as an Interrupt when streaming data + # from the workflow. + "essay": essay, # The essay we want reviewed. + # We can add any additional information that we need. + # For example, introduce a key called "action" with some instructions. + "action": "Please approve/reject the essay", + }) + + return { + "essay": essay, # The essay that was generated + "is_approved": is_approved, # Response from HIL + } + + thread_id = str(uuid.uuid4()) + + config = { + "configurable": { + "thread_id": thread_id + } + } + + for item in workflow.stream("cat", config): + print(item) + ``` + + ```pycon + {'write_essay': 'An essay about topic: cat'} + {'__interrupt__': (Interrupt(value={'essay': 'An essay about topic: cat', 'action': 'Please approve/reject the essay'}, resumable=True, ns=['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'], when='during'),)} + ``` + + An essay has been written and is ready for review. Once the review is provided, we can resume the workflow: + + ```python + from langgraph.types import Command + + # Get review from a user (e.g., via a UI) + # In this case, we're using a bool, but this can be any json-serializable value. + human_review = True + + for item in workflow.stream(Command(resume=human_review), config): + print(item) + ``` + + ```pycon + {'workflow': {'essay': 'An essay about topic: cat', 'is_approved': False}} + ``` + + The workflow has been completed and the review has been added to the essay. + +## Building Blocks + +The **Functional API** provides two primitives for building workflows: + +- **[Entrypoint](#entrypoint)**: An **entrypoint** is a decorator that designates a function as the starting point of a workflow. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](human_in_the_loop.md). +- **[Task](#task)**: Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously from within an **entrypoint**. Invoking a **task** returns a future-like object, which can be awaited to obtain the result or resolved synchronously. + +## Entrypoint + +The [`@entrypoint`][langgraph.func.entrypoint] decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](./low_level.md#interrupt). + +### Definition + +An **entrypoint** is defined by decorating a function with the `@entrypoint` decorator. + +The function **must accept a single positional argument**, which serves as the workflow input. If you need to pass multiple pieces of data, use a dictionary as the input type for the first argument. + +Decorating a function with an `entrypoint` produces a Pregel instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing). + +You will usually want to pass a **checkpointer** to the `@entrypoint` decorator to enable persistence and use features like **human-in-the-loop**. + +=== "Sync" + + ```python + from langgraph.func import entrypoint + + @entrypoint(checkpointer=checkpointer) + def my_workflow(some_input: dict) -> int: + # some logic that may involve long-running tasks like API calls, + # and may be interrupted for human-in-the-loop. + ... + return result + ``` + +=== "Async" + + ```python + from langgraph.func import entrypoint + + @entrypoint(checkpointer=checkpointer) + async def my_workflow(some_input: dict) -> int: + # some logic that may involve long-running tasks like API calls, + # and may be interrupted for human-in-the-loop + ... + return result + ``` + +!!! important "Serialization" + + The **inputs** and **outputs** of entrypoints must be JSON-serializable to support checkpointing. Please see the [serialization](#serialization) section for more details. + + +### Injectable Parameters + +When declaring an `entrypoint`, you can request access to additional parameters that will be injected automatically at run time. These parameters include: + + +| Parameter | Description | +|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------| +| **previous** | Access the the state associated with the previous `checkpoint` for the given thread. See [state management](#state-management). | +| **store** | An instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for [long-term memory](#long-term-memory). | +| **writer** | For streaming custom data, to write custom data to the `custom` stream. Useful for [streaming custom data](#streaming-custom-data). | +| **config** | For accessing run time configuration. See [RunnableConfig](https://python.langchain.com/docs/concepts/runnables/#runnableconfig) for information. | + +!!! important + + Declare the parameters with the appropriate name and type annotation. + +??? example "Requesting Injectable Parameters" + + ```python + from langchain_core.runnables import RunnableConfig + from langgraph.func import entrypoint + from langgraph.store.base import BaseStore + from langgraph.store.memory import InMemoryStore + + in_memory_store = InMemoryStore(...) # An instance of InMemoryStore for long-term memory + + @entrypoint( + checkpointer=checkpointer, # Specify the checkpointer + store=in_memory_store # Specify the store + ) + def my_workflow( + some_input: dict, # The input (e.g., passed via `invoke`) + *, + previous: Any = None, # For short-term memory + store: BaseStore, # For long-term memory + writer: StreamWriter, # For streaming custom data + config: RunnableConfig # For accessing the configuration passed to the entrypoint + ) -> ...: + ``` + +### Executing + +Using the [`@entrypoint`](#entrypoint) yields a Pregel object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods. + +=== "Invoke" + + ```python + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + my_workflow.invoke(some_input, config) # Wait for the result synchronously + ``` + +=== "Async Invoke" + + ```python + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + await my_workflow.ainvoke(some_input, config) # Await result asynchronously + ``` + +=== "Stream" + + ```python + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + for chunk in my_workflow.stream(some_input, config): + print(chunk) + ``` + +=== "Async Stream" + + ```python + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + async for chunk in my_workflow.astream(some_input, config): + print(chunk) + ``` + +### Resuming + +Resuming an execution after an [interrupt][langgraph.types.interrupt] can be done by passing a **resume** value to the [Command][langgraph.types.Command] primitive. + +=== "Invoke" + + ```python + from langgraph.types import Command + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + my_workflow.invoke(Command(resume=some_resume_value), config) + ``` + +=== "Async Invoke" + + ```python + from langgraph.types import Command + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + await my_workflow.ainvoke(Command(resume=some_resume_value), config) + ``` + +=== "Stream" + + ```python + from langgraph.types import Command + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + for chunk in my_workflow.stream(Command(resume=some_resume_value), config): + print(chunk) + ``` + +=== "Async Stream" + + ```python + from langgraph.types import Command + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + async for chunk in my_workflow.astream(Command(resume=some_resume_value), config): + print(chunk) + ``` + +**Resuming after an error** + + +To resume after an error, run the `entrypoint` with a `None` and the same **thread id** (config). + +=== "Invoke" + + ```python + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + my_workflow.invoke(None, config) + ``` + +=== "Async Invoke" + + ```python + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + await my_workflow.ainvoke(None, config) + ``` + +=== "Stream" + + ```python + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + for chunk in my_workflow.stream(None, config): + print(chunk) + ``` + +=== "Async Stream" + + ```python + + config = { + "configurable": { + "thread_id": "some_thread_id" + } + } + + async for chunk in my_workflow.astream(None, config): + print(chunk) + ``` + +### State Management + +When an `entrypoint` is defined with a `checkpointer`, it stores information between successive invocations on the same **thread id** in [checkpoints](persistence.md#checkpoints). + +This allows accessing the state from the previous invocation using the `previous` parameter. + +By default, the `previous` parameter is the return value of the previous invocation. + +```python +@entrypoint(checkpointer=checkpointer) +def my_workflow(number: int, *, previous: Any = None) -> int: + previous = previous or 0 + return number + previous + +config = { + "configurable": { + "thread_id": "some_thread_id" + } +} + +my_workflow.invoke(1, config) # 1 (previous was None) +my_workflow.invoke(2, config) # 3 (previous was 1 from the previous invocation) +``` + +#### `entrypoint.final` + +[entrypoint.final][langgraph.func.entrypoint.final] is a special primitive that can be returned from an entrypoint and allows **decoupling** the value that is **saved in the checkpoint** from the **return value of the entrypoint**. + +The first value is the return value of the entrypoint, and the second value is the value that will be saved in the checkpoint. The type annotation is `entrypoint.final[return_type, save_type]`. + +```python +@entrypoint(checkpointer=checkpointer) +def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]: + previous = previous or 0 + # This will return the previous value to the caller, saving + # 2 * number to the checkpoint, which will be used in the next invocation + # for the `previous` parameter. + return entrypoint.final(value=previous, save=2 * number) + +config = { + "configurable": { + "thread_id": "1" + } +} + +my_workflow.invoke(3, config) # 0 (previous was None) +my_workflow.invoke(1, config) # 6 (previous was 3 * 2 from the previous invocation) +``` + +## Task + +A **task** represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously. Invoking a **task** returns a future, which can be waited on to obtain the result. + +### Definition + +Tasks are defined using the `@task` decorator, which wraps a regular Python function. + +```python +from langgraph.func import task + +@task() +def slow_computation(input_value): + # Simulate a long-running operation + ... + return result +``` + +!!! important "Serialization" + + The **outputs** of tasks must be JSON-serializable to support checkpointing. + +### Execution + +**Tasks** can only be called from within an **entrypoint**, another **task**, or a [state graph node](./low_level.md#nodes). They **cannot** be called directly from the main application code. Calling a **task** produces a future-like object that can be awaited or resolved to obtain the result. + +=== "Synchronous Invocation" + + ```python + @entrypoint(checkpointer=checkpointer) + def my_workflow(some_input: int) -> int: + future = slow_computation(some_input) + return future.result() # Wait for the result synchronously + ``` + +=== "Asynchronous Invocation" + + ```python + @entrypoint(checkpointer=checkpointer) + async def my_workflow(some_input: int) -> int: + return await slow_computation(some_input) # Await result asynchronously + ``` + +## When to use a task + +**Tasks** are useful in the following scenarios: + +- **Resumable Graph Execution**: When graph execution may need to be **resumed** after being **interrupted** (e.g., for **human-in-the-loop**), **tasks** can encapsulate any source of non-determinism, such as API calls, database queries, or random number generation. See the [determinism](#determinism) for more details. +- **Retryable Work**: When work needs to be retried to handle failures or inconsistencies, **tasks** provide a way to encapsulate and manage the retry logic. +- **Parallel Execution**: For I/O-bound tasks, **tasks** enable parallel execution, allowing multiple operations to run concurrently without blocking (e.g., calling multiple APIs). + +## Serialization + +There are two key aspects to serialization in LangGraph: + +1. `@entrypoint` inputs and outputs must be JSON-serializable. +2. `@task` outputs must be JSON-serializable. + +These requirements are necessary for enabling checkpointing and workflow resumption. Use python primitives +like dictionaries, lists, strings, numbers, and booleans to ensure that your inputs and outputs are serializable. + +Serialization ensures that workflow state, such as task results and intermediate values, can be reliably saved and restored. This is critical for enabling human-in-the-loop interactions, fault tolerance, and parallel execution. + +Providing non-serializable inputs or outputs will result in a runtime error when a workflow is configured with a checkpointer. + +## Determinism + +To utilize features like **human-in-the-loop**, any randomness should be encapsulated inside of **tasks**. This guarantees that when execution is halted (e.g., for human in the loop) and then resumed, it will follow the same *sequence of steps*, even if **task** results are non-deterministic. + +LangGraph achieves this behavior by persisting **task** and [**subgraph**](./low_level.md#subgraphs) results as they execute. A well-designed workflow ensures that resuming execution follows the *same sequence of steps*, allowing previously computed results to be retrieved correctly without having to re-execute them. This is particularly useful for long-running **tasks** or **tasks** with non-deterministic results, as it avoids repeating previously done work and allows resuming from essentially the same + +While different runs of a workflow can produce different results, resuming a **specific** run should always follow the same sequence of recorded steps. This allows LangGraph to efficiently look up **task** and **subgraph** results that were executed prior to the graph being interrupted and avoid recomputing them. + +## Idempotency + +Idempotency ensures that running the same operation multiple times produces the same result. This helps prevent duplicate API calls and redundant processing if a step is rerun due to a failure. Always place API calls inside **tasks** functions for checkpointing, and design them to be idempotent in case of re-execution. Re-execution can occur if a **task** starts, but does not complete successfully. Then, if the workflow is resumed, the **task** will run again. Use idempotency keys or verify existing results to avoid duplication. + +## Functional API vs. Graph API + +The **Functional API** and the **Graph APIs** provide two different paradigms to create workflows in LangGraph. Here are some key differences: + +- **Control flow**: The Functional API does not require thinking about graph structure. You can use standard Python constructs to define workflows. +- **State management**: The **GraphAPI** requires declaring a [**State**](./low_level.md#state) and may require defining [**reducers**](./low_level.md#reducers) to manage updates to the graph state. `@entrypoint` and `@tasks` do not require explicit state management as their state is scoped to the function and is not shared across functions. +- **Checkpointing**: Both APIs generate and use checkpoints. In the **Graph API** a new checkpoint is generated after every [superstep](./low_level.md). In the **Functional API**, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint. +- **Visualization**: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime. + +## Common Pitfalls + +### Handling side effects + +Side effects, such as writing to a file or sending an email, should be encapsulated in tasks to ensure consistent execution upon resumption. + +=== "Incorrect" + + In this example, a side effect (writing to a file) is directly included in the workflow, making resumption inconsistent. + + ```python + @entrypoint(checkpointer=checkpointer) + def my_workflow(inputs: dict) -> int: + # This code will be executed a second time when resuming the workflow. + # Which is likely not what you want. + # highlight-next-line + with open("output.txt", "w") as f: + # highlight-next-line + f.write("Side effect executed") + value = interrupt("question") + return value + ``` + +=== "Correct" + + In this example, the side effect is encapsulated in a task, ensuring consistent execution upon resumption. + + ```python + from langgraph.func import task + + # highlight-next-line + @task + # highlight-next-line + def write_to_file(): + with open("output.txt", "w") as f: + f.write("Side effect executed") + + @entrypoint(checkpointer=checkpointer) + def my_workflow(inputs: dict) -> int: + # The side effect is now encapsulated in a task. + write_to_file().result() + value = interrupt("question") + return value + ``` + +### Non-deterministic control flow + +[Non-deterministic control flow](#determinism) can lead to inconsistent results when resuming a workflow. To ensure correct behavior, encapsulate non-deterministic operations (e.g., random number generation, time-based logic) inside **tasks**. + +=== "Incorrect" + + In this example, the workflow uses the current time to determine which task to execute. This is non-deterministic because the result of the workflow depends on the time at which it is executed. + + ```python + from langgraph.func import entrypoint + + @entrypoint(checkpointer=checkpointer) + def my_workflow(inputs: dict) -> int: + t0 = inputs["t0"] + # highlight-next-line + t1 = time.time() + + delta_t = t1 - t0 + + if delta_t > 1: + result = slow_task(1).result() + value = interrupt("question") + else: + result = slow_task(2).result() + value = interrupt("question") + + return { + "result": result, + "value": value + } + ``` + +=== "Correct" + + In this example, the workflow uses the input `t0` to determine which task to execute. This is deterministic because the result of the workflow depends only on the input. + + ```python + import time + + from langgraph.func import task + + # highlight-next-line + @task + # highlight-next-line + def get_time() -> float: + return time.time() + + @entrypoint(checkpointer=checkpointer) + def my_workflow(inputs: dict) -> int: + t0 = inputs["t0"] + # highlight-next-line + t1 = get_time().result() + + delta_t = t1 - t0 + + if delta_t > 1: + result = slow_task(1).result() + value = interrupt("question") + else: + result = slow_task(2).result() + value = interrupt("question") + + return { + "result": result, + "value": value + } + ``` + +## Patterns + +Below are a few simple patterns that show examples of **how to** use the **Functional API**. + +When defining an `entrypoint`, input is restricted to the first argument of the function. To pass multiple inputs, you can use a dictionary. + +```python +@entrypoint(checkpointer=checkpointer) +def my_workflow(inputs: dict) -> int: + value = inputs["value"] + another_value = inputs["another_value"] + ... + +my_workflow.invoke({"value": 1, "another_value": 2}) +``` + +### Parallel execution + +Tasks can be executed in parallel by invoking them concurrently and waiting for the results. This is useful for improving performance in IO bound tasks (e.g., calling APIs for LLMs). + +```python +@task +def add_one(number: int) -> int: + return number + 1 + +@entrypoint(checkpointer=checkpointer) +def graph(numbers: list[int]) -> list[str]: + futures = [add_one(i) for i in numbers] + return [f.result() for f in futures] +``` + +### Calling subgraphs + +The **Functional API** and the [**Graph API**](./low_level.md) can be used together in the same application as they share the same underlying runtime. + +```python +from langgraph.func import entrypoint +from langgraph.graph import StateGraph + +builder = StateGraph() +... +some_graph = builder.compile() + +@entrypoint() +def some_workflow(some_input: dict) -> int: + # Call a graph defined using the graph API + result_1 = some_graph.invoke(...) + # Call another graph defined using the graph API + result_2 = another_graph.invoke(...) + return { + "result_1": result_1, + "result_2": result_2 + } +``` + +### Calling other entrypoints + +You can call other **entrypoints** from within an **entrypoint** or a **task**. + +```python +@entrypoint() # Will automatically use the checkpointer from the parent entrypoint +def some_other_workflow(inputs: dict) -> int: + return inputs["value"] + +@entrypoint(checkpointer=checkpointer) +def my_workflow(inputs: dict) -> int: + value = some_other_workflow.invoke({"value": 1}) + return value +``` + +### Streaming custom data + +You can stream custom data from an **entrypoint** by using the `StreamWriter` type. This allows you to write custom data to the `custom` stream. + +```python +from langgraph.checkpoint.memory import MemorySaver +from langgraph.func import entrypoint, task +from langgraph.types import StreamWriter + +@task +def add_one(x): + return x + 1 + +@task +def add_two(x): + return x + 2 + +checkpointer = MemorySaver() + +@entrypoint(checkpointer=checkpointer) +def main(inputs, writer: StreamWriter) -> int: + """A simple workflow that adds one and two to a number.""" + writer("hello") # Write some data to the `custom` stream + add_one(inputs['number']).result() # Will write data to the `updates` stream + writer("world") # Write some more data to the `custom` stream + add_two(inputs['number']).result() # Will write data to the `updates` stream + return 5 + +config = { + "configurable": { + "thread_id": "1" + } +} + +for chunk in main.stream({"number": 1}, stream_mode=["custom", "updates"], config=config): + print(chunk) +``` + +```pycon +('updates', {'add_one': 2}) +('updates', {'add_two': 3}) +('custom', 'hello') +('custom', 'world') +('updates', {'main': 5}) +``` + +!!! important + + The `writer` parameter is automatically injected at run time. It will only be injected if the + parameter name appears in the function signature with that *exact* name. + + +### Retry policy + +```python +from langgraph.checkpoint.memory import MemorySaver +from langgraph.func import entrypoint, task +from langgraph.types import RetryPolicy + +attempts = 0 + +# Let's configure the RetryPolicy to retry on ValueError. +# The default RetryPolicy is optimized for retrying specific network errors. +retry_policy = RetryPolicy(retry_on=ValueError) + +@task(retry=retry_policy) +def get_info(): + global attempts + attempts += 1 + + if attempts < 2: + raise ValueError('Failure') + return "OK" + +checkpointer = MemorySaver() + +@entrypoint(checkpointer=checkpointer) +def main(inputs, writer): + return get_info().result() + +config = { + "configurable": { + "thread_id": "1" + } +} + +main.invoke({'any_input': 'foobar'}, config=config) +``` + +```pycon +'OK' +``` + +### Resuming after an error + +```python +import time +from langgraph.checkpoint.memory import MemorySaver +from langgraph.func import entrypoint, task +from langgraph.types import StreamWriter + +# Global variable to track the number of attempts +attempts = 0 + +@task() +def get_info(): + """ + Simulates a task that fails once before succeeding. + Raises an exception on the first attempt, then returns "OK" on subsequent tries. + """ + global attempts + attempts += 1 + + if attempts < 2: + raise ValueError("Failure") # Simulate a failure on the first attempt + return "OK" + +# Initialize an in-memory checkpointer for persistence +checkpointer = MemorySaver() + +@task +def slow_task(): + """ + Simulates a slow-running task by introducing a 1-second delay. + """ + time.sleep(1) + return "Ran slow task." + +@entrypoint(checkpointer=checkpointer) +def main(inputs, writer: StreamWriter): + """ + Main workflow function that runs the slow_task and get_info tasks sequentially. + + Parameters: + - inputs: Dictionary containing workflow input values. + - writer: StreamWriter for streaming custom data. + + The workflow first executes `slow_task` and then attempts to execute `get_info`, + which will fail on the first invocation. + """ + slow_task_result = slow_task().result() # Blocking call to slow_task + get_info().result() # Exception will be raised here on the first attempt + return slow_task_result + +# Workflow execution configuration with a unique thread identifier +config = { + "configurable": { + "thread_id": "1" # Unique identifier to track workflow execution + } +} + +# This invocation will take ~1 second due to the slow_task execution +try: + # First invocation will raise an exception due to the `get_info` task failing + main.invoke({'any_input': 'foobar'}, config=config) +except ValueError: + pass # Handle the failure gracefully +``` + +When we resume execution, we won't need to re-run the `slow_task` as its result is already saved in the checkpoint. + +```python +main.invoke(None, config=config) +``` + +```pycon +'Ran slow task.' +``` + +### Human-in-the-loop + +The functional API supports [human-in-the-loop](human_in_the_loop.md) workflows using the `interrupt` function and the `Command` primitive. + +Please see the following examples for more details: + +* [How to wait for user input (Functional API)](../how-tos/wait-user-input-functional.ipynb): Shows how to implement a simple human-in-the-loop workflow using the functional API. +* [How to review tool calls (Functional API)](../how-tos/review-tool-calls-functional.ipynb): Guide demonstrates how to implement human-in-the-loop workflows in a ReAct agent using the LangGraph Functional API. + +### Short-term memory + +[State management](#state-management) using the **previous** parameter and optionally using the `entrypoint.final` primitive can be used to implement [short term memory](memory.md). + +Please see the following how-to guides for more details: + +* [How to add thread-level persistence (functional API)](../how-tos/persistence-functional.ipynb): Shows how to add thread-level persistence to a functional API workflow and implements a simple chatbot. + +### Long-term memory + +[long-term memory](memory.md#long-term-memory) allows storing information across different **thread ids**. This could be useful for learning information +about a given user in one conversation and using it in another. + +Please see the following how-to guides for more details: + +* [How to add cross-thread persistence (functional API)](../how-tos/cross-thread-persistence-functional.ipynb): Shows how to add cross-thread persistence to a functional API workflow and implements a simple chatbot. + +### Workflows + +* [Workflows and agent](../tutorials/workflows/index.md) guide for more examples of how to build workflows using the Functional API. + +### Agents + +* [How to create a React agent from scratch (Functional API)](../how-tos/react-agent-from-scratch-functional.ipynb): Shows how to create a simple React agent from scratch using the functional API. +* [How to build a multi-agent network](../how-tos/multi-agent-network-functional.ipynb): Shows how to build a multi-agent network using the functional API. +* [How to add multi-turn conversation in a multi-agent application (functional API)](../how-tos/multi-agent-multi-turn-convo-functional.ipynb): allow an end-user to engage in a multi-turn conversation with one or more agents. + diff --git a/docs/docs/concepts/index.md b/docs/docs/concepts/index.md index 2d97f72b2..d44bf52bd 100644 --- a/docs/docs/concepts/index.md +++ b/docs/docs/concepts/index.md @@ -28,6 +28,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem - [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance. - [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences. - [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs. +- [Functional API (beta)](functional_api.md): An alternative to [Graph API (StateGraph)](low_level.md#stategraph) for development in LangGraph. - [FAQ](faq.md): Frequently asked questions about LangGraph. ## LangGraph Platform diff --git a/docs/docs/how-tos/cross-thread-persistence-functional.ipynb b/docs/docs/how-tos/cross-thread-persistence-functional.ipynb new file mode 100644 index 000000000..92376135a --- /dev/null +++ b/docs/docs/how-tos/cross-thread-persistence-functional.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "d2eecb96-cf0e-47ed-8116-88a7eaa4236d", + "metadata": {}, + "source": [ + "# How to add cross-thread persistence (functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + "\n", + " This guide assumes familiarity with the following:\n", + " \n", + " - [Functional API](../../concepts/functional_api/)\n", + " - [Persistence](../../concepts/persistence/)\n", + " - [Memory](../../concepts/memory/)\n", + " - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n", + "\n", + "LangGraph allows you to persist data across **different [threads](../../concepts/persistence/#threads)**. For instance, you can store information about users (their names or preferences) in a shared (cross-thread) memory and reuse them in the new threads (e.g., new conversations).\n", + "\n", + "When using the [functional API](../../concepts/functional_api/), you can set it up to store and retrieve memories by using the [Store](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore) interface:\n", + "\n", + "1. Create an instance of a `Store`\n", + "\n", + " ```python\n", + " from langgraph.store.memory import InMemoryStore, BaseStore\n", + " \n", + " store = InMemoryStore()\n", + " ```\n", + "\n", + "2. Pass the `store` instance to the `entrypoint()` decorator and expose `store` parameter in the function signature:\n", + "\n", + " ```python\n", + " from langgraph.func import entrypoint\n", + "\n", + " @entrypoint(store=store)\n", + " def workflow(inputs: dict, store: BaseStore):\n", + " my_task(inputs).result()\n", + " ...\n", + " ```\n", + " \n", + "In this guide, we will show how to construct and use a workflow that has a shared memory implemented using the [Store](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore) interface.\n", + "\n", + "!!! note Note\n", + "\n", + " Support for the [`Store`](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore) API that is used in this guide was added in LangGraph `v0.2.32`.\n", + "\n", + " Support for __index__ and __query__ arguments of the [`Store`](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore) API that is used in this guide was added in LangGraph `v0.2.54`.\n", + "\n", + "!!! tip \"Note\"\n", + "\n", + " If you need to add cross-thread persistence to a `StateGraph`, check out this [how-to guide](../cross-thread-persistence).\n", + "\n", + "## Setup\n", + "\n", + "First, let's install the required packages and set our API keys" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3457aadf", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langchain_anthropic langchain_openai langgraph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa2c64a7", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "51b6817d", + "metadata": {}, + "source": [ + "!!! tip \"Set up [LangSmith](https://smith.langchain.com) for LangGraph development\"\n", + "\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com)" + ] + }, + { + "cell_type": "markdown", + "id": "6b5b3d42-3d2c-455e-ac10-e2ae74dc1cf1", + "metadata": {}, + "source": [ + "## Example: simple chatbot with long-term memory" + ] + }, + { + "cell_type": "markdown", + "id": "c4c550b5-1954-496b-8b9d-800361af17dc", + "metadata": {}, + "source": [ + "### Define store\n", + "\n", + "In this example we will create a workflow that will be able to retrieve information about a user's preferences. We will do so by defining an `InMemoryStore` - an object that can store data in memory and query that data.\n", + "\n", + "When storing objects using the `Store` interface you define two things:\n", + "\n", + "* the namespace for the object, a tuple (similar to directories)\n", + "* the object key (similar to filenames)\n", + "\n", + "In our example, we'll be using `(\"memories\", )` as namespace and random UUID as key for each new memory.\n", + "\n", + "Importantly, to determine the user, we will be passing `user_id` via the config keyword argument of the node function.\n", + "\n", + "Let's first define our store!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a7f303d6-612e-4e34-bf36-29d4ed25d802", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.store.memory import InMemoryStore\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "in_memory_store = InMemoryStore(\n", + " index={\n", + " \"embed\": OpenAIEmbeddings(model=\"text-embedding-3-small\"),\n", + " \"dims\": 1536,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3389c9f4-226d-40c7-8bfc-ee8aac24f79d", + "metadata": {}, + "source": [ + "### Create workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2a30a362-528c-45ee-9df6-630d2d843588", + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_core.runnables import RunnableConfig\n", + "from langchain_core.messages import BaseMessage\n", + "from langgraph.func import entrypoint, task\n", + "from langgraph.graph import add_messages\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.store.base import BaseStore\n", + "\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n", + "\n", + "\n", + "@task\n", + "def call_model(messages: list[BaseMessage], memory_store: BaseStore, user_id: str):\n", + " namespace = (\"memories\", user_id)\n", + " last_message = messages[-1]\n", + " memories = memory_store.search(namespace, query=str(last_message.content))\n", + " info = \"\\n\".join([d.value[\"data\"] for d in memories])\n", + " system_msg = f\"You are a helpful assistant talking to the user. User info: {info}\"\n", + "\n", + " # Store new memories if the user asks the model to remember\n", + " if \"remember\" in last_message.content.lower():\n", + " memory = \"User name is Bob\"\n", + " memory_store.put(namespace, str(uuid.uuid4()), {\"data\": memory})\n", + "\n", + " response = model.invoke([{\"role\": \"system\", \"content\": system_msg}] + messages)\n", + " return response\n", + "\n", + "\n", + "# NOTE: we're passing the store object here when creating a workflow via entrypoint()\n", + "@entrypoint(checkpointer=MemorySaver(), store=in_memory_store)\n", + "def workflow(\n", + " inputs: list[BaseMessage],\n", + " *,\n", + " previous: list[BaseMessage],\n", + " config: RunnableConfig,\n", + " store: BaseStore,\n", + "):\n", + " user_id = config[\"configurable\"][\"user_id\"]\n", + " previous = previous or []\n", + " inputs = add_messages(previous, inputs)\n", + " response = call_model(inputs, store, user_id).result()\n", + " return entrypoint.final(value=response, save=add_messages(inputs, response))" + ] + }, + { + "cell_type": "markdown", + "id": "f22a4a18-67e4-4f0b-b655-a29bbe202e1c", + "metadata": {}, + "source": [ + "!!! note Note\n", + "\n", + " If you're using LangGraph Cloud or LangGraph Studio, you __don't need__ to pass store to the entrypoint decorator, since it's done automatically." + ] + }, + { + "cell_type": "markdown", + "id": "552d4e33-556d-4fa5-8094-2a076bc21529", + "metadata": {}, + "source": [ + "### Run the workflow!" + ] + }, + { + "cell_type": "markdown", + "id": "1842c626-6cd9-4f58-b549-58978e478098", + "metadata": {}, + "source": [ + "Now let's specify a user ID in the config and tell the model our name:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c871a073-a466-46ad-aafe-2b870831057e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Hello Bob! Nice to meet you. I'll remember that your name is Bob. How can I help you today?\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\", \"user_id\": \"1\"}}\n", + "input_message = {\"role\": \"user\", \"content\": \"Hi! Remember: my name is Bob\"}\n", + "for chunk in workflow.stream([input_message], config, stream_mode=\"values\"):\n", + " chunk.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d862be40-1f8a-4057-81c4-b7bf073dc4c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Your name is Bob.\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"2\", \"user_id\": \"1\"}}\n", + "input_message = {\"role\": \"user\", \"content\": \"what is my name?\"}\n", + "for chunk in workflow.stream([input_message], config, stream_mode=\"values\"):\n", + " chunk.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "80fd01ec-f135-4811-8743-daff8daea422", + "metadata": {}, + "source": [ + "We can now inspect our in-memory store and verify that we have in fact saved the memories for the user:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "76cde493-89cf-4709-a339-207d2b7e9ea7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': 'User name is Bob'}\n" + ] + } + ], + "source": [ + "for memory in in_memory_store.search((\"memories\", \"1\")):\n", + " print(memory.value)" + ] + }, + { + "cell_type": "markdown", + "id": "23f5d7eb-af23-4131-b8fd-2a69e74e6e55", + "metadata": {}, + "source": [ + "Let's now run the workflow for another user to verify that the memories about the first user are self contained:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d362350b-d730-48bd-9652-983812fd7811", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "I don't have any information about your name. I can only see our current conversation without any prior context or personal details about you. If you'd like me to know your name, feel free to tell me!\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"3\", \"user_id\": \"2\"}}\n", + "input_message = {\"role\": \"user\", \"content\": \"what is my name?\"}\n", + "for chunk in workflow.stream([input_message], config, stream_mode=\"values\"):\n", + " chunk.pretty_print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/how-tos/cross-thread-persistence.ipynb b/docs/docs/how-tos/cross-thread-persistence.ipynb index 213b61a9c..f1c2b33fd 100644 --- a/docs/docs/how-tos/cross-thread-persistence.ipynb +++ b/docs/docs/how-tos/cross-thread-persistence.ipynb @@ -176,7 +176,7 @@ " store.put(namespace, str(uuid.uuid4()), {\"data\": memory})\n", "\n", " response = model.invoke(\n", - " [{\"type\": \"system\", \"content\": system_msg}] + state[\"messages\"]\n", + " [{\"role\": \"system\", \"content\": system_msg}] + state[\"messages\"]\n", " )\n", " return {\"messages\": response}\n", "\n", @@ -240,7 +240,7 @@ ], "source": [ "config = {\"configurable\": {\"thread_id\": \"1\", \"user_id\": \"1\"}}\n", - "input_message = {\"type\": \"user\", \"content\": \"Hi! Remember: my name is Bob\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"Hi! Remember: my name is Bob\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] @@ -266,7 +266,7 @@ ], "source": [ "config = {\"configurable\": {\"thread_id\": \"2\", \"user_id\": \"1\"}}\n", - "input_message = {\"type\": \"user\", \"content\": \"what is my name?\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"what is my name?\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] @@ -327,7 +327,7 @@ ], "source": [ "config = {\"configurable\": {\"thread_id\": \"3\", \"user_id\": \"2\"}}\n", - "input_message = {\"type\": \"user\", \"content\": \"what is my name?\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"what is my name?\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md index 7e626905a..7cc8f33b3 100644 --- a/docs/docs/how-tos/index.md +++ b/docs/docs/how-tos/index.md @@ -31,6 +31,12 @@ These how-to guides show how to achieve that controllability. - [How to use MongoDB checkpointer for persistence](persistence_mongodb.ipynb) - [How to create a custom checkpointer using Redis](persistence_redis.ipynb) +See the below guides for how-to add persistence to your workflow using the (beta) +[Functional API](../concepts/functional_api.md): + +- [How to add thread-level persistence (functional API)](persistence-functional.ipynb) +- [How to add cross-thread persistence (functional API)](cross-thread-persistence-functional.ipynb) + ### Memory LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) in your graph. These how-to guides show how to implement different strategies for that. @@ -59,6 +65,12 @@ Other methods: - [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**. - [How to add dynamic breakpoints with `NodeInterrupt`](human_in_the_loop/dynamic_breakpoints.ipynb): **Not recommended**: Use the [`interrupt` function](../concepts/human_in_the_loop.md) instead. +See the below guides for how-to implement human-in-the-loop workflows with the (beta) +[Functional API](../concepts/functional_api.md): + +- [How to wait for user input (Functional API)](wait-user-input-functional.ipynb) +- [How to review tool calls (Functional API)](review-tool-calls-functional.ipynb) + ### Time Travel [Time travel](../concepts/time-travel.md) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph. @@ -115,6 +127,12 @@ These how-to guides show common patterns for tool calling with LangGraph: See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for implementations of other multi-agent architectures. +See the below guides for how-to implement multi-agent workflows with the (beta) +[Functional API](../concepts/functional_api.md): + +- [How to build a multi-agent network (functional API)](multi-agent-network-functional.ipynb) +- [How to add multi-turn conversation in a multi-agent application (functional API)](multi-agent-multi-turn-convo-functional.ipynb) + ### State Management - [How to use Pydantic model as graph state](state-model.ipynb) @@ -152,6 +170,11 @@ overview of its underlying implementation to help you customize for your own nee - [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb) +See the below guide for how-to build ReAct agents with the (beta) +[Functional API](../concepts/functional_api.md): + +- [How to create a ReAct agent from scratch (Functional API)](react-agent-from-scratch-functional.ipynb) + ## LangGraph Platform This section includes how-to guides for LangGraph Platform. diff --git a/docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb b/docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb new file mode 100644 index 000000000..43d9306f9 --- /dev/null +++ b/docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "a2b182eb-1e31-43c8-85b1-706508dfa370", + "metadata": {}, + "source": [ + "# How to add multi-turn conversation in a multi-agent application (functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + " This guide assumes familiarity with the following:\n", + "\n", + " - [Multi-agent systems](../../concepts/multi_agent)\n", + " - [Human-in-the-loop](../../concepts/human_in_the_loop)\n", + " - [Functional API](../../concepts/functional_api)\n", + " - [Command](../../concepts/low_level/#command)\n", + " - [LangGraph Glossary](../../concepts/low_level/)\n", + "\n", + "\n", + "In this how-to guide, we’ll build an application that allows an end-user to engage in a *multi-turn conversation* with one or more agents. We'll create a node that uses an [`interrupt`](../../reference/types/#langgraph.types.interrupt) to collect user input and routes back to the **active** agent.\n", + "\n", + "The agents will be implemented as tasks in a workflow that executes agent steps and determines the next action:\n", + "\n", + "1. **Wait for user input** to continue the conversation, or\n", + "2. **Route to another agent** (or back to itself, such as in a loop) via a [**handoff**](../../concepts/multi_agent/#handoffs).\n", + "\n", + "```python\n", + "from langgraph.func import entrypoint, task\n", + "from langgraph.prebuilt import create_react_agent\n", + "from langchain_core.tools import tool\n", + "from langgraph.types import interrupt\n", + "\n", + "\n", + "# Define a tool to signal intent to hand off to a different agent\n", + "# Note: this is not using Command(goto) syntax for navigating to different agents:\n", + "# `workflow()` below handles the handoffs explicitly\n", + "@tool(return_direct=True)\n", + "def transfer_to_hotel_advisor():\n", + " \"\"\"Ask hotel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to hotel advisor\"\n", + "\n", + "\n", + "# define an agent\n", + "travel_advisor_tools = [transfer_to_hotel_advisor, ...]\n", + "travel_advisor = create_react_agent(model, travel_advisor_tools)\n", + "\n", + "\n", + "# define a task that calls an agent\n", + "@task\n", + "def call_travel_advisor(messages):\n", + " response = travel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "# define the multi-agent network workflow\n", + "@entrypoint(checkpointer)\n", + "def workflow(messages):\n", + " call_active_agent = call_travel_advisor\n", + " while True:\n", + " agent_messages = call_active_agent(messages).result()\n", + " ai_msg = get_last_ai_msg(agent_messages)\n", + " if not ai_msg.tool_calls:\n", + " user_input = interrupt(value=\"Ready for user input.\")\n", + " messages = messages + [{\"role\": \"user\", \"content\": user_input}]\n", + " continue\n", + "\n", + " messages = messages + agent_messages\n", + " call_active_agent = get_next_agent(messages)\n", + " return entrypoint.final(value=agent_messages[-1], save=messages)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "faaa4444-cd06-4813-b9ca-c9700fe12cb7", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, let's install the required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "05038da0-31df-4066-a1a4-c4ccb5db4d3a", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-anthropic" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0bcff5d4-130e-426d-9285-40d0f72c7cd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3ec6e48-85dc-4905-ba50-985e5d4788e6", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for LangGraph development

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", + "

\n", + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c217c3fe-ca50-45a1-be91-912bc83ed8b3", + "metadata": {}, + "source": [ + "In this example we will build a team of travel assistant agents that can communicate with each other.\n", + "\n", + "We will create 2 agents:\n", + "\n", + "* `travel_advisor`: can help with travel destination recommendations. Can ask `hotel_advisor` for help.\n", + "* `hotel_advisor`: can help with hotel recommendations. Can ask `travel_advisor` for help.\n", + "\n", + "This is a fully-connected network - every agent can talk to any other agent. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb51463a-4425-44ad-91d5-f21fd5b4e3b3", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from typing_extensions import Literal\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_travel_recommendations():\n", + " \"\"\"Get recommendation for travel destinations\"\"\"\n", + " return random.choice([\"aruba\", \"turks and caicos\"])\n", + "\n", + "\n", + "@tool\n", + "def get_hotel_recommendations(location: Literal[\"aruba\", \"turks and caicos\"]):\n", + " \"\"\"Get hotel recommendations for a given destination.\"\"\"\n", + " return {\n", + " \"aruba\": [\n", + " \"The Ritz-Carlton, Aruba (Palm Beach)\"\n", + " \"Bucuti & Tara Beach Resort (Eagle Beach)\"\n", + " ],\n", + " \"turks and caicos\": [\"Grace Bay Club\", \"COMO Parrot Cay\"],\n", + " }[location]\n", + "\n", + "\n", + "@tool(return_direct=True)\n", + "def transfer_to_hotel_advisor():\n", + " \"\"\"Ask hotel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to hotel advisor\"\n", + "\n", + "\n", + "@tool(return_direct=True)\n", + "def transfer_to_travel_advisor():\n", + " \"\"\"Ask travel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to travel advisor\"" + ] + }, + { + "cell_type": "markdown", + "id": "7f5b2a7f", + "metadata": {}, + "source": [ + "!!! note \"Transfer tools\"\n", + "\n", + " You might have noticed that we're using `@tool(return_direct=True)` in the transfer tools. This is done so that individual agents (e.g., `travel_advisor`) can exit the ReAct loop early once these tools are called. This is the desired behavior, as we want to detect when the agent calls this tool and hand control off _immediately_ to a different agent. \n", + " \n", + " **NOTE**: This is meant to work with the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] -- if you are building a custom agent, make sure to manually add logic for handling early exit for tools that are marked with `return_direct`." + ] + }, + { + "cell_type": "markdown", + "id": "213d661e-6ba4-42b9-bc7f-6c8c423e3419", + "metadata": {}, + "source": [ + "Let's now create our agents using the the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] and our multi-agent workflow. Note that will be calling [`interrupt`][langgraph.types.interrupt] every time after we get the final response from each of the agents." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "aa4bdbff-9461-46cc-aee9-8a22d3c3d9ec", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import AIMessage\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langgraph.prebuilt import create_react_agent\n", + "from langgraph.graph import add_messages\n", + "from langgraph.func import entrypoint, task\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.types import interrupt, Command\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n", + "\n", + "# Define travel advisor ReAct agent\n", + "travel_advisor_tools = [\n", + " get_travel_recommendations,\n", + " transfer_to_hotel_advisor,\n", + "]\n", + "travel_advisor = create_react_agent(\n", + " model,\n", + " travel_advisor_tools,\n", + " state_modifier=(\n", + " \"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). \"\n", + " \"If you need hotel recommendations, ask 'hotel_advisor' for help. \"\n", + " \"You MUST include human-readable response before transferring to another agent.\"\n", + " ),\n", + ")\n", + "\n", + "\n", + "@task\n", + "def call_travel_advisor(messages):\n", + " # You can also add additional logic like changing the input to the agent / output from the agent, etc.\n", + " # NOTE: we're invoking the ReAct agent with the full history of messages in the state\n", + " response = travel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "# Define hotel advisor ReAct agent\n", + "hotel_advisor_tools = [get_hotel_recommendations, transfer_to_travel_advisor]\n", + "hotel_advisor = create_react_agent(\n", + " model,\n", + " hotel_advisor_tools,\n", + " state_modifier=(\n", + " \"You are a hotel expert that can provide hotel recommendations for a given destination. \"\n", + " \"If you need help picking travel destinations, ask 'travel_advisor' for help.\"\n", + " \"You MUST include human-readable response before transferring to another agent.\"\n", + " ),\n", + ")\n", + "\n", + "\n", + "@task\n", + "def call_hotel_advisor(messages):\n", + " response = hotel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "@entrypoint(checkpointer=checkpointer)\n", + "def multi_turn_graph(messages, previous):\n", + " previous = previous or []\n", + " messages = add_messages(previous, messages)\n", + "\n", + " call_active_agent = call_travel_advisor\n", + " while True:\n", + " agent_messages = call_active_agent(messages).result()\n", + " messages = add_messages(messages, agent_messages)\n", + " # Find the last AI message\n", + " # If one of the handoff tools is called, the last message returned\n", + " # by the agent will be a ToolMessage because we set them to have\n", + " # \"return_direct=True\". This means that the last AIMessage will\n", + " # have tool calls.\n", + " # Otherwise, the last returned message will be an AIMessage with\n", + " # no tool calls, which means we are ready for new input.\n", + " ai_msg = next(m for m in reversed(agent_messages) if isinstance(m, AIMessage))\n", + " if not ai_msg.tool_calls:\n", + " user_input = interrupt(value=\"Ready for user input.\")\n", + " messages = add_messages(messages, [{\"role\": \"user\", \"content\": user_input}])\n", + " continue\n", + "\n", + " tool_call = ai_msg.tool_calls[-1]\n", + " if tool_call[\"name\"] == \"transfer_to_hotel_advisor\":\n", + " call_active_agent = call_hotel_advisor\n", + " elif tool_call[\"name\"] == \"transfer_to_travel_advisor\":\n", + " call_active_agent = call_travel_advisor\n", + " else:\n", + " raise ValueError(f\"Expected transfer tool, got '{tool_call['name']}'\")\n", + "\n", + " return entrypoint.final(value=agent_messages[-1], save=messages)" + ] + }, + { + "cell_type": "markdown", + "id": "af856e1b-41fc-4041-8cbf-3818a60088e0", + "metadata": {}, + "source": [ + "## Test multi-turn conversation\n", + "\n", + "Let's test a multi turn conversation with this application." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "161e0cf1-d13a-4026-8f89-bdab67d1ad4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Conversation Turn 1 ---\n", + "\n", + "User: {'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}\n", + "\n", + "call_travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known for its perfect warm weather year-round, with consistent temperatures around 82°F (28°C) and very little rainfall. The island offers:\n", + "\n", + "1. Beautiful white-sand beaches like Eagle Beach and Palm Beach\n", + "2. Crystal clear waters perfect for swimming and snorkeling\n", + "3. Constant cooling trade winds that make the warm weather comfortable\n", + "4. A mix of luxury resorts and boutique hotels\n", + "5. Diverse activities from water sports to desert-like terrain exploration\n", + "6. Great dining and nightlife options\n", + "7. Safe and tourist-friendly environment\n", + "\n", + "Would you like me to connect you with our hotel advisor to help you find the perfect place to stay in Aruba?\n", + "\n", + "--- Conversation Turn 2 ---\n", + "\n", + "User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.')\n", + "\n", + "call_hotel_advisor: Based on the recommendations, I can highlight two excellent options in different areas:\n", + "\n", + "1. The Ritz-Carlton, Aruba - Located in Palm Beach\n", + "- Part of the high-rise hotel district\n", + "- Luxury beachfront resort with full-service spa\n", + "- Multiple restaurants and a casino\n", + "- Perfect for those who want to be in the heart of the action\n", + "- Close to shopping, dining, and nightlife\n", + "\n", + "2. Bucuti & Tara Beach Resort - Located in Eagle Beach\n", + "- Adults-only boutique resort\n", + "- Located on Eagle Beach, voted one of the best beaches in the world\n", + "- More serene and romantic atmosphere\n", + "- Perfect for couples and those seeking a quieter vacation\n", + "- Known for its excellent service and sustainability practices\n", + "\n", + "Would you like more specific information about either of these properties or would you like to explore other options in either area?\n", + "\n", + "--- Conversation Turn 3 ---\n", + "\n", + "User: Command(resume='i like the first one. could you recommend something to do near the hotel?')\n", + "\n", + "call_travel_advisor: Near the Ritz-Carlton in Palm Beach, you can enjoy several fantastic activities:\n", + "\n", + "1. Paseo Herencia Mall - A beautiful outdoor shopping and entertainment center just a short walk away\n", + "2. High-Rise Beach Strip - Perfect for beach walks and water sports\n", + "3. Bubali Bird Sanctuary - A nature preserve where you can spot local wildlife\n", + "4. The Butterfly Farm - A unique attraction featuring hundreds of exotic butterflies\n", + "5. Palm Beach Plaza Mall - Great for shopping and dining\n", + "6. Various water sports operators offering:\n", + " - Jet skiing\n", + " - Parasailing\n", + " - Snorkeling trips\n", + " - Sunset sailing cruises\n", + "\n", + "Additionally, the hotel concierge can arrange most activities directly for you. Would you like more specific information about any of these activities?\n" + ] + } + ], + "source": [ + "import uuid\n", + "\n", + "thread_config = {\"configurable\": {\"thread_id\": uuid.uuid4()}}\n", + "\n", + "inputs = [\n", + " # 1st round of conversation,\n", + " {\"role\": \"user\", \"content\": \"i wanna go somewhere warm in the caribbean\"},\n", + " # Since we're using `interrupt`, we'll need to resume using the Command primitive.\n", + " # 2nd round of conversation,\n", + " Command(\n", + " resume=\"could you recommend a nice hotel in one of the areas and tell me which area it is.\"\n", + " ),\n", + " # 3rd round of conversation,\n", + " Command(\n", + " resume=\"i like the first one. could you recommend something to do near the hotel?\"\n", + " ),\n", + "]\n", + "\n", + "for idx, user_input in enumerate(inputs):\n", + " print()\n", + " print(f\"--- Conversation Turn {idx + 1} ---\")\n", + " print()\n", + " print(f\"User: {user_input}\")\n", + " print()\n", + " for update in multi_turn_graph.stream(\n", + " user_input,\n", + " config=thread_config,\n", + " stream_mode=\"updates\",\n", + " ):\n", + " for node_id, value in update.items():\n", + " if isinstance(value, list) and value:\n", + " last_message = value[-1]\n", + " if isinstance(last_message, dict) or last_message.type != \"ai\":\n", + " continue\n", + " print(f\"{node_id}: {last_message.content}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/how-tos/multi-agent-network-functional.ipynb b/docs/docs/how-tos/multi-agent-network-functional.ipynb new file mode 100644 index 000000000..59dc274d3 --- /dev/null +++ b/docs/docs/how-tos/multi-agent-network-functional.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "87684b48-150e-4e15-b0a5-a9dd7851f8fb", + "metadata": {}, + "source": [ + "# How to build a multi-agent network (functional API)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2c65639c-9705-49f1-840a-370718852e98", + "metadata": {}, + "source": [ + "!!! info \"Prerequisites\" \n", + " This guide assumes familiarity with the following:\n", + "\n", + " - [Multi-agent systems](../../concepts/multi_agent)\n", + " - [Functional API](../../concepts/functional_api)\n", + " - [Command](../../concepts/low_level/#command)\n", + " - [LangGraph Glossary](../../concepts/low_level/)\n", + "\n", + "In this how-to guide we will demonstrate how to implement a [multi-agent network](../../concepts/multi_agent#network) architecture where each agent can communicate with every other agent (many-to-many connections) and can decide which agent to call next. We will be using [functional API](../../concepts/functional_api) — individual agents will be defined as tasks and the agent handoffs will be defined in the main [entrypoint()][langgraph.func.entrypoint]:\n", + "\n", + "```python\n", + "from langgraph.func import entrypoint\n", + "from langgraph.prebuilt import create_react_agent\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "# Define a tool to signal intent to hand off to a different agent\n", + "@tool(return_direct=True)\n", + "def transfer_to_hotel_advisor():\n", + " \"\"\"Ask hotel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to hotel advisor\"\n", + "\n", + "\n", + "# define an agent\n", + "travel_advisor_tools = [transfer_to_hotel_advisor, ...]\n", + "travel_advisor = create_react_agent(model, travel_advisor_tools)\n", + "\n", + "\n", + "# define a task that calls an agent\n", + "@task\n", + "def call_travel_advisor(messages):\n", + " response = travel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "# define the multi-agent network workflow\n", + "@entrypoint()\n", + "def workflow(messages):\n", + " call_active_agent = call_travel_advisor\n", + " while True:\n", + " agent_messages = call_active_agent(messages).result()\n", + " messages = messages + agent_messages\n", + " call_active_agent = get_next_agent(messages)\n", + " return messages\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "faaa4444-cd06-4813-b9ca-c9700fe12cb7", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, let's install the required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "05038da0-31df-4066-a1a4-c4ccb5db4d3a", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-anthropic" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0bcff5d4-130e-426d-9285-40d0f72c7cd3", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3ec6e48-85dc-4905-ba50-985e5d4788e6", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for LangGraph development

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4a53f304-3709-4df7-8714-1ca61e615743", + "metadata": {}, + "source": [ + "## Travel agent example" + ] + }, + { + "cell_type": "markdown", + "id": "34cd131b-f0c2-4b69-887f-2cbd5afb14a7", + "metadata": {}, + "source": [ + "In this example we will build a team of travel assistant agents that can communicate with each other.\n", + "\n", + "We will create 2 agents:\n", + "\n", + "* `travel_advisor`: can help with travel destination recommendations. Can ask `hotel_advisor` for help.\n", + "* `hotel_advisor`: can help with hotel recommendations. Can ask `travel_advisor` for help.\n", + "\n", + "This is a fully-connected network - every agent can talk to any other agent. " + ] + }, + { + "cell_type": "markdown", + "id": "fedc9ed0-e90c-4ee1-a7c6-f5af3c634a7b", + "metadata": {}, + "source": [ + "First, let's create some of the tools that the agents will be using:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7e31f258-ec28-4020-b86d-c91dfa9a3bfc", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from typing_extensions import Literal\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_travel_recommendations():\n", + " \"\"\"Get recommendation for travel destinations\"\"\"\n", + " return random.choice([\"aruba\", \"turks and caicos\"])\n", + "\n", + "\n", + "@tool\n", + "def get_hotel_recommendations(location: Literal[\"aruba\", \"turks and caicos\"]):\n", + " \"\"\"Get hotel recommendations for a given destination.\"\"\"\n", + " return {\n", + " \"aruba\": [\n", + " \"The Ritz-Carlton, Aruba (Palm Beach)\"\n", + " \"Bucuti & Tara Beach Resort (Eagle Beach)\"\n", + " ],\n", + " \"turks and caicos\": [\"Grace Bay Club\", \"COMO Parrot Cay\"],\n", + " }[location]\n", + "\n", + "\n", + "@tool(return_direct=True)\n", + "def transfer_to_hotel_advisor():\n", + " \"\"\"Ask hotel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to hotel advisor\"\n", + "\n", + "\n", + "@tool(return_direct=True)\n", + "def transfer_to_travel_advisor():\n", + " \"\"\"Ask travel advisor agent for help.\"\"\"\n", + " return \"Successfully transferred to travel advisor\"" + ] + }, + { + "cell_type": "markdown", + "id": "d8519a32-d23b-48b0-bd18-74f8c0dacf58", + "metadata": {}, + "source": [ + "!!! note \"Transfer tools\"\n", + "\n", + " You might have noticed that we're using `@tool(return_direct=True)` in the transfer tools. This is done so that individual agents (e.g., `travel_advisor`) can exit the ReAct loop early once these tools are called. This is the desired behavior, as we want to detect when the agent calls this tool and hand control off _immediately_ to a different agent. \n", + " \n", + " **NOTE**: This is meant to work with the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] -- if you are building a custom agent, make sure to manually add logic for handling early exit for tools that are marked with `return_direct`." + ] + }, + { + "cell_type": "markdown", + "id": "93dbc3bd-27b9-4d79-b5dd-be592bc50f74", + "metadata": {}, + "source": [ + "Now let's define our agent tasks and combine them into a single multi-agent network workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b638d6c4-3de6-4921-980c-2df1bd1cc9c7", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import AIMessage\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langgraph.prebuilt import create_react_agent\n", + "from langgraph.graph import add_messages\n", + "from langgraph.func import entrypoint, task\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n", + "\n", + "# Define travel advisor ReAct agent\n", + "travel_advisor_tools = [\n", + " get_travel_recommendations,\n", + " transfer_to_hotel_advisor,\n", + "]\n", + "travel_advisor = create_react_agent(\n", + " model,\n", + " travel_advisor_tools,\n", + " state_modifier=(\n", + " \"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). \"\n", + " \"If you need hotel recommendations, ask 'hotel_advisor' for help. \"\n", + " \"You MUST include human-readable response before transferring to another agent.\"\n", + " ),\n", + ")\n", + "\n", + "\n", + "@task\n", + "def call_travel_advisor(messages):\n", + " # You can also add additional logic like changing the input to the agent / output from the agent, etc.\n", + " # NOTE: we're invoking the ReAct agent with the full history of messages in the state\n", + " response = travel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "# Define hotel advisor ReAct agent\n", + "hotel_advisor_tools = [get_hotel_recommendations, transfer_to_travel_advisor]\n", + "hotel_advisor = create_react_agent(\n", + " model,\n", + " hotel_advisor_tools,\n", + " state_modifier=(\n", + " \"You are a hotel expert that can provide hotel recommendations for a given destination. \"\n", + " \"If you need help picking travel destinations, ask 'travel_advisor' for help.\"\n", + " \"You MUST include human-readable response before transferring to another agent.\"\n", + " ),\n", + ")\n", + "\n", + "\n", + "@task\n", + "def call_hotel_advisor(messages):\n", + " response = hotel_advisor.invoke({\"messages\": messages})\n", + " return response[\"messages\"]\n", + "\n", + "\n", + "@entrypoint()\n", + "def workflow(messages):\n", + " messages = add_messages([], messages)\n", + "\n", + " call_active_agent = call_travel_advisor\n", + " while True:\n", + " agent_messages = call_active_agent(messages).result()\n", + " messages = add_messages(messages, agent_messages)\n", + " ai_msg = next(m for m in reversed(agent_messages) if isinstance(m, AIMessage))\n", + " if not ai_msg.tool_calls:\n", + " break\n", + "\n", + " tool_call = ai_msg.tool_calls[-1]\n", + " if tool_call[\"name\"] == \"transfer_to_travel_advisor\":\n", + " call_active_agent = call_travel_advisor\n", + " elif tool_call[\"name\"] == \"transfer_to_hotel_advisor\":\n", + " call_active_agent = call_hotel_advisor\n", + " else:\n", + " raise ValueError(f\"Expected transfer tool, got '{tool_call['name']}'\")\n", + "\n", + " return messages" + ] + }, + { + "cell_type": "markdown", + "id": "9223db83-1938-434a-9d24-8666842a8eea", + "metadata": {}, + "source": [ + "Lastly, let's define a helper to render the agent outputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "058f3d96-534f-4b97-afb3-799ba81224ea", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import convert_to_messages\n", + "\n", + "\n", + "def pretty_print_messages(update):\n", + " if isinstance(update, tuple):\n", + " ns, update = update\n", + " # skip parent graph updates in the printouts\n", + " if len(ns) == 0:\n", + " return\n", + "\n", + " graph_id = ns[-1].split(\":\")[0]\n", + " print(f\"Update from subgraph {graph_id}:\")\n", + " print(\"\\n\")\n", + "\n", + " for node_name, node_update in update.items():\n", + " print(f\"Update from node {node_name}:\")\n", + " print(\"\\n\")\n", + "\n", + " for m in convert_to_messages(node_update[\"messages\"]):\n", + " m.pretty_print()\n", + " print(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "7132e2c0-d937-4325-a30e-e715c5304fe0", + "metadata": {}, + "source": [ + "Let's test it out using the same input as our original multi-agent system:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "29b47c57-ad05-4f10-83bf-c3ff6ff8eb93", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Update from subgraph call_travel_advisor:\n", + "\n", + "\n", + "Update from node agent:\n", + "\n", + "\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': \"I'll help you find a warm Caribbean destination and then get some hotel recommendations for you.\\n\\nLet me first get some destination recommendations for the Caribbean region.\", 'type': 'text'}, {'id': 'toolu_015vT8PkPq1VXvjrDvSpWUwJ', 'input': {}, 'name': 'get_travel_recommendations', 'type': 'tool_use'}]\n", + "Tool Calls:\n", + " get_travel_recommendations (toolu_015vT8PkPq1VXvjrDvSpWUwJ)\n", + " Call ID: toolu_015vT8PkPq1VXvjrDvSpWUwJ\n", + " Args:\n", + "\n", + "\n", + "Update from subgraph call_travel_advisor:\n", + "\n", + "\n", + "Update from node tools:\n", + "\n", + "\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_travel_recommendations\n", + "\n", + "turks and caicos\n", + "\n", + "\n", + "Update from subgraph call_travel_advisor:\n", + "\n", + "\n", + "Update from node agent:\n", + "\n", + "\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': \"Based on the recommendation, I suggest Turks and Caicos! This beautiful British Overseas Territory is known for its stunning white-sand beaches, crystal-clear turquoise waters, and year-round warm weather. Grace Bay Beach in Providenciales is consistently ranked among the world's best beaches. The islands offer excellent snorkeling, diving, and water sports opportunities, plus a relaxed Caribbean atmosphere.\\n\\nNow, let me connect you with our hotel advisor to get some specific hotel recommendations for Turks and Caicos.\", 'type': 'text'}, {'id': 'toolu_01JY7pNNWFuaWoe9ymxFYiPV', 'input': {}, 'name': 'transfer_to_hotel_advisor', 'type': 'tool_use'}]\n", + "Tool Calls:\n", + " transfer_to_hotel_advisor (toolu_01JY7pNNWFuaWoe9ymxFYiPV)\n", + " Call ID: toolu_01JY7pNNWFuaWoe9ymxFYiPV\n", + " Args:\n", + "\n", + "\n", + "Update from subgraph call_travel_advisor:\n", + "\n", + "\n", + "Update from node tools:\n", + "\n", + "\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: transfer_to_hotel_advisor\n", + "\n", + "Successfully transferred to hotel advisor\n", + "\n", + "\n", + "Update from subgraph call_hotel_advisor:\n", + "\n", + "\n", + "Update from node agent:\n", + "\n", + "\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': 'Let me get some hotel recommendations for Turks and Caicos:', 'type': 'text'}, {'id': 'toolu_0129ELa7jFocn16bowaGNapg', 'input': {'location': 'turks and caicos'}, 'name': 'get_hotel_recommendations', 'type': 'tool_use'}]\n", + "Tool Calls:\n", + " get_hotel_recommendations (toolu_0129ELa7jFocn16bowaGNapg)\n", + " Call ID: toolu_0129ELa7jFocn16bowaGNapg\n", + " Args:\n", + " location: turks and caicos\n", + "\n", + "\n", + "Update from subgraph call_hotel_advisor:\n", + "\n", + "\n", + "Update from node tools:\n", + "\n", + "\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_hotel_recommendations\n", + "\n", + "[\"Grace Bay Club\", \"COMO Parrot Cay\"]\n", + "\n", + "\n", + "Update from subgraph call_hotel_advisor:\n", + "\n", + "\n", + "Update from node agent:\n", + "\n", + "\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Here are two excellent hotel options in Turks and Caicos:\n", + "\n", + "1. Grace Bay Club: This luxury resort is located on the world-famous Grace Bay Beach. It offers all-oceanfront suites, exceptional dining options, and personalized service. The resort features adult-only and family-friendly sections, making it perfect for any type of traveler.\n", + "\n", + "2. COMO Parrot Cay: This exclusive private island resort offers the ultimate luxury escape. It's known for its pristine beach, world-class spa, and holistic wellness programs. The resort provides an intimate, secluded experience with top-notch amenities and service.\n", + "\n", + "Would you like more specific information about either of these properties or would you like to explore hotels in another destination?\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for chunk in workflow.stream(\n", + " [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"i wanna go somewhere warm in the caribbean. pick one destination and give me hotel recommendations\",\n", + " }\n", + " ],\n", + " subgraphs=True,\n", + "):\n", + " pretty_print_messages(chunk)" + ] + }, + { + "cell_type": "markdown", + "id": "d7d89ee0-0229-4718-9b98-bdd3f59c1014", + "metadata": {}, + "source": [ + "Voila - `travel_advisor` picks a destination and then makes a decision to call `hotel_advisor` for more info!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/how-tos/persistence-functional.ipynb b/docs/docs/how-tos/persistence-functional.ipynb new file mode 100644 index 000000000..7b9181992 --- /dev/null +++ b/docs/docs/how-tos/persistence-functional.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to add thread-level persistence (functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + "\n", + " This guide assumes familiarity with the following:\n", + " \n", + " - [Functional API](../../concepts/functional_api/)\n", + " - [Persistence](../../concepts/persistence/)\n", + " - [Memory](../../concepts/memory/)\n", + " - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n", + "\n", + "Many AI applications need memory to share context across multiple interactions on the same [thread](../../concepts/persistence#threads) (e.g., multiple turns of a conversation). In LangGraph functional API, this kind of memory can be added to any [entrypoint()][langgraph.func.entrypoint] workflow using [thread-level persistence](https://langchain-ai.github.io/langgraph/concepts/persistence).\n", + "\n", + "When creating a LangGraph workflow, you can set it up to persist its results by using a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver):\n", + "\n", + "\n", + "1. Create an instance of a checkpointer:\n", + "\n", + " ```python\n", + " from langgraph.checkpoint.memory import MemorySaver\n", + " \n", + " checkpointer = MemorySaver() \n", + " ```\n", + "\n", + "2. Pass `checkpointer` instance to the `entrypoint()` decorator:\n", + "\n", + " ```python\n", + " from langgraph.func import entrypoint\n", + " \n", + " @entrypoint(checkpointer=checkpointer)\n", + " def workflow(inputs)\n", + " ...\n", + " ```\n", + "\n", + "3. Optionally expose `previous` parameter in the workflow function signature:\n", + "\n", + " ```python\n", + " @entrypoint(checkpointer=checkpointer)\n", + " def workflow(\n", + " inputs,\n", + " *,\n", + " # you can optionally specify `previous` in the workflow function signature\n", + " # to access the return value from the workflow as of the last execution\n", + " previous\n", + " ):\n", + " previous = previous or []\n", + " combined_inputs = previous + inputs\n", + " result = do_something(combined_inputs)\n", + " ...\n", + " ```\n", + "\n", + "4. Optionally choose which values will be returned from the workflow and which will be saved by the checkpointer as `previous`:\n", + "\n", + " ```python\n", + " @entrypoint(checkpointer=checkpointer)\n", + " def workflow(inputs, *, previous):\n", + " ...\n", + " result = do_something(...)\n", + " return entrypoint.final(value=result, save=combine(inputs, result))\n", + " ```\n", + "\n", + "This guide shows how you can add thread-level persistence to your workflow.\n", + "\n", + "!!! tip \"Note\"\n", + "\n", + " If you need memory that is __shared__ across multiple conversations or users (cross-thread persistence), check out this [how-to guide](../cross-thread-persistence-functional).\n", + "\n", + "!!! tip \"Note\"\n", + "\n", + " If you need to add thread-level persistence to a `StateGraph`, check out this [how-to guide](../persistence)." + ] + }, + { + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" + ] + }, + { + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API key for Anthropic (the LLM we will use)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for LangGraph development

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4cf509bc", + "metadata": {}, + "source": [ + "## Example: simple chatbot with short-term memory\n", + "\n", + "We will be using a workflow with a single task that calls a [chat model](https://python.langchain.com/docs/concepts/chat_models/).\n", + "\n", + "Let's first define the model we'll be using:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")" + ] + }, + { + "cell_type": "markdown", + "id": "7b7a2792-982b-4e47-83eb-0c594725d1c1", + "metadata": {}, + "source": [ + "Now we can define our task and workflow. To add in persistence, we need to pass in a [Checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#langgraph.checkpoint.base.BaseCheckpointSaver) to the [entrypoint()][langgraph.func.entrypoint] decorator." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "87326ea6-34c5-46da-a41f-dda26ef9bd74", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import BaseMessage\n", + "from langgraph.graph import add_messages\n", + "from langgraph.func import entrypoint, task\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "\n", + "@task\n", + "def call_model(messages: list[BaseMessage]):\n", + " response = model.invoke(messages)\n", + " return response\n", + "\n", + "\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "@entrypoint(checkpointer=checkpointer)\n", + "def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage]):\n", + " if previous:\n", + " inputs = add_messages(previous, inputs)\n", + "\n", + " response = call_model(inputs).result()\n", + " return entrypoint.final(value=response, save=add_messages(inputs, response))" + ] + }, + { + "cell_type": "markdown", + "id": "250d8fd9-2e7a-4892-9adc-19762a1e3cce", + "metadata": {}, + "source": [ + "If we try to use this workflow, the context of the conversation will be persisted across interactions:" + ] + }, + { + "cell_type": "markdown", + "id": "7654ebcc-2179-41b4-92d1-6666f6f8634f", + "metadata": {}, + "source": [ + "!!! note Note\n", + "\n", + " If you're using LangGraph Cloud or LangGraph Studio, you __don't need__ to pass checkpointer to the entrypoint decorator, since it's done automatically." + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "We can now interact with the agent and see that it remembers previous messages!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Hi Bob! I'm Claude. Nice to meet you! How are you today?\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "input_message = {\"role\": \"user\", \"content\": \"hi! I'm bob\"}\n", + "for chunk in workflow.stream([input_message], config, stream_mode=\"values\"):\n", + " chunk.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "1bb07bf8-68b7-4049-a0f1-eb67a4879a3a", + "metadata": {}, + "source": [ + "You can always resume previous threads:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "08ae8246-11d5-40e1-8567-361e5bef8917", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Your name is Bob.\n" + ] + } + ], + "source": [ + "input_message = {\"role\": \"user\", \"content\": \"what's my name?\"}\n", + "for chunk in workflow.stream([input_message], config, stream_mode=\"values\"):\n", + " chunk.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "3f47bbfc-d9ef-4288-ba4a-ebbc0136fa9d", + "metadata": {}, + "source": [ + "If we want to start a new conversation, we can pass in a different `thread_id`. Poof! All the memories are gone!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "273d56a8-f40f-4a51-a27f-7c6bb2bda0ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "I don't know your name unless you tell me. Each conversation I have starts fresh, so I don't have access to any previous interactions or personal information unless you share it with me.\n" + ] + } + ], + "source": [ + "input_message = {\"role\": \"user\", \"content\": \"what's my name?\"}\n", + "for chunk in workflow.stream(\n", + " [input_message],\n", + " {\"configurable\": {\"thread_id\": \"2\"}},\n", + " stream_mode=\"values\",\n", + "):\n", + " chunk.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "ac7926a8-4c88-4b16-973c-53d6da3f4a08", + "metadata": {}, + "source": [ + "!!! tip \"Streaming tokens\"\n", + "\n", + " If you would like to stream LLM tokens from your chatbot, you can use `stream_mode=\"messages\"`. Check out this [how-to guide](../streaming-tokens) to learn more." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/how-tos/persistence.ipynb b/docs/docs/how-tos/persistence.ipynb index 3ca5778a4..4bbcfee9a 100644 --- a/docs/docs/how-tos/persistence.ipynb +++ b/docs/docs/how-tos/persistence.ipynb @@ -211,11 +211,11 @@ } ], "source": [ - "input_message = {\"type\": \"user\", \"content\": \"hi! I'm bob\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"hi! I'm bob\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()\n", "\n", - "input_message = {\"type\": \"user\", \"content\": \"what's my name?\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"what's my name?\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] @@ -286,7 +286,7 @@ ], "source": [ "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "input_message = {\"type\": \"user\", \"content\": \"hi! I'm bob\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"hi! I'm bob\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] @@ -319,7 +319,7 @@ } ], "source": [ - "input_message = {\"type\": \"user\", \"content\": \"what's my name?\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"what's my name?\"}\n", "for chunk in graph.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", " chunk[\"messages\"][-1].pretty_print()" ] @@ -352,7 +352,7 @@ } ], "source": [ - "input_message = {\"type\": \"user\", \"content\": \"what's my name?\"}\n", + "input_message = {\"role\": \"user\", \"content\": \"what's my name?\"}\n", "for chunk in graph.stream(\n", " {\"messages\": [input_message]},\n", " {\"configurable\": {\"thread_id\": \"2\"}},\n", diff --git a/docs/docs/how-tos/react-agent-from-scratch-functional.ipynb b/docs/docs/how-tos/react-agent-from-scratch-functional.ipynb new file mode 100644 index 000000000..0e5de1bce --- /dev/null +++ b/docs/docs/how-tos/react-agent-from-scratch-functional.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to create a ReAct agent from scratch (Functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + " This guide assumes familiarity with the following:\n", + " \n", + " - [Chat Models](https://python.langchain.com/docs/concepts/chat_models)\n", + " - [Messages](https://python.langchain.com/docs/concepts/messages)\n", + " - [Tool Calling](https://python.langchain.com/docs/concepts/tool_calling/)\n", + " - [Entrypoints](../../concepts/functional_api/#entrypoint) and [Tasks](../../concepts/functional_api/#task)\n", + "\n", + "This guide demonstrates how to implement a ReAct agent using the LangGraph [Functional API](../../concepts/functional_api).\n", + "\n", + "The ReAct agent is a [tool-calling agent](../../concepts/agentic_concepts/#tool-calling-agent) that operates as follows:\n", + "\n", + "1. Queries are issued to a chat model;\n", + "2. If the model generates no [tool calls](../../concepts/agentic_concepts/#tool-calling), we return the model response.\n", + "3. If the model generates tool calls, we execute the tool calls with available tools, append them as [tool messages](https://python.langchain.com/docs/concepts/messages/) to our message list, and repeat the process.\n", + "\n", + "This is a simple and versatile set-up that can be extended with memory, human-in-the-loop capabilities, and other features. See the dedicated [how-to guides](../../how-tos/#prebuilt-react-agent) for examples.\n", + "\n", + "## Setup\n", + "\n", + "First, let's install the required packages and set our API keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for better debugging

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM aps built with LangGraph — read more about how to get started in the docs. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create ReAct agent\n", + "\n", + "Now that you have installed the required packages and set your environment variables, we can create our agent.\n", + "\n", + "### Define model and tools\n", + "\n", + "Let's first define the tools and model we will use for our example. Here we will use a single place-holder tool that gets a description of the weather for a location.\n", + "\n", + "We will use an [OpenAI](https://python.langchain.com/docs/integrations/providers/openai/) chat model for this example, but any model [supporting tool-calling](https://python.langchain.com/docs/integrations/chat/) will suffice." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "from langchain_core.tools import tool\n", + "\n", + "model = ChatOpenAI(model=\"gpt-4o-mini\")\n", + "\n", + "\n", + "@tool\n", + "def get_weather(location: str):\n", + " \"\"\"Call to get the weather from a specific location.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " if any([city in location.lower() for city in [\"sf\", \"san francisco\"]]):\n", + " return \"It's sunny!\"\n", + " elif \"boston\" in location.lower():\n", + " return \"It's rainy!\"\n", + " else:\n", + " return f\"I am not sure what the weather is in {location}\"\n", + "\n", + "\n", + "tools = [get_weather]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define tasks\n", + "\n", + "We next define the [tasks](../../concepts/functional_api/#task) we will execute. Here there are two different tasks:\n", + "\n", + "1. **Call model**: We want to query our chat model with a list of messages.\n", + "2. **Call tool**: If our model generates tool calls, we want to execute them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import ToolMessage\n", + "from langgraph.func import entrypoint, task\n", + "\n", + "tools_by_name = {tool.name: tool for tool in tools}\n", + "\n", + "\n", + "@task\n", + "def call_model(messages):\n", + " \"\"\"Call model with a sequence of messages.\"\"\"\n", + " response = model.bind_tools(tools).invoke(messages)\n", + " return response\n", + "\n", + "\n", + "@task\n", + "def call_tool(tool_call):\n", + " tool = tools_by_name[tool_call[\"name\"]]\n", + " observation = tool.invoke(tool_call[\"args\"])\n", + " return ToolMessage(content=observation, tool_call_id=tool_call[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define entrypoint\n", + "\n", + "Our [entrypoint](../../concepts/functional_api/#entrypoint) will handle the orchestration of these two tasks. As described above, when our `call_model` task generates tool calls, the `call_tool` task will generate responses for each. We append all messages to a single messages list.\n", + "\n", + "!!! tip\n", + " Note that because tasks return future-like objects, the below implementation executes tools in parallel." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.graph.message import add_messages\n", + "\n", + "\n", + "@entrypoint()\n", + "def agent(messages):\n", + " llm_response = call_model(messages).result()\n", + " while True:\n", + " if not llm_response.tool_calls:\n", + " break\n", + "\n", + " # Execute tools\n", + " tool_result_futures = [\n", + " call_tool(tool_call) for tool_call in llm_response.tool_calls\n", + " ]\n", + " tool_results = [fut.result() for fut in tool_result_futures]\n", + "\n", + " # Append to message list\n", + " messages = add_messages(messages, [llm_response, *tool_results])\n", + "\n", + " # Call model again\n", + " llm_response = call_model(messages).result()\n", + "\n", + " return llm_response" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "To use our agent, we invoke it with a messages list. Based on our implementation, these can be LangChain [message](https://python.langchain.com/docs/concepts/messages/) objects or OpenAI-style dicts:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': \"What's the weather in san francisco?\"}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_tNnkrjnoz6MNfCHJpwfuEQ0v)\n", + " Call ID: call_tNnkrjnoz6MNfCHJpwfuEQ0v\n", + " Args:\n", + " location: san francisco\n", + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's sunny!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is sunny!\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"What's the weather in san francisco?\"}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message]):\n", + " for task_name, message in step.items():\n", + " if task_name == \"agent\":\n", + " continue # Just print task updates\n", + " print(f\"\\n{task_name}:\")\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect! The graph correctly calls the `get_weather` tool and responds to the user after receiving the information from the tool. Check out the LangSmith trace [here](https://smith.langchain.com/public/d5a0d5ea-bdaa-4032-911e-7db177c8141b/r)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add thread-level persistence\n", + "\n", + "Adding [thread-level persistence](../../concepts/persistence#threads) lets us support conversational experiences with our agent: subsequent invocations will append to the prior messages list, retaining the full conversational context.\n", + "\n", + "To add thread-level persistence to our agent:\n", + "\n", + "1. Select a [checkpointer](../../concepts/persistence#checkpointer-libraries): here we will use [MemorySaver](../../reference/checkpoints/#langgraph.checkpoint.memory.MemorySaver), a simple in-memory checkpointer.\n", + "2. Update our entrypoint to accept the previous messages state as a second argument. Here, we simply append the message updates to the previous sequence of messages.\n", + "3. Choose which values will be returned from the workflow and which will be saved by the checkpointer as `previous` using `entrypoint.final` (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "# highlight-next-line\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "# highlight-next-line\n", + "@entrypoint(checkpointer=checkpointer)\n", + "# highlight-next-line\n", + "def agent(messages, previous):\n", + " # highlight-next-line\n", + " if previous is not None:\n", + " # highlight-next-line\n", + " messages = add_messages(previous, messages)\n", + "\n", + " llm_response = call_model(messages).result()\n", + " while True:\n", + " if not llm_response.tool_calls:\n", + " break\n", + "\n", + " # Execute tools\n", + " tool_result_futures = [\n", + " call_tool(tool_call) for tool_call in llm_response.tool_calls\n", + " ]\n", + " tool_results = [fut.result() for fut in tool_result_futures]\n", + "\n", + " # Append to message list\n", + " messages = add_messages(messages, [llm_response, *tool_results])\n", + "\n", + " # Call model again\n", + " llm_response = call_model(messages).result()\n", + "\n", + " # Generate final response\n", + " messages = add_messages(messages, llm_response)\n", + " # highlight-next-line\n", + " return entrypoint.final(value=llm_response, save=messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now need to pass in a config when running our application. The config will specify an identifier for the conversational thread.\n", + "\n", + "!!! tip\n", + "\n", + " Read more about thread-level persistence in our [concepts page](../../concepts/persistence/) and [how-to guides](../../how-tos/#persistence)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start a thread the same way as before, this time passing in the config:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': \"What's the weather in san francisco?\"}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_lubbUSdDofmOhFunPEZLBz3g)\n", + " Call ID: call_lubbUSdDofmOhFunPEZLBz3g\n", + " Args:\n", + " location: San Francisco\n", + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's sunny!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is sunny!\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"What's the weather in san francisco?\"}\n", + "print(user_message)\n", + "\n", + "# highlight-next-line\n", + "for step in agent.stream([user_message], config):\n", + " for task_name, message in step.items():\n", + " if task_name == \"agent\":\n", + " continue # Just print task updates\n", + " print(f\"\\n{task_name}:\")\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we ask a follow-up conversation, the model uses the prior context to infer that we are asking about the weather:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': 'How does it compare to Boston, MA?'}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_8sTKYAhSIHOdjLD5d6gaswuV)\n", + " Call ID: call_8sTKYAhSIHOdjLD5d6gaswuV\n", + " Args:\n", + " location: Boston, MA\n", + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's rainy!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Compared to San Francisco, which is sunny, Boston, MA is experiencing rainy weather.\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"How does it compare to Boston, MA?\"}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message], config):\n", + " for task_name, message in step.items():\n", + " if task_name == \"agent\":\n", + " continue # Just print task updates\n", + " print(f\"\\n{task_name}:\")\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the [LangSmith trace](https://smith.langchain.com/public/20a1116b-bb3b-44c1-8765-7a28663439d9/r), we can see that the full conversational context is retained in each model call." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/how-tos/review-tool-calls-functional.ipynb b/docs/docs/how-tos/review-tool-calls-functional.ipynb new file mode 100644 index 000000000..bdf68432e --- /dev/null +++ b/docs/docs/how-tos/review-tool-calls-functional.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to review tool calls (Functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + " This guide assumes familiarity with the following:\n", + "\n", + " - Implementing [human-in-the-loop](../../concepts/human_in_the_loop) workflows with [interrupt](../../concepts/human_in_the_loop/#interrupt)\n", + " - [How to create a ReAct agent using the Functional API](../../how-tos/react-agent-from-scratch-functional)\n", + "\n", + "This guide demonstrates how to implement human-in-the-loop workflows in a ReAct agent using the LangGraph [Functional API](../../concepts/functional_api).\n", + "\n", + "We will build off of the agent created in the [How to create a ReAct agent using the Functional API](../../how-tos/react-agent-from-scratch-functional) guide.\n", + "\n", + "Specifically, we will demonstrate how to review [tool calls](https://python.langchain.com/docs/concepts/tool_calling/) generated by a [chat model](https://python.langchain.com/docs/concepts/chat_models/) prior to their execution. This can be accomplished through use of the [interrupt](../../concepts/human_in_the_loop/#interrupt) function at key points in our application.\n", + "\n", + "**Preview**:\n", + "\n", + "We will implement a simple function that reviews tool calls generated from our chat model and call it from inside our application's [entrypoint](../../concepts/functional_api/#entrypoint):\n", + "\n", + "```python\n", + "def review_tool_call(tool_call: ToolCall) -> Union[ToolCall, ToolMessage]:\n", + " \"\"\"Review a tool call, returning a validated version.\"\"\"\n", + " human_review = interrupt(\n", + " {\n", + " \"question\": \"Is this correct?\",\n", + " \"tool_call\": tool_call,\n", + " }\n", + " )\n", + " review_action = human_review[\"action\"]\n", + " review_data = human_review.get(\"data\")\n", + " if review_action == \"continue\":\n", + " return tool_call\n", + " elif review_action == \"update\":\n", + " updated_tool_call = {**tool_call, **{\"args\": review_data}}\n", + " return updated_tool_call\n", + " elif review_action == \"feedback\":\n", + " return ToolMessage(\n", + " content=review_data, name=tool_call[\"name\"], tool_call_id=tool_call[\"id\"]\n", + " )\n", + "```\n", + "\n", + "## Setup\n", + "\n", + "First, let's install the required packages and set our API keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for better debugging

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM aps built with LangGraph — read more about how to get started in the docs. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define model and tools\n", + "\n", + "Let's first define the tools and model we will use for our example. As in the [ReAct agent guide](../../how-tos/react-agent-from-scratch-functional), we will use a single place-holder tool that gets a description of the weather for a location.\n", + "\n", + "We will use an [OpenAI](https://python.langchain.com/docs/integrations/providers/openai/) chat model for this example, but any model [supporting tool-calling](https://python.langchain.com/docs/integrations/chat/) will suffice." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "from langchain_core.tools import tool\n", + "\n", + "model = ChatOpenAI(model=\"gpt-4o-mini\")\n", + "\n", + "\n", + "@tool\n", + "def get_weather(location: str):\n", + " \"\"\"Call to get the weather from a specific location.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " if any([city in location.lower() for city in [\"sf\", \"san francisco\"]]):\n", + " return \"It's sunny!\"\n", + " elif \"boston\" in location.lower():\n", + " return \"It's rainy!\"\n", + " else:\n", + " return f\"I am not sure what the weather is in {location}\"\n", + "\n", + "\n", + "tools = [get_weather]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define tasks\n", + "\n", + "Our [tasks](../../concepts/functional_api/#task) are unchanged from the [ReAct agent guide](../../how-tos/react-agent-from-scratch-functional):\n", + "\n", + "1. **Call model**: We want to query our chat model with a list of messages.\n", + "2. **Call tool**: If our model generates tool calls, we want to execute them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import ToolCall, ToolMessage\n", + "from langgraph.func import entrypoint, task\n", + "\n", + "\n", + "tools_by_name = {tool.name: tool for tool in tools}\n", + "\n", + "\n", + "@task\n", + "def call_model(messages):\n", + " \"\"\"Call model with a sequence of messages.\"\"\"\n", + " response = model.bind_tools(tools).invoke(messages)\n", + " return response\n", + "\n", + "\n", + "@task\n", + "def call_tool(tool_call):\n", + " tool = tools_by_name[tool_call[\"name\"]]\n", + " observation = tool.invoke(tool_call[\"args\"])\n", + " return ToolMessage(content=observation, tool_call_id=tool_call[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define entrypoint\n", + "\n", + "To review tool calls before execution, we add a `review_tool_call` function that calls [interrupt](../../concepts/human_in_the_loop/#interrupt). When this function is called, execution will be paused until we issue a command to resume it.\n", + "\n", + "Given a tool call, our function will `interrupt` for human review. At that point we can either:\n", + "\n", + "- Accept the tool call;\n", + "- Revise the tool call and continue;\n", + "- Generate a custom tool message (e.g., instructing the model to re-format its tool call).\n", + "\n", + "We will demonstrate these three cases in the [usage examples](#usage) below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Union\n", + "\n", + "\n", + "def review_tool_call(tool_call: ToolCall) -> Union[ToolCall, ToolMessage]:\n", + " \"\"\"Review a tool call, returning a validated version.\"\"\"\n", + " human_review = interrupt(\n", + " {\n", + " \"question\": \"Is this correct?\",\n", + " \"tool_call\": tool_call,\n", + " }\n", + " )\n", + " review_action = human_review[\"action\"]\n", + " review_data = human_review.get(\"data\")\n", + " if review_action == \"continue\":\n", + " return tool_call\n", + " elif review_action == \"update\":\n", + " updated_tool_call = {**tool_call, **{\"args\": review_data}}\n", + " return updated_tool_call\n", + " elif review_action == \"feedback\":\n", + " return ToolMessage(\n", + " content=review_data, name=tool_call[\"name\"], tool_call_id=tool_call[\"id\"]\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now update our [entrypoint](../../concepts/functional_api/#entrypoint) to review the generated tool calls. If a tool call is accepted or revised, we execute in the same way as before. Otherwise, we just append the `ToolMessage` supplied by the human.\n", + "\n", + "!!! tip\n", + "\n", + " The results of prior tasks — in this case the initial model call — are persisted, so that they are not run again following the `interrupt`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph.message import add_messages\n", + "from langgraph.types import Command, interrupt\n", + "\n", + "\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "@entrypoint(checkpointer=checkpointer)\n", + "def agent(messages, previous):\n", + " if previous is not None:\n", + " messages = add_messages(previous, messages)\n", + "\n", + " llm_response = call_model(messages).result()\n", + " while True:\n", + " if not llm_response.tool_calls:\n", + " break\n", + "\n", + " # Review tool calls\n", + " tool_results = []\n", + " tool_calls = []\n", + " for i, tool_call in enumerate(llm_response.tool_calls):\n", + " review = review_tool_call(tool_call)\n", + " if isinstance(review, ToolMessage):\n", + " tool_results.append(review)\n", + " else: # is a validated tool call\n", + " tool_calls.append(review)\n", + " if review != tool_call:\n", + " llm_response.tool_calls[i] = review # update message\n", + "\n", + " # Execute remaining tool calls\n", + " tool_result_futures = [call_tool(tool_call) for tool_call in tool_calls]\n", + " remaining_tool_results = [fut.result() for fut in tool_result_futures]\n", + "\n", + " # Append to message list\n", + " messages = add_messages(\n", + " messages,\n", + " [llm_response, *tool_results, *remaining_tool_results],\n", + " )\n", + "\n", + " # Call model again\n", + " llm_response = call_model(messages).result()\n", + "\n", + " # Generate final response\n", + " messages = add_messages(messages, llm_response)\n", + " return entrypoint.final(value=llm_response, save=messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "Let's demonstrate some scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def _print_step(step: dict) -> None:\n", + " for task_name, result in step.items():\n", + " if task_name == \"agent\":\n", + " continue # just stream from tasks\n", + " print(f\"\\n{task_name}:\")\n", + " if task_name in (\"__interrupt__\", \"review_tool_call\"):\n", + " print(result)\n", + " else:\n", + " result.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accept a tool call\n", + "\n", + "To accept a tool call, we just indicate in the data we provide in the `Command` that the tool call should pass through." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': \"What's the weather in san francisco?\"}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_Bh5cSwMqCpCxTjx7AjdrQTPd)\n", + " Call ID: call_Bh5cSwMqCpCxTjx7AjdrQTPd\n", + " Args:\n", + " location: San Francisco\n", + "\n", + "__interrupt__:\n", + "(Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'call_Bh5cSwMqCpCxTjx7AjdrQTPd', 'type': 'tool_call'}}, resumable=True, ns=['agent:22fcc9cd-3573-b39b-eea7-272a025903e2'], when='during'),)\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"What's the weather in san francisco?\"}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message], config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's sunny!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is sunny!\n" + ] + } + ], + "source": [ + "# highlight-next-line\n", + "human_input = Command(resume={\"action\": \"continue\"})\n", + "\n", + "for step in agent.stream(human_input, config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Revise a tool call\n", + "\n", + "To revise a tool call, we can supply updated arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"2\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': \"What's the weather in san francisco?\"}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_b9h8e18FqH0IQm3NMoeYKz6N)\n", + " Call ID: call_b9h8e18FqH0IQm3NMoeYKz6N\n", + " Args:\n", + " location: san francisco\n", + "\n", + "__interrupt__:\n", + "(Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'get_weather', 'args': {'location': 'san francisco'}, 'id': 'call_b9h8e18FqH0IQm3NMoeYKz6N', 'type': 'tool_call'}}, resumable=True, ns=['agent:9559a81d-5720-dc19-a457-457bac7bdd83'], when='during'),)\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"What's the weather in san francisco?\"}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message], config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's sunny!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is sunny!\n" + ] + } + ], + "source": [ + "# highlight-next-line\n", + "human_input = Command(resume={\"action\": \"update\", \"data\": {\"location\": \"SF, CA\"}})\n", + "\n", + "for step in agent.stream(human_input, config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The LangSmith traces for this run are particularly informative:\n", + "\n", + "- In the trace [before the interrupt](https://smith.langchain.com/public/c8b07579-5cf4-4adb-a849-282163bc9d99/r/b5b128d6-e715-480b-b58d-59e64f724275), we generate a tool call for location `\"San Francisco\"`.\n", + "- In the trace [after resuming](https://smith.langchain.com/public/b28b92e5-a555-482d-aa4d-c675a19f0eb5/r), we see that the tool call in the message has been updated to `\"SF, CA\"`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate a custom ToolMessage\n", + "\n", + "To Generate a custom `ToolMessage`, we supply the content of the message. In this case we will ask the model to reformat its tool call." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"3\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': \"What's the weather in san francisco?\"}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_VqGjKE7uu8HdWs9XuY1kMV18)\n", + " Call ID: call_VqGjKE7uu8HdWs9XuY1kMV18\n", + " Args:\n", + " location: San Francisco\n", + "\n", + "__interrupt__:\n", + "(Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'call_VqGjKE7uu8HdWs9XuY1kMV18', 'type': 'tool_call'}}, resumable=True, ns=['agent:4b3b372b-9da3-70be-5c68-3d9317346070'], when='during'),)\n" + ] + } + ], + "source": [ + "user_message = {\"role\": \"user\", \"content\": \"What's the weather in san francisco?\"}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message], config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_xoXkK8Cz0zIpvWs78qnXpvYp)\n", + " Call ID: call_xoXkK8Cz0zIpvWs78qnXpvYp\n", + " Args:\n", + " location: San Francisco, CA\n", + "\n", + "__interrupt__:\n", + "(Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'get_weather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_xoXkK8Cz0zIpvWs78qnXpvYp', 'type': 'tool_call'}}, resumable=True, ns=['agent:4b3b372b-9da3-70be-5c68-3d9317346070'], when='during'),)\n" + ] + } + ], + "source": [ + "# highlight-next-line\n", + "human_input = Command(\n", + " # highlight-next-line\n", + " resume={\n", + " # highlight-next-line\n", + " \"action\": \"feedback\",\n", + " # highlight-next-line\n", + " \"data\": \"Please format as , .\",\n", + " # highlight-next-line\n", + " },\n", + " # highlight-next-line\n", + ")\n", + "\n", + "for step in agent.stream(human_input, config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once it is re-formatted, we can accept it:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "It's sunny!\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco, CA is sunny!\n" + ] + } + ], + "source": [ + "# highlight-next-line\n", + "human_input = Command(resume={\"action\": \"continue\"})\n", + "\n", + "for step in agent.stream(human_input, config):\n", + " _print_step(step)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/how-tos/wait-user-input-functional.ipynb b/docs/docs/how-tos/wait-user-input-functional.ipynb new file mode 100644 index 000000000..33279fd74 --- /dev/null +++ b/docs/docs/how-tos/wait-user-input-functional.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to wait for user input (Functional API)\n", + "\n", + "!!! info \"Prerequisites\"\n", + " This guide assumes familiarity with the following:\n", + "\n", + " - Implementing [human-in-the-loop](../../concepts/human_in_the_loop) workflows with [interrupt](../../concepts/human_in_the_loop/#interrupt)\n", + " - [How to create a ReAct agent using the Functional API](../../how-tos/react-agent-from-scratch-functional)\n", + "\n", + "**Human-in-the-loop (HIL)** interactions are crucial for [agentic systems](../../concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n", + "\n", + "We can implement this in LangGraph using the [interrupt()][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input.\n", + "\n", + "This guide demonstrates how to implement human-in-the-loop workflows using LangGraph's [Functional API](../../concepts/functional_api). Specifically, we will demonstrate:\n", + "\n", + "1. [A simple usage example](#simple-usage)\n", + "2. [How to use with a ReAct agent](#agent)\n", + "\n", + "## Setup\n", + "\n", + "First, let's install the required packages and set our API keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for better debugging

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM aps built with LangGraph — read more about how to get started in the docs. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple usage\n", + "\n", + "Let's demonstrate a simple usage example. We will create three [tasks](../../concepts/functional_api/#task):\n", + "\n", + "1. Append `\"bar\"`.\n", + "2. Pause for human input. When resuming, append human input.\n", + "3. Append `\"qux\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.func import entrypoint, task\n", + "from langgraph.types import Command, interrupt\n", + "\n", + "\n", + "@task\n", + "def step_1(input_query):\n", + " \"\"\"Append bar.\"\"\"\n", + " return f\"{input_query} bar\"\n", + "\n", + "\n", + "@task\n", + "def human_feedback(input_query):\n", + " \"\"\"Append user input.\"\"\"\n", + " feedback = interrupt(f\"Please provide feedback: {input_query}\")\n", + " return f\"{input_query} {feedback}\"\n", + "\n", + "\n", + "@task\n", + "def step_3(input_query):\n", + " \"\"\"Append qux.\"\"\"\n", + " return f\"{input_query} qux\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compose these tasks in a simple [entrypoint](../../concepts/functional_api/#entrypoint):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "@entrypoint(checkpointer=checkpointer)\n", + "def graph(input_query):\n", + " result_1 = step_1(input_query).result()\n", + " result_2 = human_feedback(result_1).result()\n", + " result_3 = step_3(result_2).result()\n", + "\n", + " return result_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All we have done to enable human-in-the-loop workflows is called [interrupt()](../../concepts/human_in_the_loop/#interrupt) inside a task.\n", + "\n", + "!!! tip\n", + "\n", + " The results of prior tasks-- in this case `step_1`-- are persisted, so that they are not run again following the `interrupt`.\n", + "\n", + "\n", + "Let's send in a query string:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'step_1': 'foo bar'}\n", + "\n", + "\n", + "{'__interrupt__': (Interrupt(value='Please provide feedback: foo bar', resumable=True, ns=['graph:d66b2e35-0ee3-d8d6-1a22-aec9d58f13b9', 'human_feedback:e0cd4ee2-b874-e1d2-8bc4-3f7ddc06bcc2'], when='during'),)}\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for event in graph.stream(\"foo\", config):\n", + " print(event)\n", + " print(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we've paused with an `interrupt` after `step_1`. The interrupt provides instructions to resume the run. To resume, we issue a [Command](../../concepts/human_in_the_loop/#the-command-primitive) containing the data expected by the `human_feedback` task." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'human_feedback': 'foo bar baz'}\n", + "\n", + "\n", + "{'step_3': 'foo bar baz qux'}\n", + "\n", + "\n", + "{'graph': 'foo bar baz qux'}\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Continue execution\n", + "for event in graph.stream(Command(resume=\"baz\"), config):\n", + " print(event)\n", + " print(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After resuming, the run proceeds through the remaining step and terminates as expected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agent\n", + "\n", + "We will build off of the agent created in the [How to create a ReAct agent using the Functional API](../../how-tos/react-agent-from-scratch-functional) guide.\n", + "\n", + "Here we will extend the agent by allowing it to reach out to a human for assistance when needed.\n", + "\n", + "### Define model and tools\n", + "\n", + "Let's first define the tools and model we will use for our example. As in the [ReAct agent guide](../../how-tos/react-agent-from-scratch-functional), we will use a single place-holder tool that gets a description of the weather for a location.\n", + "\n", + "We will use an [OpenAI](https://python.langchain.com/docs/integrations/providers/openai/) chat model for this example, but any model [supporting tool-calling](https://python.langchain.com/docs/integrations/chat/) will suffice." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "from langchain_core.tools import tool\n", + "\n", + "model = ChatOpenAI(model=\"gpt-4o-mini\")\n", + "\n", + "\n", + "@tool\n", + "def get_weather(location: str):\n", + " \"\"\"Call to get the weather from a specific location.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " if any([city in location.lower() for city in [\"sf\", \"san francisco\"]]):\n", + " return \"It's sunny!\"\n", + " elif \"boston\" in location.lower():\n", + " return \"It's rainy!\"\n", + " else:\n", + " return f\"I am not sure what the weather is in {location}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To reach out to a human for assistance, we can simply add a tool that calls [interrupt](../../concepts/human_in_the_loop/#interrupt):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.types import Command, interrupt\n", + "\n", + "\n", + "@tool\n", + "def human_assistance(query: str) -> str:\n", + " \"\"\"Request assistance from a human.\"\"\"\n", + " human_response = interrupt({\"query\": query})\n", + " return human_response[\"data\"]\n", + "\n", + "\n", + "tools = [get_weather, human_assistance]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define tasks\n", + "\n", + "Our tasks are otherwise unchanged from the [ReAct agent guide](../../how-tos/react-agent-from-scratch-functional):\n", + "\n", + "1. **Call model**: We want to query our chat model with a list of messages.\n", + "2. **Call tool**: If our model generates tool calls, we want to execute them.\n", + "\n", + "We just have one more tool accessible to the model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import ToolMessage\n", + "from langgraph.func import entrypoint, task\n", + "\n", + "tools_by_name = {tool.name: tool for tool in tools}\n", + "\n", + "\n", + "@task\n", + "def call_model(messages):\n", + " \"\"\"Call model with a sequence of messages.\"\"\"\n", + " response = model.bind_tools(tools).invoke(messages)\n", + " return response\n", + "\n", + "\n", + "@task\n", + "def call_tool(tool_call):\n", + " tool = tools_by_name[tool_call[\"name\"]]\n", + " observation = tool.invoke(tool_call)\n", + " return ToolMessage(content=observation, tool_call_id=tool_call[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define entrypoint\n", + "\n", + "Our [entrypoint](../../concepts/functional_api/#entrypoint) is also unchanged from the [ReAct agent guide](../../how-tos/react-agent-from-scratch-functional):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph.message import add_messages\n", + "\n", + "checkpointer = MemorySaver()\n", + "\n", + "\n", + "@entrypoint(checkpointer=checkpointer)\n", + "def agent(messages, previous):\n", + " if previous is not None:\n", + " messages = add_messages(previous, messages)\n", + "\n", + " llm_response = call_model(messages).result()\n", + " while True:\n", + " if not llm_response.tool_calls:\n", + " break\n", + "\n", + " # Execute tools\n", + " tool_result_futures = [\n", + " call_tool(tool_call) for tool_call in llm_response.tool_calls\n", + " ]\n", + " tool_results = [fut.result() for fut in tool_result_futures]\n", + "\n", + " # Append to message list\n", + " messages = add_messages(messages, [llm_response, *tool_results])\n", + "\n", + " # Call model again\n", + " llm_response = call_model(messages).result()\n", + "\n", + " # Generate final response\n", + " messages = add_messages(messages, llm_response)\n", + " return entrypoint.final(value=llm_response, save=messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "Let's invoke our model with a question that requires human assistance. Our question will also require an invocation of the `get_weather` tool:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def _print_step(step: dict) -> None:\n", + " for task_name, result in step.items():\n", + " if task_name == \"agent\":\n", + " continue # just stream from tasks\n", + " print(f\"\\n{task_name}:\")\n", + " if task_name == \"__interrupt__\":\n", + " print(result)\n", + " else:\n", + " result.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'user', 'content': 'Can you reach out for human assistance: what should I feed my cat? Separately, can you check the weather in San Francisco?'}\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " human_assistance (call_joAEBVX7Abfm7TsZ0k95ZkVx)\n", + " Call ID: call_joAEBVX7Abfm7TsZ0k95ZkVx\n", + " Args:\n", + " query: What should I feed my cat?\n", + " get_weather (call_ut7zfHFCcms63BOZLrRHszGH)\n", + " Call ID: call_ut7zfHFCcms63BOZLrRHszGH\n", + " Args:\n", + " location: San Francisco\n", + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content=\"It's sunny!\" name='get_weather' tool_call_id='call_ut7zfHFCcms63BOZLrRHszGH'\n", + "\n", + "__interrupt__:\n", + "(Interrupt(value={'query': 'What should I feed my cat?'}, resumable=True, ns=['agent:aa676ccc-b038-25e3-9c8a-18e81d4e1372', 'call_tool:059d53d2-3344-13bc-e170-48b632c2dd97'], when='during'),)\n" + ] + } + ], + "source": [ + "user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": (\n", + " \"Can you reach out for human assistance: what should I feed my cat? \"\n", + " \"Separately, can you check the weather in San Francisco?\"\n", + " ),\n", + "}\n", + "print(user_message)\n", + "\n", + "for step in agent.stream([user_message], config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we generate two tool calls, and although our run is interrupted, we did not block the execution of the `get_weather` tool.\n", + "\n", + "Let's inspect where we're interrupted:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'__interrupt__': (Interrupt(value={'query': 'What should I feed my cat?'}, resumable=True, ns=['agent:aa676ccc-b038-25e3-9c8a-18e81d4e1372', 'call_tool:059d53d2-3344-13bc-e170-48b632c2dd97'], when='during'),)}\n" + ] + } + ], + "source": [ + "print(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can resume execution by issuing a [Command](../../concepts/human_in_the_loop/#the-command-primitive). Note that the data we supply in the `Command` can be customized to your needs based on the implementation of `human_assistance`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "call_tool:\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='You should feed your cat a fish.' name='human_assistance' tool_call_id='call_joAEBVX7Abfm7TsZ0k95ZkVx'\n", + "\n", + "call_model:\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "For human assistance, you should feed your cat fish. \n", + "\n", + "Regarding the weather in San Francisco, it's sunny!\n" + ] + } + ], + "source": [ + "human_response = \"You should feed your cat a fish.\"\n", + "human_command = Command(resume={\"data\": human_response})\n", + "\n", + "for step in agent.stream(human_command, config):\n", + " _print_step(step)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, when we resume we provide the final tool message, allowing the model to generate its response. Check out the LangSmith traces to see a full breakdown of the runs:\n", + "\n", + "1. [Trace from initial query](https://smith.langchain.com/public/c3d8879d-4d01-41be-807e-6d9eed15df99/r)\n", + "2. [Trace after resuming](https://smith.langchain.com/public/97c05ef9-8b4c-428e-8826-3fd417c8c75f/r)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md index 12ceaf0d5..6086a606f 100644 --- a/docs/docs/tutorials/index.md +++ b/docs/docs/tutorials/index.md @@ -9,7 +9,7 @@ New to LangGraph or LLM app development? Read this material to get up and runnin ## Get Started 🚀 {#quick-start} - [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works. -- [LangGraph Cheatsheet For Common Workflows](workflows.ipynb): Overview of the most common workflows and agent architectures in LangGraph. +- [Common Workflows](workflows/index.md): Overview of the most common workflows using LLMs implemented with LangGraph. - [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI. - [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application. - [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud. diff --git a/docs/docs/tutorials/workflows.ipynb b/docs/docs/tutorials/workflows.ipynb deleted file mode 100644 index 345aa7c07..000000000 --- a/docs/docs/tutorials/workflows.ipynb +++ /dev/null @@ -1,1240 +0,0 @@ -{ - "cells": [ - { - 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ldGE+Cgsc+pUAAEAASURBVHgB7L1ZkxxJlmZ3fQ+PPbAj18qsrqqu6ulpGXKEMjIPJOcvU4Qv5MOMtFBmYTd7utnNWjOzkMgEEksgEPvmG79PzczD3eGxAAgE3CKOAR62qapdPWqmV/Wa2tXKQEuwQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwEwTqM60dAgHAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiQDGXG4ECEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIlIIAxtwSFhIgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDDmcg9AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECgBAYy5JSgkRIQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggDGXewACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQAkIYMwtQSEhIgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABjLncAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAESkAAY24JCgkRIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhgzOUegAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQAgIYc0tQSIgIAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQABjLvcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIESEMCYW4JCQkQIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhhzuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCJSAAMbcEhQSIkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQwJjLPQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoAQEMOaWoJAQEQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAxlzuAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACJSCAMbcEhYSIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQw5nIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAoAQGMuSUoJESEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAxl3sAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAJCGDMLUEhISIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAYy53AMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABEpAAGNuCQoJESEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIYMzlHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUAICGHNLUEiICAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAYy73AAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBEhDAmFuCQkJECEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIYc7kHIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiUgADG3BIUEiJCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMCYyz0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAEBDDmlqCQEBECEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAMZc7gEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAiUggDG3BIWEiBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEMOZyD0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQKAEBjLklKCREhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAMZd7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBACQhgzC1BISEiBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAGMudwDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARKQABjbgkKCREhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCGDM5R6AAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFACAhhzS1BIiAgBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAGMu9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgRIQwJhbgkJCRAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACGHO5ByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlIAAxtwSFBIiQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDAmMs9AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgBAQw5pagkBARAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgADGXO4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIlIIAxtwSFhIgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDDmcg9AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECgBAYy5JSgkRIQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggDGXewACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQAkIYMwtQSEhIgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABjLncAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAESkAAY24JCgkRIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhgzOUegAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQAgIYc0tQSIgIAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQABjLvcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIESEMCYW4JCQkQIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhhzuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCJSAAMbcEhQSIkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQwJjLPQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoAQEMOaWoJAQEQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAxlzuAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACJSCAMbcEhYSIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQw5nIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAoAQGMuSUoJESEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAxl3sAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAJCGDMLUEhISIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAYy53AMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABEpAAGNuCQoJESEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIYMzlHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUAICGHNLUEiICAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAYy73AAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBEhDAmFuCQkJECEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIYc7kHIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiUgADG3BIUEiJCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMCYyz0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAEBDDmlqCQEBECEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAMZc7gEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAiUggDG3BIWEiBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEMOZyD0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQKAEBjLklKCREhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAMZd7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBACQhgzC1BISEiBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAGMudwDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARKQABjbgkKCREhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCGDM5R6AAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFACAhhzS1BIiAgBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAGMu9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgRIQwJhbgkJCRAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACGHO5ByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlIAAxtwSFBIiQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDAmMs9AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgBAQw5pagkBARAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgADGXO4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIlIIAxtwSFhIgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDDmcg9AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECgBAYy5JSgkRIQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggDGXewACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQAkIYMwtQSEhIgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABjLncAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAESkAAY24JCgkRIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhgzOUegAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQAgIYc0tQSIgIAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQABjLvcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIESEMCYW4JCQkQIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhhzuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCJSAAMbcEhQSIkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQwJjLPQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoAQEMOaWoJAQEQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAxlzuAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACJSCAMbcEhYSIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQw5nIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAoAQGMuSUoJESEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAxl3sAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAJCGDMLUEhISIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAYy53AMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABEpAAGNuCQoJESEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIYMzlHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUAICGHNLUEiICAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAYy73AAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBEhDAmFuCQkJECEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIYc7kHIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiUgADG3BIUEiJCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMCYyz0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAEBDDmlqCQEBECEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAMZc7gEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAiUggDG3BIWEiBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEMOZyD0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQKAEBjLklKCREhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAMZd7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBACQhgzC1BISEiBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAGMudwDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARKQABjbgkKCREhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCGDM5R6AAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFACAhhzS1BIiAgBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAGMu9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgRIQwJhbgkJCRAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACGHO5ByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlIAAxtwSFBIiQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDAmMs9AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgBAQw5pagkBARAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgADGXO4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIlIIAxtwSFhIgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDDmcg9AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECgBAYy5JSgkRIQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggDGXewACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQAkIYMwtQSEhIgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABjLncAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAESkAAY24JCgkRIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhgzOUegAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQAgIYc0tQSIgIAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAOAgicSWCgs5UzQ9zgk4Zz1gK4s+hwDgIQgAAEIACB60WgaBnRArpe5UpuIAABCEAAAhCYLQIYc2erPGZTGgy6U8rFUIouy5TT6RBdmdPIcBwCEIAABCAAgetJoGgd0Qq6nuVLriAAAQhAAAIQ+PgEcLPw8cugJBJcxHhZkqwgJgQgAAEIQAACEIAABCAAAQhAAAIQgAAESkiAkbklLLSrFzk35KahFiPjLIabxRiMScmGASZPzP7+aJZKnI3ZB42EEIAABG4ogULPVLzhn5UNCueG3g3XItvFnXwtMkMmIAABCECgfASKtpUlf+cm1Wgi5UNwIvE7AzhJYnRrFMslJz16GbYvTgBj7sVZ3byQow9ssT0oNoTDm+lB7p9SWVaj18vCV6vVqMzKQz+ShWGhjh57azmLyJMRi+PFVSbPF8dZQwACEIDATSQwCOnP6EmV9mMw8MdSdW1nuiJpkHPURrIDnwHuUtI4Rwa3Bc4Ncm6As9O4knyI47k83zMfLqrBe6ZxlSxGr1WweUN+BUrh8nVq652XxzPuWU5BAAIQgAAEziVgxZOUz5SQPi49NOgP9FMby3YI/c5trIwlNZr46PZYoHznokpvNJ2Lxpm8XpHGReOfEq5IZqIR58MnMbQ1DDcpB/uzQABj7iyUwseS4cIP58kjnYmqiFPj+qB+Q6ttP/p9dVD76po29Bsez8MN8619H3rnZVI+7Q8PDTdOrjHtWqPHvO1oI1HHRXOAyQgOMRlhNEyRwmSY4jhrCEAAAhC4UQSsDgY24h7LwNeRVmnogHTloJYwTNMg0/icpVVSGuckdFZ8X2/0He606/vYWWlcRIYrSeMcDufJ4PNlYXFWebxNPkbLzmlm+5MgK6mdl7EZRK1WvI7wlVggAAEIQAACl0zAaqj4eXNELdnc4P2ks2TM7Xa7UW/UcjvEUJPlCZystDWy+EW7lyLhybXTKRZtD20cPpafK6KMCjpMbzT+SDrFZpHGcN+JFQkW68k0RvbTpv847Mjx0e3isGGNBBtL3W9viwOWpYjj7dHj3i+W0TDFMdYfjEBloOWDpU7Cs03gbUt+LHxRyY1mMQ/gh3j4IGc7ei+mh36gw0W8ngL5mOPn8SY2vZuWYVraGwnqc4M0RGQ0QG40TpVq8QYukyHJVFz+RMCU5uhTkKLmUd64fjpgIUYFKRI9zwX1aKIpIf5AAAIQgMANIlBoj0xrea8nbXKg9aG2qtpuaVtGXek2682Tl6A3CBJZnXECRZvH7biK7lp1kvU3M+H6r9tCvn+zpmB2r894lhAPAhCAAATKQ6BoTFnT+L/3rYVGFU6x7c9KKtJbKYzDFTrMxxQoNwIkk1gRJn2K4jaYDzh+vnb0scUX0W944XzfYYq00kYRf3hwJJWROOno5L4PFjIX6YxET5sTNogxY8bEOSeRzut4Ss7X8zHtVE8sNUmv+7yXYp0HzQ6e8vciYU6JyuG3J8DI3LdndnNjFM+8CGQNd20UdcsolVQp+oSffHVU9XlDf9CJfq8T0dXoo24eSRWJK86eRu8OO6xFZTGa3uT2MExR2WmdjLo+oYpcn1FU69nIprSv69SbrajU89s9fcrqik3x8iS8Gi75seE+GxCAAAQgAIH3JDCqLjUeN3oaldvvH0s3Hui3H10p1J71YjKGWZ/ZuDvRCJ+QYUx3TZwrdocqszgwsT4vjfeN78uVIY3zOJQlH5bzffMyGr/YTn3ZYdq6mytd7Wmd2l+6VwfNqNfmo1VfiEa16T4hCwQgAAEIQODDECgMrNI1mX5yS8OKR2uvfNA2CX8BJRvEQDaIfk/tLI3UrVTctnKrrBK9TvZiUjsni+PaAJzSKoy63tPxNzRs8RLzJLrbb16yMZNFfMXODcejaSS7qtLMBqgVaaQM5Du2k3jT8hRpeisd9EZ+3no47eb7Pu92ZBbOrcua822d7XWyj8xpndtH0qHsuA6mvCb7zLQsZ5fh7wwQwJg7A4VQChH03PtZzuuIbFs1hsdieCdVHjLapvpioAZ+X5WmK053Uo8Oonu0H53jozjcP4rjvSNFyCqWrirWvb29hCCriE9oZCFO9qdvvRnKhtv5xYWQd4e01OvNWL13N2rzC1GtyR+hKq1KVaOfqo2o1FRpqcfhStIVbKq0Urw8cr6afm0fLYicHoIzEIAABCAAgXEC6kD0u7G9txnrrx/F5s7T6PR6Uau3Mq2SFKIb99JRZxh0z1NRF9FQs5DGeTKY3Xl5OS+N8+L7GrOQxnkyXCkLQUvyjK7TgX5Uq3pRr3vYRtt0nw5acXv1s7h368tYmndby53ILP5F8uR8sUAAAhCAAATOJZDaSCNa3fsy2mY/DSRLNohuZsA9Oo7e0WF0jo6ie3wcO9vbUfVAMl3Ev52tPYUvtFWRptcyvirdZID12hotXXfc8JoZWotBZJnkFdk5Mr1XpFcYZLN0JzXjm18aO51Cc2ZpVJJx2cczo+6paeTRbMTNDLk6ILmbjXosz7edgBYNfJNNZOnBg6i2F7OBcLaRNLLBb8m/sH0MpyVLZ2jYTceKfBUy5kFZXTkBjLlXjrxkF/Szmj+nfpT933+8PVBFGLVGGmk76OhN1+5mREuN96Pt6G1vRW9/N453d6Kj37G2HX5Qm4tupSlDa+a7xhXDfFUVoGvCos5IiNJ0MIXNNx2Z/JOJZYGSUElMJ1PpHkb/lT9b9TKIYxmZH3/7h2g2m7G4shxz7aWoLd+L6tx81BYWojKnESTy8WYZBnVP1CYjr2UaW7KrjR1K182unR0vKtci1FiGioOsIQABCEDgxhOQh9xaTR2I4zg82pH+6UlHVfUVSzFCxLol00uFjpuGTL6yph0+OWaleM4yC2kkKc/KS0nyYdRn8rxIPpzIGSwGSuO8Uj1ThiTkeSnk+VCwvO96sk7x1THUi/Pd3cNotWrqJKvNpRf5g8pRNBrqJA47gQ7MAgEIQAACEHg/AjaqFgOvkmGx0ITWl/r1/fVv/yB6x3JbtfUyunuyR+wdRGd7L472D2L/8CAasgVUZdTsyjCaxpUN6tHSi0gZNKTjZAfIm1QpyWTAPWmB+Yuq7JIOlAdMBzwobMQGoJfwYzrYSjQFH403up3r46RszUj7mXDeSYvTz2Qbuc6YHcL2i8zgnFoIaidkl9Rf63HvdfZif+dVVFM706N4B7H56PfRki5vy8hba81H++HPorKwrAFw87KTyLBr0WTOGKi9WvUEvWlEs0VKqecnc/l9mOXKCWDMvXLkZbygHli97eqpod5Vx9MVgvqcfrLVEe1HV2+4+pu7cbS3Fa83nsXxsQy3XY0wktNx32At1QStRjtVQtXWoiqIZX1Kmo3YqDcbsbi0pFrGFYFrm7xC8Eqdgayy1vbUxRWbKjVdx2+p0pszy6kRwMe7+6qssgrPk7A15/aSO4f9g/2U7sYf/ykarXbMyZjbmJ+LJY3cra+tqaLS5wZSBjZSR62pdJ3RXKYxGXzNoiLzelqYsQjsQAACEIAABBIBa4ysqa3RGtJV/q0urcTS8h2dOXmZaMNdpNG508Gdp3kKLTU9dnZ0FtI4TwZLel5ezkvjvPi+xvumcV78y8jHZaTxtiycr5S3kYj+XHV5YUWjyTuxtbURB/ryqpJcL/iFhDvdipF3hovCO7tN55yxQAACEIAABN4kYP1xeHiYbAONRiNqshMkV46H+9E72I7O/k5svXoR2/ppKG4yWsr8GA3ZKyoatDUng2VfzavV22tKXC/PrdRkzG3P33Io7ehA0nFJ2yX9ZTNApaqDbov5M5R0Sn9GdGE6ODRyFnLbAurt3PhqY2/a9x//nFBKTGsvZ+1PppHvJxtFFjtFTwbgQjZfT9dJl/Ofnl667sXh1npuzJVkso/s7e1q0tJadG0/6R7FT7//R5k/bB+Zj6Z4rd69G61bso/oWAz00lZfNYe/btagvHH5czlYXTkBjLlXjnwGLuhn2kuxHq1LsjPjf/PKwR4JavqsLjTzdv9YnzAcd+PlH7+Lo22NvN3aTxVqRw33mgy0LQ3Zb8pY2mjNqZJcjNay3vK0PbRf3deiDlIlU1EFUmnJaKrt7BOHXCgHSpVQIeS4SGnPFWv+cyWdtpUpjRWOOcfVOhl4VVnN7e7G3sFuHB7ojZ0q1GpX8g724/BYFdvhIHZffq/KLWJJhuX5B59F/daDqLRXo6K3VdVzHb8VAG349VJkMNuzwmCBAAQgAIGbTaDQBJl28id+0pfSVVW9kGzIYLvYXIq7Sw+kutw008tS/0ujPU7Xg0Wap5HNrnXa2ez4eWlMarTJ1KwBCy04ea7Yf980LiMfl5HGVeTjPJ5XkQ+X25n3he7RTleuQtpqD1Z3Yk8v0CuaG6Ga2l425vqTUt/D2ShiN9VYIAABCEAAAu9DoJ1sCYPoatBYV8bbnR/+FPsvHkdHg7X8ArEjdwmemqelr2+bc3NyITCnQWRzsbC8GIt6+ShDhtSTtWih4bzOJp2V1damAy1SWFZaSZ8pMY9klV0iDSBLfXyfG1VqDl+k5/heHMbr9Ee7Wue2ieyY4+dppJXD5/uOlpZiv0jDsvhE0RIZuWYKmssv2ZPOlY0lLcmOofamXGDOffapDuXpSZ7lzpEG4+3rheyWXshup1O92I0D2UwO9mTsffVYNuxKLC8uauDbp1FflX1kLrePyC1DyoOvnSeZrnfanyJMka3TwnH8rQhgzH0rXNcosB+o4qHyuniwXNF4O9UCzq9PZhXZQA98/3ArjjbX4/X6Ruxv7cio20kjcGtNd0vrceu23uBodFFzScZbDdOvVPMHPVUkDqPRtqprvGVDq0f29uV4PBldfTkt2cgNh8s6AdnR6X8dz6NDNOzWKeZxizxIeldokqGxejsZahdVmXZVQd396tPob2/EzqtXsbu5pS8P9KZP0Xa3tmN9/V+ieWs9Fh98GguK15L/3YreACbXCwWnQpzhvivUvFItRlGlc0WFW0RgDQEIQAACN5lAUrEJgPWr9ZU0okaY6P2oGs/uNKgB7heaVkoaTXHScE+R3viTgr1xVAd0IadwkeW0NN7oW5ySWFJ3p1zsvdO4hHxclMUHzYfYlYnFKUWdHVZZN/SllUdHdfSC/1A377H85/b0r3/OC4gz0+UkBCAAAQjcEALuuafeu/KbtG+acyyzi9r24F9uoEwGTPepK3EsV46vXj6P1y9/iv7Wi2j1j6JZb2jQqObMkdG2vbAWDRkf60uLMua2o6fRWj01cjpSwP5Xy0f1Om2bPY729dWujMB2IWApvK5627YLj0C1QErD9onTvy7JbQCnltwpDaR0xdMiOU3HG407uj0tXsZxGMdG6Dz+QC4kB33n2RxlP/EZTQ5fX56POyt3lWf5wG904njndWzLzrP7eks2mo6aorXY396PzVe/j+byeizf+yTaa7c1cle+duW2opKMupLVnJQfp54txVbGxoPm0lKImO+yej8CGHPfj18pYstcOpRT1ZOfs9FnPNt2fZU9g3rQZfC0cVQ1XE1xK/pUYSA/K8f6bGH90beq8DRUv9eNeqMWDbkpWFjRaNaVlagu6KEO+VeRUXegScY8IsOd0PSZqLfdYdU/15zaTHKkUfp+uIsHPG1mO0Xlk+zAOn7akvzdpmtlIVwxF8n5Mt7p2eCri3m/qQ6IXSlU27di+eGqnH8PYuf1ZhzqzdS+JmM71ijezsaLONRvW/mbl/uFpU8+jfk7dxOkZKBOPnWVmt/uqROusb7p4q6okisa7aW8JajpFH8gAAEIQAACYwTcT+lIyR0MGtHXlyCVubZ0rr9WUTD9smZxpo/HIo7sZNqnOOCI2ZKSSIa14shpa7UMTqIpUNKcKfBJ62Fa3CSgTkinW69PC6JjxWSkp5zOD6uTlDpao6GyFFM+Rg9PbidLdBZqPB8nAdPZ0wQ8CaaspFbSyJGTSO/L4sIyiOQ4ixMZLoOF85HaYCO5nL45fmeNW6IrcSQ/uTV9bTlQW3CgL6x6XbUd612Vt19AWNLsTjqRPh3iDwQgAAEI3HgC7uVrMFeyUVjXyG4gteGfPwKu1Nxv12HbDo7kC7cjVwqbG/H8+0dxrEFZ/eODaOmr3NrisgaQyUWV7BBzi0tRkVHXuipNwKk+eL/nRLL+v5FbM3n0bkfXrurnpSrjbtQzQ63+JiFG9b2PpeMKa/m82KIxugz0Zcppi0NmE5xpw8lnK22kDA6jpb2xZE+kOGlH2a6SRXnjJbyOp7ZYcX5E0buFNhAPtznNw1I4voPa7uPtgeYM6ncb0WhrMtNP7seth7KPyJWm7SMH+nV3t2Pwej02N57Gvnzqzq3diflPv4j2/U9z20dVvoiVtgfy6dp6tZvS9yXtcdcZN78kXpJDJ1jemwDG3PdGWK4EimphaCDNH3jnIj1gevgqqtDqHhgkg2d3dyt25Qd3d/2n6GxtRuNYT6EMo4uryzJw3pdzbFWATc9+2IheGs7vNz6uNJWw00rr/MFN1ZfipyW7sKqkdLI4mp8cW6WvIUaOOOZI/TRy5pxN136qrQaqZIbdTikLp7d8rx1tjSyZ12yXPTlIP3j2VK4jNuSDR+4ZNNrkYH8vWi+eqWL7REpDykLG3Ir8AAuWpclMuc6EAVs3OFFXVF6zQAACEIAABKYQ8NcqfXc49OlfX3rFLwaLCSasQqxWbNjLtqYkoEOFXs/Oju9lSugsDeukde2xpC/ayla6qWcjvWrdmqQ951rD60xcQ3lMnY3heef5ggo0jTJxcL26TlHGczNM8o2Nadd4Txbm8NFYFOVxSSyyXt8ItfEyk+nWF0pNHncS3fSxGyt53kv3k0NfsARHrsEmBCAAAQjcBAKZBj7R/ckOITVWsTU30yzRVz+892I9ttQH33r9SkZdzd0jnb8og+PCnTvRWLkdA70E1wgztaGkdaSLUhtKiQ8Hk6l94Wtl+ydkx/aTMLmJdqIJ4VNJmw2bNxbyJJ2ztoqkUjMuJZNfw2mNGjPytItwSZz8WGHIzc7JzjK8dpF6JoEP+1weLbk9ykYSp9R8Nv0rQmdtNsmj0ycpOaxGOefqfvn2kr5S7spNpSaXV1kcrX8fnc1n0T3a1cjdXuzLbtJafxWr9x7IneaS0lLEvDycRJZullhqLmYX5+8lEsCYe4kwZzWp/HlM4hUPa6oY/MD7mc2X/FGTEfdYI1cPNMz+Vez89IOG1m/qjcyOfPtVo33rTize0QO7sqxKQkPv55qp8uwqsgbsprqtpo5MNuuiDir9rK4auVBxwY+6LuRRBW+jc70aTU161tDMjQONxl1aXY2O/ey+kisGGbGPdg/icP8wbS/fvR23HtzXqGR1u5sLyoUeI4Ec9JRfAU5vuJT8QJBT3otLfdT8cnEIQAACEJgtAta6aphLUaYGtxRGat+/l5BWOEVT/r0SIvK7EHj/AtRV37XRcJllfwEZkuE6Czd6x2UdxCwXF0jlXSgTBwIQgAAESk1A2sGzkUl5qAWUBkBV9WlNpeJRuJpI3ZOp6+uP7ktNril3iHvbMuoeaw6cpVVNYHYv5uQGsaKX4BWNPhuoD+8k+rYWDnXwu2sf992v3PBocUcU6TAb71HGJ2l4ayTxPE23Oz1i2PktaJ2MIE6lIjcKso1EM+pypzBYbMXi7bZGRj+MXdlH9l9v62vmgzjaeRqHcsuwZBvRw/uyp3uU8oIG6GpS+TQKWOnLPuLrpAsVeS0umsvD6t0IYMx9N27XJlaqsJSb4nmyX9zO9qs42nouf7LPYm/rlXyitWLl1m3NArkS86t35WtG/lHsVHxuRT5mtzRCVXWwDKG1RmbMrBSvkE5q1Bnm5ZHE/oRVlmgbX/UqqtpQ5dP0el4G25VYtPuFTfnW3ZG/4M5BvPjxh2TU/eTLn8fCmuI051MNlT7pMMt82POw0prh3CMaBCAAAQjMDoFCF8+OREgCgXcjwL38btyIBQEIQOBmELAxMXWhZYfwa231xTVQrLu3qcFkP8bBxrrm6jmOel1GRH0VuzKnydVX72nglQyFcplYabWic7QnFz9HClONmic3c0oeWHUzAH7wXA66+gJHBo008M32n7p+7YWot9dieU32kfX12Hu9ERXZj/aeP4nu4U6sfvppVJcVTXahimwpycjkBkFqFLhkaB1cZsFhzL1MmjObVvbQjFZsrjSLhyltD/Swesj8iyex/UwVqJxfVzTqYkGfL7SX5eR66ba+YGjrZ8utKlz7idV0kfVWW4NSsyfU1bDtmN73Ec9+6Dc82dut7Opp9JHOjU54ZmzFcW9PLin5EeHT1fznbZc8ToqffCA4AY0h0YjjdI3kQiaX10LLHUNVRt2GKq6l+Va09hb1BkqzNusNYXfnKDZ//20crr6Kta++ipqM3BkXpV4Mf7ZOYYEABCAAAQicSsCa00rDSi7Xk16PfjZzalyfOIl3EkzHpL+vfnkXxfwhpLTyvUj+P6S8HzLtt2H2AVnkt55zmm63kf23kZCwEIAABCBwAwlIeaT5ZZx1D93saZTn+rNY//O3mrtGLhXk1nHh3sNoLq7GnOblqc3Nq19uF4fSa/bN3j8Mz71VrXqS8pSIjivR3Hm+ddNoS8D+ZLNQKfDw5NAGMQxs42UKmv0ZHh859q6bkiFLevQCb5fYuzfvxvNlW4zzPmqTKVgMryFfl4PEU3HFYdDxuqmBfhoRvdSMmuZ6aC8vyS4i37r6krknH7tb+0fyZayRup/KPnLHBmAVUsquPST3FN9yuAzencHbEbveoTHmXu/yzXOXPSxFXaTHVg+SKk0/SJrIbCCfsLvrz2Pr2SPVo1t6UI80S7FH496Nlgy51bllvf2Sn1jXuDL62sjrtFzJ1lSB2mDbd180f/J9tdMez6LiyAW70EqP/FiClv+sJauAT5Ugj1qczyqyYXp5Hlzf+5f87whTpSZH3/LH48nT5lorcbz5Og5ercdBZ13+Yo7izledaN++owpLPGTodlVtYzYLBCAAAQhA4HwCmdKxfh0uw0b/8MiUjZHw6az2R9OYEuPDHpoVvTcLcsyCDC7tDyeHU56W+rRjH/a+I3UIQAACECgVgaGi0IRkh3ux+ej3cSDfuNWOPtPXJGVrdzRPzZ3P0gTr9dZc5lZBcZLxUYYHf1nrJOxuwcdSa6hYG4RO+rz78yzvTyC5YLBLSS0JaVXG3LYmoZNht9Ge09fM85pfSUbd9deaZ34z9g6/iRXNv7R4/4G+YtbEvimWY7pUWC6LAMbcyyI5Q+lM1lmpIpN8w8fHhty+ZpDsHOp3HPsvXsTO86dyI/AyvX1ZllFy5Y7ehLUX02cMnjAsvTorakMllD2G+qRB2/ZQkA9qzScfyR/yGWLydqJYIeifjLHpn98gDTTJm0fqyql3rb4oY3dbbGrx+sVP0X+9FetHf4zbnx9pRsf70dOkcDJ5a5bNpqeymX7p0UI6Jcj0iByFAAQgAIHrRmBcDfh14PiRC+f3HaNdOH0CQgACEIAABCAAgfchYJuCbREaVNaTb9y9x9/EgebpsV2iMT8fK1//UnYIDSSTj9y+3CwMbLBVvzwNl9Io3kHVo8jcmc4aPd4a7Vq/j2g3JW42KlcEk70jy3Vh6tHBjGxmu82RFJTzhqaLwBYgGXWrbRl0m3MaPa25hOSO4fWLjYj9rdj89g9ROziI1r37MsovpFG62ejfaY3V0RKcdj4Xg9UYAYy5Yziux87oo1DkyMf8WHjAjr9kGOiNVv9Qvk5++il2f3qqN2L70W63o3V7LZbuPlAFqsrTo27lC9cRPZK3YqutjZyjF7Cf2ZGLePIzjchPy4W/Ei3iz8ja4ns2amdUnnMTM9VUOuoKS2s5A682KzG/pDHC8404ePpKrhe242Xn21iT357Ww9vRt6Nwh5Xb8OFSgBrl55PeL84NA7MBAQhAAAI3joAVbNIR/pM2bhwCMgwBCEAAAhCAwDUi4OZM6utqI7VzZJCV4fZ463Vs/fh9HMrNo78WXlxZirm796N156760a3oaWIeG3FtgXA8/7NLx8IdwDUiNFtZcVlN2ia8byN6mhupaJ/mtg59ve3J0qpyTTmnUdQrGvR2pMGCh5pvaF0G3VUZ7Oc/+yxqSysyp3gIoOPlFxi9ju8NHx89piMspxPAmHs6m9KdKR6rScH94sS2yd6BKkCtK/Iz41kid58+Tr/B0bEMk4uqOO/JmCvfuHIlMFBNaXcGdsHgJfPplzadQNpIz5ufxfRmxod83COKsi6or/nGkk5OO/FGyOGBydBpP118GGR8I8k3GasIkss+FDrbz/5mchchvT65jNOT4tG/lMGqKq3GUizea0VdBtte7XmaIO3F4z/GwuFKrD38PKq39XZKCfS6YiKe1ZqI5hcaY+NjZ4trUVggAAEIQOBaEbC+LCp/r7PtQh9lyrQ4P5nxyePaTxEnj+vg5KHJpN7Yv2iEIlx+janXfyPx7EChDIenU+Th3jsIrSh5GmMKdiTJaZtFnGnn0rEij6cGyE8U4S6DxeS1irQnj5+xX+Trwixyuc9IcrJM/OWRiaef2l12LeVjxfEzk+IkBCAAAQhcawLWXNYPqQ2inYFHe0knVTTJWW9/N45ebcTWT3Lz+PwnjZOqxq17n8iQKzuEbBKVVlO9bhn9FCfZIHJbg1Vbpt5SytpRz1xpFiov67cX507DO65TR33GOkYeexg5hR6PostOHHgj1jB62ijSLOQ0lPPU80nYLK0i/PC4RCjSPbna+JE0wC6322RhxuUu0izijxrJx7OYxUupy5iUvHWOXl12EYfw19zVOU1YJ/tITYL2ZfvY1QRpr376Uxwcrsetr/8i6otyS+mJ0Vy+xeKEh6INN3RwPD9FcNYnBDDmnrAo9VZ6gM7IQXpYXRHKNtuXX9y9l49j58Xj6B8fRHtxUQ/dnWis3VblqYdLBlpXnH7EBj29C/MrMC+qDIpK0tezaTMtNujmS2Ys1o5O5WeLU9l66sHxIJN7yYA6efCs/fHaZ3rIibphdDcTUZkq8pBOCl5e4w2734Oa3kDNSfHcjdWG3Cps1ePZD99F5dlWNOzvpy8fu0v3VFe1NFlcLoYqtZScL1JcdHR7urQchQAEIACBa0kg76hI4diwm6mFTAudnd2JMJMt8iJyClYom+LgeeuJtM8Lns7rGta9F77URQK+rRxFmheN5/BFnNMyedG0RuMrzbKxSNl8OxZuzdhw67vWzcATmuelM8qKbQhAAAIQuNYECjWa1urVa26efY3EffX9D9Hd60RTk5ot31qL+bt3ora2rH6zRuLK9mCzRVW6NJvnpyAkzWNrZlIzCjPR9rEx8nwzQCFQkebEesrpKYcmIr3dbib22ameffYtWi9nApl+lTeO6oBtwsXX1yMKX7x1QucdZ+D5mPSr1qvRvisbiIz0zYVWPP/hm+i/3Fa0g1j5/NfRXH0og+6cwtralMUfEkxl69TSxvAwG9MJYMydzqVUR327Ty4TdZsqvkHUG5pD8GAvDp89je0nP0S1MYj5tVvRvpUbcuXrZGzYe2qiq1L0k2vbplZZV1MuGoYXHH/Qkiwp3DBAuTaGr7tOxE55SrsnW65g7ITBztdr8g3TXluNllwu9HpHsfXiaWzIB3G3M4hbX/ajeeuhlFJLKUgBGZzrLP0SuXF8JxdlCwIQgAAEbgwBq4JMHVjP+IdyuDGFT0YhAAEIQAAC14xAskWoOeOv8t2qsfdB2yH213+M7Z9+iOO9nWi1lmNV7h3n1lY0F41cE3qEp8Kf2BlkeUgJ+ZWhWkb5KFOnx/JxCBTsJ23EdofhtquNu33ZSOqtluwja9FeakW3f6D5mZ6kUdg12USWdEc0Vj0xmr5i9vBBJ2pDfN70zVcfJ4MluyrG3JIV2KS4vveLh6o45zovPQQeA6+zNjsO5O+2u70RB8+eqxJ9Fn2NHF3Q7ILz9+/pc4ZlGXblXDxZa0+qz5GaNE/a6eSbw6dsuFFc/g15hieuzYYhKN+Ckf3Tnic7a1XizhdfR1/G7+1Xz2Pv1U9Rk1Ja0Fun1preQFU0k+MIv2TQncRXnL82rMgIBCAAAQhclIBVgn+ogosSIxwEIAABCEAAAjNFwI2YvCGTVjJOHB/sxt7zHzSg7Dt9JbwbSxqFu7D2INort6I671Ga+m+Dnv7lptuUJdsechvuTGURYUYJnLReszHV2ei1iga82VZ7+/Ovk4V+e/1F7G48U3n2Y142kuaq3DLU20rI8b0U62yPv+cTwJh7PqPShUiPQTLM2kWC6tLOYfT3d2Ln0R9ib+O1/JnUov3J3Vh4cD/qq2t6yOSXxqNvFcdVaFqSTfc6P1DjeTMuL/kq2znzryaEy1WN5pKTJwqBFlf7gLn35S+iIcff+y9+lJ+Y53EsI+6afu21uwpSzwziuhCK6UzAnIQABCBwzQlI5ya9q2wmJZRpIGsnd2bSp4SlIjCuVz+a6OlN6QWufnGFf4HEpgWZBR5XL8PVX3Eae45BAAIQgMBHIzCqCHpyPSjXCocvn8brH76Pjgy5i4tyrXDvrgY7ycWj7BB26Vip6YvXZInI20LS5d46W6WfXOiDq/RLgOkBd85PGRc3U09n/OaZvoZkJ/uIZheyfeT2F/KXK3eeu3Lzubu5LvuIfOv2ZJO6pRG6mjjNrpv8z/9ZLk4AY+7FWZUopB8oGXI90VlPnm012dnOD99qsrMfdLwSzU8exvKXmlFwblFGXLsKUDidsV8a1zDjz5D33nxA0xcPOlPaZSJLE7vnZuukMrai0Tso+8+1ddYG3blm3NIbqIYU09aL53G0tRHbP/xZkzfKb4zePlYqqtQKS64vPAp8dPtcKQgAAQhAAALlJSAFkJTpyBcxSd/akFvSXM1EL0WK9KPzmwUZdA996PLwLex2S3Erl/S2RWwIQAACEHhHAoW+HevD5sqh143B4V4c6IvV7R8fR0+uFeYXF2JZA8paq7c0oCybOMtKxF8I+1P9vvSWk0x2iXzg1KmSOU5+0nEzEYoj47FmpV31vnK8b/xxKhfbm050PK4HJ3gZultIpaFhufavoc+9K3ONWP3sZ2rfHseWRujub6xHvyf/uroHmqu3ZUeRfUQTqKXwY/dSSpY/pxDAmHsKmPIfljE3OqpAd/S5/zPNIvg4mrVBLKysRevO3ajPLWiEbiNVmB4OL1fjCq9KUCNMB3Zuc+HlIo/3hRMrWcAi75nSsUFXNVBmqJVCWbj7RRx3KnG4vRX7m8+i+awqn+69qC/fUTC9hWSBAAQgAAEIDAlYp+Q/W8gKFTM8zwYEZotAulvz+/RD241nK+dIAwEIQOBmEyiaKFOtBmmAWE8Tre9FZ+tFbMq1wu7mRizItePtz76IuVsy5MrI15PtoUinaP9M0yW5nTADrvbRiQn3pAySHGMBT86xdZUEihKVdUm2EZdWJU10JkuTDLcLdz6L425mHznYeh61J/1YqfaiOb8WA02KVtHE8SwXJ4Ax9+KsZjJkUYH6sfHYHr/7yLa60e929CbsRTz74+90SD5y9RZs8faDqC6uRO9Y1aD8jA9k4K3K6FtNBl09bj6oEaXZMrouHszilPZTbTtxPD99E1Ynb6Ay6oOBPw/Roj82ilcayxqh+8t49fgPEdsv4mj9SRzJR4xdLVQX1vRJgR4/d9jPWgq85wQ7KwnOQQACEIDA7BJwNT/svKiuT/sWV9sfYwTG7JJCslkjMGzCjLRRimbLrMmKPBCAAAQgcDkEhu0UJTes/n2w2NG8PYOODLk7L2Pzx2/jcOtlGpG79qkmO7t1VyNy29GTn8JutZs1dBTXUYc6ZULM8TbSiZY52VKEcwy5RRq0qybgXvLuG/aR3DrlL5krspX06mux9ulcvB58owFvL+NAE+JpeGHUPpMbhuV7KkdZtOy+kuVCBCB1IUzlCOTKqW8HrnrPNTg+jO7Wdrz63R/kZPw4mu3FWPzk86jdvpv8kvTkIze91VLN6crNQ+L9kLlSTC/TxmrH4lhRQ+c8qA1zEAUsrc1Ev4FHONdb0dfnArc+/TyW5S+3rqLpyGdx5/lP0d/bjkFXCiyP6pVLLt/N02UFAQhAAALXmYDr/KLeT58Xen9C1V4s/+8U6WJJEwoCpxDI7t/Umszaj6eE4zAEIAABCFwfAkW7JeWoaMh4nebg0de+cuHYO9iOrccy2G2+jGazHit37miyM83Voxmx+j1/F2wzlPrLbr7YDmFLbtrW/inL2HVPCcPh2SJQ3B4u3IFH6Dba0ZW/3NUHn8XS6t2o9Wsa8LYRxxsbsl8dyD4iH8vnGOZnK4cfVxpG5n5c/pdy9aLes4G2Jx+5NQ1VP375Mnb/8Meo7XVi5fbtWHr4adTmV/QcaQIujQhtNDQWVxH9gFX0PsRbWQWZV6S5ZE47GXdT7epBu1koGx69FPvZnutwx7hZS/YGylxG3o3InYIZVcS5Gu2Yv/VJ1HuN2H+lCehevIj5dluKzTN3yj9M/RRmWeHcLJjkFgIQgMBNIyAVkGnWLOOp6k/K9ywQhb7JtXHWGzorAucgcKkEfMsNm3y+X/P90Xv5Ui9IYhCAAAQgMDME3PpILRFV+rYV2E436Mlxgr5C7R3txd5Pj+UX9blC6TP62w9jUQObKpoQ3BNj9TzwyTnRSM1iJKeViL9y9ZKbG5Je8f4gVzZWNW+7DNN624iEfy8CFY/O9gjbvAAqHnBog33yStnSueVor2oOp247jja34+jlhgZsL0bzjtwsVBq6n2z8zUQYlnvRwBgeeC8Rr0VkjLnXohjzF2HKS6up4esaibv74qWcS2/ErbW1mP/0i6iuaeItzRbZ1QRdHoFbLNlW5h6gUtR2xYNSBGJ9QQLyPZzY5e4WqlZU+qRA3Kvzy9FcG8TRcSf2t7cjnj6Nar0djTvynTuY4m7B6fh3UlQXlIFgEIAABCBQFgKj6jYZxyS427qpY+S190cDlSVjyHnBGa5EAABAAElEQVRjCBS3Z7EuMl7s04wpiLCGAAQgcJ0JqNbPrbrbz1/Exk9Pw+OV1u4+iPk796LSmlebJrNDWD+4zZOmLPNk7N6eaOwMjcXXGdk1z5sNusVgN5lm07/kQUGjcSu1OQ00rEbjfjuOez/E65fPo6MG8FpjPhq3ZB+RQVeTDY0TomExzkN7GHPfQFK+A+m+1r0uO20amt55/UqTnq3r7cZczD28H9XVtaimCtQPlJdUhWrtB2TiIfHp/FAakVs8ND4+thRpFaOD8pMTFfHwlcpY3JuxI1tuUmoJSU2Tn60s6u3TagyODmJ7cysqi+uxsrykyqytnx9FsxT8onhuBiZyCQEIQOBGEyhGHoxCSB2bU/XvaEi2IfBxCPgeTT9dvmgRjjZhprQuP46gXBUCEIAABD4oAb+E7ml+nqpG5Xb39mLz6U9xqMFl9+/djfl7n2iuGH0d7MFLabJwa4fxoWVpdK4UitMZXU50S3Zi4vRoULZnlEBm0LVwmc0o2UfUWKhqArw0f1CrHvXj1aju78SeXIQ21l/E6pLsI3XfIxP2kRnN48cUa8IS9zFF4drvSsBvOjyBWbXXje72Rux//738j/Rj+cHtqC0vp5Gh+pNCXfwaTpXlXQnYgFuR2wt/SZAUj560QaumyefkXmFpQcVRiaMd+c/deaVPUg508kgh5SPmpEv0rpcmHgQgAAEIQAACEIAABCAAAQhA4IMQGDMiyYjbH3T1gq8fL775Jo5llGvPLUZr9XY+6XfmWjAz4eajltxXTsYG95THUpsq74k7hqmnOVgCApl9RANuZaX39kAG3X61H3Ma8Lb2QP5zmzVNirYRvV35z+3JNqIXBJ4LKk2K5vCKlwb7liCvVyUiI3OvivR7XyfVdkpl+jspT6bV29mUv5Hnegi24lgToN2993VUZTgMTcTlSnKgp2ZwlrFQfmoGHo47XHwt/3xs9LhTm17pTh59M+Yw8Ru1YV/C8iIU9cWFWLinkdLyqbu7tRWdR3+M25Wfx/z9BzL+qrJSpTacwXEc+Y3iRWYhAAEI3BQCWWdmPLfTjo2HYA8CH5eA71H/3O7zL7VOfazY/rjicXUIQAACEPhABFzne9Ss1yFjnA1ujXo1dp8+0dfBL6JzcBj3Pv1ZzMtXbkV2iGSE86ccSUNoRKbXSV/oz3Dx0aRJhkdONrJwWQonYeSVdcJCkemlk3hszSyBrDCj3l6I2lwjBp2jqG9v6utlGXL/3It71V9E/dZt3RG+UYoRunlufMud3Abj2zOb4Q8jWHoGP0zSpHplBHSPDzqdOHq9GRs/Po2OHobbnzzUqFwNUZ9ryjaoWSN907N8FALJl7srHBnKa3K30FxejIW7q9Fs16J7KIffm8+jv7/lACM/bY5WUtplgQAEIACB60kgtWndXs2zl9bet1pggUAJCaR7uoRyIzIEIAABCFyEgEbj2qSrAUoR3ejv7cf2E9khdndiZXUl5m5rwrPmogy5MtT19IWwjLnDicxG2ja0cy7C+nqF0Rx4J2+B+93kL7nSbkXzlkZzLzTj6GAndl49S64pB51DvTnQfZbeCCjeyL1zvai8W24w5r4bt9mKpZu6f3gUnefrcby5kwy485/cT2tPeJYGqMvbdH/SEc1s5eJaSiNPC9lYaK0bqrka0ncVvbmsrc7L3YLeVHYP42DjRRztalI0j8p16OSsWJtUVtfyniBTEIAABCAAAQhAAAIQgAAEykggGXGTKbeX/trY1v1Jg5NebkZT/dmVe3c0aGlRhzUxu/q//TTxmfq5+ZtqH7GLSJabR8ADtP3ryS7lO2Bw3I046kW92YjW7ZVor8kdZf8oDjdeRndnQwH8siA3iqT75+YxOyvHGHPPojMj53z7+mZPN3whU7qn9UdvKQbyJ3K4txuvnz+P+bm5WLlzJyrz7eSHxLe/336kLxuKuGntp+HkieCt2BicS9kpKqsC80AF4fqo76G6tWbMLa5Es9WO/e2d2N1QZZVK2GXCY3kpBUAiEIAABGacwIkWPhEUfXzCgi0IQAACEIAABGaNgPuqdrqoiazUfx0MOvHs+z9Fo9qNO/J92lpd1mkZ62S88GCyok+cm+RmLTPIc9UE1NDN7gXdIx3dP115U5ZtpLW4FAurt6K9sBwHu3vx8scnsnP1dH+N3DnjJqyrlnzmrofP3JkrkjcF8u2rdxbppneB1XSgI7cKA/mnaTarcby3EbvPftDQT/m8lRF3/u4D1Zza7mv6LdW1A00baF+5VQ0TTfe/atTMJ40/dxi5nnbGO5E+ORrAhsi861kELPZHkmHTZneZygt0QmZM0mlaanrbpEJR2cwtPojjVfk6PngStfXt6PzwNBqffCXiLidHcHgWCEAAAhB4OwJv6q63i+/QV1gBF7piVMhpx0bPp5d/IweSwpkSKenopHxGArMJgfcn4Ftu2M7Jtz1LNXfb+7MlBQhAAAKzTMCTke0fdGJB7hw9Umnr229U+e/HsabpWf7iXnQ18XpFE6K585v6tFYMucIYbamkj4bVThnqjdGTUwDYqFeYAYvTw7jFAdYzSuDEPuJiTuXmDY3kHsjvsm+XCA12W3oQnTXdX5t7Ud2UfeSnZ9H8fFFzQCkGhW1IY8sV9lbGrsvOOxDw/V78Kq4c/abr8CBNera3tSEH0nMxv7ISlYYq1qrMvq4cHSF1+jT9Vl6JFlXmmCH3HeQhygUJpDJQRaUaaCADQXVQl0G+qbprQW+g9PapvRyDg6M4frUZfa0dypUV5XNBvgSDAAQgcOkEUsV96alOS7Bom7pzVGxPC3f2sauT92w5OHuTCPh+dZvy3e/bm0SLvEIAAhC4LgTkOtCTUsnW0N14HdvP5CtXo3MX7t2SO0HphEY9fSGcGSJsxBv5VH4SAQpkksi137dJKv1U9gOP4PYACg12q/brso/MRXNhJeYXV6PW7WtOqC35zj1WwMwYzBiF8dsDY+44j5nd0xjb8Khc13e+iSu1WtTqjegcdmJ3/bXcLOxnxty1WzqpkNVG9pAku2Bf8fwA0NkThCtbXFbpl1dY2V52ebtccBk2VVG1Fpfj8LgT269VjprF0cb3gd5y6u+VycqFIAABCFw/AkUtPPs5s6QsEIAABCAAAQhAYNYJuM3SaPgL4EF01n+Kgy0NSKq1on3ngSZeb0TNI3PTsFv1fhW42J6er7yjTENoOp5rfXS00LWdZo2vRqu9FItrd6Kn/U0Zc/c3X8uW66G7smflgxOvNZa3yBzG3LeA9bGC+rM1/+xewbe8zbKuFCu9Xhzv7MSRDLlzjVa05heitrigsxrdo5G5HiuRxdW+4rJcLYFMebmssl9Wei5BP3ZeV+Q6dy5qevs0aLTjUG+ddtdfRP/oQKeorK62tLgaBCBwvQi4ji2W0e3i2GysJyUrRivMhnRIAQEIQAACEIAABN4kUJFhor+3p3lfXmqAWS0a6s9GfVF92/k0HGnMz+mb0accwVgxBcr1PpSKPLOJZPYRWbF6Gqdbb0Z9+VZU2wtyLdqN3VcvNTp3Xyxs0FUkbpXhfYExd4hitjcy2568xIx8e3+8v6+beyN6x8d6gyEXCzLkJvcL8j2SGQv9Sb8MurIL2qhb0bZ/l7I4nctK61IEmuVEikrKMhb85Uxh4PHW1WjML8Xcrbva1Pjp/d00c2P64CBZ4KmtZrlkkQ0CEPg4BLrdbtKHA/mCt170vn3J9z3bxtSlqIffZj01oUs/mLTCSFU/lHDk2KVf9D0StFipCfAeaRD1fAKTnIf3xflRryzEsEk6cq+m+/nKJOBCEIAABGabgNslPQ3ASv5e1Wbx/rH67j5W2kWVv/Oz++zH2LarR7l4XL5zX8bcVhpNWZEtwoOZRpc0EM2D0UZ/0h3FS+y0VoRRXXfe9mj6b24Xsd88c9OPzBaZXJq8HTFQA9NuKWtz85pI73Zy2bG/uREHr9d1c8hC0pNB1y4X3AAZ/lSijn/a7xoXOMbcUhSu78zspvXtnky12j3c2Y+dV6/TjTu/tKSbfi7d5KHP97O72WNzPTpXfxW+4k/7UxVZikxfQyFdeiOLysOfE9Raqqz09qmpSstvnfbWnyuQFbwKjVdPI8DYhAAEIJARqKaXltpWNZo0W95rGH3hCasPRGBClX2gq5DsFM5TDn1UTpYnf/T4AuyjlgQXhwAEZpXA0HiZNViGxkwbdUvbZrE7wIOd2Hv1TEbpbtTVh23LbeCgWlPPVa0yz91TOdvMVOgP65B3/p1a6Cn1U8/e9BMF79nj4HLTfSPbVbXWjObyWrKPdPe2M2Nu71j3Sm6xHTXczl5Grkwiu2FlmXUCRX2km9YjbG3f6x9qFNLWXnT2D2JhQaNyV1ej0mwkJ9JVfeqQOYl2RHd0MwNwiuo/2WGf+gCLEz+ZxMVbHsHjxZO7TF8KgfLzE7vT45T1aJE5y28Lu9xhNOzoeyHa8/Ox9/K1DPT9WD0+jIpnCGWBAAQgAIE3CLgxtyM3Q4eHh1Gv12NOX6c0NTKkqi8csqVYe+803ZMHncHVB1fVb5XnUWlGuTqRSbaj5/NzqeGdX7BoELzV9c8OXFxxUpKzY00/m8RTQkWa00O9eTRd25HeIu5pKNIkqOkSbyvFm3J98CMSMTXtvC6BuB+cBxeAAAQgMIWAR+Lu7u5GXyMK2xp8NadfraY+4MzWm4VGPRHw5IhG5cp/aW/rZRzsyJepfOUurt2Nmgy5PSmEgfzlekIrT/2dKYgpQC546FTTQR6/kOn05E7kPwlzfqyTsOXZKnL6Lrkr9Pdoc+3Kcp4EzqXXdjb0UAZdtefri0vRXrB95FB+mV/JFeVuVOc1P9S7ZPLKMnS1F8KYe7W83/1qxdPle102wO7Obgy2tmJOiqC9tBwNGXM9G2AatqsgxT1ePNjFhb1fnCuOXeY6jfxND6KElDyD9FZOV81nIEzZ8K6lGArjDS/5saGExfHs7HX767exlZryaCfxzVY02/NxIAR2m3GgT1bm5+z/+HozuG5lSn4gAIEPT6Df78X+wV58++038eLFi+Re6OHDh/HlF1/GkvThm4vrUVWuM7hYqvSTiAP16tygTo1qHfS6UP0fVXQLYSGzrpnWljO9qtVhf0GSyalxFGlbX5HmHdSCe4qczjnuuy5FZ2MYXwdSarpg6hBrZ/RKw3AX3Mg+7ctkd4d7+F7A8YcXL+QvrpSbXlNBOeOjYd+88DCZiVMptZS0t8w0u44ppySVs/EX4j6ahZlI6qPvFmQ+uiAIAAEIQOAjE3Bf71hzojx79iy+++67ODo6jJWV5fjqq6/iwYMHkm60HnftObr/kYUfubwly+p2T6qurX4nuuqrDo6PojG3EnP6wrSi/qxbAX31/d3/txus952APbvmiCDvsJnp0Yxr1qbSiGhtpLQncGfnT7tI1kYrzr4Z9jKkLVJ/u7WzkUbbap15G1Ou3YhRc6Kfty3dhkjhLKZ+NtF4/+NJrYtPLG4jJZlsxZfcVQ3SaMg+UtfXeL3Dg+iq7d+aW1Ss1kTMm7uLMbcsZT98+uRzR5VjZ+eVKtGX0dYo3PnFVc2O5pta51xt+gHQ2v+mPaDusHyIZSii0k8dK0vjCsS+Yd038eck+TEPMZaXQ++lzmoKl8QqOjGW/hovzr8+SzEFMxjIqFtvL8rV0KKMFFux8/JZtG/LT0xNFZaUIwsEIAABCJxotKdPnsYPPzyOVxt6Uy+/cwcH+xoVUonPP/9CX6ss5bpHXreGPuRnl95JrrJG9ej+6VIXGjIPfapeL8KdntJpZ4YxdYnsKlmbwp3TZMyt2W9xTzpchk+rbrkNqlSa0mj+OihLdSAfT1mHZ5jaaZc7/Xje6XKSfmmdOijKb++4H57qVUOBshHZOpfC5H/drrjoktpO1sV9jSayfh5o1JHy5SSydJwnp5elWfWLWDVs+nKXlLKaOh7u5Grfp6aVxzD6IHpqDzlstWZmWZrazRbtux0ntFoyE7mvMqj4gK/mgIU82vxYi8RwNiVZkirb/ljCcF0IQAACs0lga3srvv/+UTx58qOMuUexoXaLq3F/VbS2diute3pJXZuB9krSZxMYrXH8y2p66SEpp0H/IPZfb0Sl04+VB7ejqknPIhpJKdjfaWaL8FYWV6u3WpIpYyLGabJNBBvbzVSxdarnEZIsSljza2mCLeVIwnXty1j/3FaUNk46zWsvhSXCeenL9uK0qhpE57B2LZHCOE1tZLK5ITS5TJN6Mszl7Pc0UVjWnlB+PYmYkq3KhmC5e9rxSGm3mVK+3DCTuDboTmN9ORK9SypirbaOm0WW22I29eXyvNwtbO1uxtbLp3HXX6Pry+bUDnqXS1yzOBhzS1KgWQXqxr9u7u5R9A5ea6j5dswtypeIfK66E5X1nxQy1RuqmPSEOryXq6tKdLHUc/MVs6tW9NlFRR2kNAOhOkheV/R5RtT8sOqcBE/5s2NfbbmSma2KRWJ9iEW932TQdgdYHTr7PG4urkR3+7X8wmykydBqmtRuWIgfQgbShAAEIFAaAplOqUqnPHv2PBlw3ah243VnZzv+/OjPSZcMDbrKV/oCIinCQhuel9kr1ZaSV4tEK9beLbaTKvWBqUuRn0LeYn9q4Hc6OJqir2LtnJa06un1sSacqx5H3QZd6fHqQIZcKe+K2h7u+GSdOcVTRopRu28viK6czQCb2hbZCBO3bXQNHe+rbdFwbyTvaGUdt4LJxa6WQitPWcdN7RG3U5z2wL7Z1AFKbRi3UqSnbXx1stWs0+SmzKCnmKnnUeQ1JfbGxZNsEtXG4pp95ptoLev8eS9rAek6unb20l3Xc9sundAfH0/+9G3IdZvq4y7ZPZHLkKA4/85VtnidH86PsIIABCBwswhYN+zt7cb6+kvpCRnTNAjLhlu/kPa5n3/987hz544qTtX9SX8WNejHqz0LCUZLytKkl4pJBx1FVzaIw+096f96LKysSZfJkJsbc/1ly6guGE3notuTMmS6fUrsyYBvBLHU0pe5jraNotpoyEaiFkylKz3fkWG6ExXpcc85ZOOn7Sd+mZq1CbR2uVgAlZ1f5HrTBmGfr2kntdWyP29c/UQjTjl12YfUHh6kF8VucwmMZExfT0nmRMGs1Gby2TROTPnQ//Q7F+Nly3pWeiqb1A7KpJY/5nYa+f1q85V8NK/H3a+OYlCfT0WaknHZ3uAFY25JCt+dovTAqYLs7u/ImLsfDVUqfltRac2lispKwPVVsfjBvMqHM10vEzI9fqkKUW1Xa6hz1NmP49caQXW8p87XvuqXo6jPt6K1tBbVuTWZd+VfJz2L6qKdWmMXObsma1f86py5ik31kJSLfcO0ZNTtyQ9kV240agv3pma2KNebXX1NRcNBCEDg2hPox/xCO3WKah030tU+lTHv9evXqT618e2zzz6PBenHgTsV4lFNb/FnG0zSoRIx9TmsHpQvH0t9hI8oumXIFrVD/JK4PogDfSq6faAvhAbqzEnKOY3IrVdUJrWlWF6UDjtOw0qLiO+97vQOYu9wK47V6TpyL0rGzPnGgj60a8bteb0EPXI5y8jpmyH9TqQ+7+KO4dDqB6WtTq+jJI7i5cZTddz8YtzdIDWX9ZWRR1LV1OHrdA5iWe2XetVuPVrJ+Jr0uNthTnBiyZo1PifZa5047h7Ewb7aRd1jtYdORvSaml+ALy6sxlJT7SN1lPvuVSbjre91573I48RFrnjXYpjb6K8QwccKrlNwFMFYQwACELjmBAbRUP/OPnLt418DO7UMkquoH3/8IdWTNfXnV1f0la2O9/WGsOrJwz7iUtTZfoXpJbNAZHV6klGjUg802rinUa0LmvOlLp+mNoSmYZ7KUVV6SpoqV4VZGimhD/DHeuj0NpL61wrgf178YrlnHWq8MqhXZcxtVGXMPdwOOTSOQ81DdHhwFId7slMce+BZihQtfTm7cGstWnc0AnlV7Q0ZrjM1LNtF0s0mVGhCX+mqlvGSqjebagsdKaOWvaOvuOWj+VD7GjzWr9seI6NoW/5ma3MuxqyAlEe/DzfDlKerEv2s61iY9CVSVnaVuge8tTV4cS4q3a7sI5tRv+3nRUtqeGWbN/Xvx60tbir1d8m3Oqq+YQfqxBzvq8KR35CG/YgsLMoYqo7E8KkcSbx4xkcOpU0/wB9gcSXgilKCpmqzqDxdYW4+eRw//ePfx/HOcwXajaP+UczfuhX3fv5Xcf/X/1YVi3tRyp/T0J+sUvwAQs5wkml07vxctObb+gxnW+V8cKpHmKIITyviGc4mokEAAhB4LwIeafDll59rtMuOfOY+j27XX3n01bHox4a+ahh85xEufblc+Dzm5/V1gxXLsLLMdNR7CXDJkS3eG4uPFRX9Gyev9kD+KlkMxdUNbBkXN+Xm6dvHv9Nnby/U+eyor1DTCNn5+PTB1/Fv/vrf5U6fFLPIw7Q8Xigbag3IQLqpiS9+/90/xF5Hs2fLkGx76+r87fj07mdx51f/ozojMrrKGuu+V7a83QU98sadh6q/GNLL5h0Zqv/r//OfotF068p3nNso1ejqM8a5dlt57sUvvvqr+PzhbzR3QeZaIruz/Df1AJMYjpstkkf/BxVNgnP4Mr7582/jyY9PZMz1CN+sqLP7oCqD8Xz8/KvfxNcPfx0tbTuJijqMHl2UXFikm/nt8pcLcfmrQvYi5Xy/2GUNAQhA4KYTWFtbjS+++EIvATtxoP67dYTbLQcHB/HDj4+ThvnFL34Rt+RywYO3rG2yJdMqxd5VrCc1S6HBsmtnxrWeFPDe5nZ6sdmSHaIiI2L+NlSyS18qUs06WXmZTO9t8zDaPvK8AklXTyQyGsanhu0OyWKDbLIpSJCujLduwxzJjjKvyeNreim78+JpvP7uD9F/+Tx21l/F7sZm7L3e1KAqfXWUGhTVWFy9E0sPHkZdczMs/vzrWNH8DE35CO7rJbZ9AztYltdxWhNiTt1NMXJIJ3JPDTrlYEE3u27Fb6R988jIvvXySTz7//45jtZld6nIsKuvgOZvfRJf/vrfx8LthzrmyfcUX//d/CmWguXby1KkcJnrLH8DveCuaQBH022v7n4cvt6KRdmkZY1/82LFoZE8vRnoeh3BmFuS8syqTwmrDuqxFMGhFEBFfnJtyE0PbnHzjlWbw4NXmsvCoKsaLuukdI5i69mP8d3/+99i78Ujjd7RqFO9NWrfvqvR/i0Zc/+NwrmTKB8v+p0osSsV+8ovlkpHjIYdZVVK9VYz5qQYu4c7cbC7F4tDRehaabI8px278mxwQQhAAAJXSsBawn7mvv7662RUe/XqVRwfy6CoOtQdpJcv13U8tcLjC/nQXVz06EnpGOlPL3bvM0vL1EbzTDVER/SUui196e+d3Y149PiP8VQ6vds/lpGzn4y5HpD7V//qfxBkaXK3ARLo7O+7Zsntg21d7/ff/lNsHa7LmCtXU8caDVxdjP6v/6f4m1/966jo5Xbm1mFCT07snl3uzpuMuRqps7u/Ef/yu3+QH0B1/tQpzb4cqsae9HJbHYqGRuZ0escaTfUgWprMJs3bLd+9Ho+U3OnqQjZCpy+mrOXVyfL9adcQm3vP4u/+8W/jydOnqTOSPunM5fQcA/WqRjop/dvLd2Nl4a4+XG1pIJFdK4w02d8V5tkA3umsRR9illzD7XdKjUgQgAAErhOBinTGvL4W+iyOO8fJ1799/Cd9oCbJjkZP/vnPf04ZtkH39i25XFAtmrVXpDusSz9SrVrU5VY3/qV96fW+2lv7Mnp6tp6GR+X6rMVUftyekcrMtrWyISBr4zh2kaJPFIsDX2BxOhcIdhIkS9cvVNP3WYosE7pePB9Lbn0Rs3sQP/3un+PJb/85fvynv4s52SrqusbAjRi9tK36CyD9dx99/3gntl79GDu/1RdB//JpfPLXfxMPfv3XsfbZF1Frr6iNoMzbgn3BrJzIeLlbbr94ULcN3q9lpP7jP/9d7P9gu8tRdFQoCw//QkbQn8X82v0UxvKmiXclhop1dpbE0eUtoSyYy76lr5c9ul0vEfa2dmJRx9Lidr35jy1OYJYyNCbcpe+MtAwvPW0SvEQCVTXsPRqmr7dhHc3kZ0XQWNaII3VgBh6i0tSHjn4o0wNwiRd+i6RcWbsCsTG3qBwsUEWfEbYGR7GoTwu7eqNSl3+aOX+N4c9NpNhcW7qyzR48VaR+bj9iPt4iy+8Z1J+iWGHrp5SsDGptfTQqxdj5Uf6I9GbwtmZArdX1OcTUpWA29SQHIQABCFxTAvroXZ/03b1zV+83s3rw5cuXMuhan1gPaiSn6s8//emP2u7HVz/7eXJf45ehHr2pMSMp3Cz/Sc3QGdCDmQhilhrO3vMIWDWeNYL1qCOfed2tZOzsyh1BvdmKXr2rbpJ0u17U1jz5aYqihrgy9F7Z0YiazmA/DnTNSkMOFfStal+dsn5d7SKlbdcayVY/cpE0UdqFCzmL6LaIRx11uof6FFOToNV0DeXV13C7prYScdTd0+Rotfjuxz/Eb/7y38aaDLrW5Zk7BLd/fI+p7ZM6gnnrRslX1bCx4XtDXyg9evaNeLndJhcL2Xe3maSDhq4vY7UM5spS1OWmqqZRzxX93Djyi4ysazqS0Qvn8XIDZm2+ouWWrYsrpPu32GENAQhA4MYSsM6sxtLSUnypEZ2uGx99/+fkciEZbNVe8Rcf3333XXoJ/Ve/qSvsqup5xZNese688mWoXnxx/UZlUJurv38YBzKqdaWX7svNgif0rtoekYWWzkrqKr3ETLIP4+fpFRlS3q0rL7SksBcKORJIsuolarqCFLJdD1Tk9rH7+mU8+oe/j9/+l/9L2+vR29vUV1yt5A7Dbpasy/tWcKmpWNGIavnW7errI/mbPXx0EH96/iye/O538a/+1/8Q93/zmzRKd6CXrSqxkWtfcHPI5oLhTwlm24lftNsXcFXtIlml1Uw7SvaXpmwwNvjV9QLaAx2qHkmtdWavcZvipBQ8WncWbDDGkpVbPtBPX3D7y+X9Fz25w9g/EdjIh4G1nZYUU1uXBDdPdVZXGHNntWRG5fITqsUuFvr7e3LldhRNu1iwv9z0aYPOpUru4960qi9VUUr96BnyrInJoJvk8meER6oT1cFTReMpz6r6ZNDOxjP/uJkB13kscuC4flJnoUKxXB9iSXkVqyynzq225Rem3p6LtvzCHHf7cag3tvPzJzOzp08iPoQwpAkBCECgNARSbZl80N27d08Gr3pqhD958iTpn7oafW6wbm/vxLfffquJKvrxsy9/phG6+hwwGdrU03iXRvcH5GM9kIxj+TWSfsiPfcDLnpu0Oyf26GoFlekfb6ijoM/2wr7mkrFTHSbp/p5e2PZ1zDMRO1Yyarqo3nNxEu5YeVTrwL7fbExOInlb56p+2e3JVyRX1vx/hyumElB03zu+otolbseoV9rVZGcD3VN5Uyx18I71uezW3ka8kF/de2ufx3JbI5PlF/EkfiaCR+ym3rjllWuGg85efPPoG/ntk68+6XubZz2pmdtwiW/qkbnNJLbiaKmKxWGz7PnoJYAtEv5A61HZP9AlSBYCEIBACQh44rNGrK6uJsNuU6MM//SnP6VRuXPq87muPNJo18ePHydd8Mtf/KW+PtKkYorzUZdMFZ6IID016ErHb+5ES184tdoLUdFoycnOuvNjfZZMAB9ZERSDNvvS2XXZIA7W1+PP//d/jv/+n/7PmJPhtq1J5e1yYHCs2XycN+nW5H9fFjK3L6xrk99dfXlTVxukJoNob1+jQx//Kf7b/7Yef7P/H+IX/+5/kYF0TXpdRtLJJRkyBNINiomlaFPY5mFMvvK72z10DUWW6VNrtdosq14W11QIetee8pU3nSakGN999+uPp/M+e+n+8b2nxa311PSRX+mWvopqt1oyrGvwtOwjjWW5uKp/5BvM8n3kBWPuRy6At7q8KpKOPvHrH8mXqtwrtBZk5NNNndcA2fqtErzkwH7i3CHRykbaNJJFFZMrFXf8Buq89NIbH912GtWid1wKrtE0jpGMtzLupqf2/eXK6gCnXDzkk5XoJV3oXUVNncUscpJVYqY+nCziyZirCqtzcBw7mgStfeeeKqu6RjWr8+fO4sjiuEUORw6zCQEIQOCaE8hqP08q8vDhJ6mDZIPYs2fP4tgTQOTLpiZK+E4GXeuCr7/+ecyr82GjX2bULUKxPpdA0tFmrl9q7dugqxa1DI7JwGodVrPRU/s2quY9gqSFpdzcSXmvRddPL4iVbubKycZi6013XeT7baBOlF4auwPz7ovaCUlMdezUUbXsnuDaOfJ3o76eLqJfMtHKzUInHv34Tfzsk7+IW8ua/E33X6/nZrWH8+i1tWSzCwX7XfDs3l11Anc0q/l3j75LyXjkcE09Tft6dgcyjSRW2yDN4pzy4bzouNtV+jlSRtEy+JftaYMFAhCAAARmmkBm0L2l+WLm2k3V+73kXmFPE2+5Jq/LWGV3UY8ePVLtXo1f/coG3Vtq26j18hFdLZwglZR6IdmXz3pP0N3UpyNzGpVbaaovXwSyf4URFexmw/BcEeYK10lTSgAbdP2Cv7O9HY//y3+Nf/nb/xhyjhBdTcpe8wtXTaTa70of27aytBBV+dONpiY3y62fPfXHO/q0v3ugQWlqg6hHLp+6R7H35FV897eduLu8Gmv/+t/rKxuxSM0Itx6KzGce99Mo2KwRMU7AkPKl4GXjcabjfcIExylOJpNN8uZ2guCntkPW4uorYE8Wab9e76lt4byOpTS242t5MQ8vIwWZHbiSv855IUHBwC44K/qSaW5xPg7V5j846sX+5mYsLyzLPjLFgH4lks7ORTDmzk5ZnC1JepYHcayKpOPKpDEv3yEt3cR6MP3wzswy0mmTzFkV5crFO9kvfRarE9muNlTZZJOeeTub4MPVoI2/nhHTBuLspw6UwqbOmitL9RdTPNXSrtg0bkc1gCswd4zSpt4g9tLs0yO205yUqwqlO16t5eeuciXBc0rZVZU/KciG3tTWZLw9kp/ASvrcU5+xJA6qXh0lX5wDLyOHsgP8hQAEIHBtCbjGO6m/PSnU/fsPk0HXDfbvv3+sjpImqpKO1K5G6G7Hoz8/ipZefn7+2efppZhHyWRLkc5kXXw18KbV3daNs7C8KUbOKj+RrfRXAid9Ls3rzkT+ijbPgnX55eUm6XpfT+XvkavW91m3JRfqvS5lQd02UA7UeXAHwqOSfRX3UZOLDmdXx90xqzb78ezl9zLQvtTBT6Knr2lsmE3GXIWzyra+ThOzqcFypHvy1c7r2NL9aDcRbgs5naY68QdH3lbgJILSUN68OJ9Zp8py+ecAPja7iyVkgQAEIACBSQKuuyt6qbwUv/zlr5Iu+e1vf5u+IlqUocq+0g9lJPQI3Za+0Kypj78wv6g2i0YgWj9oyb7EVSr5fjp4BX+SG0UZc3vHR3Ggz9xr+lqloUm7vdhcmTwSFHJYVWnJ30FmOxf6ewnaY1I9SmZ/FTOQi8fdJ9/H7/6zJjbVvDTzC83o6Ovmntwj7FdasXT3YXz1q1/FJ3/5y5i7o1HRMuZWPKRVy7NHj+O7//6PsfH4URxuv5K7go4mJ5Xffn0V1nv1Mv7+//jf43/+2V/Kf35NL4DlAkplkxmJs2+bXOb+5z3TcqslW7TvUcE6PlAbwWZXLzbmZ8WrdoEasZY9lbfarW7Tuq2Qlb/S9LtzX03tCc8HYRuJ81vV10oeNmc5amo4+Yo1Ra44AdtW3GjT/2R/V0MlvUzWi4PZWSxLJk/akm3Eo8BtcO8faV6hnY1Y7t6P0Cj3Ilwhu3InDlkWi2PXeY0xt0ylqzuzq9kVe/tys7C6lDoINlYWQ8z9oPrZHC6pFh3uffANd1pcuWQdkryz5ZpCQmV7fhxd2dhIq6orrTWyx92lXO6KPpesVtp6O+kZJxWmqQpOn0a4g263DFZiVVWuNVU4B1J4FY3ubeptjWey9mgWOzeXZ95UUbb0tqbS8WeKmjrE52UoThWmxKj+/+y9+XNcS5bfd2pHYQcBENzXt7+eN73MqD1qaUJ7yBOywv7FEQ79Zv3q/8w/KGwrFDGyLEszGsmz9nT369f9Fj7uO4kdKBRq8+d78mZVoQiSIImlAGaShXtv3lxOnnvv2fLkSTeAv/xLVz+RYR4c4oSPLPlUHkgQumCQRuye2vMnlkfx67S2GAPLcIgR+eaMMXZw1EeR1pQSBhIGEgb2AwORdmaMwzlMgZ2gZ1GQPnQh7sHDBxjXJBhLiO1gQFu2r37za2KhNuxjljCKvkvYE9Htncd29wPGvbcR+d+OGnFoOzL7LwZoqoTz3ZJbUt9uXBGEF3hhxh/FjzShqklWLeXTzs9lFAp5qOpauIehZ1DF1nYD8vV5oS/1p5/+SMGR/KBVP3WeIgK9z/C+vq2XluCFEP/tIMwozp7/NAQqSFGaYJKVQEhWZxPaOn0V4Mmr60/t6dJ9W1//0EYLpxgxcPiLpXFLQUNuw4irXbQ36uv2Z3/zZ8T8xXunyY7mGH4byDrz7JBdf/KMeoR4oHzYEBaZh2eqt7dnUlabUpkjTjk9wqT3VoqgINR5vD5CkN6y65d8O2/ZWqqWMJAwkDCwEwMQSChlSIE3jFSqdvXqFbI69vXXX1udFUU57Z4J/2gip3z77ddsmLZln332OzYpfbYrKIheqY0D5ANqWuAKnL6exHvb7HWztb1FjNy8VWemgFmylIySWR0xTM7jaGFpfqHDYHKseIHBO73rOOxYLF73SrzuTDIenrVrz+zen/7fVlh7CrwN5EPkwrWa5Yl5P/XZj+wHPwvxb/OshCWIsR5DYP6M7cLZH9j8Jz+17//8T+2rP/sPhGq4B+9raX2xO161V5bsu//4x/bZH/2PbExfJZY/3qIRYMq4zKLHJvzB15EIsFPQfIGQmXmeLXKFHBA6bBAv+0gHb1p1L7Q26xvIEBvEwS3Y6OQC3sF4BVO7gGG3xYrtHPJKAc9ihAfHc4c8Ib9UGMF7mjJ0VOFeSfIE4+oQM1g2E8lmrZpWMxHWUSEoSuzZMyJjcZgkp/sjTD5679+9lCWbIfPpPSuOVa2FPF9fZhK9fYUfK9TB4a5pt5du14LHOzMZc4/T8+Ml5nXmw5Uxk5kWNsYIwdOZrWD2Li8iwfd5lEkG3WDM5aOTwiWK0v2Fr8oj8JHtBlUv0wexxsY32RLB6axbRTMxLE1k6owPltcV4pgnBIFmBquajWEH7Tbeq22Cmhtx7XL8ZAstMIsprxltHiJiGLgMXjMCAcOp4BQtG6Yk0MSkRP8Fp2bu2C+bDBRWiGtBiBH1TylhIGEgYeC9x4AoZiTigTAWixWbn1/wiU5NLN68ddPD04yPjbswvUGYohvffYeg3PaQC1WE7uDJ4NS3r73DR65DAD/UcZhS0EcCVGEFDQxVmdnPnwB/xLskm2hVYjQ3qta+sSxNeA5gJyi36kGdy7C7P9jzMUl5yJqTIib5St4tl85dxPP7NrFv11FmUaJQaG8SO+/a2c9tgvjN7aYLHBksagC5jABvtc6aPdt+Yo8XHzlvD7gs2EfXLtkWSzilkO18+g6Fj8jvoIi5Qth952k6pYSBhIGEgYSBIceAaHmg5wFQnSvUU8HGx6bt+vXrPjH4qy9/ZSuEL5ianPIVpVsY2u7du+dVPvzgIw+5UJShEa4QvDJDawf2N7LTfvDF6H0s8CMYZJFwER2MgvLSlT0ibP4piDBIxvoHBuDeGgbNrkavPb5rX//yL228XXdDtODNlas2de6S/f4/+yObuPwBMBeIXYyRlo3Hi4SPcAMCA9EqmmL1lF3/yR/Q2Jb9zR//H9auyVkg2Dq2CZ/027/+c2Ln/oFVLl6lPh66qO8xyd7RITZvDluGHNmK2iEOr2CJNTXCVnTwoi3jCSyGL7OI7CctDKzaQL6IsbyE0xpWWGtvPEe+xXhJxfX1bfZPIixEcZSAy9hMaLuzvopxQ3Cxinv5OTBuMMmO/QD4ZXQvI6CN59kEjUnoXEmxmgEAJ0HtzdPZrlkHWSRPXy0hbYiSXiX9/MsBZKQqIMf4TagSyfPBzjNEAB8yKKIKKR1DDMhLU3FU9Uo7vdTX333Vj3JAUmQgKAIBou8zKbrwDGW+mKQweS3K6ANVXLlKuW5Ljx7Ynbvf2Vy1YqNYN7Wjc5vlEOVzF6xIYHhpjI0nd+z5V1/a+uITGpZnLstXZmftzOc/sMIC7vcYvXPjzJJtQVVzzDhhwW1pNs0V0RdhOaqcCI4URinESnlmaPWvl3rnUUEW3lJKGEgYSBhIGAhUscik1+zsvF291sDbYNseP37sBl15USr2nJSlb775BgNbx65evWoT49OgrkdbX8msEpJ3YOBVypp0PnGyWMZ1wB21h/sicOEAo4tXOmUwH1z52B7dxYu2uYxyxJvDq/Pg0X1bXnlm1y5IqSjwvqlsfKswAiOq5VA6Hjy8iQK2yI7MKFu0lc+P2I+++Kn91V/+OQqc1KqgzGmzN61M6iWuUQbdNI7W4hJW/+1ewXSWMJAwkDCQMDD0GBABDwbd0eqkXblyxTbY4Fwbtmp/FPdAxCBYZwXq7Vu3kVfMjb5zs3MeciFyl0MbZpff6CRqoEBRJtwjjFB8T3wSk6WDJL44NAkj59rzZ/bz//j/2AgG2jHsAk0cwGrIgAUm+j/50Y9tEnuBDNGCfmRyMtgk3FCIsRAPXme/yJaFiUk7h2F9+xleun/6J5TGw5eH08auUGbS9v6Xv7TLUzPWQK4sErohTG2rVVbm3Lphtr5CPy3bJkRDE9xNnL2Ip+k8tglW/eDN2wKJmhAvCoGKv4tbbfPpY2stPQCOde+vjeG1PXvZinjpFvJhZXLt8U2rP7pjtrmE0VmyLu/W4h0rtzZtGyOwZAa9Qzk8vTvPblv9G2wNIzg5UL9R37aRU/N46TZs5PqHyCYYh4foAboMCTzafyC8XQIP/GQXgxMbvbcTdL0nKRlzj82D5q3tahR6h7l2ox8vtKYkeLE9Fu3QjkeUXb9XJY2qaesbGzZF8PGt54/sz/7tv7EiM00jxKKTAbbJUoK//6//Nxtd27Bf/ed/b4+//oWViZ1C6HKfydJMIXNQ9vP/8Me28Oln9nv/8l9Yhw3j8uMzYcmkiBmbrnXR5xRaNt+jMovughc9Zz1WEass6dkKxIx2xex0TBhIGEgYSBjoYkBinIxkeTs9fxrviJLHyZWCtEmMt0mEdMUq3YDHyKCr9PFHn1Km6ufhT4/u9mUe2Olgb7LhadXI8Aqkglj8MvtJ9lCW86uQp5BG4T6H/U7e2X43+qr2pATxNLTkh77Pzl+ycu7neOmWGDIyC8+qw5LNxfVH9oxwC6Mj82yQMppJO6wGYnlnE4+X1Y1nxEH81iamRlDW11AAgzH32sXP7a//6m954ETL0wokLbPdJXlfSQLYBTMpK2EgYSBh4DhiAHrvBt28yyk/wAlpbHTMvvz1l7a8tIK8MsFGaSMefuEuMXSlBxYwms5i0JUcE5NWbPTrizH/QI8SVJxVwReZJA/WTvF98UPJBHLmyop0AXFBoXt1KCdi0DLcLj23eze+s3lWuKyz4VxppMxkP6BPj9npP/iZFfCGzrFUv42tobO15faCaFNhJI5fF3GQFScuXbPTi8/tyS8I27X6VKwbHs9maBtP7Ntf/8qu/IN/bLltPFxdrtSYKcCz+y9//H/Z2o3fco0xl1U96zT4xd/7R/Y7f/jPrDiFQReZwEvrtRCvxwhbW35mv/7jf2c3fv5fidO7iem4ZoWZebv+D/4n+91/9C8QF7Bn4L377a/+0r4j/MP649t437JXECuG9EwqjY7hzuaSg5pdevrE/su//3fWIdRTDSe3EhsCb7LCuUwMZ8Xu/ef/87+yU1/8Pl7DvfdL0BxNynAXO3e8KM+x4wb0eOt9Pw7D03rfn8Hexg9Bqi0v2vYmRIiZpRLu+bLudaIRUgRraFL8AEWMAnHfK2gi/kSMwDu1YVPFnFVYIlCpb9oI6xWKEDoZc9vf/Nb++vs7tvzwthW3V6F3axBTqb543XJwRXi1Y9/+p/u2gUH49/6Hf2lTX/yI28TqZbdKEWgnzhF31HRlba9AHki5QHjF+uR1XSLgvTNnlMjW4rKVzk8KSpgjnsZCq5ZcHggcB9Xom70HBwVFajdhIGHgJGNAVFEz+EQQK1VRevBakFIBtdTS+I3NNZassTmFaCg889tvvvX47L/zO7/DapAR5wPysCgQZ+wwlSMXTwcI+m55R/XkBJqwKL8I8R8xnyBSHxVEu/TussYAEt8QPLWq8fmxr673Rn6e1UGjuQn74qMf2+Yv122zvWydArHvULru4RmzMHfBLi6ggBerburWewc7Z1FQztaePrf7D25Yg/BROS11hMd/fu0ntl3nHe0oHiKeTcg4rhh7395rHxTDeSrR05VcHfkpHQ/IA6zhb5JP+rGRzhMGEgb2EwMZYew2qev+POKAFips7DRC+KdrLpt8/fU3vilaQfwFPbBIWMFHjx6h47bsk08+YbIaT1KakByjVUcHk6Dk/WAKaoSn7VoNvrVlI6NMWmLIFe+KScEg28DU5QFMgAYTZVbC2+ve7c+MTbz0GPnLSwu85Eartmn1h/eIaY8ZldAVI4oLi9G2MjZpn/yUsAjjE4wD6UaCYZbaGBOiDCho/Vy3FcuWsAYzZy7bpY9+x377l3+CKQabAsbcyZFRW8dYuvXkkY2cuZKNW2Y2VSS0A7FvS1sruJuxOdl23uo44pVZiVxlAldz1IpViwAAHOFcMXlZVozdomblbZzWWjWrYqitra24d7GWAOVL2AIw5lZos7GxaHni4U5Ose8QeY0G94nF689IhmLYXAEkKrqsnkmZjcS2idM7ieyrVWsN+q/oeWYQczjC1P+OyD6iddvAz4r0ETyfcw8f+fNqY5gvTLJS23FMHaEa+8j7lpIx95g8cY9Foy/caQ0fG9KziI9+w5P0IfHrUlx9jP5lvRGIMlh2ILRl6Jg8cisYckdYvqCQDXk+0ru//AuWTGzYppYrViCiEKNtiFqVmSRNRmm3xlHe7AqeNFsP7tov/t//YD87d47ZrAVmrBToGyINQQjx5wJokWi/EaDvVFi4iSmec/TnCwqhur7k0im8vH/6mGOsdgjHBjF75OX2tvjpMseMuMqMHsd4COCnLhIGEgZONAYGhDZIqIRSGXMlrmpn6FMsH/vgQ9Z8wCfu3b2L8bZhVQxpom0Sdu+SN4pScvXKVYT8ali25uLsiUbcWw0ucCpouNNz4X4A/32tZqzMc8TGlLqiQbh8x78oQN6wZKCsg3dscUd1eC7/B2BGUW0V7PzCdRsb+bltb21YrblFtZbdfXTLrlz+yC5d+BQe55wOVqc2GoT2eGK3733H5ntsZIJnjcL7VsqT9tH13+WIIkIIKFfikHG093SbYxdXL8CwA8ojvxDm+5/1YQLkcpFbkkOvMnq0WBqrpCWZe07vofK3Z9ykggkDCQPviAFRyD4q6ewqXMt0JmOgeJj0vpHKhF2/dt1lE8X3X1tj7xiMbipRY+PNJ0+euBdviSX5p07NOlyHq8sCCfwpmNYEtugsOrsmUw+ADfsA3+kPTl54yW6v4BQFb5AxU8bmIqu2VokVe/ryNXgtfFpOXn3G3F11XnhNh1CPuK2yt8+EladOWRMZswSPJxwt7beRL2tEOli06vlriEdCCJO0Qg1/WHvj++Aohq30+zqrhIusxonilEqHfQBk3KVBxb4tEOahwL5BrP5hKzPfiFX7+sq5LYhflCVDfC+Ho8IIewo1Od8idILkl44MzXow/OeSI2GgqFvfYNNY2Rkw5Da3gRuDd4E9J4qVMRVSyaFKwQbiI+jZI3yAIS+Mbifc8c7O3KEa1r4Bk4y5+4bKg22oR1g0W8QrKgMbs3FhRkxEiP6P/I0VAPz0gb0VLDAzETxtdAZRzbeKGGZRyPmJ2CrerfaAfPToBsStahWWKxZgcoXijLW2mHnDa5mtoTHiQmpLEGvwtE4IhuXbt+zR3/7Kzv1szvJjzElJ+RIOo4Z5QI+ujy+80IPrHy9DU1bRjfV9ispbIvWFvveS4bOSGEW0NHl5eQnBAmbyFqlfyFD1weu3aDJVSRhIGEgYCBjYxQgTSaaEWxl3pGtoMlQG2yq/en2ZDTbZOAH6WyQm3RZL6r779jsXhq+hRI2y7Mz5w9sxsbd/MgM8E/Cc5ItVHXkSbK+CQ2xfQOqYjcOPZMbrIx/DWwCQDaVb03dVbpft9NwpvHAv2ubTFYy5a4y9aUtrz+3R03u2eWUN5fuU19EKmiYLI5+tPLUbN3+LYgUfZWI6z1LZBcKALCxcBmV48WLMzfPiuhmyr9P47PuyurAMzQnA6Vva8ewPCTjJxXFVlc71LS8tLbncshOEl0867CyXrhIGEgYSBvYbAxDISMy7TTvH9Cs5zLQUG1Weljghid6XWX07PjFB2MFNv6c8lVOZBw8e+KSziO6M9o8hiQ4enIeudxH+OLGnZwlaAgrjINp6OO8r9uJpNl7V6xcmsvZeLL9/OTLm1jCwFnEM02S/Nhar4kWbx5hamj9DR5lnsa/w3aVfbBBx81d276EA/LpCXN3peWtiFMZ52sqyXdBuDiPqyrNFm3Ujd2xLuKJfbBhN+igyZr0ORYSjfAtDOBPE2uBdbfOIsQFj03F7hwphaC3idUsnTYzAxRYTv6zkyctCLJRiB9ImYJiTrTg6Y2uEpazVm4SRGCdHm6hheM/sQ5LFmjyvbeBvIvviK0cosgnb0sZphF1g+3jCQeGdq1CU3rie1RAljTcDSZMguvTk71S8eD+PyZh7HJ67vnp93JgyW/5VArRc4RVbje85zP50X+shHdFeiYI+UcpC7DRUDS+wNrLIbjLubYjk6UsX7MqPft+mLl+F+eHJUq9bjWUUj/72r2zx9jfMkm3BACFoeOCKwP72L//Szvzop5afnAtMaEix1A/WDowNPF7dG8jqr/rO51rKUyR+To3lNDe+v4Fn0fKbtwmAg8bbwes3bzTVSBhIGEgYiBiASQwQSik8Umqk9GwRL10inxQj7QIt+lPFc0GToNvs/qt4dHKbWFxcDPfwzL148RKeL1qIprSj8ZC1z39Fx7s/hFL0Bsn9B0vg32IMQBZquQE9cJ+Qp3z4NTDHn8YQ1YG36OoVVYQYSQVHlKSwsWxRO12fWThv955/Y5USm5agBuWR0R49fWAP2Qxt5solf4aS2bZZGvmczUuePL9vefYV0ST1WGXKrlz6GIWs6CuFhCzhTFKeb3Qm3hn/SRbiJzlI70XA/BGNfwi7jTKF5MYmnvba8PDWrVt9kMavqy8rnSYMJAwkDBwaBqBB0uO7qf9ck8oKB5XzDdDkSFPCsFZmcywR+xEclpRaWN4UJ1fyTW0TvYx9AJp4ZH788cd46J7y+t3mD/hEXFhp5yjCtfjWy1O8F1t4ecl9uwPvbOKMtLW6yooYTaQSTgAcK6TRJJ61OXh5roTncybevKpfQd8dAaEaclNjeOYSroC4tCWM8B0Mp22eyeoy4ZcQhoKXbWwRSRTbRdhkzX1l4f9i6OHnGKFxf0162RJPrUWm5IM29bV2R17QcnDDikt4hy02cava9d/9sZ2/etGaG2zMimk3hzF48db39uBv/9bqbKBWxNjf5h2bnJ+3H/7wZ1aeP8fyIMaO4bZN+Kg8uGiDo+nzl9mIrYhM4xBF4IfyqG8FZIAYnidyqTzb39eUjLnH5cnzEUpo7fDxSjmV16aIgpRNfex6n/k7JKOJAIkYCMYAp2ANv5fBSb4UxbyikmuMeE9RJTAHES9mJdl0pHL2ql35B/+9XfzsCyuxE6UvjYASty58gOftrNXwelm78RVx2g5YqgAAQABJREFUaPjAW3hrsENog+Dna3dv2/TMXPjoXdXcX3QJ1v60g3dzw0cdy2io/YX3eq5KaoijsHpQKRg+2ni6bNoGMWnWWerTSwLAR9PLeslZeC91861G+5JWU3bCQMJAwoAwMECLIDOwSk+akJJhDNndlywWELZFhxSXrK2wPVBQ997V8jQqyavvyy+/tNOnF3wZ42HSrEHesTfqGmlqVlqCwK50OZYLeHmbv4GXyVwWPEpE13W+ozvJKHQVx+JQcS0dSRC8OxS0HRvftTX1kOHibQbZV2cQVo0/8nONp1yu2mef/MB+9e2fW2MFJYKVQHrvltjXYFkTn1y48kWc/iU2SLnz8Fus28g1jr8K4aAW7Prln1CM1VUso2zjbNMi9l1b76IzTRrTsW84wWgp87AgoL8+eI/qVI+ja4QWVAL7qABT3/yrM7GvDW5CiggcPA5i7KiAHoQjXScMJAycOAw4eYk0KI5OdFysQkY/+IV0WNF//vlENCEAJLNowzPFPNXKVCVfiZCRK01cyTN3djaEW/ACB/ynheFsEycfGc4qFRmasUX0caPuKDNGEEANYw2gqUS3FOcHT3s7TPI12fSW6Xw3hsvjGSssuEQKrGJDYI+EYFsJEL7wN5uZFtRtt0vwbIiBm6vA0xUGQePhGXn4Rnh6o0m+rMM+NOwQcYzOMIUvYQDDLPfdCKlwCjnJoVwjxznrD4zVBagcE8C5JkICHsB+jzhNnVyNOoRKwFO3jUF2dH7ORmenQ584sjE4ZFpMfJXvMcw+4x3i/aKDDobrkYUFm/30B7jlKlY/ZRTmqYRjwzb18DR29+AXkDBsGbKHgUnwHGxjyGGKdRFxPWzgHjA8yZh7wAje3+bDyyv6oI9e76zc8XmFu69vVDj2t9+3bU1A6uNS0vnrk6sqCiiHUbcjJYf6Gp9brKGNbYjOhQ8/t9nrn1tp+ozHhpFSWWAmszgyZTMfm808fmjPb35vpSaEjo9dzC/Pr8lmalrieLBm0FeMcW8o2NGAq+cRhTvuHOyFhIv9SL1m9qe9/YAptZEwkDBwUjAwILw5mcmEPIaouJnyxBA9U8kOfEDyX0xSnuShIW+X9Y11PwaedXj0KoLj8NFtPIrtiZ9L/t+dr0cYYwsaVcyLI9yPoxQxtaufzuHImnQVpA5Yf/8Z/Lqr4tmtnSXIf6vUNzZvMMLE0Tvru/9W7YdKsRVJVX6eAR/zpbjl8njXjk7bHHH4ny09xPt2ExCQMVjG+ejJffKecO8sSt02oZ5WuX4UjLa8a8XcqM1NXbHxkQU8fFGmisgpmnhGGtckg5Z+khHeA15W11f4I2j83Y2AvMMYD6RqBpfQFZ93PB4GyP0ySziPvWu0EYL+vEEsxDKD+ek6YSBhIGHgXTCwC93pKUddPVV0S8ZbTxD7Fl6ebfiKjLnSWzWRJ51W5bQHwDbhA1RGDk2hzLvA+Jq6XfIIXDI6IzPhwulwBSEl1g9jzbgnmbuMPRb14+vu7yj8VheCBd9TwhpgKGVzdcEtY7Rix8pw6VYBcBomUl/WhRAAP8Yo2mFitsPGZZbbph0cA1wOUlsyjOr5ce7MWm0xPt13mQmcOX/nQH+SouS4JkOuQjW5g17WluQMxyuGXIWezOPIlvc26R/zbMftJHjgUreN/NPGCIslBFssq8qIgUsR8iatgc2kqfeK5mQv0upmhYbIaYUaxzbwdth8Tx66+UJYkUbRLPXOYs4wHd2Iq7CU4MXxNUzAHTIsyZh7yAh/6+5ERzzx4vJhumwqukCef27K8pOs2LAcRNMijHuCKSvts5BxQBp8ZA05O80y2LHpWamUTi8VO1gEWYp6mV0Op89fsNGxCdt+RkBvCJcmyDxlRDIgL8s7wIPrl7u1z/MLhHq3m1keQxe4XX7QX1ToONAU8C6BQUHiC0UMIgrpMZj0Hr4sxUfXvT9YdvC6WzCdJAwkDCQMvCUGpOjIswHugKAuEuUboknx4Fyej4pN58uxIND99LXCssYLFy54zLqoML0lEPtSTSTUSewLtHRfmn+LRjJxuUu6+wHTPfG1njziHaisfv1F/ca7/wnPLnbQBerdG44tqAP+ew998HtcQskbrPq5eOGa3X982xZX615Q6tmjp/ftwdNbdmp2xuPpPl18ZCvsPu0SDHUqePVeufwBhlw8ZPCSaeS23KNEyyJlEBYe/f0UHHTu/etF6IMhgnjUR8k4/gMQgdcPos4F+0GnYLgNvcTzcKR3V6B1L0L2MogG8wevD3oUqf2EgYSB9xIDIlPQd8kcSpGGifk7C+KPO3lGY2CGJPEheeVOTE6Yh8Vzw2p28yAPEHxRxy6N5VyGSaSrrNc+2qkBdC8jDVaxbuaO06yBHrnuZrzLiSbrCzbCRl8beKHWG1vuOZtjIhXzacC7whXQRZBiXt6XoHaDrxzEZIHgwbgt1qtEvq07Qd4MLYWWI37E2zUZLvxpdZjjEWVfIirhc7OWkFv9rgzDZIJz3xA9w5tkBO/Yca62ZN7lfZFc4u1znzwP6aBrt4Nwnz4VakE/Kvm1L13L7r/4MPqfWYBtmP7688j+6OCpexIz3o9jMuYem+ccCEJ4T8MHpiWHgYj628xIREyP+uMDFgi4aMVgyqDO6EWEWeWlfIdr1fPTvsq644zOCRBLHJlRUhiAGstitRxFMRDlfStFPY/xcXJ2zuaIC/Pw+WPHRg8WtaQUj+HqMP+G55VBAAHVRm8xCaqdkOFV3LVEc1OMcaBErLufRy1DdlQzY1cgSPrcHMs3Rqs7u3BAe7D3bmajcFh7uS+eZVzrxRspJ2EgYSBhYI8YEA3qo0OQH+ch0FXxBAnKvuEFHo/azFG/dhtvFgy6+QHl5/z58/bhhx9hbNPSQfFS0aidFJmMA0mDPM87Udd9QzuQjt+60YzOv6o+RbpsYN/GooYOOQ08AymG/l7gKXOWDcxGq9O2uKxljFIF2XS1tmhLGw/xgPnI1jeX7cHDOx6zT0qTYuRWS2N26cJlNmkts1nriDU6GHoZVi++XjCLe57evyMY8ptieMdXCLwDKHvT5t6ovFRi4T0myYOalPHUNeZGQ0Ms9bqjkH4MEP+6YaT7CQMJA0OAgR0Usg+eQLdkbJOHrZJWCSmtr2946KdtNtRqyhNW9KiPJE1NEnf9yhXXz6Qf+wrUrK43sM9/BKnDICAQWLrWhozv9bqjlA9L8lfIDaPUeWilVzae9UqEnL6BxiLvcJQXcYFJ1E6+zP46GD47DWwI4EyjUOjKLOzWy3vlTiYS+h5pgItDrOfJ4zXipSvvOI64rwwZVTnKCLyzfWqBIM/LKrphF9wqBEMoi22EM5kJdK9NuehXRZY6zpKuPCdm7Di+/M6OYkN90R1q9uLpHevmOeQ7r4Z6MAcAXDLmHgBSD6rJSoVQAoQTaKCkShkNSS+wfsPzuTp5knbqYEX4RJSCchwgVb5SPMbzXcZBtXYzkLfqyAhEueLETUs9NDsp5heWz1JXM2UQPeEpEsrIHL27I/7zqmUcERMZXQ+PlCHltRmPLyUA+IieWPiAxiMYpFxOTU5a5foHLij0utLNCEgvt3vmlokMwBfg7M/oP+/WTicJAwkDCQN7xEDkg7G46FKgK84TkMDFB7TD7/c3vrc6ceg6HYy58qwIOpOVoa/zc/MYcj90TxcZfw877UpOX0FiDxu+2J8wo19QSw4fT957P3+JgO3nUewtG2c8iu/GZySFqo2HbaddsenJM3buzBWMuU9to75EsYbVGmv2ePGmLa5cs7WNFXv4+K7zzwJeQdWRSZufXrBT2niFfw1CQcnDRspeVPjCUAJueXW9nPKCPCMFEPw7DkLJI//bhy+HJbs+NLiEowwfI8iHly5d2sWYC1AROD8G/GaZuxxed3+XKikrYSBhIGHgpRiINCgW0LV+ojXwE3RXX1Hk12bffvedLS0u+aaOHW3chWONwkbJC3d6atquX79umoDW5JV4hzcVm963Y4DZ+b3ApB/BUGHjrFVsEA02Fgsdh3JehBwfkf5kKebH68M+5vHCLY5NWr2N0Acf7rTqbijvlBu2/viBTY6OWZ5VMmEC/0XoBL/CWhB0FvsDV/Cb9iay5NKahy8owJRlk9E0r2wSC8SkdZ09kzFf9nDE0/2enh8pXCtPBl3P8dvtlq4kcyDvqpP+pOLc86Z0xgmPSKaQrD3OaV9yi7pxOcOrZLJErKi8IUyOGh9jD7h4yQgYZECyn+v6PU3JmHtcHjxvdLFUdI/ULc41C/eCC+tQjEUfk36CLxAgEcie8se9mL0rvIMfY6ipADA6K5W17L/gDE1EU7t+ylgrw66SiF270WCWkwUUombcC7GE1OnwJFcMHT8DMEUws6MzabyQMyo8UDhcxiqDmNu18B4yA0NRl2HndwkLOxM9Rs12543elW/G02MmvRv7BWWvxXSWMJAw8L5iABr/QoLfOG0NMUw3iIVbx7ulVmN5nTbCcC8X0SZtZFW2M2fO2vVr121+/jTsQvQpCLkvNHsIGf3U0c/3QGoPAaydXaDI9Mh/gPiwbYtBcPcnBWzhmQV5SOeRI+4Eey9XWUvehI8sa0rj9XscJXtpMoApY0IljBNq4UO7df+WbTbWebfqKE01e/j0pv3m+58T665ALOYVn2DOoUiWcyN25eI1K7MrOQ1ZnQ3SOmye5vxdAKo/OnKFzJUsv/DrrjjlyosU/wDTXsZ1UGW6eAlgO45iX46/LD/mHdiRzvTNSx7U0uMow7gcuqPTSC+6WvaOu+kiYSBhIGHg4DEgQq9fTDLmhjytHlpZXWHiuW7bGEtbGBGdlnK/jRfpHCtPZcg9d06G3FFvIPLD2Nq+HftBVKNci7aWpJPiYBT0bjRzAah7/NtRRReREaj+USSAK46OWvXMGVuD544DOxYEtyG06pv25LtvbfLKBw455k4gfBnA5MupCsOqYs3W12u2fOe+FXA00yobxc7tECehzKZwC0woIgxgeFXYJPFqtSveo+NAIkv31atw60W5ilDoWnYN4Ti0E+9k7XDD6+qy75bKe/L6ccI4y4s3VZdyu0CVFRzeg49a8rqPRePIBhzHPbygHwhkSaI5ELQeVKPhxe19fH2fYN/pQfW+l3Z7ZCV+UTqGn8hVIFkDLe2APZTqkhctk6O6iKw8RdVSB2VcxloZc/ULBE7ftO4qqXSY5fR2VN+1T3UUfxlUXL5KIfHmDuoPhMiHPvgVCib1CRUPYRa46o7toIBJ7SYMJAwkDJwUDLDRAzsKr62u2t27d+zmzZu2yrk2DJFniYy42on5DAK+DLmnT592Q5Ao71GR2kDzM9rPY/DrYXkcGTCRezrfihAeIKBqutu88/CgEPkKF1mQASQqPztgegu8eV800u0va0PdOouO91CoXanzHcvKtjB/yWam5igdvKY67Ha9Wntmv7nxC/vuzm9Z2tmyInKKvEdHSqMYc6/SR4Bd7WosAXZd6P1zSCihu3G5pYbaJz+pzCCglB62dAxAHDaUJXgSBhIG3jsMoOvhcVur1ezR40f29W+/tufPn9sWE9BKJRy58vCQCVZKypCr1QfRkOsFAsvw033702VKu7Ea+FR3BlcFg26+b33vc0MFjLkj4Kw9PsZmYFrNq4lYXFyRBx/85rfWWmcD0+5q5907L8qRTJOw4tcYcFcfP7UHN287/y7iNJZXu8iWs/NzVsHgLpzoJ1uEn7ugIjuG2Lz4PNw9XHiHcjrzeLpi7Fl+cERTl6onJwUv+sIfD9+oOiT/6+GFZACOMHAU3H54SSMvtDp8GWGEAS6du6yUjdtxNnwgHxpEg2akQ+s4dfTmGPDPMrzB/hLz6YcPN3yhb97gQdQASKcfg2073E7SAonjWhtJ+oXGkTGGMEMF8WMmKs5kBYKEcZaGtRuj1g+oqgy5il+nOtGgqxsibJme1zs6PI5BigTGo6bUjv/4k9HPcPTyB/tHj036mh+zrgSLMpSnMflItR7YvXG4FwH28mE8fpr+JAwkDCQMJAx0MVCrbdr9B/ftzp279uzpM1eU5FUpviFjrpbCyZCro2KwQ3Hfd3mwi7vBE+c0zpy44zwocFEPnQSzFf8O/DTyJB3h4coMTN6b3HnlWS/941VV1xuONcW7w3ne5YBwLZUpiBC9cqHf/mt1tYOBdq8CmL17PnFM1X5zapC29Df0KTNrvlO2cWLmzs+etUppxHk2Oh0bqzTsOaEXnjx/BFx47NBBiaWeZ+bPE2LhNDH6ZNzV0Pijo1ClDfv8n1oGlkwgCZuY9GB7KcLSjYSBhIGEgYSBY4kB6XuPnzy2W7duEWf9AaGh1vHMbcAGWNqPfDI1NWUfEdP/3Llz5Gk1Ksv+DzKJ5ZBgTxlv9cuQIf5E0uZfzub9Cn1Vq2G9Ru/gt47yD/jLsWJjBCPr3JXLtoUMqFAVBRhwmYnZZ9/fsGdf/tJaWzVA1wpf/eI4NMAsiWFrwDK6P39qj3/zG1t98pQ28rZdb9paDU9qnss5ZMocq1lzGH8DmtRG1g7Cgfi5eLueq4y5Wrccboe8YJhVEdUJMlS0WUhokIE3tKhn4IEd/KiWXG7gbodx4M4Q6nMW7CvZAyXXDQw6dlMGn7fczTzSkwhRhsRMXgKkTGZy7PThsofrCHavhZhzko/SYFI6DhhwgjQCUSKuC4RdSmm7iZcRxkzN6GnZn7w/hibpO+qjHYIrfloy4nrQBB3lJu8FOUoh1JU+VohYIFky4votjlLYXG3zUAv5gpNBry7C6F1ylAbWzH667PiMVwaOZ2Tn9DJUiXG7YRqv49Z23UlxBwVRu24GiCMGI9S6jnlh/PFO/1E4fbO0W43YDy0Jh2+d1M671H/rjlPFhIGEgROHAdES0U3+yrgHbRJ5qiGYP3z4CK/ce7a0tAyPRKBF8BOHUVieU6dm7crlq3jkBkOu6K7qHkXyXvvIq2BwOX4g70XYBgsMXsca+zAuB4h2vKnYnvApTxXi07OBSFEyCHy3IM6Va8B3MWJitPRKMHFNxHLb0yCk3nx2LxQI/iyxJ5UXhyvoGfsPhYvn1W43WM24hTzRwEivmuG+tzHwJxieMaK6AqRyvv+zq0AA63qcvJ8UBE+vQlC0gLjDpLG8cBlrjnh7ypci2FYdcjvNghtzpydnbGXzkb9HQGpNvXPgoomcprPxsVkU8Y9oa4zVl7SpuhoV7eSbvJfE48tzrvfUhRjOlYJUFM4lDDnuImK8xNH9ERhdULKHqmfZzTsE0Fz2c5kxdLbzO46QZMC9EWSx7iEMInWRMJAwcIIxEOlPGKLIlUImiJhrclnxVmXIvXPnjj19+hSe0fINvUXL8niDTkxMIq9ctosXL5nigitWrXT/g0yC2HXvrBNRQ+fT4n/EmC0UCHGIMt/arFlxouJ8SRuSFwhBIF7r1FM2UbEuMdTAuTgeQcIGMDI2bpc/+sSeffVz5BLJiW0bYTex1taK3fyL/2y5yarNs29CaWI8WB80eAce/swD6zS24PVsRreyaI+//Gt7/NUvrECYhk4pZzXkkC3w0Zk9bbM//AmGXO1zg3zgxm2ayR5/vlBBLvAgD8hKwMAq4+2VVWts1KwwRyxfxfNlZzXZOTACWHsbg/7ac1tnYrhInuIm53hf2sgI5eoUYyAEYmeEH7JJvkFHyBv1hoeaLDI2oR2JxcrAon8y7zbxRl5fW+OMm9iONMYOu7phTiEHY3NTD42k4R9RcnTpDwMIqBMwkpU4NLB3sfeFwOwQ3iJfHfN7juTwgnKtpMLdF9BzTvKfZMw9Nk8Xoj5S5cXl4+WDbvBBNoitM8JmYHrdnWhkykUc0k6hNuYe1VFQhp8gcCMuxEyGXX1zmvBCl/Hk36MyIYRSl/RP5ZyxiDFQz5Wu6JWrJrK6VICGokKJSBcpx7nudRUkYPClDfrO9zkFuF/eaBfGXYoIHA1Bz7ENQ9ze2AAf7MA5Oo4Bn2cuoiYldtdOhCGliAS/eElO797LzgKu+u/uhqwX++qvsQsofbfjaF/TRl+NdJowkDDwPmEgEyj3NGQZ9RCAkfQkvG5vb3hohRs3btji4qLHdSuiEMX46vPz8/bJx59g0J1DIUF49npHS4v6e+8/f/XwB+jyrryBFrzY3lvdrU/hVQ1JpoCDcp4ZRTHkGt6phIbDoLuNHRQDqQy5ORQfuaiqqB6l+Jcv/QswD0KjDb1g1cgBuiNeJ5lGfE0KCXnKpqqGGGSCMMmbJ4SBFVooKVsoI1Js2JAlKlBU6U8ysEqQCF4qxCEETgnAeYy1OclOKESu5NGmZAR5wGjZpI9DSlaesbI8UzKFYHVvZO4TRMHmZubZDG2GWLlla4pPgxfB7/GZKcupjY5OEZLhKtpU1TB/8w8PIeQUeergvOsG3RywqD/hOHg76xj64kBSvYCf8Fw98+j+OGwRKsGp88NPL5d1IzQBe3uHTvVi3cMfT+oxYSBh4KRhINIg8QPi+aPnSdeSN+vi4rL9Bk9PGXIV178oox08qIx+f+rUKbt8SYbci27IFVfUyiJxwoNMglamPmffHKOxKAdsRYzLWomSw6LWxBhZGJ3EyMhYME7K+NyG1ytGfKCgUT89SGhf1zYGzZExO3PtA1u4cMXW734LeW/Ch9s2UcI799ZvrP4nkjlqtvAJE65lVtn4iBm1G2Xh1hjQa4tP7OlvfmV3/+K/Wu3hbasQ877NRGyLWewyjgHzP/k7Nvrxp4xfRlGMqzgO6Fm7cRaEjk3M2lphjDCRkhqY/EZeWntwz5Zv37GF01dw3MIwiyybx7jaaW5YbfmRPfrml7b85J7Ndho2Uikh6WAXwdN4+vQFxJFJXqZR8CzDB0NCEGvQb0exfSV+8SsCSwkDcoFjk/621tbt0e3b9sFP1o0dWRmnZIwg23k9yT6SuY6c/2VyjkCRXOg/DuCuie1LBu1Cddzy49MDsPa+M70V8e2T3HSSU/w+T/IYT8DY9BLysTJTo9m4HEqFiIGIC1osCmlQb47DQMV+iihUrixJgdN3F38ZAfFPLvt4g0IzQFZEoTRmCooRQva8QFByyKQdEaiw4zPX3g93pbB1lbnQbRdnO79/B6l774BPYtduVNCFFEQYvcf1YdYpR2xHUWrFCs7BKANNEotNKWEgYSBhIGFAGJDHbb2+FQy57AS9uLQIe8Rgphjr/EbHRn1zpE8//YxYuQuQ0aAwvdwQdLh4Fd8Lq1IOt9837S3yK5dJpDHwQ3VzpWW7Xbeljaf2zZ1fMRZtzsJkc1tipji/TLNRNeSSFOzPqiu2J3lAcecKdmp61ibH8TyRx4kbW1VaPB+FUfzdkUWvGHK3O+t28/6vkYeKLHkU5yepwSypaLhCwZLxmRK0En7IBIU2itEYHjVTZ71tdFXeFxmk5V3MJAEexm0pV/xaKKnyGZZ3sPrIoTRVWC01M3HK5k8t2IPHM7a6tYERVxgJ6kMJWWW0MsrGNWdsZnreKihsOWQ3waU/UjYErmDUEkvfhE+z17rNQLsyEAWUHXDmt9OfhIGEgYSBhIFjiIHAH/LExn3ajZGrTVqVcrmSVTDkzswEQ+758+eJ8S/DG3wLHhgdbvrY3L5jQBwocKFwjLxa/FMepdqMvIFXbhMvyTL2CG0KpnBVu8tTakmc+Wj0Vnm85sFfdf6iffh3/779t//zgRUbm1bxCeimjYDIZ998Zd8xyMbSExtjM9xKdRIvY3RvNjWTXLa5vmSPbvzGHn71S1vHAMvgkQmKVsHhqlko2+jCefvkp3/gRkbtzaCUF9IYelhVnLOz5y5a/eYtqz1lsrtF/3j1btDfvV/8BYbXso1duGTFsYrh5GubK8/s8Y2v7fYv/8pGkUdyjTqOXQXbxsI+dfqMVU/NB6NzAzlWL4L64U9pZJS2WRXVrNEm8kSZiWhgkcwiA/I28snazW/s/l/9f1YlZEdlbBJHhxFWs8njtWGTl65aDu9vBu9jOLo/8e3LINAgGKdkwCYTHvIMH5s+BU6Y2Mi83L2kv6hH854dHa56ky1HCUPqe08YQIFBQZDCKu8Qbe7SZCm+5s60rEEGwNcF8N5TN+9YyL+j7Bv0b4/2pHi58sU5+pBp82YdRdfl0eOUpo9taKmh8mR4zTMrVqCyDLNSatxAi9KXAw+uVunjpgkxEO+PvlROHj7ucaPljGJ+qi9mIm1I7QtG/Uiqd1RJNNjhcOADq+uwjELESjuGVkeJvaNlK816MPCKcDHWlBIGEgYSBhIGAgbkYasYuffu3TV55D7PPHI9HBFEVl65c3Nz5obchbPQVxQQhNzdFY+jx6oo/FHypZdhADYa2Kbz2x7PVQy6FksNBfetxzft4X96jHFVRkoUAlVyRU48XC3r+sWEyRS5pm2VQtV++uO/az/+/O/YRBmvCylTbtxEiEcpayH/5BTCgfK12oZ9e+tLu3v/gW1topRp2R3txx4yXcrZflcIoWtBrnudOsbY/IR98dlP7ae/9w+tiFLTYBz19hZGYmLgcWxZ3Rqca7K1gbFaYRPa8OcOXsBaPVPG+jtaHrPL5z+wOw++t9X1m8gc6gN5jbF3WHI0NjltH1752CbYiAX7M7xcSkkQRzQ0/1FJJuCCZDwyohGXGWt/GQLugJuTIPW8iMOUkzCQMJAwkDAw3BgITll4hD57Yje+u+Fyi1hjlEfk4To9P2OXCa0gQ648ceXh6fXgOSHBIyKjI0P8ab+TmldvmrJ1/qMMdYotQrxys71u9e0tGyXMEq6fVspXmEAX34dPiQlKvz/yBBxMCGtiuThB3NwvfmLz339jTzGSakxl28ZDtm1j2FSW2XhujT0WxmdmbGpmDrmR1dAwaoV3evrktj15eBtP15aVabLOyt8WcmUdo2J5YsY+/PyHNnvuAq1hacAQ39Fqse4DogLnZy5dtdVvvrOHzx5aq44FAyNkG0Pt46/+xjYf3rfJ8xetPD9txZGyLT3FK/fWTWuwEV4V4+wIk8L+fuDc9cEXP3RP6A7yCd2FH4KFZApJRlg63TN6dAoP6qkJW8ZrTlPu8gQelRzz9KH94t/+G5ucX7DJuXkbwbu1Vtsm9m/LfvRH/9JK5y/w4GUnUeNDkoC7rfdK9pHthttHxsfHgVNe0PJyHxI4jwiMoza9H9Gwj1u3GZGGoBRQVmTc20ZxbTFTA8XgJcaFnifZZJZJ3/GRJ2DQdxVA0V+uIAw6a/kHGSDsfXwqrR/EQx8rChslOecjzTx/tNTBCTLUyg23NCZF/QUGRj5NcI/+RMDdcIsCiEIlRdGVJIykMnyrR0/dk5hxQEch4FV9cU9ja8EMRazkhTvCLJsnjSvDaMhQQ69qLJQa3r/HGfbhxWqCLGHgfcCA6L4rDNBL0cytrS3f6Oy77761peUl2A358I4thDwZcqUQffrpJ3b2zBlfhqY6+g1DGqTkUjD0E7sYpiRFQXCJ72hSVTZGkOjPQdZLOLdtg2/x+lZ93WqbKBrdQYh/x5G+iHd5rig8kjyOGsTArbXWkACINeuI0LOkvoIT5Mv0i3zQkmyAPFTs2Mbmhm0RL04G0u0mcfy0esmhpEvJE5wLzl4iD7lJ8HQaeM0W21bvbNI87aGlbeNl1ES2UIxfr626UqA5+nvDe6XWwl29hyhkIOMMXj9n5i7bgwePaGMz9Esf5fyInZ46b5fPfYBHEzILirpSFzWMTW3FJC/yIpPQ8kj2sBNSiCUO0aJA8U1gBYB+KSUMJAwkDCQMDDUGgrwiXV0GOcg5fGB5acW+/fZbu4vxsC7jFHJK2NSbCUKMt1euXOmGVqjXCWGEEUuOSYHw9/iFBq72DyKJxXQNud5FYDqaUMxjcGzAJzeRvWacf5HHxKZWlYpfdRNVVCtAGOp373VPYr5KxfPuzXc68Ral+mNclUxYmZq3L/7wn9h/XV60zQd3mNDVamEMtOjbDXj/1rNFs/UNQjHcl3nFE1o5fHeLOLcNL5fHY7eVwwzMr8MKorOff27nP/sU3k5vPOCRsTE8YGvUl40itoHheOGMjV+5avl7N+hrHVmnAY8nbAbG1s2nd2wRg3Etk4Uq0v955gU2wZPTW3GUPjHkjhLS6cIXvxtWCW3XMGryTvC/jZFZK4i2t3kOwFBGDpsgRMfE6dP2hFVpTbxuZbRWmxUM7+ubK7b2uGEbTChs0T6haIG9aF/87O9a6ezZAPRR/93lVdC3I8/nHJP6ZbytuwjeAasqDr5Lg9c7Khz7i2TMPTaPkK+N93NEgcd5gesorE0CcuuFFbHoZLMoHg/2LccUGE5Ged6yjf5q+pz04Um5KrCJVxtvky2IpjxPChAT/RTbNibpbVKa8ixDlCeqlLcWhmpqoHdhgOVcR2cvlNXSAnnheMrAltqnfpr8UM1Y7shdPnp4pW3WdFG0wgjLIjYUADxUUr/eivjkS5Lj+CX39prtfWRw7qgTYYcAS0NuEpy8VqtZZXyCZQR4Juk+P3mfuSTglbNxu5Ib4I85O9relwu1fHCt7wuIqZGEgYSB9wYDEsy1SYgUIMWYu3nze/tWhtylJYTbUsjH02JsrEQssgX77LPP2OzstOOn5BtKQlJF1PvSkRt3d4ITKO5AXh+4R3gajJpteLuEDxkyFVZAGkUTjUCeJoplK3bWA7/fXJmdD7CUEksIpQyJc4+gtHhcQDcAa2LWt/GAB0p4R4lEmJcRdYQ9BBSLtyCFRoFnYeY5X/YT+vYuBAT5bpjVKb+w0gcFhs1CWsS9q46V8TRiV2p53bJ7amtbW7uNoLiNEA9PihJeRyyDLOM1nOeOJtDdA5jGFBd3m3EXC+N2fuG63Z66ZQ8f3fKxyxg7PTNrH1z6AUZjlm0yyexxBYEJyQjvGbBGGfQwFEUm6VGEayxdHWXCuSBZSHF6fVJa5myplPqrseKFwziGIgGInrUAchmOU8f7UAAXgXiFcBeLpGPCQMJAwsABYECyiibpJK8oPX++iCH3G3v86Cn5RGfFu3OrVvcwBTPzp+yjDz+y69c/8GuVr2qvnC5VPRzKLxrudFzd9XUpWp/H2FxBN23cu8PmXevYAMmUdy58q5dUSRo5k6ZU0nTvjoa4lgzmJbgf7A+92vt51hKDhW8WNNHbqtoMq2j+8b/6X+1P/s3/bq27N2yTmMXBQJgP+xAhz8iz1id9NTTkmWaOfYqQbRrYFmRAbZfGbJTQSac/+9x++E//qRWm5qiDdMBKotYmzxIZQbaI7pgZY356yq787A9sY2PJnnyVJ8TCfWQHYteCC6GuikBQEU5AVUkyBrLVCob8DrLHCjLG6Nysffjf/aEVZhesNDtPaATJWpJ7kA2KQSYoVbC3MNwCjmw5wjQufPSxrd27a8+//YrJa8Ji8K/Fs9KY2tubjIlwD6MTHr8faYh72F4kmNDmUaa+V64HBrL+NjL+xvq6jU5NWwmjOS8Ov538Pbxt/fD3t9af32v6uJ/1f3nHfSwnHH4+cLnZE8S7PI7rPLNKaDQQjU2CYRMjhcUCLuQ7wRweVBSYqcuxRKDNz+PcVst40chdXqS1xTINqI6WY8REvhuAYXDyUJUht4FS08aIqzhCIMEVMW+Aan01vQURwSIx7CoEZG9QtqWZKIzfWuYo46hxz6OCSyGi9ZhiO/2ffLx3sEf1HHuHIEuZY1ZQ0EmZVTwYLSEQwSqU+Fxj0YMFKrWeMJAwkDAwtBgQjyhBG7WJyK1bN+27777zHXo1UbgNX9QmENr0bP70PIbcz21hYQESKoNjSu+MATewwlt5BtsI1xI5tLxSAnUHwygqkxsiUYf8X68/mFeXfwXh23UdCmhitrmJ9xG1xa8rHeLGEctWq3RkyJVs00YmaNZ4httswGJs+pEjHq+zcFaxZEZR9S3vm5i8O/6Ir7t8lAn9MhhLuSzj2VtCJmgihzQUP8/GbLyCnIAis7jyiHGN0S9mV5RxeQUjXFAvyCHSdVCHkFNkuNZEc4EYh+dtZmrBHj++51AoVMJEdQav3csYa5HTpCPhraOwEfLSElyShUAb5fPE0yVMRAXvZJRBrTzyjXCQjzR2JVd4hdUwIM876j8BsgBF//lRw5X6TxhIGEgYGAYMyKtWP6Vnz57ZV199xWZnj91pJ04iy9Crzc5kyL1y9QoTlD0+5hWP8g+EXTwn2PdgfPDBAt6eoxPj7lW8vbJsZSYtcwr26uUooxPfFEfn+h1VEkD8rOGTsnlgzI3O2Cg2gX/6v/xru/3n/8W+/G9/au21VYyd27ZO2IgSfFv8OC9bhX6AT8RaQi8VrIy9pTxzlpAJH9mnf/CHNnHlCl522GYUWmFLXrHINlHUdHlDfYfUVigq8PaDP/oj+2q0ZN/91Z9Zc1Uyi1biUo5NzjpacQ2/l5jVVkipMfYOIC7v6Jmz9nv/+J/b2Ee/C/4L2AlY/SMbgdsxJM0EOIvIDS5daczYjGYuX7Fzv/d3bIPYxh7rF+OtViG1CNNZ5B8xpLCV5GxTm9kRekwG5xxG72FL+k4kE23j6CaHDW0OyEfS/2J2Qdbb1sN6N/tEnyRj7nF6vCgLCuIto+TISNXDLGyur1l1/JQTTd9UrI9oHo3yKirW+5R8yaM8dWBko7PTVm2fxjuXWHQsHcBSaXW8YYLWEwiQdoJe39i0mUliy01OWGdsnA2qIVd8tDkCrueZaVLQcX2p6uWFBAWUftfAy6cyO8vsGOodcW+KU6esicdvA2ItjqSJRM3e7EhStHZkZAQBEMXEdE8Mbb+IxM52wpWME5p1KsLYxyfZIVTGXLkV48GsOMEpJQwkDCQMvO8Y0ERXAx5y9+4d++abr11B0uYbUohkZBSRPstSsc8//4HNzs7Bb+ouAA4r3kT9JbzH5Ne6EN8JrCHeOrKj4HBQgEmTjkUMj1OTMx4SoQOPz8ngKEOlVt7gUeq80us45/SxhDF6Kz6OeKalhq3OllVYtlgtskNxjedYo8oIRlJUE3n2qL0i4ZLYxgT+SBkxcbSmNgoQOos1Chhj6Vsyh3vh6vaORD4GYnPjMDHW8FCpjLP52fgMSxLx8IXNAjyG05yNF2as0KxiVEaJItRRo8kSS965ySqbjmBgpog/l7ZCLPBPcdw0wMnJ03iAX7AnT24BNRvW4qUzhxfNHMpfXksyUeY0kLxg4KiFSW2QUgZfY8QgHC1XMSxzf0Lx8lmqiTdXE09j+ePqv5RKGcnVOadDkSQbySDtAGVAxec6FAAmIBIGEgYSBo4QA9rrRoYorRzSxPP9+/eQUxTns0fFZ/G6lCH3gw+uY/hl4jBw26OD+mVEHB6ZQ84qwJ/GiFlaX3mOp/ETOyN9vYWBDYXeY8Wiy8uu6UnMuzfULPPwDuL1YeaXSegcG5CyuievsANnPrKr/+SUXfzh79uv/+Yv7Mntm7ZM7NqclvEjM2I6hOfirYpBdwybyzSx8SdOX7QPfvz37fSHP3ADbo5wE+4KK+aMbaLjzDCOTUiUTYSnyW1N+rZZQVRgL5zrP/t7NnPxvN396kt7cONbfPPWsE1izN1ed1mnXWDCGjmnXJ6wsx9/Zl/8o39iedl6Kqdw4mOTVWSwKn0X6FeTvFrh42GpkA0ESsA3NiPgPv+Tn9qpyx/b41/80h7fv2GPn3xvuTobpCGHFLS3Ae+bnB8Ub3eF1UojwRzscA/TH3m3b2xsuJxfxbExh8zv3t39DoHDBPAhwoI8qa8speHGAI9ILh3yYoX4by0+t9VbX9sWRFQ7Gk5d/gQjL8oIrvPvQi/1Krzb6wCRJ9abUxEtA8RdRksQi5rt2Vq2zsZz3P6hd6PsqUicHW1skh8/DQPAMC3iyiyWFgrUmTGqQDxLIyhNzx/THiSG2DAybObZvVCBzLlLQ7uMFkKaIyh4Z3vD8ZXD+N2uoWhCZPPEymlB8BSmoq2lIRmRfeWzhxn1fyD6WvqvX1n3TW4ylDzMvcazfXr7po3jwTxFfJ3yxeuBF6AsinDFFNiDrgK37NLuWCAdEwYSBhIGji0GehRu9yHkPN7cb37za1tcXHJFyTfcoLCE6QsXLtgPf/gjDLmzLJ9TyB64Bkv3+1O/IqX8wesgDe/CY/obeadzn3a0J8t37ebTb2wL348zcxft9OQFeAH8TXHi4VLRmPsibx7AkdxTd0vSIt5BMlB1B0WtcOFQ4dHaKbDk0LRsb4vWCQDgzFH3URDgt6hyzisjv5TnSEg9OJWjn6SGEvUJrsDzE7zUb8vzWt6r8D0pvUVtRLZuGyg92tlbyoc2XcPciQEX2QHPWg6IBZkxl1ZiCj3TC3KG2ha8Hay3eWSmIsbZEuEU8vTX2i4hZuHxi/zRKWzScg2P3Q0mDvBiYTJ5vHwKL1rqK/YCD0bPZBu+nUe5Vcy5DobXRnPR6vWn4AF5DMRVCtN452LM1eopGXOZbCgQ81djkrjULNYI/VSzla1nxOyVXCP/YpQrvIS3iAZVbI/ShjxwaJ86Mm6r75w/74jTONLDPbbxWC5WwGZp27698xtbW39ipzFeX5j7xCZLC0DKeFJKGEgYSBh4jzEgPvHkyRP78ssv7eHDh/AN8UDR8GCIK0H3f4S8cvXaNTw/o5wySNvFAZRifrwmR7yX9KIM49lv98ebzPpygyHtO/MHdvhYu1mz1e++scX7t+Fjebv8+Y+swEZaWpujWnkmOnXm+94gNzhsDnLWpu453MoMeIjjeDuAX1ELXimDrlbPEJwJ/oqMwoxsnji0jZXHtrm+TEhDnKfYULWNg1xrdYUQCE89lKWmazsy5p6as/HT5/GUxQO5yIqdBvny8i3hsdsgVIHsChLpxJ+z1BsPE7IMW+HBtrY2rYqzWqmMnIKjlmwh7dVlW3981zaXHwCbtlBDJiKkU3l81qpnL1nx9DkesrCJnLGlSQAc2/AGVqhK9+LlfWoSWFfx9DVJrJ8Mu74XAaunJBNViqwMqoODxhrPcZNz7CQIIB1i7MqruEDYjPb6Cufs04PjWwtD73Akxsj/PLakGpMhj258zUa3eByfPWdj1z7hHraRaAvSCyrkkfSWxTdNWSG7d6YyJykNy9M6STg9gLHwAuolFTXg9SyPElOW883VJWLpMDvBR+m7J3KPz/IA+t97kwqMLhh9hogzX1qCwG+KNTexwDi4p0ky6VRiECitvqGJX4rgEl6gIoVKRAdmAHMQ48sRGkX6nGuVfLwvG2b4qFHGMAT7pwxRy49hQVYdFDd5DLXqWjAxkEQM+jJ7RDiUE8+JhGGg5jtcZh0yUycvHiKUW6e2COElRg6zacW5GToFXyit+a4ht8co1PHOKzJ2A7JvXO8AbKqaMJAwkDAwFBhoENv09u1btryywoShhFWMZgik8s6dm5+zTz/51ObwdFEaNOIOxQAGgdiNRitvN3rerRupf2YczUIIdG/v44mzddpzD1G1i2AtXlxic68S4Q4EZ79ipnlSL+uG5AyQXYzN3dvyZuH/lh81cBmFtXYmjNHbbkoFq9pkEX4upYlfDoXRHXS5RGroCfVZlzsO6kzxZ53RC7F46JClXjpNTe2G0RUUgw5gtIFqqQMfRliplAJ8zToT0CqvVjRxTAMFly3Qf5QJTJXiNEZfPIddYad8G1nEFbEQ006ey82ofFDFOiXTFgGj+fPIQ6ENYU+KVlWhlWTxDd0LKv6BXAdceOJUwskRJaFUtndBJbT2ro8IoNRtwkDCQMLAEGJgjb1QZNDVZq2+QSv0cmRkxKYxol26dMk+/vjjDOqMn4ugDksCFIdGf+A9bTegVa06jYPU118zWVnFQQpPz9EpCmBUhL+JRYmdez1ynUdkrEohhpypiW8cRhL/VZhGGFRwGcNEy3mLUAVaql+dxEkMz+kOnre5GQytxL+dvngNNsw43DYgOBmJy1jix/B+xfjXWBmkVilpA1Z53vannnEdmYHq6n1Eq425aNYpD2/PYWQtzFZtava0TXUIoSD+rsKOGvDEZHO7ofrCO6GXKnijIidoI1VNdAdpBDsHxmmHVbCpckQt4aFcjtIG8JzlCBOBlYXQDZRTP6P8SB0mznMj2ByQT3zC2HOP6o/GLxuOxo/xuoN9REu16hiiMSCtMyd+DjlfL9kgziPEGhV3/W/MO8lHYSul44IBUUdSDgG/DEHQ8r8WYQO21lZstKq4ufrY4xesb1IE8zBTZELqU3DoJ5jCjJKIko9A4wi3/Nhxwk7JHujYXnH5lzKUxRlSizF5OImAipjVPXoTivdCX/0pdinuImjkQSOaqST6nKE2ZBzSXx8vf+TBLO/jdgMPoI1VDNnyeIL8ahkB45cx15MDekjApW4SBhIGEgaGFAOKmaUwCzLgalMLLWHUT5ucabMzeeQelxTZno5RhtdRPDLyrX7eeNjjGuw78EoEfykN8jCVwC2eGgfyFgC6sdYR0N+IRPGICHVBP8TSjcqLuskNeI/0lKe9A/FinSAYyKvWHwiyhLyOu3joM8QKbpl3JUv4XLtkGa8XPFJVRyMKR0loock4Sm+TMQRP36yNHaCrRsBBmCCP1zsKDe1Fd5xDC2ECLGEgYSBh4GAxIB4Tl9+Pjip2epsVqFuot3kMuZc9rn/gFAcLxxu1LlbjaScVdy4mHRshpYCOOj2BRyuOUjUm1rU3TUehARlvC71W0kHH+XbgYi/y2qyLwzi4UCU4An93W4mPUX+QIR3atwGE+hFFr6nu4SdUBhCC6xp1HQZloudrYlypmxcuu3/px6F3YQP7Jo4MCjGl5NKSr2oSMP0AIavJBqM2taKK59FhAtllrt366a+qho8kaTRB/gpyD8Z3vpft5SVkqbaNKT4zjow7EKWx7DaeI4H/8DvNXoPD7zj1+HYY4FPETonHxuSU/7SL8iYhAzps+LLzA3679t+1lmhJ8NTIWpJXrXuoBAqxG53Q/VBGZCqch+vdoVEbmvF72W+3PoLCyesu7cl/nGuJgn5ubJaKuPPX3/7ube4O32tz1SWFvDcpiYxZz7XJkovamjY/y/NsFS8X4u7GbH2mgv21LacCCQMJAwkDJx4DEkQ//PBDYmcx6Qfv07U2O1PeOHHctDPxcUouaA/Q90z3GIphdNkmfMrlDAEnJa3LlAaAf1Ood2GwypInSpjsDLKB5IPDlnPi2OOQ4nWO8YcfWABYYUBHoahXJsvzyuL4mkoW3kLqf8ZeN97oHh0L4EDHkILcIDkJ2YHfUSZ/DRh4fDoOjV4NgDpayI4SK6nvhIGEgYSBnRhQTNxz584RgodYrNBteeT+6Ec/xiP3oyNxJtoJ3WuunLlRhqNO4XycYG7DMD1+bkERE20VY26bzamiLu+GW3RrHcN+PhRyhpcdI/M4UYwicsLB42vw+w63ezKFnoxSeEJdm0fIPFZ/XbZhmZPCYbnxHfmnUd+2lcVlHBg7NsE+GHn2F/B9hOKwNUKfOOjJV8dq0O8I7E73xXdsLFU/eAz4ewvxK+GVWx6bsa0mMxXMinWYtQgxVfvf7IOH5zj3IDPq0aXIwVo+Jyh1qIanWYc4PBOz81YZmyT2nnZsfz8J09E9l9RzwkDCwHHAwJUrV21+ft6XLmoybG5uzibYHDTsGi3aHmnscRjNcYXxAHnoATZ9kNgW2H221wPsKr3fB4jc1HTCQMJAwsC+YUDG25/85Cd29epV26xt2hjh/2ZPzSKl4MjDakzFgB/qNMiPuc7hDFWYmrICe70UtOwfOwSBBDDeEtMVY22OUEW+lL9f1xbbGmxrqAeegDt0DEiAIpaEJgJ8ckCT+vKuI5SF9jyqjBHOQ9+LVkEppfdJft0pDT8G9GIz0+MaAuea6eJlbvHbePiI3Y+XrDQ2amPa4KWYuekf2aA0I9VLPjvXu9zT2dvU2VPDfYVCzJ6+jOw0BKYXiiEi/OTx5csRXiz65jlOcHh+ntQuoRXYtKXT2rbW1obVlxWYvEjs8VGrThHDhyDk+N9QzkPxeS05Eiu91MS7G1GLXYaqifBFPKRjwkDCwLHFQJEVKpOsYNCyRSUZcWNooSN2WHxjnO5m/Nst740b3ucKzgtdBIHR8H+/eKNivUlYfx3vf939fR5uVwZ4Vb+vurff8IQJCjH03Rj9/vf2uhb1jvqPgl2ZRHmvq5juJwwkDCQMvEcYkGwyNjbGPjfEl2UjLMXNjfLK8KJBlHx3au58jz0KCnPztvrbX6PDYpB+/shmpokzz6bnbsiFKwTP0TBCVOpdEpliaZ5UQBfdjJB9AH8Pl2+HARxFnztRJ7vG62FRyLKjTk32xVA4knKZDeY2Nm2NeNM5wnuME1u4wmZ0dValV7SXUP/rEt8vXzW2ywji/V1uHfesrvx13AfyXsDf1VB5I9nIqzwqRXYcS1/D6htr1mBp6cuVq6P/ON+LZ7THQWrZZJu4vXnFvmk2rb64yk6Wq6wSKFoF7zKmn+ChBDrfY3upWMJAwkDCwPuGAQnHpVLJfzsVo+NJOaNhTM/R5U6Gobz3Ip1gQXv/n99wvRTx0cWjxqvz+Nv/8acWEwYSBhIGjhcGJK9o0lkyi466jr/jNZIMWuDPM5bRmRkr4ynZ2Ny07a11j+WqkAraRDQmir4mxQLx+Jri6faJxYCkG9ljc9hHFEK0/nwJ+8iaVUbHbJLJgxxOi9qw9iXzDCcWL68aWDLmvgo7Q3tPxK5gleqkTcyc4n1miT4bZyl2rmb8Bl9w3YesUuf9NejK43Yvv4N45DtCAzmf4lm41s6RHTEV/Le5smHNzW1iEI1bYXpWqwm0MTYpMbaDeCapzYSBhIHjiwEpQCctnbwRnbQnlMaTMJAwkDCQMJAwcBQYkA6v3xAlxcQFnOlz59mEvWrNrZqtra5Yq1nPgFTM3BA3N+myR/fcdto+huMdcnk3s4P4ngDRJhLfnGIenzY2gCdW7vbzVWvV6lbE0a08P8erxCTCsIclOeTHnYy5h4zwd+9OnwA/vkfFDilj+CtWRq1NfNX62jIv/ibGQWKvepH40er4/hpy3x3n+9RCeGxdnqag8HkMuZ3NhhW22yy/GbfqzLTlRqsYchVHqblPHadmEgYSBhIGEgaODQYi6z42ACdAEwYSBhIGEgYSBhIG3g8MBIW23cq5HaKM12QRb+PG+oa18dDtsAcM3mU4UUVsdE9iRjq+rxjg1clhvO0aIPUq8fOZAfLlgFhQGBLsIA02hc/Vtm1qfNoqxJ3OVcf8nZL9JKUeBlLM3B4uhvjM33zgy17e7KXPlQtWPnXKyusLtnzreysuPrH8xCjxdCvWyMscSAxBKGmeQNJ5zXpw/T6Q091CTezVk2uw7uD1K1+SAdoScR1pDmZbx7+IlohYjuDdnc0tayyt45XbsPLkhI2ziU+OZSsKkZznnzfZ125sM8LRuzV4J5ZQR71SfbnpNGEgYSBhYEgx8G40a6/0/tWDfzcYXt127673MkC+d8vr1Yhng5UGrncU29+xvBFfjHC87vgS8F9VbX+e86t6EPvcX9y9urfjdVeYcdFSYGfPT9cJY8frOSZoEwYSBg4TA3uhkHspE2A+Mh6lDc7aVcvhWDYyf8YWl55Z6/my2di0WRkPypLWl2pdMGPpDicwCteGPZvrLs8I9/oK7+mhHIg8sqeej77QXsY++H7spc6BjUzPHLuUjLn+tLlud98N2T24hwEkh4NiZ7NmHVYt5+otK04Rb5pwHsbGQYrp2w2r5nWz96avnQD/CxkHNqyjbjgZc4/6Cey5f72UMgFmL61PaeSsWB21kVNnrPrsudnKE2uvLVlnespyI2N8MZrf4N1XiAFmzzjZc2/HveAgsRokZq8a32DdV5XdeW8nfnfTAfUUvZT+bLMB2uqGtda32BW0jDGXXUHHJoily/KCXMF/O9vvPv1u9k5StbP/UGhniW7FdJIwkDCQMDC0GHi/6FZ3tBJy9/xMBun94HVsaO8txhp7Or6suz1VToVOBAZ4B/R2EfkxHLPrE0IZJ4MAAEAASURBVDG2NIiEgYSBhIEDw8AB8eUDg3e3hhlDruSKaXFqyirosFtPnlpnGe/c6gbOZWyExt4vuOxmloswZunGgWNQtU/meRM9fRCat9fbB1s6XtevG/cgTlX+dXUOFgMy5KoHCZA7DbraQsgNvbJZ4dm9zT5C9ZVV4jKX2fTslBXZQJDdhqifObpFQLszyjGj/xjeuf6ck3je9XI+iYM7eWPSS6lHxo+XWV9Eh6M2zJomjohijW+srfDyr7gB1907c4PhFfQBvX8pErC9HN8ZO3pMu9AP0RsRK/Y9s3wLkrSxZVtLq7YF0crNTNnI6XlmMsseEaPIro0Fnu8uzbwzeKmBhIGEgYSBhIHhwsBL2MZwAZmgSRhIGEgYSBhIGEgYSBhAaJHdodncxju3YtPnL1ixPMKG7JvsA4OHbmMbHMkGIQOc0PV+2h808qNKgzaPo4Kj26/HU+DK3XGjhUNODPppf6eW/zobNTaGX7bNOvGXT03ayPkzvGOaOGC1ebSNpNepi9ZkzO2iYthPoqoXj3qnMQgyg1EoF616asbyGHVXa5u2hTE3xw6ACq/Q0YZo0Z7Li9//YQ/7iPcTvv5xv+p8P/vsb8sNuWTkiJGrOLk6NiBWm2ub1mATtMIsXrl4VOdlxMVYz+IVN9tHUtffVjpPGEgYSBhIGDiZGBCveKWjwckcdhpVwkDCQMJAwkDCQMLAMcGA7HHNQtu2Wlt435YItXDWRmZmbWO7YXU2QmvLEIeNgj/YHsIxnsdNuY7JUI81mIM2j6MfTLRjBedEWTuCo5vCgiIAt1q2tb5qtfV1K0xUrbJwCi/vMXwYFTo02UZ2e37JmLsbVoY8r/dhQhwhkHmCjlcmJmzs7BmIqtnq8qKtPnpgeXaUzPFR6NvodIhE4sWDi32vjZ3XQz70YwUeaPfkyjm4l5O0n0O46us1W32+ZLXtbWIcj1oZz9x8pezLB/KEWBCpi/WzZtIhYSBhIGEgYeCEYSCKtf0EX3lKyagb8JD+HgMM9Aks8f09BlAnEBMGEgYSBhIG3hADWiCvJe8dCSlFTEkytJWrNnnxsrXQZTcwxq0/fcB+MIRcaLU9zqlincZftEG4geIN+07FjzsGJCEQfqPNT3GX5a3bbGKvYtN33pFtNtFbe/7c6tiwSuwDVWQ/oRx2LpeRVbZP1jjumNgv+JMxd78weUTtaOmCfvly2cZOzdkoHrqbG+u2+vghy/iZ1cCYK3f2tn58AMGwG76ESEzj8YiGcGK77dKbgHTrQKz0ANpb27bxFEMuS1Fy1YqNTE8SC2bUmWEMnyGkqH63jROLpTSwhIGEgYSBhIFBDMDWU0oYGH4MIKSEJbRp8mH4H1aCMGEgYSBhYB8wIL0WDVXL48vlCnvyFN1hrHxq1sYXTlvdmrbGpuzysGw1mqi+chzDoynTanWtlHRcR8N79aejzd87vC+dEoZcjLRcu7Mb+wUpVm7t2TPsI2uWHy1baWrc8thJOl0hA1Qle+4L70sy5r6AkuORoaDWHgRasXPdjzNnpeqIzV08Z6VK0dqNLeKNLFluG2Mu1JI5D2y62a6S1HBCSn62bUW3jdhWOB4PXAwzlJq7DO64MDF5STdZPsBmdVtLS+6JO35aBngCe4sZKkmD7yNUidE5VtKfhIGEgYSBhIGEgYSBYcOA5JVMUInh8IYNxARPwkDCQMJAwsD+YkDqqrY2wxyHmostglWlHQxyMxcvWXlqzJr5lq1trNo2sXOj01g04gqSYIdIWq5wcdJSZspwk8bg2IJzrd4a3hneG4VYKBYrhJnMW4NYy1urizgoFmz8zGkmBhasWBmxTqvRbUZvjP/USUqOgWTMPcYvQtilUAZaZjMgolhxrTo7Z1OEW2gSM3f96TNr17aYLdNiiGyNv74aUvjQMOW69E19b4O2ZPD1GZD4KR5jBB0C6JGg7FBiyAz5wqWA0GwkP3DfWGXpCcsHtutbVh4fszE2PStPTcMD2fgsI3telysdU0oYSBhIGEgYOLkY6NJ7eEXgG9mRIe/gKycXBWlkJwEDLuuEgSTZ5SQ80DSGhIGEgYSBl2FA9gOptRhzXXCROSlvLQxyxekZwi2cN2PDqtW1VatvrrkzUy7O+vU36faG/ox0ftwxIFEgz5/4i6KB5Fn9ZBHxMB28N3IodK9c3oP68pqtP3lqDWIta8Xy2JkzVhgbp0S2i5DsU7GuH/mjNrPzwSPZ700iYEVKxw0DMuL2Zrf0megFx2U9N0IM8pKdOn/NGss1vD9XMOg+sgq7A5ampihTxpuduDbRpd2JqAy54QNxQstXleL07e2N2KFoZ0RGNUM+QbphagrVHXdnbDZqxIFh2QnhLyqEVZian7WRqUnLFfs+Q68sCqfn+mLaPTeWe/XdWCodEwYSBhIGEgaGBwMSQj1lfCSygZi9+zHS+1g7Xu9eOuUmDOw7BnjlggLla5Bc9olv4773lRpMGEgYSBhIGBgaDLjEkRF8t0ug7SoWqrImz15C192yxsNHtvXkoVXaTatMnWLD9qrD77F23digOvLSTOkkYEBP0x9rnyCgBeRuwJV8qwIY9XNFzpvYmyic49hYIjzo3QdWe75mlfFxmzp9DvvIVNcW4nYSVVZ93jA17035lfJen7zq64sdyxLJM/dYPrYe0B5uQQbDHLFHclVc0THolmft9JVPWMZftcf3b9vz21+ZbS9RqUaoBeK25gi9wFutj4FPiSMzI3xtfvQvTe3rbt/XqKyUHANCUURT9GTWrp4KYxF/IBT2lLdSqUxc+Lw1G3VbxbC+vr5knXKepQPzVp07BQ9jWUpfHCF1EAlOPPajXXn9v969LDcC13/sFUpnCQMJAwkDCQNDhAHnspDvyFMEmnNfkfRXpsgJskL9NL//vMtRXtlYupkw8MYY2PGeZu+rv3pv3FKqkDCQMJAwkDBwXDDgK4PRc7v7vHCuPG3g3ZFCXBi3uQsf2CybszeePbDnt7612tIzzA/beGK20I4x77mnrnOR4zLsBOcrMCARQN64Or4suXzAKuVcmV+xafkSTtv1mm3cv2/bz1atgFPi2KnzVpleoAlWLOdwdsMJUTYVNRzbDi6I7t/7sq66Vqz4hul4UlMy5p6QJytX9bxmtwgoTbARK03P2tzVa1aoVGxrbd3W+FByxK0pQESx+PJFaBfKsBOl6kqTlA9pSPHVPyHIOaBhxIDcIk79yWelCG3RqNVANXFyCf6+/vS5LRFeYYtN0Kp45I4SCiNfZdMzN6IHQ3qXTIH+9GH2YzSdJwwkDCQMnHwMdA1hkacE1nzyB55GeGwx0C8tDipL/feO7QAT4AkDCQMJAwkDu2AgruyNAgs2XF+Rqki6RTb4Hrfp8xdsfHLCOhjsNp8+tBqbonWam3g/yaCLQW9Qgd6ll5R1cjAQ35TtrQ0sTrKPsIp8kdAKmxvWLuRsZP60Vc6cwxlxHJOIVi3LazsThPveldjOycHMu42kb333uzWUah8tBmRAdAdPzY7xQVihZCMXL9ro8nPL19btyYPnNjp7jvisFT4Y2W7l7o7JkEoKXJ7XTBqN+NIHn8842vEcl977aIsH8RbcOcdpx1rturU3tq21vGJbT5fgXeB3rGojZxaysBdc+0MLo5Xi44Z1zUCllDCQMJAwkDDwXmCgn4/EASvPJwZjRjomDAwZBvy95T2V7KJllJ6y6yDPxMx0TBhIGEgYSBg4eRgIrkda2esJpuAhF9zrNmfF+TNWXl+18cJj2+S41GnaNBujVQm5kC+O4s8U6olfpHS8MaBnOMj3Pa8vUzKtP3FsTnJ0a2Gj2nz6xJrthrVHS1Y8d9rKc/M0pIKxYqwU8KP6upNSDwPJAbCHi2N15kscMoj9xdab7W83V4Xglp7HoHv20x9Yuzhm1eqUbT5bse01ZsTw0NWMSAi3IBKsYAut7Jg+kT29CG4M75XM44lrzZYHgi+g1eTxfi7l29ZcfmabTx5bc7Nm5eqYXf7oY5s8NW2tDoTLq+sT5Jm5VsQ8pdrNmtVzTSlhIGEgYSBh4GRjINH6k/1834fRdd/hKMC8D4NOY0wYSBhIGHifMSDd1fXXnUiQjULrfdmZ3aYuXLXKqTkrjZSstb1pa08fW31lFQ9dwj5Kd07pxGBAjxP/wN5PrwejcyMuBlrZSvLtNvYRwiusr9jKvXvW2Ni0Nvs9zV6+hH1kFvsIznByapPDYX+SkKHfQOpOJA/kv0+XyTP3GD9tN+juSgcJmIAhV06fikdy5oOPbJkwCytPHtny0qLNXDhjk2fOEohcixww5sqVvfvR7NrgMcbS/oMuotTPu5xIQaDqtbqNErhbs00ylLfWntnyo/tWW96CsFVs7uwFq47PYGwnDgwxYHziMjPkhvb0LAKl0nMJaRfK1T+kWKw/b/B8sMxrmhysnq4TBhIGEgYSBg4QA4M0mq7cKWFPXQ4IvC/UUeO7dPBCuZSRMPBmGAgKWggLJbEiihb952/WYiqdMJAwkDCQMHBcMNA1HWQA9y02xayAntvCWaxQsYlz51gw3LHFuxjvltesNbJmnZExaolboBOzf0xKJwMDuAr2Eo83yrIeMEEOaxjxm+vLtvTgrtU3Vng/CjZ77bqNnTlvrWIZQy6rxWnB7VPeUpQsQrOD0uzrJOAeMCf3LBlzT8izlQ2w38DYUSwFfQ75ouUnizbNVEmTGCXbm6u2+OCeVapVq07PEHIh7DzZUYBplR/8Sk4Ifg5yGCIzeZaKjI6OWWe7abnSiLWWlmzx3h0YWZ0NzyBU5wh5MXsa4zo7eepB+VSSvHB7nrgHCWNqO2EgYSBhIGFgeDEgPuLGsQxEXYsfR0E4y06HhIFjgwF/h48NtAnQhIGEgYSBhIF9xYDviIWP7uiEjc6fs43lDauxf8wmq1bbrGAdX7iASoztASNGnni7KR1vDMi8EfcT6o2kk0W+lSG3bVsrKxhyv2eVOPsKFfM2s3DeqhNsCI/3YZsN9GQeSbJDD3t7OUtfzl6wNMxl9MZnP58h07m+BP1auiAIebFqhelTNnftMtdNK+HivnL/ntWfLVo+epEWWtYmLED4glRPr4aOAym6Ygxkn7RLxwB/ehjwHIYpvITPJhrPc7jYspcchlzCV5TL1m607Nndu7a9soS37pqd+fi6Vc+dsdyIDLnMQLaYQ2liPOf5JEX9pL05aTwJAwkDCQMJAwkDCQMJAwkDCQMJAwkD7ykGUHBzef0YP0a6/Mg0K4OvWWVyJmwQvvTMNh7c4ZzNr9gQrd1mVWvwdAoIC5bBHvIGr937TGEi0cp36Ou9Kuls/zAQ8Sxc78S339FT8J+c1Nqc6te1k8jjkBXMmytrtkToye3151bfWrH5q5dt/MIV3o1J6pYIUZnD8NumJfn3Ju9CkLCnlDxz94SmIS+k7yim+O7rqPz4jVkFD905W7j6oT36/nvbWt2wlfwTm+Z+6dQURsgRDJKEZ+DXwYtXG6GFX2hY/qP6TGUjfp+S6I/oSURr/9ijXVvBKjxhDG89f2IP73yPRy7MCW/nhStXrTg2bqUJdmbUUhIhsEvddms1NJX+JgwkDCQMJAy8nxhIk3zv53NPo04YSBhIGEgYSBg4MRiQDo15Lscq4Q6OT+VTp20WD9znN35rT588tDJL7idKZRvTvj2jo2jTZcISYnHAAhxi7mIi7FC/ixCMfIrlIA+qLPXCEsacdDxQDAj9WQduqOdcjySaNmLfKlPgRl6GD8JtbK4s2hKbnW1uPLcCoSjPnL9k1ZlZy09M8n4QGpTysjURnMOPviF8bCwdX4mBZMx9JXqG/2Y0B7rRkS/Hj7K6KoUvIxwKEMb8lJUWPrIFyOb6k/vsLLlIKIBbNrk9byOTuLgXpz22a46y5p662B5p0wNW8zEGg64+T+7vk7apuLGDBKBLJXwQu/yJg85uuVFVYGWpQ4MDReKt1x7VjOOwr6SuQ4sBoRHegAmIjnhKa8s3O7t541fWaK3Z+OS0nbrwEbsyXrAi5/KO9ojgVBJDywG0b3amttVQN6mxmMTKdtyMN3rH19zuFUxnCQMJAwkDCQPDhgEn4bsxrN3yhg34BM97jQGXvXhPXenSkR+OWK+TWt5rnKXBJwwkDCQMnFQMRONeGB+2go7MTIEpaHse4hBi0F3AkQzj3diUbT1/aFtPbluhvmJjZ68SS3eCBaw4PpVYVSxDYJuNf9pU1K5auUaGNtpTm2I2HIPRD0kKZTruO5MVPNRDm1XPJzNJSs3sKgxR3tYdvGxddo1DDo8Y51uZ1tt4WXfY5CxvRcVCxut6e/GR3b3xpTWaq+wdNG5TC5dt5PRly4/Peszc+BzlHKenqv5C6vUd8rPsPRxCzT0UPAFFkjH3BDzE+IL7q9/39spo2E2yQBYgdCz1Ly+cs0m+r/q9LWyQG/b83n2bnNq08RlMh6MQTnachIJSlY+VJvQJe9tYdkWo+1rtNn9STjS2frS9OC5KqBCIKbTwVibmT6e+RUD353bn9m8hXNxkV8azH35g5dHzVhidBe/gMyYsvzLeej99zyre3nlUqZQSBhIGEgYSBk4yBpy/MkAP2XOSB5rGduIw0H13T9zI0oASBhIGEgYSBt4FA2496PNYcrW3VLHKqXmbZPVqAUNf8/Ftqy2t2PLGdzZz/rKVJuXIVMUJSquE275aOCcbhluKM+shtgilqE9zpivPS38ODgN6DG4j0eMQxh3l2Cr8sQRf6jJGXH9229iYVtc8PvLjBzcwQTWtXSrY+WsfWGnkrBVHT2Pbr2TA/v/svUeQZNlZ/v2mL+9d++6Z6e4ZjTTSCOkvIVwIkD4iEAFEgCIggkA7dsCWDTs2LIhgI0ygCBa4YAUEAcKEQMgbZNCMxvZMe1++KqsqK833PO/Nk3kzK8t2dXea53RnXXfuuef8zjXnPvc97wmqMNKk9OHpPr5ydFvKEnM7vEb9YtqnDLUvVbhAEhR00ZUhDUF3si9r89fetcTyvG0uLMExdRIOysvQImHyji9hKR8UjV9VcJSgcIbrrXoj3efQnbO5ekNy+ZTFZc59XbUIviJa5dYnpaJVNrasBCG3tPLQVuFewTYLVunvs3Pv/aDlxucw9hxG6kziC6MnRPjVtLjGj1FNtL5acyIgAiIgAj1IIPZ46MHSq8giIAIiIAIiIALdSqCmRaCAqXTGBkfHLZe4aBvoubpy+5alt8q2cv26TZwoWGpi3EpD6NGaGnDLzQp1CLyZJ5IwoKJyiJfo6D292nKCRhHNqSV1/OcPSbM3cZRyfYCzwBzraThdrQP25rZNDP6+tmLbC3dsBT5yy1i2XM4uvPdl6588gepDvSaoj+wSauL9Ltu1uoGAxNwGHN28UFVh+WULN8Fk34DlMidtOttva9fftvzDu7a+uQph8r4Npou4wULQ5RcT/+zCKxg//yrGi5dm8N0mRKI8tRsVb1r+uPBSUrfmpmS4UXHtdsHKa8u2jVEZVxbu2wZE3f7hSZt67qL1jZ62ZAo3KfgIivZsYlU9DjYqiIAIiIAIiECNAD8W8tf01Kht14wIiIAIiIAIiIAIdAqBuJAb8pyEoJsdm7RUbtCSA6MQcq9YEYOGr8/fhfSwbenklCVpuMl3bwygFpl/Ru/nDWP6sM2E92pEC6/x4RCaHjMBt8yltbS3UFkvUXuVA5dxUDQOclaGRe72/AMrrq9hfKZFK6EXc9/QuE0/ewk+cqGPpAdQWVBW1Mg9ttqRmHtsKNsvIdjgRpmqXTCY4d3OP69gK3zWZDCq5MjFy9Y/O2nLt2/aCkaXzEOkHBqesIHJKcsNw48udikirQTE3CTN513gbb/yHilHjigAwtTfpPlgiNZV4EahCAft9J6QTqXdh+3W6oot37hhxYV560vn4N9n0GbOXrLM1IxlMdhZAkKuP1T4dYpfEHmH44qmwPrZubYpkhZFQAREQAS6lgAfQQ2PobDMEusBQQoKbUzA2zo8g/0tL8qon89tnGdlTQREQARE4PET2FMvgJZQQQ9g9hYePH0Kwm6/bT68ZQ9vvAsjs1UbWVqxobEpy548i+cL/eXSKA06BPbjHIdlZxPJtQ7M+BTLCsdMINZAdfkHbjgp5lbgIqO0XYI+Uon0EQjuhfUVW4I+sjV/z/qyOSvCYHDqmWctNT5tOfjKpe4EWQXTah5dj8J8WK5lPVrB+t2vGbxj12oau62vHaKLZiTmdlFl7l0UnNY8s8OFg6uDow9CecSNdNhycLkwlobDcVxhawsLtgYfsNtbW7j4Vmx4esrSECmZAJOgY2u/k3KNf4rBTMeGcJvglEAYMMUix4GjA2+OsmhFrAOP/PKKLd65Y2sPHtggWFX6h23i2ct4CE1Yop83KhBy4RYJMEmZWDlR/REBERABERABEeguAt6s9CKxfVi1tgntzO4qqkojAiIgAiJwTAT8rRsPEB9oPZeybHbEUn1YAVcKC9dv2joMppKFki0tLtn4DHSIMfQY5oBq6PWa5As6DMzCW/sxZUnJtCTAmgJuyhsUNiAepTAImrvthJEb9ZEK3ChsLK/a4t37tvrgrg1wZPj+ARuHf9w+WF8noJWUkQBdMURaC6eoQ1eVMHmEwDZIrweJud18BsTOcDpH8IsGE865EwH4zy2j0V023hwx6uDIhI2dh9vx0TFbv/cAF+YS3AjAl+76kg3OzFhmGs6qk334KoavYbiYOZqhj2jYFYIubyykFP38tED5EhidMlkq2fb6hm3ML7qT9tLamg3jxjQ6M2vZ6TkIuaPoIjJgJTyAcIuDOwYIwJG5CmAj3Vg9eLr6IwIiIAIiIAJOIHr24GET41F9Xlef1t5RhNGOEpDUUXc9yuG0T48RiJ1fPIO9rcm3PgUREAEREAERAIFWbRDqEBWLfOCWMaVamMIg7UNzZyw9NGrrt2/b8sOHVnyYt/LKvA3PTFvf7Cl3E8knDO1y+QGRacsq9zGeZrGPszXuZVhKwzI3gWorrm/BonrJNmBJXVxds6G+IRtFb+8MdKMc9ZH+QSvCmhfDv3tNWRkD33kbIWon+LkRzXoh4sutzpvHWNKOTVpibsdW3cEzzoshfkHQ14yLjFxbpiVFdEtMZIbgdgEXGXzlZuA6IJV9YPm7ty2Pr2KFwpZl1/M2gBEoM7gwk3Bk7Wb28IXCNOhO1x2U80NLLFSCqBlbR+vgPdv6zGzswo7v6vNN2xg9HtyXTnMCjIS80HI2dl/Csm/gxkh39cTwhwI13CsU4MB7A6Mxbq6s29ZaHt0JEjY6iQfK1JyPxpnAgyeRgSAOIbfk+cIfwODIm9DKPV3+VRABERABERCBZgL+HPWnKZ5CeGbUfpjxRwh24EfG6JndvPdBlsMz7iBxFUcEDkGAHyC8MecNJ+zItp2frYdIRFFFQAREQAS6nQCfEqFNU39iwGDK6EKBb+EpyBE5jHmWtoFcn6WzKcuMj9jKG+8YHK9afn4BRqAJGxgZQxwMoIUBxxMp9JxNoFcxLXWrqYe0PVGmy4NWV4ZWVfNTirKwW52GnZqnIePN66vLlDr8OLtsb6fVLns0ZYhajfcsxvpQjhDP7fXceA+E8cxPwO9kpbht266P5G1jNW+F1Q1LQQQZmZi0vulZy02NQ8TF2EHo8V2ikIvEWL+QfyGRQIL39m21YmJsvZqqy8312JRlLcYISMyNwejW2eYLItzMePNi4C2Q80lcaPjkBaE2iQtxCBfiqPvM3VpftZWVZZjOz9sG/KH09/dj/YhlMYhaanAU91GmAAETjfhSEdapuCm45S7vnthEv+X0t1sP0YXMzdiw4x01fuOt7dPi4vZyYT03NZcxWlPdKUTAe0cRX5L8ho0bP4yR0VUgiTwX4Wgdy7wbb29bBYOZ5ZcXId7iS1N+3TYLBZQhiwHOhvEQGbVh+MZNY7Azg79chkSa5S2RQDUfSKd6aI/Q4k/I7z7RWuypVSIgAiIgAt1EIP7M47Mhej7guYIHBJ6qWIOHV3hoHLrg0WuKnjWHBqcd9iAQGQWgveTibXSust1DP4ZRq1Jn3B74tEkEREAEeooAnwjxpwJbJlQeEugd7O/l1CDwPCnTjy7+ZUambWwIFp42aJaHaIiewvnVZdvKL1sul7W+4QFLD0CrwBg/ifQw9o10hkq5CO236Gl6ewppJiFEJCH6hicUjbvYrAqhgkOzvdWymVUz+mLseIxYabgv0+TAXq1CQ8+rXdLw/ZhQLN14Wi3TaBW3nkarrTuOzkhYWYRgTh0nmY74w6cmGGFdEqIsYW3ieQ/Dvs21efjGXbDNjbzlNwvIbtpyg4M2ODxqQzB0S49CyM1CZGei0FqCMsI5ngE4BFiFnHEBq0Pgaiz71mqU+OYQjdPq5viqnp2XmNtDVd94i8FlhSvEb6C8UsJVwTtaqg9t8YplR/stA7G2bwNfXHADzS/ctfX712xr+aGlF/ptABeu9S9Z/yB87sKnbhIWvS7q+sVKa1+a1eOfX4n4moMr2P/xuPiFrz7Nfne5rTmEVbXrP0RgWmG+Nq2uqd74WDQ/Np8TuLFELyFYCQE2hfzRiXcRN6XC2oZtrazBpcKaldaXMSJjHl+UEvAbPGKDcDExMAVH7ChrEi4WKvCjS+tbpstA1xNRiOcmPl/djAnX8uf5qq/WnAiIgAiIQI8RiD6q8rmEgkePE5/3XiP+CEEj2h/WrZ8n++OK9tvx7Nx/R8UQgT0I8LyERRVevPl2Fs7O+KeDsK56Wu+RljaJgAiIgAh0K4HmZ0DU3OFzA9a4rgzwaRHF4hzfsd3iFqsGTgzAyAo6xOiiZVYWbP3BLQi7D9Clv2wZGJVVhpYtNYQB2/GununLueUovemyzYOnVJQ6Fsocyh1iKwdQ8w2EXT0sJ/TduzN4bnx19XUf81zXGChO+togbITGXGM0PCrr6UXlracVCZxYruWjNtOQSmMa8U3IQ0iumg+m2ZxKw3J1gRpGIo24kDISKeg1dKUJcbcCUbyM8YLKGwUroncy9ZHC2oIVNlfRGxn6CLSffvRW7p8Yh140DHF9BIZuGXfhyXZBaBlQHmYIuWnIQ6wIcXyME4oTi1Kb3S2NWoQempGY2wOV3fqEj9ZGF0v1VupXDdfgFyYZOCVPZ20cF+wAujWswNl1cRVfx/JrGK1wCdEWbRtdHbgtBYvdDC7kVA7dH3JwPwBrV/pIKeMOWcYdIjpKsMrlAaqh6Q4atvBGXAvVKzq+qnaZx692vxMwFlbif7j5hv14E0zSihZCb3lj1Qr5DSttbLvj7i24kdjMb8K7wrb1D+VscBJ+gmmBjC9/GT4kYJXLB0zFy8IXGX5nqqVcy2qYiWcrrON0t/XxOJoXAREQARHodgJ8fvBDYDTls4E/vnbQwjF6vuBlBy30yHIFGw8dqqnqwXNoctphDwI4ZdlGq6AtxRdyBrbxonM5nMe+Wn9EQAREQAREoAWBqO0TPTHimyED4gWe2gENapN4987B9UIWPWQzgwO2vYhxfZbuwQ3impWXNyyTW7Ii/OxmBgYhKPZbGnpEMpeB28isP5coM/jg7Xz3r1rPMn0PmHC+JiSGlT6NNZxqSmlYV90f8bytFlmu+V7RczBsZ3zMc9HT2Lk/d/I0wiau8B1iaYT87ppG1GJEgWsh2juWBjdyO1ZFa6nS8F91J/jCpT5S2Vy3EtxLcrygInSS4hbcKmwXrAB9JAOr2374w01TH4E2lMEvhR9F3Ao+7pYhBNMSmiFqE1TL5muiP67vxPIZ2+SzIcdhypV7RG/eveeWJeb2QJXHL4ZWxQ3Wpa22+Tre5HCB9mGAtOzAMHyjrFgfBN0CLHQ3lh5aAVat2+uLlsv2WRHuCNJ9w5aF+MmbKV0xJGCxm4RfG6bBq5H5CTeOqozshwkXashvmHJ9mA/71+6ptVsFkwgpxKeYZ/452Bu+LtFtwvZWHoLulhm+MG3CWXcZ3QRKBdzAID4P9+OrHsTbHKxwB6Ym8VAYxL2XDxX68qEFSnjBjm5/0TFruYvnFLkJ+WDeFERABERABEQgEOBzg0IuLVPY8OUyf3xuRNa4QRzjmqM/TcLz6egp4PAKItBAgO+VoR3m5yeXvWUXzreG6FoQAREQAREQgV0I8LkRb6NQJaCZKNb7925XdPGenrIBjNtTHp2wzOoMxFwYlM3P431+0zYe3rcNGJ9xTB/qDuz6n8EvhWVa7Kbgv9XgQsDbWUwXPxdx8dxyN4v8gB7PQsucxiPE55F7mgGH0Lgpato1lI8R45GanpsN1mzVRD0K94nvF59nvKZ0fNewT/SEjp7bmIdom0AvbOoaZbhPyEPLKeGXgD6S2MxbhULuBnQTrCO3zNCA9Y9B3xmfssGxqUgfYfsVSVSS8FsMnSRSRlrlwTNS++O53ida2Mypx6/trZlmAhJzm4n02nKsRU5R132+VBn4hRS7mmiRmsKNsi83YX0TI+j2MGnLD8atBHP7TYi7JfhXWV5cNoM5fhZ+dvsg5KazWUvBmpfTNL7aUNBNYur+azDvLhaqN1UeNpoNB41WxJaqOUM83jz8DlKd8lIPd1LO4mW4TAfd+JWRr0QKX4s2cLOH9e02fOJuY5rGwegbppLCTX9y3PqGhiBYj2IUzQH44MFXJuTZuyig3J48s1P7+dMFS/VbzM58ck19OxYOGXameMgEFF0EREAERKAtCfD+zh4rtGTgFP7b0a2tgO6ECYz26y1kfwREVrlH/zjIZ9CjPIfaEp4y9ZQJRGcvLHgKcKUHiySOi8AX7WAcEFovYfqUs6vDi4AIiIAIdASB6OlCYZD/aVXLd366SEgk4L+VrhIGx+DqEb+501ZYWICl7gIG4kJvW7znb9GCFK4h0xvrllmGP1f41831QeSFFlHJQvbCu3+agi8HTYMIybFzktA2/GDeVMIBW4qpu7WloviJ8HWzphTEYVf3dbXYDxLfWD020qkFzseXuYEf+TndOx+MsTNwH+zPPKKdWSoWYGm7Bd0GvoXBo7K5Yfl1GrvBnQIE3CwM8LJglIYBXw7uEzKD/dY/Pg7L6BFLogd2gmMGgRFUFNdiyvSty2XqRBRyEKKWa3MZfNO+f6pJ1OIdLZXa7l0/IzG366v4AAUMVw0u8tAQb9wrunHwKw4F0wRGJqRPXZje2vjw87g/FCyPAdLya8s2NrbiomkZNwb6pF1fmXeLWNoepdHQT+OmmoRI6l9wao1/3FCrV6ofH/kJFy6P3DJgQ3RPxAzn8cdvpL7IpYqVcKMq4Ma0jQHO8LYMUTlpGdzM+zFaZiY74V0xMqPD8LXTZ31jEHHRNSOBLhl4I/H9UUokHDKG9HmMWmZwoKZQ3xbf0HptPMae882HecTk9jyWNoqACIiACDxBAhRt0TUN4m0RHx3X19ZhDcHHK270eNb6R8kkG9t4otVeFA6bPT5Emh8kh01D8UWgkQDP0Vz1hW4VbawCXqA5XkrkEiQ649hcCedt67ZlY5paEgEREAER6FUC0Qtu3Sds/YWXTSK+80dOAWAFyvF92K6BNWhu9ozlpk9ZH9w/biwtwn3iOoy5tqzI8W+24C5gE+vX0AMXz6ltiJccGq0fxlsVvOtHWkTC+tEjl64hPUm+7fOADYH6SGhHcdq43Xvwhs21/Wj0xcAN0W+/NBrT5THCcVyRQB6iKVON0gzbq2uqLiSipfreQcjl4PRlPKuL1EcKcCuJXsll+MVNw+AtDcvlLMZMyvSNQd4ZxbhJQ27c1j+Kntb0RUzBm8Zt0EWC1bTnEFwoiFdzgEkEgtvqqkljPoO0Ut3pQJPGFA60S89EkpjbM1V9DAX1Cxjp+BWFP7yo8VUGI4LZwBhcKuArWeoEXkDpdmEDTrIh6G7xZgp/K2WIqnSozRtRETcOfhEqFuFcG61/92PDZHH9u/WR3/OqN6wGHzSNZeDLAbPi34YwTwcIzFOZvlpw02GXigwGK8vhJlPGl6g+3Iyy6J6RzWGwtoFxS8AtRHIAN6gcHgze9aKaPl9IwqG8rFzAmgRHFY8C81mXnMNaTUVABERABETgIATg+sc/oMIqBNa529tFW11ZsQK7uPHxh4+h5SR/eEb6w7H2VDpI4rE4/kCNLWtWBB6dQKIE6ya8UNOXIc5Y26pgpGvD+Yo2WxBwMePzbKvV1uHQEnYfnb9SEAEREIHuJcB3bDaE+KbtygBnayHSWrmCoi6msCRND4/bUD961cJKlL8SeggXIfCW4P+1vLUCXQLj4uBXgUsBisKFQhFGX3AlgOk6fO+G55JbmNaOFGZC+4taAH6+GATMaHHH93bPJPJWE2BDGlV9wwsUK5QfKrZcOwZmeMzooNUMHSYNEuKzGPoMBnWH8gpLZBi3wXgth/GNUhByM/BH3AeXFOksLG/Tw9G4RzBwS8IAL5GhVIj9UJ4KzKTxOPdFz341N5EeFPLUmNMQRdPHR0Bi7uNj22Up8+rlzZU3L84zcJ43Wd5kErgh0NE4bqIcLIw+cyGg4luX2XbJzfYrMOv3Gye+lJXxdaxUquDr0DZE362owc+GP5NlqN4V4++wUQ5wJMx4vGo2GMeFXb/DYBusRZIQbOnaIYduAinciLK4aaVoEYwbVoLuEzb5bY5CNBJhOv4CUk2Q6WGVL+FPdOPiEh4aEHRrecQaBREQAREQARE4CgFaMqbx3BwZGjfLlSwDqwrYRVgSz6MyXgAo4rJ5zHeCuBh2uGPpiXU4Xop9IAJovyVwAqeSWRvkwLg4V7eKazaItlcGAm/42B1ekEOazcthvaYiIAIiIAIiUCeAZwy1ALSF+Aa+syXDtZQpGShW4qmDZw9aVb4uNQpxcgKLGNSrsr2CH3zCbm5CxN2OLFKL2MddDpRsC8ZnlVJVjGQ6TJKBAoOnFqZcyXjRsbkUBQqdYT5Mg9hb3XdHWjvT8MZe2L3hGNXEa8JwOFg4RnWnJsvchnzy+BjUPjPQB30EbhTgVziTwbQv6pXsA9ejx3UZvnJdnKVhHHapoHNzJLxEVNge9RCyX9Ve4oIuSxwd22f05zETwHWy41vCYz6kkm8bAtHV1pidXU8HRuYv3EBwFeM/BdHahY2tJX75wT9e4+zSkMaNNY2GvgfcNCuwyK3AgoO+aujHlqFC69uQPHfk3hRZGeKJR2uiv76Z+4V9q/O+lfuzO0DcJy82VJNkFBg9uVUwT3/+krgZ0ddLpNwyAmMhYJ9IzMU8XUug/CwfN0TCNmbjCXOxFkIitRWHn2kuf6wMh09Me4iACIiACLQTAT4lMPQELBsxIjOeQRRzowdQ9KzBuMDVx9ExPE/aqeDKS8cTKJW30Majr0GeswlbLsxbXxqDpCSHcdZGgm78FUNCbsdXuQogAiIgAsdGgO/T0Tv1ziSDD9pIrkWs6vtv1BLiQrTCpV6uRARa1VbwoZG6QhrWp9Qj3f+Pwbl7AkZclDD4h1P6eKc+gZf8CnoKR/G4HT8PQe/ggh8gNq0f36NyS8hgWLFjCm2kQdBtTCPaPyrTjl2rK+puFkLeGsXc1mnUj+MDntEuzYWNsC/93kYHCM9oX+Yh3GExd6jqIyF7jI/5mj7i67HSywdDBK+bEJnTMB8dZ7+/h99jvxS7e7vE3O6u30cvXfUCjxLCQvj0FMRWWrciMFpDVCzvuBg9Au8OTAeTxxX8nsF87XHzCJua89G8HOL5lBv5Yxm4ImwMN0SsUhABERABERCBAxKInironu7PlWin+NPlgMkomgg8VQKh6RS1iuj0ir/QRnqqWdPBRUAEREAEOo5AECxbZ5zCZaSfRs+ZhnZTeCCFXd2iFSvD+jAN21tOGelAEVvu3T4rY2R2fSRXN8S3e9GhbwQEO7ahhPF1NaE6XnLpI3Eaj2teYu7jItst6YaLuFqe8AUtfL3plmKGcgQrkt3LRyDhARPuYrpZBX6aioAIiIAIHJ4AnyxNj1tvJ4enzOFT1B4i8HgJhJZQcwsonMc6dx8vf6UuAiIgAt1LYLcnTPeWuLNL1qoV29w66OwStmvu5TO3XWumTfO1u8jZphk+ZLYOVj69ohwSq6KLgAiIgAiIgAj0CAEJuj1S0SqmCIiACDwWAhICHwvWJ5Ko6u6JYK4eRLSfJG0dSwREQAREQAREQASaCPAToT4TNkHRogiIgAiIgAiIgAiIQIcQUEv2SVeULHOfNPFOO56uyV1qTK/eu4DRahEQAREQAREQgS4nsJs1iFpHXV7xKp4IiIAIiIAINBDQk78BxxNc2K0t9gSzoEOJgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjsR0ADoO1HSNtFQAREQAREQAREQAREQAREQAREQAREQAREQAREoA0IyDK3DSpBWRABERABERABERABERABERABERABERABERABERCB/QhIzN2PkLaLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQBsQkJjbBpWgLIiACIiACIiACIiACIiACIiACIiACIiACIiACIjAfgQk5u5HSNtFQAREQAREQAREQAREQAREQAREQAREQAREQAREoA0ISMxtg0pQFkRABERABERABERABERABERABERABERABERABERgPwISc/cjpO0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0AYEJOa2QSUoCyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwHwGJufsR0nYREAEREAEREAEREAEREAEREAEREAEREAEREAERaAMCEnPboBKUBREQAREQAREQAREQAREQAREQAREQAREQAREQARHYj0B6vwjaLgIiIAIiIAIiIAIiIAKdTaByDNlPHEMaSkIEREAEREAEREAEREAEHo2AxNxH46e9RUAEREAEREAEREAEOoLAowq6EnM7opqVSREQAREQAREQARHocgJys9DlFaziiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIdAcBibndUY8qhQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQJcTkJjb5RWs4omACIiACIiACIiACIiACIiACIiACIiACIiACHQHAYm53VGPKoUIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECXE5CY2+UVrOKJgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAh0B4F0dxRDpRABERABERABERCB4yJQ3iehBLbzp9A+BPars/bJqXISCOxXZ7I5CaQ0FQEREAEREAEREIE4AbWS4jQ0LwIiIAIiIAIiIAIHIlCxSqX6s8qB9lCkJ0OA9WKqkycD+wBHCddJFPUw18ph4h4gI4oiAiIgAiIgAiIgAl1CQJa5XVKRKoYIiIAIiIAIiMBRCexnIbhbumUrlkqWSCQsncrsiBRErGRS3853wDm2Fc11V0Z9hMRbWVBLIAx0HueU5z6vC4ZisejTdDqFDyCsr4QlkymfcjkS37G2XnEeP1qM11er+vSo+iMCIiACIiACIiACPUUggQZUvJXUU4VXYUVABERABERABETArFkQPAiTurDEplQJom6zaNu8fJBUFeewBJrrLhILzZJWrhStTLE9CfEwkamKhY/S7JUof9jaiccvlYsQdrf9Osmkc/FN1fm6sMsVzeJu5NokEohb7KxVIiACIiACIiACItAzBCTm9kxVq6AiIAIiIAIiIAKtCbQSBFvHjNZS1Eu4lWGpBMEQ+iDkQltbW7Pl5WXL5/OWzWbt3NlzlsnutNjdK2VtOyyBUHdhyv0rqJOKLS0u2eraqqXTaRsdHbWB/sEmwf2wwqDE3IPWTrAVoSBLy9xiadvW19fs/v37NjAwYCdPnrBUKunXTSJBrnBZ0vRRhddUJOCGo9Y/oIQ1moqACIiACIiACIhALxKQm4VerHWVWQREQAREQARE4JEIlMtliLbrtrS0ZIuLi3b16nXb2tpyIbdQKECoSlnxR4v23HPPuYUhrXR3Who+Uha0c0sCkeXtD3/4Q3v7rbdtAXWThaB++dIle+7iRRsdGWsSdFsmopWPSGB7e9vPd57zd+7csddee80ePrzvAi6FXl4fJ0+dsNmZWZuennbr6eHh4Uc8qnYXAREQAREQAREQgd4gIMvc3qhnlVIEREAEREAERKBGIG7FyZVJ2y5uois+fXmyOz7sBCsl9+VJq8FEImVldBFfW1918XZhYdEePHhoa6urtrGxgf1ppctfJCRySpegg4OD9nP/38+5eEjr0Ea3C7IyrFXHI81EdblV2HDxMJPO2Jtvvmmvvf6aC+sU3Sko8nfmzBl7/vnLNjk5hboOVp/B7cJB6kOWuQetKl4vpTL9SSftyttv2Xe/+93Id24i8MZVhw8c4SMHpxR1Z2dn7cSJE5bL5qyvb9D3P+gxFU8EREAEREAEREAEeoWALHN7paZVThEQAREQAREQgV0IlDCAWQ7CEQdrKrjwxwGaSuVtW1letDt378Kq8AHmV1wgpH9c6rZV7RZpUiysC30UDituuZuH6PvApmemdzmuVh8XATLPpPvsjTdfsx/84P/cSjqIuDwGRd27qEeG5y6WbGZ6BkJiijWHNRQY+eO8wnEQSCbTTnR1ZcX48cMHQYOQ69dG9cJhnfAXwvXrN9wNA62qB/oHbGx8wqYgvE9NT1l/Xz8srHMu/pYhEkf78eNLyteFNDQVAREQAREQAREQgV4gIDG3F2pZZRQBERABERABEdiVwPZ2wQWhArqG09r2/oP7Nv9w3n3g0rqQbhPoQqFUjKx1o4QitwkUp3YL3I8+QmfQlVxC4W6Ujmc9LXIp5H7nO/+LwejKqM/GemG3ftbh7du3om0QFCcmJl0grAu6x5MXpRIRSEFoXcX1NL8wjw8jZfgurn/waMWIH0DW1/M+aB0/mMxDBL554yYsdPssl8vBonrSP4xMTVHcHUASUR0Hi/hW12LYFo7XKk7YpqkIiIAIiIAIiIAIdAoBibmdUlPKpwiIgAiIgAiIwGMh8M4777jf2zUM0EQLQrpO4JRCEEUlWgFynkJQXQxqFqbilp2RxSG7iq/AMnFzMw8hi/5A4XtB4bEQoGsFWuRSyKWP3Mh6mnVCC2oMrYU6pKDL9Tdv3rTt7cifMbv20+IzEnQfS9Z6NFEyL9k6/ErnIdDCVB3/YUmLQc+aBdYAKIn6gf83357P021GEtdi3j+qZHNZn969dxf1lfVB7SYnpuCWYc7oa7e/v792jYb0NBUBERABERABERCBbiUgMbdba1blEgEREAEREAERaCJQcZH22rWrtri0CAF3AeJe0V0ncMAm7woO/a8IQZAGtxT/vBs3FrYRj5a29O1JsZA2gX0QkLKZrA/etAHxaXNzy4+X68t6mpubmzY/P490S0354CKFxkhsjDYyxcjSMFrW3zqBZlbRFoqC+Xzebt26Zd/73vfh97hgmUza67RUgt9it86NxEGvW/CmGF8sbsNC96bHY0pzc3Nws5HBXKgP1UNE+Ch/63VFAX0D9bO5tWFlfgzBcjqdcsqlcsUGBwYwH12ThcK2C+2sv76+HObLVoAlNXbzeiyhztbzHHQw74Iw63H+4YLRNQOtdvkbGhpyv8i03KVoTz/VzaFZSK5/nGmOqWUREAEREAEREAERaF8CO1s57ZtX5UwEREAEREAEREAEDkWgbpWZ9G72N27cwABZb1geFn8bsBqsiTsQh4KwQymvDLGJAlLFIh+6mUzGhoeGjRaCIyMjNgF/nmPjYzY0OORpLC4t2JUrV2xpcdEFxWDNS0F3aXnJrQcpDiscDwHWG60+b0DMo2X12to6BL0suuiXIepuY77PrW8p1lN8T8EiNFhbUyCkZS79ILu4iBUcdCuVSstC93iqB9dE2S1p19fX3SqayfL6otSbhfB66tQpO3f2nIu52xByeT0u4trhx4+11TXo6hUbGIC1LeLzWmK9lmAtzw8eCVj48hr1/fARhR9YeG1R0OUHFw48SNcMrG9dcwClIAIiIAIiIAIi0HUEJOZ2XZWqQCIgAiIgAiIgAoEARb9IsKVFZtHu3LltS0tLLiK5+S2VPYS4LSZFpxQEokQqEnjZjZvd8WnxR+s/WuvS/2c/LAsj351mI6MjEJEG7N1337WrV695mrQM5LHpf3cO3cEpCAfB2CPoz5EIlCEUUiSkP1XyXlldqXWzR+d+74ZPcXZiYsIH0kpnUhAJH9rtO3dsdWW1ZmFNi+nbt+94HhKw4uVgWxSB4QwA66Lz4kgZ1E4gkHB/uatrq37O87wn4zQEc1pCX7x4yegmgZzpjqEAv9VcXyhgfmsTFrjr9gBiO33usq5paVvcLrnFLsVdXq+8Dhl8G65t+kTmcVj3Z8+e9W36IwIiIAIiIAIiIALdSEBibjfWqsokAiIgAiIgAiLgBFxEgsBDwYcWfC6oQlSqwPKWAy65MORCU90HLkXbYVjf0hKXVn78DQwMQjDsw/6w/vT9mB58fGJfCle5bJ+Lh8vLy+j6fd2FY1oKUsylIEUL0Xjg+mjf+FrNH4QAXWJQyL169aotryxbEr5VK0nYUEMQzOUyNjY2as89+6wPcMY6oO/VyYkZF2ppxbuwsOB1SKtN1sPdu/dgzVuySxAYZ2YQLztQrRsJugepj1ZxeI3QKr0IC2hed9G1VoFrkj47f/6CjY+NU8b1uktj8LoU3FzkcgP+EaVSKbnlNAcOXIcfa6bDgQjn5xdQd4tuucu6Zr3yGgwuUnhMztNPNaesW1nmtqodrRMBERABERABEeh0AhJzO70GlX8REAEREAEREIFdCVAwdTEJ1pzsbn/mzJlIbK2KPRmso0UmLWtHYIGbgjXtyPCYW3qyqzbFQO4f2e5G4h677MdFInYpL5W2IfTmkNakdxH3rv3YDb3D4WZh0cWoKJO0RKRP3kgE3jXj2hAjQFZRYDd6DmB27fo1t7CmyFdJwO9qVSyfBP+Lz12EKDvrPlODYD4EYf7M6XM19rTOZqC4T4tOisMpCIO0pp6bhXVuIjSRJehG5A/3l36JV2ExvQbLXFrfQnf1DyoDsGbnNZKi71xcB5EFdGQBn/JrgusSbl09ietybHzc64wfX+Zm142DFNK6em0NU7hjoHsNDljIH69JukDhORBdszvzHM6HnVu0RgREQAREQAREQAQ6h0BoqXZOjpVTERABERABERABETgEAQo4iQS7ZCfcXcILL7zgPjrhjRMWtwNuUUv3CfR/m4QghNgel4egdV8UwrS62DDhME5ly6T7bHR03AdGgzmhD/pEsWpzc8Py6Co+MjwKkYlpM626QNmQlBZ2IZBw0fX6jWv2+uuv2TpEPFpbs34K5WhgupMnTtpzzz1jJ06edAvqKKFQbxW4yBiBn9bzLvS5VS+sqJkGBVwOuHUHbhi8634liS7/J6J5TySksUvWtHoHga3Clm1s5nEN0IKdlvFliK1Jd0VCtxd+9vu1wF3Jt864LrjS3UkavygOhffR0VErzZZxLmy6+4W1NQywhmuNYi7PBbrWoJhLYZ5uHerXL9NQEAEREAEREAEREIHuICAxtzvqUaUQAREQAREQARE4AAEKQs8//wK6YRcQGwIsrGkpMlH4CcFlpZqIG9buMYVYTNGJAi27i8/Oztq1a9cahKSHDx9CZJp262DGqwtWe6SrTTUCFO+uXb9qr776inejp1hHoY5WzplMGmyn7NKlSxBhZ9F1PxLxGhlXXMSloHvm9Bnfj4IuLTwZr68vB8F9wwdTKxWZJt01jLcQhWtZ0sweBOhahCIrOdLlAUVz+p7mRxMGWrMD/B4pNG5iXQfxnla3/f397vpkZiYa2C64VeDxWJ/0j02rewUREAEREAEREAER6EYC/q27GwumMomACIiACIiACIhAMwEKPfR7S/cJ/HGe4lAkK1UtBA8j5PIA2C3q1g0foEhrcgKuFpoCxdxCIfLjSV+J2QttAABAAElEQVSfssxtArTH4ubWht1wi9wf2uLioovvFAM5cBatPIeHh+w9L7zHRXR3f+F8WyUYBN1Rt9C9cOGCi4sUhOnbtQjBkaIgLXRfe+01e4gBuCLRn2fHwYXHVkfutXX0Hb0GQZdsKcTS+pkCLAVdXjDuHuOQ11kQ5zkAHn88B0LadInC9Cnm+rVYrS7uE//1Wj2ovCIgAiIgAiIgAt1JQJa53VmvKpUIiIAIiIAIiMAOAlXJ1oVAWgZC3KOghGkUgmAXpthUtR6MdKcoHucjdwm0MIR1KNJLuYAY+cJlV3CGMrqWJ2H1yzj00Urr0sHBfl8O6UbH1d+IQKiHOg+6qLhx47r98LVXIeQuwYI2CwtNurUwF3Knp6dhaXvWTp8+7YaeFAlTSVpnhjQ4E9KNphT7RkbGIOhGA+NxwLrlpRWv675cnw+kduXKFU+A9USrX+4T+UmuJRwOoCkI0GqWjMidgjgtc/P5fM1XdBBzx8bGEAcuT1AV9IOb8EHoeN3UP3DwOkkgrUw663VCwNyeStH/daoq5PIcwMFwbbGOXMCt1gTrKb5cXa2JCIiACIiACIiACHQNAYm5XVOVKogIiIAIiIAIiMDeBIIQV3ZLwSguVaWwfufeFGIpJG3C/+fyyrKLUEOwLuzvG3DBiEIuBSXfAHGJYtbwyLBb/ebX8y7cUrRah7C1tLQAn58jsB6kG4AgYO08Zm+viQRXMmA3/Zs3b9ibb71h8/MLLqZSuCuXt6MBsqY42NlzduH8c4jNgbMg8MLNRRR2r9OwfXBwxM6fewbppu36tesQi5chEEYC/+bGpt3Asel+g4N5TcDaur9/0Ou3egBNYgQo5jJQSOXAZOvwEV3k6H8eojqliwUOUBZdbxRli7hikpiW/BqjX11eT9cgrvf39cNlxpxbzkPKrabDKeqHYnBYVd3C65QhfCQJ0+pmTURABERABERABESgqwhIzO2q6lRhREAEREAEREAEjpsAu/PfvXvXu94z7XPnz9nc7Jx30aflLa0AI3EWgy7ByjcH6076zX3nnXfdOrdcFZpu375tM9gvm4Wg1axGHXemOzg9CnNbWwUXU99883VbXg4WuXCHAF+o9HE8MTluly9fttOn6P92G3XAAe52C5EI2Lg1crkwjEHpaNlLwb0IX7mra6sQCzlQ16CVcCxa6G5tbdmL73kR9ZbDR4AgFjem1utLcWvYlZUV28DHi3igC4Rgsc71/OiRgihbgZDLQGtc7scPJq/DxcXQUORfd3R0rKluW9WlJ6E/IiACIiACIiACItAzBCTm9kxVq6AiIAIiIAIiIAJHIUA/rRzQjIIuLQdpMcpu5CdPnvSfD7aUjoRcpk/BL4i5VK3KsFqkpeD9B/dta3PLKsORWSEFLYWdBCie0ir2jTdeh9/ah7DOJNtIxONgZxMT43bx4iUIuSfdsrNULjYJfjvTjNbUrX5DDArxFHTPnTuPY6SMg6LRJQaPx67+WxAl6UOXPlkHBgYRd8TF+7C/phEBirkhkB8t0eNhYGAAA8qNuQE7qxJec7E5slDn9XPt2lW7f/8hLLFvwmVD0TZgGf0APosHBocsl43S9m8iXoXhwsFCmI0fTPMiIAIiIAIiIAIi0OUEJOZ2eQWreCIgAiIgAiIgAs0E9laAKOTRGpehsF2wN998027dvuWuEyKr0S27eeOm+/Q8dRKWoaVtS0BkdKUK3caZOq0Q0xC4OCBTvjoIVD6/YRub6H5e3IJ1Kf26ahxaMm4OFPFoEUsRPaoLOFGAYEvBkO4OnnPXCs9AEKSl7hbqoa85Cey7AN+txdj6Cqw9ByHI9lfXhXMgstClD91nn6U/3pL7eV2DqwCKjEMQEykuUuSdGOexB9zFQyxhzcYI8LpZW1vDYH+F2loK5hRzo8HPcJVAleUvAWtqCuiF7bxdeeeKLcwv2jaYu69pbLtx/YaNj03a+PiE130twTBTFfjDoqYiIAIiIAIiIAIi0CsEJOb2Sk2rnCIgAiIgAiIgAk5gfxGVXe6LEAsrduf2HVgZrpsPjAWBioM7cT0H3jp9+pSLTP39A1gThEP62DWbnpq2TDbjwlQaVrsUqEoQdWlpOoUBtQYGsqqNXQiwu/3K8ooVt0sQccvgSFcWKWd++fLzGLjsPHhHlp1pDJJFUZe+V+Pha1/7mrOOr/vwhz9szz//fCQkoo5cC/QIkcVuLtdvF565YCnUFy2xFxYW/PhupYsjfP/7/wdL7FMSc+NQm+bX4KYiv7Hu3JIOOOHuKeg2gX6mee0EnrB9RiUkYXmbswsXnsG19jXr6+9zNxe8/q5eve5W0/SDPAJf03Sv4V9KGo4ZRPmGlVoQAREQAREQAREQga4m0Njy7eqiqnAiIAIiIAIiIAIisD8BCogUnEpF+Mq9d9dFPYpLFJLS6bRb3Z6AiwVaGtJXLoMLUzGlieLj2bNnfR+mwy77THN+ft7F4bDf/rnpvRgUviMRN+OWzbTsnJmZthdffNEFdA6cddhA9vuFJAY7G8KgaOfOnvO6o2uFEixNaa2bhuX1zMyM5wd6pMIuBO4/eGD5qosFWt/ykqC/XLfKxXytHmr1UYFIm7EpDGZ34sQJWLOn/UMKraF5rdHPNP3oBkv5XQ6r1SIgAiIgAiIgAiLQUwQk5vZUdauwIiACIiACIiAC+xEoY1CmJLr004r24YOHGMxpwy1yKeJRYBrDoEwUnvpgaUjRKXKv0CwWJu38+fMuBLLLObWrFETJpcWlyG9utav5fnnpxe1nzpyBD9uzbg1NIXB8jIOdPW9zcyeAGn5s4VP3UYMLjTsSocsFCLqwIr1w/oIPejYJtw4pCIwUl5kv1j/F3db770iwp1aQyYOqmEteYeC/oaGhyF9ujEb9aiFz+EGGC4tLly65pft2cdtdZFD4pf/de/fuw081fE3zu4mE9BhFzYqACIiACIiACPQqAblZ6NWaV7lFQAREQAREQARaEkjBqnZ9fd3eeustH+yMfm/pO5fd77PZrI2MjPjPDXGhSlHESiSoMtUlKoMbAFoj5nI5CIHYjn/JdNItPTmAGgVBWu8q7CTAgbJ+9Ec/5kIexbyRkVEf9KxYKqAeNr3bfuNece6NW+JLwSo0TOPb6vORG4ChoRF74YUX3QqbwiQHPqOlLgMHwUtUUKNU6BWcQBECbAkW1ZsbGy5885qgmwVaVVOQp6C7eygjXgauFi6479xluNjgfuRLP8n0Tz07M+viPgfAUxABERABERABERCBXiegFlGvnwEqvwiIgAiIgAiIQAMBDsp0AwLS2vqai7oJWGvmIOJWIMrOzc1B5HvBrWzpKiHyC8rdG4XZEgZFGxwYteGhUcuvQ+CCMJVNZ7wL+erqOgaI2kaX/bjfXIrBcbNDCoW9JhY2lnkMFrkUdiMuZe+Cz274rcNBO5vxGHHO8dTivCNR99SpM7H40XaKuxJyI/cigR60V3sAS/YNiLkMuVwfrhF+/MjY4OCgDxZIq2qGOrtQF9GUFrof+9Eft3+e/2ePww8eZL2NQe7eePM16x/I2ezsrKehPyIgAiIgAiIgAiLQywQO2vLtZUYquwiIgAiIgAiIQA8RyOfX4KdzyYVc+kzd3Ni01dVVWIdO2KlTp5pItLbQDF3MuQ+75lNApLUif0zrOFwFNGVEi3sQcAERmmFkTBuJsntEr25qFtij1cFqdP/9eycGz/fl5WXb2Nyo+ryNXFHQKpdibuvQKKrTtzQt2V9++WWPTktoXi+8VtxndetEtFYEREAEREAEREAEeo6AxNyeq3IVWAREQAREQAREYC8C/f2D9oEPvGw/9ZM/ZRfOX/Au4hSkpjEIFy1z6WqBIhMFWgp7rSxoub1cLrpvXbppcB+isDLkPqtrq7DMLeyVBW17DATo6kLh8RBIYxCzlZXovA4D2BXhT3pgcMCtq+vWuLsfn/H54eP8+fN+za2srLhri8uXL9vHPvYxm56e3n1nbREBERABERABERCBHiKwW1+1HkKgooqACIiACIiACIhAnQCFJ3YTn5s76aLSzVs33Ur3BJbpGiEIua1E3HoqZhSnaJnLAbQYYJfrg0JR2I3SiMfW/O4EggjbaMm5e/wWW0ISEHRRvQqPgQAtays4t/sH+nmyu59pWtcODQ45c//+scdxg8UzP348++yzdvr0aZuZmXG/xRR5dc3sAU+bREAEREAEREAEeoqAxNyeqm4VVgREQAREQAREYH8CHLAMXnBTSR/o7GL/RR+wLBKUIl+hjZaGFBkbFUL6C6WLBg7g9P73v9++973v2eLioo2Pj9uli5d82pjG/rnqvRhkyl8QcRsZ78WDbON8C1sFH8yOflhZJ43phpQOnn7YQ9M6gfPnz8HifMvuP7hv29vbdvrUaQiyZ8A7g0i0YI/qMRJlWT+NHQRZL9zGertw4YInzGsusn6vH0dzIiACIiACIiACItDrBCTm9voZoPKLgAiIgAiIgAjsSoCCUzabw/a4oBjmd93NBUOKUqVEyV0zfPSjH3W/n3TXMDw8AgtfClwKj4sABcAkxPgQkiXMu1ZLv8UawCxwOa4pRdjp6Rmc26Pub5qMh4dGbGBgABbqJZipF/c95+PiO12ZxJeZT1nmHldtKR0REAEREAEREIFOJ5BAw2j/N5JOL6XyLwIiIAIiIAIiIAI1ApF1bW1xx8xBLDSbm0+t9ynDOpcWutSCExAYabEbhcgCccehayuYXus0a1F6Zoasm3nvXng2bRcWFmAdWvdLTEvRkZEx/EYhEoZ9m9Osi78hhqa7EWi+hvDhAud5EnC3i9tudUs/umRdLkf1F4mzgflOy9zmV5JmMXdnTqJ0d64Pa3QNBRKaioAIiIAIiIAIdBcBibndVZ8qjQiIgAiIgAiIwL4E9hOBmhOoqX+xDUGUCqtaxeE2DoRGS9Ak/OViyn9QuOqCYti/eSohqk7ksPW104qT/ouTiZRbTLMeYObZovu+xNw68/3mdoq5EeNIpC2Bt5/3tJD28z067+upRvHqyzvr7KBibhCBW8dXncYZa14EREAEREAERKA7CMjNQnfUo0ohAiIgAiIgAiKwL4G4AAtLwlLBxT2KQewKXoEFYTKZQPd8+Ols8OcZ32/fgzRFQHoQtGhlm3RL22ZRqym6Fo+FQLOwl64OQkdx3WV3r9/dBPhjyULPJRKszsm+5uf2EIib66w1QF6L9UQjIZeuM7C25ReS5mu3vm/r9LVWBERABERABERABNqfgMTc9q8j5VAEREAEREAEROBYCMSFHYqqadrNVn1xRkIuXSFgi4tDFP4YWotEh8lQJCA1D/h0mBQU99EIxOswmmedSNh7NKqNe9cZRy5EaoJuY7RHWIquR7ouoSsHXp7JFK/WBD7KJP06ruchHCZ+zXOd6jyQ0VQEREAEREAERKBzCUjM7dy6U85FQAREQAREQASOSKBU2oYAlLWH8/dtc3PLpianLJfrh9/PolUSrQbIkgh0RNTarUcJ7BRWHw1E5K6EH2ESVoYl/dr6mhW2NjHI2qCNjY/RT0OLA+i6bQFFq0RABERABERABDqcgMTcDq9AZV8EREAEREAEROBwBKj5bG5tYDCybXvj9TdsY3PTZmZmbHpq1kbHhiHq5iAYBV+bEoMOR1exReAxEaheivl83m7fvm337t2xrcKmnTp1ykZHR+DagYMLNgu6un4fU20oWREQAREQAREQgadIQGLuU4SvQ4uACIiACIiACDx5AhUMgJWEi4Wr1961a9ev+cBYS4uLNj8/b2Ojo/bccxdtaGi4mjGIQe6LU6LQk68pHbEzCdB6loayzcJqvTQt3dvWN7ecox/ru3fv2o0b113MXV1dMX5zoaXu7MysjY9PYD9ep7sft2XCWikCIiACIiACIiACHUYgmJ10WLaVXREQAREQAREQARE4OgEMmWRvvfVWrWf2Blwt3LlzB7+7trVVQMIUhYKAG+YPMz163rSnCHQjAYqu4Xe08iVsYWHBbt26ZUvLS1bGRxkKxvwIc+PmjciPbu2aPdoRtJcIiIAIiIAIiIAIdAIBibmdUEvKowiIgAiIgAiIwLERKEMAugar3JWVFctkMp5uGEDp7LmzNjg4eGzHUkLtQ4DC317Wou2T0+7ICYXbEOLzYd1hpxz4bGJiwsbGxiybycKtQtJ/JfjPvX//vv+i+q0f97DHUHwREAEREAEREAER6AQCEnM7oZaURxEQAREQAREQgWMhwEGUFuYf2quvvurCXqFQsGIJg56hZ/bM9KydOnkaPnP7juVYSqTdCKj7/ZOukUezxG3MLYXasbFRm5iccL/WvGbpUSGVSvmHmetwmbKFAdGCRb1vb0xCSyIgAiIgAiIgAiLQFQTkM7crqlGFEAEREAEREAERqBNoFu1oqRdZZVK8feON1y2f37AKhN1UOmXlcgV+c83On7sAq9wRdAXnt+7mNOqpa+5pEDicteW9e8FdRj2vHCRreHjYu/rX12ruaAT2q4/9th/+qKlUGtdqCf5x5/BBZtHyG3kf9KxULNkGrueF3KLdf3Dfzpw+63VM39gx4+DqAZuv6+PP5+FLpj1EQAREQAREQARE4HAEJOYejpdii4AIiIAIiIAItDUBijXNgg3WYFUJFriLGOjs5s3b1tfXb1ubG95N2yoJGx0dsxmIRJHbhcoxCH4783A4bBKZ6rwOz+I73/mePXz4sJ4E5j74wQ/a888/j3Mh8t3asFELhyRw+Do55AFaRk+nszY9PWNra+u2uLRoqyvLloRlrlnSlpaW7fr1GzY9NWP9/QMthFwm2eq6fDplaVlArRQBERABERABERCBAxCQm4UDQFIUERABERABERCBziXA7tmJRMU2Nwv2+uuvY7pppWIRwm3ahb3+vgF76X0v2cAgBSAJO51b03vnnOdB5FN173ja2p4EWHd0k8LfzMyMnTxx0j/GVGBZn4BpfXF72+7du2c3b910C15dy+1Zj8qVCIiACIiACIjAoxOQmPvoDJWCCIiACIiACIhAWxOgCEQxdwMWfWuw2uuPLHINFprI98lTJ23uRLDKhcdN6bltXZvKXG8SoDhL/7gc+IyDFE7PTGM6ZGW4U0jCNUo6jcEMYXh7/95924awK+G+N88TlVoEREAEREAEeoGAxNxeqGWVUQREQAREQAR6lECw5qMANDU1ZZ/85Cft3LlzNjg0CBGoYiMjI3b58qWqe4UAKa7mcj6+HOJo2mkEJO51Wo21zi9FXdbl2Oi4zc3NWTaThZCbtvHxcVzLl+3lD77som/k+7p1GlorAiIgAiIgAiIgAp1MQD5zO7n2lHcREAEREAEREIF9CVDIDb4yc7mcfeQjH7W79+7Yq6++Cv+b0xgUa6RqqRuSOg4Bt/l7eTkkXp0exzGaktSiCPQAgWChOzExYc89d9E/xGSzWTt79qyNjY31AAEVUQREQAREQAREoNcJSMzt9TNA5RcBERABERCBLibQ7DeTFn10o0B/m7Pwu1ksltyqLyCItssSN/DQVATamcDk5AQsckdxTSebPsgw160GO2vn0ihvIiACIiACIiACInAwAs1mIwfbS7FEQAREQAREQAREoAMJUNxNJFKe81Qq2+BX83i74dMSN/7rQFjKsgi0OQF3pUC3C8in3Cq0eWUpeyIgAiIgAiIgAsdGQGLusaFUQiIgAiIgAiIgAp1AgKLt5uamZzUIuGHaCflXHkVABGB3i+uYIZ3K+C/ybR2s6oMbk/hU1ERABERABERABESgOwjIzUJ31KNKIQIiIAIiIAIicEACiUTF6DuXlrN9fX2w6AsC0AETqEWjmHTQrtxBVKrtrJknSKBcLvugWBwoK4QgBoblo58HIQVNnySB6LIN1278Ogzr4rkJ6yo1EZhbVedxRpoXAREQAREQARHoFAL1Fm2n5Fj5FAEREAEREAEREIFHJBBEnDB9xOQOsHsQkw4QVVEemQAHvUulIncaTCzUc6UMMY//YNUZDYz3yIdSAm1NIIi80bRZwG/rrCtzIiACIiACIiACIrALgQQaNaGVs0sUrRYBERABERABERCBTiHAZs1hmzaH9TrVfAz6xm0OzWk2LzfH1/JxEWDTdm1tDYPbFRtOhf6BfrfE5nFaNX+D4Htc+VA6j5vAXtd5JNjTIpvifQj8pELfuqGuIz+7+tAS+GgqAiIgAiIgAiLQGQQk5nZGPSmXIiACIiACIiACByLQLLTWd6KARxGnWcgLA6LVY+43F44Rpq3iN4u3zcut9tG64ySws54Ttr29baVSyehuIW65y+MGge8486C0HieBuki78yiRmLu1teWbEkkOfBj9kpgmk6lqfbu8u3N3rREBERABERABERCBNiYgNwttXDnKmgiIgAiIgAiIwN4EKM41d6m/eeuWvfnG6y7WnTp12s6ePQPxLrNr1/pWot/eR9XWTiDQLNyznnmucL1cLHRCDR41j5HIS6tcirlX3rliDx88tJGREfvgB3/EipWSZbN0wSEh96iEtZ8IiIAIiIAIiMDTJSAx9+ny19FFQAREQAREQAQegYBbV0K7oVBHkW55edlu3bxp9+/f987Vy8sr9vDhQ3vuuedsdGzUysVoIKz4IbGbQo8QCNaZPVLcnism7wPlStmK20WIuG/b/Py83b1z19bz65bfyNvdu3dsbu4k7hdEU8Y9gxbzugH03ImiAouACIiACIhAhxOQmNvhFajsi4AIiIAIiEAvE6CFJbvNU7mlj1SKuPfu3XOxhsLO/MK8izi5vpxNTEzURN9eZqayi0A3E4AzBb8X3LlzB+LtXXetkYRoW4CV7uuw2J+cmoRldm6Hm41uZqKyiYAIiIAIiIAIdBcBOXDrrvpUaURABERABESg5wgEa8ulpSUXb/KwwqO6m8lmLJPJWDaTtYnxCbfYc+H3sREK3babLf2alx9bBpSwCPQ8AX7g4W94aNhKxZJl4B+Z9wG6Xbh3955dv3bNtgr0pRuu155HJgAiIAIiIAIiIAIdRkBibodVmLIrAiIgAiIgAiLQSIDCDa1wHzx44D92oWYvalrqUsilz9zZ2VlINwlY46XdHUMQgKMpXTTUf9HeTCH+4zGbl4MYFKZsVoVfWBem3F/hSRMI9Rzqji41orpmTuL1eZD5J517He+wBILLlCw+5Jw9e85GR0fcArdQKMB/bsE2NjfsjTfetM2NTb8/8L6hIAIiIAIiIAIiIAKdRkBibqfVmPIrAiIgAiIgAiJQIxDEmMXFRXexsLGxUdtWLpVtaGjITpw4iQGPsrDWS7mYW4uw60yzsBeP2Cz+BLGWTaow3zyN76/5p0cg1CtzEOb3mj69nOrIRyXAa8/8OqdblfMXLribBd4XUumUf8ihX+0bN27YBnzohvvHUY+m/URABERABERABETgaRCQmPs0qOuYIiACIiACIiACx0aAQg3FGQ50RjcK7E5NK9zBwUE7A6vcsbExiDhJ/0UHjQSfY8uAJ/Q40jzeHCo1EeglAmm4Vzhz5gx8ZU+6y5UUPubQUj+ZSto777xj/AD0eN2u9BJtlVUEREAEREAEROBJEpCY+yRp61giIAIiIAIiIALHSoDd6DngGcXc9fV1+MWtuEDD9ROTEy7m9Pf3Q8htPixXhF/zNi13IwFaYcoSsxtrtnWZeA8Ywged973vfTYwMOD3BbpkSaVStg6/2rdv34brhc3WO2utCIiACIiACIiACLQxgXQb501ZEwEREAEREAEREIE9CVCc28Io9fSPy0B5tlwpW64vZ7Mzsz4IEkWdKDS7SKiuPtCked+Q5oF2VqQnTOD69eu2uRkX6io2CXF/bBxW2n6WNGaofo40rtdS5xJgnabTWTt16pSdPHnSbt285WI+rXHT8J29CSFXlrmdW7/KuQiIgAiIgAj0MgGJub1c+yq7CIiACIiACHQ4AYq5MzMzxgGObt68acvLS1YqF218fNzm5ubcT27kH5UFpSAbRNhmcXYvEM1xmUZIZ6/9tO1pEXj11Vfd7Ub9+BV7+eUP2OjYqFedxNs6mW6fozXu+977Xpt/OO8DoA0NDNnU1JSdOn3K+vr6ur34Kp8IiIAIiIAIiEAXEpCY24WVqiKJgAiIgAiIQK8QoFBDn7gD/QMu4N6+fQsWmRt2+vRpjGQ/5hji3etrRrq9AqhnytksuHM5vq7qYiG+qmfY9GZBed3T8pZuFUZGRuz8+fO2sLBg586ds9PwpZvJZCyV1KtQb54dKrUIiIAIiIAIdDYBtWA6u/6UexEQAREQARHoeQIUbSjMnDp5Cla6026ly+VyubLDVy7jyiqzG0+ZZpW2WcztxjKrTHsR4HXOjz2lElywwJD+0qVLkQuWbJ9lszndB/aCp20iIAIiIAIiIAJtTUBibltXjzInAiIgAiIgAiKwHwEXZyHWcEorPA54xlAqbcNnppo6+/Hr/O3NQi5L1Gpd55dUJTg4AX64KRYLlsv148NO0YaGht1nbvQxR25SDk5SMUVABERABERABNqNgN5w2q1GlB8REAEREAEREIFDEYjEmWgXirlByEulkodKZ/fIFH4el/izmwXp4zzm7iXtvC3lpixXlxPgyl8IsdmwStPuJsD7Qi5Hn7iVmu/s+r0inBCP67rubrYqnQiIgAiIgAiIwNMlIDH36fLX0UVABERABERABESgBQGKTRKaWoB5xFXgKqyPyLCTdw8ibrwMOiHiNDQvAiIgAiIgAiLQ/gQk5rZ/HSmHIiACIiACIiACXUeAolIQlsK0WVRqtjo9LkvjroOJAsVZBZ6xchJtAnHcWpdxybKZdyy+ZkVABERABERABERABESgTQnoraBNK0bZEgEREAEREAERgNwJv5fdHVi+bi/jk6zBg7CUiPska6T9j1Xx+0x0r4mux+6/77R/rSiHIiACIiACIiACuxOQZe7ubLRFBERABERABETgiROIW1jC3hIDF6VSGeQiYVtbG5bJZDHQWXKfkegfVaw7iCB4XGDcZPS4ElM6u1jblstlSyUzlk7lwCgS7xpF9ISfVwLYbQT2uxdE95VyuQRBF/ba9LNdvfw5eGI6Hd17uo2KyiMCIiACIiACItDZBCTmdnb9KfciIAIiIAIi0NUEtrcp5rK5krArV67YO+9ctUuXLtm5c+csm6Wwu59Y09V4VLhdCCSTSZw39Q5oySTOE7hZqFSKVStMCHdJDpan0OsEaIWbyeRsaWnR3r36ruXzebtw/hk7e/Zsr6NR+UVABERABERABNqUQAINmCdpftKmGJQtERABERABERCB9iDQaJlb2C5YBtZx9x/ct29/69u2srJqxWLJ5ubm7KWXXrKpqSmIdnFR7jiaNc1pUDA+btGYx2g+zn41UBcn94vZO9vD+RKmbndrha0CrLpj69DczWZzsLTMAg1F3WZCssxtJtL+yzsq8QhZTtj8/EN7+8pbduvmTVtdXfMPRPxg9L6X3mcD/YOy2D4CVe0iAiIgAiIgAiLweAnIMvfx8lXqIiACIiACIiACByLQWtzMQHyjW4XXXnvNlpeXIdDR9ULZ7t69C0u6JfvoRz9qp0+floXugRj3RiTIspbL0Z1Cc0hYobBppVIJlpiZqsV3cxwt9xKBjc28vfnWG3YTQm6hULD+/n6/v8zPz9v9e/fQA+AC7i29RERlFQEREAEREAER6AQCEnM7oZaURxEQAREQARHoKgKthdvGIlatKhMVu3nrht2+fdPociGX64MIRz+XBTtx4oQNDw83CbnHobwcRxqNpdm5xGPwF1iE5Z0xtSYw2otEsFoO1rgRz+YOaPSDSkvuRILW3GGfvdLVtvYlwDo+bAjnR7Qf7ycz0zO2uLDo1twUdBlWV1ch8N6yoaFhWP9PxwRdXacROf0VAREQAREQARF4mgTUin2a9HVsERABERABERABEKBYR5GF03qgELe+vm6vvPIDWFOWYU2Zgp9T+kJNub/cU6dOuZhb30NzIhAI7Cb0yZ1CIKQp7jiVklv2j4+PW64v579MJo37TdFdu9y5e8cK21vuZzni1XiPEkMREAEREAEREAEReBoEJOY+Deo6pgiIgAiIgAiIQAsCjUIJxVx2f75z566lMIBVEiJuobAFC91te+GFF2xsbKxFGlolAiIgAgcjwHsMXW5wsLPR0VHfKYEPRuVyBf65V+wu7j1076IgAiIgAiIgAiIgAu1EQGJuO9WG8iICIiACIiACPU+gAiGlBAoJ9236yiuv2ODggKUhuPiAVnBgOTIyYjMzM1g/6Ja6PY9MAERABI5EoITBFHlfmYarhanJKXd8srW15etSyZT3DLgH37n8gBSF3Sy+j3R47SQCIiACIiACIiACRyIgMfdI2LSTCIiACIiACIjA8RNIoDtzwoWUYnHbXn/jddva3LJ8fsPFFPrMrUB4oRUdrXJpUZfo+NGJgg9OiUTHcz4FnlFqPD/i50hYjq87nuMqlU4kQB/KdN2SzWbs2WeftYmJSaPAS4vdBHoDbOM+dOPGDbt27VrV1YKu006sZ+VZBERABERABLqNgMTcbqtRlUcEREAEREAEOppA5NOUAxHdunXLrXOTEOTY7ZkWdLOzs3by5Ekfdb57BDkJRMd3yjaKuceXrlLqRgL0v00xl4EW//xQND4xbqk0B8gz/4jED0pr62t+//GV+iMCIiACIiACIiACT5lA+ikfX4cXAREQAREQAREQgRiBSLSl1e3lS/CLOzpm7Oa8ubVpaQgs5y9csOGRYVhbJmuDEnWPqBvDoFkREIEnToDuW27fuW0ryyu436SNA6OdOXsmcsGAe040SKM+vjzxitEBRUAEREAEREAEGghIzG3AoQUREAEREAEREIEnTyASR9i1mRa4dFyZhL/KsxBRxsZGYI17AgLLTUul0jY3O2dpTEulknefD1Z1Tz7POmKnEeD5xSDxv9Nq7snlt3+g3yYnJ21tdc0HRDt9+jRcL0zYwMBA9bzhOSQx98nViI4kAiIgAiIgAiLQikACDdvGoaNbxdI6ERABERABERABETg2AjubHmyOVCplw18XTUqlok/LpTJ84+Zsfv6Bi7nDw6NYD/s4xKd1brOYK6Hu2CqpjRLi+bLznNkrg2+//TZ8LecboszOzdrU1JQl3cKyUZCLzpvGdQ07a6FLCDSfR43LZdyDVldWbXV11QXcYbhe4KnHngLRPYfniM6TLjkZVAwREAEREAER6FgCsszt2KpTxkVABERABESgUwm0FkMozmIINC+UC7VYTmXYVKnAWm7K13OANIbdxj2rCy4eTX96lMBbb71lDx8+bCj9y8mX3eqSWtzO84fnXevzsiERLXQ4geY6bhRzKfSPjo76Lyoo40f76ENRh1e9si8CIiACIiACXURAYm4XVaaKIgIiIAIiIAKdTYCiSSSuRNaTnV0a5b69CFDor55eu2SsUdgLIt4ukbVaBERABERABERABERABJ4KAYm5TwW7DioCIiACIiACIiACItBeBJrF3ObcNVt1Nm/XsgiIgAiIgAiIgAiIgAg8fgIScx8/Yx1BBERABERABESgRqAumLmlpK+Pi2TBOjesC9NaApoRAREQgWMicLT7S7lc9uNzIMaMu4LZKztHO8ZeKWqbCIiACIiACIhAbxOQmNvb9a/Si4AIiIAIiMATJEAhty7mlssl44BD6VTaBzOLMpL0gYbq8RqFkJ2+Tp9g9nUoERCBLiPQeH85SOH4EYpiLqdbW1sQc1MH2O3wxzlAoooiAiIgAiIgAiLQowQk5vZoxavYIiACIiACItAOBLYL27ZZ2rJUKmV9fX2WTKYg7EbCR91ytx1yqjyIgAiIAAfPS/hvY2MDQm5GSERABERABERABETgiROQmPvEkeuAIiACIiACIiACJJBMJm1tbd3evvKWpdNpu3DhGRsfm/D5IOiKlAiIgAg8TQL8qBTuR7TI3dzctKWlJXv77bdtcnLSXnzxhaeZPR1bBERABERABESgBwlIzO3BSleRRUAEREAEROBpE6jA3QIt2+7fv2fXr1+39fV1W5hfsOeff4/Nzs5af38fujGXIaIkn3ZWdfwuIRAJcpFVZZcUScV4AgQo5vJHIXd5edlF3CtXrnhvgnv37tozz5zH/ar/CeREhxABERABERABERCBiIDekHQmiIAIiIAIiIAIPGYC9JPLAYOCv9yKFYsFu3vvjl29etUKhYKLJXfu3rH/+Z//tm9/+5su5JbgU1dBBNCxHRDYZD36j+NVJZNpWH2zWzz9Mid2/Hamz+PGf1hU6EkCFHJpjfvlL3/J3nzzdSuV4B5mc91WV1fs69/4Ou5nZRd7W8MJ9z/eA+P3wdaxtVYEREAEREAEREAE9iMgy9z9CGm7CIiACIiACIjAsRKgMJKHJe6DBw/c0i0J69uhoSG3dOP8xMSEHy8FNwwKInAUAtls1n0wh31pWZmCP+ZKmTbhkaUl3XwoiMB+BGjRTZ/euVzOpqamIeAuQ8wtY13aBgZydv3aNcv/yJpb5zJucMmwX7raLgIiIAIiIAIiIAJHJSAx96jktJ8IiIAIiIAIiMCRCFDMffDwod28edNHgx8YHDCKbxTXpian7Ny5cxBEOBAabHkhwimIwGEI8Jz5+Mc/Hp07sdOH51ciCbEN/3ReHYZo78YN5wkFWrpSOHv2rC0uzruVbhk9B1LplGVw7/rKl79iP/3TP2MJLEfWt73LTCUXAREQAREQARF4/ARkkvD4GesIIiACIiACj0CAL9PhhfoRktGubUKAQu56ft0eQsyln9xMNuMjwpdKJbeaPHXqFKzdhuBPdx05jilxbZJ/ZaP9CVB4o3BLa0qKbeFHIXdra8vy+Ty6xRfbvyDKYVsR4CCNE5MTNjM74x+ftrYK6GGQt1KxhIEc12x+/iEsduUapq0qTZkRAREQAREQgS4lIMvcLq1YFUsEeoUAX8wzmYx3a+TLOUU/vnBRA/JlzLg1Fl7uuc27QNIuC8tuoYWpQvsSYJ1R/GN9vfPOO/aNb3zD7t69a2NjY/aBD3zAPvjBD7Zv5pWzHQRYl7wub9++bXfv3I26vaOOCxBFWNdj02M2NzeH/cro0pzFOvqX1HfnHSC14sAEeF7FA58XfEbwnqIgAvsRaD5P+vv67NzZc3b13as2PDwMf99b1j8wYFubW/atb3/LfvInftKfT8377XccbW9vAvG2CNuUIQS3LfF1YVuvTv3DLO673hYHBLLb3t72KV2VPEpgO/ArX/mKf5AbHBy097/0fnvxxRctmaIf9MZ7va7BRyGtfUVABDqBgMTcTqgl5VEERKAlATYO2TWbDTZaxfThJSu8pJfhzy6diV7YG0RepEQrmu3idoM/xZYH0MqnToAvBXxJ4u9LX/qSfe5zn3M/qy+99JI999xzTz1/ysDhCLA+ORr8/MN5W8U1i7cvXLNZCPYlG4IwcuGZC35NM9Uk/Zs2vZwd7miKLQKtCeglvzUXrT0IgQR6DgzYe97zHvvud7+D+1XOP0bx4/BGfsM/Or744nvRvng00eogOVGcJ0dgY2PDDQf4MSi0KWn5z7YJ3bYo1AmQC5/d/JHb66+/bl//+te9fc42+i/+4i/a5ORkfYcDzvFj8CuvvGJ/8id/Alcni3b69Gn7vd/7PR+jkoOosm4UREAERKCXCEjM7aXaVllFoMsIsKG4urpqX/3qV+2b3/ymvfvuu3br1i3/Yj85MeldIS9cuOAvXS+//LL74SSC0OW2y3B0ZXHCCwFHEb9y5YpdvXrVu0nTIurZZ5/tyjJ3c6FYn9euX7MbN2+gC3zSMukMhFy8i0EIOXPmjFu8UWjjCzLjKoiACIhAOxGgQXcOQu3p06fs7Stvu4BL8YouY3jPoh/wixcvScxtp0prysudO3fs85//fNPavReD+wwK+RQiL1++bKdPnXYhl88vhUYC4YMZXdqwff7Zz37WP9TSAIOWtIcVc8mfA6a++uqr/sGE1xrb9/ywz/aChNxG/loSARHoDQISc3ujnlVKEegqAmzEzc/P2x/90R/ZD3/4QxdxKerSWoJuF/iFnl//2ejmgCXs1nXixAkfEOdTn/pUzaKT65kWu8mxi5ZCexEIL0+sI3atu379utfXyMiIW2TMzs62V4aVm30JZDJZO3/+Al6+EnYLosfq6hrmUxghfsqev/y8X7dMhHWuIAIiIALtRiCRgC/mZNoFpBff86ILVVm4hGFgb5EXXnjB2P1boX0JsC3xh3/4h4fKYHDPQuGQgW1Lirkf/n8ftk984hNe78GtwKES7sLI7DXH9jgZcZ7+8SmgU8gdHR1111lHKfa1a9fsBz/4ge86PT1t73vf+2x8fNzb/GwzMH0FERABEeglAhJze6m2VVYR6GQC0HbYxYruEb74xS/aX/zFX9hrr73mlrhsZNMqhuIsfxRxKQSyexetAtjIo/jLr/pvvfWW/eqv/qoLu8TBbSV08Yak6y9nwZqgk1F1S969+yLNoBC+973veSOeLwgcIIsvzaqrTqrpqB5ZnXz5GsBL3tzsnF27dt1WVlYg8J7HdTvo1+POeo327aTSKq/tQyCcT+EDQVhunxwqJ51GgIIuB2mcm0vhWXTRFhbn7RKscc+du2D82MiPVXuH/bbvvbe2PhoBfvS/d++etyHYruRvv0Dra7ryYruE+7Mtwt5gr73+mrdPfu3Xfs0++clP+qCLHLOBaTZb7PbKvSe4nwhM43zJLwjiYftBptyHVu804KDBBi17f+qnfsrTokFGIhWNhXGQtBRHBERABLqFgMTcbqlJlUMEup0A3n3W8+v2j//4jy7kskEX/OPyJZ1f+9m4m5mZ8UFJ6EP3/v377ldrc3PTrQNojcF1bBDy9+lPf9ob5/R3RvGXDVCF9iLAlx++NLHu+PLF+bNnz7pFxlFeCNqrdL2WGwoYFVi1UQgZqH54GfQXY16/QWxzB3i9hkblFQER6BgCfC6xRwF7Gly6dNk2N/MY9GzcMln6U932Lt+Nwp3E23aq3Kj+Ilc+bPexLcnnT/0ZtDO33IciIgP34Y/tTBoN0PKUBgNcprEAjQOahdydKXbvGrbN4iwbrwU84cHysIHteLbf6XOf9RVcXTAd9qxTe/CwRBVfBESgGwhIzO2GWlQZRKAHCNCNwj//8z/bn/3Zn9n3v/99t3qgRS59p/LrPLu50ZUCuzeyqxUtcmmt+/bbb9sXvvAF34eNQDa2/+///s8bk+ymz/3YsFT3uPY7ifgyQIsOdq27ceOGv0hRBKRV7vnz532ZltgKnUmAL8ORiBu93JUr+KCC7ssKIiACItDOBPgBuFzBIKsQlXgPGxkZgriX9nXcptDeBNjmo/hHFwC0tp2bm7Pf+Z3faRAgW5WAH/3Zk4S9vNiO5MBebFOyffrtb3/brbI/9KEPefuEbRcJjK0oHm0dx8NgzzpaRbPdf+7cOTt54qTX2VHE4aPlQnuJgAiIQHsR0FtTe9WHciMCItCCAK0xKcr++Z//uTfmggUFLTQ/85nP2E/8xE+4vzIKfaFRx0Y647Fh/eEPf9j+67/+y/7hH/7BG99sDNJFw9/93d+5AMxRqZ+Ur624tQKLGvLbotgHXsU0jyOd3Q74uNPf7bh8GeKPL0wUc/liRBcLzzzzjPti220/rX96BML53Xw+1tc35o0vx2V0kaQoUi6VIeY2bteSCIiACLQVAXahh5BbF+oqsMZlzx740YX7BVMHn7aqrlaZ4fMp1B8/KrJHFy1qDxJoIUrDALpYoIHBv/7rv7q4y3Yle4yxrfmbv/mb3nYJxzhIugeJE56j8bjNz9r4tnabZ/7D77B547gJ5Mv3AYq5bAf29fdJzD0sSMUXARHoKgISc7uqOlUYEeguAqHhyu5r//Iv/+Kj2LLhSuGHX+V/93d/137+53/eJiYmvMtbvPSMw/05sBJ/Z86ccQHwL//yL73LPhvkX//61+1v//Zv7fd///dbioNsNPJHoTc0yumbyw1vMOE03pAO4mMK3S+5jcfnfsFCg2nxxSEeQhm5LqTF+FzP5dpxq8vN+4Z48fW7zVM4C2mG43I5HI9543rGC+JaeGllPK5nCGVgeZi/kMfdjvso65n2K6+8Uhv87PTp03bhwgXPH/Ok0D4EovMIfgJRL4lE/TyvQPjgj6FS4bbox2XGSya5Leq2jNMPwf9wxoPqOZDQ9FEI6Dx6FHrat0aAz33+qz5/eM+iywWu3T/Uu/JHu8f3ic/vn5JiHJ0An1Vsv7AOQxuKxgB7hVDf7P3FNifbItyHLhYo6LJdRAtd+vf/zGc+c6R2EfPFENpUoW3GZc77edfkjzm05eJ5D+XiupDv5rTDfmF7fP+jzMfT43x8mT2owvJB02Z85o35Jld+1Kegy/X8qP+BD3ygVraDpql4IiACItBtBGQD0201qvKIQBcQoGgYGtr0UcZG3D/90z95A5fCKt0r/PZv/7b9wi/8gndra1VkNgLZAOaUPzb+fumXfsl+7ud+zsVZHmNxcdEFXX7tbw6hIRn2r23H+xa38dccauur72RcpoUwtanQkA5xwrQ5jdoyk8eP8RiYj1Yh/lLZanvzOuaDbD0/1XyFYzAu54Noy3fTsI3TUIZ4mrvlKx5nt/mQ9m7bWX8LCwt29epV79rIeCdPnnQh/1GOu9vxtP7RCSQS1RO3JsjyhSycv8Eqh+dV9GMd06KNgYMKKYiACIhAuxOIP384n05nDpjl6P4YfeDi/TB86OJ6hSdFgG2P8DGa7Zpam6dFBli/8fpmFC6zHfreF99rP/IjP+Ifl/kso/9c9iKii6/mfVok3bCK+WE+4vuF+Vr+qm0y5j/8GhLBAssT2lat4uxX3ub0dlumYQN70zBv4Zhu7BDbIbRPySb+C+WKRW05G/J/9+5d75VHFxcU02mV+8ILL/g+B02r5QG0UgREQAQ6nIAsczu8ApV9EehmAmzIccADdluj31RaPvAL/3vf+177lV/5lVoDmg3ISnJvVwNMi9a5P/MzP2Pf/OY37X//93+9cUnfZ//xH/9hH/l/H6kNWMG4DDweG6AMoZEdtrEB2dyIZNywjlMue/yqDhvS8gRb/GHckD4HdGC3vaWlJff/G6LTEoT+3Rh4DOwRNu05ZWOb8X2favma9/UyQsFluTnP/NJCl4H7czmUgduPElg+5uEggfHol46D1XE/+ia8cOGCTU9P++6B1UHSUpzHTyCq18giN6qb6KWS6yOhFlNkw+WMYOHelK3DnB9Nu2pRBERABNqSAJ+XFG790YebYHQnrGeVj+QDPhbrO2nukQnw2fQo7YiBwQGbmpxyC11aj1KQpZBLUZei434htLPYU4yCJfdlGytY/7K9F56JXH+QvLJMob3H429ubNr8wry7hmAa/f39nv7IyIgbNoS2XTyvzceJnu3xGNF8qRwZXtAwgh/eGcbGxnxwMh6H6TB9cgmhOe2wvtU0ajskXCDnR33uOz4+7u1A5l9BBERABHqdgMTcXj8DVH4RaEMC8YYjxdavfe1r3uiklSuFxl/+5V92n1kUO7lMdaj55ahVsdiQpRD80z/90y4SspHJL/3f+MY3vLFLdwwhhDzQMvh//ud/3CdaaEjSEoODp+0WuC9F2G9961t27949zyMH2fjUpz5VE0d325fHYJnfeOMN9+sbrDxCfDZg6eP30qVLdvHiRRsaGgqbdp1SEGV6fGEIDLg/LUvowoL+g/kiwYE83JIYKfElg0Luiy++aD/2Yz/movqXv/zl2na+ZHzsYx/zRjvTDLx2ywTL9JWvfMWPwfj0UfezP/uzDdFDIz+e1ne/+11vyLOuOPDZ5cuXayOFx+M1JKSFp0Igqo9Iri3jJY/nkLvpgMUtuyH7ecIuotXvD80fE55KpnVQERABEXjMBArbW7BihF/ddMoysOJNNHWVf8yHV/ItCDxq+4HtFbY7+RwLbRe2RyliBrcC+x3j+vXr7nuXA3vRYCEIwmxf0Y0D3YmxzcO2XjgGn6MMTDusixeP29nWo09f9mqjCMr2Hdu63Ma02Y5k+mxLsl01OTlZS6LmSqy2pvUMBWKmz7Ylj0V3EwxMm/lmO5ntR7YDGDhl+zJYIDdY8bLZgBDnFcrHKTnxoz4DBzpmugoiIAIiIAJmEnN1FoiACLQdATZQw9d8iqEUNLmOjUF+9f/Jn/xJz7NbjVIYYkOw2hjcrTDcnz9adb788svekGXjkGlyykbpj//4j9d253o2zJmPv/7rvzaKimyEUgSlv7RWYi7TD4EWxX/zN3/jbhyYDvNN/757BR6TLh/+7d/+za2ROc+BNhiYDzZq6WaCx+agbp/4xCfc0pgiNBvpjBMa+vHjfPWrX3XfwGzQMy+/9Vu/5VaubORTqOYozGTMxj4tSpgOG918Ifn1X/91F20pnP/VX/2Vd3Vj2rSO+IM/+AP7yEc+4uIqj9vq2IzLclEwZnxanrAMH//4xxvEXLILZQwNer7Y0EcaBWemQfZ88WAIcXxBf54igfo5z4uQIm5UNwm7/+C+LeOjRg4fMnjdDeIlMplMR9t5vWLX2CXjZVC9PsWq1KFFQASOhQCfZXxm8SbHD7sP5x94urwPjo+NY756A6weDY92hPi9lMv7NGoYReFIBMJzhu0ctiPZLtotsG3C+KxPtnHCvoy/vLJsd+7c8Y/UXM90+IGdgm6IH0+XbUgej8dl77DPf/7z/tGfYm6wyqUBAfdlGhQu2S5l25E+YoM1Ko8VzwePEdp/TOcLX/iCffGLX7T//u//ro03wLyxLPyxHBRw3//+93tbjD3WLly44FmltW0zj8AglOXVV1/1tHkMGguwjcbAPLGMZMCP/Z/+9KfdgILHC+1Klo3pcTnaiWf6zvJwG+OxDcy2KtvUTIcuFl566SXfFiWgvyIgAiLQuwQk5vZu3avkItC2BEKDmeIiG3HB1QAtCvjF/9lnn601SJsbtLsVio1C/thIpd9V+tu6deuWNygpYL755ps1MTc0NBmXgia3s7FKQZMiJq0eGKc5hAZ22J+NcjZE2XhlA5ciZvN+8fx///vft8997nPewGe3NVrOsrsd9yMTHp/H5ssDB4SjRQTzxQYzXxLDMZrzxUYzhVFyZJloJctG+N///d+7ywnmKTDniwZ/PBbX8aWDgQIyLS2+853vGMsVLKZpNUIm3Ifx4yE01plHura4cuWK86Sw/aEPfSge1bnU2BAt3mNp7cEGPPNCy2a+bNCapPlFoyEhLTxhAo3XAa4yV2jz+Q3U9zv4SHDdrejPnD7j1th8gWRdMuC9rxpqM74cvyZCDE1FQAREoFMI8FnGZ//S0oJ/KOXzkla5l/FBcnBgEEJdq8G2Gu+l/hDslAJ3WD75jGF7he0ptiv3e+awLcN2HAPbH6xftgvZ+4ptG+7PH9trbIsx7dD+iaMJ6XzpS1+yP/3TP/WP/UyHgeJtsJplm4ftX7ZL+RGe7eDf+I3f8A/gFEpDfkPbjW0yBgq5//mf/2l//Md/bG+//XZNIOYzl+1IBrbpWBZa0v77v/+7DzDLtjDTp1DaHHiM8OM2tuM++9nPumDMc5x5CW2/0A7M5/PexmQ7li7RQnuS6TCQYSgD23q7BkR/68233NiCnGi4wHcAuhprxXfXdLRBBERABLqUgMTc/5+9OwGzpCrTxB+1USBbUewgkMkmoCirgjRQgCiruKGOCu3aPaKO0+3z9MzjjI7d/e+2e1Rs7bZ1FAUR2x1QwQVEoAUBkR0XFqEA2aSg2KGo7R+/c+vLioy6WZVVlVmVmfc7VZFxb8RZ34g49z1vfOc7E/TCZrMSgfGOAKKHmLLMFXxHdvm99RZ/ZUMIgMgk36vbb799IbS+I8KmcfkcBDNIZ7dyIo59M14cl6b5uVsecYwAq2733XdfdcYZZxQhF4lnzX4c1QAAQABJREFUgaGOrFG1maiMfLNURaZNySN2fvGLXyxiGUFXmQhuEOsoo71nSWEAYtVlgrG87dXDYMJ3LhcMAIhv8pQ365Czzz67XBOYGZBYUI44u0yg6S0i7S0udeWXWB7aSxg++uijByVxbnI9Hb9MP60vL1xZ8xoECwR8WLgHMoxVBOqXJbXl7YIF86s7Z99Rza196Ll3PMeszAnzLIHC5/NYbUXWKxFIBBKB1UHA75n+7ve//10tnnUsLQla999/XzVzyQstcTKsPQTwnRA4vewmfg4VcBm/ZdPWmVb2RExc7Pzzzy8vuOWD2/htO+CAA8rn4JzNPImdZoF94hOfKBateJaX4X19fcWVgoV68S73DmtdYi4OxBUYMdM5bsJwNByJMOtzvMg3y+pf//VfC3dyTt799UtwXBLvdc8pk3gbs558/uY3v1nOffCDHyx8sllnaYi/NvU67bTTqvPOO68YCChbncyY8qLdZwK5OuO0fveJ1kRieMhDfjhq29VIt+eBlfDvb/l94ZA4KiFXWeJ2w7dZ7/ycCCQCiUAvIJBibi9c5WxjIjDOEEDUEOcnHn+iEMIQTBHFvpr0tkmg5kWcboQwzouDRCK4iCdi6TthErFtBsJiBOls8o782/uIax/nmsfic/ucspHSc845pxB2i0iw7jjwwAOLb+BDDz20DBDUFZlHkH/84x9XX/rSl4qFrvjIO1/Ae+65Z8Gtm5gb+KgHEVccGBBJTeVD9LmPIOYKBgLqFSs1+8yaWTksbaVnpUtwRa5dm2bgK1V4bl4tlN91dyHjBjKEX9Pv2gIwXCZN6Qxu1XXhgoWlnqxSnNttt90KkW+WkZ/HHgLcLMyd+0h17x/vLSJuWe26WrJYXmoXY++CZY0SgURgxBEgrvlt9zvot9tLTZzm4YcfqV+G3l/NqH9vn1db6GZYewjgMCHmelH+6U9/eoWVwXOI8l7+44wsT11XnMUL+HB/hS8RH5u8S+aETmKoF+rukQ033LC8JGe9ilvhptKybDXz6jvf+U713e9+t5TDtcGZZ55ZeBA+1OSS09eZXhE+zbaaXVvx4m841vHHH18sY/fea+9qgw03KPek+hOJv/e97xXjAW0h0hJo1d9aBs281Tvaw+qXgK1+8GMpa5aVdSyI2KyGn3ryqSLAisuNBJy4Miscr+Zy0rVxUUa34EUwjsmoQ3qGDfzlwlw+GRKBRCAR6HUEUszt9Tsg258IjFEEkLWnn3m6kFjEz4YcNxdqWJmqlwFVnQerXgQe6RTka9AVFqCRZwjGgxZpiJMrsY+623cL2sQCA0FmaSseC9R3vvOdxSeuNI4h0+psIQzCqzqz7jA4YKX7jW98o/gC7kZwo2x7m0EEAfZNb3pT9epXv7piDQIXgwHnia5RbuSnPEKvNKYWKldcvtn4ICb0NoNz82sLzVtvu7VMt0PK5assVrntwUI77SO1IAgXriGk66tF/G5+ipvp8vPaRcA1nzfv2Wr2XbPLFNEY5Lp+nrldd9m1uFpYu7XM0hOBRCARGF0E/K77jX1k7sO1kHVP+b2bOq32lVr/JrKEnFH7zd1hh/56Nkq+4RrdK7H83PEqv1N4hrURmqHNUWJWEDHUb11wI3kQYb1456qAiOqYIA9xY0+YNEtJmeKwsn3fKe+rXrDbC8oxacTHtfAqIi3BmKDLopYI7GU+HugeYxkbwYwuL/u5eSCyemn+gQ98oNyHZZZUbV3sd1gaPmcJyb5bCwE/U84PfvCD6pWvfGVkObDHGVkjf//73y9cWXvkc8wxx1Tvec97ilFA/b6i1H29dderDt7i4FJ/wuvHP/7xIhbLQ7sFbRxO8Kzgt2GVzACBmzRitbq3r9Fw8sw4iUAikAhMJATytdZEuprZlkRggiGA8AX5Q9qCANv73iRy7e9tKAhKU6dMLRYyPhNGwwo1iGXkZx9EveSzhKRGvHbey/u+ojQIuSlupt4ZJLD8OOGEE8rCYurRxEC7idJbbLFFsbiI6WaE1nPPPbcIaEOV1zzOuuHtb3972VjlIsYsh7QZNhECD99jcMJqw1RC8dTFIh7INqLeDupFkL3yqis7JL92kaDOB738oBJVnWJrplWPyy+/vPga1GaDE+kMmCJ+7Jvp8vPaQqBzny6urdAefezR6nYifL33cmD9DdYv18w1JGAMeq7q6rrH2tvaakWWmwgkAonASCCgTzPDZqsttyovX+P3U/9HFOP3nnVj3QPW/aMSU9QdCdxXJo/gU/Y4kGvjOtnwMhzHPjZ8x3WNeMRdv2usZE888cTqQx/6UFmkLK511EUaAWc5/fTTi2gawiYhtK+/r5SvHlGnYnxQi68777JzWeQW5yNeEmstkCuvZlAGPvt3f/d31ec+97liZfyRj3xkwAcu44TJU5Yu3iYvs7JmzZpV7b7b7qVdxFxWtHhoM0Sbf/jDHxZXFHCxcd3AzRYXCuIMlFGXJbj/Weuy2oUJ3GxCt/qXE60/xGttlo6QC2t1b6dvJcuviUAikAj0DAJLR+090+RsaCKQCIwHBJA3pA1BRXCFzuBnWSI4nPYU8ldzTBaoREZWDvJFQhHTppsAxyIQOcu/Bnluno94y9srW1n27bRIO4tclq4GDabaES4FftykscEjPiPGsDn44IPLdDnHYcNi9rDDDivtadYn0sX+pS99aXXQQQcNiNnyc255AUYCSxErHxuMmvqGaCuXiwdWtxG0k7UHP3QGCOrLqppFiHGrwZP2Roi6SQcr/ngNLpTLUhmRj4FApMn9WECAGOE54g9vUbneT9bX3X1icz29OHFvPO9565X7rP0MjIVWZB0SgUQgERhpBPj4fPTRuUUg85sXwbT+++69r/zWZ38YqKzZfXAN/AfPJMwG1wy+FrzId9cMT8NDbKxOjzvuuGIBayHYmO0VrYi0yvGZdelNN91U+KdjeBjrWBzQNlRgicrXvFlQBFL8FWfEi+QrL0KtNuCP6hp8jWjsvot2RRmRzmyn3ffYvfr1Nb8u+YiLG4cVcsSHEavi4KFwMMOqv7+/5N3mZsrTJi/+cVKWxfBTV1u7PlFOcy89/hjGAkRjOCsr8mnGz8+JQCKQCPQiAinm9uJVzzYnAuMAAWSNwIogBylGAAmchEEhjg+nOUEekVJkOIRSpBdxRZJXFFaGQIo7nMDnLX9lSLc0rCL4onUMsXZcO9U72gAXQilyrC02wigfa6b6rSgg8KbhCfK2tcl4Ow9x1M+g4XWve12xDiEgI/UW3XjVq141SMyVH79sRFnpEHODEoOfaFO7DMe10ZRHlsowkM9ee+01YGHSTpPf1yYCXlIYhLrXF9d+BO8qU4hdQ89o8RdZ+z7e/Pmb10L+ZkvEey8NhvdsrM2WZdmJQCKQCKwqAvF7yS+u39uHH3m4/J5HfnMfmVvNnn1n+R2eOXPT+vDyX6ZGutyPDAI4CU6Fc3AXQCj81re+NcApgxfZC/Yf+9jHiqBK7JSOtayX4twhDBXEw82sAYDrefkd94Y0rGz52hXUKYLP/vkNlQ5fxaHUFe/iP5eYK39x48U4Puu8zctwvmpn1z508cUIyvcbLa49F2P2uBYuKR1huxmUzbcubiYQjl/2spcVzteNOzqmXspy/++zzz4VH7qlXfXxZhrHugVrM+CB9jhvW8ztliaPJQKJQCLQawikmNtrVzzbmwiMEwQQPMSVyIpoIoUEIgtirUqQPgLBFCEXkEpEuGmZG/Hae3nE1j7X/t4sz7mhCCufYIhyiMxW/yXKaqu6EXTlFcQbeY+8g5AbDCDhpqQ5tqKgvfKQJoTxFaWBF2FdO1h/IPsGCbC84oorivWtxdK0Q1CPe+65p5Bxx/iRI8r29fUNlN0uU72Uo/0GFNIRnVljzNxkZkk3FI7tvPL76CPQuYfm18/OevX1Xljd/Jubi7i/Xj3w4h/SvTp93elV3w59dZxa3K3vCfdxhkQgEVgWAc+T34Lo3/V/TdFj2RR5ZKwj8Nxzz1Zbbb1Vdf8D9y+ZDbTUApOINvuuO2v3QTPq39X0ercmryUeEVzCM4bbtAXMdn2sY2BGkpfXuOnFF19cfNea+o+72NrB8+w4UTb8/yvP8SuvvLJwpxBk1cc5W9RNX+C8+I55eU6oZZnr9zSOR9nznp1XLGg///nPV7/61a/KC//IP+qmX3HMhvcqw6Zcx4IbR3x7aVjm2uOkOCBuNmXylIG6NuNH3eRpjYc99tijuuCCCwbKasYd6jMXXXggrJXFJVgYXQQ+Q6XN44lAIpAI9AoCOarqlSud7UwExiECSCCXAwQgpJCvOYIl4rmyZA7prClrIcAE4euvv75YOyDxCDGyKd9mUEb5t4RkOxfEtx030knTjFPykM+SLeLF3tQ75Bn5FUddtBepRpodR6AF+UY8ePguDSKvHUMFccRv1kEZIaxFXtKL0y2YDtgMfMTBEcFX5//8z/8sliJIPoHY9DgWxtohz1ggJARkBL2NoYEKsu7a+Kzd3Dds9/ztSv2b5efntY8AAWL69HVLRXx+4R571s/n9fX9/ES1qHa5IPDHt+FGG9b32rQl19D91f0eKwnyz7hEQL/kmutLWO97tvUL5Xt9bEWh3RcM1Q+tKJ/VPa8e2qLf0j9G3ytffZfv4kS7Svy63X5bhDhevgzzT/ioZIFH4AkxZVbtz5J/8wzjEwH3xrRp08tm8Uf3VYhwfue22Xqbavv6+orXud+zX1xTV9rzjXt5xvVTnu2hgusj8P9q5pMZR2ZF6Qu8yDb7yKJhS69jJyfXOxbu8lk6nM611k/gOHhQtxfqUaacxI2847M+Q/lmlclbEOeH5/2w+tu//dvClbVJvHaf5Lt2F5G5Tqu/iTpE3Zr9r8+4HpyIq9Ipq9Sp9o/rvM/NIK56Kcd5orHvA+nqY0KznGZ6n3FKLrq0kxWyRd+E5aUpEfJPIpAIJAI9hECKuT10sbOpicB4Q6AMeOqp+QgvcogI3nDDDWVamCluKxMszlDVnNeiTCxikERiMYJJCDUNrE1I26QxiGiJh7vWWbbTRJ0ibnwfaq+NyDGCLfCNtv/++xfx04BDPiEq+AwH9RLfZ8Fn5/bbb78BnNp1LxHbf+o2SDesuK20b3jDG8rCbURb9eQ312IYxFz1MnBxzGDC9XPctDzEXIgBSLPsIP4WVTO1zkCov7+/2mLLLYpYYkXmSN+qTn5dSwjE9bNnPcPdxu9v+X31hz/8odpog42qTWZuUq1bC77u0Yi7lqqaxY4iAq6vTfBsEy0IBPoXodnPeCnn5ZB+w/PsvhgL94a+yrReq8Xrk9XNau077bhTNWVqx0+5tkU7iSXnnXdeudf1XaYT84MZPs9Lw4fxR9uVZ5r31772teKLfPfddy8zElLMHQaAYzRK3Puqt/nmW1Zz6t803GO9eibDri+o/cBvu115Gcbn+IKFz9ULtHbcR43R5ky4annuPMvN67S8RnrejzzyyPKy+bLLLiuuC1i/XnTRRdWBBx444C4h8tB/mJEkf/2GPeFUmb7LCy9q+6eN9PaRVvzoJ/U11kuIEP2RFwUf/ehHi3sqfRluydqYAG3NgQiRj3QEZS/dvURaXuBWAY+TRnrirD7L5274aXvUy++AuogXcaXrFrwQxNX9NvCVy9WWNBZbwwWHStctrzyWCCQCiUAvIJBibi9c5WxjIjBOEUACDWZNYwvfWUiixRROOeWUlWoVcUHge4wAydIAYURILcx14AEHrhWiyGIVSUZYkV9tffOb31y9YNcXFOHZ8alTasvkJdZuvgehbX7WNtgEwfZ9ZULkOdw06kx4JoqzUuGbjUXtfvvuV9pjoQ/n5Et4t1hIuLKItirL5wgGKdLdfPPN5ToZ5HDNYHpdGRgsjRpJcj8GEIj7MHz/vXjPl9QWZ9uX+3H9DdYfGACOgapmFUYBAdffixbBQFwf60UOkcBUWS92vODRZ3iWDcwJDC8/8OXFDYfnfiwE05dNof73f//30lepF4uwEGebQq76etlkhXdijv7J4o5eaET84bYpRG8WfsQ+WBFr+BjPMH4RaP6mLlq0oHY3s0M9y+T59cvZafWLjul1/7jk5Uct5i4x7B6/je2BmrueZnBZANbLSv2aY4RQ/cBb3vKWAW4WcMQ9gJvhmfoJx/SZfMCefPLJpU+M+O29eARORgiCfGz6jOiP4vf3K1/5SrHI1S8xUCAWv+td7yo8LbiXPMIIQD5mA/zHf/zHCsVcL98IuvpE5XEZEdbMeLQ2yS+C7/75XSAYWyxXCDzK+TpOM5TZPPUh5/gExtP1yRZPhbvfjgyJQCKQCCQCgxEYGwx6cJ3yWyKQCPQ4AoTXGPzPnDmzkFFiLktaYVXEXASUtQI3DZdccskAEUV6D5t1WM0gRwd05UYI0h3f7ZVPgHUO0TeNTT3XXa8zfd0gj5A7VJAOmUaygygPFXekjx977LFlEPD4448XPImwd99zd7E+IUy4juq10047VW9605tK/ZoY+NwOptYRgwS+cq0GDh+hWFeXT/lnLCEQ911nv6gevD1TWyTNrP0E1i5D6gFenB9Ldc66jCwCns05D82pLvr5RdX3v//9MoPCojuecZa4nmECBHHX/fDtb3+7rHRuMcUTTjihCLz6irUZ9L8hrBIRiM/qHvevfs4LC/EEYoa+l1hBQGF1161PG06b9PsWR5KnjeAdM0eUH3UYTl4ZZ+whsKgWtbikmVy7e+Jf/Ln586qFz9SW6bXFt2fD8QxjGwH9k+f96KOPrqxt4AW2Z99La5ySVX64AtASfQEOROR0jVnHenmvT3GOkOockXSoIB7RFh/WB6hDM9/gfs4TQINzeYH+jne8o7iG0I81+4+mmKte8usWpFE2kTVmJqiv9BZDi0V6pW/mH3n5TfCP8IsbDlVOxH/yqScLFvL65S9/WQRg/a2XY/pD7ciQCCQCiUAiMBiBoRWCwfHyWyKQCCQCawwBhNWgWmCdRDREIg1ykWfWm//3//7fav5zHf9jQSSdQzQLAa1JaJM8Iq0Wr2BBYYoc0isdy9/XvPY1hWz73tyUzyICiVQ+6waWUwb6jodlRFhb+I5cC+qC8DqmbKKkz+rU3Ew/s8lfWtatkU5d6tjluLRCswzfxYFXEHQYNdstzmgFlrl82sJHvVwXPnQNbix+pm6mGXJhYQpyOzSxjs+XX355EbNZ5VopmlVGnOu2b+eZ30cTAYM+92Fzc6x5fHI9INug3M9Tp4RIgWrENkpvTeoSMqwZBPQ1+s/ok/Q3XpL9/f/399VHPvKRsjCQfpL4GeKCNMRdL6q8rCFesmz70pe+VH3wgx8sfiedi9DsI+NznBvNvTapq744+nL9jjp48eZzBMfUWXx9sK15PuKtaK9MvjfhAi8vv3bZeZfyGyhv5WQYXwi07wPWuH7jHZ9c76evs27dT65f9pMnddYEGNyvNvvY+Dy+MJgotXXNYvOME0pZznvZHP0a3uYFtu9e7OgXPMuuOetUHJEgKa3n3TNNsMST8Ekb7qbPiX41+iJ5CgP3T50XTul8vFjymSWr+olHOGbJirO278XIR1l4qrT4lnLE9T36PnX3XeAmS/Bd347rlUXd6plj2iN99FXiRL3FZYzhXNTF5/YWPFgceRO74Yg72zIkAolAIpAILItAirnLYpJHEoFEYC0jgMyFQIq09vf3F3+sBF5EFFE844wzqq+e+dVCnMv0rHq863yQW3kEcRSfD9af/OQnxcchsi0YnLOmMHiOdO2mK7+vr6/kjWwjr7fcckvZSxPkNcqzR+K5cTAVDyn2fcf+HbtOpZR/iJY+EzmskszKIsi1MrRRO5QJA8dYgvHZ+IMf/KC69NJLi4+xIOrtdozGdwMFi4IQbJFuU+nOOuus4vuRmKPtRFyCb4S4JvG9ueeuAW7iwMJ1YcmizctL18wjP699BOJZ6lyzpeLX2q9Z1mAkEHBd9TOCfumKX15RfepTn6rOP//80s9GHyWePpb/V25WbF782MTRD3vmr7vuuupjH/tYedFG0IxB/kjUdWXziLJjH+m1pVs/JJ7gvG1Vgnx//vOfD/jTNP16o403WmVxeFXqkGlGF4HOvdG5P+o7pdxLrHE7Ww7FRhf9kck9+gTXkrCJtxEgfcd3fvrTnxY3Lfo3oqrgHAtV3A2nIQKHwIpPmomE84g/rSH4R1r9rHT46wUXXFD6WHsv/CfXi47qO9RL/maxyZNLBP1oCLKlIq0/4tv0v9ah0BcrS3obzlrqwLq23hhO7L333mVmQpRHjGadrA5lgeElPNV5x2wMKH72s58NLPzXqsagr8Fd+f6FiTrAhbXz87dd6vN3UKL8kggkAolAjyOwdue09Tj42fxEIBEYHgJbbbVV8S3G4pMFBNKHhJ566qmFtL7+9a8vFpyIX5BUOSOTyLQpXuecc04hw0guKwSEmz9W0/99HypIP2vWrLIoDiGXwHruuedWBGYrGCPzEZTnPF+R//Zv/zYgShA0Djn0kIH6RPzYH3/88YXUm7aGSLPwYImAnLOwkG9pVy2csJTwT/tPO+20IuRqt6l6f/d3fzdooYvIf7T26kXMRbYJuaw8wgLDZ1PkLMr28pe/vFwHJL8ZYNsMFhPRLm11zeVrYJIhEUgExg4CnmPPLjHh2uuurb521tcqFvWxKrr+iK/Z4447rrwsC8FDOqIBqysvfbywEvSZPn/+858vL830t2GltaZbrY7tfmqoOgw33lDp47i+U98X4guhyMJF0e9HvNyPLwTav2/jq/ZZ2zYCcT099/zfcrfghb2X755d3NSLdQYC4kb82MvvxBNPrH70ox+VWQpmKngZ72WX550xgnzE9+zb40L6xjNq4wW+eX3v6+ur/vEf/7F6pnZnhPeJR/D14pwQSgS99957Sz+7w/Y7VBtutOFAXaJNODQurN8hyuqDtUs+8sND8WKfxV2weEERr/kEx4MF7cWFcbS+uk5+DxbV/p+94OMazO8BsRr3loe6y295QR2uuuqqIhKL78UWHsj3foZEIBFIBBKBZRFIMXdZTPJIIpAIjDEECK9W/X37299epvGatoXsIqNnnnlmdc011xQCjRQj2UgoMspqwII2xFVT0EzbQpYRViLje9/73iLKsiplLdEtiC8usio/BFNeX/ziF4slLEGYKwikmjWB6bIWxCEgI7DqyZWDxShYOLQD0dN5q6abcmdDlhFgVg1cTBjcE4SVrc0s2QwaDCIIwMSTWbUAQkBR3nAC0mwrM+SHk2CIONrO2sQgwurDBhLqqe0EWQtXxIrO7SyUj9zHnnWxAUBMrTNw0LYIpb71lxUNCCJ+7hOBRGDkEdDH6F/vmn1X9a1vf2vgJRlBwDOrz3rjG99YLOtjUTz9q0H+goULyiKPFgs7++yzSz/Hqk2/we0CX7qmIseU3pGv/fByjL5meLFXL5bfCi+x9Hv85O67777ld0w/N9z+fPVqkKnXJgKd37+1WYMse7gIBPewKK0ZR4ccckhxMYP7eVFFiCSOeoHdLeCRuB7eSszFmT7zmc+Uvs/xnXbcqfBXfal+0UsyXBDHxXsJvvpGwi2OFUE/8epXv7oYAuhLpf/6179eXrAfddRRpU+NuDgt3+Xf+973in9z9ZDefYh7Epj5qQ2uHOn07cToL3/5y+XlPS5KzOUigt9zeGyw/gYlH3yQSzP+03Fa/Bi3W15QvnoTib3g8p01cCwo6XuGRCARSAQSgcEILB0lDz6e3xKBRCARGDsI1ByOQGsVYW4IPvnJTxZii/gRWAmarBcIuTGFl6CIZCKFsVADYZCoYLD8zne+szrwwAMLOV5eQ5FcgoSVihHU2bNnF8JpahqxlWBJaCUQK88xVhHEDsSbVcGHP/zhYnnRrRx1UgYrNvkj0kg8AmyhDAN9fmNZqiHXiLfzrJSDHLPe/Yu/+IvS/m5lxDEDEZjJp4RaW67tR+L0Ku3Vn5hrKh2r3LDAkNluu+1WVqxfuKC2+qj/NQcfzrMynjSlI+bCjfCuffIM6zTxIsRAKr7nfk0hEIOoznROC/hYiX1h/Yzxhcs3boTFtWXOsmH17rFl88sjaxMBz6Fn9JprrymWV/opfZiZBGYrnHLKKcWiyjH9cAzCvcyq12IvL3f42yZcskgzw8Dzr/8khOifvZiS38qEEAOUaxMGyq7rvKIgbmyRttnnND+vKK/lnVcGwcXvg+AFICEXVtrtN8XviTBSZZbM8s8YQ6BpBd4R0/Sfk2oLx4X1Sw/P2OAQ/XAcXfE9HTFzPxgBzyAehJN4xvCi4YZ4JnGyww47rCyGZqaBZ9gLKSIp44N4kdXMl6j59tooAS+98MILy8t5vMnLHGs5zNxkZln8Vv30q7gkLuilv7T7779/maWmf2jzKfyYKxtuwMQPUVQ5fX19pT54KaMGPBa3VGdiKVE5zjES0A/hvBGUpU/1kk59iLS4uPrhrNZL4BJLHd23zuGz2slYQRncUMBO3W2BY5ShT1Q3xhK4rfKk8zvg+kSfGPFznwgkAolAIlBVbaaQmCQCiUAiMPYQqMcsyCSx1urnpnUhzPwMBjEkBlgNPAgiYioNYhkEkmUBSwquFYiFiOeKAkKJWCPtiOl3v/vdYtVAdERqickRgvAipURjYubb3va2khYZdb5bMIhH/N/61reWAT6rWySZJQZLCYsLaac8gtTaaxsrtg996EPVvvvsW7JW18Agyop6hYjrvHbZ2nEjzYr2yonAt61BhCl7IcYa6Di+3fbbFSE3Bk6Rxr6Zh6nX2qxOBgXEHvsMYwWBzvX2XN1//33VE08+XgtxW9TblrW1ZW0JVA+Mrda+rKl3Cg5j5QqOVD08t/o9MwT0u55ZwiQrKn2YF1gRmn2evia+S8NVzZvf/Obi/5EVl4E8AcNMikMPPXRYYq6+U742wkSIBNEP6oudi9+BqNdQ++iTYj9UvNU5HjhE3bnVIb4I4YN8dfLPtOMDgc495uVXhxc8V/slvffee+qXzw9Vm242s9p5p51bv89Lf3OXtjD716VYDP+T/kefEP2RZ3FVAndYOKUXMr/97W+LCMkdAlcK/+W//JdBHEf+yuU64P3vf38xPNDvsejlE9dLejxJvQQcT1/hPmGkYDbDu9/97sIrHXNOMONBwAX/+3//79XXvva16uqrry4vx7z0J9ziYzipPPU1Nt/x2j/7sz8rvni9SFO+fjhezDfLUXccnCGEeMRZoi2Oiqvqf8WPPlj9LMLG4thxvn4di3aVSjf+yB/XZeXserA+JgTjxjBxPkMikAgkAonAYARSzB2MR35LBBKBMYoAEojMbbZpZ6oXEs2ylk8uIiChFcFEGgXkV/AdEUQMTf8l5FqFGOEcblA2VwF88yKnLBEIGcokbkWIAYE4xAg+1cL6F4EVN6yxIo02qaMywhqCZRZXDTfecGP12OOPlfqLI67Bh/awxmXBxioWWV63CGndLbmkQYht2k0Md2xVyXFgHPVmNc3FBZcLpu85TsjlgiKug/ZFumg7S73Iw/REgwHBYKdpnRbxc792EfB8sSD63e9/Vz391JNlIPfww49Uu+y8SxHs127tsvQ1hYD7wPNq6q9nVl/i2ScMeAmjH1xR3xLnPef6VdZkNoGYQNjQZwwnqAOrXgKzvpFwwepXP6ec4eYTZUU/Ffs4bh/9VXzuFqcZPz4346kPzPweeCFoMSOis/rqz/3WZJj4CLgnsJVF9cswopiXZO5h09fnzn24PFPrPy99hY7GnYCH4WmeRf2Z2VWrEvApbri4OCBoeqaJm6xhudYifraD/pFLgne9612Fx7HIZSXrRbh6uS+Irfb6BIKmfgF/ZZmLUzmHU9rLb51pncV/WeeyZCWcsrAl5MrLPWUTpGfxqr/mHqGvr6/M/mLsEO4c5Bv9pr5Jf+W7/pTrrPe85z0ljRlZ/AazpI1+TX28iMf/YOA3QRvDNQS+GFyviY30BGV9vzyUgwsyuojfi2b8/JwIJAKJQCKQlrl5DyQCicB4Q6Ae37MosJIwkojcGgyb0oWsGtQjr76z1EWwkVEkG0GUti2oDgcCRJPlw+GHH14IOAKLKCPuEQgJyDehFVlnoRaicRDhiBv7OC4ewopkGwCwwuA6QtuUof6Rv6nJVvfdaeedynS+IPfy6hYsyEHcgIU8WNDFoEH8odJ1y6t9DOkXwtrCd/UhNBvkyNum3HZwXHzt5mLBoEoI67TIu50uv68dBB6vXyzcdtut5T4yDdiga+7cx8o13GP3Pco9MLmLX+i1U9ssdbQQ0BexoDIdNp5Z/RYLL8+5/lYfsKIQz7dBP9cM3OHoE4ix8lte0JcTwIjKXqz5TASWXtn6eSKIvs7vBLFmOC+wok6xX14dVvUczNRTIPr4vXLMC8rtt99+WNitatmZbmwg0HFHUwtmdXXwlt/XL8juf+D+JVaWi0sfe/9995eXonGvjI2aT4xaeNb+5//8n+W3y/NHaFzZoA8SuMF6wxveUJ5fIqVnGYfrdt30nfoWfRSO6CUYfsYSFl81MyHy0A/ikgRNfZgX5NLiS8qWl33hmPWN5ByhlL/eeNnPMpexg/7ReS/01VeeypenPPBp4jbrYnVozq7QjhBTp02tjSDqsgitRGN9N57KTQNBVxm4pvTq/MI9XlhwkJ5YDBt1VId2kB4OyvcbwkBAneRp64ZnO4/8nggkAolAryGw7Oi61xDI9iYCicC4QaBJKg3MEVPiKiHRQJ6Qa8oakmvavoGyBSSIBMgsKwIkmi8wJFZAMhFFeXQTHNvgINj77LNPIbEhHhfzmjritHWmFdGAkKCuQfblL6jzUEE9bNIhySx6CdVzH5lbpuDNX9CxjiBKsE7WDisHh1gs3yinTXqRauKw+ihjdYP8lWXzGe4IvamCRGKidxBxZYmj7YFHHIt6IPDhJ81AgJgLg3Y7In7u1zQCnSnsLNEfeODB+j7nuqQqA7MF8xd1BnEegnKfD32Pr+laZ3mjgwDXCl4ysSTzjOqTzCpo9qkrU7I+ldVZhOjH4ru9vsOmj1YuCzKWZ6Y3s2ojyIQFmT6IaODFGjGXWMJCjNsbQoI6R9+1NvoYZUYdTMcm3uiXCSOwyNA7CLgP+CB/dt6z1VNPPjUgzE2u73UvjFlph3Xn2rhXJ+qVMCOASxj9wHBfPg2FBW6j/7PJD5ds851m2nj27QmcZnGxvMVh8VT10R/oC/Cg4JPtPPSF+kQcsN1neinkZZZ+0WwaeVujYKONNyp+yuUdaez1i8Rd7rI69+RStxNtXuy8jRBNlCUEKwMf1nYcDr44quCYvg1v1i/LT/p2MKuLZbr2s8YluOcshTZK+T0RSAQSgcEIpJg7GI/8lggkAmMcgaYYGYQQcWat2gwG7n19fYWkEnG97WehwNcusnjSSScVdwuRJohtfB9qryxx7ZFWW4i5LBaiTtJ3q2s734gvP6FZD6R3s803q/3ndabq1RR6afL645RJS4WzyGdphMGfnI8yBp9ZtW/yM5CQp0EnQYUFsTrD3qCg2X7tGqqOrPyQd9eQCwyDkJxat2rXZaRTuW7EBs8PIdc1rw+VwMcjP7lEPNd28mT345KTI12RzG/MIDC7npHgxU3nmk8uVmCeW89s9AnDqexQ/cFQx+XJeouA+4UvfKHsCQXK1NcQNWwEA3noj/ijJfzq+9/xjneUmQzNF2DDqedIx9FnqjNLPJZwfo/UiR/3FHNHGu2xmV/nHu/8nhPSWIY+POfhAWF/weLOC2qWlV6WuD9G8vd7bKKy5mvlOsQspeX1O91q1uQ30gbHievUPB/pm31PcD1pxSXs2uJ4pGnuo4723UTWZlyfCcGsdM3k6jj16MSQPvJS3ygz6hx5R5zIl2ssIeL57P60dQvyjbycj8+Ot/NmEACfvr6+wgXtgwe243YrK48lAolAItCLCKSY24tXPducCPQAAkgjy1ZEFRHkg5b1gEH92WefXUgjQdd0XuebJHtF8Ig/iIx2+O2Kkq3SeWXZxmJA6OHAz1mshozUc69gNed2GIRZfZI4bYDBAsOiGkQN1h6sUTKMDQRcM5bX9z9wX3GroFaLagGNYDZt2tQi5G604UbVlFrIraNm6AEEuNdgheXeIKYSovr7+0vLm4P8kYRCvgRQ1ltf+cpXqksuuaT0iwb7XiZ4eWSmhn5fP08oJYRFXVnAevEmTlgQd6tf9LWx7xZndY/BzewR/WZMqWbJpg2EvQy9gYB7zL0Qs1kenftouW/9DpZQ/+y7j1k/eqnrGRjN+7I3UB97rXQPrG7odl8MOlbfS/iWMBLlrW59u6UnOr/1LW+tF1RdUHzuekEY4m+3+HksEUgEEoFEIH3m5j2QCCQCExQBhNVmahcRALElAFgp15Szc889txxnrcV/WFh3DRcO+Y1VUjzcNqxOPG0nlMOSv1tT8A1KuVcg5BJZVhSKpUg9jmGRZpMncUgYNBBZUUZ5ftQQcB0era1y75rd8Yk3fd3pZbDledlyqy2rl7z4JUuumYHi0qmZo1ahzHitI0Ag5X9bcB8QSFnVj/Yzq6+59NJLy6KX+gpuFLw44q+SGwUvgfQf3DAQfbnY8eIuXLh8//vfL74cvTDqNnU5gI2+3b7Zpjge8VZ1D7N5z84rVsPEXG3hY5x/yBQvVhXV8ZZOf9kR8VzzTWduWl5qeq7mPjq3fq46/IXbKC8lNp6xcVnkary1MuubCAwXAQuyzZo1a4ADZl84XOQyXiKQCPQyAmmZ28tXP9ueCExgBAiNrAcNlPnr8t0A/qc//WkRHgkSBvqsXViFsgpY2TBSg/tmuaORZzP/kfocwuv1119fmXZNRGGVyz+lxTGcbwohyu32fdKUjrWI89LEdQtxvZ1GvAxrDgFWuQ/UPlIfqX03h9XlpEmTq+nrTqt27N+pts5dp97Uh/DFOjcF3TV3ddZOSdwXEEwFz6kQ90b5Mkp/nnziyeI6gW9HgW/uk08+ufhsDItW/YfPsfAZK8czzzyz9PleOFksjY9H1rnL66NGrd+p9TuYPfHkE9W1115b6gA7fjOjDaMEX2Y75hDQZ/rdq/3tT5te389bVdts+3D1WL3Q5MIFS9yH1H3tPX+8p9pq662qvh36y2wIfW0nxH7MNSwrNMIIjGR/JC9931gL0Uaz5PDJbv3zWKtz1icRSAQSgbWNQIq5a/sKZPmJQCIwaggY2AsGywRdg+VNNtmk+FC0MARx14ITt956a7GKWZWKBAFdlbRDpRmNPIcqa1WPqyOhj/sKYi7izTKOVS7LN4JF4K+M4bTJdZIPi4yxONhYVazGczrPx0MPzamvSWeRFSKD52jzWkjbccddWk0zQGwLDO3vrST5ddwhoO/kJiDCmnhWlbH5FpsXK9wjjjii3IOs/80EaPYt+p1YAE0/cvzxxxcLXa4X9C8WbrPITn9/f6l+u4+Sl0158TnaORJ7sxEIzPrMO++8c8DPsBXohzObYSTqkHmMFQT0jR1BV430qzvtuFNxBcXlwsL6ZfSCIvROrW6/7fbq+dtsV16ejZXaj/d6NPuNkWhLs98YKr+RLnOoclZ0fLj1iL59uPHb5a5suojf7Jfbeeb3RCARSAQSgaUIpJi7FIv8lAgkAhMYAeTQwN/iXAiqN/8G944jkDF4n8AQjGjT4Mf/8NVXX10Wx5L5LrvsUllFeSSIuGsSxH5EK56ZDRsBzwRhzIrSxLunnnqy3i8sYnt/X1+dT1O87Vj6xDWLQeCwC8uI4wYBopOVysM6lzjpxQ4hVZ86GsF9xfJfH958UeQ+c28SmONlkLji2PPna9aFxXXUUZ1NXXfOv7UR1OPKK68s9TV7hJWwhY9GC7u10cYsc+UQcB/XrKQW9Nevdq85ylVXXVVNnrJOca3gpYT7Y5Jnq9nlrlwRGTsRSAQSgUQgEUgEJhgCKeZOsAuazUkEEoGlCBiwD4R6EGSAT4QkNhKpBgSn+tyguAOJ8sNQCEyup38SSIgRrJ35zXz1q19dveQlL0lhfCjQxtlxz4try22G63v//ffVCzY9W6a38+/p+cnnZpxd1BGo7vrrr1+sCIsgWvexxFQC5UB/OgJltLMoYld9vwlEz8cefax68E8PFmHWYmx85KoXYVn/rl+Ke5NLHb5pbeIOiLnN34clBUabYq+fG8mgHepw4YUXlnbA7pBDDinieNR3JMvLvMYHAqTcRQs7VuVbb71NNaPud7142GTmJtXee+1dbbDhBtXUKbU/mwalGR8ty1omAolAIpAIJAKJwGghkGLuaCGb+SYCicCYQaAIAfWCIgbL/HHZOxbi7tQpU2srmJEdtI+Zxo9SReBlle2TTjqp4MgCjp9KrisyjH8EPB9EMxuLSFbX22y7TfXo3EeKiwU+HhcvXiqYjf8WZwuGiwB3APGcuz86VttPlX7Ad2FlhEn32orSyM9GrL3nnnuqiy++uDrrrLOKuwIv6LhR8MKBiBtirnxZEccLPMdZ8MpjZepXKjdCf9TFQm4333xzebZka+EfLxcz9C4CNNrOs7O4uFLYY/c9yr26004710K/39RF9fMFH89KKrqQyJAIJAKJQCKQCPQ6Ainm9vodkO1PBHoAgSIENBba0mTHDO5tGVYNgQMPPLCyjWRYWyLLSLZhouVFZLDN2HhGtcmMTUrzXKfFiz07KSxMtOu9ovZ4cUPgF0wBJ04SWAmV0a+uKI/meQKsoC+Wvrg/6HJbyZ8I+vGPf7y67LLLinDrGIvcEJgJteoUoi6RlEUuYVdc9/Ha6PM7z8visoAbP+O+qxe3NBZ085IxQ+8iYFFJm/uUeLvjjju3wHB+4ZJjnZcfrQj1Vw9Nlwdn2Yh5ZJQQ8FyPxzBe6z0esc46JwKJQCIwkgikmDuSaGZeiUAikAgkAolAjyDQsSTrkcZmMwcQ6OvrK4sdOuAeePDBB8uiYkWMrcWkxWZBtF6eDSTu8iFWL2fhG4LrOtPWqaZMHfyi7Y477qhOO+206pJLLimil6y22mqravPNN69e/OIXF1cFIebas8o1++Lyyy+vpCXwEstik35NihjKUocbb7yxWDMr38swFsVrsh7KzTA2Ecj7YGxel6xVIpAIJAKJQCIwFhFIMXcsXpWsUyKQCCQCiUAikAgkAmMQAWKuRbssfsivJ5+0119/fXXLLbdUL3rRiwaE1uFWnfD661//urr77ruL+MrqVz79/f0DWRBlf/vb31bf/OY3i+Ut4Zibl3/9138dNDtg4YKOT/QFCxcUdwoscz/5yU9WZ5xxRu3z+f6VrttABUbgAxF57ty5xV8uUZeriuOOO660ZwSyzywmOAIp9E7wC5zNSwQSgUQgEUgEVhKBFHNXErCMnggkAolAIpAIJAKJQK8iQGzlHmDjjTcuLhYIrYRcrg+IsMMRnbgYILQSNW+//fbqn/7pn4oF7fTp06vnP//51V//9V8PEnMJsTfccMOAmwSuFd74xjdWL3vZy6oi4C6x4g1rXmIv8VRd7OXL/cKaDgN+2euyLXwGJxbI2s41xMEHH7xW6rWmccjyRgIBU/jTxcJIIJl5JAKJQCKQCCQCEwGBXPFnIlzFbEMikAgkAolAIrCKCMT0c8kXL7bQDh+m/J8SDghi9lbfWbotXrywjsuH49Jjnc/1oToQ0YYj6nVi59/xhADXCkcccUS17777FpGUuHvfffdVP//5z6vf/e53RaAlWLK4JaTaNwNrXiLnk08+WbbPfe5z1U033VQETvH3esle1R577NFMUtwvSEMEJYoq8xWveEW5x+YvmF8E3WeeeWbAP657j/sGdXV/P/3002Uvj3DnsCbvT+169NFHqx/96EcFM2L2S1/60vLZueY2qOH5JREYEHCJuYZtsfc5thB6m331UMJvQpoIJAKJQCKQCCQCEwGBFHMnwlXMNiQCiUAikAgkAquIwFKLxc7gv7MYDytGi+4QCdasb9FSYP4Zswi4J3bcccfi3oCrg3gZwBcsdwZzH5lbhNTiQ7eO6/4KEVVcvmy5GGCZ+p3vfKe4WCC2EnmJtAe+/MBql112GdR+ZRKFCbkEXfkRbx1ndass5YQfZ+KoIM1dd91VhFTfCbwR3/c1EdTRpu4WcOMXmFXzMcccUz6viTpkGeMdgaXC7IKFz9UvJx6v5i+YVy1YOL9aVL+AW2qxuzTeeG9x1j8RSAQSgUQgEUgElo9AirnLxyfPJgKJQCKQCCQCExaBEOLmzJlT3XbbbdXDDz9UBKb585+rRbrnSruJuwJBKkMi4D4gnB577LHVUa86qnwmpj722GNFnP3fH/nfxeUCpIiq7jFpCKniETMfeOCB6sMf/nD1hS98odx3xFniLovfffbZp8RtIs2tgsXOCLHyUtZ5PzyviLWsXBkrqpP85eWYPUtYrg0E6VgDE4GJvCVds5BR+ExcVidl/uEPfyh+e7WBaH3AAQcUfEah2MxyAiKwaBEL98XVlMnrFFvd+2tr+Dvrhf2eePyxcq+bVZEhEUgEEoFEIBFIBHoHgTXvQKx3sM2WJgKJQCKQCCQCYxoBghbBjd/SBx64r1hMbrX1VtXWW29dbbzRxrUA1hGjOoJuZzr4mG5QVm7UEQiXAFtssUX1xje9sbr/gfuLaEpMJVoSUK+77rrqVa96VXXYrMOqLbfaspo5c2YRT7lTuPzyy8viae65eJlg/5KXvKQ64YQTql133XXAwjYaw5J1t912q7bccsuyUNqzzz5b/eyin1V77b1Xddhhh5X7llhrm//c/CLWfuMb36jOOuus6o5a8FI34u7DDz9c6nbQQQdVfX19g8TUsOaNMkdyT3xmuUxMJuZqyw477FCE3pEsJ/OauAhMmjSlNO6hOQ9Ut956a/WnPz1UHC68+MUvLi9C3OMZEoFEIBFIBBKBRKB3EMhf/t651tnSRCARSAQSgURgAIFFCxdV605ft7rtdha5Dxdh7ZFHHinT3R988MFq5513rnbs32kgfn5IBCBAMI09UfJtb3tb8Un7i1/8ogimfNLeeeed1TnnnFNdfPHFRbxkdUtscp/Nnj27IsYKXCaw2OUj9y//8i/LgmaORRkl0pI/xE/WwKeffnrJi3XvJz7xieqrX/1qtd9++xWhl2D7xz/+sfrNb35T3CvMnTu32nTTTUv9WAQLfPsShU866aTy0mJJ9gNWxOpP2OUKQn7NoF7Oycveeda3Nu4j1L0dpGH5fumll5Y0sDjkkENKuz2DRZFbkqhbu9v55ffeRMALteuuu6Z+eXJfvfDgw/U9WL9cW7S4ftZmVxtutFG1+Wab9yYw2epEIBFIBBKBRKBHEUgxt0cvfDY7EUgEEoFEoLcRmDR5UvXcvOfK4lVPP/V0NXlKR6RjXWkaOKF3ch2nE9Iqt7fvlqWtJziGqEnA5C5gxowZ1Qte8ILq7LPPLoIti28vBu6+++5ifUqwDaGTkEsMJZY6duSRR1Ynnnhica/AnUI3QZNYut1225V4BFrWv/zsyt925x13VjM3nVlE4scff7wSh9gq7ze84Q3V+eefX1100UXFcpgILI37XJwIyiU4q6u8i/gbt39EqvcENPPcwxLSnmWx/OybgdirvRaIszgc9w7abPEzn5U/bfK0ZpL8nAgMiYB7ct48z0/9EmBxx3WJ58w9veEGGw5YqA+ZQZ5IBBKBRCARSAQSgQmDQIq5E+ZSZkMSgUQgEUgEEoHlIbBUuBKr1q5qf6W3Vo8//mj13PzaUrJ2yUhcIjZtvvnmtUC3iVjLyzDP9TACIboSYE315nbhhS98YfWzn/2suFIgMhFGiZ1ETQKpIB1XA4cffnhxxSBtf39/mSrOv2zk24TWMfclFwzve9/7inD84x//uLrmmmuK6Pqnh/5UPfPsM8WqPO5fC4y9/vWvL3VjictVg7r96U9/KmW0y1FHAivBjEjtfFPsVZ8QYNVT22wEXMe7BXlY+IyQS+BmlevZglMtiVcLF3WshaVt16dbfnmsdxHgw7yvr696aM6fyr3ERe6CBQvLvffA/Q9Um9T99TbbbJOuO3r3FsmWJwKJQCKQCPQYAinm9tgFz+YmAolAIpAI9CoCg8Xchx9+pLYYvHdAiCJITZ06pRblNq9FgW1rQet5S4BKq9xevWOG024iJMtZC5Rxg8BCl7/ce+65p7gXIGbyFSvOJptsUkRfVrZcNBBnQziVj42YGnk2y5eeGLr77ruXsvbcc8/isoFbBfkrh7BMGJY/H7zELcLx3nvvXfz28q/LSlYc7heaQT1e/vKXV6eeempJQ8hlQdsMjkXd5Ptf/+t/LW0mBPMzTaRtBnEfeuih6oorriiH1U+cjepp8Sx8m2JuM11+TgTaCEyePKXc35tvvll9vz9RLazVXC8VhDkPzynWuXxTe0bcdxlWDoF4tlcuVffYI5lX9xLyaCKQCCQCiUAiUM8SSxASgUQgERgLCCC/BsRCDFCWVy/xDfoN8G2C9M1BTPPz8vLKc8siAEv4sqqDI+uzmFa8bOyORafj3TCPvFixZVgbCISIy9JwUdkWLlxQ+168t3rq6SfraeDP1M8QIa1Tt5kzNy2i25QpnqtO2m7XdW20JMscmwjog/XJxEoiKx+4BNbHH3u8evqZpyt+aPXrG264YREyiU7Rb7db5F7rdr85Jg/pWAHPmjWrlMkPLxcHyiNkEWlZ4YobvwmOE49t4grE22aIhcnEGapuUb66iH/ooYeWOmi7NO10XpAQm6+//vpSlDKPPvrokoabk+X1qc265edeRqDTMbvnpk5bp+rv27F+QTCnmvvI3Pr+rl8u6Lvrf1yLPFIf8xKu2/PTywi2297km55Z/UT72W2naX+XRwR4R56Rn+/Deb7Fi+s11IusKGe09+oSvK9bWc7BSlBn/WHUvVv8PJYIJAKJQCIwugikmDu6+GbuiUAisBIIBCFGGJvBQMX/ZhCnSb4jTZNYIqYRmsfjWO6HRsCU6CuvvLL44IOtacqs6OA4FJZxHO5lgNK6bs3roeSIP3Qt8szIINB5DjqPw6R6MLawevBPD1Z/rC0nn6mFtgULWOROrX3mTq6ngG9RbbrZpvX0cfRg6fMzMvXIXCYaAvFM64vjeTbAn1JbEc7cZGbZSpuX9N8RJ/bd8Gj26+3z0kVa7hQE1rdDhcgr0ojHH7QQdS9flvyJ+M1jzc/aFmFFccXTjxKbicu2XXbZpTrooINKFspv5hf55j4R6IaAfttjtFF9H2233fbVs8/Mqx5/4vFq8uLJ1fR1p9f32jO11fn99f29YXkZ1y2PXj/mmWv2BfAILtk+PhysQtjUF8hH/iEMDyc/L3uiHxlIWy90J0g/nDyGU89mnMcee6zMFuDLW129gNt2220L1xMv2hRp1EuI+jTrZHbB4kkdTJvHI23uE4FEIBFIBEYXgRRzRxffzD0RSASGgQCLElNfn3rqqUIkWYG2SffkJQRXdjV9LD7jCFAsnQzOLcCz2WabDQyOg4BG8e384njul0UAVvxdfv7zny8+IVnVmZ7ct0NfNW2daYXUxwAkUgeRJ17ce++9ZXq1wc2MjWdUO/TtUG2wfkdAifi5XxsIGHRNqZ6tfXfefvvttcg0p/hcdC0N4DaorSZ33HHHarPastHj5jkrL1LWRlWzzPGNQOvl21hsTPRZo1k3v1GslP/qr/6q9JssirmaaP8+rYm6jGY7M+81gED9TC2uBV3a2va1mPvo3EfLrIr58xdUi+oF0eYtnldEOpbpLOD16+3f6TVQyzFZhN83zxw8WPDPnj27evTRR4sfbi9UvBDiJmVlAl7kd1ReAq7jZc0mMzcp5Qz3mTZTwIKMXvpIg8tuW7s5mrHJjJLvSP/hv9sCkvfff3/53ecW56STThpo/1DWxPjcTTfdVPCDIxF4hx12KAvvVTVfGG57R7o9mV8ikAgkAr2MQIq5vXz1szmeN6gAAEAASURBVO2JwBhB4K677qp++MMfVrfcckuZzt8e6Kpmmygi0o4Rc5FK/hr7+/sHfCmyJE2rp1W/wAYYLHNdCwMhIoRjxNzlhQcffLCsHC8tUZ7fyj//8z+v1n9e91Xql5dXnhtZBCbVFjQC34szZmxcPVn7GH3s8cer5+Z1psBvvfVWxQJ7+rrr1rEIuRkSgURgdRDw27T3XntXe+21V8lGfxq/ZT6zbPOgxbHVKSvTTmwEJtcv4ibVsyfMpMB7CJCPzH2kuFvwW7t+/cKUEOhcubfq+ytDB4HgMURIRgOXX3559ZOf/KQYAnD/xFf2u9/97pWCixiKt1pUEUd6/vOfX73uda+rjjvuuGHnQzi94447qnPOOaf6zW9+U64by32+xzeuf6NHo1+wAOQvfvGL6re//W2pJxFZnYnZymuXGd+JuWeeeWZ15513lnuMNe/JJ51c7fHCPZZJM2wAMmIikAgkAonAaiGQYu5qwZeJE4FEYCQQYJl77bXXVr/85S/LVLU2oRxqYIIIO0fYRdIJjgY4/BgeddRRZaEZvhIzrBwC8CeEI++wNZ3ZZ5aacA5y387VtXi8FgddywsuuKDEk49V3Pn1y7B2ETC0rz37Vc9bj1/TF9fTvmdUBnZ333V3eSFi0aj116/9LdZxisLUVc7N67h2r+LYKl1f4LnPMDQC+k0YNX/XAjfn0vp9aOzyzFIEJtXTJerHrf4N7liZbrnlVrW7nD9VC+YvLP13x7p0m+LOYyjryqW59dYnz5vZXZ5Dvq4JuBYljOcSBz355JML11keMkTbeI5vu+226rLLLquuu+66Iuay0GUVfeSRR1bT15le+zcexhC77jqvueaa6sILLyxWvuq2zz77FMvq5dVjdc65N4Lf4XO4HY62oiCNl/SMLtatX/jutNNO1fHHHb9cTriiPPN8IpAIJAKJwOohMIxfmtUrIFMnAolAIrAiBBBqliUxFc53hNN3wff43MwriLhjCClCPmfOnGJxYPrbX/zFX1T777//Cgl6M8/83EEAtjC1xXVB4IcTDBAEeQjyyLD2EeiIRksWGaytc3fYfofy8sOUXJbTm23KTUnn2nWvLSE3xdzu2PTu0RAm7TMsi0CIP+0zQx1vx8vviUATgalTO7Nj7Pt26K/9UncW/OO+A2/K57CJVuczTlJeqtR/ueXi/99sLosTOmd2GPdQfX19BT+cJ3hM5OYYLuO4z8RcVqrxHHvxLT8vSIuVa/0CmwC6vLCgXoj017/+dUmHL6kTVw1E4dG6jspRf1twtOEsThsCsHqFAQU/+xkSgUQgEUgE1h4CKeauPeyz5EQgERgCASud8+NFZEKegyyLjnzG5jsROBaZMe2NKwBE87zzzhtYNV1ebWIubYZEoFcRILN7jgzi9tj9hfXnjsXRUrE2rC1ToOvVeyTbPboIjJZYM7q1ztzHEgLEP8IhnlRcdrQqF2Jd3mv1L5vZQfW7TBa6xNKdd965iKiw4x6K1SkfsLAidDZ5J1iL+MktSq1fPvTQQ0XMJdzCWFy8Ewe9+uqrq9e85jUl/orEXPEJwnirPPpqMXmr2uJ6NK+bOtmijbhx05I72tO6lfJrIpAIJAKJwBhEIMXcMXhRskqJQK8jwDLhgx/8YJluhnAiyU2yCR+E0zkk2IIWpqrxO3bjjTcWcdc5U/333HPP4suMMJyhNxCIgZDWGiBlWBYBVroFp3pwu2CxFybhQmPZuHkkEUgEEoFEYGwh4LdNH24rv3O1yKhfz9+8oa9TCLoMBvjJvfTSSwt+zz77bPF9+6Y3vWlAhO2GIxHU8T/84Q/F1+38+fMHrG+Jwhby5S6M79w2Z41aDVyv+gC+Kg2jBKIqwwMLxsZ1XZEYHHmuzJ7rMf59GUHI3+ewzFUu7jwa5a5MHTNuIpAIJAKJwPAQSDF3eDhlrEQgEViDCPAbhmybNojkCkE2oxoWseDLNY5baKuvtmr47Gc/W3y2ItZcLlhU4oEHHqgQ2KHIdeTZ3iO1AvLeJPYIbzM0zzWP+9zMQ7qRsBCO8pXrc+zjc7s+cTzSNevYjts853Ocj32cb+dpEFnPYCz10eYoy3XoFpy3rQiPyEce7Tp0yzePNRHoYOzCdMPOQmgscp+bP688R7VHxkZin5vfG6fyYyKQCCQCicBaR8Bvrb7d72j8pqpUt/5+rVd2jFSAoItb8k2LFzIGgB8R1mf8E1dsC5rBH3FSbrxY5VrgkKALe9fCmgGE3kceeaRw2EjTbLq4cX1uvvnmsiCb8+ISVjfbbLOB6HFNxY80cVJ5bf4Ux6KM2Eca+91226165zvfWT1RL4AqPQ4da0uIj7M5HuXFPRZ5xfESZzkcQXxbu47Nuvgc+baPxzn7KLNbnDyWCCQCiUAvI5Bibi9f/Wx7IjBGEUAeEUoErk2Gg9Txe9YMCOGf/dmfFQtdqwNboReJJOgi1v39/YU0NtNEXs1jyo6AtAcZReybpDQIrjzEidDO03eWxdJrEwE64kS69nd5xbHIN/bSqJfz6mNqJb9ljlvEQv5Rz8gjyLk46q0u9r5PnVIvIlf/izRRjrSRPvbOxecypbO2BFKXSCs/AxvlCT7HwhrSOR9BnMAj0se5sicM1//kL6hzlF0ONP40r1k7TrNMSdrnG9mM84+wXYpv3dIa70UVn3yE9s5zFP7t3OMGlL5PqtZbt7NIYFyjpRiloDvOb4o1Vv2l98waKzILSgR6HgG/i82Qz2ETjaE/40nbbrttWST38ssvLxFxxauuuqo67LDDlhFyIyd8Ap8j5vKxi7sQhgWuF3AR3NMiYcTiNn+VvnCS+qf6ufnPVb/73e8GLGR333334uahyXXkF9a/8orfaMdxI8JzMzgmXuzFU8cmx7JQsE0IvhbrIchf3IULakG35pU+y0ud8LVmHYjAkW+bZ8lbfPmvt956vnYN0kVdu9278nC8fZ93zSwPJgKJQCLQgwjEyK4Hm55NTgQSgYmEQCGctaXh7rvtXvyhIYGOsZ5ggdAm1d3ajlgin0FwTUND3OVjQ4wjdCOeca7bXvkGEOrCqpgFiM30Pnkre9HCzqIU3dI3j02p2xkke1Et2GmrUAgvEXTJgCH2jqu7OiPg9tLbDCi0cagQ7Yx9O552yVsdIk/HlGOgwXJFUBd5xOa8AURpdwPXyL/kaUBRC47RVtcDdrFxseFaqT8cfZZfBgh0xNqpU9ap7wsL5vi5T2wgkyERSAQSgUSgdxHAE3CTfffdt/Cy4CwXXXTRAB/rhg6ew5rW4mf4Bp6y0047VQceeGBndkvNqYi5FjWzqGg7BA+q36EWX7kMD3BCvOiAAw4oeQXXssfd1E25wW+UifPgkxHka8OrxMURvcSNvJxrB/ngVsHDom5e8EvnvGPKUY/YO64uNu0oYdnsSxp5Rz7t8uWN+xbeWp/UpuDGwefUT5BHhkQgEUgEEoFlEUjL3GUxySOJQCIwDhFACBG/DTbcoBBRhBaxdWy4RFAaAiELDYT8hhtuKIIky4uNNtqo2mnHnaoZm8yopq/TsYZgOYpoB+HsBpuyEdYQIFmBIMGIqz3rEItubLnlliUvFsdta4tmvkj2/IXzq0cffbSU67t22lhANNsaBL6Q+xofBPmZp58pZN+59Z63XllkLgh/sxyf47h9bBHHVEVWt/BSpvxs6kWkjsGGsufOnVsWGAnSHnnYa29YbkhvAOJ/WSW53quz63H33XeXxUWUF2HGjBllERNWJgZmUV/5RIhj8b139vVgrPaF6zrAoHPPLl/QbeLWOzhlSxOBRCARmFgI6Mub/Xn8DsZ+YrV25VuDs+AMRx55ZPWFL3yhuBmA109/+tPqQx/6UOFiRM0BsbIuwnkchtXtH//4x8Kj8C5C7ote9KLqiiuuKFwFB/rP//zP6pT3nlJNX3ew5awZTVOmdqypxSfk4lGuy4477lh4oNb4Xq5hHV9dccUQOPEla0A4pj5ecuNFuBYuGtcd7xR3g/U36Ai/S4RX6cTHDQR5EF1D0NZm3FY7xMNHB+pTY0DUDc7LrQSurF1N7um8dM9bv7t7M3VUP++XLQInzJ49uwjc2mlhP5zYHs/TjiizRM4/iUAikAgkAgWBFHPzRkgEEoEJgUAQPcQQCUUWEUCEkBDbDkGUw0UBAsna4vTTT6/OPffckof0CKoNUd5///2rIw4/ojrm2GMqi7StM63jMiHIc3ug5DgR1yrJn/70p8sggMsHdUWgY4/Mm2J3/PHHl1WQkXrEup2fNqiHRTM+/OEPF/cRBiTcS/zDP/xDaSKCXtLWjLws9lEfVY68zjvvvOqb3/zmAGF+xzveUf3N3/xNSbeyfwwIfvzjH1dnnXVWGdjACIbIvTYH+eeHTn1dAwODaBNsfFb+W97ylvJZPWNAYm/AdPbZZ5fNZ22LAYj6Sk8M3m+//ao3vfFN1VFHHVVwjTLkAYteDAZjzz03v7rnnnsKBttvt0ONTUGtF+HINicCiUAi0DsI1CKZmT4LF3XcGflNZK05ddpSXhG/k70DytKW4hq4Ex+1XqZ7WezYrbfeWhYkIyA2hVwp4YV/NEXHXXfdtdp6662LqwVWvsRgvEOcm26+qXAT3EjeEXAfbgx++9vfVo899ljhMNtvv32pC1E1rov9pCmTqjtuu6P66le/WgRi3Ao//Jd/+ZcicloPAgezNsTvf//78uKcQEwo5QfXIm8f/ehHSz2ifHFPO+206he/+EUp66CDDqr+8R//scyCWjh5YeHO6vbud7+7iM24qroQrvFr7cPlrr766uoDH/hA4XbaZIuw8cYbl0Xg3v/+95fj0aY4by8+nD7ykY8UbsxthWPiKgPW1sJ4+9vfXr3qVa8qwm7TGrmZV35OBBKBRKBXEejNUW6vXu1sdyIwThBA5IiFSB0ijNQRVocKiKt4SOfFF19chFgiHgtRYp/F1NpkEmk00OGmAPn9xje+UQRX5FpegnLVBcFGxpHXa6+9trrhxhuqv/qrvyo+0Yaqk+NcPHzxi18si7KxUlBPJFf+8rW3IaiE5H/+53+uvvWtb5W8TzjhhDLYkI+yo/4+qzsS/+CDDxahbptttimDAxYfyjGICyG3mZ4FB1HUJh/iK4sK+RVLWJGHGdTZNbrrrrtKfvFdG+Ud9SRUK1dd47gilOl6EX8F8UrdazzU6aKfX1R97nOfq371q1+VgYO0BhPSwMx3ZfruurCEmTVrVvVv//Zv1cxNZpY8xVPO8kLgurw44+WcpmrPggWmQNZTFmvcf3Pzb6pn5z1bPfjAg2VgtG7tH3fevOdqrNetm9VxvRHtm0hYRJtynwgkAolALyDg91wogmA9c2bBcwvKbzORz2/wdtttV6xIxfHb2Mv9vbbjiPjhwQcfXMRSuMEFhySuEnTbGBFCb7rppsI/vKDeeeedy0t+ojDh1Mwr1wG/42qBwOtz8CE8C28y2whvwT2VS6BVF+XjNM0gPSOAO++8s5xXL9yWf18vw11bPCc2+eFV0hF08aZmIAjjpjbtk7c6aQ/+JmgDgVtdCLfyiHvGHleTv1lXNp+bXIvlMOG33R5x8Gp54tz/5//8n/Jdm1wPdZNGG5RBVBbnkksuqf7yL/+yuKJQJ3i2r02zjfk5EUgEEoFeQWDpq8JeaXG2MxFIBMY8AkHWEESkDclDNpubRiB9iKH4SOBnPvOZYgVLkCUOOk7oRBTl1QzyRVCR3q9//evFMgFBlqfynGMte/jhhxdXCFEncX7yk59Uf//3f19dcMEFhfSaKoakShukdu4jc0u8//f//l8hpUi7OAgqa2HT8ljUItDapb7SI9BEzPPPP78cQ1ilsw03NIXc4aZZlXjqBCdtVn97bXAcvr67PuLEdbS3wZNljLaLL52BhON/vPePxfLEYEdA7AnyVnnmn45vuRe84AUD1y8GAJdeemkh/AR6+fRW6LjC0OapU9ep8ZxUzb5zdi3oPlXuUeI/S+pf/erKIvb2FjbZ2kQgEUgEJjYCXnj7rRS8PGV5aVZQiG1mLJmpIXhh2usB7/BC2Iyr4CBESLgJ3cRCL90tdIa3hEC+2267VZvM2KTq6+sr3EZeBFIvmOWB/0R+vtv43cUJcSC858UvfnGxzO1WZkm85I/zeJX073rXu8q1dd21Q4iX6Y4pNwwSnFOv2HxfXpCfuhFdYeK7cnHj4HiO2XCt9iYenqc+4tsiyJORwyc+8YmChTjuTcLz3nvvXURx4rjvIRqzeGY9zKexNgixj3xznwgkAolALyKQlrm9eNWzzYnAGEfAgON73/tesVxACruRNscF/roIVSw4pUOiEUBEEkk3hYwI6Hs7ECCvv/76Qix9RkwJv6xurWhs+pxykE3WLZ/97GeL1Qbx0CCJpQuft0Tfkv+S8RHiSpAknrFwQEo333zzMm3t5JNPLnkGCUZsWXDIW10QcBYNP/jBD8r0P/7YhG4YtNsT39sDgvhuH1vEXd4+4sa+GddghiANK4MaIrdgEHnZZZeV6YraYqBzzDHHlGmIcc3Ek6cBhzwMBGBULIrr42eccUaZdgc754jepvy5nnFt4fGHP/yhWHcQvlmZuE6mSRoovO997yvXc2VwU6/xGzoDNbguWrSgfknxaDX7rtnVgvo6uR8ff/wx7xyqec/NK0186uknquet97yuz8X4xSBrnggkAolA7yJQd/8lmJ2xYEG9SNb0jluFBfMXVHMefqj8rvJDGgJjryKFF+Ab/Mm+9KUvLS/98S6chC/b++67r7xoxz+agYjKQtbvLOtdxgLy8s9LeqIsLooP/e53vyucFE8U/A7jiRZ1JfQSMAm6xFLp8M1uPLVZvrK88CeEqhtO5VpKqxxcFzfDheTFTYFjKxO0zToE3B8QcG3y9KLg1FNPLfV2TNu5P+jv7+fUq2AQ5eC3uJ+9/AR1x3dZN3/pS18q+eLdZqvxU/za17y22mTmJiU+NxS/vOKXxS0YPBldsIj+7ne/W3D3Mr/JJ6Pc3CcCiUAi0GsIpJjba1c825sIjAMEuAEg5iJrhXTXJNjnINZIMbJqE5xDigWEk0UBYv3Wt761EPVIVyI0/vDR9e1vf7sIwtIRCj/5yU9Wr3jFKwqpR2ARZcR1n332qf72b/+2fGa5oV4///nPqz333LP4z5WWjzUDBCKkvE25Q2SVf/TRR1ennHJKR0Crp9oFabefVbsHIAhzESBv5RInCZNIvgGGeMpEmqlyPgc2jSYN+2Ok7wxDOnm1Ew/EqctqB5jvvNPO1Q7b71BcOogr8I87u/aDZiDjOhG7X/3qV5dphEHqxROfCGxxOeI4/OJ6E8oNdOBO7CXkuibiNPOAPX93zrluXD7AWvo///M/L9MWo17KnMjBswBvGHqhYZVs1lfcKRTrnNqlyLSp08q0UPeT0MRyImOTbUsEEoFEYOIjsPR3epN6oVYC35yb59Qv9Do8yW8jXmHqOpGv1wPuAxN8kYsEnNNvpXDNr6+p+vr6yjkcwm+l31UGA/gdPrbXXnsVfokjikPcPfTQQ4sRgvh+j60ZcOIbTixurPAdZZk5hSfhPcTOF77whcWIYKjrIZ/YlKOOBF1i6f/6X/+rcCS/+37ftSfqq304kxflKxPkgT+feOKJJS+Ca3CvL3/5y0VYVR/txl29bG8H7Y+6FCvwmrbiKIwvGC7g6zAkZPP5u8cee5TPjpVQ38rcX8DU7DbGDeoBTy4p/vqv/7rkp15CcpkCQ/5JBBKBHkQgxdwevOjZ5ERgrCOA9IUv1SDF6hyEDVl1nhCIxHrbT/hDjBFMFpzvec97ilUu4QrxjLTRdpYTpvFfeOGFhRQSc4844ogiDCKUBFV7m7T2SCRRlmWGjRUqcmlqGNE1CK8yEO4g/er1spe9bGChiGZdCgmv60yU5P+MRamgPBYL6iVos8CFgM332OQXrhV8jrglwTD+DCd+O45ytHed6UstLxQFb20SH+4IvWNlEFOnaQaDDPloq+CzAZPrDzPpuVVgceK784LjMejipgK2BhnIvrzEVaY6RJqScAL/cf3rO7VuMyvcx6vZtaDuWXCvCAZ6sOSuYsqU+OlfOvjvDo3zzTjwH3wNu6fLo4lAIpAIJAKji0C7f+6Uhir4bd5iyy2qLf+0ZTX30dqnaW3pSEzEle644/ZqjxfuXhZEm1wvitaLAS/ATexxCCLsj370o7IgGQ6BGx573LEdMbd+KVr/uBYBlvUuXiGtF/AEVXxE4PMWVwmuRLjkisvirAJOojwzsLzslw5n8cJ6KKtcednEtcdzfSYkf/zjHy9CcHDEUsgI/VFP9RWCS2kzbPAzQbk43ArF4poyqDtOTYS+pPZ/qw3uUTPgvFhwDZTZDNPWWerq7JZbbikGAl7ymyXHcrq/v38guvyFdh4DEfJDIpAIJAITFIEY0U3Q5mWzEoFEYDwigJARBBE+5JUoFYKf9jhO6IwgDh9bBFVuFVhZEK6CjIqH7DWJHjGX4MU6wnFE/LWvfW0hqL63tyjrla98ZbGeRSaRU5YaCL6FLkoZtdgVpNfAibCofhZOe81rXlMsc61QHCFESmk33XTTJWJbZ5AhTpDUiN9tH3Xtdq7bsW55No/JT4hj9rENyq+OViyFBx1c+iXyWXpk8CfXVL6xd9bAAK4GDK4zC18DAAuNxH0QacQ3ZZF1h8GAsKIyS6SJ+KczlqkHo48XC2U4wI+lOHy5DzFgXGca33px/y1JNBHxyDYlAolAItBDCPhdjKDPtxDotttuU3y8Oo4PEXSffubp6uZ6yrqZLb0q5sIjuAIOtssuuxTOiX94UXz5Ly8v1qNefkbA4fA9geVq+T2t00bA+7jTwj0JjvifNHMenlNcHeA04nBDhXcGx+UyAH9x/aJOkWdzFpbzNnXCJfd80Z7LvEyXrnkfDOSzGh+CS8gCp5C/dtiWqe8Q5YhHuI4FbaX10pnRBe7tfm0H5UjH8tdsK+4ZYEgIZ13Oalc+QtQj2h7f23nm90QgEUgEJhoCKeZOtCua7UkEJgACu+66a/W6172uskfOEDiEOwKx75xzzimLhCGBiJv9m9/85mKlSbhC8oLYRbrmHpEsU9GXEFN5GOhYydigh1Wtcop1Y01gEW/1kKe8fUdOCbmEXQHRVY+pteUjYZbVBpcR8rJwA6vTk952UrXTzh1rU/kE6Yw2tOvsePtYsx3dPkee7XOOt8/Ju51/83t8jn07z5H+ztrD4IblM7wJudxP3H3X3dWBLz+w6uvrKxbZBHLWRosn12Iwm9RG29ZUXUe67auTn0H5vHnzqwcefKBs7sMi5tb3pGfHPccP3uDBe4i6UfJSMSCODN47v6I4yw7KBueR3xKBRCARSARGHIHSnS9daGqd6dNqcXGz2kJ382rOnIdqkXJ++S3wgs9CXtttv13NUzZf7gvZEa/jGMwQlzMzysYnrt9OnI5gSJwl8HJPcd1119U4ziktwFGItk0+hfcxKmBMAF88BEfkbuv4448vXNIxwiROKK3ZXngiQXNI3rLkJ9d5afBb1qzq2Sx/DEI7qEoMKPgKxkeCTxNmHcOF8ZVm0D444XrSSuOza+M6HXvssQPtH084NNuYnxOBRCARWF0EUsxdXQQzfSKQCIw4Agg0QsxPLUEVgWta2SJ9yDTrB5YSiByXB76bfub7kMR4SW357CIUCuL7/i//8i8DPtIee+yxctxUMOUjksolMCL0yKUyrBRNpBXH94hrut1RRx1V/H1F/Uy5u2v2XdWmm21ayCtSS7DWtv7+/mIpI91oTJvTzhVhIs5YCFxZGDgR3AnspiWaXnfe+eeVgQCrbJv7xOCJVQ2rbMeabfQZ9r0QtBNGd9d+g+M+1G4Doo023qjg1Lmvmnj4vCJxthfQyzYmAolAIjDeEWj35YurDTbcoNq+5hRz5z5Sv2Q2Rd/MocnF+vT222+vNp25eU2Axnu7V6/+fjtZxh533HGFE+J+uB5XCzgoMddiqxbadVx8Qm5/X3+ZGTRQev0e0wtTHPTrX/964Yp4pbUVCI84JNcLOCvrX3kdcMABy1j4DuTX5YOy1YcIPLm+jqvKcaSzCbHvUtxyDzXzWG7EJSeDO2u3tNry7//+78XdAjycj+BcCLw+B8f2mVVzLHjL2CJDIpAIJAK9jECKub189bPticAYRQBhQ9IIqawdatpZRLxmdYm5FhT76Ec/WsgdIsjnmUUTTEOLKfmRRp7N4I0/gug4AZYFhSn9ykQsEUZCGAEsBFrpnROCjCrXdDqbuAiodIRGC38RmFliECWVdeNNNw5YGPh+1VVXlbbx/2sgYGVlC3rxx6YuypFfBGnKP/vWFnHa+6ir44i0IG3sI59yoPEnjts369CIMuTHZtr43C2ycxHUUzDAsfjGf/zHf5QpdTCeXbvEsPlsUGRAA28uFkx5JOqyVmHRDccIzfzj2ETdezkADyL4E08/Ue4dg9Qdd9ypWv953acydkbyIQIsvRZDYxRxh46RZ7oj0H4Ol/dcNHOQLtJKE89J3NvxXZo41kyfnxOBRGDiI4AXDA6TqnWnr1dttdVW1aOPPVoLkndU02oBEJ/x4pMbBiH7j6pwMLzrM5/5TOE6+KBZWm9729sKRrghIRZWm222WXmBvFlt9RxrFYgUPJHQSuxlnStIKz+8hbUvUZgxAP7C37/8VrbflnYoTjacvLSjed1LRes/kTb2zeNxfzkX6WPfjh/pmnv4ELfDKhkeXCfg6ow25NWtTXEMdw3jDntcezjlNuuQnxOBRCARmGgIpJg70a5oticRmAAIIHUDomO9sFOQyGbTEDxi6be//e1ixUlk5TaB9avBy7bbbjsQvRvhk780ykIKCcCINWKJdIZoG5a5jguOB5klLKoHYRkxtcU5wppViq26a5od64xrrrlmIL240hKU1YW1hmMsQJB/YiZfYRYOWya0x2zLRFj2QJDuZc8M/0g3HLulbsdrfx8qTVx314Ioa0Dkel566aXFEmPgnqgHE8Rz14JwyZWFaXf803HP8IEPfKBYu7iuvRQ2rK2w+BY2ZdNURBbkhO1tal+5kycPB4sV3Vgp5K7O/eT+jmfB5xWFiFvi1dFL+oa/bf1Pr93jK8IszycCiUAHAf2HDYfYdpttqzkPPVy/VH6mXqRr++JSYNNNNysRu/VLg/qeHgBUP0qExRvvqme34BYW3WKl6zNugWvggeLx14rjNQM+55iXy6xzcTk8JdwCeFHv5T0xU1wvXvv7+7tzvGbGjc+uFR7UvGaN08P+KH1zayZ07Z1rhsLBa3oQ91Qzrc/DuV/U229WcGoGDIcccshAWe0ym+2McnFzG26dv33NK5SfE4FEoFcRSDG3V698tjsRGOcIIHL80r73ve8tgilRlDXteeedVxYjY+3AeiFCm2wSTll3BkkkerH05fIAqUQkbci7fRBHZFQax1gH+Lz1VluXeO0y5I/AI//IPasMPsJY6RokqO9DDz1ULD4Ib0gqdxHES3UIgXlQvoM5djRvQuwDV3v+67ic4IbCisdEWv6J4ca6w8a6hZsLm+/OwdFg4WMf+9iEwGRlGjF16rRi5bPhhhvVIu6WtduFuWX66Prrb1BnM4FvnJUBaS3GNaj3vHu2+Xtm1WVQP1SIfkgfNH2d6dX0daeXZ0Ia/dGgfmGoTPJ4IpAI9CgCnT5fX7HxjI0LD5k/f0F50Ykf1dJcwSX7kY5ISfQ+/PDDq9NPP31gVpSXxHgeK1t8A1aERGKu/rmJnX7ZhrfhfT/4wQ8K18NN+IXdf//9y8tpPA83xW0ImsEth3uTxu/CcOO340nfDO3vzXPL+yxdbMuLF+dgBWMcGwc2a+j973//wG8gTt2sS3yP38jAFweHcYZEIBFIBBKBqkoxN++CRCARGJcIhKh65CuOrA499NCywBiLBxaurDlZdbJSHCog0wY0rDBMgUM0uTZg1UjoFZD4+BykHRGfPKleXGpxR+wNohnliNckpIi6cvgAfvmBL68eqX3XIbKsPIi59jaDBiKu+iP/V1xxRfGhi/A3iav8lR+hXX4cH2ovfbRlqDhr4zjiLkR7Ym/gZCPUPjzn4Y6Q+2RHzIUfgfeXv/xlcWURou6FF15YveMd7yhWL2ujLWurzHJv1INJ9/Z66z2vvu82Lffwyg4W11b9J3q5VvJ2bxIF9BH6sGZf0W6/Z4ClV1xXfVFfX1+xHuPLUd/Qtg5r55HfE4FEoDcRaPYt06evW78g7at/XzuLt06qOcSCBaaqp8/RuDv8TvKbe8YZZwzwPpwsZrngKMRIC5aZ/QXfNpdyzEv83XfbvcyKYWSgv8dRTjjhhGKti6f6jd5vv/2Ku4sof7h7ZTSv7XDTjVS8VSm78LtaQ+Z+zIt5LyhxOjydqOt8cL6op2PKKnx3yaQheNtWpQ6Rb+4TgUQgEZhICKSYO5GuZrYlEehBBNaZvk71nve8p0yJI8wSYH/2s58VK4itttyqWne9jm+xEGUDIqR863r6OcIoDcL961//upo1a1ZEKeSyTRwLeTfdrP6HfPpuLx9bUzgLwukY8sr/73YbbFfiKSTIK4HYYmxE3+985zuVhUkIk6xB+FcLMbeUXaebOm3qwNQ8ZbDmNRVQOVEn+TunTlFHbSRcC0RrJDrOlYNL/kQ59s3PzTjL+6xMQVr1CvHK96iTODAZFGqyH/gFdvY2dbVFcEx+hHGLn2nPOeecU9pP5CWO99dTGHsrhMUNbOr7pBb/AseRwWHJiKpkFmWNTM69kItBLAsti7e4z10bgm48L20M3N8xJVUfIa5+i5Cw9957l41A4Hu7f2vnld8TgURgIiLQ7JO7tU8/bTGpqdU605bOVOrElLbdj68ov25lTIxjuJMZVIwAbr311jJTimssYq7FRQW++W3NBXnbrZcP0XL//fYv+fhuVtZZZ51VuKa+HA+zNsCMGTPayZf7PX7PY7/cyCN4Mrhb/F75/XJM22xCs07ORdBev3G4OItmbsekYbRw0003FRycdyzKid/EOOY2LfkvybaZf5ST+0QgEUgEehGBpeZdvdj6bHMikAiMWwSIF0H0TGnjX5YPMoSPAHr++edXt91+WxFduzWScIq4y0NerAS++93vFgIv/uJFnUGO/II4+oyYsqTlp5XYyq+aqdPNgKgTZ2+44Ybqlt/fUiwRSj71tGoh8gmSa8+lwCtf+cpCdkOUJYLalB91kJ7Ao/6EUqRXXZQnX0F+8dl3ccJqOQYlBhFNkTjKaJYjrdDtWOfMsn/hqTwbsZbYSmyOMaN6GRDYYBxYlDS1tTM3CXzNWTgOtixbtEeQN2xsrpl6EbdYuBxeT4+Me0JbDcAi3bK1nIhHOtceJnACuM8dy83OuWVb7X5sbsvGWPbIktHUsifyyAoQcD/GvR9797/rFVvc3767fl40xXMez5OXVmeffXb1qU99qvrKV75SxOHmc7SCauTpRCARmBAINPvuZT+zvp00iehW/24Wn+mDfwf8XnZ+mDsv/+rupvQ5+p1eDV4K4xMx4wGPwOPwGH3sHnvsUWZGwKgbTnHci2duFeTDCtfiaT/84Q8HXrpzEcYqdXmi8FDXQD1soxmiHQP7mrsqM1yT+S3DJf0+Cc4NxG3cP3hdqWtdXS4lDjzwwMLT/P4J3/jGNwq2fvcEcQsvXMINHZPvnIfnVLfedmvhhPghPDMkAolAIpAIpJuFvAcSgURgHCOA5CGBCOAb3/jGYgVhKrNg/4tf/KL4XiV8tgNiiWybws86AFnn2uCyyy4rizIY/BBRCKfKQVoJLKw+TZW+/vrry2ei6JFHHlncKChDXQjDl1xySdkMDgiOxMYjjjhiQIwUF9FVjny1A+kfIL8i1MG5ZpC/AYCBgLqKb5ENpPi//bf/VqxXxRHs5UsQZg1x3XXXlXZqj0U6bBHPsZEI2ovwRx1YIVqx2EIXsBTUyUCypu1FbBc34rOIYVkdvkVNJT/66KOLv79udTSogIHrE+113cR1TlkZIOCeGJlrnHiODAJe4LBU8jy7T92z7uUI8Zx4OeH+tr/xxhtLv+SzFx3EXAswvupVryrPXbu/iLxynwgkAolAIrB8BHCIfffdtxgD4IRERy/nibJ4Dfc2LHO7cZHI2Tl9+q4v2LVwLBwIF7EX9NFhfBBpVnYffGll061O/EULFxU+baFhQi4XVxbrNTOKYUATE/WzOWZGWlXTMDPKLDKMdzI+EHBpCxnPmjWrMwuFL/k6zeQpkwc4IeMJnPDyyy4vFGaLzbeoDp11aHGvtjrtybSJQCKQCEwEBNLNwkS4itmGRGCCIlDbPpR/K2oe8oeAv+IVryjkkosCAiei+KIXvagIqW2RA9HkZuG1r31thZwijCx6P/3pTxfx8yUveUnxeSZvAkvx2VpbAxBETzvttCKqqJcyCSlhaRDiofjKNxAg0iK94sh3m222GRAZLYI0ddLU4laBOMtnrvLkw3qjLUQ7RzA1GGDpoRxlnHnmmcViBFmWhoDsuPMsS4g+pvppDws/BBw2vttWNxTiXl8tZROpDH4MhlgNw6G/v7/q6+sr7XItbOJsvPHGpZ1RB8f4lyNaaavzrDCOP/74Iuj6HhhLA1OWGnzbaS+R2HkicMRb3bZNnPRta57Vv+4TB5s13xIveF73uteVgbB71TPhnm8Hz5b+jBhwSf2S6OKLLy6DYS8t9HXf+973Bvxrx3PUzmN1vsegfHXyGKm06iKMRjtHqo6ZTyIwXhDI52jwldK/NF88B58gxuKLu+yyS5kB5rv+uht+jk1bZ1rhO1wpWAhNvvrr6Oct6tp0GzW4Fku/eeHdDvKK3wmfu9WhnWZ1vytj4aKFhYPhZ8r1gtEMOC/p8VpidwTYMJiAWQRsnujr5Ty+ihd7IfmFL3yhGEaYYcc9BX4qPe6KP+LcjBUYWzjOCOOAAw+IbHOfCCQCiUBPI5Bibk9f/mx8IjA2EEAMBYQR2UXmCkGttSZiZxc+27XiRx11VBE6WKES+VjcIoBES34lm0H+xNJZtUUAEZVIQgy88sori1BCPLRoGaKKhBN6CYwXXXRRIZjqjKiKY0GMUt8lBcj3oIMOKlYI8pb+6quvLu4WXv/61xfhGdElXBoUED25bPjmN79ZzZ49u+SC9BIkw3q2WXdiLvHapt4CMfrUU08t5RpwIMXyJVSLI1+4Gpyo78EHH1wsTEriLn+0T92iXbHvErUcKlPjJnV82yL26sB6GeGH2z/90z+VY8qPeiD/rJW5lwjhVZtYOhOuiLgsQL7+9a+XdpiiRxRmDa0+YZ3omrkujiH74uy5554DeQ5V5/F63HVxfbTVtuZDisAjgbln3L2qb4p7t5lvPIOOeW4Eiz3uuOOO1T//8z+XZ8vxa6+9tvRJO+ywQ9d7fkXPbsm4/qM8cWPveBENdM+t22w4ecpHiLjuWyGe9fJlOX/a6X0v7m+W3H6R73KyWO1TpcwluCgPHiv7zDXb4bPfJtdNfjAZLh4rakwIPOoXfXe7rspfFdzaOERdVjW/SJ/7tYtA5/qxgowFXQcvLtW+f9ZubUe/dM+G2RIWw+XyCT74iueJVa6+2mfHhaGeXfmwzjXz69xzzy3xIw3+hpOut+56w2pQLLbbuVa1JLqk31AP3HjK1JWbfSS9TXB9B/j2MGrDCAC3xMuU77cHN46FzCILxgSzam7tZaV6l/uorutGG25UFqflP54ojB/ib3gq/+/yxwvhysWFOFdddVWZqaWefjNxboJu4BFl5j4RSAQSgV5EIMXcXrzq2eZEYIwhgBQaiCJwBrlEziCchQDTc5eQ56h6kNH4bo9QsrQ1hYt4yZrN9CzE+dhjjy35RnzkklDb19dXvfe97y2LMYT4eO+991ann356dUa9qrE4Bt/EUvWLOiKVBF/WdYRZ9bPJ10YwPeWUU8oxQm5YC3/2s58t5wkyLDMIrlwKcIWgHIEYTOBEWlkvNEPUgcj79re/veQblsUsGb7//e+X6PC0qZM08Zl4dPLJJ1ezaqK9oqA+0sqjG97N9M6Lp5yXvfRlBW84WuQCYWdxHFPrpBNXMGVRW6WL8K53vasMFIjbsInreMEFFxTMtthii4IhTG3qaYMVQeud73xnIf2R30Tbw9T9YtDDT7RrZGxmG9HQuUSdLJfJe5kDI1p0L2QWgp62dnu+HBOnGdzjJ510UvXlL3+5vOwwwNU3sfz3EoWA0M4rnrVmOc1nOj57Bt1LERzXN8dAvJlv5Nk8Funs5eVcM568fJffUOnaefg+UKf6fiRsTFpc33tLxOV2PspVhuPtc+3vzbLic6T1veRV+25csLAWX+upwkQTbWiGbnl2OxYvu6T1ci9+J/Rbyow2KjPwcTzq0y3PqEfEi7pJH323z80Q17N9vBmn22dlxDWN+kU859phOPVup8nvax4B/nQX1ff4otrqkgupefP44V9cZv/oS4T29V3evbjmWzCyJWqb+xyPJFSyHPUdJ8XHtt1221Kgfnl5z5B84IcH4pCd3+jOTCJCMf4mfTwnQ7UisI6+U39vC84T/cZQ6bsdl2dswbe7xWsfU1cv3y0ui7fidPLB0XA9PC8CbuulOxyjT6vqbpurBRjixribWVXyItwyRtAeWGsjfLQz8jXr65hjjqkYRMhbvqvS/qhj7hOBRCARmAgIDB4lTIQWZRsSgURg3CGAtDVJJfKGOA4MIowVh6Edic/lAUtQ1hBIIrJoBXmLnREyI0TexBHWoP/jb/5H9alTP1UW3mJ1YMAtBJFURwRTfG4SWMi95S1vKQtiiIdYDpDW+jvLBNavhFkLFfEbS2ztDJjmVbfffntpszrKU31YbPz/7J15lGVXWbf3vbfmqu4aeqweqzvppDNiSASUKcwoYfgUZIogiIJLP1FxWCyXS3H4B3GpS8QlIh9rCSxFEEQgoAgoAhJIGEJCupN0utPzXNVdc92693uf99Sue+rWra6ha7i36re7T51zz9lnD88+0/6d97yb+HQafs6EWqwU0mmSD/GYEPLufPadXqb3ve99nj6Wqjxcs52HXMobH3YpT09PjwtBfOZGXlcK1BeXBtF1AeleKWBB7YKF5Y24jUCLZQvsER4pGw/mMVDG2KFgmc4KE+WlbL/4i7/oD/Nf+fJXwrHjx9xSl/3pAGCxy77EZV8C4jr1e+ELXhhe+cpX+vrZyhzLUgvzyIvj8bjxoCNFmyKGd3VtsI5jk3WC6he3KjT5HM+9xc1Yqc1EgOsB5wmfAjMQGucnv7GO4vzgmI/nBGmUnwNxG+mwzPHEucn5TieaaxHbOLfYxjWENGL8SmmyLqbLMvtxnUuXhXRJn3RJi1BeNl8Z/9hxR5kIMW2EXF+eOC5Zjp195ognWcQpi0eIZfcfc/gT0yNfJu4BpAufxobG0LaubfKaE+sQ486UfCw79y/SokyctzEg9CDGx7LCzutB3Y0B+XBtJZ9KvEgfX5Z2V/CykS77EJ+0uL+QL9xZRxvEdqmUXixXpTl5xfqwL1zib+JH0Yr8YxlmKnel9LVuZQgMDQ2EgcGBcOrkKbOQPOzHyR233+HHYbp9V6Z0y58rx/Ztt93mL0rjswtCIl9iRatRjuvZAtc8rHmxzsVQgPOQia+2XCjnOjZLoCxcG4jPdYPnZM4zJs7lhQTSjM/b1IPrXLqdWSZOOsR1lAHjB35/xb74Qsjl5TLxKVMMcCIP0k+zYpkX0LhU4Gutv/mbvwnf/va3/ZmO+xjpcu3g2hX35ZkYi2hcU7zmNa9xq2nySacb89VcBERABNYaAYm5a63FVV8RqEICfLaFVRkPfzzIYc3AQ+PkA+XU58or1qCzo9M/1yLS4QmXBTz04XsrLeaynQdHAtt56P7jP/5jHzQNC1Dix4dvOsI8TPOAyid4WAdE37SUl3IShykdqM8N+28I7//b94f//NJ/hk9+8pM+Gi8dBPKMFlrsT/qUD5EYdxFYmPJwWx78Ydc671iJtba1uqXEtdde634zEbGjb17KQmeCiWVcFPDJG1YhdA5Ih3yZM5U/GPNgzqdszHmwpt7lcdJli+kRB1EXa+jf+73fcz+9uJo4cOCAixbpfYi7ccNGryesSIOyUm8sO37jN37DXS7cc889/qkdA24gIMSHfvaJ7PBNd9dddzk/0qXt2Ma0GkKsxtmzZ0zEO+ydKOp15sxpF3Sx9OY8WvSwOvAtOpaVSJDzg2Of4zt27Dk3WR9FutnKRVzOITrhp0+f9k40L0dIh+OHrxs4r7j+EDde39Lpsj6eVywzcb7RqedFGC5SeImGRX0sKx14OuRMLPPCJqaRTjsukyYvv/ou9Xn6MR2uC1w3eVFE+RFCKT9zzgFetCG8kMd8A3kijlN+lpnDiTrxYqunp8evnXwZgGWYB7uFEHemuoyapeP5C+edOXUgLpZpMI7twL0G8Ztt3AcoP+kzISKT9kzps557AfvSBjCDDeVmGctC5nAjT9zfkC5tzbrye9ZMzCg7edHGpM+cT6U5PmLgHkH78mKN+8ZMZY7xNV95AsXiuA9kdf937g9jo2N2PCQvBC9cvBDWrV/nzzy0/WoPnD8EjlkmBpXkmYJxBjjGMQbg2OZ8mQ8Pnhl5LuEcjM9dd955p5/nXMs5J2cLXMsQhLkOcY5z/eA6sdDzi/MTN1Rc66g3z9tRGKZ+ldJlXbzHcC383d/93fCKV7wifP7zn/dnO14ape8V3J+41sR16TThx3aeL3kZ/c///M8u6PK8Hp/v4n2OaxXP5jBkHq/rbjhgL7DS6c7GUdtFQAREYDUSyNiFPLmDrcbaqU4iIAI1SYDLEhMPavESVf7QVv6bimKhhFVWfCCdaV/i8lDMxAN2tD5l2R7l3crpvvvu8w5r3EbHt8c68zwIxwfwdDkrlYcHTh5mrTYuwvAwjACB0EGHmIdz9uPBlgdqBg5j2fex+lM+HnwRZGMgz7QlVnm+0VUEDEiLzgSCaZNZbpbHJU1EHB6c03mwnjLwYB07LuXbiZMOkXV6XTq/yCq9nbTJm7JGztSZvBED2I5ww3YEcMQVhA8m0qNNECXoNCCwkBaiCOtXY6B+9977zfDIowcDIpF/cm7HK8IY4jkdtPkFlFqptfNjtvDY7373u909Ascxx/Rv/uZvhje+8Y3+yW08V+K8Ui6cFxwDnLO4ImHQP84X9vmTP/kTHxWc61M8Z0mDbUycL5xLiHxYQn3sYx9zf4SIcgS2c/3jvMNnNelff931oaGxwYUDykscykB66TxIF2EPv9V07vFVjpDLPoR4nSX955pbmle/+tXeMaesMY5HLPvDAI8f+fBH/DrZva3bX0bdaUIIL4YYEOfjH/+4lydew7gu80IMlzoIDSzH6whJU+7yQJ1YT71gwRcU//RP/+Rfd1APrtmx7lyjEHX4zBdG27q3JaIlft1TIZ3PkcNHwkc/+tHwpS9/yTny1cK73vUuv0bhzx13PrQjPNmPuiDWMMAlo7xjwYagkeadympyEcYcVww2SfkZ9JJjJU5cI0ibgCjCiz1YIqbAaLZAOty7aGMGdKLsMb24L9diviqJ5Y7tm+YR42q+FATozs3WpZt6rI6bCxGOP770QLwdG8mbNfdQ2Lhpox/rWKOWn6OrtT3jtYCWYZnrGi9DqG98mc81bD6B6wrXDZ5LuCaSLs8qMOV6NVeW8dqfzp91s10X5lpWyhUDZYq/0+XjfKfMXAt4Tub5g/xj3FzWhOCJayHXI+LF8qXLHfNhTjxYMMcYgWsw3MmL52GOP8RjrsPUlxDLF9P2lfojAiIgAmuUgMTcNdrwqrYIVDsBHhDjQ2L6gTKWu9K6uG2u83Qe7IP4mg7xwTSuK8+z/HeMF+ex/HHO+kr7pLfHfdPx0stsL49fvj3GqbQ+pr/Yc8p0NfnFOsU55bua9BZj/8VmtLD0kg5M0fyEnjx5woS4b4ZzE24m4EPXfM/ePdbxvs0GF2lfWBaTe5Ha1M7+5CYtXDWBcjH3He94h/t3xn8inVlCfFGUzoxzgokOLh1YfDn+8i//sgswdH6xVvrABz7gVunlFq/xHKKDzKjg+NpFcOVFDZ1j1iMwMicgOPDihs7zr/7fXw13/+zd/sKEDjcvkShnNmefzpo7AwK/77v/vvDXf/3X4XOf+9xkp5vyeqffjtHY6aYsLLPPW9/61vCWt7zFO+yV6kza73//+wPMsGrr6ekJr3/96/3FDVZhvBCjTAiR5EX5eXlF+iwjUv7BH/yBi6KIAbEMpBsD5YuCA9a9+Bv/8z//c39ZBAPSYT/KhxgBr9gGfPKLKxis92BOoBzlAeGZNLE+I509e/ZMtgECPOypH/WATzwOKBdi6G/91m+Fn/o/PxU6uzonr4exTWNe7HPYrNr+7u/+zifKHq2iI1vqwRTbBXbPf/7zw9ve9ja3kJtJbCEP6o2/c75c+cxnPuPZwgGBK50my1joYVlM2yJ6I8jEtMvLHcuv+WIR4PibfgxOTX3q9Z2Bz87Y1x7/89X/mfhyhhcKeT+PsdK/8cYb/RhNt116eWratf2Lc6OW6rbY5Y3pxXm6NVmXDuW/K3Ert56N14p0OunluaRZKZ90GloWAREQgbVGIGcPu3+w1iqt+oqACNQGAR7cZnp4m2n91dQs5hfn5WmV51n+uzz+1fyulHaldVfKY77xr5QW2yo95Md9Fjsv0l2MNBcjjVjHlZknnaiRkWEfeOTcubOAcRElZ6Ja4vqjJ+zcsWsRiicxdxEgzpjE1772NRdUsdKiY3vHHXe4RTVtGAPCWXpCqEOYi1byiHaIwFiNItQhYPaY0PmmN73JXQyUH+/85rwl7/e85z0BFzKkhZiHYIs/QixBsf5EVKRcBMQ6LPYQGXG5gGDMFwb8o0yUEVEXS1aEZCxCyYcyISZifYagSvqkSTnYRh5YqiFy8mUCfsGxpC8vN2Xg6wjKTXxET+JgFYorGQRU8qFclJG6ECg3IiyW/Pfee69boVIO6lseSI+yUQ7SxYcj1q2UkzaJebAf9aV+pMsyXweQPi5u4EP6MU66LojEjNbO59rkRVkpM21BmSkrv6kL+0XrPeLRTgj3XRu6/LPvWMd0+pSNwYPe+973upBL2UmT8pNfHGiT44TywZI5YjECLfWFD1N5utSXicGNEKQ/8pGPeNtTV9oD0ZZPzxF1EXHJm3TJny9EiHfdddf5MUo5KY/CShOYKuaOjo748ceXQsN2j8G6sr4+OZc4VzkHuDakz5/y42Sla7RY+ddavRa7vDG9OJ8P15n2mWl9pbSJm55milNpvdaJgAiIwFolMLuznrVKRvUWARFY9QTKHzTpuCqsDAHaAv7lbbIypam+XE+dPhFOnDwWxkxIQ8TFv2HRxJFr9l4T9u7pMRFreNICrlT6+YonHP/xHJCwW+K4NEt8Cv+JT3zCLTARurBgTIsm8XqEeIp7BKxRER2jGMy5wjJ+trE+JR6CWvocIg38yfJZPOIjQh554ZoE696Xv/zlLrYhwjG4IGIvlq8InJQFEQ+xl1HJEXZIH1ER0ZS0qQNpI+CRBuLmr/3ar7mrgyggkj+i4V/91V+5awSsiSk3bhJwDfKqV73KxcErUWaAt+j/FYGYARZxFYBYSZ0OHjwYvvjFL7rlKOWmLIdN+P7Qhz7kguN1+65LhIKUSwTiUBZcB/zt3/6tl4k2QEjF7Q2D7SA24wuWASux3KW94ERA0P3Lv/xL54JPcnjANs2f5fSEaPYXf/EXXj5cNlB3rHsRRMnjKzaoEL7VEVopB3kxmGePCfZ33nmn50E+Udil7pQDlxSso32I+4Y3vMEHgty0cZOPII8wzKf0xPuHf/gHd4vBOixtsZ5lH/wNxwAb2hTuH/7wh911A+1J3rQxVsX4vESEZh0uGD74wQ+6SwnqiBDIfrheuPv1d3s8yhqtdGM+mq8sAdqDF4UOwSC6AABAAElEQVT4ZD1nLwqHxkYnryEI9LRr9IOcPq5XttTKXQREQAREQAREAAISc3UciIAIiIAIiEAVEkAkyaCpWjhw4KCLQo1N+I6zgVOsE95hAh6fX+PXdGpgp4kdp26Yx6+0qDuP3RR1zgS+/OUvu/UpAiDiKAIagmwMUTxBBEOgRKzDspL4WIgi9iHK4rIAgbNcyCUd9v3sZz/rAiH7cky96EUvcstQ3DvEQF5YyCIuIt787M/+rIt5CHgIyPhCxYI0BkQgBEcsThHvKD/+XX/7t3/bRUfKmBy/GReaSRvL17e//e3uMxeREsEY/65Pe+rT3Pov1jfmkZ5jsUqdER75hJ86R27E45PwFzz/BT4KPaIxPnwRGr/whS+E22+/3UVFGKUDvLAQxgUCgih1oJzPfOYzXcSOAjmcESXxNfva1742vPOd73T/jsTH//DXv/51t5wlzlwCovgv/MIvuIU1bUrbkhbcyRuBGpcJWNzCEOtnrJERT7G4jUIuecHxW9/6lovl8MBi9td//dddTKcuuMYgwAIR9s1vfrOPCo/7CiyG4frf//3fnvYrX/lKj8sfykR7PPLII5MuIhC+aWOsgOHNe5+YPqIxPqA5Hv/sz/7MufICAoEdQfzpT3/6ZNpaqB4CCPoNDfV+bu+wc+vQY4cT62u30K33c5t25FiSEF897aaSiIAIiIAIiAAE5mu2I2oiIAIisCwE6EheaVqKQlwpP7bNN8R9Zku30vZ0XnF7XBd/zzaP8RdrTn5LGcrTn61+c9m+lOVd6rQR4oL5yv3BD37gQh/086M2cN/oeGg1Uaenpyds3rTFZFs+IWcrt3Sm2E7xd5zbJoWqIYCoh/UjVqqIKiwjyMUJsREhEaEPoRfhjWUEX0TWn/mZnwn/8i//4pajCHzl5w8VPXTokFvOHjYrVdLAEpbBwRD9yIc8CCyTBoLN03/86W6VGj+fR6z8n/9JfGrGPBAZcSFw8uRJLxP7IfggEJMWxy71obwIfuRN+rt37Q7//oV/D/d+895w4OED4RMf/4RbwXohKvwhHfIiUB7ERIRQ0iIf+DCxzCCPiK0vfelLvRwIklgXInZiYVoeECcRY7/61a96+WCK6PiHf/iHkwNdkjaBeiOIYrH7O7/zO+5agbKRL9atuITgd+RTnlf8DW+E0F/91V/1NoQbaVAf6sd09913u+UxYjLHBYItbgsQqMvTR6DHCja2I2XEzynpRqGVNIhD+Ujz5ptudktkXDggxt9zzz3OLJaROe2FSI/lNW4WsK5GDEfIxlLZxV6zcsavPBMuN1gHexhyHFAXyk0+HLOxHdP5aHllCTQ3t1i7mTW/tSHXBo53rkcj5n6BY43jgOOO85xQfvytbOmV+3IRoN2vNFUqR3n8SnG0TgREQARE4OoI0MNTEAEREAEREAERqDICCCCDwyb02WewCGOj5loBQQbRpKOzw31pNjQkQlbOBCGF2iKAmIjYiGCC0MXEMm3MRJsjxCEyIvgh2DEoFmIfn8r//u//vn8az7aZAu4HHn74YRfTiIfLhKc+9akuTM60D4IOgi++MhEHKQcvFBAE6aBzXLIOn6n4xmUd5caS9IcP/dAHkuQYjeIwy5SdiTKsb1/vYvKGjRv8OK43y0DSKA9p8Q9xErcEr3vd61xwSm8r3w/rU4RHGGLJitCKFXH5PoiVR44cmRQaqcuLX/xit/6tVB7ygQeC8m233ebCKBywmkXwhMFsATEVlxXM04H8Yp5wuuuuu3wkd8pM3WHP8QLLGNhGeWgHltmGuIuvY9bzm4l2iMcQecAb4R1Bn+OJdo6idUybAe6wyPyKuX3wfawMWAxT9xiXtiyfyA8rbsrEywfKgxAIa4XqI5Acd+bipc5eJjQ1+8uK7u7uyZcDtC/HI/HKz5/qq41KJAIiIAIiIAJri4DcLKyt9lZtRUAEygjQSYlBnZVIYuXm6fZYuVKsfM4IUYg4/LvJLOk6Tbw9ePBAOHXylFvBJSJMp4k18TZeOo5XvvQqwVwIvOxlL3PhC2EN0YSQPv7xcYrlLb5UCcTBNyufs3d1doXGpsYp8T1S2R9cGSAEEnCTgDVw9D/rx5cdY4huiHDxmGNOObCqZcLCkjJgncm2KOAhAiKAIpgiNPJJ/gf+/gO+/ONP//HQ09PjoiLXVUTPKC6aBDhZStJLh3T90+sRlBhoDP+yBNKsFJey4fsVsZV6IkgiiCPm4qYh7SoC1wqI3TEdzikGpUOUpayVAkI2QinuEL75zW9OMsFdA2I2db5SgCVlI0/yYUJsTQeY404CQZo2Ii6iP1Msa4yPSBsHL6PutNH73vc+nz/vec9z1xCIr/CDWWzb8nRienHOQHfnz513q1r2Y3/4YMnLMhzK0yB/1jOHM9tpdwRz+ONGQqE6CSRtmfGXPbyY4HzmuoRrDsRdrhvx+KF9FURABERABERABFaeQOwFrnxJVAIREAEREAEREIFJCyg6z+Pj+dBoYs+unbtMTOl0YY7PYDdvts+Ym1pSggpibvRzC0R1uKv9UOJz++c///mT1q3l4hjCHILpH/3RH/mnz/z+/ve/75/d79yxMwwND7lwVr5fut6ImQwcRuC4wWoTS10sJhH2EC0RFKNAh/AXxTusKdmO2IvwiZjIPmxnPUIin2Zff/31Xi7yeOCBB1xE/X8f+n++nc/tEYNIF3+q+K+99pprfX8EwyuVnfRiQBxEtERIQuxE5CwPrKd8iKOIsriGwEUBZcUlRLmYy6BsiIwE9kOw2rNnj6dNHWfKhzJQJ4TZWH74wKtnFjGXstCmiJ7kCXvyiulQFsqPkFtnL2poJ9qGeLQDwjRicgxwwO0B1rC4jKDMCPgf+MAH3E8yYjvsiIM1MCIxAh3tRjlmCuR1+szpyWODPHG58I53vMPLyrFEudLlhks8Phg4i0A9scyt5OZipry1fuUIcKwgunOsYMWPoMu5y3FWLKTvLytXRuUsAiIgAiIgAiKQEJCYqyNBBERABCYIpDumiwVlKdJcrLIpnWoikLZQRNzB+hCxLeuCSV1dfWhf3xnWta0LeRN463L4DI2+cmM9oqDLvFKI62fqlMftcd/y33G95otBAIEEQRQBhVB+rUBAY9ArhDoGqULQQ5R8//vfHxjkC5GlfJ/yckWLTtYjGrI/UxQRY/wo5kYrUeISh4Agh/CJgEd+iHYxXyxlcfuAeIgrBsRH8iQOE2VE9CS9KMgihD7jGc/wQbqw9oxpkRfx0oHfTKSD+BhFXPYpj4tQGsu2oWtDwFUA8bEORWil/HEf9uc3FojE4Tfpk08MpFceSB8uuByIrhKIRzqIxZVCrEPMG8Y5zl2qarN0/dk/1gM/tIS4H23ENn7HfSgPAjR+hBGuaQMCbUb9sBZmP0R29sO1AnVE1MXK+4UvfKHXJabrO9uf6GeX3+zHb9JDpKX8iL20a3lAzCUtGFEGAvyZFJaawGzX69m2Y/2fcZ/OtDnXn3iceclt9ym/Z6xO+l42Y6QrbKCcs5f1Cglo0xITmNtxsMSFUPIiIAIiIAJBYq4OAhEQAREQARGoQgJ0mNKdJpYRZhB2E8vbSh3eSuti5eK2qYJZ3JrMY5ypa/Vr+QkgiiHUMdAZfl8RShHFvmJ+TLE6ReRNHx+VSoigxj6IM7hDwOoOC9cYWE8a5EXcKOZGIY5tLHPcIbwiHhLYj4Cg+RM/8RMuhH7iE58IX/7ylycFUoQ9RD/EXQJiIxaaiIm4bcDS81d+5VdCT09PxXrEPNg3lpE5A3sV7EUHgzalA9sQESkj9UEPYh2B37Hs/CZtykfd2AdhMgq5Md90fPYhsI46RQE45hctUpNYV/5LGj5oWDE7L2tH32+iPukceCHAsfCud73LB2P71Kc+5W0e68dxQ5kR57Dapc64l8DXL9a8r3/9691tRGx70iYv2pxAOmzrsXbCwpdl0iROOsANHrBmH4474nC8UUb2iYzT+2l5MQlMPScWkjLXiRjiucDveC7FbZqLgAiIgAiIgAisLAGJuSvLX7mLgAiIgAiIgAiIQEUCCCu4DHjJS14SPvKRj7hAhuuEv//7v3ffq2wvF1nSvxHkENQQ2RDzsKR905veNJkXYg3xmRDgokCXFnFYZhs+a0knxo+JYO35rGc9y7e/9KUvDfj6xSUEVpwsXzh/IYyMjrj/Wqw7Efnwr4trAixcX/Oa17hbgZhepTkCJIKkB9OrKEd9bqqrBVwMWE08Sv9A/6RgywpcKCBCxnpRB4RFJsrKNvZnOxPbK4VYd1xXYK0a00MMjunPtG95egi6meJ0C+M57z9RRuJjVYy1M2303Oc+1/3UcpzgRgKraqyxqR8uDwjwxJctlruUHWvpG2+8cbKI1AWhnuOBuIix+AnGCpvA8VCpnHF95MgcxrjbiOLwZCZaEAEREAEREAEREAERWDABibkLRqcdRUAEREAERGDpCCCWpAWTKBwtXY5KuZoIREEMv6evfOUrw1e/+lUX3xDlcLvwn//5n+GFL3hhaG4pfe6ePl6oCyIcQiYWmQhtGzdu9EGx5lvP8by5XDB9Mx6T6WORdbhQwA/r/v37XRxF6MT1APki4CIIIiRiuYtVMS4JEHU//elPh6c//en++X8UkiuVDSEXQZI6zBSP9bhWICBgkj6s2AdBMbpoYDvlhwsiKOWijIi6rE8zZDkKlOxHQOhkHyxNSROREt+iiKLpfZPYU/+Wb4/i89RY8/9FuSnLnj17fAA4eMEYa+jLly67f2VYIK5/97vf9TagDsTjWELkT4u5pAUfmJI2cwT0H/mRH5kUsCuVMrYP+6TDTG2WjqNlERABERABERABERCBuROQmDt3VoopAiIgAiIgAotOAOGDgc4QhVjOTAhSUzNKRKaokZjGNHuIespc4s6emmIsMwGEPv411DeEm266yd0tvOc973FhDatLLHVvvfVWt8ZMjiGzVjURLi2c4VYBARdRD3EPa0xEPCwx8dnq7grKDiYER4Q7d1UwUWesSBHqmFhfLkpGNJSVwbvi9rSoN9A/EG65+RaP+hVzFYF4euDAARcYGUQNMXSmQNkPHToUjh8/7v5eo2ibjk+ePqiaMSNd9onnFByib+K4DxbFDPKEywfEZlxAfOc733GmcEwHeBBgS/3xTYtgHa1Nt23b5m4oopiZ3nexlykr+ZB3uo1YT2AddaO+WMQSYjsgcGPBC+vPfOYzbi2NBTUWvLjDQPSGI2lgmUu9EICjNTWCcDmbmDb5sC9li3N4pY9H4ijUDgHacaZAu8frwUxxtF4EREAEREAERGDpCEx1eLV0+ShlERABERABERCBCgToFCOWYPo4bkII86lTslNaNEnW6O9qJoCQwiBYCJRY2L7sZS8LN998s4tjiG740cU6F6tS4lY6Pnp6etxaEwGO7Q8//LBb+CLK+WFmAFmfniJTPsFH/D18+LBbBLtbARvRnrjkh6D80EMP+eBaDz74oIvEURwmDiGKQQiNLa0t4cm3Pzk8+9nPdjGRdVjvks7I8IinG/Mun1NexNyvfe1r5ZsmfyMsEfDFi/Up5SXg5xVREmbpgNCJmweESwRHrHk///nPe5Qo3sa6Uo849fX1ua9Zyk2epLtnzx4XzdPpL9VyLAdloyywR4SmLWBEW8OLeNQrir/8xtr2hv03hDvvvNOZUE/qgLAerbfjfvhIvuGGG7wapIGo+7//+78u9PvxM1FB4hNIh2Um3FDw0gDxnYl2Vlg9BMrP79VTM9VEBERABERABGqHgCxza6etVFIREAEREIFVSAAtBAEEC7nhkeHQvbXbP+VGbSvaQE9uQrnQeic6y0L31n4rScDaDgtUhBMEx107d4W3vvWt4Z3vfKcLalie/uM//qP7zsVCF0vNKKzFYl9zzTXh9ttvD9///vfdTQEC2xe+8AUXhfmsnvhRmIn78Jtj8f3vf78LfOR97bXXhrvuusstWdkH4e6xxx4L//Ef/+EiH6Ih1rU/8eKfCPtv2D+ZLnHjRBzSLs+PfBGsywP7xcC+lAlrUtw5IGqXB8qEBfKHPvQhtzTlBQlpMDgYFrhspy4xYK2LWImLBIRZBMj/+q//Cs95znOcGXERO6MFLG4VECX/7d/+zS1/WSZN+FL3Sv6LySvWo3zu5ShVMRZr1jnlggdlgwnHAEIr67HIfdWrXjWt/MSnrOzDMmVB9GXiN20S+fCbgNiN+wXaGJYwwlfzddddN+lPN/IkPSbSYXA1RHFEXNYhmD//+c+fZhk9a0UVoeoI0L58RcK1h+OfcwcLbgUREAEREAEREIHlJyAxd/mZK0cREAEREAERSBFIPmv/wQ8e8I7yJbO227lzp4kf600MSSzeogCT2mn2xQUIRbMnqhjLTQBBjIAFKEIjfkvxc4r49sADD4TPfe5zoaenx4W88rJhXfljT/ux8I1vfMMtcrHixbqV9Qw8tm/fPrfWjPsh1tx///3hs5/9bPjgBz/oIh75vuUtb5ki+BGfAdX4PP+ee+7xbd/+9rddMEZMRPzFCpT0KD+CKMsIfOTPJ/2sQwxCNCy3mo11Zs5EXbEc/da3vuXlouwIsWnXCQjWX/rSl8K///u/T1qmkjaWwFjnRpEy1pXyPfnJTw5PfepTnSFCJwOzIWK/+c1vdncLWEQTEDOx3KWuuLdARCXgrgChEmtX6keIZfcfE3/iujiP8dK/0/FnWo7xyYsJhohqiKeUH0EZi2ra60lPepL7BI4Ds1EHxFuEX0Rr3EpEQRfXGAjBMII1c0S6H/uxH/PjjbalzbAEf+973+vCPi8K8DlMGQiI4QcPHAyf/rdP+/HDiwPK89rXvnZa+85UP62vLgIcY/GYo2TDw0N2Hpyy8/iEH3e4a+FFkoIIiIAIiIAIiMDyE5CYu/zMlaMIiIAIiMAaJoBYQic5WrXxyfLRo0+4CMNn6mfOnPWOMgLLHvt8m1AuRE231p2Lcjvd+nENN8OyVx1RBOEril9eAGuScsGkUsHYF5cLiJcIjQiXWEoixmEp+qxnPSs85SlPmeZ3lrSxGn3e854XDpu7BIQ8BNWPfexjLk4iDGO1yvGFGIhghyCK2Mcn/JQVK9g77bN8BGDSIxAfMQ//q7hu4PN+RMToh5V0d+zY4WIe8TnG+ZSfcjMAGi4QOP4RZBFamxqbiDYZypkgHCMMIkhSX+rwoz/6o+4vmHQQNCkzA6yxTFyE2Be96EUu2MKNNGP5yYhl6vBTP/VTzgZL4zggGGVlYDbqgNCMkMuEyI3rCdwOIAYTB/YbNm5wfpMVSC1EMSyKpPE3UdLLqV2mrCdOeTx+0wZYF2MxjVsJ/AQj1mN5jVBL3RHVqTtlRWxlO/5+EWVpLwJxOEaIE69NrIcr15+f+7mf831j+n/6p3/q7c1x0WMvERC0aU+EfVx/4PLh3Llzvj+CMscebi4Uao9APGc41i72XgzHjnH9OBbOnT1nx1/y5Qhti19ut6+3y0P5sVp7tVaJRUAEREAERKA2CEjMrY12UilFQAREQARWCQE6yIhBzOkk+0BQ9mkyv916rq7eP1XGqhAxqb5h6mBMJQzl4uxsgm55/FJKWlp6AghlCB2Ig7TzpHBGs8zWdESZ2BeR7gUveEH4+Mc/7mIuQiriLMcKn7SnxRSOqc6uzvCiF77IrWbZB4tJRFusOT/96U+7SIv1JmXC+hURjzRYt3v37vCGN7zBP61HIOV4jQHxj3Ignn70ox91ofj8+fMueJIuxy+Wm6SF2Mc2BEXqTR6IQD/90z/tg7ExwBqB8hJiHciP5U2bNrkFLWLkF7/4RZ+wkt28ebMLzoiHpMtEQPy94447wqtf/eqwfVviYoF0YrrEIS/q8MxnPtPLhSUylrmwQRRmkDasVRErEXIJiOfUl3phJf1zJnTu37/ft1Gn8kB+kRn5MaXLUB6f3zEO8xjiMnPqGNON9fylX/olt5jFfy7b8G2LyI51LS+F4Ic1MQIubUGZSAvLyp/8yZ90Vx38Zl8m8uA3QjCWx7w4+PCHPxwef/xx541gTBvDnzLw2T2CPdc1jiPSxx0GLx4Q/GO5I4tYL82rm0A81ijluXNn7fw46C8ECoVx8+8eQm/fRVt3wI+zTIYXlHQr53Axq+5qq3QiIAIiIAIiUBMEJObWRDOpkCIgAiIgAquFAKIP4gbCB+HosaOh92KviyitLa0uGCG48Al8fX3dpBg0e/1L4s/scRVjuQlEoQuxjGMAYctFzDlqH1EQw1r27W9/uwt2iGysR9jEShRxDdcFMUTx7Np914a7X393WNe2Lnz5K18Ojz76qIt77IsQR3kQbpgjNiPiYbGJy4SXvvSlkxa2UdyhDqQdxUDyQ2RF7EOwRdhDOEQ4Ji4iKPVnH9LfYxafr3jFK8LLX/5yF4JIN5Y1lp39CHF/xEH802JBjHUp4iQvQgjsS57kg09XPv1+29ve5handXYOTbrkTbEmXSaYURbK9clPfjIcNgtmLH9JD+tk/PASDzGbOiCYUw4EVCxPyTMG4qVDbDPWsYwYPZcAD+LH/dPz8jwQbF/4whe6SIuoD5foQ5e2oPwI/jFN5tQVERohlzbAOjqGmBecWeZ4euMb3+jiPi8DsExG8CYg/sOeMsU5LmJg9LrXvS4897nPdT6w5LgtL3v571gGzauDQGxTStPR3uHHQvLCJ3mpg3DPS5qz506HDV0brY15oZG8UCnVYOo5UVqvJREQAREQAREQgashIDH3auhpXxEQAREQARFYAIEo5iHGHXr0Mbdm47NwhKFxs3rCanH79m1u6ZTN0kGeTaidbfsCCqldFpUAn7LzST4CJMIWYilWjQgmcwkIX+xHQIhDhMNnLoIKx038jL6SmIso19PTE37xrb8Y7nzOne4O4atf/aoLuaSLSEkaWKqyP4IoYhzCHCImIYp8lJfjl8AywixiH24e+HyfMiHwUE/KRlzS4AUFlq5Y++IaABbR/QFlSAt76WXyQVgkIJ4yAByi69e//nUvPwOTxTywQH3xi1/sIqV/+m2nBYI56VH+8hDXERcrXlw3fOpTnwr33nvvpBUxdaHccKG8d5rLCQRuLI/JlzTKyx/zaWlucUEZ61/KifU0bZ7eJ5Yh7kNaCK64MYAhg+Bdv/96F53T4lqMz/60Wyw/PoPxLczxwETbEtiXawz14PqChTdtgJUx+cU2JX+WmVhPG1Lvu+++20Xaf/3Xf3U3DgjdiLoItaSNqMyED+KXvOQlHreSgBvLrXn1E6BdY+A42WXXg/7+S3ZcMvjfuB0jdbbcFx4/9HjYuGGLRUXILT/PJOZGhpqLgAiIgAiIwGISyNhDYPlddzHTV1oiIAIiIAIiIAIpAtx2mRBC+BT6wMMHwtDwgAshtsVFn+fc+Rzzh7nFRayGhuhPtHS7RnBZ/ECaS5Hu4pe01lKMbR6FOuZM8REstmecz6V+CG1xQoi90r7Ei5axCGwIi/hVPXbsmFvP8huREdGSQcEQ74iPqwXEOhctCyZamjBaSVCkvNSFfDiucVdA+gihrGN/LGARsBFz+U0gj7jsKyb+UBcGInv3u9/t1rE9PT3+yf7P//zPu6CNMIzQiYUuL0QQixGbbrzxRncrQBmd+USZSS+yjvnEdayPy8xhQdoPPfSQ541oiaiNz2BEbqxao9Uq8WcSLD1/S9u1LTut2If4USBjuTyk18X9oyBOG8cQt8XfcT94Eo/tXFsOmvsW3EKwnji0AeI7Yi7Cbnlbsl8MxKfMtF/Mm3SY4I91N35yWYY9aeN/GbcOHDcxkAYTeZW3dSx3jKt5dRO4fLkvHHzE7lkHHvbrAufC6MiY37vwk71r1x47zsotc0uC8Nxqx3kx/dyY276KJQIiIAIiIAJrh4Asc9dOW6umIiACIiACVUAAwQQBBBGKT5bHCyX/uQhhWPxt3drtIlFiiEnHlg6yOrhV0HwLKkJatIpiXhTkTNadbNoopqXjV8qQeMRBHIsCWVyXjp9OLwpyUZxDoGSaKa8oyMX9eNFwpUMwlgchGEF4LiFaGsdylu8T18e0o/iIeIsvW86VGMrrwe9MLjlnKrFhP49j8eIy8RCosPB99rOfPSOb2Ia+4yx/EMBJN9a1PHp5ueP2WDZE9koh7hcZEYc8Yl0ZXI4phnS8uI75TOvZxrGVrivpM3Fs4GoCv8QzhZgu+6fTmCm+1lc/Ac7tzZs2uxuSgf4BE/bNYt7OH9yHHHzkYOja0OWuXKq/JiqhCIiACIiACNQ+gfm+Lq39GqsGIiACIiACIrCCBBA2sLbDqg0rQDrEbW2tYWhoyMUjhLAo0CXFLFnLrWCxlfUSEHDBzsS++YQoks1nn3TcKBLGeXrblZaju4IoIl4p7mJso57pqTzNWP44L9+e/j3XMse04jydxnyXZ0tjtu1zzY90ykOaW1wujzPX35XS5xpWaf1c01S82iTAsYTldc/uHq8A96kCFud2DcP39mOPPVabFVOpRUAEREAERKAGCUjMrcFGU5FFQAREQARqm0C0zMUSF9+DfMqOBR6+UDdu3CShpLabd06lXywxb06ZVXmkyCLOq7y48y6ehM95I9MOVUqg2fxAb968NbR3tCeuQ0zYHxkZ9ZeR586dq9JSq1giIAIiIAIisPoISMxdfW2qGomACIiACFQ5AaybnvGMZ7iPz8bGJvt0uT7kbCTwm266yUo+3dKuyquj4onAohLAAjAdJIamaWhZBFaGQMYG48NNy/r2Nvd/nbPBOXMm5naYsPu0pz4tPPc5z12ZgilXERABERABEViDBOQzdw02uqosAiIgAiKwsgTwQ7p161afGDjmu9/9rvvptDHnJwpWLuiW/17Z8iv3+RMoFySjYFm+fraU5xN/PnFny3c5t8OGsvM5Pz5a8WNbiyHyj/VZrDrEdBcrvaVIpxbKuBT1ru00ywcvm37fyeXqQ6ONb4e/6ifZvevypf6wb991NkDghomqT30RU9s8VHoREAEREAERqF4CEnOrt21UMhEQAREQgTVAgMGcnva0p7l4JQFkDTS4qjgrAcTPOFAbkRlgiXVx0nkyK0JFEIElI5DL1Zmf97ZwzTXXhLpcg/nMzZqroBF3u9DU1Lxk+SphERABERABERCBEgGJuSUWWhIBERABERCBFSEgcWpFsK9opmrzmfFjkYv1OiGfz/uLDsRdJrjFaeYUqmuL2rq62kOluVoCWM3XhYYGGwjPvyaxv3a+1tWZya6CCIiACIiACIjAshCQmLssmJWJCIiACIjAWiCQthwsF3DYVh5inDgfz4+HQrHgn5bHdeX76LcIrHYCnCu33HJLeOMb3xguXLjgn3Q/5SlP0Xmx2hte9asRAuYGxUpaKGTCWH7URN0m+8VLlvJ7HL+nu2qokUqqmCIgAiIgAiJQ1QQy9sBcfuet6gKrcCIgAiIgAiJQrQS4pbr1IB3bbGJBSFnjrTbOy4Xa+Hum7dVaX5VLBJaCAOfByMhIGBwc9E+38ZuLO5Ja9Z27FIyUpggsPoHZfeZOzRPXJybXmrV85cD9cNzjZG2wNAZQmzmku6MzpTfz3toiAiIgAiIgAmuNgMTctdbiqq8IiIAIiMCSEUCESguy5Z3c8u2xIOXx4nrNRUAEREAERGB5CMxXzJ2tVJkp90N7rTmxw0xi7WzbZ8tP20VABERABERg7RCQm4W109aqqQiIgAiIwJIQoAOadEIRZaMwi3AbrZKwSMIPaNwW50tSHCUqAiIgAiIgAitMoGgug5gSFwxY5c4k4q5wQZW9CIiACIiACNQgAYm5NdhoKrIIiIAIiEA1EYjWRLFMscNasE/E8zaZf8EJMTeXy7moG2NqLgIiIAIiIAKrkQDeF06fPuODGbatawsN9Y2TLzRL9Y33y9IaLYmACIiACIiACMxOQGLu7IwUQwREQAREQARSBMrF29QmW8QSiUHM6KIeP34iHD16zAZuyoXNW7aErVu6Q2tr69Qd9EsEREAEREAEVgWBxNUQ/q4PHzkcTp085QLu7bff4WLu9Cpe+X46Pb7WiIAIiIAIiIAIQEBiro4DERABERABEZgXgSt1PpNtuFG4ePFiOPLEkXDi+HETeM1C6czpcOH8+bBnz96wceOmeeWoyCIgAiIgAiJQzQRwLTQyMmT3vSfC2TNnwoULF8Ply5d9MNCTJ0+GpqYmG8SwoawKsswtA6KfIiACIiACIjAnAlcaVnROCSiSCIiACIiACKxtAgi4pQkhNz82Zla5R8OpUyfDqC1jptt/uT+cOHHCRN4LaxuXai8CIiACIrDqCCS+4LPhmH2NgniLde7o6Ki5GhoPjz9+KFzuvzw5INqqq7wqJAIiIAIiIALLTEBi7jIDV3YiIAIiIAKrjUBJyEXULRTy4WLvhXDGLHEH+vtDPj8WigUbCMb+1dXVhda2ttUGQPURAREQARFY4wSwzCVs3bo1jOXzNo0lPuJt9dmzZ+1l5vEwPDKcoiSr3BQMLYqACIiACIjAvAhIzJ0XLkUWAREQAREQgakEku4r67ilZr0Di5B7+dLlkDPxNptlXd78BTaE7u7usGHDhqkJ6JcIiIAIiIAIrAIC2WzGXQm1t7f7vS+Xy7rP3IK90Dxy+Eg4b66G8nY/5HcUf1dBtVUFERABERABEVh2AhJzlx25MhQBERABEVgtBLwz6tZIWBhl3DfuObNAOm5+coeGh0JdLufWuPV19TYA2uawa/fu0NjQtFqqr3qIgAiIgAiIgBPAzUK9vbRsbm4O+/fvD20+2Cf3xWLI2b2wt7c3nDxxMgwNDYVxE3MRdBVEQAREQAREQAQWRkBi7sK4aS8REAEREAERcIujNIaBwcvmG/DxcOb0mTA6MuoWSMVCMbSZa4WtW7pD+/p269iqA5tmpmUREAEREIHVQsBcDRXHw47tO8PGTZtcxE3X7NSpU6Gv71Kyyt6B6n6YpqNlERABERABEZg7AYm5c2elmCIgAiIgAiIwnYBZIyW+csfN6uiEjeB9wQd8wf1CwSySMvbZKa4V8COIdZI6r9MRao0IiIAIiEDtE8A6F4tb3Avt6dnrX6aka9U/0B9OnjoRhgaH7FsW7p0KIiACIiACIiACCyEgMXch1LSPCIiACIjAGiZABzQ1FZMOKQOfMXK3meu6aItwy4TvwB07doSOjg7bD4lXHdg1fPCo6itKgPPvaqYVLbwyF4ElJpC6ry34PmUSrd0Dx8ZG3Uf8JrPORdiNydXX14fBgcEwPDzMrXLa1y1LXEElLwIiIAIiIAKrhkDdqqmJKiICIiACIiACy0JguhhbNEE3m60Lu3b1hJGRMeu3ZsPg4GDIj+fDtm3b3SoXEYlObtKrRVBKh+lpprdqWQRE4GoJRBH3atLReXo19LRvtRNYjOPbrHIzWfebyz3v1ltvDRd7L7p4m8vmwsYNGycHSONeiMt5vy1WOxqVTwREQAREQASqjEDGnNKX9yirrIgqjgiIgAiIgAhULwHcJtAZ5XbKlLGO7LlzZ8ORI0f8c9PdNujZ5s2bvQIlMbe8PovRiS5PU79FQARKBBZDzNUHbSWeWhKB6QSK5i93asiEb337W2HA3CvgM37fvuvCunXrbQC0vLscyprAi/g7Neh+OJWHfomACIiACIjAdAISc6cz0RoREAEREAERmAeB0oBmiaDLb/vMND8WGPysrq7O3S0g8s4c1HmdmY22iMBiEOBlS3KuJi9VFpLmlc7hhaSnfURgdREoF3MLhfEwMjoWGsy9QuIzHkvcxBWDmxPZrW/63W/6mtVFSbURAREQAREQgasnIDcLV89QKYiACIiACIiAE0g6qTn3nVuXMxG3od7W6wMYHR4isNwEcHMyPp62EiyGhoa6UF/f4GLScpdH+YnAWiRQsBeazU3N/sXK+PiY3xvxm1tXV29frtj5mSkG3C8kQSLuWjxGVGcREAEREIGFEZBl7sK4aS8REAEREAERmCBQssxNVlTqkFZaJ4AiIAJLReCee+4xdyfnUskXw5OffFvYv3+/D8i0MOtcWeamgGpRBCoQKL8flqJEz36JO6LSPXHqucj60rbS3loSAREQAREQARFIE9BTaZqGlkVABERABERABERABFYlgcQNiizlV2XjqlI1S0DnZc02nQouAiIgAiKwggTkZmEF4StrERABERCBaiewGIMmVXsdVT4RWA0Eyi0C+T11XbQMXA21VR1EQAREQAREQAREQATWLgGJuWu37VVzERABERCBJSFQbvmnT0aXBLMSFQEREAEREAEREAEREAEREIE1SEBi7hpsdFVZBERABERgNgKVLHITK79iMRv6B/oCA5w1NbWFYjFviWXCuA3mks1kzR9nnQZYmg2vtovAohGYan07aY2bsfU2uNJkSC1OrtOCCIjAshFIfONm7P5Ilhm7dybnLgMVjo/nQzbH/TNnA6Kpe7psjaKMREAEREAEapaA7pY123QquAiIgAiIwEoQGBsbDgcOHAj9/f2hu3tb2Lvn2lBfj4Cbsw7p2EQHteSSfurgLitRYuUpAiIgAiIgAtVEoGj3TO6TxXDu/Llw6NBjYWR4JGzfsSPsu/Y6vRCtpqZSWURABERABKqSgMTcqmwWFUoEREAERKB6CJQs/7AgeuLoE+HI4cNhcGg4XLp0KZw5czps3rwl9Ozea6JuvRkcTXWrUD5yd/XUSyURgdVEoHSeVqwVpyXWuvjRdRGJWFPPVdYoiIAILC2BfH7MX34ODg2ERx85GI4fPx5GR8fsZeh4aGhoCNu37Qitra1LWwilLgIiIAIiIAI1TkBibo03oIovAiIgAiKwlATS32YX/VNQrHKHR0bMArcYLvVdCoODg2F4eDh0b93uFrqhaAKRNKKlbBSlLQKzEEift2VR/dxku07SMjL6KQLLQsBunaFQGAuH7aXooUOPh6GhIc83l8tNvCA9E3p6emSduyytoUxEQAREQARqlUDpO9BarYHKLQIiIAIiIAJLRgDBJxMK1vm8bG4VHnn00dDX1+fWQ01Nja4HDQ0Oh5aWtkBHtOA+AK8gJC1ZOZWwCIhAicB0obZQKEz446y3MzrnUXkhU5pYlZzvM89LOWhJBERgYQTq6nJ2LmbDhq6u0N7R7olwHnKO9vb2uRujEydO+Lm5sBy0lwiIgAiIgAisfgISc1d/G6uGIiACIiACV0GgaJa2Y2OjbjF09OjRMJ4fD/l8PoyMjIZRm1rb2sKenj2hsbHROp94AFQQARFYaQLJYIQZE43ilJ3wgGKiUXHchaPpZZSYO52J1ojAYhJIfOXy8nPDhg1h06ZNfu8kBwRdXDBcvHjRXS+MTHwBU3rhkrx8WczSKC0REAEREAERqFUCcrNQqy2ncouACIiACCwLgUzGxJ/xQjhz+kw4d+6cuds0cdf8+9HBxEfuzTfdFNrWtblQlLVB0DTg2bI0izIRgRkJmOQTbr31VnvhYu5Q4usVe8vS1bXB9sm4/W3yZ8YktEEERGDJCCSCLv5xN27c6PfW0+Z7vmifwPAydGxszHzRnwnnz5+3QUa7dU9dsnZQwiIgAiIgArVMQGJuLbeeyi4CIiACIrCkBIrmNqFQGPfO5ZEjR8xqaDzk7PPQvA3U0mgd0fb29rBjx67Q3NTs5UhG517SIilxERCBWQgg127durVCrKz7vR63z7nrcnVutVshklaJgAgsOQEE3Uzo7Oh0Qbe3t9d9z8d3LwMDA+5TF8tdH1h0ycujDERABERABESgtgjIzUJttZdKKwIiIAIisKgEsAOqNMVMMmFoeCQcOHjAP/3MWucTyz4+4W4yAfeGG240f7kt3tlMLHLpoFoM+5OeYmqai4AILJRApfOUdTFEFwnJb16sxCkEHnfjI29ybk7Eslncj7mCCIjA1RFIn0+zL3Mf3b27J2zfvsPdFLlvazt3cWeEdS4vUdMhuc+m18y0PNP1Yq7rZ0pX60VABERABESgOgjEJ9vqKI1KIQIiIAIiIALLTmCmzh0jbhfD4MCQfe55wfxsFn3QFveLa7t0d28LO3fuCgzmgiCciLfLXnhlKAJrgMBM5yjrY4jC0dTfnK/pwMBLWOUmVvRxH+YKIiACV08gfU7NvpzN5tz9CffS9vYOPy8bGhtCNpd1V0a4WohhfkJu3EtzERABERABEVidBORmYXW2q2olAiIgAiKwGASsL9rY1Biu2bs3NDc3hlMnT5mrhUJg8JZrrtlrQm7DZC5z72hO7qIFERABERABEVizBPA9T+js7DSXRTvC0NCQf+nS2trqA6Rt2bLFt+v+6hj0RwREQAREQAQmCUjMnUShBREQAREQARGYSgD/uOvXrTcht8lG3d4YWlvbwkD/oHc2N2zYaFZEdERl1TeVmn6JgAiIgAiIwNwI4FoBv7ibN28Og4OD/pXL9u3b7Z67ye69iT/6uaWkWCIgAiIgAiKwdghk7I1o2Qdoa6fyqqkIiIAIiMBaJ8At8Mq3weQ2WQzjNhDauA181td7ySxy60JHR5ftO24dz5x3PkskJe6WWGhJBBaDwOznaSmXeD6XzsPyR11+l9wslPaU9V+JhZZEYDkIcC4i5nJvHRsbc8tczkMGF+ULGLYxn1+I14D57TU1dun6MXW9fomACIiACIhAdRCQmFsd7aBSiIAIiIAIrAiB2UWiSkJQuqjTxdz0Vi2LgAhcPYHZz9PZ8ig/j0sDn5X2lJhbYqElEVgOAtPPy+m5zv+8lJg7naLWiIAIiIAIrDYCcrOw2lpU9REBERCBNUVgtk7b4lvXlHcsy3+vKfyqrAiIgAiIgAgsK4Hy+/7i3+eXtTrKTAREQAREQAQWQEBi7gKgaRcREAEREIFqIDAXaz118qqhpVQGEagGAtEKUC9gqqE1VAYRWCiBcjF3oeloPxEQAREQARGoXQISc2u37VRyERABEVjjBKJQa/5sJ/zt4VuPgVRiKBYLthjj2ZIvln4n24qhksjDOok+kaTmIlBbBHp7e90HZ7rULS3NEwMqpa8B6RhaFgERWE4C3GPj/bdSvvEenMSJIm763sy5zBS3ladi93dWTQwRg69s0iqYD3zmWRvklP2Tefm++i0CIiACIiAC1UtAYm71to1KJgIiIAIiMIUAXbJ0h41OYAjDw8Ph/IVzofdib2hpbQnbureHhoYGj5t0BEv7FIt03Eq/SZ44dOzo0OVybKffV/COnv/w36V9YucybtNcBESg+gh84xvfCOfOnZtSsNuefFu4Yf8N/lInebFTOq+TiBJ5pwDTDxFYBgJzuacivI4XxlyUzZgAW5fjpS3nb3xhmz6Xo8BL4XlOGPeB1LIm5BYK+TAwOBQuXeoN4/m8Pyt0dnaGpqYWIiuIgAiIgAiIQM0QkJhbM02lgoqACIiACJQTQMg9cODhcN/994XW1lYXZq+95mK47bbbLWrJ4jbul8nQ4SsJNtFyN2cdw3x+zIThoZA16966XJ1Z6jCSduwsxhQ0FwERqFkCdjonFn41WwMVXATWHIFoQZvNNtpXOKNmcT8e8mOFiZe2BbtXp8Xb6XiymZzf10mnt/di+N73vhtOnz7lUnDeBN2X3nWXxNzp2LRGBERABESgygkkJkhVXkgVTwREQAREQAQqEbh8uS88cfRwaGzASqcQhoYGw5Ejh8Ll/ouhf+CyrUtb60xPoWAWuEzEO3/+fHjggQfCwYMHbP/LYWRkJIyODk3fSWtEQARqksDsQi7XgitNNVltFVoEapYA5yyCazLnhetIuPdb37AXuN80C9s+ezfL+Voe4kvYeC7zO2NCcCFcvHghnDh5IoxZmqN2j8+aif6DDz5UnoB+i4AIiIAIiEDVE5BlbtU3kQooAiIgAiIwEwEsZ/GXW7AO39DQcGior3MRls+rr9m7zz+tnGnfuL5oLhbyNp04cSx87/vfc995jx96POzduzfs3LnTrH8aY1TNRUAEREAEREAElokALhjq6urs/j5kL1ofDt/57nfML37OrXL37bvWLGobQ6Yua1Jt6YubmYrGy15e2iLg2o0+FO35gS9w5uLmYaY0tV4EREAEREAEVoqALHNXirzyFQEREAERuGoCjY0NYf369S7aspyzTl9DY2M4dvSYpW0dvMQx5oz51JlLBTpzp8+c8ampsckE4YZw4cIFs9Z5MNx3331T9p0tvSmR9UMEREAEREAEROCqCAwODoYvfOEL4Yc/fMj83NrLW7OwvXTpsv1+OIwMj058gHPlr3BwqTQw0O9+tMfG8m7ti1slHhG6urquqnzaWQREQAREQARWgoDE3JWgrjxFQAREQAQWgUDRhNuGsGHDBhuhvsnTy5k429d3KZw5e8YsdkfmkEfG4x86dMgF3Dqz7MUKiHSamprC7t2755CGooiACIiACIiACCwFgfr6+rC7Z7d/gcN9GZcLyb2+z76oOeFf41zJhco4X9+Mj7kbpt6+Xi8ignCL+dnPmX/86WLulYXhpaij0hQBERABERCB+RKQmDtfYoovAiIgAiJQNQSazAp348aN9tllvQ2KYtY2Y2M+QvWgjVZNp+1KHbykEkXrDB4PZ8wyF3cNI+aPb8zSaGhoCFu2bgnbt2+frKuscidRaEEEREAEREAEloVA1lwibN+2Paxbt84GMsv6YGb19uL1kr24PXr0aOjv75/0fV+pQLhVwKL3ssUbNndM0U8+zwdt69pCe0d7pd20TgREQAREQASqmoDE3KpuHhVOBERABERgZgLmJc9GqUbIbW5uDi0tzTbo2WBoNMudJrPYxVUCARGWTlsixrJPybfeqVOnbVTr097Ra25qdhGXwVaw/tmyeYv85c4MX1tEQAREQAREYMkJcM9ubW0Jmzdt9nt1nQm6fEHDff3UqVPmIum0DWY2OuPLWwY+GxgYcCGXe3vOxOHm5pZQh1VuZ5e/yC1VQla5JRZaEgEREAERqGYCEnOruXVUNhEQAREQgRQBRFhuW3FCpA3WIcNvbof70Gs0n7d03ArFjIm0Zy0u4q0NdGL/WB4dNaucQt7WBbPAHbGO4Mlw+fKlMF4Y89+ZTDE0tzSGrd1bQnf3Vls3HDJZE4Jt8lGzK46cHUfMjnN1Bg22gggsIoHycz9eA+Y+L9jpmc3iQqXezv+clY3rx9SpdG2ZKd1FrJKSEgERqECA+2e8lxbsHB23u/e4i6/XXXedD0rKeZqxCSvdcTuxHzn4qN3H+22/jN2z8/4MkD6XEYMv918OJ0+e9IHUos9c9sdNU4O9EE4c7+reXaFBtEoEREAERKBKCfC0qiACIiACIiACNUiAkaiDW+Xi866+rsFGqbYOnk18Unnp0iW31GHgk8QWt2hWvA2TlrmHDx8OR489EYaGB5N4LviGsGXLlrBv3z6zym0y6x86eQoiIAK1RoBPs9MTPjbdKt/0moIJQLz0URABEah+AvZK1n3ktrd3hmv3XeuDnBYmvrbJZjNmdTsYHnvskAu63LMZ1DQdOO/54oaB1Lgm4EaJ/erNunfz5k2mASdPCOl9tCwCIiACIiAC1U6grtoLqPKJgAiIgAiIQCUCdOaKJso0mXuErq6NLuoODQ16p28sP2Ydu8s2uFmvWe2u93UIOFjmTQx9bRY6ZqVrgo7707O0EHdaW80qd0t3WL8OH3qIxTm3CqqUv9aJgAhUJwE+v77jjjvMEj+OdJ+Uc936dZMvc1zYrc7iq1QiIAITBPiyhpe2Bft6Bs11y+at7m7h2PFj/uK2aF/TcB/HrRKuFFpb2+x+P9VWaXTUBj8zP/oFGwiN854B0bDcbWlpDW1t66Xl6mgTAREQARGoSQISc2uy2VRoERABERABhFwGLWtoqA+NDY3WiWsNw8PDDiZnIixCTl9fn3XYWlzMRZzFbQKWO3xmvXPnDos7HugUXjh/wYVhLHy7NnSFsfyouW/IuZhrfUkFERCBGiKAYDN9hHo+wrbPsO1FDy92sNRlUhABEah2Ava1zfiYfUET/IuZnTt3hnPnz9n9fsjF2R07t4ee3T2ho6PT7tncsKda2vabi4WLFy/6wGkZs9BF/G0237nt7e1mscuAp422j1wsVPtRoPKJgAiIgAhMJSAxdyoP/RIBERABEagRAnTaMpnEDQKC7saNG906ByGnrs7E3LEx95GH71tC/PQyscgrekeuoWGfzTvMv6750jMheNu2baGzo8M7hMSz/y4ATUHCSgUREIGqJpCc50kRsdQl4Ds7EXv4slrncUJHf0Wgmgkkg5fG+zdnMX5uN23aZC9sR8LWrd0+WClf4NSZ71te2BDGx/P2N3HP0Gcul86eO+vulHI26FmhOGYi7ri5WNicEn8l5jo4/REBERABEagZAhJza6apVFAREAEREIE0AT6/TPQYfOHaqNRmVYtQEwUbOnW95mZheHjErXnY5kY7lgjiDr/b2tZZB7AutHesN2sdG/ysudniNng2xJHgkyauZRGoPQJRyI0l55yWRW6kobkI1AYB7tecu/jAbzKr2v37b/Svadab65R6E3HZnoRElMX6nmcEfOUOma/c+NVOxnzl1pmv3KbmptDZ2enL7ldf73Zq40BQKUVABERABCYJSMydRKEFERABERCB2iNg0i0DnFkHrd0sahubGhPfeOZHj87d0NBQ6LvUF9atW1+xavjQw7/eunXrQi6L+wU6hAyMJCG3IjCtFAEREAEREIElJ5C2lOV+TIbJOsTYDnORQEj8405VYrmvB3sGyNiEiMsgp3Ff9vGXv51dft/Heje5509Ng3gKIiACIiACIlDNBOJrzGouo8omAiIgAiIgAjMSwBKX0GgWtfjHjRY8WRNph22Qs0v2iWWhQJx059C6byYCux++CQuexAp3ahxPeN5/1CmcNzLtIAIiIAIiIAJ+n4736+lz7tPJVznMudfGud3h7WsaJu7t3O95NzsyMmzPAUP+O3kGKLo1Lv5y6+vxlZvs5wv6IwIiIAIiIAI1REBibg01looqAiIgAiIwnQDuFPjHp9OdNgAKcwY4ST7JLIZzZ8+5/zw+u0wH38sGQstafLqEWPgyJSHpJLIcO4iJBQ+3zYxb+5w7d8598o6Zb14Cg7FZP1JBBESgiggkgk+pQPF3nJe2aEkERGCpCTAwKffKGLCiZYCys2fP2IvX3uQFq9+LZ7qZJl/jYHnLPR03CoVCfuLePSH+2o04ixumkA2X+/vd3dK4PROMm59c7udowJ1dHVaEJH5i9RtLpLkIiIAIiIAI1AYBuVmojXZSKUVABERABKYRiB0x67KZSwV83TIoyuHDh73DhqDL+gsXL/hnlvX166wTVxq9HuueurrknSYdPAIdS5bZj4nVeRtIZXRkNAyPjNh8LAwMDIZLl/vChfMXbACW0dC9rTvs3rXb/e+WBmmZVlitEAERWCEC8cVOzF5CbiShuQgsL4F4r0VIHbF76qlTp8KJE8ftvtrvfmxxn4A/27bW1rDO/OE2NzXbvZjuanK/T0pryxPWt6RXLNp9HItdfy3Loi3ZhNjLlzmXL19OXrba74Zsg/vY7TC3TEma9gKXd7cKIiACIiACIlBjBCTm1liDqbgiIAIiIALTCdBxY5RqOmj4z8U6l88rbcGscfLhUt8l6xy2WacwvW8i4NKTQ9iNVrmMgj0ymvfOH4On9fZeDIM2gAodQuaX+vq9Ezps6dORPGejZCMc33jjTSYo11sGpKveYZq0lkVABERABESgsTFxbcB9knvrwYMHXMzlXpoMTpazgUib3M99e/t692vbvr4ztLY2h9a2Vo8TiolYy8BnvHC172L8XhxdLkUxd2h4IAwODbiom7XnAvteJ+TsBS7PCbhkSnzl6m6to1IEREAERKA2CUjMrc12U6lFQAREQATKCNBZazIrHix6zpvVLB29vH1WySedvb29YevW7rI9+Mno2MUwNjZsAu5IGGGwFPOzkWsAqAAAQABJREFU22+fZiLeXrp8KVy4cMHE2nGz0B23TqF1OO3TTTqLWAITmF+0TinuFppsADa2KYiACFQfAc51gs7R6msblWjtEHDh1m6TAwMD9tXMkAm0uETgvEymUbPYPTN0Khw/fszXbdiwIXR1MXX5/bbBfN22tLaEdW3t/uI2OZ/jy1nSwFK36C9fsfgtFMftRa7d6y0PBkzr6GgPpIGYiwAcLXrXTguopiIgAiIgAquBgMTc1dCKqoMIiIAIiIAToKO2efOW0NfX575wEV/pwJ0/f96sf064G4aWlmaPyyjXfOaJhe1lc5uAgMsnmeyL+4QRs8odt88y6+oS1wykxWebOet4Mpp24mM3hDqzCG41ARlrYOtBetr6IwIisLIETp8+7edxuhTr15ulX9s6naZpKFoWgWUkgJCbvFThhWhjwFIXMZYvYri3ZrP15u/WCjSh7PLVCy9UL1y46AObNdo+69e1h87OzrBx4+ZQb1/DtDQ324vUJp/cZ35hzITcgcA1gK9y8MiARS4nfmNjU+ho77TliRc7umcvY+srKxEQAREQgcUkIDF3MWkqLREQAREQgRUlUFfXELq7u8OxY8dC36W+UF/HZ5hF79SdOXMmXHvtte7jFkscfp+/cM599SHsxgHSsOR1Edj2o0/J/oi3uWzOrW+jOwbcOtAJ5XPN7du3T1rq0kWUpLuih4EyF4Fw//33mwuUc1NI3HbbbWH//v0hZ/9knTsFjX6IwLIQSJ93m7dstgHKdpnYWjAL3eEwNmqDidrNM2NWtfYmNeqt9jvxj5S183ZsLO9f3pw/fzE89tjjXmYsdjdu3OgTou7w8GA4eeqkv8DFxz0CL2nUm6Dbvr7D4y1LZZWJCIiACIiACCwhAYm5SwhXSYuACIiACCwvAXzi8kmmd96sV+gWQPQLJ3zqPfCDB6wD+JhZ7Wb9E8+iDXhWZ+4YsL5lwDMEXfbBKCixvi2axVDBO5D1JuhigVtvn2eyjQ5ks1kEbd6yxayEOvwzzvz4mOVdb/tLzl3ellduIiACIiACtUCA+yMvRRvs3rt3796wccNGt77l5UtfX6+5NBozq3oGJDVBd/LVKAOTsh/31tL9lfs1lrt8UXPo0CG3zsWtwqi5TWL/xsYGfx7wl7SWJ4OrNZo7pEmluBaAqYwiIAIiIAIiUIGAxNwKULRKBERABESgNgkgyGbNgpbPqfnEEiudGMZNuOWTTQYxq6uvM/cIuVA0URfdlQ4hA6Uh3LLMp5sItfjDpeOJO4a21nVm1bs1dG/dbqJum21jsLPEf26hYPta+uQt/3uRuOYiUF0EOLcVREAEVp5AIuiaQGsWs+ttoLMuewl73XXXu1XthQvnw6nTp8LZs2ft97C7S8lP3J/LS+4vTu0eznYmdN58fszv4wi5bHch1+7tbTaAWmdHp+dZno5+i4AIiIAIiECtEZCYW2stpvKKgAiIgAjMSACtBt97mzdvDo8/nnyCSWQEW3eTUBwL42Pjvj9CbdY+5/R/tiOdPix2ide2rs06fut8JO2tW7eGTRs3mXjbZJY+eRdsEXEZPIXfmUydWwAh5LJv2mrIM9IfERABERABERCBKQS45/KlSxJ40VK0l6gtYdu2prDFvngZM1F2oH8gPHH0idDX2+euk3ghkx8ruHCLSMv9Fi8MRbuX12fq/cVrwV6sTlrk2iCo+L8nr3X2krfdBj9jP76uURABERABERCBWiagO1ktt57KLgIiIAIiMEnArWutk5axTzG3bd/mFj18TukirXXk6Coy2AqfXo6NjYXsuFnxWi8QC9tm83vLYCwbzHUC/m87OjrMF26rW/dEf310NLPZeNskNRN//XfGOoYNk+XQggiIgAiIgAiIwJUI2BvWslCynM+Ya4Q6u7/a/bmzwQXY0rYQThw/Zf5wT5rf+7N+jx4aGjDXDGaVa4EvY7jnj5r/Xb60QdTlK5tRu+ezvrWlze7XcrNQhl4/RUAEREAEapBA7JXWYNFVZBEQAREQAREoEaCjhqiKv9xioRi2dm8N/Zf7vbPnnUKzxOUzzIbmBrMGqndht7Or0y2ANm/a4uItqSWdRqxuEwteRFzSlsVtibWWREAEREAERGCpCXDvzWX44qUUenp6Qk/PXl9x8eLFcPTYE+aS4YwPeDg0OGQvaRNBt2Avd/0ZwGK2trbYS9pO++Kmze7t9jJ38sVsKV0tiYAIiIAIiEAtEZCYW0utpbKKgAiIgAjMSiBjHb+mpuZwww03hPvvu98sckZD0Sx0CoW6sGPnDvN52+0CbktLs4u0Uagt2qApBAZmIZQscv2n/oiACIiACIiACCwLAV6g4s6oUmB98nUMX9F0dnb6gGcMesb9/siRw+HYsaM+KFrB3DK02r1+9+7dYdeune6GoampZWL/SmlrnQiIgAiIgAjUBgGJubXRTiqlCIiACIjALASwqM2Pj4R6+4QS/7XX7dsfuru7fdAz/ONhndvY2GSWu0kHkQHL8vjcsz4h1rxMVw50HgnTPw9N1uuvCIiACIiACIjAUhIYGup3t0i4YsD/rRni2tc2dTY1hIaxkXDTjTeH/ddfH0ZM3MWlEi9s8ZHPc4B85S5lyyhtERABERCB5SQgMXc5aSsvERABERCBJSNAh62+rt7STyxrTbsN69e1p/JL3CVE33sMVobAi5ibWOcmURGCY5y4M79LcaKoG7dK3I0kNBeBaiWAr2xe2KTFnPLzvHSOV2stVC4RWK0EcI3AC1UbktRvsdxzK9e1oSH5qqZg7pT8Hs5eE/vgaomvbHhpi7ibhMTtAsuc47hQKj/Xy39P7KiZCIiACIiACFQtAYm5Vds0KpgIiIAIiMDVEpjaGUx6hulOm3XxKhraEicKPen4V1se7S8CIrA8BBB50tb2nMf8w5+2/zP1JwpBy1Mi5SICIjA7Ae7T5S9Mp+412z05cZE0sxg8NTX9EgEREAEREIHaJJCxzuqV75i1WS+VWgREQAREYNUT4PZVDbewxG3DqsetCopAjRDg0ba/vz/kx/Iu3Hqx7VLRYoMgNTU1JT8rPP7OJhLVSPVVTBGoaQKzdU1n2548F1z52aD8XC//PbO/3ppGq8KLgAiIgAisIgISc1dRY6oqIiACIrC2CEjMXVvtrdqKwPwIlIs+CDb40MTPJu4W0pa7pDxd0JlffootAiJw9QTKz9vyFGfbLjG3nJh+i4AIiIAIrEYCcrOwGltVdRIBERABERABERCBNU4AcTYt/LCMawXWy8XCGj84VP2qJXD1L1V40TuDw92qrbUKJgIiIAIiIALzIyAxd368FFsEREAEREAEREAERKBGCSAUXb1YVKOVV7FFYE0QmN3v7prAoEqKgAiIgAisagJy9Leqm1eVEwEREAEREAEREAEREAEREAEREAEREAEREAERWC0EJOaulpZUPURABERABERABERABERABERABERABERABERABFY1AblZWNXNq8qJgAiIgAiIgAiIwFogUHn0evOqYKFovnMTH5pysbAWjgXVcTUTmP0cls/c1dz+qpsIiIAIiEBCQGKujgQREAEREAEREAEREIEaJ1BZzK3xSqn4IiAC8yYgn7nzRqYdREAEREAEao6A3CzUXJOpwCIgAiIgAiIgAiIgAvMlUCxioSvRd77cFF8EREAEREAEREAERKC6CEjMra72UGlEQAREQAREQAREQASWgIALudJyl4CskhQBERABERABERABEVhOAnKzsJy0lZcIiIAIiMAiEkh8YC5igkpKBESg5giUq7PJ76NHj4bhkeGkNu4vNxO6urpCR0dHyARsGaZeP6Jv3StXf+o+V46rrSIgAitHQOfqyrFXziIgAiIgAstBQGLuclBWHiIgAiIgAktEQB22JQKrZEWgRghUFnN/8IMHwrnz50t1MEH3ybc/2cTcdltXDIl4W9o8tyVdb+bGSbFEYCUJ6DxdSfrKWwREQAREYHkIyM3C8nBWLiIgAiIgAiIgAiIgAitGIPGXa39XrATKWAREQAREQAREQAREQAQWg4DE3MWgqDREQAREQAREQAREQASqmwA6rrTc6m4jlU4EREAEREAEREAERGBWAnKzMCsiRRABERABERABERABEVgJAgxaxpQxvwhMVxN8ALQ5JpCOW8oXJfjqyjDH7BVNBERABERABERABERABGYkIDF3RjTaIAIiIAIiIAIiIAIisNwEopDKvDBeCIViIeRyOZ9mLkth5k0L3JIfz4esi8jxQ7as+9otibsLTFi7iYAIiIAIiIAIiIAIiMBVEIhPp1eRhHYVAREQAREQAREQAREQgasngIBbLBTD2NhYGBsdM68IxVBXV+dWuYUCgu3y+EnIZLKhvq4hkOfI6EhA2I0i89XXUimIgAiIgAiIgAiIgAiIwMIJyDJ34ey0pwiIgAiIgAiIgAiIwCISwOo1X8iH/v7+cPnyZbfGXbeuLbS0NIVM1mwQCtmQZb7koRhGR/Ph7NmzYXh4ODQ1N4WO9s7Q2NjkZZJ17pI3gDIQAREQAREQAREQARGYgYDE3BnAaLUIiIAIiIAIiIAIiMDyEzh//nw4ePBgOHHiRBgZGQnbt28Lt9xyc+jq6gyFCZe1CxF0SwLs7H5vscb93ve+F5544gkXltva2sKttzzJyrIjNDU1uaBcSm/5GSlHERABERABERABERCBtUtgOUwb1i5d1VwEREAEREAEREAERGBeBHp7L4aLvedDNmfDjWWL4eixJ8K9994bTpw86W4XiuZDN3G5QLK4XmDikTb9WGuCbbE0Fcx1Q9b87taZ64QowrpLh4kB1tIuFLDE/cEPfhB++NBD7vKhtaU1jAyPmLB71PIIYXx83PMv3z+dhkfUHxEQAREQAREQAREQARFYAgKyzF0CqEpSBERABERABERABERgYQTwkzuez5vP3FETZE2ENbcKvX294aEHHwp5W79r1y6TbRF0Tb5N67ep7LK5bMjVmRo8EbLmniGDFFwcd9+3CK+VrHuHh0fDgw896JbBBYuDZTBx8eE7MDDgQm5ra6uXA0G5UhoxT81FQAREQAREQAREQAREYCkISMxdCqpKUwREQAREQAREQAREYEEEOto7wsZNm8KpU6dCfmjIBiKrC+OF8XD69Em31M1mM2Hz5k2hyfzXFszvwlRBFcm2EO589p0uvKYLUF/faFa5OZsQdRlILT2YWjH0m1j78A8fDg8++KD7xW1uanYRlzRaWlrC1q1bQ87MhWWBm6aqZREQAREQAREQAREQgeUmIDF3uYkrPxEQAREQAREQAREQgRkJbN68xUXUwYHBMDoyGgrmVgEBN1tfF86cOevWsjfddFPo7u6eEHSnWshmQs4GKotWuVGwxU9uxgY1GzGRNx/q6+tdsI2FGBgcDAcOHAiHHj/kbhiiNS4WuQi5XV1d4YYbbggtrS2+PWcuG3DXIGE3EtRcBERABERABERABERguQhk7CE0PuUuV57KRwREQAREQAREQAREQARmJDA0NOADoB0+fDicPnMq4Me2pbkp5M1f7bD5r+3s7Ag333xz2L1rd2hoaDCxt97SQrDlsZZ5OrAumRCGefTFRULB0sqZ1e/w0LALuQy6NjaWCL24cyAe7hr29OyxvG4J69evNwF3Br8O6ey0LAIiIAIiIAIiIAIiIAJLSECWuUsIV0mLgAiIgAiIgAiIgAjMl0AxNDU1ueUt1q9Y0p48ddL81I7bAGZ1ti2Y/9rEkrYwXgh79+41S1zEXEK5kJteVzQx1mK4RS3+dnNhaHA4PPLIgfD44cQil20IuVjkNjc3hz1794Trr79eQq6z1R8REAEREAEREAEREIFqICAxtxpaQWUQAREQAREQAREQARGYJICoipi6xfzUFsxfLpa15y+cd6G13twtYGd74cLFcPCRgz7Q2fbtu8xyFxcIc7Ocxfft5YF+3//QocfcCje9L3nv3LnThdz29R3uszeXy5hUXEksniy2FkRABERABERABERABERgyQnM7Yl3yYuhDERABERABERABERABESgRABBt8VE1R07doX9+28IG7o2mKhaCEPmFoFtjY0N4VLfpfDAAw+EE8ePh8HBARNlC6UEJlwrJC4WWI8EnITLly+HRx99JBw5cnhioLSMW+OOm+sF3DZs37493HLLraF9faenmbX8JONGepqLgAiIgAiIgAiIgAisJAFZ5q4kfeUtAiIgAiIgAiIgAiJQRqAkmyLa4nKBQdFGR0ctXiacPXs25MfM360NQpbL1YX+/sGAv1vU1m3bukNDfYP7wkWARcCNEm5iV5sJfZd6w+HDj4cTJ4+bgDvqwnDe3Crk82OhobHRLXIRj/GRmwTb09MqlWtig2YiIAIiIAIiIAIiIAIisOwEJOYuO3JlKAIiIAIiIAIiIAIicGUCaeG0aFa4jaF72/aQyWZdwD1//nwYGR0JdSbmIt5iafvII4+Y4Dtslrw7Qmtrq8XNpVzokh5C7kW3yD127NiEOBzcJy8Wv4jGPT094dp914Wurq5JK99EyL1yabVVBERABERABERABERABJaLgMTc5SKtfERABERABERABERABBZEAEEVlwvdW7tNkk0sZbHQHTXLWgRdrHTPnT1n1rWjob6+Pmzr7g4tLSbomoZbLDLwWV3o6+sNjz72SDh06JC5ahhydwq4VWB7a2tL2Gpp79u3L3R0dpnFrgnFdXFQtQUVWTuJgAiIgAiIgAiIgAiIwJIQkJi7JFiVqAiIgAiIgAiIgAiIwGISQNAtDYqW+MaNgm7BfOUi6A4MDLhYy2BoDQ2Noc4GSyP09WGR+1h4/PFD7lsX9wwIuYToxmH/9TeEjo72MJ5H4E373vVo+iMCIiACIiACIiACIiACVUFAYm5VNIMKIQIiIAIiIAIiIAIiMBsBF3SbzEK3e5u7Xsia24UjR464AIsrhqxZ4vZe7DXR9vFw4cJ5E3htrF8TgS9cuBiO2yBpw2aRW99QH9ivYK4VEIe7zYq3Z/felGsFE4br9Ig8W1touwiIgAiIgAiIgAiIwMoQyNinZXFciJUpgXIVAREQAREQAREQAREQgRkJ8Kg69XGVp9dCYTwcPXY0PPzDH4aLvb3mGmHMXC7gU9d85U7Gt4gm8I7ZgGmjo7a9LucTw6K1mhuG7du3h717EHI3mTVuYqkbggnAFsp95WYypKsgAiIgAiIgAiIgAiIgAitLQGYHK8tfuYuACIiACNQ4gakS0/TKmI50hRD3vnKsKySgTSKw5ghgh8CUtQHOtm7ZGurNt+2BgwfCmdOnTbQdTXhMnlLJOZY1k1186UYfurhW2LZtW7j2mmtCe0dnGC+MubWuK7/xtFxzZFVhERABERABERABERCBWiAgMbcWWkllXBkC6sytDHflKgI1RqA4KRrNXHCPEq8pk/FZMW3lzIloiwiIwCQBrGbxa9vY2Gyi7HYb+Czv286cORXGzALXVVtfE88xBkIL7nahpaXFXSvs3ZsIuXnzkVswq9zGXONE+pMn6WR+WhABEVgAgdLpt4CdtYsIiMCqIKBb6qpoRlWi+ghIzK2+NlGJqoGAPXzGsU8yfG2ph9FqaBWVQQSqkkD5MyqCEROfdptbzsnrh19G7A+WgQoiIAILJ5DxGzPnGW4RCnZO5dzKFoGX8+vkyRMhP4a4m5xsiL4ZOxnZjl/d7q1bwzV7rw0bN2x2i1y21+fqJ+MvvGTaUwREYJIANz2bzBtKMCP65HT0G+FkDC2IgAisRgIT57k/D1v9uGX7s6+ef1dja6tOK0hAYu4KwlfWVUrAbkAjQ/nQ2zsURs3/Xq7O7jyY3ukBtEobTMUSgWUmMMvDaKFQNCvBolkA0oEtmnhUH9rWtYSGBrvlTuybzPirC8syt56yq3kCpRMwEW8TP7Y5c7WwY+dOO+ey5mohH86eOWM+dYsu4Npf86WbCy2tiUXunj17zEfuBj//ctm6kOPc5FfZ6Uj6CiIgAgskYOfTQH8+XDw/YCbxNqhgDmt6S6vsPFtg6tpNBESgWgnYOV60vjOGUbxAXd9RHxqbynzO6/Zara2nctUQAYm5NdRYKuryEThzeig88L0j4fSZC6694Jev4liBc/m+evmKrZxEQASWmoA9fNqzaSrYE2v6gdR6qvRTx8fziRVCGDeLwY3h1iftDd3b1tvLoYldieT7Ybo7NcWJGJqJgAjMQiAtto6PM/hZQ9hqVreEB8bHQ+/FXrt3m9VtfbO/TNm5Y2e4xnzktra2+uBpWTednyUTbRYBEVgwgSOPD9jz9GNhcLDfXrTk/LZX/tJkwYlrRxEQgSolkAnjefNT31Afdu3eGG68ZUvYZC6RPMRn5snn4CqtgoolAjVAIHYra6CoKqIILB+Bgom0uVxzaGtpt0yx7MHCLn4vzd2HYCsl5iYo9HeqoFfNPOLhW81lrOay2Wk/VXo18XbiwTSTKZhAxFY+9a4Lw8Nj9jA75kISL4QIdGKnGvvFBompcp1REAERmBuBiZPP7tN17ibBrP/M0nZbt/nQNevcI0eOhHPnz3EbD1u2bAm7d+82K/n1dl7igiGee6Wc0uJwaa2WREAEFkTATrGCvUxpaGwwl0PrLYmCP0u7m5Tpp9+CstBOIiAC1UggE4aGCnYfHgtDwyP+tcz0599qLLfKJAK1RUBibm21l0q7TASyprYUzMoH/WXHjm3WCexK3C1k0k+fsRO5TIVSNiIgAlVBIIq30wozcX1AEMqPhnDo0Ilw4uSZ5FOziqZIXE/S15RpKWqFCIjAnAjwAoVPuMdD3qziEXZ7enpCc3NL6LvUG5qbmsOGDV0mKtWH0dERE3yzZjHUYClz/ulePifEiiQC8yEwcWvLmrPMMRN0mpuawp6928P69S0hY+4WFERABFYxATv/D/zweDh9mi9cJ/7ptF/FDa6qrRQBibkrRV75VjWB/PiwfRw9aN28kdDSlg8bt0z4+THLu5BhwBW7I7mioztTVTekCicCS0BgmpjrK+zJNYt1bdEskHKhkM+G4yfM1UIRC0CuG2UF8d/plfR8ZZVbRkk/RaACgQmVaPKkKv3G4q++ruSXD2vcLVs2exrRVVLOBN1khf01AVhBBERg6QiMjY3aS5aiDTRYCJ2dudDeZefnFMOIpctbKYuACKwQAXsuztXl3U92k71MrYtjRuiWu0INomxXKwGJuau1ZVWvxSNgD53094qIuJkxS9fEGW5GxVKHcfEyU0oiIAJVTyD9MFo0ATbDtcCuE1kzx3XhttHWmWA08cKnSJyoN81YOQm5M6LRBhGYM4H0yTn7TlHglXuF2VkphggsjED5vc1uhjxLu2HEwlLUXiIgAlVOoBC/fKGcdl8u2MRzMLdoplmfiS2OggiIwKwEJObOikgR1iSBogkxhSa72djdxkTb5Atp7j4IN9yQWCaUP6Qma/VXBERgFROY8hBq1wBe7GC1Pxm4RvAjXkewBIzXjMlIWhABEVhUAlNOzEVNWYmJgAhcDQHuf5yfTCwz6fnZICiIwColEM9z5goiIAJLRUBi7lKRVbo1ToCbD9Z2TOU3ovgAGuc1XlUVXwRE4CoJJJ1Ut8C1lDLeSbXrBha5gdss8/LriK1SEAEREAEREAERWHwCk0YXFZJeNDcP3OcrpF9p1aLlWSlxrROBGiSgx+IabDQVudoISMytthZReaqEQLzD4OuyYMa4NmL95BPbhIh7pQfFKqmFiiECIrD4BOLVIUnZrgzxc9Eit1Tr2U364aSXZ9cPW8fkkm7s+E1NZPELqRRFYFUR4IRZ6EmT7Dd5Wq4qLqqMCIhA6Wu5NIuZrhl2E473YaLPVWSt+Mw/Ux7pckwsF9Nf71TYrlUiUGsE5nDuFDlv0udbrGNyW46/NBcBEVgggQlVaoF7azcRWO0E/EbFXcgmH/yMRW5Mugut9qZX/URg7gRSnTT3pa3rw9zZKaYIiIAIiMDqJbASXU3uwen7sC277/q4Pr1toeRjWnPdfyU4zLVsiicCS0DA+8qLca4tQdmUpAisEgK6s6yShlQ1loNApVeLy5Gv8hABEagNAnporY12UilFQAREQASWhkDp5aaPN6FH56XBrFRFoCYIzPelR01USoUUgaohIDG3appCBREBERABERABERABERABERABEagaAvoar2qaQgURAREQAREoEZDP3BILLYmACIiACIjAvAm4Pa6bINkSHlmYLJXopcW3zztV7SACIiACIiACa5DAbOLpHHx1XjW1aWVI38llbnzVfJXAGiKQPnfWULVVVRFYBgKyzF0GyMpCBERABERg7RDwx9Yo6FJtF3qvVH86huocXomQtomACIiACNQwgYm3nAwofOX7XTULP9Vctho+NlT0tUFAp8/aaGfVclkJyDJ3WXErMxEQAREQgdVGwGXYTOkpNflt3VVbVb7N6z4ZNXZqPZZtmtyw2hCpPiIgAiIgAqueAP5y431tJe9n8Z6aAu7FievjPLV9wYvptMrrnN624Ay0owjUJIGiPxdn7Vk4k3oepiql8yKj596abFsVunoISMytnrZQSURABETg/7P3ns9xXWma50FmApkJ7wmC3kmiJJZ8VXV5tZnq6une2ZnoiY2Y7Q8buxO7+2Ej9s/ZmIiN+bR+d9p3T3dXlbql6nLyIksiKXoLwnsgkQ77e95zL5iAQFISKRLmvWTmdece85yLvM957nve1xHYAQikIq7RVayQ9G+NvG4c6zWQ2h3QdG+CI+AIOAKOwK5GIH3ebXzY3RVwHg889yrvXscfplbKc2N7ld9XUdbD1NOvdQQeHwKr/E3oLyD9K1jvuURnN/ubeXz185IcgZ2AgIu5O6EXvQ2OgCPgCDgCjw2BaG3QWFxKVRuPNW4ncm7CW+/S10aae/do45W+7Qg4Ao6AI+AIbC8E4jPx7oSV5Fm3qa9bnn0PeoSuiT5JwlQVSvPb+Pj8TH6NB5Ltz1yz8cD9EE/TKq8073R9v+v8nCOwOxD4PH8Nm6VJ/7J2B0reSkfg4RFwMffhMfQcHAFHwBFwBByBeyJghFXTzEhxl6hqbzMqe89s/IQj4Ag4Ao6AI7DFEUiea+nDLhVc7/W8+1yPwTSzeyXeeDxN3wjVxjQb9xvT3m87zVvXb8xj4/798vFzjsDORGDjX4H9pdx9s7Ou0RvTpn9d6xL5jiPgCNwTAQ+Adk9o/IQj4Ag4Ao6AI/BgBIyokiwS1mR4ByNN3S0EG8xupKzKN73ywWV4CkfAEXAEHAFHYGsjsNkzLXn2rYm6j6EFD1KEHnT+MVTRi3AEdhsCm7Hg3YaBt9cReNQIuGXuo0bU83MEHAFHwBHYXQikA8N0zMq+wsDYkp7TTuP2mkWP09sIlH87Ao6AI+AIbH8E9Ezb+FzT/roH4FfcTMp7nMXdS6hO3UF8xa317B2BrYiA/RIkf4fR2dhWrKXXyRHY3gi4mLu9+89r7wg4Ao6AI/CkEdC4EcIq4qplNd2wHTt0j4FlY8Ikna8cAUfAEXAEHIFti4DUm4dVUu91fXr8cTw707LSjti4nx73tSPgCNwXAf50LNaE/wndFyY/6Qh8GQRczP0yqPk1joAj4Ag4Ao5AgoDxU8aWjWsZ6tjHUXIEHAFHwBFwBHYFAnoKbqbYbHbsXoDcKw+lT/N5EmLuverrxx0BR8ARcAQcgSeDgPvMfTK4e6mOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ah8IQRczP1CcHliR8ARcAQcAUfAEXAEHAFHwBFwBByBByOQWtOm6wdf4SkcAUfAEXAEHAFH4MEIuJj7YIw8hSPgCDgCjoAj8KURsEmjj2NW6JeuoV/oCDgCjoAj4Ag8CgQS0XbtmZfu30PMvcfhz1eTh7q4oYjN8llrQEO6L7K5WZ5f5HpP6wg4Ao6AI+AI3B8B95l7f3z8rCPgCDgCjoAjsA6BpnURzhTYYd3pz+4kY8IMawVK88URcAQcAUfAEdi5CERbIXtU2vMvefBtqo/qwZgiwcbdi9KDrFPbo3rDMTYb815/5hHsKfPGCquS+mw8rkNrDWDHF0fAEXAEHAFH4PEgkD4dH09pXooj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAjsA4BhFLXRdch4juOgCPgCDgCjsC9EHAx917I+HFHwBFwBBwBR8ARcAQcAUfAEXAEHIFHgIArtY8ARM/CEdh2CNhffqOh+7ZrgVfYEdiaCLiYuzX7xWvlCDgCjoAj4Ag4Ao6AI+AIOAKOwDZGgKGmuSHwIec27kSvuiPw5RGQwb0LuV8eP7/SEbgPAu4z9z7g+ClHwBFwBBwBR+BBCESLAzFVtljJN67c/pmPXNZui/QgBP28I+AIOAKOwM5BYKNw+7BPwQ2+cp8IUDzM1/nQfSKV8EIdgW2DgP7qG0Xch/0V2DYN94o6Ao8RgY1P28dYtBflCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI/B5EXDL3M+LlKdzBBwBR8ARcAQ2QUD2OqHprs1BnU3NKk3Xm1zihxwBR8AR2GUIbLRs1G/m3d/NXQbGDm+uLGkb7YXsKbnD2+zNcwQcAUfAEXAEHi8CLuY+Xry9NEfAEXAEHIFdhoAPY3dZh3tzHQFHAAQ2irfpfiriat/F3J1/qyDs2lxrfxLu/L72FjoCjoAj4Ag8TgRczH2caHtZjoAj4Ag4AtsegdUGK9zYmAcMUhPtQpa6slVy+WLb3wIP2YBU1HpQNsmN86Bkft4R2HII6B6XdWbjvZ7ez42/gBt9oTZac265RnmFPhcCad9n4oQV6+7kPmh0oPm58vJEjoAjsN0QaPyFV93tl1+BJDYsdnzDMd91BByBL4aAi7lfDC9P7Qg4Ao6AI+AIfCEEGuWML3ShJ96hCKRDmFT0SAc56fEd2mxv1i5FQPd14z2+S2HYbc1Wt9uSCPYu5KaA+NoRcAQcAUfAEXgkCLiY+0hg9Ewcga2PwGetCWOdmzZ5W7r1W+M13FEI2KCPwb4czW7HhWqbVKGvZFtNsc92bI/X+csjkGpW98ohPa9bXZ81y0Xt6GRjgvS81msXaMcXR2CLI6D71a1st3gnfQXVS3/D7MetIf/0eMMh33QEHAFHwBFwBByBh0LAxdyHgs8vdgQcAUfg8yGwimiey+VCrVb7fBfsqlRRwBJGTbgwSNfbBYJUfkvru84Lw8YxbZrI1zsfgY03RuO+tjO6ObJ80hN+s+z8m2IXtbDx5dyGW1t3/LrfyV0Ey85vqno3/U3b+a31FjoCjoAj4Ag4Ak8KARdznxTyXq4j8KgQ0CDpPrz5Xha5afHp+a1uoZvWM633xvXd+m8GSDqSvA9QGzN8RPsSJsvlsgm5mUzGxdwH4CqsstlsKBTzYFUPtfq9+yzO2rz3+QcU9ehON1ZB23x0x3nMl0cH8bbJKf2pSWYWV8qrobmFg+nxjffKgyxuG9NvGxC8oo4AP4Pcu3XeXTZhoJtpGG3YLa2v9G/CwdphCKhjN3buxv2t3+TPzzm3flu8ho7Ak0DAfgn4rU+5sK0bfgoaNp9E9bxMR2BHINBAr3ZEe7wRjsDuQiCqWbT5cT4SG8uyYdljxjwt/35lK016XtuN+19ddSXcaknXEm9bmpvDKoJkk1nhfXVlb9ucrTsb+4rewmRLnzp41uuoYhtMuKxHpRBYt6bXPjkErAkbi0+qtem5jWl9f8choN+AWzfnw5XLI/wS1UKuGQtcvZhIb9dNb4wNB82yccOxx/pbv+O6xRv0uBDQrc79W6ui5mZ4/mVroa09Hw4c2BP6+ttN4H1cVfFyHicC+r3a+Jv1OMt/fGXpp3x3tPTxYeol7SwE0iFq+qtgfy/+h7OzOtlb88QRcDH3iXeBV8AReEwIbMI6pT1ucvg+FUoeyRIZ0qf0mjpxn8se0ak4Bf/zZJa2Sqzh8S2qnxakyFgoAJcrFbM0bUJ8rGpg68sGBNRHsZ8yCN75fN52V0plfM5GUdfEXN2saym1lfaxtn1xBLYWArpdJ8aWw83rc/zdV3kpUU3eSei+vXvPp/d+/D1tuKftdtd+wzHbbtzfWm322jgCKQK6/1frvMBYzXK3yzx3JQwMtoe+3t7Q17f+rk6v8bUjsP0RuNfvM38Qjafs9337t9Zb4Ag4Ao6AI/BkEXAx98ni76U7Ao8QATFFPg1+6lK+qHWihX2mvDV+mSZeS8GBdE6Mzq1tK6iJytH5xyNOqnhZuqViaVhVHTiqE5stVtd7ndzsgkdxrCnMzy2FifFpMouorq7WQybbFPbsGQytrUXaoEGtzj3uuj2K9n31eUjwbsaSuVath+WFcpiYnAkr5WosGMiKrfnQ3tEaCoUWLJ55fKmfHcqvvmO8hC+JQDNucXOhva0YOjrbQ74FcUs/Xbboxk0+m/5e6Tc2SbqmAsTflfSorx2BLYsAPGS1nuGZlwuzs4thbm46ZDMF7uTM2t28ZevuFXs0CGz8/Wrgpo+mgK8uF9hmpLh3f7Abfo8pt5HG8ftNajsoa/RNF+PK0feO/gYMGuOx90jfmEmTrlsDs/GMbzsCjoAj4AjscgRczN3lN4A3f4cgAB80Egk5nJ8rh4V5CWBZEz9lEbaaEM27rU0JpATSRJBl4KUlm8vadMh8IYM1WTnkEdeyWbYRJpVPvFIWkyROLFEte7v64b7uxYOVayyOaZtMuy8tVcP8LG1koBjJs8huGdG01aZv1qoVE1E1E9/Uk8iztfNQS4ra5pk0hdu3psI/v/UR9WomCYVCwjs68uF73+9DgORqq9CTJ+b3b8fmrfvs0YfNZfPBiabl1rmPx0bnw9tvnw1Tk4sUjauKUA9De7vDqa8dCfsPDHJM5cc7O63bw9YozeeB641vRh5QsE5LrzPN7gFpH1i2J9g2CEjMkjW+ppcfPzHEy4iW5HczbYL+Bjb/O0hTxLXfNOvx8L0tjwAPc9ED6AeuRibCwsICz2pZ6fq9vOX77qErqD5u6GcjdhuOPXQZjy6DzX+B4by1pjAztUjMA72YkLjLiwhrlq7gRT38siWfgeM189uOQIsbnchHlSjmave7PfhV3zrXx9lG1UqNl9ZNYW6mHJi8xRLxquNkmhSWriXfzMvrHLOVdPrhDCc2b6PK3V1LRHl3tdlb6wg4AjsbARdzd3b/eut2CQKRqOFjtJYJFy7cChfOjSHEYglK+yXIRgvGlMbAQO0CviCZmSbOa6m32CrXnAn54mro6m4yEeLwoYOht6cTkTfDR9SUctAjtda3rezKR/O1UdCNbSNvswSWmBvC/PxK+Mc3Pgmh1sZx2pPBlUFuMfzeD0+Fnm6OWXLqapkph6TtqfhsKb7Ml4TYeywQ+bmZlXDhPJa59QKJJDRWQndPS/jmNzTdVNdRDxOYk/rcI6uv/PBGMfJLFfgo2iBQ0g9bvDAoV7gnGSWNTyyGSxcmw/hoiePcmwjjpaWmcOxoLTRnJZZziP5sDFJiPf1I2mbZ3/NrY8uta++ZmhNJAl238dr7XebntjcCVSzMtdT5HWjvaAqFVu098G5Rog3Ll7lmQxa+6wg8VgT0Oy4RDL/x+UTk0rP6S93/j7XiXtgjQSA+6T7zON6KP2UbeKFVEe6YhYd8ev5WuHULVzmVnImvJtbCRZr4Tc9kq/C7fDj57L5w9NiA8RcZUax7ylve5Ji+zeWFfhMBXqulGrO45sMH717FMAGhlhcdWioYIojXZDPZ0NHVEk4+NxSOH384vySRU29F4K3Jj/dr4wDj8ZbupTkCjoAj8MgRcDH3kUPqGToCTwqBSJ5v3rgdTp++xCCqDQIqEmnK691KNXC6JiOlsuLl2rq9/sd4VILaXGjOl0NvX3s4emQmnHzmSDhwaMCIKxyzYeE6BLh15LXh7MNuWlXVLEYE2k7dLNTgvu+9exoxt4tTspZYCS2FpfCtbx9HeFZwFVk2NJSeErg1C4mGc49yU4S8hmKzKtVGNS6Bq35m05/aBvAfZbnbMi/rWGouTCIusr5dZTBkA0AN/BFxdXutriKOyzJlVf504w0Yu7exk7clCF7pnYxA+rvDfW0qvn6L5T807rC+e+/vZBi8bbsUAc1E0YM4/ZnWOv3sUki82dsAAbtfJaiGMDIyHs6cvmrco1LOIOjym90EZ26S6FoNnZ1Y5RYzYf/+fmaw4UBhLdBtctPrJ97IaLzxM5kcBhSy+s3ysvpm+OC981j/6pmQiLmY6ZqYi+DbiZjb3p6JYu42gM2r6Ag4Ao6AI/D4EUgVhsdfspfoCDgCjxwB+J9N/dKISf5Htb7rQzcRDkxQiNvYzhhJbTKLGVk7yrq3jj/SbtalMDqyHMZGToexO1PhldeexQJhf+juLlq2dyufkNa7B76SLfOZu2nOlM//SqUccvyi1WppO5X48dTtbrVUXireJiK5VUEiDrVhO8rStutfXwKBNfzAcp1g/yXy8kscAUfAEXAEHicCej77susQsBdbIkNGiLZN8+tYDlSxlpVLL02rimKtyIeMCJpDabkGP54LszOl0N/ffo92pe1mdhvWEFzJS+osVr/Xw+ICPFEvq5OX1HFNeizaVzVbLj1+j5z9sCPgCDgCjsDuRsDF3N3d/976HYaA3B9kM3EK+kqpjLDZjMCJn0baWcciTJa4sgyT9S0s0f6LlAb8gYWAb10O12UWyTqXk4VpLuSy9XDzxiR5/QaRNxOeP3UI/2D8dHBJo/+7NZGNnKILBjYe4aIappa5ytbEatQ8O0YbJPbWcXUgga9QKOCOoRqqMuHVICJezFVf9UBS+ctthfqAsrHeUCRvuVvIZAvmxzeZec35J7gIt0cChYD9sktagaR/lA2HdO+oD7VONqyuSm0fvujmR1T/L1v3+19nMypJYuiorvdP7mcdAUfAEXAEHIEdhEDjU0+GBVoaj8UjW/lbVFgBWbPZXFheKptFrbn14gFfE7eEJ5dXVo0f3745QcyGDnMDds82YTQhNw3iyhNjs4jAs8yeI+hrTmKuzjEzCS4uN1MZzIJzubyVfc/8HumJxr6h4b44Ao6AI+AIbAsEXMzdFt3klXQEHoRAJGIinxIw5asxm2tGC1PgKII0tLTgNkHBGrSN4GlirgRdiWKyzpW4F/NYXCwRrGQJEtmGtassCfKhVKqG69emwgdtV0Nvb2c49tSAXasvE3RFQq2keLhRdF1L+Eg30sFBFPhU82oV8ZYpcArWprX+SRS0AYQR6Edagc9mRlktBI3r6uVn1cRxzKSb8rimkKCueqkqqlPE+bMZPL4jJjZ+4WqkmMf7JnH++xCVTipglUkHD1HItTGfKbraZ/Ajtxn8M8Feor2g5JNJL3uIWjyKS60l+uNTrVjFfevuuP0oCvE8HAFHwBFwBByBbYGAPQUbarpxv+HUFt0kFlliIJBF1EVczebN4EGTvyor5VCGF2easljlVkyYVbC/JkhJ5MLi1TRsrdnayGDJuxpycJp3CO66vCS/0hKLjRxaLIoclrgKQixBt7kZlwx27qsGSHVbq2iynfK8r7psz98RcAQcAUfgYRBwMfdh0PNrHYGtgIBEWONhjWRMFROTVPTcgKDYGp5+5mAY3NMWWtslMspqlA9LE9O5lIHsBbSM3hkjiNf1MDMNWa0QugdhuI6FryxKb1yfCNeujYe9B3rNT5jEyTrmwOm1lsFX+WVVTEXFuwUJAumnscVqTdxOU8QgWV89OR3a2xO+9/rzFK7pcbKwWMXvWSH0D3ZQNwTvVWGu2vmyOQLCJunItQTqVYZH3GvqZ2mmjZ+1ZL7hCDgCjoAjsOUR0C+6PwW3fDft+goaz2hAoVwum+BawQWZbuBCsT1UmYglMff69fkwOrYS9gzFYK1mabvuJhd5yYaV5SpztzJY846HpaUaAnFLWCrNY53LbC7Iem1VM+QQdJltphlIq+w/zBK5r/7ifHEEHAFHwBHYiQi4mLsTe9XbtGsQMIqWEEZtS+xq1MJkJYANQWjFY8JTzwyFw0fzobmFREk6ASXDSK7EwiCKnc+/0BdefvVEeOPH74UzH10LS4u4CsDCN5uBdC5Vw8TUYlhYXAn5VtwGoKvWZAWsSBEsTal1Ytyzb32Zoao2rCxtJEtSdzPOvNfJNG3j2vJJLtZx22eamjY5bDgYEFGovnuppWB3gyCsww3Z3U2/cUvXWWENJ9i3AlfDwFBn6N/TDRknXZJM0DThqqLEQCCXk6Vp+rO7MR9luUkllGyTw7ECG/O4Z8KYPK3UWoa6no8U/7WsHpRHzKpuN86908Y+TYr93Cvlt/Gji9PKybI53W48rm1fHAFHwBFwBLYKAvqltg8/6XpEimGkz2et9Uvvy05FQL2d8qz0TtiObb3LN+oy1eWu7e3pxrihFkqL3M8QvHK5HkbuTIcbN8fCwN59JInXiA6v3eN242tmXDZcvbwURkfnMZTQbLmmkIcfdne3hsXFhTA/T6YKhobwW2PGG+GI0YXTjJSb5sIldVKeOiL+dt9ls/NJHnadtjem2bh/3wLuc7KxHCX7vPluvG7jtRvPN+S78VRaO0uSptsskc5Fw4FYzzStHY65NBxKs/3Ca4q+m43qob14REYL+qVUL+vfF1ni+OuLXOFpHQFHYCcgkKoKO6Et3gZHYBciEEmAHv/6aJFPrpQYyB2u/ODKb2uxbTW0tjWFCnPEIvcTVeCkqW6KoLti18taoLO7I7z0yrPh9q1ZpoLNIvTyU8GnXmsKSwRsWEbUFa8VqdTUdy02HV4bZFmnDhkCPahSpXIV/2Ky7l3F/UFiDUwFckwla26RCwLqIc6veqR5KWfyiJx41SyETSDl8ioRhevVnFkvKH8rn2n4mYCfXNxCyFJCwwgcG6g2lqfyzynSsLVVF+l4NqzWiE5seICELIxt7MGVmuaW8Ci5RrP6c37VfPJGfBUIQ8YUmUxsk8iXMKko2jHpjMhTXj1Lu1dq1KnEtDn5IU7qZeK5ahoXYiezgWUG11ewiK7C9OWXTe3TlLss0d2EmYRzQSurX8yq7eLYB+psfeRygorQGPW9tZVVBpwz1KUpsci2C3XSmqPjYK6gG+ShvlK77VrO61wG1xx2Pe2mdaRT5ro4Sz9Rb9LXKFfuLmopVpy1ulv9NVVR9SdbilUe8T4kkeUj8CO2OhIX1U+J+SS9mp7RWsKunWo86NuOgCPgCDgCWwYB/Xr7spsRSJ7jeobreb7FF6si1Yw1FbOLOxkIi7jhsWMHwuzcXLhycRx+04wg22quyO6MTsL/hkmj4GjwIZobWR0NjuSSdCGcOX0WjgX3xW2DYj/Iddm+A8Ph+o0bYR5BV0mb4Fk6J+51z8W4UeTKim8h6g/1gj/CwSriYorXAJ+Ff+UgXuKPuWZxOdqzFj8joVexsUlR4o7KV3xasTbE0dlP+G/WjBJi0grl6LOqgkknvqeyhIH4YmiSZbH4KCtxbvJcrWXtGnHFxqUF/8Q5YZdVvXWNOCL5qmF6MSB+ay8I0vpzTr2jXdxdmABOlhXqXKlUjNMrf+OgqhNjAInpsf1ql9Bh0VeSt82g4z6lxeSbg9Nyyvh87AddE9umC3VO44PIQ8XDIxdXlpEvN7dQZyyta4qhFy8xKLQpLpzNUQ/+1WXx0sDnFbskk2HMZReBmdVDBaaZkH9OhjSxXgZCcspXjoAjsLsQcDF3d/W3t3aHIPDZN/HJAz1pn8iBSE+TIvBCPmowiaXFWYimrGlhBZxercufLoSLIF1NfEIGlsmSL7aE+bml0D/Qjp/dljjVS+lFdlabw+JSHZ+6ZXzzsm95rbELCMkqZJWpaNUm3DAUIVVliOv1MDkxH6YmF8LExAIENxtaOTe4py8cf2ooHDzYHjo6RYgjYRHPbMF6uFSCDpFfpbISzp0bgwC2UW2c/kIIyyvU2SL9ck3SzmxTG+R6JSwtL1A36sBHpK2KutvW3hKG93chUkc8RIgkTM9OlsPIrRIiN37PaHNLgWlv+Lc9dHAotLc3hRX8oo2OVsL4+AxuJ+bCSqkCUZUv4qbQ09MeDh3rZw2OJpBmzDXFzesL1DGfWCNXaEslHDjcS945puXhf3iRwgUZpDcObCTYItZK9EX4HrlZClevTOLO4naYmpo1Ul4oFsLQ3oGwd7g/DAx28imELtoif2rKSsTVyKi4LQfOn7tNPYsIwi34dYukcmBPkevyIdPMPv2paXxxgRCKgCMWK5uF+VoYv7MU5maq7DdxD6hvQujsqYaevnxoaytYmSuLi2ArQpqnnNUwNVFB/J/COuUWOMzgG073UyZ0d3WGXl4O9A10MQjaG/r6cxDYFrumuUWPIAMjVkWbRtyTtfbtvNZaLEHcXHc8OeQrR8ARcAQcgSeKQMpPGoUo+ykXLdmw6JHVuKRPpcZjvr3dEFBvq2ejqGl0dAs/rzezaLT7NYFdt60+4qPIa2F4uDO0d9bhO+PG5yQWLpYWwtWrIxw7YlwzD2dS7ApZ7jbmpRlwZz++DJeU+Mhst+XF8PSze+Bm/eHO2CiiqXhhDo7ZAk/DZYOVCJKqQPJHZBKkCX86gOCIEYMExUqlDGeswh9Hwp3bE2FkZMIE3bb2QhiAfx04MBT27e+F27fyYp08EQLz8O6VFVw72Fvx5A8UYbFWzZj7iMsXxo2vtbWJ+8+H9ra2cPBQB8GQcRkBP750aSJcvHgrLC+vwDlz8MO2sHfvIOV0wRdpg0TdXPQtXKtmw8Jc1cYX1y5Ph1u3Rqk/SxMWGIxTjh07znhAPL3F3E0ozge0NC7ihTKE0Jq0tgCV+qSMsUQLlhVVCOzIzXK4fGkmXLl8hXHKIuOXeujsZMZcfx/u5orMUOwOPb1FcxMnIVTtlg5dT8RcxRyp1Svgk6eP6uHWDXFh8Wu5weB/phyOHusJ7V0yeqgzrqqG8bGSucxYKUlELtt4qlSeDb39+XDs+D6MOTKMM5ajoQlVb24mKDVt6O7JhUNHWymbHl2hErRtlTGV9mkWfZ6lDPg9527dnMSQhiR1guXR97ncPJw6x33TbUJ7HeFeYrgvjoAjsPsQSH8md1/LvcWOwI5CICFhm7RJpKHJRNf4pju+/d2YPh1SsSZ9Z0dbmBrTG25cLODnVW+ZoRGWez4vIlS0fZEgRfvNIAzWYB+ytC0iBk9PVMOVS3PhJz/9GcTyNmSEt8+rBQhMK/m1IACuhEsXxsL7734cBvd2hG9+89lw6mv7ET5TUkppFFcpr4ZzZ2+HP//TN5iChoiK9W18g6432SIukB/qq7QK9vaf//ZnYWF5DuEUK1B4X4Y3/NVqKbR3NId/8aNvhO987wVrg75mZ8rhF/98KfzsrQ/QVYUH4m+uFA4cHA5/8id/FK5fLYe//ss3w+2RMcToafLHqgHxNE8wOXaMEP7wD15FzD3MtVxPHT49fzn8P//HW9QHwRNL1myuEg4h5P63//7fhrbW5lCCNCN/8kkXxFKETbXz8oUpgmKcD5cvjoS52RXE0LpZu+rtfDazCF7jFFEO3b358NrXnwtfe+FY2DNcgDxWwX39EPhP//SvwsoSdW3uY9reiunGuub13z0Regeom5gibUj7VAK82t/a2kYgj8nw//7fb4SJ8VLI59oZpCxhKbEY/qf/+Y/BHHIuXCmuWGzD/9tquHJlOrz3zjnzpby0sMKUw4qJ6auyVqCcpqYxcIv3oAT7Z589Hn7nd78LCW02Em0W3HZ/CZOG+5J+jaRda92Xd+/BWG+O++IIOAKOgCOw7RDwX+9t12VfvMJrj3M9u1nsGR43t/q36Mf6e1TiHTOsbEpaPQwO9mFROxluLS4lCVfD7OwiottI2LevC6FT3JhrkqZbe8nwzBkJn1WYTptxZgUjPnKkH3Gu2WY2xRgUQMUFulbcNu6tgck+BEzHrZI5eF4mXLlRCV+FXoUAAEAASURBVH/1Nz/BF++ICYwZDB8UvFjctlQeg3NXMWrIY7jQEr7+2vPh6994jvgZuVDGxYNNsrISVQYZw+fV1vNnb4S//oufwZWXjUPPzU+F558/Ff74j7+FEFsKb775Dm2esYDJmr2nQG7ifxJI9+3vD9/7wYvhay/uD1mE0AqFTI8vhrfeej+886uzoVjosxl+FEh5MiiphV/9/FwY3tcLT301PPv8MOMKcXtZrorf6pNioDXuLRCTSxhY5HNt4eKnM+HtX/4GDj7C8SYTsVts5l8ujI+Ohk8xcKjVF0PxH1bCy6+dCL/1rVMYSPTR1Mb2SzzOhBYMFGTpfP3OVPjxj38dblydojMkrMsyuxr+9R//Xnju+e7w3rs3w9mzV8JN3GsoGF4dAxf13yridEu+HJ5nTNPX1xWmJ5dwW/d+uH1zhj7OMs4pUM5q6BtsCf/+f/hXGGasF2FlhKLZcbJKlqHHdfL/h7/5ZwT6ecyAGXtxvLuvSnyOk8Tj6GKsI4MS4eOLI+AI7EYEXMzdjb3ubd4xCPC4v9sWNo33rR3RnqxwYZX2hp/U4mn2BlrrJCGinqYTNSGyajpUpglLS6xWL1+5xpSvOSxMJUCKkUoMroYiBLC1s2hipYTUJtS9GgRX083KFDU+tRJ+/YtL4c1/+nVY5s12W7EHMS8RMVclAvN2fn4JK9UCVgnVcO3ybLhz680wP/sS5O95RM8MFsQEX2OqVL5QxHKACMJYuuqNtAThuxVXA1KmjPUD7RAh7ilgFcub9foq4ixENcPbblmwSuzlYtaQHtIqUnCB/MtUWlYNBVnOFjoRnpuxTK2F//gf/4I3/QIVy+HWXtoni2CmwIlcQqYkLBvJNEuB2A8is83kGZK356rT8lIZ8l4K2YU6bYmWrJGYUh36YnFhNVy8MBr+6s9+ikUrpDYUqX8BIg455nrVWT1LLyGqFsLs9FL4+7/9Zbh8+Xr4l3/4PQJutHOaNOon6cT1XDh65Gj4+PSNsIKYWqsigmMd8u47H2NR0IEV9FH6g/SQ2HSJA4lsuIVV8W/OXCEI3jTWHrQPq+Ll5XlE49YwNNQXivSNBiISiPO5AkLubPirv3gLq+tl6leg3hK6sRiW4su9hxwNtrjlwOIYaLi3ms0P8/jYcvh3/+5Hobsfqw0pw0bUccVBtXR7WrPtblYd0xtVtdX2xn0d98URcAQcAUdgKyFgT2g9vhoWParsccWxxl/yhiS+ua0RsF6nBRvFJR3fcDNsm3bCL6m+Xk7L5RYSo730v3JlKty8dSne0LRtdnYe3nwjvPLa03BXuCacRzzbBFk1n89HH5yBcyLmQnKyCHq9BMcdPpAP08yEklVmhChiV5eYt0bUkwwMM51n+M4sN6huuHqtEv7Tf3qLGWQL5NkbCi1KqzTgjUjaSnwLCaYVXJTNMOPt7//zBwi1N8Prv/Mys+MGjTvncT9gFbTyJCKuYkCxhCi9AlcuwmMroaNtGD6ZD2//eip8+OGHWKMyC65agRfD9DCayMK/C7ickOHD6J3F8Hd/8w6z8abD7/3wxTA3VQo//fFHiKp34LV98FhZ62rMwWKcvBo6OsBhaiH8+Z/9HRh9BzH0mInPqU4p1wvR/YTGLMzgyxVDjXHH++/dgIf+FFcEeThqF81Q/VfgqljDYrEq12RZrIZzWNvWcfHw9i/PU79J+PNvhSPH9pvQnI4lKrUVmyGXb2mlPTXLoyIfYgpeDDllpBPGsMQde+NO+PnP30E4XzERN0NMEcUVsdlyqyXuk6VQxrp5hb7uG+gN+w/uDTduzNIueDIuNrJZrHmJR3L6o3Ph9d/+GmyZ/rKxFsXQ71lcKDRxf9AM+uoSOM4znmIcgys0TFmwYD4ajh4/YfeRBO98ocUsrA1Ou+EMWf9yBByBXYCAi7m7oJO9iTsdAVmnaoSUtlOEOZJmPdNXEWZXayJ+ECcLzBWJ4lp6xD+ZW9r0HWWBuDY+uhg++vCCvZG3qU06LlcMCMOFIv53i3LkIPcA+HaF49SqK4i2LWEMwvbrX14Kb/zD21iwdkK2IKy8NV5embC0IWDFEFpCAesAGbgqam8V/7cVAki8+Y8fkVcl/OC3X4CQRd+q8/MlyN0ShApGI6vcdUsqRkJ6aL80QWmIElSb8GObYUpWtLgVaZagS/UjLJaL/HWVcGBWKMhiWL68ovuInp5C+N//t78LpSXEYcRTuaOogpumPplfLl2tbcO4IcO0bjZaje4PpK7KslZrI5XycdWM+wGbLtaibHCJcCv87V+/FUZ4657LdZCr3u5L/K5Ym6L/NSwN8KNbhlCrnvl8B9PJ5rFY/mn4w//iewS2641dLuEeHL79na+Ha1jMzk5r6lW7CdYigeOj3AL0V6GN3FWELQCT1FmuEUZuTZsAX2MqmKaoFbEofvW1F/BbXAvNkNMMpFhT7K4iwv/9374TxkaXqXcrFsJMMYPQ25Q5hPTVOta+NFBTxRSpOUd+mlYoIf92dZ7Bxz+F/+a/ez32EdYjUdRNqgSsdYvivHZTc0LbjftJWl85Ao6AI+AIbFkEUgFXa18cge2JgG7eeAOLbw4MNIfePh0SN8VQAKGvDp+dm8PdFFxu774OBDa1FM4HJ5XvVS03b95BXO1hCn+OWU9zYe/+YXNnFpoWSSe+HF+GizdZfASRVmiPxZAw/qN9xGXKUm1KSyvhP/wv/x/8jFlakLvu7nZmdeGeSwUiCkogzGY0OwvuhjVwltlxmkV34TwuGCrvwSVfCyefG0II1Pw7CZ9cQsZFwjtEg4dWxOEeOLh80K6GOyMzzFQ7zT5utihDhhMFhESdk5iZR8yVuCvXW/NzNYwKRuDRXYwl5nAvcROuj/syrGk1C8zIqkCRME1dZYm8wOyu0FSyWXYy+HjuuSOhCT+/MZgzgjd1g5nbWEau2D75eCz83V//AhcEzaGdSM9zczPGKYVdoRhn+in2hAwwpMlmMTaQu7krl6bDX/75r8Jv/4tXwzMnD5MpJ2WsUmSmYaZghigSjrXIWMWIM9uKB3L5UnSBtozLNAmzcqeRo7xmfEK04DpsARdkFcYy4vKauTg83BWOn+gPn5xpD8tZOD48uLQyjcuJEnjKlZo6WP9VB9ZYNcg9hcYbwuTmjXFw0Uy32G5h0NPbjfsIDDmQgdWfy2Cq2ja6tmHXF0fAEdgFCEhl8MURcAS2OwI83S3wWaPWJcKHxW29XgwTo7gU+NlFptBDyBQhTIv509XjXwQikj7Ridsjo+bbdpxou/VKHmJWYUpUTNfdiz+soU4Im9JzHYuCQjRD4JaZcj8+uhROf3AhtBb6bQpVqaygXzUTG1948SlIzTCiH769qMKHH3xsLhgi8cMaF634zOmL+KrNhpdffYZpSvIplbc3zpqS1Mx+DSsA+YetQZ5MgIVsiXiakMu6Wl7GWUAiKiKGyvduHpcLoMN1Sbut1rQUodGmREGElMmqxF+skq/h90wkUwEL8vjSrWOpULO38vjLpS0r+MJSEIlss6xnlVkCOnnEgBXyRQwh5J8ESRG9An5n29uwfsbyYmVlFhKGtTIC78jtWvjZm+9hCYxFRbadKyRyM20OsVz+hJ8+eYC0beBVY/rYzfDh+xchyHFqmepbry8Z6f3v/8d/Za4laADCbTnsP9BNH2m7FIot5ItFrAYUly/fxEdXB37DBiGLqrn6Nba9inXy7Dw+gsfmwhIW1VUEdgmzLcV6eOGFZyDfTAOEDecRctHGw5tvYHUxWmK7k9sHLKuL9EMpHDnWBzk+BLFu4x6ohds3RsPZTy5RF2wPsOZtRrDWNLgp3Di8++7V8OrXD4ccbZZFRCrEM/yhbnLvoXus8aYW3r44Ao6AI+AIbDcE/Jd8u/XYl6mvOIWIkdaNy8b9xnNbdJsbNgOPlhGEjAOycp1lprawZqjJoUP7w7GjM/Cq23AxggsjYk7Cn65duQkHewbeJu5MfAB01WXcZl36dAyehOC7MIcASuwI+OV3vvM0s6s6saol0DByqqGkKUqIqhQM+9FfTYIdPF2irERDubhaIM7D//offoyQt4I/20UTL1fg6739LeHUC6fC4SP7zO3ZEuLi6Q/OhnOf3OEaeFamyNT8zjA5vhLOfyK3EAPGcRUbQS7T5LcXqo2lbKdxvjlewDdbDIYqvJV2IiAqmFqhqKBqRTgtFrya4QZPXsCHbB0LWbl4UKCzidGV8OZPP0BwrGLpWwKnKtwe37q4clP75ufn4eYYiGBhsAJ3zeF2TI1fxmr1/Ce3wh7cWQzjtsI4uuGAaIzoTdJw6eJE+MVbHyNoI6KaQYHwEW+UIJzDd+9e8ssh8M4hhi5wDTPFEFLz+R74/grWucvh9IfXEcA7sZzFIIJ8V1exNMZwwvi1+l9WvWYJIqMHI/zMJMQqgi6Rn9q6RFvSrdKulZVl2kKgaQThVTA0rkz50oK7u7rD/v2D4fzHc3BrXYDbNQTyK5fGwx3GZd09jD8UMM7aSJlw9kW4/ju/hPPP1EIBEbjeVOBeqtKvB8KB/UMhZ+Mfxh32TxAl9wm5+OIIOAK7BwEXc3dPX3tLdyQCenhv8gAXJ+C4LD/1lnd5aRV3BpOQtxKEai5ekxATUpAU4Q5ipaViBEVkVdaq8teqo/KHG/B/dQQhcL+JkkY0IbiaeibuOTOjKfqfQloJnAXRakHADNkyYt2z4Xd+eCgUWoncCykqEySghm+pvcMvh7/7zwRQwHeugi2IHM9Ol/BVO2pT2Pbu7UXAbULY3Q+h+68ggaQhsNoKhFnTk/6v//NvIFEImqoD/2qQsD/+t9/H0qHH3lRHn1O888efmOreTpA1Cbjr+U4jVZYYK4FULgEgUy21UKrM4GesNQx2d+MzTARwAWte8oSgKQJxgaAQxuQo3/7rlbnhakwc3CSoRwsKvaWX2FsstJqVQkuuO/z6Vx/iokAiKNPFIPl4+CV4xgDWyU+Fg/jalQ9i+RFWm7t72hB2i+Gf3njf8JM1gyyaZ6bKiOIzCL/dtJs+Jw+RupdfeY5zZxClsWagJktLS+bnbGJifzhSHcBNgvpVi/wPByygl7G2JaAHg4TVWh5LA9xtgMWhw8MEzChY/yiSsww+7tyZQPidYhoZHa3AEJD4ts5M+NZ3XuMeOYwvNvyC4caiifrUq4fDt0eeCm/89FP8mt2mrZpm1oR7iXL45JML4Ru/dcT6pFZVlGDVJi6yErEXDcm+rxwBR8ARcAQcAUfAEXi8CIiVIOgiEFoMiqTwAabPD+/vs7gKenFdhcNIoLt+9XZ44aVjcE6MCXjxX8K3qwTBn/zkLeOxmg2mWWN9/Z1wJYRPpvPLZyxMOnJUtixWhbikSHzDol0Ldgb3O//JApad84i7RQRJuG2mEjq7msO//jevw4N7MYzIKSdE5nbcZPViSXwJcfACMS1KBMLNwx8zGAmM4zt2JLzw8jCla7Yds6koNhoSxzGBjimugqxMq8zCyyGYPvXUYfzOHuVaLH0xBJiZreEy4AJuJC7AzzHwwEpXIrbcGpThkXoxXyQA8ImnjodvffsUAZAxWsByeWKyFt59+2MMFc6CjQRT+CRGDXVmE14huNqpU7Pmg1gc1SxrZYQAf5ybBWd47+1bMxyQCzb6iNmDxbY6hhBHww9+8HUzQMhheaDZbGfPjhAf4x3aTkbgKovaEhlewMfugYMDYQ/jjcpKCWvjFeopoZlk66G3YxorzcyOgW0rYwtmymH1PDjQx3hHMwKpAv5udY/IUrkdtxHNGD+0YAxz8FA/hg57wm8+usV91M0YA1d13FMKCHf2k6u4WjjOGIYxjvDnPistM/ex3My9NGkuKcq4f8szptKsyP0H98DJu++S5Vgz/3YEHIFdisDacH6Xtt+b7QhsYwREMFPpa7NmiFTIxxSv2FmWcdAvUqnAVNCU5KMzWqTYxrxiJGoJm5GUSPwsEQRr+MAwkVmHIKhKq6llyiNeJ9I2dmc2nDt3ibfOkDfU3Axkpm8wH777g6NEp8U/bmme1ESW1TSwUhNB1grhu999FXH3NCLfTcgVdYQQ37o5i6XqbBge6ocUiqDmwsEjXUbkRJYxNuWNtd6885EPXU25It8mgqr1DRawSiDSrvmmhcTB2+QeAbMKyJOIKdsifcmi83bIBE1Nw6oR9bab+i8ihr4cnj81aG4GZN2rwAgKfqGAYxJmW/Ax1tZBVFoFUFB+9qW8IyZmKW3bOpkueoeuQBdVArUF/NiehVCLndIcSKICtT33tQNh/6FBrCJ4o6/qUsci5Rf2yhLiIHVbCf/4kw8NL+E+N1sK7/z6DNPRvkvdCCpWw7oBC+BXCPJw+qNLROPFz698kzGFbHp6Lly/cZ3oyQNhoChfu7Fe6uOZmVmI/VWsHOrUiUEIQnIrge6+9rWTWJDI95cGGHEqXG11OXzruyewdKCtsvBGsO7qascapT8MDmH10SIs6Tv9w01FNjsYXnypjM+wkVCRlwn6YGlpGcuIJayTF/DJ2w7xRjDGktmqJB3XXjAkFUzh87Uj4Ag4Ao6AI+AIOAKPBQFxuvgxy8cGStLdkwv797VhxJDjBb2IDYYTiLl37oyH0dExxNrhMD03DW9sJh5CE3xnAiraaWLk8spieO7UCwS/QtRjBhSSK9fDJeF7KkI0VXwrfnTAjiL0mQEp+ZXCL37+G7OCNVFwYSZ09TaHH/3+6/hSHWSm0zKxCuYQ/wrwtyKWwMXw3e+dwMXBQvjlP5/jJb/yLpDPMi/5JxBzCVTGDDi5TZBrNDPSUGWsaLlTg1eis8ry9fjxA+H7rz/Fi/5u6kqF+D+wpwnXEqdoT4FAZpewJtW4Q8IsJ+HBsqYdPtBJ8LCX4c1FK0sidg/uKto6nrUZYO/96gqcX5wSvgqvnAHT27fKGDYwS68XLokgLlFXvPgOcR3Onb0G3xX/VxnMgGut42f3UPj2d18kwG5XQDsOGCobC3/+1H479g9/+yGxIXCDgBgulwnTuIa7dGkS7jofBoZaEV/FZ5XfvRb4P4GSQ7YS/vAPf4jQTEyNzhbGB8JGdZRBjHjyKoYguJTDSldWyxnaf+hIXzj+9ADBjhkLYWWrgNAlAjafOX0u/OD142Z4UsFdQjMDGIYbYfRmOdy8zky5BWYCyhAGsbkXvA4cKuJmQS8WGN/pHwTerXLv1V9+3BHY+QjoV9AXR8AR2LEIiACmhEx8UEEHEqJiYpkaLvLEMRPlsCTVIXPEDwviTXe+uMo0+1Ph1W8c5Y0wb6zlXsHOG42AtPCWfG4ZQnQDYXGRN/I9kKSqTTU6cnQfFgF5s7FUYARe7PN2OcsbcLKgmEOH2rBwjRF35clAESNmZ/GpenuKaLbHzG+XLIX1Vl9vty2OGvUtIzaqDsa5rMK0EhIqi4L2DvzL0mSRKy2a3l9TlAgWvfGG9di2LBYkGOpyHZFAm0GYXUZ0/s73XrLp/x2dsprAQ5dILFOgbIqdrqZgWTxDca1uMQed0BLzj9sbvnnzLv+/uUw7bSTImCyNsVTWAEBi5uAgYugexGiEYuPutEOiqDWUbDupz2EIdDPnywjiEp8l7l68eA3i+x2siWWFgIUAvnnlouIgovDifI3+qYV2AjqUCGZx49pt/Nwegnx3ULnY9/JvJlKvwGcKvqYowPl8Juw/LFcPB5k6V2KfAAuQ0jbquXfvAOS6y8iwAq5JzJWLBEVvluWu/OuqbnIvIZcMOj401B0OHTxgltginhoEzM2or8fC0HAb0+CWQ75VBJU6CULr3PtguQFa33UEHAFHwBFwBByBLYJAws3WOJEsTbflAg8xPiJzBPFGGpF8epihNYRV5/Q0QXsxmKgTIE0v2a/iauGZk/vgM3mMFCq4OTsNt4JDMXNNrhJyzPzag3jYwuwzMjdUoqs0uJFxVDFUY6nrEJNLgvnZZZvZdOPGLbhuEa6LG692Zorx0n/fflxsMZsrC49TrAcihJn6q5lPReJaDA52ww07yEOGHXL/UMUH7oK5Vhvci7CMgBtjTVArqabJIgvTSmUZS+IMFrmHw+EjWJeiOIob02Tj2RIuDx8ZCjevzYeLy2O0lRNJn3fgLuwb33yO+sFDqUu1Dt+jviVE7W6EyZdffip8QtBevEXEa+CBcvE2MT5HW5cRRtUeODiC6fxCjTLmsEqeIK2CKrMwLmlrb0YUfQ1u2wafhl/iHkIB6/KIo509LYja/QjQQ2F0BNGY8B0yclBMizFcyo2MTIYh+TlGR65JMd50gbUyZhgYaA+//6PvM0txOLQxO68GZjqu8YPGIDIgkbsKxZdYJS9h2oKF9p49GDwc7MLieJT0WNYy5sJsAp+4o5Q/H4b2t5lgLfwxgEasBg8CJytQXA4XZdlclRmNfTZLT9SdDDatpR90BByB3YWAi7m7q7+9tbsKAU2fJ/CWdExE2eaWKuQJsQ1CFkmAiAB+nZjrhO4WlhD9FMBAOx3dHdCE2XDk+F4Iy4Fw5OiQWb02t+CvFhImcVUWphIcJemWiHp7B1EOhwaQF7lewD0Db56XFirhjZ+cN1IjcU8uCUTGKhUJkUzlgpjOEsW3hWhoCyvzEBmETd4+T8/OYMm7grjHdCu84Jbl+gEhVhYDsFTetjO9iTNmdatKQPKkeWqaPtWBKOGrlvytdpCenBgWi9VXzWaRn6u1JSGcci3Q21dAyD3CtCtEaC6Q7GiiLytqYZcon3i59rWVEHKlN2uGSMJ1bUaDAJKoPE0FK0AsF+cz+Oa9HVaWJK5D0phd1kT71F8Xz0+Cpd7c6zoGD1aQviCMWBhPjM0gANN6Wb5akIwcgwjI7c1p3vzjixd2relYcpdw8tmnmUY2CtZE14U0NjW1h8nJCv7ZFKVYrjAQyLEYGBvHyuHcbapJ/0FKWwoIsa0rTOWSxS3BGir4TMMyQ22TT7YMEYSLBfBBBBbxXCZY3Mit2TA1Ncs2ftFIlyEfCey6P3RPLC1UIeErRlapCNP8eHEA4c0RKVnENEsfWZvVbMFpoi4nIMhkxSXkRFm25ryNd1LcSe6LI+AIOAKOgCPgCDxpBOwBbpUQV0oe6E+6Ul+q/EjfjMnRDHEuMRr4h3KDm+zd3xUOHx4IF86N4loX8Q5DhkXcgF27MhGWZVFJbAXxl09+c4tp83IxtWoGEgcPd2HJqhgJCo5LbgpQjLgHQyZv8VU4FIVbOewZDbQy5VasHq5euwkPXoLbiofncLWAUQTXvvP2bWa9RavNVTO6YDbbCnyYAGfUhpgLK/i/bcOiF0MEMlVei4tY8RJITcKojmkGnNayLk1aaiLlKoJpD3EzBvbIxUDkpgq+ZanE36i2pv/3DXQQ2PcmecDXdRL22c4studO7YXL64CCssHZefOv8UAGDPYO92DI0M8L/mmsZkkDh5SriUichb+QUL1WEc5nw81bt40PK/CYxGQMVzFe2GeuKyw2hWI0UKEmzq9Sljohg5uwo8cP0Rd38NVLkGT6K8O5BQItz2KIIrG4mXIi/1S949IUgbK2iHceOjRMQLO9zJ6TlQdZ4/YhxclcudllICtDFDWXiyTw5/GfrLGUAsGNEsA4h99iUINTZ3C5dp5gxq/SDvaJWTEzGcLHv7mMkcUc90hnfEmAC4j9uM/o75MbCLlTi/ej7pZYkDZ8cQQcgd2GgIu5u63Hvb27CAGJaCIZmdDT0x2OnRggkEA35EXqrkTIeE7rG9fGiDp7B1EwBiDQm/B8kcBlh/fhA/WAuVZYJcLsKqKwpvYYQVMWlgv5wFgUyMyizMpKE1G4hMB49cpt3jiPINK1QNwgNxAbkRVZX4ooKpCZjkuMNH9kEu4gPrIAXSRgQWd3lwX2EqWtQALly1bkU9FlVyXAGtNWO9ikrSJaCmom8mgkirbrmFl7is9ZyrvrZDeuyE/lKDJvJwJmlmBn0SWFcBQjS8ltJLraF5lqXJKacEgkSzYcEedYh5jSohQbgVyCoEXinMvKRxjTqrCMnZrEp7Hlm9TWmGWsvChtBXVe1gYic8JSrVnGDcbkxIyJuQVMC2QJq/7o7+9i2lsbAu4SwrnIMX7McHN74/pMODY6C4HuNn/H80TVnRiXSQQDA8Tz1aZlxFaCsD2zF7/FtBvRuEq5+TyWysk0tDI+vW7cmA0ffnTWgncsItaqTFmAqL0i9lBYgWT9ZP7kIKkViG8TqnsOIk0h1BVXGaTJccwCQKhf9R92ro/cWNgBMJHYq+lqEpDjR/cCp31xBBwBR8ARcAQcgS2CQMJfNtZmPWXaeHbL7RvPhMnAPqgblRcfU9MkDmJ8oPgAe4a6mGHWQQDgBY63MPOsFqYnV8L1a7Ph8JFOuNUKAp1EWiwsxSvDYlCg2FwzXEsv/xFj5StWs6qiJauYqAqRhCnul4IWX3jrpfaiTEuNX2ENDHlUQDAFIHv37Qvwb/GuVOyDr8pywngk+SPoajYYCWL+tGNpaQEON4F7r26omMRFhF3cl1ndLB2p1V7qYS7MVF1SCIOEGoNFFBblpkD8U7EbJEzbS3n4eJaZbW1tjAOoqzideH7MXzyPdmH5q9lfapvGJ5E7C2OQ4JAholPKGRK7Ul7iuFLRVvF8yu8gqJrqKEFV4wrla83WVRxTnXsRo2VBrCBvGq+oDlWMQ2wGH2QyitOUj1WzdY0sa9VI7agccjl85BC+eQGB+micofrFZW1D1bRrYKpcqtmC4EOZA3s6bcbc2O1Rs3TOKgN4+ce/uRpef/3V0N5NLAzun9u3F7FSlhGO6qq8VsNgfy/3Wjf1VzlJu1UJXxwBR2BXI8CvkS+OgCOwMxEQeRAFrYSOrg58SR0MTz/TA/H4rPol5/xXLo9EYkME3grWlxJbL5y/AensJdBXlwmphpOJqNqKJMKIlrEZGIcxJ5EvouHyKrqqN/5MPTM/sUz3EkUTiRMxSUmQLFIz+O7KNGm6lAivyBPioVQ68otWq9qHBHKpfUhlAi0iYyRYYjvpIhalNm5CclQ/2mXVpC5G8FQX0mqKm+qky0SeIpnUgca82bVFx9NF5bAvwq1NrY34KU0kgGlKreWGII9FbBmcRe7sTT6NUtmyjliEkEeSS1YcF7mNrjHUZpVRw/pWeYuQxzTysaWpYRJ5RaZ1ViS4ozMbDhPA7M6dOSweJOKLvObwwzWOL91x3vB3QdxrTDNbwlJhyaotfKHCiMDt+EYbjPgLD8ix3C9kmFpXwpfxmdMj4b23r4YbNyGlWEHIukRjBmrER/0cyb/y0740dvkos/42SOM9oHYb7nyZpTVpY18k/XE3AWeUNr0urpW7L46AI+AIOAKOgCPgCDx6BIzEwGzE15Lc4yHjR7JGlauF8bFp+BdiLi/O5+crWKdew6fqSfPtWuMltvhZJouwycj7yNGDWGpi2ZksKf+0/JO87VTjtnErkacGfkt9xLtgVsYnl5cUlHYFLsVagi5p40e56Vq58JL4KWFSmYs3yt0CM+7UNsYMEjRFtqJhBMeMeSmtEvDRJh+t4lc8k27rsI5E7qdt1Y1Pep02+NhYYB2/TvJfl5GOKbeGsu2I6sdcOYTdXA7jC9qTZ4bfg5b2Ds1GlGhOFQwD8U7yovEa8yhP8W9zo2b7EnjBO+X0bHd2dqhFpAUu6dVW37RxJF27SVQb8VSNfGSMkiVQXQexRw6Gi+fmmGW3jB6uGYdZ+Pkys+NuhZcI+NyK+4krl25iHIF7Obj9CrFOxOuPMlNycLAzzkCkKyN+D2qxn3cEHIGdjoCLuTu9h719uwIB0YhIeRqbK/Igf7HLkMs53vDiOwqSoGldMbWuMqrBtKhieObZfUSivQbxKJCXfDllbarYJ5/cDkUi7g7uUbCxKBhGkpZcT8EpdxEpgu6akNuOj6vOnoIF9RJZ0Zty+ZYSqdGbbhGgHE50RX4VtbWG64VAIIim7JIFKpAVqDiSRE+VKyEyLkonUqry1ZaEFH4WgJicbxFlpVZalW96q+rNP/1PWSZUDuEUkksD1xMyu3iTr5hrPKGM9FFdG9apeG5JRZTlaoEgCQSJUzk5nPvKFYYw7e7psqAJsmLQfsRVeWlJyLEso42kq93CJFpLdHYqoAK7fOSXV5YF8nN2jGAVv2G61hx+1mKkYAU7WwxjY4tmzTEzMx+uXBnBZy2WDliHKI8cVrlHjh4wCwYR3Ap+eZvpQ02JW8YP2ghWA2//8jyuIubxj6u60wabUiYxWfcYlrcNgrPuClnsLhPcQfWNlsW0AwsNud/Q1ENhH4PUkaKq9PFeM/ccmm6HwFyjDLuPSB+wivHFEXAEHAFHYGsjoOetlnQd9/x79yAgMpOQk4SJbau2r9W9gYvRgJSjtbUXzeIy18w0fficRL4qAbauXR1D4D1uoq64iyxPlWbP3n6zsGyGZ60tZC2eKq5kJG7tRONGiqGOpddKVKwT7KsZ92hFLIVx3YBv1YB1rgU/hi/KjEJcM15BEDGEZdUxlrcauvAn20MMBOgdFqRwOMRRzYSLL9fTdGl5sT7aS337xiMbv8VPlYgPa7vavrSvDXHse7U1xZlkG5eGU2q3ZqrJXUKWOsuKVYvqnbMxh7hyWmjMqJOYGooHoUWY6LzSR74fK6vj1ja7VO1gI7kHbJtrY3oZWmjbskvKSneSY0qbgsA6RxCJffv6woGDgwRfuwxPl5EK+dCGc2evMBNymD5oYlbjLWY3yqgGUxjuExlmHDzcwaxBuUtLBgwqwur22TLvlu5bjoAjsNMRSNWRnd5Ob58jsHMRiPwjIWqNzdQJTVPSdHx8zPKmPh6J37AAS1zhzXahNRu++a2nseCcDJNjeKldQXyFfJYIjnD2zBV8RHWHwYF9XCKRV+RQecRvMRkF0JIlrsgVL7shH/Gn5eTJZ8OJp/CvhSWq3k5L7BW10ZR7EZRCARIGidGsrqr8ZCF8ypVDK75yFRVXo78o/EUSJN5iH3IxUqWdZCkg/lYJjKYX6GUInqK/auq+VZUvUdlIxO5ek14b15G8Ko2ylYipJSVtMU38jvk0Hlm/TS3sXywp5mMpaI8Jy2AkP8Gqk6a0ZQiSISuBIQKLvfDSEG2XuwOuA9tIuoVNKuaq7TFPKGAUxhF/92IZ0orVgYLPybpAi6L/7iWow17ca0xNLZiLhRoW0nKHcOf2LNMCZ8PM7BTuErDKhjRqkahaIAywxFxp5rJKUHub8cVbLtcIatEU3v7VR/jovUO/o0qDU50+k/a+b/+ecPTYHus7WYVLnDWii6X1/Hw5nD17J1xngCMrFQm+0mSFr6XRWo0Fk0iu4zkbUKgvdG/oHiKNXaNjkZNbvf3LEXAEHAFHYGshkD5t9cjStj262NaO9rXpy05EIO15tc16Pllvt7aq7iIasT3iKndpJ8fYL2DpuQ+OJQ42PlrixXcrL8ArYeTmbHjzH88g2i3CWXjBjdZYqS0T7OsVXtoTuKw5vtw2RMhYfC61l137w1B5+iNR8aw14yoH35PbK82UEv/KZMrkW8UIoid847cOm+WpuaeCg8ktmrXAVEf5blU5mEEww0pt0acFtwg9fe3G95qMByqFFhWslfbTY3bEdu1ICgYZKS+7JNkQv41gxfW69BvyUzbGvVVSkqeut39JNnY9R2Q5LB5oyShT2+K8c3NzVgdtZ+HXOq4laQVcuxqWy3IZJlxibmZUwdgoi/GBLHazGBhYetpsrrxwZSZ3DQaOjkGKa4x1bLYc2cdcGgqxEpMvKyP2qiUEF6Xv7CoSiK0/nDlzlrLoE/hwlpltY6MzGF2swpFHsdqdhV9rfFDiXskRJLgff8DE12hmjIUgbN2p8g3rxkJ92xFwBHYbAi7m7rYe9/buIAREA0RWElF1k5ZJ/JKwKLKmN8IYDbBAVcQO9R8iUCOwVZbAXwpA8MKLT4V/fvNjIz1N+DNVELOJ8flw/co4kVh7eYOP9SdEMvprIhuYiVnZIsi2FlvZgbRKZKNUWeO2tbUShGtP4gZMb8lVZ/nLZVNiHwRK5NWmoEFmTMC0GioPiXYioiJS+n/3nwrWP2uEzvFRPrIcbYGkyqogTUGihkXXJNetHWVfZNU+yosrtWskSWm/3KIr1YqNi/pEgmpPdw8k8QYC6TLBxLCGFtGDeA8jvg7vb1frY12ob6wLuZFpJKF3Wyej1Qi5podVIKqycJXIC27k2Ub04WPH94WrEMSZSflRQzjGd9vI7alw9pNbXFu2ABDNza1cKxcOWaZy9eNvF5ccmnKH5ayEZwVWKxNEYxXieY3gG7KYlo83Ba3LZFa4d46El199mrrjlgML8BruPXROLjZCLYdP3+UwPVM2/8zRihcL4AfPitsIn+87Ao6AI+AIbAME7Om5yUNQx9PPJqe3Qcu8ig9GwHr/wcm2RQraIn5od21S4YRDiytqdv8efKHuPzAYRkeuM1+MGU5YnS4tLGJteYMLol9YBYTVrKojvPDOwrMUqFccTYKcOJ4EOvE7NtctKtnIpDY4J0Gxow3xld0svL5WLyMIwv1qM+HZ53rhVXmqK5KtGBcwSQm05h5NAq64OeMBYg+oHPuQ1NawTvMzq3EDLswix458WFUw7qkGa5tPtMy13fil/HQ8PWSJ2GFtV6VfwtLyUUrlL7Fch7QdS41X3L1QydVuqmaGBa2tRdohFwUKgIYBCnx0cmKCfbVFYwzaoh1qU0F81Qy4Zqyib95cxkcwvJT85L9XxiKaRSbXZ4VC9G2rvjDObY1Jt9NWISRzDcWaqBytm636a19kvW6RKJwCozbIMlhGFv0DrdR5CTdlNbh3M0JuieBst8N5LHTLK8KBFwDMrizjH1gvC3p6ZfRRplxZXuOebm224rrifMcRcAR2GQLxF3SXNdqb6wjsLARSkvHZVkWyJmohhpMQCtEqI3Y6LgkUC0lMJOXT6tTXDkEYYKYEO8tgVdmM9aSCYp05fQWfumNm+RqwsozqrMoVYVRgg9Zw4MD+hAhG8iMR8PLlyxBMpVFREnMRHEUwISiKtCs/uGXOS4wUn7MX4KQV0bIvW2tTB3Xs7mHt2GGRarbLWELojb3aYb5dldlmi6XXCTITDmCgbeUiohjzZFvgfSULIcZwZzY0tCchmC2Gm4qamsIyemqW4A4QNoRZRQ9eBTdhVWcbGdgsEGQ1DHS4ppBVQqy3RF1ZN1ewPKgjFttihH41HDm6l36VWIsvNS6QmLs4j9X1JzfCxQtj7MviWlbMTOdqbw/PPHOCaV3NpKUvIbwtuMOwPjCsyNkCdejeUDnCqYo/rz2IxoMEhqBfcgrcIJcLeokQ3TZoULFSUmAN1bduPtrWyDugx20NcJIgH3aM+3PtXNyWRUR6zDLzL0fAEXAEHAFHwBHYQgh8VfzpSTQxIZ8JV0xrADtiU0YJgdlIRSxz++BKeTiYGGWe1Lykr8s9GbOwRHDhb/v2DeJzVSIjL8ERVNcW+KaVssZvOMMBduNiG4l7BPys7sVSUyKhuJC5GUBdHJ8YJ9jtsvE2cW0Jf4EX7Y282qxM4XmK3QC14+U82dMMZa+PxFELMksVlVZGGRaMVscpT2JkLDetF+u0jsmhuKJu+qd8lSBNo7UOri12YG3vfhvmd5c6tMFRBwcH4Jf4ykUUlxItS+iZWc00m7d6R/dcYNpQlvrg1q2xsEgsD1VIsw+1tJOfxjAaz0jglWWvuG/8xNlg0fpX5yV0N9Y/Ns04KTlYW5Vpw2LpGy5pYVbinr2t4ZmTh+HHMloRic9Sr1p4/91zBCgeJx/uH2ZC5luK1K8Y9h3oISC0jCewwsbtmayDfXEEHAFHQAhoNO6LI+AIbGcEjCSIVDawBWsP+0zt0vSu6CtVBJJl7a24tvkRyBZCM2+k5eqgty8fnj11FEJ0xhzyVyqQHbK5MyIrzqsIkAR62NfJIeUl5VNkKUapPXBQ0XlxcYCfJwmNC4sl3oKXidJ6Jzz3tSGmB6l+0cpUIm4eoXiFt+ljoxNhHn+udVmSGjmu4zu201wOmFiYlCG5dU1ghUypXDVFX0aWsD64fn0ay9AeIu4iTtrJRNRM2arlzzVrWCmDZEkglCXwBq6WJIj1j3VML/qiaxUiMbdG+3oMrxb574LMyTpjcnIy3LwxGvrxT9zLtDdZGccraCMb2pbvW/lis36lC9QWYXH4yD6mYTGQMI6nvon9o37q6MwziBgIt28sIOKKPPJWHxcMk1hd43WMvImgK8e9iLI9vT0EyhvGeoCpfCbEy3I6WmpE+FQR1VkfkUvVUdYPtAt3G1nKzRLkI1p2RN/LqmWZIA4TE9PkhfUvJFxWxBo4ZDMKXqFmkJYBSOxzdoHbCLKC3NlH95rEXrVX59RPaotQ8cURcAQcAUfAEXAEtgYCei6n/Erb6Wdr1O5z1cKqf+96i3voIz5SbM1YrIeeno5wi2Cy4jZNTXJDJRiUB8QMfvPc80/BrSDKuMPSdYZLApNSaYm78Jx0SweSNPaCnTJ7eloRhVvDzDQvznO8qC/xghyL0/feuxq+//pJXG5peC+hU6IlcQzky5dZVZMTi4i+M7xYL3FexGs1FIvNFsCtA9dmdaw+c1le5FNexvgWXNG4mWoXLYzNxQDnrUpJvawtxsWSA+rvlLSy1tHY3sYLjPhxJl0ElspJ80iPxzU6LrQTg4OOPD6HOzHaINAyYxSLo4C4qfgP7793jva/ShuI0bAMD0XsbeaD1kvQ36lw4cJVE3NhqtHCFeOSnt4BBGKCAWMMISHbAvXGGlMwdVTFbTyR9Ih2k87SWvWKS7JBWnHizReVsRp6e4sYPwyH0x+dxcgCS2INteDTd0amGQ5QJnloLCJL7qMEy+vtbec63TP10MLbA/XNPYvYvGA/6gg4AjsUARdzd2jHerN2OAJiEJE9rTU00oc1VhHJh4TcehFyAKk0MqLrUpIRRcv49p3D+NZVti+8eDiMjIziK3ckzE4vQpgUEK0QLp6/ifXtAIS1g2P4nZJJqJgqfCPL9CX5cxpmKtDlS2P472IqEdP2KyuZ8POfXSDwGk7/mYJWLKpMKAri5dJClen6Y+GjDz8Jo3dmIVsSerECRUR84YWnIGuDSRvVREQ7fVuZsrxtgjy34B4AQdCIpoTAlnDmowukKpsQnMefmNqm8vKIuwocUGyjsraQmzLUSh8Jn1AnTZmyaVPpORIIV527u2hbx77MQn745JIrg46uTDh4qB/CeTMsLS7SNuj+SgW/spdDMxg899zxsGewmBStCMRY7k4ucv56eO+dT836QwMJWV20tTdjFTJEtdRm1U7tVB2jGK90x4mge+3SbLg8N8VZ8MTyAI8M5It/XpLWmarX2lYP+w6205cK4hHbmEP01TSwDH6QZfncDIGWaF+nr6KgSlqE3fGxeUjySuhvzdv4RQFAMrRVpHNmajlc/PQO9Z81UbdQlDVyAcviVfzKTYfjT7WHvO4N8repeyqaT+wL8E4sqDV9Lg4L7PSX6QC/xhFwBBwBR+ArRMCmjRs/uVuIPUHjI+XuQbYan6zrTvjONkcgdna8Ddg23pmut0PTVFfuTsQ+BebVy2YjSsar0vpHMTcVdLvwhSpjgpHbM9zXzfDUFjjWinFQCY/NheZw8pn9cEAZQzAzzV6gp1wtETy5MkqGiXBoRaXHEj4KX21ty4VjT+0J7/36PDyKgAVwfM2iOnP6Ai6y8uH404cs9oKKqjJlP/CyfXZmJXz00VUCsl2ygLfmJot+2X+wN/zu770a2slDU/gVx8I4ofgwVsD1GjOzNEOMmsllg86tg4EdcXfDizRUhk1xdh1T+7TNRboufmmDJf3rtxPsKz0z0sBujcMmhcFWOQYO8Mk8xs57hoR1O7zyNiKn/OBq5lcI775zPnR19YYDxG/o6StaYOVqLRtu3ZwK72L1eu3yOLFA1JeK7SGr6lo4eLADQ5VO48QZGZ3Q7jp9nxoTYHJAvSC9qrFmFmqmnAwwaJv6Ptae7+Qej/tp2+yyu1/KXvgg9g/saWNc1IfrsVkMWrB4VruTMQ6Z0Rfyi1wNJ54ehuPnzRAiznbTywDGBMDliyPgCDgCLub6PeAIbEMEjCaIRBhTTgieiI7+cTwKkOIWUvb04E/+1EU2ICJxEXGIhKNU4o1vRm/qm0LvQB5B9ygi2xxWoHMQFoRSRL+FhWVcLUwS0GwuHOrogXeJxMhfKmVSTEdXW3jl6yfCzVu3IDh6c4zrg1JTuH1zIbzx40/CU8/MQpi6jPRJ5JuYLDHF/yq+XG9aoDWVreN9A+2InYh9Ij1GpmMdNRVJAqLcPhSLOfxN9SCCTiE6YumJH1gFEbt+dZLAXjMIm3sQcPPkIwvUJnz9FokSuz8cOd7N9eQhURAoRKpsH6JYlxWCEdWErHJWJYusKWX8sLJtrb/sogBjWMJCsH/r28+DwwSC7gTWE4jUTNG7dmUUUloNS/NZxHOJ6RJWcUeB64WrV6bA7BbWGATSwAS3Xl9CBK2H3v69kNtCQAs2oTVWUSRai3ze5shrMPT3dYYbV2eMFErMjVbbBH2AqzZDGrt7coi+/eShABtGne0easYSQPvqgwKi68BgO/fCPPlgFaJi6O+Ln96ERDdbALTWVqasgbvcOiwj8F66MIHFxFWsKIS3TkGiGWVopti775zhngvh5dcOQGYl1or6K43sEvhH3rKi0CJSraO6z3V/6J55+P5QPr44Ao6AI+AIPCoExEPsIUuG8Qm+ec73O7f5FX506yOgp3jDkohc2+pZnbzgF1+WgBdFXXhI2jStTeyErSQ3cSeWrUeO9YRPPr4A9yXgcFUuzGQ5Cy/DL+uBg0PwW7itsrFrxGRY+DKqy0b8J+aD4GenOGLnWesi/msmUyezrV559XgYw/Di4oUleFg352thenIp/OTH72OQMQcXG0T4JNbBMj5lsVK9g9HEp+evhfHxCbgXAiv5FXn5Xiy0IgB3kTd1pbImUNqfr8TbdHaf+H7kxmvnVcEUD9vQjkYfCL/Uxfi1OLuw5PcgUun0Iq3F9LTmw6WaJRZFW/FzlaW8lCff5BENCHBZgcXC4FBvOPXioXD5ysWQrcslWAtGB4w3mH32l3/28/DM08+EUy8NWtuXljLw1dvgdAMDFfJjTCRBttC2ahbJTz8zGPYOtRI4GQMFuLa4qcYI0Z2ExiISl6OYq2BzMsiQy7MsMSXMPy9nLSixqmqL2rR+kTCcLmqTMOzoLOJq4WC4ef19K8Pcl5HI3C5QQrW2EgaxwN5/oBNr5DYIcIlzegnAvQE2+te4NJbReNy3HQFHYGcj4GLuzu5fb91uRADSI6Ii37SmsopIScA1Qi1AdF5kQgJZFDXjsfiadxXvK0ePIXweGwvzc4iIvMWu4Sc3B8m7dWMMtwnXEP3aIIGyhiULCIxITyuWsseODxHw7FA4+/EtrivZFKhcpRUiNRquXLkRBgb6ESErJk5WUfJWmHpfqUjUIzosU/PlDmD/geGwf/8weYvEisxRBOWIGErUjC4CmnibPkzk4HnqiOUD5ZvouhoJ3TUCtsn3lcRcvT3v6S0QcKANMVeEVWSOVTrA0NR+w0oYyZI34kCxGxaryYZjm+0qXbqo4gnhssNN5s5Adcpi3nr46EB4/tSxUMKXrEU8RmDN1oq0azn8YvpjML1KUIa84bO0tIigi4BLMLFMaDVfWiKU/VhEP3/qKcRYehFL19g+2L7KTYoWN1Vwh8NHh3F9MR/G7kyDpQhrfMMvwbcZa+f+gU6sq/tiHrpvEE+NgFtGIqwxw2eePYF1xyTB8Rax+MDpBkE4Ru9Mhh///ZRFVB4e3hPaOoq8AJhHXB83i+IaAxsFWSsWoMW0XYS0iIXu3OxM+OUvfxVeemUv5BjrEkrVDDIFszNfbQlhpnDuEYnzmupHGvYl7IoYW4emkPvaEXAEHAFHwBFwBJ4QAvYUX+Mfa8/nNQ76hKr1JYpd44nGM8SlaZuaZ5xI/FQvlyMHKRQy8Kc2Pj3h8sXpsLgEr4U3i++Va6Vw6tQJeI3IS+TM4lfi4OJqEgXjQl7i8CpBm8nRdCXek+Ptew4XXHKd9e3vvgZ3/CiUluBhlLVSziBYroQ3fvJrZr114Qu2HctcXA4sVYxvKx8LOIwxRg4r1MHB3nD8xBGOpoWpLemi7WT8AEe0be1rWVcxdlKeu+6krk8+6/pebU15tq61i0TxWJL804MNBWm2mPi5xOw8QXb3YQV99NjecOXShGUgtwqBMcD0ZC288/Z5DAguwnNjfvL9uwrnFX+sMgbJEdNBY5Znnz0QhjH+KK8gbhMnJHJc+ox/ZlAgIw/7qI/imCQKtxL5xVeFT6z/5/1WGVn6r629Bez3hbf+6XQs1zCMmZXLMW7J8RMHMZQp2lAufomTS1b/goV+3sp5OkfAEdh2CLiYu+26zCvsCNwLARiRnu8m3krIxQkub5BR+OAAiHYmWupakZKUYHFem1wnoqKlgD+vDMTy5VeeQvSbC1cvTXJUb77r5pPqHNP8FXzhmZP7II8SYjXtCOE0m8evU0f4/R99FxL0DtaYI5BMfOGSr3xateDIf2x0lrWCQWC1if8u+fKq40NVxEauBYaGedv+tRMWFThleEqrDzwu1pv2iZi9+NLJcPu2LAyYNlVaij6nIF0ia/EaCbN6ux7XOrZ+EV6p1YVIHGeNWxMcYY1Xa2NtZ/3lm+0ZYRW5E1FNMDaynuxSPxNnJbrSBy2IoC+/cipMT89j2XoNYZqoxBX5AWsKy1jClpYXETCXTfyU0AkFJCPqmi3zXUVQD+FrLxxCQD9CH0vspOw1Esym3RA0C3xl03royADljOArdw68yCux2K5Vy1gyZyHGB8xfsQR3WWSn1yuniKNeAoRw4sSh8OLLM+HD9y+EWQWckGkvLweWsMK9enkC0X/e0gnzLD7I1Cd9fd1Y7jIt7uKtsFxasLwZYoRCqywQZHEALVb16Qfra1ooKw3UYj7UhfNpGgyEIeZ81DW6b9cNFDjmiyPgCDgCjoAj4Ag8ZgRS3qN146L9jRys8fwW2xa5gFtEXqy6N4qZ2o+8kBRs68MCD2klkNb+A/02g6qA1SvRBHiBXw5d+Hk9fmIIriQ+SlIjnNjfYvkaiYwyIB/j6UoAwYEvri1pEZxSHlpkVfvU04d4ET4fzn2CW7T5OQwi2uDpM/Dx9jA9gYC7VE6sPvOgnwmllUX4+AozrLIWsO2Fl06YEUbkUEkhaaEyAsmID1MeL9WZa8daRg8b0ln6WCczEknGEhEXkTp9Nrtm/f0gbnkXA22nPJp0ZnzCikQKQpYFv/6B3vCtb78GF/yIGW0TcE25lMNQgOGPjXHwPQstpr0YpeBb12YSgqt8z8oS+eRzB8Kzzw3DS3FhAM+UpS0NxMAEwxH82FZwFSYuXGGGmRFO1U5B5bTmo9pv1iqdv99ibbBxT5bYGK3hyJEjYWZihAzjWEe5VqtLoYCBxdFjQ+YjOLqtEI5aVHLE23b9yxFwBHY1Ai7m7uru98bvHASgFJAUi/YKGVwNslZFviPIVCaHMJdthSfgEgEOUI+MiabLypUDIpXGSEQQoDsEu8oxd/7Q4c7w9MkhBNgpRMZliI2mYAWCdE2ETz+9YhacPTjxb+ZgxgRWYvbypl+E6ge//SrH3ycq6wR+bVfwpSqXAGSf1FHkyt6YU6SExk7ePB843Bdefe1UePrpgxxLSVIkL5FQ3yUvaucgPrN++3e+GX76k5+HBUTQyck5CJD8/opsql0RExFiWU/UEbXVTCPfybkmBeoycsYZ8VUVAXnVm3dZG8egbBwTGaXuunq9BYLOpYuIGJa1EEUF91ILNF2qCYKogG+yeI4WBfmwUCsjZEMUqWcrFs7f+c4riKnNNhVsYmwZK4EotIpISkSVj13lJXFTLhA6OlrArNXI6B/8yxdM/Kxo6hXVky8va4za2LBor6c3j59kr850AABAAElEQVSxTnwV58LsFDgZ6Ya4NpVCd2+XBVpQhF+V0yJT34ZF947ovGKitbdnwte/8awJ+VcuXwtzc0vmJqIGkZa7CE1Lk4gvS2rdM734LnvlNURnppT9wz/Uw7lzlxF+S1gDF8BHFhIdYEPPkLd0YcEslxtZROumLPevBcfjGH2g49a9SmZ1Wt/Ohir7piPgCDgCjsCTRoCfaH6q48d/rp90bzyG8tXb232RaChLUPEexDy4hzFAZnFJFBSPtEBgdlfTVnFbPvKbe/DQADxNs6g4DK+R79PDRw4xE0n7CS72d4CwC08SJY+8VL55F0mDZSgvweWnt65Aaco+uUwisOok7tbMzLPe3lz44R+8hJVnW3jv/TNw9XmMGxaYudXGeQLiIkhiOmHXiJ8qfkQzMS/6BzowBHgao4ij8DlS6A26LaqguJvaw3XZJWuDZkA1ZVbAIPJjwyW9JLlSdctIEBX3tNmAaoPqKyBovBa7RmWAZtooOyHOp3QrxvEYuACpsOYDMYyOJyKnljWsZN5m3IcdPb6f2V5t4WdvvY/xCcHdxmQYIDQ1ltF3NP7IkF8NUba1nfgZne3MZjscXnr5GDhobAQ7xxCiSnmy6Mhg/KLZZuqCrPFNuLLVV+MCuaPT2ImcbSxFgyT8b2gLie652LUKKkxZ9VoLffBc+OAdibnJJdxH+WITRjPdoX8wjwGMnG7E8UtMkSa8ZxF+whFwBHYRAi7m7qLO9qbuPATMhxZ0xUgAxEIRXLt5yzw0ROTTJiLTykk+NGB4XycWA0R15U1vCT+2a5ajEK3Ub27MRBazmsaEgAiBeAnr3OmpuXDzxjjgqRyJn7yprkCaxscRBxFepTtCnmqULzEODZnpX23hj/7LbxN0YC785vS5cPaTK5FYQXrqWGEqiJZE4FbYbbE1G77+zZPhGdwz5PNMIUs0RPl0XSNL1kARQH1ie3OQrcNHO8N//Sc/CteuToQPP/wN0/oXqVuVoAcQb+ojIbSFQGgt5Ksyxb2Ur4hZFuG5D5Fx/4Ee3tqXjLAW8C82AHmS2wVZ0JZKIo/CS9ckC5hFrNIDca22KxDZvoOdZK+AcyJgVTtWWoEEYwWb57CmgkkolrKrtYI5DNBfv/t738RVwQzRbS9h2TodlrCokIAuKwRrMxjI6qGdQAgnnzvBG/thBg09FsRMAnyACEs4tkXkOW7wTVlqOKtm2nzgcC8Rc/vC7eZZq6NIdxXrkIGBAh/8ciFEtzBdzwYqyk7NTRY1XfBxi9CubPjeD17Eh/I+grJdQLgfDQWmFWqam6wocrhSKBRyiO4d4WV8u518dohq1MOP/ujVcOhoFy8ELoUSlrzyKdfRjqhLGZpGJ0xUjvwiq38Y7nCdfJZlEIXJEwtiwaePLek62fWVI+AIPFoEom/2++dpPlLvn8TP7jIE0p9mW8dHkCGQ6j2Nz5ZdBs0Obq56O/00NjO9GxqPbeFtBDX5fe3rbw19cMKVZTiuGToUCKQLyTXBUvWnXcaTJH7CUvCN202MhmefPxSmJuZwe0CQ3mxLeOnFp+A5WHtKF2zkVWyLy7QSnLcbYbaKuCd3UgVmsIkPs0r+TJJyEsjMUACOqay6u7Phu99/2sS/t3/9YWhvI5AZs94080pxCeoENdMiIbcFXnvosNyhHcVNQS8cXjEJJFrDIdVFkC9RTtW7o7MlDO5pJQ+EUSqua7u78yZ0ioobLVYFtKjt/GHLEKSjs5kYDu1w7VTkrBGMDF59v4Wy+/rb4YvioC2MZQiQi9FDR7cC7sqIgRlriLPGdcnHAq5Rb/H7g3DafzPwOuOAmfCzN98j0Fs5zM8vRJ6djBk0I7DIeOP554/AnffT9nYbE2mcQK7kJ4y0xpgFbi4BVa4c1P7KCp2gWXFghCZs4wmlA1k+Who7NB6537fyzyGoLy9TNu0eGsbigZvAgstZH4AjfP7lV0/Cs1vJXUI6SVSOhGM6yp+390PYzzkCuwsBfg80yvfFEXAE1hDgL+LKxYXw8ZlbTElawDp1LxaLe3jo8rDXg3ZL/cXowU6FRMRYFIVWQc941Bshg4+ZqCkRT9austqUuBvfdqeCn/LQkhITTQPjzbRZ9kIfSCYLA1vUfhb9bBgMkDcJiGbRauSXcwoixhR+EULVpIS/rumpEha6y7gTmLapYAWCk3V2doaB/j6CdxXMelM5Sgi2N/sqw+qpOqmkFHQqIDIj9wC2ZkVdon/VmKwC8Uz1TxGgOM0fiKhnnT7UG25lB10DKwKR4fIgj8jNDmkUEIJgb0sLRszDKjvqdPW92QIIAOGl9fpF3M7aXQMzRMn0l1V1kHXqKtPTVAf5RtN0t0jK0jxi++q4nlA3zM+vIpYv2HS5Er6HNa2us6MdzDpCO36Fu7sQNMFK1bCgcNRP0YbjIjE0rSMJYIvplD6JsFMT+BmepZAaKnLSNk07a+9gyldvK/lqEKM+VaWTLDesdDj9CAkNGEZuT2Ohi9WCTYfLmmVvZ1fBBgtF/PVWGAjpRUELYqzKrdFPy4sV818mq5JWLBGEmT7y8KCgfJrqFnFUG3C2IJcN4tS8tFDVtURSLVxVIw4na9t5XF8a1YiVW7/KOj1jFsqn3x/B2nqSAUpreO6FPUxrbKefVElVTPe2+kz11gHhopO+7AQE6tzf7/1qxl5adDDY/ta3n8WliPpYfa7+1kd9H+9bNrbs4mLulu2aLV0xvUyVn8rzn4zjR3+UZ0GB38FB/N0TrGntd3BLN8Er94URSJ9r+n3jQc7P3UcfTIZzZ6+ZteFrrx23mAsxpsO9fvu49l6nvnB9vvwFFYiNgvFKlCwTsDYaLkiwhI9oBhb/1MrIvyUEqt7i4C1hcQFeyq74r8hLZwfXcM+v3ff2wl3XywEWRhQY5coSWNxVFIYJa5G78sgQJ5ZQHJ8Zd9sTeV3cVyxjcXUmVoW5mTqxCErwxwXWuLTiXFtbEX7XbX+DssS1iVcc1yKb4yiOqu+0yD1Bs9VhZkb8mCO0QTWw6/QYYzHOZbxH27h0qDAjkPZkQ5F2yLgkFiDuZktKb7RvYxbKS7iT/NJq7FCzYRYlka/hAA3NIpArxkIF/hhFZ9Uz4hHxA3PqLOMWjUXujNTC2P/f3ps2OXJlaXoXS+yRezK5s0hWkUVWkbV1VXdPT0/PdM9oZCYzSR9lJpnpV8lM0g+QyaSPks1IpppptVq9d6trYXVVkcXivibJ3CNjw6L3Pe4X4UAACCBjASLi8UyEu1+/y7mPuwPXXxyce/OOfq13213XmN5917PG1QWNoe1o4NIeM3rtBnUu5aRiZ5WGHpRqabl8LlDjskndivFnDC11LqJSlyuX3L3q+cjHhq09ZvVTWCG21zVp2/30P/x3/1H99y/VbIe9oe+l/+a//S/Syy8/JgFZsZeb7rtqi+ceP0N4MYO9ZdL290ocwVZclyPq0S/1/v6vPtL5eCiHosvpW69fTY89bmcZ5Y9ytj92RlRAMgQgMAkBvaWxQAACp5+APxQl0e5qBl1747Yd+kADNo9F9GHpAWJL3qr+9tk/aw+vyxhVelDkl0dZeaSlmjQws0doXapaW4OHdowUi8FPIQjLu1SiX10fyB7beExRDC607Q214fr8U/vlFU0K8awHd4pL1b6gtWyQbY5761eIksrtn+Q7/MKuZ5TVcQuY9d7IVxnCTq1cfzEKi3bdhtNsRu6NB2oNfZPvwY8HyO57kgDYsM1lV9sSce3Bu6B8G/aW1XFPYOH+Ok6rY2wtNDXxQDRorwvzK0exyjG4FCEuLEjLEttkg7yIUbtrL9v7Ym/heV2DXA9obG1eiu3CZg38FUphXWEUFIGtYKGOBDOZVgje6qvaaIlVXee0o/NVDPbVbNjvet0P2aGlEDrtEaxQC/KQuHTJQ1/3Rcd1juVEG+dyY0OzIuui8WR3sRTFi23nLve97paDOG+7/PMvXo7T4jqd5qz2QtndkWi7VYTu8ODeMZbtJe2fu9kjpaUJ8DxbtHwsopAfmCzIr+v4jq7nlsI2+KHQ/Xe9DrMQHuJqv3h4UqfMW0sWdGOHPxCAwPwSKN8/egaW93Bvf143st09e/3e45ff8PObfr/x/e9L/lwanq+/FHsQgMBMCMT9qTHLYleTzkoMlUC7vKZfumno0db4ta7xmQVWj7W6jnnrJcZzEmMl1HkcG56d8qxdk4jrGK4LGvJ52KrhaLHEeMZjNKdrfKk6l/VrM49H/X7SkReqvxAs3js8onVGHVG5/veTyK6xpUZJKr+ktX819fiT9qhdlc03XDKOx/tODJVUkf4XwrHHUx5/RgvOqfpls758X9B4eHXNv+brJkUmiPGc7fcX5vk9LARO1WlxvrlQOEo05FigoGIxvvOY10v0W88RvfyRWv4Rb4vJNQ1Qm3Ks2JQDgyfJ9fOBK9nVs8Hm9kM1ovADcgQxg0IMLO1WpW0J7vpdn+yupcefqqcXX76u+RyuFc9ADrdgu8VGZyy8e32O4psG9V0Scmz7ecbxhNv69WDHIcO2VjRelyeyxvQuG+gk+EYfir3owLRv5z6bbt9zSezI4+bXb34SffIzhDbUVlfzWzytL70uyBvaHsnup19eZAgLBCAAgQoBxNwKDDYhcNoJOLbq9rZjtvrnR+U3oOqUB5IeGDkGrGdyDW/O6OzogYEHVx7m2KPXcW0tmMUYSgMQf4Pe0lfonhjAbTkeVTnUKRFq4GHPgxiY+JtwDco0aCoGUJGsfB4UFXGgYrZYDWRqGvFpCBuDzBCcrTcOXVy/DniEJVtikOZxn/NK7HO8r2V5g3rQt6OYYS2risU4qazNgzuFF/AgSbZ3ulvp6pV15bVN9iC1eGuPibzYQzQPdnPa4NrH/X170W9Ri7o8SHb8rYgPK3vabbHqVVUO0MrRYEeib0dtF/HKNDSVC4G9m4rQA8VA2D8160iI9vn0z8D8QLEjwbSI0ztoU7nfG21abHbf7BGy51kQgrdMcSw1u3csK/bDtj1KBhYjKGIdq59Rp/MU+QqxVWcgzoseRpRcl3jrnw965mUnW9Td2bLngR5cltbVB10FPge6tsx3VwL62oWmxPWttKXztqRzWITH8LWrPHqQcliGuHbUbkzSFgL73pkaMJldCEDgRAjEjT+kpf73uCEZTk9S7320MDm+TNKmPxfLN75ibaE3BBJ/HpT9d5bKJ0rs8gcCZ5lAMTRQD2Nkps/5cj3HfY5xnsYiMQ7VGClEPN3L/jVbQ+NYa5NNeYp6CT0wtjx+sdNEIfQ5lqsnt43jqmtrR5PVKvSB6/av5Yr3BuHQfksuucsri1q3IrRBXeKhv1C304PH6Y77GmPA6mRo0aaqCbu0FtaWxrzbasfvMU0N2i0URmRZD8k8ttZbkicbtoer9rRfvIq3JPcnn6wirx0LnOLxv7/817A42ok+qq0QHqNIMUhf1vwHHvPZhhzWYdf5ZKMdSPyLLQWNiGZC4I33x7IjXvm9VWl7sXptuJ0vFB5ORjTqFqeLZ5tKqXLTxlkgLY7XJLDv6pdnyyvi6z7rOcOiqXl7JBpv1yrpX1A5pFzAiWvU42jXI2cWlfM8FktyS97a1NwNolH3r+uitCdxLvptA1SD+l59n3fqqMVjcE2ErPAQbf3y7MH9++nNX7+lzIbpOtyXjn7J88N06fKK7N+J5y9dXeUZytaPqp90CEDgvBGwAsMCAQicegIeTmj4qYHIgr0qNWiOwYX2Y5CjAaQ9UYsBgwcfzj9+UOBQAR4AhjgZgwwPZ7RocOlUfzu/qFhgHpjFOCzqK9r1gMdfMsfiwVGIqS7nxLKeOFgMiKJe/bFdS2XQXA+q+n8y36tQJaOE1o5vVaTnsZR3HVqhLWHQi3u6uFAIq+EBUdqwIIHRsWEtJGrElrYeepStAbH611S84RisxfjMgyvXlBe1nZvPSZX1XlYPZH1AmWVKRz83LXpoobMctFXKedPhETxLb3gkOEHZPLgNM7xvQ1Sdq+1oYLull+HX/RM1n+sih/K411oirShgW3xewnPbJ6yyhJ0+YWahPJsWXHMdlXyxGYNw9a1Xhcto0X4WLnqHlGxPidZumUf7y/7NnurYfKjfrmlxbF0vfrjQuF3prfDKcOeNqZiYw+fSFDzYLq5d53dSUXO1RdfGAgEITE7A91T5LqMbe//dtHf/7tXZnxahGOL9o8jRe3+IbK67fPn9I+7h8j0q3qH3ap3brV6HBixUn/1TX4cy8svxJb34gf2hvpTa3t7Sl1J6z/M7Vcknv1dHRh+JN+C8xxoCp52Ab/ri/s5DkPxJPeTNZa4663sxxp66p5vhpFCap9u6G2Nf3eM79q5Xut/HYixXjJUbCk1QKJ4SbbeL91Pf8p4XYmvbX/IXaVUGHuvtyonA7yILjQWNlex8UIyNzCzmVwgTZEB+b3bbWiyeevFkul4cZs2L7bew2Vt0vMyqsW1+342MvSx+e4qlPGFdO2sUQ+gk84q+ahUeuc4jFmYVxbS7o/5ZKnX4soAjLtZJ3QdZ5g0tHq9qZWOCm9O0lO+tTnaohKVwY9aOm/Ev9kpDfCxadPt9v5KTgO0TUlE0tsvxpatvanzcjPBe3ttbivdh1ZWZaKwdzw9LZXg1tWdHEDsjFIvacPZoXx7aZblRQu6w8EQqHmLxrsKILS1ekBfyTvrt2x+klaUn9CzTkRf0Rrp0tZGeeupC/KrQT0v6CkF/fa2537bE7G2TX06IRK1ZIACB80ig8tZ3HrtPnyFwRgj4s9yDmfyZXgpuU/XOo04v5cCqGCwUScMGDL0HUJULL80YaCl/ricXzetR6fm41r06y+3Bh95KVm2WA+NiONl/aMReUb85ZVAeYA9kjn5Y5M3paif45gFdTh+3doFeBUXGcvCZrR5aOooMGjQ0516i7C0mitOAOQsOI1jHWPBR2sitPcp1FRwGWezvYxGzLTeUKTlfzlvU0Xdd+lDvXOayrCEAgekJlO9vvs3yLReVOCHfj7nWvgw5ceD9ZzBPpf54CJ3m/XSviXnasteYPbvcU+kAmrRSX2fpI8UiRlM/m7aHnNzDdLRURebJeGyBAASGEgivUx8ZMY7yG2T/sMPvAH4/0zqGKfoT8yw4afB9UGmxDKRHuYG0nFXrGAv7sKuu1DlKSKwUPeRm+d6fheh9tUkolk1hfjDIGYqU2OttDn6O5LzleiTv4njud8HelY7mNVDzmN1CIA4To33XOare/JlVdGha9lFKVUdcXoU3+1LzYvztX/9Sns/X08P7+tVZ60H82u61117WL9oWdJ4l8ktdjl/DhU2j7BrTPQ5BAAJnngBi7pk/xXTwbBOoDI6K8cXk3R01cBqVPq5mlXHzw3y6xhU76FgevB2Ub7LjBaCeoDtRoSrfoxpIVeqcyIYpMo06d+r63gB4XH35IlJfRw7ex5V/9GOFcH+MbB7dNEpC4GwS8FtavuWzt6z3q291fh/Qz2Qd1zre4SNfFYcz9xUo9qtfLvohP9rxunwgzl887ROKq3XP0bbfW3s2F3b51ysOexP918o/brBnl38w4dA3DjPjh3F/AdXzAtMbcXYEm6PeYQoEIJAJjBpH5ePD1lOXiTfEYTUNTYuxdVlkvJPD0OKPkHjwWMx2FL/ce4Tqq0WmZDfdGL7a0P7t/IwRn2ClSD7dmdlf50Ep+fx1Orvpvfdupk2FZd7dqelXHA/S9RvL6Ue/+7ImCraYq8/ccL8O6waqtZXHbelAk+xCAAJzSQAxdy5PC0ZBAAJHTSAP2o663rNXXx4gDhtAHl9vp/VyOD5LqBkC54WA73Xf515LcPTzuzZ77wA+FMqj4j1GjMAyf9/Dd2RSxsqSK8hrHSq+THIei5tFgmNme/Z1i6R5Qh3nmNul2m/3QUJ0u11Pd+88TJ9/di99efO+Hsr1s1zFm7x4aSk99cyVdP2xdfE8WBiZ2z5jGAQOTUA3S9w7/iInbpxD10gFEDgsgVk+EzjM2LvvfJLu3bunMBwXFW7hgSauW1SIhRUJuovy3vXnsD4XD9tJykMAAmeeAGLumT/FdBACEIDAtARK0WbaYkecvz9m8hFXTnUQOO8ESnHSHqa7itV4/2473buzrZnDc1gAxzh03ICKKmtmUa76mJnFmgJo/HDVecr6IzU8/VVP/PxYs71r2vUXv35dArJylxP0nApBN4vfpZeuzE83b95Of/OXv0gffngnwiyk2k56/ImL6Q/+8LX02GMX1X2HwCkF3QHv3oIYfyFw1giUHvjRLb9X+HX+lpMcwxyqrep79fk7TX09PhTHvpqG73hy563NbcXKfUtfZm4oNrEn/tUEcY1W+uEPf0dTePjzQv+V73zeNcO5kQoBCAwngJg7nAupEIAABCAAAQhA4EwT8AOjZ1/3LOfv/vaT9Of/zz/ql52esMvLjp4oNbGNvXNzeIRIHxBnJATEY2cIAsWxfs3SIq7ETAvDmhW+JrHz9e++rElersTkYMtLnuhG/quhdw4Ix9HePP3JfS8Eb4dW8AREDx6008Y9T9rokEPaXm9poqTskWthK2/PU1+wBQIQgAAETpJAV5+TK8vL6Vvf/kZ6+unntH0p4uI6tMILLz6pueU8kdy8fw6eJDHaggAExhFAzB1Hh2MQgAAEzi0BxIdze+rp+PkhoGfGruIrLC5oohWFDPjy5gOJt560y55BWyHCej7tvdnDs5hZIsoCbl5b1tW2c/Uvej+pWxy2t+9m+uLmHcUIbKXl1aV4bPWja6dfAe4vPrAXVkz5vOvsYeZAXXl3bJ0+aFZaF83qr2PhRrLFWoWi6JpbmaGrme1DAI9alb637JvlPOJO7B0/bs+wvZaOd6tgU/LqcfN5LuILmwzLWSXgs189w95ngQAEfF+srq2mb7/2DQm3LcVar6fNrU19ftTS+npDsdbFKP+SA1wQgAAEDiCAmHsAIA5DAAIQgAAEIACBs0jAsWx35UG6s13TpF27epCsp60tT3am58l64X0aMkz8yeKMdzyju0XMejkvmI85XZOlxVH96W0V6T3vXD2o7qjNnG/jYSs1NBqtOZxDT9BVva5ixFJYMOLgqORehb2NMqf2VWFXbcdUnj7sBqoWREDhYqLPKGQBtmeEJzhzIfMo110/kWu/l8elolJvVBbnH1yqaWWZ4JLL6/g4VXqwulnv21zZEK9q12ZtF+0fIwGf7Xyy8xfD+fo9xmapGgJzSqC4G/RFZ4QVqqVlfeh19Rlb1wSjFy6spnsbD9Lysr9U1edufN7MaUcwCwIQmCsCiLlzdTowBgIQgAAEIAABCJwEgUJ8tKDbVoyDWmMn7ezekgZpr9KGxDeJMF6Xom0RKkChBCTSdPS8Wa+vpIXmsp47O9pvqY4daYy78vKtp2a4F1nMtKipBiLMguqLtXx9G7V4uZeesFs1poaORZuOFRgzpds+lZZwae00S0ORGH8GU/rFotirZNmbTkaJfWJozqT2bV/Yq+FxNU9PZPZx5/fL/ctl3YlWsBKNqCcO5aw971tZEW2oeCxFH4vtoq7CE1rb0Wa7zF/GL7Zt9pROPke57aL0PP2193GeYKj/rPRbOXhsfnvUbzd7BxEozmzvso9rPl/TB5XlOAROP4HRv7DwZ4g/O/QW3/H7v/b1ebmyuFp8qvhNML8x9m6gAR467gnSWCAAAQgg5nINQAACEIAABCAAgXNFoJDN4nFQD4zLy430zHOPpd/53W/KS1YCbdfDQ+XxuqtQCBZ0S+/Q7a1W+uD9T9Od2wqboKVjLyJ5GF26uJSeevqJdPWaJv2yQhzlJTrGQ6fUX4mdNYm9Sa8bT1zV82szhNympu62iGop14uL2gfWAm/UUZhabDvDhEu/1ulKylf/gb3a1G7o1mG3k3sNF3nc/yhrO/0qF22GQGsx1/GFbbfyhjBcabbIPVBnJFbqyjbmh/icPdi7XhUIti6TDxY1z8vffWEkbLKNG2JutecjsjiZ5VQRKM9qPt9x7aoDeX2q+oKxEDhCAvHenT89LOgupK4+GtuKPVOv6XPQMWi06KuwsY0i5I7Fw0EInCsCiLnn6nTTWQhAAAIQgAAEIJBltI48bOVJK2H1mWeupD/+kx/qZ6AWcC1K+oHScWEt5HpbZfR/e6ud/sOP76evvvww7cpF10Jms7EtEfda+sEPX0zPv3g9dTQpWCyFOlqUtZgTHnrtmHRtaVntqNriZ6cWdi3hlna5OeUtBF3XZHuyTdq0KaU4tBf8wIWKY8VG/huVqUixjlW047bURtTjbdXfE3otnOopOxaVc1Ef87pcitJFsg8Vtjs1v3LO6trWuh/Fstd2rq3SQF9j8sb1oaha2z2uuab5W4ep1e5k88v1/FmMRYcnkK/96onPaV6zQAACxRu5OeT7JK9hAwEIQGA6Aoi50/EiNwQgAAEIQAACEDgDBPyTfz1OSszspFZqLnbTY4/Lqza8S+0b5AdMSZSOnVtsyim0kTq7tbS2Xo+wCvbcXdDkabWavHTrm+nilYX02I0VeRmNxuMJ1zryUCrW3rYRFjgLcbdULLVfiLtF086jl/J6P+yKDfWhF18wi6RFjsiW/7iNqK9IKEIASFxSckhMPhwOtWVYCMUxTGJSHHW9FlMtbJcitVJiKZsq6ijayIeGr53H9XltBs5lO/SKHfUvbHV6rq8sEyErnH56luiezHWXYjt3yWmnpxtYOhUBn1nO7lTIyHzOCHB/nLMTTnchcGwEEHOPDS0VQwACEIAABCAAgfkmYH2t3bFwqcWCqSci87NmKL1VD9WG5EUJkUqvK8ZfrbaofEsSbuU/291KHf1E1K9dRVJYdHSFqEQVheer61FKVN2N2bsXFhakjzZVj2bwVrWttjMkTcbWSnfvbqR79+6lhxubMTHbwkIzXbp0IV29eimtr63FhGnNpnxcZWu73UmLS83Uljdwjq9bNF36yqpRx+jtqH637/7eu7+tNjbT5uZmevhwM21v7yj+bzNdu3I5Xbl6Ia1dWFAYCM8sLrFZQX0t/rqfLl3U6v5Nv7isBWFFDE5b244vvCwP5/vpwf3NeLXcB8UgtrK8uNjU7ObL6vdaunhxVaJ5XYw8+3lX/V0sYhXLtghzUTElx6qtJM10M2vTeT1TY2j8BAgU90l/Q05jgQAE/NnUdzdoJz4iq2j8WRNf7lUTK9tR5nCfRZXa2IQABE4xAcTcU3zyMB0CEIAABCAAAQg8OgELnH5wrBfOseGeauG1EBQdQiFioA78rD9i6EY83YWYDC1J2G1oQrS6JuZqtyWwhvNpGabA4QocKzc8aH1AYR2a9RBH6xJQw+NVzd29tZE++vCzdPPm7RA4b926lzYePJTwu5OWJdZevXYphNbHHrsq799L6YUXn5LY2ZDAKZ9eichFvNrywdhdkii6Y2XZLdYU91dG3frqYfrss1vpo49upS9u3k0PHlhEVRubWwr90EiPP3Y56n762avp8ScupaefuaaKWqkhITU01hB09TR+iKWrh3SL0F99eS/d/PT99P57t2TXZrp7ZzO1PM+ZGuom9XmlkS5fXkuXLi9rvZyeePJavC5dWhFreUbbBs9EN7C4/nkTdAdMZPfMEtCN1y9Vndme0jEIPAqBfXeIxV0nDi5DEv3e7iV+n1KWyWmDxdmHAATOBwHE3PNxnuklBCAAAQhAAAIQqBDQ06BEWsdwDY/bXrgCh02wSGhBV0u4VBYibBZqHJrBL8etrTn8gMXeCAPgfFrsapRjzoaYm0VHpau+1ZU1ecS20sJS4cn70Qd3089++l56443fpHt370ddFoX97GqzHmhysa++/FLC7Ydpba0pUfOqJmB7VTF6X0kLivdrj1bH/lXuaN522vpWS96vi4tpd6ebPnjv8/TGzz9Nb//m0/TVV7elgzqvvYlVSg11utvpi0/vpOZCJ11/7JImhLuRXv/OC+mll74mD1p7xcqj1lEYBh+8B/dLC/avzFthKiTYfv75vfQ3f/Xr9O5vP063v9pNrV1NhNNZFEt5A1tIj8nittLnn95TNdtKb6WnntIEdT/6VvrWa89LzNXwXaEg8oO81wi4+4mTAgEIQAACEIAABM4qAcTcs3pm6RcEIAABCEAAAhAYS8ACo9VIv0oxVOppV/Fr9xyDslDrPMoZ2S047sqp1q6kiqkQdfhoIfIWQq7z6JVFYddfir72QG0r7IE1308/upf+/M9+k375xkfykO2m5eVrCovgYjupLa/YCE2gajzT90JjWROw7aT33vky3b7995p4bSl997svqkxNeZ2zrZfbUZRbibuLi0uqq5HelZD74//rjfTOb+4qDETRV4ctsP5btyjqAu1G2mk5bTnd+qIjkfWj9NnHd9LmRj398EfPBZ5aQwWcN15axeJ9LT2OBacisfpXwrHq/+KzB+k//vhX6Wc/e0uhHVZUf2GPQ1c41MPuzqYEZXtKL6YteQw3mwqxIE/mDz9QWIiHbysMRUrf+cGz6cLFYghvERcht8qZ7fkikO+HvJ4v67AGArMmkL+Um7UdtA8BCJw+Aoi5p++cYTEEIAABCEAAAhA4IgJZjFR1WW+RMOnJzmIp1NvQL+0p68VJDsGwJ9R6gjAfkAdurCuCZ9SZXVothlq0VJgACZaffPww/eVf/Cb96hcfSGRtyit1WR67uxIwdxTaQOuYXE1esRJ/d7Yd+3Y5NRTSoSXh9e6tnfR//vu/lnS7nH74u0+njuwpbCpiCTYlvNYWFuWR+0X6f//sZ+m9334hIdjirmP1tiUmW4xWiAZp0RF71wLzrj1jHQvYAnVSzN6FaOPxJy6kZ5+7IiZZONXB8D4ugahX0fGeqB0J/X8Eb1N9+8nffZR+8bOP0mLjWoSRcKSE5ZVuevzxVQm0KxJzlyNersXu2wo18eDBlvoviIpPfPOzjfSTf/xVWr/USq9+2x7DsjX49jfFHgRmRyDeAOL+CRvyPZHXszOMliEwcwLxxeGAFfpUGUgZvdtXvvrxM7oIRyAAgTNMADH3DJ9cugYBCEAAAhCAAASGE7AgaUG28iCp/SzYFul+dPQTo11jLfBajNWR0Gv0R2kRokECrbcsksZrL5O2FIYhmohCsb9birN/+5cfpF/89GMJuNvyrm3Ir1YetopDcOnqYnry6SsRUmFhYUmiZyd9+un9tPWwFsJmS/FvWzvN2P/3/+4v09ee/8/SxSsLqblo+91OPSY1aym8ws/+8W152G7K1mW95Lnb3YmwCY5He+Px9bSquLsWdHe2a8r3IH2mdnZ2NCGaJifb3Kylu/c20//9p79J//l/+YN05bqFYHducIkODibGvjXfvGxvbqdf/vI9+T2vpe3NXc0110yLy7vpO995Ln3/d15Kzz5/VWwdTzilW19upTd//XH6xRtvpy+/fCDbiyH7XU0M997776cXv/GEmC0rTITP4fgFz93xfDh61ASK+6H/vURtVO6Fo26R+iBwWggMeuJO+v7scn1i7mnpMHZCAALHRgAx99jQUjEEIAABCEAAAhCYZwISAvfpkDnBa7/GiYU+bnXTL2+Xwm9sO60sW4rAka9QdtPdu6307rufKV6svGObnsjsoYTcTnryqcvpj/74tfTt159Ly6uqUVVubnQ1cdmD9P/9/dvpn37xocq0Y8K1jrxX79y6n/70T3+d/qv/+gdqbTcmYGs2JDHXmumtd96TZ+6X8rBthUjaXGzJ67WTnnr6cvq9f/ZK+v73n09LKxapuxKUu/J8vZ/+3f/+D4plezNs2t3tppXlK+n9d79Kb7/1IH3v4qW0lJ2M1bu9JbPaSxm21Vbc3Tu376Zted+2261Ub3TV7w319zvp2mNLaf2ihFyxW5S47InOLl1+UQJ1KyaGC29hex/LO3l9fVHi87b6qJjD7bZsZTg/jDdpEIAABCAAAQhA4KwSYPR3Vs8s/YIABCAAAQhAAAJHTCBkS4mIEatVLqT20p3W4c4xcT/84Kv0xRe3VLYu79IdCbFbaXGpnn7/n7+YfqAYtU1FELC422lJUF2vpZdfvagwBC+lh/Ju/flPf6v4uUsREsEhG9761Ydpd/u78nJdkLipCcOaNYVjqEuAfT998snn8uJdTju7qqu7m1bWVuUF+1z60e9qIrGmxFS14QnU5ACcXvj6xfSf/Kc/SP/z//Tj9FDhDZqNtXTv3naEZvinX7yTvvX6axJ/5Z3rPttLqtfxYkMRgytp+8F3FY94Z9eitXqtMBALi6rDQDXJ2fLqivRwexZ7sjWJ0WJ09Xo9/cm/eUXH/VIbyuu2k2IJe78W/ItzoYT5XEpG0c35tBCrToSArwCughNBTSOnikCnN/noqTIbYyEAgTkgMPTHYnNgFyZAAAIQgAAEIAABCMyYwFj5pSdmTmekohikn7/xhkRUxcVtNOVZKldUxdt96ZtPp2eeuy7vUwuV7bQkQffChWZaXbPnbldi7ZomPPu60qS8Kn5BW2kLiyuKK7uT3n77lvbl+1v35G2KN3t7O73z208UomE3Jk+zeNpc6Co8wdPp+a8/nXaUeWNb8Wgl/jaa8njViHhrZ1fhHVbS6995UcKyQjooru6F9Utp4/52+uiDzyIchAXmYlHnLehKmO0Ju/FQnvd9fD+Xmuyuy3PYE7M11O/V1Yvpb/7mn9LHH36Zvrh5T3F1t9Sf3XT/fkthHiz+diVQF6Lzrrx5d1s70Z69fJ133r1yQ8IThwg3MYTHfkKknC0CurHiG4ux7yRnq8v0BgIQgAAEIHACBPDMPQHINAEBCEAAAhCAAAROJQELcaXhIcxp28Jc9gYIkW7Kjtkz9YubX6S7dx6kC2tSbOsOL5DS8197PD377BXFgnWABomi+tdS3vBcqnfTFcXS7Ty/kC5fWU9ffL6dOhI3t7baIY6+8cav0wsvXU1LCxraSjy6/dVD1bkqgXcjbUuAXb+wGp67zzxzLT3xxEVNsOb4vrkXnvisnlZWJRovddO/+KNX0s9/9o48hlvyopXH8OKSPHTvpjff/DitX34+BObslWsmMk1MbG2/Z25ol5FRW/q/uLSUXn75pfTWr29qorOuhOLLis/7IP3kH95Nv/j5r9Izzz6lEAqX0/rapXTlyqV0UXF9L11aUriFpuxvymYJwBKr6/UiPMba0lrEzG1bxY4lWpSYnftVJs9glblUm47rRybau9jbWRav5mH7LBAYvP58tlkgAAEIQAACEDhKAoi5R0mTuiAAAQhAAAIQgAAExhLY1HxknjhtcXHRGqdEWXmr1he1r9gCEipLSbL06FMGibN1KaZyaJV3bTNdvLiq+LaaFEyipYIMpK7FTYUnsGOsvV6lwcprtZParcW00FxR/c2Izbu6tqJ4tEuq320oZm3N4QpUTpOLdSS67u4qgK+mJ5OzcKrLWTgpBm/S5G4xUVzkdTuWbC1O2QNXtutPfnk/0u2tW2QtwiI4WUXW1pbT7/3B64oV/H+ojcX0+c1P09LSirx0FQZCffztb75UnbcjfERDoSKa8hje2r4v791mevXbLyk0xCvpGxKsHS7i4ca2RF6J0vLw3RNz3RALBCAAAQhAAAIQgMBZJzD41elZ7y/9gwAEIAABCEAAAhCYIYENxaO1yrkkF9yGYt92WvI2rS1LaPWMZ4W4mzoSUbv5ZdFW3rsqU6s1JIquScctQjHUGoqRW7PoGlWGzOpti53d7kJaXrwgQVceursNicXraXllzTlVdyHiOo9kYnm4KnKtwiw4nENjoS7bZJdCP3TrCmvQ2NRrW8WUyYtdSx9hWVzupJdfWU9//G9flT79hSY8qymMwgOJyC3ZsJwWFi7LK/mqhF57FC8ozIJtvKjJ4urpr/7izfQ//vf/W/pf/5c/T7fv7KQlCdMtqcetrjyMg4vYOIauXiwQmC2B8j7pGVF8PdPbZQMCEIAABCAAgUMTwDP30AipAAIQgAAEIAABCEBgUgKbcs3thretRNqOhdVGCK5LiwsSbD3xmQRVxc2NRfmct62XhV7Hp+3EBGAWjORZa4FVoqvX1ljtHes6trck5qruRn1JXq/tiKe70FjVxGnLke7aaxZrQ9gt2tzVJGk1h+9VBUUcX9eZQye4hAVkvVR/Rzbanr0lalRtWVR1dtmq2LaKjCDPYk1s1lLYBnn7/sm/fk2xgmvp7//unyTcKvavJmBzH5oLC4qXeztCJ9TlHtxoSNB9uKOyCwq9cEVtt9Obv/okXb22nP71v/09lWuFLTKnXEoRzWEWzIsFAjMn4Gvb94avzb4bZuaWYQAEIAABCEDgNBNAzD3NZw/bIQABCEAAAhCAwBEScOzX6jKJJmjZsPqqlh+2vba2mtZW19Ldr3bCI9bi6cPNDXnT7kR4hKY8WOsrjgNrIcg/IrNEWg9Bdmenk7a3d0JUDcFSIRIcCqHZVNxbZ5X5dTn0Xry4HuuOXG4t6tak0j6Qt66F5KaK1BzOQaKwe+swC4sK37C+1lTdKX3xoKPJzuQ9LO9d1Sad2G3X5Tm7IIE15Nokfbhoy8f1T006a9jblHq7IGNqEphbimfrHI2YgG0hNRV/1/b84R+9ppALr6Qvb26mt958P739m0/lgfsg1RY1ydnutmLpavKzHVGNCeJW5cG7qzi7rXT33sP00598mJ56+rn08jefSDWFYnCYBxtTnDuLzTZkNkvv+imvo3xdDNPxZmflbNicj1Z9LVq49fWvlV/55Me291kgAAEIQAACEDgsAcTcwxKkPAQgAAEIQOCRCPjJ1g++LBA43QQm02j2pLur15Y0sZk8Z0P0KTz26gqf8NVXW2lHYurqBcXSdeDbCGsgcTJZRF0IcciTkt25c2cPmLz+HE/32vUrIZj6gOPVPnbjqvK3JBa3JN4up91WN92XWHr37kNlqIf4a4G2EIRr8pqVBFUouzG52r27G6pvUd6vXXnRNtOKJhuzQLwoQbfQKYuyhZBqD1+FOwgBa49GEf7AoRIUv1ceujW1124rpq8qqDU6aXmhlp56dl0Tu72Svvv9l1W+k5bWi3i/N28+TLe+3Exvv/Vxeu+dL9LGfavHmgCttiLRu5ZufXVXYSOeTNsK0WB2xWKWLBCYAwK926C8Jh9lpsQ56AYmQAACEIAABOaVAGLuvJ4Z7IIABCAAgTNCoPCa6+9MftLdE7j6j7MHgTNKQJf+4mItXb1yMd364nbalLdsR2Knotam3779UfrGy1fS5atPSxxVxuxharFSIuiDB7vp7bdvp9u37kqo9RDW4RUkoiqu7XPPPSlRU96AuqV2d1K6cKmhcATr6fZXdxSTV2KtPGQ3t7bSRx98lT795F565rk1taGQC2qnK3HXMXN3d7vyDN5Ov/rlu/LQ3U0ry2uqa1sxdFO6cn09Xb9+OS0uLUjg3ZWArInHZIHDLTiUQqdTirnqi72BIwyDxF17DFu0bsmz1u8EdXkINxwHQuu2hOZ2ezctry6mlTXlU/LCom2vSeBdl7DdSeurK+ne3V2JubfkaWxBWxO12UP37r1op6v2a5oszYs9hFkgMDsCvsIt3g5+rg37DJydlbQMAQhAAAIQOAsEEHPPwlmkDxCAAAQgAAEIQGBmBLKI4/XgYmGnKu50FOqglr72/DPpg/dvp7t37un4okIE1NInn9xMb7/5ZXr8iRvp0tUIXhuVtRWWoCUP2c8/vZP+4e/eUEiGrbS8tBqhElJNMWX1uvHEusTQQswMgVWa0jPPPJE++eiePHKl7kpKbUiA/fDDz9Obv74qofebmgyttEttR0mZf/OzrfSrX70bXrkOqdBpb0fM20uXL6ZLlzVRm4TYCHFb6qY9h0O76+ZKQrbVrvYbElrlPCuhuJ0e3N9Kb731WfAo4vW20vXHLsur+Gq6IK9fSb6pK0E5QjJIBLZH8BNPXg2P4G66Jfv903V593Z2Qmx+qHi6i8tVtoGLPxCYIwJ+T8ivOTILUyAAAQhAAAKnnABi7ik/gZh/PATysNNrP5zZW6Z4SFRK+bDYiwt3PCZErfnBdFQTJ2HDqLZJP90Ezsq1NQ/9KN4nQsWJiyJ+VJrfNya6TPbKTpSdTBA4QQJxdcZFrka91ktRDYo5jbwuk/dMcqZ8TQ8KjS6geLiNRvr2a19L777zUbp/byNCHNTlm7u7s5t+/vN3wwP1lW99TaKuYuuu1eSZuiMB9uP0t3/78xB0L6xeSBsPNxR/1m09TL//+99PS4p566gM0n2j+eWVlJ5+5rrEzjdT547yNiX+NpaKNn76Toihr37rGXn0XtHEYyl99ulm+qUmF3v3tzfTvTsPw1t3Z3szLa14MrTt9Id/+FpaUrzbmKys7GFd4wG//M8ibyzej5fMiCTnaWi7nTY2WunP/+wn8v6tpVV54zo0Q13hFv7wX/x++u73ZHBMkqawDk159EpIVpjd9MXNWworcT+EXIvRcjDWsUa6eGFNbGSPg/caQ2U5KGauJ5E77iVfARa7/fKVkLdDAM8ZjtsQ6p8xgXyt5fXB5szF53px84409rSMv4P6uL445MvIXnIAAsdHwNfmQe8KgyOI47OGmiFwOgkg5p7O84bVJ0AgPmQY4ZwAaZo4aQIHPSidtD1nrj29b+RBaldbowerHqaG9HvmENChM0ggvtXs79d+jWDI1e5yvYy+3iXmylv18pWV9L0fvKrYr/flDftQIquV2IX0leLE/u1f/0oTgn2ocAN16Zs7mvxLgu69XQmuCjWg8AStiKXblqjZTS+/+rX0r/7ke8rn2LS2TyEbJIQ6LMMzz11Mr7/+Qvq7h2+pHcXATfbmbWjSsYcSkd9Mb/36vXTx0nJ43t6/31LM3k0JrY6za/E1pa3Nh2ld4Rq++cpz6dlnr6lsTWKqgyvoYCmBOF8Iug7xIMmyK9sKMTd7+2ot2xqada0p1bjZXFFft9MX97flsdvW9sPU3vlZekfC9o3Hr6bHn7qmquW9vLGVtmTLW299mr780jGC65oQbUv5N9LjyvfUU09HmoJE7HuXsXXZvtgc8mfcO9OQ7EeWVNh2ZNVR0VwR2Lsv5sqsYzDGbzWn4lruvfeOgODjJ/DlzojWST5PBE7NTXOeTgp9Pe0EEHNP+xnEfghAAAIQmHMCoTAN2OjHQEa2A1DYnUcCFmMr4uWhTAyXTE9YJslVLrTfeOmGBNoXFcrgYwm6G/J6VVsSUu/e3lVM2C8UP1ZhB+R5W697ArTVmPyrpfi07Y5j1nYVV3Y1/cE//4FCFEik7d1m8jRTNXKildUL6Ue/92K6c/tu+tlPP5U4q7IWXCWqbjzYkcfrbYmzO/LmlcCsvA73UK8tqp1lbe8qbye9+uo35Tn7qjxo3XMLuUPuW7VnSv5jIbfcc0rktthrb1rbaXv/4s9/lu7c2g6P5EZjTULzZnqw8WH68IMv0/r6enjfOmavBe6HGwoR0W2mBYVdaLc20+raSnrhhScVQuKquKklu+9GK/awyxOhRdP8gcAMCPRf/zMwgCYhAIE5IBBfGOrj0t8V9D6ee1u8T8zBKcKEM0AAMfcMnES6cDwEwrNGj3fFQ1n+0PHH0d5H0vG0TK0QgMDpIuD3By96bwhFye8bkyy5nPNWtycpSx4InASBfF3mddlmTUFgFac2pc0QTlNtS/uO5ypRNMTMEbb5FlFV9nDtyqv24qWF9MPffVEipeLAStC9LVGz07Y3rif60rqxo3SJqhYtdX/53448WS9cXErXH7+e/uhffi998+UnNUFZPe1u7302Rxv645SLl9Yk6L4iYfhS+qdffJxu3bqXGhKNPSlZW92oNyzeai9sc0gEl2rJk7advvHNF9K//Fevp6efviTRVBOcDWLwvsInBIvadpTze0CtrlnMxCLE3bBC7ajPawqt8Np3nlbIhHb6Dz/+SdrecZ2yRU3uavuuwjtYyO52mkpTb8tXU57MnY4mYmu2NEHcs+m73/96uiR23Yh3oWZtspbSF7jY4S8EZkIg3yRxQ4UFNYcZmYktNAoBCMyEgD+T9EVwNYiHk/beB/a2ZmIfjULgjBBAzD0jJ5JuHI5A+RzUq8Q/1/REI426YvDJ06Wtn2vqCU/HPTgtfhZd0+zX1eXQH0tDKnikuHaDnakayfb5JDB4bVkgmJbE1AWmbWCC/IP9UJGZ3CN9dhRCTPHeUHjteZb7Ynb5Uowpu5YdHPeK721Vh7gTkCALBI6BQPV6LKvv+5zTZ56ExxA0Ld7Wb2tb4quEmo6E3VrzMR3fjZACcT/EY1vxxhH6qKp0uIG8KCxsCKeP37iQfueHz6fr1y6l9969mT768Ga6JU/almYNq1ms1OL4lLWGJiLTO9ezz11Kr3zrhfTSyzfS17/xbFpYkNDpPOVns/P749oevIVQ6nAL1xQXdyVduLCc3n77g/T551+mBw825e3r+LTyZnWnXL6xpfcUTUp2/Up68cUX0/clmj7zrIRcia/2BI53TpkU/bFNKtdJW7LtrgLZfiXBVW170jQJvLvtDZtS1K2SztuUrRcvLKTvfO9rqq+jydk20k9+8svwwO10JS7LZtfhWi02O2REUr6aYF27tJ6+/vWvpe9894X0zNcul0bouOyIV1hX8Ar+eTOM6P/zCJ8A/RWwB4GhBPwe4pcvvl1t2qO+oWu9qbXDoNR1feuY72tft87p7H2L0/cl9uUYulNUN/TQRIkDTR40tsi97Kv7sDb0VfaIOwP9MOdxZs1tPx6x+xQ7SQJjriwd6nYbxUv3c3xM2bTB6/MAcwdbmLL4AbVzGAKnnwBi7uk/h/TgCAgMflg4tp3j6sl1SA9gDXnt6JHKD7Xa7v8kqnysDFTiB7fK0QOtdPGBKqYq7wbc3jRtugzL2SdgSWBwmfY6yd9lDNZzkvvzcI/4wbP/Pq3c56WYZL2q8CS0cGX5SWXKctNyP0m+tHXeCQxend4fTLNYmdK3X5dnqGLe+vPRD2mdTksesBfStWsXdbEXomfcGb3ixV1T842Q06SIhmCi/SeeXE+XLq9LdL2cPv7ocnr//c8UAuFBr/3FxWZav7CqEARLyntFE6M9FmVc124r7jDVq7Xqty+Q/3dkhz2Aww6tn352TXF6n0svfP1iuvn5LYV3uJtu336oNtQH1dNodtP6xabCGDRUv2LXPnE9Pfm4RVN7ISvgguvLv9ZxkxJaLSQ/+dTl9M80OdqDBxvRH3vgrq6uSgS+UfbVmYtO+73BeysrzfTDH72U7r60q3aWNMFbK93SJGf37z+UCO28eknc7ardGzcuRjiJK1cupOdU59VraxqKSPjV8cjpExBNFNxVWIsTWCBw0gTKaze+iPB94xjRinfisXSSoOv7UWNjizwjr1FVEffsFKbnVqco0pc1fz5XE13nNMthbZimrXF5j2S8N64BjkEgExjzMeNPy2Lg67X+x7jABXOhye6wnDs3OVmpnJs1BM4+AcTcs3+O6eEjEIi4c51FeQbtaAISzZX9UB8n/gTRzz5j7To9+it34sNl4BPHA9apFmUfqGKq4s48zI6pK6HA2SNwFNfWYS/Oo6B6FP2wHYfsS9z6w/oTQTU9cVEr3je2tmywxa7CcDebm1ZKuext5RTWEJgNgf1X594Va4t8XD6jUgteesmesS/0zGy1FBpAhxcWFnS8rZy5LhfRdm/XamZvJ44Vgm43LS3X0lNPr6dr19fSc88/ljY2NlXYX4gU9TpW7PLyUlqSNhQfdvtunb16i/a1Hy60RcamvFuvXF2UaPx4ev7Fx9PtW5uKy2vv2eILFx9fXbeYqzaWfd9KLA1vXIdRyHW7rvzSlsy7ceNaekLCrz0PC89axwTWL3v8LbCW0FqjuP8UArM0LTVbS1evL6Q/UDzehxvddPfeQwnCmgzNnovRhnvRkZfwRdm8GpO9eb/bFWvnKG2KqiO/ElkgMBcEyntEF6cnLOwobMi2Pg83dZ0rBZ+9XAAAKGdJREFUXLUu3j0xN67f4lYJy0PIrexP0p3IXtwIk2Qfmmfk5/rQ3PsTj8KG/bU+QooMOQyKuenHI3SdInNEQBdhu63PMP3SxWGLDndVzlG/MAUCc0YAMXfOTgjmzAcB/zzTIoy0XHkJ3ddDpX5Cqp86FjHw/FNqLX0/P9WhgdFTxA2MUVGR/aC/Lj5QxUFF9h13c1M0ua88CWeTwDBPjWl7Wkgq05Y62vzzco/s51ncdbW6Y4h29L7R1YNrM9291dEERxK2pH5ZGDr0DX60OKkNAgMEqp9Avqa1P+QDxR6q7Zbvgq7EW/2KxZ52rkl57QmrSLfhxeqk4Uu1UguWbkcip16uZ2m5nm48sa6tC3oVed2GReSWvHCdZ0ExZHuLVZjY9VpHnaFvKe7YXdlse5uLfjfrSNhdkpfrSpR1kRBYVUV41cvLvrFQCLr9n/VuKBqLFpzXoq1fxc+y3Y84VO7LnD57vOPwD0U13vPm6no3Lcvr+EZXE6+V/SnKFeKvH4Zb8n4OgVn561aRK3Zop9zPtuV1cYS/EDgZAr6i8wWvR0xdy61WK8IsbD7cTr99+3ZaWVVIEoUt6eXTpZpLFDbae3c6a519yiL7GijeJfYlT5xwFDZM3NiYjIfth6ueh/HemC5y6DQQ0DPygw39WsWx5GtLusf1Tzf6Ye/T09B1bITASRJAzD1J2rR1agi05WXU0gzX29ubaeuL+4rf58lR/IHkYZJfA8M2fUANDn6m9S7wYPbQngF8UJ6aa+wkDT2Ka6sMXXmSZu9ra1760dk3GnWCRRyLuR6wSgSSZ3+3taxteek1ismMOvqSqBkzz+/rGgkQmCMCvtNGLfY6dUxYfQ5KcbGmaBF0USJpPKhZkLUw2/sw23ezDK/Y2fQmYy1WcqpejhmrJeqxUKoMas8TgcXnb7wZOEO5lPl8/xVLXkexKGIDLRg7tq7DFDT0BYvj4PrjPOf2Om5RN+MlDuQdZy1E1Jxi0ckeuV7shWSP3Cw8Ozao73/vO19pRLGKcURxvCOV2vFynS+A1jw09wjCIrnK6589ftv6YsiLY/zajmxDn/FFQ3sdihL8gcBJEfANk++m4r7whIWdzmba3NxIn3yin7rpczK89YqbPXL33i5KM+NemMLkoxif7P9cn8IAZXV3evfkdEWPNHeIuYcxRP0oztyRmkVl546AriKNhRUyW592Gg+Hh64hHObiPHcQ6TAEDiSAmHsgIjKcRwKra/rp5BONtLSyEh43Tc2UHR4xIeZ6oOoPo/4PpHJc2sOVn6l6CQdtqLo8BD4o67jjg3aMy8ux80Fg8EHpUXo9D9fVkfTDnT/kjTbcDlUa7w+q3hks6Lb10jD2sRtr8kbSzPMh4PCY9CjXH2VOmsDom8QCZUMhCUJwkQgZUQFsnj70utqJT8bBD8D+j8uibNkli5WODxsSQtweatvipu+X3htP8WDoNgpPWT0hDtyIUU+0Y9stohYNhJjqNpSgCBBpR398LKKilD//dNZcXSEkuf5C1OjEgaKyevw8XJmL3WjA3rleXM6Ctl95v6ir2I8KiyP6qwrUN8u1Fpb1ZqE0F4wUbXp4bsHWTLVVW5Rgvqj0onJPCFd8sWxuLBCYRwLFZ93FK44rvah41fI+X1rWfeIvfCrXrW6F8pbpdSLfu72EAzbidhys5IAyg4fz/T+YPs1+7+1qmkJHnPes9OOIsVDdDAjs7rRj0k/H11/WL258X/ujq7i/fcPGnTsDy2gSAmeHAGLu2TmX9OSoCOiz5cYTq4qDtxofMzHzrur2DNXjBphDP5KGJo4xdNr8g1UdcjA7WB37Z4jAWbm25qEfB9mg+9C3YuhTWodoVJZh+HqG7qmz2pWeInHAB0p8IPrCrtwQ+WltKjYuX8bQDJEytyvBp/cFqsUfvywQZSG3EFyVUCw9M3L56gGXKzPkNrwbfS3qjqPxx8JxHIwyhdhcCFO5iuIOd/1RwBsTLM7rup01b3uSKCVoorNeneHB5ONK15dCRZ+LbEU5JXmxjXEOqv11uflcspVez6+V88nudFmVz66vz5ReePFqeuGFayHidOS9HqFYdBtUx9P52nA/o3R5u03c72oFExcayJjNHkieePcobJi4sQMyHqYv89SPA7rJ4TkjMHDt7CjkmH/JE9PN+J6Ojz9fnM6YX94/zAWr4iwQOMcEEHPP8cmn62MI6HPFDjieedqePH726/h3XP7sGbHs+5lX/rwakX9f8rT591VQJoyxcVQR0s84gaO4tubhujqKfvhUH6YvtqFv2V+ZU+IVQlHhrWcfu27l6XVfNX11sgOBGRDoibjVtvdf39Wje9vlFR3Zp7lR/YQXcmncNHutZXFTKVnQtX0hxLrIXs49G5xmYVZLPl4RZQuBNg5GliKvPuhzXq0L4TbX44nRlJK9cctSxWpY+30ZtDNwl0eRnJZ5uf+7esnuSHKL7ruWCLegda8PkTr+zyRmja/hyI7alHipX+5C37b2o7tH1hoVzQcBX8/lPWiDdJIdMMVfaDocSyfuNXvm2gO9WPJ1Uu7GdZFvyZw20TpXOFHmY8o0Dzb4xjqsHYctf0x4qfYUEdA11Fzw56fue237o9v3dYR77+uGEmPxhcsCAQhMSwAxd1pi5D+TBKpjH29bxPXiIWnMZq1PohB1Y4TkHAMfOv6gcoHBZSDb4OFx+0PrG1LgEE0MqY2k80BgkmvrNFxXk/TD5/No+6JW+yosY3v2XTie8MzWSZhR3ra86zoawXrSolp+c+nLzw4E5pFAvtBH3GlOzlmO2HxXa1mz8NitVj7QYJGxsCP/dHsgS/HpXDG2ejyEUn3gRz0VESoSqhmrNlTzVeqtZontcceUwYdj0QOvJotxfNy9xYOQsv2wbW93L4+38sH+1Hnaq/ZqnuzClpMgoJAsdi4PSddeehpL6/bp5AC1uv98n8dtWDFn1J1XyZLvjmrSkW5Pct1OYueRGvUIlZ2VfjxC1ykyYwJN/6JVH2XxFiBbfC1Ocj3O2Gyah8CpIoCYe6pOF8YeFwEPyKqDsuoHTk2fRJpvJDx1j6v9UfVWHxmH5Sk152GHSIPASALV63tUptNwbc1HPyKy5wDGgp7jbMcESBH70mka2JY3dR7QeqDLAoG5IDCoqNio8CbPV2u/lXH/6U/187NfkOzPP2rPTbiuqCc+iPOXId7xY2DlJolMSvKXJRHHRMeijNL89atdf6IfvURl9RcuxRcrxQRm3nO+Iq0I2+B2XN43qMpKXPW6iHmrvH0ctN+3VPezgTlD5VjPpLxhGcu2uXpPamYbcnmvK33LRZTq95TiyyLtxOKDlXbK1Fmt8i8QqvGCw7pKH7Jt5dth3q2e6V4aG6eNQOV+jeu5sF93U3yZedD3mb5WDrqaqy0cF53Ba3OwHV/OQy7pwWwz3z+I52npx8xBYsChCexda8WdU1yb4++ik7jXD90xKoDADAkg5s4QPk1DAAIQgMBpJHDQY95p7BM2Q2AIAT9t9QmZQ/L0BMhhxw5Kq8g20Y7vrQH5YVBkjmc/5bHwWik+viVnzPdttX5VFnXoj79ZiW034PRoqKzWB3J5H46Mlfxltt7Kx/0q64rtskxPslR9IT67UPnIGm3mx9dq2Urbzn6Kl0zmFHcB06cikK/jqQqRGQIQgAAEIACBAwgg5h4AiMMQgAAEIACBRyNQevb1iUKPVhOlIDAzAqFBVoXNIZYccHhIiTIpS3uuIG9Hg+OLxFGLoQPZxt5rrneUKKqK7O2bXeX76inLZAG32mS0X9rbM7u3Uc1Z2R5TXyXXyM2w46A2RpY+sQO2MP+aPjcaWMtzNv89yFazhgAEIACBoyUw+OF9tLVTGwTOCwHE3PNypuknBCAAAQjMFwHGsvN1PrBmhgRGiawHmfSo5YbVqxtyUGEcJuAOK+o0388WhPcpzD54fhcLuH4NCrvnlwg9hwAEIAABCEAAAocngJh7eIbUcAYJ+JnMr1kv+ceWs7aD9s8WgXm5vg9LdV76MXifhp7jzvWEoRx7s0yT4fGL8sMCoDwEzgiBanzV3CXH2DwPS3/fB9/V8puIJokq31jMxWUi8m/vzeY8kKKP54HA4B0wqz4Pfq7Pyo7DtjsvPA/bD8qfPQJcm2fvnNKjkydwVj6rTp4cLUIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQOEECeOaeIGyaggAEIACB80Og3+Pu/PSbnkLgIALZy/SgfMd9/GA7smfspJbI12ikt+xgXYP7aiOShqRP2jz5IAABCEAAAhCAAATOBQE8c8/FaaaTEIAABCAAAQhAAALHSmBiIXecFY4DjKA7jhDHIAABCEAAAhCAwHkngGfueb8C6D8EIAABCExJYPz3oLV9kyY5Mli5VDZzEmsInGYCjxK7tV4bfw89Sp3TMrTn/IFxeT1z16EWi7IjhNmxdecybr+oo2AyaEzON5jOPgQgAAEIQAACEIDAWSaAmHuWzy59gwAEIACBGRA4rAA0A5NpEgLzROAkNEq3cZpu1WByEmDm6ULAFghAAAIQgAAEIACBYQQQc4dRIQ0CEIAABCAAAQhA4FgIdLoOJTDb5SS8fw/XQ3svW7zNr8PVRmkIQAACEIAABCAAgbNDYPzv3M5OP+kJBCAAAQhAAAIQgAAETgmBs+GF66gzdoDeF33mlJwFzIQABCAAAQhAAALzSAAxdx7PCjZBAAIQgAAEIAABCJxjAtkj93SKuiHg5rNXFXTL7pymCBe5G6whAAEIQAACEIDAvBBAzJ2XM4EdEIAABCAAAQhAAAIQ6BE4nUJuNn+YN24WeRFzMyXWEIAABCAAAQhAYHoCiLnTM6MEBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIETJ4CYe+LIaRACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwPQEmtMXoQQEIAABCEAAAhCAAAQgAIF+ArVuNw0Gh4iQCoOJKkaohX527EEAAhCAAAQgAIFJCSDmTkqKfBCAAAQgAAEIQAACEIDAWAKFeFuot+ME23HHxjbAQQhAAAIQgAAEIHDOCRBm4ZxfAHQfAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQOB0EEHNPx3nCSghAAAIQgAAEIAABCJwuAl3732Yf3Lw+XV3AWghAAAIQgAAEIDBvBBBz5+2MYA8EIAABCEAAAmeCwJAwoWeiX3QCAgcT2BNuaxJ04x83xMHYyAEBCEAAAhCAAAQmIEDM3AkgkQUCEIAABCAAAQhMQ6AjLSs7JXod2z0PRfkqImxNg5O8p4xASLkWcX2d65Wv93re3tN6T1nPMBcCEIAABCAAAQjMngCeubM/B1gAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQOBAAoi5ByIiAwQgAAEIQAACEDhKAqW74lFWSV0QmHcCvuxZIAABCEAAAhCAAAQOTYAwC4dGSAUQgAAEIAABCECgQqAXX6GZul0NtUK71ffntY4y+eWESb5P92/RR/0e/SQF4VG2nqQNQnHgclg7j4L3uDp87idZRvXDZSepY5wNk56zw9bh8nkp6nLcXBYIQAACEIAABCAAgcMTQMw9PENqgAAEIAABCEDgnBOoSmyOl5u6EuS6DWlvCxJ0va/E0LRaqSZRtxB5dbwicHmiqLzEhFE1i3p7aflYse5rsf/QofaGCYnD0txIVRisbh/KgAkLD7NpWNqgneOqjxM0IkOV94gskXycdZjxqD4O2jQq36TnaXw/Ot1BHnv11mq1VFdx3wc1XcO1WiPWvUvZVbNAAAIQgAAEIAABCDwyAcTcR0ZHQQhAAAIQgAAEIDCMgIStnhfu4PG2EiyEWdHyMKxUtqyFeTsLuhLEin2nD1lC6HW66xol3A0pN1HSQH1h27CCsjHszBnKvgzLeixpE9qZWU5iQ+7KYN4e78EDA/ujyjvboevIvAfaHLY7yo6jYlGtP6718noNW6rbw4wjDQIQgAAEIAABCEDgMAQQcw9Dj7IQgAAEIAABCEBgGIHarjTWbb3kY5u1LYletZpVML+UGMJtRZCMfWfWUhXLipT+v/n4pAJhf+nxe7nu8bmKo5FXNkcnJylwhHkmtXPSfONMOyt1HFk/qtftOHAcgwAEIAABCEAAAhA4agKIuUdNlPogAAEIQAACEDjfBEK8bUngzF64JQ6FXujKi7bQPS2AWlkrf67uTR+oeubG/giUbiOWwZ+75/RDrAcF4nECYNjhDOMyHcKWcUUntbPHalxl5bFR3Zi0jlHlXf081DGpDbZ3VF9cRzXMQnjmukC5xAW+11BXebsRa6QiAOe8rCEAAQhAAAIQgAAEpiaAmDs1MgpAAAIQgAAEIACB8QTsgdtpd9Ly0kra2UlpcUlaXl1iVm1Jeq1FXitl8t4tFb7QbUM8KwQvi1+FXrYnivW3WCptg0Jaf6ZH23O8333LMDuyDV6X2/vKHWPCPjuH2ej2p7HtuOqYxgbbPMyOaeoYVv7oWBTirOvz0v+FguPk1uuLejVSu93W9d9KrdZC6nTKa1qmDei9RTX8hQAEIAABCEAAAhCYiABi7kSYyAQBCEAAAhCAAAQmJ7CwsCIBqyMBqy5Bt54aoa15wjMLpQtlRRLBSs3Nk5954qi8b0fGg6W7UkQ7SkHXAmlpU2Gkd/oSStvzylaWr/A0zunHvN5n5zABumpDtrOaNrh9UB39ouVg6YLTJKz2l9xLmZT3Xon9W4fth2scX0c3+QuJ6rJ3LZtDXV9cdOScXq83U7Ppx41afDkhPTfVfSrUzXGkqjWzDQEIQAACEIAABCDQTwAxt58HexCAAAQgAAEIQOBwBBwqoduQeLWUNh+20+eftdOFCwqvoPi5IWL1VCzvS9kqY+X2kkvd0avxiwU357JIXBXTxpcaebS0o/94z6r+5HKva5FO2yFEZ1F3IOf4GooeDBTZt9urI6AMCo21Ad/QfcVlpf+NWw6uQ/6mqmD0WcksRrVS2GArDlvHqBacfnA/JLUq16PbEK2EV3S1jr1rsKYvFzrdB2l7u50ebOykRnNB177uBxXML2+Ed64rY4EABCAAAQhAAAIQmIoAYu5UuMgMAQhAAAIQgAAEDiLQTdtbW2mhWU8bGw/SGz9/M0nLkniVpSyvy6Wi7RVyoxLisH6SXsmWs/ev7SmqTL16+48O33ODfg0sfW1ZmJtwUdsKKFFmzrZUyjqpsru3uSfIRtPDMxXZq3VE5r1aii1Jk4PxcweyhMQ5DqiURcczHrfUIu7FUAOiWGHD+I7UwobD1pF5D7P2ZFgohogar/bD23qp+xZzG3I+rzfqaWdrWcKuHjeU3laZdrjmSk4eh2lYt0iDAAQgAAEIQAACEOgRQMztoWADAhCAAAQgAAEIPBqBqjZlSXBlNaXm0k66e/duWq+tp3o757DoJdl20As2dDHnyfk8adQwW6pCXpkhFxmWfV+aMkfb+w5UErKoOdQA5VN6iLi5iATdig2hLedDQ9aRVZ6d1TLO5tb88vFKddobXDqFfq3kvTqKEnv7YeJAwb3+FDbosDaK1LK8SxSbvTb6K9lfx16bZR1l+ahqL3tZTX9CZnWsdajJikn77IhjNksbhXVF7j2bdKjf7HJCs/5E53EZe9zWFQ7aMaKtf3e1bix44r9a2t3ppoVFXf/KU72S3aJfLBCAAAQgAAEIQAACBxOQg8DwR4WDi5IDAhCAAAQgAAEIQGCQgEdWD+7vpDu3H6StrZ20tKSfmVu9KhcfD/GqqpblgzmP18OGaHvVDJSYYLdfe5ugwIgssiGqqtji/Vx9loKHlXaXImaqDnYqTCJvWe+gcNhfT9FKjrs6iDALhDatYl5/Fd5TNT4etVUyer9oQXY637jlCOrIfX3kfsi+EFFtZ6Uf3j1OFp1cuRvSkps2O5/WvVMrn2iddN8DFy8uayLABcXQLQpkzi7v5UDeRTb+QgACEIAABCAAgXNPADH33F8CAIAABCAAAQhA4KgJ7GryJ0VZCGEwhK6sdrmhUsWqJg1rf5+We1CBYZVU0wbVs+qxabZlh6uqVlfdPsjMLOZawKyWq26Pq8PH8vEBTbGvvpxnWNdG1TGpDa4zi48uUy1X3R5nw1HUMaofrjvbcZANuY5p+jF4bVbPqdv2CXK9uU6Luw6tkG2KPAN/Ms+BZHYhAAEIQAACEIAABAYIEGZhAAi7EIAABCAAAQhA4LAE9MtyeUZ6IqiORCxPOGVpy4vkrFC5qtJVmVZkKPJILetmt81IHyeD9QqO3wgTsh05q/YH3UJ7h9zmYLvFfvx10TJvXxU5sTw22GJx2LFdiwzFvn6SX+bvXznVU4vtLY7S28vcV8deHm9V66uWj2PlwViNqKNa3mVG1jGifLTjP5XlqOsIG7OhI+zIh7MZI21whgnryHXltUV1F422pNzWdM1HDOHSQK8G7eg1lithDQEIQAACEIAABCAwEQHE3IkwkQkCEIAABCAAAQiMI9AvVdUlxPrn5Z5Wq5Cxqj6kpeyV1cw9GaxsoFDUSl2tSOvbGWfHAcd6beZ8qnjvN/E5cfS6Yod75l1XWUnu3+nHUtTr/CqUD0UdUVdZUT5Q5O6vO6cpazVb7kJOc519Sz5QJjp/zpMPTVuHK3AdvfKuu6y0L61ss5cx7zv7sPxKG1re5fKBgTqqyUPrrOQfrONRWAx65pbdiP4XIUVskSZJK2NAFMerX2D0SlQtYxsCEIAABCAAAQhAYAIChFmYABJZIAABCEAAAhCAwHgCVbFWmts+tatyXJN/aVYovUYIWmVyFuXGt3sER6tKYLW6EeY5i00fVaxaxbDtkPRUuDOm/mHlqmn5Z/2PWoebDp3xEP04ijoO2w8ziTpkTOUKq6I6cPtR+rHv8tb5dD1xSXsjKc6IxNzwolZAiloEpfCZj4OVtTbLJa6LvMMaAhCAAAQgAAEIQGAkATxzR6LhAAQgAAEIQAACEJiUQBapRuS3eNtbqqJWL5ENCJwBAlni99ov3xd+ldd8/gLjgNtFBVggAAEIQAACEIAABEYQQMwdAYZkCEAAAhCAAAQgMDmBA9SpPjG3rNVFst51UEMHVH9Q8b7jWW/LiZPWPWm+XO9xro/ClnmoYx5s8Hk6rB1RXhdWL86zExrlFZC/vCgbydffYdssa2cFAQhAAAIQgAAEzhsBxNzzdsbpLwQgAAEIQAACJ0BgQKka2O0ZMCI9kkcc65V91I3BerO4Nlif8o06NJh1mv2oc9CGAyoYlT2nT2Jnzlttqpp2UB3VvK6juu/tg8oPlvF+XnJd81DHRDb0RNuiB2G/CuaJ/vKUdcV+FnPL3padzX3ODFhDAAIQgAAEIAABCExGADF3Mk7kggAEIAABCEAAAhMTOLF4txNbNCbjGFVtEmFvTM1DD01bp80bZWJOP6jOg+o4qLw7ktsa7FQ1/aB6qnmr9eT0ScrnvNXy3s7pj1pHLu+6DqrDUXD3LX1J9UodlQPDN/dVRQIEIAABCEAAAhCAwGgCiLmj2XAEAhCAAAQgAAEIQAACEJiaQEW1nbosBSAAAQhAAAIQgAAExhHw755YIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYM4JIObO+QnCPAhAAAIQgAAEIACB4yVwEn6kk7QxSZ7jJXEytZ+Xfp4MTVqBAAQgAAEIQOC8Eah1tZy3TtNfCEAAAhCAAAQgAIGDCXQOznJgjoM8BzwQHTcYtfCH+HcgZjJAAAIQgAAEIAABCJwTAgeNr88JBroJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGC+CSDmzvf5wToIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAkEAMZcLAQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAKSDQPAU2YiIEIAABCEAAAhCAwCkgMFF82xwgtwyEm8scFDv3FHQfEyEAAQhAAAIQgAAEIHDsBPDMPXbENAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhA4PAEEHMPz5AaIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgcOwHE3GNHTAMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABA5PgJi5h2dIDRCAAAQgAAEIQAACowjkGLmDx3N6GTt38DD7EIAABCAAAQhAAAIQgMB+Anjm7mdCCgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBuSOAmDt3pwSDIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjsJ4CYu58JKRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE5o4AMXPn7pRgEAQgAAEIQAACEJgPAkcSznawkhwr112sHKts9nV+VHpfJnYgAAEIQAACEIAABCBwTggg5p6TE003IQABCEAAAhCAwLQEjlVIHVL5kKRpTSY/BCAAAQhAAAIQgAAEzjQBwiyc6dNL5yAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGzQgAx96ycSfoBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEInGkCiLln+vTSOQhAAAIQgAAEIDBnBIilMGcnBHMgAAEIQAACEIAABE4TAcTc03S2sBUCEIAABCAAAQicBQIIumfhLNIHCEAAAhCAAAQgAIEZEEDMnQF0moQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMC0BxNxpiZEfAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMAMCCDmzgA6TUIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQmJYAYu60xMgPAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGAGBBBzZwCdJiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITEsAMXdaYuSHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDADAoi5M4BOkxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEpiWAmDstMfJDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJgBAcTcGUCnSQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC0xJAzJ2WGPkhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMyAAGLuDKDTJAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBaQkg5k5LjPwQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABGZAADF3BtBpEgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAtAQQc6clRn4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjMggJg7A+g0CQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgWgKIudMSIz8EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgRkQQMydAXSahAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwLQHE3GmJkR8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwAwIIObOADpNQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYlgBi7rTEyA8BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYAYEEHNnAJ0mIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhMSwAxd1pi5IcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMAMCiLkzgE6TEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASmJYCYOy0x8kMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQmAEBxNwZQKdJCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALTEkDMnZYY+SEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIzIAAYu4MoNMkBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFpCSDmTkuM/BCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEZkAAMXcG0GkSAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMC0BBBzpyVGfghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACMyCAmDsD6DQJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGBaAoi50xIjPwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBGRBAzJ0BdJqEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDAtAcTcaYmRHwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDADAgg5s4AOk1CAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJiWAGLutMTIDwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgBgQQc2cAnSYhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCExLADF3WmLkhwAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwAwKIuTOATpMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABKYlgJg7LTHyQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYAQHE3BlAp0kIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAtMSQMydlhj5IQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjMgABi7gyg0yQEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgWkJIOZOS4z8EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARmQAAxdwbQaRICEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwLQEEHOnJUZ+CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIzIICYOwPoNAkBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYFoCiLnTEiM/BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEZEEDMnQF0moQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMC0BxNxpiZEfAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMAMCCDmzgA6TUIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQmJYAYu60xMgPAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGAGBBBzZwCdJiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITEsAMXdaYuSHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDADAoi5M4BOkxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEpiWAmDstMfJDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJgBAcTcGUCnSQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC0xJAzJ2WGPkhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMyAAGLuDKDTJAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBaQkg5k5LjPwQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABGZAADF3BtBpEgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAtAQQc6clRn4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjMggJg7A+g0CQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgWgKIudMSIz8EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgRkQQMydAXSahAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwLQHE3GmJkR8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwAwIIObOADpNQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYlgBi7rTEyA8BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYAYEEHNnAJ0mIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhMS+D/B62+lEB4jDsWAAAAAElFTkSuQmCC" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workflows and Agents\n", - "\n", - "This guide reviews common patterns for agentic systems. In describing these systems, it can be useful to make a distinction between \"workflows\" and \"agents\". One way to think about this difference is nicely explained [here](https://www.anthropic.com/research/building-effective-agents) by Anthropic:\n", - "\n", - "> Workflows are systems where LLMs and tools are orchestrated through predefined code paths.\n", - "> Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.\n", - "\n", - "Here is a simple way to visualize these differences:\n", - "\n", - "![Agent Workflow](../concepts/img/agent_workflow.png)\n", - "\n", - "When building agents and workflows, LangGraph [offers a number of benefits](https://langchain-ai.github.io/langgraph/concepts/high_level/) including persistence, streaming, and support for debugging as well as deployment.\n", - "\n", - "## Building Blocks: The Augmented LLM \n", - "\n", - "LLM have [augmentations](https://www.anthropic.com/research/building-effective-agents) that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic [blog](https://www.anthropic.com/research/building-effective-agents):\n", - "\n", - "![augmented_llm.jpg](attachment:ba9045a3-4f2b-408a-9efa-d232cbfbdd2f.jpg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this tutorial, you can use [any chat model](https://python.langchain.com/docs/integrations/chat/) that supports structured outputs and tool calling. Below, we show the process of installing the packages, setting API keys, and testing structured outputs / tool calling for both OpenAI and Anthropic. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! pip install langchain_core langchain-openai langchain-anthropic langgraph " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# LLM\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-4o\")\n", - "\n", - "from langchain_anthropic import ChatAnthropic\n", - "\n", - "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pydantic import BaseModel, Field\n", - "\n", - "\n", - "# Schema for structured output\n", - "class SearchQuery(BaseModel):\n", - " search_query: str = Field(None, description=\"Query that is optimized web search.\")\n", - " justification: str = Field(\n", - " None, justification=\"Why this query is relevant to the user's request.\"\n", - " )\n", - "\n", - "\n", - "# Augment the LLM with schema for structured output\n", - "structured_llm = llm.with_structured_output(SearchQuery)\n", - "\n", - "# Invoke the augmented LLM\n", - "output = structured_llm.invoke(\"How does Calcium CT score relate to high cholesterol?\")\n", - "print(output.search_query)\n", - "print(output.justification)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a tool\n", - "def multiply(a: int, b: int) -> int:\n", - " return a * b\n", - "\n", - "\n", - "# Augment the LLM with tools\n", - "llm_with_tools = llm.bind_tools([multiply])\n", - "\n", - "# Invoke the LLM with input that triggers the tool call\n", - "msg = llm_with_tools.invoke(\"What is 2 times 3?\")\n", - "\n", - "# Get the tool call\n", - "msg.tool_calls" - ] - }, - { - "attachments": { - "9aa9e4d6-0d90-4e07-ae5c-7cb0deb410ab.jpg": { - "image/jpeg": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prompt chaining\n", - "\n", - "In prompt chaining, each LLM call processes the output of the previous one. \n", - "\n", - "As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - "> Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see \"gate” in the diagram below) on any intermediate steps to ensure that the process is still on track.\n", - "\n", - "> When to use this workflow: This workflow is ideal for situations where the task can be easily and cleanly decomposed into fixed subtasks. The main goal is to trade off latency for higher accuracy, by making each LLM call an easier task.\n", - "\n", - "![prompt_chain.jpg](attachment:9aa9e4d6-0d90-4e07-ae5c-7cb0deb410ab.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**LangChain Academy**\n", - " \n", - "See our lesson on Prompt Chaining [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/chain.ipynb).\n", - "\n", - "#### Example Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from typing_extensions import TypedDict\n", - "from langgraph.graph import StateGraph, START, END\n", - "from IPython.display import Image, display\n", - "\n", - "\n", - "# Graph state\n", - "class State(TypedDict):\n", - " topic: str\n", - " joke: str\n", - " improved_joke: str\n", - " final_joke: str\n", - "\n", - "\n", - "# Nodes\n", - "def generate_joke(state: State):\n", - " \"\"\"First LLM call to generate initial joke\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Write a short joke about {state['topic']}\")\n", - " return {\"joke\": msg.content}\n", - "\n", - "\n", - "def check_punchline(state: State):\n", - " \"\"\"Gate function to check if the joke has a punchline\"\"\"\n", - "\n", - " # Simple check - does the joke contain \"?\" or \"!\"\n", - " if \"?\" in state[\"joke\"] or \"!\" in state[\"joke\"]:\n", - " return \"Pass\"\n", - " return \"Fail\"\n", - "\n", - "\n", - "def improve_joke(state: State):\n", - " \"\"\"Second LLM call to improve the joke\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Make this joke funnier by adding wordplay: {state['joke']}\")\n", - " return {\"improved_joke\": msg.content}\n", - "\n", - "\n", - "def polish_joke(state: State):\n", - " \"\"\"Third LLM call for final polish\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Add a surprising twist to this joke: {state['improved_joke']}\")\n", - " return {\"final_joke\": msg.content}\n", - "\n", - "\n", - "# Build workflow\n", - "workflow = StateGraph(State)\n", - "\n", - "# Add nodes\n", - "workflow.add_node(\"generate_joke\", generate_joke)\n", - "workflow.add_node(\"improve_joke\", improve_joke)\n", - "workflow.add_node(\"polish_joke\", polish_joke)\n", - "\n", - "# Add edges to connect nodes\n", - "workflow.add_edge(START, \"generate_joke\")\n", - "workflow.add_conditional_edges(\n", - " \"generate_joke\", check_punchline, {\"Pass\": \"improve_joke\", \"Fail\": END}\n", - ")\n", - "workflow.add_edge(\"improve_joke\", \"polish_joke\")\n", - "workflow.add_edge(\"polish_joke\", END)\n", - "\n", - "# Compile\n", - "chain = workflow.compile()\n", - "\n", - "# Show workflow\n", - "display(Image(chain.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = chain.invoke({\"topic\": \"cats\"})\n", - "print(\"Initial joke:\")\n", - "print(state[\"joke\"])\n", - "print(\"\\n--- --- ---\\n\")\n", - "if \"improved_joke\" in state:\n", - " print(\"Improved joke:\")\n", - " print(state[\"improved_joke\"])\n", - " print(\"\\n--- --- ---\\n\")\n", - "\n", - " print(\"Final joke:\")\n", - " print(state[\"final_joke\"])\n", - "else:\n", - " print(\"Joke failed quality gate - no punchline detected!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/a0281fca-3a71-46de-beee-791468607b75/r" - ] - }, - { - "attachments": { - "b2318fd5-9c93-4e3d-9a31-823ed7877932.jpg": { - "image/jpeg": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parallelization \n", - "\n", - "With parallelization, LLMs work simultaneously on a task. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - ">LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically. This workflow, parallelization, manifests in two key variations: Sectioning: Breaking a task into independent subtasks run in parallel. Voting: Running the same task multiple times to get diverse outputs.\n", - "\n", - "> When to use this workflow: Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results. For complex tasks with multiple considerations, LLMs generally perform better when each consideration is handled by a separate LLM call, allowing focused attention on each specific aspect.\n", - "\n", - "![parallelization.jpg](attachment:b2318fd5-9c93-4e3d-9a31-823ed7877932.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**Documentation**\n", - "\n", - "See our documentation on parallelization [here](https://langchain-ai.github.io/langgraph/how-tos/branching/).\n", - "\n", - "**LangChain Academy**\n", - " \n", - "See our lesson on parallelization [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/simple-graph.ipynb).\n", - "\n", - "#### Example Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Graph state\n", - "class State(TypedDict):\n", - " topic: str\n", - " joke: str\n", - " story: str\n", - " poem: str\n", - " combined_output: str\n", - "\n", - "\n", - "# Nodes\n", - "def call_llm_1(state: State):\n", - " \"\"\"First LLM call to generate initial joke\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Write a joke about {state['topic']}\")\n", - " return {\"joke\": msg.content}\n", - "\n", - "\n", - "def call_llm_2(state: State):\n", - " \"\"\"Second LLM call to generate story\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Write a story about {state['topic']}\")\n", - " return {\"story\": msg.content}\n", - "\n", - "\n", - "def call_llm_3(state: State):\n", - " \"\"\"Third LLM call to generate poem\"\"\"\n", - "\n", - " msg = llm.invoke(f\"Write a poem about {state['topic']}\")\n", - " return {\"poem\": msg.content}\n", - "\n", - "\n", - "def aggregator(state: State):\n", - " \"\"\"Combine the joke and story into a single output\"\"\"\n", - "\n", - " combined = f\"Here's a story, joke, and poem about {state['topic']}!\\n\\n\"\n", - " combined += f\"STORY:\\n{state['story']}\\n\\n\"\n", - " combined += f\"JOKE:\\n{state['joke']}\\n\\n\"\n", - " combined += f\"POEM:\\n{state['poem']}\"\n", - " return {\"combined_output\": combined}\n", - "\n", - "\n", - "# Build workflow\n", - "parallel_builder = StateGraph(State)\n", - "\n", - "# Add nodes\n", - "parallel_builder.add_node(\"call_llm_1\", call_llm_1)\n", - "parallel_builder.add_node(\"call_llm_2\", call_llm_2)\n", - "parallel_builder.add_node(\"call_llm_3\", call_llm_3)\n", - "parallel_builder.add_node(\"aggregator\", aggregator)\n", - "\n", - "# Add edges to connect nodes\n", - "parallel_builder.add_edge(START, \"call_llm_1\")\n", - "parallel_builder.add_edge(START, \"call_llm_2\")\n", - "parallel_builder.add_edge(START, \"call_llm_3\")\n", - "parallel_builder.add_edge(\"call_llm_1\", \"aggregator\")\n", - "parallel_builder.add_edge(\"call_llm_2\", \"aggregator\")\n", - "parallel_builder.add_edge(\"call_llm_3\", \"aggregator\")\n", - "parallel_builder.add_edge(\"aggregator\", END)\n", - "parallel_workflow = parallel_builder.compile()\n", - "\n", - "# Show workflow\n", - "display(Image(parallel_workflow.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = parallel_workflow.invoke({\"topic\": \"cats\"})\n", - "print(state[\"combined_output\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/3be2e53c-ca94-40dd-934f-82ff87fac277/r" - ] - }, - { - "attachments": { - "679f8364-5fce-48b2-bd58-0fb993178c25.jpg": { - "image/jpeg": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Routing \n", - "\n", - "Routing classifies an input and directs it to a followup task. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - "> Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs.\n", - "\n", - "> When to use this workflow: Routing works well for complex tasks where there are distinct categories that are better handled separately, and where classification can be handled accurately, either by an LLM or a more traditional classification model/algorithm.\n", - "\n", - "![routing.jpg](attachment:679f8364-5fce-48b2-bd58-0fb993178c25.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**LangChain Academy**\n", - "\n", - "See our lesson on routing [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/router.ipynb).\n", - "\n", - "**Examples**\n", - "\n", - "[Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is RAG workflow that routes questions. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).\n", - "\n", - "#### Example Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from typing_extensions import Literal\n", - "from langchain_core.messages import HumanMessage, SystemMessage\n", - "\n", - "\n", - "# Schema for structured output to use as routing logic\n", - "class Route(BaseModel):\n", - " step: Literal[\"poem\", \"story\", \"joke\"] = Field(\n", - " None, description=\"The next step in the routing process\"\n", - " )\n", - "\n", - "\n", - "# Augment the LLM with schema for structured output\n", - "router = llm.with_structured_output(Route)\n", - "\n", - "\n", - "# State\n", - "class State(TypedDict):\n", - " input: str\n", - " decision: str\n", - " output: str\n", - "\n", - "\n", - "# Nodes\n", - "def llm_call_1(state: State):\n", - " \"\"\"Write a story\"\"\"\n", - "\n", - " result = llm.invoke(state[\"input\"])\n", - " return {\"output\": result.content}\n", - "\n", - "\n", - "def llm_call_2(state: State):\n", - " \"\"\"Write a joke\"\"\"\n", - "\n", - " result = llm.invoke(state[\"input\"])\n", - " return {\"output\": result.content}\n", - "\n", - "\n", - "def llm_call_3(state: State):\n", - " \"\"\"Write a poem\"\"\"\n", - "\n", - " result = llm.invoke(state[\"input\"])\n", - " return {\"output\": result.content}\n", - "\n", - "\n", - "def llm_call_router(state: State):\n", - " \"\"\"Route the input to the appropriate node\"\"\"\n", - "\n", - " # Run the augmented LLM with structured output to serve as routing logic\n", - " decision = router.invoke(\n", - " [\n", - " SystemMessage(\n", - " content=\"Route the input to story, joke, or poem based on the user's request.\"\n", - " ),\n", - " HumanMessage(content=state[\"input\"]),\n", - " ]\n", - " )\n", - "\n", - " return {\"decision\": decision.step}\n", - "\n", - "\n", - "# Conditional edge function to route to the appropriate node\n", - "def route_decision(state: State):\n", - " # Return the node name you want to visit next\n", - " if state[\"decision\"] == \"story\":\n", - " return \"llm_call_1\"\n", - " elif state[\"decision\"] == \"joke\":\n", - " return \"llm_call_2\"\n", - " elif state[\"decision\"] == \"poem\":\n", - " return \"llm_call_3\"\n", - "\n", - "\n", - "# Build workflow\n", - "router_builder = StateGraph(State)\n", - "\n", - "# Add nodes\n", - "router_builder.add_node(\"llm_call_1\", llm_call_1)\n", - "router_builder.add_node(\"llm_call_2\", llm_call_2)\n", - "router_builder.add_node(\"llm_call_3\", llm_call_3)\n", - "router_builder.add_node(\"llm_call_router\", llm_call_router)\n", - "\n", - "# Add edges to connect nodes\n", - "router_builder.add_edge(START, \"llm_call_router\")\n", - "router_builder.add_conditional_edges(\n", - " \"llm_call_router\",\n", - " route_decision,\n", - " { # Name returned by route_decision : Name of next node to visit\n", - " \"llm_call_1\": \"llm_call_1\",\n", - " \"llm_call_2\": \"llm_call_2\",\n", - " \"llm_call_3\": \"llm_call_3\",\n", - " },\n", - ")\n", - "router_builder.add_edge(\"llm_call_1\", END)\n", - "router_builder.add_edge(\"llm_call_2\", END)\n", - "router_builder.add_edge(\"llm_call_3\", END)\n", - "\n", - "# Compile workflow\n", - "router_workflow = router_builder.compile()\n", - "\n", - "# Show the workflow\n", - "display(Image(router_workflow.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = router_workflow.invoke({\"input\": \"Write me a joke about cats\"})\n", - "print(state[\"output\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/c4580b74-fe91-47e4-96fe-7fac598d509c/r" - ] - }, - { - "attachments": { - "1bc16c26-6c05-408f-9cc8-12440f3e0a36.jpg": { - "image/jpeg": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Orchestrator-Worker \n", - "\n", - "With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - "> In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.\n", - "\n", - "> When to use this workflow: This workflow is well-suited for complex tasks where you can’t predict the subtasks needed (in coding, for example, the number of files that need to be changed and the nature of the change in each file likely depend on the task). Whereas it’s topographically similar, the key difference from parallelization is its flexibility—subtasks aren't pre-defined, but determined by the orchestrator based on the specific input.\n", - "\n", - "![worker.jpg](attachment:1bc16c26-6c05-408f-9cc8-12440f3e0a36.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**LangChain Academy**\n", - "\n", - "See our lesson on orchestrator-worker [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-4/map-reduce.ipynb).\n", - "\n", - "--- \n", - "\n", - "**Examples**\n", - "\n", - "[Here](https://github.com/langchain-ai/report-mAIstro) is a project that uses orchestrator-worker for report planning and writing. See our video [here](https://www.youtube.com/watch?v=wSxZ7yFbbas).\n", - "\n", - "#### Example Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Annotated, List\n", - "import operator\n", - "\n", - "\n", - "# Schema for structured output to use in planning\n", - "class Section(BaseModel):\n", - " name: str = Field(\n", - " description=\"Name for this section of the report.\",\n", - " )\n", - " description: str = Field(\n", - " description=\"Brief overview of the main topics and concepts to be covered in this section.\",\n", - " )\n", - "\n", - "\n", - "class Sections(BaseModel):\n", - " sections: List[Section] = Field(\n", - " description=\"Sections of the report.\",\n", - " )\n", - "\n", - "\n", - "# Augment the LLM with schema for structured output\n", - "planner = llm.with_structured_output(Sections)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating Workers in LangGraph\n", - "\n", - "Because orchestrator-worker workflows are common, LangGraph **has the `Send` API to support this**. It lets you dynamically create worker nodes and send each one a specific input. Each worker has its own state, and all worker outputs are written to a *shared state key* that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. As you can see below, we iterate over a list of sections and `Send` each to a worker node. See further documentation [here](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) and [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#send).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.constants import Send\n", - "\n", - "\n", - "# Graph state\n", - "class State(TypedDict):\n", - " topic: str # Report topic\n", - " sections: list[Section] # List of report sections\n", - " completed_sections: Annotated[\n", - " list, operator.add\n", - " ] # All workers write to this key in parallel\n", - " final_report: str # Final report\n", - "\n", - "\n", - "# Worker state\n", - "class WorkerState(TypedDict):\n", - " section: Section\n", - " completed_sections: Annotated[list, operator.add]\n", - "\n", - "\n", - "# Nodes\n", - "def orchestrator(state: State):\n", - " \"\"\"Orchestrator that generates a plan for the report\"\"\"\n", - "\n", - " # Generate queries\n", - " report_sections = planner.invoke(\n", - " [\n", - " SystemMessage(content=\"Generate a plan for the report.\"),\n", - " HumanMessage(content=f\"Here is the report topic: {state['topic']}\"),\n", - " ]\n", - " )\n", - "\n", - " return {\"sections\": report_sections.sections}\n", - "\n", - "\n", - "def llm_call(state: WorkerState):\n", - " \"\"\"Worker writes a section of the report\"\"\"\n", - "\n", - " # Generate section\n", - " section = llm.invoke(\n", - " [\n", - " SystemMessage(\n", - " content=\"Write a report section following the provided name and description. Include no preamble for each section. Use markdown formatting.\"\n", - " ),\n", - " HumanMessage(\n", - " content=f\"Here is the section name: {state['section'].name} and description: {state['section'].description}\"\n", - " ),\n", - " ]\n", - " )\n", - "\n", - " # Write the updated section to completed sections\n", - " return {\"completed_sections\": [section.content]}\n", - "\n", - "\n", - "def synthesizer(state: State):\n", - " \"\"\"Synthesize full report from sections\"\"\"\n", - "\n", - " # List of completed sections\n", - " completed_sections = state[\"completed_sections\"]\n", - "\n", - " # Format completed section to str to use as context for final sections\n", - " completed_report_sections = \"\\n\\n---\\n\\n\".join(completed_sections)\n", - "\n", - " return {\"final_report\": completed_report_sections}\n", - "\n", - "\n", - "# Conditional edge function to create llm_call workers that each write a section of the report\n", - "def assign_workers(state: State):\n", - " \"\"\"Assign a worker to each section in the plan\"\"\"\n", - "\n", - " # Kick off section writing in parallel via Send() API\n", - " return [Send(\"llm_call\", {\"section\": s}) for s in state[\"sections\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Build workflow\n", - "orchestrator_worker_builder = StateGraph(State)\n", - "\n", - "# Add the nodes\n", - "orchestrator_worker_builder.add_node(\"orchestrator\", orchestrator)\n", - "orchestrator_worker_builder.add_node(\"llm_call\", llm_call)\n", - "orchestrator_worker_builder.add_node(\"synthesizer\", synthesizer)\n", - "\n", - "# Add edges to connect nodes\n", - "orchestrator_worker_builder.add_edge(START, \"orchestrator\")\n", - "orchestrator_worker_builder.add_conditional_edges(\n", - " \"orchestrator\", assign_workers, [\"llm_call\"]\n", - ")\n", - "orchestrator_worker_builder.add_edge(\"llm_call\", \"synthesizer\")\n", - "orchestrator_worker_builder.add_edge(\"synthesizer\", END)\n", - "\n", - "# Compile the workflow\n", - "orchestrator_worker = orchestrator_worker_builder.compile()\n", - "\n", - "# Show the workflow\n", - "display(Image(orchestrator_worker.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = orchestrator_worker.invoke({\"topic\": \"Create a report on LLM scaling laws\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Markdown\n", - "\n", - "Markdown(state[\"final_report\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/78cbcfc3-38bf-471d-b62a-b299b144237d/r" - ] - }, - { - "attachments": { - "2858b0d1-c9c1-43d2-8bd4-f7b1f6d5912a.jpg": { - "image/jpeg": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluator-optimizer\n", - "\n", - "In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - "> In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop.\n", - "\n", - "> When to use this workflow: This workflow is particularly effective when we have clear evaluation criteria, and when iterative refinement provides measurable value. The two signs of good fit are, first, that LLM responses can be demonstrably improved when a human articulates their feedback; and second, that the LLM can provide such feedback. This is analogous to the iterative writing process a human writer might go through when producing a polished document.\n", - "\n", - "![eval.jpg](attachment:2858b0d1-c9c1-43d2-8bd4-f7b1f6d5912a.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**Examples**\n", - "\n", - "[Here](https://github.com/langchain-ai/research-rabbit) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8).\n", - "\n", - "\n", - "[Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).\n", - "\n", - "#### Example Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Graph state\n", - "class State(TypedDict):\n", - " joke: str\n", - " topic: str\n", - " feedback: str\n", - " funny_or_not: str\n", - "\n", - "\n", - "# Schema for structured output to use in evaluation\n", - "class Feedback(BaseModel):\n", - " grade: Literal[\"funny\", \"not funny\"] = Field(\n", - " description=\"Decide if the joke is funny or not.\",\n", - " )\n", - " feedback: str = Field(\n", - " description=\"If the joke is not funny, provide feedback on how to improve it.\",\n", - " )\n", - "\n", - "\n", - "# Augment the LLM with schema for structured output\n", - "evaluator = llm.with_structured_output(Feedback)\n", - "\n", - "\n", - "# Nodes\n", - "def llm_call_generator(state: State):\n", - " \"\"\"LLM generates a joke\"\"\"\n", - "\n", - " if state.get(\"feedback\"):\n", - " msg = llm.invoke(\n", - " f\"Write a joke about {state['topic']} but take into account the feedback: {state['feedback']}\"\n", - " )\n", - " else:\n", - " msg = llm.invoke(f\"Write a joke about {state['topic']}\")\n", - " return {\"joke\": msg.content}\n", - "\n", - "\n", - "def llm_call_evaluator(state: State):\n", - " \"\"\"LLM evaluates the joke\"\"\"\n", - "\n", - " grade = evaluator.invoke(f\"Grade the joke {state['joke']}\")\n", - " return {\"funny_or_not\": grade.grade, \"feedback\": grade.feedback}\n", - "\n", - "\n", - "# Conditional edge function to route back to joke generator or end based upon feedback from the evaluator\n", - "def route_joke(state: State):\n", - " \"\"\"Route back to joke generator or end based upon feedback from the evaluator\"\"\"\n", - "\n", - " if state[\"funny_or_not\"] == \"funny\":\n", - " return \"Accepted\"\n", - " elif state[\"funny_or_not\"] == \"not funny\":\n", - " return \"Rejected + Feedback\"\n", - "\n", - "\n", - "# Build workflow\n", - "optimizer_builder = StateGraph(State)\n", - "\n", - "# Add the nodes\n", - "optimizer_builder.add_node(\"llm_call_generator\", llm_call_generator)\n", - "optimizer_builder.add_node(\"llm_call_evaluator\", llm_call_evaluator)\n", - "\n", - "# Add edges to connect nodes\n", - "optimizer_builder.add_edge(START, \"llm_call_generator\")\n", - "optimizer_builder.add_edge(\"llm_call_generator\", \"llm_call_evaluator\")\n", - "optimizer_builder.add_conditional_edges(\n", - " \"llm_call_evaluator\",\n", - " route_joke,\n", - " { # Name returned by route_joke : Name of next node to visit\n", - " \"Accepted\": END,\n", - " \"Rejected + Feedback\": \"llm_call_generator\",\n", - " },\n", - ")\n", - "\n", - "# Compile the workflow\n", - "optimizer_workflow = optimizer_builder.compile()\n", - "\n", - "# Show the workflow\n", - "display(Image(optimizer_workflow.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = optimizer_workflow.invoke({\"topic\": \"Cats\"})\n", - "print(state[\"joke\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/86ab3e60-2000-4bff-b988-9b89a3269789/r" - ] - }, - { - "attachments": { - "c058c698-2cc8-4b46-beb9-7f1dd457e35d.jpg": { - "image/jpeg": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Agent \n", - "\n", - "Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): \n", - "\n", - "> Agents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop. It is therefore crucial to design toolsets and their documentation clearly and thoughtfully.\n", - "\n", - "> When to use agents: Agents can be used for open-ended problems where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path. The LLM will potentially operate for many turns, and you must have some level of trust in its decision-making. Agents' autonomy makes them ideal for scaling tasks in trusted environments.\n", - "\n", - "![agent.jpg](attachment:c058c698-2cc8-4b46-beb9-7f1dd457e35d.jpg)\n", - "\n", - "#### Resources\n", - "\n", - "**LangChain Academy**\n", - "\n", - "See our lesson on agents [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/agent.ipynb).\n", - "\n", - "**Examples**\n", - "\n", - "[Here](https://github.com/langchain-ai/memory-agent) is a project that uses a tool calling agent to create / store long-term memories.\n", - "\n", - "#### Example Implementation\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.tools import tool\n", - "\n", - "\n", - "# Define tools\n", - "@tool\n", - "def multiply(a: int, b: int) -> int:\n", - " \"\"\"Multiply a and b.\n", - "\n", - " Args:\n", - " a: first int\n", - " b: second int\n", - " \"\"\"\n", - " return a * b\n", - "\n", - "\n", - "@tool\n", - "def add(a: int, b: int) -> int:\n", - " \"\"\"Adds a and b.\n", - "\n", - " Args:\n", - " a: first int\n", - " b: second int\n", - " \"\"\"\n", - " return a + b\n", - "\n", - "\n", - "@tool\n", - "def divide(a: int, b: int) -> float:\n", - " \"\"\"Divide a and b.\n", - "\n", - " Args:\n", - " a: first int\n", - " b: second int\n", - " \"\"\"\n", - " return a / b\n", - "\n", - "\n", - "# Augment the LLM with tools\n", - "tools = [add, multiply, divide]\n", - "tools_by_name = {tool.name: tool for tool in tools}\n", - "llm_with_tools = llm.bind_tools(tools)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from langgraph.graph import MessagesState\n", - "from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage\n", - "\n", - "\n", - "# Nodes\n", - "def llm_call(state: MessagesState):\n", - " \"\"\"LLM decides whether to call a tool or not\"\"\"\n", - "\n", - " return {\n", - " \"messages\": [\n", - " llm_with_tools.invoke(\n", - " [\n", - " SystemMessage(\n", - " content=\"You are a helpful assistant tasked with performing arithmetic on a set of inputs.\"\n", - " )\n", - " ]\n", - " + state[\"messages\"]\n", - " )\n", - " ]\n", - " }\n", - "\n", - "\n", - "def tool_node(state: dict):\n", - " \"\"\"Performs the tool call\"\"\"\n", - "\n", - " result = []\n", - " for tool_call in state[\"messages\"][-1].tool_calls:\n", - " tool = tools_by_name[tool_call[\"name\"]]\n", - " observation = tool.invoke(tool_call[\"args\"])\n", - " result.append(ToolMessage(content=observation, tool_call_id=tool_call[\"id\"]))\n", - " return {\"messages\": result}\n", - "\n", - "\n", - "# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call\n", - "def should_continue(state: MessagesState) -> Literal[\"environment\", END]:\n", - " \"\"\"Decide if we should continue the loop or stop based upon whether the LLM made a tool call\"\"\"\n", - "\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " # If the LLM makes a tool call, then perform an action\n", - " if last_message.tool_calls:\n", - " return \"Action\"\n", - " # Otherwise, we stop (reply to the user)\n", - " return END\n", - "\n", - "\n", - "# Build workflow\n", - "agent_builder = StateGraph(MessagesState)\n", - "\n", - "# Add nodes\n", - "agent_builder.add_node(\"llm_call\", llm_call)\n", - "agent_builder.add_node(\"environment\", tool_node)\n", - "\n", - "# Add edges to connect nodes\n", - "agent_builder.add_edge(START, \"llm_call\")\n", - "agent_builder.add_conditional_edges(\n", - " \"llm_call\",\n", - " should_continue,\n", - " {\n", - " # Name returned by should_continue : Name of next node to visit\n", - " \"Action\": \"environment\",\n", - " END: END,\n", - " },\n", - ")\n", - "agent_builder.add_edge(\"environment\", \"llm_call\")\n", - "\n", - "# Compile the agent\n", - "agent = agent_builder.compile()\n", - "\n", - "# Show the agent\n", - "display(Image(agent.get_graph(xray=True).draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "messages = [HumanMessage(content=\"Add 3 and 4.\")]\n", - "messages = agent.invoke({\"messages\": messages})\n", - "for m in messages[\"messages\"]:\n", - " m.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/051f0391-6761-4f8c-a53b-22231b016690/r\n", - "\n", - "#### Pre-built \n", - "\n", - "We also have a **pre-built method** for creating an agent as defined above (using the `create_react_agent` method):\n", - "\n", - "https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "# Pass in:\n", - "# (1) the augmented LLM with tools\n", - "# (2) the tools list (which is used to create the tool node)\n", - "pre_built_agent = create_react_agent(llm_with_tools, tools=tools)\n", - "\n", - "# Show the agent\n", - "display(Image(pre_built_agent.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "messages = [HumanMessage(content=\"Add 3 and 4.\")]\n", - "messages = pre_built_agent.invoke({\"messages\": messages})\n", - "for m in messages[\"messages\"]:\n", - " m.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LangSmith Trace\n", - "\n", - "https://smith.langchain.com/public/abab6a44-29f6-4b97-8164-af77413e494d/r\n", - "\n", - "## What LangGraph provides\n", - "\n", - "By constructing each of the above in LangGraph, we get a few things: \n", - "\n", - "### Persistence: Human-in-the-Loop\n", - "\n", - "LangGraph persistence layer supports interruption and approval of actions (e.g., Human In The Loop). See [Module 3 of LangChain Academy](https://github.com/langchain-ai/langchain-academy/tree/main/module-3).\n", - "\n", - "### Persistence: Memory \n", - "\n", - "LangGraph persistence layer supports conversational (short-term) memory and long-term memory. See [Modules 2](https://github.com/langchain-ai/langchain-academy/tree/main/module-2) [and 5](https://github.com/langchain-ai/langchain-academy/tree/main/module-5) of LangChain Academy: \n", - "\n", - "### Streaming \n", - "\n", - "LangGraph provides several ways to stream workflow / agent outputs or intermediate state. See [Module 3 of LangChain Academy](https://github.com/langchain-ai/langchain-academy/blob/main/module-3/streaming-interruption.ipynb).\n", - "\n", - "\n", - "### Deployment\n", - "\n", - "LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/tutorials/workflows/img/agent.png b/docs/docs/tutorials/workflows/img/agent.png new file mode 100644 index 0000000000000000000000000000000000000000..35027af16d4f10499bb6170b47f413d3725bd388 GIT binary patch literal 81607 zcmeFZby$>L*FH>2Ng0$h0uoXZGBAMBAfTXhNSAao3<4^kG)O58f=YLXf`HQ9-3*=5 zzdb14_x(Qa^Spn2e|^XCa2&ueb6xw|vG!W$I@h^*{ZLUFAD03b1qB6PRz^Y@1?5r$ 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zDqepFLFcN=+9g@?n9lED{SCs1U56C%TQ$-O!^rtI8s}?*qr^t=>D6u9|_9J%G3R5&_(d_m!+95 zFiaK<>;7sazdg+A4n$D|bMeclH=HmAvf(P>3}>dAv3c3hKbYzJa#1B>Q7s*-V2Acp z*Ca_;4!t?wecLZJ)W7Rn5xTCqW(sTx|C`eqQ(LF>=2u`xjbrg&g}8=Up!L+ixXli%3>RbnyVAt8?m;L=>lpz8^EqUGM#hrhDP4l|>laow==$%PDTq_fMRC73JU~+y zS_DER%2#k?GA}5iIOgA9(|~Y+hN1Y~oRj}MGhSH&<=*7Earl3qTa?lP;xIM%- Workflows are systems where LLMs and tools are orchestrated through predefined code paths. +> Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. + +Here is a simple way to visualize these differences: + +![Agent Workflow](../../concepts/img/agent_workflow.png) + +When building agents and workflows, LangGraph [offers a number of benefits](https://langchain-ai.github.io/langgraph/concepts/high_level/) including persistence, streaming, and support for debugging as well as deployment. + +## Set up + +You can use [any chat model](https://python.langchain.com/docs/integrations/chat/) that supports structured outputs and tool calling. Below, we show the process of installing the packages, setting API keys, and testing structured outputs / tool calling for Anthropic. + +??? "Install dependencies" + + ```bash + pip install langchain_core langchain-anthropic langgraph + ``` + +Initialize an LLM + +```python +import os +import getpass + +from langchain_anthropic import ChatAnthropic + +def _set_env(var: str): + if not os.environ.get(var): + os.environ[var] = getpass.getpass(f"{var}: ") + + +_set_env("ANTHROPIC_API_KEY") + +llm = ChatAnthropic(model="claude-3-5-sonnet-latest") +``` + +## Building Blocks: The Augmented LLM + +LLM have [augmentations](https://www.anthropic.com/research/building-effective-agents) that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic [blog](https://www.anthropic.com/research/building-effective-agents): + +![augmented_llm.png](./img/augmented_llm.png) + + +```python +# Schema for structured output +from pydantic import BaseModel, Field + +class SearchQuery(BaseModel): + search_query: str = Field(None, description="Query that is optimized web search.") + justification: str = Field( + None, description="Why this query is relevant to the user's request." + ) + + +# Augment the LLM with schema for structured output +structured_llm = llm.with_structured_output(SearchQuery) + +# Invoke the augmented LLM +output = structured_llm.invoke("How does Calcium CT score relate to high cholesterol?") + +# Define a tool +def multiply(a: int, b: int) -> int: + return a * b + +# Augment the LLM with tools +llm_with_tools = llm.bind_tools([multiply]) + +# Invoke the LLM with input that triggers the tool call +msg = llm_with_tools.invoke("What is 2 times 3?") + +# Get the tool call +msg.tool_calls +``` + +## Prompt chaining + +In prompt chaining, each LLM call processes the output of the previous one. + +As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): + +> Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see "gate” in the diagram below) on any intermediate steps to ensure that the process is still on track. + +> When to use this workflow: This workflow is ideal for situations where the task can be easily and cleanly decomposed into fixed subtasks. The main goal is to trade off latency for higher accuracy, by making each LLM call an easier task. + +![prompt_chain.png](./img/prompt_chain.png) + +=== "Graph API" + + ```python + from typing_extensions import TypedDict + from langgraph.graph import StateGraph, START, END + from IPython.display import Image, display + + + # Graph state + class State(TypedDict): + topic: str + joke: str + improved_joke: str + final_joke: str + + + # Nodes + def generate_joke(state: State): + """First LLM call to generate initial joke""" + + msg = llm.invoke(f"Write a short joke about {state['topic']}") + return {"joke": msg.content} + + + def check_punchline(state: State): + """Gate function to check if the joke has a punchline""" + + # Simple check - does the joke contain "?" or "!" + if "?" in state["joke"] or "!" in state["joke"]: + return "Fail" + return "Pass" + + + def improve_joke(state: State): + """Second LLM call to improve the joke""" + + msg = llm.invoke(f"Make this joke funnier by adding wordplay: {state['joke']}") + return {"improved_joke": msg.content} + + + def polish_joke(state: State): + """Third LLM call for final polish""" + + msg = llm.invoke(f"Add a surprising twist to this joke: {state['improved_joke']}") + return {"final_joke": msg.content} + + + # Build workflow + workflow = StateGraph(State) + + # Add nodes + workflow.add_node("generate_joke", generate_joke) + workflow.add_node("improve_joke", improve_joke) + workflow.add_node("polish_joke", polish_joke) + + # Add edges to connect nodes + workflow.add_edge(START, "generate_joke") + workflow.add_conditional_edges( + "generate_joke", check_punchline, {"Fail": "improve_joke", "Pass": END} + ) + workflow.add_edge("improve_joke", "polish_joke") + workflow.add_edge("polish_joke", END) + + # Compile + chain = workflow.compile() + + # Show workflow + display(Image(chain.get_graph().draw_mermaid_png())) + + # Invoke + state = chain.invoke({"topic": "cats"}) + print("Initial joke:") + print(state["joke"]) + print("\n--- --- ---\n") + if "improved_joke" in state: + print("Improved joke:") + print(state["improved_joke"]) + print("\n--- --- ---\n") + + print("Final joke:") + print(state["final_joke"]) + else: + print("Joke failed quality gate - no punchline detected!") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/a0281fca-3a71-46de-beee-791468607b75/r + + **Resources:** + + **LangChain Academy** + + See our lesson on Prompt Chaining [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/chain.ipynb). + +=== "Functional API (beta)" + + ```python + from langgraph.func import entrypoint, task + + + # Tasks + @task + def generate_joke(topic: str): + """First LLM call to generate initial joke""" + msg = llm.invoke(f"Write a short joke about {topic}") + return msg.content + + + def check_punchline(joke: str): + """Gate function to check if the joke has a punchline""" + # Simple check - does the joke contain "?" or "!" + if "?" in joke or "!" in joke: + return "Fail" + + return "Pass" + + + @task + def improve_joke(joke: str): + """Second LLM call to improve the joke""" + msg = llm.invoke(f"Make this joke funnier by adding wordplay: {joke}") + return msg.content + + + @task + def polish_joke(joke: str): + """Third LLM call for final polish""" + msg = llm.invoke(f"Add a surprising twist to this joke: {joke}") + return msg.content + + + @entrypoint() + def parallel_workflow(topic: str): + original_joke = generate_joke(topic).result() + if check_punchline(original_joke) == "Pass": + return original_joke + + improved_joke = improve_joke(original_joke).result() + return polish_joke(improved_joke).result() + + # Invoke + for step in parallel_workflow.stream("cats", stream_mode="updates"): + print(step) + print("\n") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/332fa4fc-b6ca-416e-baa3-161625e69163/r + +## Parallelization + +With parallelization, LLMs work simultaneously on a task: + +>LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically. This workflow, parallelization, manifests in two key variations: Sectioning: Breaking a task into independent subtasks run in parallel. Voting: Running the same task multiple times to get diverse outputs. + +> When to use this workflow: Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results. For complex tasks with multiple considerations, LLMs generally perform better when each consideration is handled by a separate LLM call, allowing focused attention on each specific aspect. + +![parallelization.png](./img/parallelization.png) + +=== "Graph API" + + ```python + # Graph state + class State(TypedDict): + topic: str + joke: str + story: str + poem: str + combined_output: str + + + # Nodes + def call_llm_1(state: State): + """First LLM call to generate initial joke""" + + msg = llm.invoke(f"Write a joke about {state['topic']}") + return {"joke": msg.content} + + + def call_llm_2(state: State): + """Second LLM call to generate story""" + + msg = llm.invoke(f"Write a story about {state['topic']}") + return {"story": msg.content} + + + def call_llm_3(state: State): + """Third LLM call to generate poem""" + + msg = llm.invoke(f"Write a poem about {state['topic']}") + return {"poem": msg.content} + + + def aggregator(state: State): + """Combine the joke and story into a single output""" + + combined = f"Here's a story, joke, and poem about {state['topic']}!\n\n" + combined += f"STORY:\n{state['story']}\n\n" + combined += f"JOKE:\n{state['joke']}\n\n" + combined += f"POEM:\n{state['poem']}" + return {"combined_output": combined} + + + # Build workflow + parallel_builder = StateGraph(State) + + # Add nodes + parallel_builder.add_node("call_llm_1", call_llm_1) + parallel_builder.add_node("call_llm_2", call_llm_2) + parallel_builder.add_node("call_llm_3", call_llm_3) + parallel_builder.add_node("aggregator", aggregator) + + # Add edges to connect nodes + parallel_builder.add_edge(START, "call_llm_1") + parallel_builder.add_edge(START, "call_llm_2") + parallel_builder.add_edge(START, "call_llm_3") + parallel_builder.add_edge("call_llm_1", "aggregator") + parallel_builder.add_edge("call_llm_2", "aggregator") + parallel_builder.add_edge("call_llm_3", "aggregator") + parallel_builder.add_edge("aggregator", END) + parallel_workflow = parallel_builder.compile() + + # Show workflow + display(Image(parallel_workflow.get_graph().draw_mermaid_png())) + + # Invoke + state = parallel_workflow.invoke({"topic": "cats"}) + print(state["combined_output"]) + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/3be2e53c-ca94-40dd-934f-82ff87fac277/r + + **Resources:** + + **Documentation** + + See our documentation on parallelization [here](https://langchain-ai.github.io/langgraph/how-tos/branching/). + + **LangChain Academy** + + See our lesson on parallelization [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/simple-graph.ipynb). + +=== "Functional API (beta)" + + ```python + @task + def call_llm_1(topic: str): + """First LLM call to generate initial joke""" + msg = llm.invoke(f"Write a joke about {topic}") + return msg.content + + + @task + def call_llm_2(topic: str): + """Second LLM call to generate story""" + msg = llm.invoke(f"Write a story about {topic}") + return msg.content + + + @task + def call_llm_3(topic): + """Third LLM call to generate poem""" + msg = llm.invoke(f"Write a poem about {topic}") + return msg.content + + + @task + def aggregator(topic, joke, story, poem): + """Combine the joke and story into a single output""" + + combined = f"Here's a story, joke, and poem about {topic}!\n\n" + combined += f"STORY:\n{story}\n\n" + combined += f"JOKE:\n{joke}\n\n" + combined += f"POEM:\n{poem}" + return combined + + + # Build workflow + @entrypoint() + def parallel_workflow(topic: str): + joke_fut = call_llm_1(topic) + story_fut = call_llm_2(topic) + poem_fut = call_llm_3(topic) + return aggregator( + topic, joke_fut.result(), story_fut.result(), poem_fut.result() + ).result() + + # Invoke + for step in parallel_workflow.stream("cats", stream_mode="updates"): + print(step) + print("\n") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/623d033f-e814-41e9-80b1-75e6abb67801/r + +## Routing + +Routing classifies an input and directs it to a followup task. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): + +> Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs. + +> When to use this workflow: Routing works well for complex tasks where there are distinct categories that are better handled separately, and where classification can be handled accurately, either by an LLM or a more traditional classification model/algorithm. + +![routing.png](./img/routing.png) + + +=== "Graph API" + + ```python + from typing_extensions import Literal + from langchain_core.messages import HumanMessage, SystemMessage + + + # Schema for structured output to use as routing logic + class Route(BaseModel): + step: Literal["poem", "story", "joke"] = Field( + None, description="The next step in the routing process" + ) + + + # Augment the LLM with schema for structured output + router = llm.with_structured_output(Route) + + + # State + class State(TypedDict): + input: str + decision: str + output: str + + + # Nodes + def llm_call_1(state: State): + """Write a story""" + + result = llm.invoke(state["input"]) + return {"output": result.content} + + + def llm_call_2(state: State): + """Write a joke""" + + result = llm.invoke(state["input"]) + return {"output": result.content} + + + def llm_call_3(state: State): + """Write a poem""" + + result = llm.invoke(state["input"]) + return {"output": result.content} + + + def llm_call_router(state: State): + """Route the input to the appropriate node""" + + # Run the augmented LLM with structured output to serve as routing logic + decision = router.invoke( + [ + SystemMessage( + content="Route the input to story, joke, or poem based on the user's request." + ), + HumanMessage(content=state["input"]), + ] + ) + + return {"decision": decision.step} + + + # Conditional edge function to route to the appropriate node + def route_decision(state: State): + # Return the node name you want to visit next + if state["decision"] == "story": + return "llm_call_1" + elif state["decision"] == "joke": + return "llm_call_2" + elif state["decision"] == "poem": + return "llm_call_3" + + + # Build workflow + router_builder = StateGraph(State) + + # Add nodes + router_builder.add_node("llm_call_1", llm_call_1) + router_builder.add_node("llm_call_2", llm_call_2) + router_builder.add_node("llm_call_3", llm_call_3) + router_builder.add_node("llm_call_router", llm_call_router) + + # Add edges to connect nodes + router_builder.add_edge(START, "llm_call_router") + router_builder.add_conditional_edges( + "llm_call_router", + route_decision, + { # Name returned by route_decision : Name of next node to visit + "llm_call_1": "llm_call_1", + "llm_call_2": "llm_call_2", + "llm_call_3": "llm_call_3", + }, + ) + router_builder.add_edge("llm_call_1", END) + router_builder.add_edge("llm_call_2", END) + router_builder.add_edge("llm_call_3", END) + + # Compile workflow + router_workflow = router_builder.compile() + + # Show the workflow + display(Image(router_workflow.get_graph().draw_mermaid_png())) + + # Invoke + state = router_workflow.invoke({"input": "Write me a joke about cats"}) + print(state["output"]) + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/c4580b74-fe91-47e4-96fe-7fac598d509c/r + + **Resources:** + + **LangChain Academy** + + See our lesson on routing [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/router.ipynb). + + **Examples** + + [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is RAG workflow that routes questions. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI). + +=== "Functional API (beta)" + + ```python + from typing_extensions import Literal + from pydantic import BaseModel + from langchain_core.messages import HumanMessage, SystemMessage + + + # Schema for structured output to use as routing logic + class Route(BaseModel): + step: Literal["poem", "story", "joke"] = Field( + None, description="The next step in the routing process" + ) + + + # Augment the LLM with schema for structured output + router = llm.with_structured_output(Route) + + + @task + def llm_call_1(input_: str): + """Write a story""" + result = llm.invoke(input_) + return result.content + + + @task + def llm_call_2(input_: str): + """Write a joke""" + result = llm.invoke(input_) + return result.content + + + @task + def llm_call_3(input_: str): + """Write a poem""" + result = llm.invoke(input_) + return result.content + + + def llm_call_router(input_: str): + """Route the input to the appropriate node""" + # Run the augmented LLM with structured output to serve as routing logic + decision = router.invoke( + [ + SystemMessage( + content="Route the input to story, joke, or poem based on the user's request." + ), + HumanMessage(content=input_), + ] + ) + return decision.step + + + # Create workflow + @entrypoint() + def router_workflow(input_: str): + next_step = llm_call_router(input_) + if next_step == "story": + llm_call = llm_call_1 + elif next_step == "joke": + llm_call = llm_call_2 + elif next_step == "poem": + llm_call = llm_call_3 + + return llm_call(input_).result() + + # Invoke + for step in router_workflow.stream("Write me a joke about cats", stream_mode="updates"): + print(step) + print("\n") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/5e2eb979-82dd-402c-b1a0-a8cceaf2a28a/r + +## Orchestrator-Worker + +With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): + +> In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results. + +> When to use this workflow: This workflow is well-suited for complex tasks where you can’t predict the subtasks needed (in coding, for example, the number of files that need to be changed and the nature of the change in each file likely depend on the task). Whereas it’s topographically similar, the key difference from parallelization is its flexibility—subtasks aren't pre-defined, but determined by the orchestrator based on the specific input. + +![worker.png](./img/worker.png) + + +=== "Graph API" + + ```python + from typing import Annotated, List + import operator + + + # Schema for structured output to use in planning + class Section(BaseModel): + name: str = Field( + description="Name for this section of the report.", + ) + description: str = Field( + description="Brief overview of the main topics and concepts to be covered in this section.", + ) + + + class Sections(BaseModel): + sections: List[Section] = Field( + description="Sections of the report.", + ) + + + # Augment the LLM with schema for structured output + planner = llm.with_structured_output(Sections) + ``` + + **Creating Workers in LangGraph** + + Because orchestrator-worker workflows are common, LangGraph **has the `Send` API to support this**. It lets you dynamically create worker nodes and send each one a specific input. Each worker has its own state, and all worker outputs are written to a *shared state key* that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. As you can see below, we iterate over a list of sections and `Send` each to a worker node. See further documentation [here](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) and [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#send). + + ```python + from langgraph.constants import Send + + + # Graph state + class State(TypedDict): + topic: str # Report topic + sections: list[Section] # List of report sections + completed_sections: Annotated[ + list, operator.add + ] # All workers write to this key in parallel + final_report: str # Final report + + + # Worker state + class WorkerState(TypedDict): + section: Section + completed_sections: Annotated[list, operator.add] + + + # Nodes + def orchestrator(state: State): + """Orchestrator that generates a plan for the report""" + + # Generate queries + report_sections = planner.invoke( + [ + SystemMessage(content="Generate a plan for the report."), + HumanMessage(content=f"Here is the report topic: {state['topic']}"), + ] + ) + + return {"sections": report_sections.sections} + + + def llm_call(state: WorkerState): + """Worker writes a section of the report""" + + # Generate section + section = llm.invoke( + [ + SystemMessage( + content="Write a report section following the provided name and description. Include no preamble for each section. Use markdown formatting." + ), + HumanMessage( + content=f"Here is the section name: {state['section'].name} and description: {state['section'].description}" + ), + ] + ) + + # Write the updated section to completed sections + return {"completed_sections": [section.content]} + + + def synthesizer(state: State): + """Synthesize full report from sections""" + + # List of completed sections + completed_sections = state["completed_sections"] + + # Format completed section to str to use as context for final sections + completed_report_sections = "\n\n---\n\n".join(completed_sections) + + return {"final_report": completed_report_sections} + + + # Conditional edge function to create llm_call workers that each write a section of the report + def assign_workers(state: State): + """Assign a worker to each section in the plan""" + + # Kick off section writing in parallel via Send() API + return [Send("llm_call", {"section": s}) for s in state["sections"]] + + + # Build workflow + orchestrator_worker_builder = StateGraph(State) + + # Add the nodes + orchestrator_worker_builder.add_node("orchestrator", orchestrator) + orchestrator_worker_builder.add_node("llm_call", llm_call) + orchestrator_worker_builder.add_node("synthesizer", synthesizer) + + # Add edges to connect nodes + orchestrator_worker_builder.add_edge(START, "orchestrator") + orchestrator_worker_builder.add_conditional_edges( + "orchestrator", assign_workers, ["llm_call"] + ) + orchestrator_worker_builder.add_edge("llm_call", "synthesizer") + orchestrator_worker_builder.add_edge("synthesizer", END) + + # Compile the workflow + orchestrator_worker = orchestrator_worker_builder.compile() + + # Show the workflow + display(Image(orchestrator_worker.get_graph().draw_mermaid_png())) + + # Invoke + state = orchestrator_worker.invoke({"topic": "Create a report on LLM scaling laws"}) + + from IPython.display import Markdown + Markdown(state["final_report"]) + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/78cbcfc3-38bf-471d-b62a-b299b144237d/r + + **Resources:** + + **LangChain Academy** + + See our lesson on orchestrator-worker [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-4/map-reduce.ipynb). + + **Examples** + + [Here](https://github.com/langchain-ai/report-mAIstro) is a project that uses orchestrator-worker for report planning and writing. See our video [here](https://www.youtube.com/watch?v=wSxZ7yFbbas). + + +=== "Functional API (beta)" + + ```python + from typing import List + + + # Schema for structured output to use in planning + class Section(BaseModel): + name: str = Field( + description="Name for this section of the report.", + ) + description: str = Field( + description="Brief overview of the main topics and concepts to be covered in this section.", + ) + + + class Sections(BaseModel): + sections: List[Section] = Field( + description="Sections of the report.", + ) + + + # Augment the LLM with schema for structured output + planner = llm.with_structured_output(Sections) + + + @task + def orchestrator(topic: str): + """Orchestrator that generates a plan for the report""" + # Generate queries + report_sections = planner.invoke( + [ + SystemMessage(content="Generate a plan for the report."), + HumanMessage(content=f"Here is the report topic: {topic}"), + ] + ) + + return report_sections.sections + + + @task + def llm_call(section: Section): + """Worker writes a section of the report""" + + # Generate section + result = llm.invoke( + [ + SystemMessage(content="Write a report section."), + HumanMessage( + content=f"Here is the section name: {section.name} and description: {section.description}" + ), + ] + ) + + # Write the updated section to completed sections + return result.content + + + @task + def synthesizer(completed_sections: list[str]): + """Synthesize full report from sections""" + final_report = "\n\n---\n\n".join(completed_sections) + return final_report + + + @entrypoint() + def orchestrator_worker(topic: str): + sections = orchestrator(topic).result() + section_futures = [llm_call(section) for section in sections] + final_report = synthesizer( + [section_fut.result() for section_fut in section_futures] + ).result() + return final_report + + # Invoke + report = orchestrator_worker.invoke("Create a report on LLM scaling laws") + from IPython.display import Markdown + Markdown(report) + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/75a636d0-6179-4a12-9836-e0aa571e87c5/r + +## Evaluator-optimizer + +In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop: + +> In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop. + +> When to use this workflow: This workflow is particularly effective when we have clear evaluation criteria, and when iterative refinement provides measurable value. The two signs of good fit are, first, that LLM responses can be demonstrably improved when a human articulates their feedback; and second, that the LLM can provide such feedback. This is analogous to the iterative writing process a human writer might go through when producing a polished document. + +![evaluator_optimizer.png](./img/evaluator_optimizer.png) + +=== "Graph API" + + ```python + # Graph state + class State(TypedDict): + joke: str + topic: str + feedback: str + funny_or_not: str + + + # Schema for structured output to use in evaluation + class Feedback(BaseModel): + grade: Literal["funny", "not funny"] = Field( + description="Decide if the joke is funny or not.", + ) + feedback: str = Field( + description="If the joke is not funny, provide feedback on how to improve it.", + ) + + + # Augment the LLM with schema for structured output + evaluator = llm.with_structured_output(Feedback) + + + # Nodes + def llm_call_generator(state: State): + """LLM generates a joke""" + + if state.get("feedback"): + msg = llm.invoke( + f"Write a joke about {state['topic']} but take into account the feedback: {state['feedback']}" + ) + else: + msg = llm.invoke(f"Write a joke about {state['topic']}") + return {"joke": msg.content} + + + def llm_call_evaluator(state: State): + """LLM evaluates the joke""" + + grade = evaluator.invoke(f"Grade the joke {state['joke']}") + return {"funny_or_not": grade.grade, "feedback": grade.feedback} + + + # Conditional edge function to route back to joke generator or end based upon feedback from the evaluator + def route_joke(state: State): + """Route back to joke generator or end based upon feedback from the evaluator""" + + if state["funny_or_not"] == "funny": + return "Accepted" + elif state["funny_or_not"] == "not funny": + return "Rejected + Feedback" + + + # Build workflow + optimizer_builder = StateGraph(State) + + # Add the nodes + optimizer_builder.add_node("llm_call_generator", llm_call_generator) + optimizer_builder.add_node("llm_call_evaluator", llm_call_evaluator) + + # Add edges to connect nodes + optimizer_builder.add_edge(START, "llm_call_generator") + optimizer_builder.add_edge("llm_call_generator", "llm_call_evaluator") + optimizer_builder.add_conditional_edges( + "llm_call_evaluator", + route_joke, + { # Name returned by route_joke : Name of next node to visit + "Accepted": END, + "Rejected + Feedback": "llm_call_generator", + }, + ) + + # Compile the workflow + optimizer_workflow = optimizer_builder.compile() + + # Show the workflow + display(Image(optimizer_workflow.get_graph().draw_mermaid_png())) + + # Invoke + state = optimizer_workflow.invoke({"topic": "Cats"}) + print(state["joke"]) + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/86ab3e60-2000-4bff-b988-9b89a3269789/r + + **Resources:** + + **Examples** + + [Here](https://github.com/langchain-ai/research-rabbit) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8). + + [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI). + +=== "Functional API (beta)" + + ```python + # Schema for structured output to use in evaluation + class Feedback(BaseModel): + grade: Literal["funny", "not funny"] = Field( + description="Decide if the joke is funny or not.", + ) + feedback: str = Field( + description="If the joke is not funny, provide feedback on how to improve it.", + ) + + + # Augment the LLM with schema for structured output + evaluator = llm.with_structured_output(Feedback) + + + # Nodes + @task + def llm_call_generator(topic: str, feedback: Feedback): + """LLM generates a joke""" + if feedback: + msg = llm.invoke( + f"Write a joke about {topic} but take into account the feedback: {feedback}" + ) + else: + msg = llm.invoke(f"Write a joke about {topic}") + return msg.content + + + @task + def llm_call_evaluator(joke: str): + """LLM evaluates the joke""" + feedback = evaluator.invoke(f"Grade the joke {joke}") + return feedback + + + @entrypoint() + def optimizer_workflow(topic: str): + feedback = None + while True: + joke = llm_call_generator(topic, feedback).result() + feedback = llm_call_evaluator(joke).result() + if feedback.grade == "funny": + break + + return joke + + # Invoke + for step in optimizer_workflow.stream("Cats", stream_mode="updates"): + print(step) + print("\n") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/f66830be-4339-4a6b-8a93-389ce5ae27b4/r + +## Agent + +Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): + +> Agents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop. It is therefore crucial to design toolsets and their documentation clearly and thoughtfully. + +> When to use agents: Agents can be used for open-ended problems where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path. The LLM will potentially operate for many turns, and you must have some level of trust in its decision-making. Agents' autonomy makes them ideal for scaling tasks in trusted environments. + +![agent.png](./img/agent.png) + + +```python +from langchain_core.tools import tool + + +# Define tools +@tool +def multiply(a: int, b: int) -> int: + """Multiply a and b. + + Args: + a: first int + b: second int + """ + return a * b + + +@tool +def add(a: int, b: int) -> int: + """Adds a and b. + + Args: + a: first int + b: second int + """ + return a + b + + +@tool +def divide(a: int, b: int) -> float: + """Divide a and b. + + Args: + a: first int + b: second int + """ + return a / b + + +# Augment the LLM with tools +tools = [add, multiply, divide] +tools_by_name = {tool.name: tool for tool in tools} +llm_with_tools = llm.bind_tools(tools) +``` + +=== "Graph API" + + ```python + from langgraph.graph import MessagesState + from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage + + + # Nodes + def llm_call(state: MessagesState): + """LLM decides whether to call a tool or not""" + + return { + "messages": [ + llm_with_tools.invoke( + [ + SystemMessage( + content="You are a helpful assistant tasked with performing arithmetic on a set of inputs." + ) + ] + + state["messages"] + ) + ] + } + + + def tool_node(state: dict): + """Performs the tool call""" + + result = [] + for tool_call in state["messages"][-1].tool_calls: + tool = tools_by_name[tool_call["name"]] + observation = tool.invoke(tool_call["args"]) + result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"])) + return {"messages": result} + + + # Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call + def should_continue(state: MessagesState) -> Literal["environment", END]: + """Decide if we should continue the loop or stop based upon whether the LLM made a tool call""" + + messages = state["messages"] + last_message = messages[-1] + # If the LLM makes a tool call, then perform an action + if last_message.tool_calls: + return "Action" + # Otherwise, we stop (reply to the user) + return END + + + # Build workflow + agent_builder = StateGraph(MessagesState) + + # Add nodes + agent_builder.add_node("llm_call", llm_call) + agent_builder.add_node("environment", tool_node) + + # Add edges to connect nodes + agent_builder.add_edge(START, "llm_call") + agent_builder.add_conditional_edges( + "llm_call", + should_continue, + { + # Name returned by should_continue : Name of next node to visit + "Action": "environment", + END: END, + }, + ) + agent_builder.add_edge("environment", "llm_call") + + # Compile the agent + agent = agent_builder.compile() + + # Show the agent + display(Image(agent.get_graph(xray=True).draw_mermaid_png())) + + # Invoke + messages = [HumanMessage(content="Add 3 and 4.")] + messages = agent.invoke({"messages": messages}) + for m in messages["messages"]: + m.pretty_print() + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/051f0391-6761-4f8c-a53b-22231b016690/r + + **Resources:** + + **LangChain Academy** + + See our lesson on agents [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/agent.ipynb). + + **Examples** + + [Here](https://github.com/langchain-ai/memory-agent) is a project that uses a tool calling agent to create / store long-term memories. + +=== "Functional API (beta)" + + ```python + from langgraph.graph import add_messages + from langchain_core.messages import ( + SystemMessage, + HumanMessage, + BaseMessage, + ToolCall, + ) + + + @task + def call_llm(messages: list[BaseMessage]): + """LLM decides whether to call a tool or not""" + return llm_with_tools.invoke( + [ + SystemMessage( + content="You are a helpful assistant tasked with performing arithmetic on a set of inputs." + ) + ] + + messages + ) + + + @task + def call_tool(tool_call: ToolCall): + """Performs the tool call""" + tool = tools_by_name[tool_call["name"]] + return tool.invoke(tool_call) + + + @entrypoint() + def agent(messages: list[BaseMessage]): + llm_response = call_llm(messages).result() + + while True: + if not llm_response.tool_calls: + break + + # Execute tools + tool_result_futures = [ + call_tool(tool_call) for tool_call in llm_response.tool_calls + ] + tool_results = [fut.result() for fut in tool_result_futures] + messages = add_messages(messages, [llm_response, *tool_results]) + llm_response = call_llm(messages).result() + + messages = add_messages(messages, llm_response) + return messages + + # Invoke + messages = [HumanMessage(content="Add 3 and 4.")] + for chunk in agent.stream(messages, stream_mode="updates"): + print(chunk) + print("\n") + ``` + + **LangSmith Trace** + + https://smith.langchain.com/public/42ae8bf9-3935-4504-a081-8ddbcbfc8b2e/r + +#### Pre-built + +LangGraph also provides a **pre-built method** for creating an agent as defined above (using the [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] function): + +https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/ + +```python +from langgraph.prebuilt import create_react_agent + +# Pass in: +# (1) the augmented LLM with tools +# (2) the tools list (which is used to create the tool node) +pre_built_agent = create_react_agent(llm, tools=tools) + +# Show the agent +display(Image(pre_built_agent.get_graph().draw_mermaid_png())) + +# Invoke +messages = [HumanMessage(content="Add 3 and 4.")] +messages = pre_built_agent.invoke({"messages": messages}) +for m in messages["messages"]: + m.pretty_print() +``` + +**LangSmith Trace** + +https://smith.langchain.com/public/abab6a44-29f6-4b97-8164-af77413e494d/r + +## What LangGraph provides + +By constructing each of the above in LangGraph, we get a few things: + +### Persistence: Human-in-the-Loop + +LangGraph persistence layer supports interruption and approval of actions (e.g., Human In The Loop). See [Module 3 of LangChain Academy](https://github.com/langchain-ai/langchain-academy/tree/main/module-3). + +### Persistence: Memory + +LangGraph persistence layer supports conversational (short-term) memory and long-term memory. See [Modules 2](https://github.com/langchain-ai/langchain-academy/tree/main/module-2) [and 5](https://github.com/langchain-ai/langchain-academy/tree/main/module-5) of LangChain Academy: + +### Streaming + +LangGraph provides several ways to stream workflow / agent outputs or intermediate state. See [Module 3 of LangChain Academy](https://github.com/langchain-ai/langchain-academy/blob/main/module-3/streaming-interruption.ipynb). + + +### Deployment + +LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy. \ No newline at end of file diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 434612ac1..93451afbe 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -118,6 +118,8 @@ nav: - how-tos/persistence_postgres.ipynb - how-tos/persistence_mongodb.ipynb - how-tos/persistence_redis.ipynb + - how-tos/persistence-functional.ipynb + - how-tos/cross-thread-persistence-functional.ipynb - Memory: - Memory: how-tos#memory - how-tos/memory/manage-conversation-history.ipynb @@ -132,6 +134,8 @@ nav: - how-tos/human_in_the_loop/wait-user-input.ipynb - how-tos/human_in_the_loop/time-travel.ipynb - how-tos/human_in_the_loop/review-tool-calls.ipynb + - how-tos/wait-user-input-functional.ipynb + - how-tos/review-tool-calls-functional.ipynb - Streaming: - Streaming: how-tos#streaming - how-tos/stream-values.ipynb @@ -163,6 +167,8 @@ nav: - how-tos/agent-handoffs.ipynb - how-tos/multi-agent-network.ipynb - how-tos/multi-agent-multi-turn-convo.ipynb + - how-tos/multi-agent-network-functional.ipynb + - how-tos/multi-agent-multi-turn-convo-functional.ipynb - State Management: - State Management: how-tos#state-management - how-tos/state-model.ipynb @@ -185,6 +191,7 @@ nav: - how-tos/create-react-agent-hitl.ipynb - how-tos/create-react-agent-structured-output.ipynb - how-tos/react-agent-from-scratch.ipynb + - how-tos/react-agent-from-scratch-functional.ipynb - LangGraph Platform: - LangGraph Platform: how-tos#langgraph-platform - Application Structure: @@ -265,6 +272,7 @@ nav: - concepts/persistence.md - concepts/memory.md - concepts/streaming.md + - concepts/functional_api.md - LangGraph Platform: - LangGraph Platform: concepts#langgraph-platform - High Level: @@ -296,7 +304,7 @@ nav: - Quick Start: - Quick Start: tutorials#quick-start - tutorials/introduction.ipynb - - tutorials/workflows.ipynb + - tutorials/workflows/index.md - tutorials/langgraph-platform/local-server.md - cloud/quick_start.md - Chatbots: