From 3e1bbd3123a3696040589112154a22239d610e1c Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 13 Feb 2025 18:23:49 -0500 Subject: [PATCH] docs: cell magic to shell block conversion (#3433) * add handling for blocks that use magic commands like %pip to convert them into bash * Apply new logic to another notebook --- docs/_scripts/notebook_convert.py | 54 +- .../md_executable/index.md.j2 | 2 + docs/docs/how-tos/branching.ipynb | 541 ------------------ docs/docs/how-tos/branching.md | 286 +++++++++ docs/docs/how-tos/create-react-agent.md | 2 +- docs/docs/how-tos/index.md | 2 +- docs/mkdocs.yml | 2 +- .../unit_tests/test_notebook_conversion.py | 7 +- 8 files changed, 342 insertions(+), 554 deletions(-) delete mode 100644 docs/docs/how-tos/branching.ipynb create mode 100644 docs/docs/how-tos/branching.md diff --git a/docs/_scripts/notebook_convert.py b/docs/_scripts/notebook_convert.py index 842daa9c8..092891aa4 100644 --- a/docs/_scripts/notebook_convert.py +++ b/docs/_scripts/notebook_convert.py @@ -27,16 +27,52 @@ def _uses_input(source: str) -> bool: return False +def _rewrite_cell_magic(code: str) -> str: + """Process a code block that uses cell magic.:w + + - Lines starting with "%%capture" are ignored. + - Lines starting with "%pip" are rewritten by removing the leading "%" character. + - Any other non-empty line causes a NotImplementedError. + + Args: + code (str): The original code block. + + Returns: + str: The transformed code block. + + Raises: + NotImplementedError: If a line doesn't start with either "%%capture" or "%pip". + """ + rewritten_lines = [] + + for line in code.splitlines(): + stripped = line.strip() + # Skip empty lines + if not stripped: + continue + # Ignore %%capture lines + if stripped.startswith("%%capture"): + continue + # Rewrite %pip lines by dropping the '%' + elif stripped.startswith("%pip"): + # Drop the leading '%' character + rewritten_lines.append(stripped[1:]) + # Anything else is not supported + else: + raise NotImplementedError(f"Unhandled line: {line}") + + return "\n".join(rewritten_lines) + + class EscapePreprocessor(Preprocessor): - def __init__(self, rewrite_links: bool = True, **kwargs) -> None: + def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None: super().__init__(**kwargs) - self.rewrite_links = rewrite_links + self.markdown_exec_migration = markdown_exec_migration def preprocess_cell(self, cell, resources, cell_index): if cell.cell_type == "markdown": - if self.rewrite_links: - # We'll need to adjust the logic for this to keep markdown format - # but link to markdown files rather than ipynb files. + if not self.markdown_exec_migration: + # Old logic is to convert ipynb links to HTML links cell.source = re.sub( r"(?\1', @@ -110,6 +146,12 @@ class EscapePreprocessor(Preprocessor): ) cell.metadata["exec"] = is_exec + if self.markdown_exec_migration: + # For markdown exec migration we'll re-write cell magic as bash commands + if source.startswith("%%"): + cell.source = _rewrite_cell_magic(source) + cell.metadata["language"] = "shell" + # Remove noqa comments cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE) # escape ``` in code @@ -208,7 +250,7 @@ exporter = MarkdownExporter( md_executable = MarkdownExporter( preprocessors=[ ExtractAttachmentsPreprocessor, - EscapePreprocessor(rewrite_links=False), + EscapePreprocessor(markdown_exec_migration=True), ], template_name="md_executable", extra_template_basedirs=[ diff --git a/docs/_scripts/notebook_convert_templates/md_executable/index.md.j2 b/docs/_scripts/notebook_convert_templates/md_executable/index.md.j2 index 1d081f3e7..25f18fc97 100644 --- a/docs/_scripts/notebook_convert_templates/md_executable/index.md.j2 +++ b/docs/_scripts/notebook_convert_templates/md_executable/index.md.j2 @@ -5,6 +5,8 @@ ``` {%- if 'magics_language' in cell.metadata -%} {{ cell.metadata.magics_language}} +{%- elif cell.metadata.get('language') == "shell" -%} + shell {%- elif 'name' in nb.metadata.get('language_info', {}) -%} {{ nb.metadata.language_info.name }}{% if cell.metadata.exec|default(false) %} exec="on" source="above" session="1" result="ansi"{% endif %} {%- endif %} diff --git a/docs/docs/how-tos/branching.ipynb b/docs/docs/how-tos/branching.ipynb deleted file mode 100644 index d1d6dd494..000000000 --- a/docs/docs/how-tos/branching.ipynb +++ /dev/null @@ -1,541 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "51f122de-b2ce-4c21-a5a7-c3be70c28a91.png": { - "image/png": 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yZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+ClCweXwAAAAcaURPVAAAAAIAAAAAAAABDwAAACgAAAEPAAABDwAARV8dcCWfAABAAElEQVR4AezdB3wURfvA8UcgdAgdqaElNAEVFBSk96agf1REFJSiooAFRXxtiAooShHEBkhRULEjRTpIB0MTCC0JJEBooZcA/5mJt97mLqSQS+4uv30/8XZnd2dnvjcvnzyZ2ZmbrqlN2BBAAAEEEEAAAQQQQAABBDK9wE0EiJm+DQCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQaAgIIIICARwXOnDkjW7ZssT2jWrVqEhgYaEvjAAEEEEAAAQQyXoAAMeO/A0qAAAII+LVA+/btXQJEXeHy5ctL9erVJTg4WJo1ayY1atTwawcqhwACCCCAgC8IECD6wrdEGRFAAAEfFnjooYdk1apVSdbgrrvuMoFi8+bNTfCY5A1cgAACCCCAAAJpLkCAmOakZIgAAggg4Cxw8uRJmTRpknz88ccmOTw83HwGBQU5X2bb79q1q9x7772mVzFPnjy2cxwggAACCCCAgOcECBA9Z0vOCCCAQKYRuBAncuLsJQnbFykHDx2Wc+cvyN6wXbJ31zY5Gn1AjkRHysljMSn2qF27tjz99NOiexXZEEAAAQQQQMDzAgSInjfmCQgggIBfCcTFxcmmTZtk2fKVsnNvhOyPiJDDB1UAePSQx+rZqEkz6fZIV2nZgkDRY8hkjAACCCCAgBIgQKQZIIAAAggkKbB06VIJDQ2VjRs3yoqVK+XypUtJ3qMvuClLFilYtKQULFbC+syTr6DkzhsoOfPmV5/xP7nUca5/94+q3sZdm/6S3VvWyPpFP9ue065rHxnw/IsSUjS7LZ0DBBBAAAEEEEgbAQLEtHEkFwQQQMDvBJYsWSKLFy8W/bl///7r1k8HdyWCguXmoBApG3yLlChf2QSEBYoUv+591zs5dlBXCQtdI2WCq0tk2Dbr0oo17pBXBr0kLRvWtdLYQQABBBBAAIG0ESBATBtHckEAAQT8RuCnn36SyZMnm2Gk16tUtTsaSdU6jaWSCthKVax6vUtTde6PaWNk9+bV0qRzLzV8NVp+/uJ9uXj+rMkrW7YAeemlF6Vv376pypubEEAAAQQQQMC9AAGiexdSEUAAgUwnEBUVJYMHDzY9holVvlDx0ipge0J0cFi0VOKzkCZ2/42knzp+VOZ9M06W/zLVyqZpq3Yy6bPx1jE7CCCAAAIIIHBjAgSIN+bH3QgggIDfCOilJVaq9wsT2+q2vF9ad+svhYuXSuySdEn/e/k8+XrEQIm7dNE8r0HztjL9ywnp8mweggACCCCAgL8LECD6+zdM/RBAAIFkCLz66qsyffp0t1fqXsM2KjCs27Kz2/MZkbhn63r54q2+cvbUCfP4O5u0lZmTJkiWmzKiNDwTAQQQQAAB/xEgQPSf75KaIIAAAqkSGD9+vAwfPtztvd7Sa+iucDEHw2Xc4EflxOGD5vRtDdvK5M/HS4GcRInuvEhDAAEEEEAgOQIEiMlR4hoEEEDATwV+/vlnee6559zW7pEXRnpVr6G7Ql44d1ZGDbhfDoWHmdM6SPxpKsNN3VmRhgACCCCAQHIECBCTo8Q1CCCAgB8K6AXvO3bsKNu2/beEhKOaDw94T+5q08Vx6PWfHzzXSSJ2bjblvLtZW/nmK4JEr//SKCACCCCAgFcKECB65ddCoRBAAAHPC+ilLN544w2XB3V84mVp3qW3S7q3Jwx/qp0c3LvDFPPlt4bL048/5O1FpnwIIIAAAgh4nQABotd9JRQIAQQQ8LzA2bNnpUOHDrJnzx7bw5o/2Ec69hxkS/OVg9MnjsnHL3SRmIP7TZG///1PueOWYF8pPuVEAAEEEEDAKwQIEL3ia6AQCCCAQPoKTJw4Ud59913bQ+u37yoPPjvUluZrBxuXzpHJ7z5ril2hai1ZPPcXX6sC5UUAAQQQQCBDBQgQM5SfhyOAAALpL3D8+HHTe3jgwAHr4bc3bi+PDx5tHfvyzsyxr8vK3+KX7OjS7QkZOex1X64OZUcAAQQQQCBdBQgQ05WbhyGAAAIZLzBmzBj58MMPrYLkypNPBn70vdwcVMlK8+WdQxF7ZMKrj8uJmChTjdeHvidPdO/qy1Wi7AgggAACCKSbAAFiulHzIAQQQCDjBa5evSqNGzeW8PBwqzBtuw+Q1o/ED8u0En18Z9nPU+X78W+aWhS7uZQsXjhf8ubN6+O1ovgIIIAAAgh4XoAA0fPGPAEBBBDwGoEFCxbIk08+aZWnZPnKMvDjHyRHzlxWmr/sfDL4Mdm5cYWpzptD35Ue3R/xl6pRDwQQQAABBDwmQIDoMVoyRgABBLxPYPDgwTJjxgyrYN1eHCl3tuhsHfvTzsalv6sJa54zVapxe1357cdZ/lQ96oKAzwksWrRIPv/8c8mXL59UqFBBGjRoIMHBwVK0aFHJkiWLz9WHAiPgrwIEiP76zVIvBBBAIIGAXtqicZMmcuTwYXOmer2m0uetzxNc5T+HV+Li5N1eLSUmKn447cSvpkrrZg39p4LUBAEfE3jzzTdl0qRJbkutA8Vq1apJ1apVJSQkRCpVqiSlS5eWrFmzur2eRAQQ8JwAAaLnbMkZAQQQ8CqBX3/9Vfr162eV6dkR0yW4Vj3r2B93fv7ifVn4XXwQ3LLD/fL5uFH+WE3q5CUCG/edlDnrD8nCjYfl/IU4l1LVCikkbW4vLu3rlJBsWW9yOe/vCe+//75MmDAh2dXMkyePdOnSRe6991657bbbkn0fFyKAwI0JECDemB93I4AAAj4jMGDAAPnxxx9Nee9odp88Oui/mUx9phIpLOi+7Rvlo4H/Z+7Kmi1AFv65QMqXL5/CXLgcgZQJRJ+4IHM2HJJ5KlCMiD7jcnOxwrmkVe3i0qV+aSmaP4fLeX9N0KMYpkyZImFhYXLp0iVZvHix6LTkbE2bNpVXXnlFKleunJzLuSadBc6dOydjx46VXbt2ycsvv2x6gdO5CDwuDQUIENMQk6wQQAABbxbQs5fu27fPFPHxwWPk9sbtvLm4aVa2sYMekbDQ1Sa/Pv0GyKsvDUyzvMkIgaQElmw9Kku2xshf24/KqdOXXC5vUKuYNKtZTBrfUlRyZc9c7+F1795dli5dKvXq1ZPhw4dLTEyM7N69WzZv3iyrV6+WvXv3unjpZXp0jyKb9wicP39eHnzwQQkNDTWF+uKLL6RFixbeU0BKkmIBAsQUk3EDAggg4HsCx44dk9tvv90q+MiftkqOXP43c6lVQaedpT99LT9MeMuk3NOsrUz7KvlD3JyyYReBGxI4c+GKLNpyRJZtOyqrVbAYd/mqLb/cuQOkYc2i0rBaEWlUvYhkzeL/Q1CbqHeidRB4yy23yO+//27z0Af63Pjx4+W7776znRs3bpx06NDBlpZZDy5cuGB6Vhs2bCidO2fMhGMDBw6U2bNnW18BAaJF4bM7BIg++9VRcAQQQCD5AqtWrZKHHnrI3HDrPW2k52vjkn+zj1958uhhGfZkC7l4/qyUDKokq5Yt9PEaUXxfF4hSQ1CXql7FFf8ckw3qJ+FWtFAuubt6YWlcvajcVblQwtN+cxwUFGTqUqJECdNjmFjFdM9Ujx49RP+hy7Hpnsdy5co5DjPtp/Mf/zLC5KuvvpK33or/A5zjSyBAdEj47icBou9+d5QcAQQQSLbA119/Lf/73//M9U+8/qnUqp+5hv98OfRpCV0xz9R/ydotUr54/mTbcSECnhTYpd5R1O8rLlDvKx5TgWPCrVypfNKgWmFpUqOoVC/jP+326tWr1vvAejKa7du3J6y67VgPPe3YsaP1zuJjjz0mb7/9tu2azHjgHCB+/PHH0qlTp3Rj2LRpk9x3330uzyNAdCHxuQQCRJ/7yigwAgggkHKB1157TaZOnWpuHP1HmNyUydYcmzt9nMz5+qP4+k/6Tu5remfKEbkDAQ8KnFaznupAceHmI7J51wm3T6pRqaA0vKWIeWexZMGcbq/xlUQ9SY1e2sKxhYeHO3YT/Zw1a5a89NJL5rx+b3HmzJmJXptZTsTGxkrNmjVNdZ988knrD4Gerr+elKZVq1YSERHh8igCRBcSn0sgQPS5r4wCI4AAAikX6KImEFijJn0oERQsgz+bm/IMfPyObWsWy8TXnzS1eOqVYfLKU918skYvTt4iJ89eTnbZs6o5T3IEZJXs2bKYn4BsN5njALXEQo6ALGqpBc9NiqLfoQtQ+eu/RehPvayDTnN8mjR1UhdBl0OXzXFt/HX6nM7DcZ/K499rdXr8uf/uvybqf9dE/VwT1TklV9X+Vb3vSLM+49N0D5bzNfoefa/Ow3Gf/ow/Vmnmen2vc9p/+ZvrVIbxeTqu/7c8Jk9H2eLzcJTLyl/tXFGJugwHjp2XXQfPyL7os3L6jOvENlmVSdkSeaV+1cLSo1k5yZPD99YK1AGGXvPQse3Zs0eyZcvmOHT7+ccff0jfvn3NucTeW3R7ox8nOjvec889Mm3atHSp7aJFi0QP+3W3ESC6U/GtNAJE3/q+KC0CCPi4gPo9UXYcPC071c9SNbvhOvX+UfHCOeWHV+7yaM26PNRV1qxaKUFVbpUXRv/g0Wd5Y+b6PcTXH7nbFK1F5+7yxUdDvbGY1y3Tk59slK273fcsXfdGTvq1QFDJvFJb9Sx2vqukVLo5r8/UVc98WaVKFau8eoipHmqa2Hb8+HF59NFHZevWreYSPZOpntE04abzXbFihezYsUNOnjwpgYGBot9xrFOnjjWkNeE93nwcFxd33cDZeahuhQoVzNIh6VUfPdtsVFSU6cHs1auX9d0QIKbXN+C55xAges6WnBFAIJMLxJ6LU8HgKfVzRvUGnJawqDMSoX7cbas/auYuOc3SnujVV/6c/4eUKBcigyf+kWb5+lJG/3ukvsQePSQhterKgl9m+VLRTVnX7j4un83bp4LEkz5XdgqcPgLvP1FLLZdRJH0edoNPuXz5slSqVMnKRQcbOphLuOkAacGCBTJkyBDbJDV6cpRmzez/buqA8IEHHjDrLCbMRx/rtRSHDRsmJUuWdHfao2nr1683ywzlz59fWrZsKTfddFOiz9PrROqZW7///nurznpIrV4H8rbbbnO5r1q1ata7mckZquuSQRok6N5Lx3BTAsQ0AM3gLAgQM/gL4PEIIOD7AucvXZE9h8/KbhX87VWfew+dkT3R5+TESdcJJ9zVNleubPLAPaXlmTYV3Z1Ok7SBLwyS2d/PlMI3l5E3pixJkzx9LZPRLz4ke7ask7KVa8qcX3+RfDkS/wXNW+sWHnNOnv9ysxw8clayqF8w9e+Y+hdN50+TroZg6nT1YX3qOunrsphz6tPpPufrblIHtuME+djvj3+2zkvfo8+pgZT/PlOn/Vc2x3lH3uqUOe84dpx3l27qooaXOp6T2C/XOv3KlasSp4d6qh89ZDN+X9S+Sr8SPwTUpKvr9PDOOPWpLou/1kr7dxioGnaq71PJZuhn/P067/hhqFd0fvpZegip+o++Vn+aYaM6/d+f9GpPL3SpIv93V6n0etwNPce550tn9OKLL4oeLlmoUCEpUKCACTZ0L+CGDRusIMnxwDZt2sinn37qODSfZ8+elYcffthai892MsGBXtBdT3iTcNOTruj3HCMjI+XUqVNSsGBBKVKkiFSvXt30XgYEBCS8xXase9O+/PJL0T15Xbp0Ecf1CWf67Nevn/UupS0DdfDjjz/KgAEDEiZbx7reuv7Om17CyDHD6759+9T/D9X/WdJ5cw5Sp0yZIo0bN07nEvC4tBQgQExLTfJCAAG/F9gWecr0BDoCwf2Hz8vR4+eTrLf+xTlXzmxy9lzi7499PaiuhKj3ijyx9Xuuv/z680+Sr0ARGTZzjSce4fV5jux3r0SGbZXsOXPJ+s3/SKAPBohej0wBUy1wOe6qXIy7Jhcux6kfta9+Lqg/Pl1S6TGxl0T3IG9SvccH1R+hnLfc6g9MpYvnMcNM+7fz3B+ZnJ+ZVvuOZS5Skp/uSdMBSM6c9kl69NqII0eOtGVVtmxZ0T120dHRVgDluEBP6KIn79J/VNAT5gwaNMgEZ47zCT/1tXoY5fU2PYvoRx/FT4blmFF03rx50rt3b5fbdCBat25dW/pnn31mejhtiW4ONm7cKIULF7bOaBNdR73t3LnTZrNavXuuXXTgqK975513TBBu3ZxGO87fpZ48SD+LzXcFCBB997uj5Agg4GEB3Su4NTxWtkWeln9UYLg74pTpGUjqsTlzZpUg9S5QJfVuUME8AebeDTuOJ3qb7kHs0qisPNWqfKLX3OiJHj17yqKFC1VwlFs++HnLjWbnk/cPe7KlHI7cI7ny5Jft27aYHi+frAiFzjQCi7bEyKyVB+Tvna7/ftRVQ0n10hdNaxST/OrfEF/cnIOK5JZ/+vTp0qBBA9vlehjqnXfeaQWB+l3GX375xRrCqif+2bZtm4wfP15+//13617dQ6l7Mvv06SPLly+30h07OsDUPZOO3rl27drJJ598YoJKxzXOnxMnTpR3333XJA0ePFhatGghHTp0sIZ/Ol+b8B1KHVTpINV50xPx6OGo69ats5Vv1KhRcv/991uXNmnSRPbu3WuOnYfqfvPNN2ZYqnWh2tGBmzZMakIg53uS2tf+FSv+98eJX3/91ZpZNal7Oe+dAgSI3vm9UCoEEEhHAT3EbGfUWTVxzCnZpYaJ7lYzB+5SweAl9df7pLZihXNJsFqnLKRUXqmsPiurzxIFcprhapMXhcuMxRHX7TVsX7+UdGtYVsoVy53Uo27o/INqFlP9l2S9jZm354by8tWbX+92j5yMiZJipcrJur+W+mo1KLcfC8TEXpQ5G9WaiH8fMX+QSljVkHKB6h3DoiowLCLliyU+oUvC+7z12HlYYkrKqAOfu++On3RK37ds2TIzBNSRh37PsFs39zMV62Gkq1atMkNIn376aXNdaGio41Z59tlnpXPnzqKDQx1EJewBdPfuo+Nm5/VmdQCoJ8txBJf6Gj1ZjqOnTx/rdw2zZ89uevcSDsnUdXjooYesQE73Xs6fP1/fJkOHDpXu3bubff0fHbg6Ju/RwWTRokVNT+Tnn39uXeO8M3z4cJO3c9qN7OvhuDVq1LCy+PPPP21LmFgn2PEZAQJEn/mqKCgCCKSFwLmLV+To6UsqCDwj8zYdNu8NJhyyldhzyqngL7hkPqlSOj4QrKICwrxq2GjCbb765W7qknAJCz+V8JR1rAPL1x+uKnUqFrTSPLnj/MtF36FfSrU7G3vycV6Z9+D/qy1nT52U4Oq3yZ9zfvLKMlKozCewbPtRWa5+1qm1Dw+pd0wTbiVVIHhX1ULSpGZRqVMhff69SFgGTx07D43UvWU6qNNDQnVQdeTIEfMeog42dC9ews15IpTJkyfLG2+8YS7R7//pSW2S00Ome+yc11LUa8U2bNjQ9qg333xTJk2aZKXppTnmzJnj9j0/5yGm1g3/7ug8dB3vuOMO69TcuXPNUh/6ncgPPvjAStf7//d//2cd6x3nRelnz54ttWvXts7ra9euXWuOdU+oDmKdy2xd+O9OWs92evjwYdOD63jOX3/9JaVK+ca7sI4y82kXIEC0e3CEAAJ+LPDJH3tk6vz9SdYwm1psraIOAtVPFRUUVi0TKFXVZ1LbTtX7qHsNF6vFrq+3lVC9hd+8UFdyZk+/iQT0X5L1uyd6a9DhUenS702zn5n+83yHahJ36aLc0aCZfD/9q8xUderqRQLH1B+oVu86pn5OyDo19PzkqYsupSukRiE0UENI29UpLrWCCric95cEPVGMo/cusUXe9RBQ/V6d7p2bMWOGVXU9jFT3lunPESNGmKGf+qTuidPvKCa17d+/Xxo1amRdlrBXTp/QS2bUr1/f1guo0/VQVd1rl3B7/fXX3T5bT1zTvHlzc7l+H1H3SurNEZA69wB27dpV3nvvPXM+4X90oJw1a1bbO4b6Gt2buHRp/KgIXf8lS5ZYt+qeTB086xlRHcNf9Undi5pWs7nqmVOdA2sdrBYvXtwqAzu+J0CA6HvfGSVGAIFUCjz5yQaXJQKKFMol5YrnkgrqnUE9RLSqCgorqAkfUrLpWUwnqcDwWzWcNKlhqXnyZJeh3arJ3VX+m2AgJc9K7bX6lzDHrH2FipeWN7/OXEMsz8SekFe71DF8be/rIhNG2yezSK0r9yGQHIHtB07Lml3HZYNaxzJU/VxWE9Ak3LJnzyr1qqv3CvUQUhUc5lTH/r498cQTonsI9ZbwnTx3dXfuRdPnHUGdXgLDsUC8fmcvsaGVznnqSWJeeuklk6SXxvjwww+dT5t9PRRTB4MJNz1UVAdhCSfK6du3r/zxh30ZIT07qx626tich6E6egqdZyFNbIZVx/3uPp17EJ3P63Uj3377bdPbmXDdSXezoTrfm5J9HcBrd8emZ57Vs7+y+a4AAaLvfneUHAEEUiiwZGuMbFeTzZRUQaGeQKaiCgRz3eAvYb+tj5apKjAMT2R9Q90bmUM9wzF7ad+OleTxJkEpLHnaXO78S0j/D2dKxVviA6a0yd27c/lrzrfy7eghppC9eveV14YM9u4CUzqfF1itAsKl22JkQ9hJiVBD2hPbbq1cSBrpwFBNOHOz6jnMTJueyMXRK6gXsv/hhx+SrL6eTVT3vOnNEVQOHDhQ9LBLveleQR2EJbXpwE8HgHp7+eWXRb+P6LzpXkjdI5jYppei0M913u677z4zFNSRpt+T1IGr7vVzbCtXrhTdS6i3xx57zARwzmsI6sl29LNz507+e+mtW7eWf/75x/EI8+luMhrnVw0S67G1ZZLMAz0xjp6Mx7HpP0jqpUrYfFeAANF3vztKjgACGSiwWc1u+rUKDFeEHkm0FG3VemT71Uyo2/eeNNe0rldS3nywaqLXe/qE8y8HrR7pJ+2623+58fTzMzL/CUN6yD/rl5kiOL+7lJFl4tkIZHYB53fv9LINevmGpDbnYamO4Zg6uHPMTprc9+uc31vUw1R1UBYcHCx6ZlMdPDqGbOry6PcOda+k7iF0TAaj05966ikz86hj3cGEk+7oSWrKlCmjL7U25/f19PN0D6ruCdX/Ljk2HSy///77yZ7oxfmPfzoPXZ/Fixe7DPPUs5e++uqr5jHJdXKU6XqfusdQT+zj2PSMsXnzJv1ahuN6Pr1PgADR+74TSoQAAl4sEHsuTg0n3S+zVHCoF8F2t+np5x9pVEbmbDgsc1dHmUtqhRSUcb1ulQDVo5hRm/MU7KUrVpPnPvhWcuZO2XDajCr7jTz3RMwheaNbfZNF8RKlZO3qv24kO+5FAIFUCMTGxpoeQr0cQ0hIiJlkZc+ePbbhl7t27ZIcOXK4zV3PlKnfNXT0HuqLHLNxOv/xS6frIC9Xrlx6N9FNzyaanLX6dI+efodQT55z6NAhadu2re2dRP3+oH5nMDAwUJyX7dC9i4kteO88Oc+aNWvkwoULJt+Ek/Hcdttt0qxZM7nrrruMmS5Dwk271qxZ05asg139LmLCTU/+o4NJx5ZW7wrq4ba6N9SxJVyL0ZHOp+8IECD6zndFSRFAIIMFflwTJVMXRUjUEdcZ9XTRQoLyy8NqyYo2txeXMb/vlhl/hpsSl1ST0ozufauUUTOXZuS2fv1629pZzR/sIx172tfdysjyeerZK36bIbPG/s9k/9TT/eSVl+PfO/LU88gXAQRcBZzfE3Sc1b2GzstAjBkzxizRoIPEK1euyIkTJ+TgwYOi/+3SQYhzAKV7rPR6gHqh+4Tv/iV3Aha9gPy4ceMcxXH5bNWqlegyOb9rqAPc9u3b28qie+x0D+Zvv/1mZiPVxzr4SqwXbfTo0abs+oGOCW90UKt7RJ09EhZIv/uog0sd5OllJfSsqMePH7fNIKqHqzrex0x4vz52ntAn4XIh7q5PTprze5X6eh34J2cW2eTkzTUZI0CAmDHuPBUBBHxIYP2eEzJFTUKzbvsxt6XWMw52bVxGuqnF7vX21cJw+ey33WY/R46sMvLJWnJnJe+Ynl7/9VsP/3Fs/YZPk5Bb73Ic+uXnxNd7ybY1i0zd3A358stKUykEvEygR48esmhR/P8Pb7RoOkjSPYl6DUG96fUBdS+i3lIydPLatWvy3XffWZPVmAzUf/SQ0ueee070u32O4aOOc/pTv++n6+NY01AHhHotRj0xi14Co0qVKqYczvc471+8eNHMghoVFSV6qQu95qLedEA8YcIE0aM9krPpOut3Mh3vZepyOOfnLg/d46h7JHWwrZfl6NSpk7vLUpTmvFakLsP27dtTdD8Xe58AAaL3fSeUCAEEvETgSOwFmaKGkv6wNDLREj3cLEgebVxWCuWN/0Xl2xUH5OMfdlrXD3mkmnSoU8I6zuidhH/p1cGhDhL9dftn3VKZ8FpPU732He+VT8aO8deqUi8EvFrAudcstQXVgVvPnj3NhCgJh5DqWUl1sKcnnNHv8KVku3z5suhlLy5duiTlypUz7/Aldb8eFqp7NU+fPm2Gc+rF6VOy6SBR97I5T2DjuD8mJsa8m6jfydS9p7rX0t3mPPuqDlp1D2NyJodZuHChWWpDv7up63ujm7bQS2joXlPdC6qX3WDzbQECRN/+/ig9Agh4SODbFZEyfXGkxBw/7/YJreqWNO8ZhpT470X8+X8fkdenbLGu79WukjzRPGNmLLUKkWDH8ZfrsLAw64weZqqHm/rbdjQ6Qj5/s49E799lqqYXjm7atKm/VZP6IOATAnrIqJ74RS9VceDAATM0UvdmnTlzxvzodwx1r5bugdJr6OneOB106WGo+li/C6h/MuOmnfTIDx0E6qGoERERZohp//79JV++fJmRhDp7WIAA0cPAZI8AAr4lsGrncbXY/X4JVYtYu9vuqFZYBYZlpV5IIdvp3YfOyqMj1ogesqS3DvVLyZAHqtiu8ZYDPRvfO++8YxUnZ+68asKab0RPXONP26gBD8j+fzaZKr300iDp1+8Zf6oedUEAAQQQQMAjAgSIHmElUwQQ8DWBA6qncIqagObXlQfcFr1cqbzSTQ0lbe9muOgVNZtp89eWyfnzcebeO9WaZmPUe4feuukhUfpdRP1XaMd2c1Cw9Bn6pRQuXsqR5NOfM8e+Lit/m27qUKFSZVm8cL5P14fCI4AAAgggkF4CBIjpJc1zEEDAawW+XhIhM5ZEykn1zmHCLU/uAOmuhonqCWiyZrkp4Wlz3ObNFXIi9qLZL186n4zpVUuK5nc/VbvbDDIgUc/cp2fwc950kPjqZ3Odk3xyf9oHg2Ttgv8W3E5synefrByFRgABBBBAwMMCBIgeBiZ7BBDwXoEl247K1MXhsm1P/EL2CUvapUlZMwHN9YK9x8eslx37Ys2t+dRENaN61ZQaZQMTZuV1x0ePHjWz6Ol1vZw3Xw4ST8REyxdv9ZXIsK1WlR5++GGz4LSVwA4CCCCAAAIIXFeAAPG6PJxEAAF/FNin1jGcpJaimL822m31WtxZQh5R6xlWUcNKr7e9q2Yr/UXNWurYhj5eQ1rUKuY49PrPX375xbZItaPAFarXlgGjZjkOfeJzy6qFMmlYP4m7fMkqr546fvbs2WaiCyuRHQQQQAABBBC4rgAB4nV5OIkAAv4koN8VnKzWM5yhlq44e+6yS9VqBheUx5oESf2qhV3OJUz4ZnmkjJ4dPzumPvdsp2ATVCa8ztuP9bTwL774oksxi5UqLy+M/Uly5bl+kOxyYzonXLt6VebOGCd/TB1te3JAQICMHTtW2rRpY0vnAAEEEEAAAQSuL0CAeH0fziKAgJ8I6CUopi4Jl7DwUy41urlobunZIkg63lHS5Zy7hB/XRMnwb/+xTj3YNEgGdqhkHfvazrRp02TIkCEuxc4WkEOeHTlDyle91eWcNyTs2LBc5qngcM/W9bbi5A8MlLFjxpi1yWwnOEAAAQQQQACBJAUIEJMk4gIEEPBlgZ1RZ0yv4eIN9nftdJ2yBWSRJ1pXkG4Ny0hAtizJquYfGw/LW1P/e8etae2b5d1u1ZN1rzdf9OWXX8rbb7/ttoiPPD9C6ra63+25jEi8eP6c6jUcKwtnfeby+JtLlJQxoz+WunXrupwjAQEEEEAAAQSSFiBATNqIKxBAwAcFzl+6IpPVUNJv1JDSS2o/4fZA4zJqApogKR6Y/NlGtx84Lf0n/i2nz8S/51ajUkH5/JnbE2bts8effPKJjBgxwm35G3fqIa269pM8+Qu4PZ9eiZv/+lP1Go61TUTjeHb5ChVNcFizZk1HEp8IIIAAAgggkEIBAsQUgnE5Agh4v8Bv66PV7KQREq56DxNuDW8rLo+rIaHV1HIUKdkux12Vp1RwuHX3CXObHpY6uX8dKZAnICXZeP21n3/+uYxQy19cuhi/bIdzgUuUC1FB4rNye6O2zsnpsr9p6RxZPf87+Wf9MrfPa9ailbzxvyESFBTk9jyJCCCAAAIIIJA8AQLE5DlxFQII+IDA5vBY+VoFhitCj7iUNqRcoDzZopw0rFbE5VxyEoZ9v0N+XXnQXJo9exb5vP8dUrmkd0/gkpx6ubtm/fr18s57I2XT+tXuTstdbR+S1ipQLFj0Zrfn0zJxzfzZKjCcJXu2rEs02wEDBsrAgQMSPc8JBBBAAAEEEEi+AAFi8q24EgEEvFQg9lycTFq0X2ap4PCqmqnUeSuohpD2blNBOtVN3gQ0zvc69mevPigjZu5wHMqIXrVSHWhamXj5TlxcnAxVQeLkLz51W9IiJcpK485PyK33tJb8BVMXdLvNWCXu3rxWdmxaoXoLl0rkrv/e90x4fekyQfK66jVs1apVwlMcI4AAAggggEAqBQgQUwnHbQgg4B0CekbRqYsiJEqtbZhw69WuonRrVFZyqMloUrvtPnRGnpnwt8Seih9y+WKXKvLAXaVSm53P3Td37lzVmzhcIvfvdVv23PkKSC0VJN5av5VUrdPQ7TXJSQwLXSOhK+fJzo0r5HDkniRvadmqtbw25FWGlCYpxQUIIIAAAgikTIAAMWVeXI0AAl4isH7PCTOcdO22oy4l6lC/lPRoVk5KFszpci6lCQO+DJXVW+Of0b1VeXlazXqa2bZDhw7JmHHjZf6CBRJzKCrR6petXFP0e4rFSpaXYmUqSsnyIVK0pP2dwHOnT8mJmCg5GRMtx49ESXR4mGxWgeGp467Dgt09qGXrdvLIw11YwsIdDmkIIIAAAgikgQABYhogkgUCCKSfwFHVkzdJzUz6w9JIl4feUa2w9FZBXI2ygS7nUptw//ur5eDhs9KgVjH54PEaqc3GL+47f/68fP/bfJk3b4GsWf6nXLpwPsl65ciVW4qrYPHShQsmMLx43rWnN8lM1AUd7rtfuqnAsF69esm5nGsQQAABBBBAIJUCBIiphOM2BBBIf4FvV0TK9MWREnPcHpgElcwnT6n3DBvfkrbvwuka6iGmx9WyFndWKpT+FfbiJ+6PiJLZc+bL+vUbZPeObckaFprS6pSrGCL1GzSQLp3vlVtvvTWlt3M9AggggAACCKRCgAAxFWjcggAC6Suwaudxtdj9fgndFb/EhOPpuXMHyDPtK8r9meidQEfdvenzxPlrsnNfpGzZslU2h/4t20PXy+6tG1JVxKAKwdK2XXtp0eQeqV27dqry4CYEEEAAAQQQSL0AAWLq7bgTAQQ8LHBA9RROURPQ/LrygMuTHm1ZTno0LSe5c2R1OUdCxgtcuiISFh4lUdHRcuiQ+lGfh9XnEfU+4xn1HmKhQoWlcOFCUqRIYSmmfooXLSwVK5SX4ODgjC88JUAAAQQQQCATCxAgZuIvn6oj4M0CXy+JUMNJI6zZQx1lbXFnCendsryUKZzLkcQnAggggAACCCCAQBoJECCmESTZIIBA2ggs3hoj01RwuG3PSVuGNYMLyjNtK0otteA9GwIIIIAAAggggIBnBAgQPeNKrgggkEKBfWodw0kLw2X+2mjbncWL5JL+HYOlaY2itnQOEEAAAQQQQAABBNJegAAx7U3JEQEEUiBw5eo1NQFNuMxQw0nPnrts3Zk1603ytAoMH2lYxkpjBwEEEEAAAQQQQMCzAgSInvUldwQQuI7A/L+PyNQl4Woyk1O2qx5oXEb6tKwg+XJls6VzgAACCCCAAAIIIOBZAQJEz/qSOwIIuBHYGXXG9Bou3nDIdlYvRv+cWraibJHctnQOEEAAAQQQQAABBNJHgAAxfZx5CgIIKIHzau2DSWo46bdqOOklvQ7Cv1tIUKB6z7Ci1K5Q0JHEJwIIIIAAAggggEAGCBAgZgA6j0QgMwr8tj5apqrAMFz1Hjq2AoE5pJ/qMWxfp4QjiU8EEEAAAQQQQACBDBQgQMxAfB6NQGYQ2BweK1+rwHBF6BFbdXu1qyRPNA+ypXGAAAIIIIAAAgggkLECBIgZ68/TEfBbgdhzcWo46X6ZpYLDq2qmUsfWvn4peVatZxiYO8CRxCcCCCCAAAIIIICAlwgQIHrJF0ExEPAngdmrD8q0xZESpdY2dGx3VCssA9WyFRWK53Ek8YkAAggggAACCCDgZQIEiF72hVAcBHxZYPWuY2rZikjZ8M8xqxplS+RVE9BUkvpVCltp7CCAAAIIIIAAAgh4pwABond+L5QKAZ8SiDh6XqYsDpff/zpolTtXzqzyjOoxfOCuUlYaOwgggAACCCCAAALeLUCA6N3fD6VDwCcECBB94muikAgggAACCCCAQJICBIhJEnEBAggkJhB35ZrpOfx2aaScPnPJuqxbi3LST01Ew4YAAggggAACCCDgWwIEiL71fVFaBLxG4I+Nh2XaknDZE3naKlOzOjfLC/cGS6G82a00dhBAAAEEEEAAAQR8R4AA0Xe+K0qKgFcIhO6PX9dw5eb/1jW8pWIBGagCw+pl8ntFGSkEAggggAACCCCAQOoECBBT58ZdCGQ6gagTF1SPYYTMXhZp1b14kdzybPuK0rxWMSuNHQQQQAABBBBAAAHfFSBA9N3vjpIjkC4Cl+OuytSlETJTvWcYe/q/9wx7t68kPZsFpUsZeAgCCCCAAAIIIIBA+ggQIKaPM09BwCcFflkXLTNUYLj/oP09w5c7V5b8ubL5ZJ0oNAIIIIAAAggggEDiAgSIidtwBoFMK/DXjmMyfVmEWvD+uGVQoXQ+ebFziNxevoCVxg4CCCCAAAIIIICAfwkQIPrX90ltELghgZ1RZ1RgGCnz10RZ+eTIoRe8ryRd7i5tpbGDAAIIIIAAAggg4J8CBIj++b1SKwRSJHDizGX1nmG4fKeGk16+fNW6994GpUUPJ81yk5XEDgIIIIAAAggggIAfCxAg+vGXS9UQSI7ADNVjOHP5ATl89Jx1ec3ggvJipxAJKZHXSmMHAQQQQAABBBBAwP8FCBD9/zumhgi4FZj/9xGZod4z3LEv1jpfsEBO6d8xWFrfxrIVFgo7CCCAAAIIIIBAJhIgQMxEXzZVRUALbNx3UqYviRTnhe51+qMty8kzbSrqXTYEEEAAAQQQQACBTCpAgJhJv3iqnfkEIo+dl6lqoftfVhywVf7umkVlkBpOerPqPWRDAAEEEEAAAQQQyNwCBIiZ+/un9plA4PylKyYwnKXeNTxz9rJV4zI35zHDSRtULWylsYMAAggggAACCCCQuQUIEDP390/t/VzgR7VcxTdqZtKI6DNWTbNnzyI9W1eQx5sEWWnsIIAAAggggAACCCCgBQgQaQcI+KHAsu1HZfrSCAnddcJWu5Z1S8qg+0Ikb86stnQOEEAAAQQQQAABBBDQAgSItAME/Ehg+4HTKjCMlIXro221qlo+UAaoxe5rlStgS+cAAQQQQAABBBBAAAFnAQJEZw32EfBRgZhTF9VC9xHygwoOr1y5ZtWiYGAO6dW6vHSuV8pKYwcBBBBAAAEEEEAAgcQECBATkyEdAR8QuKZiQR0Yzlx2QI6dOG8r8f2NyshLajgpGwIIIIAAAggggAACyRUgQEyuFNch4GUCf2w8bBa6Dws/ZSvZndWLyIAOlaRC8Ty2dA4QQAABBBBAAAEEEEhKgAAxKSHOI+BlAmt3HzfvGa7ZetRWstJq2YrerSpIy1uL2dI5QAABBBBAAAEEEEAguQIEiMmV4joEMlhg/5FzZjjp738dtJUkICCLdG1WTp5qVd6WzgECCCCAAAIIIIAAAikVIEBMqRjXI5DOAqcvxJmF7r9ffkDOnftvoXtdjGZ1SsiA9hWlqJqMhg0BBBBAAAEEEEAAgRsVIEC8UUHuR8CDAt+p3sJvl0XKwcNnbU+pWqGA9FE9hvVCCtnSOUAAAQQQQAABBBBA4EYECBBvRI97EfCQwOKtMTJDzU66ZfdJ2xMKBuaUR5qUkW6NytrSOUAAAQQQQAABBBBAIC0ECBDTQpE8EEgjgS0RsWoCmgOyZOMhlxzvvae0DOwQLDnVO4dsCCCAAAIIIIAAAgh4QoAA0ROq5IlACgUOnbxg3jOcrYaT6rUNnbc7qhVWw0kryC1l8zsns48AAggggAACCCCAQJoLECCmOSkZIpB8gbgr12SaCgpnLo2UE7EXbDfqZSu6NS4r99UtaUvnAAEEEEAAAQQQQAABTwkQIHpKlnwRSELg1/XR8o0KDPceOG27Mnv2rPJg4zLyTJuKtnQOEEAAAQQQQAABBBDwtAABoqeFyR+BBAKrdx2TGcsOyNpt9oXu9WVNa98sfVtXkLJFciW4i0MEEEAAAQQQQAABBDwvQIDoeWOegIAR2H3orExTM5POXR3lIlKlfKB0bxIkTWsUdTlHAgIIIIAAAggggAAC6SVAgJhe0jwn0wqcPHtZBYbh8p3qNbx48YrNoYBa4P5htWTFY01YtsIGwwECCCCAAAIIIIBAhggQIGYIOw/NLALfrjhgFro/FHPOpcodG5SWvmqx+0J5s7ucIwEBBBBAAAEEEEAAgYwQIEDMCHWe6fcCf4YeUe8ZRsj2vbEuddXLVjyqegzvrFTI5RwJCCCAAAIIIIAAAghkpAABYkbq82y/EwjdH6uGk0bK8r8Pu9StVHG9bEUZ6VSvlMs5EhBAAAEEEEAAAQQQ8AYBAkRv+BYog88LHDh+XqYtiZCflh9wqUtAQBa1bEVZ6dOyvARky+JyngQEEEAAAQQQQAABBLxFgADRW74JyuGTAhcvXzU9hjPVYvenTl90qUMTtWxFdzWctGqpfC7nSEAAAQQQQAABBBBAwNsECBC97RuhPD4j8NPaKLPQfXjUGZcyVy4XKI+qXsPmtYq5nCMBAQQQQAABBBBAAAFvFSBA9NZvhnJ5rcCew2flw5/CZOOOYy5lLJA/hzyk3jN8XK1pyIYAAggggAACCCCAgK8JECD62jdGeTNc4LkvQmXttqMu5ehQv5Q83rSclCqU0+UcCQgggAACCCCAAAII+IIAAaIvfEuU0WsE/th4WEb9uEtOn7lklalO1ULSXQWGd1YqaKWxgwACCCCAAAIIIICALwoQIPrit0aZM0Rgwrx9MmXuXuvZetmKR9Rw0s4sW2GZsIMAAggggAACCCDg2wIEiL79/VH6dBR4e9Y/MmdVlGRTS1U8qN8zVL2G+XJlS8cS8CgEEEAAAQQQQAABBDwrQIDoWV9y9zOBtbtPqKAwQC1bkdfPakZ1EEAAAQQQQAABBBAQIUCkFSCAAAIIIIAAAggggAACCBgBAkQaAgIIIIAAAggggAACCCCAgBEgQKQhIIAAAggggAACCCCAAAIIGAECRBoCAggggAACCCCAAAIIIICAESBApCEggAACCCCAAAIIIIAAAggYAQJEGgICCCCAAAIIIIAAAggggIARIECkISCAAAIIIIAAAggggAACCBgBAkQagovA5cuXZcuWLbJp0yZZs2aNnDp1yrom5ugxiY6Oljh1Tf78+SV/YKAEBuaXwPyB6jif2tef+aV06dISFBQkZcqUkVKlSkmWLFmsPNhBAAEEEEAAAQQQQAAB7xQgQPTO7yVDSrVixQr56quvZN26dbag8EYLExAQYIJFR8Cog0f9U7t2bSlWrNiNZs/9CCCAAAIIIIAAAgggkEYCBIhpBOmr2cTFxcnatWvlu+++k9mzZydajZy5ckvBoqUkb8FCEhm2TS6cO5Potck9Ubx4cenatasMGDAgubdwHQIIIIAAAggggAACCHhQgADRg7jenHVUVJR8+umn8uNPP8up2JO2ombJklVur3eP1Lq7uRSvWEvyFSkpefIXsF0TvT9MInaGSlT4Lonev1N2bvpLrl29artGH1S+7W5p3+MlyZk7j2xa9ocs/O4zuXj+rO06HSAOHDjQlsYBAggggAACCCCAAAIIpL8AAWL6m2f4E6dMmSLjJ3wqh6KjbGWp27C53N6kk1Sq01QCsme3nUvq4NihA7Jjw3L5R/3oz0sXzrncUqtBK7mtYXu5vVFbmTTsWRUwzjHXECC6UJGAAAIIIIAAAggggECGCBAgZgh7xjx0+fLlMnHiRNGfzltQlVrS+L6eUrtJe+fkVO+fOn7UBIk7Ni6XbWsWy/mz/01yozMtUqKsHI2OsOWvexAZamoj4QABBBBAAAEEEEAAgXQXIEBMd/L0f6BjOKnuOUy4Nbn/CenU+9WEyWl2fPrEMdm6eqFsXaN+Vi+Sa9dch6E6HtawcVP535DBEhIS4kjiEwEEEEAAAQQQQAABBNJRgAAxHbEz4lHh4eHSq1cv2blzp8vjuz4/XOq1esAl3VMJMQfDrWAxLHS128dUrBQioz8eJTVq1HB7nkQEEEAAAQQQQAABBBDwnAABoudsMzznw4cPS+/eveXvv/92Kcvzo2dLOTW0NKO2yLCtqldxkWxft0TCd4S6FEPPbvryyy9LgQL2yXFcLiQBAQQQQAABBBBAAAEE0kyAADHNKL0ro9jYWOnTp4+sWrXKpWAjf94qOXLmcknPqIT9KkD8Z/1SWb/oZ4k5uN8qRs2aNeWdd96RWrUyLpC1CsMOAggggAACCCCAAAKZQIAA0Q+/5AsXLpjgcMmSpRX7IAAABR9JREFUJS61e/Pr5VKoeEmXdG9J+HXyB7LgmwlWcfLkySPvvfee3HvvvVYaOwgggAACCCCAAAIIIOAZAQJEz7hmaK6653Du3LkuZXjqna+k6h2NXNK9LWHWuDdkxa/TbMV64YUX5LnnnrOlcYAAAggggAACCCCAAAJpK0CAmLaeGZ7b1KlT5bXXXnMpx/1PvS6N7nvMJd1bE74e/rwZcupcvgceeEA+/PBD5yT2EUAAAQQQQAABBBBAIA0FCBDTEDOjszp37py069BR9u4OsxWlY89B0vzBPrY0bz+4dPGCTBjyuOzZss5W1BYtWsgXX3xhS+MAAQQQQAABBBBAAAEE0kaAADFtHL0il9GfTJRRI961laV1t+ek7aP9bWm+cqBnOp0wpIeciT1uK/LMmTOlXr16tjQOEEAAAQQQQAABBBBA4MYFCBBv3NArcgg/dEzu69hRjh8+YJUnuGZdeXbkDOvYF3fWLfxZpo543qXo27dvFz2BDRsCCCCAAAIIIIAAAgiknQABYtpZZlhOV6+JPP/WR/LjpI9tZXh2xAwJrlXXluaLB79N/lDmfzPeVvTu3bvL0KFDbWkcIIAAAggggAACCCCAwI0JECDemJ9X3L37+FXp+fB9tgXnfXloqTvUz97oLVtXL7SdGjlypHTp0sWWxgECCCCAAAIIIIAAAgikXoAAMfV2XnHnpSsiX/+5RYb2bm+Vxx+GllqV+Xfnn/XLzPuIzumFCxeW6dOnS9WqVZ2T2UcAAQQQQAABBBBAAIFUChAgphLOW24Lj70m744YJXOnjbGK5C9DS60K/bsz+b3+snHJb7Zk1ke0cXCAAAIIIIAAAggggMANCRAg3hBfxt+8+sAVGfREJ2t4qb8NLXUW1ktejH7xIeckqVOnjvzwww+2NA4QQAABBBBAAAEEEEAgdQIEiKlz84q79PDSGX9FyhvdGpjyZM+ZW179bJ4UKl7SK8rniULM+GiwrJ47y5b1smXLJCgoyJbGAQIIIIAAAggggAACCKRcgAAx5WZec8e+k9dk3MTJ8v34N02ZGnfqIZ37vuY15fNEQfbvCJVR/Tvbsn7jjTekZ8+etjQOEEAAAQQQQAABBBBAIOUCBIgpN/OaO1ZGXJFPhr8mK3+LX+vwlU/nSMnylb2mfJ4qyMfPd5G92zZY2Tdo0MBMVmMlsIMAAggggAACCCCAAAKpEiBATBWbd9w0b/cVGTXoMdmxYblUrdNQnho2yTsK5uFSLPh2gvw66QPbU8LCwiR79uy2NA4QQAABBBBAAAEEEEAgZQIEiCnz8pqrr10T+eS3v2Vkv3tNmRp37imd+wzxmvJ5siBRe3fI+0+1sz3im2++kbvvvtuWxgECCCCAAAIIIIAAAgikTIAAMWVeXnP12csiw8bPlOmjBpkyPdR/mNzd1j7Dp9cU1gMFGdmvo0SGbbNy/uqrr6RZs2bWMTsIIIAAAggggAACCCCQcgECxJSbecUdMeeuyeC33pcF335qytP/g2+lYo07vKJs6VGIeTPGy+9TPrQeNX78eGnXzt6raJ1kBwEEEEAAAQQQQAABBJIlQICYLCbvu0jPYNr/mb4SumKuKdy7s9ZL3sCC3ldQD5XoUMQeebdXSyv3UaNGyf33328ds4MAAggggAACCCCAAAIpFyBATLmZV9yx8+hVefyB1hK1b6cpz5h5e7yiXOlZiEGdasmFc2fMI4cNGybdunVLz8fzLAQQQAABBBBAAAEE/E7g/wEAAP//MpHkVwAAQABJREFU7d0HfBVV2sDhl14DodfQO1IFAYXQREBExALiooC4qOzCig0VWd1dG+IHooCLoEhTWVQEG6h0CyAKSA+9N+mEGsJ33ol3uJNGQu6dTG7+89t4Z87MPeUZF3lzWpbL5hCODCew9Vis3NK4usRcvGDV/a25WzNcG9Ja4ZcfukUO7o5r99ChQ+Whhx5Ka5Z8HwEEEEAAAQQQQACBTC2QhQAxY77/n9bukB6dWtqVz4wB4ujBPSVq1c+WwZNPPikDBgywPThBAAEEEEAAAQQQQACB1AsQIKbezBPf+HLBMvlb7252XTJjgDj59SdkxbzPLYNRo0bJHXfcYXtwggACCCCAAAIIIIAAAqkXIEBMvZknvrFw+Rrpdc9tdl0yY4A4671hMu9/71oGq1evlvDwcNuDEwQQQAABBBBAAAEEEEi9AAFi6s088Y1f1m6Ruzu1tesy7LNVkidfmH2dGU6mjXhGls2dYTV12bJlUrJkyczQbNqIAAIIIIAAAggggEDQBAgQg0Yb3Iy37d4nrZs3swvp96/xcl3TNvZ1Zjh5vX9n2bN1vdXUjz/+WJo1u+KRGdpPGxFAAAEEEEAAAQQQCLQAAWKgRV3K79ixY1K/fn27tLbd+kmXvoPt61A/iT51Qp69u6HdzLVr10pYWObqQbUbzwkCCCCAAAIIIIAAAgESIEAMEKTb2Zw/f16qVatmF1uhZn15/M1P7etQP4la+ZOMfuZ+q5kVK1aUhQsXhnqTaR8CCCCAAAIIIIAAAkEXIEAMOnHwCqhRq7acjT5tF/DSR0ulQOFi9nUon8z/ZIJ8Pv5Vq4mdO3eW0aNHh3JzaRsCCCCAAAIIIIAAAq4IECC6whycQu6+t6f88vMSO/MHnx8j9Vt0sK9D+WTUE/fK1rW/WE0cPHiw9O/fP5SbS9sQQAABBBBAAAEEEHBFgADRFebgFPL68BEyZvQoO/NmHbtLj8desa9D9WTDL4vknecftJs3adIkadWqlX3NCQIIIIAAAggggAACCFybAAHitbl54lvz58+XPn36OOoyYPiHUrVuE0daqF1MfeMpWf7dZ3azVqxYIcWKZY6htXajOUEAAQQQQAABBBBAIAgCBIhBQHUrS13JtGHDhhIbG2sXWa95e+k7dKx9HWonB/dsl9ce7iiXYi5aTbvrrrtkxIgRodZM2oMAAggggAACCCCAQLoIECCmC3vgCu3atav89ttvjgz7Dn1H6jW/xZEWKhdfTxklc6a+ZTdn2rRp0rx5c/uaEwQQQAABBBBAAAEEELh2AQLEa7fzxDcnT54sQ4cOddSlar0mMuD1Dx1poXCx+fdl8vZT99lN0XmHOv+QAwEEEEAAAQQQQAABBAIjQIAYGMd0y0X3Q7z11ltly5Ytjjr0eOxVadaxmyMto1+8/fR9snn1MrsZb7/9ttx+++32NScIIIAAAggggAACCCCQNgECxLT5eeLb48aNk1deca5emjVbdmsuYp1mbT1Rx7RW4hszrPQbM7zUd9StW1e++OIL3yWfCCCAAAIIIIAAAgggEAABAsQAIKZ3FrpYjfYi7tu3z6pKRNXrZPfmtZIrTz556J9jpXrDjD1HL/7QUm3klClTJDIyMr3pKR8BBBBAAAEEEEAAgZASIEAMkdc5cuRIefPNN63WvDV3qzx7TyOJPnlM8ocXMUHiO1Kp9vUZsqVHDuyR8S/2k33bN9n1HzJkiPTr18++5gQBBBBAAAEEEEAAAQQCI0CAGBhHT+Ty5JNPyowZM6y6aJA4sH1l67xQ8TLy1xf+K2Wr1PJEPVNTiRGP3SU7Nqyyv3LnnXeKBsMcCCCAAAIIIIAAAgggEHgBAsTAm6ZbjmfOnBFd2fPgwYNWHfyDxBIRlaXHoFczVE/i6ME9JWrVz7ZnzZo1ZerUqVK0aFE7jRMEEEAAAQQQQAABBBAInAABYuAsPZFTVFSUtGvXzq6Lf5CYI1ce6fLQYIm8/X77vldPJg97XFbMn+WoHvMOHRxcIIAAAggggAACCCAQcAECxICTpn+Gie2N6F+rph26mUDxGckXVtA/2TPns99/Xb6fPs5Rn7Fjx0qnTp0caVwggAACCCCAAAIIIIBAYAUIEAPr6Znc5s6dm+xCLrrSaZe+g6Vagxs9U2etyMSXB8jKxV876kRw6ODgAgEEEEAAAQQQQACBoAkQIAaNNv0zPnnypNzf56+yasXSRCuTJUvWuCGnXXpJ9hw5En3GrcS92zbKhyMGW9tz+JdJcOivwTkCCCCAAAIIIIAAAsEVIEAMrq8nch9hVv0c9ecWGIlVSBewadSmizRqe4cUKVEmsUeClnbuTLQ11/DLiW/ImdMnHOUQHDo4uEAAAQQQQAABBBBAIOgCBIhBJ/ZGAatWrZIx734g3341M8kK5ckXZgWJGixWrNkgyecCcePArq2yYsHnsmLebDl6cI8jy0qVKsngwYOlQ4cOjnQuEEAAAQQQQAABBBBAILgCBIjB9fVc7l9/t0jGvTdRVv28INm61b2pvdRq1FLKV68rZSrXTPbZ1Nzc9NuPJjCcJb/MmyWxl2ISfPWOO+6wgsPSpUsnuEcCAggggAACCCCAAAIIBFeAADG4vp7NfeL0WfLhlEkStebXq9axSMkIKVetrlSs1VCq1m2S4oDx0J7t1pzCvds3yP4dm+XAzs1y5MDuRMvLlSuXFRj27ds30fskIoAAAggggAACCCCAQPAFCBCDb+zpEj746FOZ8+33snzJ93Lp4oUU1TVv/oJSplKNZJ/ds3W9nI0+lewzvpsdO3YUDQwbN27sS+ITAQQQQAABBBBAAAEE0kGAADEd0L1Y5Lotu+TLOd/L4gXfydoVPwW9iiVKlJDbb79dunTpInXq1Al6eRSAAAIIIIAAAggggAACVxcgQLy6UaZ74pdVa+WrOSZQXLdetmxcJ8cO7Q2YQaNGjaygUIPD8PDwgOVLRggggAACCCCAAAIIIJB2AQLEtBuGdA4XLomsidoha9dvkLWrV8n6NavklNlf8fSpk9a2FEkNIw0rEC7lyleQypUqSPlyEVK2bFnrp3nz5iHtReMQQAABBBBAAAEEEMjIAgSIGfnteaDusbGxcuLECTlpgkb9iYmJkfLly0vhwoU9UDuqgAACCCCAAAIIIIAAAqkRIEBMjRbPIoAAAggggAACCCCAAAIhLECAGMIvl6YFTmDrwWgZPjNKbqlfXO5sWiZwGZMTAggggAACCCCAAAIeEiBA9NDLoCreFXh03EpZufGoVcFGNQvLA20qyA1VCnm3wtQMAQQQQAABBBBAAIFrECBAvAY0vpL5BLYcOC2vfRola7ccsxt/e/Oy0qt1OSlTOI+dxgkCCCCAAAIIIIAAAhlZgAAxI7896u6qgAaJPYctc5QZXjCX9GhZzgoUHTe4QAABBBBAAAEEEEAgAwoQIGbAl0aV008g+vwleXjsb7Jl10lHJWpULCgPtC4vbeoUc6RzgQACCCCAAAIIIIBARhIgQMxIb4u6ekZg2eajMui/qyQ29rKjTm0blbQCxeql8zvSuUAAAQQQQAABBBBAICMIECBmhLdEHT0rMOH7HTLhq62O+uXMmU26t4qQ3mYhm3y5sjnucYEAAggggAACCCCAgJcFCBC9/HaoW4YReOz932XpmsOO+kaUzCc9zbDTLjeUcqRzgQACCCCAAAIIIICAVwUIEL36ZqhXhhPYZvZK/Ns7q+TYiXOOuje5rqjc36qcNKrMthgOGC4QQAABBBBAAAEEPCdAgOi5V0KFMrrArOX75NWPNiRoRtdI3RajvJQMz53gHgkIIIAAAggggAACCHhBgADRC2+BOoSkwMufbJQvftzraFthExz+xeyd+JfICEc6FwgggAACCCCAAAIIeEGAANELb4E6hKzAqbMx8sg7v8nW3accbaxdOdwMOy0vrczwUw4EEEAAAQQQQAABBLwiQIDolTdBPUJaYGnUURkyaa1En7noaGe7xqXMthjlpGoptsVwwHCBAAIIIIAAAgggkC4CBIjpwk6hmVVg/Hc75L2vndti5M6t22KUlz5tykvuHFkzKw3tRgABBBBAAAEEEPCAAAGiB14CVch8Av94b7UsW/uHo+EVyoTJX8z+iZ0bsS2GA4YLBBBAAAEEEEAAAdcECBBdo6YgBJwCW822GE9PXCN7zaf/0axOMWvYaYOK4f7JnCOAAAIIIIAAAgggEHQBAsSgE1MAAskLfL5sn4ycGSXnz19yPHhXywjpbYadFiuQy5HOBQIIIIAAAggggAACwRIgQAyWLPkikEqBl2ZslC9/cm6LUbSw2RbDzE/s0aJsKnPjcQQQQAABBBBAAAEEUi9AgJh6M76BQNAEdFuMx99fLWu2HHeUUbdqIenZqpxE1mJbDAcMFwgggAACCCCAAAIBFSBADCgnmSEQGIGfNh6RYZ9ukoN/nHVk2L5JKTPstIJULJ7Xkc4FAggggAACCCCAAAKBECBADIQieSAQJIF3zbYYk+Zul0uXYu0S8ubJLj1al5feZv/EHNnZFsOG4QQBBBBAAAEEEEAgzQIEiGkmJAMEgi/w9OS1snjlQUdBlcqGWcNOb72+pCOdCwQQQAABBBBAAAEErlWAAPFa5fgeAi4LbDkQLf+ZvkE27TjhKLl5veLyQJtyUrdcQUc6FwgggAACCCCAAAIIpFaAADG1YjyPQDoL6LYY4+Zsl2PHz9k1yZJF5B4z5LS3GXpaOH9OO50TBBBAAAEEEEAAAQRSI0CAmBotnkXAQwLDZm6SmYv3OGpUomheua9VhHS/iW0xHDBcIIAAAggggAACCKRIgAAxRUw8hIA3BU6eiZEXPl4vP6857KhgvWqFpFfrCnJjjcKOdC4QQAABBBBAAAEEEEhOgAAxOR3uIZBBBH7ccERGf71Vtu855ahxpxvLyANm/8TyxdgWwwHDBQIIIIAAAggggECiAgSIibKQiEDGFJjw/Q75eOFuOR19wW5AmJmT2MMEib3MHMVsWc1kRQ4EEEAAAQQQQAABBJIQIEBMAoZkBDKywH9mbJSvftrraEKVcgWsbTE6NCjhSOcCAQQQQAABBBBAAAGfAAGiT4JPBEJMYLPZFuPNWVHy68ajjpZFmgCxl1nttHZEmCOdCwQQQAABBBBAAAEECBD5dwCBEBeYuXSvTFu0W/aYgNF3ZMueVbq1jLACxfB8OXzJfCKAAAIIIIAAAghkcgECxEz+LwDNzzwCY+dsk+kLd8n585fsRpcqnlf+YuYn3t2sjJ3GCQIIIIAAAggggEDmFSBAzLzvnpZnQoETZluMNz6Pku9+2e9ofUOzHUavNuWlSVW2xXDAcIEAAggggAACCGQyAQLETPbCaS4CKvCD2RZj8oKd8vvmYw6QzjeZbTHM/MSIInkc6VwggAACCCCAAAIIZA4BAsTM8Z5pJQKJCkz/cY98aLbFOPjHGft+wbBccm+rCOljehQ5EEAAAQQQQAABBDKXAAFi5nrftBaBRAVGfrFZZpiFbGIvXbbvV6tQQB5oVUFurlfMTuMEAQQQQAABBBBAILQFCBBD+/3SOgRSLBC1/7S89/0OWfTbQcd3Wl9f0gw7LSc1y7AthgOGCwQQQAABBBBAIAQFCBBD8KXSJATSIvD96sMyddFO2bj9hJ1NjhxZpbsZdtq7TUXJnzubnc4JAggggAACCCCAQGgJECCG1vukNQgETGCy2RLjIzPs9Njxc3aeZUrkk/tNb+IdTUrbaZwggAACCCCAAAIIhI4AAWLovEtagkDABY5HX5R3v90uny3e7ci7ca0iJlAsLzdUKeRI5wIBBBBAAAEEEEAgYwsQIGbs90ftEXBFYOX24zLFrHb60++HHOV1aVHWDDstL6XCczvSuUAAAQQQQAABBBDImAIEiBnzvVFrBNJF4Ktf98s0Eyhu23PKLr9QwVxyn+lNvL9lhJ3GCQIIIIAAAggggEDGFCBAzJjvjVojkK4Cutrpx2Z+4qnTF+x61KoULj1blZM2ddgWw0bhBAEEEEAAAQQQyGACBIgZ7IVRXQS8IrDrj7MyeeFO+fLHvY4qtW2k22KUl+ql8zvSuUAAAQQQQAABBBDwvgABovffETVEwNMCyzYflSkLdsmKDUfseubKlc1si1FOeptAMa8550AAAQQQQAABBBDIGAIEiBnjPVFLBDwv8PmyfTLNDDvdvf+0XdfyphfxPjM3scsNbItho3CCAAIIIIAAAgh4WIAA0cMvh6ohkNEEos9fMsNOd8nHC3bKeXPuO5qaeYkPtIqQhpXYFsNnwicCCCCAAAIIIOBFAQJEL74V6oRABheIMr2Ik+bvlHkrDjhacrcJEnuZoafFCrIthgOGCwQQQAABBBBAwCMCBIgeeRFUA4FQFFiw9g8zP3GHrN92wm5e0cJ5pGfrcnJv87J2GicIIIAAAggggAAC3hAgQPTGe6AWCIS0wFQzN3GaGXZ67MR5u511qxaS+1uVlxa1ithpnCCAAAIIIIAAAgikrwABYvr6UzoCmUbgwPFzMsmsdjpz8W5Hmzs0LW2tdlqheF5HOhcIIIAAAggggAAC7gsQILpvTokIZGqBFduOWdtiLDPDT31Hvrw5pIcZdvrQzRV8SXwigAACCCCAAAIIpIMAAWI6oFMkAgiIzFputsVYuFt2+W2LUTkiTO43eyd2aFACIgQQQAABBBBAAIF0ECBATAd0ikQAgTgB3RZjkpmbON1sjeG/LUaL+sWllwkUrytXACoEEEAAAQQQQAABFwUIEF3EpigEEEhcIGqf2RbDzE+ct2K//UC2bFnkHrMtxmO3VbXTOEEAAQQQQAABBBAIrgABYnB9yR0BBFIhMH/NYZlqehPXbztuf6tU8XzyFxMo3t2sjJ3GCQIIIIAAAggggEBwBAgQg+NKrgggkAaBKYt2yYdmfuIxs/Kp77i+ZhHp3ba8NK5cyJfEJwIIIIAAAggggECABQgQAwxKdgggEBiB/cd0W4yd8vmSPY4Mb29eVp6+o5pkN0NQORBAAAEEEEAAAQQCK0CAGFhPckMAgQAL/LJVt8XYKcvXHbFzDi+Qy9oWo1ercnYaJwgggAACCCCAAAJpFyBATLshOSCAgAsCny8z22Is2i27/bbFqFGxoBl2WkFa1S7qQg0oAgEEEEAAAQQQCH0BAsTQf8e0EIGQETh9LubPbTF2y4ULl+x2tW1UUgZ3rS4F8ma30wJ1csZsxfHctHVSOH8O+We3moHKlnwQQAABBBBAAAFPChAgevK1UCkEEEhOYNO+UzLZ2hbjgP1YzpxZ5V6zd2L/DpXstECcjJ2zTSbP3W5l1enGMjL0nhqByJY8EEAAAQQQQAABTwoQIHrytVApBBBIicC833VbjJ2yYfsJ+/FypfJLn5srSMeGJey0tJxEmSGtg8avliNm0Rw9HmhfMeBBaFrqx3cRQAABBBBAAIFAChAgBlKTvBBAIF0EJpu9Ez8yP8dOnLfLb3JdUXnu7hpSomAuO+1aTxat+0MGT1htf33Q3dWl+01l7WtOEEAAAQQQQACBUBEgQAyVN0k7EMjkAvvNnokfzN8ps+Jti3FXywh5ymyLkdZj+o97ZOQnm+xsXu5TV9rWLWZfc5I6gTMXRbYciZWCebJIvhwiRfOybUnqBHkaAQQQQACB4AgQIAbHlVwRQCCdBHRbjMkmUPxl/ZVtMYoWziMPtqsgdzYtnaZavfnlFvl43k4rj/z5csr/PVRH6lUIT1OeofLlszEip85flpPm5+KV9YNEt6vMnk0kpznJZdYQyp5VJJc513S9Xrk/VmIvixyOviwlw7JIqfxZpIT54UAAAQQQQACB9BEgQEwfd0pFAIEgC8xctlemLdwtew5E2yXVqRIuz5lFZioWz2enpfbk6clrZfHKg9bXIkrmkxEP1ZOIInlSm02Gf37evHmya9cu2bB1t2zdsUtOnDyZ+jaZwDB3vjDJk6+A5Mkf9zNn6ltSrc710qZNW7mlZVMpUqSIFCtWTPbt2ydHjsQF/U2bNk19WXwDAQQQQAABBFIkQICYIiYeQgCBjChw+twlM+x0h0w38xMvXoy1m9C+SSn517217OvUnByPvigDzaI1UTvjFsapU6WQjOxbV/LnDvwWG6mpl1vPzpw5Uz6YNElWrVzpVpEJyqlTp45MmDBBSpYsmeAeCQgggAACCCCQNgECxLT58W0EEMgAApv2nZZJZtjp/F+vbIuRL28O6XNLBenZslyqW7B+zykZ9O5qOXEqblGc5vWKyxu966Q6n4z0hblz58rkyZPlhx9+SHG1s2bLJoWLl5FCxUvJqWNH5OihfXLh3JkUfz+5B6dPny70JCYnxD0EEEAAAQSuTYAA8drc+BYCCGRAgflrDpv9E3fKRr9tMSpHFDDDTqtLbfOZmuP71Yfl+Q9+t79ym9kj8fkQ3SPxpZdekvHjx9ttTeykZPmqElGltlSo2UDKmk8NDAsWKZ7g0eiTx+WYCRQ1WDx6cI/1c+TPz6MH98rZ0ykbqvrYY49Jjx496EVMIEwCAggggAACaRMgQEybH99GAIEMKKDbYny4YJccP3llW4wW9UvI8F7Xpao1Hy3ZLaM+i7K/c7/pkfxbx8r2dUY/WbhwoXz66acye/ZsR1PymvmCVes3kzKVapqfWlKxVgPJX7Cw45lrvTh7+pQVNB7cs032bt8g+7ZtlN1b1snJI4cSzbJXr14yZMgQyZUr7duZJFoAiQgggAACCGQyAQLETPbCaS4CCMQJ7DMb308yvYn+22JkzZpFerevKP1uqZhiplFmZdOP/lzZVL80sGs1uS8yIsXf9+KDP/30k0wy8wznzJljVy9b9hxSu0kbue7Pn/zhgQkI7QJScPLrwi9l0qv/SPCkDjXVIaccCCCAAAIIIJB2AQLEtBuSAwIIZGCB5VuOyRQTKPpvi1GyWF4Z0r2GNK5cKEUtG/rRevlu+X772Rfuv046NixhX2eUk5Vm4RmdZ/jZZ585qnx3/xes4LBIybKO9PS4iD55Qj6f8Iosm/uJXXylSpVk+PDh0qhRIzuNEwQQQAABBBC4NgECxGtz41sIIBBiAp8v2ydTrW0xTtsta1ijsLz91/qSzfQsJnfEXLosfx+/SlZtOmo9FpY/p4x6uL7UKhuW3Nc8dW/kyJHy5ptvJqjTkAnfSokI7w2bXfLFNJkx+p+O+r788svSs2dPRxoXCCCAAAIIIJA6AQLE1HnxNAIIhLDAqXMx1iI2002geOHCld3ee9xcXv7RqUqyLd979KwMmvC77NofF2DWqhQu7zzSQHLlMDvDe/xIKjh85X8rzNzClPWipkcTf13whUx67TFH0bp4zaBBgxxpXCCAAAIIIIBAygUIEFNuxZMIIJBJBDbuPW0Fiv7bYhQMyynPda8pLWsXTVJh5fbj8oTZI/HM2RjrmU5mZdOhHl/ZNKngsPvAl+SmTj2SbKtXbiw1Q00/HDHYUR2CRAcHFwgggAACCKRKgAAxVVw8jAACmUlg3u+HZcpC57YY1SsUlNH96ktYnuyJUsxZeUhenLzGvjega1X5S2Tq91q0MwjiycSJE+XFF19MUELT9vfIfY+/liDdqwmJDTfVXkQNFDkQQAABBBBAIHUCBIip8+JpBBDIhAKTzLYYH5mf4yeubIvRpUVZefbO6olqTDJbaLwze7N1T1dGHWHmIzat5v6qn4lW7s/Ebdu2yT333CN//PGH47HqDW6Sv7022ZGWES7mf/KefD7+FUdVCRIdHFwggAACCCCQIgECxBQx8RACCGR2AZ1jqIHf7B/22BQ5c2aTZ82w08RWLH1jVpR8YuYy6hFRMp+MeaS+FC+Y2/5uep88/vjj1h6H/vUIL1ZK/j5sqhQvU8E/OcOcz/1wjHw1aYSjvrrwTteuXR1pXCCAAAIIIIBA0gIEiEnbcAcBBBBIILB8y1GzLcYux7YY5Urll9Gml7B4Qedm7U9NWiNLVsVt8N6ifgkZ3uu6BPmlR8KsWbNk4MCBCYru2m+ItL7rwQTpGSlh8utPyIp5n9tVrlKlisyYMUMKF/ZWD65dQU4QQAABBBDwmAABosdeCNVBAIGMITBz6V5rW4y9B6PtCre7oZT8p0ct+/qkWaxmoNn+YuP2E1Zarw6V5NH2Fe376XFy/Phxa2hpVFSUo/gKNRvI429e2VvQcTMDXRzYtVVGPdFdok8es2v90EMPydChQ+1rThBAAAEEEEAgaQECxKRtuIMAAggkK3DKBICTFuyU6WZ+4sWLsfazg++tKV2blLauo8y2F4+9u1qOHj9nXb/6YF1pXaeY/azbJ1OmTJHnn38+QbF9hoyWBpEdE6RnxIQFn74vM9992VF1bXdkZKQjjQsEEEAAAQQQSChAgJjQhBQEEAhhgQdGrZCoHSckX94ckt/8hOXJJmHms0C+HFLQfBbMm92kxX0WzJtTiplho9VL55fs2bIkqbJh7ymzLcYuWfDrAfuZIoXyWPMOKxTPKwvWHpZn3/vduheWP6dMfaKxlAhPn/mIjz76qHz99dd2PfWkQeSt0mfI2460jH4x9rnesvHXJXYzmjZtKtOnT7evOUEAAQQQQACBxAUIEBN3IRUBBEJUoOmgedfUsrJmoZlKZq6hBovVzE+NsmFSrIBzzuH3qw+ZbTF2ySYTgPoO39zDqYt2yejP41Y2rVutkLz7aEPfI659nj59Who2bCjnz19ZjVULH2SGllY0Q0xD6djy+3J56ynnPo6DBw+W/v37h1IzaQsCCCCAAAIBFyBADDgpGSKAgJcFdG/DFVuPyd4jZ2XXoTNy4PCZa66u9gZWMsFilVL5pHoZEzyWKWAFkB+YYacfmxVMj5+8EogN6FpN9pgyZy6OW9n01mal5Z/dal5z2dfyxblz50q/fv0cX63foqM8+PxoR1qoXHwxcbh89/F/7ebkz5/fWrCmVq0r80Ttm5wggAACCCCAgCVAgMi/CAggkKkFLsTEyk4TJG7ZHy1bDpySzftOy7b9Z+QPs63FtR6NahaRWuUKyG6T74LfDtrZ5M2TXSqWDpN1JkDV47G7qsm9zSPs+8E+efrppxMMs+z97FvSsFWnYBedLvmfO3tG3jIL1uzZut4uX4fYPvPMM/Y1Jwgg4I7A/PnzZfz48RIWFiaVKlWS5s2bS9WqVaVYsWKSNWtWdypBKQggkCIBAsQUMfEQAghkNoHT52Jks1lgZtPe0xK175Rs3H1Ktu05lWqGwmauYbasWeSwX8CZK1c2M8zzkmTPnlXefLS+NKpUKNX5XssX6tWrJ7qKqe8oXraSPPfuHMmaLZsvKeQ+F8+eIp+MedFuV8WKFWXhwoX2NScIIOCOwIsvvigTJ05MtDANFLVnv2bNmlKtWjXR7WnKli0r2UL4z6ZEIUhEwCMCBIgeeRFUAwEEMobAehMkrtt1UjbsOZn6oFHXubnsbGfliAIyxuyhGG4WyQnmsXTpUunevbujiHY9HpXOvZ90pIXaxXnTi/h6/85yeN8Ou2njxo2TDh062NecIIBA8AVee+01eeedd1JcUL58+aRbt27SpUsXadAgtOZIpxiBBxFIJwECxHSCp1gEEAgdAQ0a1+8+IRv3aI/jKdlufmJi4kWCyTS3vJnHOP2pJsk8kfZbn332mQwaNMiR0dNjZkvZKrUdaaF4MffDMfLVpBF202677TYZM2aMfc0JAikRWLn9uOhCVAvNPOYjx+K2rYn/vVqVCkrzWsWk5XVFpXKJfPFvZ+rr6OhomTRpkmzevFkuXLggCxYsEE1LydGmTRtraHj16tVT8jjPIIBAGgUIENMIyNcRQACBxAS2HoiO62U0Q1Q3mgBy8+6T1rDSxJ7VtJoVw2XiwOuTup3m9LffflveeOMNO5/aTdvIw/8ab1+H8snxPw5YvYinTxy1mpk9e3ZZt26d5M6dPluNhLJ1ZmjbGTM8fKHZumbZ5mOyIupoksFigxqFJbJWUbm5XvEEKx5nBqertfGBBx6QRYsWiW5BM2zYMDl8+LBs2bJFfv/9d9ERD9u2bUuQxVtvvWX1KCa4QYJnBNauXSv/+9//ZNWqVXLo0CHZv3+/aG9wnTp1pH379tZIFr3m8LYAAaK33w+1QwCBEBLQxXDWmUBxvZnPqEHjxh3H7Z7GFvWLy/BedYLW2ueee06mTZtm59/zyeFyQ7s77etQP/l8/Ksy/5MJdjP1L6T33nuvfc0JAtciEGsGCizddESWbj4qKzYfl23m/9/xjzxmcarIusWlbd1iVsAY/35mvW7durUVBF533XXy1VdfJWDQAHHs2LHWysP+N0ePHi2dO3f2T8q05+fOnbN6ViMjI+XOO9P3z/OjR4+K/nfmm2++SfZ9FClSRN577z2GDSerlP43CRDT/x1QAwQQyMQCGjQuWHNYercpH1SF3r17W0O6fIU8N/5bKVmusu8y5D/374iSYWYuYuylGKutzZo1k48//jjk200D3RVYvfO4/LD+iPy44YgJFhMualXBbIfTpl4JudkEi5Uy+RDU8uXj/swrVaqU1WOY1JtavXq19OnTR44cOWI/oj2PFSpUsK8z64ma6N62eqSnycGDB61fuCXW65vYu9EexDlz5ki5cuUSu02aBwQIED3wEqgCAgggEGyBW265RTZt2mQVkyNXbhk+8/eQXr00Mc/xLz4sa37+3rpVo0YN0X0hORAIlsCv247JEhMs/myCxZ1m+5z4x40mSGxrehbbmSGoOc2KxpnpiI2NFV1RWA8NFtavv7IVTWIOOvT09ttvt+cs9urVS/79738n9mimSvMPEN98803p2rVrurR/4MCBMmvWLEfZuija9ddfLyVKlLCGDf/3v/+1358+2LdvX/nnP//p+A4X3hEgQPTOu6AmCCCAQNAEdBjXqVNxPRqlK1aXZ/77ddDK8mrGs94bJvP+965VPf1Ly/Lly71aVeoVYgLLzBDUJev/MMHiUdl70LkwS7HCeaRdw+Jye+NSUqF45pibpYvU6NYWvmPnzp2+0yQ/dV7bU089Zd3XeYvTp09P8tnMcuPEiRNSt25dq7kPPfSQDB061PWmx8TESOXKztEoGgx27NjRUZd9+/ZZ8w937dplpWvv4ZIlSxzPcOEdAQJE77wLaoIAAggETcA3nEsLqNe8vfQdOjZoZXk146VzZsiHI5+xqpczZ05rNUWv1pV6ha6ADj9dsuEPWbLmDzly/MpqqLov6i2NS0pnEyg2MItWhfJx5swZa89DXxu3bt1q9oXN7rtM9FPntj3yyCPWvaTmLSb6xRBO9Hds0aKFTJ06NV1a65tPqoW3atXKWq02sYro3ENfz29Keo4Ty4M0dwQIEN1xphQEEEAgXQX8A8Sbu/WT2/sOTtf6pEfhu6LWyBsD7rCL1mFtXltNb+ychCs32hXOYCdZs2SR7FmzmM3OzY/51POs+vnndTYzqjJ71qzWfes5fdZ33zwjoj9pPy5fvizmf3Lp0mW5ZE50YZlY8w8rzXxess7j7mma3tM08z/rU2ug1740x3fNFzR/HTLpe966/+d3NM2Xn688k2Slnb8YKwfMdhkHj56TU6cvaDH2USAspxQukFMK5M1hPXubCRrvaFLavp/RT86ePSs6zNt3XO3/i7oAyv333y+6QqYeujeirmga/9B8f/jhB9m4caMcP35cChYsKDrHsVGjRvaQ1vjf8fK19s4lFzj7D9WtVKmSY565m+36+eef5emnnxbtHdQVs3U4cGLH+PHj5aWXXrJu6WI1v/32W2KPkeYBAQJED7wEqoAAAggEW8A/QOzx2KvSrGO3YBfpufwvmb9sDep0ZR+1n376ScqUKeOZeg79aL18t3y/Z+pDRbwlsHRkW29VKA21uXjxolSpUsXOQbe20GAu/qEB0nfffSdDhgxxLFLz/vvvS9u2Tg8NCO++++4kRwboXoovv/yylC7tfqC9YsUK2b59uxQoUEB0PngW88uTpA7dJ3LGjBnyySef2G3WIbXPPPNMoit/1qpVy57bl5KhukmVm9Z03y9KsmXLlmRWOnd04cKF1v30DGiTrCA3bAECRJuCEwQQQCB0BfwDxAHDP5SqdZuEbmOTadlr/drLvp1brCd0aX0dquaV4/Nl++Tt2Vsk+sxFr1SJenhEoEq5AjJ1UGOP1Cbt1fDv+dLcnnzySdHhkoULF5bw8HCrJ0p7AX/99Vc7SPKVqnPbdI6b/xEdHS09evQQXfH0akdSPVwrV6609u/bvXu3nDx5UgoVKiRFixaV2rVrW72XOXLkSDZrnWOnQyg18OnWrZv4ntdg9l//+pf93b///e/2XEo78c+TmTNnymOPPRY/2b5ObG6frmLqW+FVg9Csplfei4f/fEmtn27LMXLkSC9WlToZAQJE/jVAAAEEMoGAf4D44PNjpH6LDpmg1QmbOOzhDrJ3x2brxuzZs6VevXoJH0rHlOVbjsnAMQEYdmU6KHS4pg7p9P1k0+Gcem0P8Ywb0ql/n9R06yfePWt4qMkrbmhotivP6fP67J/l6Hn8YaLaS6JpOqTU+r4p6Mq5SdPvWt+Lq5f/PR1+qveu9dDhnBdjLkvMpVgzNNScm+GlMZcumZ+4oaIxZuynnl80N83oUOtTn03NoT0mFzRfU47mr3ldjImVC3+Wq/ldMNdJZav3T0bHyOmzMXLm7EWTT9LlP353Nel2U0Rqquf5Z/3/TEppZbUnbdKkSZI7d27HV3RvxOHDhzvSdBEU7bHTjdp9AZTvAV3Q5fnnn7d68nTBHB0eqcFZUoc++9e//jWp21a6riLqC3h8K4rqSsn9+vVL8D1dcKdJE+cv6d59912rhzPBw/ESdFimDs/0HWqibdRDV6r2t1m6dKnlooGjPqfDOzUIT49D35v/qqVqld57N6aHQ0YpkwAxo7wp6okAAgikQcD/L2PdB74kN3XqkYbcMu5XB3WqIZdi4nrooqKiJFeuXJ5rzMa9pyT6vJl7ZAVhccGTFdyZeMk/iNLzuMDO94zO6YsLxjSdw3sC+82cwx83mq0vNh6VFZuOyPnzlxKtZBmzR+L1VQvJDVUKSZNqhSUsT/ILuCSaiccT/f9MSmlVp02bJs2bN3c8rsNQb7jhBjsI1HnF+ssf3xBWDeTXrVsnY8eOFR014Du0h1J7Mh9++OFEV9PUAFN7Jn3BZadOnWTMmDFJDg8dN26cvPLKK1b2zz77rLRr1046d+5sD//0lauf8edQ6oqsGqT6Hzq6QYej/vLLL476jRgxQu666y770datW4tv/0H/obofffSRNSzVftCcaJCohsnNa/R/PlDn58+fl5YtW9qBrOa7Zs0aK4APVBnkE1gBAsTAepIbAggg4EkBnZujf9HQ47beT8gtPfp7sp7BrNT+nZvl1X5xPae6xP7338ftiRjMMskbAQ0KF649LD+ZwPDXTUcl1vQ2xj+0h7WuCQgbmYCwcdVwqVc+tFcx1fb7z52L75HctQY+N954o/3I4sWLrSGgvgSdZ9izZ0/fpeNTh5Hqgio6hLR///7Wc/7DUgcMGGD1amlwqEFU/B7AxOY++gqYPHmyvc2EBoC6WI4vuNRndLEcX0+fXutcQ11NWXv3WrVqpUn2oW2499577UBOey+//fZb6/5//vMfeeCBB+xnNXD1Ld6jf8YXK1bM6onUBWESO4YNG2blndi9YKX5L06jZeh2JTrUlsO7AgSI3n031AwBBBAImID/8KXWdz4oXR8eErC8M0pGq5bMlfdfiguMb7vtNqs3IKPUnXpmPIE1u07IrGX7Zd5vB+XsuZgEDSgcnlsamqCwcZVwq5ewpLnOTIf/0EjtLdOgToeEalB16NAhax6i/hJHe/HiHxMmTLB66DT9gw8+kBdeeMF6ROf/6aI2Kekh0x47/70Up0yZIpGRkY6iXnzxRZk4caKdVrNmTfn666/NkOmE8/z8h5jaX/jzRPPQNjZufGUe6Zw5c6ytPnRO5BtvvGF/Rc/vuece+1pPNLC94464FZg/++wzawN63wP6rG9PV91XUINY/zr7nvN9BnNxGF1Fds+ePXL69GnRcx1mruc6pNd36PDYH3/8UfLkyeNL4tODAgSIHnwpVAkBBBAItIAOp2rfvr2VbeObu8r9T135C0mgy/Jqft9PHyez33/dqt4TTzwhAwcO9GpVqVcGFli49g/59Oc98sv6IwlaUbV8AauX8AYdPlq1sDVEOMFDmSRBt0Lw9d4ltcm7DgHVeXXaO/fhhx/aMjqMVHvL9PP111+3f9mjPXE61+1qx44dO6whj77n4vfKaboGODfddJOjF1DTdaiq9trFP3R+XWJl68I1N998s/W4zkfUXkk9fAGpfw/gfffdJ6+++qp1P/4/NFDWFUL95xjqM9qbuGjRIutxbb9vlVBN0J5MDZ51RVTf8FdN117UQK7mevDgQWvbEZ3HmVhAr2X6Dp33rXM6dVgwh3cFCBC9+26oGQIIIBBQAf1Lig5rqnVDK3nkP+8FNG+vZ3bu7BkZ+dhdsn9HlFVV7VH1Bcxerzv1877A8i1H5YcNR2SJCQ73HzrjqHCNigWlRa2i0qxGEalVNsxxLzNf9O3b1x7mHX9OXmIu/r1oet8X1OkWGL4N4nXOXlJDK/3z1EVidJijHjr8/v/+7//8b1vnOhRTg8H4hw4V1SAsfqD2yCOPyDfffON4XFdn1WGrvsN/GKqvp9B/FdKkVlj1fT+xT/8eRP/7um+kbkqvvZ0a7PrvO5nYaqj+303N+YEDB6zeTf/hsyn5foMGDawFdHS4P4f3BAgQvfdOqBECCCAQFIHu3buLrmpXqFhJ+dfUH4NShlcznTNttHw9OW5Jdf3NuQ5d094HDgSuVeCXrcdk3upDsszMK4wfFJYomle6NC0tresUlYrF+fcsMWNdyMXXK6gb2X/66aeJPeZI054n7XnTwxdUDho0SHTYpR66EIoGYVc7NPDTAFCPwYMHW/MR/b8Tf8VN/3t6rltRaLn+hw4B1SDWd+g8SQ1c/fcF1KGV2kuoh+4JqAFcixYtrOG0mqa9alp23rx59TJFR4cOHWTDhg2OZxNbjMZ/HmNSPbaOTFJ4oXM5/Rf/0a/p+zx69Ki9eE5yWekWIL17907uEe6lgwABYjqgUyQCCCCQHgL6lyjfX2oeemGc1L0xbthTetTFzTIP7d1h9R5GnzxuFat/UdK/aHIgkFqBE2aPyjkrD1qB4e+bjyX4evUKBaXzDaXk7mZlEtwjwSngP/dO56Xp9g1XO/yHpfqGY/oHKCmdX+c/b1F/UaRBmfZk6VB8DR59Qza1PjrvUHsltYfQtxiMpj/66KPWyqO++YjxF93RRWoiIpxbk+hQTN/QSt9CWdoTqnMqfYcGV6+99ppVH19acp/+PZD6nLZnwYIFUqJECcfXdPXS5557zkpLqZMjgyQu4rdbFxHSOujqsP69ilqmb7XV+FnpLy91YR7f3pHx73PtvgABovvmlIgAAgiki4AuNa7/Mddl4ctWrikDXv9I8uQP/SFv/xv9gvzwxVTbPP4iD/YNThBAICgCukm69hBqgFCtWjVrkZWtW7c6hl8mt+2Mrjqqcw19vYdaSd9qnP49Y5quQd7VFkDRwEV72a52aDCncwh18RwdSnnrrbc65iTq/EGdM1iwYEHx37ZDfxGX1Ib3/ovzLFu2TM6dO2flG3/ung7BbNu2rTRr1swy0zrEP+JvPq/3NdjVuYjxD138R4NJ36EL28QPIn33UvPpP4cyqe/pAj2zZs2yFq3R+Z/ay6tBuv+hq5r6hv36p3OePgIEiOnjTqkIIIBAugjob5D1N8l6tOraR+58JLR70pbN/USmjRhsW+uiE75hbXYiJwggEFQB/3mCvoK019B/G4i33nrL2qJB9ya9dOmSHDt2TPbu3SsrVqyw5vz5B1C6wbruB6irZMaf+5fSBViGDx8uo0eP9lUnwafOUdY6+c811ABXV0D2r4v2lukQyy+//NJajVSvNfjKnz9/gjw1YdSoUVbd9dy34I0Gtdoj6u+h9/0PnfuowaUGeXXq1LFWRdVhnL4eSX1Wh6v65mP6f9d37r+gT/ztQnzPpPbTfz5nYt/VLUNmzJghJUuWdNzWXxD87W9/s4fHahAZf6iq4wtcuCpAgOgqN4UhgAAC6SsQf8+wUB5qun3DSjO09G4HeHJ7pDke5AIBBAIm0KdPH5k/f35A8tMgSXsSdQ9BPXR/QO1F1CM1QycvX75sBS7xe610SKmucKxz+3zDR63M//yHzvfT9viGT2pAqH+uFi1a1NoCQxeD0XokdehIDu1127dvn+hWFxpA6aEB8TvvvCPjxo1L6quOdN9Qed+8TK2Hf36Oh/+80B5H7ZHUAFe35ejatWtij6U6TYfTjhw50grm/b+siwbpcFn9ZUBih9ZH52Lq3E0NdDWQ5PCGAAGiN94DtUAAAQRcE9D/aOvS8XqUMUNNB4bgUNOz0adkSPcmEnPxvO2qi9Pob/mT+suK/SAnCCAQUAH/XrNrzVgDtwcffFA6d+6cYAip9mJpcKELzugcvtQcFy9eFB32eOHCBalQoUKKFq/SYaG6kumpU6es4Zy6OX1qDg0Sda9G/wVsfN8/fPiwtYiWzsnU3tOk5u35r76qQav2MIaHh/uySfJz3rx51lYbOndT2xvIQwNPDXS1FzgsLMzR+5pUOTp8WNup0x/i9zIm9R3Sgy9AgBh8Y0pAAAEEPCWgy6vrAhG+o0HkrdJnyJVrX3pG/nzpwZvl0N7tjibQe+jg4AIB1wR0yKgu/KI9RbqRug6N1N4j3URdfzRI0OBCe8F0Xpz2xmnQpb/M0WvtXfIfSulaxT1QkDqtW7fOGoqpQ1F37dplDTH9xz/+YQVhHqgiVQhBAQLEEHypNAkBBBBITkB/29yjRw/rN72+50IpSBz1RHfZunaFr2nWpy728P777zvSuEAAAQQQQACBhAIEiAlNSEEAAQRCXkDn8MTf6iEUgsRX+nWQAzs3O96fziP65JNPrJUTHTe4QAABBBBAAIEEAgSICUhIQAABBDKHgP8G074WN2x1m/R+dpTvMsN8nj5xTN56qkeC4FAbMGDAAHnyySczTFuoKAIIIIAAAukpQICYnvqUjQACCKSzQGRkpOzcudNRi9Z39ZWu/eI2VHbc8OjFgZ1bZOLLA2T/zqgENaxdu7a1eIXObeJAAAEEEEAAgasLECBe3YgnEEAAgZAV0AUPdO+s+MeNne6Tewf+J36y566jVv0sn479d6LBYUREhLz77rvW6nieqzgVQgABBBBAwKMCBIgefTFUCwEEEHBLQJeHT2wIZrnqdeXB58dI4eKl3apKqspZPHuqzBz3klyKuZjge7r64fjx45l3mECGBAQQQAABBJIXIEBM3oe7CCCAQKYQSCpIzJotu/QaPFIatLzVMw6XYmLks3Evy5LZkxOtU968eWXChAly0003JXqfRAQQQAABBBBIWoAAMWkb7iCAAAKZSmD9+vXSsWPHRNsc2eUBaXtPPylUrFSi991K3L15rcyaMEyiVv2UZJEaHLZr1y7J+9xAAAEEEEAAgaQFCBCTtuEOAgggkOkEDh06JI8++qisWOHcR1AhChUrbQWJkV3ud91lv9m64ocvp8kPX0yTy5djEy2/cePG8sQTT0izZs0SvU8iAggggAACCFxdgADx6kY8gQACCGQqgdjYWHnmmWdk+vTpiba7esPmcvM9fxX9DPZxeN8u+fHLqbLEBIcXz59Lsrj+/ftbwWH27NmTfIYbCCCAAAIIIHB1AQLEqxvxBAIIIJApBYYPHy6jR49Osu1NbrlbGkbeKjUbt0zymWu9cezwAfnxq7gewzOnTySZTY1a18ngp56QNm3aJPkMNxBAAAEEEEAg5QIEiCm34kkEEEAg0wmsWrVKpk6dau0lmFTjK9ZqKA0iO0nDlrdJgcJFk3osRenrf1kkG1YskpWLv5GTRw8l+Z2IilWl211dpU/vByQsLCzJ57iBAAIIIIAAAqkTIEBMnRdPI4AAAplSYNmyZfLeB1Nl7tezk2x/vgKFzGqnnaRC9fpSpnJNKVOpRpLP+t9Yv3yRrFn6vaz5+ftkg0L9Tt3GN8ldd3aVnt26CsNJ/RU5RwABBBBAIDACBIiBcSQXBBBAIFMILFy0RN55f4osXTj3qu3Nm7+glKpQTXQ/xWxmbuCFc+fkwvmz5vOM+dHPs7J17XKzj2HMVfNq2aGr3GN6DDvfEvjhrFctnAcQQAABBBDIRAIEiJnoZdNUBBBAIFACX3y7UBYs/lFWLPtZdkatCVS2dj6FS5SV2o0jpfENN0irG2+QelXL2Pc4QQABBBBAAIHgCRAgBs+WnBFAAIGQF4i9LLJ07Tb5bsFiWf7jYmt/Qu0ZTO2hAWGRkmWkVuPW0uTGFhLZuLaUypdFsmdLbU48jwACCCCAAAJpESBATIse30UAAQQQsAUuXBI5deGy/PDTUjl++pycOH1eTkSfl7PnzsvFC+cl5qL5MZ8XL56TwsUjpFSZslK2TBkpXz5C8uUQyZczixTKncV82llyggACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAApaKqg4AAAVzSURBVAgggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuCxAgugxOcQgggAACCCCAAAIIIICAVwUIEL36ZqgXAggggAACCCCAAAIIIOCyAAGiy+AUhwACCCCAAAIIIIAAAgh4VYAA0atvhnohgAACCCCAAAIIIIAAAi4LECC6DE5xCCCAAAIIIIAAAggggIBXBQgQvfpmqBcCCCCAAAIIIIAAAggg4LIAAaLL4BSHAAIIIIAAAggggAACCHhVgADRq2+GeiGAAAIIIIAAAggggAACLgsQILoMTnEIIIAAAggggAACCCCAgFcFCBC9+maoFwIIIIAAAggggAACCCDgsgABosvgFIcAAggggAACCCCAAAIIeFWAANGrb4Z6IYAAAggggAACCCCAAAIuC/w/OsDdHSiHP+MAAAAASUVORK5CYII=" - } - }, - "cell_type": "markdown", - "id": "710dc4f0-1c88-4386-9e9d-fec3de6bb774", - "metadata": {}, - "source": [ - "# How to create branches for parallel node execution\n", - "\n", - "
\n", - "

Prerequisites

\n", - "

\n", - " This guide assumes familiarity with the following:\n", - "

\n", - "

\n", - "
\n", - "\n", - "Parallel execution of nodes is essential to speed up overall graph operation. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out and fan-in mechanisms, utilizing both standard edges and [conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.add_conditional_edges). Below are some examples showing how to add create branching dataflows that work for you. \n", - "\n", - "![Screenshot 2024-07-09 at 2.55.56 PM.png](attachment:51f122de-b2ce-4c21-a5a7-c3be70c28a91.png)" - ] - }, - { - "cell_type": "markdown", - "id": "66b6b42d", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First, let's install the required packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bb54e2d0", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph" - ] - }, - { - "cell_type": "markdown", - "id": "73bac559", - "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": "d6c05fc4-ecd8-483f-a9fd-b1a055f922d9", - "metadata": {}, - "source": [ - "## How to run graph nodes in parallel\n", - "\n", - "In this example, we fan out from `Node A` to `B and C` and then fan in to `D`. With our state, [we specify the reducer add operation](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers). This will combine or accumulate values for the specific key in the State, rather than simply overwriting the existing value. For lists, this means concatenating the new list with the existing list. See [this guide](../../how-tos/state-reducers) for more detail on updating state with reducers." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "09372b8b-edea-4b9d-9ec3-3d93ce1ba819", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Any\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # The operator.add reducer fn makes this append-only\n", - " aggregate: Annotated[list, operator.add]\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Adding \"A\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Adding \"B\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Adding \"C\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "def d(state: State):\n", - " print(f'Adding \"D\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"D\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(c)\n", - "builder.add_node(d)\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_edge(\"a\", \"b\")\n", - "builder.add_edge(\"a\", \"c\")\n", - "builder.add_edge(\"b\", \"d\")\n", - "builder.add_edge(\"c\", \"d\")\n", - "builder.add_edge(\"d\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "66f52a20", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "74dd577b-0474-44c4-b4bc-9113090e3121", - "metadata": {}, - "source": [ - "With the reducer, you can see that the values added in each node are accumulated." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "81646784-5e7d-4096-980d-9fdfafd6e7a3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"B\" to ['A']\n", - "Adding \"C\" to ['A']\n", - "Adding \"D\" to ['A', 'B', 'C']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'C', 'D']}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": []}, {\"configurable\": {\"thread_id\": \"foo\"}})" - ] - }, - { - "cell_type": "markdown", - "id": "ea5495cf-9564-40c6-bc2d-0b2a8f72a5df", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - " In the above example, nodes `\"b\"` and `\"c\"` are executed concurrently in the same [superstep](../../concepts/low_level/#graphs). Because they are in the same step, node `\"d\"` executes after both `\"b\"` and `\"c\"` are finished.\n", - "\n", - " Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them." - ] - }, - { - "cell_type": "markdown", - "id": "c392b3d2", - "metadata": {}, - "source": [ - "
Exception handling?\n", - "

LangGraph executes nodes within \"supersteps\", meaning that while parallel branches are executed in parallel, the entire superstep is transactional. If any of these branches raises an exception, none of the updates are applied to the state (the entire superstep errors).

\n", - "

Importantly, when using a checkpointer, results from successful nodes within a superstep are saved, and don't repeat when resumed.

\n", - " If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this:
\n", - "
    \n", - "
  1. You can write regular python code within your node to catch and handle exceptions.
    2. \n", - "
  2. You can set a retry_policy to direct the graph to retry nodes that raise certain types of exceptions. Only failing branches are retried, so you needn't worry about performing redundant work.
    3. \n", - "

\n", - "Together, these let you perform parallel execution and fully control exception handling.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "08d8162e-1785-4ae1-993f-6d2ed48c22ae", - "metadata": {}, - "source": [ - "## Parallel node fan-out and fan-in with extra steps\n", - "\n", - "The above example showed how to fan-out and fan-in when each path was only one step. But what if one path had more than one step? Let's add a node `b_2` in the \"b\" branch:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "259a7704-5aa0-4e4c-aeef-cca04e8be0ff", - "metadata": {}, - "outputs": [], - "source": [ - "def b_2(state: State):\n", - " print(f'Adding \"B_2\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B_2\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(b_2)\n", - "builder.add_node(c)\n", - "builder.add_node(d)\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_edge(\"a\", \"b\")\n", - "builder.add_edge(\"a\", \"c\")\n", - "builder.add_edge(\"b\", \"b_2\")\n", - "# highlight-next-line\n", - "builder.add_edge([\"b_2\", \"c\"], \"d\")\n", - "builder.add_edge(\"d\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "83320227-8ab3-44c0-b6cf-064a7a425b9f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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aRJSCz507V1hYOHjw4ADeyx9efPHFjRs3+nw+OOFQDrL4fV3UAlu3bjWZTMuWLYMQC1kP/vzzzxcvXixAuwCAefPmXbp0qb6+HkIsNIJramq+//77mTNnIonOBUaMGPGvf/0LQiA0gv/+97+/9NJLSEJzhPT09J07d0IIhEBwaWnpjRs3xo8fDz80d1AoFCNHjvz+++/ZDoRA8FdffTVv3jz4cbkGnE4MW7DP59u3bx9vpnufhJSUlJSUlOvXr7MaBbbgzMxMIY+tHqBDhw5sL76ELfjAgQPDhw+HHJSzDBo06OTJk6yGgCq4tra2srKyS5cuMINymZiYGI1Gc+fOHfZCQBV8/vz5SZMmwYzIfdjuxLAFt23bFmZE7jNkyJDbt2+z1z5UwTdu3EhNTYUZkfu0b98+Ozvbbrez1D48wXV1dTExMTExMdAiBgupqak3btxgqXF4gouKiriwPIiDdOvWjb2rYXiCKysre/bsCS1cEMGTHlxeXs7jLQKfBJ4IpmnaaDQG8EbBoVAounbtWlhYyEbj8ARXVFSI5+DmUKlUxcXFbLQMT7BWq9VqtdDCBReJiYksCWZ9c8rp06cTBIHjeFVV1YkTJzZu3IjjOIZhQVR2AwJt2rS5fPkyGy2zLtjn8zWeXfyrkCiKGjhwINtxg4vExMTMzEw2Wmb9ED169OgHXjEYDIsWLWI7bnCRlJTEUsusC545c2ZCQkLjrzRNp6amihNKD6DVau/cueN2M19InnXBWq125MiRjb+GhYU9++yzbAcNRiIiImpqahhvFsYoetasWfHx8f6fu3XrJnbfJjEYDLW1tYw3C0OwVqsdNWqU2H1bhqUe/KSjaKvZhwWw58m4UdOPHjqbkpKSaOxoq2v9sRycACot6yN8TsFSD37MZ5Nsdd4LB813r9uNySGmCg/jaekM0rqqhpRemr7jDYw3zk327Nljt9sXLFjAbLOP00ssNQ2Zn5YNnhnTc2SERMrWQd5p85Xfde5Yd2/68niCgLozEhJwHC8pKWG+2Uf9gN3i2/Nx6fTX2hiMCvbsAgBCNJLk7tqnhxp2byhlLwp3UKvVbKzreGRD5w+YBs+KZTyP5ohtG5LQUZVzDsaDeGjRaDQ2m43xZh9ZcMENuz5CxngeLaDSScsKoO5thgRO9GC7xRfdRimVQ12qFxYtoyA9Do8STgjGMGBmYczcMhSF1dc0QA4KH41Gc/89XaZAX0ZJxI9MJrt69SrjzYqCuYJUKvV6vYw3KwrmCjKZrKGB+TORKJgr4DiO4zjj5ZVEwRyCjaO0KJhDPP3004wfpUXBHCI3N5ckSWbbFAVzCBzHGX/4QxTMIUTBPEcUzHOCUvC3e74ZPLSn0+lkOxAPSEhIYPzxLbEHc4iysrLg68EigYNhzJfvhrRycfOWv50+c8Llcvbs0efFpSuioqLhxBWB1INraqqXLHp53Ngp5y+ceXX5Ypud+bUpPCCIe/CbK98NCQkBAHTv1uOtVcszM3csmL8ETugggg3BsM/Bv/td/+iomGvXsiHHDQoIgsACeYzgUUAwyDJERDocbNX9Cmooigr6HgwAqKszh4aGwY8rTGALzsu/XVZW8swzvSHHFSyQBll/eX/VgH5DKirLv9u7MzbGOG7sFDhxRWAIHjxoOE4Qf/98PU1RvXr97oXnl6lUKghxRWAInjZ1NpjKdhCRZhFvVXKI8PBwxtsUBXMIk8nEeJuiYJ4jCuY5omCeIwrmOaJgDhEfH8+HyQaR5igpKeHDZIMITETBPEcUzHNEwTxHFMwhEhMTEY+iaRoYjApmM2gVDAO6SKiVuVBRXFyMeBSt1ksqilweF8PPsLaMqcItkfK/ViVLPPIhOrmbuq4aaqksR703rj3swwZveGTB/SYajm+rYCeZJsi/bq2+536qtw5aRJ7xyIJlCnz+qsSt7+WX5TsdVhZLDFqqPb9eqCu+aZv8Erzap/zjcZbshGgkz61pe25f7fl9Dn2krKYkoCM2RVMYwAIcJYZFyT1uMqWnetJSAW13yMaKjsdckyWR4QOnRg6cCtxOMkBn7777br9+/YYMGRLImwkCk8gEN7BiY0XHky66U4QQAb6TxhpwCSlXilfeUBG/bp4DT3BYWJhEIqyNVLgAPMFms5nxQow8IyoqivE24QmOjIyUy+XQwgUjVVVVjLcJT3B1dbXHA7tavAjUHiyTCWLOgFNA7cFsFLwWaRl4ghUKBY6LV2WwgfeNu91uxqt88YzQ0FDG2xS7FIeoq6tjvE2ogyypVAotnIgfqIMsNraNEWkZ8RDNc+AJ1uv14iG6ZYL7CX+LxSIeoltGfMJf5JERb3TwHPFGB4cI7ulCDAt0xZ1gCe7pQpqmGX8uQ6RVxJMizxEHWRyCjVOYOMjiEGycwsQuxXPEZbM8R1w2yyHi4uIYb1M8RHOI0tJSxtsU10XzHHFdNM8RD9Ecwr85HLPAEyyXy8UbHS3Dxi7L8L5xj8cj3uiADzzBERER4iCrZQgi0KfpAwee4JqaGnGQ1TIkyXwBMniCNRqNeCerZdq1axfEBcFtNpt4J6tl7t69G8QFwbVardiDWyY2lvmKYPAEW61WsQe3THl5OeNtig+Ac4jgPgeLD4C3ChvnYIzthXATJkwoLy+naRrDMIqicBynKKpHjx6bNm1iNW4Q0aNHD/96Hf+35P/v5MmT33777SdvnPUenJaW5s8YAOC/VanX65999lm24wYRPXv29P/g/5YwDIuNjZ0/fz4jjbMueO7cuffPY9M0nZKS0rdvX7bjBhELFizQ6/WNv9I03b9///j4eEYaZ11wXFxc3759G08EOp1uzpw5bAcNLtLS0jp06ND4FRmNxunTpzPVOIxB1uzZs41Go/9vs0OHDv369YMQNLiYN2+eTvdb0fO+ffsmJSUx1TIMwXFxcX6per1e7L5NkpaWlpKS4u++s2bNYrBlSJdJM2fOjI+PT05O7t+/P5yIQcfcuXNVKlVaWlpCQgKDzbZymVRT5rl6wlJ1z+2yP+lEh4/04TiOY0/0JxUeLfP56LgOyr7jDU+YDwRuXrDmX7NTJF1TGtA0mtfnk0gIDLR+r8NglJM+Or6Dss+YVooCtCS46JYja58pdWCYPkKmVHPiNjKGA0tNg63OezazatG7bRQq5idQmeL49mpCjkclKMNjFQTB8P0pDAN11R6b2XvpcO2zq5Oksma7TbOCcy9Zb/1sGz6Xo1smUCS984PCZ/+UJFNwcRnQoa8qteGy1AFhbAdyOXzfri96cV1yc29o+ttxO8lbF7lrFwCAE9jQ2dGn99SgTqQJ8q/ZlGoJBLsAAKVKMig9poXvoWnBFQVuQsL1h7Uj4pW52TbUWTTBvdsuTRi8ekIRRsWdq81+D00Ltpq8UYnML+FkFgzD2qVqass4twzI10CHx8Lbqk2hIqISlLa6pisYNT108rgpXzBM/NSbGji4ULOuugFyKQNTpYemmz7icnGEIsIgomCeIwrmOaJgniMK5jmiYJ4jCuY5omCeIwrmOaJgniMK5jmiYJ7DmODxEwd9/o+PHukjFy6cfe75OSNHp6XPGvvRx/9Tb61nKhmRRpD14Jqa6lV/fE0qkz2/5JVBA4cfOLj3L39h4EkNkQdAttIqIiLyT3/8n7TfDfAXpnA47AcO7rXb7Wq1GlVKvIRJwQUFeRmvLsrLy42IiJoxfe74cVNafn//foMbf1YolAAAkhToA8Rut3vr15t/+unHmtrqqKiYEcPHzp2zkJGqU0wKzr97J33GvKFDRv149MD6DWvcbtf0aYEuc7+Ufb59copOpw/gvXyDJMm33l72S861KZNnJrfrUFRcUFJazFRNMSYFjxg+dmb6fADA+HFTMl5d9NW/vhg3dopSqWz1g2fO/nTvXtFbb77HYDJBxKnTx69ey3799++MGT2R8cZZGWQRBDFx/DSn03n79q1W3+xyuf7+2YcdUzoNGzqKjWS4z8+XsuRy+cgR49honK1RdLghwj90avWdW/75WXV11bJlbwp20506s8kQHsFGFTQWBVssdQCAsLBWHqzIvX3ru707J02cntLhKZYy4T5qtcZcx/yuhX7YEnzq1DGNRtuuXYcW3uPz+T788M96fejC/36RpTSCgqef7uVyuY6fONL4CoPliJgcZB35cX9YWLhCobz487nz58+8kvFGy2V1dn+7Lf/unae798z8bof/ldDQsFYvrvjH8GFj9n6/63/W/ik392Zyuw4FhfmXr1zc9MU3jJyzGBMsk8nTZ8w78uP+kpLimBhjq2NCk6n231s3AQCuXsu+ei3b/2JSUlsBCpbL5R+u+8emTZ8ePXZw/4HM6OjYwYNGkCTJSN04xgTv2X0EADBj+twA3x8ebjh04CxT0YMdnVb3+9dW/f61VYy3zO6tSrvdPmtO06P/5597ddzYyaxGF2FdcEhIyMYvvmnyn7QaHauhRfywKxjH8Zho5gtsigSOOOHPc0TBPEcUzHNEwTxHFMxzRME8RxTMc0TBPEcUzHOavpMlkeIU5EIxj4VKL+Fgmmqd5Mkqcj4yunApTTX9RTSdiEpHmCs4V3/qYcrznaGR8EqOBQghxay18KpQUSRdXuDSGZr+HpoWHB4ta+4vgjs46r0xbZUcrFUZk6RwWuEt8LbUeNp1bfZpgaa/HYNRrtZLrp82s5nYk3J6T9XTg7i4jrrbQP3tS/XNlZ5jnNN7qnqOCG3uX1sqJ3xiVw1OYN0Ghkmk3Oolbofvp52VvUaEtumsQp1L0zS4qW/+eq/PuAhjOxYzdFh9J74pH5weEZPU7OLzVgqCX/rRnJNVL5HiSs2TTixSFIVh2BOuM1LrJWV5ToNR9vSg0ISOnK6mSVP08Z3Vty/ZkrqoAyynTpEkjuMggK9IGyYt/tUe00bRY1hoC3YD2hiLouj6Wq/T+qQV3zdv3tytW7devXo9SSMYhukjJSFP/NcGDYqia0oafN6AKmr+6U9/Wrp0aXR0dKvvxDAsNFqqDKAeeuvfFI5joZGy0MhAMmwJD16hDOtgTG79SRY+geNYVGKg+55bPAXhcZixxR75yAkw2JYIB4EnWKFQMPXEHF9h49loeN+42+2mOFjcmUtYrdYg3l42MjJS3AG8ZZKSkhh/BA2eYIvF4nQ6oYULRu7cuRPEgsPDw8VzcMsYjUbGD3LwvnGKokwmth6S5Ac5OTkajYbZNuEJ1uv1FosFWrigo6GhgaIohYLh7VqgDrI8niCYgkSF2Wzu2LEj481CPQcXFBRACxd0VFZWsjFGgSc4JiYGWqxgxGQytW/fnvFm4QmOj4+/cuUKg8UJeMbt27cjIiIYbxbqdUtycnJ+fj7MiEFEXl5ecPdgAECfPn3u3bsHM2IQQdN00AtOSkrKysqCGTFYqKioyM/PZ2OYAlVwr169Ll26BDNisHDp0qUnXArRHFAFR0dHJycnl5WVwQwaFBQUFKSlpbHRMuybw+3atTt27BjkoNxn165dAwYMYKNl2IJHjhx55MiRAN4oIE6dOtWnTx+5PNCVPY8EbMEpKSkajUYcS99PVlbWmDFjWGocwfzdoEGDdu3aBT8uN7HZbEeOHBk2bBhL7be+bJYNevfuff78eZYK6AYXn332mVwuX7RoEUvtoxH88ccfh4aGzp8/H35ortG3b9/jx48zPkvYCBrBLpdr/Pjx4nB627ZtZrM5IyODvRBoBAMAvvzyS4fD8fLLLyOJzgW8Xm///v0vXLjAahRkggEAw4cP37lzZ1hYGKoE0PL++++3b99+2rRprEZBuQpu1apVmzdvRpgAQoqKimpra9m2i1jwwIEDTSaTMM/Er7/++ksvvQQhEMpDtH+OrFevXtnZ2QhzgM+mTZtIknzhhRcgxEK8UBnDsA8++OCjjx5t29Kg5t69e/n5+XDsohcMABg8eLDD4cjMzESdCCSWLFny+uuvw4tHc4NJkyYVFxejzoJ13nrrrUOHDsGMiL4H+9m4ceP69etRZ8Euhw8fjo2mHtGFAAANs0lEQVSNHTUK6g5+XBEcERExZcqU5cuXo06ELXJzc7du3Qpn5Hw/iEfRD7B582av17t06VLUiTAMwosFrvRgP4sXL7bZbGfP8m0/pbfeemvHjh1oYsM84QfIrFmzcnNzUWfBGMuXLz958iSq6Nw6RDcyaNCgffv2Mf4sJXzWrVtnNBpnzZqFKgFuHaIbOXDgwMqVK1Fn8aTs3bs3LCwMoV3uClapVCtXrpw0aRLqRB6fQ4cOZWdnL1y4EHEeqM4NgXD9+vXnn38edRaPw8WLF1999VXUWdA0TXNaME3TZ8+ezcjIQJ3Fo5GTkzNv3jzUWfwG1wXTNH3kyJGVK1eiziJQCgsLp0yZgjqL/xAEgmmaPnjw4Lp161Bn0TpVVVULFy5EncX/gaODrAcYPXp0UlLSmjVrGl8ZP3481DmZZhg/fnzjzyaTae7cuVu2bEGa0YMEh2AAwNSpU5OTkzds2AAASE9P9z9vabPZEKa0cePG8vLyfv36+devL1iw4Mcff0SYT5MEjWAAwIwZM8LDw4cMGXL37l1/j0H7MOr58+f9NTj79u07atSo/fv3I0ymOYKseGRmZqbVavX/7HA4Tp48OWTIEP+vddWe29l2u8VnNTNfBkSlk0ikWHSSvHOf3zYuz8vLq6qq8tcO9Xg8nC3DydG0mmT69OmlpaWNv2IYduvWLbfbrVAobmfbrp+2xCar4lLUEgnzhyWMwCzVnrpq344PSqYtM0qk+IULF2praxvf4PP5+vTpw/Yi58cgaAQvWrSosrKSoqj7i0mZzebs7OxwWbe7NxyjF8WzmkBkvAIAENM2ZPeG0llvJFy4cIEkycbqvyRJ4jg+duzYAwcOsJrGo0KsXr0adQ4BMXHixPbt29M07XK5PB6P/8t1u91aRZS3uu2wObFw0lDppFIFnv1TxYkLO+12u/9WoMFgaNu27ezZs9euXQsnjcAJmh4MAOjfv3///v0dDse5c+cOHDiQn59fXV1dWYD91zi2ntxqkoSn1GcyK6urq9VqtcFgGDJkyJAhQ9ioQsgIHJ0uDASz2Xzq1KmCn5WjJvVP6Ah1A6UT28t/Kd89Ydrg1NRUmHEfg2DqwQ8QFhY2efLk/TXlOMNV8FvH7aQWL3w+OgnqkePxCKbrYJHHQBTMc0TBPEcUzHNEwTxHFMxzRME8RxTMc0TBPEcUzHNEwTxHFMxzRMFgevro9RvWBPDGoEQUzHNEwTwniOeDHxuSJP+9ddP+A9+53a7u3Xt63G7UGbGIEAV//MnaffszR4+a0C31mZ8vZdnsKFfPs43gBN/Jy923P3PunIWLFr4IABg5cty165dRJ8UigjsHnzlzAgAwbdqcxlf4vfE8n//fmqSqulKtVuu0OtSJQEJwgvW6ULvd3tDQgDoRSAhOcIcOTwEAjp84jDoRSAhukDV40PCtX29ev2FNYeHd9skpN2/dqK2tQZ0UiwiuBxMEsfb9T3v27PPDvm//sfFjHMd1Oj3qpFhEcD0YABAdHfP+X/5TgvyVjDeQpsMuguvBQkMUzHNEwTxHFMxzRME8RxTMc0TBPEcUzHNEwTxHFMxzRME8RxTMc4JeMCHFAPQqO4QEwA/6eAS9YIWKcFiZrz7aMlaTV60Ljom4oBccESd3WLwwI3pcpFJNqLQEzKCPTdAL7pKmy79uc9rgdeLsI7Vd0nQY/PJrj0XQCwYAzFgRf3JXhaUGxjq6C/urw6KlXfsGzaLMIK5VeT+Oet+RrZVuBxXTNoSimG9fEUJU33PhBDAmK3oOC2M+AGvwRLCf2jKPqaLB5SADeXNpaWlWVtaMGTMCebNEimtCifBYebCMrRoJsnRbxmCUG4zyAN9MXr5beyq7+8DnWE4KMXw4B4u0gCiY5whXMI7jSqUSdRasI1zBNE3LZDLUWbCOoAXX19ejzoJ1hCsYAKBQBEFN/idE0ILdvK7O4UfQgoWAcAUTBBEVFYU6C9YRrmCSJKuqqlBnwTrCFYzjuEoFdTstJAhXMEVRDocDdRasI1zBAkG4ggmCMBqNqLNgHeEKJkmyrKwMdRasI1zBAkG4ggmCiI9nd9NwLiBcwSRJlpSUoM6CdYQrWCAIVzBBELGxsaizYB3hCiZJsry8HHUWrCNcwQJBuILF2SSeI84mifAB4QoWl83yHIqiXC4X6ixYR7iCMQzT6YLmKdDHRriCxXXRInxAuILF2SSeI84m8RwMw8RHV/gMTdPioysiQY9wBeM4rtfzeUssP8IVTFGUxWJBnQXrCFcwjuNhYcFU8erxEK5gmqatVivqLFhH0IJ9PthlauHDq0p3gTBp0qR79+7hOE5RFIZh/gtikiSvXr2KOjVWEFwPXrJkiX8aGMdxDMMwDKMoKiUlBXVebCE4wWPHjn3gFrRCoZg9eza6jNhFcIIBALNnz5ZKpY2/JiYmTpgwAWlGLCJEwRMmTEhKSvL/LJPJZs6ciTojFhGiYADAzJkz/WXu4uPjJ06ciDodFhGo4IkTJyYmJspksjlz5qDOhV2C4zKJIumyuy6H1ee0khRJuxwMFHUvKCjIyclh5OxLEBghASFaSYiG0EdIw2MCrVkNAa4Lzsmqz7vmKM93RiSpSR9NSCUShZQiuZUzjmGkz0d6SdJL4hjttnvbpaqSu6tj26JflstdwVd/smTtrzUkaEJCQzQRIajTeQQ8Tq+txgl8DQRODZgcjrZDc1FwZbH78L+qlDplRHIYHiS71zSJrcZZU2Bu11U1cKoBVQ6cE3zzfP2lo/Vx3aIlsuDYeapVrFV2e7V19htoFvhxaxR954rtl4uupF5G3tgFAGij1PqE8L+/lk9TCPoSh3pw9rG6O9fdsZ0iUSfCCj4vmXemZOkH7SDH5UoPLv7VkXvZyVe7AACJlEjqEb1jHeyFupwQbLf6Lhy2xKVGo06EXZQ6hSpCe2ZvLcygnBB86ttauU6NOgsYaCLVdy7b4Wyz6Ae9YFO5p7rEo48RhGAAgKFd2OnvTNDCoRd85aQ1oh2yy8QWqDWV/P6d/7p640dmm9VFqTxurLoE0pp7xIJpGtzOrleH8/8RkvuhcUn+dUilqhELLsyx66OD6TYkI2gjVQU5kAQj3n20NN+lNrBVVz/r5z2nzn1Tb60OC419OnXEoL5zpVJ5Wfntv21esmjehoM/flZeeSdUHzN2xMtdnhrg/4jdUff9wQ03c09LJfJ2bXqwlJhCIyOkhM3s04Sx/v0j7sGVRR6pnJWbVj+e2HTgyN+6dx0+Y9Kq1M5DT575+tvv3/f/k9fr+Xrn2wPSZi5d+HmoPvqb3e84HBYAgNfX8MVXGTd/PTUgbfbYkS+b61isg+f10Fazl732G0Hcg502UhfPfA711prjp7+aM+291C5D/K/oNIY9+9ZOHLPC/+uksa917zocADBm+Isffb7gbtHV1M6Dz13YXVGZ99yCTzsk9wYAJMV3/esn6Yzn5oeQEQ4rjFXZiAW77T4JCz047+7PJOnb9u0ft337x///Gg0AqLdV+3+RSX+bqQ3VxwAArLYaAEDOr6diopL9dgEAOM7i/XBCKgzBAAcYC/OBVlstAGDR3PV63f+59xkeFldZdff+VySEFABAUSQAwFJfaYyBtUAawwCUSQDEghUhEq+blKsYHgoolVr/D5ERSYF/Sq0KtTvqmM2kOcgGn0oL4+IQ8SArREP4GkjGm23ftieGYWcv7mp8xdPQes0zY0xKSdmt6ppixvN5GNJLhmhhTIki7sExSXKzhflTkSE8vl+f9DPnd/zz69c6PzXQZqs9d/HbRfPWx8V2bOFTg/vPz7528LN/vjDgdzO1GsOVG0cYT6wRhRLXhEoDeOOTglpwW2XJUasumvkb0RNGL9PrIs9e2H07/4JWY+jSaZBO28pcpCE8bsn8j/cf+eTIiU16XVTXpwbdyb/IeGIAALetwePw6QwwBCOe8Cd99D/+cLfzsDYIc4BPbbEl1kinjYdxBx5xDyYkWPuntQ6zSxXW7ArTfYc/uXj5+4dfj4vpWFqR2+RHMpZsjopk7I/m4NHPsn7e8/DrUonc6/M0+ZF3fr9PLm/+FqzX2zYVUplM9Et2qkvch76qTuzZ7C5zdoelocH58OsY1mzyOm0kQTD2t+tw1ns8Tdw69vm8EknTh9lQfQzWzPWftcZJOWyTlkLaDwT1dTAAkfGK8FhpfaVDF930TWm1Sg9UKMvhqEJ0qhDGOpypwDzxhRimWmsV9PPBAIABk8N9AtjpFQDgMNuTu4eERcugReSEYG2YrPsATcUtnu+g4LY3WErq+0+KgBmUE4IBAO1S1W2eklfdqUGdCIvkZ5XNWZkAOSj6Qdb95Jy3/prtikjm4gqeJ8HjaMjPKnvhg3YEAftJHG4JBgBcO2XJOW+P7RKFE1w5ujwhDrPDXFQ3Z2UCDt0uFwUDAMryXUe2Vupj1GGJwV2JzlHnMhfVJaQoBk6Fet69Hy4K9lcpyz5a9/MRc2RbbUioShUaTKvyfB7SWuMAvgaqwdtvkiE6EWXyHBXshyTpX85a8q46TBUNhniV1wsImUQeIkXyFFdL4Bjp8Xk9JE2SGEbZaj1tu6jaP6NOSEG/npDTghvxuMjyuy6bxWczk94G4LTBWM0UODiByeS4ziBRaQl9hCw6iUPHm+AQLPLY8GSkKtIcomCeIwrmOaJgniMK5jmiYJ7zv0H1T1HRuVSLAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2b8659a9-bacd-4620-a160-8a08d10f7192", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"B\" to ['A']\n", - "Adding \"C\" to ['A']\n", - "Adding \"B_2\" to ['A', 'B', 'C']\n", - "Adding \"D\" to ['A', 'B', 'C', 'B_2']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'C', 'B_2', 'D']}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": []})" - ] - }, - { - "cell_type": "markdown", - "id": "00d33cb0-5a47-4057-bc55-0fc14c6034fc", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - "

In the above example, nodes `\"b\"` and `\"c\"` are executed concurrently in the same [superstep](../../concepts/low_level/#graphs). What happens in the next step?

\n", - "

We use `add_edge([\"b_2\", \"c\"], \"d\")` here to force node `\"d\"` to only run when both nodes `\"b_2\"` and `\"c\"` have finished execution. If we added two separate edges,\n", - " node `\"d\"` would run twice: after node `b2` finishes and once again after node `c` (in whichever order those nodes finish).

" - ] - }, - { - "cell_type": "markdown", - "id": "d45f4477", - "metadata": {}, - "source": [ - "## Conditional Branching\n", - "\n", - "If your fan-out is not deterministic, you can use [add_conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph.add_conditional_edges) directly." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "95f5e026", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Sequence\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " aggregate: Annotated[list, operator.add]\n", - " # Add a key to the state. We will set this key to determine\n", - " # how we branch.\n", - " which: str\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Adding \"A\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Adding \"B\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Adding \"C\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "def d(state: State):\n", - " print(f'Adding \"D\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"D\"]}\n", - "\n", - "\n", - "def e(state: State):\n", - " print(f'Adding \"E\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"E\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(c)\n", - "builder.add_node(d)\n", - "builder.add_node(e)\n", - "builder.add_edge(START, \"a\")\n", - "\n", - "\n", - "def route_bc_or_cd(state: State) -> Sequence[str]:\n", - " if state[\"which\"] == \"cd\":\n", - " return [\"c\", \"d\"]\n", - " return [\"b\", \"c\"]\n", - "\n", - "\n", - "intermediates = [\"b\", \"c\", \"d\"]\n", - "builder.add_conditional_edges(\n", - " \"a\",\n", - " route_bc_or_cd,\n", - " intermediates,\n", - ")\n", - "for node in intermediates:\n", - " builder.add_edge(node, \"e\")\n", - "\n", - "builder.add_edge(\"e\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "264da3f8-f5de-499b-8287-73797c1e3511", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ffe8e4aa-55c1-43b4-ba64-74583359a35b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"B\" to ['A']\n", - "Adding \"C\" to ['A']\n", - "Adding \"E\" to ['A', 'B', 'C']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'C', 'E'], 'which': 'bc'}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": [], \"which\": \"bc\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3bc6b3b4-a0c8-471a-9029-ddf2472c385c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"C\" to ['A']\n", - "Adding \"D\" to ['A']\n", - "Adding \"E\" to ['A', 'C', 'D']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'C', 'D', 'E'], 'which': 'cd'}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": [], \"which\": \"cd\"})" - ] - }, - { - "cell_type": "markdown", - "id": "639a3653-0fa3-4b6d-bf53-63479c6b00fe", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "- Continue with the [Graph API Basics](../../how-tos/#graph-api-basics) guides.\n", - "- Learn how to create [map-reduce](../../how-tos/map-reduce/) branches in which different states can be distributed to multiple instances of a node." - ] - } - ], - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/how-tos/branching.md b/docs/docs/how-tos/branching.md new file mode 100644 index 000000000..8cbdf939d --- /dev/null +++ b/docs/docs/how-tos/branching.md @@ -0,0 +1,286 @@ +# How to create branches for parallel node execution + +
+

Prerequisites

+

+ This guide assumes familiarity with the following: +

+

+
+ +Parallel execution of nodes is essential to speed up overall graph operation. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out and fan-in mechanisms, utilizing both standard edges and [conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.add_conditional_edges). Below are some examples showing how to add create branching dataflows that work for you. + +![Screenshot 2024-07-09 at 2.55.56 PM.png](data:image/png;base64,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) + +## Setup + +First, let's install the required packages + + +```shell +pip install -U langgraph +``` + +
+

Set up LangSmith for LangGraph development

+

+ 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. +

+
+ +## How to run graph nodes in parallel + +In this example, we fan out from `Node A` to `B and C` and then fan in to `D`. With our state, [we specify the reducer add operation](../concepts/low_level.md#reducers). This will combine or accumulate values for the specific key in the State, rather than simply overwriting the existing value. For lists, this means concatenating the new list with the existing list. See [this guide](../how-tos/state-reducers.md) for more detail on updating state with reducers. + + +```python exec="on" source="above" session="1" +import operator +from typing import Annotated, Any + +from typing_extensions import TypedDict + +from langgraph.graph import StateGraph, START, END + + +class State(TypedDict): + # The operator.add reducer fn makes this append-only + aggregate: Annotated[list, operator.add] + + +def a(state: State): + print(f'Adding "A" to {state["aggregate"]}') + return {"aggregate": ["A"]} + + +def b(state: State): + print(f'Adding "B" to {state["aggregate"]}') + return {"aggregate": ["B"]} + + +def c(state: State): + print(f'Adding "C" to {state["aggregate"]}') + return {"aggregate": ["C"]} + + +def d(state: State): + print(f'Adding "D" to {state["aggregate"]}') + return {"aggregate": ["D"]} + + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(c) +builder.add_node(d) +builder.add_edge(START, "a") +builder.add_edge("a", "b") +builder.add_edge("a", "c") +builder.add_edge("b", "d") +builder.add_edge("c", "d") +builder.add_edge("d", END) +graph = builder.compile() +``` + + +```python exec="on" source="above" session="1" result="ansi" +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![](data:image/png;base64,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) + +With the reducer, you can see that the values added in each node are accumulated. + + +```python exec="on" source="above" session="1" result="ansi" +graph.invoke({"aggregate": []}, {"configurable": {"thread_id": "foo"}}) +``` + + + + + + +!!! note + + In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). Because they are in the same step, node `"d"` executes after both `"b"` and `"c"` are finished. + + Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them. + +
Exception handling? +

LangGraph executes nodes within "supersteps", meaning that while parallel branches are executed in parallel, the entire superstep is transactional. If any of these branches raises an exception, none of the updates are applied to the state (the entire superstep errors).

+

Importantly, when using a checkpointer, results from successful nodes within a superstep are saved, and don't repeat when resumed.

+ If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this:
+
    +
  1. You can write regular python code within your node to catch and handle exceptions.
    2. +
  2. You can set a retry_policy to direct the graph to retry nodes that raise certain types of exceptions. Only failing branches are retried, so you needn't worry about performing redundant work.
    3. +

+Together, these let you perform parallel execution and fully control exception handling. +
+ +## Parallel node fan-out and fan-in with extra steps + +The above example showed how to fan-out and fan-in when each path was only one step. But what if one path had more than one step? Let's add a node `b_2` in the "b" branch: + + +```python exec="on" source="above" session="1" +def b_2(state: State): + print(f'Adding "B_2" to {state["aggregate"]}') + return {"aggregate": ["B_2"]} + + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(b_2) +builder.add_node(c) +builder.add_node(d) +builder.add_edge(START, "a") +builder.add_edge("a", "b") +builder.add_edge("a", "c") +builder.add_edge("b", "b_2") +# highlight-next-line +builder.add_edge(["b_2", "c"], "d") +builder.add_edge("d", END) +graph = builder.compile() +``` + + +```python exec="on" source="above" session="1" +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![](data:image/png;base64,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) + + +```python exec="on" source="above" session="1" result="ansi" +graph.invoke({"aggregate": []}) +``` + + + + + + +!!! note + +

In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). What happens in the next step?

+

We use `add_edge(["b_2", "c"], "d")` here to force node `"d"` to only run when both nodes `"b_2"` and `"c"` have finished execution. If we added two separate edges, + node `"d"` would run twice: after node `b2` finishes and once again after node `c` (in whichever order those nodes finish).

+ +## Conditional Branching + +If your fan-out is not deterministic, you can use [add_conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph.add_conditional_edges) directly. + + +```python exec="on" source="above" session="1" +import operator +from typing import Annotated, Sequence + +from typing_extensions import TypedDict + +from langgraph.graph import StateGraph, START, END + + +class State(TypedDict): + aggregate: Annotated[list, operator.add] + # Add a key to the state. We will set this key to determine + # how we branch. + which: str + + +def a(state: State): + print(f'Adding "A" to {state["aggregate"]}') + return {"aggregate": ["A"]} + + +def b(state: State): + print(f'Adding "B" to {state["aggregate"]}') + return {"aggregate": ["B"]} + + +def c(state: State): + print(f'Adding "C" to {state["aggregate"]}') + return {"aggregate": ["C"]} + + +def d(state: State): + print(f'Adding "D" to {state["aggregate"]}') + return {"aggregate": ["D"]} + + +def e(state: State): + print(f'Adding "E" to {state["aggregate"]}') + return {"aggregate": ["E"]} + + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(c) +builder.add_node(d) +builder.add_node(e) +builder.add_edge(START, "a") + + +def route_bc_or_cd(state: State) -> Sequence[str]: + if state["which"] == "cd": + return ["c", "d"] + return ["b", "c"] + + +intermediates = ["b", "c", "d"] +builder.add_conditional_edges( + "a", + route_bc_or_cd, + intermediates, +) +for node in intermediates: + builder.add_edge(node, "e") + +builder.add_edge("e", END) +graph = builder.compile() +``` + + +```python exec="on" source="above" session="1" +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![](data:image/png;base64,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) + + +```python exec="on" source="above" session="1" result="ansi" +graph.invoke({"aggregate": [], "which": "bc"}) +``` + + +```python exec="on" source="above" session="1" result="ansi" +graph.invoke({"aggregate": [], "which": "cd"}) +``` + +## Next steps + +- Continue with the [Graph API Basics](../how-tos/index.md#graph-api-basics) guides. +- Learn how to create [map-reduce](../how-tos/map-reduce.ipynb) branches in which different states can be distributed to multiple instances of a node. diff --git a/docs/docs/how-tos/create-react-agent.md b/docs/docs/how-tos/create-react-agent.md index 663f5f6fa..a2fc4006c 100644 --- a/docs/docs/how-tos/create-react-agent.md +++ b/docs/docs/how-tos/create-react-agent.md @@ -47,7 +47,7 @@ First let's install the required packages and set our API keys ``` -```python exec="on" source="above" session="1" +```python import getpass import os diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md index c695a508a..bf1566cf0 100644 --- a/docs/docs/how-tos/index.md +++ b/docs/docs/how-tos/index.md @@ -13,7 +13,7 @@ Here you’ll find answers to “How do I...?” types of questions. These guide - [How to update graph state from nodes](state-reducers.md) - [How to create a sequence of steps](sequence.md) -- [How to create branches for parallel execution](branching.ipynb) +- [How to create branches for parallel execution](branching.md) - [How to create and control loops with recursion limits](recursion-limit.ipynb) - [How to visualize your graph](visualization.ipynb) diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index a169e0d36..b9427b80a 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -125,7 +125,7 @@ nav: - Graph API Basics: how-tos#graph-api-basics - how-tos/state-reducers.md - how-tos/sequence.md - - how-tos/branching.ipynb + - how-tos/branching.md - how-tos/recursion-limit.ipynb - how-tos/visualization.ipynb - Controllability: diff --git a/docs/tests/unit_tests/test_notebook_conversion.py b/docs/tests/unit_tests/test_notebook_conversion.py index 554cb38e3..3176252b9 100644 --- a/docs/tests/unit_tests/test_notebook_conversion.py +++ b/docs/tests/unit_tests/test_notebook_conversion.py @@ -21,13 +21,12 @@ def test_convert_normal_code_block() -> None: # We treat cell magic as a non-executable code block. CELL_MAGIC_INPUT = """\ %%capture -print("Hello, world!")\ +%pip install numpy """ CELL_MAGIC_OUTPUT = """\ -```python -%%capture -print("Hello, world!") +```shell +pip install numpy ``` """