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Repository: pathwaycom/llm-app
Branch: main
Commit: 5fa97b75a177
Files: 83
Total size: 638.0 KB
Directory structure:
gitextract_2m1uy52v/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.yml
│ │ └── config.yml
│ ├── pull_request_template.md
│ └── workflows/
│ └── python-lint.yml
├── .gitignore
├── .vscode/
│ └── settings.json
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── cookbooks/
│ └── self-rag-agents/
│ ├── pathway_deploy_langgraph_agents.ipynb
│ └── pathway_langgraph_agentic_rag.ipynb
├── pyproject.toml
├── setup.cfg
└── templates/
├── adaptive_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── document_indexing/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ └── requirements.txt
├── document_store_mcp_server/
│ ├── Dockerfile
│ ├── README.md
│ ├── __init__.py
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ └── requirements.txt
├── drive_alert/
│ ├── Dockerfile
│ ├── README.md
│ ├── __init__.py
│ ├── app.py
│ ├── docker-compose.yml
│ └── ui/
│ ├── Dockerfile
│ └── server.py
├── multimodal_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── private_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── question_answering_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ ├── requirements.txt
│ └── ui/
│ ├── .streamlit/
│ │ └── config.toml
│ ├── Dockerfile
│ ├── requirements.txt
│ └── ui.py
├── slides_ai_search/
│ ├── .dockerignore
│ ├── .gitignore
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ ├── nginx/
│ │ ├── Dockerfile
│ │ └── nginx.conf
│ ├── pathway_slides_ai_search/
│ │ └── __init__.py
│ ├── requirements.txt
│ └── ui/
│ ├── .streamlit/
│ │ └── config.toml
│ ├── Dockerfile
│ ├── requirements.txt
│ └── ui.py
└── unstructured_to_sql_on_the_fly/
├── Dockerfile
├── README.md
├── __init__.py
├── app.py
├── docker-compose.yml
├── postgres/
│ └── init-db.sql
├── requirements.txt
└── ui/
├── Dockerfile
└── server.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/ISSUE_TEMPLATE/bug_report.yml
================================================
name: Bug Report
description: File a bug report
title: "[Bug]: "
labels: ["bug"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
- type: textarea
id: what-happened
attributes:
label: Steps to reproduce
description: What happened and how to reproduce it?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
placeholder: Tell us what you see! It would be nice if you could leave us the logs.
render: text
validations:
required: true
- type: textarea
id: what-did-you-expect
attributes:
label: What did you expect to happen?
description: Also tell us, what did you expect to happen?
placeholder: Describe your expectations.
validations:
required: true
- type: input
id: pathway_version
attributes:
label: Version
description: On which version of Pathway are you working?
validations:
required: true
- type: input
id: docker_version
attributes:
label: Docker Versions (if used)
description: On which Docker version are you working?
validations:
required: false
- type: dropdown
id: os_version
attributes:
label: OS
description: On which OS did you run Pathway?
options:
- Linux
- MacOS
validations:
required: true
- type: dropdown
id: cpu_architecture
attributes:
label: On which CPU architecture did you run Pathway?
multiple: false
options:
- x86-64
- ARM64 (AArch64, Apple silicon)
================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
blank_issues_enabled: false
contact_links:
- name: New feature? Tell us about it on GitHub Discussions
url: https://github.com/pathwaycom/pathway/discussions
about: We are using Discussions as a place to connect with other members of the Pathway community.
- name: Discord
url: https://discord.com/invite/pathway
about: Ask questions you are wondering about.
================================================
FILE: .github/pull_request_template.md
================================================
### Introduction
To contribute code to the Pathway project, start by discussing your proposed changes on Discord or by filing an issue.
Once approved, follow the fork + pull request model against the main branch, ensuring you've signed the contributor license agreement.
### Context
<!--- Why is this change required? What problem does it solve? -->
### How has this been tested?
<!--- Please describe in detail how you tested your changes. -->
### Types of changes
<!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->
- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature or improvement (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
### Related issue(s):
1.
2.
3.
### Checklist:
<!--- Go over all the following points, and put an `x` in all the boxes that apply. -->
<!--- If you're unsure about any of these, don't hesitate to ask. We're here to help! -->
- [ ] My code follows the code style of this project,
- [ ] My change requires a change to the documentation,
- [ ] I described the modification in the CHANGELOG.md file.
================================================
FILE: .github/workflows/python-lint.yml
================================================
name: lint PR
on:
pull_request:
push:
branches:
- main
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main'}}
jobs:
linter:
runs-on: ubuntu-latest
strategy:
fail-fast: false
steps:
- name: Git checkout
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install poetry
uses: abatilo/actions-poetry@v3
- name: Install linters
run: poetry install --no-root --only linters
- name: Run black
run: poetry run black --check .
- name: Run Flake8
run: poetry run flake8 .
- name: Run mypy
run: poetry run mypy .
- name: Run isort
run: poetry run isort --check .
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
**/.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
pw-env/
# llm debug
examples/ui/data/*
================================================
FILE: .vscode/settings.json
================================================
{
"python.defaultInterpreterPath": "${env:HOME}/pw-env/bin/python",
"python.formatting.provider": "none",
"editor.formatOnSave": true,
"python.linting.enabled": true,
"python.linting.flake8Enabled": true,
"python.linting.mypyEnabled": true,
"[python]": {
"files.trimTrailingWhitespace": true,
"editor.rulers": [88],
"editor.codeActionsOnSave": {
"source.organizeImports": "explicit"
},
"editor.defaultFormatter": "ms-python.black-formatter"
},
"git.autofetchPeriod": 315360000
}
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Pathway Code of Conduct
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to make participation in our project and our
community a harassment-free experience for everyone, regardless of age, body
size, disability, health condition, ethnicity, gender identity and expression, level of
experience, nationality, country of origin, personal appearance, race, religion, or sexual identity
and orientation, and any other criteria falling under discriminatory practices under the Law of France.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language.
* Being respectful of differing viewpoints and experiences.
* Gracefully accepting constructive criticism.
* Focusing on what is best for the community.
* Showing empathy towards other community members.
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances.
* Trolling, insulting/derogatory comments, and personal or political attacks.
* Public or private harassment.
* Publishing others' private information, such as a physical or electronic
address, without explicit permission.
* Conduct which could reasonably be considered inappropriate for the forum in
which it occurs.
All Pathway forums and spaces are meant for professional interactions, and any behavior which could reasonably be considered inappropriate in a professional setting is unacceptable.
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies to all content on pathway.com, Pathway’s GitHub organization, or any other official Pathway web presence allowing for community interactions, as well as at all official Pathway events, whether offline or online.
The Code of Conduct also applies within project spaces and in public spaces whenever an individual is representing Pathway or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed or de facto representative at an online or offline event.
## Conflict Resolution
Conflicts in an open source project can take many forms, from someone having a bad day and using harsh and hurtful language in the issue queue, to more serious instances such as sexist/racist statements or threats of violence, and everything in between.
If the behavior is threatening or harassing, or for other reasons requires immediate escalation, please see below.
However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute.
If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:
1. If you so wish and if you do not feel threatened or at risk of any form of personal abuse, address the perceived conflict directly with those involved, preferably in a real-time medium.
2. If this fails, get a third party (e.g. a mutual friend, and/or someone with
background on the issue, but not involved in the conflict) to intercede.
3. If you are still unable to resolve the conflict, and you believe it rises to
harassment or another code of conduct violation, report it.
Please note that if you are experiencing or witnessing a discriminatory practice that would be susceptible to be condemned by law, we ask you to directly escalate to 3.
## Reporting Violations
Violations of the Code of Conduct can be reported to Pathway via email to code_of_conduct@pathway.com. Project maintainers will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.
Violations of the Code of Conduct can occur in any setting, even those unrelated to the project. We will only consider complaints about conduct that has occurred within one year of the report.
## Enforcement
If the Project maintainers receive a report alleging a violation of the Code of Conduct, the Project maintainers will notify the accused of the report, and provide them an opportunity to discuss the report before a sanction is issued. The Project maintainers will do their utmost to keep the reporter anonymous. If the act is ongoing (such as someone engaging in harassment), or involves a threat to anyone's safety (e.g. threats of violence), the Project maintainers may issue sanctions without notice.
## Attribution
This Code of Conduct is adapted from the Tensorflow Code of Conduct, and based on Contributor Covenant, version 1.4, available at https://contributor-covenant.org/version/1/4, and includes some aspects of the Geek Feminism Code of Conduct and the Drupal Code of Conduct.
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Pathway
Welcome! This guide is intended to help developers that are new to the community
to make contributions to the Pathway project. Please be sure to also read our [code of conduct](CODE_OF_CONDUCT.md). We work hard to make this a welcoming community for all, and we're excited to have you on board!
## The basics:
* Use the [Issue Tracker](https://github.com/pathwaycom/llm-app/issues) to
report bugs, crashes, performance issues, etc... Please include as much detail
as possible.
* [Discussions](https://github.com/pathwaycom/pathway/discussions) is a great
venue to ask questions, start a design discussion, or post an RFC.
* [Discord](https://discord.com/invite/pathway) is our main gathering place - jump in and ask us anything!
## Contributing Code
We are eager to build Pathway together with the community and are excited to have you here!
Important: Before you contribute any changes to our repositories (make a Pull Request) please get in touch with us and explain what you want to work on. You can do so by filing an issue, or asking us on Discord. That way you can get coding advice and speedier reviews. If you start to work on an issue before hearing back from a Pathway core team engineer there is a chance that we will not be able to accept your changes - something we all want to avoid!
We use a standard GitHub fork + pull request model for merging and reviewing
changes. New pull requests should be made against the main upstream branch.
After a pull request is opened it will be reviewed, and merged after
passing continuous integration tests and being accepted by a project or
sub-system maintainer.
We ask that developers sign our [contributor license
agreement](https://cla-assistant.io/pathwaycom/llm-app). The
process of signing the CLA is automated, and you'll be prompted with instructions
the first time you submit a pull request to the project.
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2022 Pathway
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
<div align="center">
# Pathway AI Pipelines
<a href="https://trendshift.io/repositories/4400" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4400" alt="pathwaycom%2Fllm-app | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
[](https://discord.gg/pathway)
[](https://x.com/intent/follow?screen_name=pathway_com)
</div>
Pathway's **AI Pipelines** allow you to quickly put in production AI applications that offer **high-accuracy RAG and AI enterprise search at scale** using the most **up-to-date knowledge** available in your data sources. It provides you ready-to-deploy **LLM (Large Language Model) App Templates**. You can test them on your own machine and deploy on-cloud (GCP, AWS, Azure, Render,...) or on-premises.
The apps connect and sync (all new data additions, deletions, updates) with data sources on your **file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time data APIs**. They come with no infrastructure dependencies that would need a separate setup. They include **built-in data indexing** enabling vector search, hybrid search, and full-text search - all done in-memory, with cache.
## Application Templates
The application templates provided in this repo scale up to **millions of pages of documents**. Some of them are optimized for simplicity, some are optimized for amazing accuracy. Pick the one that suits you best. You can use it out of the box, or change some steps of the pipeline - for example, if you would like to add a new data source, or change a Vector Index into a Hybrid Index, it's just a one-line change.
| Application (template) | Description |
| --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`Question-Answering RAG App`](templates/question_answering_rag/) | Basic end-to-end RAG app. A question-answering pipeline that uses the GPT model of choice to provide answers to queries to your documents (PDF, DOCX,...) on a live connected data source (files, Google Drive, Sharepoint,...). You can also try out a [demo REST endpoint](https://pathway.com/solutions/rag-pipelines#try-it-out). |
| [`Live Document Indexing (Vector Store / Retriever)`](templates/document_indexing/) | A real-time document indexing pipeline for RAG that acts as a vector store service. It performs live indexing on your documents (PDF, DOCX,...) from a connected data source (files, Google Drive, Sharepoint,...). It can be used with any frontend, or integrated as a retriever backend for a [Langchain](https://pathway.com/blog/langchain-integration) or [Llamaindex](https://pathway.com/blog/llamaindex-pathway) application. You can also try out a [demo REST endpoint](https://pathway.com/solutions/ai-contract-management#try-it-out). |
| [`Multimodal RAG pipeline with GPT4o`](templates/multimodal_rag/) | Multimodal RAG using GPT-4o in the parsing stage to index PDFs and other documents from a connected data source files, Google Drive, Sharepoint,...). It is perfect for extracting information from unstructured financial documents in your folders (including charts and tables), updating results as documents change or new ones arrive.|
| [`Unstructured-to-SQL pipeline + SQL question-answering`](templates/unstructured_to_sql_on_the_fly/) | A RAG example which connects to unstructured financial data sources (financial report PDFs), structures the data into SQL, and loads it into a PostgreSQL table. It also answers natural language user queries to these financial documents by translating them into SQL using an LLM and executing the query on the PostgreSQL table. |
| [`Adaptive RAG App`](templates/adaptive_rag/) | A RAG application using Adaptive RAG, a technique developed by Pathway to reduce token cost in RAG up to 4x while maintaining accuracy. |
| [`Private RAG App with Mistral and Ollama`](templates/private_rag/) | A fully private (local) version of the `question_answering_rag` RAG pipeline using Pathway, Mistral, and Ollama. |
| [`Slides AI Search App`](templates/slides_ai_search/) | An indexing pipeline for retrieving slides. It performs multi-modal of PowerPoint and PDF and maintains live index of your slides."|
## How do these AI Pipelines work?
The apps can be run as **Docker containers**, and expose an **HTTP API** to connect the frontend. To allow quick testing and demos, some app templates also include an optional Streamlit UI which connects to this API.
The apps rely on the [Pathway Live Data framework](https://github.com/pathwaycom/pathway) for data source synchronization and for serving API requests (Pathway is a standalone Python library with a Rust engine built into it). They bring you a **simple and unified application logic** for back-end, embedding, retrieval, LLM tech stack. There is no need to integrate and maintain separate modules for your Gen AI app: ~Vector Database (e.g. Pinecone/Weaviate/Qdrant) + Cache (e.g. Redis) + API Framework (e.g. Fast API)~. Pathway's default choice of **built-in vector index** is based on the lightning-fast [usearch](https://github.com/unum-cloud/usearch) library, and **hybrid full-text indexes** make use of [Tantivy](https://github.com/quickwit-oss/tantivy) library. Everything works out of the box.
## Getting started
Each of the [App templates](templates/) in this repo contains a README.md with instructions on how to run it.
You can also find [more ready-to-run code templates](https://pathway.com/developers/templates/) on the Pathway website.
## Some visual highlights
Effortlessly extract and organize table and chart data from PDFs, docs, and more with multimodal RAG - in real-time:
(Check out [`Multimodal RAG pipeline with GPT4o`](templates/multimodal_rag/) to see the whole pipeline in the works. You may also check out the [`Unstructured-to-SQL pipeline`](templates/unstructured_to_sql_on_the_fly/) for a minimal example that works with non-multimodal models as well.)
Automated real-time knowledge mining and alerting:
(Check out the [`Alerting when answers change on Google Drive`](https://github.com/pathwaycom/llm-app/tree/main/templates/drive_alert) app example.)
### Do-it-Yourself Videos
▶️ [An introduction to building LLM apps with Pathway](https://www.youtube.com/watch?v=kcrJSk00duw) - by [Jan Chorowski](https://scholar.google.com/citations?user=Yc94070AAAAJ)
▶️ [Let's build a real-world LLM app in 11 minutes](https://www.youtube.com/watch?v=k1XGo7ts4tI) - by [Pau Labarta Bajo](https://substack.com/@paulabartabajo)
## Troubleshooting
To provide feedback or report a bug, please [raise an issue on our issue tracker](https://github.com/pathwaycom/pathway/issues).
## Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code cleanup, testing, or code reviews, is very much encouraged to do so. If this is your first contribution to a GitHub project, here is a [Get Started Guide](https://docs.github.com/en/get-started/quickstart/contributing-to-projects).
If you'd like to make a contribution that needs some more work, just raise your hand on the [Pathway Discord server](https://discord.com/invite/pathway) (#get-help) and let us know what you are planning!
## Supported and maintained by
<p align="center">
<a href="https://github.com/pathwaycom/"><img src="https://pathway.com/logo-light.svg" alt="Pathway"/></a>
</p>
<p align="center">
<a href="https://pathway.com/solutions/llm-app">
<img src="https://img.shields.io/badge/See%20Pathway's%20offering%20for%20AI%20applications-0000FF" alt="See Pathway's offering for AI applications"/>
</a>
</p>
================================================
FILE: cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb
================================================
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"metadata": {},
"source": [
"# Power and Deploy LangGraph Agents with Pathway: A Complete Guide\n",
"This notebook demonstrates how to use Pathway to power and deploy LangGraph Agents.\n",
"\n",
"In this notebook, you will learn:\n",
"- How to build a Pathway RAG app that indexes documents from your data sources\n",
"- How to create a Hybrid Index that is powered by semantic search and BM25 index\n",
"- How to use the Pathway LangChain Retriever in a LangGraph Agent\n",
"- How to use Pathway to serve LangGraph Agents"
]
},
{
"cell_type": "markdown",
"id": "d27aa0d3-9b11-4678-8a83-b3865b26f30c",
"metadata": {},
"source": [
"# Setup\n",
"\n",
"Install the required packages and set your OpenAI API key. \n",
"OpenAI is used for embeddings during the indexing stage and to power the LangGraph agent."
]
},
{
"cell_type": "markdown",
"id": "9e055de9-723f-44e3-ad39-70cc5f8932bf",
"metadata": {},
"source": [
"Magic library is used for detecting file types in the `UnstructuredParser` module.\n",
"\n",
"If you are running this notebook on **MacOS**, you can install it with:\n",
"> `brew install libmagic`\n",
"\n",
"If you are running the notebook on **colab** or any **linux** environment, you can install it with:\n",
"> `apt-get install libmagic1`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "209efe9f-b9d3-4760-8863-0a6052617ebd",
"metadata": {},
"outputs": [],
"source": [
"!pip install -U \"pathway[all]\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b44f689e-0a62-4bbd-94e8-5bf31ece9cd9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!pip install -U langgraph langchain-community langchainhub langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ff9da9f-66b8-4675-8c45-afa10e891462",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import json\n",
"from typing import Iterable, Literal, List\n",
"from pydantic import BaseModel, Field\n",
"\n",
"# needed for the OpenAI embedder and the LLM we will use below, you can change the embedding provider, see the documentation:\n",
"# https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
]
},
{
"cell_type": "markdown",
"id": "571d4836-4d08-4ee5-97fa-37132ccc8ef9",
"metadata": {},
"source": [
"Lets define a `DATA_PATH` folder that will store the text files/documents for indexing."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54979252-13d3-4b94-b2ad-ec7b012aa320",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# folder that we will gather our docs in\n",
"DATA_PATH = \"./data\"\n",
"\n",
"os.makedirs(DATA_PATH, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "55c2fb67-c562-47f7-a08e-6a673b4b612b",
"metadata": {},
"source": [
"## Load & save a webpage\n",
"\n",
"Define utility functions to read content from a webpage and save it locally into `DATA_PATH`.\n",
" 1. `load_page_content`: Reads the raw text from a webpage using using a `WebBaseLoader` from `langchain_community.document_loaders`.\n",
" 2. `ingest_webpage`: Saves the loaded content as a text file into the `DATA_PATH`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "64538684-5b2b-460a-b3a5-1fe737fd8269",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import re\n",
"from urllib.parse import urlparse\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"\n",
"\n",
"def load_page_content(url: str) -> str:\n",
" \"\"\"Load web page content with Langchain utilities.\"\"\"\n",
" return WebBaseLoader(url).load()[0].page_content\n",
"\n",
"\n",
"def ingest_webpage(url: str) -> None:\n",
" \"\"\"Save a webpage to local `DATA_PATH` folder.\"\"\"\n",
" text_content = load_page_content(url)\n",
"\n",
" parsed_url = urlparse(url)\n",
" file_name = parsed_url.hostname + parsed_url.path.replace(\"/\", \"_\") + \".txt\"\n",
"\n",
" with open(os.path.join(DATA_PATH, file_name), \"w\", encoding=\"utf-8\") as f:\n",
" f.write(text_content)"
]
},
{
"cell_type": "markdown",
"id": "5bf15463-7da1-4d04-9f6d-423975e377cb",
"metadata": {
"tags": []
},
"source": [
"Download the Self-RAG paper"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fff7b382-2e25-415b-a3ee-06df976b3dbd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ingest_webpage(\"https://arxiv.org/html/2310.11511\")"
]
},
{
"cell_type": "markdown",
"id": "62376b16-e5bc-463d-bf84-bec69a7c2035",
"metadata": {
"tags": []
},
"source": [
"# Build the Pathway indexing pipeline\n",
"\n",
"Set up Pathway to read and index the documents saved under the `DATA_PATH`.\n",
"\n",
"\n",
"1. [Connectors](https://pathway.com/developers/user-guide/connect/pathway-connectors): Use Pathway’s file reader to ingest all text files under the `DATA_PATH`.\n",
"2. [Parsers](https://pathway.com/developers/api-docs/pathway-xpacks-llm/parsers): Utilize the UnstructuredParser to parse the documents. This parser supports multiple file types, including PDF, DOCX, and PPTX.\n",
"3. [Text Splitters](https://pathway.com/developers/api-docs/pathway-xpacks-llm/splitters): Split the document content into chunks.\n",
"4. [Embedders](https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders): Use OpenAI API for embeddings."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27c3d42a-30e1-47f8-bf3c-bd44d4a490f0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# host and port of the RAG app\n",
"pathway_host: str = \"0.0.0.0\"\n",
"pathway_port: int = 8000"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "094ec4e1-e972-441b-9c47-ff0ce3c8d2ff",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pathway as pw\n",
"from pathway.stdlib.indexing import BruteForceKnnFactory, HybridIndexFactory\n",
"from pathway.stdlib.indexing.bm25 import TantivyBM25Factory\n",
"from pathway.udfs import DiskCache\n",
"from pathway.xpacks.llm import embedders, llms, parsers, splitters\n",
"from pathway.xpacks.llm.document_store import DocumentStore\n",
"from pathway.xpacks.llm.question_answering import BaseRAGQuestionAnswerer, RAGClient\n",
"\n",
"\n",
"# read the text files under the data folder, we can also read from Google Drive, Sharepoint, etc.\n",
"# See connectors documentation: https://pathway.com/developers/user-guide/connect/pathway-connectors to learn more\n",
"folder = pw.io.fs.read(\n",
" path=f\"{DATA_PATH}/*.txt\",\n",
" format=\"binary\",\n",
" with_metadata=True,\n",
")\n",
"\n",
"# list of data sources to be indexed\n",
"sources = [folder]\n",
"\n",
"# define the document processing steps\n",
"parser = parsers.UnstructuredParser()\n",
"\n",
"text_splitter = splitters.TokenCountSplitter(min_tokens=150, max_tokens=450)\n",
"\n",
"embedder = embedders.OpenAIEmbedder(cache_strategy=DiskCache())"
]
},
{
"cell_type": "markdown",
"id": "d3426ef8-90ee-4023-8094-5ce007d64b0e",
"metadata": {},
"source": [
"Hybrid index combines semantic search and keyword based BM25 search.\n",
"\n",
"[`HybridIndexFactory`](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.HybridIndexFactory) combines different indexes to build an hybrid index:\n",
" 1. [BM25](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.TantivyBM25) (via TantivyBM25Factory) → Keyword based BM25 search.\n",
" 2. [BruteForceKnn](https://pathway.com/developers/api-docs/indexing#pathway.stdlib.indexing.BruteForceKnn) → Vector-based semantic search"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3f7c59b-925b-4ef3-88a8-89749479925e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"index = HybridIndexFactory(\n",
" [\n",
" TantivyBM25Factory(),\n",
" BruteForceKnnFactory(embedder=embedder),\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8b55a446-cf97-4d47-b1ca-374bcf0e8a7b",
"metadata": {},
"source": [
"[DocumentStore](https://pathway.com/developers/api-docs/pathway-xpacks-llm/document_store#pathway.xpacks.llm.document_store.DocumentStore) manages document ingestion, parsing, splitting, and indexing.\n",
"\n",
"[BaseRAGQuestionAnswerer](https://pathway.com/developers/api-docs/pathway-xpacks-llm/question_answering#pathway.xpacks.llm.question_answering.BaseRAGQuestionAnswerer) creates a Pathway “RAG” application that:\n",
" * Indexes the documents (via `document_store`)\n",
" * Exposes a standard question-answering interface\n",
" * Enables us to create endpoint for the agent with Pathway."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7db0ab59-a641-43cc-901b-cc2713f03a99",
"metadata": {},
"outputs": [],
"source": [
"llm = llms.OpenAIChat(model=\"gpt-4o-mini\", cache_strategy=DiskCache())\n",
"\n",
"document_store = DocumentStore(\n",
" docs=sources, parser=parser, splitter=text_splitter, retriever_factory=index\n",
")\n",
"\n",
"# create the RAG app that will power the index, and serve the agent endpoint\n",
"rag_app = BaseRAGQuestionAnswerer(\n",
" llm=llm,\n",
" indexer=document_store,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a027e277-fba8-4198-863b-2168ae6f8505",
"metadata": {},
"source": [
"# Create the LangGraph agent"
]
},
{
"cell_type": "markdown",
"id": "cf15141f-9612-4d0d-a21b-11bd8530686a",
"metadata": {},
"source": [
"### Create the Langchain Retriever\n",
"\n",
"You can now query Pathway and access up-to-date documents for your RAG applications from LangChain using [PathwayVectorClient](https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html)\n",
"\n",
"The Langchain Retriever is a wrapper around the Pathway client, you will use it in `retrieve` part stage of the Agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fefffab6-d5a3-4925-a52d-5a5c574421ea",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_community.vectorstores import PathwayVectorClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51d365aa-5526-4ec8-932d-e65e83ee355d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"vectorstore_client = PathwayVectorClient(pathway_host, pathway_port)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59624377-8481-4407-8ee3-0f014bdff30e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# this will not be able to fetch data until the RAG app is running\n",
"retriever = vectorstore_client.as_retriever()"
]
},
{
"attachments": {
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VcApfg3qLKoxrh74hwuK6pGqFg2vjlY0a6DM7ZV5j34az0RGcKWIR5UqH8XH4uw8//BBujzzyCN5q+PDhuMXDDjsMLFXrXlxXYYFvvfVWbijwCRH92te+hu7kZlFJ6HaoDTGHaCOokLhU7iNzQXKXibgkvJEauJZVv1nA58ILLwzl0YIIaEaHqSTfeY9dQhei9phuMvbsK6+8wgOGicb9XXbZZeeff/4+nwf0HyrwxhtvpFGqgnO0iBCp+vfeey+CkhDREIPJWfpJHvppp532jW98A+sdtc7o/va3v73++uuxlhNbSgGGgP8lbpGnGo2O7oyuqvEdlWKNI7VCCUhAAhKQgAQkIAEJSEACEpCABPYPAUQMIV2XX375z3/+c5J20UlsuBXsGKeiPhGQiNQj8BAvxrIenMIbDh06FGuGRly1ahUlOYiR4UIEzccff8xciqjJ6k+oh54jk5qGos7U9k7swKP9aCeMNPQhOhjthOMwxLix4a3YmFCSTHBiPB9++GFELcnmF110Ua3GbJKSfPPNNxNpGDKRr7jiCowhDEM/CdALzpTeYuUmTZqEQyRykORl/DKhpn/5y1+44/ScAijmxx9/HBN6xBFHoOQQcKxIg1AOIw2vqFImTyQkE43IAIn+w/3FrlRDVRg9OkATNI13RgVWrBSxmTxXf/zjH4n9pBU0JZN7hgxlOnzbbbdxNvQw9IHydI+NxxgNet5554VYRbpKKjoBm5wKJVG6IaOfoWEt2ZCnf//73+fPn3/99dfX3nSfKsXA31cJSEACEpCABCQgAQlIQAISkIAEDgYCJOSGQEUMFF5m2LBhJIHGRtIRycWGnOIVX0Miahg2k9NhyhBnKDMixVBFSKURI0awvvDs2bNvuukmDCOzCuKbmHAwyleNFxlyioRWhCatswBImZVG4q1tf5VHpbGFWEtydYn6JITw8MMPr43+cC/uuOMOzCCyjPoJMBw7diwSDZNYXiliPwFL7jBsifLDMHKLuTaydRRg0RViDFGK7MM/1idS4c9+9jPqx99h/ZCJUYp07NB4SGgCAhykezwwNBpboPw+zvGxxx4jzJNTZJczKyUZ+uw///zzoENk86zylueKyFBGh68kBpNqqZwdwmyDUpwzZw6Cmw5QGA9OYUJoUZ9cThNkcDM0HlcCIbGlPNuXXHIJJWtjUynWBlXrlIAEJCABCUhAAhKQgAQkIAEJSGD/EMAKYVgwKSS04lkIoONtcE+hQ5ipkFuKdWIhl8gZYZ2I/OJUOEthsk2/9a1vkQSNpmEhFypBUZF2yqR7J598Mqeiays/VOQmxhP7g6nEYFb+wnhL0jeMWJkt9mAskwoqpxiWFr9GvB6dR8DReSI3kVYBFNoLR4aeq6CS6pyCNpMMBomGB+SGErWH8I1uE4OKxkI/6QzN0R+ykrGKrLpTZm5EbCCSjgopTIVIXh6D0EMeGEIveUKYq5GwxCj4sUz/OR61yCmwsJUpE/uW+EGiGkmrx0LSWyIof/jDHxLXSRY5iwhFPpFsZUIXeeq4azxgRNqSkU1nGAjMQ4WIQqxi2MdLkhPNyuaU5wgPMAnprFoepr+EAAOM7UbN7qsUa5antUlAAhKQgAQkIAEJSEACEpCABCSwnwmge/r27RukD2FfQbhEfSIELBxBNZ5wwgnlM0NJiA7X4on69OmDcjrppJOQU+STku2L0+EVszZo0CBiGKNqK7+TmZlJZBnmKOTA7vNCBBZSD1PGa9jCfplxlamHIYQt+C/2o52wX6Z8BW8RbYRwEutH+vCKFSswdKgxDg4YMAACBIFCErFVQQ1VPoXD/cMf/oDBpDkcHAuPcEfCwPG/wWPSGWwaBRgXO+EgxpDsYNpFIPLK84Ckw80hKAlQxYdyNwmrxMERlkgqOqdCJ2mRmQqRyFjF8ePHl/HRoQw5zkFchrc8IWTE0w28M3GCcCaUlX6GszhBHhiSr8MUihkZGczgyVmOs8QKifChGLGrTMiI4uSpoyoeMyIow1hC8jXFGDL+MZhogPP0xj7ePBtEMhK6yKN16qmnMsxazUZXKYYb56sEJCABCUhAAhKQgAQkIAEJSEACBw8B9EoYTHnvhgxi/kSsEDP0sbgz8iVMaRcNHoODnApvqQdbhJrBPTHZImuV4KTIw8UG4o+qphSpmchH6sR/RY1WvENoG6KqzBZ1suJrq3wWSUd4HY6MmD7WwsaIESuHS8WgoRERr6z7TMAgfCLaVW5rjxfS+i233IL7CwGJrJCDqsOUQZ7kZZKIQ+giwo60dGQcC1VzJMQkIuOiyD7u3ZVXXknuMOF7rBnNENB5CDsu4daTxs5q4CQgE0UYriVokY1bzDSLhJRi7hCLwI86yUMVwcff4QeJB2SKQ2YwpFpOkXR/zTXXACr09u6778ZUcjn1YEUJJGQfqqzKEtWJW0Se8kDiHEPKNlG0YeD0H8iUBAgxoeEga9RgFcuTp2+IRbao5lraUSnWElirlYAEJCABCUhAAhKQgAQkIAEJSKCeEsAiEXCHq0ID4Z5QY4ghTCLKBnkXLcRBcByLZqB4CGzE3aCHmNgOW8So0EblbU7lR4ufKuMxK39tHZQkSg44hCXiE0nOhQPTJqJiiaQjNhOfGEwiAXGxoq1mO0Yf/vSnP7GsM9hDzQg4EoG5X2GGQeL1MIOcom9sODsSihG1ZdKc8W5kwY8bN46pMClAuCLLLiNzQ6wf95EbgSxmRLyyrgutUBs6dfny5dx9vB6p01hFnhkUIY9H6AwXhh3UHoYa3worHqrg+9ghPRkPyBop+ETsIcfBReQjGzqbIeAfeZxCJdEomAYRzkQjhrhLjlM5czvyyj4yFCzhEh5XtrC/X15VivsFu41KQAISkIAEJCABCUhAAhKQgAQksN8IMAkdXgmpxMSIxKO9/PLLeEPCu1BUBB4ifRBMmCxMEGuPEICGPcSdcYTARpQiJYOB2m8DqJ2GsXXYQ4L+MGLw2RWrt5aDrI9M6jcxfeyg1VijpvZMYhgZQu2JJ55gaWbkWjTWIOaQfdGRaIf8X6wfTpD8ZbxbdBypR74zgaj4RA5ylzGMDC1EKUbFEIXEjTI0UqEZKeufvPHGG6zxzdjJ+MYtIlh5SHgwEIuYwejCUCepyqhPdGTwiRwkQJLnBA943333sUYKo0BcEuLK4uMEKvIgEZOIHg31MIUiZ6kflUlJtqh+4h/xoThcLCQHsaXBorKP04y0ZlS+LndUinVJ27YkIAEJSEACEpCABCQgAQlIQAISqFMCe5RfzGb49a9/vUOHDmQxz507l2C0qE+4G4LCMIaEg+GSCD2LToUdIhbxSmeccQaBjWVOHbhvCZcjqg6ZyEZOMdm7ZBAT0EeiMa8k3qLb2ILYqu1hYs2YyvDee+8Nkw9GzUXhitw+ohFJTKbbWDycL3NTnnfeeQhEZGjIhuYqTB/9R9hFWcD0Pwr3I/oyqjnsEB7YvXt3buvAgQORzgRpTpkyBb3Ik0CsIhtYaJFUaFqPrkVxIgdZfSWaFZFT9JxVsEnNRjUS+koPiYS94ooryGvmLKYS9UmgYqiEJVZwnaFF4GN16Tm52PhEwkLZGG8oiWmNILDPFo7vl1eV4n7BbqMSkIAEJCABCUhAAhKQgAQkIAEJ1BYBorcQQ6yli+5BDu7RKhLXRrwYUWnkz7KCRwgNI5OU46hGekZkInPecYSot+BuOIKTIt6tZ8+eFKtO4nNtjTz+eomnQ8OxGAgGDRT4LzJ5ifhDIyITQYFJrMscbW4E0xoSHBoSzBkQpg/U3NBwa+gPRo/kdKZ3ZNVsQimxbAg4DhI9SuRplE1M/9F/3KyICgMJSjH4Yu47Lg/BRxDi9OnT+/fvj0ykEtQk26GHHooHDGKRiEUiBPGA//jHPygQ5t8M66jgDbGxzH5IPVFDFOYSrCUPD50HJj6Rhy0UoMPY6qgw6dhEPoIde4iL5BbQBAMkGpTusR+V5DmMlCLmNJKSUYG63PmyW3XZqm1JQAISkIAEJCABCUhAAhKQgAQkIIFaIoBSRAl97Wtfq7h+lOJRRx2FdkQwhXxS7BVbUJCYIPJ88UqcDR6H9FjOcnz/JpxWPKjKn0XGkQKMTOSV+DvkKUqLIZPgzCrY+Ky6NImh29hALOEDDzwQ1l+G86WXXoqPgznSMCg2LBt3IdwCcooZBaKN/mPiuKEovDCXIpKOuD/S2KPZD2mCaEEqIegP84hPJECVMEbKE1FIu+Q+85aAR4ZP/RTjckIFmT4Sfcm0myRBIx9ZxYWngrbgw7PBEtgRc47QXHCa9IqNZwkZevnll1NzpLZ55CIzyLWsNoO75ELaZYtqK79DSCZbOE6WdLT+TPmSdXBEpVgHkG1CAhKQgAQkIAEJSEACEpCABCQggXpKAHmEaWIr3z9OIbPYyp86CI4QXnfnnXcS7IbzIh4TmUigHEoLZ7dfRkfMHQuh/O1vfwvxiSg2fOI3v/lNTF8QiGV6Rc+jwECUItNcovnwocyWyBSZAwYMYO7CKGU4XIvUQyky5SJLORMOSTFMX0hDJm2ZyEHiCoPgI6KQcFSeCpomb5qEZaIFg85DRBKTyBG0NV4v6hXq88QTTyQd+9lnn43iBwmKZDJEoj7RoFFJhkY3ordEPhKoSCt7HGZUjB36H5VBoUbxmLFl6mxfpVhnqG1IAhKQgAQkIAEJSEACEpCABCQgAQnUFwKk1pLni7AbPnw4cwji4/ZjzwjcQ8YFn4g1I8aQuRFJFg5J6HvsGGGGBAmGU0QdhlVZiCtE5zEBIt4Qq1j+QlzeaaedRlYyLYaFVlDGhCJiMzlC3B9CkGRnkqwJOSS1mRpohXxknGMILdwVKbgDdDhEJlgkyJFivL3ggguQmMhNjOHbb79NbcjNMWPGhCzp2J7QIgGhSEku5zjzMKIUWbmF3O3IGMaWp10EIuOiTkQnlxP/iPktI0xjL6mDfZViHUC2CQlIQAISkIAEJCABCUhAAhKQgAQkUL8IYMGIT2S1mWDW9m/nsGYsS8KkhAQDIvhOP/30sNxKBb1ikkQ0KEvrRLnAFMayscQzQZcIQYZW/nIOsoDy//t//w9RSIgiBQhFHD9+PPWwykqY4pB9tvLXhiPDhg0jnJONJXroLZnX7BOfyHEqpwOkObOP/kMUkiJNRnmZqnCOkGeBoF//+tecItzyoYceIuCRnqMpGRQFuBxJSjgkKpM0cHYoxnEcK/3HRZKzT2BpmZrr8m1juliX7dmWBCQgAQlIQAISkIAEJCABCUhAAhKQgARiCaAU58+ff9ttt6EC0XME95U3cbHl2ce4Pfzww0QX4trOOeccVnZmvsUyZfb4FhUWLCSGLhTgLVMivvPOO8wsST44y9RE8Y9RDfSHrHASojGJwR4Sn4iCJECSyEdWAYpdRyW6am879AEXefPNNzNFY1QmTN2IUiQCkdGR10zMI/YQMiGTmg5ff/31zO1Ib1l8BqUYzc8YVVJnOyrFOkNtQxKQgAQkIAEJSEACEpCABCQgAQlIQAJ7JoDFY/ETYgYJ7ttziXJHyVMmhRmlyBSKzFpYTb+G5kMRYhWxeOg8NqZZJMuYvGZmQkTzEVo4ePBg7CEHy/Ul7gN4TMIP7733XiZ2ZCLI2Jg/ctJRiiHPOqoXn0gq93XXXYdvjQ7uxx2V4n6Eb9MSkIAEJCABCUhAAhKQgAQkIAEJSEAC9Y4Ai7cQfsiUhchE4hMRnexUU1nucZCkexNryaLbuFFmh4xN4g7lUZkkO7dr1w6VOXbsWCIZ60OiOn1TKe7xhnpQAhKQgAQkIAEJSEACEpCABCQggf1MgDAxhAsRW/iU/dwVm5dArREgFpK1p9944w2WuuaZJ+iS+EQMJjngPPxkW7PMCwGSLOfC2z2u31JrXauoYpViRXQ8JwEJSEACEpCABCQgAQlIQAISkEDdEyBAbPHixbNmzZo9e/aoUaOYJq/u+2CLEqhLAiQ+89izSAsbkpHQSCZVbN26dWpqam1ER1Z/aK74XH2G1iABCUhAAhKQgAQkIAEJSEACEpBADRAgz5SwRJaeYImMt956K6SCMoXc8OHDyf2sgQasQgL1lQDhhzhEtvrawbL9UimWJeJ7CUhAAhKQgAQkIAEJSEACEpCABOqYQJCJn3/++Xvvvffuu+8uWbIkpH/SDZawIFZRpVjHd8TmJFAxAZVixXw8KwEJSEACEpCABCQgAQlIQAISkEAtEkAmrl+/Hm9IWOLUqVNZ6LaoqIiIraZNmzKjHCtRMJGcPrEWb4BVS6BKBFSKVcLmRRKQgAQkIAEJSEACEpCABCQgAQlUm8Dq1avnz58/bddWUFDQpk2bAQMGrF27ltUqCgsLWaFi5MiRl156af/+/avdlBVIQAI1SUClWJM0rUsCEpCABCQgAQlIQAISkIAEJCCBfRLYsWPHhg0bUIfPPvvslClTsIfEIR5//PH9+vV74YUX5syZw7q3VNK9e/fTTjvtiCOO2GeFFpCABOqYgEqxjoHbnAQkIAEJSEACEpCABCQgAQlIoEETYFnbVatWPf/88y+99BI5zp07dz7llFMmTJiAZ7z99ts5uGXLFgCx4u24ceOOOuqoBg3LwUugvhJQKdbXO2O/JCABCUhAAhKQgAQkIAEJSEACBxeBnJycTZs2IROfe+45ohQ7dep0/vnnn3rqqRkZGXjG++67L/KJjJuIxREjRrRu3frgYuBoJHCQEFApHiQ30mFIQAISkIAEJCABCUhAAhKQgATqLQEWXcnOzn7llVfIdF64cCEO8ZJLLmGSxMzMTPqcm5v7yCOPPPXUU6zT0qRJk507d7I2y5FHHtmrV696OyI7JoEGTkCl2MAfAIcvAQlIQAISkIAEJCABCUhAAhKoLQLIQVKbkYlvv/32pEmT3n///bS0tLPPPnvixInkOyckJNAwq7IQtPjPf/6TbGiOUICpFbt163bMMcd07NixtnpmvRKQQPUIqBSrx8+rJSABCUhAAhKQgAQkIAEJSEACEtgTgZKSEmTizJkzn3766cmTJ6emprLWyrnnnjto0KDExMRwRXFx8RtvvPGXv/xl6dKl+MT27duTHM2FgwcP7tmz555q9ZgEJFAvCKgU68VtsBMSkIAEJCABCUhAAhKQgAQkIIGDiQDBiZ9++ulDDz2ETyTwcPz48WedddZxxx1HXnM0zO3bt3/wwQe33HILqdCNGzcmbpHgxPnz57do0YKSLPcclXRHAhKobwRUivXtjtgfCUhAAhKQgAQkIAEJSEACEpDAAU+A1Vf+8z//c/Xq1azafMEFF4wdO7bMkFjfec6cOZRZtGgRPrFt27ZMrTht2rR169bhHwlR5GCZS3wrAQnUHwIqxfpzL+yJBCQgAQlIQAISkIAEJCABCUjgICFApOHVV19NgvPpp59efkjMsbhkyZLvfve7vHI2PT39+9//flJSEsuzsM8sioYolofmEQnUKwIqxXp1O+yMBCQgAQlIQAISkIAEJCABCUjgYCCAGTznnHP2NhLU4WWXXbZs2TIKkAr9gx/8YMKECXfffffatWuHDh3av3//5OTkvV3rcQlIoD4QUCnWh7tgHyQgAQlIQAISkIAEJCABCUhAAg2FQF5eHrZxxYoVYcA/+tGPmGYRmfjvf/87Nzd3xIgRXbt2bSgsHKcEDlgCX86KesAOwY5LQAISkIAEJCABCUhAAhKQgAQkcGAQYEmWk08+GZ9I7jM9vvbaa88///xWrVqxMDRWcdiwYUOGDGnevPmBMRh7KYEGTECl2IBvvkOXgAQkIAEJSEACEpCABCQgAQnULYETTjhh5cqVwScy2eJFF12UlZXFUi0oRbKh8Yms++zCLHV7T2xNAlUhoFKsCjWvkYAEJCABCUhAAhKQgAQkIAEJSCBeAiQ4M38iApELmUtx4sSJLPSMQMQnzps3r1u3bkcddVTr1q3jrdbyEpBA3RNQKdY9c1uUgAQkIAEJSEACEpCABCQgAQk0LAJoxP/4j//49NNPSXxm5BdccMEVV1zRoUMH1mbh7XvvvUfWMyGKnTp1CkcaFh1HK4EDkIBK8QC8aXZZAhKQgAQkIAEJSEACEpCABCRw4BAoKipiTed33nmnuLiYXp955pnXXHNNZA83bNiAUkxISBg1atQhhxxy4AzLnkqgQRNwxecGffsdvAQkIAEJSEACEpCABCQgAQlIoFYJFBQU/PrXv37llVcKCwtpiLVZWJIl8okcmTFjxurVq4888kgSnxMTE2u1M1YuAQnUFAGVYk2RtB4JSEACEpCABCQgAQlIQAISkIAEvkJg27Ztd91115NPPskOJ4hD/PGPf9y1a1diEqNyRC/m5eUdd9xx5EFHB92RgATqOQGVYj2/QXZPAhKQgAQkIAEJSEACEpCABCRwQBJAIz788MMPPfTQ1q1bWYNl6NChN954Y48ePWJnS5w7d+5HH33Ut2/fgQMHpqenH5DjtNMSaJAEnEuxQd52By0BCUhAAhKQgAQkIAEJSEACEqhNAgQePvXUU/feey9TJeIQDz/88P/+7//u3bt3rE+k/c8++2zTpk0jR47s2LFjmVO12TvrloAEqkug8c6dO6tbh9dLQAISkIAEJCABCUhAAhKQgAQkIIEvCOATmTzxtttuW7p0KaKwT58+v/nNb7CKxCp+UWT3/zKL4rRp04YNG8ZEirHZ0GWK+VYCEqhvBFSK9e2O2B8JSEACEpCABCQgAQlIQAISkMABTIB8ZyzhrbfeumDBAnwimc633HILWc97GxJp0ampqU2bOjPb3gh5XAL1kYCf2Pp4V+yTBCQgAQlIQAISkIAEJCABCUjgQCSQn58/ffr0O+64I/jEzp07k+9cgU9kjC1atDgQR2qfJdDACTiXYgN/ABy+BCQgAQlIQAISkIAEJCABCUigZggUFhZ++OGHf/jDH2bPnk18Iis433zzzcyTWDO1W4sEJFCfCKgU69PdsC8SkIAEJCABCUhAAhKQgAQkIIEDk0BxcfEnn3yCT5w1axZzJmZlZf385z8fO3bsgTkaey0BCeyDgEpxH4A8LQEJSEACEpCABCQgAQlIQAISkEDFBHbs2DF37lx8IlnPlGzbtu2NN9548sknV3yVZyUggQOXgHMpHrj3zp5LQAISkIAEJCABCUhAAhKQgATqBYF58+bhE99880160759++uuu+6MM86oFz2zExKQQO0QMEqxdrhaqwQkIAEJSEACEpCABCQgAQlIoGEQ+Pzzz//4xz9OnjyZ4eITr7nmmnPPPbdhDN1RSqDhElApNtx778glIAEJSEACEpCABCQgAQlIQALVJEB84p/+9KcXX3yRevCJV1xxxfnnn9+0qTmR1eTq5RKo7wRUivX9Dtk/CUhAAhKQgAQkIAEJSEACEpBA/SSAT7z77ruff/75nTt34hMnTpx44YUXNmvWrH721l5JQAI1SEClWIMwrUoCEpCABCQgAQlIQAISkIAEJNBQCMyfP/++++579tlnS0pK8InIxIsuuqh58+YNZfyOUwINm4BKsWHff0cvAQlIQAISkIAEJCABCUhAAhKIn8DChQv//ve/P/PMM4WFhe3atWPyxK9//eutWrWKvyavkIAEDkgCKsUD8rbZaQlIQAISkIAEJCABCUhAAhKQwP4isHjx4vvvv//pp5/etm1bVlYWiztffPHFiMX91R/blYAE6p6ASrHumduiBCQgAQlIQAISkIAEJCABCUjgQCWwZMmSBx544KmnnsrJyWnTps2ECRMuueSSjh07Hqjjsd8SkECVCKgUq4TNiyQgAQlIQAISkIAEJCABCUhAAg2PwMqVKx955BF84pYtW0hzPuWUU/CJ3bp1a3gkHLEEGjoBlWJDfwIcvwQkIAEJSEACEpCABCQgAQlIoDIE1q9f/+STT5LvvHHjxoyMjBNPPBGf2Lt378pcaxkJSOAgI6BSPMhuqMORgAQkIAEJSEACEpCABCQgAQnUPIHs7OwXX3yR+MRVq1a1aNHi+OOPv/TSS/v161fzLVmjBCRwIBBoeiB00j5KQAISkIAEJCABCUhAAhKQgAQk8BUCJSUleXl5BQUFycnJ6enpTZvW4g98lmGZOnXqY489tmDBAtoaPXr0xIkTDz/88K90yDcSkEBDIlCL3zgNCaNjlYAEJCABCUhAAhKQgAQkIAEJ1BGBHTt2bN68Gbs3e/bsDRs2sEbKgAEDSEBu2bJlkyY1n4yIu5w5c+aDDz74ySefpKamHnvssfjEQYMG1dFobUYCEqiXBFSK9fK22CkJSEACEpCABCQgAQlIQAISkMCeCBQVFS1btuyll15iWsOFCxeGIn369DnvvPPGjh3btWvXpKSkPV1X9WNz58695557ZsyY0axZs6OPPvob3/jG8OHDq16dV0pAAgcFgcY7d+48KAbiICQgAQlIQAISkIAEJCABCUhAAgc5ATKd58yZ89e//nXatGlNGzdu3bJlUmJiQWHhpq1btzdqdNxxx1111VXkIycmJtYUCJZ4/vnPf/7qq6+SXo1JvPrqq4lSrKnKrUcCEjhwCRileODeO3suAQlIQAISkIAEJCABCUhAAg2IQH5+PhMa3nfffXPnzGndssUpRx9z7vFjO2ZlzVu2/Olp016bMYOzTK147bXXkgddI1Zx7dq1N9100+TJk6lt4MCBV155pT6xAT1wDlUCFRIwSrFCPJ6UgAQkIAEJSEACEpCABCQgAQnUAwLEJ77++ut/+tOfFi1Y0L9nj2+eeeaoQYMSv1iSZUtu7qvTpz/44ktLV68eNnz49ddfT6xi48aNq9PxTZs2/exnP3vllVe2b9+Oo/zBD35AYnV1KvRaCUjgYCKQwD84HEzjcSwSkIAEJCABCUhAAhKQgAQkIIGDjEBubu6kSZPwiSuXLx8zZMi1F100YsCAhJiVWFKSkg7t0qVTVtbSVas/njt31apVI0aMSEtLq7JVzM7Ovvnmm8l3Li4uPvTQQ3/0ox8df/zxBxlVhyMBCVSHgEqxOvS8VgISkIAEJCABCUhAAhKQgAQkULsEyHe+995777v33m05OReefNKVZ5/dq3On8k1iGLsdckifrl1Xrlv34ezZq9esGTJkCAuqVMEqEhH5i1/8ghVgaLpz58433HDDCSecUL5Fj0hAAg2ZgEqxId99xy4BCUhAAhKQgAQkIAEJSEAC9ZoA6zvfddddDz/8cFF+/rUXXXjRSSdlZWZW0ON2rVr16NhxycqV73/yMbGNJCzHaxULCwv/53/+5/nnn8/JycnKyvrlL385bty4Clr0lAQk0DAJOJdiw7zvjloCEpCABCQgAQlIQAISkIAE6juBkpKSO+6446GHHkpPSrrx8suPOnxA04SEynR67pIlv3/ooQ/mzSe6kHjDli1bVuYqyjBt4m9/+9tHH32UiRRTU1Nvv/32E088sZLXWkwCEmhQBIxSbFC328FKQAISkIAEJCABCUhAAhKQwIFBYOfOnX/4wx8eeOABfOJNV35rxID+lfSJDC8rI6Nvt26LVix/a8a/Fy1aNGrUqOTk5H0Omxb//Oc/YzA3btxIuvTdd99tfOI+oVlAAg2WQJMGO3IHLgEJSEACEpCABCQgAQlIQAISqLcEfv/73z/44IPpycn/fdVVQ/v1q7xPDCPq3bnzTyZOHNKnz5QpU37yk5+QBL3Pkd6/a9uwYQMl8YmjR4/e5yUWkIAEGiwBlWKDvfUOXAISkIAEJCABCUhAAhKQgATqKYHf/e53+MTmKSm/uPqqwX37JDZtGm9HCTPsWWoVLx3at8/rr79+/fXXV2wVn3vuOTTiunXraOjOO+/EJzaNv9F4O2l5CUjgwCWgUjxw7509l4AEJCABCUhAAhKQgAQkIIGDkMCtt976pU/s0ycpMbFqg2yCVezUiVjFYX37Tp06tQKrOGPGjN/85jerV68m95noyLFjx6akpFStUa+SgAQaCAGXZ2kgN9phSkACEpCABCRQfwns3Nnos5VbZy3Onr8qd8nqvI3ZhQkJjdu2SuneIa1vpxZHdmvZNSu1/va+PvVs+46duQUlJdt30qn0lKbJif7zeX26PfZFAhKoHAF8IrMZttgVnzjw0EOTq+oTo9a279ixeOXK3zzwwMzPPmdSRepPT0+PzrLzxhtv/OxnP1u2bNmOHTtY6/m0005r0aIFQY6xZdyXgAQkUIaASrEMEN9KQAISkIAEJCCBOiWwNb/44WnLX/tw7ZacouKSncUlO3agGBs1SmjSOLFpE/7atEw+eWj7C47t1CypUqt81mnv61ljL3yw5sk3V6zbXEC/rj370LFHtPUXcT27RXZHAhKoiACJyf/3f//38MMP4xP/++qrjuzdOzkpqaILKn1ut1W8/4GZn39ORjNrOsdaxfz8/Oeff/5Xv/rVd7/73bPOOqtVq1b6xEqjtaAEGi4BV3xuuPfekUtAAhKQgAQksN8JzF6+9U/PLnztg3Ubs4sKi3cQXrdLJ5b2a8fORrzlYHZe8fJ120p27BzUI2O/d7ied2Dmws3TP9u0dnNhXsH2kYe16d2xuUqxnt8yuycBCUQE8Ik//OEPX3jhhZbNmtWsT6QJMqBbNm9+WI/uS1aufPf99+fNmzdmzJikL3xlYmJit27d2rVrd9JJJ7Vu3bpJE0O8o9vijgQksFcCflPsFY0nJCABCUhAAhKQQK0S2JxX/PDry96dszFnWzGRifze69Wp+dnHdbr2nEO/e1bv4we3a5ORTAeIWiRWMSO9ZgJVanVE+73ySMju957YAQlIQAKVJ8C/JmVnZ+MTp02blpGaynosR5Lv/IXvq3w9FZdMaNKkR8eOP2a1lj59mFfxxz/+cU5OTnRJamrqhAkTsrKy9IkRE3ckIIGKCRilWDEfz0pAAhKQgAQkIIHaIjBp+qops9Zl5xbTQGpKwrmjOn1jXLdjD2tzWOcWfTqmD+6Z2aVt6obcoqSmCece1/HkQe1SEk183se9+GTZ1k+XZOfll1Bu9OFZRinug5enJSCBekAAn7h58+af/vSnaL62mZm/DD6x2vMn7nFkIVaxf4/uTK34zsyZCxcuHDlyZHJyckhzJmjRfOc9cvOgBCSwRwLOpbhHLB6UgAQkIAEJSEACtUtgzZaCXz0y94P5m8luTkpMmHhC1zOOOqRNiySmUIwazivcvmhN3raCkj6dmmeklV3uk4i8vIKSZRu2LV2fT23NU5p2zmrWLSu1bcuU8tm+RSU7Fq3NozyVd22b1io9cc3mgs9W5qzaVDrtYLuM5F4d0ju1akY4ZNR6mR0SsUNzy3Y116JZ085tmnVtm9qu5R6WBF29uWB9dmHx9h1RJe0zU9q2SKb+Zeu30e7a7MLEhMadWqfSbvtdwZhRyS15xZ8szc4pKAEFv36ZQbJ186TubdNSkvbct3VbChl+aOuVWeumzlrHrJTUNnFc1xH9Wn9Js1Ejfiq3bZl8SKtmMYx3N8vslQVF21dszKd7qzYX0HTnNqld2jTr0ia1PMyoq+5IQAISqCYBfOKGDRtuvvnmyZMnZ2Vk/PdVVw6uxvrOlewMa7AsXLnyln/c//7nn48bN+6mm25y8sRKorOYBCQQS6Bp7Bv3JSABCUhAAhKQgATqhsDUT9cvXbstrE08tE/mMYe1xnaVsVdpyQn9OjUnJzqpnOnD7r01Z8NT76zKzS/JKywpKN6BoWuWnJCW3HR4v9ZfH9U5vVnTWJuGp3tw8tJFq/MY3SUndEVWvjV7/aoN+flF2zlC/GPLtMRjDmszfmj7Dq32oAhZRvnN2RueprmCkm1fNJcamjus9cWjuqSlfCWC8rWP1r7x8fq8/NLKwzbmyKyTB7eftSj75fdXM9chuhHBl5qU0Lld6qlDO4zu36Zpwu7+rtyYf+/Li3PyiinAIexeUmITlm9unZF80uB2x/VrU4bSm3M3vDxzTe62Ulu6Oa8od1tp1CfbSzPXvD17AyIxvC19bdzoqL6tzz+uU4fMr4wReh8t3vLItOWbthZCBrdIy6jM9JSE7oekX3VqD2Tol5W4JwEJSKCGCOAT165d++tf//r1KVPaZmTcfNWVQ/r2TWxa6z/SSW3u1anTTyZeesv999N0QkLCDTfcQMrzri/dGhqb1UhAAg2AQK1/WzUAhg5RAhKQgAQkIAEJxE1g3orcnPxS+UXg3jF9W3dvl1bGlIUad4m2GCm26+i67MJH31rxxkfrlq/fVn72wFWb8uetyPn/zu9LcF9UJwtJr9iQv3B1LhVM+XgdIYQLV+ZyMLTC64oNjdZsKkBfXjCqMxGI0XF2iAF87M0VKMIVNBd7Ytf+SppbnnPDBX0z075sbt3WwsVrtjFHZFS8Y9tmTZqsm/H5ptmLtwSRGk6t3pTPTlbLpCO6tgxHiMos07dwPDkpYeHKnDc/3fCjcw5NjnGsG3OLlqzJYxGbUCx6JXqRv+ht2OmSlYoxjD24Obd48kfrJr29YsnavNiOhTKEdiJerzv70J4d0giZjL3QfQlIQALVIUCo4Jo1a1h5mfjEdpmZP/3m5UP79Wua8JV/nqlO/RVfiz3s1bnzjydO/O0DD0yZPBnJyNSKLM+iVayYm2clIIFYAs6lGEvDfQlIQAISkIAEJFBHBCa9s3LZulIh2K5VyqlD2/don1bJhrcVbn/vs40PvLZ07ebSnGV+/jVPS+zQJpXZGLflb0f55RduJ9AvPT2xT8fmUSJzTn7JlI/WbcguVWxbcos3ZBfhE1ukJyUnNsGjBS9JzQQhZmUkx3aGcMh35258aMqy0BxaLZ3mWqcSEbmtYHdzqzYWNE9P7NuxedOE3bnJa7cUbMotTklpWli0PXi69NREnN3iVbn0uEObZs1SEgoKabjR9h07t+YVt0xLGtIrk77Rfwzme59vIjIxMZEfuY137Cg9yLZ9+0703/L1+ckpCYd1asGpXYcbbcwpWr2lkLYympeuYMO4wnBat0yGbUaLZI5Hf307Nx/YPaP5F86Usp8u2fKXFxbRNzK7cYZ0jNGlpzaFBvWUlOwkQ3xjXtHoAW2jOMrQrq8SkIAEqkwAn7hy5crbbrvt1VdfbZ+Z+ZPLLjv68AF15hNDt/m/j9YtW/bu0mXJqpXvvf9Bk4SEAQMGMK9ilQflhRKQQEMj8JV/gm5og3e8EpCABCQgAQlIYL8QQHvlF2zHkdF624wUkpQr3w2CEJ96b9XmXdMFcuGpwzqM6NMKwVe8feeydXn3vbJka04RNT/99sox/dsw12H52Dpyitu1bnbioHaHdy0Vcys35L82a+0ni7PpA5bzs+Vbxw1sG/Vn5caCZ6avDs01T008dVj74Yfuaq5kx5K12/766uKtBDtu3zHprZVjB2R1asM0haWmb2TfNj3bpTOB4+NvrZgxdyN6jlaKine0yUg5eUi7I7q3xDO+8cmGybPW4hOz80rWbMznCM6Oi3u0TyfmkXWuMXq8wio7v+T9BZuZJBH/SAzj49NWjOrXplMbhlbazSE9M9tnpNAW+699tO71L+ZSHD+s/Yg+rctEFrZKTyJ4s/SyXRu6cArRlxu28S41pemwvq1OGdSOYdIQMZuPv71iwYocujF97qZZi7fQkFZxNzj/RwISqAYBfOLy5cvvvPPO13b5xB9efPExde4To+7369bt+xdeePujjz41aRI+8eKLL87IyIjOuiMBCUigAgJx/PdrBbV4SgISkIAEJCABCUig8gQ25xUXfpF03KpFElMKRtdi0ojLK7PhxcKyLSTtkrw8Z+lWChCWeOzhWReN6dyxVbNQHkXItXc+Mx89t3pj/ucrc5gYsfw60Sx6ctKgdueO7Nguo3QhFyITs/OLWQeGhZIJKmQF6sLiHUQvUiczLS5YnTN3aaltTEtpOuqIrItGdzlk12SL2NABXVvS2zueno90C82xBkvSrikcmReSP656e+7GpiQpF25HHdLhEwe3O+vojm2aJ3EJATIL1+R+vHAL7pCG6EZYgiaTWR37tg4j4pWG6A9tsRrM319ZwhH8Iwu8lK6ysmv6RRRhZAnnrc4NPacYK7oM7ZVZRilyPHZDdDLfIp0hnLNP5+bfPLFb70PSgxVlgsWM5ok/+8fsouLtBFS+NXfjEd1a1nEMUWxX3ZeABA4aAhs3bnzwwQdffeWV9q0yv3fBhSMHHtG09udPrIBe/x49vnv++X96/PEnHnuMYl//+tczM0vDxt0kULMEQnDukiVLWrdu3atXL1YYr9n6ra3uCagU6565LUpAAhKQgAQk0NAJkE0cQhQBwbSACV/kCxNpOPnDtTPmbSoDqF1myrED2vTv3ILFQ5ZtyEf8USAzPelrx3WKfCJHWMOE0LyHpi7dsKUQTbZ43baRJTtTyq4U3ahTVurgXpnBJ4ar+nZu0blt6mdLt+LvsHustpycWPof+nkF25fTXHGp42zdIum8kZ2CT+QtAYLESI4f2uGBKUtZYZnmmHbwmH5tkvbyX5fEG/Y6pDnZzfjE0ssbN0J39urYnBztxo0apzVL3J2uzLmvbjSUktjk0EPSWSzln1OXh7HPW5U7un9WNWMGEa+bcopCMnir5kms/UKqeNQ4jQ7v3eqIni1nflZ6O+atzCV0MTrrjgQkIIGqEWB950ceeeT5557r0KrVNeefN2rQkXWwHss+u3p4r17fPu+8u/71ryefeILCF154IdJnn1dVvgBh+R988EFeXukSYdR82GGHsSZM5S+3ZL0lUFxczM2le4S47nMiTpL9n3jiiTfeeKN3794XXHDBkCFDantcBQUF69evX7FiBfOW0tU2bdr06NGjS5cuzKtS2003kPr38h99DWT0DlMCEpCABCQgAQnsDwLkKX9hERuRFExub+gF3u3TpVufn7G6TKe6dUhD+aEU8X1rdy05gpIjZ5lQx493hRDGlicIMbxlikOsWeypsN8pq1lmemJs+F5soB+XkKEcSpY2t2v6xdAc1ZVpDskWzWlIivT27Vy411+JA7q26JK1O6CS+llG+bSh7Qd1L12VBb/JWtWhUV5pF2m4cHVe9rZiYidLSnYkJzdJTW4a9XnFxvy9Kcio3vnC6wAAQABJREFUkn3u0Mqm3FIZysaa2inJCWVGV1Syk/kcQz2EYZaPHt1nExaQgAQkEBEgRGvVqlVPP/30v554IqNZs6vPPWf04MH1wSeGHg7s3fuqc865+8lJTz35JG7oa1/7Gv4l6nx1dghF37p16+9//3v8DvWwCMzvfve7tLTKziBcnaa9trYJsLjQO++8g6G76qqrOnToUHFz2L2PP/4YuZyTkzNixIhaVYo8dYRDMlfphx9+SFzwli1b+I+JFi1adO3addy4caeeeur+DQ2uGNQBdPbL/3Q7gDptVyUgAQlIQAISkMABTYAAw2jhFKwWbqvi4SCzWEiEMiQ+s1gzO4gwIuz+9vKS8hdm5xQHTbYxp3CPgXUtUhOj7OBwOVnVIbH6i9p2i0g6Fpoj/ZhovvLN7Wy0k5kZv2iuqNQo7n0jLTqkNociqckJ/bu04C/2ioLi7Z+vyHn9k/ULV+XiKAnnZLUZRkE+dVpKYuQ6txWWhEZjr413P6+whKVdwlXAfObdVYmR6N11FGu5eE1pTA3b1lL5uAc/G876KgEJSKBiAvjEhQsXku88ZcqUxsXF/3HB18YOGVJ/fGLo/JGHHvqts8687Z+PID2xiuedd15WVlbF46rMWaLY5s+fj3gKhVNSUlavXk2wmJFilaFXz8t88sknTz75JDGnRLbuUynyOA0cOHDz5s1EKXbv3r1Wh/bpp58+/vjjr732GqGRqampbdu2RSnS29mzZxMpfMghh9Sq0KzVodWrylWK9ep22BkJSEACEpCABBoEAZKdiYkjvo+wRFQdS46EYeP1BvXKKE0q3rWxWMonS7KjsxzDKxZ/4e0I35vx2cbdRff0PxgxlF/5M+QLR6GF5c/GHildHWVXc9SSW4nmds18GFvBl/tMUEgqcbQk9JcnYvZYYmX20q33T176wcItRV/VrCUljVhgOqZsDewiaqP4UNzlh/M3V1CpSc8VwPGUBCRQMQF84oIFC/7xj388++yzhIhfdPLJ44YNq28+MQxhcN++l02YcPeTTz7+2GOs00IwV/VXayHhdMaMGTjKnj17EixGyNisWbM6d+7s6tIVPzYH31mc4xlnnHHkkUe2atUKq1irAyQw9vPPP8/Ozh41atQxxxyDQywsLHz99ddfeOEFPozTp09XKdYIf5VijWC0EglIQAISkIAEJBAfgZa7AhWZGXDz1sIVG/KZJDGNbOiExkf1aT2w++7VNqd+up4JCmOVItGFrFkcWkpJSujTpXnTL9Kcyzd/ePcM3GX545U/EtscydqHdq6oOVYviUIvyzeR0LQxyd5fyNLy50uPkNP9yodrp3+2ERWanJTQo0Makxs2b9YUB8osk5u2Fk35eB0rpez54nJH0aAYVSZqLHdm94GUpCZR1GRSYpP2rZtltdjNtvwl6c34+V8tmOXr9IgEIgL4f5YAXrt2LUfat2/fqVOnMCsZv4GZAoyYGqaf69atW1S+Ae6AiBC//Px8tBTxTcyGdqDkLRKjh8K4//778YlFBQXDjzzy7LFjUlNS6u1NHDV4UGFx0d+fex6r2LJlS4xMenp6dXpbVFTE9Hn8P9yYMWN4zgkce/vtt08++eRYpfjZZ5/xqJNqzUZ6LCmrZEkjnvr27YuHio1nhCdekgJ8XrZt28ZnhL4R+UjhwYMHk9nKjI3vvfcelxAQxwdn7ty55JszBM7SB05RAKHJUxQ6QIXUtnTpUlwnnzvq4Rnr2LFj9IDRn3nz5tEWDTVv3pyzNLdo0aLFixcT+ManFUfGkQgR5YlIpbbQPXrCWaLk6A+XR8Uqs4ONBQutUxuLmbCkCT2ncgjQ7qGHHhoqeeutt8BFn/F0odtY7JkzZyLUOMhXB2F6UXNwY7wEioKO/vBRokCZW8zlubm5FAMdo+Bzx5FmzZqRrk7CMogI/UPYgY5XOsn27rvvcnOjVvr3789XWbhxHKeqkPYeCtA01VJhVD7sUCH0eBgYL3US1UieMqOOLbZs2TLgcy2DZSzcBbrKQJigE2+YmLh7Auk+ffqcddZZw4YNO/bYYwcNGsS95mYBh0xtesKXamyd7leZgEqxyui8UAISkIAEJCABCVSdQLd2ae+nbEYpFhbteO+zTQO6tezXqTn2Ky0lgb9Qb4tdNi22DZKFowVSKDb6iLasWxJbIHafhVCYfzD2SLz7rPLcIXP3zyQWYxkzsG3vDnttDh/XLGbp6jJtoRP3IRQbNVq9OX/655tKfWJik/7dWrC6dKlSTG2aiFIs2bF6UyGOtUy1Zd6iUMN6zRxnkkomQ0xJ3KtSZHRhWWoKJycmDOjecvyQ9mUqjN4icImyjN66I4GaJcDPdZzLyy+/jNHgB/C5556LCqEJjMC0adOmTp1KQM0111xTs40ecLURWIRrwB1gQHAEiAZ2MDWR+qmHI0KR4BMfeOABfGLO1q29u3Q594TjO9ZENnHtDTapadMTR4xgJot7Jk1iJRms4tChQ2P1X1xN82yjb8g2xS4RLIbJIvUbr4cGQmMFdU6FeMY333wT68dBJCCiKihFlBBiCD8VSmInyV3lk4Jlw7UFZ8flODueB2QfSpHwtDvuuAMB98Mf/hD5BXlsFx+oW2+9Fd9ErCh9OP/88xFeDAq9iN+kdVKz6WdQiv369cN40nSY8JE7+Le//W3dunU0hNEbP348fXvxxRcRW1gqnNdpp53GYiOcZbDEY7700kuhNrpHSTwm/ouPMPIuLqXItcw8+NRTT0EvKMUBAwaMHDny/fffx6MRQBopxXvvvZdsYo5Eqp2ePPbYY1A66aST+D4JSpGnkaqgR5RorFLEGjO9IEAYAhuDov5nnnmGjxvqMFYp8jDQKFSpASbcDgQfrzT38MMPxyrCa6+9ls9mUIqMguRoXGGon1cIs7B4+JaLDjJespUnTZpEu9wLqmXaTT7jEyZMOP7448EYSqJKWeOFRcnp9r///W8uCUoRY8sEoIcffniwirhpLmTI9Dl0g4FAiUp4NlzTPMJezZ1q/VdmNdv2cglIQAISkIAEJNBgCRzdt9Vbn6zfnFO6NPOsRVve+HQ9Eyy2a8mCiRUhQSl2bLXb8bEQ88r128YPaZeZttfYuorqqsQ5Aicjg8k0jqs25I8f3D6K7KtEBfEVIbV53ebSyfvJCj+sa8tR/b9cGQBRWLxjZ+G+QhRZdiaaJnLpuryNWws7ti4bBBH1iSVZWqYl4goZGspya15xVsvkbllfhnJEJd2RQG0TIASPyCOsIg0REMTv7eOOO459Ynk4jlKMQm9quyf1tn5cD9YGiYPLwHfgEYhgQiHhEXjFpJCfG0mH+jMKxA1K6LnnnkNdtc7IOOmoo0YecUT96d7eeoJVPGn4sHXZW5549bV//etfuKEqp6kihrhZDB+zc8QRRxBbh98h9o1bifSJHmyi2HCF2ED0EGoJM4gAYqFeiqHDCFQM+om3Dz30EDx5HvBNUQ0oLXwWJTnOpwknxdDQYbxionlmcI5UGJwaQXNoSkqGs/fddx8reNA9WuEgjowO4yupkIg8ytBhJBRBc0Qybtq0ib6FOEEuwTbSSWTZ2LFjeSBp8fbbb8dg8iiiGuHGDpcj6Sgc7/NJc0TmPv/886hPRB6jozlkJV6MKEWsOn0L20cffcRB8MIhHGGHkhwndo9bEA5S4aOPPkqFjIWAPhwiUX7oVAQu5ZkMkWFSEnlKMTw43QZy2DgOWLagWQEFH0YEWO4aWp+dWKUYO1j2qZmNy/lEQIl9HonQq/BKByB5zz33IGQ5G+wwSpQhMGpuDQMJZpA+I0bpAMNhH7AMh8FSmA5wF9C+oU7uVNQE/zzDFywimC9Vypj1HJGp5o5KsZoAvVwCEpCABCQgAQlUhUDfjs0H985ctSk/O7c4d1vxy++vRREe179Nt7ZpIdWXvF3m7yszhV9KYkLXtmld2qUuW7uNyQ0nf7A2My1xeN/WPdunpafs/u+6HTt25uSXEK3HkYoF5T77jW6jP6G5nG0lr72/NiM1cVifVj07pKcn744XoLmt+SUEIVa/OX7eoQ63N9pZXLxjw5ZCFocJYY8sz7Jkbd6jbyyLVpshtDOnoAR7WCaXun1GShQp+f6CzV3bpY0a0KZNC4IX+ZHZiLkaS3bsYA0WZCJjp7m2GSkDe2ZMn7sRq/jxwi3/nLrs+IGlgZ8t05JCTCVX5RaWsCwM+ebVhLlP2haQQCDAb2MEx/Dhw6scF3awkkSzsp1wwgkYEJwCoU+s+EGIWbdu3dANBLKxg01AeQRbVB84zJkzh6AwLEZKcvLA3r3OHD0q6YvEzPrQvQr6kJaScvboMYtXrJz+7rtkH2P0iE2roPzeTjH2sCIwUgwDGMwd2o5oPnRwpBTD5Tz8+HQC8Qg5pAzqEHnHvWafDiCkkH0YdtQSSogYNLxYEFUIKawWlVMPdeKM8GJE9uEQEU9cGCLUcE8coQyyj88XGo4mUJlYM2qjTp4ceovVIhSObGKeK+IK6czll1+O28KyUWfQ2RdffDECCz2HMg65yfBhLWMeSOonYBDJSCv0it7SLso7rsg4OowJZbCYRMLxzj77bAQZTz5LjmDlYuVdQLfPVwZOfCiTCSIKyUDncwRSbB1RnHzhcOroo48O4hjahCiClPtO3B+fKXQhDNmwk+Ex4N6xQ5l//vOfSEnGCBDMftQNoEVWEddJYZ4EOAAcSR0Vi3ZQycSukpXMePmYE/jJneVeYJABTmzpr371K45E5bl3ICVilJ7geXGgjAV9TDxjpBSjwsDE8xJWCVLOnnLKKeSqR2fdqQ4BlWJ16HmtBCQgAQlIQAISqCIBXNiJg9t9vjxn1sItJdtL4w2ffHvF/JU5h3Vp0To9CZeHTCyzNgstcRWabMKIQ+57cRGea0te8YNTln28NHtQj0zcIlMxIvhKmHYwp+iQNs3GDWxLPVXs367LaK57+7RTh3X428uLaW5zbtGDk5d+vCR7UM8M3GLU3MatRR3bNjtxYLsQIYjrfO+zjWhN6liwMicsgbJ9+86Z8zezfDMHmSexX+fm3dullekb0zW2bpm0ZmMBOcuzFmx+4q0VrVskMz9jdm7RnOVbX5yxunlaIgtMc9WqDQWT3l7Runny4N4ZXbPSmGwxVHVIZrOsjOQFq5rQ6OLVeZPeWbl8/bbObZqhEVmspqBoR1JSE+Z8JJ86lO+SlXrq0PasLs0iOUQpvjBj9fxVucN6Z7ZtmRLqBOaWbSxRs/MbJ3StYKbIMgPxrQSqTIBf5kTT8BMaDxJlNZavDSlAeA7BPhTmElQFIVH8aK8/Kq18n2vqCGFubKNHj0Y/IZvIfCRy7emnn8ZrYDEI3UKLMH8cYU24iZpqtMr1YKCIiUNytW7RfMyQIZ3btatyVXV/YdsWzceNGD5n4UKsGTPTIXp42OLqBhIqqCLMGpfzfGKFUFdEKZLmfMkll5SZwg/ldPrpp6OxsEXEvhH9h94iBxZ/FNqlAGaK2rjF1IMKLH+XMVmoRj4gXEglXIvq4i4g/tBqbFQVwhv5oCHpCJdjbWvycIMRYylqdCSijWBYdlCKqDc2LqS3HGRE3/nOd5CG9ITWsVQcR1oxWF5DP5Fo5E3zEDLeqn0qeXJIAOcDTrLzZZddxr8xUDPeHBOK3AytxPVKt1GHfGlgTiGMpqRjdBKkBHUCh1c+O4yCkRIoyr2GErnqjKLMbaJd+LOxg0IFOBuSDgW5xy5xO9g4BTEE7h7LcJuwfnyzUS2TPCAK6QkfZ/AyXtTtDTfcwOcoFibJ2t/61rd4BjCbPFHccV6J3yxTP2e50ahJ6iH4kbETo1r+sSlzlW8rSUClWElQFpOABCQgAQlIQAI1TKDPIc1PGdq+cZNGc5duRcPlF2yf8dkm/oiGI1GKpUXQcOWbJO947OFZc5dtnTlvM+GNhNfNmLuJP0rivLgEd8b+Eb0yR/ZrU02lSD2kY5c2tzzng/mbcreVEDlITB9/ZZob2Dtz1GFtglJcm114z0uLiSukTLTh+J59bxV/HMlonvStU3uUV4rtMlOG9M58YdMafkis2Vzw52cXNktJYAibtxYxKPaPG5D1wvTSGtZtyv/bK0vY+cG5h7bPRP/t/m/ajPTEYw5rs2h13or1pb/rFq3K5Y+daOvQplnj47tGSpFEchbDOXlo+1feX7N+SyHadPbibP4oH+IfQ1xkcnLCxaM7qxQjjO7UHgF+w/NLHs1BkNTeUk35tc/P7FdeeYUIOEQAP+b5Ec7veeJ6cGrxSp/aG0ut1oxUIpyNjaAkcOEp0CWEKREmRoQa/gslQYwYPojsTnb2l0EgZJItNyend+f+x8VkqtYqnBqsfHDv3u1bZU57/wN0DJosrjg7uoEhwgmS1AwEpBjf7Rw86qijcJTcMowhHpwHOOow9fPYh3xVXoMxZCoANsqgkwgx4+YymSAhhFzIA4/wQjGHzPdQD88GAosngfpxTBxkPkECDNmnBnwZNfNgcDlHggRE/CHxif6jMNoraMegI0Od0Ss10CuGwBGuQqJdeeWVqC4iZKkQ/cdIcZRM9kfl2MldMu0QznI8VodFFe5tBwOI0OTjzNML+VCMx5gHmwHu7aoKjuMoQ64xuhbvRpRoKJyTk4Om5BUDyxE6ybcQLo8AST5Td911F/+2AWQGwig4VUET1TnFjeAWMDTuJvMhhp7QVbwhzx49524ShRrLkJk3Q1wqlHhseJYQx9y+Mt3gCA8MAY98FeCOL7roovKGtMwlvq08gao8i5Wv3ZISkIAEJCABCUhAAnsjQGrtacM6dMpq9vLMNR8s2LI+uxA/SJghv7mIWwxXoRdxam1aJrVM3b2IIfF4HVs1+9Yp3Una/XjR5iVrthWyHPIu9xjiAcOF67MLojRhjny5asmu5OLdQX1f9IxW+AsbO9EKJxzhcKc2qd86ufszGSmf0NzaPTe3bkthlKNNfnHQml9UX/Z/S4cYlY45ybgAsmZTAaGCW3OZO3FnXn4Jf/yEYIHsEX1bTRjWPijF6KLSMX7Vu54wsC0hh69/tG7lhnx4RiXDTl5BydZtpXGO0dY+M/nckR1JNn9vzsYFa3Lz8oFZWmMsPX5g5hfvSE3Z++rRUXXuSKB6BNAl5BgSwMWPebLzyleGo0GcMV8bv5AxGhgW9AdLuLDxc/qnP/0p4VSxv7rL11CdI8T7RPFi1amnMteGUezxNfYgNgTJwkbUFbFIiCSmVAMO86YhGrA/aEeMDO4J1YgQIWM09vLK9KQ6ZVBd2JkP//3vHp06tt+15E51aqv7a1MSCfIuVX7oLaxTvEoR/c0d4XKeHKJKiSNjnweVr9kwx2K3ry5GHMwgNy6MFFWHY6IwWziC3jrjjDN45tGUrAdCSbrE3SdikQ2jx80N950WkVBMz8e1GEDyc3F81Ma1NMqnjAp5mMM8g+Tb8okLTfDKVbxGKjM6zg5dQl0FDrTFE0XCb1QAw8haKAwZO8Ya3zSN7ie0kFRull0ilJjLo8IV74AOp0l57GfoLeVpkQeet4yi4ss5G0ELJamNOtkn+JFM4cjkYhLRlzTEayiJQCTPGotHSCZulEZxcLCFMEGLBDlGNyiUr5FXbgTSk+80Wo/qZ4e3oX7OEmMY2xaWExrhCBdSmK/H8mQYCDeaUzw8rLqjT4xlWP19lWL1GVqDBCQgAQlIQAISqCIB/N2g7hmd26S+9/nGuctytuQWYdAKivkptDOxaePkpglJiU2I6SMbuk/Mys7k5PZqn37N+B4fLNoy9ZP1ObnFBA9u26Uj+U9/5g0kgzizeVJS0y80IQtJJycMJZ83o/Q/vkk6ZrHj2B7jK0kHbrzrJ9vh3VpyeexZFlxmesFrJvT4YOHmaZ9uQPah6r5sLqEx0xe2apEUZR+j50b0adWjfdm85qjO1JSmHdvsYdUUahjQteV3z+g9eda6JatzGRR5x/y0pLfdO6SffdQhiNbhfVoRP8iW0KRUfZLUTP51VDM7LJN98ZguXdulvj9vM6KTSrbvsoMUJsyQ9aMPafWVpjmOyuSSYb1bvTpr7frNBeRHbyviZ0kpDipndMy9WOpAOfCVpmKbdV8CNUMA7YXvQIrhDZkrsMxPaNogEZIVBkg2RCswGxobMTjMTUZUI2uqjh07FhHJr+ua6c1Xa+GLiTCuPc6D9tWCNfCu9EO+awtyocxrmVO7i+76H6wBw8ccwZBQLCwSfofp21CKzIvH2ZAeHi7BB5H4WQPd3XsV3bt3R8TM+fjj/ILCLbm5Genpey9bH898vGDB+s2bQYq+CRFhcfWS0DOyWbkEPXfjjTeWuZaZ73iAYwNIuXHhXpcpGb0lZA9nx91Eu4dbjBHj48A0fDjB//qv/8JAIctQyQg1NGjQl5g+JCA5v3xqUEvEwQUVRXM8CVRO8i//VxFZNgqwcVWIl4xaZwcUFXBAzDFMdDZSG+mJyUJrMk0hEX/k25LPC8bY2va5z4eujCOjw7RS5mD5ejB0ZZRixJZ7gVskajJcxaeAjXjAqG+Mmq8RBOjrr7/O3AJ856DziIwmsJF/uvjDH/4QMsRjGwUgXY09Eu8+4+LW0+cyQ4veRncnqpny4fZFR/a4w4V8DAlnJk6W52ePZTxYZQJf+a/JKtfihRKQgAQkIAEJSEACVSbQpnnSaUM78Ie2IoaOKQtRaeTksh5xahI50Hv2WEQvHtO3NX9Eb2RvKybIkWhFBBwKjAVJuHDXD6XdnWqRmnjN6b321sMOmSmXjO3aaOzezpcep1oyqfkjeHBXc6Wd/LK55C/FXqfWzX50bp+K6tr7OeTdYZ2as3YNKpAZIVF7AGBGRUQhF9Hiz75+GLKVH534QRQk/rR8ZaA76ch2/DHX5KbcItKZQYQcTEtJyEhL4mz5S2h3QNcW/FEyr7BkXTbTyO+galK5WzVPatGM+ZvKX+QRCdQ8AX4kEw3E5IA4CGYJxGuUaYPf9ggyfiRjGxEopPLxS548QWaCIwqJfFKsItajzFU18hZ3Sa9+97vf1UhtdVkJToTMR7bYRjEs+KzaVorop8P792/brt3bH300oGfP8SOPOVCWZ4FVdm7u82+9tXjVKhQez1gFKi0WbLTPk8njiukLj2tsdBiujYRoFBUPbZC80VX73CFqj4g/4v6ImuSTQlbsCy+8EJQiHxliGGkOR4acIiiSaEHaxScSrIpfJkYVRRVFt+Efg9AkkvTMM89ERMa2jnQrb6D4hFb8+UJWnrhrw2iztgz53azaTLjfpEmTmMKPFitjwegGXUXzoeqohzi7YAAZFDaQg7H9ZJ9e8cpzHnk9xh4CMKOSMAm3gC8NFncmazi2J3wcCHCOCuNeYcJ0CvhH3ChzLPAvGUxXynAQpgRmRtcyXlrnXnCv6Ri6M6okrh1aJKIzzIcYjZebFRbvpiq6HYYZV7UUBiM3l+R3ulqFZW3iba6hlVcpNrQ77nglIAEJSEACEqi/BPBjTJXIX1xdxHZV4aq4mogtTFRAZloSf7EHa3YfDoRVpiV/JZyQJnCIbVvuznKqTItVwAJMlq6Ols+uTCuWkUDNEiACiERdfr0TH8TP/tjKEQr8dOcXPlaF2Dd+Y3OWn9lhvWNivvjlTxBW7CU1tR/0EL2qqQorricIi9hXyvM2HAn70es+T1GADT0R8iKDdkGgRFPUhQK19MqEemPHjPnr3//+6KuvdunQfnCfKv6LSy11b2/VFpeUTP3gg5lz5ialpODCUHJ7K7m348gvfBDptDyu1113HVFvUUk84//8z/9gFdkQSRVLuugqdkIyMuVRYEgoJgrASCKzUIrcXOwbZfhEEO3LhwVfGZYcCXFq5MVjlCiG1wst0iVEG+KPDw6OkiBWtClluJZiNIGEYp8nh2cmCsTjbbB1VMsWdY+SRGVi3rmKa5GApDwzNQHmlExqNBlAKBM9w9GFe9whVDB8wIlzJOYRi0qdqFgUKkjLqDGsGbnMBBLy5dCtWzcKYP8DDbpKu/QZrwpqeoVmpT8kgwfJCC7K8BqFi/KW7xkQ4QdplAcYnoyLjz/9h3Zsh/GwAQK3gO8u3jLAQI8a2OcS3vLKVdTMPjvwZJ+NfZoOuDDXfIkxBGIhuR0cRxkT40kZblmVp3Rg+ChdnjQEMY9xNEyqdas+AZVi9RlagwQkIAEJSEACEpCABCQggZohgI/ARGC7+DnNr/Tgv0LV/BoP853xUx9VEdseHgFzQWJy+MUee6pG9lESpJFSP7/JgxOJzEgFb8uUoSeVKYxKoFjYyu/HHvmiVGnKZJny4Qj0sDxEb+FiMCzsc4RYLTwUhAlcqhE4FVfSrkOHcSee+MH7pUuc/P3Z5zpmZbVr1ariS/b7WRTap4sW/fPllzfm5Jx+xhlE3aGf4u0VEo2wQW4EMojgWQxgVAO1MR8oBUhLZybEIPiis3vbQUsxDyAfCuwYmpJPAbaIKRqJ26UVjoSEXAxX6C33Go3IskWc5fMSRdKh6kKL5MOi6vBNWMV77rmHOFZihJFiQRriK5H1aC8KsIU8az5f+DjmXuRzyuVsUW9pDlXKfAW0jrmLuocQxIQixbCE9CQqX/EOlRAZyoOKYqNvIXCPlYhefvnl8hcOHToUUcj217/+lehCeoub45mnJPsYOvqDTyQsF4DcFEIm+XqhJN8boOCjAZOwojeQGSArLNM0HeYsLpUAT2Y84CPGTUT8xXYgLKdDDc888wyj4y1+kK8pdgCIv+PTxz0Ka79Aj/5wOeVhBRbqxPTxbyc8JEyHSsApJX//+9/zbUM9r776KoNiZ+LEiQCPbbfy+wD8v//7P5bwxqL+5Cc/KfPvNJWvx5J7JKBS3CMWD0pAAhKQgAQkIAEJSEACEtg/BPjRzu9ebAteoIw6DB3iZz9b+c5hMfZ4vHzJeI9QM4Ljt7/9LdYAC8ArG5XwGt7yGrsT7YcCu05+Kf6is9FObDEOxtu9MuVRFUSoIYMwFwR7ghEngk/EkmA6MBfIhTJmpEwNNfv20D59vn7xxctWrpw5Z84/X3r5qnPPaVZVP1KzHdtbbSvWrr0H67Ry1THHHnvJJZcQp7a3khUcxyuhuXFSZJfH+kQuwSIh44h349YwySBBdhXUEx4eCnBDKX/LLbeUKczDiS8jro3J8jjFs0SwIQdxYThHGqIGbCOqkVA7ChBzx9lQkpkZsVeEwjHTImLxscceC5VjLb/73e+iFHmEmKX07rvvDsd5ZYZBNiq5+uqrr7jiiug4vozj9957b3Qk7DB2fCIl9/hZLlM4egs3ZCvxoU8//TQSkIEzCoITeYZRbFGxsMNCxsymiiVk6gM2BCJH4I8c5GuEDeBIwxEjRuDX+HTg9QhjRIxG9ZAazHcOWpZRENp56623RqfCDlQZMhZy9OjR9CQ6SydxkQQwAhBNHB3/5je/SXmUIr364x//yNyX0Sl2QM2cjOxQ1bhx44AGJfKsWRYGm8nZn//85+Eszwaf2fPPPz82IDS2qtj98DUSe4R9HrM1a9awg9zke0ClWIZPNd+WfRarWZ2XS0ACEpCABCQgAQlIQAISkEB1CBCgRLQUCoBf/qQ9RlWhEiJXQvAO9jD8tscC8Ludt5iL4EqiS2pwh5qxijVYYY1XhUJCoxCMRh4rq4LgEch2xHQQ34TNIdSOkDQMSMUCq8Z7RYUpqalHjRx52WWX3fnnP7/47jsD+xw6etAg9EdttFX9OlmP5cEXXvxk4aJ+/ftffvnlCJ0q1EnIHgYQ8kSDIqHK13DqqadyvyhDSCBncYIEwKL/YlN6cVJhZZVwyyhPhCnFeOCDOuf550NBgBvLibBFOa34KWYGwCVRgBpAjVLEDyKVaAu/Flk5YhJ//OMfE+XHXIF8pnCO9JyrqCH0hMeejxWBw2WGgNqje7EHuQolF7oXKqFdRBiRgGPGjKF78T54XIiIpCGEHUIcyci94Bn+5S9/iSqNbRppe/311//lL3/h4adRPqeXXnopfSAVGlZ0jO8NXhk1k67y3ULIHqcoDHyO00nGQp30nyMUYxTsUAOthALQIKoX5RcmZIxa59of/OAHTE+JlgVvuISDUTH8JreVvOzoktgdSjItbDjCN973vvc97hQBj0Rlcou5C9j/b3/720CIruLrkXtK52MflWAeCavkYYhKhh2EONAoz1rV9fwbrEzPD4i3pZntB0RH7aQEJCABCUhAAhKQgAQkIIGDkgA/+3/1q1+RtEhs0fe//31+VKMI77rrrkcffZTx8qsbHRbipEgh5Ac260EzDRzLPiBH+OVPSibRTPyyO/nkk1k+BS9wUFLa26CwGAgXHATQCIsjz5T8UJAig1BaiCSybnEKeI291VA3x1Gc3LIH7r+/T+fOv/rOtzt/dTGQuunDPlvZlJ395yeeePHd93r07v2f//mfBI7x+O3zqjorwEPOvSYEFfeH6uL+8rSjlmrEz+LUkGK8Ui1yimppIt6h8RwS2IgqpUvUQPfwYlWoJ7ZdaqNjKEXMIN6TiEI6SS7wDTfcEFuMrwIsamBC6/SEjwZ+MGxl+sAHBMvGRkk6yRYxBDJ4GQWv7NMuZzGDUYHYRqN9hC/ekBBIilEYgHwAo7Nx7TAQWqdCRGeNPH5UxVjCF0JcPbHwPgkYpbhPRBaQgAQkIAEJSEACEpCABCRQpwRY65nwLnIecQexDRODc/rpp5NgyLSGN9100wUXXECBP//5z/zyRyUQQ4S/iC1/EO8H8YHCIJCTzFAyK0kOxTcRqoZvJQeW4DICoPZLWOIesROEhTImF/XNqVPvevLJ6y+9NCM9fY8l99fBLTk5f3r88Rfefbd7z17oKp7AGhE6NTgcvBi6iq0G64yq4rNT/Y9PbXQPqVcmIjLqc+wON4t/Y4iOVEwJz0hyN1tUPtoBMhx4XKMjldlB2BHyWZmS+yzDQMoHG+7zqgoK0De2Cgp4qsoEVIpVRueFEpCABCQgAQlIQAISkIAEaoUAEoHwOvIZy8xBhgIYP348k6ARiEc6JBvNB8/CXHLMEtgQfjkTtEXMEQFWmMS3336bsESsIhqCwCjSXUkyhUO8QqRW7mK5SkllJQqV7OzJ06enJidfd/HF9WdSxZy8vNsfeeT5t9/p3qsXtppU3/rmE8vh9IAEJLCfCagU9/MNsHkJSEACEpCABCQgAQlIoIETwAky9RiBObxGHgf9xLoHTAtIPBGJhwERJYm8IyzxzjvvfOX/Z+884Nus7/yf2JYtWbYs771X9t4hCWSSBMLeq5TZlh5tr8e19NpyR68tvf+1tFBGOWbZK2wIgSyyyXYSJ473npIsW5Zkefzf8pMoiheOt52v8Ms8evSb7+eRY3/0+X6/X35JYCNnePXqq6++77773JOLjUqkKIk80BBREhFbKbbANpFfSZEGKwKc09LS+m40G1B0RLWjKuIB/GznrtiIyBtXLPceBv4pi83259de+3DbN0kpKaTqI5We6z4cUBoyeC8I8JZXHr3oK12EQP8SkFyK/ctTRhMCQkAICAEhIASEgBAQAkJACAwGAbKh4c4jcxnRjkgMgzHlkM5BUDN1G6gsQXFbJUkcqiuFNfAkUsoWi+KQru48JidVHEV1f/7zn5cVF//77bdfvmiRl9dQ5iu0Nzb+4aWXPti6Tevn9/e//50qFheC1/U8Lthwakq5IWq2EO9P0gPSqg6npclaLkQCIileiFdd9iwEhIAQEAJCQAgIASEgBISAEBhBBMicSD2K3bt3s+YZM2agIVIKdvr06SPUmEm9iAMHDtx7770tDsev7rpr3eJFHkMkCiNMP/r88+u3bNX4+v71r38lcpyknCPoxpClCgEhMIQERFIcQvgytRAQAkJACAgBISAEBoRAK7nVBmTgYT0ou+ZxAW58WF8VWZwQ6CcCSIrvv/8+tU1Wr15N6PcoCMslgnvXrl0/+tGPxra0PP3wL2ePH99PqM5jmFqL5TfPPvvV7j3NLS0vv/wyeiJR5OfRX5oKASFwYRMQSfHCvv6yeyEgBISAEBACQmC0EGhuaT2cX7s9s7rCaK822+ePC75+YYyvz1AG0w0m2kN5tW9vL25obArW+YyL9puXFhwVpPb0EIFxMC+CzCUEBpYAwc5MMJqyyFGtmwI7DzzwQERo6LO/+Pf0+PiBJeg2Op/BUN/57t/9LjM3Dz3x17/+9U033dR9jWC33nIoBISAEHASEElR7gMhIASEgBAQAkJACIxsAnZHy+cHyp/9NNdidSAstraO4W/FxVPDHr4uPcBXNbL31uPVf5tt/H/vniysbCB2kPhBxMSZ6UG3L4ufEqfzEGGxxxiloRAQAoNMgOrPr7/++h/+8Ifw4OBnfvmL9Li4QUiLyb8TNbW1P/zjYxk5OQi16Ik33ngjpYEGee8ynRAQAiOdgCfl4Uf6HmT9QkAICAEhIASEgBC4MAkgHdbUNT7y+vH13xTXWx1Nza0t6IltXzPHBc1NDfL28hiGZBoam1/cmP+bV469vrnwUJ4pRO8TGaju4zrRVbceqaoxN7L9lpZWUJTVWHccr/Hx9kwM16qGJYc+blm6jwgC+fn5KDVUUBkRq5VFDj4BCqFQpZrvmzZv3nrgwOxJE0MCAgZUVURPrDAYHvzfP2dkZ6MnUuuDB3fpgE46+GBlRiEgBAaBgEiKgwBZphACQkAICAEhIASEwIAQqDHb//jeyT3HaxDUXBNEh/nOmxh8+azIyCCNK/J3f67xha8K1u8u3XCwYk+WISzAJzTgnAT8uPxe/NrZILOkTufrRQPXgP1+wGp3Z9Z8m2Ww2pv9fVWTEwLiQnz7OIuPyrN5bGtz65h6W1OjA611DOqqrbH5RHGdn8YrOVLr5SmaTh8ZS/fzJnDPPfc88cQTlGOmMLGU0D1vfBdGB4Q80heOHz++oLDwUEbGjsOHZ4wfH6LXD1C1FozsZdXVD/31b4ezsvhBuWbNmocffphi2aInXhi3m+xSCPQzAa9+Hk+GEwJCQAgIASEgBISAEBgUAg325h2ZNeiJjiannkh47+wJQT+/Ik3t7emj8iCLosrzbCbB0hrroVPGcoNNaZkaoY0N8fXXnP1VsKTGuiezxmhuNNXZpyYETIwd2D3wp6xT9jvzve+Tabw9r5obvXpGBDSQR1/bVJhbWo9j0VjXuPFgRSwya1pQ32eREYRATwjg/NqyZQvFc0+cOEE5XcoTq9V99eH2ZF5pM0IJIOfp9fr/+q//wipICZqfP/7Xx378wKTkZC/Pfk6Gi55YXFHxH08/c+iMnsikAQNsihyhF0WWLQSEQE8InP09sietpY0QEAJCQAgIASEgBITAMCFgqGt8d0dJY5s/kcDeZTPDH1ibHOLvQzLBjg9yLKK1KeIjr+48UTM5MWByfICrZXPz6QaOplYCh13nR8oBu0ZFVcrRLJsSptN4vbgh/3ihGVUxs8B8PL92WmKAWtXPf5+PFDiyzsEhgHp49OjRTZs2bd26tawMK1h1c3PzpZdeGhgYKBawwbkEI3cW7pDQ0NBf/OIXFLP+y1/+8vDfn3r0B/dPSUlRefXnH+zkT3zslVcOnDhBjZs1q1f/9re/DQ4OHrnQZOVCQAgMOYH+/Ak15JuRBQgBISAEhIAQEAJC4AIhYG1sPlZkxojHfr08x05N0aMnhup6Gq18NK+2oKJhQqzOFRk9mrjhWJyTFmRtbHn164Ks4rrGppYDuSYQzUwKHE3blL0MHwJ2u/3w4cPr16/fvn17XV1dQ0MDZxCJgoKC/vVf/1WqXgyfKzXMV4LAR9llch0++cQTj/zjud/cfdc00iz2k6pYYzI98+67Ow4d9lKpbr755nvvvRcRc5gDkeUJASEwzAlITplhfoFkeUJACAgBISAEhIAQ6IQAVVk+2lPa1BbyjIJ27cKYnuuJDEcSwyP5tQQ7dzL0qDiFIXH+uODkSD8lH9mx/NqjeWYHqRblIQT6lQDOxJycnGeeeeahhx769NNPbTabVqttbGwk0hlh6Oqrr46NjfXs7/DVft2BDDa8CBCGTPHlf3vooTq7/fcvvvTxN9vrGxr6vsRKo/GZ9977aNs3Kh+fBx988P7774+IiFCGLS4uJlS/71PICEJACFyABMSleAFedNmyEBACQkAICAEhMLIJkA+LFIFHc2vZBikUQwPVc88/UeDeLMP88UFxoT2ti8Kk5oam/KqGgkoLgma43ichVBsfpvFTq7qhScB1Za09t8JSUNVAQPaEWP/USP9u2rteQvyzO5qLq60F1dZSg5VtxgX7xoVoWHCnkd2ujq4DMkVOTgo4km8qqbI22JpzyuqRUBPCerpf1zhyIAQ6JVBfX48zEVvirl27ysvLMSQuW7bswIEDmZmZ/v7+Vqs1JCQESZHKG512l5NCoCsCRMpfddVVVPV57A9/+Ps77zQ1N106f75Oq+2q/XeeL6+pef7DD9ETPVQqxMQbbriB21XpZTab//jHP15++eXp6ekif38nSWkgBIRAOwIiKbYDIk+FgBAQAkJACAgBITDcCZDusNrcSDljFopF8ZJpYUoOwZ6sW6P2am52JlWsNNqyS+tnJAcG+HanCSpj1jY4vjpc+cW35Q22Jou9mVBidVsFGD+115UXxayYGtZpALXR4nhja+GhHFOtxUExGYYixWFEkHrptPDu/YKUhMZESd+aWjvTsVNkRI23l9bHMyHS777VSeEBnaeMdCeAgjknNZCaM0iKnDfUNxrq7SIpuiOS494RMBqNyIjbtm3bv3+/xWKJj4//4Q9/mJSU9Morr+Tm5uJPnDJlCq8uXbo0ISFBLIq9g3yB98KrePHFF5PX9o+PPfbc+g/ICYuqGODXG1WxtKrqpY8/+XT7Dn1w8M/+9V8vuugil54IZGy2BQUFf/rTn8jh+OMf/5jC0179FGd9gV9B2b4QuEAIiKR4gVxo2aYQEAJCQAgIASEwegig6FWZ7cp+vFUeFGju+d4oBh0TpS2rsaHWHcytnZkaNCNJ3333UoPtta0FO4/WUDa6Y0sciyeL6+5blaT2PiejTpnR9tLG/K0ZVaZ6BxGgSsdK45j8ioaxnmNtbQpjx9E4Y7I4Nh2pfH97SV55fdM50qNzy3kVlnKD9cErUtOi/LAudjqC62RkoCbY3xs5kvnrGppwWbpekgMh0AsClZWVO3bswJl46NAh7F0xMTH4EBcvXuzr6/vPf/5zz549CIi33Xbbhg0bOMNLUui5F5Cli0KAW+iSpUtxuT799NOvfPZZUXnZzatXR4WEnBefksrKlwnI39GmJ/7sZ0uXLWNY9xFw1P7kJz/59a9/zV2NvPjAAw9MmjRJrLXuiORYCAiBbgiIpNgNHHlJCAgBISAEhIAQEALDkUCbpNiorMzLY2xkkKbnq0RfI8ngtycNSIonCp0FXibH61Se56iB7qPVWZv2ZtV8ua/CbHFwnu4+3p56P2+6K/WjCysbPt1TOiNZPy89yH2cz/aVf3O0mgBteqH9hQepg/y9zQ2OKqP9SLbpjMboPpXzmEDp7NK6NzYXMixPmU7t7RmiV3O+wmClLDVZIA9mG5/fmPefN0/8Tm8mhWvUPp5enh4s1Wx1iKTYHrc87zGB0tJSNBf0xIyMDJPJRB66W265ZcmSJXFxcYzx/PPPf/DBB+iJd911F8VYsH1hMUOaEYtijwFLw04IIP8tXrLEX6ejBvSnO3c1t7Tcunp1dFhYJ007O1VUUfHq559v3PttUmrq3ffcgz+xnZ5IJ5VKxfnf//73WBR37txJUsUf/OAH06ZN8/HpabGvzmaWc0JACFwoBERSvFCutOxTCAgBISAEhIAQGDUE3F2KRBxjxDuvrSVH+FWhJxbXWaxNxwrNM9OCErvOMEj+wY/3lCl6IvLcnHFBa2dGcsCZD/eW7jthYOraesermwsmxwfotaelSSyKe0/UmOqdeqK3t8flc6LmTwgmwpqI5uNF5o37y7NLnLWqOz4qTPavj1QVVTn1RI2P56z0oNUzI3S+KjI5Vhhtb28vPlVUhxy594ThUJ5pdmqgu4jZcTTOaNVerBZJsd7aVGd1qqLyEALnRaCoqAiphceRI0fImajT6dauXbt8+XJCRMl2V1NT89prr7377rtYce+55x5e+t3vfocuc91111Gn5bwmksZCoCMBpL1Zs2ZhJHzzzTe3HjzIxyw3r1oVGx7esWW7M4Xl5a9/8YVTT0xLw3s4Y8aMrlRCzuO0/fOf//zb3/529+7dzc3N9913H5OKx7YdUnkqBIRARwIiKXZkImeEgBAQAkJACAgBITCsCSApVtc6o4CJ+8WIRzrF81ouEcozUgKP5NViUTyUa5pXXNeVpGhtbCbfInHNjO/pOTY+XHv3ysT0aGd9FWyDoXqf/PKGapONp8fyzNll9dMS9ayHp7tOGsoMNtpwfNHE0MvnRqVF+yn5FlOi/MiNSC5IxcBIA/dHmcGKtxHR0MvLIyXG/+5Vic4AZ8yKzmotLYH+3g+/eLTR0Uzc9I7MmqmJetV3bd1P44U0WWdxWG3NfLnPJcdCoHsCJSUlxDJ/8803hDmXlZWhsCxYsIAMiQsXLoyOjsbehZ6ImIjW43A47rjjjiuuuCIvL48EizSbPXu2h0eX5t/u55VXhYA7AZIbcjtRs+Wpp57acvAgUcnXL1sWHRrq3sb9GE3wREHBGxs27D56DD2RRJ907z5DIvfqihUr+E5SxX379qGJo4/PmTOno6vRfSI5FgJCQAiIpCj3gBAQAkJACAgBISAERhgBpDprY1tawLF4VsZ+V0bBTnZHnPKWw755ZRasf6dK6+akn67+2a6pxdZUXNPgcLRw3l+jWjw5VNETeYo+iC1x2fSwt7cUogBiA8wut0yM03l5OkW+kyV19TbnCgk6Xjg+ODFC66rfEqhVIWhSs6WjpEjmRE4qGmWgn2rVjPBxbfIl4/AgC+Sc1KApKQH7TxiY8VSppamZhX2Hpujt5aFqUzkJmuZLGUq+C4HuCdTV1aEMkhIReaW4uJhYZmxcc+fORZpJTEwk/RzdDQbDRx99hEWxoaHh1ltvvfHGG7ElfvHFF1SCvvLKK5U23c8irwqBHhJAEBw3bhyy9V9Npg2796BuX7V4ccSZqs3ugzTYbAdPnnz36027MjISU1KIYkYZ7F5PdHWnZDmh+n/729/Q0J955hlSK86fP1/Mti4+ciAEhEBHAiIpdmQiZ4SAEBACQkAICAEhMKwJkD+ReF6WiLLW0tKKF/B8VcUQf59xcTqMirgdjxaYT5V2Hobc0Nhc2WaHZC4/tSdCpDsXPFiLJoa8s6WodYxTqish12GbLZFjV6bFIJ13RKCa8tDuHaMC1RF6tfsZ5djmaK6pb2RTPIho1mq8jhTUujdjfJ8zvkSKtLB391c7Pcbb2NjkbOblNZavTtvISSHQjkB1dTV64pYtW8iZuG7duokTJ06fPp20idThVVriT/z4449fffVVxEfExJtvvjk8PBzxkS5paWnYGNsNKE+FQN8JTJ069e677/7HP/7x2Y6dtkbH1UuWxIefk1exymjaeuDAx998cyw3Ny4hAQkSHbyHeqKyPHKAoir+3//9H5I6E6EqkmkRSb3vi5cRhIAQGJUERFIclZdVNiUEhIAQEAJCQAiMZgIqL48Q3enc+U0trQ32Jr82hbHneyaSeFZq4L4sA5LiqZK6rGKzp9c5qp8ylK2R0tKn68BQWjoy8BwdEItkXMjZ4qFlNVZXgWYyLSrHgTpvyrm0Wxh6aKeVVRrszoBopTF1nz/aVdouVSLp6nLLLYrmWFvf2ANFcQzlZajowpi+THqelNotW55eOASQDufNm4c4mJycHBsbS85Ed68WguMnn3xCiWej0UjOROq0REVFWa1WoqSRGvGFhZxnWd4LB6zstC8EEAeJqefHIJLfFzt3VlRXr1kwf1pams7X197YeDQ3d+PuPdsPHy4oL+fWvfPOO3EdnpeeqKxt0aJFZFdELt+2bdtzzz1HGDVnSCHal5VLXyEgBEYrAZEUR+uVlX0JASEgBISAEBACo5YAwbyhutMlWXDqIcOdr6QImuQIbXKkX0ZebX1DE6VaAvw6qfGCK1Ap60x7EhqiKp7DdOwY5xmcf21mQfRHl2mQWGmneXLMGJbasZo0+RbJzHjOUG1PqMHicMYyOx8NtqaDp4zKcaffFWGx05fcT1ptTfZGp6SIjkleRfeX5FgIdEUgKCho1apVZBXoWNGiqqrq008/RXBBT7zqqqvQE9EcGYd45w8//DAlJeXSSy/talg5LwT6SACJUPHAvv766zsOHMgvKZmckhyk01ltNiTFjFPZjpYWKrHcdNNN6Im9jr4nVlqj0ej1+s8++wz5ktSKFDcXVbGP1066C4FRSUB+rxqVl1U2JQSEgBAQAkJACIxmAuh07i7FcpMtoeuSzV2BUKs8JyUE7DtlPFVcd6zAHNBZ2Wgm8j8jwyEvUjQ52F15bB3jrAR9RkekdopLJ0RqJMUjqiJxx65o6K5W4jpPUDOZFpWnODEjgzUhAZ0InUoDP42K5bn6dnqAUxKhsy3l4hh/Xy9/39ODd9pYTgoBdwKdlrtV9ETyJ5JIkWIspFCMj4+nl91uP3z48LFjx2644Ybg4GD3ceRYCPQvAUVVJBh5/fr1GzduPPL5Fz4qFV5Cu8OBCHjx/PnXX389ORDRBPsy7+TJk3HmMuDbb7/9wgsvULmFdKK91ij7shLpKwSEwHAmIJLicL46sjYhIASEgBAQAkJACHRCACkt9EzgM5rdgRzTvLTO66t00tnt1JSEgORIbU5JfYXBVmvB3XJGHTzTRuPtERpwOsKa6s8EHceHno10xlSYWWI+03ZMVJDaVYNF5+eNFRHbosFsR9RztVEOMBh26jHU+niGBZyOraYYy4QE3WWzI9v1dT1FEv3OUtfFNdYqs12ZC21Ud0YedQ0iB0Kg5wTc9cTLLrsMPZFSLUp3s9lMKDTHhIj2fEBpKQR6RwBVcebMmYTnx8TEbN269eTJk42NjcmpqRgYV69eTcrFXsQ7d1xJUlISNznSJBr6iy++SFlzvIoUnu7YUs4IASFwwRIQSfGCvfSycSEgBISAEBACQmCkElB5jQ3y91b7eNrszUT17jpec9slcf7nnygQXTI9xn//KWOVyW6xtpWQPheJr4+Xq46KuaFpR2b1ogkhaIVKq8amli/2Vyi1WTiD2kh9Z+WlUL0PRsVGR4upzlFc3UAlaPfkiWarQ6kHfe5sY3AmBmhVyr4cza219Q7iuxPCtO2a9fzp/mxjbtnpyjNUpHHpsD0fQVoKAYVAZWUlQaBoK2RLXLt27W233UaaReUlLIr4E/fu3cuZSZMmCTEhMDgEiLKnKBAC4tGjR7kJEbgpIkRaz36cnSyixFCrVKqXX34ZryLFzVesWBEaGtqPU8hQQkAIjGgC3xEtMqL3JosXAkJACAgBISAEhMCoJEBaw2B/7ylJzuKzhBWX1TTszzH1YqcUaZmeHJgW7d9VX3TA+DBffVtMtNXe9O0Jw8ZDFRROYVLKtnz6bdn+kwbFA0ib1Eg/l9o4JT7AX+OMMiboeNPhysN5JltbQkPO0H3vSUNWSV3HSZEqMUVOa6srjVR6NNf02pai3VkGo1slFoyUdbYmQ93pwtAdB3GdqW1wHCuorTDaOEPUc1KkNuyM49LVRg6EQE8IICN++eWXZK/jABcY1q3U1FRXx9ra2i+++AKjolSxcDGRg8EhQCQyMc733HPPAw88gNLdv3qisgVinwnnp9gL9znC4kcffVRWVjY4u5NZhIAQGP4ExKU4/K+RrFAICAEhIASEgBAQAu0J4FJcPSvywClTU1MLkcXrd5akR/m1q8jcvk9nz5PCtanRfgeyjUpZ5HZNiLBOivCbPz74871lSIeVJvuzn+UWVjYgaJYZbZ/sLcOHqHRZNj08KkXC3UAAAEAASURBVFjjCnyekxb4+be+lJOmusu3Jw0UbiZoWq9VtbSMKTNadx6vyS2ztJtLeRoTolk7OzK7tL7aZKdY8xd7yziemxYYGkCgs9MdiZpppJx0S+sdS+MJju50EE7am1p2ZtacLK5TCk+Pi9OROLKb9l2NI+eFAErK5s2b33zzTQKfKb2Cnpienu7CQsBpdnb2jh07CAilGq/rvBwIgVFDAOESVZF0ii+99BKFzm02G4lEibkeNRuUjQgBIdBrAiIp9hqddBQCQkAICAEhIASEwJARII3g5PiA1Bj/zPxaBLYDJw1vbSu6ZmFMbMj5peRHZUNuiw/Xnig8mxXRfVfhep/VsyKyiuuIICbZYlmN9YUNee4NOB4fp7tmQbTGTeAL8vO+eGpYicFaUmWl145j1bsyawiF5pjVUllFq/ak0nT73I1jxrAvMjxeOityw/7yKqON2Orj+bV8MYuiVyrFXnx8PG9YFNOVREjax71ZBmTWvHKncKnx8ZyepE+L8mu3bHkqBL6TAHWct23bRrxzSUnJypUr0RPHjx/v3stkMn311VeojcuXL58wYYL7S3IsBEYNAUq1oCp6enoS/szbgQjo6667LiEhYdRsUDYiBIRA7wh4PvLII73rKb2EgBAQAkJACAgBISAEhpAAiQv5QjtDp0NoO1FktjW34CtEUGNVyG3ER7uWd6Kk7lCOiZLNWrXXkslh7sqjn49XYXVDVkm9EsIcEuAzPVmPe1Hpi5AX4KsK0nmTS7G2oQlB0DUmB1qN16zUwFuXJ0yK03meybGoNIgJ1lDuxVRP2kSKkbYyOIZBNETKLs8dF6z3U5UZbDwPC1TPSAmMCzlb9cVX7Yy2pvpzc3NLvb2pqUlZl7Oiy+kjvIrNrdcuitH6eLltcUyDvbnSbC+sslLG+vUtRZmFZsiwmAkJAatmRbh25L5+ORYC3RCwWq3YDylMkZOTQwq522+/vV2qRO7s3Nzc5557zsfH59577x03blw3o8lLQmBEEyCjIno61Voobr5//37eHRgVg4J6UxlsRHOQxQsBIeBOQFyK7jTkWAgIASEgBISAEBACI4YAiQ4Xjg/aNT4YDyASG4Ldh9tLDmSbSBqYHK69aFJomltyw8ggzfxxwSQlDPDzDtF5u2+S9IVz0oIoz6LIhdGhvuH602WXlWbUSl42NYwyKR/vLas02pAsiXdGskSdDA1S37QoloBrd2nP1euWJXGRQep9J43VzrrPzaiTdIkO1SyZFFpaY/X29EAJTYzQYml0Xw9KKAPevCSWVW044PQqEtnd0Ih641QzES5xMqKcunfhmMDtbUerjhaac8ot2cV1dRaHYoEkHHvFtPAJMbp27eWpEOieABHN+/btI9IzKysLByL1WNrpiXRHVTl+/DgGxmXLlpHSrvsB5VUhMNIJeHt7X3/99XgVn3nmmbfffhsPL2kc3fMAjPQNyvqFgBA4XwJjnR8Zy0MICAEhIASEgBAQAkJgBBJoaW0trrY+/uGpYwVmU12j+w6uuyTu/lWJfudfBtp9kI7HFGJGHyTLIdZFqp10VBI7duGM2dpUa2mkoHOg1luJVkaXtDlaSI9IikRXUZdO+/K7qsXeVFlrt7flbaQ7mRx1GlW7qfMqLL9+5eipktP1nRnKy8sjOlhz1YLolTPC6dLp4HJSCHRKAAH7wIEDTz755MGDB5ELv/e971FXt2NLxMS//e1vu3fv/o//+A9sjB0byBkhMPoItLS0rF+//u9//3thYSFq+49//OO0tDQ8jKNvp7IjISAEvpOAuBS/E5E0EAJCQAgIASEgBITAMCWAoS82xPfhG8a9tb14X6ahuq6xtr7R0dSK1EhpFCXtYP8uHREQC2Fk4PmNqtN48eXeB6chX+5nujpGOkQY/U5tlJotbJxIbw/Psb4+XoF+qphQ3zWzI/BmfmffrqaW8xcsgRMnTjz99NOoikuXLr3jjjs61RNxZlDlOT8/f8qUKUuWLLlgWcnGLzQC1Gm55ppr0BDR0zds2FBXV/fggw9OnjyZmOgLDYXsVwgIgXN+txMcQkAICAEhIASEgBAQAiOLAIpbiL/PDy5NKpwZufNkzdG8WnO9o6m5BeHPVX95ZO2od6vF6pgW408hbLWPZ3SIZlqyfnqinpDqdmbG3g0uvS4oAmROfPzxx/fu3XvJJZfceeed06ZN63T7SIpEgFKShZBnAkI7bSMnhcBoJbBu3TpURd4pu3btolrLAw88MHfuXD8/qYI1Wi+47EsIdE5AAp875yJnhYAQEAJCQAgIASEgBEYWAUKkRUAcWZdsGK62oKDg0Ucf3blzJ3ri3XffPX369GG4SFmSEBgmBDZu3PjnP/+ZfKPEPt933328awICAobJ2mQZQkAIDAIBqfg8CJBlCiEgBISAEBACQkAICIEBJyB64oAjHu0TFBcXP/LII+iJBDJTwVn0xNF+wWV/fSWQnJwcHR2NpMiDUkVarTYuLk4ioPuKVfoLgZFDQCTFkXOtZKVCQAgIASEgBISAEBACQkAIDAyBsrKyhx9+mCjOxYsX33///aInDgxmGXW0EUhMTIyPj0dSJGMAquLYsWNTU1NFVRxtl1n2IwS6ICCSYhdg5LQQEAJCQAgIASEgBISAEBACFwaBysrKn//853v27Fm0aNGPfvQj0RMvjMsuu+wfAjgTk5KSTp06lZeXh6pIasVJkyaJqtg/cGUUITC8CYikOLyvj6xOCAgBISAEhIAQEAJCQAgIgYEkYDAYKC5BPZYFCxZQu1b0xIGELWOPTgIxMTGkU0RVJBspBdMrKipmzZrl4+ODaXF0blh2JQSEQBsBkRTlRhACQkAICAEhIASEgBAQAkLgAiVgNpspw7J///558+ZhVOyqvvMFSke2LQR6TCAyMnLcuHHZ2dmoisRBZ2Zmzpw5kxrQHh4ePR5DGgoBITDCCIikOMIumCxXCAgBISAEhIAQEAJCQAgIgX4hQITmbbfddujQodmzZz/00ENTp04VU1W/gJVBLkwC4eHhhDzn5uaiKvLYvXv3lClTQkJCRFW8MO8H2fWFQEAkxQvhKssehYAQEAJCQAgIASEgBISAEDiHQGNj47XXXnvkyJEZM2b88pe/xJ8owsc5gOSJEDh/AqGhoUjz6In5+fmEP3/99ddYF2NjY+XNdf4spYcQGAEERFIcARdJligEhIAQEAJCQAgIASEgBIRAPxJobW1du3YtpSSQP37zm9/w3dPTsx/Hl6GEwAVLICgoiISkRUVF2BUtFsvmzZsjIiLGjx9/wQKRjQuBUUxAJMVRfHFla0JACAgBISAEhIAQEAJCQAh0QmDVqlWkeyNI89e//jX+RC8vr04aySkhIAR6RSAgIADzL4XUeZfZbLadO3fyFiO1Yq8Gk05CQAgMXwIiKQ7fayMrEwJCQAgIASEgBISAEBACQqDfCaxcuZLStMRj4k/ETuXt7d3vU8iAQuBCJkBOUp1Oh/nXZDJRp8XhcBw4cMBoNC5atEjSlV7IN4bsffQREElx9F1T2ZEQEAJCQAgIASEgBISAEBACnRNYsWIFRWlTUlLwJ2KbUqvVnbeTs0JACPSBANIh5Z5RFSmClJGRQepStEWkfEzBnO9GWKRc0quvvsobkxLSfZhfugoBITAYBERSHAzKMocQEAJCQAgIASEgBISAEBACQ05A0RMTExPRE6nyrNFohnxJsgAhMFoJoBtqtdrJkyc3NzcjFDY1NVGzBXkRQZ8qLl0VbCFK+p133qG0C+/T4ODg0QpH9iUERgcBkRRHx3WUXQgBISAEhIAQEAJCQAgIASHQHQFFT4yLi/vVr341b948X1/fbqxS3Q0krwkBIdAzArzFeKNNnDiR9ALffvst2mJ5eTkHOBApA91pTSTeoaiKlIomISPiI917NpW0EgJCYAgIiKQ4BNBlSiEgBISAEBACQkAICAEhIAQGjQD1nanHQrxzdHT0ww8/vHDhQsxToicOGn+Z6EImoKiK6enper1+x44dLS0tJFjcvXs3yRZRDFUqVTs4iI9Wq/XEiRPHjh2LiYlJSkrqVHls10ueCgEhMCQERFIcEuwyqRAQAkJACAgBISAEhIAQEAKDQYAkbmvXrqXyLMYo9MTFixd3n8ptMNYkcwiBC4mAoioiIPIe3LZtG6qixWI5cuQIB+PHj/fx8WkHIyQk5ODBgzSgWnRCQkJUVFS7BvJUCAiBYUJAJMVhciFkGUJACAgBISAEhIAQEAJCQAj0MwFKQ1x77bXHjx8PDw//xS9+sXTpUtET+xmxDCcEekAAVZGKK1gOiWveunUrYiLvTYT+2tpaVMV2WQhIclpfX5+Tk3Py5ElipePj4yWpYg8YSxMhMAQERFIcAugypRAQAkJACAgBISAEhIAQEAIDTQC14vbbb8frhOnp3//938ml6O/vL/HOA41dxhcCnRJQVEVch9gVURWp1oKqmJeXR3ZFREPCol0FWzhAZOSdS4Xo4uJi0hSkpqZKUsVOqcpJITC0BERSHFr+MrsQEAJCQAgIASEgBISAEBAC/U/AYDDcf//9+/btCwwM/PnPf75mzRrRE/ufsowoBM6TAGHOCIhIhNu3b7fb7Yj+HOAjJrqZMtBeXl7KeLiJjUZjbm5uZWUllkbsjfQ6z6mkuRAQAgNOQCTFAUcsEwgBISAEhIAQEAJCQAgIASEwmASqqqp++tOf7tq1Kygo6MEHH1y3bh3VY8WfOJiXQOYSAl0RoCQL4c/EOyMm8la97rrrKPG8f/9+optJtkh5FjpSkgVVkdjn/Px8Ph4gFBpvI+/irsaU80JACAwJAZEUhwS7TCoEhIAQEAJCQAgIASEgBC4UAmRDKy0tfffddzdv3ozziCquHcu89iML4ih/9atfUQWCUMof/vCHV199NQf9OL4MJQSEQB8J4EbEdYiq+Nlnn1VUVPAmxVC8Z88eykBTlp2si4yPpEhkNEZFGlCnBRUyLS1NPhjoI3npLgT6l4BIiv3LU0YTAkJACAgBISAEhIAQEAJC4CwBtIDdu3c/8cQTGzZsoIprRkbGqZMnU2JiAoKCBkIdQLt89NFHv/76a8Kc77nnHgxQGBXPrkaOhIAQGB4ESJhIXkUioPnJgFdx4cKFR48ePXToEJHRfOpA5kRkR2yJJSUlqIp8FMHnEIQ/S52W4XH1ZBVC4DQBkRTlVhACQkAICAEhIASEgBAQAkJgQAhgMtqyZcuTTz6J/6jJ4UiOilSNad136FDmyROqltbE1FTCG/txYio5/OlPf0KhoJ7DnXfeeeONN5KdrR/Hl6GEgBDoRwJ8qICqyAP/MqrixIkTiXQ+duwYaiNeRT4V4I3c2NiYnZ1dVlZmNpv5eCA9PV2JjO7HZchQQkAI9JqASIq9RicdhYAQEAJCQAgIASEgBISAEOiSQE1NzVtvvfXaa69lHj8+NS3t7ivWrV6wYO6kSaGB+g3fbM88kTm2pTV9/HgvlarLIc7nhaKioscff5w4StKu3XbbbTfffHN4ePj5DCBthYAQGAICSIoENeNlrq6ujo2N5YOBzMxMSrKgKlJbCRmR86iKGBWRIDEqUshlCFYpUwoBIdAZAZEUO6Mi54SAEBACQkAICAEhIASEgBDoAwFUgHfeeefVV18tLS5ePnfunesuXzpzZmx4eHRoaEpsbFhQ4I5DhzOOHrVbrckpKYiAfQyCLiwsfOqppz755BOysN1000233nordR76sHzpKgSEQP8TQCh85JFH8Czz84HReeMTzsx7n7yKCIUEPnN+xYoV1GMhSYLD4SACmvP0QlJEaqyrqyMUetKkSQRH9//iZEQhIATOn4BIiufPTHoIASEgBISAEBACQkAICAEh0DWByspKirHgTzQbjeuWLL51zerpaWmeHh70QD7Q+PikxcXp/Pz2kjrt8GGr1ZqalkaEY69VRfTE55577oMPPkBPJHni7bffjr+p69XJK0JACAwNgdbW1ldeeYX6zkeOHCHAGd2QNy8CItHNEyZMwKKYlZVlt9uxItbW1qIwWiwWJEU8jCaTCVWRlkQ9U6RFPjAYmusnswqBDgREUuyARE4IASEgBISAEBACQkAICAEh0FsC6Invvffem2+80WS13nTpquuWLU+NjW03GCkUqdCi1fgeyso6eOQIauO4ceOwLLVr1pOnBQUFL7744vr169EarrrqKvREBIiedJQ2QkAIDD4BdEPeoWRZPXXq1I4dO463PVAYMSFS7hlbIlHPxDiTBRWdkWO0xZSUlLCwMIq08GbHusjx5MmTKd4y+IuXGYWAEGhHQCTFdkDkqRAQAkJACAgBISAEhIAQEAK9JFBRUYG69/Zbb411OG5YueLGFSsiQoI7HQtVEanRV+1TWFZ28PARtUaD1nC+EdBIDJie3n//ffSFK664Aj2RvGydTicnhYAQGHICOJFxHVKGJTk5GbMh5Zubm5vz8/PRFTEtYlHElsh7GesixVj8/Pyo04I5kWhoCkMT7IwKSRUXzMhYGkNCQoZ8O7IAISAERFKUe0AICAEhIASEgBAQAkJACAiBfiBQXl5O9PG777xDZsTb1qy5bNFFgf7+3YzrVBXj4kL0+pyiQspA22w2UqqhI/QwAhrdgdhqHJHoDmvXrr3jjjuIl+xmOnlJCAiB4UCANz5OQ4zJaItohXwMgD9RqeyckZGBmIjOyA8TDjjv6+ubl5eHqkizpqYmFEa+o0uSUbGHPyiGw5ZlDUJgtBIQSXG0XlnZlxAQAkJACAgBISAEBopA65gx9sbm7HJLfmVDfqU1p7w+q7S+3GSvqXPYGpttjpbm1lYVsaxjxw7UCmTc4UfAXU+8/bK1l1200K8Hgcxenp7JMTFBOt3uw0e+3b+fVIs9VBXRE19//XUyNkJi9erV6IlERw4/KrIiISAEuiTA5wcEQU+ZMiU9PZ3PAzAtIiAS6YzZGYWR4GjqsdCGR05ODhkV/P396+vreZUz06ZN43uXQ8sLQkAIDAqBsWRIHZSJZBIhIASEgBC4QAnYm1p2Ha+pMdvZf1y4Nj3GX6eR9DcX6M0g2x4FBOyOlsLqhpIaa3655URJncmChthidzSjJAb4qvx8vUP8Vb4+nv4aVWiAd6C/t17rrdPynZNeaq+xns76HPIYhQQUPfGtN98k3vmOyy+7askSb5XqvPb5yqefvfDRR44xY26++eYbbrgBFxLew65GcOmJuJkuvfTS733ve0gSXTWW80JACIwIAlRfIbSZIOhDhw5xwNscSZE4aD5mICVCSUkJGqJiWuT9/pOf/GT58uUjYl+ySCEwignIH3Wj+OLK1oTAgBAwNzi8vTx8VJ5iPRkQvt0OWm9rqjDZjPWOWoujwdbER0JqH0+dr0rvpwrV+QT7e3fbe8hetNqb39hamJFXywpWzooI1nmLpDhkF0MmFgK9JdDS0mpqcOSUWQoqLYdya0+W1BWWW9wH0wdomlpbDBZbYZWN84iMLc3N/r5eoQE+oQHq8EBNCN/1PhF6p+aI+Oij6lItch9WjkcEAUVPfPPNN1tstmuXLVs5d+756ols86pLLq4yGddv3oL3kKfXX389VZs7VRXJn/jGG2+88847LS0tq1atIn+i6Ikj4j6RRQqB7gkEBQXNnTt3zpw5iImkVty3bx/lWYh65qleryeXIqoiCiNx0zgWSbwokmL3POVVITAIBERSHATIMoUQGCUEGuzNh3JNWSV1Gm/PCfG65Ag/fCijZG/DfhtNza3FNda9WYajBbWYgyqMdmNdY2tLq5/WK1yvjgrWJEdopyXrpyfqVV7D7q90pM/mlla+wNzYjC4x7HHLAoWAEDiXAM7EvArL7pOG7cery432Okujzd7sauLrqwoN1KYnhflqvFyZrRqsjto6m7nOZmxoLDGYbScMBLTq/bzjQ9XxoZqYYE2Y3icxQhsd1JsKv66p5WA4EFD0RDS+BrP5mqVLr1u+rPv8iV2t2d/X9/a1a831ls937iRDIvfSjTfeiFexXXumI3kieiL+xJUrV952223jx49v10aeCgEhMHIJ8N7HlsjjoosuInMiwuLOnTsPHz6Mh5HPGAiIZmu8/bExEiIdEBAwcncqKxcCo4CA5FIcBRdRtjBkBIqqradK6/mOxOP+VW60GTGR2Zvx8Q1EJimkGUdza3MzglIraaoGzS14KK/2iY+yvzpYsSvTUFPXmBrtF6LzGTL6F9LEFlvTsSLz+ztL3t1ekllgrjTasSiizCHV2RtbasyNhB/iATxVZuFP9Pgw7XBjQ1DkxoMVlSZn4HNSlN/05MAgv2FqqBxu6GQ9QmDICfCTprah6UCOaf2uki0Z1R6eqoRovYenh7nezk8hLy+PIL3vpLTwmZOiU+KDwoK0IYG+yldkqH9iTGB8VGBkmH9okB8eRv5BRGcsKLcczDbuOWng3xRCoVMi/YbhByFDjn0ELcClJ9YZjavmz792+bKoPpRh1arV4cFBNbXmXDxKmZlYlhLaAh5dQMit9vnnn6MnOhyOSy65BH8iJRpcr8qBEBACo4kAtsTIyMiZM2dSHpqSLNR6pmCLqu1ht9v5Ewgjs2RQHU1XXPYyEgmIS3EkXjVZ83Ah8HVG5cb9FXg32i1I5TU2yN+bUC98fBPjdMmRWkJT+zFB/cFcE6olkqKv2nN2ahAJqtotYICe7soyVJpsisdsX5Zx3VxbWrR/P+5rgJY90ofFn3g4v/afXxfwR7iS/Fbj4xkc4OOvJvBjrLmhqdpst1ibaJZfVv/ZvorFE0NH+pZl/UJACAwTAvwDp/ijNx6srDI7EmKCYsP9W8d61tY7NGoil73CQ/2S44MnJId29W+BRu2lUfsjL7KjBpujorq+uAK1yFBZU19Wbd2eUU3Shhkper4Pky3LMs6LAEUSqO9MnHKdybR6/vxbVl+aHB19XiN0bDwhMfGuK9ZxfuuBA2++8YZGrSZVYmBQkNISSRFZISQkZM2aNcuWLZs8eXLHEeSMEBACo4wA73Qqt0RERFCOidLPpFOkOnxeXt6mTZv4+TDKNivbEQIji4BIiiPreslqhxeBMoOtqLKBkpcdl5Xbdgr/4IT4gMvnRS2eGNKPee5e3lRwMMvoaGoJD9b88Q7NoEmKvt6enh6n/2wk5Bk9q+PG5Uy/EygxWDceqDhwysjIFE9Fqp6eEjg9VU/YIBHo1Fo9mGPam+ksfkII4SVTRE/s9ysgAwqBC5RAY1MLCRPX7y7NKKjT+qrTkvRBAZqTBcaCUqPKyzM2Up+SEJwcE6j26ekvk75qFb5FviJC/A5llpVV1h3OM1WZ7WvmRC6fFhahV0t2xZF1q5HL7P333yfe2aknLlhw6+pLk/qsJyoEzqqK+/e/8MILePJXr1kTGBjIq4iJFHeeN2/euHHjJOBxZN0wsloh0BcC5EC47rrrtFrtW2+9ZbFYJkyYgFsR92JfxpS+QkAI9J1AT38L7PtMMoIQGMUEEHoC/VTobeyRX3yJSqYeRSP/ax1zLL+WShpeHmNXTg/vrz+WEBMVt9ogI106OZSAtRPFZpWHx+xxQXFhvqIpDvQl4E968id+26YnEt8RGay+ckH0FfOiA85UTJ4Yq1s4LviTcN+9mYa18yJXTQt33oRjxlBFR8ld6FohxRBQhLl1KM9qsTVzE3l7jfVXq7Tqs0qx0rjO2sQXgjjXd6zHWCrxoF2qe1aQhzuzwd5EGRkC/xGdCXDW9FhuYMX0sjjfOy0sFdla6+Op9pZ8na5rKAdCYPAI8C9YTrll/a7SHZmG9KTQ+Ch9Zm5VRlYFPxYiQvw5kxITqNH00iOfGh8cqNMcz6nKKaypNFnf2lqUX2FZPTNickKA/5kfboO3VZmpVwRqamrWr1+PP7HeZFqzcCH+xP7SE5XluKuKL77wAiexJerbVMW0tLReLVk6CQEhMLIJhIaGXnHFFf7+/pgTiYa+8847R/Z+ZPVCYFQQEElxVFxG2cRQE0D1WDM3ijq2LAQdx2xtKq5sOFVSX26wIo4UVzXsPF6dFu2XHu2M/Bq5j6QI7YNXph7Jr/VTe46L1ul85QfIgF9MQg4P55iqjM7yqTqt16qZETcvjmvnD8WmesNFsSumhlNKVVkQQfGf7ytH2nNf37q50Yh0OWX1e08ZKbPQ0Ngc5KdKifCbkhiQGK5FNDzdt6X1QK5px/FqhHIydRLFj3wZGaiJDFIHaFWUgummJo8dT1NxXW55PUpEcbVVq/aamazntgn+rpBGorYram255ZbCyobCamtNnZ0Ma7HBmvgw35QoP/yYFBl334scCwEhMKAE+MihsKrh4z1l247WhARpm5tbvtqZ3dTUQpLE8SlhyTFBftq+pkNlqHlTY6LDdUdPVRSWmL46UJFbWn/1RTHzxwVFScGWAb26/TF4fX39p59+Sn1n8ieuveiiW1avTopuX0Sl7/MoqiL/EhEB/dKLLyJnr167lqqvfR9ZRhACQmCEEuAnwLp16yj0PHi55EcoKVm2EBgsAqIIDBZpmWdUE6DG5dpZEegyrl0iLH59pOqfX+efKq7Dt4UMd7KkvqOkiJsMMcXW2GxtbMaihYtM7e1BBvuOuepxb1GPw2k/a+vSZkSjbwt2MPxornmVAyROlwSDzY1kWM5CHmceXp4eqEI8x+2IGU3JBcm8Wh+vdj5KKvNSGMS9r7fn2FnJzt/mUbXYoysO+szY7f/P36UEhlsbW1iGh8cYnG5IV+1moQ9DWdC/2tbI3rsyxKGC8Tdt2+weDNJxdqYDJsVAup+u/SqH8fNjhWYKs7BAPIPj43SLJoW00xOVteMgdOmJnOHGePazXEq4uO9sfFwAIuMzn+YUVVrdr+nMtMDvr0yclqTHS0t7bsXDOcYPd5S49+XYx9tzYkLAlfOiusp6xi26J8vw2DsnTJSiPnO7bdxXHqjz/sFlKWjr7QZ0PWXG7DLL+p0lVP5pl0bAW+UxKVF/54qEKfE6sSu6iMmBEBhQArx/y4z2T/ZVfHmw0lvlSf7cY1kVwYHaCalhqXFBAf7q/pqdwZNjA0P0msyg6oyscj7wePKj7CN5oXevSowNkTLQ/YW5/8ch5HDLli3UXDZUVl6+eDH+xMQORZn7a1ZUxfuvuUbnp92wa7cSAb328ssl3rm/8Mo4QmAkEvDy8tLpdCNx5bJmITAqCYikOCovq2xq6Amgdi2bEnqsoLaoqsFqayb22WxpL/yhITpr9VY25FdaCiobqKFM9HR8qC8Z8ZLC/UiZdyZvoXM73xyvJlmeotRUUyOlTbIx1zu2Hq2i6nS7Dc9KDXTJl4x/KNeEeuhqExWsXjA+BL0ps6ju22xjucnpgIsL8aWSzKR4nXspXsK3Nxwo71h/hvYRQerpiXqq0LiGbXeASkiALT4XwtnyqxoqTHZUy/gQX4TX5Ei/iECnbKh0YSt1tqbPvi1TdocbbsG44I76kc3RQoXQ0horvUL1PlPiAxjENSnTUWUbjxsw2XJlLdN5JoT6JoT5tpvO1WVEHJjqHZRBYKk+Kk/qomIO7dGyx4719/VSdEObvVnR9/LKLZ/sLS2ptiJKIiuj8QGN0fZnGXECRgdrItt4ogxyxRF2uRz859Sx2y4MYt+BLENOad3auVH3rExs51VkqB2Z1Y/88zhqJmMigMLf03Mss6AwbjxYzhXpdOWo3RkFtS9tzGcZSkdPDw9FsGYouh/MMnDD//TqNO6KTkeQk0JACPQvAVODY3NGFT+THY5mfhRYGuyT0iMmpoZFtZVY6d+5GA2Ncuq4cH5o7DlchH1644Fys6XxlzeMDwuQgi39DrsfBqQ6Cnris88+m5+Tc8WSJbeuXp0QFdkP43Y9RHJM9A+uuUbjo16/efOLL76o8vK6/Ior1BoRnbtGJq8IASEgBISAEBgsAiIpDhZpmefCI4CqGBPiG+jnY7U1OBxOcQQBx10lRGR5c2vRu9uK22VGpI7zqtmRD16e4opFBd4LG/MLyyztWiIU0r0jWvQXVy3m/aeMT32cjWvP1Wx2elBShN/+bONf3s9iVa7z1BH+/urE2y+Od50xWx3PfZaLMug64zqYkhwYHqDuRlLE//jpt2UvbsxvsJ7jlcN3NjUl8OEbxkXpzwqCbOTvH2Vj2GT8sCD1pAcDOkqKqJNvbS08mG2iTUqM//1rktwlRYLNP95T+srXBR2nm5EW9Ivr0iPdpnPtYvgfWG1NCLusU6dVhZyrMnezeLJ6zpsYkl9uoc3RHJMiHRLOXGFwFgoPDVFHhmhyi+sUsZI2vDR/fLAiKSIg4rrlKnDPcF2Y3d62AGU6unyTUTUvPWhu2unim8p5pnjxqwKiI5WnSJbpyNP+3rlllpLKhv0nqVWtCJvK62e/I1/uPWE4eMp5WXk405LqvEMD1XUNjtKqBlZLtzKj7d0dxSIpKojkuxAYaAIHc2v5cWqqdX7a5KvxnjM1dlJKmNdAJh+geHRaYojBbD10rJR3Pb5+qpD921WSL2+gL3Vvxs/IyEDXO3LkyGWLFl2/YvlA64nKEkP0emq/GM3mj7Zt+3LDhnHp6ZOnT+/N6qWPEBACQkAICAEh0K8EJDtVv+KUwYTAuQScBS5OW/HOfWHMGJQUYz1VMs6R25RGiChUz6g02RUrYvuefX7O+Dj+zA1N7TQeztc1NLn7GXs9FXn9ncZMpnAqQuc8OGF3NFfX2t1VToQkV6w34hTaYvtuEGtCsDp9mqhnRCvXuPg9u5mOUOhqc2O/7Ms14+AcsFsXB1LGQKmH83LfBWpVoTqfEB1e19O9wA4/na8K70+Ivw9ZEV26LX/D40ZUHkjhOo0zZyKKLV8kNcM3Sp5Q98Z11vYqM/ezizAzon6G6LxxvBKOHehPFP7Yszs5dw+sigvkNES2RdOTFzI8UB2q8w53juBz+q5odQbp2xydlFY/dzB5JgSEQF8J8NObt2RTUys/OVQqT61GRdIDZ6mmAX7w883H24sZmYcfR3yYwedSAzynDH/eBGw2m8lkstvtWo1Gp9ViGDzvIXrbAd874c9atZo11BgMmCV7O5L0EwJCQAgIASEgBPqNwOD9KtBvS5aBhMDIIUD8ppvwdXbdSDv7c4zPfp57oqAtTV6bOYs/qDAhIu7wpxQZ6CgV/fSPZrgS5KlVHsgrikuRgFCX5MYUHfMTc841GWqOt7czcyImSUWSQ8ek+MbrmwvJS4gwR1tFDOLPyHe3FmFAm5kceLr72DE4U9zFu2ZGcalcrjnOPaDBqdK6l78q2HK4UnmFrTGPsjvmOpJt+tWLGf9x8wTF6cZag7Tek5L0+08aGL7GZC+ssoQGkGXrrGjIOIdyayuMzuBZFjw5PoCiH8rgTJdVUv/SV/nbjlR1Ot3hU8Zfv5Tx8E3j56SeY6xTGg/n73hIuShOd+uYMQQyo825r7bT66BceO6KH1yapDReeaKm1uLUCw+dMnIV7lqdtGZWpE7jRV7FR147vv1YNbdcpdHuCsxHyLvtkji+XHOR6zCzuO6D3aVbD1WynnKD7c0tRUunhLnESiTgg7mmwjZTJL0igtX/c/eUxDAt0/F0x4maJz88lV/e0FFf5tU9WcZ9OSYuIqMlR/ndsjR++dQwOnKvEpj/zBc5+04YuGcKyht2nqhZOjnMtSo5EAJCYCAIZJfVbzlSWVbdoPX1To4PnjUpOizobJrggZhRGTPAz2dKerjN5jiaVUFejp3HqoP9vb+/IsHdrT9ws8vIPSHQ2Ni4bdu2J554Ijc7+6pLLr5tzZqEyIENeXZfVahe//116zw9PN/btOm/f//773//+1defbVGwp/dGcmxEBACQkAICIFBJyCS4qAjlwkvGALoLEVVVmOd08+FKuftRf3c05svM9i2ZVQpeiLqCWpRWJAGIxi2vqJyS72VKiOt+Phe3lzw8ytTFeFm2fTwjMBaRV1CG2qrmuKskTIhIYCQ2HZQSXR4ZqoxiVHaS6aHGesaCXqlVgwtq8x2khISIBzo7xMf6Sz1m5FtVPwgOAff3F7skhTRMVH6qADjGv9Efi3Lcz3t9ACtigrXisCHmEjSvVC9mkBaYniLKhowuEGGxJHPfJ47I0l/2oY2dsxlcyPR/hrb5LOtx6rHxej02rOSolNgKjDDhBmxzKAnutJsUddl+9Gq7RnVvHR6ukB1WKCaP0qZrr5tuqpa+3Mb8kj+6PJCdrry4XaytsHhoq318XQvsY0rkFfbLZgbzE+tImdlu/PKUwTK1fMikQLREzlDSeX0ON3xojoDjlFHsyIrd9qRO4QrRXJP7uGPd5ZyczbYmg31jVgdlfbYHz/5tkw5RvD9lytSSZqp6ImcXDgu+ECOqaauxBVn7ZoFJTG7uC6/zJkMVK/zXjkzYtX0cOVVuk+I87/9kvjMfDMQyPB4JN8skqILnRwIgYEgwD8BB3NMvGED9RrExJkT+7+GbzfLDtH7zpgY1WBzZOfX8G/Whn3lpM29el40/9J100teGhwCDoeDFIqPP/54aVHR7WvXEvIcFRIyOFO7ZkFV/PEN10eFhrzw4UfPv/ACHzBefc01Pj7DK+cmH57ByrVm5cDb+5xPBJWTTXyu6woQaDtF0QkPKtkN4kNZLctQ4RD2dHqEh9VDWV5zczNXeZDJDCsOshghIASEwHAmIJLicL46sraRQ6DVGavLl7JipajFvhzjQaS6Nt0nhPBP3elfKJFRqJfydZuDD7kwLcb/vrXJ89OdBjqkmUN5tf/9ZmZZtZVqGF/uLbvt4jglw93tF591jd3/1IGMnFq8isF69U+vSJ0Q113JjlnJgXwxOLmxfv/mCfQgg7mRAOekKL+fXJXKS6h1J0vq7v/bAUJQSYWXW3K22Eug1vuxOya5X4Z7/7b/SJ7TU9bN41B+7fbMGiZCXYoO0dx4cdyVc6P4mxA9NKfc8vjHp/ZlGnDGFVc2bD1WtXyqU0Li78VLJoT+r7enEg29/Wj1rUviiIF1zULFFfRExWVJ4ePYMF/Xn5jkAdyBvbFtuphQ35suiV03+/R0WaX1f/ske/8Jg1PerWjYdrx62ZSRZHNjU3wpEPjfmUNnlfAtR6p+88+jLj7KgZ/G6/ZVCXe4ZcN0b8DlWD45zJ1qapQfRZzLjTbSdwafWwmBi4WwzXcmVVbgr/aaHBeApMiY3Cr5lVaXpIgOXGJwpl3j4av24qZqJ90uGBe082h1R0mx3t5koXpM2wST4nSzUwNdbyJlNLIxTk0N3HGkihsDIV45Kd+FgBAYCAL8gN14uPL9XSV+WvWCGXFp8UNQECkixG/OlBhjrbXaYKkw2t7eUpQQpp2TesY4PxDbHuox+SGL+w9NBzkJTWd46iaKnvjnP/8ZPfFH11935cUXB2gHw7va8eL4qFTXLF3qMdbjH+vX/9///R//elx33XXIYR1bntcZNsiFaKfocQYxi0vDdeHRwwGJCt+4caO7qsiwl156aUdV8dtvvy0vL3f+K9v24NJPnTo1MTGxhxP1SzPC2Hfs2FFWVjZ79uxJkyax1H4ZtvtB2HLH8JpOu5jN5q+//rqwsHDt2rWpqamdthmqk+yCe0N5sAZ21O7+GaqFybxCQAgIgUEmMBj/cgzylmQ6ITD4BLBQUZH5RJsHEAXGYHEcKzAT3muqb2QxSDnTkgLGx54W/mrq7Dll9RRr5qWYMM01i2MUPZGnhPpSyPhnV6Y99PxhfslE0yFLvSIp9uOm0In0fqrL5kUqUiN2MJLuTUwOOHjSgGylLKwv0+WXWU4WOO2Qfr6qi6eGXbsgWhkNCyE1i3+0JvlHeWYshJjmvs2uVSRFGvh4e8yZFLxlfyVSaaXBRkWOMP3ZqtDEibtKBk9L0lPH2bXC3NL6U4Wnp1s2PQxLi2s6yl4T//sv+c7piNjdn20aWZKiv8ZLqz79UxpfqrmDLdEFQTlA2iMLZ7uTrqcUXUFyJa+h68ziCSF8uZ4qB9x46HfHi82bjlRlldS1xUQ38mcusrjLK0S2NUOd894+3aWltcp4Wu9LiPSj0POZV07/P0ynxvHa7iRPeYO4NkUtoP25pmNFzkvpeuCopUQ1TzFzVLcVi3C9JAdCQAj0L4GDeabP9pYZzI650yKGRE9UthOo06QlhNQYLfwsojgYpeRnpwTyz+hofVRWViKN5eTkrFixYu7cuXFxcWgTqEs91FwGAQuy2nvvvffUU09Z6+p+dsstaxYuIIviIMzb1RSoioRdw+fp99577rnnaHbjjTf2RYolLeMnn3zChVi0aNG4ceNctseKioq9e/eWlJRwXWbMmNHVetqdRwX7xS9+oaR6RG/iVV9fX0buKCk+//zzGD/BizjFg3l/+9vfDrKkuGHDhqeffjo/P//yyy9/8MEHB1q2AwhkyIbJZgla717BpNmuXbt++tOfIhnv3r37zTffbId6CJ9yvVBjT506RbWizMxMbr/Y2NjFixenp6er1WdrDw7hCmVqISAEhMCgERBJcdBQy0SjmQBBxM9+ktPVDuPCtYsnhyaGn/4VvLahqaZNaqR9UrjfijabnqsvhUeSI7VUvEVWQ/srqnHqKf3+oFjzkgmhrmEJJZ6erMe9yO/oofpOwnNcLb/zAHWVQGnFW5ce7bd0ytlZ6MufhfjO5k0M3rS/gkYl5+5u3azInYermto0sW3HqpMjtIF+pxdzOMekRD1r1J7oidT9UFZC1LNrugmx/hdPaj9dsM5n7oTgzQcqsL+VjjSbm9bHy5fCCG0PJEVK0CjHynf3P7FPmxzcX+5wrPfzdgUjd3jx7ImjhbXkwSSRmeIJdb1Qe+amdZ1xHTB7/Zm1BfqpOv7xj32yo85Id6PF4arAcCjbxJdrzHYH3FF2u/NvM3kIASEwQARySi3ZpfXREbrEmKF0BfprvcclhZRUmguKnTb/XUerr5gblXTmH9AB2vvQDksA7PHjxzdt2oR0gllswYIFa9asQVoaJqoiYuLLL7+s9vT83Q/uXzBlymCWZOnqujhVxYuXaHy8n3rn3WeffRah6tZbb+2q8Xeet1qtr7766uHDh1G4EIbcJUW0VFS/n/zkJ9OnT+/h5aD70qVLa2pqWBV1sS0WS1cLYEwuPR7VgoIC7IpdNRvQ88h2LIApOOAxoHMxeF1d3X//93+/9dZbq1ev/vGPfzxx4sRuZkRsra93xs1woPDsi3DczUS9eAmH6W9+85usrCzW5upOmtF/+Zd/4VbU6/Wuk3IgBISAEBj1BERSHPWXWDY4xAQWTAq5c3nCpPgAlwCEhkKtZ5aFCQFtkUCzdks01jeiH3ES01lxm0WrXYM+PiWxY1CAN/kWXeOQXO/uZQnfX5rAmY56kKtZTw4wnSneTBoT05pRaM6pOKcso8nSSIpJXm2hKojhHMF0VkqQVuNFMRDMKd8crcbeqEiKOBarTHYl2d+UJH1MqMa1ErJPuoS2elvz4YLaU2eKhChtgFlU5VwAAlml8ZzpXIMM5wO1jydfNnsz21REVVaLPDdvfPBzP52trLzMYH3q01zKKXS/kQAtLljXbdh52+NFZvREAs/5BL7zFv161seLokPfsSRlQmpYuzyS/boEGUwICAEngZzy+sO5Rsqqx0bqQwelHks33PU6zfik0JLyWuzJtdYmPmEaxZJiWFjYr371q/vvv59o2S+//JL6JxiynnzySTSX22+/fcqUKT0Pue0Gaa9f2vTVV8hq6Im/ufuueZMnDwc9UdmLt0p12aJFIXr9E2+9rXgV+6Iq9ppPx44BAQFknOTfUMSmxx577KWXXurYRjnzgx/84L777uP4gw8+eOaZZwjv7arlwJ1fuXIlkdrZ2dkc4LAbuIl6MTLuTnx/P/zhD4uKim655Zbhoyd+/PHHf/zjH7GvcpV5/44fPx6La15eHprsO++8k5yczJu3F/uVLkJACAiBEUpAJMUReuFk2cOOgLeK33ZOiyOE9GIwZIkB/qoV08InxevOvOJcNvVJ0Lk4wHhFWeeTRc6iz+4PxByCfznDLyuuOrzuDfp4TJ47f42qnZDD4jsJTD3/mczWJlPD6fotJ4vqcsjMeO5M7I4oH+fuxrSilLnPQIXni6eHf7SjhDySZJOsNNmjgrC3jSUrZXWdszALjxnJ+rjQsyFXlCjB9am8lFlodtaf6Wq61vbTKb2G+XetRoXMCiirrTm3zJJX0ZAY7swjSamWiXH+yuKp3IJ58zs3Qgz1uTW02/fg0uzMrNmXZVD0xPRY/8vnR01N0Af4OtNUUY+FmkIvbMhr3w0ZmiB3rVdtWyUi5PKeq5HB/j567WnDKc7cKcn6mOCzSrf7RIih0cFno93dX5JjISAE+k7gaKGZ3B3Bel/qO5/7Q7TvY5/3CBjn46L0aYmhx09V1Dc4dh6vueGiWE0XhafOe/Rh1gHvG+Y4hAmid6+88krsil+0PT766KPPP/8c0+JNN920bNkyl3VuMJd/ZP/+Pz72WEV5+f978F/mTprkPSiJ9nq+QT6XnTFu3A0rV/z1jTedeRVbW2+77baedx+4looKzJXtXgXjVaXBEKrGkZGR1M7md1ZuwiFcRqfXAoChoaEEPnNluw+R7rT7wJ3EXIlwbDAY/u3f/g1DMZ5EAP7P//zPu+++iy5MYkrU5OEGc+BoyMhCQAgIAZEU5R4QAv1AIFjv88fvTY4PPS15PLsh78t95URskZcwt7zeUBcYojtbkRBdx1W8AuVRqXHcxSLGeg/AH1EqlbPGdBcz9vU0CqDqTCq979qds2J1u/nWzoigKA2SIr9B7jxZkxSpJcb5wCljjdkpwgb4qWJDfKkT4urFXD2eDptbv6imrskH4yA+zDcu1LfG5FRUSdC55WhVYng8x4BzBWE5k271x1pIW1ZhtFvbdN5pKYF3rIgn26aXM6OXc3gqdxO03uk81DIP1WsUSbGg3NKuxApdqGrZqc5IlLRSfpo2lJOenhRw+ezITqdgu+66fBdt5LQQEAK9IWCocxzPM1cY7NMmBCMp9maI/u4TqFNPGxdRUmGuNVtJ1YqJkgpO/T3JMBqPH7OIJn5+fjNnzqRKxt13342uiGlxz549+/fvDwoKWrduHeJFWlpax5R8A7SN7IyM//yv/8rNzX34+3fOnjCBWOMBmqgvw6q9vS+aOrWsuvof76//+9//XlVV9bOf/awvA3bfF+WIYiZvv/02ghEiL4IX6hI1VaKiorrv2JdXka5IKXjw4MGUlJSrr75auQGIIMbQykm0LSKI243/zTffcNuQoLO2tpZe3FFz5syJiIhQhDl+v8IXeezYMfdeRGFjCfT3P/1RJS8Bk3G4ASZMmIARj7uR2HAitXHOXn/99Yjgru6c/OUvf0kANVgYAZseo5GW0T2r4GeffUZ3wsBZMx2Rzv/xj38AUBmEA+L9J0+ezFOj0bh9+3Yau8Zn2SzgiiuucJ1xHXBFNm/eTBjyyZMnSc7I1NgteZu4JHjowerAgQPsd/78+QS2b926lQSIOAp5r9E4MTHRNRoH7AVTJBtnhWRLpDubCgwMRDdkjwqfhISE//3f/wUjo/GeVX5H4lin05FJkyXxkvuYcjxMCCD1EnSPLRcpn+ShXFbl2mE45Rbi/MKFC2NiYobJamUZQmAEETj7l/kIWrQsVQgMNwL8m0QlDQJLlYVdNjsiq8ickVfLLxWbj1RNiAtwTymI5yvkjDQTHaqZPzGEFHed7oj41snd/hFFJcJe/NqCnxJ5qNMZ+36SgsKuRIeB/t4TEwNQxDod1sNjTFhAe0saNYhDAn2IDWdnO47XIDDBtrSt/jWDTE0OJOrZXT/D44YspYwfpGM6fWzI2bBo93mZLkLf+UvuzYbb8ZQE3bREfUauCZ3OVNf41cGK8ED1qmlh3xnC3IuNNNidRWyUjmpvp5XVvZYLlVKwi3Y6LGJfbJgmu81va7E6dmfVkNTSvW9uuUWJ5W/XnV3otE4bpsXaVF5j23G0moo6VChq10yeCgEhMKAETpaacyst+gBNVLhOox4WyhGflOj81SR2RFK0OZqzR7uk6Lq+6BfEeyKOYE686qqr0F9QjlBzyGb4xhtvzJo1izx9S5YsCQ8Pp6WrV78f1JWX/eUvfzl+8iTllZdMn+Hv2/m/4/0+by8GDAoIWLNgQZXB+MaXXxKRiiKA4NWLcXrSBdGB9HmUNKExv5wgTJD+EqMfMdc8FHmiJ+OcVxskNhQuRECuO8qyIimS/3Hfvn3cEtHR0e6SIhri73//+6+++ooGqGPIW1SYYZFobcTXo/QhzyF4odl9+umn7sugRDWyo7ukyLzkkaQlkiL3Gxq3knURURUC2EI5qYyA9EYmUJQ7nsKEKZgRtY6Y5fh454egPBD+qIHD1MogxcXFpI9UTJq8igjIRhRJEdmRNRNB3NbP+Q1pklDijpIilV4ILWdhCHmsH/7MQkfmVQQj+pKEsbS0FBmRZlgItVot7yYasxI2snPnTpyGSK7KXEzNq48++iiLpA30ePAS60Q6fOihh5RmPGXBynnloiO/8lble0hISHBwsCLdKo3l+/AhgKRITgneIyyJO4Gbiu8cc2O88sorpDTldhJJcfhcL1nJCCIgkuIIuliy1BFDIC3KPyXaP6fMQqFhAngz8msnJ+ioqqxsIEDr5TItUn+DWpYzk7vMhe+uy7j2T7ELBLIxzWNwQSoZBl0v9eiAvHTuslyP+vS0kc5XRTlppTVmTIK+r13Q5Sd+HXUxfItLp4a9Xl2IAlVUYamqtWeV1dfUNSqf+M4kMDboHFkQGTfA97R1jukQ4K46U/G544o7TtexzXA7Q9HrWemBGQW1+04a+OA7v6z+mU+yD+eaLp8TiSsWIRtNmTSRHY2BvdgIwdTaMwHUp0rqD2Sb0qP8uAPRrfMrLa9vLdp1rEYZ1tHcUmq0Er8foPVGT6TNVXOjNu+v4FWu1OPvZWm8PeemBXGep9R7efHL/K6ygl40MYRwy82HKtnF/lNGCkkvnxG+bGpYhF6tXC+bowU1E4nZ5QLuxdakixAQAt0QqDY3YlIODfQNDfIdsH8cupm/85d81ar4SP3xrAp7Y8vJknNqwXfeYRSd5d9oVEUeF110EV4qMu4h4uDzwrSI2kJ6PtQfDDVUIsZo0+/7brFan3/mmZ379qXHx924cmVESPDA/c7Q98XjpI8JD790wYLM/LzDp7L/+c9/Igpgeev7yJ2OgOnvhhtuQFskfR5GNqpCU0KE6i4Ii8uXL++0Sx9PomqhcCER8t1lf+MAzYuT7jVVaIlignKHNIawhesQ8Qt3HnZFfHm8hKqIFsbVpAI1DVgYLjykyby8PEVlc1+qMi8NuOUQ9XBikmyRxgT2MuDrr79OSLKr/TXXXAMHND6YMBryIpH7OBlRWhXl8eKLL+YpDRDycBQiNaKPu6yOKHFJSUnKaIh38+bN4+bnKeoPtz3bZHmuuVwHH374IckBuBCsDQciuyYPKUolcieV05XtswuEJAUgAiKCLLcHjkKKNVPnGi8nnlNFUqQlOSX/8z//Ey0VkyO3ENcayQlcLJuOLucjC3AX9AFC1DNqJrOg+OPidK1QDoYVAd413CRKzR/eJtwziqTIHcIl5jwq/LBasCxGCIwUAiIpjpQrJescSQTQxVZMC8sqriNVIkLJtqNVE+N1y6eeDhIJ1HqHBZxWwQoqLBv2ladH+Ue6FUv5zq1GBGootEHGRvTE7DJLSpSfn1ss8Hd2H9AGxMn6+6q0vl4WClub7d+eNExL0k9PPI/idyunh3+wvQRJsbm5FVsc2QORmVhzWKA6NtTX1+ecn1pMp9N6KTa3mlrndFMT9VMTRpXNbWKsbsH4YDQ+ai4jHVYa7V9+W7b7WDWCHSoqVtZ6axPaq+uycld8sr/8ja8LlDOuksoZOaYfPnlA0enmTAi5aXFM9Ln6LMkWUYQJDyeVp7Gu8bU1BwMuAABAAElEQVSv878+WK7z825sbGbS2gbnVQjRq6tNttp6B+N/vKMkVO/zP3dP8VerJsbpxicGnMh3OnORgB9764RyG+CTYHkkWFTSg7oW6TpICNMunBCSXVZfVNHALU3B2TKDjRuArfE+Qs2kI9/TY/x/f8dpH4GrrxwIASHQLwRKqq2GOntCXCi5FPtlwH4ZxFvlGRHqFxTkW2uyniwcQEkR2QJxAV+V4q7iu/JQznDMgbuC0C+76+EgCBk8CKjEooVJDXEH4xhhsO+//z6uK3QTAm/xduFBw9vIH8YsFZmmjwVnd2/a9N6GLz09PH547bVJMdEc9HC1Q9WMFU5MSrx++YrcklJchC+++CLilyKZndeSXnvtNfC6lCMMbhiX3EdAWUPMBTjyE+oD8gQqFZU6cDmhoA2QpOi+gG6OWRJ7J64TZYQwYax2yHZIiiySEGMkRTJ1onsyAhrZihUruJ04RhxENEQE7GpkVBhuP4yf5Ppk+0xx1113oaiibru6oGtTaoYF8OAluFEN+ejRo6g2l1xyiSIpcoAIjp2Q6ZAUCTdGhRw3bpwyCO8vF3amo7EiCmNm5J7HZuiay3WAzvj8889jDGR8Nqs4HNevX4/gTi+ES8TTdgHpvJWUjaBXMiylzDMzM7nEIEJaYuVcR/RErJrf+973EENZEgCZkX0Bje27ZncdsCMKPWMLBfull16KudhlzHS1kYNhSAD1mR/7/LTkZ+YwXJ4sSQiMLALn/HE+spYuqxUCw5kAJZ6To/zQR5yVRmqsBEFPSQgIC3AaFTFwTUwIoBLFkRzKGLfszqyhbPHKWeEXTQgJ0nrjEMF3Ri+Kk/DLTHiH0GBGiA3VILhwgHzz6qYCgsJw9oUH+KAWNTa1UlKZA8KrO2YqHBxi05P07AWpFE3waL75rx+cWjotbOmUsMjA074zR3MrvjP0o4SwTv58ReeKi9AaLQ5CdjYdqjTVOahMwsqnJeujgtQdHTQzUwJR3DYeqEBuO5Jb+7gy3eTQiDPTwYTqLghtI9Tmxg2zdnYkUeTv7Sg+motm19pgb+YLJm00xnLG/coiwBXXWAsqG9xPckxQs8sqGB9lczSd04sGeD0WTw49VVq/81g1Mh41pusampAscUdyKUlkyZUlc+WWgzaEclM9pb0d9qaWprZQaULpf3x5yn+8nGE0Oy2lSJzVZrLVjGUcno6L12HXpdZQ+ynHOPNpYlTkHn5ve3FemYWJaKZUJVKuNd1J5ein9iS/pmfHy99uk/JUCAiB8yRQbrSVVDe0tI7V+Hx3UfjzHLtPzXm7U58qPjLwoKHBUNtYVG3tKq9FX6YhZhMxCFsTkkH3jzNKY3vBUXmOJkL3ditBiUB4avfAb4X2x0nloF2Xrp4qP+ex0iBhYAFjBCQMRZ1BW0T7YAG0YQ1E891xxx0kzutqqO7PW8zmvzz3XGll5c9uuXlKaurwTKHYcQu+avXcyZOuXbbs+Q8/RD7jmt55550dm3V/BikKx5nrOnL54OzehZec3tE2Ax2vcgUxOq1atQqFF20Ow2CnqpP7CAN3zHpQ8bg9mOLee+9FYlMWg7RKZDR3DuqJ4smiAQfKMfodQlv3q2IEzHfo1zTjHsM5S5gz6edcvbj3XG5ZnHokRkQuxC2oKHQwpAGSNw8AKtIhk6Lcdap9o+IpjRmfW533l2si9wOuFNolu0bIY7PsjlcJDEfdQ4JEKEQcbCcpsgWskUh+LEPR4vFUIk3yQwAaSEskLWUQtEXci7RkWNe+3Kd2PybOmtQEYEespDoQWnZXC3bvJcdDS4C3Bu9WPj9Ad3Yl9Oy4JO4xbLlHjhzhXuJOwH9Kvaxe/2jtOL6cEQKjhkDnP6ZHzfZkI0JgqAj4qDwWTwrBqHii0IxKQiFdjIr471gPf3RMJLvi1DB8Z1ZbE9oQcayFlZZ3thbhO/Py8kBKowviy5wJwQ+sSe64hUXjQ97bVlxrcWo35Qbr65sKPttTxoy0RMBBWZs/IfiKedH89ZVTbvloTynaJS8hAPEqB5jdPthevCOjimOsZCtnRVw2q5O/PWj802cPmxvOCTbJr7C0jTEmu9j86OvHXTkZx8fr1s2LGhftTOyN4olIdLK4Lr/cgjZKFeYKg+2LveWsEP2I7kiKJPiJCNL8712dhIeghy6ZHJpdXFfb1EIWRUWTYtgZyYGohBy0eyAULmS60vrCcguqGVksYfL5/2fvPOCjqtL+H9ImM5lkkknvISH0JkiR3sSCncVVWQvqru11m/ruu+uu+3fVffVd111dV7Gsil1ABQERqYoivRNKCAnpvU6SmTT+38mF6zBpkz5JnvuJeObeU3/n3Dtzfvf3PE8DIBeaq6+tPxcZpHvuLqvn79544KES74RMKK45vz1WQLACJVh2w1xY55RFpfNyZxZGxRj4DOXX6jCbzDE4XH/73Bii93x/rJBAq6yB+gbmET5x9tjg2aODtxzOa7Jm6Ej8fj5/95hXvzpzMKkYApe+sYzhKAZF6pfMi/3om7SDySWcaVzcT+dxxbhQ3AVsOZS3+VAealMmnWzKSrMmXFwgQFE7KrGnG9cgZwQBQaDdCKTmV+YUW5AEenp2oW++9nVP5+WZEGM8cCwThXZmUWVXUIpYWcIKYfCIs8KWOwkNYXfAfXBAkbBB5V81Ae2i0IhQThyQLGqCNAwLGdSj5UZbvkorcC4cajY4FHa8CjOinmxTAneEJ5KTJwwfNm/CBF8ndqFoNyimJsTff8YlYzft3pWRl489LKo6hfuzy9nCRwzJYZrUUpBiaPHgxdQizB1cFYQaEidMnplEKC0IKRLwUIjdepBSpA/QmkpXocNYjUoaZFgSXCWhDqRNCbg/VU6oyGAZte2qY+w4K9y8eTPWxKxwGlJ4OtYnmPAvlGKbWnQkM5wm3SAnkkyFHiWNYhEClE5iDQ3nblcPJs+oIxUcmCki25ABZBTimE6SAZNw3EfiGPFPf/oTA8dEGjCJjMSd1SSAkI+0haU2nk9BSSRvdpg750ckuuhYUdGi/sbovslOQibiRYGwLSwk1jAzi/tRFgPvKvBJ2mQROSkI9FsEhFLst1MvA+9yBMbF+WGSnJyFzaeVGjucUooJsCJU9Na4wZqVVdV+8UMWZqRkQJPIH32CmuH3DRwKdJj3BaeEdn0N8dMsnB6x7OtU4iCTlX/546eiytZgI1xcUc3uC8UiSsnENOtba/WAc0REppjK+vl4jk1o2hMTP9UoaEcpqpXAhEIvqh+1RNgwn3dBgtBlQoLRZK5d+V1GcqaJ5rBcVoyXG36QNQzPxaWiyrqvalJ3NnNk0Mdb01DJqQxURJAOphLHgmqLagLB5qQhxgqLVeZ2JquhubLqooYI0bbNIQhtrjm1KmdOwN4Oi/QN89deOS4UTR/mzMwg0ZmJo0KAGkKpQN7ptW647CR08s1TImcPb/pHkjJGgqIgGm08XkhtTJhD/TS4pERmiHs1yD18LIb4eWGbj1k0pN6EQUbmgVnmT6txUwNwU3ZYtO9jNw5GY5hbZuFfXD0GGTSUUsrie5E88MJ2Zv5sc4j7TLuoUK8cH1plqaXdgvLq6tp6QoJDlQb4eAb6aHA82rjDckYQ6BEEkPywe+dAZQMHYSdzgD9ia42FIHvUuXPnorjpkU462GhKbgVCRU8Pd5TtDhbptmwo//x8vQIDvGstNYXlF73f6qw+ICmCZcBsk3gO7aiTbxkOhVtkupUE9UB2WL/KGw41rST4lw0qi4R/1cPuI+c5A9OhUJBKNts8pDnQzkBnoNWCwaFaesKOF3ajVd1ZCyP9Yu1alrfVhWJAAMNpIaezXQL/6NCwmePGL1u7FntYqEAkY23qJOhBMajSObwQwlvZUorIALGqxtgZGhEejcob5v88VccUtKm5jmRmZSlEmFoJZxSJImcaP3Pop5qzrQnuEZWzo6yyKmwHi8gXnSYMLAQi3VDyKAmyKYm2NtpqftpS+sBg1YVKgo+sBNYwN4hdJVxSB6LctrYZOINhODFYCEeD1JHHO1f5F3oRp4233XYb0kuVqFUKMjTcJnCTQiZCPgqfaIunM6dR2mL1nJKSwuQq8lu73iJ0ZdJZBogZUTLiVZPQ59z+3Pi8cqBIO/wq2DUhHwWBvoSA7ND60mzKWHoMAaxBCXli1zye6SYPCziZXp6UWY4/uP3JxZfE+xF3QsmGnfLCyyKGRui3Hs4jtLGpokHW1SAzVDJA5Sjmn3bV8hEd3xWXhPpo3D/7IetkehkWxJxU+UTSpZU1FQ3GwlRiVYu1eDR3mfOO/xAkZjVWrmo7qOoYKfrB7ccKNh+06s4U3ZxthWCCXS051VJqItjXc3C0Dy75EDkqJ8cn+MFqNfeTGPeUuKqMDdJ9e7QAGZ2NzO3HwaH97O0yN1jmQF9P/sAEotbCfrP+HIsBUtVqr/gj/C5wc/ypeLYpoXF3DTdqIf4QBkLqgSDLm5PML0spIUwfF4K3civ3zYTzn02znHSJaTBmH1xrdfRJr+AQlRzDo30oTiVW40DbMhc6x+jgH/kjG+FfoIBZumSEU9C4u3H1Qkb5vyDQ8wiwx0astHTpUjaoiFPwumXbJ3YdL7zwAntRuCqMIm0vOVua2w2nBLyDCQvx8vR0xt+E7u5uRoMuO6eEb4QuQo/ZhErg6KL6G1er8CyN/+Ur0vak3Uf1EprKgwcP7t+/H8ILRoNsOPhDd5OQkECMC5RWjVt0/MyppKTQ4OCR8fGevdDFmEHvPSw2lsGClW3cEgeHj2QVpkDVeGLqaEvOQlOiT4RlQLz2k5/8ZOzYsWTmZuckcqcmm1C+7RTysckMyknrt+KAAVBgcMctZLO9BJ9oqxPkEjWo7zZQUNJ5mDXbIh1JU3mTxVl7mIUSqoWlSAAWvCXCtnBDHTp0iGjU8N12pdR6wAQmzu5qmz4yQIVJhEhV2VXmXeH4mCM7+o/KaV0lH5tsi+mGT8SGGlkiDClvhmCRMI8Fz3//+98YveK01LYGKvzlL3+5ePFiKCcV/CZrlpNOhQCvDXjbxxLldoZebNw35MkokdEn8g2OHwnCkfONT37kz7CQvFRo3yuoxg3JGUGgbyDgjD8f+wayMor+gMDNUyNnjwjktbTG0y3E/7yNie3AJyb4hxk0uErkpKeHKzo79SoUENzQ5CEBA0O9F0wIx44VXSFbJoUkIghJkK+n0adZBzcwcegc48L0qP8QmMAhUhCGB4UamrLwAN2gMG/awo71oQWDymbZv6pVu+HhPiA04MdeqedJwAX99a5RUFe2J5tLE+UZy2L1Kj8+kbMRKSUyUDtjZBCRrwGhsNwCRcUvMK2nK0Mz6jWq3bRaUElAPP18/sCbJkf8qFIM1KLNtMumfqQ5AEEESnOzRivNWWVusFpqcwE+GpwSqkV6ewKKDZ6t60YBEajx4O8iiQo4N7Tb9NbCtjNQkPzZnoFetP3YQppWICL5ayGPXBIEehYB1CgQN5gTYjylbE5UQgoiA1M4ztNDXGs5uW6Fbw7Ezrzg4WnJvz2LapOtEzDK18crM+scMucmM/TGkwor0VbGJz09HS+BkDWQOIojORzhERKEyLbwIKxAPsLmdBAQuJ7Kioqz+QUhRiMBazpYWzcXN1dX55UUA6+fwQCh37mtI1zC9R5EHvbROCuEeIK0wv0iBpLNNcSMcEmh/3hQ8IOkyZwY4fKggA5DbQrRZstjkp85Vc6gy4PngtXiJGQHOmjb2qhcHTKawUceeaSxVtE2f6ekoRRVG/Cf//znrENoWfBHOdvkUmSkylhYzBwdMSBlwSt3EKapRJuhXUaElAwHoyCJNasqSGzTSOk2D3YOGF44xIKCArhC4vYw0ZBK+GG0m50xY8awJJpkMNvUrmTuTgS4WXDBSTBxvqx5+df4xlTi9sAk8nRlGTDprDeeuiw2Xi3wHBBKsTvnS9pyfgR62W8F5wdUetivEIgP9eavhSFDqxlajD4MZRMdqOMPQRayLHO1VZkFmwNxA//Fvy1UjgpyaKRV+VVVXUdZftghlEQCBncJHQNlSVlrB2KakAG2UK16iZ++E4dYPVW3+6D/SAv5o2uQifSTXlIbW0Svhk62UPOgMP2gNkpGaA5tHX+2zYECzYGzUFQtoC2XBAFBoE0IsANhYz979my202zst27dilmcUgOqJYKuwstgEM2uo/FepU0NdXVmvjtwbksrVZYas6XZl09d3Y0W6kenbNBr+PIo6hrD5xaadpJLKKSQSkEjoo5hT8uGFiYFNSJkIoa6yKPw8taJ5BEhKVDovLNmTXTQkoigoPM6cyfBosVu1NXXn8nMXLf9O8i76TNmKHReiyXadpFfWXBVlFHs0GEZYK/wu4eKjZNQgVBOTA3qOeWu519c+ynpjz/+mIDLXFKmD2bK1usiTv0UB46EIcZYGyta3kxAL0JmcXCJEdEEHBzGmNBnCPFQS9Gc0hkSNAS/yXpgMUB9Ep2G+hFY8ZEuURVSPvgReDf6w9OJQ1H2QYcpakFapFqYTfJTFQeVO3KoCkFsjWkUlFiueOQEHIrDgXJAxygVUj8gQPYxFqKagIMiqqV1yqL6pDhcHuSp0jRdUpSbdBIzf07Sf/g7OsmQR44cSfTz7du3M15UuuR89913YRXJxuOXDEolDv7LQGgC6hCZKj1USd4TJ05Yf2BjseHhocymWiHde+ONNzCQx9sAkWEYpnpJEk6OAPz7+PHjiW2F5BB3nLa9ZTVyv3CPcPugulVIZBYwdxBn4BPR5Nrml7QgIAgIpShrQBBwCgT41Q6H2A4ZHcQfWr/m5H5OMTbrT0AXSD07yVvX9a2bm+u6gUjNgoAg4LQIsDkfN27clClT4BSwfGSfz+afrQjUD7wAFM+iRYs4Y9t/dv5cxTUb2bjENoZ9rx0ZxHaaPS2bWHgBhEjkRPJDWzBHGLcq8QRs6+xgmpc9ijjRbK4xX/CH28E6O7c474T8fBQ3Dg5J5ju39R6sDe4JpQzbV3QxMImKDSlMIralLDyIGBgTVpGdZqrjHb7nnnuw99xz9Og76758cOFNAQ06u45X2w01pOXkfPL1xvS8vEsnTODu6/QWQRtOgWrRij7xxBPQfFB1EGfcntzFzNdzzz0XGxv79NNPK/QZ9BMcH0wZN/Knn34KI0wa3g3S9t5770UHp/aQ9xN8hCZD8Eg9UFqwYzSHUwWYMhri9odu49LKlSsh0SA7IL94eqCx4h3GX/7yF3Jec801VIJ88qmnnmK1QGJCDcO+8QChNp4kxH3mGcKCQUP99ddfUxsdgLxTnAbydoRS9Bx9H1wkIZ7V7rWQYIyEwVWItpdeeonu0Rw1wwACC08zztDcgw8+SNMMkJysXs7Qc6hY/NkpzC9sHXIwLIgpCAhvvfWW0igIQ6lzkrvg0Ucf5SQ1EDgFooeukp+5QBFJGA0ewhC+J0+ehOaDKkJZpvCwLXTe7hKo0jR+IeFw6Q8DUeSl6IKhFFEjMk0MwbYUD2ra5SblcU0GoRRtwXHyNDcC65wva8IKcZsobwuUPrPeWLqkua8Vrl85z/0L18zdxLIkPyvEycco3RMEug0BoRS7DWppSBAQBAQBQUAQEAT6CALsbJE5wCSyJ2EfS8BT3Kuxz//888/ZveNn7aqrrrIdKkpGPDHBKpKH7Qr7bSghhGY33XQTFpRKTrb3qHvw7oQIQlFJkJN9C5sfHD+xde98SrEGStFK1RGIGM8UiucN2273eBrFvTeydv7VNusJpMc72VkdYJsKHQNPwQHnApnCwRqAUYIigYuB0mLZcHQ6k6gOgfjXSG4hVr7cvj3I3++nc+f6+/ioV502kZGXt3zjpu0HD0bHxNx1113I3xzvKvcyR6v54dq4Ybk3Yd8gxbgxIa2Ai5v9tddew0KWRwHUkqrao06YLzg+CC/mEbGe0gT3PpyFbXPQhRCCMHqKVbtyCbkiOmjSEBlQk5Ckq1atonXqQSrFJfgOFglPG2RW9IGcEF5o5WgX345I5yDIFHmdUiGRZ8jGyuGlBf23DTtDBmrmIEGfUf8pRZr7lyaUFUiC59Ldd99N3xQtLed5MOLzAeUgw4Ge5uApp/YEY+dbb72VrpIfolBpAgqPhxsUIbcAA+QZaNc0wHJwkhZ/85vfUBsJ3uhg371ixQpIPUWzCQHEo/KnP/0pfKsd/WdXoe1HquIjk0IlapfUDMwX7vZwmMt9Z8ci0WeYTYozg7bck1pWEs6MADQ3q52biDsFal7tKnPKguQjNxdEs3qeFQKZyCWWhLJm1EuSEAT6OQJCKfbzBSDDFwQEAUFAEBAEBIH2IIBgge0xqjHkNqh+SLCNZ3vPfoPYoIiM1ErZqb7yyivE02C/jYIGLgBzaSJsoD5D2AJPQVVkxoIPbRFKRkQTyJ2gGtm9sHlGFoEqjVJqhZ2VqMOWs8Gmj1aqzLUWS61O205fGZ3VJbt6CBEF1YmQn9D2dpf6zEdIKOgklgRMIqsCSoWPLBV4MTgg1hgUEuuEo0n/dJ2LA8sMRgZGeznH1xtdXQb8ZM5so3NrFTNycz/ZuHH9jh2BISEIAGGa2oQJd99DDz2E2A20lTtRKc5Nt2TJEnyuoSuEQYBo4OaFRULchDgRdgmOifsUOokbFvYBDgvNncK1KTUwX9BnqNu40yEjyED9MG5qBBglG+d5emARDPsG8tBq8FNIDmlX7Qk8L0yfwksSXBiWmTcQPCJogo6pJCA133zzzZhOww/SSUSR3Np0iXbhJZW+QYPSFtpJpXK7fxkLxTlJB+DRkEkyTDUP9aCIpGMqg8aQiV+BRk8ZIx1goZKBUpyhIKMDWJWMg4fliQeVw1MRRSGwkIdVpzRKDwH52WefVVu0SzAR3A4KoUNX6Qx4YkbNqxqgYHaoB3DUpyWjIBtk5fTp05kI0kqF5OStj/KYVaaDEUEdIvnEGJxHLtnoDPUwQAoiKldoJtv+UJyVAzlLZCS6YXtJ0s6PAHcBq4IHL9+5yu2p9Jl5Z3lA5aOi5eB5wurl1Q73FB+5BfhyF0rR+edXetidCBDBs38ZknQnuNKWICAICAKCgCAgCPRhBNjV44yJXSjbTnatyBXRB7F1/9e//oWmSR34888/j44JjcNvf/tb9ifsVdgDv/jiizAIbGsR3bAH5vfYk08+CaXIJhY9EYaH7GrYJHMeyon64RHYnKt1dkqitKLmhc9PfbXXqp8akRAydXx0gF/nE5cd6aqluu7Y6bxd+1MeuC7hhol90FUZvA8KL+hmRFgoZZh0FgMGzhwQRuxdIVBUOqYjSLapLHTP22+/jWZW6+5+87y5C+fMCTAY2lRDt2VOz8lZvmnz2u+2+xgDsNomVq9KG3VRH7gfFe0SJBTMAgwgdAMJbk9mqkmuAcKC25+r3Pvc1E3mobfUAzWpkIDwGnbzTkNUohCOZKYbHEqjdjmVgasaK4V25N8uAkRtjv7z+FIeU6QZCB3jYLyNh8xAgJERcYnBKnK/9vWQqkCYhqiEf9tXCaXosGIkDrB8VHBrzCTa1k/TjJScnf5wtm1F0p2CAOsNZSKP2ZdffhkLA25GvrJ/9atf4WKC+vnIlzKvEEhjbcBbwNOnT/NFDGMOp0waufHGjRuRDxPmG1lxp3RJKhEE+gYCXfvt0jcwklEIAoKAICAICAKCgCDQGAEEC9j9oTDCoxZRWVAYwf0hnLHlEymFDSDbTpQ7GEcrEhvs8rCwQ8WDKg15FCwS2RR2gD0tm1g0Pl2kTLQdhV7r7qf3NBo0ZksdEVqqLFZtjlMdGGMXlVZh/hxscDRehFP1v9XOIIlC2coaQAY1ePBgmETmHWmVKrNqtYauyIBAEvNhugHH/emWrQNcBtwwe1ZQQ5CQrmiu3XXiP3HF5s1f794VGhWNuBKCvqv5RLrKfWo7OwqT1fIQIB85Ws7DVW58juay0RCsh3oVGotD/dg4QWbb/I0zdO4Zu+ZaGIjSLsNxEJZW+0lVPIpbzdZqBsjNhhlodgoa1+DI7DcuJWecBAG0pXyD84bPTrSLownUspzna5qvdYS3fFnDPCrf+Fx1kv5LNwQBJ0Ggpa8iJ+midEMQEAQEAUFAEBAEBAHnRAD3dugaiJlApFE0MoT+xJmXbVexPWQ3gv4Fiz9cmKnMAhsVdrD4zlOME0ljPEiwF6xfN2zYwGYG8hFlInZ8yCIw0bKts7PSUHVGX09fbw9CP5eWm/mLDPHtrMo7pZ7qmrqcgnKNh1t8aCdQBp3Spc6thFWBmzxWDkKY7jFtdrD/GHVC0mHV+84773y+bduAAS7Xz5wZ1DXr0MEu2WZDR5aanb36m2++3rU7cmAcSiJuPQh92zySFgQEAUFARYAvWTWtJJDHYoYPb2hHKfLly6tBXu8RlxwfqWSGO+Y9H4bSvBdU3R/b1SYfBYF+i4BQiv126mXggoAgIAgIAoKAINBRBNAr4cgM92Tr1q2DHrr22mthYWwrVfcq2Fi98cYb6q4G8ysusWnhUPKjfYDEwbQKNcSXX36JGAonXyjXkEtgfgXhyK7GtuZOSQf4eroNcDH4eOEIp7C4EgrP06OTzavb3U/CxcByFhZVRgVpQ/qoShFLZ3zztRuiLi0IkY0NPnQn6/azbd/Q1nUzZgQbjV3aqCOVwyceTU5GPrk7MTFy4EDCgxAvuFVZnCM1Sx5BQBDoqwig6n3ggQewkedbVVX48vX685//nABKnFHMBRg+X7U4MOELHVe2hDPCYhoRLm/4iJGF30z1S7yvAiXjEgTaioBQim1FTPILAoKAICAICAKCgCDwIwIQf2w8oBSximpsEqXaHiJUxMwKWcSPJRtSmJcqZxCpEdcFA9gjR47g8p9NTkpKCvEo+UjwAZQRKCLtynb8o9HHU6dxM/jpamrqC4orS8rNwUZn0QNWV9dl55fX1dYFG1s3Gu04FFJDYwRYrohwz9XXv/Wf/3zewCpeO2NGSI+yivCJR5KTP1j/1Y4jR4YNH75E+MTG0yZnBAFBoBECEIX333+/3WleARL7yO4kH3mlh30AB3wiB09C9au8cWY5Iwj0cwSEUuznC0CGLwgIAk6NgMlcm1NsLiivrrLUaTxcB4Z4h/n/6ErJqbve4c6Zq+vO5ledza/08nTVe7njSY2xY6fZ4YqlAkGgkxFgo6JsNtSEbQM4dyPIBsE3EEFg4oqXOvYqthlgElXVA5o1BI+TJk3CLJr4knhaXL169a5du44ePYp7+K6gFAN9NL5aj8p6Ij9bvRYWllQ6D6Voqa7NzCv30riNjHXS2CC289hX0+ylr7r66sqKijfffBPHhQyzB1nFSrP5cFLS8k2bdh1LhE+8a8kS0Sf21YUn4xIEnAEBZNocztAT6YMg4LQICKXotFMjHRMEBIF+jUBRefXx9PKTGeVnck2wiuVVtUEGzY1TIvoPpVhhqdt1qmjVjkytl5uft0dUgHZQmH5IlO+IKB8CSPbrxSGD71UIQBfOnz///fffx6Pi3r17YRiJv4GwkfN4UYSLVOhIxoRfReKfYkkNicMBgUjsDsWLIsbRCCW6Ytwhfpq4UO9dp0tdBsCIDsD2uaa23sO98y2s29p5rJ6RTOYVlBt9NNOG9ryxbVv735fysxqvuOqq4qKijz/5pIFVHHDtjOndr1WET/zhyNGPNny1/8TJwUOGoC2aOWuW2Dv3pZUmYxEEBAFBQBDodQgIpdjrpkw63JcRqK07V1BuySu1IEkjHWb0igtxFgO0bsA9t8SSlG1yc3Xx8nQz6j1D/LzQ5fVD6uici0tytmn7sYItB/OSs0zsq1XwzdX1atqpEnhhyyisPJhc6uJi7e3kYQEBPp6ujTxht6nPFK+vr8/Mr1RK7XVxIUTD+CH+V4wLmTkySOt5kc6rTTVLZkGgmxHAQyJKQ3wpfvDBB+gNcdikxH0uLS0lWvTYsWOhETHnJGw0sVngHPkI4Wg2m3GqCAsJaYKSEUdOXdFtg85j/GDj4dTSWlePQD/3vMKKkjJzkFHXFW21qc4qc82Z9OJqS+2Q4UGDQnu+P23qfN/LDAnOMq63WJZ/9tnK81rFbmUVKy2WnUePvrtu3YETJ+Li42+++eZp4j+x760zGZEgIAgIAoJAb0NAKMXeNmPS3z6KANE2z+ZVHk8vO5NTkZxrKquoramrn3dJSL+iFE9lmV5Zl+w64JyPziMyQJsQph8Urh8S6ePt5d6viMXknIqPt2d8cyivrKJGWe8ohgzeHvHh+vCAH62eWTMZhVUIGMkD+2bwdh8eZR/s8lh6WWlFDXxfRIAWSN2JwtA1BzaTh1JLn1txAutJWvjn/WPRFbp2rDnsnaODdDGh3gg2KypracJSU/fDsYITaWVV1XVXjw+Dce6a0UitgkD7EVBNmG2rwD3ivffeu2bNGrwi7tixg7DO6lWUgTCMcIj4jN++fTt51EtKgiC2+I+//PLLiQhsd6mzPg6J0I+I8T2cVmWqqik1WdKyS/x8vXpWqIhSMjvfdDqt0OirmTE6uLNGKvV0BIGA4OBbbrnFpa5u+apVKzZv4gVSt1lAV8EnHjnyztq1B06c5Ea44447rrn2Wonv3JHZlLKCgCAgCAgCgkCnICCUYqfAKJUIAh1CoLSyZs+p4k0Hc787WsA+SqkLpV7JBUapQ7V3TWEYz8SzZQVl1VQf5KfBx5+PtqPPEw/3ATkFlZWWOuo84FLMvyMGGq4cFzpjZCB+9PqJrSuLYe3uLJVP9Na6g21cmD48UDssymdQqF6dz5KK6g0HcjYfyOMMHgbjw/SP/3QoPgfVDCQ++jb9ZHo5NN+CSWE3T43Uu1101TZnx9Msibq6cxB/VGWrrGx3zTCGI2IMt8+NSc2tzCqoTM2tOJNdQfXF5dWvfXkm1N/rsiEXxdVtd0NSUBDoIAKoCGNjY4n1TKAVrbbpQCLEXRk+fPiGDRuIuEKgZzSJkI/Yk1JQsdxEk0tMZwyfa4iTUl/PVXwvcgkr6enTpxP+hcwd7GdzxVGFj08wHk5JSc0s5f1ESkZxRIghNLAnNfLlFZbjZ/IqKiyjRgZPiLN/WdLcQOR8VyMQGBl5y623eri5fbZuHdFa6urrr5k2LSwwEGa865qmlQMnT6JP3Jd4HK3urbfdJnxi16EtNQsCgoAgIAgIAm1CoAu3l23qh2QWBPotAlBImw/lvb8lTTXwZEen83IL9vdKCP+RPwKf/FJLZpFZUYHBtsSHekM72uKWXWwmDxbTKNpC/TXemi68wZGJvbc1bUdiAR2YMTrornmxQyN8bDvTjnS4URsd4p1dZDZVQk9ZmaljKaWnM00FZZYbLguHQgKZdlTbu4rsTy7Zd6pY0Sf6entcOsS4aFrk2IF+jS3Aq2vr80osGXnn7YLR8R04UzJ9eKDteCHjyMCayS22LgzbS86fZrpDDJprJ4TR1QpL7TdHCz7ccjYp0wSrWGaqeW9L2vh4f08n8Pjm/EhKD7saAWhEWD+OlhtCq8gBXVhSUoJjRFgYbJxxpKjQMUiu7rnnnltvvRUHi3hOhFLEJTwhpGESm1Q+ttxWW682CBUNeFrA7UZOviklo8ho8PL0uOgrpq11tjt/dU1dVl55WlZJiFE795JQX68upKva3cl+WzAwOvrOO+80+vq+t3Llp1u2lldW3jBrVlx4eBexivCJx1NSP9qwAX1iUHDwTQsXXnHFFaJP7LfLTwYuCAgCgoAg4GwIdCHj4GxDlf4IAk6IAKwQNND7W8/zidilBvppiEERitlvhH7y4Isc0u9KKvrk2wzC4DIQSMPHbhqMabCtdu/bYwVf788tq6wZG++3cEpExzm+FhCD1qmvO4cqjTw1tecTLeR35FKon2bx3OhTGSZYsMzCqtScCvCxVNd9/E065No1E8KwpXWknt6bB4XmjuOFaQ0soVbjNmlYwG2zoodHOsTVlptr1+/LmTI0oE/GRIYfnzM6SKdx+/cXp8EHxvnw6RLMusfG+vUDnrn3rmjpeRMIwLzglq6JC3gwcHXVNxxNXu3Sk0G+molD/BPTSo+llpWbzGfSiyJDDVGhPSAPrKvD5Ln88Mmcc3X1YxMCpg/x69KBS+XtQMArKOjaG27gFd+yFSs+37qtssq8+KorB3YBqwifeDL17H9Wr/7+0GE/o5GA6TfddBNa4Hb0WYoIAoKAICAICAKCQFcgIJRiV6AqdQoCjiKQVWTedjg/s4FCwnFVbKj3dVPCF06OaJIVyi+zpOVUKJQiDexNKib4r6/uR5Ytt8ScmmMyVVpDA1eYrcxj7zpQnM0fE8If3d5zuvjdTamHkkvwGAiruH5PzvAon7FxiPX6slDxyFlUmeXKFA+K0F8zMcxBPhHEQOnomVI0npGBTdtd9q7F0Li3Xh5ukwYby+bF/vWj4+fOwTac+2Bb2ojbfUWo2BgrOSMItAOBEVG+eJnIK7bkFpuJ+3z6bKHRoPXW/vgV044621qE+zq/uPLQiZyM7NLB0YY5Y4K0Hn35md9WfJwnv1dw8DWwigMGvL18+YadO3F5cceCq2PDwjpRqwifeCot7fXPP/92/369wUBwGA7CoDsPCNITQUAQEAQEAUFAEBBbElkDgkCPIVBTd+50lglVGj2AQ4wK1t02J/rmKZFN8omNe7npYB6uDHuZLWvjYTRzZsIg/weviR8d5+fZEIIDoi0xray8qraZ7H3hNKbeRHkmSg+DcXMbMH6Q/5Bwh/SJ6uBNVbXr9+eoHx1JsA9EGllQXk2kl8yiKgKhYNKu+ENsrjhLjqVL1BcI8bP5lZQlckpzme3Okw+OGCehGOmnF1TllJgR1WLlbpetuY9Y+sMq4ruTDHTyhyMFOcWWBueNzZWQ84KAIOAoAv56zzljQmaPDSJAVkVl9ckz+UdP5VaazweJcrSWDuTjpi4urTqQmJ2YlKtEZZkc3wMyyQ6MoH8VhVW8+oYb7rntttioqK379r79xZqk9HRcgXYKCvgHOJ2evnTlp9/s36/z8SEsjC2fiMtRXAd0SkNSiSAgCAgCgoAgIAh0BAFRKXYEPSkrCHQIgbwS8/7k4tJya4QTP73H5eNCrh7XBnOeE2fLoJ+I5NtX494Oi/S94/LYv684SVwOINp2JH9UrN/oWENf1SniwiyroKqigTYN9sOTpo+/3iF9EIBg/44dOmzg5gO5P5sVrb3YyWZzyxSKNr2gMiWXvwoszeExowK0scE6AsJEBOq8NU24UYNuLCyvhgo/nWMiOjnEImFhJib4hxpbl0ZCPVpZyNyK1PxKuEiTudbf2zMmSBsXqh8Y6h3o49kqmY5aSe/ldtXE0GUbUhlU3blze04Xhhsjui6SdXPQyXlBoE8iQFz4ayaGl1fVbT2QC7u3/1iWywCX0YNDtF4OPYs6ggnvG8rKLQePZx9MzNJ6uY+K85s36iLPsB2pXMp2EQLaoKDrb7klNDzszXfe+eqHH/KKi+9beNPI+HhP947uL9Jyc19ZsfLbAwd0ev3ixYvhE8PDw5VREN3o6NGjRDEinJE4VeyimZVqBQFBQBAQBAQBBxHo6Fe+g81INkFAELBDgO1Tck4FqjTOQwmF4YR+TLBdnpY/Iuj45lj+kEg9UU1azqleRc9VW19vrq638L/6c5hae3m4atwxVGrJsoyuVtdQqs5cW09GYgpjgqrW2XICBspquVxTjxKNshoPNwhQxy1Vx8f5RQbpYLsIhH0spSw52zQ4Qu8gX9Zyx5zwKlSdatU+Os4Ay+ZgJ91cXQ0+HjXV9Sj+Ckote08X2wVpabIemttyJH/5tjTFdaNtnuExvj+ZHjV1WICd80rEien5lat3Zi3/Jl1VMu44WvDRlrOTRwZemuBvW4ldusJceyyt/K2NKUdSSmsvhDVX8uh1HvPGhfxkagRspodbK9p5XExecUnIuxtSWZYcBCw650KypQWstCL/CgKCgCMI4KL3pikRSJ5/SCwoKavadzSTUqOHhGq7Mt4X302mSsuhE9l7Dme4u7sOjvS94bKI6EAvRzoseXoWAVetdvKcOQM8PM698ebuQ4fQFT646Ccj4uI8OsAqFpaWLlu7dufRI1463c9+9jMkimFh1jhdynHkyJF///vfRUVFt99++4IFC4RVvACM/F8QEAQEAUFAEOgBBIRS7AHQpUlBAATgyApKLHlFZtJs1eLD9TFBOgeRgQHElxx7sO+OFhAP18FQyNYWy6sRNp7JrUjJq2DHGGH0igvx5g9Jmo+26acBYYKziqqSc0ynskypeZXQeZcO8h8crvf2clc4neb6DJmICC6joAoFHM3llFigL2ODaU6Hro1QAI4oy5CtzRgdeCbblFVYBYd1Kqt8Qpl/VKCjQDXXN+c8X2TC6Pi8CXCwQUMEHgf7CSXtp/ccFuWz9ocs+OJVu7KmDg1omSY219Qt23r2q93ZpSarkRo1wEvCzUE0s64Sz5b9u+Q0vjtvmR7FrCndYLpZCe9sPrthT7ZyBlUjHDEFoIyxQWZtNLB7TfSaWrcdLXhrQ0pGvtWsm+bcaW8ABLc1sA8Bvld/n1FUarn3yoFDWosbjjNNox4FjCvrGS4R2SMdlkMQEAQ6EYGR0b63zIxC9Xw4uaS0zLzvSCa3MFpFL274Ft8/ta8PtXX15SYL9s67DqVTf2yYz/VTIicNMrSvNinV/QgM8PCcPHOWm6fG5dVXdx048K/ly391yy1oFdvn+7i4rOzNVau+2vGDi5s7oaXhE23jsSBOLC8vZ4zp6elvvPEG5s8EbBFWsfsnXVoUBPowArW1tTiKdXNzVD/Rh6GQoQkCjiDQNIngSEnJIwgIAh1BAAd2xRVWk2eOEH/N1BFtsPDy1XtAxGAhW15Rc/RsKQydnZpMqdb2Xyiko2llBIz+9lCe7XnS2FzfMifm+olhMDV2l7BUTcwof+ajxIz8Ksgm5eq6H7L8fT1/flUcV+3yqx+Rs0ECrtmdvfK79KqLA8UgjRwW6/vwtYMISO2IXHHmiKB1O7Opjcrx9FfZEPBabagvJQpNP6oUibpDdGPHR2fQuc8fGwJFCNF28FRxbqmF0D0tFN+TVLw7sVDhE/FWGeDjGRKgZVHlFFWVmGqopLDUsjOxYESUz4SE86FpUaoeTyvbuNfqqxFOEE55YJj34ChflhY2+Jhsb9mX2xy7l1dq+fz7jMwCK59Ic0Yfz4ggnV7rTisURFwJc024IYSZ2Fy3uir4nefj7QEFyYrMLqxqrtEWhi+XBIHmEEC/zSXWGEdzefrD+XFxfl5Xx727JW3vicLScvPOA+kFRZVjhoaFBnp7OqxSdwQofKmezS7dcyQjNb2Y9wzhQd7XTQ5fMC7IkbKSx4kQcHObMG2am6eny0sv7dy3b+mnn/3pnrtDAwLaeh+VmkyvffbZ59u+qR8w4N6774ZPDAmxRmxTj/z8/O+//37v3r1wi2lpaW+++Sas4m233ebj0zbXw2qFkhAEBAFBwBYBs9l85swZDLiio6N1ur4pYrAdr6QFgY4jIJRixzGUGgSB9iDQQCme92LurXHHg5XjtSAUmTLciMFpiclquzpxiL2Bql1VCAZ/OFG49MszqdlWp4QcaAcGuLookjQopLfXn0GY9tTiEV6eF5mdHkwp+eM7R/GXp5SCDUShQm0l2MweyiOgh3Le7l+IHnztvbMpddsF+pJi/CFng7SCrjp8uuTxt4/88bbhhNqwK9v4I2wpvWJ3D3NUZKrB4WDjPH3jTFG5BXpOGQtLwnHrcoowNTHButhw/el0a7SU1buy7r8yrjlYIHwxXkZASgaQvXSI8Z75A9El8XHHiUIm7khyKaKko2fLvj1aMHagH5VzCfPzrUfyOU8aPvHKiWG/uT7BvUGyZDLXvbgmac0Oq4Fkk8fq3dk0R1GWAc3dfXnsiGhfmqYnmw7mvr/lbHKmqdJcSwSe4xnlY2JbUSdRMMCggVKkrbxis9KlJtuVk4JAmxAg5kNubi7/BgcH6/X6trIhbWrL+TMPj/L975sGr9md9cGWtApz3bFTuZm5ZRNGRYwYFMwzwa01HwWtDpA7Fz4xMbkAcWJxSSWq59AA3fVTIn469bzLvFZrkAzOhsC4iRMf/s1vzr344oGDB1/6+JPf/mxxoAH3x46y85Vm86srV36+bVv9ANdf/OIX8IlBQfbkMh4VFy1aVFJSsnHjxqqqKrSKb731FhTAPffcwz3rbIBIfwQBQaDXIbBnz55//vOfOTk5jzzyCCLoXtd/6bAg0P0IXEQfdH/z0qIg0G8RqLTUwuUpw0e35WAgDhUu3Mkh9eJ3+onUMtzbtSAYpEhafiXckMInQsfAB0UG64bGGAipqbBFGK4ePF286XCuWj8JqMOl61OwjybNjsCg9xg/1Djv0tBR8X6Y2R5KKsktappShBv6PjH/28P5FLQ2p3WPDtGNG2wcHmugoGLvjCO/19afcTDUr87LHTtZaitGpdh3KcViU43iSxHTPw+3AW21L0TYeN0kq7cpSNu1u7LQ/ZFu8jiTY8ppcE/J1QA/zcIpEQqfyMcpQwPmjws1Gqx61ZqaenhA4qgoleSWmPecLCLNYogK0j10dbzCJ3KGkCm/ujbB19te5aoUhEncd6IIGpqPYYHaW2dGj4oxsDD4yDDnXxIyb2yIpiGeTE6xGTN5pVQL/7q6uhh9zluFl1fUWl0pyiEIdAABq/G+xYI15ebNmx944IElS5bAVlRUtL4UO9Bm7yjKt8xPZ0Q/ecfIMYP8eHRD/G3bdWbdt0mn04qqa+r4jmjfMHgmYOycX1i5dVfqN7vPUC2VD47yu/+ahNtnRravTinlJAhcMn78bx55ZOy4cd8fOfLWF1/gFdHqTcOBo7q29oP1X6357juNzvvPf/7zXXfd1ZhPVKoZOXLko48+evfdd+NgEctEdv7Lli17+eWXYRgdaEeyCAKCgCDQEgK8okAEnZGRwSvGlvLJNUFAELiAgKgULyAh/xcEuheBChvDZyLdGnSOOs5Tukmg57hwPebAkFDbEwuGRPo054qRTd+B5JJvjlgJPlic8CDtrbOjF06OgNLBD92r65M37sVeFX92tcs2pl4xNkQhGcl8KLU0JbNc2TRi6fzMnSNHx1h3lVzaearo1TWnT2ZY/Rk1Pg6mln53vBAFCk1EBGlvmRV9w6RwCiKKJCLNP9ck7T1ehFwxPa/ym6P588aENK7B7oxB7wnrClNWWvGjabBdnj7w0cF9V3MjJW7J1KGBr3gmsyRKy2uYo2nDAprMnF3Cz6XzRutThgfEBF8UB4aY2qNiDVsPWg3k8YaJ/81BDW7xwV+JRk00lTEJfnZ22RqPATPGBK3dkdl484iBvwU5YkNXiEEUFai1oztjw7z5w3qa5orKz/PsTfZcPakwknzsIGhqhZLonwiwfnCZxLYBDvGLL75ITEyEW4SwiImJEcWTsiTwpnrZEGNEgNdbm1K/PZRvqa5POpN35mzhwGjjqITguCijVYSODs36zdD6wfMB0/LyiuqUjOLDp3Jy8sqZAt4YXZJgvGt+7KVxrSiUW29AcjgBAqPHjHnk0Uf//ve/r9y8Jb+o+PdL7vL3tcrSW+gaPxjWf7/jnbVrg8PD/+///m/48OEtezGLiopCxsi/SBRPnTpVWFj48ccfcy8/9thjGo2mhYbkkiAgCAgCLSPAtxKHo99qLdclVwWB/oGAUIr9Y55llM6HAAa8JQ0GxfzOthqRtVWT5uJy1aUhJ86WoiP77kjB1eNDm6MUC0rNKTkVFZVWsaHWy33y0ICfXBah4BFu9Lp7XmxiWnlGbgU/6IvLa2ADJww6H7cX49nq2vPigvuvjh8a4avwiZSdPNh4ZHRQXpmlqPS8O0hbgJFDnjxrZRvhnmaNCf7JlPPNsfMcFKZH3fZQShlKRnzz7Tld6hCl6O2uEJ2V5jqkc7Zt9aW00Ufj1SDWg8aFdGPv3eIWzH7obNd8dO5zx4fg7JKwJx9/mzZlaNN25bklFnSpSnks7n11F30RBPl6EjxHuUqY5mKTdYqRwSp6VdI6Tzc8HioZ1H/57QXNPcBlQOMILcgbVRUtIV++TSywC+t8MrMcL5lUxaoou+BgVK25cQJnd/ll5xeer4+nRHtuDJGcaRUBbjC4rby8vNWrV69YsSI1NRU+glLe3t6//OUvx4wZ02oN/SpDdKDufxYOuX5S+NcHcrcfKcDtwOmUfIjF2ChjQkxAVKjB3+DFI6jhkdXERqzhTYP1mVZQUnUqteD46bzCYuTP1j1bgMFr2qjAGyeH4123X0HatwcLL//f//3fzz777MYffigqK4NVHBQZ2cTKaECBlXEqPf1v772n8fZ2hE9UoCMkixKY5YUXXkhKSiouLuZGrqure/zxx907EGy6b8+LjE4QEARaRcD6XSWHICAItAWBi3aSbSkoeQUBQaBDCCjRdTtSxeQhAQG+msxCs9UJXXoZQsUmaytF+XWBpokN0U27OA4Mdm0LJoS+tjaZsjBZaQVVKqVIzcrXKgGpx8f526nSJiYYdx4rbEwpYphsMsNoWb+Ph0To546+yBESG05avGxkwOa9uWRSgq402W3bk9bgwA36hnPE+O27X/Qgo1CKjL3CUotfRWIo2+LQalrv5X7jZeFQighCD5wqxrq8SbTyS6EUzzttJK6LndNG7OL5U9piKolDTRrlI44slZO4PPPV2otqmR8/76a/UAj2rTKY63dl89fcQCBSHaGMWV3EdVEqCfbzaln80lxbcr6fI1BQULBy5UqUTfhig4bABTsMIwckBQopoSQaLw8eFJcM9BsT63fX3IFr9uRs3J+bmlWWnFrAH5kDA7yjwvyiwwz+vlofokvpGjzgNtRSxWOktDK/pBJXjGfSikym8zcvIaGmjwq6dkKokImN0e4DZ4YNG/aHP/zhmWee+W779t+//O//++XDcRHn3y/aje50UdF/Pf93c23tc089NXjw4Jb1ibZlPTw8rrrqKm7ep556ClYRB4uffPIJHgyef/5522ySFgQEAUGgHQg09xakHVVJEUGgbyPQ9A6wb49ZRicIOAMC8HT+DRGW4UfwKsW/bWVG8GQ3dVQgMVKgV7Ydzh9/QV1oNzrC6apOGzGvjgy4KBAwu8T40POKMzqRZeMeMb/kfOCLqBCdh4e939VAH88mCa+G5s4ryDDuPpxWdjr3vDM+pWMEe0nLszo8qq8715w3Rrsh4GQQSSMndVr3xj2xy9x7PxoJRHMB59LKGtBrEuEWBojWNTJAFx/lm5xehpRvxY7MJp2dEVIZklapp7bOReF/1WopwnpUPjZ4dbSfejWngwmtp6MiXG4B+MpWq4XpNjUofMka6u/VJi1nq5VLhr6NAIuHiLGomZYvX56ZmYkycdq0aRg+o1KET/Tz85s5cyaxWfo2CB0ZHU+OYIPnkjlRt04L33O6ODm34mRWRXpeVW5h5aHELP6sN3DDPemj9zT6emHjXGqy1PEGyeqmwOqpAN+4EcHaqcMDLx8bMjBY19Yvvo50Xsp2MwJxcXH33XcfN9fR5OR7n37mD0uWzJs4wa4PyRUVD/7lqczs7DfeeGPq1KntMFuePn06vheffvrpEydO4AJ1zZo1JpNp6dKldg3JR0FAEBAEHESAryrhEx3ESrIJAiAglKIsA0GgZxBA9EcsY6VtzELLKmvVj453iC3Z1gN5UIqnM0wEaVG1YLY14J9OsbDmJKGTDRcaVfIQHwN7WyUNl0RUX7VsualGkQT66e0MVa1ZELKpXhfVIiTKaK7ByJr0yfRyIvna2aVSJ44Uucru0uxYrBVcKCqWs77W6M9tE+7Z9s3J0wE2LC2SQAyN4W3b2meskhdOCf+/T8ogCj/fnuF3wYTZtp4QP41XQxBnTsJdmqvrbblLQrvyp+RH9oh2kjSwqxGEmD4WlW2FLadhOVUhJDGpx8QZmlvqGg83e+rpkwAAQABJREFUIre0XBvrhwDTOHa0ZhvgEh4olGLLgMnV8wjAGGZnZ3/66aeIE0lotdqFCxdOmTIFbhGhosFgQOIEnzho0CBPzzbfd/0KZUhDXjbwFTB9eODUYQG8gGD3BWWIWH7XqSJUyQVlloISCyHs84tMwf5eQX56niSRgdq4UO+4EO/oIJ2PF+4TeX+gvtroV/j1o8GiIpw8efLrr7/+v//7v998880TS5ceSj593/XX63U6UBjg43MwI+ORPz0Bn/iPf/yDm9HL66JXng4ixSsyyj755JPYWR84cIDQCsRZWrx48QcffOBgDZJNEBAEBAEVAb7R1LQkBAFBwBEEhFJ0BCXJIwh0PgJQPyqxAhWIxzr1o+ONhfpp4iN8zuZVYvu882RR+QUmyLYGdm6qD0R2fRf0Z+ez8LWJRlL5wEaRQDG2ZdXzVvLm4gMZiv2phgy0BU2p5EX2WG3VpjR7qB1rNkfDhcqq2roGr46QmwQhaTlz770KZ6eqFA+eKZk2PDA22LrvatOBAnHG8MAXPd0s1XXllVZeklmwqyHYoIG8U07ix5D9v0oXcvJsQUVawXlhKaGclUtU66M9/32BbXtyjsmuTj7WNEMzhvh5EV1HyY9iER58VIxv4+KcYU2xXJu8pJ6sqq77+uCPMfjC/PDg1koRtawk+i0CaJfefPPNVatWQSYiQiRW7LXXXgt/AQexf/9+grGkpaVBTMyaNUskio4vEistyN134ZF8SZzfyBhfvmJ4n8GOTJE/I0JUviygD6352xLLxfGeSE6nRQArZmyZn3jiCaK1fPnllx+s+3LfyVN333br5MmXrVj/5atLX2Od/O53v5s7dy72y+0eBa1ceumlf/zjHzF5/uGHH6qrq3fu3HnjjTfCKnak2nb3RwoKAoJAr0ZAWMVePX3S+e5HQCjF7sdcWhQErAh4XzB8Jo3XPEKdNA550SpS7NbmjA48llKSklO763gRIj60f3YHTJCqTCRYJ3bHttwl3uuKGyJjUIotX5iNWbS3zr3SUsvOEAVlY1rKrhX1I5UbGwy6OePv4zlioCEqSKtetU3QeRzh2Z5pMg2FBDum7E5RzNm5dGyySC89qffyMEL2NbCB2QVVBNUxDfZH3dOm4bB712vdr5gQ+sX31uDLTf4qigrUqczsnpNFc0YHDwz1xo6ehlgPh5JLj6WWKY36eXuG+Z2fPmhBH2/38opaaMojp0sQKqokI5mZoKNppTTYuLdWJayvJxbr+ElMSjcRfDzM34uwMI1zOnKGNbn6h0w1J64/W2Uh1cyS6IcIELTh7bffRopYWVmJGeaiRYuuueYao9GIw7W//OUvO3bsSEhICA8Pz8rKuuyyy0aPHi0ERLsXSQNjeIFfbHctUrDPIQBZHxsbC6uIlvDFF188dPz4H/73WY2nZ6XZzFj/9a9/TZgwoeMB1vF/Onbs2P/5n/+hiS1btuDT4PDhw7fccgvvEuQ9QZ9bUzIgQaALEcCmoQtrl6oFgb6IQNs2q30RARmTINAzCGg1rn4XIu3mlVh+OFE0KaHp+Lwt929cvH+IvxdCRVNVDVrFxmp9Imn46c5bWBNv91S2KcZG+0b8jUNnS5Um2BBGGH8kegL9vDBeg5PKyKtUI/aqncE1FuZu6kc14avzQEuofMQyekSM76IpkepVu4QjZNDOU0WEGVEK+nt72Jro2tXW2z/CBs4cFXQqoxyDcTjc/WeKLxnkNy7Or63j0ri7LpwSAaXYXEEYvWExvukFlabKWtbMG1+dIRrM7NHB8IlrdmWt3plVZbEKDv18PAdF+mAlrdQT5q+dMjxow55slsSZnIpnlh9/7KYhGGuzCCAZ1+3L/vZgXuPlp5S9eXoUq4hSSGLX7cpKzaskKBARfnAnCpNprcFcyywj3UVB2Vy3OY+e9/DZsvxia3gHKOlLhvpbw0yLSLEFyPrxJXwmvvfee+vWrSMBUfjrX/96wYIFBHQmTWwW4jlgiQmZiGIRDgLna7Nnz4Z6EPdJ/XjJyNC7CgFUhCEhITfccMOoUaPQKu7atYsbDQYQij8+Pr4d/hOb7Cis4ogRIx555BEEyLQCq3js2LElS5bAWvI6ockiclIQEAQEgcYINPk+vnE2OSMICAIKAkIpykoQBHoGAY27GyI+vc4dWoe/k2llBabqwAv6Psf7BMU2cagRRVtusbmuKUYHgi/oAk2TWVC1+0ThzBGB2LHSBMqyvBLzpn25SnOYIQ8K1atNoy48dbYM3hDC6MCZYjuFIIQU6kU1s5pA7Ea4TxSOFZW1hWWWvSeLsIYjTqiaoa2JzQfzoFyVUrjigsNqaw29KP/4eP9Nod7JWVbq7Uhy6ZaDeTB6tjyvI2NBbRodqBsS43vy7HmxoV0pmNwbJocfP1t2PK2MJcOqeH3dmQ82nyUbcwq7p6yj0bGG2aOCVNo3zOg1c2TglgO5+DEkYPSOY4X3Zezz1hLqxQXpYnlVTVXznjHHDDRcNjIQB5ElpupKc93BpOLkzHIIREhn6keDCps5wHXANRPD7pwbY9db9SMsNgbXS9edVjSzFFwyJ7YJN59qAUn0VwSwbkaWuGnTprNnrasa+0d4w4CAAJRQ7BPgE3G7tm3bNl9fXzy4bd26taioaMyYMZ0ileqvkMu4BYFWEIBD5AaE8sPPwJ133kluiD8CIqFhbKVkWy7jvXHo0KG//OUvqfyzzz6DVTx+/PiDDz74t7/9DTazLTVJXkFAEOinCAif2E8nXobdAQQ684u8A92QooJAv0MAaVVcqH7K8EBGDrWXXWTefqygfSjMGhkUbmzWgpgoz4PC9WENdqZYEH9/rODfa5Ozisx4xNt1sviFVUlFZVYNIN7yB4brB12I/syZ6yaEebpbBWB8ub6xPmXHiUJskBs+upzKMn20Nf1MdhMO9cgAh4gfQBLE8TiaWvbiqqR3t54lMjXMESc5aurOZRWbkaopH1v493S26WxOBQQWeQaGe8eH6fuw4TNjZHQsiZgQq0spRr1hb/b7W9NS8ipagKjJSxoP15umRLQg32Pt/WRGZEyIN3lg6HDlmVFQxR8BuxXCbkSsYcGksJigH51bQUOPjDEsmhGFPBDOkbVERCA0lSfSykmUV9bOvTSkuRYpe/PUyKsnhWEBTYchJVl1NJeaU5GcbYIQp4b0vIqCUsuFNWI/LIocSil96sPjxJblGrRpXKQPpGdzLdqXl8/9AwHsl4n0+qtf/QpjZ4Ku4BvxhRdeePjhh2ExFD4xLy8Pe2doRIiMV199FZ5x48aNSBQJGhsUFNS57Eb/gFxGKQi0AQGEhNCIqIM58D/QFXccTeDN4IEHHrj11lvpWV1d3alTp5Au7tu3rw0dlayCgCDQjxFg48NbEI5+jIEMXRBoAwKiUmwDWJJVEOhcBLDZnDjY+M3hfNiZ/BLL+t3ZQyP0wyKbjlzRQtOh/l7xkT5J2SbUjo2zIeYaOxCOL2DF9gwoveLymnW7s/eeKkKTiPwwtwQSx8r04YDv9jkxahgNzhB7d0iM4XByMcxgXrH55S9Of7glDfkhvS2rqEE5aG5gGBu3iLxx2ojAExnlVjawui4poxwF5Ve7c4g3TYhPvqdra8+hOAs1ej1/z+jGxdUzxRU1yzafzcivVERzs0cFx4fq4bPUDH0yMWmwcdfJIii26hprHPCN+3LO5laOijMMi/QZHG41Q1Zlg+rwgcROrAfjNnNE0EvaJLSiSjYP94t+GuHfECaawCnLt2egJMX8Wa3Nx9sD++vrJoUnhOvt4ucE+nrePD0yxF/zwda0vCKrGyz4R+oN8NPMuyTkinEhW/dj+3yeOFYrVBL4zVw8M/rSQf5f7Mran1TMWmXhWbNeyM4HWFRoa2+bCDyssbP5lcczyxPTyg4mlaTlsRisBeAo75oXA3Nq14p87LcIwBUS5nX9+vWJiYmAMG3aNMI347sN0hC9EmdYObm5uc899xzZiNZC3Gc8J65evTozMzM0NJToEDAd/RY9Gbgg0JcQgFXE0vnee+8levuyZctgFZOSknCz+PTTT0+cOPGi78K+NGwZiyAgCHQGAs39ju2MuqUOQaBvIiCUYt+cVxlVr0AAQmRwhH7CEON3R/IxdEXt9era5LvmD2yr+zw4pqnDAg4nl5yqLG9y4MF+mrmXhGQUVe1MLILHIRAwf7Y5fXTuaNbGx1+0o6Z7914R+9ePq7ILzFYdZWFVTlEVbSkBPZE0lpRXY8famEGC3pqQYMR+duV3GcmZJmxakaQpWsiGn/Lnv6whNLHUdmuGIoTrfHnN6R3HCxRpZEiAdlSswTYwsW3/+1IaVektM6KYoB2JhbW19aj/Dp8pQcq3yct9ZKzvjVMjx8YalPEGG7yWzIu9YVI4NCszaAsCtCtxcl68byx0sHIe43e7SC98pKpQP6/i2dVF5dW4MsStYaCvhpAsgQZP2EM7mpJ6mH04zasvDUUeCN0JU8xJVhd9DjRQ0OPJ20ewbLw83IZF+Vgtom2Ohso9fXX+0UE6YgQRZaWwrLq0soZFAHHpq3PHryIm3mrMa4qaa+phV19ff4a1RDQYAn8rgyG2zH1Xx08e3B7fozY9kmSfQiAlJWXt2rWYMF999dUwicOHD0cDpcZ8wNs6fCKKxa+//po4LbAM48aNw7+bQi/OmTMHzRQf+xQiMhhBoB8jAKs4cODAJUuW4Kjx9ddf5wkAq/j444//+c9/5vkgN3s/XhoydEFAEBAEBIFORuCiXWgn1y3VCQKCQGsIQK/gnw7RFjIxJFoHk0teWp00LsE/IUI/OMwHs1M7mZhSH0bKdlK90bF+kYE6iCeFQnJzhf35sW2yD430+cWVcbHB+q/2ZRc3WDorl3FmFx+hv21G1PgEY+PIJ6NjDH+6ZThmywdOFUPtwR5CACEQiwnV3zIzeuuh3J3HCznzY0sXUrBLc8cE03+suXGGWFhqUayebV/9waLCl5HzQiFkRC45xeZTOaakLBPu9o6llmKdrVydNzYYq+dG41aL9p0EFGt8qPe9VwwMMXptPZhHhBxsfq3sbXk1GlIM5FVKEfIOhG1tk21RoB50prZnGqeZ/ahALX+E3+GPRaXxcGtyyalloS8J+DM8yoMJhfJD+wWBqM7LjJGBpKGJkUmqRWwTLB6lRUhqYq3UWN02niMrPeGSWo9SxGpbXVB1OutH+3qqjQjS3jk3lob6tgm8LWiSdgSByMjIu+66y9/fXzGoVJSJSkHYBLwrvvTSS1999ZXFYnn55ZfRMMIp7N2798CBAySQKOJX0ZFWJI8gIAj0FgS4taOjo++44w60itz1/PyAVcTvAXLFGTNm4HWxtwxE+ikICALdiQDPCg40EA0yiO5sWdoSBHorAkIp9taZk373DQS8PN0mDw24ZZb5/S1p1dV1kCxE+80qrEL2haxs4fTIuaOD1ZFePiZkWLgPIkEvjRsiMvU8Cb2X293zY6+bHEYYZg5CskTaeMHjDHTh4AifAB/NtBEBmC0XlFdjUuqPHs3X09fbg7AnjflESsHyjBno95DOvXB6NdGicbTnrXEnQIqf3jM6WBcR4DVvbAgmtKFGLdyoteELB3wWQ6BsZKB2xsggCFNEbYXlFitvNWCA1tOVYC9GvcaOFcKY+rkVJ3JLzKWVtTBoqu/FqyeGXTk+tG8HZrmAnPX/MGuDwvSYCY+P899/puTgmZLM/EqCn5hr6ipsLJRti3QwzUTz16ZK6KSthbJSFnrRwUqgJlly2otWsX1RVkBZVQ2EI5ylr949Pszn0gR/Ir2wkhs3bV9YPvczBIgni40z3IGddzb4RBwsvvLKK9hEV1dXQyhcfvnlKJiABy+KxcXFGEISMqKzYs72M9RluIKAUyMAqxgREbF48WJu8L///e/0Fb+KeD/gUcCLBB4XTt176ZwgIAj0BALwiT3RrLQpCPRiBIRS7MWTJ13vAwhAl0Dq4bcOcm3t7uzU7Aqc05WaaviDXxs2sNyWUowM0PLX3KjxfMdfc1c5TyxmrFb5w4kdfCLqQoxM4TRbKMIlrFehtwaFWa1Qq2vqUJMhjlMcGo6M9h0e5csHam7SfBm9G976+OPbGTIRnaM19McAQsEMgHtqzGFll1QdTim1Zc3w67dgQhh/A4ObFmy23PneexXCLszfy6j3HBrlg0ITz4OIOjWeblC0vXdQbe05csgrLgkdHskac8EMG0ErDhn9vT2aXGxtrVzy9zEEYAkVotB2XPCJuErE7HHdunU1NTW//e1vr7vuOoU9zMjIICS02WyGYYSLbPoRZluXpAUBQaAXIsA7hrCwMEK1GAyGJ554ghGcPHnyn//8J8Ggufdt5cy9cHDSZUFAEOgSBIRV7BJYpdK+i4BQin13bmVkvQQB6Dj82V07MZwgvBBqB1JKUrJNRNXAMpQ4vF0xCBhAO7d6jrRi5R8vjoYBvehIQfLAAUFE8tdyftz5MWoA0WhcIwJ1OE9EkkZsmSA/DX1uuWyfvApiEIv89cnRtToohh8fRphv71ZzSgZBoDECCp/45ptvrlmzBvqACLCLFi1SvStu374d9SL6xMmTJ2u1/YipbwyUnBEE+jYCvDAIDAxcuHAh/g2weuZFwokTJzCF5jXDlVdeqdNdZGPRt6GQ0QkCgkCrCCh8Is8NjlYzSwZBQBAAAaEUZRkIAj2PAN9ZKLAmDzEOifCZMjygsMxSXV2Pmo+YyD3fuW7sAQ4ff3/zUISMhCf29fYMN3oFGzSNxYzd2CNpShAQBHolAvCJ6BDffvvtL774Aj7x7rvvRqZEwBZlMJwhTgvGj1dccQXhnu1spXvlgKXTgoAg0DwCUAO8Trj++usxhX722WdxrgqruHTpUh4FBHRS3zQ0X4FcEQQEgf6CAJSiwir2lwHLOAWBDiMglGKHIZQKBIFOQgBbV4yg7ZwkdlLdvaMaAv7y1zv6Kr0UBAQBZ0VA4RPfffdd+MS6urqf/exnOFMLCgpS+3v06NHDhw9HRUUR61kkiioskhAE+jYCvDzA9QEuFAnWBKWIBTQqZljFBQsWYBbdt8cuoxMEBAFBQBAQBLoIgVbsELuoValWEBAEBAFBQBAQBASBTkcAPjE9Pf29995btWoVZMHNN998++23I0W0bWjz5s2lpaUzZ86MiYlBtWR7SdJdgcCuXbtyc3O7omapUxBoKwIYOz/66KOTJk2CW4RVVLTMJSUlba1H8gsCgkCfRECRKIrVc5+cXBlUFyEglGIXASvVCgKCgCAgCAgCgkC3IqDwiR9++OHnn3+Oo7Qbb7zxjjvuIOSrbSeI8kxgFryqYfAowRlskemi9Hffffef//yHSSFUThc1IdUKAm1CAHnyww8/rIiUiQG9bNmy1atXFxUVtakSySwICAJ9FQFhFfvqzMq4uggBMXzuImClWkFAEBAEBAFBQBDoPgTYA6BP/Pjjjz/77DP0iVg4widGR0fb9gDO8Ycffjh9+vSsWbOGDRvWOEi0bWZJdxCBioqKlStXAvju3btTUlKIhGNH73awfikuCLQbgSlTpuD0wNvbe8OGDUlJSbCKOEngoUEgl3bXKQUFAUGgDyAgjhT7wCTKELoZAaEUuxlwaU4QEAQEAUFAEBAEOhkB9gBpaWnLly+HT0SfeNVVV8EnxsXF2TUDa/Dpp5/yL4FZRKJoB06nf8zPz4fhzcnJqaysJL42xM3IkSN9fHw6vSGpUBBoBwKXXHKJRqOBWCQofHJyMt4SeOUAqxgcHNyO2qSIICAI9A0E+DnB0TfGIqMQBLoHATF87h6cpRVBQBAQBAQBQUAQ6BIE+PWPPnHFihVo4sxm8/z58+ETExIS7BqDL0hMTNy5c+egQYNQzHl4eNhlkI+di0B5eTlyUbzUEVzbZDLt2LGDMNyd24TUJgh0BIHhw4ffc889ixYtIhz8mTNn3n//fXwmiN/PjkAqZQUBQUAQEAT6GwJCKfa3GZfxCgKCgCAgCAgCfQcBhU+ETFT4xHnz5sEnYtTceITQWxjhwm2hYUSIJM7XG0PUFWdgcpkjCFyibBNrG2vormhF6hQE2ocAWmZYxdtuu42g8Jjnf/DBBwiZs7Oz21eblBIEBIHejgBfWBzyC6G3z6P0vzsREEqxO9GWtgQBQUAQEAQEAUGg0xDgdz/CN8hEJIpY186ePfvOO+/EurbJBlxdXZEukgFKUbwoNglR5560WCzwicTUZm8GX0OUbYSKWEB3bitSmyDQQQTCw8Pvvffeu+66Kyws7OzZs7CKPFJkoXYQVSkuCPRSBPhd0Ut7Lt0WBHoKAfGl2FPIS7uCgCAgCAgCgoAg0CEECCKs8Ilo34i4Al04evTo5mqERpzbcDSXQc53LgIYkGLyTHBtZicqKgrj9H379mF7TlocWXYu1FJbBxHw9/dHq4hrxXfffZeFSoByaIWFCxdKQKEOAivFBYHei4AIFXvv3EnPuxkBUSl2M+DSnCAgCAgCgoAgIAh0AgLwiZgoEpIFW+bp06dj70y8hU6oV6roJASIrA2ZGBkZqdPpQhsO7EnxZUnAlk5qQaoRBDoNAeK0/LzhwNdqXl4eWkWeLeL9s9PwlYoEgV6CAK8TOHpJZ6WbgoBTICAqRaeYBumEICAICAKCgCAgCDiOAGaJ6BM/+eQTYoDAJ6JPvPTSSx0vLjm7GgHibhPiGWt0KMWioiKDweDn5weZePDgQahGhIoYRHd1H6R+QaCtCPBmAm5x2bJlJ0+eRKuI5f4tt9wiWsW2wij5BYHejoBIFHv7DEr/uxMBUSl2J9rSliAgCAgCgoAgIAh0FAHEbjhPhE/EPd+UKVPgEydOnNjRSqV8pyIA50sIXdwp4p8Ojqaurm7MmDHR0dFEwCBIC2GgO7U1qUwQ6DQECAB933334UKhrKzs448/5jnTQrQWXmns378fohwOvdN6IBUJAoJAzyGgqhSFVey5SZCWexkCQin2sgmT7goCgoAgIAgIAv0ZAZRuCp8ILTV58mSCKvBvfwbEOce+e/fuwsJCRF74UiTcM5TikCFDiMRNGv4lNTXVObstvRIEQODaa6/9xS9+gSMFGEMoRYjF5qz1Ob906dK//e1vxHUR6AQBQaAPIKBYPQuf2AemUobQbQgIpdhtUEtDgoAgIAgIAoKAINAhBPBxhoMzdvjFxcUoE5csWYJKsUM1SuEuQICoLLt27YKOoW6i4hBrG0oRj4pMGXbQx48fP3XqFALGLmhZqhQEOgeBK664As+K48ePRwoNq8jBw6dx1TDmAwcOxEp669atja/KGUFAEOh1CKgqxV7Xc+mwINBTCAil2FPIS7uCgCAgCAgCgoAg0AYE8vPzIRM5kL/hOZEIrdOmTWtDecnaXQgQMxcpIjQiZCI+E5F7kEaxOGrUqPj4eKaP0M8S+KK7ZkPaaScCxIdHqwgPDquoPHkKCgrs6goMDJwzZw7a2+3btze+apdZPgoCgoAgIAgIAn0PAaEU+96cyogEAUFAEBAEBIG+hgA81EcffcTGnn070iEERERl6WuD7CvjIawzMlIEiV5eXiqliPSDqCwTJkwg+HNiYiJBWiAZ+8qIZRx9E4GZM2fiVxHXCrhZ4PnDwYPIdqgsbzyEzp8/Hw+hn332WW1tre1VSQsCgkAvRYA3YRy9tPPSbUGgmxEQSrGbAZfmBAFBQBAQBAQBQaBtCMBPffDBB+znESqOHTuWTf6MGTPaVoXk7i4EsHqGUiTWMwEufHx8EHChVYRqUQhEhIoxMTFEboFVNJlM3dUpaUcQaCcCU6dOvf/++xFEq6wijyPbugICAmbPng1jvm7dOvGoaIuMpAWB3oiAGD73xlmTPvcsAkIp9iz+0rogIAgIAoKAICAItIQAVofvv/++wifCUj300ENIh1oqINd6FIHk5OS9e/fiQhGLUTzNKSpF+ET2afRr8ODBCQkJqD+gFCVIS49OlDTuKAKTJk2CVeSxA5n4YcNBMGi1sKenJ6GHLr/8cojylStXQqmrlyQhCAgCvQ4B5auq13VbOiwI9CACQin2IPjStCAgCAgCgoAgIAi0hAAhPt59910kiugT4RN//etfC5/YEl5OcO27775Dokh0C9zMQR3aGj7TO71eP27cOGxFYR6hYMT22QlmTLrQOgI4W3jwwQfxrlhUVMTjiMNWY+vn5zdr1qyamppVq1ZhAd16dZJDEBAEnBUBUSk668xIv5wXAaEUnXdupGeCgCAgCAgCgkB/RqCiouLtt99GogifiMHso48+Kv4TnXw9wKp8//33/HvNNdcYjUZ6q0R8Vg2fOTN06NDY2Ni0tDTIFySoTj4i6Z4goCDAK43/+q//YmGjVXyv4YA6Vy5ptVocMuCNAePozz//nPUvoAkCgoAgIAgIAv0EAaEU+8lEyzAFAUFAEBAEBIHehIDZbP7Pf/6j8IkjRoz43e9+N2XKlN40gH7Z1/379x8+fHjQoEFEdsYgFAzsDJ85g+0zQkVsopOSkhAq9kucZNC9EgHYcITSixcvRquIenrZsmU8ppSRGAyGefPmYfW8du1awp33yuFJpwUBQcDFRTF8tgZnkfAssh4EAccQEErRMZwklyAgCAgCgoAgIAh0FwLIfN544w2UQOgT4RP/+Mc/EnS1uxqXdtqJADuxb7/9FpJlzpw5BGZRalEoRVuVIufxPTdw4MCUlBShFNuJtRTrIQSIY44F9K9+9StCP7/zzju89lA0id7e3qgU8bqIdJHQz3V1dT3UQWlWEBAEOoqAuFPsKIJSvp8hIJRiP5twGa4gIAgIAoKAIODcCLAbX7p0Kdt1hU988sknCfTh3F2W3lkRwLvcli1bEGpNmDABhkUBRYn4zJzabtJQe8XFxWVnZxMhV8JZyOrpXQhg0b9kyZJnnnmmoKCAx9Srr76qEIjQ6FdffbXFYlmzZg0RinrXoKS3goAgoCBg+1UlmAgCgoAjCAil6AhKkkcQEAQEAUFAEBAEugmBV155BReKbNeHDx/Ovh0j2W5qWJrpGAKIs/CQOH/+fESIMIlKZfhSxHwMzsU2EktoaCi0Y1hYGPkzMjI61qyUFgS6GwEvL69FixYphs+wii+++CI9gEZn8fO8glVcsWJFd/dJ2hMEBIFOQgBWUayeOwlLqaZfICCUYr+YZhmkICAICAKCgCDQDgS+/vprRIKvvfZaYmIi++R21NDWIi+//DJ8Iq7KsHd+9tlnCYnQ1hokf08hgDgLq+ebbrpJtXqmJ66urgqlaCf9iImJwYY0KytLKMWemi9pt1UEtm/f/uabb27duhXFdOPMeHflYUW4Z1y+Pv/882SAVVywYAHC23Xr1u3YsaNxETkjCAgCTo6A3VeVk/dWuicIOAMC7s7QCemDICAICAKCgCAgCDgVAkQYeObppxOPH4dJhBV6/bXX7n/gAdiigICAruunyieiT4RPHDlypCgFug7tzq0ZS88TJ07gTo7QtxqNRq1cNXy2VSlyNTo6GkqR8NBCKapYScKpEMAqf+XKlevXr+cBSDQhtLdEnJ89e3ZCQoLST85fcskl//jHP/785z9/+OGHrPDHHnuMkNCffvrpyZMnP/nkE4ko5VQTKp0RBBxBAEpRYRXl54cjcEkeQQAE3P7f//t/AoQgIAgIAoKAICAICAIqApCJzz333JmUFPhE9slYrVaZzXv27GELjQs8vV6v5uzEBPbOxDpAn6jwiagUiezRifVLVV2KAIskMzPzF7/4BfJDZeLy8vI2bNgwa9YswkBXVVXNnDkzIiJC7QN6LkJCExsa2gXndOp5SQgCToIAYltoxPDwcCSKWOinp6fzDNy8efPx48d5KrJotVotj8SgoCCs+FntxHomJwueCC3IGykyZsyYqKgoISacZEKlG4KAIwjwPpXvLBT3U6dO5Q2ZI0UkjyDQzxEQlWI/XwAyfEFAEBAEBAFB4EcEiJjx+O9/v2vPnoqKipnjxv3s6qviIiKyCwre//LL7QcPvvXWW2RdvHgxXvB+LNMZKVs+ETZz2LBhwid2Bq7dVwfMC44vdTqd6kVRaVuJ+GznS5FLcDGQiUTIhVjsvl5KS4KAwwiwdGNjY3ncEcEcOS12/XANEIU5OTmbNm0iZPncuXMhEHkFwouWX//61zhYXL58Obb8OIuATD98+PDHH3982WWXsdQdblMyCgKCQA8jIBLFHp4Aab4XIiCUYi+cNOmyICAICAKCgCDQ2Qigu0lKSvrlww+npKaGBQT85Rc/Hz90qNFg8HB3DzEaIRY3/LDzP6tXE4uAH9xss6GQOqsLxHdGn1hYWMjmXOETienRWZVLPd2DAPyLwWBo3JZCKbK6Gl8SMrExJnLGqRBg9cKSwx7yEmXatGm7d+/GSSI2/jysSktLT506hWU08eghFvn3gQceID9n0DPyhIR/RNL4zTffcJXzTjUu6YwgIAg0h4DV7PncOa6Kvrg5iOS8IGCHgPxktwNEPgoCgoAgIAgIAv0OgZqamq+++go6Lysz86qpUx/8ycLwoCAvT0/lJ7W7m5vR1/faGdN1Xl5LP/2UYKbwidddd51tFI72QcYP9zcaDrboeCV76qmnhg4dKnxi+8B0zlLnl1BD3Gfn7KH0ShBoGQFkhhCLKBZDQkLQ1R44cICnJUbQeGkwmUyIFuENuYppP84WeXy99957//rXv2ASt23bxtMS80mhFFtGWK4KAoKAICAI9F4EhFLsvXMnPRcEOgeB+nMu1fUuXvIGvXPglFoEgd6HAPFJ2SH/9a9/LSspeXDRoptmzw4LDGj8fl6v1c6bOIHhvbl6NXFOidOC3Z+dlWubBo8xLOJEwknDJ06ePPmJJ54YPHiw8IltwrC3ZGadCKvSWyZL+tkkAhCLOADFVSjEIppEJIqYP2MQnZqaivNEvCgSkgUHizhtIIQL+sTy8nJMob/99ltCPxO2SJ5sTaIqJwUBZ0OgQaR4rvFPIGfrp/RHEHAeBIRSdJ65kJ4IAj2AQE2dS3ZZbX5xRXJOuaXmnKWmzlxdV11bb6mu9/fxHBblkxCmDzFo3FwH9EDnpMmeQ6DIVH0io9xSWz843MfHW1NZ4+Lm6mLQDPAU6rnnJqWLWmbf++qrr37xxRcDamufeuD+y0aN8vfxae7HtLdWO3fChFJT+Udfb8QC2t/fH81O+zoGj/nuu+++/vrrBQUFyHl+//vfE0dVWKf2gen8pZS4z87fT+mhINAyAopikcAsRGXBUcONN96IRBHe8NChQ7waKS4uJk4Lq52oLHhdJDMm/x999BEUZBdFtWq5t3JVEBAEBAFBQBDoagSEUuxqhKV+QcBJESiprP3+RMm+00VpeRUmk6XCUldff67+3Lk6/jvnwr8e7q6btO7eGrfwIN3AMO+4YG8YxhCDl7ub0ItOOqcd6RYzXl5Vm1dqScmrOJ5WnpRZnlds9vJyDw309vRw12g8fb09IwK8RkZ4xQZotMIsdgRrpynLXpdIAi+9+OKWrVuN3t5/fPi/RsbHY9rccgf1Ou2106eXmkxrv9/xwgsv3Hfffdj6tVWrSCzFDz/8EItn+MT58+c/9thjRP5l+91y03K19yIgKsXeO3fS88YI8NJFo9GEhoYGBgZi8nz55ZfjYBFPiwSKhVLkKuGtKKW4EEWlCOEIq9jW52TjduWMICAIdDUCiiNFWmnu3WpXd0DqFwR6HQJCKfa6KZMOCwIdRaDIVLM9sWDb4fyzuZXFpmozUrR6FP4unp7uUEhanYe31sNX51FhrqmsqskprU4vNB9MLtFr3AINmnEJ/pMGGxPC9UIqdXQanKZ8ZlHVnqTifUnFeUVmc01dhbmutLKmwlxbV3fOzc01LbeSX1Xubq5QzBoPV4PWzaDzCA3UDR9oHDPQL0jvpvMcICSz00xmGzqCSJCApMRZ3r9vX6i//x+WLBk3dAg+Ex2pwt/X9+bLLy8oKd24Zw8KR+yXkRk6vlvGSPCTTz6BT8RUcMGCBURKhU+U3+6OIN9787A8hDLuvdMnPW8OAcyZkSti74xBNI9BXtJgCr1v3z78LapF8Lf40ksvEYQKWbd6UhKCgCDgnAiI4bNzzov0ypkREErRmWdH+iYIdCYCWUXmjQdzMwosgQHeO47mnk4vhUnU6TyjI/xDAvRhQXotAkQ3V/48Gvij2rr62tr6moZ/cwtMGbmlx9NLz+ZVfnckf1S8/8TB/mNiDbBLndlFqasbEUCTeCKzfN/p4sSzZen5lYWlFktNvdbLIzTYJ1Cn1ZlroI1hEqsstebqmipzTZnJUldXn9Pw2lZ7tmzvqaIAH41e6zZsoHH8IP+RkTovd5GvduP8dawpFDRY6uHHMPHYsdiwsP++43bH+USlZWJA33z5vLzi4l3HjkEOIsaZM2eOI6wiu2sior755pvwiTfccMNDDz0UFxcnfGLH5rMXlBZKsRdMknSxvQjgscGv4YBYxIEDb0qOHz8OschrGxIwFKgXn3nmmccff1xYxfZiLOUEgW5CQKEUu6kxaUYQ6BMICKXYJ6ZRBiEItIZAcUXttqMFK75NN1XV6b09S8stbq6ug2KNY4eGwiLptCgTPVtwmBgSqI+LNuYXVmTllWXklGw+kHsgqWjayKAFE8KiArXCJLUGv9Nd//5E4dZDeaezTDnF5rKKGkzd/Q26IaG+0WGGIKM3ktWa2noIxQGuuNerxySefxVWsaTczF9pubmg1JyVX+HqOuBkpmnnsbyESN+RA/0ujfMNMxAj2OnGKx2yRQCHX6tWrVq+fHlqSkpCdNRvb7vt0mHDHNQn2tYzODr6xtmzCkpKDh48CDvJpVZZRfw2fvbZZ2SGT7z++usffPDB+Ph42zp7No05NmMpKSmhG7CcxHjFqpFQDL6+vhJaoYNTI5RiBwGU4r0CAR4UWEPz0ODJhqUzD7rk5GTiPnPweFENKnvFWKSTgoAgIAgIAoKAIwgIpegISpJHEOjdCJwtMK/fl7t5X3Z+iYWRIDwMNHonxAQOHhgQbPR2ZGw6aEcvD6NBGxHqGx9tPHY6P/ls4ZqdWZmF5ivGhYwdaPDRysPk/7N3HuBxFWfbtq3ee3eR3JtkueKKMc2mGEwJhBZMDwQIkJCPL/khgYSQfKGFYprB9NAxxbiDKXHvXbbcrd57l/9bnjA57Err1Tbtat9z+ZLPnjNnyjPnTHnmed+xBki3CPPVpvzP1+ZmHausa2gJDPTrkxTZOzEiKS4sOjIorM1xYvumr2haG9m9p0202MxfGMbyyvqC0uri0tp9xyoP51fDMq+KCxo3OHpEn9ARfcLF56ZbVPZPM4GF8rFjxxAJfvbZZ3m5ucP797/ryismjBhhA59IxH6+vhNHjjxeUFBaWQkT9/zzz0NWnn/++Yh1fprsf35VVFSQ7muvvcY0e/bs2bfffrtb8YnkEgUlFtmoilSO/f39YRXZ4zUjI2PmzJmye0y71WrlRShF2XvHSqwkmKcjoBYk8LHIMXLkyFGjRrGCwoYtYWFhnl40yb8g0O0REOq/21exFNDhCAgL4HBIJUJBwI0QaD3RIyuv9rO1ud9vLywuryNnSBQHp8UNTo1BjBYa7N+pvGIGGxkWyP6/YSGBkeGBe7KLVu8uzimqnZYRd05mQp+YU+zq0Km0JLAzEMBV4idrchetzzuSX4MKq09y5JC0WMhEqhWxKpJDC4lyNzDAl38qDEMuNgevqmmoqW2srm3MPlJyLL/ieGH1gdzqxCj/zAFRmf0jRvePIlYLccotVyIAn7h3714YvR9++KG0pGTEgAF3XH75pJEjbeMTVc7DQ0JmTZpcVVv76TerduzYUVdX19TUxOTZnFVE+vfFF18sWLCAvQuwCmRTF/wnurL41qRF5g8fPgylSP7Z0ZU8UyIexHpx9+7dV1xxBRvRwDNaE5WEMUFAKEUTQOSnlyBASzLs5OEl5ZViCgLdAAFhFbtBJUoRXImAUIquRFvSEgRcikDLiR57cmo+WZ37w44CLJ1Rn8XFhAztHzcoNSYqPNBm52XYR8dFBwcGJEaFB+09UHQov6Lk3zll1U1zTksakGiV5tGlKEhiPyJQXNn4r1VHlm8tLKlojI0JHdAnql9KJG40NUv4Y0Cr/uf9CQrw5V+P6BDGXvExoaXltXsOFO0/VJxfUnsov3ZDVumcyQ1nZcSHBEpHYxWkTg0ENcZ2AW+99RYuFBsbGtjZ+bZLL52ckW6/PW9yXOylM2ZU1tQsXPUtJn5vv/027wNOEo2sIhZ/uBV7/fXXCwoKkDHeeuutgwcPdmp57Yz8rLPOmjJlCpGgu1yzZg17uX7zzTds8JqUlDR8+HA7I/fOx8Xw2TvrXUotCAgCgoAnIsAol8MTcy55FgRcj4DM9FyPuaQoCLgCgfrmHjuP13yxLnfNzjY+kW1Y+veJHto/NiU+HM+J9ucArSJSR6jJjbt8MIJesSm/tr7lssnJw/uIXY/96Do+htLqxgXLDy3ZmO8f6J8+NKl/n6jk+DDehF6OGDAx6kqICeFfVERQeGjAvsPFlVUNu6ob2dqlx4keZ46KDxVW0fFV2okY4RPhxdAnbtiwobmpaVha2i2XzJkyKgPL5U7E0nHQPgkJsIrVtXUr1q+HVXznnXcIa2QV2Q0GlR984nnnnXfzzTcPGTKk48jc4g6MJ34h2UgB6MaNGwcVu3z5cjDEhpHNZAID/6PILi0tRdKILTk+IqFQ+/btSwAMpc3LkJWVhf4RhSYR4motJSUFo29cNBo3QWYj7KNHjx46dKisrAzEmpubleU1JCaW154+txFK0fytkCuCgCAgCAgC7oaASBTdrUYkP+6PgGOmE+5fTsmhIOBVCMAnbj1ag7+8DbsLMEqNjQoZ3D8WfWJsVLCFPVg6C5G/n09yfPjo1hNNTS2Hj5et2laAXe2VU3tnpIZ3NioJ72wE3lxx5JvtRYkJEbwGeE6E+HPgm6Aznxgbis9NbOq3ZxXkFVYezKv5YkPe6AFRQilqiFx/gs4OhR2bsaATZF/mtOTkq2fNnJaZ6Sg+UZVoaL9+15w3i3PNKkKBKQtoSDRsotetW4c7wptuugkTQNeDYHOKGC2OHj26srIS3hBCdufOnfn5+fhHw4r84MGDn3/+OeWCKIQNDA0NRcN42mmnXX755cnJyTpFyMEvv/xy5cqVeXl5cIUNDQ3R0dHx8fHp6elXXnkl7tUUq0i0GIavX78+NzcXghJdJ5QiNBwEJcGgFHWEHnoihs8eWnGSbUFAEBAEvAoBKEVhFb2qxqWw9iMglKL9GEoMgoB7IaD4RPwnwieyn0a/lKihA+L6946CRXJ4RpG4IXYbNSSxsbHleH7F6p1FrZhb9+gjrKLDobYnws/X5325Prd/v9j0IYl4TsQnpj2xWX6W12zEoPiw0IDte/NxsJh9vHr7karoMP+QgPZ3fbEcm9y1EwH4xMWLF2OMjJgO6go+EeLvrAkTHMsnkkl23hiRlqZZxezsbBLlOlpFNH1scgIphgtFT7QahhtldwWUlVCK4AmBCKXI30WLFr377ruwjTCJ/fr1gxP897//vW/fPqYid955p9qKBFoQ/OfNm3fkyBHIQSXPzMnJ2b9/P/4Zifn666+HXiQYG9e88cYb3OJn79691R7TEJeEQf/IXzvfhC5/XFSKXV4FkgFBQBAQBASBUyJwklE8QbfbDXreUxZWAggCDkFAKEWHwNh+JDRJzAfs91TVfuxyVRBoDwEjn8gWvUPS4uB3YJEC/J1F6MBP9U2ObGhsgb7ML6pas6c4JNAnPMQ3NS64vQzKNVcjsDqr5M2VRyIiQkYNTUqMC3OGONGkSLwSUNjIFZua2+Srn6zOiY0IzEwNC/D1eFrEpKRu/hOxG/pEEz7xwqnTItqzzLW/LOasIvbCRAurCCUHDQdZZn8qXRIDpB6ZhxsFUvatRuyJ7hJRIXyiEhsiS4RFZStt7LuhGtFjDh06lGBIDufPn4/JM7TjVVddpeyXjx8/jryRv7osqBfZMwc+kS1rLrzwQnSR7AwLngwhGEhgKK1Deu6JUIqeW3eS8y5BAKkyCxVFRUUsMOAqAR20MRvsJYXwGW8SuEdA+i3bRhnBkXNBQBAQBAQBVyLgakqRwTF9JKPn6upq+r+EhASsilxZYNekxUQCCQOulzD4mjhxYjcwWXINbpKKnQg0tfTYmVP7xfo89In4sBs2MB79IFupOHudDb6yf9/ohia8tDUXl9Wu3VsSHxlw2eSUmDDZGtXOKrX38R1HK95YcaS6oXX6hBR2UHEBn6hzjBH0hIze/n6++4+UfLYuLzrMr39ckDP1kTplOWlDAFPc1atXv//++0qf2D8l5aqZ5144dWpEaDue/hwFmWIVrz1vFlrllSf9Kr755pu0P1hAey6fCDgUgVk9U3qGLtCI9Oy4jIRDZJ6Pd8jLLruMtUO4RcSGWEYXFxdjYw6lCCG4a9cu8CeGSy+9FKNvRQowQuBB2MapU6fGxMRwlyuqClAy4pARggDjaDg4ddHT/wII5t5CKXp6PUr+XYwAzcLWrVtZuoiLi2NXq9NPP92YAT4r1mxohVitcX/vtMacy7kg4OYIQFa4eQ4le4KAuyHgUkqRJTX8jrPdJA7IWedn88S0tLSxY8fizhyrKHeDxp78MJGgm3/iiScYRjPf+Pvf/95t5gb2wCLPOhuBw8X1Szblb9hVwJ4bwwbFZwxJiIl00ZcVHOg7OC22pq6xfk9eUVn9ys0F0aF+M8ckhgW5tJFxNsIeFD8Douy8avjEPUcqR49M6d872s/XifbO7SLTLzkyMMAP+eqmfSWJUQFXTE5OiPQXpWK7WDn2In0QrBYTzs2bN2PvDJ/485nnzp42LeKnOhfHJqpig1UcnpY298ILggMCvtm4EYeDZAPNHeI7j+4HjXMMiEVWRikv+sHMzExliwDnyDkDm8bGRsycuUstYAetYEGqqcc51MgZZ5xBhMqLIgHYB4bdWqAPMIh+77334CWxfebgIn/1bjAqKo/7i003nihBiXfD4zIvGRYEugoBGhMO5My4WOWE5oVGRmUGcQbLFR9++CGNDCsQ3O2qTEq6gkC3RMDY43fLAkqhBAHHIuC62T7kGgZBTC2WLVuG6ZAqBgPl8ePHs6/iueeey7jZsWXrwtjo4ymjmnKgE2EtEaMny/kBH0bbHM4WlFnOhtz1XARKqpu+3l60dmdBc2srLvMyhyaxHbMrixMW7M/WH5XVDXsPFB0uqFm0Pj8hKmja8DYNjhwuRoAV1uMldWzJ8u+dxTjTTB+U4Ofnaj5RFZltoMelp/x705Hlm/LDA30uHJ8QK9pV578NMFnsx8IOxehc2vjEc8+9yCV8oioZHRms4s1zLkYRuXDVt/Br7AHNZBiVjYeyisDIOijdOgMVCsJ8HlaRwjKGwSZalRp+EI0hOkQ0jLhc5CJPYa+g7kI+agKRK3T0xr4eow0cTcJFwh1gUs1f+EfSwsRhypQp2DoY93tREXrQX0Z95FZUih5UZZJVd0CAhhSXEdOmTXv99ddZb9i2bRvnKmNMK5YsWYJiGu+0qKSNjYk75FzyIAh4NAKKTzzZS8siuEfXpGTedQi4jlLE5Qdr7x9//LGxcIzL8R9EN4lu8aKLLqLvpAc1BvDcc+YSKvPQi0wqLFCKBEBOsnfvXgbcY8aMQbkpLlE8t967KudQSGv3la3amtfY1DpiUELG4AQX84mq4OwoPSQttqSsNreg8mB+9Xc7i0b0DYsOFfNnV78XLa0nlm7JX745PzjIf3x6SlREEMJVV2fix/TSUiKLSmvWbzv28Q/H+sQETB0eG9BF/OaPOerm/5eWln711Vfff/+94hOxd3aNPtEE1t7x8Vecc46/n9/SNWvp5dmihAE6U2JPZBXpxLFTZuUPPhFFIeSgUiaCMMbOuuD05vykmOouJ1o9BAWJVJMrOrDJybhx47ANx24DiSJp4UCNcdEnn3yyceNG9oy+4oordFQmD3rKT+q92wzwPAVzyaenI8BSBJvI05hj4/X1118zR2DdgoYoKysLP7k0R2effTZaZmMxYRsRNqJpoC0iALMPFiRUi6SD0VKx7IGjRkhJYiMkbRotDMnheMHTZdG6mHIiCNiGAJSiYhVte1yeEgS8EAEXUYqsvSPRX7lypYKYrgsXinyuOHviL10a7pYwj7r66quxBuoGnRmFosNWheWc+YA65yKdN1fo3TnU8Lq8vPyll16CWmWygWkY7pYGDx4sI28v/BrtKXJOWf232wsLyxvwnzhmeHKX8InkH94qOT58UGpMZVV9dW3j3mOV2w5XzBgZZ0/R5NnOItB64sSx4trlmwsgmlP7RLN5jitdKLabW3ZrYffn43kV6/aVDk4J6xPbDV3otltw11+kl1m7di2zTVR1A3q36RO7hE9UBU+Ji7vmvPOS4+LeXbKUMQCsIiK+CRMmuB4We1KkB8dhCxs00y8z5cYHNLN6JU4EZIg/DA+Jn3EOU3SuRERE4CqRKwx12AlaJc1m0Jdccgn9fkc5YQBAYA6WV2EwGRHBICxdupStXWAVzzrrLI8WKlJqUSl2VPVyXRDoCAHaEKTKfP7s8oQXCyTMaJZZb2A+xdyBW7ip1c+ywgHVyBbzNFbs9USLxCoFrdM555zDioX2XM80hM3r161bh5tXmhpWO7hC04QyetasWddcc003mIVpTOREEBAEBAFBwAUIdDi6dWDa9GpI8Oj/6AWJlmElQrzJkyfDrCFdxCSKLpBZEEN2/J0zqsYO2hNVDEbEKJpWKaLExPqMK8qnO5ZTnLMYyDFixIj+/fsDCwUnGDEsXLgQBcR1112HxRMjCWOccu4pCLD2i98o6jc2NpZppwte5ubWE6t2FmUdrYyKaBMJdhWfqCooJMgPp4oVVfW79hfmltRvzi6fMDAqJNAVTY2nvCHOzmdT84nFm/MP59WgCho+IK7L+UTKi3y1X0pkSVnNpv3lqBQTowL9fDqUazkbn+4dP40PO4MdOnhwSGrq5WeeOXsa+7H8ZJ9QFxc/MjR05sSJ0eERbyz6MuvAgZdffhmxnuLgXJyTTiUHjUhLzl+6bMhQtnLGyyH9MipCqD1EPWh/oA6RE3777bfsjUBTD/LKwpcJuSogs3TMEtWmLuy7DR1JSH7SueNammk8GiJllMBPRgjkkI6Dp9izBdYStRFkoqIGCNyp/LthYKEU3bBSJEvujwC0IDQi7QzTJdp22hbaIqZUtD9QjcynVBGYWcAPPvvss0jU+dZYnKAhIiRtCMLnBx98ELf1ajiKZvy5556DVaTxUU0NzQ6RMBHjcMGQ1f0xlxx6OQJ8TSBgwarAy/GR4gsC5gg4fZ6PDpHOjE0n6eRU8oynIQ0feOABfrI4hphCeQmhJ0Or//jjjzNeZxTuWTI9Jhi4T4IWhD+lyMwEYFFVeZmToMFctGgRpTOZFcydO/fee+9lfoXZAuwqjzOvYOoCz4hckevmFSZX3B8BlpHxGcoLzMiPCSSzUHbrY/tONWhzRv6PF9dt2Fta3dAybnB0dISL9mOxUJDYyGBYxYKSmryCyr1HK9gkZFTaf9yNWXjKo2/x4fN107h1+RCktfUEXhS/3lqIhWViXFi/pIhevdyCvGN/mANHSnIKqrYdLB+SEpYQ6ZHu5OmzCgoKmIYxnevyujb/ZNCt0NVu2bx5WFraFeecPT0zs2v5RJXD4MDAyRnpLLC89vnnG7duZTZ79913u/kWpVu2bOGLpkeG0WMGjvyQJSI2W8OzIW07B6MU6EWEhMztmX4wscd/JfuQMEunaDiJpuwEY+GQaT/XcYX22GOPcQ4XCaVId4+eEf+JPEgwbNWhI6k+3iuIACLknHVWFlzhFhV9aV7dnnUFqoKSelaeJbeCQJcjQEfDYBKl4QsvvAA/SLOAryQWPEaPHo2iWWePQXAnNFEAAEAASURBVAiiZuYaXCEwB2NOmh3mFMgbuaXGorQtn3/+OcNU2hl2nIesRHat1NO0eFg90+DoOOVEEPBaBBSr6LXFl4ILAp1FwLmUIuQa62CvvfbaqlWrVM4YU8IYDhw4UE3GYM3OP/98htQPP/wwIQnDoBwr4EcffZTVe/eZsNGyMAGgOBx028wHGPdzqPExFxn3M+tgDoDQgAknagX8nqgiI1fkOof6qf7Sl8M+wKISMxZSGDsDBZEwryB+DJ2YbAilaETMg855yak73mTsSnhVeNtxf8PkE+IYblFJVBxbHIxJD+dXwx+l9Y5CJOjYyG2LLS46pG9SRGFRdV5p/aaD5cP7hvv5dFvVLd8sHy+jdhYDUlNT0RQ4jz4+ZXXUN7V+uTHvWEEtTOKwgXE+Lt/luaMcJsWFxsWE4lRx84Hy04bGxIb7u4N8sqPctnudxpx2nhkaEzw8//I5QzM544tuN/VTXkRPB721ZPHiqKDAuRdcMH3MaL+OzWxPGZtjA5CTYan97r7yipc++eSbH36g42M5bdCgQY5Nxf7YGHWogQfOyzhozLEWZI6NwGf69OlYLjMtV6nQsGNyCNWIdOjdd9/lIg/yMvBiXH/99XrPFvr6O++8E1sEDJlpJRA8GjNJc8EAgCRQO+I2kTbEeJcBBsMMLBZxCIP5gvGWJ54DJh2iJ+Zc8iwIdC0CdDR4VGQBAzcLb7zxBhIEGgRIQxhAnTEIQXbBohViBeLPf/4zy9ics/VldnY2jpV49tJLL6XPou3FzSKBGajMmDGDAIxX+TZ1PHIiCAgCfCaAwBfEIWgIAoKANQg4kVKEg2MlTfOJypZn6NCh+BJmoV5njutMz37/+9/fc889DM25/sUXX2D5CwWj1s10SAeewOXBAhC/5X6UNgVNCjQf8wFYQtQKdOSQg/AF6enpjPIhQ8kV5NEzzzxDh91RDmmSSIi5ATwpUw78lbAqyFBASxExA2e9EVkEDlDo7DknWEexyXU3RwAZC680bwXLwhAQvNXULMvCvNKTJk3izWEkx4STqaZDClJd35ydW93U2gMVWGSYY+K0P2NhwQE4VQwNLS6trN9xsKJodGNytLvkzf7SmcRAW4cWG4tOWgzG6Gxri0yJsbvrV/sZBVXUNH2zrYhBUEhwwMC+MW41HoqJDA4M8NufU7XnWOXglNDIYM+bxkAhQQzR2iNU5FtmtQBvVrTn/ESe1oVoM0VcvXo1+uj6ysrbLr3ErfhE9b1AJ/VJSLj1kktLK6uwTuAi/b5m6Ey+qa76SbNMherGmY4Ya2VYPyoavSF9t84YX/fMmTNpzHFXwioglgo8xZCA3VcJrINRapwjP//889hqwBgyfkD5yF3oQkYFuqNnUKGMM9RCIy8SAbjLkIk5P29aF75auiw2nzDWgo6n4AyoOmIVLRew3bvtXjRm0kIAC7eIoaO7HV1XibZ7t92LOpMW7lq4ZVsOLUeosyQnbogAdUczQoPDXAP5BU0HOmgEGcas0gXQN/F9MdRE2YCMUd1lrkHzgpkUplRcIQBNmfKo8OmnnzLjYHWEdowRC71YF66GGssi54JA1yLAYJ5D2syurQVJ3bMQcBalyLCY1XgW05Q+kQElQ22WyFDpm08huMuI+a677sIamnE5CMIq0ufp0bajMGVQi4oQZpC+lpG9mi3A9LEAaNKP0vWiE2T2uHv3bvrv9evXQyZSKJ0TykVzoyhFnJUgZ9C3TE6YGFBkJgbIHCAa6Lz79Omj3CcZQ6JTgFjkMF40nsOBknnMqNWEhCEF0xs4SpOcGx8xOSf/0KMMO3iWBUz+mgSQn45CAHgxJ+FArLpkyRKc4DDnxAkOTATCHFabGQ5ywjozLyFviD3p7jletetwRXh4cEpieGCAs77ozuaQhb3I8MCE2NCKyrqSyoYjRTXdmFLkM2SZhO+L+qWWcY6OPRHEIswCb4LWK3UWQxvCN7e0Hiiozitqkyj2TooIDwlwqwVWPCoGBfiydc+uI5WnDYn2OEqRyRiEETM0Vgv4qJEEYtDK+gFfNBsZozhjSobiw84v2oZ6p2vDkA1vfWVFRb8477xpmZnuo080FgcAU+Ljbp5z8bGCAtDj60CrCGjGMF17ztf6pz/9yco8qF6bhSIsFeiaacyNnKMxEkp6++23c4VxBWJSTui4GXvQC6tJC3withrcIipaEgZFRAVr6endNPlntENPx+iFoRR/VXn1VK2jE31dwah/qhPjX2MAHcx4kXMrw5uENMZmjMF43ZiQvm5yYvLT+Ig6tyZd4/zWJEKTnyax6QfNg7WbE50lfaIf1Ff0iYVbhLHnrj2PW07XcsynfNbC43Y+y5oEk5GOViJpQ9jYiqkEtswEQ5xhnEzRBdC2kDeqm01XsPTSmVFkIhMHJhHqZUAPocxoWORgLy8+Ujov1sIZtLCgQuT6WSKUQxDwWgTkQ/DaqpeC24CAswgIqCuYFMUnMovA2PnKK6+84YYbGCi3m0u6tNmzZ7MdJBQMfR4MHQwjPav6npVOkAcZ7tOtdjTIpk/lWZIzbwW4DkuIXoxdF9977z1IQGIjGAQfC3o4SILiYQSvk4NDxGoVdgD+kWjN88xFzTAyDoBTgKYkaUXz8ZfOGwsFHmSagV0zllDmkVh5hahwzkieUX2yCAkZSj7BgUEAnAXmtJwzjyV1HSGP4KeZKyw8KrjIMM/iZoXVS3IL2sxhrKcjdcxy0ikEIJ2ZSbKYjPsbptAw1Iz2GBF+/PHHaFFhkKlBrFSoEWannYpZBz5YUINQMa1fdLB7mDzrjEWEBibHhR08WlLT0FJQ4fF7C+hytXvCIj8b1iNRhE/EJxo7MkE5YQKJsSQHXyhtl/ELbTcS+y82tZw4WlRLPDCJ8Hdt/7nTEYtdfiAcSo+D+dVFFQ1DUzzSXSydBQccIhUNS0KLirdcuEX1RWOmyhdNdbtStIiIHpfEe3bv/tlZZ5192mnhbuwMC66TXclunjPnsQULPvjgAz4c+seOZtHu9PJ2mBe6Y/JvZRGgETnajYvREdQbR7t3PfQiIxOGQCihaP2UebiHFqRbZluNeNVfXUB+MmA23tIBTE5MfqoYzC+qKx1dNz6lw5hf1Lf0iXkYrqi7Ooz5iQ5jftLRsyqhjsK3e90EQBWDMX7zjHEFzcEvfvELhg06ReMJAVjNYrWSASTNJoMN413OVZwkjRcFPT1RYRT5qAeZDP5/97vfsQLKwRyH5RDmCyjc6dGYpjGS4Zs1iVx+CgJehQDfkVeVVworCNiPQPsEn/3xopiAlVN6OiZXcHY///nPO+ITVXLcnTVr1osvvsiXjKQLW2PWypB70DVCAioXIUiB6PCg0sxzSFr0ozwFiQaDaaQdiZC1cWjK+fPnY4Kqn+U6PB0H1CF+0+mhmQRCvZHcq6++CounQ3KdkTFzBjg4skTPrYxYVQDOYQyxbCUAS4is9TEyoAi/+tWvEF0ykuZZHVWnTsgMC4/wUHhZgpBiXG7yOEME5JzAC20BLKrUJMoKJPwsub322mshrcgw7lT+3//7f9jhKh0oxNYf/vAH9DWuF9SYFMEbfrL1Hrb8rAxDMzGGg9tFjQLhzoFkFWIRnQuLwwzj1DtmPSatJ3ocyK2ua2iJjw4JDnQv2WlQoG9ifGh0VHBtfWNBeTenFKkyvnQMJNlzCQ9H1DKrF7QtGDxCH2OvxJ5UNE00BXrpwvpatj4kKsXDhW2UItMLKEU3YxR7hIf4h4XQhPbKLa7LL61vbG71dxtXj9aDrEKe5Iqn06EoN1X0PqxCYc/LMhWfM3c5gR6iui13fJ1N1zw8fcSCBQsQm0wcNWrm5EkJUe6+FVJQQMDZ48ftOXTwnSVLseOjv54zZ46zUTLHTa64AAFEl6TCGqfq3axJkYGZNcEIc8qQpwygE7IypDXBHBVG5c2a2KwMaSEq81vmV8wBJ4w1wdrNns0PmmdDxe/Rfxmr42CBgTqHhYIwE1FLF7SWJqwfww+6G/QN2CHBPP7v//4vIU1iS0tL01dodZkdXHXVVRhjMR1QQ1P0jHRnrIoxa7CQDbklCHgDAu22Ud5QcCmjIGAbAk6hFCHC2O9YuSGnh2NyBeNmTqsRDKYPN2SKraNHRPrB3iyqJIxB1fcMS4gAhK6O65ib4VeoXUqRpTZINGbvzOfvv/9+tHsqHiKBvmGGjyEAUXGRhGA56cJJmskY2UBg+Le//Y21QVxiERj3IkY+kY4Zm2WsDOhoIYCQGMDc6Y6ZCOmbb731VvpmCD5jMVlIZJ5JBiiLyoyFv4y8yQbRQqQCGiHh/ig1ZadQysukepzUyTzZZuiA9BKXKBC4TGV/+9vfKuEh1+FPESRSUoqMggYukuLjqFHxicSD1y3kVJQXmsNCruSWAxHg5WEMhwNN6gu+CX0T1cp4Du0GrxzUMOaTvGPw0dAQHEZavKNsVNU1H8yp9vXzCQ8L8HW//U9CgwJiI0MOHq0rLuv+lKKqI8b6MMisfFDXNINffvklO/CiIPvXv/6F1RLrDZjDqxbPGSq25tYTx4rb/CUxMQH5jl6bLrweHhYY4O9bU9t4IK+msKKhd4yN4twuLIIxaZSJHD/72c/QpUIpwjDSd7B/C7478OaBHRmVjkKE6R/VbXzQUecsubGaRQcXGx196YwzhvbpA9HiqMidFw8WwnMvnL1p796d2Qcw+KXzhYE19qrOS1pidiUC+Pdg+KeGXmpEp/6SB32iz/UVkxP10/jXWATjdeO5jtZ4YjxXgfWVjp4lQLthTB43CXPyof+WsaPIjdd5/40/dfwmMRvDqFRMAlj+qaPV8XCiDhXbj7/+e1Hf1bdUEsaf5s8as2EMaXJuEhV3LTxoDKyDcaIO9eyPvyxdtCF8pyI3D2x+xZgHyL777ruP4Z9J5jv1ky6GORSDfwYezAIYUjJ5YbJA0oz8GU9qAQETBy4yVmGOoGYBsJDYPzHjwNupMqDuVNISWBDoZgioD5Y2maObFU2KIwg4CQGnUIr0SXBYSqLItIrpFuIdkwLwuaK0R1tB54c4EZECPR+HDsYcjN6On8j94P7Udbi2jrQMxKMkeAj08A+iKUX6TrpYRIgMamka6HRhdjAugLiB2cHbI50omWE3RuyUofNIi2yQNJydSpRu/rbbbsvMzIQxVFnSmdQn3OLQPzmh81ariIqyNN5q9xyO6fHHH+epP/7xjwwLSAjvjVyB+NM8IBcZIiCEBFVAZmmRMHCgjB6gCDFkePDBBxHIUEyFJOUiACFfeeUVAqga0amjzQR5cZuiAFH9hwZHn7R7vd2LPNLudZOL1OBJL4sTsZeEhuDlhIbAtJ+/kIy8RbxpBICJYPtgRoHYzOrMmJ/sz63C6tltOz1/Px/MsRsaWkoq6puaW/08VpJmjvwpr1B3fKcM65lRQzCxhwPG7yyKsCKCLhWzWT5kmgjaNEI6atTS3HLieGEbpejTq1d0hDvuh4N2VXHfeWV1ZdWNnaIU+ZSMyzOARpOo/56yRpwXAIEJewFzsEiAewoU+nzO9C980Sw4QSyyEkaPQ13TMut5nUPyg8kzlCKNyW9vuSVj4ECP4BMpOLUWHx31m2uv/dXf/k4XRk/37LPPOol1dQjOEonNCJhsImFzPPKgIGCOAJ2COrhlcvLjnf8usegr+kRFqH/qExWbjlNdN/7U5/rEJIzJde5yxfyivt6uVEI9YuVf+hfWMpn+YMiMERK2EYgN4fRZdmK2RQfERIzBBikiRaTVxYmtmmeh7cBiht6K/hS9RTfzvWAlehJMEDAioD9n40U5FwQEAQsIOIVShE/kUKky16JLM88BTB8kF2bO3EJnRz93wQUX4CJE9a/8RKWlptlEpbkw2D2kf+axQdvhhPHAgQPcYs6pbG045zpGpi+88AIzLn4yY4GsQZDINI+fqEvgdzAHVm5HYBWhAFiyw44YigcyTrGKWAQgNsQXJEJFnGQxJ1QZIwYLB32zIhmJROfHQni6czJJYdES0vfTr6O75IrmE5mIAib246ifFM3EuAHo0LgxPoA3hEtlGPHcc8+xMRzlUmnBkKJSZC9aFQ8++7hOYIUMSZCWYydy4E99GWf+esxkXnwLtwhs4a5tt1QGjM9ybuFQeVCvQbvBCMBdbqmQ7YbRF3VsOjwvMxsW8VYrAgJ2m1eF74KDT4CqQe/25z//2cL2BXtyqqrqm9numUwQv7sdAf4+ocH+5Ky2sbWwojElxikkF9+v+oRtKD7fsvqc9V99YkNs5o/QDigVM3wTUjJIZCr3ww8/ZCcNtM8sANAi8cGiW+QzZL1EpW4ej5VXWlpOFJXVEUlkBOsf7ri4GthWyra1osbmE2gqrSwXwfhqkFEAoH6EGRQzIppZ9bdd6LioDp768bQNFs71FZOfOph+kABWHnQQyJA5YPqU53v+stUv6zcI8ahuvGQQRtU17watusqJlfGbB8MdIR3f6VOmTBgyONSjdtzy9fEZM2TIxdNP/9fSZXheptdwbE9kjpVcEQQEgW6GgA2tdPdAwLzjoGdlqZKpClYv9JWMMTh0YR966KHLL78cKysmX7hNZF6jb6kTHqdvYkDCYqfJLfkpCHgbAmqa5m2llvIKAvYg4BRKEcaKQ2WLqb4mxYwZRVjHpEtdgRrDLBeqDjpM0TesrTFLVF0mdJ7mpyAB2zXUxXqU2BTzyAqbYgyJHFNilEGoRVRC8HSYJ0PWsChHxuBxnn76aZUisztW8OhTCfnLX/6S1BEwqjhpWfCJ9pe//AUbZPwW4xEPZR+RmPfoKhX9t1NNko6N5US6fLJKHpgzq9i4yxrmLbfcwq7ZSoHIdXhDjMqZqTKAQIYJpNjcQSA+8sgjFJAAZACW6h//+IeKhPDsZcm07a9//SuocpFywWdRHBXA/r9UAaud6HSYSIMwGeBQpBsnKn798+TNn/whgA6vztVtVUfqnOvmMZgE1iE5MQmvQqoYOHfbAyR37tyJG03qCD6io3zmlNTVN7Sc6ImotqMgXXkdSjEs2B/2Bt61ybBhugPzRFWynMBhQ5x8Vorc4S9fNIc6URc13aO+TfO/KkX1jpmkbn6RzxZRMAeya6SpqJJpfzhwtogbTXSLHKy+qE+eT1V/5iYxW/4J1GDe2NTq72/XNuKWU7HnbmCAn1Ip4kgRTaX1UdEq0pijxdai9VM+Sw1SlSDJwYnJ+ckK/0+lc0vVPidUAQta6i98JVdOmVC7AWjDqW4k5yxr8S2z8IMVPMsDLFyhQYZHZnZHjdspCWEJim5uaGq/2Eh3d6FojhKgX3/h7PeWLed7YRig1xHNQ8oVQUAQEAS8HAEGIXRMSAoQB9C7maNBbwV1iF945BpYbunJFxMc+k218srsjA6R7kmPUoiWA9fq0JFMcNQgxDxyuSIIeBUCfCDq0/CqUkthBQGbEXAKpQgXxqHyhFcOpHaa49MZ5UNlCqd+8t2iCrzzzjvVT25hK60kflxRzJS6haqLWYc6138JgPwHZ4LqCsIuTEc5h2ugT2WvFR2SnLD9LoIg2EP6VyMNgbsrfDkpSpGuGltFZvhQnEz+KYLKA9QkPo+hO6+55hoWA5kcatJBJ6FPKJTqv7miT/Rdenou8rhKkeuUSw0RoAWVqpEuXxeWaSdkKGuMGjQdFcEwcEBiCZnIg+gW+atVojoYQw1YUUyQOGHpEuKSPBBM07U6pM0nAA7/xaY0xhj0qMV48ZTntj2lorXmWQZYp8yDywKQYXUYU1Q9Ga8H9Wu8bnLO7haI0eqbWk90RvBlEonzflIKtHdBQX5+vj1DnLMhNWpi/CfgqdD8K7OzXHyPbYST4eDbUb84UV+r4qb5a3JCZrjCX2pWnZMZdUWHpHK5y2cIK8TBHAC4VJ6xA+VjtyH/ilJsaGxtbnZLjrlHD2343NjcwmYy1peRxpBWF58VOKa0/inApz3ksP4Rk5DUEVeMf/VPHVLdVT+N5zqAPoH+Y9GFgyus5dCV4EJL37XhhO6Md7K6pLSxtq7HT51v2BCbix/hdUe0il/F6tpaxgz6/XdxNiQ5QUAQEATcHwFGgzeePCxklVYUVxscDC2UQIGRBrMVOlA17mUaxWCDeQ22zyxdc5ElTDhKmERpgS0AK7cEAUFAEBAELCDgFEqRbo9DpcomLWisMPFj2mPMB4whdp10Zkywjdc5p/NjoYxJu7pOJ6f7OTVFNwlPEszQlOyOW/SLyryX3hQrOXWdGKAA1OMo+JSIT8dDj4v5M+nqhLiFfuSJJ55AcIevfeJnKqi4APR3HLhIYzbIVjBMC1U/rWPTJ6pozDDNabtly5YBC4+znKjIUxBTEKEr1BpPHRUkKRZzJhjqu2CllYYkiskDh77LCTl84IEH4BPV8iNsKVbS0LiMKpS00xjY5nNSQXpz9913K6D0JFz/5EpHF423ODc+Qn7UXfOLXNF3VRiTv+oRkzDqorGubS6yPQ+qYoI/Azvqi7pAh0WEZIwXkncYIp7laDwqWkglwM8HZ3KMHd3T8Jmckz8sHMlmaEA7i+oWimblLZYH8MSKCT/sv5WPEMzK2qfF4DDK4mhGFLGo/vJJcqIO9XkiFuagRqhZTniWz5+fOm/Uuz5XJ2RGXVR/+ZYZ4puEsfInUQX4t9HlTc0tVj7i4mBIFJVFdlPziZZO8uB8FDCtkGguyzM1wgvAh4mGjr9Qk/xVFUr9qlrmrz5RF/nJU7pOdcNlzDY1BYmGLwvjRRvO8dLIytl3mzZNHZWRGB1l5YttQ0LOeKS2vv6RV+dX1dRQs26Sc2pNVZwuLxlzk7zpLMmJICAICAKWEWAYaS7mUI8wjGFsyWE5BrkrCHgtAibDAK/FQQouCFiPgFMoRaZ8iP7gyNqYrfJydiRgRxSm/Vp4SP4Q3kOTzZ0797XXXjPJLg6GjRMMmD66RhUGLQ+2z7g71I+w6QqOyYxuQRj9K44Pa2jc5BOS7hNbM8QgbCeNvaF+Vp0we3/mmWewIDYn7EiXTXgxUoOwgATEayEqPPUUPCaOC5FA3nHHHYgEVYrGmHmWnHOF+SRskfEWmGC+B5EEXwkIJpQiZYQMNYbnHEA4TC7qn2SPzR/UTzVWMO5YzXWmnYgctTkDe9eocx60R7+jM6BPKPVdd92lf9pz0r0bdF4JKhqqGiIMelcDBYBwiHwC1Je6aHk2G9CmUmwzK2YLXffc/wR6rbqmwc83PMhpprjYlmLLz/ZK6tsHEQ6Tc/2TE31uEsz40xhMhdd/dWVZf6J4SejFk3xjG8+o2EYaAV4DzJ9xI8tCAk0QzRFtUWpqqvWRG0OiUgwOYPEGTtN0qcYYrAvPYdt4XckAr2unDJ95hK+DhSgO1+e/s82RIiJpXfm6MXmmqafvoOVXOafNx+qZ95aW2c6y0PvQ/eGL8LNvv0uIjh7Yu7edEbrs8YbGRvK8auMmviz8FNNzuSxpCwkh81fLhyoMuWL5kPGMhUfkliAgCAgCgoAgIAh0GwQY8nV21Ndtyi4FEQRsQ8AplCIzcyZLSOG++uorsoXzeHYxxt8fgn2kdjhD5ENl/sws2kim6AJcdtllqH70T+yRNRf5zjvvoIPDQZVixLD2RcCPgyqjDJBZOjwjq3NY9ar4oSBxEYLRHH4D8WmFQ67s7Gxm9cSMFzMUkXCg5FmnaHLChAclFKbQ7KSBtBD7YjzcEYZSYAqNp0ISuuKKK5juGh+kCMrtI3NL9IDGW+zUzEWuQIKgalG3mGEqTpOMoaxEFEMMOk6ki9B/ZNgYD+fIYcgDHiGVETc07uzZsyExMfHWIRHCPPzwwxpDrjNBgtLlhJyY0J36qS4/AfYuz4PDMwDFABm9YsUKWAAoBl4hKkungkEKrxlW/7w5VhYfSRrkGTEUltX0q2+KCP3vnuk62i48qa1rKq2oI4tKOue8nOBWnMPO+K3E3IZU+JDbnEGEhuoxCpu04KsBTplGgI+RhpEGioPVF37aTK+gCY0J9zuShz31f98rGzLsvEdwoajEiVFh/qFBP2kznZeo/TF39t2gU2BBi/5i7dq1LBHxmdOe4/aenpGDlSR6KOLULbzNOcSc7de//jUN/hfffZcYE33teefFR0XZHJvLHkThuW3//qdPuiVhtez3v/+9+ZKeyzJjTAhvJxg3qA6a63yMbJDluZQiAwnePV4zhkwW3mGGAQyKGB3xl/AsOrKYim81C+MiI2hyLggIAoKAICAIdBsE1FidTtNCv9ltCisFEQQcgoCzZnQQf1CKzKZg0/gykeSw+L9t2zamVchw+EQxPWbUbqQCdXmwLOMR/RljNM0cjFkZLCQDX8b3MJVYJcMbMmeDa4NW089yAs+4bt06RJHEowycGRaTLn9JGlYRnSODZpLgClP3did18JJMLRiFowGByONxQqI6xOzurLPOggt466234PKIBC4P315Mh2CCjNkgWmVWQBlh7sikTghtoyo4RVPUHg9qw2fOIZ4IAK/EFJTpDaIJdEzz588nOQb6ihwEDcgpoOAgALeICj+Pt956K7f01jeU8X/+538wpdR4En9qaqpKFzxhKgHKSDgaSyHnjkKATTmgEVeuXMlrw9vFy89LqCJnRk3FYQI/derUvn378i4ZK8tyBk6qFNsoxeKy2jo3pBTrm8qq6v18egYHOqupUfhYj5hlPJ19l2+WdwAmEUKZxQNaALy7ItHik6S1oZHhg7UnD/jWHNk3YtPe0k65KbQnxc4+iziR1penoqEUnfxWdDZv9oeHwaGbY9Xq66+/phGmGWetCKZYqd1hamAAqWUOB76xuA++7eabqysr312yNDQo+GdnnxXlQttwG0Cj9z1aUHDvU09XVlfTt2IlYOJ1xIY4HfUIq4x01iz1HTlyBLE/5/Stjorc9fFcd911NDW4WMGoQplNmOSB0tEcsTTLe8uXyUEAWiGGH9dee+1NN91EBZk8Ij8FAUFAEBAEBIHujYBHd/3du2qkdO6JgLPm+dAikHGo7SDCFH0Gh8LBLBo+BSwsfKuvvPIKe5ZpkovRLXagbJoJF0MM0GfM1mBnGPsy3FcjYHY9RkhIAGZxLLNzl1k6czaVCo8waEb8yBU1nTtlZWCnpuaEGDsjHONZtH48TrkYal9yySUQposWLWJTCKgiUiR+KCHt0JD4Cak9lTDP/OKLLy688EIIUB7BEpycEwZFIcIllRk2YOERdU7ZOSe5KVOmQMtiN0fB4RYhFgmvoqVQMI9wtcBLMZmpYi/JtIEAIAxLSFRAB1eFC0XNZqr4meUyCyXnVAccx8SJE0FP3ZK/jkUAsRKeN3mXqEc+ByqLqlSvJbUMW02tcaAHYeZGpXeWUUqKDgz0ayOhSk5Sio7NvP2x1Tc2V7ZZPfcK6XbkkfXgUN1wE3z1kIm8DHyzrB/QPJ555pksliCRVisW1kdoISSUYkZqm4eElubWxqYWfz+3MCY1Zpg2W6kUY0K7D6VInVKzNNQcyE5pmVm+gkakAedAecqnrb5uWnUjGg45p9GYcvrpeHl8ft68j1auxPz5vMmTaE0cErkzIikqK/vf554vKS9nZQsvxkDU2XbPGblSceLJRNGI2FXglYXdn5yXlgti5lVk+EFHrzod8xRZ4GR7K0xJKDUy+bS0NBYa+UlvhbMXnAzQTJk/JVcEAUFAEBAEBIHuikBHPWZ3La+USxCwHwFnUYrkDH7tlltuYUiKpTAMnc6ryYcKF4ZBMe6K8IamiDCG8hB2TLn18jiaRLZAfeqpp2DuGB+jIOBQETJPYwUezhGpF/QllKKiHZkJMG9nfIyKkPE0K/Cff/75xRdfrLNh+YRHYH/IDycvvPACzzKwhriEzkNMxOCbATdWQvB6xEOKOMmCNTBSimReu96H48P0+29/+xshyb8qJrZUlJHiq5wwmteKRWhBVXZcQEJEUhaG+KrUKBCVp0W4VIUkCOB9D8trYFRqRArOZizQsty65557dLS6yExrMR4n/0gUEXWi5RRKUYPjqBPeB0hnqGQQVuSvor+JH4oB4RJMIm8UjBJvlAnna30ehvUOCztpPVpV3YCJcUpCuFuxSHX1zZVV9THh/kP7/Ic6t75onh6SBgR90IaTB3pqWhIOKH48LbDgwcoEnDKfuWM5JtjbwSlh4aH+tXXNeUVV/ZL/s02W+4CZV1RZU9tAfngr1KvrPnmzIScs8+Cfl4MmmsaZVprlAeTG1DLNMs07zS/rWDbE3KlHaOrPOuec/IICetuXPvnE38/3vMmTOxWDywIXl5c/86/3dh88yCdw5513Ivwn8y5L/ZQJ6bVM6s59iM5TZtvmALyljJ2oC/ZSP++881huZIjy6quvwjMy8GC5VChFm7GVBwUBQUAQEAQ8EQHm12qK7YmZlzwLAl2CgBMpRYbjKO+grjDuw3EY/uPYmBKXT7AnXMe/IbZ+GPYy9YJc4yJs3a9+9StQgHGDDsNIBwmPmmxwl4Hvgw8+iJ4Oz4lwYczcUHjx8/TTTx8+fDjPMjPHVyOcGhJF3FTB0MGswbL985//pF2AO3v++eeZJCj1YrtYww/C1sGvESEHrtmUVTUTRQbWxPDBBx8ogzUihByEJ4IjUFFBDeipiLpCWphQkW0YJcKz8s+h0yUwJCNJ6EkLvBIDelIhDJlXBecvRSASfHJRLuhRCk7SKh5ghHvlKXgKCkuKip4gcuy+QZjHUcq0y1nAZlIpixcvZgJMPDpjcmI/Agg/YRIxfqQ2eXkgoKkRXle8AbRtLTFkCBXqKEYpOtQ/LTn0UEFNbX1LSXldbX2T+1CKldUNR3PLoRQHJ0al94uwH1j3jwHWGF0qNBMHsjW+fZoIXgCaMjbiYIWAr5LvlEN/+I4tFBK4QD+fIX3CNmeV5rofpVhZ3VhcUtvQ2NaCxYZ5MKVIH4TumCaUTk2pwunOWNxCcMfyGC0qn7xqwx1bvxZig7u84sor2cscpxzvL1uO7fPE9HQL4bvkVkFp6bPvvb94zZrEpCTW2HBCAlD25IRelYpgjyMiYZzAYIM+EU8jbHtFr40Ynx6f/lEnQe9J+0xnCgXMt0mtTZo0iYGE+cKbfsTyCbvAQShjIcHYhnqnP8VeQflepDWAlcPQgS6YXJGK8atH0Io7aUY7dNb0CAwt+ImenUEIbw5RcRGfm6w/GSlp/L1gr4ASlrETAVhk5VVkgEHBWTFVi5o0OKxmKUfSmG9TZLw/q/GPKgujhQsuuIBhEj9JggVIkiA25VSauyRKvRAP4w3LxZe7goAgIAgIAoKAICAICAJejoATKUWQZSQNU8ZQFb0eM2o1u+Yi41dG/9BwjONR5KkRMwNxSMNHH32UgTgeGNlRBGUiA3RCEhVhiOTSSy9lhkA8jKF5lpk59mVa6QNlc9ttt+EAiFssvBMGIRjDfdw4MqpmCvHII48wBGcvXXxaqZkMA3omHogoGaZzQsykfvPNNzM6JzMfffQR3BCDeB6HG+Jo93UhNkqnBug6AMWEMWTl/09/+pO+yAnpIjz8+c9/zsyTnOtbhEdpyOSHDKDK5Ke6RQGZd1E0okILyYH6ibtch0hl9K9kbjo8T3FO8eFqOTEmodPihMd/+9vfIiOFuhVK0YiM/ee8abxmMNTUmjqgEXkZqHreTA4qxTi3tCdF9mYZlBK2YW8plGJhSXV1bWNkWKA9ETrw2dLKupyCygB/n97xIQkR7rVvjAOLqaKCWcCuGVIAwkI5JaD94RvH9SoHn5v6TlVb5/DUjRH6+fYclRaJO8Xcwiq8wP3YkBiDdNl5XnFVVW0DyKTEBUeHB6idhbosN51PmI6ALcIQnlLRdAfouWj8aWlpt5WGnYrmAze2xp1PxPYn6Exx6UsH8f57/3pl4UJ07JMzMmyPztFP5hUXz/vwwxUbNozMyKB/B7qOuifrU6ZDpIvHsoFHgJ1vDSN0RhFUDSfED+GIbbViFblC9WHNoO7yHhKAZ1m3w965s/0gLwMrBy+//DKLBwwkIOD4utu23v7ss3vvvRdymfxADn744YekC/U8YcIE3eyT9Ntvv82LRP/LoqC6zgiEtUNIQB4kKvIGRchQBCEnS1AKE8TvtDOonln15C62yQxauAVJiotnNmrjIhnjOqQqqZAx7mK9QTaIVkUCY0hm1IiF15VXV6WoAsBX4meGPBOMbks9In8FAUFAEBAEBAEvQYDek5LSJ6pu0UtKLcUUBOxBwLmUosoZw2WmOhyWM8pkjOkQwi5MtxidYzOIVpGJB0NnxSryYVuOh2DMCowTA1byYc2QE7JKT5yoDhnuow6A3CEYTQaSIkbkTDBQmjCGVo0Io3ayCg2EahKVJaNzFGcM9AlgLALJIUaAtYRPRJBorkmhRPgxJH6UlfCSFARzV4byiBCZSKhCGSOEByQerpjwDkwtOMgwyVEKlUnCcHTU2HEdMtcYufk5gimOjmIwDy9XrERAKWehhqkCDl42/pq/HlbGdspgg5JCMCAtKOuRX1iVV1gVGxUc6O+K79pyxtgrJie/sqC4Cm+PQ/uEsROx5fAefZfG4Y033mD5gSaCNgrFEB8ymziz2Q6GhByuLB22zxMGRb29sldBUdWJHid69nAj5PMKK2vrGkFjQFJIvAeyzJA17CUCmUhjDhMEF8OaAfVLU89nrgkjV1a3SVow17j6hdT+fOHC1z77nM5iyqhRJmG65GdOYSEW2Ss3bho2ciQKfXBzCFx8bmy5RreIWA/3JugT6RbpSTHXRdQP2whPhxgfShHBHQwgYj3AocpYnqQLZimRFaD333+fqoSOpBKtBwdnxJ988gljA1Jk8YDBBusKLCZBN2MbQSNAKqx6ooJcuHAhOWFcoTtchiKoCMkJfDTrgqoXhr+j72CoAE/KXRSOBIADJVdXX301YwDyxhvIeIaECMN1BhJpaWkUHI0kSkkWQX/5y1+SHwhNxQbSLkExM3LAGF+XjrEED+qSGvsmhNVsNwcxqpY2sXLQweREEBAEBAFBQBDwEgTUXNtLCivFFATsR6DrqQdjGRiCMyBm5M0cgAkAQ2RYRVR+jM7NCTjjgx2dM6BnJM18D8dArO0zSoY65CC8ipCpiMmzUH7MRpiTEIAFf7LEkP3KK6/kKTLGOJ5WRqkjmcwQTEkFWeo3iYefTJkQFyBIRFlJccgGkwcescDiWS4mEap5hXlaNlyxkA0bYpNHNALUMof+6eyTYX3CMwZEFpQ3VNU2HS+o7JcSGRjd9d91aWU9EkX24oiLCBiacorlBGdD5Oz4YQpoNPigoBXgSpix0zhA7hjn6s7Og47ft1fP1ITgMYOj1+8uKSqpiY8J/VGcpIN0zUl5ZX1eQRWG4CQ/MCmUF6Nr8mFHqpA1COFRkcPR0DVQy6wWuFtDqvzi0WHhyXXBF1/QYU3NzLSj0A54FJLss+++/3rT5uEjR/7v738PgA7hE8kZLS2sHB8d9sUwa5D7aOtYiUxNTUUYCD0HsciCIiFZOMQknE6cr3LevHkYT5AHenb0iejyWGuEdtSk2ynLzMgBghL7YmJj3ZG3gu8dzKEm/+///g9iDpEg5ggMDDAxhlKEB8RMHjmk6uKRGeJipY3wnTKFIYRKDhtkkCFmRgvkFvEjXCeqZ6woGMbgOkPnipEM7x7cMcQooxFGSugiSWLdunWKUmQtEyaR8Ax7oD55lq2fdUKKddWx6RNITHZrAQrWQZWLau0PWoeRE0FAEBAEBAFBoHsjIHxi965fKZ0zEOh66sGkVEzO77vvPuZp7IjCuJaxMuoDLKQYr5uEtOYnkz1G3koYyPo/m64yrFcPGslEaD5mAggNsLOGFGA2opk7JgyICjloXxijM9bncUbkHMwNTjkvIgD8Aoc1uZUwgoANCAT7+0weHrvtQDmUYl5BZWl5XWwkrvq6UpvGXsNIJvOLqoIDffsnhabGh9hQLg96hPYKqRR0AK0NrMQpmwVnFy04wPeczPg1O4u37y88KzrETTiv/UdLsYXHGjcmMmBgcmhkiBttymFljdDsI6VncahLyGIrM8nrx3ZbOABBTYk17OtffMmDXcgqFpaVffLNN5+sWjVsxIj/eeABelhgtLIspwxGVHTfdM1UCoH5ABGQIhBWFQTZB/FHh84tFMRYB9Nrs8KH+E5Re/Ts2BlgFMxIQ9kInzJFFQD+DgKOJUaSg31jiMJXBpmLz0TGMAxdsB2GUiQVmEoEgwRm+AHTp9JFzEjtsFpJo0HroeKkCKoUMIaMT8gbEkv4UBSIyBKNlCLhuUu6gMk5NU6JML/AwJmf5ESvaSkzfP6yPsqhEmr3Lyl+9dVXsJ8ABQ2KZ2qIWgfWVLuJykVBQBAQBAQBQcDdEGDKz+Emg2d3A0fyIwi0i4DbUYrkkgnAXXfdxeo9voEYsqMHUaPwdgtgzUXG0wzcWW9n2M2KPaNzxAsM6LnOHIBxNiN+JTZkcsL4vt1GhIuE57AmRQkjCLgSgdFpEaMGRhUiVKxpOJ5fkRgX2rUeFfOLq/cfKamtaxrcO3T84KgAv16uRKNL0qLd4OiSpM0TRag4YVD0oD6he/YXDOob3TcpomspZnKIfjbrQGFNTZtyauzAqNSEEA+1hddkjTns7nMFjgk+6KabbqLbglWc/9lnNfX1Z4wdG+DyvZUP5+a+9dXiH7Zt6z9w4G/uv59FO6eyVNjzkoTiEznHXlhJBSHpUAWiIiR1/r700ku6sjB8xpqYIQE8oPVTCCLB4zORICfEXSN+WlSEKh6WHtVOa+DPoALXKBggs5yJITMeDFFKoj2EuUN7yAhEr0DwLPbRCBhzc3O5y7AHipCcQ18yXNEZVieIZBWfyE9WQBneENI8mMlTFn6y0Q0MJpvDkCvcOgOjneMuC2nJLUFAEBAEBAFBwP0RaJcQcP9sSw4FAdcj4I6UIh8wPsgYauOPDPUBfJ/9RB6jdobgDO5Z6meAzuSBQT+zC0UE8FdaDde/fJKioxAIDfSdNDR6x4Hy/TlVcDcD+8V0IaVYUl67O7sQq+fIML/ThsWMGeA6G3BH4enp8fTs2QMN4BkZca8sOrh1T15KQlivXg7ThdkATkND8459BUWlNS2tralJoWdmxKfEdMJpnQ0pyiPQTOjucbsBVbRu7dqauvqKquqLz5juSlbxYE7OW4sWfbN589Chw3597704MHEqn0ilYwWstXgQiwweOLgOaUinzwm8G9tDawaQK3CCDDNOKhLa3LFbeTB+UBGyQolFhaYF0frB6wE+caqoKDKiPyhFOE12isMFJ4bMrJWSEySNmqGGbXzxxRfVjs8MUbir4uREHSYZo3RIMvVFBSzF1Fc6e4JIkwMcWNPFkbTwiZ0FUMILAoKAICAIdA8EOjsk6B6lllIIAvYg4I6UoioP9oN4O7KnbObPwhsyEIdb5DC/K1cEAc9FIDM1cuzgqKKK+pLSmsM5ZTGRQWEhXaCoRZmYdahk/+GSpqaWAWnhU4fHhgd5nn2r574GOudoAGeNSfxqQ8Hh42UoRoekxvr4dJlWFJPnoznlDY0tbB0zfWTc8L7hgV4gXNV10VUnEFu458MCGsnbpo0b3126FM5sjqtYxQPHj7+9ePG3m7cMGzzkV3fckTFqlLP5RHCmf2/XHyL0nCLgGANgMgyvZ14pXDeuLHLOAXXYLk/HMqSKkHXKiRMnQsMZIyQbmEKrK5Qa42hEo/CYK1asYBMY7K8h76DtWODUy6W4ZcQyGqEihtjjx49HYsmUBkMNDmPMxnOyZ/xp4RyOktgsBOAW7lmuuuoqWE6ySqEsB5a7goAgIAgIAoJAd0VA9ZjWd7LdFQcplyBgPQLuSylaXwYJKQgIAuHBvjPS47KOVW07WH7gaGliXNigfv4uti2lDz5eULHvcHFNbUNSTNCU4bGDkv6ro5E6ciUCsA1JUYHnj098bfHBzTtzE6JDoyODumR4VFBSsz0rv7Kqzao0Y0DU5OHRMWFte9fK4QIE8M3HjsPI3EgLVvFfS5dy4gJWMfvYsXeWLIFPHDJ48O233pI5bhy+h11QXt5wLRg0Jsd1oMCJCvbIqAXh9TBANvkc9O4l6kFIQ5R6QAf9B7EIS2iMEOfOatNkZIk8CBlnDEDMmivkKbSTeHXEwhq7ZjaQwbQZhSPOnXlQ5QHOF+tpBI84XmTHGByz8giJYoms1Y7G1K0/V0ngkJpSo980KbIxHlJERoqLGDhZkSgakZFzQUAQEAQEAe9EwEKn6Z2ASKkFgY4Q6DLdSkcZkuuCgCBgGwJDUsKmjIhNiAosLa9FJ4gBsm3x2PwU5NGeA0XFpTUBfj6ZAyKnDo8JDuhKe1ubC9I9HuzVs+f5YxNHpEUWllRv3JVTU9d0Cp2SE4rNK7F68xF2DWpuae0TH3zRaUmwzL4+1qqrnJAjr4sS8gvHxDfccMPYceNg+mAVP1v1bcPJfcacgUVzS8uWrKxXFn723ZYtQwcOvPWGuWNOm+jr7ywSGcYNt4YYd0P8URwIPvUTc2YTXR6UGRsucxET47fffltZH8MzQh3yLDyaCReJSTK3iBMGcMOGDTgZhA3ct2+fcqFIeNg3vBlC1RFg8eLFuGkmclLhL6kbhX7EzMYs/CXpBQsW4CoRppJK0ZvOMWnhEThE8o+qkcgpF/wjhtKcwD+SOukSoLNV1q9fPyJE/Pjll19CUGJwTR7Y4RrdoklUbCDDrtkPPfTQRx99hLdHk7vyUxAQBAQBQUAQ8BIE6Mc5vKSwUkxBwCEIuEI44JCMSiSCgCBgGYGgAJ8zM+IO5td8s63w0LFSbJ9DgvxDXGV3XFhag9s+zGyxFBzYN3zaiNhk8ZdnucKcfxeh4pzJyU/kVu09UBQc5D8+PSXQ33VtPnzimi1H0czySvSOD77qjD6ThsSEBLouA84H2DNSUKyiyquygO7Rs8ec6dP9f6q8s78w8Ikbd+/B3nnb/v3pQ4fe/ItfjJs8xTfIiX4zcYwInXf8+HH4MvJ/6NChefPm4dgEm+JbbrlFcYKqXPB0c+bMWbNmDXtAs7Xxnj172JoZ3hB2D37tmmuuwX7ZGB6/K2rvFB6BYuMcdg+hH7wkwk/4wfT0dJwkzp8/H/khRCG7bHMXcSKSQ/jEP/3pTzo2GEPsoAm/devWZcuWQRHi5pLtnrFMV3lThtJkDOPol19+GSNorK0pEVwnakp2fP7kk0/QMCpeslPVxM4wX3zxBVliV+tt27bxJlBeft56660YxRvViGwtTbrkoaysDNtn7ZKyU8lJYEFAEBAEBAFBoHsgIBLF7lGPUgrXICCzO9fgLKkIAq5AIDk6aOqImOycqn3Hq5T5c//eUS5IGD5xy+68fYeK8aU4MCX0oonJo/tHsu+wC5KWJCwjMHlIzPdDY77fXrRtT35QgF/64PgAl7CKvBLwidl41Wz+D594dmZCRPBPrEct51zuOhABxSoiWIPz2rB+/TuLl/Ts0fPi6ac7kFVUfOKbixZt3b9/9LBhN15zzfipU/1CQhxYCvOoMGReuXIlTKK6BfennA/CDyLMNIan7LCE99xzDxQkYWDQ2HZZB8DWGKNjTQJyHcIRCg954N69e9etW6dC4hVx2rRp6hzSbfbs2URLbEQFraljQxuIqBA2UF+BPWTvaYIhqOQiltewmXq6wgl3kRCyPQviRAJQX0ggJ0yYkJWV9d1338EGQjJCAmK7reM0OSESzVHqW7hlvP766ykyEkv2j9bXzz77bMpipBQpu3qcv0arbf2InAgCgoAgIAgIAt6AgKgUvaGWpYyORUAoRcfiKbEJAl2JAB70MtMi8aDX0NSSV1LDNrtsiJEUF+brzK05FJ+YdbCoprZxUEropVN749UxKtRZpo5dia8Hps3Wz9ec0beuoWVjVunGnTn+/j5s1RLg71yDdF6J1ZuP7j9c3NzcqvSJ52YmhAuf2KXvDywVm29gmQuZtXzZMrSEaBUvPt0xrCJ84obdu9/66iv0iWNGjpx79dXjp03zCwvTJcZoF9tektZXHHKC+7877rhD8XTGCKHzzHkxxIB4MGQrFag6yEdslskVnCDmz1CKJuG5fuaZZ7JpCcbOhGSCAelGtMOGDVMJIVQkqquvvhqmD0UhYZS1NfEkJiaCszE/nCMY5BFMmzlnDxYSNQbIyMi48cYbx44diyaRtOArETYijUQCSW7JDBQnO7pQiT/72c+40qdPH/04G8Wgl0SYyV19UZ1wC0oRUSSUIjmElyQqBJsmfCKBuTJ37lwIUwro8J3xTHIlPwUBQUAQEAQEAbdFQKye3bZqJGNui0BP+Wzctm4kY4KAbQgUVzYs21KwcHVOfnljv5TI9MGJfZMjnGTxasonTuk9I0P4RNvqzYlPbTlY/uqyw9uyy6KiQoYOiO3fOzo2KthJRPPh3PKtJyWrLS2tQ/qGz5mUfE5mQliQLF85sX6tjxrrXbRyr7322orly/vEx19/4QWzp02zU6v4Xz5x3/7R6elzr7nmNPjE0P9uzYT+DqkdtsbQaqNHj7Y+t84Lif1vdXU1BB80H/QfbKPWDJokipkwpspcVPI9wpsE4CfjKIDFTpkTgsHZtRvM/EGTK1B+pEUk0JdqcxhyyE94QCK0LU6VBNnDvptSEwmRw3ual5e0yADpkpxJxuSnICAICAKCgCDgJQj88Y9/fOedd+Lj43/zm99cdtllXlJqKaYgYA8CPnj8sed5eVYQEATcDYHgAN+k6EBfv175JXXHCqsrqhqYj4aF+Pv7OXiiWHTS3hl9Yn1D0+DeYZdM6X1GRly06BPd7YXo0bb7c0xEQE5p3dG8Klwcllc29OrVMzQkABGrozKLI+uq6oZ9R0o27cw9eLQ0LjJw6ojYiyclz0iPDxX/iY5C2e54YIvYqhiNGzTflu3bj+TlJURHD+jYnPaUCZryiddee9rppxv5RGJgexA8+uHBkD1Akk4ep4zW2QEg1KDV4P7g18DEnF/TGYBiIwyHBdqRx1UwIoRStBCbjrbdE/g+HudQ7CHxkDdi5qfNcaqEiIciqPISYbuxkYpKq928yUVBQBAQBAQBQcAbEMCfyY4dOzAmYCM1bZrgDQWXMgoCNiMglKLN0MmDgoD7IqBYxdBgv+r6lrzimsKS2paWE5i7+vsjTXOAi8P6huZjBZVb9+Zj3Brg22PsoGj8J04bHit8otu+E71jgiJD/Qsq6gtK6gphFavq0SRh+op81R65Ikwiu68UltYezSvfvb9w29786pr6Uf0jZ09MvnBCUkZqRICfw1hLt8XWszIGuxQdHY2/v5ra2qzs7CO5Oclxcb3j420rxcY9e9/8chH2zqMzMuZed12bPvGnJr3o43BHuHTpUlSBuPNTrKLRz6Bt6cpTgoAgIAgIAoKAICAIOBwBRSniOQR3MUIpOhxeibBbIiCUYresVimUINAjJMC3d2ww/zDUO15YfSSvsrqmsbnlhJ+fT2AAVn62Q1RcXrs7u3Drnvy8wsqUmIBzxiZezH4sAyLDXLW7tO1Z9+4n+8YFwyqCQW1ja1FZXX5xdVlFXVNzK5IlFKydIhYxxoRWLqusLyiuyT5Swvuw52DR0dwKtvk+b1wS/PIZI2Kjw/zRVnk35G5aelhFvPXhmw+r3vWbtxw+fjwxJsYGVnH/0aPsx7Jx716cAM69/vrTpkwx4RMpP+a0OPJjExV8+SEMPHr0KHuqIFUUVtFNXw7JliAgCAgCgoAg4MUIsFUaKkWhFL34FZCidxoBoRQ7DZk8IAh4CgIIxFJigvolhAT49yqrbDxaUJVXVFXX0Izrr5bWE5i5ddbuFQrpSG7Zjn2FO7LyfXu0jh8cNfu05HNGJ/SLC/Zz5g4wngK4++cTVrF/YghWybwApZUNyFexgy6rrGMfldY2l21tr0S7RpEnTvTAN2JNfVN5ZV1BSfXx/MoDR8uyj5RmHSrG8r24tDY02Pes0QmzJiRdclpSnxjrPYVIAABAAElEQVR8yQmZ6NavA59/RETEoEGD0Cpu2Lot+8jR3vFxyBWtz3T2sWP/Wrps/Z49GZmZN9x44/jJk/0CA80fx5aWhDC5PXjwINbWmN/CKrI/ckpKirCK5nDJFUFAEBAEBAFBQBDoQgREpdiF4EvSHoqAUIoeWnGSbUHAWgTYaTc1ISQxOhCqqKq2Ka+o+nhBZWl5XWVVY2MTntDauMWTzsRMOSCUaM0trY2NrTV1jfkl1cdyK1Ci7dxX0NrU0Dc26JwxiZdMSh6VFime8qytCfcIFxHiN6x3GHJCdn9u7dGDzcFRGlK/xWW11bUNdfXNlbWN+N+sqm2orm2qqm4sqcBQujqvsOp4fsXh4+X7j5TuOVicdaDo0LFSnGnW1jVGhQcM7RN2/oTk687sO6I324ubvkjuUW7JhSkCNAjsEYxRT0tr6+YdO/ZkZyfGxuJaEderpkHNfh84fvzdJUu+27Z9WHr6TTffPH7CBFwNmoX6zwX4ROyscUvE/sVlZWX8xAL68OHDuHQUVrEj0OS6ICAICAKCgCAgCLgeAaVSZIAkvhRdD76k6KEIyC6cHlpxkm1BoBMIRIb4TRsROzA5dPvhiqzjVdk5VfllVUdyygIC/BJiQ9mhOTTEr83Lok9PJS6DZ0SShnKtsamlobGlGkqxqKqxoTEyxHf8wHA85fVPCBmQGBIi2250ohLcK2h6vwgUizsHVh7IqzlSUFNYVl9a3bg3u2DXiZ6YxiNX5HVgWwjEiey9U1vXVFffhM9EXQbYqOAg35SY4D6xQUP7RWSkho9Oi9B35cRTEKAeExISbrvttvDw8DfffPPJd9+96aKLZowda3kP6IM5OW8vXvzd1m3wiTfffDNWz0gRLRcZoeL5559PMDabzs3NhV7cvn37U089de+9944bN87ys3JXEBAEBAFBQBAQBAQB1yCAooKEGCC5JjlJRRDoBgicYhrQDUooRRAEBAEQYFcWNujg35kZcUeLancercw6VpVbXFfb0JCXizF0a31ja11Ta0/sVU+0bbgBkwiryIOI2hKiAlNj/If0jRmUEjphYHRYkLQb3eGdigj2mzI0hn+1jS15ZfWHCmoO5tXkFNcWlTdAL9bWN1Y2tqJhxNkir4SPb6+QYHjnXgG+vYICesWGB/RPDs0cGDWqb3hMWJt/Rjk8FwEIvmuuuYb8L1iw4PG332FhYfro0QEdqA4P5ebiPxE+cXhGxq233jpmzJhT8okKGRb8Z82ahZjxpZdewvAZknHXrl3/+Mc/7r//fkhJGbt77vsjORcEBAFBQBAQBLoNAlCKilXsNiWSgggCzkZAqAFnIyzxCwLuhUCgn8/g5DD+NY5rzS+vL6lqrKprrqhpOlZcl1tcW17dBIUE/4j5atvfXr36JoaMSosY2jsM5sghu0W7FxySmx49gv19BiA7TQjpkdGjsbn1aHEt0sWiyoaKmubKmqaaumbWawMDfYIDfTByh1Bm35VBSaHIVAW8boMAxsjXXXcdY2hUhP/31ts9e/lMGzM68KcW0Cwy7D1y5L2lS3/YvmN4ejraxtGjR1vJJyqg4C7POeccWMVnn30W22c2nt6zZ8+jjz76hz/8AWoSDwzdBk8piCAgCAgCgoAgIAh4LgKy0um5dSc5dz0CQim6HnNJURBwCwRQnPWNDeafzg0bdBRXNtbUN6NECwzwCfTrFeDXqX2AdUxy4qkI8FYMTAzln6cWQPJtKwJsx3zDDTf8yCq+1dSr1+QRwyMCA3uetACqa2jYeeDAix9/vPPgoTFjx95xxx2jMjM7xSeqfMFdzpgxA1bxiSeeYJ8WzK6zs7MfeeSRhx56CIISk3tbsy/PCQKCgCAgCAgCgoAgYC8CWqIorKK9UMrzXoOAbM/iNVUtBRUEToUAfSfuEaNC/cOC/YL8fdj8V7btPRVmcl8Q6D4IoBOE12ttbd2blfXFypWRcXHoCqH5isvL1+zY8fz7H2QdPTpm9Oi7f/3rzE7qE40YQUT27t07LS1t27ZtWECzQwuKxS1btgwZMgSGUbSKRqzkXBAQBAQBQUAQEARcicDKlSt37NiBe5bJkycPHTrUlUlLWoKAhyIglKKHVpxkWxAQBAQBQUAQcDACrCuwX0p8fDziwUXLV+RWVFY2NCxZvXrBwoU1jU0TJky45777MjIy7JQT8nhKSgoj9U2bNqFVZOvnnJycjRs3KlbRzsgdjIhEJwgIAoKAICAICAJeg8CKFSt27twZGRk5adIkoRS9ptqloHYhIJSiXfDJw4KAICAICAKCQDdDYNCgQZmZmaWlpfuzs9du3nzw2LHIqKiLL774vvvuGzhwoEOEhESSlJSUnp6+du3aQ4cOpaamwiquW7du+PDhEJrCKnazN0qKIwgIAoKAICAIeAQCy5cvV5SiqBQ9or4kk+6AgPhSdIdakDwIAoKAICAICAJuhADUHpuooCLEPLm8vBx9IjuoBAf/1/Wq/XmFN4S4fPLJJ++55559+/ahBcAC+oEHHnjsscdIy99fdhK3H2OJQRAQBAQBQUAQEAQEAUFAEHAiAkIpOhFciVoQEAQEAUFAEPBcBMaePJyXf7SKo0aN+uc///nrX/+a3Z/hMbGDvv/++//+97+TMjvGOC9piVkQEAQEAUFAEBAEBAEjAuzNordnMV6Xc0FAELCAQC8L9+SWICAICAKCgCAgCAgCzkMA742wis899xy2z7t372bbloqKCiys16xZU19f77x0JWZBQBAQBAQBQUAQEATaRUC2e24XFrkoCLSLgFCK7cIiFwUBQUAQEAQEAUHARQiMHDnyhRdegE9km8X+/fvX1dX99re//fe//y2soosqQJIRBAQBQUAQEAQEAQMCwioawJBTQcASAkIpWkJH7gkCgoAgIAgIAoKACxDAl+L8+fNhFfHeOGDAgObmZljFVatWQS+6IHVJQhAQBAQBQUAQEAQEAYWA8InyJggC1iMglKL1WElIQUAQEAQEAUFAEHAWAugTX3/9df5u2bKFraVJBr+K7L0orKKzEJd4BQFBQBAQBAQBQeBHBIyOFIVV/BEV+V8QOAUCQimeAiC5LQgIAoKAICAICAKuQaBv375vvfUWfOLGjRv56+fn97vf/W7RokXCKroGf0lFEBAEBAFBQBDwWgRO7s5yguLDJwql6LWvgRS8swgIpdhZxCS8ICAICAKCgCAgCDgLgeTk5HfffXfIkCHr16+HVQwNDX3ggQc+/fRTYRWdhbjEKwgIAoKAICAICAIGBIRPNIAhp4LAKRAQSvEUAMltQUAQEAQEAUFAEHAlArGxse+88056evqGDRvwrhgdHf3QQw+9//77NTU1rsyGpCUICAKCgCAgCAgC3omAsIreWe9SahsQEErRBtDkEUFAEBAEBAFBQBBwFgKM42NiYhYsWJCZmblp0yasoSEZ//rXv8IzVlVVOStViVcQEAQEAUFAEBAEBAFBQBAQBDqDgFCKnUFLwgoCgoAgIAgIAoKA8xGAVYRGfOWVV8aPH89uLSkpKfx86qmn3nzzzYqKCuenLykIAoKAICAICAKCgJciIBJFL614KbZNCAilaBNs8pAgIAgIAoKAICAIOBMBpVV8/vnnJ02atH379qSkJKSL8+bNY1fosrIyZ6YscQsCgoAgIAgIAoKA1yGgd3xmBMLhdeWXAgsCNiEglKJNsMlDgoAgIAgIAoKAIOBkBBjQK3Hi9OnTd+3ahV9F5Irz58/HJrq4uNjJiUv0goAgIAgIAoKAIOCNCAif6I21LmW2FQGhFG1FTp4TBAQBQUAQEAQEAecjEBcX95e//GXGjBnbtm0bMGAA20C/8cYbsIpFRUXOT1xSEAQEAUFAEBAEBAFvQcAoVPSWMks5BQH7EBBK0T785GlBQBAQBAQBQUAQcDICiYmJDz744Jlnnske0LCKGRkZbNWCXDE/P9/JKUv0goAgIAgIAoKAIOBdCIhK0bvqW0prHwK+9j0uTwsCgoAgIAgIAoKAIOB0BJKTk++//35/f//Vq1efffbZoaGhH3zwQWtr6w033MAtpycvCQgCgoAgIAgIAoJAt0ZASxQppbCK3bqqpXCOREAoRUeiKXEJAoKAICAICAKCgJMQwJHi3Xff7efn9/3338+cORNW8aOPPmppabnxxht79+7tpEQlWkFAEBAEBAFBQBAQBAQBQUAQaBcBoRTbhUUuCgKCgCAgCAgCgoDbIQB1+Mtf/tLHx+fbb78dP3785MmTFy5ciFYRVrFv375ul13JkCAgCAgCgoAgIAh4CAKoFJVQEYmiqBQ9pNIkm12PgFCKXV8HkgNBQBAQBAQBQUAQsBKBPn363HLLLWgV16xZk5qaOnjw4M8//1yxivy0MhIJJggIAoKAICAICAKCQLsICJ/YLixyURBoFwGhFNuFRS4KAoKAICAICAKCgJsiAKs4d+5ctIqrVq2Kjo5OSkr68ssvFavYv39/N820ZEsQEAQEAUFAEBAEPAQBYRU9pKIkm12PgFCKXV8HkgNBQBAQBAQBQUAQ6BQCsIrXXXedr6/vsmXLIiMjMYhetGiRYhUHDhzYqagksCAgCAgCgoAgIAgIAsrqGRyET5SXQRCwHoFe1geVkIKAIOCFCJSUlDBL98KCS5EFAUHAzRGAVbz66qvPP//86urqoKCgtLS0xYsXv/rqq/v27Ws3583NzcuXL6+rq2v3rlwUBAQBQUAQEAQEAUEABIRSlNdAELAeAaEUrcdKQgoC3oVAeXn5008//fDDD1dUVHhXyaW0goAg4CEIIE688sorZ82aVVNTExwcPGjQIMUq7t2716QEbAydlZX13HPPff3117JMYgKO/BQEBAFBQBAQBAQBIwLCKhrRkHNBwAICQilaAEduCQJejcBnn3325ptvsu+BiHq8+j2QwgsC7o1ASkrKFVdcAauIVhFWcdiwYUuWLEGruGfPHmPGmRugZCwqKnr99dcLCwu1cZMxjJwLAoKAICAICAKCgDcjoHd89mYQpOyCQKcQEEqxU3BJYEHAWxBobGzEMRlCRZl4e0uVSzkFAY9FIDk5+bLLLps5cyasYmho6MiRI5cuXQqruHv3bl2mXr16EeyMM87YunXrihUrEC3qW3IiCAgCgoAgIAgIAoKAcdYjKkV5HwQBKxEQStFKoCSYIOBFCNChrl69Ojs7W5kH5uTk4IPMi8ovRRUEBAFPQ4BNny+99FJYRRw1hIWFpaeno1WcP3/+rl27dFH8/f3RM7JD9HvvvZeXlyfmzxoZOREEBAFBQBAQBAQBjYDwiRoKOREETomAUIqnhEgCCAJehwD6nQ8//FBLFNeuXVtbW+t1KEiBBQFBwKMQSEhImDNnDqwibVdERASsIlpFWMWdO3eqciBURMCIUBGb6B9++EFWSjyqeiWzgoAgIAgIAoKA6xAQVtF1WEtKHo6AUIoeXoGSfUHA0QggUdy/f/+6detiY2OZgRM9c29xp+homCU+QUAQcDwCcXFxF198MX4VS0tL/fz8sIOGVXzllVd27NihEqNNw0QawhEvsYQxmjg5PjcSoyAgCAgCgoAgIAh4DgJ6VCB8oudUmuS06xEQSrHr60ByIAi4FQJIFL/66qvi4uJRo0b5+PiQN/yRHTp0qKmpya3yKZkRBAQBQcAcAdZCLrroogsvvLCgoIC77AQNq/jyyy8rVhFKMTMzc/z48du2bdu4cSNOY81jkCuCgCAgCAgCgoAg4J0ICKvonfUupbYHAaEU7UFPnhUEuhsC9KPshbps2TJfX18m3opSrKys/P7779H7dLfSSnkEAUGgOyKAt8TZs2ffcMMNw4cPp3yIrGEVX3zxRcUq4lERJSPcIpvaa/cO3REGKZMgIAgIAoKAICAIdAIB4RM7AZYEFQR+REAoxR+RkP8FAUGgRw+ci23evDkrKys1NXXgwIGKUoyKilq/fj1iH0FIEBAEBAGPQADT5gsuuODmm28eMWIEGW5oaIBVnDdvnmIVJ0+ePGzYMJo1mjsRKnpEhUomBQFBQBAQBAQBlyGA4bPYPrsMbUnI0xEQStHTa1DyLwg4EgGsnvGiSCfKDgahoaEqaowEMXw+evSo2D47EmuJSxAQBJyJAPs+41TxtttuY0sW0qH5Qn/93HPPsVsLhCMyRsjEhQsXlpSUaFWCM7MjcQsCgoAgIAgIAoKAICAICALdDQGhFLtbjUp5BAGbEWBejXXzhg0b2Nbg9NNP56+K6uyzzw4MDPz2228rKipsjlweFAQEAUHAxQiEhISce+65d955p2IVUWHDKj711FOwitOnTx8wYAArKDhVRMPo4oxJcoKAICAICAKCgCDgzgiIStGda0fy5lYICKXoVtUhmREEuhIBJIoHDhxgu+eUlBT2ZgkODla96ZgxY4YOHbp69eqcnBzCdGUWJW1BQBAQBNpDAFqQNQ9zK+agoKAzzzzzvvvuU6xia2vrihUrnnzySbwoXn755fxk62d2oxKhYnugyjVBQBAQBAQBQcCLEGAwoMYDwid6Ua1LUe1GQChFuyGUCASB7oIAhoFsgUpppkyZgs2gphRR+jAnz8vLQ8Aom7R0l9qWcggC3QqBw4cPf/zxx6tWreKkvr7eWLaAgABk1w888IBiFbn19ddfwyomJib2798foSIOFk0eMT4u54KAICAICAKCgCDgVQgIpehV1S2FtRMBoRTtBFAeFwS6DwKKUmRLFihFulKMnXXZ2M0gKSmJfZ/z8/PR9ejrciIICAKCgDsgcPz48bfffvvRRx/FWyI6xH379hkdNeDGYdKkSX/84x81qwj5+M477/AT5fWiRYtEqOgOlSh5EAQEAUFAEBAEuhABJVEkA8yDOLowJ5K0IOBBCAil6EGVJVkVBJyIAERhWVkZah32dx43bhz9KAaDujdlA+ipU6fu3bt3165ddXV1TsyHRC0ICAKCQOcRYBepP/zhD2PHjmUf50dOHp9++unu3bsLCgqUAtHX15eW7fHHH09PT1ct23fffbdnzx72ocKrAy2bCBU7j7o8IQgIAoKAICAIdE8E9CSoexZPSiUIOA4BoRQdh6XEJAh4MgL4INu0aRP+xZAoxsXFURSjSpGfWA5iAb1mzZrS0lJPLqjkXRAQBLohAuHh4WedddZDDz0Escj6BzvUP/PMM2zM8tJLL0EdomHEaUOvXr1wCzt//nx8xXIOCsrVQ01NzZdffgn5qOUJ3RAgKZIgIAgIAoKAICAIWIeA8InW4SShBIE2BIRSlPdAEBAE2hBgc4OtW7ci5MHGWSGCAzI161Y/EfgMGTKEnVJxqiibtChM5K8gIAi4FQKRkZEzZ858+OGH//znP19wwQWIr3GweO+992IQjXXzsWPHKisrExIS3n//fVhFf39/Mg/bSEOHVwe2fq6trXWr4khmBAFBQBAQBAQBQaBLEBBWsUtgl0Q9EQFfT8y05FkQEAQciwDaHMyZoRSxAZwwYYKKnGk2Dsh0h8r59OnTCfPtt99iBx0fH+/YPEhsgoAgIAg4BAF2l6KxmjhxYlZW1vLly9mMZe3atTRcI0aMQMk4a9asiIiIDz/88KabbmLLKZo+mEQOtn4eNmzYwIEDjUspDsmPRCIICAKCgCAgCAgCHoGA2Ct4RDVJJt0KAVEpulV1SGYEga5BoLm5GTtBpt/wiX379tWZUCoe/XPGjBnJycnIedD1yCYtGhY5EQQEATdEAJ11RkbGPffc88orr/zmN7+BYTxw4ADW0HPnzp03bx6rI08//TT+HFhHUQsnK1euhHY0burihoWSLAkCgoAgIAgIAoKAkxDQfKJWVDgpIYlWEOhOCAil2J1qU8oiCNiIAFbPGP1h9cys2xgFlKKxT8Vg8Nxzz2XTZxyQycTbCJScCwKCgHsiwBb2LIRce+21OFV88sknL7nkEpwqstfzLbfcgjX05ZdfjqsHvMTS0DGReOGFF6Aam5qa3LMskitBQBAQBAQBQUAQcAECjAo4XJCQJCEIdAMEhFLsBpUoRRAE7EUA0z8oRfZjOe2004xxIfMx/uQczpHNW6AUCwsLTW7JT0FAEBAE3BYBXDecccYZf/nLX3CkyLYteG9YunTp7bffzs5UcI5KkV1SUgLPyJ7RCLfdtiCSMUFAEBAEBAFBQBAQBAQBQcBNEBBK0U0qQrIhCHQZAmhz2PB0x44dmZmZbMBizAeUoskaHXsajBkzZt++fUeOHJFZtxErORcEBAH3R4AGbcCAAXfccccHH3zw2GOPnXPOOZs3bz506BCEI3pG8r9//352d2HVRFs/uX+hJIeCgCAgCAgCgoAgYD8CdP2q92/TKIpK0X5AJQbvQEAoRe+oZymlINAxAv+/vfsAk+s8zHu/O22nbe8VfYlGgCAI9k6LpChRlKhKW1bi2HHsXCd24tx7ndhxEst5nuvHj+3YcWLZSqw4VrEUSqQkkiIpir0BIEgQIOqibu91+uzM3nf2YA8GM7uD3cWW2Zn/aAWcOeU73/c7A2L3xVeUJ2p6xOHh4ZRRz7oiZeCzUYZ6Mupv2Xfeeaevr2/uUjmCAAIIZK+A/uP26KOPaqTzN77xDc2uaMyoaPz8YCzq0t3dnb21p2YIIIAAAggggAACCGSBAJFiFjwEqoDAqgpoZrG3335bHRLTI8X0gc+q6b59+zSp4pEjR4gUV/W5cXMEEFgCgb179/7e7/3ek08+qV+bm5uVKuq/bxoi3djYuASlUwQCCCCAAAIIrBEBo4uiKqtvBvRaI7WmmgissgCR4io/AG6PwKoLqJfiBx98oFHPO3fuTKnMrL0Ua2pqFD6qC4/WaTH/6k25kLcIIIDAGhJoamr6yle+8vzzz3/+859Xp8XW1tY1VHmqigACCCCAAAIIIIDAqgjYVuWu3BQBBLJEIBAIaGGWUCikvofp/xyXvkfV1k5Np/jiiy+eOXNGw6UrKyuzpC1UAwEEEFicgP6zprkUtfTzV7/6VYvFoqkVF1cOVyGAAAIIIIDAWhfQdwV6rfVWUH8EVkaASHFlnLkLAlkqoB+eq6qqlCemrPWcubq33XbbH/zBH9TX15eXl2c+k6MIIIDAGhLQwvdrqLZUFQEEEEAAAQSWSiCxOMvUlEojT1wqUsrJBwEixXx4yrQRgTkFNFuiwsRdu3ape86cJ6UdUBceDZRWHKlX2kF2IIAAAggggAACCCCAAAJrVYBUca0+Oeq94gJEiitOzg0RyCYB/X2pVFGvWSuV4W9Tm43/esxqxk4EEEAAAQQQQAABBBBYqwIZfgJaq02i3ggsmwA9jJaNloIRyBWBWCyWK02hHQgggAACCCCAAAIIIIBAqoA58FkHSBVTdXiPwBwCRIpzwLAbAQRmBILBYDwen3nH7wgggAACCCCAAAIIIIBArgkYSSJ5Yq49V9qznAIMXVxOXcpGICcEFCkacxXnRGtoBAIIILBcAvpP5djY2Msvv6wbaEKJhx9+WAtJGzdrb28/fPiwdt59990ul2u5akC5CCCAAAIIILBYAfNHHlLFxRJyXd4JECnm3SOnwQgsVMDv96uXovmD8UIv53wEEEAgfwQGBwf/9E//VO31er0NDQ179uwx2t7W1vaXf/mX+hFl9+7dRIr583mgpQgggAACa06APHHNPTIqvIoCRIqriM+tEVgbAoFAgIHPa+NRUUsEEFhtgWg02tHRoVqoQ+LTTz9tRor6D2lXV5f+Wzo5ObnadeT+CCCAAAIIIJAqYHZRTD3AewQQmFuAuRTntuEIAghMC4RCIf6K5bOAAAIILEhA2eJLL700MDCwoKs4GQEEEEAAAQRWV0C9FOmouLqPgLuvIQF6Ka6hh0VVEVgdAf1gTKS4OvTcFQEE1qaAfhSx2+3KE1955ZUvfOELGRqhLo1Hjx49d+6c/vGmqqpqx44d1113XUlJSYZLOIQAAggggAACyydAnrh8tpScewJEirn3TGkRAkssoGF6RIpLbEpxCCCQ0wIOh+O22257/fXXv/3tbz/++OM22+zfbr311ltPPvnk8ePHR0ZG9F9at9tdU1PzsY997NFHH21qasppIRqHAAIIIIBAdgno5x3zRx5Sxex6NtQmiwUY+JzFD4eqIZAdAuqlmB0VoRYIIIDA2hBwOp0PPPBAcXHxiRMn1AnR/BElufadnZ0KHF944YWzZ8/W1tbu3LkzEokcOnTom9/85quvvqp1sZJPZhsBBBBAAAEEVkZAeSKR4spQc5ccECBSzIGHSBMQWF4Beikury+lI4BAzglYrdaWlpZ77rlHKeEPfvCDWCyW3sQXX3zxvffeU3SoPom/+Zu/+Vu/9Vu/9mu/plHPihoVKZ45cyb9EvYggAACCCCAwDIJzPrvf8t0L4pFIGcEiBRz5lHSEASWS4C5FJdLlnIRQCB3BdRRUUOe9fOJosP+/v70H1TefPPN4eFhJY+f+tSnFD7eeOONOv/WW2/1eDynT58+f/587trQMgQQQAABBLJXgC6K2ftsqFn2CRApZt8zoUYIZJkAvRSz7IFQHQQQWAMCmj9x9+7d27Zt6+3t1aSKKR0VtRiLFm9RH0Z1S2xsbNRaLmpSWVnZpk2bysvLFUEODQ2tgUZSRQQQQAABBHJRgFQxF58qbVoWASLFZWGlUARySUCRYi41h7YggAACKyOguRQ//elP615agyUQCCR3VNTbeDyuQwoQtZaLWR+t9axFWoLBYMr55glsIIAAAggggMByCOivaeNvavLE5eClzFwVIFLM1SdLuxBYAgHjr1UGPi8BJUUggED+Caij4oMPPqjQ8MPpV3JHRcWIxk8s4XDYyBYNHvVb1L/iFE2/8g+MFiOAAAIIILD6AvoL2vg7evWrQg0QyHoBIsWsf0RUEIHVFiBSXO0nwP0RQGBNCugHkubm5rvvvlspocY+Ky40m6GuiOqQqFVcNG2iZlQ0/v1GJ1y8eFFDnjUCurS0lJ9nTC42EEAAAQQQQAABBLJQgEgxCx8KVUIguwQY+Jxdz4PaIIDA2hFQaPiFL3zBYrH09PQk90bUnl27dilVbGtrM9Z31tSKb7/99sGDB8fGxtavX9/U1LR2WklNEUAAAQQQyB2BRB/FwsLcaQ8tQWA5BWzLWThlI4BALgjQSzEXniJtQACB1RDQzyR79+7dsmXLqVOnkiNF1eXhhx/ev3//kSNHnnrqKQWOVVVVhw8fPnbsmGZgvPnmm1tbW1ejvtwTAQQQQAABBBBAAIH5ChApzleK8xDIWwFN9WUMystbARqOAAIIzFMgvV+Dy+X67Gc/+8d//Mf6b2lyITt37vzMZz6j7orHjx/X+i06pC6NtbW1d9xxxwMPPKCEMflkthFAAAEEEEBgWQX0847xI0+ijyK9FJfVmsJzSIBIMYceJk1BYHkEQqFQSuea5bkPpSKAAAJrXkBzID7++OMej6eystJszEMPPXTmzBlNlahVWTSLorFfi7c88cQTGuCsaRb7+/u1eIsObd++/f7779+wYQM/zJh6bCCAAAIIIIAAAghkpwCRYnY+F2qFQBYJ0Esxix4GVUEAgSwWUA5YX1//Z3/2Zyl1bGlp+aM/+qOUnXprt9sVIN53330+n09TTCiI1FLP6aexBwEEEEAAAQSWW8Dooqi78K96y01N+bkkQKSYS0+TtiCwLALqpWj+FbssN6BQBBBAII8F9KOL5k/MMYBoLD7si4YisfjUlNG0IrvVZbfo1yK7xWph2vsce+A0BwEEEMgdAf29rFfutIeWILCcAkSKy6lL2QjkhAADn3PiMdIIBBBAYNkFQtHYWGBy1BfpGgp+1D4+OB5WpmjctdzjqCp2lHkd5R6702G12TSNpH5iS/zPbi2s9NqrS4uIGpf9CXEDBBBAAIF5CBApzgOJUxBICBAp8jlAAIGrCDDw+SpAHEYAAQTyXkDdEntHwic6xk90ThxrH+8bDk9OFSozVH9EHQqGJ0OhaHwmXrSrx6LT7nAoQkyc43Fabm4tf+Sm+sZKp1N9GOkakvcfJwAQQACBlRdILM4y3bOePHHl8bnj2hUgUly7z46aI7BCAgx8XiFoboMAAgisTYGB8fDZHt+z7/W+d2qkyGErtFi2b6mx2m3qgmgtmBrzR7r6J/oHfZOxuN1msdusNqslFo9P+MJRxY3TOWP/SKhrdPK+3TVb6911pQ6XgyFna/OjQK0RQACBnBAgVcyJx0gjVkKASHEllLkHAmtagEhxTT8+Ko8AAggsk4A6c0yEJofGwz/c3/Pyh33R6FRznfe69VXjofjweGige3zMF4pGYlaLYkRLVYWnxFtUVuIqL3GWeJ2+QKSjd2xgyOfzh8ORWCAYfetw94kLoze0Vt25rWJzTZHNUlBV4vA6+U51mZ4exSKAAAIIzC5Anji7C3sRmE2Ab9RmU2EfAggkCTCXYhIGmwgggAACCQGtu9I/Gn7pw/7n3uvp6Au6XdZ9W6tOd/qeffO8fhhThujxOBprSqsq3LWV3soyl8eVGOecbLdjc7U/GO0ZmOjsG+/tHx8ZC/oD4VcPdrxzpFuxY5Ft6rO3N95zfXWZ255yYXIhbCOAAAIIILAkAsaoZ6MoUsUlIaWQfBAgUsyHp0wbEbgmAeZSvCY+LkYAAQRyTkCLrrQPBP72pQsvHuxV41oaivuHgu8eGyorc+3aWl9T6aku91SUOq1WS+ame1z2zS0V+lJHxcGRQEffWEf3aN+gb2A4EJ2M/9UzZ9t6/Y/f2tBU6VIgmbkojiKAAAIIILAkAuSJS8JIIXkiQKSYJw+aZiKweAEGPi/ejisRQACB3BLQYOfwZPx8r/8Pv3fiTMeEQkOH3RovsO7Z0djSULquoWxxPQqLHNbG2mJ93bitvnfQf6FrpLt/vKtn7MdvdR2/OP7Fu5ru2VHlYRB0bn2WaA0CCCCQVQLTq7NMZVWVqAwC2S9ApJj9z4gaIrDKAkSKq/wAuD0CCCCQHQL6Scsfnnz9o8H//J0T6qioVZvraou3b669fkvNFUOar6G2Cihb6kv0Ne4PHzjSefRk7+n28T/9/qmekdAv3NOi9aCvoWwuRQABBBBA4OoC6qVIR8WrM3EGAtMCRIp8EBBA4CoCGvgcj8evchKHEUAAAQRyXSAUib17cvg/ffOYxVJYVuK8c9/6nZtrlqnRJZ6iu29a31RXevCjzu6e8W//7GKxy/6ZWxvs1qVKL5ep4hSLAAIIILC2BcgT1/bzo/YrK0CkuLLe3A2BtSkQiUQ0FoC/X9fm06PWCCCAwBIIRCbjh86O/P7ff2SzWeqrvffcsrG5rnQJyp27CPVY3LqhqqG6+NnXT1/sGPnaj8+UuK0f2127uLHVc9+HIwgggAACCBQkD3zmpx4+EAjMU4DxI/OE4jQE8lpAkSIdFfP6E0DjEUAgvwXi8am2bt/XnjtntVi2rCv/+D1blztPNL2LPUUP3bG5vq4kEJ78k++fPnhmRPM58kIAAQQQQGBpBRQjGkmiubG05VMaAjkpQKSYk4+VRiGwxAJGL8UlLpTiEEAAAQTWiMD5/sCfP3Omsz+4Z1vNg3deV1nmWrGKFxYWlBU7P37nluoq74R/8o/+z6nTXROkiivmz40QQACBPBFI7qWYJ02mmQhcuwCR4rUbUgICuS+g6RT1t2zut5MWIoAAAgikCYwFovtPDWl95xu3VT985xa3y552yvLu0NSNleXuh+7cUlbm6hsK/s1PL8T5K2l5ySkdAQQQyF8Bo69i/rafliOwEAEixYVocS4C+SpAL8V8ffK0GwEEECho7w98/63uYrdjz7a6aHx1VkfR/Il1ld47bmzRStPvfjR44PSwNng2CCCAAAIILIcAqeJyqFJmTgoQKebkY6VRCCyxAHMpLjEoxSGAAAJrRKBzKPjMe73haOzuvY3ekuJVjPG0LMyWdZVbNlbHYvFn3ushUlwjnyCqiQACCKwNAXNIFnni2nhg1DI7BIgUs+M5UAsEsluAXorZ/XyoHQIIILAsAmP+6OsfDbx0qLey1L2usXLVf8rSGtA3bqu3WC1vfDjw4fkxUsVleeoUigACCOS9wKr/fZf3TwCANSNApLhmHhUVRWAVBaLRqPkPd6tYDW6NAAIIILCSAt0joXdODjscttYNlTaHYyVvPeu9LIWFTXUl21trI5NTz3/QF52Mz3oaOxFAAAEEEFiogLk8C3niQuk4P58FiBTz+enTdgQQQAABBBBAYHaBeHyqdzh4smPc6XTU1ZQVaOnlLHjZbdZ9OxqKHNZXPug72eWbZEbFLHgoVAEBBBDIJQFFiqSKufRAacuyChApLisvhSOAAAIIIIAAAmtSYHAicqJjIhSOl5c6y0pcWdIGBZtV5e762tJAKPbKRwORKB0Vs+TJUA0EEEAAAQQQyDsBIsW8e+Q0GAEEEEAAAQQQuKrAwFj4eMeE3W6tKHNbrVnRRdGos9Vqaa4vtVgKD5wcjDD2+aoPkhMQQAABBOYhwCxP80DiFARSBYgUU0V4jwACCCCAAAIIIDA4Hm7rmnAW2RQpZpvG+oYydVfs6A8eax9nRsVsezrUBwEEEFjTAgx8XtOPj8qvsACR4gqDczsEEEAAAQQQQCDbBUb90bM9/nF/1FVkryrzZFt1ayo9xV7n5OTU/rbhEGOfs+3xUB8EEEBgbQoYHRWJFNfm06PWqyNApLg67twVgbUlEI8zWdXaemLUFgEEELgmgcGJ8Lk+v1Zo0UIo5SXOayprGS622ywNtSVaAPr9tpFwNLYMd6BIBBBAAIH8EjAHPrM2S349eFp7bQJEitfmx9UI5IdAKBQiVcyPR00rEUAAgYRAIBwb8UW0oSkLld9lIUp5qavQUtDe5+8cDE7GprKwhlQJAQQQQGCNCpAqrtEHR7VXXiAbv0dceQXuiAACmQV8Ph+RYmYijiKAAAK5JKBlT4LhrO79p+6TAg9H4u+fHw1GsrqqufTBoC0IIIBAzguQJ+b8I6aBSyhApLiEmBSFQM4K+P0a/sbY55x9vjQMAQQQSBHQmidZHinarZbCgsQ61D0jIXoppjw+3iKAAAIIXIsAqeK16HFtXgkQKebV46axCCxSQJFiLEYfkEXqcRkCCCCw5gQik1OB7O76Z7NZpxPFgnA0PlXAwOc19xGjwggggEB2CWguRXM6xeyqGbVBIIsFiBSz+OFQNQSyRmBycjJr6kJFEEAAAQSWXSAaVS/FrP4vv2Z4TPRR1NhnRYokisv+ieAGCCCAQL4I0EUxX5407VwKASLFpVCkDAQQQAABBBBAIIcEorF4KLvnUjQXjdGKz2SKOfTRoykIIIDAKgsoUiRVXOVnwO3XjgCR4tp5VtQUAQQQQAABBBBYGYGZ4V+x2FQoK7srmhP80ktxZT4R3AUBBBDIbQFGPef286V1yyRgW6ZyKRYBBHJAwG63NzQ03H777XfddZfL5cqBFtEEBBBAAIH5CBTZrV6XbTgaCUcnx3xhZ1HWfcc4EQgb452JFOfzQDkHAQQQQGCeAvRSnCcUpyEggaz7BpGnggAC2SPwyCOP3HPPPffff39NTY3FQqfm7Hky1AQBBBBYXgGPy1ZZWjQ8HolEYmO+UG2lZ3nvt/DSh0aCxnhnl8NaaMyquPBCuAIBBBBAAAFTgI6KJgUbCMxTgEhxnlCchkA+Cjz22GP52GzajAACCOS9gLooVpUWtXVMRKKxsYlwFnoMjfqNn/3W1bgdNv7RKwsfEVVCAAEE1pKAmSfSS3EtPTbqutoCRIqr/QS4PwI5J2D+fbyglqVfZcyLnL5/QcWmnLyI0hZxiW66uKtSajuftwu90ULPv2od0gu8lgeXXtpVKzDXCYsranFXLW0dZi1tcRVb3FWzViDDzsXdZSWvylD59EOrXrHFVSC9IeaeRRSoS8L+sNcWUyGKFCd8IbO0LNmIxeMjowGjadWu6OjwQNhnzZK6UQ0EEEAAgbUoMDg4GA4n/gnN+FZ2LTaBOiOw8gJEiitvzh0RWHsCQ0NDZ8+e7ezsjEajZu0X92Oqefm1b2SoQIZDc903wyUZDi2itLkumWt/5rtnPppe5kLPVwmZL0k/uuhIMb2o9Pqn75nrqrn2p5eQvGeuq8z9s36XaR5NLirz9kIvyXx+5qOz1iT9kuV4cOl3mbUyyTszX5L5aHI5xrZx/go8tYVWTNVbqkvUusxFZT46F1ogajnnqywoWKdIcXQipF8d9izK7Cb8EX8gqrkUp+LRD954rutQ1Gph8HP6w2QPAggggMB8BUZHRzs6OoyzZ/3OYb4FcR4C+SRApJhPT5u2IrAogeHh4SeffPKll146evRoMBhcVBlchAACCCCwpgQsNmfTLSW7nigocI+NhwZHAg01xdnTgK6+CSMqjQWGf/zit+LBkeypGzVBAAEEEFjTAuSJa/rxUfkVFiBSXGFwbofA2hMYGxt77bXX9A93zc3N6bVfaP+X9BKS9xilzfoX+XLcKPnWi95e2oplqMbS3mhpS1O1jQLTn93S3iibS5vr2S1tnU3quW63oP1m3Xhws7qZPrMeXejORZS2iEsWWivj/FlvZHFErOGBKfs6fzDS0TuWPZFiLD51+uKQsTaLJTJYXlo85VqCLoqzIiyh5+KKynzVEtZZN1ra0jLUfAlvtIRFrVEBqm180pb2k5Dh07vk4CtW8yW80RIWteSeGZ7dgqqd/n1RhpI5hEA+CxAp5vPTp+0IzEtgw4YNv/zLv+z3+2c9e0F/PRslZLjEODTr3+JXvWrW6s21c67S5to/Vznan/mS9KNG69L3Z7iFcSjDJRkOXbWG6ffNUFqGQ+nlZN6TuajMR2ctea5L5to/ayHGzlkvueqDm/WqDHfRoQyXZDiU+cJZ75ihNONQ+h+6DJfMeour1mqhBWY+P/PRuWo46/4MRS3u0Kx3yeyT4UaZS0t/cBlutNC7RApdF0IVRwYtwVC0q288GJ50Fa3+940a7Dw8GmzvGonHp4ocln03NFdf/7hl6vKkHHOJZZAxLlmoT+YCF1HaXDXPUFSGQ0te2iLuteR1SC8wc60yH11EaQv6E5devrknc8XmOjrXfrPY9I0Ml6QfMluXfii95JQ9GS7JcCilEONthvMzHJq1qJQCzQaaJ2coMMMh8/KUjQyXLO5QSvnm21lLU+tm3Z/5KvNo+saKlXbVG6U/ONU281Xpzcl8iVGaftg5ffq0ZlSc9Y6zlslOBBBY/W8NeQYIIJD9Ag888ED2V5IaIoAAAggsoYA6A757avhPnjrd1R8YGPJ39o1vaalYwvIXV5R+8DtxbiAYTGSIrc3F/+bLt9WVOQuXoJPi4qrDVQgggAACOSJw4cKFr371q5rrKUfaQzMQWBEBy4rchZsggAACCCCAAAIIrCUBLXjSVOXavaFMlVZHxfbu0VgsvuoNUGfJ0xcGFSy6iqyP3dpYUewgT1z1h0IFEEAAgRwQKCkp2bRpU0VFhXop0lExBx4oTVgZASLFlXHmLggggAACCCCAwBoTqC4pumFDqdtpDUcmO7rH+oYCq9sAdZw81zEyMOhTNXZtLntgV02RjW9lV/eZcHcEEEAgRwQUJn7xi1+88847vV6v3W7PkVbRDASWWYDvw5YZmOIRQAABBBBAAIG1KeAusm5fV3LDlnL1ChweDXxwojsQmteshcvRXNVh3Bc6erpPhbudtsduaXCQJy4HNGUigAAC+SrgdDpvvPHGW265RfFivhrQbgQWJnCVaVwXVhhnI4AAAggggAACCOSQgD8Ue/3YwF89e7ZvOOR2Ofbtatp3fYPdZl3hJmpVlkAw8sqBC0dP9lgshQ/srfv9L24lUlzhp8DtEEAAAQQQQACBZAF6KSZrsI0AAggggAACCCBwWcDjtN62tfKJe1uKXTaFeh8c777QPaqAb4Vfk7H4qfNDyhM1c2JzjfsX7222WVmTZYUfArdDAAEEEEAAAQSuECBSvIKDNwgggAACCCCAAALJAmUe+8duqH38rma7zeIPRA591D0RCCefsNzbGvI8MhZ87eB55YnlxY5/9Xhra0OxhWVZltud8hFAAAEEEEAAgYwCRIoZeTiIAAIIIIAAAgjkvUBVieOT++pubC3Xos+d3WOvHrigBVtWRiWRJ46HXnjrTDgU9brs//yxLbe1ajnOlbk5d0EAAQQQQAABBBCYU4BIcU4aDiCAAAIIIIAAAggYAi3V7n/xic1ely0Wj5862//8m2ci0dhy42iE9dhE+M1D7Z3dow6H9cF9dY/urVvum1I+AggggAACCCCAwHwEWJ5lPkqcgwACCCCAAAIIIFDQNRz8d//7WFv7eEFhYevG6k/e22rVainL02dQeeLIePDld8+3nR+wWgvX13m+/X/fwjNAAAEEEEAAAQQQyBIBIsUseRBUAwEEEEAAAQQQWAMCI/7of33mzE/eTayUsnFD1b03ra8scy95qhiPT/UP+1/ef+5ix4jmcNy2vvjrv3HTGtChiggggAACCCCAQN4IECnmzaOmoQgggAACCCCAwFIIBCOxn7zf+42fXhgYDnm9zntu3rBjc/USdlYMR2Jn2offOdw+MOhzOxPjnX/n8a1LnlouhQRlIIAAAggggAAC+StApJi/z56WI4AAAggggAACixOIxuLtg8Fv/OzCTw/0Wi2F26+ru35zbVN9ibYXV6BxVSAUPdc5+lFbX3vHSHwqXlHs+EcPrf/srU026zUVey1V4loEEEAAAQQQQACBWQWIFGdlYScCCCCAAAIIIIBAJoH41FQoEv+ofezpd3te/7C/sLBw47qq7ZurNzaVa6hypitnOzbhj6hn4omzA929Y1oBRiXs3FD65fta9m1eTGmz3YF9CCCAAAIIIIAAAkspQKS4lJqUhQACCCCAAAII5JXAZGwqHI39+GDPU+90dw0ENfy5vMxVU+mtrfI21ZRUlbttc8eL4cjk4Giwb8g3MOzvHZgYHPJPKk2MT1WWOD5+S8PjtzXUljrpn5hXHycaiwACCCCAAAJrSIBIcQ09LKqKAAIIIIAAAghko4BmVxz1RX90sOfZA91Do5GCwgKtBG2zWjweR01lcbmSwSuDxUAwOjTiHxkLBkNRZYix2FQiS1SYWFp0z67qn9tds7Wp2FWkUdSMd87Gx02dEEAAAQQQQAABCRAp8jFAAAEEEEAAAQQQuFaBqakCX2jy4kDg+ff7XnyvZ9wf1R6NhlawaE2bCVGHjA6JU9qaflWVFd22o+r+66t3NJdoSRa7dcFDp6+1AVyPAAIIIIAAAgggsBABIsWFaHEuAggggAACCCCAwNwCk/GpicDksfaxIxfGjrWPn7gw7g9Nzn16QYnH3lLnua6p+JbWip0tJcUum+PK/owZruUQAggggAACCCCAwCoKECmuIj63RgABBBBAAAEEclAgHI0HIrFgeHJwPHyqy3e+zz8WiIajWs4lpokXdbSi2L6loXhTnaep0lXmcWiMs9dpK7LTMzEHPww0CQEEEEAAAQRyVYBIMVefLO1CAAEEEEAAAQRWWSAWnwqGY4oX1XtRUyXGpwr0q14aDe0usuqryGaxWJgwcZUfE7dHAAEEEEAAAQQWIUCkuAg0LkEAAQQQQAABBBBAAAEEEEAAAQQQQCB/BRhgkr/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAAAIIIIAAAggggAACCCCwCAEixUWgcQkCCCCAAAIIIIAAAggggAACCCCAAAL5K0CkmL/PnpYjgAACCCCAQG4ITE0VBCOxuH7jlTcC0dhUdDIe55nnzROnoQgggAACCGSbgC3bKkR9EEAAAQQQQAABBOYpoBQxFIl1DAbP9fpaqt1N1e4SF9/dzRNvDZ82MBY+3xeYjMXryp31FU6Xw7qGG0PVEUAAAQQQQGBtCvBN59p8btQaAQQQQACBXBcIhGP+sDKT1F5YhQWFNmviq8huddgslsJch8jYvshk/N3TQ//pWyeCoUmB/MonNvziPesK5zaR5tRMb0ZLoV4ZS+fg8ggoCJ55CAX6BC/0IeiPxl/95NzLH/QHw5OtzSW//sjGW7dW5vkfhOV5UJSKAAIIIIAAApkEiBQz6XAMAQQQQAABBFZL4I3jg88d6h0aj6RUwGErrCwpaqp0bW0q3t5cUl1a5LTn70QuE8HJZw72Kk+UUmQy9q2ftf/C3S3WuUMqX3CyfywUisR1fmOlq8xjT+Hl7XILKEwc80e7h0O6UZHdoqew0D6GR9vH2jonlCeqhNMd46c6J3ZtKPU6+a5+uR8d5SOAAAIIIIDAFQJ883EFB28QQAABBBBAIEsEuoZDZzomBsfCc9VHudl1zSVfurf57h3VnqI8HfipfCoYjl0iUt+3mPogFmSwePHDvm++dLF7MKhLfvdL2x69pWHu+HEuePZfk4DyxKfe7f7rZ86qlJYa9+8+se2GDWULKlGJ8GTsUu9ddTQNT8ajk6mdeRdUICcjgAACCCCAAAKLEMjff9VfBBaXIIAAAggggMCqCGiYs8Y4G192DXaeHuSp0aMn28f/+tlzPzrQkz4+elXqufI31cyJj93a4HHa1N+t2G3/xG0NtowjYJU9saLHyj+m5DsqDNSyKsl7Frp985byDbVud5HV6bDWVzpbG73lXnqbLlSR8xFAAAEEEEDgWgXopXitglyPAAIIIIAAAssq4LBbbt5W2Vzt1l1iU1PjgckLvf7Ofn8gmFjjuGco+ObR/hs2lG5rKl7WamRn4QqVHthVs6HW8/bJwR3NpTdtLqfXYXY+qSWslQZKq2/jgbaREV90z4ayddWuJSycohBAAAEEEEAAgXkKECnOE4rTEEAAAQQQQGB1BNxO26P76u+9vtq8vcb2vnZs8Bsvnj/VPq6dZ3r8rx8bnCtSVGdGvab0v4ICLe2ytIlbolBjyKmKTtwn8TLuqA3zXuaeRA2mz0k+7dKOmUNGmZkrfLnAggKrpXBLvVdfiXLM0i8Veuk383yjWGOvbpR4O3XlNTPVuLKAmXL0+/Q1+j1xYsaTZy1h/jtV52la1e/yUzMbYtoaBc61/9JR/Zb2mNJrkiI//9YZd79U30vlTlcwyccoPFGRmYZNbyfqZVberFJK6xJnXvr/pVPcDtu9Oy79iUg/2SwneSOldRmuunyvmfpPVzmx2/hQZ7g2+Y5sI4AAAggggEBuCxAp5vbzpXUIIIAAAgjkoICG9t69vbJvOHixzx/SwtDBycHRWaZcVPI4EYie6vadLPZCZgAAOAhJREFU6p640B8o99i3NHi2NpW0VLqXJBPRUidn+/zDvrCGYteVObc0eNVtsn88fLIzEXQq6btrW7Vxo0PnRnSyIqd1NZ7mSrfdWnixP9A5HNB6zebjaalyN1UqKbK09fgOnR1pHwzqtM313t3rS9dN99A0ztSw5YHxyPHOMfNCc0P5j9b6uJQtmnsLCjoHgxcH/ca9TnX6gpFLcy+e7Joo/siWTGGUsKnOk74KsfIkLTR8tsd3snviXJ9fTW6t97Y2eFsbizOOtE6qx7w31cZRf/RY5/jx9on+sbAG9j5wfXVVSdFkfOrdk8PReEzVu3lLhbmqifa/f3ZE64PrDpvrvPXlTpv18tw+Y4Ho+X7/iC+xzk9jhUs9Ou1JR41KqXX+0OSZHn1UfDrZYbVsaSi+rsGzuf7qrdNHq63X1z8aUp1HfYkxzV6nVevelHrsWxuL9Tg0Jj0cjXcMBjqGArqdutnqFsZ9faFJLUM06r9iDSI9BX1O1ldf8Sk9enFMTTDXiTYu169a5ltNriktMvekbySeXSh2ttd/omv8Qr9fl1zX6N3aULKuxj3rs5NDz0iiD3BNiVMfBnUNfvvU8ImO8fHgZHVJ0famkh0txRVeR/qN2IMAAggggAACeSVApJhXj5vGIoAAAgggkCMCCuwaKl0aDa2lb5XiaMljJSDaaTZPSyH/+ED33zx7TnnT9CtxRFGUErS7dlf/9qdblYkkp2nmhfPf6BwKfv2F84dPj9hthTddV/HH/2SXQrefHOr92+fOqxBnkeWb/88tihq1/fv/6yPFMeppdsuOyt/57HVapfpHB7uf298z4U+kYMbr4ZvrPn9n8xvHBr//Vue4L2L0XFMN6ypdn7ur+XO3NeouOjMam/rw/OgffPP4zHWXf7daC+/dU/sfv7QtpV0/PdL35GudY76oThWGOZfiD9/q+vHb3Zevn966+/rqX31k4/qaxDBz86UU8o1jQ//lh6fHfVETU5SK5lrqPP/5H13fUulKual57UI39OB+erjvv/34TDiimiZ6/j1fWPDXPz67b1vl731h6+/87ZHpDnsFv/PE1of21BrhoIK5v3i67UJvIrB78Oa6X3pgfXPV5bHAimi/8eKFD8+M6uhjtzf804c3lnkuB47a6Q/FXv1o4C9/fGbCf0XrNIPnxibvV7+8s6ki8RBTXvpc/cObnc8d6OkeCExOJipqPLJLp0337/vsnU2ND25QpDjsi/xof/cP3ujSUZ2psNt4jU5E/uFn7d+70s5iKfjM3c3/4hObkuPev3j6zOnO8fjlCPpSCU6n9bc/0/rxm+ouvU/7TWnm2yeH/vszZzVFQPKz08eptbnkq7+4ozYtjvzWq+1vHBmQ/7YNJZ+5vfHJ1ztPd05c+tgUJv4QbWkq/kc/t/6ObZVKvdNuyA4EEEAAAQQQyBcBIsV8edK0EwEEEEAAgRwT0ByLzpmFnmcimktN7B4O/cUzZ94+OqA8JbnVsek46rXD/UfPj/33f36j+vQlhZDJJ85rWxUocduiMS2zXDgRSISD2u4YDOpXbVuihe0DQUWKSjxDUa3Jm9hZU1akoEobCkC1aK9xpt7qNTAefvpA92FNkDceTg6nugaCL73f21LtumNrpU5LBFLxKy6cvjrxS3yqUMWab80NVUc3Sr6XcUgnGyDmmdqYjOt1RSGq2JNvdf3DK+1hxZlXHFETCs52+X75Tw/80S/v0rLFyZFucpnz3x6aiDz9bvffvXg+5cFNxiYPnRr+yfu9BqMKTOmvZ2LGEnW8spZTBdppND/dp3ck9L23Or//euesrTt5YfxX/uzgH//Krp0tpcm5n+6hD9iLB3sV1KbUJLmxboe12Jn4Zlvn69bpj0AVTX8Klvgsz1GReWJpnSsfgEq2TRam7zTroI6Tzx7o/tsXL6iX4pWnJZ7dR+dG/+l/ee8/fWWHJmQ0L9GG0lI9atV2cDzyVz8+OzIRMdl1VJ8ZLYv0jZfOK1C+a3tV8oVsI4AAAggggEBeCRAp5tXjprEIIIAAAgjkjoCSjr6RkNqjblPqLWXmWerm9s6JwTePDBg5iI6WFtvrKlyhSKy9169kRfHTwEjoW6+3//OHN5a4F79UrtNuLZ8e/qkMLjIdtyn06R4KGsTqEXZxIKDFefsn1OXwUsjVWOnUitU6YUdLScdAUENZ23sDGp2tPcMTka7BYM9gUDlpY407Go139QdUVb3aunzvHBsyIkU1p8Rj39xcYtxFv6qZF3t85tv0jW1N3p0by0anR/72j4QVWapYnVZf5SovTu2qua7O6y66/P2hkqzj7ePfe7VderpEPRM9LmtDtVsltPf4dVRtV5z6J0+d/vpv7NXC0+l3n/8e1elU58STb3QYeaLx4La2lOhe2j/hi3z9uXPzL20+Zyo1O3J+9Kk3Oo3W6Y4et00swu/oE36idXL7oydP/a/f2mdkwUaxzx7qUTCtQzMPtqC8xFFRUuRxWtVXVBVWhKdWlJdcSpCL3bbNDcU7Npbq8kg0Pjimp5AY7KwPQ12VS8F0cm1VjfW17pTuf60txXFLobJR48yewYDG+ydfNev2sYtj33uzS4PujaO6XUWpQ38EBkcSEwVoY2As/Cc/OP2///XNs2br/UMhCSiLLPbYvW67+lSqO7Au1M629okPzozqY8wI6Fnl2YkAAggggEA+CFzxHUw+NJg2IoAAAggggEAOCJzv8x84PWxMoehx2Sqnxxcb7eoaCv7D651GnqjE8Jce3vCx3TUKHNX3rnMo8Pt/f6x/JKQk6Cfv9nzqpvrrmjTtXqJT2Fsnh9RFzsz+MhPVlTt3rSt1OiyVMzPKKUXSXH4KoXqHEymnXrpd+2BiKO7gWMjs9tdUoYkUE5HiXdur922uUDTzP3564cX3epUqKsNSNTY1Fv/8vS37tpRr+4X3+77zavvQWDgciY0HopoMUTMtKhXau7n8L351d+Ie0y/N4vdP/uw9FTWzI/V33UiLQRsnqCPkD97sHJhOlL54Z9ODe+tSoit1vTTnKFRBmtLxuUO9GtCtba/b9uBNdV++p0XLTAtwcDz8Zz9q07hvHbrQ7decgPfvqlENU28/7/f9o2FNiaikWFfY7ZY9m8v+zWeu04yEupdmG/zmq+3PvpM6THveZc9+4rnewAuH+43WlXrtD+2rf+KuJk01qBBNz1H9EI+cGdXdz3X5Xjs2cO/OajO2/uDs2HhilHSi2Pv21HzujqbmKrcyR/VklKd265C+3DO9aL1FtodvrL1nR6JPnwZBP3Og5zuvtGtbvVb/5aObd7ZcDogTJRYW6BGY90rsKSj4jU9sTu4q+DcvnFffVd9031jjhPRfe0ZCB9tG9GnXIYulcGtz8b98bEuTAtPJ+Ifnxv6/751UkKpPhT54Gmn+4J7alE+CrtLnWfnmP35w/cf21Gp2SH0Iv/9290vv9ylL1edTM0LqzxqRYro8exBAAAEEEMgTASLFPHnQNBMBBBBAAIG1KqDeXh9pZeeZwEMT553vCxw+M3K222fEZC017n2t5UbzFA+1dU10DSSyPK/L9snbGx67pcEzk+woFvm3X9j2//7PD1Wm8pTjHePraz3KffpGwhrYe6Y7U1+/ZL59rRWaPVC90iqKL3VyVKQ45lekODU0GlawpPQtEJxUP0Rd1T92OalsqFAvxURLnBq1bU+kbwqPNHeeXqqP+oJ98matbV2l/o/as2tD6aEzxe+MJTqUaTZDBTpaokQXF9ksRcWXF8dQr7cZm0Q56S/FfGbSpx6ICr6Mc9Sp8KoTSvYNhw4cH9L5ihp3bSz7tel+ncb15V77v/3c1n/8JweC4ZgCpndOjdy5vcq8UXo1rrpHC5i8c3LYyOm0Is2vP7KpZWaJklKP7R8/sP7g6ZG+mU6gVy1tPid0DwY0nlpnFjmsezaX/8rH1iuDvtQ6j+PffKb1V//8kJ6LWvf2iWEN8jVjPvVFjWi5nenXA7tqrl9XqgkTM9xR5HrQRlarAfCumQ+kClQXxYqkpzlXISWuK75pVwmJ/DLjS7H7u6eGjT8jWolFyaDqqccvYc922z/7xMY/f6pNBejzo4HtD95Qa/4RSy71/r21D+2t01oxCiVVT03aeF7LB7UlYt/h8ciIP9HBlhcCCCCAAAII5KfAFd+d5CcBrUYAAQQQQACBbBbQms4/fLvr+YM9RiUVkUwHgnF1odKeihLHLVsrtSyycTQQiXWNJOaN09syj+MLdzabeaL2KE/Zu7msttLZ2RdUT7SOYQVDcUWKmjxOo3fH5p2PaHVg3VzRXpn7UrSnErT0s0aPqguY8qkbW8tfP9zfOZBY2Fe9CI2YTKmcUjx1+zKqmvKrztm5rnRbc4mRJ+qoAsRNDV6NntZ2ZWmRMp2US5b7rXA0GZ/RiU9LynzqlobSpHHiisOUkN5+ffXLh3pVea1hbbAvulYanysrXS7A9XVaTrrYpBJaVYnjvt01//DyxUWXn3Kh0TrlodqvRaI/sa8+uXX6qGiqzVt3Vr36fp9OUI+85LkI1TFWzdd0hHp95/WOYX9Ua5Vo3kxdldiVHS99pAemPRNPqtKlNbKN6km1xGW/Y1vV/3zxvM+vlWWmLvbOGabfu6NKT9n47OkpaI1vddHVhjQ0w8B8Bl9nBwa1QAABBBBAAIGlFyBSXHpTSkQAAQQQQACBJRRQ5KFBprMWuLHBq0V1799VbfT/0jnqU9Y3mujWp9xEqcfJzvFTXakpTzCsxDGROfYMKRpMbNjthTWVTn90OiLS+6u9qsqcygeV1BQ5EiORFSMqYuwfDxcmQs4C9dS7pbXytQ/6h0cTnbm03+jPVlF6aWa9uYrf1lycvFRxTWnRE3c1f3J6MV/1LkwOvOYqYWn3q2vk4EQCUy+FhsP+yKvHBo23xq/KEDUdpLHdNxwwOsQln7CgbTGqa6cuUcjbXKvQ7ooHJ+2NdVesQ72gwtNP1kB1jUE29uujMuhLbZ2Sa2MCSp0zvVzy5TJu2lJ+7NyoVnTRrlMdE71Dwafe7Cwrtm+o86pvqVaqqS1T7nb5/JXf0kdO1QsEEzXU7JxaN9z8M6I9+tOhPrzNNZ4T58d0pmZ+1CozjivBjTrrQ6hPrVl/fbY17N1iLYxPTunpX2OIbBbLBgIIIIAAAgisRQEixbX41KgzAggggAAC+SWgLmHG6GA1WytUGEGGxn5qvLPmgEseExqKxI2eborAtPTEf306MbQz5aWVMXRUL3XBM4qqKnb85qObFSGlnDnXW7fTpk5zOqqJEd0u29hERNFk/5gmU4xrOKpG6bY2eHQ00Q8uENV+o4NbfUWib9tcZWq/FntR0GOeoDVndBfjRubOldxQ/0St+WvcUZh//+KFlJ6SUjSXKPEHUtaDXlhNBag42Eh4FdeWey6P7DYKEmz6zoXd48qz/aGYZs809mnd5//94vmUDqT6kJiBqT+g3nyXr39oT63GPv/kQI/Cbj30ofGIvqz9hW2dWkhnUIveqOesMV7Y7Gh5+eIV2dJwZjXQ+OApBFSVUm6riLamzHmiYEz7lQXr+TaUO1Nqq7g80RvzymxUD+LKHSkF8xYBBBBAAAEE8kXg8ret+dJi2okAAggggAACa0pA8xL+4v3r9mwsM2r9ytGBn7zXq8VnFZoo0AmGJ5MjRaUdZuylXm9aQzlDW5WWGPPHKRnUtH0ZzpzrkMIvLesxHSnGFR1qjLYW/K2rdNeWOnWJ+ldq/YqB0bCRRjVVZRoYq2qr+5fGRc91r5Xfn8CcqY4we2ZWnpm1JoWZwtJZr7hip/K6mdWME8/ETJDNk7RTtubb+W8kMujkOHDmSrXOzMb0WVKX1Zkjs/xuOhjHyj32X7in5bbrKl79aGD/yWEtgWIk3Rq7ra/ekbCsjrWPf/7Oprunl2SZpcRl3zVVON0V17hPSv2NnVdqJiWmM3W7xmc6Uwy/I4AAAggggEBuChAp5uZzpVUIIIAAAgjkjID6SWkRld0zkaLWAu4cCLzx0aC6YB29MK4FPT59a4PZWK15oqGaxltNXHjrjkrldObRlI1tLSVux+VBnSlH5/NWi3JUFjvae9Tdcap/JOwLRdW1sLnGrf0lXru6trUPBLQstbGQdJMWBZ412pm+k81mWVxkNp96znqOcjb971KqOtsZwtF8jsYR9XTb1ORtqZozeC122zUH4mzFzGufZDTPpDFJn56sMYHjvK682kka4Kz+j+lneYpsenbGfvV43dRU3FzpSj/N2KNlW1IGYteWFenydTWeT9xUry6KZ3t9JzomTnaMa9ESdfrTutUftI1oisaNdZ6muYvVI5ieEXSu2y5+vzoY6pEpM9UtFAdrXsWUstSB0RzWrUNXXagn5XLeIoAAAggggAACRIp8BhBAAAEEEEBgLQkoo9mxvvSDc2O+QFTjVT84O3rrVq2McSn5UjbUUHEpGFKut3dz+b7NlxaDTm+kMkdN25e+f/57tJSKEUsZA5/Va1LBk+ZDVDam2evOBCa0uIo6VBqd5JRYZRj4rLDR7DQ3/wos9EyX43JwOR6MBiPx5OVrUkrTBI7VM5GiIqrNDd4v39OSco75Vk1zZVz12Dxz1g21XV0+HQ6LenoqT+xO616qTnTGsOj0y5MzYzUqOrMWs3HmqC+qnoPpV+npVxVf+tjo1tc1FX/pzqb004w9ap26sqYc1bPW0iX6UqCsKRTvvT4yPBHZf3r4Zx/0a+5FBXkX+/2dQ8GUSFHhqTmtoQbem/M5phR+jW8VzipS1B8HzZOoL6M+eohGscJUzHp+Zolzt8taNL3I+DXelMsRQAABBBBAIK8EiBTz6nHTWAQQQAABBNa8gFK8PZvKD58dfffEkKax++jC2NsnBh+/rdFoWGJlj8rEoGO9tC7zmx8N3r61Up3FjD1L/quWZ5mJFDWhXnh0PKL1K9SVT0OqNZK6rX2irdunQbXGfbWCcEpPtyWvz1ULVK9D10x41NbjUya7qS4x7eOsL9W22G3TwHNfYFIrI5/t8mmFlhvWXxqBPusl17JTq4iUeR294aDudbEvoNn9tMy0WaC6Ll7oT6ygnf7SQze7f55un9AEiFMFHmNUr1aYudjnVy/C9KvUn1Q9K7V2s5YtVuh2rts3Fohev+7S0uHp52fYo6L0MdDXVH2BuM50+RTh6fyJYEzLIqdcqKTP+Mxov7LOD8+Nfmx3Tco5S/JW83JqofBAKKC1gzQDwOELY2a8rjzxwOmR8UBi1SOFjw1V7isHQS/J/SkEAQQQQAABBHJcIPWfW3O8uTQPAQQQQAABBNa+QGuD9/oNpeqBpaYoFHu/baR39NJEeEW26VHSmxOxl5Z
gitextract_2m1uy52v/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.yml
│ │ └── config.yml
│ ├── pull_request_template.md
│ └── workflows/
│ └── python-lint.yml
├── .gitignore
├── .vscode/
│ └── settings.json
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── cookbooks/
│ └── self-rag-agents/
│ ├── pathway_deploy_langgraph_agents.ipynb
│ └── pathway_langgraph_agentic_rag.ipynb
├── pyproject.toml
├── setup.cfg
└── templates/
├── adaptive_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── document_indexing/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ └── requirements.txt
├── document_store_mcp_server/
│ ├── Dockerfile
│ ├── README.md
│ ├── __init__.py
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ └── requirements.txt
├── drive_alert/
│ ├── Dockerfile
│ ├── README.md
│ ├── __init__.py
│ ├── app.py
│ ├── docker-compose.yml
│ └── ui/
│ ├── Dockerfile
│ └── server.py
├── multimodal_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── private_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ └── requirements.txt
├── question_answering_rag/
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ ├── requirements.txt
│ └── ui/
│ ├── .streamlit/
│ │ └── config.toml
│ ├── Dockerfile
│ ├── requirements.txt
│ └── ui.py
├── slides_ai_search/
│ ├── .dockerignore
│ ├── .gitignore
│ ├── Dockerfile
│ ├── README.md
│ ├── app.py
│ ├── app.yaml
│ ├── docker-compose.yml
│ ├── nginx/
│ │ ├── Dockerfile
│ │ └── nginx.conf
│ ├── pathway_slides_ai_search/
│ │ └── __init__.py
│ ├── requirements.txt
│ └── ui/
│ ├── .streamlit/
│ │ └── config.toml
│ ├── Dockerfile
│ ├── requirements.txt
│ └── ui.py
└── unstructured_to_sql_on_the_fly/
├── Dockerfile
├── README.md
├── __init__.py
├── app.py
├── docker-compose.yml
├── postgres/
│ └── init-db.sql
├── requirements.txt
└── ui/
├── Dockerfile
└── server.py
SYMBOL INDEX (59 symbols across 14 files)
FILE: templates/adaptive_rag/app.py
class App (line 24) | class App(BaseModel):
method run (line 34) | def run(self) -> None:
FILE: templates/document_indexing/app.py
class App (line 24) | class App(BaseModel):
method run (line 34) | def run(self) -> None:
FILE: templates/document_store_mcp_server/app.py
class App (line 17) | class App(BaseModel):
method run (line 26) | def run(self) -> None:
FILE: templates/drive_alert/app.py
class DocumentInputSchema (line 49) | class DocumentInputSchema(pw.Schema):
class QueryInputSchema (line 53) | class QueryInputSchema(pw.Schema):
function build_prompt (line 60) | def build_prompt(documents, query):
function build_prompt_check_for_alert_request_and_extract_query (line 73) | def build_prompt_check_for_alert_request_and_extract_query(query: str) -...
function split_answer (line 88) | def split_answer(answer: str) -> tuple[bool, str]:
function build_prompt_compare_answers (line 94) | def build_prompt_compare_answers(new: str, old: str) -> str:
function make_query_id (line 106) | def make_query_id(user, query) -> str:
function construct_notification_message (line 111) | def construct_notification_message(query: str, response: str) -> str:
function construct_message (line 116) | def construct_message(response, alert_flag, metainfo=None):
function decision_to_bool (line 124) | def decision_to_bool(decision: str) -> bool:
function run (line 128) | def run(
FILE: templates/multimodal_rag/app.py
class App (line 24) | class App(BaseModel):
method run (line 34) | def run(self) -> None:
FILE: templates/private_rag/app.py
class App (line 24) | class App(BaseModel):
method run (line 34) | def run(self) -> None:
FILE: templates/question_answering_rag/app.py
class App (line 24) | class App(BaseModel):
method run (line 34) | def run(self) -> None:
FILE: templates/question_answering_rag/ui/ui.py
function get_indexed_files (line 74) | def get_indexed_files(metadata_list: list[dict], opt_key: str) -> list:
function get_ingested_files (line 85) | def get_ingested_files(metadata_list: list[dict], opt_key: str) -> list:
FILE: templates/slides_ai_search/app.py
class App (line 21) | class App(BaseModel):
method run (line 39) | def run(self) -> None:
FILE: templates/slides_ai_search/pathway_slides_ai_search/__init__.py
function get_model_from_file (line 17) | def get_model_from_file(file_path: str | os.PathLike[str]) -> type[BaseM...
function get_model_from_dict (line 34) | def get_model_from_dict(schema: dict[str, Any]) -> type[BaseModel]:
function get_model (line 53) | def get_model(source: str | os.PathLike[str] | dict[str, Any]) -> type[B...
function encode_str (line 72) | def encode_str(original_string: str) -> str:
function add_slide_id (line 76) | def add_slide_id(text: str, metadata: dict) -> tuple[str, dict]:
class DeckRetrieverWithFileSave (line 91) | class DeckRetrieverWithFileSave(DeckRetriever):
method dump_img_callback (line 92) | def dump_img_callback(self, key, row, time, is_addition):
method dump_file_callback (line 107) | def dump_file_callback(self, key, row, time, is_addition):
method __init__ (line 120) | def __init__(self, *args, **kwargs):
FILE: templates/slides_ai_search/ui/ui.py
function get_options_list (line 85) | def get_options_list(metadata_list: list[dict], opt_key: str) -> list:
function parse_slide_id_components (line 91) | def parse_slide_id_components(slide_id: str) -> tuple[str, int, int]:
function create_slide_url (line 98) | def create_slide_url(name: str, page: int, page_count: int) -> str:
function get_image_serve_url (line 102) | def get_image_serve_url(metadata: dict) -> str:
function get_adjacent_image_urls (line 107) | def get_adjacent_image_urls(metadata: dict) -> list[str]:
function get_slide_link (line 142) | def get_slide_link(file_name, page_num=None) -> str:
function get_all_index_files (line 150) | def get_all_index_files() -> list[str]:
function get_category_filter (line 252) | def get_category_filter(category: str) -> str:
function get_language_filter (line 257) | def get_language_filter(lang: str) -> str:
function combine_filters (line 261) | def combine_filters(*args: str | None) -> str:
function get_ext_img_with_href (line 305) | def get_ext_img_with_href(url, target_url, *args) -> str:
function log_rate_answer (line 334) | def log_rate_answer(event, idx, kwargs):
FILE: templates/unstructured_to_sql_on_the_fly/app.py
class FinancialStatementSchema (line 78) | class FinancialStatementSchema(pw.Schema):
class NLQuerySchema (line 87) | class NLQuerySchema(pw.Schema):
function build_prompt_structure (line 93) | def build_prompt_structure(
function build_prompt_query (line 130) | def build_prompt_query(postresql_table: str, query: str) -> str:
function parse_str_to_list (line 174) | def parse_str_to_list(response: str) -> list:
function structure_on_the_fly (line 179) | def structure_on_the_fly(
function unstructured_query (line 211) | def unstructured_query(
function strip_metadata (line 269) | def strip_metadata(docs: list[tuple[str, dict]]) -> list[str]:
function run (line 273) | def run(
FILE: templates/unstructured_to_sql_on_the_fly/postgres/init-db.sql
type quarterly_earnings (line 1) | CREATE TABLE IF NOT EXISTS quarterly_earnings (
FILE: templates/unstructured_to_sql_on_the_fly/ui/server.py
function json_to_table (line 45) | def json_to_table(json_value):
Condensed preview — 83 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (675K chars).
[
{
"path": ".github/ISSUE_TEMPLATE/bug_report.yml",
"chars": 1846,
"preview": "name: Bug Report\ndescription: File a bug report\ntitle: \"[Bug]: \"\nlabels: [\"bug\"]\nbody:\n - type: markdown\n attributes"
},
{
"path": ".github/ISSUE_TEMPLATE/config.yml",
"chars": 379,
"preview": "blank_issues_enabled: false\ncontact_links:\n - name: New feature? Tell us about it on GitHub Discussions\n url: https:"
},
{
"path": ".github/pull_request_template.md",
"chars": 1205,
"preview": "### Introduction\nTo contribute code to the Pathway project, start by discussing your proposed changes on Discord or by f"
},
{
"path": ".github/workflows/python-lint.yml",
"chars": 811,
"preview": "name: lint PR\non:\n pull_request:\n push:\n branches:\n - main\nconcurrency:\n group: ${{ github.workflow }}-${{ gi"
},
{
"path": ".gitignore",
"chars": 3121,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": ".vscode/settings.json",
"chars": 523,
"preview": "{\n \"python.defaultInterpreterPath\": \"${env:HOME}/pw-env/bin/python\",\n \"python.formatting.provider\": \"none\",\n \"editor."
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 5794,
"preview": "# Pathway Code of Conduct\r\n\r\nIn the interest of fostering an open and welcoming environment, we as\r\ncontributors and mai"
},
{
"path": "CONTRIBUTING.md",
"chars": 1946,
"preview": "# Contributing to Pathway\r\n\r\nWelcome! This guide is intended to help developers that are new to the community\r\nto make c"
},
{
"path": "LICENSE",
"chars": 1064,
"preview": "MIT License\n\nCopyright (c) 2022 Pathway\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof"
},
{
"path": "README.md",
"chars": 9182,
"preview": "<div align=\"center\">\n\n# Pathway AI Pipelines\n\n<a href=\"https://trendshift.io/repositories/4400\" target=\"_blank\"><img src"
},
{
"path": "cookbooks/self-rag-agents/pathway_deploy_langgraph_agents.ipynb",
"chars": 206846,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d58d3552-9168-4f0f-8a79-4dbaa4a01bbf\",\n \"metadata\": {\n \"tag"
},
{
"path": "cookbooks/self-rag-agents/pathway_langgraph_agentic_rag.ipynb",
"chars": 202539,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"09287484-c402-4d09-a26b-588a13a331f0\",\n \"metadata\": {\n \"tag"
},
{
"path": "pyproject.toml",
"chars": 1937,
"preview": "[tool.poetry]\nname = \"llm-app\"\nversion = \"0.3.6\"\ndescription = \"LLM-App is a library for creating responsive AI applicat"
},
{
"path": "setup.cfg",
"chars": 214,
"preview": "[flake8]\nexclude = \n .git\n\nmax-line-length = 119\ndocstring-convention = google\nextend-ignore =\n # Black (https://b"
},
{
"path": "templates/adaptive_rag/Dockerfile",
"chars": 317,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv tesseract-oc"
},
{
"path": "templates/adaptive_rag/README.md",
"chars": 9537,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/adaptive_rag/app.py",
"chars": 2492,
"preview": "import logging\nfrom warnings import warn\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.qu"
},
{
"path": "templates/adaptive_rag/app.yaml",
"chars": 4322,
"preview": "# This YAML configuration file is used to set up and configure the Adaptive RAG template.\n# It defines various component"
},
{
"path": "templates/adaptive_rag/requirements.txt",
"chars": 19,
"preview": "python-dotenv~=1.0\n"
},
{
"path": "templates/document_indexing/Dockerfile",
"chars": 317,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv tesseract-oc"
},
{
"path": "templates/document_indexing/README.md",
"chars": 12113,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/document_indexing/app.py",
"chars": 2461,
"preview": "import logging\nfrom warnings import warn\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.do"
},
{
"path": "templates/document_indexing/app.yaml",
"chars": 3344,
"preview": "# This YAML configuration file is used to set up and configure the Document indexing RAG template.\n# It defines various "
},
{
"path": "templates/document_indexing/docker-compose.yml",
"chars": 549,
"preview": "version: \"3.8\"\nservices:\n pathway_vector_indexer:\n build:\n context: .\n ports:\n - \"8000:8000\"\n enviro"
},
{
"path": "templates/document_indexing/requirements.txt",
"chars": 19,
"preview": "python-dotenv~=1.0\n"
},
{
"path": "templates/document_store_mcp_server/Dockerfile",
"chars": 317,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv tesseract-oc"
},
{
"path": "templates/document_store_mcp_server/README.md",
"chars": 11819,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/document_store_mcp_server/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "templates/document_store_mcp_server/app.py",
"chars": 1514,
"preview": "import logging\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.mcp_server import PathwayMcp"
},
{
"path": "templates/document_store_mcp_server/app.yaml",
"chars": 3458,
"preview": "# This YAML configuration file is used to set up and configure the Document indexing RAG template.\n# It defines various "
},
{
"path": "templates/document_store_mcp_server/docker-compose.yml",
"chars": 239,
"preview": "version: \"3.8\"\nservices:\n pathway_mcp_server:\n build:\n context: .\n ports:\n - \"8068:8068\"\n environmen"
},
{
"path": "templates/document_store_mcp_server/requirements.txt",
"chars": 19,
"preview": "python-dotenv~=1.0\n"
},
{
"path": "templates/drive_alert/Dockerfile",
"chars": 91,
"preview": "FROM pathwaycom/pathway:latest\nWORKDIR /app\nCOPY . .\nEXPOSE 8080\n\nCMD [\"python\", \"app.py\"]\n"
},
{
"path": "templates/drive_alert/README.md",
"chars": 6440,
"preview": "# Pathway + LLM + Slack notification: RAG App with real-time alerting when answers change in documents\n\nMicroservice for"
},
{
"path": "templates/drive_alert/__init__.py",
"chars": 40,
"preview": "from .app import run\n\n__all__ = [\"run\"]\n"
},
{
"path": "templates/drive_alert/app.py",
"chars": 10235,
"preview": "\"\"\"\nMicroservice for a context-aware alerting ChatGPT assistant.\n\nThis demo is very similar to the `alert` example, the "
},
{
"path": "templates/drive_alert/docker-compose.yml",
"chars": 340,
"preview": "version: \"3.8\"\nservices:\n pathway:\n build:\n context: .\n ports:\n - \"8080:8080\"\n environment:\n OP"
},
{
"path": "templates/drive_alert/ui/Dockerfile",
"chars": 190,
"preview": "FROM python:3.11\n\nWORKDIR /app\n\nRUN pip install streamlit python-dotenv\n\nCOPY . .\n\nEXPOSE 8501\n\nCMD [\"streamlit\", \"run\","
},
{
"path": "templates/drive_alert/ui/server.py",
"chars": 2047,
"preview": "import os\n\nimport requests\nimport streamlit as st\nfrom dotenv import load_dotenv\n\napi_host = \"localhost\"\napi_port = 8080"
},
{
"path": "templates/multimodal_rag/Dockerfile",
"chars": 317,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv tesseract-oc"
},
{
"path": "templates/multimodal_rag/README.md",
"chars": 15245,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/multimodal_rag/app.py",
"chars": 2492,
"preview": "import logging\nfrom warnings import warn\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.qu"
},
{
"path": "templates/multimodal_rag/app.yaml",
"chars": 4801,
"preview": "# This YAML configuration file is used to set up and configure the Multimodal RAG template.\n# It defines various compone"
},
{
"path": "templates/multimodal_rag/requirements.txt",
"chars": 44,
"preview": "pathway[all]\npython-dotenv~=1.0\nmpmath~=1.3\n"
},
{
"path": "templates/private_rag/Dockerfile",
"chars": 317,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv tesseract-oc"
},
{
"path": "templates/private_rag/README.md",
"chars": 13585,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/private_rag/app.py",
"chars": 2492,
"preview": "import logging\nfrom warnings import warn\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.qu"
},
{
"path": "templates/private_rag/app.yaml",
"chars": 4335,
"preview": "# This YAML configuration file is used to set up and configure the Private RAG template.\n# It defines various components"
},
{
"path": "templates/private_rag/requirements.txt",
"chars": 44,
"preview": "pathway[all]\npython-dotenv~=1.0\nmpmath~=1.3\n"
},
{
"path": "templates/question_answering_rag/Dockerfile",
"chars": 374,
"preview": "ARG PATHWAY_SRC_IMAGE=pathwaycom/pathway:latest\n\nFROM ${PATHWAY_SRC_IMAGE}\n\nWORKDIR /app\n\nRUN apt-get update \\\n && ap"
},
{
"path": "templates/question_answering_rag/README.md",
"chars": 21294,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/question_answering_rag/app.py",
"chars": 2492,
"preview": "import logging\nfrom warnings import warn\n\nimport pathway as pw\nfrom dotenv import load_dotenv\nfrom pathway.xpacks.llm.qu"
},
{
"path": "templates/question_answering_rag/app.yaml",
"chars": 4739,
"preview": "# This YAML configuration file is used to set up and configure the Question Answering RAG template.\n# It defines various"
},
{
"path": "templates/question_answering_rag/docker-compose.yml",
"chars": 733,
"preview": "services:\n app:\n build:\n context: .\n args:\n PATHWAY_SRC_IMAGE: ${PATHWAY_SRC_IMAGE:-pathwaycom/path"
},
{
"path": "templates/question_answering_rag/requirements.txt",
"chars": 44,
"preview": "pathway[all]\npython-dotenv~=1.0\nmpmath~=1.3\n"
},
{
"path": "templates/question_answering_rag/ui/.streamlit/config.toml",
"chars": 94,
"preview": "[client]\ntoolbarMode = \"minimal\"\n\n[server]\nenableStaticServing = true\n\n[theme]\nbase = \"light\"\n"
},
{
"path": "templates/question_answering_rag/ui/Dockerfile",
"chars": 256,
"preview": "ARG PATHWAY_SRC_IMAGE=pathwaycom/pathway:latest\n\nFROM ${PATHWAY_SRC_IMAGE}\n\nENV PYTHONUNBUFFERED=1\n\nWORKDIR /ui\n\nCOPY re"
},
{
"path": "templates/question_answering_rag/ui/requirements.txt",
"chars": 37,
"preview": "streamlit==1.37.0\nload_dotenv==0.1.0\n"
},
{
"path": "templates/question_answering_rag/ui/ui.py",
"chars": 5408,
"preview": "# Copyright © 2026 Pathway\n\nimport logging\nimport os\n\nimport streamlit as st\nfrom dotenv import load_dotenv\nfrom pathway"
},
{
"path": "templates/slides_ai_search/.dockerignore",
"chars": 6,
"preview": "Cache\n"
},
{
"path": "templates/slides_ai_search/.gitignore",
"chars": 18,
"preview": "data\n.env\nstorage\n"
},
{
"path": "templates/slides_ai_search/Dockerfile",
"chars": 348,
"preview": "ARG PATHWAY_SRC_IMAGE=pathwaycom/pathway:latest\n\nFROM ${PATHWAY_SRC_IMAGE}\n\nENV PYTHONUNBUFFERED=1\n\nWORKDIR /app\n\nRUN ap"
},
{
"path": "templates/slides_ai_search/README.md",
"chars": 17373,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/slides_ai_search/app.py",
"chars": 3472,
"preview": "#!/usr/bin/env python\n\n# Copyright © 2026 Pathway\n\n# Copied and adapted from templates/slides_ai_search/app.py\n# To use "
},
{
"path": "templates/slides_ai_search/app.yaml",
"chars": 4579,
"preview": "# This YAML configuration file is used to set up and configure the Slides Search RAG template.\n# It defines various comp"
},
{
"path": "templates/slides_ai_search/docker-compose.yml",
"chars": 946,
"preview": "services:\n app:\n build:\n context: .\n env_file:\n - .env\n environment:\n PATHWAY_PORT: \"${PATHWAY_"
},
{
"path": "templates/slides_ai_search/nginx/Dockerfile",
"chars": 137,
"preview": "FROM nginx:latest\n\nCOPY nginx.conf /etc/nginx/conf.d/default.conf\n\nRUN mkdir -p /app/pw_dump_images /app/pw_dump_files\n\n"
},
{
"path": "templates/slides_ai_search/nginx/nginx.conf",
"chars": 359,
"preview": "server {\n listen 8080;\n listen 8443;\n location = / {\n deny all;\n }\n location /images {\n "
},
{
"path": "templates/slides_ai_search/pathway_slides_ai_search/__init__.py",
"chars": 4128,
"preview": "# Copyright © 2026 Pathway\n\nimport base64\nimport logging\nimport os\nfrom pathlib import Path\nfrom typing import Any, Lite"
},
{
"path": "templates/slides_ai_search/requirements.txt",
"chars": 19,
"preview": "python-dotenv~=1.0\n"
},
{
"path": "templates/slides_ai_search/ui/.streamlit/config.toml",
"chars": 60,
"preview": "[server]\nenableStaticServing = true\n\n[theme]\nbase = \"light\"\n"
},
{
"path": "templates/slides_ai_search/ui/Dockerfile",
"chars": 251,
"preview": "ARG PATHWAY_SRC_IMAGE=pathwaycom/pathway:latest\n\nFROM ${PATHWAY_SRC_IMAGE}\n\nENV PYTHONUNBUFFERED=1\n\nWORKDIR /ui\n\nCOPY re"
},
{
"path": "templates/slides_ai_search/ui/requirements.txt",
"chars": 111,
"preview": "streamlit==1.35.0\nload_dotenv==0.1.0\nnest_asyncio==1.6.0\naiohttp==3.9.5\nbeautifulsoup4==4.12.3\nopenai==1.35.10\n"
},
{
"path": "templates/slides_ai_search/ui/ui.py",
"chars": 11886,
"preview": "# Copyright © 2026 Pathway\n\nimport logging\nimport os\nimport urllib.parse\nfrom itertools import cycle\nfrom pathlib import"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/Dockerfile",
"chars": 307,
"preview": "FROM pathwaycom/pathway:latest\n\nWORKDIR /app\n\nRUN apt-get update \\\n && apt-get install -y python3-opencv \\\n && rm "
},
{
"path": "templates/unstructured_to_sql_on_the_fly/README.md",
"chars": 4873,
"preview": "<p align=\"center\" class=\"flex items-center gap-1 justify-center flex-wrap\">\n <img src=\"../../assets/gcp-logo.svg?raw="
},
{
"path": "templates/unstructured_to_sql_on_the_fly/__init__.py",
"chars": 40,
"preview": "from .app import run\n\n__all__ = [\"run\"]\n"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/app.py",
"chars": 11549,
"preview": "\"\"\"\nMicroservice for accounting assistant.\n\nThe aim of this project is to extract and structure the data out of unstruct"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/docker-compose.yml",
"chars": 1114,
"preview": "version: \"3.8\"\nservices:\n postgres:\n container_name: postgres\n image: postgres\n ports:\n - 5432:5432\n e"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/postgres/init-db.sql",
"chars": 278,
"preview": "CREATE TABLE IF NOT EXISTS quarterly_earnings (\n company_symbol TEXT NOT NULL,\n eps FLOAT NOT NULL,\n net_income"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/requirements.txt",
"chars": 32,
"preview": "python-dotenv~=1.0\npsycopg~=3.1\n"
},
{
"path": "templates/unstructured_to_sql_on_the_fly/ui/Dockerfile",
"chars": 176,
"preview": "FROM python:3.11\n\nWORKDIR /app\n\nRUN pip install streamlit\n\nCOPY . .\n\nEXPOSE 8501\n\nCMD [\"streamlit\", \"run\", \"server.py\", "
},
{
"path": "templates/unstructured_to_sql_on_the_fly/ui/server.py",
"chars": 2502,
"preview": "import os\n\nimport pandas as pd\nimport requests\nimport streamlit as st\n\napi_host = os.environ.get(\"PATHWAY_HOST\", \"localh"
}
]
About this extraction
This page contains the full source code of the pathwaycom/llm-app GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 83 files (638.0 KB), approximately 309.9k tokens, and a symbol index with 59 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.