diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..753b249 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.ipynb merge=nbdev-merge diff --git a/.gitconfig b/.gitconfig new file mode 100644 index 0000000..9054574 --- /dev/null +++ b/.gitconfig @@ -0,0 +1,11 @@ +# Generated by nbdev_install_hooks +# +# If you need to disable this instrumentation do: +# git config --local --unset include.path +# +# To restore: +# git config --local include.path ../.gitconfig +# +[merge "nbdev-merge"] + name = resolve conflicts with nbdev_fix + driver = nbdev_merge %O %A %B %P diff --git a/README.md b/README.md index b0b572e..9dd93c1 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,90 @@ # Welcome to CausalNLP + ## What is CausalNLP? -> CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. + +> CausalNLP is a practical toolkit for causal inference with text as +> treatment, outcome, or “controlled-for” variable. ## Features -- Low-code [causal inference](https://amaiya.github.io/causalnlp/examples.html) in as little as two commands -- Out-of-the-box support for using [**text** as a "controlled-for" variable](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-a-positive-review-on-product-views?) (e.g., confounder) -- Built-in [Autocoder](https://amaiya.github.io/causalnlp/autocoder.html) that transforms raw text into useful variables for causal analyses (e.g., topics, sentiment, emotion, etc.) -- Sensitivity analysis to [assess robustness of causal estimates](https://amaiya.github.io/causalnlp/causalinference.html#CausalInferenceModel.evaluate_robustness) -- Quick and simple [key driver analysis](https://amaiya.github.io/causalnlp/key_driver_analysis.html) to yield clues on potential drivers of an outcome based on predictive power, correlations, etc. -- Can easily be applied to ["traditional" tabular datasets without text](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) (i.e., datasets with only numerical and categorical variables) -- Includes an experimental [PyTorch implementation](https://amaiya.github.io/causalnlp/core.causalbert.html) of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, and Blei (based on [reference implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. Pryzant) + +- Low-code [causal + inference](https://amaiya.github.io/causalnlp/examples.html) in as + little as two commands +- Out-of-the-box support for using [**text** as a “controlled-for” + variable](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-a-positive-review-on-product-views?) + (e.g., confounder) +- Built-in + [Autocoder](https://amaiya.github.io/causalnlp/autocoder.html) that + transforms raw text into useful variables for causal analyses (e.g., + topics, sentiment, emotion, etc.) +- Sensitivity analysis to [assess robustness of causal + estimates](https://amaiya.github.io/causalnlp/causalinference.html#CausalInferenceModel.evaluate_robustness) +- Quick and simple [key driver + analysis](https://amaiya.github.io/causalnlp/key_driver_analysis.html) + to yield clues on potential drivers of an outcome based on predictive + power, correlations, etc. +- Can easily be applied to [“traditional” tabular datasets without + text](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) + (i.e., datasets with only numerical and categorical variables) +- Includes an experimental [PyTorch + implementation](https://amaiya.github.io/causalnlp/core.causalbert.html) + of [CausalBert](https://arxiv.org/abs/1905.12741) by Veitch, Sridar, + and Blei (based on [reference + implementation](https://github.com/rpryzant/causal-bert-pytorch) by R. + Pryzant) ## Install -1. `pip install -U pip` -2. `pip install causalnlp` +1. `pip install -U pip` +2. `pip install causalnlp` -**NOTE**: On Python 3.6.x, if you get a `RuntimeError: Python version >= 3.7 required`, try ensuring NumPy is installed **before** CausalNLP (e.g., `pip install numpy==1.18.5`). +**NOTE**: On Python 3.6.x, if you get a +`RuntimeError: Python version >= 3.7 required`, try ensuring NumPy is +installed **before** CausalNLP (e.g., `pip install numpy==1.18.5`). ## Usage -To try out the [examples](https://amaiya.github.io/causalnlp/examples.html) yourself: +To try out the +[examples](https://amaiya.github.io/causalnlp/examples.html) yourself: Open In Colab ### Example: What is the causal impact of a positive review on a product click? -``` +``` python import pandas as pd -df = pd.read_csv('sample_data/music_seed50.tsv', sep='\t', error_bad_lines=False) ``` -The file `music_seed50.tsv` is a semi-simulated dataset from [here](https://github.com/rpryzant/causal-text). Columns of relevance include: -- `Y_sim`: outcome, where 1 means product was clicked and 0 means not. -- `text`: raw text of review -- `rating`: rating associated with review (1 through 5) -- `T_true`: 0 means rating less than 3, 1 means rating of 5, where `T_true` affects the outcome `Y_sim`. -- `T_ac`: an approximation of true review sentiment (`T_true`) created with [Autocoder](https://amaiya.github.io/causalnlp/autocoder.html) from raw review text -- `C_true`:confounding categorical variable (1=audio CD, 0=other) +``` python +df = pd.read_csv('sample_data/music_seed50.tsv', sep='\t', on_bad_lines='skip') +``` +The file `music_seed50.tsv` is a semi-simulated dataset from +[here](https://github.com/rpryzant/causal-text). Columns of relevance +include: - `Y_sim`: outcome, where 1 means product was clicked and 0 +means not. - `text`: raw text of review - `rating`: rating associated +with review (1 through 5) - `T_true`: 0 means rating less than 3, 1 +means rating of 5, where `T_true` affects the outcome `Y_sim`. - `T_ac`: +an approximation of true review sentiment (`T_true`) created with +[Autocoder](https://amaiya.github.io/causalnlp/autocoder.html) from raw +review text - `C_true`:confounding categorical variable (1=audio CD, +0=other) -We'll pretend the true sentiment (i.e., review rating and `T_true`) is hidden and only use `T_ac` as the treatment variable. +We’ll pretend the true sentiment (i.e., review rating and `T_true`) is +hidden and only use `T_ac` as the treatment variable. -Using the `text_col` parameter, we include the raw review text as another "controlled-for" variable. +Using the `text_col` parameter, we include the raw review text as +another “controlled-for” variable. -``` +``` python from causalnlp import CausalInferenceModel from lightgbm import LGBMClassifier +``` + +``` python cm = CausalInferenceModel(df, metalearner_type='t-learner', learner=LGBMClassifier(num_leaves=500), treatment_col='T_ac', outcome_col='Y_sim', text_col='text', @@ -65,34 +100,36 @@ cm.fit() start fitting causal inference model time to fit causal inference model: 10.361494302749634 sec - #### Estimating Treatment Effects -CausalNLP supports estimation of heterogeneous treatment effects (i.e., how causal impacts vary across observations, which could be documents, emails, posts, individuals, or organizations). +CausalNLP supports estimation of heterogeneous treatment effects (i.e., +how causal impacts vary across observations, which could be documents, +emails, posts, individuals, or organizations). -We will first calculate the overall average treatment effect (or ATE), which shows that a positive review increases the probability of a click by **13 percentage points** in this dataset. +We will first calculate the overall average treatment effect (or ATE), +which shows that a positive review increases the probability of a click +by **13 percentage points** in this dataset. **Average Treatment Effect** (or **ATE**): -``` +``` python print( cm.estimate_ate() ) ``` {'ate': 0.1309311542209525} +**Conditional Average Treatment Effect** (or **CATE**): reviews that +mention the word “toddler”: -**Conditional Average Treatment Effect** (or **CATE**): reviews that mention the word "toddler": - -``` +``` python print( cm.estimate_ate(df['text'].str.contains('toddler')) ) ``` {'ate': 0.15559234254638685} +**Individualized Treatment Effects** (or **ITE**): - **Individualized Treatment Effects** (or **ITE**): - -``` +``` python test_df = pd.DataFrame({'T_ac' : [1], 'C_true' : [1], 'text' : ['I never bought this album, but I love his music and will soon!']}) effect = cm.predict(test_df) @@ -101,10 +138,9 @@ print(effect) [[0.80538201]] - **Model Interpretability**: -``` +``` python print( cm.interpret(plot=False)[1][:10] ) ``` @@ -120,28 +156,33 @@ print( cm.interpret(plot=False)[1][:10] ) v_heard 0.028373 dtype: float64 - -Features with the `v_` prefix are word features. `C_true` is the categorical variable indicating whether or not the product is a CD. +Features with the `v_` prefix are word features. `C_true` is the +categorical variable indicating whether or not the product is a CD. ### Text is Optional in CausalNLP -Despite the "NLP" in CausalNLP, the library can be used for causal inference on data **without** text (e.g., only numerical and categorical variables). See [the examples](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) for more info. +Despite the “NLP” in CausalNLP, the library can be used for causal +inference on data **without** text (e.g., only numerical and categorical +variables). See [the +examples](https://amaiya.github.io/causalnlp/examples.html#What-is-the-causal-impact-of-having-a-PhD-on-making-over-$50K?) +for more info. ## Documentation -API documentation and additional usage examples are available at: https://amaiya.github.io/causalnlp/ -## How to Cite +API documentation and additional usage examples are available at: +https://amaiya.github.io/causalnlp/ -Please cite [the following paper](https://arxiv.org/abs/2106.08043) when using CausalNLP in your work: +## How to Cite -``` -@article{maiya2021causalnlp, - title={CausalNLP: A Practical Toolkit for Causal Inference with Text}, - author={Arun S. Maiya}, - year={2021}, - eprint={2106.08043}, - archivePrefix={arXiv}, - primaryClass={cs.CL}, - journal={arXiv preprint arXiv:2106.08043}, -} -``` +Please cite [the following paper](https://arxiv.org/abs/2106.08043) when +using CausalNLP in your work: + + @article{maiya2021causalnlp, + title={CausalNLP: A Practical Toolkit for Causal Inference with Text}, + author={Arun S. Maiya}, + year={2021}, + eprint={2106.08043}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + journal={arXiv preprint arXiv:2106.08043}, + } diff --git a/causalnlp/analyzers.py b/causalnlp/analyzers.py index 747ae40..c1cae06 100644 --- a/causalnlp/analyzers.py +++ b/causalnlp/analyzers.py @@ -333,7 +333,7 @@ def get_topics(self, n_words=10, as_string=True): Returns a list of discovered topics """ self._check_model() - feature_names = self.vectorizer.get_feature_names() + feature_names = self.vectorizer.get_feature_names_out() topic_summaries = [] for topic_idx, topic in enumerate(self.model.components_): summary = [feature_names[i] for i in topic.argsort()[:-n_words - 1:-1]] diff --git a/causalnlp/preprocessing.py b/causalnlp/preprocessing.py index 572ae1c..0707d68 100644 --- a/causalnlp/preprocessing.py +++ b/causalnlp/preprocessing.py @@ -144,7 +144,7 @@ def preprocess(self, df, v_features = self.tv.fit_transform(df[self.text_col]) else: v_features = self.tv.transform(df[self.text_col]) - vocab = self.tv.get_feature_names() + vocab = self.tv.get_feature_names_out() vocab_df = pd.DataFrame(v_features.toarray(), columns = ["v_%s" % (v) for v in vocab]) X = pd.concat([X, vocab_df], axis=1, join='inner') outcome_type = 'categorical' if self.is_classification else 'numerical' diff --git a/nbs/00a_core.causalinference.ipynb b/nbs/00a_core.causalinference.ipynb index 77d36e7..c5ba607 100644 --- a/nbs/00a_core.causalinference.ipynb +++ b/nbs/00a_core.causalinference.ipynb @@ -753,6 +753,16 @@ "Let's create a simulated dataset." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import pandas as pd" + ] + }, { "cell_type": "code", "execution_count": null, @@ -834,8 +844,6 @@ } ], "source": [ - "import itertools\n", - "import pandas as pd\n", "data = ((*a, b) for (a, b) in zip(itertools.product([0,1], [0,1], [0,1]), [36, 234, 25, 55, 6, 81, 71, 192]))\n", "df = pd.DataFrame(data, columns=['Is_Male?', 'Post_Text', 'Post_Shared?', 'N'])\n", "df = df.loc[df.index.repeat(df['N'])].reset_index(drop=True).drop(columns=['N'])\n", @@ -928,6 +936,15 @@ "Let's first use the `Autocoder` to transform the raw text into sentiment. We can then control for sentiment when estimating the causal effect." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from causalnlp.autocoder import Autocoder" + ] + }, { "cell_type": "code", "execution_count": null, @@ -949,7 +966,6 @@ } ], "source": [ - "from causalnlp.autocoder import Autocoder\n", "ac = Autocoder()" ] }, @@ -1067,6 +1083,15 @@ "Next, let's estimate the treatment effects. We will ignore the `positive` and `Post_Shared?` columns, as their information is captured by the `negative` column in this example. We will use the T-Learner. See [this paper](https://arxiv.org/abs/1706.03461) for more information on metalearner types." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from causalnlp import CausalInferenceModel" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1087,7 +1112,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -1096,7 +1121,6 @@ } ], "source": [ - "from causalnlp import CausalInferenceModel\n", "cm = CausalInferenceModel(df, method='t-learner',\n", " treatment_col='Is_Male?', outcome_col='Post_Shared?',\n", " include_cols=['negative'])\n", @@ -1138,6 +1162,15 @@ "Since this is a small, simulated, toy problem, we can manually calculate the adjusted treatment effect by controlling for the single counfounder (i.e., post negativity):" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1155,7 +1188,6 @@ } ], "source": [ - "from collections import defaultdict\n", "def ATE_adjusted(C, T, Y):\n", " x = defaultdict(list)\n", " for c, t, y in zip(C, T, Y):\n", @@ -1318,7 +1350,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -1496,7 +1528,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1518,7 +1550,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1672,7 +1704,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -1811,11 +1843,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/nbs/00b_core.causalbert.ipynb b/nbs/00b_core.causalbert.ipynb index 009e7ee..fd37226 100644 --- a/nbs/00b_core.causalbert.ipynb +++ b/nbs/00b_core.causalbert.ipynb @@ -454,6 +454,17 @@ "This implementation of `CausalBert` was adapted from [Causal Effects of Linguistic Properties](https://arxiv.org/abs/2010.12919) by Pryzant et al. `CausalBert` is essentially a kind of [S-Learner](https://arxiv.org/abs/1706.03461) that uses a DistilBert sequence classification model as the base learner." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| notest\n", + "import pandas as pd\n", + "from causalnlp.core.causalbert import CausalBertModel" + ] + }, { "cell_type": "code", "execution_count": null, @@ -486,9 +497,7 @@ ], "source": [ "#| notest\n", - "import pandas as pd\n", - "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', error_bad_lines=False)\n", - "from causalnlp.core.causalbert import CausalBertModel\n", + "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', on_bad_lines='skip')\n", "cb = CausalBertModel(batch_size=32, max_length=128)\n", "cb.train(df['text'], df['C_true'], df['T_ac'], df['Y_sim'], epochs=1, learning_rate=2e-5)\n", "print(cb.estimate_ate(df['C_true'], df['text']))" @@ -552,11 +561,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/nbs/01_autocoder.ipynb b/nbs/01_autocoder.ipynb index e9a562b..685e78b 100644 --- a/nbs/01_autocoder.ipynb +++ b/nbs/01_autocoder.ipynb @@ -3215,7 +3215,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/02_analyzers.ipynb b/nbs/02_analyzers.ipynb index a6938e2..f0d6579 100644 --- a/nbs/02_analyzers.ipynb +++ b/nbs/02_analyzers.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "raw", + "id": "f620c050", "metadata": {}, "source": [ "---\n", @@ -189,12 +190,18 @@ { "data": { "text/markdown": [ - "

ZeroShotClassifier.predict[source]

\n", + "---\n", "\n", - "> ZeroShotClassifier.predict(**`docs`**, **`labels`**=*`[]`*, **`include_labels`**=*`False`*, **`multilabel`**=*`True`*, **`max_length`**=*`512`*, **`batch_size`**=*`8`*, **`nli_template`**=*`'This text is about {}.'`*, **`topic_strings`**=*`[]`*)\n", + "[source](https://github.com/amaiya/causalnlp/tree/main/blob/main/causalnlp/analyzers.py#L43){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", - "This method performs zero-shot text classification using Natural Language Inference (NLI).\n", + "### ZeroShotClassifier.predict\n", "\n", + "> ZeroShotClassifier.predict (docs, labels=[], include_labels=False,\n", + "> multilabel=True, max_length=512,\n", + "> batch_size=8, nli_template='This text is\n", + "> about {}.', topic_strings=[])\n", + "\n", + "*This method performs zero-shot text classification using Natural Language Inference (NLI).\n", "\n", "**Parameters**:\n", " - docs(list|str): text of document or list of texts\n", @@ -210,18 +217,48 @@ " if len(topic_strings) is large.\n", " - nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference\n", " - topic_strings(list): alias for labels parameter for backwards compatibility\n", - " \n", - "**Returns:**\n", "\n", + "**Returns:**\n", "\n", - " inferred probabilities or list of inferred probabilities if doc is list" + " inferred probabilities or list of inferred probabilities if doc is list*" ], "text/plain": [ - "" + "---\n", + "\n", + "[source](https://github.com/amaiya/causalnlp/tree/main/blob/main/causalnlp/analyzers.py#L43){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### ZeroShotClassifier.predict\n", + "\n", + "> ZeroShotClassifier.predict (docs, labels=[], include_labels=False,\n", + "> multilabel=True, max_length=512,\n", + "> batch_size=8, nli_template='This text is\n", + "> about {}.', topic_strings=[])\n", + "\n", + "*This method performs zero-shot text classification using Natural Language Inference (NLI).\n", + "\n", + "**Parameters**:\n", + " - docs(list|str): text of document or list of texts\n", + " - labels(list): a list of strings representing topics of your choice\n", + " Example:\n", + " labels=['political science', 'sports', 'science']\n", + " - include_labels(bool): If True, will return topic labels along with topic probabilities\n", + " - multilabel(bool): If True, labels are considered independent and multiple labels can predicted true for document and be close to 1.\n", + " If False, scores are normalized such that probabilities sum to 1.\n", + " - max_length(int): truncate long documents to this many tokens\n", + " - batch_size(int): batch_size to use. default:8\n", + " Increase this value to speed up predictions - especially\n", + " if len(topic_strings) is large.\n", + " - nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference\n", + " - topic_strings(list): alias for labels parameter for backwards compatibility\n", + "\n", + "**Returns:**\n", + "\n", + " inferred probabilities or list of inferred probabilities if doc is list*" ] }, + "execution_count": null, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -250,11 +287,11 @@ { "data": { "text/plain": [ - "[('politics', 0.979189932346344),\n", - " ('elections', 0.9874580502510071),\n", - " ('sports', 0.0005765454261563718),\n", - " ('films', 0.002292441902682185),\n", - " ('television', 0.001054605352692306)]" + "[('politics', 0.9791897535324097),\n", + " ('elections', 0.9874581098556519),\n", + " ('sports', 0.0005765464738942683),\n", + " ('films', 0.002292431192472577),\n", + " ('television', 0.0010546175763010979)]" ] }, "execution_count": null, @@ -333,200 +370,12 @@ "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6f36dcc4e2a9407b8fcb697c5dd78486", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/748 [00:00" ] @@ -470,6 +478,16 @@ "#### Example: Variable Importances for Probability of Making Over $50K" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| notest\n", + "import pandas as pd" + ] + }, { "cell_type": "code", "execution_count": null, @@ -505,7 +523,7 @@ }, { "data": { - "image/png": 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\n", 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j6L4V/P1P29A6LxMIQW4WZAY3cCkLs8myYkoqK6lYWEjbklLaFRYDUNC1DfN69aXouw7kVhZU204nFtKZhQD0br+cz+8YzOIvlwKQG/bpMa+AVr67x2t5KINVXiu6Za2mb6lbp+SSj1hT4PpQcP939PvmeLzMum8qUzKrEN1hApWryvEyPLZ5Zz867dMrqePZUCIf/Up45FioDJMRyuTHnsMpbp3Lfk/vQY/hXZu0L8aYhmO3tTJm/cwDDgTaAYcBpwKnB8HoBGAa0B04HDgjbt3rgnUOADrjAtq3RaTFpoC+/b6ESHACqrAowq8zylPboRh/fLpsbSHEmpUVLPppdYO0O+f3EgoLXNDn+/CzFta5zus/Vd2e6udOLrjNraik36pClpd57LRydVWwCmSFoE15JR1WlVFY5tNxxWrKsjIpz8yEUIgVJT5LKrsRAQpDbSj3MoGMarmV9qvms+SLxWvH1/g+2X7VDxJkRcLg+7QrrTouZQVV9bJl3y2lYm7d+wZQ8NEiKle5594P+6x4a15S6zUk/60foDIMQFakku6lK/ErfeZ/tKjJ+2JMqvh41R7pwAJWY9aDqr6sqrNU1VfVqcDTwD7AcKA/cKmqlqjqH8DY6Hoi4gHnAher6h+qGlbVx4CFwMGN3e/CwsJGGR4yKHvteHa2R/++WY22rfoO99q6KuuZ1TqDLpvmNkj7PfvlkN2q6oOge79Qnevu1GftIP2CTGl5KMSC3LYMbB2hsE22i36jysLMz8xghecxaGkByzq2Z0b/Pvw+oA/L2rejy2Z5FLXqyts99uWVvn/mpT6HsySnC9VOB8og2g1pt3Y0O+xTQcba8UpC4HkUZbSuOk45VX3I6JdLZs+2SR2TPOmClx0TcG+bV+cxaehhb5dN104L47E82+171+06N5vXpA3bsKk/u+jKmPUgIqOBC4GBuNKabOBL4D/AnaraJ2bZkcB7quqJSFdgCbCa6hdAZQE3qOqtjdz1RvuH/+zrYvLnVrDT9q3ZdEDzusBn+ruLWDaziMF7d6N7TPC2oebPKmHa56vpvkkOO+zZoc7lC0t9/vVZGb4PB1SuouD3Qr7q0BGvbx5/3SGTPv+ppCTigQfZReW0KihdG3q+usVKXviwcm22JNOr5M6HN2Pey7P49Prv126jc1kBXdesICdSSlY2bPnbBZT62UwfP5PM1hn02qYjS1+ejTd/NXlbtCe0WScKphXQed+e5P4xn/AfK8gePYyiDxcQXrKG9n/dhqw+eQn2JrFVny9m2RvzaLdjF7oe3q8+h7PBRF7/Dv+LPygY2Jf8lTl03a4zffdt2tIEY5LUKOnPcu+sau/12f5/Wnya1QJWY+pJRPoA+cARwERVLReROwEBrgQmAR1VtSRY/lTgsSBg9YBCYC9V/SYF3bd/+Gas1/3lLFzjPlc6LSkiXOFOzedkwPQbOnLnJbNYscydzm/XqZTbHhxGwe+ref2Ad/GDs/it2mbir3D3aG2/eXsO/OCApt8RY0yyLGBNkpUEGFN/ubj/naVAhYgMB04M5n0JzAFuEZFWIjIAOD+6oqr6wL3AnSIyGEBEckVkfxGxFNBGbHWpz6J5JVBWCeEIO6wupkdFBZ0qw2xXXkrHtiH+fukmbLV9W7r3KWS73d0FUh0Gt2O3u/9Ej126MuTEQez73B70GtmL3vv3ZtdHdk3xXhljUiEda1ibx2W8xrQgqvqLiFwDvIYrBfgQeA7YVlUrReRQ4GFcQPsHrr71xpgmrsHVsb4mIpsAxbhA9x9NtxemOQpFfMKrXHa0DT5blroLmHLbutxC7745nH3pJkyYMLXaegMP68vAw/quHd/jqd2bqMfGGNM0rCTAmEYmImcC/1TVzVLdF6wkoFl77KsyLn6jlI5tPG7eKcR7E1YR8X3+dnIndtyuzdrlJkyYAMCoUaNS1VVjTMNolPRnmfe3au/1Of6DLT7NahlWYxqYiOyGu+r/D2Br3D1Xx6e0U6ZFOG2nHE7bqeqCtWP2zk1hb4wxpvmwgNWYhtcHeBbogisLeBG4JaU9MsYYs9FIl7rVWBawGtPAVPU5XE2rMcYYYxqABazGGGOMMWkl/TKsdlsrY4wxxhjTrFmG1RhjjDEmjaRjDatlWI0xxhhjTLNmGVZjjDHGmLRiGVZjjDHGGGOalGVYjTGmmVn49TJ+/Wo533XtwpDt2jO4uJjF+SVsvlMHOvduleruGWOauXSsYbWA1RhjmpG5kxfx7hmfgw9ZGRncMHwbRi5ZDsDHzy3k7Ae3THEPjTGm6VlJgDHGNCPzP1sCwa+AtwqH2W75yrXzSorCzPxqRYp6ZowxqWMBqzHGNCM9d+pSbbzPysK1w144wrf/9w3lMyJN3S1jjEkpC1iNMaaBhMs3PJDsvWu3auMdlxcAkLOmlB7zFuGtqaDky3W345eHN3jbAH5Fw7RjjEkdH6/aIx1YwGrWm4hUisiIFG7/JxE5JlXbNybWF9dPY9yWr/L87hNZ+dvq9W4no1UGXueqC6sqstylBm0Li8kurwQg1LZq+ciqUhbu/CRzcm5j8T7PECmpWK/tRr6dQ0nPKyhtdQEVl7++3v03xpjGYAGrafZEpL+I+CKySex0Vd1SVf+bqn4ZE7X8lwJ+fmom+FC8sIQp9/683m15nsfdBw3nm0G9+LlPN77ZvB8dlhWQu7p47TJ+WdXyhQ9NpfzL+QCUfjCb4qd/XK/tVlw+ARathohP5S3vEZm5dL33wRiTal7co+WzuwQYY0wtfvliJR89t5C27TM59Nz+tO+avc4ykcLyauNL5pZx61dhnp/u06u4hP1/nksrD4pnFdGucA377N+BvpcOY+J9s4j853val5ax6QVb0vv8rdz6Xdrx0L4CQF5JGXtM/a1a+wX5Ocx/pQPbbVZEO3zasZIMKimlDUTClO0+lsi3c/EGdCb7zbMI9e9c4/5V3DGJ8DOKv7SoaqIHZNfw8fDDbDjnEagIE/n7wVQ88i2UV5I1IJPQz7Nhz6Gwshh+mAMHbgdfz4DVa+CWE2CfbZI44o0rPPl3Ki59Da9tNlkPHkNoSPdUd8kYkwQLWM1aItIGuB44EmgPfA2co6ozRCQPuB8YBRQCV8etey2wm6qOjJk2GZikqjcG49sAtwM7ABnAlOjyIvIEMBLoAMwFblTVZ4OmpgV/fxURH7hNVW8QkXzgSlUdH7SxZ9D+5sBCYKyqPhTMGwFMAo4Hbga6AO8Ap6lq1VUt1fdpDHAlcB9wCdAWeAH4u6qGRaQ/MAvoo6rzYtdR1U2D8XzgUWAfYMdg+eOBLYEbgK7Ai8BZqlqZqB8mddYUVvLiLX9QWeEu23/j37M5/trB6yy3/J6f6LJsDSs7tCKrMswHoY48+olbZxqtKQnnsXv+YiBEQZtcPnp8Lv1btWX5v6cz8I9lAPxxwZfk7dyNVtKVogrIqgyTW1LGcZ/+QFFmBm0rw2vzJJHVPkWLsnn+hpmc230RYUoByGI1vD2FyKd/AOD/vIjy45+k1WcXJty/8CczqLzktXVneB5eqIaszOi74ae5brFvZhKp7E4Gawh9GdzNYFp+1bLfzaoaPvw2WDYOsrMSt9sE/HCE8sMfgYISfKD8pKdp9dVFKeuPMY0lXepWY1lJgIn1CC7YGw70AL4C3hCRLOAeYDAwFNgGOAwXdCZFRHoCHwWP/kH7t8Ys8imwLS5gvR4YJyJDg3nDgr9DVDVXVW9I0P4A4G3gQaAzMAa4RUSOjlksA9gvaG8zYDvg3Dq63g/oDgzCBZxHA8fWsU68k4G/Ax1xwff/gL2CfmwNHAo0SS1uYWGhDddjuLwkvDZYBVizujLh8hXLSum0qpRBswvoO7+Qkrjs5Jqs6uMV2ZkULFqztiY1qnJZKUtWFlFSWElmYTnbz17Gb717cO+ewsAVyxiydBFtysvI8F2f1hRWUrGker2stziufnZpUY37WDJ3GQlFfPyCksTHZ1nM97vKStxHYxIXahWWQGlF4jabaLhoeQGsKq3q07LilPbHhm3YJM8CVgOAiHQBjsNlDxerajlwHdAT2BmXFbxKVRep6irg0npu4kRghqreoqrFqlquqpOiM1X1MVVdrqphVX0e+B4YUY/2R+MytuNUtVJVvwQeAk6PW+4yVS1S1cXAq4DU0W4JcLWqlqnqDOD9JNaJ97Cq/qKqFcCzwEDgiuA4zAEmr0eb6yUvL8+G6zHcoVsOOx7cFYCsnBAjjuuVcPk+V2xLKNdlDhf06oh0gV16uGU6hCvYcd5SMjJdxiOntJwtuviMOH8zCqQ3ZUFwm7dnTzrs25tnf8qBNZUc/escdlm8guFLV3LSjNkMWrmMPqsL2GHBXAo7tAV8Ro7pTetL94Nurh+h/TYna+xRkBMEyCGPzNsOq3Ef2x65A6E9NnUjXdpCW1fukHHijoS27Jn4+Nw4GjJC4HlEjtwDMjKopC1+t45uoYHdoVt7N7xlH8gKvtdefiS0a9Pgz1G9hrt1IvPqA9xIVgaZNxyc2v7Y8EY/bJJnJQEmakDw93uRarFTVjAvB8iPmT6L+ukP/JZohoiEgGtxWcYeuNumt8WdLk9WnwR9monLBEeFVTX2SpJiIC/ow+XA5cH02aoa/TmhJaoaTrROPSyMGV6ToB9r1qNN00RGndOPPUf3JLtVBq3aJj6p0GHPngxfeByly0rZuk02eZ2z+YcH84ugS06I8lVDaNM+k+KlZWSVVdK2fy5eRogzXhBWL9iSVn6E1n1z8UIe3y2sgAyP7qVVV1b5mZn4uNLSnHAlW41eSVnXNexy+I4AtMq/Fn9ZMV7v9nihEK1W3Ib/yyIY1IVQhzY17puXk0X2h//An78Kr2suVEagYA3eJh1rPiCn7wuHD4dwmIxuHWi1vBjCEbyOrWHhSujZESrDLhPbuxOsLoHScuhRS5tNKOvag8j8226QnYnXseZjY0xLlo4lARawmqjZwd/BccEUIpIBPIwLOmcGk/vHrV+ICzJj9YoZzgeOqmHbo3GZ0P2An1U1IiJK1aWNydzcci5wUNy0gcH0Oqnqzbja1vqInteJ3e9eiRY0LVu7zuteaBUvIzeLtrnV6zP7tgPwaNMtB4DsTaoHSJlZITr1q/5vs1nPTJibwQ89OrHjfHfKvsuKgrX/DKt7diTSO5OsjKp/C691Nl6fqj56bbLxduib1L55oRBen5hgMjen7pU6V32/8jrH9L9v8B0zKxP6BO10aMu6bw2p5XVvl+ouGGPqyQJWA4CqLhGRZ4EHROR8VZ0vIh1wtZbv4U5lXyciP+JOk98a18S3wM0isgOuTvMsqrK2AOOBK0TkUuBfQCWwR1AW0C4YXwqEgguXhgFvBOsuxQWtg4F5NezCc8BVInJS0NftgTOBv63H4UiKqi4XkdnAqUGGdihwBiRT0GdMYovLPfBAe3RgVYbH0KUF9F1ZxDe9+pIdDjO7Vw/6Z5TX3ZAxxqQRq2E1sc4AfgUmi0gh8APuIiMfOA93yn16MH0CMYGZqk4G7sZd+LQQd6HSZzHzF+BqUvfFBZ2LgIuD2U/iLvCaAczHBX6fxKxbAlwFPCciBSJyRXzHVXUWLsN6DrAceBpXc/vC+h+OpJwMHAKswu3/Y428PZPmdurpcYDO4IVbXua2h99j16n54MGq1m1YmptHm03bp7qLxhjT5Dzf9+teyhiTLuwfvgX4YOCLZM1atXbcv2s3lny3kpxebRl55VAmfTgRgFGjRqWqi8aYhtEoxaarvX9We69v59/V4otarSTAGGOamS4D2rIqCFgzO+aw0zmDCWUnfRc5Y4xJO1YSYIwxzcyQcXvQ+Yj+dNinF1u+sZ8Fq8aYerKfZjXGGNPIcvrkMvTlkXUvaIwxGwkLWI0xxhhj0kg63ofVSgKMMcYYY0yzZhlWY4wxxpg0YhlWY4wxxhhjmphlWI0xxhhj0oplWI0xxhhjjGlSFrAaY0wz8/gPEYY8Vsk+L4SZV2g/TmaMqR8/7pEOLGA1xphmZO5qnzPejfDbSvhgjs8FH0ZS3SVjjEk5q2E1xphmIuL73PNthEhMSmTO6nTJjxhjmordJcAYY0yjuedbn7u/rR6gTlsKJRUWtBpjNm6WYTXGmBT5eqHPFwt89u7r0aMt/Hf6uqf/y8KwrAT6ZNXW0G9w/0RolQ05ma5orWNbGNQDTtgTMjMabR+MMc1R+mVYm0XAKiITgQ9V9fYNaCMfuFJVxzdYxzZiInI8cImqDtuANmYAN6rquPVY93JgZ1Udtb7bN6Y5+3Sez14vhKmMQE4G9GgLs1evu1zrDOid61PjB9Cnv8AeV4JfQxZ20vcw/vyG6rYxxqREswhYVfXA2HER8YHdVfXTpti+iEwGJqnqjfVYZxxQqaqnN1a/YraVD/QDdlLVr2OmHwM8D3ykqiMacpuq+gzwTEO2Wc/t35yqbZv0Nm2JT3YGbNG54TMQq8p8flkOm3eCDq2q2v9luU95GHrlwswC2KYrvDc7QmWQUC0LJw5WAUrCsKLUo0ubBDN9H/7zds3BKsDbU6qPz14CS1bB9gMhI8i8riiEKX+A54EMgvZt3fSf50I4Alv3gz8WwYoit97UWdAhyOCuLIJf50P7NlBYCtsNgKxm8dFizEYrHWtYU/quIiJZqlqRyj60IL8AZwBfx0w7I5i+XkTEAzJUtTJuuj0vJi1d8GGYe4Ia0Vt3D3HpTg1Xxj93tc/Oz4aZX+QC089HZ9CvvcdtX0W47BMXmeZkuOB0aGc4YnD19TM8CNcQd/643GdEmwQfQPtfD+9Nq71jhaVQWAJ5reGVL+HYu6GiEg7cHt64HGYsgp0vc8EoQM+O8OWt8OSHcPXzbtrB28PE7yASgb5dYM4yCIXg5uPhngmwqKBqe3sMhUnXWtBqjGlQdb6jBNm9R4F9gB2BWcDxwJbADUBX4EXgLFWtFJEngJFAB2Au7pTws0FbI4BJwCnAdcG6ebEZThGJvvu+KyIR4HlVPV1EzgP+BvQGVuKyf1eqajiZHRWRkcAdwCCgHPhOVUeKyP3A7sDOInIZMF9Vh4jIPsDNwGZAJfA+cK6qLhGRS4JjgIgcG2yiPXAVsJuqjozZbuy+dQQeBvbGHft5wXH7JIldGAf8n4hcoKpFIjIQ2BZ4MOh/dHu1Hqcge30+cCLuOdxLRJ4HHgf2wj3Hp4tIq2C9TYP1MoFLgDFAN+An4DxV1WB+FnAbcAIQAcbWtUPBOhcDJwO9gCXApar6kohcGz2WInI2cIaqbhuz7gBgBjBIVfMTtH0ucAHQBVgNPKmql4tIf9xr+Ixgf7oDk4P2lwTrdg76v1/Q3DvABaq6IpifT0z5SUybfVR1Xk2vtWDZNsD1wJG418zXwDmqOqOu42U2TGXE574pVRHh3d9GGjRgfeFXn/lBzLegCJ6f7nPpTh53f1tVl1oWvFv9vByy4zZdU7AK8ODUCCP6VF8he3Vp3cEqQHklTJoGhw+Hf73lglWAiVPgl3nw/KdVwSrAwpXw38/g7glV096aUnUzxznL3N9IBMZOgMUF1bf38c+gM2HnIXX3zRjTKNIxw5rsu/XJwN+BjsA04H+44GYYsDVwKHBMsOynuECqA+6DeZyIDI1pKwM4CNgOFyxUE1MzuZ+q5saccp8HHAi0Aw4DTgXqczr+KeA+XJDQG7gx2N45wCfADcH2ou+yZcA5uKB6a1xAdW+wzu24QPDJYJ3cJAPni4E2uNP7HYDDg/1KxgLgY2B0MH46MD7oZ6xkjtNpuOcrF5gaTDsDuBDIA15LsP3rgvYOADrjAty3gyAc4DLgEGAXYADQP9jP2tyIC3CPDvq7J/BbguWeBTYXkW1jpo0BJtcQrG4G3Aocoqp5uMD89bjFTgL2APrgAuzY2udncK/1LYJHF+DpOvYlVsLXWuARYHNgONAD+Ap4IwjeG11hYeFGO1xSXESfvLWjDGjfsO33yC4hVrT9vrnrXkjlAf1yq05sZIVqvwvAph29atvKzs6msnUWtM6udb2o4q65Qae6VU1sk0NR20wYsM7bMCU92lVftm2r6p2P6t1pnXX97My105vD827DNtych03ykj1n87Cq/gIgIs/isovDVbUYKA6yiAI8o6qPxaz3vIhcBIwAfo6ZfqmqrqpPR1X15ZjRqSLyNC7r+1CSTZTjMl7dVXURLqtW2/Zi62cXicjtuCBtQ5Tjgr0hwFRVTRSc1eYR4Jogiz0G2BcX9K6V5HG6U1VnBsNhEQF4RFWjwWtJMA1YWzpwLnCwqv4RTH5MRM4HDsYFeycBt0YzhcHzflpNOxK0eTZwjKp+H0yeR4IAXlVXishruMz8ecG6JwNX1NB8Je5jdUsRma2qBcCXcctcF7wOEJGLgd9FpFcwb39gM1VdGcy/EJguIj1VdWFN+xQj4WtNRLoAxwH9VHVxMO06XMZ7J9yXvUaVl5e3UQ+/dYTPVZ9FyMmAW/cINWj7xw9ry5KKCO/Ndlf9/2Vz1/7Lf87iso8jlFb6dGvjMbcQjtnc48jBOVz2SYRZq+Cc7UIc+r8IFQl+I6BVBly9c4iczKptlZeXQ1YGvHcNHD8WlqyGdq2huAyKSqtWzgzBrSfSdret3Pg9p0JOFsxbDheMIrd/Lzilp8uqjv8IMkJw6j60PnFv2G1L+L/xrob1n4fBI+/BstVw6I7w+jfQKRduPwme+ADe/wFKyyGvNd7f9oe+XRv8ubNhG07HYZO8ZAPW2A/pNUBYVZfGTcsTkRBwLS571wN3EqktLksZFcGVCtSLiIzGZQAHBv3OZt0gJLrsRKpOk49X1bNw2cHLgR9EZCkuCL+nlu3tgCsJGIbLinq4jOSGuAPIAp4EeorIG7gr8Rcnuf5EXAnAVUC+qv4kItUC1iSPU36CthNNi+qC2/cJQUlBVBawSTC8SWwbqlosIkti+vUTVRnXm3FlJm1JnFFN5Ang6SAQ3gOXoX4laDvmfCZnquozwV0O/gY8KiLfA9er6rsxy+UnGN6EqvzRrJj50eC+D9X/F2pS02ttQDD/+9gvBLjj2CeJds0GGtrF4+XDMhqt/QskxAVSfVrfdh7PHpJ4m//ap2p6VihxwNq5NeRk1nB6b9ctIP/hqvHCEuh8ctVpf9nUBZtR7drAg2dWb8Pz4Iqj3CPWgO7w/D+rxodvVjV82siq4UsOdw9jjGlEDV0VPxp3+nk/4GdVjYiIUv0kkq+qdd0Fu9p8EemDy+IdAUxU1XIRuROX1V1H/F0HgmnTgGOC7NxuuBrZ71X1A1wQHe954CXgaFVdLSKHADFFXQnXKcQFYbGiWTuCjPQVwBUi0iPYpztw2ck6qWpYRB7HBaynxs+vx3FK1Pfafv9xGVAMjFTVb2pYZj6uDCDal2pfVFR1y7i+ergvOoOB32vZdtR7uPKHUbis8vOqWhK0vc4XCVV9BXhFRLKBs4DXgtrUqP5UBaLRfs+Lmx+tKx0Y/I1+0Yp/nnvFDNf4WgN+DBYZHPeFzxgeP8Dj5In+2jrXqPEH1aMWLa81PPZ3uOQp6JgLD/y1YTtpjGkR0rGGtaED1na407FLgZCIjMFlKN+oZzuLcIFM9BRpLq7edilQISLDcRcNJXWFfBC0jAbeVNVlIrISF6BFPxoWAZsm2JdVQKGI9MXVaMb3cbiIhFQ1Gux9C9wcZGen4QKlaFYNERmFC4J+A4qA0pg+JOseXM1totPHG3ScaqKqvojcC9wpIqer6u8ikgvsCvygqgtwNZ4XB+UhC4DbqaVGOmjzAeB2EZmDu4irN9AppkQgdvmwiDyFK03YEVdDnZCIDMEd94+BEtzz6FM9KL9KRH4M5t+GuzBuQbD+u8BdInIy7svWXbgvANHs6rfAaBF5BmiN+wIR3XaNr7Xggr1ngQdE5HxVnS8iHYJ9eU9VYzPFZiNzzOYZHLM5bPKfyrUXb4U82LV3PS8MO3GEexhjTBpp6J9mfRJ3EckMXMZtKC64qq8rgOtFZKWIPBTUz16DuxioABc8PlfPNo/B1SEW4S7AuUZVPwrmjQVERAqCU9cAf8Vliwtxp55fjGsvekp7ebBehqpOBu4G3sadOu4OfBazziBclnY17jR0CXBpfXZCVVeq6iRVLU0wryGOU02i7b4mIqtxWdGzqHoN3YK7mv5L3On0OcDsOtq8AngBeBV3nCez7heHWE/gLsyapTH3o00gG7ga9xwU4ILcI+OO2Xjca3NusPyJMfNOCPrzKzA9aCM2C34l7ovGwqDPz8dtv7bX2hlBu5NFpBD4AXfRmf32pgHgul1DZATJkWt2DpGVkX6ZEmNM4/Lxqj3SgefXdsNpY9JM/C2oUtydVLB/+BZg6Rr3QwO98xJ/0EyY4KqTRo0a1ZTdMsY0vEaJJpd4V1V7r+/m39Dio1a7s7MxxjQzXRP9SIAxxiQt/d5DLGBtBuLualBNoguKjDHGGGM2JhawNgOJ7mpgGkfwQwPp99XTGGOMCaRj7VdDX3RljDHGGGNMg7IMqzHGGGNMGkmXOwPEsgyrMcYYY4xp1izDaowxxhiTRizDaowxxhhjTBOzDKsxxjQzUxb7FFdAh2yf5aWw+yYeGaH0y5gYYxpL+r1fWMBqjDHNyK1fRfi/TyLVph00wOONI0J4Xvp9CBljTDKsJMAYY5qR/0yLrDPtrVk+cwtT0BljTIvk41V7pAMLWI0xphnpkLPutJwM6NQqHW8FbowxybGA1RhjmpFla9adVhaG75emR5bEGNP4/LhHOrAa1mZGRK4FdlPVkRvQxn+ASlU9p8E6VvO2fGB3Vf001X3ZUA1x7NdjmycAN6pq/6bapmkeysM+f58U4bP5PocO8rhtzwzmFbqLrBLJzQ4GdAa7XTQBL+JDp02hZ0c47QFYthoGdIPfF8KeW8L9Z0BmRnKduft1eHQSDN0EHjsb2rdtkH00xpiGYgFrCyci+cCVqjo+Ok1Vz0pdj6prqL6ISH9gFtBHVefVNd2Y5u6B73we+8HlPqav8BneK8LTP/uUhtdd1gN6RmPI0WPpOGOZGz76TtiyD0z+0Y3/OMf9nT4fth0AZ+1fd0e+nQn/HOeGf5kHfbvC3aes724ZY5qBdKlbjWUBaxMTkSxVrUh1P4wxG873ff411eedWT5lYZ/denv8304hcjKrf1iM/znCe/k+PXNhUTFs3sljTUX1i6tOfztCZQ3n7nzg+6U++/TzYEXM1VfLV8OXvyZe6ern4PZXYVB3yGsNnfPgosNgSG+YNA3Gf+yC3W37V19v9hK4+ElYWQT/PBTe+Q6mzoJjdoWDdqj9gDz4Nnz5Gxz2JzhieO3LxvtpDoyd4Pp51dGQ27p+6xtj0poFrI0syIA+DuwF7AicJSJ9gTFAN+An4DxV1RrWPw/4G9AbWAk8g8uohkVkAtAXeDQ49f65qu4nIuNwp+FPD9roB9wH7AqUAC8D/6eqJcF8HzgbOAXYPOjTGFWdHsw/FrgG2ARYA7ytqifHdHMbERlbw7rxffGBC4L9HwQocIaqzqjvsa3heF1L3Gl9EZkMTFLVG0WkI/AwsDfu9T8POEtVPwkW94J9OSk4Vver6q1BO22A8cAuQBtgBnCpqr4XzB8DXIk71pcAbYEXgL+rajhY5k/AA8Gx+g54tyH226TGf6b5nPdBVeD5/hyfwvIId+1VdSp+4h8RTnwr/sp/n5F9q09ZUVb7tk6aGCH/DI+sTrmwoshNrIhAeQ0rLl3tHrMWV02bOAXeuRoOvgnKK920W09wAW1hiRv/4Ed45Ss3/NIXsCooqn32E5hyJ2zdL/H2nvoQ/v6wG376I/jsZth5SO07FVVaDvtcC4sL3Pj8FTD+/OTWNcYkkH4ZVrvoqmmcAVwI5AHbAYcBBwCdccHs20Eglcg84ECgXbDeqcDpAKo6CpgDnK6quaq6X/zKIpIJvAksAvoBw3GB651xi44BjgS6AHOBfwXrtwGeBs5W1TxgIPBoMuvW4q/AUVQF7K+LSJLFdhvsYlyw2Q/oAByOO8ZRewCLgZ64432hiBwXzAsBrwCDcc/dc8DLItI1Zv1+QHdcML4jcDRwLICItAcmAi8BnXCB+98begdrU1hYaMMNODx1YTnxflxefZkpCxIHlDML1r19VW0WFMHKUlwQGuXX83KK+Sso+fKXqmAV4Od5sCamj6tjrvpaFTNcGaZkatX3ynWOyU9zq/fr57nJH89lq6uCVYAf5zSL59eGbbixh03yLGBtGo+o6tRg+K/Axar6h6qGVfUxYCFwcKIVVfVlVZ2lqn7QxtPAPvXY9p9wAdaFqlqsqvNxWcBTRST2K9gdqjpHVcuAcYDEzKsANheRTkEbn1BdbesmcpeqzggyvJfggrudktyfn0SkIPoAvk9yvahyXLA5BPBU9TdVnRUzfyFwm6qWq+q3uGzsGABVLVLV8apaqKoVqnpH0N6OMeuXAFeralmQNX6fquNxCFAc0/43wGP17P8GycvLs+EGHD55mxxaxXzV8oCTt/SqLfOXLVvRsZUbjv5YVWYIzto2k1b1OMd14ADo1taDHh2qJubU8yTZIULro3aDwT3deFYmHL8HjNmrapldN68aHj4YcrLccL+utD6o6qW+zjH5y67QNtjRnh3hgO2SP569O8N+21Zt95S9m8Xza8M23NjDjSUd78NqJQFNIz/42wXIBSYEp8ajsnCn29chIqNx2dmBuOcrG/iyHtvuAyxV1eKYaTOBVkBXYEkwbWHM/GJcNhhVXSMiBwV9uElE/sAFnM/GLJ9w3VrkRweC9pdSw/4nsGUNF10l6w7c8X4S6CkibwCXqGr0vOlsVY19bvKBI4JttQ7WPwj3XEZw+xqbYV0SPf0fiD0emyRovz59N83Mrr09fjwlg+8W+1REfIZ2CbFN1+ofDoM7evw4JoNvF/sM7gAzC2BgB48tOnscMdjjp+U+8wojLF/jMagj/PdXnwkz193W7XsE+YWFBVUTyyvhmfNhWj4M6uHqPwuKIK8NLFoJZRUwoDu0a+Oynvts4+4c8PXt8OkvLnAd0hv2HQbH7QEZIXeHga9/d7WyI4dB/hJ3EdcuQ6BTLf/aOwyCH++BH2bDToOhW4fkD6TnwRuXw/vfu33YcXDy6xpjNgoWsDaN6Lm/ZbgAZmSQXauViPTB1UweAUxU1XIRuZPqGcy6zivOBbqKSBtVjZ7fGwiUAkuT6byqTgYmB6ftD8WdBv9KVRN8rCalf3QgKDnoSvXT8huiEFc7GqtXdCAI3K8ArhCRHrjjeweuZhWgn4h4MUFl/5i+XYgrGdgHyFdVX0SWkXyx0Pwa2jct2KAOHoM61P4S6JXr0SvXLbN556rpm3b02LSjR+zJrk/nhYm/c2JWCHpEX9W7bQ5vTXHDuw91geZxe9Sv0x3awiExbyOeB3tvXTX+p5iAcdOe7pGM/t3cY31kZcIB26/fusaYatLl3quxLGBtQkGAcy9wp4icrqq/i0gurqb0B1VdELdKLu6TbClQISLDgROBX2KWWYQ75V+Tr3EXB90lIv/E1W3eADwRl+lLSES6A7vhLlpaFZyGBwjXvFadLgguhJoP3Ar8AXy1Ae3F+ha4WUR2AKYBZwEDojNFZBTuePwGFOEC99h96QlcHFx4tRVV9cfg6ojLgOVAtohcijueyXoDd0FWtP2tgdOCNo0B4LMF6/5bVkTgy4VwyCDghYv4+ey78SI+W9x/QdN30BhjUsBqWJveNcBrwGsishr4HRdUrfNcqOovMcsXAJfhLvSJdSNwgoisFJGJCdqoxNVOboK7QOtrXHB4UZL9DeHuIJAvIoXAv4GTVTU/yfUTeRR38dJSYBhwWNxp9PUWZIPvBt7GlSp0Bz6LWWQQMAFYjTvdXwJcGjP/E1zQuggXYN4LRMsf7sY9DwtwZRVriClvSKJvBbha5WNwd3y4D3gw2fXNxqFfu8TZ2nD0XErbVsw8cmtmHL2NO9VvjDFx0rGG1fPre5WpMRugrl/GMo3O/uGbucXFPhd9FGFWgc9nMedcjhzs8dJh7gqvCRMmADBq1KhUdNEY03AaJZqc7d1c7b2+n395i49aLcNqjDHNSPe2Hk8flMHD+1W/01uGvVsbY5KUjhlWq2E1zYaIFNUw6xNVPbBJO2NMig3t4nHDriHu0ggD2sOtu1vEaozZeFnAapqUqtb4VU9Vc5uyL8Y0d1fuHOLKnS1QNcbUV3pkVWPZO6ExxhhjjGnWLMNqjDHGGJNG0vHqWsuwGmOMMcaYZs0yrMYYY4wxaSRd7gwQyzKsxhhjjDGmWbMMqzHGtCDFy8tY9k4Wvg9FO5WS261VqrtkjGlmLMNqjDEmpd74v+9ZpZms/jaTVy/8LtXdMcaYJmEZVmOMaUEW/7x67fCymUX4vo/npV82xRiz/izDaowxJqX8SMwNa9Lx3jXGGJOABazGGNPMXPlOKVveVcRpL5ZQVhkXlcZlUy27aoyJ58c90oGVBBhjTDMy4ecKbvqgHICfl0SI+D5P/KVN1QJ+9Y8fP+LzyzN/sDq/iE0P70uXrTo2ZXeNMaZJWIZ1Iyci+SJyQqr7sbEQkREiUpnqfpjma2FBpNr4uG8ree+3mJdMXEb1i5u+58trv+PncTN489iP+HXiAiKfTYd3pkI4XON2/OWF+BOm4s9c3KD9N8Y0B17co+WzgNUYk5RwxKcinC4nl+rH933KyyN1LwiES9cNEiO+v+6pfSBSUv27y7Kl5Xz2ZgGtwhFCMZnUb+fHtlm9nWnP5a+dEi4J897l03hzzEdwwA1EDrsdvyJMpCJMpLRqW/68FbD91XDoWPytLify0a9Ewj7hyur7GK6MEKmMECkLV6+djeNHfMIVyR0fY4xZH1YSYJqMiGSpasXG3oeW6LWfKjjuuRLKw3D/Ya04c3h2qrvUZGbPKeOOOxdTUBBmv33bcdKJnRMu50d8vvrbF8x9bQ65A3LZ478jaNs3l4/n+vz5tTCryuD6XUNcMTxEpCLMb4e/S8Gbc2i9ZUc6PX8AF/17FasqXQ5hF6+S+Xmt+bVTWwBy28TkFuLixrK8NpTlQevCEtoXruIv371OLoWspBNz38yhY84tgMdyP492ZwxjYLf5cNOrgLt/q1dawcyDxzN5yC6saZXNiH8OYZsjNuGnNxbwwW2/QNhn4G+L6RapYMhr+9Nu957Vtj/n6+W88X/fU1ESZrezB7PD8f0a4rAbYzZAOt4lwAJWA9BXRN4HdgLygb+q6ucikglcDowBOgJTgPNU9UcAERkHVKrq6dGGRCQfuFJVx4vIGOBK4CHgPGCViGwF3AicAuQBy4G7VPVfiToWbCMLiACHAUuBG1R1XMwyuwO3AEOBlcADwN2q6ovICGBSsL3rgK7BdmO3cR/QSlX/Gox/DPRT1X7B+CXACFU9KBj/M3AVMAhYCNyoqs8k058E+yfA/4DrVfWRRMegOThvQilrgjD/H6+XctqOWWRmpN8bYiIvv1xAQYHLcL773mr2GpFHnz7rBuyLP17E3NfmAFA0q4jp9//CDrfvyMUfhVlZ6pa58tMIZ2ztkfFWPgVvumVLflrJ5+cpqwYOWduWBy5YDU7/PzjN55yd3Lx1XkTBMiW5rdnvl0/JpRCAjqwgzHxK/Y6ATweKWPHIVOBj3Mk1n+ipwlWhXHIXrqaob2cm3/UrQ0f15MM7pxOucFvL79uZzlNnM+eSL9nqi8Orbf7j+36nvNgdn0/u/52t/tybnLb20WKMaVhWEmAATgXOBdoD7wFPBtMvBk4CDgJ6AJ8A74lIu3q03R/oBQwGdgT2BU4GdlLVPOBPwKd1tPEX4B2gE3Am8KCI7AIgIkOBt4A7cMHowcA5wIkx62cE+7Ad0D1B+5OAkUF7ucFynohsFszfN1gGEdkXeAw4P+jPycD9IrJHPfpDsOyhwBvAGU0VrBYWFq7XcOvMquC0VSYUF69fOy1xODu7at89D8rL1yRcvixSTqyM1i5oy/aqTudnhXyyMyDUunpAF8mpHvwvyc2plh+pKIms3VaNXxNCHmXZ1QPpSMxbvI+HTwg/I4RHBChlTk5Pvssbyh+t++KHXMsZOSHWrCkmMzujqumgHMCP6We0P5k5VdvIyAqxZk3ROsvYsA3bcOLhxuL+36se6cDz/Y2zJs04QUb036p6RzC+JfAj0AH4BrgjGkyJSAiYA1ysqs8lmWF9EOigqmXB/BHAS8AJwGRVLa2jf+OAQaq6e8y08cAaVf2riNwPtFHVU2Pm/xM4UFVHBtv7EJcxnVPDNtrhMr1DgC2AfwK/A98Dj+KypMNV9XsReQP4WlWvj1n/X0BrVT09yf5MAi4ELgIOVdXvajsGDWy9/uG/mlPJaS+VUlrpc9+hrTho86yG7leztXx5Jf9+cAnLl1Vy4IHtOWD/9jUu++Ot35P/31m027w9wx/YmeyOOfyy3OfEt8IsK4Gbdgtx/FAX4OVf8DkrXvyDttKVbg/uyZVjl7JgWYTiTlm816MbRHyo9GlbXsmlm4e56lh3YuDeXSbh11Au2n5VEQf//h4dMlZR1Hsg8wt6076yED/ss6ptR7pcJPTqspzI5c9Sssrjp267srRjJ/zsEAVdc6lolc2eF2zGwN26Mvur5Xx4x3QixRUMmLGELu0z2HT83rTevPpdCJb+Xsi7N/xEWWElu50zmM32SfSd0BhTg0aJJn/17q72Xj/Ev7DFR6123saAO60dVRz8zQP6ALOiM1Q1EgSkferTdjRYDdqYLCKX40oFXhCRL4HLVVVFZCIQDUzHq+pZwXB+XJv5wPbB8ABgbxE5ImZ+CJgbMx6Jjgen6yfGzBuqqnNE5BtclnULXJZ5BnA8MB0oBH6I2d5eInJhTBsZuOxzsv0JAVcA/2niYHW97dQ3kx8vzE11N1Kic+dMrr6yV1LLbnXZNmx12TbVpm3R2UNPXPettv/YXeg/dpe14/++3f1bnfhWGH72IeRBtseOa0r4674xQbLnUdP3ji0vHUa3MX8GXPq/Uw39DJ26D21xpzdq0m+nzox5addalnC6Ds7j+KeG17mcMabppGMq0gJWU5u5uFP6wNoMa3+qgq9CoEvM/EygW1wb6+SCVPVh4GERaQNcC7wC9FXVA2voR/8E4/OC4dnA46p6di374UfrR1X1EyBR5BUtC9gCV+86C3gY+A14P6b+dDYwLpqRTiCZ/kSAPXHlFaWqeksty5qNzEEDPMb/7F5uXVr5jPtnB7p3rDo9H38f1lAmRCohlOXRf+fEF4QZY0xLZwGrqc044JLgIqR84FLca+bNYP63wO0iMgBYAFyPu0CqRiLyJyAH+BoowwW9Nd8s0hkuIqOBF3CB3pEENae4C5o+EpG3gbdxXyw3A7qq6kfJ7iguYD0fKAemBNnkWbia2QtilrsHGBdkhj/HZVe3BjxV1WT7o6rTg2zv+yLSTlX/rx59NWls9BYherSFX5bDwQM9+rWv/UzesY/vxIJpBfTetgNdB+fVuqwxZuOQLnWrseyiK1ObO4DngHeBxcDewH6qujqY/wzwOu7uATNx9a3z62gzF7gXWIarG90POKaOdV7AXTS1EnfB09mq+hlAcMeCQ3DB5kJgCS7Q7prcLq71Be7/4QNVjWaFJwHtgr8E23sXOAN3bJYF2xwb7Fe9+qOq+bgSiENF5AERSb93GLNe9uob4u/bhRIHq3GTum2Wx7ZH97Fg1RiT1uyiK9OsJbqwy2wQ+4dv4f691wdUlLjvVF4Izv1sHzzPvusY00I1yj/vz9491d7rh/rnt/g3CcuwGmNMCyIn9l87vMPx/SxYNcZsFKyG1RhjWpCdTh3IPH7G92G30wanujvGmGYoHU+lWcBqmjVVHZPqPhjT3GR3TcePI2OMqZkFrMYYY4wxacTuEmCMMcYYY0wTswyrMcYYY0wasQyrMcYYY4wxTcwyrMYYY4wxaSQdL8u0DKsxxrQQEd/n1+URSsL21m2M2bhYhtUYY1qAglKfIY+HWbIGPPbj0p5TGZXqThljmiWrYTXGGJMS/5oaYckaN+zj8ejSLVLbIWOMaUIWsBpjTDPn+z4fzq5elbasMofJcyIp6pExpjnz8ao90oEFrMYY08z973efD+fFTw1xxOsWsBpjNg4WsKaAiPwkIsekuh+mcYjI5SIyoR7L54vICY3ZJ9PyTFnsc8KbYQ5+uZKbv0ocmK4pb+JOGWNaBD/ukQ7soqtGJCL9gVlAH1Vdmx9R1S2baPtjgMeBNXGz/q2qlzZFHzZGqnpzqvtgWraicp99XwyzorT25XKzm6Y/xhiTahawpr8/VHXTVHdiYyAiHpChqpWp7otpHr5b4vPBHJ9WGVAahoMGeGze2WP6cp+3Zvls3w3a5XhMnuuzW2+PHbrD2G8jvPa7X2ewClBQBitKIjzzC3RqBcdt4eF5tdSrzVsGN70EFWG47AjYtGf1eS99AQO7w6F/qpr+309h8So4dld4bxqsKIKRw+CdqdC/G/x5p8Tb8n149mO3/IkjoEPbpI6ZMWbDpUvdaqwWE7CKSGdgLLBfMOkd4AJVXSEiucC1wBFAV2AucKaqfiIiWcDFwMlAL2AJcKmqviQi44BKVT09Zjv5wJWqOj7IUF4JPAKcD2QATwOXqWpFsPwTwEigQ7DdG1X12aC5acHfX0XEB25T1RtitxG0sSdwO7A5sBAYq6oPBfNGAJOA44GbgS7Bvp+mqoXrf0RBRG4ADgN2UtUSEdkc+Ao4SlXfC4757bhj3gr4EPiHqi4O1q/xuCfYVn9ctnkMcCnQD/go2K9LgVOBCHCDqv47WGcT4FFgByAb+B44X1W/DeZfC+we9Dn6HD6oqtcE89sA44FdgDbADNxz/15Mv04DLg/6/xrg4V4TY4L5fYG7gd1wZ1YmAP+MHvvgeT0fOBHYEthLRA4AdlPVkcEy5wF/A3oDK4FncM9/uLbnx7Rs05b4DH8mTFnMs3zt5/DG4SEO+V+EwuB0fmYIKiPu78i+8HZ+8ttolw17vxBh2lI3PnWJx50jMhIvXFAM21wAK4vd+HOfwu//hl6d3Lyd/w/mLXfz7jsN/nEwXP0c3PBi0Pnnq9bNynBBL8BdY+DCQ9fd3kXj4O6gMuax9+HbOyCjhr4ZY0wdWlIN6zNAR2CL4NEFFzwCPAbsBOwDtAMOxQV+ADcCJwBHB/P2BH6rx3b7AX2BgcDOwChcABz1KbAtLmC9HhgnIkODecOCv0NUNVdVb4hvXEQGAG8DDwKdcQHdLSJydMxiGbigcRiwGbAdcG499qEm1wLLgX8Hwd1LwD1BsOoBr+KCtK1wx6EQeDZm/dqOe02OxAV/fYH+uGBzJu7LxCnAPUGQCO71+UCw7R7AFOCV4EtI1B7AnGD9Q4HLRWTXmPVfAQbjju1zwMsi0hVARPYA7gfOADoBbwF/iTYsIq2AD4CfgQHAUGAT4N64fToNOAbIBaYm2Od5wIHBMToMF5yfnmA5k0Y+mudXC1YBCsvhv7/6a4NVcMFq9O+n61xYVbuVZawNVgHeya+lWu3HOVUBJ8CaMvhmhhv+eW5VsArwznfV/0L1dSvC6y4bL3b6tHyXpTXGNBEv7tHytYiAVUR6AfsDF6rqSlVdCVwIHCQi/XBBxlmqOktVfVWdoaozgqDrbOBiVf0+mDdPVb+vx+YjwfolqjoTl3EcE52pqo+p6nJVDavq87gs4Ih6tD8amKKq41S1UlW/BB5i3YDmMlUtCrKbrwKSZPsDRKQg7nFc0PdwsP0Dgc+ARcB1wXo7BI+zVXWVqq4BLgH2FpFNRKQbNRz3Ovpzg6quUNXlwBtAhao+Euz7RFwGcrugf3NU9XVVXaOqJbhsd19cABr1m6r+J+bYfRc9NsHxGq+qhapaoap3AOXAjsG6JwEvquoHwfrP4QLoqEMAT1WvDp7/lcBVwPEiEpsqulNVZwavgbL4HVbVl2OO0VTcF6196jhOjaKwsNCGm2h4114eWXHvsG0yfY4Y7NEm5txWRvBZEvJgx57US24mDOlQdUHWrj2qqlHW6dvQTaBdm6qVW2VRtFk3N7zFJkS6tV87q3SX4F9sREy5fey6mTEv/722SnwcRmxVtczQPhS2Dq27jA3b8EY+bJLXUkoC+gR/Z8VMmxn87R78TZQ17Qq0rWFespYEwVpUPi7LhoiEcFnKY3AZQD/YXtd6tN+H6vsFbt8OixkPq2pMHoViIC/J9mfVVsOqqotE5AVcxnZvVY1++g0AcoDFItVi41Jc0Bj9ZFzn2IrI7sDEmElDY4ZjM7BrWDcju4Zg30SkC+50/AhcBjvat9jjG7/+2mMjIq2BO4CDcBn5SDAvun5vQOPWnx0zPADoKyIFccv4uOd7fjCeTy1EZDTuC9ZA3P9cNvBlbes0lry8PBtuouEd8uDjYzOYNNsnK+RT6XscMtBjWDePz4/zeOMPV8PaoZXH+7N99tjEY+decNOXESbN9ikog7JKFxuGI/B7AesoroRPjsvi8R98OreGU7bKqb1vU++Ea553GdKr/0LuFsFba8dcQl/eCs99AgN70OrY3dz0W06ALTaBRQVw4p7w1hRYUQj7bQtvT4V+XeG4Paq9Ga3d1r2nwrD+bvnTRpLXsV2THn8btuGWMNxYrIY1deYGf/vj6hDBffgDLA7+Dsaduo21FBcADQZ+T9BuIS6QAUBEMoFucct0E5E2MUFrf9wpXnDZydNxp+t/VtWIiChV+fdkbpI4FxdQxRpI1T43qqB+9hTc3QT+LSI7qmoxLnArBjrFBLGx60WP0zrHPahhzY1bvv96dO8WoCeuxnahiOQBq0n+/MaFuJKBfYB8VfVFZFnM+vNx5Qax+gJ/BMOzcRncuu7qUOPzLCJ9cHW0RwATVbVcRO4k+Qy5acGG9/IY3mvdl+uwbi5wjdo5Zplrd83g2l2rL//L8ghDn1j3ZdY6E7q28bh0pyT/JQb2gKfPTzxvQHe4/Kjq00IhGLN31fgZ+1YNbzeQWmVkVF/eGGM2QIsIWFV1gYi8C9wlIifjAo67cAHAbBF5CXgguEhqNjAoWG+GiDwA3C4ic4CfcFm1TkFZwLfBvAHAAlwNalbc5kPAbSJyCS54ugh4MpjXDpdpXAqEgu0Pw53qJpgewQV1NVWnPQdcJSIn4epDtwfOxF2k06hEpHuw/fNw+xStpT0Jl3mcBtwnIteo6vKg9nMfVX1eVZfUdtwbqIvtcF84VgYXeN22HuuX4ep0s0XkUlymNuppYGJw4dzHwFHAcKoC1jeAm0TkcuBfQBGuVvZPqvq/JPuQi3sNLQUqRGQ47gKtX+q5L2Yj9uj3iWtT/XS5waIxpkGl41tDi6hhDZyAy4j+CkwHCnCBFbiLWL7DXXVeiLvau0cw7wrgBVzdZyEwGYieIn8GeB13Mc9M3MU70dO8UbNxweYsXH3j27g6VnBB3le4rO983KnvT6IrBnWXVwHPBbWjV8TvlKrOwmVYz8EFVk8DV6nqC8kclCQMFJGiuMdzQTnDM8C7qvpEkEU9ARgpIqcF44fhvhx8KyKFuNPYI2Laru24N4SrcRnv5bja4M+BcK1rVHc37nWyAPf8riHm9L2qfoQL1h/H1c4egnudlAXz1wB7457X6cAq4H3cRXZJUdVfgGtwx6YAuAz3JcGYpOXUkFpoHf/12hhj0pTn21f0GkVva1VbDahJLyLyBTBB0/fm//YP3wIVlPoc8VqYT+ZB2AeI0C6jgv/+uTX7D2hJeQdjTJxGKTb9xvtPtff6Hf2zWnxRa4soCTCmsYjIUbiseTnu7g9CVebemGahQyuPD46perueMGECAPsPGJWqLhljTJOygLUFS3A1fqyb0zhL2JCOxP04QQautONwVU10gZ4xxhjTIqTjqTQrCTBm42L/8GkgmmEdNcoyrMa0cI1yqv7ruJKAP1lJgDHGGGOMaU4iaXgfVqvWN8YYY4wxzZplWI0xxhhj0kg6/tKVZViNMcYYY0yzZhlWY4wxxpg0ko5X11qG1RhjWoila3xOfivM9fN24JeSDqnujjHGNBnLsBpjTAvxlwlhJs8F6MZPazpydrlP2+z0q1UzxmwYq2E1xhiTEr7v8/G8qvESP4tV5anrjzHGNCULWI0xpgWYMNMnUq0wzWfJmlT1xhhjmpYFrMYY08z9uNTnnPcjcVM9Dno5nJL+GGOaNx+v2iMdWMBqjDHN3F8mhJlbuO70hcWuVMAYY9KdBaxpTER+EpFjUt2P9SEi14rIpFT3w5hU+3iuz28ra57veemRPTHGNBw/7pEO7C4BaUBE+gOzgD6quvayDFXdsgm2/R/ghGA0BLQGimMWOVNVn2nsfrRUIjIOqFTV01PdF5Nak2ZHuPijCG2z4OF9MxjaxSPi+/z5tTDhdPnEMcaY9WQBq9kgqnoWcBaAiOwGfKKquantVfMnIhmkzxdfsx7Kwz5nvRfh43k+mR78GpNF3fapMDftFuK8HTxWltbeju/7yWVZZyyEU+6H5YVw/bFw1C4btgMA//0UrnsBuraDJ86BgT02vM10Ne4DuPV/sElnGHcObNIl1T0yaSxd6lZjWcAaR0Q6A2OB/YJJ7wAXqOoKEckFrgWOALoCc3EZxE9EJAu4GDgZ6AUsAS5V1ZcSZdFEJB+4UlXHi8gY4ErgEeB8IAN4GrhMVSuC5Z8ARgIdgu3eqKrPBs1NC/7+KiI+cJuq3hC7jaCNPYHbgc2BhcBYVX0omDcCmAQcD9wMdAn2/TRVTVA9l9SxzAQuAcYA3YCfgPNUVYP5HnAG8A+gH7Aq6Pv9QROeiNwMRI/bg6p6TS3bGwncAQwCyoHvVHVkMC/+WPQnJisdPEdZQAQ4DFgK3KCq44Llx1D3c7QNcA+wHbASeBy4RVXDMds7Hfhn0MebcMcbETk22I32qmpX0mwE/jPN54kfE39nqYjAJR9H6Nam9qotj3qUBJz1EHz6ixs+4V7Ydxi0b1uPHsdZXggn3gcVlfALcPYjMPGq9W8vnc1bBqc/AOEI/DofLngCXrw41b0ypkWxGtZ1PQN0BLYIHl1wgQnAY8BOwD5AO+BQXOAHcCPu1PjRwbw9gd/qsd1+QF9gILAzMAoXAEd9CmyLC1ivB8aJyNBg3rDg7xBVzVXVG+IbF5EBwNvAg0BnXBB5i4gcHbNYBi5QHwZshgu8zq3HPsS7Dhf8HRBs83HgbRHpGMw/C/cF4G/Bfm0HfBWz/h7AHNwXgEOBy0Vk11q29xRwH9Ae6I17TurjL7ggvRNwJvCgiMSmoWp8jkSkPfAe8CHQAzgYOBW4MG4bxwF7A3m4gPUZ4Mngectt7GC1sLDQhpvJ8JLVZdRlTh3pVa8+210VU6lTVgGlFRvU/6KlK1ywGrVqzXq1szEMFy9e7oLVKDtWNtzI0vEuAZZhjSEivYD9gc1UdWUw7UJguoj0wwU0W6nqrGCVGcEyHnA2cIyqfh/Mmxc8khUBLlbVEmCmiNyOy07eDKCqj8Us+7yIXASMAH5Osv3RwJRoxhD4UkQewmX8XoxZ7jJVLQKKRORVQOqxD2sFx+Rc4GBV/SOY/JiInI8L5sbjMqs3qeqnwfxlwSPqN1X9T0x/vwv681kNmy3HZS67q+oiYHI9u/1lNAMLTBKRl3GB/efBtNqeo4OD7d+oqj7wi4jchgtY74jZxnVB3wAQWa/Du97y8vJsuJkMn/enVvxvVpifl0OrDCiN+6pyxGCPy3ZpxQ3fRKiIv6NVIFKf7d58PBxxOxSVwuVHQvcO5MW0Vd/+527eDy7+M9zxKuS1hpuOW692NobhtjsMgb/tDw++Ax1z4bpjm03fbDi1wyZ5FrBW1yf4Oytm2szgb/fgb6KsaVegbQ3zkrVEVWNvA54PbAIgIiFcJvIYXPbOD7bXtR7t96H6foHbt8NixsOqujRmvBhY3/+sLkAuMCEoU4jKItgvoD+1H7OFceNr+yMiE4Hdg+njg1raw4DLgR9EZCnwsKreU48+5ycY3z5mvMbnCHd8ZwfBatRMql5TNW3DbKS6tvH4YUwGK0qgYyufgjKPnAwI+z6VEY/OrV1WZED7SK13CUjavtvCsiehpBw6bEApQKzbT3LBb+tsyMlqmDbT1QNnwk3HQ9scyLZjZRpXOl4gYQFrdXODv/0Jsqe4078Ai4O/g1k3q7kUWBPM+z1Bu4W4AA5YW9vZLW6ZbiLSJiYg6k9VhnY0LhO6H/CzqkZERGFtnr+G/Es1c4GD4qYNpGqfG9oyXIA5UlW/qWGZfNwxe6++javqgQmmTQOOCbK7uwHvisj3qvoB7jmI/ZTulaDZ/gnGY7PktT1Hc4F+IuLFBK2Jjm/8c5XMc2fSVMjz6NIGwKNz6+jU6qfv7t8nxCEvRyhviE+gnKyGDywbKvjdGHS061GNWV8WsMZQ1QUi8i5wl4icjPvkuAuYqKqzReQl4IHgApzZuNPPqOoMEXkAuF1E5uAuLuoNdApKBL4N5g0AFuBqUOM/NULAbSJyCdATuAh4MpjXDqjEBcahYPvDgDeC+Utxgc9gai5DeA64SkROAp7FZQ7PxNWPNjhV9UXkXuBOETldVX8PLlrbFfhBVRcA/8bVpU7F1a52AgbUEuDWSESycYH9m6q6TERW4o5J9ETrt8BoEXkGd+utqxI0M1xERgMv4GqQj8Rd6BZV23P0Ju6Cq8tF5A5gAHAp8FAdXV8UbDekqha8mnXs2z/E3DN9uv9n3Yi1VUYKOmSMafbSpW41ll10ta4TcNm4X4HpQAFwUjDvVOA74KNgmddwp+gBrsAFOq8G8yYDmwbzngFeB6bgThPPAebHbXc2LtichQve3sZd0Q8uKPoKl/WdDwwFPomuGNRUXgU8JyIFInJF/E4FdbcHAecAy3EXkl2lqi8kc1DW0zW4Y/SaiKzGZZ/Poup19wBwC+5ittW447PjBmzvGFy9cRHueF+jqh8F867EBa8Lcc/N8wnWfwF3jFYGfTpbVWPrZWt8jlR1FS4DPhKXjX8HdxHY3XX0+VFc5nd58NxZCGLW0S03gx5xiUyPCI8dYG/hxpiNg2c/65d60VsmqeqmdS1rGkddN/BPo+fI/uFbqJ3GV/L1oqrx7Vst5ttzeqeuQ8aYhtAoqdAPvCeqvdfv7Z/S4lOu9vXcGGNagE07VB8vJjsl/TDGmFSwGlZTJxHZHZhYw+ybVfXmpuyPMRujYd1CPDu9qsx5l9xF1O9GIcaYjUU61rBaSYAxGxf7h2+hist9jp4Q4dP5PtvlLOCCnt/z50MPSXW3jDEbplEiy/e9cdXe6/fxx7T4CNYyrMYY0wK0zfZ460h3Td6ECdPqWNoYszFLxwyr1bAaY4wxxphmzTKsxhhjjDFpJB1v6m0ZVmOMMcYY06xZhtUYY1oYu1bWGFMbP5R+NawWsBpjTAvyv98jHD9jXyI+PP5LhOO2sBNlxpj0Z+90xhjTgpz+ToSSSCZlfianv5OOlWrGmA3le9Uf6cACVmOMaUEKSquGSypT1w9jjGlKVhJgjDHGGJNG0rGG1TKsxhjTXE2cAmc/DM98VDUt7nPIfq3QGLMxsAyrMcY0R1/9BofcBBEfHngbcrLgqF3sDgHGmDr5aZiOTMNdSp6ITBSRSzawjXwROaGh+mRaHhEpEpGdN2D9K0VkcgN2yaSDd79zwWrU0x8lXMzzPMoqff47PcKbM+0iLGNMetqoM6yqemDsuIj4wO6q+mlTbD8IUiap6o31WGccUKmqpzdWv2K2lQ/0Aoaq6oyY6ZXASFWd3Nh9aAlUNTfVfTAtwB+LIH8pDN8M2uRUn7ekAF7+EtrmQEExbNIZ7nmj+jJf/56w2Zd+reTiyZBf6MZHbx7hnxJiYREsLPbp0Aq27Raibx58vsAnLxtWlcGaSlhcDNt189ihR/rVuxmzMfMz0u9/eqMMWEUkS1UrUt2PFmI1cCtwVKo7Aql77hJt115HJmlvT4HDboXySth+IHx6E7QOgtbfF8CwC6GkvPY2FhUwfVmE+IqAoydUH39uOjw3vXqmNcMLs2kH+HVl4qav3SXENbts1CfcjDGNyPO8fYFjgW6+74/yPE+Adr7vf5BsG802YA2ye48C+wA7ArOA44EtgRuArsCLwFmqWikiTwAjgQ7AXOBGVX02aGsEMAk4BbguWDcvNsMpItOCTb8rIhHgeVU9XUTOA/4G9AZWAs8AV6pqOMn9GAncAQwCyoHvVHWkiNwP7A7sLCKXAfNVdYiI7APcDGwGVALvA+eq6pKgfOH4oN1jg020B64CdlPVkTHbjd23jsDDwN6453xecNw+SWIX7gCuFpFdVPXzGvZxd+AWYGhwjB4A7lZVX0S+AZ5R1Xtilr8W2FNV9wrG/xzswyBgIe65eyaYNwa4EngIOA9YhXsNxG5/BO75PQn32ugBvAycA9yFC7ZXAxeo6ivBOsOA+4K2MoAvgXNUdWYwfxyQBVQAhwL/FZHFwB7AFODE4O+B8Zn52o5HMP/g4Lj2BSYDa7PXJk099r4LVgGm/AFf/Q4jtnLjL35ed7AaeP7X9StgDfs1B6sA/5kWsYDVmDQSaUZ3CfA87x+4z+9HqUp+leA+g3dJtp3m/g51MvB3oCMwDfgfsBcwDNgaF0gcEyz7KbAtLmC9HhgnIkNj2soADgK2A7rHb0hVhwWD+6lqbswp93nAgUA74DDgVKA+p+Ofwj0p7XFB743B9s4BPgFuCLY3JFi+DBdodQ32sRdwb7DO7biA+clgndwkA+eLgTZAP9zxOTzYr2TMB8biAr91BMf4LVwA1hU4OOj/icEiTwBjYpb3cM/r48H4vsBjwPlAp2De/SKyR8xm+uOOw2Dcl5dEMoARuGO2BXAALgh9FeiMCyAfF5E2wfI+cC3uOekPFAHj49o8GpgY7Nc/g2l74ILqPsCR9T0eIjIIeAX3paQD7rVxRg371OAKCwttOBXDQ3qvHScnC/p1rVomdl4tIp1yGdJp/T+EMmtZdbOOzehY2bANb0TDG4nzgZG+798KRE//TAeG1LhGAs02wxp4WFV/ARCRZ3HZxeGqWgwUB1lEwWXwHotZ73kRuQgXwPwcM/1SVV1Vnw6o6ssxo1NF5Glc1vehJJsox2UOu6vqIlxGrbbtxdbPLhKR2wmCuw1QjgvahgBTVfW3eq5/G3CGiPxFVV+Im/d34EVVfS0Ynx5kj0/CBevPAXeLyHaqOhX3haMT8FKw/HnAvTHZ3q9FZHyw/sfBtArgMlUtq6OfV6jqGmBO8Npoq6pvAojIU8CDuKB3mqp+H7NemYhcB/wgIm2CNgA+VdX/BsNrRARgtqpGg/dEabG6jsexwNeqGg2O3xWRV3GBc6PLy8uz4VQMX320ux3VbwvgtJEwoDtrlzhyZ7j5eHj4XSirBM+DnEyYtYRYoRP3ZPQWIY57s/rp/q06w4wCKAtDp1awcy/IyoC5q2FRMXRoBQcNgH36hXj8B5/iCp/yMCwogpIw7NTD4669QuS1bSbHyoZteCMabizN7C4Bebgz38DaqqYsEn+G1qi5B6wLY4bXAGFVXRo3LU9EQrhs2TG408E+0BaX4YqKUHXAkiYio4ELgYG445WNy9wlWnYi7jQ/wHhVPQuXlb0cFwwtxQXh99SyvR1w2bdhuKyoB2zoRT134F4cTwI9ReQN4BJVXZzMyqpaGJzGvyUIrmINAPYWkSNipoUIjrWqrgzWOQWYGvx9XlVLYtbfS0QujFk/A5d9jloYDVZF5HhivizEXPCU6LWxOma5aMCZF7QzCHdcdgqmRf+JugKzg+H8BIdjdoJpsWo9HsAmCdqdRRMFrCZFsrPghuNqnv9/R7pHrDMfhIffqxo/YDsAQl71mwf8cEryb+P79U96UWOMaSgfA5cBN8VMOxf4sD6NNPeANVmjcafp9wN+VtWIiCjVb7HtR2sIa1Ftvoj0wZ0mPgKYqKrlInInLqu7jvi7DgTTpgHHBKfCd8Nl1L5X1Q+oSo3Heh6XfTxaVVeLyCHAhJj5idYpxAXosXrF9KEYuAK4QkR6BPt0By7rl6xHcC+ws+OmzwYeV9X46bGeAJ4Rketxx3KfuPXHqeodtay/dp+D2tZn6tHvmvwHWABso6rLRWQr4Aeqv2YSHeu67htU1/GYD+wfN61/3d01G527xsDS1fDtTPjLrnDA9m663YfVGFOHZvZLV/8AJniedwaQ53ner7i45ZD6NJIuAWs73AVKS4FQcKHOMOCN2lZKYBHulHH0tHwuLju2FKgQkeG4WsRfkmlMRLJxwfSbqrpMRFbiAp5o3ekiYNME+7IKKBSRvrhvJfF9HC4iIVWNBk/fAjcH2dlpwFm4TF+0H6NwF/b8hqvVLI3pQ1KCC9suAcZRPah7APhIRN4G3sZ9nG4GdFXV6I0j38MVWD8F5KtqbIb6Hly98ZfA57js6taAp6panz7WUzvgd6BARLrg6p4bQl3H43ncRWyjcRcNjgD+DDTmvpqWKLc1vHLputM9LGg1xrQYvu8v9DxvR+BPuIuN5wJf+75frxtHN68qh/X3JPAVLiibj7s6+5Na10jsCuB6EVkpIg8F9bPXAK8BBbjg8bl6tnkMro6xCHgduCYmkBsLiIgUiMhPwbS/4rLFhbiLc16Ma+9RXDZ1ebBeRnA/1LtxAdJC3EVln8WsMwiXpV2NOx1dAiT4JKydqk7ABcShmGk/4r4lnR9sewkuqO0as0wEF6weiMu2xrb5Lu6iozuAZUEbY9nwMoi6XIAr31iNe63U98tNQnUdj+B+tkcBV+NeUxfgnlNjkmK/dGWMqYvvVX+kmu985fv+i77vf1nfYBXAs9+hNmajYv/wLVyrsZWUBedHPCD8zww8rxl8Ihlj1kej/PO+2unZau/1f15xXMreJDzPm0sNnz2+7/dNtp10KQkwxpiNwpnDPO6b4t77T93as2DVGLOOZlbDGv/z9T1xdwh6vj6NWMC6EYu7q0E19nOjxjRP9+6dQbelnxHxPa7cb+dUd8cYY2rl+/5H8dM8z5uMK2O8N9l2LGDdiCW6q4Expvnbps0KAMuuGmMSijT/t4YyYi4OT4YFrMYYY4wxplF4nhd/F542uF8enVifdixgNcYYY4xJI82shrVP3Hgx7s5GT9enEQtYjTHGGGNMo/B9/5SGaMcCVmOMMcaYNJLqe696nrd3Msv5vv9Bsm1awGqMMS2ILvI5L39Xwr5Hp/k+u/ZuVqf+jDEG4LEklvGBgck2aAGrMca0IAe8FGZ5Wbu1w4Xn2du4MaY6P8V3EPF9v153AEhGuvw0qzHGbBSWl1YNF1WA/VqhMWZjYF/NjTHGGGPSSHO6D6vnee2Aa4E9gS7E/BxtfX6a1TKsxhhjjDGmsTwAbA9cD3QC/gHMAcbWpxHLsBpjTAtmv3ZljInXzO7Duh+whe/7yz3PC/u+/5rneQpMoB5Bq2VYjTHGGGNMYwkBq4LhIs/z2gMLgU3r04hlWFsQEakERqrq5BRt/yfgelX9byq231BEZBxQqaqnr+f6/YFZQB9VndeAXTNmHb7v88YfPqtKwfPWvcBqVWmEifnQN89jF7vFlTGG1N+HNc40XP3q+8AnuBKBIuC3+jRiAatZR00Bmapu2UTbHwM8DqwJJpUAk4BzVXVpU/TBmKbw5QKfsd9G6JULN+waIje76lNGF/nc8U2ESbN9VpQmXr/XquVM2vVp2pSU0XnJfErKCmndqx1UhGHEVnDPqdCujVu4ohJufAl+ngsn7wWHSBPsoTHGcAZVF1qdB9wMdABOqk8jFrCa5uoPVd0UQEQ6AS8A9wLHpbRXDUBEslS1ItX9MKlVUOqz/0thVpe78dVlER47IAOAonI3r6ZANapb0WqOnPJ59Ym/Fbu/sz6AyjA8dZ4bv+UVuP4FN/zaN/DjPbBZr4bZGWNMs5Lq+7DGme37fhjA9/0lwHqd3bSANYVEpA3uqrkjgfbA18A5qjpDRPKA+4FRQCFwddy61wK7qerImGmTgUmqemMwvg1wO7ADkAFMiS4vIk8AI3HfcuYCN6rqs0FT04K/v4qID9ymqjeISD5wpaqOD9rYM2h/c1w9ylhVfSiYNwKXFT0e922qC/AOcJqqFtbnOKnqChH5H3BmzL7uE7S7GVCJO9VwrqouCeZnARcDJwO9gCXApar6UtBEjog8AhwNFONKHR6KaX934BZgKLASdwrjblVNeNNLEfkbcD7QA/gFuFhVPwnmXQvsAUwBTgSmiMifgX8BfwZaAYuBy1X1xfocm/oqLCwkLy/PhpvB8Mwlxawub7X2ufltpb92meWR3DqDVYA/zZ1R+wK/LajabjAMuGzrrMUU9sxL+XGwYRvemIc3Eos8z3sReNb3/U/XtxG76Cq1HsEFe8Nxgc5XwBtBsHUPMBgXMG0DHIYLOpMiIj2Bj4JH/6D9W2MW+RTYFhewXg+ME5Ghwbxhwd8hqpqrqjckaH8A8DbwINAZGAPcIiJHxyyWgbs6cBgusNwOODfZfYjZVlfgiKDPUWXAOUBXYGtcUHpvzPwbgRNwAWk7XP1MbL3MUbgrFKO32LhfRPoF2xsKvAXcEbR/cLCtE2vo32jgBtzpjc645/XtaHuBPXBBfR/cF5STgR2BLVS1HbA38FOSh2S9xb5J2nBqh7fdpC379nNZkJAHZw4LrV2mXzs4eGAdGRLfZ5/ffnCDNS1z1v5V2z11H2iV7aZv0w923bxZHAcbtuGNebixRLzqjxTbD1ez+qznebM8z7vF87yt69uIZVhTRES64E5v91PVxcG063BZup1xmcmDVXVRMO9S4PB6bOJEYIaq3hIzbVJ0QFUfi5n+vIhcBIwAfk6y/dG4jO24YPxLEXkIl+qPzRJepqpFQJGIvAokWzg3QEQKguH2wK/AX2P6Hxu8LhKR23F1r4iIB5wNHKOq3wfLzAseUR+o6uvB8CvBtrYFZgN/B15U1deC+dNF5H5cQPpUgr6eAjykql8F44+JyOm45zd6/Ger6l3BcLmIlAO5wFAR+UJV59Z1QEx6yQh5vHVkiM/mQ/c2sHnnqk8Vz/N47c9u3odzwtz1LbTOgOO2gHumrF2IY084nz23/o2F4Sza6q8Mzo3AkN6wcCXsPhS22KRqg3tvDdPvg1lLYMdNoW0rjDGmsfm+PxWYClzied6euPjhA8/zFvq+v02y7VjAmjrR39n9XqRaDJcVzMsB8mOmz6pn+/2p4Qo8EQnhfnXiGFzm1Qfa4rKJyeqToE8zcZngqHDcRVLFQLJfLWfF1LC2wmVmvxSRLVV1iYjsgCsJGAa0wRV05wbrdg32p7YrEBfGjcf2bQCwt4gcETM/hCudSKQPrsY21sxgetTsuPnjge64e9ANFpH3gUtUtY5zvCadZIY89uyTeF5GyGOPPrBHn0yu2bVq+j1TKtcO+6EQ3Q/ciu4Ahwype4P9urmHMSatNbMa1ljTcWVzc3BnkZNmJQGpEw1gBqtqh5hHG1wwU44LOqP6x61fiAvKYsVeQZFPzS+G0bhM6JFAR1XtgKtbjb7CI0n0f26CPg2k5qBuvalqKa6GtAuwezD5eVxN6GbBKfXRMassxd1hoF7/DDFmA4/HPS/tarlLQjLHotoxVdVKVb1NVQXoF/T38fXsrzHGGNMseZ7XwfO80zzPex/4A3c29zagXt+eLcOaIkGW8FngARE5X1Xni0gHYC/gPeBZ4DoR+RF3W6db45r4Frg5yDROA86iKmsLLui9Iigl+BfuwqQ9VHUSrqazEhfYhYLbSA0D3gjWXYoLsAZT/TR6rOeAq0TkpKCv2+MuivrbehyOWgU1vWcBYarqPNvhbkRcKCJ9gcuiy6uqLyIPALeLyJxgnd5Ap5gSgdo8AHwkIm/j6nR9XA1uV1X9KMHy44B7ReR1qi6s2pbqQXT8Pu0d9P973PNbHOyfMbXzfYhmT3wf3/ft166MMdU0s/uwLgA+x8UKR/q+X7A+jViGNbXOwNVmThaRQuAH3EVCPu5eZbNw6fMfcBcIrQ1ogh8PuBsXUC3EnV7+LGb+Aty3mH1xQeci3FXzAE/iLvCaAczHXdj1Scy6JcBVwHMiUiAiV8R3XFVnAQfhLkZaDjwNXKWq8afG19dAESkSkSJgGa584WhVnR7M/ysuS1wIvEL1ulmAK3Cn6V8NlplMkr+qoao/Aofg6okX4u4wMI4aSiaCuytch/uSsBwXtB+kqvFlALG6447ZymAb/Yip0TWmJs3rc8gYY+o0yPf9kb7vP7a+wSqA5/s1Xl9qjEk/9g/fwmXfWkJFZhYAXiRC+OIsy7Aa03I1yj/vEwNfrvZef8ofR7b4NwnLsBpjTAuSW1FWNVyexM1ajTEmDVgNq2lywU35J9Yw+2ZVvbkp+2NMS7Lz0FzenhkBfLbbtI1lV40x62hmNawNwgJW0+SCX4DKrXNBY8w6Xv1zBhc/9z1h3+POo7ZNdXeMMaZJWMBqjDEtSFaGxz7t5wOQk7ldintjjGmOmtN9WD13Guh03J1zuvi+v43neXsAPXzfT/pCbathNcYYY4wxjeV64DTgYaBvMG0ecGl9GrGA1RhjjDEmjfieV+2RYmOAQ3zff56qO9XMwv3ATtKsJMAYY1qQiO/zeWF3Ir7HgRGfzFDKP4yMMaY2GUBRMBwNWHNjpiXFMqzGGNOCHP5qmFsXbM/tC7dj/xftx9GMMevyveqPFJsI3O15Xg6srWm9AfeDSEmzgNUYY1qQ12dWDX8wN3X9MMaYJF0A9MD9HHl7XGa1H/WsYbWSAGOMacEikQihkOUejDFV/GZSKuR5XgZwFHAc0A4XqM71fX9RfduydzljjDHGGNPgfN8PA3f7vl/q+/4S3/e/WZ9gFSxgNcaYFs2yq8aYeM3sLgETPM8btaGNWEmAMca0YL7v28+zGmOas1bAS57nfQHMpepOAfi+f1KyjVjAmkZE5CfgelX9b6r7Ul8ikg08DewHhFW1S4q7tFZ9jquIjAAmqar9bxljjEmJ5lLDGvgxeGwQ+1BtgUSkP+6mu31UdV50uqpu2UTbHwM8DqwJJq0AXgEuVdWy9Wz2KOBPQG9VXVPXwk1pQ46riFwL7KaqIxuuR2ZjUFTuUx72mbvaJzvkE8rwGNR+3Q+h8jCUVPp0aOXByiJonQ0FxZARgi7tYHkhdMqF+pQOlFdAUSl0ymvAPTLGbIx837+uIdqxgNWsrz9UdVMAEdkWeBdYDVwdv6CIZKlqRR3tDQRmNrdg1ZhUeGhahL+9F6k6bwa4s2j+Ost2+XeYogr45PPH2e1/b0PIg0iwXF5rKCyBrfvBh9dD5yQC0C9+hYNvcsHvWfvDg2c2wB4ZY5pUMyoT8jxv75rm+b7/QbLtbPQBq4h0BsbiTkUDvANcoKorRCQXuBY4AuiKq704U1U/EZEs4GLgZKAXsASXYXxJRMYBlap6esx28oErVXV8kKG8EngEOB/3KxBPA5dFAzsReQIYCXQItnujqj4bNDct+PuriPjAbap6Q+w2gjb2BG4HNgcWAmNV9aFg3ghgEnA8cDPQJdj301S1sD7HUFW/E5GPge1i9vVxYC9gR+B0EXkduCU4lq2BT4FzVXWOiNwP/BUIiUgR8JKqjondhoh0xP0O8d641+084KzgubgW2B34HjgJKAHuV9VbY9bfCrgL2D6Y/wxwdczx7g/cAewW9O8n4FBVXR733LUBxgO7AG2AGbjn/b344yIixwCXx+wXwDDgY+AcVf1fzLJPARWqeloyx9yktws/jA9Wa1ZUAYOWLXLBKlQFq+CCVYAfZsOj78GlR9Td4LX/dcEqwH/egXMPhi02SbrvxhgT57G48a5ANu5zPOmfZ7XLS13g0hHYInh0wQWP4A7yTsA+uPuHHYoL/ABuBE4Ajg7m7Qn8Vo/t9gP64p6snYFRuAA46lNgW1zAej0wTkSGBvOGBX+HqGquqt4Q37iIDADeBh4EOuN+y/cWETk6ZrEMXKA+DNgMF3CeW499QEQ8EdkOt//fxMw6A7gQyANew30pGB48+gHLgAkikqGq5+CC5snB/oxJsKmLcQFiP9wxORz3Yo/aA1gM9AQOAy4UkeOCPnYDPsKVLfTGHe99gf8L5rcBPsB96dgc9xr4J1CeoB+hoJ3BuOP6HPCyiHSNXzCoeY3dr1xVnYl7XcV+mWmPK4l4JMH2GlRhYaENt4Dh9jnUS3F2DhWhjNoXat82uT60b1O1TkaIIiqT6rMN27AN13+4sfghr9ojlXzfHxD7wP14wE3A/fVpZ6POsIpIL2B/YDNVXRlMuxCYLiL9gL8AW6nqrGCVGcEyHnA2cIyqfh/Mm0f1AKouEeBiVS0BZorI7cAluAAHVY39RvK8iFwEjAB+TrL90cAUVR0XjH8pIg/hAqUXY5a7TFWLgCIReRWQJNsfICIFuHOUy3AZ1Vtj5j+iqlMBRKQMl4keparzg2nn42pf/wR8kcT2ynEB4hBgqqrGfzlYiMs0+8C3IvIwLkh/Fpd1nRbNLgPzReQW4Dbcl4FDcFnV81Q1+un8ZaJOBMdqfMykO0TkUlwm+a0k9gPgUeAyEekdHI/jcOUQCbfZkPLy8my4BQy/MMrnzPfC/LYCKmMSph1yoCCuSnzPTWDJmo58e8vZDH/8JVhdAqvXQGYGDOkFBWtgzy3h9JHJ9WHsKa4Gdu4yuPRwcrfo3yyOiQ3bcDoOb4x83w97nncTLma6O9n1NuqAFegT/J0VMy36w4fdg7+JsqZdgbY1zEvWkrh6zXxgEwARCeFKEY7B/ZyZH2xvnSxeLfpQfb/A7dthMeNhVV0aM16My4gmY1a0hrUG+THDXYGc2P6oapGILAn6WS1gFZHjgYdils3Fna7PAp4EeorIG8Alqro4WGx2EKzGbj96/nMAsGsQYEd5uAwzQH9cTW4ldRCR1kFfDsJlYiO4Y5b0cxOUQbwHnILL1J9OE2RXTcux2yYeP52S+O3Zu7P6y/TDYzKC21qNgEtGbPjGe3eGd6/Z8HaMMSnTDO69Wpd9cZ+fSdvYA9a5wd/+BNlTquopooHQYNbNai7FXSE/GPg9QbuFuGAGABHJBLrFLdNNRNrEBK39qcrQjsYFMfsBP6tqREQUF2RBck/yXFxQFWsgVfvc2GL7uBQoI+Y4B/XB3RL1R1WfwZVqxE4rBq4ArhCRHrgs5x247ClAPxHxYoLW/lQdz9m4W00dXENf83EZ4wxVDdexXxfiyg/2AfJV1ReRZVQ9N/Fqeq4eAsaKyJvAUKrKUIypF7sHqzGmOfM8by7Vrxhtg7s369n1aWejDlhVdYGIvAvcJSIn44KOu4CJqjpbRF4CHggukpoNDArWmyEiDwC3i8gc3AU6vYFOQYnAt8G8AcAC3GnnrLjNh4DbROQSXN3lRbjsIbia2EpcoBcKtj8MeCOYvxQXCA2m5jKE54CrROQk3Gnx7YEzgb/V+0BtoCDgfgq4QUR+Bgpwx3k68HUybYjIKFyw+xtQBJQCscFlT+BiERkLbEVVDS3AU8A/ReRU3LEoxwW0m6nq28CbuIvTxorIVbhMswA/JbgArR0u+F4OZAflAB1q6foioK+IZKtqbE3sm8ADuHrWl6MlKcbUl/1wgDEmnu81q0uUTogbLwZ+831/dX0aaVZ7lCIn4DKiv+ICqAKqsnanAt/hLtgpxF081COYdwXwAvBqMG8yED1F/gzwOjAFdxp+DjA/bruzccHmLOAr3AVStwfzngymzQjWGwp8El0xqHu9CnhORApE5Ir4nQrqbg8CzsEFV08DV6nqC8kclEZwAaC4C7Pm4ALMQ5PIaEYNAibgbp2Vj7vS/9KY+Z8EbS7CBfb34oJTVHUR7o4Ffw7WXQn8jyCbHmRv98aVJ/yOq8mNliDEuxv3GlmAe27XUL38Id6LuCzyouC5GhBsM4wLVrfDygHMBrBg1RjTzO3o+/5HMQ/1fX+153kX1r1qFc/3k715imko0dta1VEDapLUUm/OH7wO/k9VhzThZu0fvoWLr2H1L9qoT5QZ09I1yjfO+7d/u9p7/TlTDkjZN1vP81b7vt8uwfQVvu93SrYde6czJgVEJA84D7gv1X0xLZuVBBhjmqOYHwzI8DxvL6oH5wNxZ6eTZgGrWYeI7A5MrGH2zap6c1P2J90Et/S6GffrYA+ntjempRnSEX4NKp775FlJgDFmXc3kLgGPBX9b4W59GeXjyvf+UZ/GrCTAmI2L/cO3cMtLfE57dgZh3+OR0YPo0bZZfDAZY9ZPo/wD/2uHd6q91//j2/1TWRLwlO/7J9W9ZO0sw2qMMS1I59Yep3WbDkCPtlYGb4xJoBl9j22IYBUsYDXGGGOMMY3E87x2uB9D2hN3j/q14bTv+32Tbcdua2WMMcYYk0Z8z6v2SLEHcPeCvx7ohKtdnQOMrU8jlmE1xhhjjDGNZT9gC9/3l3ueF/Z9/zXP8xR3b/Wkg1YLWI0xpgWZutjnxfc74oV9um4fYXhvO1FmjKnOD6U8qxorBKwKhos8z2sPLKTqx5aSYgGrMca0IFOOf4yn3n8LgKenTGP4Z+ekuEfGGFOrabj61fdxv0r5AO4n1n+rTyP21dwYY1qQ4z96d+3wcV98iB+JpLA3xpjmqJnVsJ5B1U+Yn4f7afUOQL3uHmAZVmOMaUHm53XimgOOIeyFuPbd/7JZ6j+MjDGmRr7v/xEzvAQ4fX3asYDVGGNakFGnXcov3fsA8FXfTZlhP81qjInTDLKqa3nuDep0YDTQxff9bTzP2wPo4fv+C8m2YyUBxhjTgvzatffa4VmduhEK2du4MaZZux44DfdT5NH7rs4DLq1PI/ZOl6ZEpFJERqRw+z+JyDGp2n5LIiJXisjkVPfDtAAlZWSHK9eOhvwIEathNcbEaWY1rGOAQ3zff56qnwefBQysTyNWEmA2iIj0x73w+qjqvOh0Vd2yibY/BrhSVTetbbqIdAVuBQ4A2gOFwHfAqaq6sCn6asyGCEd8vppaQJ+VpfzezWVZexQWEAp1T3HPjDGmVhm4uwJAVcCaGzMtKZZhNRuL8UAesJ2q5gLDgOeo+ufZYCKS1VBtmfTzw1KfG7+I8PqM+mVEpy3x2fneFWTdVcleH+UR+yPhXYtWE/58OtzwAnz4QwP3uAl99JPbh89+SXVPjEkLzSzD+hZwt+d5ObC2pvUG3A8HJM0yrC2EiLTB1YEcicsQfg2co6ozRCQPuB8YhcscXh237rXAbqo6MmbaZGCSqt4YjG8D3A7sgPs2NCW6vIg8AYzE3YZiLnCjqj4bNDUt+PuriPjAbap6g4jk4zKc44M29gza3xx3w+CxqvpQMG8EMAk4HrgZ91vD7wCnqf5/e/cdZlV19XH8u2coioAN7NIUNeprXbbY0KCJLcYWY8eu0diiiV0sUWOJpllRsSfW2I2iwRrLir0LAgqIoIL0MjPn/WPvgTOXGWYGmHLv/D7Pc5/Zp69zZu6dddfZ5xyfsijHLefHwP7uPh4g/bxjQQuk4/p3YHficT0PuBXo7+5D03HdDngLOCT93KWe44WZ7QZcSezLMxQYtpj2UVqpkT9k/PieSqbOicN37QoHrVt/vWDYxIwtb5/NzNAVAsxu34HPV1hl7vR3V+5Jtv1BUFEJIcCzF8BPNmiq3WgaL3wIO14AVVVw0f3wyqWwed+WjkpEFp/TgNuJDw9oT6ysPkMjb2ulCmvxuJmY7G0JrAS8DjyeqnrXAn2BdYENgD2JSWeDmNnKwAvp1Sut//LcLC8DGxETsIuAwWa2bpq2Yfq5trt3dveLa1l/b+Bp4HpgeWJ/lsvMbL/cbOXEx7dtCKwFbAyc1NB9aIAXgSvN7Bgz29jMGnJ8/kzsY7MO8H/Absx/XLcjJuCrE79MwAKOl5mtATxETMyXAf5CvEdds5gyZYraLdD+3zfZ3GQVYOhXWYOWfXHkDGaGeX9yZZWV5AWgXUUal2XMevbtFtvHhW3PGvJ2TFYhJt4vf9xqYlNb7aZuN5XWUGENIawEkGXZ5CzL9iIWabYE1siybK8syxp1IFRhLQJm1g04EOjp7t+kcRcCpwBbESuTu7n7uDTt98BejdjEIcAwd78sN25IdcPdb8mN/4eZnQ70Az5q4PoPIFZsB6fh18zsRuJtLu7PzXemu08FpprZvwBr4Pp7m9mkgnEdgLG54f2B3wCHExPRWWY2OG1zZuEKU0J7ELBLdVXWzM4Gflkw6yh3vzq1Z0O9x+tXwBvVlWfgmbSvq9IMunTponYLtLcgY5mOMGlWHPfTXqFBy+7YZ0m6PjeTybSHLKPzrJlM7rTU3OlZCMzu0J4Os+dAeRkdd9usVexvY9odd90MLn8kJqsd2sEO67ea2NRWu6nbJe4zoGtu+IYsy/Ze2JUpYS0OvdPP98xq5HDt07SOzHuKBMSLoBqjF3U8Is3MyoCBxIRvJWKfz6WA7o1Y/+q1xDScWAmuVunuE3LD04h9ThtiRF0XXVUPp0T4MmJltwPx4qs7gcnA+Wb2IdAzzX4pcAsx6R2VW22+Xeu4Bhyv1aj5u4J4bJolYZWWsVqXwBsHl/PEFxkbdIcdezTs5FavpQNvHLEETz02ko2/H8v149rzzz6bzJshy2j/+uXwnw/gx2vDFms10R40oa3WhlcvhZc/gX7rwcaNunBYRGrRCvqtQr7DfdRvUVamhLU4VCdFfQuSuupK4E3EpHN4Gt2rYPkpxKQpb5VceySwbx3bPoBYCd0Z+Mjdq8zMmfeH2JArSL4Cdi0Y1yeNb3buPht41MyGEE/dz3dXg3RcZxOT2Orj2oP5Fe5/fcdrDPDTgmV6LdSOSFHpu2zglE0b/09k7eUCax/WG+hN+cBneLBiDhXl7SDLYr/VDXvBRr3rW03rtlnf+BKRUrLYLmoGJaxFwd3Hm9k9wHVmdoq7jzGzZYAdgGeBe4ALzewD4jN6Ly9Yxf+AS81sU+JFUscxr2oL8Qr6c1JXgr8CFcB27j6EWM6vACYAZalyuSHweFp2AjFp60u8EXBt7gXOM7NDU6ybAMcCxy/E4VgoZvanFMf7xER0O+Lxu6y2+d29Mh3zgWb2PjAT+EMDNlXf8foHsaJ7ALE7RD/gF4AvzH5J27LUcf2puCt9RwoB9JQrEalFVtYqPhfahRB2YF7BpnCYLMueb+jKdNFV8Tga+BQYamZTiInXfsRvMCcTTyt/ksY/Bsy9OsPdhwJ/Il749DWwIvBKbvpYYuK0EzHpHAeckSbfTrzAaxixOrgu8FJu2RnEq+fvNbNJZnZOYeDuPoJYYT0R+I54Kv48d2/wI9kWgzLgNmA8MBG4DrgKuHoBy5wMfEnsLvEB8ctBBsxawDL1Ha9hxGr2+cAk4FRg0ELsj7RBPZYO1ChaBPTgABFprcYT76xzS3p9VzDcqP99IcsWa8VWpGSZ2drELwWrpiS/GOkNX+TCVXOYV6DIqPptO1VZRYpXk7x5/9jvpRqf9b8fum3Rf0ioS4BIHcysD/NuIdYNuAZ4sYiTVSkFGfl8lUzdAkSkDVDCKq2amW0LPFXH5Evd/dIm3PwSzLugbTrxXq7Nds9Ukdp0nzaZCZ2XBqDLrOmUha71LCEibU0ruUvAYqWEVVo1d3+J+Mzhltj2R8D6LbFtkbq8Wvkyx3+2GpVl5fxtqWEQ6rrBh4hI6VDCKiJSRNa8eE/Ou+Y2QlXGur89oqXDEZFWSBVWERFpcT+s2a2lQxARaVa6rZWIiIiItGqqsIqIiIiUEHUJEBGRFjdhzhK6oa6ItClKWEVEisilr1Vyzhf9gMDwFyu5dLvylg5JRFqZUqywqg+riEgROe+VeU8OuOyNDD2tUETaAlVYRUSKSJXyUxGphyqsIiIiIiLNTBVWERERkRKSlV6BVRVWEZFiUl5VObcd1H9VRNoIJawiIkXkR+NGz233nfA1oQT7qonIoslCqPEqBeoSINLEzGwgsI2792/pWKSIVVXBxffzy/fm0HXWdF7t/SM+X35FPvo2IwR48LOM9brBXn1VhxCR0qOEVUqSmY0EVgIqcqP/6O4Xt0xE0up9NhYOvha+mQQD94fDf9LSEdW01ZnwxjD69V6HK7fbHYCsvJz1Bley/bAPufnBG5lTVs4w60Ofdz7EV+7FoQedxB4bL8WV/XSvVpG2pFSqqnlKWKWUHeXud7V0EFIkfjMI3hwW20dfD7ttCiss06IhzTVxKrwRY9vtiDOZskSnGpMP86H0/XZcHHhyDACbj/2eQ5f+F+dUHsjOvarYqZcqryJSvPQJJs3CzE4ysxFmNsXMxpjZpWl8DzN7wMzGmdnXZnaTmXVJ0440s7FmtkIaXiENH7mIsWxrZi+b2fdmNtzMfmtmIU3rZ2YVZnZgmjbNzO4ws65mdrOZTTSzUWa2d259G5rZC2b2bZr+lJmtsYDtdzKzq9Lx+N7MnjazNRdlnxpqypQpatfVnjF77jgqq5j6/Q+tJ7Y5804UTO24JDVkGRuP/oLadJod9+m7KTNaz76orbbaNLWqEGq8SkHQU1KkqZnZWsA7wGbu/qGZLQOsk8Z9ANwDXAYsAdwNjHP3I9KytwOrALsCTwFj3P2wBmxzJHBuYYXVzNYFXgcOBh4H+qb1XuDud5hZP+A/wM3AKUA3wIFvgTPSvMcAlwOruPt0M9sAWB54Ne3DIGA1d98qbXMguT6sZnY3sDRwJDAROAfYH/g/d59T7wFdNHrD1+XVT+Dnl8H3U+G8/eDCX7V0RDXt8Qd4/H+sc8Y1fLrCqnFclkEIHPPGc/z94UGE9u0o/78e8MbnDF95VbY/4lw236wb9+1RRruy0vinJVJimuSNecEub9b4rL/wqc2K/gNAXQKkOVQQ35Trmdkod58EvGZm+wLB3c9P880ws/OAV83saHevBI4H3gTeANoDP2/Edm80s7/lhtcFfg3c7+6PpHGfpHkOBe7IzXuOu08HvjSzocBS7v4EgJndAVxPTHbfdff3csvNMrMLgffNrFNax1xm1g04EOjp7t+kcRcSk+MtgJcbsX+yOP14HZgwGGZXQMf2LR3N/B47B/7xMis9//3chLXLzOmM+V1nluqwM2UVP4GyAOXlMHM2ayzRgWEVGUu0K/r/UyLSSFnT5MEtSgmrNDl3/8LMDiImn4PM7D3gIqA30MPMJhUskhEvmBqTKpiDgD8BRxQmgPU4tpYKa29gx/wpfWLXmK9yw5XuPiE3PB2YnNuf6WYGUN11YQ3gSmLC2YV5VczuwKiCmHqnn++ldVRrD6ze4D2TphFC60xWq/1qG/43fN7pxBkdlqBzh7J4a6v2uY/zJTrEH0pWRaREKGGVZuHuDwEPmVkH4DjgEeBY4DN3X6+u5cxsHWAgcB1wmZk95e7jFiGUUcCt7n7CIqyj0A3AWGADd//OzNYH3qf2Uz3VCWzfgqRYpEE2GjOSl/v8CICNx4wgsHYLRyQirY3uEiCyEMxsbWJl8UVgBvADsQr5AHCemZ0N/BWYSuyvurm7P2xmnYD7gWvd/QIz6wjca2b9U3eBhXEd8IKZPQ08neJYC+ju7i8s5Dq7Ap8Dk9Ip/4vqmtHdx5vZPcB1ZnaKu49JfXp3AJ5196kLGYO0Eet98yXbjviYqlDGzHbtIKzT0iGJiDQ53SVAmkMH4Hzga2AScBKwTzq9vyOxb+knxET2OWCjtNzfgfHAhWn4N8SLmwYubCDu/gGwO7HP6Ndp/YOJp+8X1qnAtsRuAy8RL+ZakKOBT4GhZjaFWI3dD10QJQ1w41Y/5c5Nt+MfG/2YP2+3O7pwVkQKleKTrnSXAJG2RW/4IheuqqgxXPXbcj2eVaR4Ncmb99zd3qrxWX/JE5sU/YeEugSIiIiIlJBSqarmKWGVomRmTxFPw8/H3Ts3czgizeaI9eHWD2J7v7VRdVVE2gQlrFKU3H2Xlo5BpCXc8rN2rPnDy1QROHv3H7d0OCLSCmUl+D1WCauISJFZv9NEQNVVEWk7lLCKiIiIlJCqEvwyq9taiYiIiEirpgqriEgx+XYy6978GqEyA9saVl6upSMSkVZGdwkQEZGW9eOzWePzsbG9+e/hq5tbNh4RkWaghFVEpJhUJ6sAo79ruThEpNUqxQqr+rCKiIiISKumCquISDHLMijBaoqILDzdJUBERFpUVtguwX9MIiKFVGEVESlmqrCKSIFSfNKVKqwiTczMRprZwS0dhxS/0VMyKguS06yOeUVESokSVpFapCQzM7PNC8bvn8YPbaHQpAV8MSnj0WFVTJjecunhZndWsPqNlTyxziY1xofn34NZc+DJ/8H/hseR//0Unn4LKirnzfjG53GeORXNGLWItISMUONVCtQlQKRuHwNHA2/kxh2dxksb8frXGTv8s5IZFbBKZ/CDy1m5c/P+Azj8qQr8m9juN+KjmhP7XwirLw9fpVtc/WJz+Ff6k93d4LGz4c+Pwym3pvk3gH+fD2WqV4hI8VDCKkXBzE4CTgW6AZOB2939bDPrAfwJ2IZ4dvQx4LfuPsXMjgQuBjZy9/FmtgLwDnCeu9/SgM0OBs4ys1PdfaqZ9QE2Aq4Hts3FdjJwPLAqMBG4GzjX3SvnW2Ocf33gamATYEaa/3x3n9OIQyLN5J+fVDEjFSXHToVnR2Ucul7zJqxPjpjXfqXXOuz6ydvAvO4A4avc/Viffnte+3GHbyfD4P/MGzfkvXj/1h7dmy5gEWlRukuASAsws7WAy4Hd3b0LsB7wqJktATwPfAT0BtYFVgP+DJCS0meBu82sPXAP8GwDk1WAscCLwAFp+CjgLmBWwXyjgV2ArsCewBFp3tr2ZQXgBeAhYoK7FbATcFYDY1okU6ZMUbuR7fW7zfvgLwvQc8npzR7DtqvMHcV+h5zK73c9kH+tZwSIJ/val8+bYaVl5rVXWx6WWYo5a688d1TVCktD967NGr/aaqtde1saLmSZuuxL65Yqmx8ChwFPuvvUNH5f4I/uvkZu3k2BV4FO7l5pZp2AN4HZQHtgc3efXriNWrY5EjgXmARcQEwsvyQml3sB/d29Xx3LXgX0cPdf5tfl7neZ2enAru6+Y27+fdJ+rNnQY7II9IZfCNe9XcWb4zL26hv4+ZrN/z1/dmXGqjdU8u2MOLzstCk8f9NFbDh2FOHg7eCo/nDnC7DSsnDiLvCnx2DSNDhjT+i7CkydAX94MFZbT90D1l292fdBRGrVJKXQk/f9qMZn/Z8fWLfoS67qEiCtnrt/YWYHEU+7DzKz94CLiFXVHmY2qWCRDFgJGOPu081sELHbwBENSVYLPEXsAnAeMNLdPzSzvfIzmNkBwGlAH+J7qgPwWh3r6w1sXRBzAMprn11ag19v3LInozqUByac0I4z/lPBIQf+lt7fT6DL7Jlx4h0nx9tabb/+vAWuOLTmCjovCZfpRhUiUryUsEpRcPeHgIfMrANwHPAIcCzwmbuvV9dyZrYOMBC4DrjMzJ5y93GN2G6lmd1KTFiPqGX9qxO7CewNPOXus1OF1epY5ShgiLvv1tAYRKpduUM7GPdVzZEl2FdNRBZNVoKfC0pYpdUzs7WJlckXiRcp/UCsoj4AnGdmZwN/BaYCqxBP+z+cugPcD1zr7heYWUfgXjPrX9cFUXW4FngJeLmWaZ2JfcEnAHPMbEvgEOq+k8AdwG/N7Ahin9rZQC9gLXd/uhExiYiItBm66EqKQQfgfOBrYp/Sk4B90un9HYkXW31CTGSfI17JD/B3YDxwYRr+DbA8seLaYO4+0d2HuPvMWqZ9TOzj+kiK7Uzg3gWsaxywA/ALYCTxrgIPE7sTiDReVVVLRyAirUxVqPkqBbroSqRt0Ru+2IW9aw5nD7VMHCKyODRJOnniLz+u8Vn/t/t+VPRpq7oEiIgUsyxTP1YRqUF9WEVKhJk9Re7m/3nu3rmZwxFpuCU6wMzZsd2uXMmqiLQJSlilTXL3XVo6BpGF8thZzNr3j1CV0fHu01o6GhFphaqapqdBi1LCKiJSTPpvyDN3xoev7bHHZi0cjIhI81DCKiIiIlJCSrEPq25rJSIiIiKtmiqsIiIiIiWkVO69mqeEVUSkmDz6Bj/71Z2EKuD2ZWH/bVo6IhGRJqcuASIixWT/q2k/o4J2syrgoGtbOhoRaYWqQqjxKgVKWEVEisnMOfPalVXxwQEiIiVOCauISBGZE+Z9bFeUSOVERBavLIQar1KghFVEpIjk//WU6bGsItJG6KIrEZEiUpZVzW0rVRWR2pTiXQJUYRURKWbqwyoibYASVqmXmY00s4MXYrleZpaZ2WpNEVdTMLOzzeyxlo5DpFBVlvHsyJpPCA+gLgEiMp+MUONVCtQlQEqWmQ0EzgVmplHfALcDF7t7rWUpd790MW6/FzACWN3dRy+u9UrpevubjFP/U8nwSbD5yoHd+gQufrWKr6bGQmqHWbPod+RZ7PbR/zjxv88A8MKPLuDKnfZm5bLZXNFxGMuOHBPvHnDJgbBOLd8VH3kDbnwG+q4Mlx8MS3Zs3p0UEVkISlilTmbW3t3n1D9nqzbU3fubWQB2AJ4ARgO35mdK08vdvaIFYhShsirjpw9UMmFGHB79ecbDn2fkv1nN7NiRnhMncPxrz5IBM9p1YLeDz2BaxyUAmPHWVO667/4487uj4PO/19zI8HGw75VQUQlPAR3awZWHNfWuiUgzK5V7r+apS0AJMLO9zeyz3PBF6VR8nzS8uZn9YGbtzGx7M3s9DX9iZsfmlutnZhVmdoiZfQF8X8u2OpnZI2b2hJl1TuO2N7OXzOx7M/vWzAbXEeeGZvZCmmeimT1lZmvkpvc3s7fNbHKaZ0hu2klmNsLMppjZGDNrVCXU3TN3fx74ENg4rTMzs5PNzIHpcZQNrN6umZ1gZu8U7ENvM6tM1VPM7DYz+yrF9ZGZHZib/d3081Mzm2pm56VlljezW9JyE8zsPjNbsTH7s7CmTJmidittT69gbrJarbbTAH94+h+UZ/Ek3xIVs5nWYV6FdOSy3ectO2r83P6tc7c15ruYrCZzPh/TYvurttpqS2MoYS0NzwN9zKxHGt4JGAb0zw2/AKwOPA1cDywPDAAuM7P9cusqB3YlJnU1kigzWymtZyzwc3efamYbAP8GbgFWTtsYXEecGTAQWBXoBUwF7spNvwP4C7B0mueStN21gMuB3d29C7Ae8Gg9x6QGMyszs58A6wNv5iYdCewPdAbeLljsHmAdM9soN24AsWo7Mg2/DGwELANcBAw2s3XTtA3Tz7XdvbO7X5wquf8iHov1gZ7AlLStJtelSxe1W2m7S4fA0RvUrIr06lpjkPLKSiYsNW9kAPb80AFoV1nBST5k3rRT95jbv3XutrZYC7ZeJ7aX7ED7k/Zosf1VW221m04pPulKXQJKgLtPMrO3gP5m9gAxoTsJ2A24iZi4PgwcALzl7oPToq+Z2Y3AUcD9uVX+3t1/KNjMesDFwPXufkVu/HHAY7l1AgytI873coOzzOxC4H0z6+Tu04HZwBrAiu4+LreeCuL/5vXMbJS7TwJeq/uI1LC9mU0CqoCvgfPd/Y7c9KvcfXhqV5pZPt6JZvYIcDhwcko2DwPOyc1zS25d/zCz04F+wEd1xLNpevV391kAZvY74FszW019Xdu2m3Yu59cbZYyanLHe8oEeXeH1sVU8NSJj3eVh6pxyZl7bjopQRrssXoD1kE3k7Y3G061qFj2PPRq+3hcqK2H9nvNvoGN7eP5CeHckrLo8rLJcc++iiMhCUcJaOoYQE9PvgP8CTwJXpdP2WwEnAL8hXgSUNxzYMzdcBXxVy/oPB74FrisY34v5K5O1Sqf/rwS2ALow74xnd2BUiuNsYhI7AbjJ3a919y/M7CDgeGCQmb0HXOTuzzRgsy+4e/8FTB9Zz/K3AXemRHQ7YiX1obQ/ZcSK8f7ASml/lkr7U5feQEfgm3xyTLwwrAexf620YRutENhohXkVkW1XL2fb1edNf3S57uxw/EAqy8q4+YEb2f+EXdg0X0FZZqkFb6BDe9is72KOWkRaE92HVVqzIcCOxNP/z7r7eGAMcArwnbt/RExEexUs14eaCWpWxxX0ZwLvA8+a2bK58SOBhv73u4F4+nsDd+8KbJ3GBwB3f9fd9wdWAI4ldlfYMU17yN13AroB9wGPmFmnBm53Qarqmf4sMAvYg9gd4B/uXt3T8ABidXofYFl3X4bYb7X6o6K2dY8CpgHLufsyudeS7v7qIu2JtAnH7X00q0yeSK/vJ3D0vsfqtlYi0iaowlo6XgG6AocQK4EAzwFnAI+k4XuB88zsUGKfyU2IieHxDVh/BXAQcCMw1Mx2SknxjcDrZnYIMZEsA7Zw96G1rKMr8Dkwycy6Eft8AmBmHYgJ4BPu/q2ZTSQmfJVmtjaxMvkiMAP4gVjNrC/ZXGTuXmlmdxC7WGxGvNNAfn8qgAlAmZkNIPZbfTxNn5Bi7Mu8yqkTk9q/mNkF7v6dmXUHfuLu/2jq/ZHid9qLj3P6i/FP7M9b7wLnHKWkVURqqCqRe6/mqcJaIlJ/yJeJp5ar+4oOISZVQ9I8I4gXVJ1I7DpwJ3Ceu9/XwG1UufvRxET4JTPr4e7vpnUeT7zP6ZfEpLk2pwLbApOBl5iX2FXbH/jEzKYSL6q6wN1fADoA5xP7oE4iJo/7uPtMmsdtwPbACHd/Izf+duB14gVuY4B1ifsFQKrEngfca2aTzOwcd68idn0IwP/MbAqxP26/5tgRKX4nv/zk3PZvXnm6BSMREWk+IdNj/UTaEr3hi13Yu+Zw1YOqsIoUryZ58+4/YFSNz/p/Du5Z9B8SqrCKiBSTzrknU3Vop2RVRNoE9WGVopXuHHBjHZOPdfe7mzMekWbx1PlM3/tSQpax5L1ntHQ0ItIKleJdApSwStFKCamSUmlbtvkRz93ySwD26L9hPTOLiJQGJawiIiIiJaRUnm6Vpz6sIiIiItKqqcIqIiIiUkJK8T6sSlhFRIrMiJldSvIfkohIXZSwiogUkbNfrOSyUdsAMPy5Sv76k/IWjkhEWpvKEvw+qz6sIiJF5Io3590P/O9vZ+jhLyLSFqjCKiJSRCpz+alSVRGpje4SICIiIiLSzFRhFREpYqEEKykismhK8UlXqrCKiIiISKumhLUFmNlAMxuyiOu4wcz+trhiqmdbmZlt0xpiaWlmNsDMhtUzz1Qz2yq1+5lZRT3zDzOzAYsxTBERacOqCDVepUBdAoqAmY0EznX3u6rHuftxLRdRTYszFjMrA04BjgR6AzOAocB57v7R4tpOU3L3zi0dg5SIykp4/XPY4w/w/TSyTfvA/pdBrhvAQ59WsNwS8N1M6Ld6Gct3mleHmDQzY3oFrNK5NP5hiUjbpQprEzCz9i0dQxG7DTgNOBVYBlgf+AZ43cw2aMG4RJrXzNnw47Nh67Ph+2kAPDSrW41kFWCfx2CH+2Hfx6DbdVU8/FkVAA9/XsVK11ey6g2VnDG0stnDF5GWUxlCjVcpUIV1MUgV0FuBHYDNgOPMrAcwAFgB+BA42d29juVPBo4HVgUmAncTK6qVZvYY0AMYZGY3AK+6+85mNhiocPej0jp6An8BtiZWJR8EznL3GWl6BpwAHA6sk2Ia4O6fpOm/Ai4AVgOmA0+7+2G5MDcws2vqWLYwloyYcA4A1gAcONrd6zuVvg1wKNDP3V9Io78Gfm1mPwL+BPQ3s9OAn7n7zmm5O4BfAsu6+wwz+yUw0N3XTafaz03H5nfAUsB9wK/dvc7/4ma2N3A2sCYwE7jF3c/JTT+prvWl/d/W3V+uZb3tgT8CBwNVwDULOibShj3u8MbnNUZN7bhkvYud/kIVe61VxgWvVDEr/YVf5Rm/3zyjW6fS+MclIm2PKqyLz9HEymAXYGNgT+BnwPLEZPZpM1u2jmVHA7sAXdNyRwBHAbj7HsCXwFHu3rk6Scszs3bAE8A4oCewJTFxvapg1gHAPkA34Cvgr2n5TsCdwAnu3gXoAwxqyLILcAywL/MS9kfNrL5H8uwKjM4lq3l3Af3MbElgCLCNmXVM0/qnmLZNwzulear1BFYkJs+bAfsBv6orCDPbBbgdGEjc37WApxZ2fQXOBHYHfkzs8tArra9ZTJkyRe1iaXdfmkK/fPdVNhozYr7xecstkX52mPd9bKl2GZ3at+C+qK222rW2m0pVqPkqBUpYF5+b3f3t1D4GOMPdv3D3Sne/hVgp3K22Bd39QXcf4e5ZWsedwE8ase3Ngb7Aae4+zd3HEKuKR5hZ/k/1Snf/0t1nAYMBy02bA6xjZsuldbxUsI0FLVubq919WKrw/o6Y3G1RzzLdgTF1TBsLlAPLAe8DU4CtzWw9YgX0VmKiCvHY5RPWGcD57j4rVXmfqyf+3wA3uPvj7l7h7pMLqqWNXV/eocAfc8fmdJrx/u9dunRRu1ja268Hlx0MS3aYO60qBKa1nzdcaIVO8Ohe8WP9tl078NNegc1Wggf3LKdT+9A69ktttdVGGk9dAhafkelnN6Az8Fg6NVytPfF0+3zM7ABidbYP8XfSAXitEdteHZjg7tNy44YDSxCTwPFp3Ne56dOI1WDcfbqZ7Zpi+IOZfUFMOO/JzV/rsgswsrqR1j+BOvY/ZwKxW0RtVgEqge/dPTOz54iV1W+AZ4kJ6s1mtgbxeAzNLTu+4PT/3PhTN4uD0/iX3H0XYtXz4QXEWef6GmA1ah6baWY2vu7ZpU07c+/4evC/8N9PWWp343Ov+RbJTq/9Y7z3MoGn963vpIaIlKLKErkzQJ4S1sWnKv38lpjA9Hf3N+tbyMxWJ57u3ht4yt1nm9lV1KzYVdW68DxfAd3NrJO7T0/j+hArjxMaEry7DwWGptP2PwceNLPX3X14Q5avRa/qRupy0J3Y9WFBngbOMrNta6nwHgi8UN0nl5igHkdMWG8H/kdMVA8E3nT3yQ0JMt3hoPAuByOJFeumMIaax2Yp4rERqds+W8UXgC/wLmkiIiVJCetilqp/fwauMrOj3P1zM+tM7FP6vruPLVikM7FrxgRgjpltCRwCfJybZxwLTqDeAIYBV5vZb4lX118M3Obu9Z5uNrMVgW2AIe7+g5lNSpMq616qXqea2VBignY58AXw+oIWcPcXzewe4G4zOwJ4kdgF4Bxid4Jtc7MPAW4ifjk4zN2rzOwF4in2Py9C3AB/B/5hZv8hVm87ARvUdhHVQrgTOCMdm7HAFahrjoiILEaVpVdg1T/KJnIB8AjwiJlNBj4nVvHmO97u/nFu/knEi3LuLZjtEuBgM5toZk8VTMPdK4gX8qxGvEDrDWJyeHoD4y0j3kFgpJlNISZsh7n7yAYuX5tBwEPERHxDYM8FXZWfcyjxiv6/EI/HR8T92jLXRxh3/5KYBH/q7t+n0UOIF64t0kMZ3P0J4n1gLwW+Bz4Ffroo68y5DPg3scvHCOLva9RiWre0QVnWbF2gRURaTNCHnSxuC7qtk7Q4veGLXLiqZpeAqt+WE0rkPosibVCTvHm3Pn5cjc/6V65fqeg/JFRhFREREZFWTX1YpVmZ2dQ6JlVfoS8iC7B0R/hhVmwv2Q5VV0VkPqXydKs8Jayy2Ll7ne8Ud+/cnLGIlJqH9yzjlw9NoyoL3PXzTi0djohIs1DCKiJSRHboUcatawwFYJc+e7RoLCLSOpXize/Uh1VEREREWjVVWEVERERKSCn2YVWFVURERERaNVVYRUSKyGtjqzjmi+2pyuAfX1axQw/VHUSkporSK7CqwioiUkz631fFuDmdGF/RiZ89UNXS4YiINAtVWEVEisi03OW/s5WvikgtKprmAVotShVWEZEiVlWlrFVESp8qrCIiRSRUVZGVlc1tl5XpY1xEappTegVWVVhFRIpJ+8rKue1ApgqriLQJSlgbwcxGmtnBC7FcLzPLzGy1poirOaT4t8kNH2Zmo81sqpnt05KxtQZm9qGZ7d/ScUjpm1NePrddFcooK9PHuIjUNCeEGq9SoHNJbYCZdQIuAfYBlgemAx8AJ7v7+wuxvnbAdcB+7v7k4oy1tTGzocAQd79kQfO5+3rNE5G0dVnBP5+psyro3LEdlVUZsyqhU/vS+OckIpKnhLUBzKy9u89p6TgWwTXAOsB27j7KzJYBfsLCP254JaAT8N7iCU+khH0wCk6/HcrL4M9Hwpor179MlsGgITDiGzi0HyzfBa59nNHvjIN+J82bLwS6/BW2W62CN76GmZWw31rQZ5nAVqsE9lxT1VeRtqiYE5a6lFTCamZ7A5e7+1pp+CLgPGANd//CzDYHniVWGbcGriAmcl8D17j7jWm5fsAQ4HDgQqA70KVgW52Ae4nHcH93n2pm2xMrmesBVcDj7j6gljg3BP6S5isHXgNOdPfhaXp/4EpgDWA28I6790/TTgJOBboBk4Hb3f3seg7Nj4Hr3X0UgLtPAh4siGl94GpgE2AGcDdwfmGibmZbpWMD8KmZZcDy7j6rluNzEbGquzTwRtrHYWn6UOAtoDfQHxgPHAME4FqgB/AccKi7T0nLZGnfB6Rj48DR1essZGa9gBHAocBZwOrAf4HD3P3rNM/yxIR+57TYv4FT3f17M/sbsC2wlZmdCYxx97Xr2NZI4Fx3vyv393MQcCnxd/Vv4MjcvnQHLgd2ApYBhgEHuPunta1fitSEH2Cz38HM9DZ66SMYPQi6dlrwcn94AM67N7ZvehZWXBo+Gs33K/WAHeavoL44el77/s8AMiDj4T3hF32VtIpI8Su1T7LngT5m1iMN70RMBPrnhl8gJi5PA9cTk9cBwGVmtl9uXeXArsDGwIr5jZjZSmk9Y4Gfp2R1A2JScguwctrG4DrizICBwKpAL2AqcFdu+h3EhHbpNM8labtrEZOc3d29CzHhfbSeYwLwInCmmZ1sZluYWceC/Vkh7c9DaXtbEY/VWYUrcvf/pu0CrO3unQuT1eRm4peBLYkV2deBx82sfW6eQ9L+LAP8E7iTmLRuRzwuawO5chKk6fsCKwAfAo+aWTkLtn9a56rAUsREutrdwLLAj9KrW4oDdz8ReAm4OO1nrclqHcqJSfCGwFrEv6OTAMysjPh7WwbYLP0cAExpxPoXypQpU9RuzvanY+clqwBTZsaqaX3L/jf3veW7KfBRzEifW6NxPU/+OzZrmv1SW221F0u7qUwPocarFJRUhdXdJ5nZW0B/M3uAmFidBOwG3ERMXB8GDgDecvfBadHXzOxG4Cjg/twqf+/uPxRsZj3gYmLF8orc+OOAx3LrBBhaR5z5U+mzzOxC4H0z6+Tu04lV1TWAFd19XG49FcQK5HpmNipVSl+r+4jMdQrwMbHaeQlQlo7PKe4+kViBfLe6wgyMMbPLgD9SM7lrEDPrBhwI9HT3b9K4C1McWwAvp1nvc/fX0/S7iAnyle7+fRr3OGAFq786V6X9HTAxrfPVBYR0obt/m5a5h/h7xsxWAX4KrJWOA2Z2GvCJma1cXYVdBGe6+1Rgqpn9K7cvll7dcn9fzdK9okuXLmo3Z3uDnrBcZ/h+ahxeeVlYa5X6l91zc3jyrTiiz4qwejd44UM2Hz2c+pQFqMqgXRns1qes5fZdbbXVrrctDVdSCWsyhJiYfkc8/fskcJWZdSZWDk8AfkM8VZw3HNgzN1wFfFXL+g8HviVedJTXC3i7IQGa2RrEU/5bELsaZGlSd2BUiuNsYhI7AbjJ3a9N3RoOAo4HBpnZe8BF7v7MgraXTuv/DfhbqkZuS6zi/pmYrPYGtjazSbnFArFK2JD9mZobPBb4LLXfM6uRb7YnVp6r5RPC6XWMK3xnj6xuuPv0dHxWM7Ntgady861bx3am5dZZHUv+b2F4btp8CWuq3n+UG7WLu79UOB9Q6e4T6thuL2B8LV+GpNR07QQf/wUufRDal8NZ+8CSHetf7pidY1/XL8bF5LXLknDfK/z4g6+gqgpydwbYpRdc3S/w4XeB6RVgK8LLY2CzlQIbr1galRURaZwZJfjWL9WE9R7ge+BZdx9vZmOI1b3v3P0jM/uKeLo/rw81E9TM3TPmdyaxKvesme1aXZkjJlJ9GxjjDcTuBBu4+3ep/+j7xCQRd38X2N/MArAN8IyZvefuz7v7Q8BDZtaBWNV9xMyWT5XZerl7JTDUzO4nnvaHmCQPcffdGhh/4To754dTFwOAvgVJ2+LQK7edTsQkf7S7vwoUxtGLBav+ffcidh2B+HeQn1bjJpfu/mXhdhbCSGAFM+vq7pMXcV3S2q2wDFx7ZOOX2/H/4qvaoTvED4iral4r+eS+8WP8R93mjVu3GyIiJaUUE9ZXgK7E/pHbpXHPAWcAj6The4HzzOxQYnK7CbEyeHwD1l9BvJjmRmLit5O7j0/Dr5vZIcB9xP7BW7j70FrW0RX4HJiUTp/PPe2eEtEDgCfc/Vszm0hMmirNbG1iNfRF4oVRPxCrswu8c3g6Hf8c8SKnacBGwF7Mq0jeAfzWzI5Ix2M2MYlby92fbsAxqSF9SbgHuM7MTnH3MenOBDsQv0RMXfAaFujUdMHWGGL/1y+I/WMbzd3HmtkzwNVmdhjxC8PVwFO57gDjgDUXId5aN038XQwysxOJFfv1gW/dfexi3paUuCzLCCXSR01EFo/ZlN5nQqlddEW6AOhlYCbz+gUOISaJQ9I8I4gV1hOJXQfuBM5z9/sauI0qdz+amAS+ZGY9UlV0V2LS+w3wJTFprs2pxNPyk4kX9TxeMH1/Yj/KqcSLcy5w9xeADsD5xFPVk4j9c/dx95n1hDyLeOX96LTN+4EHgNPT/owjJpO/IFb/JhL7+vaZb00NdzTwKTGpn0KsIO/HvO4PC2sQ8eKwCcQLmvZMVeOFdTDxYqdPgU+Ix/XQ3PRrADOzSWb24SJsZy53rwL2IH7peCdt81YWvXIrbZCSVRFpC0KWLWr+INI80m2ttnX3l+udWeqiN3yRCwVdAipP09OuRIpYk3zjDKd8X+OzPrt2uaL/ZluKXQJERERE2q6iT0/np4S1BKQ7B9xYx+Rj3f3u5oxHRJqPugSISFugLgEibYve8EXujKGVXJVuYHLsBoEbdm7Q3edEpHVqmi4Bp02s2SXgT8sW/TdbVVhFRIrIlf3K6fPt81QROGHnHVo6HBGRZqGEVUSkyKzWcVpLhyAi0qx0aamIiIiItGqqsIqIiIiUkhK8GFMJq4hIEamsynh58kpUEvhZZUb78tL7xyQiUkgJq4hIEdnhn5W89PXGADx/dyVvHaqPcREpUILfY/VJJyJSRF7+qoq9P3iD8qyKh9bfgiwr171YRaTkKWEVESkigx64gSPeHArAfRtsCb87o2UDEpFWqPS+xOouASIiRWSf91+f297rgzdVXRWRNkEVVhGRIvLcGuvzzqq9qSwrY+sRn7BLlilpFZGaSvAjQQmriEgR+eUhp1FZHh/HWl5ZyWwlrCLSBqhLQIkzs5FmdvBCLNfLzDIzW60p4mqtzGywmQ1q6ThE6lKdrBa2RUTmCgWvEqCEVVolMxtoZkNaOg6RVifLarSfGQVZfpyISAlSwlqizKx9S8cg0mqN/ha2+D0sewice09LR1Or2ZUZ+z1ayTJ/rWDvRyqZWZHx5eSMsqyqxny7PFhFhz9V8ubXSlpFpFrplVjVh7WFmdnewOXuvlYavgg4D1jD3b8ws82BZ4Hlga2BK4B1gK+Ba9z9xrRcP2AIcDhwIdAd6FKwrU7AvcTf+/7uPtXMtgcuAdYDqoDH3X1ALXFuCPwlzVcOvAac6O7D0/T+wJXAGsBs4B1375+mnQScCnQDJgO3u/vZi3DMOgGXAXsDSwIvAye5+5dpenvgDOAwYBVgPPB7d3/AzH4CXAqsBVQAz6Vlxzdi+0cCZxOP8SPET4OK6uNmZhsA1wIbAxOBW4HL3L3SzO4Hxrj7Kbn1DSD+ztd0d2UdzeG8e+GNz2P7Dw/AXlvApmu0bEwFBn+Q8cBn8c/h4c8zBr2X8ca4jKqyXDeA1He1IoPDn67kg8P1kS4ipUkV1pb3PNDHzHqk4Z2AYUD/3PALwOrA08D1xOR1AHCZme2XW1c5sCsxUVoxvxEzWymtZyzw85SsbgD8G7gFWDltY3AdcWbAQGBVoBcwFbgrN/0OYkK7dJrnkrTdtYDLgd3dvQsx4X20nmNSn2uALdOrJ/At8JiZVf8nvwQ4GNgP6ApsD3yWps0CTiQmm/9HTGj/3NANm9l2wN+Ao4HlgCeBX+amL038gvEfYCVgN+AI4LQ0y23AgQUV8MOBwc2RrE6ZMkVtYM7MWdRQWdVqYqtuVxb8NUybOXO+cTV2IWv5mNVWW+3GtZtM6RVYCer71PLM7A3gBuABYDRwErCbu+9nZv8BHgY6p3Fb55a7DNjE3X+aKqz/AXpWVxrTPCOJVdVfAde7+xW5adcB3d09n/RWT+sFjABWd/fRtUxfH3gfWMrdp6ft3AX8zd3H5ebrA3xIrHY+6e5TG3hMBgLbVFdpc+PLgOnAHu7+bBrXGfiemJi+BkwhVpCfaMB2dgdudfcV0vBgYrX0qDrmHwR0cPdDc+NeAoa7+wAzOxD4I9CjOgE1s2OB09x97ZRUfwWc4O4Pm9kawKdAb3f/qiHHZhHpDQ/wxTjY7Q8wbBz8+mfw5yNbOqL5zJiTsdcjVQwZldFv9cAjvyjj62nQd1DF3MoqWQYhUAYM/VU5265WIv+ZRNqOJnnTht9PrvFZn/2xa9F/OOj8UeswhFhR/Q74L7Fqd1VKxLYCTgB+Q0wg84YDe+aGq4jJUKHDiVXI6wrG9wLebkiAKbG6EtiC2NWg+s3QHRiV4jgbeN/MJgA3ufu1qVvDQcDxwCAzew+4yN2fach2a9Ed6EjuWKRq8XhihXg4sBTzKqqF+7EpsUvAhkAn4odF5zrmPTvtE8Aod1+PWD32gllH5dqrp3nzHxbD03hSt4A7iL+Th4mV8ueaKVmVan1Wgo//Ojfha42WbB94et9ystxtq9bsQM14Q+Bfewb27Fte+0pEpI1qnZ9ri0JdAlqHIcCOxNP/z6b+lGOAU4Dv3P0jYiLaq2C5PtRMULM6TiufSayGPmtmy+bGjwT6NjDGG4iVyw3cvSuxPy2kd4W7v+vu+wMrAMcSuyvsmKY95O47Efuw3gc8kvqhLowJxNP6vapHpMR+BeKxmECswNa1X/8A3gLWSvtxQF0bcvdL3b1zeq2XRo8hdkPI65FrfwX0NLP8p0Xh72kw8DMzWxk4lNhNQFpCK01W8+q7x+rP19THuIiUPlVYW4dXiH0tDwG2S+OeI1449Egavhc4z8wOBe4BNiEmhsc3YP0VwEHAjcBQM9spJcU3Aq+b2SHERLIM2MLdh9ayjq7A58AkM+sGXFQ9wcw6EBO/J9z9WzObSKz2VprZ2kBv4EVgBvADsTpbRf3KzGyJgnGzif1lLzazj4BJwNXAJ8Ab7p6lrg5XmNmXxO4IqwLLuft7aT9+AKakfsNnNiCOvDuBp8zstrRP+xL70n6Rpj9BvODqbDO7Mu3774nHGgB3/8TMnNh3uAux0iqyUDI9OEBECpXgR4K+mrcC7j6LeKX7TOC9NHoIMbkakuYZQbyg6kRi14E7gfPc/b4GbqPK3Y8mJsIvmVkPd383rfN44BvgS2LSXJtTgW2JV/m/BDxeMH1/4BMzm0q8qOoCd38B6ACcT7yrwSRi/9x93H1mA8LegZjk5l+/TLE48GaKeWXihWSVablziAn4v4hV4aHAmmnaMcBRafxDwP0NiGOutE8nE6/8nwjsnrYzK03/AdiZ2MXjG+JFbXcAfypY1W3ALsA96fcvIiIiddBFVyKLyMz+Czzm7pe2dCwNoDd8kVvmokn80Cl2u+48cwaTz+msCqtI8Wqai67OmlLzoqvLuhT9h4S6BIg0kpntS7zF2GziRVNG7Isq0uTe/MtZDNz5l1SWlXHZk3cTzr2ppUMSEWlySlilRaQ7B9xYx+Rj3f3u5oynkfYBBhHvezsM2MvdP2/ZkKSt+LbLMtx9718BeHflwuv/RESgFDuxqkuASNuiN3yRe+Sl7xh75n2UVWUse/F+/LJ/95YOSUQWXhN1CZha0CWgc9FnsKqwiogUkT23XZ5//X51MmAvJasiUpuiT0/np4RVRKTIlAcVykWkbVHCKiIiIlJKSvDOIboPq4iIiIi0aqqwiogUkYkzM+6a0JdKAltOz+jeqfQqKSIihZSwiogUkR3vq+Sd7+OD296/p5JPj9LHuIiUPn3SiYgUkXfGQ7vKCgA+m9SOLMv0pCsRqakEPxKUsIqIFJF933mV3T55m6oQ+M8a6wE7tnRIIiJNTgmriEgRmbxEJw7/1QkAbDv8Q1VXRaQWpfe5oLsEiIgUkWfX3mBu+6U+67ZgJCIizUcJawOY2UAzG7KI67jBzP62uGKqZ1uZmW3TGmJZnBZH3Ba9Z2ZTzOzaxRRa4TaeMrPf5YYX+PsQaYwsX1ENoMdri8h8QsGrBKhLQBMws5HAue5+V/U4dz+u5SKqaXHFYma9gBHAZ8B67l6Rxm8DvOTuDXqbmNlAYBt377+g+RZT3JcCT7v77+qdcyG5+y5NtW6Rmv99SuQ/kYhIPZSwAmbW3t3ntHQcRWx54DigGKq2fYA7WjoIkQV6+wv4dCz03wC+nQxvj4ANe/L2r//JN/4hS8+czjXb7cZZux7I7g9V0qEcVl4q0KtrxhrLlrF7n0DHdkpmRdqsEnz7t8mENVVAbwV2ADYDjjOzHsAAYAXgQ+Bkd/c6lj8ZOB5YFZgI3E2sqFaa2WNAD2CQmd0AvOruO5vZYKDC3Y9K6+gJ/AXYGpgBPAic5e4z0vQMOAE4HFgnxTTA3T9J038FXACsBkwnVg0Py4W5gZldU8eyhbFkwKlp/9cAHDja3Yc18JBeBFxgZne4++Rajlc74Oy0/mWBt4jH9wMz2z9NKzOzqdWxu/sXtayntrjrPEa1LD8J6Mq8380vgAnE38N6QDnwGnCiuw/PbbMcmAPsDUwDTgc+Bm5O23XgIHcfm5YZCgxx90sKtl8OfJnW/3Bu/B3AHHc/sra4pY155A3Y+wqoqoKVl4XvpsDsCirKyujZcUmWmzENgDP/8wj3brQ1T9IrLVjdNaCKn/QIPLtfmS7IEpGS0Zb7sB4NnAZ0ATYG9gR+RqwW3go8bWbL1rHsaGAXYvKzJ3AEcBSAu+9BTEqOcvfO7r5z4cIpgXsCGAf0BLYkJq5XFcw6ANgH6AZ8Bfw1Ld8JuBM4wd27EKuGgxqy7AIcA+zLvIT90ZRgNcRDwKfExLM2ZwCHArsCKwEvAc+aWVd3/yfxNP3QdLw615asLsAAGrif7r4MNX83Q4j/5QcSv3z0AqYCdxUsui/xC8VywMXERPUiYC9gxbSOC+sL1N0rgVtIfysAZrZ0Wv/N9S0vbcR9r8RkFeDriTA73nO1XVUVo5devsaslWW1f4Q/92XGtzOaNEoRadVKrxNrW05Yb3b3t1P7GOAMd//C3Svd/Rbga2C32hZ09wfdfYS7Z2kddwI/acS2Nwf6Aqe5+zR3HwOcCxxhZvm/rCvd/Ut3nwUMBiw3bQ6wjpktl9bxUsE2FrRsba5292Gpwvs7YqV1i0bs0+nASalSXehw4I/u/kmK5yKgkjqObyM1dj9rcPf33P0/7j7L3X8gJp5bpi8F1Z539yfcvYrYnWAp4E53H+3u04EHGrHdQcBOZrZqGj4QGO7urzUm7oU1ZcoUtVt5e+Z6q85tZx3mnQTLgLFdl+XtVXrxwxJLMrD/vny4Um1vN+jVFdrNmTp3uDXsl9pqqz1/WxquTXYJSEamn92AzsBj6RRztfbE0+3zMbMDiNXZPsRj2IF4KrmhVgcmuPu03LjhwBJAd2B8Gvd1bvo0YjUYd59uZrumGP5gZl8QE857cvPXuuwCjKxupPVPoI79r427v5a6Q/wBuLFg8urEi7Oq561K3TJWr2t9ZvYhsfoMcKm7X1rHrLXup5ltCzyVm7auu39Zy3bWAK4kJuddmHdetTswqnAb6dgUbnc69R/f6uW/NLNniUn8JcRqa7NVV7t06aJ2K28vcdZ+sEwX+Hg04cDtYNjX8NpnhHVWZeeL7+eatbdhh2PP54cllwJgmY5ABsstCSt2gk1Xgt9aOcsu3fL7orbaai+43WRKo6haQ1tOWNM5N74lJjr93f3N+hYys9WJp4z3Bp5y99lmdhU1K2xVtS48z1dAdzPrlCp0EJPfmcQ+lfVy96HA0HTa/ufAg2b2enXfy4XQq7qRqovdiV0fGuNM4COg8Dh+VbD+sjT8VRo13/Fy9/Uaue3C5V8ifhGpzw3AWGK/2e/MbH3gfZr27X4jcI2ZPQGsS6zQi0QhwK9zN5rYam04pB8AZb/ZjdOvqqgx+/cnlquvqoiUvLbcJQAAd8+APwNXmVlfADPrbGY/NbNValmkM/G4TQDmmNmWwCEF84wjnvKvyxvAMOBqM+uUtnMxcFuKZ4HMbEUz28fMlk79IielSZX1LbsAp5rZGma2BHA58AXwemNW4O4jgOuA8womDQZ+Z2ZrmVkH4Bzil6Un0vRxQI80rbl1JX5hmWRm3YjdFZraE0BHYn/WB919YjNsU0qUklURaQvafMKaXAA8AjxiZpOBz4m3aZrv+Lj7x7n5JxGrivcWzHYJcLCZTTSzpwqmke5XujvxlPuXxAT2dWI/0IYoI14dP9LMpgB/Bw5z95ENXL42g4gXT00ANgT2TMlwY13C/MftSuIxegb4hvjw851zdxS4n1htHWdmk8ys90Jsd2GdCmwLTCZeDPZ4U28wd/HVxuhiKxERkXoFPSVFUt/dbd395ZaOpa0wswHE25it3cyb1hu+yIWCLgFVv1WXAJEi1iRv3jBwRo3P+mzgkkX/IaEKq0gzM7MuwMnE+7+KiIhIPdryRVfSALmb+Rd6SY8gbTwzO4V439lngJtaNhoRESlJJXjWRV0CRNoWveGLXPe/V8x9KECX9jD5ZNUdRIpY03QJuHBmzS4BFyxR9BmsugSIiBSR539ZztpLTKRvx0k8v395S4cjItIs9NVcRKSI/F/3wJU943NKbKU9WjgaEZHmoYRVREREpJQUfQeA+alLgIiIiIi0aqqwiogUme8rOqLrZUWkbqVXYlXCKiJSRC56tZILhu8ABD4aWskV/XThlYiUPnUJEBEpIgNfzaiunlzpKrOKSC1CwasEKGEVESkihSlqVVVVi8QhItKclLCKiIiISKumhFVEpIiFEnwEo4hIIV10JSIiIlJKSvB7rCqsIiIiItKqKWFtZcyswsz6teD2PzSz/Vtq+4vCzDqY2T/NbKKZfbsY1zvAzIYtrvXVsY3MzLZpym1I8Zs4c/67AqhLgIi0BeoS0EaZWS9gBLC6u4+uHu/u6zXT9gcAtwLT06jvgYeA37v7rIVc7b7A5sCq7j69vplFWrtpszNOHVrFp99n7NgDLnx1/nlWvr6Cf+5RxnarNUP94ZPR8NvBUJXBFYfC//Vs+m2KiKCEVVrWF+6+JoCZbQQ8A0wGzi+c0czau/ucetbXBxiuZFVKxfmvVHHze7Gq+uLo2ucZNw12e7CKKSc3Q8K675Xw4Vex/dlYGH59029TRBqvBM+8KGFtYmbWCbgI2AdYGngDONHdh5lZF+BvwB7AFAoSNTMbCGzj7v1z44YCQ9z9kjS8AXAFsClQDrxVPb+Z3Qb0B5YBvgIucfd70qreTT8/NbMM+KO7X2xmI4Fz3f2utI7t0/rXAb4GrnH3G9O0fsAQ4CDgUqAb8G/gSHef0pjj5O7vmNmLwMZp3SOJFdgdgM2Ao8zsUeAyYG9gSeBl4CR3/9LM/gYcA5SZ2VTgAXcfUHA8lwVuAnYk/u2PBo5z95fS9L2Bs4E1gZnALe5+Tm75k4DfAUsB9wG/dvfKNG0D4NoU/8QU+2UNnd5cpkyZQpcuXdQukvbISXNoyMf09AqaJZ5szHdzr+XIxnw/t90ajpXaahdjWxpOfVib3s3EZG9LYCXgdeBxM2tPTGD6AusCGwB7EpPOBjGzlYEX0qtXWv/luVleBjYiJqwXAYPNbN00bcP0c2137+zuF9ey/t7A08D1wPLAAOAyM9svN1s5sHNa31rEhOykhu5D2k4ws42B7YE3c5OOBk4DugCPANcQj+OWQE/gW+AxMyt39xOJSfPQtD8DatnUGUCntOwywF7EpBUz2wW4HRhITLzXAp7KLdsTWBFYg5hA7wf8Ki27NPAs8B/i72A34IgUe73Tm1P+Q1Lt1t8+fYuOdOkQ26suRZ0OWCc0Szzh3P1y7X2bdFtqq90W2k2mBJ90pQprEzKzbsCBQE93/yaNuxA4BdiKWJnczd3HpWm/JyZRDXUIMMzdL8uNG1LdcPdbcuP/YWanA/2Ajxq4/gOIFdvBafg1M7sROAq4Pzffme4+FZhqZv8CrIHr721mk4gP7/mWWHXMJ9w3u/vbAGY2CzgM2MPdx6RxpxD7vm4O/LcB25tNTLzXBt52989y034D3ODuj6fhycSEv9oM4PxUER1mZs+l/bybmIDOJlawM+BjM/sjMSG9sgHTRWq11SqBkUeXM24arL0cXPFmJWe/VHOe1w8MbL5Kg7/nLprf7gn7bhX7sPZesXm2KSKCEtam1jv9fM+sRg7XPk3rCIzMjR/RyPX3Aj6rbYKZlRGrhfsTq3oZ8VR290asf/VaYhpOrARXq3T3CbnhacSKaEOMqO7DWoeRuXZ34vGaG4+7TzWz8SnOGgmrmR0E3JibtzMxOWxPrKSubGaPA79LXyZ6AQ8vIJbxBafv8/u5OjAqJaPVhqfxDZkuUqfllgwst2Rsn7VFO85+qaLG9GZLVqv1XKF5tycighLWpjYq/exbkNRhZuXE/pS9iMkLqZ03hZhk5q2Sa48kXhlfmwOIldCdgY/cvcrMnHknBxryAPKvgF0LxvVJ45tDPsYJwCziMRoGYGadgRVqi8fd7yZWP/PjpgHnAOeY2UrAXcQk9lDisey7kHF+BfQ0s5BLSvPHqb7pIgstyzLd2kpESp4S1ibk7uPN7B7gOjM7xd3HmNkyxAuJngXuAS40sw+Ip5wvL1jF/4BLzWxT4kVSxzGvagsx4TondSX4K1ABbOfuQ4CuaXgC8UKkAcR+ptWnvCcQE8K+pH6ctbgXOM/MDk2xbgIcCxy/EIdjkaSE+w7gYjP7CJgEXA18QryQrV5mtgcx2f0MmEq8sKq6avp3YreJ/xB/N52ADdz95drWVeAJYn/ks83sSuLv6PfMq/DWN11koSlhFZH5lOBHgi66anpHA58CQ81sCvA+8YKdDDiZeIr7kzT+MeYlULj7UOBPxAufviZe9PNKbvpYYp/UnYhJ5zjihUUQT3u/TkzQxhAv7Hopt+wM4DzgXjObZGZzr4bPzTOCWGE9EfgOuBM4z93vW/jDsUhOBZx4YdaXwMrAzxtxpf0axGM8mVhRnUFMHHH3J4AjiRdufU/8nf20ISt19x+Ilez+wDfEOyXcQfzd1TtdZFGUleljXERKX8iy+Z+cIiIlS2/4IheuqtmHtfK0MiWtIsWrSWqh4bI5NT7rs7PaF33NVZ9yIiJFTN0BRKQtUB9WaRJmti0172Oad6m7X9qc8YiUir7LwOeTYnu1zkpYRaQWJfixoC4BIm2L3vBF7odZGUfe9RmVWWDQQX1ZfskS/M8k0nY0TZeAywu6BJxZ/F0CVGEVESkiS3cMHLZCvP3y8kuu1cLRiIg0D/VhFREREZFWTRVWERERkVJS9B0A5qcKq4iIiIi0akpYRURERKRVU8IqIiIiIq2a+rCKiIiIlBL1YRURERERaV5KWEVERESkVVPCKiIiIiKtmvqwioiIiJSSUHqdWFVhFREREWljQggjQwjrt3QcDaUKq4iIiEgpKb0CqyqsIiIiIgIhhENDCO+HEN4LITwcQlghjf9vCGGz1L4uhPBharcLIXwbQliqqWNThVWkDQkh/Bvo1tJxALRr165bRUXFty0dR10U36JRfItG8S2a1hxfQWxPZ1n2s8W9jez0do2usabuAZcDm2ZZ9nUI4WLgr8D+wHPAT4A3gW2AGSGElYFewMdZlk1bXLHXRQmrSBvSFB+MC8vM3N2tpeOoi+JbNIpv0Si+RdOa42vFse0APJll2ddp+Ebg3dR+DjgnhHA38B3wAjGB7Q083xzBqUuAiIiIiCzIq8AmwG7E5LW64vqT1G5ySlhFRERE5D/AriGEldLw0cCzAFmWzQLeAs4EhgCvAVsDG6R2k1OXABFpKTe1dAD1UHyLRvEtGsW3aFpzfK0ptiEhhIrc8FnAsyGEDPgCODY37TlgM+DNLMsqQwjDgBFZls1ujkBDlmXNsR0RERERkYWiLgEiIiIi0qopYRURERGRVk19WEWkWZhZJ+A2YFOgAjjd3R+vZb49gfOBjsTntdzq7le3ovhWBe4iXjH7eVPensbM1gJuB5Yn3krmUHf/vGCecuAvwM+ADLjc3Qc1VUwLEd/OwKXA/wF/dffTmyO2RsR3HvAroBKYA5zt7v9uRfEdDpwKVAHlwM3u/pfWEl9u3rWBt4Hrmut33MDjNxD4NTA2jXrF3U9oLfGl+X4JnEf8vMuA/u7+TXPEWExUYRWR5nI6MNnd1wT2AAaZWeda5hsH7OHu6wM/Bo43s21bUXxTiQn1gc0Q0w3A3919LeDvxPsiFjoIWBPoC2wFDDSzXs0QW0Pj+wI4CriymWLKa0h8bwCbufsGwBHAP81syVYU34PAhu6+EfH98Fsz26AVxVf9pelG4F/NFFe1BsUH3OHuG6VXsySrSb3xmZkBA4Gd0mfeNsAPzRhj0VDCKiLNZX/SB3aqMjiwS+FM7v66u49N7R+Aj4GerSi+H9z9JaBJn+xiZisQq7j3plH3ApuYWfeCWfcnVt2q3H0CMWnYrylja0x87j7M3d8hVq2bTSPi+7e7T0+D7xGrXMu3ovgmu3v11dGdgPbEKlyriC85E3gc+Kyp46rWyPiaXSPiOxW4yt3HwdzPl5nNF2nxUMIqIs2lBzAqN/wlsPqCFjCzdYAtaZ4nqTQ6via2OjDG3SsB0s+xtcTUUnE3NL6WsjDxHQoMd/fRrSk+M/u5mX1I/D1f6e7vt5b4zGxD4KfANc0QU6PjS35lZu+Z2TNmtlUri29doI+ZvWhmb5nZuWbW6MeqtgXqwyoii4WZvUVMnmqz4kKsb2XgEeDX1RXXRbG445PSYmbbAxcDO7V0LIXc/VHgUTPrAfzLzJ50909bOi4za0+8p+jh7l4Zz263OjcAf3D3OWa2E/CImf3I3b9r6cCScuLN93cCOgBPE7903tGSQbVGSlhFZLFw900WNN3MviSe2p+QRvUgPlmltnlXID5N5Qp3v7+1xddMvgJWNbPylAyUA6uk8XnVcb+Zhgsrri0dX0tpcHyp6nYXsGczJoKNPn7u/qWZvQHsDjR1nA2Jb2VgDeDJlKwuAwQz6+rux7SC+Kg+1Z7az5rZV8D6wAutIT7i+/cBd58FzDKzR4DNUcI6H3UJEJHmcj/pqSlm1pf4xJSnC2cys+WJjwP8m7vf0triay7uPh54BzggjToAeDv1U827HzjazMpS/7hfAA+0ovhaREPjM7PNgH8C+7r7W60wvh/l2t2AHYAm7xLQkPjc/Ut37+buvdy9F3AtsT91UyerjTl+q+baGwG9aPpkvzHvj3uAnc0spIr1T4B3mzq+YqQKq4g0lyuBwWY2jHgLoWPcfQqAmV0EjHX3G4gXcKwFHGtm1Y8F/LO739Ya4kuVklHE224tbWajgUHuPrAJYjoOuN3MzgcmEvtYYmZPAue7uwN3AlsA1bfLucjdRzRBLAsVn5ltA/wD6Eqsvv0KOLKZbh3VkON3HbAkcGPulPYhzdRPtCHxHZNuDTaHeEHY39z9mWaIraHxtaSGxHepmW1KfE/PJv5ux9W1whaI7x+AAR8Rb132b6A5v6gXDT2aVURERERaNXUJEBEREZFWTQmriIiIiLRqSlhFREREpFVTwioiIiIirZoSVhERERFp1ZSwioi0ciGEXiGELISwWhNv57gQwp254adCCL9rym1K7UIIw0IIAxo4b7P8fTSHEELHtO/rtHQs0rooYRWRkhFC6BNCuD+EMC6EMDWE8FUI4eEQQoc0fUAIYVgty9U1/qCUCFxQy7ShIYRZaTs/hBDeDiHs0zR71vRCCEsBFwEDq8dlWbZLlmVXtFhQ9Ui/m21aOo62oCmOdQihXwihIj8uy7JZxHsiX7k4tyXFTwmriJSSJ4GvgbWBLsBWxBtxh4Vc37HA98CRIYTyWqZfnGVZZ2B54F7gnyGEtRZyWy3tYOD9LMuGt3Qg0ubdC+wYQlizpQOR1kMJq4iUhBDC8sRE9YYsy37IotFZlt2QqjaNXd+PgG2Bw4jPTN+lrnmzLKsgPjGpHPi/WtZ1QgjhnYJxvUMIlSGEXmn4tlQRnhJC+CiEcOACYhsYQhhSMG5oCOHc3PD6IYR/hxAmhBC+DCFcFkJov4Bd/gXxkbi1rjN32vmwFN+0EMKTIYRlQwiXhxDGp8r2CbnlB6TTu78PIXyd5rk6H0d9+x1C2CCE8HTaj++r9zuEUP34ymdSlXtQHceqUwjhz2kb34YQ/hVC6FGwj1eHEB5MMQwPIexZ10HK7dOpIYTRaZmrQgjLp3VMDiF8kq9GhhDahRDODyF8EUKYGEJ4LoSwfm56+xDCn3LH8Pe1bHfbEMLL6RgMDyH8NoTQ4C9iIYR9QgjvprMB74YQ9ircp4L5B1cf07qOdQhhZNqvl9N4DyFsVts6cuNGhhAODiGsAjwFlKdlp4YQDgPIsmwy8Cbw84bun5Q+JawiUhKyLPsO+BAYFEI4NISwbmP+odfiGOC9LMseJ1Zuj61rxhC7HJxAfHxmbc8BvwdYJ4SwUW7cAGBolmUj0/DLwEbAMsRT84NDCOsuTOAhhBWAF4CHgFWJleadgLMWsNgmxMdD1mcfYBugB/G57K8Dw4FVgMOBa/MJIdAzzdsnxbEHcEZuep37HUJYOe3HC2lbKwGXA2RZtmFafucsyzpnWXZUHfFeA2yZXj2Bb4HHQs2K+WHA1cDSwN+A20MInRZwDHqmePukY/EbYvJ1JbAs8bjnHyV8BvGxnLumfXgJeDaE0DVNPxPYHfgx0Dvta8/qhdPxeDKtvzuwG3AicMgCYpwrhPBj4O60neWBs4F7QwhbNGT5eo71ccDJwHLAA8CTuf1a0DrHEr8EVqZ1ds6y7PbcLO8T/yZFACWsIlJa+gFDgVOAd4BvQgjnFSSuvUMIk/IvYnV0rhDCEsQEozrpuAXYJcx/Ucs5afnRwJ7APlmWzdcXNsuyicAjxISOFM9hwK25eW7Jsuy7LMsqsyz7B/Be2p+FcSjwbpZlN2ZZNjvLsjHAZWl8XZYFJjdg3RdnWfZ9+oLwODAny7KbsyyryLLsKeIz0zfOzV8FnJFl2YzU3eAKYrIO1LvfhwDDsiy7LMuyaWlfalSWFySEUEY8zudmWTYmy7JpxL+NHwGb52b9Z5Zlr2ZZVgXcRExc+y5g1TOAC1M87xK/pLyZZdlrWZZVAncBa4YQlk7zHw78McuyT1K1/yLis+13S9MPTdOHZVk2AzgdyD83/dfA/VmWPZKO0yfExHpBv8+8AcCDWZY9lX5PTwAPA0c0cPkFuSXLsv9lWTYb+CPx2Oy+GNY7mZgEiwBKWEWkhGRZ9m2WZWdnWbYJsQL2O+B8UqKYjMiybJn8i5gQ5O0HdCYmHhCrWxOAwireH9I6Vsiy7MdZlj22gPBuAw5Mp8N3TPE9BDGxCiFcFEL4NJ2ynQRsSKymLYzewNYFSfmtxOpeXSYC9VbGiH2Eq00vGK4e1yU3PD7Lsum54ZHAatCg/e4FfNaAmOrSHegIjKgekWXZVGA8sHpuvq9z06elZn4fCo1PyW21wuNQvb/V61i9IIYq4nGojmG1NJyPYXxufb2BAwp+nxcQu6o0RI3tJ8OpeQwW1sjqRpZlGfAl6fe7iLoS+4+LAEpYRaREZVk2PcuywcSK3UaNXPwYYn/UD0II44gV1GWp++KrhngWmEU8JT4A+EeqpgEcQEyG9wGWTUn0u9R9sdgUYKmCcavk2qOAIQWJ+dLpArG6vA0sVBeEeqxQcHq9F/F4Qv37PZIFVzqzBUyD+CVjVtomACGEzsAKwFcNCX4x+aoghrI0XB3DmILpS1Hzy8oo4NaC32fXLMvWW5jtJ31y26/v7wnqPtb5uAOx+0f177fGekMI7YjHvlo+6S+0PvFvUgRQwioiJSLEi38uC/Fio/bpQpd9iP/4XmrEetYl9kvci5joVr82J1Yod12Y+NKp4juAk4C9yXUHIFaTKogJVlkI4QhipbEu/wM2CSFsmvbzRGIVrtodgIUQjgghLJEqmX1CCD9bwDr/BfRv9I7Vrwz4YwhhyRBCH+Lp7uq+ivXt913A2iFetNUphNAhhJCPcRwLSGhTJfMO4OIQwiopcb4a+AR4YzHtX0MMBn4XQlgr9Xc+B2gHPJGm3wmcEUJYI4SwJLHbRP7/83XAr0IIe+T+ttcNIWzfwO3fDuwTQvhpCKE8hLAL8W+wusvLO8QvFrunv5W9gO0K1lHXsT4ihLBJOnNwBtApt1//A34S4gWGHYE/APkL/8YRL7rK/+0SQuhCfL892sD9kzZACauIlIrZxOrNQ8RTiROAc4GTsiy7vxHrORZ4K8uyx7IsG5d7vQfczwIuvmqA24Dtid0S8gnT7cSLl4YRq23rsoAkO8uyocCfgKeJp6JXBF7JTR8H7EC88n8k8XT/w8SqWl3uBDZMSeXiNIpYcRtB3MeniQkZ1LPf6cKcfsQLxkYTE5z8BVvnABeFeOX9jXVs/1TAiVedf0k8jf7z9AWiuVxJvFXTM8A3xC4hO6er4SH2L/438BrxOH1JPG4AZFn2AbFf6CnE3/d4YhLcoC4jWZa9QuzLexXxb+EK4OAsy15L04cTL5y6ifje+RnwYMFq6jrWNwF/SevdH9gty7If0rS7iUnnW8QuCF8Sf8/VcX0GXA+8kbo6VF9EdgDwnyzLPm/I/knbEGKXExERaetCCMcBW2dZ1qCrzxuwvgHEC550P80SFEIYSfz93lXfvI1YZ0fgA+KXio8X13ql+LVr6QBERKR1yLLsBuCGlo5D2q50F4UF9VuWNkpdAkRERESkVVOXABERERFp1VRhFREREZFWTQmriIiIiLRqSlhFREREpFVTwioiIiIirZoSVhERERFp1f4f6dwHlbhdd70AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -586,7 +604,6 @@ ], "source": [ "#| notest\n", - "import pandas as pd\n", "df = pd.read_csv('sample_data/adult-census.csv')\n", "kda = KeyDriverAnalysis(df, outcome_col='class', ignore_cols=['fnlwgt'])\n", "df_results = kda.importances(use_shap=True, plot=True)\n", @@ -603,11 +620,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/nbs/04_preprocessing.ipynb b/nbs/04_preprocessing.ipynb index 321d2cb..f48a50a 100644 --- a/nbs/04_preprocessing.ipynb +++ b/nbs/04_preprocessing.ipynb @@ -191,7 +191,7 @@ " v_features = self.tv.fit_transform(df[self.text_col])\n", " else:\n", " v_features = self.tv.transform(df[self.text_col])\n", - " vocab = self.tv.get_feature_names()\n", + " vocab = self.tv.get_feature_names_out()\n", " vocab_df = pd.DataFrame(v_features.toarray(), columns = [\"v_%s\" % (v) for v in vocab])\n", " X = pd.concat([X, vocab_df], axis=1, join='inner')\n", " outcome_type = 'categorical' if self.is_classification else 'numerical'\n", @@ -295,8 +295,16 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', error_bad_lines=False)" + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', on_bad_lines='skip')" ] }, { @@ -902,11 +910,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/nbs/05a_meta.base.ipynb b/nbs/05a_meta.base.ipynb index 3fadc96..812d617 100644 --- a/nbs/05a_meta.base.ipynb +++ b/nbs/05a_meta.base.ipynb @@ -349,7 +349,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05b_meta.tlearner.ipynb b/nbs/05b_meta.tlearner.ipynb index 789a4a6..d56f477 100644 --- a/nbs/05b_meta.tlearner.ipynb +++ b/nbs/05b_meta.tlearner.ipynb @@ -475,7 +475,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05c_meta.slearner.ipynb b/nbs/05c_meta.slearner.ipynb index 2a6bf27..391dd7d 100644 --- a/nbs/05c_meta.slearner.ipynb +++ b/nbs/05c_meta.slearner.ipynb @@ -425,7 +425,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05d_meta.xlearner.ipynb b/nbs/05d_meta.xlearner.ipynb index 41eb97d..9da4c2d 100644 --- a/nbs/05d_meta.xlearner.ipynb +++ b/nbs/05d_meta.xlearner.ipynb @@ -651,7 +651,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05e_meta.rlearner.ipynb b/nbs/05e_meta.rlearner.ipynb index 0c04ce9..24c28af 100644 --- a/nbs/05e_meta.rlearner.ipynb +++ b/nbs/05e_meta.rlearner.ipynb @@ -628,7 +628,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05f_meta.utils.ipynb b/nbs/05f_meta.utils.ipynb index 66b3f06..956aed7 100644 --- a/nbs/05f_meta.utils.ipynb +++ b/nbs/05f_meta.utils.ipynb @@ -769,7 +769,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05g_meta.explainer.ipynb b/nbs/05g_meta.explainer.ipynb index 34510fb..efef8ab 100644 --- a/nbs/05g_meta.explainer.ipynb +++ b/nbs/05g_meta.explainer.ipynb @@ -327,7 +327,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05h_meta.propensity.ipynb b/nbs/05h_meta.propensity.ipynb index 7e7d908..e6e3308 100644 --- a/nbs/05h_meta.propensity.ipynb +++ b/nbs/05h_meta.propensity.ipynb @@ -359,7 +359,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/05i_meta.sensitivity.ipynb b/nbs/05i_meta.sensitivity.ipynb index a7fe9c4..e3a4c24 100644 --- a/nbs/05i_meta.sensitivity.ipynb +++ b/nbs/05i_meta.sensitivity.ipynb @@ -633,7 +633,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } diff --git a/nbs/99_examples.ipynb b/nbs/99_examples.ipynb index 816dcfa..927bc35 100644 --- a/nbs/99_examples.ipynb +++ b/nbs/99_examples.ipynb @@ -51,8 +51,17 @@ "outputs": [], "source": [ "#| notest\n", - "import pandas as pd\n", - "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', error_bad_lines=False)" + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| notest\n", + "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', on_bad_lines='skip')" ] }, { @@ -264,7 +273,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -621,7 +630,7 @@ }, { "data": { - "image/png": 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" ] @@ -637,6 +646,16 @@ "cm.explain(test_df, row_num=0)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| include: false\n", + "import pandas as pd" + ] + }, { "cell_type": "code", "execution_count": null, @@ -659,7 +678,6 @@ ], "source": [ "#| include: false\n", - "import pandas as pd\n", "df = pd.read_csv('sample_data/houses.csv')\n", "fn = lambda x: 1 if x == 'Abnorml' else 0\n", "df['treatment'] = df['SaleCondition'].apply(fn)\n", @@ -690,6 +708,16 @@ " Note: This dataset is from the early to mid 1990s, and we are using it as a toy dataset for demonstration purposes only." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| notest\n", + "import pandas as pd" + ] + }, { "cell_type": "code", "execution_count": null, @@ -864,7 +892,6 @@ ], "source": [ "#| notest\n", - "import pandas as pd\n", "df = pd.read_csv('sample_data/adult-census.csv')\n", "df = df.rename(columns=lambda x: x.strip())\n", "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x) \n", @@ -1183,11 +1210,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/nbs/index.ipynb b/nbs/index.ipynb index 6322f9f..d34b089 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -90,8 +90,16 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', error_bad_lines=False)" + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('sample_data/music_seed50.tsv', sep='\\t', on_bad_lines='skip')" ] }, { @@ -112,6 +120,16 @@ "Using the `text_col` parameter, we include the raw review text as another \"controlled-for\" variable." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from causalnlp import CausalInferenceModel\n", + "from lightgbm import LGBMClassifier" + ] + }, { "cell_type": "code", "execution_count": null, @@ -132,8 +150,6 @@ } ], "source": [ - "from causalnlp import CausalInferenceModel\n", - "from lightgbm import LGBMClassifier\n", "cm = CausalInferenceModel(df, \n", " metalearner_type='t-learner', learner=LGBMClassifier(num_leaves=500),\n", " treatment_col='T_ac', outcome_col='Y_sim', text_col='text',\n", @@ -306,11 +322,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/settings.ini b/settings.ini index 3235e7f..e2f6514 100644 --- a/settings.ini +++ b/settings.ini @@ -26,7 +26,7 @@ status = 2 # Optional. Same format as setuptools requirements # note: torch and sentence-transformers are requested to be installed on-demand if using Analyzers, so they are not listed as requirements -requirements = transformers pandas numpy>=1.18.5 scipy>=1.4.1 matplotlib pandas>=0.24.1 scikit-learn>=0.22.0 seaborn Cython>=0.28.0 xgboost tqdm lightgbm pygam packaging +requirements = transformers pandas numpy>=1.18.5 scipy>=1.4.1 matplotlib pandas>=0.24.1 scikit-learn>=0.22.0 seaborn Cython>=0.28.0 xgboost tqdm lightgbm pygam packaging sentence-transformers shap # Optional. Same format as setuptools console_scripts # console_scripts =