{ "cells": [ { "cell_type": "markdown", "id": "domestic-remove", "metadata": {}, "source": [ "(interventional_what_if_do_operator)=\n", "# Interventional what-if analysis using the PyMC do-operator\n", "\n", ":::{post} August, 2023\n", ":tags: causal inference, do-operator, interventional analysis\n", ":category: beginner, reference\n", ":author: Shekhar Khandelwal\n", ":::" ] }, { "cell_type": "code", "execution_count": 1, "id": "elect-softball", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:37.507713Z", "iopub.status.busy": "2026-05-02T07:48:37.507364Z", "iopub.status.idle": "2026-05-02T07:48:39.805280Z", "shell.execute_reply": "2026-05-02T07:48:39.804762Z" }, "tags": [] }, "outputs": [], "source": [ "import warnings\n", "\n", "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "level-balance", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:39.806692Z", "iopub.status.busy": "2026-05-02T07:48:39.806460Z", "iopub.status.idle": "2026-05-02T07:48:39.809623Z", "shell.execute_reply": "2026-05-02T07:48:39.809249Z" }, "tags": [] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "az.style.use(\"arviz-variat\")\n", "rng = np.random.default_rng(42)\n", "SEED = 8927" ] }, { "cell_type": "markdown", "id": "sapphire-yellow", "metadata": {}, "source": [ "## Introduction\n", "\n", "In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of interventional what-if analysis using the PyMC framework, with a special focus on the `do`-operator.\n", "\n", "The `do`-operator lets us answer interventional \"what-if\" questions: *What would happen to the outcome if we forced a variable to a specific value?* By leveraging `pm.do`, we can programmatically simulate these scenarios, giving us a deeper understanding of the causal relationships between predictors and target variables.\n", "\n", "Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the `do`-operator, and validating the relationships captured by the model. Furthermore, we will explore how the `do`-operator can simulate different what-if scenarios, akin to programmatic A/B testing.\n", "\n", ":::{admonition} Interventions vs. counterfactuals: where does this notebook sit on the causal ladder?\n", ":class: important\n", "\n", "Pearl's *causal ladder* distinguishes three levels of causal reasoning:\n", "\n", "- **L1 — Association (seeing)**: What do we observe? $P(Y \\mid X)$\n", "- **L2 — Intervention (doing)**: What happens if we force a variable to a value? $P(Y \\mid \\operatorname{do}(X = x))$\n", "- **L3 — Counterfactual (imagining)**: What *would have happened* to *this specific unit* under a different action, given what we already observed? $Y_x \\mid X = x', Y = y'$\n", "\n", "`pm.do` directly supports **L2 queries**. This notebook demonstrates interventional what-if analysis — we set a variable to a new value and predict the outcome. True unit-level counterfactuals (L3) require an additional **abduction** step: inferring unit-specific exogenous terms ($U$) from observed evidence before predicting in the intervened world. That step is not demonstrated here.\n", "\n", "For readers interested in L3 counterfactual reasoning, see Pearl, Glymour & Jewell (2016), *Causal Inference in Statistics: A Primer*, Sections 4.2.3–4.2.4.\n", "\n", "## Steps\n", "\n", "The basic workflow for interventional what-if analysis using the `do`-operator in PyMC can be summarized in the following steps:\n", "\n", "- Step 1. Build a PyMC model skeleton\n", "- Step 2. Use model skeleton and generate data using `do`-operator to infuse relationship between predictors and target variable\n", "- Step 3. Use `observe`-operator to assign generated data on the model skeleton\n", "- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples\n", "- Step 5. Use `do`-operator to generate target variable with different what-if scenarios (programmatic A/B testing)" ] }, { "cell_type": "markdown", "id": "d3756a0d-447e-4ffd-8305-e0f2329dbc3a", "metadata": {}, "source": [ "### Step 1. Build a pymc model skeleton\n", "\n", "For this demo, we are building a very simple Linear Regression model.\n", "- Predictor — ‘a’, ‘b’, ‘c’\n", "- Target Variable — ‘y’\n", "- Coefficients —\n", ">- ‘beta_ay’ -> coefficient of |a|\n", ">- ‘beta_by’ -> coefficient of |b|\n", ">- ‘beta_cy’ -> coefficient of |c|" ] }, { "cell_type": "code", "execution_count": 3, "id": "21e66b38", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:39.810627Z", "iopub.status.busy": "2026-05-02T07:48:39.810561Z", "iopub.status.idle": "2026-05-02T07:48:40.477367Z", "shell.execute_reply": "2026-05-02T07:48:40.476935Z" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "clusteri (1)\n", "\n", "i (1)\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Halfnormal\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with pm.Model(coords={\"i\": [0]}) as model_generative:\n", " # priors\n", " beta_y0 = pm.Normal(\"beta_y0\")\n", " beta_ay = pm.Normal(\"beta_ay\")\n", " beta_by = pm.Normal(\"beta_by\")\n", " beta_cy = pm.Normal(\"beta_cy\")\n", " # observation noise on Y\n", " sigma_y = pm.HalfNormal(\"sigma_y\")\n", " # core nodes and causal relationships\n", " a = pm.Normal(\"a\", mu=0, sigma=1, dims=\"i\")\n", " b = pm.Normal(\"b\", mu=0, sigma=1, dims=\"i\")\n", " c = pm.Normal(\"c\", mu=0, sigma=1, dims=\"i\")\n", " y_mu = pm.Deterministic(\n", " \"y_mu\", beta_y0 + (beta_ay * a) + (beta_by * b) + (beta_cy * c), dims=\"i\"\n", " )\n", " y = pm.Normal(\"y\", mu=y_mu, sigma=sigma_y, dims=\"i\")\n", "\n", "\n", "pm.model_to_graphviz(model_generative)" ] }, { "cell_type": "markdown", "id": "e2320755-54f5-4051-b73e-feb60de8b783", "metadata": {}, "source": [ "### Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable. We will use this generated data for modelling later.\n", "\n", "Let’s first define the predictors relationship with target variable." ] }, { "cell_type": "code", "execution_count": 4, "id": "62d01fc3-9a12-4dcd-b2f1-d7a09116a3c6", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:40.478707Z", "iopub.status.busy": "2026-05-02T07:48:40.478604Z", "iopub.status.idle": "2026-05-02T07:48:40.480358Z", "shell.execute_reply": "2026-05-02T07:48:40.479996Z" } }, "outputs": [], "source": [ "true_values = {\n", " \"beta_ay\": 1.5,\n", " \"beta_by\": 0.7,\n", " \"beta_cy\": 0.3,\n", " \"sigma_y\": 0.2,\n", " \"beta_y0\": 0.0,\n", "}" ] }, { "cell_type": "markdown", "id": "4bad6441-6921-446b-82a1-6a995e74faff", "metadata": {}, "source": [ "Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.\n", "\n", "Now the magic begins. We will use do-operator to use this dictionary and sample data variables. How do we do this ? Simple by passing two arguments to pymc do-operator. First, the model skeleton object. And second, the coefficient dictionary." ] }, { "cell_type": "code", "execution_count": 5, "id": "fbf04aa4-e68f-43fd-ba70-91a287b6b12d", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:40.481268Z", "iopub.status.busy": "2026-05-02T07:48:40.481197Z", "iopub.status.idle": "2026-05-02T07:48:40.486675Z", "shell.execute_reply": "2026-05-02T07:48:40.486346Z" } }, "outputs": [], "source": [ "model_simulate = pm.do(model_generative, true_values)" ] }, { "cell_type": "markdown", "id": "a149f565-ccb3-4118-ac5a-da67733e3e5a", "metadata": {}, "source": [ "This will create a new model object with the coefficent variables values infused. " ] }, { "cell_type": "code", "execution_count": 6, "id": "113da0d7-b9d7-4cd2-98fa-7fa794169b94", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:40.487703Z", "iopub.status.busy": "2026-05-02T07:48:40.487641Z", "iopub.status.idle": "2026-05-02T07:48:40.566756Z", "shell.execute_reply": "2026-05-02T07:48:40.566368Z" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "clusteri (1)\n", "\n", "i (1)\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Data\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_simulate.to_graphviz()" ] }, { "cell_type": "markdown", "id": "af0e13da-29e6-44a6-852e-c3e965d7c462", "metadata": {}, "source": [ "The gray shades on the coefficient variables depicts the tale. Check the previous model graph, it was all white.\n", "\n", "Now, all we have to do is generate samples, the known pymc way.\n", "\n", "Lets generate 100 samples." ] }, { "cell_type": "code", "execution_count": 7, "id": "2c651c0a-29f8-4669-baf7-986687f59317", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:40.568115Z", "iopub.status.busy": "2026-05-02T07:48:40.568017Z", "iopub.status.idle": "2026-05-02T07:48:43.394459Z", "shell.execute_reply": "2026-05-02T07:48:43.393786Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [a, b, c, y]\n" ] } ], "source": [ "N = 100\n", "\n", "with model_simulate:\n", " simulate = pm.sample_prior_predictive(draws=N)" ] }, { "cell_type": "markdown", "id": "9ecd7313-e68c-4439-803c-576a5c474e4b", "metadata": {}, "source": [ "We know that this generates an ArviZ object, and since we have called sample_prior_predictive, hence the object will only contain priors." ] }, { "cell_type": "code", "execution_count": 8, "id": "99a3fede-e773-4823-bb5e-ece89805bcc6", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:43.396108Z", "iopub.status.busy": "2026-05-02T07:48:43.395985Z", "iopub.status.idle": "2026-05-02T07:48:43.407699Z", "shell.execute_reply": "2026-05-02T07:48:43.407143Z" } }, "outputs": [ { "data": { "text/html": [ "
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       "├── Group: /prior\n",
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" ], "text/plain": [ " a b c y\n", "0 0.510869 -1.311934 -0.362795 -0.306949\n", "1 -0.734647 -0.539776 0.019862 -1.162661\n", "2 -0.358604 0.343649 -0.093558 -0.278148\n", "3 -0.251229 -1.154599 -0.542470 -1.489163\n", "4 -1.316097 0.114966 -0.264377 -2.143315" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observed = {\n", " \"a\": simulate.prior[\"a\"].values.flatten(),\n", " \"b\": simulate.prior[\"b\"].values.flatten(),\n", " \"c\": simulate.prior[\"c\"].values.flatten(),\n", " \"y\": simulate.prior[\"y\"].values.flatten(),\n", "}\n", "\n", "df = pd.DataFrame(observed)\n", "print(df.shape)\n", "df.head(5)" ] }, { "cell_type": "markdown", "id": "410b2941-ee10-444b-ab5b-36f229a6dba7", "metadata": {}, "source": [ "Ok, so now we are all set with a sample data." ] }, { "cell_type": "markdown", "id": "ae564cd5-23e4-4225-9ee8-e642ae47eeb7", "metadata": {}, "source": [ "### Step 3. Use observe-operator to assign generated data on the model skeleton\n", "\n", "Now, this is another cool feature of pymc newly introduced observe method. Observe method, takes in a model skeleton and the dictionary with the data for the variables we want to infuse into the model." ] }, { "cell_type": "code", "execution_count": 10, "id": "17860fac-d25b-46bf-b4d7-8a920610a853", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:43.417935Z", "iopub.status.busy": "2026-05-02T07:48:43.417856Z", "iopub.status.idle": "2026-05-02T07:48:43.511067Z", "shell.execute_reply": "2026-05-02T07:48:43.510563Z" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "clusteri (100)\n", "\n", "i (100)\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Halfnormal\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_dict = {\"a\": df[\"a\"], \"b\": df[\"b\"], \"c\": df[\"c\"], \"y\": df[\"y\"]}\n", "model_inference = pm.observe(model_generative, data_dict)\n", "model_inference.set_dim(\"i\", N, coord_values=np.arange(N))\n", "pm.model_to_graphviz(model_inference)" ] }, { "cell_type": "markdown", "id": "5ad86c3a-ca67-406c-b114-1f5449b354a1", "metadata": {}, "source": [ "See the gray matter again. This time we have observed data infused into the model, and we have to sample for the coefficient and other parameters.\n", "\n", "So, lets sample.\n", "\n", "### Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples" ] }, { "cell_type": "code", "execution_count": 11, "id": "6ef56be2-06a9-49b8-8044-e789f1d254e9", "metadata": { "execution": { "iopub.execute_input": "2026-05-02T07:48:43.512306Z", "iopub.status.busy": "2026-05-02T07:48:43.512212Z", "iopub.status.idle": "2026-05-02T07:48:52.609628Z", "shell.execute_reply": "2026-05-02T07:48:52.609163Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "NUTS: [beta_cy, beta_by, beta_ay, beta_y0, sigma_y]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41749d81335c45b5a3cc697a4927f7b5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 7 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model_inference:\n",
    "    idata = pm.sample(random_seed=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375287a3-068a-4815-b86b-f472676a5416",
   "metadata": {},
   "source": [
    "Now, lets validate if model captured the infused coefficient values in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87348916",
   "metadata": {},
   "outputs": [
    {
     "data": {
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8UV988UXIv/vGG2+offv2Ti4PPkQORS7IOUR9Gl4ihyIXpByiRg2TkEOxW7dunf76178qNzc3ZJ+kpCR17txZ119/vU4++eSo59ixY4fmzZunjz76SJ9++qkOHTqkxx9/XBdffHHsC///qFEDMNW+fft03nnnWebI8ccfr1deeSWq67/PPvtMgwYNUkFBQcg+f/7zn/Xqq69GtV5T+PF1iTML+A35FBs/5pMdOM+Am8in2AQpnzjngKnIp9j4OZ84/4BfkE9mefPNNzV69OiQ7XXq1NGcOXPK/BB8NDgXgR+QT2ZxMp+oj8NvyCezuHX9FCvq43AT+WQWL/KJ+jhgBvLY2gsvvBDyC8srVqyorl276oILLlC7du3K/LLyUPbt26e3335b48ePD/vwiaSkJP3zn/+0NbuCVG9jDyMS7BNrQcq6INbuQn33myTVrVtXc+fOtWUe+B9ZZy1IWXdEuM8Vrlq1ypZ53MIeRiTYJ9aCmHVuefTRR/Xaa6+FbD/11FP11ltvxTUH13WIBDkXmfbt2/96n2716tXVsWNHtW3bVm3bto35ASt5eXn697//rWeffVa7du0K29/O79qmVhde0PZwomOfRCYIWUetDomMrItMELLuCGp1kQnaHgaCwJu7lV2WlJSk4447Tt27d9e9996rf/7zn/rqq6/05JNPer0039i5c2fID/327dtXrVu3dnlFv7dt2zYNHDjQ8mbUFi1aKCcnR4888khMN6NKhy/6r7jiCr3xxhuaNWuW+vXrp6ysrFiXjQRBBsXPtAzasmWLZs+ebdmnUaNGevXVV2P6QgZJOu644zRhwgTVr1/fst/777+vHTt2xDQHEgc5FFyTJk2yPFzKzs7Wa6+9FtXhkiS1bNlSr732muV1zurVq+M+QEfiIIeCLSkpSWeccYbGjRunTz/9VMOGDVPbtm2jOviRDh8c3XnnnZo8eXLYa6ADBw5oxIgR8SwbCYQMwm+tWbNG48ePL7Nt4MCBqlu3rssrQiIgh4KP+jRMRw4FDzVq+A05VLaCggLdfPPNll9eXq9ePb3xxhsaN25cTF9eLh2uufTu3VsvvfSS5s6dq1tuuUVVqlSJbdF/QI0agKmee+45y5tfa9eurYkTJ0b90OwOHTpo3LhxlvXv+fPna86cOVGNa4IgvC5JnFnAfORT9IKST9HiPANuI5+iF6R84pwDJiOfoufnfOL8A35CPpklJyfHsr1nz562PFSDcxH4AflkFrfySaI+DvORT2ZxM5+iRX0cbiOfzOJ2PlEfB8xBHkevdevWeuyxx/T555/r4YcfVocOHaJ64IQkVahQQTfccIOmTZumli1bWvYtLS3V0KFDVVxcHM+yfydI9Tb2MCLBPoleELJOonaHxELWRS8oWRcU7GFEgn0SPbIuvKKiIs2YMcOyT+/evV1aDRIdOReZhg0b6tprr9XkyZM1f/58PfTQQ+rZs2fMD9eRDtfD+vbtqw8//FDnnXde2P5jx47V/v37Y57vt6jVhRe0PZzo2CeRCVrWSdTqkFjIusgEMeuCgj0MJI5APjixbt26Ou+883TnnXdq4sSJ+uKLL/Sf//xHY8aM0YABA/SnP/2JmwijNGrUqDI/OF2jRg0NGTLE/QX9Rmlpqe655x7LJyaff/75+te//hV1ccXK8ccfr7///e9q3ry5bWMiGMgg+5mWQR988IFKSkpCticnJ+vpp59WxYoV45qnatWqeuaZZ5SUlBSyT3FxsWbNmhXXPAgecigxHDx4MOSHAI8YNWpUzB/Eadq0adjC6vjx4y0/EITERQ4lhuTkZF1wwQWaPn26XnvtNXXp0iXqA5+ynHjiiZo8ebKOO+44y34LFy7UggUL4p4PwUMGIZSSkhINGzZMRUVFR7U1a9ZM/fr182BVCCJyKLFQn4aJyKHgo0YN05FDkRk9erTWrVsXsr1du3aaNm2a2rVrZ9uctWrV0uDBg3XWWWfFPRY1agCm2rZtm+WHpFJSUjRmzBjVrFkzpvHPOOMMDRw40LLPs88+G9PYXvL76xJnFvAD8ik2fs+nWHCeAbeRT7EJSj5xzgGTkU+x8XM+cf4BvyCfzLJs2TKtWLEiZHtSUpJ69eoV9zyci8APyCezuJVP1MfhB+STWdzKp1hQH4fbyCezuJ1P1McBc5DH0fnzn/+sSZMm6Z133lGPHj2iftBEWerXr69Jkybp5JNPtuy3fv16TZ8+Pe75pGDV29jDiAT7JDpByTpqd0g0ZF10gpJ1QcIeRiTYJ9Eh6yL3ySefWNYqs7KydP7557u4IiQqci5ykydP1t133602bdpY3o8ai0qVKumpp55S9+7dLftt375dH3zwQdzzUauLXJD2cCJjn0QuSFlHrQ6JhqyLXJCyLkjYw0BiCcSDE6tXr67bbrtNL7/8shYsWKC5c+fq2Wef1Q033KDTTz897g/FJrq5c+eG/MDv0KFDlZ2d7fKKfu/tt9/Wf//735DtF1xwgZ566ilbiuRAWcggZ5mYQQsXLrRs7969u5o0aWLLXC1atNAFF1xg2ccqA5EYyKHElJOTo507d4Zs79ChQ9j8COfiiy9W+/btQ7bv2LFD06ZNi2sOBAM5lFiSkpLUuXNnvf/++3rqqads/eDfEdWqVdOrr76qChUqWPazKmIicZBBiNSkSZO0ePHio/48OTlZo0aNUmpqqgerQhCQQ4mN+jRMQA4lHmrUMA05FL158+YpJycnZPuf/vQnvfrqq0b/t6NGDcBUr7/+eplfpHlEnz59dOqpp8Y1x/XXX6+GDRuGbF+5cqXmz58f1xxu8vPrEmcW8BPyKXp+zqd4cJ4Bt5FP0QtSPnHOAZORT9Hzez5x/gG/IJ/MYpV7knTaaaepXr16tszDuQhMRz6Zxel8oj4OPyGfzOLW9VMsqI/DbeSTWdzOJ+rjgDnI48i0a9dO77zzjl599VW1bdvW9vHLly+vl156SXXr1rXsZ9d7wCDV29jDiAT7JDJByTpqd0hUZF1kgpJ1QcQeRiTYJ5Eh66IXrj56/vnnKzMz06XVIJGRc+ZISUnRQw89FPZs5N///nfcc1Griw572P/YJ+ZwI+uo1SFRkXXmcPO6LkjYw0BiCcSDE48//njdeuut6tixo6pWrer1cgIlLy9PDz74YJltnTp1Urdu3Vxe0e/t2bNHzzzzTMj2li1b6rHHHrP9Cc3Ab5FBzjE1g1asWGHZfvHFF9s6X7jxli9fbut88B9yKDFNmjTJsv3OO++0ZZ6//e1vca0DiYEcSix/+9vf9MILL6hRo0aOzlO/fn3dfPPNln0++eQT5efnO7oOmI8MQiQ2b96ssWPHltnWt29ftWrVyuUVIUjIocRFfRqmIIcSDzVqmIYcik5RUZEeeeSRkO21a9fWuHHjVK5cORdXFT1q1ABMVFhYqHfffTdke3p6ugYNGhT3PGlpaRo8eLBlH7/kk99flzizgF+QT9Hzez7FivMMuI18il6Q8olzDpiMfIpeEPKJ8w/4AflklsLCQr3//vuWfXr37m3LXJyLwHTkk1ncyCfq4/AL8sksbl4/RYv6ONxGPpnF7XyiPg6YgzyOzOWXX64333xTrVu3dnSeKlWq6O6777bss2zZMq1bty7uuYJSb2MPIxLsk8gEKeuo3SERkXWRCVLWBQ17GJFgn0SGrIvetm3bwj6IwavzGyQWcs48GRkZuuuuuyz7fPPNNzp48GBc81Criw572N/YJ+ZxOuuo1SERkXXmceu6LijYw0DiCcSDE+GcMWPGaMuWLUf9eWZmpoYPH+7Bin7vxRdfVG5ubpltaWlpeuKJJ5Senu7uogDYxsQMOnDggLZv3x6yPT09XW3btrV1znbt2iktLS1k+9atW1VYWGjrnADMtmLFCq1ZsyZke/v27dWiRQtb5mrVqpVlrq1evVorV660ZS4A/lC5cmXX5rrqqqt0zDHHhGwvKirS119/7dp6APjXiBEjVFBQcNSf16hRQ0OGDPFgRQCCgPo0AC9Qowb8b/LkyZYftnr00UdVqVIlF1cUPWrUAEz16aefas+ePSHbu3fvrurVq9sy17nnnqu6deuGbP/ss8+0e/duW+Zykt9flzizgF+QT9Hzez7FivMMuI18il6Q8olzDpiMfIqe3/OJ8w/4Bflklo8++sjy91GxYkWde+65cc/DuQj8gHwyixv5RH0cfkE+mcWt66dYUB+H28gns7idT9THAXOQx5Fx8z1gt27d1Lx5c8s+CxcujGuOINXb2MOIBPskMkHKOmp3SERkXWSClHVBwx5GJNgnkSHrojd9+nQdOnQoZPvxxx+vU045xcUVIVGRc2Y6++yzlZmZGbK9sLAwrofEUquLDXvYv9gnZnIy66jVIRGRdWZy+rouSNjDQOLhwYkI6euvv9bbb79dZtvAgQMtQ9wNubm5+te//hWy/YorrnD8Ke4AnGNqBu3bt8+yvUaNGsrIyLB1zvLly6tGjRoh20tLS8OuC0Cw/Pvf/7Zs79Wrl63z9ezZ07L9/ffft3U+ADgiPT1dHTt2tOyzbNkyl1YDwK/+/e9/a968eWW2DR06VNnZ2S6vCEAQUJ8G4BVq1IC/FRcXa8KECSHbu3btqtNOO83FFcWGGjUAU4XLp969e9s2V0pKii6++OKQ7UVFRfrwww9tm88JQXldcgtnFogH+RSdRM0nzjPgBfIpOkHKJ845YDryKTpByCfOP+AX5JNZcnJyLNu7d++ucuXKxT0P5yLwA/LJLG7lk1uojyMe5JNZTM0n6uPwAvlkFjfzifo4YBby2Exdu3a1bI/3PWCQ6m3sYUSCfWImp7POLdTuYAqyzkxByTo3sIcRCfaJmYKQdeHqo3buLcAKOWemcuXK6U9/+pNln02bNsU8PrW62LCH/Yt9Yians84t1OpgCrLOTEHJOjewh4HEw4MTUabCwkLdf//9Ki0tPaqtWbNm6tevnwer+r3JkyeroKCgzLa0tDTdcMMNLq8IgF1MzqCSkhLL9qpVqzoyb7hxi4uLHZkXgJk+/fTTkG3p6enq0qWLrfOde+65SktLi2k9ABCvM88807L9559/dmklAPxo9+7devjhh8ts69Spk7p16+byigAEBfVpAF6hRg3426xZsyxvUhs4cKCLq4kdNWoAJjp06JA+//zzkO01atTQqaeeauucf/nLXyzbTc+noLwuuYkzC8SCfIpeIuYT5xnwAvkUvSDlE+ccMBn5FL0g5BPnH/AD8sksmzZt0oIFCyz79OnTx5a5OBeB6cgns7iZT26iPo5YkE9mMTWfqI/DC+STWdzOJ+rjgDnIY3OFew/4008/xTV+UOpt7GFEgn1iLqezzk3U7uA1ss5cQco6J7GHEQn2ibn8nnVfffWV1q9fH7I9NTVVPXr0cG09SFzknNlq1apl2Z6fnx/z2NTqYsce9h/2idmczDo3UauD18g6swUl65zEHgYSEw9ORJlefPFFrV279qg/T05O1qhRo5SamurBqn7vnXfeCdl23nnnqVq1ai6uBoCdTM6gSpUqWbaX9bBHO4QbN9y6AATH9u3b9cMPP4RsP/nkk5WdnW3rnBUrVlSrVq1Ctq9evVo7duywdU4AOKJmzZqW7bt373ZpJQD86JFHHtGuXbuO+vPMzEwNHz7cgxUBCArq0wC8Qo0a8Dera4g2bdqoWbNmLq4mNtSoAZjq+++/1759+0K2d+jQQUlJSbbOecIJJ6h27doh27/44gujH7AQhNclt3FmgViQT9FLxHziPANeIJ+iF6R84pwDJiOfoheEfOL8A35APpll6tSplg9dbdasmVq2bBn3PJyLwA/IJ7O4lU9uoz6OWJBPZjE1n6iPwwvkk1nczifq44A5yGNzhfvCztzc3JjHDlK9jT2MSLBPzOVk1rmN2h28RtaZK0hZ5yT2MCLBPjGX37MuJyfHsr1jx4465phjXFoNEhk5Z7bKlStbtls9iNAKtbr4sIf9h31iNqeyzm3U6uA1ss5sQck6J7GHgcTEgxNxlNWrV+uVV14ps61v376WxQW3LF68WL/88kvI9gsvvNDF1QCwk+kZlJGRoczMzJDtThUfrMYtX768MjIyHJkXgHm++OILy/bTTz/dkXnPOOOMkG2lpaVh1wUAsQr3YcMDBw64tBIAfvPZZ59pxowZZbYNHDhQdevWdXlFAIKC+jQAL1GjBvxr69at+uqrr0K2++Uagho1AFOFy4HTTjvNkXmtci8vL0/Lli1zZN54BeV1yW2cWSAW5FN0EjGfOM+AV8in6AQpnzjngOnIp+gEJZ84/4AfkE/mKC0t1bRp0yz79OnTx5a5OBeBH5BP5nAzn9xGfRyxIJ/MYWo+UR+HV8gnc7idT9THAbOQx+aqWrWqZXs87wGDVG9jDyMS7BNzOZl1bqN2B6+RdeYKUtY5iT2MSLBPzOXnrMvPz9eHH35o2cev58vwH3LObHv37rVsr1KlSkzjUquLH3vYX9gnZnMq69xGrQ5eI+vMFpSscxJ7GEhMPDgRv1NSUqJhw4apqKjoqLYaNWpoyJAhHqzqaDNnzgzZlpmZ6VhhBYCz/JJBLVu2DNm2detW2wsQ+/fv17Zt20K2N2/e3Nb5AJht6dKllu1OPWD2pJNOsmznzTsApxw8eNCyvVy5ci6tBICfFBQUaPjw4WW2NWvWTP369XN5RQCChPo0AK9Rowb8adasWSopKQnZfs4557i4mthRowZgKvIpOkF5XXIbZxaIBfkUnUTLJ84z4CXyKTpByifOOWA68ik6Qconzj9gOvLJHAsXLtTGjRtDtqenp9v2sAt+7/AD9qk53Mwnt1EfRyzIJ3OYmE/Ux+El8skcbucT9XHALOSxuQoLCy3b43kPGKTfe5B+FjiHfWIuJ7PObdTu4DWyzlxByjonsYcRCfaJufycdbNmzVJBQUHI9urVq+uss85ycUVIZOSc2bZv327ZXq9evZjGDdLvPUg/C5zDPjGbU1nnNmp18BpZZ7agZJ2T2MNAYuLBifidN998U0uWLCmzbejQocrOznZ5RWX7/PPPQ7a1atWKi3/Ap/ySQW3btg3ZVlRUpC+//NLW+b744osyHyYZyXoABM/y5cst21u0aOHIvCeeeKJl+4oVKxyZFwC2bNli2V6xYkWXVgLAT55++mn98ssvR/15cnKyRo0apdTUVA9WBSAoqE8D8Bo1asCfrK4hjj32WNWuXdvF1cSOGjUAU1nlU1ZWlho0aODIvH7Np6C8LrmNMwvEgnyKTqLlE+cZ8BL5FJ0g5RPnHDAd+RSdIOUT5x8wHflkjpycHMv2Ll26qHLlyrbMxbkI/IB8Moeb+eQ26uOIBflkDhPzifo4vEQ+mcPtfKI+DpiFPDbX5s2bLdvjeQ8YpHobexiRYJ+Yy8mscxu1O3iNrDNXkLLOSexhRIJ9Yi4/Z124+miPHj04L4FryDlzFRcX66uvvgrZXrt2bdWtWzemsanVxY897C/sE3M5mXVuo1YHr5F15gpS1jmJPQwkJh6ciF9t2rRJTz/9dJltnTp1Urdu3dxdUAjbt2/X2rVrQ7affPLJ7i0GgG38kkGS1L17d8v2GTNm2Drf9OnTLdvPP/98W+cDYLbVq1eHbKtcubKqVq3qyLzVqlVTpUqVQravWrXKkXkBYOnSpZbt9evXd2klAPziu+++05tvvllmW9++fdWqVSuXVwQgSKhPAzABNWrAf8LdvNa6dWsXVxMfatQATFRQUKCNGzeGbG/QoIGSkpIcmfv444+3bDcxn4L0uuQ2ziwQLfIpOomWT5xnwEvkU3SClE+cc8B05FN0gpRPEucfMBv5ZI59+/bpo48+suzTu3dv2+bjXASmI5/M4XY+uY36OKJFPpnDxHyiPg4vkU/mcDufqI8DZiGPzbZs2TLL9njeAwal3sYeRiTYJ2ZzMuvcRu0OXiLrzBakrHMKexiRYJ+Yza9Zt27dOn3zzTeWffx8vgx/IefM9t///le5ubkh2zt27Bjz2NTq4sce9g/2idmczDq3UauDl8g6swUp65zCHgYSV6rXC4A5hg8froKCgqP+PDMzU8OHD/dgRWX7+uuvLdsbN24csi0vL0/z58/XggULtHr1am3cuFH79u1TUVGRMjIyVLFiRdWpU0cNGjTQySefrPbt2+u4446z+0cAUAa/ZJAknXDCCTrzzDP1+eefl9n+wQcf6IYbblCTJk3inmv58uWaNWtWyPa2bduqRYsWcc8D4LD9+/dr3rx5Wrx4sVauXKkNGzZo27Zt2r9/vySpXLlyysrKUq1atVS7dm01b95cLVu2VNu2bVW+fHnH15efn69du3aFbHf6uqV+/fr6/vvvy2zbsWOHCgoKlJmZ6egagKAzPYe88Mknn1i2N2vWzKWVAMEXhAwqKirS0KFDVVJSclRbjRo1NGTIEA9WBSBSfsgh6tNAsPkhhyRq1IAfrVixosxzsCOs/r0ePHhQCxYs0GeffaZVq1bpp59+0r59+3Tw4EGVK1dO2dnZqlOnjo499li1bt1a7dq1U9OmTZ34MahRAzDWxo0bVVpaGrLdyXzKzs5W1apVQ+bjhg0bHJs7VkF5XfICZxaIFvkUnUTKJ84z4DXyKTpByifOOWA68ik6QconifMPmI18Msf777+vAwcOhGyvU6eOzjjjDFvm4lwEfkA+mcPNfPIC9XFEi3wyh2n5RH0cXiOfzOF2PlEfB8xCHptt7ty5lu2xvgcMUr2NPYxIsE/M5lTWecGE2t2OHTu0YMECff/9979eM+/atUsHDhxQSkrKr9fNtWvXVr169dSiRQu1atVKrVq1UnJysuPrg3PIOrOZkHV79uzRokWLtGTJEq1atUobNmzQjh07dODAASUlJSkjI0MVKlRQrVq1VKdOHbVo0UInnXSSTj75ZKWnpzu+PvYwIsE+MZsJWReLnJwcy/ZTTz1VDRs2dGk1/8N1XWIi58xVXFysJ554ImR7UlKSrr766pjGplZnD/awf7BPzOVk1nmBWh28RNaZy5Sso1YXGnsY8BYPToQkacaMGZo/f36ZbQMHDlTdunVdXlFoK1eutGwv60Xrxx9/1GuvvaYZM2bo4MGDZf69/Px85efna/Pmzfr6669/LSQ3bdpUl112mXr16mXMQwCAoPFTBh1x11136YsvvlBRUdFRbSUlJbr99ts1efJkVaxYMeY5du3apcGDB4e8UE9OTtadd94Z8/gAjnbRRReV+aG7I4qKipSXl6etW7dqyZIl+vDDDyUd/tL80047Tb169VLnzp2VlpbmyPo2btxo2V6vXj1H5v3t+KEOmCTpl19+sfxwEIDwTM8ht61Zs0ZLly4N2Z6UlKS2bdu6uCIg2IKQQa+88opWr15dZtvQoUOVnZ3t8ooARMMPOUR9Ggg2P+TQEdSoAX+J5Rpi69atmjhxot59913t27evzL9XUFCggoICbdu2Td9++63+/e9//zper1691LdvX1WqVCn+H+D/o0YNwFQm5FOoG2B37dpl3BeYB+V1yW2cWSAW5FN0EimfOM+A18in6AQpnzjngOnIp+gEKZ+O4PwDpiKfzDFlyhTL9p49e9r2pR0m/N45F0E4JuxT8ukwN/PJbdTHEQvyyRym5RP1cXiNfDKH2/lEfRwwC3lsrt27d2vevHmWfdq1axfT2Cb83u2qt5nws7CHzcc+MZeTWec2E2p3mzZt0plnnhmy/dChQyosLNTevXu1ceNGffnll5o2bZokqXLlyjr77LN16aWX6pRTTnF0nXAGWWcuU7LutNNOC/uZ3X379mnTpk365ptv9P7770uSsrKy1KFDB11yySXq0KGDkpKSHFkfexiRYJ+Yy5Ssi9ahQ4c0ffp0yz69e/d2ZzG/wXVd4iLnzFRaWqpRo0aFPFuVpO7du6tRo0YxjW/C751aHdzEPjGT01nnNmp18BpZZyaTso5anTX2MOAdf37KArbatWuXHnnkkTLbmjVrpn79+rm8Ims//PCDZXudOnV+/f/v379fjz32mC688EK98847IW9GtbJq1SqNHDlS55xzjt59913LJw0DiJ7fMuiI5s2b6+abbw7ZvnbtWl133XXavHlzTOP//PPPuuaaa/Tzzz+H7NO/f3+KCIDNrN64Wzl48KDmzZunwYMHq3Pnzpo8ebKKi4ttXt3hAqSVY445xvY5oxk/3PoAhGd6Drnttddes2z/05/+pCpVqri0GiD4/J5BP/74o1588cUy2zp16qRu3bq5vCIA0fJDDlGfBoLNDzl0BDVqwF+iuYYoLi7Wyy+/rG7dumnChAkhv7zcyk8//aSnnnpKnTp10iuvvFLml4zHgho1AFN5nU/VqlWzbDctn4LyuuQ2ziwQC/IpOomST5xnwATkU3SClE+cc8B05FN0gpRPR3D+AVORT2ZYvXp12C8U6dWrl23zef1751wEkfB6n5JPh7mdT26jPo5YkE9mMC2fqI/DBOSTGbzIJ+rjgFnIY3O9/fbbOnDgQMj2evXqqUWLFjGN7fXv3c56m9c/C3vYH9gn5nIy69xmQu0unmvd3NxcTZs2TX379lXv3r31+eef27gyuIGsM5cpWRfrZ3bz8/M1e/ZsXXfddTr//PM1c+ZMR95bs4cRCfaJuUzJumjNmzdP27dvD9melZWl888/38UVHcZ1XeIi58yTl5ene+65R5MnTw7Zp2bNmrr//vtjnsPr3zu1OriNfWIeN7LObdTq4DWyzjymZR21OmvsYcA7PDgRevjhh7V79+6j/jw5OVmjRo1SamqqB6sK7ccffwzZlpSUpMqVK0s6/IHmSy+9VBMmTLDly2l37dqlYcOGacCAAdq5c2fc4wE4zG8Z9Fu33HKL+vTpE7J9yZIl6tGjhyZNmqSCgoKIxszLy9OECRPUo0cPrVixImS/Ll266M4774x6zQCct3XrVo0YMUK9evXSypUrbR27rLz8LaffvFevXt2yPdz6ALjDyRxy0w8//KBp06ZZ9unZs6dLqwEQKa8yqLS0VPfff78KCwuPasvMzNTw4cNdWwsAbzmdQ9SnAYTj5vUQNWrAP6yuIST9enPrzp07NWDAAI0ZMybif7dWCgoK9OSTT+rSSy+1/CLwSFGjBmCqcP/+w+VHvGrUqGHZblo+BeV1yU2cWSBW5FN0EiGfOM+AKcin6AQpnzjngOnIp+gEKZ9+i/MPmIh8MsOUKVMs20877TTVq1fPtvk4F4EfkE9mcDuf3ER9HLEin8xgUj5RH4cpyCczeJFP1McBs5DHZtq5c6f+8Y9/WPbp0aNHzOMHqd7GHkYk2Cdmcjrr3BS02t3SpUt1zTXX6LbbbtOuXbu8Xg4iRNaZKUhZJ0nr1q3THXfcoauvvlobNmywdWz2MCLBPjGTn7MuJyfHsv38889XZmamS6uxH9d1/kPOmWPv3r16++231a1bN82YMSNkv0qVKumll15SpUqVYp6LWp192MP+wD4xh5tZ5yZqdTABWWeOoGadRK0OgDPMfRoVXPHpp5/q3//+d5ltffv2VatWrVxeUXhbt24N2Zadna2UlBStWbNGV199tSM3ji5YsEC9e/fWxIkT1aBBA9vHBxKJHzPot5KSkjRq1CiVL19eb775Zpl9cnNzNXLkSD399NM666yzdMopp6hRo0aqVKmSMjMzlZ+frz179uiHH37QN998o08//VT5+fmW85533nl68sknjX6oJABp1apV6tOnj0aMGKFLLrnEljFzc3Mt2ytWrGjLPKFUqFDBsj3c+gC4y4kccktpaalGjhypQ4cOhexTu3ZtXXjhhS6uCkA03M6gyZMn66uvviqzbeDAgapbt67jawBgFqdyiPo0gEi5cT1EjRrwD6trCOnwjWxbt27V1VdfrfXr19s+//Lly9W7d2+98sorOvnkk2Mehxo1AFOF+/cfLj/i5bd8Csrrkls4s0A8yKfoJEI+cZ4BU5BP0QlSPnHOAdORT9EJUj79FucfMBH55L2ioiK99957ln2sHroaC85F4Afkk/e8yCe3UB9HPMgn75mWT9THYQryyXte5RP1ccAs5LGZHn/8ceXl5YVsz8zM1JVXXhnz+EGqt7GHEQn2iZmczjq3BLl2N3v2bH3zzTd64YUXjP/+OZB1pgpK1v3RF198oYsvvlhjx45Vp06dbBmTPYxIsE/M5Nes27lzp+bNm2fZx6/ny3/EdZ1/kHPO2L17d8j7VktLS1VQUKC8vDzt2LFDK1eu1LJly7RgwQIVFhZajlurVi29+OKLatGiRVzro1Znn6Du4aBhnzjD9KxzC7U6mIKscwZZVzZqdQDsxKc5E1h+fr4eeOCBMttq1KihIUOGuLugCOzfv9/yw8rlypXTli1b1L9/f0duRj1i8+bNuuKKKzR58mQde+yxjs0DBJkfM6gsycnJGjZsmM444wyNGDFC27ZtK7Pf3r179f777+v999+Pea7y5cvr7rvv1uWXXx7zGADcVVRUpGHDhmnz5s267bbb4h5vz549lu3Z2dlxzxHP+Lx5B8xjdw655c0339QXX3xh2efWW29VWlqaSysCEAu3Mmjr1q168skny2xr1qyZ+vXr59jcAMxmdw5RnwYQLTeuh6hRA/6wY8cOy/aioiINGDDAkS8vP2Lv3r0aMGCAJk6cqNatW8c0BjVqAKYin6ITlNclt3BmgXiQT9EJej5xngGTkE/RCUo+cc4BPyCfohOUfCoL5x8wDfnkvblz52r37t0h2ytVqqSuXbvaOie/d/gB+9R7XuSTW6iPIx7kk/dMyifq4zAJ+eQ9L/KJ+jhgHvLYPHPmzNH06dMt+/Tr109VqlSJeY4g/d6D9LPAOewT87iRdW4Jeu1u+/btuuqqq/Tss8+qY8eOXi8HFsg68wQp68qSn5+vm2++WSNHjtQll1wS93jsYUSCfWIeP2fdjBkzVFRUFLK9UaNGatOmjYsrchbXdf5AzjnjySef1JQpU2wds3v37hoxYoQtDzUM0u89SD8LnMM+cYbpWecWanUwBVnnDLIuNGp1AOzCgxMT2FNPPaVffvmlzLahQ4c6Hv6xCPfB7qSkJA0ePFjbt28P2adhw4bq3LmzTj75ZFWrVk1VqlTRvn37tH37dq1evVoff/yxli1bptLS0rBrufHGG/XOO+8Y+d8KMJ0fM8jKOeecozPPPFPvvPOOXn/9dW3YsMG2sTMzM3XZZZfpuuuu0zHHHGPbuAD+JykpSSeccIKaNm2qJk2a6Pjjj1elSpWUnZ2trKws5efnKzc3V7m5uVq+fLm++uorLV261PLw+bfGjRun8uXL6/rrr49rnQUFBZbtmZmZcY0fTrjx9+/f7+j8QJD5JYfcsHz58pAfiD7ipJNOUp8+fVxaERB8fs+gBx98UHl5eUf9eXJyskaNGqXUVEqggOn8kkPUp4Hg8ksOWaFGDZjr0KFDll/0JEkjRozQ2rVrQ7bXqVNH55xzjtq2bavq1auratWqKigo0I4dO7Ru3TrNmTNH33zzjQ4dOmQ5T0FBgW699VZNmTJFtWrVivpnoUYNwFTkU+SC9LrkBs4sEC/yKXKJkE+cZ8Ak5FPkgpRPnHPAD8inyAUpn6xw/gFTkE/ey8nJsWzv3r27ypUrZ+uc/N7hB+xT73mRT26gPo54kU/eMymfqI/DJOST97zIJ+rjgHnIY7Ns3rxZQ4cOtexTu3Zt3XjjjXHNE6Tfe5B+FjiHfWIWt7LODSbV7tLS0tSiRQs1adJETZo0Uf369ZWdna0KFSqoXLly2rt3r3Jzc7Vz5059++23+vLLLy3PsH/rwIEDGjRokF599VW1a9fO4Z8EsSLrzGJS1qWkpKhZs2Zq3LixmjRpogYNGqhixYqqUKGCypcvr3379ik3N1e7du3S0qVL9eWXX2rlypUqKSkJO3ZJSYmGDx+u8uXLq3v37nGtkz2MSLBPzGJS1sVi6tSplu29e/d2aSW/x3VdYiPnzJaamqpzzjlHN910k1q2bGnbuEH6vQfpZ4Fz2Cdmcyrr3ECtDiYh68zmZdZRq4sMexjwDncRJ6glS5Zo0qRJZbZ16tRJ3bp1c3lFkQn3grV9+/aQN6M2bdpUw4YNs7yo7ty5s26++WatXbtWo0eP1n//+1/L+dauXauHH35YDz/8cPjFA/iVXzMonHLlyumqq67SVVddpU8++USjRo0K+XDISNStW1dDhgxR586dVb58eRtXCkA6XNA766yz1KlTJ3Xq1Ek1atSI6O9dcMEFkqS8vDz961//0sSJE7Vt27awf2/MmDFq0qSJOnbsGPOai4uLLdud/pBgWlqaZXukDwsAcJgfc8hpe/fu1W233aaDBw+G7JOWlqbRo0crKSnJxZUBwROUDJo1a5bmzJlTZlvfvn3VqlUrW+cDYB8/5hD1aSBY/JhD4VCjBsy0f//+sF+G9NFHH5X553Xr1tV9992nrl27hvy7nTp10oABA7R582Y99thjmjVrluVc27dv17333qvXXnst6voKNWoApiKfIhek1yWncWYBO5BPkQt6PnGeAdOQT5ELUj5xzgE/IJ8iF6R8CofzD5iAfPLW1q1b9dlnn1n2ceKL4/i9ww/Yp97yKp+cRn0cdiCfvGVSPlEfh2nIJ295lU/UxwHzkMfmKCws1O23367du3db9hs5cmTcNfUg/d6D9LPAOewTc7iZdU4zoXZXuXJlnXPOOTr77LN15plnKisrK6K/17NnT0nSxo0bNXHiRE2ZMiXslwwfPHhQt912m6ZPn65atWrFvXbYj6wzhwlZl5mZqbPPPludOnXSWWedpcqVK0f09y666CJJ0s6dO/Xmm2/qrbfe0p49eyz/TklJif7+97+rUaNGat68ecxrZg8jEuwTc5iQdfFYsmSJfvjhh5Dtqampuvjii11bD9d1OIKcM1NaWpoGDBiga665RlWqVLF9/CD93oP0s8A57BMzOZ11TqNWB9OQdWbyKuuo1UWPPQx4J9nrBcB9RUVFGjZsWJlP6c3MzNTw4cM9WFVkrN4AWOnfv7+mTZsW8ZPIGzVqpNdee00jRoxQcrL1P5OcnBwtXLgwpnUBicjPGRROYWGh3nrrLfXu3Vs33XRTXF/IIEm//PKLRowYob///e+aP3++TasEUKdOHd1xxx2aN2+eXnjhBV166aURfzH+b2VnZ+vaa6/VnDlzNGjQoLDXDKWlpbr77ru1a9euWJce9s2x02/ew40frrgA4DA/55CTiouLNXjwYG3YsMGy32233aZmzZq5tCogeIKUQXv27NFDDz1UZluNGjU0ZMgQ2+YCYB8/5xD1aSAY/JxD4VCjBsxUWFgY09+74IILNGvWLMsvL/+t2rVr6+mnn9a4ceNUrlw5y74LFizQ1KlTo14TNWoApiKfIhek1yUncWYBu5BPkQtyPnGeARORT5ELUj5xzgE/IJ8iF6R8CofzD5iAfPLW9OnTdejQoZDtzZs3V8uWLW2fl987/IB96i2v8slJ1MdhF/LJW6bkE/VxmIh88pZX+UR9HDAPeWyOESNG6Ntvv7Xs89e//lVnnXVW3HMF6fcepJ8FzmGfmMPNrHOS17W7k046SY8++qg+/fRTPfLIIzr33HMj/iL236pXr56GDRumuXPnRnRuvXv3bt11112xLBkuIOvM4WXWHX/88Ro2bJjmz5+vsWPH6qKLLor4i9h/q1q1arr99tv1ySefqG/fvmH7Hzx4ULfffnvM99BI7GFEhn1iDr9f1+Xk5Fi2d+rUScccc4zj6+C6Dn9EzpmpqKhIL7/8si699FI99thj2rx5s+3jW/HT7z1IPwucwz4xk9NZ5yRqdTARWWcmt7OOWl3s2MOAd3hwYgIaP368Vq9eXWbbwIEDVbduXZdXFLlYXuxuuukm3XfffUpJSYn6715++eV69NFHw96UOnbs2KjHBhKVnzMolNLSUr311ls655xz9OCDD2rp0qW2jZ2Xl6eZM2fquuuuU69evTRv3jzbxgYSUa1atfTxxx/rpptuUrVq1WwZMz09XQMHDtSECRNUtWpVy765ubkaM2ZMzHNZfSBIUkzXO9EIN3644gIA/+eQk0aOHKn//ve/ln06dOig6667zqUVAcETtAx69NFHtX379jLbhg4dquzsbNvmAmAPv+cQ9WnA//yeQ6FQowbMFss1RI8ePfTkk0+G/SLysnTp0kXjx49X+fLlLfs9//zzUa+NGjUAU4W7wZR8+p8gvS45iTML2IV8ilyQ84nzDJiIfIpckPKJcw74AfkUuSDlUyicf8Ak5JO3wj2UtXfv3o7My7kI/IB88pZX+eQk6uOwC/nkLVPyifo4TEQ+ecurfKI+DpiHPDbDyy+/HDabmzZtqvvuu8+W+YJUb2MPIxLsEzO4nXVO8rJ21717d02ZMkU9e/aM6by5LFWrVtXzzz+v++67L+yXEX/xxReaMWOGLfPCXmSdGbzMujZt2mjWrFm66qqrbKv3ZWVl6YEHHtCYMWOUmZlp2Xf9+vV69dVXY56LPYxIsE/M4PfrugMHDuiDDz6w7OPG+Q3XdSgLOWe2n3/+WRMmTFCXLl10zz33aOfOnbaMS63OPuxhf2CfmM2prHMStTqu6UxE1pnNjayjVhcf9jDgHR6cmGDWrl2r8ePHl9nWrFkz9evXz+UVOeuss87SHXfcEdcYF198sfr372/ZZ8mSJfryyy/jmgdIBEHMoG3btum6667Tgw8+GPIDPHZZtmyZbrjhBg0fPlz79+93dC4gqFJTUx17g3v66afrlVdeCfulMFOnTtVPP/0U0xzh1l5SUhLTuJEKd8AVrrgJwP855JSXX35Z//rXvyz71KlTR08++WTYDwwCCC1IGbRgwYKQNzR26tRJ3bp1i3sOAPYLUg5Fgvo0YJ4g5hA1asB8paWlUfVv1qyZRo8eHVdenX766frb3/5m2WfTpk2aOXNmVONSowZgqnD//smn/wnS65JTOLOAncinyAU1nzjPgKnIp8gFNZ8iwTkHvEA+RS7o+cT5B0xDPnnnq6++0vr160O2p6en68ILL3Rkbs5F4Afkk3e8zCenUB+Hncgn75iST9THYSryyTum5FMkqI8DziOPvff++++HfYBrhQoV9NxzzykjI8OWOYNUb2MPIxLsE+95kXVO8bp2l56ebvuYR/Tv319Dhw4N2+/ZZ58Nu6/hPrLOe15nnZP50L17d40dOzZsrr366qvat29fTHOwhxEJ9on3vM46O3z44YfKy8sL2V69enV17NjR8XVwXYeykHP+UFxcrOnTp6t79+76+OOP4x6PWp192MP+wD7xB7uzzinU6rimMxVZ5w9OZh21uviwhwHv8MmEBFJaWqphw4apsLDwqLbk5GSNGjXK+MBNS0uLuG/FihU1evRoW+a9/fbbdfzxx1v2CfdGBUh0QcigP9qwYYMuueQSffbZZ2H7tmnTRrfccosmTJig2bNna9GiRVq2bJkWLlyo2bNn6x//+IduvvlmtWnTJuxY//rXv3TZZZdp9+7ddvwYAGx04okn6oknnlBSUlLIPiUlJZo4cWJM44e7FiouLo5p3EiFGz+aazUAznA6h5wwZcoUjRkzxrJPVlaWxo0bpypVqri0KgCxcCuDDhw4oOHDh5fZlpmZGbINQPB5/Z7st6hPA4nJ7fdk1KgBf4jmGiI1NVWPPfaYLbXWK664Qu3bt7fsE+01BDVqAKYinyIXpNclJ3BmAbuRT5ELYj5xngGTkU+RC1I+cc4BPyCfIhekfPojzj9gIvLJOzk5OZbtXbp0UeXKlR2Zm987/IB96h0v88kJ1MdhN/LJOybkE/VxmIx88o7J7+9+i/o44A7y2Fvz58/Xvffeq9LS0pB90tLSNHbsWB133HG2zRuk33uQfhY4h33iLa+yzgmJULu7/PLLdfXVV1v22bhxo/7zn/+4tCJEiqzzVpCyLpSzzz5b99xzj2Wf/Pz8mN9fs4cRCfaJt4KSdeHqoz169Aj7EDM/4LrOn8g5Zzz00ENatWpVmf9bunSpFixYoNmzZ+vNN9/UPffco7/85S+qWLFi2HF37dqlW2+9VZMmTYprfUH6vQfpZ4Fz2CfOMD3rnECt7jCu6cxE1jkjEbMuFGp1/tzDgB/w4MQE8tZbb+mbb74ps61v375q1aqVyyuKXjQvCJdddplq1qxpy7zlypXTjTfeaNnnk08+cfwFE/CzIGTQb23atElXX321tmzZYtnv3HPP1dSpUzV58mQNHjxYZ555pho0aKDKlSsrNTVVVapUUYMGDdShQwfdfvvtmjx5sqZMmaKuXbtajrtq1SoNGDBAe/bssfPHAmCDrl27qlu3bpZ9Zs6cGdN1Q7gHzBYVFUU9ZjR48w74g5M5ZLfZs2eH/cBzuXLl9OKLL6pFixYurQpAPNzIoGeffVY///xzmW0DBw5U3bp1Yx4bgP85mUPUpwFEwq33ZNSoAf+I5hri3HPPVbNmzWyZNykpSYMGDbLss3jxYu3YsSPiMalRAzAV+RS5IL0u2Y0zCziBfIpcEPOJ8wyYjHyKXJDyiXMO+AH5FLkg5dNvcf4BU5FP3sjPz9eHH35o2adPnz6Ozc/vHX7APvWG1/lkN+rjcAL55A1T8on6OExGPnnD63yiPg6Yhzz2ztdff61BgwZZ/jdOTk7Wo48+qrPOOsvWuYP0ew/SzwLnsE+842XW2S2Rand33XWX6tSpY9lnxowZLq0GkSLrvBOkrAunf//+at26tWWfWPOBPYxIsE+8E5Ss27Bhg7788kvLPr1793ZpNc7jus5/yDn3paWlqWrVqmrQoIHatWuna665RmPHjtX8+fP12GOPqWnTpmHHGDlypCZPnhzzGoL0ew/SzwLnsE/cZ0LW2Y1a3e9xTWcess59Qcy6cKjVAXACD05MEFu2bAn5FPIaNWpoyJAhLq8oNuXKlYuoX3Jysi677DJb5z7//PNVuXLlkO15eXlavHixrXMCQRGUDDqipKREd955pzZt2hSyT/ny5fXoo4/queeeU8uWLaMa/6STTtLzzz+vhx9+WOXLlw/Zb8WKFbrvvvuiGhuAOwYPHqyUlJSQ7bm5uVq0aFHU42ZmZlq2FxQURD1mNPLz8y3brTILgLucyiE7ffrpp7rzzjt16NChkH3S0tL0zDPPqH379i6uDEC8nMygZcuWaeLEiWW2NWvWTP369YtpXADB4lQOUZ8GECmn35NRowb8JdJrCEnq27evrXP/6U9/UuPGjUO2l5aW6rPPPot4PGrUAExFPkUuSK9LduLMAk4hnyIXtHziPAOmI58iF6R84pwDfkA+RS5I+XQE5x8wGfnkjZkzZ1r+t61Tp45OP/10x+bn9w4/YJ96w+t8shP1cTiFfPKGCflEfRymI5+84XU+UR8HzEMee2P58uW68cYbtX//fst+w4cPV/fu3W2fP0i/9yD9LHAO+8QbXmednRKtdpeenq5bbrnFss/nn38edm/DXWSdN4KUdZG64447LNtXr16t9evXRz0uexiRYJ94I0hZl5OTo9LS0pDtp556qho2bOjiipzFdZ3/kHPmyMjIUI8ePTR16lTdfvvtSk9Pt+w/evRorVy5Mqa5gvR7D9LPAuewT8zhZtbZiVrd0bimMw9ZZw6/Zl2kqNUBsBsPTkwQDzzwQMiwHTp0qLKzs11eUWwqVqwYUb/WrVvr2GOPtXXucuXK6bzzzrPss2TJElvnBIIiKBl0xIQJE/TNN9+EbE9LS9PLL7+snj17xjVP7969NX78eMuniM+ZM0fTpk2Lax4A9mvYsKHOOeccyz5ffvll1ONWqlTJst3pgmG48a0+vAPAXU7lkF0WLVqkQYMGqaioKGSflJQUPf744zr77LNdXBkAOziVQcXFxRo2bFiZh8bJyckaNWqUUlNTox4XQPA4lUPUpwFEyun3ZNSoAX/JyMgIewObJNWsWVPt2rWzff4LL7zQsj2aawhq1ABMRT5FLkivS3bhzAJOIp8iF6R84jwDfkA+RS5I+cQ5B/yAfIpckPLpCM4/YDLyyRs5OTmW7b169VJysnMfj+X3Dj9gn3rD63yyC/VxOIl88obX+UR9HH5APnnD63yiPg6Yhzx23w8//KBrrrlG+/bts+x39913q2/fvo6sIUi/9yD9LHAO+8R9JmSdXRK1dterVy9VrVo1ZHthYaG+++47F1eEcMg69wUp66Jx+umnq0WLFpZ9+D43OIV94r4gZV1JSUnY+9D69Onj0mrcw3Wdv5Bz5klNTdXNN9+s8ePHq1y5ciH7FRUV6a677rJ8iFcoQfq9B+lngXPYJ+ZxI+vsQq2ubFzTmYesM4+fsi4a1OoA2M38T14gbjNnztQnn3xSZlunTp3UrVs3l1cUu0hfENq0aePI/K1bt7ZsX758uSPzAn4WpAySDl+4vvTSS5Z9HnjgAdu+YKJ9+/YaMWKEZZ8xY8aosLDQlvkA2KdDhw6W7bEU98K9ed+zZ0/UY0Yj3E0E4dYHwF1O5JAdFi9erJtuukkHDhwI2ScpKUmjR4/WBRdc4OLKANjJiQyaMGFCyNpL37591apVq6jHBBBcTuQQ9WkA0XDqPRk1asCfIrmOOPnkkx2Z285rCGrUAEwV7t//3r17HZ0/3Pim5VNQXpfswJkFnEY+RSco+cR5BvyAfIpOUPKJcw74AfkUnaDkk8T5B8xHPrlv7dq1Wrx4ccj2pKQk9erVy9E1cC4CPyCf3GdCPtmB+jicRj65z4R8oj4OPyCf3GdCPlEfB8xDHrtr/fr1GjBggHbv3m3Zb9CgQbr22msdW0eQ6m3sYUSCfeIuU7LODolcu0tJSdHpp59u2YcHj5uFrHNXkLIuFl58nxt7GBL7xG1By7rPPvtMW7ZsCdmelZXlu+/jjQTXdf5CzpnrjDPO0LPPPqukpKSQfVavXq3//Oc/UY9Nrc4+7GF/YJ+Yy8msswO1Oq7p/ISsM5fpWRcLanUA7MSDEwMuNzdXo0ePLrMtMzNTw4cPd3lF8UlLS1N2dnbYfuFuHI1VuHF/+eUXR+YF/CpoGSRJU6dOtSywtm7dWn369LF1zksuuUQnnXRSyPbt27frgw8+sHVOAPEL9+Z9/fr1UY9ZtWpVy/YdO3ZEPWY0tm3bZtlepUoVR+cHEB0ncihey5Yt0/XXX6+CggLLfsOHD/fFF0YACM3uDPrpp580bty4Mttq1KihIUOGRDUegOBz4lqI+jSAaDj1nowaNeBPkdROnbqGOPHEE5WcHPrWkGiuIahRAzBVuHzavn27o/OHG9+0fArK61K8OLOAG8in6AQhnzjPgF+QT9EJQj5JnHPAH8in6AQlnyTOP2A+8sl9OTk5lu2nn3666tat6+gaOBeBH5BP7jMhn+JFfRxuIJ/c53U+UR+HX5BP7vM6nyTq44CJyGP3bNy4Uf379w/7M19//fUaOHCgo2sJUr2NPYxIsE/cY1LWxYvaXfjP5P30008urQSRIOvcE6Ssi5UX3+fGHobEPnFTELMuXH30ggsuUGZmpkurcRfXdf5BzpmtU6dOuvjiiy37TJw4MepxqdXZhz3sD+wTszmVdfGiVsc1nd+QdWYzNetiRa0OgJ14cGLAPfHEE9q5c2eZbQMHDjT+AzhlqVWrVtg+9erVc2TucONu2bLFkXkBvwpiBs2YMcOy3alDtEGDBlm2hzsUA+C+evXqqXz58iHbt2zZokOHDkU1Zu3atS3bnT5gCje+H3MdCDIncigeK1eu1DXXXGP5BVeSdO+99+ryyy93aVUAnGJ3Bo0YMUIHDhwos23o0KERfYAZQGJx6lqI+jSASDmVQ9SoAX/y8hoiOzvb8sarnTt3qqioKKKxqFEDMBX5FJ2gvC7FgzMLuIV8ik4Q8onzDPgF+RSdIOTTEZxzwHTkU3SClE+cf8B05JO7iouL9d5771n26d27t+Pr4PcOP2CfusuUfIoH9XG4hXxylwn5RH0cfkE+ucuEfDqC+jhgFvLYHVu2bFG/fv20efNmy35XXXWV/va3vzm+niD93oP0s8A57BN3mJZ18aB2d9gJJ5xg2b5x40aXVoJIkHXuCFLWxaNx48aW7b/88kvUY7KHEQn2iTuCmHW5ubmaO3euZR/Tz5fjwXWdf5Bz5vvb3/6mtLS0kO3ffvuttm7dGtWYQfq9B+lngXPYJ+ZzIuviQa3uMK7p/IWsM59pWRcPanUA7JTq9QLgrKVLl5b55/Xq1VOXLl1su6jcu3evZfvu3bst5ypfvryqVasW0VzHHnus1qxZY9mnYsWKEY0VrYyMDKWnp6uwsLDM9ry8PEfmBfwqaBmUn5+v5cuXh2yvUKGCzjjjjLDjxOLMM89UdnZ2yJxZsmSJDh48qHLlyjkyP4DYVK1aNeSb9EOHDik/Pz+q65ZwH46JpSAQjXC5zZt3wDx251Cs1q5dqwEDBig3N9ey3x133KEBAwY4vh4A7rAzg0K9v2zTpo1OPPFE295fFhQUWLbv2LHDcq7s7GxVrlzZlrUAiJ8T10LUpwFEw+4cokYN+Ff9+vXD9nGyRlOhQgXt3LmzzLaSkhIVFBSoUqVKYcehRg3AVCbnU9WqVZWZmeno/NEKyutSrDizgJvIp+gEIZ84z4BfkE/RCUI+HcE5B0xHPkUnKPnE+Qf8gHxy17x587R9+/aQ7ZUqVVLXrl0dX4fJv3eJcxEcZvI+JZ/MQ30cbiKf3GVCPlEfh1+QT+4yIZ+OoD4OmIU8dt62bdvUr1+/sNdhl112mYYNG+bKmkz+vUvR1dtM/lmCsoeDgH3iPBOzLlbU7v4n3PfAhfuuObiLrHNekLIuXpUrV1ZycrJKSkrKbI8lH9jDiAT7xHlBzbr33nsvZE1Qkho1aqQ2bdq4uCJ3cV3nH+Sc+apXr6527drp888/D9nnq6++0l/+8peIxzT59y5Rq4P92CfmcyLrYkWt7n+4pvMXss58JmVdvKjVAbATD05MUBs3btS5557r2nyPP/64Hn/88ZDtnTt31gsvvBDRWJF8uLtChQoRry1aFStWDPnE3wMHDjg2LxAkfs2g7777TocOHQrZ3r59e6WmOvPSmpqaqnbt2mnu3LllthcWFur7779X27ZtHZkfQGysvhxfkvbv3x/VB2mys7NVpUoV7d69u8z2n376Keo1RmPDhg0h26pVq8abd8BAdudQLNatW6d+/fpp165dlv1uvvlm3XTTTY6uBYC73MigxYsXq3PnznGNEY0hQ4ZYtl999dUaOnSoS6sBEI4TOUR9GkA07M4hatSAfx177LFh+zh5DRHuy8kPHDgQ0ReYU6MGYKpwN8A6mU95eXmW9e9wa/NCUF6XYsGZBdxGPkUnyPnEeQZMQz5FJ0j5xDkHTEc+RSco+cT5B/yAfHJXTk6OZXv37t1deaAp5yLwA/LJXabkUyyoj8Nt5JO7TM4n6uMwDfnkLpPyifo4YBby2Fk7d+5U//79tX79est+PXr00IMPPujOohSseht7GJFgnzjL1KyLBbW736tataplO9fPZiHrnBWkrLNDcnKyKleuHPL3Hks+sIcRCfaJs4KcdVOnTrVs7927t0sr8QbXdf5BzvnDOeecY/mAne+//z6qB+xQq7MHe9g/2Cf+YHfWxYJa3e9xTecvZJ0/mJB1dqBWB8BOyV4vAIhW48aNw/YpLS11bH4nxwZgtq1bt1q2H3/88Y7O36hRI8v2zZs3Ozo/gOg5cd3QpEmTkG27d+8OW1yN1c6dO5WbmxuyvWnTpo7MCyA+Xr9/2bBhg/r376/t27db9rvmmmt0++23u7MoAK7xOoMAwIkcoj4NIBp2/5ulRg34V5CuIahRAzBRVlaW5Y2m69atcyxnf/zxR8t2E/MpSK9L0eDMAl4gn6KTqPkEeIF8ik6Q8ilIPwuCiXyKTlD+TXP+AT8gn9yzc+dOzZs3z7JPnz59XFoN5yIwH/nkHtPyKRrUx+EF8sk9fs4nwAvkk3tMy6eg1NKAoCCPnbN79271799fa9eutex3wQUX6OGHH1ZSUpJLKzssKPU29jAiwT5xjulZFw1qd0fj2tlfyDrnBCnr7GT3fmIPIxLsE+cEOeuWLVumFStWhGxPS0tTjx493FuQB7iu8w9yzh/q169v2R5LXY1aXfzYw/7BPvEHJ7IuGtTqjsY1nb+Qdf7gddbZiVodALvw4ET4TsuWLcP22bdvn2Pz7927N2RbRkaGY/MC8J5VYVWSqlSp4uj84cbfvXu3o/MDiF64f5fly5ePesxw10LLly+PesxILF261LK9efPmjswLID5O5FCkfvnlF1199dXasmWLZb8rr7xS99xzj2PrAOAdLzMIACRv3pNJ1KcB/I/dOUSNGvAvk68hpOiuI6hRAzCVVT7l5+dr/fr1jszrx3wK0utSpDizgJfIp8glYj4BXiKfIhekfDL5ZyFncQT5FDmT/01Lkf+75vwDfkE+uWP69OkqLi4O2d68eXO1aNHCtfVwLgI/IJ/cYVo+RYr6OLxEPrnDr/kEeIl8codp+WRyLY36OBIVeWy/PXv26JprrtHq1ast+3Xp0kVPPPGEUlJSXFrZ/wSp3sYeRiTYJ/bzQ9ZFitpd2cJ9KTTXz+Yh6+wXpKyzU0lJifbs2ROyPdZ8YA8jEuwT+wU963JycizbO3bsqGrVqrm0Gm9wXecv5Jz5qlevbtke7l7cslCrix972F/YJ+ZzIusiRa2ubFzT+Q9ZZz4vs85O1OoA2IkHJ8J3GjdurHLlyln2CfcB7Fjt379fRUVFIdsrVarkyLwAzJCfn2/Znp2d7ej8FSpUsGzPy8tzdH4A0bP6spSUlBRlZWVFPWa4A6bvvvsu6jEj8f3331u2R/KhIQDucyKHIrFlyxb169dPmzZtsux36aWXatiwYY6sAYD3vMogADjCiRyiPg0gGnbnEDVqwL8qVaqk+vXrW/Zx6hoi3NgpKSlR5Qc1agCmIp8iF6TXpUhwZgGvkU+RS7R8ArxGPkUuSPnEOQf8gHyKXFDyifMP+AX55I6pU6datvfp08ellRzG7x1+wD51h2n5FAnq4/Aa+eQOP+YT4DXyyR2m5RP1ccA85LG99u3bp2uvvTbsl5l37NhRTz31lFJTU11a2e8F6fcepJ8FzmGf2MsvWRcJanehWX0eT5IqVqzo0koQKbLOXkHKOrvl5uaqpKQkZHus+cAeRiTYJ/YKetYVFhbq/ffft+yTCOc3XNf5CzlnvvT0dMv24uLiqMcM0u89SD8LnMM+MZ8TWRcJanWhcU3nP2Sd+bzKOrtRqwNgJx6cCN9JS0vTKaecYtlnw4YNjswdbtxatWo5Mi8AM3h1M/wRVk9Pl8KvD4C7Nm7cqIKCgpDtNWvWVEpKStTjtmvXzrJ94cKFUY8ZiQULFli2t2/f3pF5AcTOqRwKZ9u2berXr1/Y9089e/bUyJEjlZSUZPsaAHjPqwwCgCOcyiHq0wAi5UQOUaMG/C1cbdepa4i8vDzl5uaGbK9evXpUeUSNGoCpTMynrKwsnXjiiY7MG6+gvC6Fw5kFTEA+RSdR8gkwAfkUnaDkE+cc8APyKTpByCfOP+AX5JPzvv32W61ZsyZke3p6ui688EIXV2Tm713iXAS/Z+I+JZ+8R30cJiCfnOfHfAJMQD45z8R8oj4OmIc8tk9+fr6uv/76sF/meOaZZ+r5558P+2WcTjLx9y7FVm8z8Wfx6x4OMvaJffyUdeFQu7P2ww8/WLbXqVPHpZUgUmSdfYKUdU5wKh/Yw4gE+8Q+iZB1H330keU9adWrV9dZZ53l4oq8wXWdv5Bz5tu5c6dle2ZmZtRjmvh7l6jVwTnsE/M5kXXhUKuzxjWd/5B15vMi65xArQ6AnXhwInypQ4cOlu1OPe13yZIllu3HHXecI/MCMEN2drZl++7dux2dP9z4WVlZjs4PIDqff/65ZXv9+vVjGrdGjRo64YQTQrYvXrxYeXl5MY0dyt69ey2vg5o0aaJjjjnG1jkBxM+pHLKyc+dO9e/fX+vXr7fs1717dz388MMJd/ADJBIvMggAfsvJHKI+DSASTuQQNWrA37y6hvj+++9VUlISsj3aPKJGDcBUrVq1srxe+vzzz1VaWmrrnGvWrNHmzZtDtrdr106pqam2zmmXoLwuWeHMAqYgn6KTCPkEmIJ8ik6Q8olzDpiOfIpOEPKJ8w/4BfnkvJycHMv2rl27qlKlSi6t5jDOReAH5JPzTMwnK9THYQryyXl+yyfAFOST80zNJ+rjgFnIY3vs379fN9xwgxYvXmzZr127dnrhhRc8f+BEkOpt7GFEgn1iD79lnRVqd+HxHQX+Q9bZI0hZ5xSn8oE9jEiwT+yRKFkXrj7as2dPpaSkuLQa73Bd5y/knPl27Nhh2V61atWox6RWFx/2sP+wT8znRNZZoVYXHtd0/kPWmc/trHMKtToAduJfWMDNmDHDlXmee+45Pf/88yHbH3nkEfXq1cu2+Tp27KgnnngiZPu3335r21y/Fe6GVJ72C/xe0DKodu3alu2rV6+Oew4rK1eutGwPtz4A7po/f75le6tWrWIe+6yzztKaNWvKbCssLNTHH3+sHj16xDz+H/3nP/9RUVGR5XoAmMfJHCrL7t271b9/f61du9ay33nnnafHHntMycnJts4PwCx2Z9BXX30Vz3Iidu+992ratGkh29944w21b9/elbUAiI+T10LUpwFEwokcokYN+NuZZ56ptLS0kLVWp64hwo170kknRT0mNWoAJkpJSdGZZ56p2bNnl9m+detWff3112rbtq1tc37wwQeW7SbnU5Bel8rCmQVMQj5Fx+/5xHkG/IR8io7f8+m3OOeA6cin6AQhnzj/gF+QT87av3+/Zs6cadmnd+/eLq3m9zgXgenIJ2eZnE9loT4Ok5BPzjItn6iPw0/IJ2eZlk+/RX0cMAt5HL+DBw/qpptuCnstdsopp+ill15SRkaGSyuzFpR6G3sYkWCfxM+vWVcWanfhHTp0SP/9738t+9j9PSmIH1kXvyBlnZOc+u4A9jAiwT6JX6Jk3aZNm7RgwQLLPiadLzuF6zr/IefM980331i2H3fccTGNS60uduxh/2GfmM+prCsLtbrwuKbzJ7LOfG5mnZOo1QGwU+JdaSEQGjdurObNm4ds//bbb7VhwwZb5zx48KA++ugjyz6nnnqqrXMCMEuDBg0s27/88ksVFhY6MvfBgwfDHvT55Q0NkAjWrVunuXPnWvaJ58119+7dLdunTp0a89hlsfowoSRdeOGFts4HIH5O59Af7d27V9dee23YL6k6++yzNWbMGKWmpto2NwDzuJ1BAPBHTucQ9WkA4TiVQ9SoAX+rWLGiOnbsGLJ927ZtWrhwoe3zvv/++5btsVxDUKMGYKpw+ZSTk2PbXIcOHdKMGTNCtqempqpbt262zWe3IL0u/RFnFjAR+RS5IOcTYCLyKXJByifOOeAH5FPkgpBPnH/AT8gn58yePVt5eXkh2+vWravTTz/dxRX9D+ci8APyyTkm59MfUR+Hicgn5/gpnwATkU/OMTmfqI8D5iGPY1dYWKhbb7017BnASSedpFdeeUVZWVkurSy8INXb2MOIBPskdn7Ouj+idheZadOmadeuXSHbU1NTdfLJJ7u3IESMrItdkLLOSQsWLNDy5cst+zj5fW7sYUjsk3gkUtZNnTpVJSUlIdvbtm0b9n65IOC6zp/IObOF+/6SFi1axDQutbrYsIf9i31iNqey7o+o1UWGazr/IuvM5lbWOYlaHQC78eBE+FaPHj1CtpWWlmry5Mm2zjdz5kzl5uaGbK9bt66aNWtm65wAzHLcccepcuXKIdv3798f9k1HrP7zn//o4MGDIdsrV67MlzIABnnuued06NChkO3Z2dlxfbCnZcuWatSoUcj2RYsWacWKFTGP/1vfffed5ZfCNG7cmGsgwEBO59Bv5eXl6brrrtOyZcss+3Xo0EHPPvus0tLSbJkXgLnczCAAKIsbOUR9GoAVp3KIGjXgfz179rRst/sa4osvvtCaNWtCtmdkZOiMM86Ielxq1ABM1alTJ1WsWDFk+/vvv68dO3bYMtd//vMf/fLLLyHbO3TooKpVq9oyl1OC8rr0W5xZwFTkU3SCmE+Aqcin6AQpnzjngOnIp+j4PZ84/4CfkE/OmTJlimV7z549lZzszUdhOReBH5BPzjE5n36L+jhMRT45xy/5BJiKfHKO6flEfRwwC3kcm+LiYg0ePFjz58+37NeiRQv94x//UHZ2tksri0yQ6m3sYUSCfRIbv2fdb1G7i0xhYaFeeOEFyz6nnXaaKlSo4NKKEA2yLjZByjqnPfPMM5btDRs21AknnBDz+OxhRIJ9EptEyrrS0tKwDxjr3bu3S6vxDtd1/kXOmWv+/PmW/73S0tLUpk2bmMamVhcb9rB/sU/M5WTW/Ra1ushwTedvZJ253Mo6p1GrA2A37naGb/Xq1UtZWVkh2//1r39p69attsx18OBBjR8/3rJPuKcPAwiG9u3bW7Y///zzKikpsXXO4uJiPf/885Z92rdvr6SkJFvnBRCbOXPmaObMmZZ9zjvvPKWnp8c1zxVXXGHZPmbMmLjGj3SccOsA4D63ckg6/KVUN954o5YsWWLZ77TTTtO4ceNsmROA2dzMIAAoi1s5RH0aQChO5xA1asDfOnXqpHr16oVs/+ijj2z78EBpaamee+45yz5dunRR+fLlYxqfGjUAE6Wnp+uSSy4J2V5YWKhnn3027nmKiorC3szrh3wK0uuSxJkFzEY+RSdo+QSYjHyKTpDyiXMOmI58ik4Q8onzD/gF+eSMn3/+2fILkZKSktSrVy8XV3Q0zkVgOvLJGX7IJ4n6OMxGPjnDL/kEmIx8coYf8on6OGAW8jh6hw4d0t/+9jfNnTvXsl+TJk00YcIEVapUyaWVRSco9Tb2MCLBPoleULJOonYXjbFjx1p+ObEkXXjhhS6tBtEi66IXpKxz2ptvvqnFixdb9ok3H9jDiAT7JHqJlnULFy60vJ7JyspSt27dXFyRN7iu8y9yzkyFhYUaPXq0ZZ+OHTsqIyMj5jmo1UWHPexv7BMzuZF1ErW6aHBN529knZncyjqnUasD4AQenGiQe++9V02bNg35v3vvvdfrJRqlYsWKuvzyy0O279u3T8OGDbNlrqefflrr1q0L2Z6SkqK+ffvaMhfgFTIoMueff75l+w8//KBXX33V1jnHjRun9evXW/ZJhEMwBJ+TOWT3l6WEsnz5cv3tb39TaWlpyD5JSUkaMGBA3HP17t1bVapUCdk+f/78sF/SH86MGTO0cOHCkO1Vq1b1/ENKgJ3IoegcPHhQN998s+WHGSWpbdu2eumll4wvvgJeI4MAeI0cig71acB+5FBkqFEDznHjrCw1NVXXXXddyPbi4mLde++9KioqinuuSZMm6YsvvrDsc+WVV8Y8PjVqAKbq16+f0tLSQrZPmTJF33zzTVxzvPLKK5bv05o0aaKzzjorrjl4XYoOZxbwA/IpckHKJ8APyKfIBSmfOOeAH5BPkQtCPnH+AT8hn+yXk5NjecZ6+umnq27duq6tpyyci8APyCf7+SGfqI/DD8gn+/khnwA/IJ/s54d8oj4OmIc8jlxJSYnuu+8+zZo1y7Jfo0aNNHHiRMt6lteCVG8Lyh6Gs4KyT8i66AShdufWZ/Leeecdvfbaa5Z9atWqpb/85S+urAexIesiF4SscysfPv30Uz3yyCOWfcqXL2/L++ug7GE4Kyj7hKxzxpQpUyzbL7jgAmVmZrq0mt/jug6RIufCszoHsVtpaalGjRoV9l7XPn36xDUPtbrocE3nf0HZJ2RddKjVRY5rumAg68ILUtZRq4sN13WAOXhwInztmmuuUaVKlUK2f/rpp3r66afjmuO9997TxIkTLfv07t1btWvXjmseAP7QuXNn1ahRw7LP2LFjNXv2bFvmmz59ul544QXLPtWrV1eXLl1smQ8IqqeeekqPP/64du/e7dgcixYt0nXXXaeCggLLfhdccIEaN24c93wZGRm68cYbLfvcf//9WrNmTUzjr1q1Sg8++KBlnxtvvFHlypWLaXwg0QQth4qKijRo0CAtWLDAsl+bNm308ssvq3z58nHNByA+QcsgAP4T1ByiPg34R5ByiBo14H+9e/fWcccdF7J95cqVGjZsWFw3pS1YsEBPPvmkZZ8zzzxTbdq0iXkOatQATFWzZk399a9/Ddl+6NAhDRkyRFu3bo1p/AULFmjcuHGWfW677baYxvZCEF6XOLOAX5BP0QlCPgF+QT5FJ0j5xDkHTEc+Rcfv+cT5B/yEfLJXSUmJpk+fbtkn3i87sQPnIvAD8slefsgn6uPwC/LJXn7IJ8AvyCd7+SmfqI8DZiGPI/fAAw9oxowZln0aNGigiRMnqlq1ai6tKjZBqrexhxEJ9knkgpJ1Qand3XjjjXr77bdVVFTk2Bxvvvlm2MyWpJtvvtnyS5LhPbIuckHIuqFDh2r8+PFhP08bj5kzZ2rw4ME6dOiQZb+rrrpKVatWjXs+9jAiwT6JXBCyLhr79u3Txx9/bNnHy/oo13WIFDkX3tChQ/XSSy8pLy/P0XkOHTqk+++/X++8845lv5YtW+rss8+Oay5qdZELwh4G+yQSQcs6anWR45ouOMi68IKUddTqoheEPQwECQ9OhK9VrVpVd955p2WfF198UY8++mjYF9KyvP3227rnnnssPxxeoUIFDRo0KOqxAfhTenq6rr32Wss+paWluuOOO/Tiiy/G/NT4Q4cOacyYMRE9sf7aa69Venp6TPMAieLAgQP6xz/+obPPPlsjR46M+dClLEVFRXrxxRc1YMAA7dy507Jvdna27rnnHtvmvvLKK3X88ceHbM/Ly1P//v21cuXKqMZdvny5rrnmGuXn54fs06hRI1155ZVRjQsksiDl0KFDh3TnnXdq3rx5lv1OPPFEvfLKK8rKyoprPgDxC1IGAfCnoOYQ9WnAP4KUQ9SoAf9LT0/X8OHDLftMnz5dd955pw4ePBj1+HPmzNGNN96o/fv3h+yTmpqqu+++O+qx/4gaNQBTDRo0SFWqVAnZvnnzZg0YMEC//PJLVON+/vnnuuWWW1RcXByyzxlnnKGuXbtGNa6X/P66xJkF/IZ8ipzf8wnwG/IpckHKJ8454AfkU+T8nk+cf8BvyCf7zJ8/X1u2bAnZXqlSJWN+Xs5F4Afkk31Mzyfq4/Ab8sk+pucT4Dfkk338lE/UxwHzkMfhPfzww/rXv/5l2adevXp6/fXXVaNGDZdWFZ8g1dvYw4gE+yS8oGRdkGp327Zt0wMPPKBzzjlHL7/8ctjPzkVj9+7dGjx4sEaPHm25fyWpVatWuvTSS22bG84h68ILStbt2bNHY8eO1dlnn62nnnoq6t+plYKCAo0ePVp33HFH2C97r1u3rm655Rbb5mYPIxLsk/CCknXR+Pe//60DBw6EbD/hhBN08sknu7egP+C6DtEg56zl5ubqqaeeUufOnfXiiy/G/KAWK99++6369Omjd99917JfcnKy/v73v9syJ7W68IKyh3EY+8RakLKOWl1kuKYLJrLOWpCyjlpdYu5hIEhSvV6AXYqLiy1voizLjh07LNsLCgq0cePGqMasWLGiKlasGNXfQXwuvfRSzZo1y/Jp7a+99pr++9//atiwYWrXrl3YMdeuXauHHnpIn3/+edi+w4cPD0yxHbEjgxLLlVdeqZycHK1evTpkn0OHDunpp5/WJ598ohtuuEGdO3dWUlJS2LEPHTqk2bNn6+WXX9aKFSvC9j/hhBP48DMkkUOR2r9/vyZNmqRJkyapVatWOvfcc9WpUyedcMIJEf0b/a28vDxNmTJFr732WsT/7R955BHVrFkzlqWXKS0tTaNGjdLVV18d8sM327dv12WXXabBgwfryiuvtPwSl8LCQr3xxht69tlnLb8YJyUlRaNGjVJqamDeTsAG5FBkgpBD9913n2bPnm3Zp06dOho1apT27NmjPXv2xDVfJOrVq+f4HDAbGRSZIGQQYCpyKDJBzCHq0zAFORSZoOQQNWqYiByKTocOHdS7d2/l5OSE7DNz5kx99913uu+++9SlS5ewY27ZskWPPfaYZs6cGbbvwIED1axZs6jWXBZq1ABMValSJd1///0aMmRIyD5r165Vr169dM8996hHjx5KTk4O2TcvL08vvfSS/vGPf1h+qV1WVpYeeOCBeJbuCT+/LnFmAb8hn6Lj53wC/IZ8ik6Q8olzDpiOfIqO3/OJ8w/4CflkH6vMkqQLL7zQmIeYci4CPyCf7GN6PlEfh9+QT/YxPZ8AvyGf7OO3fKI+DpiFPLb2zDPP6PXXX7fsU7FiRT3yyCMqLi6O+v7rWNSqVSvuelWQ6m3sYUSCfWItSFkXxNrdtm3bNGbMGD3zzDP685//rM6dO+uss86K6XNyrBcVoAABAABJREFUmzZt0sSJE/Xuu++G/ZJl6fC/nTFjxlj+e4A5yDprQcq6I3Jzc/XSSy9p/Pjxateunbp27aqOHTuqfv36UY+1a9cuTZo0Sf/85z+Vm5sbtn9aWprGjh2r8uXLx7DysrGHEQn2ibUgZl0kwtVHe/fu7ej8keK6DpEg5yKTm5urp59+Ws8884xOPvlkdevWTV26dIn5/dfBgwf1ySef6L333tPcuXNVWloa9u9cd911atu2bUzz/RG1usTbw4mOfRKZIGQdtTprXNMFG1kXmSBk3RHU6hJzDwNBEJhPS23ZskWdO3e2dczZs2eHvaD9o4EDB2rQoEG2rgPWkpKSNHbsWPXs2dPyyzhXrVqlq666Sscff7y6dOmi1q1bq1q1aqpSpYr27dunHTt2aNWqVZozZ46+//77iC4kLr/8cl100UV2/jjwKTIosaSmpmrs2LG69NJLw76hX7JkiW699VbVqVNH7du316mnnqp69eqpUqVKysrKUl5envbs2aMNGzboyy+/1KJFi7Rt27aI1lG+fHmNHTtWaWlpdvxY8DlyKHrfffedvvvuOz355JPKyspS8+bN1bx5czVt2lRVqlRRhQoVVKFCBWVnZ6ugoEC7d+9Wbm6uVq5cqS+++ELff/+9ioqKIp5v0KBBOvfcc23/Odq2bavbbrtNTz31VMg+Bw4c0GOPPaYJEyboggsuUNu2bVW/fn1lZmYqPz9fP//8s7788kvNnDlTO3fuDDvn4MGDdeqpp9r5YyAAyKHo+TWHZsyYEbbPpk2b1LNnz7jnitSqVatcmwtmIoOi59cMAkxFDkUvKDlEfRqmIIei5+ccokYNE5FD0Rs+fLiWL19u+SXdGzdu1K233qq6devqnHPOUdu2bXXMMceoWrVqKigo0I4dO7Ru3TrNmTNHX3/9dcgPIvzW2WefrRtuuMG2n4MaNQBT/eUvf9HChQv1zjvvhOyTm5ur++67T+PGjdNf/vIXtWnTRnXq1FFGRoby8vL0448/atGiRfrwww+1b9++sHOOHDlSxx13nJ0/hmv8+rrEmQX8iHyKjl/zCfAj8ik6QcknzjngB+RTdPycT5x/wG/Ip/jt3r1bc+fOtexjyhfHHcG5CPyAfIqfH/KJ+jj8iHyKnx/yCfAj8il+fswn6uOAecjj0CJ5D7h3715dddVVLqzmsDlz5sT9xb5SsOpt7GFEgn0SWpCyLsi1u+LiYn3yySf65JNPJEnVq1dX8+bN1aJFCzVs2FCVKlX69TN5GRkZ2rt3r/bs2aMdO3ZoyZIl+vLLL7VmzZqIrpslKT09XU899VRMX+oM75B1oQUp6/6otLRUixYt0qJFizR69GhVrlz518/sNm7cWJUqVVLFihVVoUIFlS9f/tf7Onbt2qWlS5fqiy++0MqVKyO6R0U6/L5+1KhROvnkk+Ne+x+xhxEJ9kloQc66UFavXq2lS5eGbE9LS9PFF1/s2Pyx4LoO4ZBzkSstLdXixYu1ePFiPfLII6pSpYqaNWumZs2a6YQTTlDlypV//feUlZWloqIi5eXlKT8/X9u2bdOqVau0YsUKff/998rPz4943nPPPVd33HGHrT8LtbrE3MOJjH0SOT9nHbU6rukSHVkXOT9nXVk/C7W6xNvDgJ8F5sGJSGxVq1bV+PHj1b9/f+3evduy748//qiXX3457jnPPfdcDRs2LO5xAPhT48aN9dRTT2ngwIERfTn2pk2bNG3aNE2bNs2W+dPS0jRmzBg1bdrUlvGARJefn6+vvvpKX331lSPj33rrrRo4cKAjY0vSjTfeqHXr1mn69OmW/bZv367XX39dr7/+esxz9ezZky8HBBzg9xwC4G9kEACv+T2HqE8D/ufHHKJGDfhfRkaGXnrpJV111VX6+eefLfv+8ssvevPNN/Xmm2/GNWebNm00duxYpaSkxDXOH1GjBmCq+++/Xxs3btR///tfy34bN27U+PHj45rr1ltvVffu3eMaw0tBel0C/IB8ihz5BLiLfIpckPKJcw74AfkUOb/nE+cf8BvyKT7vvfee5b/1Fi1aqEWLFi6uKDKci8APyKf4+DWfAD8gn+JDPgHOIZ/i49d8oj4OmIc8TkxBqrexhxEJ9gmCZPv27dq+fbs+/fRT28fOyMjQs88+qzPPPNP2seE8sg65ublasGCBFixYYPvYKSkpevDBBx19qAV7GJFgn+CIKVOmWLZ36tRJ1apVc2k1seG6DmUh52Kze/dux66DjujWrZueeOIJJScn2z42tbrYBGkPJxr2SWz8nnVBxTUdQiHrYhOkrKNWF7kg7WHAT7jiQ2A0a9ZMEydOVPXq1R2fq1evXnr66af5YhwgwXXq1EkvvPCCsrOzXZ03KytLzz//vDp37uzqvACil5GRoccff1y33Xabo/MkJSXp4YcfdvxNdffu3fXQQw8pKSnJ0XkA2MetHAKAspBBALzmZg5RnwZQFqdziBo14H+1atXSG2+8oYYNGzo+V4cOHTRhwgRlZmbaPjY1agCmSk9P1wsvvKD27ds7Os91110XiBpYUF6XAD8gn6JDPgHuIZ+iE6R84pwDpiOfouP3fOL8A35CPsVn6tSplu29e/d2aSXR4VwEfkA+xcev+QT4AfkUH/IJcA7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      "text/plain": [
       "
"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 860,
       "width": 3623
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(\n",
    "    idata,\n",
    "    var_names=list(true_values.keys()),\n",
    "    col_wrap=3,\n",
    ")\n",
    "az.add_lines(pc, true_values);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89e6a59-db38-499e-a6ac-73a53a4fca19",
   "metadata": {},
   "source": [
    "Pretty nice fit!\n",
    "\n",
    "Now we can use the `do`-operator for interventional what-if analysis: generating predictions for the target variable under different hypothetical settings of the predictors.\n",
    "\n",
    "_What if all values of `b` were 0?_\n",
    "\n",
    "_What if all values of `b` were doubled?_\n",
    "\n",
    "Here's how.\n",
    "\n",
    "### Step 5. Use `do`-operator to generate target variable with different what-if scenarios\n",
    "Since we want to experiment with `b`, let's first assign observed values to `a` and `c`. Not to `y`, because that's what we want to sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0643453-0bb6-4ceb-9fb1-3adf57f031b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-02T07:48:54.142410Z",
     "iopub.status.busy": "2026-05-02T07:48:54.142347Z",
     "iopub.status.idle": "2026-05-02T07:48:54.146555Z",
     "shell.execute_reply": "2026-05-02T07:48:54.146246Z"
    }
   },
   "outputs": [],
   "source": [
    "model_counterfactual = pm.do(model_inference, {\"a\": df[\"a\"], \"c\": df[\"c\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8067a3-6430-4cf9-b53e-a5bfbd8e4488",
   "metadata": {},
   "source": [
    "Now, let’s begin the fun part — generating what-if predictions.\n",
    "\n",
    "### _Scenario 1 :- What if all values for 'b' were 0 ?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7cbeba3-d047-447f-bfe2-077604fd2fd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-02T07:48:54.147706Z",
     "iopub.status.busy": "2026-05-02T07:48:54.147647Z",
     "iopub.status.idle": "2026-05-02T07:48:54.154550Z",
     "shell.execute_reply": "2026-05-02T07:48:54.154197Z"
    }
   },
   "outputs": [],
   "source": [
    "model_b0 = pm.do(model_counterfactual, {\"b\": np.zeros(N, dtype=\"int32\")}, prune_vars=True)\n",
    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b23275-029d-40a2-8603-796c740bed29",
   "metadata": {},
   "source": [
    "Just sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edd9b175-5a4b-457a-b9d9-560251733600",
   "metadata": {
    "execution": {
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     "text": [
      "Sampling: []\n"
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       "Output()"
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       "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: []\n"
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       "Output()"
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    },
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      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Sample when 'b' was 0: P(y | (a,c), do(b=0))\n",
    "idata_b0 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b0,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
    "idata_b1 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b1,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cabe3d1-ecab-4bb8-bcea-d45e91ca0b04",
   "metadata": {},
   "source": [
    "Some basic Python and here we have the what-if predictions."
   ]
  },
  {
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   "execution_count": 16,
   "id": "4d853c28-4029-4372-a46d-132239918fe5",
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       "
abcyb_scenario_1y_scenario_1
00.510869-1.311934-0.362795-0.30694900.681877
1-0.734647-0.5397760.019862-1.1626610-1.122175
2-0.3586040.343649-0.093558-0.2781480-0.576914
3-0.251229-1.154599-0.542470-1.4891630-0.537017
4-1.3160970.114966-0.264377-2.1433150-2.093052
\n",
       "
"
      ],
      "text/plain": [
       "          a         b         c         y  b_scenario_1  y_scenario_1\n",
       "0  0.510869 -1.311934 -0.362795 -0.306949             0      0.681877\n",
       "1 -0.734647 -0.539776  0.019862 -1.162661             0     -1.122175\n",
       "2 -0.358604  0.343649 -0.093558 -0.278148             0     -0.576914\n",
       "3 -0.251229 -1.154599 -0.542470 -1.489163             0     -0.537017\n",
       "4 -1.316097  0.114966 -0.264377 -2.143315             0     -2.093052"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "df[\"b_scenario_1\"] = 0\n",
    "df[\"y_scenario_1\"] = (\n",
    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
    ")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad2d911-2948-4553-9322-121f064696e0",
   "metadata": {},
   "source": [
    "### _Scenario 2: What if ‘b’ was 5 times as observed_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c22e18f-cb96-4ca9-93f4-e8323b1528b1",
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   "source": [
    "model_b0 = pm.do(model_counterfactual, {\"b\": 5 * df[\"b\"]}, prune_vars=True)\n",
    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5422848d-e6cd-4f3e-8641-640ed227ea78",
   "metadata": {},
   "source": [
    "Sample."
   ]
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   "id": "97990259-7cd9-4801-8af0-b8e3a46ffa7b",
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abcyb_scenario_1y_scenario_1b_scenario_2y_scenario_2
00.510869-1.311934-0.362795-0.30694900.681877-6.559670-3.849755
1-0.734647-0.5397760.019862-1.1626610-1.122175-2.698878-2.986647
2-0.3586040.343649-0.093558-0.2781480-0.5769141.7182470.610107
3-0.251229-1.154599-0.542470-1.4891630-0.537017-5.772996-4.525190
4-1.3160970.114966-0.264377-2.1433150-2.0930520.574829-1.695942
\n",
       "
"
      ],
      "text/plain": [
       "          a         b         c         y  b_scenario_1  y_scenario_1  \\\n",
       "0  0.510869 -1.311934 -0.362795 -0.306949             0      0.681877   \n",
       "1 -0.734647 -0.539776  0.019862 -1.162661             0     -1.122175   \n",
       "2 -0.358604  0.343649 -0.093558 -0.278148             0     -0.576914   \n",
       "3 -0.251229 -1.154599 -0.542470 -1.489163             0     -0.537017   \n",
       "4 -1.316097  0.114966 -0.264377 -2.143315             0     -2.093052   \n",
       "\n",
       "   b_scenario_2  y_scenario_2  \n",
       "0     -6.559670     -3.849755  \n",
       "1     -2.698878     -2.986647  \n",
       "2      1.718247      0.610107  \n",
       "3     -5.772996     -4.525190  \n",
       "4      0.574829     -1.695942  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample when 'b' was 5 times b: P(y | (a,c), do(b=5*b))\n",
    "idata_b0 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b0,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
    "idata_b1 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b1,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "\n",
    "df[\"b_scenario_2\"] = 5 * df[\"b\"]\n",
    "df[\"y_scenario_2\"] = (\n",
    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
    ")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2005665d-300b-4a01-91e4-a0901dbbaa97",
   "metadata": {},
   "source": [
    "Now you've got the idea. It's an open playground — intervene on whichever variable you like and see how the predicted outcome changes.\n",
    "\n",
    "This opens the door for many possibilities in causal analytics: comparing treatment policies, sensitivity checks, and programmatic A/B testing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b743d58b-2678-4e17-9947-a8fe4ed03e21",
   "metadata": {},
   "source": [
    "## Authors\n",
    "- Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023\n",
    "- Updated by Osvaldo Martin in February 2026\n",
    "- Updated by Osvaldo Martin in April 2026\n",
    "- Revised wording around interventions and counterfactuals, by [Benjamin T. Vincent](https://github.com/drbenvincent) in March 2026"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closed-frank",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- Pearl, J., Glymour, M., & Jewell, N. P. (2016). *Causal Inference in Statistics: A Primer*. Wiley. Sections 4.2.3–4.2.4.\n",
    "- Khandelwal, S. (2023). [Counterfactuals for Causal Analysis via PyMC Do-Operator](https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80). Medium.\n",
    "- PyMC Labs. [Causal Analysis with PyMC: Answering \"What If\" with the New Do-Operator](https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0717070c-04aa-4836-ab95-6b3eff0dcaaf",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sound-calculation",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-05-02T07:48:54.387826Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon, 04 May 2026\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.14.4\n",
      "IPython version      : 9.12.0\n",
      "\n",
      "pytensor: 2.38.0+158.gb3cfb34cb\n",
      "\n",
      "arviz : 1.1.0\n",
      "numpy : 2.4.4\n",
      "pandas: 3.0.2\n",
      "pymc  : 5.28.0+58.gf58491a3\n",
      "\n",
      "Watermark: 2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4386fc-4de9-4535-a160-d929315633ef",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
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