From a35fddb7784be056b8ac2b956dec9ee69549868e Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Sun, 24 Jan 2021 16:43:29 +0000 Subject: [PATCH 1/7] create truncated regression example --- .../GLM-truncated-regression.ipynb | 1089 +++++++++++++++++ 1 file changed, 1089 insertions(+) create mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb new file mode 100644 index 000000000..9a34f145f --- /dev/null +++ b/examples/generalized_linear_models/GLM-truncated-regression.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Truncated regression\n", + "\n", + "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", + "\n", + "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on PyMC3 v3.10.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pymc3 as pm\n", + "import arviz as az\n", + "\n", + "print(f\"Running on PyMC3 v{pm.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def truncate_y(x, y, bounds):\n", + " keep = (y >= bounds[0]) & (y <= bounds[1])\n", + " return (x[keep], y[keep])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m, c, σ, N = 1, 0, 2, 200\n", + "x = np.random.uniform(-10, 10, N)\n", + "y = np.random.normal(m * x + c, σ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bounds = [-5, 5]\n", + "xt, yt = truncate_y(x, y, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", + "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", + "ax.axhline(bounds[0], c='r', ls='--')\n", + "ax.axhline(bounds[1], c='r', ls='--')\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear regression of truncated data underestimates the slope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_regression(x, y):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", + "\n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\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 12 seconds.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "linear_model, linear_trace = linear_regression(xt, yt)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", + "\n", + "Let's visualise how bad that fit is by plotting the data and posterior predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 623 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def pp_plot(x, y, trace):\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " # plot data\n", + " ax.plot(x, y, 'k.')\n", + " # plot posterior predicted... samples from posterior\n", + " xi = np.array([np.min(x), np.max(x)])\n", + " n_samples=1000\n", + " for n in range(n_samples):\n", + " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", + " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", + " # plot true\n", + " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", + " # plot bounds\n", + " ax.axhline(bounds[0], c='r', ls='--')\n", + " ax.axhline(bounds[1], c='r', ls='--')\n", + " ax.legend()\n", + " ax.set(xlabel=\"x\", ylabel=\"y\")\n", + " \n", + "pp_plot(xt, yt, linear_trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Truncated regression avoids this underestimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def truncated_regression(x, y, bounds):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + "\n", + " y_likelihood = pm.TruncatedNormal(\n", + " \"y_likelihood\",\n", + " mu=m * x + c,\n", + " sd=σ,\n", + " observed=y,\n", + " lower=bounds[0],\n", + " upper=bounds[1],\n", + " )\n", + " \n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", + "
\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 13 seconds.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And also by doing our graphical posterior predictive checks. Looks good." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pp_plot(xt, yt, truncated_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Sun Jan 24 2021\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.8.5\n", + "IPython version : 7.19.0\n", + "\n", + "pymc3 : 3.10.0\n", + "matplotlib: 3.3.2\n", + "numpy : 1.19.2\n", + "arviz : 0.11.0\n", + "\n", + "Watermark: 2.1.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From bc3d65921969fcb245e66e474c43f6315515cdb1 Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Mon, 25 Jan 2021 16:24:38 +0000 Subject: [PATCH 2/7] delete truncated regression example from main branch --- .../GLM-truncated-regression.ipynb | 1089 ----------------- 1 file changed, 1089 deletions(-) delete mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb deleted file mode 100644 index 9a34f145f..000000000 --- a/examples/generalized_linear_models/GLM-truncated-regression.ipynb +++ /dev/null @@ -1,1089 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Truncated regression\n", - "\n", - "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", - "\n", - "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on PyMC3 v3.10.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pymc3 as pm\n", - "import arviz as az\n", - "\n", - "print(f\"Running on PyMC3 v{pm.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def truncate_y(x, y, bounds):\n", - " keep = (y >= bounds[0]) & (y <= bounds[1])\n", - " return (x[keep], y[keep])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m, c, σ, N = 1, 0, 2, 200\n", - "x = np.random.uniform(-10, 10, N)\n", - "y = np.random.normal(m * x + c, σ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "bounds = [-5, 5]\n", - "xt, yt = truncate_y(x, y, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", - "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", - "ax.axhline(bounds[0], c='r', ls='--')\n", - "ax.axhline(bounds[1], c='r', ls='--')\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear regression of truncated data underestimates the slope" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def linear_regression(x, y):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", - "\n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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\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 12 seconds.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "linear_model, linear_trace = linear_regression(xt, yt)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", - "\n", - "Let's visualise how bad that fit is by plotting the data and posterior predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 623 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def pp_plot(x, y, trace):\n", - " fig, ax = plt.subplots(figsize=(10, 8))\n", - " # plot data\n", - " ax.plot(x, y, 'k.')\n", - " # plot posterior predicted... samples from posterior\n", - " xi = np.array([np.min(x), np.max(x)])\n", - " n_samples=1000\n", - " for n in range(n_samples):\n", - " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", - " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", - " # plot true\n", - " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", - " # plot bounds\n", - " ax.axhline(bounds[0], c='r', ls='--')\n", - " ax.axhline(bounds[1], c='r', ls='--')\n", - " ax.legend()\n", - " ax.set(xlabel=\"x\", ylabel=\"y\")\n", - " \n", - "pp_plot(xt, yt, linear_trace)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Truncated regression avoids this underestimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def truncated_regression(x, y, bounds):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - "\n", - " y_likelihood = pm.TruncatedNormal(\n", - " \"y_likelihood\",\n", - " mu=m * x + c,\n", - " sd=σ,\n", - " observed=y,\n", - " lower=bounds[0],\n", - " upper=bounds[1],\n", - " )\n", - " \n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " \n", - " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", - "
\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 13 seconds.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And also by doing our graphical posterior predictive checks. Looks good." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8R2b22Qe7OhERERERERF5q+4Lo95VWLb0zrGdA6uIoMdY7O8XI5i7akyl3HuypdvNky1fdgImjECw9fP453iNMYo5FaP4446M1DB7icz8/wJ//taX/4aZ/beA/zPwB4E/DfwvHvh6P3rX19fm2Y+8waWKiIiIiIiIyBt6nyOYt1tlAD1j7Ce7tdh/KiO8uh3k3XV9mUnCvZ9ja5X1TOJiBNPNKM6jD8o2ugtfQWY24H+z/vYPf8hrEREREREREZE39z7DsrtaZS06jp1GMLdW2a6W9fpuvsbWFrtrBPPyc9xulfW1VbaFZb7++dYqaxG0CB47Ncy+uv/f+l+NZIqIiIiIiIh8pB56yuTbsvRO6+ffb4v9L1tl7rArfmqEfdXF/petskxoEfe2ykZodxHiEfgjbpspMPvq/vH1v//FB70KEREREREREflK7grL3tcI5rbYv5hjF4v9p3Iei3zILrX7Ar/tUT1yDcPOf77tKqtrQ+0ySDtdL497LPExf/ZXMrM/aGa7O77+48CfXX/7V9/vVYmIiIiIiIjIm7prKf77HMGMDKqX0/u5w35yaikkL45g+h1h2csOANheo12EZdt4ZinGVJzMZG4vhmXVoT7idhk8woaZmf0E8BPrb7+z/vcPmdnPrb/+LzPzX1h//TPAP2pmfx349fVr/3Xgx9df/0uZ+X99l9crIiIiIiIiIm/X7fHFdzWCeddi/xaBk5SLEczqUF9zsf8dD2NdVfbSVlkx8HVX2e2gzBWUnTy6wAz4YeBP3fra71v/Bfh7wBaY/RXgfwD8N4A/BkzAPwD+D8Bfzsy/+a4vVkRERERERETejve5r6z1znKxqywi6JlUP49gmnFatn/XDrK7ru1lj3vICZgRwdxeXOpffQRpPYJEwdmjC8wy86eAn3rgY/9N4N98l9cjIiIiIiIiIu/e+zwFc+6dfhGWtQiMZCovLvZ/6LU9pFXWI+hfsVWWmSw9tPR/9egCMxERERERERF5XN5XWNZ7Z35FqwxujmA+pPH2kFbZ5QmXX6VVtgVtmTn2n2GPevG9AjMRERERERER+SS9zxHMpXfaRVjWI+COVtlU/BR0va1WWSSn13rICZh3tcq2oCyTEbS9g3v0MVFgJiIiIiIiIiKfnPcVlmUmcz+HUpljh5iT+AMX+3/lXWVx/ox3tcqW/mLadlerbHsv9y2MG6dsPubITIGZiIiIiIiIiHxS3tcI5l2L/YOk+sNbZbfDsgedgJkjLPsqrbIRpJ1bZdt7FUtgtM7MHntcpsBMRERERERERD4ht0Opd9UqWyJeWOzvt8KysrbKjBdbZfBiiPchWmVmiZuRaUCerifHH36l+/MpUGAmIiIiIiIiIh+99zWCGRHM/RxsRQQtkqkYZuX0vtWN4mO08VXXdbk/7NLWKtsaZW+7VeaWmDmRiQGGYTa6Ze/i9NCPiQIzEREREREREfmova8RzPsW++/qi62yN1nsvwVqeRGU3W6VuTECucx7W2Vm9pJWmZM5vraFd9u1vYt797FRYCYiIiIiIiIiH633EZbdbpVti/2LcaNVVuzNF/tv1/6yVllxw1/RKhtBWd5olY1ruNkqc7PTtW3tssjUKZkf+gJERERERERERF7X+xrBvL3Yv0cQmVT3Uyj3qsX+r9sqaz1e2SqbX9Iq28Yv4eIEzDUou69VZozHtdiuOR91y0yBmYiIiIiIiIh8VN5HWDZGHc/jjJlJi8QtmcrDFvvftQ/sQ7TKRunt/lYZQI+xQ62vL+7uVAVmIiIiIiIiIiJffx9iBLNHkMBUDBgjl19lsf8dB2WeFvvf1SozRqPszVtlSXJ3qyxiXPsWCI7ngd2+yY+MAjMRERERERER+SjcDsvexQjm5WL/bem+W1L87lbZfSHYq1pl27VvrbK4FZaV12iVtTVIu90qy7QHt8p6rNfk62joI26XgQIzEREREREREfmaex8jmLdbZZE5dn05+MVi/61VNh7z8mt6SKss8vL0yrfXKov1Dy5bZe4GeXerzEhs/ccNLf3/0BcgIiIiIiIiInKf9zGC2daW1nn3V76w2L84FHfcIOGV1/SyVtk4OIB31iobrbh17PLiwIAEet7dKjMcyHXf2RgzfcyRmQIzEREREREREflaetdhWUSw9HNotTWuysVi/8sg664RzNdtlW2tsMvPVtYxyLfVKtsacFurbDuQYIR0cQrNuGiVmSVufuNaHzMFZiIiIiIiIiLytfI+RjD7Gpa9bLG/X4Rldy32fxutsup2DrXWcckb7/EGrbJx1ZzGLs+ttcTMcHPMcj3J02/c48eemSkwExEREREREZGvjfcRlt212N9Iijtgr2yVwc2w7Ku2yoqPkIpMWr445vkmrTJg7CqLJCJo6/gl62mZIzDLU1A2QjN76/f6Y6XATERERERERES+Ft73CGZkkiSl2LrD6+Zi/7taZbcDpVe1ysZJlC+2ymzdK3Zfq6ysJ2je1yqLNOKeVllEnvasba2ybSvZCOly/Qx24/6+7d1wHzMFZiIiIiIiIiLywd0Ont520+n2Yv+tVebuGDcX+xv5ysX+mfc/Bh7YKgte+MxlVL1OY5TAxYmXI/TalvXf1Srr62tetsrcgbTTa7wwfqlW2QsUmImIiIiIiIjIB/OuRzDvapX1dbG/+32L/dcjJe+5npdd80NaZeMabj5/a5X1GA2xy/cZwZ6N64IbrbLiBpmn3WiZeWqt2cVSfzwpF0v9X9Yqy8xH3zRTYCYiIiIiIiIiH8RdwdPbHAu8q1WGQb0YwXwbi/3fdqtsa6/BuR3W42ZQZnC65u3Uyy2su1zqP07CzHXB/zn4uy8o2z5/edx5mQIzEREREREREXn/3uW+ssykxQjItgAr4iI4esBi/8tF+NtrvqpVdvm5LltlRq6B1s3nF9+ef26VnZ5/apXdvDc3xy/PrbLzCOd5qf/YVfawpf7b+25hneXjHtNUYCYiIiIiIiIi7827HsHsa6ssLsKf24v9t1bZ6TTJr7DY/3ar7EY766JVZvDgVtm21P++VplfhG/bNd3XKhvP8weNX17eq9aDhDG+qcBMREREREREROTdetdh2V0jmFs4xkWr7DJ4uu32Yv/7mmeXe8he1iprr9kqG/+zHULw4lL/iO3xo1U23N0qe9lS/8vPtjXwTq+XSWSu8eLjpMBMRERERERERN65dzmCuS32306tjC3wOb3+eZTxxmL/Cw9plW1h2bZU/75W2Vi8/2KrrK47x+5rlWUameDc3yoby/1HWGeMJxd38jVaZZfjl319vUjovZ9CwP2uvOm35aOmwExERERERERE3qm7wqetPfWmWgT9YgSzR44TJ91Ou8p83f31kMX+d7XKtsdkJstLWmW8ZFeZcd45duP5a6ss8uLUS7h5vXEOAcdetlxbbNs9TKq/fqus9Rj3LYKWrCdrjpZaj6D64+2YKTATERERERERkXfiXY5g3rXYP3Ms9jfGYv+tVba1tL7KrrKHtMrWK1pDu5vP3Vpl/VWtsotdZZcHEWytsnPYtu0qs7GbzcYutsul/peHFWz3amvfbaFiW4O3yKS1cdHmo61H2qMexwQFZiIiIiIiIiLyDtwVlr2tEczbi/1jPdaxuJFpp9MkLxf7560RzLfdKrv9/K/aKtuKd9vzLltlY7R0DcVsLOZ/6Pjl9uvWxyEBEWP8MnKEihmd6I471Pq4F/6DAjMRERERERERecve1b6yrVUWF2FSxK1dZf56i/3fRatsOwHzdVtl4zMyPt89rbJtwf/WnNve82Xjl7dbZS2S3sdndILoDu5M1U7tvLzrxj0iCsxERERERERE5K14lyOYPdZmVJ4DM+wcll2OYJZ7WmWX1/K6rTL3sYzf3XlZq2w8hxsnYMKrW2WXwdrWKuOOVpl/5VbZGL9c2sX1GEQ6pWwHFhjmSTWoRUv/RURERERERETeyLsMyy4X+59aZb61s24u9t9GGl/WcHtoq2zb+1V9C8rgvlbZeF+7cQJlrC2yXE+zfGmrbDuxcg22bE3I/OI+vqpVdvm5cg3K2uX4ZYznBQHpmCW76dwoK5a4jVnSxz2QqcBMRERERERERN7QuxrBjAh6chEkwRhJhPU8xxcW+ycGL4RZ51bZFoLdvtbIpK2tslPY5Q/bVQbn0Gu7H5evCzcDsm2/2eUS/i0M3HaVrR8RNzsdAvCypf6X19UjTqOrSw8itnsRRBheRqvMrUCCl6R6Ob9/Qvg4UOCxUmAmIiIiIiIiIl/ZXWHZFgy9icsRzG0XmFluUdIpJNsCM3j9xf5b+NTi5ghj5sN2lb2qVRa57lW7aJVtp2ZGJMnNVtkI1uzUkjuFZQ8cv8wcAVmPpK/tMtb7Nv5xar09fulriJhEBPOaGk71cZ+TqcBMRERERERERF7buxrBvGuxf2aeG1Z3tcpecR33NeDuapWZwVRe3irzdZ/YQ1pl23UUH7++HCu98flsa5GNz1leMX55+/633ln6CL228cvxgkHi43PVMX4ZmdR1/DLXh809aD3I9XlL7xeB4eOjwExEREREREREXstdYdnbGMG8f7H/1rA6L8D/qov931Wr7LxE/8VW2eWpmTcDs9tL/Udo9rJW2e2x0sxkXltlrQfRt693wHH39XPVEcx5si/nVlnPZG4jKBsTr2tq+MgpMBMRERERERGRB3sX+8ruapVti/239fNbiHTZ7rrtVYv9H9Iqy7XxlbfW3r+sVTZ2pwF5bpVt4Zqty/5j/Xx9HcUcudS5Vfa6S/1htMrmNlpl2/hlZpAG7uVGAGieVOwUhmUmc+v0fjMoM4epGpNOyRQRERERERERebl3NYK5tcoul99DjgbZFj7BRZi0DRHefR33XSdrUPayVlmuwddlWPaqVllu2/k5B2TbaZ2JXYRk3GqVcfp8fgrZ7m+VXX6miBitsh60hFxbZUHHzXGHXXW2KK8amJ1PJzi2Ts/11MyLz1kKVC9vZQfdx06BmYiIiIiIiIi81LsIy8Zr5ssX+9vrLfa/q1U2WmHjfS4/y5u2yraRTeMlrbI8L/UfrbJzSy4Tyq2l/rfv6V3jly2Cpd8cv4wcH654oZTx38zEPZncx/duHb88toA4x46ZiRdjV8p6j0dQ+abjtR87BWYiIiIiIiIicq93PYJ52SrzscjrtRf73xXowehX9XfQKht78Q13TocRlPXXl62yyHMweN5rdn78aVzyAeOXvXfmnrTW6Wmn8csgRyusgNv5M01lvFmuXzm0PgK2NUAc9bZkV51atutYR0S542Y+MgrMRERERERERORO7yIsu1zsf7tVlozW1bmtNSKou5tfr26VXe4q2x7yqlaZrcHd1hC7fO6pVWY3l/pXt1MT7GWtsi00e9VS/8vwbyz17yxtNMQyxrUHQTFnV8t4/zUDKwbu5/HLpXeWdrGnbG2Q1QK7OrF1zcb1Jcel0yL4/uKPevm/AjMRERERERERueFdjWBuJ1NuoRnkmsmM1623Fvuv73zjGrZG10NbZdvjHtIqG6/vpxAuzi9yb6vMzU5Nsq1V1iNOV3OzVfYVlvr3ZGmdjC0gjBHS2TZ+Oa63+ha82Sko6wmxLfVflQKTl9OI6xZKLi3GbrMOJMytcbXbveQ7+mlTYCYiIiIiIiIiJ3cFUW+rVXYZmp1HMM+7yi5bZS9b7P+QVtn2OHhYq8wYY479olVmF+91u1VWtqX+a0AW20mY6zMvxzq/Sqvs0Dpt6fQYIVhEjAX+peDrcv5xncmurJ9gvQfXrZP9Yk9ZJOawn5xS/Mb19QiOLZiXINdwrvXO1ZMrrr7C9/pTocBMRERERERERIC3P4J5u3U1mlg3F/sXe3mr7PIa3nerbKz8Gq2yLawrfl7qfxkARsY4ddIMty00s1Nrbnu/20v9b3+epTWOt8YvewbFjalWim2fOEfDbRu/jGDpQevbeOi4hrRkt3OmUsanX8PJyOB67hzncQJA653n1wvRgihQbv8gPDIKzERERERERESEfiu5edMRzMtdXrcX+29tqOJ+PmHyjl1l77tVFrm2rjLhVqvMHZwRuF2Old5Y6u9bq8xfWOq/jXFeXueN8cvWaMBxPo9ftgjMkqkUpgK2jl8W43yi5banrK+BYcYpeSwV9uuesnENo713XDqHuZMxgrLj3Fha0CLHTjTgGMFnr/Ud/7QoMBMRERERERF5xN72vrItQLo9gnm52H9rXT1ksf82HvlCWHZHqyzX521B1ba0/4U9aPe0yowRvrkZdtEqqz6eeFerLHMEV26JmZ8OAXjo+CXAoTWW+Y7xSzPKVKheiEzs1vhlRHDscWNP2Xavz+OX53vc+hi/XFpCJIfjMvaWxXivnRmtB7WyNtIeLwVmIiIiIiIiIo/UuxjBvGuxf3FbT4uE6luIc/9i/y2ounsEc3yh5zksG2HSOai6bH3deO2XtMqCcd1+q1VW1qX+XHymEWiNVzzvKnuxVXZ7/PJ28Nda43oJAs7jl5G4J/tdxU9XvS31X08cWIOy3sbHy+22WLLfObVs12DrHrbgsIyTNrMnrXWeHxtt6ZiPoCzMmHtjqoWr3UR5xCdkggIzERERERERkUfpbYdldy32t3VnVjJGCF+12P/y/e+6PtiaXevv3nKr7MausrUxNpb5xymg6xmn135oq+z2Z9mW+s/Li+OXu6lSfb1+A8tcP5utrbrguOTpdcg1UKt2MX457nWuQdlxDjLGc68PC8sSYDDVQgJLdBznsyd7IjpLxMVJn4+TAjMRERERERGRR+RdjGCeRhQjaOsKLXfWMAfqGy/2HwHSdmgAvNtW2QibbOxfyyTWVlnkFoSN1hw49WLx/kOX+l/PnUiDtHEYQk9qgToVpuKjZbbuKTNGINd7Z+5JrqHkNiJaytg7Zm439pTNS+ewBBFJRvLsMDMvnUjYTYWM0TSzalztJsySpQU9kqkmpqX/IiIiIiIiIvIY3BXgvEmrbFvsnzkaUrGOYNq6GN/XEcwtSMo7WmWvWux/bpWdW1UJlLWxdn7e3a2yLbS73SrrcTMgg9EqA781UrruKluX+o+DCxz383jpg5b6986xdVqM8ctTAOfJ1ZNx+mViRI7xyxF9GRmdJZPWbu0p88vxy3MYOS/jlM3WRxVvWRrPjm2Ecm5MbixLB0/2uxG0BUlveQoDe+90jWSKiIiIiIiIyKfubY5gbru2tnHF7ZTKbbE/dm6VXY5g2mu2ytbDKm8s9ofXa5VFnoNCI0nbFuPf3Sq7bMxlxmiCXSz1B7vRKnvI+OWxdY7r+GVmnppukxu7qYy2GOBrGGfrqaEtz6dfbnvKkmSajOo+Gmjr+GXrjWMLWkuij6Ds+aHRe+BlBGUBLNmpxZl2Ez06rY8GmjtEC46HBZ9s3Z/2eCkwExEREREREfnE3Q5x3mQE875W2bbYn9dc7H9XkJcjGboRoiW5jiiOgOquVhnrY+5qlcEWmp0DMnfW0cObrbLRYsvTQQXbUv/brbKXjV+OUDG4Xjq9cx6/jKQU2O0Kxf0U5NXip/HLiM5hWQ8JiPEZM8fz9lNd23PnPWXXc2NuSfSgL53rpTEfA/OxpyzWEc1ajaf7HZHBPHeS856yw6HTMnGM7HA9z3z+5MmDfy4+NQrMRERERERERD5Rb3Nf2dbmum+xfyQUG82rhyz2v+vatgbZXa2y7dCALYi6bJWZjce53WyVjcApyPXrl62y4ut1cLNVFhkj+LvRKuO1WmWRydwa13NgeR6/xJL9vlB9fV/GaOn2T/TGEhC39pRZSa5qGY+1raeXLK3zfA56D4jkuDSeHxsZY69Z5HhMqcaTqwl3WHoQfZzyaWbMx8aSSVnbb8ee1LSvHKh+KhSYiYiIiIiIiHyC3vYI5ssW+2Mw3WqVZeaN97rc83Vvq4wXW2VbyHX+TC+2ykaIdLNVZnAa1zQMu2iV+XjWjZbczfHO81L/122VzW3sKos+QrqW44TKqRpTLZj5afzSzMfJoSRLdlpfw0K2sdFkN9nFnrLReGu9cz0HvY/7uMwjKOstmIpjxWktCE+e7CulOL0HhyUoxcEgW/B87liMOz/H+KbuirHfO+aPeyhTgZmIiIiIiIjIJ+ZthmV3jWBuO8LyjlbZttj/8q1Of7a+1qWHtsq2z7R9hstWWa6nZ26tMjJp6+mX2Pmzl7Uxdhn8jc94bpWZbXvEzrvSbt+/U3vt4np7JMfWWBqn8cuxnB/2e1/HL23sKXNfR0ONHp2lXXzP1sCvFtjVcmNPWe+Nw7qnrEfSl86z64W+BGUydusJm0Gw2xVqKfTsHOYGOcZVo3WePVvARtDYIkkzpmLYzinrOGt57Z+UT4sCMxEREREREZFPxNsewby92H9rlY3YJ28s9jdebJXBOWh6k1bZaIydQzhbQzm3W7vKct3KtYZl22e/udT/5u61HlvAt72HUxzKPa2yO8cvl7Fw/3T6ZQCW1GpMxcHW+1XWtpsZGY3DvF77xZ4yK/BkDcp8Dcri1p6ytnQOx4W5JWYwTYVOsGRQ3Hi6m8h1b1kQVHd6Tw7XCz0S82TJhD6+P7sK1QsRUIqx31W8PO7ITIGZiIiIiIiIyCfgrrDsbbTKtkX4cG5zYXnHYv+b7/OmrbLIXIOkF1tltgZlN3aVrZ//rlbZttR/C/5yrXLdPgHzslV2OUJ61/2NTNo6ftm7ne4VmWvbq8D6muN1/LTXrUWntTUo2/aUeTIVY6q+NtDGHVpa53oJegsy4Po4c1j62FNWCj2DY2uUYjzZVcyNpY33KBiecLhemCOxCDpjR5qZsZ+2+1rwMhpqXgoZ7bV/Zj41CsxEREREREREPnJvawTzslV2ubfMbW1Ara/70MX+b9wqu9yBdtEq2567tcp6jiu4r1UGF7vK2AK28/gl2I1W2UPGL+femBsQRhsvjDns6nn8sqzbyrZQsWWnX45frq2yWlj3m9m6Ow16jD1lrQWZybIEz48LvQW1OF6c49IwN672lVqciOA4B8WMArTWOcxBRifcsVj3zVWoxcCc4mO3mrvT28Lh0CnVT4HoY6XATEREREREROQjdjuU+qojmHe1ym4v9r9slcHdi/3fRasMOC31v69VtjW53I1i56X+l62y8bxtJ9ndrbL7xi+391xaXxteF6dfZjKty/lzPVKg+Lak34hozG3sd4vgtKesFLia6gghT4/tXC+dtozQrLfg+fXCEkl12JVCi6ARXO0rU6203jjOfTTZfBwKcJg72YNghIN0KAVKHWOnGOynOt6zNZbex141GyHk82Xh6X7/2j9HnwoFZiIiIiIiIiIfobe1r2wLt5IxIri1yrbF/oypQcqtxf52T6vsrut6SKtsXMPdrTLjZlB2X6us+HYN6+fKvNkqu7HUf7z3fa2y2+OXvQfXSyPCyBihogFeYF/H+CUkxc7jl5kxwq02grvLPWU7M2r1U7CWGVzPC3ODaH3sHJsXDsfALJnM6es4ZzF4upswh+OykJmUWogePHs2r589RtCJsXOjVKO400h2xSnFyQhmxuMKRifJMLIH1vtr/Rx9ahSYiYiIiIiIiHxk3sUI5tYwg5uL/csbLPa/q1W2/snFQvu33yrbGnI3W2W5XqevQdnDWmU9Rkh1PQeW5/FL9zHWWIqfrnR7bTNYeqP37RrOu9+mOoKy7fObjdDr2JK2dBLjcFg4zp1kjHj2DNoSeIX95JRSmJdOWuJrqHl4NnPsATlab8YIOKfqFDPCGeObpWCZHNuyXvc2sposreEG01Qp0/RaP0ufGgVmIiIiIiIiIh+RtxWWbQFZxDk0MxL3uxf7Y7zQKrtvBDMzT2vN4lZQtu0hgzdrlW2feYxW2ilM2xpyd7fKuBiVvH+p/+X45bwu9Y8Y4ZsxRhunYqSVNShbwzcgs7N0X0NIIEcjzwvsajm9//j8ncMStJb0CJa5c5gbrQW1OkSue8qcq6uCr62wY+vj8wcsc+PYkp4N8HV8NpkK1KmSOcLJ/bojbe6dWAKfHBKyG60thI/gkbRxkuZr/TR9ehSYiYiIiIiIiHwE3vYI5u1WWXFga5XdOinyrrDsvlZZrKHSuo//dJ1bM+11WmWxNsRYf385fjn+O64ZLlplJBmXrTJ7oVX2kPHLOYKlJdHy9JnMYSpQSh2vDecTLS1pPekBfd0HljmCssmdUozqYxS0R+e4dJZ1T1lbguu5sbSgOOzcmXtgZuymwjRVIkZ4Zz0xh2UJ5hbj+ZEUyqnB5r4un4tkv6vYdn86eDWsGNnGvrTuOcYz1+9zj6AvcGyP+6RMBWYiIiIiIiIiX3NvKyy7b7H/CLSAi7DML176Mix7Watsa4LF5eMZI4m3W2Xjtez0mLtaZdvnvmyVuZ/HH2HsBuvJKYB7VavsIeOX42RJ6JnYOn7pBrX6OCjghfHLTu/bNYwRySDZ1W1P2Xmn2banrK97yua5cZg7kEw+9pQdI6gO+13F3TjMy2mXXM/geEjmZSENPB1fT/nclbF5zouzq3U9ZGAcegBQLIk2drAt1nAS0umMkI8wMjtTKUw6JVNEREREREREvq7exgjmZavs8tfbYv/RnroZKr1ssf9drbLTe3GzMQY2dmhtrbKtrWbro81eaJWxBmXJzVbZFlCBv7DUn/X5IyizU/PrVa2y7dc9grl1Wofet4MPoNTtFM9yeu1t/DKi09NHCBjnUdZSjalsrTa/saest1iDsoXDsZOnoGy0xrzC1W7sKVuWTpD42hY7zI1l7mOhP4bnenBBWU/odNiVSq7BWvQkAIuOeaX1ZLFGHRdK4NAakUYQFIxmRsVYbv/QPTIKzERERERERES+pm4HU1+1VRYvWex/Hlu8f7H/y1plW/DGejolXLbKxu9HQLeONZ5CuDwHTw9olfk2G8rNpf7jlVh3dZ2X+vsdrbLze5x/HZHMvbOsYVbAqVVWfIxcbq91HhhNehpLT3I9jXLbU1bMxqL9bfyyN54vY2l/9GReGnMb4dxUxs6xsZPM2e3Pp1ce5z6+R2kcrhda6xx6o1oZ3zcbBwJYMdIYXy+jKthbkOvoprvTcZa+UHN8xyON7J2McRhANWjrSZnHvtBao/D0tX7OPjUKzERERERERES+Zt7GCOb2Gltgdnux//ooygMX+9/XKnMbAcw2Mrg+64VW2fnaz4v/H9Iqs/V0S9adYJdL/eHcKtuaXDcPBDi3yi6vf7uu1oOlx7p7bIxfmoGXdfwSuxEmso449g6tx2lPGTYOASjVmS72lB2OnXkOkqT35PrYmFunGOy9MLcOGPupUGshMlgisBjfpzYnx7kzZyMimXwiCXZTxegjNPMyQk6DvgQ9AgqU6vQ+Tr4saRR3oic9O4YTNsZWgxEgttZYWlJ3hWJG6/3BP2ufIgVmIiIiIiIiIl8jb3sEcwuHkpuL/d1unhaZgN8xggnc2SrzkZGdgr27WmVbA+yrtsrGtd1a6v9Cq8xeaJU9dPyyB7QWZK4nRHquY5SFsrbVtvsT0WmxBocxPnyu45e7Ou5cLYXM4PlxZmnjHvQeY0/ZsWOWTGa0nrTslAK7XaW4cVyWtRVmtDk4rmFXJ6hUvCTVoLoTdIoXahmL/vsSdGMEZe5kGIe2UGyMbZIQPcZnxygZ4/7FCNBag6yOTbD0hWXEcY+aAjMRERERERGRr4m3EZZtY5eXY5jG1v4a0Vix816v7e0u22sPa5WdH7v+6sVdZQ9olfU8v/+5IXYO2rZW2Rb6bWHbXa2y8+jkzfu5BXM9kqWve8raeugBa5BoQSllPSDA1/cfoV8wQq6I01cpdXzeWuzU0lt643oOWkuyJ8d54XjsRCbVRwjVYpy2+WQap1e21mmMe5EJh+tG68GxN3ZeKTjFoBhkBXPnSZnoFsQStFxHLyOhFY7Z8IQJo3dITyKNnknJxAwioGWjd4PiWEmWmDkeGmFQElw7zERERERERETkQ3pbI5h3tcq2V9hCs+3kyG2x/5u2yi77Yw9tlW1B1vaaZrd3la1L/bdW2foa23Mvl/rf1Sp72fhl70nr5xCxlHGtpfh6b2y9Zk7PC2ycILnGi7vqeLHz+OW2p2wOYg3lDofGksHkRjVn6QkBuwqlVnrEOg4Z60hkcJwbx+hUCpMVCmO00qZx46/KRCfpvREYvXeqAVlZskM2CkkPo1tgCUtPfA0Ml4DM0ZTDHfdk7jPHY6O5jceZk8X5neOR7zz4p+/To8BMRERERERE5AN6G2HZ7T1ll60yW1tlD13s/9BW2RorncY5t71ir2qVXX7eso5dFh+7yi4Dr60lt+mZp1bZiyHb3eOXfQ0N59boYSwtIMZzkhhtMneq22n8cgRYI3RqfV3ov+4p206jnNbnRXSu587xuIZevXOc+1jiT7IvhXneTsKEelUxg6WPMCsyiQaHeWHujUjY+XjMVApWxv3aWSUs6b2vbbGgGNRSWaJD60CQ6WtQtt6HDIikM743Lcb3qZoxt5nrFswkDnhAuBHZ8d7Z18cdGT3uTy8iIiIiIiLyAb3pCOblYv8eW2trjPeZn19jC8suRzAv3+N1WmXb7y/HI0+tsouw7KGtsi00O7fKtuX/4zW2kG1rld0c3XyxVXZ5T1oESwtaCyLWENISCGopFB/7yuA8fpmMoCzWcAm7OX657Sk7zDOHeTTgiORwHOOXeFKBHnBsnToZdaoUG8GdMW7gsgRt6Vy3hch1b5rDrlawGPvOvIAb0WNcV4JbsquVpXXm1qgkmNNbknQwo2cf1+8QXDTqgMjOs2Xm2BMjcHyMhJLk0qhTpZbKvpQH/Qx+qhSYiYiIiIiIiHwAt8OyN2mVJZxGMMdplz7GJe9a7H/rPU4B2ANbZbdDtDdplW2nX27v+WKrLDCzU6vM3W6MYcLNVtnl+GXbTr/MPLXKsDV4K069GL/cntt6rCd+jvFLc6M6TJNTvYx9ar3x7NjXPWXB3DvzodPW8cvEaAHuyX4q44iF7BzmGDvLImlz59AaLYNqhVpg8oJ7ggXTVMhgDcvGZzDGHrQIOM6NjKRUp80B1kkzMvsolRmnkzmzb+OlnWPvYx9cjqZduBEE9IVaKux3eBjFjWudkikiIiIiIiIi78ubjmBeNqhuL/a/bFwZr17sfzlOefnaL2uVbSHafa0yciyZf+muMuMUlm2tsn4K/G63ys47yrbAbNtntl375b1o0Wk9T+OX5usW+xytMme0ucb1j1HKHuvhAzHuSAK1+miUrc22sdC/05aEhHlZmOdYQy8oGEuHJNjXMkY2LVkyySXIPvaJLT24bgsVZ/JCdR/ngNYxblqsnL4Xy9Ihk1KMSGNuffy5MwKvSIJx7imRLCSWnBpy7qNRdmh9be4F2ROKE54QjepOrxNOwTK5jpl5NnbrQQaPlQIzERERERERkffkrrDsdUcwX7XYP9dwaguX7muVATeu5XVaZecdads7nFtlidHHcZJ37yo77VW7aJWdDgqAHqNVVst5qb+dXuP+8cveg7mv45fJOMzAxt4zA+pUTuOXoxWXa6ssybBTAFjqaFhVP49fPjscmee8sadsXvrYqWY2luxnjD1l04T52FOW67VkONfzzGFZKOZMpTIxrtEmKGYUr+N72mMNwkZQRjpz75T15M6ltdM3I1qn4hzWgwJa72QHLwZ0ns2d3jsNqAHpRlaD3nB3KAWjsLPkmI1nXx7YYRwnZ5nnB/1MfqoUmImIiIiIiIi8B2+6r+xyZPFmw+tmIFYuRjDHe7x8sf/rtsq2AG68/4utsj62/9/ZKht7tOzGOOkm12txs1M4dt9S/+06eoygrEewtPP4ZXEj1jDLi90Yv9zu2xJJBKfxS3ej+GiWTaUQMfaUXc9BdMgYp1guLejZKT6CsrkntSRXu7oGfh0a9BZkGvO8cOydnjCVSsWoXogS4/rMMTda62RPoq6hXTeWPnaMeULvQTj42iCrGEfGvrrW+2iV2Ri/fL4sLEsj3PGeWBknX2afSTNKKRQKxZPraDx7dmSX0Ma3Egv4rePxQT+XnyoFZiIiIiIiIiLv0NscwdwW+yfnsOyyVfY6i/230AnGwvtIu/Ga97XKRpMsb7xXYkSui/LX19zaZHVNyuxi/DLWcc1ttHL7dS3npf6XrbL7xi97BsvaKstt/NJznIBpzlQc93GYQGacPvcyjo08fY7qNoIyH8Ha0haeHTu9jw9/bI35ELTsTMUoWWgtMQv2U6X42BHWIok2Dg5Y5s4hGksk1YzJjH0p9JJYdKo5pRSira2yddJywlh6YjEOGmi9E2ZY72TautR/u4fBcQlibczNvTMvC1FGUDaqdRX6zJKwq2WMXvpolD3/8khNOGYnrIzxVE8mgie73YN+Pj9VCsxERERERERE3pE3Dcsum1i3W2WYnRb7X7bKbo9gvqxVtj0sufn8l7XKMkcb61W7yoqfr+O+VlmsO8Re1iq7HL/c9pz1dan/0sfXxmMDbIROXgpTKeu1B2Zj1LOHjWX6a+RY3KjVqO6UbU/Z0lmOIxycl4VlDloEEFQvtDkJ60y1ULySJPO6RL+3JDtc93nsG0u4qoWpViJHM21fKvgYN42WNBunapZIGmvDL9Z7E0ZGYmWEgW3d0dYzyGb09TTNFsH14UjWAhGkOVkrFo2WC1MtTFbBkjkb118ex8mellRzPJ2+NvK+gbG/mnhaH3dk9Lg/vYiIiIiIiMg78iYjmHe1ys57wy5f47wQ/2WL/W+3yjJHqyzXVtnlTrNXtcrs4rHJGDu8vavsdqtsnD657lu7WOo/WmXnkzzLraX+W2B3ubOtxQjLek/IERaGJV6MUpy6tsRiPVnSGMv2Ywuf1vtXHaY6gjJIvrw+cFyAhL7uKVtax8pYpB/pLNnZVcdLxXw9WbON4M7TmJfO82XGMK5qHcEfRs/Gblex5rDuS1tIvAd1cnqCreFj2rinGUl64jZO1uwRZAa5QC8Q1unr2GgWH9+0nphXLPponbkxMWEGSzaun8/E0pjLOMhgwukEtRjfwLGrOsZpMeLBP+mfJgVmIiIiIiIiIm/ZXWFZ8VcHZdtzL1tlsI4Orn/+Oov9L3eSbdc0AiPItFP77Nwie3WrLF9oleVFK+zFVtl2AuZ2dUEAo1E2grLxvLuW+t88CTSYW9DaOWxLGzu83GxtfJ3HLw3G6GLjNMZq256yMsY1M5N5mXl+3PaZJYfTnrJGwcgoLNlxgqkUyjQONegtx56yMNrSuF4Wwo1aKlfuOEbzwN3Y+Y7o45qXCDI6067Qwsg+AryWbXxPeo5dZAQRxjE6lkEs0NwwD6Inz5d5BJ6R9AzcHDIIG0HmVHYkY4x0PnTmeSGKkbVQbYR/1eD7KeS+YIxRz9Y60fraqnu8FJiJiIiIiIiIvCVvMoJ5566yLXVbA6lNuWh63TWCue0gu31Nt1tl5ybW8FVbZael/m43WmXbCOV5JPTmUv+tVXZ7qf+NoCzGUv+5JbnOkW7X5galFGrx9foCNxuNtrA1tDyPrdZqTKXgZiMom4PexkL/pXXm4xqUFcOyjBM0WdjvdriD5Xhcb0nvaxOtd5ZMai088ULB6CWJ6Fx5JRnX0XtgbriPUzlbS9ycIJiXRjHHLenr9+jYxxhohrEEYON+zq3R+9hfZmZgjmUnxzeNamNMtEUbe9TmmSgGxSlrUOZ0fsAKeVXWXW4w986z40wEWCattVf+zH7KFJiJiIiIiIiIvAV3hWUPHcHcGlVjz9b6eheL/UdAxGlccWuPbb+/fL/7WmXjGm+2yrZw7L5Wma170jJHq2q8xoutsvE41nG+82J/2EZC48ZS/+1aL5f63zd+ufRxSuXpPq9b1oo7tZbxvlvtLoOWTo+1Dbd+nlo57Slr0flybizzeM7cGssc43RLzxGUzUm3xq5W3CeMEdq1JVlaUjAOy8IxArdkb86ulPEZvLPDYZrGfesd89GGc6AHFC8EydwWsEL19f4mHKOPMCyMSKP3RpAsESwtiIw1MHUyO+bbfTwHZa0n19cHsjq9OJMXlmw4yfd5IeoI2jJhacGz43EdAw3Kuv/t2C9u+iOkwExERERERETkDb3JvrKXLfa3deRy2y123wjmO22VxTiNcmsiwQhoirOGVXZqkF0u9TezO5f6326Vbc9LuNjXFsxLpwfr3jEjGIFYrY5bpbit0dkIolom0Y2IOO0pKw5T8bWBlnxxOLJse8pivEdrQVrHzOiLEdZxM/al4g6dTlvWz9WhRePL9ZjNJ3WiGJgXWjam4pQyQRjRA0jcRviIGT3GdR3XoGykmeNzztFp2SlWyT6CsnRj7o1jC9KgrPe1Z2DOaL1RwIwkOC6d68OR7kAtOIAFQedzKzCNU0MNOG5B2TqKWopTamW3q3w2VZ7qlEwRERERERER+Sre9ggma1g1gpYtEDqPPd612P9NW2Xnky25s1WWa1B2uTdtW+q/XVDfltJzXth/bpXZS1tll+OXY5SzM/dkXXU2HktiDrVUyjp+mWuCFxljF9h6T229V6UYu1rWvWQzh/m8p+x6bvR1T5njEIVjG6OYkxVKNfBkPnaWnlgYc2sce6Mn7L2wr2WMW9IxGldThbBxGMEa7pkbDcMCwIjs6+EDQSljsX7rQWdt5HVn7kfCjRaN47ET7tRM0n204IBaDDDSHCM5LI3rw5Hm4MXx9f4keQrKqjuYcT0vXM8LFmvYaKOpN02VJ2u4+GTa8fmTJw/4G/DpUmAmIiIiIiIi8hW8SVh212L/cxyWa2PrZqvsHGzZ6b3IJPLm/i8YrTLW3Vkva5UBa1NrPN59/fOXtMpuLPVnG53cTvA0IseOs8ul/petsi202z5/whqYJXPr9L4dPnAOEWtxSilrkJcUt3W3mY/PvyaExY1SYCoVW0ceD0vQZ8gMlqUzz0HQ1s9VRtiXC/tpolQbe8p653iIcXJlh2fLgRbJVApPa8Hc6b1TPCm1YOlEX8NIG4v+lzA8wN1puRDNKJaUakQ6cw+CpGWHZhzbjFdn6Z02d7oXSrKeljnuaymGpxE4rLvP5uPMbImtQdkWlX3DK92TUgq4cz0vHJYGfRyKkObsah2716pTS+Gq7ujROc6Nrh1mIiIiIiIiIvI6vuoI5qsW+5dbrbLLxf6Xp2yev37zVMnzaOYI2Yq/vVZZubXUf2uVRZ7ba1urbCrnEy+LQ3E/Xeft8cseQetjLJEcMVywBkTVqV5PJ3uO25T0hN5tBD+57lIrMPloSEUGXx4WlmUs9G8RzEuQEXTvWBZyGXvK6hocuXVaS1qH1hILeN6OLJG4w2fThFent04BdpODOazh4rifMcKsMBxY2oLh4/tQxqEFx7kRQI+Oh3FcFmodBxFcH44s5kwJ0cbuMzOI7IxtbZVGp7XG8TgzE5iPEy63XW5P0sjqeHHMjMPSOCzHcZrm+r3dTxO7XWHvxlQrte6gL3xxnKEnXpwvr69f56/EJ0eBmYiIiIiIiMhruCssuwyzXva8rUl1e7H/2EVlN0YutwDrdhB32R67bJVdjmZetsq2JO2yVeanAOruVtl6GOV5V9mtpf6XrTK/1SrbwjEzqK8xfmmnVlliluxqwUth67gZ4xCAjLURtwZ97jAVo9ZCRvDF9ZGlcWtPWceJ0UZrY9+Ym7OvFbOkW3A8xmmhf+sLz3uAJU/rui8tjR6d/VRGUNYhPCGCUqBj0A13J3qjBXgppwMDejgtk4xOdpjbMk4+zeTLwzXNHI8YwaYb1SFiBGVWJpa+kK1xfTjQgW6G22iVmcGUUIqP720pa1DWINZ7TnK12zHVws5hV+vYt9YXro8znkbP5BhHrqyS9XFHRo/704uIiIiIiIg80Fcdwbw8AbLFZdi2jjHeu9jfKHcu9rc11LrdKjuHXDdaZRgZ51bZeffYOZSLDNZDGl9olV0u9d8aYVurDB7WKutxbpZdjl9G53SC5DgsYIxb1lpPp2GOwK3T+hi/zFNQZtQy9ppldOZl4fkxyIBYg7Jl6bAePNCzMPdtT1kdo5EWHK4bPcBj3Ifr1mnReTJNTLaeVGlBzWQq654yT3w7csBh6Ykzmm29dTBj8qT1RmO8RusLBCy9EWuz79ky062sgWFSfDTNlmg4xlR3zH0mW+NwfaRl0LxQ3Ueg41ACdmtQ5rVynBeu5yPZ+vi5uwjKKsF+Knip0BuH44xhtAhaLoBjAXPOTLdT4UdGgZmIiIiIiIjIK9wVlj1kBPMyZNpaZawNsfEar7/Y/01aZTBiMltbZX3duwUvaZXBGngFsV7+GMEcja37WmWXbbLtPkQmrY32FTnivIixX8yLncYvM8cesIhOUoh1/HIL70qFyUf7bOkLhznoy3hea8Fx7lhJ0jpEHddunakWpmIEY/zy+RyUNYR71mZaD2pxPp8msMKSncmTao6VQkRCjCVrYUlvQSmVzM6S4xtcLIkMjgk9ILIRYbTW6OtSuHmZaVbGCGoE7mOH2HE5ArDb7TkuRywa189HULZg1GliSsg1KNub4xNYrVzPC8fnB6L1cUKnG7vdjl0t7CyptVDLnuiN43HBcZYMes6AES1o/ZpjGG6FRYGZiIiIiIiIiNznq+4r2wKj260y2FphD1vsv3398lruapWNV891J9qLrbIt/jrtKsvYfndqlZUydm/dbpW1tX42TqyM9X2NyV9slY1Ry7ixpywzxzL7SLKv45eMnV65jl/a9qHW+9sjIQtx8d447HyMX/boPD8stGWEaa2PPWURMYKyVohwei4UfOw3q8lxaRxakn0EYYf5yCGT6sbTqeKl0HuAdfbFgTKuMzu+d9o8PsMYmzVa6yTJVIzWgyUherJEw3DmpY3dYQatNeYcH3MEZeOzXLcj0TrTbs+8zMxL43hoRD9wAOpuYpcQDqXDzpwyAevo5fH5AXpw7J1ixrTfsa+VvSWlOl4K2TvzsY1rJmlxxDD60mkxM/cxVmql4O480w4zEREREREREbntTUcwL1tl21MiOY1ZXoZl8OJi/9dplZ1GMG2kY3e2ytZf32yV5Slwq+syrNutsr6NXxqnXWXVx+mPMPavFTu3yrbxy/N1j5CKHIvT0pLoMVpSZSydTxLLbfxyO/0yYWuV+XifXa306Hz5/EALI/q4pnluI6TLDmlkK7QY4dBuquAj8Hp+SJY+9pTNvfM70chMPquVUgvRoWVnVwwoEAYlR1utw9JGGGkJc+vrqZxGrGFVBsx9OQVlLceJp60vHNJwEs8xhlpK4dhnWu/spyvmZWZZGvNhofXOkaTsJnYxgrIpjJJJmRxKYV4az48HLHINypyr3QjKdjZO1MQcS2jH0YRbaLQYY5gxN2YaS4MwH6GqGTkvZHXQDjMRERERERERufRVw7K7WmVGEjECn+p+CquKbzvEXmysGTl2lXGz4bZ9/bJVti3KBzsFVQ9plcG5VbadzrmtN7tslV2OX7obU/HTdW7B2dYq6+ueshfGL/ExUsnYL1acdU/ZGKM0c5JOdCfzPH5pZtQKU6ljzHEZ45cZ4zTPZeksrY8xTgvodZxM6ck0lRESemM+JHOHsu70+p1lJjJ4UgvuFc9Ca51qULxAN7KuBw20jpUy7k8fBw9gRi0G2Zk7Y4k/Dc8yrikWanGydZ5tByJkkjY+9xIjFNvVPa0tHOaZPi8srXEkKLsdU4z9aBOGr/fMp4nDvHCcG9mD+TIoK5VqnakaWIVIshvpzjEX2nxgKhOxNI4xvi8xUrJxn3qQjJ1vT8qOH9jvH/R35VOlwExERERERETkwlcZwTydArk2sraxycy80RzrkTdaZdyx2H+8nt0I7c4PsfVUxHOrbB3wOwVxW/T1qlaZm1Esb3yuy8/wqlZZXYOy7RCAy/HL1jtL5GhokfeOX/p69eN0zkJkjL1e6/jlvjhm40TJwxLEwrpYP5hbh0x6NjwmeoegUWuhmNG901twfYBdGp7B82WhRbCfKtULRiGykdZGE63ZWK3mQUbStvvYoZGw7mwbO8vG6GXYGka25NCOBEFE8iw7SwQ7RlBZSqVF49COXE1XzG3huCz0eeHYGsvaKJsiwZKdO97HCZzUaYxpXh8hRmOvmHG12/GkTJg1dtVIm05BmZkzZ2NZ1qBsXviSI0saaQ45DlhoLSA7pTgVpzzZE2Xiy2V5yF+XT5YCMxEREREREZHVVwnL7lvsn2sTzO3cKnM7j0mO399slW1B2dbSuvz65XVctsq2YM1s+9dPz4sYu8LuapX5Or657UKLdc9WPrBVdt/45dLH6ZfYuh8tRmBWi43AbT1dc2uzRYz32k6/tALFoJaxp+xwbLRlnGDZe7DMfbyXdTIqlpWWjUzY1QJlPO76EFiHAjxfZg50npTKVXXSCpGd9IWdOUYd058E2deAL6FaYaaPSDKT4slCjNHN9TFt6Sy9jz1lGSzAEsGEMQHuhczkuh95Ol2RrXG9zPS5raOXgU+VKQFLqhklEi+Jl4m5NY6HeW2UBQW42u94WibSGvsC3SoZ4zSA3IKyvlC9kkvjt49HIo3wguU4uCDTWHpjVwqGM+33QIHesJhvBLmPkQIzERERERERefS+ygjm9pzMF0cwky1ouz2Cue4e4+Zi//F6diOwe2irLHO859qFAkbw1Pr5tSLzFLgV2xpo511op6X+GGY5gi7ub5XdNX65tEZfgzKzESBmJOawX8cvYVxHZhDho5HGGBM1g1JgKoWewfXxyNyMDOi9My9tfHYbjbZshdYXwNhVJ3fQY+H4HCKSYsYxGnMGaclTc4qV0Zoj8Ewm3xFA9o7hdEt6T2optBwHCEAy1bH77NByBGUx9rDNy0JfG3tzBEuOQKtmUkqlZ+N5O/Bk2lGicL0sZAuWZeGYHa8TE06SVHdK5AgM16BsnheyB8fWKcB+qjwtE5RgcmhWRmsxCml2Csomr/Rl4Vk/jO+Pl/ETs+5ZCzp7H8/Z7ffjZ29Z6Dnj044JZ68dZiIiIiIiIiKP111h2VdplZ2CL8C5ewTz9mL/y1bZ5TW8TqvMfYvUth1nIwCzW62yWrYTLW+2yi7HL7dWWXXwW60yMzt93u3zbyOZSyS5jl9mjK/hME0F1sMAxijj+jm38UvGCQalQjXD3DjM48TGWEaw1VqwtA4kPTvWK60tQDKVghen5ZH5Omlh1PV+fbks9Ojs3ZjqhOG0bBSMqVTcR/uumDGTROvUUrD1Pd2g+GjQXS+dlkFG0HuOUco1hFwiOGYwATvAvBAEz/qBp3XHlJXD0qAlc5s5RqNMOyYmOkExp6xBZZkqvSdfzss49bJ1HHgyVfZlwkswubMw7nGhEpnMdJbecC/E0vidfqAn4GX8XMQYq81o7OtEKRN12o+f3dZYolF2O3ZhmMVo4KlhJiIiIiIiIvI4fdURzJct9i8vtMpeXOz/kFbZNhL3VVtlW6DjbhRjffSLrTJfRyRHAGfsLoKy4lDW8ct2a/yyR9KyE43Txcd6reP0yPP4ZXHW/WRj+f+p4+YwuY8TNqPz/LqTzU57ypbW1+ttEJVoEN4opTAVZ86FeVmYu1Fi3KHreWaO0cC6qhX30fRKC3a1YrEGiWa03slSSHMcWJY+7pcD2Tl0WxuEnejJYZ7Zjk3oPfjSkl3CFEGt0xghbUeuamVnlePSyGAs9m8zXieK7/A1qJvwEZpNExHwfG6judY6xYwnU2W3BmXFoJmx9IXCRAJLdlo2wOjLwvN2TQPMCuZOxjq+Go3PdnvM95S6H9/LpdGzQRlhnBHMLZjnmZad4/X3P+Bv0KdLgZmIiIiIiIg8Sv1WreyhI5gRsZ78eBl83Vzsf3mK5eVI5froN2qVQeJud7bKSNhe8marjIe1ytbxSzOo6/UvPU5B2XZN2/il+XowQB/ji17WPWLjxIMR6hlEjCvYWmXj/ow9Za135nmhLUbP0fQ6BWV0MgvZCy0W3J29O92Dw3LkuBglDSc5LI0lg6k4n9UJt0pkozNOknScZLTlDtFHsGhGtrWtte5qi1w4hkEaLRp9Dcp6gmVfF/onkxlTD6wU8MLzZQRle584Lg3SWJaZQ1/wUrE6grJaRjjXs1PqhGfh+dLprdN64GZcTZWpTNQyFvKn+3qK50Sk04gRlJmNMG5pjFtccHOyt3HUQnb2045iBZ/2LD2weSYtaFbYlYk0mI+d4/HAdTTisDBNE18eDi/9+/OpU2AmIiIiIiIij8pX2Ve2tbpaxBr+jEBq28u1tbC2Jpnb+aTMm687wq+HtMpyDdBuPj5xu9kqG4v3t+u8WOq/vXiOfWSZSR9Lwx7UKusRo7EGN8YvW27XleOUyFzHL8toio3LTNy2YPAiKMPwMnah9UiOxyPHZkRP+ro4vwdjT1lANGfpC84Is9KTOWbmoxEBdQvKbOwp29fKziY6nc4CmUw2kRWsj2/IdVvAHWtJGnQbBxLYevLl0kdQ13syzzPHSDw7EXBtyWTOFCPYslKYYz2xsu44tAVLY5kXjrGAV0rZ4ZaUU1DW2E17LJxnvZNLZ+lBdT8HZR5AH0cjJJQ+lvkvGfQcu9xabxyOM2mOeRlhaQRJh+hcXT3BsmBlBwbz9TVlcpaEfSmkG4dj53B4zgK044LVCaaKVTj2/rC/UJ8oBWYiIiIiIiLyaLzuCOa9rbL16252Y7F/LVvgdft1cx075PRYuLtVdjmCedkqMzPc/PR6EVuodm6VFR9L/c3O45fJ2D12ul4fn+e+VllmsmxL/S9Ov2zR6e185dspmdN6+mWuL2LjuEny1vil+dZas3Hy45JkG6OarXdaC5IkokOMPWWJMbnj1ZnjyLIGZWWt0n25LKSvJ0fWKyKDQz+OUKtU2I2gLBPm7HhzrNsYK4XT6GUDosOSDdI5HI+0hOgLxZwvMqnJOL3SgyxOi4bj7MvEoS303uhL4zoWzAql7ikkVmyEaH3m6bTHcuLLpRGtsSydqVb2tbCruxGUZd+OQaBEIVhPHrWgtRGWHeaZKBUvE54QvY9stDf2+yt2ZU9YGffzeMBqwUrBgbJzrg+N6+P1CIBbYKWSBFNNqu15crXjard74N+qT5MCMxEREREREXkUXjcsu6tVdnqojV1l2y6vy8X+cMdif+wUQN14nYtW2XjPuL9VZlsENvaTXT5ma5UZ42ROMol7WmU9xq9vt8rc7Maesu0eLK0R6zVhjBMio4/TL6dy/oQGW4MuI2E9jRMbY5AwdobNLegLJElvnaXFGr41zCaiGUs2plKo1Zlj5vqw0MMoa23vsDSaBWbJZ2U37m+fSS88nXaEjR1hPYNjdDyN7NDWxl8xKNU4ZifTmJcFcI5zZ+4zPRYKxnPAejD5WMrvpdD7QqxB2XVrY9/aPIIyzKllhxG4J57G0htP6w73Pc8j6ctMa6OVeLWr7Kcr3Dpkp7NeXxZadNo4k5PjfKSYc73MZJmwMlESevTRZuyNerXns92O9EonyeOBxY19rRRPOnA4dq7nZyytkUvHpgnzwEuyq0+5utrxfVdX7PeV3/XNb97/l+kReHSBmZn9JPBPAT8M/GPA58D/PjP/Ry95zj8B/DngHweugL8N/G+Bn83Mx91RFBERERER+Zp73RHMy1ZZT15Y7D922d89gnnXYv83aZXBGI9cr+y0dy1zvKate8p8Daq2MdGtVZbryCWW65jnaHldXmP1EbzNLV4Yv1wisDRirWRlJvjYoVbWwG0d+CQDzP1i/JLRKlsPAzjOM9F93VMWtB70CMyDnk52p7FQKFxVp8fCs+tGizJOkQTm3hm9s+CpT5gVWszghVIqbj6+r5Ecs7EFeL2Pe4PDrjjXbWYJp0WQGMelM7eZFgsFZwayj1HNLftcsmOR7MrEobVxLUvjeVxTzJnKDiwxGz8nxzbzzd0TyInnOYKyZQlqKewnZ1d3GB2j0ROKGYVK743GCOKWZcbNmZeZrHvwimWSEVSD3hfqkyv2+z2RTnfI4zUHM56Uwt5HM/DwfBxGMB+W9T4UfOeUYhR7wmdP9nz+5AnVR9ibvfFNNcwenT/HCMq+BH4d+Ede9mAz++8Dfw04AP8u8D3gvwv8JeC/CfwP3+XFioiIiIiIyFd3V1j2kFbZaZcWI1Taul3Vz62yLawa73MrLFtDsVftKhvv+WKrLNdTFLdWWeZoldmtVtl5qb+dlvpvrTJbl+tvu8rcxvVv17Lu62fpeX7fbfyydyLOhxhg4zqL29iXtd6PzBjNunSMcTgAgPtoUGXCsS20bvQG0dsYD+3QGQv0czF6NiyNWgqZnUNrLLOP0x7XEzqP2cbIYd2xK1csNJJO9QLuTDZGQJcWdDpJgXXXmq/BXadzWMb3thPMc+MwL2OBfkB3OPTOVJziCeYsudB7clUqcybLMg4pOGajmHNVdoxJ2SQjWaLxjXpFK4XnmURbmOdOqYUn+4niBR+RJmmGm1GptN6InAmSthwB59gWouzAJwrQe2cyWKJjT/Z8vr8iMMIg55mDw5Np4opO78F83TguM8thppSCFccmo/jEzgrTvvL9T54yTeOU0iWSY4Nd2fPl9TU/+AM/8Fp/3z4ljzEw+7OMoOxvM5pmv3DfA83sm8D/GujAH8nMX1q//i8B/zHwk2b2z2Tmv/POr1pERERERERey+uMYG7tq5ct9vd1BHMLny4X+1+OYG4B1e1W2al19ZJWWW6tsVutsu2l+kWrrBjrUvhxET3Owddl0Ha7VbZdewCt3xy/HKGNkePAzXEfMkhjBC43bt24J2xttzVWrGUs92+9c5yD7CNs672ztCTpYzl9VpZlwd2oXkjgaDMxO5GOZdBz7DsLxojmbveUJLjOhT1OGkylQCYtg5Z9tP+aY5aYG55JWnDdc7TUIliWxjw35mywHmTQDaY09tu9IeitU0uBYrQWzPPMIRuTF3Zlx3qXIIw5Fz6rVxQK1yTZFg7HRp0qT/YTtdSx340g3agUIo2ld2AeAd7hAKWOMVifwAp1DcSsL+P5+z2f16f0gCWDnI/ErlKrcVWM1pPD85k5OsvhSPECU4HJR3uvVPa7yjefPGWqxtIXni+w8wkLiGg8bwe+tzzh9736r9kn69EFZpl5Csju+/8oXPhJ4B8C/ndbWLa+xsHM/hzwHwH/U0CBmYiIiIiIyNdIv1Ure+gIZnuhVbaGQmuzC16+2H88jputsjXQ8oe0yvzuVtm23N9t3TfmW5ON0WRbwzLDcOdGsHfZKivr+OVyEZRtS/4jA3K9/hifx2yMV7r5WCoPYySQ9bCAU0C3jneaj2ZTW+jN6Jlk7ywdknHCI1ZpSxDWqOaYO0seaQu0btTspDlzG4vuzY0nvsfcOPaFaoUnZexOKwktgzk7lUJvMDnYevgBFswk2ZPeg2PvIyiLTvaFxFkcJsZ+tGScTRm9Y8XxYmQL5mUEZQXY1RGUOeN7eMzGZ/UKxzmuQdlxbnitfHa1P7XmyD6CsiyYVea+YIym3eFwwEqh5Th51KxSsbHTLBa8FHzacVV243Mso20XteDFxljtkhyeHThkox3m0WSbCjhUqzwpE5/tJ55cPcGs02Nm7hWzidKT58cjaUZmcOXwfaW83l+6T8yjC8xe04+v//0/3fFnfwN4DvwTZrbPzOP7uywRERERERG5y+vuK7trsf99rbJtjPG+Vtm2V+zyfcd/7a21ysZbjovIban/+oHHyZhjf9ZdrTIjaf38mS/HLxMfz4vc1vavAV45n7YZMYIxLxBj/HL8ftzfiOB6GUFZJERr9BhBZNJpYWQzgoYl7GphaTPzYYxP+vqB5x4cbcHNuKoTpNFzwaOyM8eLU8xYMmgWYyYsbCz6dyctx56y3qDb2OHVOm3pLNFpbSbTaMWZMpk6WDFajlFNKz5aWW0Ef8cYwdZUJragLAKO2biqe576FccM+nFm6UGplav9xOSVjDa+xwY71qAsFlhmjjRyaXSg5Tgx1KxQgCAhFmpxSp3YTVfM80KbZyI6WR1b941FN57/znMOvdGOC6UW6uTgUHxi74XPP7vi6e4K8yQIloDqe3okx3m02tICy6QWJ0vBtMNMXuL3r//9f9/+g8xsZvZ3gH8U+H3Ad1/1Ymb2y/f80Uv3qImIiIiIiMirve4I5n2tsshcn3fHYv91L5i/pFUG5xHM122VbaOcd7XKRvC2Xf94jbH4fzx33XWP+/l9TyEfjJbXepE9giRpPcdE4RYKkpgnRlk/w0V4uAZ62/jqdnonQIvOPAe9b3vKkt4SPMZ7rKOHHuC1kNb48jhDFFiPLGiRHHIBgic+Ua2y5IxRR1vKjckK3eDYt7FOx4BSnCDYuY3gaOm0CI5tjF+2DJY+kx26FcyDKcCL0whaa1itVHNy6Sx95hgLmUmtY5k/GWQPDgRXZcfediyWxPHI3AKvlSe7wq5M9FhIgnBjbwV8NMpyOdJtPSkzgxajTYaNYBIS+kLZTex8R9ld0eaF4/U1PTteCqy71Xokx995Nj7b9YzXgk+GF6j1ismNz66u+MaTJxgBdK57Mlmlt87cDjTzMdq7XDPt9/Q0DpHUpdGOj7sXpMDs5b5v/e9v3/Pn29e//91fioiIiIiIiNzndcKyu1pllyOYxf2iCfbyEcx7W2XcbJXlegjAfa2yzPMhA3B3q8xuvMYdu8ryHL5dfp7RKuN0Lct2OuUaifVYgzLW0y1P4c14r/G623VuY6qG+7qnbOlkHwFfb522jl+aBcviLK1TzKhWoATX/Ug2J/ERQoUx95mejX3dM/mOZo0lOphRyhgp7CTH6JBBD8cpYxebJZUAkmMbhwocI1jmxtKDFgvRgm6OeTKZ4VZoBNfzDLVS64QHHJeZnsESjalM48wAgmzBQrC3ys6dXow4zhx74LXwdFeYvNKj0bIRBk+skFa4bjPW+/j6Mq5vAdwqlh0rozFoy0zZVUq9ouz2ROvMz58TGbiPYKu7k0tyePYlc2/EMg5d8L1TSqH6xFSMp0+f8Pm0wwpkbxwxnIJFcGwzhwiKJRMN3++YZ+PQgmgL0ZPuxuH2X6hHRoHZm7n8fym8Umb+6J0vMppnP/K2LkpEREREROSxeJ0RzMuwql2MPL7+Yv/zMv1Xtcq2IMxsbYStJ06+2Co7PyZz3VNm3GyPxWipbe9mdm6AXbbKxmUmPc7XuIVsY8H8WNTf1/FLt1xbdQWz9fCD2EYyHczW1xlttupO9OD6sBDd6DlaZa0DlmNPWRbaApHjFMvMZGbmeAyKFYgxBrq0ESRNpfK0fEbLhRYdc8Mc9tOOiGTJoPc2Tr4Mp/gI6Hz9zs0Z9Eiue6etQVnPxjI3shYoMJmDjZHE6+ORMu2YpgkLY17aaMm1malO1DqN8DOSYzQmCrtSiVLohyPzYWaaKle1sC8TSzTm7JiP0Uu8cN0XyDYOImidJTtLgHulZoxF/gZ2PLD7bA++x/dXY+fb82uaBdUcdyNKIY/J8y9+mz4WnwFJ2TmlVGoZQdnnT57wZJpoNHp2ljaCyozgOB+ZMygGT9zotdB60q9noo1wsvXRlCMD7/21/i5+ahSYvdzWIPu+e/78m7ceJyIiIiIiIu/JXWHZq1plPc5Nrq2FhSXOebH/FliZ2Qutsm1M8kZQZttyf7vx3ucRzDydYDnGK8+tsstRzq1VNpVt9HLbHnY+AdPWkdA8NcySYudWmRvrYy9eN4Ig6afxy9GsG1nYaHi5r0v9Y70fDtuoZGbiPs7AJIPrYx/hXUC2xnIK8jqtMUYxs+EYxZ2lL7TWCcpIBgssPZnzgJvzdHpCj8YSM0bBPdntKhkwL43Itj53tKzCx8mUSbIYZBhfzqNFdmydRqPNjW6GVePKfPTnLJiPR6xO7HZ76KNleFxmIhpTnZh2+7EHDrhuCzXHQn2bKu0yKHO7EZSlJVdWwQtzX4jeWZaFkjH2pqVjXqkWa2sNfD5Qnuwp0xNK3RM2GmXdoTrs3GleiOPC89/+LXqOnwmzwKrjPlG9st8VPt8/4WqqLHGkZSes4hjZZo7ZR1Aanak6lErDWA4zvY8xzzaOQx17z9Y9df3VByV+0hSYvdz/C/gDwH8NuLF/zMwq8HuBBvwX7//SREREREREHq+HjmBu+7YiXmyVrY/AuLnYvzhsA0Vbq2wbTUxuvv4YZRz/bH90V6vs5hL+y0DrvKus+PZ+W1DGKeSD81L/zNH4OrfUtpbc3eOXGTECoxwhlwFuI8wzHwEVZmQkY6m9n0YvE0Ygx1hMP8+dMRXZTzvRMjod6EsSJB6AF5Y+s8wxWmEJaePav1yOWAZXdYenEdHGwKAnpRQKyXHpsB0UkIXJnbCgM8Y7m4F1eLY0lmNjiTFG2ZZGlkIU2FvFHY690ZcZqxPTtIMw6MaXh+dkdqYyYbv9qYV3bDMTzr44OU3kPHP9/EAtzpNdZfKJ1heWbHRPrrKATxz6st6XBXonsnPMivlEHWv8x80/XlM+u2LafQ6ljL1oz54RxaiWTO50nOPhyHz8gtaCUicsG1Sn+A5352oq/MDTb1CK03JmjkYtV0TAPI8hzEMEZZkp+4mYdrSAOMy0ZcGmiTRnaWP/WVLI6GQmT73ymZb+y0v8x8CfBP7bwM/f+rM/DDwF/oZOyBQREREREXl/bodlrxrBvL3Y3210iEYMdHOx/9bsunzNLTi6DMu2VpnhN96zZ4wjERnhS4/RKqu3WmVjP9i64N9Go8hvhGV3L/UfnztPr2en8csk8hzubadfjnFKG4v3GaFcJ0aT6yKUi4y1qTZeN9Y9ZcWN3jvHeewpi3Xv29JyHQkNIoyldSwT90Jk4zBfAwVLG+8XcIwZSK5KpdqOYzYmq5gZkznFYW6dmRhtqhynYeJJZMPd6Wb0Ds/bwjw3eg8anTZ3uhtZRtCxK9PYr3acCS/U3Q4PJ9O4ng+QHTeHaT/uVQRLX6g4V6Vi0444HjleHyjuXFVnV3a0dUdZc3hCYbLCIRayBz0a0RqRnRZljF66gTsZgceM7yemq+/DcCLW0csCUzWKGT2d5XrmcDzQWmfa7TAP0pNSJmopTFPhd33j8/Uns9FzotieFsH14Th285nhcRhB437PMQJbjvS5QSmw240Q0Z0y7SH6CDF3V+xqxUpy1NJ/eYn/I/AzwD9jZj+bmb8EYGZXwF9cH/O/+lAXJyIiIiIi8pi8zr6yuxb7b62yzDgHQy9Z7D9OlDwHUaf3vGyVbe8XcWMPWaxjkSMIczJHy2trld3YVebba90MsG4v9d9OpdxaZXY6dIB19HMdOd12paURPU/jlxD0bpTia6Zn9J4Uz9OesZ7jPaobvY/xy95HNBMRLEtgDmmd3tcgro89ZUtvLMxjyX+W0SgLxrL7deSxWqXRR+PObCyer5XWgkOMr7c0ytr2w0ZMmWWcCnk9LxznPhbnZ2NZOjNg1ilh7KaJ3jvzfBhL/uvElGPh/fP5etzjZARifX2dvmA9udpNUCdinpmvr09BmVnFGI/rDldZKKVw6I1oo0mWvdEzaN1HUFZH+JjR8Xak7irT9DmkjfZXJLPHCMoyCQrHZwfm+UhrQd3vqDXBk6nsmbxwNVW+7/PPib5gLHQmqu1pmTx/fs1MYsXIPjPt9nTfsUTgbSGWRppjux3z4Zq9X1F3e5a2cEUy7a/WXWlQ0qhl4urq6jX+dn56Hl1gZmY/AfzE+tvvrP/9Q2b2c+uv/8vM/BcAMvN3zOx/zAjO/rqZ/TvA94D/HvD716//u+/nykVERERERB6v1xnB3FplPc8L+93OgRS3RzDvW+zPOYiCV7TKuNkqM4Nd2UK5OF371irzbVeUbbHbuVU2grrzUn+4udR/a5uN1ti5VdYiiL6OX66B3Wn8cm3TlQrkGL9MS2oZu9taH42rAmNP2dxHEBcQvdNivSM+TsFsLcnW1zDQOLSZY+9483FwQiQt+1jgX5yrekXQ6a3h1XFLahl7yQ7RiOj0LFRGiLZ9L8JG+Hd9XDgsnejnoOwY4yTOyQu17Oi9c5gPYA6l4AGFwmE5knEY932axr3qnRYLdONqquSukq1xPBypbuzdcC9jn1k0FoenVqleOPZGLDNBQB/joD0L7oVax4EKFg2zzlQrtT4hMZbjcfxcWjJVp8RolB2ePWOZF3okpRZKNbyA+cS+VnZT5fu/8TmRbXwO32E2Eb3zbDnS1wMEos9c7T+je2FujegNzFlagNk4gAFn2u1oGTzFeXr1dByg4IGv/2Cw93LatfdYPbrADPhh4E/d+trvW/8F+HvAv7D9QWb+e2b2TwH/c+CPA1fA3wb+Z8D/MvORn7MqIiIiIiLyjj00LLtvsf8ImgLwG4+7d7F/xmu3yjLztGz/slV2HpM8HxhQnXEK5Lq/7ByUbcHbuVVmdm6VnZf9Jz3G47ag7LSrLc/ttVqMnh0YwY+te8p6BtXArJCMBt4Y8RwNs+PcR1DWRlAWkTgj6GlzEJHY+rlHeDWTPkFsgeEYkzTGYvy0IKNj5vgEtZZxMmV0oh1p6RQrlHW8dIymjqbcYV543gJ6cIx1DDOCFp39VKllT++N43wc34tasIBK5dhn5vn5uE+1YgZLDyIWshu74rRpNOuO88xkzpUb2DiFs0dnMXjiFQcOJH0+jO9jm+kJPSvuzlQLGYlFJwl20w6ve5LkcH2NmdGzUUvFzWkdrp89p7dO60GpTi2GFaf4xFQL39jveXL1hGBmbteUsgfbjxHZaLQI2rJQPNntnkApHA8zy/FA2e2IGG0zKwXcsYSM4Om0o6wnYRYP3KfxmcO4KtMYIbUtuH28Hl1glpk/BfzUaz7n/wL8d97F9YiIiIiIiMjdHjqCuT1uhEc3F/uPkGmMYN612P9mWDZCLy5CsbfRKtsW/LuPxf9sz1wf0NfRz8v9ZcnNVpldXNvl+GWLXFtQ43TMPJ1+OU7FNPN1rHGMX7onUylkBD0Cd6MUaK2zLOP0y8zx3PH8pBP0BkuLcf9xluwsbSas4FmINgLJQyyQyc5H44qMsYtsMhzDzTm2Dr3RcQqVYjmu0Y3sgbkzHxtfLh16cN1n5tbJ1lkiuNpXrspTIhrP5wOOYaXgmVQqS595fnyGuRO14jb2sC2xQIN9PQdlvXcCuDoddQpGMCfsKFgmBwt67/TeINcTQq1SSGp1PBljkpbspz1RKubOcn1NJ+m5MPmElx2tdQ7Xz2nzQseok1PM8eLUaewo+8Zux5P9Fd0XWjuyq1d0D1prLD2YM4llZr+r1P2OHslhPhLHGaYJph29LVidwCcigmrG3iem/TiNs0TH6wQGO5yKU64mel+AIHryREv/RURERERERL5eHhqWvaxVlmy7yuxGWOZ3LPYfAdfW7lpf54GtsnGS5N2tstOOtItW2TkUuxy/PD8Hcl3Afx4jbRdBXlzsKUugX+wpMwvSDEvDHGw7PICglnH6ZcQI+4qNNtiyjCYZCdGTpcW4Rz72lPWWZA/cnLk15mjj2ICAWE/bPPSZYmNx/VR3WI5DFaI4E+N+9YCejdaTwmg4desU9/HJLFiy88WXR7KNRtlx6VjvzJHs9s43/Ck9Fn7n8IyJitfR/qpemZeZ54dn4IZN0xhD7J1DX7C+hqS7iYwY9yCTiaT4OMPSczTKdjlGU4+MoCz6wghTIakUC6o7xaC1mSjG1e6K8NHk6s+esayNsl2dSCaWSI5f/A7z3PBawZNSKkkwXV2xK8439nueXl1xZGZpCzvf070zt2UchpBJHK/Z7/e03Y7mTi4zMS+kV3K3I+bDWOLvld47V7Vwtb8aP/TVyN7ZlQlzuCp1hI3uRJ/pNJa2bZgbY7qPmQIzERERERER+Vp5yAjmaQQx7mqVjZE+w0+h1WiS5WnZ/+Vi/8gXW2Vjwb7fer8cEZpx5wmYL2uVZZ7f4RykrWEZuYZ1Y2zyclfZuPab45fbPYq4GC8t43DOjO35a6CXgTH2hY1f+9pfS+bWWSKhBRljXHE022I0utLo89hT1gNazMzRGd2qcRhCi05vnVqdqezIWKgYiztXZqTbeO0IWibWbQ0zG1ZGOyst6a3zxaHRlzFu2CNZ5pmO4yX5bNoTdJ4t1xScWiYMY/LKvCx88fwLSnGYKrix9M7cG6WPsJLdBL2NZl0mdQ3KWgYZjebGHqdkMpNEz7Vt1cl0Mivua/iJEzHTDK52e6hjpJHnz3lOxw2KF8jKsXWOXz5naR3c8Doe6lYo+x1XXvi+zz4bByl459gWrqYruneWeeZZWwgclgN1v8effsbcG7nMo41XKtSJ43zgqj6BMtGjs68Tn09PMEvCgl2Zxvtn8qRM4++OQcQMObFkcrw+jrFNL9A733v2jP/qP/QPfZW/wp8EBWYiIiIiIiLytXE7LHvZCObdu8q2IOrmCObYxe6vbJWxtcpuhWVbqHZfq6zHNv55bpUVS9xHw20s8L+51P/8jsO2qyxJclvabxenX0aup236jdMvzfK0oN/NxumXkRhBLSPcGiOjhhG0nsxLJ/t457mtQVmOPWXHY4yVbzmOCrhujd5m8Ao52m49Oz0DL85+2pF0PJNWKkayL34OylrDsqzfp3GjzBy3cc2/fb01yjpL77RloWFQkqc+0TCe9yMFx62sp3hWjvPMl4cv8OKU3US60Vvn2Bq+BmW2LvO3rcmXQbVKELS2ELVQwymRXOdoVEVvZC5EFowJt8CnQuljP9mSwZP9E/BCMWc5HLjuM5Mb1Zx0H6d5fvFsfA+nglfwWrEIbL/jqRe+//PPITrhC+TExJ5unevjkevWxrdgmbl68pTmT8bPwPHZWMzvlcgO0Ul3aqn0CHa1st/v8ei4N0rZ0yOo5uyt0ifGXjtPoiXd4Hg4UK3QelLdmeNIAUopr/eX9xOjwExEREREREQ+uNcZwYw4L9iHm62yly32P41q3tEqG+93T6tsDdX6Oo65tcrG68T62udl++7raZsXBwecl/7f3FV2s1WWY+QT1tDuPH65BXERRsQYmXRfLyY4vUas11SKk+nEmhgaY1n/snRaz3VEMsd4pMNCpy3ruOaaOS49mPtMUsgAW8PCHjNeKzvqKTgMq0CwdyfSWFojeqw9PydtjGh6cYIgl85v9U5fYiyw70FfGocIakmelD1hzrPlmmqFzPFcY4Riz+P5+My10tfdZ8dlwTuUYpTdGEm0GF3Dkp1iYwRy6QvUwg6nRXIgIaEvMz0bRgHb4QRlP+HLQrQDUSr7umdXJ6x12nHmWSzUXPfCmXGYG/PzL4kelKlgvVPWQw68Tnw2Vb75jc/JmAlbMC9Uv4JIDsvMl0uDCKAx7Z7Q/Slza7T5QCk7gkpmJ7JBKWR0Jhuhpe92FOs4AXUcRrCz0fzDjegzVpw299HKDIgc4WurSaMR6VSrfH6157P6uCOjx/3pRURERERE5IN76Ahm5HkU8jIAu6tVNl5j+9r59e5rlbndbJVdNsG2wwRgjFdurxNra2sLs8zWVpn5uTaWY6m/reOct12OX7YYwc1pP1rGuoQ/1qBsbbZtLx8jQCvrgQZjif/5+eRotvUMlqUROcYjM+DY+wgErLE0oy3rqGeMZf/XcQQbQU9aghXmaOP9i7PzcXAA66ECO0vCC0vvY++XFTwdbCxHq6WQ0egtOQQc54UWYzfZfJw59EYp8GTaYQbXMVPSxmJ8jKvitBa0aOM6gRiJIofjkRI2gspdJWKMmI5vX2fno1HW+wJTZWrGHMF1BhY2grL1nciJUgzf7WCeYbkmysSuPKHUHW2eycOR6z5TgakUwozjYWa+PhB9tNE8xyEC035HqTs+mwqfPf2MyIWMGbNK8UL24Nlx5tgatAbWqfunRFbaPNNbw7yQZcdhPjLt9hA+9q9Nzi4rZZro3vEMprrHSSarI9R1H2FfFiJhPrY1vHV6W+je6Szs09h74Zu7KyKhuNN0SqaIiIiIiIjIh/GQsGxrlY2wbHxtC8uMEdpknkO182J/v9Eq67GdbHn2kFZZjxFSbeFWX/eImfmNJpu7jb1p61jnNppp6yEDZJ7CrK0Rto1ftm2c0s7jl6wHCESMIMt9RG6BjXFGc3y7PxkUP7+3sQZurbMkRFvfp4974ARLD1qH6CPU6j059kankx2sjOue2wjKJofiE2QfIWExrtzoCUt02txIK1g4eIKP4KVkMi8zgfPssLBEY45kmReu+0J142pXqeYc+4Kn0UmKOU9KJVrS+kLrY/ywu0MPDvMRj9HmK9VHMy9tLe91JisE4z6mj0MK5t7p/P/Z+7Nv27bsLg/8eh9jzLn2PveGJECAAGFB2hR22jhpkDalQTIIi0pIikoVQctn56tf8oEX/gY/kDwbFSgiJCRFqEQIBKawwRgbWwaSSkiAioh79l5rzjF67/nQ59p7n3PPjbhHICHFHV9r0W6cfdaea6615jytrV/7FYGE4qMzHiS5dhxnwcdO9HukLiylUerCdj4T48J9z/hiLSkojcvOdr4Q5pSlosc1V075e2+0yu3tM8w2UGfhlJHIgLttY+uG953SCtEqqivjcs7PuzakNrbLmdoWqlbcjVYbN6LUVhgxUA2WulKRXLssBQS67+go7AF92xGUnhcTXZ2qQpHCTT1xU8sR83SipCC8zkjmZDKZTCaTyWQymUwmP7+8mwjm9TFXh9fTYn/VdJZFpN3qMxX7mz/GJp+cQcYmeReusnKNO15dZVcnWzz0o4mkUBOHteypq+zxv8cQwFV4MyOO31PJqGX36++BHa4yEahVcIEYGW1MVxuAP4pvwrX5n+FZ6h8j35dufowDCN07PoRhuX4ZAbs5PTriKRQKgvWBRaAqtFLQiHR9lcJSUizsZrnYGaBRGARSoIgiYYwwdoP7S2d3w9zZtsHmnaLK2ioLhd07926HUFM51UbsGT3cx8AFrBYwp48O3akFtGmKpEdRfcigksIPx/sevWNVMeVYAu30MVAaKoUqgtRG9A3vZ3RZWesNIoXtcqb3e3bv1BBKqQyEft7o246bUdcKpaYwulbWtvJsadycbhjjwghjrSfcBsbgrdFzVKBvtKUh65oC67jkdY4ipbCNjVIXlBRS16WxtJajDQVqKLWeaCGcyoKTQwWhMHoKg936IZQFimMMEGhaedYWTqKYaHa2YWgRqgi1VPzfwX3+i5kpmE0mk8lkMplMJpPJ5OeVdyOWXQWwVxX7Cy8KW1dXWVF46iq79n9d+8CuPBXVHs/n2in26CrLtcN34yp7lMSuotvT3jQOsSydXRwusvwNPX53uOF+FbuEOOKXeog8NrKz6nre2U12HRXIg7qn82sMY/QgJN1jlvoKGx3rSu8pyrnl67z4hrgeEUbFPGOVUoSlFCBoKFYhwqiRK5QB7MPyVaji6hRRSjgmxjaMy9a5+MAj2C+DPUaKVK2yHCubWwQjnCKFZT1RutN7xhHtEMp8ODYGvmV0U1sB0iHlboQPahQs8n1xUbQPhqYTjgisd3oMCg2VRisVKQXbL9AHta3U1ihauZzPOBu7d5oFWitdYFyFMjdKK9TS8ppqlVYq7zutnE4nbGxYGOtyS9hg6z1HDfaB+85684zQEx6O7ee8rjyFsZB0DIqnk/Dm5oZSCx4DUadqQyVjsU0KJo75jhfB3Nh3I1zwkIx6qjFisGiKk2+sC6A4wR4GmqLpTaloSZnopJpi7nuYKZhNJpPJZDKZTCaTyeTnjc8WwXzqKnu52P/RVSYvHEskMo74pNfsVa6yjF++e1fZ8WyvdJU9RDRfcpVdy/wfyv3jWrqff0wBK4WQ63na4Srzo5PMI1LUi+NoKohUiHwmP9Yp840SAs9zH8awx9fVLaiAinHZj14vd4Kgj+DeNiTS0aTkqY4wIjxddaWwSMHyXaAFDCSjiMMIlKINZyA4rRS2sdMt2Mw4945FMLbBxTsRghSlcgwKIAx3am2stR1CWWeMzgCsKGHB6APbB60IuigiStGSEc0w1ME0O8wiAHfGEcOUcGwYewwqjXIIZaqF4TvSO6ebG0KVJoV929h8o0enGpRa2MQYd2ds7/net0LRitSCauFUKs9OJ06nRt93hjttuUEjuGwb9+H4IZQtp1vMFOsd6zu5oFqx0XHNFVC3QVsap9ooS2PYDuLcLDesCCKFViouTh8XVGt+Jj2wEGwEw0cOLYhTtHHDwm1bMMAll0BplVUrIkJrJa/RcCKcbTi37fP+bW/3X9RMwWwymUwmk8lkMplMJj8vvCyWvVME81XF/vmwt4tlV7fYtdg/IrBDtHpqkCkSWVD/wnO9s6ssuMYqX3SV6eHqugpgPEQjX1zdvAp1V2/ZgxCIUPSIXx7dYREwnMNVFtSiGKARxNV1RBxi1+FqEyFEs6fMLCOXhzNtN6cglMNB5HvGOyOc0YMeg23stNryPYbsKYtgqQUplYrgkoX/TYVhwsX9WMoEkYKK4zGopWA+uL90Lu7sZvTRGd153jcUpTaloWxuDHKNs9bK0laqBaN3zlu6pIYIEkIfhu2DqtCaIKWmi88d8UGNjBrWkp1mEUYPB1WUYOwDU0dCqdKoJYWuYRthg2VZ0NooIfR957ldcDF0pKPsYjvjsmN7hypIzQVJFLRkpPHm5oZWYYyBWXCz3mAebNvOfR+4GRGDup6QuGH0Hch1UqmN2Hei5LIopVC1sC4LUgruHdy4aTcsmn+nKgwf7GxED1yg9xQjhxkqwh4dgFoqN7Jwao0QxcOIQzRe64KIULXSAHHn4hnBNZSqBev93+p+/8XOFMwmk8lkMplMJpPJZPJzyruNYLq/3VUm12J/lIjPHMF8cJU9Ea5Ur6X/L8c939lV9hirfJWrTB8HMB9e04tl/ke28aqnHa/teD3AcMMMwFPI8sixSQVFMg4pmiFPyfhlSCAlnWBECk5hwW6GD47hgHz9tQjddrwLBtgwMOFinRGDoFCl4haMMbJ/rCrLIZSJKCZOO17AxQzxQygDkHTSVS2M0Tmfd4Zk/K/bYAznre0CCFoLqyg9nH4U8KOFtq40h2HGZduhKnvVQyjr+AiKQKugteXCaASEUV3ZJai5gICHMcJBhCJB752uTkEpUSitIgHmO+FGWxdqbcgIbNt5q2+EQA1BamWzjXG+o287ZSlH/BMoSmsLN6Wwnk4UMRyj0ChlwRGebzvnvaeoJ05ZToRXou8PoxOiBaeD5WdfFmEpjVNrOWBR0hlY6w0acLOsjMg+NxdhWCdCGR5oCNswIoIuxkkbq6SYl6MSeb15CVTgVNohdh7zFCO75TYRVCqigRz32U/uO7/6Z3PDf44wBbPJZDKZTCaTyWQymfyc8Sqx7FURzHdylQmBf5Zif4Dh/rausiJBCm2P5/J4Ku/OVZbP8egqu/rIPHhlmX+Qry1L+3PlUo7jZ08ZwOE2exK/JATn+hoKcQhAFo6KUo5Cfw+DQ1Qalm6r8MACqgg7HdvAQhhjEAbdgy12wrKM3wmGDcxzXXLR7MMiwDRYjo647VgWCIcRh6hH5AqjGedto5Pxys12+giebxc8gmVphxts0BHEMyJZ20oLuPR+xDphL4IerwnL52gl0NKI470ljOLKEAgFDAJnt0GrBQ2nd2eUQBGaF7QWFMFtJ0JS5HoilN3vZ1TSTTVUuJx3tuf3+BgZu2wlP49aHoSym/WESK6GLsuJErD1wTly9dNHp7SCtpoOv8sdIuW43gSPgYYQdghlpxOndcFwQoK1FI6tTda2YGEMuxAlj9G7M0KRDpvlSIKoU0rlROPZsh49btBtEK2ySKGp0rQd11FADLoZA0G0UiRHKDqBRLAAb86VzMlkMplMJpPJZDKZTP7d89n6yl7lKnux2D8jjE9Ft5fFMr8uND5Ryt7JVZYdYK92lZmna+qpqyzP9UVXGRyi2WM9Gdcy/3jyXNnDJg8x0d38cIs57nKId9m95qSDTaMc55iClXtQiiAquGWPmJlhIwUxHxlvLA4enYuDu2DDjvcV7mxPJ1FkjHPvHUohRGhNWLXlc5Gvtwpc3MEddxiRP0NTGqzAvnfOZpgF29joFpz3nRFGqy2XSW0QqqgHrs5SFyrCNlIoM6BLzh5chTK3TlsqqjWdeYCEU1zpQER2tQnOPjq1FqpC33dMgyJK80Kth/hnG14ay+lEKQ12Y2w75/2MAFVrOuO2nctlQ8ygKlpSsNK10urKbavcnk64d1QVlYVaCvfbzq4Z57SxU2tF1xUhsO2MiaK60LcLUTSFT61oU26WW7QqYTlYsLYTqxaKpoC32c7wDY/IPretZwT3WMDYbKeIUFpljcLpWNocboSAqrLUJQXBUigIasZuA1MhKIQoiuLhjDEQUti9aYUqhWVZfnY3/ucIUzCbTCaTyWQymUwmk8m/Uz5bBPNVrjJ4LPZPV9mLj83CfuXaTZbRxnSDPRXL3slVdo1gvqOrTFLgurrKil7joK92lV1/ev1/j685HlxlQmBHHNPMMm7nWdIveixkSq4hcvyuDSNUUjg54phmDuH0btgQwo0eUAJUnC5OdGEcolofsIcxxo5IRUQZo4MqLspSBEVpWvBIUQ4JuoH3cQgzUASK5utsERjwqcMZNjxXMLdtZ0iAKk0qwwdaGgvKTrC0EwXYfXA+4p/9cNQNs3SejY62wlLXQ7wDCaMMYcdxggjP1xGOKJSSQlk0oQDVldrS1TZsQ2tlWW8ppRFbZ9sv7OOCilBLo0dwv3e2ywZmGbuUgohQ1kYrjWenldt1Ye8bGoHWhmqh98Gn9w0bgzE22rJQlxNK0PcLjkBpRN/pEaAFLYVWlFYKpVbACTfW9YZFhFqyfL/bRqhhtjNqxmZxuJgTPjA1qhTW2rgphzMMyUXRVqgqtFJpWqkiiATRO05wQaDUHIkwYwi4bbkCWoTb0wk7ditccpDhvcwUzCaTyWQymUwmk8lk8u+MzyaWXV1lGVl8MYKpR7m9v1Ts/yiWPbrK7BWusvzTqxcw3eNYsUxXGYdYd3WVPTwXcYh2h4vsKEx76irL1/TYdyYix7HlwVVm4Q/xS3OI0OwqO16LheU2ZVwXNTP2qEVpKnjAsOwpGz7woYfA6HD0lO22PwhlFo4PYbfObjuqFaRmob8ZgVAlOLVKEU0Xm0DTowPreM+GHS4+zc9IA0KCO3P2vTPCuYxB33Z2Aorm0mY4HWWlsPtA6spJoFtnmOMWbOIplLnBgPCML5bWjoEBgXB0OD0iP0kzpFQ6gWIQju2O1xTK1JTaKiMcGzvaGqebW4oWbOvs/cI+djCnricu24bdPc/BhchStrouhDv1tFC18OzmRJGgSF43pVZcBLPgfD5n1NZ26nqilRu8X4B0zeXi5QYIFhntPS3Ksq6EBBFG2OBZO4EEt8uKhYN3dhFUhW7BHgXZc2003DAJllo5iXJaVmQ4LqSA2BoV5VQqtVSUHIywsbMLxNUhGY54xlgBJILbVljWhoumgFkLd2/9DD/0Pd/J85/6Mf6///1//7P7h+BzgCmYTSaTyWQymUwmk8nk3wmfKYL5sBIZby/2v7rK4hCc3qnY/51cZXpE+648dXtBOmUeSvePX4vjeBGC+RGJPEr94fp3GQmVQ9W7lvo/LfOHFLYenW9Bt+yQusYvARzP+GU4gVOkPAh6VxdcOoIEszxfi8EYghsMGyCSPWW+40MZXeg+0CFsZlxsQ0PR49hmDqIoTmuVqiVdbxqcKFyscz4+h94NVaGUdMCpgwts3biMgYlwGTv90rmEI0UR93w9WlgdNgwtCyeC7h3xXKi8t064YBIwMnqpTamlpevt6M3S4ezhKEqMkeJqLUQMxD0ddBUKsEShLAWXYPQUytrpGVUU3wdbbOxjQx3qsnIeFy6f+jSjjwdxcDmtuBmlFk71xLI0lqrHCmlFNd97c+H5vuFjEBhtvUHKSr9/DqUiUnDLxdC8XgRVeHbKY5oZFoOburLqDQqstXEeG3s/EyXVXh8Z3fUjEjxGxwvUVjl5cDqdCPPsravpQmxSaZoRSpWAfSeKcAkHaSDZcTcIfAwa6ax8ti4MP+7NYw31H/2ff5/v+ti38P3f/XHO93eICP+f/+6/40u+5Ev+rf5d+MXKFMwmk8lkMplMJpPJZPJvzcti2WeLYD51lWV08fVdZXkMeDeusqIpc2WBfApJV8FLjur+61Gu8UuV4w/xWOr/4mu9uuHSGeWQrrIIhpGqHBklrKIYjlIO0dCITNtRNXvKMnoJ5sYwsAFuAyfjm4ax7465MsKw3RgB57GBRjrWNMv+HaVIUBXWemKYERoUlOHGvY/scrM8v1I01y/diQJ7H1zGwFXZfXC5v3CJ7AlTHDNHamPxoIchZeEGpccg3Cmh3FkHF1zA7TF62VqFkq4ncaOEsplRtCDDiBhELYSnu8ot8HpERKVSqmAK1ndkaZyePaNJpe879/0+3+cALQub7dinPk3vA1EhApabExGGFOWNdWVtjdNaMXe0NpooNpzNnG0Y3ToaTllPGY093+X1sZywbSfEiFBqbWgRWgjr7Q2XfQffOa3PHvrJRJVuO5tdEAmGCjYcLLi4E+6YDBSlLJU3JKOzo5AR0CKctBAq3MhyLLcKMTpbpOsMspsswuju+brcuVmyX05Lw8aAopzPz/nh7/0E3/XRb+If/K9/54V7OiL4s3/2z/Jn/syf+dn9o/CLnCmYTSaTyWQymUwmk8nkZ827jWC+ylWmInjkkuQ7FfsLkU4YXuEqk9dzlXGcV3bax4MgpvrEVXb8T6+1YscLKpL9Z+k6e4xfioBH9qKRXinc0l2mmsfux8KjkEuYEWCRIl6V69Jlxj/HGJgJ7imGlRDA2N3QKGwjsL5jwMUHW99Zy5IR10NcKwhLVW5KxSIwcYpmV9tmThxdaS6aAwslYDhRhK13LhcDKWw+uDzfuBySXZF0jmlbWMzoNtCysEqhxwB3NJSz9WP184jd9p3SCnVdHj8QMwqFbTi1KMVz0CCKZoedGeGOV6WpUrWiR0zU+kCWxvrmGxQXeh/c2R3DekZptbFZp999mr4ZbcmVVa0VaaBFWNotb64nVD1djVpZqmDd+ZR1iGDbzmgRSl2RcMblHrPICOm24dsOUogirEWzhL+W/Oxs5/NON+DBTV0Y4biPI3ZqdCQ/52OAwn3kaEIpnMrCs7bCcCCy802VBeW2pehVEMQGErARRChoxW2gDufRWY4M8e1SUFkylqt5z/3Tf/yjfNfHvpnv/c6Pcvf8rXe8v//cn/tz/Ok//adprb2Lfw0+t5iC2WQymUwmk8lkMplMfla8Six7OYL5Tq6ya7cXPI1gvljsn2uWLy5rvhtX2Tgilk9dZSnf6IPjTA8R7VWusmuJfyAPjxtHd5jHdQIgjznseg4pvBy2KUpR3A0zp5WK4xnbcwiFpZYcILB0FY2jg8zMMyKKZLH96BBK7wDGGHC2dD0VLdTS2PcdtGSnV4HbsgKRvV8SaMBuTojgNhgBTRQpnkKZK/sY6V5D6TbYLve8NTr1OO5gELqwemeMjtaFGxE6QdigREnXmuc4gNshlC2VchXKIEU1CpdutJpC2bbfE61QNJcwzQNXWKSwlkZodqS5OdTC7ee9iRiMbtz1DfcBpHNqH4P9rbfou1GaooUcJHi2oATPbt/g2boQPnLZtKWjbOyDn9k7IULfz6zrSm0N3PB9y547KagOtu2CakFqZamFpVYQMOsAfMHt+5AI1rawj8GwLR1z4bgXej+EP5zRO6bQSuVZwNpW/BAMd3FaKdRSWKSyaEEUdO/0Qr73lIcl1m4GHphduG0VLQUpBfHABfrlzA/+4Cf5xEe/mb/3P//Nd3WP/8RP/AQf//jH+Zqv+Zp39fjPJaZgNplMJpPJZDKZTCaT1+Yz9ZVdXWVXwSzi8e+uxf7xGSKY8iCmXUWv6/H/3bnKstT/scX/6iq7lvhfXWXDswPqGt5Mgc+PDjOO8QCyvB/P1xAcrq6MX44xUigr1042oY+Rjwtj9CyUNzeuy5suA4bk3/lgDLiMwe77Q0+Z9c4IoZR0X93WEyiEOUiW5o9DrByHY22RghbHjwXNfRi7dxylu3G5v+Oud0QLVQuBsVG5EcHGRm03NEmxxn0glIxu2oar4iPAOhRlXRdM8v30kZHLyz5oTSkR7JczdnR0uRsmgpH9aqW2fG/DkO6Um5XlthLDsd247BfMOqU0RCqXvrM/v2fsuaKZgqnCkiMHz26fcaoFcMQNakMA241Pj/34nDZqXWjLCRs7Zk6tS7q/fCMCqhZqbaytsp5OuU5pO2s78QW3nwciqCgext1+h5ZUeMeeYlaPzjDDw9Ci6FJ5UxSRgqikcArc1MqqcNJG1eyLi76x69E5RkEw3J3NDCUgYFkqUpZcB8URh3/xT/4R3/Xt38L3fPu38elP/fRr3+vf/u3fPgWzyWQymUwmk8lkMplMPhOfKYL5WOj/KJjB0wjm4d7yF0cAVLJs/2fjKpNjvfDduMrSwfRELCNL/ZXHEv+HUv/IAnZVeSKWgYcRccQoEeJYwoSg6LWnTClkF1l4nkUtKcJZGCLCGIaNwF3o1rNHTBSTweiODaHbAA8u5lxsR0UoFMwNJyjaWMRpRXNQwAeqmt1YQFjQ3bAIqhSKOo6hofQQtvOFkEJ35/7+ORdzjCNWGIZJY4lAGaArN7WxRVBGB22c9477JYUyFxgdl2BdFizDp4gFotCHIUtFI9jO91iBpdUU/TRwEU6hLLUgIvSxoy7U25XlVPDh2St2uSfCQSpaFu4u5zzGlp9/O2V/F61SRXjj5paiZFwyApYFGwbduBsZ+xy+U8tCW0/0yxmPSNFsOzPGORvuSkY2myplXY5S/gu3yzMqJ9basqvNU9RMQawxepb4j8jPwsOoWmilctKG5JQqQwyVQtPCWtvRFQfqBgR7pMgX4Uf3nOFuhBkF4XSzZG/Zcan2fuGv/qXv45Mf/Wb+57/5I699n9da+W++4iv4f/+3/y1f+qVf+tq//7nAFMwmk8lkMplMJpPJZPKu+Exi2atcZW8r9r8uRj5xlRW9imCv5yqTw1XW7RULmASq+uAqEwLRq/h2LHLGoxB3jVleO9LseC3CUeKfrVqYxcPxPYTwQDSdcXYtVxfNWKHlc4QGrRTMLCOF7uxuuB3xy/As4ifXD91gt4Bh7MM5W8+FTfRhOZNjuXNtQpOGEYSm+2lzR0awhR1ipFJUiBgohR7BdjljKC7w/O4thgebB0UgfGCysgDDNspyQxNhc2P0gdSVe3Ps8hZRa74/o2M4a10OmSyQ3aA1+hjZS+bO5f4O03RB2bbTYydaY7UU6SiFvl/QUJabE2ut7FvHwtnOdxiBaANRzpcLNjpjz8+pnpSqBWpGGJ+tJ1oRahHClWgFDeGyd7o5ve8ExtJONG1Y39n3oJYKY3C5u0NKBVVOrVBKOvksBhLG++oJYeVUGx3YvSNV6XSqLtgQfMAewdh3vAhVlEULp9Lg6O8bJe+RpTUWybVLwlHruAoDGAjiQYjlImt4fv6lUE4roA+rrj/+Y/+UT3z8W/me7/hWfvon/81r3+O/+td8MR/48Dfwwa/9Bn7zb/wPeN/NzWsf43OFKZhNJpPJZDKZTCaTyeSz8k4RTOAzusqKZlm+vxTBfCqExVH8/25cZde/e+oqu4plV1eZiGLHiaheQ46Px74KTtm1do2KyiF6xcNjAg4nWfaZXQc2s/g/e8rCg+5O1YK7Y2FZ+q+wFMXM6X3kcqY7ZsIYGfNUVUSD4QPv0COwfdBHsLvRfaAIinIZ/XAgKaUpN7JiEowiLKFcLPvSOs7wFMeaFvYYNCp7CJfLmUAxgct2Yds7ZzNaEfAO7YZFC2O/R0/PeKMtbD7Y9x1dbjn3zrj7NFEqhiD7o1AmkTFQGRAS9AjKMLx39jGIImir0Hu6x06NNjK+yKmwbRfqKLTTiVNb6NvO7p29nzF3SlkRgvu7e0YfuOWVUVellQZVaa1x0sKpKiFQSsNKOssul52zKNvlnlKEtiwEhbFdGBbU1gjvXPZ7tC6wLKyt0lrFPSDyM/wl65u5dilK98HFOy5GUSWGgJUHkXOMgRdFW+FNyRht4AxNV2NrC4sIi+bnKu6oOJcw7Ihpmvd013lgtuWgw1pzUVQViSDG4Ef+6g/y3d/2Tfztv/7DR5/fu0dV+f3/9R/k677+T/G7f9+XYq5EDDB7reN8rjEFs8lkMplMJpPJZDKZfEZeJZala+lx/fJVrjKIoxT/6Qpm5DKjZBDSjwxZ0UehTHhnV1m81FV2nbZ86iqLCDgEsXyWR1fZ0/XLh2MA3ezJazzOFz/+P5j7w8/lSfxSRClk/5gbuEDVfA+6HyXsBGOPB0eZohlT9D1dXyOXH/fh7O7stiOHVNZ7B4S1VKIKz8qCa9CBEuDDOZuDKJfjeE0qPTrmlQh43u/x0HQ72cb9/YVzBE2BGBgnlqWwb3cs6zPW9ZYunuX2yy33ttPf+hm0Lel22juuwVobEo7j6OHmG5ZdZTEGXSyFMi2EGRYdaYXFBXFBTg3rHdmD9XTDqTX63tn7zuVyR6igVBDl+VtvYe6Mnh95OxUKAk1Zl5VnpxMShgu0ttJViN3oY3D2YPSNUpRWG/igbxdEK4FmT9vljGhlOd1Qa2FdV4YbNnZulhtOeqLVhovgPjDv+QFEimTDgxGDboa5obXQ1oK6sNaW514CRakqrKWxlHosiwbhRg/DveSaajijO0T2lNVaOa2nfJ9V0IB//S9/jE9+x7fyyY9/K//mX/34a9/Xv/xX/Eo++OFv4P1f+w388l/2RTjQ97zOVSv7ERd9rzIFs8lkMplMJpPJZDKZvJLPFsE0T1fZ0X3/pKtMHlxj8ERwk6CIHg6Yp240Of57jVW+O1fZcUiuTWR2xCjlcKpd1zrzmFfx7Fi5PMS0qzvueHXA0T0W1z2Ao2vNQcSzK+w4LzmK/sMDT82KpRT2MZAIzIwxArdc2RSBgtIjnWNjgI2exfvDGTHyuKQzLSKotVEKrFoRJSOP4eDC5oEinH1QyH4zZxBRIJTz2OgOI8Bs4+7+zK4KGIyBrzesS2XsF7wu3C63uMK+b+hyw9mC7VM/SVtuMFF82x+EMnxkVDSu0dJAAqx3TAZSj7XTcNxGLlW60ErDm2BmyDaop5WbWo+Vzp3z/T0URTT7up7fPcc9MINh8OyNhgRIFdZl5XZZWA53m5eFWmB0zyECYIyNIkqtlbCB+This0Lft4zV1sq6niitsNTGCKP3C2+ub1DbEQ2F7CDzwRBnLSu9D8zBMGwMBkEtyiJCQ2hURgl6OFqVtRSWY0yhAuLGjuMIoRUbR9+ZGbhBBK1Wbk4rESmSeTh/+0f+Mt/9bd/E3/grP4i/pqglIvye/+pL+fDXf4Tf92VfTtHC3gc2HC25rBluUOG97S+bgtlkMplMJpPJZDKZTF7Bq8Syd4pgXsv2H6KR146vVxT7+6FEPY1gvq6rLI6/F00ZLA6H25NhTK5usGsg822l/vBwzKuglpFKfzhCxNGBplBKOtKGpWgWBOZOeMYvW1X8Gr90Z7dcAu2HCFIR9jAqMHq+f6MPtuFs1vNVHsLciEE9Yo6nWihVU5h0A1EscnVyC4OAJgWn55iCFO76dgg5sFtnu2zsh9hi205db2inBn3HtHDTbogq+L6DLmwO26d/mtpuiNLo+w4li+DNOq5Qo2DD2cPRIDvBBKRqDjjgDDO0FBZRSstS/H0YOoR2WjndpLh4fzmz950QpdaVfXT283NGD1AYHW7fWA4hFG7XE0uriA1OtdBp6TIcg8seuBndd5a60FrD9p19pKhIwP1+QUul1EZt0NaW154PunXet9xwaise+dnt4YR3Os5SFqwb595xgtGzawwVTgirNiwMimKSgpdqCmXLNUJJsPsgpACa3XV9sLkhbjQp1LVSKbjmUsXP/NS/5pN/8S/wyY9+Mz/xL//Fa9/Pv/SXfSHv/+DX8cGv/5N80a/84sM957goRSq6CIghKPXUqARvtvbaz/O5xBTMJpPJZDKZTCaTyWTyAu/UV5adXi9GMJ+6ygh/W7E/ElTVQwA7DipC+bdwlT24y9AHoUyO1vNr1DN/P8Wxp6X+QYpeV6Hv6kLzsEMgzHONq9CnPB7j6kob+VjXXN6s5Oqlm2FxrHZalp1JQKnQ+0Aczh5YH2wj2C1dWjyIj45qoZXGWoSlrIyw7FALw6PgY7CHY+Y5JsA4etcKl5GOqR3YbNDPGybCZXTEA60L9dSQvoMot8sNQ1P0wRcu5ozt05R6IqRifc/Vy5r/X1Zo0ti2np8hwtg2TKDUjBKqxHG+cKqNsiy4O90McWG9PdFU6WPw/Pk9JinALmXlsl247Gcu905d0hl3uzbWk4IEt+uJ09JQG6zrwtkXugc9DPNg7zslH8ppOdH3jeFHkb8P9pGvuy0ry1KprRCi+NgpAu9rtyxtIVToY4AGQwbFBaRSTLnvO3Z1galQSwqWS2sQwU5QtWaRf22spVL9+hkP7Igje0h213lGPCWy806WSqOAFjDj7/6NH+G7v+3P89d+6PswG699L/+O3/V7+dpv+Ahf+mVfgZaSfYLmFKlIzXvWa4AHrVWWpVBFKaUgtbz2830uMQWzyWQymUwmk8lkMpkA7xzBTMGIVxb7FxVUj5XMlyKYIvGCqyx40VX2OIYpLzy/HDHIXKt8XMS8ClhXce1trrIHsUwe+s3c44lwxuEgexwAMLeHMv/rOZjn85Ry7WlLESZdalexLShIbkJaLmSOAW52uNAEB8wH7Ll22C87u8NuRveOhuRrNEeL0rRSS2FVRVXoGBDYECKE3XZ6OI3CUpXhBlHYfTDGzkA424C9s3tw9ixu17KgqxKjw5AUykrQt51oC5sFffs0og1zwS7nFANLo1pHNAvq923HEJoo23bhQgp8pRSKXCN8QkMpa5bl731QQlhvUijb++D+fA8KPaBpY+tn7u5+hu0elgWosKyNVRQpcLvecNMqbjtLrYxl5bztWFF2D/p2oSiUw01o4YxtA0+32bnvQGFZFtraWFtlRODWeaPe0NY3KKUQpbDbAHO8ODUEvOawQwyGGd0dLULRoGgOKwRB90FrjRtRVi20UhB3JDw/R9EcXBgdpLCbY6NTtdBqZSnKcXHz/Kd/ik9+50f5xEe/iR/7Z//kte/jz//8L+CrP/BhPvj1H+HXfvGvZ7gRFkTkkqgqhDp4sJwqVaDWQq0VOeLWOdY5O8wmk8lkMplMJpPJZPIe553EMuCVxf5F5cHZZf7oyHpa7A/yWVxlD4rZC66yYYdI9eRx13OJyJDm9UQej/OiqyxdYvLwDN384dyvj9+HpRB2HNwtnWy1HMKZ8fA8HsdipkKtyjDDuuEEZoH1wK6ONIERBggjhHHpWeY/gh79Ieo5jgGBjO1JCmUlRbhU8EouLYaz+U6lUEUY3lGr7BFYv2CibG7YtrOP4EwnzKm6IGsD61QPTusNHcP6YFDSnXX3KJR5v0DJtUm1nvqNKf2yYyJUlLFvdHFECq0soPm+OrCKUpYFG53enSLCaV0o5ALp3fPnuAqOUFzp+8adndmeO6VBXWA9NeR4H56tN6w1xxNEQNZnKdqpZcn+fkaLUIsSbhmRtVQ2hxt977RlYV1vclkSxcMYNrihclreRGvBgIFDvxAVlIplshaLdPB1t3RLinBTWgqvh1C8tEqplVUK1QPRPJ/suhNCspNuXMVRDEVY14ZqzUkKN/7+3/lbfOKjf56/8gOfOMYeXo/f9v/8L/nw13+EP/jlf4xSKk7gI9JN1gTCkKZICLUWTmuliKKHUCcFNDyFYoS1vrclo/f2q59MJpPJZDKZTCaTySsjmLkqKa90lT0t9n9VBLN8RlfZVdx6u6ssgG6HKMdV8Hp0lT31u4g8imjw6CJDjrL+q6vMrwuXj/FLc0txS7KLLI7hgqLX2OlVfotD3HJC0nHmAWMYwx2zfG+GZwyxQjqQAO9BH0Y3YxvO8IGRgolZdrotraEqLCKUWg6RxTEr7J69WZsN9BAbh++IVIbDGBccYTPD9zN9BHd0wp0SFT0tsG1Uy14wE8fGoGuKff1yj5TGcMH2e7RW2rrifU9nIIdQRkZO+7bRNQiUogUtmYsVlCZQloUwY98HRYSbmxXMMTP2ywXTFCaLFPrlzJ07l7ccLbDc5HuBQG2VZ+sNJQwvgUlhXU/s5zNugQXYfqGWSlsbvm/sDhqgpXK+v8MRltZ4880VrYpKAR+EwI1UbtYTUkp22EngtkMR0EL0oFu+7m4GCuHGqa5ULQwb7ARLrRQtVFXWWlki0lEonteZKPsYhAijO+6dVhqlKErQWnayPf/pn+YHvucv8t3f9j/wT//xP3zte/fN972Pr/zqD/Lhr/sIv+7X/wZ6luAhUqh6rMOWQDxoa6VVoZaSMdXrnaiBQsaZj561q9PsvRzKnILZZDKZTCaTyWQymbyHeSex7NFR9ljsr/LZi/0FOTq18kt3fhGXJ66yfIbrc0OKcP0VrrLr8a5C3FVq0xdcZfIQ7Xx0lT2e/0MkU+QQyoDjZ+FP4peH0HZd2swIqDM8KCK5AOme/WeeQqGZ455CmUmwW5bw7xFsu7EdglGP7DcbnmHSR6EMSqtoBBaGhRIjlxg3G4fAJBiD8IKHsO1nRAp3vcMY7LuxqWHmFFf05gT7RtkcbWsqScMxgc2Mfr5DS8ND6JcUypZlxUfHw6lS6FuGQWvA6Hs6yrQSMWiLku1lQSEoy4nRd/ZhVFFONwsMS/HscmFooKoIwvl8R+/BvudnvD4TWimECm2p3LaVGk40Rai5wNl3zrbl+faNKkJbVqzvbPueooY5l96RUii1cXtaMjbZVqxfqCLclhu0FLQtDOuYdVSMGA5lpY4s9x/m6UaUoIijVG5OJ7p1OkZbKk0La20sAIeb7By5MokWeh84SphlLx1QSmFplQgoOD/69/4u3/nRP88Pfd93sm/ba9+3v+X/8Vv54Nd+hK/4o1/F2pZD+IMmFVkEkbzYFKE1pRalHZFLRNJN5o7UkgucgKg+uEk5BMD3MlMwm0wmk8lkMplMJpP3IJ85gsnD8uRnKvZ/EMsIisrDEmVGKPPxn8lVppKP3+2IUz5xlWV32mOpfxxf4vWx+OzhOTz8IX6ZEdEnYtyxaNnNiMem/xTSgKqBc41cZpTUPXD86EyTY6ESuo3877F8qdfnO3rOBuk+u+zZY9Zj4OMqusFSC0UKReFUK0pgEmzmSAjdRq4XitBqxcZ2RCfBxwWLQ/QaF8ygx2DDKDu02xPsO+w7ta2IOMWDbQSbGWPfkNowh63fU54IZZRClcLYO5so9OxY2yUQSdmgLorEKYVFM9rpBrNBH4OildoqxSFGp2+dUfL9id253y9sezB6dtmfnimtFFxgaYWTNloR6tIwKYQ7YU53x4ZhbKylZX9Z7+znMxrpmtqPz74tK8up0Wo9rsPBas6yvIHWkvFICcQHoxgnKt0Ut4GNncsR44wwqhaWUhFROsZmg9OyUFRZpLCQ4pKHETihBY4l127OsMFaKiGwqKJtoYlw99an+Evf811890f/PP/oR//Ba9+zz54944/+ia/hQ1/7p/gNv+E/ZhCUY/ChiBDFoKbAW0phqUI9nHAcd6BqHMMMipRr6xuEgFsuxBaVh8j1e5kpmE0mk8lkMplMJpPJe4xXimVkwf7VLXbVlorKg2AWEdiTYn8/1ivL0fH11PH12VxlQrzgKssfvugqexDLuOpc+tJzpOvrGs90BzPnqTi328g/Rf5CHA6amhoHblm8n41rwjB/iGMC+HD2ka6ysMOBptkxNkghLyzY98E2jG7BiI6PoB+iWilyFPoLa0l3FgUu3cHS1TOsMyS/pKt1ehR6wDiWLzc3ug/GbiBwN3aqKevtCWHD+06rDdQp9riUOfYNamME9Ms9VQ+hzDpQqFLpl41RKr4bPS751klBxGhNEDmlQEXQ2kqnM9xRD9b1RJiBG5e94wWUILbB3bZz2eHovOfmzUItWZK/rgsrghZhOd0wQtiGQeSwgI+OY7k46cJwY3SnasHHTjdHa6G1hbZkYX1YCl7NhJv1lrIsdHdcHPGBuSH1hPTCxdNFZ+70MRDNNc2mJ7oNTCX7yurKUionyShjuhSdCEePEYPA6aPjQBVlKdnBVmksCj/6D/4+3/2xb+YHP/kdXM73r32//ub/5D/lw1/3p/jDf/yrubm5ya62EJoUShPicJMtWtEqVJWjF0/z2i/kAEHJi16Pe+0qEktaRqlFKMfghJD3/nuZKZhNJpPJZDKZTCaTyXuIV0UwIbuh3tFVRryy2P9n4yoTyf/fP4ur7Fq2H/Gkn4xHV9m1Py17yo7zIw7HTP5994ykXZ//Wvpf9Vrofghlx/thYahoFrof3WR7z5hl6nApGtlxLBvOGEY35zIci8FwZ1ggkS6dWgqtVKo4S2uYOL07vnMIZTvdnSqgZmwGosJmO+HBxToWwdhTkLnrGy0K7XRCZcd751QXBoMS0F3ZD6EsWsMs6P2Oqi2jez4glEKlXy5QG2Gwjfvj9dVDKFNEFvTokKt1YfRcilQRSq0pulhnDMMUqhb2uzvGMO43jpgfLM+UqkoU4WZZWFXxGCzriSiNbdvyM9dC750QZykL6k63jpnTSmXsOzvpnrq5WalLQaQAjgQsUTgtK+XZwj4G5gMRw8youlK8YD0HA/ZhhBshTi2N07Kwj05EcFoWaikstXIKgaLppjPPc4xckPSRrjQ9bqgqwrI0CsJ2fs5f+v5P8l3f9j/wo//7//ra9+npdMMf+WNfxYe+/k/xm37Tf0oUKCGI5/UUGFLy3qiabrK2tLz+H8Y1HCma92YpR4cfuex6CGVXN1lRfRDIiUAe7v33LlMwm0wmk8lkMplMJpP3CPayrewotc+i+xeL/YvK8QXb8SeusmufmB4xxqtI9U6usogs1L8W+T8sYHIIYkiWzMujqyx/9XEB84VOM3lR9LPDJRM8lvh3c+KIV15fmwiU4zmv4t9VKPM8ORBhmD0U+19L/T1S/AkJhhnxEJ+Ec89+st0HYxjuUItSmrBoo4qzLoUQTQdWCITQfeDmDAk0jK0HFKGL493o7lxsED0dcs/3FMpON7d432gRaF2wGEQESOX+6ih7SShbWiPCAUVDGZeNqBVc2Lb7HArQBXRQq1D05nAkQS0NM2fvThGltEaYMbYcHXAVltrYn7/F3WVw2fOt1ArLkuKWNOXZeqIhhEKpjaq37JcLMZwQpfczIsLSFsbYOF/OKchaZA/c3qnLws3aULLUHoEmyioLUgusBS8wRqfUwIYj0qgGfeyYFProRAyC7E9rZWHg7GbcrCuFXCtda0YqPZxwg3IslroxxmALZxFFwmnLgmjhpML/9aP/gE9+/Fv4/u/6GPd3z1/7Hv2PfuNv4kNf+xH+6Fd+gNtnt8cghlLTJpaddAVWSTdZq8pSW97NAVoCJXDJsYOrSIbkfQCCqNBKCmJXN5lKCrVXO+f1d97LTMFsMplMJpPJZDKZTD7HeVUEM0il7NURTDkeES+IZVfh6bHY/9GF9ipXmT9xdcFjV9lVZuM6FHBdq4yr/HbExHgqwh0l/HYVzY71ygfBTnKV0J90m70QvxTcYAw/XDTZ2xTHgmZ4Cop9GD4846nZ9JRutof3UDjvnctuR/9ax8wwj+yNWgqVQtVgbRWRSMeXH8+B0/vAlIwJRmAouzi+53rnXR9UUpw7950aytJOYDvFs4AfDHdBtXIeHes71Jav4XxHobJcHWU0xIN+OeOlICHs2xkLp+lCaKdUoZYbNCIXOymYOX3kIiSlEDYY92esKojQKNyf7/nUfWeMR6HstCoqiq6FW220olAU1YohbJcdpEOpjLFRSmFdVva+cX++5MroGLgqEUKtjdYKpZUUajXXLmsI5bTmtVgEfDBGp5WFsALD6NLZPUU3YkcRTuvpiGEKWpTburBo4RSgrdLdGJIXSIhkrPcYcLhGMU+itNZQhL7d8yN/6fv47o99E//b3/2fXvv+XNaVL/9Df4QPfcP/i9/yn/92wFFRJJRa5RDKnIJQW2Ep2U12LfBHQHC0KCLpqayH6HW9p14VubwK21cn2dXYecUj+9Deq0zBbDKZTCaTyWQymUw+h3k5ghnx2ND1cgTz6iqTJxHNp8X+qTNJdovJz85V9tAW9sRV9vB4QNPKBVxXOa8ut8cv8+bX7rIU79xzgfJ63EcBMGhFsBHY8cxFr5HPFMVEwC3o47qAeX2/Aol46LkKh33v7EeJ/2Y77vnaRJV1SUdPVVir0oqyjcF+VKpZOH3biVZw23HXdHhhRM9+refDaAKjd+7cUM8opMRgkcC0Yt5zOEAa975jd+fsKNuN0XcKlbWtjL6n3OdC385QCkjhsl3wgHVZwHdKFVp9hu+DtioihT4GitLakhFZ79j9xlDQqlQKd+c7fnob2HYsqBY4nVLQkkV5oy7UokgtqFb6MMY+8po8esU0nCrCGJ1Ld2pRbAxMAFFKabQmtHUljiiodOfzb27xUvAIzB1VZ7dBk4XGKQcDwtmGMXwQCqtWluUZm3UiyOimCKelcaMF0xwzGD5Y68LdvoEoYxi7G4vmdVhrpZTKIvDP/sk/4hMf/xa+/zs/xluf/pnXvje/5Nf9ej70dR/hj33lB3nz8z/vwfVYSkuRTIJShKIlV1WbUks9cs2getwDeiyXXu8nHtdhVbLT7Oom0+P+fnCTPfm34OEOPlY2sjPwvcsUzCaTyWQymUwmk8nkc5SXxbJrN1gcotbVVVbLo0ssIg7x6UVX2dWxJfIYwXzacfbZXGV57HzsU1dZ/g7Hz192lR2RuCeimnsQV7GNYITjdjjQeOKUO44+/DoecF0DjFw25HitPdgPhxhHT9m1083ckFB6H+yWS5tb39gf+txgqemmWkpFJXi2NLbRues5ohAR7EcZvodhW3aIDQms7wx3LvlAfHQ+bQYe1NKQmgIaWtn6TiuVVlbubcfu3kqhzJy9P6fpwtrWPKYBCP3uHtOM8132C+ZwWhb62GlFKcsbxDBKEerNyjBHMGpbwDKKaP1CF6hVKQ7n53fcbQGWH7m2jF5WUXQpPKtLFvAriDb6GETvIByF+caqFRVluLPtRi0FN+NiRimV1goSxrI20EJxR11Y1hNyqphcS+xzkXKJlZMHewz6IQqGG1R96JAznH10lmXh5ijmv1nWjNaG4SOQkh1qZ9tSPIuOIrQIWm15jY2dv/4D38d3f/TP87/87f/xte/JWit/4Mv/MB/8uo/w2/+L302EZXSSHApwcbSAhlBbpZX8HVGFANFAcFA9fuf4+TVyKXL0mvE2N9lhRgPk4fp9ykNMOjiGAB5u5PckUzCbTCaTyWQymUwmk88x3jGCyaND7OVifyFdYO/kKrs6065F4U8jXTwR4PJJnOG85Crj1a6yh1L/t7vKnsYv0w3n6awRYTcjPE9C9MX1SxFhGLg5qocLDdDwdM55Rk3T9eTHuedZmns+jxbCgvu+s3ejj52NIMzBU9Bbl0qRQi3B7akxhnHX7YhfDoYFjjPC6JdO1YWOYT4YvbMf5eu9d/ZwcCja0GoUQLWxbRdaqax15eKDy91bRG1HdHRHpXFab+j7BfN0Ddn5wtB0Jg3b6dGzKD4GtShre4abUQqEVsYIqgZaCrgT1glzdrJsX/bO8/M9lwG2Z+xSKrQKTRVpwvuWW6jphNLSGHtn23e0KmPshCqLFko4l7Fn+ZwKYc5lDEqpnFpBcZZWCW00BDFB20pZagpKNnAGhYJGpVoOA2zu2MjFSkE4nW7oZmgaGrlZT5ykcKOKSboSd+sIyuaBC8TWGeEomZ9tWii1cVL48X/5Y3z3x76J7/2Ov8CnfvqnXvue/DVf/Gv54Nd+hK/8qg/x+V/wS7IUz6GWBTRtiKqkwFeFVgtFy0OXX4pkgqoe997RM/YggHOMTLw9cnkdzQh4278LDzaz61jAcZ+bQ6nTYTaZTCaTyWQymUwmk88RXhbLch3y7a6y7Co7nCQvrWQ+luhnKf+1J6zoZ3eVRQT9cKhdXWXpSnu1q+zB/fKk1D+O8wEOES+zatmdZvQBD/HLw1mjCu0o9M+yd1DVq5cs3Wiej92HYcNxy7L/eHCVRXaLDeH+vNG7MdzZI91JQqGqUtbCSgF1bm8bWPDW1gmLXE9E6Gbpfus7og2Xwl2/4MPoEgxLgWeQTjSVihRDJUAUG53wwmm54Wwdv9wzEHxk4X6tK7U1xn5BXKhS6PdnTAURxX0wbFBrZYxBaYXTugKBViFQhud7FqXg1lEy+trDqVrQbjz/1FtcHHyHUKgnaE2oQD1VbsuKVkVLBSns+07sZ6RWzDquNddBfXC/XSgIbpafW48UpGqltEKVQiCoO4s0WFoKRAJBxlZVCi0WAsc8OPfBiAFFqQRtPbHZwCOFt1ULSy2cSsXJaG5Euuj66PQIrA86UA9Btq4LVRXvG3/rh7+fT3z0m/if/se/8tr3YimF3/9lX84Hv/5P8Tt+13+FmIFUVDWvZ03xUUNopVKapABZynF7peCLCBKPbrJ06x1OT3175BIeuwavccsXdbJHkezqJIsngvnDveueUd73KFMwm0wmk8lkMplMJpPPEV4VwYxj7u5VrrKjvQh7KYL5sDh5CEmqn91V9uquMlCJBzeMH6VhV1cZXBf6XnSVPRSVk91fSjrIRnjGOo+4Zx7OqUUwh26BR645+iECBEb4sYxpwdbHw2DAQ4zTM6QpDn10th70MC6jE55uOZXC0ioVodTCqSnFK5dt0D0QD0aAWYovY9/QstClYH3DzLnESGFyH3SC4UGVgjCO2GA+tmpFy8o2di77HUY6sWzs1OWGtrQs+T+Esv0+o5eIEmGM7rRacQatVW6WBVQQBaFgDrVUQgs2dlQdE2H3dJTFpfPp+3s2A+8p6tQbsng/nLYUTnVFWwFVHKV3I7xTSmF3p4xBLYLH4P5ilKJ4H7hKimsIp0VorSGlUACG09qCnurD9VBL0A+xslExYI/gvO2ED6IITWuOODTF3Hl2e8tiwU1LwS0iCIWwVHV3D/yyYRGEBhqei5l1pQI/829+gu/++LfyPd/+LfzUv/nXr30f/sov+lW8/8PfyJ/46q/jC7/wC5GSTrnSVhDPa16zwL8qlHJ0kyGHMOxozcjlYxTz8b4QoL2iwP+FyCW8TThHrndcitPZAfeiSKby5N+O2WE2mUwmk8lkMplMJpNf7NiTb8fXiCHHl2M/vhF/tmJ/Dz+cXukEu3aJXX/nM7nKdnsU5+JJqT8cC5jkF3iVqxTGgwiXot2j+BbIsYCZj9nNU+xAyKKx/K00v0g6zjKfmSuKHoAdrwl8ON2N3oOrqy0Okayb0aQw+uBiwTZyabG74ZHumqrCUmv+rwgVoZtzbwMcHGH0jgnslzOUhoWy7xvDjM06A1Ab9IBuTtNKYdBUQBf6fqGVhujCUOiXu+ySiyDGTllu0PWEjx2RRgno9/eYCEjJOKLnwqUAuhTeLCdKLZgNVBQLKECtDesbSgpl3Y2lNLa7M29t9/T9EEYF1lsotaBmrEvhZn0znX2aIiUjiBhHX1wukC4KLsG2GQiMMTAX6nI43DRYlwaqFA/YB+V0g56UUhRswFKOArrCEoUt4PkY7KOnqOROqdlxVo+RgNYaq8CbdWG0FC8F0FLp1tkD6MYlnBKewwaq6Log1vk7f+0v88mPfTN/80d+KMXd10BE+L2/78v44Nf+KX7X7/syNAKVSimFwNEFKLk62bRSFqHJ4SaDjIEebjJFkePeeHSHprDcij4Rr+XBCcqDMP7ymV3vU3lwjYUI3R4fcRXJ8jM8OgvhPb2QCVMwm0wmk8lkMplMJpNf1LwcwfSHPq5XRTDfudjf47F0P+LRVaaHI+zqCnuVq8yDB7Esf+7o4U5J4Su/2BdN54s8EeH8yQLmwwpmeDpgzNmO+OXVVRaR0VARwSyjjVoEPCODeh0CcMBIoezoKbv2sOU5GyIF8eAt2/FunPedLSx/12FpKbastVFisFRBtHC391ySlMI+ehbK9z1/T2ouPVpwsS278S2dZZeRUcdyuIHWurDvF1QrVVc6ztjPDM+OLXGD0mBZcdtBFAnY33oLUyXIVUkJo9SSBfKtcHs6oUURzcXNI9dHa41+vqNIvsceShGl32/8VD/TL1BquplqhdoU3Dk14fbNzydiIKr5/m4Zq8xy/Upxp8bO7gt22fPv0OxKawtFAlWjtTUdZqNndddyoq3K0nIBFDoUpVhKm/twLn3HzAhNsbPWhhMoUGvhpjaa5jKpAaZgvVNqy9hlNyICO4S2hrAsKxLBp3/6X/P93/kxPvHxb+Ff//iPvfb998u+8Jfz1R/8er76a76eL/qiL0JaOdxkR+xSDpGsVVqVY2SjHneP571SFOKlAv/Ia11UaO8QuSQe3ZuZLX74V4HrHXoVx53A7PFx19/niDGnuS0oqg8RTo94T4tmUzCbTCaTyWQymUwmk1+kPI1gRsSDyyyFqMcI5lNXmTsPAtfVVQYZ77oW3197ka4OFpHP7iq7dopB+rzy8RyCXYobV8fYNW7XD4vb1XN2dZUJx7pmXP1njkTG1YoAKH0EEf543qpEDLY9TzDXIwduKeroEW3bx3iIuT3vG74b59Hp4Vg3RAtNsp+rItQCt6sybOF+7w/LiQ6c93t8+OHKUTpGmHHpl3Tx9A7AbkHVQq0FVaWgjH0jolHLiRHGtt9jh1CGDaQuWBFKDMQU8aDvF3o4IYp3RzTFFhlOaYV1XfPPQB8DKYWq2c4/zvfghqlAOKrKdnfP/SXwAdSjT6vAsmj2iC2VZ6db7BDKjIW+dbQKfrgGW61439hF8T0Q7el6c6dUpZU8Zl1PVBTGAKksp5UQqEWBgfUNBCpKIAx37vqeK6kETRWkwKIZi10qiwW3pzXFwszNUiMdiXsEdR9s5jiGhlBaoS0rEsbf+9t/je/56Dfx1374B3B7Yrd6l/zO3/1f8YEPfYTf+2V/kFb0wU2GOqpC6FHCL4W65ooqZCyW8CNCmQIspKb5NIpc9cXI5fUxD5L0w8DG4/1/bfZXfedesuuxriIZRzT5Wvp/vb/fuzLZI1Mwm0wmk8lkMplMJpNfhDwVy64usSt2xKpqkQfX2NNi/6u4Zk8cJFeHWVEOl8nrucoymKgPQ3vmPLjKrl/ki6ZLxtwZdhz7cMKFxFFm7oRdnymPmSJALle6BWaGiD4IgxKWHWYOEnDZO9YDF6eI4Ah7N6QIRZT7fcdGcNd3djPc0q8kRR+cSmstrEvFDe4vnc2z1y2FtjPWc/2yUNgjIIz77UyIZMk+HN1nSi35NhVg9J1FCqWko6xvdymUhSOjE6XhpVBioCjRnW0/nGpFsZHvhbb8u3aqrEuhtIJ7MMwopdLKQhRhbGfEeo4BKNQo3H36OZcB0YECcohlS0mN8mZpLMuKKlgEro2x7WgRQoIxgkYOIlyGUBz6fkGWdnxWBSosVSltRR2kD1hvaacl44YS2YHWU7CtAk7hvncuvYMGmFFrI/SICYvybD3RFBZRYi15LY+OaqNbCmTicInBEiBaOJWGiPDWp36ST3zXd/CJj38zP/4v/tlr33Nf8Et+KX/i/R/m/R/4Rn7Nr/5iZCmUKNkvJoGUoJAF/rVd3WQt7xvJJUxRfSjwT9Pk4TN7ErlU4XCDXmPQrxDKeFEke4hcHvHmd+oluwaar+u3T0oJH52f8BDHfi8zBbPJZDKZTCaTyWQy+UXEO0Yw38FVdv3C/bTYfxx/qE9cZeVdusqyWP/RVfa4yKdHlCyXKOX40g88xMnCjX3IY9dZpEB2FeHC4TomkOXogmo2nrsFfuRI5UGsc8ycEQLm7G54zxhmVfCRMTy3HALoW+cygs12tt4PAVFBhJtaEBVOpXCzVMyCbRvsxwiChPJ8v0dc2MfALRgCO8Zl3x5743xgDi4pfNCE6rCPTpNC0YUNY788JyL7rWQMtDa6KBqDooXYjbvLlkX9KlgPiviDQNJaZVmVUgvu+T4iJcVOgdE3MMnuNBXEjOdvndn2vBa0pGFLlxQGRODm9palVYoq5tlnFWOkGFQXIhy1DVfl3J1SFNs7QwVdVginLspSC0ijHCqmrjeUtrK0its4XFYge6dq49wH96MzYs++LwEJQZYVAZbWWBBu1oVyCEnposoBgG5GMWEbnSigLqylUktBCP7P/+Vv84lv+2Z+5Ie+lzH6a99zv/2/+J184EN/ki/7g1/BslRE8j2i5LWI5r3UtFCXFGVV87PNpUtBSDfZUbeW95bnZ1uFBzfZy5HLq+sryPcsXWg8iFp5X6Yz82qUeyqSvdxLJk+P9+SxV4Hu8Xl5+DfgvcoUzCaTyWQymUwmk8nkFwlPxbJXRTDhRVfZq4r9LfyIZ77aVQYv9p9dj5MLmI/dSMnRVXaIOFdXmea3+QchTjUjdlfR7iqK5QImRzTzSfxShDi+yEdkiX+kupDHV2GE4T1/r/eegpuB+UBV2QeYGUWUcOfejMu2sZtx8XSuuRtLgdYWmgqnoqgUtmHs7pg7SmUbZ9zg7rIjDtIqQ4yt77gbI6CSYiJSqAUMp4qyjwEuFG3sBNtVKIsg+gVZFi5A804tBd8Hz+/PR0RS8JENcKLgBjfPWpbGV6UPPyJ1BYkUysIGhGCasVcF7j99T+8wDMoCxaGs0FKL5NntGyxVCWA/BhKKHj1qbaVIYOfnxHqid0dqjibsYdTlhMRAq9DaDRpBicDQFMqODi4Tx23k9eBOhNJdeL6dcZwYg6IV0UKoUIuy1sYicFrWFIbMQQNxYeCc+6CGconsT1MtOTpQK59+66f5vu/+Dj758W/mn/+Tf/za99r73vd5/PGv/hDv/+Cf5Eu+5Nehh5sMzetPiqBEfhYtC/JLaUfdniOSbrJrj5jKi5FLBdb2YuQyf/dRJDtuiIf7/frAopLl/fC2XrKrRG6e1wz+Yi/Z08c9RLWfiGRP+9CeCufvRaZgNplMJpPJZDKZTCa/CHg5gmn+uDZpHi92lcn15/kF2CMwy3L9a/H+tU/s6irjs7jK7BgSSB5dZXGUk6f4djhV4MEtE25sD6aeqxCX/WMOmKVT7brQee14KlJxd8wt+8k4XDORzq9ACQ8uY2T80o2i4KH0bpSiqAh3vTP2zsVGRiddCIcqzs1ppZhz09KNNDy4G4PhGbXso2PWeb5dkBBKqexibNuZGJ0hhXZ8FkP0iJ9mLG+YHV1hlQ2j7/eEKe6O2CBqZVehWmdRwYfz1vn8EJEMexRJzODmjcYiBamaIol7Cmx+PC5yXME0+8MIuHtrw0bWhmnL/YDSUjBD4H1vvg8hBxF2BN8HKsGwgeuaC4/bHaMteCh2n91sMZzWVhhnCp327BYdhowB7YSUyqJHD9dSsK1TI1BVhMqd7Vwux2sdA0QpS7rHVJRTrSxFEVFKrXkd4WitXPqGSzAugyjgZrRSWFp2gf3D/+3v8D0f/RZ++Ac+Qd/3177P/vPf+tt4/4c+wpd/xR/ldFoRKSm6aqABUoW11XRrVaGg2V2GIxoZN3VBSnm4XuP4SFRfHbl8MIJehbJX9JJJTlxm5Pe6GPtS35gfz4Xnex1Pjnl93FWgu/acvSySXcmY9BMx7z3IFMwmk8lkMplMJpPJ5Bc4VwHr6ipL8ejRVVb0SaTqpWL/jGSla6sckUk9+s2KHtmwa9X+S64ycxjmxxfuF11lD/HLw4lSDldZeXDMBMPzXK5f6T2CwPM1PIlfXhcPVdMt5REMN+JIIBLpMNuHYSHgcLGO78HAkcO5s3dHNN07297Z9s5uxtmdMM+FTXFul0YTWJdKEcUD7veOcbjUbHAZG3e9g2cP1iDo+87olxTHIpc2z4caohUkAvMUJglhE6Hv99iQXLD0AaWwiVN9pFDWnefnHWpGJG0ckb1Dv3jjzRNNCqgwhqEeGdkMji63tOINAmxgwzmfBz4OR1mD5eaIX3oWx7/vzTeRMCQcKzkgQIwcXaiVokaMC0MKGIyRhfxxlNgbnVphOb2BdEux7HCX1ZqCUF0qMZxxubBQ2CncX3Z6bLmsyXE+6ylfk1TWWrhZGlU1xVkFjXxt93un4dz1nuMJCIso9VR56/mn+KHv+E4++bFv5p/8ox997fvr2Rtv8Ee/8v184MMf4T/8v/0GqIWmmbcsIkSBgrCUSl0EDSi1HQ6sqwNQsrsN0JJClB/3TREoL7nJRPN6edlNdr1n4XpP82RJ9tUimR/3SIpq8sIxn0YuH0WyVy1rPj73Ndb9XhbLYApmk8lkMplMJpPJZPILlqcC1tVVBvlF9lrsf12dvLrBnkYwhzkW6fR50VVGrkY+tiE9cbDlF+9hKXgJV8fatZdMH0r9X3aVPcQvzRjO4Ug74pfuEJ6CVxxF//hDJFRCECmYG+F5XqoQFgx3xkh1rsegn51BIFchwSEwqiiXvtOHs/XOvdsR58xzW2uhCJzWyqIpzF36wCIfs4+N7s6lD/qwPH/JzrJtu8ePXGQIDFLdKrWgtTDGONRDYRdh3y84JWOIkWLTRtDCWFSI7rx1vyMVKBm3VOWh3+v2zYVTWTA5hMYxqFKgVMa+HYpG4CpEv2AjuFxyMKEDpUBreUwGtJPy7OYWdUMCdgpqToSlhqIFZcdt0MchyPrAhHR5meEatFY46YK4Iy7ozRsIwU1R+uHCUoDRwQSPyqd6x3Gs70ip6RwrStHC2ho3taTYWgolyHEBS9FzIEiAS7CNzlobVUGq8v/7v/53vvcvfDM/9H3fxXY5v/b99R//338LH/zwR/hv/vAf5/aN2wc3GeVRrFqeuMmqFLTo4RxM9VFCENUnbrIcslCO0YMnJfrZhXdddH17L9n13n5V5PLl8n6LFLVTJJe3lfc/FcmuBrbPJpI9daHx5F+H9ypTMJtMJpPJZDKZTCaTX4C87Cq7OsOCR5fY02L/q4h1/d2ehWIUefziW4pQP4Or7HqcfdiDq+xaKv6Cq+zB5XY4aK7xy3C2/qIjLRc8Bx5CuB4/8yOBlk+sFDwyfpkyQApxvRtm+U1+9EEf1wVAS1fNEEIdAbo5F+9cLjuXw+XlhyKxaMnly6XQDvvWPozd8nUON3Yb6UgbhpmjtbCZs/ULoZpnFQGyED7QEkhVIhy7DAJhlMJ2uSO04eZgHWo9hDJYq2KXwafvBtIABR9QW4oV7nD7rHFTK0OUERmXrFKgnRjbhXBDJAgVog/GFlzOxtjAawplBagLsMNyo7zx+W+AddyhhyJuhASmBVVF7czeBbc4ivKdEY7WhdrPaFXqaaFIjld6gK63ZO+9UdcF6QM1P9xWK+cx2M5nQiHGQFSp60oJqKVQS+F0DAxULUdfXBBauN82JJT70dFW0BHUVllOhfvnz/nhH/guvufj38o//D/+t9e+r25ubvmKP/pVfODDf5Lf/J/8Z4hAKzXjoR5Qs4S/lYpc38vWUtw9HFrZYVZSo/K0QT4U+Ks8EbHz/kpt+rgLn7i/nhb4P4hkTyKX8uQee4hhH8fKXrIXRbJX9pLBk/v7xed9lUj28vHey0zBbDKZTCaTyWQymUx+gXEVy65fkiOuMlJ+hX252P8hGvnEVZZfnHmIb342V9n1+Z66yuSwolyf6+oqEwI9ViCvzzPMDtfLE7ebWwpmJk9WPAM5xIei2VPmbkftVgpl6fyyLK8fxh7B2JzAU6yzLJGvmq/mMgbnbc8+M4AQhhtNhFNtaFNOWoHARdh7Ps7NOftgO294BHs3tCpd4LLdA4JGEGZENCIGov7QsxY9hTK0sJ3v8LIQCLHdE1rwWikOp6bsdxufvjjSsqPMR4paY6SL6NkbjVNtDIRuhpSgSEFKoZ/PUFPkoigxjP082M5G30AWYAW1dJVhsK6V22crEUbvI+OoWApYWnJEod/TtSJORvoi6B7U2mC/p1SlvfkGZRzZWGlIraxViTCWmxvqbvTLhdBC1ZW7sdMv94AT4eCgy8KiKY6daoFWWVRpcCi6cjjIHNzYxSkerKWhQDkV/uk/+j/4vo9+C3/pe/8i57u7176n/qPf+Jv5wIc+wh/+Y1/F533++xDRozPv+B/BepPPp1Uoh5vMIwv8r0uXWpWr5GwBovqOkUvcH7vEDl52k11L/j9TLxnEK3vJ8t58d71kryOSvZeL/p8yBbPJZDKZTCaTyWQy+QXCU7eXHauS13iVR8a8PlOx/7Vv7GkEs34WV1nkHB79SVfZNYJJXDvH5SVX2WPULMLpmbZ8cNTkuQxAIVKkM3cgqE3ACyKa8csn7jYiuOz28KX+3Du+OT2cMAMKFqCSrrJz75y3nd2d3R2JfN+qKG/URinCbVtAgj0Ct8B84JEdaPtlY/OgjxwJGAqXfsllQZEsvz8cZSmUCZVgjHTvhRYuV6FMFL/cEaqwrBQLahjbNnj+6ciOMgXvoNkRTxi8+fmNU1sYnmJViKejDMHGwH2kUIYTbuyXnX0L+ga6HmKZp0utNlhvGwuKqDICREouZ6rSA0oIvt8jywloENnZRmtoFPBcm7x98334vqMW6HJCtVAlkKK0WglT7HKP03BduLtcoAys73mNSbrC1tpAlDfWhojSjjVWySo6tr0jLmw2oCpiwdIqrWWs9a98/yf53o99K//H3/+7r30/LcvKH/rDf5z3f/Ab+c9+6297cLNJFTBHmtI0i/upQlOl1ALhD06udowPXAUw8u1+iFy+XOAf8XZh61UiWTpF80+fqbxfXiGSvdtesimS/dsxBbPJZDKZTCaTyWQy+QXAVcSKw+V1/ZILj8LXC64y5wVxbXjkF2h5jGy2Iu/oKovj27e5v+AMu36ZVyBEjpGBt7vK4kmp/1Vku8YvzYEoDz1ledKeYkUogTPsKowF4Sn2dUvD0T46Y4tDBDPCC+YCxSiaDrH7faebs4UTnjFPBRYtnJqy1EYRoZN/3y2Fsn107u8vDIU+UqQzgfPYs59spFgyvEAoLk4pwiKwDc+oqyqX7Q7Tw1F2CGWynige4IOxG/fnFCG1gAwOEQRcUyhbamV4RmBFQF0IzSVNO95LEcWs47vQd2e7QF1BUwekVFibUk+N0/F5DSkZnfRcEAgpaAxiDEapCAXbdiwcrY3wQKwjbeH29IwwQx3q6Y2jJy+QAk2O6Ox2D7qwe2HfLngBzAiD0hoFoZVKLcqyNgqwoNih5YQKl97BhV2CCrSShfl1rfzYP//HfN+3fTM/+Mnv4O6tT7/2vfQlv+4/5INf+xH+2B/7Gj7/l30BIBQtQCBFaBLUUwPNa7loATk65I7HPHWTOYcgrPrKyCVyOCdFj5+9vUC/qOSoAvIQpXxZJLMA5edeJHs83hTJPhNTMJtMJpPJZDKZTCaTf8+8YwQz5IWusre5yg6hzI8IJuT35lbfhasM2I+es8eusnj4ku3xuML51FWmAn0YcSzyXX9vHNFKojx5TYHjtKKotIeYZjiEZA/T3o3hQUFx27l0x+3RoTZMkZoWtj6cixn32449DBMYAlQtnKqytEYVJZQj1gkjnD6y3+zsA3PBj5jhFo7t2yFspZgRDoijRVhwdgvOY4AUtu2C14a7Qr9nRC49FnMkBmN33npuBLCs6SIbDuIgDT7vl6600rLQ/hAM1YQohwhp2VHmBGaO7QPbYOtBW/IYEYejTOF0s7CWgklhH0bRgh4xXSkN8R3rnY6iIaDkYEKtyL4RY2O9OYEqTRQNRU43IIFirKcb6nA8eg4b6MIWNWOiBDYsy/+LclNXAjgtFVS4aS2Ft6oMD8boCJpuspJOs5MWigqjb/z1v/y9fP/Hv42//3f/1mvfQ7U1/sAf/CO8/0PfyG/7Hb+TIlnSH8fiaFGhFqWgWeB/uMki51oPYVAfCvwPbfPBTVY/Y+RSjhjki2KVHk7NF3rJ5LGX7OrGzJhzDmrkvcrDOb3uwuXxN+8okk032btnCmaTyWQymUwmk8lk8u+RdGU9FvuLXJ1lkt1ILwlfV0FtuDMsv2iX47t0eXDMpKvsGrF8lats+OEweeIqKwJ+xC9zATMOoYyj1D/YLFIo47p0GZgZ7gKUB6GMCESDtVRAcLeMmGqKRG7BXe8Psc67Mei70Yflc1lhiKNhjAH7GFy2zgDGNV5pg6U2TkVoy8JSCsMzwikju8DcBuc+uPTObkffG8aIwLYLcXUFOfSRAoiqUMMZEVy2gWhh23dMa4pp93cMVaQ2GkLYjg349PMU6NoRk+z9iVD2BQtLXThfLkCuRKajLPu7xhhoAQsjejC6YztcOqwnciBAYFlSRFlPhYaAVrYIqhuqQh+DUhthF4Y7RSrogthgqKJSIDphwe373mDsnVIrhYKUSimCYJyWFUzZ9zPDFakLFzsT/cwYPSOlVahVWetKWXK9sqiyHGMCIcJgsPd0+d270TTFTRWhnQr/8p//E77vY9/CD37i2/n0z/z0a98/X/xrv4T3f+gb+eNf+QF+2a/4wnRBagph0pQaTqsVKYf4XEqKUEXSWVf0cOopckQhr37MFHp5m5tMSLH4HSOXkn82fzFyqU9EMkhnpYi+zen1glCWB3x3ItkrhLApkv3smYLZZDKZTCaTyWQymfx74Or4euoq43CViWQP2fVL7rXY3zxe7SqLdBvVcog/HF/qyWMfRwYeXWX6xFWGQBF5KPWPOOKdRwQNAbMXRbbsWLOjeizdQvmaDNFcQhTJFUm/Pns4YbDtjgHizubGuDiddJZJKN0cIeOEFxvs3egBmzsSgrlRS+NGK6011lYJd86joy5YDIY5596533aGGY4ywhiA9y1jp6qEgZHrg1ULLYJ9DHaEsGDbN7y0FMr2e4YIuiy0CNw7vTvP7/IzOOrS6D3XJOsJ3nfbKG1hu2x0Bm1ZEAMUTIKx72gRHMO6YMPZ70nnWkmBMYDTKRcw11YIM2pdcXfEAy2KXcXQGDDyfdKyMPqeTrNS0DCIzrNnb+bi5nDWuiKtIWT0dK0NdWe73KG64NLY9gvRd7xbluBX5VQrIoW6LiwKtVZWNAWnkrFZdbi4H2uiwTOpoEp452/85R/gB7792/i7f+uvv/a9U0rh93/ZH+L9H/yT/Je/9/fSRJAjVikq6HH9FSmIFmo9Fi2v4tHTAv9DR/IjAlkk76N3ilxeQ5Rvjz6+1EvGKyKXR3n/NXL5VMR6KpI9/viY3XiFSPboYpsi2c8VUzCbTCaTyWQymUwmk59nXnaVwfULtzy4uV4VwTR3+kuuMtVr+bg8iFY/G1fZsKvDLaOSRQ93mjsjDTEPz/Eoll17yuLoZ7r2plUQcLOj4yxf7xjOsOxpGm70izFwzBxxPd6LDkdP2bYZPYQxBkb+t2rjtlZKKZyWE+LOxQZNKjE6l3AuvXPZO+fLBloxgt17lt/nriVhwShKyKCWRrVBH4PNg3C47GcoLaN04x5H0LbQBMbY2Abc3x/vywIlYO9kX9cNPDsV2nJi33bcjLYsxDieW4LeUyhDYd8GAJfngWl+puEpkJ1O6X5aNEVL1YZrJczRWhl7J9wQ7wSFIkqgmA+Qwwk4Nsq6omXNqK4H6+2bx3Xg3J4W1IPuO327h3LCdOV8OWMEMZwoQmvKja6EFtaqaBFua4qJRYVxRHNjDy5hKZoSlBCWpfITP/7P+cFv/wv8wHd9jJ/+yX/z2vfNF/2qX83XfPAb+aqv+hBf+EW/8ogdZyG/VMkCf1HQvI9KKY+OzQApSnkSb7y6ya4F/tchi6voHOGPItkrIpdCHEu0cghlL4pV5o4DEu++lyx4u8g1y/v//TAFs8lkMplMJpPJZDL5eeTqKHt0lcE1s1X17cX+ASk2PcQk89t/IG93lT0IWi+6yrpnJO5VrrJhcYgDj66ycsTAzP2h1D9dZZZl9BbIEb90d0KyGL5oPUQ2w7l2LsE+nDGuwt1gt4w62shlS0eQ4pgZZkbfnX1ktNI8sDAKhbUUTkullowjjkhBLsx4Hp19dO7ud7p1kErXLMwPd8I6odnj5aq4jhQbvTD6zi5CGFz2C2gKZd7vCFFqW9AxiLGxWQplbukgEwcf0B3WW3i2KnW5YfTOPga1FHwc1W6a3WyuAQrbNhCH7QxGPiY8nWU3JyhLoboTHkRdqIeIGaL0PsAvMAZaG0RBimbcTwQ5OuVkWVi0oaXQKIQobam4GzfLggac93uqVEJarolu5/yc+4Cm1AJLW2FpVMmOvEVqXotF6aMzXOjueMlr7Oa4FkKcv/PXf4jv/di38Hf+xl99EHDfLarK7/19f4Cv+cA38Hu+7L+miaCl5rVdhCqSgxhFEYRaHyOXEuByrMsebrJ0geW1X98hcvngJruK0DyKVUQcx3lRJHvaSxZH/5wcguVTkQx4EMVf2Ut2/RdhimT/3pmC2WQymUwmk8lkMpn8PHAVslIwe/gpkCLZy8X+1xVMO7rKAh7iY6py9Cu9e1fZ1RHjAYUgULpde84ihTeCUoRhjns8LGd6eBb9eyBSHgYHnGOZshYgbVF27TAjMAt6T5cN7mzD2PfB6JGRUwpDneJGH7Cb0ffBHoFFMDwFpabK7VIf+qecyKifO5sPdnOe31/Yek8xycFjBxG8X3IlUgpoYVhHtdBciWEMAje49DMRGYn0uGMMWG9XvHf6eQOB890htiwpVsRIgWt5BjeL0pYbeu/sNqgi4EpWuwU+drqmf6ifDYzsZrOjQP6IdLY8TRYRcMGkUGuKLiNthiCOhmepmbYUW/T4/N3AO3VZaUUQURapRClZWq9wao3qwt3YcC+UeuK8nXF2+j7AHalwWhdKWShLRcJ4trYspg9nJ7L0fgsuZtRSQYQlFK3Cz/zkT/AD3/FtfP93foyf/Fc//tr3yxf+8l/BV3/g6/kTX/O1/Kpf/aspRY+lyxTD6nXd8uom03SWXQORcghS9RClrgX+RR4dme8YuZTHyDRchSnn8JS9UOr/tLw/4jqioW8Tsa6Pv7rYfrYLl1Mk+/ljCmaTyWQymUwmk8lk8nPMVSwbT1xlQTwIZY89RI/F/ldXmfl1QQ84IpvtHVxlV+cYvLqrLMi4pVm61kTyHGrRh6ff+3U1MI/bzdK1dHQz5XnZMS4g1Gv80j07tSSjg70fgoOnKHW53+kWFNFjKdEhdkyUbparkQh7XCOaTlVlXQuLVrQquF/1InbP4z0/n7nfRy5uBvjYCK14vyBSgIJopdtOkUJTxXvHFXwE536BOERCG4wBp2crxMZ+t0GF8xlwkAV0wEgtjptncLOm4NTHTjejkI4zL4poYGNg4il43WfnWx9gh/bVSgpwtUi69AKkLPRw1lJQUXYbyHACowTpsFLJTizAhqHHKMB6LA7oMUiQ/WTBzemEjEHE4Hy5g7IistD3ey59w/aBFKU1ocWCLCtalKbB2irF0lG2jw4I+xhoa0QYix5xxoD/5W//CN/z0W/hf/rrfxk3e+175Xf9nt/P+z/wjfz+P/TlnLQSWnACVaEUoYhQasll1FYON+Rx6xzuSD2WLv36YxGWl9xkGSE+RGH0HSKXfghbQgrCb+8le0Ek48lf8s4i2cu9ZFeh+51EsuuxplD288sUzCaTyWQymUwmk8nk5xCPYJg/uMqu7q+r4PSqYv+3u8pS9GrlUVx72VXmkcLa03L+NAM5IXJ0lmlGDT2OL/KKco1vpohzPR9zyxioOXI8apiBwFIERNGihDseHP8NrAe9x9Fb5lzuN7bDyVaP5+++UWpl68boO0OV3TN+6aPT2kIVWOtCLIp6CnJisGN0cz79/I49yAinB5tdCJSwgbiDFDiEsirBWiu+71gRwpzztoOnC26MndHh5o0FYWd7a0MWuOwQZ5CWzrJxyV6xZ2/CaSmU5Yb9cgYfFHN8BF4qaGA+cBwPxy+B9yzyH55C2dJgXaHWHGxwD2ppQMZsi1S2bcdtQwtoSPZglZIroEBY+qaW04pYoK1QtRE2KG1BgbZUWgiX/Y7wgpaFkM7YLhjg3TB1lqrctBPRGoKxVuVUFginy+Gg2pw9glILWgoV0Fr41E/9G374uz7G937HR/lXP/4vXvse+YJf8kv5yq/6MO//8Dfyxb/21+aCqBZCcvwio5PpIGutgDullaNAP++ha8TxQfTinVcu3QM91mcf3WF5LrkMm5HLeEkkuwptRvaS8RlEsgdX5zuIZG9buJwi2S84pmA2mUwmk8lkMplMJj9HXEv6n3aVqb7oKnsawbx2m43DVSZPXGXX5b6rK+apqyx9aS92lamARRxfuuVhkROJQ6zLL+l+uNmuq33ulufs19ImTVcZnuJFTYcY4bj5sYIJfTdsZFwywtnOnf3ageYQIVzoWXhvRh8pvuzhjD5wGxStnEphkYKeVhpBP+KhG84YzvP7O+4dwgZmsEVHUXxkyX1ILib2vtNK4dQWfN8ZIpgZl60jAWbB3jth8P9n78+DbcvqOz/w81tr7X3OfS+TBAQ8oQnxkEolqSQECCRVqUoqIRWDSCCBlwNTStVWle2udjjCdoe77Yoqu8OOcLQ7yu5wt1suu2R3uT0UEgkk8yhmxKRCs4SUySAJXgI5vXfPOXuvtX6//uO39j33DZnkLVECMtcn4sa799yz9zn33H0TnY++v+9vfc2KwMR0caYmmGeQqYmyCnWCJPDoxwRSEiQM1FKQkkkm1F0hjANoQaWSa0GAsvPXNWcP6JUKwwCrCDHhCcCWThpXgWABUmAzZSgFibBa+WMxDGitLj5LRVHSMCIWSdE/FCOlCClwahwRq1wsE1kjYThFyRu0bshFoVYk+ThtGk7BOCBkTg+RYAlDmawQJFDnioVIBcYYSRKxYPzBb/4Gb3v9r/Kx97+HWsuJ/z6e9eM/ybmbXs3fef7P+3bO6I8RgxCTeJqupcli8rnVIH6bIT6aGUMr8JejNFmK4UhGL8lK7+hbRi4vlWQ0xSYSHrCXTDHssl6y44TgvWj7mxexvb/PFZLsKiKsS7JvHLow63Q6nU6n0+l0Op2vMT7K2JJXl41gpstSZWZytAigtGMWWSaydDU9eKpMj3WVhQCYUc23UWo7/yWpsvZefL94wGfa5pJ95HHpaqqedotRWIXQxs6g6QNUjXku/hhArZliMOVMnT2FI0SyFbK18n0V5mqeYDMjayESGCSwiolxHMAqqJLVqGLsSmGzOeRQgVooFWYKWrzMHxEs+PhiLpkUYb1ao7stOUa0VnbFRZlWYzdlMFhfu0K3E9v7Jyx5oizMHk6zCnnykclrHg1DikhMaKlEUZJBnSphSJSyIyIuykwom5YWrP77L+ZpsnGENAhazUcJUyCFQJRI1sqsFSkTEQirBFXJ6iOYqhXDKCUzDAOBQBgGRgKKS6TVODCKUPPExXlDDCOJ0fvZSqbk7KX1URhSIg1rGIQU1MWZhSb2lCCQixKTpwkHgXFIXLz3bj7w9tt5+xteyxf+9LMn/tu47tGP4YUvupEbX3Er3/3ks6Qmvwgum9bJX48QIA4RUUVS9ERXiJdslVxGkIUrC/xVFWTpCAxXJMkWSca+9Qy4srxfVY++/2C9ZO2Wq/63oEuyb066MOt0Op1Op9PpdDqdryHLOOVSGL6MYHpHlY+BXZoq0/3WTC7tKvtXTpXhaZqqTdi1Uv8g1h53eRPvIqDU0u4rXpCubctf9I2Iy/ilizhPltVqFAWtgplStLLbzeTiqSkIFCDXXUvnuKDLWsmqZG19XAarMZGCd36Bj7yZKnPObOaZ++YCWl12UdGcfRyPisTRJU8pxCGwXh9QtxtqChQzpu0GcEk1TRlTWF8zULaZ3X0TNXj5vrl3w9Q7ysYRrrvWk0MhuNQRMyJCmZU4DuTNlpgMDWCloDvIxX/3Ir45c1zB6QBp9PFVivnrOg5Eicy1UHJBopHMkJBc7qh3ddmcKeL3R2EcBoIkQusyC0FYjQMrhd28YRMSwoDUmTnvKIYn0qwwrgbWcY3FAWJhxDg1HmBamEWZ1ZBsVAkEcxE0SIBg/PFvfYK3v+G1fOS973DxdkKe9oxnce6mW3ne9ddzsFoTQjwq4Y9jJC3SDC/1j9G3W0qKWEuN7Ucupf1NhaO05vEkmBwfueRSUWa+k5UHkmTe16dcLtIW9iOXi+B6YEl2vJfsarKtS7JvbLow63Q6nU6n0+l0Op2vEWXpHmtvztU81bWU9IOnypZi/+Mfx1NlcdnuB4AXIPmoYxurtP22TTmWKisKUTxVVrWNmbGXabY/I17g7+OX2h7fWoeaijEO4WjTIGaYKrVWKpBn9RAYXmo/V/XUlnqaR82YdKZq9Z+1qJf5Y5Tq2zYjMITIahiw4IkmM+9Jy6WyzYV7dztP6+VCCVDKTDABq0gcwAKlZOI4shpX1GmHDi7cdpstglCLj15qgfXpxLwp7O7P1Ojl++3lRdro5MEBrEcYR2my0l+HpDDPShgTdd5Rc6EGoCo6t0SaetF8UVitYWUuzFQhqFKDMI4DYjDnwq5kQoIUAlT1BJsIZhVKJRNI4+hJvRgZhoGSM+OYPCU2DiRVLu42lDgS0iksT8xl6wsmtCLBx4BPrx7lY5c6sR6EyEixyqyFgFDmwpAGqlRGEWKMXLz/Ht77jjfx9ttfy59+5k9O/PdwzTXX8oIXnuPmV/0CT/mev0KK0Q1km2hcp+DSD0NCIAQXvSH4aO3V0mQBIcZwSVJTTT19iRAlXCHJlkYyo40Tc6mwWja+mrLflHmMB95wuadvuHz40YVZp9PpdDqdTqfT6fwF8ZFIu6TY30vH96kyaCOYCLUtAai6H8EE70DaJ9FcrHnq5dLRrmLW3tzvU2WYEkOkatuAGVwuhHCseNyOP1/vF8OWxxAqShIfP/RRNqCJtrlUylzRKkeibCrKPGcgEIlUM7ZlItdCEEEtULUymzKX7H1dCGOIjDGxRMysScO5FHKu3LPbUQ3mnEFgLjNiEESbwIqUkglDYpXWUAqFggVhc+ECiL8O8zxTKxwcJOZamA4LVWAuIIU2fgq1uOB61GkYRk/1CQHVyijBi/qHQM076lyoQFBPpU1bf1FLk2UxtY4yIIwQTZBB/BUKwpwLOisywKn1SMmZkBIajTxnpG2/HGL0jaLRy/erKSlExoPEkCJmymbekmTFMJwil4ldbkm8nJEU/HVencISRCpDEMZ0wFwzRSKYUNUfzxcNCCEG7vz93+Jdt9/GB9/9NuZpd+K/hx/8oR/hpptfzfOuv4FrrjmNhOiyS420iv6aRBdhQXBZBohEZOn4O5JkLqlS2o9j7iVXU2Bt5NLwa7/9FR79DdFGjI8LK2tpSWsjyEK4vJas9ZK1McqrlPd3SfbwpguzTqfT6XQ6nU6n0/kLoKrMl6XKRGB1PFV2bATTRdWlxf7hWKrMj7H25vzSVFk9Nkp51FWmILLfgGlLMXoQr3Bajm/H5VaWfyTjtMm3oKxS9CL69i7fzDwdNhdUA7V6T1lWJc+FSsDUf7hdzeSSqbTeMlWKFuaaqbUSJZAIpJQIyVNGwYxc1F/DUrlns/Vy/lIpWplLJhK8TB8hhUQphZASw2oFuZDLhJky7yaq+eswTTO1wPogYqWyOSwQYJo9KWQViD56eXAKTl/j3WIxBMQELYUhRopFZjFq9VFOE19goNnTZFZABSS5XEkCAYij3y+kQAoJ08quFEL138P6wLvQshmkQM6zr9+Mrm4OUoIQvcuriaVxWBNMKXVmW5VBBpIGqk5s1UDVN4yuRk6tT6NpQGJhEGU1HFBrRlGKhqOklYoyCsSQODy8j/e966288/bX8pk//oMT/x0cnDrF83/+Jdz0yl/gh37oqZ5YbH154xARNdKphKn38MWEC8G2qCEKPkbplz9w5cjlIslQa1KTSyTZsgUT2yfFLpdkSy+ZmXgn32UeS6RJsmO9ZJdvuFzSaw8kyZbzdFH2zU0XZp1Op9PpdDqdTqfzr0iulVL3X6t5mXu6bATTTFpX2dVTZbGlakQuTa0cT5XVY91NnlSD/Wa/liqTtqmvLRjw87QR0Fo8BdVkQ6merlExVilAkxfSEmm1FHZzRRW0QLXCVAplrn7e4g4h10quLsqsGiEmplrIVim1ImYMITGESIiBkAQrFQuBuXjy7L7txM4Mq5U5Z7Kpj17W4tIlDszThMTkI4qlULVitVLmTK4Fk8RuV1CtrEbAYNpUiDDtIAZ3KVWgTHDNo+DUYyAOPuInEtF5ZowDRvIRUqtINRRPpOUdEJfXA8IAY9gn99LgjxFSICK+bCDno5L/1YFvuiwGEsy72IYI0RNWcRiJIWBqrIcRM2NYjSQztnlLSCsIK0KZmWyi1ILmQhoCBlx77aN9dFcKp1cJIyKmzFoYQvSkYiuvX6WImXDnp3+Xd99+Gx9451vYbTcn/hv4K9/3g9z08lu5/oUv5dpHXYuk6Ne1QWxpshDFpaIASXw0kzZ+3Mr6zTwVKeYbMY8EGmDmPX8+WuwJx0s6Ai+XZOFSWeXHX9pLdtxjnaSXDLoke6TQhVmn0+l0Op1Op9PpnBAzY65tpJHlTTuMKRxtwVveM9dq1JbkKrqPqYj4xsoY/N8lTbbIsn1X2ZIQa2/y223SNmAuqRnBXPy0GUptSR4zH3XUun+uWQ0NxhBgiD4GJ24cqLWQiye/qFDMxy5rVXKunqwhUmptPWW+MVPMRcamTP6c1FhJcgnRRFlAsLYNNM8z9+1mplrJuaBWmVURBSszcRyQODDliXGVGNdrtBRUC1aqP5+SUQvsJqPUzHoFZQe7HViAPEOoQIUM1B1cex3Eb2miTAIhRrRkH7GUxIRiUbBcPFFWYbcFaWJMi49dDnhiUAWGBBikIRBNyFXZZSUkGIIhPtd3tAk1CFiKLljMkCGxSitKnlnFRFgPeICwsp13DHFFlIGaM5NWqIaWGRkiqyERx1OEAbDCKo6kOFBqgeBjl0McmLUwim/HvHjxPj789tt55+2/xh///u+c+Ppfrdc857kv5JZX/iJPffoz/JoLgYiRxkioRlp5miyIEAdPO4rPBxObmJJj0mm4yshlNT1KPC4dfHrJ0gvbd47JpcLKTFu32V6SXU4MJ5RkfcPlI4ouzDqdTqfT6XQ6nU7nBJRayZelykRgvFqxf9WW6lJqM2ESxLuijhX7Xy1VtmzsW97wH0+VgXdPWXvsRSgslU3L2GYuhVJb9b+YCx6FEGw/fukOg1qVORdPwBWhmJLnTG6iTEJwUaaFSSdUK7XaUX/UbJW5tBZ99Z6yGKM7G1x0aK3kWrmwm9jUghZlztPRGGbCHU8YV2znHWkcWa1clHk8rDKXSq2FqsKUlVKUIcIgMG1BA9TsgkszaPKvrz2AcBpSclEmIXh52YxLyegbFq14sZlUT5QV/PUxARkgHQsqrZLfvloPPkZajKkoMcB6aKO1IaJi1Ml7y8IQCebXgC9XWKNaSMB4+hqCFtQKk0AiMQhYzWxVqSVjVQkpcvrgWjQGQlBWSYgxHfXL+QUQGCRS8D65gxD5/Gf+kHfdfhvvf8eb2Vy8cOJr/+xT/grnbnk1L7rhRh7z6Ef7GKl4mmwYY0uOGWHwqFgSIbTEWQjej+abSI3Yxk9T/Cojl8KVaTIgXGXkUtUXSxz1kkm44mcIAQLLFs0uyToPTBdmnU6n0+l0Op1Op/MQMDNyK+tfvlbzibplxAyW5FcbvTw2ggm0MvNLU2XND1wiyo4SZsdSZVWNEEBNvLNJloSNj3PakVSAukg9o/U1tbHBYAwpIG0T4XJQUWU3FawKtRq5FuZSqFmx4IXoOVeyubSpgJj/LLuSmWvx0VKEaAEZEgRFg4I2UWfGhd2ObS1Mc6XUmWqQSyUAo4BKZDvtiGlgGFdQK0aFWtmVitZCtcBu8t/DEPxNbZ7BYhMrFUoGiWAKp9cuyiS41Fy60+pcUfAIWoxYrVT1L/PsHWUheJpMlaM+OGlyjghpTJSpME2ZUiAFWK8CUpWwWpOnLVYqpEBYiZfdh9DiarBOiRgT6WANWsl1okokkgi1UOpM1kotxbvA4sCwPsBEESqrISIkqvkShjENRPXx30FgTJGy3fDBd7+Vd73h1/jD3/mXJ77uh2Hk557zAm5+xS/woz/2454SjIFoXuAfqhHH6DJY2jhqW5ogwUcuWTZcLiOXQzwaudz3ivm1HHgASfYAI5f+d+jXOA/aS/bVJZkZRym3y+mS7JFHF2adTqfT6XQ6nU6n81WoquTLiv0BVkmOepaW99GlehG/WRvBfAipMjU76iBbUmXihWaezmqPV1U80YYhBE/LHHsDf7Sts9jRMWagGEMSwrKFUMBEKKrMk6fQUGGulWnOLsoEUN8kucszqpWCtTG7gKqyteJpIPXEz1GiTNroZoVclcNpYlsru1zBKtvdzoWIKqsUUIns8oQFY1ytiWqYVsyU3TS1nyswzYqpEgyiwpy9kyzgRf7TFuLgomxcwepaf13G6FsZpShmgSygpsQUqNUouZLMRVsp7rPS6OfxbaSQUnuc4Cm4WiHPhVxd3J0aW2ccggqUzZa4Hqi5EM2QlKClnk4NAwQfQaxa2E6ZIQ6IubibrXg6sRaSCGNKDKtTSNs4MAwjkYSJEcRl7SoO5DIzDAODRf7083/Cr7/xNt77tjdy8f77TnzNf9eTnszLbn4VL3nZLXzLY78FCy6ihph8P0GMfh0m8Y+ixCFCk14htJFLM19c0MTu1UYu9/KLvTTmsiTZZb1kqi48TZskg0tc2KW9ZFcmzfqGy85XowuzTqfT6XQ6nU6n03kAzKzJsv1takZs3V8LgrUNmC7VllSZmbVC/wdOlXkR+yLEPFXmfU2eKltMgNp+AyayiLcm1czItaLVH9vMu8J8+6AyxOQJn5byKcXHL6diSIWCMe1mSnXRJmY+8lgyc6lH45wxRpTKtmZMlVqVSGBII6AQKxUhmZBLZTPNHNbCXCq5FOZ58lRXVRIGw8CueH/ZOIwk84J21eLiTiuleqIM1OcjFSYDBVJsX0+0ZQV+2+paDyUtooyiWIYsfv4QhKpQJiXh2zJzS5CFVk6v1c/ZQmnE2F7uAnP1FNTBKKyCy8eKYcUwq0jydJWVwrAaiCFhtbAeR+KwQmtGrXgHnURGSWitzFrIc0bwxRGr1UGTrBCTy7Fq4tJwXPlLHgLVKrE5nY+85y285/bb+N3f/NiJr/eYEn/72c/lllf+Aj/xE3/LZXAUxIw0JqIacQxH211FhLSM9o6+1GGxyiF6Z11cEmdLGqwth4hhP3Jp5mOaRyPHti/833eMcVkvWWips0t/hgcbueySrHMSujDrdDqdTqfT6XQ6natwtRFMA4boAgyOFfury66l2N+7xYSh9ZrFlgS7WqoMaIX/LhVa81WTZ2AmLS2273WKx97Ml1opLQlVl/FLfHxziAHw8csQhFor06RMpaDFRzvnaWaqitVmjCywKbn9PAW1gJk/5rbMWKnMpiSLrOLoqbhQEIkEBC2Fi7lysRQ2eUZLZVpEmRmhVsKQ/DHzzBAHViF4Lxm+NbOWSqnCdlICilQXWLsMBC/ZF/Myfpokkwir0xDamKy/ToF5W7HE0cigGuSdb62cZpjNjy/4cXlJlDUns/LJUOrkabaUYEz+OAhkdVEWEsgqkggEDA1CDAPBjDEN2GokmLGbt4QQCBIQhTrvyCGgpWJaWcVEXK89IWVKGAJREtJmFWNIJIlIVeKQGBD+/E8/z7vf9Dre97bbue+eu098rX/bt38nL7vplbz0plfwhMc9AUuBZDCsEqIQU0RQGKKLMDVCij5CKa1HbEmTtTHM4yOXywILkaX8Xy4ZQ24te1dNky0bLq2V8/2r9JJpG3N+IEkGXZR1rqQLs06n0+l0Op1Op9O5jKuNYHqH1KXF/ma0VNYyDgmwT5WJ4Ekwt2BXTZUtXWVLqqzUtvVSgks105Zw2os6zFyUVUOrtaJ0obbnmaIQQmqF/p5Imksr8S+CKuRpYqoVzQrJE2m7Wqhq5DJjEpH2vHa1MGklV2Uksg4DbkSUCgQTpGS2Cpt55sI8g7ooK7UljlSRALMIdZ5YpRUpDagptc7kPFNKpVZhN6tv9VQobfTSO8O8T2yeva9/HACB9TU+Ljm0lycQqEXRpGhwQVKKwewLAMw/JbVUGtaEo8A6+WPF5KJsdwg1+GOdbqKuBqGoIZMh64hEJYUEKCaGhMSpECAlgghVCzUbNQS/nxm1pegEgZpJqwOSQIieQkwpIXg6CwkkiaQYKJpdhKry0Q+8k/e8/rX89ic+4l1fJyCEwN/82z/HjTe9ip/+2z9HGpKLVsQ7xtRIMWDBRyrFhDS0Av8oR9JJpI1fIqS2JVbEe9SK6n4/pSw9e3K06dIl1b537PKRy2p7SXb5yOWJy/u7JOuckC7MOp1Op9PpdDqdTqex9I7VNlq5vOlOQUjxWKrMjGoc3a+ootrSNXJpqszP+9BSZeV4qkx9W2OM0krUj54lUy6YCaWq92xhEFwABYm+vLClc0o1cs7MBbS4aNuVjE5KEWPABZVvyMyoN1ARJfhGzFooVYkqrOPg3WdSPellkVgLxeD+3cQ2F6oV5pzJbetARKhlpsaBopUxDqyH0X/eMpFbokw1sJ3a61Oa1Mpe5h/aO9fchNe49pTZeMq9XcR7xcQCtSo5qhs09d+TTsY8+etT8DHL1HrItHrv2Sr4v7RxzN0GqvkmzNUIQf32gi8NkCG0DZHWtgMIUoXTqwMIAdWK4K89MZAkgVZmm9Gi5Jw5GFdYTKQUiabIEBEJGJ6OCzGS8LlT8Yfg3q/cxbvedBu//pbbuefLd534Gn/CE76Vl9z0Sm686VWc+dYnQhISQhoj0SCNEbQ9lwAB7+mTEC9Nk2FECcS4dJbtC/xrtSMZRdsZa2pHG2VlOedVJNmD9ZIBxNCSlg/SS7Yc1sv7O38RujDrdDqdTqfT6XQ6HTxVtqTFgKM390O4dATTzI7ud7zYP8hlxf6Xpcr2fWTLG/tLU2X+eEuqzAj4GGQUr/g388ROrebiS32MTcWIGClGlgCatPHLXJTdXNHivVCHux01GxZAUEyF++eJqhUNAVUhRaGaMulMLhWrRgoJiULBxyZXMhJqIVvl4m5mWwqlJcrmqSBiBAKlzGhI1BAYg3AqHqAoOe8obezTqrCdjZwrot5NpsXFVBqWrZ9QJh+PTKe80H+RXtLGUYuCBiUEv78BNvvnYi7eJEC0/W0h+Tlie2dsFebix65H78+KEVQ80SYR4hh9TBHQhIskVU6NaxRBUZSKtM2QURK1ZGYpLuhKJsXEer1GYmSQQEiCqF9gIUSGkAhthUBKQrTIRz/0bt5z++v4lx/7IKZ6omtbRPiJn/zb3HjLq3n2s5/DsB4xMQYiIbXrNtLSXEKIwVNlEo6SkhwbZ4zhypHLqnpU3L/vHFvK+/36iw9Bkl29l8yalItcTpdknX9ddGHW6XQ6nU6n0+l0HtEs0kvVLhmZjK0D7PgIZlWO7rcU+4MRo4+iedDoylRZbXLL783RJsGqyl59CLXq0fmGJdEGVK3k4uOX1ddetp4y70mLIRCOytMruUDOmWnyB5zmibmNbxqKWOAwF2qtqHgOzKqX4U9WmEtBq7IKA6RAtky2ykFaMVKZamU7ZbY5M2khTzM5VxQlSsBqpkhAYySF4EsHgJJ35DxTzeXUZlJy4UiU1QwVH3+M1RNe88ZF2cE1MKw8WRY8UEfZ+dZKaUX9y1bPMvm/YhDG1jcGTNn/TaM//hBchpEh4+OeB2ugQkiCYeQZwgC29nHKtGwLDZG1BIZxhWJkq2jVo/STELCc2bSxxGhGSCPjEIkSoYkqQiAAElesxtXRas4YI3ff9QXe97Y38OtvfgNfPv+FE1/bj/2Wx3PDy27hZTe/iu9+0pPRYJ4mG/ZpMjFfDiH46GUMgRD36a79tsngo76L6DKjqB4JrqMtl7akJy8bubysl6y05RThASSZiDUp1yVZ5+tDF2adTqfT6XQ6nU7nEUtVL/VfSsEvLfZv45JLquzY/Y5GMGXpKvNNhl58fmmqrHXD+7ltnyrzjrSWKmvizIVbILa+JdPKXH3Eraq18UsgGEOTCTG4bahaURXffjm7VMs5s80VqYZGUKvMuZLVyHUmhIGqSgqgFLalUquxCglLA1kzxWZWYWREqLVwf65s58y2zpR5JhejaCZaQLRSqFgIRIQhJn/9ambebakEVI3tpOTJfweKb6m0VuZvxWXXPLk4OzjdkmUDSPXRyDpDCS6yYvDRzSBNlDUZl9YtWWawmdoI58DRZkYNLujyIkdHP38MQhHzEyWIK09bxSZHqwijBOLKi/mL+C9ctTKERKmVXLdUSWjJrGJCQ3BJJQEiiFYgEdOIVWOVIjG4oBuC8Jsf/RDveePr+eSH30et5cTX9bN+/Cc5d/Ot/OzPPY/VqRUhCumyNNnS/SXiP3OM6WjkctmoGkNbGMFlBf6NfTeZtb+NBx65FAGtlXK8l+wqkmyRc5ePXF5tw+XlMqxLss7Xki7MOp1Op9PpdDqdziOOy1NlsC/2H1paDDxVpoaPQR4r9hfxQvRFMMTLEjbHU2XSkl+eKvPbS5Nt4D1k4OdbetIwI9dKLdpkmYsAxVpRfWAYQitXN7R6If+cK2X2r7e5YFmpAlilTMasyjRPhNCa7QETZZsnSoUk3lWlIkw2MYTIgSSsFg6rsZszh3lCa2EqSi0zwQKi6uX9bcnAUmyPVvK8pVigqrDLlbxxSWYCOrftlMnTXQbkDIPC+qBJrhWgkMzL/svQRFn0TrMgYBl2S6Js8D6zWn3jZQBOr/3cGvy+oXqSLSY4iC7hpI1emrooM/GUXxTxJFUIHIxrFKM5HzRnJAQCESvKLkygATNhQNCUGMYRCQm1TIwBYiDKijGNBFMIRoiRe7/yJd7/jjfynjfexl1//qcnvqave/RjeNFLbubcza/iKd/zvb4kwQKxdZMNq+gJuBAJ0or8Cf6c/GKHllpMMR4Js0WGHU+TwX7j61HXnjywJFMRtFy9vB+WkUu5Ik12NUl2tfL+IF2Sdb72dGHW6XQ6nU6n0+l0HlEs0utoW+WxYv8QZJ+yaamy5X7HU2UhhJYu8zfwdmyc84FSZbRUmfrMGrUlcoIIQ4rH7q+e2iqefsMMgiBijNEFRxTfiJlzQRV200wpglVlVzJ5rq33rFIUSqlMeXbLFOJRgmiqW6oKwQJD8KTbZAUBDlJEVTlsCwE2Zca0MuXKNO2IuMSY5sklUBQGGTBTBKPMG0oBC4HNVJkOgeDpMc1QBNaDy6vm9KjVRVmMMLRFnFRPgukKGFxylexJtHkLcfRussGXVFIKTObdZKdG/LVuoozq6b+0grH6EgCtQPTeNE2QQmrbNpffqXL64DTFCjVCyYVQFYKQ4oqad+ykUkplTAlDiMnL+8cYEVGQyjgeIGqsxhGpigRhlMSnPvpB3vOW1/HxD/46JecTX89P/9Ef59zNr+Y5z30Bq9NrQhDGkJDQritRJASsGiEGkkAchktGLl347gv8Rbyov6hiakfJSTmeJsOFcGx/N5f3ktkxSYaerJdsSXI+mCTrabLOv266MOt0Op1Op9PpdDqPGFx67d+QL6my1DrAjlIxZkf3q3WfKovH7hfFxyltGcHkgVNlapCrAi4eajVCwFNlfkJqLVSDUnwEc5l8kwgpCiKR5HVPTHNBzchzYc6g1Ucs52KUqlRTkgR2WSmaqeYnUlUfXdRCrooVYxgGqla26rLm1OBbEi/OhZzbxktVdqWw22wYRIgxMO0mJAbCEBjCiJoiYpSyZd4aGiK7XJk2FRFPdNWdj1IejK2Q3zwVpgXGJr5WrcYrCUw7CCsv5w8BSgXbwbT1brI0NoFWPJkmwV+vlUFKMKsLOf/9wHrtfWkAGl3MEaE2uTSo+pNJLpQOVj7XmVFEoc47UkxYSNQ6MVUjl8KYBhdHMZCGFWYZNBPTAcOwQgxWQZDkY773X7yXD77zzbz7jbfxhc995sTX8bWPuo4XvPBlnLv51XzfD/wAEtvG0tFlakoutiT47Kk/t3hJmkzESMEF7DJyuYxVWrv4BFy2HUuTeaeZ95IdT3Zd0Ut2Qkl2RS9Zl2SdrzNdmHU6nU6n0+l0Op2HParq3Va2yLB9qkxEjjrDMKWoHKXKvONsSdLsi/2DuPi6/HxLSsxbnPZdZdo2aWq7LabAEP2xMSVXdVFW9km1EAVp5e9jim0szsilUlWZZ0ULzHlmzp4EyihBPaG21Zlcq48LYkgwoggX5wnR1s8WI4fzBCKsopDExyan0jZeamXKxeVYraQhsdlsiTG6KJPUlhYotW6ZN4bGyHau5G3FZF/cbwlOrUEy5NqEYvHusJBclJXsouziDHLgt8cAuUDc4L1ng/eTDaN/vd22YvrBRzzXp2A3Q2gbMrWlz8bYHjO6pEtDqylbDaS63/IYh8G3og6JKiBFqVa9AywEZiuUUgnm0jQKxCESGZCoiM7E9SlCXbFar0mqhJgwLfzhb/8m73nL6/iNX38neZ5OfB3/0FOfwY03vZrnvuDFnLr2gBgCQ4j7NBkViRFT874yg7j2VaMS5IoC/8tHLo+nyYBLJJkc9fUdl1ZGVU8zPlgvWS/v73wz0oVZp9PpdDqdTqfTeVizFPsfH8GUlirbp2Wa+DK5tNjfrkyVefk5R0JtEWVLqmwvEvwc3oTmHV0ixpA8VWZAbd1jJbcutTaHNkTf0hjC4NsgVdFqTLmQc6VWoZTClAu1KNkM0YpaYFMrpRaEQCBgwcXgfdsNgUSKiSrKVAuGMEZhDMKuGptSmEoh18J2mthNmVAzpMSuFIIqaZWIrf8sxIiVDbuLig2Ji1Oh7Cq0NFiZfMzx4MDl1VwA862Vw7qJstSSY9lTYjF42szaxksmT6bNBkMb18wTHOa2JGEAFFYBtoOnz6qBNAk3RP86RB+/tATBhHE9olNGS8VCZIijT6smX4QgOaNLAb0aE4UyZw7WB5j6PGccRsYAZjMxrhmGUwQRDlKkRk9wXbh4Lx98x5t4z5tez5/e+ccnvn5PnTrN869/GTfd/Gp+4Kk/jARjDAMhiScjlxWgGCLRBd4q7eWTcJRmXAr8l5HL2saMLx259E2wNFF1+cjl0ktWEUyv3kvmwrd14j1Aef9ySJdknW9UujDrdDqdTqfT6XQ6D0uWYv9l+6W2NJinXY6Nk5lSTfZl/VUpl6TKIEbfLugyYV/qD5emymIQSq2UZaRyuV/gklRZ1UopSqm08UtzIZfEk0H41sRqypyVuSpWlLkYOSs5F3JRiim0EbptUUwzar4IIEUIYmzmLRuLpJDIasx5xkQYhkBSJZtw/1TYzZlqynbascsFmydIA1MuoEYYIqEqwSIxRea6Zb5vR1gPXJyVfH9xUVY8KcYIq1PeP7bseZTqAiuuYUxe2j/PPq4ZRh+ZXF7jIG0bJq1zbIS8g+3cRFlqawsEaoRtWyYwHuDJqujJtNXKE2U1wLBKpBAo88w8Z9YtTaYoEiO1VmyzQVIkIGgtZKsE82vGBZAxrNeEANSZsDpFkgPSkBjNH1jNuPMPfpv3vOV1fOTdb2fabU98/X7f9/81zt10Ky940Us5fd1pUggMMZGCkMbgGlYCpkYchKAQh3SUJgPz+6Z4iSQzM1S1NZBduqzigXrJrlbev18AsP97C9FlaExXL+9f/l66JOt8M9CFWafT6XQ6nU6n03nYsaTKYD8yiexHMFttGGBNlh0r9m+psiV9FsSIQS5Jlan38HslWUvmCMZcWum/unpQ9cRUbF1lipf55+KJMV886OOXS3ptCNEL/YuX7c+5ogp5qkwloxV2NeObCv1cuRZAPKXWlgPMdWKXlSgREdjOM4XAGCFpxmri4lyZSvHOsmliN81Y8eUARRWdJuJqgFyIDMQUmXQi3z+5KMtKvphdlGWoBWyA1QHMOy/2R3wMcxiAFYyD14SVGYrBwdq3Yua6yBQ/T1Ev9E+Di7XDCy7J0giDuAyrEfKhp8zGg7YxsyXYUoB42tNr43pgNN9qWVJktRp9dBHDYsJKgWlCJAJCLtWlaQxEBEUJIbA6PYJlYgjE1QGia06PI5iRhoH7L9zDR975Vt775tdx56f/4MTX7frggOc+/wbO3fgqfvhHn0GMXuC/pMkkmA/7ml/AS5pMAGLb2NqutxD8GDuSu/534Nfq0jvm1x8sCcpl6cV+RNklMlftJXsokqztrOiSrPNNRxdmnU6n0+l0Op1O52HDA6XKgnCJBLOjDYgPnCrbF/vLFamy433kMQhVlSnrkYDwScw2fhldyOVaqWpH45dqRgjCEASJwhA9HVSqMs+FXP3zMrexzark3ESOtPHMWl14WMCAEAXTwv277CmgENjlgoaIhMA1VlFJbBWmnW+63MyZ3W6L5kzF00c1F09Zte4uGYS5TOQJwmrg/jlTLmaIME2+9ZIVjGuok6e50uibLMfRv5eCC7Iyu+9JA4zmMiwI0KSZVR+9DPgI57T1+w5tGYBmmIKPexJaoqyJlxTx4q2WbFvFSAhgtYIExmHA2gIFa6OWebshhYiKMNfZ02lpQMuMhUCIgfUwUPKOEBJDupYUA+sUWyed8LlP/z6//tbX88F3voXd5vDE1+1Tvvevcu6mV3P9i8/xqMc8yhcQxESKXuC/ZL8EQYJ3zYW2WdU3nrrUXQr8j49curgSxJZrH7+Ol3Fk9tth21/RZb1kcsn1Dn5tByA9iCR7sA2Xx5cFdDrfqHRh1ul0Op1Op9PpdB4WqPo2S+AoVXO82H95A7+Mox0v9neptk+fxSgtVSao2hWpsmW+TMyYqx2do01IEiMMgwuNqi0htmy/bLIiBk9dhZCI4um2kpWpVGqp1GxkVfKcmYrLOEXJxWWailsJVfw1HQABAABJREFUszY+Z4XtrlARQghMpaASMRHWViBGLmajTDtQZVsL282WebdDYqCaUXIlDZEUhSGNmFWQwnSYkSFyYa7M92fC4KIsT5BO+c9RJu8Ni4MnxFYDcMrTRyqeKlPxsUtVv0/AX69dhWRNsql3mVlpX6fWP6b+spfsybM4eJm/RO9BU4Wa/E3uOIyAbxJFhFCVeDC4KFPFVMl5IhhIDOzy3BYBDFhxWZTWa2JcFgqMnBrWDOPAynwF5zRt+PA738p73/p6/vj3fvvE1+s4rvi5517PjTffytOe9SxSFMaYCMm3WsZgSPACfxH/HY9t5JJ2PUcxhiFdIsl8wcR+5HIp2VskGXblyKWnzyomD9xLBkYMrdPtKr1k+lUkWU+Tdb7Z6MKs0+l0Op1Op9PpfNNTVD3VxX4Ecyn2h0vHJ4/LsqLVhZPs02dRrIm1y7vK7EhKhGOpMlXfMri0pHupP1RzyVWy4lOPTeAlTw15Abug5ueZilJyoWQlK9ScmXL17YVAqYWigrafR9VHOMUK25LJtXWo5cxs/nm0QhCYDMpuwkplY8rh/RcoOUMQNAXKXEgpkZIwhARiWKhMF2ckCYcZdvdXJHpya3MR0gGsTntiLBx4Ckyz/xtHkLJ3iyX76GVso5RBveR/ThDqvuR/3vn9hwE04qVkwXvO5i2sT7etmsG3acbBX/YiMK5gSCNlzmieSWkkaEFiRNZrrFbqbocEKGaoBLRWogTE43lICAynIjXPpGEkpVOMQUgxkILLx8/e+Ue8782v54PvfDOHF+4/8bX6pO8+y0tvejUvuuEmHvf4byEMgVEiKQXS4KmuNniJtOspxHAkyULgkjQZrYPM02RLL9/x6x3voAtyNBq5l2SKHivvv7yXDMy3wiJXbLm8vJcsdknWeZjRhVmn0+l0Op1Op9P5puV4qgyWTZc+nra8SZdmD5Zi82UEsy7JnYeaKmvnwJRJ94JCtRX+t1QZeP+YqlHKPukWAowpuIBoIi9XZZ4r0+wFXtOs1FwpubBVJSJMJWMItYKad5dVIKDk4ukzJFA1M2eBIAxiRKtkCUxzRotSRLj/4iHztEMxagAtFQmhJYeENKxQLczbguEF+7v7zSXTBHMGGWA45RJsfRpQUK89Q1Ze7O+dWt4ftl75NkxVL+nXVuQfY5NswDz5axwHF2dW2u2zC7q0hkdd58sBpMDB4GkyA8YkxNb7plq95D4GpI1ZSgiUwy01KAEhqyEF0ipgohRV0jgwpIjmmZhOMQ5rxmFgHSMWAvO040Pvfivve8vr+cPf+uSJr9OUBn7m557PjTe/mmf9xE+6mEzJe8ZSYIhg4ttXxaRJ1XiUJouRoxHIS9NkbeTS9kLKbP/J5b1kvuNC298JCF+lvD/28v7OI5cuzDqdTqfT6XQ6nc43JcdTZcsI5vFUmSzF/iatr6yNYJo2gXVpsX+gjfU1UQZXpsqK+qZKrX4OzAXVmAJi6lsWTcjVqKUl3QIMrYsqtFG2qkqe1YVZqWhWclamPLFTEDXMlE2t1ArgXWUt+4ZZ5f65gERMC6WGNjuojBgaBzaTt/CrBO493LCbd2BGFvVG/RC9S8qMcbVCVZm2O3KBqXWHgSe7qtekMR5451gaXIqUXSvzD949hkAVEIWUYAXU6mmweYJZ27Hix+SNnzemo6fPpH7fEP0cp07REnewWoOu/GUfkpDiQC0zIkLSiqREiANgLvJyxgRsiJSNEqO1lJZQqzKkyBh8i2mIiVMHp0kxMIaICnzuc3fygbe+ng+87Y1cuO/eE1+j3/4d38XLbnoVLz53C2ce/wQkBoYhMsTgfWu4oF2ux2WD6/E0WYqt8P/YyKWZXiLJ/G+AZWVrK/y/iiTz0rVW/H/8WL9OA74w4Ljs6pKs80ilC7NOp9PpdDqdTqfzTYWqusBp7+KXov8j2UATXZelypbUGOxFQgwuy0SCJ8KOxtraxsVFK6iyLdbSacutLVWWAtVcsmlV8jJ+KTAMPjrnE3WBWiu5GPNcXZRVpRRjzjNTNay6DMmqzLm6OKECrUzLClPOzBXUahNyEQ2VQZWQRnZlpl7cwDBw78X72M07rCozFVFPMgFIqQzrAczYbSfK7LJq2uIF/JOX9Kt4Six7uI1xAGuiTBJUbaX7LYw0DE2eLWOBwGbjI5Pgrq5OnjqLg3eXlernmXcuytZrl0UIxNZtxuDptYNV8l4vFLQwejwO0hq1yrydMKloBUkBm5UwuLA0M+KQGFKk1kxcjYwykFLiICXvWlPlQ+99O+978+v43U9+9MTXZ4yRv/nTf4cbb7mVv/E3f5phjASEcUwMyTdb2lEHmD+vGBfJK4Tg1/KSJltGLvVI5F6WJjsmyS4fuTRrfyt6TJIt1WbWuvSkS7JO52p0YdbpdDqdTqfT6XS+aaiqVL30awOGeLyE3N/qLykx7yHby4al2D8ElwVqcsl5BTvaJhiDkJcNmLqMsQkSfbzSTMmtyL/UpdTfpc+YAjH5OJyqMc2ZXLzYPxcXOrkWNnNGq6HiPWVzziCJEBQlogTMCqVUtmVJsYGERA1KqjPjODKbYRcPsTRw7+aQ3W6HYcwo1CZABKQq45gwgXnK5Bmm7N1itY1A7qq/PgdrH7008RJ/za1fLDQfJkufmi8xkPaap+S9YtsNrFa+9TIXH+uMyb+OeJIsA9POhdrBKZDgCbUVUJuUGwTSasSqginRFImCpMGlURDvaAtNXKqn1VBDklBKZVwlIi6W4jByan3AmAYSoCL82Rf+lA++7Q184G23c9/dXznxtXnmW7+Nl9z4Sl567hU88YlPJCRPk6UgDElYNK4ZhHhpmsy3uEJKgYDsJZldOXJ5dIU/oCQzaltAcTSWfJkkC+LLMBbBvHyvS7JOZ08XZp1Op9PpdDqdTucbniVFdtTPhPd/RYF49Kbf2hv5JVnmY5q1bcUMIj52Jr6BEKQV/9slqbIlgiNmbOZKVWubB10UxOjbCasqmI/25axN3MB65RIkBkOrUcSYZ2U3F0r2VFguyjRPrcTf2NXsQq4KBFcrSgSr1Fo5LLUtIagEiRRRUpkZx4iRKNsdVSL3Tzu299yLmjJbQcs+iReBceUxsCkX5h3sZheLdfYE2VxdWK3Ho0WgpOidYsMacvAeMcQ7yKq1kcy6vHaeSNtsPBU2rj1RppOfN61hDDDNsCltYUCEa67x42Iby2Ttj3FqAEnJRY9WrFbG1YCEAwhKnStlnn0MFBd6Yj7GaNEFUEzCuEoEhGG1ZoiJMfm2ScP4+Efez3vf9Fp+++Mf8cc5ASLC3/hbz+bGW27lp3/6Z0lj8i68FBkTR4sC/NpZkob7brAQjBQgxXgkaWtVTAxMjq5L/xvYfx6DEFgSlXZ0rbdn1Z7b/m+n7aNgiFeXZPvr/+oiLEiXZJ1HHl2YdTqdTqfT6XQ6nW9oLi/2NzMUuyRVZhixCYf9CKYfJ+yL/UOTUWbSkmd+vI9lSvu8pcrmSi3HZFuC1JJVVX2zYCmVUr0Da4iQhkQQl3G1ibGcK9tdxoJQ58pmnqgWUIVSM2V5nqKICIWAaKFoZSqVqkJdRBm4KBsCkiLTLmNx4MJ2y2azpZpRtPhz8l0ABIVhFQgiPgo6u7Cqbewyzz56KXi/2JCa/FIfgUwDzAVy9SL+VfIuMhEvhg8AwbvL6gRhhPWB339elgFEf33mCe4rPrJpwGMe7efGYBSwwV/jcRWIElBTaikkgTSOWBxQUeq0A61k9XPX2VNtIUApnuASq8QojGvfdBnTwEoCxMBd57/Ah97xJt7/5tdx95fvOvE1+bjHn+GGl72cl974Cr7zO76LOAZiCAxDYAjikgzx5N0QjpZQ+Mili8FhiJ6AC172f7yTr13o7dr2z2PcSzJpo6NLwnKRuVxyeOv0i3IsgXbsb8j2ibHQN1x2OlfQhVmn0+l0Op1Op9P5huVyiVBVEfapskUKRLms2F8ranKUmPGEjBEkeGLM9qmy2LrKQhC0KpupeCrKbC/bghECGAFrIqxWP0eK3jfl5zeqQc2VaS7McyWrYVnZzDOKuHhqoqy2ZFAQJUii1IxgbEqmFEFRFKGokWxmGCNiQs6VaoHD7ZbDw69QgVIyuW2iFPGk1RCFkAKlVrYT7KZ9V9g8+bbKYG1McuXHmjZRkqAuoiz4z6nqAmcQL/LX4OejgkXvJCuV1h/mzyHg57nn0McOA7C6xscua/HzxJUnz4Z19HyUKlAQYLUaCRIoWrFcKLWwKzCm9lzbIgFTiCmwHowhBOL6NGMQVml0OSrwLz/xG7zvTa/lUx/5AKr1xNfjj//1n+LcLbfy7Gc/h9WplQupFFmPPkppQVrarm1DTRCi/0whtt8HciTJDBe7y+t6uZoSEWLw611aV5laE8jHy/uPpckeanl/7JKs03lQujDrdDqdTqfT6XQ633BcnioD3255PCmj5uXoXCLLfHQTW1I1tI4ngOB9Y8uI4rHaM6GNKRajFD0SZZI8IaVV0OpjmLUqWl2GrFIkBB+gBMjZmLMnw+a5QjV2daYUf15z8dHL3IRdtEwKI7MqlndUMzZTRVsZWKlGQl20qFHmgipsph0XLlxEgd2U0eDl+FGAlgpLKbKbKtNUKcW3VU6TizINgLp0iisYAmhLehFb/1v11NhqcPElwKqlo0S8kywIaGqJsezHy+jHSvbb79l5umw9+IimVAjVxztXK3+u4zhgpqhW2rQsq9Up3wCqRp525NZNBrCKfh9pEi9EGMdICpG0GhnT6JslDe6+58tHabIvffHPT3wtPuax38KLXnIL525+Fd/9pCe3xFhgHAOpSSm/poIn7oYlBeZpsmXk0hvGBFXFVDHlEoHVKsiOjjtKk7X9qPVoJPnq5f29l6zT+drShVmn0+l0Op1Op9P5huLyVJm2sbPjqTJjnwyjjWDWWqkmR2/+l1RZDKF1mbk6WFJlNKFWq7KdK6W4xAhBkCAEKqig+Bxmzr4YQAKMoxBiBFF/fibkquwmT5WZQrHCbpep1Zhr8TRZNWpVQjJSGJiyYDa31JQnzoz9Js4hClIrZom5wmaauHDhAibCVDK5eiosmpfzr0aIY2C7VS6USs0uv3ZbF1wKLsoixGt8Q2WtLp1Uls2gQHTRA004qo88WoDNoW/NrOLpM3ITLoO/rnX2acLD7NLn9BrSyiUZGUrwLZiPWsG4XmG1kufsY5dDZL06YM4TpVbybmar/vM1l4eJ/0yLFjpYBcIwshoTKSRiSijwu//y43zgzbfxyQ+9l1rKia/DZzzzr3Pjy2/lOT/3fFanD5DgY5RjCv7YMWAFJAZiaoIrxqPR33QsTabqY8Rml/bwLeJLuFSS7XvJ9Ng9L02LLb1kl49cdknW6Xxt6MLsISAinwGe9ADfPm9m3/qX+HQ6nU6n0+l0Op2HJVdNlV02gqlmR1sBl2L/JVVmx1JlLgN8BPNqqTIRP3aXK/PsokoQYhSM6sItJbQopeiRbEvJk1uYAp5Ey8VF2S4XtEKthXkuTKVSzIVaVqNU9fG6MZDnQpGWKJszVQWlFXu1pFhAUQsUFfJ2y30XL4AE5lrZVWWgjTVWGEc4fTqx2RR2Fz0BZxUubnxE0uciPS03XovPYhaXXtYSY6X6SGWwtqkS7xULAGsXZSn66OZc8I4zAZILtbrz803Vf4bV2DZnVogKNfgWzGsipHGg1sx0OBEE1muP8qkq282GeVJm8wRcwgVcjO21aT1rgwzIOLBKiRT9re3FC/fz4Xe9hfe96TbO/9nnTnwNPupRj+b6G27ipltezff91e9HUWIKDK1zTKJvPZCWXgujCy5fKCGkYJ5sOy7Jqrbxy4Yd68sLy4bMSyXZ1XrJvpok03aRd0nW6Xxt6MLsoXMf8F9d5faLf8nPo9PpdDqdTqfTedhxReE5uKw4lipTa6mnJsuWVFlROZJo7gKMGAQ1IdcrU2VBIJfKLiu5aCvH94QPongbFdTsXWXaRFNKrUeqJYJyVkpVtttMUailMufMXJW5Vqr5z5SrEsSIAVSNqjMKbHcTswVEjFJrS0wZMQWolakKOU9cuHA/FSHnzM6MoC6gSm1Sah05vFi5sC1ohjLBtpX6Y0tqCVanfIskxTvGQtvYOFcYRu8lk+hvElMTaWEF84ajMc9agRni2Jxh8Q2Y27YQAODgtEsyK20xwApWBzCMLXqlSpkyInD6mpUnokQo24nDGVAXbUMTeao+/irRe85iHFkdrEgE4jhQSuWPfve3+MBbX8cn3vducp5PfP398I/8KDe+/Bd43vOu59Q1pxGMOAqn04iYuimsgpggyaVtbGmyGH0UUiQA4htdq2JLq75fwEiL70nwbZn767VtdLX9JtYledYlWafz9aMLs4fOvWb2j7/eT6LT6XQ6nU6n03k4cbVUmZnHrIKEo/EyERiCd5X5CKanvqrJUXm5sCwACC1x5ueLYZEQnuDZ5Eqe/XhBWkGXp8UEaeOVlVpchqxWYT8N13qmtrvCbsrkjEu7WtjmghrMtaDVjjZfBhSrgokxafHNmur5NLPKXJSDFJjNiFqZszDNM4cXD6kSyDmzxUWZzEDwMcnxQNhsjOmCj16awmbXtoQ2WZbWLtW0+EfN/lqmwTddpuhiypqMHIOPZkryxFjZ+s9c1EcqDU+f6eTnP5yX8UNYrf0+Wtpt1/g5D07566dFEXOzdur02vvg1NhuZnbVxy5DxAVmcuGm6gsJ1mMkDgNDSgwpIRI4vHA/H33r23nfm17Hn3/2jhNfe6evuZYXvPBl3PTyW/n+H/xrIJCGwEBLEUZBLCAheppscEm29I2Nw2Ujl20zqyFH194ySCkhHI1cxhiORjOX5RO0a2+RZEsaLQbxtOEl4vjBJRksSy66JOt0/iJ0YdbpdDqdTqfT6XS+LrgwufQ2Q4+SOotMWISXtjm1WpWse2FwlCoTweABU2VTruxmpVRzRyY0UeYSA6SNX/pt48oL15E2QmdQZmUzZaai1KJoUbZlZs7qWxzNqBUq2lZFBp9+tEzNSlElg3dqNUk1hEAuMxJXbKbCxfvupqaBeZrYiUuoUF1qifn45WYDFzZGbSJslz39VVvS6+CUl/VrdvFUsx87rPy+2kr8TT1Ntlq5KKstGVb8qXuirI1oVm3JsQA504r5XSoG9ccqAqdWXu4/riOmPvI6hOX+K6oqu+3EPBvb4m9Kx7blslXSIcD6FEhIrNYrBhEkeszsM3/0u7z/La/jY+99B/M0nfi6+4EffCrnXv4LXH/9DVzzqGsAYxgCq3HAWvQuEIGWbKOlyYIQgy+aiDF5WtAMK/VIkvl1YoTgEb39yKUsrpVydNHve8mOS7Kj8v4r0mQPnhjrabJO52tLF2YPnZWIvBL4LuAQ+C3gfWZ28l3EnU6n0+l0Op3OI5zLRzCXVJkcS5UBRDEgtBSOkWsr3mefGgsBxIRqXDVVpqpcyErJ1cf7RFCrSBJS8HObCrnUtkxAjsYVzYRgvjBgs83sckWLotXY5om5GqUUTJViQqmKUEGEKp4gm2ql5sKMUMrMIMnTcDFidcbiik1Wpnu+zIyPkU66xRRidXmVxJNWuwqb+7zg3ypM2QXZ1KYQDw5aoixD3vnrpNm7tqbqx0nwrZhRvHy/AnP27rKK97Tp7GmvaQN/XO4F7gTO8j3xOkgu3gKthN+ABKfGNto5ClaNeVcZBQ4OEsMwUNWYtju2O+9AOxhgHf35V/XzpejnWI1r0phIIYLAdrPl4+9/Mx948+v43J/80Ymvt4ODUzzvBTdw08t/gR/+kaf5658CYxTGFNEgreNtcOGVfLNlaGOhQ/SU19JFVqqiar50guU6dNsXYrhsRBjfcOmX8NV7yR5Qku3TZEuS8jhdknU6/xrxKGj/eLAP4DMc1W9e8nEH8FMnOM8nHuDj8OlPfKK1/33/6h+/9Et2Bb/0Sw/9+H/0j648/gUveOjH//IvX3n805/+0I9/wxuuPP4kP//HP37l8Q/1WDD7sz+79Ng/+7OTHX85H//4Qz/2iU+88vg3vOGhH//0p195/C//8kM//gUvuPL4f/SP+rXXr71+7fVrr197/drr116/9r6hrr3th3/DtnOxKRebcrW51BNdO9s7P2u7XGw3Z7tvs7Mv/94fn+j4u+7b2Jfu3diX7/d/z7/91x/6Yz/u8fam3/hDe8MHf9de94HfsV97/2/Zu/+z//ohH/9n3/0U+8f/0xvtP/lnb7T/6JffaP/JP32jveFV/+AhH//b3/9M+wf/xRvt//RfvNH+7f/bG+3f+b+/0W5/9i0P+fgP/thz7N/9J2+0f/BfvtFu+k/faGd+6Fb75cd890M+/n0vvsX+0195o/1nv/JG+ye/+k77J7/6Lvv005/1kI9/x9//d+2/ef2v23/7pvfb/+fNH7L/8L/+Z/YHj7ruIR//Ai59z/a9f+UH7L5rrn3Ix9/96x+wr9y/s7svTnZhN9nFze5E187uM59r122x7Vxsc+dnT3R8Ln69z6VaLtXKRz/20I/v/937pv7vXv/f3Mv4Gl17TwcDPmF2chfUE2YPjV8B3g/8LnABOAv8A+DvAW8RkZ8ws099HZ9fp9PpdDqdTqfzDcM0Taz+AsdHn7XEzHvJcvGNkA+VIJ5gO9wVajHkxDMhhoTANGUOt4W0Kyc5lFIrRZVdVWotPObybQYPhkLe+IjimLyQfz5Bh70Z2Ay2Ah1gN+/r1x7i02+RM/j117yG89x/gqP9BKdOjyCB7W5imow820M+PIaImvAbb38T73/LG/jMH/0eLzvZM2C1WvOc57+Im15+K0//0Wdx6sd+EC5eeEjHphQIwz5NpvLQn/vC8V6yk736zgP1knU6nb9cujB7CJjZf3LZTb8D/JsichH494B/DNzwEM7zjKvdLiKfAJ7+F3yanU6n0+l0Op3ONwT/xi/9Ev/Ok57EM5/5TMCL/U+mDpZ7GlPR1l320NnMhbwplGVG004grICalQvzzLQrLo/yQz/egE3OTKUwhAi1opzsBxC8kH970Z/+iZY+CuTo0owAB6tWpH+Sx87wx+W+Y7LsoRvHmALb7cy0g12B9bBfFPlQ+OQH38M//P/+MtvDiw/9oGO87Nwr+I//4/+UR11zLXEVGCycaFwxhYBK9C2XgNnJ5JUvpdiPVMpJf/fHnqvIyV67TqfztUXMTm7MO46IfA/waeBuM/uWv8B5PvH0pz/96Z/4xCe+dk+u0+l0Op1Op9P518x2u+XJT34y58+fv+J7Z86c4c4772RYrbgiYCW+ndI3C/pN4UgOeB9UUSNXu7SfSRQxMMLRqWKAEAQxI6uymSq1mPdLqUFs3VBBEALVvH8siG+tDPj3zPz+mzmz2xUvyjdjk2e2cwaUXA1TMIS57oC2fTIYRmA3T2RVggixKjkKtVQIRs3GbrdlKkrJGdXCZBAyTDsvz59nL97fXnRxlVvnGMHl0yDe75UCsIi0AKEAK8itrD8lL/yPye8fgXnn3WUheJeZmS8ByAUGBRugFpDo0udPpt/k7a/5MHDQXukMTLg8q/ydcz/D3/y2p5FOCas0UEphOyvTRSj4YgJTfzw1/z2NBzCmwBc+rfwv/+L/xfn6Jd8ScOErDPd9hby578TX4DCM/Ozf+XluecUv8Mwf/xvE6OnAcYgEM0gJMTmSTzEJKUQkCIIyREEkYtA2XB6XZNY6wpbrB2IUAv4zqV3ZS2bLbXjn2L7LTC753sOpuH+z2XDbbbdx5513cvbsWW644QYODg6++oGdzl8Cz3jGM/jkJz/5yQcKMD0YPWH2F+Ou9u/pr+uz6HQ6nU6n0+l0vg7cdtttV5VlAOfPn+c1v3YbN99889FtZkZoUqyqj62JcHTbIgumcmmxv5m274W9cGgbMBfZdXEqzLPLMMRlRgwuapCAqifNFnEiAaJEtBX0L+OXVqEabOeZXcnMtaDmpe2YoLUwlxmTiJhCiOxyZq5KCDAa7MTIppRSCSpcPNyg1ZhzxoBdLcjkgqri8mqbYTr05zZnF3FE2CkMwCrBkLwYf55ciqFgEWaBMnlh/sEIafAyfxEv7c/4/XcK0qZL5wlG/Jy5QJ1daA3iGzi9heY3gS1wPOKmUApnOMvpR41Meea++2d2G5DkabYx+r9qQPCU2fogMcQB1Zn/5V/8Pzm/OQ9f+SJc+DLUSj7htfed3/Vkzt38Kl5y48t57GMey7CODERSEnzZZkCa3Jrylre9+W382Rfu5Du/4ywvfvHzOVgfICFh5uO7ngrza0nCIlKDb2INfuVJCFQ1iu21l2++3JfzX77hcpFknjZ74HFLkW/OUcyPfexjXH/99Zf8d+DMmTPcfvvtRwnTTueblS7M/mL8RPv3jq/rs+h0Op1Op9PpdL4O3HHHA/yfwSEAgc/ceez7YkRxUaYtVrYfOfMkTlVlKot4aIkdtN1n2ZS5T5UFYJcL20kppQ0+tjsNgwsPqy1pJi42DN+CaU205alwcSpQoBRjLj5OuS0Z1KhmqHlSbCoTGhLJXMbMc2WXC4qxAraqvmkzgKoxz5l5Luy2GwjClA0poOqCysQTZdMGwgCbCawCAWbz5Nhq8A2Nmv0YxZNbU/HPS/aNl6cHF2WrVvaWJ9rPDFU8eVZnf3kG8fuVDKXA6gBShekixNNw6lr4Ea7jUwTOM7VfYPU7J+NMegxnv+06vvKV2bdbJt/CKQZE/xkkwanTMAqMp0+7NFLjja95C+f/5MOweWidYseJKfEzz34uN7/iF/nrf/NvedorCEOKnjkMASw0SQopCr/9Wx/nFa94Bee/cBf+igX+4T98PP/if//f+ZGnPxMz78rz1KOnwuJRItGa9Gqve3UVt4i1JS8mHE8xciTK9CFIsm+2NNlxttvtFbIMXJZff/313HnnnT1p1vmmpguzr4KI/CDwBTO7+7LbnwT8N+3L//kv/Yl1Op1Op9PpdDpfZ86ePXvljSGydJB995PPXpIqK2pH0msZwRRxyTBXpRwbwTRTT+VIOCpyEoGAj1OqGhfnQp6VWg1TxRBCMoYYEFqqrFkJrUoMgkRBq1Knwral0lBhKpndIspqRSWQ1SXJNm+RNCJEQoBZlXnKqClRATE2c0EDlFqoU2UuyrTbYGZMFXQ2RGHeAhGyQd26NNvswLb+umQ8UbZOEAfvIssFgnkaTaWl0loC73SC9SlPnlFgOzcZVvwxRAB1SZdoibTs4mw1QFSYD+HgWrj2AE6NLrtM4YXnfoE3vOa/bV1mFdLIGb6Fnzv3ixweQhr9Ocfgz1FaZ1qMsB5HhtVI1cpXvvwlPvzW1/Phd76ZC/fec+Lr7Inf9h2cu+lVvOyWV/L4JzyemAKDRIYYsCBg4iOWZqSVEBFCisybQ15x0ys5/6W7/IWwCCKcv+sebrz5Zn7rN3+H9ekDguxlVwjHG/RocnffwLckyRa5drWRywdLk32zS7LjfLWE6W233cbLX/7yv+Rn1el87ejC7KtzDvgPReQ9wJ34lsynAD8PrIE3A//l1+/pdTqdTqfT6XQ6Xx9uuOEGzpw542+aRbwAq3HmzON58YtfSIqBqi61YC8MwIVCVWUuLhoWwVBNEYxwrK0+BE8MmcIuV6ZZyUXRqiA+zpmiEVMC8260JeUTWwpJrVJmZTvPlGJYDcxV2U0zu1rQkikIVUG1sps2WEyIBQyjijHPxUdEqyffNjmjImjN1FmZ5kKeJ6oque5lV964r5lm+L3Nvfhbi7N8O9cBnmBaBU9kpQgUqJOLriUlVg3vywJODbA6BaGl1SbxzZo6wX2zp83atClS/VcztVHPz3Mv6J1QzvI98Toe9TgXdBLb+Q2iwfd9T+Tf+w//LT72J/dxH3cAZ/lrp69jfdofU8T9qOEdbAcDrE8dIDFgVfnURz7Ah952O7//mx/lpN3ZIQT+1k//LC9/1f+Bn/xbP00aIilFYljkqacYwQhRSFFIMRJjQMQQM173lrdw/stfcpMnx02Ycf6LX+ZNb34TN9104zFJ5hX9vuXSLukla7OXj3hJdpwHTJg+xO93Ot/odGH21XkP8H3A0/ARzNPAvcAHgH8O/HPrmxM6nU6n0+l0Oo9ADg4OeMMb3sALX/hizn/pS0e3nznzeG577Ws5dXCKXPWSVBlNNgQR5lrJZS8aVNVTWyE2IeJE8VRZKcpmruS5onURaxCT+TEA5qN/tIG5FATFvJS+ZOZJkRp8lHPeMpVC0YIhFPUUXC0z2RQzH/u0EJinTDYlqiJB2NTifVYlg8JcK9N2g6mR1fu76gz5EDR6kuxzd8P7XvMaznMRz5H9HmcYeda5c3xb2qfEDCjqSTLxmwitOH+dYBhcqk07F2Myuii7sPFOshS8q0wraPA0WS3wJYMPv+Y1nOdCO/PH+BRrrj/3ar792/0WVXj0Y9fkPHPfhUou8JTHXIeGp7lYMn9+cYDV2l/vUweRYRiwINx39938xjvexIfe8Sbu/cr+mnioPO7xZzh306u48eWv5sy3fSspRcYYiAgWQyvwFxBjSEKUQEyRINacmPiYpRh3fvYOF2ULpu0C8evjs5+9gxj2ksx/siVN1kYujUsK/0WEw8NDXve61/OZz9zJk598lhtuePEVo4cPV0l2nKsmTE/w/U7nG50uzL4KZvZe4L1f7+fR6XQ6nU6n0+l8o6Gq/MjTf5Q//PSnef3rX89n7ryDJ3/3WV58w4tYr9fky1JlBqQmxjYtVbZIhdpkhssylwyhlfabCttc2e0Ktfrjgvj3BWKLOak/qdZT1hYCANNuZs6KVshZ2U47sla2ZSZKJKt3q9V5ZqcZKsQUIQpTzq2LysXeYZ5druWMmVGKspt21LmiAWr1Dq/p0FNhGqFm32jpsqzgdfsuZs6z5aOveQ3XnztHiK1ev/rPsoxnJvH01rhqmy1zk4/RRzBrbdswl4NaN5pZS42J95W5LLsPV3BTe/wNt7/mV/j7/+AXue5b1kzzxJfv2aGtukyi/xwRmjzy29YrOFgNhDGhtfIHn/o4H3zrG/mdj38Iu2It6ldnPHWaf+c/+Pf5u7f+W95JliJDSxia+IZTMUOSkIKnyUIQl2RtdHdJFHrHGHzndzZhY/VIkrk0AxCe/KSzR/e/Wi9ZFCFEuWRE82Mf+yg3vPjFnD+/7H+DM2eewOtf/3qe+cxnPuwl2XEuSZhexpkzZ7jhhhu+Ds+q0/na0YVZp9PpdDqdTqfTOTFFlcWLHBwccPPNNyNiBAkUVUr73uIORCCFQG6psmUEU1WpWgkhEsI+DRRQogRyUXZTZcqKLsX+GGmAEAOhJZ6sKipe2B5j8JHK7USuoNXIszLVym6emVVRrVSFbcnUPLOzgpAIBGQVmeYZk4CIz0QeTjNqRq3VP4oy5Zk6FYq0UvgZdhe8O8xL4j3gdP8h/Bn3cZ4d/hYs4GrMV1aep/A57uM76nUofmzEZdmp0RNlolBmGFZ++KYV+Mfg6TWZ/bGyebJsWMG4hs0WTq3hjnwf5/kybtRa0ooKOnM+bPmt8/fx/fgYKG3bpRUXdCFCTP6c1gcwpkhYrbj/nrv52Bvfxofe+gbu/tLVu6wejGuueRR/9Wk/wnN/5gZe/opzjNeeJgGR4JLseHddgHFYRi6XDrzQCvmtjXxKG5/03ryXvPh6/uF/9GiXW4sYE5djZ848nhe+6IWYcakkOzZySXuVFqU27XZXyDIwzn/xi7zohS98xJXcHxwccPvttz/glsxH0mvReXjShVmn0+l0Op1Op9N5yOgxGbbHmmCQq45gxtb1tMuVqvsRzFwrcpVUWQiABg53mTkrtUAx9ZHFaIwhuBSzVsouhoRAEpcn825imxUrRi3GNhemnNnWjNRKQdjlSskzMxUhIirIKMxzJmZBxChlYipG0UqpFZ2yJ9ZqYT6c0eijl1p8w+RcXF5ZW6Z4/9bVWAXgDlw57UWZf90MGHdQeJpvXMTL84f2WpTiwmrOvlFT/WVAxMc+Q4JNgVAhrfz08wTjaXj0o2EVgd0d7XEjni5rA5/hAFix4Q5KfRqSWufZ4Hc1gfUA61OBg9VIUeWO3/stPvS2N/Kp33g/WuuJr6Fn/fhP8vJX/l2e/bznsYqROCaGEJEQjhJf/rsWUoChdZctI5feNWZHhfxHLgxjSKGlvGAcDnjtr/0aL3nJSzl/1zIeKpw58zh+9Vd/lXG99t/Cg0iy451kr3vd647JsmW003mkltw/85nP5M477+S2227jjjvu4OzZs9xwww1dlnUeFnRh1ul0Op1Op9P5qmw2G2677TbuvPPO/oboEczxVNmCiCGIp6+OpcraYkxSCJRaLyn2V1WyVlKIlwiJgIJCrjC1Yv9SvKdMwpJ0igQTL8M337q5dEzlktnOXv6lZmynzHbKzFqpWqgI27lSayGb0uJJWIJilZQDGMx1Qy4wlYyqUaYZSYnZlOlwR2lPuRYoW9hswIKPXoYIF3f77rEBH8CEs8An20+a8P1hy88uwFkicLD2YyS4jAPals02XqkwRk+RyQjbAmRPopUCZWopsOU85tssT3EWeLff2ZUcLs+WLNtZKL4hU6Lfeuo0xEFI48DmwgXe8/Y38KG33c6XvvBnJ752Hv3ox/Dil97Cy195K9/zvd+LRh8hjbHNjIoc9YVFEcbBpSgYoW3CpF1nQEuG+fUXA8QQ2tZVlm9gZjzt6T/K7//+H/K617+Bz372Dp70pLO86IXXc+r0qbYVcz9yucwNX624H+DOO+44NtJ5JY/UkvuDg4NHnCjsPDLowqzT6XQ6nU6n86B87GMfe8CRm2c+85lfx2fW+cvCzChqxwM1AATxHM7x7x0V++NdTpcX+8/F01VDjMdG7owUharBBdeslKworQhfjBSDF/Cbt4mZN5YRopBLJhelZpckmymzy4U5Z9SUWZXtXLBSmcXQWtHifVheCuazk1kndnNlLoValTLPxHFkVwv54pYquNDLnuzablpPWQXLsK0g2TNkCc+OLerlO7mOMzyK80c/1SJeCmcYeBLXeZrKvKhfDKbiY53aOsSS+AjmVmE2GCZPj1X1bZzrEdLaVZwZDGsXb+MAz/yu6/gwj+Y8rZzMfyNA4AzX8dRHX0dMLuPG07BKAUmJz/z+7/Lhd7yJT33ovb7g4IQ8/Ud/jJtefivPv/5FrIeRYRVJkrC2jIF2nUgQhkEYgn+OCGIgIR5t2DS1/UjuZZJs0V6LUFsCYAaM63XbhilXSLIlTXYkyS5zZcfL+5/ylF5y3+k8kpC+4PHrj4h84ulPf/rTP/GJT3y9n0qn0+l0Op3OJWy3W5785Cc/YKnzI62z55FIVT1Kji0sqTIDapNlR6kyfMRNVZmOFfubGbMqSYKP1zVBEYNvo8zV2M2FPFdqBcy3UcYEKSYQsLo/H1HQWtntZkxdqky5sJ0Ku3nCBGot3D8VrCgT1bdwViOkgFglqzDESKkTc65sS8WKkvNMiJHttKPsCpnWG1+9vP/wEEqbRDRrCTD8Y2ivwdLG1hwbE3AX8KnXvAb/aypA5QwrfuzcOZ40tnFOg9KK/QXfhjkEmHwXARk4aC90MRiiLwNICdZtnjMOvvRgGPz3spl9i+Zn7oZ3v+Z/5Tybo2d3hpHnn3sl3/lEWJ0SDsbE4eEhn3j/u/jw297I+T/97ImvmWuufRQvfsmN3PLKX+Sv/NW/SkjStnweT5P57yEYjGM46ibza+m4tVrK+F0WRvGOOjPz+5khQY7GM2nXmtrScyZHnWfHRy6X383VCvofaMNl/+9hp/PNxzOe8Qw++clPftLMnnHSY3vCrNPpdDqdTqfzgNx2221XfXMIj9zOnkcKD5YqM3yr5DIyuKTKvDQ9MNdKOTaCOZeCWSuLP0ryGElgrjDnwpyVnLXJsH16KJpgasc6zpqMm2fqLJj6yOdml9nO3kkW1bivZOZdprZK91oKEiMWvOx/jAPojsPtjqlqWwwwE0Ikq3qiLHh6TBWmnX/sptaxhveGter8oyYyWHJbfvuu3abAE4CfOneOe7kP7zQ7y/eE6yD5fefqfWgheNLLqou5RcgdRO8XqwZjah8jDAJxhDT4G7w0eursvkMfe6zVx06//THwyl+4hd8+9MdfcZannbmO09dAGAc+/+k/4CPvfAu/+YF3k+f5xNfMDz/16dz48lfzwpe8jNPrA1IKhBDbyK6X6/vyByGNkYgRYjgax4SAyL6839r1lqIQxHvGJOyvh33ybEmT7cv7U9tuGcOD95ItPJAkO04vue90Hll0YdbpdDqdTqfTeUC+WifPI7Wz5+HOA6XKMFCTowQPLCmeluQBplIp1UcwMWNbKkmEOu94zWvfyOc+dydP+o6zvPiGFzCnFblU5ql6N5pYExdGkoQEMJUmUQCBTZ7QySUWVrl/l8lzYaszA8KuFLabickqQSIlz5gkLBiDwEAi68T9mx1ZlZo9USYSmWuhXtwwA7StlNsN5MkTXrG9e1pEmeL9ZC03dTRsWYEN+8TdItJWAqcMHheuI6SneSrNvAtN26KAIfhGzBlPkxlwuvWZzdXHLoNAaqOWq1P++g/Rxzi3CtN97Tkp1MGFUjCXb2mEHzt9HePppzEChcJHf/3dfPgdb+LPP/MnJ75WTp06zfUvfhm3vOoX+KEfeiohgpi0jafSRmhdjAWEYRTSMUl2JKiEI0kGdpQmC014eSfekiTjkpFLsCM5toxcLrHHv6gku5xect/pPHLowqzT6XQ6nU6n84B8tU6e3tnz8MLMqGZXLfanjWCq7UcwQ2v2D+KSbb5KqmyVIp/6lx/n3LlznL/ryx7ZCgP/13/0eP7Z//gr/OD3PxMzJbRNhRJdtoh44buIYSLMeWbeKVjAUHZTZjtXNnUimFGrcc9my65mJES0lBZ5g4QhROY6e3psytRiLtNMmLRSD7dkcbmUZ5hnT5TlAgT3Lxdnl2GB/Rspw8XWMoq5JMqOd2QNeAJtBZSBtmnTX4rlfIO41DpsEii2x4gBisJq5cJtDC69xnU7bvDzXJwhZBdrS0Oa4J1qBVgnWK9hGH188/znP8NvvOstfOL972LeLc/6ofNXf+CHuOUVt3L9i1/GdY+6lmEMiETEjqXJAiQRYgpEMUJKTYoJoUUS99KLKyVZG7lc0mEPVZItx55k3PIk9JL7zsOZvuRnT+8w+wagd5h1Op1Op9P5RqV39jxyUFXKFaLMNZnavlDd7MpU2Vz1KFWmqszVxy1jiuTdjh/8oR/g/Be/eGwFY4CUOHPmsbzvHR9kfbAmhMCQPJGECKhi4oX+U1aCBt+uWQqHc2GbJ6wl4e7d7Mi1kEWgZEQS1QpDDAgBE+WwZHQulGrk3Q4j+BbMbWbGE17z7N1heeeJMu9og0Pgbu4F7gTO8gSuY8TlWGKfNqN9PuISbUX7URVKoi0t8KRXaceO7biL7JNosf2bBELyfw/W3k02Jt/EGcRHMzcTSFsOYO171kY74+iiLK0WuTnxW7/xQT7y9jfx+T/5wxNfI6v1mhe88CW89GU3c/6L9/H5ez/Dk77lLM9//rNZHZzy8V1xiRVjIAQ7SpP5tSJH143qIlfb/S+TZMvopR0bz4S97FqE7ZI8O/69qyFy9ZRZp9PZ83Bc8tM7zDqdTqfT6XQ6/1ronT2PDIrqFamypavs+AimiAsgmvhYJJuaNXFWMW0dVX5HXve62zn/xfMQBrc5qTXRq3D+C1/hbW97Fy952fXEIJgFfIDPmK2SZ8WKp76mktnmwi5nSs1A4CuHO+ZamNWIZlg139AZlQGfUTzMMzVnKsJ0uMUQ5lKpux1FWpl+Bc2w28E0u/SaW5Lpixwv6q/ApzmD8NRz53gCPjoZ8fum9jW4CFsJ5AhZwYpLskWKrdsx97bjB/ZJq4Tf97N2L+Q7iZzladdcRxy832yXXbxp9vNl/HZTF3wxwukDF2ZxgLvPf56PvuutfOJ972S3OTzx9fE93/t93PKKX+DF527i85//I/7+3/s/cv5LX8ZfbOE//398C//9P/1lnvHDP0YchNjklLX+subC/Dmbf5Ki388Fl+Bpwn0vmVa7JEkWmiRb0mQt3HjpWOdlfC3SZJ3OI4XtdnvF/9aD95Vef/31j8j/B1kXZp1Op9PpdDqdB6V39jx8UVVqG3NbuDxVdunGQaDtxyzVPwywlioLAuMQPWlmRhDls396B4SRtu4SL0JrbV8m/OmX7iBKZPEmuVbyrlKLC7tcK3MTZbt5QiRy33b2gn9VogSkKEUUSYFkAQnCNhesFOaqzNsdakLOBZ0Lk3mabJ4AhWly+TQZ5PZaFFxE7WWZsSiv8wQ+9ZrX8FPnzh0lzCouvQ6AUfzY7IE3vM3Lv58CbBTux0XZCn9TthwvwJeBD73mf+I897ffynv4JN/Cz5+7lcc9xkdG0+AiLgpE8w2bq7QfuTQmfu+TH+E33vFmPvOHv3via2McVzzn+ddzyyt/gWf++E8Qg1B2E3//3/y3OP/le0BCK25Tzv/5Xfwbt/5dPvmJTxLDASLhaKzySJLh19DYCvxtsV1Hry1HqbP9yO+lkmxJnl3efXacLsk6nX81+pKfK+nCrNPpdDqdTqfzVemdPQ8/rlbsfzxVBvsRTJ+qE++PMiUrVHXlUWqlVt9KmGJo44lKEKFo4InfehZWbfBQaQVdzSSJ8OQnnkUC5Foos5KrIipkNXIp7OaZbZ5QFba5crjbMNXqibdaqVGxCFGNIUa2pTJvdpjBbrN1UVYrdZrZtVHF7cadXa1t/NI8GbYU9tM+v5f7OE9m3wi2OnqtzrfvP5brjjrKxgi7CsX8PC3wdkm/2WF7zVftjMvHGu8lSyO84X/4nznPvcd+M6c5z443veb/x8tf/QpkhDl7kmzZqDmMnii7+4uf55Pveycff+/b2Vy8cOLr4klPfgq3vOJWXnLTLTz2sY9tm00DhvCmt7+F81+6r62crMvmBX897voKb3rzW7nxxpddsRQitX660NaLmvlyBzvWSbZIMgFifJBesmUd6zG6JOt0/uL0JT9X0oVZp9PpdDqdTqfzCMLMKGpcXmUsKGp7obGMYKbAUY+UqpGXUTkzdkUJAkMKBPHRzoCBBOaibOfMz/7Msznz+Mdx/vyXvdW+thIvEc484TE85znPZrfLFFN0NiqQc2HOmQvTBiwwq3H/ZsNWi296VMUEGAJSKuNqZFsru+2WPBfmefbRy1yocz4SZbvJN1+qwbyFre0FWeVY+TxLef/yBnEvytqr2P69gwOeRhQf4dxWFzcZf6OV2ueLtkrsxzeX1FkCTp2G8RSkCp+6+z7Oc3e7x8h+LYBwnonf3d7HD4/XIQFOrz28p5r5o9/6MB9919v4k9/91ImviZQGfvbvPI9X3Pp3+Ym/8TeJMTTJFVFcjmLGn951B9S2WeC4m2rzvJ///B0tjShEkSPRejQ7iR2lw5Y0GRwbs1xSZfzllPd3Op09fcnPlXRh1ul0Op1Op9PpPEK4aqosuCCzy2TZJakyXJaV6orjeKosBJcftSWJigrTlJlnpaoSViP/3T/9b/l7f+/vc/4Ld7Fkqs48/rH8d//Df0+2AZkLasKcK3Mp7OYduRrFhAubDRfLzFyMpMaQAiUJUisDiTkKh7st8yLKFEqp5DwzVQ+ybQ5bv9fydd0nylpP/dFo5RbPkz0KuJezwB8fe7XaKKmvCQDOUnFZFtstK/zzDNzTPl8+lqL/5eNR10I6gDC3NNoAmTvaWRaddG07m/gZ/ux9HDz+emSE++7+Ap9639v4+HvfycX77z3x9fDt3/Fd3HTLq7nxllfwhCd+q6fACIQQqOobU6OI940lTwO2EjL/17SNZvor+V3feZYxSRNZy6sLSz/ZkSSzK3vJjiYshbYcoEuyTucvkxtuuIEzZ8484JKfG2644evwrL6+dGHW6XQ6nU6n0+k8zHnQVJkuWScfwYR9qkzVMDOq+fdqrWR1ZTQkFyJVjSiemdpNhV2ulLn6JF0QogSe+kPP4Dd+/SO85a1v584v3sF3PO4sP/tzf5s0rjAVdrlSS2WXd2xKRdU4nGYu5oldUaQo6yG2An1lUCELXMwzpSh52qFVKUWZ88xUIE+w20Ip+8nB+/N+KlTwzxfRdbG9JqdwsXUIXMd1nAHOU9lnw7zW/wwHXMd1R91jkf1I5920EU32Ug587DIA15yC9SmQVtKvwcN30xbgLPCJdobSzjjhmbcVH/ngn/PY4UN84r1v5dO//ckTXwsxRn7qZ36Ol7/qF/npn342MUbSIERJLlQNUCMGIYQ2HmkgMfKiFz6Pf/SPH835819poqy9elY48/jH8dIbXni0CXP5qZeRyweXZPKAIqxLsk7nL4e+5OdKujDrdDqdTqfT6XQexlwtVbYIjSVVBktJ/z5V5v1lnirzcxiqnipDvKA9RIgYRWHKmTwVFy548kyiJ9VSHNAQef4Lf55SjKIVK5CzkufKXHdscmVWZZoLF6cdF0qBohykQB0Du5xJ4mZpp8I8zZQ8U3NBEXbTRC4w72Cz8xHMqh6COmwbKpV9Rmz5ybe4klpE2YS/SZJ2+1PPnWvF/6XdQzjDiqeeO8ejjr/O+MbLsX0srWex/TsGH71cDT6RikAuXtZP3Qe3nhKu41EccD+79mzagOc8wd1/zP33fIH/7XduP/F1cOZbv41zN7+Sm17+Kr7t27+NGAOBQErJE3mmhCDEaASDYXCRKk2cBRHi6dP8b//7/8bNN93M+fNfYhmxPHPmcfzqr/4q64N1W/jApeOWl5X3e4fZA0sy6KKs0/l60Jf8XEoXZp1Op9PpdDqdzsOQq6XKNpsNr7/t9Xz2c3fy3U8+y4te9CLW6/VRDz9HY3NGbcX+tVavHKMtBWgdVEE8nVaKtg4yvO1eXLCIKENIIOal+8UoRalVqdWY50rWmW0uTKVS1Ljv8CIXqrfmn4rCPAR2pRCLkAYhK2hWpt2OPO2wkNjuZqYJ5uJOqUyto0xhqksezFE8DVbwvJbgoizjamppDPMsl9/30cB1585xH/fhnWZn+TauA/YSbnmMNZcKuYj3jA2jn3tqE4xRPPnmG0Z9SycBhgHW18CP//VH8fYP3ePxrAvn4e4/gwtfOfE1ICL85E/9DK941S/yMz/7HMZVYogRIVBVUfHxVRGISYgxIGpIikdl/Sbik5cCUYRn/eiz+L3f+T3ecPvtfOYzd/DkJ53lhS+6nvXBAa3q7BLZtUhYsFbivy//v/L5dknW6Xy96Ut+9nRh1ul0Op1Op9PpPMyoqkfyYuETn/gYN7zoBs5/6UtHt50583h+7bWv5cee+axWSWU+eqlGqftUmWBIEDA5Kmefq5BzZp4LWoDoaSQJRkqRGKCaUbOSi/eZ1WLMc6GiTCWznQtTLlyYdhzmjKowmmFJ2JZC1ECMUE0oVZgON+Q8owjbqTJPlan6KKNml2QV2LRE2XEtE3CZdT8uspYtlRP7YctDXKAd7yHbtvs9jutY8bSjwv4de2G29J8p+zHMa05Dii7damybMFcu9VT9/jm7MDs45R1mUWG9Br32h+D82+CeL/hs6Ql53OOfwEtvfAU3v+JWvvvJ342IEQmEGKnaYndAiNLGawVCdCk6eiLMEEya/EtLIX/AzDg4dcBNN90IcHSdLXsAlutjGbncf95+G33DZafT+SahC7NOp9PpdDqdTudhxNVGMHfbQ2644VJZhsD5L57npTe8hD/8o08zrlbeV6Z4IqwuIsOOSthThLkah/de4PVveDt/evedfMfjzvJ3fvZvs77mNCH6EgHMyLOLtzwXSjGyKkUrc57ZFKWUyoXdxMV5R66QMGLw+0WNSDCKGMLAtDlk2u0gRA43M3mGbLC5CHX2JJkJbHW/U/K4LFs6ygJwwH40M+HCTIHr2Lucwr5mPwGn2Quxw3afpZtskW2r5WMN6xG0+mshAcRDc+SdJ8tCagLtwEdHo8HBgXLHH/xLfvP9b+EPP/kbR5snT8JP/I2f4uZX3spzn/sChnViTIlAQNVQMUqtR0IrpkAwkFX052mGhOg/mxwXX0vRGFgTbWpySZLscknm57h6cT90SdbpdL456MKs0+l0Op1Op9N5GHD1Yn/vJXvd629vnVMNwYWMBM7f9SVuu+31nLvpRkr1NJiZj9CZuTCJSTBTdjN84pMf5hf/jX+T81++20+klTOP/xb+6X/3/+ZpT/0xVMVTZVWppTIVxUyZS2GTZ+a5cGHOXJwnSl1K9wtFAsEEpRBiQHVkOrzIPE8ggW3O5G1mNtgeeqpsVhdRBf8X9tsoK/txycR+9HKp79/h6bHT7DvH2mTkkQAb2/EFT6Yt6bHlJYzLuQOMI8TkJ4qtD9/U+8pK8ef8Ge4F7oR8lu9L1zGegt18L5/4wDv55Afeyj13ffHEv/fHPOaxvOTcK7jllbfylO/9HiQaAwlEfLS2KjRBFVMkCUiK+J4GL/f3zjmOxieD7CWZLJcLLsoeTJL18v5Op/NwoguzTqfT6XQ6nU7nm5yrF/v7Lkg1+Mydd7RblzlNadEn1yF3fPYOdrmgFVDzLZLBU0hRjFmNkisX77/IL/69v8/5u+9t6w8VFM5/8Uv80t/7t3nfuz5ASANzrpTq3WWocZgndrmwmSYu5MxUDFSpVlGMUSJFMzVEJK7YbXbM2w0V2M6FvDF2raNsc9iEmLn8apX4R5svlxHKDd4pdtDus4izpbvsAB+XPD52Gbh0HHPCk2mp3XdhEWXrAEP0l3F9wNKBTyl+p90MOsF5hQ++5jWc5552pw/wscOJx56ufOb3P0yt5cS/8x991k9w8yt/kec993quuXZFSJFI9DFaAVPF8HTgkAJRpEXezEc0Q0QChNYttsgu2u9e2s+iTUQKNLnmYm25cV/q3yVZp9N5eNGFWafT6XQ6nU6n803KA6XKzJamLee7n3wWaMVZTZKxdEqFxLd/21m0eH+ZyH78UlF22ShzoVTjLW95N+fvutfnDQGKuoSJkfNfuZvbb38XP/vcZzNrJSJsp4lNzmymiYs5MxdPnSmVGAJJhKKVGgVYoXNmd3i3l/JPmWkDuboou3Bxnxg7/uNWXKDNuMja4mmyJVFmuOya2v1O42+CMvvEWWj3D+08M5duy4ztsVbtPoPAkEBa51gMzR9mUIG52baYII7wwX/+P3Oei1C2cO/n4O4/455pwz0n/H1f+6jruOFlN3Pzy2/lB37wBxGppDAcpclyKd4lp17gH8SIQ2opsSbGQriqJEOA9vtX9Z8nCEdpM1m+H6RLsk6n84igC7NOp9PpdDqdTuebkKulyjxRdKxgHRcYN9zwIv7P/8ETOH/Xl5v5CM1qJM488XE897nPoaoRREhRMJS5CFYruRo5G7UYn/vyHVAVrBVzpTbHR4QQueP+Oyj1pyk5c3eemXPm/u2OXNsSAQpiEBFUKyEKEgdyrsyH93gv2S4zb/xhdju4eHhpp9hS5r8kxRSXXhOeKFvj0mzE3+wsKbQD9qKsHjvPte3ruZ1reZzl3EP7SO1jHFygDStIgx9cKuTifWoM3mGG+Ne/v72X84d3wt2fhfvOc5ndfEg89Wk/ys2v/EVe8PMv4trrTvk2y2NNbdUU1IgxEWMr6Q++5sBHLYUo4UiS+cViR3LLzDATzOToZz8+cnmUJAuh95J1Op1HDF2YdTqdTqfT6XQ630RcLVXmiTK7RJRB226JsF4f8NrbbuMlL30p5+/6ChAgRM488XH8yv/4K6xPHRDiMn6pSDVqrUxzpRaQBMMA3/GYs37iYK3d389D8LnEbx/Pcu9mw6bMXNhOlGrMtVK1YrUShoQkoVoFC+SszJv7mM04PMzk2ac8t1s4PNz3hy3Jr0V4LR1ly5bLAU+I7fAU2DI+WXFxtog2Pfb5NewTazMu2dZ4Is3Y95Wdbuc5SDCuIPpCSay07Zzmn1uEU9f6UktVED3kDz75bj787tvgrrtO/Hs+ffoaXnjDjdz88lv5a099KikqKQ6uRM1ThCYGKoQQiMmIQ8RUkeA9ZDFE4pIkEzmSdUtaDISq++6xSyQZbYKTXt7f6XQemXRh1ul0Op1Op9PpfJNQVV3QHJdlrdjfuDRVFgTMvMNMzXjq036U3/nt3+O1t72JO//8Dp70xLM873nPYX3qFClCpVIKmBpzUfKsmHmKSsWYq/Kc5/00Z/7JYzl/1z0QByC0eUXhzLc9nu/969/HXYcX0WJsS6GaoTW70BkHzApmkVoN3e3YlcKFi771Uivsti7LFokVcEHmWSnvExMu7S6L7XuLWFvuu7zRKe2Ygb0IW+6fcckWj92+CLax3T4InFrDqQOoxfvJZnVJFgAZYHUA8xZyMb5y16f5vY+8ld/56HvJ83Ti3/EP/OAPc8srf5HrX/RSHv3Ya0ghtGcS0apYFDAIEojBCFEIKSJtUDUNkYARYwAuTYktqy1VPYUoRhNqe0m29JeFEK729Lok63Q6jxi6MOt0Op1Op9PpdL7BMTMvc78sVSatY+q4QPNNh5580naMmjHnTA0D17/4xUddVTH5+GVVlyilVOZZqQXCYEQRplIJQIqROB7wX/7X/xX//r//f+H8XXe7MUqBM0/4dv7df/Afs5uEooVclVIzCSENiVpnF2XZ0PmQba0cXpyZZk9kleJl/htcai3pseNpsmVcckmJpcvuu2LfRxaP3e+4KFu6znbttnDsuGXsckmWrSKMax/B1MkTb7411P9NYwvZGey2Wz79qffyWx94C1/83J+c+Pe7Pjjg51/4Um6+5Vae/qwfJYkRQkRixEqloi4DQ/RwX4IYg18DQQji4ivFyCLJ9k/We+Yul2SwT5qFIJdKsstc2F9Ekm02G2677TbuvPNOzp49yw033MDBwcGJz9PpdDp/2XRh1ul0Op1Op9PpfAOjTZZdPVW2l2VLqsw3YzbBpkZVZZcrNXs6TUQIMUDQNsopaDV2c6FkH/NLA8xmUJSUIqix285sa+X7n/J0/sX/+lre+7aP8gf5Ds5wlh945ncR4wGbaXa5Y8owJP7/7P15dN3nnd95vp/n+f1+dwFIaodWy6I3ebcWuLxUeSlbtmSbliBZtizbVZk5s3S6z3R3JmfSyaQ7PUm6e3Kmp2vOJOl0Kt2dzlJJaqKkYFuVIu3yUq5y2WWTlDdZtiyLlCVqgUSRBHDvb3u2+eP5XQBcRYjgJn5f5+CAxMW9+F1cyIf4+Pv9PN61aJ3hW7D1mMZZ6sqyPIZgU09ZOYIlVgOyghRmTUr5IQVcEzkp+JqcKznVfV7O6orlJAgLrB590JACuUnkMynyV91jRNJKZs9A1gNj0hepSsi6BE73QIfJ6wIvPr2Hh7+3nUd2/gltXa37tb3u1a/mL/3v/mPu/NSnufiiTRSZRumMGLqVSx/SKZdGd6X9oIxeCUpT55xeWcVVdCFZt1gao0q3HSMkWynvn/SSbWBINrFz5062bdvGwsLCysdmZmZ48MEHmZ2dfdmPK4QQZ4IEZkIIIYQQQpyDYoxd8HX4x040VRbj6tqmD4HGubRaGdLCnlYq9V4RMGhciLStxdoUruksfQ3b7UHmWtPUltp5nPdpxdJGytby+ve+ievb1xO9onWO2rcQAqZniNYTtCJGTbU0wjpLVTrGFXibSvKr8WpQ5kiBlyaFWg0p0Fq70GhYXcWEFHKVrPaaTQ4BmPSXuTX3GXWPPVm7NN39JiFbDgyLbv3Upoo279N9YoCQpxMvVYS6rXn8J9/mkb/YztN7H13362qyjHfc9G7+8//8r/KuD7yPQkOWGaIyBOvwKmBimhaL3TQZMaKNRmlNpulK/yfdYikc0zqNFaYQVa0EarCms0yl6TNznPL+jVy3rKrqqLAMYGFhgW3btrF3716ZNBNCnNMkMBNCCCGEEOIcc8xif1JYxlFTZSk0SSFZCr5a52mtT11hsQt+iAQdyFRq+mptoGk9zoWUBMWAdan3LNcK7wPLrcd6R4gBZ6H2LS80Y6qqRUVDaz3WdedK6oDJ00qgDZE4rmirkqqJjGvwDbgIo2UYk2Ketb1jk6DsyAmyCd+9H5DCsUl/WcVq59iksH9yGIDr/j5NmjybrF4a0jRZRuony3Kw4269U0Po0rZoIMvS9T3/7JM88tB2fv79b1CX43W/pjdsfR2f/uxvcfen7+fSSy+i38tSgb+PuACKkE65VOmQBa0gqjQBZiZrl11QprU6bD9UrV25ZLW8f21IppU6Zi/Z6eokm5+fPyosm1hYWGB+fp77779/Q7+mEEJsJAnMhBBCCCGEOIf4EI6aKgNWVjAntErBSIwR52NaY3TpZEvrPDHApM8qqkCmQKFxPmJbR9sGlI5opWhCQIVuUi0Gxm3EWoeLDoKm9YH99RLLZU2MGc56WlsTtUbrgFYaZXKapiW6hrquaNrIqEoTWy7C8lIKxQIwyQEzUvhVk4Isz2owdqQeKQSbdJEtd/fpsXqKpiIFaF21GANWw7fJRFm/e4w87ybQun1PVaQvbnQXVCmwwfLLH/45P/3edvY9/tN1v5ZZnnPbR7fx2ft/m3e97zcoVCQrMkJUONedZokiN2Y1AFMRo3U6tZSINiat0a5Mk6XVyzjJzI6xchm7jrrjhWSTzzud5f179uw5pduFEOJsk8BMCCGEEEKIc8Dxpsp0F4QcOVWmWO0pcz7gYqRuLM52U0VAiAFtUgdWiNA0Dusi3nuUUvgQsDZiMtUFRJG2cVS+QUeD9ZGD9SEOjku0LmjbQHAlTmtQHmNA6wJrHaGpqaqSqo6Uk4myAMtlCrEmq5RrO8Ua0i8kgcPXL9eahF+m+/MyKfTqs3oQgGK1n8ywGr5ZUqA2+dgAKHoQ2hTkZQPIulMhg06TZsbAC889zcPf/wo///7XqMZL634tr3vVq7n3vt/iU5/5HDMzl5HlGq106pSLqQetyPNuOgxiSK+TyQ0mppAM1MqqbXrdu+NRlV6ZotPH6CXT0N3/aGfyhMutW7ee0u1CCHG2SWAmhBBCCCHEWXasqbIIGMVRXWW6K2t3gRSU+UDTOJxPARqxm0rTkGcKArStx7bpcyEFaa2P6BDTlJWNNDYwDikoi17zYrXEgaYihix1nLkx0Wg8nugqsmJAiIpmVFLXFXULZQWuTR1lozVBmWI1+Cq7j02CsvY435PJSZiTjrOWFH6tDdg0q9Np5oivM5kmi6S+s14BbZs61HoDiBbwXUdYAI3jZ7v/gp/t3s5Tv/jRul9DYwwf/PAd3Hf/X+I3PvhBikyRFelk0BgUQSmM0t3EVyAS0gELSpP3TDcNZtaEX5Me/kk8ljrLJmHXccv7j/w+nsGQbK25uTlmZmaOuZY5MzPD3NzcGb0eIYRYLwnMhBBCCCGEOEvi5DTLNaFYiOkEzLXF/qunIqbbrU/TaNYHmtbhrCdGlcIynbrOitykQM1FWuuIPgVlNgScCxidHr91kbFrCCHV4S/VI54fjwhkuDbgfElUCqciylcUxRDrc6rRiNa2jCtomy6M8ulUyUVSzLP2lMoxq51lk1Mwj2fyS0okTYpNpsQsq0X/LasnXU6yRt3d1iOtXRa6OxQgpFMte/0ucAupJ0wBy4cW+PF3vsLPdn6VcvnQul/Dq666hk/d91t86r7Pcd0112Bylb6XShMcGKUxmUkTfyEQVTf1V2hMBG2yldd4snTbHXqZAjLOn5BsrcFgwIMPPnjcUzKl8F8Ica6TwEwIIYQQQoizIHRh2SQUO2yqbE2xf1rLS2HZJCSzLmCtx1qP8xFFOv3SaA0mYlA0tadtHSEqooLWW9o2rK5z2kgbPJVr0TFj3NQstjVNG6hdJPqKEMF6j9aWIuvjw4Clg4dw3jNuwLbQ1GBrKG06jXLSUTYJsypWy/cnAdjxTH450ayenDkgTZNVR3yuYbULDdI0me7e591bCOlBh0U6+ZLu+lCePQ/v4uHv/RG/evShw8f4ToLWmve9/zY+c/9v8cGPfJRertGZInogKIzKyIxZWacMBHyMFIUh05NJskmYFVde4/TKrJb0r4orPwPnckh2pNnZWfbu3cv8/Dx79uxh69atzM3NSVgmhDgvSGAmhBBCCCHEGXTkVNkkKDveVNlkqqi2AedTX1nrUhgWQ4pYolFonR7DR6hbj/URAlhvaZxHRZ0K4QPYGKjamoihdZ7lZsyoaqh9ILiWgMYHDzh6vRxnC5YXR1hnqbppsrpMPWBLbQrKJouDGSnsmoRn4chvwDFMusccqyuYA1K4tnzE5xpWQ7e1/WQ5aapsEtQZA/0slfu3XYo3WtzPw9/7Ko98/yuMFl88+Retc/kVV3L3vZ/js5//ba6/7jpiDsrHVLwfFEapw6fJdHoNe0ZjjEmfx6SUf1Lgz8qqpWJtUNbd3pX3mzN4wuVGGgwGchqmEOK8JIGZEEIIIYQQZ8iRU2Wh+0OmVZrAioeHIGmqLNC6FJRZ66kbhw/pdEu6gxMVkRgjTevwLj2+DY626zbLtSZEj4uRyrZEH3Des9RWLDY1jQtEa0EZbAQVW/Jc461htFTS1C1lA40FW4JtYMmn/rDJ5FhOmgQrSYHXyZiEa5P1TcNqcHbkRNnEZEVTd587OfXSdI/Ry0BP9j4VNNbz5C9+wMN/sYMnHvk+MZ5MhLdKKcV73vsBPvO5v8SHb7+DvjGE1NdPFjXaqHSSJV34qQLESFZoMq1XpslijKAiKkaMWdNJBodNm61Ok6kuMD06DJtMpAkhhDh9JDATQgghhBDiNDveVFmaKGLl4yureV0W1jiPdZHWpfXLtvHEuHpypjZp1dD5gA8K7wPRR8ZVTasUBRqtoQ2B2jZY5yEESus5WI+pXCA4l07NNIYQGrLMEKxiaXGMazxLFfgAroa6hpFLwZinC6hIwVnJidct15rkWZNgrUfqJ/Mv8Rh9Vk/L7HXfvxzIFRQmBWWxa/6vlg7y8Pf/mJ/t/ApLB44unn8pF19yKXff+zk+8/nf4jXX30DsRteMMuRGo9ErXWI+RppyxI4dX+Pphb28+rqt3LXtE5hB0YWiAUVEK5NWKlkNvJSaTJydOCQ7H6bJhBDilUQCMyGEEEIIIU6jGFNB/5FTZaYLvSZTZZNVPEUkhEjTTZVVjcXbiAshfZ42oNKyo3cRHzXOBUKAsq5pABMnZfeOxjus9cTgqF1gsakYty6dzOkcShtUpgi+ITgoqxpXW5bH3WmUNTQlLPsUlE3WLHukvy+RQq71tICtLemfnHh5IhkpGFOkEy9199Yz3Z+796jA03t+zMN/sYPHf/JdQjjZCG/V7K+9l8989i/xsW3b6Pd6WB1QUZMbhVb6sGkydMRHzyMP/4AvfP4LLCysrnn+V1dcyu///r/mllve2YVkhwdhivOjvF8IIS5UEpgJIYQQQghxmvgQ8F06FGOaMEtdZRxV6p+mjEgnX9qAdZ6mDekEzBBQxqA1hJjiqeAVIYD3jrq1VI3HKMiUwkew1lI7iwqeNiiW6pKlpk2TbtYRjUHlhtDUeOuwIdBUlrIC62Fcga1gHFdXLyGtP7akkzDh5KfKjuWlliMzVtc0p1jtSMuBQoHK04WV5SK/eOjrPPzdHRza/8y6r2PLlou4857Pct/nf4vXbX0tZJoQIMs0hU7dY7p7jUJ3dEGeG4w2uNryhc9/noWFA924GBA9CwsL3PeZz/CzRx6lv1JyH7sQTB01aTYhIZkQQpwbJDATQgghhBBigx25grnSVWaOPVUWuziqtp7WBerW4duID54QSYXxBGyIqbMsKHwIVK2jtg5CxKj0deqmoY2R6CwhapZdy+J4jPWBYD3RGMgN2BZXtjTe4prAaJxWJJeXwLcpEGu759NtOdKSyvwnk2Enq6taO2mTevshq6FZAfR0OkU0KyAQefqJn/LT727nsR/9OcGfbHPaqptu/jXu/exvs23bJxhMTeEAoxR51k2SKQ3d+mxQAaUCvSxH62zltfuDP/wjFhYOpgcM3axd93ovLOznS1/6Mvd99tMSkgkhxHlGAjMhhBBCCCE20Npi/yOnyiBlKZOpshBjOlExRqrW0bQeayPBe3yMKBTGgI8WgkGR7lNbR1W3uBBTeBWhcS1tiMS2BWVYdoGlconGunRiY0xFX9FaXNXQekfTBsbLEDUsLYFr06mUFaunTSrSyqRd87H11eaffFg2WbWcrF1Oyvx7CrIsBUtVM+KXu77BT76zgwMLT67zSmB602a23Xkv933hL3HjG25Ea01AkRlF32hMlhFD7Or3I8qENGWms8OmASev3VP79kB0qyODK884hZtP/moP2TFWLiUkE0KIc5sEZkIIIYQQQmyA402VGZ0myUJMAYnRKfSKMZ10WbcttYO2dQQPzjl8gDzTtN6iXIqRFJHGesqyxcW0GKiUorQt1oU0YRUVVYiMmmVGdUsInuACMdOAp1muCD5QW08zBhtheRHGLq1d2u655KRVy8mEmT/i/elQkCbKAAZ0K5cRihxUFnn2yV/ws+9t59Ef/BnOvlTr2dHe8tabuPf+3+aTn/gkU1s2E0gF+71MkeU5qU0MQoigArlRGJNDF2xBF/x1r2ueKTSKra/euiYsWznVAbrTOLdu3XpYwb+EZEIIcX6QwEwIIYQQQohTdKypMqUg0yqt88XVCTMfYjoZMwSWnaepPd5HgvM0Pq1WGhVpvENHQ4yB6AOjyuJCJERHQNN6T922KB+IEdoYGDUVy63Fty0RRdSaqALl0jL4QO3BN1BZGC/DoTatYU6CMtX9efL3dL7j6dWnO+my+/pD0nRZP4Paljy86094+Lvb2f/M3nU/9mAw5OPbPsWnP/cF3vK2t5FhiFpjNEwVGdqYNE0WwePRBnomR+lsJdhamRIkYjKNBjKTZu1ijGz75DZmrriMhYXnWbuOCTAzcwV33XXXymSaEEKI84cEZkIIIYQQQrxMx5oqm6xcasVKcLb2z4pI6zxVk4r9vUt/Dz6SGWhUJHOKSCQGR9l6GushehyKxgUa2xB9IIaAB8auZbGq8NaloExpgrc0VUNbWVqfBp6qBsYjONCkIKwlrTymGvsUlE36xk53UNajK+8n/VLS7973BvDs04/z3e9t59GHvoVtqnU/9uvf8KY0TbZtjosuvgSMRivoZYo8z+jOqCTEgNKR3BiMLtI0WRd4RSCGgDaKHEVmzJrQa/Wk083TU3xx/g+46667utAsmbniCr78pS8xPTVECCHE+UcCMyGEEEIIIV6G406VrS32JxKjYhLBhBApG4d1Ee8D3oUUhqmA0grr0xpiGyzBwai1KCI+RhrnaKxPE2cuYBVUtmXU1NguKAtdUFZVFbZyKSjzUNu0ennQpWDMk4IyS5owa+lWBbu/n059UlimWA3Mhhm4WPOLH/0pP/3udhaeemzdj9vr97n9jrv41P2/xc3vuJncZFBojIJ+nqG1TmFijAQVyDKDwaQifpVWZFNill6/zCiMNmtWKCNKxWOW98/OvpNf/vJxvvTFL7Jnzx5e85qtzM3NMVg5HVMIIcT5RgIzIYQQQggh1uGlpsrSgFLs1vlSQKMUtM7TtGmqzHWBWesdBoVDoV3ExoAOULUeHwIhOCrncS6s9JE5pah9Q9nUtK1P1wIE21I1JXUV8JOJshaWD8HBkEIyR/oFwAJjVsv4c1bXME+HSZE/pKBu0H3NXMPSoSfY/d0d/Hz3N2jrct2PfcNrXs+9932BO+fu5dJLLgWtybQiLwxFplmZJiOgTCRXJnWTEdNhCESIKk3/6dQWZ8wkEFvbO6aP2UE2+dj01JDPfe7+U/o+CSGEOHdIYCaEEEIIIcRJCjFNiYVjTJUpUj+ZUmlSKXQt/yGmsn7bepyPWBuw1uFCQCuF8x6PwgVP6wLWB2LwjLzD2UAIAW8tQRm8jiyXS9Q2BW4hRlxd0wRHXdoUlPkUlC0ehFFMJ1wGVvvJJpGUIoVXk1XM02VT97U0a6bLQsuvfvltfvztHTz7xCPrfsw8L/jQRz7Bp+7/Au/+tfditEbliixCf5CnIxK0JsQABEw3TWaMScFjTN8RpRTapPL+NGnWBWI6Tfod9rFjhGTSSyaEEK9cEpgJIYQQQghxEtauYK6dKjNdsb+PcaUkPnbl7631WB9xztO2EeccTQjokD639QFcwBOxzmO9p/GOtg344HHWEpXBacVyuUjjI956fIz4qqaOnqayxAi2gbKBQ4spJCtJE2SGtHK5NhTLSNNmp+vUy8lE2SRO2txdx/LBffxo9w4e3fV16nJ53Y977atu4O7PfJ677/40V1w6g841mdHkhUmhpdJpAlClwxNysxqSocCHgFY6TZIplf6sFIqINhKSCSGEWCWBmRBCCCGEECewdgXzWFNlLsSVYCgw6cKC2nmcDbTW41pP61PBPyrS+ohBY53DeYeL4Jyjsh4XAsE7vIegNUvVMjbENG2mwFYldQhUlSV4iA5GY1gepTXLyUSZIf19Ut6fkQKyyOnrKet1X9eQVi4HQOYsT/7yuzz8vR08/fiP1/2YxmS8/0Mf5d77fptf//X3kxmDziFTKSgzSqGNJpJeB5MZNKu9Yz6GrndMdWuzeuU2rVO4p3U69VJrCcmEEEIkEpgJIYQQQghxHJOpshBiOjVxzVRZiBEf06TSZOIsEvHe09pIax22mwhrrCc4T1Bp/c+7QBtbrI+03lG7QHCeyllUUCijWLJjbNvSeIhKUY+WcCjKssVa0B4WRzCuoCIFZZNTL9c2gU1OvTydZf59UvAUSZNlBVAffI4f7N7Bow99jWp0aN2PeeXV1zJ37+e45577ufrqayDr+s96BSZL33cUKSZTilzrrtg/vV5AWtVUk/ddn5wGTVz52LFCMa0kJBNCiAudBGZCCCGEEEIc4XhTZVqlCnkXVtcN05pmJMZA6yPOBRrrcI2nsQEfPT4oVAQCjF1DCJ46hG5V01EHR3SgM83YN1BbqhBoXcSOR7Q+UjWO1kGsYVx2J18GqEnrlrF7r7vrmgRl8ahnt3FyUoiVkUKzofc8+dj3eOT7O9j3y4fW/Xhaa979vg/x6c/+Nr/5/ttQRqFzRa4VvSJLU2K6S7jQXf9YF26pNOFHiGSZ7lYu1UpQhopkCowxsnIphBDiJUlgJoQQQgghxBrHmiqbhGVpikx1nzcJ1kI6BdMGWuuoa4fzpHVL67uwTdE4R4iRNnistbQuUrkG2wbyIqPSLZSO5bbCo2mriqZuqWygbbrJsWUY17DUlflPpsZaWLMWmpyuoGxS4G9IK5gFwKHnefQHX+Vnu79KuXRg3Y952RVX8sl7PsN99/4W11x3PeiIMYp+kWEyjVGgjEqnWeru75OQLMTVMFMpTGaOmibLMgnJhBBCrI8EZkIIIYQQQnD4VNlkxRLSRJlWamVaa/Lx0E2VhQh1Y2lrh3WkEzGDB5cmnpxPBf7WpzcXFGVT03rQKhILGJUlpWvS6ZnOU5c1YxtwTfqi5TKUNRzsrqEh9ZGtLe0/nZNksDq5NpkmmwqeZx57iF/s+iOe/MVuYgwnuPfRlFK88z3v555Pf46P3rYNk2l0rsgUZEVOnmsyBUproCvqn+zErjwGK9NkRuuVkEzFSJ6trlxqCcmEEEKskwRmQgghhBDnsLIsmZ+fZ+/evWzdupW5uTkGg8HZvqxXnLVTZWEleYpp/S8qfIiwMlkWIQZCVNTWYW2gaTxt47BdF5kn4oJPJf0uYJ3DhkjbNlQuogh4E6krS9WUtM7jfMDWDWMXqEsIDuoRVBZGpMmxMSkYW180deo0aZKsB6jlAzy2+6s8tvsrjBZfWPdjXXzJZXzs7nv59Ke+wOte+3oggNEURmFyQ5FpjNGk77hGqfQ6RFjtHFNpfXMShE1CsizTaQJOq8NCMQnJhBBCrJcEZkIIIYQQ56idO3eybds2FhYWVj42MzPDgw8+yOzs7Fm8sleOST/ZJDCLa8OyrsyfqADVfW4EBc4HqsZhm0DdOqxPoVjwKSiLShEiVHWNC9BYSxMiwTm8iXjrqMYNrW3TKZp1y3IbaOo0wVYeABthibRuWZNCsjMZlClWT7ycDoEX9vyIH+3azpM//x4x+Je499Fueud7uOfTn+eOj3yS3qDAGE1mFDrLMVrRK0w3yadBRQxA10Gmu5AMpci0XrlAxWqxv+4mySQkE0IIsREkMBNCCCGEOAdVVXVUWAawsLDAtm3b2Lt3r0yanaIQ00SZXzNVFifrlzGV+a8Wxnehmve0zlPXnrb1NI0nArVtsc4TtEIHqNoa6z2ltTgU0XuCCrjoqUYNZVPiXcS1lqU60FTgArRL0AZYJp10GUiB2ZmWk35RMONFnnjoj3ls91dYPvDsuh9n85aLuf3Oe/jUvV/gzTe+CaUiGI0hkuWGvNAUWqGyjOAjgYjp0rGVnrKUn6UwrJscmwRjSkGmJSQTQgix8SQwE0IIIYQ4B83Pzx8Vlk0sLCwwPz/P/ffff4av6pUhxlTm78PqVFnsRstUF4wpUhgTiSuTZdZ5ysrStpHWerwPNG1L7RwRMCictdTWUdkWHzXBB6KCJljKsqasRoSgCd6xVHrKGmKA+mA64XISlHnS388k1b0NYuTQEw+zZ9d2nnrkOwTvXuquR3nrTbPcee/9fOz2bWzetAmNIit0OuUSTd5XZEqBMkQVUSFijE5TYl1HmdagSOGZ1odPj5kj/j4p/H85ZO1ZCCHEsUhgJoQQQghxDtqzZ88p3S6ObVLsP5kqm4RnsHrKZJphmvSZpVMw69ZR1R7XBlrn8S5QuYbGBzI0MXpG1jFqG3xIfVptaKlsi2tbyrokBI1zkaVRQ9OC91AuQhuhIvWTnY2gDNLaZVEu86sffoM9u7aztH/fuh9jetNmPvKJu7n7vs/xpte9iX6Rg9ZkOpLlOdpoch3I8qLrhEsh2doTLlF0k2Ua0nAZulvB1Prwv2/ENJmsPQshhDgeCcyEEEIIIc5BW7duPaXbxdHC2rBsZQ0zrnRkTXrKIBJiwIdAYwNlbXFNxAZP0zicd4xbS64zTIyUbcPItXjnUYD1nso7gnOMRyOsjzjnGFWBuoIQYLwEdUzdZA1p9bI7EPOMymKkeern/GLXdp56+Nt4t/4F0De+9R3c9anPccfH7mTzpilyk2F6hlxpjMlQOnQnWZrUzh8jmdEYPQnJFFpFiKCNPioUW9tNtpErl7L2LIQQ4kQkMBNCCCGEOAfNzc0xMzNzzLXMmZkZ5ubmzsJVnZ/WTpX5kCbKUjCmuiAG6KbKUNBahw+Rsra0dQq7nPPU1jKyLRpDhqJuG8auobEepTUhQuksTdswHo1xzhECLI09bZtOvSxHUAZwpKmyljRVdqZPvSzqMc/++E/45c7tLC48se77D4ZT3Pbxu5j79Od58xvfQs9oskFGhqbIi3RKpY7dCZdm5fucZQYN0BX1E0OaMlt5LVa7yIxefX1ORy+ZrD0LIYQ4EQnMhBBCCCHOQYPBgAcffPC462Iy+XJyJlNlzod0GmbX7q/16lQZXU+Z857GeprW0dqIaz3WBVprKZ3F+4COYINnZCuaJhX566gYtw11U1GOS7yztC4yGpOCsgDlOAVlAIdYLfI/0xNlzdOP8eSu7Tz542/hbbPu+7/mDW/kTTe+h8vfchWv3fxG3vzGG5me7tMzGSYzqBjApKkwozVaa4zRGEXaqQQUAWPoQrLsqMJ+o1UK3Di95f2y9iyEEOJEJDATQgghhDhHzc7OsnfvXubn59mzZ48Ukq9DKuoHHwLWr65g6m5yaZLDpA6zSNU6rA20NlA3juigtA1Na7EuoIjYAJWtqa0nKCB6mgDjcommavF1TeWhrKBtIEYYVzB2KRg7ROonM5zZoMw1FQd/8i327trBoWd+ue779/oDPnT7Nt7+rnfzT//5P+XBb/0hfB3A8v/93Uv4J//kH/OOt8wSQwCjyYwiN7rrJ+uOElARo9MJpMcKyZRSRxX5n26y9iyEEOJE1OREIHH2KKV233zzzTfv3r37bF+KEEIIIcR5L8QUkLU+4H0KyiYrfkZ3q5eo1FHWOuo24FtHbT3ORhpnaazFB7CuxaFo6oYyenwIRO9pfGRcjmhbx2ix4qGnDzFmL7CVN+RbKG1audTAImmi7Ez/q3v83F6e2bmdp378TVxTrfv+17/m9czd+zk+/sl76A8KPvWpORaeXQAVSM8mQtTMzFzEt7/1XaampijyDE3spsniSv+Y0fqYIdmRRf5nUlVV3HDDDcdde5YOMyGEOP/dcsstPPTQQw/FGG9Z731lwkwIIYQQQpx1ZVkyPz/P3r17X/Yk3WSqzHqPdZNS/9WpMqNTzBNCpLGOxnq8DdTW01qPbR3WB5q2xYdIS6BtHGVw2OCI1lM6R9PUWBeox2Me3wtffuD3WKAhRT+7+RFbePu99zIglfqn6OjM8LbhxYe/zZO7tnPgqZ+v+/55UfCB2z7OPZ/5Am9/x61kRlMUmm/s+CYLzz6bwjJl0icHIHgWnn6B/+7v/W2uufIqrrt2K5/85McYDoYr3WTnwjTZscjasxBCiBORwEwIIYQQQpxVO3fuPG5oMTs7e1KPEWLE+0DjAr7rClNrgjIF+BBpnKft1i+9D1SN64KySOstIUQq31JXjlZBZRu8c7gQKasS2wbqpmRcQb0EX37gARZo6dq3gGkWgB898ABvv/deIJX6n26jF57i2Z3b2ffDr2Pr8brvf831NzD3qS9wx51zXHLxpeS5oac1/WEPAzx9cE96el5D9BB82jnttlX+t//5n3V/9vytv3kZ//4P/j033zx7WEg2OfHSnIVpsuORtWchhBDHI4GZEEIIIYQ4a6qqOiosg3RK4bZt215yLW5yAmbrPHZNMqUVZJlCx0hEUVlP2was9TiX1i7bxtHagA8e5z118IyrGoeidDUhRKz1LI+XcdbT2pbRCJwF6+Hh8SILLAK97m11Wio9m0Vgy8Z9s44QnGXhke/w9K7tHHji4XXfP8syfv2DH+We+36bm2ZnyXVGr8joZ4ZiWKBcIChNjHDdpVvTk6Zbx5yM73kPKkKcnPOpWHj+Be65+24effQxhsPBSpH/2ZwmO5HBYCCnYQohhDiKBGZCCCGEEOKsmZ+fP2aHFKTQbH5+/rhhRgiB1nlavzLo1E0xKQoDUUFlQ+olsw5vI7W1uMbRukCIgTZ4GucYlTVtjDTBYa2lbloq22LLGhc8o1GkqVNcRITFJYA9wGbWBmWH2wPcdCrfnmMqX3yGfbt28PQPvoYtl9Z9/yuvuY4777mfj995L5fPXE5GRr+f0c8NOjepmswCOhX493LD3XO389/9vYtYWHgxHfvJZMIs0M2QdX9O3WYLzy3wh1/+Evff/9lzNigTQgghTkQCMyGEEEIIcdbs2bNn3bfHGHEhUNuQspuOVpBnCk2kdoGmCTjvsW3EOk9jW1oXUFFho6dylnHVUFtHS1rVrOqaUVUSXaBuLVUJTQPOpR77xeXUS5Yq9LcCJzp1cuNOWQze8cLPv8dTO7dzYM8P131/bQzvft+HuOfTv8Ut734PPW0YFD3yXJNnGtX1jREUSkOvrzFKo7NU/DbYPOT3//Xvcd9n7mNh4fkUlimdEsoYgbCaWpJWNffu3SNhmTirNqIbUQhx4ZLATAghhBBCnDVbt544VDryduc9rYu4EA+bKjNakavUYdbaLiBrPd7HrsQfnA1EIqOmYlS1NN5ThhbnIlVVMypHBOspG09roa7SxqF3UNUpKCsB273BFmaYrF8ebqa7/VRVh55n366v8PRDX6UdHVz3/S+buZJPzt3HJz91P5fPzJApQy/LKHJFlhnQGhUVuut6KzKD0gaIaKNXPqaV4l2/9m5+9sjP+PKXH+SJJ/bw3HPP8o//p3+85qvFNaHZS7+2Z5qEJxeWjehGFEJc2FSMZ/qAa3EkpdTum2+++ebdu3ef7UsRQgghhDijqqrihhtuOOZa5szMzEqHmQ8BFyJ2Tak/gNGQ6UhAUTUeZ9OaprUhlfkHT3AhBWfestQ0lE2LDY7SOqqypm4qxmWDs9B6sE3qKXMB2hLGQAOM6E7ZPOI6f/TAA4eFZjOwUvj/cgTv2f/YLvbt3M7+X+4+LIQ6GUprZt/zPubu/Tzvfd8H6WUZmckpMk3Ry9KT0BqDIu+lCTONIpKmyzKjUwBpNFqnMz61VhBBa7VS4l9VFa997WtYeO65o65h7Wt3LjhfwxMJ+V6ek/3fFSHEK98tt9zCQw899FCM8Zb13lcmzIQQQgghzhD55fdog8GABx988LhhRtHr0TqP84dPlWkNmkgkUjYxBWTOY12gbRzOe0KMhACVdyyVFU1jaaNnuWkoq5a6HFE2lqYGH7ugzIHz0FSrq5clKWM63mmXKRxbJHWWbeXlTpbVS/t5evcfs2/3V2iW9q/7/jrL+cgn7uH/8B/9p1x3zXVopTAmI8sVhTGgQGFQOQxygzEK0ESVgrA80xitu2mztGqpNFRlzZe/9CV+9cRebti6lbvuuovhcMDUcMCDX/7ycV+7c+Vn+1QPljhbzteQ71xwKt2IQggxIYGZEEIIIcQZIL/8Ht/s7Cx79+5lfn6ePXv2sHXrVu68806K/gDrI86vTpUpBaqb8WpsxPvV9cum9fjg0UBUiqptWSorqral9YHStSyNKupyzKhyOJdWLm2TeuyrGlqbpslaVtcvT26+awsvp+A/hsCLj/+Afbu288Kj3yeGI+fXTkJvANk0oV/wg0ce4YorLiPLM/pGozODiqAzQ26g38tQShGjQinIsjRNtjYoU90z1kaze9dO7rrrrtRblq6YmSuuWPm5PdZrd64FwedjeHK+hnznipfTjSiEEEeSwEwIIYQQ4jSTX35f2mAw4P777yeEgI8QuvXLyVSZUhCDx4dIRBEDNNZjW09tPd57VABiZNy2LNVp9bKxjpFtGI8qyramKluWSzAqrV0Gn4KykU2rlpYUmFWn+fk2o4M889DX2Ld7B9XBY4c5J6QN9DdD3occcBEiLDy/wF98ayefmLsdHSNZbsgzTa8wTKJGrVTqMDMGpVTXAZc+rpRCoVZWLlfDstV+siN/biev3bnqdIYnp2tq9HwM+c4l6+1GFEKIY5HATAghhBDiNJNffl9aCIFAmvQK8fCpMqLHTQ5hjArnPK2NlK0lWI9WoEOkalc7ysZty8i2lGXFeDSiqj2jOv3jNzpoLLQOymZ1oqwlrWGeLjFGDuz9Mft27eD5n32X6N26H+PNN93KJa/dyp997TugPIQITgMBvIWoeG55D4MioygUGlCZgbA6TZYZs/J4uY6YzEDkqBMtv/jFL6af22N0qJ1PP7enKzw5nVOjMiF1aubm5piZmTluh9nc3NxZuCohxPlGAjMhhBBCiNPsQvzl92Qnb9YGZTFGfFjTVdYFZSGk6aYYobWBqrE461PPFlC3LYfKmtL7dNqltSyPx1SjZcoyUjmIHnSEsoaqTMGYI02VVZzeibK2XOKZH3yNfbu+Qvni0+u+/9SmzXzoY3dx573385bXv4lv7vgz/uyPv026eg3ag40pCQyRrZdvpd83aKUxmUJHMEU66RJS/1umIsakqTNYfTehFTyxd88JDxw4X35uT0d4crqnRmVC6tS8VDfihT7RK4Q4ORKYCSGEEOKc8kosxr/Qfvk9mcmbGCO+K+WH1amyENPqpQ0QQySGCKRS/9p6nPWgwBhN3TQcHJcsO4etWw7VFeOyphyPKetAa8E7QEEz7jrKSBNlk6DsdE2UxRg59OQj7Nu5nYVH/pzg7Lof4w1veQefuPs+brt9G5un+vTyAsj40Effzcz/cgkLCy+kqTJ8OrXAR2ZmLmJu7g6KPAVmWqtu1TKtXRoFWptjfr3UD7c6afZK+bk9HeHJ6Z4alQmpU3c+9OsJIc5tEpgJIYQQ4pzxSi3Gv5B++X2pyZs9e/ZQ9PsrQVmMsQvLIs47fEz9ZDEEApEYoGwswQUioFQKypbqkpH1lGXNuG04uDiiritGZSAEsF2hv6/hUJ0Cspb0/nR2lNlqxLM/+iZP7drO+Pkn133/wXCKD97+ST75qc/xxhvfyLDIyTIDUUFUqby/2MT/9A9/h7/8l/8yC/sOgHIQIzOXX8zv/+vfY2rTdBeQKbSCTCuUTt1kx3JkUDbxSvq53ejw5HRPjcqE1MY41/v1hBDnNgnMhBBCCHFOeCUX419Iv/yecPLmhRf4d3/wRe677z4ghWUuRFpnCUF1b4EYU2DWWI+1Pp3ZGBWttRwYL7PcepqmpWpb9h88xGhcUbfpawQPdZOCsqUGPKnIfzJZ1p6G5xxjZOnpX/DUzu089/CfEWyz7sd4zY1v5uNz9/Gbd2zj0ulpBv0BKgQwGQqNMYqiyDAasp5m9u3vZOeffo8/2v4V9j2zh1e/aiuf3PYJeoNB11PW9ZdplfrJjhGWKcXKmuaxvNJ+bjcyPDkT03cyISWEEGeXBGZCCCGEOCe80ovxL5Rffo85WaMUqLQG+MTedLsPgcZanIcYFcEHQoiEGLAuEGPAuUiICu8cB6ox47pl1FjKquLFgwcZ1y1VnQ6MJKQCf1vBuEnBWARGpNDsdARlril59sffYt/O7Sw/t/6Jol5/wPs+8gm23fNZ3vSWtzGVZeR5DjFglMHkGSYzZFphck2WKTKtyIuM3GgyM+Rzn/s0IUZUN0lmdBeNqfReoY7ZT3bkNNnxXCg/t+t1pqbvZEJKCCHOHgnMhBBCCHFOuBCK8S+EX34Pm6xZE5RNXP/qrdS2pbVxJSiLRKxz+BCJRIIPuKDwtWWxrVgsa5ablrJpWVxcZHlUM2qgyNI/ZkcjsA2M6tV+sjFpssyfhue49Ozj7Nu5nWd//C18u/7lzle95vV8bO4+Pvzxu7hi82ayXo6OoI3BKENmcnSmMEqR5YZ+rjGFSSFZZjBaAxCJKBXpmdRVpkghIRw9OXa8tcuTcSH83K7XK236TgghxNEkMBNCCCHEOeGVUjB+oUuTN1ey8ML+o26bueIqbrvto9RNOvEyhID3nhAjPniU0rQ24irLcltzqGpYqivGjWW8tMSBQxW1g0xDFqCuoBynybKWFI4tkYKyjebbmuce/jOe2rmdpad/se7750XBez98Bx+/6z7e+o5b2NQrMEajlUIrQ6EzstwQCZjMUGSKXqbJeoZMG4zRGK274CuilCIzZmV4LETgGCuWpxKUiROT6TshhHhlk8BMCCGEEOeEV1LB+IXKh0BW9Jj/0heZm5tLpzgCKM3MlTP83r/+PUxvSPAR5zw+BFARlMYHqMapwH+prVkaVSw1DePFEQcWK+oAKoCJYANUYxiVaaIM4BCnZ+1y9Pyv2LdzB8/86Bu4erzu+1/1qhv4+NxnuO0Td3PFpZeRa4UyilwbMgy5ydCZAp1K+vu9nF7PkBmFMfnKKZdag4oRpbvgjDRNFuKki+zwUOyl+snExpDpOyGEeOWSwEwIIYQQ5wRZcTp/+RAIEWK3D3jLLbP87GeP8sX5B3n8qT1ce/VWPv6x28mLPrZ1eBUgpPCntZ6qbikby6FqxOLymEN1w3h5xGjUUgfAp6AsAGUFy6N0ymUEFtn4oMzblucf+XOe2rmdQ08+su77Z1nO7Ps/xCfu/gw33/oeesbQzw0YTaEyMp2hYkT3DXlUmMJQ5Ip+LyPPDYq0Ymk0GKVSEKZAq8ODMjg6KFtPP5kQQgghjk8CMyGEEEKcMy6kFaeyLJmfn2fv3r3n7fNMq5RxJSiDtGZpQ0BlOZ+85268S7f7EGicRcc0UWato24bRrVlsRpzaHnEwbKirhuWl2vqCMpD9BADuDatYB506escYuNXL8f7n2bfrh0888OvY8uldd//iquv5cN33ssntt3DFVdcRaYVvdwQVaSnCowyBBPJjabIMnQG/X4K00xmAI0xYABjUvdbJGK6AOx4QZmsXQohhBAbTwIzIYQQQpxTLoQVp507dx53km52dvYsXtnJOVZQ5rzHx/Qx6wPBdZ/nAy54YgigNLV12NayXDWMmooXlpZZKisWF5dprMcG8A6Cp3sMaMawv0vHDpImzTbsuTjL8z//C/bt3M6BvT9e9/21Mdz83g/wsbs/w7t/7X1khSFHM+hlKGXIVZ5CL6MoMkOeG3QGg56hl2doY4A0TZYbvSYEi0zOuQxrvs9rVy0lKBNCCCFOHwnMhBBCCCHOoKqqjgrLABYWFti2bRt79+49ZyfNQoyEEA8LcNYGZamXTBFD6ihzMa1ehhix1mGtYlzXHKrGPL+4zNK4ZHk0pm08FnAecNA0abIsBFioQAMHWD0BciOUB5/j6V07ePqhr9GOD637/pdccSUf/MTdfPzOT/Hqa64nGkWmYWAMRucQVbdWqejlGSbX5H3NINNkWYbWae1SA9kxgrIoQZkQQghxVklgJoQQQghxBs3Pzx/zYANIodn8/Pw5N2F3ZFAWY8SHgF/z8eAjISha54D0cRcDrnHYFqq2Yf9okReXxyxWNcuLI1ob8CqV+CsL1kNsU3B2sEo9ZYsb+Ty8Z/8vvs9TO7fz4uM/4LARuZOgtOat73wvH537NO95z/vZNBiiMkOOp8hyNBqtsxSCFYai0BTdSZeD3HRBGWhUCszWrFVqlU6+DPHooGwSjkmRvxBCCHHmSGAmhBBCCHEG7dmz55RuP5OOH5SB92kxMgTwIeJ9OvUy+EjrHbZxeK8pm5oD42WeO7jIYl1TLpX4EGl9Csa0TyuY3kHVQNnCiI0NyurFF9i3+6s8vfsrNMsH1n3/LZdezm/c8Uk+ftdnuO6aaxnkBWSavoZCZyhVoJRZCcr6haHfzzCZop+lfjKjQGtDVZZ8+ctf5slf7eWGG7YyN3cng8EgBWVH7JpOAjUp8hdCCCHOPAnMhBBCCCHOoK1bt57S7WfCJCiLpCGsGCPOBwKrQVmMihACzgd88MQAVdvSNB6FYblsWKwrnnnxRRarmvFSSQjgYrd66SF0U2XjBsomhWTjDXoOMXj2//Ih9u3awQuP7kwnB6yDUoobb3ont915Lx/4wG0MBn36WQEq0MsyelmOjgrQKA15oSmMZjjMyXJNkRmM1hitUEoD8NBDO7nn7rtZWHh+5XpmrryS+fl5brllds3XJt0PCcqEEEKIs0UCMyGEEEKIM2hubo6ZmZljrmXOzMwwNze38vczfZLmkUHZpLTfx3T6ZdpgVF2A5tK0WYhUdYurI4HIUtPw4tIyh5aXeXE0piqbNHkGtDZ1k9m0tcmohOU2rV4uszEdZc3yAZ5+6I/Zt/sr1IeeX/f9N110Me/+yCe4/a57ed0Nr6Nf5BhlAM+gyOiZDBU1MYLKFEWumOoXFLnG5JpepjHaoHUq7IcUgDV1zT1zd7Ow8Fz6Qiqdgrmw8AJzc3M8+uhjDIcDCcqEEEKIc4QEZkIIIYQQZ9BgMODBBx887imZk0DsTJ6kGWPXQ7YmMJsEZT6ELslKQVkIjhDBOse4skSnaJ1nuSp5YXnModEyh8Yl41FFUIoQI64GG1NYFgOMx7DUrV5uxERZDIEDe3/MUzv/iBd+/j1i8Ot+jNe/7RZ+4xN38cEPfpTpwYCpwRCDwmSRYZaR6T46akChChjkGb2eocg1eWEojMEY3T3aalA2CcD+/Ze/lMKyLihba2HheR780pf4/OfPre46IYQQ4kImgZkQQgghxBk2OzvL3r17mZ+fZ8+ePUdNj52pkzSPFZSlFUsI8fCgLEZPCIHWBerG4p2iqh0HyzEHxyUvjpYZjWvKcQnG4BS040gACOAcVGNYdrAElKd89dCOF3n6B19j364dVAeeXff9h5s2864Pf4z3b7ubt77uTfSKjDzLMErRM4ph3kfrdIKlCgrdV/SNpj/IKXoZmYoUed5Nk6XvFUxOtEwF/ZNBscf37DlmWEZwADzxxLnTXXeuONMTlkIIIcRaEpgJIYQQQpwFg8HguKdhnu6TNCdBWSrxT0GZ9QHvI5EIMa1kKgUheEIMtI2nbh3ORera8mI54uCo5FBVsby0zLhqUMbQOnC1R4XUVeY9VDUsdx1l1cu+6tVrP/irn7Jv53YWHvlzonfrfowbbnwL7/v43bz7Nz/MlZsvptfrp4BLBzYVOUVWkGkDKn0PMqMY9HPyXFPkhswo8iw7blBmtAbSqZc+RGKEV9+wtpsuwhFTcCfbXXehhEhncsJSCCGEOBYJzIQQQgghzjGn6yTNI4OyEMGFYwdlMXiCgrZ1tC7SNI5q3PDCeMTBccmhpqY8uERpLcpkWAveeqJLJ2fWNp18uTiGA0Dzsq54la1GPPPDr7Nv1w7GLzy17vv3h1Pc+r4P875P3MObbryRzf0hppsOyzPFprwg0xlFnkMI6EKTKej3Mvq9jCw3GK3ItD4qKAPINOguKEvf63R66MSdd97JzBWXp7XMeHhb25HddcdzoYRIZ2rCUgghhDgRCcyEEEII8bJdKNMuZ9pGn6S5NihzIa1f+hhXgrIYWFkdjAS8i7jgaWpP6zzjUcPBcszzyyMO1RXNcklpLTFGmgbAESM4m4r9nYVDVQrK2pf1HVi97sWnfs6+XTt47uE/I7j1P9qrXnsj773tE8z+5ke5+rKLmRpM09MZVgWGRjPV61GYjExnaBPRGrKioJcp+r0CYxRKKTKjuskxOKyjTKWgLK4JysIRgZhSsGl6yINf/tJLdtcdz4UUIp3uCUshhBDiZEhgJoQQQoiX5UKZdjkb1nOS5omsdJR1p1R6vxqUhZgmypQCYiAoCC7iY6CtLJUNjMc1+xeXeLFtOTBaxtUtVd0SvKd1kymqbqKshqaGQzatXtpTeP6uLnn2x3/CUzv/iNHCE+u+f9EbcMv7fpP3fGQbr3njm7l0eshUb4BSGWSKTMPlw01kSqO1QWWRLFPkJqfXyygyjdIKRZo+O15QppRaHTKLEIiHDY+lNc/VUy9fqrvuRC6kEOl0TVgKIYQQ6yGBmRBCCCHW7UKadjkbTvYkzeM5Mihzrnvv085ljKB0Ol3ShfTeu0jbOmoXWD40ZmFpmYNNzWJZ0o5rGuuwjccDqlvddB7GoxSYHajhEKcWlC0980ue2rmd537yLXxbr/v+V13/Gt77kU9wy/tv46KLL+LSTVMMiyFaaaKJbMpzNhU9enkBRqF1JM80RWbIMkNmNNqkoCzL0vplklKxtQEYCro/EeJqUDbJ0JRS6EmotsaJuutO5EIKkTZ6wlIIIYR4OSQwE0IIIcS6XUjTLmfLy5lGOnL10vujg7JIOv3Suy5Qi+CdZ1S2jMY1zy0vc6iuWSwrquUx1lpcG7ExTVX5ANHBuEqnXh6K6dRLf9yrOjHX1jz3k2+xb+cOlp55bN33z4oeb3/X+3jvR7fx2je8mWKqz+XTU/SKKWIIZJliusiYznsUvQLdTZjlRpNlGZkxaA3aaIzWaB0PC8omAdgkKIushmDHC8pUd0LmRrqQQqSNmrAUQgghToUEZkIIIYRYtwtp2uVsOtlppJWJsq7M3/uQVi8DhwVlMaTPCSGkLjMXqFvHocUR+8uK/aMRy01LO6qo6poQwHq6lAjKMdQOqlFauxzz8ifKlheeYN/O7Tz7o2/imnLd97/86lfxrg/fwTs/8BEuufhSsqmcK4abKLIBaOjnmilTMNXvk+cZ2kBRGDKtyDKDQh8WlCkVMSp2xf2HB2UTSnWhWff9Th87vUHZxIUUIp3qhKUQQgixESQwE0IIIcS6XUjTLueylwrKQohEFSGobhoq0LoIPrJcNSyOSpaqiufHJYvjClvWlFWN82nNEgXKwHgpnXpZllACI15emb+3DQs//Tb7du7g0FM/W/f9TZbzplt/nXd/+A5e/5Z30Ov3GExlXDyYZpBNEUxgmGmm8x7DXo+sUOSZ7tYtFVprtNKgUlCWm3Sq5ZFBWQq9VsvIJiHYkUHZ5ONrQ7PT5UILkU6l700IIYTYCBKYnSSl1LXA3wFuBy4FngW+CPztGOPBs3hpQgghxBl3omkXrTXXXXfdWbiqC0cKbiY9ZRHnu4mxyEpQFlILPZGI9Z7g00TZuKpZrBsOjUbsr2oWl8bYqqEqG5oA0XeTVQVUJSwvQ92moGyZlzdRNn7hKZ7atYNnfvh1XDVa9/0vvuIaZj9wO+/84Ie49JLL0f2cfj/nqk2XkGU5WgUGuWGYD5ge9ChyRV5kKSAzilzrbsoOdJaCMkVEqUhmDLA28IpA7DrIVoOy7lt7VC/ZsXrKTpcLLUR6uX1vQgghxEaQwOwkKKVeA3wHuAL4EvBz4J3AfwbcrpR6b4zxxbN4iUIIIcQZNRgMeOCBB/jABz5ACOGw20II3HvvvVL8fxocKyizPq1chphehxBTUGa9S5/nIt55ams5WNaMRxVPlyPGoyoFZU1N1aTH9w5MDraFg8+kdcwR6W29QVlwloWffYd9O7dz8ImH1/1ctcl4/dvfzeyH7uDGt7+NQa9P3s+Z6uVcMtzCoNdDq0g/zxj0Cqb7BUWh6WmNynOMjqmUXykiCm1Ipf6KtH6p9cqK5SQoizGitTpsrTJ0JWVHrmeeyaBsLQmRhBBCiDNDArOT849IYdl/GmP8B5MPKqV+B/grwH8L/Edn6dqEEEKIs+Kpp546KiybuNCL/8uyZH5+nr17927IFNAkKPMh/dmHQOPSyFSIqbg/hkgMARsD+C5Qc4HaNhw8NKZsHU/Xy5RLNU1Z0TjH8rLHZOB9Wiv0Dg4eSKdfLpLK/I/9Cp/guR94ln27dvD0Q3+MLZfW/Vw3XzLD23/9Dn7ttg9zycUXMRgOMblmU7/PRYNNTPV7GK0Z5DnD3DAc9il6msKYNFFG7MbFNESF1ikoS8FY9+c102MhpgMRtFaYNYHYZKrsyFXLjewp2+ifEyGEEEJsHAnMXoJSaivwEeAJ4H884ub/Gvg/AV9QSv3VGOP4DF+eEEIIcdZI8f+x7dy587g9U7Ozs+t6rBgjPkbCEUFZDJEQAxHVnXjpCESCA+sDIUbGdcNouWLUtjxfLbO8VFHXLVVdMRoDKq0YKtJpl6MSXHfi5Zj1BWXBO1549Pvs27mdFx//wbqeI4DSmq1v+jVu+sDtvOGtNzHoafqb+/RMxubpaS7qDRj0Cvp5RpHlTPdzBv2cXmEwWYZWERVBGQVREaNCK8hy3U2GpVVho9RhJ1wqwBwxUTYJynQ3fbZyjRtc6L+RPydCCCGE2HgSmL203+zefzXGeNi/HWOMy0qpPycFau8Cvn6iB1JK7T7OTTee8lUKIYQQZ5gU/x+tqqqjQhBIE3fbtm076TXVEAKBrngfaJzDeVaCMqIixLR2GV0gxhSUuW71cqlsKcuShWbMeNRQVQ1lWVKWEBX4CDpAXUJVQxPhIKnI36/n+R56nqd3f4WnH/pjmuUD67hnMtx8GW9710e56QO3cenll9EvoD/Vp18UXLJ5M1t6A/JcM130KDLDsJ8xNeyTZxqlNVpHdISoNSqqNBGmoFgTlBmj0aSgbDKpd7ygTKnVkzAnTkeh/0b9nAghhBDi9NmwwEwpdUWM8fmNerxzyBu69784zu2PkQKz1/MSgZkQQghxrno5q2EnKv6fmZlhbm7udF3uOWt+fv6Y3w84uTXVYwZlLpX4+xjRqBSM+YCKEe8CLoC1Fus8i1XLqCw50JYsjVuqUcm4qqi6ibLWAz6tXI5HqZdskTRRdrJi8Ox/bDdP7dzO/sd2Q1zn0qZSXPe6W7npvbfzhtlbKTJD0YPBoGDL1BSbpqbY1Bsw1cvpG8NgkDPIDIOpHkWWYYwmxoAKIR3hOYm3FOSZRrM6UaYBrfRKUAZHB2VKTcKyowOx09VTdqo/Jy9FVj2FEEKIU7eRE2ZPKaW+CPxujPEbG/i4Z9uW7v3icW6ffPyil3qgGOMtx/p4N3l287qvTAghhNgAL3c1bDAY8OCDDx73vhfiL+gvd031REFZIK0bOufxPqKIOBvwIeKso2kcS03DclWyZBsOlQ3V8pimrVlcJq1senA1RA2jcZokWyKdfHmy6qUXefqhr/L07q9SL76wjnsm/emLufGWj3Dr+z/KpVdfQaZg0Iein7Fl0yb6meLRv9jDc+zlNf2t3Pbx93DppimKfk5mDMZoUBEVIyid1i8BFGRGkWm9Uuav1epEmQ9pcux4QVmMCjg8FNvo9csjnc51Zln1FEIIITbGRgZmvwDuBT6llHoc+F3gn10Ap0dO/jUVz+pVCCGEEC/Dqa6Gzc7OsnfvXubn59mzZ88FP82y3jXVEAI+dc4D0FhL61Jh/6QJwrlAIOKtw0eFbX06HbN1LLUNZVWx5FpePLRMYz2jxRF1C3Wdesh8C9FD2UJFCslONiiLIfDi4z9g364dvPDo94jHOeThRGZecxNvm72DN86+k6KX0R9AkUFRaC65+GKme31e2Pc0f+v/9V+xsO8F8A24lr//Dy/lX/7L/42b3nYryiiCj2mYTWu6Qy0xRmFUOgEz9ZOlsCzEFDaqLkw7MihTQIhwpoOyidO1ziyrnkIIIcTG2bDALMb4VqXUe0gl+PcC/z3w3yil/oA0dfanG/W1zrDJBNmW49y++YjPE0IIIc4bG7EaNhgMLtjTMI90smuqPnQnW8YUmrXeY23sPhYAhbUBHwPBOlxUOOvxIdI2lqW2Zty0LLcNB5dGjKuGelRRWxhXEF16bGdhbFNQdoiT/3/3mtFBnnnoa+zb/RWqg8+t+/tQTG3htTd9mLff8lEuv/5qej3o90EDw+mMyy66mE2DIVt6BUTHX/m7f52FZ55LFxw9RMXCcwt84f7Ps3PnD+gNhiilMSZNjU2CMmPSyqVSaiV1XJ0oA6P1yjVNDsAM8ejvw+noKTuR07XOfLpXPYUQQogLyYaW/scYvwN8Ryn1nwG/RQrPPgvcp5T6BfCPgX8RYzy4kV/3NHu0e//649z+uu798TrOhBBCiHOWnHS5sU60pvrlL3+ZotfD+lTS772nDQFnI95HAgGFwrmA9Z7oPc6rlZ6ytmlYrmpKb1mqKw4sj2hql8r8K6hacC2oCHUDywEcKSg7GTFGDu79Cft2bWfhZ98lerfu53/pq9/CW2bv4PVvfA/9zTlFAYMBZBo2XdRny3CKi6emmOr3mer3mM5zvvX1b7Lwq6cAD0GBCin80pqFFw/xH/7oK3zq3rtTmKWgMJrMgNamO+UzxV8R1ZX5rwZlkyAs3b46ybfW6eopO5HTtc4s/z0LIYQQG+e0nJIZY1wE/gHwD7qps/8j8Gngd4D/p1Lq3wL/MMa463R8/Q32ze79R5RSeu1JmUqpTcB7Sf/H7V+cjYsTQgghToWcdLnxjlxTveGGrdw1dxdFr48P4JyjDWF1oiwElFI4G2icgxAIUdO2geADTdsyrhsq7zgwWmKxbmgry2hUUjcwbqEpU/BTNVADI9I/Tk5GWy7xzA++zr7dX6Hcv2/dzzcbTHP9Oz7E22+9ncsvv47+FPR6kPegn8PUVI9N05u4dGrA5uEUw37B0GRMTfcY9HOeO7CnK2/zQASVpdZ+IoTI08/sQWlFriE3CtUFZTFGUlx2/KBMdeuZ50pQttbpWGeW/56FEEKIjXNaArMjvEg6qbwGBkBBmj77glLqQeB/H2Nc/znkZ0iM8XGl1FdJJ2H+J6QgcOJvA1OkldP1HDAlhBBCnBPkpMvTYzAY8NnPfpYQWenTaq3DhphWLUNMJzNGaFuHJRBaj48KFyLBtrTOsdw0VLblxeUllmqLby3L4zHlKK1atmMwCiqbivzHnFw/WYyRQ0/+LE2T/fTbBGfX/Rwvuu6NvG72dt7y5l+nyHsUA+gV0BvCcADDXp+p6Wmu2DzFlt6AYb/HVNGjP8wY9DKKIgcVuOHqrYADpUGblHT5CASIcP21Wxnmq0FZiGmnchJ2HS8omxT+H+lM9ZSdjI1eZ5b/noUQQoiNc1oCM6VUDtwD/J+B95H+7fIL4O8C/wx4B/DXgE8C/yNpbfNc9h8D3wH+vlLqQ8DPgF8DPkh6Xn/zLF6bEEII8bLJSZcbbzLRNAnKnPc0LmBdJIZIJAU+tnVU3qNDWsm0IRKdp7KW5aqkdJYXl5YZNxbXtozGdQrKWmjq9I+4kUvTZIr0/qXYesyzP/oG+3buYPT8r9b93ExvwLVv/01ef+vtvPqqG9AR+oM0TTYYwnComCr6FNNTXDk9ZMtwmmFhmC4G9KYypno5WZ5BDGgC2hg+se0OZv7uDAvPH1gNygCiZ+bSi7nnnm1EdCrxh5XVzCODskkIdj4EZaeL/PcshBBCbJwNDcyUUq8l9Zb9JeBS0mz9F4F/FGP8+ppP/RPgT5RS/w64fSOv4XTopsxuBf4O6Xo/BjwL/H3gb5/LE3JCCCHOTWVZMj8/z969e8/6yZJy0uXGmARlk0J/ax2NizgXUstWTFNldWuxIWICBJ9WM533lN5RNhVl23JgNKaqGuqmoWkalpdgcZxOvPQBGlJAFnjpibIYI0vPPMa+ndt59id/SrDNup/b5qtfx6tmb+cNb3kfF/cG5EB/GrIsdZQNpwxTeZ9804Arh32mipydf/IDXmj2csOlW/nktg8z3DRAA0ZHtM6wLtA0AZP1+Zf/4p/zhc9/noXnDqTS/2CZuWKGf/cH/46iNyCyGnatDcrWrlXGGI/ZU3amC/3PNvnvWQghhNgYKh6r1OHlPJBSXyNNXCngGeB/Af5JjPGZE9znbwD/TYzRbMhFnKeUUrtvvvnmm3fv3n22L0UIIcQZsHPnzuNOgMzOzp7FKxPrtTakmQRlTWtpXQrDYvc53nsa63ABVIy0LjXyt85RWstyM8a5yIvLI8Z1Sd1a2rplaQmWSvAN2Jj6LWpSYPZSS5SuqXjuJ9/iqZ3bWX728XU/N533uOptH+CG2Tu4+urXMgSGGvIBFAVM9WFqS0EvK+hN97ls0OPyqc08/quf8df/i7/KwlPPQ7AQIjMzF/P7/+pfcdOt78L7gHMpXFQKjFFoo3BNzX948D/wxJN7uP76rXxy2zb6w8FKUKY1ZFofMwA7V3vKhBBCCHF23XLLLTz00EMPxRhvWe99NzIwC6SC/H8EfDHG6E/iPm8Bbokx/vMNuYjzlARmQghx4aiqihtuuOG4HUN79+6VSZDzwCQoCyHiY5oia73HWQgxEANEItY6autREZx3eK+ILtIGR9m2LDclbRs4MB5TtQ2NdTRVxeIhWBpD8FD5FI61pLDspYKy5ef28NTO7Tz74z/BNydb/b9qeubVXDd7B9e97QNc3p8iA6YzyPtp7bLIYdPmHv28IJ/qc0W/x+WbL2aqV2CC57aPf4CFZ59b00MWQClmZi5n1/d+SN4fpNDLKPJMYzRkCpTWhACTbcpJ2PVygrILYf1SCCGEEC/tVAKzjVzJfGOM8dH13CHG+DDw8AZegxBCCHFOm5+fP2ZYBrCwsMD8/PyGloCLU7d2ffaGG7Zy51130e/38TFiraf1AWdTN1kMEIi0bUvjIipGgg/4oPAWatuwbKsUjLWRFxcPUHlH6zz1qGZpEcY1NE0q8NesTpS1J7hG39Y899Nvs2/ndhb3reufYwDorODKt/wG1916O5dfdyMXdWHTxUUKyianXm7eVNAv+hTDgiv6Ay6Z3sx0v2DYLyh6GTse/CMW9j1LCso8oEBnoBQL+xf5w+07uOdTd5NnCq01mYpoo/GTT+fYQdmR4VeMkWPUlElQJoQQQogNs2GB2XrDMiGEEOJCtGfPnlO6XZxZK+uzzz8PqG5SaoZ/+8C/5U1vuwXv4ko3mY+RtrXYAMo5fFT4Jq1lLjVjatdiXaCqHQeWDjIOjraxtJVl8SAs1eAtLJGCsqp7O5HR80+yb9d2nvnhN3D1+g/snrrsWq6dvYOr3/6bTA83sQWIwCV9yIsUlBV92DTdo9fr0x8WXNYfsmUwZPOwz/SgR7+fkRtNv5/z7MIeiC49uM7SyZdKd48a2ffMHgY9gyaiNfiocV1QNgm7tAajFEaro9YpTxSUXUg9ZUIIIYQ4/U7LKZlCCCGEOLatW7ee0u3izCnLkm3bPsnC8y90wY8BbVg4sMynP/d5dv75LvJ+Hxs8TesJAaJ32KgITSQEz2I7pnEe13pq63hx6RAVkaZuqJZaFpegrmDs00SZIU2TnSgo87bl+Uf+nKd27eDQr3667uelTMbMm97LtbN3cPH1b6avFBeT/lG4adCdelmk1cup6R79fp9+v+CSwRSbB0O2DHpsGhb0Bzm50fSKDJMZNHD9dVvT90qbw4KyNELmeM11W8kmQVl3GOYk7DJGkWmFVscOyo5V6A/SUyaEEEKI00MCMyGEEOIMmpubY2Zm5rgdZnNzc2fhqsRaaYop8u//4IssvLA/TUrpLIVAWkGMLDx/gPkvfYWP3/kxvPeEEFLRvw3YkE67bELENp5RXXKoGlP7QFPXlIccyzVUI1gM6UhxSCHZifrJxi8+zb5dX+GZH3wNWy6t+3kNLrmKa2+9nWtu+jDF1JZU4g8MgOkB9AbdRFkGw819ekWP4aDPJYMB070hFw96TE0VDAc5RZ5hjKLIMxQKSJNfd9xxOzNXzbDw/IukoCwCDoJn5vLL+OSd23BhNdzSCrRW5ObYQRlIob8QQgghzg4JzIQQQogzaDAY8OCDDx73lEwp/D97JkGZD+ntl3v3QNbrJqbU6niTzkHBEy/uobUO10a889TBMm5qbIjYNjCqRhyqSirnaJuW0SHPaBF+1h4C9gJb2cQWRqQ5rGMJ3vH8z/+CfTu3c2DPj9b9nJTWXH7ju7hu9mNccsPbyLSmTwrKesDmQXo6U9Mw6EN/OKDX6zHs9djS77OpP+Sifo+p6TRVVmQGnWlyrdE6TZD5GAnOYwNkvQG/93v/gs/f/3kWFl5Ip2SimLnyCh544AF63c+3UmC6oMxofYLX4xjPSXrKhBBCCHEGSGAmhBBCnGGzs7Ps3buX+fl59uzZw9atW5mbm5Ow7CwJIeAj+BAJIYVmTeu46uqtYEyXZikweSoXC4DOuHrzVsZlS2sttXc0zmOdZzQecaiqUnhmHePFSNPCw0vwwwceYCE9APBLZoC333vvUddUHVxg3+6v8PRDX6UdHVr3c+pvuTxNk918G71Nl2CAaVJQlgGbpqAooDdMQdmg36c/mKJfZFzcn2K6yNk06DG9ecDF032MVpjcYCIYY1AKfIx463GAigqjQGnF7C2/xk9++EP+8Mvb+dW+Pbzq+q3cuW0bvcFgJSgrjOoCt6NJT5kQQgghzgUSmAkhhBBnwWAwkNMwzzIfQgrJurDMOY/1AR+AEPnYRz/CzOWXs/DiIdARgk6pTc8wM3Mlb/u1t3FgtEztA855qqriYDlmbFva2lKNoFyGpSatW6awDFJYliwAP3rgAd5+770E79n/i53s27Wd/b986NiFXSeiNJe//launb2Dy157M0obctLK5RSQKxgOYaoPpgeDAQwHffq9KXq9jMv60wx6GZv7faanCy7ZPIUmYPIMrSDTJk3gxUh0AU8kRoVRCjRkJk3hGR0YTm/i3s9++rDLMxp6mT5hUHasnjIJyoQQQghxNkhgJoQQQogLRowRF1IoFkIKaFaCMh9T7Zb3uBBxKuMf/O4/5P/yn/wVFvYfTOVehWbm8sv5L//G36GOhmZcUjYth8bLlK3FtYFqDOUIDjVpOK0Elljk6Na6ZMGO+eU3/ilPP/QtmqUX1/2cepsu4ZpbPsq1t3yE/pbLAQ5bu5zK02bpIIdiAIMhDIqCXn8T/UHOZb0pBr2MYa9gy3SPizYNKIxGZ4bM5F1DWeoSCyESSN8nhUbpVNavYkTriFaGGA8PvTIDudHHXb0E6SkTQgghxLlHAjMhhBBCvOKFEHAx4n0KzVJPWcDaQCASfcTHgLchhWXe43zgDde/gwf+/YP86Y7v85jdw3Vs5a2/8QaiV+w/cJCD9Zi2sTRNpFyEcQW1SxNlk9MuU6n/nsMvKAYYPQMv/gKW97Hn5+udJlNc+pqbuW72di57/TvRxnQ3HAL2UrOVV5ktmCH0CpgaQr+Aot+nVwzJ+hlXDjcxVeRMDXpMDzIu2jQgNyadeGkMRq0GZRFFjJ4YFJBGvoxRxBC64n4NqMNCL2OgeJlBmfSUCSGEEOJsk8BMCCGEEK9Yznvcmmmy0AVlbeuJpGmyEMG7Liizjtp7go1Y57Eh4L3i5g/dwhvrt1I3LYujmqVqTOs8dRkYL0JpwbawTArKalLV2aqtwC/BVnDwMTjwC7DjdT+fYuoirrn5w1xzy+0ML7nysNt+9MADLLBMmmnbzY+Y5s577+Oa12v6RR+d5RSDHjNTm5jOcwaDPpv7GZunC3q9AqM12hgUEbUSlgExEIJKhx+oNPVF9znaKLQyh13HyQRlUugvhBBCiHOdBGZCCCGEeEU5cposdOGM8wFnfVfu74konAs4F7DW0vpIdJHK2bSmGQKlbSnrmrJuGLU1TV1TW0e1BGUFtYWmhZYUltUcfeJlDIEDe/fS+9Wf0Cw9eYzPeGmX3PA2rp29gytufBc6y1c+PgQK4FsP/FsW2A/kgAFqFniRLz3wO/yNv/XX6G2e5orBNFuKgkG/z1TPcNHmHv1+gUajtGaSbymluqDMgzLEqFOPWBeUaaO7FjZ92Lqk0VBkGq3UcdcopdBfCCGEEOcLCcyEEEII8YrgQ8D62HVopbXLSUeZcyGV1YeI9xHrI946mqbGKUNoA3XwWO9onaV2nuWypKxb6qam9Z6qqRkdhLrt3uxqUNYc43ra8SLP/ODr7Nu1nfLAs+t+PvlgE1ff9GGuvfV2pi675rDbhsAmYKDgibjYhWWaFNlV4GuIOQtxmWd+9iIf+8TrGRQ9sgwu2dJnqt9Da4PSKgVhcU1YFdMpnjHqlWkzFGitUCGiUCiVIjPVTZzlmca8RFB2rEJ/kJ4yIYQQQpybJDATQgghxHkrhBSErS3xTxNkkRACrfXEEAgRrAuEAN46qrbFWkWMkSY0WOtpfcty01K3LctVhfOBsq4ZLVnaGsoayhYIKSh7kdRPtjYDijFy6Fc/5ald21n46Z8TvVv3c7roVW/i2tk7mHnTezF5cdhtU8BmoKdgOIB8COzfAzighlBDzCAEqJagXmKZPWwafpBNm3M29/vkWU6EFIaRViCVpvs+KVLwtrqWqTWp1F9plEn/dJwEZZnRmJUOs+O8RlLoL4QQQojzkARmQgghhDivxBhTSOZXe8kma5feB0KMWO+JtgvKfArK2qahsh6covEWpyDYyNiWHCpratdSVy1eKcrxiMVDHu9hNEprl5E0v3WASZH/KluNeOZH32Dfzu2MX3hq3c8p609x9dt/k2tnb2f6iuuPuv0iuhMvDUxPgykg11AUcDHXQFhOIZkmXbBdgtaC87xm01auuXyaouh1k15dUGY0Rqfifh+AmIKy2J2CiYJMg9K6OxUzTaMZrdBKkekTB2XSUyaEEEKI85kEZkIIIYQ4L4QQCID3KYgJk7XL2PWWhYALnmgjLkS8i7jWUgdPU3u00jTO4iM4a1lsSsaNZdzU+BBpW8toacxoDDbAeIn0uSGtXB4C7JrriTGyuO9R9u3awXM/+VOCa9f9nDZf83qum72DK9/yG5iif9TtFwEDYDqD4RBMH3oGsh70Bj1efKLhmw/8K1AOxg3YZWgb8B6awMxlU3zu/o+R5wUhBCBiMk2mdfqe+QhxspMZ0zamgsyk6a9uAROt00qmVgqjedmF/tJTJoQQQojzhQRmQgghhDhnTabJQkgB2dppshAiMUZcSFNlrvU4nz6nrVtqF7A+oHykwRO9pyzHHHKWqq5pGov1EestSwdLqjpNktV16tpqfFq93M/ha5euKXn2x3/Cvp3bWX5u77qfkykGXPW293Pt7B1svuo1R99OCsoKYHMBeQb5FPSKNFHW6xXk2qBx/LN/8f/h+eX90CxB26agrI3gS664fAv/5t/8Hnm/jw+BPNcYpVGk9dQVXVAWoQvDFCEqtAKjVOouA4xRZC8RlB2rp0yCMiGEEEKcjyQwE0IIIcQ5x3e9Y5NeshDSNFmYFPrHQPAB5z3egfMRax3WB6rapSDNOaJKnWVLdclya1luKrwH21rKakw5DixVaUCrtWAdVD5NlB084pqWnvkl+3bt4Nkf/wm+rdf9nDZduZVrZ+/gqre9n6w3POr2HqmjrEcq8980Bb1NkJs08VX0MvK8oFfkYBS/2PUcCweegKYB56AJEOtU2h8j//Xf/Lu8+c03ozQMsgxixAPBd4mWUtCFj0anPrLQrWLmejUo01qR6eMX+oP0lAkhhBDilUcCMyGEEEKcE9ZOk6W1vtWQLIQIqlvL9BHrHdZGrPVY52nb9PE2OoKPxBiw1rJYVRywDW3d4Dx4a1kejagaGJcQLKBhXEHrYQwsrrkm19Y895M/Zd+u7Sw9/di6n5POe1z5lt/gutk72HzN648ZHuWkIv8+MNAwtRl6Pcjy1FOWD3KKLCPv9clVYNP0NJsHQ37IN2E8TnujtgEcK7uQWvP0C/sY9jO0Umld1afuspWgLESMTuEYKEKEbIOCMukpE0IIIcT5TgIzIYQQQpw1k2AsTY51oVmYhGWT29P6oLee1nlaC761tCHSNh4fAzYEdIz44KmalkNVxZJtsDbQ1DVV0+Aay6iBukxfK0QYlWmabNy9TSwvPMG+ndt59kffxDXlup/X1BWv4rpb7+Cqt3+QfDB91O2abuWSFJQVCjZdAkZBXsBgoMmyjCzTZL0+PQ1bpqcY9gdsyQume31eu3kr1BWokAr/J0dXRsC2vPb6rYQYaW06okDptI4ZYgofc5P6ySCFY2ZNUKbVy+8pk6BMCCGEEK8EEpgJIYQQ4oxL3WOr00kxxnSa5ZppshgCEYW1jtZ5bBuwLuC7yTIXPG2MqOix1jJuLIfqMdbDuG2xTUNZllgXGdcQmnQQZNukgaxlUkhWddfkbcPCT/+cfbt2cOjJR9b9nHSWM/Om93Lt7B1c9Ko3HXMySwPD7m1AWrecvgiKDPIc+gODQZH1copen0JFLtq0mSLL2NLrM90b0OsbdK64Z+5D/L//hy0sPH+gC8oUWAsEZmYu5/aP3oFzYSUoi91KZmEUMU6CrVTir7uuMt0FXsebKpNCfyGEEEJcKCQwE0IIIcQZceQ0GaSussPWLrt6fe8i1llaH6hLh48KZwONs8SQOsxi9LTWM2pqDlVlCsrKkqZtaeqG2kJTAyqFZHUNtYWaFJRNWsjG+/exb+d2nvnhN7DV8rqf1/DSq7n21tu5+h0fopjacszPMcA0KSibAnQBg0EKybIchlOGLMvIi9RTVhjY0p9iUORMDQZsKYYUhULnmqlexmDQQyvF7/3ev+TzX/gtFp7dD6EFFDNXzvCv/82/YjA9lb7vQCCm6TWTpsaUSidfHlbqf4L1y+MV+oP0lAkhhBDilUkCMyGEEBuiLEvm5+fZu3cvW7duZW5ujsFgcLYvS5xlk6DFHzFN5kPAhTXTZDGiUOnUyrqhCYG2DnifpsnaGMFHXHR4b2lax2JTUflA2VjapqEcjWmsp/HgSggqhWRtBXVMAdmIdPJlcJbnf/Zdntq5nYNP/GTdz0tpwxVvfDfXzt7BJTe87biBUUYKyIbAlAIzBT0FvT7oDAZDTa8oyI0iK3pM9QsGOmN6MKTXL9iUFfT6OXmmGQ5yBr0cs6Zz7Oab38nDP/wxDz74h+z51R6uu2Yrd277GL3hMEWPMab1StNNmaFQKq1+aq3TRJlWJ1yjlEJ/IYQQQlyIJDATQghxynbu3Mm2bdtYWFhY+djMzAwPPvggs7OzZ/HKxNlyrGmyEAJ+TTdZjBGlFN6nj7dVTekCrklBWmMtPgIh0vqGECNV61gulxlHTTUqKeuauqlpPAQP3qZDIssK2hpKUlDWkv5cHniOfbt28MwPvkY7PrTu59W/aIZrb/0o19x0G71NFx/383qkbrIhsDmDfDOoFgZDQEF/CgZFn7wwmMwwNZxiqDWbekP6Rc6gyOn3Cnq5pugZ+llOniu0TidZag29TKMUeN1j2113r0yIrc7pBfLcoFFM8i7TdZOdTFAmPWVCCCGEuJBJYCaEEOKUVFV1VFgGsLCwwLZt29i7d69Mmr1M59vU3rGmyQC897gI3sd0QCOgUGkd03nKtqZtIDhorcOGSPSp7N/6lkBk1LaMyoqR9ZTjkrZtWa4bbBeQGaBs0mTZqAFLCsgsMPaOFx79Pvt2bufFx3+w7ueltObyN7yTa2+9g0tfcxPqBGX4Q1bDss196A1AR8gyUBn0pxW9LCcvDEWRMxxOsSnLme716XUrmf0sT0FZnjHs52Q9Ta4zQohEBUUXlIUY8Tam0yyNXgnKFIE8M2iVp+kwWFm5nIRqJ5oOk54yIYQQQggJzIQQQpyi+fn5o8KyiYWFBebn57n//vvP8FWd/86nqb0QYwpzOLzjynp/zGmyALimZdRaXKsILtB6j/WBGCLOORweGzy2dRyqKkatZbS8TNO2VC7g2/Q1vIOqBt9C1aRJspp08uWBQ8/z9O6v8vRDX6VZPrDu59XbfBnX3vJRrrnlNvqbLzvu52lSgX9BWr/cMgV5rwuYuvXHYgDDXk4+6NMzMDWYYrrXZzrLyU1O3s8otGaQ5xT9jH7P0MuzroBfE4hkWTdBFgPOrxb1002VGR270v5s5XWZnHppummylwrKpKdMCCGEECKRwEwIIcQp2bNnzyndLo52PkztHW+aLMaIDal7DLrVwK7Q3zpH6xxN6/FW0TaONkZCN03WOkvwLU4Z6tZyqK4YVw1lWbE8KnFAsGmarK3T447Hqci/BBxQB8++x3bz1M7t7H9sdxo/Ww+luOy1t3Dt7B1c9rpb0cYc91N7pGspgE3A5s3QK1LgpBRkJgVlvSKnPxxSqMBFgyHZoM80iqn+JpSBwhgGhaE/6JEXaQLNaFa+tulOrwyA92El+KILsIwGTURrs/I9V6RAzei0gvlSa5TSUyaEEEIIcTgJzIQQQpySrVu3ntLt4mjn6tTeJCQ71jTZZO0yrNnlixGc97SNwwZH26bTL2trca3HKwjW0UaHDw4fFMtlSeUch8YVTV2zWLXQ9ZNBOu0yBqiWoQ6rE2UvLB/gqd1fZd/ur1AvvrDu51ZMX8Q1N3+Ua2/5CIOLZ074uYM17zcrmJ4Gk3XhElAU6eTLYpDT6xX0jGJzr0d/OGBaaaamNqUVyUwzzNI0Wd7PKIzBZBqjDYrYdYx1p1zGFIPpydga6etNArE4ScniZPIMsi4oO9Ea5fGCMukpE0IIIcSFTgIzIYQQp2Rubo6ZmZljBjwzMzPMzc2dhas6v51rU3uTUOXIcCXGiAthZcpsZZosRrzzWOeoW0/wiqa2tIBtPd6ntzZaSIdfcmA8ZrmsqKxjvLxM5VKnmQpp5VIrsC3YBsYudZNVIfDEnh/x1K7tvPDzvyCGdU6TAZdsfQfXzd7B5Tf+Gtoc/59FWfemgWlgqGHQh6KXri0zoEwKynqDLPWT9Qs294YM+gVDZZjqD1Fao4yibwz9fkY/z+kVmizPVkIqZUArvfK9VCnyYlIAZxQYMynsV8Su0l+h0CZd4+QEzJfTUyZBmRBCCCGEBGZCCCFO0WAw4MEHHzxu39bZXh08H50LU3tHTpONxyVf+tKX+NUTe3nV9VvZducnyPP+yumLMaZTMOvG0jhH8OBcpG4tzgZcjHjrcDHgo0NFTW0ti3XFctVQliVlVVO1oGKaKLM2hWRZgKUSrAIXYf/oEL/4wdfYt2sH1cHn1v3c8uFmrrnpw1x76+0ML736JT9/irR6OSCV+U9PQ9ZLwZVWoHQKznq9jLxXMOxnTPWGDIqCLXnBsNfD6ByvAoXWDIcF/SLD5JqeydE6YrQGHVNgNhkVA4grI2IpKMtU10umD+uGU7q7npdYv5RCfyGEEEKIkyOBmRBCiFM2OzvL3r17mZ+fZ8+ePefFiY7nsrM1tbe29H1tsLJ7907m5uZYeH5/GqNCMfN/v4zf///9G972tltwPmCto3WeGDW2DdTW0bYeHyKtbXEqomPq4Br5htGoZKluGS0vU1mPtSmw8Q24kAKz0MK4TX93MfKLvT/hiV07WHjkO0Tv1v38Lr7+LVw7ewczb3oPOstf8vOngJwUkvWB6U2gMyhyIEDRB2NA55pev8cwN2ye2kSRF1yS5/SLgizrEQhoDVO9Hr3CUPQzMjQm0xSZ7ib3AlnXQUaMxKBSiKVVF4Sl4v61rxWk1cu0mnni9Usp9BdCCCGEWB8JzIQQQmyIwWAgp2FukDM9tTcJx44VqozHY+buuoeF/QdSWhQBpVh44QD3ffbzfOtPv0Oe9wkeGu9omxSeNTaddBlURKPxreNQaFhcHjMqK8pxxaiOqY4rQlulYCyLUJdp5dI6WC6XefiHX+eJXTso9+9b93PL+lNc/Y4Pce3sHUxfft1J3WeaFJDlwEDDYAqyDPIsfW+KAvIiTcIVUwOGRcaWqU30TcZ0UbBl2EfpHB8CSkemsqybPsvIlKLIc3Kt0mmhIZDnBs3kNNH0/VVakWk1qSsjm5yGuVJWNgnK0smZJwq9pNBfCCGEEGL9JDATQgghzkGne2rveNNkEyEEnA/Mf/FBFl48mCbLlGJlpAnDwovL/NEffpOP3HEbjfXUjaV1Fq/SA6sIjbeMbctSOWZxcUzdNDQuTZBFB61LYVmRpRMwRwoaG3nyqZ/zyM4/4tmffpvg7Lqf35Zr38C1sx/jyrf8Oibvndx9SKuXPWC6AFPAIIe8B85C3odeltYds2GfYZEzPZxiymRMFX02DXJ6podXiqgi/cIwyHNMpskLTZHlZAa00bgQyDJNhlrpKouk4MuYSf9YKu5HQVw5jTR9TjoQQL3s9UvpKRNCCCGEODEJzIQQQohz1EZP7R0Zkh05TRZCICrwLuC7j+99ck8KyZROfVpZ92dS0/2jB/dw66jCOktUCqMUxEjtGg6OxyxVFdVSKvNvA8Sul8xa0B6CBhvBOVg6NOYnP/omj+3awWjhiXU/P9MbcNXbPsh1s7ez6cqX7nkzgAc2AQUpKBsa6E1BrlOBf4zp6U5PQd4zZHnGoF8wNZxis84oen2mc8N0f4BXmhADuVb0iwKTa0yh6ZmM3Ci00cQQCTFSmFTYH4grJ1tmRk/qyjDdCmYIkRjT504mwtYGZsd7naWnTAghhBDi1EhgJoQQQrzCvdQ0WYzppMsQwYUU4MQYcT5w1cxWUHkKzUw39mQMmAxMxpVsxXoPUdEGRxssBxcXWaobmqqlaR1t10fWWvB1d6Ik0Kg0Zfarxx/j4Z3befIn3yLYZt3Pb9NVr+G62Tu48q3vJ+ud3ATepMR/8tbLYHLXftFVtWkY5pAPMjJjGA769PtDNmlNfzBkyhimezmYnEBEG0Xf5OjMUBhFv5eT5RqlNHRnWRqjMEbj0w4sWilMprvwK2J0Cs5W1mRZnSKbvD/eKqX0lAkhhBBCbBwJzIQQQpy0siyZn59n7969Uux/jnupabIYI4FIDKlHK51yGfE+hWfWOawNvP+DH2Dmmsu7tcwuOCtyUIaZSzfzztveTuNbSluzNCo5dHCZJnicCzQNNA3YALqBQAql6gbKccUju77FT3du59Czj6/7+em8x1Vvez/X3noHW6553cndp3s/IPWU9TT0DBSD7oRJA70CMDDoaUyuKYqcQdGjNxiyyWgGgymGWtMvMvIix3hFNAqjITcZxkCvyMgykzrIupVKozRap/VLH1JQlor8U5imdVq/VKTbY1ydNIPD+8qORXrKhBBCCCE2lgRmQgghTsrOnTuPW0I/Ozt7Fq9MrLU2ODneNFmIER/AxxSY+RDwLnWWWe+x1uE9EBUhaP7e//A7/PX/8m+x8OJiaroHZi69iL/x1/4fLFY1h0YjqqqhcR4XAm2dgjLnANvVnvVSb9njP9vDT76/g8d//E1cU637+U3PvJprb72dq97+QfL+1Et+viKV9wMMuzejYLoHppcmzQLQ76fOsl6Rgq3+oEc/yxgMppjKcga9goHJGRYFJlfkKgMUMQv0MoPWiiJXFEVBbhTKaNCgw2pfmA8RTZogM1oTiV1PWQrOQoy4cHhQNvnz8dYvjxeUSU+ZEEIIIcSpkcBMCCHES6qq6qiwDGBhYYFt27axd+9emTQ7i15qmgwicRKShUAEvI+EELEu4EM62dL6gLeBGBSVbWmco2parrn2Rv7X3/3n7Pzmj9kb9jDDVt5467UsN4FnXthP6zzWR8ZLqZPMl10I1YNgoKoaHv7zb/Pw9/+I/fseXffz01nOzFt+g+tuvYMt1914UhNTmvSPnLx765Mmyvo5FD0wXQ1b3ksbpv2+BhWYnh6SK8Wmqc30TMawnzM0Bb0sw+RQKIPWhqADRZYBhjyDflGkEv9cE0JAA5oUnPnoUWgykzreJpdfmBSUxRjTKiyHT4QZPVnFPPb6pRT6iwmZ/hVCCCE2ngRmQghxmr0SfpGZn58/KiybWFhYYH5+fkPL6c+k8/X1OTIYO3aAEvEhEKJKU2Q+Fc57161deo+zDhsioU2P17Qt47ql8Q4bu+knpSiKHm9+341cu3w9pbXsX65wLtDawHgZIuAa8AGGU6nUf9+vnuJH397Ooz/8OrYer/s5Di+7lutuvYOr3/Gb5MNNJ3WfgnQtQ1YDs4FJp3AWw3TQgM7S9+0XBw9RspdL2cotr9/C9KaLmB4MGWQ5RWYYZjl5ltHLDbkx5CbHG48KkVxnZCpS9HKKPCPLFEp1nWTGoLTGBY8OEWM0plvBjBEyo9L6pVIr67Bry/gnU2fr6SmTQv8Ll0z/CiGEEKeHBGZCCHEavVJ+kdmzZ88p3X6uOh9fn7XB2LHCE6XSaZeh68ryIeB8JATwzuN8xHqH9wFrA96lO49tQ91YGh8IIZJlhjwGyrpksa0ZjSvGVUUIisY6ynHEe7Bl94Uz6PWhrC0Pffs7/OQ723nuVw+v+/kpkzHzpvdw7a23c/Gr33pSAVDXz48mBWWadOLlIEtBWdYH5btzCwrYvwBffuB/ZoEDQIS64hv9If+3/+t/wbU3XkpPG/p5TpEbMm3ITUY0EUUKypSCojAUWUaea/JcgdaomAKr0K24GpOmyiZBmTGKvAvKQgi4EFEcPhFmNF2v2eGk0F8ci0z/CiGEEKePBGZCCHGavJJ+kdm6desp3X4uOp9en5OZJlOk6THrU6/VpLx/Mk3mfMBZS+sCzqd1wbpuKL2jadOUmVIKoxVGwdJ4kRfKCte2tK3FBagqRzmG1oG3YCKYfuoEe/7ZZ/jht3bwyENfoymX1v0cBxdfybW33s7VN32Y3vRFJ3WfydplRiryL9Z8bGqYgqQsB5VB1DDogwrw5Qf+CQt+f3oirgJrWRgt89//zt/hn//u/8rm4TSZVmiTpcMAsq68PyoyDf1eWr/Mc4UxihhSx1jqhks9ZXmmVoIvYxQGMMZ065cB4uFB2aT0/1ik0F8czyt5+lcIIYQ42yQwE0KI0+SV9IvM3NwcMzMzx3w+MzMzzM3NnYWrOjXn+utzrJDsJafJvMdH8L6bJguR1lpCBNt6wv+fvT+PsuQ6rzvR33eGiLg3s6owEEiCAEQgKc7igKFIcRYpDgBNCCjZaIuU28vdy7Itt/nab7B6yWpZ0pIld7cky0P305O6bXdLltoSbBZAkARnEANHDBxFUhyqOJMJgAAKVZn3Rpzhe3+cuJlZA4BKkiBQ4PmtlczMeyPiRpwbzETu2nt/WdGkDDFwZOjpk5KjYp2htcLh2TpHUmT9yAZDHBiGTMjK+pGejRnEUF7XUIQocuRzt3+ET976Tr7x5U/s+BrFGM56xk9y/qWXc8bq84oo9WDbLtaFIowxfp5SHGZ+fG73chHwMOCacs7TFiYeXNfyuc/fxdrGt0ADDPOyWAA6sPatb/PxWz/F697wKpwzmEYwWcgKFqFpHE1jcU5ovAWBfmPGW69/B1/75kHOP3eVn7nicpaXl0tEUgRvilAGY3/c6EJjm87lDJgHcZXVnrLKQ/F4df9WKpVKpfJYoApmlUql8gjxePpDZjKZcP311z9ofPGx4sTaCd/P+/NI9p49nJtMpDy5mKiYshJjKqJZ1LGrLDPESBoSfVYkCzlGZjmw3g/0cezJErBOODLf4J6NDdLQ04fIrA9oho1Z4PAGpH4UyZoiSt1/zxqf/OC7+MxH38PsyH07vsZuz1mcd8nreNLFr6HbfeZDbuuASHl9KOLYZPwwjNcAdEtlW3FFzNME7QROWwLTtbTe0Uw8hzgA4QiEOaiFLJB7SGVRvjE/QNO9GoNBc3HitW2JZjoPbeuwRlGET3zidt74xp9n7e57yxuWIr/2q0/gmv/8F7zw0r2bQllxiCmwVfgPDx2/fDChrPaUVbbzeHT/ViqVSqXyWKEKZpVKpfII8Xj7Q2bv3r0cPHiQ/fv3c+DAgVOqIP9EfK/vzyPRe3YybjIzaiRhjFrmPEYtY4lfxqzEGAkxkRLElJFcJmEe7udshEjWIjJ5K/TzGQ+ocvjIEfphRogQYpmW+cADkdmsiDPWgXjIKfGlz97OnTe+g69+4c4Tl2k9FGI462mXct6ll/OEp16MGPuQm7dAT3GR5fF7DyxTrsEaMAqTXWWDpi3bkco5T5ZgMunwRmh2LzG1juVmwtNZhXmiyGwRhr5Y0lQgJlb3rOKwqBEc0HiHbYTWexoPIOQszNc3eOPP/zxrd30XshaFDmVt7Ttc/bM/y5e+9GW6riOPQtl2S9mDlfrXnrLKTnk8un8rlUqlUnmsILrT/+Ct/MARkTsuvvjii++4445H+1QqlcoPkNlsxoUXXvigf8g8ljqyfhT5Xt6fH/R7emw31UO5yZIWEax0k0FORTSb94EgSp6XCKaKkPvAgLLR98xiAjGYnDAizMLAoSEw2zhCHwIxwTAEhhA4/IAyn4N3kHNxlK0fuoc7bno3n/nIuzly6J6TvrYF7a4zOPfi13LuJa9lctrZD789RShbZkswc8AuRpEMQMokTjI0HWgeHWVdcZUtdy3GGtpdS+zxns53TLxHrCDDnP/67/8d1r7xjXIgYyAkQFhZ2c27r3sf7WSC9w7fGLwzNI3FC0SVsu4Kb/kvb+Ef/OLfhxyBUTDLefM6/vj/+hN+7k0/d1yE1siJXWW1p6zyvXIqDi+pVCqVSuWHxSWXXMKdd955p6pestN9q8OsUqlUHiEejzHGxxPfy/vzg+g9ezg32fZ6qzg6x0rMUsePTNIicsWUyeNjOhb7z1Jg1gcGVYyCM5Y0zHkgRR7YmBNjzxBSEcpCZGO9ZzYrJf5qylTJmDJf++LH+cQtN3Dgsx9DtwlBJ8uZT7mI8/ZezllPfwHGPvx/biwmXS66yJQiki0Dzo09X7YYwRoHri1CWQ7QTKHxsHs6CmVLU05rWtq2YeIaVDKtdagRJrv28Du/9Rv8k//hl1n75t0l7ymRlZUn8G//zb9iaXm5CGXe0HiPt4qKIaRS6C9jWf/Xv3mgvLhmyGnrQqT8z8GvHDhKALOm7Hes+PVgQlntKaucLI8392+lUqlUKo8VqmBWqVQqjyD1D5nHNjt9f77X3rMTRe1O5CYTSjH8djdZzpCzEmJmCIGgmdxnMgJZi3AGzIaBeShCWWscXjPzGHhgGNhYX2cgE/viQpv3cw4/kBj6kibMGSYtHLr/Pj79offwyQ+/iwfuPbEw+FD4pT2ce9FrOO/S1zE945yH3d5RYpRLFN1qsRx7KB1l3oNvwOYi5oktcUsjRatyHSzthmnjcW1L07XsaluW24bWdYhkNPZ87P2f5FvpIBdOVnnFa1/MM378+bz1z6/jlhs/zLcPHeBJe1a57HU/xWTXEm1rab3D2PI6MUHSbVMtRXEOnvLk1eIu23xTt0cvhQsuXN18Xx8sflkL/Ss/KCaTySkzRKZSqVQqlVOFKphVKpXKI0z9Q+axzU7en532nh0rijy8mywX15gKKWby6BrrhxKd1JxRICwmYMZIiJn1oQcxeLFI7Dkceo7MBjb6DVQsfR8IQ2R9Y86QYH64lOOrgqB86+Cn+PgtN/DlT32YvN0tdZKcfuFzOf/Syzj7mS/COP+w2y/ayzqgNH8Vd9kuoB1dbtYXoSwr2LY4y7wZt/WwvJvSK9Y1tF3LUtux5C3TdgnVTGuELxz8Av/sN/4pa9+6qyx0tqz8H7v517//e1z67Bdx5VWXgyjGWJrO0FqHMSBGMWIJKZfifxFUFGOhsRbnDFf/9av45f/hrC3HoRgW7+bKyllcddWVJyz1f7CeslroX6lUKpVKpfLYogpmlUqlUqmcJCdTsL0QRNbXN7j22mv5ysGDXHDhKj9z5ZVMJpPNHisocbwwimIxZpQilMWkzIeBoIoMShZIIRFyIqTMEBOzOJCSYo2jMZZZ6FmPkSMbM+aSyUHp54G+X6cPkfm8JAf7UJxbhx84xOc+9j4+ces7uf+eb+14LfxkF0+66Kc579LLWHrCeQ+7vWNLKGuAga0IZgNMfBHLbAMSi/7UtMV55m05d7Gw1MF00tK0nq5t6bqW3V1L5xtyzFgjNLaMyfxn/+Mvs7Z2d7Gl5QQpsfbtu/jv3/yPed873s9k1zKNNzTWYp1FyaO7SwgpI1ocZSKKt4bGG5wxiAhuMuHaa6/lqquuYu2urW63lZWzuHb/fnYtTU86fll7yiqVSqVSqVQee1TBrFKpVCqVk+Shes+ue+tbadqOrHD77bdx5ZVXsnbX3Wy6js4+i2uv3c+ll+4l5UxIW26ynEpPWQyJjSGQEpAyGCGGyGAg9IGQMhsxIIDBkIis9xscns3pYyBliDEyhMBsNmceMv16cTOlDFaUtYOf5c5bbuALn7iVnOKO1+C0H3sW5+29nJVnvQTrm4fdfuEigyKMLaKXS5SC/8YVsaxpIA2lq8zvgiGWGKZRSArLE5hOPW3XMJlM6BrP8nTCxFhEBBHLdOKxYvDG8d533MjaXfcVNSpFNr1sqqx9+y7e//6b+Jt/8yqstWQSIooVQ8q6WaImVnGmFP97I5jRLSYoinDJpXv5qy98ieuuu46vHDzAhReu8rM/exVL0+lRa1CFskqlUqlUKpVTjyqYVSqVSqWyA7b3nn35ywe4cHWVq666arP3bGNjgyu3u460FOav3XUXV+37WT71mc9ifVu64nMmxMQ8RGJSJEIWJY9i2hCLo2yeA5oyBosFZmFgPgRm/ZyAErPQb8zpY6QfetaPFJluHsAqrM+O8LmP3sgnPnQD937nazu+ZtdOOef5r+K8Sy9j18oFJ7VPO35eiGM9xVG2THGatRZ2LYGzQCgGsHapiGMhjtMwgeUOlpYanHe0XcvuyaR8dg4xlqzQWouz0LgWJCLA1+YHgAgplZPIWt6LqCCJtbsPgCkRSTt2jKVUhC2xihODs4KzgrfFG7cQyrIWEQyKiPrGN/4cRuQ4Aaz2lFUqlUqlUqmculTBrFKpVCqVHaCqtF3H3/y5Nx712MJFtP/a61j7zrbIpkgZ74hl7d5D7L/27ezbdxX9EJiFRM5gsiIoQyxTMfswMMTEXBNGDRZKrDIP9PM58xjpUyn4mvdz5sPAvA8MG+XlQijizne++gU+ftMNfOETtxBDv+Nr3X3uUzl/7+Ws/MTLcU13Uvt4iti1iFvm8es9jEKZh8kErICTMp3TTEBS2RYtkdGJh27icM7Rtp4zdu+hbRwTa2m8J6RMZ2yZZmkaVBOqAVFD4xyru1ZBU1HgciyfRcsZZeG8c1bxtsQwUUNKipgiMFpr8Vbw1ozutcX7LEe5xRa9Y8bIUQLYQwlltaesUqlUKpVK5dSgCmaVSqVSqTwMD1bUvv3xRTfZVw8eGJURU4QyY9mcnijCF755gHsfmCMZECXnIpQNYaBPiYAiKaNYXIaNMCcm5Ug/K11nCqkfmKXAbGODIShDX8xTIcAwbPD5227izltu4O5vPfRUzxNhm45znvtTnHfpZex+0o+f9H6erf+oaCimLkeJZApbQpkZv84RZNxJGSdgKkyXoG0tTdvRto7Tp0t0k46pczS+YQgDIpZdTYuzgsGQNWDE0pgONRlvDa+77Kf43X+9i7XvfJfNjKUCYlg5+zSuuPKyca6lJUvGAsYUp5q3BmvMNqHseBFs4Sbb7ip7sPtk+/aVR5eNjQ3279/PwYMH69TiSqVSqVQqD0kVzCqVSqXyuOCR+EP4RALIsSLZwmGkWnrIzj1/FUwDY4yvbGTBODCG809fRXIiZEghsx57YsyIQM5AVjbiwJCVEAJDGBgyhBAJMbI+3yCEQJzDkCk6kMA3Dn6Zj99yA5+/8yZCP9vxte564oWct/dyznnOT+G66cPvMOLHD6H8R0Uev949Pt80MJlCMwpiuRi6oBk/5+I2mzRFSOuWlvHOcNrSEpNJx5L3NNaRNJNyZtm3OFdGfGYElUgrDWIE68CZBueV3XuW+T//w7/j7/zt/4a1u75bzkqUlSfs4c/+458wnSyTtSygsxZjoLEGY6RENMtbAXCcq8yMrrPtrrLaU/bY57bbbjth/+D111/P3r17H8Uzq1QqlUql8likCmaVSqVSOeX5Qf4hfLJuMjuOukw5E2OijyVeedlll7PypLNZu+v+Mp3RmqIgKaycfRovf+XLOLQ+o4cy1XF0ow1hoI+B2RAJORBjJiCkkJj1czZmR4gKw2La5Qwycz7zkZv5xC3vZO0bX9jxuhnf8sSfeBnnXXo5e8572kkLO5biFHPjx+ifo6O4y+xorJuOQplfrKGFZMrzMYJX2NVC1xq66RLOCruWllnqGpbbjsYICUUFOttgbWk2UwCrtOKw1gOKNxbbGCadwVmHiHDRc/fysY/czjtueBdf+9YBnvykVa684vU00wmQsaYIZM6As3bzrYKx8mybq2x7nHK7CPZgQlntKXtsMZvNjvsZAbC2tsYVV1zBwYMHq9OsUqlUKpXKUVTBrFKpVCqnND+oP4RPJHw8lJsspjSW8iuqpTA+pUzIwv/3j/6Qf/jmf8zad+6hyDuWlXPP5Ld+839hIyZEDORMVmUeBoYQGRSGYUZISkzl+P3Qs7FxhCGUCq7YAwLfPvhV3nvjdXzrkzcT5/Mdr9nSWedz3t7LedLzXoWfLJ/0fotesoU4Zsara8fHfQPeQdsWt5jRsm0G5hm8LY9phNOmZUJmt2s3TmCpm7I8bZk2LUtNQ9CIYGgRnLUggqaMswZjFCctxijGgPce65WJ91hryJQ3TBW6yYS/cfW+UegqApYxZc5oiV/aIoAWNazcA3r0/bAQyLaLYLXQ/9Ri//79x/2MWLC2tsb+/ft505ve9EM+q0qlUqlUKo9lqmBWqVQqlVOa7+cP4Ydzk8GWSAaQUiJkZRjdZJrHx2JmCEVA0wzPfNolvOvad/Ge99zKFx84wJObVV746ktYapaJKTJLgWEYGJIypEAcBqI45kMkxcisn7HR95BgCKXvqx8GPnfbB/noTTdwz9c+u+N1EutYefZLOP/Syzntyc/eUUzQUVxlUMSxhavMAxNXRKK2hcYXQ50bn5tFCFJK/P2ornWuTMOcLi3hrcU3DWd0E6aTCdPGoyQSMDEea2yZXqngrSAenHoQcL4Iad4JTWtpnCczTrlU2Zy1YIzZcoaZ4oWzBpwRrDWYUeDKi5qzY1xlmyLbKJo92D1TC/0f2xw48NB9fg/3fKVSqVQqlR89qmBWqVQqlVOanf4hfLKRy4X4oaqElAgxEbOSIuRcplnGITEPkZR1jCUWsaWfB3oML3rlS3gxL0UwzGPPoX6DECJDVmI/IyIMShHKwox5v04fMjGW6ZEpw93f+ia3f+AGPnvb+5hvHN7x+kzPOIfzLr2cJ1300zRLe3a07yJ2mSlRy3Z83DM6yICmK/1jZpx66QQ2htFR1sDElt6y1kLXGSbLU5xxtG3LbudYXpoyaRqERBJlIh6xFrQ494zAxJaBCSIN1klxhjmH64TONSiKakZVihPMKMZsOcIWUyyVIrxZY0YBrLzHx3aVbRfKtn9de8pOXVZXV7+v5yuVSqVSqfzoUQWzSqVSqZzSnOwfwg8WoXsoN1lU6EMkJ8i5lPqHobjM+hCRhbiWMkNW+jAQk6KqCAZUOJIiaSgl/jknZkOPiqNPEIaeeT8jpsAwZOazjAAxBz7z0Y9w58038PUvfWrHayLGcvYzX8R5l17GGRc+FzFmR/svopcRmFLEMhkf75rSSyYWpl15vHHluVkPvSvTLzs/ik0Ku6bQTjuadkLbtCwby9K0Y6mb4AwkUax6WmPKK6kgCNZmWt+SUJwIzhmsNfjW0lqDsQbNGZVydmYUygCMASO2dM2NopdbFPrLWCqHHDcB00iJ+V577bV85eBBnvKUVa686iq67vhYbxXKTh327dvHysrKCd2oKysr7Nu371E4q0qlUqlUKo9lqmBWqVQqlVOah/tD+MqrriIdo5Rtd5OZ7Q6inAk5E0IiqRJDEclyhiEkYkj0OWERJGdiKvHMlBIJQTUjapingT4lhhAxGDbmG+A98z4wj4k0zBjiQB8jYRbohyLf3H/Xd7jtxnfy6Y+8l40j9+94LbrTTuO8S3+Gcy96De2u03e076KPrIMSiRy/Vsp/LDS+OMmsL5MvrRnnGQToAyQBGcU058pBllrBNy3NKI5ZEZanE5a8Y9q0ZMlIgiXbkEUgK6KgNtHaFiMKqnSNRazgnOCtpfEOI6DIZkv/lnilWGPGrrNt349C2dHy6JZrbLH/7bffxpU/cyVra98ZNzOsrJzNddddx6WXlgEStafs1GMymXD99dc/6HCQWvhfqVQqlUrlWKpgVqlUKpVTmhP+ISzCysoK11133VHOoJPpJktRSVnJSYlx4SYLoFKmQ8bMPCshBDKQkyJiiGlgnjNDCKQMOUZijgwZ+qTMZ+vE0JNyZDYPxHlxsOWc+PwdH+Nj77+Br37x48dnRR8Wgd3nwRlP42V/5+8gZmdC2cI5tphk2Y0fEfBSnFqTFrquRCyNFEdZHiCksp3a8ryhiGiNwuT0Ka7tmDQNzgjLTcdy19B6h1gQzTQ48MW9Jypkq7TWYsWjKN47jAjeGZwVjLf40S2Xc3kvjdkmlFk79pIJUuQ0nDVYY0YXmR5X2r994ul8PuPKn/mZch/Jlitvbe0urrzySr785S8znUyqq+wUZe/evRw8eJD9+/dz4MABVldX2bdvXxXLKpVKpVKpnJAqmFUqlUrllGfv3r0cOHCA/fuv5cCBA1y4uspVV13FZDI5XiQ7xk02DImomRQhpUwMRchKITFowmSBnIki9H3pK4ujKylrJmpm1s/oY8SoIcSBaIR5PzDLGU2R+cY62Qh9H5hvJDLwwHfv5s6b3s3Hb30XRw7du/OLdlM446nlwy+xAictlhlKL5mnlPkLJXa5CwgUl1hnSk/ZdArWFmHKj0nGEGBI5RiTJUo01cBEhG6pwzUdXdvSWMOSa7DecPp0gjFANnjKuMysYNWQJONNxtkGTMY6U4QxQxHLXIlWWmsYu/kxbIlW1gjGjNuY0nsmxuC2CWWL9377/bA9UikC1+6/lrW77jpKLFuw9p3vcN2119ZJiqc4k8mkvoeVSqVSqVROiiqYVSqVSuWUYWNjg/3793Pw4EFWR1Gsm0xQhbab8HNvfOPmtguhBDjKVRRTKe8vUUolpeImKzFMGGIgxyLLqArzGIgpAUJMGUQYYs+gMAyBGBPWGFKMbKiyPu9JWUlhIBth6Htm6z19D+ISX/zEnXz0xhs48Je3o5p3tgAiPOHHLyYMuzm067xNYWcFeN7VVz/s7guhbPHL31GK/JcZo5e+lPbv6qCZgFUw44RLjcVNNhSzHb4r21ordArdrinWOtrJlMZAI5amaTht2uG9wajBiUVc6XdLWUdnV2biPc4ZBMWJA2ewRoo7zFm8W7jKMojBlvRmidOa4j6z1oz9ZGBFxjL/o+OWx34/LimLRrMyIOIY99i296hOUqxUKpVKpVL50aEKZpVKpVI5Jbjtttu2YpdSZlIe2y31UG6yqMoQMiGV2GXOuukmiyGSUDQpOSWyGHKIxFSOlhmdYyh93zPPGYslxkjSxKHZQK9KiomUAkmVMARmR+aEDEfuv5c7P/Ae7rjlnTxw3907vvZm+TTOvfi1nHfJ65icvjI+egg4AKwCDz390lIEIc/WxMuG0lNmAePAWFhqoVseI5q26HE6QNAilIkbhTID1gkt0C1NsM7h2gkTa+jEYpuGXRPPpPGlbB+HGsUhRAXNivPgrcEYj4jirUFFMKI4Z3DWlhimMSiKIFtRSJHxOcEvhDJ0jFcWV1nKuhm3PPb7BaUHjc3C/wuPGiChx8Vj6yTFSqVSqVQqlR8dqmBWqVQqlcc8s9msiGXHxOUW3VJf/OKXmExLD9GJ3GR9iKObDFLMDCmTQyaQkAwhRVQNpEzIiayJlDJJIKVAxDDbmJEZ/Uc5M+TIfbMZyQi5H4g5kRWGvmf9yEDSzJc/+UnuuOUGvvDJj5Jz2vF1n7H6PM7bezlnP/2FGOePeXYPcNFD7r/oJhNKkb+j9JNtCmV2q6Ns9+nFTGVNEZEWjjLNkAVcC86C84apsTRtAxbadonGGqbWYnzD8qSh89AYjxNPJuNEUAxJFTMW91szOsqsABYjYH2JURojeGcRO3bEUUQvkbJPcZ/JWO5fIpjlardK/BfF/Fsuw+NdZcdOTb3qqqtYOfvsrcL/bdRJipVKpVKpVCo/WlTBrFKpVCoPybExyB9mSfbCMfZf3rKftbvu5ri4nAhrd93Nddddxxvf+EaUEsMcYmIYJ12mUB4LIRHHIv+MoimTRSEbNCpDjpCLSBTjwCBKnEf6GBFjAYgpsR4GNkIk54SQiUMixcx8PmP9SGK2cYg7bnwPd37wXdx397d3fM1+upsnXfRqzrv0dSydee6O91/ELqeUa8nAEkUgW6aIZEhxkHUdLO/a2g9TyvyzKQJTSmBdEcpab/DApOswRnHdlNYaJt5hXUPTePZ0FiuG1jQkVQSlNU1ZW6O0rghliI4xyyKCOQPOWqwbxbLGkXNCdfF+SynyH7fzdiGUFeVL2IpbAixMZCnrUUJZOZICcpxYJgJL0wnXX//WOkmxUqlUKpVKpVIFs0qlUqk8OEfFIEcW4sHevXsfsdddTDCEIthde+21R2+wKJ0aFZIDBw4QUyKkTB8zKSSyCofvf4B3vut9fPWeg5x7+iqvfNVLcV2Lxq3pmEkjOUMWpY+BoCV2CQa0RDdz7DkynzMDckwkTZCVYRg4vD4jD8qBz32GO25+J5//+AdJMe74mk978rM5f+/lnP3MF2N9s+P9zbgsU6CnOMqmlI6yliJ6qYXGjELZHtBE6QPL5es0bpdNcV+Jg64VnDEsTZcRAm23jPeOzhicb/DeMXGCbywTaVHRInqpZciJIQc65xBXhDInpoh21mA0Y53FjYX+zhsMSs4Z0XJexpjiFjOLaZkGZ0ocU0eRLG2LTlojmyX/2+OXC81Mdcxhbl+7baJanaRYqVQqlUqlUoEqmFUqlUrlQdiMQW4TywDW1ta44oorOHjw4A9URFi4ybbXRt1++21ceeWVrK3dtSWSse2zCIjlieeu8sDGQM4lchlT5o5Pf5Rf/IdvZm3tHsCAMaw88Qx+73d/h2c85SLIQkKZhznZWuKsJ6biyrIIIUU2wsB6DOQQUSPknMkK89mM9fWe9fsO8/Fb388dt97Ad7/zjR1fs+uWeNLzX8V5l17O8tk/9j2t2yJ22VAmXCZK5LIbH3NmdIwZWJrCntMhJ5BcyvvTHD5+9/3MOMguVnn6E/bgJmVCZuM8y8u70NTj2oaJn+KMxTceL4aus0y8ozNtGRowRi1TygxEmtZvTqp01mCsbJbuGwXr7KZQVjrMlBzZtLtZK4hRrDE0biGUlVhmznqUS2wrZqmbsdwtdJtbbdvaHeM+W1AnKVYqlUqlUqlUqmBWqVQqlROyf//+48SyBWtra+zfv//7FhVOJJItmM1mRSy7665x4/GJsfAfsWAcK088k9e+5rXMZqWkP2dltn6EX/zFN7N2930lgzjusnb3ffy/f+mX+fM/vgZp2iLspIT2iQzEFMmauX8Y6HMixhK71AwxRjZmG6wfCXztrz7PHTe/k8/ecQsxDDu+7j3nPZ3z9l7OE5/9UmzTfU9rJxRBrIXifKOIZJ6xo2xU0hxFKNt9OmgYxTIt3WRf/Sa87Zo/Zo1DFKntFlZo+fk3/n2e+Kw9QMRaYTrdjRWLdZbGWLqJZ8kZvG2wYnDGYkzpgJvrgHeOzjSlgB8tvWSAQTBWilBmwBiL94K1Qk6jACaGIokpxiiNdzRj+b8IoEo6po/fjO8vyjFimbL55m9fOzl2u0qlUqlUKpVK5WiqYFapVCqVE3LgwIHv6/mHYnvk8kTPIbB//7XFWbZAtkQyxIAIK096An/4R39ExJHnZWJlBt71zptZ++79pbhLxnGPo3Cydtdh3nPDHbzs8hcSY8KYErNcj8VNlmJCnCGlhMZEHwMbsxnr9x/hE7feyMduvoG7vvGVHV+zbSY86Xk/xbmXXs7uc763aYuOIoy1FGEsjN8vj993jO6tFjqB6TIs7QJiEcpUSwQzpBJ3LGLZ6MADYJ01PcSf/t//il//57/MZGkPnfV4axDraKxhuTV453FS+sq8cYgRIhExMHXN2FMGKFhbesmMCNbY0n9mDcYJjTNlgmmSUewaJ2JapTGWxgnOlv44IyV6mfPRa7LoJBNkUxfTUU071j22cKGdyFVWqVQqlUqlUqlspwpmlUqlUjkhq6sPLeo83PPH8lBussXzsi0id/DggW1uMlfa58sGYA2vvfxKfu83fxvXtYQYyHl8OsNXDx8oyohxoAayBSdFdXGOr3GA2fy5qMIsBWLKhJzJmkANYWPOPAwM856Dn/0iH7vpHXzmtpsJ/XxH1wyw55ynlG6y57wc1053vD8UJ1mkCGWLUn8oTjK/eFzAt6WjbLoM06XSS5YT5AjWl53VlImXHzt4iDW+S1EVe9AeeoU+subv5kuf/A4vesVZiPW0ztJ6ZbnpQMuUS49DXJkMkHOm8R5jyuTKEFKZetmMUy/Hwn6/cJdZ8N4SYgaK+6yImop3gveWxhpEBBHQnAn5BOKXgGyKfVti64kEsQeLX1YqlUqlUqlUKieiCmaVSqVSOSH79u1jZWXlhLHMlZUV9u3b97DHeDiRDEBR0C0xI49F++edvwquA6QUcFkD2GKRQnjtS1+LaRwpZySXFxlSIio8qVsFacu+jqIWeYEoYOAsVjmSBvKQCUbJKaFJGIaeed9z5PADfOKmm/nYzTfw7a9+acdrZ33Luc95BeftvYylJz31exJqFpHLRLmExRgAw+gkA1opHWWuKZe3aze07dYxci46I7587xz0oQQV5xwAhpLT7DOkUN4oKXa073AA5y9hVyt0TYPBlAmY1iHOYgwkAtZ5nAhODEOMqGZca2mNLQMAxGAMeO9Kqf8ogA1BMQpIKe+3TmlcmYBpjRnXYFHqf6xYpqOnbOvxRdG/HBO/rEJZpVKpVCqVSuV7oQpmlUqlUjkhk8mE66+//kGnZD5U4f/DC2WbhWTI2FeVcibEIniFkHj1a17Lyjlns3bPfcVhZij2KE2sPPEsfvq1L4MsqBZ3WIzKLAVSzux9xcWs/MlZrN3zAJhMkZ+KaLay+0ye/YILGGIEhP7IjCTKMOv52pe+wIff804++dH3M8xnO16z085+MufvvYwnPPeV+MnyjveH4veyFKHMAkvj18KWw6wbk6lGiqNseRm6yZYhL6eynfECWXHe0PeZBDQOuumE81mFfg5pKPY1k4pohgNVLmCV05daOlOEys5ajHUl4UoiGcGJpxFL1EwfEsYok8YjdoxYGqHxFueKaCZGyFkRzNghJiBK4w2NLRMwF64ygJSPv4eMKCJbrrI8bnBsJ1ntKatUKpVKpVKpfD9UwaxSqVQqD8revXs5ePAg+/fv58CBA6yurrJv374TimUn6yaT0QdUtlfSGIfs+1S+j8owRAKef/tH/z/e/Ob/B2vfvm8s4IKVs8/i93//d7HWE1JmPgQCiZQVkhI0g2n55X/yG/yL3/nnrN1/iEX7/crS6fyjX/gnZPH0RzYYQmQ+O8Jt77+J2265gW98+fM7XiPrPD/27Jdy7qWXM/mxZ37PbiZHEbliOVv2UKKXi46yDEzGZCoKjS9l/tMx5WksxKEcSKSIVQYYEsSY6bzQTDqccXhrueT55/C2a1rWjsxKZlNscfINM1Z27eY1r7+YiS1TLhvrERHUJqyFHIWpa0g5M08J1cyk9TS+PCYiOCc0jds8lzyqX2V1SsGZdYbGCM65TSeYjBMts+ox95KOzrNyhEUP3rEOsiqUVSqVSqVSqVR+EIg+1F82lR8KInLHxRdffPEdd9zxaJ9KpVKp7JjjhY1j2XKTLbbPWYkpETKEmMihiGYhZGLI5ZiiaEzM+zk3vvdWvnroAOcvr/KK1/wkxnTMYiCpYjAMKY6CWaLPEcEw6+ccHtb57Ee/ypc4wLms8sznnI1iCEPirm9+lZtveBsf/+D7mG8c2fF17z7zXC7ceznnPv9VxOnuHe+/wLPlKFsavxeKcDYZV88BbVc6yboWugaWd4EKOFfcY9aBFYN1hpQzKWSSQCPgpxOcmNIz5gy+sUxsx1e/+HV+/9/8C9aOfBeGAOuJlXN28Vu/9S943tMvorMesQaxGWOKkGWdw2ToYyKGTNsI065FKQKWc0LbeIwUkc9uClqyeTcYUZwztM5tE7hKef+xAyG2usq2hLLF0+YYoawW+lcqlUqlUqlUtnPJJZdw55133qmql+x03yqYPQaoglmlUjnVeDg3mchiUuF2N5CSshJSJoRUvo9KHyJDyOS02D6TNEMCYwwqmRQSCWHeD0TNZBU0JaIIKSVijMScSCkxTwGMQxX62DMkJc7n5JTo53PuuOVGPvz+G/jKX31mx9dtrOOCZ76IJ++9nN0XPIfZDsWZhSwERRhrgB7YzVi1Rpl82bIllDUdeFsq3FoHu06DrNB6Rx8jqtA6i7GGFOMYY1ScCH7SYY3BW49tLL4Rlu0Ei2C9BXFInnP7+z/DtznABc0qr3jdCzh9+fTSK2cV58bOMHEYNfQxEKIiKEudxzlDUrBWaBtbRKxyA2BdcYQJxTFmUIw1NFaw1m5zhz2IWIaOEc0tsVVP4CqrPWWVSqVSqVQqlRPx/QhmNZJZqVQqlZPiZCKXCzlIVVCFrJmUlZgycfycQy4iWVJSyAupBM25dPKrYBCyJPqUCCHRx1QmQ46Ry5gV0UzIkZyUeQrl+WzIahhCT98H+n6OZrjnO9/k5ndcx+03v4eNIw/s+Np3nb7C0y69nPMuejVx+TQCsJOGM0dxjMn4tQcGioNsiSKU5fE5ofSMtS1YO3aXedi1p3STNd4zD4F5inSuFJlpzsSYiEmLeNZ4rLE0rsFYoevKVMuuacaJlR6jPR9578dZ4yBP6Vb526/5rzitOw0xBiz48WQcFlUhxsQsFlFyuXU0nUcRkmQ6Z3HOwuI6rGAwW/eKKM4Kzhq8tZuuMt3mPtzuVDSykNCKWLZ4rgxIrUJZpVKpVCqVSuWRpwpmlUqlUnlIjnX9HMvRbjIh50waY5chJULMJYIZM/MhEmJGMxgFFSXnVKJ+YjAkQBmGxDxlhpTImSKOASklUi6OsqyZeYqoseQMMQVmoScOuQhIw8AdH7qRj7z3Br74mY/v+LrFGJ789Bfy1L2Xs2f1+cyN2ZFIBkUIW6zMYtJlz1Yn2SKOuYhktn4UylyZcOktLO8Gb8D7hrkOzFOkdQ6sQTWDQtSEM5blboqIYI1DrGHaOVrrabzHG4NiaBrPl7/4eX7zt/5ZGeaQACIr/+FM/tW//D0uesalNI2QUkbEoSrMQiAOiaa17F6aYAQiihNl2pUQ6UL4bBYqnwpiFDOupbeCNWYzOqla3GPb76/N5xgFtdGVWAS2LXGs9pRVKpVKpVKpVB5pqmBWqVQqleM4GTeZUCw/qmy6yWIq0csQU3GUDYkhRPqo5KgYBDEgZIIqFoMVQ9JEROlngdnoJpOkRDI5QdZISAlSZF0zokJSA2qIw8CR0BP6SA6Je+9e49Z3XsfHPvAuDh+6b8fXvrznLJ55yWt58sWvxe4+kweAnXrSJhRH2SJi6cfP0/HDUwr+vYGQwXlY7sA5SAmswp7TobEG6x0hDWyEgYlzo/VL0VQmi1qFSTvFGkGwWO+Yto7GWaa+gxwRY3C+oTNCyD2/+c9/hbVv3wMaS8lYFta+fhf/+B++mRvfeyOwDGIY5oGYBUxmeeLwTfnPBrXKkvcYEZKWe8FZUyK0Wu4Oa0GkTMN0RjYL/cvno4v7SypzIbkV0SzlxfTLo4Wy2lNWqVQqlUqlUvlhUAWzSqVSqQAnKZJtc5NlhZwzWYu4EWIiplymXsZMP6Ti9pLiJkOUpMVNZlQQTUQyccj0KTGkDFkQzSQRYk6QEzElYo70qjCKZCFF1vsj9H2ADCkEPnXbrXzove/g85+4jR33c4pw/lMv5Sf2Xs6ZT72E3lgeYKtv7GRpKYattDjs+NjS+HVD0acaA/MEGNi9XBxlKZRfysu7ofOCbT05RWZhwIuwNOkQkdJTFhNIpvUdxhRHmXWOSedx1rDsJngLCaVppvhxOIC3DR++4aOsffNusOMJ9BGIEGHtrnt49ztv5vLXX0afMylmJt7Stg3GWdRk2nGwgCLEpDgnWFNilopgjGJNEVKd2XKVlfXYEsu295EpZV8Zc5cLEW27OFbjl5VKpVKpVCqVHyZVMKtUKpUfcU6mwH9RIKUKOetm5DLlvFnin5IyDJEhZ3IEg2AEko5uMjUYZyAlgkLoE+uplNZLprjMEkAij0LZXHN5TgwxBkQTh8KcMAuklLj/nrv58HvexofffwOHvnv3jq99uusMnnHxa3jqJa+jO+1sHgDuOon9DCVSCVuxSsbHzPi9oQhlhjGO6cAL9KEIaqefWZ6LAWwuQtnEg+1aNAXmIdBYw/J0irWWoe+JMYEm2naCdRbGqZW+87QGlpsJ3hhEFGM9nbGIhwZH5xzqlLX+ADiFXoA5ZFPGb1oHxnHwuweY9xHrhEnncK3DIDirOD/GL7XcN403WGvICsYsYpLFTeZMGdqw8Ixtj1Bm3XKPbcV9FaWoZ9vFsSqUVSqVSqVSqVQeDapgVqlUKj+CnKybjHGbnJWMklLZL8TSTZZSKZrf6GOJXGoRS4zk0k1mBFFBSGSjhPXELEWGrEgCKG6yECNC6TMbQk+0npQgI+QU2ZgfpgfixkCOkb/61O3c/K638tk7PkLO+cEv4kE4/2kX8ayLL+eJT38Bah33AQ8X3tw+4TIDHYvWtlLgv5hyadkq8rfApCmCYFRIAqefDSQIobjNdu+CzoGbtKgm+hRwCrum0/IepcR8Y44xStt0uKZBc0LE4hrLtLFM247OekQz4iytcahRJtZhjMFZpTEWI5bzd6/CfF4uJObxxJui3hnLebtXmXYO5y3WGoyHxjlyymgusUpjzVHOMW8WN4xgDNhNgevoKZeL+26xllkXMtlCaNvqJqs9ZZVKpVKpVCqVR5MqmFUqlcqPENsnEZ6I7W6ylDKZUj6vuph0mRmGSEzK0EcGVSQWYUNUUTJJx0mXrrjCFEvolSOxH/WZIpcMqpBT6bGKA0GUkJSMJ4aIyZkH+hmzeUAU7r/3bj7y3nfwofe+nXvv+s6Or32ytIenX/Jqnn3xZbgzziEAh4CNk9xfKZrSlCKYKaWnzIwfc+4HDgKrnMYelloQhaSAhV27ikDWz8EJLC/DUgNuaYqmSJSESZmltkWcR2Mg9xE1StM1WNcAiigY37DUWKZNx8S1IAnnLQ0NWZTGGhrrwSQaY2hsQzZK11mueMMr+d3fO5O1e+4ufWhGWHjiVp5wGn/t8p/Ctw7vBUtp29cEC5eYtYK1BqQIY9aUCKUqWAPWGBTFyNGuskVMVhiFssX3xzjJqlBWqVQqlUqlUnksUAWzSqVSeZxzMpHLTcdPLqX9KetYyK6EVESykDKhTwTNaAAjpkyBtEqMERWwCFkjWEdcT2ykyDwNmASqCTWGeQzlvMj0oUeNI2HGuCEc2ThEb2TTTfaFv/wEt77rej710VtIKe74+s99ynN41gsu44KnvpjeeQJwD6WE/6EQihA2Nn2xhzLhEopQtph8OQM+ec01rKHAOvBRVtjFi6/+ec6fwHSpCGTzORgLu/dAK+AmDSJCiANWM13TYrqGHAPaB2IKGGdpmw5ES/ebLT1lE+dY9hOcLW4vLw0ZxTvLxDZkSXgjeDtBreCt0HiHOsGbhj/493/AL/7iL7J21wMlSymZlTOW+cM/+LfsPm03zoNKGQYggJgifpVif7Dj92WKZbmHvN1ykVkjx8Qoi9Msj1HezZmq2wSyWuhfqVQqlUqlUnksUQWzSqVSeRxy0pHLscA/bnOT5dFNllKmHwIxQegjIWmJV4qAZBKxiBwqWCeEnMjJEgZlPW2Q4kIAgZQzqhnNGTSykSI5C0ktOUU0JTaGno0hQkzcf89d3H7r+7nlXW/l7m9/Y8fX302Xecben+aZF1/G7tPP33STrfPwQtmifyywJZTNKbHLhatsEbvMwEeuuYY11sctMpBZY50PXXMNf+vvXk0KJbJ42h5oLLiuwVhHSD0mK0uTKRi3KZRlTRhjaf0EMQbNirWOprEsNw1LzQRny+TKzk5ImhBr2GMbVDLWZFrXIk5wIrTeoaIImayGlB3PfupFvGP/+7jx/bfyjUMHuODMVd7w+lcz3bOEkTJcwSDjfgYjlHJ/Kb1kpZsOVAU7dpcpW0La9ntsa4qqbusuO9pVVnvKKpVKpVKpVCqPNapgVqlUKo8jiivswZ/f7ibbilwe7Sbr+8CQlTRPBDIai5tMULLkcaJhxo2TLMOs5z3vvoUvHz7IuX6Vl7zqEsQ1WOuIYYAxmrceZsjYTRaykHNmPttgI2diUlLfc+Bzn+aD776ej3/oA8TwcNLW8TzpwmfyzBdcxo8/86WYtmUe4G6K+JUoYteDrg1bjrKWrehl4OhpmbvYKvb/FodY4162QpqWxazMNb7Lpw8d4oVPLPFM23qMa1ANSI5MuwnGODRFyIkUBoz1eOux3pNSAoTpckdnhKVuSms91mas8ahmkiaWfYs1hiyRzrYYA84bGucwksEoYiBHi4ZMHxMxZ6bTJa648rV0bYtvBBEd85JmvE8EYwXnDI2VzfdRVYnbXGU6Xrnb5ipbiLGqss2xyFFuMlOFskqlUqlUKpXKY5gqmFUqlcopzslGLmF0j+USo1uIZCkrwxDoUyYNypATOSjFYyQgStK45UgzAjETsuWOz93OL/3S/8jaffdvvt7Kn53Jr/7T3+DZT30WMUcGEoJnyMVNllJi3vdshIjGyKHvfpc7P/wBbnnndXzn61/Z8fW33ZRn7H0lT7vkcs466wKywizDA6EIZErxfh23LuNzC6FMKa4xBQ5v6yPr2APAaWz1mO3eDSbCtzYOsDUvs2HLgwYlwHmA3btfgHEeNCI54LsGoUwLRTIpBNQ5vG8xzpFzJuXMZNoxMULbdiz7CWIi1ggWh6oydS3OWpBEYy1qDa0zOOMQkzEuIyKkBCYZhhgIMWOy0HTCxHqMM7QO1EoRygAxBihJzc4L1hkW/WUxZ3JeOMNKF9mit2yBka1C/5TzpoC73U1mjnGiVSqVSqVSqVQqjzWqYFapVCqnICcbuSwF60UkWxSz51yK+WNMzIdAiEqKmZwzqgZy2S9qxLgi7IgRUkwYsaRZZhYjh/tD/NKv/Cpr990HCKQMCGt338Nv/sZv8Af/xx/SumWGlMi5ZxjmrMdUiv37nq9+8fPc+q63cvst7yMM/YNfyINwzpOfyk+8+HJ+7Okvx9uOqHBfKp1iCTadT9t9aouplmb8vOgnWxq/v59FH9mCL7ECPO/qqxFKrNIuDu5BWAVuH7dN4+d+fNWeJ7KKbxxGFfW+RFqzIqKkmBAtcUtjLBlIqky6jiVnaNqWRlwRraxipEEFvHF0xoHJGAHftFhRGtdirJAklW65CBghh8g8RVAwVph0DmcdrgHrDZKkZHFFRoEUmkboXBHmhOIqG5IiUgSyxUTLY11lZuwpiymX22FkIZJZI7WnrFKpVCqVSqVySlAFs0qlUjmFOOkC/9E5VpxkR7vJ+iHQx8TQp/LcOAFREHJOqAEhY4yQUsIhxCD0Q2aeB4aYEDXc8s7bWVu7rxisHGAcJAFnWJsd4mPv/xwX/dRzmM/nzEIkxsh8/Qh3fvAD3HzDfr5x4Is7vn7fdjx77yt4xt7LOWPlxyFCyHCvlp4xTzmdRd/Ydsx4mkLxgLVsecKEMi1zq7x/S9BZo+eT11zDs/721VgF48FaiAmee8YePsEu1jjEllA2Bywr7OKiZ5yGOIsYg4kZFcFgSKm4yqwxoJCtoXENUwtLkwnOODpvx9hjUyZSQpmISUJMonEt2EznfZlMqXHsEXOgRdyah0gMGesszkFrPcYLjSvvt6bx+sUCYB1MG4cxCxHMbLrKZJx8CXKUq2y7gzHENHabbd2Pi+jl8YMAKpVKpVKpVCqVxy5VMKtUvgc2NjbYv38/Bw8eZHV1lX379jGZTB7t06o8TjkZN9ki7hZzJiU9auJlVhiGMIpWQEyjkFYa2RUtRfPeYMlka4h9BjGkoMxSYB4jMWlxZ4lBNfGNdADGgnjEQjN6t6wBtXyGA1yw/hRi3/ONA1/k1nddz203vZt+PtvxGpx97gVc9LLXc+Fzfwprp6QBNgY4QvF1ufHzonZ/wcJRZhdrCXSUHrLMllAG0HNodJYtRJ05RQTbYI3MZ2eHuHTXHmIqBxzrwXjD1T/H267531njSLl+Miss8d/+7Tfjl3YjuRzTOEeYb5CNRcTirUOt4KxnYoVJ29JZy6RpSpG+8WU/yXS2wQmoyXjnMUZpGocTW4QyUYyxoIpBGGJiSAmREtP03mKtxbqMbywoaC6CGJS3r3WGxplR1Fp0lZXVNIYSIwWcKcX/2++74kArwtr2e9IYOcqFVqlUKpVKpVKpnCpUwaxS2SG33XYbV1xxBWtrW6GtlZUVrr/+evbu3fsonlnl8cROIpd6VC9ZiVxmIIRESIkhjG4ygCzbHGcZcUCI4B0aEkmEOMsMUelzT4wRFUuOuRT/m4VTLfFEVsGPQpm6MgoyW+gayJYzh3P54Dvfyi03XMtXvvDZHa+B8w3PuvRl/MSLLuf0Jz4dUunjWo9FKMsUeSpSRK/tvrCFSGbZEsambEUxMyW6yfj1EjBj0Uc2Z2suZhr3cMABVC4q+wzQeOg6OONMx//nV/4eX/zC/XyFA5zLKs9/9ln4ZoKIAwNxIZRZi2s6sihGDBNn2d12GFMEM2fBGV+ikZrprMMaDwactxgjiIXOtCCZaBJODWLGoQxZWO97NCnOGKw3NMZhXKZtLIIl5YxQ3GsIeAetdzgr4/0mpLyIVCoyRjWNAbfNVbboIAspFRFxG0bAWTmq26xSqVQqlUqlUjmVqIJZpbIDZrPZcWIZwNraGldccQUHDx6sTrPK98VOCvzjKGpsj1yGmBliZBgSUSGHBCrkLAhaJisaxRpwqiSEpCBBCUGZp1AEkJzHLjPBWMUYIWhi6HuytZCF573iGaxc80TWDj0AjQHflEhmv87k8GH+/F++mfnG+o7X4AnnnM/FL7+Mp1/005hmmTCD3MOGFqFs8YsrjB+ZIowtivthy3FmgOXxsYXAts5W4f8yJZbpgHtZBT7C0aFOPx7dA6ukoeiBvoXGW1zb0HiP0cxPPH+Z55rzMQjWejRlUj8niwFjsL4ha0QFlpqGJWuZtC3WWrwTJq5lyAkBvDE0tkGs4sTiGoOK0uCw3hBywInDG4PRjCoMMRNTxIjFOsFZg7EG56HxHs2lI02wJeJpoPUWb2V0iW25yvLY1L+IUS5cZduFshO5ymQUylwVyiqVSqVSqVQqpzhVMKtUdsD+/fuPE8sWrK2tsX//ft70pjf9kM+qcqrz/RT4p5yJqXyEkBhyJveJLILGsfheM2oAMmjGWkvuM2qE0CdiVoL2hBDJYtCYMM5iLMSUmIeBqAoqCJYYE32MbATlF//eP+UP/uhfstbfC4fvhfvugY0H2Gno0jrHMy9+CRe97HLOPPfZpCxogNmRIpIpW4LXEYpQtr2TbGFw8uPXzfg1bOvXogQshRLL7Mb9jYVdU7jY7eGTeNY4zJZQVsKcKyxx6el76HbBxFtM42nbDquJYCBjaIzBiC3TIYd5eY+MwZgi34nAtOmKUDaZ4I2l9RZrHWQlaKazltY1SMlx0ngPUrrDGtsQGUhB8a4IZwZh3pf3z6qlMQ5xBiuCceVcRSAlRcapp2Kg8SV+6a0prsTxXkoZFN2cYrm92H97/DLlTDjWVWbAG9mMa1YqlUqlUqlUKqcyVTCrVHbAgQMHvq/nK5XtZNWHFclQBRFSyptl6mXKpRKTEobIPCZSzGMv1ZZTKKOIZmxjyCGTjYGsDLPMEBIhl8gmWpxHRkvpu1jDkItQlq1FkqJi6MPALIwDA8JAihFpDvPsC8/g/vd+jH7jyI7X4Iyzz+Hil13GT7zo1Ti3h40BwhzmcatbbBGhPEyJXzqK2MX4vWEratlRxLKBLcfZfNtxNoUyKcX9Sx1Mp8UYN7XwM1f/Xd56zZ+NfWQKJFaY8NevfhOnnS7YpsF3HT4nMkr2loZS5J8RVBOp70kiGNdiyWAMzjr2tJ62bfHW0jiHl8V+0DpHY8cAqck472iMkEVojCcTyZpKoX8rtMYwJGUjD1gEh8MaGSOWQuMMTeNIWcfC/iKUWSNFLLNSJnOqknMZBhFz6ahbuMoWxf4LoWxxz4ZjXGVQkrnOWiqVSqVSqVQqlccLVTCrVHbA6urq9/V8pXIykcvtIlmmlPcvonIxKSkmQs6EmMlDcZORhExGVImiiORS0O8MsU9kIM5jcaRpJsSyX4oJ1zS4nEkpMsQiwDlrEUxxk4XAkb5nSIkUI8O851O3fZCb3vYWvvDpO3e8BsZannnRT3LRSy/n/Kc+l5gM6zOY9zCk0i2mLOrzS4RyIYwthLJF0f84ZoBdbLnQFk60hctNKRMxp4zCjodpA+14sElTBLZZgNPPhv/mF97EZ+4/xMABllll74/twXaebjKhQVEjqLUYMXjMGFHNpPmMlDO4MrWyDFGw7G49XdvhDLSuwRnBiyUKWDHs8g5RwIL3HVYyYg2CwaFkSRgsKuCtgAhHYkRipjEWRFBRxApdUyKWGCHEXFxlo1BmjNB5U4Q1pIhpqsSUyTpus81VtnCZAZvCWsxHi7zVVVapVCqVSqVSebxSBbNKZQfs27ePlZWVE8YyV1ZW2Ldv36NwVpXHOicbuSwbFCEjje6znDNhEbmMxRmWc0YzaBoL/MmoCGjCOMFlIauQY6afx81OspiLCy3lUnxmncVbS4yBmCJJpEQDw5z3v/PTfIWDPIlVnvbcJ+KbCXd965vc8u7r+dC738bh++/d8TrsOfNsLnn56/iJn3wNXXcGETj0QIkD9rE4wRYxy54SoZxxdDhyDmTuBw4SWOUM9uDY6i4zbHWbLRxlE4pI1lhYmkLTjq/jivC2PkBSaEfNx3h44YV76OwliPd439AIiLfknDHWYBXEujJcYWO9REKNxTqP8Q4rhql3LDctnQVrfRG0bEMWJQHLztNYA5Ix4rG+iFaKxSBkElaKLGg9eGOZhUhOEW8dyYwl/SbjG8eksVhrSElL/5zIplDmndA6gzFmdJ0VoSwpm64yM7rKnDVluuhm/HIx6OHo+9XXUv9KpVKpVCqVyuOYKphVKjtgMplw/fXXP+iUzFr4X9lOKeJ/8OcfzE2WR3ErZkghMaRUnGVhrLHPJaSYNKOiKIooeGeJQyQphKDEVPrJNGeSQE4JYx3OGLJmYo7MQ8SwEN6UT//lX/E7//J/Zm19A2wR2PYE5cyJ5eBf3Yk+lOp3wms0PPV5e7n05Zdx4TMvJkRLH+DIDEThSNgq53eUfjKhOL78+LGIZPbAJ6+5hjVmlODll1gBnnf11ZxFOU5PEdaWKEKZ2SaUtVMwuQhmIcF8KM61pmhWqEI3gc4bxDjatqMRMN4WwdMYGgTrGmLOhL44ytQ6XGm7pzUOZ2B319E6g3cNGGFiPWqErEpnLJ2z4AWjFoyhbRwpl6ijSEaMxRqPCDTOEHJmfejxOASDprFg3wuNd3hnUJESlRyFMjEyutoMzhp0IcbmEqtULQX+RsCYss+ir2y7UJbz1n1cSv8XDrQqllUqlUqlUqlUHr9UwaxS2SF79+7l4MGD7N+/nwMHDrC6usq+fft+ZMWyjY0N9u/fz8GDB3/k1wJO0k1GEclKaX8RJhaRy5CUHEtsMg6lY0y1RC4VUEmjxqZgwWkRQTTD+nogxEhGCKlMW4w5AVJK4EXQHBlyQkWKY0iFeY70IXDf+mF+51/+bhHLiHD/3XDfPRyKA4d2uA67TjuDS17xOi552WtZWj6LjQiH12EYigA2i1tl/EJxki0mXraUX06LSZcLQa2IZZEihUVgYA3hk9dcwwuuvpqOMvVyAoiDzsO0g2YCkqB1EDMc2Siao1OwMgplHTRewDgm0yW8EUQURRDn6JAyUVSgH3piCGRjscZiGkdrHEYzeyYNk7ZBrMOK4MVhbREoGwzTtkGcYo0vkyqtYFQIMWFRjLUYcSjQNKZES0PAqsViSYCOk0snTRHKxJTuOgAx43ttoPWGxpVesdJTlo/rKlsIZm50iy2mrS5Esu09e0bKPouOs1OJ+nOqUqlUKpVKpbJTqmBWqXwPTCaTOg0TuO222x7Ubbd3795H8cy+f3byB/bJiGRbocGxO4pSxp5SIqoSw1jkH0o/WQ4ZpfRYoZlEBiNoyhgE6wwpZPqQGJKSUiKlvKlCZc2IMVhZiCCZjTAgphwzx8QQejZiYmMYGPqB228/wNr93yxC2eH7d7xmIsJTnn0xz3vp5cxXnkqwX+PTGw1PHSBKEa36vOUYU7ail4mt2OVY58VA8ZG1wHc5RLnLzPgM47OwBvQc4kl2D8YVx9jyEri2HLjrhBCVWV+il0bAKIgtopp3YF1D13Y0jUNzQpzFWEsjhpQyPUKOgRBD6ftyDd4I3josyu7WsWs6JUsp2C9OM7O5Lqc1Hb4TNEE2DjGKM5aYilXMWbA4sIK1QmMNfQwMEZx1qGZyKu4z31imjSsWPSmdZsYuiv2FxpX97RjZXPSUpVEE2+4qc1Y2z3Mhjh37ebsDzZxiQhk8vn9OVSqVSqVSqVQeOapg9hCIyAXAwYfY5M9V9ed+SKfzmKb+6/2PHrPZ7Lg/QgHW1ta44oorOHjw4Cl7D5zsH9gPL5RtiWQLASJl3RTJUixdZSlmUsoMIaIUNxlGyDmixiApIc5gtBRORYXZeiCM28eUMMaQKaKbYSxvR+njQBKKSw0hxMgQE+t9zzwl0hC4/97vcut738a7r/tzuP/+Ha/X8u7TuOhlr+HiV76OwxtP5O3X/AlrfJwie32GT8P8iI8AAQAASURBVNDx/KufQpHKVjmTPQhFLFus0Pb5ipmtaZcLlxkcoLjKIlvV/4zfJ+AAk/YiplNo2/JKroGYYDYv/VvWFI3JuSICeQdt2+J8Q+Ndce0Zg28cjtIBFy3EFAmhR9VivC/TLY0wsY6usexpJ4grvXGdOIwtzjBjLa01TFuHWCFTlLrWGpQSixRRrCmxTCuC80LWzDwEjFqcpcR1s2IbYdo0WGtKyT8WKR640ldmoXV2dICVbriUM3F0MQpbYpk1grclVrkQxhYR4sVnkYULjaMGAJxKPJ5/TlUqlUqlUqlUHlmqYHZyfBK49gSPf+aHfB6PSeq/3v9osn///hMOP4Dyx+j+/ftPSRfew/2BfeDAAbrJ5CHdZGOQj+Imy0UoS5mQcxHJiipBSJkhRnJUUFNimjmhxqAxgxMkZpy3xJSZ94l5LJHMlDJipRThD+vc+p7b+Wo4yJP9Ki99zaWoWBJSYnyxRDXnMXGkn29Ou/z8pz7OTW9/C5/8yM3klHa8VqvPei6XvvL1PO25L8Ti6QP8p//rz1ljTmkRKz6yu0i8+5ovU8Stv2SFluddfXWZWsnWZMtFSb9QBLRFFLP4n1aBv2JLLIsUb1qJZlpWOf00aHyJW6YM/egocw5SBOOKSOYcNE2Lcx7feJwBcSU6aZJiohKtIaoSNg4DDvEtDkEMdMYz8YbltsN5izFlymVnLcYIYgzeCVNr6CYNIRQ3obGCtYaYMkKm9R4Vi0WwjcGgDDkh2WDUkHMm5yKyTaeOxnsyxZFmZCFgGRDonOCcxQgUgVbHYRELV9nRPWVlUmaJBMPRrjJgU3RbCGynWgRzweP151SlUqlUKpVK5ZGnCmYnxydU9dcf7ZN4LFL/9f5HlwMHDnxfzz9WedA/sEVYu+tu3rL/Wt74xjeeYM8tNxkKSTOHD6/zlv3X87VvHuTcJ63y115/Ga5pSTnThwjZIAhWSoQyaXEFmaxYJ2jODFnZOBLHTjIhaR4dRUrIiU/+1af51V/9Fda+c28p6RfPyp89gV/9lV/nxy94CvOsbISBWYjEIXDo/vv44Ptv4JZ37Ofub31jx+szXd7FxS99Nc99xWWcdfa5SIRZKiX+n10/xBobFBnMUDxii3BlHL9uWaP0kb3k6qs3y/0nLAKWRQZz4xEW/WYde1jBjh1mA1sV/5EVhJectwdxpcw/KWgGYyHPy8GWl8tj7bTDicV3LVYztil9Ya0YvBp6C32KxPk6SQ3qGpyUqOPEtXTOsOQdbdeBgMmCFYNxFicGZ2CpdVhvAEuIGVMUJwQh5IhzFiseESkdZBZUlGHIWLFktAhdKE0rTNsO1YxqLp1pVjbvM+egdW4Utcr9F1MipC0H5MIl5qxsTsBcOCO3D6YoQm8RxxYi2akslsHj9+dUpVKpVCqVSuWRpwpmle+L+q/3P7qsrq5+X88/VjnuD2gZxYmRg0c9f3zkMqsSYuK2Oz7Gm37+51n7zn2jRcqwsnIWf/BH/xvPfcYLS9t8jiRniUMCJxgFb8ux5vPIfCiur5QzYgQjhphCedWsxDDnV3/tl1m76xC0FryHoKzddYjf/K1/zm/9zu9gTEcYBr742U9z0zvewsdvvZEYw47X5ZwfexYvvfxynnbpS3DSQICNOYQB+lCmT5bYZMfWr5YEbDDW8B91vDXgCId4Anto2HKZGUpn2WLPRQDziQI/efXVfOSavxinZAbgflY4jb9x9T8q+8fx3TAQ+yKYLS+Xt7DtJjhj8W2DxIBxBmscXooLb5YTQ4zENBCygikCmLGG1jW01rCn9TRNSyZjMogxOO+KuGcMu1qHby1iLCkkVLRM2IxFwctAYyxi7SjCgbVKCAmMQ7SIXTmXx5cmDXaM2lpjsK4IrKpgHTTGFFfcNlfZMO6fdavY39kyCMCaMjhiIZBtj2JCEcbEFFfZwl12qvN4/TlVqVQqlUqlUnnkqYLZyfEkEfn7wJnAd4EPq+qnHuVzekxQ//X+R5d9+/axsrJyQsF0ZWWFffv2PQpn9f1z4YWrx4lk27ngwlUUpQy6lHGiYCamTMyZGJXZxgZv+vm/xdpd95fiLDx4WLv3Pn7xF/873v/Om3BNW1xHMZfeqpgYEgwhFzdZAnVSet2BoImYYungso4Yez7wzjtYu399HP8IxFE0cy1rs4EP3vQ5jtz3OW55+36+8/Wv7HgtXNdx4fNezmv/2pU88YInM2xAGuBIhvmsbJMikIurq8Qm/3Lcu6fEMqcnOLJS5LADNFxEpvwyssB8fEYpjrMzbRG+Wgc/7uCZ/91/xRfuOsQRDnAaqzzznD1gy/ZiIczAdbC0DMYJ3nc4EXzbYnIqTrDGlymYGOY5QgykHAgKiMEKOG9xxjMxhuXO0XUdRgUEGhpwgpcSg2y8sHvaEWMqPWUKakwZLJABqxg1GFv6xYwr/XJK6VhDTZliGUvMdjI1tL4haSqv5/y4fRHLWgfuGFdZSIm4zVVmRLCGEr+0i1L/cfW3ucpANx1kCzfZ40Usg8fvz6lKpVKpVCqVyg8BVa0fD/IBXMCYXDnBx43Aj+3weHc8yMf6xeecU/6h/2Q+fuEX9Dh+4RdOfv9f+7Xj93/DG05+/z/8w83d/vRP/1QBvf1k9wXVt771+NffyfXffvvx++/k9b/5zaP3/eY3d7b/sdx++8nve845x+//1ree/P4XX3z8/n/4hye//xvecPz+v/Zr3/O997GPfUz/eDJ5VO69TS6++Id2773mzDP18JF1DTFpH6Ju9GFH984509OVXWfp7/+bP9Uvfv27+pXbPrOj/T/yqa/rLZ88qB+484C+7/aD+s/+wT846X2/yfE/x96wg9f+xvlP0f/X779N/9H/8jZ98+++Tf/eb71N/+OV/+ik93/rrvOU5/y88py/oTznZ5Xn/A3945/62ZPe/7aXv07/pz9+m/7On71Df/tP3q6//cdv09tf8bqT3v+Ov/V39d+/9zb9vz/wCf2z992p//69t+lXX/DSk97/c7/ym/r+Ow7ojR//it76ia/pRz7zTV1/1nNOev+1//M/6dfXHtBvrD2gX73rfv36XQ9oWHniSe9/6OYP6wOzXvuQdIhJQ0w7unfi17+hMWUdYirH+NrXd7T/qfxz776XvlRXVlaOuvd/Z2np5F//MfQ799H4uVd/5x7Do/g7V1XrvVfvvXrv1Xuv3nv13qv33g7vvYtBgTtUd64JVYfZQ7MB/Cal8H9hlXou8OvAK4H3icjzVXX9UTm7xwCLf73nQWKZlcc3e/fu5aKf+zn4D//h0T6V7wlVBR7MS3Y8v/t7v4fzDRt9GCcPlrDhSSNADnzt/gPMZoHZRtzR+c5iJAMxDASFMzhvR/t/P+QMMUJOoBGcLlxlJ8uic0yBlhUadjOc9N7eQhYhJ2WIit10V50cnXO0CEOKpAQhxzIy8yQxGBrrsEbonKXr3GZx/kntb0AdEDOoIeWTP3eA1gvi3Oj+Kv9lsBNUlZx1a/+d7X5Kc9ppp3Hw4EH279/PgQMHWF1d5eq//Ev47d9+tE+tUqlUKpVKpfIY5nEvmInIV4An72CXP1XVvwWgqncB/+yY528WkdcCtwIvBP4u8K9P5sCqesmDnOMdwMU7OMfHDJPJhOuvvx734heXv6YrP3I4d2r9GFHVzcJzWExgPDkuWH0qR+aRPObZNO9UtQBcx8p0lRgyboeqRx8HUsoc6ef0STnv6U/Y2et/n6RRMAu5yF87n6s5ATIrOJ539dWE9//pSe+ZFUJSLNC0htnhvBO9i5ATfVKiJtBEzEUEPFmstbStZVfbYJxgvR0r8k/6CiBByoImJe3kxQEjBhlFwpSVmHf2L16LSZmLrx8fgcuTZzKZHN2n+eu//qidS6VSqVQqlUrl1EB0p/9MfYohIu8Dzt3BLm9V1V86ieP+XeB/B96iqn/9ez2/8Vh3XHzxxRffcccd389hHlVms9lR/3q/b9++Oh2z8pjiWKHs2OcWowNFSnl6ViVlpQ+JlJW0sFNpmWg5DIEAEHRzCmLKmZSUYTbjNa9/FWt33VUUDnHgG0BZOfsM9v+n62j9lKiJSEZymXxpjceoMssDKZWC9pAGQsyEMDCocrgPzI4c4qM3v4+b376fr37xczteC9+0XPzSV7L3lZdxzoVPYehLV9p8GAWxBHEANWBSEZZSgiGDShFcrMARLb1jiyVVysRLhc0if2cgZfgmhyhG3VV2s4du3HbSwKQdl8nCmadD13aEoS9TQxFizBgF2xjmG5m2BecsySRMFLqlZbyzWMC2DVhhl3FY49jIgTgkVHLpmtMidBpT1mGCofOe1gmNb3DOQBZUyvTLxlqsE5Y7j28sRgyM7sKUEoLB2jK1NKFYMThn8daQckad4IAwjM4yzSCKt6ZMyHSm9NQZg7dFFNNR3GqcwRhzlKusdOUt1lvLfWPBG1N63Niaalnu461uskXJ//bJl6f6FMxKpVKpVCqVSuWhuOSSS7jzzjvvfDAD00NxallDvgdU9acfoUPfPX5eeoSOf0px3L/eVyqPAR5OJNsuMGyKZGks8B+FCc1FlNCsDCnSx4RJsiliJIGYyr6SM64Rml1T/u3/9q9483//S6zde2/JLoqysnIG//y3/yesa+k1QM401pNViGSG2JOykHMiaiJHpQ8D6zkxzANfPfgFbrrhOj7y3ncw39h5Evyc8y/gBT/9ep7/opfjJsvEAfoZ9BFSKMJWCuV0fRniyWwowldcrJXCDPBaRLEwPr4o7TcU59lmkf+oRZ7FHhIX4SlC2bSFxoO1ZcOzTisCVhgGZvOeDAyD4qzivWGYZyRmlnY7co6QlWm7C7dkaUXQ1mONMBFD4zpmeeBIP0dyxhhhnhWNGVB845nahs5afOOYeI+xUkr9tYhU3lq8gWnnsI3BicUgxLQlnjprWUxlUIXGOhrvUDJDzjhRJAthFB2VhDUG74uoRi6F+9aU4QEL4bZ1BmuK97G4wpSYIKmW46huFvN7K6MYdrT4lcebfhEbPfb7xbGrWFapVCqVSqVSqZyYx71g9gjyk+PnOgayUnmMsXDSHMtCJIMi4hiR4jrKSoyJkDJJQZOCFJEsa6bvA1HAZHAU51cGUlBAEas0CFlgow+EIfPjF17EdX9xHTfd+BG+duQA53arvOjVl9L4BkFpcCDKPA4kLYJHiAFVGIbALEUO94Fh/Qi3f+hGbnz7fg58dufDeZ33PP9Fr+CFr/lrPPkpT6MPShpgmJcJjSFAiFuiolUgwSxAsluxS0spdXQUoSxu+9qMH4uQYWPKdEgFDuuW82xiRqHMgozDQ884DbqmIYZI34cizkXFGugmlqFPpCEz3d1ACmjMTCa7cI3D54xMWgRlagydnxLSwOH5BpozzhqCQB8TOScab5m6js4YXOfZ1bTF0YUiGNQIrfW0RnCNZdoU8cwaQ1IYQukBc0aKUGbKVVtjaVzx2iVN5d4ySorlHsIoYoXWW4xQxDARjDPY0VUGirOCM2ZzUqWM93LWEsMs79GW8OWMbMZCF0LY4h43D+IyAx5XUzArlUqlUqlUKpVHiiqYPQQi8kLg46o6HPP4q4D/5/jtf/yhn1ilUjmOB3OTnUgkW7jJhiESciblLZEMpbjMcmJIGY0lcmkXvV0xoyJIyvjWIGroY+TQPKIZVHMR2xRs0/HK1/4UUV+KquKtH0U4GPJQXEM5k8kMITKEwFwz83nga1/7MrfccB0fevfb2DhyeMfrcdY55/KS117B81/2KqZLy8zWM7OZEnIRyTQVoSwk8KYU+avCeixOswi4VISyGeXaF3FLGb9e/AKRMabpAGdLrPMIRURrKMfvGpj4IqrZDk7bXYSyMAT6IZHzVim9by0pJdKQWFpu0BTRmOjaZVzb4HOCtsFoZmKEpXYXfZhzuN8gxwTOECmRSRQaJ3RuiYmxSGPpvKMxiwJ9RyTTGYe3pghlzmIbM0ZtDf08oaoYa2iskEUxo6jljMfZMWqJoGQ0GRSDomAVZwxGFOcMooKYIppZa2CUvLwt4hwUcStT3GQlGjzeyyhWSgTTGHNUvHIzVszRYplytKusimWVSqVSqVQqlcrJUQWzh+Z/Bp4tIh8AvjE+9lzgVePXv6qqH3o0TqxSqTx05HLhMlsIBAuRLGclhMiQdHMOMePnlCJ9iKSsiIIzliSZlIqgpmTEgVMlWeXIbCBGHa1Vioglq0DORFHQiBVLIx5VJcRARMa4ZySlhEZlngIPxEhYn/HRW9/HzTdcxxc+tfNOQ2Mtz3vhy3jx697ABc98DiEE4gDrRzJ9ghy2+sj6CJ2FVqEP5XvNo6BloMkw33Zsz9aAhIVw5lzZzyp0rsz9OJTKL5YWaBqY+nHCpYJdgjOWoG1b4hDoh1jWj4Qxgms9MQTSkGiWGmzOpJjo2iVM42k1k62AcUydsNzsZogD98+OQEpkKxjvGFIqQxUsdL6lswbnGzpv6JzH2NJTFkXxRpia0l026RxNYxFrACHMI5GEoYhOVkqHmwWsKVMyVZWgGaNKFkWyLfePJKy1OG+wRhAMjO4yY7YivdYaDLIVjdRM0uJiXLjKFhFMb80Y0Ty6f2xr2qtsimYLmXi7OFYjmJVKpVKpVCqVyslTBbOH5k+AfcBe4HLK34lrwF8A/6uq3vIonlul8iPLSUcuTYlchlEki1q6oMqYwOImyzkRcmIICVXBKThjCKr0fSIbsJpwrUXUEmLkyDyXSZm68FtB0kzWSMwRg+KlLdFNFWIaiAoxJhKZHBMhK+vDnCEo3/rWV/nA29/CB9/1Ng4fum/H63Hm2efw4tf8Nfa+6nV0y8vM1xPrhwMpUV53Xi5ZM2QpApZEGAIELY6wrEUoy2l8jC1X2UIoa6Ucz/gyMVNH0S0nuC+O3WRAN4HWFWeZETATOG2PxYohxzHiGvPm1EfXeFIMhD7QThtcVrIB76a0jaNJCXUGzYZdnWdqO0IKPDBsMMwHnLNY35RJmPMBI5nlbkIrgm8avBW6psE7i+bioLMCy66h9RbbCJ23WG8xagh9Iqhic1kraw0JRQyly2wUvCJlaqdIBnWlI00U68BZhxXFWFvux7GrTBYOMcr3Rharu+hCE9LoQiz7bbnZxkDlUSKwoicQwfQo8ay6yiqVSqVSqVQqlZ1TBbOHQFX/HfDvHu3zqFQqO4tc5pxJQAyJOHaU5bglkqlCiANDyGRAUonFZSkRRQ0ZFUWcYrOiAusbgZgUSUoWgXFiJJqJkkEjRiydbdCcSSkyaGaIkaxKTBFRYaOfcyRFhj5wxwdv5KZ3XMtn7/zojtfDGMtP7H0RL33NG7jwOReBwHw9sHE4FZEsQ56XcxxSiUo6V4SujVFAC2PBf+OLy0xTEckGikPMj2vaUh5XO34RwI1uqwdGR9kuAd+WqZcWsA6aKeze5RGFHDNBEyFm7Nhh5rwn50gIgbbzdFnACt51iHc0aJnUKZ62dexyE1JOHB56wtDjvadpW2LOzPsBcmDaTWmtpXEOY4XltsVbg6biFLQCU+eL08xD1xhcazevax7LQAHnDMYpasYIpRicKR1kKSVEDCJKVkGDQcmYMSpprZaBAIv3SgRjDdYUMXZxvK17eIxzHtVVBkZ07DOTTcFr01X2IELZOJph85EqllUqlUqlUqlUKt8bVTCrVCqPWR5OJFtELmV8bNExFlMqIlneilxCmT4ZUukmMxksBnImIZAgG8VowniDEcPQR+YhgpbydqWUrKcUSSJoHsvkXYcRS9LMkCJRMzFEsoEYBlIW1sOcecx85zvf4gNv+y/c+q7rOXTvPTtek9OfcDYvevXr+clXXc6uM85g1vfMN4qbLORS5E8o4l8fofPgcxHK1hMYC0MsrrBmjFHGULrGNijiWAs4U8SllCgDD0z5hWHKIEkOp7LPkkDTwVI7lv97aCawPPUYIA4RxDDEhLNFLMJYLBlyxHlH5ww4wWkDvky9xChWHNJZTncTUo6sh4EwDDjv8G2DahmyIGQmbUsrHdYaGu9ojWHSNhiFIYM1lqn1NNbiLfjO0HhTVL8ohJhJlL4xscUpZgCxgpXRJaaZqCWemXJCsCQtZf3FdaZ4X2aGCorKosS/ONWMMaOou+Uqy4tIMNtjxKNDDBndZbJN+NIxonm0CDZKaEc9ViOYlUqlUqlUKpXK904VzCqVymOOxWS/Yx87ViQrZfNKSErKmZhzEcnGCY3FcFO6w4ZUOsxsliLkaHGW5ayoZIwrccxsYN4nUgpFRKOIZUaKEyiRikMoK4IHY8k50+dIP3ZupfEE+lngSOyZ9YlPfOQD3PTO6/j0xz6E5nzsJT8kIsKzLnkhL331FTztokvBwHwWOHy4ZxjFMUkQBxAH60OZRtlZGGbQj8cxUlxlC6NYikXk6imPdZRYppEivCVXnGCdKyJbzDDXsu1uD76BaQPk8rVrYfeutnS4hUhCSvzRaCmpB6wVxCjWOpw4sIpRjzhHI4JIxhiLTByn2w4vwpEYmc/nNN7hGg9imIUAMTLtGhoMjXOIs0xtKfUXMWSFLMLEOFrrsE5oG4N3gniDUUccBVSrJUJqbQk0GmewmPE+Kk43zYq1QsqCahH/vCnb+qKuFfF2fM+sNTROylRMdFMoW3SSLW7xrOU+LqJYucHNNlfZdrFsEcncujcW//+oYlmlUqlUKpVKpfKDpApmlUrlMcGJ3GTHPmZkSyRLm51gSoqjmww2U2maIkGVISSMgqgUYSKWg6kppfzGl2xhDMp6DEiSzemEiJBzcZPlHLGUWB5apiEmTQw5kUIgCsRY3GiHZxv0qtxzzz184Pr/zE3vvI777l7b8ZrsPv1MXvzaN/DiV72ePWeeybyf0w+BYShRypAp+UktvVwK2FCK9mfzsk1MxR22iGBuWyJmFPGrpcQ1ZTH8gCKceVNeYz6UbT2wuwPvixgnBlwD1sOeXW2Jng4BEGIq4pIxhrwpBGUa7zHSIE6RZMFaGmuxRKRkJDnTdVhgPScemM9pnKNtPNla5iGShhlt42nblsY6jLE0jWPiHa335JRJAg1CYxucNzgL04lDjCBYclRmQ0BUS9E/eYxZMsYpi7Mra9oUsDJKjGAo14YRnAFnBRWDoXS/OWMwBhoriFkU9Y9i2SiT5dGxuHCVWbO4fwUrW0MAzDgcYFHrvx1BjxPKagSzUqlUKpVKpVL5wVAFs0ql8qhx8pHL8n3MkFImqpJiBhFS0i2rjih9GAjjlEuTi3AQMkguIoXYjDFgVFBj6EMihQzJHO0mM0pMASeCBZw0GJSUlZRLP1mMkaRaOtMSPDCfsd5HPvvxj3Lj2/8Ln/jQzeScdrwuz3j+pbzs8qt41kUvACMMw8CRI/MiksUihJFK51hIRWyxppT7PzDGLlPcWpZUKtY23U9p/Ogo4leKRTgbBEwuPWTzvgwFiOPynjGBtgM3HtR10Lawe3lKyJE4xDIoIORSnm9tEcoMiFEaYxE6xINkAfE0ncNpJIlCN+F01+CNZZYSs/kc7wxN02CcY30YSBszrBF2ty1t0yCAbxy7mg7rIMTEkBKtWCbO463FWsW3xQEm1pKDklIiq+KdQYyO51hEMmsMOSlCieo6MUWgTYqowdoSyUTAWwEzuspGFdI7QzMKhbKtZ2xxTy+cZQtXmRnFxKyjq8ywGcG0ZrHv0ffHwmGZq1hWqVQqlUqlUqk8YlTBrFKp/NB5ODfZsZHLmEpxf0p5c9rjIiqHgRwDvYD2CWMMxExEkFQ2yVJmPi5idiFk5jGUiYgxF+HCKCkV0UdRDIIThyCl6D1n+pwJMRBRQk6YUSSb58Q999zLLe/czwfecS33fPubO16T5d2n8aLXvJ6XvOZnOGNlhRgDfcqkWWQeStVWikAshfobc3C+TKPs10v/mJhRSInbJmLG4npafCRK91jXlAinNSV6GSNMWugzrM/LOSVgzxS6KZhYHGh+Usr9dy2VAv75fFbWWBVrDMZZMkXISkRa6xE1iDdIBrC4xuE0oWRk0nGacXTWM2ji0GyOWKVrG4y1rA8DcWMDJ8K08XjfYo1gnWHZt3TOkHImZENrPY31ODE4B64VJq0vQmiCNCghZyyCs4L1pU8sZcVqsS+WuGxRv5wRYizTHcwYrSzF/ouGsfI5A84WMa2xZnSkAZjNeLEsnGUqm/e6NaXk38hiYmYRu0osVo6LJcOW2+zY52oEs1KpVCqVSqVS+cFSBbNKpfJD4WQilzLGz7IqMRehLFMmLGZgm5ZBiiUGmfrSB6ZRiVDsZDrKEzZiKOX+SS19jGiIaDZEZVTeMmIg5KG8vgpWXOmtQog50msixTJxU0QYQubIbJ2NmPj8Zz7O+677C+689UZSjDtel6c+5yJe9vorec6lL8F7Tz/bYKPviaNQhhlFv6FsHyKILSXywwZsSNnGSXGFiRRHmU2luD9QPgRobCnlT/NyDPFlTb0trrX1+ZYrbdcUJtPSjWYU/FJxoy0vdSRV5vMZRqWU3huDikGcKX1dJuMQPC3GOyQpqgbnHY0p77uZdEyNo7OOlBPrQyDrQNdNANiIgfn6nIkIrXU0bVeikN4yNY5JYzEY5gKtdSwZR2Ms1guuERoD3ntCKu7CIWVUM6135fzG7jkQnLPI6BwU47BWytCIpKVPzSyusQxCEDGjw6usq190ldnyZi2Eqzze2CKLe7x4JVXLjllHEU62ucpGpfhYQWzhHltEOLezcKJVKpVKpVKpVCqVHxxVMKtUKo8ox/6BfyKRbBG5TNsilycSyaDEJJMRcihCWc5KymPMT5VsEkLGeU8OJWaYh4CqYQg6lqtnVBMZJaNYNXgsKmXaoWalz4k+DCWOh0JU1sPAegjcf/8hbnnXdXzg7W/hO1//6o7XZLq8ixe9+vW89LVX8IRzz0djpA8DQ8z0fR5db6PbLpZ1WTjAdICN9RKVxIKzsLEBTTO2ZIWx3J8SYbWU0n5xRfwSIPsSvWxsEX1m83HCpYFdkzLl0o5iUDMtx54uTUdH2RyrYKwrIzTFgCs9ZEjGimDUY5otocx6ix87zGhaptYxtZ6siVlIRO1pfYczUx6IgRwTkhJLztH5FgScVRrrWW4bGmvpyRi1LInQWId1BuehaQx2jFfGADGVG8iK4BqDsUJOZdqpMRYZRVFVwdsyDTPEjKE4xpwzqJT9rZHiEMuKGsE5g7eCt2ariH+McKpu3dcg4/2um4KkUO61E7rKTuAeg+JG206NYFYqlUqlUqlUKo8cVTCrVCo/cI4VxR5MJCul+mXCZekCKwJYzltTMkUgpUgUJc0zaiDHRFJBsmzFAW3EGFNcT2KZ9QOaIEdhyLm8npb+siFHFHAqtNYXoQ3IOTHXRIyxREBR0pA50s9Zj4EvfvbT3PjWa/jYTe8lhmHH6/KUZz6Hl7/+Kp73wlcgzpJTYLaxTkxKGErMM41dYwLEUExw0ymkGdzfl2imtUXwmvcgbflBnjfANNBLEcGswFJT9Kw8lDhlmVi55Sjr50VQc24UyroinImMcc8WlpaWCDGwvrGBBYzzaEyklLHe0ToHpOKUUoPxrjjIsIgT0IQ3Gaxn2jZ0xoMmBk2EMKdpOjqzxOEYiH3ApYy3Ftc6vPWIUVrfMHWetrH0GkkJJtbhrMU7hzEZ30HjmnKvxRJ7jKpjv5tBXJmOmrIiYoo4lRMJcFImXibKejtny8ACKfeus6YML8jlPjZWcNbQOIM1WwX95b4t0y6Pc5Wx7d4fhwuY0VXmjJwwZrnY5kRTY2sEs1KpVCqVSqVSeWSpglmlUvmBcKLI5fHxscWEwNIJFnN5fiE05GIaG2cBKjFHUgZSKfmPChpAxpxikoiQcE2DJiECKSQ0CX0c3WMIRjNxdJM5tbRY1IwNVApDCsxjIBsp+yNszHvWw8ChQ4f54Pvezo1v+8988+CXd7wu3XSJF/30Zbzk8qt44rlPJqdECD05JvpZIIwdYykDsQhWOUE24F0p8r/vgbGuzRZBZz4rzjKrRSiTpiRR01BWuOuKqJaGcmxjy7RM74ootD4vP/ybBpY68M3C4VT60aZTYTJdZhh6jqyvF1HNeyRmUsqIMzRNEb6cMVhd7GwxahAjm88Z29J2ngkOYyDkyBAG2rZl2i0RsnJo3uNyxpsS22xdA6I0jWPZN0y8Y54DOUEnnqbxOGuxRmlapWkaVEf3V4KYM4ZS6m9sKe6PmtFcXGIChBSwxuKNjLFfBSN4L3jrSJowRnDGFOdjzGCExheRbOEqE9gU0jZdZaNQJiLknMkqmwMAFp14xmydy4N1lcmDRDCrWFapVCqVSqVSqTzyVMGsUql8XxzrfjleONPx8S2RrBT36+a+Y6XT2L8ViaroAJkST9QsyKIs3SpGI8ba4jCzjqEfQCx9H0sET4pIJgb6HFCERg3eurFjypByos8DKUUUQ8wQ5oH1GNgIgS/91V/yvuv+go994N0M8/mO1+WCpz6Tl73+Ki592asQ6xBgmK0zIIRZJCZKPxll2iUJyKCuCGEhwgOhOMXcWMrf96MDDCCAbaAfQPuy+3RSBDVSiWMaKY40I0UQ2xiFsraFqYd2siWUiYddyw1d0zEMPYfXD2MSWO+wGWIs0y+9swiZxlgMnmwAI8VRpuNETGfIavBdy7J4jCgxJ4aQcY1jyU1JWTk8DGhKeAzGGtqmK+fihN2+w9oiQEWgkYbGuRL9NGXwQGMMznuGkDEKMSeMCN6VqKOzpacspFzch1ZImjbjl4KQYumqc07w3qGaSWQaZ0FkjHQK1gp+dJ65ba6yNIq+MkaGFxFMNBNz2c6YIvIuJl8KRTCDh3aV1QhmpVKpVCqVSqXy6FEFs0qlsmMeLnKpYywNSuQyqpLSwl22FbkcN0GkTKhMaghDKq6cLGXMI6ZE2kzEZMUYVzqotLjOUoA+ZTIZC1hRAomQM14crfitFxrdZEEjQcswgRJN3KDXzH2HHuDW99/ATW/7z3zlC5/f8bo0XceLXnkZL/n/s/fnX7JlZ30m/rzv3vuciLy3qgQC3NjY4AYDbdMGAwabwUszCM0gBve/1+4vk4QmhARiNEYG2oZucAONwcZ4oo2NpKqbGXHO3vt9vz+8OzLzlkpSXVGAhv1ZS6tu3YyIzNxxTmnFsz7D97yFv/nlX0V3o+6Vvu80c+rW2fuAK3bXz2YtOsZocLoJt5gIHFZ49FzEI3uN55QELcG+gw+Od3gQsUsba5pFod8OKERHWSKcZ8cSsGk9gI/v+/RTC+t6pO47z948S+qgKZxU3cCzspRMFkiaY+1SgZLR7rEimeBYcixOrgtPS2EtiZvtHBHIolzpinfjUa1YN9QMUWFdV8SMnGHVwtWaSQpGuNcyyroUUEjFWZMMV6FR9wBW1Y0lRcSRJGQRqlnERBMgTu0WAHKU+qvHYw8loSqYGykpSYRmBH1MwppTADiFlOI6uh8bZrjKLuDMccxlOMHi2tMLLBO550J7XBfn2AtFMCcsm5qampqampqamvrL1QRmU1NTL0qfCpLB8NeMf2/9rpcsYpd3RfaRLxRaq5gItjt9dIfhCj4gmYwC/5QxEzwJfa8Yyvlc6e4RcQOcTh1dZIsrS14QcbAoYd/6RrNOR7Dq7K3zqG6cW+eP/u3v8bPvewf/8uc+wPnm+onP5sv+9lfxqje/nW/89leSD0fEYdvOtNbYzGm7BYDRgFQXGOIGOkDZzR69YjnDIYUbbCOil32L+OTe4abdchzKw3CXaQ9AlhU8AeOct1NwreNVFPwvh3CXeRsdZy8rHA5X1H3nY48+RupQSgZxXBKejHVdosNrwKvuHdaCukCPoYC1FMyMlpWndWXNmZt9w6uRilLSgpvx3L7h3ZBu5Jwo64Hsw90lK8elsBSlA9KVNStrKmgSNDmpwLqsiENrjndwjKyC5nCepQHPehdUFVGnV0OzsqQo++/NkSTkEquebh1BWXL0r7Ue12dO4VK7uMouK5Xd7t57Ia65gFljgGI4+iKWGWetejcM4P44+LoPw2YEc2pqampqampqauozQxOYTU1NfVLdB2MvBMmeH7n8VJAMDPNO35W99uE4G5FLwLyjGbQ7lqJIvpvRm9N2Z+sdJ5xC4kYXZ+9O0sRBE52OioIJW6+YdLbW8a5Uc07nE82djzz3HB/+xQ/yi+97J3/4u//mic+lLCvf+orX8u3f/Rb+9t/5Wva9gir1dKY57PtO6zEkiQ7I5WGaMx9dZQ1ONeKWmgJqnc8BobJEf1nOYBmutzjCnKA8GOuZNYr+e43HYdGFZluU/B8eDFB2DHdZ38J9dnz5gbWs7PvORx99DDUoKaFJMQTJRllygDKPuCUYvijaBW8B+nLWiC1m4YEcOObCqe5szcnqLIcFq43rVqEbslfSspDKQiaiigctHNfCoSQahnelJOVQEiknzC0cceuKGPR2cSs6ScNGl/RS4m9UCxec0RHiPHKOx3WLEYSUI14pKrg4Sy7AiFdawLQ1J0SEksIZBnJ7bcOl1D+cYCp30czHXGUKWTUej38cKIPHYdgLwbKkE5RNTU1NTU1NTU1N/VVoArOpqamP06eCZPcjl70bJtx2Qd2CMsCHU0fEMWuYKX03qjXapbwfQdzp0snupJKiZyoLvnd2hO3cqB4F/kWVbo3zcAUVE9alRHDTBCHR+s7ewonWqnEa0MaBf/8Hv8/PvO/H+fDP/CQ3j5574rP50r/55bzqzd/PN73itTy8eoqtVrpDrTtdlP0UoMwIl5EJ0TcmUHuAsr7DzYhjpgLJorOsOahHtFIVJMFpj+hmSrA+BVqh7aAryBZp05TDfSY1XnO9gkOBvAYosw2KwVNfdMVSFuq+85HnPkZCWFImpRyl+CkgVFkOZOIsEYOieAP6cF2JIClhCR6mlUPK1FbZWuWQFS0F74lT69ERt1V0WUjrQtYUoKwU1pQ5LhlXp5uSSFwteYCyTjo4D3MBSXgPKGUWy5eaNOKNIiSBvY/4pThGdJa5OgXBTPFmtz1kUe4Wj0kaPWe9Ryl/KUpO8TMuo9i/2+N9YoLfQk8kQBo87iq79Jxd7hXnE7vKZgRzampqampqampq6jNPE5hNTU3d6r7D5YXK/InUWQAGuIUM4RILSIaF50w0AEK1ThsRSLMRubQRovSOJkcRUpZwSPWOmVB3Y2uNS/+TWKOpcKrGopmjriAdEcUNNmsYna13eg34dLNd07rxaNv48C9+kJ9/3zv5/d/6zSc+l5Qz//A7X8Ur3/L9fPnX/D1sb7gK1zc3WA+YV7cOwG7RF3bpJxOLX7nt4TY72e2gJNZgHy6yS5l/WsJldhrwqxQox3CU1XO4zZDRe6YxBCAWjrL1KiKXeYl/UqE4HL/4ASVlaq189NmPkjSxiJLzSjPD1MkilHUliyIe84+uApbwLiwpjRXNTBfnkBce5MJ539jdKUnwkhFzqjmtNaxWtCzIWLVUgWUpo6csoUmwPuKXSVmXBbMOq/MwJSQXxITWegBInGVJIDHcoO50N/YRv3S1WBrNgnhHPDrVRJ1yyBSVeC6OpoyMUn93JxWNQQGiJy4njW60fndP3LnKHFW9XXe9xC6RS1eZXu6aj4tSXpY15ZPAshnBnJqampqampqamvqr1wRmU1Of5/pkbrL7kAygdrst778U+pvbiFwCiXD4uLFXp+2d7kZvIB7xPesdGUuQKUcnlgtQna0727nShOi2SkrtlXM3RJTFlXVdUI9CsOZQ+07rFSOxb529Nx7VWMb8D//uD/nZ97+Df/HB9/Lcxz76xGfzJX/9y3j1G7+Xb3ntG3jw4CHn00atHauV3qMzbTu3gGOjyF8sush0QC06VA+n2JIDgG3neHwe0UtagJTa4KYG/Co5yvnFIppJidimSgC42ob5SyKiWVKAtcMaZ6sOh5cHKGu18uz5WZImiihJC4bjYoga63FlTQuYYN7xBGICpiSFRYFccOsspXBIhVYre2+UksglIx6w8GZvSGtoypAyOWfUjVIyq2SuDrF02czwKiw5cbWuIE5PxoOS0FyggzWnj6L+UpSE4yIIAu5UA3VF6IgrjqK5o5qwroCNDrIo6xcc0XQb32zdEIQlKyklRLgdDghX2d21cHGV3a2sBkhLyijyvyv1v+/AvK/ng7DZVzY1NTU1NTU1NTX1masJzKamPg/1qSDZ5TO8itB7v3WTcYlcDkjmFquMgiPitObszdlrozUfkUsFM7oa4kYuCU8eTjQLmFabsbeGeBSrqzV2ic6yVTPHVBA1dICKZh2Tyt46rTu1C4+2a2prbGZ8+Bd+mp9/3zv5nd/49Sc+G02Jb/6OV/CqN30/f/vrvgFtndO+se2N3jut7pgo9bxTR/eYh3EJ64T7qwMNOrD16B1bS3SVtRrusraPgn6FugVUw+KxyxJ/lg6ViFyKhGtuG4uXSnSUZY2OsmWN8v/kcHg6QJn1xnM3jyK26ELyhKUMGkBnORSu8oFWG70ZMsr+MWEpiYTRNXrElqRcrUesNZo1dEmsOf4vpJqz1UbqjSyJnhJk5SolUlIyhQeHzJqU6k5rUHLh6rAATpPOITtXhxXpgnehtkYaEcmkjGIwiWupC44jKWKX6gkdzjE807tHSX/KpCT3opLh/Go9SvZSvnOV5QQlKebQzJ8XQbZR7K84AdNkAM9wlckt6PJP4Cq7H6+cEcypqampqampqampz3xNYDY19Xmkywf1TwbJhABZJrBXD1eYeaxW3ivxj8il00fRet0atXfcI9InDh1DMJIKqgIar9/3gGtbrXRAe3zNaOwWYG7xxPGYoXVEldqMJpXWdjqZbWvsZjyqO5jzn//zf+RD7/sx/vlPvYePfeR/PPHZfNFf+5941Ru/l29//Vs4PP0y2qMb6rZh3ai1Ybaz9QYO+xYxylQCdpUUccte47Xq6DBDA6rUHkAtK4xtAFKJOGUf7rSsAcrcBnBLcVY5eCO9xrplGWuXWS8RTIHuFIflZQ8pmui98eh0HaY/hySJrgnJ0fdV1sJBCr0be21oCpjkDlmUIoa50TTK76/yFd7DLahL4qgRp9y7c26d3CpFEi1ncDikGAxImrhaF1YFktJNKClxXDIpKYZRCjxcS9jmutB6OA6XkhAdTj1X6A1Dg05eOsgcJAluhrviCKpOGcuWKk4a0cq7hcsAryUrqooqFL24yp7v+hqRSwKKXe6fpHdDABfQdRvVfB70er5jbMKyqampqampqampqc8OTWA2NfU5rgsMC+j1iSEZ7nSc3gKSRXn/XeTyAskUx8WpzTg3p7ZObxFTE08B1dRJ4iw50X04bixA07bvdLOIaDqIOBsdupMlcZBEKiDEqqG74/3M3o1usDfhZrvm3Bu1O7/2yz/Lz/3kO/ntX/9wREifQKLKP/hH385r3vT9fPU3fivdOnXbaXuli9C2jb0b1lqsgI5OMi3QtugKwwN8YdE7dinrl7FYKQLZAqBZj69dn28TnOQC64Bq3WN9MsXLhpupRn9ZzpAfBiiL6GXEEhdgeeZhlPlvZ663M82d0g3NCza6yFSNshaOaaHVTlcnlUzHI8pocEhOByxlUlaeKkekd3bvLFk55oIk6M24robVjaKFmqO7rKSEurGWwnEpLDhpyZg5uHDQS0+ZkbJzWBJZMu5Cb4ZpJ4mii5CBTkAuMExSLFhe1jFJCIabxp8TJFEEJ2chZ8W6xzUGI34Z3WRZ9fEFTBF69/G9LvdHj3MZrrTL15Z8AVt3rrKAao9DrxeCYDOCOTU1NTU1NTU1NfXZownMpqY+B3XfQfb8D+kXh4vIcJJxgWlyC8bcDeuGXRw0OJKVfa+cO1jr1G64CzrcZM0jclmyIsMIZN1wE7ZqnPfoFRMDktD7TpPoNltIlEPCe7h/rDcandY71YV9a5xb56ZVxJw/+dM/4UPv/TF+4f3v4iN/+t+e+Hy+4OVfzCvf+FZe9T3fx/HlL6edN06nE6KJ7kK9vsZTom471qJo30f8shsjTgrbiQBmPaBMWcIN5qP7Su3yHgAKN+dwmqU1nncIjsjuAc58cBMfr2EXUPYg+s/SEdYcr3FQRY9H1rJQ9zOPbh7RrJOacSgrnmJYIamR18xVWmit08xJS6HjmAtKYlUDVSwpOQnHciA57L2SgKeWFZLgzTjvjtWdlFdcEs0N1cRSYM0La0qsS0aSkgx6g0NKrOsSQHNxjklJqYz32mm9kUQpOZFFAvA6uHXwiIQioBoxX5EAcOY6VjID2KYiFBEQxYwo5sex0VV231W2jPMxHw7KW5BsgyDrx7nKskpcw0/oKoMJy6ampqampqampqY+2zSB2dTU55BeTOQSd9yd7oQDRyQgDzYcZYCH86ZolPRXc/ZTpXXDjIhcohHbo5NUOKSECSiK9Sj931t0Y0EADOg0HGtO0cRREpqHm8wDsHUaO9Ftdm7Ged85tUbtxm/9n7/CB9/1I/xf//KXY03xCfX13/JtvPbNb+fr/9E/YcPYH504PboBkSjzt42uQtsqrVUkRVKQfge+bB9QTAKU6VgD7Q6tD3dYG0uhMFxx0TmWllHoT0Qy9xbQJOeIdHqNtcXe4+/WK1gK6BHWFEX+x7LAurAuK3U78dzpEWaG1sa6HPA14RjQosxfC3Snm6Mlyv47oCQWjXVTTQXJwpIWskNtDVHhmcOBlJVWO6cxdpDSgovS+g6aOGYlayYJXB0XNCnSY/kgaeLBcUFVsGwcipI1oZLo3amtggjLmska4KgbdGuIJwy9sKtRqM+ITkosq6ZwCapEB1nK4ULjcvbdcIGcE0mikD8r5DSgnPGYqwzvmGsslorcfm3Ncuu6vMCyy9c+latsRjCnpqampqampqamPjs1gdnU1Ge5PlXkMoYu48/d/RaS3Rb+j7+LUvNYbYzIZacZtL3Rut+6w8wdIyKXOQmiiifB9k534VQrtXUcgWZ4FqwONxnKIplUYlVQVem9YRp9ZmbCXo2bWjntFcf5s4/8Dz70vh/n59/7Tv70T/7LE5/P0y/7Ql75hrfwyje9nS/6kr/O9X7i2etrEKW7x9pl29GycL6pcQ4Ougq+j7Pt4ZRKe4CxfZyxENAFD9B1O4KQIqZZa8A0zfH1dOmtjwFH0nCk0aOw3yRcbIfjAEDHAdcUHiwLthTW5UDfTjy6eY52AWVlxY8H8I7QWA4LSzqSDZoZ5HzrZFIT8ijILynTC+ScOUim9gYp8fC4klXBnWe3Cq2hCFkTe68osORMSRlR4eHhSJYeXWndSalwVZZwYS2OeudQMllz9LG50cQoSyZpRCyjP8zi+kQxMxgdZpojvusI1iNWOfgapciAT+EUE+I9uICzkpQ0XGVZ47ozl8ccXyIDNKMkvXOOJYWS0u1jL9+zm3+cO+yF3GIvBMumq2xqampqampqamrqs0MTmE1NfRbqRUGyEbl04RZ43T7ObICLWPtLAikpfa+czOnNaO2+m0wCZmCkJJQ02vsRvDv7ZtTWogdqALnuDXB6dYokjimh6bI06Hgz9r5RMdom7M041cq5Npp1fvs3fp0PvvtH+Ne//Av03p74jL7uG7+F177l+/im73gN1Y1933n20XOA0Az6fsLwcEvVju8nACSHi8w3p7eAZToKx049opPKcIQ5HFIU+XsCBE7neE5K4SIrGbIzusQCxpUMdYAyaQHhlmX0ky2gKxSBonB1ONBLppSVvgcoq62RurGUFT0eRga0sl4dKJLIrph3elnwVkHifVyS4BhLKnhWdLj8Wmt4Mh4cV5IIEGX+rXXUHSRR3cCMZcksCDlnjsuKeOOQlGpgCMecyTkjCyTvlJwo6Riw1qF5RxAOWclJaWbUZre9dj4WJ7UoqoJ4RH/NHVW9XQwVVZY8gO3FVXZHy1DVAGTDVZYGrXw8phwOu+gqu4tZIrDGtObtImbSO1dZ0jvg9YncYjOCOTU1NTU1NTU1NfXZrQnMpqY+i3RxvlyWKi9/90KQrN+LEYrEkmAfRf7EKCUlReRy68Z+Y3g3GsAAZVH4b+G0yQlRiYhidVp3ttoi9oZgzcJp5o0GJJQimXWJLigV6L3R1dn3PVYlu/DctnHaG47xkY99lJ/7yXfyc+97B3/yn/74ic/n4dPP8MrXv4VXvvltfOnf+Epu6g3Pnk9IM0xi1dHrhquEy6m1WPO0ywFDO0Hfb2us6BKF/JrDfVdbALAlxYjB7gHHrs/gY92SFADtakQ1vcQIQM6w94hsSgu2kxc45LG6uQQkKwmO6xErJYrs+85pu2arndQah7IiSx7vz87x4RXJleyKYmxJEXPcO6WsJAHrPeKQpaAiLJppvUNyHhzXcFJh7M0CIpmTROhAt07OiaJCSZlDXsnaoxPMC83hoIllWWI9NRlalGM+YC50CzBl7nE9aCxX7rXT3EiecIkoJBqrlEki1GqR8w3HmziaAoSlpFxcZbGkCRc7V7rnKksqKOB+10fmAxTbWLJIA2I54SrLfw5X2eWx9zUjmFNTU1NTU1NTU1OffZrAbGrqM1yfCpKNvwFhLP1x21d2W+rfGVuIowtKhVob581oPYrg6YDfxdGQTk5CTgkXx80xg/1s7LXR3aE5ft9N1sJNdjXcZDlnxC0cXrWyu9HPwjYK/Pe9U73zu7/1G/z0e36EX/uFD9FqfeIz+tq//4285s1v41tf8d2IZk7tzEefexZxxxD2Wul9J6WF7p391NABw6QTZ9eg7ndg69wCmmmK86sNkgUo6z0eqwlGYhHN3AKWhyNq2VOASQSagG/x+q5QDuHsyyVA2VoiPnhcjvSUSCjJKmff2Wonm1NUSccHgCNeuToeI3opGfdKXXKcn3RyyqgIyXtEYQ8ruLHmQu8dd+NqyRyWBRHnpjZaM8QdTZndd7oHmLrKhZITa1ooGTQp6oo5LJK4GqBMk7GsmSSJJJneHTzcYzkrh5JvI8DVOtGvH47DJE5KCUkj14pgPr5XilBxUSWlAG42yvZUuHWLyXCVqcarprGGed9VNu6c6EEbvWaXddWS4t+f7yq7vK8XOPaJANjsK5uampqampqampr63NEEZlNTn6Ey97sSfl4Ykj2+hnnXV2budLtz0lzAQauN3YxaYwWzOQHKkHAViaECS05oiqJzq0az0WnWojzeexTHi3eaODrcZMclSqeUWDhs3ti3DTOlNud627huDTfjY4+e5Zfe/24+9L538J/+6A+f+HyuHjzkla9/C69689v4G1/x1Wz7xnWvcHPG3akObd9jqpIcC5H7CSOA1f0lytagV1gPcK7DRZYDlLUWHWJFRrF/BTRimEY4xHKkGHmwxhBAT5ByDAI0A7kHytYH8eeyBnBbF1hzZkkFKwVFUW+c2sa5dRZi+VFLQZOCV9b1SNKVNS14r7QsdBPwCygboA6nrGuAP1GEiDiuOXFcV8SN3Yxt38NxqIXdKrVXRCJeuZZElkxOSsnCqgvVO4rwcFkQEWRxSkqUrCQpOE7vTvdOyspxyQElLYYlauskybg4qqAqJAko5q5xsLeusqj+zzmhGiDt4vZywjnpyJ2rTJycBfFx/doFIvvoQfNYdx0AzHFSErLqYwuyMdhwB84u+kSushnBnJqampqampqamvrc0gRmU1OfQXohN9njH8Q/ASQbkTsTiTorPNwzY+VyqwN29R7xQwM3CdeYGyTIOdxkaACxZrCfI3LZgb53XATzTmxqOkUyB9UASmuG2mgJ9vPO2Tp+UprBdb3hvHWad37/936bD737R/mVn/0g+3Z+4jP6qv/l63jdW36Af/Tq11KWh2zbIz56/Rypxs9UzbG24yJ4Us6nM+p7gLJLvK5FfLLW+HMu4Je4JCAaLrIy+szMoRJfO52i50zKiFW2WLBsAjXBYQlIZgp+GvBlFPlj0VXmAlcrHNeVkgpNNRxlYpzqmdNeWURZHMiJpBmRxpILy3KgaNC8pnLrHlxyAZysgouwLBkbTq3kUYZfUmZdMipOp7PtDXej6HDeWcXMOSyZgyQ0JXJOLFkokulmdOtc5YWSE56NtSQkOYe0jNjlgL0Kh5xj2MGMXo3ucTBKQnCyBCwTjcgkCDZ+B/SyQBo/h14ilMRIQESLHVQpF1fZgMOKhLPSfDjR4p4wDwfZ/cVNRcKxds9Jdkl3vhhX2cffo6EJy6ampqampqampqY+uzWB2dTUX7FeTOTSfJRsXSDZeO4tJGvcAgAZH9R7N/bWqXsfrx8F/bfxNDFyFg6akDQgQTNavXOT2YhhWnyVjqEoWTKlKKRwP7kZe2/UtuG7UqtzvVduakVc+Mj1s/zyT7+PD733x/mjf/t7T3xG6/HIK173Jl71prfyFV/7v2LdeW675ny6hr3TRLipFbyhmjARzjen6OCKKqwAOaPEv+8RmSwZrEQhvxBF/OpwzFHe30YPXHXYb6LEPx1HqT/hInMFy7BEQpXqQA33muZwktHjny5wKHB1PLCkQgNEEmWAsnNtLKosCJoSKRVEGiUnlmVl1YJZp4sMMGWUlFGDosLWO+vVFXvv4EoeDsNDKqRFyap0nNPWxipqIomy94qZUUrmKik5F0pKaHIOqSA43Z01FdalYNlinVITS0oIQjO/BbClpNs4Y2/G1qKwTcbqgY4OspQClJnF+TtOFgF1iqaIX8p4/y6usnFti0Sk8uIqC5ca4Srz4WbzgFw2hgN0ADrcxxhAuMouTrJLF9qLdZXNCObU1NTU1NTU1NTU564mMJua+iuSjRL+TwTJPAqeIjrIbZ85cnluBxmf8FMakcvWaN1p3cIZ1sEtFjKtR+l6SbAsGRHFJSBZr06rTmtGc8er0UVw7/H9MAqFNS+AkdeMtk5X53Te2KzjPVE7nNo5gIwbf/Bvf4efefeP8ss/837Op5snPqOv+Kqv4bve9kN8+2teR1kf0r3xkZvnkLOBCLsbba+IGCqZhrOfbgCJfjIct3B89RbATCwcYprDBUaPqKUKHBTqBieLIv9zg/0Eywp6iPcma8AxU7AUAwAXUCZtgLIC63CU5QJyiDXNhw8eBMBJCTEhq3Pez2y1kSWxIIgoeV1RaeQsLPnImhfcjUosUYp1kggH0bhOkqKlsJjSzVlEb4v985JYU6Z6Y9srLqBaSBjnVqOHLCcelEwuC0kTIp1jzoBj4qxkjktGi5AUDiWTxMlpoZvH8IOCJuGYUnS2NcPdOTcjvpsjyckpISOKaZfxU3FUFFOi0F8TmqKMv1msc+qAW253XWUXyJWTDjBGXOeAjJun2YBpw4HGAFqfyFV2H3h9Mvj1QrBsusqmpqampqampqamPnc0gdnU1F+i7FLCfzGMfRwkc0TC1YVIlKYz3DcW0Ug3otA8620es7fG1ozeInLZLUARFuDNRuRyTQnRAAG9O/u50yFcaOZjLTKibtUrmUTSxCEXJAtFYt1wa41aN9iVWuF6b2ztTO/wsdOzfPhnP8DPvOfH+IPf/TdPfEbLeuA7XvN6XveWt/OVf/frsKac+omPnU+UUUq/4fQ94pwiiWYNemNvHZKgDrRYv2zD7VXScIOlOFAhestUYZF43E2N7rJq8Nw1HA6QDxHdLCVgWTfwFcqIaXYD6hgIyFCOkRTMS5zzmuDhUw9JCKYScURxzv1M3R01yBenVF5BO0tSlvKAJRXcOjWCojgBjha5dM5BWZcIyLqTCddUESWXxLEUtrpxrh0HVDIusFulmVFUucqJnAtZM0mNRYWSDlTrFJRDXtCiiBhLSpQFSi70HkupJoYmYcl611NWYW8Ncb3tIStJY0FTHbPoXLvviBRx1qSoJpIGwGpmqMiIAselqSrkpDBK/bPKLexqZrf2S2N0m+mdW+zS5wd3MUoZUU14HHh9Mvg1I5hTU1NTU1NTU1NTn/uawGxq6i9Y7hFn+2SQLDxgAEIbq42YPw7JLp1LY3ax10Yd7p5uPnq5/LZDyiXcZCllRCOCZr1Td6d32GuLx17cZNZjDRNnkcLD5YiKkUtGzenq3JxPnHtHLNG78Fw93/Zg/bt/9/t86D0/xi9+4L3cXD964nP6sq/4Sr7rrT/IK7/7e1gePkNvxkfP5+js3zvdOid3vG6oZoSEtRPdlG6dHjSFVg2vsO/xuiVBz9AAT6PvrUavWB7l/3uNuKQ5nLaIWh6O0PaAZqrRUdaARWG/xGJHF1ou8T8s3Gii4VZ78NRTJIkOL9UM6py3G5oLYvHGai6ILEg2VoGyHFnLgrizuyOiKIakTHFnt3CUHcqKq+MWBfdLTmBwXBaWpDRrnPuOe5TkZw2XWTfD3Xi4LCwpx8hAEkoS1rRGzNech2UlLQnEWLKS18yqGtdOi+ilEsX8RYXmBl1iQdWUrBlXu3OAlYhkth4Xv+DhkBSnJI2FzDEYEahLbt1dPqxfOeltIf9jrrIRv7yU+EfX2QWWBVwTuHWp2WVVUwR3edGuMvh4WDYjmFNTU1NTU1NTU1Ofm5rAbGrqL0j33WSfCJLFv4w4GHcPCAg2IBmQksQHfTNqM6oZ1gwbMMT6WAD0iMaVJOSSQRwx6O5sW6d3p7YWz7vkQcVpXkmaKGSWJYHCoooj7K2xtx3rQu/Czd7Z2kZrzvV2w4d/8QN86N3v4Hd/6zee+IxyKXzbK7+L173l+/nqb/gH0JWtnfno+YZUDa+dMx6upV5RyVAyvlf2vXFqxpKNauHqanvEUDG4OsDWYrHSRpm/OpTROeYjptk67G10mi3RTVZrlPcva0QvO7GE2cb7qS1imHkNB5p7QLZlCbfag6eeJolil0XGnDjtNzQTxGI4YcmFpAuSOguwrEcOZQ1QFhZDzDopFVZRtm54Ug5liQXTHoB0zQk35VgWUpin2CScVtYdSIgap7rhbiylcEgF1YQilEU4poVuAcoOZSHlhCdjXaNw/5AUJ7G3gG06VinziDi2bjSL9yF5IiUCtKUEGZI51gJuiQa4AicnQTShOrrKYMBjCQfguGkukcpLYX9SuV2x7O7j0X7XVabhtAMi/imPu8q4OPbuFftfQNwnconNvrKpqampqampqampzy9NYDY19RLqU0EyGyXsF0h2+9mdiLJd/l1EAjqM16h7o4vT905HqLvF+p/HMqTo6HLScAupKmadWo3anLb3sXRp0dtlHdNw+RQKD5YjSZ1UEtqHm6yead3pVajdedR22tZo3vnjP/73fOg9P8Yv/NS7ee7Zjz3xOX3pl3053/XWH+AV3/1GHn7By+ndOZ1PbM1ZXdDu3PRO7zviY/lTnbqfImbpPdxICvsWi5bdiPL7ElHJ3aELJI//ZYly/9YCTrYGdZT/pxKOM+vxuKs14NglvmqMr3kAtnIYgwEezrKkcMhwfPgMWRPdekAYhW0/0ZtCV/BGyoWcVhigbF2PEYeU6NTq7jTrJM0cU2Z3o4tSkg63H/QaEccsmeOyksRJmjjT8OaISdjpvFFtR9wHUJPRUSakLFxpwRUad4X+5BF1lMRxETRl6m50a8iIQ8roDzNzrBnb3mMMIimmnSXn4e4CE6W538JfF9AEJSVAb3vIfCy7ukfEsltc+/l2AfMOlpl7vCcDlgUIuxT53wE5leiEeyFXGdzBsk8Vp5wRzKmpqampqampqanPP01gNjX159T9yOXzIRmMD9s4Mhb7uvllEvB2yQ9GEbkEpDGLdcFbJ5k5rTndwVqPJUAzNMvofQJJQXZa7+EmM6fWRu/cwQKF7pWUMqsn8qqkJOQUBe/bvnP2Cnt0TJ32nc2M1pxH2zW/+s8/xM++5x389r/+tSc+p5Qy3/Kdr+K7v/eH+F+/8R9iCFvdeXa7gWpoh9YaJzes7SQt4IKqsW87BuzWSZpi7fIc0VIzUGDJ4DmAjGSicJ/4pwPnPc62Afs5HGmkcIbhsBDPMw14pnYPwklAOb2CqwHQJMGqcFiF4/EpVBPD5oeocKo3dFOkK26VnAukA5rjey2HI0USMtxPTZy9VQ6pxGO5c7YtJYeDrkfH3ZIyD5YVESOpcPZO6qAIjoIZm0V525IyOelYk4wesWNO5JTpwIKyloLmOJ8lJQ6HREopYGu1iPeWPK7j6EvrFWpvmEf8EjFEnUMupOQIyl7DO5mS4sPtmBWSRjY2usXuYBcI3f3WVfZ8UCZAG6DYh0Ms+tsuj7lzlQky4NhYoB3ONb9X7P9iHGITlk1NTU1NTU1NTU19fmoCs6mpT0MXt9j47P5xcS0fUUec2w/pBni3UdJvIxR217HkAwLsW4vXbp3qQq2Gdx+LmYZHyo1lSahAyilK/7dObU7dWrijutzCui6GuFFk4eFyJKujS0a7YwlO55twk3XhtDdOZrStYTj/8T//B37uvT/Oz/3ku/joR/7HE5/Vl3zpX+e1b/4BXvmGt/CFX/TF1Go82s5UBTk3xIVT7zRv9FrRtJByRnH26zPbJWonQu/Qtj7WEAMmaQ7Q5R1a9MuTGmABCvtlZTQFKMMCkjmjnN/vFjNTCgC394hvLgxodoSyDpCm8feHVTlcPRUQilvqyc12jXkapWkNyYmUVnTRgFPHlYySUo5ie+9srXLMC4e8UgSqO1kiyinmKAnzylUuLHkZLiqhOpgbmRTXmBmNjnlHEZaysOQcDV7iHEumpBRLky48yJlUojg/ZSWvyrEUujl7NbobJWVsLEgIcb3XbrRK9J8pSHEyibwKarC36BIL52OcT0pCKmmArOE+c0NVwOMeCZjsd/HLe7Cs3wNXPlxl5uEiK0kGwPLhGLuLYNq4MZ9f5v+poNeMYE5NTU1NTU1NTU19fmsCs6mpF6kXgmSfqJdMEG4H+8YDWrfbMv/LB3Z3w9zotdNwrHZclH0PKOStYyOuVkQoxxLlWyLhsnHn+manN6e1TmsRVbs4nRqVkgrFC2mN71k08pvn80bVTr923JWbbWdzo1fnZj/xr371l/jpd/0ov/mr/+KJz0pV+eZvewWve+sP8g3f8q1IKrTaee50YjdnNUgG163TWh2uH6XkjPfGeduptUWhGGPpsjkdwjmmAbm0QN3iMWYBv8L+Fc+xFCCsnkY/1ej4cmLlkuGq0hRwbauwJG7fVyl3pf+i4WQ7HjOHw4NRxj8isUk57ze0XVBXaA1NCXQhrYnF4Hg8kCQFKHOn4extp0jiqqxkhGaGaGLJsUBQJNHYyRilHFmWHNeMxPusRM60mgUYpSPGiGkKIgkE1pI4aKJ7XJSHUkgl3YKysowFVVG2vY9C/7FwiaMSXWytd1qLs8lJMTpLSmgWFlW6wdYi6ps14WIkAU2KoLcdZDIAsUrMYV5cZSKQRpn/BZThTjO/hXWX+xBGBPnSQTZg2ce5yp5X5v9ioNcLwbLpKpuampqampqampr6/NIEZlNTn0LuHsX6nwCSmYf7S0dVuROPB25dMJdeMh0RNLOIE1YzvBu9Gc0icuet4kmgRRYwe7jIdLiKzGDbK7U5vRrNAmZghg03mbpTpPDU4Qp1Iy9lECXhtG20zeiNWzdZrx0H/st//WM+9L538LPveyf/40//2xOf1Rd+8Zfwuje9nde86e28/K/9NZo5W2tYP7HvnWTRJ/Zs22i9oSRAKBlqrZz3zvlcyQeljzVL2wlwZrAuUDvIEiuYqQ4Q1qPMv1o8x3OU/fct+CIFvIWjbCmjnyy2DWjEa4tBIcBYkoBlmsMJpQKHq5XDekQQEtDx6P/qG/UsEYesHVdBy4IuypUkDocVEWXJhW5Gs05zQw0OeaEgNAxEyUuOdUhVeq+oG1flimXNkQmVAGPJo4drN0MEujcSsORCzkIaoEyzcCUaC50Cx1TIS4xBLCVTFNYloUnH0mof70ceUcSOm9DN6N0xF9TH2SUoObOWcLdte6DapIIPt1dREB3rlyIkcUZ9GCopIslGuMpSOMNUGA6zcJxdENXt+uvoJrvENWWsx8a+5t39CnwcLHsx0GtGMKempqampqampqamYAKzqakX1H03mdknhmSXXjLHbz9oR2F5wANhxMMkXDfdwgnm3MGuWsfzLLqhyEp2Jx1yLPzlBD1Ay3nr9ObUGouXfXSjIeEmy5o5SvRRpazkoCbstbJTqY9suMm2WzfZVjd+41/9Ch/8iR/mX3/4n2PjZ3+xEhH+wbd+O6996w/yzf/4O0kl4x2uz2fOGEvltnPqUT3RuqEkyrJAN/Zt41ThvO+kJKCw3Ri9BpRxg3UU+ZuAa7CjNKrghmmPbY+e+13AbsYPV0bs1WPxshKPOTD+PDrKxMOttqRYvxSN7+kOxwcHDssh3kuI5css9FZpNQYJrHYQSDmTjpmjCYfjERGh5IL3ztZbXCfNSCWxaqLFO4jqcHOJ4tYR6xzzyvG4QItobErRW5e1UM3o3sNtZhYuQg1nFhKvd0hCzsvoMdMYdMhCTomMczgmSs7U1tlrH89LcZ22HpDShdY6vQcMzimGFlLSAHMQgwAe5fyXmHFJIHpxlcXKKxcHpse7Vs2ICGXEkm+XMAnABXFvXWKV3XzAtOe5ym43Ne8A9X24pcItXPtUej4smxHMqampqampqampqc9fTWA2NTX0qSCZj38Ct64X5+6xZpdesoBksSLYMResWywgNqP1cJP16qOTTPBuiEqUu4tFd5UHLDjdbOE+G5DMnucmSwJFMsfDVawlloyb061zthqdXw1u9jrcZA0Q/uT/+y/87Pvfyc+89x386Z/8lyc+r5d94ct59Ru+j9e99Qf4a1/6pdRu1No49TO7d64skZtz3SvdGr07irKM3207ndn3neYB6GycSWvx+slhzVBHrFJzvB9q4fqqBiTYzvF1F6jXsVqph4h8OpCXKM9vCocUz+vjNcwgLbCOvxeFYwkAejisrOsRRUa8EboY5o1913hPezigVJV8LFyJcnW8wjxWKxMxVABGr4Zk4eHhQLOGm5NzIiF0xgiBGmtaWA8F3zeMKPUX65AK7kJtje6GWWfJsXx56exSUXKGY17AonNsXQqShSxKyUo5ZA45Y+ZsNQ5bNY2eMKOZocIAuoKNQn3VKOLPWShJMRHO1RD3KPWH4RJzhPRY/BIuEOyeqwxDVW8BWBozmsMTdu9+dMBjnfNerPKFXGXOHeB6ElfZ7Cubmpqampqampqamnq+JjCb+rzWBYRdHCzPX7j0W8fKAGF6cbwwStYvrhluAQE++qQMeosVzN46e3d6c3w8x7sji0bp/BoF/jkrbgFFznuArt6dvdk9SNfp0kkoV2m5dZMlF0SVWiu7N/o5gMe5Vk69YQZtr/zmv/4wH3jXj/Drv/wL9N6e+Mz+/jf/I1735h/gW/7JK0jLCsSaZvNG60YxyCgfq2daq5grSYQlK2bGea9s53OsgTrUnVj39LF2mSAn6GPRUuKXhhZfrz0K/3uL1KoUaKcAX+kQi5atQ87xmqbxmr2PpcthSxOF41W8BgIPrhLWO8taOKxHlIA5SaB5o3qjtXgMfnkfYTkuHFU5Hq7ovaOayGY0kega2+ObPjweqN5prZNy5DxdQF0i3khhWQvaKo5RSgFVukdBf93DaXZZgFyWhTSWIBFhycKi8Z90ScpSYh1TVShFKYdEQdGk7L3j5regTXBqb7gJYGx1VOWpkxJjSTVe083Yq+PipKBKMZ6QHBGNCCbcLmBGRb/iTnS0SbyuoqOv7A6CMX6ai6ssABjAnatM5QLf7lxll+jmBXD9eVxll+fPCObU1NTU1NTU1NTU57cmMJv6vNN9SHa/xP/xr8eH6Ev5+K3b5QLK/PHIZUQHLbrOumEQRf7dRq+W4x6wwFUQg+WQASPlaJw3cR5dn2kutL1j3WOXcEQkm/RwDOXCVV5QdfJwCrXe2HD6qdEabK3xqPVbN9mf/vf/xs9/4Cf46ff8OP/1P/3xE5/ZU8+8jFe/4W289s3fz5f9rS8P4JISN+eN6o3FIpqoonxsu2GMHSIpU3Csd07nxrZvwVBU2TfDW0AxdTiU8aUcK5VJw03Wx2Jltzvo1ccYgO2gS0CzzHitHEDFNFY0YSxq5hHfJCKDI4XI1YOCWWNZCiU/iGL+HLHFSuPcK80KvRvq4N3pXjk+dcXBnQfHBzQ3kiYWhyZCz0rbGqqwloITUdycBUtEX5lIwC9PrGVhwWl0Uim4esCrJjSLCK8qiDk55eFKs4BzWThIIuUC6hQjnIoq5CVRRFiKoinhbtQevWSqihArndadpDqgmWI4ZYnlypKFkpWUErV2mo+ONwkgpSkcZviIX47+sctK7H1XmWOIKFkTSQfyugxg3HOV+S1oC7p5B8DirruoWwDp+2DsrgNtwrKpqampqampqampqU9fE5hNfd7o4lr5RJDssrwnIsMxFh/I44N8LFrGVN9d5NKtDwdZ9EtZt4gmNseajwjnWK5USCJoEtKqJEnDTdY57w1rsNeIcMr4WWL3sFNUWdNCLjHVmFEcZ6+Vaj3icx1Oe2XrnWZBmH7zN3+ND77rh/nwL3yI1uoTn9nXfcM385o3vZ1vf/XrKSWz985pbzTp1O3EA1mwruw4p/MN3cMxlQ4Z6UatG3vtbG0fZzgWL7vRoq6NYx5xyBxuLyMAmW3hKJME9XwXqxSDtkG6Ajyil2bgy93j81jDpI0/5xHPzLBXSCmApXmn5MSSjyRJ5JzJ4nTp3NQN90J3RXrHq1OlcTweOCg8dbgaZf2J1RxTpSnUrYI4aymojrhtUlwlur8GsFWHklaWBKaOSCJL9JJhKaCkRyQzHFSJJY/VSlVElKMK63Kk90YRyCWTcgpHGFDWxDoK/LuPw0XJSajWMbPgWu5sm+EuqBhlAKfjoqgq5s5WewweaIDilISksbAKOuKYghDxYyFhZvF9icK5LMNVRrjRLnDKL4X+F4AlsZr6eKwSLrDM7y1n3o9gphfpKpsRzKmpqampqampqampT6UJzKY+p/XJ3GS3X/P7H7x9FPjfGrsIyiC3H+BFwi12iVyiQt0qtUcM05pjjAilCurCWjQK/GW4eqxz2nZaj96u3qM4XTyibkaPwviUb91kKSUcodads3SsdmqLHqrrHt1kLsJH/+xP+fkPvpcPvvtH+Y9/9O+e+MwePPU0r/ruN/OaN30/X/GVX8Ue2TxO285OB3NyB9XMR+qJ1hpmgqKje6uybxutNeq+hdtuxChruziA4ErAS/x78XHKnSBjHVQjrukKm4C0gGisAZtSjfdIDgHCcgxuhjNwuMekXEDK+N4KVw+j2D4npZQDWTLrslCSsPWNm7phLJgrYoZVo3njcHXkYUk8WA50cboruRu+CJ4Kba90NQ6lkFLCWsVJeFaKJvQSHRQ46ELJgiioFsTqcGRl9lZx6aMLzBGP58f1F7C2JDjmFfHoG1sPhZwzSYWShLQm1hSjEeZGbYagJBXMOqeto0kQN1qPGDBq5ByRzTT69Nyd1iw61iDWLhFUInoKiZTuRSoloJuI0mx0vInfdZUNV5lIQDNGVPMxWMbFuXnnKou/D5jVLe69+06wJ3GVTVg2NTU1NTU1NTU1NfViNIHZ1OecXgwkuy3vF4l1v3uF/xAF/ojcRsJEHPzSRxbl/r2Fk6s2j8ilRIk/GvamkhJg5CWRJGHWqTW6yXqL12kDkkH40IxOVmVJKymHi0dQHGMfa4l9gLKbvYabzQzvxv/zb36DD7zrh/nln/sg+7Y98bl97dd9Pa9509v5zte+gcO6ULtxcsN6p/YNNUddEE1c93PERi3sQEtWrDX20w03pxOK00XZ9wBl7nEsSyY62w4p+ric8TvFGahBF6gbeI7opW2BSuqIWBaLhUtdA7TJeN12vsAYSAOUaYJ9g5LheAxgk3Oi5AWVzHFZKEU5tzPPbTtGwTwhFg7B5pX1sPKy9cjDcqCJ0VzI3fFFyWXlvFeaOIecWbWAdbobrsKa0u1AgyscvLAOl6BoBmuItNslUxFDVejeEVNUEyqBlrIkcoZDKgGLslJypuSMukfssiiLKjqcaM0s4pcD1FZrNIuuvNYN6xpdZCWus5J1LGEmerPoT4MBycJdpsmBAsQyZkC8uDfw6KnrFveDKGRNqF4WLTVeQ2JM4M7ZORY2B3h7vqtM5M5VBpd+NMafYznzxWhGMKempqampqampqamXqwmMJv6nNAdCLvrNfpkkOx+L1m12xfBZZSdSwAHER8AQKKjrBvNnFaHw6xHL5mNvqWkSsqKJGdUn9Na52bf2Bt4c2o33CFFeA2hoyIcSianFdUBjzSxt4p5Q9zZ94hDnrrRasNEuH72Y/zcB9/DB979o/zRH/y/T3xuxwcPeOXr3sSr3/R2/uev+VoQpffOo61i6rRauSLHqqcq23am7g1JGc2ZksB65bxv3FzfhAcoJ87nHq45oGiAj6IgJdYV3QIu9uE48x5F/WrQErDAfhOLly3BUuEA9HzXV6YpIph9OM1SgjT+i6YJ2h7f8+oqiu9zSpSyICQerCu5KOd+5nSuEb00Rcwj3uo7h8OBpw9HnlmP7DSaC9qcvCY0Fa5PG7vCqok1Jdw7ooqLkURZSDFaoMJRFlKJdUkPmhs/ay/U2mg0iirmhhiUlFFkuM6EnJWHOSOaYuRBhCVlNAm5KEWVkoVcotzN3GnNb3vGmjVq9+hN6529a3TOZSOrQlIOKXrNukNtPa7oAY3v1i8Dn4Wja9wjdMwVkHuusoBpEV324UAbcJoYe7htKnPGvXbfVXYZDBjF/hbLsvfh1uV7vFjY9XxYNl1lU1NTU1NTU1NTU1OfTBOYTX3W6j4kM4ui/bsY5ePl/XcLlo7h1H73Qf3ug/nAAepAQLdao72+bpXqThvQy9XB/NYRs5YEbuQc0Tnrjb03tq3RUfo2Ym3mKEKjR1F8Vg75gKQRKUMxj6/1vWEt+tAetYpVp7mBOb/3O/83P/XuH+WXfvonOZ9PT3x2X/U1f5fveusP8I9f/T089eABHagCvjd2r7gbB88kWbnplW3faLWjKVMOK26d3ne21jnf3ATsShGhbOeOOywFVgkHGElw1YCUNb6uPtYux3vRR1G/N3ABDuA7LAosQIIEMFYv93M4x3IKsCYDntTdwol2lQKqpEROBdXMg3VlKYmbfuJ0rpgXzGJY4QLK1nXhC66e4Zn1yObxvmcT0qJQVs5bZW/OISklZcwintjHqEMh4yK4OMWUsmaWlPCxdkmOX9Sqs7VKTkJx2K1RUkFlOMwir8mVCst6RKyHg0yEvGRSSWQgF2VZCmD4Ze3VA95269zUThIluVGr4q64GuWQQISisC4les56uMOUcc+kcIZpAkj3QBlA/Izu0VXWuuHyuONLiDGBuLYZDs7LAqbgA5TB/Vjl5SqNPzQbgPnTdJXNCObU1NTU1NTU1NTU1KejCcymPqv0JJAMJNwqw8tygWQR74pesltQJo4QH8ybSzjJaqMj9H24yWyUl3NXSJ5LLGSqR79UbY1zNVpz+m40ogwttgKiwj+pcsiJkg8RwUuKd+gWBf9izn6u3NR+6yZzEfabGz70wXfzwff8GP/2d//NE5/dejjyitd+D695y/fztX/v6+lu9O5ct05W4VTPFNdwxknmY9sZ7051h+6sxwO9Vfb9FDDx5prq4QyzGh1hvcG6RmDPFVKWADRi2N7j7EZJfx2uMkthpKLfQbNUY8myL1BKONAkjFAR8WywLOEq0yS4CNINTc7hEBHArImUMikVrpaFw5I59RPPnq7pFCBjzWjVqL5zWBde/uALuCorjcrJjOJKWRJO5lwrZzeOIjyVFyA66TwZTYyjFBClSScR/XOHY8F69L6lLIgLvUHtDVFYS+Zcd5a0sKThMNNElsyS4KocEe+ksbZJSeSkJJy8KIecx+E5rRt4QFdwzrWCC4sKtRvWFDR+DlCyxvqliFKbhePPoytPPM46fsfoJEtycXP56PFT3MNV1oerrKRwpIlCEr2FYBdXGZd+wHuussfL+l/YVXaBWzqGMz5dVxnMCObU1NTU1NTU1NTU1IvTBGZTnxW6uEQuMcr+cZDsErkMSJaE28eNOrLoUBIwD2AQa4Px6b27YH2Atb1Ta/SLWevxUPNwMLmQSkC4suYAJHXn3Dv7dg+wefRzKRIQbCwbHtOKFh1uN6F3OPca7iODrXYe1UqrUdbu5vzh7/8OP/WeH+XnP/Bebq4fPfHZfcVXfjWvf+sP8I9f+wZe9vQzNGIooHVjk4aak7qyUOhinPaNvjU8JRA4lEL1Mzc317RW6bXSHboJbY/3JWmArYPCcrXQvcf4gUDbezjGGiBgbfT6y3hvLMr/zSFfAMkhHisWkDOX8Y7ZKPjPAIKoQuskdWQtpKSIQ86FpJlDzjy8OnBuJ57dzjQP+NVrREarVQ5r4QsfPMNVOQKNhpEtUZaEIZxqZXdjRXimHHDv8b5ao2ehkEhaMMKZtUqmZEWJ6GlJ0d7lpuy93QKibg1zWFKmWyOpkrWQk/Mgr4AjKTrL0ppjYVKju2zNsTbJ6Phq3RAPB1/tld4F1ejF2/YRrSxOFsVFOGSGK44RRXZcJJxoGveQSJTyJwG95yqLov7oKqvjRswaj3Hi/cuqI84Z14dKRDC73Y1sXCKYz3eVuTvdHXt+sb/G675YTVg2NTU1NTU1NTU1NfXn0QRmU5+xepLyfrgr7+8e7qUwrPhwlEXb0sUdc7Ga7bvFB/na6KLUrWMG3Q2Nl8WBkqNxTJeEmgDGea/stQc42jpdwLsFKMEwMTLKYRnF7BgMN1ntne6OOvStcd0a5+b03unm9G3nFz70Xt7/rh/ld3/7N5/47Mqy8E9e/Xpe99Yf4O/8vW8gq2AGW4sC+q1v8bN5dIqdrFHrTq+NJkIphZwSrZ65Pm2cb67BIrZJF/Ya0b8lByhLDfJhCaBpFkuGzcHDDSYSbjInXkNaOMYkeu8pLdxiXeKf8R4HGEs63vcR84y4o6DdUHVkyaSUopQ+FVQTD3Pm6uGR2nc+evMcJonehV4bZs7edw5r4WVXz/DUekS80TGSJ9Yl0U041Z2KkxwelgNgYB0Tx5NTtJBTDlDmTtLoGitEDjUnaG44hVorpMsVGGckkri0gmUtpCw8lUs4DsVZc6EsCXEhawCykoVSMqIBh3uL609FaL1TzcLhRaO1hLuAGkkVkRQOvCXHeMWwZrr5+HllVO9fgJbeAiohosk+uspq79iI4eakt8awJendYuZ4pgLNIoaJ3C1qXlxlI5gJXCD348X+IjwP2n1yzQjm1NTU1NTU1NTU1NRLoQnMpj6j9CTl/Rc3mYxespH4A3dU5XbBEeexyKW5xEJl63SHtke/mLeKp+gZk6RgTlkUwShJQRKtVjaD7TxcVgMcqQjqQpf4IVISVl1JJQrnrTvNofYa8TxR9q3y3H03Gc4f/7s/4P3v/hF+9v3v4rlnP/bE5/dlf+sr+J63/iDf+fq38MzTz1A9LFrXtUcXW90oWjhIFPk/6hveRncVTkmZrMJez+wNTs89h1nEJluFNrJ1S4ZjiQjmYS3oMWNu1LrfrmJ6DVjWPZ7vCtQAZSbRT1YSaI7HaIoopwzjVNLbt5OSA5SZCKlHjFVyGQXximompRyg7MGB6o2PPXqWJgkzofdGb0btlWXJfOFTT/Gyw4MAZWaUXCgqNFfOrbKbIe48zAtNnOSOidNVWDSRRG9XUVUitnlImW5OSUpTwy3T20bTRkpC94gCJxKMTi+VhCThoMqxrLg3UlaOa46y/5JIRPR3XQqu4UTsPSCyXJxe1nATkhiG0lpCxEmZOJsMRTUckXYH7hhR4qRRts8AU0kGKBMAC/DmMQjRhruz5ABfZvE+pnuuMpG478ydZvcGN7hzlekAYn5vLbM/z1X2JF1l8VoTlk1NTU1NTU1NTU1NvTSawGzqr1z3Idnlg/N9UBbRqschmfJ45DJ6jsZridz2miUNl1N3aN1xN1qz6M9qseh3idJB1EHlNUUf1jEHnDDj3BrbuQVs20fIshuK4nQaxqKZNWeWpSDWGZm4cOO4IS743rlplZsaLiPvRq2VX/q59/NT7/4xfvs3fv2Jzy/nwre/4rW8/m0/wFd//TdRNNMF6t7Y3HHpWG8sklh0wdx41Hba3hFNNDOWlBEaWz3TcbbnrgOOIWwnx3OU9K8HIQ8isVyt4TgS53w+Rbwy9hKiyL+DZR5zmXUB2wYAK1HuL6PE3xnRvlFlZUQnlqtgwBLIEy0llkhFUMnklHm4FI6HlUrjudMj9hEbbLXRu1PbzroWXvbgAS9/+DT0neZGSQtFobuytcpmFXXnYVkDlBErBE2cJWcWAwaMDcdX4ipFYb6KYqnRAN+dXWI1MxErlaoFkmDWERRV5VASV3kFOlLgYT5AUkTDMZlK9N1pCu8XNkrwe1zre4+cqxu4NapHMb8uA8YBhxIdZN1AxAZgjq4zTQM6w1iz1LFsya2tzz16/h5zlWnETBE4jPciYs9xzeDhKgtnp38cABP8sfv7hVxlT7KAeXmdGcGcmpqampqampqamnqpNIHZ1F+JPhUkc4/41sUlduk74nmRS8FHP9IdJFN1ZICz2glXTOu4C3WP6KRVi/VGDzeZuFOKolnABF0TtVZah712WrUAAP3uw7+oYdYpS6ZQyEWRrFgzeh9ush4l6ttp59qMukdc0XH+yx//ET/17h/lp9/3Tj720T974jP80r/xN3n9W3+Q17zhrRyfeVn8zpq52SuuQvcaANAFpNC8sdf4n4vGQmJKZDqneqL3xn5zohJdYXUDEycXyKuSm5HLgiRo1VARrm9OsX5Z42fqHhFLV4Zbj5i2TAHKRKAsMFKLlHI32rDmeHwHcoq4nilkC8egq5BTHpG+TEmZh6VwPK64GI8GKLMutNaotdGsc1gyzzx15Iuffhmt7TRrLHnhiNNJ7L2y9YpjPMwHmnjAUHF2OmtZWHzsqY4RieO6cEiJbhGqTCmuYbrQJa7JhLNbJ0tCNdPbTikLmgtJnWNaKDlsdQcyeQnHXFJBVDguKZx04uP6tnD7DcjbuseSpjXMFCGhaTj0XCjZWUqJwQof4xLuOHerleEqiyVLlcjCxv0Uoxh+cZX1eM9KDipmLpQUC6SX58joU+sWIxm3rrIB0/ILuMq6xX1/3wV212v26cOy6SqbmpqampqampqamvrzagKzqb9UXT7YXoBZN38MksV43mXKcjhO3OnA3u56yVQvka/44K0peqRQjbiaOWaNvTnWnOaOt4jVSVQ2AZAWRR1SyShCa5WGsN1UbMQ1TQWqIShdOipOypksmbJkpI9uModaGzZielTnZt+4bmFxMzN6b/zKL32I9/3ED/Obv/YrT3x+mhLf9k9ezfe89Qf5u9/0rYhIxEm7Ux32viNWUVMyYJo4W4Xe2WvDNIryNSWsbdycd+p2Qzt3qka32HnAr5ThuCZS7SxXRxSntU6tdcQbewCuGsSrNZAS3f7JQFIAtH4T5605yvuN0VM21hLXEiubqKB5fAEoZnQEcooSeYSUV5IoTy2F47ogRXn2+hEViehl62zbTnfjsGSeWq94+VNPY1Zp1lnLirjRJdGscdPOuDhP5RVT8GakBKZOTpmDA6MzTTxcZVc5B05yYcnQcNRiLVIFxAwXMO8oSvdOkkwpKyrOVU7kkiHDipCXEouSAyiVIqzLgtMRiThvtwF8W4+FVxfwjplgHkut4CRNpAxLjn601h1RyBJLmYqQlRiwAESdJOkWajHizbjEYqzducqSCu4R31wGKLsb04gbqg2o94KuMrnc5/HfAbO7QYCPX8t8cZoRzKmpqampqampqampvyhNYDb1F6775f33/3kfnN13o1wiYt2dOnrJLp1W9yOXqk6+uFrco1i8VXqP3qS699sP1LcRsBQOngSQBbVYA6y9UXendaNXY7eR4yTACGq4G0vJUWyugqSAGebC3ho0I+dEPVeetU7bYuXSBf7bf/3P/OS7fpgPvufH+bP/8adPfIZf/Ne+lNe/9ft59Ru+j5e9/IsBx1KinXeqgGF4a5SykLSwW2dzo2+N1p3unUNZ6ArbecPaTt/ObFulqmAVKoDBeiCcXFsLmHNYaK3R3KhbpbugFi4zSVBHgX8HFg0YZh3aDeQVymEASgnOo4OHHtZMPzdchHLMEc90p/ROF8VyIkvE/XJZySnxsBTWpSBFee76Ea1qfK/W2bcdA9aSOK4HvvDpZ7C24zhLDlBmKM2Nre0YnQflMNhTOOZqMlQzV8QYgl9ciAIP1iVWOXvAuM06CwmzjpmPcK5g3kiy0LxRtCAe5flLThxKwbyzZGU9LHdLkSLkRVlLQXXEdRHcw61VW8SJfcSTI9aZcIFcIjKpCkuK+KV5DE+oxFJn94h46ijlR4ysiXBvMu43C5gsGqMUMYhJTgQ0dGFJkC6uMg0w5R7ut0tXWSxl3rnKLr1md11lAczgLoL56bjKXgiWzQjm1NTU1NTU1NTU1NRLpQnMpv5C9MSQbHzA9tuScEYp+uXxz+slI2KYtTu9t1E476NcP9xkHSfJqDcXyItE9Gy401pr7CZsNQrhe4u4pzqoC54tSt2z3LnJzLEB6cLtE5E8qcbNvnFzcqxGGXw349d+5Rd43zv/f/yfH/6lez1sL04iwrd+xyt4/dt+iH/wD78NktK7YSLUvcWyp1WSKIdUaElw69zUyt6iqN1H9xZmPDpfYzi2V06nDU+JtkElXHeHNaKQYk45FHQtiAjbcG25RbFYdGBBddAeQwBXK1BhO0V3mWZYjuEmizcy+s2SwrJkfG/03kmHgqjgZqQe3XCWcziuHgNlmfWwoDlxff2IvYajrNbGvu0B3YpyXDIvf+YLcGvgxlIeB2XnvmPeeZAWRAvSOmkp0V0mmQdpwdBwiJmhKjxcD9FVZ50kQi8agKw7m/aRKRWqNZLkuFbdKFJIIqxr4aAJV2dZlaWs4AGV0vN6ymTEHSGux+jdG9e7BfjEBJEEEucpLuTklJKxAa9EI2bZbMQt4dbBBkbS+E//5f6Ke0Xp7uy14wNIq96NApR7rjINLDZcaCMq6j7uWXkBV5m8ZK4ymH1lU1NTU1NTU1NTU1N/8ZrAbOol0/Mh2V0/2SeHZDaid26M8vGRyByRPTPQ5CRiKfFS3t/NaM1pzaMXzPzWuYIHtEkqiIOWhI4Y296MvfaAa9VoHlE8VY2eNBokJWtiOSzocKZZd3pzqhpeO6UU9tPGI+vUzWC4yf77n/5//NS7f5T3v+tH+NP/9idPfI4v/6Iv4bvf8nZe++a380Vf8tepvWKSRuxS2OuOWiWnzJJKlPT3OkrZw+WECCkluu1c7ze0Vqnnna12BGE7g0unZLhaEsmMlJV8XFEL2HbaN1o3pDk+YKXZAGWjmD8fIsZ5ei6+Lin+zhgxTIE03s91XWCvscS4FlBFzPFWQVOAMgJqprwEKFsy6xqg7HRzzXkTrMNeG32vdHeWJbGWAGVYRaxT8gLWo0sL2KzSrHGlBUkZ70ZJQs3QvXGVV3y4rTCDrDwsC1lTrKZmsKRRTt+NXYSEIAbNjSTptmdMNaHDUbaKspRMKXE9RVeYBBxLwnHJY8Agvq1buMvimh6wTAUb762IIhpOvSRKzpCSjlL/EU3WRO+d3ocDc4A5Ebvtf7vEFruPGKkLW+3xM4hTckRgRYWigo6lyqRx734iVxmM4YbbYv/HXWV+D5apDIg3I5hTU1NTU1NTU1NTU5+BmsBs6iWRj0jk4w6yx3vJfJT3X3rJ7DZyOf6OAdw8oJumcHulJNGF5T5WDyOm1rvRWziuvBsiihPPW0qU2ssgEd06e3X2FuChNgtA58OVomGXElWKZnLJ6Pie3aBbBxxRQZtzs1f+7GYbrqsAG7/64V/iJ9/1w/zqL/881vsTn+E3/6Pv4A3f+0P8w+94JSoaQJAARJvXKODvo4cL6Bin1qitw4jnOdFz1vad837G2s5+3th6FPm3Bh1nWcPplWojr5lcCt473p2bvdHMSN2iwP9S5J9Gab9GV1nf4fRsQLKUQQuYQJboMVOHnCGXBXo4vlhzrDA6tH1DUwk4mXK4k/JCyZmHJXE4rIjAaTuz70LrUPdKuwVlmausfOHLvgC1jloj54WEcO5B+HY6bp1CYs0LmFFSomFUMRYyaEK9xwKqRqF/loS6kXKiecNwsgkbRhbFekeSYha5xd0aSyoBw0Q4JmVZCq7OVSkBY7PENmUS1qIsawG3gLweLrvWnToGKYyIXmKAS/TiSbgGc1bWHNY9DzTFMI8NCDa66gaUigXMfBu/dPfxOKV2Czhn0WlWUgrnlzxe6p9Ubu/rF3KVqRJdc89zlV06y+ASuxyPf0JH2IRlU1NTU1NTU1NTU1N/mZrAbOolU78HzJBwy1w+PV8cJWZG7fJY5PL+cxkf/EsSDKKXzNqtm6wbWI8P631E5nCHNMrFXcglIR6QqxvszWj7AGWAtFjeRB2h4ylRJFGWcBORwhVTq9PU8dbJObOdNq6tU5tBC6DxkT/773zwfT/O+37iR/iT//wfn/jMXvYFX8h3vfn7+K63/CD/05d+GXvfoz8NsNbZpSPeyCiiOVw/GKfaqL0HcGIsfopwPp+wzbB9Zzvv7EA/QxvnvKxw1ER2I5VMvjrivdO6sW+V2p3kHa+MMYGocpMy3EVj7XJ7bhTBJ8gLmMbXGWuKx6IkzRGHdIOcSKJR9F8rrgnNiZQSSkKGo+ypJXM4Hkni3JxPNBd2g33baduOOSzHhaskPPP0MxQ3igiSC1kTN3sFnB3DvHMgI6ng7hTNdBousVyJJBIWQEeEJQtrWqIvryTMnOqNxTMnr4gr3o2mimJYF7beuSorilCGq2zJmVygaI7fb/TmqUDOymEtw301liit0wxaC4iswujh66gH+BINeJtVKDlceI7ibnHuKJ2OipIuzi0NiIWDSLq912zcY/Y8V1ke/WYql0VLvY1MCtw6ReOejBv4+a6ykXa+jV5eINefx1UGM4I5NTU1NTU1NTU1NfWXrwnMpl4S+XCMmY11PL/rKHo8cikfF7l09ygQBxChW/SLdY8IpNlwtDTDiA/jgkQvljpFUzzZJRYQzWi7sfceC5nNovg/cmOIWJT/ZyV7IZVEEonCfwNzG44dQbtzqo0/uz4HABw/72/8q3/J+975f/AvfuFDtFaf+Ly+/pu+lTe+/X/jW77jVaSU6d6p7jQLl9zuEbtUzWQtuHcc59watTaQFCuYbpg45+06Oq5q5XzaaQhtC3OSJjisStFE8Y6sC0tZ6PvGdt6izL92dHSU1UgdhstpgDDJUeJfGyBQUkReXeP1vcX5PjxkSAnpnY7hWVnLitfKvm9IypB19GolSIWcEk+vhePxCOqcTzd0h3OLn6ttO65KOSwsSXj6qac5KOQxvVk0cb3t7HSqRKfdQTKiC65Odg3b26B54gkVQ0xoKqwJ1rSgEuuhCWezxkLGrLFZIxFdb044vnZ3VhXWXFCBpSwcU0IzrEuAsqyXgv2LIyyRs8Y17GPYokf3Xu9j+RWnjhxp1sSFERnOYVFyClCGxN8mTbgZjgWQvI1fgoojpBGNjV61S+fe3kfk0+LeyyngWEl62z0mMH6muE+7xf1xG6vmcVfZBaq9lK6y+N6Pw7LpKpuampqampqampqa+svQBGZTL5387oNsgLIo8I8P1o9HLm8h2YBYlw/kZv0WXPXu9NYxEWgWs3ziaCZcRapIWGUwN1p1qo3nNGfHSV0CpKXoc2riZIabLH5k3J19N7o61qOb7Hxz4saNWg0f3VEf+8if8dPvfwfvfecP85/+w79/4uN56ulneN0b38Z3v+0H+Vtf/pVR0G+GmdCbsdNBnGTGkgp5PdJbpZmx7RsuGcHJGguOW+/s2xk3Yz+fOW8Nc6jb6KFKUDIsSyGrsF49oFuDblyfT9TW8B7UMolEXNMHADPIJWJ5/Qz1HPAt54BnOUX8Ui1caM88WOkAfSyTlohAamucbq5BE1ISSVOA0VQoOfPMklmPR3JSTucbmsMJYT/t9G3HkrIcFooIL3vmaRbxWHccHXM3552dFtFLnAdSaJohCdkVHzlFN4/vLQYEnU3qXOWFJAMSYTQ6eERGd+9xgYhQeyNJYnNIOKvmcEKWhcWdwyGTkpJSuo00ioIm4VAyS0k4UeKvo5OstgBlInFp1xb5V5XL6IAjCqUklhwF+46gcnFt6XCp3bm7kgqiFzCtY8UyOv7i/hNqM3r3OIOspAHUStJbF9jdsuXjcetLBFNEPs5V1u0uMnn/cffdZTOCOTU1NTU1NTU1NTX12aIJzKZeEl3cZL0bqMRSogoprGYjohWffnN8csYIQGPDTVZbrFz6cKr1ftd/pgqpRMk6w7njY1WxVWfbazjRmgXjMEZnVMfzcJOhLFnJquyt482xFC4tBaRDrY2P3pwD6nUHd377t36D97zzn/FLH/op6r4/8dn8va//Rt70ff+Ub3vld3NYD5zbxqlWIOHN2GiAsWrCNeE0zDvnrbH1StEFkRygBOXcN3rv1P1E73Bz2rAeNWEmkFfCzSSwHBZSWRHv4ShrnVOtqDm9esT5DM7VKWsU+KtGcX/b4LRHP1kp4EusYvp4jnd4+qlDQNDWISm+JJa0kHrjfLrBRJGkqCpJBEkLuSSeKZn1cCQl4bxvnHbn5M5+s9POG54T69WBIsIzTz2kiLNoQlNEPW/OG5VGpePmHDSBagBRSeSc6dYC2pBQcVQSlIQqrFpQTSQUlYCl3gUlFiPdwtWFGNaNauBqFMmkJGTNFHGuDjmWLnMmi5LyXXfYsiRKSbfl+tFDBrUF3HUP51mzzt76WAYNB5oDKYf7Le6VAFiiHkBPYiFDiLhmjlwm8ch0C6fCVTY6y0aM2dwjIqoBvdJwld1frozbTh5zlV2K/eU2sikf5yq7H8G8RC8/Hcj1QrBsRjCnpqampqampqampv4yNYHZ1EsisygOjwm/4aIZPUfeDVEhAYiMZT2Llb1qAc3M6c0CVnRHRkZT1Ud/l8YHdhU6zr73OzeZQe2GmoxlPydlMHHKZYVw/JzdnW0LQOfeyRT2U+XEXc8Zqjz77Ef56fe/k59854/wR3/4+098HlcPHvLaN7yVN3zfP+XLv+LvRMyyVc57p5HG8mVDrCGayZpR76CZm92o1hEyglK94wJb3TAz9ptr3BM35419j2irLpAOcJUzGTgcVyRlBMPMud42tj3cZTFhCQk4b1AOsMaoJMsBzjewNSKquMZr31IcwhV4dbVGzK91vCi6ZooWtFfO5xMVwYcLLBxlC+uSeSonlsOBnJS9VvZmPLLOfr3RzhssheXBkQI89fRDVoXikJYVlcTpvONsbN5QoCCkstDpJIPDsmLWqdZRSSQgSRpR3KjIP6SFLIokx3C6C+KCyYBKY67VWqUSa4+r5tEJpiyqPDgu5Cw4ypoSKQcEDteZsq6ZrEq1HuDHofW43ptFF5nj7Hulm5NTGt1fccBrSZQsmMVS5bC6DVdklPo7cusOE3y4uNJYsbxzlZlLdKINN1tOSh69aknksU6x+8uWz3eVqcT3u8A1+HhXGXDb1/bpuMourzP7yqampqampqampqam/qo1gdnUS6KUEjpKy83BzUgp4pIyIpc23GTNjNbButN7OMK8R1zML71kJTrPong8ImzmsPWxlDkim+6OeCwPihie4s9FEylpuNCcOzeZGeogLpz2xvlmo5ujJnR3fu93fov3vOOf8fMffB/bdn7ic/iav/f3eePbfoDvfN0buTpcUVvlbA2asJvTfMfFyO4oibwc0LpTRTi3Tt1vWHMhueHqbGa0/YyjnB49h7lSW2c/t1tQlrNwKAvqnWVdKOsRbxvNJZxYe4+oZI8o5e5Qa8CxwwpuAt2pG9wYLBoALZdwm43aMlaF5cEV6karjVYSaUkc84Hezmzb6XbwIeUUcdu8sOTE0yWzris5Z877hnXh2baznyr7tqNLJj84clTl+OCKqywUElIyWRPnc8W8sdERhAUhl4VmDe/9dlGztYZfUOMozNcUIOiYYljAB2SqvaESjj5vsRrJKPTfquEqHDTjIwarqjy1ZHJRUg53WlkyMEBUSSxFKSkNQNpRCVDWx7UOMWix907vRtZEGhFYT1BWYVEFifXLlEZMFkXUxljGXd9YuMw87hMufWMBSS8dga332MVQGe644S4bJf/AvQ6yWLbso9j/+a6yCwS7RDA/mavsEsl8sZoRzKmpqampqampqampzyRNYDb1kshG8XiMTMpteb8ZmLVYvKx2C7kuEU03wv0zOqWSSHR1SXwAN5w6Cvxr62ARExMf63wpPpk7kHMK90uY3AJUNMe5c5O1c+URTt0b4uAo14+e40MfeBc/+RM/wr/9vf/niX/3w/GKV3/3m/ie7/un/J2v/jqSOM/VjVPtOAlrlZN1snv0W2mmiNDcaa1z3eqIyGVWOq13dgknmXmn187NzRZOuhbgMGU4HDJryigeMGoptG3j5uaG5sa+ncHaiMdCG4uXuUA+CmJC341tczpQFJYSr606auOABVgeHKMDrlb6ktElc7U+oG7XnPcTrdaIXpaCupPySk7C06VwOBzQnNi3jb4bj1plO1XqtiNLphxXjqJcPbzimIVFEpIzJWXO58oj26jeQQcoS4VmDVrjcFhRnNYb7kLSBGZoyuQkuBirFErKSBbUoVq/XZg0i7EHSQlxp+7Rm1Zyvht+SIkHWVnWMlYjE+sSS5eIk1KKUYWUBmyy22tjr0ZvhktEH5t1ttaRcZ+4RcQ1JWddEgxnXiJinGZRrB/XtD4GriDuFwhYdhne8NFV1nqn9zB95qyUdIlhXqDWHSyDAGAXx9jFVXZxi90fArjvKrtENXOSW0D2UkUwJyybmpqampqampqamvqr1ARmUy+ZBEXFw9HlUaDfzOguWI3IZncDk4hcKVHGr3Ib5QTA/TZCVmsbK5mxUCkOkgTpBlnRLGQUzRrxQIFaHdfobxLCgXazN/ZTxXvHLfrT/uD3f4f3vOOf8aGfek8U0z+h/ue/8zW88ft+iFd9z1u5OjzE6Jz7BpZwMue6xcqk2XC8CWksfZ72nXNr5JTJacWt00zYvVL3nbad6S5c35yxGvBKUvzv4ZpZcqIUJaUFUcHNeHRzQzWjnmtYliS65GoFLVBWoAjJhXpj7O3iBmMUuAekbBKxy1WhXD0g9R6jCqqktbCklVZPnOuZfa+ICGldb0FZyvBMLhyPByQFKMOdm964vtloW0WWTD6srCo8ePiQQ+ocNJOWQkI5b43Tdmb3jmr0zmXJdKt4axwOh3AUtsbmzpIKNor5l8NCtUpW4ZivIEE22LtRAfERxexg0lGFvu9sIhxyJhGjEmjikIQHxzX6upKypBy9bxKurlKUtSREA4ZFC1qsu7YeYxSqgriz1Qo2Bi5V488JDksarq0BsjzuoctQhiBYh5QY0U2PiOIo9r/ELy1WAejmNIulzZTkNn6pelfsf3fPRgTzxbjKzC/3djz3fifh5TVnBHNqampqampqampq6nNFE5hNvWSK6FlAsmYjBnnvg7gguMTKXy4avVEefWViRh+wa6+N1qMkvZuhouABdVyGo2VJ5NFzZubUGv1jY4cTccXOlWuMvncg4mbnm42f/Zn38J53/B/83r/5v5/4d1zWlVd91xt5/dt+kK/5um9AXWhWOdcxOuBO9x0nSuq7OKlkihnNlZsehfvmSkkZc2M3Y+9blMufruldudl2eh2umxxQa13CmXZ1WJC0IGJYd077zlYb7VzDBSSJXiOyKgOUyZJQd+rJOFe/dS3lJSAkwW/A4ZCEsh5JHs5BK0pyyFJo7czOzr5XVDv5AsqWAyrG0yVAmaZE3XfcnLN1nnt0HT/fBZQl5amrB5TcOaTEUg6AsJ0b115p4rg4qypZC63vKJ1cVjQBvYfrTDLZOwllXY90j164l61XEb0cXVxnIOM0h25Cs4aqY83YREgCV5pwgayJosLDq0LOGRMomsh5uKxEyFk5rhlVpVun9x6g12zcBzaWJJXdG9ZidMB1AGOBdQ2QxXB7CR71ci7kS9m/CSgsw+HlA8Cp6Limfbg7AYTao9MvIqJKSdE3lkf/2kUXFmW3QwCPd5DdusuGG62ZcTHOXdxgt/1pn6ar7PLzPx+WJZ2gbGpqampqampqamrqr14TmE29JDIzznuNgvE2IpdmUSAO0bWkRhZ9zE0mAr0FZKitxXrgeE2xEbnE8QSahISSUkAKR+g1QBvuEZEzuN53qgOtY+P7/9Ef/j7vfuc/46ff9xNcP3ruiX+/v/W3v5I3fe8P8Zo3fS8PHzyDW2dvjW6CoGxtp4mRLICLqVAEMsrWjeu6oZLQ0YmFGze9sm83aMqcHl1jJM6nRt0CnKQFlqIsOZYYj8dDOJis4USRf6tO2zYQ0JSxvVF7Jx8h9QCLmNPPnZt9rC8q6CFWQSGWNYvAIWekLGQCVPqiqEGSAl7Z+06tDek+QBnkZUUwHi6Zq3Wl5My+bTSHvTc+en3C9o6URDkeWFR4+sFD1twpmijlgCKctyjqrwOUHVJGUdwq1itLWZEEyYXeG92jQyxLIq0Lbo1E48FyCAeiGyKZ3RrijiLsI55rEi38dQDdY4oesiRxbT11zJQByrIquaRbIKSqEb/MmW7G3vpwaSm9x3XsOCUnujX23W6BGB7Lkzk7hzWPgn8hp0TrPVY0NVxhPpxcsWKZuPSkBaC6c5V5XPqYeURL4dZRlpOGK03TY9fyC7nKLh1k9yGZSsCxvd8RrQtUK+nxSOeMYE5NTU1NTU1NTU1Nfa5pArOpl0StNWozWgtIho9uJh2OEdWxlqkwFjW7wVZbfGAf3Wb46DMToABcispTdKSZs+19gIIOGILSt1i67NXGMieczzu/+HM/xXt+/J/x2//Xv3ri36mUwj95zet549t+kK/7pm/Fu2PeOLcaUUeE3vZbJ1OSRF6UxaAr3Gw7e+/klFnySm8RCT17Y99OdKCdK9Uq27lxPtcAJgWWRblalnCn5UTOBdvP7GZsdac1aNs2AEti3zpmjbTC4agxZKBOP3X2HUhQFiCHo8zGUmZOsC4riFJEqThkyGSSC06jt52tVsShrCuKkJblMVC2loVa9xgu6J2PPPcs3gSSBChLysPDkbV01pTIZcWbUXen9nCUNYyDlmjk8k63Ti4LqlBQat85d6UIHEsZMcFGwjkMWIcbjRhY0B6OOzOh945pxDF77bgIeTxeiD8/OCSOZaG5kVQ5LgUk1ieTKusa8NLcadbxsQrbq1GtwWWpEudcd/AAX9GVJ3h2rkrELztCyQmzTmth3bq4ykba+BaUcYlg3nOVdbPbwv3eB/DSiF/mpHHfySd2ldnoO7tEMC8dZJ/IVXb5vpc+s4s+nejkC8GyGcGcmpqampqampqamvpM0wRmUy+JVDX6jUaETDR6u/ooBZexEFir0Vu4iVqLziXgticJArRpEhQh53DfhINmvJbECicGN7VSDdSMOr7+n/74j3jPO/93PvDed/LsRz/yxL/LX/+bX86bvveHeN2bvo+nn/kCxJy91fHzCucecAScrImqHq4ed8zgUdvBHJNEyYqbsbUG3jm1yn460buxtegb20fHWF5gPWSOpZBKoqTMoSzUWjmfz5xrpVXDx/dPKmybg3aWFbRk2t5wAzs7dQfPsB7AEpG5HEe+ZmEZoCy501XpYpRUyKK0Gi6xW1B2CEdZWhaSwlXOXC0L67KytcpNrez7zrPnE32PvF5eCiUrT61H0tJ5kBcuHHTbwxFVMboYi2YKiSzObp2SF0qBgtCtct2FReBqWe7cTxhXeWFZCgr0MaLgIiTCzbW3jmsAIdsruyhrzowUKikXHhTlUDKeFBPhwbKi+bL0qJQlvi4iVOtgY9HVhGad3n2A4cTWKtYYYxbxfFdYMnEtaBT9JyIKiccSZhKlD4i5ljTuibg5wnUl97rKAPx2KfbiKgtQFnHO9DxX2aWDzIYb7X6xf/4UrrJLsX8SbgHcSxnBnLBsampqampqampqauozUROYTb0kUg3nj2VwlaBeHlDCLFwwe20RAeuR/xKTgBlKFPqnywpg0J3usG3Pc5OJUk+Vsxp9t+EggvO28y9+6Wd494//7/zGr//LJ/75U8p8+ytfw5u+95/y97/l28CcRAAjcwk3WOs06WS5WzMsomRi7fPUa6wapoyqYK2yO2ztTHdju76JCCnKzXNbsMICywHWw8qqSjks5JQpCLU2Pnr9HLVZdLRZC3ePOa2BiXO4EqRkrDa8dqzB+WToAvkw3gYBxlriMSeW45HuTjKniWD6/2fvX2Nt3bb0LOxprfXevzHmXHufMhjHYBKT2MTEIYq5CUOwwGUuLl+rykBVokSBHwlRCPkRhBTJRMmf/AKF/IilSEGKiG0hoAy2MXEQAiWAYiQgBCuJELGjCsGXBNtVdc5ec47v6723lh/tG3PNtffa55x9ap3inDr9qdpaa8/LGGNedkn16H3f5hQzilT62Dlm0McAJBNlAdY2qsBmyuO20drGMTpPozOOg5+7izLlFGXGJ+2CtMknraEBVZQ9hLe9s8+RY/xa2CQvfR4+calsW6WSO2NPw2kC19pomtXIGZOmRmsPVAm6B/uceV1SJa+JeuDiqAX9+eCpVVoxLiG5mSfCtRpvtkKoIqZcrGAlxVFEUIqyFaWUktI2MkGW9ctJn5m4qqZM79xuEz8vVAqSMqzmqL/HfecrxWScokzgPBgQX6hf5tVJPRNZfl57PcXXmSqz16JM79cs35dPcsq1uyS7p8q+1VYZnFJNoH4XUmWrgrlYLBaLxWKxWCy+l1nCbPHRCALuA/6eF/V8TA4fzPRdLztQuCMlx8+RTGqZKWNOxphMIDzQ8wJkeHA702T13C4bIfyZP/Uf8Yf/wO/nX/qD/ww/8xf+/Fd+zb/sL/0V/PYf+3v5kd/59/LpX/LLkOn0Oc50UnAg+OyMcDY1iqYMuyAMgn3k+zSMwDAJeu8cPtmPZxBlv92ytjeD56cOgDRoJjxcr0g428OVWitydED4mafPcpx+70wfqCgy8rBCnJJNa2OOju+dfYd5QL3mP3rKmLzEKFyqoduW3/vzmqgb1FLRqDiT/fb8cl2xXC7gQamNonAx5c3lQq2NMTMp954oM8HuoqxueB18ujU0hCrGUOXtbec2B2FBaw2JoIkwmQSF66VRVdmPneeZMvRaK00rIYHgmAqP9YGmmSjbpzNPUTSmcwRMPAf4e6dbXt68SEosPS9dfnLNmusQp4lRt4Le05AqXJvljlkExxxnoDB3yvo58F8tj070MRkzJZKcdWSp8NByG83JnTL3yRyB6CnLJOWeEmzF3o36yyntkJf6JeffM+CWMcF7qqyW8wqm6Hu/23KmxeaXpMruqc77yH6f/upQbX58Xk9dsmyxWCwWi8VisVj84LGE2eKjEBGApFDo87wUGPh0xHI8XixTNnggNVMxJsqcA0TepckIFECg3w5ukttkRQyfk2/0wR/7N/5V/uA/9/v4t//Yv34+97ePqvI3/obfyI/+3f8N/rpf/7cyCcSDGIMZwhxCD+eYnWYFE8VPKbOJcEznic4YjoiBltyiisB9chu3rFw+74yRxxCOHcSyetmqcm0NK0qtFbNCBW7HwfPtRh9B9JkXH0NgpiiTC7THiiCM/cBvnePI1JEW0CuUkkkidZAiXFpBWkvhNic9BmZCLZn6cp/0/WBEEB6Uy4YElFJRCR6Lcb1stLrhHtzmYOwHP7s/MfdAimJboZrxWBpaJ1972NC5UcXoqnx229nnIApctg2fg02EiTNm1j2rGX0M3u47iHBpjaYVxxHJyuNDvVJFmJFirMdE3CGUw2HMiUruyR0AYlRVBFCUWozHTbm0jWNOROGhNopp/s6pvQz6BzA8q8CghAdjjhRcClo0r5reUg7rWZlEhdaEVjUPN4gR3DfHhFJSFDkCEtSiLxcyhXvC7Exl+pkq89ebZZkqM1OKCe1MZZ569IV7qiw+lyq7b5CJvEuYpZT73H/LAs3e3z9bFczFYrFYLBaLxWLxg8QSZouPQkRwOwb7MZieQ+cSgpih7kjRez+T1ixraHNmTRNgTlQFwvHh3OakR1BciHA8lP/Pn/qP+cP//O/nj/zBf5Y/9//7s1/5Nf7SX/af4Ud+9O/hd/zoT/BDv/wvY/bB9Aki3PrAJS8wHjGpYhTVF6lhHiDC27FDGGIFM5hzMsckfPDknf70TO+DPoPbc6ePFGXWYNsKD7XSLimwmmbdbh8jt7+m4L3jOIpChxGBbXD9ZCMC5n5k+m1PqVI3MkV2/pescSakLhVKxVSJ4bhOpMDFNjgrfmPfOTyQEHRLgWZWKCZcRHh4eGCr+bz7HMw++ZnnbzB7PsddlD1YpV7gk22jSkE9mMV4ezt4np0owqVsiE+qwoiUW7VttEuKsqfbM4LSamXTSmgmysC51o3NCggc7sw5MyUlwhHKHONMbeWGmCIUEawYSB6MuDThzeVCDycE3jxsmazKI6xsm3FpFYA+58sBClCmO306JlBbPt9+SwkloqfczfrltVnum5Gpr/DctRMNaskLl/fklkjWL1VfSpz5Mz4TmgI492RYSqZMlQn1vIT5hfrlq1TZXZbNM971OlWmpywb/r7Q8oj3tsrgOxdcS5YtFovFYrFYLBaL72eWMFt8FOac9NvMbSYENLJW5oFWfdlICiLF1G3iACpoBH5PkxEwHFEjxuSzMfm3/9i/zh/8534vf+zf+Nfw1wNL3wYiwl/3N/0GfvuP/wS//m/5TZRWGcfIi5UBfQYzghmT4cGGsllBVKicsg7o4XAEoQUTmMOZMXl7e8YNbt94y+yTKYXb27xiSYXrg7BdL1QRtocrGoLNAcDzsfN05DGBmCl9iCAO6OJYg4eHK+HOeNrBjKfPAilBbVADBufBgIAw46FVwsqZeErhIk1pYkgEEc7cD25zIgi2NZTc1qrVeCzGdr1wKQ0PcldsOD/z9HX6EZgZWiVFWWmUDT5pjWu94GMwRbn1g+fnG1GFVhv4YFNhRtD7oLSNrRijD55uT4DRSuWqjamOnlcor9vGpTaC83X083dGyLpq77hmLfHYD9yMdia0TBREuG6FT68b3Z0hwWNrmL0TNlsziinFLEVVOHiKMndnzAlAPbfN9j7pPSuSKpk8kyJcqlCqpRw7Ldyc+VpLOS9cSr69nq9Rzq2ye5XSPZjh5+94pjWn85IGU1WqQTvrm98sVRanKPO4X6rVl5TY/QDA+FyqLHgn1fK/ne8sVbYqmIvFYrFYLBaLxeIXA0uYLT4KpRSkCDaCSAMDBK0oetbtenemCIx5JlyCOZxjzkzwIGedTvhzf+bP8If/hX+aP/Iv/NP82T/9p77y6/mhX/IX83f9zr+bH/1d/3V++V/+V/A0dgjNgwMIvUfWLsdBMcu9LhNUjSbQfXLzM2nkjltW+m4964XHOBgEx97pt4OpxvNngxkDbXC5CJfrFSO4PDxSzJB9R1T5Rj/YR4oXiXlW8oTjaaIG+gCX7YGIyfH2GTHj+RmkTNpDBvVGZHJtC/Ky4+MjIYKcZ0RnTMpWEBcsYIQzjs5xjvmXc8xfRNmq8Ukp1MuFrVTcU1DNGfzM09c5bpNSCmVLWfhQGqXBQzU+vTwy+qB7cHTnNg6iQG0Ni0ktxhzO3ju1bTwWY47J29sThlGs8mAbE0dwxCetVX5oe0BFeeoHTMfP3xcPGMMZBJsZc79x00JpBSNlrahy2QqPreTvnjsPW8VMMVU8HDPl0oxiWZk8ZicGiBkRefQggGIpeuZ03h4zBRSZDAyFtimtaqbKQsl5viBcUA2KKc598D9TZUKQAS5FziH/ce70BeAh+FnB1HOQv5jQip57Yx8QT5GbfvB+qqzaF1Nld5F2xyOPFxT9+VcwPyTLVqpssVgsFovFYrFYfD+yhNnioyAi1Cp0h1L0vBKZNcfjqeflwKLI9Jc02S4OM2WRD+cA/p1/69/kD/1zv5d/4//4rzDH+Mqv49f99X8Tv+3v/gl+42/8zVBr1ueOWw7PT+dpDJxgEFhAKxVRsKJoOJPgs3FgGBKaiSMRxnT63LnFoPeD/elg9MkIeH7bUevIBheFx4dHTIKHhzcpIgLGGPyF23NeK5y5lyaaI/HeJ67Q3ihWNghnf3rCVNlvIJaiTBCG5yXKBzNcgjdv3mSiaDpyDuhbMa40wqHHYByTY3RUDW0NA1SM1oxHE7brA1tphAfdc8D+Z55+jr577qtdKipwLZW2KQ8mfHp9w+iDY8Ixgn3sTHNKUQopX9ydox+UuvGmGD4mT8cz4UIR5bFeCQUJR3G21ri0B8zheQzIebIUNx4cI3DNKmb0zk3Bag76A6gapRifVMVqZUpQBD592PK6JbnN9bDlcYIAjt6JEEQNJJgjk4aqQjNhevB8DMbImux50BXblGvLpFhECqmITJWJQW15KTYEap7CzMuXck+gpVAeHjnif69enlcqRTkFX9Yv65dtlZ1pMQ/5YKoM7rtlcu6yvV+TDOK9VNn941cFc7FYLBaLxWKxWPygs4TZ4qPg7tRSAWf2yb5P3M40mWXVcI7J0SfDHUXQEHoEf+4/+U/4l/7wP8Mf/uf/af7Uf/TTX/m5P/3aL+Hv+G0/xu/4XT/Jr/wr/koGTu8D6QNDz7TUkZU7hRaaIquU3ImaHSmZghMKqpU4YzJB0I8bB87+dOPYD/oMxpzsT47Uc5+sKQ8PD7RaqaVhOBpwc+cvvP0MnzBnED5xdyQC30EvQnmsCAY+GftB9Env0NVpD5rfu4iUcK2ABJfrAyJKzAm1gkhWA90QhO6dcYxzCF+xbcMCRI2tFa447fGRq9VTpOTBg5/5LEWZlkK95CXLaym0pjxW4WvXTzjGYEy4jWAfN7pOqhoXLCVNTPrslLZxVYXpvD2ec/Rehcd2RVWYPtGA0owHu1DUGHPy5J77WuEw4Zgp8sp5MOKGpMg7k1MiWav8ZDPKKcpEhE+2lldWyZ/l1t7tlO29p7CUc2R/OsNTOm5FCFWOY7D3iYRgZBLNzlH/Wg0PedFX99H8UnLXDASzs4YcvIz6y6nc3D13xtzPzTNe6saq+bmmmSor5/j+F25bhDP8y1Nlr4f9P58qi8g66MdIlXE+/mtWBXOxWCwWi8VisVh8v7OE2eKjMOdk753jOfeXMEE8t8nG0dljnmkyBYdbOP/+v/Nv8S/8s/87/k//6r9M78dXfs7/8q/76/ltP/4T/PDf/luolyvPs7OPnrLAYXdnkumqYkZIZGXPlKtKirSITJw971AVu18MnIO3txtdg/5849g7E6XfOvstoEJ7gMv1wsUK23VDrFLHgRA8D+ezp7eM4efIfsDMmqMJsCmXh5bpsNHpc+BH0D0vXm7XTGh5pHi71orH4PLwmDW+OaHlNUc1sBDMjX3uua92ijKplYIQalxa4SGC+vjAQ2kpWWLSHT57/oz9NsAMu4sysxdR9kOXT9l9cDjcerDPG10mTY0HKahlQu/wQamVB6swJ7dxAAoefG17OI89DFSMbTOutlFNGeG8HT131uCsJsIxxinFguN8x6ZCaF6gFBUem/Fw3TiGg8Gj1VNoQUiwVaOWlHnTJ4M8dIDrKZpyZP9SFVTo3Xm+7fiEqobjeTWyGVtTiNxQ01ej/vf6Jecemb1Kg9n95CtZK+2eBwMQCN7VL0VAVClnBbPoeTjgA3tg91QZvJNl9wuY8K1TZfdNwTvfaRJs7ZUtFovFYrFYLBaLX6wsYbb4aMwBYmc1bDh9TEYERRTxrBP+hZ/9c/zRP/xT/KE/8Pv5f/+//sRXfo7HN5/wd/zWH+O3/q6f4Ff/6v8SEcHhg9E7mxozgqfjICTFhCE54l8M8UBiEBI8+UQciPOiYTGOMZl+8DQ7ffRMwz0f9ICxH+xHpsmunxjbZWMzY7teEVXqcSAavHX47LOfY/QAyeMFPgazO0WhbEJrF0KEcdtBhH6b9HE+9qUwx2BKpqweS8U12K4PSED4RErNvTJxiiqVypO/ZR/Hy2C81IqdVy+3zbgGtIcHHqylIPTBCOEbT5+x7wPUkG1DIrfBWlE+3YRPrp/S52CP4Plw+ux0JoZylUItCu5Mn3gpPNS8tnnM/L7pmDxeLli70H1Sw9k248E2minDndsczDERS7HWIxh9IiaUYuzHgalR9axeiuECD1vh2iogzHDeXCulGCKCR9ZDL9t5KZTgNjoamsk8MvHoEVgRrtXow7ndDnyCnvtiHo5V5dIEUzslVabF5gxEoVRQKXAm0F5G84nz75n2cneOCRH5M4qzginkVUo7a5jlrGDCN0+Vva5gFs3H+HyqLF49xj1VZrIqmIvFYrFYLBaLxWLxrVjCbPFRqLUSPHEcnRFOzLxUqB7cfPDH/71/hz/0B34//+q//C9y7PtXfvxf81f/V/ntP/YT/PDf9Tt4eHxD98E+OiZGo+S22L4zPTfSKsqmYFoz3DMOtFT6hBiBmjJ8nvtkgcxndnFu+87teafvA0foe6dPsAqPnxau25bD928+yeuSY+DufGNM3r59S0zwOTAr9D6xOVJSbML18RPmzNcpCMdtMsmE2HYRxgi6Dwy4lkY0aJdTlM2J1soUQJ0WhqixH0887zvhTg5mFUqAWKU15QGhXa9crTHDGTEYCN94+xnjmLga0jbUg62kAPukBm8un+ShAA9uh3PMzhRHRblqpZggERwzDwJctEAER0zGDGwOPrk+Yu3KDEcItiI8tI1mRiDs4yBccc1LkfuY+AxcAjNlxGT2oJbycslTRGjFeLOVlJESXJrRaskNt3CqCluttJJ1y310CEElB/3DnRmBFrhaZfrk+dazfonm91tTfpWaddtAM0kV94JnHgSQUz6p3if8syaanFtlkaLMX+zSmSrjneQqmjXhal+eKpvuxOdSZSLQ7Iupss9XMPlAquznkwRbsmyxWCwWi8VisVj8YmcJs8VH4Xa78XzL+p1MYUbwMz/7s/zRf+kP8Id+6vfxJ//D/+ArP+b14ZEf/pHfwW/7XT/Br/k1fzUqxm0ePD09UVrFXHiOjnsmgZxz/0mz+mmm+Oi45m7UGJNx1uKmg+P4fuMWzn678fy8M6fjCLe3A1ewAp980milcq2VerlS3InjYJrx5z77DA/oM5DzSIEQzKcdKyCbsl0eGT64PT/B8POAQCaTNjH6mIQJTZRWDa2FWtu5rzYRK0QxQiabGiFK953xfBBz5pbWXZSVSjV4tMJ2uXApW9YQY+CifP3tW/opyjjTYK0ULrXwWJzH65vz+ymMHtzmziTFYrVClbzSufdBbZWHko+xx2RMp/jkzeWKcSEk01TVgsvWuKAgxjEOpku+3wfDwbvTNWiaInOG5mVRIkUZQinK164NJCuZ1TJBdn7TEYLrVlKeAX0MsvmYm2LuWb8Uha0YEBx98rQfxOBMkGVVsjbjUgXkLsryae71S7VMnMFZvzxFUZH7h8qL5OojznTXu60y5V398n4F88tSZRF+jvrLB1NlkLXPc+LtPZkl5zEC/UipslXBXCwWi8VisVgsFj8oLGG2+CioKjFhn4P/x//t3+cP/dTv41/53/8hnp+fvvJj/apf82v5bT/2k/zwj/x2Pv3ka0x3bj6pERQxKMbzsdMDnFMGAFup4IHioHJWDx1cGCJYnGW6mLy9PTHcOW47+55iZQxnvw0G0Db45M2VaymUWij1QpuT6AcHws89vU3REyDTkXle9BxBFNAHo7Ur0yfHfsOPnnU8oFRhM2PMwVDnujVMA71UWs0rme4Ts4obiAXVFVHj6M/4VObR8QCtleKO1kbV4LE06tZ4KBszHI/BFOHpduP51vGzQ6hAfSXKPrk+nOkrYQzneZyiDKGcoswCegSo8XhpEMHwPOJQgGurNK5MnAinFaOWymZKscrt2F/VETPl1bszNWgqmDtHgBXDziSXmGFFeVMLrZWs+BbhsTTMJL//Emy1UIpi50bacNBIKZU7YxOXoJqipszh3PaDPu7JtSAIajO2BsVKJg/J5bF5H8mvWQkVeNkM84gXARURpJybDD/l2XkZM7fKAiv5+fa5VBl8uSyDD6fKUlblc/vLxc0k9Zy87Jq9+/jvTG59SJatVNlisVgsFovFYrH4xcoSZouPwtPTEz/1U7+Pf/6f+X38B//3P/6VP3/bLvytf+dv47f/+E/wa/8rvy6FhU+ejoOtVC5ReGYy94NQYQBKsOm7oXX1DmIpikYgpoQogSAuHP2ZrnDbd/bnnaMP+kiB8/TU0Qrtwfjaw4WLGe1yyXF6YIzOLYSfe/sZw0lTdg72RwQGDIX2plHrxnHsjH7Qnw+mQ1im1SRyzH8yebhckBiUS6WWjTkHc3RquxAxwJzqmcoa7PT9wPdOiKQoC9BSqeo81krdKg/lyowJOCHCN56f2ffOCAEtKWhK4dIKj+p8+vCY6TwXxnSeeoqyQDE1NhX0FCVuhU0tL3yGs49BFeFSCk0rM5wQz5F9NWpRmhZuozPmwZyeldII+ghGTIoIheCpD2orVA+EvF6pRXhTCqUaIopL8Olle6ksugStZo20WA7633pHRc/rlIEPJzQoBs1qHqfYO7cxic7LxxUTyiZcW+6UDY93j0G81C9N9bxpcQou8ojD662w4c6Y+Ya7THL8rG4qJu+nyu7JMN6TUfk9v1cw8zDB+6mylFUAkntlr1Jl9ysCq4K5WCwWi8VisVgsFt8ZS5gtfl78yT/5J/kn/ol/gt/7e38vX//617/y5//K/8JfyW/98Z/kb/8tv5NPPv0ainDEZL89U0uloTwfOw6MOUGV4o6oUKRk7XJ2MMVH5Mc5yP1yYAj78zc4irLvO/128Hz0vDI4nNvhWIHHTyvb1nioje3hIZNqxw4G3+iDt89vz2qdU0qj92cUKKLQUlwVUXwM9mNnPh+MCX6mkkSVOZ1iwkO9IEzqpaJ6JebAfVJKYzIRc8pQhBRlYz8YxyAA2zbEAy2VZsG1VLZWuNYHZkyCSYjwc8+fcTsGI0hRBmy1UqvyxuCxbaDCdJgOb/uOR9Y7zYyrSl73BNAUYBIpfvY5MDUea02xBQTOVpRmRqtGs8o+B8/9IDzlFgLHcHxOUDDk5WLl49ZyV0wVVeVqyvVSmSFYIRNklu9zCYoKrVaqpYA8xoDIsf7wFFnufg7pWz53HzzfDnym+ArNBFndjK3kgYQ+4lwiyx0wVTDLuua9eXk/ImBnxTJOmRQxOWb+XouQks8zVmimyLkhdk+VFdOXa6CveZ0qiwjGmSqrJudmGudO2rvk2V1m6Snv7ttqd34+cmt+7gWuCuZisVgsFovFYrH4QWAJs8XPi5/+6Z/m9/ye3/OVPqe2xm/4TT/Cb/+xn+Cv/mv+eooVPIKjD1SEzSrUwtt9B1V2d0yVpopYgYCKA4GLE551uh6ChuPkpcH+9A2OcJ6Pg/3rB2MGfQxmz7qcFXjzaeNy2bjWSrtciOnYsYOVHPL/7DPmBMIRySuOvt8oqkSBdn2kjwNmcHhH+qQPJwTapWSaLBxFuLZGUajXBmJ5VnROSrsy/DhFmUAoLp1+ijJE0NZSlKnRNuGhVFotPLRHpg9gMsLZ91uKMg+wgoaz1YoV4dHga3XDiwLGnM5tHByey26qhYdzCytEAKWonuLM6TEpnqIMMTRSxBRTTIW2VWoIQ+Ht8YxEwQXG7BwBMiOTdqpMnBGwmTLPS5JGXqu8bDUH/DW4VGNr+e8hgYRzbZVyJrOOMQi/yyBJEeqOmrBt5eVi6350jhEohmoKtVKU1nLDbXgwZ6bKpmfFspT79UoFCZS7JEpZ5mf9MkiZO/28oClprZx5jvrLeQnzA6my9/hwqsyUl1rl61TZ64qkyPlzg4+WKlt7ZYvFYrFYLBaLxeIHmSXMFj8vfuNv/I386l/9q/kTf+JPfMuP/RX/uf88v+XHf5Lf/Ft+J3/RL/1LMhmD8HQctFK5WOXZB8ftBiYcEugcXErJtlqA+CBEmAI+ITwQ1UzhoPR50OfB1/vk+fbMGMHok+lBPzqHw7Ypn143HraNS6toKUiAHjteNn72drAfn9GngzuiBuEwBmpKVGO7PNDHQd933B3GKfwMaivMMRgxKFYpUmhF0cuGnqJMwtF6IRiEDJqUHKWXgY/JcTsA3okyM9qmPJQctb+2R8InEYNJ8Nl+Yz8GPh23gqizlZK1RhM+rRtUhTDmDI55cPgEh1KM65mgGsR5UTLrgyMmA8dQ3lgjRNH7npflkQIrxqZZf92PGzIK4UL3gwF5OVSDIpmW8lBKMZqCAwWhVePaaoooE1pVWi0YuVOm4nl0oGbdsp/JLd7bKXPEglZTUvl0jjF5PgYyznqi5LXIuuk5/C90j/PCZW6A2dnyLWqvNsLeSbNMhWWqa55bZSCI5oEB95n1y/Oqp4pgCkW/PFXm8e4C5j1VBu9SZQAqka+DD6fK8u+rgrlYLBaLxWKxWCwWH4MlzBY/L1SVf+Af+Af4R/6Rf+SD7y+l8jf/bX8Hv/XHf5K/9m/4m86kjbKPgURQrLKp5RXJPhkxcVGqB1UEVHEPinBuZHFeWIyUJCqIwzxuHBLsY/L82Vv2YzDdCZTb80EA20Phl1waD6VwuV4RzXH5uR9Mq/yFtzf28Vluk81MlHkEcuyIKXpptO1C7zvH83NeeRzO8xgUEbZWGb0zZFDqhimnKLuc6SRH3NFyIWQAg0rJS4qeI/59z000LZmkK6VQFR4ujU0L13rNPSwmU5y3+42jT2I6YSVlkxlaK5+o8KZdkCoQxhj+nigzU1ozisA4BYmqUNSYBLt3RJU3WlOUpRGiaGGraZUulntjt74DlkcDvNNJodll0kKQgGM6tRXKOYYvKNWEx1ZRBWqO4D+29rJThuVFy3uKzQm6O/nNz6kun7nwX4umPAvoY/J8O5hdEE2ZpSqowvVqmBa6O3Km0zzi/N2EctZCRfJCZ56KyG21d6LMzzTb61RZ4BJnIu1VqkyhFsv3f6F+GS9JNbg/7ruDAvBu2B/0C6kyffXaP1YFc8myxWKxWCwWi8VisVjCbPER+Pv+vr+P3/27fzfHcby87Zf/ZX85P/LjP8lv/i0/xl/yy385kDKg90kx2KwxffLUOwrsMSiqmCvNChFgkiJgKMwxUS3scyA+mWqUooy33+AQeDqO3CfbOwj4DJ5vAwSubxrXS+WhNOqlYRQKQe8HzxPe3p7Yz5OXEnImhKBJPrfVjXa9cttvHLdb1vyOzs2DZsJmZ6JMndq2rN0VxbaNPAoZ6AQpjdBBRKdQmAR9dEJgv/Ws01lWHa1WqkKrhcdSedgez6uVeYHybR/s+8EYk7DCVGiaqaw3d1HWFPG8CHl455iT8EBN2U5R1iM4HKopRQoDp/vEiTN1ZqjmBphKJsqaGtVAxOje82sMZTDp7owZgGMiGMqBoyJca8HPTqGK8KZUalMcoZw7ZXbulIUEzRQ7ZVnulE2Ed9ck81mcVhVVwyNSCvbBPh2dhuiZhitKbXlJdXp+3L0WqWQFMyuTBsRLqkwkRdk9VeaRo/6ejeD3UmV6T5XBOfAP9UyVATjvC6csEsPnZVnRD6fKXsuy86jmeaFzVTAXi8VisVgsFovF4mOzhNni580v/aW/lB/90R/lp/7AH+DX/4Yf5kd+7Cf5G3/9f41SKh7OCGf2SSuVrSjz3BULnCMc9chdJzEwwCdmlkPxU/CRaa+jd0IVFI79Mz5z53nf2W+DcQxGOOGwH4Na4M0nuU/2yeWCtoq5UnxwzM5n7nzj7WeMAO8jhcucFDNqKfS5I9tG08Yck9vzMz6cOTpd8mVuqoQ7UYW2Xag1x+TLthF5eQBDEBpRc2eskFc876LstqdkFDU0glIL1bIu+Fgb1/bAPP+HcD4bg2M/8vqjFaYGVZTajF9SKw91QzYlBszujBgcYxKRo/uPW8MEDne6k2k0zde0+8wrl5JJrWqFIFNWrRhNlFrzeubunRgDD6VHZ8wUZRF+JsGUMSdiWSPt58BWQXhoDSuSqTsNHi71JdXlEihBbQU7rdAYA3cQPXfK/LxcqdDO/bveJ9Odt/tA57v6ZTHFLLg0Ra3Qp59pv5RVxfI1FDFU8+0q93TZeY3zTFz16bjn9cn7BUyJYBK59XamykSEapkqu++KvU5s5ZGAOE8LvKtg5jGAL6bK4N1r+HyqTD9SquxDsmylyhaLxWKxWCwWi8UPMkuYLT4K/+g/+o/y9/z9/0N+yS/7SwmfoMaMwKdTS1buDp/EDPY4MrkkknLGUqIVnEFewJz+rq42z8ZahNP7Tg/42Z/9OaYLvXdmkCmq7rSr8unXLly3jU+uVyiKhKDHwQjhszH5xttv4CJ4n+i53aUxMYHuB4+PnyJuuQd2dGaf+Jx0zd2u4vlabKs5fK9BbZairDvgmBZCFGqHmNgpyvb9BqYcx8DORBJA3QpVU0w91salXolXoux5Dvre8zWJ0TUvRV6s8mmrXEqhXipMYex5AOAYmSgLhWstmJwbZShNLSunRCbPxGmiFGugip7D9qpCVWNrhaLGEYNx7EQY0weHT/oMJPKOATM3wYoGWynMSDFVRWjF2LaKAlKywlpMXnbBssKaokwkE1ciAqfACs+tr2LndVIC96DPyb53xsjH0pLiygy2i1KtMuZkzNwge1dhvFcfCypn1kv0/LtkpTciL4m+qilmU1gJshpaXokyVWimqMoHh/3vqbL4iKmy+/tWBXOxWCwWi8VisVgsPh5LmC0+Cr/qV/0qftb/LPsIxnQKQrXCcOftcVBE2KMjYRQpKRwcBMeUrFF6UKRyjE7EIEQxBJ07b+fB03Bun33GcJjTmTPoI0XI9XHj4RPlzeXC1hpWCrjDfoBVfu7Webs/MyYvQ/4meVVTRJjiXB7foOOgH0eKuqMT4QzAqlFnEO5YbVSrxNy5PFwprWUKjkDtFGVlIO5UbRzRz0MGmgPzMU9dotRroalSTXlsG5dyIZi50RbB4ZP9duBjMkQZZM3vYpU3rfJghXYphOt7oszPRNnWcqfNCboY25maCqC7E+JctaKaPxPcEQ9azaSdFKGJ4eLsPUWZD+eIwZEhupSZ7oyZtcZNLEWdgFpeN73UmvXGkuKslfz+O+du2KudshnBjDw+EPFOnOq5CZdrcDAdjuNgH47OFF0CebHzIlQxHKGPee6dxVktzbRaNXsvyaXnQYC89ZAia3q8pMqEyD20U9Rlik1e5NXnU2X+uVTZ662ye6pMeF+WyQeG/e+psjjl48eqYL5+jo/1eIvFYrFYLBaLxWLxi4UlzBYfBXd/GZZqVvGYPPfBnANX2CdUM0LySiAebJYCaEzH1DjIS5QuhkrQjyfe9sln/WAcndvtwIFw4RgH4bA9NL72sPFJvaSMqRfEJ74fHKJ8/enGGN9gRA70i2QKqUTWB6UW2nZhH8dLWk2Gs/cjR/BNsXDcJ61tmBgwuF4rWq/4MSEC04KLIDbAnaKFIZN9vxEiDICRH4sY27Vk/VOFa914qBc8BqF5zODwTt87c0w6cooypZrxaSk81nqKMqEfTo/OnH5eHoVWhIYwIphWXkTZPKuA3TtXqzTbcoD/RZSV3JIr+VyYMI6Oz7xEOul0z2qix8Qc3HLjbLNMsfn9KqQIW6kUhTChmlBLoRZNS6WwmWH3Qf9IAXqXSqlTAyQH/U01K74jmGPydAyYKZtEz5+V8lK/nNNfrqsiKdJC8nWZZa5NRDlbvngIfThBpso+f4lSJVNlIoqme/u2UmV+VjA/v1VmKq/kVAo4Ef1Cquy9owTfxQrmkmWLxWKxWCwWi8Vi8Y4lzBYfBVVFMCIGPZzb3FExkFNOmREEhfPAoTiDgvskRNjHzFuE4RzHEzOCz247+61z9IGHEy7s+4EYXB8uXLfKm8s1K3xa8pLmvnMT4etvn/Py5CSjSCJUyVRRnwfUSmtXjnFwHJ0hgu6dY6Q0q7XAHOeY/oZZDshvW0PskgJk+pkoE5ABHqgWBjOvZZ5JqXgZ2zfqxWilYgYXqzzWKzAJA3PleaYouyfKegTVjFYKn5hxKYXLpUAo/XAmkSP0c+Z4vglbPiLdzkQZwvAgRAgmmxSu9YJ7EDgaSm2Vqkqp+Vx5uXMQQ/EJBz0TfdMJJhqBaGXqpCiYlJdh/KZCLS2l2Xn5sm2VYnlpMyQvoJZ2prEiUm6FnPtgKZ1EnKKCWSF80vsgAvbeOTqIpygzS2lVNs0DDJ5SDTg32HLPTCSl7Xl3ABHFzm7m9GC6n+mye/fyXf1SeZcQe53yqgblfEz4Zqmyc4/N80To51Nl+rkK5utU2ccc9odVwVwsFovFYrFYLBaLb4clzBYfBfe8rngbO0jBtOb/U+8pBArClJlXCaVy9IFK7oqpCnMe7GMnQvjZr3+d4ZLVSMnxeO8Da8YnX7vSauUv/vRreAwUg5iZOCL4xtMTRz/wGWeaSzEzNlNufSdqpV0ecwutD8YMbAxmDI4Qaqvo0VP0bRdagDWltg1Nl4MiiFSmZr0SoKAMceYYud0Wgc+ZlzfN2C5GKZVShKaFN6cokyKUKDyNTu+dcUyGCEdkAu9SjUcrbMVSlGGMPnGJrFSekqcW4UpWFbsZjbz4eExnRmCSj6dsSAghTlGl1oKqcCmGSB4tGOMAjJjKPnPQv88cpS8aTLLuahJYKSl4Igf0q7ZMvoVTq1FredkpA1KulfIifcb0HNuP3CQLT8lVLNNWAvnzj6zh3o5JDF6JMqE02EoBVcb0M1GWaUcRUAM7R/1Ncj/MNJ8/L2ZmhXU6L+G2iHiXABNS5vHhVNl9hP+1hPI4f//OB3R35j1Vdrdrr1Jl98+J+OapsiXLFovFYrFYLBaLxeIXhiXMFh+FOSfHyBF9U2POiURQSmHQc8CdwoiRUum8/qcxeHra+cbo7E9PzCnMOZmeQmiOTm2FN7/kkTd14+Hxmp8ZWV3so/M0Ok+350xaDc+0W6QwqiYcPqEY1+0Nve8whDHA5sGcnQOhtYruB0ijNKWoIk1ptQFpMUyUCCVMwAcSUBEOd4Y7k3Obqg8AxIx6v6B5SptP2hXiFGWkKLvNST8mRwSDoKhxrcaDFR5qoW0FRXPEH+fwFHOO0ixopzgaolSETZVjOE8+MIXNFJOS0ko873aWDVW4bvXl4iNMenemKzMG+xgZziNH/IfDDNgKmaw7zz8WFTbLBBkamEEtlWp6yh3BTCjl3AzTe4osECQlWeQcfh4ZyIuTTjBnCq39GPhMeaUKVhQxuBRFSyGmn5KUc6fsrF+qoJpVzbsUqpopsXEeDHAXXoXKQPJapZypsnC+sFX2OlU2X8XK7jtj8G7Y/6ukyu6XQV+Sca9YFczFYrFYLBaLxWKx+IVjCbPFR6HWilaFI9DIwXckN6RKFDqTow/EDJhIdD67dT7bbxy3g72P8/+pF/roEEG7bvzQpw881gt1K7iAOfR+MIDbMbgdt5RVPffJiijFDDHHJShtI3zQe0e8cHRH/Zk5Jz2EUiuMjCxZ21JqmNEuDSJQ1dwj82AKFEkhYgjd8yBADsM7Pmb6mlPUbe1CKSnKHusFxREThMI+Orc5zv2xFGWmxmbGRZU3rVGbImQlcoazT2eMQaAUEx7OxfopWXW8AMd0ejiqwdUUDUVUs+nowWYbanCxghmAIJVMrc0cyN89k3caWZ31kHMXLZ+HM6VkRanktlgxkKJsRVHVl50yNaGVco7Zn2mrmTtgd39zH/7Px9H82XRHgGMM9iNe6pelnKmukqmyQLJaeo6Vyb1+eYo3vY/6i5x/z1RZH5N7CDH3xe5XMoVqmXYbnpXITIF9+6myeJUqc/I576myfDxeZNnnU2XfjWH/JcsWi8VisVgsFovF4quzhNnioyAibEXxMXEmxRr7sTO9Mx3MjEkn+s4xJl9/fmbfRw69Hx0PGL0jBg8PFx4uG2+2C65Cs4IC8zi4MXl7OzjGTh/gvYMa1YxSDHwwcba20X2w906I5CD+eMan42dN0xC0NqoWVAK7VmpriAemiojhDh2nmjAFZMZZPw1mwJiDMQcE2LahPtguF6oJpsZju2AEUgxCcyNtDkZ3Dg8mgarRTGgiPG4bl6YYyoyUO8ec9DHACqpwOeuKAzBRLiL0GfSz2bgVwcJyQ04DIajWsApVlFpSCk4ZCM5xC3wEe3TmBJ+BaF7WDIxSgipG3BNhApsqpgXRwFoeLyjVaHYKOgWLvH4Z+Hnx8r5Tdg7bA3amwEyVcKePSZyD+/s+mD0va9630KxmQuwlVXZevhTJy5cR5NGC85AApIzKgwG5wXbMfD1+2i4502f1FIu5ZxYvFVXIVJnpu8f8fKrMX18XIIf9I3iRaxGRwu98PODl+e+P+bGH/e+PuSqYi8VisVgsFovFYvHVWcJs8VHovSORw//DB33cCAwJwA9uxzNPx8E3np+ZnntUY0xm5GVMKcrjpw88Xq98erkS4pgWpk/onafpfHbb6acoizkQMVprmUrygYtT2wY+GHOCKPvxjETufIkqcqbGpFaKB+qTcmmoKeU0L1ZqbltJUPRM45DJqIis8vmYjNERJJNpONulIVRaqTzWDSXQUvCYmYqbA5/B83Cc8wiAvi/K1AMPY3dnnKJMrCAKVxMgr3EGwlWEY8JNAgyqQqWe6aUADapWkOChFMQCswLqxBzMnl/TPvLy5QzQ8yplYKilbCso7imWqoBao9RMeVUtWLF3Vyw9qJKClFPMCGd+K5Tgvu111iXvo/tz4p6iafTJMbJeWWpKqlDYqmKlEB7vUmWvklL3dJjpuzub5ZRRYzr7yOeA91NlplnfjXD65AupsnrKty9Llb27gHnKsuxwvtRAP58qu6fI7ntoH0qVwceXZStVtlgsFovFYrFYLBbfPkuYLT4KEcF+7OwzkNC8hOmDp9uN5955fr7Rp6eImimDhImacf2hNzzUxqePj3AmfAg4bjsDeHvbedqfwBUfKcq22ihFGXMgtdC40MfOHE6Ycdyewf0UPYqOgVwKUh4oBFUFaSl7zAR3UCnMALeUFRGRu2VnEqzPrF3O0UGU2i4YznZtOYhfCm/KlomyWnEfjNkZYzDG5Hk4ISBNKZ4prUtrvLkUzINB4YiZe3C9n3ILNg2KbQx3RCxroa48+8RMaZq1T1Aszn0szWH9zYzLxUCUycS944cwRtDHeKmV4nnAwKWkQDqvQ4qkZMyLl9tZLwyqKbXYKbQ0k10CdklBJ9xFWSAoEY571mbl/vhn/XJ6Jtz6WcWMCSi0qogKalCLZY3zlGV3I6ZnskwBM01Blv/7kio7xqSf+2Z3fyR6ijI14H4h88tTZfI5WfahVJm751GLc9g/IuXj51Nlr1NkcT7gqmAuFovFYrFYLBaLxfcWS5gtPh5S8NgRBp893Xh7HBy3nrKpT8KDvR+YKq0Zjw+PPLRLDs+LIAHH6GgIT2PyvN/Y9xtxbkmZKm27UEzoc6AqbOXCMXZCGh6Kjx2/DRwjRNE5kFKQ65UqkuLFDK2KmiIeKAUIwoQSQfdJPS98uqTIiemMfoAqdbuiMWnXRhWQUvi05EYZpRAx8ZGJsn4MDid3xapiLmwo26Xx5lIp7vQwuk+mj3eiTIRWwKSlaASa6rnxNtGqNA9arYzpVJTQoEiKsmrnVc0QXIPK5Dhyp+wYg8Pny6C/MpmiCIVWhKLK8PvlSyjaTskTtGpYya2xUhRIAbbV3CnLUTAgM3T5r5KxLjV9qUdGOEefEDmK34+UZapybrfldctqoKUi3GuQeRjgvs6vAuVMgJ1htRcxdPTBMePdS7pfzdQUoXq+juFfTJUVFcp5tCAi3qtgvlQ55d31zM+nyjwC+yapsvvjvP53WBXMxWKxWCwWi8Visfhe4QdKmIlIBf77wK8D/hrg1wIV+O9ExD/5LT73vw38g+fnTODfA/7xiPgj383X/P2CquJ+8I2nt3x2uzGOeVYKsxrZ+0GtxvXhwput8Xi5UmpJ+eHCMQ7mdPYZ3PYn9uNgdD8TPrlPVoplSkmFVi70caBuuCvDb3jvDBe0GDIGVi/ItVLO0fXSKlpSlJWQvLpIihlV5ZgDU8FCzn0ypwfMvoMIdbsiTLZLZdOGi/JD7YoJHASiCnMwYtJvB/vM/TOtSpnGJsrl2njYCjWCEcrNg+mDPmfKlbsoozACJkIzBZQ+BxSjqVFUcGloyHklU7Meqsr1UiiSX4NGZx7Cs0OfwfPcwVNkuR+IFpTKZkERY6Th4lpAJVNdooHVwlbtTHGd6TOT3IFTIU9JKnAO+kdeuZSzj6kmmJzXKWfulE135gz2PsHPIwKmhATFhFpLbqbFfVMs02Rpv+TcPst/ELBTeLmfBxLuqbLTVJnd98xSkH4+VXbeUHipYH5Zqkz43LD/51Jl0+Ple3T/PHg/Vfaxh/1hybLFYrFYLBaLxWKx+Jj8QAkz4BH4X55///8Cfxb4z36rTxKRfxz4h4H/GPjfAA34SeBfFJF/KCL+V9+VV/t9xJ//83+eP/0zP8s3nnbmdHqfOAHhqAQPDxs/9MmnPLaNICWaniKtd2d35+n2lj4G45iYFba60c5rixPPHahaOMZAKcwR9HgmxmSiuQkmAVqRrdLUkJLVQTUhVLhoZR+Oi2AFVI1bP9g0N8y6pyiL6cxx5GNuF4hJa4VLSVH2abtgInTyxGGTFG79dnDMFGhahTKNJsr1oXGpxiYwxNjnoM+Owyl1glqCi1kmnszYzqpoirKglryOGZ7fu6o5rqYUzJSHrWICI7IOK6YcuzAn3ObOMXLsXmTgJiiNokFToQd4QC1CE8NRrApahEvJmmU+v+EBRc+dMiKlmtw3wVJqRqSIuouyrNpmRdbvVyp7CjMxoTY7x/ehFcM0jwzM8Jc9MtXcblN9lyq7C6G7bNrHoI8vpsqK5nGF/LCgz3g/VSZQ7Junyu6XOO+y7EOpMhFOwZnE/XLn/d9XBXOxWCwWi8VisVgsvi/4QRNmT8BvAf6vEfFnROR/BvxPv9kniMjfTMqyPwn8DRHxM+fb/zHg3wX+cRH5IxHx09/NF/69jojw9ecjLxvOiSoQzuPjlTeXB65bS+HhDmr46OzD+Wzv7P2ZCKHvByLG5XKlFSMk8JhUKagax5ioVEa/4XGAR26PFcGmo3bBLGhWcINilgJEoZWN56NzRNCqEsAxBw1oVnLLa3pe0RwHYka7XMEHtZ6iTJVPypaVRYKpeXVyhPP8dOM2ggOnNKMMpb6IMqWJMM+vYYzBfNVerOpczJjAsMLlvOp5jMAVtpqJMlwRlGopyiIyGfXQKiqBC0xxTIXjEOKAp75nasxTXB0yKVLRmFyLMc89tFqCTYxQzbpiCZoopRi1CnpWGE2gVkMkzkH/zFuB4u7AzLQhkjtxIkyf+OQ8mJD1yzFTNtVmuRengZlQrbxKdqXQyk2y/LOeibKsiPIivOac3EZ+TrxKlZWSFdD7otr9AibwlVJl77bK3j2OiHB3Ul+WKntPln1OnsHPPwG2ZNlisVgsFovFYrFYfHf4gRJmEXEAf/Qrftp/7/zzf36XZedj/bSI/B7gfwL8/XwL8faLnTdv3tD7DkApwqdvHtnahWs9ryWidB+ow23vPO07e9/xCXMOFOPSNkotOI7LpIQQonR3RCp9fwY9wFMWScxTejSsQa05km9qXCxTT9U29uncplOLQmRiq6hRzwri7IN5JsqsFNrDAz46rRiXuoEoj6VmugrHJa9DTpzb043n7vRwbDPqEEoIl0smyi4qDFGOcwetn3tZIYKpZ5pLFNdCI6+HdlFchU2gal6oRJSmQhhAprEeS6EWwU1BAgN6hzHgaT9wgnFehXSZGEZB2UoKpOmgClsBSkUmaIUqknVZE2pJSSd6Du8rEDnqrxK4p0Rycn/MT7HVVM9LpXJevwzmyN2yiJRVxRQ0MBHMFLMzVeaZKlM9hVyQRwdepco4U2Xuef1yfG6rrOSvQqbfyO/58Di3xT6cKoP3a42vU2Uvw/7h+bsn765mfrupstdvWxXMxWKxWCwWi8Visfje5gdKmH2H/PD55//hA+/7o6Qw+2G+DWEmIv/ul7zrr/rOXtr3DmbGL33zhinKpW6UIqgZMuFp3FCMYzhPtyeOcTDONJBhXNuG1UIuXAVNC8fsTMn63+hPzNszSu6OacysA+qFIlBLIUwwNR5qwX1QSmOfzh6OWRAOwyeGvGx1zT4ZY+KjY62wPTwSs9NU2B4fUTUuarRamTEJnGJ3UfbEU3cmjjSjDEkZtTWuzXioRvdgP3e0ugsSZxVUnSqWo/hmFAJ3p5/XRavCg+UO2CSvabpJiiuBixYum4IoQxzDOXoQQ7iNweEjN8QiMHGmKyZGLYJhzMiLks0gX/Upua5GLQUxqJapspedMtPzQIC+CCH3nPZPZfZOlAV5KEHOHbg5nOlOH46JYlVf6pel2HmpMj9WhTOdeKa5BFp5J8r0ZcMMjjHok/dTZUC1s375Kg02ZpzVzPwe5oVMeUmVvU5qfbNU2f3QAHx7qbLvxrD//XFfy7KVKlssFovFYrFYLBaLj8sSZt8EEXkEfgXwWUT8mQ98yP/z/PO/+Av3qr43qbXyy/6ir/H21gkzCLgde+6MzeDW37IfN3xmfc/EqCbUVpk+UBzVwjEOHMPDGMcTPiYihoSiEjn6rhcKkYmnaim2qhFzoKUxu3L4O1E2w6kIKkYPZxz9TJR1ylZo2wP4pJmwtQfMjGaFrRT66AROtcoU5/b0zNMxmTjWCnVmWqttlUtR3rRGDz/rgUKfgcRkIiiDTQsZgzMMR8KJMKZ3rCgXNUCZApW8JhozMISmyvVaEYSOUxn4BJ9C787T2DM1JkpEJ0JxDCtw1ZJ7awKbZF3VXShFsRLUUjMp1VI8Bnkp8i7KkLsoCkQMP7fFlKyHljO1NTwH/ePcCZsjGGPgkSP+pil7VIVWynkswlPu6b3iKIScO2mqL2/XswI6w3OHzgP3c6QsoJa7NPpAqky/darsPsYfnxv2J5z5uVQZpFR8J76+WLcM4gsSa1UwF4vFYrFYLBaLxeL7gyXMvjlfO//8uS95//3tP/TtPFhE/HUfevuZPPtrv9Ir+x7D3XEUF8mqZYcRkUP+R+6aiRZaLVRJETN9AJNWG3s/EAEPpe9v4RRrSMEkGAiUhsWk1YKcFb6tFRgD1coM45iTcoqycM90VAh7OD4jE2VzYFuh1QtKUFW5tEY1o5RKU2XOQYRzaRcmk+N24+0xOGJiW6GOlH6lKVsRHmvDCW7uhAt9OuEDF0Oi00pDpSJWiD6xU5T1U5S9qQ0RxQkslK0YMZ2YQTPjeimYCFODGAM15dihj+B53OgeKcp8J0rB3SgF3pSNZx8Mh2vNlFpIvnba5FJL1i1rCiS5CyrLLJmctUjuO2XhiHiKI06pJpIJvCl4vKtf9mMQKhQtFAlEAwnOa5uZKps+84rmvX5JSqVmiqpgwstrmJ7HJMZ5KcHPa5kicKmZznt9VfM7SZXdjwLE+bjTs4L5OlVmp8h7x7vK5v2x4ONXMD8ky1YFc7FYLBaLxWKxWCy+O3zfCTMR+WngV36FT/n9EfHf/C69nDvxrT/kFzf7vrPvB7fn4+Xi5Rg57m7kxctSU5JIzEwzbRtH78wJHkLf3yIzEJQZnuJLCqHGFpN6aUgEWgtbVQxhSkE0d86a5QaYh58pK+Xmk5jB6IMgsGZYaTn0XgqXUqil5O5ZKYzREYStXXBx+u3GZ0fn8Ik24xot01dVuZQc3A9gDydmChX3QYihGlRxTDdCBBwsHFej+0ihVRuiyoygoNSzkhgzqFZ4eKjnNU7HfQK5UdZ359Z3ejjimim2EiAVAd5sFQ9nH4OHqmgVXBWdQtSgVUPUsCI00/PSJVg5R/g9ZZlKEKHnL7if6ixl0mbGmIPhmfAanvVLDzh8YJI7ZXJekTRTiubxgDnzdyD/Ue43NutL+ovzqEC+lhFBn5kqu9cvc1ctjxl45GaZRzBPYfZVU2WQqcBTuzEd8grml6fK3lU272/5+MP+r1/vx37cxWKxWCwWi8VisVh8mO87YUZeq7x9hY//0z+P57onyL72Je//Vgm0HxjmnPzs0xM/9/REjMkIQTGurdFaBQLmgVgFbTlu34M5JjMO6I5opoQMR7cNApqC1YZI1tsuW8VQJudAfDimgUVwDKeIIlLYZ89EWR9AoK0gnntTRZRrqzQrmBWaKu4Dwt8XZX0w3ZlVuERjkttfl6I8toqLcpxCLiKvQU4RigLibFqZZ42wCPhZWdQCV4xWKt0nFnJWMVPBmBrXa6UITAnGnITBdOg7PB0HIxxCcydMJiYFC2hVKAhjBqUoWzMmgrigFpjB1jbEoJkgainK7kP4ZFINSxkUCBHn5YBTlFXLGuUxBxKZ/HLPDbLRB5z1y1KFeW54VVNKKWf6a+YW2fmYESkJqynF5L3NrxnBGP65VFkeAWglf1/uu2JzelZgv0Wq7C7I3pdQ8fL1ZZWTr5wqy3TcqmAuFovFYrFYLBaLxS8Gvu+EWUT8pl/A53orIn8K+BUi8pd+YMfsrzz//A9/oV7T9yr7vvP26cZxTIoal2K0ywYE6oMQhbrliP+AOTozJvSJlppXHCUIzcTWRQVt9UwZBZdWKBgTyxSR+FlbHCmhUKoVbqMzR8fH5C7K1EFUaTUTZbmdZS+iTBBq3VCF4/nG85h0d0bJgX09k1BvauGTWhA1Dp+MMXKZbE78fJ1FoInieo7ri0AI3SelGA3NbbQ5mdO5aEFMcI9MuW2FS1E6zogULiHK2J3nvdNjZOIrAMk6o2FsRahqDAQX55NWc6TeFTPQGmy1nfVLeRnazyF9RTW/R3LunIER7mf06t1OmWpeOwWFM/U1x5ksIzAtWb0k98VqNdopytzzQqaeBwzuqa1W7ukzOXf+U8JNz9Ta61SZKtSimKRAVFJdDXemx7uv5wOpsrt8+nyq7J4Su6fK4v59P3+3Xw/73z1VJtF4edz8Xn73K5hLli0Wi8VisVgsFovFLwzfd8LsPwX+NeC/Bfxm4H/7uff9yKuP+YHmcrlQzXjcDGsVFfDjRqmNsHKmvYI590z2HB2rjTDAJ1YvBMHFDCsGmiP/rQhNKh1lBrgG1YSYwa3viCibFt72g7k74Vnu02ZoCKjQWuWhFMzyQEDVrDCagNUNFTj2nec+GR7MEmxWEA9E4bEUPt0qIkr3yegpyiKCwydGUFWokmkuP5NHJlktJSatZOVzejBncNGKmBDuhCgPW+G6VWY4BzPTXjPoHfY+eRo3cEFEmd5RMSKUVoSH0riF4xI8mBBizCkoilTnUlsKryp5UfQUQmqWokzu62HnblwEgeclTDgTWpqJshlwT9ONcyvMB4phKpjl5hgoVcFKwcPzsSUPEhCccg2qGabvJJCfI/3uKeNSbmWCzFSollItEFQzVTbczyMFH06Vwbs02bdKlaWQ4mXPrLx6DHmpbL5jVTAXi8VisVgsFovF4hcnS5h9a/7XpDD73SLyByPiZwBE5K8A/kFg54si7QeOh4cHPv2hR77x9kDGgbVGr43u5wh8f2YANhytlSgG4ufmlnOpBbVM8VQVzITNNnYPRmTCSUzQCI7jIEROUda5jYNwUlVVo6BgkhtgZti5UWYigNNECK0UFY7eedo7uwdRgmYV3EGCx1r42qUhptyOAw/HxPCYDJ8QTitCQXGESe51qQuTYPqgNeOiF5wcoW+iSFEiJkjhUiutGdYK++hYZBtyzOB2OPvY2adTrBAMJg4YZinKuk92HzwWA1M8sn5JDbaaly9tE6qcO2VAqblTxnkoAElRlvtkkR7p/J6384rl0UcKtHvya/q5Q6aYFPT+f0kixdVL/XJm0s/M3m2PSdZBS9H8mZw3BcaZJJvuL3+HU36ZfCFVNj0rmCZZl/xWqTJ/JcqynvnhVJkH71Uwv1mq7GMP+8MXZdlKlS0Wi8VisVgsFovFLzw/cMJMRP7HwF91/uuvO//8+0Xkbzn//m9GxD95//iI+D+LyP8C+B8Bf1xEfgpowE8AfxHwD0XET/9CvPbvZW63G1vAW4Gphd49a3Vjz0H2GZRawQYxB2INwWnVMKuoGkUz0bOVxj6dHoIoQO6X+RigWb186oPn2zOEIDjajEIFVYoZD5qiTM0oIoikrEIMEzjG5OvPB31moqxaYXgwxF9EGSo8HwcyDaUAzm0MiKAWsFBCJC9hiqMujDPhtlVlk7ycOQhKKLVoCikVrtrYtkKphds48KNjKoTD231y9INnn1QtVHEO70gI1ZStGhLBEZOHmom8OQI5B/23lt8DKcLl3CnjlC4qKR31HOOPADkV1PnSc6esZIKun3VXJNNkvXsmwOZEtYAEeko2K0a1HPV3nyBy1i/tpX5ZSn4Nmdw6E2XOi9S6D/sj91SZnptwwAdSZdXepcpMyCMD30aqDDivYOZbX6fKXg/7fyhV5ucu2y/EFcwlyxaLxWKxWCwWi8XiPx1+4IQZWa38Wz/3tr/5/OfOP/n6nRHxD4vIHwf+B8B/F3Dg/wL8YxHxR76Lr/X7huM4+MaRiZ/hjs/O3DsAdbvg0Yk5EatZYSyGlkYxwwi2WhBRhkNHiIxAYSKMMUGNKspTnxzH8ZKWkqYULHerzHi0kqJMM72kErT7XpcKRx9Z35zBsMBqQT2YEjzUwietYMXYx0F4irII2OfACapBQQhVgtzSihmgBji1Kg9amORlR0NpxRBywH7TwsO1YKZMH9z2A6u56fV0G9z2nSMmglEBnx0PaKVQLWufnayqPrTK6I53wARTuLaG6imm7vVLTVEmKqje65cQKCJBRI72i8lLBXHGJDw3y8bI+uWYnlVNstpqGoQJGpLJrlJI8TbP5zjrl5HHBormqL+pEpFJtbuwep0q07MCqgpKIHp/nA+nysxSwn3zVFl+xfetsrukk9MQfjupsnyE+OjD/q9f88d+3MVisVgsFovFYrFYfGf8wAmziPjbvsPP+6eAf+rjvppfPJRSuPWDY+zMPdNQVjeUyZwz/x6e9UhTrBgFaMUIUdzh7DMSOEVgzMnUgiHcxuTpOOAuyopQKKgoWlKU1dYAviDKTIW9D263TJSFgVVDZuASXM4x/1KNYxz0ARoFD+hzvoiyi2SibEyhiuDTwQroRAo8WsOBEZko05K7XnNOTIxPHhq1pCgbHoQKTYzbc+dpPzjo+MzK4ZTBDKhWeFSoWugAGrwpmdgandwyK87juVNWq1KKcW9Wmuk7YSaRgoyUS+6TODfXzKCY0cc4K5JZv/QhjOnnDlmmxcxyQ4yAIkqt+X3OUf+gFMP93Wh+K1m/LHpPsr2rQb5OlYm8E3a5awaQBw5eNs14lyrT8yLp61TZXTx9eKssP+Yu6SQXyM4rnvn+Vw3R92qRv5AVTFiybLFYLBaLxWKxWCz+0+YHTpgtvjuoKhwHMgK1gkkOu2ONGk4phlpFVNhU2Gqln3U8TAn1s9YI0wehFQnofbIfOe6PCFaVIgUJ0Go8aKG0hkSkKNNgkxQ4okKfk6fngyMiN8hKwT2YEVyK8clWKcUYs7N3ECmEwxFZKS0mXLUgBEcIFcE001OhgVjwYA0hRZk41FJQFcInROHxsnFpRuB0cVxARzCO4G0fPB037itik8lAKBjXImxmeURAgsezZjlCkBC0OFutKcQqNDnrl7wTZRKBWiqgTOUJEnlRkrPy2jQvVh5jnPood+dGz49LiaSgQTZkAzWjmmBmKdNOwaPY+dhglqP+9yuTfqbK7nLoniqDd9Irh/ZJG/WBVFmxFKZm8nKM4M79WMCHUmVZuYxTlp0bZq+OCchZAc3Hef93+7s17L8qmIvFYrFYLBaLxWLxvcsSZouPwpwT3Sr4gYqBCE2FUitxr80Bl9aYCGM4YoZUiHDMhT577mIF7GOy7wcqillBDKoq6kAxHrVQt41wB5xi0CSTU6Z5zXK/7dw8ZY6qEVMIyVTbp6co63Owj4lSTuHidHeaKZeaFcMeYPelL1Fggjqflky0pYMRmhbU8utRUdq28bBVsOBwJ4ajAeHw2TG5HTszgmKFPgYHjqGUojzWltIK59Ot4ir4DKKDlKA2pVhDinAtmuk8ycucnBIo98M45VPKsxlBeKCaqS8gDxig56h/MI7cCJNTskUEKpGHF5Cs0Z4pLw9HCSLysTwiK6Em1KIv9ctMqeXvyj1VFhFZpT0F0T1Vdhdqr1Nlr69eftVUmcCrIwLvNsyK3ZN3Kak+n/T6UKoMlixbLBaLxWKxWCwWix8EljBbfBTcHZGCyKQWy+RPLRQVqkRKLzVmn7gp1grTJzbPi5IAHuwzN8r0PtqvUNWQADHlWgtt287drUFVaNpeRNkI57PbMzf3FBBq+AzcgnYmymopdO8ccyKR9cXDJ9MdNeGTVhkx6ZGpLxNQMiGlCk2NizUGDp6SSuW+h6VcWmOrhhZhxCSOTGaFC0/7ZO8He0w2a8xxcBudgnCpNS+EijFwHi4VNWUekxiAQdty0F8t65dqSoRgrzbK7Nx/UxEiUkjNcOaM3Ckr+bY+ZqbHRJmeO2ZjZq1S7pJNAc6kmlhKMMvLmRFxCjF9kUu1pCi71y+nTzzkc6kyPw8QfDFVFmeVdM5MlamcFUy+s1RZcJdld1H24VTZ/Jy9WhXMxWKxWCwWi8VisfjBZgmzxUfhcrlwVSG2BkVpqpS7KDPDj5GbViXH8RkAQfcgfNBDOXpHJUWZae6iyQykGFeUerkgr0WZtbOGqfRw9tszewROvIiysKAW5U0rtFLoTPbRiTCU90XZ49YIHxweudWlYGRCCg2u1bjIRmcyw3PRSzN5JpqXH6/Xhhr0MZiHwPkYb/fBfnSOGBiGITwfNxBlq5Um0LTSxakGzSpzBt0966g1uNaaSbuqFFPCyWqmaQogsgYpAhGWtcNwxoxzzyxlk0cwfJ61xMirk32+u/7IOeR1DqEVNazm5wqR3y8BMPyUUaoptqrlzyPCGf5uNP91qsz0XTosh/bJzbPXm2bxfqrM9L5d9s1SZfma76my+SLS7mkzXi5v3uXXh5Je360KJnxYlpkuUbZYLBaLxWKxWCwW32ssYbb4KJgZ7bHht0mBrFuaET1X7N1yh6t4bpvllpVzeMolFaOUiknuY5mnhLro+6KsKFysUV6JstspyuJMS8UMsLzE+aYZxQzX3OjyUEyMYw6OSFH20CqC0+eEV6IMgtCgmfBgV/aYecEy7uP0gORY/sOlYacoG+OdmHk68vLlPjqCosBtHogqtVY2SSHlKqDOo+bFz8PPlNRZRa2tIBpsJmReLZNiguQ1yZIpMDlTW4TTR5x7Y0J9LcrOz3cPxnCc+0J/CjSIvL9ApsnKuYeG5KiYYkTEKZZyML/d65enULsP66eQihSMou9doSyaEsrvqTJ/lyprZwru/vivU2UR8UqGvbw1v18iH0yV5fO9S5XJt1nB/FipslXBXCwWi8VisVgsFovvL5YwW3w03pQNtz1jTiMlzFSQcArCPOtzEU53OMaRUqZUCo4Vg+loMS7xTpSFD0TgapVqiqkxCZ72G7vnxhWqMMH1LsoKrWS10SOYQ9AwiOC5H0gRHqyCT4Y7ueEVWSU8ZVAtwWN9YMbkNjqmiorCuQ1WTNlqodRMkh3dMw2Fsh+T5/3GbeyZVhNlH4NQoZasql6KEaYQzptaCFNGn4wRqEEpSq0pylrJi5ioYNxTV7lTZpJXK+V87dMdn4GoUIuljAonzkuUr+uXkIP+jiOaiTCVrF8Wk/PipedWnCiQFzqJTIa1eq9f5o6d865+ed+EC8DuhxheUl75zClOv71U2f0xp3/7qTLgpYJ5vxZ6F27vP8IXU2XfTVm2KpiLxWKxWCwWi8Vi8b3NEmaLj0LvPS8eRm5fueYgfEEYEczh+We8EmW1Utyxoojn4vtVG3U7E2UxMBGqVpopeoqy577zPDOVFqLnNcWgmvFQlEurKYgI5pTzsiM89w4G163iczDCySyZ51XL80akluCxXAmcffR8rapYMTTAitDUqE0RyzH+cczcUBvB8+3gNg+GByaFYwymZFJsq4VmimBgk2tRtDTGPoh5bnttSlXLK5NNz4rlWSPUTHuZCXLfKSM31O7JLlFedsrGdETyBqdHJsT6mDjkAQI4U2XkpVG1rG5a1i8h65YRZ6osMn3WqlJPsQWkDH1Vv8zEWKbZ7tLpLspyUy0rmO7O8RFTZe7vUmJyF2XfJFXG+fXfr4Pe+W5WMJcsWywWi8VisVgsFovvfZYwW3wUzIypihtoOOYB2QxkzMFwGN7RSFFWI9BTgmHCxYy6be+Jsk0qW7EcpSe49Z3nU8I4Cp5CoqhytcKbrTJwZjjDs6IIwj4HLsHWCuIjr0KGYZEpLKWcg/mTh3IBnMPnWXe0FC4qKMJWldpy0N9nMLsjqojDZ8+d57HjgM9M2HUGIsK1GJspRSvCpG2C6YV5DLrPrCuWrE5aEeqlIGeqSyKw8v5OWUqskjLNnWMCBHaO4k8PRuTjQiav5nSm+ymxUlYBiMp5dVIppyxDUiJF5D9+Wh9T2OpZrZS8ZHmvX8KZKpsTRDIRBy/iyjIAxzg/vs/5UVJl9y0zj3cpsc+nyu6PsyqYi8VisVgsFovFYrH4dljCbPFRMDNqBMcMpgQSwTEmYwYjBkVKVi/DcxcrhKlw1UrZGrgTMVCHayk0szNRBr3v59XLrATOs+YnAo+l8rhVJp7XNqeg53j94XnxsZVCEejuBLmTRhGMrDiqOm/qBqdkAqHcZc+5Z9aKsm0lL1+OeQocgRk83wbP42DMiXvueg13FKHWkqJMCmiwbWBRmZ6v7y7KTCRFnAlNwSOFk5qi506ZlftVx5RgKmSV0d+Jsnv9MsmdMvdgzJE5qshDAIGACCZxbovdr2dmtVI0k2txSiY7U2vN8jky8cWLgLrLp6x22svvxbtUGS8f/zpVVu2rpcrygicvqbLp7+qVX5Yq+4Ue9l+ybLFYLBaLxWKxWCy+/1nCbPFROI4D8dwLO8bEQzhmp2o9RVmkKKMwNNhOURZzEp6i7FJKpsBEcWCfnecxcsgfzTQTQRHhapaiLCZB4C5wbnTtnhcfmxlbMZ57xylUK4SAhmRyy5xP6gYBIyYReTRAIHe81GhVaefgvohw9AGe+/fHdJ6Pg94HiBIe9FM2tZLVy6oZB6ubUMOIsyZ5nwSzIrSWV0HN8tABpEDTVztlReMc+0/x0udkzhz03+q5Uzade/Jqnkv6c05m5ID//fLlmR2jmCFnuivTWFmtFH2/frlVxc466D1p9jpVFuQu3fT8GHgniPJ1yxdSZabyMsL/VVJleqbs7rLsq6bK7o+zKpiLxWKxWCwWi8VisfhmLGG2+Ci4O8/TeeqT4Z2ilVIrGsFFDQ1hGDQpPLwWZQGX2qiS9UdU2PvB8xiYZPVSPBgEBeGNGY/bxoisWYYr3QPDuPmBE2xqtGLceufWlWIVOY88KooU502tOc5/ijJ4V3ksZqgG29YomrXNOWZeekQYnjtlfQx6gCD0PgiFVitVhWYGprQmbKJMlH50QjNRZU2oZlSFcu6UEXlFU1VzzN8Uk/kiDHOTbNInoEGxvBQ6Zhq8FFops9zzMiWRSSufjnNepzRFxDLhVTTrndwlmL436r/Vc4PsVVLrnRDKyqtHHk24h8NUUv6ZpCi7p8r6zE8sZ5Lsfimz2LtEGrzbYnufeJFgH0qV3RNl92rnh1JeEKcU/fgVTFiybLFYLBaLxWKxWCx+MbGE2eKj4O7cvGeqqFY0oKm+iLJNKg9bQyJwHxSUVipNBdOCCxy9czs6RYxAz92tSTHj0Yw3W2PGxCXwKcQpyvo82Bk0NZoaY6Yos1OU5VXJTJRtplzsyu6d6ZOQ+9VJKKpYgVqMUjLZNbtnhVGMOZ2n28E+Oh45mj/GxAWaGbUIVQRrDRHnYoJQGMMZMilWCD0llQm1FSwmwZlqMz3H/GFrKX2Qci7zB8d0pgfVBJWUY46nbEOYM142xNzPOiJBeGaqTAPTctY7MzWWP7t4Vb98f9T/vDiQ1c5TBuWb8gKmh7zspH0+VXYXZPdUmb5KlamSxw9eyaSIYHzJVll+vfHBVJneE3LfJFV2z9W9dlergrlYLBaLxWKxWCwWiy9jCbPFR2HbNh5L4WePg4dSEIdpsGnjWg0F3AcWytY2NuFFlN1652kcNC14GIc7c07MjGsxPrk0HJgCc6ZQEoxjHOwyqSq8sZbibCqmlSKBkaIlZHItsNkDt+jc+oGLUPJWJcWUUqDWUyaZ5jj9nDhCdOe2T44Y9OnM6YTDLkFV41KEZnnMQMXZCijnTllMNO5psftOmWLigIAY5dzxUoVShGLCdLk3KBnuzJk7ZVtREMnx/ghwwfO+AD7neZlTQR333CqTcysMyccudi81klXXuG965Y5YPZNrcW7BzVepMjlTZfNMld2d0OtU2Qxw/3CqDKDad5oq8xRvX5Iqg29v2P/+er9bsmylyhaLxWKxWCwWi8Xi+58lzBYfja1UHkowJHioDW0FIpgxqRhb3biooFoI4DYOnvrOphUo7HMy3Sma22Ofbg1RpRO5GzZTlPXes6JpwmOpHMeNp8gLlCYTk0JRw5lsFa7lkd07++xE3FNQQlE7BVVevjQRQoQ5Bu6CD6cPeJ4HfcxTUgkdp4jRinIxo0ihGNQSmFQmcMwJoRRVpMBly4uWqnnxEr9vbZ1ps6oUnInizkv9coxADNpdlE1HNOVZVi+BCIZPwnP7DAl85JXSIoGcqbJSNC9VIkTw7vrlWb+s5yVOkJex/3fVx/wZ+3Tm51Jlpnk8gI+UKrsP+9u5meaeH5Pbcu+G/V+nyr6sgvl5cbUqmIvFYrFYLBaLxWKx+HZYwmzxUYgIxpy0Uri2ioczfbBhbKXxWEoO4wN733menSoFpLLPyQinacFM+NrWMDP2cPQubEKZ4ezeKSp8WhvHsXMbE7VGwSmqqBghQSvBtTzSfXAb/bwQGZgIxc7KJbBd68tG1xyDOQWfzgh47p0xJsecmCiHOyZCs7x8WdWoZpQGVQsjOLfMBAlFqlA1L2xqzQpp0ZpbamYvO2VV8lCAu6EizDlz+0siX6fqWZt0VFNy9Xmmv+bAp5xbXnm50oNz38xe5FI5+5f5OHdhlvXLWpT0cco9eXbfKhPJWwHuuYEWn0uVFUuxNdxfUmV3AfadpMqCOKXdF1Nl9+e8y7IvS5XdH4nv0rD//bW9ZlUwF4vFYrFYLBaLxeIXF0uYLT4KqsrD9cpne2fOQRWjlJb1zPPqZe87t9kpUggqh0+GT0wrNeDTrVLNOMJxDyQgMMInz/PIRFmrxBw89YFSMEkRY5KS7tLgYg907+xzMMmh/xQtuTOmAtulYgIuSsxBD4FI+bL3yVM/8kqjByOCYHI5L1+mNKvULeVbOPQRDMnkWQjUTanVEAXFkSiYnYP+mq+htXOQPwqEEOH0mV97ebVTNsPzPuaZ+AqAcI7pCFm/zA0zEA2aKmimyWo19Fzw8oizgnmvSZ57aq9EWZzfgxRAgNyvUcJdQOXhAEFPKXWcJzCH58ZaJs6+PFV2/1j/nCwTyTqpRzCnMzMk914F05QXCfehVNmHKpgfU2atvbLFYrFYLBaLxWKx+MFgCbPFR2HbNgSnhFBqijLVwsAZI4fyTQpIY/ikzwMrjULwQ1tBpTJV2E8hpKHMOThixzRFGT7Yx0BCaWeCqkgmz8wmX6uPjLN6OTmvYorkmL8KtUDZKlUgVPNSpwYjhDmccQyee6dHHhsghElQzGj6bqfsYTNUhZgwDscl0FAMRQyul4qoIEzMKhH63k5Za0rR+wXJ3CrrczLHObhflODcKRNy/+2lJhmMmOD5eBGOz3x7UVArmfwqmjVJOffEzsRYRGQ980ydvUuVyUtS6771P8/LmhGnOSPfVy2F1OutshmBO5jl9xs+nCrLBNrnf3vuW2XC9GC4v1Rn789p9k7CwZfIslXBXCwWi8VisVgsFovFR2IJs8VHYd93rlTqJcf8O86cuVFmZKJszMkxO2KFqoWv1YJdGkOgzyDcMSmMo7MzURUeakXceR4dDaMUMElR5gTFJp+UK0FweF6vdA9U8gqkmVFKULbCJkKYoCjdJ30EcXT67ux0jiMYPk5Rllczr0XYzJBiPGwFwfOggWcFVVEEJSy41EIpChqoGILke+3dTlmTYCKMmaJlzMmYAQq1ZhLrvlNWTBger3bKBj6yogmOe0orUzDL/5Tvo/55yTJroHkUIE1PO69/mujL2+6psvvFSQhmBPNzqTLTPB6QX3t+7jxTZSJCfUnQfbVU2f0Cpns+L58b9i/K+TUnX1bBlO9iBXPJssVisVgsFovFYrH4wWIJs8VHYds2tFViON07b4/bS/UyE2UD1Cha+KQWaq1MgmOCi2NiMCZPHJgK11LBJ/sYaBhVjaJ2ih7QMvmkPSAzGGTKakagQFWlWMFKULdCUyUUBGGOydEnHo4P4Xl2Rp/c5kBCmJrbZpdy3ycrbGZUCzSEEZppKgKTQljQTKmW4/7hQcEQUtblTplQxbEijJEpqQjnGE5InMLLcA88HFPBCfo498R8prwKRdXPi5iAetYvxVJSVUPCz42ywO+j/vBSZTTJ70MO679+f4qy+wYZfDFVdh/1vyfV7gLs81tlpu/Lsi9LleUz5GsYM7fKhJRQ91RZeSXKvmzY//VrhVXBXCwWi8VisVgsFovFz58lzBYfjaqTP397S5GCSKPPgYfTEaoaj81oJUXZ3h0KGAoDnuKgmPFglXDnGB0Jo5hSJHe2hECLcy0bRmOGn/tamUqqqpRSMHNqM6opnKJHxuQ2Yc6JivHZ3vHpPPeO3hNlAk2VaymI5tXPVu8pIqUHjHAqBXCsQK0FPfe8JAw9R/pVBDW4NkVUmVOZM0XZcGeOFGWZnBI80iipkOmtM710zA6uiARoMI4gJKgqiOX1S1OhFX2pTwZZlXxdv9TzomR6nrtQu1chI6ud3Mfs30+VFcujA/eB/tepsla++lYZ51ZZRJwVzDi/9q+WKovvcqrsQ7JspcoWi8VisVgsFovF4geDJcwWH4Wnpye+cfNXomwwRLCAN1X55HrlmCNrhJq7YD4nT35gYjzWSiCMOQhX1IwqmvIJCHUe20YRY5J1yun+kkjaSsWKYyZctg2AEEGncxuO44gLR58cfefmAz+cMM2RfjMuapQqNClcLoYUJWYweoDcd8MUUefhUnMkjcDUEMmU1V2UXVq+7hkCEzKdNZmnDGs1d8rcPcfuNaXdXUTN2ZmeX/+MSURWIdWgWSF4N9ovpMwKgniVKqsGVrI0yqtkWb7uU1ARZ+0z3mW+zsF/O+XQnHFuoAUzsgqalyq/eapsnrtpd+Je+zwrmGPmlcy7hLpXQou+v1UWfPFx4IvialUwF4vFYrFYLBaLxWLxsVjCbPFRUFWOPhijM0QoCJ8U43G7cMxB705YihCZ8OT7iygTUY5+EGGIKbUo1TLFFRpcS6VZ5YhBH5MRjiJnwqli6pQtP6eKMEXQU9g894k49D547oPDJz4df9kWU6oKTYXaCtetggk6g+OW1U0RhVBEg+tWsWxBngtluQemmttgrSpVAkdSlpHy6BieO2XlnigLgnf1y/FSdXQODyQUlVOEueDiWftUO9Nvhsr9+iXvpcrM3q9fwitZdv79Lvv6Kcte1y+zIqrE/Spn8FLVzK2yd6my8kqc3ckE2vu/HykFM1U2pr+kykw/fAETvryC+d0c9s/X//6TrgrmYrFYLBaLxWKxWPzgsYTZ4qNQa4XqxBC+VgvXtnGMztEnswhlAgOe/UAQLmpYKfTjxgjDaqEC1SrgiEwupbK1xj46Rx/Mc3crAFOjVqVUoVilqhIqcCahnnuehpx9cvPBcUyGzxRMQFXDNGil0qpy3RqigQzofTIEREE9d8q2WjDJK5ZZ0Mw0mZ2D/sXgUu3/397dR1n25XV9/3z3Pufcqp6BEYgKEsIPCTIEUBCBMCAPw4pBk4AmghhAhhUMZhEYjCtLEoNANCtPBAliBB1lFDSYYIAQZ5QsYUAkPgGDCwURmAnPKBCRme6uumfvb/747nPvubduPfatqlvd7xerV3VX3Xvq9K+KO92f/ny/W25SrWkdMrXdatmiieYuudUIhjyCsjggMlpltcTSfCVXGSN86pLUdZ3MYl9YhG5SrSaXb4xfTmFXts1W2XSC5JT7jOVsqyynaHmZtGqVSdJYy5lWWZfjUIR5eBV7zXY1s1zWWmWlxo/5rrLtVpl0/mL/+a6yuPbtjmASlgEAAADAi4nADHthZvrVRy/Tk1x1WpY6GYs8J3XFVEfXk7qUqqvPsWesLE/0+KQq506LlNSnCMqSVfXJdLQ4Vq1FJ6dx8mWpVZZMWabF0LegrO0p60xWTXLX49NRKhE2Pa0nGk9dbytLZTdVa8/vkoac1C06vXwYJC9KcnmRTl1SkpJnVasaBlPOWZbjMICkHPeRIzDrOuloyEopTr70ul6IX0qMIA7dek+ZKdaqlVYLm49fWkoyq/JqESol12Lo5C7lLA1dllRXp1rG+GX89+/aCZXZJGvBYQxcrhtZ1sK10sIoP9Mqs0tbZWaKAC/nja//ea2yZBEexvhljFdOAVSyzQMDpPtZ7C8xggkAAAAA2ERghr0wM53WpZ6MkuWkrkaA9KQuVUo7SfKo13hyoqdLyVKnIzMNeVCkO1VHXdIwLFS96GS5bKFNNL36qVGWpH7oNPRZNUnJk7xUPT5dSiUCoSfjqcax6u3jUlaiwWQm9TnpuMtKvell3UJdliybaklaVsnLqJx7jdVkqjrq45TLCOqSZKYuz/eUZWVzFZfKGP8dxlo1jlWy2fhl21PWJVNxnwVLrpOxyDzJVCMA2xq/lEmLvrXOJElJ1edL/SOU6qZAS5sjl6Zocbm0HvtU7AS7SqssRiXPX+y/q1UWH/bVrrJ5q2xa7J+v2CrbtdifsAwAAAAAcNsIzLAX4zhKNalTkUrWk3qqWqqGrtPRUady8lQno8m6XkeS+jy0UKdq0ScthmN5bTvKWqI0BSO9ZXW96+goxieLV7mZbKx6Uk5VS2uHlVFldP3y8kRaVqUc+74sJR3lrK6XHvVHGnqTd0kaq05PRrlJqpKUVWqJPWVZkSxJyp6iUZZMqTMt+qwhxVPGOo1HtvFLeTvhMbdRyKoupzgds0xNKVeto8Yx9nZVc9V2gmYyxf62VftqGqOMa8Ryf0lyddmUUlZuI53rECk+T5vKjKX+LSmbArGpVZZSPGdXq6zL62v2+WqtMq0CLlu1yqpvLvaP0c6rjWDe9SmYjGACAAAAACQCM+xJ3/eqZlq6a7l8qqHv1B8tVMtSJ6VIqdNgitHLHOHQcZ80DMdSLapl1OnosZjeo52ULWtYSP2i16KPhfFVpk5Zj09GVXfV4louR5XqetvpiWppTabcDhAw02KRNaSsR0eD3BSHADwd5ZYijKppvacstTiqurquW+8p62IscdHFc8Zqq4CpVNforixXnyJUctU4OVNtBLJKMlMto0a3OCygi2ZadclS1ZB7VY+TMBd9J1eV3FojbN1MS20fWjKP9pzU2mWtj9WaZtVdyzLFa7tbZbXerFVW6mbYdF6rTFov9p+CsrR1rfsYwdz1eWmVAQAAAAAmBGbYi3Ecdbpcysz08uNjlbLU0zLKqtRb1aI/krLJalXXuY6HR7JaVMpSYzEta5G51FmMIi6Okvqhi91ZktykVE1jdT1etj1lpep0HHWyHPV4LDF+mFJcw6qGPut46HQ0DNEYq5KPVSdtTFFu8mTq+jgxM6fY/5UtR9jVWmVdG79MJhU31dJCI3eNY5XJ236x2FMmj9Mjq6SxTP+FXMsxxi7Nq1wWY6AeDbSUOslciy7FCGWNx0zBToRdcdJkSqmFO2k14ihJ7u33pVmrTOtWWU5aLdmfrluv0SqLxty6jTYxuZKZ3KNVVtuOtfli/+u0ym5zsf95n5ewDAAAAAAwR2CGvcg569Gw0OOTUz0pEWj1yTX0C1lOEZRl12IxKLmvgrJoOFX1KcksqRtMw5BjKX+2GL2ssbPr7csiX1bVWnVSRp2cjHpai8xjMX/yWH7f56y+z3r58SPlFCGWF9eyatVeK27qzNX1WTmZpkVfOeUWYMW45NGQ1WVTqdKyhV+l7SmrbWwyWVatVbI41GAaz2zHVEarrJiyuarFnrL1qZab45dqQdgU6kRBzOPky5QinGotrWnkss6aY2X1uTdbZV2aB2tahXDP2iozxQmY0y6zOlvsf51WmXs7LfSOwzJGMAEAAAAAuxCYYS9qrXpalzotVV2ShmFQlzt5LUo26vj4SNlMxYuWniJwqkVdzuosqxtiN1g/ZCV5nBjppuqup6WonNQ2YrjU05Oipz7KR5csgp1s0qLr1PWulx8fxzWy5KVq2ZbwZzONxeSpatGCMJlJXpWtU86mlE05WwRlSZJMp+P69zjtKUtJGtqesqqqnJPktS3Ab0mWV52OLnMpZakUqYxVObu6tqcsmTT0WS5fnWxZZi2taIVFoyx+pNUYprcfU6tsWerOVllO03ijr07YfNZW2XRJU1rtMptaZemCVtl5I5jbodhdjGASlgEAAAAAzkNghr0Yx1GdZx0NWcMUlOVRR8NCfZdVVbUcpeWyyr1IZupzr643LYasxdCpS9IoqbNOVa4np0uNy6paXaMXnTwd9faylGrs6upyVkqmPiU9GpL6fqGhT1KSVExlWVTclOSqNalkaVjE7q/cFvF3lpVyjvHLbOo701Efi/yL2+o0yrG6xlqV5eosxiJdVZKpN1ORq3o7jlJVy3YiZVIENWUZ8Vbfmczy6hTNbHH9aUn/usUVbbXphM/UlvpP4dh8kf5VWmXTc6ZWWYxvXtwqi2BtV6uscWlZ66pVZlJr5q3vlRFMAAAAAMBDRGCGvVgsFjpadHp6UpSsqF/0Ouo7jao6WVbVUlS8qioW/8di+6SjR4OyXNVcSp06dz09HXW6HFWrVMqosUpvOz3ROBYlS7K2x6tL0Y56h+MjLXKKwwSqR1BmSalGU624qUtSP+SIZiyW7ndd30YdTV0LynKK3WJTUFbcVcZobsWoZoxfutc2yiktxynw8givqpS8SpbiVMs2ftnlTm6uri3dT3kK2BThU3G5XF1KrVXm67CshVBTiysnW7XEzmuVTSHRrlbZPNA6r1W2HZatW2W20SqLj1lrw63vdX69+1jsLxGWAQAAAABuhsAMe9H3vR49ypKbFkMvs6rTZdW4LBoVI5GdRYvsaEg6OuojuMiSPKuXtDwterocVYtrLKPG4nr7uNTpsijJlHMnk9SZNCw6HQ9ZR12vlEw1EieNrTllMhU35Vo1LLIGiyX8MYKY2zL/aJUd9Ul921N20sYvV3vK2v6wwdr4pVd1XZLaTi+PpWMyuU7HKnk8flzG4v7UwquUkixJi249ful1NvJYIhzrc2rBkdqCf2sL9SMYmwKlsVaVrVZZStEqm95nLWDbbpVN1zyvVTYFbFPQNG+VeTsRdHrM1Cpb3e8NW2WMYAIAAAAADgmBGfbi9PRUSb2OFqNOT4tKqSoxk6jsEVAtetMwdBr6rNRJ1ZOyS+NY9SvjqHIaza1lKTpdFj0pEZ4NXR8tLZMWfa++c73s6Eh9zqoq8rbzS4rQqBaTrGoYsvpscou2VNf1EV5lU9clDZ1pyCZZ0mkboay1qngEUilJvUXzqqpGi0rTAnxJiv1ntUqju8xdJtNy2U7KzKaUcpxumafxywiU3KbxyAidui618ciprWWrUyZd6xZXrTHuOeVNU1jWpen0y/XXpLRWWWn3ddNW2bSMf6x11bybFvunFLvhrtYqm+74dkcwCcsAAAAAAM+KwAx7kXPWsox68qRIJhVzJZeSZw296dFRr36RJdV28qXJ3PX201FlWTWWEqdfLqueLE9VqytZVpclM9fRoteQXceLQUPXqSpOpRyXVa6kLFepJs9Szqahj9HN2vaU5T4pd0k5J3VZOh6yzEzL4qrFZ6c8xk6uGL+M55tMnVlbfm/yGvdUq7fdYRFIuUulVFmS+j72lKUUBxJYiufLI5Qaa4Re61ZWxEh5tlfM3dvpkRGazVtlaiddpiQNXVqFWNPzqkulFlW3GGM1XbtVFgFT/D7LdqvMrIV0Z1tljGACAAAAAB46AjPsxXK51FiqRhXlmpSVtRhMx0d97A6zKpcreezneny61PK0RBhTR50sq54ulxq9KikrZ0VoZTEyeXw8aEhZlto+rlK1NCm7VE0aPYKyfojnFHdll4auV+6SupTUddJRn2NPWWkjlW3UcZztKev62FNW256ynExjWS/jN5NOS5WXGqFalarXGEvsTLkFUl22FoQl1eqyFCOSY3UlbY4w5mRtSf80Urk+OXLeKpvGLKVolcUus/j19LEIuKrkl7fK4iCAHbvKtN7htqtV1qW0euwhjGDu+ty0ygAAAAAAN0Vghr1IKUkydd5r6KTjR4P6IavPsQ8sWyeXdHI66mR5qlIiKDsdXU/LqNOxKCvFgQAWTaspKOtTjHS6uepYtVRSalOJYzUlqzHqma2NRkqLrovRyxR7yo6HpC4nuUsnYwQrpTW2qmoLlNr45WpPWZUknY4tkGptq2WJx5ulaJi5x/hlTtG+ykmptc6ioxWWY5Up9pSpjZhOgVxuAZTkq7HNWuOQhLFEq0seY5Y5RZgY9+qrVlmprlrbyaAWO9Nu0ipzeewqq+vHbrfKtsOo88Yg42OMYAIAAAAAHhYCM+xFSkmPjnrVXlosYrl/xD2dkiWdnBadLkctxyp50ZNl0WkpWpZomQ1toX9O0lHfaVjEQv/c9pRN448mUzZX9aRai/qhU2cuT9JYqvq+V84thOqSjnrT0GXVdgrl1JYaq2tZSoxAWoxfuqKV1adprNFUZ+OXS48gKVtSqVW1eNuHlqNJlpK6pNUpnB5ZWjzWt8YvUxw0sA7KpKmJZbZe6j+1ui5rlU2L/a/aKpM2w7JdrbLpMaZo7123VbYdlDGCCQAAAAB4KAjMsBeLxUIvf3Si5akrpSpLnbqadHLSgrHTquqjSpGejKOW46hljf1iQxtdPBoGLfp423e9xrqUmassq6RYij+OrmquTqbjRSe3CLX6FDvKui7Cr76Xjvss2TR+uQ7Kpj1l/WxPmbvHvrFk7TERlKVkOi0u1SrzCMKWJU7t7DpT6nJb0h/7yuTRKYsF+lVFkvk0XipJpi7HSOM61Fnv96q1aqzroMoklbrZKivtoIDp5/NWmdoJmynFgQZpFshN4dI8NJu3ytQOIZgeM4V157XK5tfcdPuL/c/73IRlAAAAAIB9IDDDXpRSNHRZY6lKblouXSenS41jValFZSx6WqWT5anGUtVZp6GFSEPf6Xjo1PdJizxIqaqUUVZdT0vVkJLGKp2OVZ1Mw6JTag2wbKZu6Nopk0m5kx71McJZaowzxsmPHmOgXpVTUt9NI41VXU6y1og6LVNzqy31H0dZSpLHXjSfDgToTNZGJ7O5TElqQZMkLWuVtVaZma9OvJyHWNsji7taZWZSn6IxVuvmx7ZbZVOg1WWtxkvj+he3ylaL/bdaZd0FrbKrLvbf9dxnxQgmAAAAAOC2EZhhL8Zx1MmJpCrMg1w1AAAwqElEQVQ9Xo46ORkjqKpFj5dVZVzqaS3qlNXnTp3FyORi6HU0JA05dpe5VXlxjZKyJWVJT5dVZtJi6NSptbZkWgxdjArmLMvSoz5OwTyzp6x6nHbp6z1lMo/xyxwhS2mtrqTYBTa2wwByyiq1ytvS/r5v45eWlJIrJ2uBl8tlq/HLZKY0W/q/HWLZbGTxvFZZl6S81SqbPnZeq6xPcWLp5MJWmU+ng+rcVpl0tRFMa7//jffdQohFWAYAAAAAuAsEZtiLnLOeLp/oyZNRxavKctSySsvlqFMvqtU0pE6WpK7LOuqyFkPSUTco5aSqolKkYpLauOTpsqpa1WLo1SVXNakU05CzuhzhWMqmRScdDX2c0rk1fjm28cmcUnwerzLFiZSStfDJWwvMdFrjBM4kU5JpWdvzc1LuYtG/WYRZUo4F+VIs5K9V8ml0ct3SGmYBVrLoYV3WKhta6+2qrbKctfF5LmqVWRvBrL67VZbN2qEGly/2bx85E5YxggkAAAAAeMgIzLAXtVYtl0uNZdTpaYRAj+tStUqL1MlyVd8nDdapX5gWQ68+x56yZKZaFM2sNmr5ZFk05KSjoVeVVKtFSNYn9V2O8cZBOuo6mWm1pyxGCyMoc813hbncaxwGYFL12FUWgUuER+NylCzFUn+Ppf5TUJYtyaUYv7TUGlm17SqLFCfNxi9zTups3faKppnWIdaVWmVanU4ZI6VnW2Ux5rm7VTb/+bxVVmtVO9xTiv8yqz1s+RlHMG+r7bUdltEqAwAAAADcJgIz7MVyudSTk1FPnladlGUbb0waOlMy1yL3Wiw6DUPWkAe5RlWN0uha1qps0ck6KVWDScdHnVKtqjVOhewWWX1bsN/10qM+grLz9pQlS61FFoFQTknZXLKkZWlL/ttesOK1LdbP0UwbiyyZhkVWNpPLWqssSUrRKnOPRlmatbFSjF/OT6acgjJXhGXTKZTzVpm7S+e0yuLz+LVbZfOfn9cqm0Ywc4rG3a5WmXT1xf6MYAIAAAAAnhcEZtiLWqtOTpZ6eynqPavPpk7SUd8rDUnHQ1KfhtbmGmOEUZJyNLqeLEdlScd9p2w1Qh1LGvoIynJKyr10nGO00l1ajhGmTHvDSi0ybzvNFM0xd1OXI2iawrQWgWksVaXEiGa2pLE9P+ekvjPJIhzrsyTltvOrtco8mmYmkywaWn0bZ5zv/pLWrbLYp7ZulU2nUya7+1bZdA99tmu1yrYPKpgwggkAAAAAeJ4QmGEvzEzVkgaTumQ66jt1Q6c+uxbdQrlLEZQpaWxL8bOk02XV0pda9J365BqTVEu0u7oUe8osmY57aTH0Ku00x2n32PaeMkvWtoqZcjJZ21N22oKyZG1vWamqklIbv/RSZSmpH5K6lNqS/ao+5xi/VJX75vilFPvGckpnTr+c7xyb9qltN8ekaK2d1yorXuStVSZFWNR1du1WWQSKtnrMdqts/vjJuYv9t4KyuxrB3HWPAAAAAADcFgIz7MUwDHrHo05vO6k67nvlXHW86JVSlltR9WhXmUmdS8ux6tRjuf+0p6wUU5+T+j6pa3vKjgZp0feSvO0p89WeslKrao1xy5Smkyp9tadsGr+UpmX7ptNxlNc4ZdMkjSXGL7s+q0/RPau1tjCpU23trNU5kC2wsalV1qU2qrkev5Qi0Iu3u1tlOZlSauGYn22VRe8ttRM542PXaZXF+2o7AMFWj9lulR3aYn9GMAEAAAAAh4DADHvh7hqOOr1DcvV9Vp+O5BrlVuQt6OosgrKlVw1mGo57qRTVGq20vEjq+y72lA3Sy/q+NaRaUNbGL8dSW3yT1HVJU1SWzNQlySyplKritS34l5altNMwo7E11iKvpq5L6ru0amOZZrvMSpHMVtu6kkWrLCdbjWCmrXHG67TKplBtHrJVL6q+2fy6rFUWp27OWmVtv5rLNhb7X9Yqu+pi/13P3QfCMgAAAADAoSAww16klHQ0LNSbJNVY6F8jQFJ1JUt6PBZluY6HXuZFdXQlmRaLrG5rT1nuclvkr1VgNp1+mSx2jk2nX0qmIZvMksZSVVUlb3vKaiz0d5dyyq11VWWWtDhKymat4VXVpSzJWtg0tbd8FZSZqY2J2qpVthlSeQt9Ymw0oqzdrbKxbo5u1raHTddolU0BU4yFrltltWpjBNPamOx5rbLt605sR6uMEUwAAAAAwIuAwAx7Ya1xtSxLJU8x9phMyaXT4io+atF3SlZVTeqUNfRJQ9cpJVPKpuPe1HcxBjlujV8ua1VyKVuMX3oLsFJrjMlMp6WuulBuprEU1XEd8ozjKMmUO9PQ5dXy/mn80t21rLEPbfo9SRE6ddnU5aRsEQ5ujj5G2FN9fc+rVpnOtsrmbbB4TpF7Wv23vKxVNv+cq+vU6TCCzcX+yaQ+p2u1yu5ysf/89zL//LTKAAAAAAD3icAMe+HuKpJqUUuspNNlUZVryFmLPscIomcNXdKQ88ZC/6HtKRvrfEdZGy1se8osm5JFwJJTBFjuauOXvjqVchxjzNNMSinCpFJdOcepmzGSWSOYaqFUqfGcnKYRzDQbY0zKKZb7T3vKplZZbeOX08jovFUm83Zq5marLE+/LmXVKpNu3ior3g4kmD1GtrmrLO7p8sX+usPF/oxgAgAAAAAOFYEZ9qKUorqs6mRx8qWqejMNQyerVV5MfZdjSX4XoVU/SI/anrJpjDHaZW1PmcfOsdzlGLG0+HWfLE63LK7iLmvjl7VWjZFaKaXUxhMjpVr0aRWGFZ+W+qdVmBbhmiRFmJWSqzNTzmm192s+fjmFTaWNUk6nZ1oLyiL4uX6rrE9po9l2WausrEKy9WOySTmnVfB0iCOYu8IyRjABAAAAAIeCwAx7YWZapqqny6Js0nHfyevYTm40HR11saMsJXW99KjvZMlUSm0nU0a7bCx1dqLk+vRLmambAivF+KXcIxhTLPVXsbb/y7QcR5ksQqg+vs2rx8hml3IL16IOl9IUUEVDbGjL/OetsrQVYs2X+k8Zk5lFqKfrtcpM0tDZqu0W93pJq6ydELoRcJmrT2kjeHoII5i3+bkAAAAAALgJAjPsT4l9WSl57ClLnfJsT1nXm46yqWt7ykpx1RrhT6musVaZRzhlyWSKMCpbjBa6YvyyTjFR20vWDrOUTPLqGt2Vu6Q+5xjhrDXCpnZQwLKUKYOTWWtnmZRTjGimFkylZBvjl9NplHG/bfRRkqmNX7YTOuVXa5WZSV02Dfn8Vtn0643DBaaTOLV+TtzzDVtljGACAAAAALCBwAx7kXPW8dDpbXWUKWnISV2O8UtLpkdDLPR3rfd9lVpjub9XeXGlttMsRgNj51g0s9pJme7Ry/Joi03jl9ZqWrVWeTItclLOSe5V3gK3afxyWWoEYRYp2RRGRdBnEZTNxi+lddC03Spbt7hcZmm1X63saJVF2LY5gnlRq2z6tXR5q6y7QausfeTMCOZtNb0IywAAAAAADwmBGfZi2gU2pKS+z7GnLJmGwfSo72OfWAuTpuBpOZbV82PJvcvkcWqmRburVrVdZG38slaNtcrruv1Vq6sWX49fehwWkE1KluP0y/a58nz80KRFF+2unNqJm+1zx2OiVVbr5o61KLNZC3zWrbIxpkQ3WmW1FtU7apVJV1vszwgmAAAAAAAXIzDDXuSc1fed+i4Wzve9dNx1MTZZytk9ZXU61XI9fmlmylOQYqax1FUTzExajqNqWe/98tY6y53paOhbwyuW8HftdMhp3HMdPkWo1bXxSzOpS5tBmbQOeep0AmaN8CxZ25Om81tl8fwICOseWmWltpnRZjq9M7fDCKTDHME87x6m3xsAAAAAAIeKwAx7kVLS0SKpFOm4iz1lvtrlZSq1ajnWdqrlOXvKcoqQqkZDrK3FVylFY52W6cfnK7XKkzTkrJzjNMqqSGb6nFfjizKp71JribXQqo1frhtaOtP2mu69+rpVth7VjFaZSRrbrrIpdPK2qyyCsnWYdZNWWYycboZlthrBPL9VdgiL/RnBBAAAAAA8ZARm2Asz08sXQ2tizcKmGrvGxlqlqrY/rO0QM1e2OJFS7RAAl8trtLeqV41jkbtFeGbr8ciuHR4wtcqkOFXTTVqOsTNsaquNxWUm9dlWAdl2Q0u6vFU2NeKmVtkYn3Y2gnmzVtk0+rlup/mqabbeMeZK7fCDfbTK5ve9b7vCMkYwAQAAAAAPCYEZ9mbaY1Z93ZA6HeNEyhQLxyIsm8Yv2ymUUjS1bBrRNGlZRtW2pywlqZa4fsqmo65fjUUWj8bV6mCA6krm6rscTTWPoGzaTzYFZWlHI+vyVpntbJVJrlJGVaVVMBXjmVObLW18no3/ZrPPs27kbS7jN/NLd5Wdt9jfdiz2v+sRTMIyAAAAAMBDQ2CGvYgmWYz8jbVqHCM8mxbzWzLJ44TKztJqRrBWl7eGmBT7zkp1ySOomkY0Za6hy0opKVmMX7pL2VIs468xkNllk1p4Zlov9U9tLDLZ2VbZ1CS7SavMVVswt7nYf+hM/RVaZVNwNbXKXBe3yuafe34tRjABAAAAANgfAjPsRZxWWXSyrLFLzK2NGMb4pSUpKcWeslUw5ZJLMpN7jVFKj1DL2/ily9XlpJy7FqBVldqCGLPNVlnOKm3nV5emoClGHbut8csp4JkCs4taZfMTMOetslqLittGKJVThHQXtcq87Vq7rFVmehiL/QnLAAAAAADPGwIz7EWtVaelqNZZ0GQuayc6TuOMtUZGVr3KLLUArKqU6SAAqZZokOVsGnK/anoVr60tFidueq1tqb9JHuFZShZNsjbu2bW9ZRv3uhWSzVtl20FZfDyeN7WzzOKkz1LnAdc1W2W23pXms8X+LQdUsv0t9mcEEwAAAACA6yEww954jTHGafwyW4qW2Wz8UqpymcySSq0aS52NX7bwxVx9Sso5K1kESlUmc5PLVGtVdalL01ikJJm6vD71MmdTtxWUXdQqm/aaxamd61aZtNkqkxedlv20yuoqKGthmVqrzG7QKluFZZvvv83watd9TAcXAAAAAADwkBGYYS9SSuo70zhWWTJ1ltuoZZx6KZvCMItdYNP4ZUqStRaXu1JO6nK3apVNoZK3gwSqV5lcQ5/j13V94mVqoVeXtAqvJpe1ytIU9Kw+3n5fLXBKFocYjOVsq6yb7RjbFSKpBWFTq6xuhGXXa5W5zoZijGACAAAAALBfBGbYG5PaSZRJasFR5CreAq+qUtY7yMxcXqN9lbIpW7caeTS1xf2WYoSzJVg5RbBUa5y02We1gwB2j19euVUWDz7TKjOTvBadFNsIqnKK0ze7NoK5+5TK6TRNm91HbeFWXFt+tlUmHeZi//PugxFMAAAAAMDzhsAMe+HurS0Wv45QJcYv5VJV1XKssnbipOSqksyquhSnX5q55LEELfKxtGpjpRZgxf4zU06b45fbgVPcQ4RkpbXJ9tUq65I0dHkVhO1qfbXTDOL3uaNV1s5DaGHf5vVvuth/fu+3gX1lAAAAAIAXBYEZ9mLaJRZ7yqZeWQRK41hUp/FLRTBlcuWclFOMX5riRMziUqrTeOO6VRbhlLXAylqTzZTt7Pjl6uRJqS3V32yV5ViYNnvs+rmrIM2rTsbNICwlabhGq2wKmOJthGVt3/+swXaDVtnWiZrT+xnBBAAAAABgPwjMsDcRn/hqMX9py/nNo6112filNJ2AWeUudVktIYpxyHmrbNf4pbQ+eXIav5waYLmdmpnTagBzo1U2BUDRKqsay+Z1uywtrtEqm06/nC/2nwptZtq492sv9t8Ky+56BJOwDAAAAADwvCMww16sWl0uFa+x/F8xOulyeQvHsqXV+OW01H+spmRptdRfFvvB3NUOBthslXXp7Pjl+uRJbTS7TOvntnMun6lVdvEJlee3ymKc1C/dVXbu57jjxf7n3QcjmAAAAACAFwGBGfbC3eUee8qqYvzSphFLRdCSUxe7zszltWopaydDtmX4VUrJ1eWsVjhTZ1LObVdZun6rbNpXtgrKfB2IzVtlxauW4+Z1r9Iqm4KsqYkVj5vCrXWrTFcIy66z2J+wDAAAAACA20Nghr1wjwX53hbyT+OIXTaZRegkcw056bQWSWk1filJlqSuM0lJpW6OX057x26jVWY7WmVm0qK7fqtsPoI5b5VFKHf9xf7roO/s+xnBBAAAAADg9hCYYS+iUWYyi4aZmdTlJLNY9J/MZXKdLMvqNM3q064yV0pdC5tiHHO11D/ZzrBm2o92UatMJnnd3SrLSatdZfNAartVdnap/9lWmRS/l1qn0zWnQOtmrbLpc9Q7bpXtuhdaZQAAAACAFxGBGfanhSsprXeSmbmsjULK42PuNUYMk6vvosVVqqtLEbzN22W7WmXj7OTL2k7DnLfKkq0X75/XKjsdtfGxZ2mVFXeZYtfa9Lkj3Lp4sf91RjBvO7hiBBMAAAAAgDUCM+xNjlRoNX7ZmVTkKm6r8cQaR2UqWVVurTIzacgpWmkbodemUqtKXQdNUyNtOgQgAh7FCZ3ntMqqu062WmU5S0dXaJVJtmq0Tffjvtkqe5bF/vcxgilNJ5SuMYIJAAAAAHjREZhhbyyZzL3tMJOWtTWvbN0qS8mVc7faP9alWOq/GXpdrVUW+83SLODZ3SrLOe7rdNz8mCQtOqnv4v8NzmuVmaTqEZad1yqLEzFd3dahBFdd7B+f4+z773oEk7AMAAAAAAACM+yJmSmbyc011hrjl5bkXlso4+pykpRV3ZVaq2wavzwvqHmWVplJ6nKMT56Mfu1W2dT4KlutMkktCJy1yrYOJbjqYv/pIXe9O4wRTAAAAAAAzkdghr1wd9VaNVaP3V02LcJ39Z2UbD1+2Zkp5wjYppMsr9Iqq64dAVs8rm7tI+taq2xZYrH/3KKTuhxh2cWtss1gqXrV/ATMeKwr2/VbZdL68IBthGUAAAAAANwvAjPsRTS82u4xj5MyXa5ha6l/zknZYrn/eSHNea2yPp9tlY1bI5bJ4nHVXU93tMqi1ZZWI6HbdrXKNkYwZydgJrN2Cmi4zmL/acxz+/2MYAIAAAAAcP8IzLAXOWflUrQsVe6ulOzMUv/Lxi/nAdhNW2XZTCdjOdMqG7LUd9dvlU2L/ecHEZhFePYsi/3vegRzV1hGqwwAAAAAgN0IzLAX7i55Vewq22yVdV1aLcM/L6QZa1XdapWZSf2OXWUXtcoeL+uNWmXT/e5qleWk1a6zCO0ezmJ/iRFMAAAAAACui8AMe1FrlSupS9polXUpxi8va5W5x8mX0wmY09jms7TKuiwtLmmVJTOVGgcTXNQqu43F/vcRljGCCQAAAADA5QjMsBc5Z1kZY4TSpL6LVpe0u800NbhqXYc661ZZWjWyzCT3quVWGHZRq8xMWnSmLudLW2VjqWdaZcls456vsthfungE864bXuwrAwAAAADg5gjMsDd9Tq0dFk2sm7bKpjDJ5FreUassDi2Quo0W2T4W++/+/LeJEUwAAAAAAJ4NgRn2wsxWjTLpnAZWrarSmVZZMqmbjV9Ou8pOb6lVFgcTrN8/1irTZlhm5pfuKpMOa7H/RfdDWAYAAAAAwNURmGFvpqBoV4Nqe6m/u0t2TqusnN8qOx3LmfHMq7bKtg8MmAK8qREXzrb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" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pp_plot(xt, yt, truncated_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last updated: Sun Jan 24 2021\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.8.5\n", - "IPython version : 7.19.0\n", - "\n", - "pymc3 : 3.10.0\n", - "matplotlib: 3.3.2\n", - "numpy : 1.19.2\n", - "arviz : 0.11.0\n", - "\n", - "Watermark: 2.1.0\n", - "\n" - ] - } - ], - "source": [ - "%load_ext watermark\n", - "%watermark -n -u -v -iv -w" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From d84d852ef3dca88b243e7a2fdc03826ab404708f Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Sun, 24 Jan 2021 16:43:29 +0000 Subject: [PATCH 3/7] create truncated regression example --- .../GLM-truncated-regression.ipynb | 1089 +++++++++++++++++ 1 file changed, 1089 insertions(+) create mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb new file mode 100644 index 000000000..9a34f145f --- /dev/null +++ b/examples/generalized_linear_models/GLM-truncated-regression.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Truncated regression\n", + "\n", + "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", + "\n", + "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on PyMC3 v3.10.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pymc3 as pm\n", + "import arviz as az\n", + "\n", + "print(f\"Running on PyMC3 v{pm.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def truncate_y(x, y, bounds):\n", + " keep = (y >= bounds[0]) & (y <= bounds[1])\n", + " return (x[keep], y[keep])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m, c, σ, N = 1, 0, 2, 200\n", + "x = np.random.uniform(-10, 10, N)\n", + "y = np.random.normal(m * x + c, σ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bounds = [-5, 5]\n", + "xt, yt = truncate_y(x, y, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", + "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", + "ax.axhline(bounds[0], c='r', ls='--')\n", + "ax.axhline(bounds[1], c='r', ls='--')\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear regression of truncated data underestimates the slope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_regression(x, y):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", + "\n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\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 12 seconds.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "linear_model, linear_trace = linear_regression(xt, yt)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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d06OxiVlVDtf0aKzw4NwVonM3Hdbe46kmZgQAAABUHBQgAQCAHA6HJn+3SYfznPvYrE6QnruioywWzn08G38fL918Xu4qSLtD+nApqyABAAAAiQIkAACQ9NXq/Vq4Pd6I/bytendsNwX5eZuYVeVyfZ8mCvHPfb++X39QR5LSTcwIAAAAqBgoQAIAUM3FJqTo2V+3u4w9dSnnPpZUiL+PJvaLMuJsm0Mf/bXXvIQAAACACoJlDQAAVGPZNrvun71R6dk2Y+zCdvV0XS/OfSyJNWvWaMeOHUqOO6CM6EOyB4bJr3EHzVq9X/ec30K1guggDgAAgOqLAiQAANXY24t2K/pgkhGHB/vphdGc+1hcH3/8sV566SXt2bOn4JNe3gps0Udvtw/Sk2MGlPlrR0VFad++fed0j88++0wTJ050+9yUKVP09NNPl/ievXv31sqVK88pLwAAAFQtbMEGAKCaWrfvpN5Z7Fo4e+XqTqod7GdSRpVHVlaWRo8erVtuucV98VGSbDlK2/m3pt40UnPnzS/fBIspNDTU7BQAAABQDbACEgCAaiglM0f/mb1Rdkfu2A19IzWkdV3zkqpEbr31Vv34449GHB4ervHjx6tNmzY6deqU3p35gw5sWS1JsqWf1lVXXa3VK5erY8eOZZZDVFSUvL1L9qNcTExuZ+7Q0FCNGDGiWPO8vb0VGRlZrGsbN2b7PgAAAFxRgAQAoBp6es5WHTiZ26G5eZ0gPXJRWxMzqjy++eYbzZgxw4jPP/98/fjjj6pRI7dpzyXjb9fgO1/U8V+nSbZsZaSlaOzYsYqOjpbVWjYbUJYsWVKi65cvX67+/fsb8ZgxYxQQEFCsuY0aNSp8pScAAABwFmzBBgCgmpm/5ai+XXfQiL2tFr05pqsCfL1MzKpysNlsevLJJ404IiKiQPFRkto3DNXwSy5XrYHjjbEtW7Zo1qxZ5ZZrfp9++qlLfNNNN5mUCQAAAKobVkACAFBCycnJWrJkifbv36/Tp08rIiJCQ4YMUURERKFzTp06pSVLligmJkY2m02NGjXS0KFDVb9+/VLlkJ2dreXLlys2NlbHjh2Tv7+/GjZsqIEDB6pevXqFzjuZmqXHftzsMvZ/F7ZSh0ahOnXqlDZt2qRdu3bp5MmTcjgcql27tpo3b66+ffsWe7VccXJfsmSJYmNjderUKYWHh6tXr17q1KlTmdzfk37//Xft3LnTiJ966qkCxcczbh3YTMt2jVLyul9kS06QJL355psaN25cueSaV1pamr755hsjbt++vXr16lXueQAAAKB6ogAJAIAbEydONLbZDho0SEuWLFFqaqomT56sGTNmKC0tzeV6Ly8vTZgwQW+++aaCg4ON8cTERD300EP67LPPlJ2d7TLHarXqlltu0bRp0xQUFFSsvI4ePaopU6Zo1qxZSk5OLvC81WrV+eefr2nTprkt6E2Zs1UnUrOMuH1who4vm6Ue//1ZGzZskN1ud/u6vr6+GjdunJ566qlinQWYt4NyZGSk4uLi5HA49Morr2jatGk6duxYgTldunTR+++/rz59+pz1/mb56aefjMdBQUEaM2ZModcOaBmudo1qKanDUCUtd658XLt2rQ4fPqyGDRt6OlUX3333nU6fPm3EN954Y7m+PgAAAKo3tmADAFAM8fHx6t27t95///0CxUfJuTX3008/1bBhw5SRkSFJ2r9/v3r06KH//e9/BYqPkmS32/Xhhx9q1KhRbp/Pb86cOWrZsqU+/PBDt8XHM/dcuHChunXr5nJOoSQt2HpUc6IPG7G/j1X2VV/oqSef1Lp16wotPkrOrs+fffaZunbtqqVLl5411/wyMzN1ySWX6KGHHnJbfJSkjRs36vzzz9fixYtLfP/y8ttvvxmP+/bt61Jszs9isejWgU3l37SLMeZwODRv3jxPpujWZ599Zjz29vbW+PHji7gaAAAAKFusgAQA4CxsNpvGjBmjrVu3KiAgQKNHj1bv3r0VEBCg7du3a8aMGTpx4oQkaeXKlXr66af1+OOP6+KLL1ZMTIwCAgJ0xRVXqHfv3goMDNSOHTs0ffp0Y86iRYv0xhtvaPLkyYXmMHv2bI0bN042m80Y69evny688EJFREQoLS1Nq1ev1vfff6/09HTZbDbdeOONCg4O1pVXXqnEtCw99tMWl3tOHt5G87bn/ijQrl079e3bV23btlWtWrWUmZmp2NhYzZ07V9u3b5fk3Eo+atQobdq0SU2aNCn2e3jnnXcaxbuhQ4fqggsuUN26dXXixAnNmTNHf//9tyQpPT1d48eP1/bt2xUSElLs+5eHU6dO6dChQ0ZcnJWal3RqqJdadVS81UuyO3/v/l69XjfffLPH8swvNjbWpWg8cuRI1a1Lt3MAAACUI4fDUdovAACqrAkTJjgkOSQ5LBaLQ5KjU6dOjr179xa49siRI47mzZsb1wcFBTkmTZrkkOTo3Lmz2zmHDx92NGvWzJgTHh7uyMrKcpvL7t27HcHBwca19erVcyxatMjttbGxsY727dsb19auXdsRHx/vuH/2BkfkQ78aX6Pf+8eRY7M7xo4d67jzzjsdW7ZsKfS9sNvtjo8//tjh6+tr3Peaa64p8v176qmnCrx/derUcSxbtszt9S+99JJxvSTHG2+8UeT9zfDPP/+45Dh9+vRizfvorxiHV426xrymnft6OFNXTzzxhEveP//8c7Hm5f09DA0NdYwdO9bRvHlzR2BgoCMgIMARERHhGDhwoOOJJ55wbN261cO/CgAAAFQQpaojWhwOR6lrl6WdCABARZf3DEhJCg8P19atWwtdOfbrr7/q0ksvdRmrU6eOtm7dqjp16ridM2fOHI0aNcqI58+fr+HDhxe4buTIkcbqwZCQEK1evVpt2rQpNPejR4+qffv2OnnypCTpulvu0fKw3Pv6eVv1230D1LxOsDIyMuTv71/ovfL66KOPdOutt0qSfHx8tH///kKb6OQ9A/LM9StXrlS3bt0Kvf/gwYONlXq9e/fWypUrC7123LhxWrVqVbHyLqkvv/xSvXv3djt+/fXXG/Gff/6pIUOGnPV+KZk5qteqi9L2b5Uk+dRqqEP7YlUnxK/ski6E3W5X06ZNtX//fklSvXr1dPDgQXl7n30TTP7fw6JYLBaNHj1a77//fqHf7wAAAKgSLKWZxBmQAAAUw+OPP17kttWLLrpItWrVKjCnqGLMxRdfrJo1axrx6tWrC1yzc+dOlzMDp0yZUmTxUZLq16+vxx57zIi/++oLOey5W7f/b1grNa/jPLuwuMVHSZo0aZKaNWsmydnJ+s8//yz23FtuuaXI4qMkl23JGzduVE5OTqHXHjp0SDExMR75Sk9Pd/ua+c/dzP/7XZhgP29FNsj93rFlpWnG8rhizT1XixYtMoqPkjR+/PhiFR/d8fLyUt26dRUZGVmg87fD4dD333+vrl27asuWLYXcAQAAANUVBUgAAIph7NixRT7v5eWlDh06lGiOt7e3OnbsaMQ7d+4scM3s2bN1ZreCv7+/sQLxbPI2GclOTVTWsb2SpE4Robr5vKbFukd+FovFZcXfunXrij33hhtuOOs1eVcdZmZmKi4urkT5eVpKSopLXJLibcuGYcZjR1aGZq7ap7SswgusZSVv8xmp5N2v27Rpo6lTp2r16tVKSUlRfHy84uLilJSUpNjYWL366qsuq2APHTqkSy65pNBGQwAAAKieKEACAHAWUVFRxdpWWq9ePeNx06ZNFR4eXqI5iYmJBZ4/05xFks4777wiuy7nVadOHTVqHGnEWUd2yWqRnr+io7y9Sv/Hf/5iU3H4+PicdfWjJDVq1Mgldvd+nLFkyZJzOce6yK/Bgwe7fc0z3c3P8PX1Peuv6YzQ4EDjsSMnS4lp2fpu3cFizy+NpKQk/fjjj0bcu3dvtWvXrtjz7777bm3fvl1PPPGEevbsWaDg2rRpUz3wwAPatGmT+vfvb4zv27fPZQUuAAAAQAESAICzyFskLEpQUJDxuLhdhvPOSU1NLfB8dHS08bht27bFuqfk3BKb7pVbrMw5fVwT+kWpQ6NQt9cnJibq448/1nXXXacOHTooPDxcvr6+slgsLl/PPfecMScpKalYuYSFhcnHx+es1+V9LyT374eZ8hfgsrKyij03MzPTeGzxdhYuP/l7r2x2zx2pPWvWLJeiaUlXPxangC45i91z5sxRRESEMTZ9+nQdPny4RK8HAACAqosCJAAAZ1GSrbbnMsddY7gzjWQk6e233y5QECzsy2q16mTc1tx87On6v2Gt3L7ma6+9psaNG+uWW27R119/ra1bt+rEiRPKzs4uMt/8KwILU5r34kxuFUn+1afF/fVLcjlX0uLrfD/2nUjTH9viyyY5Nz799FPjcUBAgMaMGeOx1woLC3NZ9ZiTk6P58+d77PUAAABQuVCABACggkpNTS2yEUtJdKofoBD/gqsQ77rrLj3wwAMFzje0WCwKDw9X48aN1bx5c+Mrb+OVilYg9LT8jVdOnTpV7Ll5t5NbfQOMxx8viz3nvNzZunWr1qxZY8SjR49WaKj71a9l5YorrnCJi+piDgAAgOqldG0QAQCAxwUEBMhqtcput0tybnXNXwRz50RKllIycwuXAb5e6toqssB1v/32m95//30jbtasme677z4NHTpULVu2dLtt+qmnntLUqVNL88up9Jo2dW3ek7e79NnkvTa0bu5W5bX7TmnD/lPq2qR4HbWLK3/zmZtuuqlM7+9OvXr1FBoaamzNpxENAAAAzqAACQBABWW1WlWzZk1jG/b999+vRx55pMg5q/ee1DUfrtCZtW5+3lYt/L9BahwWWODaN99803jcoUMH/fPPP2ctcBbVGKY8jRs3TqtWrfLIvb/88kuXjtxn5D+DMyYmplj3y8zMdGnY07trR23N8/zHy/bq3XFlV4DMycnRzJkzjTgqKsqle7knBQQEGAXIvNvOAQAAUL1RgAQAoAJr06aNli9fLknasWNHkddm2+x6/KfNLmP3XtDSbfHRbrdryZIlRvz4448Xa3Xl3r17i5G15x06dKjYBcCSKqxwVqtWLTVq1MgoJq5YsaJY91uzZo3LVvpLB/fW8RN+ik92NqaZt+WIDpxMc/v7VBpz585VfHzu2ZITJ06UxWIpk3sXxWaz6cSJE0Zc3CY2AAAAqPo4AxIAgAps0KBBxuP58+fLZrMVeu3Mlfu0Kz73LMcWdYN1y4Bmbq89ceKESxfnzp07nzWXrKws/fPPP8VJu8q66KKLjMcrVqwoVqfuP/74w3hssVh06SUjNaFflDFmd0if/lN2hd28268tFosmTpxYZvcuyqpVq1waF+Xfsg4AAIDqiwIkAAAV2DXXXGM8PnbsmGbMmOH2uqS0bL25aLfL2LOXd5Cvt/s/6vM3kClOR+dZs2a5dOU205IlS+RwODzyNXjw4EJf9/LLLzcep6amatasWUXmabPZNH36dCPu3r27GjVqpHG9IhXo62WMf7PmgJLSi+46XhzHjh3Tb7/9ZsTnn3++IiMLnv/pCW+//bZLPHTo0HJ5XQAAAFR8FCABAKjAunTpohEjRhjxAw884HYr9tt/7lZiWm4Ba2THBurTrLYk98XF2rVrKzAwd8vv3Llzi8zj8OHDmjx5conzr2pGjBihVq1aGfHTTz+t5OTkQq9/4403XBrQ3HvvvZKk0EAfXdOjsTGemmXTrNX7je3SZ77i4uJKlN/MmTNdViGWtvlMSc9vnDVrlr7++msjbtasmc4777xSvTYAAACqHgqQAABUcG+99ZZxPmNiYqL69++v2bNnG92x446nasaKOON6Xy+rHhrRRuvXr9e9996rAQMGFLinl5eXS2OSF154QUuXLnX7+hs3btTAgQOVkJAgq7V6/+jg5eWlp59+2ogPHjyo0aNHuy1Cfv/993rssceMuF27dho3bpwR39S/qax5jmb87J+9sttdV6aWVN7t16GhobriiitKdZ977rlHEydO1Jo1a4q8LiMjQ88995zGjx/vMv7888/L25ujxgEAAODET4YAAFRwLVu21Jdffqkrr7xSWVlZOnnypMaMGaNHH31UF154oTanBOvUyRw5crJkS0tSYx3XeV/cpgMHDkiSWrdu7fa+Dz74oLHyMTU1Veeff74uvfRSDR48WDVr1lRCQoIWL16s33//XXa7XQ0bNtRll12mDz74oNx+7RXRmDFjNHfuXKPT9KJFi9SiRQvdcMMNat26tRITE7VgwQItXLjQmBMcHKyvvvrKpYDbpHaghrevr3lbjkqS4pMzFXgqrdR5rVmzRlu2bDHi6667TgEBAaW6V05OjmbMmKEZM2YoKipKffv2Vfv27VW7dm35+/srMTFR0dHRmjt3rhISElzm3n///br22mtL/esAAABA1UMBEgCASuCSSy7Rn3/+qSuvvNLocBwbG+u2GJiUL/by8ipwjSQNHDhQU6dO1ZNPPinJ2Rn7559/1s8//1zg2jp16uiHH37QvHnzzu0XUkV8/PHHSk5O1pw5cyRJCQkJmjZtmttra9asqVmzZrlt9DNpQDOjACnJpYlQSeVd/ShJN954Y6nvlVdcXFyxtoL7+Pho6tSpeuihh8rkdQEAAFB1VO99VAAAVCL9+/fXnj179Oyzz6px48ZFXuvn56chQ4bo7bff1l9//VXodU888YRmzpxZ6P38/Px07bXXKjo6Wr179z6n/KsSPz8//fzzz/rwww/VrJn7TuO+vr668sorFR0d7XKOZ17dI2upe2QtIy5tI5qMjAyXhjjt27dXr169SnUvSRo9erRGjx6tBg0anPXakJAQ3XrrrYqOjtbDDz8si8Vy1jkAAACoXiz5u2CWwLkdUgQAAM7JBz//pSc++UW2tCTZs9JVp1YNPXZlb7Vv21YdOnQo0fbbnJwcrVy5UtHR0UpKSlKtWrXUqFEjDRw4UDVr1vTcL6KKWL16tXbs2KEjR44oKChIERERGjhwoMLCws46d/6WI7p95nojHty6jqbfWPriYVmLi4vT1q1bdfDgQSUmJio7O1shISEKCwtTx44d1bFjx0JX2QIAAKDKKdW/NlOABACgEsq22XXh639p7/FUY+yjG3poWLt6JmaF0rDZHTp/2hLtO5F7/uOC+weqVb0QE7MCAAAA3CpVAZIt2AAAVELfrTvoUnzsHllLQ9vWNTEjlJaX1aKb+jd1GftwaaxJ2QAAAABljwIkAACVTEa2TW8s3OUy9tCINpy9V4ld3SNCtQJ9jPjnjYd0ODHdxIwAAACAskMBEgCASmbG8jjFJ2ca8flt6qpX07OfNYiKK9DXWzf0jTLiHLtDHy/ba15CAAAAQBmiAAkAQCWSlJ6t95bEGLHFIk0e3trEjFBWJvSLUoBPbjOXWav361RqlokZAQAAAGWDAiQAAJXI//6KUVJ6thGP6txQbRvUMDEjlJWwIF9d27OxEadn2/T5in0mZgQAAACUDQqQAABUEseSM/Tp33FG7G216P+GsfqxKpk0oKm8rblneU5fvldpWTkmZgQAAACcOwqQAABUEm//uUfp2TYjHtu7iZrUDjQxI5S1iFqBuqxLQyM+lZatb9YcMDEjAAAA4NxRgAQAoBLYdyJVs1bvN+IAHy/dfX4LEzOCp9w+qLlL/NGyvcq22U3KBgAAADh3FCABAKgEXv9jl3LsDiO++bymqhvib2JG8JRW9UI0tG1dIz6UmK5fNx02MSMAAADg3FCABACggttzLEU/R+cWoGoG+ujWQc1MzAieln8V5PtLYmTPU4AGAAAAKhMKkAAAVHDvLd4jR57a020Dm6uGv495CcHjekSFqWdULSPeFZ+ixTuPmZgRAAAAUHoUIAEAqMD2Hk/VTxsPGXGtQB/d0DfSxIxQXtytggQAAAAqIwqQAABUYO8u3qO8O28nDWimID9v8xJCuRnSuq5a1wsx4rX7Tmll7AkTMwIAAABKhwIkAAAV1P4TafpxQ+7qx9AAVj9WJ1arRbcPdj3r861Fu03KBgAAACg9CpAAAFRQ7y7eI1u+ztchnP1YrVzaqaGiagca8fKYE1oTd9LEjAAAAICSowAJAEAFdOBkmr5ff9CIQ/y9NaFflHkJwRTeXlbdNaSFyxirIAEAAFDZUIAEAKACen9pjHLyrH68qX9ThQaw+rE6uqJrIzUJy10FuWz3ca3bd8rEjAAAAICSoQAJAEAFcygxXd+uPWDEwX7euql/UxMzgpm8vay6m1WQAAAAqMQoQAIAUMF8sCRG2bbc1Y8T+0UpNJDVj9XZFd0aKaJWgBEv3ZWgjQcSzUsIAAAAKAEKkAAAVCBHkzI0e03u6scgXy/dfB6rH6s7HzdnQb65cJdJ2QAAAAAlQwESAIAK5MO/YpRlsxvxDf2iVCvI18SMUFFc2S1CjWrmroJcvDOBsyABAABQKVCABACggjiZmqWvV+eufgzw8dIkVj/iX77eBVdBTluw06RsAAAAgOKjAAkAQAUxfXmc0rNtRjy2dxPVDvYzMSNUNFf3iHDpiL085oSW7zluYkYAAADA2VGABACgAkjNzNGM5XFG7ONl0aQBrH6EKx8vq/4ztKXL2CsLdsrhcBQyAwAAADAfBUgAACqAWav3Kyk924gv79JIDUIDipiB6mpUl0ZqUTfYiDfsT9SfO46ZmBEAAEA19s+b0oLHnV//vGl2NhUWBUgAAEyWlWPXx8v2GrHFIt02qJmJGaEi87Ja9H/DWrmMTVuwS3Y7qyABAADKXWqClHzE+ZWaYHY2FRYFSAAATPbTxkM6mpxhxBe2q6cWdUNMzAgV3Yj29dWuQQ0j3nYkWfO2HDUxIwAAAKBwFCABADCRze7QB0tjXMbuGNyikKsBJ6vVov8Od10F+crvO5SVYzcpIwAAAKBwFCABADDRH9uOKjYh1Yj7Na+tLo1rmpcQKo0hreuqe2QtI447kaaZK/eZmBEAAADgHgVIAABM4nA49P6S/Ksfm5uUDSobi8WiRy9u4zL21p+7lZSWXcgMAAAAwBwUIAEAMMmKmBOKPphkxB0a1dB5LcJNzAiVTffIMI3s2MCIE9Oy9fafu03MCAAAACiIAiQAACZ5P//Zj4NayGKxmJQNKquHRrSRr1fuj3QzVsQp7nhqETMAAACA8kUBEgAAE2w5lKRlu48bcVTtQI3oUN/EjFBZNakdqIn9o4w42+bQS/N3mJcQAAAAkA8FSAAATPC/v2Jd4tsGNZeXldWPKJ27hrRQrUAfI5635ajWxJ00MSMAAAAgFwVIAADK2aHEdM3dfMSIw4P9dEXXRiZmhMouNMBH/xnaymXs2V+3yW53mJQRAAAAkIsCJAAA5Wz6P3tly1MYmtA3Uv4+XiZmhKpgbO8malYnyIijDybpl02HTcwIAAAAcKIACQBAOTqdka2vVx8wYn8fq67vE2liRqgqfLyseuSiti5jL8/fqYxsm0kZAQAAAE4UIAEAKEez1xzQ6cwcI76qe4RqBfmamBGqkqFt66pPszAjPpSYro/ynTcKAAAAlDcKkAAAlJMcm12f/RNnxBaLdPN5zcxLCFWOxWLR4yPbyZKnn9G7S/bowMk085ICAABAtUcBEgCAcvLblqM6lJhuxEPb1lPT8KAiZgAl16FRqMb0bGzEGdl2PfPrNhMzAgAAQHVHARIAgHLgcDj08TLXrbC3DGD1Izxj8vA2qhnoY8QLtsVr8Y5jJmYEAACA6owCJAAA5WD13pPadDDJiDtHhKpnVC0TM0JVFhbkq8nDW7uMTfllKw1pAAAAYAoKkAAAlIOPlu11iScNaCZL3oP6gDI2pmcTdYoINeJ9J9JoSAMAAABTUIAEAMDDYhJStGhHvBE3qhmgizrUNzEjVAdeVoueGdXBpSHNO4v3aP8JGtIAAACgfFGABADAwz75e68cjtz4xv5R8vbij2B4XufGNTWmZxMjzsyx6/Gft8iR9xsSAAAA8DD+9gMAgAedSMnU9+sOGnGIn7euzdOhGPC0B4e3Vq08DWn+2pWgOdGHTcwIAAAA1Q0FSAAAPGjmyv3KzLEb8djeTRTi71PEDKBs1Qry1eMj27mMPfPrNiWmZZmUEQAAAKobCpAAAHhIRrZNX6yMM2Jvq0UT+0eZlg+qr9HdGql/i9pGfDwlSy/8tsPEjAAAAFCdUIAEAMBDftpwSMdTcleZXdKpgRqEBpiYEaori8Wi5y7vKD/v3B/9Zq89oJWxJ0zMCgAAANUFBUgAADzAbnfo47/3uoxNGtDMpGwAKSo8SPde0NJl7NEfNysj22ZSRgAAAKguKEACAOABS3cnaM+xFCPu26y2OjQKNTEjQLplQDO1qhdsxLEJqXpj4W4TMwIAAEB1QAESAAAP+GRZ/tWPTU3KBMjl623VC6M7yWLJHfvfXzHaeCDRtJwAAABQ9VGABACgjG07nKy/9xw34mZ1gjSkdV0TMwJydY+spZv75xbE7Q5p8rfRbMUGAACAx1CABACgjH2S/+zH85rJarUUcjVQ/h64sLWahgcZ8e5jKXprEVuxAQAA4BkUIAEAKEPHkjM0J/qQEdcK9NHobo1MzAgoKMDXSy9f5boV+4OlMYpmKzYAAAA8gAIkAABlaMaKOGXbHEY8vk+k/H28TMwIcK9nVJhu7Oe6FfuBb6OVnsVWbAAAAJQtCpAAAJSRtKwcfblqvxH7elk1vm+UeQkBZzF5eGtF1g404j3HUvT8b9tNzAgAAABVEQVIAADKyPfrDioxLduIL+/aUHVC/EzMCChagK+XXr26s/IeUfrFyn36Y1u8eUkBAACgyqEACQBAGbDbHQWbzwxoZlI2QPH1jArT3UNauIw99P0mHUvOMCkjAAAAVDUUIAEAKAOLdhxT3Ik0Ix7Yqo5a1QsxMSOg+O69oKW6NqlpxCdTs/TAt9Gy2x2FTwIAAACKiQIkAABl4KNlsS7xpPOaFnIlUPF4e1n15rVdFeznbYwt231cn/6zt4hZAAAAQPF4n/0SAADKSPxWKWGnlJogZaVKQXWkkAZS416Sf42yeY2MZOnAainlqJRyTPIJlILrSuGtpPodyuY18tl0MFGr95404tb1QjSgZbj7iz3xHqSfkvYtlxL3O+8ZUEuq31Fq2E3yKuEf9Yc3SDvn58ZN+kjNh5QuL1QqTWoH6pnL2+v+2dHG2Evzd6hPs9rqUNdPOrBKSj4spcRLXj5ScD2pVlOpYVfJavK/aZ+IkeK3OD/zmclSYG0puL4U0UMKKuSzeDaZKdL+ldLJGCnztOQfKtVpLTXuLXmX8GzXk7FS9OzcuG5bqf3lpcsLAACgEqIACQDVkd0uHd8pHVovHVonHV7vLIzZstxfPyWp9K+VlSr9/Ya06WtngcwdL1+p6UBpwANSZL/Svc6+5dKyadLevwr/ddRsInW6Vjrvfsk3qHSv48bHy1xXid08oKksljxdPTz1HqSfkv54Uto4S7JnF3w+pKE05BGp2w3Fu58tR/rxDinh3y7I3gFSl7HFm1vZnYhxfh4O//uZOLJJykl3f+19m6RakeWbXzm5omuEluxM0M8bD0uSmtn36cRnb8thiZYlO9X9pKC6UvsrpEEPSUG1yy9ZW7a06gNpw0wpYYf7ayxezoJhv7ulNiOLd9/sDGnxc9Kaj6XstILPB9SS+t8n9buv+IXXuf+VYhadSUq6+Y/izQMAAKgiLA5Hqc/24VAgAKiMfrxD2j5Hykop/pzSFiBjFks/3yUlHyrmBIvUZZx0yWvFX2GUnSHN/T9p45fFz6tGhHTF+86C3zk6nJiuAS8vlu3fs/LCg/30z8ND5Oft5bzAU+/B6aPSpyOkU8XYIttzkjRy2tmvW/6OtOCx3Hjwo9Lgh84+rzJbNFVa84mUkVj8OVW4AClJyRnZGvnGUo1Lma6bvX6Tj8VWvIkBtZzfZx2u9GyCkrNA/P0k5z+kFFerEdIVH0oBNQu/JjNF+vwyZxH6rPe7SBrzpWT1Kvq6bXOkb8bnxl2uly5/t1gpAwCASmDB41LyEefjGg2kC581Nx/Ps5z9koI4AxIAqpujm0tWfCyt3Qulr64tQeFNkhzSxpnS7OulnMyzX56dIX09tmTFR0lKPih9ebUU82fJ5rkxY3mcUXyUpAl9I3OLj558D769MV/x0SK1GyX1/49Ut53rtWs+dq4SK8rpeGnpS7lxzUjnKq+q7tiOkhUfq4Eavl76MeJr3e79S/GLj5JzRe73k5wrcj3p8EZpxqUlKz5K0q750heXS+mJhV8z94GCxcfmFzhXTUf0yne/ea6fGXey0qTf8xT1/UKloVNKkDQAAEDVQAESAFD2TsRIs8dJtmIUEd3ZvUCaV4yVd7/l3dZYQjkZ0tfjpJOlb7KRkpmjr1bnbqn297FqXJ9/V8Z58j3Ys0jav9x17MJnpGs+l4Y9Ld26VGrQxfX5JS9K9iKKSQsed56dd8aIFyUf/1Kljkpu2TSF7/m2dHMddunnO6X9q8o2pzPSTkpfXlX6ovHhDdL3N7t/7vhuadNs17Fet0rjf3AWDW9eILW+2PX5Fe86C6+FWTZNSspz7MKQR6XgOqVKHQAAoDKjAAkAcDZqCapbdveb+3/OAp877UZJY7+RblogXfSys1GEO+umF13E2Le88FV9wfWcBbSbFkhjv3WeT+dOdppzxVMpfbPmgE5n5Bjxld0iFBbk6ww8+R5s/8U19gt1brM+w9tX6neP6zVJB5wrx9zZt1za/E1u3PJCqc3F7q+tTBIPSCkJJZ/n5etsDFTRZSRLx/eU7T1PxEjLXnX71GlHgKb7X6/M8XOl8T9JPW6S2x04Drv063+cZzSWtT+ecDZwcqfpQOmaL6SbF0qXvSPVbuH+uj0Lpc3fFRzf/otcThiyWJ0rio3Y4lwJmVdWivOYBXdOxkrL386N63WQet3i/loAAIAqjiY0AFDdePlI9TtJjbo5uyQ36ubcsrv0ZWnpi+d+/5g/pdgl7p/rdat08Su5cZPeUqvh0gcDXFffSZIc0sIp0k3z3N9r4RS5PY7Yr4Z00+9SWNPcsVYXOrtNr/6fm3wXSbFLpWaDCv0luWOzO/TpP66rJ28679/X9PR7cHSza9ygk+QT4DrWON92UUk6Gi1FdHcds9uk3ybnxl5+zuJtZZWZIm37WYqeJcX9LU34pegVZ1arVKdN7mehUTepXkdp87fOlXwVjd3m/P6KniXt+M25TX7II2V3/yUvuC2c5zisGp/1iDZmttDO6FC9MLqjszt6WDPn6tn8jm2Tor+Wuo0v+FxpHd8jbSjkuIXWF0vXfpnbFKZxT2fTmQ8Hua5APGPh086zKvM2i8r/uQptLIU2ch1r1MPZ2MaRZzXx0U1Sh9EFX2PeQ64roC96+eznRQIAAFRRFCABoLqZtNCzfwne+JX7cZ8g6YInC47XipJ63y799XLB5/Yvd64iCmvmOn4iRjpQyOrIPne4Fh/PuOBJ54pJd11tN35Z4gLk71uP6uCp3C7JF7Spq+Z1gv+9n4ffg/xbPoPdrF4NrldwzN1W0dUfSfFbcuN+90i1m7tNv8Ky26W9S5xnD+741f3vcWGunlE5ikJHtziLjpu/lVLiPfMaGcnS9l/dPvWzvb82OpwrCmet3q8+zcI0qksjqc9dzsK+u+7uG78s2wJk9FcqtAfi8OcLdqQODJMGPSjNubvg9Un7pb1LpWaDc8eK87myWqWgcNffA3efqx2/OY9ROKPjNVJUf/e5AwAAVANswQaA6saTxZbs9EILGGpxvuQX4v65dqMKv+cmN2fRbS7ifLrC7uUXIjU/3/1z239xNrQpgY+XxbrEkwb8WyAsj/fAO9/ZjFluCm5ZqQXH8s9LSZAWP58b14iQBpR+S3q5O7Zd+uNJ6fX20hdXOLeRl6T4KFXs4mPKMecZg++fJ33QX1rxjueKj5KzeJuT7vYp7w6Xu8SP/rBZMQkpzoJcm0vd32//CveFydIq7HPfoLP7f3SQpLaXOrdSu1Oaz5VU8LOVf152hjT/4dzYN8R5RisAAEA1RgESAFB2jm4ptIChhl0Ln1e3reQd4P65g2sKjh1Y7f5a7wDndtrCFJZDdpoUv7Xwefms23dK6/cnGnH7hjXUp1mYMyiP9yD/ttDEfQXnnIorOBYa4Rr/8aSUmZQbj3he8g0sPMeKIPW4tPID59ba9/pI/7wpnT5sdlZlJztD2vKDs0v7a22l3x+V4jeffV5ZKOxzJemSESPVr3ltI07NsumuL9crI9tW9Pf1wbVlk9vp+MKLmUW9fkBNqVYhxcmzfa6SDhZs3JR6wnnuo8u8fJ+rv193/UwOfkgKKeScVwAAgGqCAiQAoOwc2Vj4c4U1hJCcq9BqRRVyz+iCY0c3ub82rGnRK9qKyqGo3PP55O/8qx+bynLmLLnyeA+iBrjGx7YVLKDmb7JhsUpN+uXG+1c5t/Se0Wxw0aswzZST6TzX8asx0rQ20vyHzv77Vb+TNOwZ5+q4ymDfCmnOvdKrraTvbnRu37XnFH59jQip371S52vLLgd3nzVJ8g2WV2gDvTGmi8KD/YzhHUdP67Eft8hR1Jb9wu5ZVrlJRX+uinr+xG7X1Yz5P1eZSa7bqCX3qzCjzst9fCpO+ueN3Di8tfN4BQAAgGqOMyABAGXn+K7Cnztbl+3gOlLC9oLjqcektJPO89wk5+PCuuAGFdFsRHJ/ptsZCTuLnvuvAyfTNH/LUSOuX8NfIzs2zL2gPN6DLuOczULybjf+8XZp9P+cq712/Cqt+dj1Hu0uz23GYrdLv/1Xxnl6Vh/poldU4RxY4zz3b8sPUkbi2a8PayZ1uErqdI0U3tLj6Z2zk3ulTbOdzVpO7T379QFhziJxx6ulyH6uDVTKQmHfu/9+ruqG+OutMV007pNVcvz7rfP9+oPqF15bVxZ2z2J+rs6eWxH3Kc7nyh2HXTq+W2rYxRm3vliq0UhKPpR7zbwHpcBwZyE7blnBRl2NeriuwJz3sGsTn4tfdjb+AgAAqOYoQAIAyk56YuHP+YcWPbeo5zOScotv7ho+lNVrFMOn/+yVPU8fjAn9ouTrnWdDQXm8B0G1peHPSb/en/v80U3OLcnuBNWRLnw2N177iesq0j53SHVaFZ1beUncL0XPdq7OPBlz9uuD60vtr3AW5fJ3+K6IMpKkrT85f337V5z9ep8gqfVFzl9fiws8V8yy5RTcWnxGnu/Lfi3C9cCwVnp1QW6xcurCQ7rSt5D7FvNzdVbn9LmqWfhzefPz9pUufVP66hpncVJyfj9+MtT9XO8A6dI3cuNdC6RdeTrWt7vctckNAABANUYBEgBQdjJPF/6ct1/hz0mSVxHPZyaX72sUIik9W9+sOWDEgb5eGturSb77lFN+PW6ScrKkBY9L9uzC59VqKo35Kvd8u9QT0p95ipEhDaRBDxWdl6dlnnZusY7+Wor7W4V2Oj7DL9TZXKTjVVLTQQW7H1c0dpu0Z5Gz6LjzN9cVcu5YfZwNkzpeLbW5WPIN8nyORX3/5/u+vXNwC206mKQF25wNcdLsRfw4WYzPVbEU+bkqrPr5L68ins+fX8th0lWfST/fVXhBVnIW9a+eIdXv6IxzMp1HA5zhE+j8RwIAAABIogAJAChLRXUgPlu34aJWduXtRptdSIMXSbKe5Y81ryKed9c1Op+vV+9XalZuU4prejRWaGC+vMvjPTijz+1S6xHS6o+k2KXO1VrZaVJALalee6ntJVLX8a4FpIVPuW5nvvBZyS84Nz4dL635yFkwOxnrfF8Cakp120ltRkrdbpB8CmmWU1pfj5X2/lX0Nd7+UqvhzqJcywvPXsytSJZNkxafrRhlkZr0dRZV21+Ru9q1vJTgc2W1WjTtms4a9e4/ik1IVbaK+L4uxueqePkV9bk62+e+hJ+r9pdLkf3//RwslE7EOIuRfjWczaJaXuj8BwD/Grlz/nnL+Xk5Y+B/XZvTZCRJaz91rpI8vlPKSJb8QqTwVlKrC6UeNzs/ZwAAAFUUBUgAQNnxKaKDsq2IhhqSZMsq/Lm8nZmLKn7ZilgJeLbnz7LKLNtm1/TlcUZssUg39o8qeGF5vAd51Yoq/kqrg+ukDTNz48j+zoLXGVu+l36+R8rOVzRKTZD2LnV+rXhHuvZLqUGn4r1mcdjt7sctXlKzQc6iY9tLnQWbyih/J+W86nV0/h50vKpgN+XyVMLPVYi/j/43vrtGvfOPsrOKWNFZVqs3i/xcne1zX4rPVXAdacijzq+zSdwv/f1abhzWXOp7T2689y/p24lS2gnXeeknpQMrnV/L35Gu+lRqPuTsrwcAAFAJVfA9SwCASqWoAlFRRQDJuYWx0PvmWWlUHq/hxm+bj+hIUm6hZXi7+oqs7aa4YlJ+Z2W3S789oNzGM97SxXkaz+z6Xfru5oLFx/wS90ufj3L+19M6XSMNnSJ1GVt5i49FieglDZsi9b3b3OKjVPT3VyHfty3qhmjaNV3kqyIKgOf6fWvcp6jP1VkKkDlFfO7KIr/5j7iu0Lzopdxt4Yc3Sl9eXbD4mF/6SWnWGOnQ+nPPBwAAoAKiAAkAKDtFbSE8WxfjoppV5G0yEVDrHF6jiOeLaGThcDj08TLXLsWTBjR1f3F5vAelsX66dHhDbtzzFuc2bclZ+PzlPrmcvVirqXTLYumxeOdZd97+uc+ln5R+L8bKsHMVPUv6cKD0Tk9pyUvOrbBVycHV0swrpdfaSHP/K+1fKaO9dHnz8pZ8g90/V8T37YgO9XV33/DC73uu37dnnNPnqojnzzW/PQudXefPaD3SeY7kGb/+x/XMz8Da0vifpMePSTfMccZn5GS4NpYCAACoQihAAgDKTngRnZRT4ouem3LM/XhQXdfz8ALDnA0gSnKP4jxfp3WhT63ee1KbD+UWB7s0rqnukYUUQsvjPSiptJPSomfy3K+ONOSR3HjHXOn0Edc5F78iNeom+fg7z8Trdavr89t/dZ4XWRY6jHZuJS/M8V3Skuelt7tJ/xssrXhXSj5S+PUVTdR5UkTPwp9PTXCeN/jpcOmNTtIfT0lHt5RffmcU9r17ls/VLd2KWJ1YxOeqRMKLuE9pP1cWqxTesvQ55WRJ8/I0nvH2l0Y8nxsfXOda9JecW7qbD3GeYdpskDT4Edfnj2x0zgMAAKhiKEACAMpOgy6FP3diT+HP2XKkU3GF3LNzwbH6hZw/eCqu6PP2isqhiNw/crP60WKxlPg+ZfoelMSiqc5Vi2cMm+q68ivub9fr/WtKzS9wHetwZb6bOqT9y88trzN63izdFy3dOM/Z5MaviFVphzc4V1++3k6afom0brqUfqps8vCUpgOkSQule9ZLA/4rhTYp/Nqk/dI/b0gf9Jfe7S399Yp0cm/h15elwr7PstOk5MOFTvM6Wfj3dXa9juealVNRn4GiPleSdGK3+/HaLc/tjMoV77i+9nn3uxbS45YVnJP/c5T3DNYz9v1dcAwAAKCSowAJACg79TtI3oU0szi0wf24JB3bKtkKOf/Q3cqxxr3cX5uTIcVvLfx18q9GOsMnMHc7cj6xCSlatCN3hVWjmgEa0b5+4a9RXu9BcR3eIK2fkedevaTO17lek7+4VKORZM33I0JNN0WzIopSpRLZT7rsbem/u6QrP5FaDHU2onHHYXcWeH65T3q1lfTVGGnzd+67GlcUtZtLFzwh/WeTNOFXqcs4ybeI1YMJO6Q/n5Xe6iJ9dIG08v2yW3XqTmGfK6noswkL+1xJem1bDTnKYlt5SD3334NS0Z+rtJPSqX3unzuXz1XSIemvV3PjmpFS//+4XpP/8+EbUvAIiYBaBbe+l/XnCgAAoAKgCzYAoOz4BEhtL5E2f1vwuZg/pYxkyd9N04etPxV+T3crhDpeLS15wf31235y36E5I0mKWex+TttLnVuN3bBMv0R7/fIUODIkPSMp8jzpxrkFJ5TXe1AcDofzbEHHv12mLVZp5KvOFt555eTrYuyuM7C7lWL555UVH//cztCn46XN30gbZzmLtO7YsqRd85xfPkFSm4ulDldJLS6QvHw8k+O5sFicqyKbDpAuflXa/ovzvMu9S3N/r/I7tNb59fujUtSA3M7gRZ2NWFJtLpG8/0/KSS/43LafnN/X+dltzu34bqy2t9b7G7PVMGq/xveJdH3yxzuk6K8KTgptIt2/2X1+Ha+Wlk0rOB6/2Xk+aO3mbvL+WS5nm+bV6Wr348Xx+6OuDZtGvFjw/yHF+VxJzs9WVkrh8wAAAKoAVkACAMpWl7Hux3PSpYVTCo6fjJVW/8/9nCb93BcVajeXGvd2P2fVh8575rfwafeFFcm5Es2NU6lZSkgpRTGgPN6D4tjwhbNodUb3G91vZc1/vmRqQsFr3J2jF3AO51IWV0g9qd890p3LpduWSX3udJ6JWZjsVGfxd9a10qstzTlLsSR8A6XO10o3/CTdv9XZ9btOm8Kvd9idhco5dztXfq7/vOxy8a/hvsgoSVt+kA6sLji+/G0p+aDbKd/aBkmSnp6zVatiz9IFujg6j5VUyNEHvz9a8PiFtJOuqxTzCm0iNR1UujxilzgLsme0GOYseueX/3OVdqJgkyGHo2CH7PL4XAEAAJQzVkACQHWz8gNp718Fx4/vKnzOrEIKape+KQXnawjT/HznX+z3Li14/dpPnMWtruOdK7cOb3CuaMq7+sdgkYY+VXhOFzwlTR+pAqubslKkT4ZLAx6QGnaVMpOdhbhtP7u/T/Pznc0g3Phi5T71cqjQmkehyus9KEr6KWfR9YzA2tL5j7u/tn4nacv3uXHiAefKw5B6uWN5C5lnuFtp6kkNOjm/hj3j7D4cPUvaOa/wrevpp85+PuTm75zFtfySDhQ+55f7nNv28zv/caleu6Jfryg1GjrPETzvfueW5+ivpS3fFSxQnWHLdG4FLkuDH5G2zSn4njps0hejpfP+I0X2dz6/9Sdp3Wdub3PIJ0o/ZpwnScqxO3Tnl+s1557z1KhmIccTFEd4C6nrOGnDzILP7ZovfT5K6n2bFFzfuX3979cKLY5q6FMFVwIXhy1b+u3B3NjLT7roJffX5j+r1p7j/Lw36pY7dni9czyv8v5cAQAAlAMKkABQ3RyJlna62TpclMKuH1HINuhLXpfe7+d+K+H2Oc6vs+k+UWrSp/Dno/pLXa93FhfzSz0mzX+o4Hh+PoHSyNfcPpWeZdP05XEq4lS8opXHe1CUP5+V0o7nxhc8WXgn7TaXSIuezt3+67BJK9+Thv1bwLTbnIXrvEKbFN1wx5O8vKXWI5xf6YnS1h+cxboDq0p+r+O7S/55iC1kK3+fO0r++oVp1M35Nfw5afcCaeNXzv/assruNdyp3VwaOFla/GzB57JOS38+U3A8P4tVNa99T1FzcrTnmLOwfiI1S7d+vlbf3d5PAb6FnOtZHMOekXb97n6Vbtwy941f8mt+QemPNVj5nnR8Z27c7+7CVyg3H+I83zHvPy6seEe66tM88buuc3yDpWZDSpcbAABABcYWbABA2avdXLp2pnN1UGm0GFb4qqK8Ln7VudqwNLz9pTFfSmFN3T79zdoDOpl6DsWe8noP3DmySVqbZ2Vaw65S1xsKvz68hdTpWtexf95wNnZZOEX66HzpwErX5wc/LFnPoZBUVgJqSj1ukm5e4OwyPfDBwpuVVEZePlKbkc7v1Qd2Or/nG3X37GsOeKDQYwnOymKVRr2roBb99dENPVTDP/ffurceTtaD3286t6Y0gWHSuO+cndpLo0EX1wJgSSQfkZa+nBvXiHB2NS9MQC2p712uY1u+lz4b6VydPP0S15XHktT37rI91xMAAKCCoAAJAPCMlsOksbOlkIYlm9dlnLPY4l2Mwp2PvzRmVsGuzmdTo5E09ptCi5c5Nrs+WubmHMmSKo/3ID+HQ/rtv85VjJIki3TxtIJdrfO7+FWpYTfXsV3zpL9fl45sdB3vOcm5Fbaiqd1cOv8x6b5N0sS5zhWy7rZKV1aBYVKvW6Rb/pTuXiud939SSBEd2UvLapUue0fqd2/hXcjdCagljf7IOAO1aXiQ3rquq6x5djr/En1YH/51jp+thl2kCXOk8FYlm9dyuPOszdIW+BY87rqacfhzhTeWOWPQQ1Kri1zH9v3t3B6ef7Vmq4ukQQ8KAACgKqIACQDwnOZDpLvXOLd0hhaxKs3L17kt8sZ50uXvlazw5uMvXfGBc27zC5z3KkxoE2cud68p9NxHSZq7+YgOniqkYU1Jlcd7kFf0LNetyF2vlyKKsWLOL9j52v3uKbxoF9LAee7nSDediCsSi0WKOk8a9W7xfu2VUXhL5zmGPW70zP2tVunCZ6Tb/pLaXlZ0ITeortTrNunudQW2Ng9uXVcPjnBtqvPS/B06mnyOn68GnaU7lksXPlt0IdJilZr0la79Uhr3jbNIWhpxfzvP4zyj6SCp/eVnn2f1ksZ85TyztrBVm/6hziMSxnxVMVYVAwAAeIDlHLbBnMP+GQBAtXR0s7PZTUqClJ0mBYU7i1qNezs78JaFjGRnAe70ESn1uOQTIAXVkeq0lup3POt0h8Ohi95cph1HTxtj39zWV72allFnWk+/B2s+dt77jF63OF+jJDJTpH3LpZMxUlaqc8VY3fZSRE/n+YuofrLTpf0rpeTDzvMXrd5ScD3nEQYNuxW5wtbhcOi+rzdqTvRhYyzE31s/39VfzeoEl01+x/dI8VucuWUmOztJB9dzfs/mb5RVGpu/c54Xekana0renT47w3mUQcIuKTNJ8qvhLJ426ev8hxQAAFA5LXjceVSLJNVo4PwH0qqtFJ38KEACAOBi8c5juvGzNUbcrUlNfX9HP1lK0zEXgCRnU6erPliurYeTjbHW9UL00139z60pDQAAgNkoQBYLW7ABAMjjgyUxLvEdg1tQfATOUYCvl/53Qw/VDso9ImFn/Gk9+fMWE7MCAABAeaEACQDAv9btO6VVe08accu6wbqgTV0TMwKqjkY1A/TO2G4uTWm+XXdQ36w9YF5SAAAAKBcUIAEA+Nc7f+52iW8d2ExWK6sfgbLSt3lt/d8w16YxT/68RTuOJhcyAwAAAFUBBUgAACRtOZSkxTtzm7c0qhmgy7s2MjEjoGq6c3ALDWyV2xgmI9uuO79cr5TMHBOzAgAAgCdRgAQAQNI7f+5xie8Y3Fw+XvwxCZQ1q9WiN67tovo1cjs/xyak6pEfNuscmiMCAACgAuNvVgCAam9X/GnN33rUiOvV8NNV3SNMzAio2sKCfPXuuK7yznPEwS/Rh/Xlqv0mZgUAAABPoQAJAKj23l3suvrx1oHN5e/jZVI2QPXQPTJMD41o4zI29Zdt2nIoyaSMAAAA4CkUIAEA1dre46n6JfqwEdcO8tV1vRqbmBFQfUwa0FTD2tUz4iyb8zzI5IxsE7MCAABAWaMACQCo1t5fskf2PMfO3TygqQJ9vc1LCKhGLBaLXr2qsyJqBRhj+0+m6cFvN3EeJAAAQBVCARIAUG0dSkzXD+sPGXENf2+N7xNpYkZA9RMa6KN3x3aTj1fueZDztx7VzJX7TMwKAAAAZYkCJACg2vpwaYxy8ix/nNi/qUL8fUzMCKieOjeuqcdHtnMZe+637YpNSDEpIwAAAJQlCpAAgGrpWHKGvl5zwIiDfL10Y78o8xICqrkb+kZqePvc8yAzsu26/5to5djsJmYFAACAskABEgBQLX20LFZZObmFjev7RqpWkK+JGQHVm8Vi0fNXdFR4sJ8xFn0gUe8ujjExKwAAAJQFCpAAgGon4XSmvshzvpyft1WTzmtmYkYAJKl2sJ9evqqjy9hbf+5W9IFEcxICAABAmaAACQCodj5cGqOM7NzVj9f1aqI6IX5FzABQXs5vU09jezcxYpvdofu/2aiMbJuJWQEAAOBcUIAEAFQrx05naOYq19WPdw5ubmJGAPJ77OK2iqwdaMSxCal6/Y9dJmYEAACAc0EBEgBQrXywJNZl9eO43pGqW8PfxIwA5Bfk563XrukiqyV37KNlsdp0MNG0nAAAAFB6FCABANVGfLLr6kd/H6tuH8zZj0BF1D2ylm4+r6kR2x3Sg99tcmkeBQAAgMqBAiQAoNp4f0mMS/FifJ9I1Q1h9SNQUf3fsNYuW7F3HD2tD5bSFRsAAKCyoQAJAKgWjiZl6KvV+404wMdLtw3i7EegIgvw9dILo127Yr/9527tjj9tUkYAAAAoDQqQAIBq4d3Fe1xWP97QN1LhwXS+Biq6fs3DdV2v3K7Y2TaHHvx+k+x2h4lZAQAAoCQoQAIAqry446malWf1Y6Cvl24dyNmPQGXxyMVtVD9Ps6gN+xP19ZoDJmYEAACAkqAACQCo8qb9sUs5eVZL3dg/SrVZ/QhUGjX8ffTs5R1cxl7+fYdOpmaZlBEAAABKggIkAKBK23IoSb9EHzbimoE+nP0IVEJD29XT0Lb1jDgxLVsvz99hYkYAAAAoLgqQAIAq7eXfd7rEdw1uoRr+PiZlA+BcPHVpO/n75P74+vWaA1q//5SJGQEAAKA4KEACAKqs5THH9deuBCNuEOqv8X0jTcwIwLloHBaou4e0cBl74qctstGQBgAAoEKjAAkAqJIcDodemu+6+vH+oa3k7+NlUkYAysItA5upWXiQEW89nKyZK/eZmBEAAADOhgIkAKBKmr/lqKIPJBpxi7rBGt2tkXkJASgTft5emnJZe5exaQt26hQNaQAAACosCpAAgConx2bXKwtcVz9OHt5a3l78sQdUBQNb1dHFHesbcXJGjt5YuMvEjAAAAFAU/iYGAKhyvlt3ULEJqUbctUlNXdiuXhEzAFQ2j17cVn7euT/Kzly1X7vjT5uYEQAAAApDARIAUKVkZNv0xsLdLmMPjWgji8ViUkYAPCGiVqBuGdDMiG12h577bbuJGQEAAKAwFCABAFXKJ3/v1dHkDCMe3LqO+jSrbWJGADzljsHNVSfEz4iX7EzQ4p3HTMwIAAAA7lCABABUGceSM/Tu4j0uY5OHtzYpGwCeFuTnrQfzfcafm7td2Ta7SRkBAADAHQqQAIAq45Xfdyoty2bEV3WPUPuGoSZmBMDTruwWoQ6NahjxnmMp+mrVfhMzAgAAQH4UIAEAVcLmg0n6bv1BIw709WL1I1ANWK0WPTGyncvYW4t2KyUzx6SMAAAAkB8FSABApedwODT1161yOHLH7hrSQvVq+JuXFIBy07tZbV3Uob4Rn0jN0ifL9pqYEQAAAPKiAAkAqPR+23xUa+JOGXGjmgG6+bymJmYEoLz9d3hreVlzu91/tCxWJ1IyTcwIAAAAZ1CABABUahnZNj3/23aXsUcvbit/Hy+TMgJghuZ1gnV19wgjTsnM0buLY0zMCAAAAGdQgAQAVGqf/L1XhxLTjbhXVJgu7li/iBkAqqr7hraUr3fuj7czV+7TwVNpJmYEAAAAiQIkAKASi0/O0LuL9xixxSI9eWk7WSyWImYBqKoahAZoYr8oI86y2fXGwt3mJQQAAABJFCABAJXYK7/vVFqWzYiv7h6hDo1CTcwIgNnuGNRcIX7eRvzD+oPaFX/axIwAAABAARIAUCltOpio79YdNOIgXy/9d3hrEzMCUBHUCvLVbYOaGbHdIb3+xy4TMwIAAAAFSABApeNwODT1l20uY3ed30J1Q/xNyghARXLTeU0VHuxnxPO2HNW2w8kmZgQAAFC9UYAEAFQ6v246orX7Thlx47AA3dS/qYkZAahIAn29dXueVZCS9OYiVkECAACYhQIkAKBSScvK0YvzdriMPXpRW/n7eJmUEYCK6Po+kaoTkrsK8vet8dp6OMnEjAAAAKovCpAAgErljYW7dSgx3Yh7Nw3TiA71TcwIQEXk7+OlOwY1dxmjIzYAAIA5KEACACqNLYeS9PGyWCP2slr05KXtZLFYTMwKQEU1tncT1auRuwryj23x2nKIVZAAAADljQIkAKBSyLHZ9cgPm2V35I5NOq+p2jcMNS8pABWav4+X7hzcwmXsjYWcBQkAAFDeKEACACqFGSv2aXOelUuNwwJ039CWJmYEoDK4tmdj1a/hb8QLtx/TpoOJ5iUEAABQDVGABABUeAdPpWnagp0uY89e3lGBvt4mZQSgsvD38dJdQ1zPgnznzz0mZQMAAFA9UYAEAFRoDodDT/68VWlZNmNsVJeGGtSqjolZAahMrsm3CnLBtnjtij9tYkYAAADVCwVIAECF9tvmo/pzxzEjDg3w0ROXtDMxIwCVjZ+3l24d2Mxl7L3FrIIEAAAoLxQgAQAVVlJatp6as9Vl7LGL2yo82K+QGQDg3phejRUW5GvEc6IPa9+JVBMzAgAAqD4oQAIAKqwX5+/Q8ZRMI+7TLExX94gwMSMAlVWgr7duPq+pEdsd0gdLY0zMCAAAoPqgAAkAqJBW7z2pWav3G7Gvt1XPX9FRFovFxKwAVGbj+0YqxD+3edV36w7qaFKGiRkBAABUDxQgAQAVTmaOTY/+uNll7O4hLdSsTrBJGQGoCmr4+2hC3ygjzrY59L+/Ys1LCAAAoJqgAAkAqHA+WBKrPcdSjLhl3WDdPqi5iRkBqCpu7B+lAB8vI/5q9T6dyHPUAwAAAMoeBUgAQIWy51iK3s3XnfaF0R3l680fWQDOXe1gP13Xq4kRZ2Tb9dk/ceYlBAAAUA3wtzkAQIVhtzv06I+blWWzG2NjezdRj6gwE7MCUNXcOrCZfL1yfwyesSJOyRnZJmYEAABQtVGABABUGN+uO6DVe08acZ0QPz00oo2JGQGoiuqH+uvK7hFGfDojR1+s2GdiRgAAAFUbBUgAQIWQcDpTz83d7jL29GXtFRrgY1JGAKqy2wc1k9WSG3/y916lZeWYlxAAAEAVRgESAFAhPPPrNiVn5P7l/4I2dXVRh/omZgSgKousHaTLOjc04pOpWfpmzQETMwIAAKi6KEACAEy3eOcxzYk+bMSBvl6aenkHWSyWImYBwLm5c0gLl/ijZXuVnecMWgAAAJQNCpAAAFOlZeXo8R+3uIz998LWalQzwKSMAFQXreqFaGjbekZ8KDFdv+T5xxAAAACUDQqQAABTvf7HLh1KTDfiThGhmtAvyryEAFQrdwxu7hJ/sDRGdrvDpGwAAACqJgqQAADTbDmUpE/+3mvEXlaLXhjdUV5Wtl4DKB/dI2upV9MwI94Vn6I/dxwzMSMAAICqhwIkAMAUOTa7Hvlhs/IuNJp0XlO1bxhqXlIAqqX8qyDfW7JHDgerIAEAAMoKBUgAgCmmL4/T5kNJRtw4LED3DW1pYkYAqqvBreqoTf0QI16/P1Fr4k6ZmBEAAEDVQgESAFDuDp5K02t/7HIZe/byjgr09TYpIwDVmcViKbAK8v0le0zKBgAAoOqhAAkAKFcOh0NP/rxVaVk2Y2xUl4Ya1KqOiVkBqO5GdmygxmEBRrx4Z4K2H0k2MSMAAICqgwIkAKBczd18xKXBQ2iAj564pJ2JGQGA5O1l1a0DC3bEBgAAwLmjAAkAKDdJadmaMmeby9hjF7dVeLCfSRkBQK6ru0coPNjXiH+JPqz9J9JMzAgAAKBqoAAJACg3L87foeMpmUbcp1mYru4RYWJGAJDL38dLN/ZvasR2h/TRslgTMwIAAKgaKEACAMrF6r0nNWv1fiP29bbq+Ss6ymKxmJgVALi6vk+kgv1yG2J9s/aAEk5nFjEDAAAAZ0MBEgDgcZk5Nj3ywyaXsbuHtFCzOsEmZQQA7oUG+GhcnyZGnJlj1/Tle03MCAAAoPKjAAkA8LgPlsQqJiHViFvWDdbtg5oXMQMAzHNz/6by9cr9MfnzFft0OiPbxIwAAAAqNwqQAACP2nMsRe8u3uMy9sLojvL15o8gABVT3Rr+urJ77vm0pzNy9OWq/UXMAAAAQFH42x8AwGPsdoce/XGzsmx2Y2xs7ybqERVmYlYAcHa3DWwma54jaj/5e68ysm3mJQQAAFCJUYAEAHjMt+sOaPXek0ZcJ8RPD41oY2JGAFA8UeFBuqhjAyNOOJ2pH9YfMjEjAACAyosCJADAIxJOZ+q5udtdxp6+rL1CA3xMyggASuaOfGfVfvhXjGx2h0nZAAAAVF4UIAEAHjH1121Kzsgx4qFt6+qiDvVNzAgASqZDo1ANbFXHiPedSNO8LUdMzAgAAKByogAJAChzi3ce0y/Rh4040NdLT4/qIIvFUsQsAKh48q+CfH9JjBwOVkECAACUBAVIAECZSsvK0eM/bnEZ+++FrdWoZoBJGQFA6fVpFqYujWsa8dbDyVq2+7h5CQEAAFRCFCABAGXq9T926VBiuhF3igjVhH5R5iUEAOfAYrHojsEFV0ECAACg+ChAAgDKzJZDSfrk771G7GW16IXRHeVlZes1gMprWNt6al4nyIhXxJ7Qhv2nTMwIAACgcqEACQAoEzk2ux75YbPyNoiddF5TtW8Yal5SAFAGrFaLbs93FuQHS1kFCQAAUFwUIAEAZWL68jhtPpRkxI3DAnTf0JYmZgQAZWdUl0ZqEOpvxL9vjdeeY6dNzAgAAKDyoAAJADhnhxPT9dofu1zGnr28owJ9vU3KCADKlq+3VZMGNHMZ+3BprEnZAAAAVC4UIAEA5+zpX7YqLctmxJd1bqhBreqYmBEAlL0xPRurZqCPEf+08ZAO52m6BQAAAPcoQAIAzsnCbfH6fWu8EYf4e+uJS9qZmBEAeEaQn7cm9I0y4mybw6XxFgAAANyjAAkAKLW0rBw9NWery9hDI9qoToifSRkBgGdN6BelAB8vI561er9OpWaZmBEAAEDFRwESAFBqby3ao0N5th92bVJTY3s1MTEjAPCssCBfjenV2IjTsmyasSLOvIQAAAAqAQqQAIBS2Xn0tD5eltuAwctq0XOXd5TVajExKwDwvEkDmsk7z//rpi+PU1pWjokZAQAAVGwUIAEAJWa3O/TYj5uVY3cYYzf2i1K7hjVMzAoAykejmgEa1aWRESemZevr1QdMzAgAAKBiowAJACixb9cd0Np9p4y4Qai//jOslYkZAUD5un1QM5f442Wxysqxm5QNAABAxUYBEgBQIidSMvXCvB0uY09d2l7Bft4mZQQA5a9lvRANa1fPiA8nZWhO9GETMwIAAKi4KEACAErkhXk7lJiWbcQXtKmr4e3rFTEDAKqmOwY3d4k/WBoje56jKQAAAOBEARIAUGwrY0/ou3UHjdjfx6opl7WXxULjGQDVT7cmtdS7aZgR7zmWogXb4k3MCAAAoGKiAAkAKJasHLse/2mLy9h9F7RS47BAkzICAPPdOaSFS/z2n7vlcLAKEgAAIC8KkACAYvloWaz2HEsx4lb1gjVpQFMTMwIA8w1sGa6OjUKNeOvhZC3afszEjAAAACoeCpAAgLPafyJNby3a7TL27OUd5ePFHyMAqjeLxaJ7L2jpMvbmIlZBAgAA5MXfHAEARXI4HHpyzhZl5tiNsWt6RKhXnnPPAKA6G9q2rto1qGHEmw8lacnOBBMzAgAAqFgoQAIAijR/y1GXv0jXCvTRwxe1NTEjAKhY3K2CfINVkAAAAAYKkACAQqVk5mjKL1tdxh65uK3CgnxNyggAKqYL29VTm/ohRhx9IFFLd7EKEgAAQKIACQAowmsLdik+OdOIe0WF6apuESZmBAAVk9XKWZAAAACFoQAJAHBry6EkTV++14i9rRY9e0UHWa0WE7MCgIprRPv6alUv2Ig37E/UElZBAgAAyNvsBAAAFY/N7tBjP26WPc/CnVsGNlOreiGFT0K5iY2N1cqVKxUfH6/s7Gw1bNhQbdq0UY8ePcxOza3ExEQtXLhQe/fulZeXl1q3bq3zzz9fAQEBJbpPdna2Xn75ZWVnZyssLEz33nuvhzIGSsdqteie81vqnlkbjLFpC3ZqcKs6slj4xxsAAFB9sQISAFDAV6v3K/pgkhFH1ArQvee3LGIGysM333yjDh06qHnz5ho3bpz+7//+Tw899JDGjx+vnj17qkWLFnrvvffKdMvnsWPHFBYWJovFYnxFRUUVe/6LL76oRo0a6eqrr9aDDz6oBx54QJdccokaN26szz//vES5vPHGG3r88cf19NNPy9vbc/+GGhcX5/LrnTJlSonvMX36dJd7LFmypNBrp0yZ4nJt/i8fHx+FhISoSZMm6tWrl8aNG6eXX35ZK1eulN1uL/S+hVmyZInL/adPn17ie6BwIzs2cDkLcsuhZM3fctTEjAAAAMxHARIA4OLY6Qy9PH+Hy9jUUe0V4OtlUkZIT0/XmDFjdO2112rr1q2FXhcTE6O77rpLw4cPV0pKSpm89n/+8x+dOnWqVHPvv/9+PfLII0pLSyvw3IkTJzRhwgS99dZbxbrXoUOHNHXqVElS165ddfvtt5cqp8ooJydHKSkpOnDggNasWaOvvvpKDz30kPr27avGjRvriSeeUEIC23wrCqvVov9e2Npl7NUFO2WzcxYkAACovihAAgBcPDd3u05n5BjxiPb1dX6beiZmVL05HA6NHTtWs2fPNsYCAwN1ww036O2339ZHH32khx9+WC1atDCe/+OPPzRmzBjZbLZzeu3ff/9ds2bNKtXcRYsW6Y033jDiESNG6P3339ebb76pXr16GeOTJ0/Wzp07z3q/Bx54QCkpKbJYLHrvvfdktVbdH2EiIyPVvHlz46tp06YKCwtzu+rz8OHDevbZZ9WqVSt98sknJmQLdy5oW1ddm9Q04piEVP244ZB5CQEAAJis6v70DgAosb93H9fPGw8bcZCvl566rJ2JGeG9997TTz/9ZMRdu3bVjh07NGPGDN19992aNGmSXnjhBW3btk2TJ082rps7d65LAbCk0tLSdMcdd0iS/Pz8SrTtWpJeffVV4/Fdd92lefPm6fbbb9e9996rFStW6KKLLpIkZWVl6c033yzyXosXLzYKsDfeeKP69OlTolwqmyVLlmjPnj3GV2xsrE6cOKHs7Gzt27dPs2fP1s033+xyhmZiYqImTZrk8j0A81gsFk0e7roK8vU/dikz59z+UQAAAKCyogAJAJAkZWTb9MTPW1zG7h/WSg1CS9YoBGUnMzNTzz//vBHXqVNH8+fPV+PGjQtc6+Pjo5dfflnXX3+9Mfb8888rKSmpwLXFMWXKFO3d6+yC/vDDDysyMrJEeS9evFiSc7Vm/jMUrVarXnzxRSOeP39+offKzs7W3XffLUmqWbOmy7zqqEmTJrrmmmv08ccfa//+/brppptcnn/11Vf1wQcfmJQd8urXPFzntQg34kOJ6Zq95oCJGQEAAJiHAiQAQJL0wdIY7T2easRtG9TQxH5R5iUE/fnnnzp8OHdF6uTJk1W3bt0i57zwwgvGVt2TJ0+WqsFIdHS0Xn/9dUlSixYt9Mgjj5Ro/p49e5SZmSlJ6tKli8LDwwtc06lTJ9WvX1+StHfvXrfnRErSm2++qW3btkmSnn32WdWpU6dEuVRl4eHh+uSTTwqco3nPPfdoz549JmWFvP6bbxXkW4v2KC0rp5CrAQAAqi4KkAAA7T2eqvcWxxixxSI9f0UHeXvxx4SZ8ndOvvLKK886JyIiwmWL8vfff1+i17Tb7br11luVk+Mskrz33nvy8/Mr0T0SExNd8ilM3pWceeeccfjw4WrbeKYk7rnnHpeVkDk5OXruuedMzAhndGlcUxe2yz1D93hKpmYs32diRgAAAObgb5YAUM05HA498dMWZdnsxtjYXk3UtUktE7OCJMXFxRmPg4OD1axZs2LN69Spk/H4n3/+KVEX63fffVerV6+WJF177bUaNmxYseeekbdgefr06UKvy/ucv79/gef/+9//6vTp07JYLHr33Xfl5UUn9sK89NJLLu/hzJkzdfToURMzwhkPXNhaFktu/MHSGCWlZ5uXEAAAgAkoQAJANTcn+rD+3nPciMODffXg8DYmZoQz8hYOQ0NDiz2vZs2axmO73a4tW7YUfnEehw4d0mOPPSZJqlGjhrENu6QaNmxoPN61a5fbazIzM7Vvn3MlWEBAgEvOknP155kO3BMnTlTfvn1LlUt1ER4errFjxxpxTk5OgRW0MEfr+iG6vEsjI05Kz9bHy2JNzAgAAKD8UYAEgGosKT1bz/y63WXs8ZHtFBroY1JGyCtvl+OMjIxiz0tPT3eJt2/fXsiVru6++25jVeKzzz6rBg0aFPs182rYsKGxvTomJkZ//PFHgWs+++wzI8+ePXvKas39kSQnJ4fGM6WQf7Xq0qVLTcoE+f1naEt5W3OXQX68bK/ik4v/mQYAAKjsKEACQDX26u87dTwl04j7Na+tUV0aFjED5Slvw5WTJ08Wu6P1me7VZ8TGnn211Y8//qiffvpJktStWzfdeeedxU/UjfHjxxuPb731Vq1du9aI58+f79LY5oYbbnCZ+9Zbb2nr1q2SnIXQszXegVPesz8lacOGDSZlgvwiawfp2p65Z56mZ9s0bcFOEzMCAAAoXxQgAaCa2nggUTNX5TZD8PWy6pnLO8iS97AymKp79+7GY4fDoT///POsc7KysrRs2TKXseTk5CLnnD59Wvfcc48kyWq16v333z/n8xYfeOABYwVlXFycevbsqQYNGig8PFwXXXSR0XSma9euLgXII0eOaMqUKZKcHbQrQuOZp59+WhaLpURfN954Y7nnGRkZ6bKS9Pjx40VcjfJ239CWCvLN/Vx9u+6gth8p+rMJAABQVVCABIBqKMdm12M/bpbDkTt2x+Dmal4n2LykUMCwYcNcCsKvv/66HHl/09z47LPPdOLECZexohrBSNKjjz6qQ4cOSZJuu+029erVq5QZ5woLC9Ovv/7qsorz6NGjLrm1bt1aP/30k3x8crf8n63xTGpqqv7++2/98ssvWrFihTIzMwUni8WikJAQIz558qSJ2SC/uiH+un1QcyN2OKTnf9t+1s80AABAVeBtdgIAgPL3+Yp92no4d+VNVO1A3TG4eREzYIYWLVrokksu0S+//CJJWrZsmZ588kk988wzbq9fs2aNJk+eXGA8/5mQea1atUrvvfeeJKlevXp6/vnnyyBzp27dumnbtm169dVXNWfOHMXFxcnLy0utWrXS1VdfrXvvvVeBgYHG9X/99Ze++uorSdKECRPUr18/47nExEQ98sgjmj59ust5mMHBwbr33nv15JNPunTfLku1atVSWFhYieacPn1ax44d80g+RQkODja26p+t8IzyN2lAM325ar+O/nv+47Ldx7VkV4KGtOaYAQAAULVRgASAauZoUkaBs8eeubyD/H3ObcstPOPVV1/VkiVLXJrDbNiwQffff7969Oghf39/xcTE6Ouvv9a0adOUlpYmb29veXt7G4W64GD3K1tzcnJ06623ym63S5KmTZtWoBv1uQoPD9eLL7541kYyOTk5uuuuuyQ5G8+89NJLxnOJiYkaPHiwoqOjC8xLSUnR888/r7Vr12ru3Lny9i77H23uvfdeY1t4cU2fPt2Ubdh5i441atQo99dH0QJ8vTR5eGs98G3u9/Lzc7drQItweXuxMQkAAFRd/KQDANXM1F+3KjXLZsSXdW6oAS3rFDEDZmrVqpW++uorl47Yc+fO1dChQ1WzZk35+/urffv2euaZZ5SWliZJeuedd1y2NRdWVJw2bZo2bdokSRoyZIjGjRvnuV/IWbz99tvasmWLJOmZZ55xaTxz3333GcXH888/X5s3b1ZGRoZWrVqlzp07S5IWLFigF154ofwTr0DsdrtLAbKkqzZRPq7o2kjtG+YWh3cfS9HstQdMzAgAAMDzKEACQDWyeMcx/bb5qBGH+Hvr8UvampgRiuOSSy7RX3/9pW7duhV5XVhYmGbPnq3rr7/epRAVHh5e4NrY2Fg9/fTTkiRfX19jG7YZjh49aqww7Ny5s+644w7jubi4OM2cOVOS1LBhQ/3666/q0KGD/Pz81KtXL/3222/G1uszK0Crq3379rmcJ+ju9x3ms1otemyk6/93py3YpaS0bJMyAgAA8DwKkABQTaRn2fTknC0uYw8Ob626If4mZYSS6NGjh9auXasFCxboP//5j4YMGaKOHTuqW7duuvzyy/XBBx8oJiZG11xzjbZv3+4yt0uXLgXu98ADDxhnQ06ePFlt2rQpj1+GW5MnT1ZycrLbxjM///yzsUX8jjvucFkJKjmLkmPHjpUkJSUlaeHCheWXeAWzYsUKlzhvF3VULP2ah2to23pGfDI1S6/9sbOIGQAAAJUbZ0ACQDXxzuLdOnAytxlJ54hQje0daWJGKCmLxaJhw4Zp2LBhRV63atUql7hnz54Frtm7d6/x+PPPP9fXX39d5D3PdMk+87hFixZGPGzYML3//vtFzi/MsmXLjBWON9xwg/r37+/y/Lp164zHvXv3dnuPPn366LPPPpMkrV+/XpdddlmpcqnsFixY4BIPGjTIpExQHI+PbKu/diUoy+YssH+xcp/G9Gqitg04uxMAAFQ9FCABoBrYHX9a//sr1oitFum5KzrKy2oxMSt4yrx584zH7du3V7169Yq4WjpwoGTnz+Xk5CgmJsaIO3ToULIE/2Wz2YzGM6GhoS6NZ85ISEgwHkdERLi9T97xvNdXJwkJCZo9e7YR+/j4aPDgweYlhLOKCg/SrQOb6Z3FeyRJdof01M9bNfu2PrJY+H8zAACoWtiCDQBVnMPh0GM/bVG2LfdsuAn9otShUaiJWcFTjhw5ovnz5xvxzTffbGI2RXvnnXe0efNmSc7GM+4KpWe2X0sytoznl3fcZrO5vaaqe/jhh42u55I0YcIE1alDc6mK7s4hzdUwNPcYjNVxJzUn+rCJGQEAAHgGBUgAqOK+X39Iq/eeNOJ6Nfz0f8NamZgRPOmRRx4xinCBgYEaP3682+s2btwoh8NR7K+823kjIyNdnvvpp59KnGd8fLyeeuopSc7GM3feeafb6/J2ct6/f7/ba/Ku4KyOnZ/ffvttffrpp0bs7e2tRx55xMSMUFyBvt56bGQ7l7Hnf9uulMwckzICAADwDAqQAFCFnUrN0vO/uTYkeerS9grx9zEpI3jSzJkz9fnnnxvx1KlTK2wn5MmTJyspKclt45m8OnbsaDz+/vvv3V7z3XffGY87depUtolWYCdOnNCkSZN07733uoy/++67atasmUlZoaQu7lhf/ZrXNuL45Ey9/eduEzMCAAAoexQgAaAKe3HeDp1MzTLiwa3r6KIO9U3MCCWVnZ2tp556SgcPHiz0mszMTE2dOlUTJ06Uw+Hcat+rVy/95z//KacsS+bvv//WF198IUkaP358gcYzeY0cOdJ4PHv2bG3cuNHl+d9++03//POPJMnPz08XXHBB2SdcgRw4cEDffvutJk2apMaNG+uTTz5xef7hhx/WrbfealJ2KA2LxaIpl7V3OZP307/3KiYhxcSsAAAAyhZNaACgiloVe0Kz1+ZuTfXztmrqZR1oblDJ2Gw2TZ06Vc8884y6d++ufv36qWXLlgoODtaJEye0bds2/fLLLy7NVzp06KC5c+cWuqrQTDabTXfffbckZ+OZl19+ucjrO3furKFDh2rhwoXKzs7WwIEDddddd6lly5aKjo7WBx98YFw7ceLEKnHu4eDBg+Xtnfsjmt1uV3JyspKSkpST435rbq1atTRt2jTdeOON5ZUmylCreiGa2C9Kn/zt7E6fbXPo6V+2acaNPfl/NgAAqBIoQAJAFZSZY9OjP252Gbv3gpZqUjvQpIxwrhwOh9auXau1a9cWed2IESM0Y8aMCrv1+t1331V0dLQk5xbxs3XolqSPPvpIffr0UXx8vE6fPq0XX3yxwDXt2rU7azGzsti3b1+xr23YsKFuvvlm3XvvvRX29xzFc9/Qlvp542EdT8mUJP21K0G/b43XCFatAwCAKoAt2ABQBf1vaaxiElKNuFW9YN0ygDPhKiMfHx9NmDBBERERhV5jsVjUp08fff3115o3b57q1q1bjhkW37Fjx/Tkk09Kcp7VeNdddxVrXlRUlJYtW1boVu3LLrtMS5YsUY0aNcos14rEy8tLgYGBatSokXr06KHrrrtOL730klauXKmDBw9W6LM+UXw1/H308EVtXMaemrNFpzOyTcoIAACg7FjOnBVVCqWeCADwnL3HUzX8jb+UlWM3xr67va96RFW/7sBVzc6dO7Vjxw7Fx8frxIkTCg0NVYMGDdSzZ88iC5QVxbJly7Ro0SJJ0qhRo9S1a9cS32PDhg1auXKlTp06pTp16mjQoEFq1Yqu7qga7HaHrvlwhdbuO2WM3dA3UlNHdTAxKwAAUKQFj0vJR5yPazSQLnzW3Hw8r1Tnw1CABIAqxOFw6PpPVumfPSeMset6NdELozsWMQsAUFHsjj+ti99apmyb80dti0X67vZ+6h5Zy+TMAACAWxQgi4Ut2ABQhfy08ZBL8TE82FcPj2hTxAwAQEXSsl6I7hjcwogdDumRHza5rGoHAACobChAAkAVcSo1S8/8ut1l7IlL2ik00MekjAAApXHn4OZqVifIiHfFp+jDpTEmZgQAAHBuKEACQBXx4rwdOpmaZcQDWobrss4NTcwIAFAa/j5eeuEK16Mz3v5zj/YcSzEpIwAAgHNDARIAqoBVsSc0e+0BI/bzturZyzvIYinV8RwAAJP1blZb1/VqbMRZNrse/C5aNjvHsAMAgMqHAiQAVHKZOTY9+uNml7F7L2ipyNpBhcwAAFQGD1/UVnVD/Ix4/f5EffbPXhMzAgAAKB0KkABQyf1vaaxiElKNuFW9YN0yoJmJGQEAykJogI+ez7cV+5Xfdyo2ga3YAACgcqEACQCVWGxCit5evMdl7PkrOsrXm/+9A0BVMLRdPV3RtZERZ+bY9eB3m9iKDQAAKhX+hgoAlZTd7tBD329SVo7dGLuuVxP1iAozMSsAQFl76tJ2Cg/O3Yq9dt8pTV8eZ15CAAAAJUQBEgAqqZmr9mlN3CkjDg/208Mj2piYEQDAE2oG+ur5Kzq4jL3y+w66YgMAgEqDAiQAVEIHT6XppXk7XMaeGdVeoYE+JmUEAPCkC9vX12WdGxpxRrZd/5m9wWUVPAAAQEVFARIAKhmHw6FHftis1CybMXZRh/q6qGMDE7MCAHja05e1d+mKveVQst5ctMvEjAAAAIqHAiQAVDLfrTuoZbuPG3FogI+eHtXexIwAAOWhVpCvXrm6s8vY+0titCbupEkZAQAAFA8FSACoROKTM/TMr9tcxp68pJ3qhviblBEAoDwNalVHE/tFGbHdId0/e6NOZ2SblxQAAMBZUIAEgErC4XB2vU7OyDHGBrWqo9HdGpmYFQCgvD18URu1qBtsxAdPpWvKnG1FzAAAADAXBUgAqCRmrzmgJTsTjDjEz1vPj+4oi8ViYlYAgPLm7+OlN67tIh+v3P//f7/+oOZuOmJiVgAAAIWjAAkAlcCBk2kFt15f2k6NagaYlBEAwEwdGoXqgQtbu4w9+uNmHU3KMCkjAACAwlGABIAKzm536L/fRrt0vR7atq6u6h5hYlYAALPdMqCZejcNM+Kk9Gw98O1G2e0OE7MCAAAoiAIkAFRw05fHadXe3A6ntQJ92HoNAJCX1aLXru2iEH9vY+yfPSf0/tIYE7MCAAAoiAIkAFRge46l6KX5O1zGnr28I12vAQCSpEY1A/Ts5R1cxl77Y5fW7TtlUkYAAAAFUYAEgAoqx2bXA99GKzPHboxd2rmhRnZqYGJWAICKZlSXRro6z7EcNrtD987aoKT0bBOzAgAAyEUBEgAqqA+Wxij6QKIR1wnx09TL2puXEACgwnp6VHs1qxNkxIcS0/XID5vkcHAeJAAAMB8FSACogNbtO6XXF+52GXvpyo6qFeRrUkYAgIos0Ndbb1/XVb5euT/e/7b5qL5ctd/ErAAAAJwoQAJABZOUnq17Z22QLU8X02t7NNb5beqZmBUAoKJr3zBUj41s6zI29Zdt2nQw0ZyEAAAA/kUBEgAqEIfDoYe/36RDienGWLPwID15aTsTswIAVBY39I3UsHa5/2CVZbPrjpnrlZiWZWJWAACguqMACQAVyJer9mvelqNG7Otl1dtjuyrIz9vErAAAlYXFYtGrV3VW47AAY+xQYrrun71RdjvnQQIAAHNQgASACmLH0WRN/XWby9hjI9uqfcNQkzICAFRGoYE+en9cd/l65/6ov3hngt5bssfErAAAQHVGARIAKoDTGdm6c+Z6ZeXYjbEL29XTDX0jTcwKAFBZdWgUqqmXtXcZe+2PXfpnz3GTMgIAANUZBUgAMJnD4dCD321S7PFUY6xRzQC9fFUnWSwWEzMDAFRm1/ZsrKu6Rxix3SHdO2uDjiZlmJgVAACojihAAoDJPl621+XcRx8vi966rqtqBvqamBUAoLKzWCx6ZlQHtakfYoydSM3SnV+uc1lxDwAA4GkUIAHARKtiT+jF+Ttcxh4f2U7dI2uZlBEAoCoJ8PXSB9d3V0ieZmbr9yfq2bnbipgFAABQtihAAoBJjiVn6O5ZG2TL05X0ss4NOfcRAFCmosKD9Oo1nV3GPl+xT9+sOWBSRgAAoLqhAAkAJsjItumWL9Yp4XSmMdaybrBeGN2Rcx8BAGVuePv6un1Qc5exx3/aog37T5mUEQAAqE4oQAJAOXM4HJr83SZFH0g0xoJ8vfT+9d0VlGeLHAAAZWny8NYa2KqOEWfZ7Lp95jodS6YpDQAA8CwKkABQzt5ctFu/RB82YotFeu3aLmpRN9jErAAAVZ2X1aK3x3RVZO1AYyw+OVN3fLmepjQAAMCjKEACQDmaE31Ybyzc7TL24PA2Gt6+vkkZAQCqk9BAH/1vfA8F+noZY+v2ndKUX7aamBUAAKjqKEACQDlZt++k/vtttMvYld0idPugZiZlBACojlrXD9Fr+ZrSfLVqv75ctc+kjAAAQFVHARIAysGu+NO6afpaly1uvaLC9PzoDjSdAQCUuxEdGuie81u4jE2Zs1UrY0+YlBEAAKjKKEACgIcdTkzXhE9XKyk92xhrHBag96/vJj9vryJmAgDgOfcPbaUL2tQ14mybQ7fPXKe9x1NNzAoAAFRFFCABwINOpWbphk9X60hSbofR2kG++vym3qod7GdiZgCA6s5qtej1MV3UvE6QMZaYlq2bp69RYlqWiZkBAICqhgIkAHhIamaObpqxRnuOpRhjQb5emn5jLzUNDypiJgAA5aOGv48+ndhTtQJ9jLHY46m6YyadsQEAQNmhAAkAHpCeZdNN09dow/5EY8zHy6IPx/dQx4hQ8xIDACCfyNpB+nB8D/l45Z5JvCL2hJ74aYscDoeJmQEAgKqCAiQAlLGMbJtu+XytVu09aYxZLNJr13TReS3DTcwMAAD3ejUN04ujO7mMzV57QB8tizUpIwAAUJVQgASAMpSZY9NtX6zT33uOu4w/d3lHXdq5oUlZAQBwdld2j9BdQ5q7jL0wb4d+33rUpIwAAEBVQQESAMpIRrZNd85cr6W7ElzGn76svcb2bmJSVgAAFN8Dw1rr4o71jdjhkO77eoPW7TtZxCwAAICiUYAEgDKQmpmjm2es0aIdx1zGHx/ZVhP6RZmTFAAAJWS1WjTt6i7qnOe84oxsu26avla74k+bmBkAAKjMKEACwDlKSs/W+E9W6Z89J1zGJw9vrUkDmpmUFQAApRPg66WPJvRQ47AAYywpPVs3fLJahxLTTcwMAABUVhQgAeAcHE/J1HX/W6n1ebpdS9IjF7XRXUNamJMUAADnqG6Ivz6/qbdqB/kaY0eTM3TDJ6t0MjXLxMwAAEBlRAESAErpSFK6rv1whbYdSTbGLBbp2cs76LZBzYuYCQBAxdc0PEjTb+ylIF8vYywmIVU3TV+jtKwcEzMDAACVDQVIACiFfSdSdfUHKxSTkGqMeVkteu2azrq+T6SJmQEAUHY6RoTqw/E95ONlMcY2HkjUnV+uV7bNbmJmAACgMqEACQAltH7/KY1+b7kOnso9B8vXy6p3x3bTFV0jTMwMAICyd17LcL1+bRdZcmuQWrIzQffP3iib3WFeYgAAoNKgAAkAJTB/yxFd97+VOpHn/Ct/H6s+ntBDIzrUNzEzAAA855JODTXl0vYuY79uOqLJ30XLThESAACcBQVIACgGh8Ohj5fF6o4v1yszJ3fLWWiAj764ubcGtqpjYnYAAHjehH5Ruvd81wZrP6w/pEd/3EwREgAAFMnb7AQAoKKz2R165tdtmr48zmW8cViAPpvYSy3qBpuTGAAA5ez+Ya2Unm3TR8v2GmNfrzkgHy+rpo5qL0vefdoAAAD/ogAJAEVIzczRfV9v1MLt8S7jnRvX1CcTeig82M+kzAAAKH8Wi0WPXtxW2TaHyz/MfbFyn3y9rXp8ZFuKkAAAoAAKkABQiLjjqbr1i7XaFZ/iMn5hu3p6c0xXBfh6mZQZAADmsVgseurSdsrMsWvW6v3G+Cd/75Wft1WTh7emCAkAAFxQgAQANxbvOKZ7v96g0xk5LuM39W+qx0a2lZeVv1gBAKovi8Wi5y7voGybXd+tO2iMv7ckRja7Qw9f1IYiJAAAMFCABIA87HaH3l28R68t3CVHnvP0vawWPTGyrSb2b2pecgAAVCBWq0UvXdlJ2Ta7ft542Bj/8K9YpWTm6JlRHWTlH+wAAIAoQAKA4XRGth74JloLtrme91g7yFfvjO2mvs1rm5QZAAAVk5fVomlXd1aOzaG5m48Y41+u2q/0LJtevqqTvL2sJmYIAAAqAgqQACBpz7EU3fbFWsUkpLqMd4oI1QfXd1fDmgEmZQYAQMXm7WXVm2O6yN/HS9+vz92O/cOGQ0rLsunN67rIz5tzkwEAqM7450gA1d6CrUd1+bv/FCg+XtU9Qt/c1pfiIwAAZ+HtZdUrV3XSDX0jXcbnbz2qWz5fp/Qsm0mZAQCAioACJIBqy2536LU/dunWL9YpJTO32Yy31aJnRrXXK1d1kr8PKzYAACgOq9Wipy9rrzsGN3cZ/2tXgiZ8ulpJ6dkmZQYAAMxGARJAtZSUnq1bPl+rtxbtdhkPD/bTrFv7aHzfKLp3AgBQQhaLRQ+NaKPJw1u7jK+OO6lrPlihI0npJmUGAADMRAESQLWzK/60Ln/3Hy3accxlvGuTmvr1nvPUMyrMpMwAAKga7hrSQk9d2s5lbGf8aV353nLtjj9tUlYAAMAsFCABVCvzNh/R5e/+o73HXc97vK5XE319ax/VD/U3KTMAAKqWG/s31WvXdJa3NXdHweGkDF31wQqt23fSxMwAAEB5owAJoFqw2R16ef4O3fHleqXlOQjfx8ui56/oqBdGd6RDJwAAZWx0twh9PKGHAn1z/4xNSs/W2I9W6Y9t8SZmBgAAyhMFSABVXmJalm6cvkbvLYlxGa9Xw09f39pXY3s3MSkzAACqvsGt62rWLX1UO8jXGMvMseu2L9Zq5sp9JmYGAADKCwVIAFXa9iPJuvSdv/XXrgSX8Z5RtfTLPeepe2QtkzIDAKD66Ny4pr67o58ahwUYY3aH9PhPWzRlzlbl2OwmZgcAADyNAiSAKmtO9GGNfm+5Dpx07bh5Q99IfTmpj+qGcN4jAADlpWl4kL6/o5/aN6zhMj59eZxumrFWSenZJmUGAAA8jQIkgConx2bX879t172zNig9O/e8R19vq165qpOmjuogX2/+9wcAQHmrG+Kv2bf11ZDWdVzG/9qVoNHv/aO4fE3iAABA1cDfwIH/b+++w6Os8v//v046qQQSIJDQm9IFbKBYEVnFAogF7HVt635W/a1lsf10XfXzWV11XVcXd62rqICuqMiiFFGK0gWlEwIJJY2E1DnfP2YymQmZMGRmMgk8H9eVK/c5c859Tq5LD/e871NwVCkordC105bq1fmbvfIzUuI0/dZTNHFYVph6BgAAJCkxNkqvXTNcN47s5pW/aU+JLn55kb7dtDdMPQMAAKFCABLAUWPD7mKNe3GRFm70/uJyUrc2+uTOkRqY2To8HQMAAF4iI4weuuB4PT1+gKIjjTu/oLRSV7++RG8u3iprbRh7CAAAgokAJICjwudrduuSlxdp+/5Sr/zrRnTVWzeepLTE2DD1DAAA+DJpeGe9dcNJSo2PdudVOawenrlW905fpTKPrVQAAEDLRQASQIvmcFj935yfdetby1Va4b3f43MTB2nqhf0UHclQBwBAc3VS97aaeftI9WqX6JU/fXm2Jr6yWDsLDvqoCQAAWgq+lQNosQ6UV+nWt5br+bm/eOW3T47V+7ecovFDM8PUMwAAcCQ6t43XR78+VaOPb++Vv3pnoS78y0L2hQQAoIUzAeytwqYsQJg5HA4tWrRImzZt0u7du5WamqqsrCyNGjVKCQkJYenTgQMHtGjRIu3cuVN5eXlKSkpS+/bt1blzZw0ePFgxMTGNvre1VsuXL9cvv/yidRu36d9Lt2m/TVBkQqpi2nVTZHyKTujcWq9MHqp2yXFB/KsAAEBTcDisXpq3Uf/71c/y/JoSGWH0+/P76oaR3WSM8X2DeuTn52v+/PnKzs7WgQMH1LFjR/Xt21fDhw8Pcu8bVl1drdWrV2vlypXau3evysrKlJycrMzMTA0fPlyZmbw4BYAW6cuHpKJdzuvkDGn0E+HtT+gd2T/ELlHB7gWA0Kuurtazzz6rF154QTk5OYd8npCQoCuuuEJ/+tOflJqa2iR9WrFihR577DHNnj1bZWVl9ZaJjY3VmWeeqRdffFE9evTw+97FxcV66qmn9NZbb2nHjh0+y6Vl9dDYRx5Uu+QRR9x/AAAQfhERRnee3Uv9M1N097s/qqisSpJU7bB64j8/aVV2of44foDiYw7/NWbHjh36n//5H82cOVMVFRWHfN6zZ0/df//9uvHGG4P+d3gqLi7WH//4R73++uvKzc31WW748OG69957NXHixKC0+/e//10333yzV97UqVP1yCOPBOX+AAAcCWZAAi1MQUGBLrjgAi1atOiwZTMzMzVr1iwNGTIkZP2prq7WAw88oOeee07V1f5tFD9nzhydc845fpe9/vrrlZ2d7Vf5q666Sm+99ZZfZQEAQPO1dW+Jbn1rudbvLvbK79shSa9OGabObeN91v3yyy81adIkFRQUHLadSy65RO+9915AqzR8Wbt2rcaOHavt27f7XWfChAl6++23A+pPbm6ujjvuOOXn53vlE4AEgBBgBqRfmAEJtCBVVVWaOHGiV/Cxc+fOmjx5srp27ao9e/ZoxowZWrp0qSQpOztbF1xwgZYuXaqOHTsGvT8Oh0PXXXed3nzzTXdeXFyczj77bJ166qlq3769KisrtXPnTi1btkxff/21z9mR9fnss890ySWXeM1aiE7vqlbdhyoyKU0R0bGKrSrVoMRirV66yO8gJQAAaP66piXoo1+fqvumr9Knq3a589fvLtaFLy7U85cP1hl92h1Sb/Xq1ZowYYKKi2sDl+eee67OOeccpaSkaP369XrzzTe1b98+SdLHH3+sW265RdOmTQtq//Py8nT22Wd7zXps06aNJkyYoIEDByo+Pl55eXmaN2+evvzyS9VMDJk+fbpiY2MDeqH6m9/85pDgIwAA4cQMSKAF+dOf/qT777/fnb7yyis1bdq0Q96Qv/DCC/rNb37jfpAdO3as/vOf/wS9P/fdd5+eeeYZd/qSSy7Riy++6DPYWVpaqnfeeUcnnXSSBgwY0OC9f/jhB5166qkqLy+XJLVKba+kc36tVt2Husv07ZCkv189TFltnDMgFi1apI0bN+qaa64J9E8DAADNhLVWry3Yoqdm/ySHxzcQY6Q7z+qlu8/upcgI52QMh8OhQYMGac2aNZKc27+8/fbbGj9+vNc9i4qKdPHFF2vevHnuvPfffz9oy58l6eabb9bf//53d3rs2LF65513lJKSckjZRYsWady4cdq/f7877+uvv9aoUaOOuN3PP/9c559/viSpb9++Wr9+vfszZkACQAgwA9K/SgQggZahqKhI3bp1cz+YDhkyREuWLFFUVP0Tme+88069+OKL7vTChQs1YkTw9kZctmyZTj75ZPey6+uuu06vv/76EW8MX5/q6mqdeOKJ+uGHHyRJMSntlH7lHxWVXDvL4VcDMvTMxIF+7QEFAABavkUb9+qOd35QfmmlV/6Inm3150lDlJ7knDU4ZcoU92fPPfecfvvb39Z7v8LCQvXr1087d+6UJPXp00dr165VZGRkwH2tqKhQenq6ioqKJDm3xdmwYYPi430vG585c6Yuvvhid/qmm27Sq6++ekTtlpaWql+/ftq6datiYmI0a9YsjRkzxv05AUgACAECkH6JCHYvAITGW2+95fVW/E9/+pPP4KMkPfHEE14Puc8//3xQ+3PHHXe4g489evTQyy+/HJTgo+TcNL0m+ChJrc+70x18NEa697w+evHKIQQfAQA4hozomaZP7hypAZ28ZxAu2rhPv3phgb7fvE8vvPCCO79z5866++67fd4vJSXFKxi3YcMGffHFF0Hp66ZNm9zBR8m5aqWh4KMkjRs3Tu3a1b5sXbly5RG3O3XqVG3dulWSdP/996tPnz5HfA8AAEKBACTQQsyYMcN93bVrV5199tkNlk9JSdGECRPc6c8//7zeEyAb48cff9T333/vTj/66KOKi4sLyr0l6eW//tV9HddlsFp1cx6ikxgbpdeuHqbbz+wZtGAnAABoOTJT4/XBrado8smdvfLziss18X8/1bJly9x511133WFnM15++eVegcGZM2cGpZ+eL40l58vawzHGqHv37u70ke7huGLFCv35z3+WJHXv3l0PPPDAEdUHACCUCEACLcDBgwf19ddfu9PnnHOOXwG4c889131dXFysBQsWBKU/r7/+uvs6OTlZl156aVDuK0lfzf9Wq1etcqcTBzpPy+6elqAZt4/Q2ce1D1pbAACg5YmLjtQTFw/Q85cPVnxMbYCxZNNyeW4v5fkc5EtiYqJOOeUUd/qzzz4LSh9TU1O90iUlJX7V8yyXnp7ud3sOh0M333yzqqqqJEkvvfRSUF8OAwAQKAKQQAuwfv16VVbW7nd08skn+1XP84Facp4KGQxz5851X48aNUqtWrUKyn3X5hTqhqe8T6CM63aCzuiTro9vH6Ge7RKD0g4AAGj5LhrcSbPuGKne7Z3PB5V7tro/MxGRimp3+FmHkvfzUnZ2dlBOj+7Tp49XENLzRbIveXl5WrdunTt9+umn+93eiy++qKVLl0qSJkyY4LXvIwAAzQEBSKAF+Omnn7zSPXv29Kte165dvZYe1b1PYxQVFWnDhg3udE0wtKioSC+99JJOO+00ZWRkKDY2Vh07dtTpp5+uJ554Qjk5OQ3e95OVORr/12+1d3Ptg3dUakfdPmaIXr9muL5fME9XXHGFevbsqVatWqlNmzbq16+fbrvtNr8e6gEAwNGnZ7tEzbh9hC49oZMq92W78yOS0jT5jR80bdEWHe7QzbrLoz1PjW6syMhI3XLLLe70J598oq+++qrBOvfcc497f+34+HjdeuutfrWVnZ2thx56SJKUlJTkXoYNAEBzQgASaAG2bNnile7cubOPkt4iIyOVkZHhTm/evDngvqxatcrrQb537976+uuv1b9/f91xxx1auHChdu/erYqKCu3atUsLFizQww8/rJ49e+rJJ5885H7VDqunP1+vO9/9UWWVDlXk1f6tg/sfp5tOTNeE8ZfqvPPO03vvvadNmzaprKxM+fn5WrdunV555RWdeeaZGj16tHJzcwP++wAAQMsSHxOl5yYOUuvq2pmLUcnpqqy2evSTdbr1reUqKPW9D3ZWVpZXOhjPS5L08MMPa/DgwZIka60uvPBCPfnkk9q9e7e7jMPh0Pfff6+xY8fqnXfekeTcC/LVV19Vly5d/Grn9ttvV3FxsSTpscceU6dOnYLSfwAAgokAJNACeJ6iKB26r1BDPMvWPJwGYs+ePV7pTZs2acyYMdqxY4c7Ly0tTRkZGYqIqB1iDh48qAcffFDXX3+9O6+orFI3/nOp/vr1JndedWmh+7p3Vnudf/75XgfwJCQkKCsr65CTJOfMmaOTTjrJqx8AAODYYIyRo7zUnY6Iq9225Yu1uRr7/AIt3bq/vqqHPFcF43lJcs5inDt3rnuv7LKyMj344IPKyMhQ+/bt1bVrVyUnJ+vkk0/W7NmzJTkPj/nss8901VVX+dXGRx99pFmzZkmSBg0apDvvvDMofQcAINgIQAItwIEDB7zSR7KpuOf+jHXv0xgFBQVe6Ycffljl5eWKjIzUfffdpx07dmjPnj3KycnR3r179ec//1lJSUnu8tOmTdNLL72kjXkHdPGLizRvQ21A01qHbOVBd/qDDz5w72c0YsQILViwQMXFxdq+fbuKioo0d+5cDR061F1+27ZtmjRpknsDdgAAcOzwfM7p1DbJ67OcwjJN+tti/WXuL6p2eC/JrruXdTCel2q0adNGH374of773/+qV69e7vy8vDxt27bN69CZq666SkuXLvV7/8aioiLdddddkpwB2FdeeeWwp34DABAuBCCBFqCsrMwrHRMT43fd2NhY9/XBgwcbKOmfuqc41hyO8+abb+rpp59WZmam+7PU1FTdfffdmjdvnteMxf/vgQc17n/naPNe73tNGtxO8ljeXXPvCy64QPPmzdPIkSPdp39HRkbqrLPO0oIFCzRq1Ch3ncWLF+u9994L+O8EAAAti+fz0im9Oujxi/srJqr2647DSs/N+VlXvfadcotqy3o+K0nBeV6qUVpaqnvvvVcXXHCBfvnllwbLvv322+revbuee+65w+5bKUm///3vtXPnTknSTTfd5PchhQAAhAMBSKAFqDvjsaLC9z5GdZWXl7uvg3FadX2zL6+88kpdccUVPusMHTpUf/jDH9zpA0WFyv1hjjsdHWn05CUD9OTEoYfUTU5O1rRp0xQdHV3vvVu1aqU333zTKyj7wgsv+PW3AACAo4fnM0pFRYWmnNxFM28foZ7tEr3Kfbd5v8b8eb4+X+Pci9HzWUkKzvOSJO3du1ennHKKnn32WZWWlioqKkq33XabFi5cqIKCAlVUVCg7O1vvvfeeO3hYWFio3/3ud5oyZUqDQcjvvvtOr7zyiiQpPT1df/zjH4PSZwAAQoUAJNACJCZ6PzjXnRHZEM+3+HXvE4y+SNIdd9xx2HpTrrtBEVG1QcSybSslSWmJMXrnppN15UmdFRUVdcgshMsvv1xpaWkN3jsrK0sXX3yxO718+XIVFhb6rgAAAI46ns8oNc9Kx2Uka9YdIzRpmPdBM/mllbr1reX63Qcrtbeg2Od9GstaqyuuuEKrVq2S5AxqfvXVV3r55Zc1YsQIpaSkKDo6Wp06ddKkSZO0aNEir+ept99+2+cL1aqqKt18881yOBySpGefffaI9gcHACAcCEACLUBycrJXOj8/30fJQ3nu2ei5F2Ow+hIfH6/hw4c3WGfH/lLd8O5Pikrv6s6ryNusAZ1SNOuOkRretY3P+59xxhl+9ctzGbbD4XA/8AMAgGOD5zOE57NSfEyUnp4wUH+5YoiSYqO86kxfnq3b/rHAKy8Yz0uzZ8/WV1995U4/9dRTXs8qdUVEROj555/XkCFD3HmPP/54vcvBn332Wa1evVqS8/nn6quvDri/AACEGgFIoAXo1q2bV3r79u1+1auurlZOTo473b1794D7UvceHTt2VFRUlI/S0sJf9uqilxbpp11FikpKd+dHlB/QB7eeoo6tvZc51b1/586d/epXVpb3zIa9e/f6VQ8AABwdPJ+X6ntWunBQR31292ka3tV7tuDunGyvdDCelzz3o46JidGNN9542DoRERG67bbb3Ol9+/ZpwQLv4Oju3bv12GOPSZKio6P18ssvB9xXAACaAgFIoAXo27evV3rTpk1+1du6dauqq6t93qcxevbs6bXfYt0l0zUcDqsX//uLpvzje+0vce1Z6bEEW9UVios+9KTG448/3ivt6/511d2b8kiWqQMAgJbP8zknOzv7kL0dJSmrTbzeu/kU3T+mr6IjnQfbVeXv8iqT7WgdcF9qZihK0nHHHaeEhAS/6g0bNswrvW7dOq/07t273bMiIyIiNG7cOPXs2dPnT92VJC+88ILX556zNAEACCXf05YANBt9+/ZVdHS0+1ToxYsX6/rrrz9svcWLF3ulBwwYEHBfoqKidPzxx2vFihWS6l8OXlBaod++v1L/XZ/nlR9RUeq+btOmTd1qkqRBgwZ5pf1dbl63nK/7AwCAo9PAgQPd11VVVVq2bJlGjBhxSLnICKPbzuih03un6Z5/r9CCnPW1nyW21b2fbNacTSV67KL+6pBy6OF7/igpKXFf+xt8lA7df7KhE7nLy8v9fildIz8/3+uZ6cCBA0dUHwCAxmIGJNACxMfHe+0bNHfu3AZPRqwxZ07tSdOJiYk67bTTgtKfCy64wH2dk5OjPXv2uNOrswt1wV8WHhJ8PC4jWQkHapc41V1WXt+9JWnlypV+9aluOV/3BwAAR6cxY8Z4pT2fg+rTr2OK3r1usBy5P7vzWvVwzkD8cl2uzvnfb/SPhVtUWe044r54HgqTm5vrd71du7xnY/JCFQBwtCAACbQQnqc8b9myRXPnzm2wfGFhoaZPn+5Ojxkzxu/lzIdz6aWXeqWnT58ua63e+X67xr/yrbLzvd/WTxyaqfuGSLm7ax+qfR0u06NHD68ZDB988IFfffrwww/d1x07dlTv3r39qgcAAI4OmZmZXkuYp02b5rUVTX0+nv6BKspqn1ta9TrZfX2gvEqPfbpOY/48X/M25NVX3aeePXu6rzdt2qTNmzf7Ve/LL7/0Svfq1csrPXjwYFlr/f7ZsmWLV/2pU6d6fe75fAkAQCgRgARaiMmTJ3u9Tb///vtVVVXls/xDDz2k0tLaJc933XVXg/c/44wzZIxx/zRkyJAhXjMyn/j/n9T1r36jBz5erYqq2lkCMVERenr8AD09foCmPvSgO98Yo8mTJ/u8/z333OO+XrJkiVdwsT6vvfaaNmzY4E5PmTKlwfIAAODodOedd7qvt2/frueff95n2aKiIj3yyCPudO/evfW/91yjpDjvXao27SnRddOWasIzM7yela699lqf9x49erRX+qGHHjps33fs2KGXXnrJnU5ISKh3CTkAAC0RAUighUhJSdF9993nTv/www+69tpr3ftCevrLX/7i9QA7ZsyYoC2/rvH000+7A5U5O7P17uO/VnVJ7Z5Cmamt9NFtp+rCfmm65pprvE5xnDRp0iGHzXi6+uqr1b9/f3f62muv1ezZs+stO336dN1xxx3udEpKin772982+u8CAAAt1+TJk72eMR544AF99NFHh5QrKirSJZdcop07d7rzHnvsMU0+tZvm/naUfjUw45A6323e73c/LrvsMnXs2NGdfvfdd3XXXXfVezCOJK1du1ajR49WQUGBO+/Xv/510FavAAAQbsaffeR8aHRFAI1TWVmp8847T/PmzXPndenSRZMnT1bXrl21Z88ezZgxQ0uWLHF/npGRoSVLligzM7PBe59xxhn65ptv3OnDjQ0VVQ5dcP09mvPmC+48ExOvhONO08BBgzXpxK7avmWj3nvvPa+H++7du2v58uVq3bp1g/dfu3atRowYocLCQnfeOeeco9GjRystLU25ubmaPXu25s+fX9u+MZoxY4bGjRvX4L0BAMDRa+XKlRo5cqTXASujR4/Wueeeq+TkZG3YsEH/+te/tHfvXvfnU6ZM0b/+9S+v+yzZsl+PfbpWa3YWSZKqCnO185Ub3J8POHOc/jP9XWW1ia+3HzNnztSll14qh6N2dUj79u01fvx49evXT/Hx8crLy9OCBQs0e/Zsr+Xiffv21eLFiw/7vHQ4W7du9doXe+rUqV6zPgEAQfDlQ1KRa7ux5Axp9BPh7U/oNbxk0lclApBAy5Kfn69f/epXh5xwXZ+OHTtq1qxZGjp06GHLHkkActOeA7rn3yu0KrtQ+fP+oaIlH8ufIWHgwIH69NNPlZWVddiykvTNN99o4sSJXofc+BIXF6c33nhDkyZN8uveAADg6PX555/r8ssv93qR6cu4ceP0/vvv1zvb0OGw+vCHbD3zxQblZG/3CkAm9D9b7S64R+cPyNAtp3fXwMzWh9R/8803deutt3pti3M4Q4YM0ccff6wuXbr4XccXApAA0AQIQPqFJdhAC5OamqoFCxboySefVEbGocuDJOeeQTfccINWr17tV/DRX5XVDr00b6POf36BVmU7H+hTz7xe7SY9rpQuvpdUp6en66mnntLixYv9Dj5K0qhRo7RmzRpde+21SkhIqLdMVFSULrvsMq1YsYLgIwAAkOTcfmbVqlUaP368YmJi6i3TvXt3/e1vf9PMmTN9LnWOiDCaOCxL39x7pm4b1eOQzx1W+s+qXRr34iJd9rfFmrUyR+VVtTMZp0yZopUrV+qGG25QfHz9MyVr9OzZU//3f/+n7777LijBRwAAmhNmQAItWHV1tRYtWqSNGzcqNzdXqampysrK0qhRo5SYmBjUttbsLNS901fpp11Fh3x2/Yhuum9MH+Xs2KalS5cqJydHFRUVSktL04ABAzRs2DBFRAT2vqOkpETz58/X9u3btW/fPqWkpKhLly46/fTTlZycHNC9AQDA0Wv//v2aP3++srOzVVJSooyMDPXt21cnnnjiEd+rqKxSr83frNcXblFJRf0nbLdJiNHEoZm6bHiWeqTXPo+Vl5drxYoVWrt2rfbv36/y8nIlJSWpQ4cOGjZsmLp3797ovxEAEEbMgPSvEgFIAA0pPFip/5vzs978bpuqHd7/26cnxeqZCQN1Rp92YeodAABA0yssrdTbS7bpjUVblVdc/8EykjQwM0UXDe6kCwdmqF1yXBP2EADQZAhA+leJACSA+jgcVu8v26E/fbFB+0sqDvl80rAsPTD2OKXER4ehdwAAAOFXXlWtmSty9I+FW7R+d7HPchFGOrFbG40+voNG92uvzNSGl2MDAFoQApD+VSIACaCubzfu1R8/X+/e59FTVptWeuqSgRrZKy0MPQMAAGh+rLX6cUeB3v1+uz5ZlaOySkeD5ft3StZZfdrp9N7pGpzVWlGRbM0PAC0WAUj/KhGABFBjxY4CPfPFei3auO+Qz2IiI3TDad1051k9FR8TFYbeAQAANH+FByv1n1W7NGPFTi3Zsv+w5ZPiojSyZ5pO752u03unq1PrVk3QSwBA0BCA9K8SAUgAy7fl669fb9JXP+XW+/lZfdvp4QuOV7e0+k+iBgAAwKFyCg5q1socfbZ6V70rS+rTIz1Bo3q306g+6TqpWxvFRUeGuJcAgIAQgPSvEgFI4NjkcFjN25Cnv32zWUu21v92vle7RP1+bF+d1bd9E/cOAADg6JJTcFBz1uXqi7W7tWTLflU5Dv91KjYqQid1b6tRvdM1qne6eqQnyJhGfe8DAIQKAUj/KhGABI4t+SUVmr48W+8u2a7Ne0vqLZOZ2kr3nNNbFw/ppMgIHnIBAACCqbisUos37dP8X/bom5/3aMf+g37V69S6lU7vna4z+6TrtF7pahXD7EgACDsCkP5VIgAJHP2qqh1avHmfPlyerc/W7FZFVf0bo2ekxOnWUT10+YlZio3igRYAACDUrLXauq9U83/eo/k/79G3m/bpYGX1YevFRkXotF5pOvf49jqrb3ulJ8U2QW8BAIcgAOlfJQKQwNHJ4bD6YXu+e9+hvQcqfJbt3T5Rt5zeQ+MGd1Q0pzACAACETXlVtZZvzdc3PztnR67fXXzYOsZIQ7Ja6/z+GfrVwAx15CAbAGg6BCD9q0QAEjh6lFVWa+EvezVnXa7mrs9tMOhojDSqd7quPqWLzuzTjv2EAAAAmqHdhWXupdoLf9mrwoOVh60zvGuqLhzUUef3z2BmJACEGgFI/yoRgARaLofDat2uIi3auFcLN+7Vki37Ve5jeXWNtMRYXT48S5OGZymrTXwT9RQAAACBqnatcPlqXa7m/JSrzXvq38+7RoSRTu2RpktP6KTz+2ewZyQAhAIBSP8qEYAEWg6Hw2rTngNaujVf327aq2837dP+Et+zHGskxUbpvP4ddOGgjhrRo62iWGYNAADQ4m3ac8AZjFyXq+X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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", + "\n", + "Let's visualise how bad that fit is by plotting the data and posterior predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 623 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def pp_plot(x, y, trace):\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " # plot data\n", + " ax.plot(x, y, 'k.')\n", + " # plot posterior predicted... samples from posterior\n", + " xi = np.array([np.min(x), np.max(x)])\n", + " n_samples=1000\n", + " for n in range(n_samples):\n", + " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", + " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", + " # plot true\n", + " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", + " # plot bounds\n", + " ax.axhline(bounds[0], c='r', ls='--')\n", + " ax.axhline(bounds[1], c='r', ls='--')\n", + " ax.legend()\n", + " ax.set(xlabel=\"x\", ylabel=\"y\")\n", + " \n", + "pp_plot(xt, yt, linear_trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Truncated regression avoids this underestimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def truncated_regression(x, y, bounds):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + "\n", + " y_likelihood = pm.TruncatedNormal(\n", + " \"y_likelihood\",\n", + " mu=m * x + c,\n", + " sd=σ,\n", + " observed=y,\n", + " lower=bounds[0],\n", + " upper=bounds[1],\n", + " )\n", + " \n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", + "
\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 13 seconds.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And also by doing our graphical posterior predictive checks. Looks good." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pp_plot(xt, yt, truncated_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Sun Jan 24 2021\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.8.5\n", + "IPython version : 7.19.0\n", + "\n", + "pymc3 : 3.10.0\n", + "matplotlib: 3.3.2\n", + "numpy : 1.19.2\n", + "arviz : 0.11.0\n", + "\n", + "Watermark: 2.1.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From d3dabca59f38a0a46e8aae6bde713979017e7bf5 Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Mon, 25 Jan 2021 16:24:38 +0000 Subject: [PATCH 4/7] delete truncated regression example from main branch --- .../GLM-truncated-regression.ipynb | 1089 ----------------- 1 file changed, 1089 deletions(-) delete mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb deleted file mode 100644 index 9a34f145f..000000000 --- a/examples/generalized_linear_models/GLM-truncated-regression.ipynb +++ /dev/null @@ -1,1089 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Truncated regression\n", - "\n", - "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", - "\n", - "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on PyMC3 v3.10.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pymc3 as pm\n", - "import arviz as az\n", - "\n", - "print(f\"Running on PyMC3 v{pm.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def truncate_y(x, y, bounds):\n", - " keep = (y >= bounds[0]) & (y <= bounds[1])\n", - " return (x[keep], y[keep])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m, c, σ, N = 1, 0, 2, 200\n", - "x = np.random.uniform(-10, 10, N)\n", - "y = np.random.normal(m * x + c, σ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "bounds = [-5, 5]\n", - "xt, yt = truncate_y(x, y, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", - "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", - "ax.axhline(bounds[0], c='r', ls='--')\n", - "ax.axhline(bounds[1], c='r', ls='--')\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear regression of truncated data underestimates the slope" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def linear_regression(x, y):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", - "\n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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\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 12 seconds.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "linear_model, linear_trace = linear_regression(xt, yt)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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d06OxiVlVDtf0aKzw4NwVonM3Hdbe46kmZgQAAABUHBQgAQCAHA6HJn+3SYfznPvYrE6QnruioywWzn08G38fL918Xu4qSLtD+nApqyABAAAAiQIkAACQ9NXq/Vq4Pd6I/bytendsNwX5eZuYVeVyfZ8mCvHPfb++X39QR5LSTcwIAAAAqBgoQAIAUM3FJqTo2V+3u4w9dSnnPpZUiL+PJvaLMuJsm0Mf/bXXvIQAAACACoJlDQAAVGPZNrvun71R6dk2Y+zCdvV0XS/OfSyJNWvWaMeOHUqOO6CM6EOyB4bJr3EHzVq9X/ec30K1guggDgAAgOqLAiQAANXY24t2K/pgkhGHB/vphdGc+1hcH3/8sV566SXt2bOn4JNe3gps0Udvtw/Sk2MGlPlrR0VFad++fed0j88++0wTJ050+9yUKVP09NNPl/ievXv31sqVK88pLwAAAFQtbMEGAKCaWrfvpN5Z7Fo4e+XqTqod7GdSRpVHVlaWRo8erVtuucV98VGSbDlK2/m3pt40UnPnzS/fBIspNDTU7BQAAABQDbACEgCAaiglM0f/mb1Rdkfu2A19IzWkdV3zkqpEbr31Vv34449GHB4ervHjx6tNmzY6deqU3p35gw5sWS1JsqWf1lVXXa3VK5erY8eOZZZDVFSUvL1L9qNcTExuZ+7Q0FCNGDGiWPO8vb0VGRlZrGsbN2b7PgAAAFxRgAQAoBp6es5WHTiZ26G5eZ0gPXJRWxMzqjy++eYbzZgxw4jPP/98/fjjj6pRI7dpzyXjb9fgO1/U8V+nSbZsZaSlaOzYsYqOjpbVWjYbUJYsWVKi65cvX67+/fsb8ZgxYxQQEFCsuY0aNSp8pScAAABwFmzBBgCgmpm/5ai+XXfQiL2tFr05pqsCfL1MzKpysNlsevLJJ404IiKiQPFRkto3DNXwSy5XrYHjjbEtW7Zo1qxZ5ZZrfp9++qlLfNNNN5mUCQAAAKobVkACAFBCycnJWrJkifbv36/Tp08rIiJCQ4YMUURERKFzTp06pSVLligmJkY2m02NGjXS0KFDVb9+/VLlkJ2dreXLlys2NlbHjh2Tv7+/GjZsqIEDB6pevXqFzjuZmqXHftzsMvZ/F7ZSh0ahOnXqlDZt2qRdu3bp5MmTcjgcql27tpo3b66+ffsWe7VccXJfsmSJYmNjderUKYWHh6tXr17q1KlTmdzfk37//Xft3LnTiJ966qkCxcczbh3YTMt2jVLyul9kS06QJL355psaN25cueSaV1pamr755hsjbt++vXr16lXueQAAAKB6ogAJAIAbEydONLbZDho0SEuWLFFqaqomT56sGTNmKC0tzeV6Ly8vTZgwQW+++aaCg4ON8cTERD300EP67LPPlJ2d7TLHarXqlltu0bRp0xQUFFSsvI4ePaopU6Zo1qxZSk5OLvC81WrV+eefr2nTprkt6E2Zs1UnUrOMuH1who4vm6Ue//1ZGzZskN1ud/u6vr6+GjdunJ566qlinQWYt4NyZGSk4uLi5HA49Morr2jatGk6duxYgTldunTR+++/rz59+pz1/mb56aefjMdBQUEaM2ZModcOaBmudo1qKanDUCUtd658XLt2rQ4fPqyGDRt6OlUX3333nU6fPm3EN954Y7m+PgAAAKo3tmADAFAM8fHx6t27t95///0CxUfJuTX3008/1bBhw5SRkSFJ2r9/v3r06KH//e9/BYqPkmS32/Xhhx9q1KhRbp/Pb86cOWrZsqU+/PBDt8XHM/dcuHChunXr5nJOoSQt2HpUc6IPG7G/j1X2VV/oqSef1Lp16wotPkrOrs+fffaZunbtqqVLl5411/wyMzN1ySWX6KGHHnJbfJSkjRs36vzzz9fixYtLfP/y8ttvvxmP+/bt61Jszs9isejWgU3l37SLMeZwODRv3jxPpujWZ599Zjz29vbW+PHji7gaAAAAKFusgAQA4CxsNpvGjBmjrVu3KiAgQKNHj1bv3r0VEBCg7du3a8aMGTpx4oQkaeXKlXr66af1+OOP6+KLL1ZMTIwCAgJ0xRVXqHfv3goMDNSOHTs0ffp0Y86iRYv0xhtvaPLkyYXmMHv2bI0bN042m80Y69evny688EJFREQoLS1Nq1ev1vfff6/09HTZbDbdeOONCg4O1pVXXqnEtCw99tMWl3tOHt5G87bn/ijQrl079e3bV23btlWtWrWUmZmp2NhYzZ07V9u3b5fk3Eo+atQobdq0SU2aNCn2e3jnnXcaxbuhQ4fqggsuUN26dXXixAnNmTNHf//9tyQpPT1d48eP1/bt2xUSElLs+5eHU6dO6dChQ0ZcnJWal3RqqJdadVS81UuyO3/v/l69XjfffLPH8swvNjbWpWg8cuRI1a1Lt3MAAACUI4fDUdovAACqrAkTJjgkOSQ5LBaLQ5KjU6dOjr179xa49siRI47mzZsb1wcFBTkmTZrkkOTo3Lmz2zmHDx92NGvWzJgTHh7uyMrKcpvL7t27HcHBwca19erVcyxatMjttbGxsY727dsb19auXdsRHx/vuH/2BkfkQ78aX6Pf+8eRY7M7xo4d67jzzjsdW7ZsKfS9sNvtjo8//tjh6+tr3Peaa64p8v176qmnCrx/derUcSxbtszt9S+99JJxvSTHG2+8UeT9zfDPP/+45Dh9+vRizfvorxiHV426xrymnft6OFNXTzzxhEveP//8c7Hm5f09DA0NdYwdO9bRvHlzR2BgoCMgIMARERHhGDhwoOOJJ55wbN261cO/CgAAAFQQpaojWhwOR6lrl6WdCABARZf3DEhJCg8P19atWwtdOfbrr7/q0ksvdRmrU6eOtm7dqjp16ridM2fOHI0aNcqI58+fr+HDhxe4buTIkcbqwZCQEK1evVpt2rQpNPejR4+qffv2OnnypCTpulvu0fKw3Pv6eVv1230D1LxOsDIyMuTv71/ovfL66KOPdOutt0qSfHx8tH///kKb6OQ9A/LM9StXrlS3bt0Kvf/gwYONlXq9e/fWypUrC7123LhxWrVqVbHyLqkvv/xSvXv3djt+/fXXG/Gff/6pIUOGnPV+KZk5qteqi9L2b5Uk+dRqqEP7YlUnxK/ski6E3W5X06ZNtX//fklSvXr1dPDgQXl7n30TTP7fw6JYLBaNHj1a77//fqHf7wAAAKgSLKWZxBmQAAAUw+OPP17kttWLLrpItWrVKjCnqGLMxRdfrJo1axrx6tWrC1yzc+dOlzMDp0yZUmTxUZLq16+vxx57zIi/++oLOey5W7f/b1grNa/jPLuwuMVHSZo0aZKaNWsmydnJ+s8//yz23FtuuaXI4qMkl23JGzduVE5OTqHXHjp0SDExMR75Sk9Pd/ua+c/dzP/7XZhgP29FNsj93rFlpWnG8rhizT1XixYtMoqPkjR+/PhiFR/d8fLyUt26dRUZGVmg87fD4dD333+vrl27asuWLYXcAQAAANUVBUgAAIph7NixRT7v5eWlDh06lGiOt7e3OnbsaMQ7d+4scM3s2bN1ZreCv7+/sQLxbPI2GclOTVTWsb2SpE4Robr5vKbFukd+FovFZcXfunXrij33hhtuOOs1eVcdZmZmKi4urkT5eVpKSopLXJLibcuGYcZjR1aGZq7ap7SswgusZSVv8xmp5N2v27Rpo6lTp2r16tVKSUlRfHy84uLilJSUpNjYWL366qsuq2APHTqkSy65pNBGQwAAAKieKEACAHAWUVFRxdpWWq9ePeNx06ZNFR4eXqI5iYmJBZ4/05xFks4777wiuy7nVadOHTVqHGnEWUd2yWqRnr+io7y9Sv/Hf/5iU3H4+PicdfWjJDVq1Mgldvd+nLFkyZJzOce6yK/Bgwe7fc0z3c3P8PX1Peuv6YzQ4EDjsSMnS4lp2fpu3cFizy+NpKQk/fjjj0bcu3dvtWvXrtjz7777bm3fvl1PPPGEevbsWaDg2rRpUz3wwAPatGmT+vfvb4zv27fPZQUuAAAAQAESAICzyFskLEpQUJDxuLhdhvPOSU1NLfB8dHS08bht27bFuqfk3BKb7pVbrMw5fVwT+kWpQ6NQt9cnJibq448/1nXXXacOHTooPDxcvr6+slgsLl/PPfecMScpKalYuYSFhcnHx+es1+V9LyT374eZ8hfgsrKyij03MzPTeGzxdhYuP/l7r2x2zx2pPWvWLJeiaUlXPxangC45i91z5sxRRESEMTZ9+nQdPny4RK8HAACAqosCJAAAZ1GSrbbnMsddY7gzjWQk6e233y5QECzsy2q16mTc1tx87On6v2Gt3L7ma6+9psaNG+uWW27R119/ra1bt+rEiRPKzs4uMt/8KwILU5r34kxuFUn+1afF/fVLcjlX0uLrfD/2nUjTH9viyyY5Nz799FPjcUBAgMaMGeOx1woLC3NZ9ZiTk6P58+d77PUAAABQuVCABACggkpNTS2yEUtJdKofoBD/gqsQ77rrLj3wwAMFzje0WCwKDw9X48aN1bx5c+Mrb+OVilYg9LT8jVdOnTpV7Ll5t5NbfQOMxx8viz3nvNzZunWr1qxZY8SjR49WaKj71a9l5YorrnCJi+piDgAAgOqldG0QAQCAxwUEBMhqtcput0tybnXNXwRz50RKllIycwuXAb5e6toqssB1v/32m95//30jbtasme677z4NHTpULVu2dLtt+qmnntLUqVNL88up9Jo2dW3ek7e79NnkvTa0bu5W5bX7TmnD/lPq2qR4HbWLK3/zmZtuuqlM7+9OvXr1FBoaamzNpxENAAAAzqAACQBABWW1WlWzZk1jG/b999+vRx55pMg5q/ee1DUfrtCZtW5+3lYt/L9BahwWWODaN99803jcoUMH/fPPP2ctcBbVGKY8jRs3TqtWrfLIvb/88kuXjtxn5D+DMyYmplj3y8zMdGnY07trR23N8/zHy/bq3XFlV4DMycnRzJkzjTgqKsqle7knBQQEGAXIvNvOAQAAUL1RgAQAoAJr06aNli9fLknasWNHkddm2+x6/KfNLmP3XtDSbfHRbrdryZIlRvz4448Xa3Xl3r17i5G15x06dKjYBcCSKqxwVqtWLTVq1MgoJq5YsaJY91uzZo3LVvpLB/fW8RN+ik92NqaZt+WIDpxMc/v7VBpz585VfHzu2ZITJ06UxWIpk3sXxWaz6cSJE0Zc3CY2AAAAqPo4AxIAgAps0KBBxuP58+fLZrMVeu3Mlfu0Kz73LMcWdYN1y4Bmbq89ceKESxfnzp07nzWXrKws/fPPP8VJu8q66KKLjMcrVqwoVqfuP/74w3hssVh06SUjNaFflDFmd0if/lN2hd28268tFosmTpxYZvcuyqpVq1waF+Xfsg4AAIDqiwIkAAAV2DXXXGM8PnbsmGbMmOH2uqS0bL25aLfL2LOXd5Cvt/s/6vM3kClOR+dZs2a5dOU205IlS+RwODzyNXjw4EJf9/LLLzcep6amatasWUXmabPZNH36dCPu3r27GjVqpHG9IhXo62WMf7PmgJLSi+46XhzHjh3Tb7/9ZsTnn3++IiMLnv/pCW+//bZLPHTo0HJ5XQAAAFR8FCABAKjAunTpohEjRhjxAw884HYr9tt/7lZiWm4Ba2THBurTrLYk98XF2rVrKzAwd8vv3Llzi8zj8OHDmjx5conzr2pGjBihVq1aGfHTTz+t5OTkQq9/4403XBrQ3HvvvZKk0EAfXdOjsTGemmXTrNX7je3SZ77i4uJKlN/MmTNdViGWtvlMSc9vnDVrlr7++msjbtasmc4777xSvTYAAACqHgqQAABUcG+99ZZxPmNiYqL69++v2bNnG92x446nasaKOON6Xy+rHhrRRuvXr9e9996rAQMGFLinl5eXS2OSF154QUuXLnX7+hs3btTAgQOVkJAgq7V6/+jg5eWlp59+2ogPHjyo0aNHuy1Cfv/993rssceMuF27dho3bpwR39S/qax5jmb87J+9sttdV6aWVN7t16GhobriiitKdZ977rlHEydO1Jo1a4q8LiMjQ88995zGjx/vMv7888/L25ujxgEAAODET4YAAFRwLVu21Jdffqkrr7xSWVlZOnnypMaMGaNHH31UF154oTanBOvUyRw5crJkS0tSYx3XeV/cpgMHDkiSWrdu7fa+Dz74oLHyMTU1Veeff74uvfRSDR48WDVr1lRCQoIWL16s33//XXa7XQ0bNtRll12mDz74oNx+7RXRmDFjNHfuXKPT9KJFi9SiRQvdcMMNat26tRITE7VgwQItXLjQmBMcHKyvvvrKpYDbpHaghrevr3lbjkqS4pMzFXgqrdR5rVmzRlu2bDHi6667TgEBAaW6V05OjmbMmKEZM2YoKipKffv2Vfv27VW7dm35+/srMTFR0dHRmjt3rhISElzm3n///br22mtL/esAAABA1UMBEgCASuCSSy7Rn3/+qSuvvNLocBwbG+u2GJiUL/by8ipwjSQNHDhQU6dO1ZNPPinJ2Rn7559/1s8//1zg2jp16uiHH37QvHnzzu0XUkV8/PHHSk5O1pw5cyRJCQkJmjZtmttra9asqVmzZrlt9DNpQDOjACnJpYlQSeVd/ShJN954Y6nvlVdcXFyxtoL7+Pho6tSpeuihh8rkdQEAAFB1VO99VAAAVCL9+/fXnj179Oyzz6px48ZFXuvn56chQ4bo7bff1l9//VXodU888YRmzpxZ6P38/Px07bXXKjo6Wr179z6n/KsSPz8//fzzz/rwww/VrJn7TuO+vr668sorFR0d7XKOZ17dI2upe2QtIy5tI5qMjAyXhjjt27dXr169SnUvSRo9erRGjx6tBg0anPXakJAQ3XrrrYqOjtbDDz8si8Vy1jkAAACoXiz5u2CWwLkdUgQAAM7JBz//pSc++UW2tCTZs9JVp1YNPXZlb7Vv21YdOnQo0fbbnJwcrVy5UtHR0UpKSlKtWrXUqFEjDRw4UDVr1vTcL6KKWL16tXbs2KEjR44oKChIERERGjhwoMLCws46d/6WI7p95nojHty6jqbfWPriYVmLi4vT1q1bdfDgQSUmJio7O1shISEKCwtTx44d1bFjx0JX2QIAAKDKKdW/NlOABACgEsq22XXh639p7/FUY+yjG3poWLt6JmaF0rDZHTp/2hLtO5F7/uOC+weqVb0QE7MCAAAA3CpVAZIt2AAAVELfrTvoUnzsHllLQ9vWNTEjlJaX1aKb+jd1GftwaaxJ2QAAAABljwIkAACVTEa2TW8s3OUy9tCINpy9V4ld3SNCtQJ9jPjnjYd0ODHdxIwAAACAskMBEgCASmbG8jjFJ2ca8flt6qpX07OfNYiKK9DXWzf0jTLiHLtDHy/ba15CAAAAQBmiAAkAQCWSlJ6t95bEGLHFIk0e3trEjFBWJvSLUoBPbjOXWav361RqlokZAQAAAGWDAiQAAJXI//6KUVJ6thGP6txQbRvUMDEjlJWwIF9d27OxEadn2/T5in0mZgQAAACUDQqQAABUEseSM/Tp33FG7G216P+GsfqxKpk0oKm8rblneU5fvldpWTkmZgQAAACcOwqQAABUEm//uUfp2TYjHtu7iZrUDjQxI5S1iFqBuqxLQyM+lZatb9YcMDEjAAAA4NxRgAQAoBLYdyJVs1bvN+IAHy/dfX4LEzOCp9w+qLlL/NGyvcq22U3KBgAAADh3FCABAKgEXv9jl3LsDiO++bymqhvib2JG8JRW9UI0tG1dIz6UmK5fNx02MSMAAADg3FCABACggttzLEU/R+cWoGoG+ujWQc1MzAieln8V5PtLYmTPU4AGAAAAKhMKkAAAVHDvLd4jR57a020Dm6uGv495CcHjekSFqWdULSPeFZ+ixTuPmZgRAAAAUHoUIAEAqMD2Hk/VTxsPGXGtQB/d0DfSxIxQXtytggQAAAAqIwqQAABUYO8u3qO8O28nDWimID9v8xJCuRnSuq5a1wsx4rX7Tmll7AkTMwIAAABKhwIkAAAV1P4TafpxQ+7qx9AAVj9WJ1arRbcPdj3r861Fu03KBgAAACg9CpAAAFRQ7y7eI1u+ztchnP1YrVzaqaGiagca8fKYE1oTd9LEjAAAAICSowAJAEAFdOBkmr5ff9CIQ/y9NaFflHkJwRTeXlbdNaSFyxirIAEAAFDZUIAEAKACen9pjHLyrH68qX9ThQaw+rE6uqJrIzUJy10FuWz3ca3bd8rEjAAAAICSoQAJAEAFcygxXd+uPWDEwX7euql/UxMzgpm8vay6m1WQAAAAqMQoQAIAUMF8sCRG2bbc1Y8T+0UpNJDVj9XZFd0aKaJWgBEv3ZWgjQcSzUsIAAAAKAEKkAAAVCBHkzI0e03u6scgXy/dfB6rH6s7HzdnQb65cJdJ2QAAAAAlQwESAIAK5MO/YpRlsxvxDf2iVCvI18SMUFFc2S1CjWrmroJcvDOBsyABAABQKVCABACggjiZmqWvV+eufgzw8dIkVj/iX77eBVdBTluw06RsAAAAgOKjAAkAQAUxfXmc0rNtRjy2dxPVDvYzMSNUNFf3iHDpiL085oSW7zluYkYAAADA2VGABACgAkjNzNGM5XFG7ONl0aQBrH6EKx8vq/4ztKXL2CsLdsrhcBQyAwAAADAfBUgAACqAWav3Kyk924gv79JIDUIDipiB6mpUl0ZqUTfYiDfsT9SfO46ZmBEAAEA19s+b0oLHnV//vGl2NhUWBUgAAEyWlWPXx8v2GrHFIt02qJmJGaEi87Ja9H/DWrmMTVuwS3Y7qyABAADKXWqClHzE+ZWaYHY2FRYFSAAATPbTxkM6mpxhxBe2q6cWdUNMzAgV3Yj29dWuQQ0j3nYkWfO2HDUxIwAAAKBwFCABADCRze7QB0tjXMbuGNyikKsBJ6vVov8Od10F+crvO5SVYzcpIwAAAKBwFCABADDRH9uOKjYh1Yj7Na+tLo1rmpcQKo0hreuqe2QtI447kaaZK/eZmBEAAADgHgVIAABM4nA49P6S/Ksfm5uUDSobi8WiRy9u4zL21p+7lZSWXcgMAAAAwBwUIAEAMMmKmBOKPphkxB0a1dB5LcJNzAiVTffIMI3s2MCIE9Oy9fafu03MCAAAACiIAiQAACZ5P//Zj4NayGKxmJQNKquHRrSRr1fuj3QzVsQp7nhqETMAAACA8kUBEgAAE2w5lKRlu48bcVTtQI3oUN/EjFBZNakdqIn9o4w42+bQS/N3mJcQAAAAkA8FSAAATPC/v2Jd4tsGNZeXldWPKJ27hrRQrUAfI5635ajWxJ00MSMAAAAgFwVIAADK2aHEdM3dfMSIw4P9dEXXRiZmhMouNMBH/xnaymXs2V+3yW53mJQRAAAAkIsCJAAA5Wz6P3tly1MYmtA3Uv4+XiZmhKpgbO8malYnyIijDybpl02HTcwIAAAAcKIACQBAOTqdka2vVx8wYn8fq67vE2liRqgqfLyseuSiti5jL8/fqYxsm0kZAQAAAE4UIAEAKEez1xzQ6cwcI76qe4RqBfmamBGqkqFt66pPszAjPpSYro/ynTcKAAAAlDcKkAAAlJMcm12f/RNnxBaLdPN5zcxLCFWOxWLR4yPbyZKnn9G7S/bowMk085ICAABAtUcBEgCAcvLblqM6lJhuxEPb1lPT8KAiZgAl16FRqMb0bGzEGdl2PfPrNhMzAgAAQHVHARIAgHLgcDj08TLXrbC3DGD1Izxj8vA2qhnoY8QLtsVr8Y5jJmYEAACA6owCJAAA5WD13pPadDDJiDtHhKpnVC0TM0JVFhbkq8nDW7uMTfllKw1pAAAAYAoKkAAAlIOPlu11iScNaCZL3oP6gDI2pmcTdYoINeJ9J9JoSAMAAABTUIAEAMDDYhJStGhHvBE3qhmgizrUNzEjVAdeVoueGdXBpSHNO4v3aP8JGtIAAACgfFGABADAwz75e68cjtz4xv5R8vbij2B4XufGNTWmZxMjzsyx6/Gft8iR9xsSAAAA8DD+9gMAgAedSMnU9+sOGnGIn7euzdOhGPC0B4e3Vq08DWn+2pWgOdGHTcwIAAAA1Q0FSAAAPGjmyv3KzLEb8djeTRTi71PEDKBs1Qry1eMj27mMPfPrNiWmZZmUEQAAAKobCpAAAHhIRrZNX6yMM2Jvq0UT+0eZlg+qr9HdGql/i9pGfDwlSy/8tsPEjAAAAFCdUIAEAMBDftpwSMdTcleZXdKpgRqEBpiYEaori8Wi5y7vKD/v3B/9Zq89oJWxJ0zMCgAAANUFBUgAADzAbnfo47/3uoxNGtDMpGwAKSo8SPde0NJl7NEfNysj22ZSRgAAAKguKEACAOABS3cnaM+xFCPu26y2OjQKNTEjQLplQDO1qhdsxLEJqXpj4W4TMwIAAEB1QAESAAAP+GRZ/tWPTU3KBMjl623VC6M7yWLJHfvfXzHaeCDRtJwAAABQ9VGABACgjG07nKy/9xw34mZ1gjSkdV0TMwJydY+spZv75xbE7Q5p8rfRbMUGAACAx1CABACgjH2S/+zH85rJarUUcjVQ/h64sLWahgcZ8e5jKXprEVuxAQAA4BkUIAEAKEPHkjM0J/qQEdcK9NHobo1MzAgoKMDXSy9f5boV+4OlMYpmKzYAAAA8gAIkAABlaMaKOGXbHEY8vk+k/H28TMwIcK9nVJhu7Oe6FfuBb6OVnsVWbAAAAJQtCpAAAJSRtKwcfblqvxH7elk1vm+UeQkBZzF5eGtF1g404j3HUvT8b9tNzAgAAABVEQVIAADKyPfrDioxLduIL+/aUHVC/EzMCChagK+XXr26s/IeUfrFyn36Y1u8eUkBAACgyqEACQBAGbDbHQWbzwxoZlI2QPH1jArT3UNauIw99P0mHUvOMCkjAAAAVDUUIAEAKAOLdhxT3Ik0Ix7Yqo5a1QsxMSOg+O69oKW6NqlpxCdTs/TAt9Gy2x2FTwIAAACKiQIkAABl4KNlsS7xpPOaFnIlUPF4e1n15rVdFeznbYwt231cn/6zt4hZAAAAQPF4n/0SAADKSPxWKWGnlJogZaVKQXWkkAZS416Sf42yeY2MZOnAainlqJRyTPIJlILrSuGtpPodyuY18tl0MFGr95404tb1QjSgZbj7iz3xHqSfkvYtlxL3O+8ZUEuq31Fq2E3yKuEf9Yc3SDvn58ZN+kjNh5QuL1QqTWoH6pnL2+v+2dHG2Evzd6hPs9rqUNdPOrBKSj4spcRLXj5ScD2pVlOpYVfJavK/aZ+IkeK3OD/zmclSYG0puL4U0UMKKuSzeDaZKdL+ldLJGCnztOQfKtVpLTXuLXmX8GzXk7FS9OzcuG5bqf3lpcsLAACgEqIACQDVkd0uHd8pHVovHVonHV7vLIzZstxfPyWp9K+VlSr9/Ya06WtngcwdL1+p6UBpwANSZL/Svc6+5dKyadLevwr/ddRsInW6Vjrvfsk3qHSv48bHy1xXid08oKksljxdPTz1HqSfkv54Uto4S7JnF3w+pKE05BGp2w3Fu58tR/rxDinh3y7I3gFSl7HFm1vZnYhxfh4O//uZOLJJykl3f+19m6RakeWbXzm5omuEluxM0M8bD0uSmtn36cRnb8thiZYlO9X9pKC6UvsrpEEPSUG1yy9ZW7a06gNpw0wpYYf7ayxezoJhv7ulNiOLd9/sDGnxc9Kaj6XstILPB9SS+t8n9buv+IXXuf+VYhadSUq6+Y/izQMAAKgiLA5Hqc/24VAgAKiMfrxD2j5Hykop/pzSFiBjFks/3yUlHyrmBIvUZZx0yWvFX2GUnSHN/T9p45fFz6tGhHTF+86C3zk6nJiuAS8vlu3fs/LCg/30z8ND5Oft5bzAU+/B6aPSpyOkU8XYIttzkjRy2tmvW/6OtOCx3Hjwo9Lgh84+rzJbNFVa84mUkVj8OVW4AClJyRnZGvnGUo1Lma6bvX6Tj8VWvIkBtZzfZx2u9GyCkrNA/P0k5z+kFFerEdIVH0oBNQu/JjNF+vwyZxH6rPe7SBrzpWT1Kvq6bXOkb8bnxl2uly5/t1gpAwCASmDB41LyEefjGg2kC581Nx/Ps5z9koI4AxIAqpujm0tWfCyt3Qulr64tQeFNkhzSxpnS7OulnMyzX56dIX09tmTFR0lKPih9ebUU82fJ5rkxY3mcUXyUpAl9I3OLj558D769MV/x0SK1GyX1/49Ut53rtWs+dq4SK8rpeGnpS7lxzUjnKq+q7tiOkhUfq4Eavl76MeJr3e79S/GLj5JzRe73k5wrcj3p8EZpxqUlKz5K0q750heXS+mJhV8z94GCxcfmFzhXTUf0yne/ea6fGXey0qTf8xT1/UKloVNKkDQAAEDVQAESAFD2TsRIs8dJtmIUEd3ZvUCaV4yVd7/l3dZYQjkZ0tfjpJOlb7KRkpmjr1bnbqn297FqXJ9/V8Z58j3Ys0jav9x17MJnpGs+l4Y9Ld26VGrQxfX5JS9K9iKKSQsed56dd8aIFyUf/1Kljkpu2TSF7/m2dHMddunnO6X9q8o2pzPSTkpfXlX6ovHhDdL3N7t/7vhuadNs17Fet0rjf3AWDW9eILW+2PX5Fe86C6+FWTZNSspz7MKQR6XgOqVKHQAAoDKjAAkAcDZqCapbdveb+3/OAp877UZJY7+RblogXfSys1GEO+umF13E2Le88FV9wfWcBbSbFkhjv3WeT+dOdppzxVMpfbPmgE5n5Bjxld0iFBbk6ww8+R5s/8U19gt1brM+w9tX6neP6zVJB5wrx9zZt1za/E1u3PJCqc3F7q+tTBIPSCkJJZ/n5etsDFTRZSRLx/eU7T1PxEjLXnX71GlHgKb7X6/M8XOl8T9JPW6S2x04Drv063+cZzSWtT+ecDZwcqfpQOmaL6SbF0qXvSPVbuH+uj0Lpc3fFRzf/otcThiyWJ0rio3Y4lwJmVdWivOYBXdOxkrL386N63WQet3i/loAAIAqjiY0AFDdePlI9TtJjbo5uyQ36ubcsrv0ZWnpi+d+/5g/pdgl7p/rdat08Su5cZPeUqvh0gcDXFffSZIc0sIp0k3z3N9r4RS5PY7Yr4Z00+9SWNPcsVYXOrtNr/6fm3wXSbFLpWaDCv0luWOzO/TpP66rJ28679/X9PR7cHSza9ygk+QT4DrWON92UUk6Gi1FdHcds9uk3ybnxl5+zuJtZZWZIm37WYqeJcX9LU34pegVZ1arVKdN7mehUTepXkdp87fOlXwVjd3m/P6KniXt+M25TX7II2V3/yUvuC2c5zisGp/1iDZmttDO6FC9MLqjszt6WDPn6tn8jm2Tor+Wuo0v+FxpHd8jbSjkuIXWF0vXfpnbFKZxT2fTmQ8Hua5APGPh086zKvM2i8r/uQptLIU2ch1r1MPZ2MaRZzXx0U1Sh9EFX2PeQ64roC96+eznRQIAAFRRFCABoLqZtNCzfwne+JX7cZ8g6YInC47XipJ63y799XLB5/Yvd64iCmvmOn4iRjpQyOrIPne4Fh/PuOBJ54pJd11tN35Z4gLk71uP6uCp3C7JF7Spq+Z1gv+9n4ffg/xbPoPdrF4NrldwzN1W0dUfSfFbcuN+90i1m7tNv8Ky26W9S5xnD+741f3vcWGunlE5ikJHtziLjpu/lVLiPfMaGcnS9l/dPvWzvb82OpwrCmet3q8+zcI0qksjqc9dzsK+u+7uG78s2wJk9FcqtAfi8OcLdqQODJMGPSjNubvg9Un7pb1LpWaDc8eK87myWqWgcNffA3efqx2/OY9ROKPjNVJUf/e5AwAAVANswQaA6saTxZbs9EILGGpxvuQX4v65dqMKv+cmN2fRbS7ifLrC7uUXIjU/3/1z239xNrQpgY+XxbrEkwb8WyAsj/fAO9/ZjFluCm5ZqQXH8s9LSZAWP58b14iQBpR+S3q5O7Zd+uNJ6fX20hdXOLeRl6T4KFXs4mPKMecZg++fJ33QX1rxjueKj5KzeJuT7vYp7w6Xu8SP/rBZMQkpzoJcm0vd32//CveFydIq7HPfoLP7f3SQpLaXOrdSu1Oaz5VU8LOVf152hjT/4dzYN8R5RisAAEA1RgESAFB2jm4ptIChhl0Ln1e3reQd4P65g2sKjh1Y7f5a7wDndtrCFJZDdpoUv7Xwefms23dK6/cnGnH7hjXUp1mYMyiP9yD/ttDEfQXnnIorOBYa4Rr/8aSUmZQbj3he8g0sPMeKIPW4tPID59ba9/pI/7wpnT5sdlZlJztD2vKDs0v7a22l3x+V4jeffV5ZKOxzJemSESPVr3ltI07NsumuL9crI9tW9Pf1wbVlk9vp+MKLmUW9fkBNqVYhxcmzfa6SDhZs3JR6wnnuo8u8fJ+rv193/UwOfkgKKeScVwAAgGqCAiQAoOwc2Vj4c4U1hJCcq9BqRRVyz+iCY0c3ub82rGnRK9qKyqGo3PP55O/8qx+bynLmLLnyeA+iBrjGx7YVLKDmb7JhsUpN+uXG+1c5t/Se0Wxw0aswzZST6TzX8asx0rQ20vyHzv77Vb+TNOwZ5+q4ymDfCmnOvdKrraTvbnRu37XnFH59jQip371S52vLLgd3nzVJ8g2WV2gDvTGmi8KD/YzhHUdP67Eft8hR1Jb9wu5ZVrlJRX+uinr+xG7X1Yz5P1eZSa7bqCX3qzCjzst9fCpO+ueN3Di8tfN4BQAAgGqOMyABAGXn+K7Cnztbl+3gOlLC9oLjqcektJPO89wk5+PCuuAGFdFsRHJ/ptsZCTuLnvuvAyfTNH/LUSOuX8NfIzs2zL2gPN6DLuOczULybjf+8XZp9P+cq712/Cqt+dj1Hu0uz23GYrdLv/1Xxnl6Vh/poldU4RxY4zz3b8sPUkbi2a8PayZ1uErqdI0U3tLj6Z2zk3ulTbOdzVpO7T379QFhziJxx6ulyH6uDVTKQmHfu/9+ruqG+OutMV007pNVcvz7rfP9+oPqF15bVxZ2z2J+rs6eWxH3Kc7nyh2HXTq+W2rYxRm3vliq0UhKPpR7zbwHpcBwZyE7blnBRl2NeriuwJz3sGsTn4tfdjb+AgAAqOYoQAIAyk56YuHP+YcWPbeo5zOScotv7ho+lNVrFMOn/+yVPU8fjAn9ouTrnWdDQXm8B0G1peHPSb/en/v80U3OLcnuBNWRLnw2N177iesq0j53SHVaFZ1beUncL0XPdq7OPBlz9uuD60vtr3AW5fJ3+K6IMpKkrT85f337V5z9ep8gqfVFzl9fiws8V8yy5RTcWnxGnu/Lfi3C9cCwVnp1QW6xcurCQ7rSt5D7FvNzdVbn9LmqWfhzefPz9pUufVP66hpncVJyfj9+MtT9XO8A6dI3cuNdC6RdeTrWt7vctckNAABANUYBEgBQdjJPF/6ct1/hz0mSVxHPZyaX72sUIik9W9+sOWDEgb5eGturSb77lFN+PW6ScrKkBY9L9uzC59VqKo35Kvd8u9QT0p95ipEhDaRBDxWdl6dlnnZusY7+Wor7W4V2Oj7DL9TZXKTjVVLTQQW7H1c0dpu0Z5Gz6LjzN9cVcu5YfZwNkzpeLbW5WPIN8nyORX3/5/u+vXNwC206mKQF25wNcdLsRfw4WYzPVbEU+bkqrPr5L68ins+fX8th0lWfST/fVXhBVnIW9a+eIdXv6IxzMp1HA5zhE+j8RwIAAABIogAJAChLRXUgPlu34aJWduXtRptdSIMXSbKe5Y81ryKed9c1Op+vV+9XalZuU4prejRWaGC+vMvjPTijz+1S6xHS6o+k2KXO1VrZaVJALalee6ntJVLX8a4FpIVPuW5nvvBZyS84Nz4dL635yFkwOxnrfF8Cakp120ltRkrdbpB8CmmWU1pfj5X2/lX0Nd7+UqvhzqJcywvPXsytSJZNkxafrRhlkZr0dRZV21+Ru9q1vJTgc2W1WjTtms4a9e4/ik1IVbaK+L4uxueqePkV9bk62+e+hJ+r9pdLkf3//RwslE7EOIuRfjWczaJaXuj8BwD/Grlz/nnL+Xk5Y+B/XZvTZCRJaz91rpI8vlPKSJb8QqTwVlKrC6UeNzs/ZwAAAFUUBUgAQNnxKaKDsq2IhhqSZMsq/Lm8nZmLKn7ZilgJeLbnz7LKLNtm1/TlcUZssUg39o8qeGF5vAd51Yoq/kqrg+ukDTNz48j+zoLXGVu+l36+R8rOVzRKTZD2LnV+rXhHuvZLqUGn4r1mcdjt7sctXlKzQc6iY9tLnQWbyih/J+W86nV0/h50vKpgN+XyVMLPVYi/j/43vrtGvfOPsrOKWNFZVqs3i/xcne1zX4rPVXAdacijzq+zSdwv/f1abhzWXOp7T2689y/p24lS2gnXeeknpQMrnV/L35Gu+lRqPuTsrwcAAFAJVfA9SwCASqWoAlFRRQDJuYWx0PvmWWlUHq/hxm+bj+hIUm6hZXi7+oqs7aa4YlJ+Z2W3S789oNzGM97SxXkaz+z6Xfru5oLFx/wS90ufj3L+19M6XSMNnSJ1GVt5i49FieglDZsi9b3b3OKjVPT3VyHfty3qhmjaNV3kqyIKgOf6fWvcp6jP1VkKkDlFfO7KIr/5j7iu0Lzopdxt4Yc3Sl9eXbD4mF/6SWnWGOnQ+nPPBwAAoAKiAAkAKDtFbSE8WxfjoppV5G0yEVDrHF6jiOeLaGThcDj08TLXLsWTBjR1f3F5vAelsX66dHhDbtzzFuc2bclZ+PzlPrmcvVirqXTLYumxeOdZd97+uc+ln5R+L8bKsHMVPUv6cKD0Tk9pyUvOrbBVycHV0swrpdfaSHP/K+1fKaO9dHnz8pZ8g90/V8T37YgO9XV33/DC73uu37dnnNPnqojnzzW/PQudXefPaD3SeY7kGb/+x/XMz8Da0vifpMePSTfMccZn5GS4NpYCAACoQihAAgDKTngRnZRT4ouem3LM/XhQXdfz8ALDnA0gSnKP4jxfp3WhT63ee1KbD+UWB7s0rqnukYUUQsvjPSiptJPSomfy3K+ONOSR3HjHXOn0Edc5F78iNeom+fg7z8Trdavr89t/dZ4XWRY6jHZuJS/M8V3Skuelt7tJ/xssrXhXSj5S+PUVTdR5UkTPwp9PTXCeN/jpcOmNTtIfT0lHt5RffmcU9r17ls/VLd2KWJ1YxOeqRMKLuE9pP1cWqxTesvQ55WRJ8/I0nvH2l0Y8nxsfXOda9JecW7qbD3GeYdpskDT4Edfnj2x0zgMAAKhiKEACAMpOgy6FP3diT+HP2XKkU3GF3LNzwbH6hZw/eCqu6PP2isqhiNw/crP60WKxlPg+ZfoelMSiqc5Vi2cMm+q68ivub9fr/WtKzS9wHetwZb6bOqT9y88trzN63izdFy3dOM/Z5MaviFVphzc4V1++3k6afom0brqUfqps8vCUpgOkSQule9ZLA/4rhTYp/Nqk/dI/b0gf9Jfe7S399Yp0cm/h15elwr7PstOk5MOFTvM6Wfj3dXa9juealVNRn4GiPleSdGK3+/HaLc/tjMoV77i+9nn3uxbS45YVnJP/c5T3DNYz9v1dcAwAAKCSowAJACg79TtI3oU0szi0wf24JB3bKtkKOf/Q3cqxxr3cX5uTIcVvLfx18q9GOsMnMHc7cj6xCSlatCN3hVWjmgEa0b5+4a9RXu9BcR3eIK2fkedevaTO17lek7+4VKORZM33I0JNN0WzIopSpRLZT7rsbem/u6QrP5FaDHU2onHHYXcWeH65T3q1lfTVGGnzd+67GlcUtZtLFzwh/WeTNOFXqcs4ybeI1YMJO6Q/n5Xe6iJ9dIG08v2yW3XqTmGfK6noswkL+1xJem1bDTnKYlt5SD3334NS0Z+rtJPSqX3unzuXz1XSIemvV3PjmpFS//+4XpP/8+EbUvAIiYBaBbe+l/XnCgAAoAKgCzYAoOz4BEhtL5E2f1vwuZg/pYxkyd9N04etPxV+T3crhDpeLS15wf31235y36E5I0mKWex+TttLnVuN3bBMv0R7/fIUODIkPSMp8jzpxrkFJ5TXe1AcDofzbEHHv12mLVZp5KvOFt555eTrYuyuM7C7lWL555UVH//cztCn46XN30gbZzmLtO7YsqRd85xfPkFSm4ulDldJLS6QvHw8k+O5sFicqyKbDpAuflXa/ovzvMu9S3N/r/I7tNb59fujUtSA3M7gRZ2NWFJtLpG8/0/KSS/43LafnN/X+dltzu34bqy2t9b7G7PVMGq/xveJdH3yxzuk6K8KTgptIt2/2X1+Ha+Wlk0rOB6/2Xk+aO3mbvL+WS5nm+bV6Wr348Xx+6OuDZtGvFjw/yHF+VxJzs9WVkrh8wAAAKoAVkACAMpWl7Hux3PSpYVTCo6fjJVW/8/9nCb93BcVajeXGvd2P2fVh8575rfwafeFFcm5Es2NU6lZSkgpRTGgPN6D4tjwhbNodUb3G91vZc1/vmRqQsFr3J2jF3AO51IWV0g9qd890p3LpduWSX3udJ6JWZjsVGfxd9a10qstzTlLsSR8A6XO10o3/CTdv9XZ9btOm8Kvd9idhco5dztXfq7/vOxy8a/hvsgoSVt+kA6sLji+/G0p+aDbKd/aBkmSnp6zVatiz9IFujg6j5VUyNEHvz9a8PiFtJOuqxTzCm0iNR1UujxilzgLsme0GOYseueX/3OVdqJgkyGHo2CH7PL4XAEAAJQzVkACQHWz8gNp718Fx4/vKnzOrEIKape+KQXnawjT/HznX+z3Li14/dpPnMWtruOdK7cOb3CuaMq7+sdgkYY+VXhOFzwlTR+pAqubslKkT4ZLAx6QGnaVMpOdhbhtP7u/T/Pznc0g3Phi5T71cqjQmkehyus9KEr6KWfR9YzA2tL5j7u/tn4nacv3uXHiAefKw5B6uWN5C5lnuFtp6kkNOjm/hj3j7D4cPUvaOa/wrevpp85+PuTm75zFtfySDhQ+55f7nNv28zv/caleu6Jfryg1GjrPETzvfueW5+ivpS3fFSxQnWHLdG4FLkuDH5G2zSn4njps0hejpfP+I0X2dz6/9Sdp3Wdub3PIJ0o/ZpwnScqxO3Tnl+s1557z1KhmIccTFEd4C6nrOGnDzILP7ZovfT5K6n2bFFzfuX3979cKLY5q6FMFVwIXhy1b+u3B3NjLT7roJffX5j+r1p7j/Lw36pY7dni9czyv8v5cAQAAlAMKkABQ3RyJlna62TpclMKuH1HINuhLXpfe7+d+K+H2Oc6vs+k+UWrSp/Dno/pLXa93FhfzSz0mzX+o4Hh+PoHSyNfcPpWeZdP05XEq4lS8opXHe1CUP5+V0o7nxhc8WXgn7TaXSIuezt3+67BJK9+Thv1bwLTbnIXrvEKbFN1wx5O8vKXWI5xf6YnS1h+cxboDq0p+r+O7S/55iC1kK3+fO0r++oVp1M35Nfw5afcCaeNXzv/assruNdyp3VwaOFla/GzB57JOS38+U3A8P4tVNa99T1FzcrTnmLOwfiI1S7d+vlbf3d5PAb6FnOtZHMOekXb97n6Vbtwy941f8mt+QemPNVj5nnR8Z27c7+7CVyg3H+I83zHvPy6seEe66tM88buuc3yDpWZDSpcbAABABcYWbABA2avdXLp2pnN1UGm0GFb4qqK8Ln7VudqwNLz9pTFfSmFN3T79zdoDOpl6DsWe8noP3DmySVqbZ2Vaw65S1xsKvz68hdTpWtexf95wNnZZOEX66HzpwErX5wc/LFnPoZBUVgJqSj1ukm5e4OwyPfDBwpuVVEZePlKbkc7v1Qd2Or/nG3X37GsOeKDQYwnOymKVRr2roBb99dENPVTDP/ffurceTtaD3286t6Y0gWHSuO+cndpLo0EX1wJgSSQfkZa+nBvXiHB2NS9MQC2p712uY1u+lz4b6VydPP0S15XHktT37rI91xMAAKCCoAAJAPCMlsOksbOlkIYlm9dlnLPY4l2Mwp2PvzRmVsGuzmdTo5E09ptCi5c5Nrs+WubmHMmSKo/3ID+HQ/rtv85VjJIki3TxtIJdrfO7+FWpYTfXsV3zpL9fl45sdB3vOcm5Fbaiqd1cOv8x6b5N0sS5zhWy7rZKV1aBYVKvW6Rb/pTuXiud939SSBEd2UvLapUue0fqd2/hXcjdCagljf7IOAO1aXiQ3rquq6x5djr/En1YH/51jp+thl2kCXOk8FYlm9dyuPOszdIW+BY87rqacfhzhTeWOWPQQ1Kri1zH9v3t3B6ef7Vmq4ukQQ8KAACgKqIACQDwnOZDpLvXOLd0hhaxKs3L17kt8sZ50uXvlazw5uMvXfGBc27zC5z3KkxoE2cud68p9NxHSZq7+YgOniqkYU1Jlcd7kFf0LNetyF2vlyKKsWLOL9j52v3uKbxoF9LAee7nSDediCsSi0WKOk8a9W7xfu2VUXhL5zmGPW70zP2tVunCZ6Tb/pLaXlZ0ITeortTrNunudQW2Ng9uXVcPjnBtqvPS/B06mnyOn68GnaU7lksXPlt0IdJilZr0la79Uhr3jbNIWhpxfzvP4zyj6SCp/eVnn2f1ksZ85TyztrBVm/6hziMSxnxVMVYVAwAAeIDlHLbBnMP+GQBAtXR0s7PZTUqClJ0mBYU7i1qNezs78JaFjGRnAe70ESn1uOQTIAXVkeq0lup3POt0h8Ohi95cph1HTxtj39zWV72allFnWk+/B2s+dt77jF63OF+jJDJTpH3LpZMxUlaqc8VY3fZSRE/n+YuofrLTpf0rpeTDzvMXrd5ScD3nEQYNuxW5wtbhcOi+rzdqTvRhYyzE31s/39VfzeoEl01+x/dI8VucuWUmOztJB9dzfs/mb5RVGpu/c54Xekana0renT47w3mUQcIuKTNJ8qvhLJ426ev8hxQAAFA5LXjceVSLJNVo4PwH0qqtFJ38KEACAOBi8c5juvGzNUbcrUlNfX9HP1lK0zEXgCRnU6erPliurYeTjbHW9UL00139z60pDQAAgNkoQBYLW7ABAMjjgyUxLvEdg1tQfATOUYCvl/53Qw/VDso9ImFn/Gk9+fMWE7MCAABAeaEACQDAv9btO6VVe08accu6wbqgTV0TMwKqjkY1A/TO2G4uTWm+XXdQ36w9YF5SAAAAKBcUIAEA+Nc7f+52iW8d2ExWK6sfgbLSt3lt/d8w16YxT/68RTuOJhcyAwAAAFUBBUgAACRtOZSkxTtzm7c0qhmgy7s2MjEjoGq6c3ALDWyV2xgmI9uuO79cr5TMHBOzAgAAgCdRgAQAQNI7f+5xie8Y3Fw+XvwxCZQ1q9WiN67tovo1cjs/xyak6pEfNuscmiMCAACgAuNvVgCAam9X/GnN33rUiOvV8NNV3SNMzAio2sKCfPXuuK7yznPEwS/Rh/Xlqv0mZgUAAABPoQAJAKj23l3suvrx1oHN5e/jZVI2QPXQPTJMD41o4zI29Zdt2nIoyaSMAAAA4CkUIAEA1dre46n6JfqwEdcO8tV1vRqbmBFQfUwa0FTD2tUz4iyb8zzI5IxsE7MCAABAWaMACQCo1t5fskf2PMfO3TygqQJ9vc1LCKhGLBaLXr2qsyJqBRhj+0+m6cFvN3EeJAAAQBVCARIAUG0dSkzXD+sPGXENf2+N7xNpYkZA9RMa6KN3x3aTj1fueZDztx7VzJX7TMwKAAAAZYkCJACg2vpwaYxy8ix/nNi/qUL8fUzMCKieOjeuqcdHtnMZe+637YpNSDEpIwAAAJQlCpAAgGrpWHKGvl5zwIiDfL10Y78o8xICqrkb+kZqePvc8yAzsu26/5to5djsJmYFAACAskABEgBQLX20LFZZObmFjev7RqpWkK+JGQHVm8Vi0fNXdFR4sJ8xFn0gUe8ujjExKwAAAJQFCpAAgGon4XSmvshzvpyft1WTzmtmYkYAJKl2sJ9evqqjy9hbf+5W9IFEcxICAABAmaAACQCodj5cGqOM7NzVj9f1aqI6IX5FzABQXs5vU09jezcxYpvdofu/2aiMbJuJWQEAAOBcUIAEAFQrx05naOYq19WPdw5ubmJGAPJ77OK2iqwdaMSxCal6/Y9dJmYEAACAc0EBEgBQrXywJNZl9eO43pGqW8PfxIwA5Bfk563XrukiqyV37KNlsdp0MNG0nAAAAFB6FCABANVGfLLr6kd/H6tuH8zZj0BF1D2ylm4+r6kR2x3Sg99tcmkeBQAAgMqBAiQAoNp4f0mMS/FifJ9I1Q1h9SNQUf3fsNYuW7F3HD2tD5bSFRsAAKCyoQAJAKgWjiZl6KvV+404wMdLtw3i7EegIgvw9dILo127Yr/9527tjj9tUkYAAAAoDQqQAIBq4d3Fe1xWP97QN1LhwXS+Biq6fs3DdV2v3K7Y2TaHHvx+k+x2h4lZAQAAoCQoQAIAqry446malWf1Y6Cvl24dyNmPQGXxyMVtVD9Ps6gN+xP19ZoDJmYEAACAkqAACQCo8qb9sUs5eVZL3dg/SrVZ/QhUGjX8ffTs5R1cxl7+fYdOpmaZlBEAAABKggIkAKBK23IoSb9EHzbimoE+nP0IVEJD29XT0Lb1jDgxLVsvz99hYkYAAAAoLgqQAIAq7eXfd7rEdw1uoRr+PiZlA+BcPHVpO/n75P74+vWaA1q//5SJGQEAAKA4KEACAKqs5THH9deuBCNuEOqv8X0jTcwIwLloHBaou4e0cBl74qctstGQBgAAoEKjAAkAqJIcDodemu+6+vH+oa3k7+NlUkYAysItA5upWXiQEW89nKyZK/eZmBEAAADOhgIkAKBKmr/lqKIPJBpxi7rBGt2tkXkJASgTft5emnJZe5exaQt26hQNaQAAACosCpAAgConx2bXKwtcVz9OHt5a3l78sQdUBQNb1dHFHesbcXJGjt5YuMvEjAAAAFAU/iYGAKhyvlt3ULEJqUbctUlNXdiuXhEzAFQ2j17cVn7euT/Kzly1X7vjT5uYEQAAAApDARIAUKVkZNv0xsLdLmMPjWgji8ViUkYAPCGiVqBuGdDMiG12h577bbuJGQEAAKAwFCABAFXKJ3/v1dHkDCMe3LqO+jSrbWJGADzljsHNVSfEz4iX7EzQ4p3HTMwIAAAA7lCABABUGceSM/Tu4j0uY5OHtzYpGwCeFuTnrQfzfcafm7td2Ta7SRkBAADAHQqQAIAq45Xfdyoty2bEV3WPUPuGoSZmBMDTruwWoQ6NahjxnmMp+mrVfhMzAgAAQH4UIAEAVcLmg0n6bv1BIw709WL1I1ANWK0WPTGyncvYW4t2KyUzx6SMAAAAkB8FSABApedwODT1161yOHLH7hrSQvVq+JuXFIBy07tZbV3Uob4Rn0jN0ifL9pqYEQAAAPKiAAkAqPR+23xUa+JOGXGjmgG6+bymJmYEoLz9d3hreVlzu91/tCxWJ1IyTcwIAAAAZ1CABABUahnZNj3/23aXsUcvbit/Hy+TMgJghuZ1gnV19wgjTsnM0buLY0zMCAAAAGdQgAQAVGqf/L1XhxLTjbhXVJgu7li/iBkAqqr7hraUr3fuj7czV+7TwVNpJmYEAAAAiQIkAKASi0/O0LuL9xixxSI9eWk7WSyWImYBqKoahAZoYr8oI86y2fXGwt3mJQQAAABJFCABAJXYK7/vVFqWzYiv7h6hDo1CTcwIgNnuGNRcIX7eRvzD+oPaFX/axIwAAABAARIAUCltOpio79YdNOIgXy/9d3hrEzMCUBHUCvLVbYOaGbHdIb3+xy4TMwIAAAAFSABApeNwODT1l20uY3ed30J1Q/xNyghARXLTeU0VHuxnxPO2HNW2w8kmZgQAAFC9UYAEAFQ6v246orX7Thlx47AA3dS/qYkZAahIAn29dXueVZCS9OYiVkECAACYhQIkAKBSScvK0YvzdriMPXpRW/n7eJmUEYCK6Po+kaoTkrsK8vet8dp6OMnEjAAAAKovCpAAgErljYW7dSgx3Yh7Nw3TiA71TcwIQEXk7+OlOwY1dxmjIzYAAIA5KEACACqNLYeS9PGyWCP2slr05KXtZLFYTMwKQEU1tncT1auRuwryj23x2nKIVZAAAADljQIkAKBSyLHZ9cgPm2V35I5NOq+p2jcMNS8pABWav4+X7hzcwmXsjYWcBQkAAFDeKEACACqFGSv2aXOelUuNwwJ039CWJmYEoDK4tmdj1a/hb8QLtx/TpoOJ5iUEAABQDVGABABUeAdPpWnagp0uY89e3lGBvt4mZQSgsvD38dJdQ1zPgnznzz0mZQMAAFA9UYAEAFRoDodDT/68VWlZNmNsVJeGGtSqjolZAahMrsm3CnLBtnjtij9tYkYAAADVCwVIAECF9tvmo/pzxzEjDg3w0ROXtDMxIwCVjZ+3l24d2Mxl7L3FrIIEAAAoLxQgAQAVVlJatp6as9Vl7LGL2yo82K+QGQDg3phejRUW5GvEc6IPa9+JVBMzAgAAqD4oQAIAKqwX5+/Q8ZRMI+7TLExX94gwMSMAlVWgr7duPq+pEdsd0gdLY0zMCAAAoPqgAAkAqJBW7z2pWav3G7Gvt1XPX9FRFovFxKwAVGbj+0YqxD+3edV36w7qaFKGiRkBAABUDxQgAQAVTmaOTY/+uNll7O4hLdSsTrBJGQGoCmr4+2hC3ygjzrY59L+/Ys1LCAAAoJqgAAkAqHA+WBKrPcdSjLhl3WDdPqi5iRkBqCpu7B+lAB8vI/5q9T6dyHPUAwAAAMoeBUgAQIWy51iK3s3XnfaF0R3l680fWQDOXe1gP13Xq4kRZ2Tb9dk/ceYlBAAAUA3wtzkAQIVhtzv06I+blWWzG2NjezdRj6gwE7MCUNXcOrCZfL1yfwyesSJOyRnZJmYEAABQtVGABABUGN+uO6DVe08acZ0QPz00oo2JGQGoiuqH+uvK7hFGfDojR1+s2GdiRgAAAFUbBUgAQIWQcDpTz83d7jL29GXtFRrgY1JGAKqy2wc1k9WSG3/y916lZeWYlxAAAEAVRgESAFAhPPPrNiVn5P7l/4I2dXVRh/omZgSgKousHaTLOjc04pOpWfpmzQETMwIAAKi6KEACAEy3eOcxzYk+bMSBvl6aenkHWSyWImYBwLm5c0gLl/ijZXuVnecMWgAAAJQNCpAAAFOlZeXo8R+3uIz998LWalQzwKSMAFQXreqFaGjbekZ8KDFdv+T5xxAAAACUDQqQAABTvf7HLh1KTDfiThGhmtAvyryEAFQrdwxu7hJ/sDRGdrvDpGwAAACqJgqQAADTbDmUpE/+3mvEXlaLXhjdUV5Wtl4DKB/dI2upV9MwI94Vn6I/dxwzMSMAAICqhwIkAMAUOTa7Hvlhs/IuNJp0XlO1bxhqXlIAqqX8qyDfW7JHDgerIAEAAMoKBUgAgCmmL4/T5kNJRtw4LED3DW1pYkYAqqvBreqoTf0QI16/P1Fr4k6ZmBEAAEDVQgESAFDuDp5K02t/7HIZe/byjgr09TYpIwDVmcViKbAK8v0le0zKBgAAoOqhAAkAKFcOh0NP/rxVaVk2Y2xUl4Ya1KqOiVkBqO5GdmygxmEBRrx4Z4K2H0k2MSMAAICqgwIkAKBczd18xKXBQ2iAj564pJ2JGQGA5O1l1a0DC3bEBgAAwLmjAAkAKDdJadmaMmeby9hjF7dVeLCfSRkBQK6ru0coPNjXiH+JPqz9J9JMzAgAAKBqoAAJACg3L87foeMpmUbcp1mYru4RYWJGAJDL38dLN/ZvasR2h/TRslgTMwIAAKgaKEACAMrF6r0nNWv1fiP29bbq+Ss6ymKxmJgVALi6vk+kgv1yG2J9s/aAEk5nFjEDAAAAZ0MBEgDgcZk5Nj3ywyaXsbuHtFCzOsEmZQQA7oUG+GhcnyZGnJlj1/Tle03MCAAAoPKjAAkA8LgPlsQqJiHViFvWDdbtg5oXMQMAzHNz/6by9cr9MfnzFft0OiPbxIwAAAAqNwqQAACP2nMsRe8u3uMy9sLojvL15o8gABVT3Rr+urJ77vm0pzNy9OWq/UXMAAAAQFH42x8AwGPsdoce/XGzsmx2Y2xs7ybqERVmYlYAcHa3DWwma54jaj/5e68ysm3mJQQAAFCJUYAEAHjMt+sOaPXek0ZcJ8RPD41oY2JGAFA8UeFBuqhjAyNOOJ2pH9YfMjEjAACAyosCJADAIxJOZ+q5udtdxp6+rL1CA3xMyggASuaOfGfVfvhXjGx2h0nZAAAAVF4UIAEAHjH1121Kzsgx4qFt6+qiDvVNzAgASqZDo1ANbFXHiPedSNO8LUdMzAgAAKByogAJAChzi3ce0y/Rh4040NdLT4/qIIvFUsQsAKh48q+CfH9JjBwOVkECAACUBAVIAECZSsvK0eM/bnEZ+++FrdWoZoBJGQFA6fVpFqYujWsa8dbDyVq2+7h5CQEAAFRCFCABAGXq9T926VBiuhF3igjVhH5R5iUEAOfAYrHojsEFV0ECAACg+ChAAgDKzJZDSfrk771G7GW16IXRHeVlZes1gMprWNt6al4nyIhXxJ7Qhv2nTMwIAACgcqEACQAoEzk2ux75YbPyNoiddF5TtW8Yal5SAFAGrFaLbs93FuQHS1kFCQAAUFwUIAEAZWL68jhtPpRkxI3DAnTf0JYmZgQAZWdUl0ZqEOpvxL9vjdeeY6dNzAgAAKDyoAAJADhnhxPT9dofu1zGnr28owJ9vU3KCADKlq+3VZMGNHMZ+3BprEnZAAAAVC4UIAEA5+zpX7YqLctmxJd1bqhBreqYmBEAlL0xPRurZqCPEf+08ZAO52m6BQAAAPcoQAIAzsnCbfH6fWu8EYf4e+uJS9qZmBEAeEaQn7cm9I0y4mybw6XxFgAAANyjAAkAKLW0rBw9NWery9hDI9qoToifSRkBgGdN6BelAB8vI561er9OpWaZmBEAAEDFRwESAFBqby3ao0N5th92bVJTY3s1MTEjAPCssCBfjenV2IjTsmyasSLOvIQAAAAqAQqQAIBS2Xn0tD5eltuAwctq0XOXd5TVajExKwDwvEkDmsk7z//rpi+PU1pWjokZAQAAVGwUIAEAJWa3O/TYj5uVY3cYYzf2i1K7hjVMzAoAykejmgEa1aWRESemZevr1QdMzAgAAKBiowAJACixb9cd0Np9p4y4Qai//jOslYkZAUD5un1QM5f442Wxysqxm5QNAABAxUYBEgBQIidSMvXCvB0uY09d2l7Bft4mZQQA5a9lvRANa1fPiA8nZWhO9GETMwIAAKi4KEACAErkhXk7lJiWbcQXtKmr4e3rFTEDAKqmOwY3d4k/WBoje56jKQAAAOBEARIAUGwrY0/ou3UHjdjfx6opl7WXxULjGQDVT7cmtdS7aZgR7zmWogXb4k3MCAAAoGKiAAkAKJasHLse/2mLy9h9F7RS47BAkzICAPPdOaSFS/z2n7vlcLAKEgAAIC8KkACAYvloWaz2HEsx4lb1gjVpQFMTMwIA8w1sGa6OjUKNeOvhZC3afszEjAAAACoeCpAAgLPafyJNby3a7TL27OUd5ePFHyMAqjeLxaJ7L2jpMvbmIlZBAgAA5MXfHAEARXI4HHpyzhZl5tiNsWt6RKhXnnPPAKA6G9q2rto1qGHEmw8lacnOBBMzAgAAqFgoQAIAijR/y1GXv0jXCvTRwxe1NTEjAKhY3K2CfINVkAAAAAYKkACAQqVk5mjKL1tdxh65uK3CgnxNyggAKqYL29VTm/ohRhx9IFFLd7EKEgAAQKIACQAowmsLdik+OdOIe0WF6apuESZmBAAVk9XKWZAAAACFoQAJAHBry6EkTV++14i9rRY9e0UHWa0WE7MCgIprRPv6alUv2Ig37E/UElZBAgAAyNvsBAAAFY/N7tBjP26WPc/CnVsGNlOreiGFT0K5iY2N1cqVKxUfH6/s7Gw1bNhQbdq0UY8ePcxOza3ExEQtXLhQe/fulZeXl1q3bq3zzz9fAQEBJbpPdna2Xn75ZWVnZyssLEz33nuvhzIGSsdqteie81vqnlkbjLFpC3ZqcKs6slj4xxsAAFB9sQISAFDAV6v3K/pgkhFH1ArQvee3LGIGysM333yjDh06qHnz5ho3bpz+7//+Tw899JDGjx+vnj17qkWLFnrvvffKdMvnsWPHFBYWJovFYnxFRUUVe/6LL76oRo0a6eqrr9aDDz6oBx54QJdccokaN26szz//vES5vPHGG3r88cf19NNPy9vbc/+GGhcX5/LrnTJlSonvMX36dJd7LFmypNBrp0yZ4nJt/i8fHx+FhISoSZMm6tWrl8aNG6eXX35ZK1eulN1uL/S+hVmyZInL/adPn17ie6BwIzs2cDkLcsuhZM3fctTEjAAAAMxHARIA4OLY6Qy9PH+Hy9jUUe0V4OtlUkZIT0/XmDFjdO2112rr1q2FXhcTE6O77rpLw4cPV0pKSpm89n/+8x+dOnWqVHPvv/9+PfLII0pLSyvw3IkTJzRhwgS99dZbxbrXoUOHNHXqVElS165ddfvtt5cqp8ooJydHKSkpOnDggNasWaOvvvpKDz30kPr27avGjRvriSeeUEIC23wrCqvVov9e2Npl7NUFO2WzcxYkAACovihAAgBcPDd3u05n5BjxiPb1dX6beiZmVL05HA6NHTtWs2fPNsYCAwN1ww036O2339ZHH32khx9+WC1atDCe/+OPPzRmzBjZbLZzeu3ff/9ds2bNKtXcRYsW6Y033jDiESNG6P3339ebb76pXr16GeOTJ0/Wzp07z3q/Bx54QCkpKbJYLHrvvfdktVbdH2EiIyPVvHlz46tp06YKCwtzu+rz8OHDevbZZ9WqVSt98sknJmQLdy5oW1ddm9Q04piEVP244ZB5CQEAAJis6v70DgAosb93H9fPGw8bcZCvl566rJ2JGeG9997TTz/9ZMRdu3bVjh07NGPGDN19992aNGmSXnjhBW3btk2TJ082rps7d65LAbCk0tLSdMcdd0iS/Pz8SrTtWpJeffVV4/Fdd92lefPm6fbbb9e9996rFStW6KKLLpIkZWVl6c033yzyXosXLzYKsDfeeKP69OlTolwqmyVLlmjPnj3GV2xsrE6cOKHs7Gzt27dPs2fP1s033+xyhmZiYqImTZrk8j0A81gsFk0e7roK8vU/dikz59z+UQAAAKCyogAJAJAkZWTb9MTPW1zG7h/WSg1CS9YoBGUnMzNTzz//vBHXqVNH8+fPV+PGjQtc6+Pjo5dfflnXX3+9Mfb8888rKSmpwLXFMWXKFO3d6+yC/vDDDysyMrJEeS9evFiSc7Vm/jMUrVarXnzxRSOeP39+offKzs7W3XffLUmqWbOmy7zqqEmTJrrmmmv08ccfa//+/brppptcnn/11Vf1wQcfmJQd8urXPFzntQg34kOJ6Zq95oCJGQEAAJiHAiQAQJL0wdIY7T2easRtG9TQxH5R5iUE/fnnnzp8OHdF6uTJk1W3bt0i57zwwgvGVt2TJ0+WqsFIdHS0Xn/9dUlSixYt9Mgjj5Ro/p49e5SZmSlJ6tKli8LDwwtc06lTJ9WvX1+StHfvXrfnRErSm2++qW3btkmSnn32WdWpU6dEuVRl4eHh+uSTTwqco3nPPfdoz549JmWFvP6bbxXkW4v2KC0rp5CrAQAAqi4KkAAA7T2eqvcWxxixxSI9f0UHeXvxx4SZ8ndOvvLKK886JyIiwmWL8vfff1+i17Tb7br11luVk+Mskrz33nvy8/Mr0T0SExNd8ilM3pWceeeccfjw4WrbeKYk7rnnHpeVkDk5OXruuedMzAhndGlcUxe2yz1D93hKpmYs32diRgAAAObgb5YAUM05HA498dMWZdnsxtjYXk3UtUktE7OCJMXFxRmPg4OD1axZs2LN69Spk/H4n3/+KVEX63fffVerV6+WJF177bUaNmxYseeekbdgefr06UKvy/ucv79/gef/+9//6vTp07JYLHr33Xfl5UUn9sK89NJLLu/hzJkzdfToURMzwhkPXNhaFktu/MHSGCWlZ5uXEAAAgAkoQAJANTcn+rD+3nPciMODffXg8DYmZoQz8hYOQ0NDiz2vZs2axmO73a4tW7YUfnEehw4d0mOPPSZJqlGjhrENu6QaNmxoPN61a5fbazIzM7Vvn3MlWEBAgEvOknP155kO3BMnTlTfvn1LlUt1ER4errFjxxpxTk5OgRW0MEfr+iG6vEsjI05Kz9bHy2JNzAgAAKD8UYAEgGosKT1bz/y63WXs8ZHtFBroY1JGyCtvl+OMjIxiz0tPT3eJt2/fXsiVru6++25jVeKzzz6rBg0aFPs182rYsKGxvTomJkZ//PFHgWs+++wzI8+ePXvKas39kSQnJ4fGM6WQf7Xq0qVLTcoE+f1naEt5W3OXQX68bK/ik4v/mQYAAKjsKEACQDX26u87dTwl04j7Na+tUV0aFjED5Slvw5WTJ08Wu6P1me7VZ8TGnn211Y8//qiffvpJktStWzfdeeedxU/UjfHjxxuPb731Vq1du9aI58+f79LY5oYbbnCZ+9Zbb2nr1q2SnIXQszXegVPesz8lacOGDSZlgvwiawfp2p65Z56mZ9s0bcFOEzMCAAAoXxQgAaCa2nggUTNX5TZD8PWy6pnLO8iS97AymKp79+7GY4fDoT///POsc7KysrRs2TKXseTk5CLnnD59Wvfcc48kyWq16v333z/n8xYfeOABYwVlXFycevbsqQYNGig8PFwXXXSR0XSma9euLgXII0eOaMqUKZKcHbQrQuOZp59+WhaLpURfN954Y7nnGRkZ6bKS9Pjx40VcjfJ239CWCvLN/Vx9u+6gth8p+rMJAABQVVCABIBqKMdm12M/bpbDkTt2x+Dmal4n2LykUMCwYcNcCsKvv/66HHl/09z47LPPdOLECZexohrBSNKjjz6qQ4cOSZJuu+029erVq5QZ5woLC9Ovv/7qsorz6NGjLrm1bt1aP/30k3x8crf8n63xTGpqqv7++2/98ssvWrFihTIzMwUni8WikJAQIz558qSJ2SC/uiH+un1QcyN2OKTnf9t+1s80AABAVeBtdgIAgPL3+Yp92no4d+VNVO1A3TG4eREzYIYWLVrokksu0S+//CJJWrZsmZ588kk988wzbq9fs2aNJk+eXGA8/5mQea1atUrvvfeeJKlevXp6/vnnyyBzp27dumnbtm169dVXNWfOHMXFxcnLy0utWrXS1VdfrXvvvVeBgYHG9X/99Ze++uorSdKECRPUr18/47nExEQ98sgjmj59ust5mMHBwbr33nv15JNPunTfLku1atVSWFhYieacPn1ax44d80g+RQkODja26p+t8IzyN2lAM325ar+O/nv+47Ldx7VkV4KGtOaYAQAAULVRgASAauZoUkaBs8eeubyD/H3ObcstPOPVV1/VkiVLXJrDbNiwQffff7969Oghf39/xcTE6Ouvv9a0adOUlpYmb29veXt7G4W64GD3K1tzcnJ06623ym63S5KmTZtWoBv1uQoPD9eLL7541kYyOTk5uuuuuyQ5G8+89NJLxnOJiYkaPHiwoqOjC8xLSUnR888/r7Vr12ru3Lny9i77H23uvfdeY1t4cU2fPt2Ubdh5i441atQo99dH0QJ8vTR5eGs98G3u9/Lzc7drQItweXuxMQkAAFRd/KQDANXM1F+3KjXLZsSXdW6oAS3rFDEDZmrVqpW++uorl47Yc+fO1dChQ1WzZk35+/urffv2euaZZ5SWliZJeuedd1y2NRdWVJw2bZo2bdokSRoyZIjGjRvnuV/IWbz99tvasmWLJOmZZ55xaTxz3333GcXH888/X5s3b1ZGRoZWrVqlzp07S5IWLFigF154ofwTr0DsdrtLAbKkqzZRPq7o2kjtG+YWh3cfS9HstQdMzAgAAMDzKEACQDWyeMcx/bb5qBGH+Hvr8UvampgRiuOSSy7RX3/9pW7duhV5XVhYmGbPnq3rr7/epRAVHh5e4NrY2Fg9/fTTkiRfX19jG7YZjh49aqww7Ny5s+644w7jubi4OM2cOVOS1LBhQ/3666/q0KGD/Pz81KtXL/3222/G1uszK0Crq3379rmcJ+ju9x3ms1otemyk6/93py3YpaS0bJMyAgAA8DwKkABQTaRn2fTknC0uYw8Ob626If4mZYSS6NGjh9auXasFCxboP//5j4YMGaKOHTuqW7duuvzyy/XBBx8oJiZG11xzjbZv3+4yt0uXLgXu98ADDxhnQ06ePFlt2rQpj1+GW5MnT1ZycrLbxjM///yzsUX8jjvucFkJKjmLkmPHjpUkJSUlaeHCheWXeAWzYsUKlzhvF3VULP2ah2to23pGfDI1S6/9sbOIGQAAAJUbZ0ACQDXxzuLdOnAytxlJ54hQje0daWJGKCmLxaJhw4Zp2LBhRV63atUql7hnz54Frtm7d6/x+PPPP9fXX39d5D3PdMk+87hFixZGPGzYML3//vtFzi/MsmXLjBWON9xwg/r37+/y/Lp164zHvXv3dnuPPn366LPPPpMkrV+/XpdddlmpcqnsFixY4BIPGjTIpExQHI+PbKu/diUoy+YssH+xcp/G9Gqitg04uxMAAFQ9FCABoBrYHX9a//sr1oitFum5KzrKy2oxMSt4yrx584zH7du3V7169Yq4WjpwoGTnz+Xk5CgmJsaIO3ToULIE/2Wz2YzGM6GhoS6NZ85ISEgwHkdERLi9T97xvNdXJwkJCZo9e7YR+/j4aPDgweYlhLOKCg/SrQOb6Z3FeyRJdof01M9bNfu2PrJY+H8zAACoWtiCDQBVnMPh0GM/bVG2LfdsuAn9otShUaiJWcFTjhw5ovnz5xvxzTffbGI2RXvnnXe0efNmSc7GM+4KpWe2X0sytoznl3fcZrO5vaaqe/jhh42u55I0YcIE1alDc6mK7s4hzdUwNPcYjNVxJzUn+rCJGQEAAHgGBUgAqOK+X39Iq/eeNOJ6Nfz0f8NamZgRPOmRRx4xinCBgYEaP3682+s2btwoh8NR7K+823kjIyNdnvvpp59KnGd8fLyeeuopSc7GM3feeafb6/J2ct6/f7/ba/Ku4KyOnZ/ffvttffrpp0bs7e2tRx55xMSMUFyBvt56bGQ7l7Hnf9uulMwckzICAADwDAqQAFCFnUrN0vO/uTYkeerS9grx9zEpI3jSzJkz9fnnnxvx1KlTK2wn5MmTJyspKclt45m8OnbsaDz+/vvv3V7z3XffGY87depUtolWYCdOnNCkSZN07733uoy/++67atasmUlZoaQu7lhf/ZrXNuL45Ey9/eduEzMCAAAoexQgAaAKe3HeDp1MzTLiwa3r6KIO9U3MCCWVnZ2tp556SgcPHiz0mszMTE2dOlUTJ06Uw+Hcat+rVy/95z//KacsS+bvv//WF198IUkaP358gcYzeY0cOdJ4PHv2bG3cuNHl+d9++03//POPJMnPz08XXHBB2SdcgRw4cEDffvutJk2apMaNG+uTTz5xef7hhx/WrbfealJ2KA2LxaIpl7V3OZP307/3KiYhxcSsAAAAyhZNaACgiloVe0Kz1+ZuTfXztmrqZR1oblDJ2Gw2TZ06Vc8884y6d++ufv36qWXLlgoODtaJEye0bds2/fLLLy7NVzp06KC5c+cWuqrQTDabTXfffbckZ+OZl19+ucjrO3furKFDh2rhwoXKzs7WwIEDddddd6lly5aKjo7WBx98YFw7ceLEKnHu4eDBg+Xtnfsjmt1uV3JyspKSkpST435rbq1atTRt2jTdeOON5ZUmylCreiGa2C9Kn/zt7E6fbXPo6V+2acaNPfl/NgAAqBIoQAJAFZSZY9OjP252Gbv3gpZqUjvQpIxwrhwOh9auXau1a9cWed2IESM0Y8aMCrv1+t1331V0dLQk5xbxs3XolqSPPvpIffr0UXx8vE6fPq0XX3yxwDXt2rU7azGzsti3b1+xr23YsKFuvvlm3XvvvRX29xzFc9/Qlvp542EdT8mUJP21K0G/b43XCFatAwCAKoAt2ABQBf1vaaxiElKNuFW9YN0ygDPhKiMfHx9NmDBBERERhV5jsVjUp08fff3115o3b57q1q1bjhkW37Fjx/Tkk09Kcp7VeNdddxVrXlRUlJYtW1boVu3LLrtMS5YsUY0aNcos14rEy8tLgYGBatSokXr06KHrrrtOL730klauXKmDBw9W6LM+UXw1/H308EVtXMaemrNFpzOyTcoIAACg7FjOnBVVCqWeCADwnL3HUzX8jb+UlWM3xr67va96RFW/7sBVzc6dO7Vjxw7Fx8frxIkTCg0NVYMGDdSzZ88iC5QVxbJly7Ro0SJJ0qhRo9S1a9cS32PDhg1auXKlTp06pTp16mjQoEFq1Yqu7qga7HaHrvlwhdbuO2WM3dA3UlNHdTAxKwAAUKQFj0vJR5yPazSQLnzW3Hw8r1Tnw1CABIAqxOFw6PpPVumfPSeMset6NdELozsWMQsAUFHsjj+ti99apmyb80dti0X67vZ+6h5Zy+TMAACAWxQgi4Ut2ABQhfy08ZBL8TE82FcPj2hTxAwAQEXSsl6I7hjcwogdDumRHza5rGoHAACobChAAkAVcSo1S8/8ut1l7IlL2ik00MekjAAApXHn4OZqVifIiHfFp+jDpTEmZgQAAHBuKEACQBXx4rwdOpmaZcQDWobrss4NTcwIAFAa/j5eeuEK16Mz3v5zj/YcSzEpIwAAgHNDARIAqoBVsSc0e+0BI/bzturZyzvIYinV8RwAAJP1blZb1/VqbMRZNrse/C5aNjvHsAMAgMqHAiQAVHKZOTY9+uNml7F7L2ipyNpBhcwAAFQGD1/UVnVD/Ix4/f5EffbPXhMzAgAAKB0KkABQyf1vaaxiElKNuFW9YN0yoJmJGQEAykJogI+ez7cV+5Xfdyo2ga3YAACgcqEACQCVWGxCit5evMdl7PkrOsrXm/+9A0BVMLRdPV3RtZERZ+bY9eB3m9iKDQAAKhX+hgoAlZTd7tBD329SVo7dGLuuVxP1iAozMSsAQFl76tJ2Cg/O3Yq9dt8pTV8eZ15CAAAAJUQBEgAqqZmr9mlN3CkjDg/208Mj2piYEQDAE2oG+ur5Kzq4jL3y+w66YgMAgEqDAiQAVEIHT6XppXk7XMaeGdVeoYE+JmUEAPCkC9vX12WdGxpxRrZd/5m9wWUVPAAAQEVFARIAKhmHw6FHftis1CybMXZRh/q6qGMDE7MCAHja05e1d+mKveVQst5ctMvEjAAAAIqHAiQAVDLfrTuoZbuPG3FogI+eHtXexIwAAOWhVpCvXrm6s8vY+0titCbupEkZAQAAFA8FSACoROKTM/TMr9tcxp68pJ3qhviblBEAoDwNalVHE/tFGbHdId0/e6NOZ2SblxQAAMBZUIAEgErC4XB2vU7OyDHGBrWqo9HdGpmYFQCgvD18URu1qBtsxAdPpWvKnG1FzAAAADAXBUgAqCRmrzmgJTsTjDjEz1vPj+4oi8ViYlYAgPLm7+OlN67tIh+v3P//f7/+oOZuOmJiVgAAAIWjAAkAlcCBk2kFt15f2k6NagaYlBEAwEwdGoXqgQtbu4w9+uNmHU3KMCkjAACAwlGABIAKzm536L/fRrt0vR7atq6u6h5hYlYAALPdMqCZejcNM+Kk9Gw98O1G2e0OE7MCAAAoiAIkAFRw05fHadXe3A6ntQJ92HoNAJCX1aLXru2iEH9vY+yfPSf0/tIYE7MCAAAoiAIkAFRge46l6KX5O1zGnr28I12vAQCSpEY1A/Ts5R1cxl77Y5fW7TtlUkYAAAAFUYAEgAoqx2bXA99GKzPHboxd2rmhRnZqYGJWAICKZlSXRro6z7EcNrtD987aoKT0bBOzAgAAyEUBEgAqqA+Wxij6QKIR1wnx09TL2puXEACgwnp6VHs1qxNkxIcS0/XID5vkcHAeJAAAMB8FSACogNbtO6XXF+52GXvpyo6qFeRrUkYAgIos0Ndbb1/XVb5euT/e/7b5qL5ctd/ErAAAAJwoQAJABZOUnq17Z22QLU8X02t7NNb5beqZmBUAoKJr3zBUj41s6zI29Zdt2nQw0ZyEAAAA/kUBEgAqEIfDoYe/36RDienGWLPwID15aTsTswIAVBY39I3UsHa5/2CVZbPrjpnrlZiWZWJWAACguqMACQAVyJer9mvelqNG7Otl1dtjuyrIz9vErAAAlYXFYtGrV3VW47AAY+xQYrrun71RdjvnQQIAAHNQgASACmLH0WRN/XWby9hjI9uqfcNQkzICAFRGoYE+en9cd/l65/6ov3hngt5bssfErAAAQHVGARIAKoDTGdm6c+Z6ZeXYjbEL29XTDX0jTcwKAFBZdWgUqqmXtXcZe+2PXfpnz3GTMgIAANUZBUgAMJnD4dCD321S7PFUY6xRzQC9fFUnWSwWEzMDAFRm1/ZsrKu6Rxix3SHdO2uDjiZlmJgVAACojihAAoDJPl621+XcRx8vi966rqtqBvqamBUAoLKzWCx6ZlQHtakfYoydSM3SnV+uc1lxDwAA4GkUIAHARKtiT+jF+Ttcxh4f2U7dI2uZlBEAoCoJ8PXSB9d3V0ieZmbr9yfq2bnbipgFAABQtihAAoBJjiVn6O5ZG2TL05X0ss4NOfcRAFCmosKD9Oo1nV3GPl+xT9+sOWBSRgAAoLqhAAkAJsjItumWL9Yp4XSmMdaybrBeGN2Rcx8BAGVuePv6un1Qc5exx3/aog37T5mUEQAAqE4oQAJAOXM4HJr83SZFH0g0xoJ8vfT+9d0VlGeLHAAAZWny8NYa2KqOEWfZ7Lp95jodS6YpDQAA8CwKkABQzt5ctFu/RB82YotFeu3aLmpRN9jErAAAVZ2X1aK3x3RVZO1AYyw+OVN3fLmepjQAAMCjKEACQDmaE31Ybyzc7TL24PA2Gt6+vkkZAQCqk9BAH/1vfA8F+noZY+v2ndKUX7aamBUAAKjqKEACQDlZt++k/vtttMvYld0idPugZiZlBACojlrXD9Fr+ZrSfLVqv75ctc+kjAAAQFVHARIAysGu+NO6afpaly1uvaLC9PzoDjSdAQCUuxEdGuie81u4jE2Zs1UrY0+YlBEAAKjKKEACgIcdTkzXhE9XKyk92xhrHBag96/vJj9vryJmAgDgOfcPbaUL2tQ14mybQ7fPXKe9x1NNzAoAAFRFFCABwINOpWbphk9X60hSbofR2kG++vym3qod7GdiZgCA6s5qtej1MV3UvE6QMZaYlq2bp69RYlqWiZkBAICqhgIkAHhIamaObpqxRnuOpRhjQb5emn5jLzUNDypiJgAA5aOGv48+ndhTtQJ9jLHY46m6YyadsQEAQNmhAAkAHpCeZdNN09dow/5EY8zHy6IPx/dQx4hQ8xIDACCfyNpB+nB8D/l45Z5JvCL2hJ74aYscDoeJmQEAgKqCAiQAlLGMbJtu+XytVu09aYxZLNJr13TReS3DTcwMAAD3ejUN04ujO7mMzV57QB8tizUpIwAAUJVQgASAMpSZY9NtX6zT33uOu4w/d3lHXdq5oUlZAQBwdld2j9BdQ5q7jL0wb4d+33rUpIwAAEBVQQESAMpIRrZNd85cr6W7ElzGn76svcb2bmJSVgAAFN8Dw1rr4o71jdjhkO77eoPW7TtZxCwAAICiUYAEgDKQmpmjm2es0aIdx1zGHx/ZVhP6RZmTFAAAJWS1WjTt6i7qnOe84oxsu26avla74k+bmBkAAKjMKEACwDlKSs/W+E9W6Z89J1zGJw9vrUkDmpmUFQAApRPg66WPJvRQ47AAYywpPVs3fLJahxLTTcwMAABUVhQgAeAcHE/J1HX/W6n1ebpdS9IjF7XRXUNamJMUAADnqG6Ivz6/qbdqB/kaY0eTM3TDJ6t0MjXLxMwAAEBlRAESAErpSFK6rv1whbYdSTbGLBbp2cs76LZBzYuYCQBAxdc0PEjTb+ylIF8vYywmIVU3TV+jtKwcEzMDAACVDQVIACiFfSdSdfUHKxSTkGqMeVkteu2azrq+T6SJmQEAUHY6RoTqw/E95ONlMcY2HkjUnV+uV7bNbmJmAACgMqEACQAltH7/KY1+b7kOnso9B8vXy6p3x3bTFV0jTMwMAICyd17LcL1+bRdZcmuQWrIzQffP3iib3WFeYgAAoNKgAAkAJTB/yxFd97+VOpHn/Ct/H6s+ntBDIzrUNzEzAAA855JODTXl0vYuY79uOqLJ30XLThESAACcBQVIACgGh8Ohj5fF6o4v1yszJ3fLWWiAj764ubcGtqpjYnYAAHjehH5Ruvd81wZrP6w/pEd/3EwREgAAFMnb7AQAoKKz2R165tdtmr48zmW8cViAPpvYSy3qBpuTGAAA5ez+Ya2Unm3TR8v2GmNfrzkgHy+rpo5qL0vefdoAAAD/ogAJAEVIzczRfV9v1MLt8S7jnRvX1CcTeig82M+kzAAAKH8Wi0WPXtxW2TaHyz/MfbFyn3y9rXp8ZFuKkAAAoAAKkABQiLjjqbr1i7XaFZ/iMn5hu3p6c0xXBfh6mZQZAADmsVgseurSdsrMsWvW6v3G+Cd/75Wft1WTh7emCAkAAFxQgAQANxbvOKZ7v96g0xk5LuM39W+qx0a2lZeVv1gBAKovi8Wi5y7voGybXd+tO2iMv7ckRja7Qw9f1IYiJAAAMFCABIA87HaH3l28R68t3CVHnvP0vawWPTGyrSb2b2pecgAAVCBWq0UvXdlJ2Ta7ft542Bj/8K9YpWTm6JlRHWTlH+wAAIAoQAKA4XRGth74JloLtrme91g7yFfvjO2mvs1rm5QZAAAVk5fVomlXd1aOzaG5m48Y41+u2q/0LJtevqqTvL2sJmYIAAAqAgqQACBpz7EU3fbFWsUkpLqMd4oI1QfXd1fDmgEmZQYAQMXm7WXVm2O6yN/HS9+vz92O/cOGQ0rLsunN67rIz5tzkwEAqM7450gA1d6CrUd1+bv/FCg+XtU9Qt/c1pfiIwAAZ+HtZdUrV3XSDX0jXcbnbz2qWz5fp/Qsm0mZAQCAioACJIBqy2536LU/dunWL9YpJTO32Yy31aJnRrXXK1d1kr8PKzYAACgOq9Wipy9rrzsGN3cZ/2tXgiZ8ulpJ6dkmZQYAAMxGARJAtZSUnq1bPl+rtxbtdhkPD/bTrFv7aHzfKLp3AgBQQhaLRQ+NaKPJw1u7jK+OO6lrPlihI0npJmUGAADMRAESQLWzK/60Ln/3Hy3accxlvGuTmvr1nvPUMyrMpMwAAKga7hrSQk9d2s5lbGf8aV353nLtjj9tUlYAAMAsFCABVCvzNh/R5e/+o73HXc97vK5XE319ax/VD/U3KTMAAKqWG/s31WvXdJa3NXdHweGkDF31wQqt23fSxMwAAEB5owAJoFqw2R16ef4O3fHleqXlOQjfx8ui56/oqBdGd6RDJwAAZWx0twh9PKGHAn1z/4xNSs/W2I9W6Y9t8SZmBgAAyhMFSABVXmJalm6cvkbvLYlxGa9Xw09f39pXY3s3MSkzAACqvsGt62rWLX1UO8jXGMvMseu2L9Zq5sp9JmYGAADKCwVIAFXa9iPJuvSdv/XXrgSX8Z5RtfTLPeepe2QtkzIDAKD66Ny4pr67o58ahwUYY3aH9PhPWzRlzlbl2OwmZgcAADyNAiSAKmtO9GGNfm+5Dpx07bh5Q99IfTmpj+qGcN4jAADlpWl4kL6/o5/aN6zhMj59eZxumrFWSenZJmUGAAA8jQIkgConx2bX879t172zNig9O/e8R19vq165qpOmjuogX2/+9wcAQHmrG+Kv2bf11ZDWdVzG/9qVoNHv/aO4fE3iAABA1cDfwIH/b+++w6Os8v//v046qQQSIJDQm9IFbKBYEVnFAogF7HVt635W/a1lsf10XfXzWV11XVcXd62rqICuqMiiFFGK0gWlEwIJJY2E1DnfP2YymQmZMGRmMgk8H9eVK/c5c859Tq5LD/e871NwVCkordC105bq1fmbvfIzUuI0/dZTNHFYVph6BgAAJCkxNkqvXTNcN47s5pW/aU+JLn55kb7dtDdMPQMAAKFCABLAUWPD7mKNe3GRFm70/uJyUrc2+uTOkRqY2To8HQMAAF4iI4weuuB4PT1+gKIjjTu/oLRSV7++RG8u3iprbRh7CAAAgokAJICjwudrduuSlxdp+/5Sr/zrRnTVWzeepLTE2DD1DAAA+DJpeGe9dcNJSo2PdudVOawenrlW905fpTKPrVQAAEDLRQASQIvmcFj935yfdetby1Va4b3f43MTB2nqhf0UHclQBwBAc3VS97aaeftI9WqX6JU/fXm2Jr6yWDsLDvqoCQAAWgq+lQNosQ6UV+nWt5br+bm/eOW3T47V+7ecovFDM8PUMwAAcCQ6t43XR78+VaOPb++Vv3pnoS78y0L2hQQAoIUzAeytwqYsQJg5HA4tWrRImzZt0u7du5WamqqsrCyNGjVKCQkJYenTgQMHtGjRIu3cuVN5eXlKSkpS+/bt1blzZw0ePFgxMTGNvre1VsuXL9cvv/yidRu36d9Lt2m/TVBkQqpi2nVTZHyKTujcWq9MHqp2yXFB/KsAAEBTcDisXpq3Uf/71c/y/JoSGWH0+/P76oaR3WSM8X2DeuTn52v+/PnKzs7WgQMH1LFjR/Xt21fDhw8Pcu8bVl1drdWrV2vlypXau3evysrKlJycrMzMTA0fPlyZmbw4BYAW6cuHpKJdzuvkDGn0E+HtT+gd2T/ELlHB7gWA0Kuurtazzz6rF154QTk5OYd8npCQoCuuuEJ/+tOflJqa2iR9WrFihR577DHNnj1bZWVl9ZaJjY3VmWeeqRdffFE9evTw+97FxcV66qmn9NZbb2nHjh0+y6Vl9dDYRx5Uu+QRR9x/AAAQfhERRnee3Uv9M1N097s/qqisSpJU7bB64j8/aVV2of44foDiYw7/NWbHjh36n//5H82cOVMVFRWHfN6zZ0/df//9uvHGG4P+d3gqLi7WH//4R73++uvKzc31WW748OG69957NXHixKC0+/e//10333yzV97UqVP1yCOPBOX+AAAcCWZAAi1MQUGBLrjgAi1atOiwZTMzMzVr1iwNGTIkZP2prq7WAw88oOeee07V1f5tFD9nzhydc845fpe9/vrrlZ2d7Vf5q666Sm+99ZZfZQEAQPO1dW+Jbn1rudbvLvbK79shSa9OGabObeN91v3yyy81adIkFRQUHLadSy65RO+9915AqzR8Wbt2rcaOHavt27f7XWfChAl6++23A+pPbm6ujjvuOOXn53vlE4AEgBBgBqRfmAEJtCBVVVWaOHGiV/Cxc+fOmjx5srp27ao9e/ZoxowZWrp0qSQpOztbF1xwgZYuXaqOHTsGvT8Oh0PXXXed3nzzTXdeXFyczj77bJ166qlq3769KisrtXPnTi1btkxff/21z9mR9fnss890ySWXeM1aiE7vqlbdhyoyKU0R0bGKrSrVoMRirV66yO8gJQAAaP66piXoo1+fqvumr9Knq3a589fvLtaFLy7U85cP1hl92h1Sb/Xq1ZowYYKKi2sDl+eee67OOeccpaSkaP369XrzzTe1b98+SdLHH3+sW265RdOmTQtq//Py8nT22Wd7zXps06aNJkyYoIEDByo+Pl55eXmaN2+evvzyS9VMDJk+fbpiY2MDeqH6m9/85pDgIwAA4cQMSKAF+dOf/qT777/fnb7yyis1bdq0Q96Qv/DCC/rNb37jfpAdO3as/vOf/wS9P/fdd5+eeeYZd/qSSy7Riy++6DPYWVpaqnfeeUcnnXSSBgwY0OC9f/jhB5166qkqLy+XJLVKba+kc36tVt2Husv07ZCkv189TFltnDMgFi1apI0bN+qaa64J9E8DAADNhLVWry3Yoqdm/ySHxzcQY6Q7z+qlu8/upcgI52QMh8OhQYMGac2aNZKc27+8/fbbGj9+vNc9i4qKdPHFF2vevHnuvPfffz9oy58l6eabb9bf//53d3rs2LF65513lJKSckjZRYsWady4cdq/f7877+uvv9aoUaOOuN3PP/9c559/viSpb9++Wr9+vfszZkACQAgwA9K/SgQggZahqKhI3bp1cz+YDhkyREuWLFFUVP0Tme+88069+OKL7vTChQs1YkTw9kZctmyZTj75ZPey6+uuu06vv/76EW8MX5/q6mqdeOKJ+uGHHyRJMSntlH7lHxWVXDvL4VcDMvTMxIF+7QEFAABavkUb9+qOd35QfmmlV/6Inm3150lDlJ7knDU4ZcoU92fPPfecfvvb39Z7v8LCQvXr1087d+6UJPXp00dr165VZGRkwH2tqKhQenq6ioqKJDm3xdmwYYPi430vG585c6Yuvvhid/qmm27Sq6++ekTtlpaWql+/ftq6datiYmI0a9YsjRkzxv05AUgACAECkH6JCHYvAITGW2+95fVW/E9/+pPP4KMkPfHEE14Puc8//3xQ+3PHHXe4g489evTQyy+/HJTgo+TcNL0m+ChJrc+70x18NEa697w+evHKIQQfAQA4hozomaZP7hypAZ28ZxAu2rhPv3phgb7fvE8vvPCCO79z5866++67fd4vJSXFKxi3YcMGffHFF0Hp66ZNm9zBR8m5aqWh4KMkjRs3Tu3a1b5sXbly5RG3O3XqVG3dulWSdP/996tPnz5HfA8AAEKBACTQQsyYMcN93bVrV5199tkNlk9JSdGECRPc6c8//7zeEyAb48cff9T333/vTj/66KOKi4sLyr0l6eW//tV9HddlsFp1cx6ikxgbpdeuHqbbz+wZtGAnAABoOTJT4/XBrado8smdvfLziss18X8/1bJly9x511133WFnM15++eVegcGZM2cGpZ+eL40l58vawzHGqHv37u70ke7huGLFCv35z3+WJHXv3l0PPPDAEdUHACCUCEACLcDBgwf19ddfu9PnnHOOXwG4c889131dXFysBQsWBKU/r7/+uvs6OTlZl156aVDuK0lfzf9Wq1etcqcTBzpPy+6elqAZt4/Q2ce1D1pbAACg5YmLjtQTFw/Q85cPVnxMbYCxZNNyeW4v5fkc5EtiYqJOOeUUd/qzzz4LSh9TU1O90iUlJX7V8yyXnp7ud3sOh0M333yzqqqqJEkvvfRSUF8OAwAQKAKQQAuwfv16VVbW7nd08skn+1XP84Facp4KGQxz5851X48aNUqtWrUKyn3X5hTqhqe8T6CM63aCzuiTro9vH6Ge7RKD0g4AAGj5LhrcSbPuGKne7Z3PB5V7tro/MxGRimp3+FmHkvfzUnZ2dlBOj+7Tp49XENLzRbIveXl5WrdunTt9+umn+93eiy++qKVLl0qSJkyY4LXvIwAAzQEBSKAF+Omnn7zSPXv29Kte165dvZYe1b1PYxQVFWnDhg3udE0wtKioSC+99JJOO+00ZWRkKDY2Vh07dtTpp5+uJ554Qjk5OQ3e95OVORr/12+1d3Ptg3dUakfdPmaIXr9muL5fME9XXHGFevbsqVatWqlNmzbq16+fbrvtNr8e6gEAwNGnZ7tEzbh9hC49oZMq92W78yOS0jT5jR80bdEWHe7QzbrLoz1PjW6syMhI3XLLLe70J598oq+++qrBOvfcc497f+34+HjdeuutfrWVnZ2thx56SJKUlJTkXoYNAEBzQgASaAG2bNnile7cubOPkt4iIyOVkZHhTm/evDngvqxatcrrQb537976+uuv1b9/f91xxx1auHChdu/erYqKCu3atUsLFizQww8/rJ49e+rJJ5885H7VDqunP1+vO9/9UWWVDlXk1f6tg/sfp5tOTNeE8ZfqvPPO03vvvadNmzaprKxM+fn5WrdunV555RWdeeaZGj16tHJzcwP++wAAQMsSHxOl5yYOUuvq2pmLUcnpqqy2evSTdbr1reUqKPW9D3ZWVpZXOhjPS5L08MMPa/DgwZIka60uvPBCPfnkk9q9e7e7jMPh0Pfff6+xY8fqnXfekeTcC/LVV19Vly5d/Grn9ttvV3FxsSTpscceU6dOnYLSfwAAgokAJNACeJ6iKB26r1BDPMvWPJwGYs+ePV7pTZs2acyYMdqxY4c7Ly0tTRkZGYqIqB1iDh48qAcffFDXX3+9O6+orFI3/nOp/vr1JndedWmh+7p3Vnudf/75XgfwJCQkKCsr65CTJOfMmaOTTjrJqx8AAODYYIyRo7zUnY6Iq9225Yu1uRr7/AIt3bq/vqqHPFcF43lJcs5inDt3rnuv7LKyMj344IPKyMhQ+/bt1bVrVyUnJ+vkk0/W7NmzJTkPj/nss8901VVX+dXGRx99pFmzZkmSBg0apDvvvDMofQcAINgIQAItwIEDB7zSR7KpuOf+jHXv0xgFBQVe6Ycffljl5eWKjIzUfffdpx07dmjPnj3KycnR3r179ec//1lJSUnu8tOmTdNLL72kjXkHdPGLizRvQ21A01qHbOVBd/qDDz5w72c0YsQILViwQMXFxdq+fbuKioo0d+5cDR061F1+27ZtmjRpknsDdgAAcOzwfM7p1DbJ67OcwjJN+tti/WXuL6p2eC/JrruXdTCel2q0adNGH374of773/+qV69e7vy8vDxt27bN69CZq666SkuXLvV7/8aioiLdddddkpwB2FdeeeWwp34DABAuBCCBFqCsrMwrHRMT43fd2NhY9/XBgwcbKOmfuqc41hyO8+abb+rpp59WZmam+7PU1FTdfffdmjdvnteMxf/vgQc17n/naPNe73tNGtxO8ljeXXPvCy64QPPmzdPIkSPdp39HRkbqrLPO0oIFCzRq1Ch3ncWLF+u9994L+O8EAAAti+fz0im9Oujxi/srJqr2647DSs/N+VlXvfadcotqy3o+K0nBeV6qUVpaqnvvvVcXXHCBfvnllwbLvv322+revbuee+65w+5bKUm///3vtXPnTknSTTfd5PchhQAAhAMBSKAFqDvjsaLC9z5GdZWXl7uvg3FadX2zL6+88kpdccUVPusMHTpUf/jDH9zpA0WFyv1hjjsdHWn05CUD9OTEoYfUTU5O1rRp0xQdHV3vvVu1aqU333zTKyj7wgsv+PW3AACAo4fnM0pFRYWmnNxFM28foZ7tEr3Kfbd5v8b8eb4+X+Pci9HzWUkKzvOSJO3du1ennHKKnn32WZWWlioqKkq33XabFi5cqIKCAlVUVCg7O1vvvfeeO3hYWFio3/3ud5oyZUqDQcjvvvtOr7zyiiQpPT1df/zjH4PSZwAAQoUAJNACJCZ6PzjXnRHZEM+3+HXvE4y+SNIdd9xx2HpTrrtBEVG1QcSybSslSWmJMXrnppN15UmdFRUVdcgshMsvv1xpaWkN3jsrK0sXX3yxO718+XIVFhb6rgAAAI46ns8oNc9Kx2Uka9YdIzRpmPdBM/mllbr1reX63Qcrtbeg2Od9GstaqyuuuEKrVq2S5AxqfvXVV3r55Zc1YsQIpaSkKDo6Wp06ddKkSZO0aNEir+ept99+2+cL1aqqKt18881yOBySpGefffaI9gcHACAcCEACLUBycrJXOj8/30fJQ3nu2ei5F2Ow+hIfH6/hw4c3WGfH/lLd8O5Pikrv6s6ryNusAZ1SNOuOkRretY3P+59xxhl+9ctzGbbD4XA/8AMAgGOD5zOE57NSfEyUnp4wUH+5YoiSYqO86kxfnq3b/rHAKy8Yz0uzZ8/WV1995U4/9dRTXs8qdUVEROj555/XkCFD3HmPP/54vcvBn332Wa1evVqS8/nn6quvDri/AACEGgFIoAXo1q2bV3r79u1+1auurlZOTo473b1794D7UvceHTt2VFRUlI/S0sJf9uqilxbpp11FikpKd+dHlB/QB7eeoo6tvZc51b1/586d/epXVpb3zIa9e/f6VQ8AABwdPJ+X6ntWunBQR31292ka3tV7tuDunGyvdDCelzz3o46JidGNN9542DoRERG67bbb3Ol9+/ZpwQLv4Oju3bv12GOPSZKio6P18ssvB9xXAACaAgFIoAXo27evV3rTpk1+1du6dauqq6t93qcxevbs6bXfYt0l0zUcDqsX//uLpvzje+0vce1Z6bEEW9UVios+9KTG448/3ivt6/511d2b8kiWqQMAgJbP8zknOzv7kL0dJSmrTbzeu/kU3T+mr6IjnQfbVeXv8iqT7WgdcF9qZihK0nHHHaeEhAS/6g0bNswrvW7dOq/07t273bMiIyIiNG7cOPXs2dPnT92VJC+88ILX556zNAEACCXf05YANBt9+/ZVdHS0+1ToxYsX6/rrrz9svcWLF3ulBwwYEHBfoqKidPzxx2vFihWS6l8OXlBaod++v1L/XZ/nlR9RUeq+btOmTd1qkqRBgwZ5pf1dbl63nK/7AwCAo9PAgQPd11VVVVq2bJlGjBhxSLnICKPbzuih03un6Z5/r9CCnPW1nyW21b2fbNacTSV67KL+6pBy6OF7/igpKXFf+xt8lA7df7KhE7nLy8v9fildIz8/3+uZ6cCBA0dUHwCAxmIGJNACxMfHe+0bNHfu3AZPRqwxZ07tSdOJiYk67bTTgtKfCy64wH2dk5OjPXv2uNOrswt1wV8WHhJ8PC4jWQkHapc41V1WXt+9JWnlypV+9aluOV/3BwAAR6cxY8Z4pT2fg+rTr2OK3r1usBy5P7vzWvVwzkD8cl2uzvnfb/SPhVtUWe044r54HgqTm5vrd71du7xnY/JCFQBwtCAACbQQnqc8b9myRXPnzm2wfGFhoaZPn+5Ojxkzxu/lzIdz6aWXeqWnT58ua63e+X67xr/yrbLzvd/WTxyaqfuGSLm7ax+qfR0u06NHD68ZDB988IFfffrwww/d1x07dlTv3r39qgcAAI4OmZmZXkuYp02b5rUVTX0+nv6BKspqn1ta9TrZfX2gvEqPfbpOY/48X/M25NVX3aeePXu6rzdt2qTNmzf7Ve/LL7/0Svfq1csrPXjwYFlr/f7ZsmWLV/2pU6d6fe75fAkAQCgRgARaiMmTJ3u9Tb///vtVVVXls/xDDz2k0tLaJc933XVXg/c/44wzZIxx/zRkyJAhXjMyn/j/n9T1r36jBz5erYqq2lkCMVERenr8AD09foCmPvSgO98Yo8mTJ/u8/z333OO+XrJkiVdwsT6vvfaaNmzY4E5PmTKlwfIAAODodOedd7qvt2/frueff95n2aKiIj3yyCPudO/evfW/91yjpDjvXao27SnRddOWasIzM7yela699lqf9x49erRX+qGHHjps33fs2KGXXnrJnU5ISKh3CTkAAC0RAUighUhJSdF9993nTv/www+69tpr3ftCevrLX/7i9QA7ZsyYoC2/rvH000+7A5U5O7P17uO/VnVJ7Z5Cmamt9NFtp+rCfmm65pprvE5xnDRp0iGHzXi6+uqr1b9/f3f62muv1ezZs+stO336dN1xxx3udEpKin772982+u8CAAAt1+TJk72eMR544AF99NFHh5QrKirSJZdcop07d7rzHnvsMU0+tZvm/naUfjUw45A6323e73c/LrvsMnXs2NGdfvfdd3XXXXfVezCOJK1du1ajR49WQUGBO+/Xv/510FavAAAQbsaffeR8aHRFAI1TWVmp8847T/PmzXPndenSRZMnT1bXrl21Z88ezZgxQ0uWLHF/npGRoSVLligzM7PBe59xxhn65ptv3OnDjQ0VVQ5dcP09mvPmC+48ExOvhONO08BBgzXpxK7avmWj3nvvPa+H++7du2v58uVq3bp1g/dfu3atRowYocLCQnfeOeeco9GjRystLU25ubmaPXu25s+fX9u+MZoxY4bGjRvX4L0BAMDRa+XKlRo5cqTXASujR4/Wueeeq+TkZG3YsEH/+te/tHfvXvfnU6ZM0b/+9S+v+yzZsl+PfbpWa3YWSZKqCnO185Ub3J8POHOc/jP9XWW1ia+3HzNnztSll14qh6N2dUj79u01fvx49evXT/Hx8crLy9OCBQs0e/Zsr+Xiffv21eLFiw/7vHQ4W7du9doXe+rUqV6zPgEAQfDlQ1KRa7ux5Axp9BPh7U/oNbxk0lclApBAy5Kfn69f/epXh5xwXZ+OHTtq1qxZGjp06GHLHkkActOeA7rn3yu0KrtQ+fP+oaIlH8ufIWHgwIH69NNPlZWVddiykvTNN99o4sSJXofc+BIXF6c33nhDkyZN8uveAADg6PX555/r8ssv93qR6cu4ceP0/vvv1zvb0OGw+vCHbD3zxQblZG/3CkAm9D9b7S64R+cPyNAtp3fXwMzWh9R/8803deutt3pti3M4Q4YM0ccff6wuXbr4XccXApAA0AQIQPqFJdhAC5OamqoFCxboySefVEbGocuDJOeeQTfccINWr17tV/DRX5XVDr00b6POf36BVmU7H+hTz7xe7SY9rpQuvpdUp6en66mnntLixYv9Dj5K0qhRo7RmzRpde+21SkhIqLdMVFSULrvsMq1YsYLgIwAAkOTcfmbVqlUaP368YmJi6i3TvXt3/e1vf9PMmTN9LnWOiDCaOCxL39x7pm4b1eOQzx1W+s+qXRr34iJd9rfFmrUyR+VVtTMZp0yZopUrV+qGG25QfHz9MyVr9OzZU//3f/+n7777LijBRwAAmhNmQAItWHV1tRYtWqSNGzcqNzdXqampysrK0qhRo5SYmBjUttbsLNS901fpp11Fh3x2/Yhuum9MH+Xs2KalS5cqJydHFRUVSktL04ABAzRs2DBFRAT2vqOkpETz58/X9u3btW/fPqWkpKhLly46/fTTlZycHNC9AQDA0Wv//v2aP3++srOzVVJSooyMDPXt21cnnnjiEd+rqKxSr83frNcXblFJRf0nbLdJiNHEoZm6bHiWeqTXPo+Vl5drxYoVWrt2rfbv36/y8nIlJSWpQ4cOGjZsmLp3797ovxEAEEbMgPSvEgFIAA0pPFip/5vzs978bpuqHd7/26cnxeqZCQN1Rp92YeodAABA0yssrdTbS7bpjUVblVdc/8EykjQwM0UXDe6kCwdmqF1yXBP2EADQZAhA+leJACSA+jgcVu8v26E/fbFB+0sqDvl80rAsPTD2OKXER4ehdwAAAOFXXlWtmSty9I+FW7R+d7HPchFGOrFbG40+voNG92uvzNSGl2MDAFoQApD+VSIACaCubzfu1R8/X+/e59FTVptWeuqSgRrZKy0MPQMAAGh+rLX6cUeB3v1+uz5ZlaOySkeD5ft3StZZfdrp9N7pGpzVWlGRbM0PAC0WAUj/KhGABFBjxY4CPfPFei3auO+Qz2IiI3TDad1051k9FR8TFYbeAQAANH+FByv1n1W7NGPFTi3Zsv+w5ZPiojSyZ5pO752u03unq1PrVk3QSwBA0BCA9K8SAUgAy7fl669fb9JXP+XW+/lZfdvp4QuOV7e0+k+iBgAAwKFyCg5q1socfbZ6V70rS+rTIz1Bo3q306g+6TqpWxvFRUeGuJcAgIAQgPSvEgFI4NjkcFjN25Cnv32zWUu21v92vle7RP1+bF+d1bd9E/cOAADg6JJTcFBz1uXqi7W7tWTLflU5Dv91KjYqQid1b6tRvdM1qne6eqQnyJhGfe8DAIQKAUj/KhGABI4t+SUVmr48W+8u2a7Ne0vqLZOZ2kr3nNNbFw/ppMgIHnIBAACCqbisUos37dP8X/bom5/3aMf+g37V69S6lU7vna4z+6TrtF7pahXD7EgACDsCkP5VIgAJHP2qqh1avHmfPlyerc/W7FZFVf0bo2ekxOnWUT10+YlZio3igRYAACDUrLXauq9U83/eo/k/79G3m/bpYGX1YevFRkXotF5pOvf49jqrb3ulJ8U2QW8BAIcgAOlfJQKQwNHJ4bD6YXu+e9+hvQcqfJbt3T5Rt5zeQ+MGd1Q0pzACAACETXlVtZZvzdc3PztnR67fXXzYOsZIQ7Ja6/z+GfrVwAx15CAbAGg6BCD9q0QAEjh6lFVWa+EvezVnXa7mrs9tMOhojDSqd7quPqWLzuzTjv2EAAAAmqHdhWXupdoLf9mrwoOVh60zvGuqLhzUUef3z2BmJACEGgFI/yoRgARaLofDat2uIi3auFcLN+7Vki37Ve5jeXWNtMRYXT48S5OGZymrTXwT9RQAAACBqnatcPlqXa7m/JSrzXvq38+7RoSRTu2RpktP6KTz+2ewZyQAhAIBSP8qEYAEWg6Hw2rTngNaujVf327aq2837dP+Et+zHGskxUbpvP4ddOGgjhrRo62iWGYNAADQ4m3ac8AZjFyXq+X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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", - "\n", - "Let's visualise how bad that fit is by plotting the data and posterior predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 623 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def pp_plot(x, y, trace):\n", - " fig, ax = plt.subplots(figsize=(10, 8))\n", - " # plot data\n", - " ax.plot(x, y, 'k.')\n", - " # plot posterior predicted... samples from posterior\n", - " xi = np.array([np.min(x), np.max(x)])\n", - " n_samples=1000\n", - " for n in range(n_samples):\n", - " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", - " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", - " # plot true\n", - " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", - " # plot bounds\n", - " ax.axhline(bounds[0], c='r', ls='--')\n", - " ax.axhline(bounds[1], c='r', ls='--')\n", - " ax.legend()\n", - " ax.set(xlabel=\"x\", ylabel=\"y\")\n", - " \n", - "pp_plot(xt, yt, linear_trace)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Truncated regression avoids this underestimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def truncated_regression(x, y, bounds):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - "\n", - " y_likelihood = pm.TruncatedNormal(\n", - " \"y_likelihood\",\n", - " mu=m * x + c,\n", - " sd=σ,\n", - " observed=y,\n", - " lower=bounds[0],\n", - " upper=bounds[1],\n", - " )\n", - " \n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " \n", - " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", - "
\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 13 seconds.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And also by doing our graphical posterior predictive checks. Looks good." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pp_plot(xt, yt, truncated_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last updated: Sun Jan 24 2021\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.8.5\n", - "IPython version : 7.19.0\n", - "\n", - "pymc3 : 3.10.0\n", - "matplotlib: 3.3.2\n", - "numpy : 1.19.2\n", - "arviz : 0.11.0\n", - "\n", - "Watermark: 2.1.0\n", - "\n" - ] - } - ], - "source": [ - "%load_ext watermark\n", - "%watermark -n -u -v -iv -w" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 664ab97019402a10a8482e01ec9ec2e860b1ffc1 Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Sun, 24 Jan 2021 16:43:29 +0000 Subject: [PATCH 5/7] create truncated regression example --- .../GLM-truncated-regression.ipynb | 1089 +++++++++++++++++ 1 file changed, 1089 insertions(+) create mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb new file mode 100644 index 000000000..9a34f145f --- /dev/null +++ b/examples/generalized_linear_models/GLM-truncated-regression.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Truncated regression\n", + "\n", + "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", + "\n", + "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on PyMC3 v3.10.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pymc3 as pm\n", + "import arviz as az\n", + "\n", + "print(f\"Running on PyMC3 v{pm.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def truncate_y(x, y, bounds):\n", + " keep = (y >= bounds[0]) & (y <= bounds[1])\n", + " return (x[keep], y[keep])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m, c, σ, N = 1, 0, 2, 200\n", + "x = np.random.uniform(-10, 10, N)\n", + "y = np.random.normal(m * x + c, σ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bounds = [-5, 5]\n", + "xt, yt = truncate_y(x, y, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", + "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", + "ax.axhline(bounds[0], c='r', ls='--')\n", + "ax.axhline(bounds[1], c='r', ls='--')\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear regression of truncated data underestimates the slope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_regression(x, y):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", + "\n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\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 12 seconds.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "linear_model, linear_trace = linear_regression(xt, yt)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", + "\n", + "Let's visualise how bad that fit is by plotting the data and posterior predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 623 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def pp_plot(x, y, trace):\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " # plot data\n", + " ax.plot(x, y, 'k.')\n", + " # plot posterior predicted... samples from posterior\n", + " xi = np.array([np.min(x), np.max(x)])\n", + " n_samples=1000\n", + " for n in range(n_samples):\n", + " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", + " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", + " # plot true\n", + " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", + " # plot bounds\n", + " ax.axhline(bounds[0], c='r', ls='--')\n", + " ax.axhline(bounds[1], c='r', ls='--')\n", + " ax.legend()\n", + " ax.set(xlabel=\"x\", ylabel=\"y\")\n", + " \n", + "pp_plot(xt, yt, linear_trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Truncated regression avoids this underestimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def truncated_regression(x, y, bounds):\n", + "\n", + " with pm.Model() as model:\n", + " m = pm.Normal(\"m\", mu=0, sd=1)\n", + " c = pm.Normal(\"c\", mu=0, sd=1)\n", + " σ = pm.HalfNormal(\"σ\", sd=1)\n", + "\n", + " y_likelihood = pm.TruncatedNormal(\n", + " \"y_likelihood\",\n", + " mu=m * x + c,\n", + " sd=σ,\n", + " observed=y,\n", + " lower=bounds[0],\n", + " upper=bounds[1],\n", + " )\n", + " \n", + " with model:\n", + " trace = pm.sample()\n", + "\n", + " return model, trace" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + " warnings.warn(\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [σ, c, m]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", + "
\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 13 seconds.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + } + ], + "source": [ + "# run the model on the truncated data (xt, yt)\n", + "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " warnings.warn(\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", + "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 296, + "width": 656 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And also by doing our graphical posterior predictive checks. Looks good." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 479, + "width": 614 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pp_plot(xt, yt, truncated_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Sun Jan 24 2021\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.8.5\n", + "IPython version : 7.19.0\n", + "\n", + "pymc3 : 3.10.0\n", + "matplotlib: 3.3.2\n", + "numpy : 1.19.2\n", + "arviz : 0.11.0\n", + "\n", + "Watermark: 2.1.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From cc6693fe22d0913e7d7219e84ebdc27289834109 Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Mon, 25 Jan 2021 16:24:38 +0000 Subject: [PATCH 6/7] delete truncated regression example from main branch --- .../GLM-truncated-regression.ipynb | 1089 ----------------- 1 file changed, 1089 deletions(-) delete mode 100644 examples/generalized_linear_models/GLM-truncated-regression.ipynb diff --git a/examples/generalized_linear_models/GLM-truncated-regression.ipynb b/examples/generalized_linear_models/GLM-truncated-regression.ipynb deleted file mode 100644 index 9a34f145f..000000000 --- a/examples/generalized_linear_models/GLM-truncated-regression.ipynb +++ /dev/null @@ -1,1089 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Truncated regression\n", - "\n", - "**Author:** [Ben Vincent](https://github.com/drbenvincent)\n", - "\n", - "The notebook provides an example of how to conduct linear regression when you have a truncated outcome variable. Truncation is a type of missing data problem where you are simply unaware of any data that falls outside of a certain set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on PyMC3 v3.10.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pymc3 as pm\n", - "import arviz as az\n", - "\n", - "print(f\"Running on PyMC3 v{pm.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example of `(x, y)` scatter data, we can describe the truncation process as simply filtering out any data for which our outcome variable `y` falls outside of a set of bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def truncate_y(x, y, bounds):\n", - " keep = (y >= bounds[0]) & (y <= bounds[1])\n", - " return (x[keep], y[keep])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate some true (latent) data before any truncation takes place. In the real world, you would not have access to this `(x, y)` data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m, c, σ, N = 1, 0, 2, 200\n", - "x = np.random.uniform(-10, 10, N)\n", - "y = np.random.normal(m * x + c, σ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rather, in a real world context, you would have access to truncated data, where our outcome variable `y` falls within the bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "bounds = [-5, 5]\n", - "xt, yt = truncate_y(x, y, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualise this latent data (in grey) and the remaining truncated data (black) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "ax.plot(x, y, '.', c=[0.7, 0.7, 0.7], label=\"all data\")\n", - "ax.plot(xt, yt, '.', c=[0, 0, 0], label=\"truncated data\")\n", - "ax.axhline(bounds[0], c='r', ls='--')\n", - "ax.axhline(bounds[1], c='r', ls='--')\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear regression of truncated data underestimates the slope" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we get into truncated regression, it is useful to understand why it is needed. If you haven't guessed already from the plot above, then a regression on the truncated data is likely to underestimate the true regression slope. Let's see that in action." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def linear_regression(x, y):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - " y_likelihood = pm.Normal(\"y_likelihood\", mu=m*x+c, sd=σ, observed=y)\n", - "\n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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\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 12 seconds.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "linear_model, linear_trace = linear_regression(xt, yt)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(linear_trace, var_names=['m'], ref_val=m, figsize=(9, 4));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the posterior of the regression slope `m` is underestimated, by quite lot in this example.\n", - "\n", - "Let's visualise how bad that fit is by plotting the data and posterior predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 623 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def pp_plot(x, y, trace):\n", - " fig, ax = plt.subplots(figsize=(10, 8))\n", - " # plot data\n", - " ax.plot(x, y, 'k.')\n", - " # plot posterior predicted... samples from posterior\n", - " xi = np.array([np.min(x), np.max(x)])\n", - " n_samples=1000\n", - " for n in range(n_samples):\n", - " y_ppc = xi * trace[\"m\"][n] + trace[\"c\"][n]\n", - " ax.plot(xi, y_ppc, c=\"steelblue\", alpha=0.01, rasterized=True)\n", - " # plot true\n", - " ax.plot(xi, m * xi + c, \"k\", lw=3, label=\"True\")\n", - " # plot bounds\n", - " ax.axhline(bounds[0], c='r', ls='--')\n", - " ax.axhline(bounds[1], c='r', ls='--')\n", - " ax.legend()\n", - " ax.set(xlabel=\"x\", ylabel=\"y\")\n", - " \n", - "pp_plot(xt, yt, linear_trace)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the degree of estimation bias will depend upon a number of things, including the truncation boundaries and the measurement noise. In some situations with high measurement precision and/or little measurement noise, the estimation bias may not be very large. Otherwise, this could have a negative impact upon your research conclusions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Truncated regression avoids this underestimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Truncated regression solves this problem. By using a truncated normal likelihood distribution we are explicity stating our knowledge about the generative process which gave rise to your dataset. We can impliment a [truncated regression model](https://en.wikipedia.org/wiki/Truncated_regression_model) as below." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def truncated_regression(x, y, bounds):\n", - "\n", - " with pm.Model() as model:\n", - " m = pm.Normal(\"m\", mu=0, sd=1)\n", - " c = pm.Normal(\"c\", mu=0, sd=1)\n", - " σ = pm.HalfNormal(\"σ\", sd=1)\n", - "\n", - " y_likelihood = pm.TruncatedNormal(\n", - " \"y_likelihood\",\n", - " mu=m * x + c,\n", - " sd=σ,\n", - " observed=y,\n", - " lower=bounds[0],\n", - " upper=bounds[1],\n", - " )\n", - " \n", - " with model:\n", - " trace = pm.sample()\n", - "\n", - " return model, trace" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", - " warnings.warn(\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [σ, c, m]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " \n", - " 100.00% [8000/8000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", - "
\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 13 seconds.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - } - ], - "source": [ - "# run the model on the truncated data (xt, yt)\n", - "truncated_model, truncated_trace = truncated_regression(xt, yt, bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can check that the inferences are much better by examining the posterior distribution over our slope parameter `m`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benjamv/opt/anaconda3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:88: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", - " warnings.warn(\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n", - "WARNING (theano.tensor.opt): The Op erfcx does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 296, - "width": 656 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_posterior(truncated_trace, var_names=['m'], ref_val=m, figsize=(9, 4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And also by doing our graphical posterior predictive checks. Looks good." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 479, - "width": 614 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pp_plot(xt, yt, truncated_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last updated: Sun Jan 24 2021\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.8.5\n", - "IPython version : 7.19.0\n", - "\n", - "pymc3 : 3.10.0\n", - "matplotlib: 3.3.2\n", - "numpy : 1.19.2\n", - "arviz : 0.11.0\n", - "\n", - "Watermark: 2.1.0\n", - "\n" - ] - } - ], - "source": [ - "%load_ext watermark\n", - "%watermark -n -u -v -iv -w" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 3afaf7c1894b7b7befcc40b0f14fbbbe571135cd Mon Sep 17 00:00:00 2001 From: "Benjamin T. Vincent" Date: Sun, 17 Apr 2022 16:48:47 +0100 Subject: [PATCH 7/7] add *.DS_Store to .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 5bdb132f5..75d9d25c9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ *.pyc .ipynb_checkpoints .vscode/* +*.DS_Store _build jupyter_execute