From 7f148be67cfe7ed1df79bec4a3b925c2793af8be Mon Sep 17 00:00:00 2001 From: aevans1 Date: Thu, 10 Aug 2023 11:01:11 -0400 Subject: [PATCH] more posterior plots --- .../comparing_estimator_posteriors.ipynb | 270 ++++++++++++ Lukes_folder/comparing_estimators copy.ipynb | 392 ------------------ Lukes_folder/comparing_estimators_MMD.ipynb | 392 ++++++++++++++++++ .../figures/posterior_uniform_bife_0.png | Bin 0 -> 74370 bytes .../figures/posterior_uniform_bife_1.png | Bin 0 -> 77599 bytes .../figures/posterior_uniform_bife_2.png | Bin 0 -> 73076 bytes .../figures/posterior_uniform_bife_3.png | Bin 0 -> 73163 bytes 7 files changed, 662 insertions(+), 392 deletions(-) create mode 100644 Lukes_folder/comparing_estimator_posteriors.ipynb delete mode 100644 Lukes_folder/comparing_estimators copy.ipynb create mode 100644 Lukes_folder/comparing_estimators_MMD.ipynb create mode 100644 Lukes_folder/figures/posterior_uniform_bife_0.png create mode 100644 Lukes_folder/figures/posterior_uniform_bife_1.png create mode 100644 Lukes_folder/figures/posterior_uniform_bife_2.png create mode 100644 Lukes_folder/figures/posterior_uniform_bife_3.png diff --git a/Lukes_folder/comparing_estimator_posteriors.ipynb b/Lukes_folder/comparing_estimator_posteriors.ipynb new file mode 100644 index 0000000..631db78 --- /dev/null +++ b/Lukes_folder/comparing_estimator_posteriors.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "import zuko\n", + "\n", + "from cryo_sbi.wpa_simulator.cryo_em_simulator import CryoEmSimulator\n", + "from cryo_sbi.inference.train_npe_model import npe_train_no_saving\n", + "from cryo_sbi.inference.models import build_models\n", + "from cryo_sbi.inference import priors\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "plt.rcParams['figure.dpi'] = 100 #75 is default\n", + "plt.style.use('default')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "num_samples_stats = 5000 # Number of simulations for computing posterior stats\n", + "num_samples_SBC = 2000 # Number of simulations for SBC\n", + "num_posterior_samples_SBC = 4096 # Number of posterior samples for each SBC simulation\n", + "num_samples_posterior = 10000 # Number of samples to draw from posterior\n", + "batch_size_sampling = 100 # Batch size for sampling posterior\n", + "batch_size_latent = 1000 # Batch size for calculating latent representation\n", + "num_samples_umap = 10000 # Number of simualtions for UMAP analysis\n", + "num_workers = 24 # Number of CPU cores\n", + "device = \"cuda\" # Device for computations\n", + "save_figures = False\n", + "\n", + "# empty cache just in case\n", + "torch.cuda.empty_cache()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load up Hsp 90 model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "torch.set_num_threads(1)\n", + "config = json.load(open(\"Lars_hsp90/image_params_train.json\"))\n", + "models = torch.from_numpy(np.load(\"../data/protein_models/hsp90_models.npy\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load up both learned posterior $q_{\\phi}(\\theta \\mid x)$:\n", + "### - 1. neural posterior trained with $p(\\theta_{CMA})$ uniform$\n", + "### - 2. neural posterior trained with $p(\\theta_{CMA})$ from free energy in $\\theta_{CMA}$ from Cryo-BIFE paper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_config_uniform = json.load(open(\"Lars_hsp90/resnet18_encoder.json\"))\n", + "estimator_uniform = build_models.build_npe_flow_model(train_config_uniform)\n", + "estimator_uniform.load_state_dict(torch.load(\"Lars_hsp90/hsp90_posterior.estimator\"))\n", + "estimator_uniform.cuda()\n", + "estimator_uniform.eval();\n", + "\n", + "\n", + "train_config_bife = json.load(open(\"resnet18_encoder.json\"))\n", + "estimator_bife = build_models.build_npe_flow_model(train_config_bife)\n", + "estimator_bife.load_state_dict(torch.load(\"cryobife_estimator.estimator\"))\n", + "estimator_bife.cuda()\n", + "estimator_bife.eval();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialize Simulator, with prior sampler defined from uniform distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sim = CryoEmSimulator(\"Lars_hsp90/image_params_train.json\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "num_sim = 80\n", + "images = sim.simulate(num_sim)\n", + "images, parameters = sim.simulate(num_sim, return_parameters=True)\n", + "indices = parameters[0].round().long().flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing Posterior samples from both neural posterior estimators" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "theta_samples_uniform = []\n", + "theta_samples_bife = []\n", + "with torch.no_grad():\n", + " for batched_images in torch.split(\n", + " images, split_size_or_sections=batch_size_sampling, dim=0\n", + " ):\n", + " samples_uniform = estimator_uniform.sample(\n", + " batched_images.cuda(non_blocking=True), shape=(num_samples_posterior,)\n", + " ).cpu()\n", + " theta_samples_uniform.append(samples_uniform)\n", + "\n", + " samples_bife = estimator_bife.sample(\n", + " batched_images.cuda(non_blocking=True), shape=(num_samples_posterior,)\n", + " ).cpu()\n", + " theta_samples_bife.append(samples_bife)\n", + " samples_uniform = torch.cat(theta_samples_uniform, dim=1)\n", + " samples_bife = torch.cat(theta_samples_bife, dim=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([10000, 80, 1])\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(samples_uniform.shape)\n", + "samples_uniform_split = torch.split(samples_uniform, 20, dim=1)\n", + "samples_bife_split = torch.split(samples_bife, 20, dim=1)\n", + "for i in range(4):\n", + " fig, axes = plt.subplots(4, 5, figsize=(10, 8), sharex=True)\n", + " for idx, ax in enumerate(axes.reshape(1, -1)[0]):\n", + " ax.hist(\n", + " samples_uniform_split[i][:, idx].detach().cpu().numpy(),\n", + " bins=np.arange(0, 20, 0.5),\n", + " histtype=\"step\",\n", + " color=\"blue\",\n", + " label=\"all\",\n", + " )\n", + " ax.hist(\n", + " samples_bife_split[i][:, idx].detach().cpu().numpy(),\n", + " bins=np.arange(0, 20, 0.5),\n", + " histtype=\"step\",\n", + " color=\"k\",\n", + " label=\"all\",\n", + " )\n", + " ax.set_yticks([])\n", + " ax.set_yticks([])\n", + " ax.set_xticks(range(0, 20, 4))\n", + " ax.axvline(indices.detach().cpu()[idx], color=\"red\")\n", + " plt.savefig(f'figures/posterior_uniform_bife_{i}.png', dpi=300)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 64-bit", + "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": "3.9.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "7b7fbdd20bcc2083504065e64dd68e11295ac29c39a09e225403f090756a3e6a" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lukes_folder/comparing_estimators copy.ipynb b/Lukes_folder/comparing_estimators copy.ipynb deleted file mode 100644 index 3a61a0c..0000000 --- a/Lukes_folder/comparing_estimators copy.ipynb +++ /dev/null @@ -1,392 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import json\n", - "import zuko\n", - "\n", - "from cryo_sbi.wpa_simulator.cryo_em_simulator import CryoEmSimulator\n", - "from cryo_sbi.inference.train_npe_model import npe_train_no_saving\n", - "from cryo_sbi.inference.models import build_models\n", - "from cryo_sbi.inference import priors\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "plt.rcParams['figure.dpi'] = 100 #75 is default\n", - "plt.style.use('default')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "num_samples_stats = 5000 # Number of simulations for computing posterior stats\n", - "num_samples_SBC = 2000 # Number of simulations for SBC\n", - "num_posterior_samples_SBC = 4096 # Number of posterior samples for each SBC simulation\n", - "num_samples_posterior = 10000 # Number of samples to draw from posterior\n", - "batch_size_sampling = 100 # Batch size for sampling posterior\n", - "batch_size_latent = 1000 # Batch size for calculating latent representation\n", - "num_samples_umap = 10000 # Number of simualtions for UMAP analysis\n", - "num_workers = 24 # Number of CPU cores\n", - "device = \"cuda\" # Device for computations\n", - "save_figures = False\n", - "\n", - "# empty cache just in case\n", - "torch.cuda.empty_cache()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load up Hsp 90 model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "torch.set_num_threads(1)\n", - "config = json.load(open(\"Lars_hsp90/image_params_train.json\"))\n", - "models = torch.from_numpy(np.load(\"../data/protein_models/hsp90_models.npy\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load up both learned posterior $q_{\\phi}(\\theta \\mid x)$:\n", - "### - 1. neural posterior trained with $p(\\theta_{CMA})$ uniform$\n", - "### - 2. neural posterior trained with $p(\\theta_{CMA})$ from free energy in $\\theta_{CMA}$ from Cryo-BIFE paper\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train_config_uniform = json.load(open(\"Lars_hsp90/resnet18_encoder.json\"))\n", - "estimator_uniform = build_models.build_npe_flow_model(train_config_uniform)\n", - "estimator_uniform.load_state_dict(torch.load(\"Lars_hsp90/hsp90_posterior.estimator\"))\n", - "estimator_uniform.cuda()\n", - "estimator_uniform.eval();\n", - "\n", - "\n", - "train_config_bife = json.load(open(\"resnet18_encoder.json\"))\n", - "estimator_bife = build_models.build_npe_flow_model(train_config_bife)\n", - "estimator_bife.load_state_dict(torch.load(\"cryobife_estimator.estimator\"))\n", - "estimator_bife.cuda()\n", - "estimator_bife.eval();\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Initialize Simulator, with prior sampler defined from uniform distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "sim = CryoEmSimulator(\"Lars_hsp90/image_params_train.json\", device=device)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Defining Maximum Mean Discrepancy with a Gaussian Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def max_mean_discrep_gaussian(X, Y, bandwidth=None):\n", - " \n", - " # Compute all pairwise distances \n", - " Xdists = torch.cdist(X, X)**2\n", - " Ydists = torch.cdist(Y, Y)**2\n", - " XYdists = torch.cdist(X, Y)**2\n", - " # Use heuristic for bandwidth if none provided\n", - " if bandwidth == None:\n", - " bandwidth = torch.sqrt(torch.median(Xdists)*2)\n", - "\n", - " # Compute all kernel sums\n", - " Xterm = torch.exp(-Xdists[None, ...]/bandwidth**2).mean()\n", - " Yterm = torch.exp(-Ydists[None, ...]/bandwidth**2).mean()\n", - " XYterm = torch.exp(-XYdists[None, ...]/bandwidth**2).mean()\n", - " \n", - " return Xterm + Yterm - 2*XYterm\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recompute reference simulated samples, with specified parameter" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "num_test_obs = 5000\n", - "indices = torch.tensor(10, dtype=torch.float32).repeat(num_test_obs)\n", - "indices = indices.reshape(num_test_obs, 1)\n", - "obs_images = sim.simulate(num_test_obs, indices)\n", - "# transformation to latent space\n", - "batches = torch.split(obs_images, split_size_or_sections=batch_size_latent,dim=0)\n", - "obs_latent_samples_uniform = torch.zeros((batch_size_latent, 256, len(batches)))\n", - "obs_latent_samples_bife = torch.zeros((batch_size_latent, 256, len(batches)))\n", - "with torch.no_grad():\n", - " for i in range(len(batches)):\n", - " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " obs_latent_samples_uniform[:,:, i]= samples\n", - " \n", - " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " obs_latent_samples_bife[:,:, i]= samples\n", - "\n", - "\n", - " obs_latent_samples_uniform = obs_latent_samples_uniform.swapaxes(1,2)\n", - " obs_latent_samples_uniform = torch.reshape(obs_latent_samples_uniform, (num_test_obs, 256))\n", - " obs_latent_samples_bife = obs_latent_samples_bife.swapaxes(1,2)\n", - " obs_latent_samples_bife = torch.reshape(obs_latent_samples_bife, (num_test_obs, 256))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compute different 'observed' datasets with a different parameter for each dataset\n", - "- for several fixed indices of the parameter (hsp90, this is the arm angle) $j$\n", - " - simulate $X^{(j)} = \\{x_i\\}_{i=1}^N \\sim p(x \\mid \\theta = \\theta_j)$\n", - " - Compute feature space embedding $Z^{(j)} = \\{z_i\\}_{i=1}^N = \\{h_{\\psi}(x_i)\\}_{i=1}^N$\n", - " - compute MMD of that feature embedded data-set to a reference feature embedded- data set generated from $p(x \\mid \\theta = \\theta^*)$\n", - " - i.e compute $\\text{MMD}(Z^{(j)}, Z)$\n", - "- feature embedding $h_{\\psi}(x)$ comes from the learned neural posterior estimator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "combined_samples_uniform = obs_latent_samples_uniform\n", - "cv_list = torch.arange(0, 20, 1)\n", - "distances_uniform = np.zeros(cv_list.shape[0])\n", - "num_test_sim = 5000\n", - "for idx in range(len(cv_list)):\n", - " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", - " indices = indices.reshape(num_test_sim, 1)\n", - " sim_images = sim.simulate(num_test_sim, indices)\n", - " \n", - " # transformation to latent space\n", - " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", - " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", - " with torch.no_grad():\n", - " for i in range(len(batches)):\n", - " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " sim_latent_samples[:,:, i]= samples\n", - "\n", - " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", - " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", - " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_uniform)\n", - " distances_uniform[idx] = mmd.item()\n", - " combined_samples_uniform = torch.cat((combined_samples_uniform,sim_latent_samples))\n", - " # empty cache just in case\n", - " torch.cuda.empty_cache()\n", - "\n", - "combined_samples_bife = obs_latent_samples_bife\n", - "distances_bife = np.zeros(cv_list.shape[0])\n", - "num_test_sim = 5000\n", - "for idx in range(len(cv_list)):\n", - " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", - " indices = indices.reshape(num_test_sim, 1)\n", - " sim_images = sim.simulate(num_test_sim, indices)\n", - " \n", - " # transformation to latent space\n", - " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", - " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", - " with torch.no_grad():\n", - " for i in range(len(batches)):\n", - " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " sim_latent_samples[:,:, i]= samples\n", - "\n", - " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", - " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", - " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_bife)\n", - " distances_bife[idx] = mmd.item()\n", - " combined_samples_bife = torch.cat((combined_samples_bife,sim_latent_samples))\n", - " # empty cache just in case\n", - " torch.cuda.empty_cache()\n", - "\n", - "# Try same thing, but for comparing with OTHER feature space\n", - "cv_list = torch.arange(0, 20, 1)\n", - "distances_uniform_bife_obs = np.zeros(cv_list.shape[0])\n", - "num_test_sim = 5000\n", - "for idx in range(len(cv_list)):\n", - " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", - " indices = indices.reshape(num_test_sim, 1)\n", - " sim_images = sim.simulate(num_test_sim, indices)\n", - " \n", - " # transformation to latent space\n", - " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", - " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", - " with torch.no_grad():\n", - " for i in range(len(batches)):\n", - " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " sim_latent_samples[:,:, i]= samples\n", - "\n", - " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", - " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", - " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_bife)\n", - " distances_uniform_bife_obs[idx] = mmd.item()\n", - " # empty cache just in case\n", - " torch.cuda.empty_cache()\n", - "\n", - "distances_bife_uniform_obs = np.zeros(cv_list.shape[0])\n", - "num_test_sim = 5000\n", - "for idx in range(len(cv_list)):\n", - " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", - " indices = indices.reshape(num_test_sim, 1)\n", - " sim_images = sim.simulate(num_test_sim, indices)\n", - " \n", - " # transformation to latent space\n", - " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", - " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", - " with torch.no_grad():\n", - " for i in range(len(batches)):\n", - " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", - " sim_latent_samples[:,:, i]= samples\n", - "\n", - " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", - " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", - " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_uniform)\n", - " distances_bife_uniform_obs[idx] = mmd.item()\n", - " # empty cache just in case\n", - " torch.cuda.empty_cache()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot MMD, highlighting the true index, where the MMD should be smallest" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(cv_list,distances_uniform)\n", - "plt.scatter(cv_list,distances_bife)\n", - "plt.scatter(cv_list,distances_uniform_bife_obs)\n", - "plt.scatter(cv_list,distances_bife_uniform_obs)\n", - "plt.vlines(10.0, ymin=0,ymax=0.9, linestyles='dotted')\n", - "plt.xlabel(\"$\\\\theta_{CMA}$\")\n", - "plt.ylabel(\"MMD to embeded ref. data\")\n", - "\n", - "plt.legend([\"uniform feature, uniform obs\", \"bife feature, bife obs\", \"uniform feature, bife obs\", \"bife feature, uniform obs\"], bbox_to_anchor=(1.00, 1.0), loc='upper left')\n", - "fname=\"figures/mmd_comparison.png\"\n", - "plt.tight_layout()\n", - "plt.savefig(fname, dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(cv_list,distances_uniform_bife_obs)\n", - "#plt.scatter(cv_list,distances_bife_uniform_obs)\n", - "plt.vlines(10.0, ymin=0,ymax=np.max(distances_bife), linestyles='dotted')\n", - "plt.xlabel(\"parameter for embedded, observed data-set\")\n", - "plt.ylabel(\"MMD to embeded ref. data\")\n", - "\n", - "#plt.legend([\"uniform feature, bife obs\", \"bife feature, uniform obs\"], bbox_to_anchor=(1.00, 1.0), loc='upper left')\n", - "fname=\"figures/mmd_comparison_swapped.png\"\n", - "plt.savefig(fname, dpi=300)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.15 64-bit", - "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": "3.9.15" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "7b7fbdd20bcc2083504065e64dd68e11295ac29c39a09e225403f090756a3e6a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Lukes_folder/comparing_estimators_MMD.ipynb b/Lukes_folder/comparing_estimators_MMD.ipynb new file mode 100644 index 0000000..67876a3 --- /dev/null +++ b/Lukes_folder/comparing_estimators_MMD.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "import zuko\n", + "\n", + "from cryo_sbi.wpa_simulator.cryo_em_simulator import CryoEmSimulator\n", + "from cryo_sbi.inference.train_npe_model import npe_train_no_saving\n", + "from cryo_sbi.inference.models import build_models\n", + "from cryo_sbi.inference import priors\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "plt.rcParams['figure.dpi'] = 100 #75 is default\n", + "plt.style.use('default')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "num_samples_stats = 5000 # Number of simulations for computing posterior stats\n", + "num_samples_SBC = 2000 # Number of simulations for SBC\n", + "num_posterior_samples_SBC = 4096 # Number of posterior samples for each SBC simulation\n", + "num_samples_posterior = 10000 # Number of samples to draw from posterior\n", + "batch_size_sampling = 100 # Batch size for sampling posterior\n", + "batch_size_latent = 1000 # Batch size for calculating latent representation\n", + "num_samples_umap = 10000 # Number of simualtions for UMAP analysis\n", + "num_workers = 24 # Number of CPU cores\n", + "device = \"cuda\" # Device for computations\n", + "save_figures = False\n", + "\n", + "# empty cache just in case\n", + "torch.cuda.empty_cache()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load up Hsp 90 model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "torch.set_num_threads(1)\n", + "config = json.load(open(\"Lars_hsp90/image_params_train.json\"))\n", + "models = torch.from_numpy(np.load(\"../data/protein_models/hsp90_models.npy\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load up both learned posterior $q_{\\phi}(\\theta \\mid x)$:\n", + "### - 1. neural posterior trained with $p(\\theta_{CMA})$ uniform$\n", + "### - 2. neural posterior trained with $p(\\theta_{CMA})$ from free energy in $\\theta_{CMA}$ from Cryo-BIFE paper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_config_uniform = json.load(open(\"Lars_hsp90/resnet18_encoder.json\"))\n", + "estimator_uniform = build_models.build_npe_flow_model(train_config_uniform)\n", + "estimator_uniform.load_state_dict(torch.load(\"Lars_hsp90/hsp90_posterior.estimator\"))\n", + "estimator_uniform.cuda()\n", + "estimator_uniform.eval();\n", + "\n", + "\n", + "train_config_bife = json.load(open(\"resnet18_encoder.json\"))\n", + "estimator_bife = build_models.build_npe_flow_model(train_config_bife)\n", + "estimator_bife.load_state_dict(torch.load(\"cryobife_estimator.estimator\"))\n", + "estimator_bife.cuda()\n", + "estimator_bife.eval();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialize Simulator, with prior sampler defined from uniform distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sim = CryoEmSimulator(\"Lars_hsp90/image_params_train.json\", device=device)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining Maximum Mean Discrepancy with a Gaussian Kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def max_mean_discrep_gaussian(X, Y, bandwidth=None):\n", + " \n", + " # Compute all pairwise distances \n", + " Xdists = torch.cdist(X, X)**2\n", + " Ydists = torch.cdist(Y, Y)**2\n", + " XYdists = torch.cdist(X, Y)**2\n", + " # Use heuristic for bandwidth if none provided\n", + " if bandwidth == None:\n", + " bandwidth = torch.sqrt(torch.median(Xdists)*2)\n", + "\n", + " # Compute all kernel sums\n", + " Xterm = torch.exp(-Xdists[None, ...]/bandwidth**2).mean()\n", + " Yterm = torch.exp(-Ydists[None, ...]/bandwidth**2).mean()\n", + " XYterm = torch.exp(-XYdists[None, ...]/bandwidth**2).mean()\n", + " \n", + " return Xterm + Yterm - 2*XYterm\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recompute reference simulated samples, with specified parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "num_test_obs = 5000\n", + "indices = torch.tensor(10, dtype=torch.float32).repeat(num_test_obs)\n", + "indices = indices.reshape(num_test_obs, 1)\n", + "obs_images = sim.simulate(num_test_obs, indices)\n", + "# transformation to latent space\n", + "batches = torch.split(obs_images, split_size_or_sections=batch_size_latent,dim=0)\n", + "obs_latent_samples_uniform = torch.zeros((batch_size_latent, 256, len(batches)))\n", + "obs_latent_samples_bife = torch.zeros((batch_size_latent, 256, len(batches)))\n", + "with torch.no_grad():\n", + " for i in range(len(batches)):\n", + " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " obs_latent_samples_uniform[:,:, i]= samples\n", + " \n", + " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " obs_latent_samples_bife[:,:, i]= samples\n", + "\n", + "\n", + " obs_latent_samples_uniform = obs_latent_samples_uniform.swapaxes(1,2)\n", + " obs_latent_samples_uniform = torch.reshape(obs_latent_samples_uniform, (num_test_obs, 256))\n", + " obs_latent_samples_bife = obs_latent_samples_bife.swapaxes(1,2)\n", + " obs_latent_samples_bife = torch.reshape(obs_latent_samples_bife, (num_test_obs, 256))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compute different 'observed' datasets with a different parameter for each dataset\n", + "- for several fixed indices of the parameter (hsp90, this is the arm angle) $j$\n", + " - simulate $X^{(j)} = \\{x_i\\}_{i=1}^N \\sim p(x \\mid \\theta = \\theta_j)$\n", + " - Compute feature space embedding $Z^{(j)} = \\{z_i\\}_{i=1}^N = \\{h_{\\psi}(x_i)\\}_{i=1}^N$\n", + " - compute MMD of that feature embedded data-set to a reference feature embedded- data set generated from $p(x \\mid \\theta = \\theta^*)$\n", + " - i.e compute $\\text{MMD}(Z^{(j)}, Z)$\n", + "- feature embedding $h_{\\psi}(x)$ comes from the learned neural posterior estimator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "combined_samples_uniform = obs_latent_samples_uniform\n", + "cv_list = torch.arange(0, 20, 1)\n", + "distances_uniform = np.zeros(cv_list.shape[0])\n", + "num_test_sim = 5000\n", + "for idx in range(len(cv_list)):\n", + " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", + " indices = indices.reshape(num_test_sim, 1)\n", + " sim_images = sim.simulate(num_test_sim, indices)\n", + " \n", + " # transformation to latent space\n", + " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", + " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", + " with torch.no_grad():\n", + " for i in range(len(batches)):\n", + " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " sim_latent_samples[:,:, i]= samples\n", + "\n", + " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", + " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", + " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_uniform)\n", + " distances_uniform[idx] = mmd.item()\n", + " combined_samples_uniform = torch.cat((combined_samples_uniform,sim_latent_samples))\n", + " # empty cache just in case\n", + " torch.cuda.empty_cache()\n", + "\n", + "combined_samples_bife = obs_latent_samples_bife\n", + "distances_bife = np.zeros(cv_list.shape[0])\n", + "num_test_sim = 5000\n", + "for idx in range(len(cv_list)):\n", + " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", + " indices = indices.reshape(num_test_sim, 1)\n", + " sim_images = sim.simulate(num_test_sim, indices)\n", + " \n", + " # transformation to latent space\n", + " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", + " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", + " with torch.no_grad():\n", + " for i in range(len(batches)):\n", + " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " sim_latent_samples[:,:, i]= samples\n", + "\n", + " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", + " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", + " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_bife)\n", + " distances_bife[idx] = mmd.item()\n", + " combined_samples_bife = torch.cat((combined_samples_bife,sim_latent_samples))\n", + " # empty cache just in case\n", + " torch.cuda.empty_cache()\n", + "\n", + "# Try same thing, but for comparing with OTHER feature space\n", + "cv_list = torch.arange(0, 20, 1)\n", + "distances_uniform_bife_obs = np.zeros(cv_list.shape[0])\n", + "num_test_sim = 5000\n", + "for idx in range(len(cv_list)):\n", + " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", + " indices = indices.reshape(num_test_sim, 1)\n", + " sim_images = sim.simulate(num_test_sim, indices)\n", + " \n", + " # transformation to latent space\n", + " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", + " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", + " with torch.no_grad():\n", + " for i in range(len(batches)):\n", + " samples = estimator_uniform.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " sim_latent_samples[:,:, i]= samples\n", + "\n", + " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", + " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", + " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_bife)\n", + " distances_uniform_bife_obs[idx] = mmd.item()\n", + " # empty cache just in case\n", + " torch.cuda.empty_cache()\n", + "\n", + "distances_bife_uniform_obs = np.zeros(cv_list.shape[0])\n", + "num_test_sim = 5000\n", + "for idx in range(len(cv_list)):\n", + " indices = torch.tensor([cv_list[idx]], dtype=torch.float32).repeat(num_test_sim)\n", + " indices = indices.reshape(num_test_sim, 1)\n", + " sim_images = sim.simulate(num_test_sim, indices)\n", + " \n", + " # transformation to latent space\n", + " batches = torch.split(sim_images, split_size_or_sections=batch_size_latent,dim=0)\n", + " sim_latent_samples = torch.zeros((batch_size_latent, 256, len(batches)))\n", + " with torch.no_grad():\n", + " for i in range(len(batches)):\n", + " samples = estimator_bife.embedding(batches[i].cuda(non_blocking=True)).cpu()\n", + " sim_latent_samples[:,:, i]= samples\n", + "\n", + " sim_latent_samples = sim_latent_samples.swapaxes(1,2)\n", + " sim_latent_samples = torch.reshape(sim_latent_samples, (num_test_sim, 256))\n", + " mmd = max_mean_discrep_gaussian(sim_latent_samples, obs_latent_samples_uniform)\n", + " distances_bife_uniform_obs[idx] = mmd.item()\n", + " # empty cache just in case\n", + " torch.cuda.empty_cache()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot MMD, highlighting the true index, where the MMD should be smallest" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(cv_list,distances_uniform)\n", + "plt.scatter(cv_list,distances_bife)\n", + "plt.scatter(cv_list,distances_uniform_bife_obs)\n", + "plt.scatter(cv_list,distances_bife_uniform_obs)\n", + "plt.vlines(10.0, ymin=0,ymax=0.9, linestyles='dotted')\n", + "plt.xlabel(\"$\\\\theta_{CMA}$\")\n", + "plt.ylabel(\"MMD to embeded ref. data\")\n", + "\n", + "plt.legend([\"uniform feature, uniform obs\", \"bife feature, bife obs\", \"uniform feature, bife obs\", \"bife feature, uniform obs\"], bbox_to_anchor=(1.00, 1.0), loc='upper left')\n", + "fname=\"figures/mmd_comparison.png\"\n", + "plt.tight_layout()\n", + "plt.savefig(fname, dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(cv_list,distances_uniform_bife_obs)\n", + "#plt.scatter(cv_list,distances_bife_uniform_obs)\n", + "plt.vlines(10.0, ymin=0,ymax=np.max(distances_bife), linestyles='dotted')\n", + "plt.xlabel(\"parameter for embedded, observed data-set\")\n", + "plt.ylabel(\"MMD to embeded ref. data\")\n", + "\n", + "#plt.legend([\"uniform feature, bife obs\", \"bife feature, uniform obs\"], bbox_to_anchor=(1.00, 1.0), loc='upper left')\n", + "fname=\"figures/mmd_comparison_swapped.png\"\n", + "plt.savefig(fname, dpi=300)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 64-bit", + "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": "3.9.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "7b7fbdd20bcc2083504065e64dd68e11295ac29c39a09e225403f090756a3e6a" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lukes_folder/figures/posterior_uniform_bife_0.png b/Lukes_folder/figures/posterior_uniform_bife_0.png new file mode 100644 index 0000000000000000000000000000000000000000..68cbbe86c7d5b61d917708d5eaf39ac131336a95 GIT binary patch literal 74370 zcmeFac|4Wd+dsZ2X(Y{q=x|ixph1zTL6o8*k;pEUq}Y+6OuJbLg~}97C}|^^r@K^6 zDJroQvZaV^OxdQ*_geS8ZJj>n_j#V@_x*fdujh}mUa#!E?|a>AxYo6<;eB1}?%ZZ0 z{Rv}djYSAeSier!7@=_)2#ukQ9tBrsG#r}*AL>WenjbN7+;_xnm(yNkxa$c0pyQE) zcDv`g?samub97juv`lG<;@tg5j?kS~C@UWlR8VqsvQ;kmk@o`{8AD&U-5DVzS^Q7P zJx0eB2_dvzchy$+$mZ(g;Ms+jiCQ$P&yta%I=d9-tS~xZt-<_^B1G%hF# z3ijfHAMiy&*zKpEK@&4Z4F9}%f|xV>i!o(n>+mmhdoEpc_~%@a;~K-iko@SXpE_N) ziY+#LbLH5-bZPugm;Vs?ZZ*5BhaS5BBui4)*0s4F0e#Djsq1PwRniS4COj-Z^ne5nA0ZdK|~I$5oun zzo6y5s!N0YnEcwk@qNkhkP}vQx5jsTQZL+~U@hk~|@zu&X6;Z}?3m zHE|Sqf+vQGD>e0-`wnDm+@&MRrg?7~iD-rZ?PSa?UHWui%F*(gIfaXQ^K`!HBKiP4 zTBG~KdV0htWK`fSijK~4N?LC(QPAFzt*V{#y3$*WFzMEkKn&F}L&#Ckv4KbPY;@J$ zjReA}>FgcE_}frX)A}pkRF$BG5Z$7(Wmy)}qkX6SA7^%%+sPm`AL-V_FAdSMt*zUl zMx%fd=(?fhRfMlu;>Ua?9sN@+k_=@wR~MlxGIUYKl0_xX8%rNL8``%7bF-L!d2O3i zry;g z>uCTmrIkT%Gd!JeTWXg@+r$%Pqzi zB)tZfz=e?OZf#Za{?O5*7Vw74+uaFV<_+|upEi)sTPjRd0`)Y9&yFR_3?(}2mY3+? zmvD%p@KG^2is0<`dD(tiW+nO=y5q^`)I#vSXWUaAV!A^&s0kI<1Ul9{>;JY_H>c0r zWyYCYAKR#!>WN)j9VU!G85?1+2r4YjO zHoGrTyTY9uEqfiwM(xK1yr}iHrk5m63SJHm)48}FaW<{eS3E!7#eN1NevopG5Y1vv zjwHvKkie1gG#!WN*Q$>H8%UTG_6TKc)yeak-C1-sKiA>@Q@Doa8IC}b8}6&#Y-3UMfGWjjfVz`HW964cy!v26K861f@x!};E+Uq>eZ}LJ zw+JO{7>20Lh*xkW^5g?hlfz|V?MOGY&OW&oI)#NIoaLVN;ELOA;68wUb;%(33YqMyG!Oinlv*xzTwI z$88bnlK&q7|Bqkcr}>3dOA5@c#Ct!!rvNJj4-eM-Ny{PKnnW$wdSRDSe~Jen#^}EY z==H<%hF1R{VV$QV_(NcO8kSD!^qU^&z0=;`I5L&m2$Fi#8WSmAZf?_cTDm<+DUy7Y zoF}8RzCvb3L2*Pzms2_o{mFL`oC32@9>*( z#-#zX9W!ERMcC)dW=ZfqnBX>ZO#V}9fRxF&Ni~0R%jI%l%Wb5Fn-op;&0Y;8k9f7O zWf883kl40=+xuZ`gAzUa@3&Z)z!GV$5%3OC3VWMzaza6p+bdG$3YS82?UJNwugqqa zXAw)u9*{WSRA8=SxkzoqyAh$c3;!*blY(V#oQ3^`x(v|r*h<(hL$wBfm9D{Dx)Y2I zzI)|&%@AC+y!4AC_^SpT^A|?{Ump45OIDa0!+z;6ddEp%l_t=_V=E3Wql}7+2EQ0A z^b`D0AMrFZ-?5`exy|~j-TADKn75HApSKaR3}q#r6-~@P=bO)5JO*wNr2-)kRN;{R z#-+MgHFbp_mTMW7e946vzyBmWX{vj{8W=8fACDOdLE5%-rRhECX6LpnyUD?}+@R!P zxAbNju3RDrO{&?%g!q1y&SAMo@Xk8&dO0_j;Py1l{(eeCSww@c-Ld}8#cSVvc}Dfj z3N-_;@U6uT8n(c?bL`Gn9F!XDkMQm07#HAP5i&Cb0W*Ion0`=|x&(g- z#}_{dQ-oHY;;Uk~$5`_2!+>h4-V}X?Fp8YY=cMQg2idIF>gb{piNonln5^yRc2(r> zla%cpz!I3Q3Dh#LaLx8_tvOAIrIw4t!dMw4oGfu+hPBxo$`Hzs!L`g;E^jo3SsZEq zY8*xmD?Z8ROV_?s{>bYY7;qnSxGzzc;b90iIz1rG;dr`z<7(lGah?2JEAM%rz5Rwn8c3b<7P2>e`a6xM z%@zTQebJfu6Hok4R0$=+i^w(Zt+}Uq(_K?wgfJ#cWQ-uDhR6E8s&Djh#H8--gz-^j zrS7RE=Kt+pDJgT7;!Fq7)a4NZ@y6iDf`u?{afw9fLmw;q=saLeFQ5J}#NgKVlybGx zm3&(#I-0*1H`sHsv0j+|g9l$GX3Ee8I7ySrg%~;`G`qrJHS6`;OjEJy~7` z2Km+Jh-hhf;GqhAHSCR&jkV*3kI27*Sx#*nFdX~NnGu1J;Q1X>mBzy%j7p~@zfZsx z0wSCwBXD*FzpNZ<1eUQ_SBGjkGgpMMSMota`K&FP&-iOXgke!4I{QrOe8k`!`BO?0 zG^h`Zm%UZtiDCf~Fa;U4`t$=#Nb|DxXjDKUT@;<@d98`HZcCbfWhQIrug zr0b%dN>OGVB2;0tL|ABpub1c>sm_TXj4cn3L-t9cBj^)`hO3DU5Zm41GR5ELS|Hll zzP@Ss{SQ10@2eWf7mXi@#rYr|RGsM9-xrbDGJCJV6pO>A|iFpL=^xMf@Rzm|?(B#TBlz zliTK6oP8~jQ*n15H8Ok^LS8gjlNG=&w@7E|H-@&tt$EI11PxXgmAScN}sAILJ z|09#$=G^wGv(Z+Fk?O@4Uly{@4i9FP%bcklIn*|B4)fHl6-#es=LdF|3~IJ0BKlo} zb6@*Xi!#073ID7hg!GpG~q1%>tIT@**{ThwdOYH=;wHQ^&UlYBS6AgdkpOj zHEFz&-b1J2zFig?Jj|g~iOm21Z;A+{ZieO1X10AmHcO-e@2(W?lw4$(&62FZq*%GI zKt=MQ12&Kco|ICZzbBn8toO>J1Z;!KzvP)w_sPHiUuH9XCZ8wnr_MOD>-agcGQ$}Q z!`t97GAnaq%lGaBW@4-fF}~T>M?xoznuo3xSr)Pe>aYrkwBOgeSe*j>Ko)k7SnkS$ zlDt8XVTzh=qxqNNgAy&<4*N)hd6Lb_7{%W{z?)fkeNA(`bA9~naoQDCm_O?qkJLO3 zUHfRv7vDh|wqte6FYM;apU&(@j18NAF>kRNRb+Q{m2&ZHJO)3gGKQW(xW{0H=*%Ut z97}P*D2X-E&tD1ZAVfPl+znj*dqS? zXjgSGUBZgnTJojAH)Ds(khhdE1KL!(3AjLYzJ0nrvRq?as2Y&`^8sBfT$y)Mm6lR$LHU5D56ipYqMrO- z66jM}1Ey?t9>uh3Hb5-e)B=@et0e)qR0N#NK94tBiYu|IT|#1u{iy;re-H>cVROQ^ zg>jNCE3+Fk%P5SVkmX{8^F~26Kqnr7mjTNJKx36})bGdLALla3&23q2PD1QwR^7VB~&$v+6s)}vM z#hr*g2gug?o?&6XsDc3yg&>ub;p@BL=^4oCcfx4I@S!XU(Z6T`+#$mrv;%yI zAGsFXgfBp${qYOmtp*YZ%F0Q@TOKa{B0>d2!1WnZNNAll=6;K{iM?(|H)*ZnJH!E? z6Ja_7Z<$3m+cxQ7Ll<(9hop&XiJ;CkdK8DUMPm!?ir#B_`_qX&< z!U{?eZ~f>+CH9+O-J^c?JrVs_O?CCtO&Z$LVB_bH#A0e3cAadi00$I8?V)^B_QP-b z7l*cGBdJc*6v)8Pb`QSEjMyVFMl`bdh+|X1V3X;$Tqz z8wCDn(o0P|#J0k8M;q^+#WFI$W8HFC!<%1%d5R5fh^?Y{!^ejBdUKa}!d=Zbnk9=2 zOFz2YPZf!MpIF{>bq!pdI|0~Q@@rQ)w+~$`8xloPx-fRyZ$Ax~kpe@4m1}mfnw~rV z4Lpep*aQF8@f{945HXb`=JC;m^4GP?DbK&~9C-d%Cs6G@5x^hrPgI(%mjpZt3gqAahKI+mofLv?Ty8knGP}Y6=yYF4?w9AOe9@|OfLx=BxDekw(g2kr_Z<7v zOH$Xlty~`Um`*HORi~!cHuxvMQF=Wo&@ChXY74qAbh|pV;!u+eIWQjbBRL^;nkjBX zpr3_o!1p?SI{>5j!o=6vLx{s3q*Pv8;P=k1#F>S@vOF~a;)YFv%b6 zZ=}%47+ZNrm(^lz#%Z*D9~0vA@N7C_zjarkgq0ErOET_(qqm&j+n>d2?+ZFc=r$Yh z{JE!x`uO9_n$}x7UmJ3>)4aJQerj(61yf-o*sOC;ldvGW#Lv7P$E4bI`H&-ngIU!# zG}gE0X&;Nr<-EdMVrU%9%P>KiPZ$C&mlCLS#I*by0E!5vtljhaV+0!^3U9>rkp=XM ztiu<+_LUY3+MwIPB21Lf$parF4;-Gj-#WoB$M6=RYA0$LW@_*MDT`7F8T(-ZzP=W+ zyG!gU2L{z)e38X;;_`O9s)9PkT0c>TG%^TTEWCIe3qLjX^;LqqUD%=0gb>5>nh``p zDz?yTTJG|1(<$7)eb3pCI~NB?%^1Npfw&siGOWzP?~%)PvKHNi@t_0dUq^GX z<>s2G{(aORP=T>Q0I-@r8rFyxH~bCFKs-UHtu6zg6acEgmBIx}1S))Ji($3Nq~1z0 zk*BO7Ttj?o&Bc4S9eYKh!Mk=p=3bk8A(V6&oh54BGecBf_Y&@*1(?a?JrMX)ss?466{`=Wc z$E(@)A-b-nwtK5y=yWJY7aE&Zzg#iih5O34`>8teb8@z!bRDtC(pm)397a|Nnfph| ziWxc%uGj6aJr~XE|5DUlCvrfrdC-e7z{5}!SC%f_!ur9rcC&ioz@@)x^S&yk-9Km7 zZLDGtQgIZfjNx&cSuO`h?R(~!{tR2oP>Qx0T9IG)e{{dH#fTTGhEHnZ(#@>(pA_*p z-hDj{Z+zMxJ>nYdqq@U&iM7yo(g7FVv;AF;Q9G|1FD|$SSa^GQh|Izze2<0Z)Y>M5 zF029eqse5$y|f?A6y$|o9`A!(lDdhoDAVpM*ac7ITJ-ztLc+{ zOI)T>cUF$!auJG^1)-#vaf(sdhU_%f8;hS}j%r0kw*wp~`8 z@aw7CEmw`tzc@n;3=tbKE4DjgYiN5rloxz$ctwBdn*=ZE-1JAy&y>{|^?moZlE+RP(+ z;lBB_Y8#ULG%p}NbFk zKgaWE)olLiL1WYLI^T8*sBlo`4!BqizP{8K;i$y*qX`*EPg&oXH%({4TOgm(C6|+Y%(1h{cF5EXuYUkCrUqG4`%QpD7xHc^WVLln26LTs~b_p7X zG;e%YUv8oxgrZ5FGLnm zcYyDX0+o^b^*sk~UOI;)@9lZvJ4P06madmyt3m%%4jVKw15bat43MOKxz(x%8ZlGK zUIbUmabqrQ99ewBPNu21mfIx~;pp(}-npVBdq*;iAG-iaK|1PaKll1QW~s4i+}-$v z%P6XhfjvWC?>l+qhLo7%5%@Zo72=xIsH-UQBxaRha5$N!zWam;E-) zcb8n8lY1>z3`IHKU2bQPJ#Czf@P>D=B?67Ea5ou$aDGf}p0Ete(%Nl9`OXfj>Pk7GJ!%!6u@F>SI=+Bx-y&)Oo!a<8uKlcl|udfMt_rj#rR z@f>9+e=}amdK{5CM`P*)6e>y)@x^FZ*N{lrw932p7)1v1Yb(=Ea=;_GcBS6&D6h9= zwyPD_mFu=j*F3IJD96iZP^W)$xj z5ub=?D=Mr|t&@mO_KPon{FrCBe)OW%6D1f< zre7k3QT^(Iaj2eM(Z{fQq<6*md^}Ay1yU;T7GQ|>4&-U{T?`6t*>KO^$~r7D$Zto( z)?+uCGq_6QX101%XI6c@!qcAK&F(kQp8c}cS*Y)iAG`keO&1w$Ii~IUymJ9!+s)dE zlJ>~GTKysIk1Qp7$Y692hh-A~ESX##!0m&5ZF%vIw1(pNxLMhgTRE&mQRR?1vp z-};KP>u*`NgXCp_@bhoK)d`SVDXes(t!Ch3p3l{T*N}?8P2%=^FDcL16Vvw8WDXA0 zU6gtJd>RT_(6h02#XOXx2c2ua<~oYLD{ee|u{BLmRWx$d`qA1qHAnkg$jkScQGYQ~ z*1N;cufNweUWlw2ztb4;8v-$ESlXm9kYBikvO0Y`{_xt4yoXA>Tk5NR{`;zD- zWe@y)3+C^5?1>cLt4sGTe7|!;oMfej5JL_WBKvrDcKV0|F_y$xmjx3ZCsuN1WIcDy zsq}Sszpz1=7wz4?XDD^CUDO|nHh9j??>=4f!v32YJ;|{|tszy+V^ri-Eq5y;EphF*>}-XDx!MnE z`s=$MMY%&zsHv<#Rn8+RcWUe|@%r%FAvfpD`0c$*^$zi)m$kqDh^Wa42=VULNOjz# zxaF9A$;UB8`@}~v{TUj>dC$IH%u~-@z%;Jmb|en=Cf?%Z<@bM4?&v6#4(#e$rW=L6AA_F5k8>J!=;f?K~x*jATpQopLnT63~% zTj&`rcZ;i8(*1okJ`!GoFAWdGHk`AGD2Os2=+oo55Ml369=GolukqqmyO~TEB940Y zHpX?_v*sOWT8zw(Z_@60a&k%uO?(vFviVVo&EJ0#Ri-A>VK)eh1Tf}sdHF=g6Pm9v zv>J&TI#~Gws@^<~wa?%WoIc+DCeZtPGv=y9sS9a+pMy7jDiGJv)IWEx&it}-!V5K{ zFOv5o)oHmU68Jzg^A0!2Rgqm~~>>Gbv5&!uUuV)8gcXb!yijWqoNEyF+~& zJmM5!I$v2{ifUc)TlZoyinh^S2E5ipsaD=i}k?Xrb*L^q%^gD>bqFVQ`4jU zJJ~U`e4*~h3Xk@V=0~M42Xa7*14fnZGfO2CZ5XVztZvq z2(jUkG8bEKLxoWM>!aiTV~;-8fCVP{A@8}nZIG9*(;d#^llX3=&C^*u&Q`DjLnKbP zq{E_CfF#6|e}8)1X8&GZ z@+Enl^kDZRR`LL=Y{Z5sv*7 zG(UP7R5BxJbQim{;$&iEu9(_!tWjv@YSFhsFOpvww%;-vJi2H!I}jt=KI`lBzB!qw z)8Ag;-tpzfzwvb0YT=z}2Djq5+pO=hM4ASa8ijNlU6pg$@ zeodll>o1b+VmAQAG=odDHa!siFQ@k(lj;9CP10Wc4!q)`GOpL>$iQQV4CLcqq`0Dl zlW^##Uamosm)$0138n+ea46ha(xE;H8NsjXr$Cd@A4WPuy3?(lm1&1SxhydoiAs~^ z3llm0B&8IDUD(YZBl`lZ55$4*a6Xv4q^|h{8=p@*BnsG#S(Pc^ zT8v%8EF8GiG)5i5d&J0}v@#?3yO*?j!%6!r=4?-CldXtU;EbvJ!NVS?Peq64tX^ zPTCtAY%%SfB&_0tSL!IeI**rV7k-BW*q^b1K5G=Q0^^EQiG1UbKHIXCWM7GRAx7!v zNSo|xsUIJ*m@RHdGeVzq+xeA4Mdl3IEfhjl;$vkXak<Iast9Dop+(ADI4yYCDBn(b=>j>h*un{PE7m&jJvf{+$ zyPOXLqQukTTle1J-73y(%yPI5Fd%YQ5-gkSS27hL-^N)#3(nv5Rbv9J5)K}XQvm>zc$@?JBS9pU&)VH6_;W4!Z{D_cgf9v<@O-ZPZm zgBYgZzv72zTlt$d=(#o72+_-@rAj<0!tNGPZSTADi{bhjPWQ}Mh4D+Rj95-qMT@Jf zDL?TM_+Ao|Zn~MNDM-JeCUAH4Lt@U7lv9sXqQm93oJ=mca z+3XwmtU&155M$sNx*^L2xJNG@KxQcJ=Rv?EtmDTl-~y4b)pRPHpus}heGP>c$@`mN z>U0iodqDqWHBVxkTKiW%|2u(ol?2wcGL&MLuEwIDD(y>Q1_I>4eUT5WngRz zm+?&i%|{Prg~-G=k)jYVV;8H4xx&P4YIwf`hFjUi4p+`^{#16aw&NXANyFURkk33M zTP02jegkT>>*C{QHM_rck)4XgH?iMfmh=(O|JR=3Jw7arQhxAwx1)cP`l@V?lS>FK zfe>TA$4W1blzORr(ZF6{IC%?8$Ftf+BD?wsmex>;Fk}sd*zz+^(+vN1OW=x(C3fL` z>vh%`XL;hys2D6*6Z$llImxh=cqSWujZ6-2Ot|}BUPFB!OlPx=?ZWW|cq3(G~#(1LB>=;m=?Oe`6;GZ0d!t5rc)hIIqL)U(tZ zz5=N9JUTAqW_-Maf-u`qKwqFrS$9v-)sDguI*z1yvi}EazsR4kBMz%zApP(GI4vBv z)Nt~TTjETs>B*H^mTt~<1q$%EF@M_aSF!?L-yVw?nfEwZcd`$d`fnbO6p6gE1yUQb zW+4@9u~CIqmgV4XXmFw(C0I8W>_RGXKivW?r+Uc#yaclJtMMK<`d!*{q6Ch`F+Udw zqg(uCCfi}^89Wa@gqG~fpaOJM6ibJI$TY7zb>ErX$MzQdIL7}*?(r{QhLT06xkU?kcXPX%vFU8>iW@#_;=c+{8T zn|84wN*y;OM&K^(i?P8QF&DG*ef!2~kOQ1LSpesRnwy#?zp;W)D`meS+WlT_nTA>y zE4LoLf-vAOcf!_U35WulQXPgs8|?V6mZB(f&A%u~#P|U;7`i=dLXeMci~AcL4R_e_ zOAU3c#1l6(eoLkL(8MupDD<^IZ({}zL{G-C6)XWI2~8Mc(k#9{<#uteyv=N_7|hGd z-#AQ8()v%(qvl{)oID6;RVCW;eFlzEoxxvr6|_uy<`nR?CU>wVJ-xIXn1jfX)Mx}< zVbGleR3f&LRlElle+o32zUT&%{lXpF|Ff_{VjSJx_xlnEkbdc$2hEuP?=miF6(2NH zmoYSX(cI#i{Ydpt<+FV$GfDLY9U9{Z=j+9Jw-<>a%acJ~OCEnbdj73VoV+x#7tiSc z7H(%*w=b1`30zXCbIw;svS()^L~_Gg1_1RoOAPRY{-7|}zR|;E8S;^lWbJ zgce^`PMQ~{^`%wm6OXIB0|*coR@Yz%H*ka*?Nf~LH9sucyIf!nD@ct1eMk2%*M2@8 zY?dC9S~GxJ>=VZ4XZ(3k|A&iij$};a_InWQWS++=03n}JaA`*TQ5Srr15&Q`_U${B z>l__)##18vo6v>z(2vrc_^~U(JeL^All76GF)OuR4Q>82@2KX49#oIV0)>&XAZ*Vx ziH@I)5@38XdJA{e{BEw_2k8`76G4hc-TUl(zX+Oe8UJX z1ThVZ_Yw~K;ew;+ zr@w-J$2lrk+z%CO&N0^hDX`6`>+GQMUetr+@Bad}tGx!+C&NY+cC_@);J0f09gPy! z3Pi76IvjpH2g5T@5I8fqX_^&Qd|-5>xrk4P49%nahI4Sxzu?IN+T%y(!ub}Q)j|nI zVkv*_aEEd7C$Flp>3Hf3?73dc8jnIl1iBv4H(TjbOF^jKiB-Z)4i%Xrut%Pf$snSv zs%jTmQ;LJ}{RV7qjDr|^i>n4e6R-)UGc2~6t> zoAk;FOdR%(c!h0iu-D?5_AF_Xu~49|iKl#j+#t3AH$6u%DF)cj{7E1*4Lcpv5=ck6 zFELx`Q^)u35fMwB_~IHriJ9FT;@V)FN?MwjQfAmVi2kM>!k(y-Yhbh@1?Lp=tqI0G zGt)Bmn$HN?Uv<_UY~>BMX4#S6#WB*P!y`r=omVA%Bpcm^oJewt2-WLSTN7%`FtuXM z`wCx@u9CF23|n64-xh4FYwB+%j+=nekHh$oEf99&aNVKVH;&IjHk_SYOD_f|q>E!Q!8b0g!nwe}uDd8s$SG2Ctms}BP z8nCYJ@(5z)@RA_nG>ID+Sv@=OS@Q5#`T!&*(Vx|@xQ;%FpO2mjX`NqyHXFel%r9UU zN+W|w`Nms^d}Bq!VhLV0CEPA&*MG}zODU1j?)`cexO;bcc7L0QxrRV|V=+8rxb(Mmld4Q7 zrzH6XY=Y&&KTssZl7|L?RK{WB?kaDN{Mu8r6~sXxoXXurQCY*{?Ft0380-Bz-VM3t zz`Nsrbqj~#4`SvKD$Vc@_2afU6@{Oe#OE%uV@5EkV3zGp$@Kh#$a?eIEQnbAthQ z@^x{@a$5fu*>78XZc&VHJa%n13~La&-id>jC;3({ZNNb4yWNh#Sm7MZ+02U$d>G*B z64#6O71q52Maf|X>*j(H%2e9TN^RfHo?3RaD{^i1pl7#nle zy>qjgu5s=dQnO&?@^Z7YJ2)s~n?PKmaa#b+d3Z*I;rA_a2J!G$mru|S9@D;0SWO9v zIk?hF4vv71$geeRTsyv9a=2MIxMBeLoM!<@1r^)wk+`#^OWAu3WHP=#7?2>w{t1+X z_Z8fYm-$>GRR6T4?Q_DKt#33}LxPZ%5Y}vz?ZX z5WfBsh-)eI$oRVMFHVf5G&~ua%y;P8KbM#o$Zn&g#mnvMZR;26XJo_K6Jr(}r4+Z0 zoAM^D07Ry5b*qz$5M2v40PUHvfq2O*Ax7EJDv`Z4PirC~4%=g0aOLjpC7qjOK8f2* z8G#b?OwTDH6$(8KeO;%tpl@s0kg;*0AU3 zbyV8~kbZMv1`OK;c1hrWnnGG5zjL;om-M2g2cKS_it5+F>MTgBoRDZ&seg^eufTZo zMRHfgShN8agm~&cY3Ot*-~?hoP^D+V1XYt2gEjpzNQSaIbpS4z%LrDAtvD1U@!Pz( zQH{Uz>GFZx>>!>v!XwKyKEJ=Rz&;D)=GON@7VG=$5R-)8O=LZR3Z)>G{wM?@Uu8!0Q6#FE0FF&PWhg`0$7&56Z) zWds&6lq9F&T{S)AF9tGwr(AHGOt}t^xhLPF$py8~6U>S+E`Fg&fKd6%&)FVb8mKtt z@1017?2sAqGL-5ril2|bxCMEzvz_Cp;VmKaiplW+__Hv~bNa5{Ij9-uUqdo7favUS zob9N}$M%Sc!EK1`4g=?B|HaNdD}q)YhY&zU?4cdgU*J|5YZqn?=4>*M==G-C0bniF zJ;LHGxB&}5G80<_Xb$d_5u?#H;9RNqweL|35JzFI5W2-55kvN}>;4V$Yh&)u#Wkr6 z=;HVz-#%2-32zVay(HBQc(iLvf}L7#&PfA(8U;iOy|Pk=khxpY&~~ zxj*!!r<+ewo45?o!~hIPYk8z<#hQAy5#B5Kv1-73l7vraJeDXKlhhmF1u`Yx*E|vt zArGvg77=^y4yVBWt)vCuPA>s1Q0gVOu~}OOuYsxHnD+F$gQKG;OEI&^1Dp`?k-rBq zMj}a^NSJb$b6CTPZz3cawdyEW`b}3JMOL+9`Q4v`t_+zrW69{jBNu>ujCQQT(}tIm z;K4r(kr~^{3Dj0f`kg-aAY4{)T+=|A3=f@JeQZq|i-8_Qia>apx(pmMBC%N>kCmRD zzIINf5F!Cc{d@6814O1^gUdYck#n)5o&r|*MnTR`T>JW2sV#@OHp+AEByv9R%n|!0 zOdjq+q178aK2M_bFDomKADf(3Xx2ONWXUL`cv+yGUREXVT8lE4!p#O}A!Ki+vX$aK zstKh{qCrQkXt3RdwYJ%{w5-4m(k<`xE;2rU=iyxuKI%(8weJq+Mfn*0$KT$$ zz(IkJZIkchCtzr`4#Kr{18_25PT+UF~9ng1E<*h>OW1eq2cW?7Zp} z9_X+dRlJxalcd`>Z*qu{0?pHFu#0OjFt~X8Zcpv$C<$k`@(Z!RVE$wkaW^+?Fb=K> zmO~7aVbc`7ykOK=RKApJRaW!PMd}-3Od5W4@`lE|x{isp>oTNP#3=8u>Khz|0`MsX zs;DH#Pdas~;ccxXqFoaZ89aD}Ya83&*`NJN*Y+>b_Urz7OFD1a_D?z3sw=QJkZOeq z^PK?PP;v2|6E0bq9fKUXFQ5U90HEE7btJ!-$GkOS$ybYhi!*-PvNP^`r$*Y2V>Q;X zQs`&AJ;@;EczfElG=-ju>ejxzK;N^^TfJ7bW@kU1KXO`Ag+rqNB4T(9PmoAxqHV6`M zNx!XJob)+s1rchT)z+nToAV<3Y|6$s%acc|{@@iU%kIoQmQmlqLc~#Qfc3v(vKMXx zyQO14breSdXH3T}Iez-?LM!H`j`y~Pp5M3d*dBdSqcSqr%WhN6E3AI2RU(4wejb#%) z5a|^fvLA?3Ro$>rs3QR4iSJo<+04^vn;<%>tEt&8H|Ld!!IlXRujq8mdim~1)G6#TfyxFMQ+c%O=b1Sbr2!^8-jb#cE$3C`aMY<}V!|{&{y@7hi=Z<>DfCSE_X~vD238jB+py=hsb>B%3zt6^>B_Bx9P;T=I6WKb#Q2^$m!Vg zDB0?Z5aWwLV1NOmXF9=ccl528a;-Dec7si}W`K-ki?VLZ>A)d~aX1jX@#g2dy>~U{ z@;4TJrkk5p)yF(uu1!@{q;kJCOI+jG2#Pl#hKtRa9+$aa$2L@hu^v=RQ4;O@T3_GN z24}cd)_wDJ&z^kDfXj6S|0KOlBLA2vy>tewC?;h|lEV6WeK;Q!)im^^opmBJOPeXQ z-P9`W@cU{j&9C3R-P|&3;9Sy>Si2X5!HHT``Ih`JainrOFMn{n^SWBb7G!?SEHRb| z2t)*kYLy*w(>Wp-OmYXSSav`yDfWh?S;!pZQv7r*z+>mua`N0JrR z%5mnc05s zgS;Y^F#Wtu+{v0MOD1%H*QdQ!SJh}0#dxwF_TMX) z*K9N!PgO^j#|vM5jS4abteyEB=Y2T4v7Ys{iEBO@MPEy567~eS=^0j4enl6*X5eNef&==F(Kw=G72n=#t<9WDqv}E(!-)19oDkC6cx9QYcZG3s6L`P+b zPT;^v@u>dH;&ixjfp6c!*9`F_k4fICt*u!Zxmpmzn__C}Z-P;!$v_?V>u_vEYA1_A z!Esw^fo6%WCjs;J_dhEM6FdhJsD0(nK^|O-g&h^zE_`HT@qtToiA|s``I{!|g z%^=vEg?(S3)=Q)3?%iG{IiLb&aHAEZm%YBr8Lj04onrHdcG1u3<#s!pOKEesCCLHv z;enjHpEci1QHF0oP1xY(r(^AIdfQOL#ut;k1KTER%guZaM=^bYYY5$)`QE(iOKS2q z_vT4-_2rqdQk8tb5?y2H{L>UOH;qOKm&p2n=CYs8v#Jn6wD%CX8p90*r4iZrNVQKY z4V#=m`sLWE0;6w#C4}0hFWRT188hY3`duD7m&d+UF%V>w5s_p<6UOmEBImb8e4-x5 zoH)+dw6odM+_X&Q*}&yd^FO3nJ@ElBm9YHdizb!tR=>{Pi0qB}vu_m`p^UHvGk5uX zO%+_ zg`yX`XD(R*_Wl9V6rtbzDR+ow2tquIDbA#l3ki4FQS5ctGu>bI!LD>H^(Y|$nM{jj zQW}_k8x}J40L5l-pOJ#a8T*Lamg4&$Gfm!(|4IccF&h!Tkt6|2 z8FCko|F)AHM#Ecv@=aIjXKZ<%=8J2!)mHj5V0#hfaCo%ZAr5D}Ate$bzlIH~_Hd@c z?57X`rV2?*Mj4pQQ>ejlOrdYm*q((E6-EP=E!`2!*%1BmVnpq-dM@Pe3u`NeR~=px zVYq(D^~Xis@bwlpGrA+HAw#0FNzD3ynbS&HNTsV#yF=D2f%lQ)tWPR42gKZP(iiP` zQ&#y(fb1D%0;R*S#VI?2kRC(zr#mkMAw6>p&#}RBd6RY)*c<3 zmGfqDRxkj@d=NH-6lR)Gg%4p0S^G_=;(VB=$8b`WtoZ)tH8CX1zUu>uN#6VXPA8_% zc_TS=a;UUG;HXzqPj3l1a>TK+>nqZNl}R=jwK?^^=pE=qZR1H*AQyj_jF~F6W6>2- z8^FQ+XW$a|yrxY+Y$}#Z6Oj`A6dzf@bAqGLlbMLt)AZp8W-c|#7f&GSR#stfFnBBM zI5v!yWpV+d|n=&;4;)l1DsP$we^5lH^X(Yxw~ z0!%0Q-pdN!XAch*)Cj>j@dt$Ss}_z*N6F|D`D68blW!svKSN zL6~?j1hX^(MTnz0$4t8(8LwH+wxGa|iD=kyXB0$5#_vdZ`1W&@(gdXj+ZEiEf6&XhhgKI2gl; z>p$Nul?ZgSvHLp8atREA`6AioGvmjCO!PYKe(|iN)minDn?l7=t7+K}!}r3B)Xdyh z9h(M^RC%2s^9F0q3YE9CGW{yS5SjH95go1E*Jq;@I3HZ*GnQj<4R4+v(+PA>n&#$a zVg|@#lj(M@u>bCiZF0e@4abNeH5|DmMjNcCX*vc{w=>C6vR%8?tW(lzIoP0mqD5l) zn;y+R9T1M_V*)jHTx~5v31`;>vB_6$%s7s%*d)nkLe694#FwVGv`6P!)ds&=aX^^p zx$K&TZFwW`SBNr&nX=P&g-9xHfo$I6xK zZE_xG)&qZ*f^cCs&6F*C3oo@Gex&6Z=v5~gnwskKcuJYHFyr%0AH)_|-VO3`t8PHF zu@2zRY98^3sEZ!3>uYAg3GX@5Uf}Fjyq#c*b&Rh??zJ6nA)uz;A!Fr1lw%}CL zhBk=K;rEI#%IG%H_LrQWEMiqcKhBAtS9?{aNd+8NINY3{jp%k%^KNcc@G1*SjV~*z zmmfabeKw!T^Mc95VyyT;Msmrl7LR##U9SBr;Nk)}`n~4a*^ncrAuOcvUGK)Nc_5t{ zwR76Q&ILxmX7#>}+OSBpqw!6eR?7F{R~&QN`g}@u$lIDMS2)mdc_ZE5HzQl4W(v!s zYZpAkUWzvk{ZvrWx>E$mFctrxfYw8mV_SuiM#mY_56Rrr&iGUh|04sE!Gc6P$2Jt+ zXQzUL5R9qryxv`IM9>y)mBLKbDmcYkdy#4rEI@c!yK#K=#bbHF8 z*X#P(_GcqgZh2H@$&cLFP*Eb%&nBa1;{E-RrSlECr2EDamCbG}yXN(l^Gt;332)ok zF$El=r%|ft?Qo1^KG=yg2}@u0tgU5EkGOW~0DT|nGVp*$xBoH(P&IMrj z$+xhLKXpagS+Mss9L)OO`-7h3(=^c&_Qi>=-b0m7OT4?)6arXvA;U7d5uV%2a zu+7J(>PJWPz|yB0ZI<;l89^m7UOv*6TE91_dv1HLyPl|a6-ttdy$0zR;=meW8Tl){ znpM#){o|A51~-PBVk>i6lK#~8=_&fMu$$|Xuhrewp);7$-WT3qng6PGUWaFO^&D>P zo|>8)m6e^^4^yTUejK@?%h~36%F4j(*N-hLPO|DBcCv5Qur0$bFC6nIy42CTbA|CY z$9B~e(Zr%zB`19b`zpJ;>*@v$^?xsAb(BW$tS@mdPVexuR;*1~x8BWl{y7wv-G8)Q zT|I78IV7*27&suv6Jw~uTA5#h|MIHq5etr@m8I0w?J~O(y;iqp!DIE_Pkzehvy?V9 zmH2q~+G0op6mQw%10Y562RKJRa+H0}LD!u*tmG@s_noe%mW?^%pgGX$maoyslWv?`{J8G6nCG+Z!GXd--vJ$7oUd0W89(sy4RJuPkj0dg^Ozo)7ne6M3%gMcxL&w zydT%#KPmJ3+Sf4n1s@GO+J0D@_r0{c@PuKV&On_GvmtU8^XX$9?&pJW3?H(qhYyf9 z#zj9%^!{-Vn>QlbJ)eWqg%dSj!P&yhij&cWfB$(r^WHDXR9IpOO6dP#@6F?>?AnIm zQ;J5?fMmErBGM+AMTJr*6;XzbkeST$+0`UtgS0b512SaH9CoA1R7uEKip-Uy624=d zdvDZx-`D%zzwh~;_xs~nf0TU=YprvwW1iO8pzlMFgB)8jAFJ(x%tZTNKOs+jvLA8? zD?i^5nE2Q{JkoH`OoiKV?CH9Mhj0>_cf_)Tl|;_7YaAqa_lyn}c07hm$duuV$qyTY zRt-Zgr2D)La4gyHspcpvvTIgOxKHzmJ(leJ2}BM%WKg@t)e+B_x z(P`Q7?%z7Qnop$A^0tWt_xk1&kv8mCug=Uy*M^-X`sN?5PNZ4jQwe3IJ+585>REm{ zY{i+^UfrUx-mod}_y%%n`0IuF6qg&PdIW0BzMNdmvo{xyzg?8)tAeYNsEH*ZN@tnZ zeUST@lVt66G3AGzN1C?@&U8Lt=a%(_-XR&|%5=*s@SRy+e%LJyH1KpwoKJ{ejNdC} z%usL%7o0Jc!Oy*OfjG>xkeLJ{xTwbQ8#IW{c-GMy8s5^LOjwjnj^RUr$xmkv7ZxX2 zUaHG^)+PVJErTLd(fETm9rk!j<@(63hrB<^YI_Xf#uo(xAKrD{Pli3<2IB>(9f4UR z3I?MYT2mIM@Fx3-mvOqTTxc4F3LS zdH*|?ch6%<(2IVwbWC!Z1^)PuRjB&&7m>31j+ zQYn7aq8c*YpYi(hx{yev7;B)J^Ubly_EH9yg@?cz*D>XrVrPs{`76-g+JJaZO$*RnMYx>zp}{(sH}l~CLi|K#)kU&u0lAOa$Gj7CW)ukbT#X)ibN;IO3}gXFOJGcr@>d)e61vD*fP&Qr32 zItR_u-*w35U7_Mv=df8EPo$71^BsfffvidNE!$Gd_7KgqJD@O%8d2weT+QZSWQ@ zq=k$U<1K#Ix85ShdpJTsfPZ}Spqa+h=SrwnP~ezhkUql6`kRD@zq=V#@bVM%KL`5f zZt~^RYaDiJIY!R|pv06%ek^ITl#MmF16(r&bS0BL;wu;vQ#%Vj(qi6H@LhD@;sM;6 zX}=dKN?ba#x_H&|g28qv2*+Pv_6A<)+&8^gq~Hd-d@pSpA-KhY??0R+)c3P(5Q5zU z`zuO#3cwqiOiwaGL&+a9*)eeV#*vF`aPXt+Gq9X6+MixON+Sr-hkO8B0cVCCX&mIgcz%^)$X zfghYVu{`vV1#U-w(g{$HdYo9fLY8a7a8=T%W9-G?;Q)T0MGpyJz@_QvNz#HKyki3m zJ;b|IE&XX^CoXU(LRVKtIb8kHzYdzbNY4zFG2RNh2_7ODojf;f2rN2un}wc=jdh}= z2$yaB9_l6)rELW!83u)gyE4vRt$|;X6$blZ{?u2u8~bl{m<3GYQ3EN6 z3PJS{y1R}n3*|BGX>>u`@DFB=T#$>M;9L5}haonBHwP$%yl$h$*F9d5T4L8<0uW-g zkW~a#40y(15ija@0fnKGKLI&xdfw5tNulqe-c}(;V&iW8IxrIjXc-KOztvFu0Vt-#$9V&ukrB9>wqf)=K*up4O$S-(f;{U zH~e?>G#N_z>eLVLH4-F<33*^etNI)Jy(jOsD?skaUf2L_M)c;9TWRZI^d>79V?}>o z+PC5b7=GhVu%G5>XeKLuuki3+tn9)7)n+Y20uYgt#7Bn)W8`3fR_m1iMm5=1kTZ0o zagO$OKw>!TQOdElkA^7ZeRQfKN&~n`!i@>5N+H%;?YO%n4LLAL6siCU?f-HGTX$)n zdi?-@eE~;$18Ecu3ay?720#w!yU(sE25@aw-5zpENnqBh`^;%} zltXYwF69!fN8;cANCRbHGVx}Chl%>s*X}w;zpUH0d|tI{?gq``HYW!muXDR+Vs?Y%RDE z4ZbgYdQBHXX(@M;LN{&k&q(mCMnN$|Q@aLLa6TMUpi$`)ssFesf7p@;vktAB_lL6@ zp;UY=FzDlHXs6{jFhmc0L2ADu139rcjekMCX?@JIZSfqYfFt@+pc7mn?U?@P6_)vj z2KgY*Sb{T@iRk{cwHEyYhSoCg*r58lU`gdiA(9^z~!1&`5;`{#RCtu9LFMhi~+k#9Km`z1gcM=M&hKez2?D2k6FEH8ia$MJJV2AeryczDzil;+3`#& zjlZJ%k|=nm^+vy7bX87z)5dj2685QE)iF|FFiZpm{fjuvqGRxqaz>3|(p9J)zutow zLo$vIWO2>wEid`PYG>~P4d$;F>-xu|qeD`h5Php1$pA!{wnCPvk@jNR#-FpL;W0#A z)g+g0P9E+uID^LxqSHla4POoGaV`}~E0{F}1++b~%jX>!-5+u^rp@w=te|Bj5&uU# zDvFX4wbk|8*qRU+SV86HJey0tAm!wu`9K?@V;jCa4fu#bDm)hVszE;_xFz&#V}ERX zQINTm6h@RNAfAVRG#~|D&}pMcrhO?HD&!+d9QPb{;^zb>)7Z}Gtw4`!hoYN%O&SaS znpQe-=Knj)>EDtSnCE{){QeX3@BbeLovlf{8sji2^wVx>pF)Wwa!pjPYU zp9h+IkyHu}`#+H8cazHRl2*tpp3uof6PYKzIl>WST@48K7Eh-);1-Y)V)1#%vn7Ja zd?qnUHHoT?oBC$;mFyHkl`EiGf{cm&$LD}5W;XS!CHT81{YVM2@;@^X27!*U&9m+L z`LnxDzfnVRq(6UfQc6tYz#$Y81C2!pzTOIP=B5DNvJ3iUGj#}vgUzjvKA z+ta9tr+Nq7+kbN2PAuweL;77;MF+!Q!Ik9R}5R+ZUnV+|fxj4TAI>M-`xDJj2 zA*jB5Y-XtZ*Q`dFl0;^bbFosO18X~zXgv~{2ieuyA24CuC~biQ93YMil2&G-mV)DPKQt7+3G{{5HCnYh!rY&1Z? zq;Zh9zF<(TAxfMk@sE9TT%Bclo8=bL%;l#=H=Kj8?;RVE4uNWKz}CvTG7!hYeHhgH zIE4CSo-WhpI)7T622mjl&4(EhQ%#om1+C`ju-?*Gp8ab(Bx|Krs)7k1aYak1S8HMH zjt+KIPb`IzkFmU9qr}uXIcJd5TgKYBSeS4H1$T49laI`?wTEthiv)nXs-hIM0rnV$ z$)aYQ$hGltCWen^%o41t>#i+&ASf8qJ@k$Q&vbsB-o=G_z@BfS06KgTtxOKdEg+~q z$>nkZUot1YJAYz}U&r{6FW=|g589FV>+>y_7$;Glw&jrI$Wis7Ngp#VXt0~2ltg^b zHy@6yENdS({}CMA-yh7iF~Uz5x|f0|L7CFZy8){qq8}*)E?ek8L)uh$|I2xM?a|)_0jx<>ap~MW|pta<>`E#(#c7EpEFc(thpJq2A zOAZgcW7^>^*19$cOhA;_DERv9N*#Tq5WUn{_oEWY9DcvL*aB3rZmYbrMdg8BN;b8K zNIa%xnYkQ?pz9p&(uKHBT3VG~qzNpzZOns+-E;zdEsK?U_pa||PXqx}RPBjan7om| z!gdpPUt;$~5#NP6RF03!Gr(?S{24tf()lP9aK4OqJ_=P<3l0v}e{A|rr~cvnm1aaQ z=$~3f%FmPNmpY9jCP=2(Fmdm7e{e~l*Z5Dx3QHwy#KXg5tb9+4QN-^aLX>kLLc=+ z-HR}P5vp_G{a$<aJ0NlX?V^X;{En*s8`>yFi+FnqrZyWe@LDqDRyfff5x;sy-&EL6vcT0B=DN|xNwms@zBNkslNZ6-A*%7FhDfvEU1P-a4sE<``$(h^vrPZtKj^prEaWj)8 z2bV*bBTCN=^-2k?Iho|{cF!YWy!;%E%Ss2(016|^6gTc3v;$MzUf5}}W}4j=^8hp` z(Ijc2HJfFa`?}m>A{m-XM&t6!Mur)chVOJQ+hvl$NrK9 z&za*6HEP89G2e<3yg%nJcqDP@3?>nFa-><_x_Nt#H6fO`pCLw+reL8q1NF9ZT$t|#2yiKv4$8D0X&Oa7%n!Ah`KPBCX?mSb8V@n zoF^VQ756LcE9qUvPW?(F<98fSh8w_3ovw^|M?|eQb>La@ovHhJ+T9c+&nSH4AVz}6 zps8`7(ns3;ql$ec)7CTEEsUfJOAU%tzhRR-Di087C56Odz&ctw0vPxnTDVB+gM^S5 zWZa^eVbGJsird<@+;s&Zvcbn5@vmhJ3aq?MU&FA4iF)lAm+I#5A7C@WtLSb9sxY#1 z$9ESWQ%apf6&nQ;Q$dfg+X{@=28pM>Q$?Wr7u16BhY>z<@fDrFQQsox$p_Cd7VOJi z-%E1EvO3Eb9R@nO;$H4{VzRVb!66Z3o0jqw= zaGgnTG9N&Xzt3Jwqr=3j|C^AU*~*6@VRz`E(CiKekln#$MpqHZN=q}8;Iu@UT^Mib zKRaQjr-$b7&r0HqTe0Qmmh1hVk+v>|tK_2$Jm2J~={JtD5H_oA zzPr(;=XjLhuPowKNrsiTx_b8VJQ$arY=oL6-_4kD4L#_~cs+wlV4`8aUlB1!>O-65 zKAvQP#Li@3P)?S-z6M~AR{}tf|D61l4~MfdoJAtw`ve;_h^NR6c6|LiE-{UPA7;xz z?HCTy>Db}k_IC4u2J^^yY?sG}J@XVm7(Z}wm`AL zS~I|gGjx@byf;7~C&k?VC^viQZQx@88+KTc(pk55m}5y$Y}G=wWt@(Bt|*fV-5 zM~FX@-1fj*Ue}~&o)_MmnwBF@-gRtFjM$GdAUO<_qJ*^$9hw7P#;ESmeh=j!(_y~L z`*;$0x&>`|B9e1N!TX2X(&o`AO7n#hG+y_deBA84&>h43!@Qo&hbc)hq0an$^gp=5{wM|sFoFoL}D+}Mfe^Y&lCFRgf_42`~ z&77yu+F_UJq8Y3pNHW~R^)kkR?UvPziL zId{R>qyXARtq@yxmtzf;2`jxrQ>q(&*h5xP(pAv){Nw{9mq;cbP`AL2S0^SeFEzfy<&cyPSC3B%vQ{FOuM_>Gn8EVcfZW zU`=z;SOGOIZbsdFhqE^17X~kb0vu19IJ*vnMm@P)YWXxEk{~z~Av#)6z(*;u6AtOx zP4+$JxxCGRChUN{$Rt$lG33wwJHwWzW=`kdj>95v3OHI~%}h;} z90p+sYI-sl5Z9U%rSZ|xQSfNEUC4{QrNhq+5?uK1r^CoUV8vhqlJ~E*H8YW)6?iT8 z-dR6jhO{tjJMAvzJZQ&$??I}s;3S*=++wk(tAAi95gvfXPJ>y>`I6VJ?DCPBq0hg^ z>>ll|i8Z_{cdx*4b$Y93(eiV7@@t`Jk0R0zp#iL@sTZ9?W`$4Z9$C1GcW#kWFq%lt z31$PF2Kg(vrNynyD?U441LSuh1lEH0PoV^?&On8G(Lfc4Z-)#MMu^YBT@DrPQiP|l zY-|)#9rl&{kPJ#Fs411is_O_V!eH@Tkkj|_(D%J_YkJt7-Ssksig8eOtLnqCN}-6n z315iE_k9g(P$Z$Bq_+gdsz+(0(5PEM%TWo>+TLX^siB)P9$fF4K!{t@ki@pHQSs#c zjmH$*cElBo|5^h%ur#yl2&6+pb3x^ST#g$*0Z9w%zDc-Vhl^Sz``rxmIg{aj3*pG~ zulr`~nztHw(_*AuN}gMU6oR{1!^7=`@yyYS$`=C@`<4*`C5}rE{}@N#JUaQjdQBD- z@B&lH4pliwKy~%dk1VNz%r6JJcwXcZThlf^(C2X_Elp9hDYtFaM7eISM?lH*>Te@v zW@ZWReF#A2X812%qP8oR?CBjX_~2d=DSx*`1$_c_x=@Mt$?ZyQ%pdv*EbhLlgX9KG z9-SGA8rie`uFu7l+xCb-APi|G)EGe8)pq+$2RG0l`5;;mhT){LzmcOZVVGZtOZzEj z=qbs5?1<5A1Ea2(WT`@UjEd3Wy{M~i{kFi+ap=M}<^;dosm89rD>dOlN%L#Mp={Ij zn&j|)U}po}kk5f?JE8X1VSW7*o>_c9M~lp?-MWtQgj!usbip3(#1Gsm_p67qHa@n(78ZOI*!=am;n~cz=QI|3hk0tlnPIld5@tZ>x)p{l!Y+t`2 zi%lC(p2sG8sYP8o#Wd<={3`PuV@Izd7>Yyn7P&r@FJd2a{Y)$~|DG{3$*qLzyi)Q6 zv%dc3W({?C(gl?!DiW1M915d4iM5@4J_amU4dl0|ZbIU^*gpExx+mJKsfHON+&1XR zO$}olt5#omgmgQ+_ttv8*G%1#^p6A<%Tc(;xBra~2~$JeqZh2fo8<*ie> zB`%CvW>WwY@e6c{QGEa`cIRK*S9#-hc@7tx_h86-j^CkWVoGHHuApH>1^%|6xACLC zd>$slychTJ5Z)c!7J#ku-3MduU>Ee6cyF^qN$~Uge90myH=u?R>#3eST4QG3DVhB* zrgRWPsYV_ZuDVkMBB6OYh3K@011ilxwI2sA>aoxs0+~p>_U!H?S?)OYu_#q9SOie-l%YW-RpoH@`kly zV}w;3_|r^y7uWl~w1`vDF?++((tgDvY{k4Ib*zbnLqCu#K3yV_x&he$hNO_ub;*7t zR9~jmT!6nQ`ABu2=z8<*S3dOs9E*$`%vjI)yx<-}>_93a-a_9J5z%$G3Y!;$wJj=> z=3;_KI(W45z8r>srsInCxkOqZ6KThvMsCK87TjzxY6I41zcg3C^CZQ&j}(nsjAo+wF*+>)Ks%J0*?Mq)w$r!CUL{`Ci$# zlx}^Qo^EbGr}lvt$>nZI0ijCww=YaTHo+>F&e&q~6*%IQ!_wA+~MowmhYxaMwi@Fj?dp zV@2Q!zfe*v)+Ol}D{y))cm4XVnrGM7z-4)aWak`96?8gM46~fguH7vuDTNyT1x9AS zz@=t_!h%|SMZc6xGGV_2lUCG}M|rCoo0-{oI0)>6YFmW$Lm3$@&kZ_hmVuk=ps1tR zLd-Z9O7YnCufaMWrlq}H!tMESJk~_bE9Ws13doA|-CHPhiY4F2VJzMTo{;%%2t(VC3xBPG04RM1Ggx?Mh|NtnI@f zwMl*n6SR5JI66wocQIl!k#V<8dzP6A?-S(YqJCq!%si;`u{4eu>s-niAQiox3svaD zP+l-LbSdxltqoAp;v4co55tE$l`tPuT(zVNR?q1KJO)io0cMZ;H5-7)Bm)`Oqt0^` z7D>fER@eyD7Rr3`ri|-&_H$o8^%R~}rpt%qIlu?UNpPgb;)s@QuOG)g{Ely?6>iY%i>O)Ap*Muyv!uaGM&%(WoDHc-i}TYXDgbo zuitEP`e}>lSC544rFmlk0&Q2HzL_HpqvIoVsrKgFo=$V%CsA6gBBFB7V^NqOIIAx^ z`MqcqgP73W7dP*C;#2X}6ajxJ5Hr$h#eR@X*D07>jc;$?*=ybM6U>$%sbliSL$%^M z^@fmNdM9pd_b>{1$VMey@~-6p%Xhn8x9(xHOn5elw5al~*@bmBj%xLWq=k!n z8qy~$?`~uw%1z-$^+rH~6kdElbHX0q39>(1zK7%5Dad=3qEE2)A%PmCb>oH6O%5^M z-?T5o$q2+}u4`zoztdSxR}itdT}b&Gk{S#6rr_Rl3Vi=9BYzJDY^22!l3amAP_=G< zliL8sWg_Hb1Ud6u)+6zN?T4?4#2J4`_?bndhqyGsMES^)V{GGJ%-v#1!7BB;?l?|* z%*QQF2<|}r0AN8rAZdYSPn7{y7rMP573U88%)J-(&S6XltWlte0nVXR?XYJWw2<4* z3wN%VptD4@4$cV^v8lXX=-lEx`s_8>;Zl%37iK}&UdFi1+7a8qNOI}=rb}~ed6cY< zJZYOJzqXBXyz*&t&J*4DP$U}x1uvA1SJ7D{DEN+`>`on*>z`6DkHM^pM<;5AEXG^C z?tvp5)a}UKz1by0c>p}^BY*csq19P_uGsXi7?JyHfe+%gNEBf+(DyN8={JV4qkvT- zz4&nB1C}EY@Hd|uH>xsH=5&2C?+BV(6zJvnyP{x%J-PUtHdSUZj*hcd7Hg9kgKK2; z;VUZXAFi897du~jM#UNKf~sQ8$bn~g+kp9id+{;0;aIuxEWHKyn8$zpsPpMro)8%G z?&)%8AjD(95y;DqNp30nv^O(7eFaBh1+`=7)o_&`h8?Tzg>q)`qFRZ50#FO%BPiM6 zfU$Bp4zZ4>GPkUQc(2F4$B%*>GhV>85quUr;W2m{q~m>Q$6xo0O#VnHy9N;;=tHzz zKAXRBnzOU8BJM@10ERm%EaHz%RxpwmUN`Vpk;|0=uJ>Dnks?Ui(GvP*=&<{1I6o#s znz^$e?aL~VwK|s}{JYUq?&33bsv4PDp13|UIR)xU-F_<%F4o$vxJAd@+II^M>|?1u zTzkEsCi$#QoiI03`(;1L;=w2-^Yc?a6X*Eu9V_1`{{i{Bqfp6kZVlbSY8uQo&cV)g zMU^h}Fgdv-q*J>28XO8>+!D&4m6e6gb&U+XD(BpOWbowXl)wDJntyEYSOA|Z^=)-Y zIfrXg1i8}RjLZtOgscnrCBNE4jMz8%!tGv%57B&)J$uCXj92pt*;Iz|7GL|GZ0j`d zZAzIZjt+ZNz%#f4?M8e(H_;FGPA&+wjt~G_{+KX zvbC*A%Iwd`eC?9THc@z~MQTf>LKf5elbw;SPg19(1h|gz$G)uTSS=sFZcp0xo*+-i z&L(W4yuzSjmXp8a82eKy4rYBz37!1eC~{S01OSAzSzmvN@0OsU&N=lEbEV!oiN~&9 z-^|v+%Y)6KMntmm z%9biG7P8SxnHLYSa)A4t(XAUM|rsSXy50$ z39~5=zJx$uLz5Q&t#h%<{m(P2T1pp!ThuylqP4g7UER2@b0a8st5=*_67ofLbyM#d z<5(Roj>7G|#ap8Wvumy$^Aruu-o1*XqY(PWf7I(rgwc_Lnz2gb+(D;akaJjBs|%+# zT>9kP@7vEjDiu@_SNo+UVQtVz?3UO46OARZvE!Mp4rX#n)d3w`yd}&=wb$%<7Ckf< zRR~ptOi;Pcy(y(rdL@q+&_ZSkc^e&vbky%oz5S^u$Un8V>p9;B`HDEFD`x`tO#bLe z>7RRXWFAM_ zU*F<#R8q2A#JW4jh#XVBNY>MNhbF`Xb=`D-rl<~fj9MQMiYw$F`SJe1n&5!a#aB*L zdO%_mrA{#Ar%G&1&tcuFv3bVlEZak;I;O_=f1T*+*j{t5a+M!j?dM9>NvXh**p30- z5lFoq&Fv<;RY~yuRPz7Wb9*r8@YGn)6jwsf$lY61dnNz7*cpC4sNYRAVa zaO3->P=eCG{GT}~{Tn-h`iO=j0f(-r(|#t-pZYWcDV)ll99Z1rj>&I@-@jCC3@V#| zY^+DD0{)*q_P~leJ2^Uz(KEA-g)FnF%y|Y$qONg}B$_f>zen{EI~*a}KZp(o{|rgJ z5eJ_?q?|Z2lSi6TPb8HoE6=2o#z!lBm+$U;JCjY?dxyw4V~5mK+MDvaDOPMO$=xW_ z&P5S&*3A6@D%ScePm6GPU)hI(5<_3AH$`SRStM8rJ zgwD{O?zVCht3WL;9KAiA*vePO>o4zya+VpHuTZgE#7ejKV~VuYU1GQXvs_;$VH?d^ZyKrmixa8{y-3zY5y}Qu@-+Oo&1+YYZBRcE{Z|pEgkQwHdg!^ z{kB6OU2)R+_n7Im#Xg*!$GPLiC#_RGV|v^ysF7c`}DtND-3o6?ByQ`0+C42 z=?;gE+yLf{a)#eu+@qOxTI*jW_u4>Nu8!6>uigE0&E?!asb3k!=TOkZ%U!yUbe`f=duqM^by+?CJtT>iFZTt`5in^~ z`3aGRx7ORg0dG2lj!gE}vj#Z@&cny~el~Bu|7#(Z^vOg9!@y7cj+*5!m^44-ET)Vq zylSK3b;gnc6Gh3kkl-12O%S7G5fL4pzgpqFF{Q70_9uU=#CE+cJqpGMILza?*$;38 z%j?YT%*mhySb%yomFG3K^+Mce05zyFPWb(D-{PTFpOY{CePmU*`qIQAZ-wJNQlbx| z62q9Am`*jSf8buX@VM|w*6fQ5U8(1Gi7s`Xckjcy29D%~;+mTGFU)INC%l-)ybM3L zVQD~Hdftm(ld>OO&)aK;+O>y=s@?0Vd-iKIj1Fwdt8m;f9^X5uQit^E=L>llA+HEA zkIq{%n_Atu|1B9zVzOBs!#kp_#49!Q)Uz7xp|&oCy>XyzRf|567Yo=Y7FLPwBL?W91$F8bzFt9Tp1%wz68m@)Wna1IzHVJR~E>bfIefS zq4$dCa!imh#(m~yFEWZV+F#qI!#2>R4LN0S8~?0TFY`rNKp|5!!?T*)NJim$dC!& zdExVGX6jdBrsSb0t5t1IULPqJh2FhRC4xm@@dKa$TuagMJw}>gMKx}QKJ5-}Td*$r zH1WqPHxDHz+wz=UTZCq9*Szb7JSL+>U;Oa?QD33T_dfi5yIqLSpcF6&RH3`vy~ZD1O>T0qKzU$F4O$n@CN1|edXeU?43ma z@KyS@F`8FE$ot}D`tJ1SVf3%Sg&>AUc5f7lfd=1CA7b2{{}y2d<6Qf zw)=OOSV1jB}ay&mQHLa zzGS_fF`rJo%(8m@=3noBXTknGK3s(>1O#rJD352~l(39{T1V>!{w3)F<-~)70rkbFwkQZXwY~2*4w`t={91#<^+^?Dq8POV z?Vx2tZ<8*~;tB8KHN5>=3;RL6cZb55Awt-}6{8N~xep>ErCL0B<-(~OxKW>iLvIeSzh_d0jrVzm*pHQ~2Ln51-sE*xPT z35!*4T9k2bYquwMCjqu1k;7vPsoUqIy#&z{vTg&ZJmcWI^r<6{7K#0V>e+wKsK?b|X(6IjFSx)Uws4@i>@GlWlIs={aH{I^w6yEIIWmUkMznPyhoXLx`X&4A;AJ-_9p@# zJ{VKH&?0z0fWT4w$|c|yit#7d2Rb^8As5dON*7xhMerV6@n{81iGn~xO`Z3slUZ@& z+bwiCMlGFSl7$FJMRaFT>pN=t8exZSbqG#i)k?EA;`tn_Rp!?MF;0^|zQcSTEyx0< zh+la%JSJ3?d%o+XOguVUiossd2a{Q5(Bd%yAV#}$ph7^(?pk2zS7ri#7}rr?pt=#8 z!2T8X0#&*57mYGcvpw{PM3F^VZJ)vfTG-3?0x zSB(6(cG5I;gz(ZoIou2gK>wV26$0n29#r1!MM6-)ES{CE_EIKXxo$^cAaDLFo;jcR^Uo-Cds<+i>YWg2aE6i~l1=I}4~_QhB!|gwpsB(bp7? z7G!B!I%|8EKzb(v`DD=$F@0u-hxN5*zu!BT)dReBQ~oimd)BZx%OxaV5ukJE-eWGt z_rm`KQ>Fbrs;;jQ_2F^CSNuqBpDpf*G1_*?&_% zP|EhJR1}ufx}S-{I%^dUB@Kib;0(1FiaC(vM~2jA;xet<=!VP&9Quz{BxW z<{1-iP?am8SRlcKah{vrkL2r({SDlg&a5Q?nvz{-CbK!SWc|CZ_3Zpy8tS*py@{zJ z{!g^T&7MPH9}S665>A7E3;H5|>C`_N@&8C!?JQw2Hq;)4iZaYdVc=DjBt&u$)E-5J zAUa{K`ENUe>4g#;QSR4W866?5jD|(jt;BLrl=Snzr3$2l*E#}i=hpCUKll6V#Do#jK--Q3sw^vuQNUZ7g| zqZ~IIC}vzg5q=4+q+3cylx!W5Rpw_ zJ5@W??Yy_Gtztez05c}=uc5doW>`KscnFxRHCekQ+q%M(XwbW!;MmCGBf^~(DhUY z8tL3X%m8Vo>gp>rq>hD&bOi^h3RY=G?yi5@C~Z%$qteu#ZbB%k2D_6Z4Q_nWH!cJ4 z&XSY2?y3EmbugNvA>u)hijENHuM%ZU+xUX;182=4y6A?lENkHEY`pgLJ2fD5p6Wa- z+m_yohvYKnn`y+OrK!`FjdOlWyu8d1V$lPxfE zAA$bV^|m}5nT<5Et_{N2_DCi}WtcDiI&I|2=HH|%tgA8ak|dUnx;~U%Yty70nJvVR zIUexxfFm!2r0B^{&=T;EWso%M*KTk3SdaoPNQDMg)A+~??luS7H=&AxyLAwDF$OA!5?S$`Urvci= zEo{iv*VorDiWaJDxYBe^`0NyfEg;pf>0GYFjW;=6*=8LB663;WKds`#jr6_p&tDg{ zGkHZLSw}BQ&Eo z>RiJag$>uC85S>)VPZKU)=!XyC$6?Jy4PaD zQ#UmW*11(QVoP2kZ3)zmZ|eE>2h}N7>}s2U)&(Ey%xPHU_{zwKwvl+v(_C$4d2IO& z;;UgS|DE#^x9;9;rS8tkzo`SF$cWZ`0ueePWUjbTY4mDZmBD4vIBqwQ2KSnlqZ=POS@J?Rw@@lMe!WZF}ODt8#yNrCc}Igk^6{ zp7$H{6EuSH-qBNoI&{e(FELHhz zeC1yL1SEnG`)>nh?HV(KgGG^;W%^(xQ}vcA&3O<}TOsQcTpChIc4$H(d*~B#*oUOPi#~(bLA@3k zd4p9)06N*r%ELq1YV>8KidY%JeBD=qT&<4Or%zI7D%dR1lia=b>{%OliNsF4^xSS@ z^7th^fr9p3_DWv5g(7HFXk=;nZ6jIV(R|@6x*Oo)bC!uf)!gkiC$fZe>OmZXOC(d` z`^YC?eJC;w-t54!kASulua|cZeUWt(!FQSxO`X3|lm*JZpy1&$*&PKPUa_t#mLK3tYEHnszODTgXqu}bwX>^7a} zA%*{$^u-D$jBPW_CRZY~7R~04=^rA~A1c%=5gqno#;lZh_4+M2NgbhOyOA6UYk7jP zk6|B2`@*Y6f^}_jfyYCm3L93;Qd4n*Kl+@O3(>=LJ22H?g+`gHEzfz%8=%0{#RTrD zWFp*qO6N=UNF<`zdN_9y0(J8tc$*w|;h~KY{6V>iwDGr=eUc?CSXkurrhp2f%K+Jo z=zrEA2=lF)2~48rM&(Mx=zsX4DMCxU{hM_~EdcSxE9Zs5f(m9_TFch=Se*{hrovhx z#x+|f0@b6J2wY6APqj)XB#IXIRamz9Ec~XVnbtTKk0?jht~j`@N_tTilUjYUTs{rC zXQBQvzpLmixO zLg=e7JW4$RYM;;5NYHULzWSDJ2&zzA-puY_u^}k^%0)sO1|t!EjlRujLL@2B67y5v zsC!}*$A6G#A73YA6`{%`Tg6jA8mwvNXbhhRl@+3_6x_A-R_sqph9e(>^;8!8MD|?y z1Jiy5axz`5Fm$mp(kyIoED3x4G3mWx0O>lj9$x59-XR0Qm5!1FP zeBLx|$6t%uIed-=6PuPa`cQ~DoHl8z*~6lqxBQ}9*n$j))ne0RS0aOF(c>M6=o!i& zX^fzo)xmTCHa*FT%YGaa%60f!BoptCNE!8AS`OJ?UH!e!NsTTbROBzPDbz60s$ftc z*FzRmZZ(k=4v%7Fq^yxMLSm|(q<|&9%oq_ijPp=T^7)=0&!vzHXh^yrE>Ww|RKxV! zVi)^d-BFoahMvO~ZO~fQh;%EQX&O`w=iBl~S={@Mn8^H4RL(?g6Pm!{el*MP6q$H} z;KDa1=Z4+#ox_^S{JxB$d(fyEXv;9{ za1@Zj+K;XpQjBFbNV4Vm5xmk0L_^aoxug&b7nw!(6J50~sHfD1NJDr$0z*rh&1*Iq zqV7qD5$JGR8ClCJz|T9{G*+x36;Int{4ISmNsZ5>uLGr^iL{9kf4J4|FC0SMVbxa% z(ckhzk?8Bymridc6IT-h7`+_4>RGGs`Na7fUxGhsmm{Mqp1`o_7zQP8!6X=hx@4Uw z1670wtI>0fw&0|s{K!$?ddPDlOpd7I>PvJX?otuQhWy06_bNc%5 z;3JCjn5r>inhsiE%`D<_Za4s)8txzrC9&WDCU={7=s)aDVHdFEaF2P}KdKpFbm&>v z!{s{em^X!h^a4;GT0;paBe##z8PWkR4{b`0Y=*b`9m@};}o@fzegQWj9`e!M!gsjL)1ADsm~ z2K9j3b>o(OUJbB@fBcI7V3bv}_NxV$BjH!Yl8^M% z$_TO?yku0GZ#P2w5c`Ja10WmHeK1sI8WY?Prd##JnXhxLtB^r3@ls#)jykslbW#{O zy_#=^_J9zW0YpGz?%~Qg)tR7}8AUJQ0~=Np;S4~N5kXGm?4=HTi_o`)MyNhVSD|Gu zrNcff`I?tssnFQKi#(3DA)isavad8H?E(&ZBvU6Vfa(gkn69D2m`=4qVRd@ zbb-e012PRI_6l07)s$0r*3S<(4oXN?WBO)EAmi?e%fa0b{@8W&No3=8-{xsv-SKe| zn6${ClYx-P#<$OfK=ij2&9+GS~q zR^mMH-@NdYNq4)rhM4&NTQG6l=Rz`v)Vr?t*K1OA>w|d$j`!=L@P)Nn?+!^}Bn}Yy zyk0m#9KvFT*5D?oCHQq7%$Mr@yGUCtFzfU7?vJ+eq%h$N4rS{Y(ggzaaYx0$La4SI zTz*2HGNDGzFC^$j=If$h%hmx_eKZoBiM$+o`V4KiTR*3E-uF>{WPJ%QGD@g@R^y9N zkiR%F2@uQFQ)c&thu<797jBPkuMHbC;=JFzC(NIi;atMvw7oK;@q4vHdTHmOXsaP- zX0->?KnfDpjcLM7HJ_%)u+A1>OMZC$2M34wTYorcVU~Ei9NaRTHLO@4joAVWBTcb7 zTuyVKU4@Vyj#en9#~!A3Od;?%r1sh4zP^;X;fp95j|fg2Qkx?I@}YBU6=JaN^C*OkB$H)nXhF)eHV zDCIL$fvP|)rVH6*-5Ey->envsGm&A1{)R@Fq78S%Z)kPwU?4lV%#uJ=d~Lo6S&?z( zzUt3=>VqF%&A}unGT~1~-dctTUT=&g&a5PDq;HMHIbun|c`qZ|xjE-6+h>GM3u2gB ztngV#e8g}qI!I=#AsPg;MlCUk=)uKsR=5vw1C)0pq~1!cY?isOf+VT8c5wuE&7iBlYvTYG#(tdrbu7`u8 ze?zddoFPV&Rev3bkDm`wA6g>BS-RK7tAfo#hvLB=r<;z1C&Rrrwj>Bdw2_(c+i2-N z?Wb+A8REAu7(;P0g%Q3wJwmT{TDMGXH|+?N(yzeixp z z4p#@GLF2Y_&m4DC(k0Lb%!!6{lp=;zd-3WruOBb>@vpmjbOC^9dgJQ& zETjc;qA{nRM}Ly%pv6Jzaxl-xFuJgYCb4if7w5rIws0X3c~L+hJ1_iHy*%iGdec(G zpk$Fd3Rd&U$swX*)HYB=I8}fVeB8!LzIOn;(h>XzEi<_>Y7+sHiQtq+>!u(H=DGv* z{J>wfSPXxmiM->{%xWecUqB~3w8RB^C9yN)eSDPuwo$YZpCcd0WXc;2s+q{I8;sUE z#rYguX9mpRXdx2aKAY#dd?Qh7d2Stz*_RSq4|+V8K3d^M6kr_iJ!J5 z#jpVc*}h|t)2@F>A2vl;E17<#3D`OvJ-HesLP-Y~V@H``ST&HOL}csS#J|80)yEan z9~!O6EVHZDi-g)UJAhJfrx12)_KT_P7ol1KqPE&s9NU1+t(u3)JYDaacl$B#=o7c8 z@Z*p4^z8)>J4^44U$70Mz6B*pN&+NGB*i6HFY&|P>_XM-sNRHIv-ZeeFd{aMQ!skI zVrkKvdn>V{i(q0BQFM%4IDTK(WX70KC%pr`2W_}4l zXrqphCVa>BVB$Mq!D|={{#CTF!31+t7)_&k(e={(OFn6JpMz{{GHWg(_T4iA@q(7( z`nhXllWUzseTK$hckmx{MuF#m0F%qBl38na6U_&lvB1x$_ZdEv)By9dKXteZPQh$A z^%yom^kB6#`oO!0a!l#krRA-4IO%9UEkej{XK*x3MpTDgB2HRc@0Hm}^i9?@<1Fgq z=-vxWD^cVbKb>OZXM2CM;ehN(#8k+KDIUJB*~AH6mZO?37UU3k^WPS@bz{p$L-1o04y9)1ty(&67q^Z;k>{}pYw)QX)d=l_XO35p8 z>}b|&x69FAGHSA|fQ~_42TiM|Q{Tr@;u$|!IA&`*DmF~0*wT%vqy!RRUDL_*K zwAJ)1IobeEa(F1wid;n|tylTJFykVK)Ib1CPu|kdH8djz=uZu$K_K6g8fC|em8t~4 z!dc!Uh+c)A@4nY7Zs)m2+b=d#QLbmC^^pkK*SNFWeD`jtW1+h*W@CmB{E8yE?q%#; zHmu4-=8-6vQ=La(Wo%Y&SgZeSWT|p~c*RB+igw}!gn8qpkIPTr&6j=hz@NjL?Kk#O zN(4ceLp4`LQ{?_VeC_yHo~qL7CElU8ZZArGWuCJ#aS$xd#}3$dWlR;ySLGHLSJ=Fi z$Ec5K#CO$2THIV~xEyDWpI6yaGDbb*5!3dj=_3pEYrL-=r%jxc0Ce|c+j{IGh{o<7 zp3IAziZ_J^-y2s3FN(%WcKsC^gJWs5?*=5G2y5B3?aeC2_>f@h^p~AG!IXvAB%(>) zDq>XwC2&+4APU%el35G9`I1HZ99N<>-2M8nkzGDv1SGN1gdm9xIuM17_h$*Ted=?Q zgP?67EL6q5)`!`GeP5Af=$7sm%A~A`mW9Y>Z19d-&znjEIl)Z11?jcpaWX3Bj$;zC z;=7qh&U7+wnDEE|dy`Td@F93KtN3-i%#fl4mK{v=+WPQlim;Z$INS4WAbl&mTNr3= zZ7B4*^s`P$j(C0`lA9nG`nw5CBm67c7 zpwJB=EZB1tA6C7j#sk1KTfym#y(h%SA8b1t;<*#HIdKD`t1=Ix`>EE3CM)5YTOGo4xF3g@JeDhd+?AFrdA4!4UaMCcxbo>1$E12);`Jugt=lN zSG=L|cujW6`mX3?)-TqS(HInAVyzV~QV<-hoF!=SikjcZvteHj_Z#o{Rf$@Tt>&dp z-NS9GUAwPbOnv(kO8a!UIj_PxKf$y}%ioa3RjXup>T*rPGa>rLaR)m|gm5Trda&={ z|I^-=M>TzK`(inbtH&Va*n&}p3M#cAK~Mn+P(@3*R;X10WmHf~5ilYUAP_07QW>J6 zfFj^fL1hT2OcDrGNI-_Dh(V?RF{H>4!Vm(P?*7Kw)7!h&d*`fo*L&-&)vWyU(~x}k z{_ed$pS|~Y5PGh0rn8oOduuCs`I#W|SIe_97FRG^lUC~f8up=Pcn;2w@FxD6cWoyz zXJpQKYl+hf-;dh8jMYvTL-Jk!RrmNfr9I+!<3Me!Q(D>L^3rd&_IhO=y^>uW9{RxQ zhT5G#ufoAD@9NN6##p3<|EjIFuSz;%wSSxkv^r6AurE%&Na_x%r+5VU4LO*9!<qKNP14dSx|O3-yQuKkR;VF5L{93) z@4IpDc|zBt*%tg6=%7_B?(vQY`&#!qIwLRE=*X1PGeY>lE1ExdA%Ei0RP(oi*M=Cc z$gjG&=bcBgosT64S*F)77cQ=3X%s(N=N*0fz@;oR9KknVWFyJX7Bd$FVPfId=YuQsSFPOG=*3)3r29j=ayVWge5=&LO(o zxkP*5jA7Shhkka86CCNDKDVl4ezKUNapFaAaXwK`%})$*Im-y$fm{zaKy&H0Iksa( z4=QG5&b_rYN$Q3BH7&^zms2H^QAS^wzO--%Wyq&|YedW>xwOE&^#@pON)ZQAww`Ls zi5kGco>WD?4~3qZ&{_V<*?9AqEq9?KBbTww(k<}sk5i2jGc%Uk^>|=hV{gC2lZTcO zfBC=|s~67Fi%Rk07ZI18kJ_Ec@X)vtT9Oh%DbG{R^jq^U4OK5Zbqsb0b34$ORv$ydfkDxVCzb@13{^$q#$zNjB&CM)G z{sHD{Ji|n*FaFD8LdAn6(6;~m_2*p?-4%2?z3-HkS_-Q!PC8uZD}VDupJuV_v{7t^aXeA1>e!p9n+F*#zOXGo`4{*A<9H7 zEBB`<7aBSRUR`hBJVI(Fc3>5b5Z7_w?c1)SlhiU;{4ab6iI4VaFa;7Gl?vO^DdV)* zo}hxJD}lK-Vor{e>MQL!_QiE{y|kOV>l*Uthb!w6v;XkpAvxFq0!}6y zb9afz1}j2(ubPFA^hFDD5C*XJSoMFjd-rZ4T$Iifu?!4yT#R$Y4-1;^7UcGv`l{;%5n|n9OBfgKPUnzqyMe#9fy+3-9HJsrYdQkDAz`k6TQAt0#V8f2? zn7*Yycc)C|(nnFsTQhxPMyF}YMH)UYdgO{DJ7}k=gSd?9ttjR$7W3hTc_*HH2`k_m z!RQIatgG_WLT3DEW=3Tg*M63MJLr+@iF@721yAncOAm%aieX~q7Z}Zp4Hl%8IBa6T z)XV0b+~!M(_>2pKY?Ci`rr1$}M8Ms#Kee}#+nVMoEtkW!B#6Sd~aUK_KNj$yz2%e=L|#H;~kO)TzX9!5FMfJUTVh?B}r?6PAOuM# z^=|%p>GrOLal%+`vJNPp*t)S2ONf7c!HKyqclr{k&0l{7>`*_21{+#6bf#+VrDuWL z>YdGl#PbbH|6u!XwL`#JkirAZLyQwEReCjc#Dt|`^C!SSS_Z*P0-6WgFEprym)z}$ zQR^An7#7`HZ`x&)W_@IVBBv?K$=tYkjg%V2;uz<+I~1y?k>8*BER4#jos2LM1U&Ek zuDt)O{p^h`mU(&d@jyJg%|r=`=^DCIt)qEWr0C>+oo1pZ2wpWzGnPBAAm8oz5Cw14CR!j@<@20`d8jNv32TuM<>Tf=kSxqbho&-AJDb@R2YPP)X#=~sby zwX!o-^ur6o$#|c-k*mQOLWWWSF2*36>TG`0KRI0kN5k;J+6rHWpS$T{&r7ky)XlHR zlQi=0-P+ zXn7YmhCV?aMne7u>QQ4tUAX?e*!Ex_%c(O;6zA-xZ76aMrIrlFxtyHD;AeQ|e;p$2 zi{N;x8w^5Rs!WqRzA{}XWGogYcr~r5SjIrc@?yaF{w22(FmlVer+(-vis; z0CQcKzuzL@M>s5a0??zxfKsgfF3jWY7Np>SDHJQ5z}Qpv{X@<4XZT5D`}u0N20>g= zSsU_t#s$uLAdT+!eRNU=SGzL}WDN!>HS{Y}28zd&B+szgEry1X{tE8eTMGnh#(dk3k+bHH6R5tqCU{9PzL63k!Ad>MDWTUDmk7R5{ zHu{^45CI|A(SD(nQk3|~I_czP9zruwqp^M2%Wz+54`;cqa>zh2l|E&w~7_9~4ZvZ%^|Daemg;Zb?O30rWeucR>U z3k|_7gOLDNEB$<+>frG;0>gR{UiJ(AMf_y7Aa`|dWf%-k+!M@$W1vJ;{}Eg(ovgw8 z%s=A5-Gy3O#D@2E8khM@eSfC#yMbE(mI*wd7?t91cWQriI2tuZ!C0k5RLbYA_P3lO zzcw(XNj_ zE`RK>=%oBKZQGkWu$(5eY7LU^5W5z9`XybPN1SQ5($sYr*+dPxb8*z8mOp@34CA-% z|3Ehv(Qdl^KNUI?Ftzwk6}G20jHNeBfFGMr7$NdabH!AqgR`p5z}oWq?;j1FS&d0^ zwVimVVW)&Rt7knqFZC=5(k8=+S}9QsX}<{zLb23vPbbFYJF1i2o4ul|kUa4v1F^zX z6grLa<`JGM6Db~0M9@vxm>J; z4%K48HxH1+5AyU7((>-yN;~ILup%%g!i_$S2#8$^`-0)KJSH%nDddF0z`>fi0Fje7Je4yTrR@>+GsR z7WY)=9?z1(34J)gh15UhqoZjr@!Prc8d$Oj#UTy%E;ER{HN%(oxg4~KAM@m{%JhzW zb(rNaRj)3}x2oMxQY-Gc%*vgo3!3oCX}s(GwjYV2CLIht*Bv0Yl<_vVpK8$H+0*n+npfDZ zHzxbc-XsBM(n9FX>*XpU`^YoxqB$DHZa_LP&rHvq{aJ#5d|H;LT*#xc<0iw)LV7Fm z;7l2fw?-YvPOhhM>)ckWmir8vW!>47B36G7rt&Np?!$EKfG&=36&tFFJkuE225B>{Pye6O58AQ=ZDF#a z(5BDRqn>i60;1QzRB%yzhim+F`o^vokx)uhv{DxECM?E`EyL2>GAGf+hJQ)Iw5rnr z3uW;f4Hm!l3UA*cy22)}x1YJf))m_XU1jdl@%~6T$(7eY5@VF_F+qN$R7iP<`PVD) zuJ8>cFqa;`1L>8EM<$MDdTUdpm6V#3GL-oEC>J>_Ky5t~5qiTIc4oD+&n5Ls?MtVl zL^sarpW{kWxk=tNkxjdOrY{C%u)jawu-Z5gJQ)0VX|rF?mzc0?*b+#gT`>lqBnBDb z=tPSuaDD=gd>Wgd8ZgCd83gZ#7_bDazfNWSh4^m?(-)|HAbs8Sy@6ZDi=bqu8c7`7 z@1~{Y8r#!3+#VJm{`f$7ucM~jN1ArCU+YG^*o!96!^g*dyc@w+JcwcIv+%_IQP!sQ zVWh{a0dj}&ZfORhBqx9m(N7w*woSoqzE4S#BTUHIhAA?tQcEf3DtZ%W%09N-iy(|< zC#xi!>9EB^+kEFvpxLbVb-X^T9Eb_ljU6xBcQ>bL4XUkxn4uY1`_D0*U!al;lIvK@ zpgM5Wwg1cit|#xl3R6B3a!g9w)nhSEQLvRbUr@V|;Sr#gBEZ_U`Y__nUI$|3aJFK! zimyWs$3%APbl`v^OWDqq?V*dyX{l%;ob0DBY&c-Uh##wrH?d!6aa~S8B2ki2DyVT_ zv)x�It?^rcm25$_;Hp*gh%X6dG119FD!VeM}o90CcNkp$%+!TQ9r=?$xm60_GO zaxOMJyn@wt!GxtCd3|B7F7okPGZgT5UITGpJsDxP?8%v`ATKPZ#dU< zAau?TJID)^4^NVx?b2xiyXyDazHz>QvREMcUWfikXzkp~%Pjr*Dppg?z+6ccprk*y zE)Ibml;rxf08E3tPBgp^L^MI;DCAkI!^phVyggwtolkKJUF6tx3B@anjvOsy>?$4L z-tec|ZAE4f`#Rip+vv}vR?h*8scxZCA*2>>C1oMQd@BcdY2Ls3q?QZ|%%8U?r~46k^j7?Nm(E>Gx0lTCrP83&}JN)X9-nwzx+Dbqhw<{X+tD zbRd~;;(PFq6f06%%aztbBU1Q8gs8d%LdOc0e(d;Dft`HRmFfydZ>m#kXL5wI+fS+^ zn`AO%naY!Xw-Y&a;__vcw|hXAT*YhwCtd-Nhl($4_5SFP$jU?7*z*l@&4Z~>QN4gd z%CUrZ0sxABjP2kDI6JMlu7U( zhTtHrusuymMBJg?4{h z_<-sG$-DPJ*Ast+IGCYr0DpG`sty`%TjOAJ^5RvNoA33N{4#3XaT$QkAse&SldTaA zH{OY8iITiOh`1&ZNORkJ@8%GT$_U4_n+IdwdT%qN!shiyps!PUy~CQ9QnmP|&^;}z zZec{j`>6z+rE&x=M7hyB83*~nIm(o#qT!4$Puv^kJ3|kq)o$&umyQ=_a6XgH3#lkc z94r$!C>K`8r0UQI-#5|OY-k8otFV;Y7181aHpSjY{>GgJ@%Z6%H%<10ri!s&UTo^U zBWSZ^d(Cw3b+*t+OJ$0}<@ z52wyeT8#z?Oo*{+F~&3lGS%Yf@n=By&iMGxNp5~x@y+u-=`DxFTdML0u&2Q%{&*0Z zr>Pj!9M+eS--8k@E^qM)mxWuHO4#u8}SV!+fPQ2v@koH5j_ zQLJk#I^U3zj?J~l(KE2e+?xHE&KTD`6cp7>wV{u>>HjS3;QTezR;X5U?!b#fHvxiJGp+ zJIr}&6AN}@404fWs79EH*k_yR+p(MB$Rt2sBD7sRzQ)HqOCAQ|;bVi8s*M~l*lr&2 z-+ECi#2RA8%T)kM$q@H65li`ronLMfym2kh|NzV`;$alTycBaoEX|*q) zHl`yu<7jww|9W5&z!*XMvj~>VAAF9XLVQba=rlsG1lAAK3Ie$+6~Ph+{MN1r5EQI& z*ivhRMtAZSuH+q@VJaEQd8PeywABE~U&d8RDeh@dchpB_vrW91WyhFW#xOr2yP+9( zM;tDMT8jjkOaiOJZoZFrX^VT^ViyfsV0i}fkW4)BXU7iA45JPR6n*Z247_S~T(WK$&EC4WSQ(-VOZ8*rW4;E_xtehUAa||=>a_PyUR&#oFfIGb)DOWi z@HoOjiz@x!9m5nwrgx5!f#NneIxNn}hJMwrGvN;N;UdL6gYtUmTo;P`TOajv*Rnew z`^?>QZ-TRr=@$Ex&>l+*1P(4r&?J8nFVdk9{l-xrEk{TWqzmJOt4T7%U}0z5DhI9O zY1m`efd7{`Vjcs8f%jTeEk}hA z;%>&&w~*sL;Qu@&J&VYwn#`phL7|xbOyp9_LZz`4Gv*~hHgNC z7%7FwOSM`~s0-$lSWCwrK$@pVlulH#P+>jcz?V>uQpyA@hy<&8E3=n931}IF&Z`B< z1fgJ7^qNE#@R-43#0vaIe#GkIK`)g%kUd?|u^`bwj{|>4c8B={?N(YQM8TarVwv}o z6@5qph0qwjh`{DO-;MX4x9JbHf=s}*Dyv~tK~Cf$@?4>Bsykz`t1OV6dzn>u?;P}k zLfhM9j*wbx9DrPliAd&^nyl?o*a`wU_CeVLikR(oY|?3GUu16xu{WerPaKWPy~B{6 zo=(Pn^&^^G^N=-(SKe36?L+v7w~@G0`>p>Qmis0b((1OE zK`qQ04plAKzhP$b=X4jCk19iZC>%4PJ@Wc-T5O|{z80R;unhHhza!7yGSKTzTd{r* z!n~}C^C-YV^U1FVg4SCw30n3JJn}aJy&(5ikbhO|fWx4;O2s@68JukDtV4=iI?qA{ zENeJtl?O%946DWC-CTJ;w<*^yc38?)#-mz4>JzK1)%eJ5 zcF2W6*Km2d{#$@54iuik% z6^^|4|Dl2Vf2#-j|ETr)*U!E)$bYB5Em2PNsP%zfY2eJ!u+b-kA7 z>vi3Q95C23clMIm7>3Q=w^zpq!{*R1Y?jr`8E{9c_3Q%ppSs&FGdE)=TQ^T@mlK%2 zwHxV-liL})<10K)xVYLmIVve^QBYdH;*^^k$yH1=!uUSCV;`M_Bv-XL3kd|vX8m!i)&f7qeW>_Xm< z(=cZ2l=pc$Q7++#RLc)iD&$F`?geG<4%R%uZw%W$dz$Cu6=pP_COUa}C>r+~O`jM^?=_t0H!Pd`a5MEWCS9g7H2bvTs_aUvYyap6ouLM=st=SodrJ)QtUH2X46w(QueDzVzdBY7T>l$~ z7*>o$WE0o9GyYD62}~)n-=HQxul#=uHOW767Q>!fNnMaC8n$IqjC2oOtGpmWUvjxw zs#sBa;3&fvBNLx(`WUsbOE;wF#4@_~P~lQ?8u1QbTY&%%$$kvtu$v=A?Q^M@676hn zh+$qgt)-4JT^26bIbnpBYt|O;TL~RY@7mQqn z14-nK`97R())FT-KJDnjoV7jm1OnVbiWj*>AmF}190 zhda~`vC78A#;19n?4KwzF|tjTLrJ7%-GJY@W9nGh*>=4D=X90Xn3W$a(0oEdL~!Df zv*H-*a(ixA%bS71hy~`1F29?q8W{F9**?bw``a7d!Y1FrFyj4`l>7$*W)3;DUt-F- zV}x&zwLiTw`vp%wDTdjI7iW0tGdW872yY{<5~61W!Y1*ZXSPJc#)3jM%M!kYuIGQt zKC^_cp@;JLTB2Vtq*O{-m#Xa0U^%5Awipgna zzH`hGW4M`1>Q}!~gto9NSHlawfgYC$K}&Lt-8Ykoxi|?+n%xvmhE*cmX)#3k)w$IA zyhJt^`ccyV0Xj(gUGNt9uhf*6)LcZBOS2k8tl|sV5>)vF9TO8Jy~!a&?g7wpp}x9o zR)q}H^s8q!%@JJuD}IWvGHGqI+A~G*%5h&6@q@N8lQ5~*VjzvAjnsvAXSP(1 zk8%jTeI=HuIH#FcbNtTa)W7HxuoH%HVQ{QjCMLV8mG_5F-Zo7*3R`xv1b+)HmLSnj zcE#5}EI7EGVY#ZQ$kIzz_yS8E7*pY>Ipd9T`B)&+|CooA@R?Qmcf$F}JRast4+-T7 zggPW4#=P-2sMq);RZA9Zd1^Rx-$;!*w`C9I4Jn&|5xq^P}wP~;Dz;6-d_&i1OaH!vH&X9=Z_9uteAKSH+UUk7b zxHsBKRyN+#4FUNXio&Yy=6kA@C1SUl>^!2Y!&qj);CKX!dA*ciGfOw_mp~Nl3E=Y# zJ;lVNh8LiJhD6AY^`;FC~q%M|H=v395{O>)=2SWvi(lA&?!a~8)!3D%YW`yl=PGq}$rH2|PRBA7 zCZ*+ytj6w+Rykj}wYdsUPR3(c?XJoA!FpTiQmG5pY)Yf!6OS$soj>p=0Yl4(!r5^B zHkiXcVFbcV=U;`vdVN8QblH@Yi-~sEmy43Gjr6Si*i~N{&eh^|YbdA$(xakW|6Juh z5sbKeYJbk7Bab9x>S&LWC%p3OcQrI=5nWx4D>Py0_fOfDm)>_3`gadzhXs$-XPVY{ zw}@a?M_|HgxA~&yv%#cH@kO&;^RUrTg4&j=K(U=Lm?SnS^o-18O^DujOcBAu`0&T< zb#I*f3D$cxdPJ6N=Z=TVm=@1(+KDr%+WqKgr0F0Eb{!NidDS(e_>SBH`;o%%E|0C% zIvAZ;m|qwmL(ty1Wfq@zh(E!crIS-qPMtwK%`a<=|JyLr$8wxWw%aB|e97&8AD8Yw zrt)!&yLb&hR7Ec14R9My|5(eOdZ)g*f|*KE|63V%P9VzBP`7{|==R@}2WLap#Za3P#r#qCga-Bx zNaZd@M&$2Kq*UwR&DEN4153y47uM;yJ4 zDD{U3s<7O(661v_SA;fF!}Ich$&rld!C9K7!dXw-E%dq?_pzRbmq1}UB85qoy!V}<-^mvj>p!(5>gaG-kFpE2OyS!D<-Ot45V#;slJaf~SmeDTvX}q<}dvS;#Xd7ZmPJN=Z~@?+4zVMZXozr?M~H zP}?AQlYa57`!fA%eg{XsH+ykjusZR~`LeatAoYFM2(UkD5sJY+L?U{Mc=7z`a@)2| z_)Rq{=~eSOO&1Udd5yy-K{9R=s!WXjV(!j!W4ajTmHZvo{u{3mTC+GVUF6?g9JIyJ zv0ZyP+k=pkBf)5Bh)^;_rU5GX*YVXg5lUoRcE{S2QTPbgQyo^dk9=_TlX=jn<=fq2 zTX+MQbBgkL6%Wfb#=WLO7ZI>7d< z8S$TJ4i0vX0f|qxUqceJ%Bsr_&82d{o_&REbiCMSH7>Wvtl#C!?Lq{mc|gO5Q>h)N zDEu{uy5#^%FlB5)tAy3ruJHuq3@Xw_-wFJ8kA>+0G}#0lx5jamI4w|u6-r>3LVzAd3P*QvFb+D3%e)XK957 z-0^tqK4yIEN1P70S*4hn?DXT-T;wFGU7OVWXry3vkZ(BT1cl$fL~;`j5@R5no?m*- zM@KIpGzPZTZHcF9Yj<<~zs@*`VR}GBL6xB$ut|G+O1g5xNHbM2`Y?Y=tl3!(O9vv4 zNod`P*j$Xuw&`qnmm{>j+c3 z<9u1_LSzv&A~;233<--Hlk}@NB_+Eh8Xkho|4AGK#Ztjm#OPNOJ1hJ>RT1l8k(0C} z-L>&N2@_%2{g<{HX3Ocd(W&yIerpW4}$hmr+NmHN}x#OPeH!+U@MndmRD; zgPb*~lVT2-*WUy>v&_ElP9T+_b&UM;MtcMbN17ljAMMJ_>792JGjg5JrO-UfJ!*DE2dQ3K{D6l0-*NZVV#G2Gf`H{Uj; zm+22(-mRk(vEYL%c&+q=Nki^u4RJQU4xI;Ijx9Y`nd~1L%*hND znlL;Q)8|ml?~UpH(GK`{<)li3t-XdjbRQK-!YJu~3ki~D0amJCX~r%c9LP9H2~H5_ zE6K*+LlM_To#)SncGNGxE3}a)QhOUrvbQhN%;Y&&$5#5uz<4t*mol|A2gXX&D!^l* zuNJr~ruMqhwyLI>)jX5W_Vb=QzT`;cnU~}+V*@~N?3bJ5c>_o3(qO6Onw+=P*0G2? z*MPuF`05pwH$KZj$2f7Jhc}!cMubaIpa%SIoix~}O&&g8<)z^S z{l1<@^47jHrul;>wPA9s3-f()`hO^mUeM#&$Pm#6itua`Xa;`ogVi1?9}D-)JAriI z&B^!Z`$l`TK1L=4L31_2+jJ9oFx3XrALU*0;yA{7LmNYSLg_}lmU8hFWz0TNr-y5i zcUL}><5%LbwGzpdjrbm45sKu^n7v1yw=L3rtX%yNR_4*ZPWX*ezDnvsFsNvGgOgLB zK@$~l5Kw6ys17GIdvdh>+wYR2qlZsU!>r8lv61Ao^8;jxq|K_`&6TsOacQlfBTsnH zdOc}O3nX<(M?>7B{#=PDo-FCj~mG6KP<22Vb0$O>X>M z-JR9-_Z(svj>8s!sSYS-(xwxtL$l&6K|Hc_g`)S*dmVLRZZx&h?ipY*(A@?87)Dwo z^kxh-Hlf8=+a^?crnghpZv9lW1KIMerAY3fF~(5%W87>1(jd>{{dBVTTlaI7VpwNR z{eZKlrm)>&^qbY>g~bR^Va86?Y?ohdz#(?D0VZiK&HzgF8VUR-8r zs(Y?t#I32oN7e4;%_i7>wMcE~)n*gi$A+Dmd(PyP@u)=4)q7N_`T2*3;#Y?hbBJjDfa)FHG7wsCY`aH+A*7~Jii~Q2m-$F|m>zjQrK-|W5xd(W9 zs(P0Wsv~ffP(K`L6n}K&FEBc=Swf{mc7uR~?c;X;(=`7xUQGAK_#)69kz0no9HX!zK{GUGk#@Xf(K)ThY13N$?I~BIY zhtVdsX$=l>`rfkBarcL^=jOl>z;61(k?H9b0fIiYVNCLA$xQ>JuJ0VPh80LNWa;ZsKbFlEgOI}NE1ND z7P`g@y8#jMXVIr5%YgIc9E?%!D=`_i`=B-06);dULptw$H(?VCNj{Z9Ew z!I-+{JVu-`;4LY(Q8O!=U)wEX?kTBs1?Y%ggA7u_riorE{s!GG=y-K2(j+n#rV6Ll z0Rrk~?wM_QjD`fC1;}Qz5CjXz=TWuYn{(OI0ZxQc8uj<3LFj*C%^MkY0P&1+kgSLd=p@iftaW586TAH}Esd3mc!6Akw| z)go;?-*o4e6`K0Y<-|KVR&abY2ic1{u5QW&MtrU-)Y@3*z-PNkw^{N(nkV_qA#t*CajOz9l@PUCQ=xU)c1iY;Dc# z>rAGxmcM3x>S%-y7c2ya>=HDA zb%T5__T3HKS!Ctc4f>nbGY7`{X>()yhS)X6`AA04SA&e8U)?P2e%93noK}T$DT2ht zjPP*Bt~iY{w9-EvVszKIoE^6Sa+2-?50LH}FL&2ieVy55G0vPu9#|~<@x%1mFhSZn z3Bd(d=no!@R_Q*H7?g09-iR4`w?{cJ#4)n_*>?N9QXCMq=uVPZ^^Dye;A_W>)-K#_ zUdNUopV0R!>c@@FJ`hzTd1y*8vv6YMQC{9~tJ?V#7tOU;sq3pP82xik7LfVc0)pfS zn`Zwb)~Y-(R+ZGWY6iyghGmMnanl+x55{tjojskgK!WOxf*DZ=o-=rUFbN}_MLYiU z^ubv;9th7ioAniNR4S2VbwzwL>*m*c)!{NS<3|mjepOXX8g|-X{nAl6p+TlYjP9P= zp|Rv-2$EuHwfrz#fkWqq;{Y)Xv%p*#%vh*Qnk}RCuy@d5jP)pK)#+omKKNOp3Ot*o7oiwU!(yjeg|aP`K%I)kC&NB!7ncXxMc zYvQ>Hc(rFMU(*t!a;Y0NVnm;JCzn6!I7+H;_u_OnR;_|M87{Dgf`fS$$-CXMeXhJ2 zWIS}vFS5+9JlZXz9hQxwBb&J*kL(g!pz;gH9`hBPcT^kM%BkQ zr-Qx_8z-U2$n>!fZ{0Usi9Iq=@_^v!={ZLa%y>l^feEqQ$e`#|YD!9KctMiRIM<@g zI31e8~C!uN-$Jq+;}u-D0t zJ0Wc)BBSl^+hZMA$nF{kL6L^Q%a0RHz8Q}ZzK>zyZ{2kU=cc5desZ~}eDmy#D~WcU ziC{?+#RWsd=ug0sUZxRdU1<;fD+oXYn60&m$@}x*Os$appJT+O9o%siFI(gxG|MYj z0D~rHF6IHEwz{{<`ykRN#N#tBCkLF@4S79`n0@=aW+`XZptFu%(FCp+K7$A(Z4ofN zLc_Ud+Oo2_3LpR28h{!%yJj6s;^Me_(z{Xt_5%;TkN)5>Pu8Jlz2j!}t%r8%+;EEd zYh8j4!`+9`k6YG>A*em9HkF;U4evQ0*639V7|k7LA^#<;>~#<_NXJ_dmJ~j6&GlZ& zS_%ZrF7LXw!1-2hcraJ>w%a&rB16GpDL#h=<{HiEocV|jE}-5NQ*){5T2(DxCW}6# z!(za4B<6ZdNDLy%`0aTQsNn(=lbhDaPF3TXq3ciOT%zUG7bQk;CC;LD81^8jJGZ_Z zTx`zp%Nl+iVOGfIN>1cpQI0tsYZ{ggu3U|=G;kx)>erl{Acosbb8&c)T&=$4x){sN z(FxKkGzXv-*&cSg)gnmYd7T_>=nfF8z%(jbu_v zXSW3XtnZlZ0K@*-105Ze%I)xezL`10qpjsr_MXlvh}U<&o~P(zfd;e~*Fjdl8aJgi zb>$8X9=rVmM&Ga3_&FPR>2vWpjCdg>#rGX75V=Nx`AME^r{){z$`#mH1Q*wmtlF?QIWDgVGaQq$~ZRIsX%ulqFV{;D;?uD^O z-6X>%{LAQK)G6YE;`s-MeT4(S)~tXLq%{?VdvOQz7}HADC)mjaw*>s`K6i>(s^%p4^lWzH%}(seKkrG($rpS7u|vj}RlLLWWqjNB_aop2wIS^Kr2l z3Ar2q$q|*lJfB9NzO*zrWT&~#sqHfmE)FK-MP>bk_YbL{G0_j|$|G<~K@_VR90qyBZzsFX?Z5S~-FR!&eS3$Ow zZLyUKCQ7B^+xz?F@(bDI_2XsaI}rFLe14P7VatT;*LMNK z2aFc_+0+Osm0qy+`Z_X9an`$CDvyyIV3E{bI+{Y9;#7ULB3Fz1EQLSo3`an1D%53V zDa`RLA1`hEiTY*40W$Ca@{)0CdZ=_r6f8WnfzNk#Wb_=;U5@M5tbOwF>?FqAojSXg z!#-2kQK4QpX!vWp_Nevy*wGh|=+0QH-|UMoEsNMM3hxMt)gm{N z80)vDA~s|l`I7W??GjI8WMc>s8O=*ye1X3_WczDI7g3tfaF2B`mwR#38^{0w-Y5U{ zD6i}drCb~{h1^wu>iJRQTorvK?;`W!#lRmkfj?fYNC*zOFlUoE;n-YBLD%G^aYgqTSv!hBV)oeF~*+dx0F}97R@!p8k!#CSu$77p5+T?UJmoGIm7NM)i zCmqCi{F;TNo zcle$lMvsw?{S3p?(JArByGew^$cIMy#kNm}2`-mk{X}KFgQ2Ydu+EPsf>FKA_%LBdmM^(r?V_(M zPf5IS8{vK!h7VCTA-FKkLRB2XAHyBc82MPneYF?e+$Lqf^5F6YTG=$6Y8`2x^!)hu z#Jl~yZ1ez=g^al78wLq?Kz@4Jrx^W{z}8J;evKNO*&^iqAhzgtV>%4nrY8U{(2Q!~g|VG4 ze`&M$>IXvb$CTFMgdk_m=Z6#>cT_9CS#lDlPP}&ebd6eKu;TS)7)=}zKvE7^)7BMO z(=fVxt~(tgzGtFE82tolo~Ee3 z&{;3@QC(ZRUqAyx%u0*vc-+lgry@M3%ZD`!qv7Db@qIySwE-vnaRap2DT-E!!tyZKu(U=^GG}q|}^SXsr9@ z)TS9EW1&vM$V)#zw#V+NhS}(7YB_dq)zk2x!sC&`I3njm@q7+vks)ImmKFg!=vTE2~#A~jE zh3NKzMygIpbF*!E#LC*+c9ayK2h=W6EE4HK)@+sX-aD^Cp^*c+=X}8J&e?FX6(LX{ zk;CzQ2@_cSR?nTU8h{`XAN#22uZr>#4O@1N%H2pz zqF#!kabyTG6MmUmT)EE93BQ}NPd1(cV=~vfX9?Ec*r@K<^aq#Qra}P)ZI6sJQnBFW zn6bQq-v*3LMuK}nXOI){EgWb+xQ)(ZTZX`Busy6BQf`~+wiqV&+))*DYkzKpk@7Rz zlk+$HgLBy(a%9RUaV#bpX!jRSLJQ;!J;M2N)TtRuD8DyRsoZz>Cn_)8cHE4M3 z7nX?_<}7_jPxI+EuZv4D*44bc1W@%}%Mi!T(`~bnys&f7ed2!CjI?4=dKrkqxRKA& zGCP+a>jKP-JO`!|nnV-9EOcDuF;*2~(EX}H~BIag~}$%j4}4hJJM(1tTe$=-j4 z$@#+@By*l;wSD2pX^r35vOq&pn@h!>o9iMM+Z{Jwtq`e~TgnS1*ZN=pqcmY0a# zrBvEfI6XQ6HbJb{#jW132*7d25g{+#d(?K!wK&rK6s7D@@^}x4lD;bA#eE3K>dK2T z-p=qg3rb?9k1-2>Bm@P#@XnZtu^u?+uiYj)Qpngxu#j)S z>R;rL7i8__@mEeuBHPr`Po*Pt?e8OfscrrK6$)%Z61Ohv<|TWYSLy!CHYGYu`R(z} z_lV{K0Y|Q7y-&c(}CIzmsWN-R3twe^iRFktxP*()U!Qnk>97xtHdz@^~P) zdTf+2&J9qjc_*r%SI#Nu=CG3g_VJgPmN8@Fu(+uM|&V6_%5$0Q~t&$&E@Wm=@sVBT9aD*58tepB5N&(JDEnY95+C`XFf3bko1 zC-0z1EQvN4TYfGQ$SbtYI(lCJw#y(HaY^;crT!G9l@v)T|yq7}W#D zaRcuFHZ6T1)zWW~e8Fh`-R|+v&I-K^VP`7>e?JDcGHKU1eBnpy?AQRqvS`R2&g*6Q zWOu0+4ZB~q*~Pt(#pMJ{^vC(ZQH3DwSEgm5@4I3rh~_C-7k+J}+TZ6|Tl94YCeJaS z__}f8YfAb=e^hUO)D!C@wHeyuCc}R*7CkI|v03?=|5%5mY3P=M+3UtHJ2%<%cWRI6i#Y5%K0B>mo-Ly> zqo^XKTWl+};j(>hg>PNoy@AG!1tqRq+tnpzIF?)b_LZwL*PH)7^hoqxvK&3$ zwi%;Mv-BVSym+S+qjVEGYbfr&xD=f=+%$AT8l5#PWkouR!(l|}z~8g%uPY#3C@8YX z$`Tf{kfNcMXs5y>xic|?W=_$B-{{xKqS3*D@PN_gp~n*bJs;EifcV-=95|mNEuXc< z1^8cFkRIk|QdyW{qg|7kZqNZ$ZAQC2i@1XWmHva7LvOZA8|yzC_PEql+yVv+FX3afsN5t) z`?(x?pDf$Y@vREpt#vq7B6v(+i}bp%WX$H9ySzJTe|+<5Dj*HQs^FvkU%cv@?Iwby z8P;d_!j|BKTV(PGjV}>xw?DOGlB6N_pIURL%<(@w#vJP>*>G~S%KL>b6}n0IcAnpo z=znpZ{}&|s=T+Y7{eQ=V|4{hj|t!$jN|XJUD8+<0~V0H)g3kzVAlT-)Ea z>3p$t;E~mFn^>}HaJ=27C)71f)Z#Hd@()jjrJw#G9cT$Ek#*j;dw6mppvD6Cq-#%% z_Mx!rj+z5qLE5AJaRq)$d*deh&Lm)?@2hQQ2dU4<)YY<~?*hs!E@drkH4}@uV$)Nm zHlz4_pZdi`JS|I>{Q9W%um7xAXMH>4U-^&$P6D!t03*OJA^7@@PDbd-WI6o5_v+A@ zHgK=aD%5sIeErG+G}G(Q0?ceyoE?JRHoYVi=>f1abYdEq>}sYhRcdQQF%}n%&CyIA zW#KUy9D;+-2msw-AC%^jW&`9S-CG6+A5V>KhLoHWG3#9IV*Y~Tm43^ur@)v0VSXPF zGe8n84@s2(bTyB-4U&<1Zp=_S4=`5%JMsl`vdew|Z|^hl#{T9QIIQl0{F9!&6$FPo zw?*LaG@}854~|B{(iIG!X8!+#?tXG|ini=ADpu5^?vvkD{Y3=)IRsAeR~t@Vp)d+W zYaQw<4gj2#^s!fGk`u81zzSN&a)p)2J0~$ZfD)5s;b0DqMe=HxWhL6hJJctX0pVvL z)tz4lOySbtV+_gD)wMzkhA4-4(c(V9tSvOopx-%LSnS`@|HeK%53mlIIvNv14%FXBu(KG? zihj1sS`>qHu&c@hE?%V`nOrou4*pg$te#{Jcxd)Nk_=PWfo#78P(^7ACEglkcuZPraBv5Z zY_x^2$lKqI1)>gc8!u{sfYC&3e&i^1VKIaeSVmKH{Tes}%14xlQbO59zTpmlWDw)7 zvLWD_81Aa_+EUDnS%uUc3}sbxM&#G|5)!}x z5%KDSyuQ%exD+GTaN7)-`shr4-Qh@~zN7tiln63HP%>s~;8Q}uj?u1;iD7M-6pd^l zF&Kz@$h$tr+;OOPvXm(P=NP)E0gYSBc1;6YjqxX2CAxU zMN~hWU`Gi)CF4-09`C?O$I(pG40TT?xKxv6{F2*^5D0_8SA@iiKQIV{W;J}H;8p3vXn(zegOj@I`L>p)qQkT6+fYV&v}$X*&>2<3 zgLI1{7Qp!o^QZaW1VFHyGZ4dEXt+!wRs%cSvj@V-YhpDFa)$U_uIGi;Fmm)?u3@#trFh>N8sBeJp=?LbzxTFz ztB_W&7Isiqon|X_;f5f{iB74aSH-Rw!8duRBsKmH_}xy;WH(-#^b6XsCMO(RFF_OnwxJ1fzoVq zeyq@}0V!H9{S$>hsRed<9Lu7zeKHZbhK9}^&rJ}(mF7O+(efOG1yPGDbV}ROp*lZQJG=~ z!Mg;sz4MDm}+hSH$yN zu0u_3A^T2^iwwF=hCJbeFZ*+?)dG-y%+Z-#{}3ZM^orXz|EjjW-3`6?IO+Qv8xw-R z$T&cfMvP24N;j$R@;xscb~-MvpW0F6Wb5|SBjafdg;s&3e;iaf_I^!5AB5;o$~n-L zHoN#hj)QciCD`Op82d7TrsO`{?pk{$$GyWNFId$HPjR5jU$)VaFD+2zaUWh^QH-z1 z%x<9eNjM_V+TS@n=XuJ}Vl5v3MJL0@#_#|z-YjOY6Uf7w%@0f$ zE515V-ln3jQ*V%-_j&@n1+pRHK^do|klW&M<1GFX>n)lP^#F)D_&yVoc%FvP+{>5#z2Ft(6dP`sqDyw4}b%iNr3OwHwjFE)elyNE2M`YJr5>!d>kL|g+vd6b?j=e zP0}Guv?Tw;7v8Y&NFO=1rt`%;CevvR6V8D3wuCxzVDR}aVTxY(^vTE2;A0MfOpbdP zjdM)f0iGtnwwLtD!)H@ITwjIJ9q{PaTFCP8aK(oTX)sYP0LJG1J0VItJ~bapwG#ci z0(hxKA#)fLBm*qWTo?3VI2-2h;X@C@Kt6txgy3r{m;qW7ES6RjX91ao3*UPS-e;Zf zj}cfHaPQwUk69AZ#V(|BMRB1#hDmQgQy5&!PuNh)o6OolzUEd-eO@Pescog#P;x{O zkkJ5H7G^s791es=sau?Fx&9?+-w14~TNe6(@r9mD(B4&`sp)h1YUAw>uPtqkIcFt< zPi*cW)Tgw?AOc8%Lj8eNw6uvvb7Ps(r9TqnX7HGRPC+`q2G`<`A;AP`GO}Un4XKCt z0*3sG#)K*NNM)ah4$8J)+Y4N&oC*w>J= z;!?5W3kb8jl#pL#w8WhQoR0J5TpEfy($7QcD+&$r)n^%u{!=pn8lh{rnU|DUwFL*O zxzLosK|~C}dJ^%CZU#+Bu$NwMUcG&7!jJHIG;o3G4oLFwi(?%xD`xD|&p-@|%2nqD zPvHOG!0g`of2v z4)pbqB=`sbE(-bYHarCPsT}e&TFTL0r@15D-GIW@egUNNAc>s~OjBab512&)${`5g zo|TGan&s#1S26{A0hL$#pmTC`El4^CA5wWCAp)bfeU4cZUZ$^P%onh9*@gw*NHgRH z@JpczN%kuU>=+4>FgO<4ZDtSnscfqo6|+^hZ{;A) z)>NT+g5!+z9T%+ChA3s#%9S~vrEee97QFu!=jP{!mpTdVkoO>wU4f^TAyF~ocMtT4 z)l#GPx1Rj2OeZ4hoU{qvk+LvMq#lE@lgbx#bF!84Nmn4p(YLNc_Clnr(7q&1HjHZbS3L)9XA#sSTA-vFK{{b)j?`oUY|@ms+Ji z^h?9ikJMvjZjnj}H!j>s?cLW6{0goSz5}>Te?BcnE=&EIE9w^2a*>-m{}H0#@Q3vySnw z3ug^*%d^~V&r`dYOOP9h4h}P`bD|TTuFNvxBE+AE;`c2}wP0 zF?jsVb;>1{;ao7{l1xDqKrw3&AhG2)Qa=z~!d|amR*^o)q*x{a$Dt*no#F;|=<8?} z&W`-^k|TPD?!Y%x-p(kEJX{}x0${7}!fLVN(Qc8{=KzslX7tO_Ji_}|RgiT~si;_DI^=;n|xzQK@x<_%{+-if- z?;|;hT7}x>?LO7j@LRThxl0>-z03MWxTXCF+f{1@*05H%5e~z71>WhCUp*b5HfONx9RRMI`W)@LS!HA5wFH%_Ne*p=;|jM7Jz0a zH}i!9{ThgCxo8zZ(l+gf*4X6-ly30U-2EA>>eI_z76U4=Xw5;D*54{hG2TfC)W0be zPS>=8sr{5M+2n}+jw-j`6|&>bciN9n3=*yfMXG<^sZj9(Onzk9@d|ATpSsQqBqSt! zath!Z+D|R*EzRJ#hE*uA2J4Th=$v$F;=u!ubL@lOrWKx)?StXKGhdh`nA8B5(U%Tlf9G5Rz8yA{}QKN3d|ZQm*r zx(!i1lB4>`UynHc5eILPJ8ZOIzX(g}tw5wZP%*WWy6^&A%~MMXKENe>vq>QTX}aL& zxWH$+0W3!O^cbe1d4VrA(G@QW(KC(v?0xNJnoyftQ9w`A=e)g~DZIr}1EON2-73q* zA$uG?fs40BAJ7$eRJX1o1)CNFZ)VI!nAiuEMe5dOH!0C4gwo%a$xEGvQ$evi0lO@F ztsL5MX5D94Z1i0xkmqST_^uj8+K9}iLpkym$gSfuumG zWnR9=GuhI%q~qWd{J~!{+FJR9%)1H5IyD$S0n@V0tiOh8+Qc_%+{I5WMQxucI0=AH zi9B5;^aaSOfb5fMO86b#pI4JXOpLrx>cZ{Q03A?vI~ieQfwo}nHJ7~F2btp)2q>g) z;w6f=EsXwDlKAr55S9Y(loy=IkZ+yo4YXqY+1PmES9`#`c`A?`-S0;1FKm0$93}hY z-YMP~1ctS0vMsS6zML4Xsu6`24>}{ljLDgBXbuhwy=QD#$a}#GvV`GO3W{x|P&U9S zUq#lZhY(7KT9tFJEi;2!${_0>nFBt4d}&J?&Xo{#Lw3gccyI!=^5qf) zXU-~4!UFyhp1sZQxmOP!2?;>(?0VVO;E)*2RwwU$+0yB;T6Rl#c1>p! z?0-YIvVFOqp(c@!8`!FbRpxACH>sik61@wB@fuAPc2~UTz}m28@SHfsyvFX~E#(9r z5y}59+Jkp)WeZLmeC7cT{v{}*7zljWS4fne<2=wBnu61BV6es({sJ`=VtF-Ed&d3X zi=IJ@Q%%6(8Ow#lr)MGKIP7r!jT2Hh$3(Kb6?XJ#;XPtB2iTDGH*U80*jR_f7c~;` zWx{0zeSoj}K$OiANTntE)9IH_C)p+a_+jz-sp06fw*m_}Q|*-?gFrS`ONNAw{a}o= zkHe{eb8%-@T8Z$*CPw}%WfrT`W11R<9e@++=mV{rZyl|07tu982Sei7J9h`&ZTV0+ z$Yv66q~a(*lB+)U{*Z26NvB#fcf(s#j?E6m0!C3&exeFa z>7hzXD@R3ROmqngD=l}Glm>;4f6CoLtD z^R|kBoq6x7Yppr_AEO;bvJRaK)yIR&cDf;M{Y=HN$E@z|FB|8PjKKgp20G0r z{;v&iM2Wsyh_*q#39__YN#(HeMX?WN+zf!Zdu>R%&Kn&5_unI&=uyWNBORs;q<4VX zt2uxX^QHjd#qw``i_mWB=-dU#{zJv#6WahMSRSDG1sTl&p%GQy7e#4SAad3iWW#4c zx&$*5oEQoY4o(cg6K-hB_(s>`Sv5Z_fUEUzOFnB13 zeHem|8m@y!L|9UQBSfMv)CJ8!v{b&P_Q*TZG8QtQSDdt-md>xo@Q10+t*9hb~okRg) zB|J#XEOg|p2g(CQ#cCfq+!s{bQ8Zk>Qzt(A`mfc@heKu0fi1p6n|)pfa*{yv1pr z;blGf@~n__k?+~}6C5xU`l~u;a>5y-1C~jrj>XNz z)I#g}nj&OlDAV8{Z2|H?p_i{)eY|aVUVTzX);5U9Wp8UpScK8$f%DZ|NLq;{9dbSj zpSp#QE57{^Q(FV`ymgeZ%QyYVD+{@sh|Um9z6@}vVcwTm9|}q)NjndNJFwxT;YVEN zNF$CkUZx`}+a~K`GB}-~?JE^~zNTn=#QSVP_9X>)gNVq77qU{B`kvIl=jCvA#VM~S zk_d_uzTL#L!>z)pT($DfD>l)cFjI5{820D=+WYWk%&(v)k`mD8`%yST;3bo{xrC{i z-f2vo}-GVePyPD#h>hUe=QgG)1Q5L33+%f_{VE1odX9@G^=qD!C1!i^tB`s*9v=H=< z;h6k@Wk>_vSIA!G^$R;d7oE`+od*955^y0sPAZzGlHzy+dWE9ScwsF9H(B$lufd;{ zl7@Gru%S~@i43r+l~Fnl4SjoobeAu554hTbh5;_w1uMI5SR8ABkTj%Z`6$9MQ)DPf zC-ZF#RO_cC9~q=D2n+q}23;-#f05kV+-a;Gincv&pSXNYMgTDWhHi zn&_6Oi=}93YBCQi-9*Gu<0q=YPsiF#=1dXR;k*6LbuU&HA5eWi2fKA01`u-qfjg51 z9_bMd3apvPmtP%Y4Gci{MzM7Tb3obcfm>MTAZbV=4{@d`SOT`0!=K`C*?gv^$1j)j zf7pBTc&gU+e|$-Es^g?6(?NqIZKcdZCp4j?9m=pvM49Kg-RV?9oF*hw3ZW30r(KCM zOG3sHTV|OFzw2IW+c`bY?|FXD=lA`-UY|dn`wzof_gd>7uKT*)*L2@3)(7(@yKIE} zKkY4YgzxIig@!}~_jv%_)vBnm9lzn_zuIJX8R62$-K$U&UZ{d%2#_~!$U>Tbj7Bco zcxzw(BCM-N)>E;4uLRbr#Uxia&IG)1?ia%kZp27%W(SO^ECjMlA%?v+_N)CG&9Ucl z@XjZ2*2+ygI02|5J^#k|XP>w+-iGyv*}}db;n01(o0+mrAaPEiUJoj_|F&s)$U%tF zQA~H7dc_~tLU&(JJ-IvF#iJQfr_>H&sm6h?Qb=Gq_zH*aaYBk`>gpC`Uk=`-J+w2m zVr4YPv*dRy)Ll&S17|6Oq7L5W_{L)gin)BxSknsma5g zQ(n97g7SzGrHR5mDBkF)wq`0ZPe#l+Ez#~m`S0csE*xC=j2n(5fJW7UDk!{W`}y6WzsYdu-h7_8VXu^ywi0F5TWJ{Nn6o8liMn+HRml>I!i`{{64`Yz5TY!6haL zlCudm9clHRC|!X)fv6~y?2Q70!XLf7u=!35g!^f658qL^S{cK(Pd@@oXqISw2*AdY zx1zt{ozwdZ7~Axh3O+tTXlMdotGi>i03Tm;ZRoCrE8I_3OYB#49+MczJJK?C&Mhg? z%wA^+hKDy|*sP-4SasVQhrT7_4yPAve_qwA_2svv8<)H}`$x%OahNf;f&N)emq7ov z#Ek;+zird6IM(dq>`-LKGJDD9Mwf_z)t^cSkKR@}6)bRHJ{WfvYCyp%-F~^6Icq6TXN(itqMnx;i|y?rX1`ReBXh*#IPs z@5grAW=lk~-RP-+C=Cu8#l8$a9xb-T9L_q!d|P4#25S?KthmzBsGQ?GI8aNz`-==l zl8+I4)UYzAzio;gQ@aMl!U^GvI$;mx)sKK5aC&?+f2?VtL84XqRe5DC7Yv&Ws6M?@ z^2<`1`=&oDjo;A|hoA&ECuf&WHcoG^-31q)0H87NH|B4f%4oas!+=LaiFW#YbNHc?b zwVqc_f4I7`-iN^Vd8{L}VLkmQuew#Vz6%NV*9CNfvLc0h#u_J5B4&@A z*c$fdwGYMTUNS-hM=gRV&e66pfBHJ*g%O6*frgHWO^uEA4ST0Yx_i4z{U?V0)McUe zs~c-N97-Q+TJ2Ig_AsP2vwt<#1S$??4{Y++ap3TX$F}m?ZihQXAFB&`MeL~xox#8l z{fAJmxw5=g1g_cRm7(uWX%e$OP_7Ydr1R#vTkqyEyMf~}R~KOWvfi57G#dwwMRLvL z-2=;9C0eH@aORv&Smm)?(GuDClCdUaWx9#riY9t#jaW>dLh3`&?}Y=d}F=!kfBXn)iL5UGYKVzVJX!4#nPu(qq5tuhNGt z)3_dr$!-cCni}TPH>5w^$?aNVoWa3wz%BJ$=%ZDhpQ)nkcLTfCf*tM&{th-8+Ts=Hx^V}Z2okNu2a;zCuQu^Qxnhq z+|;)dF&AaD;#Y}%%%8GbQ_(k3-;l2Uy=8XVCUJ!HL@eK_;hC4T4O?G@NpSC1Xt(&N`FC% zf>{l^2}e#^b`LhBTk(JAgB>%!DCRdrOvZL)G`$@CtF0kHMcIAAxS(aa>6195kByER zTW}Bh&pMCqew{xfZ7@6L<~iP>4#zK;)GR68KeK8r`UR^e|Q_#`=%O*1(Sakdy2tY0r*@6a^Z3uFwD8zT=u0 zD|J1c9;E>okuA3iu5x(}+zxsCSlY%2H5EIya;Ehxsl zqbnzGsxN!II(%mMRh!}W&zysAw}i84R~ASu9;?u6-=#fKO6D7~H-Ir|<7!m2yNh3R zDXn&qUaaV6WIbC-R=_WcAE6WDJdHmC7mMUV1C2JoH z9U|e3Za|i{La5Qez>l{0)4ABx4vO0&jdHI16tRDR>B>eGNPo}F_WgTi_Cuzj0M(@l z|DSslADls5(RG9IhSTS}#i#DYyLX8AyTJSe3C$)9;r~KEjl%w;e=)HJf1tcE%T7Vm zVn&JJ%9{HV{r)MrXd@F@qnliRPcBlO7q$a1U6bLgL^Z*h!13QliIMp4FcIVMUy@*q z1BU%f63#n;D}_}2Bd3<2s9=NS1hDheB1`f zhPy@sZBlS5xd_OJ^RNO=j}1 z+>bXTPft{^&yM8x&DO&nI_$Hb6o*f(w!Yb%=aHTI^lKlUpMA-v)$PI-kZ(oeLApbk zD?%Zk8YKF4TXPmw8uY}(jm^<_L1zdO6#$8LeXH8fZz1nF+M^3<&Y6hwZ$<};#%6ma zhP-CS`Q5fHYIVyl;#g(Jwpi?s{-?RtxV$teql*p0uz<+iQv7TYmq1bLwR^qdb>hn- zocvzU>elY#WX=ct@%P;+eE;6p|9iY1diSIJ8wn$~9$0m!Rzn(iCl4m7Z&lIM;a;pH z`Ek$Zf_=vyaeVq^5ms6jOCKn2frzrU_ub?KG7J4ZM49qmbnmYS1rt^&^0VUFpjhCR z+_bnj9np2?5|(0?yVVP>UwiyGZs|2)jAE%Ff6WPY)VAh}F-ZLA^tAZ1O6ze&w~3uI z)~)n%*f6&Ph3f1C)rp6x)aXXs^;NK>Az6ZHzHCSM@^XS=uUh%-IL-~cX8EHj3x)>9_zv4WZ&0k z#9?Cs>;1Z_Qpzk?PN3bYaBj!Pam6E`!JmOX+fEGBh7P%s%Rj47@4S1lIxIw`e4P|4 zriONf>wz%JxVTk*HV@X~U2K)u@Cyt5FT4B?3(jHGugK~l?;UA<6Inxv;%YL>um$4( ztL|ka`fi4u#^9(BUhP_k4{7l2K=WKb;gVNI$GRZ47+j-hA2n2NALmdJUFY+l1gub3 zl6?nimUE>rT2zX*AVnn?&aMuQmX?Z6=njj65vME{wOtHxnG8Z2jsrzf3@vuNw(rsZ z2=5@|ftL@U-x|AEs~w7sIgekUK49P*Qbw|<(K8)3MzS0!=?rjzABD&X#Q$$y2;0dk zr;O<7xA1EByCIuz-;d}QxjK_-sYREwzr#YBq+)8LMAn*6atnjCvj_(Kp% z11Cy^_I>q&>X_AT>eUj@^u+vEH$KFPcS|k|Ak`E1$F!S2i$w=hL9zZCy!mrS$8?)V zx{k)zZ(yjV4dZp5?i_7;FMy1oD3k{jnL+Uwdl&c?Ws(V3sJnFcE8i0rbI?aGTZ23% zM3JkjscE8r=-ydu>$armrmr+2@%F=g6AQ#$;Z5{nbT1>!B;O9>&yIuY&;xyI9e(%`DhoZId6xGU2(v1Mn6o z)D&>~2b8o|8WS&DbAnBu_>1_d{C5ERE0Y#rq|CLow+hBRg}!?w%YBEv0G(8zAEdc0 zJ7iM8)Fl^iX!(ncV5ryTP*E053)@!W_4>@LGrYWmkxoD@zhk_9MeIiWd%tP@N0j-W z@X5b-I%+F9+0U_vyf1?vsSB;xak@Pan)w? zV0(E2&OMeCeU}pSY&pCePT@p;E26MHwh;1@kn+pwiwTI;O%y7&oywH75==-;4{tE< z%E*-dZm=~<*!y$NB47vu6qvEf6j%E4=iA5d=UbD3;oe+a#Azn^K7Yp-L$oXgZE9=d zA6_&dlb_XjpztDB6b~bR>S@}Jh>y0oiv(*;s2Nm~fqT0c&N=jFYU2HgJUTX+41_0I z!q3ME8;li{1hSrhSk5|_e-byo6qXddPpBi<6Q?q#J;fZC34=4&t@3#jxB!QNAfwj* zeO<_Xbmbk~d$}zaxN|^kX4WBgIPfi9HrzqK=+Gp@PgCvyr-;}GRNqz#(Zh4POZ0{bCDNc4DhQJPd{}GI_`!PI6tt##<^|Z6A-nV;* z91sqZpz#mX30bN<;8z3DvQdlMd5|-!07zEb7>#j`%LGyhe zBcWU8=?R!xr50PovG}-;GA6)#ke7}qCT1XxHk>eTfCOg+v6M^_T-IC2yuH~Nap-{k zMNxNLA9r}FuX(1o>Lj8GH9zLK-#!-59ubPp*P-^!;~ux6VR3POesH|diFoqrwGbXi zY?6zAnDWg#GU?S4jK;=%MCl05Ar-`;z)lJ;6KBc-YS$-7gV`SO-IJX`caCvfQ9!{Q z7*>h==BS93pHG#MiN+~R9*^V4F!v+f>+ze!kDnE$pS~G3C(7&zq{X9E>+s}6e3%Ha z_WMxq4;0#ywJN}M&UEg?-%4fsj|Pr8_~_w(V;dSkx)Gyf9k0E{zhC*e1wM(^D4CEb zrAj!Iud*4*vmYycdZMVvd=LAPa7V)xr7Gp;$~Sp710O{m5TcM+)vF6^GkLPuT7Mre zWx*o0{oH)CUkOp3)xWo~vvY=weDB|P>;*~`1Ng(ZmvZx%GMSfe2yCn)BcpbR1NXy8 zi2ywJ9|v|s6aVo=Eqt-d$%sY0-?w{O*xSo!{V=fqbr}-U;nH@KInW4hW^KJEGP}Np z5KFn^dvWr68=lS^6~Iu+nRX(oPwL61$%SO{rss%#$^V!}vEmxCx7YdZOhl0_iV58^ zZY+qM(HT<*f9lA$g@b6@il|uI4W+!Q}f?47O&J>*Id73(bawjw~jzuj8gud`m(hK6KCNXpo|qZDFUek8 z`>Fdm6b%_)fhUT=m@7Y9)#l6Q!j3Kx73I{CMzlB^+M07>+a_IW3{S(eyBH~e zw2ipAiC?=G@maOY{j24a5w@>$O%N4Y*KlNF4ETsO+D*nMXgbw+E*LyphVC{PUc7xOHr<@ukP~={JzhDZ=psTZ!r6 zzyT=6QjkLFc2L&k;}1MG1{krrb&8di^YnyETb#(5XPs$a9r*^hg?9Z|R_+^{1NwAB z5`32W01gi-fwt`|j}%?>D@MJ>XdsGe27`l`^BmX0XwnQ8M&onv7`IVYZWzVcIIP7v zC|qZhDx+YMa@~3I`4obYQeshFaWV=C2}9)*u#V=UGM%2(b_xpaf&nED;-NJ|!pm3iomR757nwOvFCvQw?h` z`g#RQ_n%NM7(F(d{2b}p*rct4!&qk1YdU+n{>&3a5YEuDYShhZwrOcTR&HAXBw?Ga z41?UY4>e6ssMw52`&8g7t2GB+jOqt1JZ%fVCU2$q1oGwp`a)H;y^9nm~w^^0TwW|qX;crBMc<_L6K1_3`@O=KEo)L ziVW2{!pOnHUF{J2l{DDP1d5&%V~jiN&O5lPA+&)5H@Iblkj0v`hG`2!$&9pK+$ao} zsYXbfbf3)_H&V{wc>*F=71{DOi^fbfFi8o$8spIlkj>Wikk#1=GyWqy5DqozX>f20 zza!`!*#Jv38s9qyUTi>E&~I}u<$6fKxUBMqdk|nnHG3;x8y^>{F$($nj5BPg1`J)Z zY3p(UG~BSn(dKn(${J(oKj0r+US$ThX`zO+_gc+qiztQeSK*K-6tf@X71C#wAb`&X zX&q=W$wd~YK8`xZ1ntw?zvNBz*f}ZC_!xFdOttkDO}|R{(WgT!)Pv`Pm+GovlXp^9 zI2H6xG@q~W06byG04%gtT-}!2TqZX-eY);b5&PiOgXQp<-wv0HHDZ_?GPGVUt{0OK zJd^SjKfRYCWxN#vWNGg2`7JB#{9*N}RN==XRU?D*Q(HSa0 zii8mQ%X%Gde}SlBR7l25K8(>;e;n5WR1G1m%vh~cLwuEis7$O_JIw|rOHj_y}7yNX4kNkz6`79V8BQ=;>{sb_q zi@V;TrrMpHkmc^_4w`a0?6iwf6>^=$?V)w0mk{ZIuSGEUDSh= z3_7w`&G`&h_)N)hjt!y+KpAIWG&O`2Wwf=8 zMMXz&UWz_K^+8;27tU#cMK_vxSj-V* z_0M#8__XQ_aT$u=6}z=mfLU$62@+lWhd`Y<8$Wdrw47*Zx#$F3^MGa3qFwco?IA@!bNJQJJK8~e3z%~1 zM4fq`Lg1Re0M|f~lDzNFkS*w6(%gg^dDxmJy_W8RPB zUKt^GX&3SrO={N`6+MF017an_zr}*sXTo|9jt}`BUf_n;tG$ZXX0Hh6=WCO@$m9L; zcgT}I9GDXHEY98P&)C7aHC`Sq8Y%@%rRnMEOIIwx3Ll_p9?u97ONV#R#u<>X^fs_#$n`qPv)G@+gLcJG1*7G*Y>i&l`b#_hrxg8L< z0K$!1iHgIQ~Ytla>CEB96?i`z~DjXl%(RZ=RIqaID6i;r>i1XGF z&4O)7P{~6al(Vy|tIrORP2NSl+6l=lyesH-D-V!h>w?~f6FH&|Mnsd4!AfCs)btpV zJd?;rbJp$?;Abe5U|Yw&ry?ui4)qfvntHu)Sf*67LtjTjrh~G|Wzq50lzf?(3p&b> zC|W7ahEe7gT~KCZ(r>Wyv$^f>Nehxl^6{nQS7h?*m)AF7Gy?{G!8hbfmc3X`gKp3H zIpO_#pW^`nx2wKvFzN2-*nFYUGG+|NITyNQ1XHR2kSQfXPpG+gdtl@)Fu0X(tqYK zi7$UrtSl{)Wz$&Oj&i*I&HH9%P2~0#R7363l^*!jc6y5? zVQWRtY<_G3hPURFzmNq7&KzYR)S!qmla`cjFX7dBeO)3Bngk-G>B8917UB9b_;?{k zC}i{kJmz~~J^Qs&XE{|A@1nQBqChff%)H>I&ciS@VEh5c2?gKH2HvJR4^*~t zY*pbgL*I4aeXv)6X1ujz>4XAQYW;CtG$Xq4(?*1nf*MlOjv;0s>)0a zMgLJX33tI*YNvUJOqBY})~GXl8hEMzaTRP57c z!Q@oSr|XxFnX*t%qse@DltU&FzH43IwfbWtC}d`|lkl+s8KaVg*>Vf`Z7;4P7A|}S z%1K0xH_%Zk0j3m_LY|<2YgHQPX%Ki4e=!hKzmyo`rUPgSSB`#X~4* zf1>;@sau%0j9}UtCEQ=7PX^{5v(R2>J}P#y$GD%hZA)XAxi&t-G zwWI%_qXrzz4k>u1iZfDz?nM}u)-KV^z`CHkp*#Y$lYLhJ@cS?M{YooYyrj<6@o15bYwNs`;&8rpMR_f=Q-#j3gfhOaVG>9EeiG6 z{2wCupq{|{kwJ0Cy2TiBjMM5-R90`|xu@kB%OT|PB^h5uPz{{5y!=MjK#@>DkTZ>3 zNcIA+>Z#7Z-u0^Z8ChBi8sv070&1kb$Z<{eqgV8XEXG1wtny}LK2|7XkJ0mBQ!)r2fV5uaX8IqBTb3Huy67Vol9dLbPO_OJ9(2))uzKm0Sf}ctgHy*bt_Kge|6;+| zkTp6ewDC7Nm=Uf!#?8{cCguYMs%u^yc-xP3E{w)8hjeb=O4)Xbtc!tLxN?cCIV&+Z zJ#aX9*BdfGwUjh;UNNxZ4TO>JZT&$wgOvx30lq->grf2X0B(26-9w&wHRH4WR zm8v6qG15K;fD4SkNn*S;NZKQP8>|L4{PSWsPNXf$$Wv%8PmU952nVkNt->JBSCMCZ zTQ)C7e~u!=fJ=#;jgO@MMS>I_~;ruQOZkw}B%?6-+yheH5SCN(tIuCylnV zH)Vi6V{N%_kjc9{bM?mzSg6;4QBn>!q_J0-%contaOxAWMla903w=87q+K!+6ro&h zEY`FD9~IbQr4;QAaEjrTEE{j$!dk4mQG9rh3p__T9ytqR#dnwJHLk`MQ z_0x(+V)KEkYruiBSO;2`T*?}?84uFm!lD{CAUq7xp)XN>Y#sNN&}s>G&$Bm)2TCGdN3`r z6X(LdBH0!nkC!%!C zRl`D4$N+Ik`$2nlIr}Olq^uk<;G$;X9^j4Gml89DVcc>6qY)@%CzNvF>mKFR7#s4m zx8Af(qm|suR>ueQRZGhiuGW3hk{N zL__}kj<)7)M8b3H&8)@+RxPjF@$h8683VF$yh5vdHlSmrypVxFYu3t`CSJrV`rM&7Ey8i(KjgiEiH5D7ffjkP3s%S!>Q}8vlXmT@V~ul z)6~nAj;C3(Zl$o6+GY-A4qK$}X2IG5?9>ILIIi4g$0!TY!mF~qxv14$W965gwxRV@ z>9;GxJHEWTAAP<3n#Hlx$~~>sx%cl+SJYowDYcQF{U)zpAiF|$-x7@Hc1loYoCw{I zVXJ%CE?D9cI3Z|7T-oZL747p#dsK7J;%$uegWc&qG>=5y3AEod^?I@Vm_p!#icfas6-Rmp z0!zfqNglKPv#EpGExaod_UCxGtzl22Vufw6A;76}!)hX@Q zn07DM{Y&CX({3z7&DdouyQl1i*b~xbN$J>LHv^|)>F<^L?fgRYM7Q%tUrNgbiqKnT zOsXwbPUV3~R_=sl%CoKELZN5aWa%9t4fQWp5UmjVz5I1h(+csJ=3#}JSp6^X&EL9Q zWk+~2wbI?zOm}RaO}*Ak7cXC%)iP#4GRZt;9M;|9U@2^-!TExPD;x;s%v4Epp3&#n z(|gw1o7mseklNmL zeo9ViM~?ls9ZugTYZKe@zX!Hm5~mhrL16LC>g_RpUW2_!E7<6I^aEO%Gm2N;5~T}W zC%W!T9qOxCfI~x7K)1eWUmyj>iF5pDi)4{gcCq5-qZhl>hk0UJOe^dX%oORfd4{vD zZY$`~*T*L+DuxX7C#LelY2@9iJH{T`N!HnR(UtqoHRDO;->i79j`q(q7Nod&jsn7* z9lLn3@q)b3CoVaUbe6Q<@*aK1hCiN_AS%z}40MX^&o9dBe+xO->h3cWTOD%zyj~SO zx0@N6nF!~J(j^Jknle1LtwYIDbL2D7j}xdHAgRYz5>>jE^?VoWFNny>@Cg?Cn;8?^C6l z*huSQ3`D<9=B}C`61e;Mb~*ZQ<=OMvXuwE8q~G(4{Z!`EQ23PZW%2Pb_Q8Beo>%<- zs${%Y!Dl^tQR`_32R4NmtGtQm+9c90TY=gYzyQ&)-EvYLEi=6>C}X)lHu9@f|JWDL ze7o>Ay?SwH9lF%wi=A5(C*D3ZP+V(qYSv;b=oJi*>|~#{K+(F^@4+4JwPL)N`OkUR zMy5!qt~|pwcD$)Ahs~pD2X9wJx6crz_1*W*rfjEvfcVs*@oyzN+TfV&c1YJCu|k^m z73?kRzt7kk{c-Xch^3!narw~>xJ{KRkl6*f|AuXl8aVlBFecn{1LTBPU2dWGHl&|A z)!>1W1bgaJ?Cwa(;tAD8Vc+h>4^8%E_kTVDd5Ek1FTyF~1zW=1ck1n3^FK-fM@fo5 z<&x9hrhp4dIXP4vu;%Val(Z35a%oSaMD6${;%ryjl zQ-5|x06bw9>diT$EPm-{D*JDaI5ATH&4-_(n18=*zzl5I#3%YzxB1#^y~!ql#7g7r zbAMv`3zC&mepx6iCiZKP&t8G(g~mLzRlg)$FBaF}Sa48e6-9RKXR5&6qrX`$x^hu0 z!7nL3$^7h}f~Btm{v!1(wPz6!{lz0o`@%!ncOfnOKIMHOc8<~XkMg_1kKB#xRIsXW z(XbnJKbIBmX*kzX3^QH060~V%k@#aVVPhh$Y}m`(7aLu;5%J^nQ&=>rm4G^#MM=n% zuo4hgBG@GWkugi<(hWp(5^?2!=Vwo!YXZ}){M1;#KWu#BZZrk^b@)B>lZh+z#&kHw z8tsmtbHwZFJPUV)u@^y>vB#qATLZG|(dTtc(;I&s%%NM%rYg=3 zd0kU<&l|yZIlWpbIlTQsmb4a^+TtC;E=we+Y+=4^1~$n&LBwB2lh>GH>*dz3XFK|E z(M84WmoKS2e8Ee(&g1Z2)1v`|rYlIN81(*ZN8XtW=R(+RKAPEWTlBc7L+vzaeNMy| z-b$sN&JsJFU%$1bc%L0GJv;EntxzS_x2R*(4cqId%+|9>=pS>!b$bS7{PN&$Bz5(F zB}qAc(8sV}s=7xaMN4k_!_L^Z3Wm;Ow9%X*jB;~j$+JhILZL?TSmjx=!t7fO%ydt! z9SgSV9juO(M+1zXH}u~0FBy_M`Sb%DBy*^NI#E7)W^Bc+z!Pm z%@gPgFrF`i^2}9(VgF_Y*6G4%(nhnIEYIquJ=5nPetWOmuy}IHq&fL)x*3cVrzawlckaD7pk)L2?xj zYtENF*Z?ov0|z(WG_I$Kn+2E0Zoncm$eTs4lgyT3Njgm4CHj|WiJUzfcnnjQ9TrrB zL^{!R%)W1XB3jbf`_Z8;@&4dicwx|%AGdlAo(tz~;U93=m4_Db&?B7APv`_k-=t5_ z#8otp8+1UlGF~6&0og4&rd$hDuE%}$o$gE2ldE8uj6nKB*>4V^yhj)>P6f#*bymOB)dml z=vS?ByaMegLn21}k?d=l4)-dAPCotmfuP!wH}2v;-h=%tmvKpj?khF9=6WUb$VCfx z;MQ@qQ<|UlSpbo3>^KZthQ<)wm3kMl@_R=+iyX4qbwA`M3Jc1FetnNxT3s1Cuy=RG z5O4vz$BQuviP0rew5X4DKMF|(CsU`T<6vLeZ*u%n(Zb%H_A+*HOEHQBi~!{-pQK7*VDL?MI&qC zvLP}N<5Iu1vFh8Ub(jui9&|XNS97eX+C+&eQOg+=6n(n#v_8+zc@#uJ7t*oJLCF)q>^ zJze~WZSa;pi6Y+gJO@KZRwY>uhdsYci+L*O7KF*XE!>+l)CAOUzab;-TkYX9c zcDSh%jHCqJK%G}c`O-G9v!$Aanb&7*JC z<2Z~$Ojk!{-t{^`dkh=0ahu^NIjc(e%8fL*)iN;QIU>y&|2o=@#!h;nq#d833T zV&0^IM|r7iH%>-6asX2{1V)=}yiNyaCSD$^;%kPK%%FexF5<*QDS63(FJ8vK|Aq@C zAq57&gb5&OkPI>jgk*S(yOZI%G3px#0^x(V7$6mYFKs!(ek&qteS}M17MBc46_3=p8{kO!f1$Rdvmou76Z8?h*l_U7DlYsHQ=}%MB6@dA zsE4Eb#WxI?8bt({MabayX{7*@?S0qdJ33OMzv2RyCQeScxB&8S8C7s`RT_b#bqRGQ z#G`Dw$2T`i@FA)y5<{Yy9#euJK3oNr#;f3=6>7kx_=KFDt}!9WNSTNttDsh4)JZcK z)=f_3(lv$cF3Rz{QF0B$XTOse5g=sJI)VdHM1XP1RV?unzl;-5(qn>eS;O+MgUBw5 z3%m#(jTVF{y|*2IR_B;*K<47*7-`8rTU@a{&AASS>D4RjF; z1;$laUFe(pKoBuf1f`1p${;S1%E4c&0F4s>RW$l1Ir@?fHn7q8J7yz3+=0-8#PI;# zavm(=AdWYuGbC;tc=CX;vW{NI^UMeq?=#~;Fh{rQA&D3B-OSYUq0Jmbn^Q$Z>>XpwHoU#%x3dGc( zVF-l795Yt&R&PNWve+LBFhakNFd2Vu5H8H?^ZZ0If3=KI3voxK9$do;nrw+cp1 z!JaCN0ZFoT>`xOR$XcSxHWA;>9K9V1?Ii(%B@WD2Qc2s)Hr%9NhDD_uvn$+{sEP#> zRxgPI3|3}j0*!M&aLx%pv+lR@zKz#SQ@iHYLWB$Pb-UYuj_e&7)MU-|GiRBDmiRX| zoIz$kD3=|wo8Baww}LuA$4^*67fb!{%BC5A(`Dhh-oI8VnOq0GQ3)7Qx~qlle||Cr?WTnaWnC$pEXgcNLP0pa=xoIoK7U{|)TFGnhk+3J z{z-lM6H%9#gQPPI?TlJ;6O`cZjGH3F6021DXVZuC4j2H|P1o;r5Dru3?vWc zVl?$-fYo2Mk`~bF3N3#1uM}Q|m4-1XV#CAguWY=xK}gttfcDakeLFD6Ur-BfQ1F@* zff3gw0ad(@UdD~$4HL~EPy(ll@)ecS81*U|6lLX$L)ynwAC25y09aU8^?3t`1XMkV zXOHCd{h}HVodZ;!pF|hckh=4m;N*@&Asg5wYZx(!+k}4L+mxlD4p>sF#C29oooS4r zp4@ow*X^N5)ul1UP4s5u<2_>PLbuMQ5r9PE%7qv#Ns1WN!w(zelYIMrGm{BF+4b)0 zaqd8R05`r$RkfoubFQm@P6%B<`;4g9sEYwGRBHCq=%VVY-GvoHzI1p=xmmV@Vxu0A zIf!?ibeO#1^=@d^ZTzV@WzBcf!K->|8Ag^-crEmUwwTGeFzhe@IId(amr_^pZ)J)3 zO=I07CJ^2@(f{{tz9@HEgW^^{~{Y|Urr`S6&s*}KBqs%L0@qu;KpZq*eQ#~=G^Js&M@f# z;V@%CU=faN7sn&e;B!IV@@`!T@LO_zN1rs@kUA^=%_7Q3Of@W;-9^E<)G1HPqtFq< z=2sIf5j08Va=FEJu{ScPY-}+Wa2tIgMdV1~e&1R9ISu0BS7_?W%$xVKuFIx1WvBNw z^fga>JXsJXKNPzRGZlkZlFp^L?X(Vm#D0kT))AXq z7gW_Q#>x|b|S^9hGh56TVLCpU1qKT`3CvRw+8xOK+v08xD5UYGcqE@@#@5((5 zfkoH1WW}SI#VYN|+J&G&{#qTyN;_1AOKkiGO^+N}w-NNmdt$uU3Ge+4_*LSE=|ZLk zx8h;LjW@}c8&udeZN62A=Nn2U@5}$R2TYRsFprAW-70t52W(Wtr%nc_pzgPdr+DTs z!^{cTWO@h8Km3mS1q2ha6TIxoZO|G$p^jGO(05PW=N%I(!J7Y2ROn-GR=keb!JRE* z0hrQmeC(*I2X8$(Hsq!2g*R&5zMAXm-jQB@U*EA%+6nZ4-=LVpX_%uUm2UpT`VjjK zFwo? zSTi@?5j~w6EyI()(Yrbb8%L!unx0w)_`@)V16?>mCq4(~CL>k+@Fu1~3#ro&7$6Sn zJVs_Q(VI+ldhRh_!a7>%jKs%C|2A*0GM{%n&GPxdj2b}-IEgE@8S5(Fy>kB6+0i*I zi6lEmeNZ(bGLlPSv$}fm0<4V?C&cq>9dKZhdiNj;^{JfDJd`{qx$t@ss<{Trx~urK zIFPL|!gAi5&mRY6(6Nb3QYZ5tI8g1i@gAJFkz`5?*pmnmN$o}*e6gRg*xMJhRPW5VqPw48Z!OkK>7O;6q|qwSnW#g(KV zsQSvpR*>ax!)^aqNjLhmi{_)RmlhA5_7;q+z6Y8ANu3O4n8|1-CCoLKb(hvEEe>3w zZ@#;cPZjbD86fvdBUlz9%n%AsKsMCJEgjcnVIoAAHXCKyTnU`qi%E+?0wiC5WQqWX%R$lpCI%H z;&#xpje|$GV8;m=*FhKt=Xr>r76azdLLicV&O_9i1d543g^ATR_0^>ZWLDrB8nP!M z4Q-DKMq3#7B|h^A7chY1&TgQ%ed=Y$d=AX@0b{p`n6L~vmog+5SrhCjkK0}^oY=7n zvm;sNz5KNCZBMfsi_)6X&h>N16g8LY2E)J)979J}b=Z-2yhTORYCF1z4{U^Llci*` zD7O^&q@>Ba+;W>Q6<*&fp#}F#Yj2nPje9n!pvvEU@!@%_8DNZCa*QmQvqD)G*DfNg@kvT&2a@j;y=%(PQg&1Yy+-y>#P8jcw_44XYY{^PIB;*vf zUvwy?L^rKbkso`%7hF<2OvpotH4k&}u-Ja4BFy#tz2h|<-^&6#PPkwc2adG??-5(J z^28`+Opq3E=&o+<{fI+DlNn_b4@*TZ*4$;!)mk+>BEdU71)0e&I4L#uA~~7Jfm3po*t0KsH!;*4D)V%tqxv*41(R(ja z(x?XlCb-*sn;_qGC$RZvT)PUaEcFf*d$}meK3T>|HP#+EoglLEHE9RAxx`g*u~HMU zZeU;!CvdV)H=Wbgd*+b(*kj6DiXJlK0gmy9l4BAN>MiWr-o>)pRQKdHr74zt{K%Fs zfJrE6oq#+gojuI4ymB{siAfO8BjMss`^oHza{2U+-CN!ch~K}TUHd_Bv?q3SvaK#V zJ^yRovXtW5>1h^f`(I$%-`Cs-aovC2 zUZC&|ZD%?%%9=_fq`mGb-Mezo^5?oZIstDm zd3NpX2$=A{&T*=qQl00}rc7>~Q>QFN-=xbxo49VX=ozID# zAOqv}$KRLdacb=8?Qlwy~PtFx_N`h9z15Q5icz6_BJ)Nw5IE8xoVorTV{Jy z2YYx1r_{ZQLlBzmd~D;$qotUV5iz>!j)7&`_8KQuJk+jyU`fO`xp3VNIy2nZ<`j6l zqk3U{G-3pUeq}>5b|<5BJ?qO|;&Uys{RSCPM-&+g0s^C`jDUg^DN-z;gHlwaDNUt_G!f|~IE;cKFv5rw0V^m~K#G(E zq}M1Nf&__x)X)QjBzK<#=AG}m>$~@T|G4+B$;xv1ES@|$`<%1){_Wr1XNQ%a)BgMf zx6P%BKd`#s8S>reft#Isl1MB#+!L(WxUQxu{-ED^n=>N$CjLc(1TXahqk$_Q%Y_v5 zX8fbG>hP~M7wNwj8UxO^RD(Gy-8SAk{u>)!XkUdVjmO@y?Fv?O;#?SvX?~)!HCA0! zyP!mznHNQ;0t0ExolpceY4!D2CZqHMvE*oIaIv!m7(4gR>kvWck{q%YbNyW-#b?i+ z2UnM4rj4{>g`+?F_MpR%JJCv`SJWAn-u3k6OWhAPBt-+OJV#lB>HIV*@C#lA>(fg#L1DtC^d% z2IbFH!l<4F*~_jaEec;FBz(PAMNb1G8zwZQ7-0Pgq1e8|24e032G-MCF*aH&Mgj1t z=tt;YbWCGgoN{KKeZ|OJbMVeyx5w7?Gks1Gk_5sU>97@YbO*5@GV!8Usnt<|0S?2z zl8Kn83&|&j+BPYCd$gDSrMD$EH-AKX*xtU)#)0JE8gB!!aIB4^}`r~@0vHj zdq1)ctbR4@d*e>hTjj;t?pS^zNAzaaw3ePZAg(K#2+0{O88XVcL`R`GrrFEwi<{DHG&a0Ps3yCTCg$^8aXPz^H>wgZOu@z)5e??VOV z&Z9VS+4)ng>h{iUG5YyOHv`ST-wS^g$B)^($}(dD9=|gqx4vlTJUSYqZ>#01b}m`U z??C@U*?ZwL3hVag*HDZw*V!PlwBcgRT0*EOjL+C9_Dg^==$Z2tOz8_YK5l4u(eT>} zi+oIW#UdL*F3rf8v*f>|gLWF62eKk*onbgTWQPYv8G><w@M-b;^^S4q)f!(Xz z@Ed6_b= zRZQc`lmS}5v41J!iIC!l5 zw&)%~#jDhb+iI^duCOHgHThrtl9Dd{m zI%n#&lr^&6B5KoF+oFsjjHk9*n!v6McyNUDo-}d5LXXK=dSjJGI(F?M2*84;i>;gu zx>43Phu1!Pr1JY(OzIV?Q)7DR_mqaye?lzro#RaqOwXG)bviyGPJo)a)0CpZ)C`hIg{G`qF%z$%Qpx; zekC{G{wtz>r@}T?qV+Ooy;)!jmT`sq%saoW@)H!gor>7IQu-2Q#7;0d?|jmmLQ}h+sAuLdNy@c?$*x~v$nHi1xu_s8$=-` zl)c2COft`Z4`ylQ<;Et{CDPa_-_0i9t?hs=fno`i5htCzgYGLGyA8!n?l+5m*>l7q zfmq^$lh5jW@PMyqwOU9gB8m5;6R*ZCSB0&nig5loG5=D&C!S_x^)lvu%Sm)B%-vR_?8x#4ET|f5ij0P+}Iu^p0KfSt~7$ zp4TbvSF^&-?0g2?qXzRrWSkf=Lz#A@v{-d`ESSQx;GHm;kFjR9|2`g6IVJQmu;e$Fz5EGIjk@k}pDll#)x+SnvCr=KAMyxxxmAq-+k zkc%(=O&lZ5chX2c=s@8}U2yM#bqtU58lE<5EN`*Rg7NiLy3Z#X$@}arZuYg)QNIabnKBofuj?gBB?IL`*LTEnB%NWq@r^Hk$(y)*ISz}G`slP~barsg-Sufen=4?vcPh|#i-#U90&$h{k_)zBwKNxPa!LExs z*@m~a(fA>tcCXe|L&E_6u8D|loZn0)v7>yA$=^J6=RmR7@JymqtH&^>*5aqfo!e*^sCm@9Y7hw?}JBJ zXAHfAVp0Co1I2YC2?mZ%VHPl<-S#cq2L5K#JtVz=lPdvd09Q(3#9e+~j%P}FaV2g! z$P=oHVJ2l;9HQ0*ShGWOAg)mD9+VO|nJZKd?)xnt742DnepqzzAm(BHnH`vSo+-Zj z8)kgdVy<%R_`H{AVxm_yF4pq##Shh4LOkC%&anw^>0_%|x*L+bDma%XDQhiNO3W*s z(K5C8zs5)g4CFiT2yvFC#C$6=E6Kh234WiEmZ4`9?_k~Ivnviya+fRPls*fKYJ8yF zDNNmxgzM_a9!cqOJ)_Tumfz68

)y(;JmeXI{m;Y0OrI;@r~yx7me5U&G>}3{&Ob zfAuX|r{2-tUbW;8zx2tkT$Lh6OwGPk>7Htc`Go35C9FFeT?Uiuf^t<9$|d2c=rtEJ zIFgX#5S1zO@@S4iZQXXvE_TT-`s1mKuaBqy@?)b7rt}^FXU=FIR!B{K8>GJ znS5x1?2?(i5~j7{WHsb#R?8wwcV9|3B&mkE7`=YGWO)YF8P$HjyXIXpJmlT<<`N}F zz>DBc_-%lRFEgojU9apa{vebS6I@|9iWj3a9eS`OFWWC*-aHBlGr1jG20a}O?9PbX z9(!J8_SnX?oc7fr;V|Q-ycdbH^f~L1OHN@teL1ZQ5wE(HSndbtm0W&Ff-4U^lPHjX zpky<9UTMOZBkXzD$g(0|$>O@}s~^V1pu&RyP5l9ZSEJ#;b3I^rIb-P+2HF5_uyir- z`n8tx447@1*a7((r+h(^s7*pol7=0A1Y}-7xfv zvGfYTm!6(pnV{-(%AcMh@!ar=%$k4!_9>Ns#}}<{(N)54;vcgTtGlx2m*t%0)?m^e zq%>MWcTlf_rW-PtWBPlwE)19bDNgar^C9WG?Q0d+J_QLn_6RU(@bh0URa0|^zz!R? z*}NM!*5Se!%S9GbIaoSo`5Nj1gTE&}aw^(zuHno&OzUe%YsuW1$fH;kehHQoWGvY! zo10yft!?mySwUw6GL9EA)FFn8n><0khvj0e_czY*CxRj3QJ)I1TZNdR}4F-4b3s}tV-rsNvnb@J)#RtBw(l3k7@LC*22`a?E6EWWw{eutg6qm5Lqu6t_x2WVZzFSR-+gMRbPB9n z*B`k<`t}4)>HVpWC-5#tkzLDMYP!3-TkQpBmvR{X$8rozD$B}ml-lg)$2>#zvy!3a zQdc|0qajU^9A)kP{b3&QaOy9x6>imJH#7pDt;C=I*}hjcYN9L0_C}`lK3QYm9CP_K zmI5g%4|9YlCo!ntCEzubd67-k7n`3HA~b3HDnwf13+KJY>-L7{F^a|FJl`BWoT0D} zR>R3N72zStpCn?AtEQN~eAvlQ-dDOufO_RA91)!_;nAv->3P7x!aixmUy2%kU59C? zF5vlQZSs4*1RP{ck$d^d?)UpO7|TURr>2w}j*gCL{KR~kzvg*u>%2u5Tg&u1?ew(Y zTr$tEk$ljYT9KBX9y+HSM_LyT>D*A0t(cccQ{+yp<`@nSwHb{>RZH)7_RlL?bY67Fq@+wQDiKsQ*2#sm9l3G@j*^&)EyWB{ zi5bTopC-{#s^Sm(_f+{Le&w)SN}4(+gqdT1T|~j#&Bt6{*@;I-zul`k=Q%4fQJ}ftPmJ*h>@JWk4kLO}WL4_lpfcG_{+5m95LI#m>I`_@K7MMKH{Gg; zztn@0;H(oe@rmCtIrkN;8739&_akN2cQqxzJl7TQoK^Tr3oVN1DsH^x?rFT;Wblss zv&H#|FGOnojb?+Z?E1GLdZEE(%qbuU1MW&sgi<60)fQ6-TVC3AKg=@_6mD*1c0eV5 zUG^s}ldAWF8&amt_g$s+bT1$4b=SWBLuvM3P&@jGm%@srxe>rBgKSJ#zh89H+8M3K~v6S;yUP+y89(U;dLmhVTq zdnu70zInY|n&ybkLYn`ru^i#syB#Ub#kjm3hbJn84TNg~&tLQ-*jMc|-ee%W^kCA< z>nELsqPV1WYShiyUs7O(>n&%v(+qT!^+)BjU8|PFho0B@T%ej6M%w>b`XZ${+P{Fj z*T#oueA78oUK3XfRs2^VG$_aJ7={% zsx&NpTrReK8|QJ7u9)Y8S6Ut)zmr(o;8uTyU$#K9ni*r$mHh63>Xt~g0EM?xj4GkN z85x~#uF|E`tSz`{?q#umivT6nY-6V&k8k^z1a9*~>(r7Rs9w)Er0&85ZMb{8{$LQZ+pWV< zvLrXNJ8~WIM*rF@S3-Kxnw_M4++6zoFw#^0IN7^pR+Co?6*l)|5E=FznialLvCH%M zJaSbZ7596oA92?4Whw}*lkx1q*2{Rx$av-{$2*^{SUxe5lNhS__4l%CP9<}HVC}c7 z7wNWS`)$ss8?uhA8S3-a5e;-Mb{QqLj%G65XT8;aZq(k?wIS7ErX;y?kz0MtFO^W{ z7JG_|$`lt~*rn1~xwgk=ZB%;zwS`F%Q{yd()O`>fOmr$b*aSz!ahXOjzf3-L<-&d$ zvb1a)Gq$j>$ShiqNh0SY(jAH??@Fb?VS%X9NGO*y0o4@9lETn&~x)7so9;+!q?@g|I%>FFD}E1Mr}$) zZHjvEZ;Km2m0H2$KN%Q0ZmUA(cUhjQ?lAiF+oQ6XAyWhPo2;Vq1f4=mP#;QBX1xW5 z>Fh4-@{|i>aY&Eu20zU^ z+nHlaXtnU*U;QvVCDNyVYof8ohK*;rI{yBUr`*Dc+!=|k2Jx(S=!~2azls{a>EB0l zOUBbzrog|+z>W2mZ)*8wCH?N#|6bk;`une!+<$Z&jvd>seENSY6kCBI>_7i!hiF;n zaz3e~8%p#}N9E5t=BTbr@muAg2iD)Hh3qMZe+7o_|A*HbEOWK7N0d0dTpiLB!7s%n^hN;{2yqhEY&xd$^Eb%V_I3SH zMt)Q#7td`IYc=Um(>DbNZuezx#;DJ{)$_YSKcMGT=dIyDBSGJT4yl5^-{x|2a|_Qp zzIsypZQtdluD8hvmpn{z7pI@#ix~uHVPc^^T_egvJGz~Bln1*|-oYHj(lbY`P1?<- zFZOtnLq|oJ3)LN@g(`&%buvG}7+Z5vSmrV5z7M6ugC24AkO?!Cv=BO{>NC%+lFXaM zco(GVK+nh0@;k7zFGX~y>z+oK#3z?8*5jA!=PVcl7UrZ6u0y3WQAxu~XDybR=bU}X zg8FoZG}wlR+!JDf){{TW>|CnttF7ttmoetz*%SCY@qhdiM>MKNZ^uqYCZ^$MUrPxs zaSY#K>Z(jt%$+^-;nhU8-~gD*vAm%h+oi5A&nByzv4<;k)cqnO-n)-FLST#$Mh;;m zpP0FKEx9Jrdgh=}2@hSVdNF=s#0X#L|8axZ!`o^i3lvyS3ub4v+7Y^lH+PPj9?|6T z<;&R5A?9+^UOp5b($U8hp7NT$+SG| z!J&AMUX}WUPNW&wDL(AsoDLuwMvoftD8dFIyM+6gs?0v;2_=UeJp{l`BC}YgF1u%E z4wlLAT&%Yc_DZO}%V(~bBaj+B=hZz{M4~&XP(KhtVcc2H%txn%`B|d80w?z}TuphN zLK5jP_i^mG-J;`~x@nKqq+AWm+sclP)#cLDMNr05MJ75avMe&P zY~89dK;l26$r}^<9C>LS8OR9?_ zw?U_oDy5jY>`OXS1{II3k0hY>FCF;BMrS8}8tTGAhkGZNT&jgS6mVahGASfH{@zG6 zHN&ICo|udaR~~sYcUpDi{8py!@^B(!xN53;mutgPss(*y?yOYFZFul$7nip zN`yJJbN~)oyiUL`gy5q;>K(XjdsK$ehU&tA?e64Ig$xmw3SK|w1u5J@e|Z>wrs-(DY3qU!bHt)CtAB8Dc}N}a za>#w6=feOeF~#hY;`sZPJky2(YOl{a;TZ+&jKb(?A-9Vp7iTPuICOF9cJZhg<&t`T zuxL9$c{FXV12>;L%4c@_^OAv)S*|~&gqmUD-es--;Tar1?jTKtd8Cp2Y2%O4QDu6A zEMbh+8}8L`tsQD*YDm~iHhu}4=lc2Zp7oUeuZOH0hLS;g4U)*cM`*q2pjE;eT;<1+ zYz2wjQDuQOmWsckokEQ)lkwcb&kb7w6mG?Aa73eYrNr3wOixXcAOpK7#T@u_4L1`{ zNfYt<_Q+49-?@0IO6ej|C7P%^Be&pem+D`Vc7nMbh=51zBn_>k0`#T+Cp z$Kl8c^Y4=z3%ZB9^PNdcW*%R1$2*dyjy@Xl8raAmR0zJJJ#z0bje(_iU{$9_wd>Oe zEu3mIF@)KYcF;Yg_QgOGR@(@cAg5S@$gl+|XA1(*5D5Z(CXgU2_D>XmUD95Rv6jad zHzVJrmc$7nbx;BC_1(qxsmmz@1baGh6dbKTobf$8$*lLpf*x~Ny;4Wv%Qjx#aNlpD zDk5~vOi7PmiZy3Bq)-C=y5J^^+SH-1{BpZ9UN>f*OZ zTAnlb;kT_C6Lo!XWr2n zhuNs}b6~YtV7^#joEBHTW$bCLa6K+(JuZbvPyo4bgll^fxSjUHgxd)tOk1qV7HM^$ zmq|;8q=|AhLC%SVbgsBD9bK^wl#&vPz(htslqn8yD);r~1nriaE7IN@Hjwd4JI9AhrC`e^S|Sm?M1^l5vPWEZ+!zWSu_^dFv;66RT1 ze$Y#;I!JBy`Sa>8SSEc6%OEg^iPSnH+^vzh^cP@FvB3l^W5$o>|CP`GdD0CVO4Mbi z;mfxuyy2b|Rn5WbjHz(48O7U31uShi+D$0v(M)Z7P^~tfHMP`%S33mOGSo!TvfXOZ z(`xc-Z663E6AhvP5`(}i?lf8$Eri%aU@3&rQtanf5$5;xlqv86tHot(gZBC-X!v#& zy1x0*fcp9Kk@>Zmh0#pBe}7>t!j>UM_@y=%k{?x5qrz(_o)157k2iERXq=wU$E)fd z9e*D>jtrE`@areS?#swqc|xNz5hDw~xhkBk>Zr+-1b$i~N7B{W6zK@~b0-H8j~SC)VPM0899rG6g0eQIglcd$lS%fD>9Cc4El?ko$PIe6u~YO2Lie znb6AbeOi?dMDW50WA5oM&Gx1EQGDz(-gvoFdW8U%nW%i-pcfn*Tr^QAkw7gTsyu5x z9kI}wthSxBJg7jvC+HN6`i4z`XJ&2g${;}Yp=2y9_ii;`L=dI%Er{6_T2@|j*b zPu<#0kdZGKh`QVl#_T?9h!R089|g~3EHsjq8x;rs)bC#_tF%i)#ButZve+ zCOKrH_eYeyj4W%b?E{$3X(g?*H}JGrU}|L99DuSK%h+GJ!gaZf!~#df0(t%@!_-5e zU+ItCzQ@UBz!B7BW-b19>T&WFKGe3Pqj*r>1=s@(jf%*G*;k(Bg1zTp&$~0})M^BV z5!x8*%o2q#iRb7%>d{16j3?cH0vX6dHrloJ5KAqL;u)i%Or5n5;U(mb8YWRS=R-|K8|Bz*PnV2tDG z`Iisb>>5Gy1mVx#u%iCVv2v?gl56DM;+TKCUX2$+d2=uKi znzCTjW4k(WlOH#d({h?pGN0X}p>WT$$u(iC!k61ERXC)QQ^=5^5T+9kT{$6sbQw+0 zuiE#-^d~g;*M^!nH`bxpWYnBmh)kRMG;PIR^!Z8C=>;Y%3vD;6v=eQ_ueU(3DG6N^Sa4c*S-jQ(n7Rr(H95czl2MhlZs1 zFp(?I{rWgXf;UPPzjG$7{(Eg0JMwG%4ArxPQAJ5h}`|j`ED- zB!=HKbY=CT$|Sk|-78EgoZ&D1QTcY)>)>G6?y?9oAqd*t zPTcnwA^qQ9hG%5Gu%QL2F9aGx$IMRuhHJuGk=;Q&g^*`YB>aD&LQL&*KE=`5CypmV zbOD0m9k9chqqvyOuw#wVdm?TS=|bKRm76Xu=<6!&(^VdRh4UjZ=JUl4Zd|JAyTMu( zKbC4~{v4PJs`aRB?h#V09e21hK($VBb$~nlInd+>nF3HP*r+0G)jEhe76dH9K?Q_Z z3&!(^N9Moy&On+s#@5h3l!9f_{D@HXt*)NC6x)B$*{i;a>Cc!+6hVE>NEhshD7FId zRYbuZtX3Jj!V@AIu5#;=^^vM%wYj&rakQRO(EF{RIg8*wpF{3(C4@?36ztivxncd( zR5(_pck-Tsc1e@<47xz#YG!s2JE_PFO@W1zb|33Jf6aQvql(|wp(}rJO{FxCPiJ{Q zvOQJqCQ(~E)&@nCwV@txecmYMl@2+^Wh5GSS~PHUcOXwhndF+))eSSk@!`as3pj+OJR~?-yj8FS^T)AX53D5uxAP;|!9#_sU z|G_0^T2$LvWz3#E{aN9D4)ALgxar4r)xDrjQ;0|6vsLvn~5D_;7V0$0f2dJS7*zVTr zH8kglI>e^=j)7u-rtTiupyzo4X?%-#=-S&rd^>k}R10}LoE2aNmGg(Icgez7#}T2k zJd(+{fk>z?om2v&OFcx-w|^_d4L77d&7jEJ*@x0w@;JN%#SMUJz7t{dY|sv5F@3(Z zedUp8YakAAF#^F(&Sh{BX2KPNfK|}=H)GNA4lFW&zaO_WTDz@ zZ|c@&;75F^HiZ{gV=fY^GneZ!5o9dNqI@9uIr*YJ1G0hx`TFeFrcRLn8NRX%u|M)S(V+Z6EGV(~s4TD+DZelaI}DxX;@OWx^iXfMMAV@L#rRE*+Ogbgg zcPu@>R{%RLhHcKLfJVokGC4bddNo4BV|9eSL;VQe?K%CZw<4OEcuEh=<*MLo<$3;j$181$m{yP8*pA};Q2*uGgF<7_9o)OD_`O=izKm$ z7;}hUNlZGpbQ~`)FLK2XjEeU# z2AvuE8D#8oxJ$E^n4{8QcKx9caGuf`9|mJ_bZ7sWIP&jXb)!#r>CYgPi-IV}E-avn zc}EhM2A38c1xK0yLNCxd(V7XnS%^8C$Y{ZZsD=B_z9g$sya=CoM&eW<=+1EafHz4< zUROM3Efs?Xu zJHwH}zT?g~!Bq}e88lR;;74F|Shl6UPuxm+*U!iRNd~3@IM%KYn}b(j2+SLmgvA#3 zt(6K^r{$=R5=l<^%3AnAsGU_gO!XcO#vE2v}NCILt18zp|1Z#H%LnDdOI-gI0!rNGA zgtdnt+t=BdIOJt|t9BnzxCLhHr3n2^2)~2N(r|M1z!M8$DePiB8A4ykvyaa8$J%Ff zOOrwIxd;yBgryyeP zsN^0z!6mB$4D=r>w%9WE#H#JJ`;ot456hh-9k7Bt_gOf08?Yqg+HjZ!8vtDBRU+_I z9tIVw9;P?T)=mL4CxN06CBvFMz=H~Jg{e2{2NABT3>?tA4_ONCU0AeinarikYUGa# zRxnzE{%gr1gnw}hl~PqS7@Q~2PS{Qt9`8?q{ov1aP5l=Krk?Zoy>V93jh07#<9{40 z0FV`-F!herOV39)avam}g_K4JQramX!le;zgi3W0fn+#2s4{M+H*+fS5gnA(&(v$+ z@l*-LPYO}`sBtc9zi!o4+${`oN)=g{s;GBbmaoIi!_0Rkj`R?*a#Ju5dtt^|Y)g~U zPz5-9BM+}FyfV?l5oL0Bxovn^>e8}}@wDnoY5Hw3#2U_W_*0irABn5W=N6nu8 zYqIkHWxL`aCf@8QtGapTH)qvQq19hxN8^6?^S|1X{d2XllK;%hN=f-YJLDhjVC%#` zc4E~w{4*2(%)~#Qa&-^my(aNojWa-vQB^71YT}lxZO-e>eCHvTSuI?5}(OpClZ78yoZH#S- zQV~UrjAf)uj5S%u@;m3cW=8z)r|0{8|IhFDdtSfSeO|9H*LALQ&hC%1! zoq4mxXCnwQZ_jRRLj;*aMvz(NGX=mEF{ami@Jq#I=V2Ekhtn?Z$DK|g`o~>Jwhk_~ zRwtIZopN%vaBBMDA?TsGsZ+QcVBc4pmA!|wir zAvAGw+w5uX6Q2;n$y5IM&y@cV_8<27M@W!s|EPk0bnHKt1o=l5{G$qxf7~tPA64*= zDnR~G1^=i55Ip`9ApEy^1s8U7%|JXf{6|^Gnsd7H*+adwWB!uIeESH+R}u0ee>T1G z^9KK+lJc1G?f|VK2XMh}PQ93=0#g2UuHTZs|1DsDl`6>y_8F*m?}^tMeLNtJ)SY!G z7~L{9Sgh4=)_hv|9G}PfGRB7HxdMy~<#PpBHjAOO&vVB%m6;=EL7T;1C;u?}2_!&d zCSeaN9KYouND7mcQsCFso7L#Iw9%(mKi+QsaLo9n!7P9F4=wh9f5>3Gf33STsy#Ns z)G8P-sT3|>SmrFCrR@I-F{3US*;K}mROv$1n&E47Uv-M+pQU{6*s7`}?WTNW*)nrR zH9#>sUmF8jG%y|VhgSKGvm4!OSKVtIY3Utr@1O_z4~5qs;(Og$xcNeF)N|pon2o6M z7x)tRz)bAVrC-$r=Q|1b-#TcS>O6xHr*iIWyDp&lL|e9f5ea(ErJI%#9vRhZgwzqT98(b?2}iSY`ezn62zmrF$JQ-rV(vj0 znSh39K>9k7;^uJwMs=Ml&8#kdgqFHaPa?*V@vc|m0lzSk{8nWiQg@CymaOrji_RC_ zoUNs|YtVB(LS8Rb@)IESYm}+!J;1Wibq4W)#|OVU+lW+tbdW~K7b{yA7N!X0R-5bv z3*G%3n@QN8Ce<|1fzW<5m0uU}i=K|y#{l4oA${@p6B6DId(AL_jVpkAG2l;o%DNycWZReK)VhTE{ z-Z>$5F-B{=dq~1^!mAVbX>&nKOL)h)p z5OpB{4^l-32VI}Uv}AIq!Kl_4aC1Y@@CgJkGJjn2a|gY5b4F(xeDeW4o@5Ajx~RTX zn5*SK))cU~t08aPq;ji6Zr(z)WQ6IT?|(YoZHWGi58>&FZpOvqfj!16PuNI?^D$bk zJ_xvvpv(nut-F#$dUG|HymcIiG7u^7YAs$}MJbm@sJ`>DPO|ok;LC$Hm~p&&;1NP~ zoyA2p8A4B3;w8^)%QC9nY9JyUt;1WWyZMou%2FYDG;kRemCb{@!ENZwNigZ2$uKF# zz<5`NszJQb+`?29W1ITpOTo38Rb03gRfD|QT;n|tTqYlG1)6Z8X-^(;vSRr^0=#mh zFgn^~WUzVYmWfU1=Ouq|7l_)xy&fsdaj$GCRWIKd+>CP=c3)kJ7Q*FxaA7L`BgCa- zvJG+h&99z~M{Tqni8!#VCIW^PZhaRiF4F6LOzn=$#isD`q>P9-tHzgLD8}rYD?fYq zGz^qE7b)j=YGTuMs{m1C{2pc~^Le!3jBi>IbqrAd(0ZMWK)_)w@0 zCP=8@A>B)k65c7?QAI=!a0!cd>H36@0vCQg|3CQszwe&oz6L_wQ0;p4)RD5X z^lGcT65U z`b7pe+kPV%!VZ6Ce>p9NjmR6dv#heR_*l-DRTI~Tp%p;3ImK7XUOcX>W@J-u1@$n3 z*z4yo7SVv;6Wg9T521Qu^1j<%?4nrlQ{d-+y-Cn=Vy)W-b12&L1aJs| z@q4akvm0-%X!}nB;C{bayjMNuj#bi2MSV59=}7mfNq0l%KcF4uN4DoHA5Gi=wav9SDh$kxp1gRRv_K8%zBa{WeKD^Sq0T!3m;Wis+sj(bmglEYZJ?Fi z3cLia8N}a$p*v-a$<}F#+p7!%0LDtp40borOoAff2AB=GkJaq^Dus?(UtJ@$bviyS zFwnQ4*JXt{JCV-b0bisA`eQbgi-3?>+{4jQ7RH8x<*xe9z8Ue1jGawz>N)Z++M5pp$m6H7O%#EDIX0y4!Lh zJ!34rfago~!IH?Dyw$GA0)BK>q6?Uf>^VgXHwoZAA9)EFsKq+zz-2XiY*l0p&i;}` zDiw)}TcN7;_-cos-cHb*0h>MW34|wT@r28<)CKsQHIFJ-7YM@&+8&yKtms_cf=TBt z9`CMR3Yx%-U7{Mo1bwXzGqJ^0@o zA!#?n90hFb$L0+51Gf~r&BbJ1^#DstA6LuRwXlohNFes1n3KTRW$N^Rt?m%;o%7g{ z?DC(Yf?ucQvFIK=H+UHMygq($8G%-{meTk@s0QvV&X-$>2R+#3)RbSA1QcPxVAjJ7 zw-2UCO1eGke&?(Tp<;f#&snS6^0yYS)zu@9rBS7=r5M_hu zFn={&EYdkOaI5_^ zk#_jz*Y>?OcJX#(nb_#1)ume*u=5f-LElSO>&E?rc`dJyA4#Nr*|@7ILa9AJ|f5l+=2&#$PD^k&#%g@ zeBsZzN!9qTgi61xD8WQ~naqiwh=8E$!=p=p(SF$ri*8^u-37GuLkL7bA|Qz9?YP$a z*`CoB62)sBIL3^cN4kKSbo`P31b4(_-KT7lF_kj@J2s}~LGQw9Ug2-e%Kc{ih?JS^ z%Ucp6lOlL9CF+e43?Wl-p7PKLFB;6)73#$C>=9&43)fWqx8pi)c?Od_wctwYVq8ZD zZDJ@33y-elL#23%8kndrt*y-|L?>ARgesML@~3x07uI1OLHAy0nWADIyT;(*1JLSc zaYb|sM@OC}RR?-%YE|Ag{*6PXQJnQVO=Yu0K4aHLea3x@=vj2<29sHcX(aYMMnlH) zJkRF)bCG3&JR^o6Io2J|vQ7NG^j0F>%AD19U*@`idgF_s#_Ig2NZhiu-2SE@sUCJc>+-A?UiO(Qjq!4xIaYko4H2m#}p@G0Q z%|J+NIV&Tb0*=Nvhwqp;nHx>9c>i_)&Rt$!67UkDVyx_V^p}F}4e(MtO6EJN_cw54 z6rCdalXcpZ(k)K#{Q8^AWwDDqcm)hfeSkmb^Fm=Fq66@e-jvtk8glzu3UH#U%lrip zC(Qd(QLB{r1_n~+LK}6d@$n%ATlCpoe29AEvvrQ@Qr;rX^YQh^DC*NU_ewO`{lKqN zZ5#`J77kgsqvRz}<FDLJ zdU!SoK~CZx%rfiI)+UOxNlyFk8u)A|6APb^-(o$S=i-;%ANw1YB0EBDV_RP3HA>7J z8=dN+0LoqgQb*NadJziY%S=Oii+%WT;8ygnP5w`0-jJ0nQu0iLR@>(ewwSv(Fc4Mp z@Xf&Hrvs2O-JAXmQwloQ`KVam2H0}E)#8qA9M2%{ELQ`CAB&Wn-8 z#Vz!fq zr?g&QYX;~Do5yZUCK!ZJ!xIv~j@qgM@a?#Fay6?98{>UpihJx@&G}h03Hgb~dE6E?a%)sVDSrF(m{lmu2 zIj(v*BvJ%l1o(+nuA07ddG1}q^bGzG=8m}A9l3efzc(^Q>)3Z>A*ZAICQLd^-;58@ z{I8C8=5hVXx@*5wryRF8c}i@Jje7EFvdy{@C%3qa%u3+#4nbpR_9%2dX4=v7V_p6u zI#0$&$}KIelho|69or_%3+SouFa=Bv`)IOUeLC0a5``f&U`gBXe3k6#K*Qu5j>}JC z<5ozWj#)#qGn^N*#UH>vwSU8)a_)TBU@Cyq%yppc3JU-p^uZ~37rx7k?HRjDdx2vs zQt85tcu+QOZy7Qmw6)oU4@J`dB|lGbYw zg{t*-)=z-~Nh-!^SSS3)R3MIH<~)L3uO7Y$^n3@FMSy)%c3@&u_Yl`+e8^~?r_u5g zeqA=aK)h11BL~M+)NvjKSkGbEl`^je^0&+tX&!9##3p_HuRR0T7N`!>U>!~y?580f5Wv6NzhGPu>6}rGufd1o&mc(ZjpJgN^nz~N zOn^Y&4broJ5Dh&v$=t39n9vuzp&IAm~vxTk+DY{O{K0gMZ-_t z^~rG0FOv~m7|_Bd$qt}W4;GfUVsZm0qlO9a`zoF~{r4j1&oTj*t@?% z_g=qp%$svP6~+hKR(G7UvD9*%(?O{YT*F=mmwkl8kkVthf=IV`wbBVzh|HMSv~A}% z^CM(mzyqj3+??e5zE}oxsXNGW-7U}?GX%c>=OL}TH%=Y-?4b@r7mxiV^P56s5Yrd8 z_>ekPFx|C3k4{HkTeIS-pRGIKUMJZ3X-4_@OOX1F9tZD9_6*PMgUFjv8Cat*%$q%A zEapR=)lwe!`Rf$+-LLE^*ou6i={&Nj$RabKoM%X`r9{sLoZP~y~=vqYOArwa@T1Y;?IOX z4MerJ8a)}V@}-dT^XX4d=LM6qWj)7d=A;8oxsqaa`w`2FWcuvgOoTLDf5AqVHJ=rx zqk)xXn<@4lS6HzK$!t2X4ZHResu)`mA`MDSkA>&?)7z})AW=6%?L&@dwo(HBlw|R( zBGCF6ObrCJcx=PG$hXG%tTw42Q zr4d118R!<%%h!npImtm`IZ@!#Og?wS{FOm=5P~o}4C1RD@^58E%z0|&?&!Q(T|RbU znC}_T%lwe8n+mW!sQ-M-)}QVgraU=!l%6Rc!I7va1uzhLi%jtvG_9<2d**LU+gTIK z$&e~VCV0T?0TQUd5l)H$=AW428HOmOmDhy>RB_TVSi0_|zVv~ua& zD#GQ!9PjsA-gT6%>T09NAL_ryF@?%&rvbbNp*}fCSR~>;a5u?@Ucni{bQ)L_Xa>7z zVORBJ?c??;sXenWzPw?5JCM?#xhmGt z`nXww+=-_V6nKgVH1oaqsvfqP1TMiZp+~Dea&11mvh6$y0UU1I++*7YUl#3sKOmyw zAYJL&{Bat>ynE8_YHB`41)QZ2^xSKe6WLKQ|8HvNdD4!6!c^W;nYRQ+hWh=gc=^q%tcT^ zr?RJ}^6~PJE6Reg2}p|i3l7le#Saw?)*~b#-RkPVXML`OXZ1U^)9sedISOEWq+599 z*1b7L#F3f+&NHr(zY7Z@0Ty;(W^?PwcSRGs*soO_B#kOA2}B&y6|u|l-AmiKaElz= z8dPQ_Gh%s^r2yg~hb>V4P?^e2u!pA*?*zH}E1(3#^UUp4ZIlMpRk4bON{B?Hb18xp z{|-2Q!sb*O{BiRe|kbw7! z`X62MT=(i)J+yAQ*R|Y9O3GN=gddSNvf8Zm99S7fXjqg*FgdpuywE=QLVG{^AqLCr zwOFo2@80!qRAummK4zJ0z3uH3d{{m{8FEOO@tb#F%O3DY2{0r0kp9)+DT$A9!PAKY zsd{wl*yYcECms_H`M@WPI^Qt7oN1R8+)7FI(18wf#O276t*qIGqq4D&TwvgY$s+ZS z*4;qc#U>=&30BjU57r@Xg8_=s+H9*lJGvlK(2!y`{Ony^i#oVkdJgP2b#JK4y{my9 zy>US-8$cCv<5-rhp8!ZW4L82bhkTBbBrVs zHGDe9eA-aKZI7dt-z`4c5enh|&Qb3&Rl3aO1bjbRxmd%Z;4m-54KOGk5~9 zSP*T%_sdy3ebuAZe8_T2dha*c*fVrDAR4R_vCtEzeu7$Vi_`J(^w6mH$5QAXVoFlff+D*#`58rDqAapYGFKspgf@Zj zl>AT|Vs;@=@qz)RQKQq6IbqI4vHJ=&MT_<9mwAs4_c7RWo|o^?71=SM>Owrf3x$ee zl>$;~w8!X1V6TJivpsJbRbq?XN(7LWU6^7s*xS1EN@QexLxGh&Al$0h`UZAgu1C)a z(q4L|>ic)w2s;NY!MwWJo@1M4=}dEufBdFdSKK%~$@x4&l7o6rzQ~KX*u}+3%6u%? zSL8wRoiEVOGuUCZdi1(v_4(z1AzJG17Nsh^{hPLyU7Ci3h>!%)Iq9tGgnLL$*5LbilW@v|gv^418~W5xx4cLD=kE`y(Ri4-r%Ys&y`!?R@3lD{)vbkcBh1R@!l&(N z%>k6;ki~zh#@c-?7FrqlWk=oN>C|N1;5jlvqKDOEmB#Dp>sVr`Lov!bi~7%9v1ph{ z^x9E$FJC&fKACpV-PL~U_V6fCNp{Qfck2*_3ozr|kNn36dadUTOOM@dZq~`~@3#vc z?=29lu(VvkB#zs$0tcI>7rHk}K9o4!l2(54oOi)kA8YrXKWz;BzAqS;Vt%QsX6z^# ziu>kXwZonEw77BHs>|dnT`wkbr2SM;jG&pn7Rj%@vpwj-Z=I{aD~6qCwY z9oeV)d3dytLJFJ5`nTG2Z%m>U6VK^M$gSD; zn6BhOYd&hz?f2w)bN0vbkHxBfHN{W0_H|KiUdjX4D@!7ms}eb6@eSjZyM&uV%DPET!LaUc((F53h-dM~|F4Yh>v~vV(KyGiu zI}W|WHXBDR*P`L%$Ef>X`iO*g=rsC*dTk@opE>8(-sxnv`rr1>|92?;šQ~%l5 z{{bS;Ai*zhCAYEMr!fxtusgDP!V5;evWvCGhsvAJ32R0q-(6ra zPr%%`nlXC#9A8ZsRK@0q8>Vi>P$JZI%NatXUHK#5Oa8Z581+aBf(S8zMak_Q4BC(% z2sF6Ied{&-w1QnDv~kLi&3LoF_DKA=qP+%nrx!5nckJ}LQewm^#?p9~{stsz$s zvB?cAon|`UMgR#oN#sK!3Wy<}WF)fqkf{6lAvIsSH^j~i@I$oVYHN(J2>Qk`TBhB) z#X-vtPN$k4*^DT!V9iFTBOP9)i<`*v5XQo<5>HE-j?s51|KJ}y>+Lu39!SKm-wR(H z^AQBdhS|kL+Dq#U%H}v0<@vxK*`0*!)gp|juqR89{ckDL5C#7M?n=|*E_n=er?6xs zhWTt^`V%ZZ)z%yV7*RI9aMsdqzNXCUTd5@~#h=}k&7X;83RvGyzm zkl?FSm|}r3GYW4plJmQkGjm<{(p;AlFl+zAw}>j6LwYO3P{7c-Yuu<<{!n7o?nQvg z&NrQdzdES^uM9M*))$GUP)VvX)_FgH04Vsl1m3|~M20nn;BY@L+dhd~ZsDW>cjwyt z3|fE-+bXr^Mm?Jo-;P8Z;Lk9oe{_`$dU>J^1DsL+N35CPu29NtAZrsXa54j~n#{Yu z-l)lsPC>u^`yOqA4jv%gV-{P}sq9=cC7Iqjs%I8%csapInu~wM>tk=-9}0=>nLcnV z>Cicg+RUpjPdKJ?>sHL?noOLnL6E7tJjH}}<6s&?LP>vtD^WODPgcXD4n(Uo7ouol zm37K6hlu9E7|j?n0T%S~M9&u*fsj$jLhc@e*%mUh9iOS5~7WBu|DVtZ02rlSK^#GP!c5eH|l&D`9X_acz_U) z0z!GTb;3!#@fN^`KEtfqE!(9*XSDaT=4osg{Kx4(1i5;Pab|cda|e41aVvoP0KgN& zbAw5WDmv@Jjf}w>sgEZ(>2$o6OxtikF;VoXiM}y}Uz>lvnYxZadZ46oSAn zY}D7cTi~3sSB(Jpg7nJpeC;a<6VGG2cKQj6a^DfCzl65eRoKJeanDwEU{pZyBC~u! z1_Jl?v~7mTqN_IaEsIU+xt(r&k$7rqK%9YeRkSSNTm--VKMFm1B1nk3Jw zUxk4n3^G8AGEA=S$CK0{Z&Zq%0746PC-^R)h?j?{F2G$G^KqODAVG)M#bLNAoLk<6 zs6+MTly+w8U zAP+xxB+{<#hfiY-2lG9&Co?ZdFu3O({sAo3XTO2jNdGDx*DlbN1=j0m@izgG8KA~OaUh%fO8?bcc`V#e zn;@Al1(%BI9VCR6#el!Mg`q5qu44v%{oa~lL!U`AfgNB&cR(Z(LEQlg?@Dx#ykRK* zn5!Mh>u?niOi8wz#RUKsJf7m)xPa#w(U1btQtreeYb7Fw0AOif;nEnvlwiP{7@CaD zN!G^3MUdsKY)zT(cbJsUCUT!w;SKCABDxLd2J;mx*WkdfcbJC~tpJV+Tf}mlC(#9| zW;%6sw$eAi>s~d&b28M89DNjpvRF+U?Up`0EWs&RLM=X&K=6^%&?E2hx^naF0EOy# zv$_wg0pwyRTAQvO!ey6wki!Y9TwDpjW#Fkru74JBD^#Y|jUDlh*nqFiAQIPP)Y5~5 z^KrQ9p{`7rLMcZ>M%kSQ?c+ge6>rgy2#mRXzS?Dl&ANZKMLs!H zXi>Wb_%0oz_3{;*s~0)1Q6p-G!*9WCfe!GnDta}z;kX;t#T;{I+60> z3gxvcwLkWClS4Z-lM@18epXfjbM{ub)-xgi3>|iLJGKP05J0I2`8U_~j{tXs9$jQx z`4M=4KJW3#gm&BfEaqJsqtX23D2rlaj&O9p#yri4dX@-sX*#FRJHm95RJR|O8?;p}JlK1 z59gyLE9>yrN-g*o^(mMWB)7n;NQGA%&p(EzJu9aD2DV`ZCOp(jKwU!Bau6Ki@A>`} z_^aI!?561Cpu!Pyk?ERc6lj)MaIitRTLk|JH1*<20087iij5k?V6vj*K;`>fGt zv%yBhmN38b!_N8ezfq-q)OIJ4GZJYz>OU*sV+ZYL4C`}_`@_d%~ zH+=8Qw6kck-iS>=iu3CwXpetrKDN&)*97t4D0%a3z|YAA8AIGzKvH8uWwxhCqo459 zHX?Qn7#kIQ+kt7A>OJF{MZU}-e^_ofMDucBAZEt(O}VvZb=Wvy;f9xgw%Ae}r>5o) zD0=-HIlc;boowD6BArt)&3ON75uY3+a8(vg*8jsHB|bp2F1 zO9Jg!_9D?(B*y2N?_OP)PRE-OmNX6y3~XbiEH}%KBFi9d5Z@#TBqz&oTvXT-ZH06A zo2ietL|O&$nOjbMW1c?@dvBfsk@6gSY$)fCuJw7v!o@#)XOCXy^ZOY>CQSzs38~bp z{#7qCNF;$zd7k|c_hrGeQ<;EQnLzcRB-@(mii9neg;N068E76f=p#sIMuF6>t`T%y zR-ep}TT06m33};h0|!N&f&PT4Ur(5#WxcrJf{5w@`$gWZ!aOV=ZWPX)IqFhE!ikOV zmA0p7pvSjF;875q1}uo9C6!T*PaLlSEV1B0C!bp&tgB!H3xifdYETb^wzwHyCV|X9 z>U@n8NxM%esF0{mU@S|vqaR+k7-zZw_-MB z0RQL86U;+pMTA!D24Mv0Ehn!Dp$jFSd{`#fvKLSY&5K>Qkwjc6BSVqLRwBQLNV8X* zfiRXOBskg50voUsiodx7VNnye4g?h3rA$@$&>4I>Kd;vmY7pTNP{UJYtz>_k z`xb~d6guUN1j5du&t=Z8l1~rYO5v^(=t7eFJ5~qpKBt;zvo>uV7%z)#xoa1&dR6R? zkUNj?p)W$}fCgOGK;XH`wOD0{OZB$K7oNkzZS*qM57oPC2X>Ly((Yo1jYv8)*K-(6 zJ9$UBQ!#Ugs)3l55dl>8)5BLdQ3Z8}?|65%yZ7Kh6`8BXLWm&82k#Ds9%z$e>y_l- zSWsL;2Rm_jpH`mQw=q*!qOr>Ztb(`Q2I`2M_&7gcLd6rs5FyB>pfMy}B^|9I4<01E zY0L(tMCarkld*-O&3|jn(V&|XuufNJE7&Nk>ENNI=#zv5zfyM-;1)R^L|P4+ajaL5XCPR3HC*Nl zeZzW5ch|68PiCVQn2N*}+{rB#LXTp2nwW$!VfjLS>IV)Z0Io&#i95RV_7v|257dP7 zblTiC9pbQ>slE831U3}@qx4TKR#&*5AM$#rH3igSIR&YLTMAF%t^$b@hYk@^h7WXQ z1uu|My9+tbDA9%q-%nX{v=RK`wul)}TO-P{+`=T*t^}^7QC@-3%s`h*M9xDxIEfd) zBCN3{N9b=C3@I9cjisAz6+NQqVg zbZ8Rv*N<^F)lAp@E_p+2V1lZTM)=*6Wr-5^EP;Z-T0B;(f#$VS_;=EGouTAMURN1+ z)E=?Aa)Q%?f&O^QT|KyjYuN~ls!fivk`pI7Pz$W??&(jV8fpX_{yW8lvU`?r%bc;}X%+56o#Y4u<-5uTz3O&4Pz>S724?eiznqxZd*Mx>0M?I`h*>=_yA z!K@A716UOV5UJQTvv7>u7?%x8QN@ascX+x(N9FWe^Or zmo)MnbuB6hUrtbzUdc3We+?|Z0u~{I$ABUfNJzN+a{qRAq{Az4%nd=@a1{g4G7;C& z!sf`xLN{AMR7JNn^TF3G;DnIru@FuKN{Yta^h<5EZdHhBbb`3qIK@5+54O=>9PRU# zYdw?GkrCjL!iIIb2jt!v`G;u`?Z{OHs!l8{6UD%M&0Y zp6rO^rSUf(x2p=;9o@1K!d2D*Z^2`*al-4UJJ;X4X&1fI2W2V6DVAk0N{kt!?9*CL zFU$d!@ysobENsKoM_F0&Jv)2^#M}g%OFiM?0x#$X2i(A&_fa;!Pl&>5yo2uQAH6@) z!bSX}DO}IJ23kXOPne640W7IJHb)_ksP<}Lw8`3IR29Uppt7;hw?Ks>VDz}bAC;go zisL3<-Tymh2~4P&!7yKz(cP47XGcZr|Irs283-zD#?1T&3+tC2-;y^Fx2=DbeM0t~ z_?!aw#K6OILG>PNHH~qvc10$ShAJBIGZZnNin?}Ph?ygsMK*dhP&H!ZIb*4QX#brF}bUD|hl z!Emddtjx$#sh65L>#pPlaojdpW-`?A7a?CK4ftAg-6XwUYPw}cFspW+qHWv* zs8#`pm!G(n+0G|_3BCsP(t^&5L*D=v<7Ts@aX?lk{R$^wmB+&;()9_Er8lD}5PbMFYk+l0{r0Rndns7`G<@T{s zdajMF`JLMTgw^+&o;FQE4P!~_jXO?axyejX3Qie4=pw4vmCvRgz~Lc@4~fS5KHuNe z-|PWZW-leLX34g?^g>*NkTKw0?~h?~V02%+yX6Bw_H>6E70MYSl~n}EAn*0f?6D>? zJHx%66hGptTAyy`9iL2WWR0;g(gN9qC@v7#rNF0{eT1byuY6yu8)}!FY^a^?9!+=Y zJwS7PDo)|NHyMvgsD~gP(@oZwEKW%dHd=u69wZR7>++5k&x3`Kh;1bR>$_Zb*ZAZe z|I%@UFCr8pS1N(fA+@*eCS*nZqMP;Ib1UWb!vb3&CC~XhD zEM;xasKHhd5#Wp_eA%GbZI+YIaRax>Vph@o5UhJ{a$G4{y>E|u*M{`gE*fBq%&f%Hz(ZCvsIE^f1W3S6Nv%O{2=a)fN*0&7zSpMjvTm^XS0%d=(M+4Fr zssbW;e4D}dGq&(S-5a;p!X}Q z?>LLnl~yjFn!)KvI>6~@OaReH<+1o#IDaG5cCm?xUf=uzhS?#)qg=7Z29w{fE5iS&HYw!-v12$byuvQL&-nnz%L&Y9N51&7 zvKoTSU7E)8A9YazBwLLWo5s5%^QEI6aN?FMmcJ?7Fa*hmDMy5KHX-53(k&9VWAdDg zjb&w`{kBra+5WQj_MKWscNW!O()y^+uTp%mYv6r${X^>437j7cfu><&Ap7q=WRJwj ze=|1D@hwR364Tw#b8JlS6FF0(^qoMA@1g3a!w&v>pbq#vG#;RF(By4ri8+5SbZtP3MKGeNju>5*QXh$<#+BW9>kPwu<;H&nD8HG2bOE9 zH4M-RjXDdz7)kAJoo~MlJlPO7k<6qnh9bCp%`D;@n`z|NbGvFYEJl8>>Z=!T@yfmK zTqIwhTI)8f(vl7mXni^EXKk{R7+E(rs@HghhHgqUY|lh>`Ca@Q(={@jJ54Fnz|Q;jIqfh=u01`y8et zV+aAV**sp*+&0I>B_$!@(b0@ZN7UVJ_)cnTcD|P1dIX8YFmRaH!*dvzD@c73W_r@? z?5&L;N#|*vc){Qq`v9k0TDIOTR_i>>F&pd9Iu^GH_8SlTJLCm}VqP-9D{T#Ft{>jz zhA(3Vj*Zf_*nxo1IIO(NNy{bUQtm1=sI*iEIG~v(HJ|$WNSi)mdpLA_ey6KslQGa= z)Jm)pGhQB;6*LI4f`Z7swTS#5dvtn%!V0jazq>Loi)02jwvG?O*+6z>6BJ_fmfWY- z-(T1Zm(s13B&_Y}hr-8}1IOCV-Mc=bAwO@4L~qLY?a!Y!Z`B672pTmThKFSajRSu< zH>k!vj9f?k*!}~N&?Ve?)pcWhtnt{~V5{3({J!5wFXV8F&fgpxoM z3X}NMG)uzBm(|=pNGBwW{B|Nu^(`0eK}w@A^-Jo(0<`LlAn%1`&A^z{uNb(6$eT@$ zElhL&CTIu!4PJXmIyyTKWG6?)!6*kbMmMI35alH_nh_8PtV!I!rlbn|1Y&SpIL0R2 zSlRBM97K@U{9R}QvaTPistTF6SKUtzI2Kva#NycR;C;~N`ozMs5&f&Dp=r;o>{3{P zFHi6ee>lRK6uc9wx+5|dxp88P2we`>tZlUypuWVnXBEL|lNvZ7l!U8f^Gxp94|FrW z-FvMGsRSK*=F?PyAoa-6l;!sJnIU^*UyK@H?@MaHb$c4&nWXjk^#}M`7?}UCi`$QLhCUo8S?GWGk5lyOt`~Q))oNQ18{W&Ow4VFlUBe zLBw#Zz%=xF$NuHEmu8dpMyig#gfVa{}}BjRWLhe8ZpKb9lvz z8M6_FIw5TR7kuX#3$gcCxWq+9V?`307IHI}?h3G%e9)bI#9Z_CsuSO?|5dUR3Ev3? z4Bhk5)vkDE|Mj1&z^rGLqDZL@@n|?7Sr3y!1U(Q7&dHn!A8OGTrP1KqU1{BV%y-eK zj`Yr=j^s0IJ4(PD5h&nr>JqtGLE7YPpwcKhnRWRUcrYPhxiNoxLPEmxJEpV8bNCU` zHrbUO@Jk6JpcrCLAXM2;3$5knsXCkKI+WSat9w9J1GcDTxKGosyW<^Ca)b_WlqJFm z_@k~;l3hDE;-q~1KM_P&8mnGkiBT#5snn@Cb4_T*Sunk(olJyDrU--vgm(OtPfKtIZ zk#xHpSdWCMMNU*!6Nc#yWOdRcU<#4g_UlS-T&-}H@t>5(;6lymOI^oY`mK(ty2^eCx=^i<=a!$B+f$k!01H96TCoGpHz1c&dJT8 zpR%!oh5!fch)GOY{`u>pm5_%`A7Uv1P+!IP6TVvei8I zX)qe!#tQUMIido_SAf|v^ajYk(}h4jGq9>{un+jUlxmiQNQqjx%0+O#RG)*p>E${; zg=IGG(SxU-8G?uJ210IJ9iPxiyapClW&p%$y?f>%m~%8_9Y7d@Sl*zJAKXNb;MN2& zcZ0Z0rrObh4_+9Ui= z<#7tHK&1yLdZ=lH;T;s*n!<9kT_c|6;Be!hr2yJX1EE)6f23$V8Hy8LK14O+nlo^T zahBrND+B(B5?`9i^AP}*=BQ%OIO#7+a?ZeDQ$+s0dpevP{}pTvz^zM%g8zhOr|zFG zc2Asf4bMsf{9gV(qX5Kyb8B6kf2;!K0iEGs^)*?`w0eT?G zZ%3~^B}kqL!xUh54W`*GZHhwb~><)~2a7It5B{3mtI%+{KSpRj^ z8$V8!4MG(H$^pIc0c7_6yxDf=W}CO{t%A@Btxh>X`2%kZpc zMOSkx7q~Dm7(sa}+^c>2X_Nu(Rv{8WK)aACAmn~G)~mwM_rZiod?pBd7Q89I)8CAT zRj!hA)>pY7B7_YAXSS>XbBtTu!jt{mC^HB8ry=nzV;kSC8ZH3S^kE>It#q>(KWc$H z`QRN0nAvzSnh(kro%ix15`|S^7ITp}9PUMsr5G@?MG$;D6xlX-C11oj94RREBBq|< z!xF4;Mds(QHFXWZeDh^sg+!6J`WkAX=#m^52B0?*xjv3}Kbcs`ol z#ttaY^QtPUB8-kU<7ez55))m%yhY5_pn#L(zsT&M?}~bp0j$q5dk1sZE5Zm>UP5o0 zf-$SFkeLjkh?;zo)sGp}kI*QAMph<1uhs|a6G;AcvmJ3|o5>5ocp?C@BzTV`+J)8f zi1}&ArXT?=vLj|dZwC~%sAvLs(2oA<#N9=fZ-n+|b;TyG2H*RsNF_M&FGZ*-ueqg? z%HnX3Wxh%@^=eXynD=)MV2&^)tdJKO8L1Ys)w0D<`^-Bbq<95Y=@@oQ7gj3l+$sjt z;x_Cwy{LRF-QY6ah3IL#OZgNi{B>J110k>2V3xZ^tZLVJi!}&i6M^v6XFd`Yf#vy5 zvc>X(k%-&S$cZK;$e055M}qB~2L^HsDWg}Wfy_XDLA|F-c(`$fv`HyY|$4eKw*%l{!u%`xvO8U0B?;=KJy%uObOCSx zQDla4AePr|a%B3o^T#Ix2T?*+#f-MS6;ZeV;sGhudrPy5+rZ&~hY$by;FZ^U;*_LF z;cD5aSJUyZAx$B`zHs}juZLzN3DPq)Owf};x|9vOzENi)@twmI+Yed;7SiPDD~S!@ z^yskqbVTA9krw=1#dBr2hT;J}vby*ZKIr%>EC?96)-aGo&$f5|@&p(@WOYjfsIEhc z-`yLUwKw)l!yf&Ez#-6s`Ix9XQOoPzgRqQx(2z1u#;($}@NC@mrR1!tyOpbv_-z3d z#Y*7IARf@MuN9z<kqS%Ia4ZA6N-en3ruVz5Do<&(s2u%(U6Ii&HmLGa5CjT}`a#k;=u?HTXFK@DjE-%r=OCtZP3dF& zzC+4rZEL>R(`^0+>rbns6aC|>fjZJUgB&FSJ3!9)R^a98ef%2{pyf20yQrt=B8F4_ z__`k;CoULLJi9Sr_Z|hmO3S~9Ij`oF@KaB_N%)$+aXk12VtW(}vzigg(()$sF^j z8?|`kySLsp_TgV|<5tN9jBNN!7F3?W_Q}s-Yx+K+xi@}mYisIyo?oDmm6a0ZJ})^u zp?NI3ggp{0$cO?47M_K6wDHh*0_x-M?TEx8V zYfF*M|G1F_{x1L_%?)KcV^WCc%myN3Y%2z=zIP8MZ@qY+^IGx#lw$?azG-3lLY~3h zzZGj#GkQb`}|`3hFi^2;!6v(i^hGv z*o^l(Y-sksdZe^{L|#Uq_SV8S3O%PQYe`$wi{@(Ybfx!~D5dW&XL;w1a}Fg3 z96mQbSj?X;`02BI0h^fa_knm5ocQiZ0d>he@3uBqO$)ZW1tSjYNYf>x5d&x%jW9ip6fe^*f8Gv+DWps70qW zj&|;5CT;kk{5Jbrp~S%9bMdFc5)7o4gR(=TGE+-9d7+vT6Ns_KRWp)6~j9X z+?tA=84D>l1qWoo;n*d;E{CyGam5qogH_kzhq@;nUKUeUDhIW{XyFj!obcz~axJYv zw%`ne(ny3GG5^30W2CAmfV&3}^7UU|cJr%#>QdH7X8PIhtc z6R}&1{}MENqd4x-5z$<#$K)dvoZs-D zlTTFMo$n5`1@)`XU{ioYt{)gy-G9vI9yr?^prt)B+Nd>_=RZ;$STIuJ$>OuG*b4q@ zx>uQT!pwz2n+5r|KKEo1BH+4DSB}(QNZceCQ!J5d*s}QUH1HO-ZbDmh_)PDph#g+a z=Z?ZUl(Gs~y)yn@PwAYLJ3Q$)=Qj4>? z>?jd4k{@cQcs1h|byJ+|=kr>`zYM_-Bz1kfS*12PneVQBv+}DSfMG`RJ?DZzqo-61 zoe$M&>1m1Lnhv*pevJxqKe*N}lAES%FR|QUuyRr1Z%z9|5!;f-0I^B7K_1aJfUg?y zb7!zyz##+qmzymR-NO|>nTr2}DCDD%bL{DU1tFPs{yuk{gY&yYpUh;CM>RjM_NT}& zXZlkR#ukm-xJx7=KXrJdg;o&UDq0XYvlrA9_x6GX2SA%~_#izKEVSYjqP_=!nBdF! zwaIaO=1UEA<$jQOqLEvIMdDAI2mB5?ac&^;oWv2BA~0~bEcl<>1dG2!J?LWJ!m=**Se0B= ziHbBhv35Dm>hvQF2Fpyey8X?T2vOQ~7(m??=5kK_;`4J`8Fxqhr0wYWJxY>He2zBb ziiPX9TPUJQ(2k;l{p`S))8@{>*ZeQ^f%MUKSfdSQUnFOD_~Axz>K16Wsr&#ijOJjV zfGx_+xNidc9>h+$CV*P_<6=U=Z&XAb{@`_ef_-rv!Rq!ZO7c<85nBXVyq~j%^*j=T zaT4yziX-nJ-|`xcMDRX_{nGR|*|!4lf?*OTdqP4uz{1M-KVd8r5?rEEzW?2EyQLpo_2AK8+ar~VAF{i#8POY%=%h?X z35zWHdfq7!B$e|GgjgqPTUfhXG=~XLBZzo#TOw^>^uxwN1#Oz^=7apy$Ln^0_vE&_ zvmrK5TErgJtj*L=m5){2fuUybK2;o>coBe$tFdDN_BDz-uQXRSf_fy24KRmAKEiXi zOzV_)wY%-}Z&c9!c83p1)vQ!}>2dOcMcL2^tqrl@KLd~$_4I8#)^0gXLHMTthOrJP zo6gWh!KZEtX9Aip+7%0JtlRx6Q1keFJ zl5VF>TMv#QBhH_?20o0rTxecysJofKPc`}3@e~E$)!=eSd0Myj|8GV^%w;Qo^wXR) zN5nZRB*Rh=ApF%VnuC8*7c+&sC1lzZo%8dOko}i(pmjmxZ^va7+;sj#;|zzOUT{}a zRY71qnvowSMNYGfMdmSO z2q7|u(||G-2^otBiISm&_g;ISqv7}ee}BLK`&`%azSs3U`*Pu&z4qE`xW~_ZueC@~ zr9Z^p-40|4RIxed*wyEpCIWS%L0P zpWke>X?g1>uUG~fT4N8XZdalG4}brO&TbxgD!Ohx_~Lawn+-l<~IQ zx5wnlqcK&?*4O1%^+m{DOvf}biM{eOY|%KT)if+Ja^3pek=Kwdy_Zd zPdJ%gIqv zpmwb{Ba&Wa(YB=37AuJTlX_9GPlvL`i`H0sE;OSlv0GS32m#km2_Fe0Qcg%6?!wiUy7zL(B@E}IkLowdn}BUH)wyy}W?5igm^ zjk8vC9JNBPshsDULm$V7^2;WI{r98q>l*$L8feij1xw| z^-o51!3nTEf#&H%;=>^FrzwAdq_xe*)h<~bSHWi|$?~%frN&Twx3aqC(;LT)ZZ$*F z%KVIxb3DG8KHbN|%@fuzZbgR)N(8W;@uXAmL<56g($zWUtXCyBqmP13|5X#0qLH#W zYp<5wJRiOp#n-;e>4SOJYDbtDSW7rqcl$FlUkou~8S{1zRQqmEdj_;hWd1u+Ij%I9 z2Uu3@5a&cw`g%TmN-D2#@awQWZC_)g^t7z@2$B#K_y3W-{!3>rCFB}O?`K?BU zt&VkQe-(u52vIh=QeE_?58*Hy60W!0eZwUAL4xHgCnr9S8Ul0FDvt;Vxay3KZ<4dpd< z>#;({+3YtLNGvN&XC#DE)Te4|C1r0nuNnBI#UxqA35 z;@5AM<3A~FC@vWMTU!W%D@ceAL>Vym^C6uFU58*38T|d-XVBnx`>4wDGF{|4MBK%& z6|A=o^3y)R-~Y(?7d`^VLp?EZ4EQkZj;A-E3dp%A_k_%78R-qK4+`Mom)>^72xdFg z;iO?VwYuhe8`u6IgL|&-)bTg13^^YWm1q?1q};njbx<^fU1K8)8>Ku`ihpgbx%KJv>7c-GJ*q^6~MKZecg!kdLVXNKl&nK90U>rezIa z3b~M~(g!p`$ z5t53d1xYgJw%Tg$!cGxu`rteCwPGRZvV!Us&bBvnazuI+af8A(?&;-R19HFhHA}Y& zRY(=c5H2Gma*AsbE+Zw5D*Wu7-sCmO3|&BALdM!>G70hLiG1{jc(yE5D}A6%S#LXD zKV{Rr$b`@9V52w!H`r%qu#22CFZ$6v*xKDWFN;3>GIg9GGpKRZj)1F`y>bVU=?@2u z(6(!8DG2I5qxclIDA<1d7*!UztpzeBb4en5aV3ks)gBnPCay~F4w?Y!mL>|oDD%%l z8svWwz%&8Q;4!YP*vw6M`SzZ_HHBhB&5R*)JwO&UcrGwi7Z(+9^swItFCJMf_SI-C z-JSru zaCITtOTm8vB{N`{6wQt+C@S5~CB+0XrGs!@Q^M8@(i#AN|CuWZF@}n3NKu97*sCv_ zA@T0XX9t;mUuVXNe16hyYX3<}pxg##CcQjn-1f_r2SpV~s)IW_(E}od>>Zx8rGhW* z9mK5NwdJr?R8x(-`rG?Tp30Gc08ZY&A0v{mZIuqV-!>cydyK_e7fujVhgty)^!wJv zS_BjXH^|lBzf^k%?va%fNOV7iOE7XWdPBE0qM6Fd_a31BF)Z(sRN1VHTGyx<1_~GcMF8RU2foivg`e>LPz07PLHpf8fEqEAWPZ{+s(DmPa_KKJ z{7+kVe#}r=@;BHP0)$#GAUtEQ=rJOo*zP^EC&4B#@W23wRs6DxO7eD)lN0E7KUKI| zosB>}>~Q|;7I<)v>j7TsP!0{`(>wY<(IW(QPPte8Z7TdGFK0DMxQDA?$FC6I7@AJ< zWx}X`=`xRKMfyQgp zBA#}O-@ZR=`I8kLONSRkDTntQbju_y*F zjOSc)ksgjN@85!16Gj_IDQ~2)N5iA}mPx)@&?5476BMK(PtXV-0OVO4x6l6gejl64 zF&}&FoJxZ{P=x|WrnNt~d4y@-n?-%mx0|=E0OD3uWp&_R)hV8HlkkPsqJ>@*bx0SN z9bRzj)W2vOY(h%djClT&&RaCNFmdlB?$)8{5O-n_uJ>k+;ro*PzFWHb00F~rNuo46#|6_cYbD}Yz$e3+j75DpN|P!UHe*ppg| zl3iF?cQCPP48ut)R=1%D?fTO|A%Y%omz?~JAT^tFcv&49tf?0QCWRS0Z_y{9{88I2 ziR@S20>MgfrAsC!1RUQANh-E1C_N0!PO@u?N$X)l>f9ZY=%4^BAd>hJ9;(XKzSpBu?Fk73}wL#{I<#lp8G?_LF$(2*dE;tF;f#a9!Cb zdvJasg35y++;Sz}OTgOJU@+_m=y~ZN(~o8Y>%IkMlXL59KUT|91YaZKMzqK!PPMDx zn{Muk9q(S4w&C!BHi6b?5v%A}{xt#v50Hm(NkfLigRf-_A;mykwzNEjG;!Sbaz0k) zjz^2&kpoCv6tMmQy7zNc?r<%JaMiJzb3C7utMmFMSwDYHc#qdd;YE7PR*@Fb0#V9h z!}Ynxw2f!&Rz&q+pyOLj;QwNjCN<5c((8G`#t?2ZNUWd5Z&8~3Xf7-b3(w;Cqb0z@ zv=BlThfSMuvkMI!;RF3jJuid<3)&M;hgxBjb6X#_cK9GycYK#V!`iA?iKzGWhVCefvo!%D3J1YhfF=b z6>HWu6fEhj_}kTlj_gkjf{__H=S@?x1SgP&tq5k{gPPy%jkg6T3bo}HFX@14WIF8q zB#!GHa+`PnYvc6f+&vB?-m`G1MLGdug5c?jk^P1A*Y0Fuon?Zq+ixxVA^i@#cQg#6 zaj0%-i^xyAq`4E)h#er|aJkm!6X!OMqDZLqw+)yaK5Yy%7zD$H@{oNCJ2xU12wt}h z(Gs83nj?u)Yd)UjdbxJpr4#HQg;?gzBYRVu;$a4)yD8%jY=nO>QPgoMO^&$Z+;zG4 zdUSPB1iG0>XpzpD8PzC!7iuno`FC1!#uz|@Q_xD-GpnI!3Jypgq&B2T7N1m%G69gL zGz8(s3_y3sDzA4 z!VnN<3i?-M6V<3-!WC&a&C<8e`Nc*<1r;ux<3oR`*MDknhrt z&9?>ZOjIYLTRv-IT5OlYU|DKXt=DI@%MzDGP-dYVW%jzmN7s zt0OI~mmuZixB z#KH|Ca-zqvyafL5KJy#WRG_qpSq>q$wM5MmZIl(KJ)hn%f5Ga&<4unZY844ris~&~L>cC& zToQG_NVhF-tw8|;W`KvZxl|u>TtNQ*|Y$Ci0p&gM|~jcc+O3u!8mmwnmpdGaB>clKeYMkEl+{(-YBKxRJGN z-jsE2FmKz0W>*>xV%@R6`2Gsk*4oQy!UG@nFeSrEnQ-EqKi&R{ZJ7O1(8H$_lLK9^ zy*-wgoL%%32Fbb}yiq3;SPhL^P8liu=}|{~l#9;$JDIG7v=rfi6>D!D!1X0%2O58S zCC|SepJ2a|=5p(j!LI@eH5qQp;_R!FY2)$;vJ|>q2?^Th$NKslgq}BT=@_}`y1(vy zaR%L?G>k(iY4${n)g(`j1L{QftMG>eJX856crtv-kwyyO}CP8U9hYr zQm%C5H?5nloZdofA7v|SBQ7ho26kY@o4FB;+W`J`Agd2ZDJT})XT&IH23Fu$w*79R zgnHwWA;MDt3B*BTR!q_i!5{Kvw&!?QdA8$Qaw+VfHWefL!#=uFl3YJ}j1y6Rf5o{d z%n`_q+_SA81#3pH|HSer%p5dLyq?DH=;=TlG#LJrqcaIh?NF;Q`$dUTIhu$CAwq&l z8$NgUcZzh}lzeTAk~A{AUD!d7zv-Z-q@xB49nStD|9H@gocrO|$jYzJL1vXb-phQn zLOOn>&MeiwJka{n9mj|?7StxVY>&0QQD26o5Zi+x)NmdjK{cg!;doQ=^qr>NSTx#x z5rT_2s|7$73QADXT8RC4g(A}jbnmll>X)dqtV&$v@KJn6|B|b~$|OSwYiblvHwjrf zUq7PQcntPW!^h>&{h*LJR=dpE?E@O@xAtzNB#Nu|C`)2lOY5w*!J3_Q8sy;>mlj|x zSAs70J?V~IPf|gHwtAP#`?61$P}8xnO-qn1i@Vbpxe85rV9r^jLxGIgg zwaXOi{zR}PqpKI}LuI-{sXPu4En}v|x?68=cN^RV_T;QXr{my)eKEc9wywg3 zr48{Bi*zu`#=15vUIV8#n9K{@YAp$xYo!PI8QP12%C%Y7=+RN)Zhmw#%ZCF!@SSL2 zSVU~gy7o;6ZypYGNe>vk!%c3Z1{?-5z{egg%H^&H)cJFZiq_4MW%SKvS9!@&sLM6+ zy^DRKHs=q$Et@eZTh@4!_pv{Chk0o>1|8aT-K;chhf;(FW%J=xssra39+<4RdUohb zxP0O?cRS1dy{wcd)K9bS#mlbVY)^NaIRlt@JF17|j@VpoyjEtdeE6b*)yq!{yNBKU zsJ8&ix)U@e*>By}Af7y8f5>s#gx%JPVG-ds>2VmW1SZHoRaVw@WM)NuSY{?zgpbW! zH8#t{uXh!eF?{|mbqQlS;BNMvi?M2?1*y+iNvYd;4Zr=nqjJrsynD8~R-uf#CdoGw zWb*7L{biDa*HPB!9b1G=s&bxCvbeGcOQ)U7kOTXc4QE>B6?rCdyIei7!uVl)5Ln%l z8_$q-e>}AS%O6<7c|yH$m5GYa-1(sL1|;H5HXok*rf;vZJhL`=OZmCEJ{G0=*b+}0 zTUf@6lc~rZe01A1vtzzA8}(21vPlW)1qMacCU(dkD60P;2fci+8hdg1MV8N5lCBbT zSl%hf+K9RSZf-Cwvr|uGrp4)6p3T;jK9MaQMtgaJ#@d?S*%?K_F?@X^306+T9Ev<; zzS*Xg9g{t}&B|%8wy#p&YP!Q5BUP&wrvfw^&L+SW4_(yFteDz92iv&nMFp+%)^Nm3KSm?1 zjb}+pHzvxijcd$p34My){4^U68mRf}_wj*|87dB1@wj6goN0UO`cDN$D>~)(F9;P` zd2owT&mA)zSRuz3ZMOgdtg4YGV%{tfepl4|Dsfj)h+v_>LM#u@P0;Bcw=$@BqGKpT z(Qeqd?e8#YOhfle{7kfYYkh3Q?YukS>}dR8@ML%Mg1E(Iui4Ne=tXpB_qQPgX<<=*a@F^@3HtgYLCy&}~*&2Fmq z)M&~Z=9+wV;575ul&^&gqiDY2^Yz79t^1ySE@_C{J$609aZ~dU+;1vh zv($FWlw0k#5!U3gk&TQ6`l^;!8#LuyYtP`$GpYdHI8%{2BZ z)A@vZYqXJL$BZ5`?BTWN#LYctcaOLCN7l7UFr6B2k$Gog6qN+}^AK84*Es-3Kz`q* zmH%Px`xEy1uFoZQr5ru5>sMD?ZfvA~Puf&*nL)LVQziX6PcONLhZ}OGjXPp|x43+~ z8y4keaHMga$6#0IRjKy6rO!qucrQ4uQSzM0Rnm^Ga3AWKZS&g6V5g$)G@2NlqdYZS zIIXQH=lSq4Z*CySIbjpQfhCLK0)^r?D&d0xK9hpXq|xWn9+?Q@Q= zr)wYe?_kJ$GSpnQ^^K&5@YqcGkGIQ5W;>gW_Wk%AIM(E-1UgQBrcs`I-5Af@XCBy7 zDy~-RWzVHOeWykEPqG{jm6_6FG!pKjv^x&EoW5Jwf3bRzz+1{W*^8hGux-Kf;Bzdp_=;c2Y{v7RZ}F`&uRb8cqx^KgFG?9}(hgYy2%_e_xr zFX&XPlav3s(8&-+M6R#+A0e5$mI*HNs3;_5{kfoeR(= zmPl1N6p1*AW=T>PIE+4GKm%>~!zoVQYdx%&E|}`Ky+K_`1uLxTCiA_f^ACh&9qsFW z|I3R!`nnqWmCdxp$&5M+DMvm2 z8$rxOxr@tMdV3+KQTLihSaj7okM*E5lEoP4tsvHswGkw~5<1)%q7#(G4?Vt#?V0|V zlUS|C{J*GLP>NJHr1FnGkvd)m)yB$ul^&8vf5S7o;`5}}+|=akXCA)UFWdX?F%%Tp zm455V!ant^~tr5C~rUtTR($S zHzzBGKs!~!sp)G{uV|m0n>p<@ayyWB_Iswe@FKm6Co-3x)I1V4Eu|MaiFD4y=j^HjSJnC}U2Vhjj{vgV|vC}KC z*Tb(0wn(5c)8*R3YfjqvGR)#B#8cMRvIQBC*`Vj5hXnky8!sdyn9ycDILXD&( zJg_7JdU%unjf%lz4)!v5F!)0O1cN>0_bTBJqF;y5p_h`#9YV36`L79-4L({|NeApG znVSvFbgE<2A)qBWTEHOcOLd@pK-X64B5(VkKg|kacPyx8jTjXZulv?pzunyq5)+i| zP=;U%wXVRG3&A_gu)!A{=uZDA9ie9Z79U09L7}G2jtc&@f^_fP*pN}>S6`!b>w=F& zQqN)Q-a=K%EjMo-xm1oob%0V;Cjx03Kf%YJP(F6ZpGY=^8qR#Rg2hK3MkrP$p9VQ! zSa%Uo*9z38)R$=bCVM9()SVip;V!v|u#Yw@q1q3>#zn}%IH9gz7wi-ZEh8~PP2{<_ zl3mLqd$*0y&sygMUEk#Vuw!Qs#!aUmufJr$sv3a-%o%f3*W`-JX^@@Gp#BA$d^7yY=znS6cg#_z-DawUrb-N!0@T~BwK zBz3u)C!ICh6DOo?Y38+i+IpzzocWp9b3~E=gvo=lMQ(T4Hp2oj(S1QhL|zW`2X$!) z8vrnnW~vi%&x_A`j(V?#g&AR%*v_igWfVCsSGI#kLT_l?Uyr`SNV4MK`or}?`ibp3 z83|t7$fFYQk>cuH-0>{ix;s+bWk?pwk|F9qB{4tsP}7&Eat9#>9)4J*;};@^SkqGc zT~3McGS<8~>b*!6e6u>ry8GkXMeA8gu5B*-*5{p5pHaw**GOuownCKcyY0KevAQ$9 zs3WjfF7Mr9{2%HsZ&BhbJ4{l0Rrw1E%Sh#*@J$3g_axA6y&Q19lJR^@?&!VOt57}w zj7&Kx9ghl8tMQMgmc>7lm0tjZ&tD3BA2X;gO)-c}y3zlQBB69xF}X3~2*Gh#;ndb< zjRx^w(@E1ljYm2#`1r?s{4!`5CjRu(FSki@XG*-Y+h-jtId8CsEyKjgi&0)4dGX-* znZqpK)}#vhY|+jc<7^Q3f0}QC8Yk*t%JI_HZmvKs&?2`2ay%)nmi7$f#o-}+n_&Bv zWRYr%z%N^0-E@(SQ62gs7Z_?+N%V=l5!$OjU!ywqGdoEw$nzF4#CREtg#_s&r8oNu zRzI@xF_z86Z;WNO`9I6P80gEJ(0DgY;ti+d~=>_i49 zqY$$iPgm68EwXEBs%VIJ;?q57G=7m~;q~xec=qwkwXE;2YnO|w;v?Yg?Va#$({pkE z{Xx%PFTg}3iCNRk%RZv4SUggwkxX56$D3+&yJpN=$0{4f##f!#e-UbA+^F98Y|P_B zQG#GXkH$&(Cgn0dndrT1{tn`QDdxBa{^i{IUlMaXYeqo=mwEIJZntBrSYtHiA`=sx ztF-a9jR~sreeswos?E?`fT=}NNks2xRYKD#=3~JvzPLJJzsP@$-BD&G7B9((O8}t{ zxL-<&srtL^1n*qmQ!Z)kdl5V zee)`%9f_yl3$p$#yiSs${+gGkno!_aVP}Xb$FOVhC#1N@9Ji_O6x!b?{zZ4vlH5mDt>fkoK8Ps3AL}TC+NwuiM_N2a~y?SJlW-8Jl7QFL3 zsirh9#^C#`JD+}aBscs$KfS0{W}?;TU%387U=Wl=+{EHpU7HQj{}ef4AOHG$>1t(E z=|a{gD3o+THl|1zFCK1KZl{Ix_ys(>57_VKNK6$1QPBaQXh*JT%P#ev)MP;A+rStOm+h&BA zv;spyyxBCuJz)Kd2Ql-a0+vOu+2z#^TmM*TVQDD_2sD1xtR%+DB#8g^LC=FhpMg8z z614)E3>ocN?5H+YR!@V17*1BHCY_}i<)o?pB{~JXQBO8K zA04`TwdM#t#?DBzFG_DN(QmsYR*_62w%K>M;vrwYLl7I=Rcm`q{g5lQ-sRSB(!ZE% zP={Ngqbl3awb+mME;UtgN#PLM8F+jksVrQ|FdvqgJh%>|L_#Uql0MI^G2 z7z4Hk-po-dK_cyOgf7C^1OJ(z?-&0o<}T$=43^`fTA}!j7fS?aA|1$!4aiHbo*`q453g)Y-A&j6p@NXL3iL2~*GDW81bIF7 z;dCz&1jN<^Vg!t#zlb++3aZfVGBpe{U*Vie z%r6{~8jg#?nv~<)6GRv?xA+Xujy$Be5qxQO1nE5&w0Iv~6xh;qE%7h$oAsCef>c>T z%nr4L_+zRCz#e8R#aq2NQ>Ag>)se7-l)P=~d@FBLT?6u!mqaW4MSLxC2Qw0m$7tlb zxPLlEeP^wS#;>pZ4{aIc;3QV}JsyUK6`f^+(V?Q~nxDcH{%hz}R7xg!{Iel*Xd_NI*pwas&`);@^pf*43BGYbKaI^<+1IznPq!nlv z=U?-M(N}+Vt;O&(J_c6@k$RgdN*U$%3Ep~T4y_GA z_x{4jFNroJCH=JF{GNQ@?kE}jrU!-p;Y4ZuP$jEp6B|^-5{)r(At4>ZBhj=BWf*&; zdfcF=yaQbXdbc16^lmzj9RntkE+loyPC#8^1(1iR7s2!xg?*FCyTyVS$w0Yeqg*}T z!(n&t-7#Ao|Z+D~Sn5}-l}@s`}G zY;T>kPeM>QUjKF1iqePH#=GEy?@u}r_v3mDGp?ugb2_~f1g~Te8ui&o93uNSXhEvz z0cz8MZCB7lnZ&L8Ln_7zyRe+wk3CP^NV5927|V(U`X-$ulx(^$Sg!arb{CcCx9wbj zNhX6{VaHA2+uTFOxU#{^3=vZReXGi_!$jkJjHfvDLHkoQe!EeFtgQO6^Hx3y-Wtg} z6MZ6YRq^PC!boLlLO;mL>@L|+Dx!FmegvIVYzOp~XeW1xP(ZjL_GXQd~-hjqh*F75MtsNg4Pm7%@}IWyVhUKTGf9 z_r+MwCY6&?@M#>8HruPA@dT0TTSLows-<$zQF~k42Z$cJ@L?cbplRow)$k6;PrP+?b2hwzvSVG78;TF&7D_(1w7cR$wb;qydQYDh8Fw73+rVH*ue<*r2 zh0@Ub$n}rLTXM{&BIV-zxf=GxoXkAWLl$Ux&TIvDvr_O!O!e2?)1R6^7Dol8pX7S# zRRu-gvKt23JH^#0lUp7mE75Gx3X4}smyX+sjYHuV93X5Abu z>ZVSZTwAyA``DT@(`C0)J8~?|Vo>YR;W-a*KKThr3`tW|_E4a|`Tl6m_q~!i*;4Uc68|TWv ztZ{j^{7Fh??yO4%zUl$exCliIN3jo$^D1-X?S5`36dEeIr}@>r{(ev6?uT>Vphp0> z_yaqjtM913vY(js*`Q)A>(|nmFFEmV(YWr+7!3Us99_hKy>JF=`;N@BFOE-RcQ?Wg z)c8dbMtmRfdO*uS>iONX7^WV^|D=mi+w#J8pTUtbjPw#W7t==y!Rp@mw_AqSy~9T4 z?ZoCSqRhm=r@UIYoN{vY>bxxn8pT%{+|IJC7e;jVZRy{NE`j^v)rl~9b-k9VAp@L&bhI&dyRy(LW(cU08SaZ2%fkfdcX z(p?oy-HWC#ggTxn@koDvey9T~ba`u1I&uXQ(t}cqOKn_4P)%4Z!3^xaLYg+_mI*fb zZNS-!GgMrj($(aS7v?^cD{a<5zTy_H!X2KT3lE-Y&L5X&j4DYU7*8$L*)h5U%Nh0& z=B<-98Rg{J)U0)lB5qji4mGdw#T!8qsgP;PTbeD>=+>g39fdWD`zx+pn6>vx^1;qK zT#c29RrQanRG%*0tmVE{UJo0ywzQPYzPB`0(KvQj98}{=WyzhlPic~3`)vMH-=i{(ya8c!{B{x!?S%;k5p$!bfV^|y;g@?PQcv33IhZXkgq)# z)3o6*Y)Ya4Io)Pl&~l6ahQ8b#go{WhCF-ieh@H#AC})v*m4^)HtgQe(?K(Bw<$J2@ zJ!I|d7hkmCw(Qp&t4sBnthW!QcztM5I)d|(4Nrdd%KH!HJPac5Zr=0SJ#L5)0tv2(R*A>`%}UzT zmWG+?!+{j^fsNw4^;dcF-TCD3XD^}`=WSBwOOQZ#KdjL;*A>{R!h7^w1%CfuP|2Uh z8r)adJC7fdi)c=sxOy^mnKyO9$(KX$mlsvzRd$>Iv&sDvJB;5m@2eH!t4=7rG}}Ut zL^Ep$4-IN2z2wNnZD#x}Fs6iugDLcUJ;Q2RIG(xLR8h9Xkz@^)N+dvFeu4{9nzAN& zSy_dJ5taaaKof49$%q;6dCk1?F0edWPGNVEeFAo?VwwcFS1oVgAt+7hB_^#;C$rht zvE^Bbw_vK@kwxUiDUtes4H_?;I@b2l{8m-{J{(zn(fmnk;3-b+;b_XBf_9;5u{X+} z=tTFWN^S5W5{&iL$Z}A3HR^eKdgc@()m_uLm?|O zd{~T(8mQ};Pr{obaevYH6)ks>k@uH9+=Mwb4Nwzc5xpRgB$3 z)ABMPckePvZvhADzQA5+l1ThO|?vkAKYR*++kTSH08PuXI?S7BJ^? z=A4J??wi;54pAwjFA!Rzr|=}{970~y;mB~zOgTs^3NuJ#Ek(}N=T}ryw`MspSqe{F zz1;U7Rx4d&wzo;y4YdcP%Qzvo=$YGQ;N1FwA&^w*b27W`z`dM1gD-B18*%|IFM{F{ zh)3UtyxjHvrnk2s*mb26@CH&;>PHr1^A+qO)k(!Bs)s*&Re`2|=?=m{>(F>wpIp8P zKPH0wAqB;+2WM1m1H5yjjKY%crU50XDTb8FG)9v4v~VvpJJ__5!98|gbuJ%P@w8tN z+E$L79&c|K&9YDFxv=1CjTT-UDWUCE8%>(iG~5=$(p?XYh2T`E5z^9Tm-A%UUk%P* zaxqbEoI)56BXOrA>I~PXrpq_}nuz(B5*VTs)G5XjH-`%lSqN-ZZ_ZHh za|8z&c!O%00n8W7&(Xy^sU7*=H{s0&bPpnGGYtKvTBXie}R zE*XT=sMAJSSCuVt^qx+KY0V`ePfqvsZ=Hb>#{^VX#&OfdpQkEmOmvOXWrtBD2 zVZ1|l;i(_f7Xm`@vrx>kp6)R@sg10#ko*V$w5}c6@OEm94)aqAx#rB8)Bw)Z=W~eJ zA_*s1G#JMtidHV;UW#d&@^XtCMJo4x>y}VrJ$z!tE{fWh-%G=o=4C9z$TliLG!Ggy zIZa%WZNNbMvazSzH^Do=VH76)FXSX#Y`!zldu~o>*Gc7;O>9`oRe}iN!L2Gm790%A zvuw1v7J&uCuOa{eZkOlTXHM&8?-lq7^RcHkw@tNw?@X^flbfn`$2a_=mTY9&;8V)D z_zyZ!jmC3jM_kN(l`P-)G*Q)+N5BVg?A9ByWCKh09j#agh3AK*D9*EW`F)ZY@ggG zhl|bZgB7m#;@Arw;!wm+VU?Bs7ogfKby`UkSR&nQ+^)t1Jp>VA4d0Ujkg}Bl@C^hd zd}(Ho&*kuJ#jv0cc>i9t4Gqp##yfCWInWvBGc0ExFm=rh0+<0}BfDQWeUhBB`ML9{ z6BEez5pKV1q*cHuud%kh*><0d<01GR5`TKdZD z2_@m}ABgYM(fm1Opa>y?yg)o+wVI~cnRij>2VH8&h7NbqH#4xnNLB*V3$R86T&3Bs zJvX?`10!*zc7_8$qaxM{04>H&C>5;c!Jn+yq6VQF$F;L+`y0JCzT{-JV!r%tHMvN; zHbG8m1;8969jeYw+N6?jBIxp~p?kwmcRVv|6e;e=?I^vnL-DL@!ON=-2UaqZQM(j8 zsZ(DQPlY5yEsw=W*@9+-ixf-L_FXIDqgCp5^c7qC+U)v{onKvQk1lSEK3`Sd z{&@_h8u)@A8FW(-LQmc|INSUUQ3M4x(}-)Zt_0n*Mz+o9&X~GU3HJ>Bg|J9O+g8=j z-h>ywjnz=Gjv7B9sZGiSJ6MUY&1f!_KQRVqr)c{Vfi}Web3cU z+KDh|V7)vtN(?q0x)HY)V=vRVyfyucT-YNhQNxWy?%%47KqC*61bKx#@Zc?F&6yJa zdTsXo>nu4>Y}kQbEFwUZ3|g|+1`9upJ9@`86APCN9(kYibcvL}0)^mv>DC?@sY^@bvPQCG10ZJ>(A=x-%VyA>j(2+ zt4G%T=uRrJljyWN-0N(Gt@njIdaQel6{6fIu$kL~8c8lEu;MlQX>!x~RI4gg>!rQ9 z$$(jBXx_iwVk_*|)^Ow!&ldC$?&HIs^C$JkqOQ$rJ#XRut21`^d2!kqKGzP2uEB=1Q2&{Ms+@bVD;+v@w( zf<6P7@F*wO_SxYQU43TCD#^B7v_4MB6L`&Jrr@|KvajliKI{9HdNt025jbBCV zONP)8S0}6mIvkQO4(MyG0VWbFB;}QC$$4#8GDhOZhnQSZoo(HlB)3L0q~h{?P!gj! ze?4Or?96bVbtxup5Sh_oFTVeHJ`1@JAaq&r`l1&iO( z;5@9{@RtwdJVa>hrWijLlgftkGjAec#;TnM03?bF)m%Y|1T%IUD#p)21^`f?-1f!0 zv=SZO;)w?_8oNbV_Rt>*gAi3B8>GrOs3a7wB7eT?xYOK{24jVM@a0UhXZ8yw@T6z1eitl5?V>qWS0v`XB@a-FT;+7c;to(rePSpzpTs5 zEfc!Fzd#jVLQXDJ>W)`Rs?dtyUB`MqTP(J#{*?>dh)g1=z5O)u3UQkPMha}DOB+my1b0p`qCpOLzD1^}`FQ(x%6vXae?;UgM za6w?1Ln`dv{`#(T;ih^gse1aFYrxs2`YgvZ$05Q+O&lUGzWb&8NtMrb$R`SCRtaLV z34Nst!_h;4NeIVbkHAHa#R?;E0Prj88Q#8)>U>u|#&fe;X;uh*8l`m;qhM&_@~-o< z?sa{!7(GC)Ky0-huEO&m!b;tHono?q`8O!6fTd7XzujF=-p)fb7mx?(U_ zzW10R4w*}!a^1$jz&N`sy-?8?#O$JZB`btXU5?Kr$a zeYk)jr*0b`gnI}Yp!|JQe~Yo6UByEwvqQtxgG-F`=t1rR27dNRM#SWnk=2Vk_9-gH z?yLE1Nxn=91NZ1V)u`Jz+_CGS@QF^Youj6Rhru3dgj*<;42M8*@r1q>-I@`2@Hh@E zQ6#FXt3w3x=&>kaA={-EYgMoor1!+NLR*VgqQd0zp6^q=n^cVRg|P4}4f83MAXe8K z3(2*_Tgh9$o^jEIa)$ziG&_A0QraI%`J&*`7L+gM9p2N3t$U36*GM4HlgdigQqMb_MkqOASK`Z+q@vW1cW(4&e8VW|RN582IPcXbrUA{c@A41Dd^e#C zv=h7bXBegpXf0ie>Dl8VA^tC9k=+|nf0ecbbew+ZOWXV*NZYg^3rUr3GUXV!3DI&r zaYR;z2QX10do2Dh>`r2$b}HB$oI_O zh3gk_qmUD8luo!YVKrh@F#*HK*I=KrnHf3ZcuSz_Yc3c{e`0B$6U zx3AV!1bx#jab`80xA!yf!DX#5a*JD!f#X;$E3CCLQ4;Y<5EQxx&Mah4*`p@a5cyp% z><=7BB12tSySZ#VipT1{`<%E1=`$!X^0box6pV7*B@TaRmp|zOBxxs5EqARBW7@tr zphl2AYz@Imi~Kg1Fo1I+grr{HOE0)aNM9f%lqN@7kjgZ5u+f`80%Bp}m0uh=HphMt zkcIk5d;Z0_1Rw=Xrzo9vMq@t}LA3}72_cfg*yF%tc>g!jzMXWof1d1TFup2+2pe8cm z@kAb|z94>0I8S7dkByGRho)=SO%ySAQ%8v+5S}8pI+m~2N7E4X;mFJ|syBK-tH2_* zzwqr|tY&w_5{wJc@2MRJPv6?Ca>;5b=6A|cB>=SmE(+3VC=$x5FX_?o@Zc$zwP;LW zVx9@rEbsv&@Bz?z)_%N{X~dR$_)W|=<4rp*}KT0P&Y&ofm zg`0}8LTWd36RsDcL|71IE^N_892Cs2^1yE+k4jVi3<`7x-&;hE3BCqd;~?5d@7gcJ zV_CaF0T+YW$OI`K1LM3`+H-UZ-Pf+sZpzg7BKLP0zt-m4phywLiaLDy8yBj2a;6+_ zV|VKTbfo?P;_>5GTuJ69R{=gj$YE|gg_moR4KQ@&S=2O<1pXZ-xZ`cLlfNVb&$rPW zPznpAKHBu0LuIh|`>CITAiqF$0l5e&BC~=8V9?BbWSqqw?y@KSj*XEW4m9Q_Rh{rj zI1>!?Oc6oW2cJ;&l8Z;_$lkeJT^OkW!hWHjPhb>3KwzO`Yd^J`9Nu*8+M`38ITm3r z))7|2RUI879m8yIsAYFzQNm-LLK<0wpB7t&#mgWahEWuq`HY_x#Chu-75jq5fwYqv zZ5Vc5NLf7Z4qpb?FbPEn71V{8Vy}rCwgs&s>)%v*I=WqVW;f(GQPN?YQTmQhvjQC^ zuu(;=mw~L02)!bu&9JS=_WG;qI_HwA_{nmo-y)+4vk3jEhQf8B*(ASgwVh&EO_Y%4 z01{TG%U83SNALJC-thx*2t}h+0vx6A%|s8$hm#w&ZY}%_Mi4n>2&GpSjuoXev@T-~ zgM8`?Ki_ z<=XF)E+em5n*nn|>7tZXAka_V{0o8(msZ30xJEo!jMd=TEmtxRG(HEPO4Kg(BPpoA zFsd92=R&PZ+Mr@Ry;L*}gt{V=mEx;QOB5so?$KdmLeow%Oc55>&bnilVb2}FoI>Ov#AyZqgp<;%oI~7C@q;7%T=0qY#Mn~QRZ$k29q)G#EMf9+L=*S+RHo~`qKYi!R=@+u zmm7v`Cal-6K9P6-?ooMXlgik(3z$f1w%-Tt+=&rk;c=8e>uX_}=>7iv+aYl?><|nH zUHED3#m|&#npenRePL*-s?3;A37nwEhIY>fOhvLv&PteNvD0*alS(v&nRblfGPgmC}35{m}OPRNa5uZu3r0CsCe{oQyfiTSmZ! zi$su=)9q%1;wAFW%-Gs3uOL)FOjVJO71P#sH@be4b-=lb&g|(M$*(hAv*r<3U@ZE& zvSMl^i$6P+yt-|Pq`$D+n^;c)JuJTun z*6(_rPWCUr0v5X02B){amxm?$77EnRS5c+P!P-`4>V^1RflWKc z?Bm~zhmudRxhYqBK;7Y?;HXeOp*D(pahY3=&lJg5Im3COeCf*=>4jC5yA9R+ZPAx{`z2+N+aUR3YtOzkfytzTK%=B(vT^u2Ke|D=< zQ-vVuY!Lt5nUe3)QTqG71(@X2?`<~J9p3q}e$Iol`1JHj^4rwoCKz-*w#4_oX_@;gS8}X!oSJ4)^RvruE66``lNH@mQ>>bs4iWP4x4g-5$=IzJNS% zs-eGIsLD|WQl}i_3`^FY?@JLImo)yWGSU+rx~uB+x@M!Y&qv!KVIpAI)u}nLpCT^i z_Pse=D)W|Y^(Q?OvIFx5t|qgRk3BUh)!Vz)TFOpGIJ9gvjV>+E?6BXs38{{fG}kZD!672TSz|ElQ_5Mm~L*8jrZYN0VJ`ar4MK-?9}44qV*n zRnl82AK=*^SN3iD>+sVGo~dOW?FFOXhsxHLbWX_YKI@L**tfXLt>>h?xyQ)auCWNG zBb?t`SGVlpkUQ(a68m68>1kb;yvJKc)=Hi)hh@Hf-g@88H}Fw5n`=_@PW4d z9BN$Y-X9}%pEGHO`zlk$Ri;jhPCF(i!&@A{lN-HiD~I#GuDd6t( zK|E#^tI?Zx&BsSCC0UyIgM1x8C3$Z@z$* ziJ^A;C(ND(ZH92Br^EMwlk%Qm<&2Y9Q7i>1-<<$K-CvN7W^rwQJ zL1BxEXWau%8L7mNLu|!?#Up-9v3vScW$_=*e#?g^N9+EBLZYHp_@b2_r*?&J@fp~-b^52)@{FxMv zjDh*Ge4> z)G@v*+wbGv+h0VCIsE4AzxF@;yp(i*_^6@b|I^-;hc$hr>w3AuZAGKC6(f>VP?;(f z4Js&sjCDZ=fw8utWmQnB2uKkLkVTuRXceL|ih_i;R!~_YDtkykAz@LYAO>X#5G4YV z1qcaQ?{{Kxmgkw7KKDLz??2`b^{2$&S-$W6-tRr)#0@dZA;Q?Pp4b6tg*y-*sZONq5LhDe#fnGsn)pz z;maip8IJ2MnVT$I4-+oNo~un?!0>WlOzCe}@}B;F1WEU-4P9WKKdX3vWMZl&ylnFJXHX%L@U9Y$Q*jjic3!tpEu!-U56MqB*)wX%spFc2)}8K+hDn#gypyp%5$tQ)G``EUpX=oHd(6EYYF6;SZjkB zG-!mKmKb}+l=e@7i4_>f@<>6{`lZ1u#w=Ran;^Enj~ch7Yp-jQ4Oj8KkFd=?_))pZ zsPm^AM09Grs3_<>Jr}2~(590n(kq+sje9eKO0{=wxQF8R@8AC_C+^QVCu1lQ+k63@ zt}lHkU2mEXbC;i^f8i)FrQm9R5w`BTkdfgZGJY!~O2?u0Sm5V2zv)GKtyNo=PK+J6n!)d`P9FMQ)iwm(FyrgG_O84;ICy|Fa-Mx!NR`Fe(uS2xI^}X= z|E1U$XTA7f5P^v=&m)`0Yg8$;A{Zd&x(X@F{lTq-*O`$;CsBQ-?DipNXJ>VGe#(3( zWuvTf^jzUfRMw%kTybhHA8A?&zga}0m$H-E^6iDA<%+x09Cw6$obKs2WazOXoj@ZW zD@k=7_n<>D9D%tj1HYvAH8?O&8+2+xi743Cwd-)}lLL(Z!V+f?G@roPGy-`g;bLxj zE#Na|(!lqx#d6~Q@jp-L4-Cpyy|?mSK=;T6Zq=6<_t!VuOe(0rH3*TZqbyp?rP`L} z!?qgL(}0-5>m&W-uA$(Bp37U40t$0=k5WAE^gfhEcns~z7f(D)SX}_c2z36W@nU8n zF8Osab*N)_e9&mnMP8#FmQ&@)uGK?22#h(|l#o0R=^uOHtF@J)>S$Gq zw{uYSP*K(Df?-usK3$VSFHdUvH1FUwsh?@gPoT)N>jL}vX@nR;y&r~aRcA6OFC5;y z&}a~w47q~NBOOusc*83%{~T38L?KPVz;`~DW4;0>d1Fw-6V}IR1hO<-vF?p0{FGze zy#`A;^B0%#wZt!E>{Wb1n0qu9^p~48(Htee?cNOc0{L_kRI1{<)Bom zf3!53ohzBu^6kc?piG z=BlgnQ|ykk5AKS{F3}!$W5Ir1I1N6PmVQcG<9)9=MpwnxPuW;8E@g`*zq6R>whE7Wpf6%5L!a#-3k& zzV{Pk4U^#Dw%JmC*;?lKrl&K`A;Zyu|G@xRhF*s7;B?cUr){IgXrS<#NxGgnVN~;Y zNnns_>It5bZ=3hWs8JLJsQ4bS_Gwv~Vf{0wi6N-9#w8_hC>(}vDi*O%jTZl;mhYQd<$%IvDQ|A7QZ6kCd+&42;_+HLnN#dBL$JU z?H|79^a~89yQjKBsPa&kmQ_c+ijBA&yNNS))!9R#Ic9}BLDDcta}yF>dP#b?^%7ib z`-Tm~{uRuV2^@)C$|_Hl2`{_&C#B3}_`#2o)w1Da*{$Yfw*I?1Ru7DhoiS$%zo;3>-=IH2i>S;)xR zV%Z#;>4o@e_hw7ibnvN_Of+$vv)0jzVM?Z;LjXso#y2j?81pdJhv@5@1gyA+Yl>l@ zESPMrc2LvkMy4Q)2L?KmDf@9)BH%dK!d5`6Rw99Xm_P)murhKf`PyAGg%1P>Q2~}No zqLtIQs=}Ch1RoYmF+k47%-mHwp3G;G>m2&p*fiBKfx0u!k1}=zZ1yuAF({a%R$g_cUYA#dXuc1*c0IGP>g9z1ZE0sRe2E$wS%)IKvRln{WdY#U zUjd79gYLum-TK*(fRbCYx<5k2{f>P)fBAkCqsWnj@;fF>WLa|$2 zAkk>(DQxWNKU+(N zyYHI40qkO$0eq1ssmzoie@1qrtg*ms*nq;bEl-=z`#SEc2O~uZ)k}Tdhws2(0Alg4 zW&~Hh)av9sQukP{Ykix42(_m|3P7Q3=INW$o#J)}9)7$rRuq(AN2a_k7VzU(qnGC` z%`8~T=G>AiFGaAhkXKKtI{d;T;Mh&5M0S0~WRT5rvh zLxl0C069!MQ4%%bsW^rKvmAB$?6Z1cYIFQ(Bk4ecpmk|B)vq?jLj5fP;=h`Q3EbJP zorl%~brYXJ!O6^AfPFLV+>AD~UYowS99q3I=fwFeCd@yDiqu|BK3=eucrY3{PQNqR(Dq^#s#a;`SK(T!9w#8EI>oB zaB@KY)l&_p105IMD%QG=d*Kpal!k;ERv_?8_;PzIoXmr|wl8_}$$EF*s5>a=ln|b@;+NEi02d|ZbYxJG- zUUm{g>9Mdf22-GZq{Njbaqau_cHfg3oEM8)opPowV3he;`a=%w$PhJzW^T5O$+0f- zwG{bTvd`A8JZs(Ui48>gv~{opW4}F?LnV~2#RdYi-(+cF-gQ3h8^hF86mL3csVbY5 zH#(=gqooFOQ=llBtGzu-`y`K_1^v#7c43^(GD8T@t2$R38NS?SA*0O8JPH|;efA)bb>WAq!xi(e*2~36h53O!Y)ciOgn)BPUSS#+8v}?#yM#Bc|2*()Fk(0aRd(&&!~e+_qJH^{ zbKZUJVN4H2CuFSAk!y25x5O+)trmHsz_*`(C0;~*40J}ZF1ImU5lSE@n|rW{aB*>Z zEu#^MOVD52#?Ye^pzDWDv7?SXhB(I6$)b3CZP-&@KSu*tGSf6bGUQn>oxv*Ih6-*E zF1^-kd@VFOu}pfYzz5h#PlcDq;5H>fvmywY?L0airIoQD?$s~K2>g(p_#kqRT2?ZB zFJxMh)EG(_XTT?B9yx^+kSx}9i^}5nY?$9Z7&GhOxcbow!Ti}~dmd{?CIOj1*_ek~ z$1~}|@~7kM8Tsk89S)44MaWGZf9q3?JuKheJR11u+P?VsRoxoCD<5tO{CL(ZX5#&@ zK07(tt9s+Ac1Wnq$yTzgLRu5&oY?67bH^~hqkFs*^&5?MDzI)RU$@>rksqH5{s;)K zzznb}eK7M!dogr38UnpE(%r2(MG9%YO)z=4I1Z(99I1!=I!t88Yxz$Z)vx!SH_?(* z`5|5Ea#(L=NyDz2dv^A1&u*`?^mq7mI-5iHF|3P#&oHfvGJwI{x5Nel7cgd$NuS&gV%}VHs_wNKE)sd8{iWy-7L(jg@=4QtG<`Cn^F@a6c;cfF1Uz~-YdyuD2 z=Gnmh6(DOyC6g(n^t&MUuqMj(`z3!((YEHul4aR)N*?B1hXS!6<4c}CgPJ}gSod7C ztnhq~`{rnb$jb*j!H_p90S%#5ianYpogr2xcLF|DT}D~7uUk?$t95`y|Qxl(A- z3z>!7Tr$KKT8}tH-)AEj$jj+2YkR&gx@x$TE5HyHHOIqn6AfN`sNw2aM6i{Bta(r@ zDXSiiQo;;zT8f3q-J>9@KRns&%p=-AUHmy^aC^=VuDpo{VeGhW^Mi;xUIMId22mZ! z8;?|;Wl|O%8Gim%*xuFzJ~rYYIodGwhu!A4FyZEI3ug1J&e)(sznknb1|@U*<}~yi zFr!RTLk}Z-`R(6N4$yj(-CdeWD4&Prco52QC_T2mxf3Nk*AQ^P%h?d_K|((`^c^1q z8RT@fYs`$UBl1%r`aOCEU4bbz%q#2sw)waOc^@*>BVPx@Ix&6sEA>*6 zsb(tdxsRcZW`$jkVo{BwIZXfR!LO7(3=EDe^QXP^@0*>Y*|4y&FY3wd2q9~_&@Q%a z7BZ(HOxK=S{Ee0vv(Kr{fWouCu&eZd5 zQk3m@k+hGg!l?1vYZ|nw{h6|xOWHSEy719$J6;M_dS9n zv#hrQnjROxhCe5ad3b7HT}k+&B~aIA!<)++S<3rREvB1}NsW!92}k9CAARi+dGPD$ zNx@*GYLD)+n2?uYP_45Y=(nww6+l1spZs;^=2jvGimU^*{a-!Q^nomW^T*(^pKa>T z0{iGuig-qq2FS1fKs&NUDig_CskiU&5hfQD6-5u_KpI~lUxj4K>5=_W?v*D_TuEEI zByCOqMXnUtPgFJkEB~N2HMuH5@B9NAZ8Q6f6;suAWZC!5l-(3$HlQ|g_)VF;FP-U$ zVu|;%ieE;G0b(+A4= z#%)E2pg4qR1rY-6afsec=ul7FCZw2M*UgGX%Ohg9zJTIELYv zoC6P5ik1S&({zaqU7GPSp53fjC~bkV?rV`UwZv$E`9@lPlv_VY6{n^-bCwrX^k;IV z;_=e5dZhqQO1&lMk1>tMNFcjvSLmb4x}K`d%1Ksq<^EQY8@MBEG?^|$R}(0iCM8K$ z^=cIH!)}vNjWPVl?Y;+5@#`{O){DfXhNU4BP353P%;>yM2b`^QVJS~*YOFkGI+JUs zX`jl(kdT3%$k%~qK_h^@d*!uWS2%w6*bE-cJxjuugCGKO|ApGG8bWtEAh2;_?xQw-OJVTbna+e>(ma94(=1Fs9vRoydVn zB%tLBbhU?K!RkFxwl~4x%VRR@oZN=*wDuvmOba&CVhW^4VujwlTbV|dx{E8Wh-eLH z@lVs_&m~+C&8uGs3MU*Ri6Ba_v0d>Zl?NRp&^k~Fk+Ae7#GTKSKO221vo={Jznp|oIbef~$m3+C(208lN3=S3B58 zL)qo>iTb#tJ|{d5wLFXo;>zy*E~n)&$~M+d6?0b~TGHyME! zo_g3!8DN3swi38eRC7G6-^A8VwW&+s>AKuN-N@Uy;Pwlo<5LVnAxu9?-)to;dUyb% zSarw%D2@e=!JC6zrVsSjA7CcG{lj^J9I<{hB_AT`PqM`?spY~DL@YSt2nyO__Vcs+x)+B^P8Wb&B2qO zcTev|0yYJ|vx#5L;N3{P^M`l-FlhttM&f@o5;Hyh@vJ}2@`*y<-m=kmL(%7<-~Bfe CvUb@3 literal 0 HcmV?d00001 diff --git a/Lukes_folder/figures/posterior_uniform_bife_3.png b/Lukes_folder/figures/posterior_uniform_bife_3.png new file mode 100644 index 0000000000000000000000000000000000000000..40986fa162bafa14df5c8533a50aed181013e3f7 GIT binary patch literal 73163 zcmeFac|6o>+&6wnrA4HTvZj+#j-_PDda`sX; zN@c556Jr^~&|paRWq#N7{mw|;_kHf?c|G^@yncV2>-9RB`CixeT0iUi^SR~(?={-F zWYL;M0DvXC{?s)EU~xVG3vCz7hfjp)zRTbr)iZkLXZE?CIOBEX^l@N#d^^B9l zQ3=oEr`;V~UAD?9$!?XAIC!83Ar|e*psQo-I8~9d=N zDt}TX+pG1w-XbFrmDa#Nvhs@36qoH@f9=xyK``I4AwA|?_pKv0N53_*MpCt=x{cug zGi7u7C$X6?@Pp*szFA*?TT9^i@l|5pS+yVEfb@dKSv~#v2aWGXrS_sZUO&D8)1~>` zKfYf5i!A!%8*#-?L*AA6cgTM?_FwY(mr20CqTpW{3;xw4;9pVjuP6Zjih_Sd0r*!G zfPY27zoG#AD+>MxML}zM12-V3vZh*CqGhZpChOafYv5q)=*O(UCvCdG=>=<=$$BZy z8m%bvpWn7BDTu4wHX2}gj!PxVv40|eJ~u&@LK|^gGM{Fd`IP(# zIFSMEf8=6|4Yk9d%h`8HrNuV&r7RbKX zHtGy}%wIvVl_)yZI}2j~5<7kZ8J3Ad@Pb%5Rt@Ld!)Tf6^BSPGOnW9fEE~^ag^X4N zsZW+oRhH>a4%x`5@oP(>g-rLD*F{lQgt;#%bq{Zy?Gv3&^`98+Hk@&(}nRO(d19#VUoCreA2cN}6i@0kn8C8{I# z?-yuA%^7*Y><3q)7>b~ZKBx_7*4{Gw|1~z$e*`B7^JaP84U`Deq_m!Jc59iPkTe88 zcvyY#n2O4}bcB@ad@ybSAo>k{vwu%2*NUncQ+>jmI2qHKG{|+V)yo^as`OX`I%6k% zBmg-QQY!7QuZ5bcL3O?K)@Lra0En}%sl5wEnd`x^PPCu`mpLHsPdLir*onKvOXcih8SHF+SAfbmoLOu^m#5s+brM3-QKdUCzAaP)(F6{@;9j+pM$pofb2hebPBcs zJ=;>RaRbL$s9R$}Vdm|%oO$^kgJ}~qwPs*6zge9?1;9flI*EU~Idw7ikxtI2R4W{mwCSoW(kjQamvv)x1$rFSLDN zn-PXx!&myQVK3VrL1&VoF%KVJTXPl}37eob)xvYvp8wNhGc!fQoM9mV?0?0LdoT*V zx`U$kVQAK1F1NA4B+eFw5l+eDLlKy4N+OUsY@9YP1>0ug1RMS(ayR5^6r@0g6?uvu zfS}#s)%g3^zouyKA@wh*XReEnH_F<)KIzdA`>3vlVuR8ys%zF9ri=&}x;I&{Uu`zP z<_XA6o0D~0Ms`wo%a=M+WfcVgp(;?TO1&gaw_|h@Z2Wlz8|?PVN=rK_;>-nN2S!zx zi8-ZAt9Ce!tsTAy5k=ne*V{aG*mNzs3+q4Igr7glhEYf=44iI|*L^&wr0kz^dqApa zEt_wne!~vu^F1BmREo#>W)XaTpEt2b1;9Z=J%ZGgP~tiIJ?+S+UHf4JZI_wDc%PQG zNwP*Q?Np4c+G1Em2*9h7B9;ZTh+Eky>jEHgxu7hjXGB0;%i3!dY(sm5Pg&Q@?42UL zm&E2#f;78VC5M<4p^AYog-3)|MWa9mEFxv+MCJf+`#;wCsr9a0x7S0?3s{bXuyKUw2GKx#^j>uc^SuDKLE>zUy(yM?!2CK~eQR@C|)hxoq9oz|@!pzHsnc%rswEHBQcFpzyW*V#C; zd!pGP0+8$Be6x>es<7t?X(aM<&B~c?@HE)lia)x`QNY;OzWP}^Q0~%= z$(edVY7Xc-3%O9~84oVXoL2Ned!k3B zV_l}iJ7|s5-D$Sm&fYaK#h^5Jj;Ugu5i12a@|@u2Zgkk~Jqj}RU+1ihc-g%HJt2hs zV){Pg>%%%|yeelSsTr`nBr}3hi%XMYT*%UPq(~nXG0qac2rCF#Vq%hzLZtCg-Eq2+ zJtndaa&anr_{y=yVYAU8xKQE*REJnIU@KsB7%koC{y&BV2iSI)iv<$*-5$x7K}f*B zLhQkJ^x%Gq0MryK@TY+K&t}Ij3@{_*(A)pK`!nZ%Vy((rt zkkI-e9mqFBXBb`S&VOPY=swTe(3uL|1UVbEX||An6OG&e?GMuEL_%N)%hC_YVTqej zwPoVIVu3M{w{VTzcY#e3d?%@Oj>8kAaPcfHBn zYejjdSlwV}Sxx8?dMt>nrH^x9qr>ZFHF}2(0&2@a1KpSU#kbvL4z-LZi;i&vI+T>! zx|7thX~#W5gUUN8Dx4`39fc}LO?|yg#>9dvDu96ep`}4#l&Me1;_yn3a5W(-*p%t3 zYszj#PpSsKE4o^Jy4}nL_}Ju57)ks3J%?GIT_pn>$W!RGgP-cVMZxD~v$j+8Rtm{Y zomnF1bxtl>H>l10F(yq0r~iVQW`=J7_^r-Ez+-BPk(xTDmYc98OrWUAhZ#^H><=~D zXE8C5FY%*O?JXWt>c8>ez6cx|72+3zv0T4#>GGxTaw*V&r5&0@N3YFpiZ*f0SRxX) z9b%4(RNR3_a3s+IzDyhTu+p{C3aWPL2jn&E7w>NUp&yAwCKOK+4>*1AADQT9opXVo zW4*bbL4u6c!8k%XNu@iAiM@HY4;M#lqak0XoZxFHEpT&?Mb!OT7?$D?mO0S!JGQdV zO4!QDqP_*~18W1s-ICU^4ZQq~e)U)r{GWdgDsbd{ex|A9`1EKDn#%D`I1BSPfm+Y& z>ISwYN8H=wR}PCaDR;{P-Q zwwK`uU9}l81PzbtQ`19^&P9_@Z5^K3DceCYF$3N2wbsb8nw}n#=Y6+n5uAg>HuiaU ztCWfY@AR}Z$t0iQLsrn-Xhm5T+D2@V^4`rKw`UqRz&9H6k92x^CP!#7U2vHV$lC(O zmJM{H^~r2q478Qtd--m?RlU9ArQt7CGuUf5IMTngbyx_j7>LAJ6^U^M<&){CTz0qaR%C`16wv2 z@PJoHDk|Sa23>-sGOx~K&8M)A=;h}hH!|S?H}_cjAY40K{aUn^t7&PCGHfvp&C?uw zfH-HNixvz_4%|#dt%)6b_B~gLx6W&^35s--Uf=d%ZCm-$R^SF`*V(UR-1ecTG5u1P zp!S4)FBOjF9rg>Mbn14OeJ_nl+w`o$7m(kx7a(6=t`kh6U(iOHV9=vnzA6wPfhKDh z!3Ugh#RR|!=mtr~3(oQQeGROGv)G;X3vb|7A9)XEMIaLtS`qW06#?5?7WPpcY_!%~ zBt?lwX{gI6F9#U31nZ)cp>fW!gCsHv!=eWT4w+d&ZA8rYb_ZqYaLRvS#ldRwHs11q ztaXwp4!58UOTVZi*vXAHY}joc(GM4{=AYS?j=L*T!u^#E!{1}3G~3!MkP23()D-O9 z3B3s@deJ6t!6lW9)6Wj%)v%nyoY}y?zb4GEtv PYHFFC>c@`=7BydX@P(s|7uE> zKzMj%6*urdXb?R8;lg&wiwNKlj9vgxnj z#-g;4lQHd%d>W+rz|scF$hgA2++gw+reL6Jmf1wZ2nqUz*8gZ22hRwp!+Q;MTN12C zh8&-_w-3>=*NKO5rv?yLr)^4dFv1NHY{_|Sw+nq~yn;Z2yt1qaSAH#sJX%2>a(M|i zSZr)X(wd5mi)+}-sIAFH4FXt`$Fdw^B-_R`zUrjNB<8GOGlBN}Eb&9Uy!v9Dqe1XJr`kn8WLD}? z-yuZLBUzW$COK1L?HQeuC5#DDO2IT_`eA5V<~vwX^rq;>YtytQ*S4i59EfLoQ$(k) z<69mMMafzDkMB>gJZRvJ9t>_91Kd>F%zbZ&C*45k9vI5KTn6gPju+vI6Uv7B2_?56J#dT= z*o4Mowi5b{CE=Ch-`2O)r7~^AeY2sY{(Y7VH1+jXVIJWIwnge&*&Bj7|33)gb2L$| zZH(m00jP=0PjF1l!?=`S$oNdaR*lUxw?a_>u> znH3Sf%xBnwQyHbV*LWI8 zBk4)>`Ek}{!!!m7;&9NlYEJr340U`kn0dLRyuXb>aZI^?zQme~<~eg@-cbliq-QPB z(WPnz^|l@Agst45kj;2x;^REy9hI-1IPd~>K|Z>SZt)9 zWKeYjg0RA}q+d8}0of~OvC{!4?h&>)Hts>Hs2Hwy3=dB&^Fk^CS{fdY48qew#)?_Zx+3lz3`Gz!~;iRHz-iK6a2)Gnm;?Q!9?6v-S%l(OpdG*CWSrWbLvs($l0JUR;+3mVXU66ZYH$=F`{MLq*(BfIXCl3RlUEq*2t?n{ zT!$Rx&mp!J?(={nju{a=dP9qLLau7B5r7kTj|ufNM;AAZ4%=>qZ;};e&Z@bi zHF21{2`C?DJBzBRtLA`y&zS~*DE`A&?3;1-;qJtmV5(G^SHRhC$_Ehh@I57%KvT%L zQDe=;wTJMD5;xyV!)cgiiT5j;2F;&F4K}7&Gedk3El>Q(NoYsW&k}HD-S&-<#XNOL zGgsKVkm*!&qIV+Tc||yqOsH&O*sn)V&W)=ZMQ$x>{AMf(eSxHqV?opLxEPS`;9UYe2k zKF#(oXV4fRfAA;Sz>MUACyB6jxaoCZ1dQL`L?8C)4nKJt=igjJNdl$Al7 z5EZf#!r1aRQyP2iT&f055h_0B170OUsMcxQyvAU+(9gY};^zRG^YDzr*KrQPIpT5Cp1F52vsX<1TB4STKUJM=oD^?Ulezej(bq%nk#cjX$?&l}M z0D*)^&HALh)8}Ip4={S7=G+khm-nzKHLqo-|71Q?^4j|J`0paErR#tQ(jB($oqa@k z<$P|UDrFVrW_hQ&(5hCcC5<_3Raeen1*&8I7F?ZQ@nrF6x5I5!Ks$gIs`9n5{d;D? zZib(yws}KkG=1+YZt{5U8E3A~F@^CX#LN3&Y<|5u3S!tl5RrVw;{)C0z7Z}wBmm#0 zG2hB&thO6-j1?Y)vjl*0(9BGCl#J{b#2n-$s>)(vpFOp!JT13GmPMGB2C(dU3NiC0 z???^(tbhOpIavpZflmrhv~6&Cz*nCARJdiw}3!JfOLWKfg>p?ZsN;u@0})f2BJI()(77}mZT|F734Nj zJzktUf+k8Xxm7e!kvPHy=tx#ORVfBgGgui0W2=&_o>kK1%4IV=e$VHoJ+j0dzU%=W zAY%+?k_{~)x0>MK0M_`qDAk9PiFdr^R!<%e?Z5;Ibp2eQ_Ne`=krA=%W3s0)H*qUu z=lmGnqCJD##lloXmWqc&8>@bgckrDUIif*-`7P?0mnE7J0G~d=$giC1gVMbS|IQDE ztl;%>Nh=f!Zp#IUE8&T$RK|HvbxXGm(({6tIz@ZjOA7}2lRc}Nnd>wIi>pT1TEu*Z z73D8$(`<-Dz`<4OIy?j7q30!c;*#e)@tS&Tk!7kQy61FirYNobWrvxuYrmP5=_Y)1+g;Z6r`9Fqzuis)|9B& zspB$1e@ds8)vosn4I0xFKaF?t#jDrwZ(<$;hL6Nu7C>>8@kWez3^KXQS9(Ii&U}n& z;zvq4=Vnqu@8vy+a8(%b6CEFnouC(VL{EOQ+7wy##a=>_b5X{-tT6MATaFm<3+%(P zs?jPTh_|LCXYpVhZ75Rmt7^qa_0)ugS=89XQ9fC`~* z;H{&3m5&XyJCx}hknHBJeD;y_ z^1}xygAJD)o0aDiGf*7E-p|{6sv(j8F5inUyP4(z)%^4N2DmOn$rjj67PL5a^Q+3` zbK$#BZdy9{%zd{j9}hST4HDa&&Yo9O?Q(y86lz_|s@(p4@%KortKXhJ9fR){H{r+u z#Qq+B?<9cic~B84IU4 z<=ZHx_JO^VOcNN_7A)-ce$AW*qM&3Xj;=B^E`tDL-M;7aT342)oYzR8@l_4`OXC^& zAHqH&zsAZX)4ghnezcE!kyCkG(8cf)L7WrKDe~_0N2Uh*M;_NTK7&ToK&U9sQ~_ud zgwxV}-ojcjkZ^FcboF07yfsY^$2;hYFL@f*evK5{OU`8AU1WRo-WR1zKVVLz_nr*A zU)`FEu%z1prj~=X^N9YC3>!oDl`S|BL?R`u627!Jzf-Q)T~kHHYeNv;0r2jQRQmL2 zohG0_OonzMZc?v5XqE7VCHcwxP=LC;h!d7{xrnuWAXgqX3NQn)yJw`uvu65T6;DtW zVilfud4V#Y4-`+jkF8HGp7N`$@0hwCqybM$nSPNJyA~gdK!x+UJ2;v@X*1X|z3u3D zY`Y^gJ{Ti@2q#al&iknC+imwMrqfsLA+>{z{Mz~%NWZK7Tn&>t>ea5fYald5=g-#K zc>r$G^8~wZ7j|?OmiC?;Z?t<=)AV~lIzL-H0fMNRnd-tV!BMeaL>m;dG981gfjT62 z;$q)1zt2_GRSS!f+2?w-mIK=vZ*NH(=^?pp@B}pwO(rh>=C>M#DYn98*quDa1Ex?4l&yCj8qn+VO$FT*2RdXw}99kSZzGu+@44EorbUGfx3cT_%=*QQZ% zl`ZuFnPtxm^c5+nQ8y_Qy|-BIu^WBi7Wz?JpH-*0P829-97<{ZXnR0octdp7dz)R> ze0^`W9x_Pp4pB%uu6_NTknyp(RN>ZV?q}axy?wOl)}EKUq}s*D)YByguZ|tqe?UzC zIkT9@Hh~DO)h$-a>_Lg7NhzO8);xf@9PY>RL?)bQyy zexkboYO_-B{NNT?Nn4?;9G;M#MtU^a>Q<-gq}2WqW1WtiBMh2VkjyfR*68+=>dP zy!H>!hcv4D9=@x)|1V!)F3H95?j(;2y4Yl;X3hb`5oe_-yRQv31YpVMn2W|>m*{yA zxbT*+XS^d7_hzvln#Y$;ywnrE_Hr}KVcWcp#z*GP6~lbDq+E;56Sg-%QeD3oTTLTRN*#{ae7SsLt4q~+f z%F6AZ<^sZf(~x_Nc00ZRvC7^JS50+I;tmLdP(4mJ0pFzB822v&FyAavApPTj!VXgy zA~0Dol~BU>+Ih=fN@|G~2z`BgmgfRa`~lrt-(FXZ@ra0vS#Z|oDRI4S&R3uj4diW@ zEy}W`!6fAykW5Fg2kGd_Ue)vAVb6E9ng z4!yYna}08w{VKMpHo>f(=uk)`sBD;M8zl9WHzmY&4*V4vuDQwX_z(gO)}5p(ePRrK zI8LUSHf-bVcJDe=ZQ3EbXP30zk@UU97jS6wI_;RW-t%jdg%iPJ!>%913SFrl(vl)5 zg(d%lo?AfC5tl7D#yzaynGv|=;t3+Q^G~~caLMy%Y%bh|76r&1%dpO94^HP+l-$j5 z!wHu-2x$>k%O0TUJ$7d0r>riGbUb~9+K%LdyfH*UEFw$VO%2xV=N`P_CmLX5P~`X& z$j`;-hj#Rd^K)Mf4-6b3?|<_s^rj1M|J{9AcPK-ID@LXUCL_+v*9aKaJ%4#15eV*L zJ8$}t?stZpilqAb8t)>4$=q$SVpR&TRAm#TkVVavhLqL!;SRzQEdU?Suk)~a?Cf`D zPrDHe%q?ym#KUvD^MY1Wk2{0TKcQvx^s%!rW61X_uiKu!R*@wrqbNio^Jl_%za zeA&!9-11M3e&vWvG%JZ6KkyMin*(t;XR79ehjjABMN{XvQecnddueGQk7UBbc1d5^ z58dbD(AqroCa5wqfZkFABQnh)h9{iKWVR16D7AqJg_(9w4&6?d0r7C7h$c6Wch4n~ zy!}mmNGsm~a^sjkJV(oOwVLT|?cu-o%>(8!Nu>PT-JfHtPkdblRJh?b*)CJvaPxpJ zCLi`NJ72b010eRM%Rx|}$fbY0fvewg4)pS{^46~CN`9G^rao~o$X``(Sr0x04Rh{thuv5gE#%6_sr^se)|PmI*E={nNrsRu`41ED=FbLh-%)%{NZWi5Rq z_;wfgyaA&l^g1~Bx9i!hTQq>`&GiWj5>{+5(+f}E%VpYCGxb$;sY;$8mOWr_^ z`J4{J8gs_vmAlL+tU$e+(-B=znp?rgM~ z+iA}#>zfLA&Va&>@&U%5RQS;WkK z#=~X1NtN;-kGkE()$kM0;fN%_olOuY2C&IN{hP#}Q@wYS;pFB2)=|?Fz5X7qyfX5A z=C0QYI!s;r)V;(?iCOD^V`M@uSn=X}nQ_mGMTFICv4j>+toNN5cH0=x1q1$jkTQtA z00~FK@8&kc<-+!<00y9)JZJgEdM3}`h}ptZuw(a>P#iq!`&IIU6g|eHE_6RH zFy2)8UPIqph;otZ)!HA^1~PB%t6hjM+&l1=@wJdu4G-j`7Jt7CwhLrQL@#3EcVh87 z{+22Gik>ognr+(XTFjsvt?7qh)WgmUu6P|x(3#)Oy<(0uD>2vk&IIioiFERT%$mkT zJO8e1FRPFRx1XZjNXc!|Z)`TQnn!*F6+7WNI#Y+J?i3}vS5vAv9nc6S%WL`!N|nYJ zk~Tkj?WB;~^p$tMcB!0Zc$a45RDpw-*W==X)n!t!FPbmpD8ltzXS)n5sjXN2?8-73 z=C>ayL)#3u z7wPl1C*7K(5|VKfKz<2@PE5S}xM==qKGsA~>gYrn(=SI#^4ppru6{*~vS~;6Ls}IR zzwy4E-m`bEWm@LtuGc)ey5~co*vd0*E)LF0m7POO?;IDAk4RE!nZt@eYTb3X&&nER zfQI3^PFCOItFB)+-vt7{2F0VCGH3VOTmc!~Ip;WK&6%*Xo2GM`>$ zZj%x^vrtigc%P%|poPU(-o@zXS?Y!STK?Y{H32}yjN_3~@<24)@5=!n+yDF}_F}ZxLFOc17@cPY0Fdx44fWnwQ%agCk0|+IEL#NyJ z+LHnb{pWzt4aua!KJk%0tLIHk{X^vBxAn11=W)s$z2|*i4--}c?;a$PCoV6Hl?tjs zTHF(M&^1(VZ{|Pg7UZZ*EmIJbPQK@NeJ@o1CzzSfrrUKo-*#;E+FTLvYz)ZgQS`p- z@hGsex5!L-wRr!y1@LwX5*TT1xd~0#4Rgr%`D|Qzwf^Yd?k*3=$>VUVP0T6=7nQLB zma|5~5BxDl1e%=MS+T;0%O1+;nS_pS^*cpVuSncGe*ds)&DuDApxU6+_T1{S`0LxL z?w+Sxzny9NuoCcYuov*s6&0vi&P9tm-AU`sg*=OfSnoTOMoO5fvezI+A<^%hxR0Bz zdj9>GwM*ZY^zB`q0L1$Ur4E#%iEk;<>EM+U*;b1LMCT&h$bbE2ZYKs0|95-qiaF3HA# z8jH?gzLF7DwW|1rfI!yWvGrCxw~6%Er%HtcJVcKe&LwCRlDt(|J;pb5YU-*g`f{ER zZXoAgX%?QpDqP`a+|>q51C%gV1e9&|VtR;Yg%{TwuWL&$b$!vc-|Tr{%3TMsgrF~R z7smq@%4bYW$WAwvsZF=GroM}*wLeXC==3Rse>~QKnHY@Exn%bK|5BNj1UUkxCkL(r)!yZ@js(V)3z} zzJVlCT*58ZfR<@vv!*(w!9~2Zwz|6gsx_r|M<9R25X)%gD7W{?5j9Gm*={$lD6h7O z7S@DIjoQoNg}g6}YyjAUBwD_Ko{8m$w9csvkxP8^gst8|H6w4_Y6#QPEpinOhU0Q{ z`YjS`Ov@wA`Z|kYLmll3&I%YiF3f_RJ-s)y=f3m0z=6w0nkRBHgwHuR}0MdC_7V6_1V=M<W5X(zSx#F;-Jpq5`biZ}c(@9v+A+b8!daq#(IpQ&e! zd&2a{qgPkp<+Y%|cZ}+%a&~;yQiEfP4W3<&PdW`>9_Ney1SfGRb$*fRXvDsAc7tzN z-9EkdDJJk9TcVSZEG6lznzX z1#i+_Rvq>E&;JTV7yW9?vpo|i#-K#LH5$t-o#-oqJ|#_k`g^L@3s-nctxR+JwQ=Aw zt?_r^@l%>Mfny!#GI}+qzSoU*-3hNTX?x-J;G60JACXl5u#Y}dW3@qHT9d8(K>?GM zHq%=IC)>r1559dCeXfFT-r>$}Ik*MeDE}}s&5BJ4b~h{Bw#&KqSN<FxwE^-g*A9>e9k1y5Si zu~QfmNmmbB9`&l$13v<6jsHzGmt|hrSYHvdG`4V7QV!S%4`f!6{CU|6{~s;;=TZM( zZT7#T;wS%qkDUMA;s2!_#<-q72!EJOIuPD0o*o)74*Ygw^j&zM;t@R{^CE#YNgpj2 zpYEqrludTXwO43O51g6zd#I`qao??THZMhZ$@i;}UYbwa-${#7;Nd20a);6uT4{n` z^2(;1C&wve@rArVmYkXpxCTky6j?ik|6I{<6wrUGaJ;v9aDlj5G*zB(Ew)aPO1tR9 z8qX47b!&-EHv~<=Ptz^ohxrLNODDfo@=#;?YNbW&`A$Uoza;9(BTX{;<`#n}hb&M}?wYxld>saI9!A@IS2S`M+ufhYr-+`lycir)x8XwaAc(wN&fBbYqh4{|N zODy?A(x8Q!uur8%q)KKil#eJ8&~dQ zM{Dpy5@4eS__wO_GdK=1FS{zddJI!AKBPG<_ z+CVD<>5yj)`(Aah)&O}g%`89;QxyQTEpg)ovd`?*fD!aIU>4PT%xAJM%Y=?~#sLV& zN4;ik1Gats)?CEf?$yC_%mAp{KICq7o*@e2^f|hXSikHZ1T=^Y9BBk#_35GtCBv;d zV16nrgj;OxmMGs{Sb@WgKIoCHHU_{31vFxw>cY)`ReqV7pixa+ewlDclL-J2{$URp zj<%Zjo=hd29<+%BYky2HPE}#UiuHTv0r}r4+kw#anM1iO2_FIwwVOR%qcivXde%xO z3*b>qVh07{HE>=D;3kFZ?K+Fbn=femOF?@IpMEp0?@DqaM*H(=~}G|K9dB zKGu^TV?ikVd_|Hin!{N1zOx zx-~WTp*6%zxPchf18oxoD;~IcSTTe!h!VD0{_Z-YN=_mEYCU1XkW(wqkI~Z3in|f| zY4eGWzo8=}zks0zD@sXwOiFO|iLE>I@Ya=kBac5gkbb`s&_b|q62T2Zwym5b$W`~E z5%bIx($7|#6q}my`u}GJ-~X*qTR$QKCO2Xj|8SUN%!%_bVUD==i+|t9Ep(M1fQuON ztMP-v$VC4>jIxM6|7;cpADk^YE#p%heBl8lZT$Xq9*qZyLc(8ZX-P7b&2DUdym)o6 zpv68A4oXaQR*G#_@UW^l^#l=O>KZL>y5x ztW%RpUUA`*2YL!X&`r}2c(pdnAx26mw))SNT-p0EG2R~^cR3AC&;YxK1E*ZJDdgGI za!`Y&3Wi~R-*F2u(Ho{~?jt?|(NURxla?zsoDA_na6l2wn0HCTwS}v-CwGC=3s0!B6?7$E68~JE|^YQt9C4z)F%x82>lRha^LFh1;m3U z`wLP8!q8U58_!61!GeO|>|JOX1ioJ-9{Z{(0F!zs^y9w=MS1xXiV9X|LfF;_-oA78 zgAYXlMRDi?b^=#UB5e#Ief;_uMv>*^oP{4+Yq2~qz-f%ZMf?%SvAo;4clUb@7Y4>UCtNUA_U(~_m4Iek&24Czj6_E zW;%j2u_)sB^HW3R?I}L$C$PZLw5GJ)iWfeP+(hO~z&YC+B7X2HWps+e(VY6w{Q`iz zrQ^(*EXJije1OUZ4q1U!SsO#lcUhn#XsiE?ebpaLpq8|5FOr(kqPo{R1}D<_TI)t#7_F0BRi^!udP2m$$Qe$LjS>mcFG*kq9(5~YY)hy zvsU>Zy;Gd`0pT_l%k!&9j?llyi6cj&h#LS>CLCFb4+l=%{b%X;lT^+YF}$HHn@rjv zY>mbJ*nPmP&D{DgS77%3$5WpVfqeB(`m-i2zj!+*#xEa|(k*_&R5#HK(W)gU6b69) z!}{+K$cOKb9)dMQA3UCg_s?hGkhkEWaov~SPtGN3&a$}3f9M^#Bnn=d{h|!JX7oM4 zgYhv^M{`!Eioxz|f5diFijqj|2r4jNgLVgom1pZZAa8FqGFlKqd3B1^UNxkIKnl)*X66l8>u&F&+$uD)F= zSz0?SzKspkJXC)ELx z@}U~TmN?;3y~O3Ru#tQTJGuuhVy*#{WqR&)xEB<8=YK;25DHPQ!ll}6hR;t7_7}v) z4xE}p+{c!w1dn0Y@xoTLWEl%I`Kk7hqmZFzHZp&^s&YV6KeDietm_m@?4UWGZ_Z8F zHnV6*1G}7t^FFHm4Skl5$M<+>lIHJc$y~AwVs3I9WGZLsa9Cyi8>IZO5{-h$U{&gD zdjVC%-I_%@^7&ZU|ID5`#fSAy`e_S@s%%k1OM?6&(0h&^BQ-3>#H22{ex)AF(K!W^ zG@?1_91UPu*jP7_;LUOay^nMXrij&C5+l>A=7k>wwcY*>bEve%*y<;<#y-JpV4$Mooq}|7PJq_!lh~*dnCApvJxK3N z3B9SPipBs{VKXbe@ux%H<<|;^H}ZsG>ds)D;*_0f$i|RXM=skR6c(*(qWgLOJU~0$ zA+!I1W5Pl%ZZnOK0yJqwlt0ENsil@#_F1hft#Es;*Y3Nn&Yix;2 z+`ab~k^Qq<&Gui2$6*>T{3#$LX|Cb!9qc7?51J!U(BUV&2@S`;f1%^I*iG~J42PIz zxOp2FG2UQ%2N@OL0@^L5SVLwsmdSDj^TW_2E%~smugRPpwTJw28V{%6c=!%w`jC&} zj*#bZi?7y*1nr^m33H>&P#2-8|H({Bxy9yi2Sl0#Q;$d6sUX=a;b{)A$qV?aUr%09$y3yByF6ZiEMxwr-yb58sf+?t9bJU(V9>kI^Z0bejlOXC%XYo4qC|Xas`@rwA7nj zQqysF*c&6r6In95Z(oC(@5D|~z~)7~v0}Dc0DpXHM&T3IBZu3AnUC98dy$aO{$Ylu z?}Y(t%yWa!!`M@#CHW8d=#I4VhsR1CE#qQTSLbiT56*`zCh|8*>**TXeM%Ba#_ulD zZdpFV92jgq5X3)&tYq`6nU~>@D3r;rw=!U#JaG&|2h6V`ahSj=EDSTxsIlQVsASdd zmRa5WH(<}R^!sS*X~$<-b!RYOc?2_-V5S4sjNIR#bQIb_@a`p+{Y2h#6B^#zJXWIG!5h>kipiuT zpd= z9Kcwj|1LV}cDtC)eg=BC=0B*(a!kzAcKza-z_yvjCSB%>Kz}JpW?}4IAyAzts|T_H zrg0B$Ox^0I`+RB&7vu2qY!=in3GWjZfK`}VNLzHf;TA44;c@>tW1SxS8#)1gxo4mQ zju|?D52HMqMRVyD^Nc|6Ni0_Z(TioslzMBT{}Bpe<@T<^CRxXdxUH_u5WI8lMyy+f zaVV&LbV!3Y-?0b&V3Js@AuAfDj}3dmGZ)%P)qfQW@6z+l&VK}Qn4*U_h@q$S(xaBp zv>e!Or#dem8JU6^k|t0w%|lF)gTRw!=H9tru@oX2ZQY({y_tSuIK(C68I_^M0Y-f9b{c4=V0jr()LGWi2)04VbG9MIS5-_&sTf0U((h{D#v z-{AY9S!z14guN#W+3}K;;LlDk(>Ymw=2D19^y@Gr^?oHW{c)M+mpI2=A>4lF5UqQZ zGC7@F@}Q=~&42rG2s(Q%&v*}~8qVNX_w51(r;t4`gpO^>b0$iTKpPXi-y#y4{wcED zvIck_CZ|3HL&OM2LL{FRBB_dsZSX%cI7YB|Q#jFo#NP(CMNu*5S)K#F9&3eT8 z?m|*-z&L}QNDVtV>jbl~bEzaM6`2-dY#xukjmg*Pag@*TFa&LZt?4hcC%^-4?pKiiWy2?eJnB{4uFh8$;-2$Y4Q52sb0oZ4M#1Z_|Le zEkomDS?P?yk9hU0u2&^Fe2Zrk!b3(?#I@KwJn)*15P|%9uw? z^=NY8d8Ao`Wo?ULa?keDX&rP~OltvltDGpK(aD2lXGupzod#H63u2O;uFJ%Br3;LiJ_lH4| zd%7mGS+B8a30u?Ss)ShoYKfbuuP98|k`5`pP`>5^w~UTiucri~T?BbX`JR|CuhMrR zAw3_NB6E8X9n6_Zzdrdx(0iP`tFUOujqw+9 z;}svCIB|>%V96aIKTF*Fie=;QP_}>2M4r`tGpbr&rCjW2Ez9TpU&KFCVCM zyE8Z~w$x0_6@!rOx=$Mv6S5c%=f<$-C9=aFh4$D37v~z*Lp$y=F))Aw03ZyK1&5j@?e+yhE%*q)Co{6#CX5!A7tMH3SZ3=$Nj7 z@-TWa6kWv4Ze7n_VL5DdAGqc3Mo4}w42lz7gEb~fJCx1?;iwbKU>!mI)7*bgoLK-m zGdEho{7<~edbD5pN9X*FzLK9f#uU3r_h#NlM~oXQ#E~zJA3Svsi_2D=xS^bxxs4Z2 zkf?w$9~1yCr-~9$_z0Eq83P}FIl*|#S9}TQS6gqiNEst|W1`omnekSSW9@dXv*&3h zp)B-a&(F||h(~_F<+>R+CiRWQ<5LQofc$xEX_j?gF5QED69>_}VZvcia4J0Aa{sd3 zi~s6j1@tjf?{nv?t13TfdOM#um~(O^h&C|Mt)i#|_x*f&rE?O!1^g!CJKg(|&QoT6AxdA0?z zE2RQDrSNu>kp|gkw2VVt?P%)WJE-~(DEkgRy}m;4yj4Q$w^{YlzqmD+14jHq(XiRw zBY^fA0se~|Kd@JnkE2=gMCb=J)&@0eWn%$qxd#hCKFU&^nu3n4p1BU@i;)}AqSSR} zV6qF94_n@i?Y%_DY!evuhDqSeWrG!3DfuNg;WW{{SfkC1Pn21w__m|*bfLOyo}upyP~BAje{Xx8x3IzTwekwRchq^GeCCt0kxv>PzRMx*5F7wR8ey-^MEnGwNT-ez&wlq`*Tunbruvo(KFmd!}`yABN-i^$w&c1xkN^N#^b zn0$)Xh?QvRC9iM=K~6F=dp^BmXkwx`td~=bN7u4X9$)R2dEu*}`=kQ9MxGy8bzgaE zOIm)*8tsfFDZ9Ru<0T3rU34N5Dzo9y=ti>7?06V82%`VwGw>*eUj-k1mwQeB0ir#k zk93Hnq?qS(zqh|JsjfS-77_=MXg-mM5ahw)gC=oV=}^#uI&^hqr_l=Rs^I~g(c~k4 zh1L*=z$wIASmq7b_osAHrm-JhLrHZQuYi1eaw{5L4mti+2XMDJb$-od$)iuU_dWqp z$Qwl=sT=O}RlxQ)C^p8TmcM9=b(L%`zLhTtXfeo;|Jv}x9`@ob4Y$;m0M9xqQ9U9- zX1DSja1l=?fkAfQ9SLR1&{S|+UW?J;s(GJX)}i$9Ze3(00dh2=f~QZM&-1Q_U#lvc zteREWsHM=2_|2#dyHx#!g&O7EXIOqkhU7j%1VE}8}|v$Z^K@$${>!+?1C zjQikdfeyWs4YJB_L(5!t{Qd;oeR4ALZP>lCK0_Ni#z+4#Hp}8tt+Igl#S5#v3&}E)5tkI{B@O7`3X=IbVXaN=)H(1NxsUuw&h5z4Yt^XF!Bg0Z|Q zP&|quR?58MC<*=xSl2ub%DeH=Xv||WKsYnmMzOlQ!YV2$O)UO-OQY@zzl<(CA%oxn ztFw_&MShJYJWoK^q+j&dO9aZYT~65+4{JQW=sDML^BiCCwGcU9cTHT?Bu`3<`6D zRxV1q%!O-Ei-!3M&vAfDHAhqVgMDT?D9}2MnT58wiud8WaN!tTS>DRM(xvG=&VXVq z9zK@WFx&+H5FIfH1sv?Wp)6n{KIUsR;?#|-5?$z0blLp~D(0_1<5-jg#0rvjJ3vT= zSl8Hsq?IU$To2>;Hgn`8n@b$q`ivIwgVQT6%xgn~pj)XDhw%+~=2D3Pu&lSQ7tJGH z9v=5#LD|S<3-2D1%wB$JQe3wJw-a_Ea;kr0dbM{=#PM4zeSxjIq5gXrnB8D@0(N@v zj~mDvpk2oZmR!Zoxv-bpJI=n-%VZ92&xL)-i^rfVxgZKd(?RAQ1A(7?g#a~|u z{m0DkaEBODsMI&q7?%E{aIKINUO?a>a~R%i=ESA6foy0$3|IP|3ldISX9(svcNG3+%cdfm1L;ZTN)QpK9kS+3`+*+rcx}nhe z33AfMQz%D9wo))5NUZ6Sxc+mOBS&iN<-kickAwHv;{WZMLHK_dc^trcb&bNc0Pwvo zf&YI2fxaAW@@bJn(FYMRt zRK(XYA8Y6=ytWRhlcjO)b3j~0qTbn6i=YG{^JAMweEOyFZ)54Cjd)zL(>6!-yHf~h z{OMM<=L|RX68xb_8C@8^9(KA$F&~?c!#=TAl)oz$qsb2 z)iM+6sd$m7W-$myqL=goZDUGbl(& z7zSW-hYW&Zse&#+i|bOmLVU-NcXdnx5{JGjJ?_(6*_P=~U6(u!Ag>qIZtKGS;)QS-If1VoackyWd#7)*g1ituA`f}iY)4fh}!`v)KT-CZ? z%MpIN+od-0`2DjV=W6;aN7s%p%ks`mZZZmx5HGy@xUkD^Ew8)hDSBa}czuLt3(mJS zM>31JxX9}FA*qFHaW{yYCzBA`|XlqvLy({{?u$qW&-1%0nm0n^~Ie|ivso9F2CRGP& zWCNh(9Y=$-lr4eQCBL;O){vij{qg(j6v)!nk31g0d#!*_-|1VD4D+^L==Ybz%@^{p znz%Iwd>(v37ihXvq*dl8=;oyilXBD%=t$r?;{p4Nyo&+cpI>$BF9OarX>r2>;)vf z$t$y$c7E&iD$w|J#knt}?Yl7dYPB4E*MYnpT(rC$yqc>~vi#)T$BSXm)@k^9Q$^uJ zt0JAl6`g0!X&qw?np1i;%WuQXSz80uva%isSjuOhTfT?mn_615zJBI|l(riQgRR4_ zV^s=Qc2V>Ai((fE1Mt1FX}eOYu)md-e-^?#jnf@jCE(@v>n%1*1s>wpJna+&fqHGb zYXOuQX!!}{263RFwl%1Jvg^MbB|ZAFSvee zKyajs{3BKPiX7D-9`$iYLVs$OT3!z%-8a!}Q}5L(un zO)#mS4>@pS{c8S^tCvA)F*rt(_d07W!han-kbiv}xO1gv6=<4qte`S1A<*fH zw#fQwVF*$i+DVP~dS6}FdPd(upQcOjMH)!MZm*KHnf0po&*Oyp*&xVI-#?j~%J`Qt z<(%WOU}@Y2UfakVp*56Sm6Ljt!^%2f+^m~nhei^unq))|d7i+W+l|{5mXPb$eT{-O0S3r`Fm{zpUb4gNu$Yhzk-e&FA!6TZO>4 zfHhhtr6aSyat;M4Y(oV2>#W`6R5usu70+X07sJT19@6i=Zm*IU5?RPxG59N!72r

q~p|7p2ryX7v(6Dks^vv16=D)gY1 zuIj#Du#VC%=L%KX1y?Ph^f_Yh1Ii=~x-$k3iv<7#BNzLW#@Kk1tdKdPk3+irM*SG* zMvJY>-nL#YXm2W?_t2uTZG6W9r8$Kqh`G;l#pH8BiXZ5gJPo!sUBig2Ed_vSL&l=L zsl4w6@!TYLK@k=~M3(dCnveB;DT?6wwJPgZ>wB|d{#6S_LlC&J)!-r03)2|Np2XBM z@5>A6?-wtunPA?i$n_(G#oIqr6Wigw} z)Hg<8iYl|ooiC(p+)K_qPRrfi|1lw8&&!A?hhGz834^hV-rY(mvb#KaN9NFxW!d4& zetF+z2Xw}%_D@Za^IkEuLz6VaN-6w51v#xwq-jpU-lx6Q@|G1zg zNp;T(ayuG{*r(+k(xEFQiZbqhIn9`eOoX+Y*f{p9rVZ3J`EqxjTA5m7c+EevU;$+; zBzI44nnk0m<~DR#LuBSy?)s2iu57r*D)^IT_##n6N4BqyeqA5I7Vso%&X${M7RNL0 zGhp98PFyMlY1Vgq&8u5jStbY^bw=UhK4FdKy!O(yrbiW4P0oAT&xutDM5lBdEPkkZ z3&PH$>%XxjY<5)rU}Y!u-hmgpzAr^t=P4Q>>*{Pc$fNvuT-3 z@898N1ARTU6PAzumXSGB%&fucuxq`4&&R&vtgJfkI9r8%33Dkqb+F+Zub=+7S6GU> zp1R|uoZ{a+WbKu;43q)jS<*S8uYR)qH3y3?qBB*OdvX)rq8;auesQaxYMXh#WMGvc72YI!9vyN_zZVJ8bvluv@xH)tV{|%jNrx z%6Zv99)objrj=j2>oWWbtfkufSc|_n+>5*Ns?q!CB20V9;?a?YFSUH}`q&aMd65-4 zer|YImw9ZfcgXiq;3@3d16*E3Q37GVT&|~l_|_5MRnsbElr|8tBVq22=&((-hQT9U zlS0Y$mFJzh=5xlse6quy`CB=2SaGA)CAy5Ub9wh1vO|7``O>72x3?kodgon{+_;AV z-rHdk!^*y>>N@aTWlm_fOabedj1+fEyQL~ayF?y^*I5K) zi+6TBkm#g-K|@+SPs825Q%vSd=Dz;;^3Fk^RGtYw&e`!ECx!0ykDTf-83W&zDwV)H zSy>ZX_P7?$!L2MC#0U1bx(bZ1k66Lo>;IZ*gK%j?FDp%VOwP#v#u3hT$s)*;ZL7b$ zPY8EBU$>R*InZ+1d`aVajfvjOnP<-$v9tj{jsNToKH9=WROo?+I-)~)eF!onQF=Ykx>#%<3`qz_+XKdcpQzIl~a8w2akbuCa9XT)<)*b_dj*u`Aw?8KR~i6qP|8Zg*zk6Jh~c!8K~u zHXk<&SZ;A~tBP<5PZD#K(&3`kQ-i*0Kg)jiP+l+F@viNUy0?p(G}F>_6zb$$cC(*) z=UXuP*{jgY3B$ zw|;hEG2SERW;<%T1oMV~7s}2Iy^UYfGxEE>o{!N;V7A9<#+T|B>u}}u`U}695M_|T z{M*Wljihu+te9%JH+u|*=zo>15UGjqHOL>YbLlkdtf%NTQYUk6^|~@i&DV3tKC?us zPf@k8@D^3ckL_~F!j~-j?}wRuv}KL%4bJ=ui3`FsAGq@$gwSur0lBQP#(;^iXoWql zB3sMPr8ET0ebLnxwkC2QO2<}FfHTSh19!%-;vq3{U^$(Lv@I|NDl z?3Z#=o`c@3DQrw{;}YvHJ(_y1oYaPDxeZ2zo(|vdsOzx%xI8Vj)fwo^e zvV2jW2dak`^Jf<;uo~MPr8d_tP34=P?&$npD%K|Q~steX8H@om)M%M zq~*FS7rfg)dYVbse~aw=*Xw^3}dOV zX+msCCPlOmQ0tNO;ia<0jk}&zWViXpAX|ak4A7frn?nGa+a6 zg9%69Xn`KjWPO#Mk~39jXit4Z*u<~GEkhe*gBdw1dHML7V-tEa(|HS4I)|;PY)Tt% zW-oJQ=K18y{qCw`_KRw#y9`y*AMCzS`5fN4Op3Pc4a^vY07T2m@ux4_?~R_YxyCK- zB4KoVYok)DcJ!|_H?IpnnkPy=40H9YZT_fO>E`9hKL4F^pGNM+F6pb{7f#5|-y`*6 zGWgoUc-~M)?v~t%s&SJXv@&uI)&k$=QB3im_!7BEXYjJrBnOD-z4NNKh`Qhhx57qf zy8GxeF_Hu-n_4v8891(s4{xs|VOLKxF&>~&E)onQFndr$Wr1u?m+Rl}NWwbagOKN| zkZ_>!9lM_MkJmjuvZDeGPJP)AdD|h271cY{Y2>GYjMI>*=Ti1O?N%`jhe9=3Yw@_g z+)3G-k+xu7bGM-yqaQEl^D1k6EYx(*Gm8ItFV?wjqmRQ%fAL|N%BCOq*YJ!F`e9cM zm}Gs#FThjy3KavRPieWA=ri#UU+=}rw4KweB$3R+kniK`ktTPR=C@w#?sB~;MkUQ7 z{q6AAQd;ZqelZn7@vSZgcx4gNZiZl6vjjN*O5YXo_-rylkeZ+UY3bz zz*kx8yLmF9B`ck!O}-D{U!mfbL+vSybFGh}5cBEsd&t<~-+jTZ|K=nu&x)%d=r z9_FYx)AESjUx38Ssj_UQ*A_0f9vw%kj_z@*67Lwi1{r`8Xn-?mrdOe zw}6~6JTcbS5^Rw(@@2><++_u?^Y@oF^NMZ&MNq>rDnz?iHuVEV*nSrwVG^clz{J=F z@yn!LIm69fkbJ8I0#V8HE&z6aapA}5&aH;8S1zF(j(eGx#5h-UnaTI_aWAnYZ&y06 zG7-i0z;BVUv+UQuBfK|vz;Twe9a5H*X4i`7<=ikT2~#=7R5S52z|(Em!^4g0Gs*Fz zEZiH#yMyn5OP^+0z4(>!xR(_5?>U2faWC7G7*o#b+JAX~|5@Vqe4YRq#_px#{`3gO zZbTnGB2G?e;X9pHl!VKLKVF%fJvq^mI|iP3xg)`)2N;H9r<=81hLc6DZ3$VT8}TWL z8JICFI>RJee7cHcvW(IZ_fq~_%%5C-7yTB`{C^6~{}Hxcp{2&ip9WnNszU7={rEQ4 z9ynDA7qInSX20ln^q4N_*dM6sM6Zpjz_k6l_JfkivU5zLuLj+43zmi^a=aI);j%2s z!1d&H<=h)^>x5?bcfOC?RN`KMwooxNQ&mYbmLXlJ>JIqjeJQ7!HKZ_53Jm&frSUJ@cOi2og#0K+ zf=7e{E7&YM$7D0(Yj;GM)!MrnhBDqi7SGRs6}nT+z)FR)JloX{2nI!Qeoe9ooJZcF zQ}vb>mY_gauk_e)+3NI72f;$n6Ae@XuVlt^uSc3v(4No8ElDx$fo4;| zg)DgCra9o54QfngQ|Z8v)@ z@K;5*J~hJsAU^&D6*xqPZDGO4Wh!Oe+~Ca%qq((_*km5U+JqsGf#tMlpNo(>zbo@$ z9|TauSY1Fam6X4yPy*irI8q5!N|2^%WiW0>WPEUQ#&EDdb3|YAr&0{~8E4h+)v1!J zGlw$~Ty%LkT4kYNq}E1-0$$4A^vHZngXZQ*J~HUSt5ekj)98VOl6YB93nV5T`|SYS zm;cSBAMVhlH;un_Wftc)wNR*i0A{H5bnydD zYDm-aFUv%rWpWaZRnTxW@)iQ-!Y~8e)n~DB?Q4j~dK^e;3pal+$2&WA+)mX_5eK-M^EI46VUQcH4}Q)_C_+lv|u$ zmZ5yt^s)jH0L*tC>P^o%6k#ERDbENfBP-ULN@%s1TLce(e_e3}Qc(=i;Gn59((l2! zAaqWOCbq7^+uBYT`(ZEOQg_X#Hpo{5oow}4f&d=8B>w}V1HgxSnYsZl0gd3IaW2Tc zC1)b@<{`QI8xYE5xdh*IfjUF$;bzC_2{mO7j%Uf$)(3>0t-F8{5z5?z3zPlLB(xF z6I;L!rRIhj3%F_1kpM~a45>RD)Qf0aV)Bw%Bqv*rd;cd2maxGXzxG`#>1_Z z$USokmLc@#KRep!m1PgjSRmr!!KV*sPMTAp806ii5+29_)Yx_e_Z*DEk@OCT@W^bC z+Ykv3+V3mco(+3>!YTaxi`La){Qc@9RnjDe{*?T4KiE+g^$%VI++o-ig!GzB;V!i* z+xdB|cI*W4fS%*=bQMMr=7}ekM?<5;Jz7?}Z4Az}LR$o`vGBsjKxy8t1q7Qc1^1xg zfdEkV10Lr_X%-qkbItGQHjfj(-s+Iu9u^UKPQYmve~yVk(0SamVVX^tbP=7pbWa?; z^RA-j#X)^zBxd4SX1wk6AiarR)Gd?g`U5{aHiI17bOyNtp(3F7zYdAcIDoKI|5$;7 zhC_-6#D~H^V?J3Ei_J{KjAS-E{1f)a_<5$KII4_^x_y3&|3Q4rR?Ezy)42Ky)hyhF zI8UkV@n*HvrV?w95vx-`W2Z1q@I#nlUVsMwBUa=etN{)FJs{4)Qu%;j%!dgT`eXvd z5MW$St1)Fp*gu{7Qw6Laa`T*-Wk$?- z^jljKN*eO~1Q9h%d4b6MVLv#5Dk=8L;1gc6YU0!1^tJ=>TrJm+s^3*TPXEo4o?2l0 z_M7I#MOqK(LGHS8#T5il>tY zKA-QjZJ!a!Jg`#+#VwFqW}|@L7q*$ki?*Dr9cl_hlx61+Y!!jjQFQ)VbpmLFF5--% z_O0J&H#sA=>vIr+1eP4wf!Jhvxjr+F)2kG4mi>jgo}N~SAM&0l9Gte%A?{?LJi%!J zDdOk>xKgiS3mCQyQ#haS(Hg8KqM=s6orgrOFxu}m|0(ZAJk(6@k$4c_Hwn2LB!b7} zoJMV41Yu;4DV_{`V$Pza;^xs%4}fbms!Ob9VEEZ&_&;YzEy2VDlEUu{DJ=}N@W5|W z4Y)`0KjD_?bglifc^V`wLow{rLGY@0PBTh3as!5q zosS}`%@*7&iI@U78Hphz1BAO?#$tI>U=RI@($ia|C;kR{_@F*SE}wB=|AW{$JvY)m zq>0FEfHYX(OoZNDMUNR|ggYjp&JB4(HU%OXmNs#<2Tj7`r-_JU2(em!jG%V>F?4|% z9|iV%47H02TScuIIQD@5_4Zn5;O ze?FpwtXo*}dxP4#jY>B4Kd9i7`^k_XwX1PWfl_RjG*Gs30~prXOkr?Lns8NN`|?x_ z?@V4c+a3Rd`1m*FH!i9=5Xo0I8sXCdy%hlw84VNcTKb<2&qrup=00)O*B5TEc#cA^`KHo>wh z=J#B3wYDbMQ+UJ?fRm=fe%6`y!#7GEbK6%NHc2-PRuSIK>5gibJ88`71)_c;M}jUe z-iVfhkeunr$A@N|Gnuj!Y*2U+ATl*2a>Q1QlbsfJW&-sf$^jht%5MR!WPBF_9lJ-y z0{MG%KJwoFgeV+dnyl~>DggNtanYuwv(=OjWHxp6wa2WDt_yc$M2zkc=7cCQ&$&*! zdF~#4<2lJr>-r3V+j?tECFY|XA+bqp?Q4Vij=9Cfdu_Kpw6Dx>5+$n8FQ`%IB%v4< zasiUuTB*=N+FXcerWFw`tC?!35J^RK8yYxwJtn3hvoUhh++w)YA?{gR4)70j z1}OiWi0gy)5|N>g8t1ZZR@bwAN_F6FrnMG@5)DxJzRnj3_Wav#-f(P8jOpsYTlF`! zSj@^Hu6r^7JmoBTJ}YDvygZFY`I=Y`VMECUR@M*oo!;mSx5~Htnx7Tl^$G;%mXvEK z4G4({Y@odLLQWXq|hZcqw(B>k5pQaQf zU~K(#%WSN-BusXB++QS~108lW=SWcroS!4l`9}XmdoBJ8DM_{G(nQsmuwt>yGc!Eq z4wH#<>Eeiq={mjZAPP#vuroOBB5$h1#lFImT3Jri;JG><`vT{a9jaeEFY9P${gIcy z5g!lv8xA9s<&Y+EaCCUAzG1KP@E0Oa6trboKbu>kahiBf*rFfE{tAa_BqRG~dPc@% zN@{4-3bOS8sN%VKB+E^-dvmG2eta=ko(W0W^7wC?FBo|?sI9b7!)?O)4S)CzO=8G? z1Xf?MkzZ!`Fv&XW=Ho723%7CGH__n@yeUpxqoU2e(M3|12+KN5&_2z_YOmdGUF2jQi|wE|hn7Xiwhy}P=dK`TycNxu^5!oOOrWCH zKm|Kx2_ac6?t@|NaG)NZT@{BQq9afgkD|c#GeS<#c_xb#oWF-?~x54 z;O%+`>>0XU%+2Ey^qp!Oa$T&gNA*Lk8`Hl1MVx(F9P_-7+`)p7K525oC(VBDW6F=1 z4_%{sB!a*2m^?0G0Dn}`Dpdv=c+rl!?8M6qp!H2sK}DL z*1k3DM1+?xK9R39&ar8o)R*2Q_xjk1^p}%$cQ)9R;yO_>u0eyEA?x_-T#OTUL}rbJ zS>;CXm}u0t&HqdsyE1|pS^76l3O|^vnb^WIPxWF!7c$oD*dAYW?yYQc%*yzd5nrEq z2u(jWU_+aqxDLlYmQguA@82`;y;!r0LjmG$wl7kg=J|{gk07ly5#^3qM#X!jr*@Of zqZ@If%@<>gv`UKGW-UHgvQ$CFM9*hQu!P}zWBE+Q&=@Yy&^#x;AaMcA&f`)&iwnO@ zm70ppN0=PB_4oQ1Ha~P_Z)HABK}rw_@TjlBC61Tpr?M(z>4f|xOv<{6My_g?Xhv;h zBjbC)({hs z5Dl`b#>5K?_WPp2*bwam*K0QLW4zpN`BtH$7X{t&?~| zG$bk<4+8Yi`t%8%+EkQnQ#2dd|9B_IOV&2~=r1SWX&%1gP0iqaJ(%+syBt!Ft7g%) zM1_Q*VA-P>K0K!o-aJM=H(PWjvRa9*H4BCum|1MBSlbzd+p;Eg#-;dV?>!?{F_7SY z?^MGYPKv*Lu5{qsWn*$iHRL3Kq3ddvu5Z zV2qqboUp|0W=5hZietS;xo5@-E!O=S4gAm}UsVby3$Weg6_w!oh|4hQnaz zcayp&Yld@j7uWC}?3l2LE_S%=0N60_fGV5!g78|bX$`((iAt)00#rV6%Micb-5sDt z(Y?=2I*`@JE<8T*Wu*VLzG{63Bz^4~{6S4g>X^|?ulPaKiVo3Ap42J6CK>fsFK&{Q zDY{4%jvF14C~>VJnf)9}FY#gf5e7f9*1>PR9E~oYk5kW(q+BPnJSU9g8geqOU6}Ya zoOiDF#d+%m6o<= zUMXM{*_?77xRY{9{YufqACkS!iG6+oiLzs2AU!$R0510$1z*4&??y+zKiMHE*^jKk zP-v0@*^AEbZ89@wSY6xb@^k;MZD5+o72N#i996F0*HK@F?W^1=LwBaja zIfq8ZHPd50EOu&V{KA{rC^#znpliq{fBWAXYNaM!G#dWKk2`4_zFS~rQ209ki^nHo zUu(_#q@VTmAMTjAJ?WCM%lVW;x~^HFeJ; zVa8fwyMF39sb!vA%Y3fZzA89`={UFDfShng4KpebEF_#)ljJ%1^5W^N+j*;GZ zStM^!RRpM*?&h3Ce8c_2_-bii=^29g)LKa!cly&czIaLmtZ8Gpt!{t{w>SeP+!Arq zU4o666>+f0Z$VlEL!3hgupw7l#DNBo*C2MPhgW}(I95or^m=KiJ4A1X=nyLj&Cvd8 zDXZPwg7r`D!T_wnyOu~m5P(9jP_blKZ<_ewTS()!vMLyh(8)$>DYtPJ59`y)E=(Rr z8kTQWdmi7b-OPr+;0dR$@^k!L>>hq5(@(6oDgO)8e4r+|bYd_Euc9qNJ3*ww5=&tM zqEW;GCxM}+4c8QqPvcymTQ1-Th#R#mvXX93qw%aDh&(ajddfiBk2;9gL<8Cf_xd6E z+y2WCVnoGnF0O**v<4go|Rg8Mju zuTz83uDHFT$=Vo}D*)`xK zx8w;MgoPeVeFXFQo3)hcVxyC0uU!1(FlxKd1E=vRw~s26t>U4690&7>qH48RE{-y7i5k4XH5hmQ_>QFnDb*esCyp$L`(%shGLu%)0BrQKdg77lme?8VJlwv%nAR zcA|07Adq|JFI6|IHM{~5zuUu@9-^-0zY*xH^q6|2nh<2oG66uexr9K3+(l?2wdlgI z^-|!-DC+N};-b&l1ht=^DOaMlmZ-pnW8@Ub;Mz4f+A+4%&mq8lKDJ$V3R6^neRQ3{ z@P51r{~okuLV@_a@AxLn!8rD+t6vI3&0s=%35!eN$;LxE_}0RYiOX;zZRuZI`(zV* zKX4ZotDCnhRrLZsO;=(u=9#X2AS=4zCIt6jq7gCI>1m*`KsFo@cq#~K>os>>muGeO zX@|j$A|1ECHqp9iGRDE|`(wmvi1b&--c|KRLxMuVuOKZ{RaN@t>6x1CyqWTv5FH^L zd@4;~HCi%Cw|4TwZK5}#57`=_g+#?$W0sFD?p8#(HA5Lg&69DFHdnzCxBUk_7e+%# z0!XRd!>cAGa%1<`zZ8%eC+U`SD%RcJ5URdGNVltMUB( zSR#j{q$Jh6-OhD>p!-ah3jh0;*^c5>NpzJAIK9bjZ;K#9s)`z&W`-!Zijr*&$FYU{ zkMMdFTwO}ugiN6J7ed;qgi5O!j@096#-IWL{Mr#{SKuO39)E)S%*x2f@2qy+pH<() z+xb_*4q;+=@`T6ueF1g#q75B8?IbS%Vc({@6+t~MsT|4QYDc!5jT|uJ8C`9AqHkeB z7w;6J*9oME1x8BQI?g9Q(UQbZ2A&rR!}$14BffA<4#WX-i>%Oa#k#s*7qVZjhw9*11`dud4q~33S(anwY9+zJ!V+9Y>3ia>Gh5nBSJW^?+9P;5 z=k`#gtmErq%6`>)l7Tg$)R4m8#1=vHfsOz-d1*-I%Ma3wer*wV3VG-XsU{_yk}WQy zvU;pEHr#goYHV8Vk)6@6r7@w>-jx@ut;LS4$6iAQJzQ-Vh1+NnMRnNE@7|+AQc_hK zOj=kEa#}>?n>AGs8+%M$yt;(}UM(=MP&~UAiF<{(ji~$mLY)O_mPN>gXnwKfp<)XU zulEZ%WCtWar<&cm=z_%;_?jF6XNgaCoTP;MfckKyH@wj($pzMgXU|Mr%Wtzr`K36R zmil>%Y1(Nialf!JN#i~2*dE8h&Wh~8yf#AnNZ3I{*CO5GSl*d4$1h$}R~H}I%L-yW z$0xXI!nTNzr!Z0mzD$OXy7(%)TKdxJ_m&R=oe4e8CbSEtm)AQVJGeDKymaqow87+7 zT%nde$Ci6d?OdNt5f4Y>GyYn_py>}QpjMRxY;y4Ov-8sp1r=90);yh9#TtI&C?e1l zE!0iAu~fw3Q;V>u!>2oVFe|M*NK&uP$yJ-ng=efKryx3IS#enyOJ|g@VUW+6;cpfr zjHk)PD7HAM3e1>hcF0?6KF*_rFl8Ls1o<$mt=SO3#JuzL)JV`Zc$#(zLb;)g;Y6@d zTgPbC9%jo+sb+4&jp|j3y}F#C77QGpe7T+@ImmJ6d;OjdOm8Js^jmNNpTdJ55lp6| zM>QQONMyIhcV%`ox=ni2M%(4UHI^88j7x=A-&z1VNj!sUFZQby7jG}vijntB^g;~F zA-Lxde*H1Dw=A!E)gq`Tgi^jy=IH!(cCY! z*L;E0_g9M^i-vhL6vz&w5a_}suKw_DL>=;Dz8vOntE0Ag7y~vZ$?OTsywk4^!jbHp zIhHE;_Qv~Kawm2jKAbSxAHG(0#y-*wmL@hm>Z6(SCmp63ZL{9 zS-UTVA^2@>Z!sxT7+$b|B94~)zJLqw`N!u&uYM9bQfw{vs-HJK#C=zGUyjp&M($*N zAISS9L$VWsI);aO&N_AG&d2ntd-&XN5RzYLrFuD03Ng)u1h2u@{(jIiOQa&I+?F-y zfb~ro>P4x8f=r)eJ0d8J{p}@(9EEhu7z8Q~uc@fF?cXBdva9B*3<~tcNP=ie@oxl` zfO)&IU*kZ0g|@`QIR^PUl4he1j%7p-el-UP07CyenXwxwhnV*j`hn~Ht6K&_un;h$0+^!K0bee3v#6jgz-&*D~t$U91VE$;> zxIj?xmqf#22kskCrx>^ma1WMCkw*7izAv^r}kbJ~>gRrQ#$vr_HJUm2zGleM_ZBg`Id0Xt}yFA9Ar z>|J}JUf-xYM4SUle~}TkU;(gXd&C<*IOy3^H8bws6`>Bqh1|R*)!)2wJ$4X;FUrG- zk#Mo2t3rC@enom2`TXh~>=K@6oTN@X9uy6Y&$A|7QTrAmrAM!s;4yDnkxdbI2BhMRwc!j=t$W<|@5bgZpINz+V|Zxa60g)6=jE)goiFZ8O~}byeDiMa(9qDMvDV8BcDcvu20h^Z z)kkh)33gRCkcjP{1AvNT=k`VP(01+i)&?*AOs4^4gBK#Ia*=i z%Ju`I3MaNqwoeL;1(o$SewM0wz@ig8C*H%PwCqGD^<|ETPSl#l+M+L6ugVhSn5*7* zXymT^dP)F-@=)Mje_K&7TsKSN$Q>!NXznkO3x}IfRy4I11pI)AwkHLFOhi}(Z{B|z zb+N=R{?DFnGkR!r9W#Rf5q7z{3AnhnIh)$BWAr>8VR1GK#R@{`+*N_|=V_5|5W)9) z^uGum`B#B?D4^tD#rH9`wIPQY(GV86X2zf@bEmIe!mj!KZoc2F|rSCuMK~J4qUk+F&+F<{{IDMyZ^>o{a@Lj|LGLY&cE>6 z{hvKW@bzyviIBfIkM*PhV%Ga*Irh*;_Byq}$pYNJ1HJyM*y)+UdJrc)GnNlxqJh(0 z@zqNt$Mg3{aCQ;6uU6vsV7*yXMf7mypS|jE2du08rAoZ@S>r!j3oZPscV(?U@*GVR!<}PQY1Om@4>~mZ z@Oj}f%1UP*6H&@#!wJ{VE0+{PWC7*U3s?fGxg3oi91$=4Jk}>Sk>w-jJsh0N`E(g+ zB?{QrayR8NZ_^M^N!t>JS4aX4UD$+vkKe{nUPlt=0hLaA^8j2&PCri)l2MR~<|MJ$C`9 z<$g_e*SjPN8MyF&Z!}|WVtQZD6Sm7QP0^o1-gH)?bur1=s9y8zTcM!1 zC2U=iE#^{u@1Cn+TmO>>AA0+3!W(;}i|wP@t4ijIN6;OS#cyxX~6Meo}Yn2v{f3XMw84k1#a2eH#2lyZ83#`jR`mW$#_q{q$hQFE!TUXk3lNTN3;(;#O22~3Y zyJ5X?#&UvsH6)I{D@3EOV8Wm}o6|)dJEC4uecnK;_&ykR@baz{ha90eNWujGK?^*@ zFmd`=ho87Y3YgcMDCJt+up;l1O{EDh7$H{rS&)0KuLQE!CW?)8i%-{}W2y1;C^e*` z!6-|&+yx`RRM{_z?@;2g%zAUUn$b=>tX&MhLvurPqtD}*o7};$vVve5!S<2yvrl>g z39z3MyMU3Jr!dH6p8}}#NFHyFa--tjkpv+l2-y*Bu&uIfdBbL(fT`iz%1CUhrZy}r zY2o15_~X4c#|+e$k1T;z)RYLmcZ3BC%r>iy!*ETp=q9`#gsS$ia;xXEecXuQa)p$Dw2r9E~JhtDag554By`>U=*TpKPsLcvD}*DvD4;@!5v zZMyn-UmbYvMIUFt+OAJy98xbpMUuW_c0;v#Fl>ld9_V1E%Dzj}HQ~qG_i4+4S0Jy8 z+om&Bl_CIupHRgm6$Pl)Mp`Up0eF89#}2QvZZkj*1ic1;DrX6OXl*BQ8+~G8-g$2- zuwtyh>m_p1fCJDo(23ffD0Bv3Z{{RN(H0T^PF_GinE2Oqr>Brqn~s+M^LMQdJX(em zYtTco3w<0yA?9gZibrm62egkUxavihJrR$u>QaoY2zuX#y8~+~(0jk4w(QDYEBtk- zGfc{OC&b5pxe64S{C*jNT1PK@$|UKo;-hpjJ%=GTgF{^ zR(!!h-~TRcp-F-RW31}pj)e^?i}{}v-{{?8H_+_4R-#g#1>;iZpW83P*S`e0L8(Pe z9ZmJ+r2y0Out!hciD{O0DVkccBCGY|q7-6DQQsK0ZWnDB#Mk|r=Q4@E$cldL4g-$a zxAe`kpWhHG?kIfBhn|oJ=-9#kcpBG{We@Ydj=U=lHPCu?;^{5@4_5l^jO3#C{hnJ& zNQzo}ZDibGCh~LXl;mKn0N=ZT`*SI;^0$rv>Xgs?^ZxDreRQ)(J-t7d15Yb!zR-0<4hZZs`tpH&7o{kDo1{K7g@aKN1_mC+u zLiZ;MexflsAALK7@%3N8EFJXl z%CjlapTYfq-K(1jhA8p<=rQ^rR(LDH`7X`27}cbuV| zDCmyzqSNM`n|U`**9`HCEDODHm?SUEPmVjTzw74yVcHPfLDo4^!OgsHqo{Ook$`~A>+-X-uKY3PI1FZ>Ampt_Vx z-w-D{o|mD=h>3C$N5TJawAhT}3*15^VoIyrLdFc>y1Fm-c29TnAF%qYZKmxN#irFk zAwkj+F4VP=PQhs(cC-wU!(KBgpy;57#Ol5@&{Bx4Aeb`W_y?9OOxWt>S_Feb>s0=C zj(2Gi&hgS723PbY)NA+;g4E;7JdNThb+t+QJvpjIL$<)aO3iMNli;Yyml5rcG5#6| zH#8puXo6BkN0j@3o|KxW1rre@WKKF!fI32tx*#av$D1E5Kb=RPL$VH^X}U*RV^+JO z)L0w^`*#rtBJkk2t6WUXN<`~8)l&}U@@|QGo<4OW|64>=bPXT>so-S7xx} zNsQ;419Qk%%7OF?T)(CNQzrZfaj2DV(~*Dt3_b`p?b0k3?{^$|jfTO=`@h*`!Ci#! z;jEWD4$=X6Xy?I=1)i!#7aCK|Qs~$4tyag+2Kma7Wr~2oqY5~VE@QPl@F6aR=Bj{* zdth9$kjc%0ewnR0+At5wUpO^#)h?<A0ccONAYK}@;h+Ol9>Jvy?U935@2aS+=xWXb*a8xcOVi;l>?@a@al7P76D7~ z6`$tW;4OvU_hOc4N|#Ik0HbO;Z3G>NF__PB_5N&#*C4Fbs3DE^6^2jlJ@tZOwYYUPEHRfXS+|wrx z;PFXD;gN(YFMs}n4AZ!9^%`5lz-?LU&^QwxPc`|dc*z~ZfWP*srPbbiAh6dvzu$Rp z1yrFp)5$i5u^nD^Wxg-`NV-FR558E7OdV(~lU8Uqh|X-l!$2o3f35vsR%W1Y$Ju?M zoEwUSaYsoZHaZ?;+VUD75hd~8!VJ)I)I*^>pZtE$Lce|R4U@--idq%m*tGV5Slb*^ zz-k!DMLP5c$N;ckGz3w!d{FdKE7PZ!g`U{|*1oW`3r4N|Mw}L|pW% zpx;y10<D7LD}f>+dWD zNw?x2X;aDCWYZ}+T7Xb#qGhrWq4FY+XtZCX=$1H%;L89(%Y}rdi})(Pw2;OF0!ae98+tyowr0hpIk3kCc&5EB#`@xkBgqgYOe; z7+IwZZ%ck^kmcQx_>4gw_?wusg1=&*Hccu$8#H|m$O{Du9D$dpdIW-4TqMpJf!^m7 zAtLXHCq(4Opfsrt$h63J+PP`t9IV@m)&Tjkf!2!6q4*Rjw+Vs5mz@0W^ILe|u>Le{ z>K6e5$n!q+Q6I*{B(-#IgQy45lc6h3*r|>?Q_=uNP+6=3>nG$pExw>1DX(yN{FR=3 zeK0cty{?EN=U|NYLv^ZX1QIQ(z3vg2M=(uOh%AHZT zZU-wOmVI4G%r^8iF&(Lc%#phDC;?jg!eEpn0EzL(?^JR*SR(~F@TRNXY3oOVP=V{I z-;fs-EHHT%swDJe=n*D=8rL1~+(0O3Qs^56kM_Z}i_cD|XfR;%Y{ZPqKlUjdtLWHb zAO#v(5|3?Gy#U^Q2CaL4V*kl2>EjL5Q#_(jFyMuEb>r5bQPVUp;ZegssB#;?E z;586e0le&S{}xc=C>X}77uU94l6+dim7*d6m8Mr+kcQ# z{%d89ufL2>$KYNcxGq-JNMaD)O_Z4?Wqatr2+l6I=tp)vKC+Scy-KJn@;PD?;l(#N zPT83j%xi!g%X|m;SX0YoWj=>(jv&2|-gBcIV3$GkoO4$80^( z)Vt{MjLCsSpK5lJhc!FG1?iHz8-xqCN{1GkE`~PA3+W4qVb=mx1>WITc+4Lc+ab76#)2DnavLT zSGo|g4ZfXI3><1eQUo>fp+d{&Eq6N>zSf23HNW>H+H zX_ch&1Rc-`Ep|S1@XNg-+Mw6UXg?>RPeyS5pR%G zOkW$Q>wgs=CK9sGzL^OzHKvO|QizlB;pj&r_cK5vhqA9?dd%c{v^F=lx7_$)Zv;6v z4W2V#OK%&nHiV(ArHrO0FoNVk3}!FFxYtjA4Fp9QO|%Dzjv(XPVgwe&rlQuN{LRWb z8!SLQK<2DY-R$Gp@G%GNw4Fr&E?1`F>3{Ioe3t>Oqm%>bWqlgqqR3im+~XpHKS3V_ z`==Qh(et=T3hXq<%tjuW!SMyA)7dey!=3*>7$5JjAfooBz;(JZ!gs?A21|Jv9oIin zo*q$ROkkz)c2oE1m7~KURs1i&N$A8AAtTqd?<0<;#Af$f!DH zD{)YTvgs2fO;k7thyt20SpxUvhW^G-W29U3BSPZ)jj0sXLPU{E3e0VkUo#bXvvCJq zxv>n1np*$~m(W;WILE}ONg{+H^!h0SEpP5S?G9(|DUM;}Z?j|#7L##1Etl?R2;`z`af;_^2zdw{?7JwBrb?QikDZSXIEk}qwa91zg|d_mtjfng0f9qq(s;M~G9?eIx&YvCV-=@5=o?WHr5QVy`r$ftFm#YR zW@vH<%(^0!&eeniU;1%5{-zkAYy%3AX)bxXP~kf(>#427HDE>Q2Jiqe7YI^f2yGMJ z6^O%2LUa`crF^;K0@u+`=|Mi49n}v9vAxR~5=DMDk{Z&1iE{gWvd{^z!5d(Q_Z(5U zkPlWk66b7lxgkh;8{&#@KVdy59DIV{fZAAh;VS_j%qiBqBr(A3N%?Ey{Ejq0xXxbm=iujiz zG;N{CS=^COrZgK@n}dQsZYw|?t=0kfBS-of(EJO8e56^Jha4LoG87)g(fm8_f_`V* z;+4pMXD^_a12kY3ig+OKpf#Tl*1?^&f2!^hV}_p=Od)u9^eEZT=qRuy_hgr99=1I$ zm-KUCqB5QD#8wbo8Ho>RQ1qirV`@3(Ve%)WZKJ$zcUs64#SPTk4qkbHaiPelq^+> zukVNu>+hfsGr-Y3CFJoGRRN~vd=AOvR)+cCfpZ?H|GSh3w&&A9 z)Frq*`eOvZ|KqXy!4=YEhhvo&ZXqS9h$V&vBeO!A=8I z$xs-rdXqgvH=(_s1EXwk(Ed#g8c351$xmR-#Rj}#&5g!T8`z3)I?$`lkWkL$&tQfe z2x|riIDQX4zFC45JT5-K&3e2GYg0p#SwEi!Ae$Ey2244G))s7LeOx(%P%=>RRz+q| zxe$c)N(B;ZhR+*FA+0K2I0*GZBDw;bkiGDqI|(G0DE}d-th5~rto|w)D=~?Oi1_FW z-p))Qyd^|g2yhbeRgG3N(I)tvR%617W+sTU7?}SJy8RJvdfz2ng-?$BO~nh4q&PnD z*~LK0kqg5%5PmL+$7VRd?5pzTHlf-|rzE)l=#)L{+s>*bQ4iCg)un_G+zoD$&6D)S z7Dv?f>^%a5b#^~^i(oWwXsrY+_UQ;URWHG)nPrO04^-3gS2Vepb2p|;^jwr6sb;z2 zmWrwgges%$9N{pXLiFSOq33&n?+F7%+I=a4>h9YeS`$^c4eey&c_6;EYEMB`raDco zML*eADTZ8VrfXI-O%b{{bGLOklBHk9N2q`>Kw&NE;c&-ZIw;l5wHhybGS4H)K+w-R@)j-FiH{7PHO8c zlrVrv-9k!JG921tv zLTrQo;ZTv!&iRaqTL44W)Vl9gJfhrVn-;?-16(B(iVT`pBD1YgazX!sJE3zZHWl}- zRSG@Bl#hnKgG*<7YSZ!rqeHg3Ux)LQcR&yGdRm-)wDJ5L45v782h|k^SUwNt1|*$| zYrB34EboEc%dn^d%4vi( z31@>!M_)j$IskR@b2 zd42_3@Z^RhZ@AEuA2t4cq)4(+X6X5a%1z>*3IJ+S%4%I;xs zEYwDaz(0h8t=&-XFYM~0#7`QbYGZYbLdnup9m!FxZxW;av2*wZ|MEkhEqFP;#AtGH{G^7RrLkGPpFN(89&f!%}4oK?%?^js>`fn@G~_8 zC?aA851!R7(>$)&-IW8XIfdPldpe5+c64!d`X8USdY-MJQKN3O$=P??zC}JP4ltE$ zbhdsfQghVQ@_QK5*`xLIGduT*7N1fN<-BBpdtPw3HiAKVu-Kw)A8D!KIOVg9T*%HM zLuITj@6qvv)_qJ^H9KrcciHB&VLO}ScHNpz^P4ieGLs-=MNoTm++2ntQ_uFD`EJ_nrm>vAyTvH2 z{`lL6EIixF9HSy)QuFQ^c`q1O2`p#laC*4kA}3^P=grnTreDW3b8}4$U!RZJ*jy-9 zW!vc)Sm-3d`9kx|cjtRH{(U_u4%}QtZ_bAoojOu}Z_S0iNx79&H(|~fs3)P1{ z{v&0-ZtN(sjyASnDYF=mu5;|N(tYhWvg$=(e3o$Y8@4>3{_M)`iqLKo{q~08B=Ot_ z+y&Ig!aGrohYCof8aPUtt6AiJeg8OQmR6toq(9WL^GUFDSdmoAjjep6`g?DNjT=AY zxt7)3<}&`h^5ojyT(^z6_95Im>`aHNGjD37JiE0)s5+p}xqU~lv};LWwU`=*3S&w1 z+O^FG$A)X*!YJz>54Bc^c}R!$y?fT+W%|xOH_K$jp-Ky}zUQ6$evE5!w=|^WHyP$P zwMqmFP3~4$C2OS+ZGGrb;5FO#Do4+c^*A~4ZjIMB)L^t2=+k7h@Lm#jNOpc-ZJb<7 z@j>{_x_O=4WQ9(td(982bget@o3gX1F)@AKI=1|$Wp%TTJ}<2*&%EPOw5c&*>!Bc5 zxy|ku4N?q5IhF(o&mrdzzhck%JHesIBuE&pXR&;{T;mPg6kTS+X}t&TWf9zZ!R3r7 zc8AZsO6j++7t3d})dZHL;qHBJ{9lqp&%#|_8}!?YLx0>&a5k_VgK#15xhyi4_3arw zskK-AQ?r_{Z)(2GrW&YlZE@c7n3Cr)dA9Hw6z9R~x_zR}V#3gNOi{$~$SS+)Q$@?S z8J+pwopF4S_?NDFXT7_m5@Si2iF!Ex&!=mf*47Kw|3)Q_KuM&UqkKgTpD&hbSN`>a zKg4o(h}8!{Hn~1NtEZjTPdm?t!ly)^REdLX+4{oGoOLo)F|znSo~m>GXTHD7Z)%${ zoM3);NmWKkm;~`J&-Swn=Zo`O(;ec~UGRT9NR?gxOH>^!A))9m$HHG0lJaeU7I2Nk;dV_rHNyGF4Wa zWKR7Je|Qp10_`Wl&N%h_pLuK%+#~Jf|AnvOe$4wysJWBl>EEJ)n|rhRyvKU8{=fFF zJ*o*a%iD1pwrh~8Qy~gugbLGwLQw=EB%_Fy0;bg}S{}gyq9OzefgpsmO)Dq}1x7@9 z4fTN)B|eZhi7AhOh@dFpotP+y5JGr_KuEIp3p(AIv$MPB?Abm0hyKAiNR;p1@7~}2 z-QV}JNh3@)J1V*ZCGXnMw~cYN8n1KwytoOTp5#hy_-UW;s`cY=W{&={C`tU2>+(}E zH74zG52H{TByaF3-|RQkUj@e&BtyB>G#Nt{e=Crmru*>C`sJYXebCMrkCL2kPdMDU z-eQEK8c$&})uZw{8N&wsw?8_d%Qc9oO&t@OBx zQ0^AT7k}Bi)4wy>h@c)J;}-x2hD5ta^N)hG->tH{F|MC>tpYjlBE!?>CQT z56_9Rq?@r0RZMT@q$Trl^xWESW}bQ*_{&dwW6Q((wkU*E8QfEB zhBTB_Z$L`$6HJ}bE_WWg$+INQwX|q2It26zj#A6v{b9N^lu|Q38Z&bF-0tVI^^SIQ zK$&nDw=4yF@-A4>A{q#~i5)b=VMAL)ZCSh;7{WZA zvSuDt6L=s@0T0)g<%U;(q?smbqmK;>ISf_D-4nmQqi6hn`-!eQ;-lpFuZ&N;EP_?} z{)l>g*kb-FC*L`?X{}J%1iR)VAGsNN-Dq;U!pK)d^G#wKtiYX>YDHg?0v~bjoyhr- zVt&RhQydxu`ez#|`d7vd#tlC>#`+f=;XOOL1CC%~M-L3L{RL)N=bFEzKyBPV|M61m z$}3Kz?|fSCJSa~qD*kd7u3F$}=PBCIiHUjxgEKYo0;Ry(8}|E(Al<{SPDcAi@ZK#E z1ogHayF3oBa!y>ZENpigz0RxPs-^aU=y@_8AKnV(6(yNEZRQMw8^nzF_xyQg?W|^& z%Vd)oc)I92RtRzGe6A)}DIsYjPbF}YRm6MO(AT!|)wL3g0b%0agZE=gMQYR16(% zLRmhU!-dybjg7%?X(?=xw3?cdFIhf$5F-1s>kB zy0&Tp4x}?von^I~%*@hA8fE44Opi4;ijT(jNAf!qaCFb4JyCHd)Fd9D0~^Ma#S3!} z_}z(6K1<^7EtvevFZl>@w&|IajY|sDVdYxu02l66j8{@Dw%?RJ>0qsn8Vd^*PkkA%q&EjY9Zl+)d35y6GiNFpHRRp-J zg<2I;@|~XPfq_J*S$NC*=shPs%$IHYJ)LWv3e?POwV)&iTb0JU&o; zmv{O?tX4g15Qe8c`QmQ63xIl3IlOr|cxTj+W9dWg>zl}4}jxJlY0dwF>p!L~LVmVX! zMbcrCb`E^>@~+a%4!j2$XGUvdd2Fl-nmHu`aspAHT2uNbeXSCr#}Qz`yMkKv zBAX8EC)7lBhMExAR+W?57hV%~MAdAbe*9`?Pj^&H-8nd$ufOkiDvSTwFrf zhaPFEyO`#5jCQmEl~aUgB!sw*udZGc-&<;s3ce{WIB~328b(dADy{sQwt9IEbh@9| z(S<<~Cs4R}p>j3BX%S5~LnvK9OiaYc;*DhR>~c1@Ad&{|=`?2r+ukz+t>ESlF=O2; z5Qg-|KKO)xebhKUO$#Pgj8yNLMR^Pbd{gZhCf@kRzk^?nB(U@ ztySD*pV>=xp*N1nT^FMbHGMMI;Z3aQbcX z>2{7L2rDdffkD9KEeEV{&wAskByiP|lyG!1DW}dw*i9X%rVR{tPvsOYJh#lf_7M%1 z8xn_O6GLdK3Z|+eslYR&LXzgZk)xSm^Ix2!m;yaXk^sZnnJ~1e@Kif|RS{qZTjk52 zQ^W{A@hUhejxF)?Oeai!nioG_<*MZp#w#V_d|5&WYoC{}z#)`f{py+$8U9piSSxy0 zKIFjNii$Rx2yto6_-UZZb{p%aA3qf|qFhQ;3z#lxTZeM!GbI$4*2#k_fm;VfFrY2A zqZ{HpPX|rRw}|G0B7o{{w9ENJi)bc`Hv^q@i^wm?_M=SRs3mJOi-*WAbS90kF(BYQ zX`HL%BXZpR>h@d1`e&TT^$$y64+2VZ$+IMUT5>WfZ*rK>x@XrOE0mISz9gMws%J_M z0It3sT2CV16e3>k!=3Sw&kMMNZK?0YwfYRM)YaVTI=JDa(UK@XMQeZX2~|;BByVU- zh1RS=tHf)~H3M8#tSd>|Q>gBtw|MxkfC(M$%iBX>P37J98uG3<1#KMB`wjjK(;TDT#N1v%mH-%K`V3CI`+4~)zVN`((v zWZz6RZ)I-i%six>g4gVoy#mha6)UW)tmKc<6E{O+#XVW_nE>#pkdL@Iin;}Wr#L7Z z84@itP!yOu&myNXuwxe&x)e`M0L7Ult!|KjuTNkkmi1Z^W$U@)QtqQ{7_Na}goyRN9HORV> zK109gFY;n~kG6gAmr9cA4k_ha-B@tsaDHs*czn~Qf)%<9Z(2)DMcf})<;vOqD6((d zi?ce{-@jk_ZZPyyrc}nLk0gv5-`8xp&{JWMAX@6`VY##6)gESR0F~_u)AQ*<7ggN_ za7mnhN#{Ec0z;W)YG*C?uO)252rOS437Dh{!clWHg@8%A2!%Oq%LH(qM&Ua#-wg8? z0QPx;-eA4XVe=~-79;Pf;Mki2)rH!vQA#A49vhFAU;QCW$Z;8*IO)&;UM;JZJ!L1- zAa*L@on%Q_=k65v+s%kyJHkmlF;dQa6X#w;O79AG77Um06P}ynPYKln!Z2~6t$;Y0 zk>22D=(o8{_Ukg6)LGC0^!II!kB>Jw0XXKPni=te64wy}%fxU4K*%6o2SPGzJge8_=IcG%$*s2R6tqg@!Q&-!HQL0O|h+UH$Cb{e^)d=Nrhh4P|V zQUIl>v6!SDDPqVHXKNTg`6iJQC$r>i%~S503nLEEV9C^|2-{t{ z#L8|(2c>P};|lnI8m77)mV+QNaqx?jkI28!#ylwVj~%BTY?Abb zvd<*@ZQ71>kkhC~T*Z!hr27W$Y$DC8HLp0RZ8(#jIDPR}aBDWkDz|PGnGo5=aiM;m zs{1AI6a0;E?|pYiszoht=5G@c3UcE9_{JgYkQ(rtL$J8o+<)hg!_6Qh*xvgFR^e}6 z+6`5_QZqjaRINr@P8;Z`grd=vM}0(y>#4Izl&8oIG(6IQ*{!Ik+w%wLk~5LLM~lPC zYcKUPwQ3NkD-5LZBOa9M(x}?8U>l#hq7^IT=*A(lyBlt|Gc{9k)W(@Yhx(MEYyJwA z4VGrD+0>$fy}P#tw{egr451XYkah0B5AGQ}iLM}kNw&BLwLks$zs274&9)g?0-6S* z8Antl6!kG_Ak;LCvV=Qdk}(`uHP&Ah)y64ykscj&;ulC-Z})q;FO#KfUZ#unsdQHY z=yoM4|EUXt2Gj>NfKVa`2yWZDFi|hLdNFNHZ$$;S?F)r*;qaVA$@h1IY_mROg^&>n zo;TD9ZFc}OGI4dhPENBe^hJMruZDTDW`21_1KP0Ngilm{XRaX?Z1pe{Tj-;!d zWRgo7q`dptCS#|uoAq5|HP8?XXh>6}9#KKpHV8wN!d+%xFHG8Kzd-_2iApmK=Ee%Nn@rlZ3l}Kn;NaI+)x7enW_{tH-3>f-OH>Tq)h+;yR zAX%^{6EV#5dW&|fXU%$HmRcLY0+Gin1VYd*(P%oCCd}3dZ_vhoQ4q`2P>EtPFRw=` z|0hP+t{LG|Z;DHFT41_MOk0kdPufN^@n2cXPrEMP8gxW>#$m9sG%5i&h*7Vo#8|WF5zN9Q$jdh` zB@Tro9&TH!)oOtKbW6hPX1bIlxPt9^IQ#nb2KK^zwr4GIsNH7aR~zVXmG$^i#ux}{ ztC-Wfm;ksBHnr#NIJoSE6=m+Tyr_p;!0_4JH+4Bq{rnGl&rqnCce|p?M*2$Dg;v5D1Y373qQ4u7e?s zc&!|7(8jqnwtxSAF;^qd!VUFp)Wj%s+M5rRN0ws4oNF!VJDA-Fw-6D|u35JovkA(6 zHA1PJI56--9KjD2Ae7dgnb?Aj2(b_QgMscC@;PiNEHYWAqhn})-3sI}VWEk-(A2T_ z8j1q(Y@%#djH-FZ48S75l7G0ar64ui>g{E~sUP28#jesXrJAv1@q7o9CRt1;%Nl?22D&^55S{Sb}EIKKz?-%3%x#gbWo5mk+ z(*Oq!IidnFJq3EfDE6=;xCoA0+N-Fw@+PIF_IV+8V0sQwL8VBKEHAFMq4SoBp&>bg8eZ4H*ztZGDt!`YO7C@OP&t5Oy~h?(0zkL~ zWC@Niu``OvK%UCx(Ll~|Ln(X!BUr~eWQcM$0Oy+ovh|*FESTBpwxlBuQEq>jI)33o zOCeTDt>}~I4HGO2=ARTdnZ)+55H1WkNLn%?@4cgk^xoi}n=U$K1KK(|O$9_~U^wZv zTXbJoMI+Lw?D4nnSf?-fSD3NWR;5N4yuLOy24P6f z!Y>5u9oFd-ldU$CJ6d2or((IPfn2BARsPdI8#dYtd^cr8g*AYCvS71_&LdquRFht~ zR)ybiv^amYo=NoQ{WO7}{4Sdjc4T#Ca`HJ46eB?^X}W(pWZU_TIKn}20`(*cmpgw7 zng~KFI8GNrYh^aH}46l%^DpdZEi@P2PIzj_#5$Oo796+UhrGYNPVIaEmg5^}<@z(4O zI(kQ(_|D+LKpXZkY#H^XJ>4g7*UphBjBiM0Vz@x^(85F#sed>UX*j+oMlOdRwq2zI z*h`QaD3?EExkG(GDejKR`q#zG&%|1M0E|nHmRSEhwH)MN<&$FK2ZbP`NuakKB=TW3 zX2ZtXiC>i}VQ<43mc2B`u?+aAc^#q#h^ zMvuMOU5+iN<-EUSM+0i1mCzmHp!M8mjiw4_Vu9gxx4M& KQMx_g`+ox(QQb5E literal 0 HcmV?d00001