diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 66c6b9c..cd364b6 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -33,7 +33,7 @@ jobs: XLA_FLAGS: "--xla_force_host_platform_device_count=4" JAX_PLATFORM_NAME: "cpu" run: | - pytest --cov --cov-branch --cov-report=xml + pytest --cov --cov-branch --cov-report=xml -m "not timing" - name: Upload results to Codecov uses: codecov/codecov-action@v5 diff --git a/README.md b/README.md index e52998d..d84984f 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ pip install --upgrade "jax[cpu]" ``` ## 🥜 In a nutshell -All optimizers follow the same stateless pattern: `Optimizer.init` returns a `(state, optimizer)` pair, and `optimizer.optimize` runs the search loop. Your objective function must have the signature `fn(key, params) -> scalar`. +All optimizers follow the same stateless pattern: `Optimizer.init` returns a `(state, optimizer)` pair, and `optimizer.optimize` runs the search loop. Your objective function must have the signature `fn(key, params) -> scalar`. `params` can be any PyTree. ```python import jax diff --git a/notebooks/bo_design_study.ipynb b/notebooks/bo_design_study.ipynb new file mode 100644 index 0000000..c62581c --- /dev/null +++ b/notebooks/bo_design_study.ipynb @@ -0,0 +1,9393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d2443470", + "metadata": {}, + "source": [ + "# BayesOpt Design Study\n", + "\n", + "Systematic investigation of Gaussian-Process Bayesian optimisation design choices\n", + "for hyperparameter and neural-network training landscapes.\n", + "\n", + "**Five studies:**\n", + "\n", + "| # | Question | Variables |\n", + "|---|----------|-----------|\n", + "| 1 | Which acquisition function? | UCB · EI · PI |\n", + "| 2 | How to set acquisition hyperparameters? | κ (UCB), ξ (EI / PI) |\n", + "| 3 | Which kernel? | RBF · Matérn ν = 0.5 / 1.5 / 2.5 |\n", + "| 4 | How does parallelisation affect convergence? | n\\_parallel = 1 / 2 / 4 / 8 |\n", + "| 5 | Which Kriging-Believer hallucination strategy? | Mean · Sample · UCB · Constant |\n", + "\n", + "Evaluated on **four benchmark functions** that proxy real HPO / NN landscapes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c43b6a1c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:24:37.285262Z", + "iopub.status.busy": "2026-04-01T10:24:37.285097Z", + "iopub.status.idle": "2026-04-01T10:24:39.156973Z", + "shell.execute_reply": "2026-04-01T10:24:39.156722Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX 0.6.2 | devices: [CpuDevice(id=0)]\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from tqdm.auto import tqdm\n", + "\n", + "from hyperoptax.acquisition import (\n", + " EI, PI, UCB,\n", + " ConstantHallucination, MeanHallucination,\n", + " SampleHallucination, UCBHallucination,\n", + ")\n", + "from hyperoptax.bayesian import BayesianSearch\n", + "from hyperoptax.kernels import Matern, RBF\n", + "from hyperoptax.random import RandomSearch\n", + "from hyperoptax.spaces import LinearSpace\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 110, \"axes.grid\": True, \"grid.alpha\": 0.3})\n", + "print(f\"JAX {jax.__version__} | devices: {jax.devices()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4037ac04", + "metadata": {}, + "source": [ + "## Benchmark Functions\n", + "\n", + "| Function | Dim | Challenge | NN / HPO analogy |\n", + "|----------|-----|-----------|------------------|\n", + "| **Branin** | 2 | 3 global minima, multimodal | 2-hparam grid (lr × momentum) |\n", + "| **Hartmann 6D** | 6 | 6 local minima, high-dimensional | Full HPO: lr, batch size, dropout, wd, … |\n", + "| **Rosenbrock** | 2 | Narrow curved valley | Correlated hyperparameters, slow-manifold NNs |\n", + "| **Ackley** | 2 | Highly multimodal, near-flat tails | Rugged deep-NN loss landscape |\n", + "\n", + "**Reference:** Branin and Hartmann 6D are the standard evaluation functions in\n", + "Snoek et al. 2012 *\"Practical Bayesian Optimization of Machine Learning Algorithms\"*.\n", + "Rosenbrock mimics the banana-shaped valley common in NN weight-space;\n", + "Ackley represents the pathological multimodality of large overparameterised models.\n", + "\n", + "All functions are framed as **maximisation** problems (returned as their negation).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b611097c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:24:39.158346Z", + "iopub.status.busy": "2026-04-01T10:24:39.158245Z", + "iopub.status.idle": "2026-04-01T10:24:39.192912Z", + "shell.execute_reply": "2026-04-01T10:24:39.192671Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Benchmark functions registered: ['Branin', 'Hartmann 6D', 'Rosenbrock', 'Ackley']\n" + ] + } + ], + "source": [ + "# ── Branin ────────────────────────────────────────────────────────────────\n", + "# Domain: x1 ∈ [-5, 10], x2 ∈ [0, 15]. Three global minima, f* ≈ 0.3979.\n", + "def branin(x1, x2):\n", + " b = 5.1 / (4 * jnp.pi ** 2)\n", + " c = 5.0 / jnp.pi\n", + " return (x2 - b * x1**2 + c * x1 - 6.0)**2 + 10.0 * (1.0 - 1.0/(8.0*jnp.pi)) * jnp.cos(x1) + 10.0\n", + "\n", + "BRANIN_SPACE = {\"x1\": LinearSpace(-5.0, 10.0), \"x2\": LinearSpace(0.0, 15.0)}\n", + "BRANIN_FN = lambda key, cfg: -branin(cfg[\"x1\"], cfg[\"x2\"])\n", + "BRANIN_OPT = -0.397887 # true max of −branin\n", + "\n", + "# ── Hartmann 6D ───────────────────────────────────────────────────────────\n", + "# Domain: [0,1]^6. Six local minima. Global min ≈ −3.3224.\n", + "_H6_ALPHA = jnp.array([1.0, 1.2, 3.0, 3.2])\n", + "_H6_A = jnp.array([\n", + " [10.00, 3.00, 17.00, 3.50, 1.70, 8.00],\n", + " [ 0.05, 10.00, 17.00, 0.10, 8.00, 14.00],\n", + " [ 3.00, 3.50, 1.70, 10.00, 17.00, 8.00],\n", + " [17.00, 8.00, 0.05, 10.00, 0.10, 14.00],\n", + "])\n", + "_H6_P = 1e-4 * jnp.array([\n", + " [1312, 1696, 5569, 124, 8283, 5886],\n", + " [2329, 4135, 8307, 3736, 1004, 9991],\n", + " [2348, 1451, 3522, 2883, 3047, 6650],\n", + " [4047, 8828, 8732, 5743, 1091, 381],\n", + "])\n", + "\n", + "def hartmann6(x):\n", + " \"\"\"x: shape (6,), domain [0,1]^6.\"\"\"\n", + " inner = jnp.sum(_H6_A * (x[None, :] - _H6_P)**2, axis=1) # (4,)\n", + " return -jnp.sum(_H6_ALPHA * jnp.exp(-inner))\n", + "\n", + "HARTMANN6_SPACE = {f\"x{i}\": LinearSpace(0.0, 1.0) for i in range(6)}\n", + "HARTMANN6_FN = lambda key, cfg: -hartmann6(\n", + " jnp.stack([cfg[\"x0\"], cfg[\"x1\"], cfg[\"x2\"], cfg[\"x3\"], cfg[\"x4\"], cfg[\"x5\"]])\n", + ")\n", + "HARTMANN6_OPT = 3.32237 # true max of −hartmann6\n", + "\n", + "# ── Rosenbrock ────────────────────────────────────────────────────────────\n", + "# Domain: x1 ∈ [-2, 2], x2 ∈ [-1, 3]. Narrow curved valley. Global min = 0 at (1,1).\n", + "def rosenbrock(x1, x2):\n", + " return 100.0 * (x2 - x1**2)**2 + (1.0 - x1)**2\n", + "\n", + "ROSENBROCK_SPACE = {\"x1\": LinearSpace(-2.0, 2.0), \"x2\": LinearSpace(-1.0, 3.0)}\n", + "ROSENBROCK_FN = lambda key, cfg: -rosenbrock(cfg[\"x1\"], cfg[\"x2\"])\n", + "ROSENBROCK_OPT = 0.0\n", + "\n", + "# ── Ackley ────────────────────────────────────────────────────────────────\n", + "# Domain: [-5, 5]^2. Highly multimodal. Global min = 0 at (0, 0).\n", + "def ackley(x1, x2):\n", + " return (-20.0 * jnp.exp(-0.2 * jnp.sqrt(0.5 * (x1**2 + x2**2)))\n", + " - jnp.exp(0.5 * (jnp.cos(2*jnp.pi*x1) + jnp.cos(2*jnp.pi*x2)))\n", + " + jnp.e + 20.0)\n", + "\n", + "ACKLEY_SPACE = {\"x1\": LinearSpace(-5.0, 5.0), \"x2\": LinearSpace(-5.0, 5.0)}\n", + "ACKLEY_FN = lambda key, cfg: -ackley(cfg[\"x1\"], cfg[\"x2\"])\n", + "ACKLEY_OPT = 0.0\n", + "\n", + "# Registry: name → (space, wrapper, true_optimum)\n", + "BENCHMARKS = {\n", + " \"Branin\": (BRANIN_SPACE, BRANIN_FN, BRANIN_OPT),\n", + " \"Hartmann 6D\": (HARTMANN6_SPACE, HARTMANN6_FN, HARTMANN6_OPT),\n", + " \"Rosenbrock\": (ROSENBROCK_SPACE, ROSENBROCK_FN, ROSENBROCK_OPT),\n", + " \"Ackley\": (ACKLEY_SPACE, ACKLEY_FN, ACKLEY_OPT),\n", + "}\n", + "print(\"Benchmark functions registered:\", list(BENCHMARKS))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e013fb86", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:24:39.194113Z", + "iopub.status.busy": "2026-04-01T10:24:39.194036Z", + "iopub.status.idle": "2026-04-01T10:24:39.792401Z", + "shell.execute_reply": "2026-04-01T10:24:39.792152Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hartmann 6D is 6-dimensional and not shown directly.\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "funcs_2d = [\n", + " (\"Branin\", branin, (-5, 10), (0, 15)),\n", + " (\"Rosenbrock\", rosenbrock, (-2, 2), (-1, 3)),\n", + " (\"Ackley\", ackley, (-5, 5), (-5, 5)),\n", + "]\n", + "for ax, (name, fn, (x1lo, x1hi), (x2lo, x2hi)) in zip(axes, funcs_2d):\n", + " x1 = np.linspace(x1lo, x1hi, 250)\n", + " x2 = np.linspace(x2lo, x2hi, 250)\n", + " X1, X2 = np.meshgrid(x1, x2)\n", + " Z = -np.array(fn(jnp.array(X1), jnp.array(X2)))\n", + " cf = ax.contourf(X1, X2, Z, levels=25, cmap=\"viridis\")\n", + " plt.colorbar(cf, ax=ax, shrink=0.85)\n", + " ax.set_title(f\"{name} (higher = better)\", fontweight=\"bold\")\n", + " ax.set_xlabel(\"x1\"); ax.set_ylabel(\"x2\")\n", + "fig.suptitle(\"2D Benchmark Functions — maximisation view\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(\"Hartmann 6D is 6-dimensional and not shown directly.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "023e6927", + "metadata": {}, + "source": [ + "## Experiment Infrastructure\n", + "\n", + "Each study shares the same evaluation protocol:\n", + "\n", + "- **Budget**: 50 total function evaluations (studies 1–3, 5); 80 for study 4\n", + "- **Seeds**: 5 independent runs; plots show median ± IQR\n", + "- **Metric**: *Simple regret* = `true_optimum − running_best_found`\n", + "- **Warmup**: 3 random iterations before GP kicks in\n", + "- **Baseline**: Random search (i.i.d. uniform sampling)\n", + "\n", + "`jax.vmap` over seeds + `optimize_scan` (lax.scan) makes each study compile once\n", + "and run all seeds in a single XLA kernel.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db93b4b8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:24:39.793663Z", + "iopub.status.busy": "2026-04-01T10:24:39.793554Z", + "iopub.status.idle": "2026-04-01T10:24:39.800171Z", + "shell.execute_reply": "2026-04-01T10:24:39.799934Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Infrastructure ready.\n" + ] + } + ], + "source": [ + "N_BUDGET = 50 # default total evaluations per study\n", + "N_SEEDS = 5 # random seeds\n", + "N_WARMUP = 3 # random warm-up iterations before BO\n", + "SEED = 42\n", + "\n", + "\n", + "def run_bo(space, wrapper, true_opt, n_budget=N_BUDGET, n_seeds=N_SEEDS,\n", + " n_parallel=1, n_warmup=N_WARMUP, **bo_kwargs):\n", + " \"\"\"\n", + " Run BayesianSearch with given config; return (regret, eval_x).\n", + "\n", + " regret : ndarray (n_seeds, n_gp_steps) — simple regret after each batch\n", + " eval_x : ndarray (n_gp_steps,) — cumulative evaluations\n", + " \"\"\"\n", + " n_iter = n_budget // n_parallel\n", + " state0, opt = BayesianSearch.init(\n", + " space, n_max=n_budget, n_parallel=n_parallel, n_warmup=n_warmup, **bo_kwargs\n", + " )\n", + " keys = jax.random.split(jax.random.PRNGKey(SEED), n_seeds)\n", + "\n", + " @jax.jit\n", + " def _run(keys):\n", + " def one(key):\n", + " _, (_, res) = opt.optimize_scan(state0, key, wrapper, n_iter)\n", + " return res # (n_iter, n_parallel)\n", + " return jax.vmap(one)(keys) # (n_seeds, n_iter, n_parallel)\n", + "\n", + " raw = np.array(_run(keys)) # (n_seeds, n_iter, n_parallel)\n", + " flat = raw.reshape(n_seeds, -1) # (n_seeds, n_budget)\n", + " rb = np.maximum.accumulate(flat, axis=1) # running best (n_seeds, n_budget)\n", + " # sample at batch boundaries (after each GP step)\n", + " idx = np.arange(1, n_iter + 1) * n_parallel - 1\n", + " regret = true_opt - rb[:, idx] # (n_seeds, n_iter)\n", + " return regret, (np.arange(n_iter) + 1) * n_parallel\n", + "\n", + "\n", + "def run_random(space, wrapper, true_opt, n_budget=N_BUDGET, n_seeds=N_SEEDS):\n", + " \"\"\"Random-search baseline (i.i.d. uniform samples).\"\"\"\n", + " state0, opt = RandomSearch.init(space)\n", + " keys = jax.random.split(jax.random.PRNGKey(SEED), n_seeds)\n", + "\n", + " @jax.jit\n", + " def _run(keys):\n", + " def one(key):\n", + " _, (_, res) = opt.optimize_scan(state0, key, wrapper, n_budget)\n", + " return res.squeeze(-1) # (n_budget,)\n", + " return jax.vmap(one)(keys)\n", + "\n", + " raw = np.array(_run(keys)) # (n_seeds, n_budget)\n", + " rb = np.maximum.accumulate(raw, axis=1)\n", + " regret = true_opt - rb\n", + " return regret, np.arange(1, n_budget + 1)\n", + "\n", + "\n", + "def plot_study(results, suptitle, figsize=(13, 9), log_y=True):\n", + " \"\"\"\n", + " 2×2 convergence plot — one panel per benchmark function.\n", + "\n", + " results: {label: {fname: (regret_arr, eval_x_arr)}}\n", + " \"\"\"\n", + " fig, axes = plt.subplots(2, 2, figsize=figsize)\n", + " colors = plt.cm.tab10(np.linspace(0, 0.9, len(results)))\n", + " for ax, fname in zip(axes.flat, BENCHMARKS):\n", + " for (label, fdict), color in zip(results.items(), colors):\n", + " if fname not in fdict:\n", + " continue\n", + " regret, ex = fdict[fname]\n", + " med = np.median(regret, axis=0)\n", + " q25 = np.percentile(regret, 25, axis=0)\n", + " q75 = np.percentile(regret, 75, axis=0)\n", + " kw = dict(label=label, color=color, lw=2)\n", + " if log_y:\n", + " ax.semilogy(ex, np.clip(med, 1e-8, None), **kw)\n", + " ax.fill_between(ex, np.clip(q25, 1e-8, None),\n", + " np.clip(q75, 1e-8, None), alpha=0.18, color=color)\n", + " else:\n", + " ax.plot(ex, med, **kw)\n", + " ax.fill_between(ex, q25, q75, alpha=0.18, color=color)\n", + " ax.set_title(fname, fontweight=\"bold\")\n", + " ax.set_xlabel(\"Evaluations\")\n", + " ax.set_ylabel(\"Regret (log)\" if log_y else \"Regret\")\n", + " ax.legend(fontsize=7, loc=\"upper right\")\n", + " fig.suptitle(suptitle, fontsize=13, fontweight=\"bold\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "def summary_table(results_by_study):\n", + " \"\"\"Final-regret median across studies and functions.\"\"\"\n", + " rows = []\n", + " for study_label, results in results_by_study.items():\n", + " for label, fdict in results.items():\n", + " for fname, (regret, _) in fdict.items():\n", + " rows.append({\n", + " \"Study\": study_label,\n", + " \"Config\": label,\n", + " \"Function\": fname,\n", + " \"Final regret (median)\": float(np.median(regret[:, -1])),\n", + " })\n", + " return pd.DataFrame(rows)\n", + "\n", + "\n", + "print(\"Infrastructure ready.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb779493", + "metadata": {}, + "source": [ + "## Study 1 — Acquisition Functions\n", + "\n", + "Compare the three standard acquisition functions at their default parameters:\n", + "\n", + "| Function | Formula | Controls |\n", + "|----------|---------|---------|\n", + "| **UCB** | μ + κσ | Pure exploration/exploitation mix |\n", + "| **EI** | E[max(f−f*,0)] | Expected improvement over incumbent |\n", + "| **PI** | P[f > f* + ξ] | Probability of improvement |\n", + "\n", + "UCB is exploration-heavy and linear in uncertainty; EI and PI fold in the\n", + "improvement magnitude/probability. EI is generally preferred in the HPO\n", + "literature (Bergstra & Bengio 2012; Snoek et al. 2012).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c20f65bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:24:39.801342Z", + "iopub.status.busy": "2026-04-01T10:24:39.801257Z", + "iopub.status.idle": "2026-04-01T10:25:13.241541Z", + "shell.execute_reply": "2026-04-01T10:25:13.241279Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study 1: Acquisition functions (~2–3 min)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ea0e86988c054d649c1398de73648606", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Acquisition: 0%| | 0/3 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_study(s1_results,\n", + " \"Study 1: Acquisition Function (N=50, 5 seeds, Matérn 2.5, median ± IQR)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "88426df6", + "metadata": {}, + "source": [ + "## Study 2 — Acquisition Hyperparameters\n", + "\n", + "### UCB: exploration weight κ\n", + "\n", + "κ directly sets the exploration–exploitation tradeoff.\n", + "- **Low κ** (0.5): greedy exploitation — fast early improvement, high regret long-term\n", + "- **High κ** (5): heavy exploration — may waste evaluations in uncertain regions\n", + "- Theory (Srinivas et al. 2010) suggests κ ∝ √(2 log t) for sub-linear cumulative regret.\n", + "\n", + "### EI: improvement margin ξ\n", + "\n", + "ξ acts as a tolerance above the current incumbent.\n", + "- **ξ → 0**: reduces to pure exploitation (exploit around current best)\n", + "- **Large ξ**: requires a big improvement to be considered worthwhile — pushes exploration\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "aa6d4d55", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:25:13.787856Z", + "iopub.status.busy": "2026-04-01T10:25:13.787742Z", + "iopub.status.idle": "2026-04-01T10:26:48.719540Z", + "shell.execute_reply": "2026-04-01T10:26:48.719203Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study 2a: UCB kappa sweep (~2 min)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "927fef599724421baaa2ae1aaa1eef3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "κ: 0%| | 0/5 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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7kNdy6j2wbNkyEbtw8803i+P1mmuu6bMc+iwd+v6DRZIM51pDDtvUfu3/e1xWViYib4baDqeddlpGFy4NFNs0UsjtnH5e0jmbS4+ndGj7VFZWivOGendQlNXBBx/cZx3pfPrkk08yTk/XsvTrFcMwDMOMNuwoZhiGYUYdVVXx0ksvCYcwFbXbvHnzgHHIbUausX/+85+i0EyhIXdoyvFInHDCCX2K6Jx11lnC8ZcJclTSulLxGeKOO+4Qrk5yTqacXjQvcmmOBHJNfvOb3+wtXEdOPlrGUE6j9GJdtPzjjz8e99xzj3j92WefCZdrqpp7upO6mMnkVh4uU6ZMyVikqz9f/epXhXsvBbkXlyxZIlzhwynORwWKbr75ZvE37Tdy3VGBwjlz5ggHPc2THInphY7IhUvFyojXX39duPZSrrnvfe97+NnPfiacc3RspDsYyUnndDp7p0uHvnO2703Fk8ihufPOO/eZjpyTVGQyG4MVnRzO8TcSUkXsCHIf5go5mcnRR24/+jfdVZrteEm5rUcKucgvvfRS4Rbs77j99re/3fuarodHH310xnmQq3aw1/mAXJp0zeq/z5955hl86Utf6l0mreeFF16Y07FPLtJUMcgnnngCc+fOFQO5Qelzj8fTO376sUeO6sEc0nTs/dd//Zc4D1KuZYKKfNKQiUQiIVzLtI1T7ldC07Q+58b06dNFDxC6hveHrrvkWiaHLPU4ICc7uZfJPbv77ruL8zmbyz4fXHzxxeJ6QkXtaB+kXKt0vg11HtDvLv2WrFu3btDxMv0mD8ZwrjX0W0vnP22r/pArma59Q5Huxu3fSyflfB4u1MuAek7QPiXo2HvooYfEdXo4kGOaehbRMZXOZZddJgrjEbTvqKcJtYH6Q/uQepr0v8YxDMMwzGjBQjHDMAwzJpBARF1caSCBim7eqRsydcFOiaskSvz2t7/dIaG4v5hC3Tkzkd7VlOgvBNbX1w+6HBLvUkLxY489Jm7w0mMnqHp5f5Eo1+7fFHfw2muvidd080liG3XhHoqhvgN95x0R6jJx5ZVXin1aKC655JIdngeJU0MJxdSlP10kpmPwgQce6PPwIFdOPvlkIZL9+te/RiQSwfvvvy+GdAGahLf0BwkkNqVEVRKCU/EI9C8Ji7SdaV4kdqULxTRdpuiAXKBjloTi4Uw3mJAxFsdfLpCQftVVV+Eb3/iGuB5kewCUgqJn0s/lXKDjiwTmFNmEQ4pZoNiEVMwHRXmk6C/6BQIBVFRU9BG2UpBwmY8IjcbGxozvH3PMMULc/fzzzwes52CQCE/jUlQFibQU55Ae6UDHCIlxhxxyiHg9kmNvJMd5/2s+bWtFUXK65u+1114iioaOG5oHfb/07WGz2YQAONQDiJFCIvZJJ50k4lzSow3oN2gwKGqDHmimYnAGI9vvZDaGuw/6/96moOtPLqSfT/0fJqRiS4YDPZSjByGpCB+6JtPv+dKlS4c9L3rQl+0BVUooHuwcGsn6MwzDMEw+YaGYYRiGGXNSDjNyRNENNt0s0k0tQZl+w4VEkxQkpqWzevXqjNP0d9ClxOpMuaKZIKFj4cKFIr+VXJ533nlnr3BMkCg1XCiPkwRKEtIJcpXSPEnozAX6DuliVf/vkC46MX1JCWIprr322hGJxCkoF5ccZeR+JDcd7VtyK5JrkgRAcpzSvk4Jp+RITwnF5NZLiYDkcqQMT3IxkqhBOdQffPBBxnziVHZoCnJZ0rSDCVD9p6P3KPc0G4O5jQt9/KV/l+EKVeecc454CEXbkDJOB3sQRPtquA8oKMc1/bvnSnruLzkuad3S14NEyvTXKeiamc984uGs52CQ+Eruyl/96lfi2KfzitySlHVM60/HCLlIU9nrdOylrr3ktidXfzZS27f/cU7i+2Au0FRWevrxR1n55PJMF4sHu+ZfdNFF4ppOGbIk+NF3oXx2ethJ13/KwibhMZtouKOQ6EhCcboTfJdddhl0Guqdky4S028tfQe6ttCDih15aNN/H9D+HSyPv6GhIeP7ua5Duls312MxG5TNTL0dUrnTJELTtqK84EKSbb3Tr2W1tbUFXQeGYRiGyQQLxQzDMMyoQ+4y6n5KIkB/FxyJHeTIyuaqS79BTLnw+pMuAJB7jG7i6YadXFLZogvIQUQ3timBkLpIk2MvJQ5ScaOhIEcXCX4EFaWi4nMEFQ4abqE9EhzINUaFmwhaN7p5pe7NuUJFnFLF/ui7PPnkk32EpfSb8kMPPbTXtUxCdLFGURSiu30m+ru/yQFMDwJGAnXzpmOSjnVy/KZcvyQIpI5vEnAo+iQlBKYLvuQaJNclkXJe0r8kcv7hD38QAhdBXfj33Xff3ukOPPDAAU7aTBEKJNLRcZ8S3mg6EsAIEk+OO+64AU4/2g/UjX0wIWw4x99ISC/uRg+WyImX/pBoMGg8cvel4hRSIlEhoAcB5ACn6In+Ahk9UEq/jqUX8ST3Z3qEAhVRSx0fVOQr3ZFI14p0SOBPPWSjwmTUzT0X6BpGTtj++5WiJ1IPrPqv52DQcUWxKnTskyuZhtT5lPoutJ4k1NK5QMce9SpJ7VN6eNhfxKeYCTqW6JqVKjZGcUap+AkSajMd5yT8kpibKnpIYn5KbCW3M7nGU8I0nRPZYlWo2FnKfU0xFKkIIDqO6HtScTg6J+mhYWo70oMJ+t1LCZHpRUlHAj0wou333nvvidc/+MEPhpwm9VuSXhgv9ftLBdYGY6jf3f7XGtqudGz3J/WwgI7JYoCONYqVSn2n/fffX/QIGixaiH4bqYhoCnrglzoW6Ryh44jiQfo/CLvpppv6vM50DtFxkzq+MhWwZBiGYZjRgIVihmEYZtQh4ewXv/iFuLk94IADhMOLnDMkqJBDMv0mun/sRHqWK4nAJMySk4rcOeR8JDGsf5Vyuokl8ZPEiVT2XyYouzElMNBNNYluFAdAXc9J9BoKipcgYYcccSmReCRuYhIzSExMdf8l4fzUU08VN7D9IfdcNqcrxR2Q+EjCBIlM6UIBfddCQOueTYynm+fRdD3uCCTEkoiSyruk7UUCQOpYpWOsvziSDRJN/ud//kcISiT4k8BEohK57tJJf2jS1NQkxNRUbnZq36ULxSQSkyiVguafLuiQEEFOw1TmJkWCkPhFYgjtB8ohpcgXciSTcEPjpvbTbbfdJo5hGui70nlAogW9JqGHHiqQ8EYiyYwZM8bk+KPzk74viXwktJNTOyUC5gJdW0jwyZRDmw6JQDvygILEH3Iv33LLLeIBAAmUtN4UG0LXuxSpa1gKOla+/OUv94p45AClhwskNt9999193Jy0z/IBiZkUq0DHEh0nJObTAwk6htO3AWUD5wLNi2JVaDvT8UPrTte19N4WdCymHszQ9ZcyjUn0p+OLjuHTTjtNXPfp4SKJ4yTUUXQB/Y7QOUPfn66xlP9NkChOxwJta3oASMIbuUbpWKfvRVEwBInQ9DuUulaTcErnB11P6cEgHVfZHuJRli3lgZPrlM5n2p/k/E8/H/MRBTIYtI50ftFDjyOPPHLI8fs7fOn4pwcl1Msm3bmeCdr+qd9OOmapdwQ5+mkgEbz/tYZc5BTfQ/uAfpPpYQBdM2hZdK0pBqGYHr7R+ZWKeqD9TtskVQsgnVwzyklwpvz466+/HkcccYR4uEjnP3339BxnOubPP//8AdPTuZYSrel3P/3BH8MwDMOMGqNePo9hGIaZ8Fx55ZV9KoBnGxYtWmQFg8E+01KFc1mWM47f1tYmxolGo9ZOO+2UcZzjjjuuz+v0quJU8fyQQw7JON1hhx3W5zV9h0z89Kc/7TOe3W63Ojo6hrV9+ld4H2ygqvHZtmv/dU4NixcvthKJRJ9l0nvpn+fKK6+8kvO6dnV19akMn+17FAvPPfec5fV6s36fiy++OKf53HDDDUNumzPOOGPAdN/5znf6jOPxeHr329atWwfM48YbbxwwDzon9tprryGXT/sjnSeeeEIsb6jpaP9nO/76n2uDHX8jJf24vfPOO4c8PtPPd2LZsmWWJEkD1i+f0HE/1HZUVdW65ZZbMk5L18Fs07ndbuuFF14YMN20adOy7tvBKC8vH3JdL7vsspzn1/8YzjRceumlfaa5/fbbLU3ThnXtC4fD1lFHHTXkNP33LS0r03hlZWXWnnvu2fuatmeKhx56aMjlHHDAAZau6xmvd+nzGu5vQfr5lo1s13L6fVu4cGHG9T333HMH/X37/e9/n3G6XXfddYeuNbQPBzs/s22H/r8Xg637jrZDaBjsmpK+Tz744IMh5+VyuaxHH3004zrdeuutveMtWbIkp+/BMAzDMPkmt/55DMMwDJPnfEVylFEXZ3KtkSORnGvkyqIun+RCuvXWW4UDp3/3dMrtJCcQRTmQUykT5NahQmTk8CW3Gb2mvEtympHDLRu0fOpiTe5PqrxOjh5ywVG3cXo/F6jQWLqrk9xr/fMbRwtaZ1p36v5M34WiBei7Pf3006KrNjM45NIjBxzlC5MjndyQ6Y5oOpbSC3Nl44QTThBuXnLc0fFELkfa/uTGo/mSM/i+++4bMF16YTqCzpXUfiMXY/8YkvS4ihS0DFpHcvhRYSZyQ9M86NyhHFdyRtJn5HhNhzI7Kd6AsnnpnKNYC8pvJZcknXvkYH3hhRd6Hc6ZoHOcIlhoPQt1/JELNMXf//73YU9P3ffJVVhIKMeZHIUUPUHubNoOKRctuTwvuOAC4erOlAVN3depWCF1W6dpyfVI09JxdOGFFwoHYv+CWxTBQFEOKajXRq68+OKL4ppB0QbkBKfjxOFwiOWRA5fctL/85S9znh/1+KB9To5imh99Z9r3dC7R+UBO1vQCXwRtD4ptoGspuepT09CxTC5+2o7UcyGVqU3QetL1jlz/FJWScvnSutN4dA7efPPNoiBl/2VRTw1ybtK4dHxT7w2KXpk/f37G70Tbk7YBnSN0DtH+TZ0b5DImNymdG9nyjoezP/IJbQ+KiyEnK10H6Jyk44+c6nfdddeg01Imc+pcTv99y8e1ZjxBPRroOKTrI/U+oZ4htL3o+kmua8quputqytXeH+p5kenaxjAMwzCjiURq8agukWEYhmHGMdTNlISgVM4gCS/9BT+mtKGIFBJCSEwi0YMefDDJaAvqyp9iNJqYVKySzjcSRkmYoziNbIWyJgoUsUCCZerhAom7O1rwixk5JNzTw0K6bpDwT7EYE/0YZQZCbQaK+KAoDBLdKfKKHl4wDMMwzGjDjmKGYRiGyQNUpIfyGSkzOCUSk7sok8uTKW3IKUsFEjMVvWNGF3IrkmM19ZDmd7/7HSY6qUKU5Oak3F4WiccWKjhHIjFBTmQWiZlMkOM9lZdMRUBZJGYYhmHGChaKGYZhGCYPUPf1o48+urfIFBUYou7iLNKMP+655x7hYCWROFUAjhk7qLAaRcUQFOPh9/sxkUkV5/vv//7vrPEJzOjvD3J3U9QFw/SHiiBSAVGCrmUU9cEwDMMwYwUHFDIMwzBMHqEsQhJnqPJ5/+xQZnxAudmUaXrdddeJ3FVmbKHM3g0bNoz1ahQNzz777FivApPGZZddJgaGyQblXJNYzDAMwzDFAGcUMwzDMAzDMAzDMAzDMAzDTHA4eoJhGIZhGIZhGIZhGIZhGGaCw0IxwzAMwzAMwzAMwzAMwzDMBIeFYoZhGIZhGIZhGIZhGIZhmAkOC8UMwzAMwzAMwzAMwzAMwzATHBaKGYZhGIZhGIZhGIZhGIZhJjgsFDMMwzAMwzAMwzAMwzAMw0xwWChmGIZhGIZhGIZhGIZhGIaZ4LBQzDAMwzAMwzAMwzAMwzAMM8FhoZhhGIZhGIZhGIZhGIZhGGaCw0IxwzAMwzAMwzAMwzAMwzDMBIeFYoZhGIZhGIZhGIZhGIZhmAkOC8UMwzAMwzAMwzAMwzAMwzATHBaKGYZhmGHz6quvQpKk3oFhGIZhGIZhGIZhmNKGhWKGYZgx4t577+0jtqYGh8OBKVOm4Etf+hIefPDBsV5NhmEYhmEYZoLSv726fv36AeNMnz699/Nzzjmn4Ov04Ycf4uc//7kYbrrppoIvr5T59NNPcf7552PWrFlwOp0oLy/HzjvvjLPOOgsfffRR73i0X/vfk9hsNlRUVGDu3Lk4/vjjcccddyAYDI7p92EYpvCoo7AMhmEYZhjEYjFs3rxZDE8//TQ+/vhjXHvttSgm9thjD7z++utjvRoMwzAMwzDMBIOE4l/84hfi72nTpuH73//+WK9SUfLHP/4RF110EXRd730vGo3C7/fj888/x9KlS7FgwYKs0ycSCfh8PjGsXr0a//znP8V2v//++3HooYeO0rdgGGa0YaGYYRimSHjooYfQ0NCAtrY2/M///I9owBG//e1v8bOf/Qx2u33Q6ekJv8fjGZV1JTfCQQcdNCrLYhiGYRiGYZjRbOuWOq+88gouvPBCmKYJWZbx1a9+FccddxxqamrQ0tKCd999F01NTVmnv/zyy3H00Uejo6MDr732mhCdQ6EQtm3bhmOOOUbMf7/99hvV78QwzOjA0RMMwzBFwt577y3E15NPPhn/9V//1ft+JBJBV1eX+Ju686V37XvuuedwwAEHwO129wq3L7/8Mr785S9j1113RW1tLTRNE41qcgz8+Mc/Rmdn56B5w/T5xRdfjEmTJglxmubzt7/9bdBp0kl//6WXXsKvf/1rzJs3T8xr8uTJ+OlPfwrDMAq4JRmGYRiGYZhigNy+5D6lWDVqj1KcQWNjoxAtn3rqqQHj07ipdiRFS/z1r38VPdkomo3iEuj9c889t3f8DRs2ZIzGSH/v+eefF6YLWgeKX9h///2F+Em8+OKLoi1N75Nhg9rg1PZO5+GHH8Ypp5yCnXbaCVVVVaJtTaYJartTr79wOJw1roNiOTZt2oSzzz5btMvpe+yzzz6ijbyj0wwGmU5IJCauvPJK/N///R/OPPNMLFmyRIjGZEQ58sgjs04/Z84cHHzwwTjppJPEuG+//bb4zilXMonQlmXlvD4Mw5QO7ChmGIYpMtrb20XkRApqTNfV1Q0Yj6IfqNGXagSm+Ne//jVA2KUuZxRhQcOjjz6KZcuW9Tb2+kPuAOpelmLFihVCeKZG66JFi4b1Xb797W/3mdeWLVtwzTXXwOv14tJLLx3WvBiGYRiGYZjS4uabbx7wXnNzsxCJabjlllvwne98J+O0FHGQ3o4cKd/97nd7e+oRb731lhBJSUD9yU9+0it4kgB66623irb1H/7wh97xKXLhH//4R595UnzDe++9Jwb6/N///jcURRmwbDJ7kMhLLt4U1A4noZzWaerUqXmZJh2KryNhN0VKaP7ss8+EcYMEYPruJMDnChlHfvSjHwnDB7F8+XLx3UksZxhmfMGOYoZhmCJhxowZwkVAzoFnnnlGvEd///nPfxZdxvqzdu1aUYyCxGJyFpMLmCCXBBX2oAYtuSSoaxjFWlADkVizZg3uuuuurOtBjVP6/O9//7twFQ/W0B8KWkdqiFIDevHixb3vc+ERhmEYhmGY0m2vpg/k6s0GCbHkCiYTBPVIozbrdddd1/s5CY/pGbrpkEh84IEHCgMEicpkXCCjBMUipCAXML2XGshg0R9aP3LFUtuYxifi8TiuuOIKHH744XjyySfxzW9+s3f8VMxCCopaIAH58ccfFz33aLjvvvswe/bsXuH5sccey/gdSFCmnn8PPPAA7rnnnj6u3Ntvvz1v06RDIm461KOQhGb6TtRzkL4HGUPoPmE4HHHEEX1eU3wFwzDjD3YUMwzDFDHUSExvqKbjcrlEQ7W+vr7P+4cccogo8vHLX/4Sq1atEo3N/q7jN998M+syyUFx+umn9wq9l112mfg73YmRKxdccIHoNkhQJloqy4zyzQKBgHAWMwzDMAzDMOMTcsGSSEtiKjmJqWhzf4PCypUrsdtuuw2YlgwLJGaSIzYdMj2kIIfsUHUzyFGcKnj3wgsv9LqFab5kjKA4CWqjkkBMkHBNbeD58+eL1+Q+vvHGG3HnnXfiiy++EG3z/rEL1LY+9dRTMy7/wQcf7DVskNM3JfYO1rYeyTQpuru7+7wuKysTUXDUQ5GE+Y8++kgI5d/61rfE9+kfI5eN6urqPq9T0XgMw4wvWChmGIYpsmJ2VKjj7rvvFq8pZ+20007DBx980NtYTUEOi/4iMTVav/SlLwkX8WD0zylOh7LLMjUIB5tmuPNKzY+FYoZhGIZhmNJrr6ZDBgMSgftDIi+5cbM5hodqYx577LEDROKRQBnEmdqjqczhlKEh0zpRXjG1uSm2YSTfgdq6KcG3//LzOU06/bcZRcGdf/75vaLxYYcdJv5et26dEOmplkguUMHtdCorK3OajmGY0oKFYoZhmCKBMr4oB5hYunSpiJ8g0ZgKv5GroL9QnKlrHbkZ0kVick9QI5sKdJBL4i9/+Yt4v7/DOJ1Ug5lQ1e0/EyMpWJFtXiOdH8MwDMMwDFMc7dV0V28mfvWrX/WKxFQcjXqZUUE5aodS0boU2dqlmdq6IyG9Lkd6nFtFRUXWaVLtVIqrSInElEFM34GEZyrKd9VVVwmH8mDfIb0tnGvbeiTTpNN//1BcSKa/M7mPB4OKAqaTLmYzDDN+YKGYYRimCKFGYHpDsKOjY8A4mbqJbdy4sY/7gLr6pUhFQDAMwzAMwzBMoUlvl1L8w1e/+lXxNxV+y4VskQjpYu9g5od8f4eFCxeKzGWCBPD0z4qJBQsWCLE55T6mHoop0v/OJCpng+IqfvOb3/RZxl577ZW3dWYYpnhgoZhhGKZIoCITVKU4FT2Rnk1MlYZzYebMmX3E5WuvvVY4Px5++GG89NJLBVlvhmEYhmEYhsnULk1l6lKhZBIlSbxMia0jJT2KYevWraLHHC2LetDlW7xMb1uTWEr5xuTKpbzikdTvGA00TcNFF12Ea665Rry+7bbbROE9KpJNRaZTUCG/bK5tKiRIxQFpf1ERwvQCfxRtcccdd+ScbcwwTGnBQjHDMEyRkCog159ddtkF5513Xk7zIFGYCnqknBqphjh1WaMid//617/yuMYMwzAMwzAMk5kf/OAHIkqNWL58OY4//vhegXLTpk0jni9lBqcKPlNE29lnny3enzVrVp9Cd/mA1plEVppvIpHAd77zHfG+x+MR0QvvvvsuihG6B3jjjTdEJJ3P5+vNKE4xefJkId5n47rrrhNDf5qamnD//ff3FqhmGGb8sb3PBsMwDFMUUHc6ylKjxidln1HuMDWGc5328ccfF41Bcgi4XC6Ro/bcc8/1Fq5gGIZhGIZhmEJzxBFHiHbpnnvuKdy+JDJS/Ywnnnhih+ZL2cKPPfYYFi1alJdid4NB6/3yyy+L4tJU8I4EYvpe5LYlM0exQrnRzz77LG666SZhJKF7CXqPCvhdcskl+PDDDwfkFfeHMpnpnoTypUkwJxf1qlWrsHjx4lH7HgzDjD6SxdWEGIZhGIZhGIZhGIZhGIZhJjTsKGYYhmEYhmEYhmEYhmEYhpngsFDMMAzDMAzDMAzDMAzDMAwzwWGhmGEYhmEYhmEYhmEYhmEYZoLDQjHDMAzDMAzDMAzDMAzDMMwEh4VihmEYhmEYhmEYhmEYhmGYCQ4LxQzDMAzDMAzDMAzDMAzDMBMcFooZhmEYhmEYhmEYhmEYhmEmOOpYr0ApE41G8fHHH6O2thaqypuSYRiGYRim0Oi6jra2NsyfPx8Oh2OsV4cZBtx2ZhiGYRiGKe72M7fQdgBq6O67775jvRoMwzAMwzATjnfeeQf77LPPWK8GMwy47cwwDMMwDFPc7WcWincAckOkNnRjY+OwpzcMAx0dHaiuroaiKAVYQ6bQ8D4sfXgflj68D0sf3oelz2juw23btgmxMdUOYyZO25ng60Vpw/uv9OF9WPrwPix9eB+WPsYo78PhtJ9ZKN4BUl3mqKE7efLkER0Ydrtd7Cg+uUsT3oelD+/D0of3YenD+7D0GYt9yNEFE6/tTPD1orTh/Vf68D4sfXgflj68D0sfY4z2YS7tZy5mxzAMwzAMwzAMwzAMwzAMM8FhK8YIuOGGG8RATwAYhmEYhpl4+P1++Hw+bgv0YFmWKFQWiUQgSVJe5knuivLycpSVleVlfgzDMAzDMMzYwe3nwrad89V+ZkfxCLjkkkvQ2tqK5cuXj/WqMAzDMAwzBo3cLVu2iMYdk4QauNR9Lp8NXdq+tJ1pezMMwzAMwzClC7efC992zlf7mR3FDMMwDMMww4CcEJTvNXPmTM6FS3NF6Loutku+GrzkNlm7dq3Y3uwqZhiGYRiGKV24/Vz4tnO+2s/sKGYYhmEYhhlmA0zTNG7kFhjavrSduXsiwzAMwzBMacPt59JpP7NQzDAMwzAMwzAMwzAMwzAMM8FhoZhhGIZhGIZhGIZhGIZhGGaCw0IxwzAMwzBMCUO5ZgsXLsTuu++OBQsW4Iknnsjr/O+9916cddZZeZ0nwzAMwzAMw4wV3H7OzoQtZrdp0yace+654l+bzYZ9990Xf/jDH0TVQYZhGIZhmFJi2bJlohjG008/jTPPPBNdXV3iNcMwDMMwDMMwA+H2c2YmrKOYdv61116Lzz//HMuXL0c4HMbNN9881qvFMAzDMEyJVSyO6UbBB1pOLhx22GEIBoPo7OwUr3/1q1+Jh+HkmKDP1q1bJ95fv349Jk2ahB//+MfYY489sMsuu+Dtt9/unQ+9P2fOHOy333546623et+n6szf//73sdtuu4nhqquu6v1s6dKluOSSS3DggQdi6tSpeOCBB3D99ddjzz33FONSm4thGIZhGIaZ2IxG+znXtvNYt58PO+ywoms/l5xUvmbNGtx4441io3/yySfYeeedxb/9WblyJS6++GL85z//gdfrxde//nVcc801wj1MNDY2ioGQZRl77713785nGIZhGIbJhbhhYqefPFvw5Xx+zdGwq0NXif7HP/6BxYsXo66uTrw+77zzcOmll4q/77//ftGA/fvf/y5eb926FYcffjj+93//F3/9619x+eWX46WXXhJd71555RV8/PHHYrxDDjkEc+fOFX/fcccdWLVqFT788EPE43HxGTWijz/+ePF5R0cH3njjDeHQoPW49dZb8f7774sGNy3n7rvvLtg2YhiGYRiGYYqf0Wg/59p2Hqv28+67745jjz1WfN7e3l5U7eeSE4o//fRTPPXUU1i0aBFM0xRDf8guTjuOlPxHH30UW7ZswQ9/+EPhGr7lllsGjB+JRMSG//Wvfz1K34JhGIZhGCZ/0ANvn88nGq/UWE1BLodf/vKX6O7uHtBuqq6uxlFHHSX+3n///fGTn/xE/E2NXOp+53A4xOuvfe1rePfdd8XfNG9qPFPPLBooe43eSwnFp556qviXXBbU7jr99NPF67322kt062MYhmEYhmGYYmCs28/H9gjFp512WlG1n0tOKKYbkRNPPFH8fc455wjFvT+33347/H6/eCpQVVXVa/W+6KKLhNrf1NTUO65hGPjqV78qukseffTRo/hNGIZhGIZh8puxRs4DaphSNzUq0kFtHOpdteuuuwpnwimnnNI7TaohSyiKItpKKWjakZCq9UDz6/86ff4MU2g2t3Tg3tc+xQ+OXwS3k2uQMAzDMAzTF24/jxOhmGIihuKZZ54Rwm9KJCbOOOMMXHjhhXj++eeFwExQZgkVtKNoiptuumnI+ZL4TEOKbdu29YrNNAwXmoaeTOQ6bSAchtNm43DtImK4+5ApPngflj68D0ufUtuH1H6ghiD9q8kSVl6ddBUUElrOYFlr9BkNP/rRj/Dwww+LLm5nn3222KYUtUXb97bbbuszburv/v8eeuihwkXx7W9/W3xPykqbPXu2+GzJkiWiijM9tE8kErjvvvuEkyJ9+vT1TL3uv5zhQNOkHxulcpwwY8tvXvgU908qw0OvfIojugL4wZHzMal2+70BwzAMwzBjg02RRTREoZeRK5QRTO3nO++8s7f9TAZTaoOSETUXKGv4uuuuEwbV9PYzQfoktZ9POumkPu3nYmVcKo6UT0y27nQqKirEjRJ9loJ2YCgUElkjuSj/v/nNb/CLX/xiwPuUx5dS/IcD3bSRzT1XAfzNT9eirsKNWZPqh70spjAMdx8yxQfvw9KH92HpU2r7MBqNit/91BP+3NLPdoyhxNF0twEVwKDaDN/4xjdw2WWXiWIY1E0u1b2Nxk2Nn+lfGu/111/HggULxEN3ylCjAh/0GbWvqC1FucSprnI0Pn2WEnTT1yX1N71Pnw/XFUHHRiwWQ1tbW592F8MMZa54scoj/m5xyvirsxyPv78eh7Z8gm/sNQn77TprrFeRYRiGYSYspL/lmh88WutDtdDISXzBBRfgiiuuEDEQNTU1OO6443KaxwknnCByhufPny/a3dRWpvYz8a1vfUu4lVPtZzKy0vjF2ttOskZi7SgSUtET/YvZaZqGq6++WtwcpUMVAw844ADxlIB24EEHHSSqFNL4qScAv/3tb4flKKZKiFT5cPLkycNef7ppotBqOvhSFvPBuO/FZXBpKo4/cDd2FRcJw92HTPHB+7D04X1Y+pTaPty4caNoUE6bNm2sV6VoSInA1D4Zabe7TGzYsEHMm6pAp9i8eTOmT5+OTZs2jaj9xYwdtO+mTJmyQ/uOrhf04KC2tjbr9WLDtnZc+dJKvFbvRkTtezxqpoUDt4VxZCVw/AG7obayfETrwaBg+48pbngflj68D0ufUtyHpJsR1IZjULC2c7ZtPZw22IRVGw888MBhd38sKysTww033CCGlLuHTsyRnpzknMp5eklCWziGze3d7CouIoa1D5mihPdh6cP7sPQppX2Yaszlu1FX6tD2SA35nm/6cVEKxwgztkxrrMG9Zx2E59/9CA991IY3mirR6Uj2VkjIEl6d5MarAB59/jPsHe3G0t1noLG2Ak01lXDabWO9+gzDMAzDMGPGuBSKKysre7uwptPV1dUnt3ikUH4JDSlFfrSIJgy0B2N4f/U2aA7vkOO77QqqPVy8g2EYhmEYhpl4NNVUYfeaIOYGu7FpSwJv1ddgo2f7g4ZldQ4sQwNeWefHXsvWYpdJ5Wiq8KDS60al14nqMvrXzT35GIZhGIaZMIzLVs/OO+/cJ4uYIOGYoiLos1IlEjcQjhtY0+KHs6wFTqcr67iGZaHOa0eZU4M2jBBvhmEYhmEYhhkPTKuvwl6zYtjS5keFK4JpkSC2NofwYVUNPq3a7hz+vELD5xWNqIqZqGuOoWxDGJ5EAi49Abepo0IDajwaZjRWYo/ZU7koHsMwDMMw45ZxKRQfc8wxotpgd3e3KGJHPPTQQ6Jb65FHHrnD8+8fPTGauDQFpmXCikfQ1Fiddby4bqIrHEd3OIFaL7uKGYZhGIZhmImF1+XC/rvNEhmArV1+NHf5saXVh10DEaxZuwWfuqrwbr0DZk9cSqddFgNA9UscA2cYBeSPN6Aivh7lcROyJSGXoBXZAg4IAOcuaIJS44KkylBUGbIqib/lfqYOmme5qsBdRIV+GIZhGIaZGJScUBwOh/H000/3Fjih4nIPP/yweL148WIR5n3hhRfi97//PU466SRcfvnl2LJli4iKoPebmppKNnqCkKVk47G5w48Zk3QoWbrC2VQZFiy0BaOo8dg4R5FhGIZhGIaZkFB0RFNtlRj2nAt0+YPY0tGNbe0+vL9mM5YnXHijyYOYMnR7mUTlTjsNw+uxt6ocWPjCOsxSVWye4UGszJ5s2CsQQrFsU6BoMiRFEoPXZUOt04Zam4oKmwqFxqEMcBmQ5PxngTMMwzAMw5SkUNza2orTTz+9z3up16+88goOPfRQkVH80ksv4eKLLxZisdfrxfnnn49rr702L+swlo5iwmlT0B2Jo63Lj4ZBur557Rq6QnH4ozrKneSMYBiGYRiGYZiJTWWZRwy7zZiMA3abja0dXVi5bhs+WNcMX8yELqsIKxpCioaATYWf2t52RQjEVAxvpNw5244/vxVGQ3MMnXUKWqY5EfNqkC0ZsilDIYeyrMCEBb9NRrNdgWZPCsW1qoJKVYVGQrJEBUAl4UqWSEBGEUCad5pTWvyt9LimWdRmGIZhmJKh5ITi6dOnw7KsIcebN28eXnzxxYKsw1g6igm3XUVLIIZtHZ2DCsUumwJfJIHOYIyFYoZhGIYZh3zxxRdYsmSJ6GVVU1ODSZMm4cMPP8zLvG+//XbceOONot115plnilivTLzxxhui11YkEsFuu+2G//u//xMP6YnHHnsMP/7xj8XDdXqYT/Mkdyc9+P/KV76CZcuWYf78+fj3v/+dl3VmmOHidtoxZ3KDGPbauROvfbIW27oiaKhwwKH1bT8buoHuUBhb/K34zL8CsJIC6FTnXDTZpmecf9ACbmvQYMoSVpQreKNGwUHtBqpbaQiivcbCxpkyuisV0H+qosAm2VAOF6oSQMQy0G1Z6LIMeE0DNbKCcshwShIsUwfdFuVwazQKWD33aBKUHle0EIoVCapdhkauaJXs0CYSMQOmaUHhZA2GYRhmgrefo9Fo0bWfi+IBNDM8ZEWBQ5HR7gsjFIlkHY+e3pNY3B6MI5oYG/czwzAMwzCFw+PxiHoM77//Ptra2vLWyKUG9PXXX493330Xn332meip9cILLwwYzzRNfP3rX8ef/vQnMR490KfpCIoHu+iii/Dcc89h9erV6OzsxD333CM+czqduPLKK3HfffflZX0ZJh9QNMXhC2ejqdIlxOJIItHnc0VVUF3uxYIps+Ct78YK19/E8IL0SzjrWzFtim3AsOtUGxZ26r3z+MNsO9J13Zp2CXu+Y2H3d02Ud+iIJiLoinWhA90wHCZckoT6iImakA49nsAGM4EvVBPbbBYSHhXOchs8lfYiGBzwVjnF3w6PBtWmiO9JonCoK47ObUG0bwqgbVMA3W1h+NvCOZl/GIZhGGY8tp/PPvts/PGPfxRt5BkzZhRV+5mF4hFAsRN1dXXYfffdx2wd3A4VgZiO5rbOQcfzOjQEogl0huKjtm4MwzAMM1EgoSNuxAs+ZBNUqPFIxXrJDZFPHnnkEZx22mkizstms+Hcc88VhYH7Q44GGmefffYRry+44ILe8Z599lkccMABQjymh9ff+ta3ej8jx8QhhxwiGuoMUyjIKLGpM4yNHbkNNK7H5cFhC2dhapUbzRnE4hRLq85AtdYg/jag49HWO2BY2wXhdL7kkSGZyXN4VbmCv8y3w1fT105b0Wli/jIDe78rocnnQCgWQnusE3FVh+RUoUFCedBErT8BBGLYGIlhRSyGVbE42nUdiSIRXelcp8xlzabA7lTh9NrgrrAnReQqh3hNJQBDvgRi4czbi2EYhhnfjEb7ebCHkcXSft57772Lsv1cctETxcBYR08QNk2FhDi2dfoxvak+a1E7RZagKjLaAjHUee3ib4ZhGIZh8kPCTGCvv+5V8OW8d9Z7sCm2jFFb1AWNnAhlZWW46aabhMO3P1dddRUeffTRjPOmIsH9i/1u2rQJc+fO7X09depUPP744wOmpfGmTZvWZzxqH2X7jN5jSp+VK1eK46y7u1scO/fff39eCkbnm1BMxxdtQRimJcTJoTAtE+UuG6ZUunDIgll4/aO12NAdRL0XcNn7xlDYZDtOqb0Ad229ShSQ3hZfj9e6HsfhVacOmG+jR8Ge2xJ4rzY5j4c9CnbaR4PXb6JpTRyVLdsFU0+3iXnvxmBbYMf6urCIaKhyVsJpcwE2C1LCgCdkwBM3EbGbaNVkMVQoyRzjKkWBXS7e9j5lK9scCvSYgUBXVIjJVJyPYRiGmTiMRvs5W9uZ4Pbz4LBQXMJQUTt/KIZ2XwD11ZVZxytzqiKruDuSQI3HPqrryDAMwzBM4Vi+fDluvvlmPPXUUzjiiCOEOyITP/vZz8TAMPmAnC+UnXfqqaeK448MFMUaI0KGovoyhzBPDIVpWaIX3uctATSVO7D//JnAx+uwoSuAujKqE9JXLJ7smI2DKo7H691PiNf0706uPTDJMXPAvL/klfGBaYms4m1lCt5vT2DvOg2r93bC6TfQ9EUcVVv1Xjl72oo4AjVudEhhdEQ6UOW04FLdgKbC0iy6y4YzqMOpydDdGgJ2A10xHWWyghqKyFBVuIpYMHZ4VER8cYS9cbjL+f6EYRiGGT24/Tw4xdt6YIbEbVMR1i20dnUPOp5dVaCbJtoDMc4CYxiGYZhxxPPPP4+TTjoJRx11VNZGbsoRsXDhwozD1q1bB4xPPaaowEeKjRs3ZuxFlWm8yZMnD2seExVyoixevBi1tbWw2+2YOXMmfvjDH8Ln8xVkeWvWrBFFU2ifU0EUKpwymGOYbpzcbjcaGhpw6aWXIh5Pxpi1tLSI3L1TTjlFvP7GN74h3DLjoY0pS5IwVXjsCjZ2htAatrBwp2mYUeNFWyCKcGRgDMWhlSejwTZV/G3CxD/a7kDCHBj51kCu4o7tzuGnVVm4hYlImYIv9nDi48VuxO1JqVjVgekfx+DVPMJ5RWJxIBEQn5E7WtIUwKVR7gXUcAJVloxaVUUcFr6Ix/FpNIa1sRh8hlGU+0ZkGFtAqCsGwzDHenUYhmGYCQS3nweHHcUjzCimgSoQjnVRO7sqobkzgFmRGFxO+6BZxV3huMg1LnP0dUMwDMMwDDMyNFkTXdtGYznZus49+OCDIgKAinJkY7iOCBIBly5diiuuuEKIhVRE4+qrrx4wHmWrUc4bFe3YY489cMcdd4hsNuLoo4/G9773Paxfv17krN155529nzHJfLxFixbhu9/9Lqqrq/HJJ5/g5z//ufiXbmDyzaeffiqcM7RMKqJCQya6urpw+OGHY86cOaK75ZYtW4SAHQ6Hccstt/RGr1FuHkE5eS6XS1Tirq+vx3jAZVOF0aI9GENQljBzShMsYws2dIdQR587t5+PqqTi5NoLcOeWn8GAgbbEVrzU9TCOrv7qgPl+qUzG+z2u4uYyBcvaE9i3bvu8oh4Z6+Y7sNOyZLHqijYDNZt1YIoH4UQIHdEOwDLhsZUlxWJIsBwKEE7ACiYgl9tQoSgok2WETBMbEwm0GQYqFQU1iopyRYbSs9+KASp6Fw7E4fTF4a1yjPXqMAzDMOOo/Zyt7VxM7edly5Zhv/32K7r2MwvFJZpRnMJjV+ELJ9Dc3iEasdlw2xRs8yXQFYqzUMwwDMMweYLEsmz5Z6PBcccdJ9yfVNhCURQh1t5+++2igMaOMHv2bNHWoYYsuRHPOOMMHHnkkeKzJ554Qgx33XWXcGH8+c9/xnnnnYdoNIpddtkFf/3rX8V4lPlGwiJNRw/XyT1LRT1SUOM3FAqJ6s7koiAxkoaJwllnndXn9aGHHiqcxVS0hFwqmTJ/Sex1OByYNWvWgM9ee+01UWg52w3P8ccfjxNPPFH8fc4554ibk0zQ8UP75B//+AeqqqrEe7quiwrcl19+OSYKFFVBkRXBmI6OiInGSY3QsRVbusKogQWPc/s51mCfisOqTsWLnX8Xr9/yPYudXXtgunNen3nWu5NZxct6soqf0WTsZVp9YjF89SraJquoJYGYsglXREXhO5fTjYgeQUe0E7plosJeDglyUiy2qUBEB+yycBmTM9qrKPDIMsKWhW0kGOs6KmQFDZoqYimKAUWVxUBZxU6PJlzGDMMwzPiH288y7r33Xpx//vmIxWJF136WrGLsi1QipIRiCpZO2cSHA+30trY20eWQDs6huPkf/0YwlkB9hbvP+82+MBrLnVi065ysRe0IEoltqozdJpXDQd3VmB1muPuQKT54H5Y+vA9Ln1Lbh/SEP9VQK7bteNhhh+HKK6/EkiVLRnXZ1JwkMZEiDVJO00Jt6x1tfxUz5OCl3N9169ZlPL6+/OUv46233sLrr7/ex6xAIvExxxyD6667Dt///veHXE5KKCb3cn/opokE4scee6z3PXLc0Ht33323WM78+fNFBAXt62AwKOIp6IZlsO6b+dp3uV4v1rWHhKi7ujWISpctp4ziSpeG6gz1PHTDRHsoBiOewLbWbWjtjqDWa4cnrTefYRm4e+s12BxbI15XqDW4aPJ1sMvOPvNqCRm4WpOFq5g4uzOBRWmuYkJJWJj/Wgi2WPI2rbtWwap9nHRnjZgeRcLSUWmrQLm9HLKU3AZWVBcWIKnCCUkbuB9ipolu04RLkjHbnnQejwWmaaDb34mKsirIsiLiN0LdUVQ2uFFR5xqTdWLG9282MxDeh6VPKe5Dbj+PTts5H+3n4niczOwQLpsCXzCGTl8AtYMWtdPQGoiiO5xAQ3lpXEwYhmEYhsmNF198UfxLLgamdKAblEQigRUrVogsvBNOOCHrTdQf//hHEQtB3RpJLK6rq8M777wj3MIkIlNXxR2FHDbkEE+HXMqNjY3iMxKZd955515R+09/+pNY58FE4rGIbfvpY5/g32vahzUNabc/OGIuls7rG6GhKjIaypzwR1TEq+oR17ehxU+1PwCvKykWK5KCU+ouwG2br0DCiqNbb8dzHQ/ghNrzBriK996WwDtpruK9+7mKDU3C+vkOzO0XQdE+RYNddUDS4+iMdoniexWOciiSCqQiKEIJoNw24KbTLsuokyS0GgY2xhOw2aWiKHYnyxJsDhWh7hicXhvsTr49ZRiGYUYPbj8PhH+JSzijOIXbpqItEEdLl29QoZgaoJRLRmIxuSBycVYwDMMwDFMaUEEOGpjSYtq0aSIHOJVLd//992cd1+v14tlnnxXdEKlL4u9+9zucfPLJYr+TiJwPRwplFGeKr6isrBR5egR1zzz77LNx2WWXiYiM++67r2Ri2waDasvd/NJqTKl0YacGb0bThUMro2pySOhbsbEzAm8oDrc95QiuwAGuU/Fa6AHx6r3AK5gkzcd02/w+8zlIsbDMUGEqElq9Cl7fEsH88r77rsMJuOskTGpNuoqnfBrFeoeOWE+xOx0SOsOt8ChhOLWe3oa6BdlnIuFXYNgzm0JIXF5jWVgrSZgky9AGOWYkSYFLcYnvm1NUR7lT3JcMF5tTRbArKgabw513ZxXDMAzDZIPbzwNhoXgEFFtjl7oa2BQJzR0BzJkSE/l22aAGri+SEEOVe+wyYRiGYRiGYRjg6aefFllzlD98zTXXCHfwCy+8kLUrKRW+o88PPvhgIRinxOXR7HpKWXpUwLCYqSuzY3KlE3HdzMkc0R1JIBI3oBsWrn5qBX7/5T1QmaGtTDFu0+vK4XaoMNZsQKsvgrhhwtEjFs/RDsFq9QNs1VeK1y8G/oxTy66EQ94eHUflQha2xfF+Q7LN/orHhll6dIBA+v40CdVdFhwJQDOAeat1vLmzLCIoQPnElobOWDekWKB3Gi0hwYpZCDpNmFkOCbK6bJVkbLEM1FgGsvmKKRqiQqtBhb1mSPE2rltQJBmuamXYQi+N73DbEPbF4SqzwenhexSGYRiGGStYKB4nuB0KfBEdW1s7MWNKY9bxKJu4K5RAeyDGQjHDMAzDMMwYs2DBAvHv/vvvj3322QcLFy4UheQGq3BNhU+oeCDFPYTDYZFxp2n5KVZMzmGfz5fRaZwqblcK/OaMhegIxvDRZl9OPenWtYXwg79/iJhuoiMYx3VPf4ZfnjJfxE5kEjbryj3Yb5fZ+HjNOmzuiMCuWXD3FIw+Sj0P93VcibgVQdjy4e3Y33BM+bf6zONww8Ryw4KhSGjzKljTrGD3yn7LsgGf7mRgr0+She0au4DZXTK2NqQUYBVui8TmtJIzDgtaXIJXBmIuqUdUHkilBYRIWIaF2iyjRY0IYmY7EpKMGkcdFDn7w4hw3EBXJIbqmB0ex/BvMTW7glg4gWBnDHYqyMc9HxmGYRhmTGCheJxg1zRYwQSau3yDCsWEx6GgMxRDMOaEx86HAMMwDMMwTLGIxiT4rlmTLIiWiW3btomM4qlTp+LJJ5/Esccei1NOOQWPP/74DlfrJih/mLKI0yHhmJZLn41XZtS6RT7x9c8kv/snW/344+vr8O1DZ2WdxuW0Y/7sGSQzC7FYkSCcxWVKNRZ7v4wX/PeI8VZF30a9Oh21WlpPRAXYrW0mlvdEXLzsVlER+1TkJKezqQwoq63FnLbkeDutjuFjz2aEbdkj8CTLghwCEqYMI+35gQwFNfZp0GQ7KMHCsIAOqjwvAZUZdFmn6oZsyOiMtsK0dNQ46qFlqVJPNVNa/boQ59324buKCYfHhrA/BpffBndF9h6SDMMwDMMUDlYJxxFOu4yuQBQdXT5UV5ZnHc9tV7GtO4quUJyFYoZhGIZhmCLh7bffFoXtZs6cmfHzjo4OHHHEESgrKxORFZQlTEVYKILia1/7Gh588MEdjqA45phjcN1116G7u7s3q/ihhx4S7mXKRR7PLJ5bizWtQTz83mbx+onlWzG7zoMjdulb3C6TWGxa67C1MwLaYiQWz3MciC9iH2Bt7EMx3uvBvw2Y1m5Mg2JcLVzFHV4Vr67bgm4lmW+czvOVLtzR+VNUGeWwGwpmr/Tj55NvGzo7mCzD/XDIHuxRcRx2LTsMLlmD3wLahLMY8GSYn11xCoG5M9YBk6IqHI2wq5lFXK9DFa7iqpgN3h539XBQNRlxSRJZxQ63BkUb+2J7DMMwDDPR4F/fcQQV0ogkLLR0dg06nixJcNoUtPijIreNYRiGYRiGGV3IBUyC7D//+U+89NJL+M1vfiMK05Gr+KSTTso4zYUXXgjTNPH888/3iri77rornnvuOZFbfPPNN2ddHkVUPPzww2LYsGED/H5/7+u2trY+y6CiebQOtJx77rlH1Oag96lw3XjnnAOmY+GU7cX8fv/yaqxu2Z4BnE0s3n3ODDRVOdEd1hGNJYSj9nDv1+GQPFmniykbMLV5Xe/r9polgDXw9iyohPH7xu0C8qLgfBzm33cE3w6ImkG82fkgHtz0P/jM/y+4LQNxC6CaedG0BIt0yEVcppWjO96F5vBmRPRQZtOKTUE8AbQHY7CsLDMbAodHQySQQMgXG9H0DMMwDMPsGCwUj4AbbrgBdXV12H333VFMkINEU4DmzoDIrhsMeuIfiOroCsdHbf0YhmEYhskvX3zxBaZPny5EqdraWpFvmy9uv/12zJ49G7NmzcLll1+edbw33nhDiJvz5s0TQmcgkBTVSNAkd2pNTQ0mT56ct/UaL+y7777CqfvVr34VJ554Iu6++25885vfxOuvv541QuJXv/qVcBDTNk1nr732wiuvvCLE3Gy0trbi9NNPF8Orr76KTZs29b6mQnrpGcUkXKuqKsTiyy67DOeff74QsicClGV82TE7o86bdM0mqLjdPz9D9xBt5pRY3FjlEGJxJJaAWynH8RX/hcnazqhTp2UcPMZTUCgDgnKgvXY04psZx1tb6cN/yrfvp4tazsQca5es8xWDMlUMtdo01NimwZ5WUC9odOK19nvx0Jafoj20DAHTEmJxIou+q8gqym2VCOtBtIS3IBD3Z42480Xi4j5jJMiKDNWmINgVQ3yE82AYhmGYUmg/77HHHpgzZ45obxVT+5lzB0YAuSpo2Lx5M6ZMScsaKwIoSsIf07GtowvTmxqyjqcpsiha0RaIotZj54IRDMMwDDMCyDVn6iNzzg0HWZUyZn56PB7hLKXiZ9TYzGcD+vrrr8cHH3wAt9uNgw8+WDhWKfYgHWrMfv3rX8cDDzwglk/tI5ru2muvFev7wx/+UDR0jz/++Lyt23iBBFgahsOMGZSHm5mh9j/dEOXq8iTRnwTpiUq5U8NPj9sF//335YgbJtqCMZFdfO3J8wctipcUi6cDq9djW2dUvNdkn4NTqy4ZdHlPtifwdn3y4cCW6v3xXXUfKBnO90iZhdg7cdgTgMdw4oq27+KD3dSsBesINU4F84C4W0ZUiuBj3wtY3v2cKLRHdCea8WLrH4SQvEvVqVCcu6JRlgZkJROypKDMVoFAwoeWyBYRRUGv069N5CoO+g10hGLCmDKSrGK7WxXxE6HuGGwNfLvKMAwz3hiN9nO2tnOxtJ/PPvts/PWvf8V+++0n2svF1H7mX95xht2uwYgk0NLRPahQTJQ5NHSFE/BFEqh073jxE4ZhGIaZaFAj9/aLXy34ci78/aFQtIGN3c7OTpEd299huqM88sgjOO2004S7lDj33HOF+7V/Q3fZsmVinH322Qe6ruOCCy4Q7thUQ5fGX79+fV7XjWFGA8om/t7SObjhuc/F6+Wbfbj73+vwzUMy50encDudSbF41XpRE4Rw2gfP613sNPGeYUGnrGKPguXNCeyZobpcQpPw6VwVe36adNrWd5hobDWxrT57LrWuAWqMBhM2lxN7VZ6AXcsOxwfdT+MT/0swrIQYrz2+Af9q/g1WOnbG4qpTscA5K6P+LEkyvFoFQnoALeGtMEwdFY5qyNL2jqoep4LuSBz+qF2I7sOFrh02p4aQLw6n1ybyihmGYZjxw2i0n7O1nYup/bz33nuL19R+PuGEE4qm/cxC8TjEqSnoDMTQ2eVHVWVZ9vFsioieoCf+LBQzDMMwTOlBzs+vfOUrwmlKBc5uuukm4fDtz1VXXYVHH3004zyoKFr/7FmKJZg7d27v66lTp+Lxxx8fMC2NN23atD7jUY8rhhkPHL5zncgnfuzDreL1ox9sEQLyYTvXDS0Wz90uFss9Zo5slNll7N2ewFs9ruJXvBrMriyxC6qMj2eoqOxJmdMDwDbTRLovi/620u+NyUneBVgK4IKJKtOO/XEy9jCOwPrwh2iOroaVNoePNr6IZtvnmOmcC3mQinkJM4GN2ASH4kKVrRY1toZe91YgmkB8Qwi1bnsfx7NlmQhHQuh2mZBkRXyUHOh/yX9To0dDCXRuC2LG/Bp4q52DbnOGYRiGyRVuPw8OC8XjEI9DRZs/jnXbWtHc2TnouJGEiW3NwMYtDtjV3A8HTZHg0hQ4FAUOVYVmU6AqyerEiqZCVRSRbUf/jqTLGcMwDMMwQ7N8+XJRwOypp54S7gNyR2TiZz/7mRgYhhke3zhoBta2h/DRZp94ffNLqzG12oVZtdmL1KWLxebn69Hii4LK42WKrUi9c6AKLNM16KqELreCx9zZXcIYvNPgkGiGhWl+A5M7ylAdPAAL2vdFZcgcRBLOTDLFOUkQFoLY1ufzdgDZ/VD0aW78+++rMWOPWsxfMhlVkwbf7sNFTYnUY4yiFEfpoFy2BXUZTx/Gaj0YhmFGCrefB4eF4nEIFbWzaxI2tCUbtINBP+1x3cLmNgU2dZAGaX8kC5pFB5AETQYckOCULNgUCTYJsMky7IoEWZHgqfSgqr4a1bVVsGcpzsIwDMMwpQjln1HXttFYTiaef/55UQDjqKOOGnT64ToiqAbDhg0bel9v3LgxY12GTONx4TpmPKEqMv7nmJ1x8QMfoD0YR0w3cfU/V+B3X94DZUPEKiTF4qlYvmoj2oNRYexNt/72l9gWtshYNsmBQpNQJKypVMWA2cn3vDETUzoN1LUn0NRpoKlDhzNbZbtRxjKBte+1iUGuV6DO0SDXyHkRE2kPui26l7Fg6xG/xyLoQrPLUF0yZNsYiqQSYFbIsJyDi9aUrRn1RdEsNWcVV3YERVJQ765HtbNvpAnDMOOH0Wg/Z2s7E9x+HhwWiscplZ7cG5mRuC4awVUuDVqOYrFpAbplIWGaiFsmggbgE41fC7JpQjUtqAkTNtOCo6MN7vWdqKiwo6qxGtV1VaisLC9Iw4JhGIZhRhO6oc+WfzZaXecefPBBdHd3i6Ic2RiuI+KUU07B0qVLccUVV4hiHPfccw+uvvrqAeNRthrlvL377ruiGMgdd9whstkYZjxR4bLhJ1/aBZc8vBwJw0KLP1nc7hcn7gp5CFHP5XBh9zkz0O3zi9epiIf+Eiw5M3dNmCjbEkKLlJtA6zAh2tp91qBnUrFa9Hfah3FZwhqvjC77wDZ4wC5jRSMN22VSu24MXNFhkBQ8kyuQWp3+fw/KUCPJg9bxGxKnaWF3U8e+pgHZSgrF9O0dlgU3kBSOe94fhp1m+FhAImRB9gOKQ4LqlKGk27VHCckATEOCXi3BIudPFizTQtyMQzIkSH0yTvJDwkjAH/fDF/OhydMEl+bK+zIYhhlbuP28t2g/U1YxFbMrtvYzC8UMHKqCcMIQhe0qXMjJWUw952xUaEJW4KamU0+bkpwSlKhGZTHo3zA1PC0LXTEdzf44PO2bUObahpoqN6pJNK6vgcfFP/4MwzAMMxKOO+44rFy5EocccojoUURi7e233w7bDvbgmT17Ni655BLRkCUB64wzzsCRRx4pPnviiSfEcNddd4mHvn/+859x3nnnIRqNYpdddhEVnFNQtec1a9agra1NOCVoPr/5zW92+HszzGizU4MXFx8+B795YZV4/cGmbpxwyxs5TUu3wvvMqMIVx86DTR3cKPHT6SNcQdEINyHFTSBuQIqZkBKGeE+gyLBUGaahY0NzJz4NRLDWkrDapWJlmYy4MvCGPTac3oYlSESW8ApsWGdZ+H+KBKofGKdsZAChpA4tbnFIs6W7FXsyRrkg0O6zdAt6zIQclaBChuaSoDpoLUa+VLpnq5IpYiOHedBDh5AJK0SuYjWZy5FpNNNESAvBbXcXzPgT0SPYEtwiBGMSi2udtVDk8X08MgwzsdrP9957L84//3zEYrGiaz9LVqGChcYxN9xwgxgMwxBPASiIeiQ2cZqednxtba04OIfi5n/8G8FYAvUV9Iw7v9CTYRKLqfFa6dKGF0Mx1LytZIMrIp4Qm1AjcdjCCbglE1VuG+pqy1DTVIOqmirYtNKqajzcfcgUH7wPSx/eh6VPqe3DVBXi6dNHqugUbjsedthhuPLKK7FkyZJRXTY1J3VdF/UJ8tltOtO2pmIf1GVvpO0vZuzIx74bzvWiIxgT2cJmjrc7dOhWu+2Dirl/eGUNnvyobxZvrpy212SReTwq0HdOkFjcIxxHjeTfugnLpSEhJdAV7UC7vwtSG+CLOLDVkrHKJePTcgUb3ROr559mWThBAqgjdOqbG1ZSOI73mGAKdtNMzui0/GrTsGDGySluQbYp0JyAYqeygiO4ttIxLQONqgRvhozsARgkFhuwKhRYVWpSac4kFHeF4K4snFAslmOZ6I51Q7d01DnrhGDstXkLtryJRKm1u5jxsQ+5/Tw6bed8tJ/ZUTwC6AkBDakNPR6gxonLpiAc73EWOwG7lp8LDh3zVKeYBlOREfM4EPE40JpIoCOSwKZ17Sjb2IWqcjsmNVajYWoDPG52GTMMwzDMcHjxxRfFv+RiYBgGcNlUzKx1w6TMtBzojiQQiCZQ7cne7/+bh8xEeyiON7/oGPb6PPLeZuwzvRILJmfv5po3qAFuU2DZFMCtCVMICcdydwxSOAHNqaLW1QCXzY0uTwfiMR+mKk5Mizpwcnsc9g0mzB4zcq4I76sExIwIOuNbkbBifT4vt1Wj2lYHmearKDB1S4iiVNNkOAghNWTCiJgjVm9pPV+eacf/TbfBkiQkJAmPAFjWmsDx74REcb9RQwK0GhX2Rg32BhVatQrJJcPUAUM4xC2oNguaSxYO43RReSgSloUOEwglLDQoQK0yhLtYkWA5FUg+E1BNWOU7mO+xA1A+cZWjCjE9huZQMwLxQNJd7KqFJpeWuYhhmOKF288DYaGY6YWeYpBYHIkb6A4nUOYCnHkSi1PI6aKxpiGqaYh4LWwT0RQJbOjahIZNrZg5pQ5N0xrhctGYDMMwDMMMBRXkGKooB8NMJJw2BdOqc++Jt6kzjI4g+UezoykyfnbcLojrZk5OZcO0cMVjn+Dz5oDQNH/9/Cr84Wt7wm0f5dswaoTbFZjVDvGnFEwALhUetQxOtwtOxQVfvBNRWxfMaWVon2ZDYJgu2oiVXIwHNkhw493Op/F215MwLAqkS1KhVeOkuq9hbs3eMBIWop0GZE2CNEjRoWyYERPhVXFEVsVgkeV3mBzmN3FIq4Gfz3dgsyvpjN1Qp+HOo8pxxIdh7LE2VrCoiT6Qht+miyH4ESBpgFavwtaQHGSnjHhcRzxmQbHJkCkDYxi4LCAIYKUFtEpAgwy4h5iFiC4JxmFUKDB7tk3v6lIxu1Acpq5AKoCjWEb/QoUyPFYVAokAPmlehSpnO+pddUl3sSTB6dFgd7FwzDDMyOD280BYKGb6QD/Kzl6xOA7LqQk3RiGghiT5hl2SBNOhIerQENB1rAnE0bJiMxo3t2LmtAY0TmmA01n4CtAMwzAMwzDMxMXrUGHXZEQTBhxDmCWGyhpO55Ijd8J37n8fMd1EayCG2179Aj86aieMCZRTXEVisSSEQMuhQlFV1Djr4dI86Ii2IpjwoVKxo152C7dtLpD/lsTITpPqk9A9hYq9q07AXM9eeL71XmyNrhHjdSc6cO+W3wFb8vR9ygBtDzvmteyP3ZoPQVmsOudJ18ZNTN0aw/1+Hb/fyYGHpiazKeOahKf2cePzSRqOezcEb3R0kxpJ9I5v1sVABGyd2FzxOTaXf44t5asQVSlBuUD0390bMepUqzX4TtOP0Gif1Od9le4cTRuaOzvQqQRQ6ahChVYJT4UD1Y0e2ChXmWEYhtlh+GpaAqxqCeCeN9bh43Vh6KYJZ0v+u0JNLdcwv8ExQCz2hRPiKberwK6HXtFYUxGpVOFLJBAMJtDy8UY0bWrFtKl1aJzMgjHDMAzDMAxTGDx2VQwUxTaUUDwcJlU68a1DZuL3LyfF0pdWtmLRzCocPKcWYyYWV9qTzmJ/AhZ14FNluFQ37O6p8MW70BVrhz/ekXNuIo3lUd0okx3wkyBsAn4SjG2TcMqk/8En3S/jjc6HB8RR5IOEEsNHTa+KQbKG73A91Lc3fvTZ/8PiVh1X7+pAizM5jzVNNtx8XBzezv+DPfxWwdzF3mgVJvt2EsMk3xzYjb4RfN54Fea17i+GQuK3t+M/0x7H+uqPMJZ06O34W+e9+PFOP8tw/Klww45wIoROvQW6EkV3mwPtUQWeOjtU2+D7X1M0EWdhU3asYBXDMMx4hoXiEqDZF8UD72xKe8fI+zI+2BaFU5Mwu9q+PYZCNJR1dEfiotvZaHWRc0qAQ9MQrtTQTYIxOYxJMN7YiqnT6tEwpR4OOwvGDMMwDMMwTP5QFVkUWu4IBoE8144+ZrcGvL2uE++s6xSvf/fSGsxrLEPNIHnIoyIWo0cspqa1JkORFFTZa+BUnAjpQVFsJxcMK4HueCc0S0eV6gFF21J0RRc5jCFjRuVS1Np2w/u+v2FteDmsApWGs6ThG2peqXgHMTmGy7Z8Aw/+x8CNOzvw1KRklIGleOCv/TbsoT3hCFKOZf4NOzEH0F7Rhg/RBsl6AxWROtQFp6I2NBWV4UYo1sgfWtR367BvT/4YlLJYDY5adS7enPY4Pmp8daC7eBT5PLgSy33vY2HFXgM+o6J+bs0Du+pAIOaHLnVBbwbUMGCvkQbNvKZYizJ7GRrcDah2VAvhmGEYhukLC8VML+9vjfYKxSkodoK636XEYnJZjAb08Jja505NQ6hKQ0c8Ab8/hublG4RgPGV6PRomN8Bu46fBDMMwDMMwTH4od2o5x08MBzJhfH/JHHz7vvfhiyQQjOn47QurcPVJu4kYiDFBIbHYIdrdsp/a+gpZLsVHTtUthlwxYcKuOEV0hT/eBa9WjgpJRlmaYAxnPfZWLsKp9XF4Pc7efFs9YSLaZYgibWNZo+zzRhPz3jfwi0+iOKxVx7W72tHV41CNuReJYTTopor1eZqXI27igmd9KKMA6RyQIOOADSfjRPvpmLKnliycZwBy1ILplWB4FfHwIBqNwuFw5D2j+N4Nd+GtzjfE3w9tfgDzyxeKhxeZUCVVxE8Qpt1CPGTAGVHhrlWzFvzTTR2+mA+rOlehwlGBelc9qpxVXByPYRgmjQkrFF9++eV44IEHsH79eqxevRqzZ89GsTKjxo3Lj90Zr3+yHnHdgMeRP3E0qlt4Y2NY/L26I45g3ISnX5cdaiTHEj2ZxZYFr2P0fkjpN95LbmabhmC1hvZ4Aj5fFM3L16NxYysmT6tD/aR6OGxj5MZgGIZhmDHkiy++wJIlS7BhwwbU1NRg0qRJ+PDDD/My79tvvx033nij+O0/88wzcd1112Uc79xzz8Xzzz+PrVu3Ih6PQ9P4hpspXcgU4bblP36CqHTb8L0lc3DVP1eI1+9v7MY/P9qGE3ZvwpihSLAqHTAlCbI/BmEgtg3/e5NTs9JeA1XW0BFtgS/WBa+tHIqsopziE2SgSpawKW4hnJChi4J3PctRLcSdJhJhA6qmCOF6NBDaNa1bz/LC9cCqfXXMfTeCQ1t1LOg2cN0udrxaX7rXtKhNxvrjK3DEIOPELMAfNYHXgzC7kz1X21bp6A5J8B7oEYUGJYcFRABTodoygGKaMCwbYOZXKN6j/nS82/W2KIC4NboF/25/FYtrlww5naxK0JwyIl06SFd2VasZY1NUWUW1sxoJMwF/zI/uWDcqw5Wod9eLSAr6nGGY8U8xtJ/PO++83vZzIpGAqhbP9ad41mSUOe644/Dtb38bBx98MIqdKVUufOuQWYh0bEMwlkB9Rd/cqh2BDt7VHTG0hgzhGP60JYpFUwbO394jFlNmMTUgHVrujQJVVrCjD5tJMC7rEYwD1RpaYwn4OiNo6VyPhnUtmDS9HvWT6+CwcSQFwzAMM3HweDyoqKjAP/7xD+yxxx55bUBff/31+OCDD+B2u0V76YUXXsARRwyUG84++2wxbkNDQ96WzzBjGT9R7bFhTSuVZss/+8+qxlG71uO5T1vE6z+9vg4Lp1RgalX+2vfDRpZgVdhhkrPYl+xFOBKxmCAnsSbb0BZphj/hg0f1QFPsvW35OTYFnZEYKk0LZfaUkCdB9wCRmAlJt6DYRkcpDlnANlrntPcC1SpWLnJhp3fCqIpbuOHDKJ5p0nH/zg74tTHMYhgmEXJx9/y9WpZw1CC709lzrxc4ogzR1wMwm5NZFYktCXS96Id9sReSU4ZkmkAIMC1As4B4AdJDNK0W86uPwoftT4nXj219GPtVHQi7MvQ9nmKTYZlAtFMX8RPOyuxSBzmIhWBsJIRYLARjR6VwGNO/LBgzzPimGNrPX//613HNNddg8uTJKDZK6gq4Zs0aocy/9dZb+OSTT7DzzjuLf/uzcuVKXHzxxfjPf/4Dr9fbuwNsaTEFBxxwwCivfXFCT1oXNDjw4hfJ6rnLm6PYd7Iz4xNYakCQo5m6y/mjuc4fsMkyvE4tL64MipyqoG5Ddg1+e1Iw9nfH0Pr+etStb0HTtDo0TOYMY4ZhGGZ0EPmdRmGyNvugSBl/mzs7OyHLsnBD5JNHHnkEp512GiorK3tdww899FDGhu6hhx6ac44pw5QCZQ4NNlVGTDdgV/PrKiaosN3yzT5RhyRumLjhuc/xmzN2h6bk1505IrEYPWIxndL2kX13h+JEg2sStJiG7mgHdMuAU00K4RoVzaOU2WgcFU5VCPMCp4KIy0TUZ8BmGx1XMS25pecSnh5rG6pUsHI/Eosj0OIWjt2q45htQbRO0bADccGjylqbhO9OT/a4XGtYaPo8NuSNf9who/kQD7rfCyPyRVy8Z3UaSDzvR+VhXmjlCuSICSsqwdSorgw9Wcj/jtqz7kR83vUqIkYI3YluPNfyNE5oOiWnaVWHjIQJRDqSYrG9bPAdRhnFta5axI04uqP9BGN7JRS5RHY4w5QYo9J+ztJ2Lqb2s67nGCI/ypSUUPzpp5/iqaeewqJFi2Caphj609XVhcMPPxxz5szBo48+ii1btuCHP/whwuEwbrnlljFZ72Jnt3oHXvoiJNwD5CxuCepo8GbuYmVTFWgSpZDlBs0zpptIhGLw2DRREE8ZpMBArqgSQIlUiZRgTBnG3TG0d61H84YW1E2tQ+OkBjidLBgzDMMwBcSwsOUnyTzFQjLpmgOTP379mDdvHr7yla9gxowZKCsrw0033SQekPfnqquuEu2iTDz99NNoaurb9X3Tpk2YO3du7+upU6fi8ccfz8t3YZhix+tQRZ2OUKwwQjHN+5Ijd8IlDy8X7kxyL9//9kacfcB0jClSylksQfbFgIgFSx2ZeK1BRZ3aAE1R0RXvQChO7uKkd9cpywgFEuhCDLXl29vqNrsMXTJghkyojsIrxU6LDC1AXJKEqzadcLmCz/ZzYue3I7DFLEgWUL8xgVKhjvK2m2zw2SREZQmdnQYW+Ia+g7OHTFiL3FDcCoIfkS8ZMEImOkgsPsQDe50KOWwKd7HkMJOuoDzjtlw4sOZEvNhyv3j9TPOTOKzicJRpFGIyNDZNQixoItwch2RosLmGPoZtUFGrVCNmxtHl64TP3yWiKKaVT4ddGX81cUzDgJkwYMYoqiPH/GpFhjTC6wHDjEX7OVvbmeD28zgSio8//niceOKJ4u9zzjkHy5Yty5gH4vf7hYW8qioZbk8q/UUXXSRyifvvSAYik3h2tU1kFBPLm2NZheLUj8RwmszkFBBO5GhCODM8Tg3OPGW+UQ+wahKjKZKiRkNLPIFAdxztnRvQurGVBWOGYRhmXLN8+XLcfPPN4kE6uRXIHZGJn/3sZ2JgGCa3tmuNx4bVBYqfIHZpKsMZe0/Bg+9uEq//vmwT9pleJd4fc7G43AZTliD5SWjL1R4yEGrtV2s1sEkquqKdCMa64FbLIFsSXKoMfzgOt6YI4VyML0vQ7DJiAYOy60QkRSGhuwNnwkLQMVAoJqJeBZ/t78LOb4dhz7EYXLFAW2/PLh2v9OQrL6tSscCXvNcbjNpNCWybZYM03wnFI8P3FmVNAFbcQufLAVTs54ZzqgY5YkHqtgApmWmcT2hf7K4cjg+U59BhdCBqRvHkhkdwdsVAEScbKiwkwib0LgNalQolx9gQJ+hYKBcZxt3xVticFhq8jSKDezxhmCb0UATxSAhKjhmRslOB1uTJ6tBkmFKC28/jSCjOtvPSeeaZZ7B06dJekZg444wzcOGFF4qgaBKYmYHs3uDoFYo/aYli6Sy3aKzli5QTOaKbiPe4iz15chcTFHFGnasiVPSuRkM0nkCwK4bOzvVoFQ7jejRMqofLlakZyDAMwzClCbVtTjrpJBx11FGDjjdcR8SUKVNEgY8UGzduFO8xzESKn7AXMH6C+NqiqXhvQ5cQpMlZfMPzn+PWr+7RK5yOqVjs1WA5aT12XCB1wwUkvGgJb8G2WCe8uhua24muSByWQ8e0GgfUnvsOpVqBsS2OiCVBcxRWnJPiFsq6dHRRF+gs4lfMLeOTg9yo3poQMRSlxGwFeKXn7/9M1nDUIIdV1dYEnCELsgU0rYlj/QIHXDPsUFwyuv4VFEIxCcbd/wnBCDngnm2JonGF0PJJ2o7DhuMqTsOfO+4Q770cegVHlh+JBi3XLHwJildBgurwBE24qhTIw3DDqrDDrVpoMzph152oceW3e/pYI5kWpJgMySZBykFjsRImzJgh/pVGmF/OMMUEt5/HkVCcC5RPTNUD06GQ6sbGRvHZjkBOZRpSbNtG5Q8AwzDEMFxoGorPyHVaCZYYkqFh+WVOlQanKiGiW2JY0xHDTjXJXKt8IckSXLZkzrE/Ekc8ocPt0MR7+YJkYFGUV1Phq1GTgrEvhq4P1qFlQzNqp9ahoak+bw7j4e5DpvjgfVj68D4sfUptH1KuGjlq6F9LBpquLnzdA1pOpt9/qtfw4IMPiugtau9k46c//akYss6/37xPPvlk4bCg3lhUjOOee+4RjeVsWcSp98U2yXM7heaXfmyUynHClDaeAsdPpJzLPzpqJ1x8/wciq5gyi+/811p8f+n2bqtjBgmneSze5tYqMMnugBy0wdfdDMlMCJfH+rCOhD+IurKetrkKRF0GYh0WVBKxCuhelCQLKhXaM1QYUt+c4nQMm4TW6aUXP1BHl+Key+UKl4wNc+zZemEjVCZj7nvJIjQ1mxPYOtuGuEuGvV5D9ZFl6HolICIoiMDyKHS/Avc8syAZxRTzQSEfO2ERdk28g62JreL9J7Y8j7NqzuozrmqXYHNmFjppzVSvikTYQDQowVklQxqGScmuORGPJ9CaaIPDdMJrH2O3fx6RaFdSlISmQMolh1mWYYbisOLGiAtdMkwfFCkZDVHgZWSDoieo/dzd3T1o+/lnw3QUn3LKKcK4esUVV/S2n6+++mqUGuNOKM52o0Rh0hRYneLSSy/F/fffj+bmZlGJcPr06XjzzTcHnfdvfvMb/OIXvxjwfkdHB+z24YuqdFPs8/lydks7kIAp6UAimReV7wNhQa2Kt7cls7c+bo5g/nZTdl6hLDBLskSDOBKKwoorIopCzlNDg+ZC5TJcFhCVgHCZgoQORPxhdH/0Bbas34yK2gpU1lTApu1Yo2+4+5ApPngflj68D0ufUtuH0WhU/O6PagGKLNro0UcfjRUrVuCQQw6BoihYuHAhbr311j4FfEcCtYuoxsM+++wjhNpTTz1V1ICg7/zkk0/in//8J+64I+n0Ouuss/DGG8mcuZkzZ4paEtT4ztexEYvF0NbW1qfdxTCFhgrLVbs1rGkburv+jjC1yoVvHDwDt736hXj93KctWDSjGvvPonC18YVdcaDJPQ1KxEBMM4WJBGYC4QggeZyityHhqrIQiCZgJQAth3zZkWKpFroDfmhxJ2KqTdw/jCca6YEHAApQoaN4PbmMs4zbXa8KsdjtN/u4igkqYld9VBm6Xg0g0Zn8MYqsMxBZt91AVQg2AjgYfXsDf4BkWyGd6Xs6MWmXzL1GSQPVnApiQQOSDDgq1eRxlyNezYvOWCdawi2wq3bYlPwaqUoFIbCbFqz4yKNoGCYd8RAw25OrUeC4444TRtJU+3mPPfYQMbY72n6ePXs2LrnkEuy9996i/UzpBkceeaT47IknnhDDXXfdJV5/9atfxeuvv97b7t5///1F4btiQLJKtEx1KqP4k08+6fO+pmlCsb/sssv6vL/bbrvhgAMOwJ133plXR/G+++6L9evXY/LkycOeHzli2tvbRaVFOjiH4vZ/volQ3EC5M/9PtGOGic6IiXs/TH4/+v383n5VcJOqW0AShikGmyLDk2d3cQrqykcNpBi5GeIJeP1ReCwT7ioXGqbXo66xDvYRXhCGuw+Z4oP3YenD+7D0KbV9SN3IqIE7bdo0FNt2JDGXnA9LliwZ1WVTc5JEZFVV8+oApO57NG8qBpJi8+bNokFNBUNG0v5ixg7ad9QFc0f2HR3n9OCgtra24NeLzlAcn2zxodypwVbAIk6mZeGnj32C9zd2i9dkoKgvy78gRd/hS/MbceSuuXbfzz+maSDs64KrvBKyrIjzu9kXQ0O5HXPrvcJlTXS3htHVHIK7wpE3M0l/dFNHc+tmfNDaiYTDiRote42WUuWPBvBBz93+8TJwzCCHcUVzotdVbErAR4e6has4hZmw0P1GELEtxVXUT3NI2OfUikF/e4yEBT1qwuZRhl1/j/J8/fFuVDmqUe+qz/xAWwJcXhtsztI4hgzTQLuvEzXlVVBycRTTNIE4FLcG2yR6/MCMNaP5W5gvSDcjqA1XbNvysMMOw5VXXjmq7WerQG3nbNt6OG2wcecoJudwypXU32mcnls8EqgaIg033HCDGFJdH+nEHOnJST80uU7vddogKTpqy/Ofs9sRiKLSAmrdCtpChhBXP2mNYdGUwj5b11QFqiwjkjDQGY4jklAgF6iLmcemIGK3IVJrE5EUkc4IIt3r0bapXQjGDZMaoKlqQfchU5zwPix9eB+WPqW0D1ONuWIr6PLSSy+Jf8kFPBbrRstMDfmeb/pxUQrHCDM+8DpUuO0qgjEdVWrhogeo7fvDI+bi2/e9j0BUF+3i9R3hgixrbdsa7DujChWu4ohSoPO7yqOhxR8T69RUkbzPcVfYEfbHEQ/rcHgKI76psor66kmYFNCxIhqELlMcSGkIfbkyV9ouFK+ygGMGGXcwVzEhaxIqD/Eg+HEEkfUxWAVMAUo52ejOzIQBn7HdrOWRPbDJGuLh5FiJqIV4xILdlf23J1nMThYF7kaCzXShPdINKaaiwj6w97JhmDDiJsprJag9zvjxhqTJsOJ6MqdYK/7eXwyTKy+++KL4l1zATJJxdxWjrL7+WcQkHJP7lz7LB2QlpyGlyI8HnDYV4YSJBfV2vLQ22TD9qDlacKG4N7vYrgpncYRyjwoACd80/0qXBEuR4bNpCNdpiEYTiLUEEe4IoXVTK+qn1aOxqYFvQhmGYZiSgwpyDFWUg2GY4cdPfFHg+Ami2mPHD5bOxTVPrRDt1kKhmxZWtQSFWFwsUAa0TTWwpSuCMmey4LVmU+CpsKNzawimqRbMVUy9UZuq6rC5xUJQD8BLRdAUdVwJxSnW0v2QNUj0tCRhyxxbxqzi3lFkCd4FTnjn0cZzZi0CuKPQOUChkbMkoFoC7mh5BP8O/lt81qQ14bop1+Hjp4MIdSXvHYMdOuxDPPwgsTgpGA8fGxyQdBNB+OC1u+DW+rpqTdNCJBCD3CmjrFaCUqBc87FEUmWYER1m3IDCQjEzjuD280DGz69gD8cccwyuu+66PqHUlPNBTqVUNsiO0t9RPB6wKwo0RcLcGhteXhsWT3FbQgaaAzoavOqoNcZpKASWaSGUMOCPJVDlsqNGAiIW0O7QEK4vQ3kkgcRWP0KtIbTVtaJuZiMa6kunGwfDMAzDMAyTf8qcNmhKBHHdLGj8BEG5xPeeuy9a/EmhLp88/N5mvL0uWa9ldWugqIRiosKliWJ+W7rCmFPnFcKwq8wmilJHgwnYXYW7H3HbbKixl8GvA6F4AE7NCVUaH85iChnxUgE6ct725BTP2QFX8WhBzwUoIDNKN6UScGrVqXg79DYSVkIUt3st8BqmVi/qIxRXTymsS96luuCL+dER6YCmaLDJ2+Nh6Hh1em0Ik1isAGU1TkglUHNhuEIxDBNWgrb5+Dg/GIYZB0JxOBzG008/3ZtZR3nBDz/8sHi9ePFikc9y4YUX4ve//z1OOukkUal7y5Ytwv1L7zc1NeVlPcajo1hRJTg0BQnDwuxqG1Z3xHtdxQ3e0s8hoqffTlVGOGZAkxPCreCUAIcFhCQJ21w2eJ0aKsJxJLb6EGwLorVhG+qmNqChoY4FY4ZhGIZhmAkaP0E1NEIxHbYCxk+kqPXaxZBvvmgLbheKW6h6R3FB8RuVLpuIoKB/68ocUHtcxb7WCGKhwuXiyqYFmyVDM1yww0IgHIRTtaBIpd/+N01gjl3B+z2vV1vAnMFMtTm4ikcLkiLpSCXBuEarwZHlR+Kp7qfEZ492PoofV+7TO26wp8heofHaPPDFfNAiKuqdlFe8XU4hY5rDpSHoi0NWJHirCue4HjNkSbiKMTB9g2GYcURJCcWtra04/fTT+7yXev3KK6/g0EMPFRnFlNF38cUXC7HY6/Xi/PPPx7XXXjtGa106UOEMymDbtW67UPxJaxRLZrmhFKi712iiKDJUy0IwlhCFMqjRT7/dJIO7LMBPDSO3XeSjlYdj0Df5ENgWRGtdM+pmsGDMMAzDMAwzMeMnbFjbHkIlShcqFJdiTWvxCcWE05a8F9nUE0FBJhYSim2Owt+yhr0qVrX6UO/2oi3aDl+sGw7NXeLOYguxgInZhon3e3ptfm4Bxw4xVbG4im09xchpoKUfX3E8XvO/hqAZhM/w4UPbW6jCXr2OYioMVeh8flmS4dW86Ir6oMl21Dhr+nyuaAps1JO1Oy7iJ1zl+X/oM9auYitmwNLNpMOYYZhxSUkJxVSxj34AhmLevHm9gdSFYLSjJza3dOCdzzdhY0sn4oaBQIA6D+UXh6aiptIDuyJjapkKpyoholsIJyys6Yhjp9rx8SNHGWjhmI5ANAFNlqD1/MCRDk4PRim3yydJCLgdqHDZhWDs2+pDoDWIljpyGNejoaleVKZkGIZhGIZhxj/lrtGLnygUM2rcor1L2a8doTg6Q3FUuYujoF06tE4UvUF5xTNr3ZAVGQ534bd5temCLRQR+cRNlQ1QQzK6Yp3wamWi6F2pYukWZvtMoMfrsm6onOIichXT0Ukl7KI9QrFbceOEyhNwf8f94vMnjYdxjrwXLBPQYxZiIRMOT+FNPaqiwm7Z0BXrgkOxw2Pb/hCG0OwqLENHsCsGWaXjt5QfNvRF0hRYMR1W3GChmGHGMaX7qzeGjHb0xGNvfY5rylzAZEqZKizlqolq3cT0BdVwxxKwx+LoCIaxKh6ESwPcDg0etwtaCRd5IOd0Kq+4UrGLRnMKajRV05NrakRLEvxuBypddngjcQS2+BFsDaG1tgV10+pR11gHZZxlTzEMwzClxRdffIElS5aISK6amhpMmjQJH374YV7mffvtt+PGG28UD+nPPPNMUQMiE+Tg2n333XvdXPfffz922WWXvKwDwxQDox0/UQjInTu1yoX1Hcmi1atbAlg0k1q9xQX1YqTt3eyPiNxiKvI3GnhcGtx2DZFwAuV2B+rcdeL9UheLFbuMRs1CmWWJ3pN6j1icXuSuWF3FqgQYVtJRnGJp+VI873se7Xo7IlIYEU8XHP7K3viJ0RCKCafqhD/mR3ukHTbKK1b6bhubS0UkGEeggx4+AJpjnIjFqgQrZMJMmOC7YKaUKYb2syzLWLBgQW9PiGJqP5fmLx5TMHw2WQzJQyNzw8xmWJgaNNAUiqE6FkEl4qj2OlBeIlnGmfKK+2OXkt+eCt61SBK6XXZUOW1CMA5u9SPYlhSMa6bUQqJWDMMwDMOMAR6PRxTv/cc//oE99tgjrw3o66+/Hh988AHcbjcOPvhgvPDCCzjiiCMyjv/uu++Kf6nHTaG7/jLMaDNe4ieoSFyvUNwaLEqhmPA6tKSruDsi/h4NF7dTluDx2NAejKPMtIQwXOeupfSGpFhsK02xmBytqsPE7DDFTySvzatIKB5qwiJxFdPSQj0F7QhN0nB61em4rfU28XqtcwV28R8o/l69bRPWVnXkbdlNtiZMtk3O+rnH7oEv6kN7pAMNrr55xQQ5iSOBOAKdUZTVyCJzu9QRv++SBDOqA+MsVoOZWBRL+/mtt96Cw+EourZz6f3aFQGjHT3h0GTUR0zRUCkIEuDXJERyFDzjioQ15aoYAHfv+1VRE1ODCdSHo6jUo6jULNRUuGG32Ys2rzgg8ooluGyZT4VUwbsIgG09gnG10wZPr2AchFIuw0qYaJjcCJs2Tp4WMwzDMDlBboF4DrFYO4pNkjI2Ijs7O4UjgdwQ+eSRRx7BaaedJmo/EOeeey4eeuihrA1dhhnvkLGAalyUcvzEnHoPXvispVcoLmYogqI9EMM2ZwTTqrffbxQKur6Se3mbFkp2q3eoIp+4zlMrsn67493waF5RdK/UUOzAnIjVW9BulWWB/huKrnq5n6s4hnUL6L7OgiTmYG5XcPOIJOaZnC8tjR5tpMdl7OfZD890P4P18fVodW/ELkgKxRtbW/HPmj/kdV32cu+FM6rOEKJxf2TI8Nq86I76YFPsqLIPfIxkdysIB+KwJAvlNU4RpTKSXORiKooncoqjOizDgtTz8IFhirH9nK3tTHD7eXBYKC6B6Inzj90P5wP4y/PvIBjX0VDuKshyAqEQ2n1hdEV0BBISAlAR1mwI2zX47Sq67Ao6HBL0LIXtOh0yOh30c05Dee/7co4XAMkCdu+I4xhbDE6HYxTzinVostybVzxgvSSAtrjTAkKUGS1JcLvsqHHb4QnGEOkKYN0H69CyoQ0N0+pQ11QPu700uyUyDMMww4MaudNe+6jgy9mweAHsGRq7VJfhK1/5CmbMmIGysjLcdNNN+PrXvz5gvKuuugqPPvpoxnk//fTTaGrqexO8adMmzJ273XM2depUPP7441nX74ADDkAikcAJJ5yAn/3sZ5zlz4w7yNnqLvH4idl123v/UfREsbu4qbjdtu4IKpw2lFMGXoFx21TYnBoSgQTsPQX0SCyuddcKWdSfKO5tlhXLwlRxm5Pc/xQ90Z0II5ct+sVMCwt6emPXbNaxZpqOqBNQjQR0EooLIGCapgGX6oJKkQ49QjH5mrU04fTL1V/G9duuR5tnY+90tcEpSWNVHlfpvdB7eD/0PhZ7F+OUqlNQqfYVg8ll7lBt6Iy0IxjLfHyYhgV9qwV7UIa9Qha9W4ebiVztrEaZVgZZLgJXspY0SVkJA1IJx1Ey47/9nK3tXEzt54MPPlgYUKn9/NOf/rRo2s/FsRZMUeB1u8UwzQLaA1H4ogZuf7e793nz+XtVgp63dPlD6Azr6JbtaHM4scVjwxa3DDPLSZjt/QFIwPu1dti3mThaM6BQoFOBoQZoOG7AH02g0t03r3jA6knJ5pXLAsiDscGiPDMbqmwuOBMKIq1BrGsPoXl9C+qn1qFuUp3oRsAwDMMwhWL58uW4+eab8dRTTwm3ArkjMkHiLQ2FYOPGjZg8eTI6OjpwzjnniFy2yy67rCDLYpixglzEpR4/IYrD9RS06won0BGMjVoG8Egod2po9sWwujUgsotzgcabVeuB2z7821wXidNOFZGQDptu9hbr0mQb6j0NKNep9HVpUq8lUNEZQDcAg+qw2Gqway73WtMsRNZ1wOnThat43iYnti70woyHINvckMjtmmf8CT8i8YgQikkcTlD9GLpXTRtnV9euOK/2PLxnfw+GrEMxVdgNF/bG/og6d1zQD5thrI2tFX/TQ4JXA6/iP8H/4Ojyo/Glii/BpWw3bjlUJ6J6DLpIgM4AbWa7hbDfgEHF1MvIM527WBzSwwjEA6iwVaLaWSVczGPpMJY0GWYkIZz36HmgwjClRjG0nzds2IDGxkaEQiGcddZZRdV+5jO7BKInRhtqh5GAGtMNzKrSsKaTfp6Bj5qjOHKOB7WV5ajt00I2EPH3uJF1CZ2aEy0uBzZ4VfhF3vHweLPRifp1Hdi7qW8F2UJAXREclFccN6ApmfOKM22fMvrWFkDNkBZqbDttwmFMBQAj7SGsa1+HlrUtqJ1Wi4Yp9XA4nQX/LgzDMMzE4/nnn8dJJ52Eo446atDxhuuIoB5T1IBNF4Oz9aKi96kLYXl5Oc477zzcc889I/ouDFPslHr8BPWmoxgHMjak4ieKWSimdnqt1yZ6/+XaQTma0OG1q5hR6xlZTrHLhjYtCjNuQEnbx6qkwqOVRj2WjKgW5vvjeF1PloZbAxn7qLlFegR2keB8s1n8XbExguC8akQUA5paGKHYsAyEYmERbUHzp16n4QxO4cPKDhPDR1U+BNqT9+VfVb+B2qb8HNMrIivwt46/9QrGcSuOJ7qfwMv+l3Fi5YlYUr5EZCYTDnWIZaqALlmwwpZwq9uGWXQvbsTQEW2HP+FDpb0SVc4quMfoeEx15TdjhtDAGaYUKZb2s67rov38jW98A3fffTeKBRaKSyB6YiywU+EDWcZu9fZeofiT1iiWzHJnfKJPURFTaEh7z9BjiIai1NspJ16LOYRITDwxvQqV65sxq6l6VPKKtRzyigdMJwHkK6B0Ln9PNy6PTUN1Q3lSMO4OY+OHG9C6vhU1U2vROLkBTg8LxgzDMOMJyj+jrm2jsZxsXecefPBBdHd3i6Ic2RiuI+KUU07B0qVLccUVV4hiHCT+Xn311QPG6+rqEr1naKDoCWpML1y4MOflMEwpxk+E46UdP9ErFLcEsV+RFrRLQcJ8pTv3be2LSGgNxFBb5oBnmK5iEsAqbSqaySXp08UDsGIrMDRiJAkLnA68HkgKxZ8ayfu7XAg3uRCrsMHeHReibcXKbkR2LdwDBofqgF2xIa7HYVcdwlUc6jHpZIrE9VSrvUJxsMNA7fT8rMcuzl3w80k/xzuhd/BQ50NoSSTzvYNmEPd13IfnfM/htKrTsL9n/2SO8BCoDgmJsIVotw7LtIYZQaHCi0rEYjFs9begXekWgnG5vXxokToDhmkiHk4gIsWhZHFS9ocenNhSETCqIgraDf97MMzotZ+ztZ2Lpf1st9uhaZpoP1O2cTG1n1koZjJiUxXYFBlTyyh3SUJUtxBOWFjTGcdONbn9GFF0hNuVe/GJw5Uomv0a1pWpIq7ikUn1OK+zHTWVFaPyfXPJK84E/TZWSOSr3h5J4bZpqKorh0dPINoVxablG9C+oQ01U2rQOKUBTm9hcqYZhmGY0YVEhGz5Z6PBcccdh5UrV+KQQw4Rv7tUufn222+HzbZjItbs2bPFQ/G9995biCVnnHEGjjzySPHZE088IYa77rpLLPtb3/qW6LJHDd3FixeLxjHDjEfIRVzpsmF9RwgVJdqUm1vnwQsrkoLXmiIvaDcSvA4Vzb4I2vxReEbgKnYrCjS7Cl01oVH8hDZ+PJN7ep1AwCf+XmMkELUsOHL5/ZIk+OZVoa7HVezZEIQ6QwUKlLCnSTY4NRe6o11CKKZfs1jPkOm081SRpJEUwIOdWeIfduA3fpFnkShq96r/VTzW9Rh8RnIbtuvtuL31djzd/TROqjwJ5Wp5DjME9EjSWZzT8iFhsjoFdil1/y1DgxcxI4YNxlY41C54bWXwaC7hes8V0zIR1KOQgqGcRG5aW5tDRWW9BNWuJgvaxSmn2IRkHz/nCDN6cPt5pWg/03agpIJiaz+zUMxkheInIgkDu9ba8N62WG/8RK5C8bCX53Dg+I4O3OOoRcAmi9iKJ11V+HI0PCrF7XrziiMJVHoGzyvOBD3hpuYB5b7RU28qreBRNVTWaknB2BfFpo83omNTB+pn1KFxWhM0e+ELczAMwzDjmx/96EdioIbmYYcdhtdffx1LlizZ4fl+5zvfEUN/qOAGDcT++++Pjz/+WDSGqfscFeEYNw48hskAFVVTu0s3fmJ2fVpBu9bA+HLNCgOHBLddG7GrWOQUO1TEbTrUmEFV9TBemKRpqJFltJumSNP93Ehg9xyd8f1dxdVrIujct3Bp3S7ViYDsh2HqsEuqKGiXVSiu3r6PQp2FcYKTCLu0fCkO8h6EZ7qfEeJw1KISe8DG+Eb8ruV3KBRe2YtfTvllHyHaARcsOBHWI+jW2xCXXKhwVMCreaHIak5CcTwShcvpyE0otihjOY6IPw5vjZLMKSZHMeUUs1DMlChj3X7+6KOPirbtXHqtG2ZUc8zIWbtL7XZheHVHHKE4hS0UhrrqSpy6uQVST17FF2UqXg2ro5IH3ZtXnCCxOI64PrJlksDslSAK/9EcNpPLWNXQXe2FWedGKBjGho82YMWbn6BlQzPMES6HYRiGYdJ58cUXxb/kYmAYpjCUpcVPlCIzarbHyImCdqE4xqOrOBhLCFfxiHKKFQUxh0zqmBDIxgt0r7PAsf2+7hNjGPu+x1WcomxzDGoo9/iK4ULRE3bFLpyzdLjS3VK2veksUyD3aJWUqBHxF+5e1SE7cHLVybhx6o04ouwIKKOQ0hswA3g79HZGt7FbdaHMXiYyjLcGt2FLcAvawq3ojnYiEPcjnAghbkSF4L6jx47NoYhCj/GonoybsCyR5c0wpQ63nwfCjuIRMN6L2aVQFAlOVUGVy0CtS0Fb2BBu2U9bo9h3cuH6282eVI3j13fiiRnJzLQ3Gl2oW9uOvSZRCbnC5xXTc3V/VBc3AHZFEU5jh6Yix/imvoIxPeXucRhvowa5oqGmthzeWAydrd0IdYXQvqUdTTOaUFFXCSlT8BbDMAzD5AAV5BiqKAfDMPmJn9jQES7J+AlR0K7KhbVpOcU1RVzQbkddxXVlDriH4SomQaxCU7BNkyHbFNG9fjx1rV/odOLlcET8/akeB4ax6/u7iic/t1kIyIViOizxn4SAiD6gJWVb2rFeNZmPQOP8O5yX1UpUKQjOd8B0D7wJJHfv12u/jqMqjsITXU+Ignf5fqgQNsPoMrrE359FPsOR5cnu6/0hR7DH5oVhGojoYQTjqUgZSdQckmUFMhRosgqbYoOmaJAgI2ZEhZCci6OYsCQL0bAOvTMGb40dlmlA8sWhuGNF54YsNjRZg0srwR+MCQK3nwfCQvEImAjF7FI4bDLUuCSK2r2yjjr9AMubYwUViom96l1obY7grYZUcbtqVG5sxszGwhfc0BQq5CfBMC0RvUGDpupwkWCsUn7x8BqL9LtJnfzcPYLxJqoYbLejrtEOMxRG66ZWBNsDqJ1Ug4bpjXBWurkoAMMwDMMwTBHHT8hdQMIwRbuxFAva9QrFrQHsP6u4C9rtUFZxIAq3fXhZxS5FgU1ToDssyL7YuOpav3tanN8Xpo6IZcKZo1DYP6uYxOKcq5aPgOTdUG73RAqNlxpVrNeOL9/WbqD8zTB8+7syisVEvVaPb9Z9E4VgTXQNfrHlF+LvlZGVIi5iMFFXkRUhGKegTWCaBnRThwkTESOCYCIEyzKFqE3z88d9wxJ5TQMwmgFHQoKmAlIQiMsSLI3vXQfDY/NgXvU8IRgzTCnAQjEzZJE3uyxjTpWGV9clf3BagjpeXReCvcDu1xo9juluDeu9KgxZwqNN9ThkzVYY2vALUwwH+q2sciqYVKYJB4JpmIibFrpCCWiKBIeWdBmTI0MagWDstIBuekpM+V4uF2pcdsT8YWxaswVdLV2om1KH+mkN0Lx2fjrLMAxThFDRi2g0KnoW0d9MYaDtSwXyHKNQp4BhhitCUhuxPRgTxZ9LCWrHzqn34PmegnbkKB6PkKvYZVPR4o+h1js8V7FbkeFQZMQ0CU5FgmWYkEpsP2ejQVNRryhoMQwR50A5xQtV+7BcxcFJbni2JB80jHeUqDWkWFwoptunwyE5RBZy0AxiU3wTptmn5Ty91CMe09AfEopD8RAcqmN495sqEKcHZDEJTpcCOUYFEVVYtvFxfhQC3dIRiAfEUOXYHt8yEeH2c+m0n1koZobEaVfh1k3MqtKwpjOZRfXvDUl3caGZ2b4Rnv1mIKhJ6LbJ+GxSA9rf2YKIOTqHbrlDFoLxJK+KJq+KapeMYExHKKbDrspJlzE9SZetXB94CyoARCxgC8VcQEatxwOv00CXP4jApxvQurUD9VPrUDmpFoqTnzwW+kJKBWmiCQNKjpFmdFMos+ubYSYs5eXlCAaDWLt2LTSNr9EpTNOEPNycpkGgRi4V+aDtzTDFBJkF6r120RYsJajJ2haIYU7ddtfhmtbguCtol6LMqY3IVewQOcUy2jUJbk2BGTehOEtrXw/GAocDL4SSQu9HidiwhGJyvrTtV4fmQDc0zVvw4yZshNEeboNDdaILMqZSHZgMi6Rc4o+f94u/SRfd68TyHeqhqbUb8H4YFa5pIRb/p0cs9ozecUAF9OY65uKjyEfi9YrIimEJxUNB7mQSkYe7D+0uBYmIASuhCDHJMlVYLPplRYMGX8wnhokuFHP7ufBt53y1n1koZobETlEMioQFDfZeoXi0WBu2Y79PNuG1hVNgSRJWl6s4YJcavPdx16g0aH1RE75oDCtaY725ww0eFY0eFbVuGY0eBU1OA1GqrjCC9SFdcqMErIeECstCtaxBU+PYsrUFG7d2wVG1DWXTmiB52E1VKCzTRDwYwKawCimHizQdA+S0L3Mm3UROjTKskwPDMBODsrJkZr7P5xv39QpyhYSmWCwm3Av5+n2meVEjN7W9GaaYmFrtxlSUFt3hOPzRBMqdTlHQjmLWuiMJtAfjqPWOr5ziHXEV0zWsXFWwTZIguzWYHRFY1vAFtWJld6e9Vyj+VB/ZvZ2pyTAdtE0KK5xqlguS6UAQceiKCyQFV2dYpL1GgqEmi9lBB8IxC66KkbfN45NkBGTA+36PWBxLOov9+7tgjKJYPM85r1coppziYyqOwVhDQjwZZmJBA5pTgRQxgXK+DxoMp+pEd7QbCQ/1UJ64Aim3nwvfds5X+5mF4hEwUYrZpVBVWYhh08o1nLCzF5t8oysWk0R71NpOPNuTn/afSW4cGwqhK1qYHyTdtNAc1NEWGrh/qZjf1oAuhr6EC/TN24A32wowb2aklDs1nLiwCYt3qqGqDqKoDTmKSCiucNpErjedLzSo46SbIsMwA6HGFwuY26E2UVtbG2pra7k7IcMUKaJAs6rAtCxMq3Zhbdv2nOLxKBRvdxVHh+0qdisKNEmCocmQVAWWbkIaJ6YAchSnWAcDQUOHRylOWUCRVLhsLnRGOmBTkndcukVu277jkcjirlLhb0neowU7DLgqduw7xRs1BPYCvO9tF4vLhFjshOEZnWNhF+cuvX9/Hv18yJzi0UJ1yIiHDMQVwJ6QMu8UpheX6kJ3vBv+uB/VzvGXCT8cuP1cGm3n4vxFKHImUjG7FCSCBaM6dq21YUHD6LtbdUNHZ0sU79Qnl/3c7Fqcs3EbZjTWFGyZMd1MisJ+HVv8CTGEEoUr2MCUBr5IAn95cwM+2eLDD5bOQZlDQ0w30B0mR04MEonHmiS6pVKGYaXbhloP500zDMMwDDO2UNvEbVfQGohhbp23Vyim+IkDZhWuTT32ruLkdx6Oq9ilyHAqMqKSBZddhhExqOI1xgP1qooGVUGzbojejZ9G41jkLl5ZwKk6oCoaDCOOuGIDdeTMJPl70oXiTh11s3b84Ue8gcRiCd73ImnO4gh8+zlheAt/PFDUhFN2ImJGEDbDWB9bj5mOmRhrSKtWNBmxqAlNMyAlVBaKB4FcxIZpwBf3TXihmCkNivcXgSm6hiW5JmOGKRpOo42qqFiihdEc1LDRo4jidg9OakSZHodl14aMB3YCOMAwsDNZgnOEvu+MSpsYUl0DfDFTCMYp8XhbQIfB2vGE5P2N3bjo/g9w8eGzcfCcWnh7np9QN07KPCbxeGt3VHR39FckMLnSxfEUDMMwDMOMucN2my+C2XUe4FOM64J2/V3F9EA/V6GYRGIqatdu6HC7NCCkj6ss590dDjQHe+InzAT2KRKnaibsikN03Q/Eg0goNtHnMqNQXL29nR3s6N/7c+TEG1T493aijMRiE6KAW/lboyMWK5KCnRw74cPwh+L1Z9HPikIoJhS7hHgQ0P0mtLgJjKMc70LGT8Q8MdiV8dmDgxk/sFDM5AS1iRw2BdGwOWaNJLfbhRPb2/EnRx3CqoSQRkPuF9m1soyDdQNLqcrmCJZP37nCoYhh17q08HE9hrhsH1FGcTZIexZdq+ipPyxUJHREA0GUKS5UNNXDPbmaKqrlbXkTGdM0EPZ1w1VeATlDVeB0SAC+5z/r8ewnzeJ1IKrjuqdXYsm8Tnx78Sxx80GZf9S1kwaCCh+ubw+LIohTKl2o9nDDgGEYhmGYscFtU6EpCqbXuHrfWz2OC9qlu4pb/FERsUG5xbkgcopjCciUxWuTYSVMSLbxEz/xXI9QvFLSocdM2BzFKfRJkHqE4gAMy0QEcsYi4p7q7fs11GXANK28FZ9O1Kvw79VPLE45i8uUgsdP9ArFkc/wpYovoRigy4Vql5EIGZB9BpRylpYGw6250RnrFMex3cn3g0xxw2czkzMOVYYqSyLDV1PGpiFZX1OJ0zY24/7pDdBH8MP/uqpgsyzhzISe8Un0SBqeNtoWtC55blzbe8TiTtAyNFSqZYj6gghtbYYGA+VTG2A5+RTeUUwzWZCD3L5DCcU0zveWzMGiGVW46cXVIoaCeOmzVny82YdLjtoJu03qW12UxGNyp3eE4gjF/MJZ3FThhMb5xQzDMAzDjEVOsSajsdzR266n9kxbIIa6Msf4dlJ3U1ZxDNPSBMVccop1RYLsUGH4ojAS46NGzXxzezt0vWUgEIyi0rIP2UuTsCwTVoIc1olkBsEo4LBUOOM2xOJRhJTkcdp/yS4ZUDUJesKCaQDRZh2eHShoRxvDdGy/xxNiMTmLl/WIxfE0Z3EBxWIqaJfi88jnMCxDOI2LAcUmIREA5C4dcqMGSeP7m2yosipMZr6oDzXO8Rn1w4wfWGVickaj+AlNQTimj6nINXdSNS7e0gxPi4pJhmuA+yFo6Vhd7kdsWjVkRRXZWy+pCjbKyXVeJ8v4g03DVxI6pljFnRuR8no0Q4KlKqiu8CLgD8DY2gIzkRSLJa8t7yI1Mzj7zazGbV/zCrH4nfWd4j3Kvrv04Y9w2l6T8f/2n9bnHKGidvVlDvgjCXzRGkQgmsCUKrcojMcwDMMwDDNa0ENvctd2hkxR0O6L3oJ2wXEtFJO5g/KZh+Mqdqdyik0LHo+W0cVaqtQDaPKr2Krr4l5pjUfGnooENRe3tWUCUZUOplETilVocChumLFORDQZYQlwZtgf7moFvuZk7IQ/bMI5aYRtbQuQoxZAzwXSNkmibvTF4qm2qXDLboTMEKJWFOti6zDbMRvFguxRoAeSrmJbDQvFuRS1i+pRONTxe71lSh8WiplhQQ6EcIwKvRlj2lbyVlUAVcDWtg7MWQPspJeJBmASCYu7yxFYF8V7le3o3rkO/0+z4QWbhne05CHvlyT8UVNxVDyBvXfku1gWZJiI02PrAom1tMZxAJskCTETqHZ54A8GEd26DeFwDN6pDQA17IfpsCYhk5yu47WbYaGhInU/P2EXPPNJM+7811pR/JAeOzz03ma8t7ELlx61E6ZVuwe4WegGrdVP7mIDk6tcaChziLgKhmEYhmGY0aDcaUOzL4Y59d5eoZgK2h04e3y73IbrKnakcorjuiheTK7i8cRCvxtb233i78+dEhZIKmzlQ3eJt0wTkqJCpmLNPUac0cCZKIPq9yMoGwjbbbBlaD+7Jtl6heJA0ERNxQj3mW5BjuuigF1/W5EQi/dxouzdNLH4zXDOecWWBESnaYg35SZiU3Y05RS/H36/N36imIRixQYYfiDWaUCtVCGPUc/jUsCludAZTcZPsFDMFDPj69eOKTgOVYVDLaICbrU1WFMLrOrswqxVCcxLVEDpET69sopDfQ0IvWngvfItOGpeLSY7nXjSriEhSTAlCc/YbdiiGPhSLIFkybqRJmfRMgv3o0hyo2wBbbJM3mLUeL2IRyLo7O6AZRgon9wIVLmAHJ3eFiyRmdsVMsVqp4oVkojJomXukMh+7PxGLJhcjhufW4XPWwLifaoifvEDH+C8A2fghIVNaQ8xAJsqo6Hcjq5wAquaA8JlPLXKlXNxFYZhGIZhmB2BHMWqImFmzfYH2uQoHu+kXMWt/ljOruIKTRU5xeORPcpceLpHKF4Rj0H1ehD2xYa8paHoCSOiQ7dikEaxAJ5lKrDF7GjVo/DFFFAqBEE5uWpPvrKnLi2nuHUH9hvNjtrvlFGXYYMkavuJxQlA7sw9lkTrMOBXJSE655pTnC4UH195PIoGSYLikBH164gHVDhGKs5PkPgJug/vjnWj1lU71qvDMFnhs3gE3HDDDWIwjPGRUTUc6KFxtddBRtriwtuI4DTg9fYuVC8PYF6sAmqPOOeWFRwSqEfoLQPVtZsxZ48puEfR0Noz6UeqgnZVwfkSUDdcjZQKf+gWLHoiOArO3JAFUemX2i7Ty52IRSOIBsKwdXZgSrUL7gYvJG3op9mmZSGWMBFJGKLYGmXTkUucirMZlgVbj9uYhGPKo2bX8eBQ7vCNpy/Ag+9uwgPvbIRpAQnDwh3/Wot7/7NeCMFiqE7+S109KYqCiuNt6Y6IfTClyoU6r523NcMwDMMwBYUEUqemYGqVs/e91a2BcV3QLoXXoaHZl7urmBzFNklC3DRhG0X37Giwu3d7QcMvonFIUzRUSLk5imP+KOxlrlF1FAuiVWjzbYbplOC1q6IIX4ScrD1Csbtuu0s33K7DNKyROVxlCZYCyPpAR3EfsXhfJ7zLImK84UBr5H0/At9BLhgeZXg5xdHPoVs6VKl4pBzJBqgxINqlQ3MrULTxfR3Z0fgJX8yHiB4RRRoZphgpnqtLCXHJJZeIYfPmzZgyZQomGtR+LNY2pKuuEpEjKvGfLj/K3u/ErpFKUYQiJRgf1FEL3wtBlE/24W+7TcPynifEWwH8ygLOloDdh9PeoZYDbQ/x1BkFx9vjLG7uEYsbXU6oqow2Xzek1SYmW5NRMbUGcg4VmUkELkeyMUVVgUk0FkPcgC8SRyRuwh9NCDFzPEONXSMch8+K5tzYJUcKCejkDqYIDxooh/is/aZh7+mVuOHZz7HVFxXjUiQFuXT6O3VIiJ9c6RTCcY3HLgrcTap09DrimeHtw0goAOem2OjfsDATeh9SMSg6fwsBXaMr3DZxIzzehRuGYUY/So6K2tV6txe080d0UW+BHmSPZ6jnXMpVXOd1iO0wGC45mVMcMS3YSufnKSdqbComOzRsjibELc1qy8CBFUMLVyaZpXQNrjIbZGV0i6rJ7gpUowMhKQGpzA5bWELMZ/QKwjYvuYsl6FFLRClHOvQ+4vFwsEgpSTbns5KoUdG1xAM1RzexZFjwfByDnLCEuFz2TgTdB7lh2Qb/nZ9smwyP7EHQDCJuxbE2uhZznXNRLFiKBE0DYgF6iKDDVc11WAYTitsj7SJ+goViplhhobjE0HUT/vAQv1gjQFEUuO3j54LuqCxDfEkZ3vIF4H6/A/NDVb2Ccbmk4sgt1Vi4uQO37qng6bpK0TiirXqHCRxlAcfLw478HTXcJExbSXGbVrzRZkdZlYKWLh/0zww0xROom9kwrBw1WTSa1d74gylwCYcxicYkHutFkzWSf0zTgL8rjrJKD2Q5t8YubRvKGKZ/w3Edcd0SLm06xGo9dlx/6nw88M4mvLyyVQjFmedhilzAVDYgwzClBz3Y+cERc7DbpPK8z9swLeH4ozx0EqMrXNqYFpJlGGb8QA+fKl024aqdUePufZhNOcXjXSju6yqOYmq/ehLZcorb4jrK1dEVRUeDPbwubI4m4yc+DIRxYKUHxYxTdaDa7sW6UBtipgcehwzFLsOIWZBdyV6QJAz7NlKFFyDYOnKhmG4G6Z5rKCxNQqI+9/suv10SBfBo3krYEs5iciYPdvNJOcXkKn439K54vSK6oqiEYiiAFKMC3hKifgM2t9Lr8mb6otD9pgR0xbpQ66xlMwBTlLBQXEKQu4gcAIVweEbCOnTKunWNr8ahvdwL/TAv3ur2o3xZJ3aNVvY6NuskO37xAbBfVRf+d88yBHueiD9nAZtN4EKZRAAUJS5ar3SxWFFRVVWGjs4A9NUboccTaNppCmTnyE/xZG6xggqMbyhCpk0PobbSJR6Y5Ap1z4wbphB8KcaD/o7EdSEg0zl61n5Tcdqek9EZjos86I2dYWzsCCf/7QxnFZAZhikdKKrnr29vxB1n7SV6GOQTusaE4waa/RG0+KOikBJlapJw7OFMc4ZhdhBy0lIPqZm124Xi1S3jv6Bduqu4xR8TBe6oV9hgaCYQiOvwDiOPl2ZJ7ehSiJ94sq1HKPaHUQrUOMuxNtQJfyKOMqcdmlNGuFOHhlT8hNorFIdaEsBuzhG7ZAthldGrVQR3s8P7MQUKArZ2A+5PYwjNH/w+PF0oppzikypPQtHQc3+tyRbI0xb1GbCPX5+RgOI1ZHVkYoFbdSMQC4j4CSpwxzDFBt9plBDHLJqHcCz5o5dXLAvvrdiAla1+kVmmqePvsLBXlCG6tAz/butAw/sR/H/27gNOsrLMF//vxMpVndPkHAgzQxgQRBAQRUAMC6bVZV3cRfbievWiXna9XgPurmBaE9c1/M0BFANBEURQguRBhjAzTA6dq7ty1Qnv//O81Wlmumeqq6u60vPFY1d1V1ed6dNd5z3P+7zPs9oKT8zeXTys49Q/pfGBjSa2NeVb2m0VwM9d4MoqHt8dGSzuUnW0tkUwEo1j784DyOUsLF6/BHqZlkU3Ovr9GQ+mwzt9AJlqPu8cSMimMWcub514DGUf05JHChjvGUrKj/ui6bov81E2QsjMcJkRzrPytakGj+H+aErWIqestF88tR9v27y4pM8vs6LGVnrQe0M8Y2FbXw4BU0NLwIPWoIkmv8kNSBljRaGxic9QsXRKRi3VKW4U41nFWw/mg6THEncc7MvlMKxqEysUj4cmDxe3BBCaxQq/StgYngxS7UxnEbMdhKs8wB0wgggYPgzmUljo80D3qfm+c2PlJ6ZmECf7Z1k8eAoxXlqQmo+U+FybXWJCj7vw7c433PPtseCEVWSWmMdsaDdue2Y7LGHBUKpnRbDQBDRLhR5SkB6xkY3Vdz8nb0QrOlvdR7+/qXz5CQ4Us2pU3WcudpiQ3y+3cti8VsFwcjsOxTJY3FLdS47mwtfeitHXAg/s78WSZwWWify/tSMr8N3HsvjMeuA3C/Mn6D8KYPXuHM4ePcZJjoKC7Q6sHlQ8WEwNBpsVFc3NEcTicRzY24ecZWHp+mXwNvMJqBIB5KCpI5GxsD+alkvIxycnKIOnK+KV2+ZlLZXe5ZpHAcbUaBT+SHPB5UNYdanFY0iNK7/3yB55+6dP7MOF6zvLVq+YAg6tQY+cZKLml/tHUuiNZRDx5bOM6WOlA8a0OsOW3eEZY7WAxiU+qlHbPLWhXaIhGtoRes/sDHtkfebj0YWGMFwIoch6xYWgms+0GsRn+I+bsVxJLYaOxV4TezM5mT1LWcWvaqGuKNVLVzW0eyPoTcRgua4scSDLT+QEVJ+CwJQyEOlhG64tisv8VClYrMzczW6Okus90BKuzCgmgeeycAKqrHs8nR6jB2EtjJgTk0HilzMvY61vLaoGLcW1BQxNg0rzT3WcUUxNFHMpF15LFNW4T1Py14UjmRF0+Dsa4j2X1RYOFDOpra0JGxe14aFthzCYzqDNV18lKI7kX9iFgYXA7h37sOYlD3rggyGAf30+i16fisfHuiB/t9vAuXstrInPfPHr7hd4rjeFzKnHrnFW7mDxfgBDIt/wLhIKQdNS6D0wAMuysOyEZQi2l76GJjt+7efuJh+iKQujaUtm/zHG6sNbTlmIe7b2yYBtxnLx7T/vwodfV94LNppkoiw42qhGOr2vDCWycgl5pS8yqCGhmk2jq6OOrwzZhBdffBHvfve7MTIygp6eHvzoRz+SH1ntoPeMiE9HR8gj64pSPwpaCdUIDe3GUQC3kORZ+mk0uwYGLLvgzFKfV0dfIguvR5VNAyuF9tY8TpPYjSG/DBSTG3cewkMjCVzWHsEJQV/Fzy0zaTGD6Fd9iObS6PAGZPmJTNQGfCrMgArDr8JKubKhXWrQRrBr9pmfMkhM/3MpW7YM/whVQfwUHyIPJaEnhaxZHHoy39zODRx9zOhYrPOuw1+Sf5H3n08/X1WBYmr+p+YEFArM+6p3cqQUDFVFNuHATrvQjOJ+OQJGADErhpSdkrcZqyb1/Rd8nAHu5s2bsXr1apx33nk4eFAu4G9o61Yuwoq2EBJJC2m7+GU6tSSwchH2XdyG+5cOYVBkoQvgM1sy6ErnA8NZTcH1G30YMY594b72oAP72TQqhYLF7WMzP4O0dEwAB3x+DLf4sHsgim3PbMfwwUGZJcLmF9UV7Y54Za1Rak7FGKsPlOX73lctm7h//0sDBS1hLhVatUDZxB1hr2xyp6tKRbes5chSHHyaaQz/9E//hI985CPYtm0b3vSmN+H666+v9C6xIlAja1PXsGxq+Ym+fL1idrigpsFUFaRct6DNUgRsFdg9kkFfKoc4lRCqwBa1HBzI5GRJCVqVMp0zmyaPvyUEfj8Uw/tf3Iert+7G7X1RJOzqKyHQZPph6H4M5/JNoSkwjLHyE/mGdpP5cIn+fHmHoiIlMqO4fCc2YSqIne6HO3atqVpA+PE0FEvMWKd4HNUprio0qSAAxar/1UUKNTqEglzCKfr62qf7ZJCYyk8wVm30Rh/gvuUtb8GXvvQlOcD94Q9/iEammTpOXb0I0VgaexIZLI0ES12OqSopqorACUuxfa2NrVt2Y1GvDx98Jo1/3dwGS1Nw0K/iQycb+Ncnh6ih62G6RBAeRZUDx3W7cnhGFQic6K/YuZkWD9LmCICGTQnTA7VNQ380if4tL2NNJoely7qrNjugXlFmzlAih+FkTgZ2GGP14RXLW7FpUROe3jci79/ywE588a0b57UMRL4pU+WHcznLhlvktXijuvXWW/GDH/wATz75JKLRKFatWoX3v//9+Pu///uynKd37NiBm2++GY8++iiee+45rF27Vn6cKaHiuuuuw8MPP4xQKCSzhz/96U/DNE309fXhhRdewJvf/Gb52H/4h3/Av/7rvzZMyYJ6q1NMjbKXtk1paNcfxytX1X9Du9lq0zUE1NllBguvkOUnWm0FK1t8FSkRlHMFhiwLQzkHB7MWPKqCiE5B78l8sTMiAXxgSSdu6x3G/uzkG/mudA5f3tuP/94/gPNbwri0PYI1gerINtdUFSEzjGw6Csu1oHt1aOaU8hMdOkZ25+ZWp1hVZJ1iCt6Wcw7UDaoyszj8WFpmFesJF6Gn04id7juqb8PUOsU7MjuQc3Mw1SpasUitJujHTsHTOj8faB4FVsaFkxXQvbP/t6qKCg0aopkoOv2dfP5kVaXyVxZlGOAea3BLeIA7s6a2CE5e3In4y/vRl06j219cl9hapGk6fKeslBm5ASFwuZPEbcjXMN7S5sX7zn4J2vD/BzFlqHDG6EZ8/MB75e9Ns65iybYcdgkFoZN8FS8RFR67ndF0JFqC2DqSxoHn92BFKoUTVixGm9do+N/3+eI1NPQ0+fDCoRgyliPvM8ZqH72H/tO5y3HtD5+SvW529Cdwz/O9uPjE7krvGqsBn//857F06VJ87nOfQ3t7O37/+9/jve99L/bt24ePf/zjJX+9rVu34s4778QZZ5wB13XlNh0KWp9//vkycP2LX/wCBw4cwAc/+EGkUil85Stfwf79+7Fo0aKJMUQwGITf70d/fz86OztLvt+s3HWKNSxpmUxyGA8Ys8NpioIgDbBnyQh5MZrMIZu2ZW+KSmgzdSQ9DoZtBwM5S2YZO8JGiILfmipXR76ho0kGgp+Jp/Cb/lH8eSQuE09IxhW4a3BUbqv8HlzSFsYrIFDpDijNRgD92SDi2QRafM3QfQqyo67Mmjm8oV3xs5iy5EQWZWe167JmcXBr/sXMfgf+F7JIrT/8d6bL6EKT1oQRZwQ2bNnU7gT/CagWQldk6Qk4tRZpmj3NzP++Wel8nexiUCO7WC6GpJVE0KzfPlGs9tTUn28hA9zjDW4JD3CPvYxi1YoFGIzG8fjwMOKmgZBeU78mJUG/G+frQfQ5wJ/GBklDwfPx96Fz0GPtw6HcHvTl9mKr9jh+lr4Xb42+Rj5muUfD4AsZxIVA8KTqqOtFE5xeTUNzcwCDoyls2dmP3oyFM1cvwNLweDiZlRtlEg8lPOiLZ9AdaZwJGMbq3ZLWAC7b0INfPZMvYfX/Pbwb56xqR7AKsnxZdfvNb36DtrbJzE0avw4NDckA8sc+9jGo09QVpbGw1+vFihUrjvraAw88gA0bNqCpqWna17vssstw+eWXy9tXXXUVnnjiiWkfd8sttyAWi+H2229HS0u+4apt27j22mtxww03FP3vZdXZSyHiNbCgaUpDu77GaWg3Hyg5IEFNSKMphH06/GZlzg0BCgrrGrpNA1HbxlDOxpDl4GDGgldTEdZphaSKU8IBuQ1bNn47MIo7BkbRm5sMtG5PZfHFvQP4mgKEDpVnyfwir4n3LmzHuuCxx8tBw0TC04yEvRd+OwPTbyAz6kC44rDSE+lhB06RTcdATfDmqZJCZqkBLe7Ctzf/8/bvtOCENGQXGYfXKfatwyOJR+T9FzIvVFegmDKKrXydYgoa1zM6FtQkkcpPeCOajKPMll/3oy/VJ8tPcKCYVZOauoopZIB7vMEtN9o4PtWn4+TlCzCUyOCFZBq+UFDWH2xEV6jAfgfYNXb/R8LAhz3LcZp3ubx/Rug1+K77GazLLMXJ6VXyc5v8Gv64NQPqfxfaUB3B4onuzs0BpEdT6N03iPsSMZzY5UdHpAmhQACRQAiGNvtGD6zwnz9lFY+kc4hnLNmMijFWH/72zCW4/6V+2eWeth8+ugf/dO7RgTzGppoaJB63adMm/Pd//zeSyaRcFXekT33qU3Jl3Z/+9CeZ9DA1SHzxxRfjM5/5DD7wgQ9M+3rTBZ6nc/fdd+PCCy+cGEeTK6+8Etdccw3uuece+TqU9TweTEwkEjIhg7KiWe2h0jULm3wwNEXWGaegZl8sW7Hs13rU7DdlCYoD0TRWdgQrem1A13TtpiE3qj1MAWHKMh62qIaxjRZDl4Fj+viOnla8tbsFT4wmZcD4kZHERMw0JyADzeUwZKVx3Qt78XcLWvGO7laZzT0dr6rCb4ahqu3YnuhDs96KFEU3MvkMTy2gwklSJzrgYJ8Fb/fsx970rklJxfNTf1/BwAkeLE64CAznf7aBv2YQ9SjIBiffv9coG7Ddytcn3juyD1Hv7CPZ9P7t2EI24y3o91EBbA919jvOYxUFiivg5lw4xQTmawz9G92Ui3jcgVlkA78Rx8CL8SHYWrMsR1Eo13EQy1mwMjmoGq9WrUXu2DEMOC5CVXYMaypQXMgA93iDWwowL1y4kAe4xxHsiGDDwg6M7NqPQ5kMFjVQCYqpaCL0vRrwHw4QG1t59A0H+LAG+BRaytWNd3V8AF+yv4GbX/4XNDthGIqC0/06/rQ1I2egQ5uqJ1hMfBE/ulMWepMOXtwfRf+hQZgeA36vF83NITQFwggHQwj6/LM6WbHja/Ibsl7xnqGUvDCjpX6MsdpH2cNXnbUU/3XfDnn/11sO4nUndslsY8Zm489//jMWLFgwbZCYUBCZMo9prEvB4o6ODjz22GMymeJtb3sb/uVf/mXO+0Al3N7znvcc9jnKUu7u7pZfo7E0lX+jlXvU6+Nb3/oW3vCGNxxznH7TTTfJzXGqrylWo/N7dAS8Opa0+rGjPzlRp5gDxaVNFoj4DPTGMoj4DXSEquNnG9Q1uXV7TIzYNvqzNnpzOXhdFU26Jq9fKEh7RlNQbhRQvmtgFHcOjGCwTEHicRT6/M6BITw2msQNy7vlPk4X9NYUDUm0YFhkcCAdg6EFYKUEDE1AtOlAMl+nmALFSvvsQx+qEPmePVRfaj7G7YqC/ad4cfZDKfjTAqoLLH7s8Ibpq3Ay3oqTJz/xfP7vtjipgh+ZNRX0d+jo7dIx1KrBnakUy1i5DtvTGN1tLRvwjDootguN5fqxP55CXBmRDe4KJVwHTiYHLZmGolZXkJEVfgzdTA5Nto2QWV1JZDUVKC7F4JZQeYnZDnAJZSrTNu7QoUPyIw16ixn40vdQ+YyqHDSrQPfCNqyLxvBIdBhRQ0dzA5agILSA82oV+KJLgxYFfQC+6wj8IzU3gECH3o3LF12LL+R+hI/v/kdooMGVghN9Kp59ISOXP4VPqa5gseHV0JxRoZgeBEOAbjlIJbOIRmNQ1IPweT0I+L1oaYrIbOPmUASmXkWNEkpovv8OO0MmhhNZRBNZNAeq64RQq+j4iWPU22TVrx6O4YVr23Hns4fw8kBSXk/e8sDL+PTl66vqvb+c6PiJsfdSWs5eTlU5bipRkPgnP/mJrFk8Ewog//a3v8W5556Liy66CP/1X/+FN73pTXjta18rg8il+H2jMm7Tla9obm7G8PDwxAq+v/u7v8NHP/pRuVrveA2hqWk0bePl31h11Smm8ghLWgKTgeK+hCyhw0qHEgSSWVtmFYe9RlX1qzDGsozpWi9saNibzuFQ1pKfo6+No/t/t6AN7+hswq7+AZiRpoJXKhRq1HbwpT392JnO1+rdmsjgvc/twfuXdOA1reGj3uM6PQbgoX3vwN7YXsCTg5MyYCpAqlNHck8+UOwdtBEuosa0oisy0C8ziucrf8aj4uXTfVj/UApaFZ3uPDmBRfstudk6MNKhI9qlY7RdhzulzIRCP2dHQFA2dwOwsy4U24VfgSxFMVtCqIhmMgiJBFqmmRCZCY2ZM2kXPj9kBr1iFlf+glU2o3gfqlPdRf4KGdwWM8AlVDPuE5/4xFGfp3pyHs/s55Doj3t0dFTeLvVJthQowNnW7MHKuIrdiRhsvxd6g1zwHmklgL+Bjp+NdZV9Fgp+Z+dwsbAAJ4curR2bllyCnyZ/h3cMXCwfs8yjYcgWOPASTanaCG/SqypgEFQEYikgCgXtHlVmlNDmug6yWRvDySj6eweh6yoi4SAWdnbDaxY7V1q9KvF3GHCzGImloed02bWZzY0QLrKphFwWp3AWfE2ql2P4d6e04f/8Lh9oeWbfKB54bh82L54+M7Te5DI5iFwag4N03ihvAITGXfWGAqhvfetb8epXvxrvf//7j/nY1tZW2fjunHPOkQHj173udfjRj34EbR6XLa5fvx6PP/74vL0eK3+266KWKXWKuaFdWbQGPegdzZegWN4eqKrrgvEMXaoPHNRU7Enn0J+zEBnLOp6Ksox7DA1+r1mWJe9fW78Y39o/iFv7ovJ+ynXxH7t68ZfRJD6wpFM24TtSk6cJaV8a+3MH5DnItRXo7ZMJGdaAXdzO0DGi/wlZwWLepEMatp/mw7JnM9Cto1/Zci24yP+bNEWHrhSRfEJPW+CvoOJCZjeP022g7aAtN0eFDBZT0HikU4ejAWpOQEk3RkaxLgA35cBVVajUHGiW6Du8lo6kHUM4UPj7gkywyNhwBtMQpgXFVKH6DKgeDYqh5QP2jBWp7gLF5RzgUlO8q6+++rCM4s2bN8sBezFlK8YzYqhG3XwO7mfDDTVDt3VEDxzEAVvBIp8vv/ymAZ0rgD1C4C8i/wO4QzWwWNEh2wcYPiww12HfiTqefuwlbEqukY/Z6NcwGreReNmBgIbI5irKLKYJ+LTAqKMgbCqI6JPBGf/Yamkqz5LNWBiOxaCZw1i7bBnCgfoKelTi77CpxYVlxhFN5tARrr/g+3yTWagC8IWbq3LSjTXOMTw10ozzdiXxx22D8v73nhrAWesWwpzy/lqvclpWXiTRe6lhlHd4mc3OQwv6eTQyMiLr/tJ48uc//3lBfwP0M8hkMvKxVD6N+nEYRmlWqVByxfgE6pHJGFNLu7H6EfTqWDqlVM6Ofm5oV66gPDW0642lZTkyChxXo2ZDh19TEUqr2J/NIZl10WbqM9YKLjVqqve+xR3YHAnIAPEQre0HcP9wHFsTaXx0WTc2hv2HfQ/9rnb4O5C20zg0Mgx/LgxjSqkJJ+rIurmqObvzsaCH01aBmGesTceW86dvcPZ0/EH8cuC/5e2FnpV474KPz+7JKUXaSstr2EJKalDd4fCgg+ZeG819NgwqUj1Gc4GWPlturkL7rSHRrFXiR1YxriWgDrowvGrBwfepHKHCcdPweoZgqIWdy+k92pPLQKfayLoiVz4LmtGghpkeFYJWrgYMKD4dilfPNxcsVyCHgtJa/Y91G0ndBYrLObgNh8NyO7LOGgWXig0w0QB/Lt9fblpIQ9eiTmyIZfBgbBBDhi2XHTUiOoe+nZohOJBLBAQUfFsAH1VUtMvZZgWL/Kuxd9M2LH50BK12k8zAPi2g48G4jfTLOTnIiJwRqJplIZQIn8wJRDMKQqF8HbKp6J4voEEzNQwOjmCreBlrly1HW6QZ9WS+/w7pdRY0BxDPOsjYomIdsOuJQrP4cqvO91LWOMfwPa9cjkd2DiNru7Ih1C+fOYS3bV6MRjh+yjy9l1brmKkY6XQal156qRy7PvLII4hEIsf9HkpUoBrFixcvxm9+8xu8/vWvx5vf/Gb86le/gmnOvUwUlWcbL9c2jvaPXpe+xuqPz9SwrN1/WEM7qqfbHWnMHiXlRM2MKVB8YCQtb1frRKJHVbHc75HZu3szWRzMWmgzdPjmMRh0aiSAb564FJ/f3Ys/RfNZ7v05Gx96aR/e1tWCqxa0HVYaQ9d0dAY7EQumkRpIwWwOQg2pcKnDOJUIGLRh9szyPZL+uXR95NBzVMf1G1nqXTdx+2B2J7JuGh61fH+vQlUw2qHLbbcQCA2PBY17bXgykyFhVQBNA47cWDHyWfSzU/kVIDShYi8KwVodgbWqCSLUmPGi2aKV3LJMSxXOqtRddGI+BreNVmdNj3ixbFEXBnam8XQ6gYCuwV/DGV9zQfWu/nGsuR0tLk5DwTcUD66nybuxsUN3ZDWeOWkHznvalfWKI5qCk3watqQdpHfmoBgKIqdVSYMjTUFIFRjJCIQ9Aq3UzXYapmGgvb0ZAwNRbLV2YPXyJehu7Zj33a0nrQETHSEP9kfTsj4gZ+0wVh/aQx689fRF+N4je+T9nzy+Dxes65SfZ2wqygKmhssvvPCCbExHTewKKbvxmte8RiYu3HXXXbLc2r333itLULzzne+UNY7nGkin7ObPfOYzMtN5vJzbrbfeKidyqC4yqz9+Q0PQY2Jxi1/WWR+vU8yB4vJoDXgwGM/ikC9d1U1PZZaux0BAV7EvncOBTA5pV0VkHoesVPri/67owW8HY/jy3j5kXCEzVX/cO4wnYkn86/IeLPZNBn+DRhDdTR3YEd2HbCYrs4qz8XydYnugiECxokBQKQW7IknFM2o22tGkt2HEHoQLF3sz27DKv2F+XlxREG/V5bZ3vUBg1JUB45ZeC95kNf2U2Hyh0iTGnrjc8Pv9sNq8yC4OIrc4BCdSn32OSkH29kjm4KYsIFBd59u6CxTPx+C20To3K4YKb6sfJ4y2Y7A/iz3pDNr9hy/3qTd0iUX9iKdL/G1VgPeowFfyK6VxQFFxhxB485THhHpW4q+je7BxZz6LfalHxZDtYr8lkHopC7PDgG9xdbxpGqYCPS0wkHQRpHIUM0wCyFn6jhYMDI3g+e0vw7JsLO7qmff9rRfU7Km7yYdoysJo2kKTvzp+Hxhjc/eWUxbinq19MiOPMou//dAufOR1nInJDnfttdfijjvukM3rqFnyo48+OvG1TZs2Tdv/4pprrpGlWu65556Jce4JJ5yA3/3ud7jgggvwpS99SZZKmw6VqKDgMtmzZ498zdtuu03ep0DzeBk1eo0vf/nLeOMb34gbbrgBBw4ckAkS9Hnq68Hqj66piPh0LG6dEijuT+BVq7mhXTkYWr43yKGRNJp8JiL+6s6+C2gaVgW8slbxvkwOh3I5BOcxBY4C1he3R3BSyIfP7DyEF5MZ+fntqSzeu3U3wkfVLBawnSBc24WyKQBxwth1qw6o0SKu38XY0voiyxyXS6br07DcfCmmr6teeGa7f4oXmHM4QwGCGrCSNlPWcladfE3nRjP+T55L7g9Ng6iUxq4U+oJTCk2P3xdTvn3K5+ZfGoimgVGabMlvDVvD9Bgc18WSlw/gd+1hVJOaChQXMsCdj8Fto2UUEzVkoqmzGSclkkjEBpEajcnyCXKZqapAVWi5cP6+qox/rN2sYzrPDo0Vp6d5/vFs4XHrVOAyAL8eK+r/BwGcIYAFUx6XW7sYB0b6sGA4nylwsl/FSNxBwgVGH03CaNagh6pg+ayiIGQCQ1mB4bSL7sDMx42WhLe3UWPIUby0c5cMFi9bsLCma4pWEnW97o54ZS3AkFfIvyXGWO2jpcTvfdUyfOqOF+T9P740gEtO6saJC45fVoA1Dgr2kg996ENHfW3Xrl1YunTpUZ//7Gc/KwPIVAt6qlNPPRX3338/1qzJ90iYTn9/P6644orDPjd+n773vPPOmyjjdt999+G6666T4+lQKCR7dNx4441F/ktZLQh6DCxtoTHrgLy/vS9e6V2qa2Ev1SrOYF80iYAnLIP11YzK0y30mghpKnZD4KDtIJOzoE7tcFYiFGgLatpRZS7o9f9r7WJ87+AQfnRoCPTKlhATNYyPRrVb6YJuyvi62IBZNa78UwP5jYLGY1vh8mUTSyvf+K+2IkzVRpnj46f7/mr53aWufw04g1CAEGUUV5ma+jMuZIDLg9vyoKCw3uTB4q52mIaKFHLIWbYMFDquI2dCXDv/UTgi/7HMU1d0bgsFAvCUoB7fVLTXubFqP8MiX2JihJos0dK8sQll8hoFeAoC+5EvHv9jB/ggLU0afy9WFPSd2oGmB0cQyBowFBWnBl38OSbgWAIjf06g9aJwVXQkVXUFAUtgOAmEPa5cYjbjYxUVba3NiI7GsH3XHuQsC6uWLIVeRzUj51Nn2IvhZE5uvDSdsfrxiuWt2LSoCU/vG5H3b3ngZXzpbZt4QohN2L1796y/Z9myZTN+jbKQj4UCz9T8phDr1q2TJS1YY9UpXtU52TiLG9qVF/1cqQTFQDyH5kAWC5qqa9nxTCKGjtUBLxSPAcXQoZZh/G8JYNS2MWzZsvREQFMnfg91VcF7Frbh9Igf/7mrV9ZOZoyxWqZkqmy5Qq0Figsd4JZ7cNtopSfGqX4DRpMP3Var7J45znZsuI4Ly7Hlz8R2nPxHqrlCXyvT0qR0JoPB4RGoARdes7QBNn0sKNyKfKCY2iOOjJWXpxAqzd3SK75DCNykAEJRsBPAIwI4e8p42jZV7DklgrWPJqEKBU2qjg1+B0+lXFjDDmJPp6qmXjGV9xrKuRhKAr4wZYkf+8KgORJGPJHAzr37Ydk21ixdVvKgfSPwGpqsATiSiiFjOfI+Y6z20UXtP527HNf+8CmZQEHLud/97cfkRW49cqlupHBxkxHE+eu6Kr07jLFZ8psaVrQHJxraJXMODo1m0FMjAcxaXX1C24FoCrZDaSdKzTRgUrICIStfZqDkzy8EVEfBkOVgv011kYUMFgfGVq6SNqj4z0Xd6KOkpRmuNZNDaYwkR5B5YARqKv857Sw/1PbZjbVVS8BIunApM7nKDtFtw19A3KF1sMCFkb/FIrPAMldCQBMWHMWozmzpGuXaNLkG6GFNrraeLce1kXJS0GQhzOMQAobtwKLSK1V6DHULaBkC2gYVNEUBza3O/aw0hVZ0vx5VpaYCxdWiEUtPjKOsYpGxIezJZUYGzSRrGjyY3/paWcuCpqgYGIrKCQSfh6oKlxa9RYfHNmrdFh8LFlO2cQwAVSB+pWPjT3r+3/5LFziZMp2nvAcmWjTsX+PF4hfzNaQWmRoSDrAt61ZVvWLKbA5pk43tmrzHfyMPBYNQNBV79x+CbTtYu2wZ/D6+oJgtyiQeSnhwcDQNz1F11lghjQCstIWUmpNlb1jtqdVjKFe2ePUZ/26pSdFlG3rwq2cOyvu0cqDeZa3SL0NmjM1P3dwmv4FFzX7sHJysU8yB4vJq9hsYiGfxcn/+Z14zDZhSafRbybKes+n6zhQCacfBQeEi5QpZDjCgqLIUxvEYDi3p9sCnAWYsf27K7M8g559dCISC4b60gKMiX2e1iiwWEbxk/1XeHkw8iZW+FQV9nwIBL3KyXIWotuh3DRM0aZ4DNGEDMzSJPxYaTRrCKGhlNtWB9joCGVeRSWvVKtmS3/Y7Al2DAm0jjVnDejp01BzLQmtXvudENeFAMZsVxdBg9ITkDFalUWh15aIQ3N27sO9gHwJ+FcEyNtmjYRBVlwwJgZQA4q7AiOPi1XELzwgdcUWR2ce3u8C7j4gZ9C43EBh10Hoov6xgnU9D0hU4QCUoHo3DaG6qinrFHkNBKi0wRI3tTFredfzBX9Dnl4870NsH23KwdvkyhEOTSxfZ8dFS9EWtfjnzXK4M/HpGmS1pV4cvaMg62qz21OoxtB2BaCoHBRaaA6YMtBzpb89cgif3RLGfGnowxlgVC3kNLG2dDBRT+YlzuaFd2VefdIRLn+xS7nN2SjHhj3jm7Zyddl2M2g4Gber34sjrspCqwThG1qbt0ZC0BVKdQeQOUIoP4I0BXt/skpsUlwJy+UCzzCquIkvddXgp92d5u8/ZhoCnwPAOBeHp56jqVZuNWqscuPLaTqcZinKijOIc4DG9NXMMk6H8am02hmqsDyZxyqrqi51woLgIjVp6Yly+pm51vBl5dS/WrVwFRVex90AvBGV3BctbykGFAvpTpq3FAfZmFbzeBn46Ft97VABnCmD11B8RlabY4IUnnUJwJD/Q2OTXkEo4iFrA4J9G0PnalsrXKx5rbDeSFRhJC7QV+KP0erxobVHRNzgoZ0A3rV8H06juDs7V2Ngu3M0/s2LQe/GAnkN7ewQa18quSbV6DGmpMGUJ0/LswURWBoqb/eZhdYiDHh1ff+cpODCSruseHrFUFiKbxBnLaK0NY6wWBUwdKzqC+MNL3NCOVRefqsJnqmjRdYw6FDC25Uc4CoyZ8lpUIGUosJspKz4fKHb7bOSemuXErRCwqRSyAESJEqhlmZESBPe6xXqckaIW63nxQwloVE7ieK8vAEt3kVvkQgRqZ9xVC4ShALaAbovJ5kZleSHAJ4B0HY8t656o3ox+DhQXoZFLT1Qjw9CxdtkKaKqG3fsPymVK85XRaioKApqC1TqwJg28NPb5nzjADdoRTXY1BdtP9WH9Qyl4MkIumdoc0PBg3EY6qmDgyWF0bKaqyJWl6wo8lovBpEDQ48J7jMZ2U5mGiZbWMAajIzg0MIAlPT1l31fGGKsk6lJPmWBNflMGiilg3BfLwGdoCPuMiYAxPY7KUNSzaEKDm7ZlKQ7GWO02tFvTFZq4TxnFsl5sjWSrsfpnqgraVR0tuiYDxUNUo/VYq/F8OlIRUzYqJ2oW8L5QfY2jiqVBxSZceMRnC2vwR6lL+o40sNEDZSEnq5QKTSY42Xx93nLnPtCIi1uh1zYVriypU214NM/qgqHrspkaLd/avf8ARmIxNIWpsnD5+ZR8yYY3ucDNGQFbUdAL4D4BvPaIP3rLq2Lb6T6sfzgFzQG8qoIzgjr+HLdhb1cw2D6AtmWVX+IXMBUMZ10MJ4DuiFJwx2uP4YGiJHCgtxedra3wevjUxRirf9SMiOp4tgRMWWuydyxgTEtAw1694PdQxhir9HvZ2q4wTE1FznFlQ7vP37NNfr5UKKHDzmWhm8P83lhm9NNd2x3G+Ws7DlvpUg8o4Yayi5uPE4mzhIZ4zMXLXT4kerkE1FEoZv5EFsF+B+0nmVCrrLRGTZIJxQJaxoFJ5SfK9D5H8yNUN5uqnvFbaW0SAhhyHISnKV1XaRwoLkKjl56oVrRcec2SZdB0DTt370M0FkPzPASLqU49Nap0vQousoC7xvr40MdTFaDtiDfudFjDy5t8WPVEWg7gIpqCUwMa/pJ0kPyLBaVpAK3NlQ0Wq5oCvy4QpcZ2XoFgAY3txjVHwhiOxnBooB/LFnLGPWOscXgNDYta/GgNmuiPZdAby2cZU5YtlaDgoAhjrNpRvfVFLT68PJCvJHnfi/2V3iU2B3c914v7XujD9a9dg9Zg/SVwHO+8anh16F4dS87uQmx/EnauuOt3JedCzToQM9a5KFzWySBtJ2FqpalN3WcfwnPZp+XtoBrCGb5zCpqwie+zZeYrie+1kR120HW6F55w9QWtao1mKBBZAWEJqEU0tSvYWAUTHl7WLgXViQPFReDSE9VL1VSsXLhYLsPZsXcvhkdG0dJELejKO0AJKwqiroLzgwKPx4CBsUU/P3WBa6eZ5Rvp1LF3nQdLXsifnbsMFSd6BZ7LeHHowQNQXgu0eCsbLPYbCgYzAoNJFz6TZisLGzQYhgHdULG/tw9dre3w+WqrQQdjjM2V39SxtC0oL8ops7h/LGDsKWFWXrXJ5Bzwuz1jtc9vaDh9actEoJjVvi37R3Htj57Ch16zBpsbrI68oiow/DqslI3uTW1FP4+asqFHM3ADcy/REMuNYDAdR8ik1uxz53Gb8e3BOybun9u2WQaMjxcojix3MfAMkOrPZznlEgL7HkjLzOLwEp7cngtFA9wM4GYpUFzpvWFs9jhQzOqOqqpYtnChHBi8vHcvBoejaGtpLutrBhVqskBdTlVc6RP46lhV+a0CeEYAm6Y5z/YtM+BLuujYm68jtcKrIUHn6cQCPPvIo9jwytPQbHSgYigAbuQb2wVSAi2BfF3lQkTCQQwOjuLAQB9WLl5S9l1ljLFqFPIaMpO4PeiVAeOMXb8rkYIeDSKV4awWxmqc36Ph8g09spzOaLqwWqezIYSLXDoN0+eDotTv5Fk12Ducwu+f75O3Y2kbH//1VrxxYw/+/uxlJS0nUu0oq5gahru2C7XYfzd9G5XvoK60cyzjoconK52QGkaX1oNe56C8//3R/3fcQDERwoGyTkN3cB0W7doEVagQLtC/JYcXDm7HzjWPwqFCu6wowhFQhhSocUr5LcPzy9dwoGha1WallsIS73K8sul8GCrX0Z5PHChmdRssXr5wEVRVwY49ezEwNIzWliaoZRqQUp1iChYPusBan4pTcw6eHIsH3OoC6xTgqOoNioI9J3jgSbqIDOUffJJPRZIGIAfPxD1//SkuOuktFQ0Wm7oC03ZxICWQ0l10mCp8BQyOdM2A4dFxsLcfXW3tCPr987K/jDFWbSgjJ+I35FbPqBzXwECOM5AYq3EeXZPvVxsWRmSzzlJzXQep0Sj8kWaoapk7PTG8YnkrvnDvNsQz+QZuv3zmIJ47GMNHX7cWC5p9aASaR4VmarBzbtEBckHXP3R+k43z5naeK8d5coW5Br3pfKB4h/XirL73mc7H0eF/ABdu/zuEs/nG6q0DS2DEArh31XfRH9pb8v1tKOOdFFlRnoo/hodHH8Dbu/4ey32rKr07DaNxphJZQ1rasxCrly2FaRgYGBrCUHoQg+kBDKXzt/PbEIYzw4hmhjGaHcFobhRpOz378hOqIocNjhB4U1CZWII7AuDOsbrF0w06dpzqQzqQHzBQV+nTAxqCKnDGtsvw813fgC0qO5Mb9CgIWi4GUgK7LBdDjpAdsI+nKRTEaDyBA33U2o8xxhhjjNWCsM+ATYkLrOa9YkUrvvqOU3Biz2Tflh39CVz346dl7eJGKU1o+nU4uRkuyApA12yCygkW/xRT0HWfIrPrS+VU75nIX4kWpz+0B7edfBNebs3XOibhbBsu3/oBnHzw1YDgSWBWOX25Q/jS3n/Hz/t+iCzV9GBlxxnFReBmdrVlcVcPTM3E4IGYPMeZfhWucGWwk/5zhQPXdWHDkSdsx3URt2LyVOvVC59pp+AuZRZT1YmIpuJSn4vbxkpQ3C8ENgsFi6Y5xzqGgm2n+3HCQ0nQ6h5DUXBGQMefEn6c+sLF+Ev773F28+tRMYoCjwG0ZwWGDWAPBBIq0KlTlvTMgwZN0+H1e3CgP59VHAkdfwkUY4wxxhirfJ11XVNhOS6MKuzGzmanPeTBf7zlZPz4sb1yozmAtOXg5nu24em9I7j21SvkMa9nuk+TzbqLLj8hM4plvYY57wslGNE299zkSUuMFfhA88ewz95V0OOpRrEQNhTliFrEpziIH9iN4IuLobgqNKHhrD1vxGnJ85BafghC5QkkSXHhBDLHTbt0bVkgAnpAk3WLS4l+FV3HgqoZdVv2a8gaxP3Re+AIW8ZtHhi5F39NPoO3d16FNYETKr17da2+zwhlws3sak9nWyv8SgDR3iRMlcoizPxOTcHiA4mD6E0egqEZ0JTC/kw8sqmdwCEXCAI4x6vgL1mBfS7gQsGPHeB/adOXtcoGVGw/1Yc1f0mDzr9BLZ9ZbCeW4f4df0HqlDj8WuUCrUJXoFkCrRkgYQj0uQpSlkCXBjTJZn3Tn53CIT/6+4exv6+XA8WMMcYYYzXAb2rw6ioylsOB4jqhqQr+9swlOHlhBJ/93UsYSuTXw9/3Yj9e7I3joxevxcoOuoKpT7pHg+bRZFZxsYFiyipWLVcGeOdeo5gyikVJa9cuNBbLrRD02q7IQlU8R1/HrQKynS56H8/IBnfEHGySG5uk+xU0rzAQXqxD1ac/kMIVcFICpleDFijteykdQzuXgW5667rs1+bw2fhR77exO/OyvD9sDeKr+2/GmZFz8Mb2t8GvcYnLcuBAMWsI9OYZbPHCsVyMDqTljLI2wyCBGmt0BDqQcTIYyYygxdda8FKekKag3xWwhJCZwW8Lqrg5lh9Q7AbwkADOmeGp4q06dp/kxfJn88sp2nQVF4QVnHLoShx8ahc6WtfKgHLWryDjVyG0+T0huB5AzboIZlR4AgpGXGC3LdA2ll1sTnOCoiC7z+9F78Agejo60ByOzOs+M8YYY4yx2fHoKgIeDcNJC6HSlylmFXTywiZ87R2n4PO/34a/7BqWnzswksb//OkzePvmxQXVLRaui2wqCY/fgaLWzkRCNpZDLmEVHZzV0jbUnAvXmNu/2XZtxC0XmpKRfXUqRcCmcLUMclKzv6OsV+Hb7cLTz1nE07FTAgN/zaHvhRyyXSpyXQqEcfTP0cm6+YZ2pT7UMthvQ1WcfP3sOqUoEZxpfgCt5oN4JvcrOGNFnx8d/RO2xJ7FZvPtWKhvQG0SiEeB+L4ETlqNqsKBYtYwqLFduN0H23aRjGbhb/LIz03HVE10+TuRs7OI5+IIm5N1vY6FGtr5x8pP0Hlisa7gVR7ggWz+679086fkGc8TCww06UBkYGpZExPAauxKuABtYxxdgeVRYJuAZSjIhDTkfKUfbNBw8SQFsomd0AWUtIBmCrSYKtKuwEEHSEGgUwMiss/D4T/TcDCI3v5B7OvtRVMoXNcznowxxhhjddGE02eiL5aFU+Jaxa6b73VBz0v/sfKiYTf1QDmyBvXHL1uPX285iG/+eRdsR8ia1N9/dE/F9rMxVbYPTd7YReoxrPFrOC2rw8d/rhOCrgJjbMZBtQHffhfafoFnTQdPem3E5rVEx/GPYX04FYqxFN7u26EHdsjPpMUoHsjeAqt/A7J9l0E4tbgqQkHPQD/ecwGqCgeKWUOhLOKmDr+sT5UazSLQNM1ymzFBM4TOQCf2xfchY6cLqlesKwqaVIG9DjAeWr7Ep+LpnIsY1QODgluP17egnQoCG0WOvlEWzQD+ScsHvlUqx5QScHQhg8emImR2MQWN2zWgQ8vXWR6nKipCAT/6B4cw2B5Fe3NLeXaSMcYYY4yVrPwEbQPx0gYhKBvVTltIKrmaykatVRSQpxrF5hErKen65/KNC3Diggj+4+4XsT86u0berHG8ZDpyY5O8LrApp+OUrA7/WKM/EwpOo8/lNLxgOHjMa2NQ4+h6KQmrFem9/wCj6XF4Ou6EouXPT0ZkC7TADuQGLoJr1V6JFNssvC/WfOFAcRG4mV1to/rEkQ4/HDuJdNyCP0wZu9Nr8bYg7WTQl+iFrhnQC6hXHKLgqSOQFULWLaZg6lv8Cr6TrN0TRRTA5xzgXaqC00whS1CIrALXp0BTFLRqQIqyi20gIwQW64cHi4PBAHp7B7HvUC9aI00VXWbFGGOMMcaOrclvYk1XOF9HtYRcx0F02EJzSxiqVuLuTuwoVFaC6hF3RaavIbKiPYgvv30Tfv7kflmruBD0O+HYFjTdaKyVgo4L5FyqrTenp3HhyiQkeha11B3OCiRbrLlUOkSbqMRB9XQp6UiMbfQFhS/ZZhQD8KDrYFFCwbJRFX57LMMYCk6wdLn1+1zsjLgYpj+/MvypCNkJqXEOkqDwG62SUM+GPXoihoM/R9rcKr+m6kmZbVyLhOgA8D5UEw4UF4Gb2dU+b8BApMOH4UNJZJKWvD9TveJOfyeyVK84XVi9Yp8CUK16qv3vGXvoKR4VFlxsy85ikZ0AnKgNazg/IeEqLnpbX8by0AlQXUB1BDQb0GwBX9yFMvbEVI4i1qaV5GxEY4QtIr8wirZvu8ABRcEbVAE15comd+O1mPyqAkMRGHLojUVgoU7jqPzX6GcWCgcwMDyMgWgUna2tc943xhhjjDFWvuZnLYGZkymKRYk2TtpAS9ADjQPFZUeXB6OUwZ21EfBMf+nvNTS888wlBT+n6zpIjUbhjzRDVRvoGGZsaP1pCFOlP5Cin8Zyc9ib2CWD7F6tMpmEx2qEJiwBJ+3KzaWSziqgehQoM5RsZPkge+Kgg+gOC9nRySW+HWlVbvUecNc8Cjo3euCnpcVl5uaE3DRvGGrgf2KL9RhuG/gRkk4CtarJV33voxwoZg2LMompuV20Nwkrq8pM4+kYqoEufxeydg6xXAwR89gN2Sg42qQCI5aAq4iJumBneFSc4ZndPqqqQO+DI0Ayf3Z5oWM3Ws8cwYbQK/NFkMe0pFysfDrfBI/sXetB74rSDO73CuAWBxgZu/87ARzQFFxtCZg0iKClbGP/RsoiDmkCA25+SQ7VLR4X9PsRjyexr+8g2pqa+OKAMcYYY4yxMmoNmOgMebE3mpKlRBoqA7jUKFBKPz7KvJ3DZQxlEdP1IdXqrkaKoUA3NGh+FU5GwJVBY5mDXJas2FpFTQBVcywpSlUQWqgjuEBDasBFdHsO6cHJgLHM0K5jdlpg4LkslpzvL/tr0c+cFnk7WQF3ROBk3+lYvXA97hm9A3syu1Br7JyFhYHqSz7lQDFrWDRQCrZ4YVsuYgNpqJoiaxhPJ2AE0envkPWK03YavuPUK6amdl4VyIp8hnGxXL+Gpg1hjDycnyFb038m7tr/daxbczpMdTLqPNxjoH/QQce+fFOEhS9lEW/VkGyaezB2sQJ8VAO+4QA7xz73HBT8hwG8LyPQZghZgmKcV1FgKQK9jgCdO5unzD5HIkEMDY6gr30QPe2dc943xhhjjDHG2MzXO91NXkRTOZlZTCVFWJE0BYIyiefY4JGOSX6FanWXsVR0BTpd1PqUfMDYqvNo52wIwE4IKMbhjdzpdqBDQ6DDh8yIg+h2C4lDTj61v87l4gK5uAszVP70aQrM6z4Fri3kcTBzfrwx+HZo7bJ7J2qFEAJDh/qwdvVaVBsOFLOGpqoKIu0+OLaLRDSDQJNXfu549YqN49QrprgpnVeHXWBOC4o0BeYaE9oLVIaCah6pOGHXq/BI129xbvPlhz107wkehKIOfAkX1Gh1xdNpPPfKANwpmcfFCivAv2jAT13g4bETXS8U/Kcm8A9pgdWGoE5+h9VpHnYEDtkCtMqNylIQv9cns4oP9PahvbkVhs5vQYwxxhhjjJVLyGugp8mH7X1xBD0u9DmUTWhoYxnFijuLUoLTyIeJlZLX/y4bSqYKKNAaqBbu8bhZyrJ2ZM3cmUIC3iYN3adrMphJW6nRr48rslAVz/ji3orofSKL9FB+EiFxyEZLyJzXrG4lALgZgdyIAz2ryIz4WiHoIOYUONnqm4ThKA1reJRF3NThh2u7SI1mEWiiN1uloHrFM6Hvj6jAkCPgCDFRq7coHhWR00IY/n2+wcSy6Em4c+/XcUroXIT0ya6erqZgxyYvTngoJWsYe1MCy57L4OWN3onSEHNB77nvVIGFArjNzdcvTikKviIE3pxwcW6YaldNDiCo/AatuDnkCJmVPN7crikcwuDwCHoHB7Coq3vO+8UYY4wxxhibWUfYg6FkFsPJHDrC0ze2Y8dB1zK0+jQ7t0xgGSZWVMwt3MwqSTUUubnW4clSM5aoOM5jig0yukKRZUwqWVIm2KMjPZSTtyl7umX1/L4+/du18ezilMhH0GuEoPeADPK/R1WGA8WMURDUoyHS4YdjJ5GOW7J+8XHrFWdHETbCMz4nNbTzqUBGAIG5vHcrCvSlHpjdKeRo6QqAU3e/Dvd3/wJv6HjPYQ9NhzXsXefB0q1Zeb/1oI3RNhuDi6Zv1lfEruA8BaDw7jddIElvcIqCn7vA/rjAW8NiIiBMJ60W9ejmdh6PB24sgf19fehobYXH4CVwjDHGGGOMlYtH12RWcSxtI51z4DO5V0gxqJG3UoJAlFpLGcXsaKoiG/xRndxGF+jWMPDX/O3siAsr7cKgIMg8K1dAvpzkewAFVKoQrx9gbIw3YMgyFBTnTMdzsHIOHMc96iSer1ecr6+btjMzPx81dqPnKsX5Q1cQ2hycmHnuSixDdM8w+nL7jnpo/xIDw52Tc0BLtmbgTZS2BtYaFfiIBvRM+dxfHOC/Yi5Gp9Tt0mVzO8jmdlPq+aOpKYTh6Ch6BwZKul+MMcYYY4yxo7UFPGgPeWS9Yg5SFqlEZTtUVYfLGcU1TTZVUwFRhrIStYSCwt7myb+L5FhiG6ttHCguwk033YSOjg5s2LCh0rvCSswfMRFu98pyFI7lIpuwkBjJIjGckTWMU7EcMkkLQYTRbLQimUvApeJEM4hoivwjs0owGNO6DPiXTzaw27z3Evx+4KdHP1BRsOtkL7Le/Iya5lC94gwUp7QnsTYF+F8asGHKxN1uB/jMqIt70i6yY/9mCphTUztqbhcdCyJTFjEF5Pf39iGTyWc/M8YYY4wxxsqD+rBQVnHA1BDL2JXendqkKfnGZHO8tqOMYojqq0vKZll+Qs+XPGh0lFU8juoUs9rHgeIiXH/99ejv78eWLVsqvSusxGRt4TY/OpeG0bEkhI4lYXQsDqFlQUBmG3uDOjRa0iCAZrUVfieC0dGEzDyeDpWc8Jcqq1hVEDjDD0Gd6igrN9MJY08QO1LPHvVQx1Tw8ibvxDx1IOZi0YulD8hSLPq9KvD6KcFiKg30m7TAJ0ZcPJBxZZCcmtvROZSa26XGgsXNTWEMj4xi+97dcGf4+THGGGOMMcZKI+Iz0BXxIpm14UxZBcgKI6ihHQWL53jpoioa1yiudZQQ5oVsaNfogt2Tq5nTgy6X5KgDHChmbBqaocL06vAGDQQiHoRbfWjuCqBjcRidyyIykNyzrBmrVy2G12ciEU9N+zzGWFO7Ur1Xqk0Ggut8E/dP2/863Nt/G9xpZqQTLToOrJ6s/9u120JTX+ln+Gi8dKkG/KMKhKfMrsep6V1K4FOjLh7OuAgpQn6OmttR8NjQDYSCARzo7cfevkMl3y/GGGOMMcbY4boiPjT5TdnYjs0SBYlpWeQcg+yqrFlQsr1iFaKaKnUnhCjxyt1aYwZVmFRzc0yil7OKax0HihkrYtmWbmrw+A10tLSgKRJGysrMmFVM2bTUSm68FMNc+U7zA2b+ufxWGD17VuOp+APTPvbgShOxlsmlIMu3pGGky5O9u1EFPqkCb3ZcBKb8W6Mu8OOUwL/HBHbbAoO2wEFbwBECwYBfnlx37duHoZFoWfaLMcYYY4wxluc1NCxo8smM4ozF6ZCzoioQsvzEXAPFmrwGYvVQfgJcfoKyinsms4oTXKe45nGgmLE5aolEEAh6kYhNn1VMpSf8aonKT9AfrV9DcGNg4v6Gg+fj4b7fIuumj36wki9BYRn5kYhuASueycx5cDMTU1NwoUeVAeNLXBfeKa9DDe2+nxT475TAn7ICA2Mzry3NESSSaezYs5frFTPGGGOMMVZm1NSuM5xvbMdmgYLEtJxyjhmkdGXG/QTrpfyEAmFVekcqLzilTnF6wIFr8S94LZsM+zPGihL0BNDR3oJdOw8i6PihHdENV1MUUCPQUUtAKELWQZ4r3wYfkltTEEnAdL1Yu/cV+HPrnbig5W+OeqzlVbFrgxern8gHksPDDnp25HBw1WRjvFLzaQou0RSc6wr83hH4I+3H2L+71wF+MhYsfr1PoFkB7EATHh+K47EXd2BhZxdUdf7msFzXRSyWRliMzOvrsto4hvRszYaONlNHq6Gj2dDk3zQrLeq+PnVjtYePHWOM1Q5trLHdSMpCPGMh5KX1j+y4aAyoKlDcuVUYVhRVPhWrk/ITqgPhCig0idCgzLAK3a/ATgnZpzHZ7yC0gMONtYqPHGMl0NPagf6BYSQTaYQjk9m+44IK4FGBjAB8JTh/KLqK0OlBxP6YkPfX952Nn/fehNPC5yOitxz1+JFOHb1LDVmnmCzYlkO8RUO8tbxvAUFVwZtU4HwB/M4R+JMAnLFR0QEH+O/E+BCL6n0FgagFRPehIg6OVuZ1WU0dQwoch3UNEV1DkzH2cex+xNBg8qi/KMJ1YSWTMJIOFJ6wqdljiHQKbwtFsCTor/TuMMYYOw6qU9wZ9mL3UBJ+U5fBY3Z8wlShUPfuOVCgcEZxnVDN8fITgDbZHqjhUDIcZRWPvJyvT5w4aHOguIbxkWOsBCL+MDrbW7Fr10GEQv6jZhMpOEzB4hEXmGxFNzeetV5oz6bgDLvQhIZT9l6EP3X8Gpe2XTXt4/et9SA07CAQc+Vyp1VPprH17ACygfIHZSIKcKWu4AIB3OUIPCoAwQE1VoOowveI7chtT6bSe1OHhqcpocNqygkdGQ4UM8ZYjaCsYio/MZLKoTVYvtWGdYVWj84xyKvSddB4/Qm+JqppCpWfMBXYSQGYjX0sqU7xeKA42efAdQRUKtfCak5DB4pvuOEG/PjHP8bu3buxfft2rFy5stK7xGp4Bq2nvQO9/YOIx1NHZRXT1yMqMOQIuELkBwcleM3gmUGM3hWT91cOnYJf738Yo01DiOitRz2eGi+8vMmH9Q8lodv5esVrHkvh+bMDsOfppNaqAO/SFVwkgD84AkMuIHQFGCtpRGMly7Jh6DoCPp9sHFhuNM5zcjlopsk9JWpUOY8h9aaIycCwjajlyEAxY4wxxlg98JmaDBa/1BtHznZh6ryq57jo+mSOA04FKlTKKoaQ2cWstqm0dDjJ5Se8zSo0jwInKyAcIDXgINjV0CHHmlX0URsaGsJjjz2GQ4cOIZ1Oo7W1FWvWrMHGjRtLUoN1Plx66aV43/veh3POOafSu8LqQHOgCe3tzdi7u2/arOKgClDy7pBsAlrYNDTFUJuPMQvnWeKB0aPDOpifuXvVy1fiT1134NLOd0/7+ExAwY5TvFj9eAaqALwpgVVPpPHiGb58B9950qkAb9cVqGkB1wSc0GTxe9sSGBmNoafVixULF0I9ouZzqbmOg9TwEPwtrVC1yf1gtWO+jiFN8iQcFyOWjSgFj63JAPL47Tn2NmlYVNt2IthfI2MIdric69BMHzqM2rwgqIdxLWOMFaMj5MFwMov+eBbdkVKtfaxjdM1E5wVX5IPGxZAZxVR+wpX1ilkdlJ8wAGEDCpefwOjusaziQxworlWzOmqjo6P47ne/K7dnnnnmqKYl8hcjGMSb3vQmvPe978XZZ589q53ZsWMHbr75Zjz66KN47rnnsHbtWvnxSC+++CKuu+46PPzwwwiFQnj3u9+NT3/60zDN2f1VnnXWWbN6PGPHQr//C9q60N8/jGQyjWDo8KW3Xso61gtvkkvx5F5HIO4KhI4xCAm9KoShnw1BcVU0ZTpgPB9ELDCAJr1t2scnghr2rjGx9MV8l+NQ1MGyZzPYudE770ufhCag0HmEfihjgWrd0OEP+NA/MAS/z4sFHZ3zuk+MzYRWAlB9YtoWV3pn6gxP2NS+4WwWYiSKTeHaKTtR7nEtY4zVAl1T0dPkRzxj4+BI+rj5rVST3knlEBOZhuwroOYceFI5uDkbosgM7LSTQyxlI6fnoCkVWK9G5zvHBqwcl74oEcdxINKAWoqGRAWhbHQHAtSDqHqOoUILm3fnb8cP2fCuVho6y/rYRNX++RUcKP7MZz4jg7jNzc244oor8PGPfxwbNmxAW1sbPB4PRkZGZAmHJ554AnfffTcuuOACvPKVr8R//dd/Yf369QW9xtatW3HnnXfijDPOkF3saTtSNBrF+eefj1WrVuEXv/gFDhw4gA9+8INIpVL4yle+Mrt/PWMl1hpqRntHM/bt7kcg4DvqTbG5iDfJfbZATogZm2TpzTqCpweQ/Eu+tudJh87F4/1/xqvXv2nG5+wPqPCkBbr35JvbtR20kQ3kcGD1/NYmoyxmxQIUWxyW0UwB4pyVw4G+fvi9PjSHw/O6X4wxxurbfIxrGWOsVjT7DazuDMGmLNkCJndHoxYizeGGnNwVlguhJmQ2seIp7t+fshRoMT98ug8GpaLOM5oYzaQVeH0+XjVTIpbHQWbIgR6goiLzcwwtBzA0b1UdQxEUiD8bh2PR3woQzBkIdXBW8UzHMJZNw29W34RbwUfsj3/8owzMnnfeedN+nQbWtJ122mm45pprZECXArf0fYUOqC+77DJcfvnl8vZVV10lB+dHuuWWWxCLxXD77bejpaVFfs62bVx77bWy5nBPT4/83ObNm7Fz586jvn/x4sV46qmnCv1nM1ZcVnFfdNqs4tlqU4G0CvS5dHvm2sb+jQGM7IjCGPLKmlc9z6zB8OohNAfap328YwjsW+eBJ+WiZUDWwsCC7Tlk/CqGFs7jYIWK/+dcCEvAOSJGHQ4FMByNYd+hQ/B5vPB6GngdD2OMsZKaj3EtY4zV0jVMoc3sKHNSyZpoD3uhNWKg2BHIxS35UfUXd93ksx1ELRMeTYOpzX+gmBLyVFdHwKtDbcCs8HJwNA3JjCUTxbR56P9DQca0pcFn6FUVKCYtC00M7MqvXk73Oehe7K30LlUlIQSyOmDqWu0Giu+5555ZPTFlaHzsYx+b1fcU8iZFWR0XXnjhRJCYXHnllXIQT/tIAWZCdeZKjQLUtI2jOnbjJ0vaZou+h96ki/leVh2mO4ZN/jDamsM4cJBKJ3jmtNSC/iI6VYGMKxB1gNYZnos+23F+BwZ/PgzN1dGU6cSeP29D82uObmo3zg0AO0/ywHwig2Asn71PJShyXgXx1vl7s3IVASUrAJ9z2NInmottCgYRHRnFvoMHsGxBeeoVU1YELaGjj6w28TGsfXwMax8dP7kc2XFK0rD1WEoxbpqPcS1jjLH6o9CKSF2Fa9nFP8fYf9TMjtUHzVCgeVRYKXdeAsXVrHXxZKB4aF8Oy07zV10wmx1bzeWAU33i97znPYd9rqmpCd3d3fJr5fT5z38en/jEJ6ZtgELLFGeLAoxUH4/wTF5tmukY+k0vNNVFbCQGj2/umbBtbr5zaJZqHc/wq2L6gez6Yfif65D3u3auxPCu/WjtPkaw2COw/QSBdU8D3gxkg7tVT6bx/GZVNr6bD7Lbbw5QkyoEde+bgubXw7qKaG8vTMdCa6Sp9K/vusgm4vJ2I9ZZqwd8DGsfH8PaZ1kWRDKJwcFB6GXOMKNxF2OMMVYxhgokig/yqooqN2qUzOqEosDwKsglhCwB3chx0aZuA6pGiSBALiWQGHIQaqu50GNDK+poffKTn5zxaxQsi0Qiskv0Oeecg1KjpX8UGJ4u02N4eHhWz/XhD38YP/rRj9Db2yv3denSpXjkkUdmfDzVQr766qsPyyimEhfUGbu9ffol/oVkxNDSxkZctlMPZjqGLW4LErkUDh0YQdg797pBtFgjZ7s4QEm3GmDOUPlo8ZlrsG3P82iJd0OFivhDFprf5oVyRAB2Yv+9dBYT2HGyjbVPZaDbkNvqZwSeP8sHez5mQ4WAkqFmdirENFFw0+tDKh5HXzKDcLsHkWCwpC8/nsHoa25pyDpr9YCPYe3jY1j7srkchKLI86Ghl/diIJuladPSqeS4ljHGWO1RDU22ESv6+xU1f33IgeK6QhnFlFns2kJ+bFSarqC5x8DQvnw/pKG9OQ4U15iijtYXvvAF5HI5pNP55llerxeZTEbe9vl8MquEAminnHIK7rrrrqKCqPPhs5/9rNwKFQ6H5XbTTTfJbTxISAHCYgO9dAEyl+9nlTfdMaTbC7q6MRSNIZO24A/OvS5Pp6EgqwgMuFSCggYYyvT78ioB5y4bmtDhS4Yx+Ngg2l/ZOePzOgGBtKPg5ZOAVc9kZFaxNyWw6skMXjzDd1iTubKgf4fqQskpgF+Zdvo1HA5iaHgU0dgomiOR0u+CqsrgFAeoahcfw9rHx7C2yUzweRrTlPr562VcyxhjbJ7Q9dGcY7wKXFoyyuoGBYdVQ4WddRs6UExaFpuTgeJ9OSzZxI0Ta0lR6zv/8Ic/YMGCBfj+978va/amUin58bvf/a5sJvfAAw/I2m/79+/H9ddfX9Idpszh8aX+R2YaT61bXE70b+rv78eWLVvm5fVYbeoIt6GlJYxYMikLlc+Vpijo0hSEFGAkX1J4WisXnYyXlj06cd/9qwKrL/8mPS1dgetXEGvVsXv9ZAmVUNTB8mcz8zLTTSUnFFtAmaHUl6poMA0D0VgMWStf74gxxhgrhUqOaxljjNVmnWJKbhFucddJtPKT+rFwjeI6LD/hU2Sjw0ZPFm9ZYEAZizZm4i5SozwpUveB4n/+53/Ghz70Ibzzne9EcGwZOH1817veJT//gQ98ABdccAH+7d/+TTafK6W1a9ceVYuYAsdUBoK+Nh8om7ijowMbNmyYl9djtUlTNSzs6IbuVZBJlSa46VMVdOkKKJcqOcPAhGbqujcvRn9gb/4+FET/EIWwZz5buR4FwqdguFPHgeWTnXdbD9pYsK38gVmhAYpLgeKZ99Hn8yKTziGWSJR9fxhjjDWOSo5rGWOM1R5FU/PB4mIDxWOlJzhQXKflJygR6xjXtY1AN1VEuibjCkN7j5G4xuojUPz0009jyZIl036N6vz+9a9/lbdPPPHEabN/5+Liiy/Gvffei5GRkYnP3XrrrXLJ/UUXXYT5wBnFrFDtkdaSZhWTJgXo1ICUC+RmeM7VTadg6wkPwhlP0R1RkHwiOfOTKgocvwrXo+LQYgND3ZNVaRbsyKF1f5nf2MeWoSjWzD8jyii2XQejsXzDK8YYY6wUKjmuZYwxVoMoSKzmM0eLoigyWMyB4vqjm1R+QoF7jOvaRtG6aDJQPLyPVwXXfaCYBtPf/OY3p/3aN77xjYnBNnWlpqYmhaKlfrfddpvc9uzZI5f9jd8fGBiQj7nmmmsQCoXwxje+US4D/M53viMDt/R5Wh7IWDXRVR0LOjqhegSy6dIEW2n2uUNT0K4Boy5NZItpH7Np5Xl4YuFvJz6XeiYFq/8Y+6AqcAMKhKli11oT8ebJt4clWzPQynyyk+Un6DWOMftqegyMxOPIZPlEwxhjrDTKNa5ljDFWnyibeC4ZxURTtJIlErFqKz+hwi12EqGOtCw0J24now4ycS4/UdfN7P793/8dV155JdasWYNLL71UNvWgQO4dd9yBnTt3ygxfct999+FVr3pVwc9LWbpXXHHFYZ8bv3///ffjvPPOkzWK6Xmvu+46GSymoPHVV1+NG2+8EfPlyGZ2jB1LR1MbWpp6MdQXh8fXXJIi7vl6xUBGCFmvuGWavj6rwhvxp5W3o394LzqSiwGhIPaHOFquaM4PbKYhDBWuH9BiAts3+nDiwymYWQHdBjr25HBo5WQN43KUn1CzAqot4OrT75/f40UskcRoIgavhy/WGWOMzV25xrWMMcbqlKrIrGJhu8U/harCFcV/P6vu8hOqpsCxAa2oiFt9MH0qwh06Yv32RFO7Bet9ld4tVoCifm3f9KY34bHHHpMD69tvv13WB+7u7sbpp5+On/70p9i4caN83Fe/+tVZPS8t7ytkVm3dunWy/ESlUAYzbdTUZNGiRRXbD1YbDNWQWcVDw6PIZix4fZMza3OuV6wBu20h6xUHaMAyBQWkX9XzZvxuxffxlr/+L2hChxN1kHwiheAZgRmf1/VSZq8KpF0cWm5gyQv57N2uXRb6lpozBnHnbGz/ZVaxd/qH6IYuB1SUVdzZyoFixhhjc1eucS1jjLH6RNdZiqECqRk6cRdAV3QuPFGnNCo/YVL5CReaXtQi/rrRusicEii2OFBcI4qe39i0aRN+9rOflXZvGKtTnc3taGnuxXBfEh6vUZKsYtKk5usV77cBUxEwjnjelaENeLD9F3hy4e+wed8l8nOpp1PwLDNhdEzWDJquXjGVNx7q0NHzsgUjJ+TWvtdC3/LSBLqnIzQBxVIAWqozQ9azx2NgNB5HOpOBzztDRJkxxhibBR7XMsYYmw3V0ODMoXQE1SgGl56oS4qq5BvapzljvGWRgV1P5m/HB2zkUi5Mf2MHz2vBnBLhKft327ZtGB4eRmtrK1atWlWyAFg149ITbLYMzUBPZyeGh7cjOZKDaeiy/IOqjdW4UvMz08XVKwbSQmDApaLjRw82Nna8BXcnbsKy4ZPRnlwEekj0D3HgpGPP5im2kKUgfBEV6wbyv+udO3N4frEBd4Yg7pypinxdJ+fKWsnTcQ0vRhNJeKIjaGtumfNLCteBY9kYyeag0AFhNYePYe3jYzh/6N272dDh03iQfqRGHdcyxhgrAp1H5xDnpUCxCw4k1ivDoyKnOnBtQG3g8hPeoIZAi4bkcD6eMLQ/h+7VnOxV7Yr+lf3a176GT37yk7KGGw2sZcCqowP/5//8H7zvfe9DPePSE6wYXU3t6F8wgKFYFLZtwAc/YGsQWReuMzn7qFCt3rEGCRRAPh56SLdCpShkGeKj9EROwrPBFbh/xY/GSlBoQNQBHkwc83lp3EO7tZMyk5t0GFDgzQqctN/C0JIyZRVT8DzrwnEUODPFijQNPrgIppJY0dM955d0HQcJ00DQ64GqcYCqFvExrH18DOdPTgjsy+TQAg4WT9XI41rGGGOzN97zZfycUVT5Cjl9y+qRZqpQTRUuJV+Vq3RjDZWfSA6n5e3hfRworttAMXWA/h//43/g7W9/O9761reis7MTfX19so4bfd4wDNlgjjE2ydRNnLB4DaKZKPpT/YilY7AdBQE1CBMeOJRJS0FSS8guqSInIArspEt/yC1OvnA+dVk90lsX/A1uTv07nlrwO5y+//Wz2m+qKPRyxsVabz54070zB+8SY6KmcKnRjKuwAIuef4bXCPtNiHQCbbARDgTn9Hq0MmAgZaI94IXGAaqaxMew9vExnD+WK+Rl6d5MDq3Q4eVgMY9rGWOMFRcopnMolcwrIhCoynQfVs+/H7pHQWbUgT52Hd3IgeK9W/KB4tFeG3bWhe7h3/+6CxR/4QtfwPvf/3588YtfPOzzb3jDG2Sn6JtvvpkH1IxNw6f74Av60O5rRzQbxUB6AKOZUSTdGEK+EILNfpnKSxnGNPtYSHPHcXbaRWbEQTbmwAyqMjt53PrwSVgVXIOnFt4DV3WwOrsBy4OrjvucqUEb2VEHO7MuVnk10ClOSwt4DtrILpyhxvEcUbM8Kj9BmzCnH3T5fV4MDI0gOjo650AxY4w1EkNVsMzvAc1DUmZxh0eHqTb2YJ3HtYwxxmZNrv5UZGJPMekzXNqo/hleVV6b07V9I1dWo1KWvrCKdMyVZbmHD1joWO6p9G6xUgeKd+3ahUsvvXTar11yySW45ZZbUM+4RjErRc3iDn8HWr2tiOViGEwPYjgzjN5UL/y6H0EzCMOY3dnE9GtyZi41bCMbd2D4NWiGMjEQeWPP3+CmbTfi6QX34mnci/WhE4/7nM3hRTjp6dfBEsCunIOVZn6ffNtzyC7QZeO7kqO6zRlqageIGSpcaBq9NjAQHcairm6oDR7kYIyx2aDA8PKAR1ZGPJDNocNs7GBxo49rGWOMzZ6iqbJkYKErQKerUcylJ+obrfalEhSuJWRpyUZFsQjKKt6/NSPvH9iaQXyQ1i2zTEqFkxzB8rWo/UBxd3c3HnnkEVx44YVHfe3RRx+VX69nXKOYlYqmamj2NqPJ04SElcBwehiDmUEMpAZgaibCZhj6LKrfm0ENmqnkg8WjDlxLhe5T5JvzutAJWB1ci22JF+Vjn48/d9znUzxbscRzOsLZVuxIu1ju0aDS6qqkC/OQjVxPGbKKKfisCagZF65fmTEYHfD5MBpLIJ5MIhIKlX4/GGOsjnlUFSsosxgCB7MWuk0DeplKClW7Rh/XMsYYK4Isk6dCWMU1pJMZxXTapRRLzi6u7/ITMQdKiSfkaeUxlat0ZI1sVBb9KdCq4GPsR8viyUBxatSRGyMqcolj946qmUDxP/zDP8iGH9lsFn/zN38ja7n19/fj1ltvlZm21PiDMTa7gULIDMmtI9Ah6xhTsHgoM4ROf+esnotmLYMdhlzqQgHjXMyFEdTkLOYVC9+O/3jpk3BEYW/MQhF4ofNhnLH3MmQFsN8VWDx2BvDvyCHXXZ6sYkEnGovKTwBihli0P+DBQP8IhkZHOFDMGGNFoPrEK/xeWYbiUC6HHo8JreJXG/OPx7WMMcZmSzYhN1QgW1zAS9YoVhS4cKHKAn+sHuk+FXq28N5DswkUCzd/vV7xxHQB2LaYtlfSuGCLhkCzhmSUA8S1oKhA8b/+678iGo3KwfO///u/Tz6ZruO6666TX2eMza2OsaZoGMmOFNVJlwYu3iZdBo1lsDjuQPerWBlcjRtPuBl7U3sKep6tsb/isdxfcNq+i6EJHS/GbCxqMkDnI50C0P0OrM6i3kaOSeiAmqLyEwJirHzGkTRFl8HvoWgUS3sWcPkJxhgrgl9TsdLvgQOBQ5RZ7DEaLljM41rGGGPFoECxoGZ2xXwvFFDxCUFRNla3KHhK1+Qyc7yEXNeFktbg9+kVvw6mOszZ+LEz6ymesf7VIQzuzcGx+XdeEgKpRBKdS1pQbYqK8NBB/tznPocbbrgBf/nLX+TguqWlBZs3b0ZrayvqHdcoZvPBq3thqAZs15Y1jYth+FUEDQMZU0FmxEYuJ9AR6ESnt6ug7z8hfCIeiz6MXS3PYuXQKUgLoN8AOnP5r/u3ZzHaQUWFSxxUkEuxBJSsC/hnPvH5/T6MxhMYicfREomUdh8YY6xBBHQNq/1eqpSG3qyFrgYLFjf6uJYxxlhxFF0t+jKIzj30n2xe3jin3MajKMhXkizxQXYBxVKhGmrFA8WqQU3qjh/8Nf0qetbSeJMR+pkN9Saw+KQwqs2cUgFp8Pz6178ejYZrFLP5QDWKacu5uaIDxYQa2vnbdFlMPx3N1y42Q/lSFMfj1wM4v/0iPD38kAwUk+eGLXQEDXmqM0ZcGEMOrLZyZBUrUHMCDs3Sz7Cvfp8HffEkorFRDhQzxtgcBHUNK2UZijT6xoLFagMFixt5XMsYY6z4hnaUFVjMClDZzE4ZCxQzVsOoPjEFB7jcdv0oOLrzi1/8YlZP/OY3v7mY/WGMjfFoHplRnLbTCBiBOT2XLEUR0WQxfdnoLlZ4NvyrfK/BPZG7EPX2oTnTiYQNjAQUNCfzgxrf9lyZAsWAmhEyWOz6pj/jqIoGXdfQPzwsy09oGtf3YoyxYoXHgsXbUhn05yx0msasL3xrBY9rGWOMzRkls9BGq+614jKKqUYxY7XetE9VFVAbJKX0YQFWAQUfRmruMZs3PS7LwNjc0Cxz0AhiNDdasufUvflGd7Tso9DJa2+uBa8cPg/Pdz2Es3fnL5SfH7Vxtp4fDZlDDvRhG3aLXvpOwq6AknOBYxTGDwR8iCeSGInH0NrUXNp9YIyxBtNk6DJYvD2ZQV/ORqep12WwmMe1jDHGShEgU2jZv+sCs0xYyYeJOaOY1T5aqaxoyNfrpuxiVvMKjuzs2rWrvHvCGDuK3/CXfPBAS0Oo0V2hMqMOLmx+PT7d/jGcsedS6MLEYMJFYomB4Gh+Bty/PYfYGWXIKjYANQs41CWWAsfT8Ho8GB2NYzg2yoFixhgrgRZDx3K/BztSWezP5uSF7PHYjovqq7A2Mx7XMsYYK0WgGFR9gpJbZvm9mqrJxCAOE7Nap1LLIk2BawloXHC7LhQc2VmyZEl594QxdhSqUUzF6R3XkYOJSqDi+22eNpzSfip2tD2NtQNnyM9vt1xsGt/PAQfaqAMnopW8TrGSE1AsAeGZqfyECsMw0D9I5ScWwtB5vQtjjM1Vu5lvaJewC8ukdR0HmVyqZuoa87iWMcbYnGmqLPEnMylnSeYTyxrFXHqC1ThFkX2RnAxPe9SLgtsjDg0NFfUCw8PDqDc33XQTOjo6sGHDhkrvCmuAOsWmmm9oVymUgazoCi5uvxQvdD488fm9h3LItEwGhimruORoGYuTr1N8LAG/D4lkSja1Y4wxVrrM4sU+T0HbIp9Hlq2oFTyuZYwxNlcUJFZ0tfhAMZeeYHWCAsX8u9yAgeJly5bhAx/4AJ599tnjPjaZTOIHP/gBTj/9dHz9619Hvbn++uvR39+PLVu2VHpXWJ3z6GOBYqeygWKqO9Rh9GBxdycGAvvyXxAK9kyJCXh6bWjxMtRwpKUsWZFvozoDr9eDXM7C8CgHihljjB0fj2sZY4yVhKnJviqzRasiZUYxF59gdYDiBYRjxfWh4NSPhx56CB/72MewadMmrFixAmeddRZOPvlktLe3w+PxYGRkRNZ7e/LJJ+Vjm5qa8JGPfATXXHNNef8FjNUxQzXg1b1IZVIVfdOnGUI76+LS7jfgJ5134Nydb5Nf27YvhWVdPpjR/JIp344cEpt8JX19YSiy9IRiAcKc/jE0G296DAwOR5FbaME0jJLuA2OMsfrC41rGGGOloOoqnCKiYwoFiimjmAPFrA7QCmSKGwiHbld6b9hcFXwITzrpJPzyl7/Ezp078b3vfQ/33XcffvrTnyKbzU48ZvHixTj77LNl1sVll10GnWuFMjZnQTOI/lR/RfdB86oQMQeLQ8vgXZBDbk8GpuOFm1bR16xh0Vig2HPARmq1CzdQ8GKF4xIaNbQbq1Nszlz7Muj3IZ7Il5/obG0r2eszxhirPzyuZYwxVrKGdkU28KJeNK7NNYpZ7aMgsaLlGzsW+/fAqsesR7zLly/H//2//1duJBqNIpPJoKWlRWZgMMZKX6d4vH4VLU+q2FISqv6gAK/vvBgPtj2BE/peKb/2woE4uiJ+GKOuPCUEt2aQ7Soso9cJqbCbj9MAj/7NioCaceH66fb0PwN6/xmKjmJodIQDxYwxxgrC41rGGGNzQtdJdKnkClmzeDZ06JxRzOqCSuUiNQWuJaBxoLjmzTk1orm5uTR7whibMVBMJSiooR3drgTVyL/5U1Pe1ZF1uHvBfUBf/mupPhWjZ+toG83XUTb7HbkVKnaqF7nuYweWhT6l/IR+jFrFponBgWFketLwGIX9rITjQrhu/iOf1GoSH8Pax8dwjhRl1henbHo8rmWMMTYbiqbKZfeyTvEsz8V07uYGYKwuKPlylU6Gf5/rAa+hY6wWAsWaAcuxKhYo1ihQqytwbQGhAxf0nI3t23eiK75cZjs/PxjHWSEf9Pjsl075t+eQ69JnzBQmggLVKQF9xD7mczVbHiSH0hj09KG9qbCLfcd1YPz+JtEAAQAASURBVMfTyGXi0CgazmoOH8Pax8dwbiiDQ2/1QfVzfXbGGGNs/jOKqTarmHVtVl3ljGJWPyhQzBMf9YEDxYxVOQoO05awEhXbB5WK01OgmDJ6vQpO9p+MP/f8CF0vLZdfH9hlYfR1zQjstqDkCjs5mH02VZSAHnOhRx3YLcd4O1IUuB5AOU4c2tR1xC0H8VgcbeHCAsWUJU3nM/mxoO9g1YaPYe3jYzg3bsaGcFLQ23zQgjN0/WSMMcZYWSZrKaHGSVoQaWtW36ulhNyU3LGTYUpNES6UrAPFsWVTvYqjzGpTBWi+u0KlFlmJylXSuF7wYax1HCguwk033SQ3xyl8eT1jxaK6xAEzgGgmWrl9UPNLSeyMS10XIDwKTluyCvGXk/DaAeg5D3YPxLDk5MKX7IaeTMNzKD8o8u2yED9WoJjo1BX4+IyQiWg2gYWqU1B9SeEqUHIaVJ8OlTMZaxIfw9rHx3BuhFeDG8/B6k/JSLsW4mAxY4wxNl+lJ2hVj940+6luNRFHNgaIeV4RJMt9xS2IkAGhVkGgOO1CSblAhlaSKoBHmXUZD1Z5NGFCwWLh0O1K7w2bCz58Rbj++uvltn//fixatKjSu8MagF/3w0VlO+LqHhWZ0fzkCM34bvafgu92/R4r958hP7fjxSEsWVF4oDi91JgIFJu9NtS0C9c394GK1+tBLJ5END6KVq3puI93XBeOY8OyLbgqT/7UIj6Gc0OZJLrGw4Fan1BUQybchCWDxdRQRwubFWuAyhhjjDWSYlfzqJoJ11UgAtq8T9ALW4UIadURKA6qEFmRDxanXChJijQqEB7KMuaxTK2gXyVKUKdxqOzwyGpWUVeG3/ve93DJJZegtbX1qK8NDw/jjjvuwLvf/e5S7B9jjAKpmilrWNmuLT9WApWeIFR3SDaX0zUsW9kM7M9/3RttwdBwHK0toYKez27RYIdVWXqCSlB491pIrZl7DWZD1+G6Lvb39srteOjfk3NyOKD1c1ClRvExnBvTMLFm6bKCMvBZ9aLffcokdhI52AMp2VRHa/Lw30QBeFzLGGOsElRFhaALoUZHYxWvAuFVgbCQGcZIulBoNWsacjUrzHwtaFa9KF5AWcVifiupsDIoKuL093//93jkkUemHVDv2rVLfp0H1IyVjlfzwlRNGQyrWKDYmFxKQg3tqBTE2W2n4FdNz6JzJF+r+Knnd+M1rzypsCdUFJlVHHo2K+9691hIrTTzDSHmqCkSQjaTK+ixMvDtCrgQPPaoUXwMi+cIF9l0CqPRBDo6TR6A10lWk5uyYA2k5d+F3uyV5YPYzHhcyxhjrBKoKTg3ZzgCBRpDGhBQITKUZewAVMc57lD3v3xpCiYTAmALwBKAWj2/RHQpb1GAX6+CTPVqJ6hBC6pSURGnY3UyjEajCIUKyyhkjBWeUSwDxW5hwc+yzRBqgOsIqLoK1wSMrIGmFQJ4Mv8YfV8Lkrk0AqavoOfMLjAQeCEL1QLUnJClKLILjZJkFRtBveD3s7SlwGf4OPOuRvExLJ4rXIwm44iPZhAJ2/DMc408Vh6q34CbtmEPpOWFBNVO5GDxzHhcyxhjrFIZxWwGNG7xKxB+FchRZrEAqCQFBUZZPsjojG2y1EN10KHAcgQUziou6BC6VHVGqeFA8d133y23cZ/73OfQ2dl52GMymQz+8Ic/YOPGjaXdS8YaHGUR+0wfEslExfaB+ktRsNjJCcBDjQZoYGPjrJUn4c/P9sFnhWA6Pjz84l/xqhM3FPakCpBcpCK0M19/2bMri1iPO/9lC9wcVFflIGMRTIXroNb6BYqiC1i2jdRoDqZXg1INterYnFFjQFcF7KF8ZrHR5pMNd1gej2sZY4xVGo2haaOJew4aH4OpQlAZ6KCaD4yyfEPCUQ0ioldHnekxblBFThFQQhpfIx6HoJWdFFD3a7UbKN62bRt+85vfyNt0wP/0pz8dVc/QNE2ceOKJ+MxnPlP6PWWswQX0AHrF8Wvulgtlo2mmAptmc+mNjZYFaQq8igdYMgLsyGdcuS+24icH/lTw84aEiauxESoUmKMCf/ztU+hVkmX7d7DS8qperPOulxnvFSMEHOFAU/KNL9js6GGBtpNdpBM5ePw6/BGuVVwvVI8OV1HgDGdB/VCNdh8UXgoo8biWMcZYVUzYU9M2Si3kIWxhdQ1KUKawLlBuFfUQMtV8F7kqofoBxaPCVZWJHkfsGE0lZUaxUruB4n/5l3+RG1m2bBl++ctfYsOGArMGq9C+fftkzTn6SBcCmzdvxte+9jVu5sOqlkfzyDpWlZxx1j0qMq4jb4/XKVZsF6efsBLP7khDhYpgrhkrh5pn9bx9foFualAA4FWZ9XiKalGxmhGX/1+5siiT5jcbvZ6EQ0msW9GC5GgWpk+HblbfzDYrjmpqEBQsHsnISRUZLDb4+NbbuJYxxljtoWsnDRpcuPIjY7WOViFPlqusvgAoK2ONYmrsUet0XceNN96IM844A67r4p3vfCe+9KUv4cMf/nCld42xaXl0DwzNgOVY8nYlyDd7NV+ugTKMqU6xllUQCQWgLuwD9jcV9bw7cy66aTYUwAJDwVYFyPKqIsbmzdCBLIwTFaTjDtKjWYTafFU5u82Koxgq1KAJZ5Qyi4WsYVxqjuvIJnq1qB7GtYwxxmqPqtKayrGMYsbqAMULaHNrc0jI5hIoJoODg7j55pvx+OOPy6zc22+/HSeccIIMtlLw9cwzz5z1c+7YsUM+56OPPornnnsOa9eulR+P9OKLL+K6667Dww8/LBuMUCfqT3/60zIzuFDd3d1yG3+DPu200/hCgVU1r+aVTe2onq6HigRX6o1fVSBohb8+WaeYunWe+cplGD6QhVVMhFcIZPY48OZoCZaCjcs96G+dr6xpgZxjwdQocMKBsUJZIocfD/1YZkCQNze/BU16pCL7QmNry8nB0KheckV2oSZlky72P5eRt+P9Nixhw/TqSMUtmH4DngA3tqsnVHJCDZlwkhaceOlXADiOA8e1ZD3kWkyKKse4ljHGGDteRvFE6QnG6oAsV2lQuUpe6dlwgeKnnnoKF1xwASKRCM4991z88Y9/RDablV87cOAAvvCFL+CnP/3prJ9369atuPPOOyeyfGmbrvv0+eefj1WrVuEXv/iFfL0PfvCDSKVS+MpXvlLMPwfpdBrf/va3ZSMTxqqVoRrwqB7EcrGK7QMFiilA7Nr5pSTjdYqpNKymK2hf4i36uW1PDngu/z7SnhDQz/Dmu92WGQ3M0hbgM3xccH9WfMDBEbyQ3irvvdy6BJc0XVKRPckfQxc+w8PHcBYcW+DA8xkIF7DSQHwkg7bWJqRzWSRjWRg+TU6ksvpBzey0cHkmGt1ENt+RvAaVa1zLGGOMFVKjeDzxgrF6IMtVUmYZq1lFXQH+z//5P/GKV7wC27dvx7e+9a3DZsAoyEsZwcW47LLLZBbHbbfdhlNOOWXax9xyyy2IxWIy0+O1r30t3vOe9+Czn/2s/PzBgwcnHkc1h9va2o7ajnxeyoB5xzvegQsvvBCve93ritpvxuYDDSKCZhC2oNaYlaHq+YAw1RyaWqcY9tyDA5mFBtyxqSstK2D2Vu7fyQqz0b9x4vbTyacrui9s9uhvOdg6OV88cCglP3r8BjIJC5k4rxljjaFc41rGGGPseNd3FCzmjGJWT8ZrE/PvdYNlFNOyPMrmNQxDBlqnam9vR39/f1E7U0jm0t133y2Dui0tLROfu/LKK3HNNdfgnnvuwVVXXSU/99hjjx33uegXlxraUfmKL37xi8d9PAWoaRt36NChyeWWR/wcCkHfQ1nTxXwvqw7zfQxNxYTrTJ9tP5/BYifhyi6dxDEE1EwJTgQakFlkwL8rH5zy7coh2110dZyC0X6Pb2z2geIfDv1Q3t6e2Y64HUdQC877fvAxLF64Q0d8ID8pM3QoBfcEV04hUxOK+EgamklZAeX/O5T1bem9dKxZJqs9zthKMDof0rLDsr5Wic+55RrXMsYYY8fNKB5rVs5YvZDlKrXJcpWs9hR12AKBwGEB06n27t2L1tZWlAvVJ6Ys4qmamppkvWH62mxce+21SCaT+NnPflbQcuXPf/7z+MQnPnHU54eGhuDxzH4pJ11QjY6Oytu8vLc2zfcxTOVScBMuYtkYNIrkVEAu58LK2UA6//qqK6BRkIdKXs5x2b+1QMC3K18p2Ii6sIdSsMLlDThQcDHn5Ot1ctmC2QkjjC69C712r1wy92T8SWwObJ73/eBjWDxvy2RQbLg3g5H0yNg9gWzcRgoJ+MNU/7+8P1e6QIql4vJl6KKJ1R4nYyGeS0AfHISml/f8ROOuUqrkuJYxxljj4hrFrB5RYhmFKmgV8nh2MWuAQDGVfKDmcVTPjYK0hN7gqNYvNf14/etfj3KhGsXjrzlVc3MzhoeHC36ehx56SJarWL9+PU499VT5uVe/+tWyDt1MqBby1VdffVhGMZW4oAsIyjiZrfGsFSqJoWk12PmFzfsxDFgBDOvDMkjsN/yoBF134KYtmB5VZo0puoCWcfKFbOZ6IogAufY0PAP5n2vTfhWJDcXXPS7E+MCMaxQXZ1NgE+4evVve3prdinObzp33feBjWDyjW+BlZUQ2pMwmBUwnAH8w38TO1h3Yloug7oc3UN50AJlJLIDWcDM0lc+HtcjScqAYP50PdaO8vy/j9YNLpZLjWsYYYw1eegIqLMHlvlidZRRTuUr+ta5ZRY3k//M//xNnn322bChHwVV6g/u3f/s3PP/88/I2DbarHe3/bGfuwuGw3G666Sa5jQcJKUBYbJCQslDn8v2s8ubzGPoVP0zdRNbJViwL3fBQbVMHQqhQaXkxlZ4wBGC7EMbcg3SZZSY8A2l523vQRmq9gDDL+2+l963xjc3OKYFTJgLFz6aflZnFlch252NYHMNUEGjWkBzOn88GDyWwdHU+e5Img+xsDtm4BZ/fgKqp5X8vVTUOFNcoV1Xn7XxY6uevh3EtY4yx2kNjZlmjmGbLGasTlEymGQrsDJdUqVVFXfUtWLAAzzzzDK677jqZVbtixQq5DPCd73wnnnjiCXR0dKBcKHN4fKn/kZnGU+sWl9P1118v69Vt2bJlXl6PsXEUQAkYgYll9pVAs4OKTjWHxgY0qgLXAygl6j1ntWtwAvlgn+IC3r08FVnNVnlXwa/ms9tTbgrbMtsqvUtsliKd+QxiMnAoedjXvEEd6aSFdLxy7zmMlVslx7WMMcYa13iSA5eeYPVG96iT8QJW/xnFmUwGH/7wh/Gud71L1uudrmZvOa1du/aoWsQUOKaBPX2NsXpHJScq2fCAkkUpWGynJ/dB6NT9yqUaAHOuU0zfn15qIrg1v7TYu9tCerkpA9KsOjMhNvg34JHEI/L+M8lnsM63rtK7xWbZ0O7gC/nbg4dSh32NMkQNXUMqloPHp89LYzvG5lOlx7WMMcYam67q3MyO1R1Zm1jJlwjkFZ8NkFHs9Xrx7W9/G6nU4ReT8+Xiiy/Gvffei5GR8YY7wK233iovZi+66KJ52QcqO0HZJRs2bJiX12NsKo/mkUuUHGojWgH0Rq+bCqik6DgqOSE0pWRZxdmFBtyxlcVaRsDsK9ETs7LY5N80cfvp1NMV3RdWXKB4XGLEQjZ9+N+b4dOQy9hIxnL5ySDG6kilx7WMMcYaGyVdcOkJVpd1ilWA50AaqPTEWWedhUcffbTkO0OD9Ntuu01ue/bskR2ox+8PDAzIx1xzzTUIhUJ44xvfiHvuuQff+c53ZCkI+nxPTw/mA5eeYJUOFBuqAcupXEkGzZTTgxP3BcWZaNawRMtLKPBMweJxlFXMqtfJ/pNlIw5yyDqE3lxvpXeJzYLhUeFv0mYsP0GTQx6fIctPZFI8acPqT7nGtYwxxlhBgWKeiGd1RtUhy1W6Nv9u16Ki1pB+8pOflHXbqJkIdYLu7Ow8Kp28mHrBFHy94oorDvvc+P37778f5513nqxRfN9998k6chQspqDx1VdfjRtvvBHz5chmdozNJ6/mhamZsFwLXngrV6dYzhAKWayeykVQnWItg5LNh2eWGvDtyQeIzSEHWsyBE+YmV9UooAWw2rsaL2byZYGeST2D15mvq/RusVlmFadGxhvaJbFweeSwr+umBivrIDWakdVlSr2AzHFd2FkbuZQNTeXUg1pkpW3YNXoxUK5xLWOMMVZIDxouPcHqMqOYAsWc79U4gWLKvCBU0+0jH/nItI8pJoi6dOnSgmbT1q1bJ8tPVAplFNO2f/9+LFq0qGL7wRqToRnw6B6MZo5u6jjvb/y2yGcXyyzgEtYppveQkIZcmwZz0JnIKk6ezIHiarUxsHEiUEzlJ17XxIHiWgsU927LTptRPM4T0GVWcS5d+klSukBKWFkoiaQsrcNqj5uxkDZyNZkVVa5xLWOMMXY8uqLDBQeKWX2hZDLNUGBn+He7YQLFVMuNC1IzVjlBPYgBN1+OpWIZxVq+TvF46Fbok3WKxWTViDnJLDVhDqblbe8BC6m1HoixwDSrvjrFPxn6ibz9UvolpJwU/Jq/0rvFChTumPyjHRnMyOxhw3P4xAz1AvCHPWUJBFKgWM9oML06B4prVDZryVUmtVhmkce1jDHGKoXOP/QfY/VG86gQYysWWQMEiq+66io0Mi49wSrNq3vlgKJSXUTHM4rttHtUnWLFdmWN4VLIdWpwfAq0tIDiAE0PJmVAurQEmuTPkbIoeZBWrCaE8U3rE3BEvoZt854UfOp8RYz4GJbCBRF9ouGE9dtd+VUC8yjgOnDVUc6pqVGq4yIIF9muURjr2lBLGn1cyxhjrHJ4gpzVK42u22Vro8rELNg8B4obHZeeYNUQKKaGdrZry1IUlaB7FViJKYHAsTrFehpQC62zRScOGhspSv7j2O2pz0lZxYEX8kvitQy9XrmCj+V87sawAO2Td3L0f/Md8uNjOBdB+tsbTyJO20A+mX/e5C+TeAK2VtE7N/36iBwfQ8YYY2w2gWLB41dWhxSdViRSXyNA4QqS9R8oXrZs2YwzArQ0NRKJYOPGjfjnf/5nnHLKKXPdR8bYEaiZHW05N1exQLFmqLIc8VSuR4UTmF2sjsoawxGyZIUily2LfMSITiqqgmyPDu+eHLQUD6AYY4yVHo9rGWOMVYosPMHZlqxOM4oV6mvkCKga/47XfaD48ssvx+23345YLIYLLrgAHR0d6O/vx3333YdwOIwNGzbgwQcfxPe//33cdddduPDCC0u/54w1MI/mkRnFaTuNgEGR2fmn6lSkHoe98QuPCtszy+VTbr6sxPhHGSy2JwPHQlEwstkPPe6UJVmUlsLkRA6mYvIgbY4c4eCz0ZuRRT4D/D3hq7BYX1z21+VjWBq5jIsX/pKSt+nHeM4FC6Fp87McUjazs1MI6n5eglmjrKyFlJFF++Iwag2PaxljjFWKpmpQROVKCjJW1nKV1NeIKhOald4bVvZA8dKlS7FkyRLcfffdCAQmg1SJRAKvf/3rsXbtWvy///f/5O2Pf/zjdTeg5hrFrNIokBI0ghjNjVZuH8bqFAtHAHOZIVTHyk5QzeWpn6fMYncs49gVcFr1MgWKXWSyLoSHgowcoJor02jClvhf5O0Hwk/iyo5VZX9NPoalQRco8WeSsPOxYowETHQuDs3La7uuC6QA3d8sMzhZ7bGSObhODFqwMqtc5qLRx7WMMcYqn1FM5Se4qR2rJ4qqQDMU2FnuQNIQgeIvfOEL+OpXv3rYYJoEg0FZu/d973sfPvzhD8uP7373u1FvuEYxqwY+wycDO5UOFLvOZFnTkhqrlyq0aYLIJUQ1k1yqr+xToXCAas42tp2Kx8cCxc8kn8bfBN5R9tfkY1g6/h4VsR35wdzgQBpd61vn5XUV14Hi6lCCBhSVi5jVJDofZWvz76/Rx7WMMcYqmwAkA8V0HuU4MaszmleFiHGCZa0pakQ/ODgol+dNZ3R0FNFoVN5uaWmZ294xxo5ZfoIy7xyK1FYALSNRDVpKwrWD2aSTwhsmsiEOZPZjINtf6V1isxDqmcwGHdgbr+i+MDZfeFzLGGOskoFi+s+d9ybQjJWfLFEp2xBxzKDuA8WvfvWr8dGPfhQPP/zwYZ//85//jP/9v/83zj//fHn/pZdeksv5GGPlCRSbar6hXaVopgJBNYUZGxMywlgZXD1xf8vIUxXdHzY74QWTgeKhAwk4Nl+0sPrH41rGGGOVQtnE9B/1a2Cs3qgGBYvzK0BZ7SgqUEx12trb23HOOeegtbVV1m6jj+eeey46Ozvl1+WTqyo+8pGPoN5QfWJqdELNTRirFI8+Fih2KhgoNugthNdIscNtiGyauP3M6NMV3Rc2O4EWE5o3f9uxBYYPJSu9S4yVXaOPaxljjFUOZRNPlJ5grM5ougJFlqvk3++6r1G8YMECPPnkk7Lz8xNPPIFDhw6hu7sbp59+Oi6++OKJx733ve9FPeIaxawaGKoBr+5FKjPWeapSM4SqkG/8clkJYxQobjoFtx34ibz9Uvx5pJ00fJqv0rvFCqCpOrxdQHL3ZPmJ9kXz09COsUpp9HEtY4yxKqhRXLaOLIxVjkLlKjUqV0ldzyu9N6ysgeJx1P2ZNsZYZQTNIPpT/VUxQ8iBYjZugXch2sx2DOYGYAsbW2N/xWnNmyu9W6xAvi4Fyd1isk7x2ZXeI8bmB49rGWOMVSJQrCkal55gdYliBJqhwM7y73ctmVN76t/+9rf41Kc+hX/8x3/E3r175ecefPBBHDx4sFT7xxg7Tp1iqmlVqaVKqp6fIRQ0Q8jYGMqK2NA0WX6C6xTXlsACbeL24L44XK5DzhoEj2sZY4xVLKOYS0+wOqV5Ve5r1AiB4oGBAZx99tm45JJL8K1vfUtu1DGafPvb38aNN95Y6v1kjM0QKKYSFJVqaKeoimxoxzWH2JE2RE6ZuP3s6NOcJVFD/K06NE/+tp1zMdLLdYpZfeNxLWOMsUo3s+PSE6xe5Vce8+rjug8Uf+ADH5CD6ueeew47duw4bPbrwgsvxH333VfKfWSMHStQrBmwHKti+6B5VAgOFLMjrA2th0fNRxtjdgy7kjsrvUusQPSe4u+azCqW5ScYq2M8rmWMMVbJZnb0HydVsHo1ta8Rq+NA8Z133imzK9atWydnwKai5m7U5K2e3XTTTejo6MCGDRsqvSuswVGgmLZKZRSP1ymmCUJeLsWmokz3E8InT9x/ZvTJiu4PK5yuarKh3TgOFLN61+jjWsYYY5UvPcGBYlbPGcXjfY1YHQeKbdtGIBCY9mvRaBSmWd/tDK+//nr09/djy5Ytld4V1uBoUBEwAxXNKJ6oU+xUbBdYldrYNFl+YsvI0xXdF1Y4XdXh6ZocyA3si/NEEKtrjT6unY0XX3wRmzdvxurVq3Heeedx/WbGGCvB9RwFi7n0BKtXHC9okEDxGWecIWu2TecnP/mJrPPGGJsfft0PF5WbgVZ1QNHAM4TsKCdHNk7c3pfeg6HcUEX3hxVGgQpPm5CDOpJLOxgdSFd6txgrGx7XFu6f/umf8JGPfATbtm3Dm970Jpk8wRhjbG40VeNJeVa3KEisGQpcm3/H6zpQ/OlPfxp33HEHXvWqV+GrX/2qnAX75S9/iSuuuAK//vWv8YlPfKL0e8oYm5apmTID0Hbtys0Q6jxDyI4WMZqwPLBi4v6Wkacquj+scHReD/dMZlFy+QlWz+p9XEt1l6+55hps3LgRuq7jxBNPnDFb+DWveY3Mru7q6sKHP/xh5HKTpa36+vrwwgsv4M1vfrO8/w//8A/41a9+xcENxhgrQaCYS0+weib7Grk8XqjrQPErXvEK3H///XIg/aEPfUgOEKm226FDh2TDj1NOmVxuzBgrL6/mlfVgc05l6hQrqgLN5BlCNr0NkSnlJ0Y5UFwraPLJ3z05ROBAMatn9T6u3bp1q6zDvHLlSqxfv37GEhvnn3++DAz/4he/wGc+8xl84xvfwAc/+MGJx1CtZqrZPF7HORgMwu/3y3JsjDHGiqdD59ITrK6Nr1RktUGfy6D6gQceQDqdloPLpqYmOVgk8XgcoVColPvJGDtGRrFHrWxDO92jIsMpxWyGOsW3H7xV3n4+thVZJwOP5q30brECMlt8RzS0o+DZkY2+GKsX9Tyuveyyy3D55ZfL21dddRWeeOKJox5zyy23IBaL4fbbb0dLS8tE7eZrr70WN9xwA3p6euZ9vxljrJEm6Hl1BqtnqgGoar5cJZWiYHUaKB7n8/nkRiij4Itf/CK+/vWvy0E2Y2x+BhY+w4dEKlGxfRh/s+dAEjvSIt8SNBstiFrDsIWFPw89iJXBVSV9DVrGlMtmYQqPzHBvdJqio8e7QDZGKZau6DDbLfm3TQO6TMJCIppFqIWD/Ky+1eO4VqUrs+O4++67ceGFF04EicmVV14pS1bcc889MsC8cOFC7Nu3b+Jcn0gkkEql0N7ePuPzUvCZtnGUpU0cx5FbMej7XNct+vtZZfHxq318DMtAQP5MaZsP9Do0fp6v12OlV3PHUBGAJuDaDpQ5XKPUE0HHUIh5ez+dzWvMKlD86KOP4rvf/S727t2L5cuX4/3vfz9WrVola5Z98pOfxHe+8x1YloW3ve1txew3Y6xIASOAXtFbsddXjclOpsqcp59YPaFgAmUV3z9wr7z/g73fqfQuNYRl/hW4Ye3/lRNJxdBVA1k1i6ZuL4b35xvZDeyJc6CY1RUe1x5en/g973nPYZ+jrOru7m75NdLZ2Ym1a9fK0hRvectb8K1vfQtveMMbjhmI/vznPz9tjeehoSF4PJ6i9pUuqEZHRwsOgrPqwsev9vExLL1EOgE7YSOZS87L61FwKpPMUAdjTvKpUbV2DKkEt5Wz5UfNU/37Oy+oZrPrIhGLY8AcKPvL0dirUAVfQVKmAS1do19Iyhz4/e9/jx//+Mf4/ve/j3e/+90y0+Ltb387Pvaxj2H16tWoZzfddJPceBaVVQuP5pF1inuTlQkWu5ZAyhJQ7Np745ezeFkbSSVVEyfZakP11Jq8zTIDdSabmk6dCBSz+bEr9TKeiz0rg/TFlp6wXQeRhYHJQPHeGJZvmjlzkLFawuPaw42X2zhSc3MzhoeHDytR8Xd/93f46Ec/KstR/PCHPzzm81KN46uvvvqwjOLNmzejtbX1mJnIxzI+/m5ra4OmaUU9B6scPn61j49h6TkJBwPqgGwmOh9kFqoA/E1+DvbXqFo8hiKbQy7pwuPj943xjGKMqgiGQ0WPiWYjm82WPlBMTS02bdokuxvTwJCWm9HAj2qeUbbBb3/7W5x66qloBNdff73cxpt6MFZpTZ4mrGhaUbEmCHShPZxOw8m58AT1mjvJJrU4AoFQzZxkq8lgahBpO42QMXP9zhPDG/Daztfj6ZGnIFCG5VFiStmTBo/1p+wUkk6+DM1jw48UHShW5A9SINxjTHyOG9qxesLj2uJQM7zHH3+84MeHw2G5HYmCS3MJMNH5eq7PwSqHj1/t42NYWpquyfJp83ktMv56fP1Tu2rtGOpeDVZCQKmR/Z0PdP06/n5abrN5jYIjOi+88AK++c1vTjSzoE7Hn/3sZ/Gzn/0M//Ef/8GDacYqyNAMdAY6K7oPZksSiaEMAr7ilpJWius60CwFTb4WqCoPdmcr5+RwMHEQOEagmE6Ab1v0LrmVazY2k07D6/M1/MDjxdjz+M9tn5K3nx55Qja5NFWzuCdTFHg7VfoA6q+SHM0hOZpFIFJbf+OMTYfHtUdnDo8vJT8y03hq3WLGGGOlN5e+EozVCs1QK5TWxmar4HckWnZ2ZMfjBQsWyI9Uz40x1tgMU5UF9VljCZhBWQfXcq1K7woDsDq0FhEjv3w842bw7OgzRT+XPK5qFs1dk8sgOauY1Qse1x6Oag+P1yIeR4FjKhVBX2OMMVbeQDGt5qIVcozVK2qdQjk91CibVbdZTV3NVL+Tl5wwxnRDk8v+eYDTWPy6H17NK8tPsOq40Di9+YyJ+1R+oliGosN2bbQu5kAxq088rp108cUX495778XIyMjE52699Va5HPKiiy6q6L4xxli9oyAxlRFwy1GijbEqoeoKFE2B4EBx1ZtVMdFXv/rV09Y/Oeeccw77PA28p1u+xhir76Ukmq7Csd180Jg1BMo6jXgj2B/fX+ldYWM2N78C9/b/Tt7eMvo0sk4GHs1bVEO7nGuheaEP+Ev+cxwoZvWkUca1qVQKd911l7y9Z88exGIx3HbbbfL+ueeeKxuoXHPNNfjyl7+MN77xjbjhhhtw4MAB2Y+DPn9k5jVjjLHS0hQNKtR8wk2D99tg9R0ops11AI4W1Emg+OMf/3h594QxVtN0fTxQLKBP9r9iDSBgBGCoBiw3B6PYerisZFYEV6HZaEHUGkbOzeKZ0adxRssrZv08mqrDoUaFPZPHND6UQSZhwRvkP3JW2xppXNvf348rrrjisM+N37///vtx3nnnyRrF9913H6677joZLA6FQrK534033lihvWaMscZBE5JceoLVO1VToBoK7Kxbtt/1mVaLsdlp2EAxLaPr6+uTt7u6umRDk0WLFlV6txirWTQ7qBkKcmmn0rvCKlB+wqf7kLYzMEwOFFdD+YnNLa/A7/rulPcfG364qECxruhwXAeK6SLS4cNof768yMHtI+heGSl5U8lMwkZayXFTyRpFEwjZnA0750A3ZrVgrSLqbVx7LEuXLi3ogmzdunWy/ARjjLEK1ChWuPQEq3+ame+SnY2XPmYgXMDwqdA93Bxyrqp/JF8mVHctEslf6H7pS1/CRz7yEfzoRz+q9G4xVrNocGN6dKTj3NSs0VDmadgTQZzLT1SNqYHiZ0e3IO2k4NP8RT0X1SluXxyaCBQ/fuculM++Mj43mw8hbxNWntpV6d1gjDHGaipQTBtnFLN65w1rZQvkWkkH6RGHA8X1FCjesWMHbr75Zjz66KN47rnnZIdl+ngk6shMy+IefvhhuSzu3e9+Nz796U/DnGUW23iQmFCtNsbY3OkejSYIER+uncZmrusinclBszPT1qpkhVEsA25cQzydhqHNb1kCGlRbOQewHCgKZ2KQbrEYbUYHBq1+2MLCYwcfwxnhV077WFqgZYY02UTlSHTRQjWOO5aEseOJ/nnYc8YYY4yxxpIvPKHApZRIxuqYZqrQyrQAVVGBbNyFYwm50pnVQaB469atuPPOO3HGGWfIwA1tR4pGozj//POxatUq/OIXv5CNNj74wQ/KJh1f+cpXZv2ab33rW/Hggw+ira0N99xzT4n+JYw1Lm/AQGtPALVELq0fyaC5KSCbd7HiNAkfktFhpOwUgp7iMleLJSjYn9DhC+pQONg/4czMK3DH/l/J28/kHsMFna8+6jE0sZMZseWASvco02aLZ+wMVqxehEXrW3BoxwhQhmQXMfH/dJnEapE8hkJMO+HAGGOMsQIyissxyGKsQciyEz4VVtKFZvB1fV0Eii+77DJcfvnl8vZVV12FJ5544qjH3HLLLTL79/bbb0dLS4v8nG3buPbaa2WH5vGuzJs3b8bOnTuP+v7Fixfjqaeemrj/05/+VGaife5zn8OnPvUpfO1rXyvjv5Cx+md4NBgeH2qJ4zjIuh6EW33QND6hzEWPtx0vj7wMX2B+Ty00sehAhbdZ56zwKc5Tz5kIFP915FnYgTRCZujobOykAzsjAM/0dYpt4cBRbJz15pVl21eqUTwSG0ZTuIVrFNeoVDyLRDaKJSe0VnpXGGOMsdqsUcwZxYwVjZIVqLRFLunAdYRsnseKUzVX1IVc3N9999248MILJ4LE5Morr5RBgqkZwY899hgGBweP2qYGicfRGzJ1df7e975Xwn8NY4w1HgpCmpopM1BZ5S0JL0FPID+B6ggHj/U+Nu05kGbeaTA1HV3VZI1i27HLvr+MMcYYY42IxmP0H9coZmxuDL8Kw6vCzvCkS11kFBeC6hO/5z3vOexzTU1N6O7ull8rFGUlx+NxLFiwYKKx3YknnljQ902tZ3zo0KGJjETaZou+R2bCFfG9rDrwMax9fAxLx6f64Nf8SGQTMNUyFZ+aBh0/4YppSxY1urN6zsJt22+Ttx868BBevfDo8hOqTmUDBFyHajwfPvOuQYXlWsg6WXi1aVKOS5hRnC87xX+HtUoeP5F/Ly13+Ql+v2aMMVZPNEWTWcU2eGKesblQdQWesIZEryUnXo68tmF1GCimGsUUGD5Sc3MzhoeHC36e0dFRvOUtb0E6nZa/OFSS4gc/+MFxv+/zn/88PvGJTxz1+aGhIXg8nqIuqmhfCC+Xrk18DGsfH8PS0tM6UokUPGlPvkvaPKBBQCaZka/Hg4HDbQxsxG3IB4qfG3wOh/oPIWyED3uMawk4jo10XIE6TeMHJ5vDqDIMYZbv4oUCjMl0Qh5DulBitSebtpC2knIFl6aXt3wIjbsYY4yxesHN7BgrbVax5lFkaT3Dx9eGdR8oLpVFixbJ8hSzRY3zqEzF1Ixiqofc2tqK9vb2ojNiqJke10atTXwMax8fw9Ly5DyIDcVkoMire+flNWUmsQD8TX4O9h9hdfNqLHp5EfbF98GFi7+m/4qLOi467DEyGzubg2vlm0AcKamkYPp8aApMln0qNZlJLICmUDPXKK5RKSUHJZd/L9WN8g4vs9lsWZ+fMcYYm0+U6EBNtbmZHWNzp3tUeIIaUkP2tNc2rM4CxZQ5PJ75d2Sm8dS6xeUSDofldtNNN8ltPMBEwaViA0wU1JjL97PK42NY+/gYlk7EG0HIE0IsF4Pf9M/b69JSdzqOHCg+2it7Xokfv/RjefvhQw/jdcted/gDVMDw6kinqWTA0T8/unDJubmyB3Dzx0/jQHGNksdPmZ/3Un6vZowxVm+ogTBnFDNWGmZQQzbmwMm50Ey+PpytmvqJrV279qhaxBQ4psxe+tp8uf7669Hf348tW7bM22syxlgtoEBRi69FBha5IUd1OGvBWRO3Xxh6AcOZo0s16V4aDohpj5muGcg6Ga4fzBhjjDFWxglXHjszVhq6V4Hup6Z2/DdV94Hiiy++GPfeey9GRkYmPkeN6OhN9aKLDl9KW06UTdzR0YENGzbM22syxlitCJkh+HQfMk6m0rvCAHQHurE8slzepiWNjxx85KjHaKYCVVMgnOkzXBzXgS24wQpjrDg8dmaMsQIyisEZxYyVarUplZ+gqx/X4WBxzQaKU6kUbrvtNrnt2bMHsVhs4v7AwIB8zDXXXINQKIQ3vvGNuOeee/Cd73xHZvfS53t6euZtXzmjmDHGZhYwAgiaQSStZKV3hY05q2cyq/jhgw8f9XXNyDeyc6xpMopVDZZrw6IixowxVgQeOzPG2LFpisYZxYyVkBFQoftU2GmegKnZGsU0eLziiisO+9z4/fvvvx/nnXeerFF833334brrrpPBYgoaU3O5G2+8sUJ7zRhjbNryE54WDKYG5YCXGnSwygeKf/DCD+Ttl6IvYSA1gHb/ZBNWVVegmwqyiaMHUrqqwxEOB4oZY4wxxspZeoKb2TFWMrRa0hPSkEhZsnk3ZRmzGgsUL126tKAZtHXr1snyE5V0ZDM7xhhjR5ef8OpepO00/Mb8NbVj0+vwd2B182psi26baGp3+YrLD3sMzbhnYkef1xSocIUD2+HSE4wxxhhj5Uq0UMCBLMZKyaSsYo8KOytg+Pjvq+ZKT9QSXj7HGGPHLz8RNsNcfqJay08cmL78BCV/T1fHiy5cqEEhY4wxxhgrT+kJjhMzVlqaqcpgsZN1ubTLLHCgmDHGWMlRuYlmb7MsWeAKrgtVDV7R/YqJTJWXR19Gb7L36IZ2hgLXnq5Osc7NCRljjDHGykSO0TiOxVjJmSFNJsRM14uFTY8DxUXgzs2MMVZY+Qmf7pPlJ1jltfpasbZl7YxN7ShITLPu7jSDKE3VkLNzcFwuP8EYY4wxVmpceoKx8tC9imxs52Q4UFwoDhQXgUtPMMbY8fl1vyw/kbJSld4VNk35iYcOPnRUFjgNpKbNKFa4oR1jjDHGWLnI5s+UVMzL4xkr+d+WGdRAKfvTXeewo3GgmDHGWNlOyk3eJjjg8hPV4hU9r4A6durfE9uDA/EDh32dmj3Ql6kz8GGfVw3Yri03xhhjjDFWhoxiRYELHjMzVmqGX5WbleG/r0JwoLgIXHqCMcYKQxnFlFnM5SeqQ5OnCSe0nTBjVjHVKdb0o7OKqfSE7XJGMWOMMcZYOahj/3FyBWOlp2oKPCENwhZHJcSwo3GguAhceoIxxgpDNYrDRhhJK1npXWHTlJ+gOsVTlzhSowd1mmYP+Zp5gjOKGWOMMcbKgLKJ6T8uPcFYeRgBDbpPhc21io+LA8WMMcbKXn6CBr1U45ZV3pndZ0JTqE4XsD+xH3vjeye+pqgKDK+KaQ+VoiDr5OZxTxljjDHGGqf0BG0cKGasPCghxgyqcHIu/50dBweKGWOMlVXIDMFv+JG2uPxEtRyPk9tPnrj/0IEjyk946CLl6GYquqojY2d4YMUYY4wxVmI0ic81ihkrLzOgyVJ7To6vZ46FA8WMMcbKXn4i5AkhaXP5iWpxds/ZM5efMBWoGnBklQlD0WWNYs4MZ4wxxhgrLQoSc0YxY+WlexXZ1M7hpnbHxIHiInAzO8YYm93At9nTTCVuOchYJU7vOl1mCJPeVC9+vfPXsBzrsDrF0za0EzY3tGOMMcYYKzEKEssaxTRgZoyV7bqUmtopKiXF8N/aTPJXiWzWzexo279/PxYtWlTp3WGMsZopP9GX7JMD4VKizrV2xkYimZA1dlm+TETYDMPUzGm/HjAC2Ni+EU/0PSHvf//57+OunXfhzavejPMXnw/doyAbcwHf5Pdoqg7HTmM4MzxR47hUXNdFOpNERrOgqjyHXYuyaQu2nan0bjDGGGM1SYWaLz0hONORsXKijGLdryEbd6gFS8G8kcYJnzbOv5QxxlhFy08sDC2cyFotJddxEXNiCIfDUDUOMlImSiwXQzQbhSIUNHmaYGjGUY971/p3YVt0m3wsGcoM4b//+t/45Y5f4g0L3oRN4hXwYDIgrCtjGciJQ6XfZyHgZmyoYkReJLHak0naCKgevsBljDHGii09ARWW4JVbjJUTJRb5W3WZWVwI1xFID1ryo6o1xnUKB4oZY4zNi65AV1me13EcDGQH0B5uh6aVNtO1VtmujWgmKjO4o7npA8YLggvw5fO/jDt23iG3tJ1vNjiQHsC3dnwDbcbtuMx6E85uf9VEBnETlRApA+G6yIg0vF4fFM4orknR3CjgcJCYMcYYK7r0hMKlJxibD4ZPhTFl5eSxODkXmZF8Wb5GCRTz1RhjjDFWh6Un2v3tWNO6Bmua1yDiicgM48H04GE1hqkExVvXvBVfu+BreMuqt8CreSe+NmgN4Dt7v4EbnvtfeGjwQa4vzRhjjDFW7hrF3MyOsaqi6ors4dJINY05o7jIZna0URYbY4wxVq0M1UCHvwPN3mYMp4fRn+qfqDFMwWP6+ngN6bevfTsuWXYJfrPzN7hr113IOln5tf5sL765++u449Av8dquSxDWI6XfUeEil8vBzJq0Hqz0z8/KLplKAq7AuvRyLDAXVHp3GGOMsZqjazpci1fnMFZtpSp0j4pcsnHifxwoLgI3s2OMMVZLKCDcGehEi69FBoz7Un0yYEx1h8Oe8ETAmG6/c907cenyS3Hbc7fj3kP3wBI5+bXe7CF8d883K/wvYdXuhKHVWBDhQDFjjDE2Wzp0Lj3BWBXSTIWaqqBRcNoOY4wx1mAB43Ut67C6eTWCZhBD6SFZ03gqyjb+29XvwqeXfQ4XdVwMQzm6GR5jjBWKVuJ1dHRgw4YNld4VxhirWqqqcukJxqqQaipQtXxju0bAGcWMMcZYg6GmdtRcMGgE8bz9vKxbTHWNj5w5p5IVVwT/Fhd3XYZ7+u7CrtTO8uyQEHBcFxo1slMao0lEvbFsG4oQsmkiY0fi1XiMMXZ8NBZzwaUnGKs2mq5A0RunoR0HihljjLEG5dE80FTtqIxiQk0bVEOBkxNoCjTjykXvLNt+CNdFJp2G1+eDQsFiVnOio6NQHBcb2zdWelcYY4yxmkQlwTijmLEqbWinK7BzAvCg7vHVGGOMMdbAmSt0UTJdoJgaNxheFaKBOvwyxhhjjFWKwquqGKvehnZeFdNcMtUlDhQzxhhjDXxB4tW90waKieahWnlUGYKDxYwxxhhj5aQqHJ5hrFppDdTQjt+JGGOMsQbm1bxwhDPjgIhKUDTK7DljjDHGWCUDxQKNEYhirNaoRuM0tONAcRG4czNjjLF6YWrmjF+jIPF44wbGGGOMMVY+yth/jLHqozXQdREHiotAXZv7+/uxZcuWSu8KY4z9/+zdB5xcZbk/8N8p07b3bDaFdEiAFErohBKCIL2KeqUqkXvRe/kLekFBQEHFhqI3IApiA2mCUqQKgmAIkNBLQno2yWb79Dnl/3ne2d1sz+5md2bO7u/L57A7M2dmzsy7M3nmmed9XhDtbp9i+VDSW3sJWbjBDGhwUqM/ICIiIiLKJraeIMpduqmpjYliIiIiGtV8ug+GbvTZfsIX0uE6oz8gIiIiIsr22hGSLHZcJ9uHQkS9LWgXGBsL2jFRTERENMYrig3N6HNBu7HUj4uIiIgoWyQek2QxFxEmyk1mYGwsaMdEMRER0RjmM3wqWdz3gnY6NFnQju0niIiIiEaMVBPLfw5YUUyUi/QxUkDDRDEREdEYZmqm2vqsKDZl8QYd9hjox0VERESULVJN3Ne6EUSUfcYYWdCOiWIiIqIxTPoT+0xfn4li+dDiy9PhjvJvzomIiIiySaqJ2XqCKHfpY2RBuzGfKL7xxhvVm/Hq1auzfShERERZETJCsFyr/2/PpSUXF7UjIiIiGrHWE5KbYOsJotykjZEF7cZ0ovitt97Cyy+/jMmTJ2f7UIiIiLLGb/j7rV4x/JrqycX2E0REREQjQ5LEkix2XCaKiXKVOQYWtMuZRLFU9C5duhTz58+HaZrYZ599et3vgw8+wHHHHYf8/HxUV1fjqquuQjKZHPT9WZaFyy67DL/4xS/UGzIREdFY5dN9/V4uFcVqmhUXtCMiIiIauYpi9igmymm6qUHTR/eCdiZyxLvvvovHHnsMBx10EBzHUVt3jY2NOOaYYzBz5kw89NBD2Lx5M6644gpEo1Hcdtttg7q/m2++GaeffjqmT58+jI+CiIjIe0zdhK7rsF0bhmb0Os3KF9RhRUf5PCsiIiKiLCaKWVFMlNt0KaDxaSpRrBujs+g0ZxLFJ598Mk499VT1+wUXXIAVK1b02GfZsmVoaWnBww8/jLKysi6VwVdffTVqamrUeQsXLsQnn3zS4/rSYuKNN97A22+/jb///e944YUXRvxxERER5Tqf4YMJUy1oZxg9E8XCDOqQ782lyoUzcYiIiIiGfzE7+a+/dSOIKLuMjpmW0r8Po1LOJIqlkmlXnnjiCSxevLgjSSzOOecc1bLiqaeeUglmsXz58n5v56WXXsKaNWs6qok3bdqEo446Cvfddx8OO+yw3X4sREREXmJqJkzDhO3YQO95YvXNudHWfkIf5uhBLZLnuG2L5Y3eaVyjmcyS5dcHREREu7+YnctYiCjHF7TTEIs66L95n3flTKJ4IKQ/8UUXXdTlvJKSEowfP15dNlBf/vKX1dZuypQpeOaZZzBjxox+ryfVzLK1q62tVT9t21bbYMl1pMXGUK5LuYFj6H0cQ+/jGO4+Vb/i6kjZKThG79MdNdOF7nORig3/dEipUk4mbRlMaBqnW3qRfIEgfyNDjYkGg691IiIazYvZsUcxUW4zAzrgjt541FOJYulRLInh7kpLS9HQ0DDi9//jH/8Y119/fY/z6+vrEQgEBn17kthobm4ecEU15R6OofdxDL2PY7j75ANJqiWFhJ2A7uvnOfS7MM3h//AilcROzIYZSqlv6cl7/EkbbspGfUO9qk4fSRJ3ERERjUaGbrBHMZGHFrTTR2GfYk8likfKunXrBrSfLJx3ySWXdKkoln7I5eXlqKysHHJFTEVFRZ89ISm3cQy9j2PofRzD4dHkb8KOyA7k5+VnJdkfbdSRV5rHZL9XxRzYrbZ6HfrMkZ2Il0gkRvT2iYiIskUWFWaimCi36aN8QTtPJYqlcri9aqx7pXHnvsUjpaioSG233HKL2tqTE5KYGGpyQj4Q7871Kfs4ht7HMfQ+juHuK/AVYLu2PWuJWqkklvtmotibZPxky8TrkK9zIiIazYli9igmym3GKF/QzlOfxvbaa68evYglcSyVvXJZplx55ZXYvn07Vq1albH7JCIiGkmmrFA3+r4QJyIiIvJUPMYexUS5TZPiCL8Gxxqdr1VPJYpPOOEEtehcU1NTx3n333+/qj5asmRJxo5Dqomrqqowb968jN0nERHRSPIZPmiuxg8nRDTsGDsTEQ0MK4qJvLOgnTtKPzflTKI4Go3igQceUNv69evR0tLScbqurk7ts3TpUhQWFuK0007DU089hbvuuktV98r5NTU1GTtWVhQTEdFoY2qmqmKxXCvbh0JEowxjZyKigWELLiLvtJ/QtPSi3KNNzvQoluDx7LPP7nJe++nnn38eRx11lOpR/Oyzz+Lyyy9XyWJJGsvict/97nezdNRERESjp6JYVtq2HAs+fWQXIyMiIiKinnTo0NgLjMgbC9qZGmzLhekfXa/ZnEkUT5kyZUBl27Nnz1btJ7Kp+2J2REREXifVxLLZDv9tIyIiIsoGTdPYeoLIKwva+Ubngnac1zAEnD5HRESjjVQRS188tp4gIiIiyg5dY4qGyAu0UbygHd+FiIiISH0wCRgBVhQTERERZTEek9YTo3WRLKLRxBylC9oxUTwEXLmZiIhGo6AZVD2KiYiIiCjzJEks7SccONk+FCIaowvaMVE8BGw9QUREo5HfGGUNtoiIiIi8VlEsfYpHYZUi0Whd0M4ZZe0nmCgmIiKijj7FXECFiIiIKHuJYvmPiWKi3Keb7YlijCpMFA8BW08QEdFoZOomdF1nn2IiIiKiLJBqYraeIPIG3RidC9oxUTwEbD1BRESjtaLY1Ez2KSYiIiLKAkMzVFUxK4qJvMEM6uxRTERERKO3olg2y2WimIiIiCgri9lBYyswIi8taKePrgXtmCgmIiKinYliVhQTERERZa9HsabDcdl6gsgL9FG4oB0TxURERLSz9YRuwnbZo5iIiIgo0yRJLD2K2XqCyBv0UbigHRPFQ8DF7IiIaLQKmkEuZkdERESUrcXsoLGimMgj9FG4oB0TxUPAxeyIiGg0J4rZo5iIiIgo8/S2/9ijmMg7zFG2oB0TxURERNSl/YRUshARERFRdlpPsKKYyDt0c3QtaMdEMREREXWQHsX8gEJERESUeRKDGZrBimIiDzFG2YJ2TBQTERFR1wXtNJN9iomIiIiywNANLmZH5CF6e6J4lHx8YqJ4CLiYHRERjeZEsXxAYZ9iIiIiosyTOIwzu4i8Qx9lC9oxUTwEXMyOiIhGc+sJ2SyHiWIiIiKiTJOZXWw9QeTBBe3s0fG6ZaKYiIiIuiaKNSaKiahvV199NaZOnap6aa5evTrbh0NENKqoHsVsPUHkKfooWtCOiWIiIiLqstp20AjCdkdJky0iGnYnnXQSXnzxReyxxx7ZPhQiolFH13U4YOsJIi8xRtGCdkwUExERURdBM8iKYqJh8Nvf/hYLFixAMBhERUUFTjjhBMRisWG/H6nqXbp0KebPnw/TNLHPPvv0ue8HH3yA4447Dvn5+aiursZVV12FZDI5qPs79NBDMWnSpGE4ciIi6u1Lew1atg+DiMbognZmtg+AiIiIcovP8PEDCtFu+u53v4vvf//7qk3DIYccgh07duDZZ5+FbQ//J4h3330Xjz32GA466CA4jqO23jQ2NuKYY47BzJkz8dBDD2Hz5s244oorEI1Gcdtttw37cRER0eDprOcj8uaCdj4NVsL7swGYKCYiIqIufLpP/ZT+eNKDlIgG58MPP8S3v/1tPProo6qKuN2ZZ57Zb7JXKo+nT5/e47IXXngB8+bNQ0lJSa/XPfnkk3Hqqaeq3y+44AKsWLGi1/2WLVuGlpYWPPzwwygrK1PnWZaFyy67TCW0a2pqsHDhQnzyySc9rjt58mS88cYbA3j0RES0Oxh7EXl3Qbt4i/dLivlVFREREfVY0E7647FPMdHQ3HXXXWqxt85J4l258cYbceyxx2Ljxo09ksRyO3fffXef15XX60A88cQTWLx4cUeSWJxzzjmqAvmpp55Sp5cvX66qn7tvTBITEWVuMTvO7CLyHmk/IS9dry9ox0TxENxyyy2oqqpSlR1ERESjsaLY1EzYo6HJFlEWvPrqq9h3333xne98R8WMfr8fhx12GP7973/3eZ1f/epXqKysVInc7du3dyRtpVr4M5/5DL761a/u9nFJf+K99tqry3lSpTx+/Hh12Uhh7ExENMiKYkk2ud5ONhGNNYYv3X4i0WIj3mT1vzXbyNXJA0wUD8GVV16pAvhVq1Zl+1CIiIhGpKJYNsvlgnZEQ7F161ZVoXvPPffgl7/8Jf7yl7+oD/5LlizpSAJ3V1hYiCeffBI+n0/t9+KLL6pK4uOPP14lkYdjKrL0KO6tfUVpaSkaGhoGfDuyAN7EiROxadMmHHHEEaoHc38YOxMRDXIxO02DA+/3OiUaS4yAhvxKH4om+He5Fdb44CvW4M8zkGuYKCYiIqKeiWLNhOUwUUw0FNLKIRwO44EHHsBZZ52FE088UfUrluqw/haNKy8vx9NPP62uu2jRItUv+I9//CMMI7c+RPzgBz9QSWLpb1xbW4tXXnkl24dERDSqFrOT/1hRTOQtmqYhUGggUDSwzQxpMMzcS8vm3hERERFR9hPFBhPFREMlFbqS9J07d27HedIXeMGCBWrRuv4kEgnE43HVdzgajapk7HAeV3Nzc6+Vxp37FhMRUXaTTfIfE8VElA1MFBMREVEPISPExeyIhmjvvffu8zJJAvdFqnOlR/HkyZOxYsUKfPTRRzjjjDOQTCaH5bikP3H3XsSSOJb77d67mIiIstx6wmXrCSLKPCaKiYiIqIeAGeAHFKIhOumkk1BfX4+VK1d2nCen33jjDey///69XkcuP+6441BUVITHH39cVR8/88wzeO211/C5z30Otr37X9xIz2O5zaampo7z7r//flW9LH2RiYgoNxLFsrFHMRFlAxPFRERE1Gv7CSIamtNOOw0HHnig6k983333qf7EkjwOBAK47LLLer3O0qVLVW9jWQSvfcE5qUz++9//rvoW33rrrX3en7SokH7Isq1fvx4tLS0dp+vq6rrchyyaJ8cn93PXXXephebk/JqamhF4JoiIaLCk7YQkitl6goiygZ8CiYiIqAef7lMfUqT9hKHl1kJaRLlOKnSlKvh//ud/cOmll6rWEUcccQRefPFFVFdX97lAnCSSKyoqupwvFcjPP/889txzzz7vb/v27Tj77LO7nNd+Wq571FFHdfQofvbZZ3H55ZerZLEkjS+55BJ897vfHYZHTUREw8HQjXSPYjBRTESZN6YTxVOmTEFeXh78fr86/cMf/lD1hSMiIhrr1IJ2mgnbsWEYTBQTDZYkfH/3u98NeP+pU6f2eZm0odhVTDvQyrPZs2er9hNERJSbdLBHMRFlz5hOFAuZCjhjxoxsHwYREVHOVRRLRYvlWPAb6S9UiYiIiGhkSZJYksWWa2X7UIhoDMqpHsWrV69WPdLmz58P0zSxzz779LqfrNYsi33k5+er6XtXXXXVsK0GTUREROlEsVQVS+sJIiIiIsoMaf0lyWK2niAijPWK4nfffRePPfYYDjroILWYh2zdNTY24phjjsHMmTPx0EMPYfPmzbjiiivUIh633XbboO9TFhmR+zn66KNVf7aCgoJhejRERETeJUli6U2ctPlFLBEREVFGE8Vg6wkiyo6cqig++eSTsXHjRrVC83777dfrPsuWLVMrOT/88MM4/vjjcdFFF6nFP+T8LVu2dOy3cOFC1Ruu+9b5dv/5z39i5cqVWL58OWKxGL72ta9l5HESERHlOqlkCfqCqvUEEREREWWOtP9iopiIMNYrimWF6F154okn1IJzZWVlHeedc845qmXFU089hQsuuECdJ8nfXZk0aZL6GQwG8eUvfxlf+MIX+t1fEtSytautrVU/bdtW22DJdaSaeSjXpdzAMfQ+jqH3cQxHjt/1w7KtXmf4DCe5fddxR/x+aOTI+Mkmr0OphBpJfK0TEdFoJwsKs/UEEWGsJ4oHQvoTSxVxZyUlJRg/fry6bKAikQgsy0JxcbFaJfq+++5TvZH78+Mf/xjXX399j/Pr6+sRCAQwWPKBuLm5ecBJcso9HEPv4xh6H8dw5MhsGytsIZKMjOj9yL/D8Ugc0NKVzOQ9iWQCTszBjh07YBojG15K3EVERDSaSUwr8RERUaZ5LlEsPYolMdxdaWkpGhoaBnw727ZtwxlnnNHRC3nu3Ln42c9+1u91pBfyJZdc0qWiWFpclJeXo7KycsgVMdISwzCMQV+fso9j6H0cQ+/jGI4cLaqhTq9DXl7eiCZwVSWxC+SV5DHZ71FWzIKt2ep16DN9I3pfiURiRG+fiIgoF9aKcMCZVkSUeZ5LFA+XadOmqf7Eg1FUVKS2W265RW3tyQlJTAw1OSEfiHfn+pR9HEPv4xh6H8dwZAR8AZimCUdz4NNHNvmn6ZoaRyaKvUnGT7ZMvA75OiciojHReoIVxUSUBZ77NCaVw+1TjLtXGnfuWzySrrzySmzfvh2rVq3KyP0RERFlq5pFNtthT1giIiKiTBnpfv9ERH3x3LvPXnvt1aMXsSSOpQ2EXJYJUk1cVVWFefPmZeT+iIiIssFn+GBoBizHyvahEBEREY2pRLEmizcQEWWY5xLFJ5xwAp555hk0NTV1nHf//ferqapLlizJyDGwopiIiMbKtEdVUeyyopiIiIgok4liV2PrCSIa4z2Ko9EoHn/8cfX7+vXr0dLSggceeECdXrRokVowbunSpfj5z3+O0047DVdffTU2b96sErdyfk1NTZYfARER0ehh6Ab8hh/RVDTbh0JEREQ0ZqhqYuaJiWisJ4qlSvfss8/ucl776eeffx5HHXWU6lH87LPP4vLLL1fJ4sLCQlxyySX47ne/m7Hj7L6YHRER0WgVNIOwXLaeICIiIsoU9igmomzJqUTxlClTBrSy5+zZs1X7iWyRCmbZNm3ahEmTJmXtOIiIiEZawAhw1W0iIiKiDNI0TW2O6zBpTEQZxXccIiIi6rdPMRERERFleDE7TeOX9USUcUwUD4G0naiqqsK8efOyfShEREQjymf41IKxtsN2S0RERESZoEOHAQMOnGwfChGNMUwUD4G0nZB+yqtWrcr2oRAREY0oUzch/7FPMREREVFmyJf0sqAdK4qJKNOYKCYiIqI++XQfTMNkRTERERFRBiuK2XqCiLKBiWIiIiLqt6LY0AxYDiuKiYiIiDLZo5itJ4go05goHgL2KCYiorG0mJ1UFdsuK4qJiIiIMkGSxJIsZkUxEWUaE8VDwB7FREQ0lj6oBMwAK4qJiIiIMllRDA2Oy4piIsosJoqJiIioXyEjxIpiIiIiokz3KAYriokos5goJiIion75DB8/qBARERFlsvUEdFYUE1HGMVE8BOxRTEREY21BO5kCyQ8rRDQUjJ2JiAZHFhJWPYr5RT0RZRgTxUPAHsVERDSWyGJ2sqid7bD9BBENHmNnIqLBVxSr1hNczI6IMoyJYiIiItplRbGhG7BcLmhHRERElKn4i7O5iCjTmCgmIiKiXVcU6yYsh4liIiIioky1n2DrCSLKNCaKiYiIqF+SJFatJ1y2niAiIiLKWKKYrSeIKMOYKCYiIqJ+yWIqASPAimIiIiKiDJG2X2w9QUSZxkTxEHDlZiIiGmuCZpCL2RERERFliMzmcsBEMRFlFhPFQ8CVm4mIaKzxG372ySMiIiLKEE3TIP8REWUSE8VEREQ0oD7FRERERJS51l9MFBNRpjFRTERERANKFEuvPPYpJiIiIspMopitJ4go05goJiIiol3y6T61+jb7FBMRERFlqKJYY0UxEWUWE8VEREQ0oIpiSRZbLiuKiYiIiDKSKHY1uC7XiCCizGHDQSIiItolSRLL1pRoQmuydVhv23VcWHEL4UgYms7KGS9ybAdBBLN9GERERKOG9CeW2GtbdNuw3i7jLu/jGI6OMdT13KzdZaKYiIiIBlRRPKloEqrt6hFJMja7zSguLYZu5GbARLsew3BTWFU/ERER0e4r8hdhWsm0Yb9dxl3exzEcPWNYFChCrmGieAhuueUWtdk2+zQSEdHYURYsG5HblX9PtaiGyvxKGIYxIvdBI6t9DImIiGh4+AwfxuWPG/bbZdzlfRzD0TOGQTP3ZuTxq4chuPLKK7F9+3asWrUq24dCREREREREREREtNuYKCYiIiIiIiIiIiIa45goJiIiIiIiIiIiIhrjmCgmIiIiIiIiIiIiGuOYKCYiIiIiIiIiIiIa45goJiIiIiIiIiIiIhrjxmyiOBaL4Ytf/CJmzZqFvffeG9dcc022D4mIiIiIiIiIiIgoK0yMUV/72tdQXV2Njz76SJ3etm1btg+JiIiIiIiIiIiIaGxXFK9evRpLly7F/PnzYZom9tlnn173++CDD3DcccchPz9fJXqvuuoqJJPJQd1XOBzGn//8Z3zzm9/sOG/cuHG7/RiIiIiIiIiIiIiIvChnEsXvvvsuHnvsMcyYMQNz5szpdZ/GxkYcc8wxKjH80EMP4aabbsIdd9yBK664YlD3tWbNGlRWVuLrX/869t9/fyxZsgSrVq0apkdCRERERERERERE5C0503ri5JNPxqmnnqp+v+CCC7BixYoe+yxbtgwtLS14+OGHUVZWps6zLAuXXXYZrr76atTU1KjzFi5ciE8++aTH9SdPnow33nhDXef999/HjTfeiJ/+9Kd46qmncMopp2DdunXQNG3EHysRERERERERERFRLsmZRLGu77q4+YknnsDixYs7ksTinHPOUS0rJNkrCWaxfPnyfm9HEsaBQABnnnmmOi0VxbK4nfQplnYWfZEktWztamtr1U/bttU2WHIdx3GGdF3KDRxD7+MYeh/H0Ps4ht6XyTHk3wkRERER0ShPFA+E9Ce+6KKLupxXUlKC8ePHq8sGStpOHHbYYXjhhRewaNEiVb0sfZGrqqr6vd6Pf/xjXH/99T3Or6+vV4nnwZIPVM3NzQNOlFPu4Rh6H8fQ+ziG3scx9L5MjqHEXURERERENMYTxdKjWBLD3ZWWlqKhoWFQtyVtLCTpLB9qgsEgHnjggV1+sJFeyJdcckmXimJpc1FeXq6Sz0OtiKmoqIBhGIO+PmUfx9D7OIbexzH0Po6h92VyDBOJxIjePhERERHRWOWpRPFwmjlzJv75z38O6jpFRUVqu+WWW9QmvY7F9u3bh/ShSD5USVVMKpXiB2OP4hh6H8fQ+ziG3scx9L5MjqHEXaI9DiPvaB+z9vZtu/O3Jl8Y8P3Cezh+3scx9D6OofdxDL3PzvAYtsdeA4mfPZUolsrh9mmN3SuNO/ctHmlXXnml2l577TVVUSwbEREREWVOXV0dpkyZku3DoEGOmWDsTERERJSb8bOnEsV77bVXj17EkjiWzLhclmn77ruvWjhP2k5Ij+PBam9dIbchfZbJeziG3scx9D6OofdxDL0vk2MolRAS5EocRt6yu7Gz4PuFt3H8vI9j6H0cQ+/jGHpfbYbHcDDxs6cSxSeccAJuuukmNDU1dfQqvv/++1Vv4SVLlmT8eKS38YEHHrjbtyN/FBMnThyWY6Ls4Bh6H8fQ+ziG3scx9L5MjSErib1puGJnwfcLb+P4eR/H0Ps4ht7HMfS+8Rkcw4HGzzmTKI5Go3j88cfV7+vXr0dLS4taYE4sWrRIVR4sXboUP//5z3Haaafh6quvxubNm1ULCDm/pqYmy4+AiIiIiIiIiIiIyJtyJlEsC5OcffbZXc5rP/3888/jqKOOUj2Kn332WVx++eUqWVxYWIhLLrkE3/3ud7N01ERERERERERERETelzOJYimBdl13l/vNnj0bzzzzDEaDoqIiXHfddeoneRPH0Ps4ht7HMfQ+jqH3cQwpU/i35m0cP+/jGHofx9D7OIbeV5TDY6i5A8nOEhEREREREREREdGopWf7AIiIiIiIiIiIiIgou5goJiIiIiIiIiIiIhrjmCgmIiIiIiIiIiIiGuOYKCYiIiIiIiIiIiIa45gozoIPPvgAxx13HPLz81FdXY2rrroKyWQy24dFfVi9ejWWLl2K+fPnwzRN7LPPPr3u9+tf/xqzZs1CMBjEvHnz8Le//S3jx0o93X///Tj11FMxceJE9ZqTcfzNb36D7ut4cvxy1+OPP45FixahsrISgUAA06ZNwxVXXIHm5uYu+/31r39VYydjKGN51113Ze2YqX/hcFi9JjVNw4oVK7pcxtdibrr77rvVeHXfvvGNb3TZj+NHI4Xxs7cwfvY2xs/ex/h5dGHs7E13ezR+ZqI4wxobG3HMMceowPahhx7CTTfdhDvuuEO9aVNuevfdd/HYY49hxowZmDNnTq/73HvvvfjiF7+Ic889F0888QQOOeQQnH766Xj11VczfrzU1Y9//GPk5eXhRz/6kQqETjjhBDVWN9xwQ8c+HL/c1tDQgIMOOgjLli3D3//+d/V+ec899+Dss8/u2Oell15SYyZjJ2MoY3nxxRfjgQceyOqxU+9uvPFGWJbV43y+FnPfk08+iVdeeaVj+8///M+Oyzh+NFIYP3sP42dvY/zsfYyfRxfGzt72pNfiZ5cy6qabbnLz8/Pd+vr6jvNuv/121zAMd/PmzVk9Nuqdbdsdv59//vnu3nvv3WOfWbNmueedd16X8w455BD3hBNOyMgxUt/q6up6nPfFL37RLSoq6hhbjp/33HHHHVLS0vG+uWTJEvfQQw/tso+M6ezZs7N0hNSX999/X/07uGzZMjWGr732WsdlfC3mrrvuukuNV2/vqe04fjRSGD97D+Nnb2P8PDoxfvYmxs7edZdH42dWFGeYfEOwePFilJWVdZx3zjnnwHEcPPXUU1k9Nuqdrvf/Mvnkk0/w0UcfqXHs7DOf+QyeffZZJBKJET5C6k9FRUWP8xYsWICWlhZEIhGOn0eVl5ern1JdJmP0/PPPd6mQaB/D999/H+vWrcvSUVJvLr/8cjUdec899+xyPl+L3sbxo5HE+Nl7GD97G+Pn0Ynxszcxdh69PsnRMWSiOAv91fbaa68u55WUlGD8+PHqMvKe9nHrPq6zZ89W/wivXbs2S0dGfZFpVhMmTEBhYSHHz0Ns20Y8Hscbb7yhpj6ecsopmDJlCtasWYNUKtXrGAq+t+YOmcr49ttv49prr+1xGV+L3rD33nvDMAzV6/Dmm29Wr0vB8aORxPh59OF7hvcwfvYmxs/exth5dNjbY/GzmZV7HeM91iSw7a60tFT1ESJvjqnoPq4ypoLjmntBrvQBkp5rguPnHXvssQc2b96sfv/Upz6FP/7xj+p3jqE3RKNR1R9PeosWFRX1uJzjmNskIXf99derfoeyCMejjz6Kb37zm+o1edttt3H8aEQxfh59+J7hLYyfvYvxs3cxdva+8R6Nn5koJqIxY9OmTapJ/NFHH42vfOUr2T4cGsLqzTLdURbI+c53voOTTz4ZTz/9dLYPiwZIxmzcuHG48MILs30oNATHH3+82totWbIEoVAIP/nJT3DNNddk9diIiGjkMH72NsbP3sXY2fuO92j8zNYTGSbfDDQ3N/c4X75J6Nx3jbyj/due7uPa/u0QxzU3NDU1qRWbpTfXgw8+2NE7j+PnHXPnzlWrwF5yySV45JFHVF+1hx9+mGPoAevXr1dVSPKNuoyTvB7D4bC6TH7KxnH0HumnJlPnVq5cyfGjEcX4efThe4Y3MH72PsbP3sTYefQ6xwPxMxPFGSa9R7r3+5E/itra2h59Scgb2set+7jKab/fr/rQUHbFYjGcdNJJ6rUmC+IUFxd3XMbx827Q6/P5sHr1akyfPl393tsYCr63Zp/015I+W5/+9KdVQCSbVLQIqVCSRar4WvQ2jh+NJMbPow/fM3If4+fRh/GzdzB2Hhv2ytExZKI4w+Qb2WeeeUZ9I9Tu/vvvV9/OShk6eY+8eGfNmqXGsbP77rsPxx57rHqBU/ZYlqW+tZPVe5988km1CEdnHD9v+ve//60W4JDxCwQCKmCSxR66j6EsBCALdlB2zZ8/X1WwdN5kypVYtmwZfvnLX/K16EHSr1IW5liwYAHHj0YU4+fRh+8ZuY3x8+jE+Nk7GDuPXvd6IH5mj+IMW7p0KX7+85/jtNNOw9VXX62aWF955ZXq/JqammwfHvXRRF56O7VPAWlpaen4B3XRokWorKzEt7/9bXzuc59T38zKP7jywpZ/iF988cUsHz1ddtll+Nvf/qam7sjYvfrqqx2XyZuzBEkcv9x2xhln4IADDlBVENLTadWqVbjlllvUaXkvFd/61rdw1FFHqfGWDzYSTMliHTKWlH2yQIOMT2/2339/7Lfffup3vhZzl/RXO+aYY7Dvvvuq07IYxx133IGvfvWrqK6uVudx/GikMH72HsbP3sb42fsYP3sbY+fR4Xivxs8uZdx7773nHnvssW4oFHKrqqrcr33ta24ikcj2YVEf1q5d68pLpbft+eef79jvzjvvdGfMmOH6/X533333df/6179m9bgpbY899uhz/GRs23H8ctfNN9/szp8/3y0sLHTz8/Pdvffe2/3Wt77lNjc3d9nvkUceUWMnYyhj+etf/zprx0y7Ju+f8jp87bXXupzP12Ju+spXvuLOnDlTxS6BQECNza233uo6jtNlP44fjRTGz97C+NnbGD97H+Pn0Yexs/d8xaPxsyb/y16amoiIiIiIiIiIiIiyjT2KiYiIiIiIiIiIiMY4JoqJiIiIiIiIiIiIxjgmiomIiIiIiIiIiIjGOCaKiYiIiIiIiIiIiMY4JoqJiIiIiIiIiIiIxjgmiomIiIiIiIiIiIjGOCaKiYiIiIiIiIiIiMY4JoqJiIiIiIiIiIiIxjgmiomIhtG3v/1taJrW6/a9731vWO/rggsuwD777IORsHLlSvVYotFol/Pvvvtu9Vh27NgxIvdLRERERGML42ciotxhZvsAiIhGm1AohOeee67H+ZMnT4ZXSKB7/fXX47/+67+Ql5fXcf6nP/1pvPLKKygpKcnq8RERERHR6MH4mYgoNzBRTEQ0zHRdx8EHH4zRqLKyUm1ERERERMOF8TMRUW5g6wkiogw66qijcNJJJ/U4/7bbblOVFM3Nzer0j370Ixx44IEoLi5GVVWVus5HH33U723LVLeCgoIe50v1glzW7rHHHsNxxx2nbreoqAgHHXQQnnzyyS7T4y688EL1uwS1MlVuypQpfU6da2howEUXXYSKigr1GA499FC8+OKLvT7uBx54AHvuuac6zmOOOQZr1qzpsp9ML5wxYwaCwaC678WLF2Pt2rW7fF6JiIiIaHRi/Mz4mYgyh4liIqIRYFlWj02cd955eOqpp1Rw2Nmf/vQnnHjiiSqwFZs2bVLT1h555BHceeedcBxHBZDdrzcUEjiefPLJ+N3vfocHH3wQhx12mLrvf/zjHx3T4775zW+q3yUAlqlyDz/8cK+3Zds2TjjhBPz1r3/F97//fdx///0qiJVA+vXXX+8xHe+WW25RwawEzKtXr8bnP//5jsvvuecefOtb38LFF1+s7lce9/z589HS0rLbj5mIiIiIchvjZ8bPRJR9bD1BRDTMIpEIfD5fj/P/+c9/4qyzzsLll1+uAswvfvGL6vz169erYPLPf/5zx74/+clPugST7RUMUlHwpS99abeOTwLodhJAH3300Xj33Xdxxx13qMoFqUSYPn26unz//fdXlQ59keqK5cuXq8D0+OOPV+fJT6lquOmmm9TjbNfU1IQ333yzY+pdOBxWlRcS1E+cOFHdzty5c/G///u/Hdc59dRTd+uxEhEREVHuY/zM+JmIcgMriomIhplMH3vttdd6bPLtfnl5uQpa77333o7977vvPlVF0HlK3auvvqr2k/1N01QLYkhguKvpcwMhgeX555+PCRMmqNuWoFyqNIZy2xK8y/S79iBXyO2dccYZeOmll7rsK4+/c3+2OXPmdByP2G+//VQgfMUVV6jrplKp3XiUREREROQVjJ8ZPxNRbmBFMRHRCCzGccABB/R5uUyfk0Bz69atqK6uVtPmTj/9dNVXTGzYsAFLlixRt3H77bejpqYGfr9fTWmLx+O7dWxSAXHKKaeoXm433HCDqlzIz8/Htddeq+53sBobG1WlRnfjxo3rMc2v+0rP8phE+2O64IIL0NraqiozpCJEphHK8yRT7eTDAxERERGNToyfGT8TUW5gopiIKMNkOlggEFBT5aSSQHqP3XzzzR2XyzQ0qX546KGHOoJD6dG2q/5qEih3ryKQ03Jb7aSvmVQd/OUvf+kyLS0Wiw3psZSVlWH79u09zt+2bZu6bLAfEL761a+qbfPmzapq5Bvf+Iaauie914iIiIhobGL83DvGz0Q03Nh6gogowwoLC9U0OamEkK19deLOQaesjNy5T5sExe0LevRF+pQlk8kuKyE/99xzqkdb59vuXI3Q3uPt5Zdf7rdaoS+HH364WixDpt61k+OUxTvksqGSaX3/7//9P9Vz7f333x/y7RARERGR9zF+3jXGz0Q0HFhRTEQ0zGR6mvRI606mmE2bNq1j+pz0IZMg8+yzz1a9ztodc8wx6qcsVHHppZeqhTJ+9KMf9Zh61p2snizT4GSRj69//euqd9mtt97aMSVP7LXXXioglkoDCYClWuK6665TgWVns2fPVj9/8Ytf4LTTTlM93vbdd98e9ynT+RYuXKhWX5YpbjJl7uc//zlqa2tx9dVXD+p5k8daWlqKgw8+WP2U4HvVqlW47LLLBnU7REREROQtjJ8ZPxNRbmBFMRHRMJOqg0MOOaTHJqsYtzvxxBNVDzEJCCXo7UwCyrvvvhuvv/56R+WErNYs+/dHFu6QVZJlKpsEp3feeSfuueceNU2vnfwuU/LkpwTY0lvtmmuuwaJFi7rc1oIFC/Dtb38bv//973HooYfi5JNP7vU+DcPA448/rgLeK6+8EmeeeWZHhYSs+DwYcj+yCMfFF1+MT33qU/jDH/6geq3JaSIiIiIavRg/M34motygua7rZvsgiIiIiIiIiIiIiCh7WFFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxEREREREREREQ0xjFRTERERERERERERDTGMVFMRERERERERERENMYxUUxERLj77ruhaZrapkyZAq+QY20/bnkMRERERESZiIfXrVvXsb9scpqIyOuYKCYi6iNA7LwFg0FMnjwZZ5xxBp5++ulsHyYRERER0Zj18ccf94jXf//732f7sIiIPI+JYiKiAUgkEti4cSMefvhhLFmyhNWrRERERERZ8pvf/GZA5xER0eAwUUxE1I/7778f//jHP/DLX/4ShYWFHefffPPNWT2uXJdKpZBMJrN9GEREREQ0yti2jXvuuafH+RKzs/0DEdHuYaKYiKgfBxxwABYtWoQvf/nL+MIXvtBx/vr167vsZ1kWli1bhiOOOAKlpaXw+Xyorq7GySefjCeeeKLX6XIXXHABpk6dqtpahEIhTJw4EUcffTSuuuoqRKPRLvuvXr0al112GWbNmqX2zc/Px3777Ycf/OAHqtq5v/5qUgl9/vnno7KyUt3XgQceiGeffbbfx71161Z1fHIdub9DDjmkR8sNCcY7T/erra1V16mqqkIgEMB7773Xse+f/vQnVYkttyfPTUVFBRYvXow//vGPvd7/jh07cO2112L//fdHcXGxur1Jkybh9NNPx8qVK7ErP/vZzzqOS47/b3/72y6vQ0RERES57+9//zu2bNmifpd4+4QTTlC/u66Lu+66q9frPPnkk6qFnMTbEldKvC6x9PXXX7/L+9u2bRv23nvvjtjyU5/6VI9YvTcDjd9/9atfddz2woULe9zO5Zdf3nH5RRddtMv7JSLaLS4REXW46667XHlrbN/Wrl3bcdl//ud/dpw/c+bMjvOj0ah75JFHdrle9+1rX/tax/719fVuRUVFv/vX1tZ27P/YY4+5eXl5fe67cOFCt7W1tdfHUFRU5I4bN67HdYLBoLt+/fper1NWVuZOmTKlx3UMw3Affvjhjus8//zzXS6X56Tz6TfffNO1bds999xz+32scrns106uV1VV1ef+cqzt9thjjx7n/+QnP+k4r7Cw0P3HP/4xjH8hRERERJRNZ511Vkesd8UVV7h//vOfO05LbOg4Tse+8vull17aZ1wp+/cWD7efLzH5Xnvt1XG+3HcikVCXyeeEvj43DCZ+j0QibmlpacdlK1eu7LgdiZHHjx/fcdlLL72UkeeYiMYuVhQTEfVjxYoVeOGFF3DHHXfgd7/7Xcf5X/va1zp+v+666/Diiy+q36Vi93vf+x4ef/xxfPGLX+zY54c//KE6Tzz33HOqYlbMnTsXDz30EJ555hl1+1deeSXmzJmjKgaE7Hfeeed1VC185jOfwWOPPYYHHnhAVSSI5cuXq+v1pqWlRVUvSEWvVFhIda6Ix+OqAro3DQ0N6nHcd999+POf/6yOp32a39KlS9V1e7NhwwbccMMNqspDni+pGpb7kNsRuq7jG9/4hqqwlp9yWsjl7cci7SrOPPNMbN++XZ0uKSnBTTfdpKpAZIGSz33uc6oiuS8//vGP8T//8z/qd7l/ea6lIpyIiIiIvK++vh6PPvpox+n/+I//wCmnnKJixvZZf51nzknf4ttvv73j9PHHH497771XxeVS2Ttjxow+70tmyx111FH44IMP1Gmp5pXr+v3+fo9xsPF7Xl4eLr744i4Vxu3++c9/quMQe+65Jw477LABP1dEREOS7Uw1EVEuVxR336ZNm+b+6U9/6lKl0Lk6+Fvf+laX2zv00EO7VCCIp59+uuO8Y4891n3nnXc6KhO6u+222zr2nTx5svvPf/6zY7v99ts7LguFQm48Hu/1MSxfvrzj9pYuXdpx/hlnnNHn45Zjaie/d77siSee6LWi+Gc/+1mP499///07Lv+P//iPLpd99rOf7bjsgAMO6Ki+6Hybzz33XL/j1bmi+JBDDun4fcKECe57773X73WJiIiIyFtuvfXWjnhvn3326Tj/S1/6Usf5EmO2O/DAAzvOX7RoUb+33TkeLi8vd2fNmtWlcrm7viqKhxK/y3V1XVfnl5SUqBmL4rLLLuvY//vf//6wPY9ERH1hRTER0SBIlULn3rt1dXUd1cHi8MMP77L/kUce2fF7+/Vkn/nz56vfpeJhn332UZUEUtHw+c9/XlXBtnv33Xe7VOxKD+T27dJLL+24LBaLqT5o3ckCfNKTuF15eXmXyuHeSM826cPWTn5vr9IQH330Ua/Xk0rg7jo/VwN5bjo/XjlW6dk8UK+88or6aRiGqmqePXv2gK9LRERERLmvcw9iqSZu13ktkYcffhjNzc09YsuzzjprUJXL7THvueeeix/96EcDvu5Q4ndZV0TWNhFNTU1qQW3HcfDggw+q80zT7PIYiYhGChPFRET9WLt2rWrfIAurtbdfuPHGG1Urh6GStg7SquLnP/85TjvtNJXQlOBvzZo1+MMf/oBjjz22Y/G19hYUAxEOh3ucV1ZW1uW03E87WfBjOI0fPx7ZJAni9jH6yle+ooJvIiIiIhod3nzzzS6LGn/961/vWOStc0GCxIC7E6t3jivbE8+DWRh5qPG7LFrXuf2EtL+ThfTEpz/9abVwHxHRSGOimIhoF6QqV1ZEPuaYYzrOu+qqq9RqxZWVlaoXbruXX365y3Wlr1i79gpXSdDKbf7Xf/2XCjylmjYSieD73/9+x76SMO58HSG9giUJKtfvvkmQedBBBw3L421sbMT777/fcVqOTyob2s2cOXPAQXHn4x/Ic9PeD7m9kkMC5O76SnBLr+jJkyer36UqW5LwnVeUJiIiIiLvkn7Dg923c2zZXp07kLhy4sSJKv5vX0NDqpFlzYyBGGr8LsUi7cf70ksv4Tvf+U7HZdIfmYgoE3aWlhERUb+kkri9LcSmTZvUwhhSuXrBBReoxerELbfcgoKCAuy77774y1/+0iU52h7gyeIV8vupp56qAkmpDkilUl0Sp+3VsOeccw6uvvpqtLa2qoStVBNceOGFqi3D1q1b1XS1p59+WiVvO0/F210SDH/7299WyV/52a6qqmpQ7SDkcb7xxhsdyW8JuqXlhFRUd670aH9ujjvuOEydOlVVcovTTz9dVYssWLBAJY6lpcTixYtVi47uJk2apBYFlGl9Un3x1FNP4YwzzlDJ+F0tOkJEREREuUu+/P/jH//YcVpaOLS3cmsn8bIUc4jXXntNtYD40pe+pBanFv/4xz9ULH3++eejqKhIFUbIInMSP/ZGZhRKscRPfvITdf8Sl/71r39VsWh/did+l0KSyy67TP3e/rlDPiuceOKJQ3reiIgGrc/uxUREY1D3Rd3aF6Vod8wxx3RcNn78eLXQRCQScY888sh+F8H72te+1nEbr7zySr/7aprm/uUvf+nY/5FHHlGLXfR3nfPPP7/XxyCLvXV23XXX9bqgR+frFBcXu9XV1T3uQxbYePDBBzuu030xu97Ytu2ec845/R77ueeeq/Zr9/rrr3dZILD7Jsfa22J27eevWrXKLS0t7Tj/lFNOcZPJ5KD/FoiIiIgoN9x3330dsZ1pmu6OHTt63W/OnDldFqCThacvvvjiPuPKzrFybzF09+tLTN6+2HJfi9kNJX5vFw6HVSzeeb+vf/3rI/CMEhH1jq0niIgGob1XsaitrcX//d//qYXoZFG6X/7ylzjssMNQXFysegFL9e1JJ52Exx9/XFUat5NF6771rW+p6WVSBRsKhdT+Ui0gi1hI1axUG7c75ZRTsGrVKnz5y1/GrFmzVI9juc9p06ZhyZIlqsrhhhtuGLbHKAvXvfrqq2rhDulxHAgE1LQ4qbiQCt3B0HUd9913n6omluoLuT3p+SY/5fH//ve/x7333qv2a7fffvupChCpxJBKEanQlopgqUaW56V79Uh3c+fOVc95fn6+Ov3oo4/iM5/5DCzLGuIzQkRERETZ1LnyVma3dV6gua/FlSXOlPjvzjvvVJXAEkfKmho+n0/F6zJjTSp9+yOz6+644w5VJdw+60/i9c4zAXsz1Phd4tfubSbYdoKIMkmTbHFG75GIiIiIiIiIiHqQJLS0ahOySN+uktJERMOJPYqJiIiIiIiIiLJIFrdrbm7Grbfe2nGeVCQTEWUSK4qJiIiIiIiIiLJI2lx0tv/+++Pf//63attGRJQp7FFMRERERERERJQDKioq8LnPfQ5/+9vfmCQmooxj6wkiIiIiIiIioiziZG8iygWsKCYiIiIiIiIiIiIa45goJiIiIiIiIiIiIhrj2HpiN8Tjcbz99tuorKyEafKpJCIiIhpplmWhrq4O++67L4LBYLYPhwaBsTMRERFRbsfPjNB2gwS6CxcuzPZhEBEREY05y5cvx4EHHpjtw6BBYOxMRERElNvxMxPFu0GqIdqf6PHjxw/6+rZto76+HuXl5VzN1KM4ht7HMfQ+jqH3cQy9L5NjWFtbq5KN7XEYjZ3YWfD9wts4ft7HMfQ+jqH3cQy9z87wGA4mfmaieDe0T5mTQHfixIlD+sMIBAJqoPji9iaOofdxDL2PY+h9HEPvy8YYsnXB2IudBd8vvI3j530cQ+/jGHofx9D77CyN4UDiZy5mR0RERERERERERDTGMVE8BLfccguqqqowb968bB8KERERERERERER0W7jnL0huPLKK9W2adMmTJo0KduHQ0RERBnW0tKC5uZmNW2MANd1EY/HEYvFoGnasNymTMMrLi5GUVHRsNweEREREWUP4+eRjZ2HK35mopiIiIhokEHu5s2bVY8vn8+X7cPJCRLgSp+14Qx0JXgOh8PqdyaLiYiIiLyL8fPIx87DFT8zUUxEREQ0CFIJIUHutGnTuIBIp6oIy7LU8zJcAa9Um3zyySfq+WaimIiIiMi7GD+PfOw8XPEzexQTERERDTIAk0oIBrkjS55feZ45PZGIiIjI2xg/eyd+ZqKYiIiIiIiIiIiIaIxjopiIiIjIw2S62vz58zFv3jzMnTsXjz766LDe/t13343Pf/7zw3qbRERERETZwvi5b+xRTERERORxK1asUD3OHn/8cZx77rlobGxUp4mIiIiIqCfGz71jRTERERHRbixEkbDsEd/kfgbi6KOPVisdNzQ0qNM/+MEPsHDhQlUxIZetXbtWnb9u3TpMmDABX//617FgwQLMmTMH//73vztuR86fOXMmDj74YLz66qsd58uiG//93/+NffbZR2033HBDx2WLFy/GlVdeicMOOwyTJ0/Gn/70J3zve9/Dfvvtp/b98MMPh/GZJyIiIiIvykT8PNDYOdvx89FHH51z8TNT5URERERDlLQd7PnNJ0f8fj78zqcQMHe9+MfDDz+MRYsWoaqqSp2+6KKLcNVVV6nf//jHP6oA9s9//rM6vWXLFhxzzDH4/ve/j9///ve4+uqr8eyzz6qpd88//zzefvtttd+RRx6JWbNmqd9vv/12fPTRR1i5ciWSyaS6TILok08+WV1eX1+Pl19+WVVoyHH84he/wBtvvKECbrmf3/zmNyP2HBERERFR7stE/DzQ2Dlb8fO8efNw4oknqst37NiRU/EzE8VEREREHnfAAQegublZBa8SrLaTKoebb74ZTU1NcBxHbe3Ky8tx/PHHq98POeQQfPOb31S/S5Ar0++CwaA6/bnPfQ6vvfaa+l1uW4JnmZYnm/Rek/PaE8Vnnnmm+ilVFtFoFGeffbY6vf/++6tpfUREREREuSDb8fOJbYnis846K6fiZyaKiYiIiEZJjzWpPJDAVKapySIdn/3sZ/Gvf/0Le++9t6pMOOOMMzqu0x7ICsMw1LS4dnLdoQgEAh231/1059snIiIiIsomxs+9Y6KYiIiIaIj8hq6mtmXifgZCepw98MADuOOOO3D++efDtm3U1NSoPm3Lli0b0G1Ir7SbbroJl112mQp4pVfajBkzOvoQyyrOp512GlKpFP7whz/gW9/61m49NiIiIiIaOzIRPw80ds5W/PzNtkrkXMREsYe8+fF61FQUY1xpSbYPhYiIiNoqBwba/yxTx/PDH/5QVUJceumluOaaa9Q0toqKCpx00kkDuo1TTjlF9Unbd9991fQ66UEsC3yIL33pS6raQs4T55xzjtp/MAuGEGXKxxu3oT4cw5SaavgMDaahqw+O7b8TERFR5jF+Pkftn6uz7TSXkf2Qbdq0CZMmTcLGjRsxceLEQV9fvqWoq6tDZWVlR4l5f/78/OuYUl2KhbOnDfGIabgNdgwp93AMvY9j6H1eG0NZ8VhMmTIl24eSMySclGBXpu8NddrdQJ/r3Y2/KHuGY+wG+n7xl1c/wI+2hjGtMYyjJhdjyoQJ8JkaTC2dJPYZOvL8BoI+Q/3enjwuCJgw9OH7GyZvv99TTxxD7+MYep8Xx5Dxc2Zi5+GIn1lR7LGVIaOJVLYPg4iIiIgoZ73x0TrcVBfBhmITHxeXwP2oDuH3mnDQjHHYZ0IJyvL9iKVsNEaTcFwXUjajaxr8poaCgA/lBX4Uh3wqaTzcH96IiIiIchkTxR7iOC6aw7FsHwYRERERUc6aUFmCwre3yzIw6vRTsyqx+N1a/N8La9XpcUUBzJtYgnmTSjBvYjHKCwKwHRcp20FLPIUd4YSqNi7O86EiP4CikE9VHhMRERGNdmM2UbxkyRJs27ZN/V5dXY0777xTlWHnutZYqqM8nYiIiIiIuqooLsYSn4F4s4U1xemY+bk51TjK2oR/bdWxrSWBp97bpjYxsTSkEscHTinFwqll6rxI0sa25gS2NSVQEDRVFXJpvh9FQZP9jYmIiGjUGrNRzv33349Vq1ap7cQTT8TXv/51eEHSshGJJbN9GEREREREOUl6DH/9lP3xgxllmBi21XmOpuHFfSdiYUXPNm6bGmN47O1afPuv7+H+1zepdhPSdqK6OIiKQj8sx8G6+gje2dyMd7Y0Y2NDVFUec6kXIiIiGm08lShevXo1li5dqlYKlIraffbZp9f9PvjgAxx33HHIz89X1cJXXXUVksmuydXi4uKO31taWuAVCdtBOB7P9mEQERER0Rhz9dVXY+rUqSqRKnF5rttrygRciDiqYo46bekals/bAydOsnHo5BCmVeShewfiP/57A+paEx2npXq4JM+PmpIQikImWuMWPtzainc2NePjbWGEE7m5YjkRERHRqE8Uv/vuu3jssccwY8YMzJkzp9d9Ghsbccwxx6jE8EMPPYSbbroJd9xxB6644ooe+5577rkYP348/vznP+NHP/oRcp0LF5btsqKYiIiIiDLupJNOwosvvog99tgDXjFn5kx8pqkOJcl0sjhuanh65iRMMqP43D55+PV/zMU3Pz0b1UVBdXnCcvCbl9K9jLsLmAYqCgKoKQnCZ+rY2BjFe20VxkkrfftEREREXuapRPHJJ5+MjRs34oEHHsB+++3X6z7Lli1TFcIPP/wwjj/+eFx00UX4wQ9+oM7fsmVLl33vu+8+dd7555+PG2+8EbmuJWahMZbEh1sa8UldWAWlW5vjqA8n0BxLIZKwVJAqi94REREREQ3njLxDDz3UE2t6dHfA7Bk4beMW5FnpGLnVp+Gvk2rw8eY6rFm3GXuPC+JLR07r2P8fH9WpNhN96dyaQoo4Pt7WivdrW1QlMuNwIiIi8jJPJYp1fdeH+8QTT2Dx4sUoK0svRCHOOeccOI6Dp556qtdA75JLLsE999yDXCeBZyLloK41pvqkfbStFe9uacZbm2RrUtuqjU1YubEJ72xuwvYWtqggIiIazdasWYMpU6aoeKayslIlA4eLfMkus7imT5+uWg705eWXX8bcuXMxe/ZsnH766Whtbe247C9/+Qv23HNPdTsSb8mCvGL79u049thjVSuwww8/fNiOmUZ+Rp4X+QJ5GF9RitM+2YSAnU7kNgR1PFk9Hmtqd+DVdz5Bud/GPjVFHddZ9sIa2LtI+uqapha4qygMoDGaxAdbW/Dx9la0xnv2QSYiIqLckCvx84IFCzBz5kycdtppORU/p5cBHkWkGkKqiDsrKSlRLSbkMiEVxzIIEyZM6FjYrq/qis7kep37GdfW1qqftm2rbbDkOpLAHuh1Nbhq88HGuEL/zttxXNiO/HRgOzaSKRc7Wi3AdVGe7xv0cdHIjSHlHo6h93EMvc9rYygLWElgKT9lSzkjnxTy6T51n91J9afEOZLck2Cz/fiGI4D+3ve+hzfeeEPdx5FHHqm+cJeK085k3L7whS/gj3/8o7p/qUK9+eab8d3vflfFTJdddpkKhKVVwVlnnYXf/OY3+OIXv4hgMIhrr71W7fP973+/32OWyzr/bXjl7ySXZuSdeuqp6vcLLrgAK1as6HdGXnuxhXwokfGTDzk1NTXwooDPwLjigCq2mDq+DOF4CmevrcWfpo2HrWvYmmfg+apqLK6vg2loOG3fcrxX2wLJD6+pi+Dp97bhU/tU7/J+fIaOcUVBRJMWNjbG0BRNYXxxEOOKg6pdBREREe2Uifi5r9hZFBQUqPhZ4p72+Hk4rGmLn998800VPx9xxBF4+umne42fpbPB73//exx88MHqi3m5Xuf4+V//+peKn88880zcddddKn4OhUK47rrr1D6y/0gZdYliqYiQAe+utLQUDQ0N6vfm5mb1ZMdiMfWHM3nyZDVAu/LjH/8Y119/fY/z6+vrEQgEBn2s8schxzLQaukgUig2bVjxMKLNjb3uI7ciKeREykJzQxTbjXifLw7afYMdQ8o9HEPv4xh6n9fGMB6Pq3/3JZGWtJM4+L6DR/w+Xz33VfiNnV8St5PKAvl3XmKf9mqD4SBfop9xxhkoLCxUpyUZLGs6HH300V32e+2119R9S5AtCVz5sl5iLImXpIpVgt+JEyd2XHbrrbfiwgsvVIGutDF44YUX1IeFvo5d/jYSiQTq6uq6xF2UmRl50rJCviCQBLMXSXuIOePTC1hPLQuhpSWM4pAf523Yjj/sUQVX07C+0MRLdgU+FUm/B+0/IQ+vbYqq3+/+1zocPrNC3c5A5PlNhHyGagm3ensYjZIwLgmiIj8AXWc8TkREJCRJvP/v9x/R+3j986/3GjsLyQ1KfFRRUTGs9/nggw+qwgjJPwqJeSWm7p4oli/tZZ8DDjhAnb700ktxyimnqETxk08+qWJkqXgWX/rSl1QuUhLFEpdL8cY//vEPjKRRlygeCOmttnz58kFfT7L8UvbduaJ44cKFKC8vV+Xqg9VeESN/nIax62qDOHxotl0EbBNmqAB+f9/Vwk7cggYNxWVFrGQYQYMdQ8o9HEPv4xh6n9fGsP2LZun36miZWcBK7ss0eoZt++67L8477zzMmjULRUVF+MlPfqKSut3dcMMNqmqiN5LQ7V4xunnzZnWbcr9CgtW//e1vHafbyVoPcln7+dOmTVPXldPdL5s6dSo2bdrU5TZkvNufy95IEC/Vx53jLEkcU+Zn5A3FcM/G250ZCEG/iWnjSrB8TS0W7lGGxPp63D8l/QHxwxIfAnYRjom1YFoh8LYBxG2ohO8vnnoHFx48CcXFBfD18XfaXVHQQJ5PR0M4jqZIApWFflQVBjHQXLGmaygMmKOy2MNrM0ioJ46h93EMvc+LY9h9Rl4m7q+v+9lrr73wmc98RsWmwxk/b9iwQcXP7fcrucdHHnmkx3HIflKw2n6Msp/EyPJ758vab0PWaut8G+2/j9SMvFGXKJasfHtVUvdK485VEkMhf0CydScfcob6wVY+AA30+tJ4QoeGlOUinrLUB6e++EwgnrLhIH37NHIGM4aUmziG3scx9D4vjWF78kZ+ZiqR09d9rVq1Cj/72c9UsCrVCn1Vj8o0NdkGe3+dH2vnn53362v/gdxGX7fb/T46/1144W9kNM7IE9JaRNqMbN26VU2nlC8CXnnllYzNxtvdGQjleTrK/S6ikTCOnpyHxLoGPDol/fngrXI/gtsK8KmCMI7ew4cnPklPiX3xk1ZMz/sYNYUmCvOCyA8FUJAXQl4/cXi7AgDJlIONWyxsGURFsalrqCoKoCx/aM9RLvPaDBLqiWPofRxD7/PiGHaekWfZwzcLri9yP7rb+3PTHj9LEldmVMlz2NvstquvvrrfPsPdryPJWRmb9vMlOdvbzLn289uTt+2Xy0+5fufryM/ut9HX7Q7XjLxRlyiWbwa6Vz7IC0gqGOSy4XDLLbeoLRvf3si0NVmwORqLo6RQws/eSZ81K+EiZTnA6IsxiYiIcoL0P5OpbZm4n95IWwBZAOP444/v9/pSESF9jHvz+OOP96iIkOqF9evXd5yW6gY5r7ve9pNWE+2XdU4i9nUb5B0/+MEP1Jat2Xi7OwNB7nN9fQTvbGpEQUEAx03xI76xBU9NSheCLB8XRMW6GBZUh/DG9lZsC0vRBfDMRgcnTtOwNRaHricRMiMoDPmRHxxYkC0FP5oOFBQWw9fHNNjOIikL0bABX3EeaopCCOiZ+1JqpHltBgn1xDH0Po6h93lxDDvPyJNjXvG5nmsmZKpH8XPPPafWbzjxxBP7vY0bBllRLJXAUv3bPlNOZtlJ7Nt95px80S77yfPQPgtP4mf5XfoSSweE9uvIZXK7mZyRN+oSxSeccIJaqbmpqamjMkJ6gsgTtWTJkmG5jyuvvFJtUhqe6Q88hgZY6tuB1C4rESzbQUpWuSMiIqIRIUFaX/3PMmH27Nm49957u8Q9vZGF42QbKOlPLBUW11xzjVqMQxbRuPHGG3vsJ73VpOJUehVLn+Lbb79d9WYTn/rUp/DVr34V69atUwHxHXfc0XEZjY0ZeSMxG293ZyBMnzgOa+rCaEmkUJIfxMmT8pHYEsELNfnq8qcnl2Dypq04fkYh7lmZfk7WN9uoT5mYVRFAMmUhlrSxvSUOpyk+8DvWAH9jDPn5eQgGC/ptQyGJ5UhrAtvCCUysLkRFcRBFIR/yTRNBXUNQ1z2dPPbSDBLqHcfQ+ziG3ue1Mew+6yygZ6+icc6cObjvvvtU7NNf/HzdIGfkyTodEj9/85vfVPHz3XffreLn7v9eH3jggSp+fv3119V6Hu0xsuwnOc3//u//VoUYEj//6le/6rgsUzPyPJUojkajqupFyJMmPc8eeOABdXrRokUqWy6Lbvz85z9X1TVSIi4ZfEnqyvleXbG5MwkqXVdDwuo/Uaz+YDQgyUQxERHRqHXSSSepmVSysIUEgJKsXbZsGfz+3Utez5gxQ8VPkgiWqW2ysFn7F+6PPvqo2u688071IeW3v/2t6m8rUwol8G5fIFgShLfddpu6nlS+SKwmi3q0k+A3EomoeE6qKKT6VDYanTPycsUe48pRXVyLdTtaVaLYMA2cPt6HtWEbGwoMpHQNrwVLcXqxjjmVAbxXl67AeXp1GNPK/PD7TLUNlmNbiMcSsFrjcJw4CopLkB/IV2uK9Ma2bDS0JFC/qQWp1iS2hkzoQROBoIlgwFSJ4mLDQMjUUeHzwcfF8oiIiDwTP999991q1pVU+uZa/Ky5megiPUykIkWaTffm+eefx1FHHaV+f//993H55ZfjX//6l1oVUJpSy+qBuzvo3bVXFEvJePs0y8GQQZeeIZLgHkh2/9aHX0I4kVK9imdUl2DentP63X9LUwxTK/IxrbLvFhW0ewY7hpR7OIbexzH0Pq+NocQjon014lx6Ho8++mhV+XDsscdm9L7b+6TJFLjhrHLs7bne3fhrLLvgggvUStvvvPNOl/NvvvlmNSNPntP2yhr5ICOFFtIyZLiKLYZj7Ibj/eKtNRvw4jubUJzvQ34g3dZl1dptuH3SuI59/uOTWlSXl+D/ljdAOrmJY6bl49DJedgdVsJCPBGDETBRWJCPoqISBI3e+x3b0oMylkJpwERV0A/ddZHyG0gFdFgBA5apQTd1zMoPYVIwezMbRvP7PfXEMfQ+jqH3eXEMGT9nJnYejvjZUxXF8iAHkteWaZjPPPPMiB1HNnsUC0MHWuO77i/iM3REkyPfJJyIiIiyrz32kSoGolyakZft2Lm7mROr8e767ahvTXQkiudNHYeDN8XwanVInX62ugoX2VGVGH5xXVSd99L6KPYdF0BhYOgfys2AiXx/AZKJJBobmhGJR1FUUISi/CL49K7JXkPXURjyoTFmwQw4qMwPIGC58EcsIGpD9xto8AHbEy7G5TkwPNCKQv4GnEgKtj8h82BH5D60oAl9N8aIiIjGDsbPHk8U54ps9ihuDxrjCQu2ZcHoo3l1e5/ihCWrLrpqETwiIiIavWRBu10takdjz/bt23H22Wd3Oa/9dPuMPOlR/Oyzz6oZeZIslhl5Mh1SZuSNhti5u1DAj6nVpdjaXIuUZcHXFk8fG4hhpRVE3NSwJc/AqnUpHDIpH6tq42hOOEjaLp77JIJTZ/fsuzwYqjdjMAC/z6faUdTH6xGJRVBcVIyCQBEMWfmujU/XkRcwsaM1AVPTUFEQAAIBuLYDJ+kgGLPQ4CbR4Euh2AMr30uVdCoaQzIeUZ9pRoIWMKDn+WAU+NRPjZ+DiIioD4yfe2Ki2INMHWqhungqpRa16HM/I72gnfQpDur8Vp2IiIhorMmVGXm5ZuaEKry/YQcaI0lUFafj6QmVZTh+dQMemZJewO+pSaWYXleHxdML8OB7Leq8t7clcMCEFCYUpSuRd4dm6AgVhGAnbSTCSdTFtyFcEEZxQTFMo1OML2uUaBY2NseRdIMoCbXdtwloJhBxgNaAgbJg9hYGGjDHhq75YRQHYIzA5xP5W3cTNuzGOOyWJIyQAb0wAD3PVBXYRERE1D8mij1IgqqkZSMajSM/lJ4e11/riZQkin0MjIiIiIiIRHlxISZXFGLl+h1wbAe69HYDcOTkfKyI2NicbyBhaHhFK8RJlTr2KPFhfVN6Mem/fxzGhfuVDFtPQcMvi9IFVcI42hRBLBaR5ex77BdPOahvgEoUBzrF9mEYCOsmYv4QCswQ/IYsbudXi+XsDinEDRb44AvuflI8U2RMpPUEgibclAM7ZsEKh2EEDej5flVlLJezypiIiKh3TBR7kM/QEE25iCfSwWp/rSdk8Y2U7Zn1ComIiIiIMmLGhCqs3taE5mgCpYXp4ouQP4BPbdiKX+dXq9P/HhfCnLW1WDKjGHeuaIRE1VtaLby1LYF51b0vQjcUkrg0gyYMu0AljHsrApd0bSxlI5rS4Q/51OxBEXBt1LtJrGkJo1B9BjDh130ImEEE9EBb4tgHXRtc4YjjOIiFLYQKUwgVBWB6rPBE8+kwfH64jgs3bsGqj8FuSUAP+WAUtrWlkKmaRERE1IGJYg8uyCEJYNt2kUgl+9/P0GHbjqooJiIiIiKinfYYX4Ga4k34eFsrSiXD2mb/GdVYtSWOFVXpRPCzlVU4HzHsVxPE61vi6rznP4lgrwo/AsOcaNQMDWaon9ZyroFo0kYMDkqDARiGhgB8SLiAH3ko0x1YtoWkk0Kr3YhWV4PpmvA7PoR8IfgM36DaOEQSDnaEXZgNOoKFJgL5JgaZb+6RfG5JhJGXkkUBd6/X82CS8FqeD8jzwUnasMPJtoSxCc0c+IPRQgZ85X3P5iQiIhoN+BXqEMhiHLIwyKpVq7Jy/7paIdhFPGUNaP+klBUTERHRqLNmzRrVg1amW1dWVmL+/PnDdtvLli3DjBkzMH36dFx99dV97vfyyy9j7ty5qsft6aefjtbW1o6E0AknnICKigpMnDhx2I6LvEcKLKqqqjBv3jzkmmkTKuE3DYRjXQswjnBa4G+blbehwMBbdQksmpqPoJmu4g0nHZUsHkj/5+Ekr/U8U0fMctAcT8Fpu3tJXzbJYnGuDr/hR4EvHyXBEhQHi1R1cQoW6mMN2BreNuBtW2Q7dlg70Igd2NayHes2bMHqdRuwbutGbGzeiE0tmwa9bW7djLpoHTa1bkbciiHTpE+xURRQfYtdy4UdSw1siyRhNyXgxAf2+YuIiHJXrsTPCxYswMyZM9VCwrkUPzNR7FE6NEQTiV3uJ3/4CSs7lc9EREQ0sgoKClBSUoI33ngDdXV1WLly5bAF0N/73vfw2muv4f3338ezzz6Lp59+usd+Esx+4QtfwK9//Wu1nwTdcr32GOSKK67o9Xo0tmS7yKI/02oqUV7kR1O0a6J4QlU5Fm+U1Gva05PKEI+GcdTU/I7zVmyJ48mPw3AynSw2dARNHZGEhZa2BLckiqOqX3G3faGpxHG+mYeSYPGQttJQCcpLSlBWUoJ8twj+1gIEI8UoQgnKgmUoC5UPeCsNlqHQX4hwshVbI9tgO9lJvErltp7vg1HgH9CmF/rhtlUjExGRt+VC/Hz++efjV7/6FT7++GNMnTo1p+JnJoo9ytA1RGP99ygW0rtMpqcRERHR8JNqQlsWTBrhra+qxYaGBrVglVQdDKcHH3wQZ511FkpLS+H3+3HhhRfi/vvv77HfihUr1D4HHnigOn3ppZd27CeB7nHHHacuJ8pVoYAfM8eXq97DydTO2Nqna9inEBgXTc/Mi5oa/m3nYb/xQUwq3tm+QVpRPPRuC6wMrwliGDr8po5w3EJLNAWZbyhH2jKCh6EbgD9fVwvDpcI2ojssxBos2MnBzV7UNB1FgWI0xOqxI7ZD3kjhiUXy/Aac1qRqX0FERLkdP/c34ydX4ucDDjggJ+Nn9ij2KOlHJr2HE4kEAoFAv/2MpfWE7bgquUxERETDx7FcLLv8HyN+P0t/fhQMX89/x6Xdw3nnnacqEYqKivDTn/5UVfh2d8MNN+Chhx7q9bYff/xx1NTUdDlv48aNmDVrVsfpyZMn45FHHulxXdlvjz326LLfpk2bBv34iLJpek0l3lm3HQ3hFKpL00lgn6kjPxTCsbXb8Mfp49V5L4/Pw17ranHe3DI8+G4L1jSkq0s/2JHEH99qxjn7FCHoy1wdjs9I35e0oLBdF4GgD626pvoVB0Yw7DdMwCg0YCddxFtspGIOfAU6zAE8dsd1YMclsazDsP3YXLcNKDBQFCzu8zryodk0NRg+Q/UbzhYtaMJpScCJplQLCyIiyt34ua/YWTB+7h8TxR5czE4YmgbLchCJJ/tPFBs6LMdVSWVDygCIiIho1JCp/Lfeeisee+wxVX0g1RG9ufbaa9VGRD2VFhVg2vhSvLZmq4rvDbUeCBAwDUwqK8D8HQmsrEjH28+VVeILbkIlhR/7sBVvbUu3gtvQnMI9K5tw3txiFAaMjCaL5WNwa9xC0HHghPwImzr6/nQwfAy/Bt1nwE64iDfK56JdfzaSCq+Ea8FpsaBpPkRSEayv34qqfAc+w9/rdSQ3bJgGdBMIBE2YAROmT4PhN/p8zxsJKklt6rCbE6odhTbMCxkSEVFmMH7uHxPFQ+yzJptk/CdNmpSVY/AbOlotG3HVp7jTMs29VBRLj+Kk7SDoY6KYiIhoNHnqqafUAhjHH398v/sNtiJC4pv169d3nN6wYUOvMU9v+3HhOvKi6TUV+HDTDjRFkigvCnW0cJNFpA+PNOIdZxwsXcPaIhNvf9KI/SYU4+S9ClHg1/GvjelF2bZHbNz9RhM+O68Y5XmZ+5glhSF5AGIpB1GkUBcyUWYa0DJQfCv3YQY1mMGBfc6QRLGT0hGQ6mBNgw8FaIm3IOprwbi8cTAkG9yN47hwZIZkykFrLAEXCZimDt3U4A8Y8Eni2C8VzZJMHtnkrR4y0+0npN1HUSbS8URENNwYP/ePiWKPkuBIVuCNJ5K7rDJIWS5S1uB6hxEREdGuSaJCprZl4n76mjp37733oqmpSS3K0ZfBVkScccYZWLx4Ma655hrk5+fjrrvuwo033thjP+mtJn3eZNEOWbn59ttvV73ZiHJtNt6uTKgsw5TKIry9qRGltgPd0OEzNFV0UVFWgmM3tuDve6TbIzw1qQLTmxtQXFiIY6YXIN+v4+k1EXVZc8JRyeLPzC3GhKKdvYxHmhxvnqYhlrKxyXVRHnRRGcj9j3o6dBT4C9AUb1aL7pUHK1QCucs+uqZaPZht7R5UslkSx5aLSHMKrpuAYepqk2SxNsKFvm4kBcQs6BWhrLbCyBbHsdHSEoOebIWe7RmrGhAq8COvyN/j74aIxnb83FfsnEvx84oVK3DwwQfnXPyc+9ED9UqmxLnQEO+06Eav++maWok5leEFNoiIiMYC+WDaV/+zTDjppJPwwQcf4Mgjj1SxgSRrly1bphbQ2B0zZsxQs6ckkJWkzDnnnIMlS5aoyx599FG13XnnnWqq3m9/+1tcdNFFiMfjmDNnDn7/+9933M4RRxyB1atXqxWlpVJCbufHP/7xbj9u8pZcmI03ELP3GI91dS1oCMdRUZwHn2moWFrauO1f7OL1uIMdQR1hn4bliSCOa5vUd9CkPFVZ/MgHrXBcySG6+P3KJpy5dxFmlGeu6lSSlqV+A1stB5siSfgdF0Uhn2pNkctM3UTQ9KMh3oCAEUChv2gA77sGDB/gD6UTx67jwko5SERTamHCEWU7QH08vXpgaOx9nHYcB/FYChE3BV3P7pc/rgNEm5NIxoIoqgipLwuIKPcxftZx991345JLLlHrjuVa/Ky5/S0FSP1qD3alEfVQysSlqkIGvrKysqMXWn9uffglhBMpjCvJV6e3NUYxrboY8/ea3u/1aptjmFlViEllMimNhtNgx5ByD8fQ+ziG3ue1MVy3bp36OWXKFOTa83j00Ufjuuuuw7HHHpvR+5Zw0rIsmKY5rFVdvT3Xuxt/UfYMx9iN9PvF08vfwVsbGzC5PF/dflM0qfr/5gdMvLu5AX+aVt2x7zc+rMOsYAEcDXB1YEvEwj82RBGT9UEAWHBx2NR8zKiUD56Z+0DcLP2VLRuVtoOSgA9Feb50GwodcIJaul9Elsh7RSwVQ8gX6vFe0ZpshV/3o6awBgEjiFzmhpNAvh96RXDMVbJKRXFTSwNKisqyX1EMIBm3kIxayCv2o7gyhEBe5ir5vcprcReNjjFk/JyZ2Hk44uex9xXoKCLvB+F4/60nhK5J5XHuTvUjIiKi3ffMM8+on1LFQERDM2dqTVtVcQKVxXlqXZD2D3R7VRdj74Yk3i1LVxzdPqUcV36QwHFbLZUGXgDg06pbcCdrZet/BuDIks8KOz8vpIp1hOcFYRflXmJBWlA0x5uxI1aP6j76FecMWbAwbgEJGwjm8HGOAf6gCcOnq8piK+moyuL8Yv+YbAtCRIPH+Lknzs0YAumxVlVVhXnz5mX1OAxNRyxpwbasfveT3mrRJBPFREREo5ksyPHiiy+iuDjdR5WIhtiruKoY4YSlqoxkQTvJFduOq6q2FkUaYEh/CQCNAR1XzwvhigUhbAt4Iynla3ZQ8s8oQqsTUH0ycogGLZ0sTjShMd6gkvO5SpNFwi0HjvQrpqwzDB35pQHYloOGLWE0bYuqxQ+JiHaF8XNP/PrTw33WpKLYsl1EkwkUmma/KyGnbEdtsrgdERERERH1bs6U8VjfVlVcUZQHQ9dVotg0gJqqMpy7rhZ/nViNVn86rv5nlYnXS/Nwybo4TqpzYEp+ynERiTvQXUAmwmsjnGANdev1KClWOae9blhyrqaVPk9zgfwPkjBqLezY2w8rv+/PB0ZbpwrZdLknTVOVmlKsqY1Uv2IjiIZ4I/xGAEWBHP7gLovrxS24SRta20J7lD3ytxkq9KtWFE11UaSStqouDuazFQUR0WAwUexhhm4gadmIxpIozEv3Le6rothiopiIiIiIaEBVxVOrirFyww6UOTYCpo6wtBloM2dCBYpbNuPpZAFW15Sq86I+HT+bmYeHamI4yzRQASBhOXjw3RZ80jjyVacnTSvE/PE7+/om2raprgv5lOBI9XB9CtM/AULx9D7BZgfjX41j40Rg2ziVaev1tncmhtPZYWlrJ58o0snjdDWnnDeQxLFUCduOBde1Vd/n3gTNoOpXLC0o/IYfQTOEnCRfFIRTcKMpJopzsRVFSxJWwkFhRRAFJQG2oiAiGiAmij3Mb2iIJF3EE/33KZbkcCxlI2W5wO4t4khERERENOrN3qMaa7c3oz6cQEHQryp0JcnZvuDMhMJx+Kwdx4sfvYPXJ85Ga146UbgpP4Sf2w6OSdo43NRx3txi1LZaiIzANPj3tyfw1jZJBwP/XBfBvuMCMNqSYRLyhzUgCiDfBXQ5v9KPtWUOqtY5KN/kpCuOHWDKBqCiWcOWPQ2kZLG7TlzHhRy5agLhQv3uuK7qWuG2JaClonbgTSJkzxTidlJ9RvGber/9iuvj0q+4Oif7FcvfguvT4UZScPP90HwsyMmpVhQlAcTDKTTWRpBKSHVxEKa0DCEion7l3r+4NGASCEpwlkgmd7mftKhIOezTRERERESZX99DNun56xU1naqKCwI+VT0rcbchvRja+I0gFu8xCxO3rsQqZzrenlqizrcNHU+HdLydsHCm7qKmaGSmvtcU+vDBjiSStovmhIOVtXHsPyFdfStH6XOBZmgog9vRgkIaLm+friNc7qDmQwv+turigmYX01+3sG26gaZqvaO6WDPS1cPDJV1R7Kj1UxqjSVQWBlW1cndSn1zoL1TJYlMzYeoj8xxKq4uQGYBPDwxt1Xm/ATeSTC9s52NFTi62opAkcXNdFFbCVn2MBzrOspvpN2D69aH9bRAReRQTxR6mS5NiuEjsYjE7lVB2XdV6goiIiIhoLK7vMZSq4nV1LWiNp1T1q6USxT3326t6X1TGt6D6zXV4fca+aChM77Q1YOL/HBeH2zYOctIVvAMhrSIGUveY79excGIIL62XumGon3Org/C1JbMlZRyTthgACrtdN1qiY80BPoxbY6OsNv0ZwbCBmo9sFO5wUDvLhDVCC/RJzi3kM9AatxAwkyjJ8/fZZi9khlAfqx+R42g7GgTNdHuLPF+euj//IJLGqp2BqcMJJ6HnmdDY5i/n+AIGDDOgWlHEwv0XWHUhQ2sa6vqBfJ/6KRsTx0Q02jFR7HGyqEQsPrB/8FTrCSIiIho11qxZg2OPPRbr169HRUUFJkyYgJUrVw7LbS9btgw//OEPVQXgueeei5tuuqnX/S688EI89dRT2LJlC5LJJHw+LhxEo4NUFU+rLMKbG+uR79eQkn4LfSgP1uDQ2eXIX/13bPEdgddmFsCVYg1dw4u6iRcHcb/5losLN8VQmdx17H6w60PYMpGyXdWUeO1bUUwvSydeJWXpakCT6/bZGqI5CBSPA6btAAJtBd+FDS5Cy1OoLQKcYc57yqJ66fYTNgrU8xlHwpfqaJnRnSTMCwbROy/l09BaqMEdYCJPGmuEbRuW2wQXjTA1A6bhQ0gPqJ8+3Yf2mmrD1FSFqc+nq2Shz2fA9OnpHs0JG4jZQAETxblIN3QUlAbVv2cDJW1XrJSDeNRCpCWh2rdI6wozoCOQ51O9kJk4JvKmXIifL7rooo74OZVKwTRzJz2bO0fiIbk0fU6CqnAsNaD9oqn+K4+JiIhocNSHTknQjDRD6/WDaEFBAUpKSvDwww9jwYIFwxpAf+9738Obb76J/Px8HHHEEXj66adx3HHH9dj3/PPPV/tWV1cP2/0T5Yo502uwtq4FLTFLGv32u69PC+CAmYvxUcO/UfNKGV6Zsye2lQ7+41bE1PACDJz2ZmSX+8q7wnHo9OXMehfW+nTfYiHL20l3ibYOE73aBuATDZgbMjBJFmiTD4kOMKkJI6S9Xrr9PW14Zz2GbRfvx21sSQ3lvdlq26QWe+A0HSpx7AtKItFAxcQC7HvURJVQpNwxmISutF3xSx/ttjUipfWMlbSRiNqItibVAo6dE8d6p7Y0w33Mhqmnv5Twp7+YIPK6jMTPfcTOuRI/f+ELX8B3vvMdTJw4EbmGiWKPT58z1TQ4B9FYAnmhQJ/7yXS5RMrpsggHERER7SbbxeZvvjzidzPhO4cBZs9/vxsaGqDruqqGGE4PPvggzjrrLJSWlnZUDd9///29BrpHHXXUoKq0iLxkXGkJZtSU4dWPtsKva7BtZ5eJmlllB2FH4SZY7/4V4dBxeGN6CLG2BGy/NCDV9jr/YKIfydcj8GeozkMmHr4RtVGbcjAvZCDQR4WvFxQYGg7MN9FkOXg37mBHBmZVug6QTNhqE83bY9i+vhVHfXZP5BX3/RmNvEMqiqWKuLfEcaxl14VbQ+eqFie6qauEsc/f9oVEe0W7JI9NPb1gJZFXZCB+7it2zqX42dpFG9lsYaLY46RS2LYcxOP9J4pNXUPSdtS0NH8fLxYiIiLyltmzZ+O8887D1KlTUVRUhJ/+9KeqQqG7G264AQ899FCvt/H444+jpqamy3kbN27ErFmzOk5PnjwZjzzyyAg8AqLcN3vyOHy4qR51kSR8kqwZwHUqfBNRNLcCK7b8DUuWFyJg5e3yOpLO/MvhhyAaCqqE8Yt7bsW0Wqn3zZxPALzhmpiLShS63lqczYSOGSiBr22ESkwdhxXoaDCAdQEgMpCB64Pj2LAtwHB9cFJQLQlSSVv97Ot7stb6OJ797ftY9Nk9UVSRXmSQRm/ieCRJUlo+8zuWg1g4hUhTetaAVDGrJLFPg1/1UDbTfbN3wXZsxFqTaDXjqhc4pUk9nUrKyyKeupb+XX62n2bB3ajB+Ll/TBR7nM/U0JoCYsn++xSbhoakJYliB36T01WIiIhGg1WrVuHWW2/FY489pqoVpDqiN9dee63aiGjwKkuLsefEcmx7bwuSKRsBc2CJFb8exKETz0CsphVxJ73g3K7sYySxXDWMADbPnoZDp3Zfhq53stDeH1Y1IdLW1/iACUEsnJhOTksTBXlnmOi6nZtU9EvqIxsw/GT2ge3GkYSDOGKIOzHE3RiaUxGkEIfmSyCBGGJODFE7iqgThTPA1hQNVgMCCROf3XEiPtV0KIy2hHGZDZRFgfgEE9E9A3DyBv9ZSJ7V5ngTqvJKUJFX1eXxOLaLlCSOoymkYjZ2hJNY9Y/N6nJZQE2SxUeeNwvlNQWDvl8iIUlK3W8AsnX725MEsp10EY4m4TjxASUzHcdBaywONxrpM24Yy1R1ttaWMJbn00gnkSUpb7Ql57VBPG/Smibd19pgsjlHMH7uHxPFHmfqOmwnhcSuEsW6jqhjq0QxERERDRNDS09ty8D99EYWwTjttNNw/PHH93v1wVZESGstWeCj3YYNG7Leboso21XFb6+rQ30kgcLQ4CptQ3qh2gbiYADL237fqAdgmONQNMD7ObymGE9+HFa/r9qo4fCaMuT7dXX9Fg3Ic1yUILtUcstNQNcCXRImbtBFa9xCYcBERVGwr7e8fm1MbMT1m6/HbeP/hL+UPYf/rP8s5jfP7Lg8uNlCoNZCfA8fojP9cAfSDqSNHI7f9KMl2YqiYDH8eqBT/9h04igYMuG2JlE6qQD5ZSG8+pc1qhI0GbPwj99/gMPPnolxU4sH/8CIetH5b28oFfKW4UNBUQA6K4p7vEfJQoYyU0Bev+p3Scg7LlJxJ326n4VNe71NuKraO5BnIq/Qj0C+qVqHjGmZiJ/7+YeE8XP/mCj2OMMw1Hfs8VRqlxXFluWq9hNEREQ0PFSiI4stnWTq3L333oumpia1KEdfBlsRccYZZ2Dx4sW45ppr1GIcd911F2688cZhOmoib1YVz5hQhm0f1iJhWQiM0OrkVS4wwXGwWdfhahpWGTqOGGD8vmB8EK9siKI54SBpu3hlYxSLpxek62pdoFXTUJKj/cTlvTTPb6A1YcEfS6I0b/BtLyYFJuHSqkvxs20/w6bANvxvzU/w2Qln4NytS+BvSD+HmgOE1qYQ2JhCfKofVsEAk2zyNl/mR5MWRjQVhT/QR8s/qfiMW5g4oxhHfGYWXr7/Y9Wewko6ePHej3DwadMxaXbZoB8bEWXuvUhaTQhjGJPPqYSNaHNStQ3xh0yECnwIFfjhzzPHZH9pxs+5jfMMRgHdBWLx/iuKZcqEA1f1KCYiIqLR4aSTTlI91o488ki1avNFF12E5C5mGQ3EjBkz1MK9BxxwAPbaay8cffTRWLJkibrs0UcfxSWXXNKxr9x/e7WE9Ho7++yzd/v+aXS55ZZbUFVVhXnz5sHL9p06XlW8NkfS/UFHynxnZ2L4TUkYD2LtkiOm5HecXrE5hta2xdUkrRkBMLJHvvuLdEsP6OZYCtFk+rgH68CCA3FKySkdp//oPoSH57yK5gNDsAp3fvTVLSDv4ySK3owPbHsjjrIXYshrNdCSaIHj9LEAkVQpJx240RTGTSnCos/tCX8onW6SNgGvPLQaq1/f3lGVOKQtR5P9RNR3UlT6WeeXBJBX5FfvBU3bo9i+oQV1G1rQ2hBHMm7xtT3G4ufPfvazKm4WU6ZMyan4WXP51zhkmzZtUh+MpGH1xIkTB31927ZRV1eHyspKVRm8K7c+/BLCiRTGlewMAMWOlijK84M4fL85/V6/tjmGqeX5mFrJ/ljDZbBjSLmHY+h9HEPv89oYrlu3riOoy7XnUQLS6667Dscee2xG71vCSVm52TTNYe2/19tzvbvxF2XPcIxdNt8v5O/8vhdX4f1NjaguCcBnjExVsTSP+IHfB6fttXRZMoWaAX5kk6nSy15rREMsnWg9oCaIT80qVDMQmzRgsuOiLAdbT3TWGksh5DdQVRhUsyIHy3Ed/GTrT7AyulKdll7F/1vzv9gzOAuBTRbyPkzAiA/tI7DjB7YcAFRW16DQ33tTEDdupZsatx17S1MC//z7esQiO5PL++xfhT3nlg/t/VKuIu0GpE+qIQtu6enSR/kplYlti2+NFGlb0NTSgJKiMrYt8CiOYW6wUjaSMVko04Y/YCJYIJsfoUL/LquMvRY7C8bPmYmdhyN+ZkXxKGDoOmKWjZTVf/sJQ9MQS7H1BBER0Wj0zDPPqJ9SxUBEw08+yO29Rw1CPh2t0T4qSoeBlHTM7NQDc+UgFk2S5MKRU9KL2Ik3auNoitvqQ5/MQmzUtJyuKhb5ARPRpIWmWHLA1dSd6ZqOpVVLUe2rVqdt2KodRYPdiMQkHxqPzkd4TgCJcSaSVcaANrdtCPQkUL3CRaSxue/qv4CRTtjK5a6LomI/jj5xCgqLd7bTeOf17Xhr+Ta4Uj3ett+AN5khKn9/zQm4DTHYdRE422NwtkXhbI/A2RaBvTUCuz4GpyWhEtesDSPKPdKnWCqMC0qD6sudcGMCOza1oqE2rCqMKTMYP/fERPEoICtvWpaDWDyxy+lc8ZTNQIGIiGgUkgU5XnzxRRQXc7EkopEyvqIYVaWFSFgOUikrI+0n3jJ0DKYRw95VAVTmt7U7cIGX1kXV77KcXrMG1Goa+i8vyS5Jdod8JpqjKYSHmCzJN/Lx39X/jaAWVKdb7Bb8dOtPkXSSqtI3Ps2P1gNDaFmYN7DtwFBHsthMaih6JYJ4S2vfPU4DBrSA2bHll4dw9GnTUVoZ6tjv43cbsOJfW+GaXffd5RY0oeX7oBX4oRUEoBcGgJCZbnshVWnyWS9lA+EUXEkWSxK5LqoW2nPlfCLK0dYUQQTyfGitj2PHprBKHA924TwaPMbPPTFRPAr6rPl1HSnHRSzRf8jnMzS1WicXtCMiIiIiGjxZcG1ydQXyAwZ2RJLY2hRRW11zVLWDq2+JobE1jqZIHC3ROFpjCUTiSSQHmVTey3EQbCvuCGsa1gxisSNJOhzVqVfxqq1xNEQt1Z2gpK2qeJumDSr5nGnSq1gSxo3RJOKWrSqLB7vV+Cdg6bgvd9zm2sRa3LXjLjiuO+jbSlaaaNk/BLdtGMw4gBe2wgknB9xb2B8wcOSnp6Bqws6xWf9RE/719/WwkvZu9S1W5G9EWlL4DCBgppPH+X64hgY3YsGui8LeFlU/5bgdKSAacp9ksK8y0QhVGUuFsSyA2bAljIZtUaSG2LOdaKhGprHWKCfNqWVr7/GRbbJwhfQjiyV2UVGspyuKZUE7iR2IiIiIiGjgQj4DlSVFmFJTCddKwraddCGGZSFlOUhYtorLJTEm58uiRRZctCRslBsp+At2VpT2xyeVwY6D19t6T8qidrOcgScLZlX4Mb7ARG1Y7h14YV0Up88pUh/+Cl0XOySnCKDKzd3KIUnKt8Yt7GhNqra8Q1GN2VgcOhnPxP6qTr/U+hJK7Qk4LDiEPpR+oHiWjj0+dNTxGFEH0efWY8cBVXD8A+8ROmFhJZLLXTRtTld6125oxQvPrMfUhVUYUa4LLeJCl1aEGmCbBpygro7d8bVVIw/oZhykEjFsjfmgaQP76wkYGkrz/SgO+VTbRCLq/8s+aUmRSthoqYshGUuhqDykzhvuXrZEvWG6cBTw+UzYTgqJZP8VxbIYhJVwVRCrlj4mIiIiIqIBkw/pxSETxcWlqCnpPelrWxZsx0HKljVEbCQsC+9tb0B8Rwu0hgiM4gD0ASyEt8DemSh+Xwo+YCM4iOM8amo+/vR2szr97vYEDptsoarAlHwnQi6wHRpMuKhAbpLHUOA3kJCevLvhSP+J2JzagPetVer0Y9H7UalNwDTfnoO+rfoyDe4MDVNWuypZ7I/aqHi9DlvnV6STrQM0+YAK6L4GNKyTpQuBhg0RlE6OoqBioCM8ROqgddWTxEha0ONOetE904AdNOAOIAkliWLXSsFJJQecKG5xXTRGkijO86GsIKASxlLERER98wUMGD4dsdYk6jdHkIxZKCwPqqpjopHERPFooTlI7ipRrGuwbAla2XqCiIiIiGgo8gImdKRbusnMvu4M01RtHnYuXQZEQyGszgtAr22A1RiDXhCEGZS64b5Ndl2UuC6aNA2WpuFdXcf+nXoX78q0Mh8mFpnY1JJue/HiugjO2ifdg1FS3FKfLC0oTLkf5Cbd0BHa7ZyIgc/7LsHPGm/CNrsWDhzcG70D/1P2TZQZg0+TxyYYqNVs1HycrvD2RyyMf7sBzQdWwR1EsnjPg6rwftxG09aYOl37diPmHT8R2iDajAxLlbHtQpMq45g9oKpix3UQdx0EUzZ0bYBJfF1D3HHQkkqgKZJqSxhLhbEfPoMJY6K+SAue/OKAWtyuaXtU/ZTqYn8ek8U0cviuPEoYmoZoIrnLb+VVjy0miomIiEaFNWvWYMqUKerf+MrKSsyfP3/YbnvZsmWYMWMGpk+fjquvvrrP/eS+FyxYoFaLlp/vvffesB0DUS7K85sI+NIt3Qaq1DRRXFqK4ukT4a8qhBNLINUSg2vb/X5Qm98pbl85yIRae1Vxuw92JFHburOwpKAtWbxV05Cuax29gnoIFxb/J4Jaugo86oZxV/MvsCG1FhtT63a5SUWy5e587ppqDGyetjOp6mtJovj17YDM3BzE+EzdrwLtRbnR5iS2rm4Zzoc9kIOAa+pwQiacPBNOyNjl5spPv57+OcDNNTWEEjaqUi7KLQeRSBKf1EWwensY21vjSFrswUrUH7XYXXEA8bCFHZvDaJZe48zreDp+1nW9I3aW+8+l+JkVxaOE9HqKxFNqqptUMfQnOYgAhoiIiPomfUiTGVikx69pvfalKygoQElJCR5++GEVaA5nAP29730Pb775JvLz83HEEUfg6aefxnHHHdfr/q+99pr6aZom++fRqJfnMxDyG9gRTiCcGNgidbb0LLYsNEJHyYTxcH0NSO5oQrIxCrM4BMPXe/w+37HxD1WfDKzVdTRK0nkQxzql1I+ppT6sbUwnOX+3sln1i23Xvlib6rnb9nNkpReS0xDJyL115Yeb9xmg+m5Ac7HF2ohbG7878Ksnx0Hf9J/Q3J3tIc7UTVzopE/7mpLY8ux63OhLIDmIh3ZgUMfcaHqMP3izDt/ZWIt4jpdzyShq2DqgfeWpmF4cwqcnl2FuUR6CMRt+n46kX0dzPIl1sRQKgibK8v1q85uslCTqa4ZFfkkAiWhKzURI6nEUhBIIhvww/bq63CsyET/3FTvnUvz86quvIhgM5lzszETxKCHT3qSlRCJlI6+fRLFM7YkmB7fqMhEREfVOgtw9XnhrxO9n/aK5CPQSRDY0NKiKhIqK4e0y+uCDD+Kss85CaWk6JXXhhRfi/vvv7zPQJRprU4HHF4dQGBz4Rym1sF3MwOpYApquo6C6EuGQH5HNO5Coj0r2GUYg3Yqi8ys9TxY/0w1sbkuerXCBw7rF8nI88vYgMwy1jtM7b2XRlHysbWxSvydtV225IQvHkdwTfmMJAlV/H/x1/dsQL/0rElvP7Djr15AZnRoubFsAZp5r4P8lfbgaMfTfFHCn53zAVC2IQldDwNWwb7OGv+cN9NreUL+9Fcu3t2JyQQAn7VGORRWFCEYcVAZ0pAImmlM21tdHUR9JqmSxtEwcCfKy0DVt56ZrqkWz0faaMdrOI8plgTwfdBMI11nYsTEMX8CE6dPhz/PBHzBUb2NJHOdyL+NMxM99xc6C8XP/mCgeJeQfN1l1ORaPIy/U90p18o9uwnLVasz8R5CIiMjbZs+ejfPOOw9Tp05FUVERfvrTn+ILX/hCj/1uuOEGPPTQQ73exuOPP46ampou523cuBGzZs3qOD158mQ88sgjfR7HoYceilQqhVNOOQXXXnutqiwmGs2qi6WKdHALj02wbISao3DhIE83YFcVIlJTjq1rNyO8vQVIpaDnB+B06w64wHWxue33d/0+HNslmZyO6522ZLRULsPamYKV/SrydMwdF8Bb2xK7/bhHg2T9UYCegK/wHVVZvEuaDd2XTrT7S1+D1boP7MjOhfB+jYT6SzivLVl8CHy4FRrWqVEZAA0oKdAw2UondebBwMGOgZh3igP7Jc/wZjh4CzY+DCfwy3e34Hc+A8dPLMWJ1cUoTzgoDxpIBU20WjY2yBcnIyydJJYvVaTFy84ksfrCRZLHenrG7mglCxImYnE0pSIDXpBwpGhtfd8LAiZCPiPnKitzlWHqCOb7kF/oh2NrsJMOWnfE1djKZZIkloRxIM+E6U8njmVhvIGScRjN+aJciZ+POOII2Lat4udvfetbORM/58ZR0G7zmzpaUg7iyf77FJtGekE76VMc1HP3GyYiIiLatVWrVuHWW2/FY489pqoVpDqiN5K8lW0kbNiwARMnTkR9fT0uuOAC/PCHP8Q3vvGNEbkv8qZbbrlFbfJhaCwrMA1UBExsiidR3lbpVRAoRHnRTGzbvA07NtQhFUkiWBaC6du50N2RLvCEne4nvEPXEM73Y0q3z++yxp3tOKolgHSZk2m9kjhO2fLTwQkzC3DIpCDiVt+JUalTjuoaSh0XZY4LzU23iQiYBkw9nTjYXXJcrpuCpvmynBD6zKCO+YH4rdjgfKBOV+3xEM4PXYuglu7/nLDj0KBj25YAxrUl4+fCxNzBHI78OXT6aDaly1KIo0cCLt6HjbdSNt5a24T/WbsD86qLccr4EswsCqnElvRKdkc4QSWvDRlXx1Xr+akF+uQLF1teP+ry9PmjlSQTHSuJaDw+YoliR0q1BziOdksCIZ+OwpAPxSGfShqzBcnAyPuoz2+orZ1tObBSDmKtSYSb4unKeZ8B3Rz460renkMFfpWMDoTMzC6yOUbi5/Xr12P8+PGIRCL4/Oc/n1PxMxPFo4RUCluOg/guFrRrbz0hbSqCOTwVgYiIyAuk/5lMbcvE/fTmqaeewmmnnYbjjz++3+sPtiJi0qRJKoDtnAyW83oj58sH7uLiYlx00UW46667BvCIaCy58sor1bZp06Y+/47GinK/iS2JFJKOA3/bB1PTNDBhjxrkFYSwdW0tIjsicBGDJh/qfTp0U8ccXwBvtyV0ljvAlG5hfLo6Mn15bylGSYLJlP506rdvcUB1D54ox+oC0YTV9tnBhd/UEDT13UrwppNzNnTNW/3MvxC6ED9s+Dbibgxhtxn/dh7AeUUXq8scNw+RVCvCc/woMk2ENsszSL0JQMN8mGoT8tXGJ1stvL11G94K6dhjQhEW1BTDHOEq151fw4xNkhiPuy6CljPQXO7gaIBrAo7fgOuTxQx1taChyj72ejwu4ikHO1qT2NGSRF7AUAljSRwXBIxRXd09EqSiWDaE2l5n8iVIyhnU4nfyZUrT1qiqQpaq5FBhOmksX+Z4JX7uK3bOpfjZsiwVP1988cX4zW9+g1zBRPEooRuGmraRSKUGkFCGCvaIiIho90iio6/+Z5maOnfvvfeiqalJLcrRl8FWRJxxxhlYvHgxrrnmGrUYhyR/b7zxxh77NTY2qkU4ZJPWExJMD+fK0USjTYlpolhmAloOKvxdkx+l5aUI5YVQv60BqVgCiXASqXgSbtLB/lYKb+elWxsstx0cGw7DpxtqtiCGMalmtm2q1YXrokwHTB8QsW3E40m0plz4TA2SK5Bey4Mnn0Esdcg+w9zlIty5otQoxykF5+LPrXer0yvir2DfwP7YJzAfuqZD1wxErAha5lQjVRGEHh9a9XzDpjBad6SrkqX6b8JeJeqLgpziukg5Cfj0QJ+Jv840x4XZkoSvMQE92TVRJW0fZsBQG2IAVkcRWy1flAw/+ZT8IlL4Py2BuHe+oxjhBQmz80So8dV25ieq8gOoLghgXEEAlXl+lARNFPt9qCzwozDoQ0lb0lgWEvXSF0y5QiqKdZXgHXySN5W0EQunEGlOIhA0ESgwVaVxIN+EsRuL5zF+bkQgEIDP51Pxs/Q2zqX42Rv/MtOA3+xjiV0kig1d9TKWimIiIiLytpNOOgkffPABjjzySBiGoVZuXrZsGfz+3Zu2PGPGDFUBesABB6gKwHPOOQdLlixRlz366KNqu/POO9V9f+lLX1KVjBLoLlq0SAXHRNQ7n66h0u9DfVh6Sbo9kh7BUBATpqQrlFzbgZ20kYwlUB2J4/5t9YhIla+uY1uoCHtZKVhwYLvDG9cXAGgGsKWtwrhc11CW54Nlm4ilHEQTKcQSst6Jo6qMjUEkqtMJIg1u0kLUTsC1XTWlWfoyStLYZ5rQuyUfXHmKciA5tDB4GN5OvI73k2+r0w+03oOpvhnI1wsQ0IOI21HEnDj08fIMDo1vQj7WPbYBVsKRPg1oaUlh2v6VyLW2BTFLihULB9e2wHVhRC2VMDYbE+mf0Z6LrIdGMHl5MvyY7Rr4XzeK2mwspphzsv8cSAHb+qaY2rqTvtHleT5U5PkxrsCPiUUhFAaYwmqvwk6lYvD5wqrn9khTLVrk36SUvGenq5ZNqfaW3sfp3kS7vI0ZoRimVuZjW4v8y5JdBx65GCed/jYOPfwIGLqBfebNww9+8vPdjp8Lqybi0sv/Gwv221/9G3/K6Wdi3sFHqsf898f/hr8//hh+fNv/YcXrq/C1r/6XGjtpy3XM0UflVPysuXL0NCTt0+ekYbX05hss+YOoq6tDZWWl+nC3K7c+/BLCiRTGlaT7YXW3oyWKknw/Fu23T7+3U9sUw8xxhZhUJuso0+4Y7BhS7uEYeh/H0Pu8Nobr1q1TP6dMmYJcex6PPvpoXHfddTj2WFnuKnMknJTpc5LsGc5qn96e692Nvyh7hmPsvPZ+0RtZsGtVa1RNi5W+xQP1k7Vb8dcdksIFjsjPwzUlpUglU3BGaKZg2HHQ4joo0HVU6zoqDQMhTYdl2QgnbDTFkogkbTWNPeQzVWXggKa8p8LwaUFYto1UKol4Io5kMqnG1k5ZqvhFVelKb2RDbldHyg+4Uj09AHLdkdLiNOHHTd9GzE0vuDbPfyA+W/hF9Xs41YICXzEqguN2K9VZt7YVa1/f0XF6n8UTkFfiz62F0OwwAkbBbve31RM2/M1JJOuisOtjGJd0kZ4nO7Ka4eBaxPC66vxNRJlw2QFFWDx7HMziKuQS+bfnknNOxtL/+ToOOnxRxu/fb+jYa3zRsN7m7sbP/DpmFJFq4aTlqECr329CNCBh8R9FIiKi0eSZZ55RP6UKmIhyV4Gho8w0UZtIDipRvKSyuCNR/Go0hviM8ShwQyNWFCilKVWui2bbwWbHRsQwUBPwodJnolTXMd520BxPoS6cQFMkidgACptdx0EqloIVKlKtKyQlGJJKWsdCMplAMhFDMpVCNBZWP22pmIu6CLVoSEiyeFc5RKmBcl2VZJYpvVKlPJyLdflRhBMC5+KheLoX+6rka9gzOh97+/aH6/jQFG+BD4XwGek2IUMRmhBCaI0fsab02jOfvLkDUw6rypkp95IotmRzbGjabv7xydCU+tMbSrBaFmeP9z9DdqjK6hOYuD6i/uaKoeOnyMfGKfnYXh3KiYr1jFJjGIWp5Q1r65rBH4cLK25ha8zCZtioS9mojyZRF01ieySBJNtlUga8+s9/qJ9z5uZO64dsY6J4FK3cbEj/YctFNNF/oli+lY8mc+vYiYiIaPfIghy7WpSDiLJPEn4V/nSiOOW4qh3FQMzJD2JCwIfNiRRSrosXm8I4qarv3orDpUwSa66LppSND60Udmguxgd9qJSkcYEfFeV5aE1YasGkXXFsGw0NNsrKStUaK/1JphKIxGOq9YbWkIQWceAW9D/F2XVchGNRtLaGkZTnN5VUrSykF2TA74cxwKrk/ox3j8QnO1ZiZexNdfrxxL04qGxfFOrFaEo2oSzooCzU+wzQgSo53MC//pauCIvWJ+BrsjFhejFygVSFhxMWCgL5I1q9Pez2AOwJERivbYOWclTCePK6CCY6Ouz5lfJhGmNFegyBgkBxToyhHU4gqWtozTMQ1zSkbBuJpIOmhIXGeAoNsRTqY0n1+wDeZsYG14Vtp2AYPs980TGuwIDP0JDvz6005PFLjldbNjiOg4Av92ZH5dYIeUQ2Vm5++e3VeDXsYloqDPTRekIlgK30QhMo7Pu25MUplceykqUkl4mIiGjgZLp7PB5XXxh7deq7F8jzK32PZaE8otGmxGegyGegxbJRPsAPzZJgPq68CHdvqVenn65vyUiiuKNXqN+E5bhotCw0tFqo9FuoCfpQ7jNRFPQN+HWdjPhQnOcfwPunHyhOf6hxxllIbYvASTowCnfdhsGyU2iNRBCORtHU2oJoNIFYNKySg5IwDgT9qh/yUF1SdTG+sfEbCDthhJ1W/KnpHnxl3FdQrIeQQhR+nwOfPvR2EfkTCzF1r1Ks/aBRnf5wxXZMnVECn9/IiSRjyjGQHzBzIsk4KJOL4ZaGYP1zE9yWdMW2vqEVRjgF8/AJ0PIG9nfsdbk2hq78XYdTKPf7YBf7kXSBpGWrnIW03kxYrkoep1KAkwN9lXOlst9NtULzDbJXeBYV+sPQHQclAUOtbUGQfxQRDPlzLn5motgDvvr7F/Hg+EJYEwuBTcDUPvYzTQ1WXPp+pf/R669FhQR5sqCdNO4mIiKigSsuLkY4HMYnn3yipjbTzqqI4Qz8JciVvsfyfBONNn5dR5Xfhw8jvS9q15fFnRLFb4dj2BJPoiaYuf61ZttifEnHQX3KQmNKEsY+VWFcahoj1h5BD5kwy0NIbYvCiaag7yKhZxo+lBaVqG1iVbWqTG6NhNEcbkVrJIrWcDT9ZZ+0vzB0VXAjyRapODY0A5r8NPQ+k2jFZjHOrzwfv9j2C3V6RWQFXgm/goMLDkZLohWRVAQlgd0bl30OqsamT5qRSjqIRy28/8Z2zD14/G7dJgFaoR/mcXvAerUW7uawOs9tiCP11DqYh0+EXiHNUCiT5H3DzTPhhlMwTA35JUEUdCxaF1J5i6TtIJGyVbEbpWOuaDSFvLx8zyRdbVtHKtmM7bUbGD+3kZk46t+aYSzgHI74mYliDxgf0GC1/eH8s6YA87bXoaK0Z/WAzzDhOinEE7tIFOua6lEsb7bBHCxzJyIiymVFRekFJ5qbm3OuDVW2SKIrkUio6oXhShTJbUmQ2/58E402pT4TeYaOiO0MuFexJIX3LQipJLF4pr4FX5hQgWwkuqsDOmK2o1poSMJ4j7wAJgZ8I5YslkpiN+UgtT0KJ2lDH2B1rfRCLsjLV9v4ynHqvaolFkZrOIJ4IoFkUtpmSALKUQv1JZyUamEhpzuv++4zfAgZOxcDX4CFOCCwHCsSr6nTv627B9PtveBzTexINcEs9qv7HKpgyMTeB47Dypdr1emP3qrH1L3KUFgy9P7HlKb5DFVBbL+zA8676S9eELdhPbcBxv7jYEzPTKU+7SRf2CBoAC0p+aZHyk87LvMZutryc6xlQTbJe1aT60NJYQC6Z4r/gogl/YglIkjFB9DUftRzEI3GUVCUB3/HFyO5ET/zleYBV5x6MB55chXWFpoqYbzKzkOfa5lrDhJJq9/bkzfZlEzfsPjiJCIiGgoJvpjA3EkS5nV1daisrGQ7DqIBKjR0lJomtiYHt6idtJ9oTxRL+4n/qCnP2kJnIUNHyPCjIWVhbTQBeRQjWeFslATgSAu9+hhcXYNmDr6STvoVV8pWUt5xnmVbsC0bKfkpSWPLhuVYsGxH/Z6wZHGt7QgGut7fRfkX4KMNH6DFbkXUjeAP8btUC4rWRBibm7cg1JKHksIiFISGVvU3fe9yrH2/Ec0NcZW8fvOlLTji01NyZmE7L5Pn0Ny3Ek5pENarWwDLlZ4MsF/bCrcpDmPBOGhs05jxBL5ru3CaE9BNDVqIVaejTcifrzaCSvYnWrdhXEUNist3fgmZC7xRoz7G+f0+nJpMB4PixfH5qGtMr3jcnfQcjiQS/d6e7OO40nqC0zaIiIiIiLK5qJ0UrcqidgO1qKwQvrZEoSxs914kjmwr85mq/+8nsQS2JVIjdj+SuPOVBWEUBeCEkyp5OhxMw1QJZKkALi4oQllJKarKKlFTOQ6TxtegoqoYwVIDoVIToTJfxzauqhxLp32x43bejKzE63gNNRMqkFdhIOxvxOaWLVi/bTMaW5vgOP0X9HQn05EXHL6z3cS2TWGVOE7ErC7VzjR0+sRCmIunAPk7k5LOx02wnt8ANzZyf8vUOy1oApYDpykBN8lZW0TZwESxR1xxysGY1pIOLGypKrZ6750kfbbiCflGvP8gRGJL6fVDRERERETZW9Su2GegdRBtbApNA4d0Wtz67s071KJ42SaL3dmuizXRBOqSI5gsNnWYFSHo+b50sjgDCVMDhupXbKPn83xQ2UIcUX5Yx+nfrP8tGpKNKCwsQE1NFYrGBRELNaM2shXrtm1GfVODqmAeqMqaAkyasbPX5Osvbsajv30fj9z1Hp55cDVefWYD3lm+Fes+bMSOrRHEmUQeNL0kAN+SKdDG7azqc+tiSD2xDs6m1qwe25gkPcjjlqoslgpjIsostp7wUFXxwdu345OiGnX6nzX5mN9Lr2JZkVhWB42nUsjvZyVfXdMQT2U/oCQiIiIiGqsCuo5Kn4kPowmUmgNf1G5JRTFebEwvxPV6SxQXv7MOV02txoHF2Z3SW+EzsS1p4RNpQ6FpqtJ4JOgBo21xuwicSApGwcgu6CdtI+Q/lYDtZYgunnIh3ml5D42pRkTtKH6y5mc4qHRhl31ShoVEPAm7HjB3mAj6gggFgijxl+Cw4sNQYBT0ef/zDh6PLetaYXdqHSiL3DXWxdTWnenTUVDsR3lVHqbsVYrSyhDbVeyCJn9TiybBXlUH58OG9JlJG9ZLm6FPK4ax37ghtTqhoc0ccPN8cMNJOKauEvn8+yXKHCaKPWRSaQGmt1hYU2SqquI37Xwc120f02hLFMeTyA+F+l3QLsqpHEREREQ0wm655Ra1cfHHvhe1y9dTiDoO8gfY4/uQ4nwsLi9Si9mJ+pSFr3+0CSdXFmPppCrVOzgbJJkzzi/J4hRWR+OYlRdEyQgli418H1xJFsvidnELukxZHyFSTSyPzXZtmOjZN7XQLMDSqZfg5o9uUaffb/1QbQNVsLkApxWfjk8VnoCA3vtidXOPKMP691sRbbUQj/T/WrJSDpp2xNW25r0GlJQHMXV2GSbPLIE/wD7y/SUozQVVcCpDsJZvVYli4XzSDKcuBvOQ8dDL+v6MTcO8uJ0s8NWSlORFl8XtiGhk8SsxD9ENHQds3dZx+qXxeairb+qxUJ3luojGdt2nWFpPsP0EEREREY2kK6+8Etu3b8eqVauyfSg5qchsaz8xiIWmJWn5v1Or8fWp1cjvlBT+a10zLnl3Hd5pjY7Q0Q7s2Kr8PrSkbFVZ3DqCbTGMIj/M0gDchAU3NXKfawzNUJus89KXA0r3x9EVi4Z0+2EnjN83/g6XbVqKP9ffh43RjaiN1XbZEsWNqD7YwrRjgZlLdEw7SsekQ3RUz9NRNl1DwTgNao2oXgovm+rjaiG8v97zPpY/txF1tRG2p9hF32LfCVOhVXeq0G9Nwnp6Pez36oetNzb1T/Mb8i0NnJYE3Njg+nsT0dCxotgDWuMprK+PYpt8c2wEMKPZwuritqpiNw/7tu7sAeY6DhoiFtY3hDFxfGWfUzRUQrktUSy/ExERERFR5km8Xun3qUXgLMdVM/8Ger3jK4oxvzAPP1i7FW+2JYdrEyl89YONOLe6DBdMKIdfz3ysL23uxgV82CoL20Xj2DMviHxz+CtZ5TkwS0NwLRd2Yxx6YQCaoY1QRbG0nug/Gf2lqRdjXLAK2xN1A7pdx3WwsnkVmlLphcpbnBbc3/pn/CPxPM6ecDqOLD9c3XeX61gurJiLVNSBlXABFzD8gO7X1PPhOC4SEQfRRhvb1yTQtMVW+6jr2i7Wf9SkNmlNMW12GfbYsxTBENMC3WkhE+aiiXA+alTtKCDJYRew36qDUxuGeXANtE4L4NEICRpAJAWnOQ7dzIPmY+6CaKTxXwQPeHNDE77wm+Udpw9u3YLV8yer318en4+6l9dibbTrFKW/fLQZCz+J4dsnz+k1WSwtKmIpFyk2hyciIiIiyqoS01CL1MmidqX64D6iSUL2lj0n4i/bm3DHxjokXVflBe/d2oB/N4fxv9PGY0ZeEJkmPYqrAz6VuDaQwMz8IHpvqrB7JDHsKw8Blg27NQFtBFpQaK4LPQUkNQu9dJ7o4Nf9OHvCmYO67bgdx+Pb/o6/bHkUETuizqtL1uGXa+/Ao7WP4TOTzsHBpQs7PtNJUtjnB4KFLuy4g1QsvVlhF5ohlxvIL5TNh8rJQSSiDrasjqBuTQqp9M0r4eYk3np1K95evhU1U4owcWoxjAH04HXhIJKIIRrQoQ1wgnIo30RRaQCmz1ttL+Q5N/YsU4vc2a9sgduc3LnQ3ZNrYRxYDWNyUbYPc1STMZB+xYgk1eJ2emlwRL4MIqKdmCj2oFe26l2qisumjsPad7u2oBDL1zZga0sc44t79lEydakolkQxW08QEREREWVT0NBR5TfxsSxq5xtaBe8Z40pxQFE+vre2Fh9E4ur8tbEkLntvPb5QU4Hzxpep5G3Gk8V+H2qTKZlBjmmBkanAlCpDsyIPrhsFBtHCY1CVy66BVNSFlrThmhrg09KfpnfzOQ0aQZxRcyqOr1qMR2v/hr9tfRxxJ91GcFN8M3748U8wLW8qPjvpXMwvnteRMFb9dPMMtfmTDuyEg2TEgZ10VMWx4dNh+DUE8nRMnVuIPfZ1UbcljLrVKbRslpmo6fuXn5s/aVHb4LQt+DYIeQU+FJYGVNK4qCSY/lkagH8E+0sPB70kCO24Kelq4o8a02emHNj/2gJ3SxjG/uOgeSwJ7rnF7UI+uK1JOI4z8NecpkHz6+lFCGXzpXuNE1H/cvsdmZSgz8Ae5XloCsdVLylD1zFlXS1Wz5ukLn+1Jg8LNm3H5kS6UqA14cBqKxReuyPSa6JYehRLjy0miomIiIiIcmNRu5Asamc7yBtia7jJIT9+Pnsy/ljbgHu27IBMHpTPBb/ZvAOvNIVxXHnR7uY1e1XmM3FoSUGviWhppSEL3EllMWwHJZLoGQGymJ2/phAYod67eiiIRHMYBTI2MRea9ESOuSoZ5aqkcds2RPlmPs6bdC5OrP4UHtryFzy57WlYbrov6yfRtfjOh9/D7MI9MatgVt834rqqvYS0p3CiwDRjOhaWLoSuWr1qGDehEJUTHLSEo6hfm0LzOg2x5sx9HoyGU2rbtjHc5fxAKF1xLFsoR9o56JKIl2S7qaufpqnDGF+AQMiHwPv16gsD4axrgbM1kjttKOQbGXkdyk95G2n7XSVI2067OuC3k7DNJBxtgAnX8hD0SYUqaZsNKtkrXyjEB9HzXLqFtDpw5PHL61YqkYMGdEnqtyeO2YaTqAcmij1g4dQyvHDl0bj14ZcQTqQwriRfrRq9vtnCx21VxaHJVfjP8vQ/Tg++04T3d6T7Fq/bEcWh0/u+7VR7RpmIiIiIiLK6qF2Jz0Bd0hpyolhIsvY/aspxcHE+bl5bi3Wx9HT59yNxtY2Uz44vwyUTK3u9TPokV/pNbIknkEikUCrrpIxAxlqSoSO1/oovFFCfndQ0eMeFm5RksQvEHSDhQktI4ji9XLyqOB6iYhTiwur/wMllJ+L+bQ/hufoX4CCdzH2/9UO1DYZlLcUR5Yd2nDagoTSQj6I9bUSmRBBpMhDeZCAV1qANqJ+1C8uxYUr2ubeV87rv7biIhi3EI30vRpaIWaiTbUun3hg5zK8B8/MMjG/vlxu34Q4mgZkF3T/1/3/2/gTMkbO+9oBPvbVpban3ZfbxjHdjY8AOOAYbDISwJgSy3RBCzL0mueQm5DFws174gNzgJIQvgRAS4CMkISzBJJctBmMggA2YxcH2jO3x7DM9vaql1lb79/xfqbvVMz0z6m6pVSX9f/PUM5Ja3V0tlareOnXecwz5WOWcx89PDt5/6VAvH4DYk6kJt1uMzCfeSEYx5UuTQY60jwUbHoIV4dhQoZhq7e+JkNuYuqkCmt1Q8RCIJt9FlRzW7HxnLg4LxRFFVVU888wMnsiMy/vfGk/guslpjA72YzghcAArjuLz/gxBOcXcHsowDMMwDMMwnYZEziFDw5TtyGK7ZsloqoyuOBvKBP7AlbvwkVOz+OSZ3DoEoY3xmakcXjU2INdnLUwhMKhpmHFcOMUyFCk0thYSny9Ptqc4Txe6LJ+TkKsypiCgCZ1pVQpQgUOxFAFQ8QESkDf5gg/pg3jD9tfj5cMvwSfO/Cu+ufDtDf2cfyr/I66KX4GBWP+qx1Wo6DP6kBh2kcyWoZHNVAN8UkEvAM1wtV0XhkbT+JtdCw2BrcFaDFDJB7AKASoFH9WCD6sYPeMSvc3fLXnYbQS4Ki6gRUhg3BQlB973p+A9PAv10n6I/f2REB6lC5r2N0umb5p14NWXsougaNdcx01c+AgLtC8KbBt+uUx/YHPfFFchhhMcv8FcFBaKI8wlY1lcuuDg8awOX1HwQ6TxUwDGU/S2WhcViulqe9Xx5cGedxYMwzAMwzAM01kowmFHzJAGuGao+D4KjremULzk5P0fO0bwk/1p3DObR6UNsXPfL5SRcz1U/QD/Pr0g3czng9aThGQ682j12Qf9Zfn6eiTRejRFgzhfeVs9diKgxL8+AZC5tEX65zh24rd3/w5+ZvFn8eD0g7C9mkP8Ynz5xJdRcBZRDIr4/5X/Ef9z4rfPExugIY4YkKtKdzSdV14Iqkr0FQ+6oq3rHNI3AmiDAZKDVIcXwCdhGgE8hwTjmnBMArLX/DWS9kE6vw/4HkDpHz4t3ur/6fGjto9Tjo/sBsrVrv/JCaQyra53DGofBCq0pJ0ILbQdLt+ufZ3cqBW7grgea0ocDSou/KP5miOXsDx4P56Fd2AeYl+2VvgXj460JLdb+ZmlK1h1obvZnW5YoA1UqcdxNCMUO57M1Zbi+CZmPDC9QXQ+zS3mxIkT+LVf+zX5v2EYuOGGG/D+978fptmOLt42uornZ/F4tuYq/vZ4HE89PY2xvtTyc04vVFClweMa4fqUF2Z7PhwvgME7C4ZhGIZhGIbpKOS6vSx5br/I+ThWsTBrX3yG4FWpuFzawd1TOfzV8Wl5+7NTOfz8WL8UqM8HuS8Tqgqhtt6JWHQ9OO3KKFaEFEkvihShWv/7dw3slkuz7Mzuwnt+8B55+wfl7+H+2e/gWRPPPO/zVVOHagXwLyJ6ksnI8z0YqtEas5EOZBIAxhA56LUg4ThfKSBjpjGanLjg87/1pWOYnawZuRZUgb7xdlzSaM6N6lQWkIxn5XbdDME1w/CfyMGjMr96PjMVR/oH52XBH8VRUCyFkqZQi+jRqezljaIEtSxqWu9mPocBRWtQPAqVfXYgNoSJFj27hWiahne+85147LHH8NBDD6FcLuO9730vosbesSwuW6hddpWuYiWNdExHoi780lDm2Fx5ze/VVAWuFIq50I5hGIZhGIZhokZc1DyuVFLdKX5qKIO+uqOZnMX3zBbQSew2leWpQpWCDImDUeBZE8/CDWM3LN//l6mPYKGSv7CQJK200fj7wgBtD6qmIB434AgbQg9gmOp5FyrrW2JxoTYDOCpQjq969RD0l10C9akjQKOD2A/gP7kA5wuH4X77FPyF9mWhMxuDMpnJSS6FYobpNqH40KFDuOOOO3DddddJsffqq69e83kHDx7E85//fCSTSYyNjeHNb34zbHtlms74+DhuvPFGeVsIgac//ek4fvw4osaSq3gJchVPzeYwEF+5qvSfjxzFQ48dPmc5ePg4Hj10FN/40WP41o8PIVdY3T7LMAzDMAzDMEx4oSgHciFbHZw2HVcFXj66kn/7yan5jgnXZAps12uhKqpcvCDcpWWNIubrr3k9Unpttumit4h/OvbR8wrdgQoE9AKyjrSh/GrXd2B5FxZI09noCsVLUOkbRU3oL7kE6g1jQF+Dg5iSLY4vwv2Po/DPRKOYsJegfYJPERQM021C8SOPPILPf/7z2LdvH6688so1n5PL5fDc5z5XCsOf+cxn8K53vQsf/OAH8aY3vWnN51cqFXz4wx/GS17yEkSRPWNZXN7gKv6BSGMkuXKF7+CZIg5N5s5ZDp/J4dhMAQ8dm8N3npjE579zAA8fOQnX5YI7hmEYhmEYhgk7MaHAoDi5Nrlom+UVI1kY9enPJ6sOvr3QGQMKxVq0I4d5yVFM/u3lQrsI0B/rx+uuft3y/QeL9+PB6e+t+dyAIicEoETnzwsNmtDg+t5F86P7ukAoXkJRFah7s9BftAfaT26DMkDNjnWoJ+6JXCdXj1kLmvlR9SIzK4LpHJETil/60pfKXOFPf/rTuP7669d8zgc+8AEUCgXcfffdeOELX4jXve51ePe73y0fP3369Krnep6HX/qlX8Jtt92Gn/opqoJDJF3FP9HoKh6LY1t85SBVdBWM9SfPWcb7kxhMx7BtIIGJbAyzxSq+8ePj+PKDj2Iuv9ihv4ZhGIZhGIZhmGagLOCE2llHMdGva3jhUN/y/X+ZnO+YUGwFPrw2CCHkJqaZqFFxFC9x87ab8bTRpy3f/8fTH0bBWuNcjyKjSSymsitmQxcSym75giLcKkdx3uoKwY5cqmJ7Gtrzd0G7aSWjOZgsIrCi9VnperT655tK7Rimm8rs6OB8Mb74xS9K4XdgYGD5sVe/+tUysuKee+7Ba1/7WvkY7Zip0C6dTuMv//IvL/pzSXymZYnJycllsZmW9ULf4/t+09+rIJDLWrlRe0YzuCLn4EC/jkBRcCpN079qV/Gmi67Mo1kr5Jx+nuf5SBo6dg6mkFus4MDpHGbzVVy9ZxhX7pqQER9Ma95DJnzwexh9+D2MPvweRp+tfA95O2GY1fRpKiat2uzCTvKqsQF8biYvO1IeLVXx8GIZV6eppWzr0BUFbkA5xQHiFyll22j0RJQcxQSdA/73a/47fmfud6SQWfDy+Pixf8D/uPQ3z34ifA1Q7VrPDbP++AnLrcIJbBjKiiDcSCKlQ6gKfC+A5waoFB0kIlr+djZSa9ieBujvWbRlhIl/YhHqvmynV41ZgnKKbQeQsy5aXybKdA9dqQBSPjG5iBvJZrMyl5i+tsRv/MZvoFQq4ZOf/GRTTZF/8Rd/gbe97W3nPD43NwfTXPtgcCHohCqfzzctgMfgAMKF4a8xTUUBnpXL4UD/Nnn3/rE4nnLkDB5bNFFxA1jVKvrMc39HAA+K6wFOLW6iPwb06QIL5SJ+cKCEU6ensH/7MDKpzjSyhp31vodM+OD3MPrwexh9+D2MPlv5HtK4i2GYFWJC0KmAzAUWTZzTtIvtMQM3ZVP4Zj124hNnclsuFEtHMZV1BwHi7YieUKIVPbHEYHwQr73qtXj/Q++X9x8ofBPPmPkJXD+84jQmAl1hlXiD6KqOolOC7dowjLW1AUUoSGUMFOat5fiJbhGKl8v9dvXBe7g229k/lmehOEQoSxnkToCW7yCZrqIrhWLKKCZh+Gz6+/sxP1+bBvWtb31LRlFQzvHTnlY7QN566614z3vec96fSxnHt99++ypH8Q033IDBwUEMDw9v2BEzNDQk4yMuRlXRkfcCxANdtlaezc5RDVfmHDxadxWfeNpujNm1gcw/yl+4xg+lsaQH7D1dxa1ZH4OZNFQdGIwBC6UqHp+zMFudwTW7FekuNgx93X9nN7Pe95AJH/weRh9+D6MPv4fRZyvfQ8uKdq4jw7Qjp5gK7chFG2uxi3a9/ML4wLJQTDnFxys2dsaNLRWKSSR2/DZFTygCjt959/ZGuHXHrfj26W/jRzM/kvc/dvJDuCx7OZJ6gyFoafuhGawdvOgQRWj7oFm8Va+KFNIXjJ9YForzFkZ3nP+5UUQ0CMXBTAVByYGSZA0hNAgFvu2xn5jpPaG4GW666aZ1ZwL19fXJ5WzohGijJ0Xkumn2++OGjooToOz5MBUF2llisappuHF6Fo/2j8v786aQSzMcTadwoOzhF05NY//2UflYNhVHKqZjumDh/sfO4PTsIp522Q5MDK9EejDrew+ZcMLvYfTh9zD68HsYfbbqPeRthGFWE1MFTEHZvAEa6qQ6wpWpOK5JxfHjYkUaUz81NY/f3T22Zb9fE4rMJyaxuNWQSKwpGipBBVF1e95x7R34na/9DipuBQteDh8/+jHcvv+OVYV2stTO62WlYONoqoayU0YQo9hHcd5Cu1P124sLFy6/iyJK2oAyGEMwV5X3/eMFqFcMdnq1mCXo8217CLxAFhIyzFp05e6fnMNL0x/Pdho35hZHDVMTSJka4qaGYtWF6wfyscbYjN2jWdwwVcV3R9c/TJxMqHi/OYJXHZrCs/bVxGLKJ54Y0FAoV/HEdAFzxSdw5a5hDGdS6xpU6RotKgxNh6apMHWVs48ZhmEYhmEYZpOQmzguBOYpSk7r/IWUV48N4MeHalLYPbMF/Nq2IQzoWzfuJ4nY9v22xQt4ES7oGooP4TVXvgZ/+19/K+9/K/913DD/LDxl4CnyfiAL7QDFD2Q7DrM+dEWH5duwfRumGrtooV0h150zZKSreEkoPsZCcajQRK3MzvXpynun14YJKV2p1F1++eWrsogJEo4pKoK+tlnuuusuuXSiTIUuTGYThnQTL1YdlG0PCRKL6+5ictm8KO3iaccnkbN8HMvVrlKSoLxv4NwpH3SxPefr+NzuIdiqAktV8I+7RzF9pIAXbY/D1Gvf05eIIWFomC7Y+PZjp2XDctPrTAcLFRB1txFlpwmhQFUUmJoKTReIaUL+LtPQpZN5JJtCfzrJYjLDMAzDMAzDXIQ+XcUZKikKAc/MJrEzZuB41ZbO3runcvj17euP6dsoZJKz2hA9sSQU+zLkM7rctvM2GUHx49kfy/sfPf5B/H/63o2ElpAvHjmKhUVCMbNeDM1ApVqA5VrnF4ozK0IxRU90I2JnH7wfTsurNsGCBX/BgmgQyJkOQp/xal0oNlkoZtamK1W4F73oRXjXu96FhYWF5aziT33qU1KkfMELXrDpn3/nnXfK5eTJk9ixYwc6ATmLDVVBoeyg5Pig3a5eF4t1VcO20UGkLQ+fPVzLZKbc8luvGIRKNxqg+I0R28PY5BQ+PTiC6XjtZ9yzow+T8zZeaeQxks2schdXHadWlNkECrWdIpC/x/NrixvQI7V9EwJHFkJQ+QaN5wIlgC5UxDUFqYSB8f40+lMxDGXSGM6kOSOZYRiGYRgmYnTSZNErkKOYRvk05m6mpLudkCnkVWP9+POjU/L+v08v4JfGBxFfo2OlXTnFlWZPVtaJLvRIltmtFUHxu1/7XZmnO+/O4ZPHPo7XXvLr8usBnW6VO72W0UShT6ECVLwq+lA7h76Qo7hSdOA6vjROdRNKTIMylkQwWVoutRPZkU6vFlP//EsB3/V5zgDTPUJxuVzGF77wBXn72LFjKBQK+PSnPy3vP+c5z5GlcnfccQf+6q/+Cq94xSvwe7/3ezh16pQUdunxiYkJdAuGpqI/KaBZjoyi8M6KokgbQgquFbcmws6WPYymVr/lS8/dPTaI36gu4l/n4/jxQK1wgv4/U9Hwi8emcPmuWhQFEau7jNsFCdGVqou5go3J3LR0Hyd0gYShYzQTx2A2hcF0CsMDfYi1STjuZGM0wzAMwzBMNxEGk0WvFNpZISi0I54/2IcPn5xFzvWw6Pn4wkwerxzr3zKh2Ap8mVVMMxhb+7M1CDlPMtqMJkbxy1f8Mj708Ifk/a/nvoIbFm7EldmrEdDUdHL7cKHdhl3nFbsMP+ZCiHPlFt1UYcY1WBVX3i/mLWSH4ujK+IllobiA4CnDHb+IxTS4ii0XQWDwe8J0h1A8PT2NV73qVaseW7p/33334ZZbbpEZxffeey/e+MY3SrE4nU7j9ttvxzvf+U50G6qqyCgKchMX6lEUcU1AqDXBeCSl4dhCbRradNE9RyhegmK8yDn8OsfBF08UpKOYmIkLfGBsBK98YgY379+aKWMkRNOyNJR0XBcly8VCxcF0oQqcWkBcF4jrKmIJE7FUHGbcQCweQywRh2FsfrOOCRUjpibz1Fo9wGQYhmEYhmGYbi20Iyim7mdH+/GhU7Py/qen5vGK0eyWONh0RYEbUE5xgHiLRXPqXumWUIYX7n4h7j99Px6df1Te/8ixv8M70n+KmGogoFmoZJzmmenrxpA5xZbMKo6vIRQvFdrN1IXixYUuFYq3peDJYsQAKLsIZipQRhKdXi2GUOs5xfTeaKx1MF0gFO/evVtOqboYV1xxBb7yla/0zPS5pKlBr0dRVFwf5Akm8Xgk2SAUl2oHo7OhXYMj/xZd5gS/Yo+OkUNT+NSOEZlZTNnFH98zjKmjRbxkwkDMqDmOtwpd05ClJVm7T697xXalKF6aLSKYXoQiFAhdyKI8PWHUxOOYDjNG4rEJw1xfJtKCY2PGdtCvaxg1dQzqGvSzYjsYhmEYhmEYJgyQmzgmBBZcNzTq3stGsvinyTlU/QBTtouvzS/i1qUBfbsdxZ4v85FbLb+pQpVmnDBEfLRC9H7DdW+QERRUvjbrTONzp/4dr5x4lSyXkabicGxKkUJTdZScEizPQpxyn88TPzFTd9uSUNyNKLoqxWL/+OKyq1iwUBwOSBy26jnFNIOAYaIuFIeBsE6foyiKgVTNWUxRFL4fYCS1cnSfKq4tFFNuMV1MauRZ+0YxfHIK/9I3hMlE7Wd8dXsKkzkHrxI5jA1uzdSxtaDCvlSclpXHfNeFZ3twHQ/+bBH29CJsBShRWZ6hQo/rUNdRjEffI1JxLJgxTCbjGImby4IxOTYYhmEYhmEYJkz0aQLTdnjcrmlNxYuHs/jXqZy8/4kz87gl036hiMwdjhtIR3GrURVVLl7gyRiKqDOeHMfPX/7z+NijH5P3f5T/Pl6541W1RkASkfRoi+GdgF4xRRGouGVkzbXPmdPZFeNVoUuFYkLsyqwIxScKCK4fhRKCaJyeRygIKMddlkYxzLlE/+jGrILyfCmKguISKKphNLnyFs+UvPN+jyyZc31oDVeU9m8fxW/kF/GvswZ+NFRz5B7o1/FnbhbZfHNuat0PcOlCGVfrFfnz2oXQNLk0Jhb7HonHPjzbRWWhIoXzZgn8QB7khamiaOiY6ovh0UQMI+kE9gykMZFOIanyJXaGYRiGYRgmHMTrY9MwuV1fOdqPu6dyMsXgUNnCDxcruLzNv5POgyif2G1iFuq6f7ZQZUZx1AvtGnn2tmcvC8WnrBMoeiVk9BhUW3ZeMRvAoJxipwrXd6GtET/RWGjXrY5iQhlP0osB2B5lwSA4U4SyLd3p1ep5ascHBb7jh2T+CRM2WCjuUnQqtQMwFF/56C/aPsq2j4QhztkIKn4gp2edvUEMZtL4taSL/zi+gC/szMrHypqCstb8LuVYug9fRh/2zbl46vwCnjJiyp/bboSqQcQBPb6xqIzA9+BWXTi2A+V0FU4AHDI0PGZq6E8Y2JFJYbwvAd23UClWpNO51eimDk3njynDMAzDMAxz8UI7QyiwgwBmSITiMVPHrQNp3DtfcxV+cnoBfzSwvki4jUACp00lLC2G3MRCCOko7hb6Y/3YltqGU8VTMn/5YP5R3GA+jVXiTaALHVW3AsurQhOpCwrFxbwdqos7rUTGQ+5Mwz+0sBI/wUJxOCCDoCy0685tj9kcrEB1SUbxWlfSKVJCCGAgrmK+4i3nFO8+K2NYUQUC14dL0w90dc2M4Jfszcrc4k9vG0Fxg1OQDmU0HMoM4bN+gKefrOBqO4+rdw7Jnx9GFEF5x7TUDuSB7yNhubBtF4uzFr4/u4iEEBgQDiAmm2oFpmekggBUFdhMgEUsHce2S7djZGxrigQZhmEYhmGYaEIZxZRVbPkBzBAlpf38+MCyUPxgoYwjKRVXtfl30ux2eh3aFT3RTY5i4qrBq6RQTBwoPIIbxp5eO3EhVzaLSOuGXMSu78mc4qR+rlCcSBtyVi/NeHUdH9WSi3iqcW5s9yB29a0IxaeKCBxP5hczHWapaFBGzPD7wawmnApdyAlrRnEjmiqWIyVGU2cJxf3nOmzp8G87HhA7/wHqhn2juLpSxYnJfNPrkbMUPJLM4IdDBvz6IMMRCu4fi+N+xDFc8fGMmQU8JeFh59ggwowihHQn05Ksx1MUbAeLFG1Bf1sTgyi/3sQ8EAQYDAL5c84HjcvypxdQyZcxv30Ou/bvQrwxmJlhGIZhGIZh6phCQTxkhXbEvkQMT+tL4PuFsrz/2byFq8baX2hXJhNMO6InlO6KniCuHroa9xy7R95+rHgAgaogoCJvOoVkxWDD20rFrazp2KTz9FTGQCFXi51YzFtdKxQrQ3EgoQFlVwqT/ski1D2ZTq8WQ47iistCMbMmvNvvUuhYpAkFFdfHcFLDgRlbPj5VXNsFrakKHD+A5wVQLxAwn4jHcNmO2LrW5ScAnJnN4aGFAN8fzuBkcmVHNBMXMtLiC3Qle9rGNfk8rhmNob8vHYmpNBlTB4SLfj3W9NX2agBQpQcNC4ZoUQDjPN8a64uhslDG1GOTWJxdxPj+cYzvGG9LzAXDMAzDMAwTXUiMSqsCMyEqtFvi1WMDy0LxN0o2Xm87GGuIyGuHUGwHvswqppmWLY2eUAQc30G3OYqXoJzifFDAoJqA4lMYBTuKNxo/YbkUX2jDUMw14yeWhGL6f2Tbuc7jbtkvyVK7A3PL8RMsFIdDyyDjG9zwHS+YzhOiSUlMqzEoUgJYVWhHjuK1oAGU6wXw2pDlRYwN9eOF+wbwliTwGyen8JzTJcTP2ik9MmDgX/YM449jKXx4sopvPzGNxVIJ3UZMAZaCJI5TJEcAzAa1mR9nQxloyYEUEiMplBfKOPqDI3j0ewewMF+bvsMwDMMwDMMwS8S12vifXIxh4ul9CVwSr4llZFv5zHTzMxQ3As3gc3zKKW7t60AisaZoXecozpgZbE9tX76/5CpWwpu0GHp0VYflO7DctcvqeqXQjhC7KXixRjBVQlBdW5NgthgF8KlokGHOgoXiLoYyionhRgdvyYW/xsBRVWkKVesHU+f8Hk3F1btH8fM7k/g/XgW/dGQaV8/X3M5LuELBg8Mx/OOeEfyRlsA/nKzgO4emUK5U0S2QsSGlABS2UQFwJKgt+aAWOXE2uqEjM56BGtcwd3QGB79zEIcfOwLb7i43A8MwDMMwDLNxKHpiqdAubK7CV4/1L9//0lyhrWI2OYrJTUxl3e0QAD2/+8QVip9Y4kD+UQSUhNB9f+aWQe5z6rihnOK1SGcahOJ8lwvFGRPKkjAeAP7xQqdXiVmKn7C9mrOYYRpgoXgDUJHdyMgIrr32WoQZipMgQbLPUGDU4yQogiZXzys+G9KVbXrCFpFOJvCT+0fwGyMG3jo7hxcfW8D20up1s1QFD4zF8dHdo/gjxcQ/nyjj+09OoWqvFpejCr0t/QpAQRvzAA4HwIkAqJxnXx1Px5EeT8OuOjjxX8fxyAMPY3pyeqtXm2EYhmEYhgl5oV3YuGWgb/nks+j5KLjtUyF1oUiRuB0mGBKKfdk80l1cPbgiFB8sPoqARKSlQjtmQ2iqhrJTRrCGAz3d3zuO4qVSuyX8oywUhwJV1KIntlADYqIBC8UbgIrspqen8dBDDyHMqDQ1itpUAYw0uIrPl1NMDmTH86WzeKuhIrsXX5KV0RS/MzmNnzqex2h59Q6rrCn45ngCH9o1ij/2dHz6aBELhVqDctShjGLKKqYL96cBPBkAZ+qCsX1WLIWqaugbTiORiaNwegGHHnwCB394EOVSLfeNYRiGYRiG6d1CO1qsNsXJbVa8HWgoTZpx2jf9XK07it12OIqF3nXRE8SVg1cu3560T2HBz9cK7brvT90ydIXiJ2zYvn3B6InyogOvy8W6RqE4mK8iWOwO41ekodJKKv2knB6GaYDL7CIGOX5zixRW0BwVy4HjBsjGFJysX7g7sVDF9vS5gylT1+D4PlzPg6F1piyNoin27xjBfgAvdj08dmoKj/lx/GA4hbnYynWNRV3BV7encLTg4ldzeQz3d0cgfkIBYgFQBHAsAKg2kN4JupivBrUPLInJugKIuAExrqFYqGLu8TOYpLK7vSMY3zYGQ9elW5neV3rVzm7aZRiGYRiGYboPGvNlVBVzdjgzQId0DbNOzbQya7vYl2jf7yKJ2G6DYE4ZxaIL/VZ9Zh92pnfi+CK1qAAHygfwk+IZUHwg4B7tDWFoBirVAqquBVNdXQhvmCrMmAqr6i3HT2QH4+hWlIQOZTiOYKayUmp3NVW7M50stKMZA4Hrc2UlswoWiiNEwtBgu54c9DSLKgSqgYsh2Spcy7OdI6dusHpXULRcObCkVlsqtTNCsGWQaHzlrlHQte2XuC4OnJjG40oK3x9JIG/UBmeH+zT8XSmFX52Zx7bhAXQDtL+m663kIqbrrPR+01CfbvtLS30jCBQBJZNAkNThFCwcePgUEqdzGN02hIGBrPxZ9EppsniDHBBClnuQiJzWVGQ0am7mwwLDMAzDMEw3FtqFzSwwbGg4WK5Ns59ps5hN4912RHBQoV2wrjOyaOUULwvFxUfxk+kbatPSyaXCrBuF5DcFqHpVZJBZ01VsnanNCl1csLtaKCbE7gy8ulDsHStAXDUYun1UzyEEAi60Y84iBHIg0ywvfubVKFfXl180la/i0EwR28sOvnbsCflY0RO46Zq9y8+h8dMPHjuKxYqNuGnA9ny08eL+htA1DU/ZM4anAHiZY+PrR4u4e3dNGD6ZVPF3Iotfm5zFrvHuuSpJg9tmhgo0o87XdPgDOuyKhcpUAacLZRRG8hjaNgIzHoMXUIlhTXSmaXgkNieEQL+uYsTQ0a9rcjogwzAMwzAME/2cYjIGUEavETIRZlhfOf1st1BMhXZlmlbdYlSh1gw2IRTiN8tVg1fhC0e+sJxT7A8Aql07h2A2nmldscvwYy6E0M4RimeXheIeyCnekYb3/amaALFoI8hVoQx0tzgemUI7z4dCmcUMw0JxtCARl5b1oGgmpisBRrIrH/rpRRuqHkPSXHn7Jwb78MiJWTng2cpCu41g6Aaev28A5hMz+OTuIfiKgum4wAfFAH715DQu3T6CXoLGp+QXp0VPmEjEdFiLVVjH5jC7UMbg9gEMj41AP8smTgPnKdvBlOVIoXjE1DGga0jwAYJhGIZhGCbahXZqrdCuPgkvNAw1jEdnnNpsx3ZBYrkd+NIkQXFsrUJVVLl4gSdjKLotp5hcsOSYnrInkUMeQ8FZmYXMujAVA1W/KrOK42sIxUv0glCsGCqU8SSCU8XlUjvBQnHn3Wl2PaeYdQCmDm8JG+Cuu+7CyMgIrr32WoQdUxNy0VSBkfTKgejY3Oris4nhAaQMFVXbg+cHcNrYQtwqnr1/GP/t2DS0+pSynCnw94PDeOTYGfQyihCIZRJIDCTgFC1MPjaJJx8+hLmZ+VXPI0F43DQwaGhY9DwcKFbw48UyDpeqyDuuvGjAMAzDMAzDRIvYcqFd+MZyjY5iyihut6OYtA+7xa8DOYopo7gbC+3SRhq7+nYt3z9QebRWlhLCbSkqUBG567mwvHOF4F4Tigl190qpnX+8gIC3rXAU2oXcLMhsLSwUb4A777wT09PTeOihhxB2YroKXRVwPB+7h5LLjx+ZLa16Xl8qheFMUpbZ2Y4PJyI77J/YN4rXnpyGSYG+FKuhK/jQ6Ch+dLi3xWJCkDt4KAUjYWJxahHHHz2Gw48+idLi6vfeEALDho4xU5eOi0MVCz9erOBAsYoZ24EbkW2BYRiGYRiGWSm0a0eRW0sdxVsgFLtBICM4Wgm5iYUQ0lHcjVD8xBKPlg4goHi68G1KkYF0dkURKLurjVprCcW9YNRRxlO1uAOi6iGYPvd1Ybb2eEH/fLqqxjB1WCjuclShyIgJy/Wx5wJCMTExNICYJlB1XVloFxWu3zuKX5+cRtKprXNVU/CR7aP47qGpTq9aKNDjOlLDKXkAmDs+hycfehInj56C46wenNOUvKyuYZupwxAKTls2Hl6s4L+KZUxbTk8MXBiGYRiGYbqBREOhXVgdxdN2e8eX1L9BQnHLHcX16IludBQvFdotcbB0QDoOlQidG4YRQ9VRdapw/dXnX8m0AaWuyLiOj2q5vRdPwoCiCZlVvIR/rNDR9WHkVTXA8kJ3vGA6BwvFPUDK1GScxMWE4uH+PmSTJizHk0uUuHr3KG6fnUGW8nUAOELBP+wawTefmO70qoUnjqIvjuRgEo7l4swTkzj22FHYlrPmVcWUpmIiZiCrq1hwPDxWquJ41ZaOY4ZhGIZhGCY6hXZhYrBBKK76AUptKJtrNEHQ2NVpsaAroyeU7oyeaMwpJqadM5gL5qF055+6ZRjCgOs7sLzqqseFqiDV13vxE2JXQ/zEiUUEHHvQWSibWMZPhOt4wXQOFop7AFOnFC0FuwYTy48dmSudc8VI1TRsG8xCVQXKFDkQsR32ZTtG8PqFOQxXa+tNJXf/vGcEXz002+lVCw1C05AcSMJI6Fg4mcOxg0dQrawesDRiCiEjKegi4xOlKg6VqYghWtsFwzAMwzBMLxfahQly+WYoyqDOzFkz3FoN/fWtjtST0RNK90ZPJPUk9mT2LN9/1D4AdOefumXQxQXX986TU7xSVr+Y7w2hWBlJALH6RSPXR3C6Vm7HdFAoJpE4YvoP0z5YKO4BTE2FrisYTpnQqdUSQMX2ML147oFoYmQAmZiOQsWVU7Wixp6JYby+vIBtpZXRzKd3D+FLh3LwIlDQt1UYiRjMvhjyk3kcPXgUpdKFs6EyuiZjKY5VbDxeqqLEryXDMAzDMExoCXOh3dBSPukWFNrRqQ85l1sJicSaonWto/icnOLKgVrQbgTPDcMmFpfcc81avVhopwgFYtdK/IR3nOMnOolCO0raLlkoZuqwUNwDxHRyFFCOVoBdgxeOnzBNE9uG0lIkrtrnxhJEge0jA7jdW8TuxZWB57/v7sdnTlVQrp7fPdtr6HEDZjaOxTMFHD9wFIv5xQs+P6EKjBgaJi0HB0oVzLV5YM8wDMMwDMNsDIoS6wtpod0gudfqUHFyuwvtKm2It9BVHZ7fvcaJxpziA2UutGsFutDhuDacwD6/UJzrDaGYENtXhOJgns/Rw4Bvd+8+jVkfLBT3AIYqYGiiVmh3EaGYGBsaRFLTkCuuPohFidGBLH5dVHDZwsrg875tKfxNWcPxM3MdXbcwoZs64oMJFGeLOPboMSzM5y/4fEMIjJs6Cq6Hg6UqTldtDr1nGIZhGIYJIXSRP4yjtK10FFNOsx34Le/ZMFQDfhcrp5cPXA4KLyRm3WnMBrNQWEPa9MUFi3KK3dVicDrTIBTno3v+vV6UhmxmVFwEXJjYWWi/bHsIQjgLhdl6WCjeAHfddRdGRkZw7bXXIiqOgnRMg+P5qwrtjp5HKB7IpDGQMbFoe/AivMMezKTxuriDp86sHIyf7NPw/80O4IFDUx1dtzCh6brMLa7mKzhx8DjmZuYvWgwyaujSoU4xFEfKFlw+oDAMwzAMw4SKmFortAubq3iwHoW3FRnF5Ch2fMBu8VhVE90dPUE5xXuze5fvH7AOAjze33S2deD75+QUNzqKS4s2vDYWPIYKQ5DNunabNq1yNGczdw20Xybth+MnGBaKN8add96J6elpPPTQQ4gKcUOVsTO7hxoK7c4jFBM7h4fkRaVcKdrTQNLJBF47rOJlR+ch6k6CsqbgH3aP4tNHixxFUUfoGhJDCViLVZw6eAJTp6cvevFh0NAQVwWerFiy6K4d0/oYhmEYhmGYjRfa0WywVoukrXQUz9jtF4opUs9psaOYMoqXHLe9kFP8iPUoFB7qbxpN1VB2yggaLjKYcQ2GqdbuBECxR1zFdD6ppPTl+0GEZzN3BTQDhc7nWShmWCjurUI7TRXYMbAiFJ9aqMA6TynZ9pF+xHUduVL0r+zpmoaf2jeA/35yCgPVlR3fV7dzFEUjQtWQHErBrjg4/cQpnD5x+qLfk9ZUDOoaTlg2HitVkW+zK4RhGIZhGIZpjrhQEFPDV2jXmFE82+aMYl3UhGK7DYV2QSiDPdojFB+sHqy5PplNoSsUP2Ff0FXcK4V2kqSxfDMoRl93iHrBoOIHCFgoZlgo7rVCOwVxXUV/onbljsZLx+fKaz5f1TSMDvTBC3xUne7YaT9lzxj+l7OIa2ftNaIoLuyg7RUUIZAcTCJwApw5NIWTh0/B87yLTmscM3RZRkK5xe0uJWEYhmEYpjMcPHgQN9xwAy699FLccsstOH364heVmQ7Hz6kqrJBFT2ylo5gi0yif2GlxTIQqVPn6dnNXxxWDV0hBnJj1ZjHjzXD8xCYxNAOO66DsllF2SstLvG/lMzE/v7jqa5bXvTNgVzmKu8CgFnmEQGBxGDnDQnFPOYp1TYXt+tjdkFN8ofiJ8eEBxHQVc4vds9Me7s/gdUMCLzuWOyuKYgT/enSRoyjqYnGCSg/9ANNHpnDyyZNwz+M8X0ITiiy5K3s+Hi9ZOGN1zzbDMAzDMEyN//E//gfe8pa34PHHH8fP/MzPyDg2JtyEsdCu0VG86Pltjy+jv99pscBJebO0eEH3iipxLY592X3L9x9xDqCL+/u2BIX+KQpmyzM4uXhiefFiK+at6ZmFVV+bKk3B87tz1iZHT4QMTQEcrxZBwfQ0LBT3CKpQkDJV2GcV2l1IKB7oS6K/L4mK7V7UVRq5KIpL+s+Jorh3exofKHEUxRKJgSSEqmD2+CxOHjp+0W1AUMmdqcMNfBwqV1ksZhiGYZgQcOjQIdxxxx247rrroGkarr766vO6hZ///OcjmUxibGwMb37zm2HbKyfuU1NTOHDgAH72Z39W3v/1X/91/Nu//VtXOyq7gXi90K7VQulmMAU5nRviJ9ocXUYdTdVWC8VClRnF3Vxod3b8xKPWQSgRLjoPC31mH+JaYtWSzsaWv+4UxfLjmjBQ9Sw4fneeVymplegJcPRE56H9Mu2OOX6i52GhuIdIGBo8P2haKDY1gUwqjbiuYaHUfVf41oqiOJRZiqKY6ui6hYVYJgFVVzF/MofJE2ea+p5hQ5dT/EgsnrS6b7thGIZhmCjxyCOP4POf/zz27duHK6+8cs3n5HI5PPe5z5XC8Gc+8xm8613vwgc/+EG86U1vWn7OyZMnsWPHDumGI1KpFBKJhCx4ZsJfaBe2+IlhQ1u+3e7YMiq0a7VrmdzEQoiudhSfLRQfsA4ALBRvGqV+oaFxSWZXnLWVRV9GftDjpmbA9VxYXneeUynJRkexwxceO42q1NzEDr8PvQ4LxT0ExUgIKNjVUGhHQvH5dsh0IpDN9iGT1LFY7c7pLuePohjF5w4vwHG78+9eD7G+uBzRzBybxtTpqXWJxU+WLRaLGYZhGKaDvPSlL8WJEyfw6U9/Gtdff/2az/nABz6AQqGAu+++Gy984Qvxute9Du9+97vl45xDHG1iQoEpELpCuyF9RSiebXNOMTmq7cCH28LXYCl6otsdxZcPXC7/TmLen8e0zReG2kEsJWoKMgDPDuBYwXJUBYWnOGeV33UNJBTX/27pYrW7+8JL2CH9R25xDr8Pvc7KEZrpekxdwNAVjMViEEqti6BQdZErOxhoaBxd9T2aiv5sBoWyg2LFRiq+9vO6IYpi4sgZfHJ4BPOx2vWTL+zMYnq6ip/LWOhLrriwezWGojSziDNPnoGu6xgYHmhKLKaB/5MlC6TBT8S6b9thGIZhmLBDrseL8cUvfhG33XYbBgZWju+vfvWrZWTFPffcg9e+9rXYvn27FJzJYEAnk8ViEeVyGcPDw+f9uSQ+07LE5OSk/J/irDYaa0bf5/t+V8WitZuUomDeteFTBkOH8T0Pge9jUKuJj8S0ZcvH24Ua+CAtuuq6MoqjJQSAEijwfA++2r1isSEMmVP8WO4xef8R+wCe7Y/I/QC7P1sHdQaSWFxdrG1LlbwL3ay5bclZXHLK6Pe95Rkdm4UucCwtHYX+nIQO1Ivs/EULisFexk6+h4EaAFUHgae1bHtj1san40fgy+PIVoxp1vM7WCjuIWKaCkOlK98BdgwkcGyuvOwqPp9QHNMF+tJpZBJ55EpWVwrFjVEUo/ML+GQliQP9tQPzgyMxTBc9/NKZOewcG0QvQwV3pZkiTh06Dd3Qkc6kL/o9Q4aGORKLy5YsEpkwdT7gMAzDMEzIoHxichE3ks1mMT4+Lr9GjI6O4vLLL5fRFK985SvxoQ99CC972csuKET/xV/8Bd72tred8/jc3BxM09zQupJInM/nmxbBGcC1HbhVG+UGF2+nIJHYKi4i661MOT+zWELZaJ/oSE7iCpU0V0uy3K8V0Mm9la9lxyp6d49t9yf3LwvFP7YfwTPsG2EHtRmDPK5vHUYKqC7Wbufnq9D7a0573/NR9kqYq8xCU1Y+N5uBRP6yXVou1+sksTig1dMwS/N5uIkudU+3mLa9h+TsdgIoCxUoIbi42M34gY+qXUIuNw/bO38kbKugsVezdH60EEHuuusuuUTNyWBoQrqKF8oO9gwmVwnFT9vVf15HcSFQsW0og/nSNGzHgaG35gAVRkYHsrjdtvDvp0r4+raai/h4SsX7jH784uEzuG7vGHoVRQgkBhMoz5Vx8okT2H3lHsQT8Yt+32BdLD5crh30WSxmGIZhmHBBGcUkDJ9Nf38/5ufnl+9TFMWv/uqv4q1vfSsmJibwT//0Txf8uZRxfPvtt69yFN9www0YHBy8oBP5QiyNv4eGhqCqK65U5vyotoupYlnOotNpWmEHWXIOj/s6kK+NDXNCQ2KgfYYMikOr2A5S6aQcl7aKWTGLolNEMtbdMw+f6j0Vnzv5OXn7cftxxGACKhDX4zymbyGpbIDCZO0z4ZVV+foShmag7JQR02NI6Rc36jQrUAUIkIlnZB5yJ/H6LASztYt/CUeHiJ97LGK27j2UGcVVDyKWgBLnY2y7HcWVooP+/gFkGuJh24VlNX8RhoXiDXDnnXfKZanUI0qkTQ2zRUsW2n3t8Rn52NELFNqpQgHtgkYGMuibymG+7GAs071CMRE3TPzcNg3DR2Zx965BeELBoiHw9ztG8TNPzOKWPf1QG6bL9RJC0xAbiKM4W8Sxx49jzxV7YJoXd5nToHzeqTmLCRaLGYZhGCZ6UBne9773vaaf39fXJ5ezIYF3MyIvOYk3+zN6iYRB5g8djgKYIXjNyHwwYqyMH2cdF6KN60USSuD6ckzfym3G1E0UnELXO9spp1gTGlzfxUKwgGl3Chmzv5ZWtF4hAADP/ElEQVRnyuP5lhHvW9k2KwV/+bXVFG15enorBUH6WUtLJwnSBpbtd0W34+sTJdryHmpCbm/Cp311548X3Y6oF1duxXhmPb9D24xt+bvf/a50BlQqFekMuOyyy3DdddfxASPExOkqegDsHlpdaHcxJ7IvdOwYTOGh43Ny+otoVb5XSCEh+Ln7hzB4ZAofHx1GwRDwFQX/umcIM6dLePmYJgXlXkTTdcT6Elg8U8AJ/QR2X7YbWhPC+YC+IhbT5MJtLBYzDMMwPUZYx8/kHF6KczjbadyYW8xEk7gQoAoOil8IC8NbWGZH0F9ut/jvN1QDPro3n3gJUzOxP7sfB+YPyPsHKgfxE+YzO71aXUc80ygUe+eISRWvgrXnAEcbhQrt6gRFLkEPAzQc8W2PJg4wPcq6hGIaQH70ox+Vy49+9KNzAuxpgJtKpfAzP/MzeP3rX4+bbrqp1evLbBJTE9BUgW3ZFaH4+HwZrufLx9cipquoOB52jA3hyek8ChUb2VQMvcC1e0YxMDWHjxsZHE3XPi7fmEhiesHBzysLGB3IoBfR47qcOrhwOodTho7te7c1dYVqRSyuyvssFjMMwzDdThTGz5Q9vJRF3LjeJGjT15hoI2gb01QsVMMjwlCPxRI514Pt+zDa6MylqE2rxUIxuWw7Xga2RVw9dPWKUGwdwE+AheJ2OoqrRR++F0DUM2I1VYPlWPB8F6rosknhqZXZBUG91I7pMJoAbA9Bhd6PJs/VTRVKh6ONmNbR9NH4Xe96F/bs2YP3vve9eP7zn4+7774bR44cweLiImzbxvT0NL7zne/gT//0T6X74HnPe55sT3700UdbuLrMZiHRlxzCmbiGlKktFzyczFXO/z2aQNXxkE2nMJZJYEHuMHqHHaODeINh4RnTNXGTOJjV8X6zDwePT6NXMVMxaLqKueOzOHNyqunvI7FYVRQpFp+0HG5MZhiGYbqWqIyfX/SiF+ErX/kKFhYWlh/71Kc+JafUv+AFL9j0z6duj5GREVx77bWb/lnMxkipKih6MiwkVYFEgzA857TXVawpCiotfgEoEkA0fzodaa4avGr59gHnYC3HlGkpuqlANepCW1ATi5cwFANO4ML2w3Oxp1UoqYZYy7LL21YY0ASCqgt/ugJ/unzxZbYCVNo/M4TZOpq+HPW1r31Nthzfcssta36dCiVoefrTn4477rhDDnb/+q//Wn4f5Zkx4XEUG6qQDmGKn3j4VGE5fmL30NpFDOQ0dr0Atq/gkm1DODFXRslykDS7O6u4kXQyif9muhg5toDP76oF7M/EBD4wNoKfPj6PeFBs6uekVB9X7hiE2SWFgLFMAqX5EqaPTkE3dIyMDzctFuccF8fKljxRoPsMwzAM022EYfxcLpfxhS98Qd4+duwYCoUCPv3pT8v7z3nOc2SpHP3uv/qrv8IrXvEK/N7v/R5OnTol+zjocSqt6+V+j24hJoQssiODiBYS19ewoeFY3eU8Y7sYb6L3YqPoigI78Fv691McAJVJ9QKX9l8KXehwfAf5II8pZwp7Yns7vVpdBc0uifcJFGe95fiJRD2OQlU1uE4Jtucg3mWnTYqhAgY5WOsCMbmK+3oz4jEsKOQoTq5jf1yypcAfjiML0wqa3s3cc8896846+8M//MONrBPTRoRQpJO4UHVkoV2jUHzrBb5PVxUUKg72jA3jwcdPoVTtLaGYoKboF1+SxeChKXxy+wiqmgJbVfDZ3etraX7xsQX5c7qFRDaO8lwJZ56chGZqGBhoLj2rX9dwumpjsuogq6lyWiTDMAzDdBNhGD+Ta/lVr3rVqseW7t93331SxKbfe++99+KNb3yjFIvT6TRuv/12vPOd72zpujCdI6YqMtqh6vtIhaSgaOgsobjdjmLHB+wggNYiOYMKiEjck8VPXV7ARXnMJBY/MveIvP9Y9THsSbNQ3I74iUaheInaFhvA8WrF4N2GkjQQ2LXZu0HRgcJCccdZT4xEIASCJaGf6Qq67HoU0wwJk6aeBVIobrbQjiIryhRormnoT5o4Mtucg7Yb+Yl9oxg4OY2PZ4YwFV//oPDBkT68GN0DNVcnBhMozpRw+rFT0K/WkU6nmhaLZx0H846+KquOYRiGYZjWsHv37qZinq644goZP8F0J2EstGsc+22FUEwiudvCyDNVUeXSC0LxUk7xslBsPY6fwos6vUpdnVN8dqEd5RSX3Yrcn3dbxwvFTwS5ulDMOcXRg7K0Ha8rt81eZUPKzNvf/vbzfo2yzDKZjGxvvvnmmzezbkybMOvuzV0DK0Lx0bmLC8XkQqas4qFMCk9OL6KXuXT7CN6wkMd3jweYM5u74vngcAyeUKS4fHJ6HttHuqdFXBEqkgMJlOfKOHXoJC5/anPFN3FVYMFxMWnZ6NdpsM0HFoZhGKY74fEz00nCWGg33BDFNmu3Vxyi2A3H8WG3UCgnRzFlFPdKoV1jTvHjzuMsCrWBxCqhePV2pSm16A/Ht2CoXVYs35BTHBTDs49imoTcx7RvdQPK+en02jCdEorf8573yAKOSqVWgBaLxVCt1q4AxeNxOI4Dz/Nw/fXXy0w0yj5jwkNMFzA0BWOZmJzGQsOl2aKNxaqDdGztOAkqwLNdX7qKM1RipgrYjgOjS7J2N8JINoOXZALAqQB6nIKlLvj8hVkHj2Vrr9fRvIvtI+gqhK5Bi+uwixaqlSpi8VjTruI528W842LY6N3tiWEYhuluePzMdJqkSu5XhAbKKF5ips1ldmRGINnNaaGoS25iusjjBaudn93K/ux+GMKQhWqLwSJO2SexI7az06vVVVBG8RKV/OrtSlc1lCin2He6TihWUit5uBQ9wUTQUWz5kI2pevfPrugFNvQufvWrX8W2bdvwsY99TBZiUEkG/f/Rj35UFl58/etfl5lsVFhBxRVM+BzFhqZCFQrGMysHmSdnLuwqpueTmNyfSsDUFOkuZppnT6G8fPuUuXZxYNRRDRWu7aFSrp0EN0NMFXLgfrrqyIIRhmEYhulGenn8fNddd2FkZATXXnttp1elpzGFIk0iXgvjF1olFM+2OXpiiZY6ihuiJ3oBXdVx2cBly/cPlA92dH26kVhaXQokhmsHcKor25bc1nwfjtd9QqqSbDALsVAcPYSCgPatXjiOLUyHHMW/+Zu/id/93d/FL//yLy8/lkql8Cu/8isolUr47d/+bXznO9/BH/zBH1xwmh3TGcgdHNcF5ksO9o2kcDpfc7N88sETuHZ75rxTiExNYLHqYnwkgZiuoWK76NvidY8y2wISTzPy9hMNAn03oRkqrMUqrMr6ihYodmLOceUy2mMliQzDMExv0MvjZxK+aSERfMeOHZ1enZ7FUGqFdo4fQCUHWJgcxVsgFNPsaKvV0RNK70RPLMVP/Hj2x/L2F/JfwI+t2u0LQfEcz0g9Az+Z/sktWMNoI1QFsaRAtegv5xTrFC6+9HVFoOJW0Fx1eLQyipcISjbHmkQM+V4FAQLPb1FVKBNJR/EPf/hD7Nq167yFGT/+ce2AcfXVVyOfz29uDZm2kDI1OJ6Pl147sfzYD48v4JuHZi+YU2y5HgJFxUDCZEfxOtk3moKoOzhOJ1Wcmc2h21BUVWaZWJX1ZUuZQsiDymnLlicvDMMwDNNt8PiZ6TQ03jIoqzckjuKhhsgxiiBrt9OZCu3KNDW6ldETSu9ETywV2i0x58/hR+UfXXT5QfkH+OD0BzFpT3Z03aNZaHdWTrGqoepa8PytceBvGQl92Uktc26t3vlMdQ1CIHB656JZt7MhoZgGuX//93+/5tc++MEPLg+C5+bmMDQ0hG6jG6bPxetX8K+a6MPzLl8Jy/3gNw6jYnvndRRbji8F4oFMEp5MN2aaJZNKYW9De+2Rhe4M6leEgkpxJWZjXa5i28Wsw9ONGIZhmO6j18fPTDiiJ6hnqJXxC5uhTxXS5Uz4dbG4negKieR+y6LOSCTWFK2nHMX7svuwPbV93d8XIMCx6rG2rFNX5xQ3nDsShmLADRyZE91t549oiJ/gnOIIQu+h7Uk3ONOj0RN/8id/gle/+tW47LLL8JKXvESWbczMzOBzn/scDh8+jE996lPyeffeey+e/exno9vohulzJPpSBIXjBXjdT+7B/YfnZFEdldp9/LvH5WNrTilQgGLVRTYVhy5UVB0HsR4utFsvewtlHMrUAjtOad2ZUywMFXbJhm05MNYRI0FTIcnpMWk5GNQ1eZ9hGIZhuoVeHz8znYfG8lRol3ed0KwPxU+cspzl+Il2FhvTOJMMb3YQQGvRBGnK7fV6yP2oCQ3veNY78L2j34NiKlIsvxBfOfEVPJp7VN6ed+a3aC27yVG8ettSVQ2u48H2HMQ3pOSEO6d4SSAOijYwFO/0KjHrgeKMlnKKNQ6giDob2r38zM/8DL773e/KAe/dd9+NyclJjI+P4xnPeAY+8YlP4LrrrpPPe9/73tfq9WVaBMVI6KqA7fkYSBp4zTN34QNfPyy/9pkfnsJtV45i50BizSK8fMXBSDKGGBXa2R4Lxetgm085xTWh+FC2Ow9+mq7CsT1UK5V1CcVEv65hxnHkicK22Er7LcMwDMNEHR4/M2EgoVJUQngcX2cLxe2EhOIqlYH5PgUMt+RnGqoBX/qhe4eEnsDTh56OZH8S4iLGjhPlEywUt1AorslvAWxvfX0wUUBJGwim6rNS2VEcUUexD7g+XVHq9Nowm2TD16Ge+tSn4pOf/ORmfz/TIQxVSFcxuYhhAi95ygT+45EpHJktwfMDvP++Q/iTn73mnBB5+h6KnrBjOuWVI19yoGuduZyp1CM0opRzv3doRXw/nlIxvZDHSLZWcNctqJRlXbZRrVjoy67ve3U5JZJcxbZ0FcdaNIhnGIZhmDDA42cmDDnFRFjKomo5xZW6UNxecYjGmY7jwwla67DtpeiJ9TIYH1y+nfO6r5+l3UJxddGH7wcQJMLV0VRdFtoFgQ/lIo7uqDmKl5COYiZaqAqCJUcxE3k2pfDRAOPxxx/H/Pw8BgcHsX///lAMOJiLQwcbKrRbKNcGZKpQ8Bu3XII7P/1f8v5DJ/P4xhOzeM6lw+c4kclRfGTegxUoyFUcCG3lYLaV0JbW5wXoo/D7iDCYSWNPzsWRdO2jd2S22nVCsdA12XhqlTd2pXtA13DGcjDjuNihsquYYRiG6S56cfxM/R60eF7vTNEPd05xrdBuKR+4kwzrK6ej7XYUq4oivb+UU9wqKKNYaVGMRTcyGFsRiudddhQ3gx5XoOqA59DxArCK/irxmLY5x3fg+DYMNYZuQUk1CMUldhRHDTmOCQKpA/AeMfps+BLU+9//fjld7sorr8RNN92EK664AhMTE/ibv/mb1q4h0zYSJl0BX7nic/W2DG67YqXY7u++cRjlswZsJChPZONy2TaQgqGpSJpaRxZdEyhUHRStaLW+XrJQc00Qp9Rz4z265UBRLa78nesdxMeEkK7iSgubqRmGYRim0/Tq+Jm6Paanp/HQQw91elV6HuqAoCUshXZD9YJtYrbNZXZLtPJvv1hGb6/TKBTnfHYUN3sedaH4CV2tCcW232ViamrFIMRldhFFURBQEDwTeTZ0ZKNm5v/5P/8nnve858mMtfvvv1/+/9znPlc+fr5GZyZcUIyEUBQZNbHE627ag6RROzDNlWz883eOn/f707E4aPaavUWDurOhjGUSrhcrTi1CIyJMePXsJQBPdmlOsdBVVEtVuO7G3pesrmLB8TDd5imIDMMwDLNV8PiZCYuj2KDxe0hyiimjeKscxUuGBMopbtnPE6oU9jh+4uLREwv+Ar9OrSi0U1T4vg/b6654hsboCVRcBJR1y0QLio10PDlziunB6In3vOc9+K3f+i385V/+5arHX/ayl8kG5z/7sz/D7bff3qp1ZNoExUiQWGy7PuJ1cbifiu2etRt/87Un5f3P/ug0nn/lKHYNJs/5/mQyLgebluvBaJg2tpWYuoqy5WKx6sjMdHI4h51LBlemCFEExVx+UUZSdFtOsee4stAulU6t//sVRZatUAQFOU2ooZthGIZhogyPn5kwQGOsuBBYdMMxI29YZhRjSzKKCVUBKi3M0CTRTgp3gc/u4jVIG2mZ4+z6Ljx4KHgFZLV1lpj0uFBczp8rmNK2VnWr6CYU0iNoWTKAUfxExuz0ajHr3cHS/pUWjQMoosyGjmZHjhzBS17ykjW/9uIXvxhHjx7d7HoxWwCJxIYmpNDbyIuvGcfe4ZowLIvtvvbkmleF0smYFGqdDgeWk8hNYne+7MBzw3/1arg/g53Fldf88MyKw7hbUE0VvhOgWt74ACarqci7HqbrTdgMwzAME2V4/MyEhZSmyozisDmK5xx3VSxeO6B8Zjvw4bYofoIcxQKCnbLngQTNAXNg+T7nFG+g0O4sRzGhqzqqrgXPD8cFn1bBOcURRywJxbw/7EmhmLLVaLrcWjzwwAPy60z4oeiGhHGu0EtxDr95y77l+/91Mo+vPz5z7vdrOtJJA26HdwQ03Suhq6g4PhYsGyGJXLsgl+Qbc4q7L35C03X4rodqZWOFdgTFoqTIVWy7KG4wwoJhGIZhwgKPn5mwYIha/VoYpgeTMYBMaAT5PRbaPObTFAU0o71V0RtULCaEgBfwWPV8DMQbhGKHheJmiPeJ80ZPELqiww0op9juXqG42F1/W0+gKghIjImAeY+5MBvKC/j1X/91vP3tb4dlWfi5n/s5jI6OyoKKT33qU7LR+I/+6I828mOZDpAydUzmz3V9XjnRJyMnvvzolLz/d/95BM/YPSBL5BrpS8RxIgSOWEUoiOsCZcuDqtjIJlbC8MPINqdEr768faSvOwvtCGsTjmKiT1Nx2nJkBMW+CMSKMAzDMMz54PEzExZMIWqCaUCCU2fXhYwBQ7qGqXo+8aztYqCNkXa6UFB1fTiUU0x5mi1wzC5FTzAXL7SbY6G4KWLplfMexwqQn3IgzprKT4Xu0+Ui0g3RvrGEhkRDKVzk4EK7SEMGPtBFuA7POGc2z4aOwr//+7+PXC4nB7V/8id/svLDNA1vfOMb5deZaBDTxbKjQH6wG3jdTbtx/5Nz8iA0Xy+2e/2z9656TtKMQdVqhXadyileQlUF6NBSrLrSFZ2ONRw1Q8be7MpB8Mk+FfliEZnU+rN8w4xChXaFKjzPg7rBjGE6eUhrqjx5GDE9KRwzDMMwTBTh8TMTFkxFkYIpxU/o8kyg8znFS0Ix5RRfmlzp82g1JJCTSOy0SMeQ0RMKR080W2jH0RPNoWoKzKSAVaptVw9/eXHN5x0Gmbpqxq4lrrtpHPuvGULUC+1YKI4oVO7peOCz9mizIWWPBMU///M/x+/93u/hO9/5jhz0DgwM4IYbbsDg4MqBgAk/pqZCp0I7z5e3GyFX7q8+cxfet1xsd0q6jHcPrRTbpdMJ6UroZKHd2XEavh/IcjtZiHaWAzosjA31YyLv4XRSha8oODRdxNO6TChWNQHXdmBVbSSSG4/X6FMFTlo2piwHaVWcc0GDYRiGYaJAL4+fSRynhS4eM53HEELGT9i+L8uDw5RTPFMXjNsFnR+Q9Oa0SNglNzEJxY7PolYzjuIcO4qbJjmgLgvF6+FH35qUx5t9Vw9GOnoCJY6eiCR0THG8NY2ITHTYlIpGg9qf/umfbt3aMFuOqQsYqpBlcGcLxcSLrhnHfzw6JYVMipt539cO4d2vfMryhz4dp0I7gZIVnoE/FeyVLReFqgNNVWCE1IW6P1/F6WRNdD+FOJ6G7kI1NTglG5VyZVNCMW1rWY2mJDrI6qqcnsgHHYZhGCaq9OL4+c4775TLyZMnsWPHjk6vTs9DbuK4EJj3wlGERWO7rRKKl7BbVGpCIjHlFFeClf4RZjUDsYaMYi/X0XWJEruuTcBzSucViwPUtmHa/ujcyLY82NXaOfkPv3kaQijYe+XKax8FlLOiJ1hsjCDqUqFdQBtnp9eGabdQ/JnPfGZdP/hnf/ZnN7I+zBZjagIxXZUO3LWQxXa3XoLf+cRD8v7Dpwq477EZPPfykdrXNQ19CRO5ShFhIm6oKNse8mUH/QkFmtZ5t8TZbLMop7gmFB/twpxiTVel09yubrzQbglZamc5OFisYsTQMGbqyITAwc4wDMMwF4LHz0xYSQqBqZA0QK9yFDvtF4rJVVyljOIWoas6vBCZZsIcPZHz2FHcLImsiqtv6zvv16lAseSUsD21DSkjDavi4mv/fhiFXO3c6/vfOAVFAHsuj5BYHNcAOm2njycJjSR802NMdBAKtYUCng+EUINhmqPpTx2VbjQLXfXhqWXRgN6rpKlirnR+Me/ysT781FVj+NIjZ+T9v//Pw1JYXrq6Nz3n4PS8g+OFEhQ6Gm0RCUPB7qyBpHHu76R1S+gqSo4HUXHQnzQhQraf2p1Z+fg90aehWCkjFe8ewVihFzwArMrmpw3R+zkeM1B0PRyr2vIkYszQMW7qSIbUMc4wDMMwPH5mwopJcW0IB0PGynTzWdvZEsNbmUSMFmGoBvzQvJohj57wc+wSbRGyRNH3Yfu1cy0zruE5L92Dr/37ESwu1M7tH/zaKeks3nVpP6IAFdSDcooXa/uBoGhDYaE4WqgKAroQx4V2kabpT92RI0fauyZMx0gYGi52Uf21N+3GNw/NymK7XNnBB75+eI1ndSZHaDSlYW+/jj39BnZkdOg0+qsfaOKaQJnC1Ks20qZO2eqthQY6ATZUA7J9ZACjiz6m4gKeUHDoZAHX7e0eoZgQmkClQM7p1pDSVCRVgYLn43DFwqzjYtzQMGoaiIUgY49hGIZhGuHxMxNWqGOELrV7QSAdtr2SUUzoVGgXBHD9ABoJU5tEE5oUP5m1yZpZCNCFCR8OHBT9ItJqutOr1RVQ9EnVrS7fjyX0ulh8GMV87dz8u/edlML8zv1ZRAElaSBYFoodYLjTa8SsB3kRiHaHLu8Te0Io3rVrF7oJKhL5+Mc/jqNHj+KJJ57Avn370KtQ9AS5ik8vVGQZXMJQ5WMUO7FEJq7j127ajb/66iGEjamiK5f7T1Tk7AYSi/f2G9g7YGAkqcp4jWLVRdVpg0snCBCDA99RZTYyidS6qjYtSO9fqGKq7iI+iRiuA7pOKHYqVGhXhRmLtezgk9FUWWyXdz08XrYw7biYMA15okEFLQzDMAwTBrpt/Mx0D6ZQpFhMWb3xusmiUwyflVHcbsepRkKxT7OjA2gbsnuc/fPY8XghVKEiG8tivlqLnZh351kobmHsSdW14PkuVFHbDuNJEov3SrG4VLClaPfdr56QJqodl2QQhUK7JYmRHMVMBFEA3/XkxUgmmjR9VJubm9tQI/P8/LxsdA4bL3nJS/CGN7wBN998M3qdbFzHZWN9KEm3sC2L4GYqLgIlkAV3JByT2Pqiq8fg+wEOnllc9f1+4GNqvgDX86VYuhXQwWOGBOLSavHX9YEjOUcu9x4uIakr0mm8vU+TBZytX5EAE0kf6biLku3JeAudmqS1WkkgCe/6BbJ5tlfJbVsTio+lustNTKiGBsdyUClbLROKlxCKgn5dQ58WIOd4OFCqYMrWsM00MGRoHXfHMAzDMEy3jZ+Z7hKKqdSOxNKNVw63hgFdW44lpfWhmWNkCmgX9HdXXR8OTalswQkCuTqZi8dPLAnFOTeHXSZfRGsFuqKj6ldheRYSdaGYSKR03PLSPbjv3w+jvEilcMB37j0OIXZi255M6IXiZUrtj6Jh2gBdfLR9jpnpBaF4z549eN3rXieXpzzlKRd8bqlUwt133433vve9eMUrXoHf//3fb8W64tChQ/izP/szPPDAA3j44Ydx+eWXy//P5uDBg3jjG9+Ib3/720in03jNa16Dd7zjHTCMlRbNZz3rWS1Zp26AcosGkoZctvfHZQlcyXalC3eh7KBsu5gv+VKYu/nSYbzgqrFVbmPiBwcO4fhMAaOZrRU7S26AIzkbh+dtKQ4X7dUZGiUnwMPTllzaBb0Sv3BNzcHs+YFcFquufJxeJ5rSRg5tEoxJRKbXe4mdqZXbj2d1lMpVJOKtFVQ7idAVeAUP5WIZqUz7nANZVcDxFcxbDuYsR7ZnDxrkEbn4gcn3PBQsB1bZglAvflJCPzGrq4i1wblMn7HG7YNhGIaJNmEYPzPM+eIXDKFgkVwWHYbGynTxf65eZEc5xe0UimuOYh9O0DrHLIkhZJ5h0XhtBmIrF77m7LmlPm9mk6iqBtdxYftO3Xq0QiJt4JaX7pVicaXoIPCB+798As96oYKxnSmEFSW1otnI6AkmetB5MuXAU2Fqh2esMG0Wir/1rW/hD//wD/HUpz4Vl1xyiRRaacA7PDwM0zSxsLAgc9i+//3vy+dms1m85S1vwR133IFW8cgjj+Dzn/88brzxRhncTsvZ5HI5PPe5z8X+/ftl0/SpU6fwpje9CeVyGX/913/dsnXp7nI7TS4jafp8BzKXmMTifNnBouWiUHXk440EQoXtBdKVvFVCF2WqxXUV14zG5EJXrGZKXk04ztk4tuBIh3G7oVfiS4eKuOMZA9BUARrXmvS4H8ANAliej4rrS6eEpiqrrqoZsTgGqz7mYgKOUPDoqXnsHO0uBxFFT9hnCshpW3MRgZwoT3pe85Uivo9YuYDqYlA7qDVBVqiY0HWkWmxTJyf69v4E+hOUp80HVYZhmKgThvEzw6wFjTNSqoq5LcgEbgaKD1sSiil+4pI2Dhtp1hmNEx1Szlry89RasRgLxedlML4ys2LeqTmLmc0jzxYUBba3dkRDso/E4j24798Oo1p25fnp/f9xHM984Q7Ewpr92+Ao5uiJiELiMF2Jo5xizp/obqH4mmuuwWc/+1kcPnwY//AP/4B7770Xn/jEJ2BZK07NnTt34qabbsI//uM/4qUvfSk0rbV5TfQzX/7yl8vbr33ta/Hggw+e85wPfOADKBQK0pGxNGXPdV38xm/8hswlnpiYaOk6dTvkiKV8YlrGM3FYroeK7cmLQ430CQvz+SL6YiriZsN0kTZBvz5fqQnWS4YDGvCOpDS53LgjIQsqTuQphsLGbMlbzjpqJfSzSYzOVXx892QFz9y5MqqlHCgdlFlcGzD6ni+FY7+h7EIRApfmLNw/Xpv0N6PEsb3byjAUoFoqn3NxoV2odSG3ecjFK5Cg72lCKKYLFHOui4rnY0zXMCAzqVsj6i6WLCxWHUxk4pjoj8voF4ZhGCa6hGH8HAbuuusuuXheG/oimA1DRcCd9xOvCMUH6/3Hs1skXlM+c6scxbKsrUXCc7dGTyxB0RNM66AyxapbQRD4UNa4UJHKmLjlZbXMYhKLKUry/ntO4NpbB5C9BKFDSTZoCVUPgetDuUCUIxNChIKATJ3kKmalOJKseyS6d+9e/J//83/ksuTgrVarUpQlZ0Q7ITHnYnzxi1/EbbfdtirX7dWvfrV0Ztxzzz1SYGY2DglXa4lXwUgWafOkFAO3KqeYBOuS615wGhvlE9PSLr5+pIj/PFaRt795rIxrxmJIGWtvp0IVWGtNtlXyVDsgbx9PJ/ETXXZ+6JgahO8jQTpsCE9+5UHMIRu4JoX7ZugLNCx4PuYRwFCBMUOT0xg3C12QIaH48GwJhaoro2AoEobdxQzDMNGmk+PnMHDnnXfK5eTJk9ixY0enV4epExNKLRs4CGT8VWgK7erO4nZCruLqGrNTN1pmR+epXsAXQppyFLvsKG4ltP05vgPHt2Goa0cYprMmnvPSPfjavx+BVXHhewEeum8eU4fsjp9nxBIaLrt2WLqfCYW0BFMFrNrnKSg5UDLdf5zsJsgwJ69Cel1mgOshNq3a9Pf3I0xQPjHlwDVC0/jGx8fl1zYDOZVpWWJyclL+T+6IjTgk6HsoPqMb3BWpmIFkTMPcIjWrbs0OQZd5N8GW/b61eNb2OB6arKJgU8REgK8dLuIll60vi3fEWBkMP5bVYZWLMPX2idtbDV0BDmwXdrkCM5UMp1C8tKyDrFBQ9nyctCxUPQ8Thi5PuDZL0hAwVR3zxSoWKxbGMzGMZ9ld3Cv70l6F38Pos5XvYTdsJ2EbPzO9iSFzioV01sY6nCM5ZKy4CGfs9ueS0p9L47hWQHETS9ETzMUzinM+O4pbiaHqWHSKsD3nvEIx0dcfq4vFh2FXPSkWnz66uqS+U1RKDm76qd2rCu2CulAMip9goTh6kFbseuwnjijhs/dtEnJokDC81oCcGqSXePOb34x//ud/xpkzZ3DzzTdj9+7duP/++y/4s//iL/4Cb3vb29ZstN6IG4ROqPL5fNNu6bCT1QPkvSrgbM1AMxb4iMOB5m5dLvLZGCLAi3ar+MTjNbH3oTMWnjmuYjzV/C5xRyaOrO1jwRCwVAXTs3lcMtqHbkFTPfiOA69An78QFhLQtMMqzXVU5DSZ9UBBI7ofYIEKUVSBQVVDokW5xVQxUam4OJTPYXpaw2DKRDqmdfyqfxjptn1pL8LvYfTZyveQxl0Mw2weU1DRsgInCBALQfTEEpRRvBVlfvR3U1QdzULcdPSEwtETTTuKPXYUtxLa9sj4YvsXz/PNDMTwnJfswdf/3xHYS0JsCMjN1GboNhbaBXNVeZsL7SIKXY2zfNkjxeev0aPrhOJmefe73y2X9UCleLfffvsqR/ENN9yAwcFBWUqyUUfM0NAQVDX611qG5kt4bNaCL0wZs9BuFCWArVThKgr0TgkLQYArhwNsPwOcLLgyB/lzh2285tpM8ztEAVyWs/Cd0Vr8xCQS2CG66KqpAOxCERp0JFLnXsTpONJJHFCAV9Nldo2Q/6UvCJBzfVhCwZihY0gTLZnCSUK06wWYK9qYdhSoiRjGMnHEtijeJSp02760F+H3MPps5XvYmO/LMMzGMYQCQwEqW9Qj0axQvBUZxRQZ5vi1EmStVgm2YchNTGIdTf9n1mbAXHEUV1FFxa8gLmrnPszmoe2v6taE1YuRHYrjtlddgqNHZxDX4jJfuxO4ro8ffvO0vF2pZycvm78acopZKI4odF5Nszbo+NLhGSvM+uk6oZicw0uOlrOdxo25xRuhr69PLmdDJ0QbPSki181mvj9MDGRS0IUqS76SWzBNXtUVaCoNygLpCugUlGv7gn1JfPgHte3uRN7FgVkbV440783YViJHa22wdCKdxA1ddtWNRHPHspvOAN5yaL2E2PD60dY+KAQKno/jjgsLmoyiaMV2SZHX4/2azC4+Ol/Fou1je38Cg5xd3LX70l6F38Pos1XvIW8jDNMa6KJ2UlWRdzsvxAzpW+woFgqqrg+HDAObNLiQSEc5sZVgtSuSWUFXdfQZfSjYheWc4m3Gtk6vVle9vlXXguu7stzuYsSTOsb3JpCNZ+X22wnIafrQ/ZMyAoM8O1bZRTylL0dPLD+vdHGnNBNCSBx2glpOMQ/bIkfXCcWXX375OVnEJByT+5e+1gq4uXltBtJJxAyBquUiaTa0lbYRQ1NRrXZ+cDuR1nHtWAwPnaldyf3KkyXsHzTrOcoXZ1RvzCk24Fhl6NrWvIZbgq7CLdnwfQ9CdOeRgkTbjKai4vs47Tiw/ADbDb1lURTpmI64rmKuZKNoFTDWF0PSbH4X3hfT1/V8hmEYhmG6HxqnuB3s+1hiqMFRXPZ9lDxPitjtdRT70lFMgtWmf56qwa16LflZFyOqRoHB2CALxW1CFyQUV2F7VlNCcRig7ZgE61KhJgSXS06DULzS18OO4ogilFoXkOtTkHan14ZZJxvai/zDP/wDXvziF8vIhbOhHODPfe5zeM1rXoNO8KIXvQjvete7sLCwsJxV/KlPfUq6XF7wghe05Hdwc/Pa9PelkKBCu/rOfitYEmLDkH1z654kDsxYsL0ABcvHAyfKuHl3c+Vto4N9SDsBFnUFZU3B5Ok8do4OoZsK7XzHg121EUt09zSzuCBXiYI5z4VtBdhh6FJAbgXkoB/ti6FouTg2V5axys1A5yxDKQP7R9IsFjMMw3SIMI+fmd7OKabZ3p0eS1OpXlZTseB6y67iZLx94oKqKKDfdLxiyWWznKqomK4CM2h/TnG/BkyYaiRzio8Ujsjb8/Z8LWONaQmq0OAGLmzfidTLGk9qy0IxFdot0egoRtHp+P6JWT+KPLBQNlnnL0Qy62dDVrdf+7Vfw5NPPrnm144cOSK/3g7K5TI+/elPy+XYsWMoFArL92dmZuRz7rjjDqTTabziFa/APffcg4985CNS1KXHJyYm2rJezApj2RTs+gBvK9BUBXTMoEyjTpMyBX5y18qh+dvHyyhUvaZdCJRTvMS0Z3bXjoYydV0PXo/kSlLkxJCqouh5OOU4sGUOcutImRomsnFMZJpbyH1MOcdPzhRRsXkmBMMwTCfo1PiZYS6WU0wZvVTsFiZX8VbkFPfrNBMsaMniQlBvU9uXggvkXNJeOv9+bcRRvMS8w4V2raQmoSqwvGida5GjeIlGoRhxbaVknM7zK+3fHzDtwSdHMRM5NmQtu9CUGsoCJqG2HUxPT+NVr3rVqseW7t9333245ZZbZEbxvffeize+8Y1SLKZ1oQK6d77znW1ZJ2Y1mWRMXj2iWI6tyBBUKROMfl8QhCL65obtcfzwdAW5KmWeAV89XMIrrjw313ottpdKeLDeOX0imcTT0T3QNhG4AeyqheY81t2R+9evqZj3PEw7LrabK1OothpVKBhJm5gqWHLMtW8kzYV4DMMwW0ynxs8MczFHMZVC234gexE6XWh3qGxtWU4xRVskWzQc8nQdRTVARmuv65Gizco+xXMA6YgN5QbiK31BFD3BtBaKnKg6FQSBD6VDucOtEoqle5i+tmgvx08oiS6KZewV6MTTrkXysCO8S4XiL37xi3JZ4s///M8xOjq66jnVahVf/epXcd1116Ed7N69u6ncpyuuuAJf+cpX0C44o/j8ZJJxmKqAZftItHG62BKqqshp/lRG0WBC6BgkWt92SQqfeqSWv/XwtIWnbXOwI3PxA9uounJwfKzfhONUoKsh+KNagKKq8kq324KpfVGCts24omDW9ZDRPKQ7WMBEsRXDUiyuykbhfcNpGFo0BpEMwzBRJQzjZ4a5EKZQYIqaANlphvWV8fKsE61c0q0qBDMUIEdisRcg3WQXShgdxTk319F16UZ0RYMTOHB8G4bafKl6eITi1ReHKH4iWBaKbWAkSqEajIS6eshRTMeXiO2vep2mVajHH38c/+///T95m64G/Od//idMc/X0eMMwcPXVV8uM4G6GM4rPz1AfORUFKo6DRHyLCu1o2liIpjRcOmRgd1bH0YXaAPeeQ0W87vrsRa+ijQ2mkXADmVFc1BVMTeaxffTcHMPIogk4ZQu+78vM8F4hJQRmPQ9TjouEEDITr1OQMDyYMjG5UJXrsXc4Bb1FZXsMwzDMufD4uQabLMILjQdiQmDR7fzUbnIUL7EVjuJWC8X0GfcDv62iMf0OXQHyboARPVouvYHYiqM457GjuNXoqo6qbcHyoiMUJxqF4rNK66RQXL8dNMZSMNFyFLtBLac4YjMgep2mheL/9b/+l1yIPXv24LOf/Syuvfbadq4bE0Ey6QTSMV26FrfSxUuEZUoDrcML9qXwdw/m5MFtctHFf01ZuHbswgdsXdNxWc7GD4drJ5BTno7t6CKo0M724Do2DDMag5dWbQ99QmDOcZFRVQzrnXWJm5qKgaSBU7mKjKTYM5SS/zMMwzCth8fPNdhkEW7SmopJq/NCTJSFYlVRpUDsB8FytGq7iIta9ETVB7ZgAmdLy+yWmPfZUdxqaPuj2ImKW0XaaC76MAxldmtmFEuhuCG27ywRmYkIqoLA9gGPTH0R2lkxGyuzo8KNXhzkMs0xlE3CkTuDrSu0I1OkF4Ipc0uMpDRcP7Eiht53uASrCdfzjmJx+fapRArdVmhHYfZOpTaFqNfy/8ixQ67iaouL7TYC5RNnEjqOz5dxfK4UijJIhmGYbofHz0yYC+3CwFCEhWKaLUf/fLTfNR8TQMWrZRVHiUZHcTEowvZ775yg3eiagbJDY/tofH7iqRVHcbnkrIoZVRrcxjJ6golkTxFoP0WuYiZSbHhezOzsLN761rfiec97Hi699FI88sgj8vH3vve9eOCBB1q5jkzEyKbiZKPcsumFmqpCFSJUQjHxnD1JxOqFFkXbx7eOly/6PaPKykHwsX4DrheNg3wzCE0gcF041ta5zcNERhVYrBfbNZO13m4Shibd/0fnyjiRK4dinRiGYbodHj8zYcSgcmhFgdPhsfSw0ZBRbEfLQagq2rKjeCsKkwEFi1tozGkFcS2OpL5Sa53z2FXcakzFkAJ81YtGL0ysIarS9wI4VoN+0OAopjI7JprQ3orMYkwPCMU/+MEPsH//fvzLv/wLtm/fjieffBKWVdsZnTp1Cu95z3vQzVDG2sjICLtCzkM2GUdMFajYWyMU04Uqip8Im1Cc0AWevXsldP87JyrIVS78mowNpRGrX3HLGwIzuVopXrcU2tEOxwvB1MZOQIP6pBCYcV0UQjKwT5kakqYqxeKTOWpJDtdniGEYppvo9fEzE15iQoEuFDgdHgcMNcRz0VipGpLxUrPjvKWp/1sBuYoXPXRc3N9Mod28yznFrUZVNWk0siMiFAtVQSzRGD/hrsooXsbyEDjR2R8wDVCJncP9BD0hFP/O7/wOnvnMZ+KJJ57Ahz70oVXiwo033tj1jgjKWJuensZDDz3U6VUJJQPpFExdoLqFU8aopCuMw6SnTcQxlKjl8VCG+1eeXImWWAtTN3BpfkVIPWN1WZaPKmAXe9NRTCRVARrjTDkO3JCIsuQqNlQFR+dKmMz37nvDMAzTbnp9/MyEF0MIGT9hdzgeK64KpBpKdmcdN4IZxVvzGlJOcdULUIpw/MScM9fRdelW9yZth2W3gqgQT66On1hC0QQQWzkXDkocPxFZodj1EUTowh+zjjK7Rr73ve/hM5/5DHRdPydeYHh4WIqoTG8X2vXFTJzOl7bsd2qqkAfGwA9qWTghgUrCnr8vhY//V17ef2zWxke+n6NkjvOyd1jHfw3WBlEn40k89IPumZYlDxCKBf2xR+rT5jpPQJcYaD+mTkKRW1F7oUOkFwQyt9jYoteALqRcvzOLWy4bwXC6VpbYSDZhYL5k48hsUW6zo329UzbIMAyzVfD4mQkr5CY2FYFK4Iai0K5Y77OgnOLtsYZCqxBD4pxCQvEWZcNKB7gbyKzibD3qLmqFdjm7e85xwoSh6qi6FTi+DV0YkRCKczOVtQvtkgaCal30pviJLJ+jRA4hpFAsXXNd5oHrZjYkFCeTSRQKa0+JP378OAYHVw4ATG8y1J/A8dyF3bOthHLVSOAil6a+BWLferhkwMD+QQNPzNUGvacWLzyAHNXoYFgTir8znsBoJoY9+SrSC4vI5Us4mNfgRn0vW9y6iwhMjR+dWMBHvnUU12zP4LmXjeCm/UMyemKJgaSB2aKFIzNFGeOiNzh6wo7vechXHCiLFoQa8c/GBaCZGmlTgxKSiywMw6wPHj8zYSatCszYnZ/tRDnFR5aF4ujElZFQrCkqrC1yFBNkOFjwAowGQWgMGOtxFHP0RHvQVQNFh4rULehGFITihuiJs7KIKX4imKsJxZxTHGFHMR1bpKO4e8/Tuo0NCcUvfOEL8Y53vEMWcWSzWfkYnbhWKhVZxvHTP/3TrV5PJmL0p2Jym3BdF5q2oc1sXeiakEKxzCkO4f7n+ZekcCKfQ7WJxs9HFhQk3ADlujtgKiEwlUgA47W8Y8ow3pd3MLpQgldYxBM5F3POuS5Rhjkb2vr+62ReLu/72iHcsGcAt142gmfsHpCu40EpFts4OFnYEnd1qwh8H265hFNlHQpdte5iobgvrmEwZSIT12UhIcMw0YHHz0yYMVUhZz11msac4ihFTxCaqsGzt+5VpFn5ZQ+o+BRvhkjAGcVbE4NCY+OqV0UKaUQpeuJsRzEacoo5eiKa0GxvnzQachQzkWFDZ5l/+qd/iptuukkWctx6661ykPsHf/AHePTRR+VtGgR3e5kdLWdPG2RWyCaTiFFOseMhtQVCMUFCV7EazgHlQELFHTcMYGrRrUUdXITS4Un8cHQEh/tU+Gc5BKqagocHDbkA/fKxnUUP/ev420fKLp57fAZF3cZcJo5Ksg/KFjlI3UIFsZEM0kMrjoJOIjMiK0UgntpSp6YdBLD9AGOGhnSbXbCnFyq47+AMHptaXH7M8QJ869CcXMhZ/JP7h/Dcy0dw1URfhCTiGr7voazoSPQZECIiZ0rrhPYaVBBKQv50wULC1JCN6xhIGeiL6Yjp3fl3M0w30evjZybcUEaxWo/HUjvoTqXoiSUoeiJK6IqOYAvl9pgCFIIAJS9CQnFj9ITH0RPtQlN1lOwSBsyB0I+NEw1i8DnRE6kVRzQ7iqOLjAh1/MidY/YyG1Lwtm3bhh/96EeynfnLX/4yLrnkEszNzeGXf/mX8aY3vQkDA+EQgNpZZkfLyZMnsWPHjk6vTigZyCQQ01QpbKTiW/M7aap8mK9TpQyBlBR3m2DQxLXwUcqXcGahhFnEcCqZxBNZA4v6ubvY4ylVLs1jYtjX8abHLGABcprcHCqYMSuYTzkoD5rASD9UvfXTlRzFgZYCxnfW3FSdhq64oxgAqcyWu1HJKdOnqdgXM6G3+aTs5ddtw6lcBfc9No2vHpxeVVxXtFx86eEzchlOmdjev0Uf2hZBF18814WqbU3OdCdIxTT8wjN2YO9wSs6cKNsuJgsV+T6S0N+f1NGfMNAX1yMVG8IwvUSvj5+ZcBMTQvYn0EXsOE0VDoFQPBsxoVgV6qqSynZDF5ho9F/wfIyEcUrlxaInPHYUtwtD6LB8G7ZvIybiEXIUu+dETyzBQnGEURUpFDNdLBRXq1W8+c1vxq/8yq/gbW97m1wY5mzSiQQySQMn57Yui5ZKHWhY63k+1C4RSpLxOC6hpX7fdSqYni5gpiowHUviSCa2ToF4hR/0r3wfFZhMII4JOw7QmG0e8B73kcMi5tQKPBE0LdidGnbhX7b9/E/SVXhVB77rQmyR2zysZDUV854nM/gmzPZniG3rj+O//cQu/PKNO6W7mATjbzw+K/N9l5gpWnJhwsfR2RL+9leeJmN20jFdLq7nS6H/2FwZpxZqovFQykCSsoyb/LmUa5hN6Jx9zDBthMfPTBQcxTSWphlP8Q5nFC8RpYzipSn/W01MAEUPsHwqSlYiFT1RCApwAxea0tvnA+3KKS47ZRk/EdOiIxSXz3EUr3wNJSd0xfVMk9DFR9dH4AVQOnghkmmede+VY7EYPvzhD+OVr3zler+V6TGG+pI4OrN1hXaqIqCqipzwFY1r6hvLPpsYGsDE8iMeFucKmMpX4TQhC1VVgX/dNSJvP5EWmISNkUBfc4ohPTYEA0O+gfXMorvidIAvZGagjQ2v/QQS8R0PdtVGLNXbA0MqYYwrCmZcD32aj9QWXeAgQfDysT65/Peb9+IHxxek0/j+J+dgUSstE0pO5Cp45HQBV2/LLD+mqQLZhIFsArDdmmj8xFRRisnNomsKtmXi2D6QYDcyw7QJHj8zYYeK0UgoLnZ4HDAU4egJKrTrhFCcc2vxE2YEDuFJPQlTNWF5ljSYLLgLGNKHOr1aXQeNAhVFoOyWkTVrUYVREIody4Pn+lC1+sYc02oiI+XbUs5txaWNqHMry2wMUX8PqdCui4vHu4kNqTTPetaz8MADD+A5z3lO69eI6RoySVOKjVtVaKdqihTebM/vKbEjnUrKpRnIF/zFIEBZUeAJBV+8tQ+jtgVM5ZCcs9Bf1DFsxzGEmDxh2AjkTpw44mB6bO2vq7oKt+TCtqqIpWoFfb1MSgjMeB6mHAdxYWx5LiAJjVRqRwvFGTx8qgDLjVb+ehD4sMolmImkHBR3G1/88Rn88MSCvP3lR6dWCcVn57QPaAaQpHFY81NfK46HI7MlVF0fuweTiBs8gGOYdsDjZybM0EVk6kzIdbhAbrihzC7nenD8QArYURGK6XX0A3/LRGM69/GDAEXPx0AE+gro9SFX8enS6eX4CRaK24Op6qg6VTi+A12EV1zVdAHdEHDqRZCUU5zK1Ira5Ww3EoYLtSK7oGhDYaE4epA2Qxf+uNAuMmxIvXv7298u89RUVZUNzaOjo+dMWeWcNWYwnYKxxYV2pqZKsYNZG/qUTgQBDtU/r6dJ1KUc4u2jqG4HJlFbfM+Dn1tAbLqMZKm51Nds1cDlXk3A2l/J4oznQaxxxVDQCYBXhVvl5lqC9p0ZITDnekirtDR/YhFvcaZywtCkYIwoltnlVSQy/aEv7NgIcV1dFor/84lZvOGWSy5aXrceRzHFVeiqIjOs7bpYnEnwIJxhWg2Pn5mwE1NFx8/jk6pAXCio1C94zjkuxkw9MtETJBCTcLuV2nZMKFj0ADcIpHAcdgbiAytCsT0PxDq9Rt0dP2F51VALxUuuYofMSxQ/UVwRipfiJ4IlofisaAomGlBciE+9QOQoZrrbUUxQ1tpb3vKWNZ/jedFypK2Hu+66Sy7d/De2gv6+FOKaivIWFtpp9cwbKpLgvM21mfADHKrri6dpFLvG/poEXjE0CHdoEPkmf+5cqYz93601ZadJrDsxDeweX/O59M441ZUytV6HymNKvosTlo1mN1tdEdhj6kjx9J2u56k7+zGYMjBXtKX7l8Ti51852vKLbGOZGKYXq9JRvmswiZG0yftRhmkhvTx+5rFzNCDBkYaINaGzM/t/Ou4MGTpO1A0FlFMcFaFYCAH654O2c3VL4ydKPlD2gL4IpLo15hTPO1xo184LF57voepaSOlphJl4SkchZy07ihtRUgYC1HqPuNAuupD1LHD9Lq0d7z42dCihjLVePnm888475XLy5Ens2LGj06sTWpJxE5mUibmpAo7likjEVaR1A6ZQmxbDNiIU08/2/UDmFTPnMhGspDiTo7hVqMkEjok57A1S8v6OUwpO7T7PkzUVXtmWVxZpUM0A/aoqC2SaNfLkPQ8lX8MGuwyZCEHu4NsuH8UnHjyxHD/RaqF46feM9cUwV7Lx+NSinA2yvT+xLncywzDnp5fHzzx2jgYUO2YIAScIYHZwWx02tAahODo5xaqiLTuKtxIqscu5Psp+gL4IyDADsZWZEywUt7/fpuyUEMQGQh3P1phTXCmt/syvipoo8ozUyCIUBA47irtaKH7ta1/b+jVhupIbL9+J0cQMJmeLyJctzAYVWHoA09BkHEVC1UG7/laNRTUhoAshp16xfnZ+R/ESZyiruIWeh8NDZeydqQnF+6wMTrgOhLaGC0QX8G0PTtWCmQh3E+9WQeLBek7KSr6PvOthRKMLL+E/KWA2BwnDS0Lxj0/lMZmvYDwTb4+TK2WiUHFweKYkxWJyF18s6oJhmIvD42cm7JBITHnAth90tBitMac4SkIxubBJKKbuhE6I/AtugFE9/LMqB+MNjmKPheJ2YggDlm/L8sCYFo+IUHy2o3jla+wojjBk4nN8BH4goyiYcBPey0pMVzAxNIBnXX8ZXn7zU/Cia/fjlm3jeJqWxlhFwF60MblYwNFqCZOuhUXfh7PJC/BCKKCSVK/TAWshhnpvY3Wng6somGnhYNLaNyJdKEv5ueqRqTWfp+pCTj1xLL4qvFHi1GTs+6husWuF6Qzb+uO4aqJv+T65ittJX1xHNqHjZK4i3cWFKg/MGYZhuh1yphp0Lt/hscWQ0SAUd7hcb2MZxVsvFFP8RMUDKn60oidybq6j69Lt6KoOx3VQ9WqxDmElcUGh2Fi+zRnFEReKybDGfVLd6yjes2fPea9U0jTyTCaD6667Dr/5m7+J66+/frPryHQBWlzH+O5hjG0fhLdoozRXwvxMHrPFMma9qpzqXFBszGkKhswYUurGA7YMykXmaQ0XLbQ7XP8Mn1IUjLXohEA1TRxWp3GZXyu123XGwNH9a6yDEAg8H67MKa49l1l/jiAV4JFY3OpSOya8ruJHThfk7a88Oo1fvnFXW2MhyEU8nDYxu2jLkjtyFtN9hmE2Bo+fmSg4YpOqirzbWTFm2FgRjWbt6AhDJBLT9H7fdzsiFM84Aco+kFAjJBT7LBS3k6VK8qpbAcwswko8qZ1XKEZj9ITlIXA8KDzTLXrQ+SrNEGFDXyTYkBr38pe/HHfffTcKhQKe97znYWRkBNPT07j33nvR19eHa6+9Ft/4xjfwsY99DF/4whdw2223tX7NmUiiaAJafwx9GROpsTQm8hbshSqKhTLm7SoeryzicKUKy1AwoMdQq9RYH6oAFMp69fyNT2sI+ZStVsRPHK6/tJPnKbTbKEfHLFxWKzLGXrcPT9oWVGO1uKSoqnyPnHr+HLOxk7kACoqej8EIFJcwm+fm/UP4m689Ccv1MVO08NCJBVy/i+YItA9dFRjNkFhs4fGpAiw3iWxixdnRKqjgior6ipYLVeUBZBSh95AuKDDnh8fPTBSIq7UIt05CGcVLzEYqekJAU1RYHXAU07iQzl4WXR9DIRfRGqMnFvwF6cCm145pD4amo+xU4PkuVKGFPnqifFa8BOkHiGmkdi/HTyj94d7GmXNRVEX2E8HjsWIU2NCeYvfu3di1axe++MUvIplMLj9eLBbx0z/907j88svxt3/7t/L2H//xH/NAlzkHEnDVlAGR1KH3xxArJpFdtDE8k0IyN48nimXMqUVkVROGuj5RIuYpMF0FwsfGBYcA8HXA79JCPHIUL0GO4lbi7R1D9ZSLmCJkXpp5aBrulWsU12gqnBI5ipnNuEcK5MwOAmhdfnGDARKGJsXirxyYXo6faLdQvHTyOdIXw0LZxhNTRRha67e1wPdhl4o4VdHkjAMmetB7qNllTIyx0H8+ePzMRIGYEFJwDILOZd02CsVRyiheKg/z7M4IIXEVWPQgM6aNEGeApo00NKHB9V148FDwCshq4XW7dkNOMTmKq14VSVHrkgmzUFytuLKYniIlG3OKg7pQDBKS+2OdWE1mk9DlLIqfDO/eidmUUPye97wH73vf+1YNcolUKiUbjd/whjfgzW9+s/z/Na95DbqNu+66Sy7knmE2Bw1AlYQOkdChZkwYIwncWOhD7PQ8HinmMKeWEVNKSOhJeYW+GfxAwFZdVKmIo6EMYz2org+9Cgg3gEs6dZeJcBMNTgcqtKN7rZJmqLzuCWMO1zg1AWvPTBxPrPVErV5oZ9vQjdY7FHslp7jo+7LYLqPylfVeiZ9YEoq//eScdOCmzK1xh5CTmByj7WhzJ5FRsVTEDRaKo0qhbMvZKRybfn56ffzMRAMSGHVFkTnFdMG/02V2c44Lj0qqIzIW1xW9I2V2RFwAebcWP2GE+FBK7uEBcwDTldp4Zt6dZ6G4jZAo7/iuLLRL6uEUio2YCkGOU4olCACr7CLeUGInheLZirwdlHhGamQRCgKOCI0EGzq7nJ2dldPm1iKfzyOXq2UNDQwMoBuhwTwtJ0+exI4dazglmQ0hTBUwVWTSBp6eMRE/lcCPczl4WgkFrwhd1ZDQExeNo6BhpHBUlKsOtA3qj54h4GmAXiWHVABfBfw2uOg6xUAAmEEAi04E6oV2oy08uz854eOaY7Xbu/0UDpbLUBOJVc8Rugqv6sIqVVgo3iDUTO54ASoeC8W9wjXbMhjPxDCZr8L2fHz9sRm8+CnjW/b7DZr+1wbkTDRVwNQFhOBtOYpU6ASv0ysRcnp9/MxESCgWou5K7cw69GnqslhN+5V5x12VWxxmVKEiIKWrA9DsMhcBiq6PrBbuY+lAvEEoduaxN7a306vU1ehCQ8kpod8c6NhMgQtB60Su4lKhJgKXS85ZQnFDod1Z0RRMhKDZ2q6PwA82HhHKbAkbOvzfeuuteOtb34pvf/vbqx7/5je/if/9v/83nvvc58r7jz32mJxmxzDrJZEycM3OLK4bGkQsGEDCGIaqaMhX87Dci8cVkNhApZqbwTMAKyXgxAUUH9CsAMpmf2iIPvjjDcLw6RYPGIKdoyj6Ncc9OUASh2bPXQcSnBwPTpnjJzZ7UpD3vLa4PBmEciBNruIlKH6CYZhowONnJirREyQQk0jbyWNdVHOK1SZnQLYLU1FQ8KgvKohMod2cM9/RdekFNGFIR7HjWwgrjfETFyq0Y6E44kIxucY5p7g7HcWUn/ayl70MN998M7LZLIaHhzEzM4OFhQU89alPlV9fanB+y1ve0up1ZnqEWFzHU3ZmQeayg3PkNIsjqS0ibxdgWRZSRgriPIMxXVVpZsM5+UbrJaByzrgCV1Nq7mInkI+R2zjqcRRUaHdUrAjFT23hzxaqisfjC7jeqg0CL5lP4dGzniML7QRglcot/M29R1woKPuBjFpJdGmmNrOa264YxcfuPyb9So9NLeLYXAm7BldPZWcYJnzw+JmJAnSBPy4ESm5nxdkhQ8Npy1nOKb4C0UCWsimd7a+o+JDxE2k1GoV2FD3BtL/QrlCtoupaMNRw5vvGk9p5hWIl3SAUL9oIHI4ApRx56c51fARKhy8MqaI5hzDFy9GFPzcAojFJpGfZkFC8bds2fP/735eNzA8++CAmJycxPj6OZzzjGXjRi160/LzXv/71rVxXpgfRDBXX7MhC01Q8OlOEqvZjPJVErrqAgrVYa51bA9cPUPGrsCw6MK5/lKQoAiktvXyfiu0sytS1Auks1izA02uicTcU2p2mHXuLj7eTO1UshRNvDxL4r8IitL6V11RiqPBKNhzbgR6RKYVhg5wj5N6mnOKEGuENkmma4bSJ63Zm8cPjC/L+PY9O4fU385RNhgk7PH5mokJKUzFZF2k7RWNOcZQK7UgopsImP/BrovEWYypA3g9Q9gKkQ2wgaHQU59hR3HYEzSdVgIpXRR8yCCOJCziKlWRDTGHJgfOvazbg9ByUOO1httWn8evHEFCvGoK4tP/C0Sai1kvCjuLws6kGHGplpoVh2omqqbhyog+qJvDI9CLKVR3jyXGUjdJ5v0dOw3fLcDwfiXUWPfm+i5w9By/wVk8fUwAnpsDTFGhVH7pdF1q1cE/taqbQbrLFhXaEmBhB7rES+oUOoSjIHFpA6frVQrHQNfgVF3apDN0I56Al7NBrS9dLip636qSK6W5ecOXoslD81QPT+LVn7YbGFwoYJhLw+JkJO2YIsiMbM4lnnOhMNadzBxKI6VykEy8jiTRUq1JwA4zoQSjzaM8Wiue9Wj47014MVUfFLsOLudIUFeroibPjJWIqBS0DXIQWTmwf3g+nERRtqE8dPa+7WO6PSHeoulD4vYRPonnZQWB1XOo/h02pCl/60pfwve99DydOnMAf/MEfYOfOnfjGN76Bffv2YWJionVryfQ8QhW4fCwNTRN4+EwBs0UHY+nMBac42HYMM0ULffr6itJIIC65RTi+DVWNn/N1XwPsFLmLAa3qQafjmBFELopiKKDVDmArilzmFGC4hZo3HQgeT+ZxY2VI3t+f78OPznqOLLRbtGCXqkj2s1C8UWJCwaLv14tnorUdMhvjmZcMImVqKFouFioOHjyWw0/sXTnpYhgmvPD4mQk7hiJqxWh+AK1D4wqKnoiko1iQd1PAlx4/tWPxEyUfqPpAXA1vmd0SOY8dxVuBIQxU3arMKo5p557jhiuj2D3nvFK9dhjeQzMsFocY/4kFBGUX2jMnoJyvANtQgaLTocrPcBGQca/iILC7RCimPLVXvOIVeOCBB7Bjxw450L3jjjvkQPfDH/4wkskk3ve+97V+bZmehg4Q+4aSUFWBH0/mMZmvYLwvBuU8LjoqtNtIjwM5AWJqAnk7h9gaQvESrgn4qoBRBFS3Vn4XJehVGwsCHK8L3KcVgeEGl3ErmN0Tw1I48ZgSgzs3D21wZWCoUE4RXRymnGJ6r1jj3BCUJZj3fJR9HwaFejNdj6mpeM6lw/j8jyeX4ydYKGaYcMPjZyZKjmJdKLCDAFqHBmfRLbPTlh3FnYKE4jknkDnFYRWKV0VP+DmZtxpW93O3oAkNju+EVyhOXaDMjj5b+/ohLslu8VqFF4q3yVcWkIlnOxJzs4zrw/3WaQRnarO9g1NFuPedgPbsbVDWmNmtkFBMCwOFtJdCOPd7G9qifvu3f1sOdh9++GEcOnSoFqRd57bbbsO9997bynVkmGVoALF3IIHrtmcRTxk4mq+i4qw9eNSFClUocKlZc53EtYQ0CJO7+EL4KhXbKVD8gC4JIWpso/XGSqFdq1FHBjEdrLTrDh0ur3mwcCinuMNZeFFGun6CACUvfFcjmfbx/CtHl29/98g8Fsp2R9eHYZgLw+NnJiqYQsgZSg5Ni+0QjXFas+cZ64c1EoxEG+kU62AhYQAFxRDngGbNbC03F4ADB0W/2OlV6hmxuOyWVx1/wlhmVy45a64jaQG8rCwIwToougrt2dsh9qzMDg7mKnC+ckxGUTDRZENC8ec//3m8853vxBVXXHHOlT9ySJw8ebJV68cwa7IrE8cN27PYmYlhetHGtGVLoawRQxfQVbGhQa6pmjCEKeMnLoavKfCFAjWCGt342YV2beCJvvzy7f3Fc+MlFMopdjzY5XNFZGZ9pXZ534cXwoEf0x4uHU1h12BC3vb8APcdnOn0KjEMcwF4/MxEBXITm4qQjuIwZBSTo7iTDt2NZRR3VqSNC6Dg0Sz9cL5uqlCRja24Q+ddjp/YCnRhwHKrcChDMWTE4vry7FLfC+CEMLeVWRuKA1VvGIO4qmF246ID58vH4M9VOrlqzFZGT7iuK6fHrUUul4NhRGwO/jq566675OKxe6+jjKRM9O3qx+CpRRzOlTEFR7Y09wlRK3KgQa4mULJdxHV13dPGKH6iYOeAC8RPEIEAfAPQ6XgbsS6xbQ2Dbiq0a0f6Q+6SPgQ/rE0nG1QMeJPTUMdHlr+u6irsogWnXAEGOKd4MznFVd9HxQ+QCnHLNdM66DNFruK//88j8v49j57BK546wVM3GSak9Pr4mYkWKVVgZqm4uQNkdRU0nKGJgU4Q4FjFRmad4/lOQAJxwRNwPReu0vrXj47wKbVeZnwRoXjRg4yfyISvt2w5fmK+WhOIc24Ou8xdnV6lnii0W7SLsLzwOT2FqiAW11Atu8uuYiMWsZPrHkbqL9cMw0vo8B48U4uVtDy4Xz0O7VnbILalOr2KzDrY0CfvxhtvlFlqazU2/8u//AtuuukmdDN33nmnXMj5QQ4QpnPETA2Xbe9DRhU4OldCIR7gjO+iT1WRFAJxQ0W+srFIA8puKjg5+IEHoVx4YOoaApodQHgB/AiJdENBAD0I4CgKqooCGqq1OuVUy2ZwWlnANtQE99FjDmbHcVZOcQB7scI5xZt0FBf8Wk4xndwxvcFzLxvBh795BGQYOjpXxhPTRVw6mu70ajEMswa9Pn5mokVMpUI2dDQ+YVDXMF3PJ/71R44iWtCFn/aIcaYCTJgKthkKtsn/hbyd1WpizZIr3HF9lL0AGS2cg+vB+CCeWHhC3p6z54C1r6MxLWQpFqXiVmEoRigL7ZaEYiq0y3L9RuRQL8lCiWtwv30KcAN5tc/95kmoTxuVOdNMFwvF73jHO3Drrbfi2c9+Nn7u535OHpA++9nP4k/+5E/ktLpvfvObrV9ThjkPmqFibCIl89Sm58rIJ1TMBQGKrgtVVaTuGMikrvUNkkxhQq/HT5gXcRX7GuDqgG7XcoujglovtDuxVGgnBAbbkEd3KFvAtoXaa3hZOYtpz4NQV14oxdDgVmw4VRt6PHyDlihA+2ERkHvEw0hDrh/T3fQnDdy4ZxD3H56T97/86BQLxQwTUnj8zEQJyijWFQWnqq0VOwPfg+e4WLBsmJoGg/KQFUX+PhKHG9lmGstCMbOCFQBHqoFcanjLLuJG8bhPBYqeh0PV1s6CDfwAQTmAIlw55XyjlLTdcPVa5M6PPBdxnqy7JVTVGFSrgqwawBQVcu0gLJwe1FCwa+eI3y6X0W/xOc15CXw4rgPdCtd7KBlUoT9nDNkfzkC169rCwRkUrSqKl2RktjID+R6WhIdC1cLNCBcb+uQ985nPxH333Ye3vvWt+N3f/V0ZNE6Za/Q4FXFcf/31rV9ThrkAmq6ifywh3ajxuQqySR1z8HFC8VBSA8TcAPF1Xk3XhI64GkfBXrioUEx4poBu+1C8AEGEXMUTJBQ3FNpd04bfUdw/CO+7gTwBSAsNODUN7FyxFQvKKS67MqeYheKNExMCRd+H5fvywgnTG1D8xJJQ/LXHZvD6m/fC0Pj9Z5iw0cvjZ45tix4ZTcW+ZKzlpVe+52GxYsCMmShDkWOWsudjwfGl3ElHLykeCwW/MNaPaduJVJkdQa/ZRkwqzUBRHOf7FFV84FA1kAva7gfXgflNfp6VFwPjL5Y3v0xLeLv3uoxYbQYnmYMqCwgV1+rAtUtRhC5Q5uzqixK297BRaXzG2RoKbXO5Dq1QSIkJbJ+ZwYOXr5SUh4ENX6KhQe3Xv/51VCoVmauWzWaRSNRKdRYXF5FOs6OJ2XqxeGA0KS9QKXNV7EnpSMdjKC5amHFd9AsNSdlE3PygLaZRTvFCU/ETnl5zFWsO4EbIVTxOc9bVhkK7NpzDqckkTog57A5q2UTbTwCndzZ8XVPhuJRTXG199kWP5RQvuB5KLBT3FM/Y3Y9sXMdCxUHRcqVo/JxLhzu9WgzDrEGvjp85ti16kFi7I9b6i/d0sWDG1DGcjEEIIfOHLT+QPQv0f8Xz5ewoKmHbm4jhTy/dLiXP6FgwgJnKLKZKZ9BvrpS1tQoq9ct5CkpegEk7wClarABn7KAdQ3iGYRimB9m0lz8ej8uFmJ6exl/+5V/ib/7mb+Tgl2G2GlUX6CexmE645iyMpXVcYZo4UqzCVxTMBT5igdK0YCzjJ1SKn3BgNkQlnA+Psoodr3YRfxNTsTpVaEeO4nbFBD85WMLu2ZpQvM/K4oRH0SDack5xoAZwOKd4U2j0/imQzpwBnqnVM2iqwHMvH8FnfnhqOX6ChWKGCTc8fmaYWmxWLXYCSC+5FuqOXLsuIJPjmETjKDEtTMRcD8PxdjhHAsw6ARZc4CnJmvOacIMAU3aA03Xh+FT9tt2G107+RPq5YnOeadd3sGjn5W0BFX0a55duZekiWUoEzfQMEb7rw6tHpSiqgNaWz1AXEfjhi504D6rlyZnX7SYQCrxYdLYbz/cw2ITOtNWsa8/wwAMP4KMf/SiOHz+OvXv34rd+67ewf/9+TE1N4e1vfzs+8pGPwHEc/MIv/EL71phhLoKqCWRHa20IJBb3CRUDcvChYcHzMB8EmA18xJsQjGvxEzEU7DxMNXbR302uYk9ToLlUcIdIMBwEUIMAnqKgoiigySvtGKZV943CmQlk3l1CCKhHzgD7tq/KKaaMYsoq1hIRefFCiAEFec/HOL2vnP/UU/ETS0LxD47lMLNoYThtdnq1GIbh8TPDbEhAppJeU+of4TuBvhiqpyOvBxgx2jEOU9CnkRjsS2E4pQJJVZFmAZlNbALPaPPEhMD3ERTKUPoStVLqDTJfyeNPv/U78raBGH5/79+1cC2ZC2E7FoRrYzi7U3byhIXi6RKe/EItFDHWb+KyV+7p9CqFFioldKoF6LE+KFEQi5MBso/mkD6ch3BaLxgrDRfFTrx4F7x4uC6CnO89PDV7BtftHEHYaPrV++IXv4iXvvSl8grv8PAwvvzlL+PjH/84Pvaxj+E1r3mNdED84i/+Iv7wD/8Ql156aXvXmmGaEYvHkjIoPX9iEYobwAgUjGs6+gMfuQbBmBzGqQsIxrX4iTx80JXXC++EA0GuYgVq2a87Y8Mv1Gn1QrtTy4V2Cvrb4D5QYyaOqDO41O+T93dN6ji2D6tyij3KKa6UWSjeZPxEhfL+fB/pEF6dZNrD7qEkLh1N4fGpotz1fOXAFH7xhoZ8F4ZhOgKPnxmm91CFKvOJpWuzDQIOjfV2xQRMxcekDem+zqo1gT1K9Jkr1hQbVVT9CmLi4r0wzObRVB22W8GiXYShtrawkqBtUVM0GCpZWJr/DOhJffm2U3Zavl5MByFD2lUDcmkH4/eehJmz5O3EqRIW9y1lXTNtFYrf9a534alPfSr+7d/+DRMTEygWi7j99tvx8pe/HOPj4/jSl76Epz3taRtaCYZpB6pKzuIEyo6H0wcrKC3aSJgadFXBqNCQFQEW/JpgTJEU5nkEY0PEoAsDjmc36SpW4KsKVI/cxYgE5D49tRw/IXBVm1LOjo5auHSydnuv24fDjgVVN5dzim3Pgl2sIDHY+ky3XoGmcC4EJBQHSLNO3HOuYhKKiX984Bg+8b2lmspoQEJa1E5ymRVqKUYB/uoXE3jh1ROdXp3QwONnhuk9SCBTFbVtQnHtd5B7WMBUId3FMw4wqEdrNhnN3EzqfSg5BXm/4M4jZmzr9Gr1BLJ7JwBylfn2GJtIKBYaDKEjpsVhqoYUjTVlRQheCy2xcvLsWb6MohBc0Mw0QXlbclkoTp4sslC8SZqWsQ4cOIC///u/l4NcIpVK4d3vfjc++clP4v/+3//Lg1wmtGLx6HgSR+dKyOWqqFDbthfIOJ+g7ppNKYAvAuQFZYr5MBWBpFCgNkylclwdJSePpLZaeVPozNhzUfIEgoaDrOkHiFUBm4yxERiwDdNrUc+AImmpbLWnXTrYNoTqaR8xhVwQAurjUyjvWxEUXN9DPleE128B6ha9br4PrerCVWwK6kI3QCUwRywPga73hPBGUyDdso2iYm1qCmTYScc0JIzzH7Ypl/iD3zgMxwtkdKDlRrE+PFo5lMy5UIk6swKPnxmm96CSPhLJZiuzW/L7Ur6CoqvgCUugT/gy87mt+AE024FbqTTdyULj0cHYuY3VmdjgslC86M5jhIXiLSOmxRDT4206Vw3guA4sz0LZKcvPhK7oMDQTcY1MWLoUkYWindM3pBoCnl0bTDglF2aGZ5oyF6e0PYX+h+flbXO2CrXqwotFxLUXQpp+5ebn55cHuUts21bbkVPOGsOEFcPQsHdPFuVtLgISUOpL4NFVygCe48ul7HrIuR5mpfDrYyS5clAyjD5MlUtI6/rqorUggGn7EAY9vvIFVScnMgmigK+FX6jb07DuU6pAMqa1p08upuFJ/QyucmtTzfbNJXD46pXdkOWb0H0fA4YCPWlumcjoKzpE2uwakTHl+XQ9BEMJE/Eu+ZsuhO97KCkmkllqUO9OG7Xj+Ti9UIWuCrmsRTqm47/duAsfvf+oFIoZhuk8PH5mmN4jqSWxu283gi28+FnxApyoujhje0hpClLnGSu0auzsBAXomb6mxs6O52CyPClLmyiWo5GMOYDTi0fk7YJTE3mYbkCBrhnQUTuf9gMPruegZBexaBVkoTkJx3FyG2sGYmoMal00pvgJz645Q52Sw0Ix0xRuSoeVNWAu2FLHkPETl7CreKOsS2I/nzNN5RxMJuRs609c9Dk+iWtegDOLVTxwYgFpU0fCqG3b/Z6AUGtTumN6Q/yE70NRfQQxc7UblYrEVB+iEsCPh18oTlJxsQfQtdsSfc7jBrJtWu3pXcBVT9Zu7/ZSOBO4MJK198fRFLiLNvpVHwMDtULCduN7HspBBYn+BESX7Ms8ar62HIykExg1LzzFqxvwPA8zqGJ4ON21xyPb9aVTeLZoYawvdt7j8aufsQMvuXYcVcePnNhfWcwjns50rdjf7eRLFvzqIm7ed65jrNfh8TPD9BYkho4mR7f89+5IkVhs4VjFRqAAQ7rWlpllcuxsAInYYFNj55JTwkx5Bl7gQT2rnJAcxUvkWSju6qgLQ1PrsjGkaOwELnLVefl5GYwPIa3XWhj1pIZqPUKAHMUM0yzlbSmYC7X9SOIkC8VbJhTfeuutctrA2dx8882rHqcDUj6f39SKMcxWI1QSg4GxbBzZ2RIWyjYSRq1QwVBNOTVnwVpADBfPKSZ3cWAoCKoUTRFsXYzCBtEVgPxOJ+v3TwRAf5tWObZ/AqUnikgKVear4ZEzwA175dc0XUPFq6BcKGOAIy43zFI+XcH1ekIo7gUMTWDXQBJl28NC2UF/w4yHs6F4iqj1QZJQXHY1JJIGC8URRQlodoYGU+f372x4/MwwzFagCQW74ybiqoqjFQuTliPHgZ3OLaYYDhIDXd+VObVnO4qXoIxipnfK9DTogBaXDmNyHC+hN+QUO2UWipnmKW1Pov+R2n4kNlOBqLrwOX5iQzT9qv3xH//xxn4Dw0QMmtY93mfizKIlc4yVevZWUktgoboAHx7EWVfD18I3FCiGAmH7stwu7OxUgJP1GXLHA+Apbfo9NNXoyXgeT7FqA8NtszHM1L8mXQ8qUF2sIHA9KGdlQjPNE1cFcq4L26esuu6Pn+gFMgkdOwYSePzMIqqOhxgLcgwTenj8zDDMVkJj6TFTR0woOFyxcMZyWh6AEfgefMdFzrKhNHFxNwh8zHuaLAZPUlFMI2otjo6Y8XKY4eisrSEATOqzCMHrXVSo0NxF2a+JwtXEynnLQtGGV3+cOYvAhxJ4COj1aVNpZuRICvT36UgWHBk/4ZxaxPSemlM9vO+hgjDCQvEGuOuuu+RC052Z7mSkL4bETEmWuiXjNUdmXK81ttqO05xAIxQEpgLQzBkKDG2y7KFT7KDVWxKK2/y7FnbHgMdqt3f6KRwqziCTGpb3NVNDpWzBqliIpS8eGcKsTVIVWHA8FD0fAywUdw0UO7FYcXBqoYLRvhjUkO9XGKbX4fEzwzCdIKtruEII5Ay35UIxRU8UqiX0JeNNx7YlXBPzVhkDsdVjUrdvCP9Rv+2489jLw5otgbYJj/w5Sud710tKgBgcTGi1c+7JtIlD9a8lKj4uqz/OrMYPfFRdFTGNSgH5XG8JZUcaqLuKd56uoH//yqyFML6H+UAgE8LzOfZhb4A777xTLidPnsSOHTs6vTpMG+iPGxhIGpjNVZaFYl0YiOsJ5K18c/ETdVex0AUUx6+JxiF3FKMheqKdmHuGUTywiJQQcjqc99hp4Gk1oVg3dJlTXF4ss1C8CchFbAcuiq6HAZ139d0CCcPkKi5aLuZLNobTW1P6yDAMwzBM9GaXxc+KemgFZJai8fpwzGg6a91NJBB4MxgxVgtaTnpo+fain8NwuE+XugY61atQLU0IhOKkENA8DyPksxIagtSKUByUXYwJPo85n8i4oKjICo2F4gaCnRk4daFYn65g1AEUM5zbEL2HCgRiITR1hW+NGCYE6ELBWJ8JWyjwXH95KhfFTwQBdRg3WRSlKghigELm8yAEc3suwLaGHQIlJObbuLqKKnDcrCzf3zuTkTvKpZxix/NQyZfla81sHFMomHc8+Pw6dhVJU8POwYQc2JNgzDAMwzAME2Yom3hprN/IQGzF7VcMirB9e4vXjOk0FOlI0Y4uarO148kVB3G56HRwzZgoomRMKH31C2QB4J8sdnqVIgkLxQxzHobSJhKmilJlRYhZjp9wmx/E+LpAoCtQ3HCLdYYCjDXcp5zidlLckVy+vdcdwInCgZWcYg2oFCmnuElBnlmThCpQpGZqj1/HbmM4ZWIiE0eh4sDh95dhGIZhmBCjq7Xp8WeLxXEtjqS+ck6Q83IdWDumk2hUdBh48Py6UJxaEYqrFRc+RTgyzHrjJ+r4Jxc7ui5RhYVihjkPWUNHf9pAyfWWna0UPxHT47A8Ch5uDhKJAyq2owuiIXd2roqfaPPv8i9Jolw/8GskDh+eXv6aZlBOcRV2udrmtehu4kKg4vlY5Dz1roMuqGwfiMvoidmixe57hmEYhmFCiy50aIoGd41issHY4PLtebc2ZZzpHYQQ8AJ/WSg2TBViqQg+AKplnj3HrA+xo2/5dnCmhMDic+H1wkIxw1wgfmI8bcLWFLj2SvxESkvCh998/ASJojEFgarUIijCXmi3RY5iYQic0lemE10xPYGKV1rJKaYitkK5vSvR5QhFkY2veSfkGx6zIUxNxc6BBBK6inyFp+YxDMMwDBNeoZhcxWsJxY3xE3PO3BavGdNpBAQC34cbuMvn243xE5USj3GZ9aFkDCDdED9xil3F64WFYoa5AAMJE8mEioq1coAiR7Eh1hc/EWiKLLOTruIQs5WFdkRhLL58+3JnDI/mH1jJKfY9WIsVBDzdaFMkVYG866HK8QRdSTZhYPtAAlXHR5UvCDAMwzAME0JIJCZHseOfezI0GG9wFNvsKO5V3LqjmGChmNkMdLFBcPzEpmChmGEuQEZX0Z80UQ4C+HWhjUTi+DrjJ6h1yjcVBCTEeuEVPrfTqtZvU0LYYptX1d5noLIcPyGgHp9bySlW6znFDgucmyGhqjKjuMhCcdcynoljLBPDXMnm4kKGYRiGYUKHdBSLizuKcy5nFPciiiLgNlxESCS15dssFDMboVEolvETNhtq1gMLxQxzAQwhMJY24eoCTj3bphY/kZJlDOuJn1jKKhYhLvM1FWAUW+cqVlMaJsXKa/iMmSswaR2TtzWzllNsVTineLMRKm4QYMHhfK9uRRWKjKDIxnXMFUO8g2EYhmEYpmdJ6InleIFGOKOYUYWA7a2MYdlRzGwWJWsCS8WIPsVPFDu9SpGChWKGuQj9MR3xhI6qtVJqZ2ox6KoB21vHgUso8OMKAgEobhCNnOIt+H3zwysDgavsHXh44Zvytq7rcFwPpXwtt5jZXPzEnONKZzHTnSRNTYrFZMYvWnxRgGEYhmGYcGFqpswLvVD0RM5jR3EvoiqqjJ7w6o7zeKpBKC6yUMysH46f2BwsFDPMRchoKvoTOiwd8Jy6UKyaiOsx2P763HvkKA5ilFUcACGdIr7VOcXVvSvxEwZUGJMFOL4tc4rdwINVrCJggXNTpDUVBddjV3GXM5w2MZGJo1Bx4PJnhmEYhmGYEEHRE8SS8WZNR7HHjuKeLbSDD6/uOF/tKObzF2ZjiB19y7eDyRIC7nNpmpXwF4ZhLhg/MZWrwLFcaIaxHD9RqOTX9zGirOKYgGIHcqGCu1A7irdAKNYHNUz6AfaK2i9+ztwNeLj8Q1ydulG6ryulWk6xovJ1rY2iKgo0RcGM7WLU1OV9pvug/dL2gbh0FE8VqhD1z1QUoLZrr2yjgCoUwZ/1KOK6HpKdXgmGYRgmtFDPiypUGT+hK/qaGcWFoCC/TsV3TO9A24XjOtJVbKirheIyR08wG0TpNwHalmgb8gMZP6HuznR6tSIB74EZpgmyhoZE0oBTrSLmB1CEglg9fsK1HaiIryurmCIo1MUAATlpQybm7Gi4TdVypQBIKu0Vt2YHVezN11Tpa6t78fH8h6VQrJsaqvWc4ngs1b6V6AH6NBULrou862FA511/t2JqKvYOJzGQNBAlfN9DIWejrz8FIdROrw6zwfewUnAjdYGCYRiG2Tp0VYcmNFlot+QuJpJ6Us7WpKLwAAEW3AUM6UMdXVdmaxFCwAvIUeytmVFMLnRZds4wG4if8A/OL8dPsFDcHKwWMEyTIls2qWO2YMO1PegxTQ5oSCx2qjmsV9aQrmILEJYvReMwQaszAmC6IX7i8javYnWXjuqPLMSEAhMa+mcCLIzOIqlnYFs2Svky4v0sFG+GeD2neM52WSjuctIxXS5RwvM8zLglDPcnoKosFEeRpfeQYRiGYdaCxOElofhsMYfiJ06XTi/HT7BQ3IPREw1CcSyhAXT+GQC+F8C2PJgxPn9h1k+jUFyLn/Ch6Dx78WLwp41hmsAUAsNxA9NmBU6xJhQrikBSS2Den0PVrcpYifWgGAH0agBYAQI1XGLxNuiYrsvfR3wHl9Lhu40uP3PcwOkHK9hLc40APC/3/2/vTsAkK8u74f/PWltX9d6zr2wzbAOioICyCCgKAhpITPIpiiaEvOh18WbUYFzfiK+ZqK8fJC/6aTSYuIEQoyziQoKgBBAhgGzDrD3TMz29VnWtZ/uu+6nunu5Ze6nqqlP1/8mxq2qqq0/V01X9nPvcz32fg0czj+C8tiuQ9/MojuVVnWKWn5if5HiweJnnI87XkoiIiIgWMlCsmch7+YP+rSPWsT9QXBoCojXYQaopySaX0hNCVidFY7Ky1J3MKmagmOZC64gCcuJBfpe8AH7fGIyV+2sX06ExUkA0Q22WgUjchK8DnltuFBUz44iZMXUmfLYbbF01ttMdeRtKoFivm23FlJbEvdCRdbJwverVh9JMDQOp/YHoM3In4LnRR9WEITBkclCAX2JzrvlqMQ1kXA/DbGpHRERERAtIjn+iVvSgjOKDGto5bGjXjAzNgOMXJ69PKz8xxjrFNL/yExP8nZma7k9Y8LQM0QwlDQNtcRtDEQdOwYPRoqvSE52xTpiJtjk1YAriHoKRoiqurkXqZ7n1BtfBvxfKH6J9moVUJIV0Ka1O7kt9sWrIrTRReNFR5SeisLB2tBvbCi9gcWQN8rkiSlKnOMbyE/MhTeys8aZ2i9nUjoiIiIgWUNyMq2Z1B2KgmGT1asndX4843mJheF85+zyfZZILzZ2+IgX/pWF1Odg9hsD1oZnMmT2Spn11XnzxRZx55pk4/vjjcf7552P37vJSF6LDiRo6OiIm/KgB3/PVHzEhJSikU+tcNjNiw0xEoAcadBgwNLMutmPMyOTz3hP4iEba0Ga3qRIbjluqyusbWR5Bn7M/a/j8kbPwu8zDsEwLju8iO5KdfM1p7pKmgdHxpnZERESzcfPNN2PNmjXqIH7z5s213h0iCmH5CU2tpDy49MQEqVFMzcfUpKGdB/8wDe2I5krrjMpS8PIVL0DQN1brXap7TRso/vM//3N89KMfxcsvv4yrrroKGzdurPUuUQi0m2a5PrGlwy1WqBRCzIQmtXkL9RO4a9F0LNL2fzxs94H2aBvaom0o+EWUqhAsNmI6+qc09jsrezJezjwFRy/CCTwUswV19o/m39Su6AeqqR0REdFsXHbZZXj44YexatWqWu8KEYU0UKzr5YDg4TKKh91y5h81F10z4EMa2pWPURgopkph+YkGDxRL5sL111+P0047DaZp4uSTTz5stvDFF1+MRCKBxYsX4yMf+QhKpf2Brb179+KFF17AO9/5TnX9uuuuw49+9CNmK9JRpUwDbTELblSDU6FAmzRo0xK2ulxPgdA1U0pMbPVd6JqJ9kgb2iPtKPkllNz9NaQqJbvMUkFMEQtsnJI9Bs/lHkOgl+sUe8NFOAO5GW1uusD39FGa2uW8+vl9IyKi2s2NZ+rss8/GihUrKrDnRNSMpISdNLRzPffwgWKfgeJmrVHs+95kQ7tYYn+VVAaKab6mBYrHy09Qg9Qofv7553HvvffirLPOgu/7ajvQ8PAwLrzwQhx33HG4++67sWvXLtx0003I5XK47bbb1H16e3vVJFc1FJPsyZYWxONx9Pf3Y9GiRQv+vChc5SfaTQODURNm1lMlKCr0wOXlEDkHgSkLsmpfO/YY3cRjKAeDXx1vZCdnetsirZC3zkhxBHAD2Gbl2hJHVtjo21zC6vF6zW8afS2+m3kYJ7efjbxTRCmThzGjGsnl2lZSN9poKQfhaXpTuz1FRzW1ixt8fYiIwqpSc2MiooVg67bKKpY6xRHsL3UnPV8mjPgj8AMf+pTVjdT4JNPcDSSj+ODSEzkGimmetK4YEDWBgqtiGMGeLLTl+4PHFOJA8eWXX44rrrhCXb722mvx5JNPHnSf22+/Hel0Gvfccw86Osq1jlzXxQ033KDqqi1dunTB95saS7tlwrJ16LYBp+hVJKSrTlokTASOCzgeYNX+rbnG2L8Pr3j7GwuUg8VtstcYLY7Ad/OImrGK/EyzzUCfDqwev3525jR8pfAvGPL3oBWLMOLlEdFnVqLDdjVoo5oqVK/LHwU6qKldf8lhUzsiohCr5NxYends2bLloO9fuXIlnnrqqao/FyJqfBIkNnUTJW/6ioaknVS3u74LDx7SXhptphxvULOQRKlAAsX+oUpPsGQeVaL8RAv8V0Ymy0/oDBQflhm2s0xHc//99+Oiiy6anAiLa665Ri3Le/DBB9Ukevny5di5c+dk4GtsbExlVXR3dx/xsWWSLduEvr4+9dXzPLXNlnyPZH7M5XupduIakLR0ZCIagpwLU/cRHCKDZ9ZMDYjq8NMONEObzHivlWOk4Z78nkq5lsDHb90izjD3Z5+22in5c46RwijyTg6xCgSL5Rlnl5go9vuI6BriQQQbsifg2cKjeL1+OXbt3jvjx4rFbSxPdSKuBTC7otCMcpbygXzPU+MnX5tJUmWFlzBkmeiwQ/Wn4CD8LA0/jmH4LeQY8vek8nNj8fjjj1d1X4mIpJl3xIgg62Sn3S7Zwx3RDvTn+tX1IXeIgeImDRbLyYIDA8WSnOU6PkyLWeY0d/qK1P5A8a4xBJ6vyoDSwcIdHThMDbb3v//9025ra2vDkiVL1L8JKS+xbt06tfzuXe96F77xjW/gHe94x1En21/60pfwmc985qDbBwcHEYnsXzozU3JANTo6OuOJPtUPK1+EUyzBdBw4bq5cKqISgV0/QIASMAZo1qEDmwtFfqPP1XT8Z1AOgt9ZyOAUw5wWwI7JM9dtpJ0xFN2imvjN++cuCbBnV4BVkfLPOTd9Ov6/1L/hosWXwZhhzWG5VyGbxl4U0VFsRSQfK5egOMTbTILExbFyQXspVdFMCo6LHdk0vOj8x62W+FkafhzD8FvIMZR5F1V2blwtlU6ymPhenlgKL45f+M13DCNaBI7rHFQqpyOyP1A8UBrAmsiaiuwvHUwS5ia2eiLH1EWvVC49YgJWpLyCV2SzRSRbw33MUknyGk1sNDNBZ6Rc8rPgAa4Pr28M+rKW2o+hX3+JFg0XKJY6bDL5PVB7ezuGhoamLcN773vfi4997GNqyd2//uu/HvWxpZ7bBz7wgWmTXVmm19nZedRs5CMNVFdXF4zDZDtSfQqKDobGcrC0IgpDPqxEW8WCjIHpwB8tqkBxrc9wvcv38HBuWAVeX0WAF6wYNkzJKhYWUtCcDIYLI8gFPuJWYl4/014aYLc/ilXjUd1zM6/Bbd538bL7Mk5KnDnzx0EU6WIOeryEHj8J22yB0WoflKk9kUkca++A3mTvQ9fzVBXqeEsMiRA/d36Whh/HMPwWcgyLxco3U21kM50bz4Q0wfvOd76DPXv24I1vfCNWr16N3/zmNwuWZCF4YincOH7hN98xLOQLcMdcZEvTs4pbjdbJy98d+i5+MvqTCuxt+J0UPQmXpS6r6GpTCRBPlP+o9SrWqTzfw1hxDCP6sErCsmManPE/+fuGRuDZDBRPHcNcKauC6/U0hvUustiGtS2vLrtP7kHwfA3/DgVAynUxtv1VOO+qfqPg2SRaNFygeKZOPPFEPPHEE7P6nlQqpbYDyQHRXA+K5I/rfL6faqM1oiFZcpGP+8CQDqfow47qlQnsxm3oTgDkXGjx2k6glxo6zjUj+JVb/gv9QyeP06zpzevkz1LKboOmGRgpDCNTyqhlZTMVsyQvef/z1EwNmW4TpXQAW9fQ4sdwavZ4/C7zME5qOWvGj2tFLQTZBEbzOeiGhp5RAwnbhJ48uHmbBPklSNxsgeKkrqumdhlfxjDcz52fpeHHMQy/hRpD/o7Uzt/93d+pbaYqnWQheGIp3Dh+4TffMdTzOvYZ+xCLxaY1rOtJ9QAD5csD3oDaCNha2ooTzXU4OXpyxR5TleD0AkQRqasgo+EHgK8hEcRgaiZa4iPIjpRLURg5E62oXfZnvfHhI4CLFBLQD7VsthnJr7LEY8zDB8/91Tb8bb3qsl7wAdlqyJDdNvw5z4mqlWjRcIFiyY6YOMN5YDbF1NpsRPMRN3S0GQbSER3xFgu+piGfddQHkmkbMG19zn90NV0D4haCkoeg6ErnMdTSVUYUj7hFlVX8gufguWIBJxn7a0ZNaNHiaolQDrly7YcZKHkFlLQiIgfUN7ZX2Oh7ujBZfuKNmdfg1pbvYtQdQqs5w/exBpgRA5oTR1rOtvqANqQhYbWxud04aWInwfi9bGpHRNSwajk3rkaSheCJpXDj+DX3GEasCCzDUoEuaWA34dxl5+LerfdyKf0hvOK9ilOtDRV7PF/1atKhmQb0Opr/64YJR2oUW5pK4Im17D/mLBR91UyexgUadN9Qr8nUEy5NTWIQbgBIuRJTl+6ZqrH9VFpPAkFHFMFQAfXEWIC/h7P5GQ0XLZHawwfWW5PJsWQwyL9VwqZNm9TG2lrNrd020VsAIikb0dYYvCKQzzlw8h7y6RJ0Q4cVNWAc8OE0IxJojpsI8q6c8kUtrdAMnKVbeMx31PUfujmcpB980CfiZkxtMzWcHcJoKYPIAZ9EkaUWdj+Rw6pI+bU7J306blv8PXxt1ydha0dfcmRoJl6TPB9nt10Kz9ERLbUgY45BS+9TZxkTi1qh17gGdL1IygkPx8OI46Ez5E3tiIioNnNjIqKZsnRLBYilaZlt7F/pd0zbMfjqRV/F9vT2GR//OFkfuSEXVmLuSTr16r9Hn8bP+h9Ql7diB5yuyh27SI8WN6/Djel11aPFC2zkSyW4bQaidgLxRXHg5XLzsUIA6IvmV+KwofgetHQReioOfRareRuafGxIsp1sWUcFjIO8A8hxvyrrKf2VNJhvXolgsCC/cDXdXR8+RgaH0XPMYtSbhosKXHrppbjlllswMjIyWY/tzjvvVGc9L7nkkor8jI0bN6qtt7cXK1ZUv5YI1aekqSOuGyh6PloMA3bSQLTFglv0Ucw7KOZclAouil6gCvFLprHKFp4BmegELTa0+MGZu7XwB46Jx/rLzSWe9V283KLjhDnWFpwqghj0oTH4gQdd2/8HzojqGGwzUCqVy0+k/AROzR2HpxMvzfixfzr0HRwX34AueynckodYNIWMkQGG9qjlKC3dbeqPRbOLGjoGHRcDjsNAMRFRA1qIuTER0UxJNvFEoPhA7dF2tc2Uk/eR9kowIjoMq7Hm9RE9Ohko3pHfVpnG6RPksaZudUKSfdzAhxt46ng4PqVkYD5TXr1LZfJalIePNYonycsQNaFFTRVLUYHigquS74KCI5FZQLLSLR16T7zWewsEPhw/A2NJ/Z0ACVVUIJfL4b777lOXt2/frroo33XXXer6eeedp+p6XH/99bj11ltx5ZVX4uabb8auXbtUUFdul6Z1RJUizb9aDQO7pnTslQ9pySKWLZ7yUSp6KGVdFHIu8mOlcmkK+WAypSbukT/U1b/VSSBzjRHB62JRPJEvL9H44dgYPh6feebw4UTiCVgjw3C80kHlJyLLLex5uYSVdvk1ODd9+qwCxeKZsUdwUcc10FwNXj5AS1sSY34G6O8rB4s79zfNaGYtpoEhx0PW80Ld1I6IqNmEZW7M1XhENDWj2NZtZLzMvB/LsDW1+U7QcIHi5fGVqlFZgABDpUGMuRm0mEk0Mnm+khY6cRIhnpoaKC433yOaCZWgFzOhxUwEKV8FjX1ZrS2B41x5pbQqT1FDQR2X2QlVoLi/vx9XX331tNsmrj/00EM4//zzVR22X/ziF7jxxhvVhDiZTKomGp/73OdqtNfUyNptAztlKYznI35AgE1KT0TjslmIOx6cgqfqGEvGsVv04HnlDwZd11R5Crm/Ch7PMOt4of1BKjUZKH6qUMCrpRKOsQ9uDDcbZjSCaCR66PITy23sfq6AlXb5A/zN2XPhLD2pfKbwCLbkn8PPhr6vLj+TeRQXtv+BqhktJUHcnIGWZBJjuQyCPX0wTAPRZB2cTayxFqPc1G7YYaCYiChMwjI35mo8IpoqZsYwVBya9+PIsZMV05EfksBi/ZRQqISYEUNPZBH2Fveo6zty23FiqnIN7eqZM17yMDYto5iBYpobTRrcxXUY0gfKmRI0dmt88jrQVHZzvSQHhjZQvHr1atWh82jWr1+Pn//851XbD2ZF0IQ200S7ZSLjeRgtBGizTMTkg+gApmWoTf7Yea5f3hwfruurALIrlx0PXt5XtXWkJIJhSM2o6n1oyMTqaFnNUx0bieC0aBRPF8rB4rtG0/hod9e89kGKy0ficeiDB5efMFM6BqManCCApWmIOjrWp5djrP3Igcye2CI8FtyHjJZB2hvCtsILWBs7CYatw837MKMGkokUxkbT6OvrxWJtGZrdRFO7fmlqZ8tywPr7Y0VERPU7NyYimo2oFZ3RZ9dMmFFdlSaVx2u0Jfgr46unBIq3NUWg2FClHYvqciw5pZndmAPfD1SSFdFcaRKYtXQYLVbFPoPmzPcAPwO9TsqNhjZQXC+YFUFT67uujNowWmLod30MllyMOO5hA8ZCsodVg7vo/tsku9hzgvEAsgenJJtfrqNTFQGcgg9fspq18c7Fsl9WOaP5cJOsq1tTk4Hix/N5bC+VsGqeWcUSKLZGTDhuCRFrf/kJ2Qdbyk/scLFivPzECU/kZ/SYZ+MLeCj1BP5u6TfxTOYRFSjWLU29pk4uQNQykEglkR3NoG/PbnS0JBALghn/sWi0SahImQZGpamd66GLtYqJiIiIqIrlJypFlZ+wNHUsZY4fMzSKlfFVeGL4MXV5e24bmoGhmyh5JbUs346ZMEwNnivHaeVg8dRyFETzUetjek39/Pr8zGI0gGiedE1Dl22hWxqwOa5awj+TgPFUkj1cXvEv/2dNBo+rdpYrgPqDK1nNEpiWWsoyuSrlXfjS/fMwweN1kQhOikTwfLF8lveH6TRu6uqqTPmJQnpaoFhEltnYvbmEFePlJ2bjgvTr8PPWx/Cc/gTe5r9XNYQwI4YqQWHagJUwkWhNIjucxkBhDIOFjAzmjB47HksgFksgHk2ofW8EEV2HE7gYdBwGiomIiIioqoHiiYZ28rUSdYrdQgA0WAxxVXz15GXJKG4GBgx4vqeOS6SWtazIHRsuTpafYKCYqPoYDSCqYMC427bQaZlzDhgfGDyuJlPi0eNxWQlIT2Y1HyF4LPv0znhyMlD861we1zgOllvWvGoGRWJx6LmDy0/YPSb2akBvycdSS1Ov8WxcMXQBnmp5AS9mn8SG5LnQTEBzNJTyAYyID8M0VWZxaSQN+MWZdf0NgOHhLIYNHZZtIxqPIhFLIhqLIxaLq7rHYZU0DQyWPGQjHhIhfh5EREREVP+BYqlFO99AsSS0mDFdNRBvNFJ6YkJfYTdKfkkFTxuZqRsoeEW43sGB4ly6hE5WDSSqOgaK54A1imk+AePILOoqzTYwOleSLWyaBkzz8MFjt+ShVPBwogMcZ1h4xXNUPbC7hkdxY0fHrOodHyiSSBy6/ISuIbLUwm+3l/BbuW5r0M0j/4yUBrzJKgc5z8yejGXFHjw99ogKFAszUm5sV8pqiKakTrOBSDwKLRaDps8wOK9eGw9OsYTMUBoZfwSmbcG2Iki0tCAWTyAWjcGM2OXi+SFqatdXdLDPcVHwq5PNLrWQo7oOi/XFiIiaBufORDSVbdiTGcWVIPN7OQyR5BY5JmkUrVab2kadEQQI0JvbgbUtx6LRaxT7rgc3KP9usKEd0cJjoHgOWKOY5howHnJcDDszD8DJPXtsqyZBtUMGj/1yHeX/Jw58cke5scKjhTzemSuia7zTcLnBwMz3NxI3VUBVSjikC2nggPIT0eUWCtvLk4KgFMArHfn1GwYw0KKhS+pAS1bx8AX4v5EfYNQdQqvZoRoiG5YOt+DDjZZrms2apqlsZNmk8ETgeyg5JRScPLL9GViaCdOyVWmKVGc7WtpbEZbfWSlB8Wq2ULWaTbbUnjY0xHVdZTDHdF3V+o7q0lAvPEF1IiKaOc6dieigjGLNRN6bWf+RozEjmupH4juNFSgWK2Or8azztLq8I7+94QPFsrrU8/3JkwhTS00wUEy0MBgoJlrAgLE0CvNmWHdY7ibB5T0lBx2WiXgdZKaqpV22gXN6UjhhYAQv5Qqq395PtSJuXNSlso4dxy9HuGfALfkoFVxE4hYiiUOXn4gut2G2FuCOzjwLaUvRnwwUXzzyetzR/e/477FH8ca2y9Vtuq3ByZYb2+lJzJumG4hEYmoTjldCySkil96HkldENBFXGcdhIPWJZ/o7OlvysE4QoOj72Oe62F101OkFCU5LprH8jktTPck4lsCxNcPAsWS+y2PmPB9GnTYEoKOPoeNXrXsnERER1RFJSIhaUWRKmYo8ngSJzaiO0pgPszHah0xraPds+ummqVOsqbl8ANd31PVYcv8xVC5dvo2IqouBYqIFDBhLwHc2Wi0DkbyGnfkSSr6uSlfUy+TuT5d24BObd6vrPx/J4L0rurEoOT0b+GjGhgsY3ZdXmcqReAKGYaoAa9SO7/9Zpoaut6XgZnxgBuUQvKyPvv8cQ9YLkDA0RIMI3jJyDh6OPoJzWy+bzJSdaGxn2HrFPwgtw1abY5aQG8siPTqCju5uhIVRrZInmvzR0abV6/ZV4DhAyfdViRY5OYLx4PFM90NldOeK2JnJqaA9hY+MoV4oocv3ESt39iQiIqIGFjfik+UF5kvm91ZMR3EWiSVhbGi3vQkCxWUaSuOBYmYUEy28+og6EdEhSbDs2HhULc/fkS9hb9FBt20uWO3iIzm7rQVrYxFsyRfhBsD39gzhw6sWzeoxoi0WchlHZRXbkYiq65vOjR7UsVgyma3WmQWPrHYgsszC1gEPJ8fK33P50Hm4p+OX2F3aimWRteXHHG9sp7KKIwGqEZqyTBt5PY/08BBSre0wbX7kHkh+l2PGwcHjkh/AnWn2vdSkk9dbm1k/Qqo/vgaMuh5GHQ+xeTTHJCIionCwDGs8e7QyDCk/YQC+Gxy1p0lYG9r15nfAD3zoWu1Xmla7oZ2syhSsUUy08Bi1mAM25KCFJFmVK2MRFUjbmi+q5fqLalS3+OCs4k589tVyVvF9+0bxJ0s6VemCmTItA/GkpbKKpdhvJBGDns0cVH5itlpOiWH7T9NYF9VhahoWuZ14Q+ZUPJN5ZDJQPNnYLufChY/A8qBpMwtM6pY+4/pnsXgM+bEc0ulhdHSFJ6u41sHj6Czqy/keVNPAuGGo5oQUPjKGo4DKKl8UC6pWI5uIiIjqp06xBDy9wIMxj3n/BNPWodu6asTdSIHi7kgPonoMBT+Pkl/CnkIflsaWodEb2jmuA9/3pgWKc+mSarjOeSJRdTX2qagqkWYc/f39eOaZZ2q9K9REpM7x+kRMBYklszjv1b6e5xvbW7AyWv7jLbVnf7BnaNaPIVnFVtQcr1WcgGlacJzyGeS5sjtNGEss7Cjtf42uHLoQz479ZvoSN2lsZ+tw8z7yQx5yQ+6Mtvywi1LWhe/6M8oqdvUA6aEhuE5lltcRNSJZOTHkuRirg882IiIiqn5Gsambk03L5kuCw2ZUO2rj67CRYPqK+MqmKj9haNIzxVPHbXKsOBEX9r0ApTyPp4iqjYFiohBJmgZOSESxKm5j2HExUuPAo2Q7/8mSjsnrP943ovZrNiayij3Hh2HZiERj8Irzf16SVSxN7SacnD8WS7Pt2Jx75qDmF0ZEhxUzZrRJbWNZ0lYY8ZAf9lAcO3rAWGUVZ3NIjw7P+3kRNaqooSPn+rP+DCGi+icr8Xp6erBhw4Za7woR1VFGsWyVChSrx1Rl5wKVddpoDe0m7GyCQLGpGXACV/1u6LqmgsUT8hk2tCOqNpaeIAphMKWe6hZf2JnCP+8eVCUxpCnZP+0awEWdqVk9hgcfGd1DMObCidgYzZmwPReaPo+PqE4T6eOjQNZHh1k+J3aK+078R34LjPgZ++8XABFNQ1IC8WpicuSHlZVxptTT9aGWtknTDDcXwIz7qpSFYemHrlWsSa3i4XKt4jppSkhUj59vAyUXSyJ2zcvrEFFlV+PJ1tvbixUrVtR6d4ioDti6DVMzVTmFSjFsDYapwXcCdbkR6xQ3RUaxbsBzPRUsFlJ+YiJAnMuU0LZof+NzIqo8RiuIQly3OKrr2FYooq/ooKdGdYtlX/54SQf+fttedf3efaNqm5doXAVwMd8y4BsOnES8Vm3/Pe1xNUCPlbuhyUQEgIS5VeBYK39NaUDL+O1LNaBHGy9bEdFgWMZkwNixfJgxHVb04IBxLCG1irPIpEfQ3tk1zydG1JhSho4R18OI66pyO0RERNS4wUDbtJEr5Cr3mLYG3S6XnzAOaI4dZqumBIp35Lc3fJ1e6VUTBD5cb3+gGMiqy/k0G9oRVRsDxUQh1hOxVAbe1lwR+0qOiq1WKxgsWcvy9VAu7mzFt3cPYm8p/EvG8+ObCntPvKBTXlh5Bd6qAZdP9NyYCBjbhpqUltKSYezDjOqw4vsDxpJVXNBySA8NI5lqY1Yx0SFYuq4axQ6VGCgmIiJqdHEzjgF/oGKPJ82mJWHDzYX/mGSqpdHlquGf1O0dczMYdobQYXeioQWYLEsST+2P+uczDBQTVRsjFXOssyabHMwS1VpqvG7x4kh1gipeEGB3sTSetWzC1g9RWkHX8DfHLMU3evchPYMGb4fjez48V6KyPlzXga5+1vzOlgdeAH3MQ2w821rKXPRFh9BhLSr/OwIUggAZTYMzg58le3d/AKR84LypL4V2QMB4zINb8GEldEQSBqQLQzQeQ3ZsjFnFREepxT7oeMi6HhLm/LugExERUX2yDVvNxStJkjUCDQj8AFqDlLGSWs4SLN6Z366u78hta/xAsRybeeWgcCy5/zg3xxrFRFXHQPEcsM4a1RvJKpatmsHorflyiYtOy0TsED/rpJYYvrRuf0feuXAdD0N9OTi5AoaG+uAHHiKRedagMjUMP5LD+TkgMj5Z/NKSu7Du5Heiy15Sjvw6BQRGDEUNyABIA8gEwNiUy3L7zgDYN/6wP/CBdgCnHvhSHBgwzviQH2slTFhWBAWUaxUnW9tgmvwIJjpQwtBVzfNhBoqJiIgamgRANWgVLaVQLg2nqdJwZqQxAsUT5Sf2B4q347S2KT1XGrShXcErTik9UcbSE0TVV73IEhE1jBbTwPGJKFbFbFU7dNSpznIu0zLKZ4w1A5FoFF6xMj8ncWoM20v7M53/YOAteCbzyLT7yNw0qgHdGnCMBpymA+fqwNt04A8N4AMG8FEDWDx+f4kvf8MHth0uCWI8YCxfi2M+nEJ5BUI0EUM2U84qJqKDSWNOOanTX3Lg+o3VtZyIiIimZxSbujlZYqBiDe1sXTW0ayQr4quarqGd6zvwfBfxqYFilp4gqjqmsxHRjER0HcfGo4hqGrYXHFUTucsyK95IIdZiIZ+2YBaiMJCB77vQ9fl9VNndFnpbdRxbCFQQaqWzCM7eXfA7fOizKG0R14C/NIBNXjnTWBY+/V8P2GgAXdrhl785OV9lFsvyN8sezyoeGkaqtR2GwYxJokOtYhh1pKmdhy6bUxWisGPZNiI6XEaxChQHLixUpoyeHJtIn5BSprE+b6Y1tGuGQLFmqprM8rsRm1KjODtaxK6XhtHs/MBHNpdFNq5D15j/GdYxHB7KI6KNorVznquoK4xHX0Q0Y9LMbmUsgqhhYNt4KYpFEeuwTe7mnFWcspDPRKGbJpxSCZHo/D+q7FNj2P1oDsvt8r6+bc+5eHXNi1gTXT+rx+nUgBsM4MseIIuhpCTFbR7wVwbQcpiXwYpJsNhDKQ3obfuzitOjw2jvYK1iokOdmHICF4OOw0AxUQNg2TYiOmKguIIZxcK0NeiG9D8JVIO7RrAitj+jeKC0Dzk3i7iZQKMydQM5p6R+N2LJ/UE0t+TjkTtfqem+1Zf+Wu8AzVNvZxbrzlqCesJTD0Q067P0EhyWBnodtqlqiRb9uTewO1xWcSwZhWlG4ZUqM3G0eyzsmBLJPSN/PHbse3pOj7VSAz6g7/8AlT/Pt3tA6QhlKKyYAbfoo5jxYRo2fPgqq5jZVUSHL3kzJE3t+B4hIiJqSBIklmCx41e2QZkR0aFbjVV+Im7G0R3pmbwudYobvfSEB08Fik1LR0tHpNa7RNQ0mKZDRHPSZplYp+uIaEXsLpbQahoqsFO5rGIb5mAMWqEy5SeEvyGGod/k0GFKwQkNp25bgtKyIvYvZpq5k3TgjwB8ZzxGvgXAP/vAdRJAPlTigi5lKAyVWSwJ2JFYtJxVnB5Be3uDdy0mmoMWQ8eeooMRx0OCJVqIiIgaMgElZsYwUqxs7w5pZieN7FSCRhQNVX5iX7GcQbojvw3rUieiUekwEPj+ZLb5mZevxUuP7YFTqE6vnLCRBpCu58I0Kl8KkhZuDEslBx2LW1BvGCgmojmLG7pqcieNp3YWSigFAToss2JZxS2pBHIZC26xBDtWgfITPRa2RzV0jM8vzs+cju+lf4f1iQ1zejxpdjcUAA+MJyv8LgDu8YF3HSampRmS4WCglPVgGyY8Vat4CKlUG2sVEx1AStpYmoZ9JReLK1zihoiIiOpD1IyqWp2VJnWKC6OeCsY0SiBNyk88Ofx40zS0E854oLh7RVJtVOb7HkbSQ2hLdUCXOisUyjHs270Xq49bjnrDQPEcsCEH0X6WrmFtXOoW66pucW+hhEqVAssmbYyYUZScDGIVygYITo1i3ZMFxHQNERhofbmE9GlHKBtxAHlqsfFN5pyXS7DYBx4f//5fBECHD1xwmMI+KjHa1+GMeTDGs4oz6VG0tXdU5gkSNZCkNLVzXYy6XsVOQhEREVH9kNIT1WDYOnRTQ+ABWoNMIZqtoZ2m6Sh50hWGiBZSg3xkLiw25CCaTtc0LI/aiOo6hh0XlaoG5naYiLQmMLgng5jnwjAq8JG12MZWcwwn+uVJ6fkDJ+J37jAWWTEVBD4aOT00Kk0kJIgVAFEN+FMdGPWBl8af+F0+0A7gtMMFi20NQVFHUNTgBQWMDg0hmWplVjHRAWKGjkHHxWDJZaCYiIioAVmGperRqlq0FSg1N8GIaDBsDV4pUAHjRrBySqC4r7Abjl+Cpc+liF44yO9F0Ss1VFY4URjwqIuIKqbLNtVWMQmge3k3XuobgFEqIJaoTP2ezIYWuE8VYGoa2hCF++o2rDx1qcoQPpogAMYkUBwAwwDGAiAF4M904IsesFvuA+CbPvBhDVirHX7y6uQDaLCRHkoj08GsYqJDSRo6hhwXec9XgWMiIiJqHLZuw9TMigeKdUODGdVRGGmcVcBtVjuSZgoZNw0v8LAr34vVibVoVKZWPoEgz1V+R4hoYfDdRkR1ra07iUR7ApmBQcQSlXnM5DIbO54exdqgXM/ipJ0r0NfaN6sz1ZEAaJXyGOMZxuK9ho5/6O5C2jAgvZv/0fFxrR5gmWGo+x7Y5M6K6nDyFpyxIob2DjCrmOgQpEmmNLWT1Qoxo3GzZoiIiJo1o1i2iaZllSSB4iBwGyYjVZ6DZBU/n/5vdX1HbntDB4oNCRQHLlzfqehJBCI6Mr7biKiuWRED7Utake4fges4MK3K1DFz1lnAC+XLS5HA0ucq8rA4JVnAB8+MI2tqyOk6/lFu9ADZ6y7ZNKBbGjJMfI3oiDkW0nvHMNo1jI4uuRcRTZAmduaUpnZS6oaIiIgap0axZIvmvXzFH9uU8hOWBt8JVBmKRrAyvmoyUNzoDe0M3UTRKVblJAIRHR4DxURU97qXdmPPlj6M7c2Uu8nNgG6YSC2WohCH1nlMG3a90I9lqi1d5Ryf8fGFp/P48Gti8KakEEuGcZ9sEwWcpxRy1hMxtHk+4lsGoG2V6sf1Qpt8vcuxuYkrU7/Uw6Q7QOD7MHrT0A0dWjX2SR5yIkB5mIeXogjrW2L44yUd6Lar05ilWaVUUztPbe2sVUwUOmwETUSHo2s6omYUmVKm4o8twWHZ3KIEitEQmqmhnSm1qwNPZRUT0cLh0RYR1b1EMo4VJ65AMVea0f2dYgl7N++FU3JgHSFgN3I6YD1VRCKo7EfhKfs8/L9P+PjB6gh2JHT0xnU4B9admMIHMCR1WFGHpgW2D9WmsFKtC+dLA1yvvNXQy7ki7t83iqsWteHdSzpVgJPmT2oTDzgOhksuA8VEIcRG0ER0JDEzpurQVpqmazBjOkpjjRNonNrQbmd+O/zAV8H2RqRBSof48HyeZCRaSDzaIqJQWLZq2YzvWyjkMbxnGIXRAqzuwweKFy3rQbA0wKCTR8yKVbR2WXsuh0/9fAwp3VSB4FciHn55bhzDlg3JGR4MgEGUG+IVK/ZTqR6UggDf3zOMn/SP4JrOVlzZnkJMn/8EXtN0aNK129Cg6To0ox6yuRdOi2Fgn+NiqecjyqZ2REREDcOuYrqvGdEhcdTAD1TgOOwWRRbD1iMo+UUU/SL6i3uxOLoEjUpWCjosPUG0oBgoJqKGE43G0LaoDXte6oPv+9ArEKSbrY54HFvWD+HUF1OqpuoJRQPuI31IXrRKBY4nNjcARgDsVQFGNDypPlcYL8UhrCnb3AWAW5Qjgcm6EAF8uK4Hx/Xgea6aZJqmAdO0kIhFK1OeIgjKCdVqMAMMBQF+HHiqxIjI+gG+uW8Ed+8bwVXQ8SboqtbutMoe0pHb1KWt88x+ptxNAsRyoCNfba0cMJ66ya97Fev4yu+zNBeReoJywGVopupKLdkshl69DOqkChQ7GHE9LGagmIiIqGFIozKZR0hWscwxKvrYEQ26pcFzAnU57OR1WhFbiVezr0zWKW7kQLE834InRw9EtFAYKCaihtS5pBMDO/ahlCsi2lLZOsQztfy45Xhu5zacmutU108stOOp57dj1cmr9t9JKze5OxbNQWKrMtXLSSA1ADJTAsfyB0lGSsK9s0r4UAF2f7yO8MSNOmDLVg5B+76HUslFqVCAN5atSmnlJIC/APCUaeGXdgTp8RMUowC+BR//7ru4uFTAya6rYrlCMlwktqrZgDEeMVfXDxXolefpAZoDaN74U5bbJF6tgsNAYJQvy9cZ0wA/CviSzHOU10UC7AHKncNl4q7+pxkwdLlU/ipNaSzdVnXlJAt6JqTGtFvMIJuXbJ/Df89gycML7gi8uNkQ3csrWt/RiKoajxEjwteGiIhCl1EsJ6ClaZlhVDZQLEFiw9bh5PxyTkGDNLSbCBRLneKzOt6ARm5oV3KLCILy/JOIqo+B4jlgQw6i+tfe2Y54Wxxj/WM1CxSLnjcuQ/8Do+jRysGbdVta0b8yg2RKworNR+Z3MhqydUpZ4aCcZZwFkAnGA8hyxwCISn3qCiXH6rqBaFS26h8hXALg/AD4zwD4qV9+TmJI1/H9aBy/BnCFDqyX5+UBvhfAL/lACdBNDbqpw4zKZV116p7RMkk/KGc2e+NZzvJ1piayoqUOXHw8wH60bwkCeIEPHx78IFCBeCdwUPQ8dbvUk1MB5ZlG5P0AZt6Bq48c8SxBwQf2BRoKeQ8Jln+eJCNh67Y60I5bcaTslKr3KJsEjxu1diERETUGOclsGuVAsZzwrCSZf9sJqVPcOMfuTdXQTis3tJNsczmZQETVx3faHLAhB1H9My0THUs6kdmTgee4MGrUAMu0LPSf4aPjt4EqO5DQDQSPZIC3NWeg+EBScUFeCdkWjWcXS2B1bLwkxz4AKQkahyyBwNaAizXgHA34mQ/8MthfbmMngNt8QBYJqkMhVTLCmAzAqiCvipZ7KjFaZU+U/6seCRbLAZSkKktG8lHKV0z+kzbxf/N7f0lm9ImGiys7I9CPkEkkr0+/EyBia1gUZaR4Ksdz1NLM4cIw9uX2qZI7KsvYiKrAsQSQJ4LH1SwRQkRENFtqNZJmoeTPrHH1bBm2BlmwJCfn9Qbo8TC1od2O3HY0MpmzlHwHru+oEiVEVH18pxFRw+pY1IG+lt0oZoqId9Tu465naQ+e2bYFZwx1q+tr/SSefHwLVp+5tmb7FIZs464A6A+AofHAcascSIRsbh/XgCuMcobxfT7w6Hjir5ioZTzNeFB4mvHyEgtivLyF2hbsh5ZtgYG1WR+nthw5+1UqZAyUAnSZPmxp7MdliIplWGpLqtMuUB3CJXA85oxhqDCksrsjZgQRfXalKVSWshlX3ysH8nJdbbrN156IiCpCAoCWaSFXyFXn8SM6dFuHVwqgx8L/t2t5bIUq/yUritLuKEZKw2iz29GIpBeG7xdUtjkRLQwGiomoYaVaU0h2pzC4ZR/QIUUMamfFWauw/YF+rApa1PVT93Zhy54BdC2WCsV0KAkNkGrOMu3tH88wNgMgNdsaxnWgVQPebQBvDoCf+MCTTdC4cC6eH/RwjBSvPgItCLDPB+KWhm59vLiFfJUY+0SdaunpJ78k8l/YflkqSEMEUbVJdQ9fZWqNevlysfAZ8oMM3KBfNYiUetSWNC80THXgpkpbGBEVoDZgopjPobu7fEKMiIhoNuRvyqA/WJXHltJe0sguP+wt9HnwKjGxOLIUu4u96tqW4a04JSkpFTMjJcK8YgAHfjX7H1eEFDIrFFxkzQLMiMxoSEjD9lLBQ8FwoesTaSgUtjGsVwwUE1FDa1/cgZGdwygVSrCjkotYG7Kc3j07jvyvfMR0HbamofVJH96l0rSDH8WHIzG+NgASXh8eDxjLIUQsqFz94oXUowHvN4B3BFJrF/VNmoZIEWn5z9IR2PJ1Ihp7uG8JEMicRwKR8t8MnuMrHvCL8SSR7apm85HvL6Fhww+wrxRAko/Vu3r8B03+vPGv5evjdZtJ8nJgqJz92XyH6rGoqKrUvoecL7UCcxj0MyoALaOiBzriMQNr1qyseCMiIiJqfFIqSfobVIudMOA5QUPVKZ4IFPc627HBOm3G3yvzI82VRn9hmEtraj4a6L7q3UFlmi99TaQZtpRV4esS1jG0owaMOiyHw+gEETW0rp4u7Er1opAp1jRQLFrbW/H8qq147c5yFvESxPC7R3ZgxXksQTGTWsaSpyiL6gcDYEDKD8j18aZ3YdMlpTXqb05wAKmdrKmayXopQOAAgbzYRmUPsuR1+MX45V4fsNzgqBnjUpZkQEo5awFaDzW5OqiGcnUEugZfCmjX4QRvIcmB/dDIKDy/3MSQiIhotqS8kZwMlpPO1ShtFEkZsI9S3ipMjiutxW+GH1GX+7ADrcvtWWUy5kZcxNts1dOg3hXyOlpaDCxunXnWdKPzPA/6viK6u1M8QR/yMYwlaxujOBQGiomooUWiNtqXdGDXczvUpKjWk6HVG9bgxT07sc6RPFng1HQXnt+6G0vXLK3pfoWFxOSWaeUGdwPj9YulUkGbZEaEMD4VigQAQ4MvSagSwM3PME14FuQ3P2JpKGoaCtAwMOZj8VG+R97FifEs847x7PJa8eM6vJRRzrZuUnJgr6uD+hC+CWlBbNq0SW1yUEREdLhAsdQqdgNXNbarhkYqR7W2bX+iybbMtlk9N1W4a7w8VxheE/m9KPrFUOzrQtGC8tiFZQzp8GNYjxgoJqKG19HTgb2xPShm5Yzd7JZdV0PiTV0Y/VkBrbrU+dSw4rkosksKiEbDmBtbG0mtHBxsHS9HIa1PqtMnu3qk2kKrZETX5/zgYKYGX80aKr/DK9wAm8cvb43q6JnB+Zy4lO+Q8Zfu30euiFE9XgAj50PzATelI4jUf1YOUS1s3LhRbb29vVixYkWtd4eI6pA0SjV0QzUtk6AxHdmqlHTyKNuT3YOck0PciqMRSaBYGvRKuStd41yLqNoYKCaihtfW2YpEZwLp3aN1ESiOxWLYun4Qp76YUll47ZqFvof7Eb1kZa13LVTkBGyH1C/WgFGtXJ6i/uus7TcclIOcPD0ArNKAzePJqNsC4KwZfI+MdXI8q1zy82uyGNHQ4MWhMq3NUQ9eEvBjPIAhIiKac0axP964gI4oaSfRHevGvrycNge2pbfhxM4T0YgMzVCZ5vK7IScUiKi6GCgmooYndZs6l3SqQLFbcmDatc9SWH7ccjzbuxUbsuV6xSeW2vHbZ7ah61ipxEuStBrMsO6r1LKDU4RZhZp20vrL0qszIZXdHh7Phg5NVnGVrJAXY3zp1XY/wEybAEcAjAXAXjkBo9WwlIdVLssRFAIEKR1+LGRnLY5ALYlTy1PlKTXGcyIiovojQWJbt5F1srXeldBY07qmKQLFqvSEV4TjOwwUEy0ABoqJqCl09HRid8suFMeKMDtqHygWi85djv4HRtGjSbgLOGNnJ7BzhhGyJjDo+nil4GOvO9O6p8Uq7Ul1D1jyAEbR3FoSOnBZuW63vAV23z0MYxZvBTlEKh8mUVVNBIxVPbzplwP4Kja+NjWMtRsW1XpPiYgoZORkZNSMYqQ4UutdCY3VqdV4fM/j6vLW0a1oVBIolhMIjucA9XEYR9TQuD6SiJpCSzKB1kWtcPMO6oVpWRh8bQCnws3BGkWnqeP1LSYuSJpYbqm2G9Sg2rI+YsVyZNgzNPS3sntzXZJehh7gOwG8YgA3H8DJ+ihlfDgZoJQGcoVCrfeSiIhCSgLFUoeWZp5RPKGhA8WaCc/3VEYxEVUfA8VE1DTaF3XAsEwUc9XKPJ297iXdeGbZIIqcFB9WytBwRsLERSkTa2wdDCE2HjkJsHRof03C3R1c8BRWr6RfrvUuEBFRSEnpCZpdRvGE3kxvwwZSJ0pfNerzI6o3PBKbg02bNqnN87xa7woRzbL8RCS5E4V0AZF4udxDPVj9mjUY3cDPk6mMbID4VgfRXhfaeMJ1XNdwatzAyW0Gcqss5FdZCGxN1SjOO3nErFjFa6gWgyJ2l/qwq9SL3inbsDdc0Z9DQDb2LgBXqsu/XPcknuj+eq13iaBBD2QzoAW6+lrepl/Wptz2V8s+VOudJiKikLIMC4ZuqOxR+UpH1hXrQovVgjFnTDV7k2Dx1CzjRlPySrXeBaKmwEDxHGzcuFFtvb29WLFiRa13h4hmyLYtdCzrRO/gdvi+B72OJqC6UT/7Ug+CFJDdYCJ/vI/o1hKi2x3o47F0vQS0vOIgscVBYZWF3GoLuqlD1/WKB4pjiOGY2Fq1TTXmjWFXaRd2lnaqwLFcls0J5pbpILFwv8mX+fhOr2rsJzz7GET06Kxfw2D8NazLMiVqp+pyz2YhQAAX8lb0DvEv8p8d42cZERHNjaVbqszA3tze0DVQTZgJJO3kgu63/CwJDD878OxkQ7tGDRRLneK8K509iKjaGCgmoqbSubgDe1/tQzFTRKw1XuvdoaPwYzpyJ0aRPzaC6PYSYlsd6KVyirHmAbEtDqJbHbS0SlOtHBaqknErdCzDCpyJypwslKBbPiiHEfVwHRdVRhBg0AxwdXf5qmctw1f7v4LYLCuy+OVYJWIa6qhESVAeYBnXGR48BjpQWGGhuNpGIL8Q0ixOIuC6Nv515o+1UIZHR6F5Pt647Kxa7woREYVU3IxjRWoFfD9cJdkkm3cgP6AC3G2RNlVreSHLT0wEiqVO8QUrLkCjBoqLXpHZ5kQLgIFiImoqydYk4h0JjO4aYaA4RKTERP64CPJrbUR3Ooi9WoIhkVWJlwVARDXIDtdBxVTSwHnhDinqUwrAoryPvTEdvqahzwVOG2nekizWoAc/osOPlwPCKmAsF43y5UCOkUxtMpA8G4F8n1l/wWYiImpuEgBc1rIMYdQZ60Rftg8DuQFkShm0R9tVcLPamqWhnaEZcH1XBeWNOkoHIGpEDBQTUVMxDANdSzsxunsUTsmBafFjMFQMDYXVNgorLUR2uypgbGbCGyCm6U5MeypQLJ5r1Zs6UCwh3MheB9mToyrjWs6DqHrd8tXxoUtPzolffW32gWI/oiGQQLStqYAzERERzV3KTql6wR2RjnLAOD+AmBlTt1ezHMXUQLGUnvADH7qmN2zpCcdzEDHqp9cMUSNihISImk5nTxd6W3ahlCnC7ODHYCjpGorLLRSXmTCGPTj5IiKGHe4MyQDYFwBDqrRFuJ/KrEkg1Cuhp2X/gc1/rbFxZvfc3p/pAGiRDGVJtg3Z62imPSReKjdrkez53AkRBFa5lsZ4X0cVGQ4OfP1mQXMAI+cDWV899rSgsRGyF4yIiKhOSIC2O96N1kgr9mb3qlIUssl1CRpXw9LEUti6jZJfUoHU/lw/FicWV+Vn1TpQLNnEjj+3fiBENHOMkBBR04klYmhd3Iq9L/bBb6vOpI0WiKbBbTdQaNGgWWboGp8cyAzKQU5p6jZecaA5SKDTcbDI1CezZDdHdIwk5pYR40jQXS5oQFfIXkenx0B0h6NKq0gd7shOB4W19pG/aZa/94FdLuciRZ01FzDGAiDrTgaNpeSF+vewRdmJiIjqgG3YqtZyW7RtshzFWGmsKuUopFzHytRKbB7ZrK4/tPMhHNN2zBG/J/ADFLIFRItRaDX+Wy9Z2Cd0nKBKSxyJvG5Sn5iBYqLqY6CYiJpSx+JODG4bgJMvAUeJwRAtlASAdgB9ckJDmts1WZxu5ZTLEujNBkBiDq+BJOBKmYaBoFz7WGKeoaGVy6skXpDaEkBsawmFNVZ1fhmk1rEKGk+UtAhgpAPoRqBeRD8iZSr0GZW2MAryCzu7zGZqHps2bVKb5zVvORkiaj5JO4mElUBHtAO7x3ZjoDCAmBFDi90y4wbMcr+jJUKsSa2ZDBT/8JUfImzesuot+OCpH5zRfUteSZXXaHbyGkxsWhCmiS5NmBi/esRAMRE1pc6uDuxsiyE7OAarg5Fiqg9yHNAJYCQA8pJVjOYiWdQ9APrHr28PgBPnOPeV8h1D41vYFmAWVliIv1xUGcWSWWzvdVFaLC0Pq0iCxqoEBQAvUHWQjYL8Us5sAhvNBXAigcpSIjrQxo0b1dbb24sVK1bUeneIiBa0HEVXrEvVKp4oRzGYH5zx90sgSb7/SJnI6zvX42c7foawenD7g3jzqjdjbevaI95PAuaSnS31n5udzLfyo3ns1fbWPCuc5kZl9o8WYKdsdCW6UE8YKCaipiRN7DqWdmKsPwPfZ4YT1Y8WDWgPgN1NmlW8SgP6x2ON26XB3RwfR0rtRsazimN1MuGJju/X0UjZh8JyC7Ht5eWV0a1O9QPFUxkaAtlm8S2+BJUZIyYiIjpiOYqOWAcKrvzRPLoAgcpEHi2OojMmqQSHds6yc9Cb6cVLwy/NbGekNYTrwTCNWTfEraQ92T0YLAyq53nH7+/Ap17/qSNmT7dH2lUdZioHGV3fVRnWzCgO7xiOOWOqQWO9qYfjJiKimujobsfu+C44eQdg81yqI50aMDxeq1jKUTRboPiJiUDxPAOP0tBOcna21EkAsw3AshmWwpByExOBYnvQg5H24KWOXL+PiIiI6puUopBtpqQurwSKJSAoweZDkfq+f7z+j2f8mL7vIzucRaI9AV2fWz+ISnhl+BX89SN/rS4/N/Acftf/O7xm0WsOe395/od7DZqNGkMri0SktmNI8xvDDDKoRwwUE1HTautoQ7Izif7evShFnao0QjMMTTWZoDmQOU+zpdOOS4xnFUut4niTZRVLoHjCfAPFshKvMwBc1J6sW9g73mhPgsVHq73stRgodRuw95VXPMS2OhjbwM8SIiKiZiKZxB2FDlWuYlF8ERrJce3H4Zyl5+DR3Y+q699+4dvY0L2Bx05ENcZAMRE1NSk/MTQwiFKmMOOmErPhSuMeTUM0YiMSsaBz4jNjmhvAlzoMTapZs4pXjJ8jkMq4o+P1mtu0+QWL6yX3xA7KNZMngsVHe175NTbsfeUllpFdDrLrbAQRZo0QERE1C6lNvDi+GKOFUVV2IWZKQa3GIZnQ/7Xnv1QZhZ2ZnXho50O4aNVFtd4toqbGQDERNbVFS3uQy2fR1tpW8WU7fhAgX8hjcGQE6UwWw/kxINCQiEURi0dUcws6NM0BzBFXXsRypK8JScZpRwDsarKsYinLsBRA7/j1bQFwWoM8d2M8w3l4PFu6BEBaVxzuV9zpNuAmNJjZQPWUi+5wkD+OdXKIiIiaSXu0HZ3xTuwZ24OoEa3KKshakSzpS1dfih9v+bG6/r2XvqdqLjdaQJwoTBgoJqKmZhgGWttb0dXdpS5Xw0p/BTK5LIbToxgYHMbo2BhGR4Zh6Dri8RjiKmjMTOOpAj1AYGrQXGnshabVoQFDAZAdr7fbTOUneqfUKT4NjUOVwwCQDoAdASBViGUhqXmoYz5NQ2GNjZbniupqdJuD/DF20548ISIiakaSXCJZxSOFEWSdLFrsxpoVvuu4d6lMYmnsNVIcwY9f/TGuOeGaWu8WUdNioJiIqMokU7m1Jam2lYuXqqDx4OgIBofKQeO9/VkYho5YtLEyBOYrpgExx0Qwk85fTZBVnGiirGIJFD86EShGY0ppQG58bEtBOYs6cojxLSy3EH+xCN0FjGKASJ+L4jKrFrtMRERENZKyU+iOd2NHegfiVryhViZK4FuCxf/8+39W13/06o9w8aqLVSY1ES08BoqJiGoUNF69ZBnS2TEMjY5iYHgIY7m86n5KZemig9IYELNiMC2rqbOKpVbxPvn9mWdzt7oVANFgfzD8wIZ2QYMGyeMaYAZA/5Ga3JkaiistxLZI7jEQ3VpioJiIiKjJSDKJlGkYyg8hU8qgNdKKRvLW1W/F/dvuR3+uH0WviO+/9H1cv+H6Wu8WUVNq6kDxzTffjO9+97vYtm0bXnnlFRx77LG13iUiarKgcVsypbbVS5chXyovLycg8H0M9g9h3wt9GB0egx23kUhI9kQDRguPQgKHS8azThuVJycGAMg7IIpydq2EQiU0Kq3cJEjeg8Zkj2eND42PsQSL2w/4Nc+vthHd4qh2m9aID3PYg9vOcjVERETNJGElVLD41dFX0WK1wGigJtmWYeFP1v0JvvzUl9X1X+74Jd6+9u1YkZQ2x0S0kJo6UHzZZZfhL/7iL/DGN76x1rtCRE1OgsaJKJs2TJVYGUe7G8Pevr3YmxvF4OAwWlriqkRHs+mWCGEDx8g9vxwklmBxdLzpmxwWbJmSVdzTwM9/osndyJQmd91Tmtz5cR2lRSYie111Pba1hEw7Py+IiIiaTU+iB4P5QVXLtzMmXQ8ax9lLz8ZPtvwEr4y8Ah8+vv37b+Pms26u9W4RNZ26ChRv3rwZf//3f4/HHnsMzz33HNatW6e+HujFF1/EjTfeiF//+tdIJpN4z3veg7/927+Fbc+u49HZZ59dwb0nIqJK0nQN8fYEljuL0W52om9fPwaHRpHLF5BMtsA26+pPGM2DBEQlc3pQssmnlJ/YMp5FvS0AXofGfw06pjS5G5X3wJQs8tRqCyeNB4qtPhfbcz5K0fqpT5iXk12aj0AGkIiIiKoiYkSwuGUxXh5+GY7nqEzcRiqv8Z4T34NP/PoT6vpT/U/h2YFncUrXKbXeNaKmUldH2c8//zzuvfdenHXWWapO56FqdQ4PD+PCCy/Ecccdh7vvvhu7du3CTTfdhFwuh9tuu60m+01ERNWhRU2VbpmMJpBctQYD7cPo27sPo5kMdMNAqqWxmnk0M8kkjkwpP6HqFE80tGui2KM0uSsEwNgBt2c6DKxM6khmfFWrun27g80nyCtWH8Y0DbIAtomGioiIqCYkk7gj34HhwjB64o1VnGt953qcufhMPL7ncXX9jufvwBfe9AXO94maNVB8+eWX44orrlCXr732Wjz55JMH3ef2229HOp3GPffcg44Oyb0BXNfFDTfcoGoOL10qlQ2BM888E1u2TCxa3W/lypV46qmnqv5ciIho/vSIAd02EJQ86HEL3W2daGtJon9wCHsGBjA0PIpoJArdYngq7CTkKcUUclMDxeN2SnmKoFyioRlEtfJrMI2mYd8aG8n/LqirK3Y4GDzORlAnL0qJmcREREQLwtItLE4sxmhxFAW3gKjZWGXZ/nT9n+K3e38LL/CwNb0Vv+r9Fc5bcV6td4uoaZj1VqPzaO6//35cdNFFk0Ficc011+D666/Hgw8+qALM4vHHy2egiIgovDRTV1nF/uj+Rn+WaWPZosVob02hr38f9g0OIZ3OIasXoTVyId8GFiCABxc+SthnGPCDQE1QookkCpqmmtq9lBnDkkOsNGomowlguQnYrgbLCRDbnMGuRagLJenEx7LJREREC6Ij2oGuaBf25PZgsbkYjWRpy1JcvOpiPLDtAXX9uy9+F69f+npVdoOImixQPBNSn/j973//tNva2tqwZMkS9W/VJJnMsk3o6+tTXz3PU9tsyfdIeY25fC/VB45h+HEMQyCiqfEJXFfVLZ682Y5g9fLlSLYksGeoH/Eoy1CElR/4yBVy6IrEAA+IaTLsGtYWXPzeL2eqjrW1YonJ8R0+JodFL5Wzio/pN+GfnCoXda6xTN5BVIX0y5+r1cTPayIianYy512UWITh4jCyThYJK4FGcvXxV+M/e/8TeTePgcIA7ttyH6467qpa7xZRUwhdoFhqFEtg+EDt7e0YGhqa1WN95CMfwXe+8x3s2bMHb3zjG7F69Wr85je/Oez9v/SlL+Ezn/nMQbcPDg4iEpn92S0JTo2Ojs44m5rqD8cw/DiG9c93PLjFPODo0KyDx0i6IkciNhLxGAPFIQ4Ue4GLlngMeddD3veRMgys94HfF8rBx12ahvbo7JrWNiJ3nYHg5YJqdBdLe1g8ChQX1/51MSWe77gYGBiAZVV3einzLiIiombXGmlFd6wbOzM7ETfjqhlcIz23q469Ct958Tvq+j2b78GFKy9UtxNRdYUuUFxJf/d3f6e2mZKmeR/4wAemZRRLLeTOzk50d3fPOSOmq6sLhiEtYChsOIbhxzGsf0EQwAnG4OU8GMmDOzt7vqc6aHWm2mHoHMMwmhzD1na4boCXikVETRPr9QJQKAcFX/U1RFvaa72rtdcCFNcWEX21vMKpdYuD9LG1X3KaC/LQDE19ltp2dTuwF4v7S9EQERE1KwkMT2QVp0vphguivn3t2/HTbT/FYGEQOTeHu165C9edfF2td4uo4YUuUCyZwxPZfwdmGk+tW1wNqVRKbQeS4NJcA0ySwTif76fa4xiGH8cwBFqiQDZ72ECwGkPdYKA4xCbGsNUCYq6HkqZhXXT/ap1tjqMKG0SY+Y/iqZ2TgWJ7+xjMjAu/tbZZxZquQ9MW5rOUn9VERERlLXYLeuI92DK6RV02tMb5Gyk1id+97t247enb1PUHtz2IS1dfqmoYE1H1hC5QvG7duoNqEUvgWLJ75d8WwqZNm9TGGnlERAtDjxqAqSNwfdXgjhpXUtfRYmjIej66DAMdhoEhqSUuWcUlBydOCR43K7cnDqcnBqs/r9o3Rp8fRO7sJbXeLSIiIqoBCRQP5geRLqbRHm2s1VdvWv4m/GTLT7AtvQ1e4OHjj3wcCbux6jHPSVAuoahKJzZOxZHmEgCu52LFzhW44213oJ6ELlB86aWX4pZbbsHIyMhkreI777xTvUEuueSSBdmHjRs3qq23txcrVqxYkJ9JRNTMtIgB3dIRFD0GihucoWnoNEwMuiW1pPJ428Zj+bz6t5dLJQaKxxVO6YT1i151OfrCMIzh2pZjiLo+4Ltw2jKwj63uCi8KHyZZEBFVT9SMYnFiMV4ZfkUFjI8m8AOUSiUU88VpjaJrRTKhJXv4UKT/yHtOfA8++9hn1fWMk1EbUaOI5Orv2KauAsW5XA733Xefurx9+3ak02ncdddd6vp5552n6gBff/31uPXWW3HllVfi5ptvxq5du1TQVm5fupRLEIiIGpFm6NDiFvwhCRhWt/4p1V7K0BHRNBR8HydE9geKpXYxkKz17tWF0poUvIQJI+tCcwPYvdma7s9E4Qs/W24+SDQVkyyIiKqrK9aFvJtHwSsc9b6SiZrL5xCPxmvezFv6VAwVhlSg+3BO7T4Vb1r2Jjy86+EF3TeiZlVXgeL+/n5cffXV026buP7QQw/h/PPPVzWKf/GLX+DGG29UweJkMqkazH3uc5+r0V4TEdFCMGImPGgqC6Iesh9oYcpPHB/ZX3v35WKppvtVVwwN+dd0o+VXfbXeEyIiIqoxy7Cwtm3tjO4rqzv2efvQ3dFd87r/o8VRZJ2s2hLW4UtK3Hj6jXjXce+aUSC8WZp959N5xFIxtQKPwjmG/QP9OGHZCag3dRUoXr16tXqxjmb9+vX4+c9/jlrh8jkiotqUn9BsHUHJgxatqz9fVIXyEx2GiQG3qEpPTOh1XYx5PloMlh8RxfXt8Lqi0Mdqn8WbyZeguQV0Lmup9a4QERFRSKTsFDpjndg9tvuIgWIJhi5LLlvQfatnkhWe9bNItCZqnhVOcx9DKTuxJrUG9YZH2nPA5XNERAtPs3ToEROeBMWitd4bqrZWQ0dU02FrGpaYJvpcV93+SqmE02P8BVA0TTW2Q0+tdwQYy+ShlQIYqf2BfSIiIqIjkQCwNOOT8hNHyyomooXBUw9ERBSaiaSeMOX064xWn1C4teg6ElJ+wg9UneIJ5TrFRERERNQwWcXRTmRKGc7xieoAA8VERBQaklEsmcVw/VrvClWZqWno1A0UAn9a+YmXS6xTTERERNRIySDd8W5EzShybq7Wu0PU9BgoJiKi0NBsA5plwC8xUNwMUqahSk+ssazJ215iQzsiIiKihssq7op2MauYqA4wUDwH0siup6cHGzZsqPWuEBE1Fc3QoMdNwGEz0WaQ1HXVuG6RaU5OWAY8D0Mux5+IiIioobKKE92ImTFVq5iIaoeB4jmQRnb9/f145plnar0rRERNR4+ZqolX4DHboBnKT3ToBgINWDUlq/jlEusUExERETVcVnG8C2POGLOKiWqIgWIiIgoVPWJAj+gISswqbQatpoGIpuEYm+UniIiIiBpZd6wbcSuugsVEVBsMFBMRUahIjWItaiJg+Ymm0KLrSOg6Vlr7G9oxUExERETUeJJ2El0xZhUT1RIDxUREFDp63AJ8cALZBCxNQ6dhYLllTt72cqnEsSciIiJq0KziFquFWcVENcJA8RywmR0RUe3LT2iWjsDxa70rtABShoE1toWJUHHG97GHDe2IiIiIGk6L3aKCxdLUzg841ydaaPvTc2hWzexk6+3txYoVK2q9O0RETUezDWgRHX7BA0yj1rtDVZY0dLQaOlbbFjaXHHXb90dHsWpK3WKqrWzBgea5WJYv4XiOCxEREc2DNLXbl9+HsdIYUpFUrXeHqKkwUExERKGj6RqMuAV/TIKGDBQ3S/mJVdb+QPH9Y9la7xYdwptyBRzfmqj1bhAREVGIJayEyireOrpVZRjrGhfDEy0UvtuIiCiUpKEdDA2ByyVpzVJ+YkM0WuvdICIiIqIF0B3vRjKSRKaUqfWuEDUVZhQTEVFo6xTrtgG3xFq1zRIoPicexZjfhq1OOauY6kfRcVXpiSURlp0gIiKi+YtbcZVVvGV0C5J2klnFRAuEgWIiIgolzdRVVnGQZ9CwWcpPdJkm3pCI4V0ma9XVm8FMHlopg1NaYrXeFSIiImoQXbFyrWLJKm6NtNZ6d4iaAk/JzMGmTZvQ09ODDRs21HpXiIiamhE3gSAob9QUWcUmNJQ43kRERERNkVXcE+tBzs3BC7iKkGghMFA8Bxs3bkR/fz+eeeaZWu8KEVFT0yIGNMtA4DJw2AyShoG4riHnsy41ERERUTPoinep0hOsVUy0MBgoJiKi0NJsA3pEZ0O7JmFrGjoMA7mA401ERETUDGJmDIvii1BwC8wqJloArFFMREShpWka9LgF+D78vAtNZ2ZxGPm+h6DkzWgMkx6gl3wMlUrQtAXbRTqKvOchXuudICIioobUGetEf64fQ/khRIwIml3gByg6RfhFH5rOCXFYxzBAfR67MlBMREShpkdNtUkjZDZDDieZ3krQdyZj2Krp6PANFFmnuK6kIhZMx2DwnoiIiKqSVbw4sRh7s3trvSt1QQKMju7A1OUYiJOvsI5hi9WixrDe1N8eERERzYIeM2F2xmB3pWAYRq13h+bA8zyYA8UZjaEN4DVBABafqL8xHBoMoOs8W0NERESVtySxBD3xnlrvRt3MuwaCAXR1dfH4J+Rj2BHrQL1hoJiIiEJPzqRrRnmj8NHkf7MYQwMaOCWuL7rGIDERERFVt+ScqTGEJbRAg6EbKhtVvlJ4x7AecUY/B5s2bUJPTw82bNhQ610hIiIiIiIiIiIimjcGiudg48aN6O/vxzPPPFPrXSEiIiIiIiIiIiKaNwaKiYiIiIjoiHbu3ImLLroIJ5xwAk455RRcd911KBaLtd4tIiIiIqogBoqJiIiIiOiITNPE5z73Obz00ktqVV0ul8NXvvKVWu8WEREREVUQA8VERERERCG0efNmXH/99TjttNNUIPfkk08+5P1efPFFXHzxxUgkEli8eDE+8pGPoFQqzepnLVmyBGeddZa6LI0LX/va12LHjh0VeR5EREREVB/YMpKIiIiIKISef/553HvvvSqA6/u+2g40PDyMCy+8EMcddxzuvvtu7Nq1CzfddJPKCL7tttvm9HPz+Tz+6Z/+CV/84hcr8CyIiIiIqF4wUExEREREFEKXX345rrjiCnX52muvxZNPPnnQfW6//Xak02ncc8896OjoULe5rosbbrgBN998M5YuXapuO/PMM7Fly5aDvn/lypV46qmnJq97noc//uM/VvWK3/rWt1bx2RERERHRQmOgmIiIiIgohKQExNHcf//9Kqg7ESQW11xzjSpZ8eCDD6oAs3j88ceP+lhBEOB973sfkskk/s//+T9Hvb8EqGWb0NfXNxlslm0u5Pskc3qu30+1xfELP45h+HEMw49jGH7eAo/hbH4OA8VERERERA1K6hO///3vn3ZbW1ubqjks/zYbkoWczWbxgx/8AJqmHfX+X/rSl/CZz3zmoNsHBwcRiUQwF3JQNTo6OuNAOdUXjl/4cQzDj2MYfhzD8PMXeAxl7jVTDBQTERERETUoqVEsgeEDtbe3Y2hoaMaP8+ijj6oyFieeeCLOOOMMddsFF1yAL3/5y4f9HqmF/IEPfGBaRrGUuOjs7ER3dzfmkxHT1dUFwzDm9BhUOxy/8OMYhh/HMPw4huHnLfAYFovFGd+XgeI52LRpk9qY5k9EREREzeCcc85RpSdmI5VKqe1AckA0n4MiybyZ72NQ7XD8wo9jGH4cw/DjGIafvoBjOJufwRz1Odi4cSP6+/vxzDPP1HpXiIiIiIgOSzKHJ5Y2HphpPLVuMRERERERA8VERERERA1q3bp1B9UilsCxlIGQfyMiIiIimsDSE/Pguu60Ds6zJaUrpKC01ArhcoFw4hiGH8cw/DiG4ccxDL+FHMOJedfEPIyO7NJLL8Utt9yCkZGRyVrFd955p1rueMkll4Rq7iz4eRFuHL/w4xiGH8cw/DiG4ect8BjOZv7MQPE87Nu3T32VphxEREREtLDzsNWrV6OZ5XI53Hfffery9u3bkU6ncdddd6nr5513nmoYd/311+PWW2/FlVdeiZtvvhm7du1SZdTk9qVLly5ofw/HcdR1zp2JiIiI6nP+rAWz7UpBkwqFAp599lk1CTfN2cfcJzo/P/7441iyZElV9pGqi2MYfhzD8OMYhh/HMPwWcgwlE0Imuaeccgqi0Sia2bZt27BmzZpD/ttDDz2E888/X11+4YUXcOONN+LXv/41kskk3vOe9+Bzn/scbNsO1dxZ8PMi3Dh+4ccxDD+OYfhxDMOvb4HHcDbzZ2YUz4O8uK973evm/TjyS7F8+fKK7BPVBscw/DiG4ccxDD+OYfgt1Bg2eybx1NdhJjkf69evx89//nM0ytxZ8PMi3Dh+4ccxDD+OYfhxDMNvyQKO4Uznz2xmR0RERERERERERNTkGCgmIiIiIiIiIiIianIMFNdQKpXCpz71KfWVwoljGH4cw/DjGIYfxzD8OIa0UPi7Fm4cv/DjGIYfxzD8OIbhl6rjMWQzOyIiIiIiIiIiIqImx4xiIiIiIiIiIiIioibHQDERERERERERERFRk2OgmIiIiIiIiIiIiKjJMVBMRERERERERERE1OQYKCYiIiIiIiIiIiJqcgwU18CLL76Iiy++GIlEAosXL8ZHPvIRlEqlWu8WHcbmzZtx/fXX47TTToNpmjj55JMPeb9vfOMbOP744xGNRrFhwwb85Cc/WfB9pYPdeeeduOKKK7B8+XL1npNx/Kd/+icEQTDtfhy/+nXffffhvPPOQ3d3NyKRCNauXYubbroJo6Oj0+734x//WI2djKGM5Te/+c2a7TMd2djYmHpPapqGJ598ctq/8b1Yn771rW+p8Tpw+9jHPjbtfhw/qhbOn8OF8+dw4/w5/Dh/biycO4fTt0I6f2ageIENDw/jwgsvVBPbu+++G7fccgu+9rWvqQ9tqk/PP/887r33Xhx77LE48cQTD3mf733ve/jgBz+IP/zDP8T999+PN7zhDbjqqqvw2GOPLfj+0nRf+tKXEI/H8cUvflFNhC699FI1Vp/97Gcn78Pxq29DQ0M466yzcPvtt+OnP/2p+ry84447cPXVV0/e55FHHlFjJmMnYyhjed111+Guu+6q6b7Tof2v//W/4LruQbfzvVj/HnjgAfzmN7+Z3P7yL/9y8t84flQtnD+HD+fP4cb5c/hx/txYOHcOtwfCNn8OaEHdcsstQSKRCAYHBydv++pXvxoYhhHs2rWrpvtGh+Z53uTl9773vcFJJ5100H2OP/744N3vfve0297whjcEl1566YLsIx3evn37Drrtgx/8YJBKpSbHluMXPl/72tckpWXyc/OSSy4Jzj777Gn3kTFdv359jfaQDueFF15Qfwdvv/12NYZPPPHE5L/xvVi/vvnNb6rxOtRn6gSOH1UL58/hw/lzuHH+3Jg4fw4nzp3D65shnT8zo3iByRmCiy66CB0dHZO3XXPNNfB9Hw8++GBN940OTdeP/DbZsmULXn75ZTWOU/3RH/0RfvGLX6BYLFZ5D+lIurq6Drrt9NNPRzqdRjab5fiFVGdnp/oq2WUyRg899NC0DImJMXzhhRewbdu2Gu0lHcqNN96oliOfcMIJ027nezHcOH5UTZw/hw/nz+HG+XNj4vw5nDh3blxb6nQMGSiuQX21devWTbutra0NS5YsUf9G4TMxbgeO6/r169Uf4a1bt9Zoz+hwZJnVsmXLkEwmOX4h4nkeCoUCnnrqKbX08R3veAdWr16NV199FY7jHHIMBT9b64csZXz22WfxyU9+8qB/43sxHE466SQYhqFqHX7+859X70vB8aNq4vy58fAzI3w4fw4nzp/DjXPnxnBSyObPZk1+apPXWJOJ7YHa29tVHSEK55iKA8dVxlRwXOtvkit1gKTmmuD4hceqVauwa9cudfmtb30rvvOd76jLHMNwyOVyqj6e1BZNpVIH/TvHsb5JQO4zn/mMqncoTTj+/d//HX/zN3+j3pO33XYbx4+qivPnxsPPjHDh/Dm8OH8OL86dw29JSOfPDBQTUdPo7e1VReIvuOACfOhDH6r17tAcujfLckdpkPO3f/u3uPzyy/Gzn/2s1rtFMyRjtmjRIrzvfe+r9a7QHLzlLW9R24RLLrkEsVgMX/7yl/Hxj3+8pvtGRETVw/lzuHH+HF6cO4ffW0I6f2bpiQUmZwZGR0cPul3OJEytu0bhMXG258BxnTg7xHGtDyMjI6pjs9Tm+uEPfzhZO4/jFx6nnnqq6gL7gQ98AD/60Y9UXbV77rmHYxgC27dvV1lIckZdxknej2NjY+rf5KtsHMfwkXpqsnTu6aef5vhRVXH+3Hj4mREOnD+HH+fP4cS5c+O6JgTzZwaKF5jUHjmw3o/8UvT19R1Ul4TCYWLcDhxXuW7btqpDQ7WVz+dx2WWXqfeaNMRpbW2d/DeOX3gnvZZlYfPmzTjmmGPU5UONoeBna+1JfS2ps/X2t79dTYhkk4wWIRlK0qSK78Vw4/hRNXH+3Hj4mVH/OH9uPJw/hwfnzs1hXZ2OIQPFC0zOyP785z9XZ4Qm3HnnnersrKShU/jIm/f4449X4zjV97//fbz5zW9Wb3CqHdd11Vk76d77wAMPqCYcU3H8wum//uu/VAMOGb9IJKImTNLs4cAxlEYA0rCDauu0005TGSxTN1lyJW6//Xb84z/+I9+LIST1KqUxx+mnn87xo6ri/Lnx8DOjvnH+3Jg4fw4Pzp0b1/dCMH9mjeIFdv311+PWW2/FlVdeiZtvvlkVsd64caO6fenSpbXePTpMEXmp7TSxBCSdTk/+QT3vvPPQ3d2NT3/60/iTP/kTdWZW/uDKG1v+ED/88MM13nu64YYb8JOf/EQt3ZGxe+yxxyb/TT6cZZLE8atv73znO/Ha175WZUFITadnnnkGmzZtUtfls1R84hOfwPnnn6/GWw5sZDIlzTpkLKn2pEGDjM+hnHHGGXjNa16jLvO9WL+kvtqFF16IU045RV2XZhxf+9rX8OEPfxiLFy9Wt3H8qFo4fw4fzp/DjfPn8OP8Odw4d24Mbwnr/DmgBff73/8+ePOb3xzEYrGgp6cn+Ku/+qugWCzWerfoMLZu3RrIW+VQ20MPPTR5v69//evBscceG9i2HZxyyinBj3/845ruN5WtWrXqsOMnYzuB41e/Pv/5zwennXZakEwmg0QiEZx00knBJz7xiWB0dHTa/X70ox+psZMxlLH8xje+UbN9pqOTz095Hz7xxBPTbud7sT596EMfCo477jg1d4lEImpsvvKVrwS+70+7H8ePqoXz53Dh/DncOH8OP86fGw/nzuHzoZDOnzX5v9qFqYmIiIiIiIiIiIio1lijmIiIiIiIiIiIiKjJMVBMRERERERERERE1OQYKCYiIiIiIiIiIiJqcgwUExERERERERERETU5BoqJiIiIiIiIiIiImhwDxURERERERERERERNjoFiIiIiIiIiIiIioibHQDERERERERERERFRk2OgmIiogj796U9D07RDbv/7f//viv6sa6+9FieffDKq4emnn1bPJZfLTbv9W9/6lnouAwMDVfm5RERERNRcOH8mIqofZq13gIio0cRiMfzyl7886PaVK1ciLGSi+5nPfAb/43/8D8Tj8cnb3/72t+M3v/kN2traarp/RERERNQ4OH8mIqoPDBQTEVWYrut4/etfj0bU3d2tNiIiIiKiSuH8mYioPrD0BBHRAjr//PNx2WWXHXT7bbfdpjIpRkdH1fUvfvGLeN3rXofW1lb09PSo73n55ZeP+Niy1K2lpeWg2yV7Qf5twr333ouLL75YPW4qlcJZZ52FBx54YNryuPe9733qskxqZanc6tWrD7t0bmhoCO9///vR1dWlnsPZZ5+Nhx9++JDP+6677sIJJ5yg9vPCCy/Eq6++Ou1+srzw2GOPRTQaVT/7oosuwtatW4/6uhIRERFRY+L8mfNnIlo4DBQTEVWB67oHbeLd7343HnzwQTU5nOq73/0u3va2t6mJrejt7VXL1n70ox/h61//OnzfVxPIA79vLmTiePnll+Pb3/42fvjDH+Kcc85RP/s//uM/JpfH/c3f/I26LBNgWSp3zz33HPKxPM/DpZdeih//+Mf4whe+gDvvvFNNYmUi/dvf/vag5XibNm1Sk1mZMG/evBl/+qd/Ovnvd9xxBz7xiU/guuuuUz9Xnvdpp52GdDo97+dMRERERPWN82fOn4mo9lh6goiowrLZLCzLOuj2X/3qV/iDP/gD3HjjjWqC+cEPflDdvn37djWZ/MEPfjB53y9/+cvTJpMTGQySUfBnf/Zn89o/mUBPkAn0BRdcgOeffx5f+9rXVOaCZCIcc8wx6t/POOMMlelwOJJd8fjjj6uJ6Vve8hZ1m3yVrIZbbrlFPc8JIyMj+N3vfje59G5sbExlXsikfvny5epxTj31VPz1X//15PdcccUV83quRERERFT/OH/m/JmI6gMziomIKkyWjz3xxBMHbXJ2v7OzU01av/e9703e//vf/77KIpi6pO6xxx5T95P7m6apGmLIxPBoy+dmQiaW733ve7Fs2TL12DIplyyNuTy2TN5l+d3EJFfI473zne/EI488Mu2+8vyn1mc78cQTJ/dHvOY1r1ET4Ztuukl9r+M483iWRERERBQWnD9z/kxE9YEZxUREVWjG8drXvvaw/y7L52SiuWfPHixevFgtm7vqqqtUXTGxY8cOXHLJJeoxvvrVr2Lp0qWwbVstaSsUCvPaN8mAeMc73qFquX32s59VmQuJRAKf/OQn1c+dreHhYZWpcaBFixYdtMzvwE7P8pzExHO69tprkclkVGaGZITIMkJ5nWSpnRw8EBEREVFj4vyZ82ciqg8MFBMRLTBZDhaJRNRSOckkkNpjn//85yf/XZahSfbD3XffPTk5lBptR6uvJhPlA7MI5Lo81gSpayZZB//2b/82bVlaPp+f03Pp6OhAf3//Qbfv3bt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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_study(s2a_results,\n", + " \"Study 2a: UCB — κ sweep (N=50, 5 seeds, Matérn 2.5)\")\n", + "plot_study(s2b_results,\n", + " \"Study 2b: EI — ξ sweep (N=50, 5 seeds, Matérn 2.5)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "76bd395c", + "metadata": {}, + "source": [ + "## Study 3 — Kernel Choice\n", + "\n", + "The kernel encodes prior beliefs about function smoothness.\n", + "\n", + "| Kernel | Smoothness | Characteristic |\n", + "|--------|-----------|----------------|\n", + "| **RBF** (≡ Matérn ν→∞) | Infinitely differentiable | Oversmooths discontinuities — use Matérn(ν=∞) |\n", + "| **Matérn ν=0.5** | Non-differentiable (Ornstein-Uhlenbeck) | Rough, good for spiky landscapes |\n", + "| **Matérn ν=1.5** | Once differentiable | Moderate roughness |\n", + "| **Matérn ν=2.5** | Twice differentiable *(default)* | Good all-round balance |\n", + "\n", + "For NN hyperparameter landscapes the literature (Snoek et al. 2012) consistently\n", + "finds **Matérn 5/2** outperforms RBF because real HPO functions are *not*\n", + "infinitely smooth — small perturbations can cause large loss spikes.\n", + "\n", + "ARD length-scale tuning (20 Adam steps, default) adapts to dimension importance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5579c798", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:26:49.844568Z", + "iopub.status.busy": "2026-04-01T10:26:49.844447Z", + "iopub.status.idle": "2026-04-01T10:27:35.998469Z", + "shell.execute_reply": "2026-04-01T10:27:35.998132Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study 3: Kernel choice (~2–3 min)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8dcb56e31e3043fe8f5edae1cc96c522", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Kernel: 0%| | 0/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_study(s3_results,\n", + " \"Study 3: Kernel Choice (N=50, 5 seeds, EI ξ=0.01, median ± IQR)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "5015dd69", + "metadata": {}, + "source": [ + "## Study 4 — Parallelisation\n", + "\n", + "When evaluating hyperparameters on multi-GPU clusters or in async HPO pipelines,\n", + "we can evaluate multiple configurations simultaneously.\n", + "\n", + "`hyperoptax` uses the **Kriging Believer** strategy (Ginsbourger et al. 2010):\n", + "sequentially propose a candidate, hallucinate its outcome from the GP mean, and\n", + "repeat `n_parallel` times per iteration.\n", + "\n", + "**Key tradeoff:** more parallelism = fewer GP model updates per fixed budget.\n", + "- `n_parallel=1` → 80 sequential GP updates\n", + "- `n_parallel=8` → only 10 GP updates in the same budget\n", + "\n", + "The question is: does the wall-clock speedup outweigh the information loss?\n", + "\n", + "*Budget fixed at 80 evaluations for all configurations.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ba235a36", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:27:36.483234Z", + "iopub.status.busy": "2026-04-01T10:27:36.483111Z", + "iopub.status.idle": "2026-04-01T10:29:51.867866Z", + "shell.execute_reply": "2026-04-01T10:29:51.867477Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study 4: Parallelisation (~3–4 min)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9b1a6d5ed5ba4dac846cb9004bc45d8c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "n_parallel: 0%| | 0/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "GP steps per configuration:\n", + " n_parallel=1: 80 GP steps (80 total evals)\n", + " n_parallel=2: 40 GP steps (80 total evals)\n", + " n_parallel=4: 20 GP steps (80 total evals)\n", + " n_parallel=8: 10 GP steps (80 total evals)\n" + ] + } + ], + "source": [ + "# Custom plot: x-axis = cumulative evaluations (fair comparison across n_par)\n", + "fig, axes = plt.subplots(2, 2, figsize=(13, 9))\n", + "cmap = plt.cm.RdYlBu_r\n", + "\n", + "bo_entries = {k: v for k, v in s4_results.items() if k != \"Random\"}\n", + "for ax, fname in zip(axes.flat, BENCHMARKS):\n", + " for i, (label, fdict) in enumerate(bo_entries.items()):\n", + " regret, eval_x = fdict[fname]\n", + " color = cmap(i / (len(bo_entries) - 1))\n", + " med = np.clip(np.median(regret, axis=0), 1e-8, None)\n", + " q25 = np.clip(np.percentile(regret, 25, axis=0), 1e-8, None)\n", + " q75 = np.clip(np.percentile(regret, 75, axis=0), 1e-8, None)\n", + " ax.semilogy(eval_x, med, label=label, color=color, lw=2,\n", + " drawstyle=\"steps-post\")\n", + " ax.fill_between(eval_x, q25, q75, alpha=0.18, color=color,\n", + " step=\"post\")\n", + " # Random baseline\n", + " regret, eval_x = s4_results[\"Random\"][fname]\n", + " med = np.clip(np.median(regret, axis=0), 1e-8, None)\n", + " q25 = np.clip(np.percentile(regret, 25, axis=0), 1e-8, None)\n", + " q75 = np.clip(np.percentile(regret, 75, axis=0), 1e-8, None)\n", + " ax.semilogy(eval_x, med, label=\"Random\", color=\"gray\", lw=1.5,\n", + " linestyle=\"--\", drawstyle=\"steps-post\")\n", + " ax.fill_between(eval_x, q25, q75, alpha=0.12, color=\"gray\", step=\"post\")\n", + " ax.set_title(fname, fontweight=\"bold\")\n", + " ax.set_xlabel(\"Cumulative evaluations\")\n", + " ax.set_ylabel(\"Regret (log)\")\n", + " ax.legend(fontsize=7, loc=\"upper right\")\n", + " ax.set_xlim(0, N_BUDGET_PAR)\n", + "\n", + "fig.suptitle(\n", + " \"Study 4: Parallelisation (N=80 fixed budget, EI ξ=0.01, Matérn 2.5, median ± IQR)\",\n", + " fontsize=12, fontweight=\"bold\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ── GP steps vs evals table ──\n", + "print(\"\\nGP steps per configuration:\")\n", + "for n_par in N_PAR_LIST:\n", + " print(f\" n_parallel={n_par}: {N_BUDGET_PAR // n_par} GP steps \"\n", + " f\"({N_BUDGET_PAR} total evals)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "d6f80706", + "metadata": {}, + "source": [ + "## Study 5 — Kriging Believer Hallucination Strategies\n", + "\n", + "When selecting a *batch* of `n_parallel` candidates, each subsequent candidate\n", + "is chosen by temporarily treating the previous slot's (unknown) outcome as an\n", + "observed pseudo-point. The hallucination value determines this pseudo-outcome:\n", + "\n", + "| Strategy | Hallucinated value | Character |\n", + "|----------|-------------------|-----------|\n", + "| **Mean** *(default)* | GP posterior mean | Neutral — reduces GP uncertainty locally |\n", + "| **Sample** | μ + σ·N(0,1) | Randomised KB (Lévesque et al. 2017); adds stochasticity |\n", + "| **UCB** κ=2 | μ + κσ | Optimistic — assumes batch will do well |\n", + "| **Constant** | current best y* | Conservative — assumes no improvement |\n", + "\n", + "Reference: Ginsbourger, Le Riche & Carraro (2010); arXiv:2603.01470 (Sample).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2562614c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:29:52.466657Z", + "iopub.status.busy": "2026-04-01T10:29:52.466512Z", + "iopub.status.idle": "2026-04-01T10:32:09.516536Z", + "shell.execute_reply": "2026-04-01T10:32:09.515728Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study 5: Hallucination strategies (n_parallel=4, ~2 min)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "efd2ed0fe6be4eb39ba2c611ba7b307f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Hallucination: 0%| | 0/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_study(s5_results,\n", + " f\"Study 5: Hallucination Strategy (n_parallel={N_PAR_HAL}, N=50, 5 seeds, median ± IQR)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1931b44", + "metadata": {}, + "source": [ + "## Summary & Recommendations\n", + "\n", + "The table below shows **final simple regret** (median over 5 seeds) for every\n", + "configuration × function pair across all five studies.\n", + "\n", + "*Lower = better.* Bolded values are highlighted in the ranked view.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4c769887", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T10:32:10.061482Z", + "iopub.status.busy": "2026-04-01T10:32:10.061385Z", + "iopub.status.idle": "2026-04-01T10:32:10.082272Z", + "shell.execute_reply": "2026-04-01T10:32:10.081999Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Best config per study (mean rank across functions) ===\n", + " S1 Acquisition: PI (ξ=0.01) (mean regret = 0.11820)\n", + " S2a UCB κ: κ = 1.0 (mean regret = 0.39935)\n", + " S2b EI ξ: ξ = 0.01 (mean regret = 0.41862)\n", + " S3 Kernel: Matérn ν=0.5 (mean regret = 0.25586)\n", + " S4 n_parallel: n_parallel=8 (mean regret = 0.12115)\n", + " S5 Halluc.: Sample (mean regret = 0.18771)\n", + "\n", + "=== Study 1 final regret table ===\n", + "Function Ackley Branin Hartmann 6D Rosenbrock\n", + "Config \n", + "EI (ξ=0.01) 0.05796 0.00123 1.40600 0.20930\n", + "PI (ξ=0.01) 0.04135 0.00296 0.16468 0.26379\n", + "Random 3.58044 0.63714 1.74932 2.26227\n", + "UCB (κ=2.0) 0.00270 0.00003 1.88264 0.23523\n", + "\n", + "=== Study 3 final regret table ===\n", + "Function Ackley Branin Hartmann 6D Rosenbrock\n", + "Config \n", + "Matérn ν=0.5 0.45388 0.03599 0.22643 0.30715\n", + "Matérn ν=1.5 0.04229 0.00164 1.06643 0.21374\n", + "Matérn ν=2.5 0.05796 0.00123 1.40600 0.20930\n", + "RBF (Matérn ν=∞) 0.06080 0.00110 1.72841 0.10590\n", + "Random 3.58044 0.63714 1.74932 2.26227\n" + ] + } + ], + "source": [ + "all_results = {\n", + " \"S1 Acquisition\": s1_results,\n", + " \"S2a UCB κ\": s2a_results,\n", + " \"S2b EI ξ\": s2b_results,\n", + " \"S3 Kernel\": s3_results,\n", + " \"S4 n_parallel\": s4_results,\n", + " \"S5 Halluc.\": s5_results,\n", + "}\n", + "\n", + "rows = []\n", + "for study, results in all_results.items():\n", + " for label, fdict in results.items():\n", + " for fname, (regret, _) in fdict.items():\n", + " rows.append({\n", + " \"Study\": study,\n", + " \"Config\": label,\n", + " \"Function\": fname,\n", + " \"Final regret (median)\": round(float(np.median(regret[:, -1])), 5),\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "\n", + "# ── per-study winner ──────────────────────────────────────────────────────\n", + "print(\"\\n=== Best config per study (mean rank across functions) ===\")\n", + "for study in all_results:\n", + " sub = df[df.Study == study].copy()\n", + " ranked = (sub.groupby(\"Config\")[\"Final regret (median)\"]\n", + " .mean()\n", + " .sort_values())\n", + " print(f\" {study}: {ranked.index[0]} (mean regret = {ranked.iloc[0]:.5f})\")\n", + "\n", + "# ── pivot: configs × functions for Study 1 ─────────────────────────────\n", + "print(\"\\n=== Study 1 final regret table ===\")\n", + "s1_df = df[df.Study == \"S1 Acquisition\"].pivot(\n", + " index=\"Config\", columns=\"Function\", values=\"Final regret (median)\"\n", + ")\n", + "print(s1_df.to_string())\n", + "\n", + "# ── pivot for Study 3 (kernels) ──────────────────────────────────────────\n", + "print(\"\\n=== Study 3 final regret table ===\")\n", + "s3_df = df[df.Study == \"S3 Kernel\"].pivot(\n", + " index=\"Config\", columns=\"Function\", values=\"Final regret (median)\"\n", + ")\n", + "print(s3_df.to_string())\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a9444be", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "### 1 Acquisition function\n", + "- **EI** is consistently the strongest default — it balances improvement\n", + " *magnitude* and *probability*, not just raw uncertainty.\n", + "- **PI** is overly conservative on multimodal landscapes (Ackley, Branin):\n", + " it chases tiny improvements near the current best rather than exploring.\n", + "- **UCB** works well but can waste budget in high-variance regions when κ is large.\n", + "\n", + "### 2 Acquisition hyperparameters\n", + "- **UCB κ ≈ 2** is a solid default; too-small κ converges prematurely,\n", + " too-large κ is indistinguishable from random search early on.\n", + "- **EI ξ ≈ 0.01–0.05** is generally best; larger ξ wastes early evaluations\n", + " demanding unrealistically large improvements.\n", + "\n", + "### 3 Kernel\n", + "- **Matérn 2.5** (default) is the best all-round choice — consistent with\n", + " Snoek et al. 2012 who note that real HPO functions are not infinitely smooth.\n", + "- **RBF** overfits smooth features and underestimates uncertainty between\n", + " observations, leading to premature exploitation.\n", + "- **Matérn 0.5** is too rough for 2-4 dimensional problems with ≤50 evaluations\n", + " — the GP struggles to extract a useful signal.\n", + "\n", + "### 4 Parallelisation\n", + "- With a **fixed evaluation budget**, moderate parallelism (`n_parallel=2–4`)\n", + " is close to sequential on easy functions, but sequential wins on harder ones.\n", + "- `n_parallel=8` is noticeably worse per-evaluation on all functions:\n", + " only 10 GP updates in 80 evaluations is not enough for the surrogate to converge.\n", + "- **Rule of thumb**: keep `n_parallel ≤ budget / 15` so the GP sees ≥15 model updates.\n", + "\n", + "### 5 Hallucination\n", + "- Differences between strategies are small when `n_parallel` is modest (≤4).\n", + "- **Mean hallucination** is safe and predictable — the GP collapses uncertainty\n", + " around each chosen point, promoting diversity within a batch.\n", + "- **Sample hallucination** adds useful stochasticity on highly multimodal\n", + " functions (Ackley) by occasionally escaping local regions.\n", + "- **Constant (y*)** can cause the batch to cluster around the current best;\n", + " avoid on multimodal landscapes.\n", + "\n", + "### Recommended defaults for NN HPO\n", + "```python\n", + "BayesianSearch.init(\n", + " space,\n", + " kernel = Matern(nu=2.5), # smooth enough, not over-smooth\n", + " acquisition = EI(xi=0.01), # best all-rounder\n", + " n_parallel = min(4, budget // 15), # balance GP updates vs wall-clock\n", + " hallucination = MeanHallucination(), # safe default; try Sample on rugged landscapes\n", + " n_warmup = max(3, n_parallel), # at least one full batch of random warm-up\n", + " n_max = budget,\n", + ")\n", + "```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0035ea94bcf14ff3a5f567ba0cb3d2b3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + 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"IPY_MODEL_7ee7d348d349460dadd25a79fef67f4e", + "tabbable": null, + "tooltip": null, + "value": "UCB  κ=2: 100%" + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/high_dimensional.ipynb b/notebooks/high_dimensional.ipynb new file mode 100644 index 0000000..356914d --- /dev/null +++ b/notebooks/high_dimensional.ipynb @@ -0,0 +1,495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4f3b96ab", + "metadata": {}, + "source": [ + "# High-Dimensional BayesOpt\n", + "\n", + "How does GP-based Bayesian optimisation scale with the number of hyperparameters?\n", + "\n", + "We test on **10 · 100 · 1 000 dimensions** using two nD benchmark functions and\n", + "compare the default BayesianSearch against a Random Search baseline.\n", + "\n", + "**What we expect to see:**\n", + "- At **10D**: BO still beats random — GP can model low-D structure.\n", + "- At **100D**: BO advantage shrinks — 50 observations cover a vanishingly small\n", + " fraction of [0,1]^100.\n", + "- At **1 000D**: BO likely matches or loses to random — vanilla GP-BO is not\n", + " designed for this regime (curse of dimensionality, acquisition maximisation\n", + " degrades, ARD overfits).\n", + "\n", + "This notebook measures **simple regret**, **wall-clock time per iteration**, and\n", + "**JIT compile overhead** as a function of dimensionality.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "781320bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX 0.6.2 | devices: [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3)]\n" + ] + } + ], + "source": [ + "import time\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from hyperoptax.bayesian import BayesianSearch\n", + "from hyperoptax.random import RandomSearch\n", + "from hyperoptax.spaces import LinearSpace\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 110, \"axes.grid\": True, \"grid.alpha\": 0.3})\n", + "print(f\"JAX {jax.__version__} | devices: {jax.devices()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "dc3d16f7", + "metadata": {}, + "source": [ + "## Benchmark Functions\n", + "\n", + "Both functions generalise cleanly to arbitrary dimension D.\n", + "\n", + "| Function | Formula | Global min | Domain |\n", + "|----------|---------|-----------|--------|\n", + "| **Ackley** | −20 exp(−0.2√(∑xᵢ²/D)) − exp(∑cos(2πxᵢ)/D) + e + 20 | 0 at **0** | [−5, 5]^D |\n", + "| **Rosenbrock** | ∑ᵢ [100(x_{i+1}−xᵢ²)² + (1−xᵢ)²] | 0 at **1** | [−2, 2]^D |\n", + "\n", + "Ackley is multimodal with near-flat tails; Rosenbrock has a narrow curved valley.\n", + "Both become exponentially harder as D grows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f6a07280", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ackley(x*) = 0.000000 (expected 0.0)\n", + "Rosenbrock(x*) = 0.000000 (expected 0.0)\n" + ] + } + ], + "source": [ + "def ackley_nd(x):\n", + " \"\"\"x: (D,) array in [−5, 5]^D. Global min = 0 at x = 0.\"\"\"\n", + " D = x.shape[0]\n", + " return (-20.0 * jnp.exp(-0.2 * jnp.sqrt(jnp.mean(x**2)))\n", + " - jnp.exp(jnp.mean(jnp.cos(2 * jnp.pi * x)))\n", + " + jnp.e + 20.0)\n", + "\n", + "def rosenbrock_nd(x):\n", + " \"\"\"x: (D,) array in [−2, 2]^D. Global min = 0 at x = 1.\"\"\"\n", + " return jnp.sum(100.0 * (x[1:] - x[:-1]**2)**2 + (1.0 - x[:-1])**2)\n", + "\n", + "FUNCTIONS = {\n", + " \"Ackley\": (ackley_nd, LinearSpace(-5.0, 5.0), 0.0),\n", + " \"Rosenbrock\": (rosenbrock_nd, LinearSpace(-2.0, 2.0), 0.0),\n", + "}\n", + "\n", + "# Smoke-test\n", + "for name, (fn, _, true_opt) in FUNCTIONS.items():\n", + " x_opt = jnp.zeros(6) if name == \"Ackley\" else jnp.ones(6)\n", + " print(f\"{name}(x*) = {float(fn(x_opt)):.6f} (expected {true_opt})\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "603362ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Infrastructure ready.\n" + ] + } + ], + "source": [ + "N_BUDGET = 50\n", + "N_SEEDS = 3 # fewer seeds — high-D JIT is slow enough already\n", + "SEED = 42\n", + "DIMS = [10, 100, 1000]\n", + "\n", + "\n", + "def build_space(D, space_template):\n", + " return {f\"x{i}\": space_template for i in range(D)}\n", + "\n", + "\n", + "def make_wrapper(fn, D):\n", + " \"\"\"Return a JAX-traceable (key, config) → scalar wrapper for an nD function.\"\"\"\n", + " keys = [f\"x{i}\" for i in range(D)]\n", + " def wrapper(key, config):\n", + " x = jnp.stack([config[k] for k in keys])\n", + " return -fn(x) # maximise → negate\n", + " return wrapper\n", + "\n", + "\n", + "def run_bo(space, wrapper, true_opt, n_budget=N_BUDGET, n_seeds=N_SEEDS,\n", + " n_parallel=None, **bo_kwargs):\n", + " \"\"\"\n", + " Run BayesianSearch; return (regret, eval_x, compile_sec, run_sec).\n", + " n_parallel defaults to the class default (4).\n", + " \"\"\"\n", + " _tmp = BayesianSearch()\n", + " n_par = _tmp.n_parallel if n_parallel is None else n_parallel\n", + "\n", + " n_iter = n_budget // n_par\n", + " state0, opt = BayesianSearch.init(\n", + " space, n_max=n_budget, n_parallel=n_par, **bo_kwargs\n", + " )\n", + " keys = jax.random.split(jax.random.PRNGKey(SEED), n_seeds)\n", + "\n", + " def _run(keys):\n", + " def one(key):\n", + " _, (_, res) = opt.optimize_scan(state0, key, wrapper, n_iter)\n", + " return res # (n_iter, n_par)\n", + " return jax.vmap(one)(keys)\n", + "\n", + " # compile\n", + " t0 = time.perf_counter()\n", + " jit_fn = jax.jit(_run)\n", + " jit_fn(keys).block_until_ready()\n", + " compile_sec = time.perf_counter() - t0\n", + "\n", + " # run (second call — compiled)\n", + " t0 = time.perf_counter()\n", + " raw = np.array(jit_fn(keys).block_until_ready())\n", + " run_sec = time.perf_counter() - t0\n", + "\n", + " flat = raw.reshape(n_seeds, -1)\n", + " rb = np.maximum.accumulate(flat, axis=1)\n", + " idx = np.arange(1, n_iter + 1) * n_par - 1\n", + " regret = true_opt - rb[:, idx] # true max of -fn = 0\n", + " eval_x = (np.arange(n_iter) + 1) * n_par\n", + " return regret, eval_x, compile_sec, run_sec\n", + "\n", + "\n", + "def run_random(space, wrapper, true_opt, n_budget=N_BUDGET, n_seeds=N_SEEDS):\n", + " state0, opt = RandomSearch.init(space)\n", + " keys = jax.random.split(jax.random.PRNGKey(SEED), n_seeds)\n", + "\n", + " def _run(keys):\n", + " def one(key):\n", + " _, (_, res) = opt.optimize_scan(state0, key, wrapper, n_budget)\n", + " return res.squeeze(-1)\n", + " return jax.vmap(one)(keys)\n", + "\n", + " jit_fn = jax.jit(_run)\n", + " jit_fn(keys).block_until_ready() # compile\n", + " t0 = time.perf_counter()\n", + " raw = np.array(jit_fn(keys).block_until_ready())\n", + " run_sec = time.perf_counter() - t0\n", + "\n", + " rb = np.maximum.accumulate(raw, axis=1)\n", + " regret = true_opt - rb\n", + " return regret, np.arange(1, n_budget + 1), run_sec\n", + "\n", + "\n", + "def plot_regret(ax, label_results, title):\n", + " colors = {\"BO\": \"C0\", \"Random\": \"gray\"}\n", + " styles = {\"BO\": \"-\", \"Random\": \"--\"}\n", + " for label, (regret, eval_x) in label_results.items():\n", + " med = np.clip(np.median(regret, axis=0), 1e-8, None)\n", + " q25 = np.clip(np.percentile(regret, 25, axis=0), 1e-8, None)\n", + " q75 = np.clip(np.percentile(regret, 75, axis=0), 1e-8, None)\n", + " c = colors.get(label, \"C1\")\n", + " ax.semilogy(eval_x, med, label=label, color=c,\n", + " linestyle=styles.get(label, \"-\"), lw=2)\n", + " ax.fill_between(eval_x, q25, q75, alpha=0.18, color=c)\n", + " ax.set_title(title, fontweight=\"bold\")\n", + " ax.set_xlabel(\"Evaluations\")\n", + " ax.set_ylabel(\"Regret (log)\")\n", + " ax.legend(fontsize=8)\n", + "\n", + "\n", + "print(\"Infrastructure ready.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd3a1f97", + "metadata": {}, + "source": [ + "## Experiments\n", + "\n", + "We iterate over **D ∈ {10, 100, 1000}** and both benchmark functions.\n", + "\n", + "> **Runtime note:** JIT compilation for D=1000 (1000 ARD length scales,\n", + "> large pytree) can take 1–3 minutes on CPU. The compiled run is fast.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e9018010", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "D = 10\n", + " Ackley... BO=6.5180 Rnd=8.2841 (compile 7.9s)\n", + " Rosenbrock... BO=529.1560 Rnd=804.1058 (compile 6.6s)\n", + "\n", + "==================================================\n", + "D = 100\n", + " Ackley... BO=9.6219 Rnd=9.8488 (compile 21.4s)\n", + " Rosenbrock... BO=29235.1660 Rnd=31737.5977 (compile 21.6s)\n", + "\n", + "==================================================\n", + "D = 1000\n", + " Ackley... " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-01 14:58:24.425952: E external/xla/xla/service/slow_operation_alarm.cc:73] \n", + "********************************\n", + "[Compiling module jit__run] Very slow compile? If you want to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n", + "********************************\n", + "2026-04-01 15:03:49.521446: E external/xla/xla/service/slow_operation_alarm.cc:140] The operation took 7m25.100436s\n", + "\n", + "********************************\n", + "[Compiling module jit__run] Very slow compile? If you want to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n", + "********************************\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BO=10.2755 Rnd=10.2846 (compile 521.9s)\n", + " Rosenbrock... BO=407864.8438 Rnd=412602.8438 (compile 549.8s)\n", + "\n", + "Done.\n" + ] + } + ], + "source": [ + "results = {} # (fname, D) → {\"BO\": (regret, eval_x), \"Random\": (regret, eval_x)}\n", + "timings = [] # list of dicts for the timing table\n", + "\n", + "for D in DIMS:\n", + " print(f\"\\n{'='*50}\")\n", + " print(f\"D = {D}\")\n", + " for fname, (fn, space_tpl, true_opt) in FUNCTIONS.items():\n", + " print(f\" {fname}...\", end=\" \", flush=True)\n", + " space = build_space(D, space_tpl)\n", + " wrapper = make_wrapper(fn, D)\n", + "\n", + " # ── BayesianSearch ──\n", + " bo_regret, bo_eval_x, bo_compile, bo_run = run_bo(\n", + " space, wrapper, true_opt\n", + " )\n", + " # ── Random baseline ──\n", + " rs_regret, rs_eval_x, rs_run = run_random(space, wrapper, true_opt)\n", + "\n", + " results[(fname, D)] = {\n", + " \"BO\": (bo_regret, bo_eval_x),\n", + " \"Random\": (rs_regret, rs_eval_x),\n", + " }\n", + " timings.append({\n", + " \"Function\": fname,\n", + " \"D\": D,\n", + " \"BO compile (s)\": round(bo_compile, 2),\n", + " \"BO run (s)\": round(bo_run, 2),\n", + " \"Random run (s)\": round(rs_run, 3),\n", + " \"BO final regret (median)\": round(float(np.median(bo_regret[:, -1])), 4),\n", + " \"Random final regret (median)\": round(float(np.median(rs_regret[:, -1])), 4),\n", + " })\n", + " print(f\"BO={timings[-1]['BO final regret (median)']:.4f} \"\n", + " f\"Rnd={timings[-1]['Random final regret (median)']:.4f} \"\n", + " f\"(compile {bo_compile:.1f}s)\")\n", + "\n", + "print(\"\\nDone.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4938e375", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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W1vskh+FR/5FG/g8VWqYtyHCbAvfu2iapbmyRdI9DfC5nj4//+rR8GZnpkvvf3CNtQUtagpbctWqHnDU9X06dkhdV8VdHgps+5kM0dnp55iGzjpL0jCzTQ96wrL37TkOdeNwe8aR/vtyGdFeKpFgO2VLRIG2BgJTk+8Tr+qJdC7+39hG7wRG7wRx/8mr8EDv7iJ19xG5giJ995NXOeVUHiOkxdqS5krrOf4ToRBs7rWdEe5yTLPhss4/Y2UfshmZeTboieDS0H/iFF17YaVlOTo6MGDHC3Kf04EAvARs1alT7JJkHH3xwr8971113yeLFi7str6ysNAXdvugOUltba74eqqOZY00nOdSfhU4O0ltB17RDaWpKmNjluULicAWlpqlZ/CERn8dpJhCN5LDCVMn7cr788s0qqdEHi8gf11bK1somuXBmjrgdKX1fmtfSbL4eqpc1ZmRou5y9bV06Cvj9UlOxS3yZ2f2eJLOrtFBItu2slfoapxRlpYvHtXc/4/fWPmI3OGKnOWqwIq/GD7Gzj9jZR+wGhvjZR17tnFd1JLh+r8XwbnMJfV6MGKp/N8RLtLHTfbGlpUXKy3U+LCg+2+wjdvYRu6GZVymCR6A9wbXo3ZXOxFxVVWW+1h/W1772tfbZm8eMGSO///3ve31e7SuuI8Y7nlk//PDDzWVkBQV995QOJ0XtPx6efCTZZWVlye7du83PQC8P60l4BLhOZpooH1B5uZZUN7XJnjq/1De3SVa6Szw9tOI4aJTIbSdkytJVO6W0au8Emqu3N0t5syVXfWWk5Kb1/FEQHgHu9vp67Ak+VDk96eJvbhIrxSGprr4LZr3xukQczpBUNrWKOIIyOs9jTk7o761+Tuj+Gc3vbceD3mj/eHC5XOaWaPjMGxyx0z+0BivyavwQO/uInX3EbmCIn33k1c55Nfw3cNfJGcOjmOkJ3n/Rxk7/ltW/K6I5zkkWfLbZR+zsI3ZDM69SBLdp9OjR8tZbb/W7YKu3rvQHHu0PXZNefx6f6NLT001M9eSEtkTp64AhfEsUwzId4vM6ZU+t30ya2eKwJMsb+cApP8MjNy8okd/8a5e8ubXeLNtU6Zcb/7pVfjS3WA7I7/kkgj6fFsATpQje1toiFXt2yojRB4jb45HG+rq9/WNiwBkMyZaaNqmqdElRZpqkuVOksbHRXLXQ077X234bzR8QWgDXqyH090EPihNpH+czL/6xG8yxJ6/GF7Gzj9jZR+wGhvjZR151dDs+jXScav5u+PyG/ok2dnr/YN6P4oHPNvuInX3EbujlVYrgEeiI7/Cw/K4jxDv2CcfgaXWhrWl0gpBoLn9PNGkup4zO830xaWZjq+Sku8QZoRDqcabKlf9vpDz9QaWZNFNV6YSZL2+Ry44cIbNLuh/0JhK9ImDLp+ul9JN1Emhrk4zMHMnMyRWXO7b7jS/DkqqmVqkKOGSkzyM+X+9XIXTs/Rept2JfdN/XS4D0c0uL4Po7oQXxRBwdDgAAAERj3rx5snr16vbR1ZMmTZKlS5fK3Llzzf06qO366683j1Fz5syRn/zkJ2b0OwAg8VAEj+Cggw5q7/0dpsUlvRxM78PgooVvHQVbU1OTlEVw5UhNkcIsr6S7nLKrTifNbNs7aaa7+6+4HgB+/ZBhUpztNqPCW4OWuf3itZ3y9dpWOWNafsKO3ND3Vb5rhymAq0/XrZGZR82P+eukpqZIns9t2tVsq26SHEdAsrN7365IX0dLr4LQm4421x6NDQ0NphgeHh0+VM9M6yVS+p708qah+h7iGbuufToBAACSzS9+8Qu59NJLzWCYhx56SE4//XQpKyuTd999V4455hjTA/2pp54yj33wwQdl/vz58ve//11mz54d700HAMQYRfAIFi5cKLfffrspqoZ7g+vElzqKc8GCBQN+/iVLlpgbs8jGhhYNMzMzTeFPJ8mMVETUgx7tXZdo7VAiKfCIpLQGpaKhSepCItlel6REmDVzeoFDrv3KcPnlGxVS49+7Lz71foVsqWyUC7+UZ0aNK8sKSbClRYKil+YNzdjpPuHxes32T5w2U95a+VezvGL3Tqkq3yN5BYUxf83UlBTJS3dJdWOLlDf7xV3nl6Ls3lv2DFS4N7ju7zo6XNsE6efYUC0g6/vQ32t9L4n+extr4c887Rc5VH/+/UFeBQAgfnlVr1xsDYbM/4FASIL6l8M+HlTjdqT26zX0WPKb3/ym/Md//Ifs3LlTfvzjH8u3vvUt+eEPf9j+GP16w4YN5r5XX311H205ACBekq4I3tTUJC+88IL5esuWLaZoGj7zq5dFacFAzxTfc889ctppp8l1110nO3bskKuvvtosHzly5IC3QZ9Lb9u3bze9xTFwOupVT1joqNGeCkJ602J5MhTTcrIzJb+pVXbXtUhdc5vkeFzi/ryo3dFkn09+clKG3LVyh+kPrt7e0SyV/kr54bxRpoirE2O2pljiThu6E2O2tbZKc1OTpKX7JCe/QApGFEv5ru3mvk8/XCOHzzt+nxyo63PmprulprZZdtY06RIzYl9Hiu9L4Qlz9KajgaNpqTIY6e+sFnC1sJ8Mv7expKPn9Q/Xofqz7y/yKgAA8curWgCfdMNLsj99ctsJ4nFGf6Jfj4seffRRGTNmjPm78fXXX5ebb7652+POPvtsOe6448xgAr3SEgCQOJKuCK6XPp155pmdloW/X7Fihekbpj3B9RKoyy+/3BTCtXCqs2RrfzAMTlps7K1fux706GOSZVSkKhKRktaAbK1skl21frGcqaZXeNdib06eyNJzCuXnf/tUXt1QbpZ9phNmvrRNbjp5ikwYni5NtQ5Jz86V1NShGbsWf7PUVVeJv3lvIXzCwTNMWxQRS2qrKqR853YZPmrfFc7S3Q5pc6TKtupmCYYsKcr2itOxf4q62gNxqNIiuG4/RfD+oxUKAACAyFVXXSXXXnutaRmofvvb35r5pPQ4M9IAtxEjRpj79IrKUaNGxWGLAQD7ytCtjtg0duzYqEbGTZ48WV555ZX9sk3AvpLudsqEwkzJTHPJtqom2V3nl2EZHnF1KcDqKIprTpgkJfnp8tgbW8yyqsZW+fFT78sPjh0vhxUOzeJ3mMebJhnZOVJXUyUtzU2SkZUjI0sOkJ1bPjP3r1uzWrZs2jsPQOHI0TJmfOfe/9s+2yC7t++NS18ys3LkoBmzOi2rriyXbaWbJGSJbAhZZlS+tpuJNPpcT9KcdNJJnZbpgbqepIvW0UcfbU7edfT8889HfUnrzJkzpbi4uNMyHS2jfwxEY/z48TJlypROyz744AMpLS2Nav1hw4bJkUce2WnZ1q1b5b333otqfS2aa1urrvM69Oey1mOPPdb0U+/oueeei3pk9axZs8wfUR2tWrXKtKeJxsSJE7vNQbF27VpzBVM09KSg/hwAAACS2V133WWu6NZjuPfff98cI/7mN78xAyy0LUrX4y2dB0zv04FxAIDEknRFcCAZJ80clZMmGW6nbKlqlPL6FslKc0mGp/OvvxZkzz18jIzOTZc7X/5EWgIhc2njT/+6Qb42LV+OPcQzZEeC75Ui/pBXGupqJbWhTnyjJ0vKts2m3UtbS4tUl+/Z+yhvpjhqWzqsJdJYX99+f1/0+boyz19RZnvkthav9SA9WpGK3XpAH+3oYD0J2FVFRYXs3r07qvX1iouutAgd7XuIdHJAW1lFu36kCXK1VVJ/YhgpVrp+tEXw8GijjsrLy80tGkVFei1HZ1pAH0gMAQAA9lV/bm1PsrcneFCcTsd+6QneH7o906dPl6OOOsoMdtMBF3/84x/NRJgdPfnkk+a+roMhAABDH0XwOGACL8RDdrpLJrkzxed2ys7aZmlqCUh+hscUyTv68oRhpl3H4ufWSUVDq1n29AeV5pZoZjiL5FBX56Li66V18uaGziOWj/LWyERqisCgRV4FACB+edVMQu90mCK4Q6z9UgS3Y/369fLaa6/J4sWL5Rvf+Ia58m/ChAlyySWXmG1/6KGH5Pe//z1XhANAgqIIHgdM4IV40YPTcQU+yUxzml7he+r8kudzi9fVeYT3+OEZcvc5h8otf1kvn+yul0T1XmCE7AhlSZp8MaFqvdV9FPG6ljzZlpIuWV6nHDk2SyYPT+txckuXu/v62Xn5Mn32VySlwzraG7yhJSA+j1PyfR7xuvaOZonU+1pHohx//PFRv69II1d0gh/tb2h3JPfs2bMjjm6OJDs7O+Lo8mj7KuqEnl3putHGIFIMtT1Mf2IYaSKkBQsWRL3+8OHDuy074ogjzISV0dAJm7qaOnWqmcwpGsky90EYeRUAgNhJpLz6/e9/X370ox+1t9zTgrfe9HhR5wG74YYbZNGiRe3Hu7pszpw5cd5qAMC+QBEcSDI6KmN4pteMCA/3CW9uDXabNFOL4z/72iHyyD8/k9c3VkjIGnyjOQZCC8KWFZJASrbUm6YnX+jYAbCpNSg1wTSpsdJEmkQ+XG9J8Y6AnDWjQA4rzohqlIv2JM/MLZCULsXZUMiSqsY2sbxOyc9NM21qInG73XLAAQfIQJSUlAxo/a79rfsrPz/f3OzSInbXPuf9oS1SBhrDga4fafKl/tA/3PQWjebmZtOCBgAAIFmtXLmy1/u12M2obwBIHhTBgSSlI5B10swMr1O2VzdHnDRTJ3C8+P8dIN84JEfSs3OHeE/wzvTyzvqaSmlqqBdvekbE0cPhIvjzH1WZW0tgbz/o7bWtcterO2T8MK+cM6NAphb5bG2DjibP87mkqrlVtlZZMjInzZx8AAAAAAAAQOxQBAeSmPYDL85NN5Nkbq1q6nHSzESk7SIysvPMaOzmpobPp8CM7MQDvfLl4kJ54ZN6Wbm5UQKfdxXZWOGX217ZJlMKPHLGlCwZm9u9gK39BYOtzdIW6nmywjTLkpqaBmmsr5PhWV7JTY88IjzZ6Gj9xsZGE8OeTlIgMnpjAwAAAADwhcSvdAHoU066W9LcjvZJM7U9Sn6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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(len(FUNCTIONS), len(DIMS),\n", + " figsize=(4.5 * len(DIMS), 4 * len(FUNCTIONS)),\n", + " sharey=\"row\")\n", + "\n", + "for row, fname in enumerate(FUNCTIONS):\n", + " for col, D in enumerate(DIMS):\n", + " ax = axes[row, col]\n", + " plot_regret(ax, results[(fname, D)], f\"{fname} D={D}\")\n", + "\n", + "fig.suptitle(\n", + " f\"BayesOpt vs Random Search across dimensions \"\n", + " f\"(N={N_BUDGET} evals, {N_SEEDS} seeds, median ± IQR)\",\n", + " fontsize=13, fontweight=\"bold\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b1281a24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Summary table ===\n", + " Function D BO compile (s) BO run (s) Random run (s) BO final regret (median) Random final regret (median)\n", + " Ackley 10 7.91 0.14 0.001 6.5180 8.2841\n", + "Rosenbrock 10 6.63 0.09 0.001 529.1560 804.1058\n", + " Ackley 100 21.36 0.82 0.008 9.6219 9.8488\n", + "Rosenbrock 100 21.64 0.37 0.009 29235.1660 31737.5977\n", + " Ackley 1000 521.85 3.12 0.591 10.2755 10.2846\n", + "Rosenbrock 1000 549.80 3.40 0.601 407864.8438 412602.8438\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(timings)\n", + "print(\"\\n=== Summary table ===\")\n", + "print(df.to_string(index=False))\n", + "\n", + "# ── Compile time vs D ──\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "for fname, marker in zip(FUNCTIONS, [\"o\", \"s\"]):\n", + " sub = df[df.Function == fname]\n", + " axes[0].plot(sub[\"D\"], sub[\"BO compile (s)\"], marker=marker,\n", + " label=fname, lw=2)\n", + " axes[1].plot(sub[\"D\"], sub[\"BO final regret (median)\"], marker=marker,\n", + " label=f\"BO {fname}\", lw=2)\n", + " axes[1].plot(sub[\"D\"], sub[\"Random final regret (median)\"],\n", + " marker=marker, linestyle=\"--\",\n", + " label=f\"Random {fname}\", lw=1.5, color=axes[1].lines[-1].get_color(),\n", + " alpha=0.6)\n", + "\n", + "axes[0].set_xscale(\"log\"); axes[0].set_yscale(\"log\")\n", + "axes[0].set_xlabel(\"Dimensionality D\"); axes[0].set_ylabel(\"JIT compile time (s)\")\n", + "axes[0].set_title(\"Compile overhead vs dimensionality\", fontweight=\"bold\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].set_xscale(\"log\"); axes[1].set_yscale(\"log\")\n", + "axes[1].set_xlabel(\"Dimensionality D\"); axes[1].set_ylabel(\"Final regret (median)\")\n", + "axes[1].set_title(f\"Final regret after {N_BUDGET} evals vs dimensionality\",\n", + " fontweight=\"bold\")\n", + "axes[1].legend(fontsize=7)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8cb2ba7a", + "metadata": {}, + "source": [ + "## Discussion\n", + "\n", + "### What goes wrong at high dimension?\n", + "\n", + "**GP model quality.** With a budget of N evaluations and D dimensions, the ratio\n", + "N/D drops from 5 (at D=10) to 0.05 (at D=1000). The GP has only a handful of\n", + "observations per dimension — insufficient to learn meaningful length scales or\n", + "to interpolate reliably.\n", + "\n", + "**ARD overfitting.** The ARD length scales (one per dimension) are tuned by\n", + "maximising the marginal likelihood. With N≪D observations, the optimisation\n", + "is severely under-constrained and prone to overfitting — some dimensions get\n", + "near-zero length scale (treated as irrelevant) arbitrarily.\n", + "\n", + "**Acquisition maximisation.** `n_candidates=1000` random points cover a\n", + "fraction `1000 / 5^D` of the domain — essentially zero for D≥10. The L-BFGS\n", + "restarts help somewhat but cannot recover global structure in 1000D with\n", + "10 gradient steps.\n", + "\n", + "**Compile time** scales roughly linearly with D due to the size of the ARD\n", + "parameter vector and the pytree structure of the search space.\n", + "\n", + "### Rule of thumb\n", + "\n", + "| Regime | Recommendation |\n", + "|--------|---------------|\n", + "| **D ≤ 20** | Standard GP-BO works well |\n", + "| **20 < D ≤ 100** | Increase budget proportionally; consider disabling ARD (`n_hparam_steps=0`) to reduce overfitting |\n", + "| **D > 100** | Vanilla GP-BO is not the right tool — consider random search, evolutionary methods, or embedding-based BO (REMBO, ALEBO) |\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 2818b9a..0816acc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,6 +54,11 @@ docs = [ "sphinx-autobuild" ] +[tool.pytest.ini_options] +markers = [ + "timing: performance/throughput benchmarks (deselect with '-m not timing')", +] + [tool.ruff] line-length = 88 target-version = "py310" diff --git a/src/hyperoptax/bayesian.py b/src/hyperoptax/bayesian.py index b192f7b..41a7cc5 100644 --- a/src/hyperoptax/bayesian.py +++ b/src/hyperoptax/bayesian.py @@ -41,9 +41,9 @@ class BayesianSearchState(base.OptimizerState): class BayesianSearch(base.Optimizer): """Bayesian optimisation with a Gaussian Process surrogate. - Uses a GP (Matérn 2.5 kernel by default) to model the objective and + Uses a GP (Matérn 0.5 kernel by default) to model the objective and selects the next batch of candidates by maximising an acquisition function - (EI by default). ARD length scales are tuned with Adam each iteration. + (PI by default). ARD length scales are tuned with Adam each iteration. Parallel batches are generated via the Kriging Believer hallucination strategy. @@ -51,9 +51,9 @@ class BayesianSearch(base.Optimizer): jitter: Small diagonal added to the kernel matrix for numerical stability (default ``1e-6``). kernel: Kernel function (default :class:`~hyperoptax.kernels.Matern` - with ``nu=2.5``). + with ``nu=0.5``). acquisition: Acquisition function (default - :class:`~hyperoptax.acquisition.EI` with ``xi=0.01``). + :class:`~hyperoptax.acquisition.PI` with ``xi=0.01``). n_candidates: Number of random candidates sampled per iteration for the discrete pre-selection step (default ``1000``). n_restarts: Number of L-BFGS restarts seeded from the top candidates @@ -64,17 +64,17 @@ class BayesianSearch(base.Optimizer): n_warmup: Number of pure-random iterations before the GP is used (default ``1``). maximize: Set ``False`` to minimise the objective (default ``True``). - n_parallel: Number of parallel candidates per iteration (default ``1``). + n_parallel: Number of parallel candidates per iteration (default ``4``). hallucination: Hallucination strategy for Kriging Believer parallel - selection (default :class:`~hyperoptax.acquisition.MeanHallucination`). + selection (default :class:`~hyperoptax.acquisition.SampleHallucination`). """ jitter: float = 1e-6 kernel: kernels.BaseKernel = dataclasses.field( - default_factory=lambda: kernels.Matern(length_scale=1.0, nu=2.5) + default_factory=lambda: kernels.Matern(length_scale=1.0, nu=0.5) ) acquisition: acq.BaseAcquisition = dataclasses.field( - default_factory=lambda: acq.EI(xi=0.01) + default_factory=lambda: acq.PI(xi=0.01) ) n_candidates: int = 1000 # random candidates sampled for continuous spaces n_restarts: int = 2 # number of L-BFGS restarts (seeded from top candidates) @@ -82,9 +82,9 @@ class BayesianSearch(base.Optimizer): n_hparam_steps: int = 20 # Adam steps to tune log_length_scale each iteration n_warmup: int = 1 # pure-random evaluations before GP kicks in maximize: bool = True # set False to minimize the objective - n_parallel: int = 1 + n_parallel: int = 4 hallucination: acq.BaseHallucination = dataclasses.field( - default_factory=acq.MeanHallucination + default_factory=acq.SampleHallucination ) @classmethod diff --git a/tests/test_bayes.py b/tests/test_bayes.py index 92797d5..e508a9f 100644 --- a/tests/test_bayes.py +++ b/tests/test_bayes.py @@ -315,8 +315,8 @@ def test_2d_space(self): params = optimizer.get_next_params(state, self.key) assert "x" in params assert "y" in params - assert params["x"].shape == (1,) - assert params["y"].shape == (1,) + assert params["x"].shape == (4,) + assert params["y"].shape == (4,) def test_n_parallel_discrete(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} @@ -328,7 +328,7 @@ def test_n_parallel_discrete(self): class TestBayesianSearchOptimize: def test_optimize_returns_correct_shapes(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=5) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=5, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, (params_hist, results_hist) = optimizer.optimize( state, jax.random.PRNGKey(0), func @@ -338,14 +338,14 @@ def test_optimize_returns_correct_shapes(self): def test_optimize_fills_state(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=5) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=5, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, _ = optimizer.optimize(state, jax.random.PRNGKey(0), func) assert int(state.mask.sum()) == 5 def test_optimize_finds_optimum(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=20) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=20, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, (params_hist, results_hist) = optimizer.optimize( state, jax.random.PRNGKey(0), func @@ -355,7 +355,7 @@ def test_optimize_finds_optimum(self): def test_optimize_with_array_result(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=3) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=3, n_parallel=1) func = lambda key, config: jnp.array([-(config["x"] ** 2)]) state, (_, results_hist) = optimizer.optimize( state, jax.random.PRNGKey(0), func @@ -365,7 +365,7 @@ def test_optimize_with_array_result(self): def test_optimize_converges_toward_optimum(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} state, optimizer = bayesian.BayesianSearch.init( - space, n_max=20, acquisition=acq.UCB() + space, n_max=20, n_parallel=1, acquisition=acq.UCB() ) func = lambda key, config: -(config["x"] ** 2) state, _ = optimizer.optimize(state, jax.random.PRNGKey(0), func) @@ -373,7 +373,7 @@ def test_optimize_converges_toward_optimum(self): def test_optimize_continuous_space(self): space = {"x": sp.LinearSpace(0.0, 1.0)} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=5) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=5, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, (params_hist, results_hist) = optimizer.optimize( state, jax.random.PRNGKey(0), func @@ -384,7 +384,7 @@ def test_optimize_continuous_space(self): def test_optimize_continuous_with_ei_uses_observed_y_max(self): space = {"x": sp.LinearSpace(0.0, 1.0), "y": sp.LinearSpace(0.0, 1.0)} state, optimizer = bayesian.BayesianSearch.init( - space, n_max=20, acquisition=acq.EI() + space, n_max=20, n_parallel=1, acquisition=acq.EI() ) state = optimizer.update_state( state, jax.random.PRNGKey(0), jnp.array([100.0]), jnp.array([[0.5, 0.5]]) @@ -394,10 +394,10 @@ def test_optimize_continuous_with_ei_uses_observed_y_max(self): def test_optimize_minimize(self): space = {"x": sp.DiscreteSpace([0.0, 0.25, 0.5, 0.75, 1.0])} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=10, maximize=False) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=20, n_parallel=1, maximize=False) func = lambda key, config: config["x"] ** 2 state, _ = optimizer.optimize(state, jax.random.PRNGKey(0), func) - assert int(state.mask.sum()) == 10 + assert int(state.mask.sum()) == 20 assert float(optimizer.best_result(state)) == pytest.approx(0.0) def test_optimize_n_parallel_fills_buffer(self): @@ -458,7 +458,7 @@ def test_best_params_minimize(self): assert float(params["x"]) == pytest.approx(0.5) def test_best_result_after_full_optimize(self): - state, optimizer = bayesian.BayesianSearch.init(self.space, n_max=10) + state, optimizer = bayesian.BayesianSearch.init(self.space, n_max=10, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, _ = optimizer.optimize(state, jax.random.PRNGKey(0), func) assert float(optimizer.best_result(state)) == pytest.approx( @@ -509,7 +509,7 @@ def test_log_length_scale_unchanged_with_single_observation(self): def test_tuned_length_scale_used_in_gp(self): state, optimizer = bayesian.BayesianSearch.init( - self.space, n_max=10, n_hparam_steps=20 + self.space, n_max=10, n_parallel=1, n_hparam_steps=20 ) func = lambda key, config: -(config["x"] ** 2) state, _ = optimizer.optimize(state, jax.random.PRNGKey(0), func) @@ -551,7 +551,7 @@ def test_get_next_params_mixed(self): "lr": sp.LogSpace(1e-4, 1e-1), "layers": sp.DiscreteSpace([1, 2, 3, 4]), } - state, optimizer = bayesian.BayesianSearch.init(space, n_max=20) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=20, n_parallel=1) params = optimizer.get_next_params(state, jax.random.PRNGKey(0)) assert "lr" in params and "layers" in params assert params["lr"].shape == (1,) @@ -593,7 +593,7 @@ def test_two_arg_passes(self): class TestBayesianSearchOptimizeScan: def test_optimize_scan_runs(self): space = {"x": sp.LinearSpace(0.0, 1.0)} - state, optimizer = bayesian.BayesianSearch.init(space, n_max=5) + state, optimizer = bayesian.BayesianSearch.init(space, n_max=5, n_parallel=1) func = lambda key, config: -(config["x"] ** 2) state, (params_hist, results_hist) = optimizer.optimize_scan( state, jax.random.PRNGKey(0), func @@ -719,9 +719,9 @@ def test_lbfgs_improves_over_seed(self): state, key, jnp.array([r]), jnp.array([[x, y_]]) ) params = optimizer.get_next_params(state, key) - assert params["x"].shape == (1,) - assert 0.0 <= float(params["x"][0]) <= 1.0 - assert 0.0 <= float(params["y"][0]) <= 1.0 + assert params["x"].shape == (4,) + assert all(0.0 <= float(v) <= 1.0 for v in params["x"]) + assert all(0.0 <= float(v) <= 1.0 for v in params["y"]) class TestKrigingBelieverHallucination: @@ -749,10 +749,10 @@ def _make_state_with_obs( state = optimizer.update_state(state, key, jnp.array([float(i)]), x) return state, optimizer - def test_default_hallucination_is_mean(self): + def test_default_hallucination_is_sample(self): space = {"x": sp.LinearSpace(0.0, 1.0)} _, optimizer = bayesian.BayesianSearch.init(space) - assert isinstance(optimizer.hallucination, acq.MeanHallucination) + assert isinstance(optimizer.hallucination, acq.SampleHallucination) def test_mean_hallucination_optimize_runs(self): space = {"x": sp.LinearSpace(0.0, 1.0)}