{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ad46f0f",
   "metadata": {},
   "source": [
    "# Ch 05 - Decision Trees, SVMs, and Kernels (notebook)\n",
    "\n",
    "`[← 04 training-models]` · **this notebook** · `[06 ensemble-learning-and-random-forests →]`\n",
    "\n",
    "Runs top-to-bottom in ~2 min on free Colab CPU. Last verified 2026-06-11.\n",
    "\n",
    "**What you'll build**\n",
    "- A CART decision tree from scratch (Gini, greedy splits, leaf vote) that you check against scikit-learn on the same data, plus the depth-vs-overfitting curve that explains why a depth-30 tree scores 100% on train and worse on test.\n",
    "- A linear SVM dual solved as a quadratic program with `scipy`, whose weight vector and support-vector set match `SVC(kernel=\"linear\")` to four decimals. That match proves the SVM sees the data only through inner products.\n",
    "- The polynomial kernel's explicit feature map, written out by hand, shown to reproduce `(x·z+1)²` exactly, then an RBF SVM whose decision boundary you sweep with `gamma`.\n",
    "- A deliberate footgun: an RBF SVM on an unscaled feature, watched failing, then fixed with a `StandardScaler` pipeline.\n",
    "\n",
    "**How this notebook works.** Code cells with a `# TODO` are yours to fill in. Run the cell to grade yourself: `[ ok ]` passed, `[FAIL]` shows what went wrong, `[ -- ]` means not attempted yet. Every exercise has a hint ladder (open only as many as you need) and a folded solution below it. The notebook runs top-to-bottom even if you fill in nothing: the solution cells redefine the functions so later cells work. See Ch 00 for the full protocol.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65167b50",
   "metadata": {},
   "source": [
    "## Before you start\n",
    "\n",
    "1. You scale every feature of a dataset by 1000. Which is affected: a decision tree's splits, or an SVM's margin? <details><summary>Answer</summary>The SVM. Trees split on raw thresholds, so scaling a feature by 1000 just multiplies the threshold by 1000 and the tree is structurally identical. The SVM minimizes $\\|\\mathbf{w}\\|_2$, a Euclidean quantity, so an unscaled feature with a huge range dominates the margin. SVMs need standardization; trees do not.</details>\n",
    "2. A decision tree of unlimited depth on 100 distinct points reaches what training accuracy? <details><summary>Answer</summary>100%. With no depth limit it keeps splitting until every leaf is pure, memorizing the training set. The test accuracy is usually much lower; that gap is the whole regularization story in Part 2.</details>\n",
    "3. The SVM dual objective contains the term $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$ and nothing else about the inputs. Why does that one fact make the kernel trick possible? <details><summary>Answer</summary>Because the algorithm never needs the raw vectors, only their inner products. Replace $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$ with any valid kernel $K(\\mathbf{x}_i,\\mathbf{x}_j)$ and you are silently computing inner products in a different (possibly infinite-dimensional) feature space, without ever building the features. Part 5 makes this concrete.</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcd8778",
   "metadata": {},
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "261098b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:13.905759Z",
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     "iopub.status.idle": "2026-06-10T18:50:14.510819Z",
     "shell.execute_reply": "2026-06-10T18:50:14.510372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy 2.2.6 · sklearn 1.7.2 · scipy 1.15.3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn, scipy\n",
    "print(f\"numpy {np.__version__} · sklearn {sklearn.__version__} · scipy {scipy.__version__}\")\n",
    "if np.__version__ < \"2.0\":\n",
    "    print(\"WARN: written for NumPy 2.x; older versions may differ slightly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50c771a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.511930Z",
     "iopub.status.busy": "2026-06-10T18:50:14.511803Z",
     "iopub.status.idle": "2026-06-10T18:50:14.517396Z",
     "shell.execute_reply": "2026-06-10T18:50:14.516979Z"
    }
   },
   "outputs": [],
   "source": [
    "import os, random\n",
    "SEED = 0\n",
    "FAST = bool(os.environ.get('NB_FAST'))  # CI smoke mode: smaller grids, same code paths\n",
    "GRID_RES = 80 if FAST else 200  # pixels per side for decision-boundary heatmaps\n",
    "rng = np.random.default_rng(SEED)\n",
    "random.seed(SEED)\n",
    "\n",
    "# ── house self-check harness (identical across all chapter notebooks) ──\n",
    "import numpy as _np\n",
    "\n",
    "def check(label, test_fn, required=False):\n",
    "    \"\"\"Run one self-check. test_fn raises AssertionError (with a teaching\n",
    "    message) on failure, NotImplementedError if the stub is unfilled.\n",
    "    required=True is used only in solution cells; it is what CI grades.\"\"\"\n",
    "    try:\n",
    "        test_fn()\n",
    "    except NotImplementedError:\n",
    "        if required:\n",
    "            raise AssertionError(f\"{label}: reference solution incomplete\")\n",
    "        print(f\"[ -- ] {label}: not attempted yet — fill in the TODO above, then re-run.\")\n",
    "        return False\n",
    "    except AssertionError as e:\n",
    "        if required:\n",
    "            raise\n",
    "        print(f\"[FAIL] {label}: {e}\")\n",
    "        return False\n",
    "    print(f\"[ ok ] {label}\")\n",
    "    return True\n",
    "\n",
    "def attempted(*vals):\n",
    "    \"\"\"Treat None placeholders as 'not attempted'.\"\"\"\n",
    "    if any(v is None for v in vals):\n",
    "        raise NotImplementedError\n",
    "\n",
    "def check_shape(x, want):\n",
    "    assert tuple(x.shape) == tuple(want), \\\n",
    "        f\"shape {tuple(x.shape)}, expected {tuple(want)} — check your reshape/transpose order\"\n",
    "\n",
    "def check_close(got, want, atol=1e-5, rtol=1e-4, msg=\"\"):\n",
    "    g, w = _np.asarray(got, dtype=float), _np.asarray(want, dtype=float)\n",
    "    assert g.shape == w.shape, f\"shape {g.shape} vs expected {w.shape}. {msg}\"\n",
    "    bad = ~_np.isclose(g, w, atol=atol, rtol=rtol)\n",
    "    assert not bad.any(), \\\n",
    "        f\"{bad.mean():.2%} of values wrong (max diff {abs(g - w).max():.3g}). {msg}\"\n",
    "\n",
    "\n",
    "# ── small plot helpers (defined here, never imported) ──\n",
    "def plot_boundary(ax, clf, X, y, res=None, title=\"\"):\n",
    "    \"\"\"Paint a fitted classifier's decision regions behind a 2D scatter.\"\"\"\n",
    "    res = GRID_RES if res is None else res\n",
    "    x0, x1 = X[:, 0], X[:, 1]\n",
    "    pad0, pad1 = (x0.max() - x0.min()) * 0.1, (x1.max() - x1.min()) * 0.1\n",
    "    xs = np.linspace(x0.min() - pad0, x0.max() + pad0, res)\n",
    "    ys = np.linspace(x1.min() - pad1, x1.max() + pad1, res)\n",
    "    gx, gy = np.meshgrid(xs, ys)\n",
    "    grid = np.c_[gx.ravel(), gy.ravel()]\n",
    "    zz = clf.predict(grid).reshape(gx.shape)\n",
    "    ax.contourf(gx, gy, zz, alpha=0.25, cmap=\"coolwarm\", levels=1)\n",
    "    ax.scatter(x0, x1, c=y, cmap=\"coolwarm\", edgecolor=\"k\", s=18, linewidth=0.3)\n",
    "    ax.set_title(title)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b64796",
   "metadata": {},
   "source": [
    "> **Note:** seeds make this notebook's printed numbers reproduce on CPU. Library versions and BLAS threading can shift the last digit or two; quoted numbers hold for the pinned environment. If your test accuracy is 0.933 and the page says 0.940, you did nothing wrong.\n",
    "\n",
    "> **Caveat:** the SVM dual in Part 4 is solved with `scipy.optimize.minimize`, a general nonlinear solver, not a dedicated QP package. It is exact enough to match scikit-learn to four decimals on the toy problem, which is the point, but it is not how a production SVM is trained (scikit-learn wraps libsvm). We use it because it runs anywhere with no extra install and keeps the dual visible as plain math.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223b4dce",
   "metadata": {},
   "source": [
    "## The map\n",
    "\n",
    "> **Part 1: The tree that is its own visualization.** Fit a tree on Iris, read the splits in English, then build CART (Gini + greedy splits) from scratch and check it against scikit-learn.\n",
    "> **Part 2: Depth, overfitting, regularization, instability.** Watch a deep tree memorize, sweep `max_depth`, grid-search the regularizers, and see two trees on resampled data disagree.\n",
    "> **Part 3: The maximum-margin classifier.** Derive the margin, fit a linear SVM, and see why it needs scaled features.\n",
    "> **Part 4: The dual and the support vectors.** Solve the dual QP by hand, recover $\\mathbf{w}$ from the $\\alpha$'s, and confirm the data enters only as inner products.\n",
    "> **Part 5: The kernel trick.** Write the polynomial kernel's implicit feature map, prove it reproduces $(x\\cdot z+1)^2$, then sweep the RBF `gamma` and break-then-fix an unscaled SVM.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55750c1d",
   "metadata": {},
   "source": [
    "## Part 1: The tree that is its own visualization\n",
    "\n",
    "> **Objectives.** Fit a `DecisionTreeClassifier` on Iris and read its rules as plain English. Understand the CART recipe: greedy binary splits chosen to minimize weighted Gini impurity. Reimplement CART in numpy and check it against scikit-learn on the same data.\n",
    "\n",
    "A decision tree partitions the input space into axis-aligned rectangles, with a constant prediction in each. To classify a point you start at the root, answer one yes/no question per node (\"is petal length ≤ 2.45?\"), and follow branches to a leaf. The leaf predicts the majority class of training points that landed there. That is the entire inference algorithm: a function from $\\mathbb{R}^d$ to a class label defined by a sequence of axis-aligned splits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97495796",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.518440Z",
     "iopub.status.busy": "2026-06-10T18:50:14.518334Z",
     "iopub.status.idle": "2026-06-10T18:50:14.569615Z",
     "shell.execute_reply": "2026-06-10T18:50:14.569108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- petal_width <= 0.80\n",
      "|   |--- class: 0\n",
      "|--- petal_width >  0.80\n",
      "|   |--- petal_width <= 1.75\n",
      "|   |   |--- class: 1\n",
      "|   |--- petal_width >  1.75\n",
      "|   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, export_text\n",
    "\n",
    "iris = load_iris(as_frame=True)\n",
    "feat = [\"petal length (cm)\", \"petal width (cm)\"]\n",
    "X_iris = iris.data[feat].values        # (150, 2)\n",
    "y_iris = iris.target.values            # (150,) in {0,1,2}: setosa, versicolor, virginica\n",
    "\n",
    "tree2 = DecisionTreeClassifier(max_depth=2, random_state=SEED).fit(X_iris, y_iris)\n",
    "print(export_text(tree2, feature_names=[\"petal_length\", \"petal_width\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128a8016",
   "metadata": {},
   "source": [
    "> **Interpretation.** The whole fitted model is four leaves and three questions, printed as text. The first split isolates setosa (petal length below the threshold); the second separates versicolor from virginica by petal width. This is the readability story: no other model in the book hands you its full decision logic as a flowchart you can read aloud.\n",
    "\n",
    "> **Note:** trees do not require feature standardization. Splits are raw thresholds, so scaling a feature by 100 just scales the threshold by 100. We prove it in the next cell rather than assert it in prose.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "805a56a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.570699Z",
     "iopub.status.busy": "2026-06-10T18:50:14.570620Z",
     "iopub.status.idle": "2026-06-10T18:50:14.573896Z",
     "shell.execute_reply": "2026-06-10T18:50:14.573573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node count raw 15 == scaled 15: tree structure is invariant to per-feature scaling\n"
     ]
    }
   ],
   "source": [
    "# claim under test: scaling a feature does not change a tree's STRUCTURE\n",
    "tree_raw = DecisionTreeClassifier(random_state=SEED).fit(X_iris, y_iris)\n",
    "tree_scaled = DecisionTreeClassifier(random_state=SEED).fit(X_iris * 1000.0, y_iris)\n",
    "assert tree_raw.tree_.node_count == tree_scaled.tree_.node_count, \\\n",
    "    \"scaling changed the tree size; it should not, since splits are scale-equivariant thresholds\"\n",
    "print(f\"node count raw {tree_raw.tree_.node_count} == scaled {tree_scaled.tree_.node_count}: \"\n",
    "      \"tree structure is invariant to per-feature scaling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e769d2b",
   "metadata": {},
   "source": [
    "### 1.1 Gini impurity, the split criterion\n",
    "\n",
    "CART (Classification And Regression Trees) is what scikit-learn implements. At each node it is greedy: pick the feature $j$ and threshold $t$ minimizing a count-weighted child impurity\n",
    "\n",
    "$$J(j, t) = \\frac{m_{\\text{left}}}{m}\\,G_{\\text{left}} + \\frac{m_{\\text{right}}}{m}\\,G_{\\text{right}}$$\n",
    "\n",
    "where $m$ is the node's point count and $G$ is the **Gini impurity** of the labels there:\n",
    "\n",
    "$$G = 1 - \\sum_{k=1}^{K} p_k^2,$$\n",
    "\n",
    "$p_k$ being the fraction of class $k$. $G$ is 0 for a pure node and maximal ($1 - 1/K$) when classes are uniform. Below, `p` is the vector of $p_k$ and `1 - (p**2).sum()` is the formula verbatim.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966e5a2c",
   "metadata": {},
   "source": [
    "### Exercise 5.1: Gini impurity from scratch\n",
    "`Difficulty 1/5 · ~6 min`\n",
    "\n",
    "Fill in `gini(y)` for a 1-D integer label array. Return a Python `float`. Empty input returns `0.0`. The hand-computed checks below pin three values you can verify on paper.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b247a50a",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-06-10T18:50:14.574643Z",
     "iopub.status.idle": "2026-06-10T18:50:14.579135Z",
     "shell.execute_reply": "2026-06-10T18:50:14.578812Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.1 gini (toy): not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gini(y):\n",
    "    \"\"\"Gini impurity 1 - sum(p_k^2) of a label vector y (ints). gini([])==0.0.\"\"\"\n",
    "    y = np.asarray(y)\n",
    "    if len(y) == 0:\n",
    "        return 0.0\n",
    "    # TODO 1: counts per class with np.unique(..., return_counts=True)\n",
    "    counts = None\n",
    "    # TODO 2: class proportions p = counts / len(y)\n",
    "    p = None\n",
    "    # TODO 3: return float(1 - sum of p squared)\n",
    "    result = None\n",
    "    attempted(counts, p, result)\n",
    "    return result\n",
    "\n",
    "def _gini_toy():\n",
    "    # pure node -> 0; balanced 2-class -> 1 - (0.25+0.25) = 0.5\n",
    "    check_close(gini([0, 0, 0, 0]), 0.0, msg=\"a pure node has Gini 0\")\n",
    "    check_close(gini([0, 0, 1, 1]), 0.5, msg=\"50/50 two-class: 1-(.5^2+.5^2)=0.5\")\n",
    "    # 3-class uniform -> 1 - 3*(1/3)^2 = 2/3\n",
    "    check_close(gini([0, 1, 2]), 2/3, msg=\"uniform over 3 classes: 1-1/3=2/3\")\n",
    "\n",
    "check(\"5.1 gini (toy)\", _gini_toy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef3255f",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`np.unique(y, return_counts=True)` gives you the per-class counts. Divide by `len(y)` for the proportions, square, sum, subtract from 1.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "_, counts = np.unique(y, return_counts=True)\n",
    "p = counts / len(y)\n",
    "return float(1.0 - (p ** 2).sum())\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"got 0.5 but expected 0.6666...\"</summary>Check you are dividing by `len(y)`, the total count, not by the number of classes. The 3-class case has counts `[1,1,1]` over 3 points, so each $p_k = 1/3$.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aaa4b30f",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-06-10T18:50:14.579964Z",
     "iopub.status.idle": "2026-06-10T18:50:14.583077Z",
     "shell.execute_reply": "2026-06-10T18:50:14.582694Z"
    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.1 gini (toy)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines gini; the check below re-verifies the reference.\n",
    "def gini(y):\n",
    "    y = np.asarray(y)\n",
    "    if len(y) == 0:\n",
    "        return 0.0\n",
    "    _, counts = np.unique(y, return_counts=True)\n",
    "    p = counts / len(y)\n",
    "    return float(1.0 - (p ** 2).sum())\n",
    "\n",
    "check(\"5.1 gini (toy)\", _gini_toy, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc005733",
   "metadata": {},
   "source": [
    "### Exercise 5.2: The greedy split\n",
    "`Difficulty 3/5 · ~18 min`\n",
    "\n",
    "Fill in `best_split(X, y)`. It tries every feature and every candidate threshold (each unique value in that column), computes the **information gain** `parent_gini - weighted_child_gini`, and returns `(feature, threshold, gain)` of the best improving split, or `(None, None, 0.0)` if none helps.\n",
    "\n",
    "A split that puts everything on one side is not a split; skip it. The toy check below uses a 1-D dataset where the optimal split is obvious so you can verify the gain by hand: `[0,0,1,1]` at feature 0 splits perfectly, so weighted child Gini is 0 and the gain equals the parent Gini, 0.5.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c107058c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.583869Z",
     "iopub.status.busy": "2026-06-10T18:50:14.583796Z",
     "iopub.status.idle": "2026-06-10T18:50:14.587961Z",
     "shell.execute_reply": "2026-06-10T18:50:14.587540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.2 best_split (toy): not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def best_split(X, y):\n",
    "    \"\"\"Return (feature, threshold, gain) maximizing Gini information gain.\n",
    "    X: (m, d) float; y: (m,) int. Returns (None, None, 0.0) if no split helps.\"\"\"\n",
    "    X, y = np.asarray(X, float), np.asarray(y)\n",
    "    m, d = X.shape\n",
    "    parent = gini(y)\n",
    "    best_feat, best_thr, best_gain = None, None, 0.0\n",
    "    for j in range(d):\n",
    "        for t in np.unique(X[:, j]):\n",
    "            mask = X[:, j] <= t\n",
    "            # TODO 1: skip degenerate splits (all points on one side)\n",
    "            if None:  # replace None with the skip condition\n",
    "                continue\n",
    "            # TODO 2: weighted child Gini, then gain = parent - weighted\n",
    "            weighted = None\n",
    "            gain = None\n",
    "            attempted(weighted, gain)\n",
    "            # TODO 3: keep this split if it beats best_gain\n",
    "            if None:  # replace None with the comparison\n",
    "                best_feat, best_thr, best_gain = j, float(t), gain\n",
    "    return best_feat, best_thr, best_gain\n",
    "\n",
    "def _split_toy():\n",
    "    X = np.array([[0.0], [0.5], [1.0], [1.5]])\n",
    "    y = np.array([0, 0, 1, 1])\n",
    "    f, t, g = best_split(X, y)\n",
    "    assert f == 0, f\"feature {f}, expected 0 (only one feature exists)\"\n",
    "    assert 0.5 <= t < 1.0, f\"threshold {t} should sit between the 0.5 and 1.0 points\"\n",
    "    check_close(g, 0.5, msg=\"a perfect split drops child Gini to 0, so gain == parent Gini == 0.5\")\n",
    "\n",
    "check(\"5.2 best_split (toy)\", _split_toy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbb5613",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`mask.sum()` is the count on the left. A split is degenerate when that count is 0 or equals `m` (everything on one side). The weighted child Gini is `(n_left*gini(left) + n_right*gini(right)) / m`.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "n_left = mask.sum()\n",
    "if n_left == 0 or n_left == m:\n",
    "    continue\n",
    "weighted = (n_left * gini(y[mask]) + (~mask).sum() * gini(y[~mask])) / m\n",
    "gain = parent - weighted\n",
    "if gain > best_gain:\n",
    "    ...\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"gain is always 0 or my loop never updates best\"</summary>Print `parent`, `weighted`, and `gain` inside the loop. If `weighted` always equals `parent`, your masks are probably not splitting the labels (check `X[:, j] <= t` selects rows, not columns). If `gain` is right but `best` never updates, your comparison should be strictly `>` against `best_gain`, which starts at 0.0.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7796ef6d",
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    "execution": {
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    },
    "collapsed": true,
    "jupyter": {
     "source_hidden": true
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    "tags": [
     "hide-input"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.2 best_split (toy)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Solution { display-mode: \"form\" }\n",
    "# solution: redefines best_split; the check below re-verifies the reference.\n",
    "def best_split(X, y):\n",
    "    X, y = np.asarray(X, float), np.asarray(y)\n",
    "    m, d = X.shape\n",
    "    parent = gini(y)\n",
    "    best_feat, best_thr, best_gain = None, None, 0.0\n",
    "    for j in range(d):\n",
    "        for t in np.unique(X[:, j]):\n",
    "            mask = X[:, j] <= t\n",
    "            n_left = int(mask.sum())\n",
    "            if n_left == 0 or n_left == m:\n",
    "                continue\n",
    "            weighted = (n_left * gini(y[mask]) + (m - n_left) * gini(y[~mask])) / m\n",
    "            gain = parent - weighted\n",
    "            if gain > best_gain:\n",
    "                best_feat, best_thr, best_gain = j, float(t), gain\n",
    "    return best_feat, best_thr, best_gain\n",
    "\n",
    "check(\"5.2 best_split (toy)\", _split_toy, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6c99cb",
   "metadata": {},
   "source": [
    "### 1.2 Growing the whole tree\n",
    "\n",
    "With `gini` and `best_split` in hand, CART is a short recursion. Build the methods as standalone tested functions first (done), then assemble the recursive grower in one cell and re-instantiate, rather than editing a class in place. The recursion stops at the depth limit, at a pure node, or when no split improves Gini. The leaf predicts the majority class.\n",
    "\n",
    "> **Stop and think:** what happens if you forget the \"no improving split\" stop condition? `best_split` returns gain 0, you split anyway into two children that contain the same labels, and the recursion never terminates on data with duplicate feature rows. The `gain <= 0` check is the sentinel that prevents the infinite loop; we read it adversarially in the next interpretation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65ddb815",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-06-10T18:50:14.596437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tree builder defined\n"
     ]
    }
   ],
   "source": [
    "class Node:\n",
    "    # a CART node: either an internal split (feature, threshold, left, right)\n",
    "    # or a leaf (prediction set, children None)\n",
    "    def __init__(self, feature=None, threshold=None, left=None, right=None, prediction=None):\n",
    "        self.feature, self.threshold = feature, threshold\n",
    "        self.left, self.right, self.prediction = left, right, prediction\n",
    "\n",
    "def _majority(y):\n",
    "    vals, counts = np.unique(y, return_counts=True)\n",
    "    return int(vals[counts.argmax()])\n",
    "\n",
    "def grow_tree(X, y, max_depth=5, depth=0):\n",
    "    # stop: depth limit, pure node, or <=1 sample -> make a leaf\n",
    "    if depth >= max_depth or len(np.unique(y)) == 1 or len(y) <= 1:\n",
    "        return Node(prediction=_majority(y))\n",
    "    j, t, gain = best_split(X, y)\n",
    "    if j is None or gain <= 0:          # SENTINEL: no improving split -> leaf, not recurse\n",
    "        return Node(prediction=_majority(y))\n",
    "    mask = X[:, j] <= t\n",
    "    return Node(j, t,\n",
    "                left=grow_tree(X[mask], y[mask], max_depth, depth + 1),\n",
    "                right=grow_tree(X[~mask], y[~mask], max_depth, depth + 1))\n",
    "\n",
    "def predict_one(node, x):\n",
    "    while node.prediction is None:      # walk to a leaf\n",
    "        node = node.left if x[node.feature] <= node.threshold else node.right\n",
    "    return node.prediction\n",
    "\n",
    "def predict_tree(node, X):\n",
    "    return np.array([predict_one(node, x) for x in X])\n",
    "\n",
    "print(\"tree builder defined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f147b1",
   "metadata": {},
   "source": [
    "> **Common confusion:** the stop condition is three clauses OR'd together, and the `gain <= 0` clause is the one that actually guarantees termination. Depth and purity alone do not: a node with two identical feature rows but different labels is impure and below the depth limit, yet has no split that separates them. Without `gain <= 0` you would recurse forever on it. (This is the same family of bug as a k-means loop that exits on a sentinel equal to a valid state. Name the exit condition and convince yourself it fires.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1bb207c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.597613Z",
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     "shell.execute_reply": "2026-06-10T18:50:14.603253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scratch depth-2 acc 0.960  ·  sklearn depth-2 acc 0.960\n",
      "[ ok ] scratch CART reproduces sklearn's Iris depth-2 accuracy exactly\n"
     ]
    }
   ],
   "source": [
    "# agreement check: scratch CART vs sklearn on Iris (depth 2)\n",
    "scratch2 = grow_tree(X_iris, y_iris, max_depth=2)\n",
    "acc_scratch = (predict_tree(scratch2, X_iris) == y_iris).mean()\n",
    "acc_sklearn = tree2.score(X_iris, y_iris)\n",
    "print(f\"scratch depth-2 acc {acc_scratch:.3f}  ·  sklearn depth-2 acc {acc_sklearn:.3f}\")\n",
    "assert abs(acc_scratch - acc_sklearn) < 1e-9, \\\n",
    "    \"scratch and sklearn disagree on Iris depth-2 accuracy, check best_split tie-handling\"\n",
    "print(\"[ ok ] scratch CART reproduces sklearn's Iris depth-2 accuracy exactly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9479c0b",
   "metadata": {},
   "source": [
    "> **Caveat:** equal accuracy does not mean an identical tree. On Iris, petal length and petal width both separate setosa perfectly, so the two implementations can pick different (equally good) root features and still agree on every prediction. We check the behavior (accuracy), not the byte-identical structure, because the structure is genuinely non-unique under ties. The hand-computed gain in Exercise 5.2 is where we pinned an exact number.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07711ab",
   "metadata": {},
   "source": [
    "> **Key takeaways**\n",
    "> - A decision tree is a sequence of axis-aligned splits; the fitted model is literally a readable flowchart (`export_text`).\n",
    "> - CART chooses each split greedily to minimize count-weighted Gini impurity; the from-scratch version is `gini` + `best_split` + a short recursion.\n",
    "> - The `gain <= 0` stop condition, not depth or purity, is what guarantees the recursion terminates.\n",
    "> - Scaling features leaves a tree's structure unchanged; trees never need standardization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fedc056d",
   "metadata": {},
   "source": [
    "## Part 2: Depth, overfitting, regularization, instability\n",
    "\n",
    "> **Objectives.** See an unconstrained tree memorize the training set. Sweep `max_depth` to draw the train/test gap. Grid-search the regularizers with cross-validation. Watch two trees fit on resampled data disagree, which motivates Ch 06's ensembles.\n",
    "\n",
    "An unconstrained tree keeps splitting until every leaf is pure, so it reaches 100% training accuracy by memorization. Test accuracy is the honest signal. We use the full 4-feature Iris and a 50/50 split so the gap is visible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71d9154d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.604430Z",
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     "shell.execute_reply": "2026-06-10T18:50:14.608579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unconstrained tree: train 1.000  test 0.893\n",
      "leaves: 4  depth: 3\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "X4, y4 = load_iris(return_X_y=True)     # all four features, (150, 4)\n",
    "Xtr, Xte, ytr, yte = train_test_split(\n",
    "    X4, y4, test_size=0.5, random_state=SEED, stratify=y4)\n",
    "\n",
    "deep = DecisionTreeClassifier(max_depth=None, random_state=SEED).fit(Xtr, ytr)\n",
    "print(f\"unconstrained tree: train {deep.score(Xtr, ytr):.3f}  test {deep.score(Xte, yte):.3f}\")\n",
    "print(f\"leaves: {deep.get_n_leaves()}  depth: {deep.get_depth()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fdfe70",
   "metadata": {},
   "source": [
    "> **Predict:** before running the depth sweep below, where do you expect test accuracy to peak as a function of `max_depth`? <details><summary>Answer</summary>At a small depth (2-3 on Iris). Below it the tree underfits (too few questions to separate three classes); above it the extra splits chase training noise and test accuracy plateaus or dips while training accuracy climbs to 1.0. The peak is the bias-variance sweet spot.</details>\n"
   ]
  },
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train reaches 1.00; best test 0.93 at depth 2\n"
     ]
    }
   ],
   "source": [
    "# viz: train/test accuracy vs depth, the overfitting curve\n",
    "depths = [1, 2, 3, 4, 5, 7, 10, 15]\n",
    "tr_acc = [DecisionTreeClassifier(max_depth=d, random_state=SEED).fit(Xtr, ytr).score(Xtr, ytr) for d in depths]\n",
    "te_acc = [DecisionTreeClassifier(max_depth=d, random_state=SEED).fit(Xtr, ytr).score(Xte, yte) for d in depths]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(depths, tr_acc, \"o-\", color=\"#1E40FF\", label=\"train\")\n",
    "ax.plot(depths, te_acc, \"s--\", color=\"#C026D3\", label=\"test\")\n",
    "ax.set_xlabel(\"max_depth\"); ax.set_ylabel(\"accuracy\"); ax.set_ylim(0.55, 1.02)\n",
    "ax.set_title(\"Iris: deeper trees memorize train, plateau on test\"); ax.legend()\n",
    "plt.tight_layout(); plt.show()\n",
    "print(f\"train reaches {max(tr_acc):.2f}; best test {max(te_acc):.2f} at depth {depths[int(np.argmax(te_acc))]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2e415c",
   "metadata": {},
   "source": [
    "> **Interpretation.** Training accuracy climbs to 1.00 and stays. Test accuracy peaks early and then flattens or slips: the deep splits are fitting noise specific to these 75 training points. The gap between the two curves is the overfitting you regularize away.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b368e8c4",
   "metadata": {},
   "source": [
    "### Exercise 5.3: Quantify the overfitting gap\n",
    "`Difficulty 1/5 · ~6 min`\n",
    "\n",
    "Fill in `overfit_gap(depth)`: fit a tree of that depth on `(Xtr, ytr)` and return `train_acc - test_acc`. The check asserts the gap is (weakly) larger at depth 15 than at depth 2. That is the two curves you just plotted, restated as a property.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4912085",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.689176Z",
     "iopub.status.busy": "2026-06-10T18:50:14.689099Z",
     "iopub.status.idle": "2026-06-10T18:50:14.692480Z",
     "shell.execute_reply": "2026-06-10T18:50:14.692095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.3 overfit gap: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def overfit_gap(depth):\n",
    "    \"\"\"Fit a DecisionTreeClassifier(max_depth=depth, random_state=SEED) on the\n",
    "    training split and return (train_accuracy - test_accuracy).\"\"\"\n",
    "    # TODO 1: fit the tree on Xtr, ytr\n",
    "    clf = None\n",
    "    attempted(clf)\n",
    "    # TODO 2: return train score minus test score\n",
    "    return clf.score(Xtr, ytr) - clf.score(Xte, yte)\n",
    "\n",
    "def _gap():\n",
    "    g_shallow = overfit_gap(2)\n",
    "    g_deep = overfit_gap(15)\n",
    "    assert g_deep >= g_shallow - 1e-9, \\\n",
    "        f\"depth-15 gap ({g_deep:.3f}) should be >= depth-2 gap ({g_shallow:.3f}); deeper overfits more\"\n",
    "    assert g_deep > 0.0, f\"a depth-15 tree should overfit (gap {g_deep:.3f} > 0)\"\n",
    "\n",
    "check(\"5.3 overfit gap\", _gap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2ba859",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1</summary>`DecisionTreeClassifier(max_depth=depth, random_state=SEED).fit(Xtr, ytr)` then `.score(...)` on each split.</details>\n",
    "\n",
    "<details><summary>Solution</summary>\n",
    "\n",
    "```python\n",
    "def overfit_gap(depth):\n",
    "    clf = DecisionTreeClassifier(max_depth=depth, random_state=SEED).fit(Xtr, ytr)\n",
    "    return clf.score(Xtr, ytr) - clf.score(Xte, yte)\n",
    "```\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34edd12d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.693354Z",
     "iopub.status.busy": "2026-06-10T18:50:14.693287Z",
     "iopub.status.idle": "2026-06-10T18:50:14.698851Z",
     "shell.execute_reply": "2026-06-10T18:50:14.698569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.3 overfit gap\n",
      "gap at depth 2: 0.053  ·  at depth 15: 0.107\n"
     ]
    }
   ],
   "source": [
    "def overfit_gap(depth):\n",
    "    clf = DecisionTreeClassifier(max_depth=depth, random_state=SEED).fit(Xtr, ytr)\n",
    "    return clf.score(Xtr, ytr) - clf.score(Xte, yte)\n",
    "\n",
    "check(\"5.3 overfit gap\", _gap, required=True)\n",
    "print(f\"gap at depth 2: {overfit_gap(2):.3f}  ·  at depth 15: {overfit_gap(15):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9dbcf6a",
   "metadata": {},
   "source": [
    "### 2.1 The regularization grid\n",
    "\n",
    "The growth-constraining hyperparameters (`max_depth`, `min_samples_split`, `min_samples_leaf`, `max_leaf_nodes`, `max_features`, `ccp_alpha`) all cap complexity. Tune them with cross-validation rather than by eye. `random_state=SEED` on the estimator and a fixed `cv` make the search reproducible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "672f6620",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.699454Z",
     "iopub.status.busy": "2026-06-10T18:50:14.699391Z",
     "iopub.status.idle": "2026-06-10T18:50:14.770361Z",
     "shell.execute_reply": "2026-06-10T18:50:14.769971Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params: {'max_depth': 2, 'min_samples_leaf': 1}\n",
      "best CV accuracy 0.987  ·  held-out test 0.933\n"
     ]
    }
   ],
   "source": [
    "params = {\"max_depth\": [2, 3, 4, 5, None], \"min_samples_leaf\": [1, 3, 5, 10]}\n",
    "grid = GridSearchCV(\n",
    "    DecisionTreeClassifier(random_state=SEED), params, cv=5, n_jobs=1)\n",
    "grid.fit(Xtr, ytr)\n",
    "print(\"best params:\", grid.best_params_)\n",
    "print(f\"best CV accuracy {grid.best_score_:.3f}  ·  held-out test {grid.score(Xte, yte):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc7e7a3",
   "metadata": {},
   "source": [
    "> **Interpretation.** Cross-validation picks a shallow, leaf-constrained tree, not the deepest one. Its held-out test accuracy is at or above the hand-picked depths above, with no peeking at the test set during selection. That is the regularization workflow in five lines.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdafd1a4",
   "metadata": {},
   "source": [
    "### 2.2 The instability of single trees\n",
    "\n",
    "Trees have high variance: the greedy root split is exquisitely sensitive to which points are in the training set, and every later decision is conditioned on it. Fit two trees on two different resamples and watch the decision boundaries diverge. This is not a bug in CART; it is the cost of greedy choices over discrete splits, and it is exactly why averaging many trees (Ch 06) helps.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9136aea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.771397Z",
     "iopub.status.busy": "2026-06-10T18:50:14.771326Z",
     "iopub.status.idle": "2026-06-10T18:50:14.887308Z",
     "shell.execute_reply": "2026-06-10T18:50:14.886936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two resamples, two visibly different boundaries: that is the variance ensembles average away\n"
     ]
    }
   ],
   "source": [
    "# viz: two trees on two bootstrap resamples of the SAME Iris 2D data\n",
    "from sklearn.utils import resample\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "for ax, s in zip(axes, [SEED, SEED + 1]):\n",
    "    Xb, yb = resample(X_iris, y_iris, replace=True, random_state=s, n_samples=80)\n",
    "    t = DecisionTreeClassifier(max_depth=4, random_state=SEED).fit(Xb, yb)\n",
    "    plot_boundary(ax, t, X_iris, y_iris, title=f\"resample seed {s} (root thr={t.tree_.threshold[0]:.2f})\")\n",
    "    ax.set_xlabel(\"petal length\"); ax.set_ylabel(\"petal width\")\n",
    "plt.tight_layout(); plt.show()\n",
    "print(\"two resamples, two visibly different boundaries: that is the variance ensembles average away\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef5c811",
   "metadata": {},
   "source": [
    "> **Interpretation.** Same dataset, two bootstrap resamples, two different decision surfaces, often with different root thresholds. The mean of many noisy measurements is precise; the aggregate of many unstable trees is stable. That sentence is the entire motivation for random forests.\n",
    "\n",
    "> **Key takeaways**\n",
    "> - An unconstrained tree reaches 100% train accuracy by memorization; the train/test gap is the overfitting.\n",
    "> - Regularize by capping growth (`max_depth`, `min_samples_leaf`, …) and tune with cross-validation, not by eye.\n",
    "> - Single trees are high-variance: resampling the data changes the boundary. Averaging trees (Ch 06) fixes this.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563a8686",
   "metadata": {},
   "source": [
    "## Part 3: The maximum-margin classifier\n",
    "\n",
    "> **Objectives.** State the hard- and soft-margin SVM objectives. Fit a linear SVM and see why it needs scaled features. Connect the SVM to a regularized hinge-loss linear model.\n",
    "\n",
    "Among all linear boundaries separating two classes, the SVM picks the one whose **margin** (the distance to the nearest training point on either side) is largest. For labels $y_i \\in \\{-1, +1\\}$ the model is $\\hat{y} = \\operatorname{sign}(\\mathbf{w}^{\\top}\\mathbf{x} + b)$, and the hard-margin problem is\n",
    "\n",
    "$$\\min_{\\mathbf{w}, b}\\ \\tfrac{1}{2}\\|\\mathbf{w}\\|_2^2 \\quad\\text{s.t.}\\quad y_i(\\mathbf{w}^{\\top}\\mathbf{x}_i + b) \\ge 1\\ \\ \\forall i.$$\n",
    "\n",
    "The constraint forces every point onto the correct side, at least margin 1 away in scaled units; minimizing $\\|\\mathbf{w}\\|$ maximizes the geometric margin, which equals $1/\\|\\mathbf{w}\\|$. Real data is not separable, so we add slack $\\xi_i \\ge 0$ and a penalty $C$:\n",
    "\n",
    "$$\\min_{\\mathbf{w}, b, \\boldsymbol\\xi}\\ \\tfrac{1}{2}\\|\\mathbf{w}\\|_2^2 + C\\sum_i \\xi_i \\quad\\text{s.t.}\\quad y_i(\\mathbf{w}^{\\top}\\mathbf{x}_i + b) \\ge 1 - \\xi_i,\\ \\ \\xi_i \\ge 0.$$\n",
    "\n",
    "Small $C$ tolerates violations (wide, soft margin); large $C$ forces a tight fit (hard margin). This is what `SVC(kernel=\"linear\")` solves.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7bd7ffeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.888541Z",
     "iopub.status.busy": "2026-06-10T18:50:14.888465Z",
     "iopub.status.idle": "2026-06-10T18:50:14.892442Z",
     "shell.execute_reply": "2026-06-10T18:50:14.892157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [ 0.57  -1.698]  b = 3.960\n",
      "geometric margin 1/||w|| = 0.558  ·  support vectors: 20\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# linearly separable 2D blobs in {-1, +1}\n",
    "Xb, yb = make_blobs(n_samples=80, centers=2, cluster_std=1.1, random_state=SEED)\n",
    "yb = np.where(yb == 0, -1, 1)\n",
    "\n",
    "lin = SVC(kernel=\"linear\", C=1.0).fit(Xb, yb)\n",
    "w, b = lin.coef_[0], lin.intercept_[0]\n",
    "margin = 1.0 / np.linalg.norm(w)\n",
    "print(f\"w = {w.round(3)}  b = {b:.3f}\")\n",
    "print(f\"geometric margin 1/||w|| = {margin:.3f}  ·  support vectors: {len(lin.support_)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d0fef91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.893391Z",
     "iopub.status.busy": "2026-06-10T18:50:14.893319Z",
     "iopub.status.idle": "2026-06-10T18:50:14.956861Z",
     "shell.execute_reply": "2026-06-10T18:50:14.956331Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viz: hyperplane, the two margin lines, and the support vectors\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "ax.scatter(Xb[:, 0], Xb[:, 1], c=yb, cmap=\"coolwarm\", edgecolor=\"k\", s=25)\n",
    "ax.scatter(lin.support_vectors_[:, 0], lin.support_vectors_[:, 1],\n",
    "           s=160, facecolors=\"none\", edgecolors=\"k\", linewidths=1.4, label=\"support vectors\")\n",
    "xx = np.linspace(Xb[:, 0].min() - 1, Xb[:, 0].max() + 1, 50)\n",
    "for off, ls in [(0, \"-\"), (1, \"--\"), (-1, \"--\")]:   # w.x + b = off  =>  decision and margins\n",
    "    ax.plot(xx, -(w[0] * xx + b - off) / w[1], ls, color=\"#333\", linewidth=1)\n",
    "ax.set_title(\"linear SVM: decision line (solid) and margins (dashed)\"); ax.legend()\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac96c04",
   "metadata": {},
   "source": [
    "> **Interpretation.** Only the circled points (the support vectors) touch or cross the margin lines. Every other point could be deleted and the boundary would not move. That is the SVM's defining property and the hinge that Part 4 turns into the kernel trick.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5205d3",
   "metadata": {},
   "source": [
    "### 3.1 Why the SVM needs scaled features (proved, not asserted)\n",
    "\n",
    "The objective lives in the $\\|\\mathbf{w}\\|_2$ metric, which is not scale-invariant. Blow up one feature's range and it dominates the margin computation, distorting the boundary. We check this directly: fit on raw blobs, fit on blobs with feature 0 multiplied by 1000, and confirm the learned direction changes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9804643",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:14.958153Z",
     "iopub.status.busy": "2026-06-10T18:50:14.958059Z",
     "iopub.status.idle": "2026-06-10T18:50:17.352482Z",
     "shell.execute_reply": "2026-06-10T18:50:17.351890Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight-direction agreement after un-scaling: |cos| = 0.997\n",
      "un-scaled and scaled SVMs are NOT the same model in the original metric (unlike trees, where scaling was a no-op)\n"
     ]
    }
   ],
   "source": [
    "Xbig = Xb.copy(); Xbig[:, 0] *= 1000.0\n",
    "lin_big = SVC(kernel=\"linear\", C=1.0).fit(Xbig, yb)\n",
    "# compare unit normals of the two weight vectors via |cosine|\n",
    "def unit(v): return v / np.linalg.norm(v)\n",
    "cos = abs(unit(lin.coef_[0]) @ unit(lin_big.coef_[0] * np.array([1000.0, 1.0])))\n",
    "print(f\"weight-direction agreement after un-scaling: |cos| = {cos:.3f}\")\n",
    "print(\"un-scaled and scaled SVMs are NOT the same model in the original metric \"\n",
    "      \"(unlike trees, where scaling was a no-op)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993a004e",
   "metadata": {},
   "source": [
    "> **Common confusion:** \"but I corrected for the scale factor when comparing.\" Yes, and the directions still differ, because the *regularization* $\\tfrac{1}{2}\\|\\mathbf{w}\\|^2$ was applied in the scaled space, not the original. There is no post-hoc rescaling that recovers the unscaled fit. The only fix is to scale before fitting. Always wrap `SVC` in a `StandardScaler` pipeline; Part 5 shows what happens when you forget.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fd910c",
   "metadata": {},
   "source": [
    "### 3.2 The SVM as a regularized hinge-loss model\n",
    "\n",
    "There is an equivalent unconstrained view. The soft-margin SVM minimizes L2-regularized **hinge loss**:\n",
    "\n",
    "$$\\min_{\\mathbf{w}, b}\\ \\tfrac{1}{2}\\|\\mathbf{w}\\|^2 + C\\sum_i \\max\\!\\big(0,\\ 1 - y_i(\\mathbf{w}^{\\top}\\mathbf{x}_i + b)\\big).$$\n",
    "\n",
    "The hinge term is zero for points classified with margin $\\ge 1$ and grows linearly otherwise. This is the same shape as logistic regression (log-loss + L2); only the loss differs. Seeing it this way means you can train an SVM with plain SGD, which is what `SGDClassifier(loss=\"hinge\")` does. That route scales to datasets the dual solver cannot touch.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b271d50c",
   "metadata": {},
   "source": [
    "### Exercise 5.4: Hinge loss from the formula\n",
    "`Difficulty 2/5 · ~10 min`\n",
    "\n",
    "Fill in `hinge_objective(w, b, X, y, C)` returning $\\tfrac{1}{2}\\|\\mathbf{w}\\|^2 + C\\sum_i \\max(0, 1 - y_i(\\mathbf{x}_i^{\\top}\\mathbf{w} + b))$, with `y` in $\\{-1, +1\\}$. The hand-computed check sets `C=0` so the loss is exactly $\\tfrac12\\|\\mathbf{w}\\|^2$, and a second case pins a margin you can verify on paper.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f28b743",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.353359Z",
     "iopub.status.busy": "2026-06-10T18:50:17.353238Z",
     "iopub.status.idle": "2026-06-10T18:50:17.357730Z",
     "shell.execute_reply": "2026-06-10T18:50:17.357385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.4 hinge: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hinge_objective(w, b, X, y, C):\n",
    "    \"\"\"L2-regularized hinge loss. w:(d,) b:scalar X:(m,d) y:(m,) in {-1,+1}.\"\"\"\n",
    "    w, X, y = np.asarray(w, float), np.asarray(X, float), np.asarray(y, float)\n",
    "    # TODO 1: margins_i = 1 - y_i * (X_i . w + b)\n",
    "    margins = None\n",
    "    # TODO 2: hinge = sum of max(0, margins)\n",
    "    hinge = None\n",
    "    # TODO 3: return 0.5 * (w . w) + C * hinge\n",
    "    result = None\n",
    "    attempted(margins, hinge, result)\n",
    "    return result\n",
    "\n",
    "def _hinge():\n",
    "    # C=0 -> pure regularizer: 0.5 * ||[2]||^2 = 2.0, regardless of the data\n",
    "    Xt = np.array([[1.0], [-1.0]]); yt = np.array([1.0, -1.0]); wt = np.array([2.0])\n",
    "    check_close(hinge_objective(wt, 0.0, Xt, yt, C=0.0), 2.0,\n",
    "                msg=\"with C=0 the loss is just 0.5*||w||^2 = 0.5*4 = 2.0\")\n",
    "    # one point exactly on the margin (y*(x.w+b)=1) contributes 0 hinge;\n",
    "    # here y=1, x=[0.5], w=[2], b=0 -> margin term = 1-1*1 = 0\n",
    "    check_close(hinge_objective(np.array([2.0]), 0.0, np.array([[0.5]]), np.array([1.0]), C=10.0),\n",
    "                2.0, msg=\"a point exactly on the margin adds 0 hinge; loss stays 0.5*4=2.0\")\n",
    "\n",
    "check(\"5.4 hinge\", _hinge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b302aad",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`X @ w + b` is the vector of raw scores. Multiply elementwise by `y`, subtract from 1, clip negatives to 0 with `np.maximum(0, ...)`, sum.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "margins = 1 - y * (X @ w + b)\n",
    "hinge = np.maximum(0, margins).sum()\n",
    "return float(0.5 * (w @ w) + C * hinge)\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"C=0 case gives something other than 2.0\"</summary>With `C=0` the entire hinge term drops out, so the result is `0.5 * (w @ w)`. If you are not getting 2.0 for `w=[2]`, you are likely adding the hinge before multiplying by `C`, or computing `w @ w` as `w.sum()`.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52498547",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.358583Z",
     "iopub.status.busy": "2026-06-10T18:50:17.358511Z",
     "iopub.status.idle": "2026-06-10T18:50:17.361956Z",
     "shell.execute_reply": "2026-06-10T18:50:17.361573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.4 hinge\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hinge_objective(w, b, X, y, C):\n",
    "    w, X, y = np.asarray(w, float), np.asarray(X, float), np.asarray(y, float)\n",
    "    margins = 1 - y * (X @ w + b)\n",
    "    hinge = np.maximum(0, margins).sum()\n",
    "    return float(0.5 * (w @ w) + C * hinge)\n",
    "\n",
    "check(\"5.4 hinge\", _hinge, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cceae2",
   "metadata": {},
   "source": [
    "> **Key takeaways**\n",
    "> - The SVM maximizes the margin, which equals $1/\\|\\mathbf{w}\\|$; soft margin trades violations against margin width via $C$.\n",
    "> - Only support vectors (points on or inside the margin) shape the boundary.\n",
    "> - SVMs need scaled features; trees do not. We proved both rather than asserting them.\n",
    "> - The SVM equals a regularized hinge-loss linear model, putting it on the same footing as logistic regression.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f24d79",
   "metadata": {},
   "source": [
    "## Part 4: The dual and the support vectors\n",
    "\n",
    "> **Objectives.** Write the SVM dual, solve it as a quadratic program, recover $\\mathbf{w}$ and $b$ from the multipliers, and confirm the solution matches `SVC`. The dual is where the data appears only as inner products. That fact is what the kernel trick exploits.\n",
    "\n",
    "Every convex quadratic program has a dual. For the soft-margin SVM the dual is\n",
    "\n",
    "$$\\max_{\\boldsymbol\\alpha}\\ \\sum_i \\alpha_i - \\tfrac12 \\sum_{i,j}\\alpha_i\\alpha_j\\, y_i y_j\\, \\mathbf{x}_i^{\\top}\\mathbf{x}_j \\quad\\text{s.t.}\\quad 0 \\le \\alpha_i \\le C,\\ \\ \\sum_i \\alpha_i y_i = 0,$$\n",
    "\n",
    "one multiplier $\\alpha_i$ per point. Three things to notice. **The data appears only as the inner products $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$**: the entire dependence on the inputs is the Gram matrix $K_{ij} = \\mathbf{x}_i^{\\top}\\mathbf{x}_j$. **Most $\\alpha_i$ are zero**; the nonzero ones are the support vectors. **The recovered weights are $\\mathbf{w} = \\sum_i \\alpha_i y_i \\mathbf{x}_i$**, a weighted sum over support vectors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3238f06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.362732Z",
     "iopub.status.busy": "2026-06-10T18:50:17.362646Z",
     "iopub.status.idle": "2026-06-10T18:50:17.365294Z",
     "shell.execute_reply": "2026-06-10T18:50:17.364982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gram matrix K = X X^T (the inputs appear only here):\n",
      "[[ 5  8  9  4  7  6]\n",
      " [ 8 13 15  7 12 11]\n",
      " [ 9 15 18  9 15 15]\n",
      " [ 4  7  9  5  8  9]\n",
      " [ 7 12 15  8 13 14]\n",
      " [ 6 11 15  9 14 17]]\n"
     ]
    }
   ],
   "source": [
    "# the same toy 2D separable set used to teach the dual in the draft\n",
    "Xd = np.array([[1., 2], [2, 3], [3, 3], [2, 1], [3, 2], [4, 1]])\n",
    "yd = np.array([1., 1, 1, -1, -1, -1])\n",
    "m = len(yd)\n",
    "C_dual = 10.0\n",
    "K = Xd @ Xd.T                 # Gram matrix: the ONLY place the inputs enter\n",
    "print(\"Gram matrix K = X X^T (the inputs appear only here):\")\n",
    "print(K.astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ca399f",
   "metadata": {},
   "source": [
    "We minimize the negative dual. Writing $P_{ij} = y_i y_j K_{ij}$, the objective is $\\tfrac12\\boldsymbol\\alpha^{\\top}P\\boldsymbol\\alpha - \\mathbf{1}^{\\top}\\boldsymbol\\alpha$, with box bounds $0 \\le \\alpha_i \\le C$ and the linear equality $\\boldsymbol\\alpha^{\\top}\\mathbf{y} = 0$. `scipy.optimize.minimize` with SLSQP handles bounds plus a linear equality directly. We hand it the analytic gradient so it converges tightly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddf0ab72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.366089Z",
     "iopub.status.busy": "2026-06-10T18:50:17.366019Z",
     "iopub.status.idle": "2026-06-10T18:50:17.369636Z",
     "shell.execute_reply": "2026-06-10T18:50:17.369301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alphas: [0.5 0.  2.  0.  2.5 0. ]\n",
      "success: True\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "P = np.outer(yd, yd) * K\n",
    "def neg_dual(a):       return 0.5 * a @ P @ a - a.sum()\n",
    "def neg_dual_grad(a):  return P @ a - np.ones(m)\n",
    "\n",
    "res = minimize(\n",
    "    neg_dual, np.zeros(m), jac=neg_dual_grad, method=\"SLSQP\",\n",
    "    bounds=[(0.0, C_dual)] * m,\n",
    "    constraints=[{\"type\": \"eq\", \"fun\": lambda a: a @ yd, \"jac\": lambda a: yd}],\n",
    "    options={\"maxiter\": 500, \"ftol\": 1e-10})\n",
    "alpha = res.x\n",
    "alpha[alpha < 1e-6] = 0.0          # tiny multipliers are numerical zero\n",
    "print(\"alphas:\", alpha.round(3))\n",
    "print(\"success:\", res.success)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60ecf077",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.370433Z",
     "iopub.status.busy": "2026-06-10T18:50:17.370366Z",
     "iopub.status.idle": "2026-06-10T18:50:17.372746Z",
     "shell.execute_reply": "2026-06-10T18:50:17.372374Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w (from dual) = [-1.  2.]   b = -2.000\n",
      "support-vector rows: [0, 2, 4]\n"
     ]
    }
   ],
   "source": [
    "# recover w from alpha, then b from any point strictly on the margin (0 < alpha < C)\n",
    "w_dual = (alpha * yd) @ Xd                       # w = sum_i alpha_i y_i x_i\n",
    "on_margin = (alpha > 1e-6) & (alpha < C_dual - 1e-6)\n",
    "b_dual = float(np.mean(yd[on_margin] - Xd[on_margin] @ w_dual))\n",
    "sv_idx = np.where(alpha > 1e-6)[0]\n",
    "print(f\"w (from dual) = {w_dual.round(3)}   b = {b_dual:.3f}\")\n",
    "print(f\"support-vector rows: {sv_idx.tolist()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f11786e",
   "metadata": {},
   "source": [
    "> **Predict:** will `SVC(kernel=\"linear\", C=10)` produce the same `w`, `b`, and support-vector set? <details><summary>Answer</summary>Yes, to floating-point tolerance. The dual we solved is exactly the problem libsvm solves; scikit-learn just uses a specialized QP solver. Matching it is the strongest possible self-check: our code is right iff it reproduces a real implementation.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7d940dce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.373532Z",
     "iopub.status.busy": "2026-06-10T18:50:17.373467Z",
     "iopub.status.idle": "2026-06-10T18:50:17.376608Z",
     "shell.execute_reply": "2026-06-10T18:50:17.376162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn  w = [-1.  2.]   b = -2.000\n",
      "[ ok ] dual QP matches SVC on w, b, and the support-vector set\n"
     ]
    }
   ],
   "source": [
    "sk = SVC(kernel=\"linear\", C=C_dual).fit(Xd, yd)\n",
    "print(f\"sklearn  w = {sk.coef_[0].round(3)}   b = {float(sk.intercept_[0]):.3f}\")\n",
    "assert np.allclose(w_dual, sk.coef_[0], atol=1e-3), \"dual w disagrees with sklearn\"\n",
    "assert np.allclose(b_dual, sk.intercept_[0], atol=1e-3), \"dual b disagrees with sklearn\"\n",
    "assert set(sv_idx.tolist()) == set(sk.support_.tolist()), \"support-vector sets disagree\"\n",
    "print(\"[ ok ] dual QP matches SVC on w, b, and the support-vector set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28cd9d32",
   "metadata": {},
   "source": [
    "### Exercise 5.5: The decision function uses only inner products\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "The dual decision function is $\\hat f(\\mathbf{x}) = \\sum_{i\\in SV}\\alpha_i y_i\\, \\mathbf{x}_i^{\\top}\\mathbf{x} + b$. Fill in `decision_dual(Xq)` that evaluates it for a batch of query points using **only** the precomputed `alpha`, `yd`, `Xd`, `b_dual`, plus the inner products `Xd @ Xq.T`. The check compares your scores to `sk.decision_function` on the training points. This is the cell where \"data enters only as inner products\" stops being a slogan.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eff6e4e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.377513Z",
     "iopub.status.busy": "2026-06-10T18:50:17.377440Z",
     "iopub.status.idle": "2026-06-10T18:50:17.380422Z",
     "shell.execute_reply": "2026-06-10T18:50:17.380174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.5 decision via inner products: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def decision_dual(Xq):\n",
    "    \"\"\"Signed SVM scores for query rows Xq:(q,d), via the dual decision function.\n",
    "    Use only alpha, yd, Xd, b_dual, and the inner products Xd @ Xq.T.\"\"\"\n",
    "    Xq = np.asarray(Xq, float)\n",
    "    # TODO 1: inner products between every training row and every query: (m, q)\n",
    "    inner = None\n",
    "    # TODO 2: weight each training row by alpha_i * y_i, sum over training rows,\n",
    "    #         then add b_dual. Result shape (q,).\n",
    "    scores = None\n",
    "    attempted(inner, scores)\n",
    "    return scores\n",
    "\n",
    "def _decision():\n",
    "    got = decision_dual(Xd)                      # score the training points\n",
    "    want = sk.decision_function(Xd)\n",
    "    check_close(got, want, atol=1e-3,\n",
    "                msg=\"dual decision function should match SVC.decision_function\")\n",
    "\n",
    "check(\"5.5 decision via inner products\", _decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f2c9c1",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`Xd @ Xq.T` has shape `(m, q)`: row $i$, column $k$ is $\\mathbf{x}_i^{\\top}\\mathbf{x}_k$. The coefficient on training row $i$ is `alpha[i] * yd[i]`. Sum the weighted inner products over the training axis (axis 0), then add `b_dual`.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "inner = Xd @ Xq.T                      # (m, q)\n",
    "scores = (alpha * yd) @ inner + b_dual # (q,)\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"shape (m,) instead of (q,)\" or \"off by a constant\"</summary>You are summing over the wrong axis or forgetting `b_dual`. `(alpha * yd)` is `(m,)`; matrix-multiplying it by `inner` of shape `(m, q)` contracts the `m` axis and leaves `(q,)`. Add the scalar `b_dual` at the end.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "10d642e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.381188Z",
     "iopub.status.busy": "2026-06-10T18:50:17.381110Z",
     "iopub.status.idle": "2026-06-10T18:50:17.383548Z",
     "shell.execute_reply": "2026-06-10T18:50:17.383252Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.5 decision via inner products\n",
      "scores on training points: [ 1.  2.  1. -2. -1. -4.]\n"
     ]
    }
   ],
   "source": [
    "def decision_dual(Xq):\n",
    "    Xq = np.asarray(Xq, float)\n",
    "    inner = Xd @ Xq.T                  # (m, q): every training-query inner product\n",
    "    scores = (alpha * yd) @ inner + b_dual\n",
    "    return scores\n",
    "\n",
    "check(\"5.5 decision via inner products\", _decision, required=True)\n",
    "print(\"scores on training points:\", decision_dual(Xd).round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f83a6f",
   "metadata": {},
   "source": [
    "> **Key takeaways**\n",
    "> - The SVM dual depends on the inputs only through the Gram matrix $K_{ij} = \\mathbf{x}_i^{\\top}\\mathbf{x}_j$.\n",
    "> - Solving the dual recovers the same $\\mathbf{w}$, $b$, and support vectors as `SVC` (we matched to four decimals).\n",
    "> - The decision function is a weighted sum of inner products with support vectors. Replace the inner product with a kernel and you have Part 5.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3768dad",
   "metadata": {},
   "source": [
    "## Part 5: The kernel trick\n",
    "\n",
    "> **Objectives.** See that swapping the inner product for a kernel buys non-linear boundaries for free. Write the polynomial kernel's implicit feature map by hand and prove it reproduces $(x\\cdot z+1)^2$. Sweep the RBF `gamma`. Then break an unscaled RBF SVM and fix it.\n",
    "\n",
    "Part 4 showed the SVM touches the data only through inner products $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$. Replace each one with a **kernel** $K(\\mathbf{x}, \\mathbf{z})$ and you are computing inner products in some feature space $\\phi$, since a valid kernel satisfies $K(\\mathbf{x}, \\mathbf{z}) = \\phi(\\mathbf{x})^{\\top}\\phi(\\mathbf{z})$, without ever building $\\phi$. The polynomial kernel is $K(\\mathbf{x}, \\mathbf{z}) = (\\mathbf{x}^{\\top}\\mathbf{z} + c)^k$; the RBF kernel is $K(\\mathbf{x}, \\mathbf{z}) = \\exp(-\\gamma\\|\\mathbf{x} - \\mathbf{z}\\|^2)$, whose implicit space is infinite-dimensional.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "628873ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.384234Z",
     "iopub.status.busy": "2026-06-10T18:50:17.384168Z",
     "iopub.status.idle": "2026-06-10T18:50:17.385831Z",
     "shell.execute_reply": "2026-06-10T18:50:17.385560Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explicit degree-5 features in 100 dims: 96,560,646 columns\n",
      "the kernel evaluates the inner product in O(d) and never builds that matrix\n"
     ]
    }
   ],
   "source": [
    "from math import comb\n",
    "# why you cannot just build the features: degree-k polynomial in d dims has comb(d+k, k) of them\n",
    "print(f\"explicit degree-5 features in 100 dims: {comb(100 + 5, 5):,} columns\")\n",
    "print(\"the kernel evaluates the inner product in O(d) and never builds that matrix\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701181fb",
   "metadata": {},
   "source": [
    "### Exercise 5.6: The polynomial kernel's hidden feature map\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "For 2D inputs the degree-2 kernel $(\\mathbf{x}^{\\top}\\mathbf{z} + 1)^2$ equals $\\phi(\\mathbf{x})^{\\top}\\phi(\\mathbf{z})$ for the explicit map\n",
    "\n",
    "$$\\phi(\\mathbf{x}) = \\big[\\,1,\\ \\sqrt2\\,x_1,\\ \\sqrt2\\,x_2,\\ x_1^2,\\ \\sqrt2\\,x_1 x_2,\\ x_2^2\\,\\big].$$\n",
    "\n",
    "The $\\sqrt2$ factors are exactly the multinomial coefficients you would miss with a naive expansion. Fill in `phi(A)` for a batch `A:(n,2)` and the check confirms `phi(A) @ phi(A).T` reproduces `(A @ A.T + 1)**2` to numerical zero. Internalizing this is internalizing the kernel trick: the kernel computes this inner product without you ever materializing these six columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1112f66a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.386631Z",
     "iopub.status.busy": "2026-06-10T18:50:17.386549Z",
     "iopub.status.idle": "2026-06-10T18:50:17.390101Z",
     "shell.execute_reply": "2026-06-10T18:50:17.389801Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.6 poly feature map: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def phi(A):\n",
    "    \"\"\"Explicit feature map for the degree-2 poly kernel (x.z+1)^2 in 2D.\n",
    "    A:(n,2) -> (n,6) with columns [1, sqrt2*x1, sqrt2*x2, x1^2, sqrt2*x1*x2, x2^2].\"\"\"\n",
    "    A = np.asarray(A, float)\n",
    "    x1, x2 = A[:, 0], A[:, 1]\n",
    "    s2 = np.sqrt(2.0)\n",
    "    # TODO 1: stack the six columns in the order above with np.column_stack\n",
    "    cols = None\n",
    "    attempted(cols)\n",
    "    return cols\n",
    "\n",
    "def _phi():\n",
    "    Xt = np.array([[1.0, 0.5], [-0.5, 2.0], [0.3, -1.0]])\n",
    "    K_explicit = phi(Xt) @ phi(Xt).T\n",
    "    K_kernel = (Xt @ Xt.T + 1.0) ** 2\n",
    "    check_close(K_explicit, K_kernel, atol=1e-9,\n",
    "                msg=\"phi(x).phi(z) must equal (x.z+1)^2; check the sqrt(2) coefficients\")\n",
    "\n",
    "check(\"5.6 poly feature map\", _phi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae29215f",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`np.column_stack([...])` glues 1-D arrays as columns. The constant column is `np.ones(len(A))`. The cross term carries `s2 = sqrt(2)`; so do the two linear terms.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "cols = np.column_stack([\n",
    "    np.ones(len(A)), s2 * x1, s2 * x2, x1 ** 2, s2 * x1 * x2, x2 ** 2])\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"max diff is small but not zero / about 2x off on some entries\"</summary>The $\\sqrt2$ on the linear and cross terms is not optional. Expand $(x_1z_1 + x_2z_2 + 1)^2$ and collect: the cross term $2x_1x_2z_1z_2$ forces a $\\sqrt2$ on the $x_1x_2$ feature so that $(\\sqrt2 x_1x_2)(\\sqrt2 z_1z_2) = 2x_1x_2z_1z_2$. Same logic puts $\\sqrt2$ on the two linear features (the $+1$ contributes the $2x_iz_i$ cross terms).</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "929a9cdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.390846Z",
     "iopub.status.busy": "2026-06-10T18:50:17.390781Z",
     "iopub.status.idle": "2026-06-10T18:50:17.393293Z",
     "shell.execute_reply": "2026-06-10T18:50:17.392916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.6 poly feature map\n",
      "[ ok ] the degree-2 kernel is an inner product in this explicit 6-D space\n"
     ]
    }
   ],
   "source": [
    "def phi(A):\n",
    "    A = np.asarray(A, float)\n",
    "    x1, x2 = A[:, 0], A[:, 1]\n",
    "    s2 = np.sqrt(2.0)\n",
    "    return np.column_stack([np.ones(len(A)), s2 * x1, s2 * x2,\n",
    "                            x1 ** 2, s2 * x1 * x2, x2 ** 2])\n",
    "\n",
    "check(\"5.6 poly feature map\", _phi, required=True)\n",
    "print(\"[ ok ] the degree-2 kernel is an inner product in this explicit 6-D space\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303344bf",
   "metadata": {},
   "source": [
    "### 5.1 The RBF kernel and the gamma knob\n",
    "\n",
    "The RBF kernel $\\exp(-\\gamma\\|\\mathbf{x} - \\mathbf{z}\\|^2)$ has an infinite-dimensional implicit space, yet evaluates in $O(d)$. Its one knob, `gamma`, is the kernel width: large `gamma` makes sharp bumps around each support vector (a wiggly, overfit boundary); small `gamma` is nearly linear. We sweep it on the moons dataset and read overfitting off the boundary shape.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5fc8130",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.394049Z",
     "iopub.status.busy": "2026-06-10T18:50:17.393975Z",
     "iopub.status.idle": "2026-06-10T18:50:17.948568Z",
     "shell.execute_reply": "2026-06-10T18:50:17.947998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small gamma -> smooth, almost linear; large gamma -> bubbles around points (overfit)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "Xm, ym = make_moons(n_samples=300, noise=0.30, random_state=SEED)\n",
    "gammas = [0.1, 1.0, 100.0]\n",
    "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n",
    "for ax, g in zip(axes, gammas):\n",
    "    clf = make_pipeline(StandardScaler(), SVC(kernel=\"rbf\", gamma=g, C=1.0)).fit(Xm, ym)\n",
    "    plot_boundary(ax, clf, Xm, ym, title=f\"gamma={g}  (train acc {clf.score(Xm, ym):.2f})\")\n",
    "plt.tight_layout(); plt.show()\n",
    "print(\"small gamma -> smooth, almost linear; large gamma -> bubbles around points (overfit)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc23a4ed",
   "metadata": {},
   "source": [
    "> **Interpretation.** At `gamma=0.1` the boundary is smooth and slightly underfit. At `gamma=1` it follows the two moons cleanly. At `gamma=100` it curls into little islands around individual training points: high train accuracy, poor generalization. That island shape is what RBF overfitting looks like.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c96fde70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:17.949826Z",
     "iopub.status.busy": "2026-06-10T18:50:17.949753Z",
     "iopub.status.idle": "2026-06-10T18:50:18.014766Z",
     "shell.execute_reply": "2026-06-10T18:50:18.014380Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma |  train  |  5-fold CV\n",
      " 0.01 |  0.833  |   0.827\n",
      "  0.1 |  0.840  |   0.833\n",
      "  1.0 |  0.940  |   0.930\n",
      " 10.0 |  0.950  |   0.930\n",
      "100.0 |  0.993  |   0.857\n"
     ]
    }
   ],
   "source": [
    "# quantify it: cross-validated accuracy vs gamma (the eye-test, made a number)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print(\"gamma |  train  |  5-fold CV\")\n",
    "for g in [0.01, 0.1, 1.0, 10.0, 100.0]:\n",
    "    clf = make_pipeline(StandardScaler(), SVC(kernel=\"rbf\", gamma=g, C=1.0))\n",
    "    clf.fit(Xm, ym)\n",
    "    cv = cross_val_score(clf, Xm, ym, cv=5).mean()\n",
    "    print(f\"{g:>5} |  {clf.score(Xm, ym):.3f}  |   {cv:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02016b3",
   "metadata": {},
   "source": [
    "> **Interpretation.** Train accuracy rises monotonically with `gamma`; cross-validated accuracy peaks in the middle and then falls as the model memorizes. The CV column, not the train column, is what picks `gamma`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f754b6",
   "metadata": {},
   "source": [
    "### 5.2 A deliberate failure: the unscaled RBF SVM\n",
    "\n",
    "Part 3 proved the SVM is not scale-invariant. Here is that fact biting in production. We take moons, multiply one feature by 1000 (a stand-in for a feature measured in different units), and fit an RBF SVM **without** scaling. The RBF kernel's $\\|\\mathbf{x} - \\mathbf{z}\\|^2$ is then dominated by the inflated feature, collapsing the effective kernel. Watch it fail, then fix it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a316111e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.015629Z",
     "iopub.status.busy": "2026-06-10T18:50:18.015558Z",
     "iopub.status.idle": "2026-06-10T18:50:18.020732Z",
     "shell.execute_reply": "2026-06-10T18:50:18.020370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BROKEN (unscaled) test accuracy: 0.700\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "Xm2, ym2 = make_moons(n_samples=400, noise=0.25, random_state=SEED)\n",
    "Xm2 = Xm2.copy(); Xm2[:, 0] *= 1000.0                       # one feature on a wild scale\n",
    "Xtr2, Xte2, ytr2, yte2 = tts(Xm2, ym2, test_size=0.3, random_state=SEED, stratify=ym2)\n",
    "\n",
    "# BROKEN: raw RBF SVM, no scaling\n",
    "broken = SVC(kernel=\"rbf\", gamma=\"scale\", C=1.0).fit(Xtr2, ytr2)\n",
    "acc_broken = broken.score(Xte2, yte2)\n",
    "print(f\"BROKEN (unscaled) test accuracy: {acc_broken:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d838bf2f",
   "metadata": {},
   "source": [
    "> **Interpretation.** Around 0.70 on a problem a scaled RBF SVM solves at ~0.91. The inflated feature 0 swamps the distance, so points that should be neighbors look far apart and the kernel sees almost no structure. The model did not error; it quietly underperformed, which is the dangerous failure mode. The fix is one line: scale first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7078c06c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.021447Z",
     "iopub.status.busy": "2026-06-10T18:50:18.021381Z",
     "iopub.status.idle": "2026-06-10T18:50:18.025707Z",
     "shell.execute_reply": "2026-06-10T18:50:18.025415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIXED (scaled) test accuracy: 0.908\n",
      "scaling recovered +0.208 accuracy. Always scale before an SVM.\n"
     ]
    }
   ],
   "source": [
    "# FIXED: the exact same model behind a StandardScaler\n",
    "fixed = make_pipeline(StandardScaler(), SVC(kernel=\"rbf\", gamma=\"scale\", C=1.0)).fit(Xtr2, ytr2)\n",
    "acc_fixed = fixed.score(Xte2, yte2)\n",
    "print(f\"FIXED (scaled) test accuracy: {acc_fixed:.3f}\")\n",
    "assert acc_fixed > acc_broken + 0.10, \\\n",
    "    \"scaling should recover a large chunk of accuracy on the inflated-feature problem\"\n",
    "print(f\"scaling recovered {acc_fixed - acc_broken:+.3f} accuracy. Always scale before an SVM.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ac0e48",
   "metadata": {},
   "source": [
    "### 5.3 Linear, poly, RBF on moons, side by side\n",
    "\n",
    "To close the loop, fit the three kernels on the same scaled moons and compare boundaries and support-vector counts. Linear cannot bend; poly and RBF can.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50efce03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.026539Z",
     "iopub.status.busy": "2026-06-10T18:50:18.026474Z",
     "iopub.status.idle": "2026-06-10T18:50:18.367718Z",
     "shell.execute_reply": "2026-06-10T18:50:18.367056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KlClwcHDAX3/9laF2rq6uePjwYZp7e7m5uaF69erYt28f1Gq1zmvJHVznzp21Ze3atcPhw4fRq1cvrFmzBkOHDkVsbKzeXpAVKlQAAL0EOSEkdR07dtTu+9a0aVOsWLECf/75p06dvn37YurUqShfvjyWLl0KLy8vzJs3L9X9Z01MTNCmTRvs3btX73d99+7dYIzp9WffYoyhVatW2L59O7p27YrZs2cjICAAPXr00Ksrl8sRFhaWrj+GREVFITQ0FM+fP0ffvn0RExMDb2/v1P7aUnTr1i1wHIdy5crplMfExGDDhg2oU6cO5s+fj+nTpyM0NBSNGjXSe8DRp08fDB8+HAUKFMD8+fMxfvx4SCQS7d54ADBjxgx069YNQqEQM2fOxIwZM1CgQAFcunQp1fgeP36M8uXLg8fT/VpauXJlyGQyvH37NsW2yUnE7wdHxsbGAJIe9qQlODg4032fh4cHXFxcsHjxYhw/fhwBAQG4d+8e+vfvDzc3N51/jxcuXICnpydOnToFFxcXmJqawtraGlOmTIFGo0n1Pq6urgCA/fv3G/x+9K2UHn6p1Wrs27cPtWrVQsGCBQEAL1++RPPmzSGXyzFz5kwsXrwYLVu2NNhvVahQAS9evNAOaAkh2atDhw6QyWSYO3euzmEiarUajRs3hr29PRYsWIAKFSpg2rRpmDZtGgCgePHi2L59O2xsbFC2bFls374d27dvh62tbYbuf+vWLVhbW2s/j5L5+fnhyJEjaN68OZYsWYIxY8bg+fPn8PLy0u55lxxn8+bNMWPGDFSoUAGLFy/GsGHDEB0drfNdPT39zfc0Gg2ePXumN44CkvoSX19f7YMdQzLal6jVagwZMgR9+/ZF6dKlU7xuetWuXRs8Hg/Dhg3DnTt3EBAQgFOnTmHOnDlo3bo1PD09tXXr1KmDM2fOYOXKlXj//j1ev36NQYMGITo62uCD0G8l/7fbtm1bmg+qunTpgvDwcJw9e1an/Pnz53jx4oXO96X169dj6NChKFGiBJYtW4YZM2agbNmyuHv3rt51y5cvT2OjX0HOTWYkuYmhpcffL9e6fPkyA8CKFy+us3fh8uXLGQD2/PlzxljSEpgiRYqwRo0a6UyJlslkzM3NjTVo0ECn7Hu3b99mANi2bdv04qtZsyZTqVSZeo9KpZJxHMdGjRql95qhOHbv3s0AsGvXrmnLunfvzng8Hrt//75e/eT3mjxF/Nulvt/XMWT//v1690vWoUMH5uDgkGLbZCdOnGCOjo4MgPZPo0aN9JZaGdKnTx/G5/PTtR9UWtJaevyttJYeP336lPH5fO3S62nTpqV76fG5c+cYn89nfD6fVatWjY0dO5adPXuWKRQKvbqrV6/WW+KtVquZs7Mzq1atmrYsMjKSAUjXflKMMSYSidiAAQPSVZeQ/C759/v7pTMDBw5kALRLf548ecIAsL59++rUGz16NAPALl26pC37vi87e/YsA8BOnz6t0/a3335Lc4nykSNHGAC2YMECbZlKpWK1atXS+8xL7rfS88eQYsWKaV83MTFhkydP1tmjKCO6du3KrK2t9cpVKpXeXsSRkZHM3t6e9e7dW1uWvIzW0JLZ5H7Nx8eH8Xg81qZNG70409p+QSqV6twv2cmTJxkAdubMmRTbPnz4kAFgs2bN0ik/c+aM9u8uNdeuXWMcx/3Qktq7d+8yDw8Pnf+mFSpU0G7HkczMzIxZWloysVjMpkyZwg4cOMA6d+7MALDx48eneZ/u3bszAMzS0pK1adOGLVq0iP33338G61aqVIm5uLjo/LdI/jv5559/tGVLly5Nd5+6a9cuBoDdvXs3zbqEkJ8nuW80tBVD8j563+4Vp9FoWLNmzZhIJNL53XZ1df2hpcc1a9ZkFSpU0CtPTEzU+9z39/dnYrGYzZw5U1uWvB3St1syfBszY+nrbwwJDQ1lAHTulyz5O/7r169TbH/w4EEGgG3fvl2nfN26dQwAK1WqlE75qlWrmLm5Ofvy5QtjjP3w0mPGGNuwYQOzsLDQ6Ut69OjBlEqlTr2QkBDm7e2tU8/GxobdunUrzXvIZDLt9wtXV1fWs2dPtnHjRp3tK5JFREQwsVis9+9u/PjxDAB78+aNtqxVq1bpfv9//vknMzIySlddknNoRiHJsF69ekEkEml/rlWrFgBoT5R68uQJfHx80LlzZ4SHh2tnTMTHx8Pb2xvXrl3TPjn/9qmNUqlEeHg4ChcuDAsLC4NLZvr16wc+n5+puCMiIsAYg6Wlpd5r38aRmJiIsLAwVK1aFQC0cWg0Ghw5cgQtWrQw+LQqefr3wYMHUaZMGe1SXkN1DElISACQtIT3exKJRPt6amxtbVGuXDnMmTMHR44cwfTp03H9+nX06tUr1Xa7du3Cxo0bMWrUKBQpUiTN+2SnoUOHokmTJmjYsGGG2zZo0AC3b99Gy5Yt8fTpUyxYsACNGjWCs7Mzjh07plO3U6dOEAqFOjMwrl69isDAQJ0nZkZGRhCJRLhy5QoiIyPTjMHS0jLdJzQTQpIMGjRI5+chQ4YAAE6dOqXzv9+fRD5q1CgASPXkv/r168PJyQk7d+7Ulr148QLPnj1D165dU43r1KlTEAgEGDBggLaMz+dr4/tWo0aNcP78+XT9MWTz5s04c+YM1qxZg+LFiyMhIUFvFmR6hYeHG+z7+Hy+tj/XaDSIiIjQzmz/tg8+ePAgOI7Tzk75VnK/duTIEWg0GkydOlVvZmBaJ9snJCSk2Pclv56S8uXLo0qVKpg/fz42b96M9+/f4/Tp0/jrr78gFApTbfvlyxd07twZbm5uGDt2bKoxpsbS0hJly5bF+PHjceTIESxatAjv379Hhw4ddJbMx8XFITIyEjNmzMDMmTPRrl077Ny5E40bN8by5ctTne0CJP2bWLVqFdzc3LQrO4oXLw5vb28EBgbq1O3atSsCAgJw7do1bdmuXbsgEonQoUMHbVny1hxHjx5Nc1Zj8r8h6tMIyRn9+/dP8bXBgwdr/z/HcRg8eDAUCgUuXLjw0+6fUl8iFou1n/tqtRrh4eHabQy+70tsbGwM9pnfjqPS6m8MSWsc9W0dQ5o2bQpXV1eMHj0ahw4dwocPH7Bv3z5MmjQJAoFAp214eDimTp2KKVOmZHhWZmqcnZ1RuXJlLFu2DIcPH8bIkSOxc+dOvVV0xsbGKFasGHr06IH9+/dj06ZN2iXcqZ14DySNY+7evYsxY8YASNreq0+fPnB0dMSQIUN0tvqwtLRE06ZNcezYMcTHxwNIWlmxZ88eVKxYEUWLFtXWtbCwQEBAAO7fv5/m+7S0tERCQkKas+NJDsvpTCXJHTIyo3DPnj06bZM3cd+yZQtj7Oum5Kn9iYiIYIwlPdWYMmUKc3FxYRzH6dTp1auXXnyGZtulV0hIiMFZB4wxFh4ezoYOHcrs7Oz0Yk0+OTA4OJgBYJMmTUr1PhKJhHXp0iXD8f3ojEJfX19mbGzMDhw4oFO+ZcsWBoCdOnXKYLtr164xiUTCGjVqpPfEKrN+1ozCPXv2MKFQqPPEKiMzCr8ll8vZvXv32IQJE5hEImFCoZC9fPlSp07z5s2ZmZkZS0hIYIwx1rdvXyYQCLRPC5MtXbqU8Xg8JhQKWa1atdj8+fP1Zo4ks7OzYx07dsxQrITkV8m/335+fjrlCoWC8Xg87abaf/31F+PxeAZnB1tYWLD27dtrfzZ0mMmYMWOYiYkJi4+PZ4wlPR2XSCQsKioq1fgaNWrEChQooFf+9OnTLD3MJCIigtnb2xucEZ8eTZo00Tnp/VtbtmxhpUuXZkKhUKfvc3Nz09Zp3Lgxc3Z2TvUe/fv3ZzweT2+GYnr8yIxCxpIOFKlRo4Y2dj6fz8aMGcMqV67MzM3NDbaJi4tjlSpVYubm5toVEZkRFRXF7O3t2aJFi3TKr1y5wgCwNWvWaMukUikDwD58+KBTN/kwsZROlzQkLCyMHT16lDVp0kS74uJbISEhjM/naw8/SUhIYKampqxVq1Y69WQymfbvzsbGhnXq1Int3bvX4OzV5JOPU/o+QQjJGsl948ePH/Ve69GjB+PxeHrf4X19fRkANm/ePG3Zj84oLF68OPP29tYrV6vVbMmSJaxw4cKMz+fr9CV169bV1vP09Ezz4Kj09DeG/OiMQsYYe/HiBStRooQ2drFYzJYvX87s7OxYmTJltPX69+/PChcurNPf/eiMwhs3bjA+n6+3am369OmM4zidMUvjxo1Z8+bNdeqFh4czKyurDI853r9/zzZu3MiKFy9ucJx74MABhm8OP7lx4wYDwJYuXapT79WrV8zZ2ZkBYIULF2YDBw5kN27cMHjPsWPHMgAGV/SR3INmFJIMS2lGH/v/PgfJT6QXLlyY4uyJ5M3EhwwZgjlz5qBjx47Yt28fzp07h/Pnz8Pa2trgk+2MHIDxPSsrK3AcZ3AWWMeOHbF+/Xr0798fhw4dwrlz53DmzBmd95PVkjerDQoK0nstKCgITk5OqbbfsmULEhMT0bx5c53yli1bAjC8T97Tp0/RsmVLlCpVCgcOHIBAIMhs+FlizJgx6NChA0QiEd6/f4/3799rN/P/9OmTzr4naRGJRKhUqRLmzp2LtWvXQqlUYv/+/Tp1unbtipiYGJw4cQIKhQIHDx5Ew4YN9Z4WDh8+HG/fvsW8efMgkUgwZcoUFC9e3OA+klFRUbly30dCfiUpzSJIa6ZaSrp37464uDgcOXIEjDHs2rULzZs3h7m5+Y+EqSMhIQHBwcHp+pMWS0tL1KtXT2cWZEZYW1sb7Pt27NiBnj17wsPDAxs3bsSZM2dw/vx51KtXL9v6PiCp/0up7wOQZv/n7OyMGzdu4O3bt7h27RoCAgKwYMECfPr0SWfGQzKFQoG2bdvi2bNnOHr0KEqVKpXp2A8ePIiQkBBtX5vMy8sLZmZmOn1v8vv4frP65INI0jNLPZm1tTVatmyJU6dOwcvLCzdu3MCHDx90rtmgQQMcPHgQSqUSx48fR2xsrN4enEZGRrh27RouXLiAbt264dmzZ+jUqRMaNGigN4M1OT7q0wjJGT8yDvoZUupL5s6di5EjR6J27drYsWMHzp49i/Pnz6NkyZLZ1pdYWVlBLBb/UF9SsmRJvHjxAi9evMD169fx+fNn9OvXD2FhYdq+xMfHB//++y+GDh2Kz58/a8cniYmJUCqVeP/+PSIiIjIc/z///AN7e3u9VWstW7YEYwy3bt0CkLSC78yZM3p9jpWVFWrWrJnhvf9cXV3Ru3dv3Lx5ExYWFnrfM5K/GyWvuNq1axf4fL7eftDFixfHmzdvsGfPHtSsWRMHDx5EzZo1Dc4MjYyMhLGxcY7/eyapy11ZAZInJJ8obGZmluYBGgcOHECPHj2wePFibVliYmKWnCApEAjg4eEBf39/nfLIyEhcvHgRM2bMwNSpU7XlPj4+OvVsbW1hZmaW5sEYHh4eadYxpFSpUhAIBHjw4AE6duyoLVcoFHjy5IlOmSEhISFgjOl9sU8+qUqlUumU+/r6onHjxrCzs8OpU6f0ToLMDT59+oRdu3YZPI24fPnyKFOmTKZOFE7uhL//MtGyZUuYmppi165dEAqFiIyMTPFgAw8PD4waNQqjRo2Cj48PypYti8WLF2PHjh3aOoGBgVAoFChevHiGYyQkP/Px8YGbm5v253fv3kGj0aBQoUIAkr7YajQa+Pj46Px+hYSEICoqSm+j9e+VKlUK5cqVw86dO+Hi4oKPHz9i5cqVacbl6uqKixcvIi4uTucz882bN3p19+7dm+a2D8lYOk6+TUhIQHR0dLqu9z1PT0/s3LkT0dHROsnQAwcOwN3dHYcOHdJJun7/xd7DwwNnz55FREQErKysDN7Dw8MDGo0Gr169QtmyZTMUX9myZXH9+nVoNBqdZct3796FsbGxwWSfIUWKFNFun/Hq1SsEBQWhZ8+eOnU0Gg26d++OixcvYt++ffDy8spQrN8LCQkBAL2+N7k//rbvrVChAnx8fBAYGAh3d3dtefJDr8wuYatYsSKuXr2KoKAgnX/7Xbp0wZkzZ3D69Gns2rULZmZmaNGihV57Ho8Hb29veHt7Y8mSJZg7dy4mTZqEy5cv63yP8/f3B4/HS/d/D0JI9tBoNPDz89P53Uw+BCq53/wZPD09cfDgQb3yAwcOoG7duti4caNO+fcPyz08PHD37l0olUoIhUKD90hPf2MIj8dD6dKlDZ6MfPfuXbi7u8PU1DTN63Ach5IlS2p/PnXqFDQajfazMDAwEBqNBkOHDsXQoUP12ru5uWHYsGEZPgk5JCTE4PYi34/jUupzkut+P95LL0tLS4NjWLFYjPbt22Pbtm0ICQnB/v37Ua9ePTg4OOhdQyqVolOnTujUqZP2gdycOXMwYcIE7fJvIKkvobFR7kczCslPV6FCBXh4eGDRokWIi4vTez00NFT7//l8vt4AaeXKlZnehykt1apV0+tAkmdIfh/H9x/wPB4PrVu3xvHjxw12Qsnt27Vrh6dPn+Lw4cMp1jHE3Nwc9evXx44dO3T2Kdq+fTvi4uJ09hSSyWR4/fq1zj5BRYsWBWMM+/bt07nu7t27AUDntMvg4GA0bNgQPB4PZ8+e/an7a/xMhw8f1vvTqVMnAEmndS1dujTV9pcvXzb4d568v1mxYsV0yo2MjNCmTRucOnUKa9euhVQqRatWrXTqyGQynT2ngKQvNaampjr7egBfT0irXr16Ot4tISTZ6tWrdX5OTuI1adIEQNJeQoD+5/SSJUsAAM2aNUvzHt26dcO5c+ewbNkyWFtba6+dmqZNm0KlUmHt2rXaMrVabTDJmNk9Cr98+aJ3rffv3+PixYsG98dNj2rVqoExpndqo6H+7+7du7h9+7ZOvXbt2oExhhkzZuhdO7lt69atwePxMHPmTL0ZJGklQtu3b4+QkBAcOnRIWxYWFob9+/ejRYsWOntO+fr6wtfXN9XraTQajB07FsbGxnp7eg0ZMgR79+7FmjVr0LZt21Svkx7JA/M9e/bolCfv6fRt35vcf307mNZoNNi8eTOsrKxQoUKFFO8THByMV69e6ZUrFApcvHgRPB4PhQsX1nmtdevWMDY2xpo1a3D69Gm0bdtWZ7AGwODMl+REr6E+rWTJkj915i0h5OdYtWqV9v8zxrBq1SoIhUJ4e3v/tHtUq1YNkZGR2n3pkxkaz+3fv19v79R27dohLCxMJ9ZvY06uk1Z/k5L27dvj/v37OuO0N2/e4NKlSzrjKAB4/fo1Pn78mOr1EhISMGXKFDg6OuKPP/4AkPSg0dD4pGTJkihYsCAOHz6MPn36pHpdQ4oWLYqQkBBcuXJFp/z7cVzhwoXB4/Gwd+9enb+PgIAAXL9+XafPMeTp06cG95n98OEDXr16pTc2ApIeOimVSvz1118IDQ01OIkiPDxc52eRSIQSJUqAMaZNdiZ79OgRjY1+ATSjkPx0PB4PGzZsQJMmTVCyZEn06tULzs7OCAwMxOXLl2FmZobjx48DSJrOvH37dpibm6NEiRK4ffs2Lly4AGtr6yyJrVWrVti+fTvevn2r/XJvZmaG2rVrY8GCBVAqlXB2dsa5c+f0Zh4CSVPrz507By8vL/z5558oXrw4goKCsH//fty4cQMWFhYYM2YMDhw4gA4dOqB3796oUKECIiIicOzYMaxbtw5lypRJMb45c+agevXq2usHBARg8eLFaNiwIRo3bqytd+/ePdStWxfTpk3D9OnTAQA9e/bEokWL8Ndff+Hx48coWbIkHj16hA0bNqBkyZI6h6s0btwYfn5+GDt2LG7cuIEbN25oX7O3t0eDBg20P/fs2RNbt26Fv79/mk8lV61ahaioKO3siOPHjyMgIABA0uAseXDx4cMHbN++HQC0nfns2bMBJM3Y6datG4CkQc73kmcQNmnSJM3lT0OGDIFMJkObNm3g6ekJhUKBW7duYe/evShUqJDB2T5du3bFtm3bcPbsWXTp0gVSqVTn9bdv38Lb2xsdO3ZEiRIlIBAIcPjwYYSEhOhNwz9//jwKFiyYZqdNCNHl7++Pli1bonHjxrh9+zZ27NiBzp07az8/y5Qpgx49euDff/9FVFQUvLy8cO/ePWzduhWtW7dG3bp107xH586dMXbsWBw+fBgDBgxIcXbDt1q0aIEaNWpg/PjxeP/+PUqUKIFDhw4ZnOnn6Oio3VIiI0qXLg1vb2+ULVsWlpaW8PHxwcaNG6FUKvH3339n+HoAULNmTVhbW+PChQuoV6+etrx58+Y4dOgQ2rRpg2bNmsHf3x/r1q1DiRIldB701a1bF926dcOKFSvg4+ODxo0bQ6PR4Pr166hbty4GDx6MwoULY9KkSZg1axZq1aqFtm3bQiwW4/79+3BycsK8efNSjK99+/aoWrUqevXqhVevXsHGxgZr1qyBWq3WGywmD3rfv3+vLRs2bBgSExNRtmxZKJVK7Nq1S/vvoWDBgtp6y5Ytw5o1a1CtWjUYGxvrzAAHgDZt2mg/869cuaLXzxrSokULlCxZEjNnzsSHDx9QtWpVvHv3DqtWrYKjo6POgLFVq1bw9vbGvHnzEBYWhjJlyuDIkSO4ceMG/vnnH4Ob8CcLCAhA5cqVUa9ePXh7e8PBwQFfvnzB7t278fTpUwwfPlyvTzQxMUHr1q21s/INDe5mzpyJa9euoVmzZnB1dcWXL1+wZs0auLi4oGbNmtp6SqUSV69excCBA1OMkRCSMyQSCc6cOYMePXqgSpUqOH36NE6ePImJEyf+1MkAzZo1g0AgwIULF/Dnn39qy5s3b46ZM2eiV69eqF69Op4/f46dO3fqzJwGkrb92LZtG0aOHIl79+6hVq1aiI+Px4ULFzBw4EC0atUqXf1NSgYOHIj169ejWbNmGD16NIRCIZYsWQJ7e3vtYWfJihcvDi8vL53EXMeOHeHk5IQSJUogJiYGmzZtgp+fH06ePKmdjWhjY2NwfJL84PL716ZPn44ZM2bg8uXLqFOnToqxDx48GJs3b0aLFi0wZMgQuLq64urVq9i9ezcaNGiAKlWqAEiaed67d29s2LAB3t7eaNu2LWJjY7FmzRokJCRgwoQJKd4DSBqbTJs2DS1btkTVqlVhYmICPz8/bNq0CXK53GB/5+XlBRcXFxw9ehRGRkYGH7I1bNgQDg4OqFGjBuzt7fHff/9h1apVaNasmc5MzocPHyIiIkJvIgbJhbJvO0SSm2XkMJP9+/frtE0+zOT7TdwfP37M2rZty6ytrZlYLGaurq6sY8eO7OLFi9o6kZGRrFevXszGxoaZmJiwRo0asdevXzNXV1fWo0cPvfi+3+A1o+RyObOxsdE70CQgIIC1adOGWVhYMHNzc9ahQwf2+fNnBoBNmzZNp+6HDx9Y9+7dma2tLROLxczd3Z0NGjRIZ0Pb8PBwNnjwYObs7MxEIhFzcXFhPXr0YGFhYWnGeP36dVa9enUmkUiYra0tGzRoEIuJidGpk/zf4vvYAgICWO/evZmbmxsTiUTM0dGR9evXT+/gD6Ry0Mz3m/63a9eOGRkZscjIyDRjd3V1TfG63/7bSo4/Pff/XkYOMzl9+jTr3bs38/T0ZCYmJkwkErHChQuzIUOGsJCQEINtVCoVc3R0THHD9rCwMDZo0CDm6enJpFIpMzc3Z1WqVGH79u3TqadWq5mjoyObPHlymnESQpIk/36/evWKtW/fnpmamjJLS0s2ePBg7SFDyZRKJZsxYwZzc3NjQqGQFShQgE2YMIElJibq1DN0mEmypk2bMgDs1q1b6Y4xPDycdevWjZmZmTFzc3PWrVs39vjx4592mMm0adNYxYoVmaWlJRMIBMzJyYn9/vvv7NmzZz903aFDh7LChQvrlGk0GjZ37lzm6urKxGIxK1euHDtx4gTr0aMHc3V11amrUqnYwoULmaenJxOJRMzW1pY1adKEPXz4UKfepk2bWLly5ZhYLGaWlpbMy8uLnT9/Ps34IiIiWJ8+fZi1tTUzNjZmXl5eBvt8V1dXvdg2b97MypQpw6RSKTM1NWXe3t7s0qVLem179OiRav/3bT91/PhxBoCtW7cuXbGPGDGCFS1alInFYmZjY8N+//13vUN5GGMsNjaWDRs2jDk4ODCRSMRKly7NduzYkeY9YmJi2PLly1mjRo2Yi4sLEwqFzNTUlFWrVo2tX7+eaTQag+2SD4RxdHQ0eEDJxYsXWatWrZiTkxMTiUTMycmJ/fHHH+zt27c69U6fPs0AMB8fnzRjJYT8XKl99+3RoweTSqXM19eXNWzYkBkbGzN7e3s2bdo0vd/5Hz3MhDHGWrZsqXegSWJiIhs1ahRzdHRkRkZGrEaNGuz27dsG+1+ZTMYmTZqk7bsdHBxY+/btma+vr7ZOevsbQz59+sTat2/PzMzMmImJCWvevLnBzy1DY4758+czT09PJpFImKWlJWvZsiV7/Phxuv5eUjrMZNSoUYzjOPbff/+leY3Xr1+z9u3bswIFCjChUMhcXV3Z6NGjtQevJVMqlWzlypWsbNmyzMTEhJmYmLC6desa7Pe+5+fnx6ZOncqqVq3K7OzsmEAgYLa2tqxZs2apth8zZgwDkOJhKf/88w+rXbu2dtzv4eHBxowZw6Kjo3XqjRs3jhUsWDDFPovkHhxj6dgYh5A8ZNasWdi8eTN8fHxSPJiFfGVvb4/u3btj4cKFOR3KL+XIkSPo3LkzfH19MzWriJD8KPnJe2hoaLYcmNCmTRs8f/4c7969y/J75TQ/Pz94enri9OnTP3UpWl41duxY7N69G+/evUt1pl9+0bp1a3AcZ3BbFUJI/nH9+nXUqVMHr1+/1u4JS1JWuXJluLq66h2gmB/J5XIUKlQI48ePx7Bhw3I6HJIG2qOQ5DsjRoxAXFyc3n5CRN/Lly+RkJCAcePG5XQov5z58+dj8ODBlCQkJJcKCgrCyZMntVsd5HXu7u7o06dPppcv5zeXL1/GlClTKEkI4L///sOJEycwa9asnA6FEJLDatWqhYYNG2LBggU5HUquFxMTg6dPn2LmzJk5HUqusHnzZgiFQr29g0nuRDMKCSGEEJIrZMeMQn9/f9y8eRMbNmzA/fv34evra/D0PkIIIYQQQvIjmlFICCGEkHzj6tWr6NatG/z9/bF161ZKEhJCCCGEEPINmlFICCGEEEIIIYQQQgihGYWEEEIIIYQQQgghhBBKFBJCCCGEEEIIIYQQQgAIcjqAn02j0eDz588wNTUFx3E5HQ4hhJAMYowhNjYWTk5O4PHy9vMs6rMIIeTXll/6LOqvCCHk15aR/irPJQo/f/6MAgUK5HQYhBBCftCnT5/g4uKS02FkKeqzCCEkb8jrfRb1V4QQkjekp7/Kc4lCU1NTAMDdpx9hYmqWw9EQQgjJqLjYGFQpU1D7eZ6XUZ9FyM8jCPMHANia0Wwnkn1i4uJQqGLNPN9nJb+/9w9uwMzEJIejSVloDIPKxi2nwyDkl0D9Zv6Skf4qzyUKk6fCm5iawZQGXYQQ8svKD0ubqM8i5CcyLQNBqB8SNYCded7//CC5S17vs5Lfn5mJCcxyaVL0SzSDqQmgov6UkPShfjNfSk9/lXc30iCEEEIIIfmKytYdQFLCgBCSfyT/zid/BhBC0of6TWIIJQoJIYQQQkieQYMeQvIXShIS8mOo3yTfo0QhIYQQQgjJU1S27lDZuuNLNKOBDyH5ACUJCfkxlCwk36JEISGEEEIIyZNo4EMIIYSkz7cP2Uj+RolCQgghhBCSZ1GykJC8KXnGMM0mJISQn4sShYQQQgghJE+jZCEheRMlCQnJGtRf5m+UKCSEEEIIIXkeJQsJIYSQtFF/SShRSAghhBBC8gUa/BCSN9DvMCFZi/rL/I0ShYQQQgghJN+gwQ8hv7bk311adkxI1qL+Mv+iRCEhhBBCCMlXvj3ZkQZAhPw6KElISPai37X8iRKFhBBCCCEkX6LZEoT8OpJPOKbEBSHZj/rJ/IUShYQQQgghJN+iZCEhhBCSMuon8x9KFBJCCCGEkHyNZigRkrtRgoKQnEX9ZP5CiUJCCCGEEEIIIbkS7UtICCHZixKFhBBCCCGEgGYtEZLbUJKQkNyDDgHLPyhRSAghhBBC8j06CZmQ3IWShITkPvT7mD9QopAQQgghhJD/o03bCck9KClBCCHZjxKFhBBCCCGEfIOSE4TkLErUE5K70e9o3kaJQkIIIYQQQgghuQItOSYkd6OZ93kfJQoJIYQQQggxgAZBhGQvShIS8mug39G8jRKFhBBCCCGEfIdmTBCSvShJSAghuYMgpwMgJDsxxrB18zbcu+8LpgGKFbXFoKH9IRQKczo0QgghRMf9e/exe9cJxMcDlpY8DBzUEwVdXXM6rHxFmywM9YOdOZfD0RCSd1GS8NcWFRWJ5UvW4XNwIsRioGnTamjcpHFOh0UIySRKFJJ8ZdniVbjxwBIio/oAgBuPIhE8cTb+XjgjhyMjhBBCvnr65AkWLDwJgYk3ACD6iwZjxyzFP+unw9zcImeDI4SQn4QShL8+tVqNEUOnQYYW4PHFgBLYsOUJVEo1mrdsltPhEUIygZYek3xDqVTi/sPPEBkV1JYJRZbw8RchOPhzDkZGCCGE6Nq18yj40rranzmOB7WoAbZv3ZWDUeVvtASZkJ+LkoR5w7kzZxAtr5iUJPw/oXFZnDx1OwejIoT8CEoUknwjPj4OcqWRXrlCbYOgwE85EBEhhBBiWEIiB47TXeoqEJoiLCwmhyLK32i/QkJ+LkoS5h3vfPwgFDvplcsSciAYkq1Utu7UL+ZRlCgk+Ya5uQXMTeP1yk0kH1C8xG85EBEhhBBimKODEdQq3VFWYvwHlC9fPIciIpTQIOTnoCRh3tKwkTeUssc6ZYxpYGvNz6GISHaiZGHeRIlCkm9wHIfu3ZtCFXsaapUMGo0SipjLaNm8DCRG+jMNCSGEkJwyeOhfkHInIU9I2hpDHv8WhRxfonnLFjkcGaEBESGZR0nCvKdY8RKoVBZIjH0ExhgU8ghwiYcxZGivnA6NEJJJdJgJyVfq1PVC6dIlsGvnPsjlCnTo2B2uhdxyOixCCCFEh6mpGf7duBhHDx+Bz9u7qFqtPOrU66O3HJlkL5WtOwShfvgSzegUZEIyiJKEedeEyWPw+NFDnD19Cfb2Nvi98zwYGRvndFiEkEyiRCHJd6xtbDFk2KCcDoMQQghJlVAoRPuOHXI6DPKd5GQhIST9KEmY95UrXwHlylfI6TAIIT8BJQoJIYQQQgghhPx0lCAkhJBfD+1RSAghhBBCSAbRXoWEpI6ShIQQ8muiRCEhhBBCCCEZkJz4oGQhIYZRkpAQQn5dtPSYEEIIIYSQDKK9CgnR9X3inJKEhBDya6JEISGEEEIIIZlEJyATQjMICSEkL6Glx4QQQgghhGQCLUEmhJKEhBCS19CMQkIIIYQQQjKJliCT/IiWGRNCSN6VpTMKr127hhYtWsDJyQkcx+HIkSOp1r9y5Qo4jtP7ExwcnJVhklyEMYYtG7dg6ODpGDZ0Gg7s25/TIRFC8gnqs0hG3b1zF6NHTsfggdMxf95ixMfF5XRIhJB8IKf7q29nECb/IblbeFgoZk7/G0MGzcC4MTPw6uWLnA6J5BH0oCxvytJEYXx8PMqUKYPVq1dnqN2bN28QFBSk/WNnZ5dFEZLcZvaMBTh+QYLQ+Hr4EuuN3YdisWr52pwOixCSD1CfRTLiyuWrWLTkEgIi6iI8oR4evCqJoUMmQaPR5HRohJA8Lif7q9AYWmb8q0mQyTBs6Ey89KuIMFldfAitg2nT9+Dli+c5HRr5xSUnCWmf3rwnS5ceN2nSBE2aNMlwOzs7O1hYWPz8gEiuFhkRjqcvEyAycdKWiYwK49ad0/hzgAIikSgHoyOE5HXUZ5GMOHjgAgTSBtqfBQJjRMrK4+zp02jSrFkORkZyCh1qQrJLTvZXKhs3qEzNfugaJHvt3rUXCfCGiJ80luI4DnxpA2zbchDzF5XO4ejIr476vbwpVx5mUrZsWTg6OqJBgwa4efNmqnXlcjliYmJ0/pBf0+fAT5CrbPXKE5WmiI6KzIGICCEkbdRn5U/xMv0ykZErXr18k/3BkBxHh5qQXwH1V/lTYMAXiMRWOmUcxyHOQD9GCCFALksUOjo6Yt26dTh48CAOHjyIAgUKoE6dOnj06FGKbebNmwdzc3PtnwIFCmRjxORncvcoCqk4UK/c1DgG1jb6CURCCMlJ1Gflb5YWHBjTTQop4l+gTt0aORQRyWm0FJPkVtRf5W+/lSkGueyjTplGo4StDa3WIoQYxrHvv+Vm1Y04DocPH0br1q0z1M7LywsFCxbE9u3bDb4ul8shl8u1P8fExKBAgQJ46RcFU5oW/8vZsmk7jp36DJFJFTCmgSr+Onp2LY8WrVvkdGiEkGwSGxuDku4WiI6OhplZznyOU59F0uL7zgcTJ66FRlQfAqEJEuN94FnoI+YtmJHToZEcJAj1o2VY+UxMbCysPMvmWJ9F/RVJi1qtxrDB4/A5ohzExgWgVERBrLmApcsmwc7ePqfDI78w6vN+LRnpr7J0j8KfoXLlyrhx40aKr4vFYojF4myMiGSlnr27oWKlZzh88DQEAh5+79wFbu6FczosQghJF+qz8g+PwkXwz79TsG3LLkSEx6J6jbJo0Kh3TodFcpjK1h1faOBEfgHUX+UffD4fy1fNx7EjR/H06S04OVqjS7e5kJqY5HRohJBcKtcnCp88eQJHR8ecDoNko1Klf0Op0r/ldBiEEJJh1GflLxYWlhg6fFBOh0FyGUoWkl8B9Vf5C5/PR5t2bdGmXU5HQgj5FWRpojAuLg7v3r3T/uzv748nT57AysoKBQsWxIQJExAYGIht27YBAJYtWwY3NzeULFkSiYmJ2LBhAy5duoRz585lZZiEEEII9VmEEEJ+CdRfEUJymiDUL6dDIFkoSxOFDx48QN26dbU/jxw5EgDQo0cPbNmyBUFBQfj48evGqgqFAqNGjUJgYCCMjY3x22+/4cKFCzrXIIQQQrIC9VmEEEJ+BdRfEUJyUnKSkGbO513ZdphJdomJiYG5uTlttEuyHGMMiQkJEEsk4PFy1QHihPzScsNhJtmF+iySXeRyOTiOg0hEp1xmB9rgPf/I6cNMsgv1VyS7qNVqKORyGBkb53QoJAXUx/2a8tRhJoTkRlcuXcb27WcQFSuGkViOWjWL4K8BfXM6LEIIIURHRHgYZs1YioAgBg6Aa0E+ps0YCxMT05wOLU+jfQoJISRjGGNYsXQ17j74BLlcCCtLJfr1a4fKVSrndGiE5Ds0DYqQDAoOCsSqNechQzOITOtDLWqG81d5OLj/QE6HRgghhOiYNGE+PkfXg8CkMfgmjfEhtBamTvo7p8MihBBCdGxcvxnX71mCiZtBZNYQcepmWLRoH2JionM6NELyHUoUEpJBO7bvA89Yd08XoVFhXL32PIciIoQQQvQFBnxEcLg1eDyhtozPl+DjZwENvAghhOQq9x/4QWhUQKdMI6qDPbv25VBExBA6xCR/oEQhIRkklyt1Bl3JNOocCOb7GDSanA6BEEJILpGYmAAGsV65hgmhVChyIKJvYqD+ihBCyDcMdQt8vhgymSz7g/kGYwx57FiHTKNDTPIP2qOQkAxq07YJHk47BbFpNW2ZQh6G4uUdciymbZt34OLlF4iXcbC25NC9RzPUqFkDjDHcvnkDt2/fR8VK5VDbqw44jj7YCSEkP3D3KAoL6b+Qo4K2jDEGG4toWNvY5khMz54+xdo1exAaDhhJGKpXc0f/gf3AcRw+B37CwQNHYWlhjnYd2tFG9oQQko94uFng4atYCERf99BVyW6iY6c+ORJPgkyG2bMWw9cvDhoGuBYQY/LUkTA3t0BiYiKOHDyEL1/C0LptSxR0LZQjMeYEShLmD5QoJCSDSpQsjWYNH+DcxXOQKd0g5AWjqLsCfw2YkiPxHDtyDMfOxENo3BRCUyBGBSxbdgLu7q6YP28lPga7QSQtj5v332LfnpNYumIunXpJCCH5AMdxGD26B+Yv2IwYWREAGliY+GLCxME5Ek9cXCzmzNkKnrQ1hKaACsCF6+9hbLwdfL4Ah4++BV9aA2plHE6cmoSpU/ugRMlSORLrz/IlmtGgihBC0mH4qMGYMG4m3geYQ8VsIBX5okObsnByLpB24ywwbcrf8A+pBoGxFHwAH0PlmDh+HkaO6ovJk1dBwdWCQFgCV25sR9OGzuj7V+8ciZOQrMCxPDaPNiYmBubm5njpFwVT09SPfCbkR8TGxuD508co5OaeYx0YAAwbOg1fYr11ytTqRJjxDiAi0RtiI0dtuVIejkZe4ej3F53QTHKv2NgYlHS3QHR0NMzM8vbnOPVZJDtoNBo8efQAAqEApX8rl2Mzyzdv2ISTl2wgFFvolJvwTyM6hg++tKG2jDEGK8kZrFk3L5uj/LlomVbeFxMbCyvPsnm+z6L+imSXD+/98DkwAGXKVoCxVJojMcTFxaJXr4UQmNTXKU+MuQFr8yDEadrr9KXK2ItYt3YQbGztsjvUbCUI9aP+7BeWkf6K9igkJJNMTc1QvaZXjiYJAUCl1C/j8UQIDonSSRICgFBsjbdvg7IpMkIIIbkBj8dD+YqV8VuZ8jm6/URUdAz4Qv1BX2RELBSshE4Zx3EIDf/194VS2brndAiEEPJLcS3kjmo1audYkhAA5ImJ0DADe/zCGKFfEvX6Uo2wDC5euJRd4eUIOsQkf6Glx4TkgGtXruLo0ctQKAEPNyv0H/QnJBJJpq5VrKgtrj2IhFBkqS2Txz2Eh7szguNU4PG+/pozpoaRhJ4PEEIISZ/goM9Yu2YLIiKUMDXl0LdfZ7h7FM7Utdp3aIPrt7dCbFpHW6ZSyVCwgBH8A0MBuOjUl4hB++oSQghJF7VajQ3/bMTLV5/B5wPe9SuieYvmmbqWtY0trMyjEKtmOv2QhXEAeCbWSPyuPlN9gatr6R+IPnej2fH5D2UMCMlmJ4+fwoo1D/A5qh7C4uvhxiN3jBo+JdOzJgYM/guutvcgj72LhLhPUMVfhHdtY4weNxwa2Xmduur4y+javd3PeBuEEELyuKjICIwYPh+vP1ZFmKwu/IJrY/z4f/Hh/ftMXa9AQVe0auYOdfw5yOI+IjH2IexMLmLOvBlwsPoEtSpBW1eZ+BFVq7j9pHdCCCEkr5s8YSbOXbdCaHw9BMfUw+YdAdi6eUemrzd6TB8IlUcgi30NWawPeIlHMGRoJ9SqVRzKBF9tPY1aDmuT16hSrfrPeBu5FiUJ8xeaUUjID2CMYcumrbh/3w8qNUPRwlYYOmJQqoeFHDt2E0JpI+3PQpEZPoe74f7dO6hctVqK7VIiFAqxeNkc+L57C5+3PqhcZQisrG0AAJMmdsTWLYcRFcNgZsKhS//mKFrMM+NvlBBCyC/v3t172L3rJOLjAQsLDgMGdoObu0eK9bds2gmVqAkE/5+ZznE88IwbY9PGXZgxa2KmYujRqyvatIvGnVu3ULBgOXiWKAkAWLRkOhYuWIGPn2QQCjlUqeGBXn3+zNQ9CCGE/NqioiKxdNEafA5OhEQMNGpUBc1bpjw78NPH93jrJ4HI9OsegSLjErh46SS692SZmp1ezNMTW7cvxt3bN6BUqlG9Zg8IBAJUrFQJIuFO3Lh5Dgol4FJAglFjp9MMeJKnUKKQkB+wYulqXLtvCZEkaaPbO8+iETJ+JhYumZ1im7h4Bny35YVA5IqXL15nKlGYzKNwUXgULqpTVrZcOZQtV06n7HPgJ4QEf0bxkmUyvdyZEELIr+X506dYuOgUBCZJh1/FhDGMH78a69ZNgqWVtcE2YeGxEAiMdco4ngCxcZofisXMzBwNGzfRKZOamGD6TN3kY3xcHN68fgGXAoVgZ+/wQ/ckhBDya9BoNBgxbDpkrDl4fDHiZMCWnc+hVB5Bm3atDbbxfecLFZzx/VQNWYIASqUy1UkcqeHxeKhWo7ZeedceXdC1x9efGWN49eIp+AIBihYrQUlD8sujRCEhmaRSqXD3fgBEkt+0ZUKROXw/GCMw4COcXQoabGdlyUOYTLdMLX8Br7ptszJcyOVyTBw3E/4BUijVVjCRHEaHdlXQtn2bLL0vIYSQnLdz51HwpXW1P3McB7W4IbZt2Y1hIwcbbFPYwwn/+YdCJLHVlqlVMjg6GBus/zNt3rgNZ8+/QbzCFSL+BRQvwjBj1kTw+fwsv3dW+BLNaNkWIYSkw4Vz5xCdUA5i6deZFULj0jhz9myKicKy5cpBxF0FUEin3MJck+kkYXq9evkC8//ehMh4N3CcGtamGzF9xjAUdHXN0vtmFzrEJH+iPQoJyaTExATIlfoz8pQaWwQGfEqxXd9+7aGOOwG1KgGMMSTGvUS50gzuHkWyMlwsW7IKH0KrQWxaEyYWJQBJU+zc8whfQoKz9L6EEEJyXkKi/sEgAoExIiLjUmzzR9c/YG18HfKEIACAUh4OCTuJvwb0ydJY/3v5AsdPfwZn1Agm5p4QmdTG6/fFseGfjVl636ySfPLxl+hf+wRnQgjJDr6+/hCIHfXKExIMVP4/C0sreNVyRGLMAzDGoFEroIw9jy6dG2RhpEkzCRcs2Aw5vxWk5mVgbFYeMrTE3Dmrs/S+2YUOMcm/aEYhyXUePXiAbduOIjqGwdyMhx49W6Nc+fI5Fk/olxD4vnsLz+IlYWFppS2XSk1gaZ6I+O9WYElFfij9W5cUr1embFmsWeOEbVv3IDZWhjp1q6C2V50siv4rn3eREIrMdMr4xrWwd/cBDBlueDYJIYSQlMXGxmDR/JX4FCiDSMihRg1PdO3eJceWHMnlcjx78hC2tnYo5K57MnEBZ1N8fh4PgVD6tb7sI8qWLfr9ZbTEYjFWr1uIg/sP4s2b23AtaI/fOy+AxMgoy94DABw6dAoikyo6ZUKxHV68fJ6l981KKlt3mpVBCMkxjDFs27oDt2+9gULJULCAFKPHDoGJiWmOxfP29UvEx8fjt7IVIBB8TUs0alwf5y8fBN+0+jf1NbC1SX1G+eChA1C9+j2cOHEJYrEQXbr2hUuBrJ3V5+f7FlGxLjD6JpHGcTyERpggMiI8xa09fiWUJMyfKFFIclxMTDREIjEkEgn8/Xwxb/4RCEwaAhyQGAvMnXcQC+dboJC7e7bGxRjD7BkL8PSlHHK1M4wEF1Cjmh2GjRgEIGlmRq9eLbB8+TEwcR3w+CIo426gQ9syMDJOfVmWja0dRo4emh1vQ4vHA/BdUpOplZBIxAbrE0II0ZWYkAClSglTUzMwxjBqxHREKRqDz5cgQQkcPeMPeeJm9P2rd7bHdurEaezYeQWxcg/wuWg424Zi/qIp2kHggEH98N/gSYhMqAKxkQvk8W/g6uCLlq3npHpdoVCI3zv/nh1v4Zt7CsA0anDfLTPm8WiwQggh6aFWqxEbEw0zcwvweDz8u24jzl8VQmjUEADw6n0iRg2fhnXrF2f7w60vIcGYOGEBwqIKQAMjmBntx6BB7VCtetJe7e4eRVCtkgS37t2H2LQClPJIiNgVDBk6LM1rl69YGeUrVs7qt6AlEonBcUq9co5Tgy+gVAv5ddG/XpJjXr18gWVLtyM8yhgCvgKeRaVgjIFvXE+nHt/YG1u37sW0GROyNb69u/fiyWtniEwKQggAKIKrt56jfPmrqOXlBQCoUbMGSpYsjl079iJRLkf7Dj1Q0LVQtsaZXhXKFcS560EQSb5O5ecUV9Cp8+QcjIoQQnK/xMREzJw2H+/8FVBrhLC1kqFps6oIjfKAxPTrFhRCiRuu3zyFvn9lb3wxMdHYsu0q+NJmMPl/OKGyRMyfuwyz5k4BkHRYyD8bFuPEseN48/oeqlQtD6+6fXPlhuudu7TH3REbIDKtry1TJvqhRqMSORgVIYT8GrZv3YVz558jIVEKY6NYtGheGTdv+UBo1Exbh8+X4EuUG54+foSy5Stka3xz56xCnLo5jMyTUhFqlMCqVQdRsVJFCIVJo64x44bjxbOnOHXyPBwcbNGh07w0J2LkhAIFC8HO8guilSpwvP+/H7UcBZxUMDMzz+HoCMk8ShSSHKFUKjF71gZoJG0gMk0apLz0i4Qy+iCMbWvp1OV4AiQk/NgJi5lx//5biIzq6JSJpKVw7uxNbaIQSNoTY+CQAdkcXcb9OaAfYmKW49GTJ1CoJLA0T0CfEW2pEyOEkDTMm70YPoEVIZCagg8gSsGwdvUKSCw76dVNTMz++E4dPwkVvzK+nX/H50vw/qPuhk4CgQCt2+b+A6xcChTEX31rYveek4iJM4FELEO9Gm7o+HvPnA6NEEJytWtXr+LIyTCIpE0gFAJKALv334ZaoYDx96tg+Xb4+PFTtiYKVSoVAoPUEJjopiFi5cXw4O5tVKv59YThUr+VQanfymRbbJk1a84YzJ65DEFfhAA0KOjCYdqMsTkd1g+j7TLyN0oUkhxx4dxZyNQVYPTNTAahyBLRCQkQJOqesKhI/ILixV2yPUa+waN+NOALct/si/TgOA5jxg+HUqmETBYPMzPzXDmThBBCchPGGN6+i4XA6Os+ThzHgW9UA+qEB4Bxc526drbZfyqvsdQYTCPTK/9FDwgGADRs3BANGjVAdHQUTExMdfavIoQQYtjJE1chktbRKROZVEVkwFoYWTGd7/4CzQvU9hqRrfHxeDzwePoHO3GQZ/n+t1nFzt4eK1bPgyw+HhzH5cqZjxlFh5gQOvWY5IjERAU4nv5R9TZ2bnCyvAN5/OukE4HjX8PZ6h46d+2c7TE2aVIDirinOmXKuFvo1KlFtsfyMwmFQpibW1CSkBBC0kltYFI7X2iJ0iVEkMdcSTpdUR4OLuEwhgzplu3xNW7aFBLcA2NfB1+KxFCULmWf7bH8TBzHwcLCMs8kCVW27nTyMSEkSzGm//2e4zh4FHYAl3AYisRwaNQKyGMuo2njIjoHNWYHHo+Hoh5SqJSx2jLGNLCUvkPZ8hWzNZafzVgqzRNJwmSUJMzf8sY3L/LLadS4EfbsnQ7ga9JNrU5EEVcTzJ43BdevXsX9ew9RuUoF1KzdL9NJLbVaDf7/p1R88PfDhfOX4FHEHV516qZ5zbr1vRH4OQTnzp2CTC6BqVSO37tWQ/GSpTIVS0ZdOHceR49eQ0IiBxtrPoYN7wtHJ+dsuTchhJAkHMehgLMQARFK8HhCbblU+BLTZs5FSHAQDh08BhtrS7TrMC/TMyLUajV4PB44joMsPh7Hjx0D0zC0aNUSUhOTVNuKRCJMmdoXK5bvQFiECEKhChVKWGL4yJGZiiWjPgcGYMXyjQgNU8PYiKF1ay94N6ifdsN8SGXrji+hfjQAI4RkierVS2HbXl+IjT20ZfL4l2jfpTmqVa+OQwcOITwiAG3b9YJLgYKZugdjDIwx8Hg8MMZw7eoV+LzxRf0GdVHI3SPN9pOmjsHsGQvh45cAtZoPe1slJkwcmS2TGNRqNVavWIsXr0IAAKVKOmDQkP7a8SIhJAnHvn38nAfExMTA3NwcL/2iYGpqltPhkFRcungJmzaeRnSCGwS8WDjbR2De/Ek/5b/b4YNHcPz4HcTE8WBuqoFElICgcHvwJWWhVgTBUvoMy1bMSte9NBoNEmQyGEul2TYL79KFi1iz/gmExpUAAIypwUs8jE2b5/+y0/IJSa/Y2BiUdLdAdHQ0zMzy9uc49Vm/hqjICIwfNw/BYXZQMyNYGL/HwG9OaPwR7/39sGjheoR8AYRCDWysFAgJ5UPJqwqAg1BzB4MGtUBtr9ppXgsAEmQyCEWibJuFlyCToU/v8dBI2oDjkgZayvh7GNy/AurUq5stMfxqBJQozFNiYmNh5Vk2z/dZ1F/9GhhjWPD3Ujx4HI1EpROMRYGoXtUew0YM+uFrK5VKzJ+3FC9ehUOt5sHGSgFZfAJiFeXBFzlBnfgMVSuKMXZ8+pYzKxQKaNTqbB3bTJs8By98PbXbXCnloShV+A2mz5yYbTHkdrTsOO/KSH9FMwpJjqnnXQ+1vWrj1cunsLCwQkFXt59y3ft372LHHh+ITJpCaAqExvghURYIa4eaAACBsAhi1QWxdPFqTJ2e9knKPB4vzdkcP9uRo1chNP46G4Pj+EjkamPfnn3o3qtHtsZCCCH5nYWlFdb9uxDv/d4hLi4WxUsO+CmzD9RqNSZPWg6VqA0EpjxoGMOjJ9tQoGgPiLW1WmDjhuOoWasmeLy0d4zJ7mVPe3fvg5yrCxH39e9DYFwJhw5foEQhIYRkM47jMG7CSERGhOPDe1+4eXSCubnFT7n2gnlL8eRNUQiNrCAA8Or1AdgXbAWxNCnRJxBWx+0Hj/D40UOUS8cBKSKR/jZUWSk6Ogr/vVVAJP26F75QbIsX/z1BbGwMJcC/QUlCQnsUkhwlEAjwW5kKOknC/16+wPYt2/Dq5fNMXfPQoXMQSitrf46JeAor+xo6dfh8MQICE75vmmt8e2pmQnwgAt7tRHjQDezYeQ2LFyyDRpP9p0ATkl0EYf45HQIhBhVyL4xSv5XTJgmjoiKxZ9dunDt7Bmq1OsPXu3LxIuKU5cBxSV/H5AkhMDYrrFcvOt4BH9/nztMHg4K+QChO2uNKo1His/9BBPkfxPMXIRg+dBKCg4NyOEJCCMl/LK2sUbZ8ZW2SUK1W4/y5s9izczeiIiMydc2Xr8MhFH2zpyHHA1+gOxtQbFIOZ05fymzYWSoyPAwK9ddkYETILQT67kXolwgM6D8V586cy8HoCMldaEYhyTU0Gg0mjpuON/7m4IuL4vCJCyhSaB/mzp+uHZR9/PAet27cRomSnvitbDmD11GpobNEmC+QQqmIhkhsqVNPLErfqvuI8DD8s24zwsITYWdjjD/794SllXUm32X62NuJEB2gBMAhLPAiXIp0076n20+CIVy+BkNHDM7SGAjJTsnLHADA1oyeYpLc79CBw9i99z7Ugopg6hBs2zoCc+eNQkFXVwBJS3LPnT0LsUgE74YNIRQK9a4RExMLjvd1BqBAKIVKGaNXT8CPg7mFpV759zQaDXZu34mnT/0hEnJo37EJylfI2s3h69argXtPn0BiUhrBH47B1skbQrEFACA4WoWJ4xdg4+YldIAWIYTkkMCATxg/bhHilOXA45vj4JHF6Ni+PDp0agcgabny7Vs38OljIOo38Ia1ja3eNRhjUCkBvvjbMv0HZCplDKytLdIV18MH93Fg/xmolEDZcu74o8sf6Zo5n1kFXN1gavwFKgAxES/AcXw4e3QCAKgB/LvhMtzcXVGkaLEsi4GQXwXNKCTZijGG61evYvbMBVi/bj3i4r6eeLV/7z68/eQJiWklCEXmEJtWgM+n4ti7ey8YY5g3eyGGj9qJ/SckmD77GoYOHgeFQqF3j8oVi0Ke8EH7s7VDTQS/P6RzGqQs+jE+fXyP7t0mYeSwKfDzfWcw3siIcAwaNAuP35TF50gvPHxdGoMGzsj0k7hvPX/6BIsXLMHxo0f1ZqIMH/EXRKojCAs8D2tHL50BllDsgMdPP//w/QnJDQShftr9upL/EJJbfA78hEULlmL+vMXw8/XRlsfGxmDPvnsQmDSGWGIDidQNKlFbLF70LwDg8sVL6NVzGrbslmPd1nD06DYGr16+0Lt+g8YNIWSPtD8LhKZQyqOgVERry5SKGMREPMWwYUvw15+TcPTw0RTjnTR+Bo6cESIwsg78Qmpjzt/ncerE6R/+e4iMCMe61evw79p/ERUVqfNalWrVUbpoJBJiHwGMaZOEAMDxBIiIdceLZ09+OAZCCCEpS0xIwLbN2zBn5kKcOXVKZ/XRwgX/QClsA4nUHSKJNQQmjbD/wAPExEQjKioSfXuPwMJlr7D3mAh/DViFjRu26F2f4zg4OwrBNCptmdTUDVGhd7U/M8YQFbQf1669Qc8ek/D3nEUGx2oAcOLoCcydfwn+IbUREOmFQyd5mDpp9g//PahUKhw5dBiLFy7ByxfPdF7j8/no/HtdKGPPIDbiBSztqui8LjCpjd27Dv9wDL+ybx/ck/yNEoUkW02bPBtLVv2HF35VcPa6Pfr1mYwP798DAB4/egexkYtOfbGxC5488cONa9dw74kUYtNaEEmsITYth88RNbBuzb9692jfqSN+K/oJitjrkMV9hCbxAWpUc4KD+QUIVOegjN6NuBgfGNn0g1LQCJ+j62PSxJWQxcfrXWv9P1uhFjUD//+Pz/h8CVTCptiwflum/w4YY5g+dQ6mzrqM+6/KYfOuWPTpNUJn8GVtY4tNW5agXCk5BEL9/RFVKr0iQn4ZyclB2iyZ5GanT57BkGHrcf9FaTx+XQ6jx+3F7p17AADXrlyBAr/p1Oc4HoJC1FCpVNiw8RQ4aQtIpE4wkhaCRtIGy5bo9xsmJqbo0b02mOwkZLF+SIh9iiIeQpRyfwKx5gwEylMI/fAvLJwGQClohBhlI2zf8wFnTukn/3zevsEbPxOIJE7/j4eDyKQGjhy59kN/D+fOnMOffy7CxduuOH/TGf36zcelCxe/ed8cZs6ZjEljq0As1p9dooExomOi9coJIYT8HBHhYejbZxyOnjfBC/8q+HfrF4wcPlGbLAwKUWm3uEim5JXFpQuXsGj+KsSpm0NiUhxiiQ2EJt44dfoDAgM+6t1n3MRBMMYxJMQ+gyzWDxamwahRWQkz4TkIlOcQGbAaErMaUItbQs5rhIevimL6lHkGYz5y7BZEJtW0kyFERs545SOGv5/hyRvpERkRjj69RmLbvgTcf1kOk6efx5yZ83XqNGnWGOvWDoe1lf4WVBzHh0KR8W1E8gr6Xk6+RUuPSbZ5/OgBXrwxg8S0BABAKLIAE7bBqpWbsXDxDIjEHBhTa09NBADGNBCLOVy6dBtiE93TJYViC/i8e4TvcRyH6TMnIjDgI169fIXSZerBwcFJ+/rQwdMgNPfWqa/g18OeXXvRu19vnWuFhSeAz5folPEFxggN1U8qpte9O7fx9D9zSEyTBpli44JIUNtjxbJ1OoerCIVCTJ4+Bf36LQTQWFuuUctRwFny/WUJydW+f0JJX0JIbqbRaLBn72WITJtpy8SmtXH8xGm0adcatrY24FggAGeddmIR8N/LZ4hJcIPJN8uzOI5DeJQxYmKiYWZmrtOmWfOm8Paui9u3b8LaygqlywzWDpzW/7MBZ1l18AVfLyY0LotTpy+gcdMmOtd5+vgpIHDXey/RsZn/XVOr1di+4wIEps2/FgqbYuu2E6hTr67OErFKlauhWOFjCJXp9uMmoteoXKVbpmMghBCSulUr1kMpaAkRP+lwELGxOz4GA2dOnkLTFs0hFgHK79owTRTs7EsgMCgRPL7uoSJ8aXUcOXQcg4bqnpTs4OCIjVuW4unjh4iKika1Gn9ALE7qn6IiI9DvzxUQmBTX1heILODjp0F0dJTegSoxsQx86Xcx8d3w7OlzuLnr79WbHksXr0MirxXERklbfUhMK+HBs8d4/OgBypX/ug2Hja0d2rapiyNnP2sfrgGAPP4/1KlbKVP3zivo+zlJRjMKSba5eP4qRCZldMo4jofwiKSnXV27toUqXnfmgyruGv7o3ApSYzE0Gv2p6yL9LZ+0nF0KokGjxjpJQgCQG5gBLxSZ48uXcL3ygI9voVLJdGNSxsHRIfOnYl24cANiaWmdMh5fjMDP+k+2LCws0fn3ytDITkEW+x4JMY9hLjqDsROGZPr+hGS3b59Q0vJi8isIDwtFrMxcrzw20Rm+Pq9RqUo1WBq/geabJViKxGBUKOcMcwtLCHhxem0FAgVEIrFeOQBIjIxQt159/Fa2vM5WE2GhERAI9eOQy/WvwRfwEBf5RK/c4gcOcfzw3hexCY565THxtvgc+EmvfMKkQZBojkIW+xKyWF8g8Tj+7Ncs20+2JISQ/CTki1wv2SeWuuPBg5cAgIrlXaCUf922SKNRwdL4FapVr2lwLKVSRMLOzsbgvTiOQ9nyFVGnnrc2SQgkJQqVGlO9+kqNCWKio3TK4uPiEBqif3AdT/MO5cob3oM+PYK/yMHj6b4hsUlZnD1zWa9ul+5dUdzNB/LYG0iI+wRl3BVUKReP+g0aZvr+hOQlNKOQZJuixQrj1qNAiI0L6JQb//+wrKKexTF4YB3s3XMG0TGAuRmHjgO8UaJkKVhZW+Hu4BWAaVNtO6XsEVp088pwHM6OEkT6y7XLiQFAHvsITZrV16n3+NEDxMqLIiJwH5zc2kEgNIVKEYvo4O3os1Z/yXN6OTjY4NF/UXqHq0hSmCTYpl1rNGrcALdv3oCdfWmULpP5DpSQ7EIzCMmvzMzcAmKhfrJPLAyHo5MLOI7DwsWTMH/eKgQGKyAUcKhexRmDhg4Cj8eDs30EvsQlamekqxRRKFFECklKH/QpaNy0Hu7NvQGJaXltmVqdCBdXY516Go0GR47cgTxRjvgYX0jNPMCYGqGBJ9B3XNPvL5tuVlY2EPD0lw0L+bGwsLTSK3dydsHmrUvx6ME9xMbGoVqNzjoDSUIIIT+fsTEQnsB0HjQp5JFwdEo6fHHI8EHgrViHh4+fQa5gKOgowthxE8FxHKrX8MSxM34QGSXNSGeMQYKbaNlmQYZiKFjIHWbGwXozF82lX+BSwFWnbPGiVRBJK+BLwDnYOnuD4/iIi36LYq4xKOhaKMPvP5lEDMR+tz2TUhEOZyd7vbo8Hg+z507Bxw/v8fq//1C2nDfs7B0yfW9C8hpKFJJs06xFcxw6NAIytZ02SaeMv4vWnb8m++rU9UKduvrJP6nUBEMGN8bevWcRHslgKgWadaqCOvXqZjiO4aMGYsTQqYiILwOh2AmqhCeoXkmMMt+donzqxEVILWtCYloeYUGXwdQK8PhiuBdyhamp7hSNJ48fY9fOo0hM5ODsZIKBQ/rp1Un2+x8dceHCJDDWRrtfiDL+Plp1rZNizMZSKbwbNsrweyUkO1FykOQVYrEYlSs44saDjxAZFQQAKBWhKFlUACvrpFkW1ja2WLB4hl5buVyOMWP/wj/rtuPDxwTweBxKF7fCmHFjMxxHufIVUbX8Vdx9dAsCo7JQygNhJX2G4aNm6dTz832LqHhnOLqVR+SXO4gOfwIAkBoJUKpUCZ260dFRWLNyPT4Hy2AsAbp1b4NSv+nut5jMwtIKnoX5eP0xAkJRUmJQIQ9D6WISmJjozxwBkmabVKhUxeBr5Ksv0Yw+IwkhP0WXrq0wZ94x8I3rgOM4MI0KYs1FdOmatD8gx3EYPGyAXjvGGJo1awR54nHcuXMaCYmAvS0Pw6cOzfBDHh6Ph969m2PN2uNQ86sDAATqW+g3uKXeqfefAuJhZd8AsriPCHp/BAAgMXKAg73uacuMMezesRt3778FB6BateLo+HtHvesla9a8BtZvfgShcfn/t1dDrLmCDp3mG6wPAAVdC/1QcpKQvIpj3x4FmwfExMTA3NwcL/2iUkzUkJwTHR2Flcv+QWCQDBIx0LatN2p51U6xfljoF8yYvgSfQ8TgwOBkr8T0maO0A7XM0mg0uHzxInze+qJRk/oG98Lo328QwhNbQCDQnblhZ3IBy1fO1P58985dLFx8FgJp0unEapUMUu4U1m9aAoHAcC7+44cPWLVyE0LD1JAaAy1b1kbDxjTVnfy6fuYGyDGxsbDyLIvo6GiYmeXtz3Hqs3Ivxhh2bN2Je/d9oNEw/FbaBf3699XZl+/7+osWLMfDx1+gVElhJo1B9+6NUNe73g/H4vvOB+fPXkTRYoVR19tbb5B0++Z1TJ15CjZOuvsWquIuYNOm0dp/W3K5HH/2HYMErjn4fAkYY1DHX8LECa1Qrnx5GKJUKrFi2Wr89zoMHICSJR0wZNgA8Pl8g/VJ+tCm8XlHfumzqL/K3R49fIhdO48jXsZgYy3EoCF94OCgv3VEsvNnz2PnrvOIiTODWCRDpQoOGDFqSIpJuPSKi4vF0UNHAQCt27aG1ET3UEa5XI7WLfrBwrmvTrlSHoVm9cLQq+/X/eLnz1uCe09tITJKmpGoSPBHzYoxGDE65S2YTp04jRMnbyIxEbC1FmDo8D5wdimQYn3ylSDUj/qkPC4j/RUlCskPk8XH49TJkxAIhGjctEmGl1YBSRumB30OgLW1LYyMvybmBvw5BhHyJuDxkhJuGo0StsbnsGptyk+GfoYrFy9hycrbiIoMhrN7B225PO45end3RfMWXzd2Hz50KkJidZctKxIC0aW9CG3bt/vpsd27ew/79p5CQgLg6GCEwcP+hIWFZdoNCfnJsmoGYX4ZdAHUZ2U3xhhuXLsGf7/3aNDIG45OLpm6TuiXEPB4PFjbfJ39sOHfTTh92QgiydcDTpSxp/HPupE69X42uVyOXj3G4mNAApzc2oEvSNrPQ6WMhbvjPcxf+HXW464du3DghBBio6/LsBhjcLa8hMVL9WdH/qioyAisWP4vgkMSYWwE/PFHM1SolL83iv8WDcryhvzSZ1F/lf0+vPfH1cvXUMyzCCpXrZapJF5cXCyiIiPg5FxA+6ArOPgzBg9eDaHp19VKisRPaNlAhR69e/y0+A2ZNnkOrt9JgNTUFSYWxQAk9UOQHcHGTbO0icW4uFj06bMAPOMGOu1Zwlls3jRJZ7z4MzDGsG3zNjx4lPTdtlLFwujWo+sPJ05/BfTgKv/ISH9FS4/JD7ly+SrWrj0BJa8yAAX27p2I0aP/yNBA4Ojhozhw8DbiEqwhFkajQjkbjB47DCEhQQiJsIDY9Os/Ux5PiPeBQuzeuQ2tWreDsVSaypWTMMYy/CF/8tQNSC3rgPF8EOC7B3yeCBqNEm4F5WjeQvcEsHiZfnuhxAlv397P0D3T4/at21iy7CIE0qQZKmG+cgwbMg0bNi2GUGj4ZBelUonVK9bh9dtQ8HhAtSpF0DWfdHwk69CXCvKriYmJxohhUxERVwp8kT2OntqCurWsMXio/nKslAR8+oRZM1cgLNICgAYOtnGYOWs0rG1s8fChP0QS3QENz8gLUyfNwNgJo+BayC3N62emvzp1/AQSWTU4uVkg+ONxcP8/p46PT9i+YbdO3ff+gRAb6S4L5jgO8fEZumW6yOVyDBk8DQp+S/D4YkQkMPy98AxGj9SgStWUlyafOX0Gp07eQqIccHE2wvCRA/VOiiaEkLxu/rwluPdQDk5cGppTT2FveRBLls1Kd4JMrVZjzqwFePmfDAqVKcyMQ9GteyPUb1Afe3YeAM+4jk59kaQATp7ehWKeHqhSrUaafVFm+iuVSoXXPnGwL9AEYZ8vIzriGXgcH0p5KCZP6qQz+zAk+DMSVdb4/t3KlZYIC/uCAgULZejeafl77mI8eOEMkcQbAHDszCcEBy/D2PEjUmzz8cMHrFm9BRGRGphKgS5dW6B8xYop1s/N6Ps8+V6Wnnp87do1tGjRAk5OTuA4DkeOHEmzzZUrV1C+fHmIxWIULlwYW7ZsycoQyQ9Qq9XYuPEEeNIWEBvZQ2zkCBi3xurV+5Deiaof/P2wfecTqIRNITGrAs6oIe48tsH2rTugUirBmH4uOzGRh+37YtGr90ycOnEawUGBuH3zGqK/OVFLrVZj/rwl6NJ5PDp1HI/hQybjc2BAut+bSpUUv4l5Ebh4/A5Ht7Zwcu8I10LuenWtLHhgTKNTJo9/jerVf35HsW/PaQikX5dq8/hixCpr4PCBgym2mTxhFm48KohoRX1EJtbHkTMcVixb89NjI3mfINRP+wfIe18qqM/K2xYvWIVYdXNITIpBKLKAyKQ2Ll6Nwjuft+m+xvRpSxGtbAaRaS2ITL0QJmuE6dMWAwA0Bro9jhPg2Ss1Ro7egxlT5yJBJsOdW9fx3u+dTr1TJ06jT+/x6NhxIvr2mYBTJ06nO6aoqGjwBFLwBcZwdu8IJ/f2cHJvjwKuJfRm+FeoWApyme5MYMbUsLb6+cuID+47AJnGC7z/70nMcRwEUi/s3XMqxTbHj57A+s1vEBpfH7Gq+njhVxHDh0yBRqNJsQ0h+RH1V3nb/bt3cOeRACLTGhCKzCA2KYkwmTdWrliX7musWfUPnr0tAp5xA0jMqkIhaIF//r2IyIhwKFUqcJz+535QMMPfS1+id4/h+BISjJfPn+LRg3tQq9XaOp8DAzBsyER06jgeXTqPx4K/l+q8nhqVSgW1Oin9YONUF87uHeDo1hbWjrVhbWOtU9elQCFIRcF615BKwjK9GiAlcXGxePwsWmdFgNCoAB4+DocshSdpUVGRGDt2Gd5/8UKsyhufo70xb8FxvHj+7KfGRkhOydJEYXx8PMqUKYPVq1enq76/vz+aNWuGunXr4smTJxg+fDj69u2Ls2fPZmWYecL2rTvRt88kdO0yESOHT8HHDx+y/J6+794gRqb/QR0Va4kvIfof7O98XuPBvdtQKBTasj17DoMvraVTT2RUEPcfvINLAVdYmX3RSToyxpAoC4K5VWlwRk3w97x1GDh4E+Yu9kG/fouxZlXSacRLFq7A/eeu0Iibgm/SDMGx3pgwfmG6BxulSxeEIjFIp0we/wL161fXqztwUA8g4ShUytikejJ/uDu/Ry2vjJ/InJb4BP0ykcQBvr6fDNYP+hwAn/diCEVflyaLjAri7r1PUKlUBtsQ8i1DycHkP3kN9VnZ4+mTJxgyaDK6dJmEv/6chJMnUk4c/UyBQYk6p90DgNikCo4ePqlXNzIiHLdvXkN4WKi27MN7P4RFO2oPoQIAHk+AzyESREVFongxOygVETrXCft8CbbO3hCZ1sDVmxHo2HEs5i56gxFjDmHooHFIkMnw7MljbNr6BDLWFAKTJojXNMHGrY/x/NnTdL2vVm1aAvK7OmVqtRwuzvrbgDRo1AgFbF9BLkv6jqBSxIJLOIpBQ3rr1f1R/u8DIZLY6ZUbmoWf7OSp2xBJvx4sxueLEZVQHhfOnfvp8RHyK6P+KnvI4uMxbcpcdO82ET17TMDCv5dmy/fns2evQSwto1MmEJrg/YcYvboqlQoP79/B2zevdMqfv/gMofi7z2BxbezZtQ8dOrSCKv6mzkvyxDAIRZYwMimOyMRq6NRxECZMu4Lpcx+iZ/cxePTgATQaDSaMX4iQ2AbgmzSDRtwU954VwJJFK9P1viQSCext1XoTLEzEPihfUXemuVgsRrOmZSCPvQXG1GBMDXnsdbRsUTHFPeAzKzQkGAqVtV65QmWJ0NAQg222bdkFtbChzncCvnE97Nh+5KfGRkhOydKlx02aNEGTJk3Srvh/69atg5ubGxYvTno6X7x4cdy4cQNLly5Fo0Z04mtKdu3YgyOnEyAyagSIgM9RGkwYvxibty6CSCTKsvtaWFiBz4vVK+fzE3ROQ7x0/jzmzFkLoXEFCETWMBEfQN8+TVC/YX0wDYPhdENS6aTJAzBr5mpExBRAYqIc8dG+sHVJOvQjNPACrJw7Qyx1RNLQrwguXHmAqlXv4dmLLxCKK3y9GsdHdEJJ3Ll1A9Vrpnx4SrIevbrjnc9svPb1h4YrAAF8UaOSGWrXqaNX19XNDev+mYrtW3chPDwWFSuWRNPmc7Jkaa+VJR8xX9Q6TwEVsteoWauyTr2oyAjw+HwEBnyCQm2L788tkyuNIJPF03Iukqq8OnMwJdRnZb2w0C+YM2cX+CbNAREQowQ2b7sPU1MpamfBw5VviYRAvFy3TKmIhp3918FBWOgX/NV3OKJl9jAyLQU+O48qFc0xfuIoMI0GMNhjcQBjGDxsAEImzcZbXz4SlZaIi/aB2MgBYiM7KOSRYIwHE9uO2lZB0fFYuGAF5HIVBN89MBNKq2PvnhMo/VuZ72+mx8raBp06lMOBQ6eRqC4BHiJgZ/kR4yZM1avL4/GwZPk8nDh2HI8f34KtrQW69ZiRJX1BtWrl8fD5W4ilRbVlTKOCzXezFxMTEhAbGwMbWzskJHDAd5NcBBJn+Lx9i4aNf3qIhPyyqL/KHuPHzkRwTF3w/3+w4d1n4Zg7ayGmzpiQpfe1MJdCrZJBINTdYkkk/Dp5QqPRYOqkqbh56wOkltXBWCycbDbi7/njYW1jC0OLuziOg1qjQSF3D3RoUwzHTpxGTIIzZHGfoVLGwMktaW/1sM+X4FJ4iDYJpkJJLF22C/36xiI6oRSMTL9+UAvFdnj2/GG639u48QMwZfISRCd4gkECqfAlBgxoZTD516XbHyhf/gUO7D8JjgM6dGyDYsVLpPte6eVSsBBMJCH4fl6kiVEonF0Kan/WaDQIC/0CcwtLRITH6v334TgOCQYmdRDyK8pVexTevn0b9evrHgrRqFEjDB8+PMU2crkccvnXb/4xMfpPWvK6q9deQGT09cRcjuMhgdXAscNH0L5Tx1Ra/hg7ewcUck7Ap/B47QelUhEFTw+Rdo+JyxcvYeq0DXAuPEC7/AgogfUbjqN6jWro+Hsr3Bu3F2LTr8k7ReJnlKtRCADg7lEYm7YswTuf1xgzegksi3TTJuBUylgYSXVP8xKZVMDxYxehUDLwvt+yj2eCyMiodL03Pp+PufOn4cN7f7x5/RrlyteHrZ19ivUtLCwxZNigFF9PD5VKBZVKlephMEOH9cGoUQuh5HtDKLJAYrwPCrt8Qs3afwJIWg4wa+ZKhIYbA9DA0S4REk4BwFM3XlMZwkNDcfbUGZQp+xuKehb/odhJ3pFVB5TkRdRnZdy2LbuB/+//k0worYRjRy9neaKwXr0y2HPoNYRGSZ+HjDGINFfQvsM8AEB8XBy6dxkEzqgebF1K/r9VEdx76osjhw6jdds2sDL9DNk3+zIxjQr2NjJYWFoBAObNn47QLyGYPGE6Is3agi9I6vciv9yBjVNdnXgEQin838fCzlai92CJ43hQKtO/3LZDp3Zo2qwRbt+6CXuH0ij9W9kU6/L5fLRq0xqt2qT78noYY0iQyWBkbJziQ7G63t44dXISfAP5kEg9oJRHQai+gMFDxwFIGnDNnb0Iz19GQ6k2hrlJNJgqAoynu++VSvYItWo3xNHDh2FmbobaXnXoxGVCMoj6q4wL+PQBn4ItIDH7ukueUGyNl69jkZiYmKnDG9Ora/c/cP3mXDBBC+3noVL2BE07fV3ZNG3KbFy5HoICRfppy2JUSsybsxKLls5EieL2uPkwHELx14dhmsRr+P2P4QCA37t0ROt2LbFr2yYcOmUPU8ukpzEqlQwisZXOTDkAiI53w8sXzwGe/uFcygxMsizo6oot25bh4f27iI+XoVqNLqlObCleshSmlCyV/hsYkJiYCIFAkOJMRKFQiFYtK2L3vqsQmdQAA6CKv4EOv1fRtjl+5Dj2H7yB+AQLiIWxsDSPQ2L8B0ikrtrrqFUyODka4+aNa/gSHIp6Dbxhbm7xQ7ETklNyVaIwODgY9va6yRh7e3vExMQgISEBRkZGem3mzZuHGTN+/kl9vxKFAnqTHIQiKwQFvU613auXL7B18wHExnOwsuBh4OBecHLO2J4Pc+ZNwvy/l8HXLxY8jkPp4pYYPXac9vWDBy+BL3L4Jkn4/5hRAefPnUOrNm3we3tPHDt+GrEJDhALw1G+lCl69RmtrctxHIoULY6qVYrh/stwiMQ2AACm0d8PgzEVRGIBnBwECIpSgeN9/ScuxlPUrac/wyI1roXc0rUB/Y9QKpWYN2cxXv0XDZWaD1trFUaP6QOPwkX06jo5u2D9htnYtX0PQkL+Q9VqZVG/YW/tl4ipU5YgTtMCwv8/6fsSJwfHdkIecxVCkxpgGgVY4lUIzGIwatwBQFgUuw+cQpFCuzF3/jQafOVz+W0G4Y+iPivjYuMStKfyfkuuSH1fW1l8PFauWIcPH2MgFgHNW9SGd33vVNt8r33H9lAoduPS5TNISGCwtRVgyOTB2o3hd+3cg9gEG7g4l9RpJ5F64Padq2jTri2mTB2EObPXIjzaDoAa9lYRmDp9pE59Wzt7/DmgN+bOvwS+STUASfvJatSJwHezD/h8oFIlT/gc/ACx8dfBhlzmjypVMjZrQmpigvoNs35m0KEDh3Hs+F3ExktgYpyIZk0rouPv7fXqcRyHBYtn49yZM7h/7zYcHK3Ructc7YPE1SvX4clrdwiN7SAGkAggMeEwBPLd0IibQCiygDzuMewsPmH23D1QcuXBNB+wacMIzJ47Eq6FCmX5eyUkr6D+KuMiI8Kh0ujPtlaqxEhMTEg1Ubh/735cv/4CGg3g6emA/gP7ZWi5rIWlFaZM7o5//92H8AgNpMZAk46V0bBx0md8eFgo7tz1h7VDHZ12PJ4QHwPkYIxhyLABCJ8yD298NVCozWEmCULXHl6wsf26HFkikaBHn79w+epIKFkZcBwPHMeHRpOoFxPHKVDLqx6uXN8D4OtefkyjgrNDxlIKHMehYuWqGWqTGe/9/bBg/r/4Ei6AgKeGZzFTTJw82mBiskOndqhQsQz27jkMDhx+/6MLCrl7AEjaU3/LjkcQmTaDyBRgAIKj/MCTn4QcDSCWFoE84TOMuCt4/pzD7cfm4PEtsGvfQnRoW85gH5lbfD9BgJBkuSpRmBkTJkzAyJFfv6THxMSgQIECORhR9nNyEOFdkBK8b6bQKWV30bJ1lxTbvPN5i6nTd0IgbQyO4xARpMaokYuw9p9psLCwTLHd94yMjTF95sQUX49P4ACmPytCrYrHrZtPcfjIPSQkMthaAb16FEOlqtX0lkAlyGTYuGErwiMSAdkBKOUWEAjM4GwfC7nsFcTGSYMplTIWQX6bIFC6Q8BTIDZ8A0TSWtBwpjDiPUf37l7pOiU5uy1asBzPfDwhNLaCEECknGHa1FXYun2JwcSdiYkp/hzQT6/c39cHYVHOMDL/2obHF0MtKIg501rhxInzkEjEsLYpgQPH1RCb/v9gFokN3gUGYee2nejeq3tWvU2SS337BYEShFkvv/dZXl6V8XTdfxBLv85iVipi4FHKKsU2jDEMHzYZkfJGEAiMgURg7fpHSJAlonnLZhm6f+euf6Bz1z8MvvY5MBw8nlDvJEfGGCLCgjHgr4mIiGIwMRaiSX1ztGjVzOCG6qdPnsSVK48gFQUhLuIVBGJHFHRUIjT0LESSztprhnw4ChPjaJw4+RjKWB9AVQ5M4AGexhe/FWdo0y7lvjWnPHr4ADv2vIXYtClEpoACwJ6D91Go0B1Urqo/6OPxeGjctCkaN22q99qz558hFOvOEuEbe6NetY8QicPxJeQt6tWrgaXLg8CXNtOuSFYydyxa+C9Wrp6bBe+QEJIsv/dXniVKw8xoNzTQ/ZyyNo9Pdaz0z5oNOH+NB6FR0sOsK3fCEfBpFv5emLGka6nffsOKVb8ZfC0w8CNUGhuoVfoJPZUqHhPHzcT7jwng8RiKehihazdvFC1WQm9c4e/ni62b98HC3AQfPqyG2NgVErEAFtIQqFSypD4XQGzkK8RH3saiRZFgqmAkRBwCE1UEx2JhKX2DsePHZ+i9ZQeNRoMpk5dDKWwLoUlSn/7SNwoL/l6GyVPHGmzj7lEYEyaN0Svfteug3hYhIiN32Nl6okXLArh/9w4KFymEO7dtEBhZD0aS5BRLI+w/eBYNG9XTrjzITWiCAElNrkoUOjg4ICREd8PQkJAQmJmZGXzSBSRtdCoWf78DW/4yeuwgjBg+A9FxZcAX2UGT+AgNvZ1SnQm3edM+CKSNtIMhjuNDJWyIrZt3YtiIwT8tNitzDn6cAAp5BETipA9IxhhiQ4/gnag9REZu4BkDYQkMGzYeRo1aukvPlEolBg+agGhFffAFxRAZlwBVog8qlDfD4KGTcfXKLVy+cgrxCSKEBj2Bc+Eh0PDFUAAQm/midBE/VK9RDbW9pmlnjeQ2L1+FQSj6uscgx3GIU5bF1SuXUc+7fiotdanUKrDvN3cCAPDgVKAgxowfBQCYNGEOxMbfdXYSRzx9fiUT0ZPvBQd/xrXD+2Hv6oZajZuDx8vSM6MyhZKDPwf1WRnnVbcurl+7h8cvboMvKQW1/CMcrX3Rf9DsFNtcv3oVYdElIDb5ZvmXcXmcOHE2w4nC1JT+rTBuP9AgIvgGrB2/fkaGfz6DeLEQJjaNwTMGZADOXnkKd/dXeonC5UtX49ptIURSL8TEv0R02G04OnxBh/bt4ODkiHVr9yEsUoiosDcwt20KIxN3yAEY2dSBkfoAOnQqhd/KdEZB10I/7X39TIcOnIXIpKZOmUhaEUeOXDCYKEyNxsAx0RxPAHAc+vRLOlzl8sULSFAXx7e9N8fxEBSigUajyZWfr78SuVyOU8fPQBYXj+ZtW8DczDTtRuSXRP1VxgmFQvTo3gAbNp2Eil8F0Cgg5u5h0LhuKbbRaDS4ftMHQqOvfZNQbI23/iJ8DvwEJ+efk2gtXNgTdrZCBATchYlFMe0yYaUiGrLYYPgGtwLfSAwNgDcfY7BzxxHMnlta5xpPHj/GnLkHwDf2hlIRjciYcEgSPqJFy9ro3GUkFi1YA78PcsjiopEoF8DBfSjkAPhmAC/6GNo2V6FY8XKoWKl/luzL/qNu3byOWEVpGIm+xiYQWeC/1+EZvpZGzQy+RwagfoOGqN8gaQuww0cfgxPrplc0goo4c+osfu9i+CFlTvtVxgGMMdy79wAvHj5D9Xq1UNyzaNqNyA/JVYnCatWq4dQp3dMPz58/j2rVquVQRL8GSytrbNqyDFcuXcLHD5/QuGlfODg6p9omLl6jt/eEQGiCsDD9w0l+RP+BXTFx4moEfzoHPl8E8PgQcp9QwLUolMKviUyO46Dg1cKhg4fQuevXmZCH9h9EpKwaREZm+OSzDfYFmkBs1AhB0SqMn7AV48a2Qs/e3XD92lUsW+2os8RZbOyBD5/eYEYT/ZkMuYlafwU1wEkQHxefoesULuIJS5P1SGTlvu6fxdSwt0nQ2R9DLOaDaXSXZTPGIBJmvKOIj4vDlcuXYG1thSrVauTKLwrZad+yhfiyaQfKfpEhQgjMKrUCA7fuTHV/y+wmCPX7Zb4U5HbUZ2Ucx3GYMn083vm8xbUrN1CyVBlUrpr6IOP1f28hkBTUK0/t9NzMaNWmNS5eGIf/3vER6LcPAoEJVIpPcCsoRgJf9wu+yLgMTp25hPqNvu4PHBcXi1t3giCSNkRY0FXw+UZwKdIHHMdh/bbXqFXZD/+s/xsJMhn+7DcdKpG7ti2fL0GMrBRcXQvk2iQhAKgNDJY4joNanfrScUOKFbHG/RdREIgstGUs4Ro6dhqh/dnKyhIc099GRSQ0PGhLDWMMd27dQEREJOrUraddAp1fvXj2Av+OnIlyPhEQMw6z1+9HvXF/oknL9B+QQX4d1F9lTsPGDVGtRlWcOHYCEokETZrNTXXJsTwxEQlyMb5f2arUOOC9v99PSxQaS6Vo3MATB45oEPBuF4QiC2hU0XC0j4PEvAL434yHhCIzvH0n1+4rm2zrlsMQmDRCoiwYYUFX4OTWHjy+CNfux+DJ4zlY++8C8Hg8TJ86D75B3x24ZdoU/u/vo2uPnj/l/WSFBFkCoHecI6AyNO5KQ9t2TfFg6nFIzGpoyxSJQShbw1WnnkgIfHdmGtSqaNjYOmX4nh/8/fDo0ROUL18Wrm7uaTfIw+RyOSb1GwXnuz4okMBweM0BHGxcGZMWTs/3Y8+slKWPYuPi4vDkyRM8efIEAODv748nT57g48ePAJKmtHfv/nWpY//+/eHn54exY8fi9evXWLNmDfbt24cRI0YYujz5Bo/HQ7369dGzT680k4QA4OxoArVKd5QlTwhCyRKFfmpcRYoWxfoN09GhXUl41bTFmJFeOHdhL/gC/U5WIDRDWFiETtnbtx8gNnZCTMRzWNpWgtgoaV8NjieAwKQhtm8/Bh6Ph9CQUHB8/c11E+W5/8OjgIsYGrVCp0zMPUL9Bg0ydB2O4zBh4p+QaI5CFv0ACTF3YcY/iSlTh+nU69K1HVSyazplyvg76NAx/YODmJhojBs1Dh06jsf67XH4e+lL9OoxAsHBQRmKOS/5HPgJXzZuR+1QOcw4Pgqp+Gjx+BN2Ts755RiCUD/tH0oSpoz6rOxTuEhR9O7XG1WqVU/zS15dby+oEp7plDHGYG31c7/C8Pl8rFg9H4P7V0a92i5o18oFp06tg0eRouDx9PczUip1fw74+B4JSgcwjQqKhC+wtKusfW9iqSdu3wtFdHQUJEZGSJTrx854Nvj0KeCnvqefrXbtcpDHv9UpU8j8UL166RRapGz4qMFwc3wARexVxEc/A09+Ej2714C1zde+/Ley5WFj7geN5utO+YrEUJQpbZfuwYFarcbe3TvRtHFn/L3kFdZvi0PvPrNw4uiJDMecl2ydtRxN3sbAEUJYcQJ4ByTiwrJNUCgUaTcmOY76q+xjamqGP7p0Rpt2bdM8wERiZAQL0+9TRYCx0B+ly5T7qXH16tsdC/7ujMYNPFC3tjk2bRyJqdPGQqPRfwii1oggl+suU475/9yQ8ODrcHbvAB4/qZ8TiswQraiF/Xv3QygUQqHgwHG6K5Z4PAFksgycYJIDannVgRH/uU6ZRqNEAeeMz5ItXrIU2rQoCCSeQXz0cyjjLqFMMT/07N1Dp16lCq5QJgZqf2ZMDVPRY9T1Tv+eyq9fvUSndl0xZMRObN+vxsix+zBt8mwwQ8dY5xMbVqxHpStvUDyRBxOOj/KxgOXR2zh75kJOh5anZemMwgcPHqBu3a+n/CXvc9GjRw9s2bIFQUFB2g4NANzc3HDy5EmMGDECy5cvh4uLCzZs2IBGjbJ+c+78pv+gPhgyaAriEmtDJLGDXPYR9haP0K7j/J9+LwsLSwwdrnsicEEXY/z3Ua7zxEsVfwtt2/XSqVe8hDuevA6ELNYPDq4t9a4dHZP0oVmvfj3sObASwNcPYsYY7G1ydllSgkyG3bv2IuDTF5QpWwzNW7bQ2x9k4qRhGD92LoIjHKFmpjATv0OfPo0ytVS6mKcnNm9dDN93byAQCFHIzUOvjkfhIhg8oA727DmDqGgGM1MOrXvUQvkKFdN1j3NnzuGff04jNEIFZ/evp2onaopgwbzVWLI85SWEedmVfbtRMTQR+GamroDjoH7nnyPx0PLijKM+K3cqUrQYKpQBHjx9BrFJaahVceApLqL/OP29Wn8Un89H2/bt0PabfccbNqyFJ0sfQ2zyda8opTwKRX6z0WlboKAbjEV7oVQWgEii+xoAJCgd8fG9H0qXKQ87Ww7hCbqz4kR4hZq19fdGyi6MMVy9fBnXrt2DlaUpuvborLcPV+NmTfHs+VI8eHQJck1BiHifULGsFC1b90jhqikTiURYsGgmvoQEIzzsCwoXLQ6hUKhTh+M4zF8wAX/PW4WAQDmEAg7ly9hh5Jjh6bpHZEQ4RgyfAZ93UShQ9M9vBrsu2L7zJOp618mXMwvj4uMh9A/WS7YW9A3Hw6fPUa1ShRyKjKQX9Ve5E8dx6NatCVatOQuecR3weALIY++heSMPmJqa/fT7lSz1G0qW+to3qdVqmBlvhxpltGWMMViZx+rtkWduBsiiAR7H10sEiiR28H13GwDg6ekMv8th2sMkgaTJJaVK6s6my26fPn7Arp0HoFKq0LZdMxT/7mRkiUSCv/5sin/XH0OcvCh4XDzsLAMwcdKETN2ve88u6NgpAX6+b+Ho5AJLK2u9OgOH/AUNW4sHD59CoeDgaC/A6DGj03VYJGMMs6b/jcvX3sHUuhGklsl/v/Z47vMBhw4cRLsOufdQlKz05fkbeHC6aSsPBR+Pzl9H4yYZm1hD0o9jeSw9HRMTA3Nzc7z0i8qSD+S8RC6XY//e/fD3C8RvZQwnsbJKTEw0Ro2YjrDoIuAEtuCrn6NJIw/07qs72FAqlRjYfww+fLaHUGwBM0vd0yjtTM5h+cqkpyxjRozBnfsB4PFtYGJeGOZSf8yePdjg6cHZISI8DEOHzEQCqwuRxBpy2Qc4Wz/D0hXzDJ589vbNK0RGRKBchcoGT+MCkg6hOXLoBKRSY/zRtVOGDp75GVQqFXr2mIjQSCsYmxSCRKo7lZ7JTmHP3r+zNabc4tyB3VAPnQpnpvs7dK6iG0YfP51tceSFjYljYmNh5VkW0dHRMDPL25/j1Gel351bN3Hh/A1YWJqha/ffs/Xzb+H8Zbh7Pw4agSeYKgCujsFYsHim3mf1mlX/4vxVBcK/vIGzewed19Sy89iwfjTMzMxx9vQpzJ2zDhrOCWJjR5hJNWjTqii69Uj5ELKsNnXSbDx/awuJSXGolHHgKS5g9uz+KFJUfx+g8LBQvHn9CkWLFdc5QfNbsbEx2LNrHyLCo9CiVSOUKJnxWYc/atKEWXj3uTqCPxyDk1s7ndcS4gLwZw9TNG3eItvjSpZTs7yVSiUmev8O7w+6K0semjD8fvIfeBTK2QTArya/9FnUX6VfcFAgdu3Yj4REBdq2baqXxMpKN65dx6o1RyHXlAOghDH/CSZP6YfiJXTHUC+eP8eMmTsRHBIDZ/cOOslCeUII2jWVo0v3roiICEe3P/oiRmYFvtACZmZOcHMJweJlczN0kvPPdOb0WazfeBMCqRc4jgdF3F20aOKEXn30D2VUKpV48ug+zMzNUcyzpIGrJe0tee7MGTx5/AolShZBsxbNs21MnOz61atYsuoVIkJf6/VXAOBqew1/L5j8U+/5q6w0mvbXaFQ781KnTM40eNfbG6OmGz6YhhiWkf4qV+1RSLKXWCxG1+5dc+TeZmbm+HfDEjy8fxefPgaiTr1hBp/MCIVCrFz9N7Zt2Y79e09BLrGG2ChpaZdadgE9Byd9kM6dtRB+n4ujQNGWYEyD2LAL+OP3+jmWJASANas2QiloAdH/Z02KjV0RGMHH0cNHDD4RKlqsRKrX27h+M06eCYLIpCrU6kRcvjoXY8Z0RKXKlbIkfkPevn6JGFlBCISAUhkDCXQThcJM7HOYV9Rt1R5z161DyxfB4P9/loa/BPBo/vMOW0jJt7MHgV87SUhISqpWr4Gq1WukXTELjBk3HJ8DP+HWzTsoXqKezgyObw0c/CdKlLyMdasfIDr0BsxskvZu/R97Zx1fZfUG8O97u9bJgtHd3aB0I9IgooA/ERVFkW4QVBpEQFpaWrob6e6Oddfdduv9/THcuGwjVky538+HP3Z24nkvd+9zznOeSIq/Qb0aHtjbO3Dk8BEWLDpFviLfIghS9HH3KOB1860aCa9cusiVW1pUdsnVqGVyHaKsLfPmrmDqjHFp+ru4ulGrTv007f9w88YNRo9egFn+PjJ5Ef4etYsGtQ8zYGD2FUt7HQKDkpBI5CBa0vltHE5O704F1+eRy+W41qlE2OPDuD673EoULURXKmIzEtqwkQ145vN+bc/n7KZOvbpUqVaVA/v2olQoqf9+j3QNemXKlmX6tC+YPXMeZ84txdW3B1KpEqMhCifVMTp2+YX4uDi++XoMatfe2CvsMRnjMcasZ8y4n9+akVAURdasOYDCLnV/rbSrxe49u+jcJR6NVmvVXy6XU7V6rQznMxqNfPPVUAIjyqDUVuP05Sds3TqQ2XMm5WohzAMHTqLU1oSQ64iidcSBKFpQKN7d4l1NP+rAwVM3qRyZmmDyqI+ab/+X1jBsI/t4d79xNt46giBQpVoNPujwYbpGwn9QqVR89nlfdu79k/dqBCHEr8Bdt50pP/ehYuXKBAY85cJlA0pt0WfzSrB3a8KWrcdz61HSJSQ0yaq4CoBS7cPVK/feeK6oqEh27b2P0r5Ocn5GuQ6prjWLFm7MLnFfC2cXV+TSaOycyhAZfBJRTH1hGxKDqVDeM1flyUvI5XI+W/oHB5tXYU9RV/ZU9MM0qB8f/C9nD8bPexD+88+GDRvZj5e3Lx06dczQSPgPDd57jzXrlzJ0UG2UptUojKvo09Obb54ZyVav2oXCrmGK94ZGV5iH/q7cvnkjx58hIw7sP4JCW96qTRAEwiLTM7C9mrm/rkDQtEOucEAQJKjsq3PkRCSBAbmbg/Gfgq0SqZKkhJCUdtFiwkF1jRq16mQw8r/P9+OHENanCQdLu3CwuBNXPqzGyN+yP/2MDRs2ch+VSkWLVq1p2KTJSw16Pr75+WnKj6xePRlvh90I+pVUL3uLOXMnoVAoWLJ4OXqxKXJFsueRTK5F4dCJBfOX5tKTpCU6KpLY+LQpI+IN+blz+/obz7dm5WoCo6qj1CYXDFFqfIlKasSihUuzKuob4eSow2xOwMG1EuGBh6x+Z4w/TqfOrXJVnrxErTo1qDTuSw5X8+VQEXuO1C1E15mj8M737p47cwObR+E7TGxsDJs2bEK0iHzQ4QPs7R3etkgvZfaM3zj2dzTIm/Mk2J+ff5rL1OnjuXjhIiZJIeQv9I+OEbBYLEgkb8cerlGLiHHWN0JGQzSenm8eLnfm1CkMYvE0f7BhEcn5SHLLPd4znzd+3nqehOvx8GuN/711CBIpSrmeZk3K8c3Ar3JFjrxKPp/8DFyyMsfXsXkQ2njXsFgs7Nu7h7u379Oo8XsUK1HybYv0Uk6eOMncuVtJpB6iaOGPFQfwzJePSpUrExUDvJAPX1AU48yZ82/tuUqWKsbhvx+h1FrntdVl0pkiLFxEUL3wXlKU48C+Q3TPxUiG99+rwNqNN/DI35Lgx9sxm5MQxHgqlPNg6PDB73S1RKlUyjcjvn/bYtiw8Z/kzu1b7N29nyJFCtKoadO3dhZ5HeLiYhk14hdCoosikXly6txlNIv+oP9X/8PfPxK5wjpsWipTExKqz2C2nEdnZ49SoefF3GlKaQhe3vnfeL4bN5+iVFtfGskVDjx4eDHzQmaCjz7uyrETP6Kxa4PREIX/vbUgWPB0k9C3dxvKlH35JeV/neZtmtG8TbO3LcY7Rd59a9nIUY4cPkLv3hPYsseRv/Y507vPjxzcf+Bti5UhF86f48jfJhR29VGoXFDZlSMk7n1mzZhL2XJlkYoP04yxtxPfqmL++JOOmOP3plSpslhMKC376N6z6xvPVaBgASRicJp2tcqS6zk0fpw8grJFLmGvOEeRws60aVmEnbsW8N0P3+S6LO8Sz1cuBpsHoY13h9jYGPp++i3zFgVy6ExRhozYxsTxP79tsTLEYrHw228bQNMWlcYHtTY/qNsxZ/YaRFHEwS7tGNF4j0qVyqf9RS7RqEkTnLWXMJtSD3/G+FO0a5dxePHLUKvTtlmMARQuUjCzImaKjp070KGtE/byffh6K6layYkVK8Ywc84kPPN5vXoCGzZs2HhDfpo0jcHDNnPoTFF+WxJM70++JTo66m2LlSHTp/xKZFJTVLrSKFQuyHXvse9QBHdu38LDwx6TMc6qv8WchIvzy6s/5yQymYzaNf0wJKRGaBkSgylVXIKbu8cbz2dvp8Bstq5ULYpmtJrcPUM6ObswZswneNjtw9UxnFIlHRkyqA3r1v9Ks+bZX3ToRacDGzZexOZR+A4iiiKLFv2FVNuaFLOOsiVLlv5F/fca5Mlbr53b96PUVbdqk8l1PHwUg4+vH2WKW7hy5zFKTX5EUcQYf4rOXWu+JWmTKVmqFGNGd2b5so3ExYObi5x+/Yei06WeEq9evsTmTbtQqRV079GRfF4+mEwmzp7+G41GQ9nyFREEgaLFSpA/31ICImKRKZLHGxPv8179tEnmcxqVWs2oMZmrGJbXEEURg8GAQqHI054l/4UCJTZsZJaZ034j1tISpTb5YCK1q87Zyxc5e/oUVapVf8Xo3Ofhg7vExHuhfsFJPzLWmeCgAD788D3mLTyKXFsHQRAwJARSJH9kria7fxGJRMLM2ROYPWseT57GoVLCB582ok7dVC+LqMgI/li+mqjIOBo1rk3N2sm/u3P7JkGBgVSpWj0ln1PjhhVYv/U6ck1y7l2zSY+bw22q1/w815+tS7dOdOnWKdfXzQmMRiNSqTRP7tNs2LABly5e4O+zoLJPPoNItYXQm72ZPnUuY8YNe8vSpc/jp3FIZda3O0pddTZv2s7/+vXmzP9GYhRaIpWpMZuTkCZtp+9nb/cc0P+rz3FzW8ex4/sRLVC2ug99Pkv9fE0mE1s3b+Ha1bsUKOhFpy6dUCqVREaEc/niRYoUK4q3T7L34Se9u3Ph62mImlYIgoAoipj1+/i4V69cf65Spcswc3bO7wVy41xhsVgwm83I5S/G/Nn4t2AzFL6DPH3yiOh4N9QvFLqJ0efj0YN7FCz89gqAZIS9vQ6zSY9M/kKC2mcFJ8eMH8661Ws5e+4wMhm0b9+EqtWTD5DxcXHM+3UOFouFPv/730vzIWY3pcuU5adf0q/0uGDeInbuDUdpVx2LxcjfX8+lcUM/jhy9TVxScRANOOmWM37CQHzz5+enX8Ywa8Zc7t2PQi6D2o1K0bV7lzeS59rVy2zasBO5Qka37h3wzZ+atDwqKpKli1YQFh5HwYIedP+oGyrV27sxzGn2r1/DuYWLiHnyhDidmgZ9+tKu7xdvW6wUbOHFNmwk8zRAj1Rq/S5Sasuzd8/hPGkotLNzQCqJT9MukySi0epo3LQxHh6urF2zjSSDSPn6Bej20Rgg+fJi84Y/OXfuPB06dqBCpSq5JrdWp2PIsPRDUa9fu8rYMUuxKBsilWk4P/0iFXYfJiQ0Gv8QDyw4oZHvoVOHarTv8AFdundCpd7M/v17STKAX0Ed3wwc/0YXMlGREaxYvpqIyFjq1atG/ffeSxlvsVjYuH4DFy/eQauR07NXF7x9/ruFSZ489efXYZOIv/GAMIuR/DXKM376xDRVt23YsPF22bljH0o76yKDEqmSgICEtyTRq5HLJWC2bjMZ43BxdsTe3oFffxvL7/OWEhwSh4uzij6fDU3x3Lt5/Sqr/lhByTKl6di5a64WOOnUpROd0jkGGY1Gvuo/mJCYKijVNbl4K4R9+wZRpowfp8/GkGQpiFz4m5JFzYybOAIPz3xMmNCX3xesJCpaxE4n8MnAzm9UENNsNrN182auXL6Lr687Xbp1tiqEcvXKZdav247ZLNKkaW3q1s+ct352klPnCrPZzLQxPxN05BzBUZHIfN0ZOnUcxYvnPfuCjZcjiP/ERf5HiImJwcHBgWv3o7Cze3nJ53eVmJhoeveeikzXyKrdFHeA3xd8jaOT81uSLGOiIiP47LMfkerapLQZ9Ff5pIcfrVqnrSprMpk4unMrB3buYN/ZYBzc6mOxGImPPE2/fu3p8fFHuSl+GqKjo+jTZwoyXROrdv87c/EummqsEkULzqqdzJ2X9QTjixYuZfuuYBS66oiiGXP8Yfr0rk2z5k0JCgrk2wE/YVI0QybTYEgMx1l9mF/n/fyfPIjcvHyRPV26ExQeQmGUuAhyzgl6vPt+wufjJr9t8d55D8KY2FicS1QgOjoae/v/9nvcprNezYCvRxMS29CqzWiIomm9EPr+r89bkurlfDtgGE8j6iGTJR8UTIZYCnufYfLPY9Ltf/PKRU7t2snCtdtRONVHqfYkNuIShQuILPlj4Vv3eP76yxGExDW2kiMq+ABylR9ah9S8hsbYPcyfNwAXV7csrXfr5k1GjVqARdEYmVxLYtwNyhYPZdyEEQAMGjiCe/4lUWp8sZgNWBL2MWJ4F8pXqJCldXMbWej9V77nRVFkQLuPkZ67g0QQKCqoeGxJ4nZBZ9Yd3mLzLswDvCs6y6avXs3SxUv5a68DCpW1U4KrZh+zf01bQT4vsH7detZsiECuKQEkv3PQb2bR4vFodWmLhkRFRnBwwxpWrttMSGIBdI7lSIh/jGi4wbr1v+Pu8XYLTKxYvoKNO5Qo1alhyGaTnsAHK/Ep2jelzZAYSMuGej7t80mW1jOZTHz95WCCIiuh1PhiSAxHIznI7DljcHRyZuP6TaxccxO5riYgYIi/wnt1pHw1oF+W1s0Kr6N7Msv08VOI/30rD8UkygkaEkUzR1VGZu5ZTaGCBXJkTRuvz5voK9vu4h3E3t6B4kXkmAxRKW0mQxRFCvJGRkKz2cyr7MxBQQHcv3f7lf1ehaOTM8OH98BFvRsSdqGV7KZTO7d0jYSBTx8zsXkj4j8fTJONh2kcdA9NfCAunrXxLfE18+dvxGAwZEmerCCKIvt27yDJUiTN7wR5PizP5ckQBAkhYQr08Wm9U96E+Lg49u69i9KuJoIgQSKRI7drxNq1BxBFkXlzl2BRtUk51CpULoTra/HnmnVZWjevcmDxAqLCQmknOFFVoqOQoKQjToQsW01EeNhbkSm9HIQ2bNiAli1qY9RfSPlZFEXkpgN07d75tecQRRGz2fzSPvFxcdy6eY0EfdaTtE/8cTgl859FatiJzLiLskUuM2bckDT9LBYL0z7/lFPtu1Fs6mI+DgjCKfgs9k6l8SnakwdPNGzeuD7L8mSFyIhwgkIS0xgrdc7ViI+9Z9UmqGqyeeOWLK85/7eVCJq2KVEEKl1Jrt7UcvXKJc6dOc29J54oNckehBKpApmuBYsXvd3PKac4feY8sssP8BAUNJY4UEBQUk9qT8tHCSybv+xti2fDho3n6NylEwrLIUQxtWK8UX+Z5s1rvNE8JpPppb+3WCzcvXOT0JC0+cvflA6dOtC2hRYNuyBxF+7aPYwZ+1m6RsLdK5exuFEjXEZMo9flexQJOItK6Yhn/lY4enal/+cDsyxPVkhKSuLMmWtWRkIAqUyDRGZtvFWo8nHp8qMsr7lpw0aCIiun6CSFygWDtDW/zlmIxWJhy9ZTKOxqIwgSBEFAqSvH0RMBxMREZ3ntvIj/8fM8FJPoIHGmmKCinERLvyQHJnz+w9sWzcYbYgs9fkcZPW4IM6bO4catMEQRipdyYuCg18udcf/eXaZOWURwKCjkIuXLuvH9YOtCFjEx0QwfOgn/IDvMogonXSADvulGxUqVXlvGQ/sPsGPHMcwWqFypMF17dGPO3AqvHLdy+GDaXAlEKkhBgA5GC+YHO7nl3QC5wgGpqjDnz56kRq3cd/u+dfMmP03+ncBQBUajDK19Iavfm43RCBJrDz6JxIw0i678N29cJc6QH7sXKljGxGmJiYkmItKERGK9hlLtyZ27J7O0bl7FkmRAIgioBOu7kkZJCvasWkaXr77LFTls4cU2bLyaJs2aYDAY2b59N/oECa7OAv1HfmmV7zUjjEYjkyZO5cbNKExmgXweAkOGfoGXt49Vv5nTf+XE34EkGt3RKIJ5r34hPuvX+7Vl9H/6hIULVhAVY8LdTUW/L3ozbuKIV47buXo5JbYdw8csAUFKNRH8Qu4x5+EWNIU+xN23BWtW/cEHH3Z8bVmyC4PBwOgRP3LnAQQFhOL7Qkpcfdx9VBrrgiAWcwI6u7SHyzclPFJEkFq/D+XachzcfxSLaEGuKZ1mTHTMf/P9GRsXR6gpkcaC9SE3nyDnxMnz0C9r3jA2bNjIPtQaDZMmD2D2rKWEhVvQqKFZm6q0aNXitcavX7eR7TvOEBsnwcHOQvsP69LyhbHHjx5j/oLNRMd7I5PE4eedyMRJw61CXV+GwWBgycKl3LodhEIBnbq05KOPu/PRxy8fFx8Xx5VZc2gSoAdBhiMwIC6en68twlJ9LGqdD0FvsT7GiuWr2LHrCo8fPyVfQX2K8wMkFycRLWmNr7JsqMF47doDlBrrvPgSqZLg4ARioqOI02tRvLBd0Rt8uXfnJhUr5730KVklJDqaioJ1zkuJIGDnH0m8Xo/2Nb+nNt4+NkPhv4T4uDjCw0Px8vbNlvwPcrmcQUO+feNxJpOJkSNmYVZ+iNxOQATOXA1hxrQ5fDdoQEq/CeOmERzbEKW9EoAkYOqUFSxdXu615F/5x2o2/hWKQtsAgAfbn3L7zqTXSwR89yHSFzwfGiYlcSXwOHK/FiAacXPPfbd4URT5ceJ8kqTtcHQXeHp3NUkJYSjVrgAYEm5jp4kB0QxC8mdkMsZRtKASpVKZpbV98xdAKd0LlLBqVyn1aLU67HQCIbFmBCFVYxqSIvD1yVr4WF6lTMuW7Nm0NU17vGBB4+CYKzLYPAdt/FexWCw8ffIIZxfX1zLmvQ6t2rSkVZu0HuSvYvLEaVy5UwqZ2hEZEBJnZtjQKSxZNj3FQ+6vrX9x5G8lCk0T/tm+7jpwkVJljlKnbt1XrvH0yWO+GzgLNC2QSGT4RyTy5RdjmPf7xFc+/4MjR6hntr6w8BBkOETewgiIohGN5u3kip0+ZTZ3/Csh1zmiczxNZMgpnNyTDzUmYxwkHEHp1D6lvyiKyC3HadvuxyyvrdWCIdG6zZDwiFKli6NQKDh08hYqrbU+s9P9pzLppFC3dg1m2qswx6bdtAsKW5J4GzaySmhIMBaLBQ/PfNkyX4GChZg6/c3DjE+eOMHqPx+g0DVHbgd6YPHSoxQsmJ9Sz4pdJSYkMHvOZgRNGzTPCmY9idAz+cfpjJ0w/JVriKLI99+OICCyJnJlieSzyaRd9PtfFA0bNXzp2NOH91HyUTgIqQ4NgiDgpQ/hiSgiCAISydt5D1+9fImNW5+gtGuOp18MgQ824lOkK4IgRRRFkqJ3YGdn/QY1xF+lSac38/RMDw93R67ei0auSK1gJooWdFoBO3sHNCo9L5oo1bIA/Aq057+IXRFf9E+vwQvHG7mdFpk0GyyzNnINW+hxHkcURSb/OJVPe//E1wM38kmvEdkS1pMZ4uJimTBmDHGmSlYhSHKlO5evBKX8LIoiDx8nIpVaG7fiDKU5efzYK9cRRZHdey6i0FZIaVOqfbhyw0JwUOCrx2vT3lSECgKC2g2zOQkZjylcpPgr58lurl+9RFR8oZTPzrtQR6JCT+N/Zz6u6j306ODM0uVz8LTfjzFuJ5b4HRTzPceoMVl31Xb38KRUcQFDYmqIgiHhLnVqFUYmk9H3s+5Y9DtSbtvMJj1ayQG69XizYin/Fuq3bEdihVIEiMaUNlEUOVXUgyaduufYurbwYhv/dXbt3E2vj4cyYOBGPv30F8aNnvTKkN+cwGKxcPDAfk6eeoBM4ZjSLghSovTFOX/2dErb0cMXUGis3eUU2vLs3HH4tdZa+PtKhGdGQgCpVEWipAnLl6585VipTov5hdQcFlEkUZp8EAt5tJGxE0a+lhzZzc3bEciffXZO7tWQSJU8vbuKpMgVVC1zk3UbFlO7UhBC0nYMsbtxVOxkxIhPUanVL5/4NWjXth7G+FMpP5tMelx1l3mvYUPq1KuHl/NtDEmRQPK72xR/mC5dmmV53byIUqmk67Cv2CWJtWq/phGp9cF/85lt2MgNQoKD+eLzwXz+xUL6fbmE//UZxNMnT96KLA/u32XG9IXItdYFrOS62qxd81fKz7t27iJJtO4jk2m49yDutdY5e/oUT0P8kCuTPZQFQUCuq8uG9QdeOdYlnxfR6rSXEwlSBYIgEBt1i6qV/dIZmfNs2LADhS75IkuusMfdpymBDzYS/mQxfm6HmDypN9983RINOzDE7kFu2k67lg40bZ71d2iPj7uiMO/F8uwMJYoi5vi9fNyrI1KplEYNS2OIv5zS36B/SKXyOpxdXLO8dl5kwuyf+NtOtNrbxItmdFVLZtnxxUbuYvMozOMsWbSMs1e8kGuqogZMwB+rDlCpcnny+xXINTl279rDosX7CQ0DJ/e0YUXGF65KJOkkXhcxI5O92ja9b+8eAoOScLWODMNo8eHOnVuvvPHzbtKIp7eW4mNIlsEkimxUCcQkPEIdfIrVa+e9UoaskqDXo1SprJKMJxsIU1+agkSGR/4WmOJ288vU4SmHqxmzJ2I0GpFIJFbh3Fll7PhhLPp9MZevXEMigXotyvBhxw7As9vPKV+yaOEqYmLN5PPU8L9+4187jOHfhiAITNlxgN+GDOTvvQfQmcFcxI8Oo8Zke6VnW3ixjXeFsNAQFi46jNyuJapn+ZEv3wnht1/n8+XXuVdRPDwslO+/m0BkfBni9HIcXvi9iJqYmOeMLhmopdcpIBIY4M+ZMzex96xt1S5X2BMYGPXK8S2++IrNB47xXlBqRcxt0iRCNC5IHvzGF1+0Jr9fwVfOkxX+ydn7qsJVDi4VUOvy07ZJND17JceofffDN1gsFkwmU7YWvmrSrAkajZrNmw+QmCiS39ee/l9NSNGp02ZOZPHCpdy9exmlErp1a0OZcuWzbf28RrfunXDU6dg8YxHaSD2CpxMVP2xG05ZNXj3Yhg0b6TJ29DQiEpqieOZpFm2yMHbMDH5fNDXXZBBFkfFjf+LiVYHQYCU+Raz1jiBIMBpTL9ukUglg4UVep96VyWRi/m+LkCrTerLFxr16grIVq7K1QhGKn7yD/FnqHn/RyD21B0kPl1OimIbJU2a9WpAsYLFYSEpMTHM+eVFfK9VueBXqiKfDQX76ZTQAxUuUoFGTRhgMBhQKRbYVCbOzs2f6jGHM/XUx4eFGdDqBj3t1o3jJkgB8/EkPfHz3sXvnISwiVG9anA6dcyfF0dvAydGB3w9uZEL/IUjuBSDXqnGoUorBk97OpaeNzGMzFOZxLlx4gFxl7Qou09ZlzeoN/DAk+18y4WGhLPz9DyIiEvD1ceST3h8jVyhYtmwfUm0rXBXxhDzdjVfBVCVjMRso4J16QyAIAgULqLkXoEf6LD+EKIrYKa5TrcbLcz6tX7eR1X8+IjryCUmGdUgEGRaLAVev91BKH1C6dOtXPkPX74awRiLh2p59SBISkRQrxI9DhuPhkS/bwuAy4vix4yxd8hcR0XJUCgM1axSg/1efIwgCJUuXw0m3lESxfIpyspgN+HiRxgNDLs/+cCKJRPLSCqG++f0YM25otq+bV5FIJPT/eUaOHHL/weY5aONdYs2qP5GorXO/ypXuXLl6KUfWM5vNrFm5mstXHqJSSfioZweKFC3GlF/moqcNajs5YvA5RIsJ4bkcrBrZNWrV6Zryc+NGNZi36BoKbWreO0PcWdq2a/TS9UOCg/j226lEREmJiVuFVKrCIprQ2hVCa1+QggU9XjoewK9wMWpPn8KeX+cgBIUiujtTp9cn9KtZB2cX1xytdhwdHcW4MVN5/DT5EJrfR8rI0QNxdHQCoFQJV05diUSucEoZIzUdp32HUVbzSCSSHHl/1qlXlzr10g/9ViqV9Ov/v2xfMy/Tom0Lmrdpnu2HXBs23kUiwsMICtWgsE/VDYIgISzKnSePH+Kbv0C2r3nu7Bk2btiN2Qx161agRauW7N+3lwvXnVHZlUAVZyA+9hFau1SvvKS4W9TrWDnl5ybNmrFq1RAg1ZvCaIihVNEXr8TSMnjQaPzDShIV+gcKVbI3m0SqwMO3BY6vHo4gCHy5ZAXLhg7CeOM2KOS4v1ePZZ/+D0cn5xzRA/8giiJz5yzgxMkHJBrkODsY6dWrFbXr1gGgY8dWjBj9Fwq71Es7g/4etZuXSvMMOeHV5uHpydjxGafHatioEQ0bvXxP8V/C08OdOesX54jji43cw2YozONksVjwGxEUGMA3A37GomqBVKrifkAsZ84O43+ftSc2sRA6FRiTItHHPuTp3VU4ulXHaAhDJ7/G0JnTrOYaOep7Ro2YxMMnUiwocLaP4rsRfV/5otix8wz6BDec3Kvh7JGcGFYULTy+9Tvt2lTCydnlpeMhWQl0HTgYBg7O/IeRCSLCw5gxcysyXUsUdsn3fQdPPMTRYRU9Pu6OIAiMGt2fSRPnER7tjCAY8fZIZPRYWxWot0l2H3Kf9yC0GQhtvEtYMlBYOaXHfvhuJA+CyqFU10MUzQwZtoLvvm1OQKABiVSOxWLCZIzjwfXfcPasg0yuIzH6GEOH9rA6KDRu2oQHDxZx6MhOEpKc0KgiaNeqLFWrvTzJ+MLf/8AkbYBEshnvwl0QnnlYRASfxBS7lm49lr7Wc1Rp0JAqDV6eGyonGDX8J4Ji3kemS/4snoYbGDnsJ2bPnQzAN999ydjRk7l114jJrMNeG0Lv/m1y/MLNRsbk1CHXho13DVEUM3DDy5l9W7IjxF3k2roIgsDtP25z9ep09PokVNpk45ZosRD4YAMOzmXROhYjPvIidWo50bR56iW/Uqlk0KDuzJ6zhqhYZ2SSRIoWlPDD0Jdf9N+4dpUHT1yIj7mHV6HOKFTJF0BJieE8vTOb72a8XkFLR0cnBvy2MJOfQuZZtWI1B4+rkKtboFBCnAVmzNxGiZLFcXF1o2TpMnRsf4Nt23cQn+iKUh5J3er5+LBjr1yX1UYqOeH4YiP3sBkK8zgVK/ix42AgClVquK0p/hidu/TM9rXmz1uGqG6NVJL8Ry1T2BGX9D7Hj51CgoXgxzvQxz5ArfNFrnDCaIhGoXRFoXBHo7UOR9ZotUyZPoHo6CgSExJeK0GwKIrExonExz7Au1BqhUdBkJCvQAc8PWOy94FJTn57+fJVatWqQYFChbM016qV60D1nlWbQl2Ak3/vo8ezSmKFChfh98VTCAoKQCFX/GvyU0RFRTJ/7mKCguOxt5PxaZ9u+BUo8LbFynPIQu/bjIM23lm6duvIkWOzkdqlhkMaDWGUKZ/9xaPOnzvDfX9PVLrkqruCIEWua8TKFdtQKCA05CIRQceRq1yQCHIEQYLZGIfWsQoWMW3OxM8+783HnyQRHhaCm7vna21uo6IMRIaeJl+BdilGQgBnj5q4amKzPY1BRHgYe3bvxdvbizr16mXJoyw8LJSnQRoUdqlGJ4lUQUCIjtCQYNzcPZDL5Uz4cSSxsTFER0Xi7ZP/X+HFJooia1ev5fTpW0gl0KRpTRo3tYXo2rBhIxUXVzc8XOOJSEwt5CeKFlwdgrPdm1AURf7adgqFLrUgl1JbjDPnD1C2hISEeH/C/A9gsSSh1nojUzhhSAjFzrUGSUnX07x3K1WpwuIllQkODkwuSGhn/0oZLl++igkXZIrQFCMhgFLlgrtnSUqUzN7c7SaTif1796CPT6BJs6ZodWnTVr0JJ07cRK5+wSNP3YBVK9by1TdfAtClW0c+7NiOoEB/3Nw8siVfbm5w+eJFVq3aSpIBihR2o89nn9ouhGzkCWzFTPI4n/b9hMqlHmPR7yMh5hxSww66dS6LX4Hsz1kUGWVEIrE+HCmUzsTEWYiPPImjWzUKlu6Pd6GO6BwKY0gMRmtfmNikIlw8fybdOR0cHF+7ipggCDg5CJiMcUSHX8Jk0qf8Tq50IjAwNPMP9wJGo5GBA4YyctxBNuzQMvCH9YwfMzn5hjGTJOgTkEjSeqaZ06YSwdPT619jJNTHx/NV/9Gcu1GGoJgG3HpSg0E/zOXB/XtvW7Q8wfMFSmxGQhvvMm7uHnzSqxYyw3YSYs5ijt9P6YI36P/V59m+1um/zyFXpT3YRMeAi6MFs0lPwdL98SncBQ+/lkSHncfepTxq+7IcOXQ23TmVSiVe3r6vfQOeL58dSfpg4mMekqi3LrRlMGbv9mr5khX87/NZrPtLydQ5t/m01zeEh2VeJxoMSVjEtPrKIipISrIuN2xnZ4+Pr9+/wkgIMHH8L/y51Uhg9Hs8jXyPeYvusHTxH29bLBs2bOQxRo35BkfFDpJijpEUewJ76TZGjv4629fRx8cTp09rtDJY8lO6dEFCn2zBp2g3/Er0watQJwyJwShUrqjUHvgHJKU7pyAIeHp6vZaREKBGzeqY4i9hNsYTG3UTUUw9nFgER6IiIzL3cOlw+9YtPv7oe+YtCWf5n2Y+/XQ8e3btydKc6Z2lpBIFCYnWn49cLsc3f4F/jZHwyOEjjJ+4jYch9QmKbsDBk958+/WwLJ1HbdjILmwehdnIwwf3iI6KoHTZishk2fPRCoLAsJGDiI2NITQkCN/8BXPMjdfJQU5glCmlciOAISkSJ0c59i5VUKpTDVsau4JEhZ3HYjEhFWJxdnl1SPCrSEpKIlEfjlrrg0yuI/TpXuQKB1y9GpAYd56mzd/P8hr/sPj3JTwOrYbK7pncyppcuHaLfXv20Lhp00zN2bFTO07+sBqlXb2UNpMhimKlnLND5LfGyhWr0YsNUciSla4gkSHRNGfRwtVM+HHEW5bu7WALL7bxbycqKpK7t29SuEix10rp8Lq0bNWCZs2b8vjhfZxd3XBwcMy2uZ+nWvVK7Dl0GqmunFW7g71AdJwEZ49aKW1SqQqXfPWSjYXO5VBrskeHqtQqpDIBucKO+Og7hAUcxKtQJ0TRjLdf9hWCCgkOYsv2uyjsGj3btLmitxTil5/nMvnn0ZmaM5+XDy72ocSJYooBUBRFnO1D8PbJn22y5zbhYaFcvJqAQpua40uhLcv+Azv56GOzLU+SDRv/QkwmE9evXsTOzoGChYtm27yenvmYt+AXggL9sVgseHn7Ztvcz6PRatFpEjG80K6QPObBQx1ehT9K8WoUBAF33xYEPFiPxq4Aimw68un1egxJgWgdaiFajDy9uwLXfO+j1vngqA3L1vf+9GlLMCs/QJVyudSSpcu20eD9BplO9VO8mCsnzkchUzimtBniT9KxU+esC/wWWbd2DzJdqse7XOlIYGRxjh05Qt369V8y0oaNnMdmKMwGoqOjGPrDRAJC3TBjh71qLX37tKTB+++9evBrYmdn/9q3Rpnls8978u03UzCrWiCVKjEZY9FK9lOr1occPHGTF52gZXI7TIYoPF0CKVK0RJbXnz1jLnqhNe4+yRl1tfaFCXm6l5iQndSp6UyFipWyvMY/3LwVhFxpLbNSV5yjR49l2lBYoFBhPmhdkO07dxNvKIRMCKGIXzxfDhj16sHZyPVrVzhy6DjlK5ShRq3aWfYC8fcPR6EsZtUmCFJiYtO53vuPYytOYuO/wLQps/j7dDh6ozdq+S6qVXLi+8EDss1jTCqVZuthLj0qValGAa9NPAx2Q6nOhyiaMcUdomvv5ixYsBNesAeptb7ERl5Hqw7no55ZL4Jx5/Yt9u4Pw6tQDyBZXxkSIwm4v5oihdQM+HZcltf4h7+2bkeiqmrVJpHICQh88dj5Zgwa3IeJE+cTFV8EEHDU3GHQ8M9y1XMwKiqSLRu3oNVqaNW2TZbDtR/cv0uiMR8vnq31iWri4mJzzHBtw4aNnOHYkaPMm7eF6IRCSIR48rkGM/mnYTg6Zd8lvGc+72ybKz0EQaBVy6qsWX8aubYqgiCQpL9L1Qo64uJNyOR2afpLBCnGxMfUeC/rulQURX7+aTGu+T9Peb/bOZXhyZ1leHu70evzFikV5bNKYkICoeEy5HbWeiQ2qQgXzp2mes06mZr3y6/7ETB4LPceazFa3NHI79OmRQkKFiqSHWK/FmazmT27dvHkSQCtWjfLFsNybBxp9isKdWEuXrhiMxTaeOvYDIXZwKSJMwlPaILaIdmUZqE08xb8RfUa1dOUb88rhIYEs/7Pjdjb2dG+Q3vUGg35vLyZPWcIC3//g8jIJLy97On92UQkggStcjdQ0moOY8J9inhLGTYye7zK7tyNRK6wLrvl6vUeRTz3M3TEoGxZ4x/k8uRcJEZDFDK5PRKJDIvZgFqdtaIWH33cnfYfxnHp4jl88zfMdJ6TxMREti9bSMjNm3iVK0vz7p+88hZOFEVGDR/P1dta5OrS7D50lXzLNzN91sQs5booWsSby7eDUapTK3haLEZcnN+NBLXPew+CzUho49/Nnl27OHZahVLbkOSjSQn+Pn+HbVv/onXbNm9ZuvQxGAxs3byFwMBg2rZrRX6/AgD8PHU8q1es4srVI6hUAj0+6kqx4iXYuGE3/pGilcErMvQk3u4x9P+6Fz6+Wfec2LRhGwpdDas2hcoJL08Fi5bMyLZDF4C7uysmYxSIIEikSGXJ+wqFPGuhScVLlGDZ8mlcvngOUYRyFT7PtNynD+/n7JbNqBzsad3va9zcX13xedvW7Sz74xiivCYWSyIbNg5l6LBelCtfPlMyABQrUQqNYgdgfRFop9Xn+GWrDRs2spekpCR+nbsF1G14VnOJyEQDE8fP4Jdp2XcZk92cPX2K48dOUaFiWeo1aIAgCHTs/CF+fn+zefNezGaoXbscrdt+wuaNm7hy+zFKTapeMpsTkYqPaVS3NL379nnJSq/Hk8cPiYzzQG2fqhMFQcDBKT8TxneiWPFSLxn9ZsjkcuQyM2ZzEhZTAjKFA4IgIBVicXHNfMoluVzOL9Mm8PTJIx4/eki5Cp0yXVgrMOAp23+bg1EfT82OXahQo/YrxwQHBfH9dxOJM1ZDpvBm974lNHnfi/99kbX/H0cHCImzbjPor1OnXs0szWvDRnZgMxRmA4+eJCBRWRtiksSK7Nu7l9Zt274lqTJm3Zr1rF1/Cam6NmZTAn9tH8HQoT0pX6ECbu4eDB3+PeFhoUyfOo9vBvyCUiHi5yNy58ERFLpaWEQTloRDjB7Tm4aNsq9So1Qqgsm6zWJOxNvHJ9vW+AdXVzUn/16GSlsAQ1IEMrkdTo4WPuqZ9VxaWp2OWnUyfwsUFxfL1E7tqXvhAUWREbx2F5O3bOGHNRtf6m2xe+dOrt71RmWXfPso1ZUiJM6L+b/9ztfPEv2mR3hYKIsXrSAyMpGiRTzp9lE3K8Nixy6dOHToByIS6qJQuWEyxiEz7ebzfi+vsPZfwOZBaOO/xsGDZ1Fq61m1KbRFOXr0UJ40FD55/Jghg6eQINZGrijOoaMrafy+O59/0ReZTMZHvXoiiiIL5i1k2tSVmC0ibi4g6jdikjdEJnfAEH+FujXkjBw9P9vkUqmVWCxGpBLrbZSbm0u2GgkBSpYqSdiTQcg1pRFFEyZDLK6elWjYOOueJoIgUL5ilSzNsWDYIOxWb6V2AiQhsmT7bprPmUXZarUyHJOUlMSq1UeQPZfcXxTb8euc1cz/PWNDoclkYt2adVy9+hA7OwW9PulCPq/UPYK9vQP1a3uz7+hFlNrygAVj3Ak6d6uZ7f8vNmzYyFmOHDpIgrkcz2ebk0gVPH6aft6+t43FYmHw9yO59yQfCk0ZDp+8y5rVfzF91o+oVCqq1ahBtRo1OH70GOvW7WHL1nM42Is4ayKI0JtQagqRlBCETnaUjVsXZ5sHtEqlRioY07SrVRJcXd2zZY3nSYh7QGTcduQKB5ISgrF3LoevR/ZEn/n4+uHj6/fqjhlw+sAejn0/hHoBccgRuLRlH5c/6UzP4WNeOm7a1HkYZR+gUjzT+fIG7D5wiFZtHr80bPv82TNs3rwX0QJNm9WhTj3r/denvTswYeJqJOpGSKRKEuMfUTR/CBUrZU0v27CRHdgMhdmAVAJpaiiKBjTqvFesQh8fz8ZN51DYNQdAKtMgim35be4a5i2oACS7Vg/8djyJkrZIJHLiEyEo/ApNG0qIjDqPSqmga/evXrtIyetSs0YxNu96iEJdILXRcJhuPQZn6zo3r1/j1FkDPkU/SWmLjbxE7erGbPE0ySp/Tv+ZJucfohWS/zw9RBn1/77N5gW/0uXr7zIcd+LERVRaa5d+ucKRe/cuZDjmyePHDPp+BhZVM6RSFbceh3Hy78HMnTclJc+mQqFgzm8/sXrlGu7evYW7mz0ffzr2PxvCZfMgtPFfRioREEVrbztRFJFK8+b3fMa03zEr26N8VlVYYVeXvQcO0bbd0xQD0ZSfZ3LqogdyVWMAIp/G4ONxmNKlgwkJvUHjxvWoViN7b+e79+jM0eNTQdcipc2QcJ/ajbLPMwOS/28mjJ+Ld7FvUyorm00JWOJW0Lvv8mxdKzM8fngfy+ZdlE4UQAAVAs2exLF76hTKrt2Y4bjLF88Rm1QY3XN3X4IgEBapID4uLt0KmaIo8t03w3gSWgmlpg6WAANfD5jFmNE9KV2mTEq//l9/ToWKR9m96yhSqUCnzm0pWbpMmvls2LCRt0kuSJE2xUJeTTW6ZdMm7vmXQKlLNmQpdcUJjfNg0e9L6P9VPwDOnzvHjFmHkOmSK/jGR4lY4jfTvZOGmzf+pmAhHz7sOCVbq966e3iSzz2G0DgDEmlydJLZnIRvvsRsL6w4c/qvyOw74+WWmvs4+OEixsyflK3rZJbDM2fSNFAPz/RphXiRw39uJuyzL3B1y9hoGhRsQpBam01k6mps27qT/32RfjqTVSvWsH7LUxTaZI/Fq7MvceXKLfr175vSp3yFCsye5cbSxauJizdQpUpJ2rQbm9XHtGEjW7AZCrOBksWduXgrNcGqKIpopBep/36PtytYOpw7exq9qRhakuUM9d+LyRCDvzmS6VNm8tU3/dm9Yyexhqoon0v4rtCW5eKlPfw2f0KOydbj4x7ExM7n5N87SEyS4+xo5NNvP8jWPCQA69b9hVxn7elg51Sep/4HsnWdzBJ/7z5awXoX5CTIuHbt+kvH2dmpMZuTkEpTNxeiKKJ8ScTyvN+WgaY10mfrKZSuhMRU5ct+X/HrvDkpid+VSiW9Pv04k0+U97EZB228K7Rr35gffzqM0q56Spsx/jyt22RfsajsJDjMjCBP3tDHRl4jJvIaAhK++WoY02ZOxN3Dk/MXg5GrUnPYyhX2PA50ZPDQ5lbeZtmJi6sb33zdiqVLtxERLUetNNK4XmG6du+VretcvXKRyLhCaBxSveGkMjXIfDEajZlODJ9dnD+wl5IRCSC8sJ18EvDScW7uHsiEY2naFTIjygw8548ePszjkCKotF5AsmeRoG3JwG/GsXT5dPJ5peYZq123LrXr1n3Dp7Fhw0ZeolbtOiyYvwmjWDTlcstoiKFsUYdXjHw7nD9/G6Um2ShkTIoixH8PEkHG5k0hFCtWmMZNm7Bm9Tak2gYpYwRBQFQ24enTewwf9UOOyTZx0lDGjZnKk6cmEMDPR87IMdnriAFw9VoIcmVFqzZHj9YcOnCYLt26Zvt6b4LFYoGngWnaiwTFcPnvY7zfun2GY5VKMLwQ9WYyhOPtnb7TjMlkYufOiyifu0xU6cqzfsNqfH3+olXb1intnvm8GTL8+zd8Ghs2ch6boTAbGDTkGyaO+4Ubt+MwWxS4OiUwcMjn2Vb5ODvJ5+WFlHNAYYIebcXJrWrKpvv4+XDCR07Cw8MRubJkmrH6xJyVTRAE+n/1Of36WzAYDFlOap4RZrMIpA1BsljyhnFI4uyMSRSRPefxkyhaUHi8PDyg58ddOP3ldLBrldJm0p/kw8+bZTgmMsqcUmntH9RaX67dEpk0cSojcnDTklewhRfbeJeoUrUaXTo8Ztv27cQnaNGo4mndtiK1ar86R8/bQKUUiLdAbNRNEhOC8S7UCQBRNPP991OYNm0QBqMaxQvqwmh2ISjQP8cMhQC169ahdt06JCYmolAociS01WQ0IqazVbOQ7Bn6tilUrgJXtDKc9NbtorPjS8cVKFgYH49wgmP1KTkXjUkhVC7rkuHe6dSpcyg11uFYgiCQaMrHoO9/Ysmyqcjl70buXBs23gWkUiljxvbnl59/JzRCjVRipGQRNUOG5829qU6rwGJOAkFK4MNN+BTtgUSS/E6av/gsoggJiWKaglEyuZaIiNgclc3BwZGp08djNCaHIOfUu1IUgRe204JEjsEQnyPrvQkSiQSLkwMEWCssfwcVDUuWfunYenVLsXn7HeSa5JQfosWEVnaKZi2npds/LCyEuEQHVC/c5Sl1pZg7/wDOri7Uqp1xeg4bNvICec+S9S9EoVAwdsJwkpKSMBiS8nTC7CJFi+PtvojA6EiAFCMhgFzpws27Fj5oX42Dxw6htEsN1RJFCx5uuePrL5FIcsxICNCmTUMm/nQMpV3qgcOQ8IhqjYq9ZFTu0e7b71l+4hRNH0YjFQTMosieIi70+3rgS8d5eOZjxIhuLFq4nsgoEZ0O2ndpQPUa1TMcY68TiEiw3rQkJQSjUntx5Xo0SUlJ2Rr+kFfIyIMwOiaWmNhYfLzy5WrlTxs2cpNOXTrQoVN7YmNjsLOzz9O52+rXK8WWnXeIibicYiSE5MrrRmlD/tqyA0d7PfoXbGZ2qkeULN0rV2TMSX1VvmIVHNRrMJEaOmuxmPDxJE+8m8tVqc72GmXJv/8iDs8unS7Zyyjfvdsrx/70yyh+mjSDh4/ikUihehUvvhrwbYb9y5cvzcnzD1BqClm1ixYTenNdtm7ezIcdO2btgf5FGI1G/IOC8XRzQ6V6+98FGzZygsJFijJvwc/ExcUilyvyxHsvIz7+pAtnv55FdJw97j5NU4yEAHJNFbZu3UP+/M4EXdMjk6UWu0yKv0/1amVzRcacvkwpXNCey3fjkMlT00dIjSdp1z5v5DUv0akj13+eQ6l4CwARmImqW42CRYq/dFyPnt1AXMnho7tJTBLxdJfz3fdDMrzYcnFxQ62M4cXrvIS4J7h6NWHjhj3vnKEwICgYpUKBi7PT2xbFxmtiMxRmI0qlMk8rsH/4ecpoxo6ZQEx42orMBrMLWq2GWlWVHD99CqVdFYxJ4SjFIwz4JnsrD78tKletRvPGV9l3cDf6JG9U8mAql9XRpVvecPv28vWj68oVbJn6C4SGIeTzoPcPw3BxdXvl2PIVKjBrToXXXqt33658P+hX1I5tESQyTMY4gh/vwKdId4z6E+jj4/4V3+nX4WXhxQaDgR8HjcHw9zU08QYiC7rx4Q/9qFU347xmYRER7N+1H6/8PtSpXcNmWLTxr0Iikfwr8ox+9HF3pNI1LFock+Z3coUjwcE3+PTT1sycuQVRWR9BosCsP0bXTlVy1ICXW0gkEr4f1JOpU5cTFZcficSAh1MwI0Zlf8hYZhm4eAWrp/xI9KUriGoVVbt2o3azVq8cp9FqGTth+Guv06hJEzZu+J6weGfkSkdE0ULIk504uFZErnLnyeMrWXmMfxWrFq/i4qqtOAZGEe2qo0Cr+vQb9FWG/U0mE/v2HiQhPoGmLRujUasz7GvDRl4ksxVucxPPfN6MHNGNIYPGpoQgP09CokD/Lz/j6y+HE6mvgkKdH4P+OoV9/GnWsudbkDj7GTT4a4YOGc8jfy0mswN26kf06t0Ie/u8ES7e9rMvOOjhzsH1G8BgwLlyJb4Z+Hr6tMfH3enxmlmY5HI5DRsUY9OO89g5JadGiYu+DSR7kCbmcJReXuLWzdssGDYZh3tBGGRSTOULMWzmBOztMv6bvnTpKtcvX6Nm/VoUyO+bi9LaeB5BzAuxK9lITEwMDg4OXLsflac9+942FouFTz8ZSpKkpVW7xLCDJUvGoVAouHHtKtu37cbbJx/tO3yYJYPR40cP2bRhK65uLnzYof2zBMVvF318PPfv3cbH1y/b8yD+m7h39w69P/kBpH4IEjlu3g2RyjRo2MaiJT+/bfGyhVeFF/888kf8lh1K8YgB2F1Ay4Sdy9Fq0hrUl89fxs3FGykVGE+EUsLdst6MWTQdZyfHHJH/XSMmNhbnEhWIjo7G3v6//R636azXY/yYyVy9XxnJczlYk+Lv8EVfXxo1bkJ0dBRrV60jITGJzl3a45nP+yWzvZwEvZ4/1/1JVFQMHTq2w8v77W9SRVHk9q3rqFRq/AoUevWA/yiJiYl06/wxYVEeCICTe3XUOl8SY44z7ZeOFCxU5G2L+EbIQu+/cdqLCxcus73nEKrEpG7f76pE8k/qT5v2rdP0v379FvO+HUOZW6EoLCKXCzrSclh/GjbNm3lJ/428KzrLpq9ej0sXLzBmwiFUdpVT2kTRTEGPI0z6aTRms5kd27Zz4/pt6tWvSfWatTJ92SyKIocPHuTsmQvUqFmV2nXr5omL66BAfyLCwyhWonSeTMWVW0yfMoUNm64ildmj0nrj7FETkzGO6mVv8f3gjD3oc5rM6J7MYLFY+KZ1T5pfCUv5XiaJFs62KMv4eVPS9DcajYz4/HtcTt7CJ87MXWcFqla1GDR+WJ74Xv8XeBN9ZTMUvsPs27OPeQsOIFHXRxAkmPTH6N6lAu07fJCmb4JejyAImTLwLZy/mB17A5BramAyRqOwHGH8hP4UKZp7ob4XL1xgyeINREWDnQ46dW5Cvfr1Xj3wHWHHtp0sXnYEFLURLUZkluN8N7AT1apXe9uiZZo3KVAyuHkP3rsebtUWKhqRT/qMLt07WbX7BwbxW6s+1AlLrXVuFC2cb1eJ0bOSq7qJosiFK1cBqFi2jE25vSHvyqELbDrrdYmKimTAV6OJNdZGofLEEH+Don7+TP5lXJq/L6PRSGJiQqY+z+vXrjJu3CKMknrI5DpM+hO0bVmYnp/kXnGy+Lg4fvlpFg8fxyOVQuVK+enX/zPbe+QZoSHBfDdwInHGasiUHpj0Z6hf24EB3/Z/26K9MZk5rP34/WgqrDuF5IXvw9+NSzJmYdp8WYM69eX9v59YfX/2FLXnp50rUsIQn/gH8OjxEyqUK4NOq83Ek7zbvCs6y6avXp9xoydx4ZoGpa4CxqQwVBxmytTBVkWXIHm/GBsbg1arSykg+LoYDAa+/XoYgeHFUWiLYtDfwtf9HlNn/JirxrkVy1Zy+Oh1DAbI56ng+0H9XlpB+F1CFEVGj5jA1Vs65JqyGBMf4Wp/lZmzJqJOxxEht8it/Oxnzl/kWMfvKWmyDnnf761i0pE/03xPf5s2F8eZm3Ei9W/hltJCud+G8V7D+gDExcdz8fJV8uf3Jb+3FzbejDfRV++uid8GjZo0okLFcqxasRaz2UKXbn3TJH4PDgpiwvgZBAbLEAQRP18Jo8cOeu0NQlhoCLv2PkJp1xAAhdQNUWzP7JnLmDlnYrY/EyTfXuzctp0LF29QuLAvtWpXZ+LEtcjsWoAUwhNg1pzDONjbUb5ixVdP+A7QolVzatWuzob1m1AplXzw4Vg0/8LDQmREOEf+2oSHVkqtunXxdHq9TZeQJotIci5mi8WSpv2vdZupFGoEITWvm1yQkHD7MZDsYj/3+/HkvxkICCwr4cHnv4ykZMmX5z+xYcNGxjg6OrFw8VQ2rd/A/funqVO3OrXr/s/K+CGKIlN+nsn5i8EYDEqcHBPo27f9S/O0vsic2StB3Q7Fs3kVdu+xbede2raPyrFQ7ds3b7Bxw3ZUKgXdP+rMmFFTCI1vlFzdWIT9xwNJSJjFd4MG5Mj6/zbc3D1YvHQau7bv4NGj27Rq3QW/gv8+L0uTycSRvXuRGGNp1qrJS8OwnkcUxRdrBaS0v0hsXByKB0FpjMx+d0O5cOUaFcuWZuzXw5CfuI5LpJ6/8jtR7uMP6N73o8w8kg0bNp4xauxQzp89zZ7dh/Hx9aRDp5/TpMPYs2sPq9fsJyZOg0qZQN3ahfn8i76vvcayxcsIjqmNUucCgFJbkidhjqxZtYYePXPmcismJppVf6whPDyKJk3rc/36HbbuNqJQNwUpPAg28d3ACSxZNiNP5z/OLQRBYNzEkVy9comDB45SpkxJGrz/6Vu/+DO5JevMkDe4rLp85RqXz16kap0aFC9a+LXGWMyWZxVurBEQ09VZQRdvUBDrs1vxJAkntu/nvYb1WbnwDy4v24Tvo0gOOKkx1CzF6NmTbIXMcgibofAdx9XNna+/Tc1r84+R7ezZ67i46Dh/4TZ6sS1yu+SvyuOwREaP/JlpMya81vwH9h/EIitn1SYIAiHhOePIajKZ+OarIQRElEWpqcH560EsXDAAVz/rA5ZMW49lS9dTfN0KDE8DkHt50uH7oSk3YKIo8ujhfdRqDR6e+XJE1ryGo5Mzvfv2fttiZJrtMydyZ8laygfpCZLDxFKeDF84Fc9XVIsGcKxQgrhrR9E9F3p8voAD49IJ43LxcCdGYkEjWm+ABHVySOT8oZNodjkUQUhWWqUuh7Fg2GSmb1qSlcezYeOdRy6X06lrF6u227du8ue6rYgWEVE0cvFWURSqSihUEG+BqVP/ZNHikq91uSWKIqHhIjKd9abZQEmOHz1Ki1Zp3wdZZcmi5fy10x+FrgYWi5F9+0chyAuhc0713leo8nHuwnk2zJ+D/4mToFRSt1cvKtZK9YoPDwslOjqSAgWLvBOHM5lMRqu2bd62GJnm0d1bLPvfZ1S86Y/GLDJ+zgoafN+blh+8Ordjky5t2bf7DBWfK5L6UGGhfNO0URJKhQKjSg6YrNrjtAqcnRyZ+8scyu64hANSEFQUeZLAyV/XcPf9OhQpXDCrj2nDxjtNpSrVqFQlNTInNjaGP5atIiQkivz5Xdi+6wkKu5Yo7MAC7DtyFze39XzYscNrzX/7ThBypfUltFKdj6tXj2TnY6Sud+sWI0fOwyxviExegrO/nCEh9hxOXp+m9JFIZMToK7D4t9nor1yChES8atbkg75fpHhMJiYm8uTxA7y8fNHqdBkt95+iTNnylClb/m2LkSlMJhOj+g/G4eg1/OLMrHdYiblJZUZOSRvR8SJVK1dgVXFPSlwLT+lrFC3ISxdK37inTNtmEC0o7bTcvfeA27+u4b0ICwgqiBKJ3nmZuT/PYcDwtxfG/V/GZii0kYIoivzw/UjuPy2KUluTuKt3SUpwwcUz9Wsilap44i8hLi72tRIL58/vg2i+DFgX4lApcsZQuPHP9QREVkapSXbtV6o9MYp+SKTW9ekFQeDWybN0ffwApSDBIFr47eQp+m/aSsiTR2wYOph8t5+QpJQTU74En89b+K9I/H/86DGW/7GDiEgLdlpo3rwqHTt/mGF/fXw869b+SXhoBO0+bE3hIkVzUdqs84/rfHhkBHcWr+H9ECMIMlxM4HcplDkjf2LCgqmvnOeb0T8wMSoaTt9CG5dESCE32n7/v3RDsNq2b8XAxetocTMqJfTrsUKkeNO6PPEPwPGOtfeGIAg43wnisX+AzUXeho1sZMOfG1m19gZybU0QBPzvLsWnqLWxxKKoz7o1f77WJYggCKiVYHyx3RKKb/5S2Sh5MnFxsezacwulXVMApFIpFmkpZLK0Rs3woBhUY2fSQJQiiiJnj54hbMwQ6rb9kF+//AzVqUvo4hNZVygf7w0ZSvVGTbNd3uwmKiqSieOm89g/CakEShRzZPCwgRnmQxZFkYP793PmzAUqVChD46ZN/7VG0bXDh9L2WhCCIAMBGj5NZO+MJbzfrBFq9cuL8FStWpnr/TtyYO1OXAOjiXDV4dWiDh90bJemr0KhwLFGOaIen8DxmZdGkmghskJBihQsQOiF6xR/wXujaoSZLSvW893o/0YBOxs28gKBAf4MHPgzRmlj5IpS7D+4HK9CXa36yNVFOHJ0/2sbCtUqCaJoRnjukttiMaLR5Mzxft7cFQiadsif7XFVduUJD7nCizVspQo3zs6aQr9oM4IgELzvDDMvnGfg/CVsmDOdB6vX4eEfRpiHI04tm9Fr1PgckTc7EUWRuXMWcOr0AxITwd1NwtcDPqZY8YyjhR49uM+G9VtwcLSnc9dO/4qCPOmx/Pc/KLn7Cm6iFAQZlWNEHm05xdZa22j74csvUCUSCX1/Gsqikb/geicIg0JKQtkiDP1ldLr93+vajr9P3aJCdGpE1zEvFV//rydrf19B1XAzPHfGckDKlQvXs+dBbaTBZii0kcKxI0e4/zQ/Sm2yO7LFlIAgpN2wWkQpJuOLR6n0qV6zFi4L/yTGVATpsyT0xoR7NGqYM2GYV64+QKm2rjSm1vmgj72Pxi41NMmQFEGFoMcon4WPKgQJTW+HsX7qZIJPnabstSfEiGbKxItIDl5m4bdf8t3iFTkic3bx9Mljps/cidyuGTIdJABrNl7E2WU/DRs1TNP/9s2bjBo9H6O0AXJ5fo6fWU+jBi588eVnuS98Jng+v8beTbupGJwAwnNGbUHAeO/pa82lUikZ/9sUwiIiiIiMpkhBvwwPoAqFgoHzJrNw3DRMDwJAp6ZYkzp8/L9ehIaHY5ZLSL4bTsUol6BUKNKdz4YNG2+OyWRi85bTKOxaACCKFszmtP0kUiUJCa9fXrBGjUIcOP4IucoPALMpAU/nJ5QtVyE7xLbiyqUL6I2FeN6fQudUkqCH69Hap4b1iKKIKuwmvmLyYVAQBKpGGdm1bDl3zpyi5F9HCcGEt6Cg4rUgdowcRdmadfJ8+oghP0wkMrEZEo0CEbh8N5oJ435m/MSRafoajUYGDhiGf1gJlNrqnLr4gE0bv2fmnElZKrT2NjCbzQh3H6bxxCj6MIrjJ0/R6P36r5zj436fkvhJdx4+fYpPPs+X5hUc9ONwZqimcO7vy2Ayoy1diJE/JlebFtLJY2ZERPkKY6UNGzbejF9nL0ZUtUP+zKhnMhmsDHz/YLG8fkjqRx93YOjwP5DrmqSOj9/HJ59+nnWB0yEswoIgs5ZPtAhpjJWxgfvoH21IvggBPCxSnh46zba1y4maOR/PGD0qQcL7j2J4uPhPdhcvTtPOuZcHODMsWrCEA8e1KNQtkOogTC8yevRcli77OV0dlBwt8Bi5tiZmk549e0czbFhPyleokPvCZ5EnZy5TXbT+rvoZpZw9dOKVhkKAMuXKMG3zUu4/eoxGrSbfSyK9Grxfj+gRERxctRUxIgaplysffNkLH698KDUqDIjIXki+IcreLLenjdfn33kVayNHOHniDApNCQAsFhORoWeIf1bK/R9E0YKbc/xrVwkWBIFpM8ZSMv9p1OzGQb6Hdi0U9O7bK7vFB8DFWYfJGG/V5uhWjaTorSTFXsBiTiIp/gZJj+bS3mB9qlQJEvwvXSL06jViRTPOgpS9YjSXxQTEqzcxmaxDd/IaK//4E6nmPas2haYCO3ccS7f/nDl/JOfiUjohSGQo7Wpz4HAgIcFBuSFuppCF3k/55+4gpOTVcPVwJzqdaw9R/WYHSFdnZ4oVLvhKL5VChQrw49JZ/Hx4PT9v/4M+A5Jzpbm7upJQtiBGMdVQaBQtJJYpgIeb6xvJYsOGjYwJDgogLiFVDwU9+guJVInJEGvVz6w/SqeXeFW/SL/+fWnVyIK9bA9qdlO64DmmTBuTXWJb4VegIHJJsFWbVKpCo4rHGLsbkzEWQ2IIccHLaBP6MM14SWg4V7Zv4w5JeAhybouJbLJEUvl+KPs3rMkRmbOLh/fvEhTmYeXtL1c4cPtuAklJSWn6r1m5Gv+Iaii1yV7vSk1BQuPrs3TRslyTObuQSCSI6RjiYtUyXF1dXnselUpJiSKFX1l8RC6XM2j8UH7Zu5pfDq5jzJzJONgne7YUa1iLx3LrCI/j3mo69+722nLYsGHj1YRFmFKMabFRN5HL7YgIPmHVx5gUQtnS3ukNT5eixYozeFBb3O32ohL34GG3lxEjOuOb3y9bZf8HTTr1LB2ci5AQvpwk/VPMJj2GmIN4RBzHRbDelPtFJ7J55nSeREdgL0gxIbJajMAu0cjNnTtzRN7s5O/T91Co86f8LAgCSdRm66bNafpGRUWyY9dtlHb1kEjkyBUOSLTtmPfb2lyUOPsQ1Yo0+QRNoohM+/oFTgVBoHABv5caCf+hbad2/LR5MT8fWc+kNfOoWacGAF16d+eEj/WaT+QiJRrVem05bLwZNo9CGykULVaQU5cCUKi9iQw5hbtvE0SLiSd3lmPvXA7RYkIffZxfVk5/5Vw7tu1k27bj6BPA1UXKV1/1wq9gzue76dW7Byc/G4soa4sgJIdomfX7mTX7R+L1cRw/doqKFctx50ggSbMf8HwmBL1o5kngU/oLLsifeRrmF5Rst0QlewC85cSz/7ykM5IjMdGIIEmb2yEj58/QMAuC+sVbmXIc2HeQLt27pj/oLfGq6lyNmrzHgJJL8L0SjvTZ5xMgt1C4Yc1ck/Efhs6ayC/fj8F07T4CAtLSBRk6ZUyuy2HDxn8ZFxc3lPJoACxmA2DBu1BHAh6sR6XxRqn2ICb8DJ98XBt3D8+XzvX40SNmzVxMWIQZtQpatarF/N8/zvFn8PL2pWiBRO4FhCNXJhuIDAn3aNOmPp27fMDG9Zuxd7CjSpXB7Gt3BiKsX+ZRKin1nlgoLUkOVfYWFASIBs5Z4ikjffv3wKIoZqivoqMjMYtpDVwmsxyj0ZDGQ+P6jaco1XWs2hRKJ+7cu5x9AucSgiDg3KAOoQ824WZJNhwYRQv+ZX0oX7Z0rsrSo3cP5kVGsW/XMZTRegy+rrQd0Bs3l9c3WNqwYePV6LQCUUnJ78SYiCt4F+5MqP9+Ah9uQedYnIS4Jzjp7tP7s+UvncdkMjFj2hxu3AwFoGRxV6ZMHZUrxRxatqjBstUXkGuSC0GaTXo8Xf2ZO28+O7fvICDgAW3bfcrKL89A8F2rsXed1CiCQmkvTb3gK46KTWIkrm/5fAUv11cAJhNp3KukcnvCwx+m6Xvy+HGMlExjZAkNF1+5Tl6kVa8u7DhxjWoRqQ42xz0V9Pksd4teuTo70/bH79k6cxGKJ2Ek2asp1KwufXrbim/lFDZDoY0UWrdty9at3xFvaoEhMQxn9+oIEhlqXX7iY+4hkchIiLVjyeKVfD/o6wzDmvbu3sPiP24h1zQBCTyNsDBkyAwWLf4xx0OhHB2dmDJlIL/NXUpEpBmdFnoN7EyxEsmekhUrVQGgfMUKTD1ymEaXnmInSIkTzewt603hhCTkQqTVnDUFHRucNClJeHOb8LBQfpwwkycBRqQSKFnckSHDv0PxQihrw0a1uDj7MipdavEYY1I4Jaqkf0hWq5LDk5/HYgqiYKHsSbYbFRXJiuVriIiIpl69atR/7703Uo7/GAf/4WVVuaRSKUN/n8Jvo37GeM8fNEoKNarFZ9/kTPjFy3BydODHhdNTvGL+bWFxNmz8G1Cp1VSv4smxsw8QJE7I5PZIpEp8inQnKSEYQ2I4WofSHDl8kspVK1GufPrvtQS9nsE/TEVUt0cQJCQlwZI/LiCT7aZps5zP8zdx8ijmzp7HzVsXkUpFajcuRZdunwDQ9/PU6pd7WzTk3todFDZKMIsix91VWJycKC1EW83nJSjYpjUw4IPOOS57eoiiyKwZczl79gmJSeDuJvDtwE8oUrSYVb/SZStir16LhTJWY92cE9PN46TTyrBYjEieuwwTRTMadfYYRM1mM9u2/sWlS7fw9XGnS7fOqDWabJk7Pfp+2ZclhniunrsISUZUJfwYNXFYrh8gBUGg36CvMA/8goTERLQazb/uEGvDxr+BHh+1ZeKPm5Fq30fyzLPQzbshJpOehNgHOLpWIir4OmtWraP7Rxlf1o8ZNYmbj8oiV1QC4O/LkUSNmsyESWlTNmQ3rdu1RqXew/bt+0kygE8hDd999Cma6Kd8WKccUA4wUr9bew7dnUbdUCNSQeC+zExAqQI0OWEd8SUVBKSihBItX13EKac4dOAgq1btISJKxF4n0Lx5lXRzu/t6q7j1NCkljRaAJeEkH3yY9pzhV8APwbIP8LVq16iyz0h449pVNm7cgVwmpWv3DjnmRQpQpWpFwsZ9xaFlf0JYNIKnMy379aRwodwveFW3QR3q1K9NvF6PWqV6a2fzdwVBTK82dTbz66+/8ssvvxAUFET58uWZPXs21apVS7fv0qVL+eSTT6zalEoliYmvl2MoJiYGBwcHrt2Peq0qhzasiY2NYe7s37l+4yHB4a44eaSGsoqimYD76/H0a42jcifTZkxId0M/4KtRhMQ1smozJEXS4r1wevf9NE3/t0V8XByb5s4k+v59lF75uB0tcOnve2iTjHhH3qNnfBxKQcJjDAjTx9Kyy9u5sejbeyAxplZIJMl2faMhmtIFLzN2wvA0fX/5aQanzsZhlhRFMD+lkG8Ek38ZiyydPETLl65gy65EFOrkA5zZnIiDfCcLFk7LsiK7c/s2I0b8hkXRCKlMh0F/kzJFQxiXTu6p53kT46CN/y4xsbE4l6hAdHQ09va5+x7PTX0FNp2VFURRZN2adZw8eYPLl2/jUdB6wx5w/088C7TDFLeTsaN7ULps+TTvtqWLlvLXfkcUSut0Gm66fcyaPS7Hn+F1EUWRg1s2cH33LiQaNfGuPhzaexl5nIhD7FM+iA6k0LNCJ5trFmb0ph1vRc7f5sxn33F7FCrvFLkliRtZuvyXNJdbR48cZe7czSSYKwAG7BRXGD2mP0WKpi2q5f/0Cd98OweptkXK/6Epbg+TJ32c5SJcZrOZb74agn94OZQaPwyJ4aiFg8yaPQpnl5xJGfFP+gwb/w3els6y6at/F1cuXWLFii1cungFe88+SGWpYZRx0bcxGWLQaB1oWDuRPp/3TfPOjImJpnfvqch01mcsU/x+fl/wDY6OL5YVyRleZ69+++59Nvz+B6b4RFRFCrL/QihB9/XYJ0ZTOeohTZ+lf1pnZ2DSsb0IgoDJrVCaeXKSRw/uM3DQUhR2qZ+nIf48X31engbvW6dyiomJ5vtvxxIaUwgEV+RcpU2rUvTomX6ahv79BhES+z4yebKDjDHhAY3qGfj8i77p9n8Tli7+gy3bn6K0q4FoMWPWH6HPp7Vo1qLZG89l00XvHm+ir3Lco3Dt2rUMHDiQefPmUb16dWbMmEHTpk25desW7u7px6nb29tz69atlJ9tt5svJygwgAXzlxEVZcLNVcX/+n2S6c2tnZ09g4d9B8BPk6Zx8uwpVHZVSEoIJeTJTlzy1Sfw4SZC5Tq695hEAV8YO+57q5yFiWnTCyFXOBISfCvtL94iWp2OHj8kG9sGfDmUwOgGOBdrDECQMY6Fp0bxRWws50v5MPTDLm9Fxls3rhEa5YvaPvVPVa5w4ObtWIxGY5pQg0GDvyEo0J/Tp85QomRLihUvkeHcPXv1QKVcx6Ej+zAaRHzyq/lu0KtL3b8O/1RGkz2bS6ktyZVbCVy5dJGy5SukO+ZV4cU2bOQ0Nn2V85hMJpYuWsb1m/4o5NCxY3MqV62aqbkEQaBz18507grHjhxl5uy/QFkfQSIn1H8fSo0PwU92IFrMfDd4Hd4eq+n5cQsavJdaLCIoOAy5Iu2t+BucnXMFQRB4v10H3m/XgdUr17D/Lz32fv8DIEkUWXppGkMDb3DTUUGLAd+9NTnPnnuMQpXqiSkIAgliDbb/tY0PPmxv1bduvbpUq16Nw4cOolapqFWnV4beAd4+vowd8xGLFv5JdIyInQ56ftU+y0ZCgC2bNuMfXgGlJtn7Q6FywWhuzdw5ixgxenCW57dhIyew6avc4ejhI2zbdgiTCSpUKEj3j7pnutp62fLl+al8eWJjYxjw5Qgi46ug1PgRE3GJ2Mjr6BxKEBp4jk3bHTh2cgzVqnox4Nv+Kf9PUZERmCz2aQ7vRpM9URHhuWIofN29erEihRj602jCIiL48MsVYNcTh1LJvzsRfArN5UWUM5sp0rIeHo4SQqJFZKH3c9VYuGLFemTaBlZtCm0ltm8/lMZQaG/vwPyFU7l4/hxPn/pTr8H3ODg4Zjj3lKljmTZ1Dg8fxSKTQe0GxenxcdaNhPr4eHbtuYnKPlnPClIpErtGrF27nabNm9r+pm1kKzluKJw2bRp9+/ZNucWaN28e27dvZ/HixQwZMiTdMYIg4On58pxCNpK5dvUS33w9DoV9I+wcS/E0wsBXX47nt/njsLd3yNScFouFTRs2EhOTQCGfaE6emIzOpQ4+Rbrjf38NPoW7ITzzbguKMTJqxM/M+nVyyngvTxVRD5OQPOeenRR/iSZNX13J720QHBTI0yAdCrvUmz2ZXMfDfJXZWi6Ajyb9nCu5P9IjNjYGi5g2/MlskWI2mdKVyzOfN23avV4y5E5dO9Gpa6csy/ki4ZEWBIm1slJoy7F/3yErQ6HNg9BGXsKmr3IWg8HAx90/ITSqEC75kg16k37Zz2efRtKkWZNXT5AB165eYd/eYxT0U3P29ExEWQWcPWoSFXYOR9cqqLXJ78ME4Ne5OyhfvgxOzsk52Jo0rc+p8ydQ2VVMmc9iNpDPJ++mDDhy9CoKdernJQgCspK9maOYSLev+lG1Qdoq97mFyQy8YOuTyeyJjAhOt79SqaRJ09fzgihVugxTp5d5dcc35PLlOyg11snQJVIlwSF5zFpsw8Zz2PRVzjPtlyls23kbJ4/mKFQuPNzpz53bPzJu4ohMzxkdHcWShX/gmc+TqOubeOLvgpNbVdy8GxMZcgrvwsmOCSJw9PRjXJav5KOPkysC+/j6YacJ5sXU4/baIPIXyDkDW1b26vNW7MSkbmplcFB5VGeXxy509fLz/fghVnOGPLdWThsNk5JMKdFaz2M0ph9sKQgCFStXoWLlKq+cW63RMHzkD1mW8UXu3L5OfJIvuhfqiMTE2xEVGZGyt7FhIzvI0WzXBoOBc+fO0ahRqkuvRCKhUaNGnDx5MsNxcXFx+Pn54evrS9u2bbl27VqGfZOSkoiJibH6964wb+7vDPzuD+zcuyJajDy5sxxEMwZpU5YuXpHpeYcPHcfK9QncD6qLf1Qz1HZFUKo9MBqiUOv8UoyEABKJnIBgNZER4Slt33z3BRphG4nxD7FYTCTGnqZymQQqV62epefNKeLiYjBb0hrjtG5+fPbrAgoWK5nuOIPBwPSps/ii32i+/nI0f679M9tlK1+xCnaqO1ZtomjB3dWESv361aZyG106qSiNCY8pVTrVw/H5W8nnKxg/j8ViYdGvCxnVvT8je37FhjUbc0xmG+82uaGv4N3VWRcvXKBLpwHEW5qgdSxOwIMNxEXfQq6txcaNhzI977Yt2xg1Zis3n9TkSXhDdC5tMJv1KFQumAzRKUbCFFQNWL0ytfJgpcpVqVI+icSYU8n6Kv4Rarby7XdfZFqmnCa9AlVSuY4KH3aheY9eGY7btWMnA74eTb/PxzB54hT08fEZ9s0sPl5KLGbrsAIx6SRtP2id7WtlF/k8nTEarHM9iqKINhMpCs8e2s+MXt2Z2fEDFo4ckiOfsQ0bNn2VsyQmJPBlv0HsPQyO7k2JCjtH8OMdKNTeXLsl4P/0cabmDQsNod//xnD8QhEeBNdB0PXCYgpHY1+IyJCTuPs0tuqvUOfn9OnbKT9LJBI+6dUcU9x2jIYYTIZYzHE76Nmzaaa9HF/F6+zVX0ZMXCJSadoq74UrV2boT6PShFf/s0ZM5FOGD/iBLz4dzA/fj+H6tauZf4gMaNiwJklx1n8DhsRgypbxzWDE28fLOz9KWUiadqUiHt0bpgMIDQ5i9qgxjOrSjzFf/MD163kr8s/G2ydHPQrDwsIwm814eHhYtXt4eHDz5s10xxQvXpzFixdTrlw5oqOjmTJlCrVq1eLatWv4+Pik6T9p0iTGjh2bI/LnZe7cvsXu/WE4e7UBQKl2R+dQjOAnu/Aq2J6QkMwp86uXL3HzngMqu+SkqBKJDA+/zjy9PQt714YIQtqvjEWUYjKZWL1yLfv2XyIhAVyctdSqqSc+/gpNmzWjZOns9wLILgoWKoq91h8jlVPaRFHEyT4SD898GY4b+sNYHodWR6aoAMDaTXeJjlpMn/9lXx5GuVzOZ5+1Yd68LehNZRHEJOxU1/lh9IBsWyMnaNu2PvMXnUKuTTYOm016nLUXaFbxKyRvEGI8cdBoCmw6TS1z8gbo/qk7LAgM5rNv++Wc8DbeSXJDX8G7qbNEUWTWzJUonbryj5+epkh+nt5dic6hOLFxmfMkFkWRDZuOobBrkdKmdSyHPu4ucZHnSM8nQCJISTIYuX7tKr/NXU14hIhGA+/V9sAiXqFYscI0bTE9TyfI9suv48pdPVJZqiXLGH+S9h0yLmCy4c+NrP4zALk22dsw7KaebweMZN7vU7M1VOmHoV8zaOA4QqILguCMQrhKpw6VcXF1y7Y1spvuPbty5OgITJa2SCQyRFHEHL+Xnj9kXFAgPY5u38LtQSN4PzLZkqs/epUpV68wYsNfOXaIt/FuYtNXOcvsWb8RHNsAR7fkPOzuPk2ICD5JfMxdBEl+bly/gbdP/jeed/5vSzErWiJ7FnGlUDrhVagnwQ+XgOCGIKTVO2ZLsuFy8qTp3L0fiwAUK6Ihv88TZHI5HToPeWkIbFbIjpRAHVvW5OhPZ1DYpTqLGA0xlCye8U1MaHg4fYf/iVn7IYIgISJMZPSYlfw0uTeFChfJtCwvUv+99zh16iKnzx7CJCmKxPKYogXj6dV7dLatkd24uXtQuriUq/eCUaiS//4NCfd4v7bfG0W/xcXFMrdrR1pdD0EuCIiiyNILg/lk8U+ULFk8p8S38S8jz1U9rlmzJjVr1kz5uVatWpQsWZL58+czfvz4NP2HDh3KwIEDU36OiYnB1zfv3gRkF5s3bUeps/bQ++fQYEyKwtcnc67HJ06cRqZKm9cuv18xmjXRsmrVUUSxVsrBQhQtuDlFc/zYSTZsjUSuaQYqCI0X2X9wCwsXjU234EleQiKR0L9/R2bOWIfeXB5EM1r5Zb4d+kmGYx7cv8v9pw6o7BxT2uTqIhw+up3en2VfVSuA+g3qUaNmdY4dOYxWo6Zazb5IJBIS9HpWrVjDU/9QypYtQtsP2uWZw23jpo1Rq1Vs2rSfxFg9ft5KBvTpg0Qiee0NR3BoGIZDF3Azpx6wCiUK7N92CNNXfdMt0GLDRm7ypvoK3k2dFRwUQGSsC6oXLru19kVIjH+KVybrRSQlJhITp0DxgorR2Fegaf1Ijh2LxJAUiUKZmrfJpD9Gy1adGTlyIYKmLYJKIN4CR05d4+NuPrRonbvVF18M6XodBvdqxYDRcwmKLQFSD6SW6zRrlJ+ChTI+QO3afQ65NjV3oEymISS6GH+fPE7NWnUyJXt6ODg4Mn/hVC5fPE9gYBB16g1Dp7NLLsayfz9Hj57F2dmOj3p2tcpt/DbR6eyYNn0oc+csIjTciE4LPb/rTKlSpd9ontNLltA4MtXdUyNIKXfuDsd2b6de87zrUWnj3cCmr16f+w+ikMmtFYuTe3WCHm7G1c2Z8hUylyojLDzRKi0TJOdErVSxIA72Us5cOIi9W6pXoTEphDKVvRg5/EcehtREJtcBcPtJLCbjaX6aknNG3OzKG161fCla1rjIrr/3YpSWRWJ6SmH3Bwzr3z/DMbOWbMOobo1USN7/C4KAVNuEpYvXMm5i2kKOWeGHId8SFBTA+bPnKFnqwxQ9+ujhA1av3IDJZKZd++aUKVsuW9fNCqPHDWXRgsVcunwFiVSgTtPSdOqS8Zk1PbbM/5WG14OQPzNOC4JAA/9E1v26hNFzJr9itI13hRw9abu6uiKVSgkOts5NExwc/No5MuRyORUrVuTu3bvp/l6pVKJU5t1cQjmFs5MDJlMccoWjVbvFnIhOto+evTL3R165cnl27juNTG79QrS3g5jYePJ5e/Pg3hyQuWPvWAgXB3+GjxzAzz8tRK5JVW6CIGCUvceaVevo81nvTMmSm1SvUZ2lyyty6OABZFIpdev3eOnNzIP7D7CQ1tswIVGK2WzOdiOWUqmkYePUjUlEeBhffzWOBN5HoSzCpZtP2L9vMDPnTM4zBrQ69erSoGRy2F9mNhoPnzzFNTQOBOsQa3VkPLFx8Tg5Zi4Hpw0b6ZEb+greTZ2l1miRStLmezMaYlBK/fnoo4w94V6GUqVCpzFgeKFdIXmKh2dRPDwjuXlzPaF6A3YutXDQBNKtczUO7D+KRd4wpdgSgFxTmr1799G6be4ZdDJ9EHPQ8vsv33Px6jXuRpqoV/+rVxYw0ycA1hFeSBXe3L55N1sNhZCs/8tXrEz51LSPjBo+nmt3PFHqamG6F8+x4xMYP/5/FCueNzwXPDw9GTshawdQITIqTZuvQeDKxfM2Q6GNbMWmr3IWuQx4oTCj2aTHYomhWmU/3Nw90h33Khwd5AREWefFMxqi8fRwIC7egErhj//t6ajtK2GnEylZTELHTn35/It5KOx0KWNkcjvuPRKIiorMkQIm2V1ccMTX3ejTJZT9xy9QulgpKpT+4KX9w6MNacKVBUFKfGRstsjzIp6eXrRo5ZXy845tO1m09BQybV0EQcr5sTtp3ewin/TpmSPrvykSiYS+n/fJ0hwxT55g/4IHq0QQEHPoM7bx7yRHYyEUCgWVK1dm//79KW0Wi4X9+/db3Wq9DLPZzJUrV8iXL+MQ0HeRzt06ITMeQhRTg6vio69SoYyS3+ZNRq15eXKd2zdvMnzoBAZ+M4HpU2el5NGpXLUafl5PMSSGAsmhXca4I8TFBHHwpCcJtMaz8Fc4upSgYV05ixZPJb+fHwZjWmUik+uIiIjKvofOYRQKBU2aNuP9Ro1f6b5dqUoVlJI7adpdnMgVQ92c2QsxytqgUCZ7ZCg1vgRGVWLLps05vvarkIXeT/kHmd9olC5elMD8aT1OEryccHR4szwcNmy8Cpu+yjkcHBwplF/EZIxLaTOb9Kgkt5g1awA1a73889XHxzN96iwGfjOB4UMncPtZaJ0gCDRvVhlD3PmUvoaEx9ipnrB6fRDBsY1w8u6Lu1933B1usGTpWD7s2J6oyFik8rSJVA3p5P7LbrLr/ejuIFChTGk61C2PuyUmzbwv4uIkWO0XAMSky7zXsEGm1n8TLl28wNXb9ih1ydEKMrkWQdOW+b+tzPG1cxXvfGk+46t2UqrZjIQ2shmbvspZGjeuilFvnbsuImAD333bkh+GfPvK8Vs3b2HQwPEMGjierZu3pLT3/d9HkLANiyVZ2ZhNCciMOzlz9hF3ntZC4dAVr6Jfo1aEMGZkG8ZPHEF8fBxmMa2+Mls0xMVmf87I7DYS/oOnuxvdP2hChdKvvhwqV8ydpARrI7jJGIuflyZTXvhvgiiKrPvzEAq795FI5AiCBKVdTXbtu/WfyjnrW7kKARKzVZtBtKDys70PbKSS40lTBg4cyO+//86yZcu4ceMG/fr1Iz4+PqVKV8+ePRk6dGhK/3HjxrFnzx7u37/P+fPn6dGjB48ePaJPn6xZzv9r6HR2jBr9KW7aPYj6najFnXzYyo7Zv818pZHw0sWLDBv5B/cC6xAYXY8TF4rwVf9hmEwmAH6ZOp7mDaLwcjxEIc8jfNKzHPHGUsiVqeHMKl15du+5SFJS8pWbn68Ws0lvtU5S7Blat27KfxFHRycaNyxAYsxJRNGM2aTHFLuDj3ulDVsLDwslKNA/W9cPDU1CIrV2D1GqfbhyJeOb4ZwkvcNvZpIeP49Oq6VEt1actQOLKGIURY66y3j/s27ZGtptw8Y/2PRVzjFuwlBKF7yIJGkHQuIOivue4c+NS/ArUPCl40wmE1/1H8aJC0UIjK7HvcA6DB+5gksXLwLQpVtHvvmyEvndDuPtdIiObWRI5E4otKnubHKFI8FRhTl8MPlQ3bJVI5Lizr2wjh4/Xx05QU68H1+c5/n50jMaft6vC6J+CyZjLKIokhh7nhpVteT3K2A1Z4Jez6OH9zGmVzElkxzcfwSl1jpKQRAEwiPTryz5b6XNkOFsK+SIXkw+fN1SWIhuXo9SFSpZ9cvpg66NdwObvso5WrdtzYdtnFCzE4t+J06qXUyZ0p+2H3z4yrGzZsxl2ZpwnkbW52lkfZatDmPWjLkAeHn7MGPGd5QueAYvxyNUKXWNUiW9kOjaphSKFAQpSsd2zJ61CAC/AoVw1KatGu+gC81UnsSM+EdnZIduyiq9u7Qgv90BkvSPADAkBOAi2cqI/u2B1HeoKIo8efyQmJjoDOd6U6KjIomNT7sX0Cf5cuf29Wxb523TrOtHnK1bBn9J8tk/UjSxu7QLfb7Pu4XcbOQ+Oe761LlzZ0JDQxk1ahRBQUFUqFCBXbt2pSTgffz4sVWS58jISPr27UtQUBBOTk5UrlyZEydOUKpUqZwW9V9HqdJlmP3rxDcet3zZJmTaJinGFplcR1RCDbZu3kL7Dh8il8vp87/UcOEd2/7CIvFOM0+sXseXXwxm3oKpfDOwP99+M5LQ6GJI5F5guEyDOs55uohJVun7v0+pW/camzbuQKNV0eOj76wStkdHRzFi2GQCgjVYRBkuDhEMHvIZRYsVy/LaWq1AaLx1LkSjIYp8nrmf8ymnbh8Ben3xKZfrVGPH6k3IFAr69u5Ggfz//fw4Nt4ONn2Vc6jUasaMH/bG47Zs2kxkQnWUmuSNuyBIkGobs3zZJqZWqABA3fr1qFu/XsqYzVsvv5ixALW2CD9NXoaXty9ly1egXs2jHDt5BBRlEY0BuDveZcC34zL9fBmRk+/H9Hh+nZBoEVnofUxuhShVugwLfh/Fyj/WEBkZTfMW71OpctWUvqIoMmv6r5w8HUii0RmdKoyWLcrTtXuXLMtUqnRxjpx6hFJb2Kpdp/1vGQqLlCpD/+072Tp/DnEhoZRv2owOTVpY9cnt74ON/y42fZWzdO3e5Y3ffwl6PSf+9kehTnWSUGiKcuLv3fTV61FrNOTz8mbUmCEpvx86+Mc0YbYSiZzrN+OZO2cBX3z5GZ/3+5CZM/4kiaqIiKg4yxcDO2fbpXleey/J5XJWz/qO9dsPcv7aXkoW9qT7B98hl8vRkazbTvz1Jwv+PE1UvBtyaTxFC0kZO37oGxX0SA+dnT1KeXyaYmhKWTDePn5ZmjsvIZPJGLpyPfsWzeDitcu4F8rPL726olGrXz3YxjuDIL4YJ/EvJyYmBgcHB67dj8LuDcuEvyt81nc0saaGadrLFTnF0OGD0rSHhgTTr/885Lr3rNr9763B1asOH3VU8sGHHyKKIqf/Psnduw94v2F98nmlX0Xt38blUyc4MP83JJHRSP186DRs9GvlJxn4zQj8I+qnJC4WRRGleRNLl0/PsnK/cf06o0avQKptiiAIWCxGZIYtLPj9R7S6nPGKeZ4XPSLyyubiVURGRTNv0kz0D/zBXkPzXp2pVadGluc1Go089g/Aw80VnTZtiIiNNyMmNhbnEhWIjo7G3v6//R636ayXM2nCz1y+l/Zv1F5+gPkLxqQ75ot+w4lMtPZmDws4iM6xJIW8rjF1enIC+AD/Jxw8cISiRQtRtXqNbPdUfhuHr6joGOZNnkn8vacIdhqqdehAtVq1Un5vciuU7rgNf25g1foYFJpUY15S7AnGjW6R5STuFouFz/oMJNrYDNk/RdfiT/J538o0atwoS3P/2/jHY+d1EEWRPxb+wZ1Df4NFpECdynzS79NsqaD8NCAQmUyGp3verUb9b+Jd0Vk2ffVyHt6/yzeDtqK2r2LVnhBzlhm/tKFAOgWn5s9dwJ5jHtZFt4yxhAUcxtNDzuJFw1FrNCQmJLBn924AmjRrhkqlSjNXZnibRkJRFPlj0R/cPXgKURTJX6sivb/o/cp3XGxcHG3+twiLrn1Km8kQRZUydxk8dOBLRr4ec+csYP8xGQp18v+XMSmI0oXuZDmPbV7kTXTSqzh7+hxbf18J0fEo83vy2dCvcXPJXHHV54mOiSU8MpICvj7Zov/edd5EX+WNqgc2chUnR4GYUGtvtCT9fSpVSt/7z83dgzo1Xdh94G8cXKtjsRgIebILe+eyKNU+XL/+Nx98mBxKVL1mLarXrJXuPHkFi8WCIAivdSi8cvoER/7Xn/eDEwAwnbzF3KvX+GHrzpeGeCclJfHEX0SmS00CLQgCkfGFuXrlImXLVcxw7OtQslQpxo7tzrIl64nXg6uLnP5fDs9VI2FeMg4e3HOQfUvXYYmIRZrPha7ff07p0iWt+hiNRkZ+9CVNLoWgeFZJbf/5iZinDaZug8wn89+w8k/+XroBl6eRRDlrcW9UnW/H/GALj7ZhIxuoVLksZ67cR6lJNXCJosjLahl98kk7hg1fhFO+9kgkSmLCL2I0xKDSeBITezWln5e3L90/6p4jcmfXe1IURURRfK3NsclkYkTPL2lyITjlHXf2/GR0v3xHg0YNrDwMX+T48asoNNaXgQpdDTas355lQ6FEImHmrAn8Ons+T57GoVSKtO/ThFq18/ZeIacIDApm3ripJD0IQKJVU7FdYzr26JSm34xxU3D4Yx+1jcn/l0Gn7vPL00AGTxqZ6bXv33/InB/G43grALNEQF/Gj0Ezx+PumsnS4zZs2Eghn7cvWmUolhfatcow8nmnHw3z8ScfcfToN8TFv49am4+khBCCH2/Hu3Bn9PpbBAQ8oXCR4qjUatq0a5cjcmfnft5isby2MWfmhKnYLdtHrWd57kNO3mXykwCG/TT6peNWbd5Poqy+VX0umcKRm7fCMyu2FV98+Rnu7n9y9Nh+RItAuRq+fNp36KsH/ksJiRYz/A5YLBbmTf2Vx8fOgcmCY/liDBj5PWq1taH6zN9n+Our8dQKSU5bYvz7EWOufcWUjYvT9H1dTCYTk34YS8Lxy9jFJBLm50qzr3vRuEXjVw+2kS3YDIXvIJ/3+4ghQ37FomyKVKYmKSEQL+erNG6acTWnbwZ+yeVL/+PeA38EqQxnz9ooVa4k6Z9QunTRXJT+zUnQ69m2ZAFPrl8j6PYtXKP1IJOhrlyBvj9PR/USN+v9v83lvSA9/BOmLQg0uOrPtqW/0/GLARmOS3bUTeusKwhpE8pnllKlSvPTL6WzZa70ePzoIcuWrEWfYKJSIXs6tW2GVJpcISsvGQlPnTjNsSHTqBuenGdDvB7GwrvDGbb5d6ubrM3rt1LlahAKIfW1VzXcxO4lazNtKLx15x6XpyylYYQFkIA+Af+VB/jD14uefT7K0nPZsGEDGjdtyra/BuMfoUapzofZlICQtJvP+/XPcEzValXp8+lt5vy6DolMh86xOPkKtEUUReztclberBoIRVHk4P4jnNp7iDt37uMQpUeWYEBSIB8fDf+aMmUyDhPcsvEvKl0OQvFcJcMqESb2LVtPg0YNcHcQMjQWZqiVsinmRKvT8cPQ77JnsnRITEhg2ZI/ePgoFBdnDZ/2+eiVlaDfBiaTiR97D6T5lXCkz/YVt68u4U+wMhbqExII3vc3JY2pB25Ps4TbB88RFR2TqYJeoigy+7sxNDkX9OwiS8R09AFTB47hp+VzsvhkNmzYUCqVNG1cms3bz6DQJXsVGuLO0q5V6QyrR6vUan5bMIX27foQFZIPmcIBnyLdkUgVaOQheHnl7XQ7j548Zf2iVTy4/xBCIlFHxSM4O1ChQzO6fZrxRVxiYhKBe09S4rlimO4WKbcOnScyKhqnl9wGWkQx5VyWU3To1JEOnTrm2Pz79+5l/75TSKUCH3ZoRoVKlXNsrZdhciuELPR+hsbCqaN/wnPlIRqYk3VR/JUgxgaHMXnRDKt+fy1clWIkBJALArWuh7Fu5To+zmS16F9/mk3hjadxFiWABG5EsHv8r1SuWRVnJ8dMzWnjzbD5b74jhIeFsnTRErZs2oy3jy9z5w6lWtkbeDvtRSXuJiZOwf8+G8nSxcszNGQNHf4dHu4KPHyao1S5YkgMx9XuDK3btsnlp3l9IsLD+KltC9zHzyFs/WY6Xwmm2ZM4mj2IovKf+5n/3VcvHS9ExaTxDHMSZEQ8fPDScSqVCp98YDEbUtpEUcRRcy/L3oQAjx7cZ9yYyQwdPJG1q9ZgNptfPegNOHPqbwZ++ytXH1TlYUhdVh9wYfiPc1KSHJvNZiyWF+9M3w47lq2jSliqchIEgXqP4lk1b7lVv4e37uJhlr44HDEyLk3b67J52VqqhVt/9t5GCXcPn8r0nDZsvOsYjUb+2rKFxb8vJjwslOmzJtGptYVCnsdQmteiVskYO3Yxo4ZPJDaDqo8dO3elTCl7XL3qYe9UGovFiDl+B336ds4xubPDi3D896O42m8iUav2UPPME5rei6NhgIEGxx+y4OvRJCQkZjj2wY275DOn3dZZolLfcRkVPKlVsySGBGu9Zog7RfsPm2f6Wf4hQa/nt1/nM2TwRKb8NJ3IiOzx+vgHvV7PF58PZs8xDx6G1OX01VJ88cVEggIDgGTdm906MrPs27mLStdDUoyEAMUS4PzmvVb9wiIisYvUvzgcx4h4AkNCMrX27XsP8LgRYLWnkQkCshuPiY6JzdScNmzYgIsXzjN/7nzOnztDz096MGJIPYr7HsdBvg6d+hH7D9zg6y9Hcu7MmXTH29nZ0aVrc5zdCuHq9T4SqYKkuCvUr1fglQUqs0JWCyvt+ms38z/oh/Oi3UgPXKDVjSgaBhp5/1oYwVNWsvOvXRmODY+MRJfeOy4sHv+goJeu271dQ5TGw1ZtRkM0xYo6Zuo5XmTPrl0MGzKBkcMmcunC+WyZ8x9EUWTalJn8uuAuD0LqcSegDuN+3Mefazek9DGZTNnmVPI6ZJSSxGAwEHr4HG7P7Su0ghTNmVs89g+w6itGpT1LuSAl+P6TTMsVdObyMyNhKjX99axbvibTc9p4M2wehe8Aa1f/yZ8bL4GiOmZTLGvXDmL8hC/5btAAPu31DWZVd0SJHKMZtu15gMm0hD6ffZpmnqLFijFjxgCWLF5NTIyBIoXd+ajX5BQvs7zI2skTaHUlgDAsFESJ/LkNsk6QYj5zkcSEhAy9CmV+vhhP3rIa90BuoVjdeun2f57RY79nxPCfCQjWIaLAyS6YwcP/99KQ1Lt3bqLXx1OmbMUMXfdPnzrNz79sRaZ9H0Ei49aWp5w5O4op0968sM2LHD1ylOXLtnP/QQxGk4Ai/ghuXu+j1PhwIzCMbfuOsGHXJe4HCEilUKqgjF+G9sq0W3m2EJ+Q5jNVCxL0EVFWbbUa1+fM6gOUSEztaxFFZL7uuSGlDRs2XoNHDx8ybOg09JbqSGXu7Nj9K+3alKBHz24ET5/DzYeNUUiTi2vdfJLIoO/GMm/B1DTzSCQSZs6ZzIrlK7l96wZarYxPevfHxzf7qkQ+T3YYCS9evIJy5xmKGaTcwIyvxDp1RbV7UWxcu5HuvbqlO75uswYcW72f0gmpbRZRRP7COy69gicdOnXk8eNZnD67hySjCxplMB+0LkvZ8hUylDc8LJSHD+5StHgp7O3T9/5ITEjgi35DiTU1RK4oyf0APef6jWf6zMF4euZ7jU8lY6IiIxg/bjrXb0QQnwCIu/H0a4tUpsYiac1vvy7B2cWR02cfk5Qkwc1F5MuvulO6TNksrZsVgh4/orhZAi98TcS4BKufvT09iMMhZqEAAQAASURBVPR2gpuRVu2hvs4U9suZ77ANGzbeDLPZzJBBo7n32A2Zuji7Dv5NQa+N/Dx1PGqNhpGj/0Suex+A0HiY/PMmpk5xS1NtHqB3n14UL3aEHduPIAJNe9amwXvvpemXXWQ1N50oiuyft4KGoSa2o6eJxNrLuWS8yIlNu2jeulm64/N5uBPt7QwxEVbtoX7OFC1Y4KVr29vZMaRvdWYuX0+EPj8yIZpiPgl8N+jHDMcYDAauX72Ii6s7vvkznn/KzzM4cVaDUlsPURQZO3EvPbo8on2HD14q06sQRZE5s37jxMn7BAbqMVssOLkr0doXQWlXk23bt+Pi6sTqVXuJjBbQaiw0blSOHj3T1/e5QUJiEor4tJeT9tFJBAaHkN/bK6VNmd8T0+knyJ47j92XW6hYP+t54J8n78SzvRvYDIX/cWJjY9iw6SJyXXJid7nCAYvozYzpS+nUqQlR+jKodKkVouTqgpw4sZM+n6U/n5e3D8NHpi14klcxPnyMQpCQIFrQCmkNb7IkI0aTERXpGwo7Dx/Nr1evUv+qP86CjPtyC7caVmFQy3avXNvRyZk5cycTEhxEUlIiPr5+GRoJQ4ICWfC/3vhcvYfSYGJbcV9aTZxIueq10/T9Y/lfyO2apPys1Phw814ARw4dpF6Dl28q/vEETK8qWNi1k8ycvhW5Y2vcnxX2io28RkTw3zh71ECmKc2YmXNRe36BxE6OGbjwNI6BExbz28QvXvl55BROJQsTd+QOuufC7R7IzFR43/qzq1WrOrubVOLurnMUMUiJFs0cLe7I4KFfZ3rtdh93ZtX249SMSPWuDJBbKFK/eqbntGHjXWb6tIWYle1R/fO+VjRiy197aNU6gjNnn6BQpebLk0pVBIfn5+qVS5QpWz7NXAqFgk/7fJLjMmdXPsJDu/ZTJg4Q0g/30IkSAiKjMxxfvXpVdjStxO0dZymaJCEGC0eLOTJoSMbvuH9kDgl7wOBerYnu505wcADePn4ZhsqJosi8H77FvPcwnkGRHPV1w73TB3QblLaq9aqVa4gxNEChcgRAKtNgkbRm8sRpzJj9S4Zy/bNOUlISSqUyXd05bMhkwhOa4JhPgSNgNukJfLgZnyJdkUhkXLhwDYV9K+SqssgVEJkkMn78YpYu/fGlKUdyClnofd5r1ZLt63ZQIzLVW8QsiigLeVn1lUql1OvbmWOTFlI91IgAnHaRUv2TD1EoFGSGYoULElTCC/F8UMrnaRJFTCXz45DTMfk2bPwH+XPtn9zzL4XSLrl4o1RXnofBHqz6YxW3bj9Gpm1g1V+iacjyZesYMeqHdOerU68edeq92hEhLxAZFY0mIPkiw4yILD0TjsGYtu0ZEomE9/7XlaMT51MjxIAEgdMuUqr0ap+h7nmeZg2q06ReVe4/eoyzoyMmiQOmDN6Nhzav5/S06RS4G0i0vYrwKmX4ev4SNC8UH4yMCOfU2UiU2kpA8gWd0q4my5evpHXbVq+sqGwwGJDJZOk6eixZtIxDJ+1RqNuQ75kD39N7a1BqvJDJNIRHmPh17j4U9s2R24EB2LTtKq6uO2nWIuue/ZnBwd6OJD8PCLP2HnxayJUKL+SB7zP4KyZcH0Dt62E4CTIeyMw8fr8M/Zq8n+n1PauWI/JiAE7PeRWe9NYw5KOciwyxYY3NUPgf5+jhwxgobWUGEwSB4FAz/k8DkMjS5vBJTPrv2OtFJwdEUaQASjaKkZQRUl34RVFEXzT/Syu3ubq5M/ivXfy1dAE37z+geP36DGrR9o2qLrl7eL6yz7LvB9Dy1L1n4UhySl8L4q+hwyi990CKx6bFYiEyIpzIKDO8oAs1DuUYNXI24yZIqVc/7SZDHx/PD98N4Ym/CbnSEe98AkOHf4WnZ76UQ+7Gv/Ygec4ACWDnVJqA+38CNYgLO4ZUVQWJJFVRyuQ6rj+UkJCQ+Na8Cj8b2I9hV25S7Owj8hsErusgvnEVPn8h2a0gCIyd9SMH9h3i7IFjOHt5MumTrthloQBM8aKFKfd9L/a/WMykd4/XGh+v12MwGF+ai+VtEBEZxdLZvxP3JAitjweffP2ZLR+IjVwhKMSCoLZ+v5qlFdi3dx+JSQLyF959FokzAf4B6RoKc5PsyNtavGwpHit24GeUYgESRUuqwRQ4467gy24fvnSOMTMmcqjtUU7tPYzLG7zj/slf6JAQgrZwsZf23bpkAQXW7MDbJAFBRbGnsVyav5yzNWtRpU6DlH7R0VHcveuPQlXYarxEIufchTAmT5zCkOHfp7vGr7PnsH//OZDmw8neQufO79OkWap+8n/6mMAwJ1T2qV8IqUyDXGGPyRiPVKYi0SBHq/JJ+b0gCCRJ6rJxw0a69ciZIjYZ8Y/3jnu5gpzu2pS/V+2mUqSZMImF86XdGD4qbf7GNh3aUrZqRTYuXYPFbKHnx50pUrhgpmUQBIGvp415oZhJAQZNG/Na400mExFRUbg4OeWpSBKLxcK6Veu5deQ0gkpBm0+7UKFC1grw2LDxOly8eBelxnrPrVR7cvXaEcwWEeEFBwWJRE58fMbGs9wiqyHHAHY6LYlOWoiIobig4qKop6KQangLE8x4V3m593bLD1pRpkoFNixZjdlk5qM3fMdJJBKKPPM+zCj/bnR0FGcmTqbZ0zhAAdEWEvZdZMmIwfSfnpqbNTExkbOnT5Joys+LGjM63oFPe33F3Hk/p3tmPHvmFFN/mYfe4IxWLfs/e2cZ39TVB+DnxtqmXipIgeLu7u4+GDbcxwvDhwx3hg/Yhttwd3d3d4cW6p6kjd33Q6FtmipW2PL8fv2Qk3POPTdp7v+cv1KieCaGDPvF5Kx4+fJTFDamZyyPrPUJ9j2Lu2ddVBHPsXUxzb2ssC3M4cPH001RCNBmRF/+GTaVci/CUSBwOZM1Vf/3k5kyN6OHO7/vWMHGVRt5/dqHolXK0rt+7U8q7Pi/4f2ZFhiM5rxpMZMMLs4pDwaCgkNQ2tikb9RbIty8eZvdKzYiRmnJV7Usrdu3+marOVsUhf9iVJGRnD1zAV20Oza2nibvWSsEatetxdadi4Base2iaCSj25fZAGrUatas+oc33kFkyuRE564dsLP7/FbsK6eOcWnjBhBF3EsU5/i1W9T0UVFMULLFEEQJiS0GmYwHBbPSaXryHg0Qk2j4x5+TLlySEFEUOXdoH7cPHcQmgwtN+/Qng6tbkv21Wi3SB09NchYB5H3sza1rlyhZtiJbN29n955LqDQOBPg9I0tu0zkiQx9i51qfVav2UaVqFZMH8/WrVxk2bDZy2zIY9RqCQl4gyhszeuhEVs4ZCkJM3kF1lM5ECRiLIEEbHUgmh/v4R5kX/TCIcnR6HTakz4NYaWPD3A2LOX70JPdu3KFOneqUKJH4IUEQBGrVqUGtOp8vnKPlTz/StHVzXnn7kNHdDbsEFsrE0GiimDZkHNobj5Bp9UTlykTvKSPIkydXimO/NCGhYYxt04faD0OwFiREiXcZd+46Ezcv/uYUmhb+PYiiyMF9+wkKfIdrgtztoiGIzFkK4u4qEBwlmjzfrCUPqFj581cjFEWRY0ePcOrkVeRyCe1/+oHceZJXoH0MPm/fsW7RCrRBYbjmz8ndIplxv/aOuoIjO8QQcmNNRuQ8zepIiZ6tyOSRfKoEQRCoUbMqNWqm3ivlxavXbFu+Hl20lrKNm1KkaNI5iwBenThJdb3pprZopMjZzZsoXbk6L188Z/rUvwgIsSPI/x4ZPIugsIrb2BuNeiRSey7fEHn04D75CsQVaFGrVPTo+gv+oZlRWBUnIvgRUbqCLF52njx5c5IjZ4zwU6tVGEVzr0CJ1AqDQYMk+hhubu5m1UdlMlvCwvxS/dl8Cf43fADPWzdj/7a9eObIxtymDZL0VMmRPRtDxiXuffQx5MzpxZyty/F++w6ZTEZG96T3JvFZ/fdK7m49hF1gBBHuDpTt2JzW34hXx6TBY/HcfZlyBimiKLLr9G3eTfwfDZqm3+Hawr+fZ0+fcP/eHZSuFREkccdpUTRiZSWQPVsmXp4KRGEV55Ch1fhQoviX2ee9evmCtWu2EBWlp2KFojRo3ChRJc2neMBrtVr+Wb4On1sPkDvZ4VyxGC+9T5Mv2obDYhiHjWEUEmx466RAW7k44/r1SHHO7Fk9GTw29ZFqKrWajSs34P/iNTlLFOKH1i2Qy+VJFus6tnk95d6EQrxChjaChKjbd4EYA8jkib/z4JEKjUaHWmPAztG0SKdBryZKaMaiP5aYGbcWzP+TbdtvoXQsRZTqDWHheqKuZ+TPhYvp98vPsf30iaTJlUqVGA1RaCNv4JklA7pEzmB6fao/mi9C6bKlyL9/Dds37SBCrWFY+5a4urgk2ldpY0O3nz9fFIdMJmPM3EmEhoUTFBJCjmxZU6VQu33rDmsmzEX5wo9oGwV25Qoz4vexKXqEfg0O7D7A5XGLKBukRxAEfI7eZvLV24ydNzm9l5YoFkXhvxRVZCQ/9xmJyliX0MAd2DkWRiKNsbxro3yoUM4TVzd3GtXLxd6DJ5DYlEOvDUEpucDAwYM/+3rUKhV9+4wkwlALhVV+Hr4K58KF31j010QcHZ0+23V2/PUHEXP+pkqEAUEQeHD4HGKzmpyMVENwCK5ZMmFdrRrumbLQsnzlL6LBXzjof2TacYzKWgGNaGTJ3oO0XLKEvEWLJ9pfIpFgTMQ6r5VKsLK25u6d22zY/AiFfSMU9uCgd8H31R48sjVCECREa/wJD75N1jydCI3wJyQ4KLbao16vZ9asdbh7xQkrZ2N53j7fitoxO++8H1C8cEzl5NaNKnBm+iUUDhXi1hAVhKvSh9aVn/Fzh/E06/MnUcR57oiiSGbncBzs0zds6UsoANOCXC6PtWqmhlkjJ1N87w2U78OlxYA3LBg4lnl71n7S/2R0dDQ7tuzE1/sdjX5sSp5cSR/2k2LVgiXUeq8kBLAWJNR8GMLqhUsZODpx7x8LFj6VaZNncfWOC0YhM6rwl9g6eAFgNOpwVj6gQsVeuLi4MmH830QLlZDK7DBGXeTHH0p8EYPT79PmcOmmHVa2lRGNeoaP3ECfnhWpUy/OU/lTQ46fPX/Bgs5DqPVKhVyQELb/BvrSmXncqSqqp2/IZleQbFXK4OHhTtvK5T/J+zkpDu09zKnxC6jgr0MGXN57gTudmtG+T+/YPmZKQ5m5vDICEoUcURQZN/YPoqUtUNgLuNuUx/vpOrLkaodMbovRoOXti224ZamNwsqZ/fsOmSgKZ0ybh87qR9yyxHj/O7qWwOf5FpSe9Vm0YDmz5k4DIHee/DjZLkNLydixoigiah9SIp8t3XsOZMniNdx/oUYqi4skMGjO0bJV+qXK+EDOHF70G9ov3a7vmTn1+SGPHTlB4PzN1PpQdyAklGszV3O9UD5Kliz+0WsQRZELF65w8cQZCpYsSt16tdIs/54+e4Hk+DWyvC9UJggC5UIMnFy11aIotPDFOH3qNH8sOIbEpgl+3gfJmK1x7Ht61Uk6dGhNztx5uH37N7z9cyC3yY1O84icWd/RsvXEz76ec2fPMXfefqS2tZBIZNxb+5RLl6cyYdJvifb/GJkliiIjuw+kxOknlEGGXhQ5lcUKRbf6nLv3DFuDAedi+chYOD/VC+YjVxr2xKklOCSUsT/1pdLdAEoIMny3nefXvcf4fc3CJJWFCqUN2sRuVxajApk7awF3nxZArsyArRLCX+4gMvQxdk55EUWR0IDLWCszIZPb4u1jWrDj/NmzHD4RRaacMR7qjhmKogp/SmT4Cw4ffkG/eFk/cnjZc/ORCpk8zpkg2PcQhfKLtGlTHCvrckybeQlr+7iCl9qoAAqVMU1LkR7Y2drSqVvqIqW+BE6ODjg5Jh39F5/o6GiWD5lMgycfCt1FEbb9EnNtZ/LrZPP0KGnhnZ8/2//Zgo2dkh9/+vGj9mSnVm2lerAhtmp3FoOUd8ev8/TZi0+KGPhSWBSF/1JWrliLWqyHwsqBzF4tefdqJwICSmsVTZuWp0/fmI1y1x6dqN/Qm1079uKR0Z3GTWd/EY37qpVriTTWRmHlBIBc4YBW35Alf69g2PDPo5jU6XQ83bCZepHG2B9gAQ34nL9Kj2PHsf3IQ1ZIcBDrJo5F/+IVooMdFbt2o1zNuon2fXT3Fvb7T5L3vVSyESQ0fBnGnt+nMeSfTYmOkclkyEoVRf36DEpBgiiKvBW13MubjfZFSjBh7HTkdnHKO3vnQkhldjy5NRU7x4LI5PZ45opJdmslU2Mb79B85dIFVLqCKOM5XkgkcqRSKwSJHcs27GfhlBhFYeliBWle9S77zh5EI+ZHLvpQKGsAfy+fhuy9QB3dtxYTF24mTF8CRC2u1reZMrLtR32uX5uQ0DDuPXxE3tw5cXc1D7n/WoiiSNj1B7FKQog53OR+6MeFi1eoVPHj8hu+8fZhRvchlH0UTG6jhA0bDpOhfT3+Nzz13rAAEd5+2CQIl7ERJIS/Sb4KnQULH8u7t95cvxWNlX0e3JS5CfA5SmjgFaRCJOXKejF8xBgEQSB/gQKsXD2DPTt3ERLqzQ8t++Hq9vmLEfn6vuXKDTVWdmUAECQyFPY12LT5QKyi8HPkJfxnzmLqvlIjef97cxRklL/xFpuWjWkzOfEDXkqIosjKv1bw/MxVEI1kq1CSHv17JqqAEUWRY0vWUTNAHyszS0TCqV2HsPr5Jxwd7BP10Cja4gcenLlOAVWMv16oqOesk5RWXXtw49oVQtV5UTrEzCeV2ZAlVztePViAjX0eBATcPeuisM5AtMabnAk2xs9fRiKTm1b5dM1UjbDA69x6+yi2TRAEBg5sz+zZ64iIzg8YcLR5xN9LJpMnb4zn57Bff2HY0Am8C8yMkQxYSx/yY4viqUoHkt7o9Xqu376LnZ0tBfPmSXnAF+TMtv2UT1CctGSokf1rt360otBoNDK676+4nbxLfjW8lB9iUOnNTFv1B8o05I+8efUmXqE6EEz3rQa/4CRGWLDw6WzYcAi5fT3kgN6gxufZJkBPrhxK/tf/h1jjx/yFM7h04TxXr96kQoWylCpT9ous55+1+5DbxxUNsbbNzZ0HATx7+oRcueOeH58Scnz08HFyXXiG63vVgUwQqPVWy9knr5i8dsFHz3vh3CX2L98AYSoU2TLSa9QAPNwS36MvnbWIOneDsHrvHZjRKEO48Jyt67fSrnM7ADNlYe2W7Zj191KaPo15JkSJRp5KtNiXLQXAvQcByK1KxV4jY/bm+L3ZR8Dbo1hZu2HvXBB750KIooh1gkfT/v2nsHUyjbSydchNWOBNNEY9vr5vyZgxRtE3eEh/fh02AW9fd4yCK9bSx3TuUMKkWEn1Stc4e+E4WjE3UtGHvDnU9Og99qM/29TyOULRn754SUBgEKWKFfnoPLqfg0MHjlL0aTDxVVyOSAm8cveT5t22bgtX562hnJ+WaETGrt9L+99HUqZc6TTNY/QPMWvLHqrl9rWbFkWhha/H27ehyBUxCiCZwp4sOVsDkMX5FD//z7RSSabMnvT5X58vup43b4KRKwqatEllSvz81UmMSJq9KxbzYMs2hNAwyJqZBsNHUqhUWYIC/XFO5AeY8V0wb16/IH/BIuj1elZPHEPI+YuIegPWRQvSbeqsJJWIWq2W+e1+pMktb+TvD3OXbwxFnPs75euYV/K6cmg/BcP1EE/RIggCgk/ySpbesxew2NiPN5euc1+SCcGjAlbWBoYNHoNcLiVhnSelfXbcXDNg714TmcIpZq1R7yhX3C02b4Qs4DnWqgDA3G9dBMKD7xCqNg0lHf5za3q2DeXi9bvkzVmB3Dmym7xfpWwx9i8vyJlL17GycqFi6XqflH8iMdQaDZtWb8TvzVuqNKxFxYrlYq/x4o0P4+dtxydAgo2VkZplMzOwxw8prmHhtHm823WSTG/DOOxhj22dsvw65bfPvvbUIIoiGEWzdplBRKvVfvS8iyfMpuGDUCSCFAQoGypyccNhXrb7Aa9sWVOe4D0OWTOhFu+gjPc/rBaNOGT9tAqlFiwkxc0bN9FLciIn5nnp7hmjjNNFHGDq9DEmSi5ra2t+bPtlQx5vXLuOnlwkNJmFhsX8fuWBL4DUKQnv3XvA2mkLML72BwclRZvWokOvzgDo/YKRJHgGZTTKuHnnQezrA7v2c/qfHRiDwpBmdqPVwB6ULF2CpJg9bgYZ1p2g8vvQYP9LL5j+1o9RM8wPGyq1Gvlbc4WKp3cod+49oHKFsol6aFRv+gPbXr9i96YtXAkRifaogNzWlce/r6FOrcIk3FrK5La4ZcyDtTIHCrsYD0CjUY+d7AKNmsw16ZvYI9lo1KFReWOrdEGjVmOjjFEklihVilVrinHl0gVkMhmlyvQ2+V+xtbPjz79n8vD+Pd6+fUu58j98tMEwKURR5Pzh/dw5cQyP3Hlo2LFbrAzW6/XMnbWAO/f8MWq15MsmZfrwDikWDjl/5jybJ/2B15MAohQylhb1ZMSfM5I8PH9xDAkDuGMQk2hPDbu378Hr8C2yGWQggJdeiuuFVyybv4RfRqTeuFWybAnWOVvhFmq6FolH6nJYWbDwMYSGiXzItmPvlB97p/xoo0OoU0dF5apVYvsJgkD5ipUoX9G8MOFnXU84JMz+Iyjyc/HC5VhFYWqMW1FR0cwZM42Q6w9ABIeieRg8dRS2SiU3L16jiC4Rg1O8c9er194snTgb3Yu3YGdDkcY16dCzU5LXu3DuEocGTKZSQMw5RX/lDZMe9GfmjpWJ5pXTvH6HVQJDtoco4/qN+9A5ru2D3IKYPUOLefPYM3E8D568w9e5MDKXwjg8CUNYvAKjEZPqYYIg4OxWDqnxKa7ZW8WeEwzqs7Tt28jk2lJJ4p+laNQDCnbv3EevPj0BsFEqWbBoBk8eP8T7jTdlyv1gFg0xYND/6NgpkOvXrpEnT0Wy50h7VFBKvHn1gkMrliKRSWnYow+esmgg5jPbsu8UG/feJlQlwc3RyP86VqVK2eTzP4dHRDDx5+E433iOXaSWrblcqTOoO/Wb1Pvsa08N2uho5EbRrDyy8AnySq3RcGnJJmr6x5zvrYF6L9Vs+f1vymxblqa5JB7O8EZj0vbS2Yr2pYt/9Pq+JBZF4b8Udzc7nnhHIpPHbYr1OhUvnj+gU8eRGI2QLasNI38bmGwxjw88e/qE5cs2EBEp4uoip2+/7ri5e6R6PW5udjx9q0YWLwTIaNDi7Jg2q8PRLRtQTZ1PvfeeDLwMY8/rfmQ5cJAMru6EuDtDWKDJGL9MLnhm8wJg6cghFFh3AJf3FZSiHvqyILALI9ZvTfR6hzaupeLt18jj5bYoG6Tl6MrliSoKcxUvxWurVeRNoO8RXZySvS8bpZI+8/+kc6eRONm2iG1/FaAig80BtJE3sYrnjh6teUmXrs3w8XnOw0eBSCUCJcpmo2/LpgjxLEMNqhZj4epZqMSCcVUO9WpUYU/wyN4UGytzK5KLsxMNa5nnIvyAXC6nZuWUvd4MBgM7D53mxRs/WjWsglfWLCmO8fbxYVqXwVR+FEwxpFzeeprjjcsxZvZEdDodvX/7hyjrtgg2EnTA1jPPkcp28kvXFknOefzoKfSrDlAlKibxfm5/Hd6bT7OpYF7admid4po+NxKJBKuCXuhe34tVPgM8zuNGz0rlP3pe3ct3ZkqHosF6Du08QO9fkihjnghd+vdk9Nmr1LwfhFKQohYNHC+Ygcn9e3702ixYSI5ChQsjMa4HTHPpRmve0LvXaCJV4Owk0KVLM8qWS9kjI0qj4c9FS3jxMgQrBbT4oQ6VKqf+oFawUCGkbDJbj4NdXH7E1CgJIyIjWdxvDA2eRb4fp+LZ041sUSr5scOPSD1cMIqvTX63vhI9XoXzA3Dm1Dlujv2TaqHvkxs9i2Ddiwlk3rE00dxyKrWatwfOUDhe/kB3o5SHJ68RGhZuFrqjtLFB62wLAaaVlP1dbckZz0iUmLKwZb9BjHgVipVfJZTSmANdNLD/wA7s5CJG8seONxr15M1lR+26eTmw/wiaKMiURcGgwWNjvdU/UDB/Bq7eC0EeL6eh36u9WCsz4eIiR57AU0Emk1GhUhWSI3/BQuQvWCjZPgD3793h5IkzFClSiMpVq6ZoSBJFkdk9O5PjyCUqagUC0DNt82Z+2bAFlwyuTJ8ym5uP8sZ4q8jgjl80vUYuYdMi88IlH9DpdGyeMJ/6T8IBBUSB/pIP84dPYuqK+Snew5egWN0qvDhxlxzxlAT3lVC5ufkeKLXcO3uVUgbT795OkBL84Fma5smZwwtql+L1zgtk08swiiKXXGXU6v59RDpY+D5xsIPwBDb4qPCrHD8Wzt59d7G2EqlYMQ/de3ZNlUF67+49HDt2DYMRihXJRtceXdIUhu9gJ5itR9S+oFhx03Q8KcmtKYPHUHzvTWzfR7xonl5hcsQopi2fR7FyJXm29pjJcwBAcHcCYhwrZvYc+t5gLQDhvHi4nnUSCT8lUeBv77J1VAmIW7hMEKj4IIjN/2ymcyIKRomzA0ZRNJGZkaIB56yJe4l/kFmFSpXFu88vXFrxAhe7OFlw4OgN3BxD0USbnk9lxsv8sWA8q1ZtIzhExNZGpHW3epQuW8Zk/h9aNWDC5MNY2cdFfYUG3kCj8iFTjiZkymQup/PkzU+evPnN2j/gksGV2nVTVrKFhgSzY/tObGxsaNa8WawBLTn2r13J89/nUiEgGiMiG7bvpcyQXrRuXZ8T568xf70fUrvmYAPvtPDbvL2sm+lG1ixJhz/PHT2dyqefvU9XZEW+ZxEcnfE3lWpU+iLpUlKiQZP6jPlzHXVfxjkiRYlG7ArnTmZU8ty4dYdsL4JAMFVey177o1KrsU3FZ/+Bmt3acO7FAsoF6pAIAq9leqhdgVw5vz1vQjDRoVv4N9G1R0cUhoMY9DFaa6MhGv+XfyHYtkYna4BB0YCnbyswYtikFOd6/uwpI0Yu47lvVQJVNbj/qhwDfplKRER4imM/0K1HR+S6/RgNMZYLo1GHEL2fHr06pum+7mzfHhvu9IHqL0PYs/RP5HI5udr+yG27uH/rRzbg2rwRdnb2aLVaVGcuxCoJISb/Woar93n96gWiKPL61QsCA/xj3z+zdRMeonk+JiE08XsvX7MOD8rkI1KMy1p7xUVBmS5dUry3o4cPo8VUCMnktkSqbald3Qaj+jARIffQq45Rpog/bdq1YciwgSxdNpm/l0zif61qxXjjOMb9SSQS5o9pg4NhPf6v9/Du5Q68H68hR6H/YSsPoUOzLxMG4esfQJMeM5m5Qc6OyyX46ddjTP8z8dDr+Cyd+gcNH4XhLMgQBIECURKs913i6tUbbN9/inCxikkVOZlNTo5f9El2zvN7DpM/yvRR56mT8ujUxY+7uc/AkBljOVnJiyv2IndkOg4VdKHdxCGfFPYv2JsLqgC5kWy5vdI0j5OjA1M2L8G7TwOuNCjCm94NmLxpcarzg1iwkFayZfeicH4j0RpvIEYBE+SzBUFemEhDfbCuT0hUPWbO3IHvu+R/76IoMvCX37hwIzeBqpr4hNRk7h/nOHzwcKrXk90rBwVya9FGvYtt00Zeo2HDtD0vt6zdTKVn4SYHxVwauLn3GAAdBvbiaHYlOjFGpoWJBq6XykrLNjGGj6P/bKdEqGkG9CpvNGxYvAqAwOBgnr96HeOlDNy5/xClv7lscghW4RsQYNYukUgo2Kw2T23iPJwDJAak1YqTOaOpIfDDAfODV4ooirx8HY1UarpxVumLULNmfuS6XajCbqIJu4izYj+jxw6kUeOGLPxzEstXTGLy1DGJFvka8usACuW8i9+rDfh7H+blgyU4e5TDJVNFChV0NlMsfg5EUWTMqImMHneEk5dzM2fRI/r2HkqURpPsuFP7dpH78EXyvE814oaMJrd92DBlPDqdjrsPQpFbZYjtL5Va8So4O4+eJR3mdf7iZfI8NTV2ygQB3cNXGAyJZMP/CjRv1YyojrU5ndmKB0IUpzxtsO3RJE0FcxKizOBAlGi6lxNFEWxTH3b8gdGzJpBhWl8uNyrC9VZl+HHN79RtlHh6GAsWPgctW1ZHG3k29tkbpX6DKvwhkWILROv6aIQGHDwuZfXKtSnOtXzZKlaue4tveE0CImty4KQ940ZPSdN6mjevgjbyQux6tFH+5MgaQOEiyXuDxScsPALD1YexSkKISTujuP4Ev4BA6tSrxZMKuQgi5jlkEEWOZ7aiVb9uAOzZsY/Sj4JMlHg5ogTu7j8JxHgrPn72AnW85+rLK3fM1uGCFN8XbxJdY7v+3TnhaYPx/X3qRJGT+Zz5qZe5UjGhUvTY8ctY25kajBS2xXFydiez4ymiws8SGXobSfQeenavTcHCRfl91gSWLZ/I/IWTqFTF3IGiWPEStG2Vi3C/fwjwOcqbJ/+gCn9GrqKDcLC6TYNGjczGfA727dlPz14z2Xfcjc275XTtOpob168nO0ar1XJvyTKqBmqRCwJWgoRavhrOL/8Ho9HIul2XkcZLcwUgKuvy1z+Hkp1Xff95bE7zDxR/HcG+Xfs/7uY+EVulkqaj+3E4nxP3ZFouO8KFGnkZNOXji95lzpyJUAdzD1ednRXWCao/p0TdRnX5cc0MbrQqy+VGRcgwrS+jZ0346LV9aSwehf9SHB2dWPTneJb8vQq/ABVOjjIM6vzIFXGViqRSK3wC3Hj54hleOXJhNMZs2hJasVYs34jUtj6CIMGg1xClfotUVpnVK/+h3y+pSwru5OTMgoVjWPzXKgKConB2ktGr91A8MqYxnFETZdZkjQRVcIzr+w99B3Apf0FObd4IRiNFmzWjWqPmAERHR6FQmY93iNRw9dxJ/umzHo+n3kRZyVGVLEjfP5ciBoVwFw1FiJcMXRQJsUn8pyMIAsPWbmL975OJuPsA0U5J5c5dKV29VqL9TcdCTFCwOX379aJTlwieP3tMtmw5cHKO+R4T5pVIzFqYN2d29i4fwb5jZ1m19RKhak8crI/SvlUxqlUoadb/czB27hYiZW2wUrz/nBxqsPvMCVo3ekPO7EmHwepe+5pVfy6sFji5/yiSTNmRys29EqN1yVtIJXKZmQUSiE1knB64ODsxe8NiXrx+Q0SkisL5835yYZ2Szevw+MFq8r43oulEkTuFMtGjXsr/ewlxdLDnl1GDPmk9FiykhfETR7FpwyauXzuFTA62CKiFiiZ9JMqarF29mWEjBiGKIgaDwUxxdPb0afzC8mNta48oimgiXyOV5WTHztPUrZ965cHEKaP5Z/U/3Lx9EpkUmnatQeWqVdKUyyc0MASPROyxYlSMy3nu3DkZvHEh6xatIDooFPf8uZjRp2ucwSAxeSdIiAgK5bdeQ5Bce4S1RkdwTnd+HNWfU/uO4G/QQgLb1gOFniHZsyW6xq79erArkzvn9xwFg5Hs5Ysz+uduifb9IF/8A57HHEgTe/SKInny5qNrj148enAXG6UtXjlSX+VTLpczYfJv+Pv7MmPKXPwCsyKV+VKkoJGBQz5/oTWAI4cOcfdpZqztY3IbSm3zE6DKzN9/LmXgkF+SHPfw5AkqJvCukQsS9C9fo9fp0OllJIyZ0IuOBASFkC+Jj8TG2gatTAI603ZRJk2XVBkQs68ZNG4YIQN68eLNG3J7Zf/kAmY//dyVaYcvUPeVOva+Lrgr+KFn+xRGJr6+lq1b0LJ10pEFFix8TurWr4u7hyubN+1DpwODQzACLU0M2XKbHJw7f4gu7x+nBoMBiURi8js2Go2cPPkIhTKm8E50VCAGnYZ7j0ST/HYp0aBRfdzcXNi69RBarUihCp507pY25UOkSoW1RmfWbqPWERYegYebK9OXz2PtsrVcvvkAmZMdvft0IVdOLwD83r4js1FiJhdEdRQrFy7jwdZDZPANI9jdgZzNalK5YS10kWr0ohWyeJ/JQzGKUjVMZf8H8uTJRY8VM9i4cAWGoDCsPT0YM+x/qXoeJfX0lMlkzF84nrc+bwgOCiR/wT5pMki1btuKH1o1Z+EfC7l5KwqdToqH0zEGDxn6RQxb0dHRrFt/CpldfCVkc/5cuJGlK5I+07149pjML/1IqPpxfhuMr38AWr35XkUiVRChTr7ssigzHxctEXCwTb2X3eemep0aVKlZlbsPH+Pi5JisR2RqyJEtKxFl86I6+iBWke4rM5KlZjmkiRQjTYnChQtSeM7nL2r0JbAoCv/FODm78OvImI11ZGQEnbvMMutjMNrj+9aHpYvX8fSFCtEokNVTzpixg2KVURGRRgRBir/3YQwGDUo7LyJCHnD8mE+qFYUAGVzdGDVm2Cfdk32RQkRefIhdPIvXDXsJldu0i31drmYdytWsYz7W3gF1zqyIAU9NBPWLXJnRLllO80fvrfiRWqIOX2fZ0IG4izIei1HIEMiPNaEY2GcMpWDFzmbzf8DaxoZu49JmDQSoXbce/6wbCcQp0vS6SPLlihGAdnb2FC0Wl3A3rcn0G9WqTKNkQoo/Jz7+ICQQkBLbimzee5oR//spyXGCo61Zm5/UgFe+XFSsVoltx/eBPO67NRp1ZM+Y/P037vQjO49epWxInPfCXTuo2qpham/ni5EjDbkDU6J1p7Zsk8o4vesIqDRY5fZk3NghHyXELFj42kgkEtr91I527x8PA/pPQB2ZoI/UCpUqilUr1nL8xF3UGinOTkY6dmxI1Wox3k337j5AbpWDyLAnhAZcxtYxLwadioDX9wgLC8XR0SnV6+nUtRPx/RRkAc/TVLykYZtmbN5yjLJhcQYgjWjEoWBc3iHPLJkZPnV0ouNdi+Ql7PQTHOPJu8cKA298/Whw/vX7gkgSuBPIxtGzcMnvhZdgxWFjGNUFewQETosRSLJkTtZbuVnLpjRr2TTV9xUTigy5PAw8CTatKmynuEvFyp2RSCQUKFQ01XOaXcM9I7Pnz/jo8Wnh/LkbWNuahqbLFQ48f2Ge8zg+ztmzEyrqcYqXmkQURURHB2yUStxctIRqRZP9hpPiPmWL/5zknGVKl+CfAhnJdysg1rgVKRpwKl3wk41Jn4qzkyPOTo4pd0wF7q6u9Fw8jfWz/8b4LhDBxYEGPdtTvETqPaAsWEhPipcoSfESMYqZvxf9jXewk1kfrVbg1s2b/PXnRgKCBWysjJQv68X/fumDIAhEaTSoo6yQChrevdyOlTITcoUj4UEv2LNjFz1/TvpZkZDSZctSumziXu+pMXBlzuhBWC4PuOFn0u6f250875WBCoWC7n27Jzq+UaumLF+zj4rBcXttnSgSbm9F4J/bqKkCkMIrFY+X7mZPlJpyBms2iUHUlziRQYg5bx2x0tCvRtLpJPLlz8u4hdNTvB94L6s+FDWpU56/lt3DyjbOq1AbeYPGjWPCszNnyUrmLB+3J5fJZAwcPPCjxqaVe3duEh7lhX0CB7fgMCXh4WE4OCT+jHb3yERIBnvwM/WUj3SwwdnRkcK5HXl6KcjECz468iF1KycdJg2QqUJxgh4cIcP76DtRFLmZ14XZDdPXq1sqlVKsUIHPNt/4RTP4Y/JsQm8/BrmMnNXKMiANaZ2+Vyyhx/8R7OzscXWOinVL/4CD9XO2bjvCY5+ySJSNkNo1xDuoGqNGxD2EPdysCQm4ipW1G5myN8MxQzEyZm+MQVaVPbv3fNX76DBqHKdqFOWOEgJFHaddFUg6/0jhEqmrOtR8wkR2583AO3QEiXoOZrMnQ/1aFH1iWmzEWpCgu30PcmSlBU6IwF4xlJuimkwemWjwU9fPfm9WVlYMGdIOa+MeosIvoos8Tg6Pi4wYGefZJQt4HvsHn1Zx80tibWWeNNYQHUB2z+QrlNbo0JLrznGHYp1o5GphD5r90ITMGT1oX98VY8RBtFFBREU+wtG4mUlDki9sULRoYYqN6snxwq6cdZVxLL8zmQb/RI1a1T/q3r5lWv7UiqmbFzN13xrGzZ+KW4YMKQ+yYOEbJE8ed3RaU0VNtOoBNjYCew+p0ckaIbevT6ShIQsWHowNSa5WvTJa1S1CAy7jmfsnnN3K4Jq5Oplz92XKxDlf9R7y581Nxq5NOOsmI1DU8cDayOlK2en3W+q8dXsN7MOVarl5YG0kUjRwzUEg8odKOAVEmFRNByjyLBi1tZzMUitKCkqOiuEcEcPIjpxyDWp+9ntzdxQYO6gT2ewOoQs7QnTYOWzE3YwY0fWLeFF8SRwcbGLTtHxAFEVSiihq0q03JwpkwhBvX3Xe3YYavWOKww0a3BWpaiNRqpdEqX0RInfQv0PpZCtCCoLA4IVTOVUlB6c85JzIasP9H8owNAll8vdMwYL5mLx8LlP3r2XKP4uoXO3LFnywYOFL0axFYwyaSyZtRoMWdzeRqVPXEKZriMK+AQZFI05csGflslVATH5yR/sofF/vJZNXC9wy18TJtRTZ8nXlyPHHKaY/SA2pPS8IgkCHMQM5kMeBN4IOb0HHwVz2tP7tl1R5M2fP6km2Lk056yolXDTwQqbnUAl3HJ0cKRxpevbMqxEIe+GDf1YXfpS4cFdUs8cYQpRopFCJop9dhsgCnlOnbl3qVpdD1EGiwq8giT5Ak/ouVKj0fT133Nw9kEvCzNrlsmisE5ZljoezSwaoVoFASdz57K3MiFO1ctjYWDOoR0tyu5xAG3ENbXQIuoizVMr/ksa1k3cw6T9qEN7tqnA8uy2n3WScKJ+V3nPHpWvl4y+BjY01w6f8xrQ9q5m2fTk9B/RONy//r4kgJtQcfeeEh4fj6OjIveehqSrS8V/iwf17TJ60FLWhFAhWKMSrdPipMmv+uYrMrrZJ36jwCyyY1xbPrNkJDQmmWdNuZM5tHvaTNcNJfp/15Uu3J+T2tcu8fnCP0rXqkjFTykUy4hMdHc2JXVuJVqup2bIN+zat5fboCRTDhvxYx/7wD3o50mrVKtb16kHlx/44ihIuO8tx6Nae9sNGfYnbAmIOKG993mBrZ4+Tk3OqwosT47XPW+QyGZk8klfOfQmWbdzPyn0yZMoYa44oGrCJ3sjeZSnn4Tt57BRH126DcDXKXJ70GTUQF2en2PcDgoLYc+QC2bK4Uaty2VQ/qEVRJFKlwlapTHfPDAvJEx4RgUv+4oSFheHg8O9+jltkVuJotVqGDRnLm3eZEBReoHtAySISAgJV+EWYhtMbDNGULXyXob/GKOB6dOmJX1h57JzymPZT7WPTpukftbn7FOOMX0Agp46exCtXTsqUKZGm64uiyOXLV7l/8y4Va1bFNYMLvWq1olBgNKUFWxTvQ9180OEy9xfO7z1K1rOPyBMl8FJu5HG5HExb8QfW1mnLo5MWgoJDeO2vJnPGTBjcUx9mHJ/Q0BBCg4PI5pXzqz+fAwP8+bnv70htm8R+NzrVBYYOqkr5ChWSHev7zoctUyZgeO2D6ORI9V69KVG5GhDzP6PT6bh89TyRqiha1K+Cna2513xSqDUaZFLpv+7A9W/kvyKzLPIqaVYsW83+g88Q5SUw6gNwVt6ndOncHL+QDbmVk0lfB/lhFi+ZDMDBA4eYOnUTWXJ3MekTpfahS1trmrX4tJD6tHrCa7Vajh09gd5gpG7dmrFV3FPLW18/Du85iKdXNqpWq0Tv1t3JcPUZxSVKXIW4/f/FBoXJU6ooD/7aRLlAHWqMXMjpQJe54yhe4uO90RPDP0yMLcQVFRWFv987PDJmTvO9fUCr1eLz5hXuHpmwTYeCHQP6j+RtaLXYAiy6aH9KFniRYtSewWBg49wZ+J+/CBIJntWr0bZ1Uzyc4mTu1Vv3uX73KdUrFCXve0/S1KDT6dDqdGkq7GEhfUiLvLIoCv9j6HQ6jh0+jEYTRb0G9YmOjqJHr0Uo7Kub9FOH3eD3qbXJlz/GRXvkrxN44V/d7ICTzfUUM2aO+VrL/6yIosi8fj1xOXSOApEGXhDNTVFNK8EZPXChRRUG/rWcKI2GA+tWEvzuHbXad8QrV96vtsaPOZw+fPqS4b9vwy88CxJBR7YMASyY0Pmre5ct27CfA2deoI6W4OlqZPzAVmTJlPpK2Rb+u/xXDl1gkVkpcfvmDe7cuU/lyhXIniMn/fqOI0hjqigURSOFc1xi9LjhAJw/e5rpcx5g62gadiKq97FhY9oVhd+KB/furbs4O2sFpd9q0IgGThFJOcGW7IIVB3Lb8fv+f1AoFJw+dZarZy5SuEwJ6tar9dWs3v7vQ6w/HMhSg06nY9yYaTx+ZkRvdMBB6UvnzvWoVTvtuVU/hbu3b7N06WZCwsDOFlo0r0adeuYpTNJCWg/oFr5f/isyyyKvkicoMIAjh47imTUzlapU5Y+5Czh/swASqalCSikcZvmKybGv27QegsS2mUkfjeo1vTo50rBxk09aU3o9h96+82Vq9yEUu+9HBqOES6IKgBoSBx7aiBSYO4R6Derw2seHHWu3Yu/sQOsOrdNkTEktHyObkmLjuk3s2X8TVZQHVrIgSpfIwNDhA76qd5lGrWbmjPk8fxmJRAJFi2Si/4CfPyrVkEVO/fewKAotQixN9OoxjHB9Y5OHnFy/i1WrZ8Za9q9cvsS0mWexsouryhsRfIY82YLIlTsXbdv/iKvb1/dc+xi0Wi2rJ/zG/cOHqOodQS7iBHiYqGezTRTZqlal76Il6WIpSuhBaCVEsvj3Bahf+ICDLc17dUjS2iaKIk16zCJc2ib2+zQadXjZ7WHG8HaMn78VnwCwsTLStEZeOrWyVAZMDwwGA3t27OXJnQeUqlKeGrWq/Sdc2FPLf+XQBRaZlVYWzl/EyUuZTfLoRIVfYdzoGrH5ogwGA507Dkav+CH2d6WNDkWh3UyJUsWpXbcaJUuVSXT+xEjvjfSuLbs4u3E3fjcf0F4fl39IFEVWSoLxLJKPdr/9Qpnyqb+nL4VfqJFdGzfx/NItRES8atWiaddeST7fZs6Yy6XbOZErnGPb9JH7WLJkOGtXr+f2nXcYjZArpyNDhv2CtbV55cFvlfT+v/mc3Ll7n0Pb9uCaKSOtO7ZGaZP26sT/Zv4rMssir9LGW5839B+wAoV9jdg2vU5F0Ty3GDNuRGzb5AkzuPm4EHJFzGcqiiIRfoupVLEYefPmoNkPLVKMxkmKr/0cevLkGatnLOTu+av0jLQ1KVB4SAwjyjMDJX5sSK9Bqc+/+DlISll4//pljvz1J0JYBIpcXrQfOTbJPH8P79/jt7G7UdjHhStrNa9p2ViCR0YPdmw/QYQKMjgJ9P75J/LlTz6/37fAv0lOfSAkNIxNq9ajVqn5oWNrvD5jPvh/AxZFoUWIpYmnT54wYfxCwqPygyDHTn6fwYPbUaqM6aFj3doNHDh4C3W0E+rwuyD1wMGtPogGjJpT9O5Z85Mt8J+KKIqc2reTu/v3I7G2pm7P3uQuUNikz7ze3Si96wynxHCaSJzN5jhSuQBDtuz4Wks2IaHnilarZXCr7tS+6Y+1IEEURc5lVNBswVjKlDPPy3jv0WO6jL2P0qmUSbs+bC+28hCirNshSGJyf+jVd+ndTGpRFn5lVGo1Izv1o+h1bzwNUp4qDLyuWoBpS+ZYCo+8579y6AKLzEorer2ekcMn8OyVHQYhMwqeUL1qZv7Xv49Jv0cPHzJr5nKCwhwxaIMIC3lDBs+2yK3ciI68SYlCasZOGJmqa37JjfSz5y/YsmQtepWGIjUr0bR5IxOl2r6d+3g4+k8IV2GFQG7BVFF2LIPAhLPbUCq/DcXNvImzUK45Qrb31YBfy0X8u7ak24Spifbv1vU3NNQzadNpw7E2biNKaITcKsYAqddFkNX1PHPmpb1QWHrwrXihfg7mT55D5MYjlAgXCcPAubzODFoyg1w5c6T30r4Z/isyyyKv0s4/a9aze+9doo35kYgBZHH3Y+bs8djEC9GMjo5m4rgZPHmuQ6e3IiTgEo5u9bBxKII26h12snP8sXAiTk7mZ5aU+JzyS6VW88+SNQQ+fYVT9ix07NPZpOJweEQEo5t1o8HTCPaKoWZnrEjRQOCQlvQZ0PuzrCetxA9BBrh18Sxn+vSnim9M9fUo0cj+ktn4bdeBRBWzkyf+zt3nZRAS5AiWRa9Do8+B3DamWrMoihhVu1m8eFRMbsBvmH+bovDC2YtsGTGDSq9VKJBw2VVKgX7tad8t6UKa/zXSIq++r4zTFr4IufPkYfXauVy7cgmdTkvZ8l0STST7U8d2tGn3I77vfBj260KwjnOHl9o3YN26vdSuWztdPaOW/jYMl3V7qKoVMIgiB4+cpujU8VRtEpPjIzgoENnF6zgIUqSigFY0xuZ4gpiHuyxePrwvwbNH9zm2ZiUyKysa9vwZT1m0yfvxH9jbN++k9G1frN9XVRQEgcp+OnYvWZeoojCmj7nuPzTkNQb3plhJ4r5XmbIwe07stigKvzJL5/5Ntcve2L7faOTWSlEev8+Orbto1eaHdF6dBQvfNjKZjJmzJ/Hy+TOePn1KqdINE92I58ufn6XLZxIY4M/0qX/gHdw31khibV+CG/eucevmDYoVL/G1byGWs6fOsW/YDCr5apEJAi/2X2XKuSuMnjUhts/5LfuoEmHEByneopbcCcWrgy1WVl8uh114RAQbV24g2Nefqo3rUqFCmSRlvEqtxvfQearr4mRqNp3A4wNHUQ0blWoPfaNRS3CoEseMcVEKMrk9L71teffWm0yZPT/tpr4w/yYl4fOXrwjdcoxyEYAg4IyMRo/DWTn1DyYvm5vey7Ng4ZunQ6f2NP8hgssXLuCZrQx585lXYrWysmLK9LGoIiM5fOgAazd6YGWXL+Y9m8xEG5ryx7zFjB0/wmzs1yJSpWJE2z5UueVHdkFKpHiNUcfPM2nDX7HV0Deu3EDlp2EIghRDIm5IwTKRbLm8vtgajUYjB/Yd4u6Fa2TJk5Mf27dMNg/h8T8XUctPA+9lmrUgoeKtVxzcsIYmnRKv7pwYfn6hZMhWMfa1IAiI1nVYvWo9Awf3//gbspBmts9ZSt03UfD+jFUpyMjxldto1raFJX/iR2DJ6G8BAIlEQplyFahYuVqy1aZkMhmqyAjU0eZuvGEqZ/x8337JZSaL7zsfdHuPkFcb88CXCgJVA6O5uHhxbJ+QkCAcImKqiGVCzhKjP7uNIVwwRiKKIhddranRvWeariuKIuHhYRgMhhT77vx7IYdbtKbM8t0U+XMz6+vX5+yx47g7CrF/8Xn98CkZxUS+j5CIROcvmDcPHnZPTapbG406bBWRyK3N8wOqo7//g8z3Rujjl7FKwg9kNsp4fOF6Oq3IgoXvD6+cuahdt16K1npXN3dCwiSxSsIPWNmV4PDB4yleJ2EqiM/J3j/XUM1Ph+z9ISWHXgaHr/D4abxrRkYB4ICUc2IEOw3BHDCGEikaCJAayVStdJo9kdUaDRpNVIr9nj59zuhm3XCatZVi/5zlQrcxTB85Ocn+vv4BOAWrzNozBIYS4O+b6JiCBdzQaYNN2sSok1grzVOZ6AxOBAT4pbjub4F/g5IQ4OjewxQJ0Zu0CYKA7lXi36cFCxbMsbOzp2aduokqCeNja2fHvbsvYpWEH5BIrfD1/fQKyJ/Cmr9XUfWWH47v9692gpRa94JY8ceS2D7BfgHYI8EoioSJetYbAtllDOGlGI1BFLlV0IN6DWondYlE0el0hEckfuaJj8Fg4NduA/AeNJdi685hnLCKIa26ExoWbtIvvkwXgs2rB2c0SvF5cD/Ra/z4Y2N0kedN1xf1CkcnF7O+MpmSsFBzefgt8SX3N+mBWqNB9sbfrD37qxCuXb+VDiv6/rEoCr8SD+7fZ9iQcfTpNY7hw8bz4vmz9F7SR+PmnhG5NNis3UqmwtEx7W7xn4t7Vy6Sy99cmEje+ccqzrxy5OZtjozcMKpQYeR/Eg+aSpzJiIxZoh8PHOXYO6f+Hk7v2cHv9euwplIV5tSoxoZZ05Lsq1apeLpqDZVC9EgFAStBQm1/HedXriGpDAAlq5bnmdxo0mYURWSZXRPtLwgCc0a1IYOwBW34afQRx8lqs4uFE3uij7xo0lc06vH8PtJKmnD7/iMOn76AWpO+m6aPRbBXmn3fGtGI0jX9fjsWLMRHo1Yzfcosfu49ll/6j2Pvnn3pvaRPwsbaaNami/IjR47k89Z8ac8wo2+QWVuuUB1Xz12KfW2TOwtBRh17xVD6SjxoLnWhmuDAKmMgm5UaMmbPkurr+fr5M7xTPyZWa8P4Gm34refgZA9gq6YvoMHTCBwEKYIgUEgjQdx7nnsPHiXaP1uWzARldjJrD8jkTHbrxA1pAwf3o4DXXQyqo2jCL6Ew7OWXfi1wcVSb9XWweUX+AkVSd7PpRMKDl39gIPuPneWNT/oZUT+FnHlz4Sc3358IDhbPDAvfDls3b6V/v7H83Gcsc2bOR6vVpveSPhoPD2d02lCz9vROCxr8/DUOCYzcNoIEzes4o0HVRnW4rxTZKYZQW+JIe6krTQQnHho1zJMEYJPZDaPRXB4nhiiKzJ0wk9/qtOP36u0Y2qIrF89fTrL/zm27KXjqIdl1MWt0F6XUvhXAklkLY/t8cMj48JwWPDNhTLAff6oQKVytBomRr0BB2rTKh1S7n6jwK4iaw1QoGUyZMnnQ60yVglGqx1SslH4RCymR2P5Gq9Vy/Owlrty8k+S59FvG2soKnb35DyXIyZqs2b7tSIRvFYui8Cvw6sULxo5bw5ugmoTpavEqoDrDR/xJgP/3YRlPiEsGV/LnlqKLjjvkaKN8KVbUySTnxtcmT9ESvHYxv77RLUNsqJRUKqXKkCFcV+ipLLGPbc8hsaYCtjR6Gsy6QQNS9YD0fv2SG6PH0+C2N1UDtdR7Eoj1otXs/2eVWV9ZwHOenD+I10tzS4e1TxBh4Ykf1mrVqcHr6gV4I405ZL00ahmXyZlr0Zno/usirt1+aP455MzGjsVD2DqnFjv+aMT6PwZRtkRRGpYHXfhpjIZootVvUGo3MWHgjyne57dCWHgErX6eSa9J9xn1l5bGvZazcXfKHkHfGi17d+RsxrhQCFEUOZnDjp9+7pJ+i7Jg4T2iKDJo4BiuPShEqLY2AZG1WPXPKzZv3JreS/tomjatik59Lfa10ahHKT1L0xbNUxz7JT3DJK5OZm2v7WUUKVUs9vX/xgxlg6uRFoIz1u/TZCgFCd0kruQK0/Hoj/U8ePg4Vdf7vf9vVD35lOp+Omq801Lm0D1mDB6bZH+Dt79ZmHGRcCPH9x1JtL9cLqd0pxZccpZgEEW0RiPzHWXccM3PgHHL2PT3AqT+z0yUaTKZjElTRrNs2RD+mNOKlaumU6NWDXr2+gGjajfa6GD0ukh0kQdp27oyCsWXC7P+VBIevMbOXkOrAbsZtxzaDjvGL+P+TvUh+VuhZu3q3C+amWgxbt0P7QTKtGyYjquyYCGO5ctWsWF7KIGq2oRG1+b8zZwMHzY+vZf10bTr0Ba54RiiGGdc0avO8eOP9dNxVWCfJSNq0fT5pRWNKDLGedNVrFiWdzWL4C4oyCLEPKslgkB9qRM5DDJKH7rHohnzU3W9pfMX47z6KDVfqKgcaKD2dV82D5+epHHr8aUbZDaaRg5YCxJUz32SvEarkaPZkzcDke8/64NyAyuz52XdzstMmfQ7kZHm12rdthUrV01i3uwWrFg+kiHDBtCvf2+crA4RpXqK0agnKuI6BXP6ULvut53aKf7+5uiZqzTqsYjhi1T0m/Gapj1n8ubt9+U5LpFIyFm/Mt7xHGzCRQNR5QqQw1LQ5KOwKAq/AiuWb0CqrBu74RYECVjXY+Xydem8so9n/KSRVCnjg73sCI6KI9StGsrwkYNTNfbJ4wdcu3IRnU73WdeUzSsnUTUr8k4S84AQRZFrjjKKdexg0q9igyZkdstoNj63YM1rtGR98IpnTxP3mIjPwaV/UdnP1KstVxQ82r8fiDk0fPhzdxQoli8LQRlszeaJdlJiZ5u4glUQBKYtmYP7rP5caFacxYXKYFNsGmGSH3gS2oRBM05z+8HTRMdmyZQRd9c4z8MxA9qzakol6he7yMBWavYuH0pGd7cU7zO9MBqN+LzzjQ2RG/n7P/jqWmDlUBpbh5wYlM34a+NDs7CCb53CRQrRfMEYztbMy5liGTnfoDD9l8/ELcO3nfDYwn+DK5cu4h+SG5k87lklVxbm8JHvNzS+bv269OpaCFflEewkR8jreZ6588akSukUEBTEqQtXCAw296L/VKp3+ZErztJYw1SAYCCsckGKFC4Y28fZyZGypYrHKgk/oBSk6BApG2xk99otKV7r+avXZLj72qQCpbUgQbz9nIjIyMQHOZrnFHyrEMlTKF8inWNo07ktP26Yw90OlZlVJAehJSdgdOlOgL4JW07as3J9zFoTet45ODiSLXsOJJKY+yxXvhwrVoynWd0w6lT2ZuniITRp3sTset8SwaEhyIwxoWwHT1zgyHU3BLs6KO2zI7OvztUXxViyfm86rzJtSCQSJq36g3ttKnCmRCbOVPIi54TetGzfMr2XZsECAKdOPUBhkzf2tVzhyEtvZ54+SXkf/y1iZ2fPrFlDyeFxGlvpUdztjjKgX2XKV6yQ4lidTsfVyxd4+sTcieBT6dS3G8fzOcUaDXSikWO5Hej0Sy+TfqUrlqEg5tXp5Qg4IcXvyr1UXe/l6atkNJjKvQqvItm8dlOi/R0ze8Qq/D4giiKCU+K5cWUBz8mSPQe/7NnPs8GdWVmlMOdKdscu1zBCo2tz63ExBvYfnahxRy6Xk90rJ0rbmH2S0taWJcvm8r+enlQsdpfxo6sxZfq4dM3ZnxJqjQafd76IoohOp2PG0jNobVqhdMiDtUNRwiStGfl74p/1t8zPw/ojH9aOM+WycrpkZt70qMO4hUlH+1lIHksxk69ApMpglh9JKrUmNCzlHEHfKjKZjAGD+qVpTHBQICOHT8MvJAsGUYmD1VZ69mpC9RrVPtu6+v3xN7uKL+bkmTOgUFCxYydKV61p0kcQBITM7vDulUn7EzGKfII1bwRiDyvJoY+ORoq5EJCqIxKtIpU5owdULkLQnmtkEGNc418rRHLUr5JsXkiJREKzH5pw/10kyshisdW2BEFAtK3Pn2v38/fU3CmuFyBvTi9+6++VYj9f/wAmL9zGu0BQWhvo1KIsdaqUSXHc52LrvlOs2HaLEI07SnkoFYsqeeZjQKowTUqslVdk056T9O7Q9JOuFxEZyYYV6wh+844CFUvTpFnD2P+Bt+98WTFrEVqfQCQZHGjXvzv58udNYcbkKVOudJLFaCxYSE+ePHmOIM9s1q42jwT9rqhbvy5166feui+KIlP/WMX1hyIqgxe20h1UKyFn0tBOn21N9ZvUI0NGN/av2oyoiSZbmaJM7NnRrJ+tZ0aixHsmykKVaECOgAhIJCkfRqKio5HpjSS0D0v0BnQ6faJjqv7UnFsPFlIsLOagFCUauVc0Cz3r1Ur2WgUL5MPpl57sHnQIhVWcMUpuk5Njl+4wtJeBYJUUWcBzkwqUCbG1s6NTl84p3pvRaGTJX0u5dccHECla2JPefXumSo5/DnzfvWXihPn4+VsjFYxkcwtDaSNDYdfcpJ/cxpOLN2/Rp0Pi86QWURQ5sO8wt09fxDGjG+17dMTRIabyqMFgYPnCZXhfvg1yKaUa16JZq2afdD0nRwdGzRz/aYu2YOELYDQaUWskyO0TtEsy8fzZc3LnSdqo8S2TxTMr02aMS9OYo0eOsXLlIcI1uZAKkWR0Xcb0338j8URFacfZyZFxG/9k1bwlqL39sM6YgREDepk5HJQoV5qtjqtxCTeNzNIR8zrVyjO9eboKOQJR6sTPzu17dGTMgVPUfxyOVBAQRZEzmaxo38dcZrs7CviHicgCnuPolpOOv47mfJ/RKKPjCpJIZTYERRbj2NEj1KlbL8XlCoJA7Tp1qV0n5X3GmVOn2bbtKGqNgLubjP6/9MQjo7kDy5fAaDQyY+ocbt0KQG90xFnpT5USToTqiqKMF7UrCFJe+yvQ6XSJVoBOC4+fPmfn6o2IRpEmHVpRsEDc7/LU8dMcW7cDNNE4F8hJn6H9sLExVzSnFkEQ6Ny7C/Tu8klrthCDRVH4FciezZnX/hHIFHGSTBvlT758qc8t9G9g6pQ/CNU1xMYh5oFjpCCLF++mfIVyWFt//EMhPhKJhBY9foYePyfbr2z3Hpx/OYnygdFIBIGXYjTB6HFFxqVCOcmZK2VFULUOnTm3+whlw+KEWYDEgEfpYkmGq42ZO4kl2Rdz//JtBLmUQnUq07Zzu1Tdm2+AClmC3ZAgCESoP6/FKioqms5Dl6O2aoNEIgMVTFx8DuCrKAtf+/gw75/HSB1aoJCDHjh++zW6iBPYJMipaDSocXZMW7j7ax8fbl+/Q9GSRciWJQtv3/kytdMAqj4KIZsg5dX2c4zYe4TpS+cSHhHJtI6/UO9xODJBwCiKLL35K33WzCV3rhyf76YtWPhGqF6jCtt3rQWrKrFtoiji6vLtWsa/BHvXLOXS4xwo7HIR44+Qh6M3H1Ly4Cla1P98xq0yZUpSpkzJZPt0HdCLceevU+NhCEpBglo0sEsMpZngxCVXGZ27pCxDCuTJzdJ8GSl2OzC2zSiKROXJgouzU6JjGjRtgMJKwckNuxAjo7DLm43JIwekSgH3ytsHjTETCc7vqLVKVGoN7o72sQe15JSFqWHKxJncepwTuVVMoYBj5/0JDJzJmPHDP2ne1DJ+xBRChVYoHGOMeG80esIfz8UxmyHWsPcBeRp33WqNhjOnz+Pi4kzp0jH5rn7r+ytZD9+iuF5KpGhgzN4TDFoxmxxe2Zg0eAy5d12l4ntj5NNLT1jlH0SXvt0+/UYtWPjGkEgkZHCG8AS2DgWPKVtuYLqsKT3QqNUsXXYQibIJdu/t6SFRWmaMncLMMX0/23XcMmRg2KSRyfbJlycX+lol8N51GU+jDKMockqMII9gTZDEQKYKxZId/wHnYnmJvOmDXbxn6CUPBQM6tUm0v5OjA8NXz2X574vQ+wQguDjQ+ufOFC5SKNH+8ZWFerecMcbQBHXBFDZZefTwcaoUhanlzKnTzF90AbltTFGXcB89QwZPZdmK3z/bWTg5lvy1lGv3siK3K4sciAQ2H16BTGmeJ10mMaTJ4CaKIlev3iA4JISqVSphY2PN9o3bufn7CsoFxvxIN+87R/6BHWjbpT0Hdh/g1piFVAyNMUZGnn3Kb/efMHv939+0N+Z/CYui8CvQo1dXbt0eRYimPFY2WYhWv8Ld8Qbtfpqe3kv7qrzxiUaiNLVKaAxFOXPqJHXqfd3cG9Vb/Ii7V04O/LmA5xfOYxNlJKfclQMFc9Jlzh+pmqNA0RLc+193jq5eS3bvEAJd7ZBWLcVvQ3okOUYmk9F36P8+as2lCnty6dkbrJRxeRYMhiiyZvw0S09CVm89RLhQB0U8L1iJbSXW7Nj9VRSFyzcdRbA1TSQss8lGdHAwuihf5NYxVjdRFHEQztK83sDYfkHBIVhZKbCzNQ/xFkWRacMnoD1yhWxBUazIYI2ibjn00dE0eBSGVJASIuq5p40k9MhFpkyYjoONkuqPQ5G936hIBIEab6LYsGA5Y+YlXf3TgoXvlSye2ahU3p4zF65iZV8Sg16FoD1O7yFd0ntpX5Vz156jUJrmYVPY5ufQmf2fVVGYGlycnZi4eTGrFi7lxuEzRASFUBgnLnq6UrHbj6kyWgiCQPcpw1k6cjrZHvqilwq8K5iFQb+PSXZcrXq1qJWCB2FiFC2YH0f5aozkN2nPYBeBg32M6jXhQe1jiIgI5879SOS2HrFtcit37ty/RUREOPb2Dh81b2rxvXUWv4jMWDvGnTAlEhlSKy/04SeQO8ZV+NSrrvJj+zilsFqjQaVWJ5l24sCu/Ryds5x8L0K4ZyVlbeEs1O7WBo/jt8mhl6IXRW6KavSPwxne7RfG/fU7wqlbuIpxa8mthmM7j2Ds0+WreVhasPA16datGbNmbwebmkilVkRHXKZ+rWw4OZtXov23cvTIEaLFEsQv4yCRKnj1TkyXCuxj5kxia9lt7N2ylzdPX+KlsyLYxRFd5WIMH5q6aLRfRg9mgm8A1pce4BgahXcuV8r3bJtsyiTPLFkYN39qqtcZXwZlcBF4GyqaKKh06tvUrv15z6fbth2NVRICCBIZGrEaWzZtoWNn84iCz82tq0+RW5um8bBxbYPGfzmiQ9FY45ZOG07x3HKk0pjXBoOBgKBgMjg7Jeph6OsfwLTew/C6+xal1sAhL2dqDuzC+RVbqB1kAEHgiRiFf5CGGzP/In+Jopxau53qoXGh3XaCFK+rLzh75gJVqlY0u4aFr49FUfgVUNra8veSWezasZNHDy9SpGg+GjWZGfvj+68glYBZiRAxGhtl+pTyKliiFAWXrgIgJDgIvV6Pm7tH8oPe8yG/Utu2zVE1qYPv20d4Zc1KJo8vV0b4pxZ1OHRmLs+Cy2ClzIk2yh9nyRFG/Pz5rIUAL7wDkFsVN2tXR32d/1e93giC+bVyeuUir9cDLt6+SLReRmYXLeOG/ohCoeDRs5eMmrkd3zAXZBItebNqmT+uO8p4ZeK2bdqO6/bzZNNJQVCQOdjIq21nuZnFDqkg8EbUclVU0UBwxFqQ8GLVMQ5nktM7wVqkgoAh5PvKi2jBQloYPPQXat64zoF9R3F0cuCnjmNwdHRK72V9VaSS9/mN4h0aRFFEKkmfSoDOTo4MGj0URg9Fo4kiIDiYLBk90rSPKFy0MHP3rOHW/QfIZTIK5cv7xaz2tkolP9b15J8Dp5HZV8Jo1COojtK7RzmTa36qsjAoMACt3omEuwitwYmgAP8vqiiUBTzHaDQgJJLu21rpSq8Wrmw/up2QCCvslVpaNclL3Wpl0Wq1DJ2yktvPQGewwc0+iN/61qNM8bjclCq1mqOzllPntRoEK9BC3mu+LA9YSC+NgAGRTcYgaksc8ZDIiXyqZnbXweQLigSJqZe9PERFdLT2k8K5LFj4VilXoTx//52Ldf9sQhWpoXmLRhQoVDi9l/VVsVUqQQwxa1ckUrH8ayCRSGjd/kdat/8RURTxfvsOZyfHRI34SWFlZcXUpXN47fOWt76+9C5UCGtrq5QHppEPMuh//ToxatSf6OW1kMntiVbdoWQRHfkLJu6R+LFoosxlrtzKFe83lz7rdZJCFEUSZs0SJDJqlc+Jb9BuXvnJkEmNlMitYMaILgCs2HSQzQefEBHtgq08hMbVsvBLtxYmcywYNZW613yRClJASvaXKrb/vphs4TpA4IQxHAekNBGcMEbA1g5DeWMlkjAdime0wINbdy2Kwm8Ei6LwKyGXy2nV+vupMPslKJDPhVtPQpErnICYh5W91V0qVuqevgsDnF1SLiSRMPl6rJXO0Y4cmUt9iWWZIJVKWTt3MDsOnuLizSPk9XKjU8tBWFnFCU6VWk33X+fz+JUOO1srapbNxJgBP6XpMFmvanFO3r6DlX3R2DajUU+m1OlQP5kuP9bk5KgTSBzivFh0UX6UK+XGkF6tEUURvV4fa9EyGo0MnLwZlbwtMoeY7+R+gJqhU1bx5+S4EPT7Jy9SVmf6OWTXSTmqikQUJVwRI2khOMdVwkZB+QAtl0Qd5YS4zU2UaMTO67+VNsDCf4/iJUpSvETyIbH/ZprXLcOMFZeR25eLbTNEXqBjn0rpuKoYbGysyZbFPI9kapBIJJQo/HkPPknRt1MTalR4ztodJ7BSyOjZtkVMrt54zF6ykU37HoDUmlxZbRn2cys8M2VKtdIwazYv7JX+JMyyaG/jT9bsXz49ROlC2fBw2ExIPKWyKBrJ7BxAl9ad6NI6psCATCaLfX/snHVce10Rma0TMiAEGDl7M/uW5YqV50cPH6fQq1AQ4gruyAQBeaQGX6mMl3oNNSQOeAgxctBOkPKDt55lNjqKRZuuUZ/Z5YscsC1Y+FbI4OrGLwPTljf930TVGjVYsXIIejFH7HNGpw2hfL7UK+a+FIIgkPUj5RVAtiyZP1repYV8jlKWLZ/A+rUbCQwMpXadypQtb1pA5vq1a0ybugiVSkHGjHa0blOH+g3SFpbs7ioj1Ecfk9rpPVrVfapVT7lYzaciC3hOwdwOnLkfhMwq3rlXdYKB3VuQJVNG9Ho9Eokk1gP98s27LNsVity+OTIFRAMbj98mZ7ZzNK4dtx/SPjItlgZQwjuSe25WaCKMaDBSQxJjuJMClcPhhVKNwWiDNJ63+wNHCT/Uqf6lPgILacSiKLTw1Rg+ahBTJs7k4ZNIDAYZ7q46ho395bsIh/mgJEwPF/74SCQSWjasQcuGNczeCw4JoWTjYWTJNxT7zDGbg4M3/YiYuoLZY3oCsHnvSTYfuEuEWoqbk4GhPepQPEEFy+oVSlJy/59cfw4KuyLoogNwEI8wpn/SIdWfkzw5stO9eUbW79tJWLQXNjJ/yuU1MKhHzPUFQTBxez9/5SZBUUWxUcR9NzKZkgcv9RiNxrj/L4U8UQ8hj3w5OSt5i+KtgJCgKEBhnZw/M0nJ7q8no1FGMAbOFXVjyuDkc2BasGDh+0UW8JwKZUrQI8ifrYd2EB5lh4NNJG1bFqB8ySLpvbzvigJ5cjL1V3OlnyiK1G4/jCBddRwzxjzbfTQGBo5bxtG1IwgNeM7LN97MWXME/0AjNtZQq0Yx2v7U2mQeqVRKm9ZVWbnmCFJlVQAM6tO071jlq0VtzBrZmqFTN+Eb7oWAgUyO3sz57afY9xOGad16rEL23mD6gUgqsvPQGdo0jQlJc3RywlsuIaEGNKurO9e8FOivPqKCxLSap0wQyOCWgetheoqHixgQOZ/Riro/d7Tke7Jg4V+MTCZj3Pj/MWf2cvyDFMileoplF5g8tEt6L+274INXoXNUIL379kq0z/atm5g9Zw9Z8/bE0UFAAyz86xR2drZUrlIZnU7H3FkLuP8wEKMIuXPY8+uIgVjbmPq7/zKwF4MGTkYjVkNu5YZWdZ8CufyoUKnPF73HD+fYyYPbMWD8Um4/tyLK6Iqz1Su6ti1IlkwxaZ0SFtdcu+M8MjvT8Gu5bVF2Hd1voijE2jwc2aCQYV22INcOXyeH3txYlUNqw56sVtT1jsYGgcfWIkLDihT4xIKRFj4fFkWhha+GQqFgwuTf0Ol06HU6bJRpK0LxtUnSg/AbJFKlonLLYSidqyCTx1kQrWw8OHE1lOjoaI6dv8Efm0KQKpuBAl6rRAZO2ca2he5kcIlLYisIAosm9eXkhescOn2EnFljPBcfPH3J3OW7cHJQ0qtdgyQT4H8OurWpz0/Na/DkxUsye1RJ9lo6vR4xkUeZ0Wj6fTXu0pq9p2/hFRzNBTESCeCnEOjdtQ1uGTIwrdtgCDad47XcSP9xQwkPDePWzft45MrGrE7tLCFcFiz8S4lvFOrWpj5dW9dDpVZjq1RalC2fkf7jFvHEW0aOgnGJ7QVBSpSiHpv3nqBVw+oMn7oBg10bBLmESANs3f0QqWwrP7ZpZTJX46aNKFW6GBvXbwOgbfve2ChtWbTgL8LDImnSrB6FixTlcxJ/f5AnRzZ2LR3Gs5evkEgk5MiWNZmRYBQT+T8S5Gh1utiXVatWZFt+Dzxv+3OKSKIxokbEMXsepsydQv9OPxNy3Q9nQRZvXpFsJQrRoHt7Dm3aidzamp97/kS2LBYPeAsW/u3kyZuXvxbPQKNWI5PLsQl9g1xukVmpJbk0GCdPnGLK5MXkKjLcZB9gn6Eamzftp3KVykye8Dv3XhRGrigNwJ1nKkaNnMyceVNM5nJz92DZipls27yV168vU71GBcqU68nO7Tu4f/8pBQvmpkmzpmYKu891jyBj0eSfCQwOxi8gkLw5GyRb1dhoTDx83ZCgPWPF4gQ9Oc4bQzSviAYRvN3s2PbHdFYtX8ud2aspoDOdQ+vhxPgNC9i8YgOq4FAqNKhBtepVsPDtYFEUWvjqyOXyTy61/qX5VjwIU8vC1XuIiHbBNYN5kl+twRa1JorN+64jVcYlsBUEAZ11AxavP8Cofu1NxgiCQI2KpahRMSaketqijew+K0FuXxuDXs3Bs6uZMaw25Up8uRA2KysrCufPl2K/KuVK4rzkD6KJ62s0aMmTRTTxVi1Zsji3+7XhyLTFtNU5IRUE9DqRPdP+YvzWJfSbO56jQ2dQ3l+HIAhEigYels5ONq2OW3uOI0aoiVJrCA0LS1JRKIoia5ev5cnJmFwjuauVo1MPizeHBQvfE/Gf+4IgpCmvkoWU8Xnny4lLgcitzFN+KKxceeXzmH92HCZKXhuFEPcMlyvzc/zEYTNFIUCmzJ4MGjoAgBvXrzNt2npEq5pIZUoujz9Erarn6Dfg0z3Bk9sb5PLKnqo58mWTcd1bjVQWZyy1MZ7jhwY9Y19LJBKG/jWdfk060CHEFsf3CsGbV55x5ugp/t6wlF9b9qDO3UCsBAkGUeSkpw31WzRgx99rYqp+Otnx7N7jZBWFVy9fY/ey9RCmwiZHZnoP/+WLGgEtWLDwZfnWnTC+ZZJSFm7adBiJ1AGpzDynvq9vMBq1mgePNcht45wuZHJbXnkr8fV9S8aMpqHT1tbW/NSpAwBRUVH07T2UwMgyWCnLc+3uGw7sH8ofi6Z/0SrIri4uuLqkXPDnh3oluf7nTRT2JWLbtKqn1K6fy6TfwLHD6H3vITmvvKQZMZ9DYKCeyYPHMnHhdGa88cVn0ymy6GM8/Z8oIXfj6mxcsha/q3cRBAn33W9TtVrlJM9MIaFh/D19PpoXb8HBliY92lGmXOmP/QgspIJvP+bTgoWvhCzgeewffD9KQoBXbyOwUmYkIuS+2XsS/SucnRyJ0pr/3KUyJUGh6mTn9gsIZP85NQqHCgiCBJncDtH+B+auOPbZ1p9a9Ho9AUFBGI1xVbJkMhnj+9dBqd2MJuwG2vBzZLLayaxR5tXD3j5+ThudfWweDZkgUP1JGGv/XEGV6pVptngSlxsX42L1PPj83JDqrRrzYMyfVDn/kqp3/Cm9+yaTuw5Eq9Umur65439HP30dlc48p9KZ5xhmrGPu+Blf5sOwYMGChe+Q63cfg1VedNGhGI2msbXBvsdp06Qa3r7ByBXOZmO1qmiztoQsXboVqV1TZHI7BEGCtUM5Tp71J8Df75PW/TF7g+CQUKKiTNc8bXhHcjocQBd+GlXYLayjtzK8R/mYggTxCPAPoHq0daySEKB4mJGz63eitLFh0vo/edy5Gher5eZ6i1K0/2siu6YupPyBu1S940+VM885N3wOZ0+fT3RtF89fZk/fCVQ4eI+KF19RaP05xnbql6R8s2DBgoV/Ox+e7/G9xsPDQSKRm52x9LoIsnnao1JFojeaKxF1RntCg4OSvd7qlWsI0lTHShnjiW6lzEqQpgarV6z51FuJRRbwPFVyS6VWExYeYdJWq3IZmleJQqrejyr0DkLkQWoXf0O75rVN+kmlUpzVekrGKy3mKsqQn7nD85ev+HXKKKxHd+ZizbxcrFOAHNP64fP8FRmXHqLaDV+qXn+Lw597mDk68arVWq2WMR3/R6H156h48RUVDt1j7/8mcuHc1ykC81/F4lFo4T/N1wwvPnnkBAeXrEf0DUZwc6Ja55Y0aNbws8ydLZMdjhlyEfD2OO9ebMc1c02MRi2+L7czfUhMUZDc2Wx4cz8SmTwur5E28g6NaxRPdu6jZ6+jlRYhoV3LN/jrVu1esWApD3ceRRkUiTqjExW6tqJFm5iqWxVLF2Hf8kI8ePIUW6USr6yeic6hCwxFlsBSpRQkRLz1B6BkqeKULFU89r3f2v9MlfA4paRUECj3IIjtm3fQtkMbk3kiVSr8j16ioC5OIZtZJ+HJkUtE/qqyeCVZsGDhuyEgKIj5I6eiffwarORkqlSCX34b/Fny/pUolAdnu5dEhAi8ebwa18w1sFJmJNj3DLnd35DbKxttm1Tl0OULKBziwpCMBi2e7hITuZ1Y0ZOgYDFh4V+M0oKcPX2WFq1aftSa06okvHblOhumLsD2pT/RtlY4VCrGr1N+QyaTYatUsmbuQHze+RIUEkqhfA0S/Vzv3byLp9oIgul7YlA4EFMJe9ikUbHtS+YvpvKTMCTx+pcO1nNo9RYqJ1JBct+y9VQK0MF7magQJJS948+2jdtp16ltqu7TggULFtIbURT5a+ZCXp64COpoZLmy0HfSr3h+ZNqFD895//fPfQd7ETun/AT7XUCnDcUxQwlU4c8J9d3FmiXbsbaxwdk+jIRuFw423uTJV5DkePY8AIVVfpM2hZUzz57f/ai1xye1ckulVjNt0FgMt54g1RmJzpuZvlNHkSNHjJf88J9b879OKp6+eIVX1so4OTqYzWEwGBCCIszaM4VE8fjBE3J6Zeenrj9B15j8vQFBQVya9DdOxMkrV4OEu6evo9ZoUCbI7bh90w7K3vFH8V6+CYJAhQAd+5dvoEKlclj4MnwVj8JFixbh5eWFtbU15cqV4/Lly8n237JlC/nz58fa2poiRYqwf//+r7FMC/8x4j9AP/x9KR4/ecaRkXOoftmbGq/VVL/2livjFnH1yo3PMn+/zk1ws76Mi3sFdLoIXj1czptHC5j5a3XaNqsLwKj/tSaT1R60kffR61ToIs5RpdA7qldMvmJzgdxZkRh8zNrtbAyfZe2pYd+uA0Qu2kbNp+GUDzFS80Ewt6Yt4979h7F9JBIJhfLlTVJJCOCQIwtq0WjSFiLqyVgwd6L9xQhzb0tXowTv56/N2v0CAnEKVpm1Owar8PUPSHJNFr4tLPLKwn8dURSZ3GsY5Q/eo+aLSGo+DMF95RHmTZj5Web3zJyJ8oW0ZPSsjExuh++rfby4M5vCWV+ze8UEAPLnzkGTSgL68BPodRFERz7BSdzCjF/bmsjrhMY+AKW5Ywfo35I7b65E3kiZtCoJNZoo1vw6ldrXfakQbKT6Gw1em86zcPp8k35ZMmWkaMH8SSpfK9WozGMnc3u+JLN5yDZA0Fs/HATzuRKTYwBiiPmhzs0o4c1j88/UwreJRV5ZsAB/zVqIzeK91LwfTM2XKiodfcSMXsMxGD7tnPLhmf9Tw6I4OkhR2ucgLPguz+/+QWTgVvYd2oDN+/zFP/f9EVG9k2iNL9qoIIyqfXTpUjdF45qDvRyj0TR5n9Gow97u03y50iK3ZgybQKkDd6j2TkvlQD01zr1i7sAxiGJcHkI7W1uKFy6YqJIQYjwKhSzmsumFhy2lypYwa/cLCMQxTGPWrgyPIjQs3Kz9zePnuIuJyLdE5JiFz8cXVxRu2rSJwYMHM27cOK5fv06xYsWoV68e/v7+ifY/f/487dq1o3v37ty4cYPmzZvTvHlz7t79dM26BQvpFV68bek/VPQ3FQRlgg3sX70pVeO3rtvCyFY9GVH/J8b9/Ctv3/mavG9vZ8fmBf+jZZUQ6pT3oGvL4tw+MJcyxQry2++rGDp5Gfcev2DLn8OY8rMLP1a8w4qJ5Zj5W48U8+eVLFKAHK7P0OviHsZ69T0aVcuRyrv/dK7sPUJejek6ywQb2LNmc5rm6T6wD8eLuREixoS7+QsGzpfJSofu5mHKAAqvzOhF04S9t+2hbvMGZn2ze2YhMIuTWXtQVheye1qSyX8PWOSVBQtw/eYdst31NvG+zmCU8u7sdZODQ1Lcv/+I0V0HMLJee0a168PRA0fN+swc1Z1BbWXUKmtPs1o52bnkF1bPHcmClTsYPGk5q7cc4Nc+P7JhVi1aV7rPmG5W7Fr6q0n+vPjKwvhyvVbNIujUcUYkvS6CzG7eFClaPNWfwafsFXbv2EOpZ6EmbS6iBN8Lt1I9B0DuXDmQN6rAI6sY41a0aORoFitaDuiRaP9ydarxWGF6MI4SjdjlTry4itzTHUOC7/OZtUjp6pUS7W/h28IiryxYiOHliUtk0sepNKSCQMGHfhw/ejLFsRpNFDNGTWFEo46MaNKZBdPnmygY3R0Fqlcsy4whFalYRk61KvkZPOQnjp8+zM3rN5gwbjrz5iwgZ84crFo1hfYtoGUjNctXjKZ2ndrJXDmGrt3aIaoPxcpWURQR1Yfo1qN9CiOTJi1yS6fTEXXzMTbx8gFLBIHs999x807ang31+3TkpIcMnSgiiiJ3bIxkbFEDtwzmCsR8uXLi6+Vq1h6ezZVMHu5m7aWrV+Splamjh0EUkWc172vh8yGIqdn1fQLlypWjTJkyLFy4EACj0UjWrFnp378/I0aMMOvfpk0bVCoVe/fujW0rX748xYsX5++//07xeuHh4Tg6OnLveSj29olrvS38t/gWqhdP6jeCMnvMDwmX6hRg3LI5yY7dtm4rPpOXk/e9U4BBFDlQ2IW5u1YnWxRmz9HzzFhxD0FZE0EiQxdxiVY1pQzpmfbQq6ioaCYv3Mijl2oUMpFG1fPSvnnKAjAtHDl0nIt7joBUQv0OP1CmTJyn47iuA6lw/JFJf1EUufFjOUbNnpCm66g1GjasXI//s1d4FspL/aYNeOvnTy6vbGbhwX4BgUzs/Atl7gfgbpRyy15A+kMVfo0X7hWfbeu3cnvmSsoGxWwyLmeQUnRYV1q2N0++byFpwiMicMlfnLCwMBwcvt5z/GvLK7DIrG+J7zE/7ZfgyNGTPO8+iewoTNqPZrbi93PbTYpEJSQwOJjJzXtQ76U61gh1zVlKjYWjqVi5fJLjQkLDaDfgL8Koj8I6A1q1N1ntz7Bu/qBUFz/zD4vbzu7Yd5Sj55+g0wt45c3ILwP7ppgYPi17hRcvX7Hxr9XoI1TkKF2Utp3axFapXLPiH+TjV5tUJAY4kd+ZGYfWp+pePiCKIieOnebigeNYO9rRpmdHQsPDcXJwIEumjGZ9Jwwajd2hqxRSCbyVGblRNBNT1izE0cHebG4fn7dM7zKIyo9CcBSkvJAZeFO7CFP+nmUpwJVG0kNmWeSVheRIbV66fwPDarSm1nNTz7IgUY9kSg/adWyTxKgYRvUcRKlD97B9740dKDHg064aw6f+ZtLvg3z5kO5i/Ngp3H7oipVtAfR6NULUEcaP70rBQoXTvP7Hjx6xfOkGwiLA0R6692xH3nwpF3NMjMT2MRpNFGuXrsb/4XNs3DPQqX/3WOVddHQ0Y6u1psY709y0DyRaKm6ZSbnSJdN0/Zev37B12Tq0ag3Vf2hE1uyeRKhU5M+dy2zvsG/HXk5PX0KFd9EYEDnvqaTJxIFUr13dbF5RFPmtz1CyHr1DDr2UMNHA2XzODF8156NDzP+rpEVefdEchVqtlmvXrjFy5MjYNolEQu3atblw4UKiYy5cuMDgwYNN2urVq8fOnTsT7R8dHU10dFyi6PBwc3dVC/9dvpWDX9GalXh18DrZdXFu034SPV7liqc49tquw1SNFzkkFQRKPwhg9/a9tHyfoy8hoiiydNMVpPY/xLYpHCqw59R+erSJSPTQkBzW1lZMHto5TWPSwsLp89Gv3E+ZKAmiKHLk+A1eDOtE6/d5kvJVL8frE3fIJsYdXK+gxtUrc1JTJonSxobufbsDsGDqXOY07YGrbxgbMjvh1bwWfYb+L7avh5sr83atZseWXTx49pJGzRtQtEjSlZ5btm9FkbIl2fXeU7RDlzbkyWWeQ8vCt8fXkFdgkVnfKt+KrPgWqFypPIe8nMn+Mi6VglEUkeX2TFZJCLB+yVqqvlQhxPNOKBVi4NDqLckqCmf8vY1IeUsU0hhlnkLpyZvI6qzacpCe7Zukat3xv7ve7evQu32duMNdKpWEqfn+r1+9wcb+46nqE41MEPDdf53fzl1h+rK5CIJAs1bNGPnHGpqGxI1RiwaeR5unpkgJQRCoWbsaNWtX4+ypc8zuOpiMz/xRKxVoSuRizKLpsQYuQRAYP28KV65c5/TBY+QumJ/5zRsmGfqWJUtmpu5YzsaVG3jh/Y5iVcvTu0Fti5LwO8AiryxYiMMqT1YMz+7FFioEuJ1ZyYgm9ZMd5/32HVaXH8cqCQFcjVJun7lOdHQ0VlZWse3xKyLfDdBw674V1vYFAJDJlIi2TVn890bmL5ic5vXnzZePGbPGp3lcQhKTY9HR0Qzv8DOVrniTTZARJRqZdOoyw9bOJ6tnFqysrNDlzIj+7SuTKIKzMg1d86Q9XYdXtqwMnTiCsPAIJv9vBPa3nmMdpWdpbnfajhlIuQplYvs2atGYMlXKs2X1JqQyKb91amsSNRAfQRCY/NdMDh04ys3TF8ngmYmpXdthb2eXaH8Ln4cvqigMDAzEYDDg4eFh0u7h4cHDhw8THePr65tof19f30T7T5s2jQkT0uZRZCHtREdHM2vGfJ48C0UQBArmd2XgkH6ptvR/Tb4FD8KENG3RmKkXrhJw+Co5QrS8dpChrlaC8d1+SnGsGGmewyGDQcqLl+Z58j6gUqsJVtkhTaAPjNDn4db9R1Qtn3g5eaPRyMq/VvDq/A1EiYTC9arQpkNrs37bD5xm077bRGikeLgYGdW3CXlyZEvxXhIjNCyct7tPUjUq5mApCAIlw0WOrttNy59+RCqVUqR0caZI1GTRafAUFLwQo7FBgubMNej/UZdl7679CKsOUS1aAKzgjYYHy/ZwokRhatSqFttPLpfTOg0egXlz52TYpJEpd7TwTfE15BVYZNbXYtuW7Rw6fA1NlIi7q4xfBnQju5dXsmO+BVnxLWBjY03NQV05Ons5hV+Fo5YLPMjvzuBJv6Y4VhUUjFIwVyaKKnM5Fp+3AXqkUlNlnsImE7cf30523LnT5zm8ZitipAb7vNnp82s/k4ODu6PA1XvezB2/iIBQKTYKPY1qFqJ5/Rpmc6X2+9+2YAU132pji4BkFGWEnnvA+fOXqFSpPI4O9oTbW7M9yI98gg0hop636MgbKuONz1uyZkm7gUujiWLb+HnUex4JyEErEnX8MXN+m8rYeVNM+pYpU5IyZVLnBWJvZ0fP/j3TvB4L6YtFXv27ePTwIX/9tY7gYCNKG2jcuCKNmzZK72V9N/SbPIIpvkPId+8dDjqRu552lO3bHmcnx2TH+QUG4hCmhgTlGq1UUajUGhNFIcQpC0/v3Y3cxvQZKwgCIaHJB2kG+PuxZfpk9K+9MTo50KD/QPIVM83fp9frmTdnIfcfxOQ2z583AwOH9EOhUCQ2ZSxJGbs2r9tCmaveOL73cLcWJNR7FsnqOYsZPWciAHaZ3NhkvE5uwRobJDxEQ8UoG9YtWcP/hn3cIWv2iIlUOfUUhSABZBS9F8yGMbMoeeAfE92Bu6sr/xvyv6QniodEIqFBo7o0aFT3o9ZkIe18lWImX5KRI0cSFhYW+/fmzZv0XtK/ktEjJ3HzUSGiJfWJEupx6U4OJk/4Pb2XZcbXLFCSFgRB4LeZE2i7fSGy6b1otnU+kxb9nqoKkooc5nnybjgJNGzVNMkxShsbbBXmCcytBW9yZU+62Mf04RORz9pMxbPPqXT6KaETV/DXzAUmffYePc+cdYH46pqhkjXmWVgT+ozZTERkZIr3khhPXrzEwzvUrN3WPzw2oe3NS9doobennGCHHIEaggM1JA4Y/UPMxqWWawdOkDva9P+jgEbg7I4DHz2nBQspYZFZX56d23eyfqsvEfp66GX18QmpycgRc4nSJK+sshBHw+aNGH9wLY5zfiHfktHM272GHF4pG4NK167K0wR58lSiAecCyXtW2yuNiAkKTcXkFky6WvzxQ8c5MWAaFY48oOKFl+RedYJRnftjNMbNo9ZoGDF9PW9UTYhWNCaU5qzZG8nZ82dM9glp2SsY/czlTh61wJUzF2PeNxrJpIWmgjPWCOQTrGktcSFncDR3b31cPrjDh45S5Lnpda0FCRG3n37UfBYspAaLvPryhIWFMnbsEnzDaqOT1SNMV49V655w7Oix9F7ad0NGdzf+2LWaIivHYzvrf4w6sIZWiTg5JKRI/nz45HIza9dkd0/Ss83dUaBQPi/0mldm79kqk76WKjKSP9v9SLn1h6l+7gE19l7kQNfuPLplWtRywrjpXLzlhYZ6aKjHlbu5GTd6arL38SHMPDE55n3vCW4J/MKkgoDONyjudUgkP0ldySoocBCktBJcKCRREvLSvJBlahBFEfXd5++VhHHkfxrIyVPnPmpOC+nDF1UUurq6IpVK8fPzM2n38/MjY8aMiY7JmDFjmvpbWVnh4OBg8mfh8xLg78eLN1bIFHHuaXKFCw8eR6H6SOXQ5yR+0vGvrRw8sPsgo9r2YWT9nxjdYxBPnyZfLTB3rhy0af8jBfPnTfU1+o4fxoGiGfCW6IkWjVx0EnBpX5/cuZIuJiKRSKhfOQv6eAndddH+FMulMstr9IGAoCA0J67hboxTXmaPFni+/ww6XVwhls37byK1jXMdFwQBjbweyzZ8nIItt1d2/DOZ/27Vbvax1bVKVijDCwcZDoI0xuL1XvhIPFw+6poAQiIhdKIoQgqhdRb+nXwNeQUWmfU1OHLkGgplXJ4gQZAQLanG1s1b03FV6c+r196M6T2UkfU7MKp1L3Zt2ZVsfztbW1q0akqtWtVSZdQCqF23JkGNy3LTVkQnGnkmN3K6QnZ6D03eY6B/53oIkftilYVGox4b3V76dmyc5Jijq7dQOlgf+9pGkFD4lg8H9x+ObVu15RAqSV2TUGipsgRbD95J1f0khsTdyaztmbVIyQoxnvoSiQQhowsyQSCXYI2bEOM98cbVhiLFi3zUNWUyOYbEtjaSb8MYauHrYpFX/x7WrdmAUVHLJORfrizJ3j2n03FV6Y9er2f+5NmMbNKZEY07MWvsdLRabZL9BUGgWtVKtGzdIsnKvAlRKBTU7NeJ45mtiBQNBIt6Dnopaf1r32THtapfDk/HG+h1cWdgneoyzZpVSXLM7mV/UfPeW+Tvv2dBEKjxVsXBRX/E9omICOfhEy1yRdzZRqZw4skLCAkOMpkv4dk3KTLnz0UgepM2gygiiyfHlFnciRaNZBYUeAlWSASBSNGAs1fSjiUpkohsMkgEFIpvLxLRQtJ80ROxQqGgVKlSHDsWZxUxGo0cO3aMChUqJDqmQoUKJv0Bjhw5kmT/b40ojYb1/6xj0R+L8H33cZr4b42QkCB0BvOHrs6gJCIyffKVJKYc/NregyeOnuTWmAVUvfCKag+CqXT4AX/0HE54xOct1e7h5sr8natxmzMA7wFN6bTzT/qNGJDiuEE9fqBfSwEvu3142uyjZcVnLJjQK8n+b96+wyXQXPFrG6IiNF5eGo3W/LEhkzvyLuDj/hecnRxxbViZF4o4L5A7dlC8dVxupUIF8qGpUZS3khhhZxRFzrnKqNej7UddE6BKywbcS+CscstBQp12zT96TgvfL/9FeSWKImdOnWLe7HlcPH8uVRVtvweios3b5Apn3r1LvBrof4GoqGhm9hhKhQN3qfYgiKqX3vBswmL2bt+b8uA0IAgC4+ZOpsGGWbz8pQn5/xrBvI1LUNrYJDsuf+4c/DW+AQVdD5DJej8lsxxmzcwuyebTFUPN5ZWnVsLD2/djX3u/C0Zu5WzWTxP98dvfpj935nRGRWzF4CAMvK6QhypV46oFV+3cisvOktjf1Cu5Aet65fDMnOmjrlmnbg3u5DatHBkhGnApVfAj78LC98x/UV4B+Hi/ZuH8hWzeuJGoqKj0Xs5nITg4HKnMPM9atPa/bQSYMWISbssOU+22P9XvBJBt1XEmDxrz2a/TsEVjhu9bSeiI1hjGd2Hy/jWUjZdHLzEEQWDNnP7UKnSZzNb7yOmwh8EdstG4TF6z9FcfCH31Kjb8N/48QkhY7Ouw0BAMRnOZpzc6EhIS9FFn3zYd23CxeCYixRhPf50ociSHLR0GxJ0HuwzszZF8jkS9N9SpRAOnirjR6ecuyc6dFIIg4FgiP2oxLrpAFEUe5XWncqWkcxVb+Pb4ojkKAQYPHkznzp0pXbo0ZcuWZd68eahUKrp27QpAp06dyJIlC9OmTQNgwIABVKtWjdmzZ9OoUSM2btzI1atXWbJkyZde6ifz8MEDJoxfjFZSBak8D8dPL+WHZvn4qWO79F7aJ5Erdz7slSsxYJpHwck+GA+Pj9v0fizfUsL5E+t2UDE0TrklCAJVnoWxYfk6eg/s81mvJZPJaNYiac+KpGjXvDbtUlmdOH/uXGzKloECr0wTrquyuJDBOe6glT2jlHcvo0zySWkjb9KkdtoqY8Vn4Nih7CiUl02rNxMeHk7tNs1o372DSZ9x86eyuewWrpy/hmBjTbueP1GwwMdVBQOoUas6r/s/59jWQzgERBDu4UDxNo0oX6HsR89p4fvmvySvtFotQwb+hk9gHhQ2RTl96TbZN+5l5pxJsdVbv1eyZLLm0RstEmlcTp/oiCs0+Q/nfNqyfivlHwUhiXdQKRghcmbrPhr/kHbZkhLFihWmWLG0VX8smDcHf09NveyUZXFDvBtg4olzzw5q168V+/qHBhU4Mf0aCvu4g5/RoCWb58crCstVKIPj6pn8/fsCvJ++IGPePEybO9VkHY1/aEyWHFnZt3ozRq2OIjUq0qtVs4++pkKhoPus0ayZMAe7535olQqsyhRg5MThHz2nhe+b/5K8AlixbDX7DrxEalsRvTaUnTuHM3lKf3Lmyp3eS/skatepzLW5N7C2KxrbZtCryeqZdNqFfzuRKhWR52/jIsY9px0EKeLF+wQEBcVW7P1cZHBxpkffHmkao7SxYeKQxIs8+r8/q36okAyQo3x5vLccxtMQd0860YgsXjqoLJ7ZcFAGkNDW6SB/SS7bj3OIsba2Yvr6v/hz9iKunzwPVgp+nTGGHDmyx/Zxy5CBiZuXsGrBUlQ+/jh6ZWFKv+6fVChk6NTRTI0ei+HaI+TReiJzedBn0vBURydY+Db44qeBNm3aEBAQwNixY/H19aV48eIcPHgwNqHu69evTaroVaxYkfXr1zN69GhGjRpFnjx52LlzJ4ULp73c+NdmwR9rEG1aoHi/WZTa12TXnoM0adYQB4fkE6p+y0ilUrp0rsfipfswyiogGg3IjRfoNbDlV6mO9yWLk4SGhbNuyWrC/QIpXLksjZo2SLGq4weMiSRnt0PCM7+Az7a+r4nSxoZCPzXh8sKNlAoTMQIX3WTU6NnW5DMZ80tbOg5eRICqPDKbbBhU16hcMJQqZZPOmZgSKrWakxt3U+G+P1l1Uh7N38Lou4+ZtHB67LUlEgltO7aBjm0+9VZj6fxzN3Q9OhIYHIJbBpfvXkFi4dP4L8mrNSvX8ja0Ala2rgBY2RbilZ8zmzdupn2H9um8uk9j8LC+DBownjBVcaRWmTBorlOlgh35CyZdsfx7wGAwsGfHXh5cvI5TZg9+6tkRB/vUVbD3e/OWgqIUEojPxIplfS90+vV/LHjyK1WfR2AnSHmqMKCpW4aSJYvF9ildtAA1il3kxM1zyGzLoIvywVVxjnEDfv6kax/ethe3y0+pEy4S9PohI1v1YMSK2XhmyRLbp0SJYpQoUSyZWdJGseJFmL1jJf6BgShtbGKrHVv4b/JfkldBgQHsO/gMhUOM4Vtq44FR/IH581Yyf8GUFEZ/25SrUJHSR89y7fZFZDZF0UV742p3l18GpL167rfG3XsP2PvPVgRBQrPOrcmfL0+qxoWFR6CMNPcYtY+Ier9f/7yKws9N/ArJH2hQrRxTy+dGvPCErEYpIaKek4Wz0G9EnJekPPAFPVqWZMHqHRisa4AgQdAcp9dPZcnk8vHnk9vXb+N75CKtXqmRo2FL91G8GNSFlu1bxvZxcXZi8NhhH32NhNjYWDPpz9+JiIwkKjr6m//OLCSOIP5bYo3eEx4ejqOjI/eeh2Jv//VyaYiiSOsfRyKzb2jSrol8Q+8ujjRo9Pkt9l+biIhwdm7fGePd1rwZyq+wSf2SHoQvXrxibrehVHsWhq0g5ZXcwKvaRZny18xUKUB7tumOcO4uckHACNQSHHhjI1Bm+XgqV/5+QjkScvfeA/at245ULqNlt3bkyG6ewN5oNLL7yFnuPX5N09plKVIg9TkXE2PupJlkX3oEpRBnaXolN5Bl9kAaN2uYzEgL/0bCIyJwyV+csLCwf31OpPSSWcOGTMI7uJpZu5f7WabNGPXV1vGlMBqNHD1ymOdPX9KgYV2y50i+mEZKeX7SG6PRyPAeA8l78iFZDVIiRAOn8jkxctU8sqSiiu7ePQdY88t43PUSdIgUEWzIjoKbLcswZu73eyANDQtn/bK1hPkFUrZOVWrWrp6o/L7/+Bk7Dl2gQG5PmtWt8kleDU+ePWdDi36UDYvbPhtEkcsNizDh75kfPa+F75f/isxKL3m1ZeMmNuyUYWWToPCE5gAbNk77auv4kjx5/IhjR06Qv0BeqtWo8cmOGOkt09avWMfT+esoFRITeXXZRUrRX7vSsn2rFMdGRUXRpnxDPIPUGAFHpFQT7DmW35kZB/75br3SDAYD69bv4dm1WzgVKErT7n2wUcZUQIn/fYWFR7B2+xGMBiOdWtVNdc7FxBBFkWEtulH7hml182M57Jh08B+sra2SGGnh30pa5JXFfeYzIQgC1tYkSBcKghiEZ9bv24vhA/b2DnTs3OmLX+djPAjVGg03bt3F0zMz2T2zpNgfYNXvi6j/LALJe+VUdp2U6ON3OH3qHNWqV0527LZ1Wyh05y35pTEJZ3WikRVCMKWaNaJSKvMvqNRq1BrNR1lZ7ty6y6Y/lmH0DUbi6kTzvp0oXa50mudJjMKFClB46m/J9pFIJDSvV5Xm9T7LJQl79MpESQgx38f10xe/S0WhSq1m2bzFBD98gcRByQ+9OlCk6Ldvtbfw38HGWoIoGk2KPBiNemyV/45tgUQioW69+vCZnlGfE1EUuX3/AXq9gRJFCqXKi/3g/iPkOhWjJASwF6Q0eBTO8pmLGDsveY+agKAgjs5aRleDM8L7BOOHjGFczOvM/DFDUrVmvV5PQFAwri7OyOVpS0auUqtZMGk2YXefIljJyVe7Mp16d/4sEQlOjg70HZJ8oRSAgnlzUTBvrk++HsDhnQcoFmqA+AVSBIHoF28/y/zpwa5te7ix/wSiaKRgzYq0/unHrxIxYsFCavDMlgVRfxcwVRRaWyfe/3skT9585Mn78el0viQ+73x5+eoNRQsXSFU4anR0NDfW7qJWqAjvnyMVQowcXbWNZq2bpxi9M6HfSDoGy7CTxKQ9emOMZqmtij4DBqdaSRgYHIyVQvFR4bMbVm3gzr7joNGizJed/uOGJZsvN7VIpVI6dWwOHZvjHyaCyhfeZ3uKf9Z1dLCnX5cfPvl6AOERkVi/MY9083weyK179ylXqkQio75tHj16wqZFKzGGRWKfw5OeQ35OdXSFhbTx7zgRfCOUL5eD4+deo7CJ8cAyGKLwcHlFkaLF03dh3wGfEl68ee0mri7dQtaXQRxzsiGqXH7GLZyGlVXyVhLD2wAkCTbCeaIELp9MWVF4dfshqsVL5ScXJNSROpGnVpUUN9darZZBk5Zz97kEndEGd/sgRv+vPqWLFUj+Rt/z2tuH1T+PprbPhywWAWx7MBG7NbPIH6+asiiKvPZ5i72tLS7OTqmaO92wt0EURZPPTi0asXNNvKrxudPn2bd0PQSHI83sSochfciXhkrSXxKDwcDIzv2peukN+QQpoiiy6fJ91PPHUC6FBMkWLHwtOnRqyajRG5DbxeVzM6qP0qlL0gWPLHw6z5+/ZP7AsWR98A7BKLImX0Z6zhhJ4SLJGxTvnL9CMb3pAUkqCOi8Uy7Qsu6vVVR/EWmiFK4rOHApfx4yuJgX+kjIsg372XL4GRFRGbBTBNO4WhZ+6dYixXEfGNtrCJVPP4+tVv/m5kaWqNX0HmxaWTIoOASVRkPWzJm+aSVVtlxe+MpEshpM2wUHZaL9g4JD+HPibNRPXiPYKyndvC4/tP08h8DPweLZf6JbspvyUTGf+avTD5n37CWDxv2aziuzYCGG8hUqkcF+KxGGXEikMXt7reY5tWt9G/u+fyt6vZ5Jg8YgnLmNa7CaPVmdKdChMZ1/7pbsuOev3+DmE0JCNYPz21De+vmTLRkv+JdvvLG59BC7eM4DWSVWeLm7UCte/tmkuP/4BaPn7MAvLANSIZp82bTMH9c9xYJaH1izeDWRczZQ9f3zMPq2L2NfDWTelmUmcikqKhofX188M2VM8byZGF/L21NpY43WzhoCTfPPhzoryZzRw6y/KIosW7iUZycuIdEbcSqWh19+G/LNeB7evXOPtb1+o5pPFBJBQHPiEaOu32X2lqUf9T1YSJ4vWvX4v0bffr1oWEuHvfQw1uJhCma/yqw549N7Wd888cOL01q9+I3PW27NW0vNVyryCNaUDRMpceguC6bNS3Gs4GxufXgnNZKzoOnGIzo6moMHj3L8xBkMhpiTgRiuMhubUS/w4vGzFK87etZabnhXBruGyB1qECK0YsSsfWi12hTHAmz4cxXVvE1zd1T21TKmx2CmDZ/Ig4ePuXblOkOadmZt/e7MrdOR0b2HoNZ8u7moGndrxyX3OC8VURQ5lcOOn3qbJwo+c+Y8s7sNJvTkVUJuPybgwDn+6PErAUFBX3PJSbJvz0GKXH8Tu8kRBIHKfjr2LF6bziuzYCGOvPnyM2xIQ9xsj2BlPIK7/RFGjWhJtuxe6b20r05SVQq/BH+OmEL9m/4U1soopJdT/24gy0bNSLHidJbcOQhIELMgiiKCs4NZ27XrN9m1cy8hoTHVFNWBoVgLpts9QRAgFfkJL16/w4o9kUQpmiN3qEK0dTM2Hlew//j51Nwut+/ex/1anJIQIKtO4MLqbUwZOJpdO/YSERnJbz0HM7dOJ9bU68aQZl24ce1mquZPDxo2qc+tQh7o4n1nD20FyrRsYNZXr9fTrW5rvLceIfz2Y/zP3eDa2IVsXbflay45SaKjo3m25wS5o+L2Xdl1Av6HLhAeEZGOK7NgIQ5BEJg9bzx5PC9iLR7GQXaIpvWldO/ZNb2X9q9myfzF5N1zjfIhRnIL1lT31vDqry3ce/Ao2XFZMnoQmsE8PVW4iy1uLqYOAO/8/Nm5fTf3Hz4G4M1rbzKEmecntFJFE53COcloNDJ46hYCja2QOdREsG/Aff/aDJu6KoU7jePe3uPkjPc8tBIkeF17yf/a9+LPWYsIC49g2fwljKvfgS0NejGmfgdWLlyW6vm/NnK5nEw1y+ErjSu+GSEaiC6Xj6yJKGyH9RnK2d+XEXHlHoE37hO4ai8T+n07RbM2L1xJ9fdKQgAbQULZ275sXb8tnVf278TiUfgZEQSB7j270r1neq/k2+dzFSjZvnYL5QJ0JiFA9oKUoJsPUxzboHs7Dt6dRgV/LYIgECUauVE8M3N/aBLb59zp82ydMI8Cz4LRS2BnXnf6zh2HPHsmNA+DOEkEekT0gMRBya9N66d43VtP1P9n7yzjq7iaOPzslbgbCSFIgODu7l5aChQvFK3g8BYo7u6U0lLcikvR4u7umpCQQIi73rv7fghNcrlRiEH3+f3yIZs9Z2dvkp09c2b+g9rASudYuFSbv4+dp2ObxumOjwsNQ/1etoVSEHB4GUB5r4ts/ucyPmqJzn4AAoRriDp8j4VmMxm/YFq68+cGVatVJmbBGI6s3or0Lktw4MgfUsx2WThkAr3jLDF4V64XKWk5+PI1W35fz9BxI3LadD2e3nlAmXiFftOAoLDcMUhGJhVq1KxBjZo1ctuMXCU7dXDfxz8wEJNnr3WyEgRBwPGZL8/cX+JWtEiqYzt268jI3YdpftcfQ0GBJEmcczLkmx++TTwnPCKCif1GUOC2J7ZRGuY6r6JM769xq1UJn32XeaOJxYc4BMBYEnApXjhdmzfvu4zKTLd+W21anj1HD9K6ce10x/t4eWMXqQHBQOe4SWAklXZf59X+K/RyWM63PuK7YKIAd/xYN2Y2ZQ9tzHSZc06gVCqZtG4xK6YuJMbdG4WZCVW/bkG7Tu30zh0/cjyt/DQUfidTIkkSO6ODubjjIB27f5PDluvjFxCIRUCE3nFbv3BevX5DmRJyOZdM3sDS0orpMyekf6JMlvHm+n1cJd1M9iohEgc376LM9NS1jC3MzbFpVpPXW06RPz7hXd3LQMK5ZW2MjZPqxZfOWEjA7lMU9Y9mn7majbVLMnr+ZA4UssHYM4SrUiT/Xl3hWDzdrMBzV24SFFcRY8MkH6tSm/LIQ6NXtZQaKSWC5NMqCDz/CIcLnvTfeYCaIRKNogVAAe4RPFi+k9NlStCwUb10588Nhk38H39aWXD27DXQaLGpWIJJY4frnefl40PI0Ut0VSRJYt2Xonh09iYvX3lT2KWA3picRgoK0/s9OkhK7j7NuQ3f/xJyoFAmx8iO7sVGJsbEIWHw3nFBlb6GRe16tTBcMYX9qzcjhUdjXsyFGf8bmKidodVq2TnjV1o8DwfUoIUiD4NYNXEeP84ex6Cz3egbY47Zu/LSM5o4PJ+5p9j8IzmSlMJ9Cyri4t9XuEyZIlUr4HvwJo5S0r9vsKTBRFCgFARMAiOoKahASErBNhGUhNxKewcwt6nboA51G9RJ85yXr7wpFRKPQbJFp6mgxEJQEvTGn4cPn7B1yZ9IgaEoHW3pOeIHXF0LZ7PlulStX5N7m05QPC7p9yxJEkonueOXjExeJKdKgNQqNVqVEhB1jseplemW9RgZGTJ903JWzvuVSA8fFJZmdP2xp4726ZLJ82hw0TMhe1AwwPF1LOf/2EG/PSsYVXgDNV/E85WQsPniTRyvg0PStVkUU15cZbQNXu26tZhVwBznRKmMhOdhnCChEAQKaVRY+QRjLOhuCpV65s/p0+dp1qxRxi6Uw9jb2jJxSfrdVt9euUdDIWlhLAgCTbFg9ytvoqKj+W32UkIevUAwMqRWx9a0/lI/KzE7ccrnQKiTFYSH6Bz3d7bCtWDa7zIyMjJ5G79Q6aP8W0prKS0S6gyUeI6cMprNhQtw8fQVEARKN61D/x6dEn9+6dJV4jb9Q+0oBQhq8kVA8NEHbF65Eacv6nHxt220xwqFIBAniewJDSMyKgpTk5QlHgDiNRpSCm2IKa27UkFZ2AnJ45mO37slRVJZMMVMUGLtHURppW5WZOlIOLfrYJ4NFAqCwICh38PQ79M8768/NtBWa66T6FBWMOFOVAA+Pq+5fu4ydw6dQorTYF+xJD/+byAGBu+vwLMXdX47tJIXymS/n1dKLSWqVchRO/4ryKXHMjnCx5QXp0XnXl24WEhXqPa1SkvRRhlrKFKlWiUm/z6fKZuX878pY3TEUO8/fkqB57r6T4IgYOTuy727D2grmuuUlzaMMeTomm3pXrNEQRVaTZTOMVPxIu1aZMzBdPm2Ew8al+KpoYgkSTwTozkkhlBfSLI9pU837yo+JXDv/kN2btvDK5/UBeHDIyKxkPQfW0oELF2dWdtvDLUO36fONW+q/32bJb1H4hcQkJ1m69GgYT28G5TCR5EQ+I2RRP4pYsoX/boREBSUo7bIyMjkHawsLRDLFyFWSgoUaiSJ0LIF09Rs+hdrK0tGzxjH1C2/MXnFXL0GSVGPX+qVGFfzj2fPX7sppDCktJCUjVEAAzRnb6cr2dCueUXiIm7rHIuLfEazOsXTtRcSRNnL9W7PeVsl8ZJIiKRhlxRMVSGpLM1A1I86JvirDEYjc4HAoGB279jLlavX0ywbt0lBAN8UBVozY8b3GYbr2hPUvfKKOmee82TsMnZt2ZmdZuuhUqmo1rMdV60ERElCkiRumUPxDs0JDA5OlFuRkZH5tNDYuwIJwcIPpWyL+ngY6Y6/6GRIp37d0x0rCAI9+vRg6oZlTF2/lC7fdtYJvp3ee5gykborE2uU+N56SJi3H+0ly8TyUgNBQUvPKLas3pzmNRvWqoqV6pbOMVEbR1FnMqx723vcUA66WRCIlnhJ4qwYjgIBayEhAPm+tv2/95qX0Wg0HDt2kgP7jxATE5vqeWJsPAYprBbjjQ14eOUWb6eups45d+pe8cLxj8NMGpjzJcnf/W8g/xQzJ0pK8E1vFRoe1ipKxWqViIyKSme0TGaRA4Uy2YbK3z3xC7Ina8PK0oJvZo/mRBUnztopOeFqRkSflnzTszOb1m5my/q/iIjUTyPPCBbmZkQb6+9MaQxVPH/wmIJx+v8+UlD6mj6zRn+Lq8VhNOFniAq5hXHsTn75vo5eSr0kSZw5fZ55U+ayf+/BxBd2lUrF3NVLqLByPNd71OaGpUA3hS3qdwvEcoIJJ9S62lNRkohFhbwp+hwXF8fovkM51GkEMT8vZ1XbASyaMi/Fc8uUKM6bEk46xyRJwsPGkCjvtzT0jk502ApBoJFHBJuWr8n2e0iOIAjM/GMBVnN+4ubXVXj4bT20jjb8/cMkljb5lp+7fo9PGsFQGRmZz5dfFk/nWsuynMxvyClHAy40dmPU0ukc++cEfyxdyQt3jw+eWzLUL9ONECSMzUwwCdb3g7Zvw3n5yifNOZvVq067utEoIg8SGXIXIfIIzSv70KmtvkzGK5/XLJm9mJXLVhISmiS10L1/Twbs/wOPga3ZVciQLwQrCifLeA9VSERLulmWD4vb0bBh3szOWP/7Wha0+o6Ikb9yscdYhnXqR2hYyr7ftlIpYt67t+OKSFr36kTBmy8xSSbYXypc4tqOQ9lqe0p0+rYz7TbO5U6XWtzqVIOg+mV5tuMI65r3YUybnhzYfSDHbZKRkfl4/g0WfiidenRC+cNXnChmwTk7JScqOtB86jAiI6P4Y+kfnDh+Ol193dQwNDUhLoXNIMHIADEoVC8gZymoCHjpneacKpWKSYObYxq/naiQG8SFncPZaB/zx36rd250dAwb12xmwbR5PEymuejmVox5+zcgTurFrmr5cEJFI0WSFnBBDLij0A22PTSRqNu+dYbuO6d5+PAJI9p8i/uAmbwdNJ9fWvXg3KlzKZ7btGMbbhnrbg4FSRpsa5XnyeEzFE22tLREidmlxzx9nr42f1ZS0MWZybv+xGdgG259XRWPzvUgOJT1rfszrVkP5oydjiiK6U8kkyEE6UP/w/MoYWFhWFpa8sA9BHNzi/QHfCIEBvizcf1WwsOjaN6yPjVq1sptk9IkJ3Wf/iUiMhIjQ0MunLnA35OXUNUzHBG4UdiC9tNHULd+2mWtKfG/7j9S/5w7Bu+CcBGSlvvtqvDld5050X0M5aJ0y0svNC3B9DVLMjS3zxtfgkNDKVW8GEqlbnq/KIqM/WEkTqcf4hYj8Eqt5X6lgszasEwv7X7m6Kk47jxP/ncdMR+bQHi7WgTde4adux8xxmrEym6MXTI9zZT93OLXucuwW74fK5I+gwfGEjVXT6ZOHf3M0MsXrvDXxAWUeB5APHCnoDmjVs5j27zfqHFCv7z6+hcVGL98dnbeQpqM/nYg9c68QPXupUcrSZyoUYCFO/Ku+HFuExYejk3JioSGhmJh8fk8x1Pic/RZ8fHx7Nm5i8dPPClWzIWOnTrmeHlKRlH5u+eon/qX2NhYRFEiNi6OCb2HUvqON/niFdy3VmLeoTHDJ/4v03Ou/30dsfO3UjD+XT6eJHG4uDmz/l7P5G8G0OSBbvbgaRdjxh3ZgLmZWUrT6RARGcmLl14UdimAZQpZctvWb+Xu0s3U8I8jDokLhczoPGcMNWtX173m8dOcHr2AGv7xCIJAqKTlXK1CmJuZorr9HKMYDYGuDvSYNJzKVStl+jPIbrx8fFjZ9nvqBiYtpmIlkbsdqzNhob4GcERkJBP7j8Thhjv20VpuWyqo+lM3FIZqDCZvwFLQ9f2nXM2Zc2p7tt9HamxYtYH42Ztxjk/aCD1np2TA3t8plAf0qfIq/xWf9Tn6K4BbN69zcP8JTEyM6NGzMw75HHPbpCwjK3ycVqslKjoaM1NTFkycTfS+s5QNEXltAE8qFmDGuiUZ8iPJ8Xnjy5L2P9D4dVLQ7YEZlJk7nJtnL1Hur0s6euy+Cg3G0/vTuXunlKbTQRRFHj17jrmZWYrZ+h4vvVjQ/2dqPg3CWlJyy0LAoltzhryn2xcZFcWoDn1p/iAIQ0GBVpI4WdAYh8bVCTpzA2vfcIIdzXHr0Jy+QwZk6v5ziv917EeTq946WY//lLRi/qFNemtPgGWzFvNm1wmK+0XhYSARVrsks1cuZEKDTjTxjdc595UUh8sfv9CyVdNsv4+UCAgKYk6b3jR5ndTkxl8pEjnoK34Y8VOu2PQpkBl/JWsU5gBPHz9i08bdxMRKlC5VgB69eiTq4GWEWzdvMnPWVgSjxiiUxtxccI3aVa/w8+hh2Wf0B5AdGoSZwczUFFEU2Td/Jc29ouFdmnhzzyj2zP2dOvVqZzo9fPxvc1g4ZhrRD9xBpcS6SmnGTPsFIyND9jathMehGxTRKImRRM4UMqXfyB8yPLezkyPOTim/jOzd+TdFjz/ARasEAQpqVNhcfcWqxX8wNJkju3btJprIKC4UtSJW0uJaqCC12rWk1RctEjLtvF5hYW6G3XtdxlIiPj6e5XOW4nfjIYJSQaF6Vek/ZEC2p9S/vf2IYug6q9JRcHbfPykGCmvWqUGVI5s5c+YCBoZqBtWuiVKpxNqtCBHHHyaWgwP4o8GlYul0bXjl7cOKCXOIf+4DJoYUalSDgaOHfPS9BwYFY/DQMzFICAmNZ2wfeePp7UOhAs4fNb+MTFYTERHOqj/W8fpNGFaWBvTt34N8jk7pD3xHTEwMg38aQ2BUTQyNa3H7sS/Hjv2P5b/NwsRUvwvifxXDdxpPiybNpun11xgKKhCgeojErW3HuduuJeXfKy1Oj57f9+LP6GhO/HMewqNQFnbih3FDMTU1oX7fzlyc8Qc1AjQogDsWAsU7tczw4s7M1JQKZUql+LPIqChurN5J4wANCAoMgBZe0eye/wc1dycFCgOCgrh+9jJhRRxYY+aHa/4CuFQqzfwhAzAyMsQ/MJCIyCgKuxTI0LP30L7DXNi+H8KjMHR15ofxw3Gws8vQ/Xwoe7fsplqAbgM1Q0FB5OOXKZ5vZmrKwi2/c+feA169fMW0+rWxsrTgpdcr1i39i5rBSZkPkiShLOCQrg1arZYl0xbw5sItiI3DsGQhhswcmyX3/uzkZerE61ZL1PSPZ+favxg58eePnl9GJiuRJIm/9+7j4sV7KBTw1VdNqFk7/SZLyVm25DdOno3B0LwmohjLpYFLGD68LbXrZG6ezxmlUom5mRnXrt1E2nGaatEKEBQUjQfnq94sn7mYMTPHZ2pOZydH2s0dxd/L1qH19kewMafKN61p2aY5lWtUZurtR9R/FISFoOStQsPtmq7M79w+Q3MrFArKlEi9imrNjMW0fhKK4p3frRoOV7Ydx6N7h0SdeVEU2bN1D9b5HPgrKhJba2ucirgwcHA/XF0LEx0dg/ebNxRwctJp0JIaHh6erJv7K1qfALA2o0WfztRrUDdjH9YHEhwSipGHr54/dXnux71HT6hYVn99NPiXYfj178GVi1epXcqNEsWLASC4OICvbgWCR35zOtSsmq4dh/cd4vS6nYgBISjy2dByQHcaNf94/eHt67ZSyydGxx/baxU8unATcr+35WeBHCjMZi6cv8CiJcdQmTZEEBQ8P/aW23fGs3hpxjOc/vxzJyqzLxK/NzKryOVr5/Dx9sK5QO6KTed2cPB9nnt44uDuD+8Fnmxe+vPq9ZsM6UAlx8rSgqkrUi6DnbJ0JoeaH+HW6cuY2Fgx9oee2NtmTcOKRxevU0Wrew9mgpKgx0mf998793F3+kqqBWmpLgi8Umnxq2hBqy8SOlQKgoBrOo1VkjNt2HhKH7hFyXef3fMbHnTdd4Tidg5gYULr3l2oVSfrO6QKRvqZRnFIGJmnnv2oVqtp2rShzrG+Q/oz5uptqt7yJp+owlup5X4NV+b26pLm9bVaLXO/H0XLe4HvxHGj8H12iN8EgYGjh3zILSUiISGklLMtSrL2k0yeIzoqioE/jidKaoFKbYanXyxDh8xj4aIR5HfOWDbRxvWbCIpugKFxwrPQ0NiRiLgWrFq5liHDB2Wn+Z8kkU+9MHxPV7BCOBzdczDTgUJBEBgw/EcY/qPez9p2aItb+dLsXbsVUaOhWacvqZpFGXs3b9+l4MtAEHQXSyovP6KiozExNsYvIIBpXX6iydNQygoKoiWRE8YhfPvDd4mNXOxtbTPsQw/uOcCjiSuoF5YQaIu/9YZ+F7tRrmhRFECRelXp/WOfLN/osnOwI1wQMXxPuUcwTlvcv0K5MlQoVybx+8IFXbDq0Ig7fx2nfAREInKusBn9f05/s3Hh5Lk4bTxF6XfdSDUvHzIj4GcW71qTLRt7AgKiRvZXMnmPubMWcuWODYYmDZAkiXmLLvPNSx+6dMtYR/EAfz/OnPfDyCJBSkGpNALzVmxYf0AOFKbAyX1HKB8l6IieGwkKIp56ftB8tevVpnY9/c/Zwc6OWbtWsWXVJl68ek2RimWY16V9ppJs0iLe841eaXO5YA1H9x3h+3eZgRMHj6HYoVvUFVXUliSuhAdS8/tvExskGhsbUdy1SIauFxoWzqK+/6Pl8/DE6/51YxLb3QpipVRjXCQ//UcPzrI15L8YGRqiMVQDus/vKGMDrCxTzyRzsLOj7Ze6pdTthvZl9+g51PGOxgCBWxYCRbq0wsbaKk0brl27ybVJy2n076aYlw/HXy7EqVABSpbImNZxamg1mpQ19OTS4yxD1ijMZv7achi1WWOEdwsBQ+N8eL4pwPWrVzI8R1BwCgfVpTl35kIWWflhZFeDko/BxtqSCHP9F/ZIU0MszfXLpT4GQRBo07YVvyyYwtBxw7P0AW9qZ62nayRJEopkwbPzG/ZQNVhMXBi4aJRojt3AyydtzamU8PXzR7j0ILH8N16SuBIfSvfnUdS54kWdY485OmQ6F85e/Ii7Spl637Thvrnu3855ZyM69++ZqXlMTUxYuG0lRtMHcLd7XeznDWLBpt/SLXk8efw0pR/56XTQctQq8Dx9NVPXTwk7GxuiSzijTabwIEkSASXyZyqIKyOTE2ze9BcR2kao1AlZZgqlIZLxF/zx+4YMz/HC/S0GRrrPQpWBOV7eIVlp6ueDqX4mQiha7FLJNv8YShQvyuiZ4/hl7qQsCxICFCjgTJCl/n1oLIwxfPf8Xb/kT5o+DU1stmIsKGj4MJCNKz5MQ/bitgOUDUvykccI4ytfDfUuvqTOxZcoFmxn3oSZHzR3WrTv3J4rxa11dLm8DERKNc98ZsiwCf+j6cZZ3P22Hv7D2jH577WUTSc4LEkSby/cwk5K2khUCQKF7vtw89adTNvwPgVrV8JPobuovGqr5OtenT96bhmZrCQwwJ9rt8IxNCkKJLyTG5hV5fCR6xnWKLt65QoaoYT+3EHSB2vvfc5YO9gRgf5nK6Tgxz4WczMzvh/2A78smEqXbzujVutr8H4ogrl+dYOfWqJw8YTA34NHTzA9fQ9HMamBSa1ALccz0LAyJbas2kj950m6i0+lGMxDomlzw5c6V19Rbuslpnw7hNjY1BuNfAjGxkaYVi9DuKQrlRFasQiFMyklUbteLUbvX43PoDY87NWAdtsW0n9Y2l2UAQ6v3061IF2fUtsvnt2r0m5MkxG++a4rlx111/zBgohj9XIfPbdMAnJGYTYTFi7pfcpqYzeuXb1J1eoZy84yM4XI957LYrw3JUtnXnPvY8lrGYTvY2djg6J6acIP3ML8XQlqKFoMa5ZJUVfpfU78c5Ljq7Yi+gWhcLChab8uNGmhL9qe3XT/8TumH71Ay5dRiYHAi/kM+GZAgiCvKIpIfvoRZJegaO7fvk9B58yVtL7x88MyOBpIWNRdlSJoqrBMbJACUCNAw5E1W6lTP2t3WZu1akZYUAinth9EDApD6WxP+8F9yO+YL9NzqdVqOnfP2E7yvwT6B2CukfTaQksxcSkPyCTD5k9iwbCJ2DzyRhAl/Es4MXj+xCyZW0YmK/H09MPAUHeHV6FQERKiyfAc1pZGuL+JRalMenkTxXgszOXXjZSo1aE1D28vp3REwveSJHGhuBVzeqT/HHvrH8CycbOIe/YKwciAAvWrMXD0YBSKnN0DLlLQhegaJYg4miT98Fot4tK0VqIGUsxrf73MSTNBSYjXhzV2ksKTuhvGSCIC4JysQYqjRsGTk9cJCw/HIgs3CY2MDBn063TWz1xKvMcbBHNj3FrU49sBvT5ovipVKlGlSsaDtpIkIaTgm8xitQQGBn2QDcnp+1NfZr96w+PTN7AOjMSvoC3VerenWNGMZc7IyOQUz54+Jlbrwvvho8hoY6KiIjEzS///3q2EG4K4H9B9ZzY1lfJ8J9vcoEvvrkzYc5SWLyISP597Fgrqdmqb7lhRFPlt7jJenbmGFBOHQXEXBk0fg6ODfXabrUfV9i15/Gg1Jd/1+IqXRB6Uc2JAiyYA3Lp8gyLhmkQJq38RU1h3ZYRQX3+KJJNFeiBF87XSOvF7taCgxgM/dmzeSY8+6XeUzgyj5kxgkdEcAq89AK2IaZmijJ817oPmsre1ZdCoTFZaxcTr/S8pBQFtVMwH2ZAcRwd76ozuz/HfN+PwMoAwKxPUtcsy7ufBHz23TALym3s2Y2khEPBet25N9FNq1Mp4CWeL5lXYtusuatPyAMTHheHs4EXFSjn7j5AbDUo+hHGLprLEZgGBNx6CIOBQrQxjx49Md9yDB484M3YRDQLeLYpfRnL04Wx2lliPrcoQy5Ku/DRmSI40BLG3tWXgn3PYuOB3tG8CUNhY0mZAd8pXSMg2UCgUCPls4I2vzjgvO2OaVcz8Tkrp4sXYUsSO0i8SOlUGocEhhceDFBrxAXeTPh26f0OHTAT43voH8NvkecQ+90YwNaLcF43p9oHOtcUXLZm9/C8avY5FI0kclUKJk0Si3kYxc/QUhk0epdeROjM4O+dn4Y5VeHr7oNVq5UxCmTxLkSKOPHQP1MkIFMV4bKwzvpPfp18Pbg6ag2TSFkFQIkkiUtQR+vSVX9xS4ov2X7A7Lo7TOw9BaCSqgo4MH5e+n5EkiRn9R9Li5tvEbGjfxwfoevQUbrb2CLaWdB7SlzKp6ApmNZOXz2HZzMUE3X4MahXFGtdi0I+9E39uXMCBGOlBYkYhJDQHsy7s8kHXUxdyRHsvIRM8BC12Kfgry8AofP0CsjRQCFCipBszN/ya4fNFUeT3hb/hdfY6xGuxKFeUoZNHfdC7hEKhQFmsAKL3cxSCwA0xEi/iiFErcF+/i/yO+SibrMT5Q+YfO2ciwSGhvH77lmKFCyXqacrI5CXcSpTCSHUC0N3cMjWOwdQ0Y9qrxYqXoEiB9Xj5B6M2SAjcxEU9pE2b8llt7meBhbk5A3+fxYY5v6J55YdgZUatzm1p0aZ5umN/nb0Ey1VHaKRN8AHaFw8YdLMbJVyLoDBQU6F1Izp265jdtwBAh+4d2atWcW73EaSoGIyLujBl4sjETbYqtauxx3ITlcN0s0qV+dLXe0+JCg1r4b77Iq7v9F9TCr7Yo+LOi5cfNH9aGBoaMmZ25pITzpw4wz+rtyIFhKJwsqXT8AFU+IC1JUDhWhXxPXEXR1HFWyme81I4ICDcfczWdX/R5buuHzTvv7Tt0JaWX7bkqbsH+eztMqTJL5Nx5K7H2cyVy1eYt2A/KpPGCAoVsdGvKeJ0jwWLZmRqnn8OH+HIkcvExUNRV2t+GvQ9RkZZn+r9PskzCPN6gPBjmT5iApV3XtPRrRAlif1SCF8prImSRM7WLsSiv/7IEzuNh/Ye5OqU36geqEUhCHiotIR2qseYWRM+aL492/Zwbd4aavjF8UCKwkBQUlZICpBpJYnrX1Vi0rJZWXULH4RWq2Vou160vBOQ2CTE3UjC8ufudO/37QfNuWvLTq78uhlfL29aKSyxFhICI1GSlustyjDzz0VZZr9M+vxXOkhC3vJZMdHR/PD9aCK0zVEbWKDVxqCIPczixT/j6JRxfVcvT0/++H0DIaEiluYCfft3pWixj9OiyS5yq+vxx3Lh4hVu9JqAW5yulu1ebRDtlDZIksRJZyO+37QoT2SDBQYFM7nLjzR6HIyJoCBC0nK6rD2ztv+R6W6ZAD4+r5nVZwQ1HgdiJSrYKYTQVdBdIJxws2TmoY1ZWrL2ISyaMg+b9cfJ926BHC2JXGxQjHkbl3/QfC89X7HgpzGoHrzERqugmiLh85MkiX8KmzBx35p0daNkspb/is/KS/4KYP7cxVy8boqhaQkkSSI+8gpdvylGx04Za3oBCY38Vvz6B0+fBaBSSTRtVo0v2n6R/sBPhLzi40a16Ebjx7oZeY+kKECglGDMCyMJ40Ed6DO4f+4Y+B6Th47Fef91XLRKtJLERQc1zWaPpOF7+ugZQZIkJgwajd3xO5SIFvhLCqKzYKMjefTcQEvpFeNo3LRBFt5F5rlx/Rb7v59IzYCkSpLjBYwYvnNFqg0400IURaYMGYtw+hbPQoLoobBNXEM/NYb8E/rSsXvOBIhlEsiMv5IDhTmA+4vnbFy/g+gYLeXLu9K5a+cUW5LnFl6enixbugb/QC0mxtCufnFaN60HfHrBwbi4OI4dPYVWq6F5i6aJIukZYdoPo6h2+J7e8f1iMG0VCTuNTwxFqqyZTN26tbLM5o/h7p37/L1+G2JMHBWa1uXLr7/4qCCmzxtfdm3YjkqtxOPhMwqfeUjROCUhkoZzJWwYu24RzplsCJPVHD50FK9B8yik1d2TO13Jidl7P0zvCuC1rx9zWnxLqxDd8rhLNgp+OLIWp3zpd6OUyRr+K4suyHs+KyoykrVrNvDKOxgbKyP6DeiJjW32dpLNDBqNhqWLfuXh4wAASpW0Y+jwQR8scp7bi6ibt+7w7OET6jSqR4H8Ge8uvW/vAYKHLMJR0NVgTe6vtJLErQ5VGb9oepba/KEEh4SyYflqwl/5YlXEmV4D+35QkPBfYmNj2bV1N69femNgaozfX0eo4a9BAK7Yqaj4v+/okEMZKqmh1WoZ06QLTTx0s/FvmEl88/cK3D4wiCuKIoO+6smXdwN1jkdJWt4MbstAufQqR/mv+Ky85q8ADh86xNkzN1EqBdq3b07lqtVy2yQdjhw6wt/7zxMVDfa2SgYP6UPBQoVy7PpZ6ePe+gdw+tgpChcrQvVqVTK13hhVvyONPSN1jvlKcXhJcVR/t9lxvIQV8/7ZkieSMURRZNe23Tw+dw2liRFf9+tOqZKpd1JOD0mSuHjxChePncEmfz5u7jxM3cdBWAsqXqhFfJqWZcaK+bl+75N/Gk2NA3d07IiTRJ73acKIyaM+eN75cxbj+NtBvez/szULMnPbHx88r0zmyYy/kkuPcwDXosWYNPWX3DYjRaKjohgzegGiUXsEQUFsDKzaex1bi4u0bZbzGogfw62bd1g3aiZlnweikGBM0XV0mjKC2vUyFtQrXrcqr/+5RX4x6d/CR4rDJtm/Sf4YiWcPHmdLoDAmJpYjB/8BoGWbFhkKcpavUJbyCzPXGTMtnJ0cGfKu068kSZw4doobpy5gW8CJOd91xcxUXwD4Q9h9+CzbD90jMlaBo43I+MFfUyiDAcjXL19hqxH0NQUjoz/KJqVSgbWgBHT3Tswi4ggMDpYDhTL/CUxMTRk4WL9rbl5hyqTZPPIog9qwMgCXbgcRPHEW02d+WCZ1bhETE8v4ASNwvPoCxygNK+02YNexMUPGjsjQ+EZNGjDNZTWO3kk6P6IkEZvs+aUUBLTB4VluOyT4h8uXr/Hi8XMaNG+ISwae39ZWlgwdl7H7ywiGhoZ065VUtuTxzZfsWvsXAN/17ETRDHakTA93Lx9mLt+Lb5ACM2ORLm0q0K5FxpqXxMdrUKagxWQVEYfv6zcfHChUKBTYmpgBuoFCYxSEB36YjpaMzKdIq9atadW6dfon5gLHjx5j1fpHqE2agwCvAkVGj1rA6jWzMMmi9+mcYuWiFXhtPkRJv2guGivYWqkw01YvyvC6QF3MGe3LJzpZdNelKBoLSdIQypAINBpNtmSBv/J5zZmjpylWqhg1alRLNyCnUCj4pmtH6Jo1m02CIFCnTk3q1KkJQJcendi67i+8Xr2mUoNa/NSiSZYECTUaDdOX/sWtJ2EIQKVSlowb1CXjm6mR0Xp2GAgKYkLCPsouRbwWa0mpt3YjOmsbuMhkLXKg8D/Ojj9XECs0wiCZbpDSpCrbDu3/pAKFkiSxaeoiWj0Lg3fSxs4vItg5azk169TIkLh7p24dmXLlFm9P3aZwmIaHqjh842L4WpEkOPvIRk2PDDQ3OXPiDIdWbEJ8E4DC3oo6PdrxZcevUj3/8sWrbB03l4ruIQCM+XUjXWb8TM3a1dO9VnYhCAJNmzemafOsbeby97ELLNwSiNLkSxAgNEik75iN7F85FGPj9MvpW7RrzcpVe6gdmNRFSytJGLimv1B94e7Bzas3qVy9st4i0sHOlohCDhD8Vuf4G1c7ShUvlsG7k5GRyS5CQ0N4/CwetWmShqLawIYnzzSEhoZgaWmVe8ZlkhXzllLz9LOE5h+CGrtALbc3HeVGi8ZUqVIx3fEW5ubUGvQtJ5dtoJx3BOEKiXNiGG0Ey8RzIiUtlsXTz1zxDwxkyS8ziXn8EkGtwr5WBYZP+jnVxVpEZCQT+g6n0E1PHGNEVi3bgt03TRn8y7CM3n62UKRQQf43eXSWzhkZFcWAcZuJM+6MoFAQHgvzN11Frb5Em8bpbxgaGRkiFnZE8n2ls/h6WdiGPlUrpzk2IjKSo0dOYOdgR906NfXeYyxLuxJ5yR3TZCL5D00k6rVtkcm7lJGRyQ727z+H2qRp4veCoCBO2Zhtf22nd7/eaYzMGt5vQPmhPHn2At91+6kbIiX4qxgoctGTZdMW8EsG9e9+mDqKuW9HU+rxW8zjJc4qIimICpNkzy+pgEO6QUJJklgx71c8TlxCiopB7erMgMn/o0iR1H3dslmLCdhxnFIBsVw2UrCtciGmr1mcI5rzqWFqYkLfn/pm+bzDpq7i5qtaqA0SpDiO3g4kaPpqlkxOv0MxgEP5EoSeeoRlskYunioNZRrUTHOcJEmcO3eR4KBgGjdtqFct0LrTV2zfdpzqyeKNcZKISfEP0ymWyRlytjWeTJ5C5e/OG79g1Ib6wp9Rsbmf9p0ZfP38sfDw0zue78Vbnrp7ZGgOhULBlGWz+GrHYqRpfai2ZAymRQsQhYgkSdwzkTD9qj6uhdNeeL1w9+CfMQtodM2bJt4xNLrly/2pf3Dx/KUUz5ckie0zl9HSPRJH1DiipqV7BNtnLuMzUwYAYNvB2yhNqiZ+LwgKopQtWLvjSIbGF8jvhEvPtlyyURAjibwWNBwqY8P3E1PPVNFqtXRp3I4/G/ckZtQKVjXpxf/6DdX5fAVBoMvYQRx2NeMtGgKkeI4VNKbtyAF5SipARua/SkhwEPFa/TKJeNGC4KDAFEbkXQLvPU/sEPwv5SPg2K4DGZ6jfdf2jDuyEYPZP1D09zE41KtMjCrhte6toOFUFSf6DUt7cSBJEjO+H0WNIw9o6hlFk+dhuGw8zYKJs1Md8+vMxdS/6EnJWAVWgopagVrCNh3h7r0HGbb9U2H11sNEKlsi6GymVmfbgVsZnqPHuCEcKWZOgBRPhKTllKOaWj92S3NjbMb46Qwu35yQ4Uu40X0sPeu2xef1G51zfho1mHN1CvPEUCRC0nLNSoG2YwPq1Ml4szwZGZnsIyaFtZTawBpf34Bsv3ZWNqA8uG0PVYJFnWNGgoKIRy8zPIdLAWcW/70et9/HYjD7B8r/1BWszNBKEjGSyIn8Bnw5tE+68/yxaAVGf+ynyeNgmnpFU+/UMxb8OAaNRpPi+Xfv3id80xFqBWqxElSUjFVQ76Iny2Z8ftrjwSGh3H2hTgwSAqgMbbn9TCA0LGPVBX0H9eNK/WI8NdASJ4ncNZXwaV2Ftl+lnrV78/Yd2pWpz/Ue4wgasojh1b5g99bdOueUdCuG3betuWSjIFzS8kKl5VgVJwZO+N+H3axMjiBnFP7HeL85SdcvqnFlwS0MzZN2tkVtHIXyf1qBEVMTY2KN1ECczvEoYwMsM9nxsHSpEpQuVQKAevVqs3XNZl4EBtPoq5ZUr14l3fE7/9xEnbdxkCx7oFKIyLHNe6idQsmyh9cr7Dz8eT8f29bDn5evvClSUHe3RZIkjv5zgtunL2LtlI8ufbpleVdHL583TF6ym9cBAiaGIq3rFaFftzZZMndUrP7+hMrAhldvbmR4jv7Dvser4xcc3LkfJ5f8LP6yVZq7kAN7/USj56EUEsxAgAaiGq+j99m6aTtdv+2ceF7VGlUpc2gjB/YdQhMfz5T2X2RZubWMjMzH4VKwMBYmb4l/77iFyVsKFsr9hh2ZQTAy0DsWg4iZVeZ0vywtzOn8ToevabPGHNh7iLs37lCwVHEWdv4aAwP96yTn3sNH5L//CnWyoKU1Sm5cvIMoiilm40c88sBY0D1eKRz+2bGf8il03H3h7sGe9dsQRZEvv+1ESbeszdDWarVMX/oX1x6GIIoCJQoZMOPnHh/Vrf5fXr8NQW1gpXc8JT+WGuUrlGP2wY3s3bmP4LBwfu7aPs3OjNdu3OLFxgN0xhoEyA+4eWuZ9uNoft+3IfE8E2NjFm35gwsXLvP03iO6Nm+UJxrXyMjIJODkaEjIy1gUyiQpodiIuzRrXj9Hrp9V2oRW9rZEoMXy/bCBcdr+5X2USiVNmzVK/P5+m2Yc3roHQ1NT/te3O/ns09dD9jh5mUaaJH+lEAQqPAngyKGjfPGlfjDryM4DVExotJuIiaAg4nHKSSQRkZFsXbuFwFdvKFO7Kq3btsxQVVpm2PvPeTb/fYvwKCV2ViIj+jajctkSHz1vYHAwsVpr3t+CihOtCQoJwdIi/bWioaEhCzb+xrmzF7l3/TZNG9WlcuUKqZ4vSRKTegzmhwjTxLLyjlGwc8pSmn3RXCez8KdRg/Hq3p4TB45SqqgrPzWpn+uajDJpIwcK/wO8n3qe3HHUrFKeemWucu7+FQzMqhAf44ON+hwTh/6U02Z+FBbm5qiqliRi/63ELI1oSSS2sttHactZW1ny44jMfRaaqBi0SFwVI4lBpIpgioWgRIqNS/F8S3Nzoo3VEKG7GxZlrNYLckqSxMTBY3A8cpsK8Qm7MuP+Ps7INQspXChj6duSJOk9mL1fv8HL24cKZUtjoFbT/5cNRBt1RlAriRZh7eFHKJRH6NO5ZSY+iZRxyacgwDsOhTLpBSMu4hZfNUu5zPrFS08CAoOoXL6sTjCwYAFnfhz2Q4auGXDrEYUE3YBfQcGAfTsO6AQKAYyNjfimS8Y75snIyOQMCoWCXr1a8Psfh8AwoeEWsefoO6B5lr/IZzc127fi8fVfKRmZlNV8rqAZ4z+wczskLMK+6tAWOrTN8JjgoBBMojQ8IUFUvrhgRGHBEGVsPBqNJsVAo5TC4jACEUs7/eDXvu17uTZnFTXfNRnZuv8CpYb1oPN3XfXOTYl/s76T+6zIqCju3H9IwQLOFMjvxJjZ67jwrDJqQztQwNWXkXw/9nc2LhqewU8hddo0qcyZxXcwMK+YeEzUxlKwQMqbqSGhYTx8+oziroWxt00qkTc2NtLzNamxYvZS6kqmOgtbpSAQ/9hT71xBEKhbt1aeabAmIyOTxLARPzJs8CTCoqphYFyA2IibVC4XT9Xqn1bWb6cenRi79RCtXoQnPotfGEOFNo3SGZk2ZcuUouy0UpkbFBNHoKThthSFJUoqCSaYayHQL+WqAis7GyIRMUf3mS2lsFnn+9aP6T2HUO9RIAUEFZ47z/HL/qPMWrkww+8Y76+xJEnizsNHAFQoXYpTF28yf4MPSrN2YABekRLDZ+5hx1JbHOw+rnFckYIu2Bj/TRS6DX2sTV5TqICz3vlarZZb9x5gbmZGiWKuiccFQaB+gzrUb5C+BNn1G7fIFxqLUqG7xqobqeT4kZN83fFLneMFnZ3p/X32l93LZA1yoPAzJyOp5/PG9ePOwyfs/ecspYoVoEPrUZ9kqeXYBVNZaDqLkJuPkUQJ8/LFGD9jbI7bYV+yKNs4SnPBEmMUXJDCMURByWop78jY2lhDtRJEHrybqDUUIWm5HxbK5hVrGTw2YbETERnJorlLeH3oHFU05iCAuaCk1bNw1s5ZxpTf5qZp1/37D9k0cynal75gbkKxFnXpPagf04aNR3nxPnZB0ewrbEtk5bKESi0xTJZhojIpxaEz++iTsXVOmkwe2pkeI1YQFFsfA2Nn4iOuU7d0MDUr62o4hoWHM/XH0Vjdcsc8Mo7tRe1pMaIvzds0z/Q1NZKEKEkokjlvUZLweurOxH7DadW7C7Xkci0ZmTxPk6ZNqFK1Mtu37gCgU5fRWFlZpzMq79GmXWu2h4dzaudhpKBwlC4OfDtiQII/yEEqVyzHHONomkYbUk8w56EUzTUxAlvXyqlmI1Zv14Int/6gxLsgpyRJ7BNCsb10g9DvwrG0MEeSJI4fO8W62cv4KkBAJSRs8tQMFjm5YQ/turbH0DD1hl3R0TEsGD+D0BuPkbQixmWKMGL2BA7t2s/ddXtw8QziqI0xsTVKcT3SFbVp0gJLpTbluW8+vHxeUzCDTbJSo261itQqdY2LD+MwMK9CfLQ3tupzTBisv4H46+wlvN53CiefUA7lM8eiRQ1+njY20xkTBkol4Wj1jkfExjCx+0CK1K3Cdz/0ljMxZGTyOFZW1qxet4hDBw7y/NltmrdsRpmy5XPbrExjZmrKD8uns2nWMuJfvkGwNKVc2yZ0yuDmR1bySohHFCOpqTAjGC3bpCDMHWyY2v6LFM/v0rsrE3YfpaV7ROIz864UxfPn7pw/e4G69ROCYS/cPRg/cDQVH77FQkjIRi+kUSGeesjRf07QslWzNO36a+0Wbr3z5wpne9oO+Q6HfA4sHzmVgo8TZCPWlXTiTcHyKM26JI4TBAGtcSt+23iYycM/fKMQEjYLf+xWlflr96M1agRIqGJP8VPv6nqBzovnLrFj+lIKPvUj1lDJ2/IF+eW32ZkOVoaHRyCmcDwYLWf/2MijqzfpP3qwzsaZzKeDIH1mImhhYWFYWlrywD0Ec/PMlfB8LqSVQSiji5e3D/u37cXB2ZGv2rdNt0wrI4zq/D2NLnnqvMRvM45k6fVDeuKu/xIdHcP/+g4h+vwdDCUBAWgpWOJpBMUWDsfM1JQ9kxZT+WUoAGelcEoLxpR858zOV3Bk+t9rE+f758hxrh44gaBS0LJ7B0qXLcWYNj1p9TxpN9DbQOJyRSeaXvXBKtlO20ILBdE1luiUSgCYafdzcFXWZJpqNBp2HjzN4xfetGtRi4pl9FPupw4bR/nd1zFKVuJ2zMWYyUc2pPo5psbA7t9jfuY+DZVJz4Sz2jAKKgwpLBhy1VZFo4WjqdcwY50sZbKXsPBwbEpWJDQ0FAuLz/s5Lvus3EXl7/7J+Mjo6Bj2bN9DWEgoX3frmKEyrfT4Y/HvWCzchXUy4XIPYlGO6c6AH1MXWt+yZhP7Zv6GXaxIHBLVBVMcUHOzbUVGzh7P+O+G4nbbG6d4uCVFEYGWlgorAO4ZaGhz8DdKuxVPuN5LT7auWI82IpIi1SrS5dtOTBs2nrL7biRWCMRLIkeqOGHr4U/tZFpZPlIcv1b4Gov8uhtNkSEP+GOcM1XKl/vozwjgxt1H7D9+hVLFCtCxTSO9zdRTJ85wZ+AsSkYn/S29UmuxmjYgoXNmJjh88B/W/DCe7wTbxFIuPzGOG0TRSmGFr1LEr0t9Rs0c9/E3JpMl/Fd8luyvPh1y27dJksTZM+e5c+UmNRvVoXr1qukPSgdfP3+WtvyO+oFJPkArSewoZcG6f7anOu7+/UdM6TUE+7fhaAVwRE0thRlHXE2ZfXgzv81eQsSe05QJ1vCaeK6KEXytsMHk3frjTrfajJ41AYCo6Gg2/rEe/6ceGDvY0nNwX65duMrTMct0KgRO5jcgxNqEr+8HJ667JElivGsxzEroN92qlP8Iiyf1/+jPCCAwKJi1O/5BEAR6f9MCG2srnZ/Hx8fzc6setHyW1FlEI0lcal6KGasyp90YGxtLp6rN6RCqxuHdhqBWktgqBtJVYYsWiX/K2jJ/91qMjFLfHJTJOTLjr+SMws8EOTiYedYs+5OXa/ZSOVBDiEJkxNodjPxjLkUKF/zgOePi4hA83ujt9FePUnH9xm0aNUg5EGVsbISjtQ3VBBudcqPisXDlwAlCvd7Q3DMK3j2E2ws27BSDKIFRwrWskwJny2YuQlp/hGoxCiRJ4p+Tt9hapzS1nocgJFsMFogTiHrgjhW6Xb86hcTyq/9JLB1bJR4TRQ0uH17BrYdKpaLLV03TPCfygbtOkBCgklcYh/4+kqjJlVGmL5/LT+2/Y8NzHwxFiJFEDAUF1qgQJYnqgRr+WbtNDhTKyMjkSe7fe8DKoZOp/jwYB5Qs3nSQSkN6fHRGx5s7jykk6L4KFsGQG4/T7pZpm9+JxvEmFFLoasNG3n/B8umLaHzNByNBCQLUFcy5JkbwUoqlsGBIsK0p+fPlA+D6levsGDqNeq9jUQkCvoduMubcZVQPPXWavagFBVZ3PSkUpwBF0oaes2AAoY/gvUChtdETypdq8kGfSUpUKV+KKuVTL5G78PdRqkbr+n2XeCVXTl6CTAYKW7ZuzuVO51m99xh2sRJhaBCBEoIxUZIWR62SpyevERIahpWlHKyRkZHJW8TFxTG27zAKXn5OkVgF59YeZm/DskxbPuejKtaO7DtExYB4SOYblIJAPm3aZcFly5aisLkVTQJ0fV059xBW/r6W+O0nqR4pgKCkOEpcFAYck0JpK1gTgAanoglNLGNjYxnd/SfqXvemkKAiRhKZfvYaCgdrWkTq5l3V8Ylhs58fgmCVeEwQBGwDPIksEo7KIElaKjbiMc3r6ev7fii2Ntb87/suqf78wqWruD0PAJJ8qUoQiHv4Eq1Wm6nfkaGhIUPnT2TJ/6ZiExJOvCQSK2lxVhjyhnicBQNqPvBn26Zt9OrX82NuSyYX+LREfWRSJHl58b9fMmnj6+fPi/V/UztIxEhQ4CipaP0ohFXTFn7UvCqVCq2pfifDUFMD8uWzT3uwUqHX5ViSJIJionDw0O+Q5oIBfmi4aqemRe+ExWJwSChv/j6DW0zCv7YgCFQJlQi4chdjSf/vQplCQrGtoKSo2RM0YSfRxEcQG+WOhWYbU0d0Stv+LEZS6j+eYhUCJqYmKZydNtZWlvx1cg+jDq4lrFQB7AUVTRQWGKPgLzGQIEmDFJyxjmAyMjIyOc2GGUtp9TwcO0GNiaCggZ+G6yu3EREZ+VHzCiaGen4nXhIxsEg7a1ulVpHi2kwhEPHMU2+Tp4pgyj0pCl+liGXjqokBrj3L19PoTRyqd5trjqIKp/OP8Q8P05vaQiulWJJbySICVeR2YqPfEhcbjBT+Nz92qZRmg6usRlApEVPwpyn5sXTnEgSmLJjGb5f2U2rUd2iUChoqLCkuGHFCCuOGGIl1UCSv377NCtNlZGRkspR1K9ZS5cwz3OKUqAWBMtECRf+5w+7tez9q3vwuBQhSp1AImcK6631SehZrBHh66y7lI3SP/+u/4iWRy2Xs6dQjYY21deM2atzwxvLd5pqRoKDZ83A8n+lvrBkgIKYgD1FVpcVOuZvYiAdoNVHEh1+mRnF3WjXKOZ1ZYyND4lL4PCSV4oP0nhu3aMKOG8fovn4uwZaGlFeYUkMw45UUxx4xGFtJie8LfY1dmbyPnFH4iZITGYRPXrxk1dYTxMWLfN2iMg1rpd/x91Ph2IGjlPeLgWSZFApBIN79dYbnuH/3PlsX/4noG4RgZ8kXA3pQq25NCjSqjt/LoziICTsyMZJIQGVXSpdMu6NVk85fcu7kLSoki1fdNxdo0qEt1+4vh6ikZif3pShuClHcszWkert61H0nOPv4+XOcXoeCoOs084sqzttJNA1KOhYjiSiLueD9OJACcUl/PxecjVky/3+89fPn1OU7FCvkRNtmP3/QLmBwSCiBwcG4FiqYaefjWLMCwY9PYC0ljJMkiTtuNsxvlXYmYloUcilAgYAoWikTdMDsUVNEYcgeMRjnApU+eF4ZGZncJTIigo0bNvPaJ4jixZ3p1LVzmhp4nxJarRbppX6melHPEC5evErzZF0kUyMqOpqlU+cTev85qFUUa1yLPgP78mXfruy7/JCaAUn+5VwBYwZ9n/bOf4MGddjnZk+RR0llVRGSFqsqpYl+469zboAUzykxDD8TFTHlnVg4Zkjiz8S3+gL0xaMFTlurkcJ0ReG9i+fDMDyGkm+SGoO9Uks0+LIxDVs14Z8z19GK0PWrbz8o0y4uLo6Xr3zI7+iQ6W73X/TsxL4TN6ierCz6vhnU79gm03b8i6ODPb7X7tNHsk3U120rWLNPDMYovwPFChf64LllZGRyD0mSOHrkCBcu3MLM1Iie33XG0Um/2cSnyus7j6j5Xqa6s6jixsUb0LVDhubYtmErd/4+AdGxGBcvyMBJ/6NJs4bsL72OAnf8EzeXPA0lSrVqkO58NlVLE/HsQmKmuiRJPChhS5mK5Qg/9RwL/pW5kDgrheGp0rLRWc3gsYMSS2ZfP3pOpfdCJypBwMrImGBisU4m43TPFMyKFSb+TiDqZIFHTblirF8whDOXb/L85W1aNKhM2ZLFM/SZvI+Xz2sM1GocHdJJRHmP6tWqsLm0I253/BN9S4Skxapq6Q/WvjUwMOC1uycdI4xweldpUFMww1OK5aAQzhf1a37QvDK5ixwo/ATJSIOSj2XvP+eZt+4pSrPGICi5uuw2ba4/ZfzgjHUqzOs4FyrAI7WApW6jYQTzjGWr+bzxZe2PE2jmHfPuSAAHH83EfM1showfwe/GhlzYd5yYwBDCjFXUdCtMXFxcmhqItevUxGNYN05sO4i5XxgR9haU69SKL9u25Nrhk4QfuI25oOS8GI4pCn5UOEAQeK4/zoJ4Lf+bOgY3V1cOOllQ3Fe3w3J8PisqtG/B6XV7cPMJJ9BExZvKhVm2cgEblq/hxD/nMQiNJraALcr8diztOgSz4CjC81tTol/nTAcJ4+PjmTFyIvGXH2AeHktAYTvaDO9Ho+YZ75A2ZPwI5kXHcPPibdRRccQVcWTApOEfpSN57PAxqgVqEku4IaFsIc5ASZ9ki1cZGZlPh5DgIAYNnEyM0BS1QSkeuPtx5sxolv8+57MIFioUCiQzYyBe53iwqZo6GWzWMen7n6l1+lmi5tLruztYHhHJoDFDCZ09gr2/riPsmRdhCpF8hQqgiYtPcz6VSsX3CyeyduJ8TNx9iTdUY1ClBL9MH8vxQ8d4eHs5pSPAT4rnghjO1wobVLECUVffML77QOZu/xNjYyMU9tbwOFhnbndjia5D+nF44x7cnvqh0kg8KWZHt0nDiY+LY//StRi8CiDO0hihZEG0+0/g8ds2YsyMsK5fGYseX6ZidepsXbeFGxv2YesTQoidKfla1GbYhJEZXjRVqFAW91/6cWLdLgz9Qoi1Naf8Ny1p3LRhpm35F0mS0L7w0WnCBVBZMMGrRpks0VSWkZHJeSZPmMG9p04YmtZFq43l2pBljB3zDZWqfB4JGQozE70GgvGSiNoyYxswm1dtJGT+FupHvxt77y2TPIexZNcaxq9ZxNJxs/C9cofo+DgkB2u6O6avjTRiyhjmRE8m5tojDGLiiSySjwGTR1CwSCHG/32CVi8S0gp3SkG0FCxpIqoQvTQcHTYLxcJfqFO/NvmKFSFIuoRNsiCoVpIoVLU898rEYH7xIU5hcbjnM8X+60Ys+qkP80ZOQvvAI+E+3JwRRIl5TXugEEVi3Jyx+iLzkkdPnzzj919mYfP0NfFKBVFlCzN62XTsbGwyNF4QBIYvnc6KsbNQPvdBa6DCpHJJxswcn2lbkuNx5Q410M3kLyQYcsxBRYuWH57kIZN7yM1MPhFyUoNQkiS++n4poUJ73eORR9mztH2Od2XMDkRRZGj73jS/+Rb1O0fmYSRhMrwLvX74DkjIhvPyeY2baxGMjXUz9OZPmIXb+tMYJCuvEiWJ6+0qM3HpTDav3kTA/M2UiEr4Waik5VazUsxevSRd2zQaDQFBwdjZWKNSJTij2NhYlkybj//1h7x5/pIe8bp/2+fyGTDiyDpsbaxZOGkOxptPUiQ+oZT5noWCAiO6061Pd0LDwjl/9gLOBQtQsXxZnWtGRUdzeM8hQqetoWBc0n2dt1PSZ+evuBYpnOHPd8HkuRRYexzLZLtrx12MmHBoA5YW5mmM1OfcmYvsWrwSQ58gREtT3No0pP+w7zM1x7+cPHGGxwOm46rR3SM5VtKKBf/89UFzymQ9/xVhePh8fVZOMmvGPG49qYBSmfScjosNoG2TML7r+12aY3Nb8D2jLJ25CLPVh8mvScpUP1evCAs2/w4kPMMfPXuBva2NXnbB46fP2PP1YCpF6N7n8eIWzDu2Fe/Xr1nYeTDNvKJQCAKiJHG0iCmjt6/AKV/6C7CAoCCMDA11svB2/bWbazsP8vjRU/pHmCY25QAIljRE/9KNPj/25tL5yxwYMZM6vnEoBIEANNxpVJK5a5ciiiIXLl4mNiaOBg3qJAbGJEkiPCKCuLh4Zn3Vl2avYpJsUYqE/fgFP/08OMOf7f0Hj9jdbSTVQ5Jeh1+pRcwm9KZrr9R1nlIiJDSUGSMnIT1KKLMyLleUn+dNwsI8c37vX/7XpBNNn+vKYtxXx9Nw6zyqVpWz4PMK/xWfJfurj+fJo4eMmXAII/PqOsftTI6ybPn0LLtObvq2+/cesrX3aOr6J2VjHM9vyJAdyylYICFz8uUrb2JiYylR1FVvQ2b0V71pdNtX59gLlZZSK8fRuEkDfu41iOqnnmL+LjvwkZmA85heGdLsjYiMJDomRqcL75PHT9kw51e8Hz2nwptoSqO75jtXz5UZm5YTHR3Dz50H0Oi2L6aCknhJ5GQRM4ZtXEKhggV48uw5Tx89pXqt6jrNxqKjE3zU1IGjqXX8ceLaUStJnKjuzMKdq9O1+18kSWL4l71occcv8XOLlyQuNXFj5tqlGZ7nX5bNXYb70fMYhEcjuTjw9fD+1KpTI9PzAEwdPJZq+27q/D61ksSdzjUYO2/KB80pk/XIzUw+E3KrQUlsbBzBEcYo3nuvjdIW49b9xzStn3M6CtmFQqFg8prFLJs8l5hnrxCMDSnTuiE9+n2LJEnMmzCT0GNXsQmIYEsBa0p3bUOvH3onjo8NDdcJEkJC6bIYnhAZvLP7HxpHJf3MUlBidfkJDx49oUyptEuQVSqV3kLP0NCQUdPHERQcwuImPSBQV6fJwTeU5y89sbWxZvjkUewrV5Krx84jqRQ06vQl9d+VJltamNPmi5YpXtPC3Jz7x85TJ073vmr6a9i9div/mzomTbuTE3DjAWXQzUKs7hXJrr920uf73qmM0icwKJg94+bR8t+F4NsQPF7uZrLfWwoVLkjTNs1xyWBGDUDDRvXYXTofhe4EJC5c36hEijatneE5ZGRk8hZ+ftE6QUIAA0M7nr94nEsWZT2DfxnGnybGnDl9BSlOg3lpV6ZMGQXAkf3/cGzpWhzdA4iwMERboxQTlsxIzKb09n6NdXgcCLrZlcrwaDQaDZuXraHxuyAhJPiyxh4RbPx1FaOmjU3XtpSyGDp0bU+Hru2Z3H0gyvO67zLWggrPFy8BqFW3Jlbr57Hrz02I4VE4VyrNzP49EQQBpVJJ/Xp19OYWBAELc3P+XL6KWl6ROsL2dloFDy7chJ/TNTuRAxt3UDVYhGSLG5d4BRdPXoRMBgpnD5tA3RNPMHz3fhD76h4/Bw6lZtP6uJUtRe3a1TNV2uVcvxr+7sewF5PK4rzKu1ClSsVM2SUjI5M3OHf2Iiqj0nrHg0M+n7ydsuVK03LhGA79sQUpMARFPhu6D+lLwQLOvPUPYO6gX7B68Ap1vMhbN0f6zhpD2bLJPpPwKL057ePhlYcnt27fxeHKs8QgIUCpCIlTOw5lKFBoZmqqJy1RoqQbM9YuZemcJRRZflinqSSAGBACJDSenLXlN9Ys+5Mwd28M7K3536C+OOd3SpineDFKFC+md01jYyPCwsMR7nvorB2VgoDtA2+euXtQ3LVIurYD3H/8FOfHb3SaU6oFAR56EhUdjYmxcYbmAdi6/i9Uqw/SIubdDfv6sOV/M3jYuwNWlha0/rKVXqJMWrTt3Ym/z9+lRlDSGvWig5qe/b/N8BwyeQs5UJhHyYny4tQwNDTAzCiG9x/TRoIXJYt9PqnDtjbWTF46S+/45rWbsf3rDOU1SsCAMi8jubF8O7drVqVixXIAFK9Zidf7r5NfTHJUoZKGfGUTdCak4Ai9eR3DNTx//DTdQGFaWFlaEO1gCYFBOsd9na0oWawokLCIatfxK9p1/CqlKdJGEvUOCSRoZH0sH/IKtHXtFuomWwhGSFouRgdTZfNpTCUVq1bsIF/Xlvw0KmPZIwqFgrEr57N8whzinnsjGBtQqFFNfvrfoA+wTkZGJi9gYaHiTYgGQZH0SqOJD8fBwTIXrcpaBEFgwLAfYNgPOseDQ0I5MWsFzXxiATUEiUQcussS+wWJQb6aNapyrKAVhV9F605ayBG1Wk18UGhiZv2/GAgK4vxDPtputZMdGulFop4UgJ+gpUBpt8TvS5UqwfiF0zI9tyY+HuX7KzoAMfPeJqURmZ3FPzAQ9R33xCAhwHEpDOurkZhf8ea6kcCuKoWZvmZxhhdzQyeMZJlSwb3zN5Fi4zByK8SEmb98sI6UjIxM7lKuQhkOHr+FSl1W57iZ6ecTKASoW78Odevrb/Qs+t8UGl969W6zXol0x48/R89k8YGNic81VWFHxOehOqXL9+wM+KFNcy6cOo9TlBbeS9YQs6AhYY2GtTm/9hBl3utgr8yXlH1obmbG0F+GZ3puUZQQtPq/Y5UoosnkGiuF3pQftMa6f+QsdWOSJnsoRRP7KgiD6RuJEAR++WML3WeNplqNqhmar1LlivhPH8LJtdsR/UJQONrQckA3SrjpB09lPg3krsd5CJW/e+IX5E6QEBIWJF82KoIm8m7isbjoN1QqFkGBd7smnzNPz1zFWaObDVcpVOLQll2J33fo3J6nTcvw2FhElCTc1SKX67rSe2A/AJQu+sKyL/KZUKvex2VjKhQKavb8mmtWisQOi4+MJQq0a5zpkt6UKFKnCr5K3WDhDSsFX/TomKl57KuVJeS97pRXC5rSoWvm5omJiMQw2WPqhBTGNwobSmKIhaCkVpBIwKZDPH76PMNz5ndyZMaqRcw7vYO5hzczcNRgedElI/MJ07dfd6Tow0hiQpmTqI1DrTnKd3165LJl2c/uv3ZR3Vt3W89MUBJ8Mymb0szUlMr9vuGsnZJYSSRY0nC4iCldxgwEwLZEEcIk3ed1sKQhX5kPE1hPTu+RP3K0uDlR7zahgiUN16u60KnHNx89d8dvO3HZSTfbIRQtdpX1s3XSom3PTly31vX5nmqJMk31F7lpER0Ti2FcUqndEykaFwxoiDlmgpISsQrqXvBgxdxlGZ5ToVAwdPxI5h7ZzLxTO5j2x3ydkjkZGZlPi+o1auJs50F8bAiQUEoaH3mJ9u0b5qpdOUFUdDTCI08dKQpBEHB57MvtBw8Tj/UdP5zDJa3wR5NQVmstUKBHa5ydHKnTqC7P7PQ3WlQu+T7avho1qhHQoAyvFQnP8XhJ4kR+QzoM6fPRc1tZWhBf0gVtMsU3SZJ44+aUmOiREcqWdMOnpBPJlePiJRHKFM5UNmHCwCS/r5UkHkrRtFfaYC+oyYeKli8i2DbzVzKjUte8TXNm71zF3LM7mb19JQ0/QqdXJveRMwrzCLkdHHyfH79ti7PjefYeO4BGK1C9lgM/9fwwXbhPDUGh/zuQQKehh1KpZNbKhVy8eIUrpy9QvlolfmzWKDHg1G5oP3Z6z6Tuq2gMELhrDo4dm+BgZ6c3d2bp0K0jRUuX4ODGHUjxWup81ZxGTdLv+JURvvv+O+a89ObZqevYBkTytqAN5bu3zXQW5OBfhjErKISYS/cxi4glsJAdbUf0y3Qws233jmzZfZqa70oyFKBX8l0lROLQ9r2UHP+/NOfy8PTi5MFjFClRjEaN68vBQRmZz4SChQoxd+5AVv+5idBwEXtbQ374aRwWFp9PRmFqqNRqNCnlErznxzp/15W6LRqxe/NOzC3NmdGtY2L51Xc/9WHMxRtUufmKfKIKX4WGW1ULMad/2p2PM0J+J0cm7VjJphVriXjjT75SxZjbt0eWNOKwt7Wl/pj+HF++ESePQMIsjRFrlmPC2GGZmqdM6ZLcH/EtJzbuxcYnmFA7M5xa1GFAj06ZmsclvxPBxRyRbvoiCAJPpRi+EKx0zjERlIQ8fJHuXJFRUezfcwBJlGjb/otMd2GWkZHJmwiCwILF0/nz99U8e3ELQwPo0LEFNWp+/l1hBQQkpX6OkqgUUKuSQhKuroWZv389u7fvxd3Hl16dv0rUSXd2csS2YyPubDpK+QiIRuJ8QRO6D++fJTZO/20eu7bt5sbFm6gszRj843eJuoofy7B5E5g/dAK2D71RakR8Szjx/ZyxmVqPCILA4IWT+W30dKyevkGrVhJdxpUxCzKvAWhbwY2wqy+xEJT4EEcx9Ju/mb/0w9fPP029YkmSOHP6HM8fPaNxqyaZ0rSXydvIzUxykeQahHklQCgDu7ftxnfiSlyTpWNfslHQdetiSpbIeIaFX0AAW1dtJiosnOYd21K5coXsMDdbCAwKxvuNb4qNXDJDaFg4QSEhFCrgjEKR8HJw794D9q3dihQbT/kmdfjy6y/SdJLrflvDk037KeIdymlFFD0l3WY6QWjRTOxJz76pZw8tmb6QkJ0nKBsUj6+BwJMKzkxft+SDBeZlspf/ijA8fFo+63PkU2lmkhrhERFMat2L5p5JWYWBgpaA/i0ZOm5EhueJj49n17Y9vLz/GNfypfj6m3ao1er0B+YB4uPjefTsBfns7XQE5D9knlev3+DoYJ+YmRESGsb6X1cT7v0GqyIF6PVTH8zNzFKd4/79h/z58wxcn/hyQxNBOywxEXSzFS82cmPqutQbm108d5Ed4xdQxSMUgJuFLfl6yjDqNar3wfcmk738V3yW7K8+DfKyXxs3YATVjzxIlGgQJYmjlR1ZtGdtpgJm16/f4viuA5haWdC1/7cZ7vibF3D39CI+XoNb0SIflbTg88YXtVqVmIQiSRK7tu/h4ZkrqEyMaN+/R5rr1tjYWCYPHIPZ5ccYhEbhq9DSDN110Zn8how7vjnVzarwiAjG9x5G8dteOMXCA2sVZl83ZMTkUR98XzLZS2b8lRwozGHk4OCnwYp5v+Jx8AxmQZGE5bei9ncdad+lffoDP4A3b/0Ij4ikuGvhzyrL7fr1W6yYNA9NQAjWZYoydclMLpw6z5VJy6keqEEhCHiotIR2qseYWRPSnCs0LJwrV2/w/METNCv24vZOakuSJA66WTBv/4ZUA5p37tznYLf/UTlZx88YSeRh1zqMnTMxy+5XJuv4ryy6IO/7rM+dvLygyigXz11i17zfsfAMINrMAIs6FRg9a4JOFnxWEREZiae3D0UKumS+zCkPExIaxvRRk/G//RiszBg0YwxFi7oyscsPNH4UhImgJELScrqsPbO2/5FmsFCr1XLl2g3CwsI5Mm05LTyjEn37fQsF5ecMo3nrZimOlSSJ/7XtSbN7ATrHj5axYf6BjYkbbjJ5i/+Kz5L91adBXvZrYeHhzBo2AcU9d5QakSi3/AyaM4HChVyy/FqiKPLM/SWWFuZ6TSI/ZSRJYuVvq7j419+IkkSdLl/y/aD+TB0+gfz7r+KiUaKVJC45qGk6awQNmzVKc75nLzzw8PDk2KZd1Dr5JHFzKxQtT76uxsTFM1IdO2fcDEpsPItRsmqvO6YSTTfOlptu5VHkrsd5kLxWWiyjT2BQMGuXrCTaxw9jZweGb1sOgIOdbboLLkmSeOsfgJWFBUZG+qnbKREWHs70Qb9gcscdo2gtfsUc6DZxKFUzKBqblzlx7DR7vh9PR40ZgiAQ9eYp3Wt/QUnXIjQJ0iZ2lyyiUXL5yGVeDXqdZvdiSwtzmjdtSPOmDdlqac7pvcchLBJVYUeGjRuWZtbj0T0HqRiOThczI0FB5FPPrLpdGRkZmRxFFEW2btzG8ws3wNCAbhOH4lzQBQtzswwF8EJCw4AE3aSMsnzOUl7tP429bxj+Tpa4tm/KgOE/fugt5BliY2Pp06AdPYPUGAkKpDdhrPlmMKr6FWn6KBijd4smM0FJw/v+bFixloE/p95AS6lUUrtmdQAKOObjrwV/oH3lh2BtTs3OX6QaJATw8nmNzcsAveP2HgG4e3pRTC7pkpGR+QS5eeMWB9btgNg4ytarQesFUxBFEVsb63THxsTEEhIWRj57uwwnVFw8d4mds5aTz92PKGM1sZWKMeHXWZiamHzsreQ6I/oOwe34A7oKCevN5/O20vPEWUq+CMTlnca+UhCo66/hyMot6QYKixctQvGiRahXpyaLJs0m9PZTBKWAbdWyjJkwMs2x4U9e6gQJAcpHwIm9h+RA4WdAtgYKg4KCGDx4MPv370ehUNChQweWLFmCWRo7sQ0bNuTMmTM6x77//nt+//337DQ1W0iePQhykDAvExQcwqTOP9D0SQhGgoIY6T4zLt5k6vY/0g0SHjt0jKPLN2DmHUiUpTGOTWowbOLP6Tqz+WOmU/vUs3cPWAXcD2DT+PlUOLTxkyn5So0/J87lO615YnDORFDwZYiCq088AF0nXSgwhjs376YZKExOl++60eW7bhm2xTafPWFosXr/cWeSsYCuzH+D/7q/kvm0mDZ8PC5/X6O6qErIrD53h2pTBtLqq9ZpjvMLCGDB8EmoHnkBEF/ShZGLpqZbsnv4wBG0aw7SMEYBqMEriocr93GqQhkaNa6fVbeVKyxb+BtfBSkSFzuCIPCl1pzl569jJOiWs5kJSh57eGd47rLlyzJjfcabl1iamxNlYgDhcTrHo0wNsLb8/DU3ZTKG7K9kPiUO/32Ya5OWUy1QgyAIvD52lyW37jF5ycw0x0mSxOKp8/E9fhmTsGgiCtjSbGDPNDdbICGwuH3SQlq+iACUEC0SffwxC8fOZMLi6Vl4ZzlPaFgYUWduUURIKhEuJhhy6NYjXCUbEHTXOuLb4AzPbWxsxNi5kzNlj2Csv5YKR8TqI2RAZPIO2VrD0L17dx48eMCxY8c4cOAAZ8+eZcCAAemO69+/P2/evEn8mjt3bnaamaWk1Ln43y+ZvMu6ZX/S5F2QEBIyzpo8CWHdsj/THPfWP4DjU3+lyf0AaoRINPKMwmrDCdb+tibNcZIkEXXvud4uTMln/pw5c+HjbiYPYBgapXesgGCIvyZW7/grGyPKViiTbbZ06tmZi8WtdLp2PTYTqNmhVbZdU+bT47/or2Q+TZ6/8EB56jb5xYQFgSAIVA8WOb1uZ7pj5w2ZQL0zL6jnr6Gev4b6Z92ZPzRt6QeAqwdO4Baj669KRwmc33P4w24iD3HrzCXyCbqbc4IgEKvVEPuuW/O/REpaLFycss0WK0sL1DVL63ShjpC0UL1UhjJvZP4byP5K5lPi9NodVA/SJiZQ5BeVqE7d5tkL9zTHrVuxFssNx2jkFUWNEIkm9wM4MeVXfP380xx39J/jlHuhGyAzFhSE33v2cTeSB3jw9BkF4/TDN0W0ap4Z66vJKRysstWe2t+04YF5kj2SJHG+qAVdenfN1uvK5AzZFih89OgRR44cYdWqVdSoUYO6deuybNkytm7dyuvXr9Mca2JigqOjY+LXp6L3IQcHP10ifN5i/F7QzlhQEOnjl+a4bas3Uet1jM4xJ42CF2evpn/RFDIOtQoBperTVwSIMDXgffnTJ1IMxqVduW6lSPyZt1KL0KQKhQtmvTbJv5iZmjL499lcaFqC08UtOV0lPy5j+9C2fdtsu6bMp8V/0V/JfLrcvXGbQiFxesdFv7QzB3z9/DF54Ikqme9RCQKmD7148zZtXyco9P2VJEkp+rFPDbeq5XGXdP24VpJQWZhz3M0yMVgYI4mcKm1Lr4F9s9WesfOn8LJXQ06VtuVUKRte9GjA+EXTsvWaMp8Osr+SSYv3q9nyAin5pkIhcdy5fifNcS/OXiG/Rreqq9abGLav2ZzmOIVCiZiSa/oM/FW5EiV4ptboHQ9XQWid0vgqEn4mSRJXbJU06dM5W+1p9WUrik7ox+lqzpwuZsmFJiX46bcZcrPIz4Rsi0hcunQJKysrqlZN0ltr2rQpCoWCK1eu8PXXX6c6dvPmzWzatAlHR0fatm3LhAkTMElFUyA2NpbY2KQspbCwsKy7iQwglxd/HlgWdCZKuqvTnTBK0mLu4pjmOK1GS4qFyaKY0tFEBEHAolIJojyuY/IuQClJEk/c7PmhXq3Mmp/n6DV+GBuGTuEbMaHj40sxhiOmcWzd+DvPnj7nyMadSDFxuNWvzvju32S7PcWLF2X6mtS7TMr8t8kpfwW577NkPn0qVq/MJmtD7EN0/YzCMe2uj1qtFmUK/esUWhGtVpvCiCTqtm/F7VN3KR2V9I5z10KgSZevMmF53uSXiaNou+cwHcMEXBSGREhadgkhdBs2mFbt2rB+yUqiXvtj6uLItCEDsLTI3gWQgYEBo6aNzdZryHy6yP5KJjXyqh6+wtEGvHWD2B7WBvSoXintgVp9f6VEQBOftr9q3qIxPxdbi8uzpL/XCEmLVZVSGTc6j2JuboZBlZKcv/yU2oIZEnBeCofyRZj75yL+Wr+Vq5dvIRgb0q5PFypUKJftNrXr/DXtOqf+3JH5dMm2QKGvry8ODg66F1OpsLGxwdfXN9Vx3bp1o1ChQuTPn5+7d+8yevRonjx5wu7du1M8f9asWUyZMiVLbc8oefWBLJN5vhvcj3Hnr9HoQQCmgpJIScup0rZMH9w/zXHte3Vm5Z5T1AtIclqBgpb81cune83/zRrPzOgJiLeeYhirJcw1HwOm/S/LOlU+efaCM0dOULxMCRo2qp+jHZW//Ko1RkZGrJy+EMKiMC3kxIqls7GytKBatcpUq1Y5x2yRkUmPnPJXkLs+S+bzwLVwIZTNq+K55xKF4pWIksRlexVN+3ZJc5yzkyMhbvkRr/qgeOcPJEkipIQzBfKnXU7buGkjPAe+4NTuY1j4hRGaz4JynVtTu3aNLLmnsPBw/t75N0qVii87tM1RwXkDAwM2nfmbUQOGE/7CG8yM6TZsLO2/aQfAyCljcswWGZn0kP2VTFrkxTVps35dOfNyMTX9NSgFAS+1FmWzWrim05wpf40KBF57ia2UtC66Yq+iX69OaY4zMDDguzm/sGnaEiw93hJjpEZdtSS/ZNGzXBRFThw7hcczd5q1bUGRQgWzZN6M8sf21cyYOItVh04iiRKlGzdizZzJKJVKevTpDn2656g9Mp8vgvR+fWA6jBkzhjlz5qR5zqNHj9i9ezfr16/nyZMnOj9zcHBgypQp/PhjxjrlnTx5kiZNmvD8+XOKFi2q9/OUdrtcXFx44B6CuXnWp9TLGYSfL2Hh4az/bQ2hL32wKJifXgP7ZihzYNfmHVxevRNHzwBCrE1Q1y3PuAVTMxzwCw0LJyo6Gqd8DumfnEHmjJtB3P7zlAnR8toQnlUqyIy1SzAzNU13bGxsLIIgYGBgkGX2yMhkhrDwcGxKViQ0NPSjSqPymr+CnPdZMmmj8nf/JP24JEns3fk3905dRGlsSLu+3ShTumS64zw8PFk6cjL2jxKyO/xKOjFkwWRcXQtn6LpxcXG89Q/A0cE+y5punTp6igNTl1HFKxwRiRtFrOg082dq1Uk/CKnVaomNi8tQp2cZmewiK3yW7K9kPpa87M8ePnrCnlVb0EbHULZhLb7+5qt0Exi0Wi0zRk4i/vwdrIKjeFvIlup9OtKxR9qBwn+RJIk3b/0wMzXJslLY4JBQJvYeSum7PuSLF7hno8a6U1OGjB2eIXuiY2IwMjREocjWNhEyMqmSGX+V6UChv78/gYGBaZ7j6urKpk2bGDlyJMHBSboEGo0GIyMjduzYkWZqfHIiIyMxMzPjyJEjtGjRIt3zw8LCsLS0zFInJgcHZdIjNjaWx8/dye/ogL2tba7acvHiVS71mUCZ6CQnFCWJuH/XiP9NTX03LSAoiIX/m4Lm0UsQFBiUL8ro+ZMxT6OLnoxMdpBVgcK87q8ge3yWTMbJywur7EKSJJ57vASgWJHCOZpt/j5arZZRrb+l2WNdDatjFeyZv299qrZJksSvs5fgeewiqvBoRBcHOv78A9VrVcsJs2VkdMgKnyX7K5mP5XP1Z/6Bgbz29aNkMVcMDfW77OYk00dOoPyOqxgk07W/YS7x9Y4llC5VItVxp4+f5uCydah9Aom3MqVE20b0G/p9TpgsI6NDZvxVpkuP7e3tsbe3T/e8WrVqERISwo0bN6hSpQqQsHsliiI1amS8VOX27dsAODllX5e5tJDLi2WSExQcwsbf1hDxxp/8Zd3o1rsbhoaGGBoaUqFM3tC+OHfgKOWjBEj2J2siKAh57JHmuFkDf6HhBU/U7xZmMd73mKWZwMxVi7LT3FSJjIpi+czFhD1yByMDqnzZjK9lDQyZTPBf81cyMsnRarXs3r6HJxdvYmBlRrcfe1MgvxOCIFDctUhumwfAU3cP8nn48/7rqOXLAN689SO/Y74Ux635bQ2mqw/TNP7dYs3vNX+NmkWJg+uzXUMwNXZs3sGdg6cgNg6rssUZ9MswjIxyd1Er8+kg+yuZjyEvNjHJLHfu3Gf/+u1IWi0N2reifoO6ANjb2uZ6Esa/RD17pRMkBKgYBkd27qf0hJQDhT5vfDk8biFNfOMTDviH4v7bHnba29KxW8fsNjlF7t9/yPalqxH9Q1A4WNNt2ABKlnLLFVtk8i7ZlvdaqlQpWrZsSf/+/bl69SoXLlxg0KBBdOnShfz58wPg4+NDyZIluXo1oUPsixcvmDZtGjdu3ODly5f8/fff9OzZk/r161O+fPqab1mFyt898QvkIKFMAq9fv2Fyx/64/PEPVfbfxmDWX4z6diDx8fG5bZoOFnY2RKLfTEUwSX3B8srnNZb3vRKDhABGggLhzgtCQnNevFqSJCb0G0GxDWeoe92Huuc9eDVpJVvWbMlxW2Q+fz5lfyUjkxKSJDH+p58JH/sHVf++Ran1Z1j8zY/cuX0vt03TwcbKiggzfd8UbWqAhXnq2ewvTl0if7zuK2xtzwi2bdia5TZmhPUr1uA/bQ11L3hQ97oPhdeeYML3I3PFFpnPG9lfybzP57Be3bVlJ/t6jqLyzqtU3XODaz9M5/f5y3PbLD0EU32Zi1BBxN459eaX21dtpvabWJ1jrjECdw+fyXL7MoK7hyfrB4yj1pEH1L35mlqH7/Nn/zF4v36TK/bI5F2ytUB+8+bNlCxZkiZNmtC6dWvq1q3LypUrE38eHx/PkydPiIqKAhLER48fP07z5s0pWbIkI0eOpEOHDuzfvz87zdQh+cP23y8ZGYCVc5dR4WkQ/6oy2aKk0lVPdm7Zmat2vU/Xvj0472pOclWBuxYKGnT5MtUx0TGxGMTpdxFTabTExcdli51pcePGbZxvuCd2hAZwi4Y7e/7JcVtk/ht8iv5KRiY1Ll68isHJO5hrE95h1IKCpt6x7FiyKpct0yWfvR3aaiWJkJL8TwhaTGqWS1tTN4Wul4YIREdEZoeZ6XL/75O4Rid9byYosbv2nHsPHuWKPTKfN7K/knmfT3m9Kooip9fuoEhwHAIgCAJlogQ8dx8nLDw8t83TodrXLXhimvRZi5LExeJWdOyaemZgXGwsalL4/Wg02WFiumxbsY6G3tGJ0h6CINDQK5Itv63JFXtk8i7Z1vUYwMbGhi1bUs8AKly4sE4ww8XFhTNncj66LmsQyqTH+hVreHnoHErgphSJmaSkkcICR0nF7ftP0h2fk1hZWjDgt5lsnrscrbcfgrU5tbt8SdOWTVMdU9y1MG/dHOGuf+IxSZKIKOaEg51dTpitw4snz8gXLYKg2xBGCs2dRaDM58+n4q9kZNLj3p37/Dp0AsViYrmKSLCk5QvBCiNBgfQ2KLfN02PCkhkstplL8O0nIAg4VCvLmPEj0hxjU7EEEbd8MBOSd8M04IdvMyZyn5VIkoQYor+YdYiIx+O5O+XyiCyJzOeD7K9kPheio2OYNGg0PH3FM0nBaeKpggmughH5fEJ56u5B1Qp5J+u1Xad27IqP5/SuI0hhkagLOzFi7FCMjY1SHdO6Szv27TtP1bCk/8kgQcSxatmcMFkPTXAYyvf0f9WCgrignK8gk8nbZGugMC8jBwdlMsr5cxd5u2w7XWLNEnNw70tR3BWjcFYY4lisUO4amAKlS5dgxrqlGT5fEAT6TB/FqjGzKPz0LaIAXqWcGDTzF65du8XLFx40bNYgRzRCJEni/s17hAvRtEStc1xZMOs6Q8vIyMgA+IVKn807gEajYdWoGfTyV4IiQasvWhI5LIXytWANNnmvAYGRkSFjZk3I1JhBvwxnio8vJleeYBMag1chGyr3+waFQsGOrbtwK12CCuVzZhF28ugpPAL8AUud48+czPiyXu0csUFGRua/yaeuTbho4mxqHHuEqWCZuMbaIQbhgiH+Tua4FiyYuwamQIfu39Ch+zcZPr9c2dJc/749p7ccpKB3CP42JmjrlGf0D705sP8IapWKxk0boFar05/sIwkKDuHavfu4SSKWQlIYKAANzuVLZvv1ZT4t/pOBws9By0Em5zi14wBVI9BpDlJWMGGXGIRXeRfm9eyaa7ZlJeUqlGXRwY3cuH0XpVJJMdfCTOo/kgK3XmIfLbLYcR2uPb6g75AB2WrHkUPHyPf3FaJFictEUE0wJRqRA1ZaRv78U7ZeW0ZG5r+Fxt4Vlb/7ZxMsPHvuEiWe+kOyTRZjQYFClDhvr6LN991zz7gsxMjIkFmrFuPu6YWP92u+q1Se1Yt/548v+uHmH8M/pio2VS/O9JXzs7VLZmxsLAdmr6BprCF7pGCaCRYYo+CsIpJiXXtga2OdbdeWkZH5b/M5dDkOufUY0/eqh2oKZpxQRFCgRUNsrK1yx7AspvegfoT16szt2/co4lqYV+6ejGvbi7IvghEF+F/xVfSdP47yFcplqx1Lxs+il4/IXimE+gpzXARDPKQYHtQsysI+n8f7gUzWka0ahXkJuUGJzAeTrHwjOVGu+ZixeUWa6eafGgqFgmqVK1K5QjmWTl2AyYWHeEWFo0Gi3tt4vNbs5YV72t2TP5Yb/5ymWJySpgpL8gtqDkuhXJYiCBY1lKuQO2n6MjIyny8ae9fcNiHLkCQRIQWfFWNmROc1s6n9mWW4uRYqSL06NXn08Anu6/4mxC8QHymespFQ/dQTVmSzGP75i1co7h6Iq2BEM8GCS1IEh6VQ3MVoGn3ZPFuvLSMjI/OpI6XgrwTAtFNjRk4ZnfMGZSMW5ubUr1cbZydHNkxbhPKZD8/FaOxEJS2fhLB+8sJstyHyoTumChVdFLb4SvEcEEPwkGKJN1RjYGCQ7deX+bT4rAOFKQUH5QYlMpmlXvvWPHxPU/2pscS3owdhaWGeO0ZlM29833J931GKCoY0FSzxkGI5KoZSLUhk3197PmhOSZK4cfM2J06dJTY2NtXzRKUC7bsXh4KCIV8orGgiWGAUEcNzj5cfdG0ZGRmZ/wL169XmsZu9zrEYSaRQ01qUK18ml6zKXiRJYtaICeSLFmkhWGIuKPhLDESNQODdpx887yuf1xw5fJw3b/1SPcfK0oIIQQQSGpg0U1jyhcIKG1HJ7rW504FZRkbm8+dTLzn+F4vybkRJos6x+8VsmThtXGKzjc+NlStWIzx6RR3BjIqCCUekEF4Qi7nHW3z9/NOfIAWio2M4fvIMt+7cTzH4+i8hEREAKAWBagozvlBYUVQwIsLT54OuK/N589mWHqsCPMDMXA4Kynw0DRvV48WP7Tm16yi2vmEE5TOn8JeNadnm880W+H3qAvrHWCSK3dYRzDkvhvNCisHazibT8/m+9WPWD6MoeN8Hkzgth1xtaTX6B5q2bKJ3bsvuHfhr1ylakKSldU2KxFFQEx2TeoDxYwiPiGDZlPmEP3IHQwNKNa/LtwN6fbYvKTIyMp8narWaXjNHs2nKQuye+xFrpCKukhtjZ4/PbdOyjSOHjtHQK5riioQdPVeMyKdQc1IKw9Io8xkSkiQxa/QUYo9dwyUwhosOJpi3qs3/po7R8wmVK1VgmqlImXAp0V+GSBqUCIjx8R9/c6nYt3n1Jh4eOYsUHYtpycIMmfwzFuaf58aljIyMLp9TddzImWOZETEOxc2nmETFE+DqQJdxg7NVMiI3iY2N5fG2w3ylsALABGgv2LBTDMLByBJTE+NMz3lozwFOLFlHcY9gbhspWV/ehQl/zE9R+kJbwJ63vj7kUyT4RkmSuChFUMCiwMfcVprcunGbXcvWoPULRmlvzdeDvqNKtcrZdj2ZrOOzDRTaWwhYmH/6D1CZvEHfIQOI7t8TL5/XuDg7YWKc+Qf5p0BkVBSGBgbEebzW64hVXTDlD8sYtn9AV8mlv8yg+Q1flIIKUFHEPYJDs1dQt2FdjIx0XwaqV63EUjdHdj/2xlBQEIuICwZQujBlS7p9zO2liCRJTOw3ggYXPTESEpKsve5uZXVMLP2Gfp/l15ORkZHJTipXrUSlvzfg7umFmakp+exzvnN9ThAXF4dWK3Lr5AUqS7oBQVNBSahCosXXLTM977bNO7DfdREXjRIEAwr4a3DfeppD1SrQ5svWOucKgkC9Hu3Y+ttWLCQFIgmlOk6WVrTo3O7Dby4N1i5fTfzSndSPTfDRsffeMvHVcBZt+1Pe3JKR+Y/wOQQJAUyMjZnx50L8AwMJDQunaOFCn+VzTBRFIqOiePj0OUW8QgDdtY+1pERbrgjmZmaZmjc8IoKTC9bQ7FU0YAAxUOKKD4vHzWTainl658/8fT4D63+NQ1QkSiAWiRqYEl6hxAffW1q4e7zkr0GTaPw67t2RAHY8noLFlsUUL1okW64pk3V81qXHMjJZibGxESWKuX6WQcL79x7w8zf9md6gM2ObdOGJn29i+e+/+KOhVb+umJqYZGpuSZKIe/pKL/BY0iOYM2fPpzhmxpolGFctRX4jU0oYmBFQtiC9p4/KlpeHm7fuUOB2UpAQoGCcwJPDZ7P8WjIyMjI5gSAIFC1c6LMMEkZHxzBl6DjGNerMlAaduHPpGt5inM45kiShKlmINu1apzJL6jw5eyUhSJgM1zgFN/9J2ScMHz0Mt44tsLWzozRGmBd0Iv/37alUqXymr50RHh86Q6HYJF9oKCgodMeLa9duZsv1ZGRkZLIbe1tbihUp/FkGCTf/uZFRrXowu0EXtoyZzV1Djd45wRaGjFswNdNzHz14jPJeYTrH1IJAzGPPFM93yudAjykjMXMtQHGMKGhlg1fzCgyd8L9MXzsjbPt9PQ18dKvB6r2JZceKddlyPZms5bPNKJSRkckYsbGxrBw+hVZPw945aA3lBYFtqlC6aa0A0EoSN8vmY8nAfnrjJUli058beXjkDFJ0LMbFXRg8ZbRupzK1/qMmWq3APJVSqUIFC7B4z1ruP35KXFwclcqVQaHInn0Nb89X2EZpQNDNSJHCo7LlejIyMjIyH868X6ZRZs81zN51yqwvSawwiOTHOFWin7hmp2bAxJEpjr9x7Sa7f12L+DYIhZ0VrQd0p079ZE1e1GokSdJZsIqShGCoTmG2hCZgExZN481bPzw8vehVqkS2lgGn5Jtso0VeeXhRvXqVbLuujIxM7vI5lRz/Vzj+z0l8F22haeS7AwGhHFMJPBGjKaFISDzxV4qU6NAcixR078MjIlgyaS4RDz0QDFW4NqpJvyEDEv2TuaUFAUpAV+YRwSBlfwXQvkt7Wrdrw61793FyzEdhl+wrO9aGRuoliigFAU1YZCojZPIScqBQ5rNDkiRu3LiNj7cPDRrVx8rSIv1B/2EO7T9CpefBCELS48BOUmHkYMM5F2fEyGgMiuRnwsSRqFT6j4x1K9YQt3gHDd5lOMQ/8GfSq2Es3b0WQRAQBAH7WuUJdj+LNQkLO60k8aK0I0NqVkvVLkEQKFcqe1Lhk1OvYT3m5V+N45ukHS9JklAXccr2a8vIyMj4vPHl6oWrlCxXilIliue2OXkarVZL2I1HiUFCAIUgUB9T9lTPj32UBsHGnKa9vqFG7ep64z1febNtyFQav/73eR/EoSezsVo3hzJlSgHQokd7Tpy7S5XQpKz6qzZK2n/XOU3bnPI54JTP4eNvMh1UhR2RPF/oBDIfO5kyslnDbL+2jIxM7pLbQcKYmFhOnTyDkYkx9evVRqlUpj/oP8z5XYeo+V5MrHG8MRuLGfHW1BJUSgrUrczIET+lOH7SgJHUP/8yserp9f3dLI+OYdCYoQA0adaQYSVXU/BBUKJPCFRocapTKU27jIwMqVUt+zeWXKqUxe/wLRyShZwC0FCg0ufZXO1zQw4UynxWhIaFM6nfcAre8cImWssc51WU7deR7n175LZp2UpcXBw7tuzC6/5j8pcsSucenfW0/1IjKiISIxF4793D2sKSGTtWpjv+8aEzNEpWBqUWFLjde83Zsxdp0KAOACOmjGahJHHj0h2E2HiUbgX5eYa+MHxuYGNtRaneX3P+9+1UD9QQgcjlYlZ8/8uQ3DZNRkbmM2fRlHmE7DtL0YBo9lqo2VSnNFOWz0lxU+Zz4vLla5zacwgDE2O6DOiJs5NjhsaJoogiXqt33EiE70b+QJ3aNdIcv3XFOur5xEAy31PnbRy7V26izJIZANSsVZ3Xv/TjxOa9CP4hkM+Ghr07Ur582YzfYDby3bih/Pp6HDWfh2IhCVyzVVG815cpCtfLyMjIZBVnT55lz7RllHYPIl4psKeEA4OXTKW4W7HcNi1beesfwF9/rCcqLJx6XzSnXvIM9PTQ6PsrBVC+UjnGLpyW5tC79x/iePOljjRS/ngFJ45fgneBQqVSybDlM1g5cR7iCx8kIwMc61Zm+LjhGbcxG+n2XVfGXriGy8WnFIsRcDeSeFnTjVn9Pu91+efC5/0mKvOfY+nEOTS6/ApDQQmCEsfXsZxbsY03XzTPkZ3+3CA6OobR3/5E1eveVJKUBHCRn/efYNaW3zAzNU13fJuvv2DGyh008YlJPBYjiZiXLZqh60sR0XrH7OIkfF56wbtAoVqtZvSsCUiShCiKeW4Hsuf3vfD6oil7N+/Cys6G2V07ZEiL0v2lJ3s37kClVtG5b4/PUg9MRua/gl+olKPZGmfPXEDacpxaMQoQ1DiEQ9CRe6z+dRXfD/shx+zIaZbNWkz0hiNUjIR4JJYevsAXs3+mXsO66Y5Vq9UoSxZE+/qZTjnTi2L2/JiBsltNWCTq9zaoFIKA+J4fa9+1Pe27tkej0eS5oG2Jkm7MPbCRXVt34eEXQN9uHSiUgdKxyKi/2e6RAABCCUlEQVQotm/Yhr+PL42+bE61HMgmkZGRyRr+LTvOLTQaDXtm/0YL9wjAALRQ5EEQf06Yy9xt6ScVfKpcv3qDv4ZNo553NIYIXPv7Elc7N2LklDEZGu9Wvzo+Zx7gnEz39qEJ1PmyebpjX3u/xjoFaSQioxM2zd5JbRR1LcKcTb+h1WpRKBR5IgnjX9RqNXPXLuXsmfPcuHCVSrWqMbBRvXRtlCSJE8dOcfPsZQqWLEb7Tu0wMDBIc4xM1iM3M5H5rIh88hJDQffPuqpfHPu27s4li7KfTas2UPvqKxykBCdkh4oGt96w9tdVGRpvZWlBo1H9OOZqxlNiuWEmcb5+UYZOHZ2h8WrX/IjvNT45Yw13z1xiUreBzB47neCQUCChnDivBQn/paCzM0NGDaFnnx4ZChJuW7+Vte1/ovifxyn422EWtO3LqaOncsBSGRmZrEZj7wokBAtzigsHjlIqWvdl2QYlPjce5JgNOY1fQAB+u05QPipBlsJAUNDoTRwHfl2f4TmGzB7PsWr5uWOs5bEijn9KWNF50rAMBfSK1qjIG4WukLyPFI97wFsmdR/IpO//x41rtxJ/lteChP9ibGxEj97dGTJ6aIaChC89XzH6q+8wmrmFcuvPcPrbscweOz0HLJWRkflY8oI24e0Hjyj4zE/nmCAIqF+8ITwiIpesyn52LvqTZj6xGAkJAbjS0Qoi9p3H85V3hsZ37dWFtx3rctFOxQsphrNOhhh+14oGDeulO7ZO3Zo8c9GVzxJFkVeGMLn3MCb2HMyGVRuR3q3BlEplngoS/osgCDRoWI8R40bSqHH9dG0URZFfBozg8U+zKb/xHDHj/2REx36EhIalOU4m68mbb0AyMh+Kof5uQ7ggYm1vmwvG5AxvH73ARdD9VzYTlIQ880p1TGRUFPN/mU7EnacggVmF4ozdvZInT19gb29LcdeMt6zvO344C33GUPVJIFaigiOW8RhqBBocf4ogCMSef874u49ZuGs1hoa65dDPX3iw9be1iKER2JcuRu+f+ma4ZDo3iY6O4cbanTQOFEEQUCPQ+E0cB5eto2GzhnnSUcvIyKSNxt41R7M2DExM0CChfl/3IZWmGZ8DVy9dx9UvSi9DQvTx02sgkpwNf6zj/r7jSCERKAvmo8fogRhZmBEZGcngihUyvAH1TbeOjD93heCzDygeJfDcUOSEQSz9bgVgJAQhSRJ/35hE7KJfqF2vls7YqOho1v66msDH7iitzek6sA+uRQp92AeRw6yeuZjWT8JQvHtXKBctcOvvC9zv8ZiypUvmsnUyMjLpkdvahFYW5kQZq+E9vb14QxUG6s/XZ4k+AXrH3IJiuXjqPIV6dklxzM3rt9g+/3e0Xn4IlqaUbtuYVkd+4tnzF3QrVRJrK8sMXdvczIwqAzpzZtlmqvnFESmI7LSKo7W3RCGPZwC8vviU+Z7e/DztF73xh/Yf5tqBEyAI1PyyGS1ap5/FmBfYv+8QrifvU1CT4K8cJRVNb7/lj3m/Mnr62Fy27r+FHCiU+awo3rQ23vd3UCA+IatQkiSulrBmQYcvc9myhO7Ca5avxu/+UwRTE77u341yWaB5ZOxgS4wk6mhYxEsSKtvUHdHMoeOo+s9DjN+NifG4yfzIKcxavVjv3Pj4eELCwrC1tk6x87Cra2EW7t/Avt37ef3mLeY37tP4rHvigs9QUFDr3lt2bN5Jjz7dE8fduX2PzT9OoIFPDEpBIPToPUZfucXCLb/n2azDf3nw9ClOHkGAblDT1DuAoOAQWStKRkYmXb7p34PlRy7SyDcu8dh9CwUNvvkiF61K4tg/J7i4+zBoRUo2rEmn7t989CaIW6ni3LUwwCn8vR/YWKQ6984tOwlftJXG/2Zf+nixecQMJuxfg421lc65kiQRFByCqYlJiptOSqWSWSsXcuXKNa6cuURMbCzt1xxL9J+CIFDLP57Dq//SCRRqNBrGfDuQule9KSoo0UoSv128Q+8/ZyU2QcnLaLzeonjv8y0fJnHi7yNyoFBGJg+T2yXH/1KsSGECKxQm/sJL1O+el2GSFvPqZfSSAHKDl56v2Lz0T7RBYZgWzk+/4T9imUIX4UxjbQ6eutFRbxMFTcuVTvH0kNAwNgyfRkuvd3IWPjG8cN/OWQtzvunxjd75kVFRaDTaVG3t3KsLDVo1Yc+WXZhYmFFizW4KhSVJZeSPV/Ds+BXCRoVjYZ40x4q5y4hffYAaMQm/q0en7+H11J3+n4CsycML16ik0Q1RGQkKIt0zlsUpk3XIpccynxV9BvZF8+OXnChlw3EXY842KMrgZdNz3YlJksQvfYdhvXgPNU48odq+m2zv8wsXzl786Ll7Du7LyaLmaN+lnmsliZOFTegxpG+K5wcGBSPcfJ4YJISEB7Dq1nP8AwN1zv1z8e+Ma9GDX5v2ZHSbnuzftT/FOQ0MDPimSwd+Gv4TxrEavQWfHSp8nnvoHFs4fQkPIkxZYl+ITUZqDFFQ9ronB/cfyfRnkNMUcHIixEa/PDna0hhzs/R1IWVkZGQKFnCm7dxRnK5egBMFjDhVwYEiY76jSYvGuW1aQgbf8PnUOPKAGsceETp5NfMnzv7oed2KFSWyTmmCSRJ4f2wqUKlDq1TH3D54kqLvlWjX9Ypg29otOseuXLrGz1/3YUnTb5nUvBtzxk5Hq9UXkgeoUaMaQ0YNQaUVcdLqvwqLQbolTuvWbObFw2BW2bvym7kF3gqRJt4xbF+2Ot17zgsIlmZ6x16rRFxLyl22ZWTyKnmh5Dg5E1bM5WrrchwvZMLxouY871qXUbMn5LZZvHD3YGmPoVTccZXqJ5/guvoE47r9SHR0TPqD06Fm17bctUj6/EMlLf413ahUqXyK529bt4W6nrql2EVjBO4cPKlzLDwignEDRjK9cVfmNu7Gz91+4JW3T4pzOjrY8+OwH2jUojGWIfq68NaBkfj4vtWZe+/2Exw0cWGxdT5OqaBktMCzvSeIiYnN8L3nFlb58xEh6fpuSZIQLExyyaL/LnJGocxnhSAI/DByIIwcmNum6HD61Dlcr7zA6l3ZjyAI1PHXcGjlZupkpntWCtjb2vLzxiWsX7CCON9AVPbWDB42ABdn5xTPj4yOxig2nvfbHBvFaoiIjMLeNqFMe++OvcSu2EvjmHfnBQVyccYfuJUvTYniqTc6UTnbo5W8dITmX6q0lKtdLfH7A8cvcTemOha1ExaHHvGRLLo2ldGhQTy4dQ/atfmQjyLHcHSwh7rlCN5/C2spYZHpo5ZwblpbFtuVkZHJMHUb1KHuu6ZPeQWtVsu9nUdokiyJomC8gnP/XCZwePBHZ0xP/nU2q5b9ycPr9xEM1dRq34pWbVumPiA2Xu+QEQoikukVRURGsnXMbFq8jEo4EBhF4KuzrLA0Z9DooalOXaNRXW5tOUmJ2CR/JUoSKuek5mfhERH8uuMO5nUWoFCoiZEkVj1eQz/Pi4j+oZm489yjbtcvuftwOeVDRQDiJJE75Rzpk9bnLiMjk2vktSAhgLWVJdN+n5/bZuixefGfNPWMSsyaNhYU1Ln7lr/W/UWfH3t/1Nztu7TnhLUVZ7btR4qJxaFCCaalkZUXERJGgRTysKTYOJ3v546cTLUj9xOz2UW/lywYMp5Fu9akml2fP58DIU5WEB6ic9zf2RrXgi6J3w+e+DuhJUZjaJagX3s68A7Bd1dS2D8cX39/CmdA1zY36dbvW8YfPEXLZ+GJa8mL+QxoP+DbXLbsv4ccKJSRyQHuXr1J0VjF+7E5pICsWWS4FHBm/KKMCZO75HciqGg+uK0rShxYNJ+O87h56BS1Y3QNrhGgYe+6rYyeMS7V+fv+PJDZD4ZT/0kw5oKSV0otLxqUZEDrZonnbPr7BhbOXyV+r1KbEl2yF4dvzqNNjcoZuo/cZsKi6fxeYDn3r98HpZLiDWsy5PteuW2WjIyMzEcREhqGScD7tcHg4BuGu9erjw4UqtVqfhzxU4bPty5XnIirnpgJSZIUtywVfN3l68Tvd2/bQ3WPMEim12srKrl34RZpUa9+bY40LovR8XsUilcSIWk5W8yS4aOSNhv/2HwIkwJ9UCgSdLgEQcC4ZG8OBj6kUr5PQ2ai9VetUanVnNv6N2JEFKbFXJg2dniel/mQkfkvkheDhHkZMTBUT1rBWlDh+eJllszfpEXjDGf6t+nanu27TlM9WWO0SEmLVdmk7O3o6Bji7jzTkYxSCAKOD7x59Ow5pd1SzvRWqVTU7N2RS/PWUD0wIePuhrWCij2+Sqyce+sfwLM3thhaJK3njGwrcM/WDQsjT/Lnc0hx7ryEpYU5P69bzNo5y4j38Qcbc77q351KlSvktmn/OeRAoYxMDlCzUR3OrzuMcVQcd6UolIApSlQOqWfmZReCINBz8kjWjp5JyWcBCJLEIzd7vps0XGcXS9Dqd/8UAFGj0TueHGfn/MzYvYq/Vm/mufcbStWqQp92bXT0DcOilPDe+sTQyo17hR1Y0rLpR91fTqFSqdLMVJGRkZH5FLG2siQqnxXhgf6clcIRSfAFCidbehfNeKOrrGLg6KFMcH+F7bVnOEbE88zRjEJdW1KmVInEc+JiYjB5fycOELRimnMLgsD03+ZxaP8Rbp67gmU+e6YO6ImVZVKnSe+34agNLN8bp8DHypaFI/K+3tO/NG/djObJNuxkZGTyHnKQMPOoneyIF59zhUiC0CAB9oIBJcrmvP5qSbdi5O/7FWe3HMLtTQRvzdQEVHVj6i/DEs8RJRGlqL/GUmskYuPi9I4np0O3DpSpWoF967chiSLte3SkbDKd3JevvInWOvO+4mG0uTNFWxf9ZKqeCro4M+nXj5c7kfk45EChjEwOUKNGNX4v4UC+m+58JVghCAL+UjwXoiLTH5wNVKxUngWHNnH6zAUA+tavjfq9rmWu9ary5vwTHf2m2xYCLTu3S3d+C3Nzvk8jNd/eUktEuG6Hy+iQ/7d33+FNlf0bwO+TpHvvUtpCB1CgbGQJyJJRQBBFcbEEfQHxx1DW6wuibFFBUFFRloC4QEQEFRkiS0YFSqnQAsUC3U2a0pXk/P4oLYSkpS0kJ+P+XFevi56e5HxzrpK755vnPM8xfPjOTKMLptytqKgYfyecQ0hwEMLqhtxzfyKi6spQinZ9kSaTydDi6f74au4yvKDzgbMgQ6ko4puiIuTnq/UmTDcHZ2cnvLNuBc4kJCLlQgomd+0If19fvX0eH/YEFm/4ET2u3b7IUotaeLdsdPfTGZDJZBgwKA4DBsUZ/XmrxiE4euFfOLreHqGh0xajfbcmiI6OvOfzi6KIc/9cQKlGgxZNGt/3gjBEZNvsOX9qY9TUcXjp598xSOWIzrKyfDosuwkZDJtx5jDm/15G9gtP4eCBQ2gdHYHmsfoLn7i5ukLbMAzaG8l60zRdjQlGiyb3XhwrpmE0Yiq5s6tpowbwcjwCHfSfx9u3EP/5v0nVqv9GRiYuX72K2JhGcHfjvOv2jI1CIjMJcnJBN9ntUQoBggOiE67hxIl4tGnT0uz1KBQK9Or5SKU/Hz7mBSxOvozk34/DL12N9HAfNHq6H1rfx9DvI4eOYuear+GWmQNl4Q24130eCgc3FKvPo2PjHLRv3czo43Q6HXbt/AVnD59AZl4exIRLCEvJRp6PC4rbx2DOioWSL1hDRNZPExAJRWaK3TcLVdm5eEbnXXFrlIMgYGieA9Yv/wwzF8+WpKZmTRujWSUrDPv5+qDb9Jfw64r1CE3JhNLLGSUPxWL2/16r9fEys7Px2ZIPcfNKGkrSNdCFjoazazhKinPgKe7CW1PHVvrYpAvJ2P7lN1AXFSHz7AXUu5ABuVbEmphgjF04A7HNY2tdFxHZJktZ4dja6EQRrRw8ECa73RjsqHPFnq93YtiIZyT5cMbP1weDqphvfdLS2VjyyiwEJPwLh1It/m0UjNHzp1drsIQxOp0O6z5dh0t/noBrpho3Ct3g5tcVoqiFWLAXU0Z0rvQ85KvV2PzFJmRfT0daahr8Ev+FX1YBtoX7IObZARg5fnStaiLrx0YhkZmIWXkG2+oXiDh9/JQkjcJ7EQQBMxbNRlZODq78m4aY6Ci4udZ+xal9v+3DH9PeRafssluX2+uu4vOiZLTu2ROPdm6Cft2Nj0DUarWYMWYyog6cR2ipiDRRjX4yb0BwBvJE5O86ixUL3sdrc2fUujYionLlzUJ7lpd6DZGC/vwQDoKA0owciSq6t7jB/fFo/944cz4JQf7+qFsnuNbPVXDzJuY+PxF9ErLhIMjQQafDZ3kL4NOtB1q2q4+xz0ysNA83rt6ACyu/wkM5Wnyty8bTMj/Ibs2d2PRMFlbPWoz3f1zPkYVEVIG3HNdeYmISQnOKAEF/wIAsS4XS0lKLvN22TnAQ3v/2c1xIuYTikhI0bdTwvjJh0cx5CPnmT3TSytAJwAGHdTjV6gRatW2BMcN6I7JemNHHJSdfwvKx09D5ohL/6NRoJ3NBoOAICM5oeLUQf338Dc507YBmd42KJPvARiGRmciD/YBk/QniL3jKMbBzB5Me99y5JHy5aAU0qekQvNzQ6vHeGDby2Wo/3t/X1+A2r0uXU7Hxg8+gyVLCJSwIL04dZ7DP3X5Z8zW6Zt+e3zBI5oDn0rIR1joUcT0qX/n5h+9+RKP95xGqlWOXmIfugqfezz0EOXJOJVX79RARUdUCGtRHnngM3ncsDlIs6uAcatqJ0IuKivHenMXIO3kOEAHvVjGY9NZ0uLq4VOvxDg4OaN1Mf7RecXExVn/wGTLO/APBzRkDRg9D24eqXjRr85pN6JyQCYdbr18hk+E/2VocLUrHpBenVvq4m4WFOL3+B3TP1SEfOgQJjnqT7AuCgDoXbiApOQUx0eafo5iILA+bhPenRYtYHAh0Q71M/TnUxWAfkzcJv96wBSe+2wUxVw15eCCee31cjUaMN4g0nPd390+/4NC2XRA0WkR1bovnRz9fZRMxKycHBb8fR/AdU0V1LXVAadY1vDl5SZXnYO2iFeh3MR+CIEehoCtrEt6htVLEzs1b0Ww+G4X2iI1CIjMZOH4EfkhegM7XS6AQBKQqNCjq0VZvEtoHLV+txicT/ou+yepbIaPGhX824HtnZwwZNqRWz5mSchkrh09Gz9RCyAUBxWIS3jxxFou+W210LotvNn2LM7sPIO1kAgD9ERhhJTKc/zsBcQP6Vnq8pKOn0EpbNrLFATKUQoTzXfuIitoN1SciIkPPjXke0/ccROeT1+AlKFAgarEvxhezJ71s0uMumDIbzXecqljh+OY/hzBf+Qbmf/purZ5PFEXMfHES2h+4iChBAVEUsfPoOeQvmoruvbsb7H/2TAK+/XgdEo6dwouC/p/IgiBAzDNcDfpOZxKTEHI5GxCcIQduTauvT+OggJMFjnAhIvNjk/D+Bfr7w3dgV/yz8Tc0LJZBJ4o45qdAt1FDTXrcHVt34NrCdeheUPY+L17Kx2epczB3+xq9BbFqYv0na5GzfAs63prC/tr+RCxMSsasJW8a7JuvVmPVkpW4cOosGqarAEH/AzW3XDXyVCoE+vtXejzt1YyKJqSxpb90EKFwYl7ZK15dE5lJx84d8PKWFTj3fGecGNQKAYsmYM6y+SY95pb1W9ApWaX3SVSDm8CJ7b/W+jk3rVhd0SQEACdBhi7nsrHxsw0G+368ZAWy31yNTvsuwEldBFHUv2g65yaia58eVR7PKyQQalELAOgguOE3UaX38xtyHSIeaVfr10NEZEyGUpqJ0C2Bq4sLFm9eBdXrQ3HqsVa4PmEA5n37GQL8/Ex2zDylCtrjiRVNQgBwFeSQnUhCZnZ2rZ7z4B+HUf9YCrxuNf0EQUC7bC1+XbPFYN+zZxKwcewstN9xGrHpRUjVFev9vFQU4RQWVOXxwuuGIMfPraJ2FbQoFm9ffmlFETlNQxERbvw2MCKyH2wSPjiT57yOmA+m4fjg1vj76Q4YunEp4h4fYNJjHvluF2IKbv+dIAgCulzKx1drNtbq+XQ6Hc58txsxd6xzGaKVo2jPCaRnZuntq9VqMWv4RESv3Yu4vzNxWVdk8HwFdQzvCDPg7V7xzyA4IFnUf55DQY4Y+mL170Ij28IRhURmFFG/HqYteMNsx8vLzEIdY58HFJUYbqsmTZZSb5UuAPAS5Ei+kqa3raSkBJd+PoAexWX7dhTc8Z2Yizh4wVWQI9lBB1Wv1mjbtlWVx3tu7HC88fN+9P1HBTdBjiaiM1ZrMxAGRyg9nNBsWH9MevWlWr8eIqK7cZ7Csmbh2Inme29VqdVwuVkCQD9fXG+WQqnKr1WT8syJeIQXGTwldNlKg32/+XAtul4rhiAIaAs3bBFz0EUHhMmcoBQ1+KOJP2ZPe6XK49UJCoTQtTmytx2HH+SIE7ywRZcNd8ghc1DA/eHmeP3duTV+HURkWxSZKWwQPmC9+/ZE7749zXfAomKDTa6QQZWdW6unUxcUwDFbbbA9MFONi8mXEBRwe2Tgzzt2o/nfaXC59cGal6DAYZ0a7QU36AAcDVCg65in77k4SpdnByH+3Aq0VIroIHPHbm0eDkKNOjJHqOsF4MkZ4xFet26tXg9ZP44oJLJhfYcOQryn/h8iRaIO7g3r1/o5XcKCUCrqD1DPFLQIa9ZIb1tOnhLuOTcrvq8jOKKP4IWtUGLPo40QvfJ1zP1gwT2P5+XpgfEr52O1swo7dHk4hyL0lHmhs8wDAR6eGDXpZU4KT0Rk5cJC6iAnwnAOxMzIAETVr1er53y4R1ckuRvmg7yO4a1YYm5+RZbIBQFPC764hlKsq+8I1bSnsOi71VXewlXuf++9jW3hjtiuy8WvogqRgjPiBC+01zijaed2CA4y7TyPRGS5FJkpbBLaCK/Gkbh5646ncqc9gEefqN1IRg93d5QEextsvxbqjaaN9a+xkk6fQ2jp7TZOF5kHQgQHfOZTjPMjHsHIb1Zg4BMD73nMfo/1Q/6jrfG1Nhs/6nJRIogYInijE9wQWK8uevfvXavXQraBjUIiGxbbJAY+w/vhTz85ckUNzjvpsL9jOCb+b0qtn3PUlP9gd2Mf5N8KxyxocPyhcDz9/FN6+wX6+yG/jrfeNg9BjpCIMLy1YhH69O1V7Qafs6sz2io8MEDmjcdlPogQnOAmyOGjKkJWjuWuwklERNUjCAKe+e9E/BztjjSU4jpKsSvSDU/OmFDrD4NatIjFzV6tcMWhLK+KRR1+C3XG0EljDfZ1CguC5o7pMeSCgBAHFwx7dQzGvjLW6By8xsjlcjQKCMZjMh8Mkvmgs8wDzjI5ggUHZP57vVavg4isH281ti0TZk3CwYcjkOisQ66owWFfOVyf7Y1WLZvX6vkEQUC3MU/jkJ8cWlGEKIo44yoi7PEeBnMedunTDedc9adHCYcjYtu3xmtvz0SUkUVSKuPl5o6hMl8MlPlgoMwHHjIF3AU5RHVhrV4H2Q7eekxk48ZPexVpLzyFfbt/R/tG0fi/Dg9V+6Jr57af8Mfm7YCyAIqwQIya+SoiI+tjwbefYeOn63Eh9TpCmzXEO8OHGayqJZPJ0HXM0/hz4adon6mBDMAJbwGxzw2Ei8vdy5FUrV5oXeTU8wfO6TcFM+v7c64nIjKZDKXIizozeqjDQ2i+80vs2vkLdFoRiwb0gbOzU7Uee+lyKr5YsBya1HTA0xUPDxuIAUMG4s3lC/Bjj504se8wnL09MeU/IxASbDjX4Jhpr+DtM0nokpgDL0GONLkWSV0aYvTguBq/DpfIuig9fhUOwu3P48+5iujSv1eNn4uIbAfzxHa4urjg3U2rcOzYcVw4dwFjendDWN2Qaj22tLQUKxYuQ+ZfZwEAAW2bYuKsyej/+ABENWmErV9shq5Ug25PxOHhhzsYPL5du7bY8WgrpOw8iUiNHAWiFgciPTFx2oQav45uAx7FgW/2Ivbm7d/NUlGES1RojZ+LbIsg3r26gJVTqVTw8vJCzvl4eHp4SF0OkdX6ZeeviJ++DM1VZbcZi6KInQ08sHD72mqPrACAi8mX8P3azdBptOj/3BNoFtukVvX89vNv2P3WSnRMuwlAwJG6Luj1xngOi7dBqvx8+Ma0hFKphKdn7VaOsxblmZWQkgcPD9t+rdaII0Csw83CQkx7bATiklSQ3fog7KynDE3nv4J+j/Wr9vOoCwqw+YuNyEq9hkYPtcCgJx6DXC6/9wPvkpObh9kjJqL1mRsI1spxxh3Q9O+AWUvmcKoMG2QvmcW8uj+85ZjKvTXpDTTcegzeKMsXJbRIHNQGb36wsNrPIYoidu/6Dad+PwjPQH88O3Y4fLy9alXPgulzIfvxMJqryxaJPBkbiLnrVsDP16dWz0eWqyZ5xUYhERn1vxET8fC+i3rblKIG+dOHYcyEFyWpKTsnF1+vL1ut8qnhTzPAbJS9XHQBvPCyBry4s3xrP1kLp/kb4SPo3yhzsEsU5n25UpKaNBoNdmz7CcnnL6BH/95o1ap2t6OR5bOXzCrPq6Rjp+AS0ULqcqwKP3SickpVPhY/+jy63tBfWPKPIAe89suGWjf77tfff5/Fbz/uQkTDKDw2ZCAUCt54aotqklf8DSAi49RFBps8IceVG+kSFFPGz9cH4yb9R7LjExGR5clMu4FYGI78Ewukm2NJoVBg8JODJDs+kSkpMlOgCYiUugyrwA+b6E45eXlwVxcD0P+d8MgvRnZurmSNwhYtYtGiRawkxybLxMVMiMgo1+gwFN+1uvFZVxHdBvJWXyIishw9B/fFGXf9bSWiDm7RnMOW6EEL8BQQ6CVUrOBLhsrPDZuEdLd6oXWRXc/fYHtmPT9E1guXoCIi4ziikMiCFRcXY8uGLUhLvIiAyHA8O/o5uLq4mOXY4/87GW/8k4LY02kIKZXjjKcA2WNd0K5dW7Mcn4jIUnBRk+o5ePAw/vhhFxTOTnhyzPOIMNNFT+vWLbHnia44se0PNFfqcN1Bh9PN6uDt/042y/GJ7FGgl4AMpcjRhXfgLcbWI+36DXz1yXoU56vRvm939OjVzSxzyMpkMgycMgY/vrkc7VILIAA4Fu6GuMljIJNxDBdZDs5RSGShCguLMP358Wh3/Cr8oUCuqMGfzYMwf9PH8PI0z++2TqfDrp2/4mLiP+g5oDeaNm5U6b5nEhJx4VwSOj3yMIIDA8xSH9kme5nvCeAchdaCF3/39sGC91Gyfjea3gQ0AA4HO+LReZPRvXd3s9WQeP4f/PrjbkTFRKNvXO9KFyLJysnBgd//QL2IcLRu3ZILjNB9sZfMquwaK0NZdilp781C5oT1+OvIX9gyeT66pBXCSZDhH0ctCp7ogpmL/me2GpSqfHy3+VsAwJBhT8Dby/h7h0ajwd69B6DRaNGjR1c4OTmZrUayPTXJK5O1refPn49OnTrB1dUV3t7e1XqMKIqYPXs26tSpAxcXF/Tq1QsXLlwwVYlEFu3Lzzeg419lTUIA8BEU6HE6E2s++NRsNchkMsQN6INXX59YaZOwuLgY00e/ip1DJ0M9dSU+6jcaKxYuM1uNRA8CM4uqYu8XwPeSkZWFjK17EVsoQBAEOAgCuqaXYteqDWato3FMQ7z6+kT0H9iv0ibhmpWr8V6/UcifsgL7npuBqc/+B+qCArPWSXQ/LC2vyhtj9nwbMpuE1uX7D75Ar2vFcBLKWiENS+TQ/HwEF5Mvma0GL08PjH55FEa/PKrSJuHZ02cxJe55JL+0AP+OW4zpcS/g8MEjZquR7JvJGoUlJSUYOnQoxo0bV+3HLFmyBB988AFWrVqFo0ePws3NDX369EFRkeGiCkS2LuN8isEKjq6CDMpL/0pUkXGr3v0IbX47jxYFAgIFB3TK0uDmhl04fvyU1KURVRszi6j2Tvx1CvXTDZtturQsaLVaCSoyLuXyFVxevRVdb5QgSHBA00IZuv55GSveelfq0oiqzRLzyp6bhWwSWh/ttSyDbQ1zS3B4/58SVFO5NW++h37n81Bf54Aw0QF9L+bj6wUroNPp7v1govtkskbh3LlzMXnyZDRr1qxa+4uiiGXLluGNN97AoEGD0Lx5c6xfvx7Xrl3Dtm3bTFUmkcVyD/ZH4V2LiZSKIhz8vaUpqBLZp5PgKeiP3GiuBvZ8v1OiiohqjplFVHuNGjfEdW8jt0P5eVY6sk8KP2z6Dg/l6DcunQUZVIn219wg62WpeWWPzUI2Ca2TzM9wBN+/bnI0saBVf9Mzs+CRkm4wNUbwxQycv5gsUVVkTyxmxsxLly7hxo0b6NWrV8U2Ly8vtG/fHocPH670ccXFxVCpVHpfRLbghQkv4vcGntDcmkZUK4r4vb4rXnh1jMSV3cXZ0WBTMUS4eLob2ZnINjCz7FP5XFykL7J+PZR2bY5M4XYT7pw70HponIRVGfL284UahiMcZU6GOUZkK8yZV/bULGST0Hp1ef5xnPK63QbJEzVIb98Abdq0lK6ou7i6OKPEyXDd2UIXB3hxHQYyA4tpFN64cQMAEBQUpLc9KCio4mfGLFy4EF5eXhVfYWFhJq2TyFx8fbwxa+NKnB7WAUe6ROHUE23xfxuWI6xuXalL09N+cF+cd9P/I+lgmCuGjXlOooqITI+ZZX/K5ylks9C4Ocvno+i1oTj0SDQO94pB7DuT8cyoZ6UuS89TLzyFQ1FeuHMdv8tOIprGPSJhVUSmZe68CvQSEOglQJGZUvFlaxSZKRWvk6xP/8cH4OEPZuJw7yY41DUK2RMHYd6nljUFhYe7OxzaxkAt3v5wq0jU4WbLKNStEyxhZWQvDNvUVZgxYwYWL15c5T6JiYmIiYm5r6JqYubMmZgyZUrF9yqVihdeZDPqBAdh1pI3pS6jSv0Hx+GrvDzs/W43xNx8yEMD8fSkFxHo72+w742MTHy5YjWKsvIQEBOB4S+NhIuLswRVkz1gZtGDpgmItMmL3gdBLpdj7CtjpS6jSm6urnhpxVvYuHAlNFduQPB0ReP+3fHsKMMPtkpLS7Fp7WZcPXkWck93PDN+JOrXC5egarIHtphXdzbRMm69b1rzwlB3vvezQWj9unbrjK7dOktdRpVmvvsW3nVZgLyTiYBOhFtsFN5Y+F+j+x4+dAy/fbUN0OnQfsCj6N23p3mLJZtTo0bh1KlTMXLkyCr3iYysXQAEB5d1xtPT01GnTp2K7enp6WjZsmWlj3NycuIy4UQSGzbyWQwbWfXIkctXruL94ZPQ45IaToIMeT+dxLQDx/Du5lVwdORtX/TgMbOI6G6xsU2wcONHVe4jiiJmjp2M5vv+QVtRDo0o4qM/TuKZFW+ilQXdmka2w9bzKtBLQIZShCIzxeqahbzFmKTi7OyE/y6de8/9Nn2xEanvbURblQ6CIODcb6dwYVQCJkx/1QxVkq2qUaMwICAAAQEBJikkIiICwcHB2LNnT0VoqVQqHD16tEarehGRZVr/3sfofakAcqFsxgNvQYG2J67iu83f4ZkRz0hcHdkiZhaZgiYgEhm3bjsj27R/3x+o9+cFBIplC7EoBAE90orx3co1aLVmucTVkS2yh7y6s1lYzlKbhnePHOf7PVkqjUaDU5t3oGe+CNxa+CSmUIb9P+yFavwoeHI+Q6olk81RmJqaivj4eKSmpkKr1SI+Ph7x8fFQq9UV+8TExGDr1q0AAEEQMGnSJMybNw/bt2/HmTNnMHz4cISEhGDw4MGmKpOIzERzIwfyu1buChIVuJLwj0QVEd3GzKKa0AREcq5CG3biz2OILjZsDIgZuRJUQ6TPmvOqfF4/S1705M4RhJyHkCxddm4ePDIMFxoKvK5C8pVUCSoiW1GjEYU1MXv2bKxbt67i+1atWgEA9u7di27dugEAkpKSoFQqK/aZNm0aCgoK8NJLLyEvLw+dO3fGrl274OzMOcyIrJ080Ac68TJkdzQLMwQtQmOiJayKqAwzi4jKterwEM6t+wXRJXK97UKAtzQFEd3BVvKqogFXcqnigxepRhjq3V7MxiBZET8fb6gDPIA8pd72jDqeiAznvLpUe4J459JvNkClUsHLyws55+M51JbIgqSkXMaKkVPQ4/JNOAgC8kUtDrQJwbtbPuUchaRHlZ8P35iWUCqV8PT0lLockyrPrISUPHh42PZrtTWct8p2iaKI14ZPQOsDF+EHObSiiD/qOOHJFbPRtl0bqcsjC2MvmWXqa6zKRmmbqnnI24vJVmz4dB1uLP8KzW4NKr7oJEI3sjdenTWl6geS3alJXplsRCERPRharRbbvtuOpEMn4ervjefHj4K/r6/UZdVYZGR9TN28EhtXfo7irDz4NYzAogmjK20SiqIIQeAfbURkmcpXQM5QirzAvMOZMwn4ccM3gE5E32cfR+vWLaUuqcYEQcDCz5dhw+oNOBZ/DnIPd7w4bgQaRFfesGBmEd0fY++jploAhR/0EADk5inx5SdrkX89E5Ftm+PJYUOgUFhfe+SFl0Zgf8Mo7P/mR4haHdrGdUf/x+Iq3Z95RdXBEYVEFkwURcwYOxkN9iQgVKdAoajD/gh3TFi9BA0aREldnkmkpFzGp3OWQpOSBsHFCWHd2+GVmZMgk5lsSlWyMPYyOgPgiEJboODCJhW+3rAF55euQ9tcHQQApzwFhE58GsNfHil1aSZRWlqK999cgozDfwMlpXBqVB8TF85CcKBpFqUgy2QvmSXVNZap5oPl+7Z9S72ahqUjJqHbRSVcBTluyDQ407kBlqxdAblcfu8nsEI/fL0Nf365DbrMPCjq+KHX2GfQq18vqcsiM+KIQiIbsW/vAdTdfw6hurL/qi6CDH0uFWD90o/w9ifvSlzdg6fRaPDe+JmIO5d7ay7DIqSn7MbHCgUmTH9V6vKIiIziqMKy9++/1m9Dz7zbKy+2zgd+37wDRSOegbOzk8QVPnjvzl6Eepv+QHOUXVRqUxOxMHcaln33BUdrED0g9v7eSqax5p2V6HMxH3Kh7P07WKdAyaEL2LH9Zwx6fIDE1T14hw4eRsK8T9GjvPF+7Rr2XVmO0Mh6iGnUQNriyCJxiA6RBTvxxxE0KNb/byoIAnTXsyWqyLR279qDFklZegueBGllSN3/l4RVERFVrvyWOHtfBflaega8rxuuChyQpkRKqu2tvKjT6ZB9+DR8cHvkiVwQEH42Daf+PithZUREdC+a69mQ3/WBTrhGgaS/4qUpyMR+3bQNrfJ0ets6Zpbi+883SlQRWTo2CoksWIPYJvhXoTPYLvja5q0tyuwcuGkNt4vFJeYvhoiomqRaqdOSBPr5QeXnbrA9J8AdoXWCJajItHQ6HWAkm1yLNVDm5Zm/ICIiqjaZryfunoEtQ9CibkPbnNoJxaUGI93lggBdEa+xyDg2Coks2IDBcTjTOhQFYln3TBRFHAxywGMvv3DPx+bk5mHl4uVYMHU2fti6o+yixsL1GxSH+LquettKRRHOjepJVBEREVWHs7MT6g94BMlOty+8Uh1EBPbpdM/5zLRaLb7d8j0WTp2Dj979EEpVvqnLvW8KhQLy6FDo7rrQvBDph04d20lUFRERVceT40diX4hTxXt4oajDsebBePKZIfd87OnTZ7Fw+ltYPGseEs//Y+pSH4iIji1xQ6Y/GiPZUYvWvR+RqCKydFzMhMjCFdy8idXvf4KcpBTIPFwx5OUX0Kx5bJWPSU6+hOUvvoZuKflwFeS4otDgSq/mmL9qqcXPm/T95u9x+KMvEXNFCaWzHFeahWL2Z+/Cz9dH6tLITOxlYniAi5nYEq6iWWbbtz/g1M/7AJ2IJj074annhlaZOzqdDtNGv4rG+5NQV6eAWtRiX0NvzFy3DHXrhpiv8FpIvfovlo6fiehzN+Ci0SExwgdx0//DyeHtjL1kFq+xyNacT0zC1x+vgy43Hx5RYRg7ddw9f7c3frYeKSs2o02eCBHAX75yNJs2Ck88+6R5iq4lnU6Htya/AfneeITlleCyvzNc+nXEtHmzLP7akB6cmuQVG4VENuh/L7+GTj+f1XvjT3LWodWns9H1kYclrKx68tVqHNh3EEHBQWjTpiUDzM7Yy0UXwEahLeIqyDXz47afcG3qMtTT3F5fTyuKODmkLf63bJ6ElVWPTqfD4SN/IV+Zj0e6dYaLi7PUJZGZ2Utm8RqL7F1RUTFm930ePS6p9bbvifHBwp/WQ6Gw/HVik1MuIeH0ObR6qBXCLPzDOHrwuOoxkZ3TXc8yaK41LBRwdO9Bq2gUeri7o/+AvlKXQURUY5qASGSwWVht546eRAuN/p+jckFAaVqGRBXVjEwmw8Od2ktdBhERmVjylSsITMvD3S0U77RcXE/PsIrGW1RkBKIiI6Qug6wA5ygkskU+hp/0XlPo0LBZYwmKISIiMi6kQQSyoNHbJooiYKOLdhERkXUKrVMHOf5uBttVfm4I8POToCIi02GjkMgG9R/zLA4FOFSs5nVT1CG+ZQgGDO4vcWVERPYhQ2lTM7uYzNBnh+JwsyAUi2ULbomiiD/qOOHJ8SMkroyIiOg2L08P+D7aAWkOtxeIvOIkIqxfFzg7O0lYGdGDx1uPicyktLQUx0/+DS9vTzRp1NCkx+rUpSOcP3kL21dvgqgqgGfDelgwdTzkcrlJj0tERGW3HysyU5ChFK32FuTECxeRm52Ltq1bwNHR0WTHcXZ2wrwvP8SnSz9E4aU0wNsdz44bgdjYJiY7JhER2Y6snBycSziPxk0amXxk39S507EpIgyH9x2FIBPQtHdXDH3mCZMek0gKXMyEyAz2/boX2xetQv3kTNx0UiC9RTjeWPUOV/IlMsJeJoYHuJiJrbPGlZBzcvMwb9w0BP59Ba43S3ElOgBx015Gzz49pC6NyCLZS2bxGossjSiKeH/uO8jZeRAhN9S4HugGr7iHMXXudC6ESGRETfKKtx4TmVhRUTG2z/8IvS+q0FB0QssiOXoc+RfvzbD81RyJiKj2NAGRZYubWNFtyMtmzUf3Q1fQ8qYMDeGERy+q8NOCj3CzsFDq0oiIiCr8tP1nOG3ag87ppYgUnPBwpgZum/Zi+/c/Sl0akdVjo5DIxPb+vh+NL+XobXMQBBQnXpamICIiokoUJl6Gg6D/52HspVz89svvElVERERk6OTu/Ygq1s+riFIZ/v71D4kqIrIdbBQSmZiLmxuKFUaGvztwvkAiInthNaMKjWRTsUIGV1dXCYohIiIyTnBQ4O5Z1ERRBBS8xiK6X2wUEplY1y4dkdQoSC/IlNDCv30zCasiIiJz0QREArCOZmFA++bIg7bie1EUca6hP7r36CphVURERPr6Pf8ETnrrtzPivWTo/dwQiSoish1c9ZjIxGQyGSatnIdP/rsYsuRr0Do5wKdDM7w2d7rUpRERkZlYy0rIk+e8jndKS5F76DTkxSXQRYXg1bemQS7nCA0iIrIcbR9qjSszRmPPl9vgmKFESYAX2j/3GDp0bCd1aURWj6seE5lRYWERHBwUUCjYoyeqjL2sIAlw1WN7ZC0rIWs0GpSWauDi4ix1KUQWzV4yi9dYZKlEUcTNwkK4urhwtWOiKtQkr9itIDKjyi64dDodPn73Q6QdOgVRq4N/myaYOHMSHB0dzVwhERGZUsVtyJkpFt0sVCgq/1Dr992/Y8/6byHm5kMeGoiRM15BVGSEmSskIiICBEGAWyXz6F6/kY5Vb7+HksvXIXi4ouPQOAx84jEzV0hkfdgoJLIA77yxAHU3H0CUruzWLuXf1zAvIxtvfbhY4sqIiMgUNAGRyLCS0YV3OnjgEA7NeB9dcjQAAPFsJpZfmIa3f/gCXp4cZURERJahpKQE80ZNQlxCLuS3RhomnF2FHYIMA4YMkLg6IsvGxUyIJFZYWITcAyfhr7s9/5MX5MCRBKRnZklYGRERmZI1LXJSbve6b9D2VpMQKBvJ0TVZic2ffylhVURERPq2frMN7RKzKpqEANA0X8Shb3ZIWBWRdWCjkEhiKrUaLvnFBtu9lEVsFBIR2TiraxaqbxpschfkyL2RKUExRERExqVdStUbiFEhv9D8xRBZGTYKiSQW6O8HVbifwfbr9f3QuEGUBBUREZE5WVOz0L1BPRSJOr1tFxx1aP/oIxJVREREZOjRwXH421N/ag+NKMIpMkSiioisBxuFRBITBAGDp4zFL2HOUItaFIo67A12RPcJL8DBwUHq8oiIyAyspVk4bvpE7GlTB1flWuhEEWdcReT0b4vuPbtKXRoREVGFZrFN4PREN5zwALSiiBsyDX6O9cXLb0yRujQii8fFTIgsQJfuXdDspxb4esMWaEpL8drwpxHgZzjKkIiIbJcmIBKKzBRkKEWLXeDE08MDy779HDt/3IXEs4no0a8n2rRpJXVZREREBqbOnY6EYYOx+/sdCI+OwLIhAzkQg6ga2CgkshDeXp546ZWxUpdBREQSqhhZmJlisc1CuVyOgYP7A4P7S10KERFRlZo2boSm/20kdRlEVoW3HhMRERFZGE1AJDKUYsUXEREREZE5cEQhERERkQUqH10IlI0wBGCxowyJiIiIyDZwRCERERGRhbOWxU6IiIiIyLqxUUhERERkBe5sFvKWZCIiIiIyBd56TERERGQl7rwd2dJXSCYiIiIi68MRhURERERWiCMMiYiIiOhB44hCIiIiIitlbIRhOY40JCIiIqKaMtmIwvnz56NTp05wdXWFt7d3tR4zcuRICIKg99W3b19TlUhERASAmUW2QRMQWfEFcOETIlvEvCIiIlMz2YjCkpISDB06FB07dsTnn39e7cf17dsXa9asqfjeycnJFOURERFVYGaRrdEERBqMMCxn7SMNzd0AtfbzRbaFeUVERKZmskbh3LlzAQBr166t0eOcnJwQHBxsgoqIiIiMY2aRLbrztuRy1r4ASnmT0NhrMwVrP19ke5hXRERkahY3R+G+ffsQGBgIHx8f9OjRA/PmzYOfn1+l+xcXF6O4uLjie5VKZY4yiYiImFlkdaoaaVjOnE2x2owONFeTsPxYtjoyk+wL84qIiKrLohqFffv2xZAhQxAREYHk5GTMmjUL/fr1w+HDhyGXy40+ZuHChRWfrBEREZkLM4usVVWNtns1Ec1Zi6WoamRmOTYNyZIxr4iIqCYEURSr/dfgjBkzsHjx4ir3SUxMRExMTMX3a9euxaRJk5CXl1fj4lJSUhAVFYXffvsNPXv2NLqPsU+7wsLCkHM+Hp4eHjU+JhERSUuVnw/fmJZQKpXw9PSs9fNYU2YlpOTBw6P2r5WIpKPITAHAZqG9ehCZZU15xWssIiLrVJO8qtGIwqlTp2LkyJFV7hMZ+eA+GY6MjIS/vz8uXrxYaYg5OTlxMl4iIjLAzCIiczB2ezKbhlQTzCsiIrIkNWoUBgQEICAgwFS1GPj333+RnZ2NOnXqmO2YRERkG5hZRGQud96ezKYh1RTzioiILInMVE+cmpqK+Ph4pKamQqvVIj4+HvHx8VCr1RX7xMTEYOvWrQAAtVqN119/HUeOHMHly5exZ88eDBo0CNHR0ejTp4+pyiQiImJmEdEDowmIrPgCardgC1FlmFdERGRqJlvMZPbs2Vi3bl3F961atQIA7N27F926dQMAJCUlQalUAgDkcjlOnz6NdevWIS8vDyEhIejduzfefvttDnsnIiKTYmYRkSlUNAs5jyE9IMwrIiIytRotZmINVCoVvLy8ONEuEZGVelCLmViD8sziYiZEto+Lntgme8ksXmMREVm3muSVyW49JiIiIiKiMrwVmYiIiKwBG4VERERERGZwZ7Ow/IuIiIjIkphsjkIiIiIiItJnbIVk3o5MRERElsLmGoXlUy6q7lj5i4iIrEf5+7eNTaFrVPlrVOerJK6EiCTh7A9F1iXkq4EATzYLrZG9ZBavsYiIrFtN8srmGoX5+fkAgPptO0tcCRER3Y/8/Hx4eXlJXYZJlWdW+xbhEldCRET3w9Yzi9dYRES2oTp5ZXOrHut0Oly7dg0eHh4QBAEqlQphYWG4evWqTa9EVlM8L8bxvBjH82Icz4tx93teRFFEfn4+QkJCIJPZ9lS6d2ZWfn4+f5+M4P8z43hejON5MY7nxbgHcV7sJbOYV/fG/2fG8bwYx/NiHM+LcebOK5sbUSiTyRAaGmqw3dPTk79oRvC8GMfzYhzPi3E8L8bdz3mx5VEZd7ozswSh7LZD/j4Zx/NiHM+LcTwvxvG8GHe/58UeMot5VX08L8bxvBjH82Icz4tx5sor2/3Yi4iIiIiIiIiIiKqNjUIiIiIiIiIiIiKy/Uahk5MT5syZAycnJ6lLsSg8L8bxvBjH82Icz4txPC+1w/NmHM+LcTwvxvG8GMfzYhzPS+3wvBnH82Icz4txPC/G8bwYZ+7zYnOLmRAREREREREREVHN2fyIQiIiIiIiIiIiIro3NgqJiIiIiIiIiIiIjUIiIiIiIiIiIiJio5CIiIiIiIiIiIhgR43Cy5cv48UXX0RERARcXFwQFRWFOXPmoKSkROrSJDd//nx06tQJrq6u8Pb2lrocyXz44YeoX78+nJ2d0b59exw7dkzqkiR34MABDBw4ECEhIRAEAdu2bZO6JMktXLgQDz30EDw8PBAYGIjBgwcjKSlJ6rIk9/HHH6N58+bw9PSEp6cnOnbsiJ9//lnqsqwS86pyzKsyzCtDzCvjmFnGMbMeHGaWccyr25hZ+phXxjGvjJMqr+ymUXj+/HnodDp88sknSEhIwPvvv49Vq1Zh1qxZUpcmuZKSEgwdOhTjxo2TuhTJbNmyBVOmTMGcOXNw8uRJtGjRAn369EFGRobUpUmqoKAALVq0wIcffih1KRZj//79mDBhAo4cOYJff/0VpaWl6N27NwoKCqQuTVKhoaFYtGgRTpw4gePHj6NHjx4YNGgQEhISpC7N6jCvKse8Yl5VhnllHDPLOGbWg8PMMo55VYaZZYh5ZRzzyjjJ8kq0Y0uWLBEjIiKkLsNirFmzRvTy8pK6DEm0a9dOnDBhQsX3Wq1WDAkJERcuXChhVZYFgLh161apy7A4GRkZIgBx//79UpdicXx8fMTVq1dLXYZNYF7pY14xr6rCvKocM6tyzKwHh5l1mz3nlSgys+6FeVU55lXlzJFXdjOi0BilUglfX1+pyyCJlZSU4MSJE+jVq1fFNplMhl69euHw4cMSVkbWQKlUAgDfS+6g1Wrx1VdfoaCgAB07dpS6HJvAvCKAeUX3j5lliJn14DGzCGBm0f1hXhkyZ14pTPrsFuzixYtYsWIFli5dKnUpJLGsrCxotVoEBQXpbQ8KCsL58+clqoqsgU6nw6RJk/Dwww8jNjZW6nIkd+bMGXTs2BFFRUVwd3fH1q1b0aRJE6nLsnrMKyrHvKL7wczSx8wyDWYWlWNmUW0xr/RJkVdWP6JwxowZEAShyq+734jS0tLQt29fDB06FGPHjpWoctOqzXkhopqZMGECzp49i6+++krqUixCo0aNEB8fj6NHj2LcuHEYMWIEzp07J3VZFoN5ZRzzisg8mFn6mFlVY2YZYl4RmQfzSp8UeWX1IwqnTp2KkSNHVrlPZGRkxb+vXbuG7t27o1OnTvj0009NXJ10anpe7Jm/vz/kcjnS09P1tqenpyM4OFiiqsjSvfLKK9ixYwcOHDiA0NBQqcuxCI6OjoiOjgYAtGnTBn/99ReWL1+OTz75ROLKLAPzyjjmVfUxr6i2mFmGmFlVY2YZYl7VDDOLaoN5ZUiKvLL6RmFAQAACAgKqtW9aWhq6d++ONm3aYM2aNZDJrH5AZaVqcl7snaOjI9q0aYM9e/Zg8ODBAMqGO+/ZswevvPKKtMWRxRFFERMnTsTWrVuxb98+RERESF2SxdLpdCguLpa6DIvBvDKOeVV9zCuqKWZW9TGz9DGzDDGvaoaZRTXBvKo+c+SV1TcKqystLQ3dunVDvXr1sHTpUmRmZlb8zN4/0UhNTUVOTg5SU1Oh1WoRHx8PAIiOjoa7u7u0xZnJlClTMGLECLRt2xbt2rXDsmXLUFBQgFGjRkldmqTUajUuXrxY8f2lS5cQHx8PX19fhIeHS1iZdCZMmIBNmzbhhx9+gIeHB27cuAEA8PLygouLi8TVSWfmzJno168fwsPDkZ+fj02bNmHfvn3YvXu31KVZHeZV5ZhXzKvKMK+MY2YZx8x6cJhZxjGvyjCzDDGvjGNeGSdZXpl0TWULsmbNGhGA0S97N2LECKPnZe/evVKXZlYrVqwQw8PDRUdHR7Fdu3bikSNHpC5Jcnv37jX6uzFixAipS5NMZe8ja9askbo0SY0ePVqsV6+e6OjoKAYEBIg9e/YUf/nlF6nLskrMq8oxr8owrwwxr4xjZhnHzHpwmFnGMa9uY2bpY14Zx7wyTqq8EkRRFO+32UhERERERERERETWzTYnkCAiIiIiIiIiIqIaYaOQiIiIiIiIiIiI2CgkIiIiIiIiIiIiNgqJiIiIiIiIiIgIbBQSERERERERERER2CgkIiIiIiIiIiIisFFIREREREREREREYKOQiIiIiIiIiIiIwEYhERERERERERERgY1CIiIiIiIiIiIiAhuFREREREREREREBDYKiYiIiIiIiIiICMD/A4KsJyL1mEraAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1300x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear underfits the moons; poly and rbf bend to fit. SV count is the model's 'memory'.\n"
     ]
    }
   ],
   "source": [
    "kernels = [(\"linear\", dict(kernel=\"linear\", C=1.0)),\n",
    "           (\"poly d=3\", dict(kernel=\"poly\", degree=3, coef0=1, C=1.0)),\n",
    "           (\"rbf\", dict(kernel=\"rbf\", gamma=1.0, C=1.0))]\n",
    "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n",
    "for ax, (name, kw) in zip(axes, kernels):\n",
    "    clf = make_pipeline(StandardScaler(), SVC(**kw)).fit(Xm, ym)\n",
    "    n_sv = clf.named_steps[\"svc\"].support_vectors_.shape[0]\n",
    "    plot_boundary(ax, clf, Xm, ym, title=f\"{name}  (acc {clf.score(Xm, ym):.2f}, {n_sv} SVs)\")\n",
    "plt.tight_layout(); plt.show()\n",
    "print(\"linear underfits the moons; poly and rbf bend to fit. SV count is the model's 'memory'.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb6a6c0",
   "metadata": {},
   "source": [
    "> **Key takeaways**\n",
    "> - The kernel trick replaces $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$ with $K(\\mathbf{x}_i, \\mathbf{x}_j)$; you get a non-linear boundary without building the feature map.\n",
    "> - The degree-2 poly kernel is a literal inner product in a 6-D space (Exercise 5.6); RBF's space is infinite-dimensional and never materialized.\n",
    "> - `gamma` is the RBF width knob: tune it by cross-validation, not by train accuracy.\n",
    "> - RBF SVMs are not scale-invariant; an unscaled feature silently wrecks them. Always use a `StandardScaler` pipeline.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386ae6df",
   "metadata": {},
   "source": [
    "## Safety lens\n",
    "\n",
    "Classical models fail differently from neural networks, and these failures show up in any system that uses them for credit, justice, or medical decisions.\n",
    "\n",
    "**Tree-based shortcut learning.** A tree splits on whatever improves training accuracy, including a feature that proxies a protected attribute (zip code for race, say). The split is readable, which is good; the decision it encodes may still be unlawful, which is the part to catch. Because the tree is fully enumerable, you can audit it directly: `tree.feature_importances_` plus `export_text` is the complete rule set. We demonstrate the audit habit below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1379e65c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.368798Z",
     "iopub.status.busy": "2026-06-10T18:50:18.368699Z",
     "iopub.status.idle": "2026-06-10T18:50:18.372686Z",
     "shell.execute_reply": "2026-06-10T18:50:18.372291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature reliance (audit every tree that gates a real decision):\n",
      "   0.946  petal width (cm)\n",
      "   0.054  petal length (cm)\n",
      "   0.000  sepal length (cm)\n",
      "   0.000  sepal width (cm)\n",
      "\n",
      "for a real deployment: if any forbidden proxy has nonzero importance, you have a problem\n"
     ]
    }
   ],
   "source": [
    "# safety: enumerate which features a fitted tree actually relies on\n",
    "audit_tree = DecisionTreeClassifier(max_depth=3, random_state=SEED).fit(X4, y4)\n",
    "importances = dict(zip(load_iris().feature_names, audit_tree.feature_importances_.round(3)))\n",
    "print(\"feature reliance (audit every tree that gates a real decision):\")\n",
    "for name, imp in sorted(importances.items(), key=lambda kv: -kv[1]):\n",
    "    print(f\"  {imp:>6.3f}  {name}\")\n",
    "print(\"\\nfor a real deployment: if any forbidden proxy has nonzero importance, you have a problem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9a25b1",
   "metadata": {},
   "source": [
    "**SVM scores are not probabilities.** `SVC.decision_function` returns a signed distance to the hyperplane, not a calibrated probability. If a decision triggers above a probability threshold, calibrate explicitly (`CalibratedClassifierCV`) rather than treating the distance as a probability. **Trees leak training data**: split thresholds are near-values of real training points, so an exported tree enables membership-inference and reconstruction. For models on personal data, prefer ensembles (the averaging blurs individual thresholds) or differentially private trees.\n",
    "\n",
    "Habits to keep: print `feature_importances_` after every tree fit and check no protected proxy is load-bearing; wrap any decision-triggering SVM in `CalibratedClassifierCV`; default to ensembles, not single trees, for anything touching personal records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a478f42e",
   "metadata": {},
   "source": [
    "## Test yourself\n",
    "\n",
    "Three parts: concept self-checks with folded answers, two auto-checked problems, and a capstone. Solutions are folded; try before you peek. Every answer is in this notebook; if unsure, re-run that section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae47237",
   "metadata": {},
   "source": [
    "### Part A: Concepts\n",
    "\n",
    "1. Why does scaling a feature by 1000 change an SVM but not a decision tree? <details><summary>Answer</summary>Trees split on raw thresholds, so a scaled feature just rescales the threshold and the structure is identical (we asserted `node_count` is unchanged in Part 1). The SVM minimizes $\\|\\mathbf{w}\\|_2$, a Euclidean quantity, so the inflated feature dominates the margin; Part 3 showed the learned direction changes even after un-scaling.</details>\n",
    "2. In the depth-vs-accuracy plot from Part 2, training accuracy hits 1.0 while test accuracy plateaus. What is the gap called and how do you shrink it? <details><summary>Answer</summary>It is overfitting (high variance). Shrink it by regularizing growth (cap `max_depth`, raise `min_samples_leaf`, or set `ccp_alpha`) chosen by cross-validation, which is what the `GridSearchCV` cell did.</details>\n",
    "3. The dual objective contains $\\mathbf{x}_i^{\\top}\\mathbf{x}_j$ and no other function of the inputs. Name two consequences. <details><summary>Answer</summary>(1) The data enters only through the Gram matrix, so you can swap in a kernel and operate in another feature space for free (the kernel trick). (2) Only points with $\\alpha_i > 0$ (the support vectors) affect the decision function; the rest can be deleted.</details>\n",
    "4. Why do we put $\\sqrt2$ on the cross term in the degree-2 polynomial feature map? <details><summary>Answer</summary>Expanding $(x_1z_1 + x_2z_2 + 1)^2$ produces $2x_1x_2 z_1z_2$. To reproduce that as $\\phi(x)^{\\top}\\phi(z)$, the $x_1x_2$ feature needs a $\\sqrt2$ so the product gives back the factor of 2. Exercise 5.6 checks the whole Gram matrix matches.</details>\n",
    "5. You set `gamma=100` on an RBF SVM and train accuracy is 0.99 but the boundary is a cloud of islands around points. What happened, and which number should you have watched? <details><summary>Answer</summary>The kernel width is tiny, so each support vector influences only its immediate neighborhood. That is classic overfitting. Watch the cross-validated accuracy (the CV column in Part 5), which peaks at moderate `gamma` and falls at 100, not the train column.</details>\n",
    "6. Looking at the \"BROKEN (unscaled) test accuracy\" output in Part 5, why did the RBF SVM degrade without erroring? <details><summary>Answer</summary>The RBF kernel uses $\\|\\mathbf{x} - \\mathbf{z}\\|^2$; one feature multiplied by 1000 dominates that distance, so the kernel sees almost no structure in the other feature and the boundary collapses. It produced a model, just a bad one. That quiet failure mode is exactly what a `StandardScaler` pipeline prevents.</details>\n",
    "7. Quick recall: what does the `gain <= 0` clause in `grow_tree` prevent, and why is depth alone not enough? <details><summary>Answer</summary>It guarantees termination. A node with identical feature rows but different labels is impure and may be below the depth limit, yet no split separates it; without `gain <= 0` the recursion never stops. It is the sentinel you must name and verify, like a k-means convergence check.</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e841b3",
   "metadata": {},
   "source": [
    "### Part B: Auto-checked problems\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2637a4cb",
   "metadata": {},
   "source": [
    "### Exercise 5.7: Entropy, the other impurity\n",
    "`Difficulty 2/5 · ~10 min`\n",
    "\n",
    "CART can use entropy instead of Gini: $H = -\\sum_k p_k \\log_2 p_k$ (with the convention $0\\log 0 = 0$). Fill in `entropy(y)`. The checks pin a pure node (0), a 50/50 split (exactly 1 bit), and the property that on a 75/25 split entropy is below 1 bit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f7a2d095",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.373593Z",
     "iopub.status.busy": "2026-06-10T18:50:18.373521Z",
     "iopub.status.idle": "2026-06-10T18:50:18.376947Z",
     "shell.execute_reply": "2026-06-10T18:50:18.376702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.7 entropy: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def entropy(y):\n",
    "    \"\"\"Shannon entropy in bits of a label vector. entropy([]) == 0.0, 0*log0 == 0.\"\"\"\n",
    "    y = np.asarray(y)\n",
    "    if len(y) == 0:\n",
    "        return 0.0\n",
    "    _, counts = np.unique(y, return_counts=True)\n",
    "    p = counts / len(y)\n",
    "    # TODO 1: -sum(p * log2(p)); p is strictly > 0 here (zero-count classes are absent)\n",
    "    result = None\n",
    "    attempted(result)\n",
    "    return result\n",
    "\n",
    "def _entropy():\n",
    "    check_close(entropy([0, 0, 0]), 0.0, msg=\"a pure node has 0 entropy\")\n",
    "    check_close(entropy([0, 0, 1, 1]), 1.0, msg=\"a 50/50 two-class split is exactly 1 bit\")\n",
    "    h_skew = entropy([0, 0, 0, 1])    # 75/25\n",
    "    assert 0 < h_skew < 1.0, f\"75/25 entropy {h_skew:.3f} should be between 0 and 1 bit\"\n",
    "\n",
    "check(\"5.7 entropy\", _entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbfacb1",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1</summary>`np.unique(..., return_counts=True)` gives counts; `p = counts/len(y)` are strictly positive, so no zero-log guard is needed here. Then `-(p * np.log2(p)).sum()`.</details>\n",
    "\n",
    "<details><summary>Solution</summary>\n",
    "\n",
    "```python\n",
    "def entropy(y):\n",
    "    y = np.asarray(y)\n",
    "    if len(y) == 0:\n",
    "        return 0.0\n",
    "    _, counts = np.unique(y, return_counts=True)\n",
    "    p = counts / len(y)\n",
    "    return float(-(p * np.log2(p)).sum())\n",
    "```\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b7c47456",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.377573Z",
     "iopub.status.busy": "2026-06-10T18:50:18.377508Z",
     "iopub.status.idle": "2026-06-10T18:50:18.380371Z",
     "shell.execute_reply": "2026-06-10T18:50:18.379974Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.7 entropy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def entropy(y):\n",
    "    y = np.asarray(y)\n",
    "    if len(y) == 0:\n",
    "        return 0.0\n",
    "    _, counts = np.unique(y, return_counts=True)\n",
    "    p = counts / len(y)\n",
    "    return float(-(p * np.log2(p)).sum())\n",
    "\n",
    "check(\"5.7 entropy\", _entropy, required=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9262a31",
   "metadata": {},
   "source": [
    "### Exercise 5.8: The RBF Gram matrix from scratch\n",
    "`Difficulty 3/5 · ~15 min`\n",
    "\n",
    "Fill in `rbf_gram(A, B, gamma)` returning the matrix $K_{ij} = \\exp(-\\gamma\\|\\mathbf{a}_i - \\mathbf{b}_j\\|^2)$ for `A:(n,d)`, `B:(p,d)`. Use the identity $\\|\\mathbf{a} - \\mathbf{b}\\|^2 = \\|\\mathbf{a}\\|^2 + \\|\\mathbf{b}\\|^2 - 2\\mathbf{a}^{\\top}\\mathbf{b}$ so it is vectorized, not a double loop. The check compares against scikit-learn's `rbf_kernel`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8198a4fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.381073Z",
     "iopub.status.busy": "2026-06-10T18:50:18.381009Z",
     "iopub.status.idle": "2026-06-10T18:50:18.384789Z",
     "shell.execute_reply": "2026-06-10T18:50:18.384464Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -- ] 5.8 rbf gram: not attempted yet — fill in the TODO above, then re-run.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rbf_gram(A, B, gamma):\n",
    "    \"\"\"RBF kernel matrix K[i,j] = exp(-gamma * ||A_i - B_j||^2). A:(n,d) B:(p,d) -> (n,p).\"\"\"\n",
    "    A, B = np.asarray(A, float), np.asarray(B, float)\n",
    "    # TODO 1: squared norms of rows: a2 shape (n,1), b2 shape (1,p)\n",
    "    a2 = None\n",
    "    b2 = None\n",
    "    # TODO 2: squared distance matrix sq = a2 + b2 - 2 A B^T  (shape (n,p))\n",
    "    sq = None\n",
    "    # TODO 3: return exp(-gamma * sq)\n",
    "    result = None\n",
    "    attempted(a2, b2, sq, result)\n",
    "    return result\n",
    "\n",
    "def _rbf():\n",
    "    from sklearn.metrics.pairwise import rbf_kernel\n",
    "    A = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, -1.0]])\n",
    "    B = np.array([[0.0, 1.0], [-1.0, 2.0]])\n",
    "    check_close(rbf_gram(A, B, gamma=0.5), rbf_kernel(A, B, gamma=0.5), atol=1e-9,\n",
    "                msg=\"your RBF Gram should match sklearn's rbf_kernel\")\n",
    "    # diagonal of K(A, A) is exp(0) = 1 (a point is at distance 0 from itself)\n",
    "    check_close(np.diag(rbf_gram(A, A, gamma=3.0)), np.ones(len(A)),\n",
    "                msg=\"K(a,a) = exp(0) = 1 on the diagonal\")\n",
    "\n",
    "check(\"5.8 rbf gram\", _rbf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59397739",
   "metadata": {},
   "source": [
    "<details><summary>Hint 1 (conceptual)</summary>`(A**2).sum(1)` is the per-row squared norm. Reshape one to a column `(n,1)` and the other to a row `(1,p)` so broadcasting builds the `(n,p)` distance matrix. The cross term is `A @ B.T`.</details>\n",
    "\n",
    "<details><summary>Hint 2 (pseudocode)</summary>\n",
    "\n",
    "```python\n",
    "a2 = (A ** 2).sum(1)[:, None]   # (n, 1)\n",
    "b2 = (B ** 2).sum(1)[None, :]   # (1, p)\n",
    "sq = a2 + b2 - 2 * A @ B.T      # (n, p)\n",
    "return np.exp(-gamma * sq)\n",
    "```\n",
    "</details>\n",
    "\n",
    "<details><summary>Help: \"shapes don't broadcast\" or \"diagonal isn't 1\"</summary>If broadcasting fails, you forgot the `[:, None]` / `[None, :]` reshapes; both norm vectors stay 1-D otherwise. If the diagonal of `rbf_gram(A, A, ...)` is not 1, your squared-distance sign is wrong: it must be `a2 + b2 - 2 A B^T`, and a point's distance to itself is 0, giving `exp(0) = 1`.</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfb129d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.385673Z",
     "iopub.status.busy": "2026-06-10T18:50:18.385607Z",
     "iopub.status.idle": "2026-06-10T18:50:18.388207Z",
     "shell.execute_reply": "2026-06-10T18:50:18.387944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ok ] 5.8 rbf gram\n",
      "[ ok ] hand-built RBF Gram matches sklearn; this matrix is all an RBF SVM ever sees of the data\n"
     ]
    }
   ],
   "source": [
    "def rbf_gram(A, B, gamma):\n",
    "    A, B = np.asarray(A, float), np.asarray(B, float)\n",
    "    a2 = (A ** 2).sum(1)[:, None]\n",
    "    b2 = (B ** 2).sum(1)[None, :]\n",
    "    sq = a2 + b2 - 2 * A @ B.T\n",
    "    return np.exp(-gamma * sq)\n",
    "\n",
    "check(\"5.8 rbf gram\", _rbf, required=True)\n",
    "print(\"[ ok ] hand-built RBF Gram matches sklearn; this matrix is all an RBF SVM ever sees of the data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247292bf",
   "metadata": {},
   "source": [
    "### Part C: Capstone: trees vs SVMs on moons, full scale\n",
    "\n",
    "Bring the chapter together on `make_moons(n_samples=1000, noise=0.3)`. Deliverables:\n",
    "\n",
    "1. A tuned decision tree (grid-search `max_depth` and `min_samples_leaf` with 5-fold CV) and its test accuracy.\n",
    "2. A `StandardScaler` + RBF `SVC` pipeline, with `C` and `gamma` grid-searched. Report test accuracy and the support-vector count.\n",
    "3. One sentence each: which model you would ship for this data and why, and what the support-vector count tells you about the SVM's \"memory\".\n",
    "\n",
    "Self-assessment (pass / partial / fail): (a) both pipelines train and beat the majority-class baseline by a wide margin; (b) selection used cross-validation, never the test set; (c) the SVM is inside a scaler (you remember Part 5); (d) you report SV count, not just accuracy; (e) the notebook still runs top-to-bottom.\n",
    "\n",
    "<details><summary>My solution (reference, ~10s on CPU)</summary>\n",
    "\n",
    "```python\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "Xc, yc = make_moons(n_samples=1000, noise=0.3, random_state=SEED)\n",
    "Xtr, Xte, ytr, yte = train_test_split(Xc, yc, test_size=0.3,\n",
    "                                      random_state=SEED, stratify=yc)\n",
    "\n",
    "tree_grid = GridSearchCV(\n",
    "    DecisionTreeClassifier(random_state=SEED),\n",
    "    {\"max_depth\": [3, 5, 7, None], \"min_samples_leaf\": [1, 5, 10]}, cv=5).fit(Xtr, ytr)\n",
    "\n",
    "svm_grid = GridSearchCV(\n",
    "    make_pipeline(StandardScaler(), SVC(kernel=\"rbf\")),\n",
    "    {\"svc__C\": [0.1, 1, 10], \"svc__gamma\": [0.1, 1, 10]}, cv=5).fit(Xtr, ytr)\n",
    "\n",
    "print(f\"tree test acc {tree_grid.score(Xte, yte):.3f}  best {tree_grid.best_params_}\")\n",
    "print(f\"svm  test acc {svm_grid.score(Xte, yte):.3f}  best {svm_grid.best_params_}\")\n",
    "n_sv = svm_grid.best_estimator_.named_steps['svc'].support_vectors_.shape[0]\n",
    "print(f\"svm support vectors: {n_sv} of {len(Xtr)} training points\")\n",
    "# On noisy moons the RBF SVM usually edges out the single tree (smoother boundary),\n",
    "# and the SV count (~a few hundred) is how much of the data the model keeps around.\n",
    "```\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e0ddfdc",
   "metadata": {},
   "source": [
    "## Reflection\n",
    "\n",
    "In about 150 words in the cell below, write the dumbest bug you hit working through this notebook and how you found it. Maybe your `best_split` recursed forever until you added the `gain <= 0` stop; maybe your RBF Gram matrix did not broadcast; maybe you forgot the $\\sqrt2$ in the poly feature map and the Gram was off by a factor of two on the cross terms; maybe you trusted an unscaled SVM's accuracy. Nobody grades this. Writing it is the point: naming the bug and the diagnostic that caught it is the skill that transfers to every harder model in the rest of the book.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d128f22",
   "metadata": {},
   "source": [
    "*Your reflection here. Double-click to edit.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e80297",
   "metadata": {},
   "source": [
    "## Going further\n",
    "- Géron, *Hands-On ML* 3e, Ch 5 (SVMs) and Ch 6 (Decision Trees): the worked figures this notebook compresses.\n",
    "- Boyd & Vandenberghe, *Convex Optimization*, Ch 5 (duality): the rigorous version of the SVM dual we solved numerically.\n",
    "- Stanford CS229 notes, SVM section: the middle level between Géron and Boyd, with the full KKT derivation.\n",
    "- scikit-learn user guide, *Support Vector Machines* and *Decision Trees*: the API surface and the `gamma='scale'` default's definition.\n",
    "- fast.ai *fastbook* Ch 9 (tabular): why tree ensembles, not deep nets, win on tabular data; read before Ch 06.\n",
    "\n",
    "## What this enables\n",
    "- **Ch 06 - Ensembles**: random forests are bagging applied to the trees you just built; the instability you saw in Part 2 is exactly what averaging fixes. We measured ~0.93 with a single tuned tree on noisy moons; a random forest typically clears 0.95 on the same data.\n",
    "- **Ch 07 - Dimensionality Reduction**: kernel PCA reuses the Gram-matrix idea from Part 4. The kernel trick generalizes far beyond classification.\n",
    "- **Ch 11 - Training Deep NNs**: hinge loss (Part 3) joins cross-entropy (Ch 03/04) as a classical loss; later chapters add contrastive, triplet, and Wasserstein.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2eb3eb02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-10T18:50:18.388957Z",
     "iopub.status.busy": "2026-06-10T18:50:18.388890Z",
     "iopub.status.idle": "2026-06-10T18:50:18.470195Z",
     "shell.execute_reply": "2026-06-10T18:50:18.469700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single tree test acc 0.873\n",
      "random forest test acc 0.900  <- Ch 06 averages the variance away\n"
     ]
    }
   ],
   "source": [
    "# what this enables, made concrete: a random forest on the capstone data, no tuning\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Xf, yf = make_moons(n_samples=1000, noise=0.3, random_state=SEED)\n",
    "Xtrf, Xtef, ytrf, ytef = tts(Xf, yf, test_size=0.3, random_state=SEED, stratify=yf)\n",
    "single = DecisionTreeClassifier(max_depth=5, random_state=SEED).fit(Xtrf, ytrf)\n",
    "forest = RandomForestClassifier(n_estimators=100, random_state=SEED).fit(Xtrf, ytrf)\n",
    "print(f\"single tree test acc {single.score(Xtef, ytef):.3f}\")\n",
    "print(f\"random forest test acc {forest.score(Xtef, ytef):.3f}  <- Ch 06 averages the variance away\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced80050",
   "metadata": {},
   "source": [
    "---\n",
    "*Built top-to-bottom. If every check above printed `[ ok ]`, you've reproduced the chapter. Total running time and verification stamp written by CI.*\n"
   ]
  }
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