diff --git a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb
index 70b02102501..d9d4524cac9 100644
--- a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb
+++ b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb
@@ -15,7 +15,22 @@
     "\n",
     "# Pauli Correlation Encoding to reduce Maxcut requirements\n",
     "\n",
-    "*Usage estimate: 30 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
+    "*Usage estimate: 35 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning Outcomes\n",
+    "After going through this tutorial users should understand:\n",
+    "- Understand the theoretical principles behind Pauli Correlation Encoding (PCE), including how multi‑body Pauli strings enable polynomial compression of classical optimization problems.\n",
+    "- Implement PCE in practice to encode and solve large‑scale optimization tasks on near‑term quantum hardware.\n",
+    "\n",
+    "## Prerequisites \n",
+    "We suggest that users are familiar with the following topics before going through this tutorial:  \n",
+    "- [Variational Quantum Algorithms](/learning/courses/variational-algorithm-design)\n",
+    "- [QAOA and Maxcut](/docs/tutorials/quantum-approximate-optimization-algorithm)\n"
    ]
   },
   {
@@ -54,11 +69,11 @@
    "id": "1f28c0b3-b6dd-4627-90e6-db70b9114cd0",
    "metadata": {},
    "source": [
-    "in the Figure 1 from [\\[1\\]](#references) , Max-Cut problem is used as an example to illustrate the PCE approach. The Max-Cut problem with $m=9$ nodes is encoded into a Pauli correlation space, representing the optimization problem as a correlation matrix, specifically, 2-body Pauli-matrix correlations across $n=3$ qubits $(Q_1, Q_2, Q_3)$. Node colors indicate the Pauli string used for each encoded node.\n",
+    "in the Figure 1 from [\\[1\\]](#references) , [Max-Cut](/docs/tutorials/quantum-approximate-optimization-algorithm) problem is used as an example to illustrate the PCE approach. The Max-Cut problem with $m=9$ nodes is encoded into a Pauli correlation space, representing the optimization problem as a correlation matrix, specifically, 2-body Pauli-matrix correlations across $n=3$ qubits $(Q_1, Q_2, Q_3)$. Node colors indicate the Pauli string used for each encoded node.\n",
     "For example, that node 1, which corresponds to binary variable $x_1$, is encoded by the expectation value of $Z_1 \\otimes Z_2 \\otimes I_3$, while $x_8$ is encoded by $I_1 \\otimes Y_2 \\otimes Y_3$.\n",
-    "This corresponds to compressing the problem’s $m$ variables into $ n = O(m^{1/2})$ qubits. More broadly, $k $-body correlations enable polynomial compressions of order $k$. The chosen Pauli set comprises three subsets of mutually-commuting Pauli strings, allowing all $m$ correlations to be experimentally estimated with only three measurement settings.\n",
+    "This corresponds to compressing the problem’s $m$ variables into $ n = O(m^{1/2})$ qubits. More broadly, $k $-body correlations enable polynomial compressions of order $k$, with $k>1$. The chosen Pauli set comprises three subsets of mutually-commuting Pauli strings, allowing all $m$ correlations to be experimentally estimated with only three measurement settings.\n",
     "\n",
-    "A loss function $\\mathcal{L}$ of Pauli expectation values that imitates the original Max-Cut objective function is constructed. The loss function is then optimized using a quantum-classical optimization solver, such as the  Variational Quantum Eigensolver (VQE).\n",
+    "A loss function $\\mathcal{L}$ of Pauli expectation values that imitates the original Max-Cut objective function is constructed. The loss function is then optimized using a quantum-classical optimization solver, such as the  [Variational Quantum Eigensolver (VQE)](/learning/courses/quantum-diagonalization-algorithms/vqe).\n",
     "\n",
     "Once the optimization is complete, the solution is decoded back to the original optimization space, yielding the optimal Max-Cut solution.\n",
     "\n",
@@ -79,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5abb33b8-080d-4375-ac16-7788f2f1516a",
    "metadata": {},
    "outputs": [],
@@ -88,7 +103,9 @@
     "\n",
     "import numpy as np\n",
     "import rustworkx as rx\n",
-    "from scipy.optimize import minimize\n",
+    "import networkx as nx\n",
+    "\n",
+    "from scipy.optimize import minimize, OptimizeResult\n",
     "\n",
     "from qiskit.circuit.library import efficient_su2\n",
     "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
@@ -97,16 +114,12 @@
     "from qiskit_ibm_runtime import QiskitRuntimeService\n",
     "from qiskit_ibm_runtime import Session\n",
     "from rustworkx.visualization import mpl_draw\n",
-    "\n",
-    "service = QiskitRuntimeService()\n",
-    "backend = service.least_busy(\n",
-    "    operational=True, simulator=False, min_num_qubits=127\n",
-    ")"
+    "from qiskit_aer import AerSimulator\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "89c2999b-d309-4cf4-820e-9ef8a5cd5807",
    "metadata": {},
    "outputs": [],
@@ -123,6 +136,35 @@
     "    return cut_size"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Small Scale Simulator Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We are using the aer_simulator_from(ibm_pittsburgh)\n"
+     ]
+    }
+   ],
+   "source": [
+    "service = QiskitRuntimeService()\n",
+    "real_backend = service.least_busy(\n",
+    "    operational=True, simulator=False, min_num_qubits=156\n",
+    ")\n",
+    "backend = AerSimulator.from_backend(real_backend)\n",
+    "print(f\"We are using the {backend.name}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c8084430-5386-4788-97c3-c6e4fb7cb191",
@@ -138,21 +180,20 @@
     "\n",
     "\n",
     "### Graph -> Hamiltonian\n",
-    "This tutorial uses a random graph with 1000 nodes.\n",
-    "\n",
-    "The problem size might be hard to visualize, so below is a graph with 100 nodes. (Rendering a graph with 1,000 nodes directly would make it too dense to see anything!) The graph we are working with is ten times larger."
+    "Let us first consider a random graph with 100 nodes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "37edb718-2bab-49d7-ad66-5f2f67d2aeff",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/pauli-correlation-encoding-for-qaoa/extracted-outputs/37edb718-2bab-49d7-ad66-5f2f67d2aeff-0.avif\" alt=\"Output of the previous code cell\" />"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -160,36 +201,26 @@
     }
    ],
    "source": [
-    "mpl_draw(rx.undirected_gnp_random_graph(100, 0.1, seed=42))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ddbb83ea-848d-4ab7-8c69-52b1683b09eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_nodes = 1000  # Number of nodes in graph\n",
-    "graph = rx.undirected_gnp_random_graph(num_nodes, 0.1, seed=42)"
+    "num_nodes = 100  # Number of nodes in graph\n",
+    "seed = 42\n",
+    "graph = rx.undirected_gnp_random_graph(num_nodes, 0.1, seed=seed)\n",
+    "mpl_draw(graph)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "eb7a80dc-74ea-472b-a13f-11cb8d4c0ca9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import networkx as nx\n",
-    "\n",
     "nx_graph = nx.Graph()\n",
     "nx_graph.add_nodes_from(range(num_nodes))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "id": "63451877-908a-4e80-9e11-23eb0d288bfc",
    "metadata": {},
    "outputs": [],
@@ -200,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "id": "515e7220-586f-4e2a-82b6-3885e3e38566",
    "metadata": {},
    "outputs": [
@@ -208,7 +239,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial cut size: 28075\n"
+      "Initial cut size: 345\n"
      ]
     }
    ],
@@ -222,9 +253,9 @@
    "id": "57e3804e-61ae-45c5-9d94-3665cf01784b",
    "metadata": {},
    "source": [
-    "We encode the graph with 1000 nodes into 2-body Pauli-matrix correlations across 100 qubits. The graph is represented as a correlation matrix, where each node is encoded by a Pauli string. The sign of the expectation value of the Pauli string indicates the partition of the node. For example, node 0 is encoded by a Pauli string, $\\prod_0 = I_{19} \\otimes ... I_2 \\otimes X_1 \\otimes X_0$. The sign of the expectation value of this Pauli string indicates the partition of node 0. We define a *Pauli-correlation encoding* (PCE) relative to $\\prod$ as\n",
+    "We encode the graph with 100 nodes into 2-body Pauli-matrix correlations across 9 qubits (see explanation below). The graph is represented as a correlation matrix, where each node is encoded by a Pauli string. The sign of the expectation value of the Pauli string indicates the partition of the node. For example, node 0 is encoded by a Pauli string, $\\prod_0 = I_{8} \\otimes ... I_2 \\otimes X_1 \\otimes X_0$. The sign of the expectation value of this Pauli string indicates the partition of node 0. We define a *Pauli-correlation encoding* (PCE) relative to $\\prod$ as\n",
     "\n",
-    "$ x_i \\coloneqq \\textit{sgn}(\\langle\\prod_i \\rangle) $\n",
+    "$$ x_i \\coloneqq \\textit{sgn}(\\langle\\prod_i \\rangle) $$\n",
     "\n",
     "where $x_i$ is the partition of node $i$ and $\\langle \\prod_i \\rangle \\coloneqq  \\langle \\psi |\\prod_i| \\psi \\rangle $ is the expectation value of the Pauli string encoding node $i$ over a quantum state $|\\psi \\rangle$."
    ]
@@ -239,9 +270,34 @@
     "Then, we encode the nodes in each set using the Pauli strings with $X$, $Y$, and $Z$, respectively."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For that we need to extract a relationship between the nuber of nodes and qubits that we will need to encode all the nodes. Using all possible permutations for the encoding yields to:\n",
+    "\n",
+    "$$ \n",
+    "m=3\\binom{n}{k}.\n",
+    "$$\n",
+    "\n",
+    "In this notebook we consider $k=2$, hence:\n",
+    "\n",
+    "$$\n",
+    "m  = \\frac{3}{2} n(n-1).\n",
+    "$$\n",
+    "\n",
+    "Therefore, the number of qubits $n$ needed to express a certain number of nodes $m$ read as:\n",
+    "\n",
+    "$$\n",
+    "n = \\left\\lceil \\frac{1 + \\sqrt{1 + \\tfrac{8}{3}m}}{2} \\right\\rceil.\n",
+    "$$\n",
+    "\n",
+    "*Note that the $\\lceil \\cdot \\rceil$ symbol represents the ceiling function, which rounds any real number up to the next integer. This is required to ensure that the number of qubits is an integer.*"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "8ea1f545-3e9f-4620-bde8-755178ad3ec9",
    "metadata": {},
    "outputs": [
@@ -249,20 +305,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "List 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332]\n",
-      "List 2: [333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665]\n",
-      "List 3: [666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]\n"
+      "Number of qubits: 9\n",
+      "List 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]\n",
+      "List 2: [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]\n",
+      "List 3: [66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
      ]
     }
    ],
    "source": [
-    "num_qubits = 100\n",
+    "num_qubits = int(np.ceil((1 + np.sqrt(1 + (8/3) * num_nodes)) / 2))\n",
     "\n",
     "list_size = num_nodes // 3\n",
     "node_x = [i for i in range(list_size)]\n",
     "node_y = [i for i in range(list_size, 2 * list_size)]\n",
     "node_z = [i for i in range(2 * list_size, num_nodes)]\n",
     "\n",
+    "print(f\"Number of qubits: {num_qubits}\")\n",
     "print(\"List 1:\", node_x)\n",
     "print(\"List 2:\", node_y)\n",
     "print(\"List 3:\", node_z)"
@@ -270,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "d2649acb-7857-4cf4-88dc-4ef381a8552f",
    "metadata": {},
    "outputs": [],
@@ -318,18 +376,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "035f6b4a-4de0-452a-b60f-7260f9e3103a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ueV8a+btg6YwuWPHuA3XOtUokBTZ5ndntxk5dEye7u7tj7969eO211zBy5EgoFAoMGjQIS5YsgVxuk30wIiIiIsmKivTF3u8G4MK1fERvu4bP15xHWYUGbi72iN/0BBoHGPhcdCKSBDs7Od575T68Pa4dfv87ES/PP4TScjVclAqs+agXBvVsAoWCn/NI2mzyL/hujR0AaNGiBbZu3YqSkhIUFBRgzZo18PX1NWWIRERERCSiiBZe+HBKVPXtVu4u9mzqENkQRwc7jB4UAk83BwC3Jkoe2jeYTR2yCjZ9xU7bttZ1D6VXRFM88OnLsHdVojQ1BwcmfwGv0MboF/0Oiq6l4++R81FxswhO3u7o8cVkuDUNgKayCkdmfIPs47cmk46aPQbBQx5A7umr2D/hMzO/Iv2UJZ5F8lcToC0vhoNvUwS//jMqbsQjYd4AODUMRcv3/4a9px9KE04g5dup0JYXA3I5Gr2wGO5t+wIAUn+YjryDa+HS8n60eHu9mV8RERER0d3pWv+1mfIEQob1gnvzQOx5bhFSd5+sHsOtqT+6fvIynLxcYefkgJTdJxE772dAEBDx4iCEjX0YlUVl2PrIDDO+0rrpWv8lLXsBJRcPQe6ohJ3SHY0nLINz8/YAgGsLn4Iq82r1mOVJ59Bi5h/w7DwYWZuXIGf7V7Bz8UT44lgzvUqiu9M1D3RbMhHebVsAWi20ag1OfhiNjINx1eO0eu5hhI97FIJGC0ErYNuAt6FRVVlNHrjt5t4fkbR0LFq8+yc8Ow0CACQtHYuis7uhcPcBALi3ewiNnv8EACSRB2yysXP48GFzh2AU3T+fhEOvf4W8+CSEjOyLTrPH4Ora/Si4klrjDdhx1mjknEzArmc+hHe7Fujz7ZvY0OVVCBotYuf/jILLKWj0UEczvhLDJC0di+ApP8C5eXvk7v4eaaunw7vvWCgbR1S/AQVBwLWPHkezqT/DrW0fVKRewpU5D6H1iiuQOyrR6PlP4NQkEoUntpr51RARERHdm671X0ZMHJI2H0K3xRNrjRH13rNI+esELn63HXaO9hi0YyEy+sQhbe9pXPhmK/LiEhH13rOmfFk606X+AwDPzkPR9NVvILNToODEVlz/ZDhar7gCADW+zCtNiEXC3Efgft/DAAD/Ia/DuXkHpP7wpmlfGJEedM0DJ95bjcqiMgBAg8hg9F83B79FvgAAaPxwJzR/oge2DXwHVcVlcPJ2h7ZKAwBWkwcAoDI3FTk7V8KlVZda4wQ8+Tb8Bk6qtVwKeYDXnVmJBq2bQV1Wgbz4JADAtXX70bh/FGR1XFoYPLgrLq7eCQC4efYaynMK4H+/tB/lXnb9NOyUrtXfvHj3eQ4Fx7dA0KhrbKcpvgl1cR7c2vYBADg1CoOdqycKT+0wdchERERE9aJP/Zd7OgHFyVl3HMve3RkAYOfkALm9AuXZBcYIWVS61n8A4Nl5MGR2t77Tdm3VBZXZyRC02lrb3dzzPbx7j4bcnk9II2nQJw/cbuoAgIO7CyAI1T+3njgYZz5di6riW9tU3Cyq8z1iafTJAwCQvPxFNB63BDIre4/b5BU71si1iR+8wppg8K5PqpcpnJ2g9K/5hAdHL1fI5HKobhZVLytJyYFLIx+TxWoMqqxElCfH4cLU9tXLtBWlqMrPqLGdwt0H9p7+yD+8AV4PPInSy8egSruCyuwk0wZMREREVE+61n/3cnzOD3jwx5kIe7Y/HDxccPbzDcg7nyh2uKLTtf77r6w/l8Kj4wDI/vNgFG1lBfIO/IpWCw4YI1wio9A3D3R8ZxSaPtYVjh4u2Dfu0+rlni0bwaddC7R/cwTsHO1x7fd/cPG77UaPv770yQM5O1ZA2TgSLq061zlW1ubFyP3rGzj4NkHQqA+qm0VSwMaOFck6cQl7xnxU/fPI89/VuZ3wr84sAKt5rJ9LWDe0nLOt+uezY+qe8LrFrM1I+3EGMn7/EMqmbeAa0R0yO3tThUlEREQkGl3rv7tpNaY/rq3/B/ErtsDJ2x0Pr38fOSevIPPQeTFDNQpd67/bbu5fg/yD69Dqo5ha6/IPb4BjUEsog9uIHieRMemTB04uiMbJBdEI7NEGUXPGYPvgd6GtUkOmsINbcAB2Pj4H9u7OeHTjXBQlZiBt72lTvIR60SUPqLISkfP3KoQtPFTnGEGjP4S9VyBkcjnyj2xCwrxH0XpFAuyU0phkn7diWYmS5Cy4NvzfVTf2rkrYKR1RnpVfYztVfgkAwNHbvXqZSyMflKbmmiZQI3H0b47K3BvVP2vKiqBVlcHeK7DWts7BbdHyvR2IWHIKzab+iKq8dDg1jjBluERERET1pmv9dy/h4x7FtXX7Ady6/SJt72kEdLX82kif+g8A8mLWIuO3uQidt6vGRKq33dz9HXz6jTNavETGYGgeyIiJg72rEl7hTQAApWm5uL7pIAStFpUFJUjdexq+HVoaNXYx6JoHSi8dQVVeOuInhSNuQjBKLx9F8hfjkPP3KgCAg3fD6qv4vLo+DjulOyrSLpvuhdQTGztWIi8+CYJWQEC31gCAsLEPI2nzYQjq2vdFJv95BOFjHwEAeLdrAaWfJ7KOXzRpvGJzbt4eMpkcRef2AgByti+HV/cR1fdS/1tVfmb1/8/5exXkji5w+/+nYhERERFJhT71390UJ2ejYd8OAACF0hEB3Vsj/1KK6PGKTZ/6L+/gOqRHv4uW83bDwbdJrfWqrESUJhxHg55PGz1uIjHpmgdkdnK4NQuo/tmnfQicvN2r5966vukgGvZpDwCwc7RHQNdI5F1IMslrqA9d80CDXs+g3Y+ZaLMqCW1WJcGlVRc0nfwdfPtPAHBrUuXbSi4fhbr4JpwCQ0z3QuqJt2JZkZjJX6Db5xPh4OaM4qRMHJi0DF6tap+4Yj9Ygx5fTsETh76ApkqNmMlfQNBY/sRY9xL8+s9IXvY8bpQWwjEwBM2mrUF5cu1LiHP+Wom8f6IBQYBTo3C0mLnJam5HIyIiItuia/3XZvLjCBv7CJy83dF96avQVFRhS//pUN0swsHXvkTnD8ch8uXBkNvb4cbO40jeesQMr0Z/utZ/iYtHwd4zANcWDKleFjpvDxTu3gCA3N3fw7Prk7Bzdq+1L5Gl0yUPyOzk6P75JDi4O0PQaKEuU2HfhM9QWVgK4NaTr7p+/BKG/rMEgnDrYoAbO46b4+XoTdc8cDdJS8eiqjALMrkd5A5KtHjrd9i5eBgpYvGxsWNF8i8mY+vDM+65XUVuIXaNnG+CiEzLObgtwhefvOd2QSPfQ9DI90wQEREREZFx6Vr/xX2xCXFfbKpzXd75ROwY8q7YoZmErvVfx41Vd13fcJT11cZkO3TJA9pK9V3f55qKShyc8oXYoZmErnng31p9uL/Gz6Hzd4sYkenxViwrp6lSQ+njgcG7PoGT972/gYiaPQZtJj9e3bmVOpnCAVUFWbgwtT2qCrLvuX3qD9ORuf4jKFz1e5oEERERkaXQt/67m4gXB6HLwvFQ5RXde2MLoW/9dzdZm5fgxtcToXCX9hNkyfYwD9hWHpAJ/31EElmEqrIKRLcYbe4wdDbq2hrYOzuJNl65Guhh+U/XqyFmAKDkNXBEREQWrVG/X5GWXYaGfs5I3W2586lIrRYEWA8CrAelQCo5AGAeYA7QHa/YISIiIiIiIiKSKDZ2iIiIiIiIiIgkirdiWShBEKAuV5k7DJ0plI6iPllKEIAKjWjDmYSTHcCHaxEREVk2qdyGIbVaEGA9CLAelAKp5ACAeYA5QHe8A9RCyWQyUe9RlhqZjPcnExERke2y9VoQYD1IZOt5gDlAd7wVi4iIiIiIiIhIotjYISIiIiIiIiKSKDZ2iIiIiIiIiIgkio0dIiIiIiIiIiKJYmOHiIiIiIiIiEii2NghIiIiIiIiIpIoNnaIiIiIiIiIiCSKjR0iIiIiIiIiIoliY4eIiIiIiIiISKLY2CEiIiIiIiIikig2doiIiIiIiIiIJIqNHSIiIiIiIiIiiWJjh4iIiIiIiIhIotjYISIiIiIiIiKSKDZ2iIiIiIiIiIgkio0dIiIiIiIiIiKJYmOHiIiIiIiIiEii2NghIiIiIiIiIpIohbkDoLoJggB1ucrcYehMoXSETCYTbTxBACo0og1nEk52gIi/AiIiIrJhUqsFAdaDAOtBEpet5wHmAN2xsWOh1OUqRLcYbe4wdDbq2hrYOzuJNl6FBuixXbThTCJmAKDkO4qIiIhEILVaEGA9CLAeJHHZeh5gDtAdb8UiIiIiIiIiIpIoNnaIiIiIiIiIiCSKjR0iIiIiIiIiIoliY4eIiIiIiIiISKLY2CEiIiIiqyYIAm5klODvw6koV6kBABWVGmTfLDdzZERkKhUqNU6cz6nOAeUqDc5cuomqKq2ZIyOqP87ZTkRERERWR1WpwfpdiYjedg2x8bnIya+osf5mgQr+fX5BI38XdGnri7FDWuKRbo1gZ8fvPYmsRUJyIVb+fgm7j6Uj/lo+1Gqhel1eoQodhv8BB3s52oY2wKPdG+HFJ8PQKMDFjBETGYaNHSIiIiKyGmXlanz03Vms/P1SrWZOXVKzSrF+VynW70pCs4ZumDo6Eq+ODGeDh0jCjpzNwvsrTuPvw2n33LaySovY+FzExudiwbdnMbh3E8x/tSMiQ7xMECmRONjYsSIBXSPxyMa5NZZVlZaj6HoGrq0/gIvfbYegsd5LDYvj9uPKu31qLJM7ucAxKBTevcfAb9BkyOz4J09ERGStYk5m4vk5B3Atpdig/RPTivHaoqP4Zfs1rP6gJ8KaeYoboAmwHmQ9aMvKK9SY/eVJLP75PATh3tv/l0YjYNOeZGw7kIL3X7kP08e2gUIhvSYv84Dt5QHrejUEAEj84xBSdscCMhmUvp4IGdYL988dC4+WDXFk+kpzh2d0Xt1HwKPTIEAQUJWfiZv7f0Lq99NQkXoRTV/9xtzhERERkcgEQcCHq85gzlenDPow91/H4nLQftgf+G5ud4waGFL/Ac2A9SDrQVtzI6MEj7zyFy5eL6j3WJVVWryzLBZ//nMDf37xELw9neofoBkwD9hOHpBe+5HuKS8+Edc3xOD6+gOIX7EF2wa+g5K0HIQ+8yAcvd3NHZ7ROTdrD+/eo+HdZwwCnpiOsI+Pwt6nMXJ3fYuqwhxzh0dEREQiEgQBM5fGYvaX4jR1blNVajB65j/4dsNl8QY1IdaDrAdtSWJqMbo/t1WUps6/HTmbjd4vbEeuDrd1WiLmAdvJA2zs2AB1uQq5p65CJpfDvam/ucMxOTsnF7iEdgYEAarMa+YOh4iIiES0+KfzWPT9OaON/+K8g9i8L9lo45sK60HWg9Yqv0iFh17agZTMUqOMf/5qPh6d+Bcq/v9pWlLGPGC9eYCNHRvhFnzrjVuRb9g951J3+42rcPM2cyREREQklnNX8jBzaaxe+5z4dTBSdo3EiV8H67S9IADj3z9oFY9GZz3IetAavbbwqF7zaumbAwAgNj4X874+Y0B0lod5wDrzgE00dnJzc/Hmm2+iRYsWcHJyQuPGjfHGG2+gvLwco0ePhkwmw7fffmvuMEVjp3SEYwM3OHq7wzOsCTovGA/vNs2RczoBxYmZ5g7P6LSqMqiLclFVmIPypDjc+PpVlF8/DeeWneAU1NLc4REREZEIqqq0GPvuAVSp9ZsANMDHGY38XRDg46zzPrn5FXh1wWF9QzQr1oOsB23Bn/tv4OetV/Xax5AcAACLfjiHE+eldfsO84Dt5AGrnzz53Llz6N+/P7KysuDi4oKIiAikpaVh8eLFSEpKwo0bNwAA7dq1M3Ok4mk/bRjaTxtWY1nyjmM4OmOVmSIyrYx185Gxbn6NZZ6dh6LJKyvMFBEREZF5abUC/jqUij3H0lFcVgUXpQLdOwRgcO8mknziCwCs3nwFpy/dNNnx1u9KwoHYDPSMCjTZMeuD9SDrwf/KulmOn/+8iqT0Ymi1AoL8nPHMgBZo3kiac61oNFq8tuioyY6n1Qp4/ZNjOPjjIJMds76YB2wnD1h1Yyc3NxeDBg1CVlYW3nrrLcyePRuurq4AgA8++ACzZ8+GQqGAXC5H69atzRyteK5E70bi5kOQK+zg2aoJ2kweCqWPJ9QVldXb9P7mDQiCgH9eWly9zMHTFUP3L0HsvJ9wfWOMOUIXhXe/cWjQYyQErRrlyeeRuWEhqgqyIHdQVm9THB+Dq/MerbWvoK6EoNWg4yaNKUMmIiIyCkEQ8M36y/j4h3O4nlrzsvslP8ejoZ8zXhsViTeeawO5XGamKPUnCAK+WnvR5Mf9au1FyTR2WA+yHrwtJbMEby0+gQ27k2pd4Tbnq1N4tHsjLHytE9qENjBThIb563AaEtNMezvRodNZOHv5Jtq1ksZtPMwDtpMHpPkVjY6mTJmClJQUTJs2DYsWLapu6gDArFmzEBYWBrVajdDQUCiVyruMJC3FSZnIiIlD2r4ziP96C/aM+Qg+94Wg68IJ1dscmbES/p3D0Wxot+plXRaMR/bxS5J+8wKAU2AI3Nv3g8d9jyDg8TcRMnsrShOOIfnrV6q3cYvsgQ5rS2r8i1x+BQo3HwQ9M/8uoxMREUmDIAh4bdFRvDz/UK2mzm1p2WV4a8kJjJ65HxqNfrc0mdORs9k4eznP5MfduCcJGTllJj+uIVgPsh4EgEuJBegy+k/8tvN6nbctCgKwPSYV3Z7bipiT0ro1Z7kZmrvmPK4hmAdsJw9YbWPnwoULWLt2Lfz8/DB/fu3/IDKZDB07dgRQ8zas1NRUTJkyBQ888ACcnZ0hk8mQlJRkqrCNIudUAq5viEHzJ3rA975b9xKq8ktwaNpydP5wPJT+Xmg6sAsCHojEkRkrzRyt+FxbdUGDXqORf+BXlFyu+3JNbZUK1xc+AdeI7ggc9o6JIyQiIhLfZz+exxe/XNBp2193XMfbn+s3CbE5bdpjnqdUqdUCth1IMcux64v1oO3VgwVFKjz6yl9Iz753M7K4tAqDp+zC9dQiE0RWf+UVauw8lGqWY2/akwxBEMxy7PpiHrDePGC1jZ3o6GhotVqMHj0azs51T4zl6OgIoGZj5+rVq1i7di08PT3Rq1cvk8RqCmeXrIdWrUGHt0ZWL0vbexrJW4+g55dT0GXhBBx+YwVU+SVmjNJ4AkfMBuR2SI+eXef6G8tfhraqAsGvrTZtYEREREZQVq7Ggm/P6LXPsl/ikZMnjSc/nbyQa5PHri/Wg7ZVD/6wOQFJ6br/tyworsSSn+ONGJF4zl7Og0ZjnuZKTn4FUrOM82h1U2AesM48YLWNnT179gAA+vTpc8dtUlNvdXn/3djp2bMnsrKysH37dowYMcK4QZpQcVImEjcfQlCvdvDrHF69/MT7P8KtWQDS9p5G6p5TZozQuJwCQ9Cgx0gUn92N4vialxRm/7kMhbFb0WLmH5A76jc7PhERkSVa+9d15BdV3nvDf6ms0uL7P64YKSLxCIKAUyacNPm/Tl6UbmOH9aDt1INarYAV6/S/ZejHLQkoKasyQkTiMvf7UMoNXuYB68wDVtvYSU6+dYlucHBwnetVKhWOHTsGoGZjRy632l8Jzi3dAK1Ggw7T/9ewUperUJKcjfyLN8wYmWkEDJsFyOVI/2VO9bLic/uQ+tMMNJ++Do7+weYLjoiISESb9xl2q9Ife81zi5M+8gpVKCzWr2klpsQ0aX+LzXrQNurBi9cLkJCs/21VxaVV2Hc8wwgRietO84aZCvOAtFljHpAJUr1B8B7c3d1RXFyMEydOICoqqtb67777DuPHj4e3tzdyc+vuuK5evRrPP/88EhMT79gg0kVUVBQyM/WbjMxekOM97f0GH1Mfj2yYi5RdJxH/9RaDx5grP44qmXiTLsoclPD/PEG08eqiykrCpTc7IXDke/AbOKne42VNbQmhUhqXsBMRkXXLcXsBlfZN9d5PocmFf+EXRohIPBqZGzK93rzj+hO/DkaAz52/aQ3wUUJhJ4dao0Vm7p3P25m5Zej0dO3aSCaoEJS/QL+gDWDKWhBgPWht9aBK0QS57uMM2tezZCNcKs+KHJG4CpwHotSp7veHWDkAuHMecC/bDbcK408sbOt5wBQ5ABA3D9QnBwQEBCA21rD57qz2cecBAQEoLi5GbGxsrcZOSkoKZs6cCQBo27at0WPJzMxEWlqaXvs4yOwAfyMFZATpGemoFMR7FJzc0dmoL1+rKsO1j4bC4/7BopzEASA9PR1alTSelEFERFYuuASw1383dWWZ3jWLydm5AV53Xh3g44xG/i73HEZhJ9dpu/8StGqT/I6kVgsCrAcBC6oHlfaAu2G7FuRlo6DQwvNAYBHgVPcqY+cAACgqzEdRLvNAXcTMA8bOAYD4ecBcOcBqGzv9+vVDQkICFixYgL59+yI0NBQAcPToUYwZMwYFBQUAat6GZSwBAQF672MvyAHpPHUUQYFBon9DY0z5hzegPPEsKtKuIP/g2lrrI7+8AAffJnqNGRQUZBHf0BARERUqCmDIjQJKeR4aNGwoejxiEqBAuqAFZHXfPp+Ze/eCWp8rdupiBxUCTPA7klotCLAeBCynHtTK7JApVEGQ6dHhFQRAJoOfexXsXS07DxQ5yXGnm7HEygF3G8vTzQEujswDdREzDxg7BwDi54H65ABD+ga3We2tWDdu3ED79u2Rn58PhUKBsLAwlJeX49q1a3jsscdQUlKCffv24YcffsDYsWPrHEOsW7EMUVVWgegWo016zPoYdW0N7J3v0DY3QLka6LFdtOFMImYAoLTaVikREUlJQnIhQh9br/d+h38ehK7tLP/r4TZPbMT5q/kG7ZuyayQa+bsgNasUjR/6Te/9n+wXjPWLHzTo2PqQWi0IsB4ELKseHPdeDL7fpN+E6L07BWLfdwOMFJF4/tx/A4On7DJo3/rmAAA4+dsQ3BfhY9C++rD1PMAcoDurnSm4SZMmiImJwYABA+Dk5ITk5GT4+vri22+/xebNm3Hp0iUAprlih4iIiMiUWjb1wKPdG+m1T8cIH3Rp62ekiMTV0QQfqCzx2ET6eHVE+L03+o9JI/Xfxxw6Rnib7dgO9nK0bnmX+0GJzMBqGzsAEBkZiW3btqG4uBhFRUU4cuQIxo0bh/z8fGRkZEChUCAiIsLcYRIRERGJ7ru5PdA0yFWnbX28nPDbx30gk8mMHJU4enY0/HL1+upxn+Vf0UQEAPdF+OCzN3WfePfVkeF4ol+w8QISUZCfC1o0djPLsTu38YWDvZ1Zjk10J1bd2LmTuLg4AEBYWBgcHR1rrV+/fj3Wr19fPSP1jh07avxMREREZOkCfZ1xcPUgtA9rcNftQpq44+DqgQhpYuBMq2Yw/OFmcHc1YHboeopo4YluHdjYIemY9mwbfPlOV9gr7vyxTyYDZrzQFsve7iqZ5i4AvPhkmFmO+9Iw8xyX6G4s5A5Q07rd2LnTbVjDhg2r8fPEiRMBAM899xxWr15t1NiIiIiIxNIowAUnfxuKvw+nYfnai9hzLB1lFWoAgKO9HL9+3AeP9WoCxV0+9FkiV2d7PDe4Jb745YJJjztxRLikPvgSAcCrIyPwZL9gfLfxCr7bdAWJabemHZbLZXjzudZ4aVgYmjeSTmP3thceD8Wc5aegqhTvSWz34uPlhKceamay4xHpSlpncZHcq7EjCEKd/9jUISIiIqmRy2V4pHsjbPniIZQefw5BvreeMuLj5YTHHwyWXFPntqmjI6F0Mt3tEA39nPHsYyEmOx6RmAJ8nDHrxfa4vmN4dQ4I9FFi0ev3S7KpA9zKYS891cqkx5z+XBs4OvA2LLI8Nn3FTtu2bc0cibi8IprigU9fhr2rEqWpOTgw+Qt4hTZGv+h3UHQtHX+PnI+Km0VoM+UJhAzrBffmgdjz3CKk7j5ZPUbU7DEIHvIAck9fxf4Jn5nx1eimLPEskr+aAG15MRx8myL49Z9RcSMeCfMGwKlhKFq+/zfsPf2Q8fsC3Nz7I1QZCWgxaws8Ow2qHkOrKkPSF+NQlnACkMvR8NmF8Or6BAAga/MS5Gz/CnYunghfzFvxiIhI+qzlipPmjdzx0ZQoTP34mEmOt+q97nBzcTDJsfQhRv3n1iwAXT+aACcfD8gUdji7+HckbTkMAIh4cRDCxj6MyqIybH1khrle5h3pWgsmLXsBJRcPQe6ohJ3SHY0nLINz8/YAAFXGNSQvfxHq4pvQVlbAI2ogGo39BDK53CprQWvJAQDw4ZQo/PlPSvVVSMbUqbUPpj3b2ujHMYSueaDbkonwbtsC0GqhVWtw8sNoZBy89dm496o34B4cUGPMvc9/jJS/Y60mD9x2c++PSFo6Fi3e/bPG58Ls7cuRs+0LQG4HmUyOsE+PQ+7gJIk8YJONncOHD5s7BKPo/vkkHHr9K+TFJyFkZF90mj0GV9fuR8GV1BpvwIyYOCRtPoRuiyfWGiN2/s8ouJyCRg91NGXoBktaOhbBU36Ac/P2yN39PdJWT4d337FQNo6o8aZzb/sgGnQfgaQvx9UaI3PTp5DbO6L1yqtQZSXi0ltd4damDxSuXvAf8jqcm3dA6g9vmvJlERERkQ4mPxOJDbuTEHMqS+d9MnPLavyvLl54PBSP9misd3ymIEb91/3zSUiI3o2r6/bDyccDg3YuRPbxSyjLzMOFb7YiLy4RUe89a8qXpTNda0HPzkPR9NVvILNToODEVlz/ZDhar7j1KPDUH96E5/1D4PfYFGgrK3DxzU4oOvUgPKIGsBa0cK7O9vh+Xg/0Hb8dgqDbPobkAEcHO/wwr6fFXuGoax448d5qVBbdet0NIoPRf90c/Bb5AgDU+FLfu10LPPTLLKTtPwMAVpMHAKAyNxU5O1fCpVWXGssLjm1G3j/RCPv4KOxcPFBVkA2Z3a253KSQByzzL5P01qB1M6jLKpAXnwQAuLZuPxr3j4KsjuSTezoBxcm6F0CWquz6adgpXau/bfHu8xwKjm+BoFHX2talVWc4Braoc5z8g2vhO+BVAICjfzO4RfZEwbHNRoubiIiIxCGXy7D+swcR2tRD5306Pb0FjR/6DZ2e3qLT9r07BeLLmV0NDdGoxKr/GkQ2ReqeUwCAitxC5McnI3jIA0aLWyz61IKenQdDZnfrO23XVl1QmZ0MQautXq8pKwQAaCvLIairYN8g0PgvgETRu1MgvnpH979XfXOAnZ0Mvy7qjcgQy3zEuT554HZTBwAc3F1wp25Yy5F9cH3DAWgra7+XLI0+eQAAkpe/iMbjlkBmX/MhSpmbPkHQ03Nh53LrfGLv6QeZnXRuu7PJK3askWsTP3iFNcHgXZ9UL1M4O0Hpb5kJSAyqrESUJ8fhwtT21cu0FaWoys/Qa5zKnBtw8G1a/bODXzAqc26IFSYREREZkZ+3Evu+exQPvbQTF64ViDr2g52D8MfSflA6WWbJLFb9l3vmGpo/1RMXVm6FW1N/+Ea1QvGNbLHDFZ2htWDWn0vh0XEAZPJbH3wbjf8c1z54DDk7VkBdmo/A4bPh3LyDMUMnkb0yIhyCIODVBUdEHddeIccvC3vj8QeDRR1XTPrmgY7vjELTx7rC0cMF+8Z9Wmu9naM9mg3tjh2PzzFazGLSJw/k7FgBZeNIuLTqXGtdRcoFlF2NRfqv70GoUsG7z7Pwe2yKMUMXlWWepcggWScuYc+Yj6p/Hnn+OzNGYxouYd3Qcs626p/PjvE1bKB/32us63WcREREZBGC/Fxw8MdBmPzREURvu1bv8W4/LWjeqx0tfqJUMeq/g1O/RKc5zyFkVy8U38hGRsw5aNWW/009oH8teHP/GuQfXIdWH8VUL8v9ayUa9B6DgCemo6ogG1dm94VLqy5wb9vXaHGT+CaOjEBwQzdMmHsQ6dm632Z1Jy2bumP1/J54oL2/CNEZlz554OSCaJxcEI3AHm0QNWcMtg9+F9qq/73fmw7sgqLEDBRcks4X3brkAVVWInL+XoWwhYfqHEPQqKHKvIZWCw5AU1aIy7N6wTEwBB5RA4wWt5h4K5aVKEnOgmtDn+qf7V2VsFM6ojwr34xRGZejf3NU5v4v4WjKiqBVlcHeS79LZx18m6AyO6n658qcZDj4NhErTCIiIjIBL3dHrPmoN/5Y2g+Bvs4GjxPe3BOHfhyERa/fb/FNHbHqv9LUXOx/8TNseWg69o37BA7uzii8kip2uKLTtxbMi1mLjN/mInTerhoTqWZvXQbvvs8BuHX7hcd9j6Lk/D/GDZ6MYkCPxji/8Qk8N7ilwWPY2cnw+phInFn3uCSaOobmgYyYONi7KuEVXvNzT8un+yLh171GidUYdM0DpZeOoCovHfGTwhE3IRill48i+YtxyPl7FYBbnwm9ej4NmZ0dFG4N4HHfoyhNOG7S11IfbOxYibz4JAhaAQHdbs3UHjb2YSRtPgxBrb3HntLl3Lw9ZDI5is7dSjw525fDq/uI6vundeXVbRhytn8F4FYnt/j8P/C8f7Do8RIREZHxDenTFIk7hiP6o97o1kG3D2UyGTCwZ2Ns/6o/zm98Al3a+d17JwsgVv3n5PO/OYqCereDR2gjXN90UNRYjUGfWjDv4DqkR7+LlvN21/oCz9G/OYpO7QAAaCpKURy3F8qmlvn0I7o3L3dHrP6gJ678+RSmPdsaXu66Pc0uwEeJOS91QNKOEVg8vQucldK4uUXXPCCzk8Ot2f+eeuXTPgRO3u415t5ybewHnw4hSJTA+/82XfNAg17PoN2PmWizKgltViXBpVUXNJ38HXz7T7i1vuczKDq1EwCgraxA8fl/oAxuZ9oXUw/S+GslncRM/gLdPp8IBzdnFCdl4sCkZfBqVfvKkzaTH0fY2Efg5O2O7ktfhaaiClv6T4fqZpEZoq6f4Nd/RvKy53GjtBCOgSFoNm0NypPP19ouY/1HyNn+FdSFOUhaOhZyByeELzkNew9f+D8+HcnLXsD5l0IAuRxNXvoKCrcGZng1REREJAZHBzs8M7AFnhnYAklpxYiNz8XJi7m4cK0AZRVqyOUyuCrt0aalF6IifdCptS/8vZXmDtsgYtR/jftHoc2koRA0WpRl5WP3qAXQVFSa4dXoT9daMHHxKNh7BuDagiHVy0Ln7YHC3RvBU3/EjZWTkPXHZxA0VfDsPBSeDzxlypdBRtCyqQc+e7MzPpzcEacv3cTJCzcRG5+L7LxyVFZp4eggR0M/F3SM8EHHCG+0C/WGvb00r3vQJQ/I7OTo/vkkOLg7Q9BooS5TYd+Ez1BZWFq9Tcun+yJ52zFUlZSb+iXUi6554G78B7+O5OUvIf7VCEAmg1e3YfDq+riRIhYfGztWJP9iMrY+POOe28V9sQlxX2wyQUTG5xzcFuGLT95zu8CnZiLwqZl1rrNzckHzt9aKHRoRERFZgOCGbghu6Ian+jczdyhGIUb9l/DLHiT8skfs0ExC11qw48aqO4/RvAPCFtU97wZJn5OjAl3b+aNrO8u/rcpQuuQBbaUaO4a8e9dtTn/8m5hhmYyueeDfWn24v8bPckclmr3+k4hRmZY0W5KkM02VGkofDwze9QmcvN3vuX3U7DFoM/nxGp1bqZEpHFBVkIULU9ujqqB+T3TI2rwEN76eCIW7z703JiIiIrIA+tZ/dxPx4iB0WTgeqjzpXNnNWpCIecDW8oBMEPgIIEtUVVaB6BajzR2GzkZdWwN7ZyfRxitXAz22izacScQMACRyKy4REdmwRv1+RVp2GRr6OSN199PmDofuQGq1IMB6EJBGPcgcIB22ngeYA3THK3aIiIiIiIiIiCSKjR0iIiIiIiIiIoliY4eIiIiIiIiISKI4x46FEgQB6nKVucPQmULpCJlMJtp4ggBUaEQbziSc7AARfwVERERGwfk1pEFqtSDAehCQRj3IHCAdtp4HmAN0Z+FTe9kumUwm6uRzUiOTWf7Ec0RERETGYuu1IMB6kMjW8wBzgO54KxYRERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRLGxQ0REREREREQkUWzsEBERERERERFJFBs7REREREREREQSxcYOEREREREREZFEsbFDRERERERERCRRbOwQEREREREREUkUGztERERERERERBLFxg4RERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRCnMHQDVTRAEqMtV5g5DZwqlI2QymWjjCQJQoRFtOJNwsgNE/BUQERHZNKnVQoC49RBrISLp5QF+JmIeMBc2diyUulyF6BajzR2GzkZdWwN7ZyfRxqvQAD22izacScQMAJR8RxEREYlCarUQIG49xFqISHp5gJ+JmAfMhbdiERERERERERFJFBs7REREREREREQSxcYOEREREREREZFEsbFDRERERERERCRRbOwQEREREREREUkU56smIiIislLXUorw9+E0nLyQi9gLuUjLLsPNggoAQNbNcjw36x90jPBBvy5BiGjhZeZoiUhsVVVa7DqahqPnsnHyQi7iEvKRnlMGAMjILUe/CTsQFemDTpE+eLR7YzjzcUZEksR3LhEREZEV0WoFbP3nBpavvYi/DqfdcTu1RsBPf17FT39eBQD07BiAiSPC8cSDwbC350XdRFKWnl2Kb9ZfxjcbLiPj/xs5/6XVCthzLB17jqUDADzdHPD80JZ4ZXg4Wjb1MGW4RFRPbOxYkYCukXhk49way6pKy1F0PQPX1h/Axe+2Q9BozRSd8RXH7ceVd/vUWCZ3coFjUCi8e4+B36DJkNnxT56IiKzX1RtFeH72ARw8naX3vgdOZuLAyUy0D2uA1fN7ol0rbyNEaHysh1gP2TKtVsCKdRcxY8kJlJar9dq3oLgSS36Ox7JfLuCd8e3w7ovt4WBvZ6RIjYc5gDnAFvG/qBVK/OMQUnbHAjIZlL6eCBnWC/fPHQuPlg1xZPpKc4dndF7dR8Cj0yBAEFCVn4mb+39C6vfTUJF6EU1f/cbc4RERERnF8t8u4M3Fx1FeoanXOGcu5SHq6c14/+X78M6EdpDJZCJFaFqsh1gP2Zq0rFKMfucf7D+RUa9xNBoB81eewR97k/Hroj6IDJHmbZrMAcwBtoTX2VqhvPhEXN8Qg+vrDyB+xRZsG/gOStJyEPrMg3D0djd3eEbn3Kw9vHuPhnefMQh4YjrCPj4Ke5/GyN31LaoKc8wdHhERkagEQcDMpSfw6oIj9W7q3KZWC3j3y5N4ce5BaLWCKGOaGush1kO25FpKEbo/t7XeTZ1/i0vIR4+xW3E8Tpp/L8wBzAG2hI0dG6AuVyH31FXI5HK4N/U3dzgmZ+fkApfQzoAgQJV5zdzhEBERiWre16ex8LtzRhn7241XMGnBYQiCNJs7/8Z6iPWQtUrLKsWDE3YgKb1E9LHziyrx8Ms7ce5KnuhjmxpzAHOANWNjx0a4Bd9KXhX5xWaOxDxuJy+FmzTnCyAiIqrLjpgUvL/itFGPsWLdJfz8/xMsSx3rIdZD1karFfDM2/uRbISmzm0FxZV4ctoelOk5Z48lYg5gDrBWnGPHCtkpHeHYwK36ftJWz/aHd5vmyDmdgOLETHOHZ3RaVRnURbkQBAHq/Ezk7Pwa5ddPw7llJzgFtTR3eERERKIoKFJhwtyDeu934tfBCPBxRmZuGTo9vUWnfV5bdBT9ugQhyM9F7+OZC+sh1kO24KvfLuDASf3+ng3JAVdvFOHdL2OxeHoXQ8I0C+YA5gBbYhONndzcXCxcuBCbNm1CWloafH19MXz4cHzwwQeYMGECoqOjsWrVKowfP97coYqi/bRhaD9tWI1lyTuO4eiMVWaKyLQy1s1Hxrr5NZZ5dh6KJq+sMFNERERkCfIKVUhOL0aVWoBfAycEN3Qzd0j18s6yWKRl1/0Y47sJ8HFGI3/9GjQFxZWY/NFRbFjyoN7HMxfWQ6yH/ksQBCQkFyGvUAVnpQLNG7nB1dne3GEZLDWzFG9/Hqv3fobkAAD4fE08nhnQAlGRvnrvaw7MAcwBtsTqGzvnzp1D//79kZWVBRcXF0RERCAtLQ2LFy9GUlISbty4AQBo166dmSMVz5Xo3UjcfAhyhR08WzVBm8lDofTxhLqisnqb3t+8AUEQ8M9Li6uXOXi6Yuj+JYid9xOub4wxR+ii8O43Dg16jISgVaM8+TwyNyxEVUEW5A7K6m2K42Nwdd6jtfYV1JUQtBp03CTO5JNERGR+h05nYfnai/j970RUqf/3iNvObXwxcUQ4hj/cDE6O0iqJbhZU4IfNCSY95qa9SbieWoTmjaQx6SjrIdZDtxUWV+LHLQlYse4iLiUWVi93dVbg2cdaYuKIcEk++Wnl+ksoqzDd7VGCcKu5s+aj3iY7Zn0wBzAH2BKrnmMnNzcXgwYNQlZWFt566y1kZmbi1KlTyMrKwvz587Fx40acOXMGcrkcrVu3Nne4oilOykRGTBzS9p1B/NdbsGfMR/C5LwRdF06o3ubIjJXw7xyOZkO7VS/rsmA8so9fknQCAwCnwBC4t+8Hj/seQcDjbyJk9laUJhxD8tevVG/jFtkDHdaW1PgXufwKFG4+CHpm/l1GJyIiqdBqBby1+Di6P7cVv2y/VqOpAwDH4nLw3LsH0PP5bci+WW6mKA2zenMCKlSmLbgFAVj5+yWTHrM+WA+xHgKAS4kFaD9sE15bdLRGUwcASsrUWL72Ito+tQkr1l40U4SGqazSYNWGyyY/7u9/J0omXzIHMAfYEqtu7EyZMgUpKSmYNm0aFi1aBFdX1+p1s2bNQlhYGNRqNUJDQ6FUKu8ykrTlnErA9Q0xaP5ED/jed+t+SlV+CQ5NW47OH46H0t8LTQd2QcADkTgyY6WZoxWfa6suaNBrNPIP/IqSy0fr3EZbpcL1hU/ANaI7Aoe9Y+IIiYjIGN5ZFotPVsfdc7sT53Px8Cs7UVxaec9tLcUPf1wx03ETJPuELNZDtlcP3cgoQd/x935alFYrYOKHh/H9JvO8rwyx82AqsszQYKms0uKX7dJ8ohJzgO3lAFtitY2dCxcuYO3atfDz88P8+bW7jTKZDB07dgRQ8zas9evX48knn0TTpk3h7OyMsLAwzJo1CyUlxptp3hTOLlkPrVqDDm+NrF6Wtvc0krceQc8vp6DLwgk4/MYKqPKl/TrvJHDEbEBuh/To2XWuv7H8ZWirKhD82mrTBkZEREZx5tJNLPpe90eAn7mUh49/uHcTyBIUlVQi/lqBWY6dk1+BxDTpPk2G9ZBt1UNvfHoMGTm6z0P16oLDuFlQYcSIxHP0XI75jh2XbbZj1xdzgG3lAFtitY2d6OhoaLVajB49Gs7OznVu4+joCKBmY+fTTz+FnZ0dFixYgB07duDll1/G8uXL8fDDD0Or1dY5jhQUJ2UicfMhBPVqB7/O4dXLT7z/I9yaBSBt72mk7jllxgiNyykwBA16jETx2d0ojq95WWX2n8tQGLsVLWb+Ablj3X8rREQkLcsNuK1i1YbLqKyy/PkETl+6adbjn7xg3uPXB+sh26mH0rNL8cfeZL32qVBpsNrEc1cZ6uTFXPMdmzlAsmwpB9gaq23s7NmzBwDQp0+fO26TmpoKoGZj588//8S6deswatQo9OrVC1OnTsUXX3yBw4cP4+BB/R8paknOLd0ArUaDDtNHVC9Tl6tQkpyN/Is3zBiZaQQMmwXI5Uj/ZU71suJz+5D60ww0n74Ojv7B5guOiIhEU16hRvQ2/W8VyLpZjm0HUowQkbjiEvLNevxzV/LMevz6Yj1kG/XQz1uvQq3R/7bBbzeaft4aQ5gzD1y9UYSyctNN2iw25gDbyAG2RiZI9UbpewgMDERmZibi4uLqnBhZpVLB398fhYWFSE1NRcOGDe841sWLFxEREYFffvkFTz/9tN6xREVFITMzU6997AU53tPer/exDPHIhrlI2XUS8V9vMXiMufLjqJKJd0WTzEEJ/8+N+42JKisJl97shMCR78Fv4KR6j5c1tSWESmlMJkdEZM3Ucg9keU4zaF/3sp1wqzgickTiKnbqgSLnfnWuO/HrYAT43P2b1gAfJRR2cqg1WmTm3vm8lZlbhk5P164NXCqOwLNsp35BG8CUtRBgefWQKWohQNx6yJJqoXznx1DmFKX/jkIVGuZ/IH5AIkv3egeCzLHOdffKA/XNAQAQkL8IdoLut7kZip+J+JnIlgQEBCA2NtagfaX1bE89lJaWAgAqKuq+T3bNmjUoLCyEt7f3XZs6AHDgwAEAQFhYmEGxZGZmIi0tTa99HGR2gL9BhzOL9Ix0VAriXb4ud3Q26svXqspw7aOh8Lh/sCgJDADS09OhVRn/BEdERPdgrwI8Ddu1qLAYRbn6nbNNzrcYuMNntgAfZzTyd9FpGIWdXOdt/620pAylGcb/HUmtFgLErYeMXQsB4tdDFlULNSwHnAzZUaZ33W4WngBkda/SNQ8YmgMAIDMzC1AXGbSvPqSWB/iZyMLygA2x2sZOQEAAiouLERsbi6iomt36lJQUzJw5EwDQtm3bu46TlpaGOXPmoH///ujQoYPBsejLXpADEprSJygwSPTutDHlH96A8sSzqEi7gvyDa2utj/zyAhx8m+g1ZlBQELvTREQWQAsHZAhaQKb/HedebnI4O979Cx9zK3FUovAO6zJz711M6/NtfV1cXRzgcY8vxcQgtVoIELceMnYtBIhfD1lSLVSk1MKQab7ttCUIMMHfd31lQA0t6r5i5155oL45AAAC/b0hh5tuwdaD1PIAPxNZVh6QGkP6BrdZ7a1YEydOxIoVK9C4cWPs3r0boaGhAICjR49izJgxSE5ORlVVFaZOnYolS5bUOUZJSQl69+6NzMxMHD9+HEFBQSaLv6qsAtEtRpvsePU16toa2Dsb9LVIncrVQI/tog1nEjEDAKXVtkqJiKTlydf3YOOeJL32cXVWIG3303B3dTBOUCLZeTAVj078y+D9U3aNRCN/F6RmlaLxQ7/pvf+PH/TEs4NbGnx8XUmtFgLErYdYC9XPpcQChA/ZoPd+M8e1w4LXDLiFy8R6jt2KmFNZBu1b3xzQ0M8Zqbv1n57CEFLLA/xMZFl5wJZY7eTJb7/9Nry8vJCSkoLIyEi0adMGISEh6Nq1K8LDw9G9e3cANSdO/reKigoMGTIEiYmJ+Pvvv03a1CEiIqL6mTgi/N4b/ceYQSEW39QBgI4R3mY+vo9Zj0+ki7Bmnuh7f6Be+8jlMrw0rJWRIhKXOd+HzAFElsdqGztNmjRBTEwMBgwYACcnJyQnJ8PX1xfffvstNm/ejEuXLgGou7FTVVWF4cOH4/jx49ixYwciIiJMHT4RERHVQ9/OgXjiwWCdtw/yc8Y749sbLR4x+TZQommQq1mO7epsj7BmHmY5NpG+Pp52P5yddL904K3n26BpkPFvLxJDp0hf8x27NRs7RJbGahs7ABAZGYlt27ahuLgYRUVFOHLkCMaNG4f8/HxkZGRAoVDUatpotVqMGTMGu3btwpYtW3D//aZ7GgMRERGJQyaTYc1HvTCoZ+N7btvQzxl/rXgYjQIMm0TUHEYNaGGW4458pBns7Ky6fCQr0jHCB5uX9YOr872bOxNHhOPDyZZ/C9Ztg3o1hquzvVmO/fSj5sk/RHRnNnlmjouLA3DrKVeOjjUnHZs0aRLWrl2LN954A0qlEkePHq3+l5qaao5wiYiIyABKJwU2fd4PK959AG1aetVa38DDEdPHtsHxXwajdcsGZojQcC8+1QqyOzwRx5heGa7/LW5E5tSvS0Oc+HUIJjzZqs6rd3rc5491n/bFl+90hVxuhjeVgdxdHTBmkOkbLI90a4QWjd1NflwiujubbuzUdRvW9u23Zqf68MMP0bVr1xr/vv32W5PGSURERPWjUMjx8vBwnF3/OI78/Bi83G7NodPAwwGpu0bi42n3I8hPOlfq3NY0yA1D+zY16TEfaO+H+zi3BklQWDNPfPNed6TtHon1n/WF5//nAb8GTjiwehCG9W8GmTk6pfX06sgIkzejJj/DKSqILBEbO/+RlJQEQRDq/Pf++++bOFIiIiISg0wmQ5d2fnD+/0d1KB0VUOox94YlWjK9M9xcTHMrhr1Cjq/f7WaSYxEZi6e7I558qBlc/j8P2Cuk/VEoMsQL08a0NtnxnuwXjEe7NzLZ8YhId9KuaAx0u7HTtm1bM0ciLq+Ipnjg05dh76pEaWoODkz+Al6hjdEv+h0UXUvH3yPno+JmEbotngi/Tq2gqahEZUk5jr/7PfLikwAAUbPHIHjIA8g9fRX7J3xm3hekg7LEs0j+agK05cVw8G2K4Nd/RsWNeCTMGwCnhqFo+f7fsPf0Q8bvC3Bz749QZSSgxawt8Ow0qHqM3N3fI2vLElSkXETj8Z/Db+Ck6nVZm5cgZ/tXsHPxRPjiWHO8RCIiojo1DXLDp2/cj5fmHdJrv8zcshr/q4s5L7dHm1DLvF1NjPrHTumAbp9NhE/7FhC0Ak4uiMaN7ccAABEvDkLY2IdRWVSGrY/MMOMrrZuutVDSshdQcvEQ5I5K2Cnd0XjCMjg3bw8AqEhPwI2vJ0JdmA1Bo0bgiDlo0GMEANZClm7eq/fhz39u4HJSoc77GJIDvD0d8dWsByzyyiadc8CSifBu2wLQaqFVa3Dyw2hkHLz1udCtqT+6fvIynLxcYefkgJTdJxE772dAECw+BwC654Hbbu79EUlLx6LFu39Wfy66tvApqDKvVm9TnnQOLWb+Ac/Og5kHJMAmGzuHDx82dwhG0f3zSTj0+lfIi09CyMi+6DR7DK6u3Y+CK6k1ktCNncdxePrXEDRaNOrXEb1WTsOm7lMAALHzf0bB5RQ0eqijuV6GXpKWjkXwlB/g3Lw9cnd/j7TV0+HddyyUjSNqJB33tg+iQfcRSPpyXK0xnFt0RPPp65C5/qNa6/yHvA7n5h2Q+sObRn0dREREhpjwZCvsO56B33Ze13mfTk9v0esYD3UNwozna1/lbCnEqH9avzwY2soqbHxgMlwb+2Hg1g+Reeg8KgtLceGbrciLS0TUe8+a6yXela61kGfnoWj66jeQ2SlQcGIrrn8yHK1XXKkew6f/BPg8OBZVBVm4+EYnuEZ0h4N3Q9ZCFk7ppMCvi3qj1wvbUVxapdM++uYAhUKGnz/sBX9vpSEhGp2uOeDEe6tRWXSrmdUgMhj9183Bb5EvAACi3nsWKX+dwMXvtsPO0R6DdixERp84pO09bfE5ANA9DwBAZW4qcnauhEurLjWWt3h7ffX/L02IRcLcR+B+38MA+JlICqR9/SFVa9C6GdRlFdXfPF1btx+N+0dBVsclpil/x0LQaAEAOaeuwLWRL8wyA2M9lV0/DTula/W3Td59nkPB8S0QNOpa27q06gzHwLonmHNu1g7KxuGQyfl2ICIiaZHJZPjxw55Gm2+nV1QANi3pB3t7yzxHilX/BA/phos/7AQAlKRkI/PoRTR5uJNpXkQ96FMLeXYeDJndre90XVt1QWV2MgTtrd9HedJZeHQcAACw9/SHc7N2yD+41jQvguqtQ7gPtn3Z3yhPyVIoZIj+qDce7XHvJwyagz454HZTBwAc3F0AQaix3t7dGQBg5+QAub0C5dkFRotbTPrkAQBIXv4iGo9bApm9Y53rAeDmnu/h3Xs05HfZhiyLTV6xY41cm/jBK6wJBu/6pHqZwtkJSv/aTwH5t4jxA5G691StxCYFqqxElCfH4cLU9tXLtBWlqMrPMF9QREREJuZgb4d1n/TFlIVH8PXvl0Qbd/jDzbB6fk+LnotIrPrHtaEPSlNzqteXpGTDpaHlTxRtaC2U9edSeHQcUP2llnNIJ+T9swb+Q6ZBlXENJZcOw8GvmTFDJ5H16BiAvd8+iqfe2IMbGaWijNnAwxE/fdgTA3s2EWU8Y9A3B3R8ZxSaPtYVjh4u2Dfu0+rlx+f8gAd/nImwZ/vDwcMFZz/fgLzziUaPXwz65IGcHSugbBwJl1ad7zietrICeQd+RasFB4wRLhmJ5Z6pSW9ZJy5hz5j/3U408vx3d92++ZM9EPxYV+wYOtvYoRmNS1g3tJyzrfrns2N8zRgNERGRedjby7FidjcM6dMUE+YeRGqW4R/sfLycsHzWAxjWXxof7MWqf/79FZclziNyJ/rWQjf3r0H+wXVo9VFM9bLg11Yj9Yc3cXNfezj6N4d7236QKUwzMTeJp1NrX5zf+ASmLz6BlfVs8g7t2xQr3n0AAT7OIkVnPPrkgJMLonFyQTQCe7RB1Jwx2D74XWir1Gg1pj+urf8H8Su2wMnbHQ+vfx85J68g89B5U7yEetMlD6iyEpHz9yqELbz7vGz5hzfAMagllMFtRI+TjMcyr6slvZUkZ8H1X98s2bsqYad0RHlWfp3bBw9+AO2nDa+eTEyKHP2bozL3RvXPmrIiaFVlsPcKNGNURERE5vNI90Y4v/EJfDi5IxoH6PcYd39vJd59sT3iNz4hmaaOWPVPSVrurVuz/p9LI1+UpuUaL3CR6FsL5cWsRcZvcxE6b1eNiVQd/ZqixYzfEfH5GbSYuRGaskIoG/Ox1lLk5uKAr2d3Q8zqgXjiwWDY2enXpHyoaxC2LHsIG5c8KImmjr454LaMmDjYuyrhFX7raqTwcY/i2rr9AICKm0VI23saAV2l8R7QNQ+UXjqCqrx0xE8KR9yEYJRePorkL8Yh5+9VNba7ufs7+PSrPS8pWTZesWMl8uKTIGgFBHRrjcxD5xE29mEkbT4MQa2ttW3wY11x34yn8deIuZIoWu7EuXl7yGRyFJ3bC/e2fZGzfTm8uo+ovn+ciIjIFnm4OeCdCe3x1vNtsT0mBTsPpeLkhZs4eyUPqkpN9Xb2CjnatPTCfeHe6NelIR5/sCkc7O3MGLn+xKp/kv88gvDnH8HBqV/BtbEfArpG4MiMb0z1MgymTy2Ud3Ad0qPfRct5u+HgW/PWmqqCLCg8/CCTyVB46i+Up1xAi57PmOplkBF0vy8A3e8LQGpmKaK3X8WxuBycvJBb6zYtf28lOkZ4o1OkL55+tDlaNfM0T8AG0jUHyOzkcG3ih+LETACAT/sQOHm7ozg5CwBQnJyNhn074Nrv/0ChdERA99aI++IPU78cg+iaBxr0egYNev3vfX15Vm/4D32zxtOCVVmJKE04jhbv/GGq8Ekk/ARsRWImf4Fun0+Eg5szipMycWDSMni1qn1PbM+vXkN5dgEe/OF/s8T/NXwuVPklpgxXFMGv/4zkZc/jRmkhHAND0GzaGpQn175kMmP9R8jZ/hXUhTlIWjoWcgcnhC85DXsPX9zcvwZpP70NTUk+Co5tRuaGhQh59084N+9ghldEREQkDoVCjsF9mmJwn1sTK1dVaZFXpIKqUgMHezm83B3h6CCtRk5dxKh/zi/fjG5LJuKJw19A0Ao4OvNbVBZIoy7StRZKXDwK9p4BuLZgSPWy0Hl7oHD3RuHxP5G5YSFgp4C9VyBaztkOuaNlPgGJ9NMowAUzXvjfU+0KiytRUlYFQQBcnBXwdHOQ1K2HddElB8js5Oj++SQ4uDtD0GihLlNh34TPUFl4q9F18LUv0fnDcYh8eTDk9na4sfM4krceMcfLMYiueeBecnd/D8+uT8LO2d0IUZIxsbFjRfIvJmPrwzPuud1PTUaaIBrTcA5ui/DFJ++5XeBTMxH41Mw613n3Hg3v3qPFDo2IiMii2NvLLfZxxfUhRv2jLlfhn5eXiBmWyehaC3XceOdHYfv0Hw+f/uPFDIsslIebAzzcHMwdhqh0yQHaSjV2DHn3juvzzifedb2l0zUP/FurD/fXWtZw1HyRIiJT4xw7Vk5TpYbSxwODd30CJ+97d16jZo9Bm8mPV3evpUimcEBVQRYuTG2PqoLseo2VtXkJbnw9EQp3y38yBhEREd2ib/1zNxEvDkKXheOhypPOnISshcjW2XoOAJgHbI1MECT4nGsbUFVWgegW0rmKZNS1NbB3dhJtvHI10GO7aMOZRMwAQMlr4IiILFqjfr8iLbsMDf2ckbr7aXOHQ3chtVoIELceYi1kPMwD0iG1PMDPRNLJA9aGV+wQEREREREREUkUGztERERERERERBLFW7EslCAIUJerzB2GzhRKR1Fn1BcEoEJz7+0siZMdIPGHChARWT3egiEdUquFAHHrIdZCxsM8IB1SywP8TCSdPGBtePebhZLJZKLenyk1MhnvzSQiIrJlrIVYCxExDzAPkG54KxYRERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRLGxQ0REREREREQkUWzsEBERERERERFJFBs7REREREREREQSxcYOEREREREREZFEsbFDRERERERERCRRbOwQEREREREREUkUGztERERERERERBLFxg4RERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRLGxQ0REREREREQkUQpzB0B1EwQB6nKVucPQmULpCJlMJtp4ggBUaEQbziSc7AARfwVERGTjWAuwFiDbJrUcADAPMAeQubCxY6HU5SpEtxht7jB0NuraGtg7O4k2XoUG6LFdtOFMImYAoOQ7ioiIRMJagLUA2Tap5QCAeYA5gMyFt2IREREREREREUkUGztERERERERERBLFxg4RERERERERkUSxsUNEREREREREJFGc2omIiIisliAISMsqw8mLuTh7OQ+FxZUAgMKSSqzefAVRET4Ia+YJhYLfdRFZK1WlBnEJeTh54SYS04pR8P95oLi0Cn8dSkXHCB/4eIk34S8RkamxsUNERERWJyOnDKs2XMZ3my7jRkZprfUlZWo8PzsGAODspMBTDwVj4ohw3N/GV9RH9RKReWi1AnYdScPytRex42AqqtTaWtsUlVbhkVf+AgBEtPDES0+F4dnHQuDp7mjqcImI6oWNHSsS0DUSj2ycW2NZVWk5iq5n4Nr6A7j43XYImtonNWtRHLcfV97tU2OZ3MkFjkGh8O49Bn6DJkNmxz95IiJrlleowpufHcPPW69CrRZ02qesQo2f/ryKn/68iqhIHyyf9QA6tfY1cqTGYeu1AMB6gIBtB25g6sfHcPVGkc77XLhWgNcWHcXMpbGY/EwE3n+lA5wcpfl3Yut5gDmAbBH/oq1Q4h+HkLI7FpDJoPT1RMiwXrh/7lh4tGyII9NXmjs8o/PqPgIenQYBgoCq/Ezc3P8TUr+fhorUi2j66jfmDo+IiIxk875kvDTvELJulhs8Rmx8LrqO+RNvjW2L917pAEcHOxEjNB1brwUA1gO2KL9IhamLjuKnP68aPEZZhRqLvj+HzfuS8cO8nujSzk/ECE3L1vMAcwDZEt5QboXy4hNxfUMMrq8/gPgVW7Bt4DsoSctB6DMPwtHb3dzhGZ1zs/bw7j0a3n3GIOCJ6Qj7+CjsfRojd9e3qCrMMXd4REQkMkEQMOerkxj62u56NXVu02gEfPTdWfQdvx0FRSoRIjQ9W68FANYDtiYprRidnt5cr6bOv11KLES357bix80JooxnDraeB5gDyJawsWMD1OUq5J66CplcDvem/uYOx+TsnFzgEtoZEASoMq+ZOxwiIhLZrGUnMX/lGdHHPXwmG/1e3ImikkrRxzY1W68FANYD1iw5vRg9n9+GaynFoo6r1QoYO/sAfvjjiqjjmout5wHmALJmbOzYCLfgW8m7Il/cE55U3E7eCjdvM0dCRERiWrX+Ej767qzRxj95IRfD39wLQdBtvh5LZuu1AMB6wBqVlavx6MS/kZJZe5J0sYx//yD2Hks32vimZOt5gDmArBXn2LFCdkpHODZwq76fttWz/eHdpjlyTiegODHT3OEZnVZVBnVRLgRBgDo/Ezk7v0b59dNwbtkJTkEtzR0eERGJJCmtGK9/ckyvfU78OhgBPs7IzC1Dp6e36LTPX4fTsGrDZbz4VJghYZqFrdcCAOsBWzH7q5O4eL1Ar330zQNarYAX3otB3IbH4ebiYGCkpmfreYA5gGyJTTR2cnNzsXDhQmzatAlpaWnw9fXF8OHD8cEHH2DChAmIjo7GqlWrMH78eHOHKor204ah/bRhNZYl7ziGozNWmSki08pYNx8Z6+bXWObZeSiavLLCTBEREZHYBEHAuPdiUFqu1mu/AB9nNPJ30ft4b3x6HA8/0BBNg9z03tccbL0WAFgP2ILDZ7Kw5Ofzeu9nSB5ITi/BW4tPYMXsbnofz1xsPQ8wB5AtsfrGzrlz59C/f39kZWXBxcUFERERSEtLw+LFi5GUlIQbN24AANq1a2fmSMVzJXo3EjcfglxhB89WTdBm8lAofTyhrvjfHAG9v3kDgiDgn5cWVy9z8HTF0P1LEDvvJ1zfGGOO0EXh3W8cGvQYCUGrRnnyeWRuWIiqgizIHZTV2xTHx+DqvEdr7SuoKyFoNei4SWPKkImIjKqwuBI/b72K3/9ORG5BBRzs5Qhv5omXh4WhR8cAyGQyc4eot33HM7D3eIbJjldSVoWPf4jDV7MeMNkx68PWawGA9cB/nTifgxXrLuLs5TyUqzTwcnfAkN5N8cLjofDxcjJ3eAaZ89UpmPIuyW82XMbb49pKpsFr63mAOYBsiVU3dnJzczFo0CBkZWXhrbfewuzZs+Hq6goA+OCDDzB79mwoFArI5XK0bt3azNGKpzgpExkxcQCAtH1nkH38Ih7d8gG6LpyAA68uBQAcmbESQ/YtQbOh3ZD4xyEAQJcF45F9/JKkEzgAOAWGwL19PwCAx32PwDWiOy6/3Q3JX7+C5m/8AgBwi+yBDmtLauxXeTMdl96Igu/ASSaPmYjIGARBwMLvzuHDVWdqXdly5lIeft1xHW1aeuGXhb3RumUDM0VpmOVrL5r8mD/9eRULp0ZJ4lYMW68FANYDtyWmFuOZt/fh6LnaTwE6fCYbc5afwpRnIvDRa1Gws5PO9JuXEguwx8Tz3mi1Ar5ZfxkfToky6XENZet5gDmAbIl0srcBpkyZgpSUFEybNg2LFi2qbuoAwKxZsxAWFga1Wo3Q0FAolcq7jCRtOacScH1DDJo/0QO+9926n1SVX4JD05aj84fjofT3QtOBXRDwQCSOzFhp5mjF59qqCxr0Go38A7+i5PLROrfRVqlwfeETcI3ojsBh75g4QiIi8QmCgKmLjuKdZbF3vV0pLiEf3Z7bilMXck0YXf2kZ5fij33JJj9uSVkV1myV5pNUbL0WAGyzHrh6owhdx/xZZ1PnNlWlBp+sjsOYd/6BViudScJX/n7JLMddteEyKqukeRWHrecBW8wBZDustrFz4cIFrF27Fn5+fpg/f36t9TKZDB07dgRQ8zasmJgYPPTQQwgMDISjoyMaNWqEESNG4OJF038zKKazS9ZDq9agw1sjq5el7T2N5K1H0PPLKeiycAIOv7ECqvySu4wiXYEjZgNyO6RHz65z/Y3lL0NbVYHg11abNjAiIiP5actVLPvlgk7bFpVUYdDkXSgtqzJyVOLYfyITGo15PoDuPirdJ+PYei0A2FY9oNFo8djkv5F1s1yn7X/dcR2fro4zclTi2XUkzSzHzcmvQFxCvlmOLQZbzwO2lAPItlhtYyc6OhparRajR4+Gs7Nznds4OjoCqNnYyc/PR+vWrbFs2TL8/fffWLRoEeLj49G1a1ekpqaaJHZjKE7KROLmQwjq1Q5+ncOrl594/0e4NQtA2t7TSN1zyowRGpdTYAga9BiJ4rO7URxf87LS7D+XoTB2K1rM/ANyx7r/VoiIpEQQBHz6o34f0DJyyvDbzutGikhcJy+a7+oicx67vmy9FgBsqx7YHpOKS4mFeu2z9Jd4VFVpjRSReErLqnBRz9cmppMSusLxv2w9D9hSDiDbYrWNnT179gAA+vTpc8dtbjdq/t3YGTx4MJYsWYJhw4ahV69eGDVqFDZs2IDCwkJs2LDBuEEb2bmlG6DVaNBh+ojqZepyFUqSs5F/8YYZIzONgGGzALkc6b/MqV5WfG4fUn+agebT18HRP9h8wRERiejgqSycv6r/N8pf/XYRgilnIjXQqYs3zXbs5PQS5OZXmO349WXrtQBgO/XAV7/pdsXev6Vnl2GzGW5z1NfZK3lmvW1Myo0dgHnAVnIA2RaZIIUKzgCBgYHIzMxEXFxcnRMjq1Qq+Pv7o7CwEKmpqWjYsOEdx7p58yZ8fHzw5Zdf4tVXX9U7lqioKGRmZuq1j70gx3va+/U+liEe2TAXKbtOIv7rLQaPMVd+HFUy8b7hkTko4f95gmjj1UWVlYRLb3ZC4Mj34CfC5GhZU1tCqNTtcmciImMqduqBIud+Bu0bmPcB5LDsW7KyPCZBbedb57oTvw5GgM+dv2kN8FFCYSeHWqNFZu7dc3Zmbhk6PV373OhX+CXsNXees0QsrAWMXwsA4tYDllQLpHvNgiDTf6Jvl4oj8CzbaYSIxFNuH448t5F1rrtXDgB0zwN3ygFOlRfhXfKbfkEbwJQ5ALDdPGCtOYCkJyAgALGxsQbta7VPxSotLQUAVFTU/a3amjVrUFhYCG9v7zqbOhqNBlqtFikpKXjnnXcQFBSEESNG1DHSvWVmZiItTb/7gB1kdoC/QYczi/SMdFQK4k0kJ3d0NurL16rKcO2jofC4f7AoTR0ASE9Ph1ZVJspYRET14q8CDLyKPCMrD1AXiRuP2FwEwK7uVQE+zmjk73LPIRR2cp22q0t29k2gwvjze7AWMG4tAIhfD1hOLSADGhj29LbSMjVK9axbTc4jCLjDE8d1zQGA4XmgQqXWu7Y3hNRyACC9PGC9OYBsjdU2dgICAlBcXIzY2FhERdV8JGFKSgpmzpwJAGjbtm2d+/fq1QuHDt165F9ISAj27t0LHx8fg2PRl70gByz/FudqQYFBonfnjSn/8AaUJ55FRdoV5B9cW2t95JcX4ODbRK8xg4KC2KEnIotQ7OQAQ1szgf4NIL/TJyYLkaWQ4U7P+crMvXtBre8VO3Xx8/WGvdb4J2nWAsZ/YqnY9YAl1QLpggqCzFHv/VycFfC8y5XslqDc3gN5d1h3rxwA6HfFTl2cHBXwNsHvSGo5AJBeHrDmHEDSY0jf4DarvRVr4sSJWLFiBRo3bozdu3cjNDQUAHD06FGMGTMGycnJqKqqwtSpU7FkyZJa+1++fBkFBQVITEzEJ598gpycHBw8eBBNmuj3Yd9QVWUViG4x2iTHEsOoa2tg7+wk2njlaqDHdtGGM4mYAYDSalulRCQlx+Ny0HmU/pfSd2nriyNrBhshInH1m7ADe44Z9nSqlF0j0cjfBalZpWj8kGG3UuQdHA0vd/0/MOuLtQBrgfoYMmUXtuzXf76UP794CIN6mabeNdSxc9noMvpPg/evbx54ZXgYlr/bzeDj60pqOQBgHrCkHEC2xWonT3777bfh5eWFlJQUREZGok2bNggJCUHXrl0RHh6O7t27A6g5cfK/tWrVCp07d8bIkSOxZ88eFBUV4eOPPzblSyAiIjJIp9Y+6Bih/1WmE0eE33sjC3BfuLfZjt28kZtJmjpE9WXI+7lpkCse7d7ICNGIq21oA9jZycx2fEPyKxGRMVltY6dJkyaIiYnBgAED4OTkhOTkZPj6+uLbb7/F5s2bcenSJQB3buz8m6enJ0JCQnD16lVjh01ERFRvMpkMbz3fRq99goNcMax/MyNFJC5zfqjiBzqSioe6NkSHMP2aoG8+1wZ2dpb/8UDppEBkCy+zHZ95gIgsjVVfKBYZGYlt27bVWp6Xl4eMjAwoFApERETcc5zs7GxcvnwZnTt3NkaYREREohv+cHOcv5qP+SvP3HNbXy8nbF/+MJwcpVEW9OkUCHuFHFVq008+8Ug3y7+agQgA5HIZtix7CN3HbkVyesk9t39pWBheHSmNq/YA4JFuDXHuyp1m2jGeID9ntA4xX1OJiKgult+SN4K4uDgAQFhYGBwda15OPXr0aLz//vv4448/sH//fqxatQq9e/eGQqHA66+/bo5wiYiIDDLv1Y5YPusB+Hjdeb6DHvf542j0Ywhv7mm6wOrJz1tplquLPN0cMPKR5iY/LpGhGgW44Oiax+7akPRwc8CCKVFY8e4DkMnMd3uTvl4aFgZzhPvSU2FQKGzyIxQRWTCbzEq3Gzt13YbVpUsXbN++Hc8//zweffRRfPLJJ+jRowfOnDmDkJAQU4dKRERUL6+MCEfqrpFY81EvDOnTBA72t079LkoFzq5/HAdWD0LzRu5mjlJ/5pgP6PmhLeHMWTFJYgJ8nLFjxcO4vOUpTHu2NRz/Pwc4OsjxzZxuSNs1EjPHt5NUUwcAmjdyN/l8QAqFDOOfCDXpMYmIdMHGzn9MmjQJx48fR35+PsrLy3HlyhWsXLkSTZs2NXWYREREonB0sMOogSH4Y+lD8P3/q3c83RzQNrSBmSMz3APt/TCoZ2OTHc/L3QHTx7Y12fGIxBYa7IHP3uxcfQWfj6cTJjwVBhdnezNHZrj5r3Y06STKU56JRJCfi8mOR0SkK5v82ul2Y6dtW+sq0LwimuKBT1+GvasSpak5ODD5C3iFNka/6HdQdC0df4+cj4qbRei2ZCK827YAtFpo1Rqc/DAaGQdv/U6iZo9B8JAHkHv6KvZP+MzMr+jeyhLPIvmrCdCWF8PBtymCX/8ZFTfikTBvAJwahqLl+3/D3tMPGb8vwM29P0KVkYAWs7bAs9Og6jHSfn4HBUc3QaZwgMzeEQ2fXQj3tn0BAFmblyBn+1ewc/FE+OJYc71MIiL6D5lMhpVzuiHy8Y0oKK40+vGWvd0Vgb7ORj+OvnQ+9y+eCL9OraCpqERlSTmOv/s98uKTAAAhI/si8sWB8GjZCMfn/IBLP+ysHj/ixUEIG/swKovKsPWRGWZ6lXenay2QtPR5lF07CcjlkNnZ3zrft3sQAKBVlSHpi3EoSzgByOVo+OxCeHV9AgBrAUt2X4QPZo5rhw++OWP0Y4U29cAHkzoa/TiGEOMzgFuzAHT9aAKcfDwgU9jh7OLfkbTlMADLzwO65oDbbu79EUlLx6LFu39WfyZQZVxD8vIXoS6+CW1lBTyiBqLR2E8gk8uZA0gSbLKxc/jwYXOHYBTdP5+EQ69/hbz4JISM7ItOs8fg6tr9KLiSWiMJn3hvNSqLygAADSKD0X/dHPwW+QIAIHb+zyi4nIJGD1nmieu/kpaORfCUH+DcvD1yd3+PtNXT4d13LJSNI2okXve2D6JB9xFI+nJcrTFcI3ogcPhsyB2VKEs8iyuzeqPt6gzIHZzgP+R1ODfvgNQf3jTlyyIiIh0E+bngq3cewKiZ+3XeJzO3rMb/6uKJB4MxamALfcMzCV3P/Td2Hsfh6V9D0GjRqF9H9Fo5DZu6TwEA3Dx3DftfWow2k5+oNf6Fb7YiLy4RUe89a7LXpC9da4FG45ZA4eoJACi7fgZX5vRDu59zIJPJkLnpU8jtHdF65VWoshJx6a2ucGvTBwpXL9YCFm72S+2xPSYFpy7e1HkfffOAvUKO1R/0gNLJMj86ifEZoPvnk5AQvRtX1+2Hk48HBu1ciOzjl1CWmWfxeUDXHAAAlbmpyNm5Ei6tutRYnvrDm/C8fwj8HpsCbWUFLr7ZCUWnHoRH1ADmAJIEm7wVyxo1aN0M6rKK6m/frq3bj8b9oyCrY3K32wkdABzcXQBBMFWYoiq7fhp2Slc4N28PAPDu8xwKjm+BoFHX2talVWc4BtZdlHt0fBRyRyUAQNm0DQStBuqiXKPFTURE4nlmYAssnBql8/adnt6Cxg/9hk5Pb9Fp+54dA/Dzgl4WOf+IPuf+lL9jIWhuPUUs59QVuDbyxe2ZZ/MvJKMwIQ3Qmv4pY/WlTy1wu6kDAJrSghr/TfMProXvgFcBAI7+zeAW2RMFxzYbNXYSh4O9HbYvfxgtm+o+V5g+eUAul+GXhb3RtZ1/fcI0GrE+AzSIbIrUPacAABW5hciPT0bwkAeMG7wI9MkBAJC8/EU0HrcEMnvHWus0ZYUAAG1lOQR1FewbBBotbiKxWWbbmfTm2sQPXmFNMHjXJ9XLFM5OUPrX/TjGju+MQtPHusLRwwX7xn1qqjBFpcpKRHlyHC5MbV+9TFtRiqr8DIPHvLnnBzgGtICDDx9nS0QkFTNeaAcHeztM++SYqOP2f6AhNi5+0GInTNb33H9bxPiBSN17SrJf7PybvrVA6o9vI//w79CU5KPF2xurmzuVOTfg4Pu/+RQd/IJRmXPDqLGTePy9lfjn+4F4+OWdiEvIF21cB3s5fvu4Dx5/MFi0McUm1meA3DPX0PypnriwcivcmvrDN6oVim9kGz3++tInB+TsWAFl40i4tOpca12j8Z/j2gePIWfHCqhL8xE4fDacm3cwZuhEorLMSoUMknXiEvaM+aj655Hnv7vjticXROPkgmgE9miDqDljsH3wu9BW1d3ZtmQuYd3Qcs626p/PjvE1eKyis3uQ/ttchM7dJUZoRERkQq+PaY0OYd54YU4MEtOK6zWWo4Md5r96H6Y92xp2dpZ9cbM+534AaP5kDwQ/1hU7hs42dmgmo08t0Oi5hWj03EIUndmN1NXT0WrhIcjtHW6t/PdVWVbQ9LI1gb7OOLpmMN5ZFotlv8TX+z9hVKQPfpjXA61bWv4k82J8Bjg49Ut0mvMcQnb1QvGNbGTEnINWLY3PBrrkAFVWInL+XoWwhYfqHCP3r5Vo0HsMAp6YjqqCbFyZ3RcurbpUz7tJZOksu1ohnZUkZ8G1oU/1z/auStgpHVGedfdvLTJi4mDvqoRXeBNjhyg6R//mqMz937dpmrIiaFVlsPfS/7LJ4vP/IGnZ8wh59084NWolZphERGQivTsF4tyGx/H6mEg4GzgXxkNdg3B63VBMf76txTd19D33Bw9+AO2nDa+eSNUaGFoLuLfvB015McqTb00c6+DbBJXZSdXrK3OS4eArvdrI1jkrFfh8Rhf88/1AdIzwufcOdfD2dMRHr0XhyM+PSaKpI9ZngNLUXOx/8TNseWg69o37BA7uzii8kmrU2MWgaw4ovXQEVXnpiJ8UjrgJwSi9fBTJX4xDzt+rAADZW5fBu+9zAAB7Tz943PcoSs7/Y7oXQlRPll2xkM7y4pMgaAUEdGsNAAgb+zCSNh+GoK55v7zMTg63ZgHVP/u0D4GTtzuKk7NMGq8YnJu3h0wmR9G5vQCAnO3L4dV9BGR2+hXzxfEHkLhkDEJmbYZzs3bGCJWIiEzE1dkei6d3Qfqep7Hs7S5oG9oA95oex99biddGReLi5ifx98pHEd7c0ySx1peu534ACH6sK+6b8TT+GjEXpWnWM4+crrWAoFGjIj2h+ufSK8ehLsyGY0BzAIBXt2HI2f4VgFvf7Bef/wee9w820asgsfXoGIATvw7GsejBeG5wS3i4Odx1e4VChgfa++GnD3sidddIvD2uHRR1zFFjicT6DODk41G9Lqh3O3iENsL1TQdN8ArqR9cc0KDXM2j3YybarEpCm1VJcGnVBU0nfwff/hMA3GoQFZ3aAQDQVJSiOG4vlE1bm/bFENUDb8WyIjGTv0C3zyfCwc0ZxUmZODBpGbxa1fy2SWYnR/fPJ8HB3RmCRgt1mQr7JnyGysJSM0VdP8Gv/4zkZc/jRmkhHAND0GzaGpQnn6+1Xcb6j5Cz/SuoC3OQtHQs5A5OCF9yGvYevkj+YhyEKhWSlj1fvX2zqT9DGdzGlC+FiIhE5OHmgMnPRGLyM5EoLq3E6Ys3cfZKHopKqqDRClA62iE02ANRET4I8nO2yMmRdaHLuR8Aen71GsqzC/DgD/97Qs5fw+dClV+C5k/2QMd3RsPB0wWNH+6ENpMex57nFiLvfKIpX4rBdKkFBK0GSUvHQlNWCJncDnJHFzSfsR4K11vzkPg/Ph3Jy17A+ZdCALkcTV76Cgo3y79ag+5MJpPh/ja+uL+NLwRBwPXUYpy8kIvrqcVQVWpgr5CjgYcj7gv3QdtQLzg5SvdjkRifARr3j0KbSUMhaLQoy8rH7lELoKmoNMfL0ZuunwfuOsbUH3Fj5SRk/fEZBE0VPDsPhecDTxkpYiLxSTeDUS35F5Ox9eEZd91GW6nGjiHvmigi43MObovwxSfvuV3gUzMR+NTMOte1/jqhzuVERGQd3Fwc0DMqED2jrO8JJ7qc+wHgpyYj77ju+oYYXN8QI2ZYJqVLLSC3d0TYorrn1gAAOycXNH9rrdihkYWQyWRo0dgdLRrr/uQsKRHjM0DCL3uQ8MsesUMzCV0/D/xbqw/31xyjeYe75ggiSyeNawzJYJoqNZQ+Hhi86xM4ed/7ZBY1ewzaTH5cslfwAIBM4YCqgixcmNoeVQX1m80/a/MS3Ph6IhTuht2nTUREZGr6nvvvJuLFQeiycDxUedKak4e1ANk6W88DzAFka2SCwGn/LVFVWQWiW4w2dxg6G3VtDeydnUQbr1wN9Ngu2nAmETMAsNAn4hIRVWvU71ekZZehoZ8zUnc/be5w6C5YC7AWMAbmAOmQWg4AmAekkAPIOvGKHSIiIiIiIiIiiWJjh4iIiIiIiIhIongrloUSBAHqcpW5w9CZQuko6hNFBAGo0Ig2nEk42eGej9QlIjI33oYhHawFWAsYA3OAdEgtBwDMA1LIAWSdeAeghZLJZKLenyo1MhnvTyUiItvGWoC1ANk2W88BAPMAka54KxYRERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRLGxQ0REREREREQkUWzsEBERERERERFJFBs7REREREREREQSxcYOEREREREREZFEsbFDRERERERERCRRbOwQEREREREREUkUGztERERERERERBLFxg4RERERERERkUSxsUNEREREREREJFFs7BARERERERERSRQbO0REREREREREEsXGDhERERERERGRRLGxQ0REREREREQkUQpzB0B1EwQB6nKVucPQmULpCJlMJtp4ggBUaEQbziSc7AARfwVERDZNaudBgOdCngdJbLaeB6SWAwDmASJzYWPHQqnLVYhuMdrcYehs1LU1sHd2Em28Cg3QY7tow5lEzABAyXcUEZEopHYeBHgu5HmQxGbreUBqOQBgHiAyF96KRUREREREREQkUWzsEBERERERERFJFBs7REREREREREQSxcYOEREREREREZFEsbFDRERkA1SVGiQkF6JKrQUAaDRaCIJg5qiIyJSybpZX54AqtRblFWozR0RERGLgnOVERERWSKsVsOdYOtbvSkRsfC7iEvKrP9ABQObNCvj3/gUdI3zQrYMfxg4ORaMAFzNGTERiy8krx49bruLAyUzEXshFRk5Z9brsvAq4df0JEc09ERXpg6F9mmJgz8aws+P3vkREUsPGDhERkRVRVWqwYu1FLF93EQnJRXfdNie/AjsPpWLnoVS8v+I0hvRuiulj26BLOz8TRUtExnDuSh4+/uEcfv87EZVV2jtup9EIiEvIR1xCPn74IwGNA1zw0lNhmDo6Ei7O9iaMmIiI6oONHSsS0DUSj2ycW2NZVWk5iq5n4Nr6A7j43XYImjuf3KWuOG4/rrzbp8YyuZMLHINC4d17DPwGTYbMjn/yRGS9Tl7Ixdh3D+D81Xy999VoBGzck4RNe5Pw2qhIfDg5Cs5K6eVMngt5LrRllVUaLFh1Fh9+ewZqtf63WqZkluLdL0/i+z+u4Pt5PdArKtAIURqXrecAgHmAyBbxHW2FEv84hJTdsYBMBqWvJ0KG9cL9c8fCo2VDHJm+0tzhGZ1X9xHw6DQIEARU5Wfi5v6fkPr9NFSkXkTTV78xd3hERKITBAEf/3AOs744CY2mfvPmCALw+Zp4bD2Qgi3LHkJ4c09xgjQxngt5LrQ1NzJKMOS1XThzKa/eY11PLUbvF7bjrefb4KPXOkEul4kQoWnZeg4AmAeIbAlvorVCefGJuL4hBtfXH0D8ii3YNvAdlKTlIPSZB+Ho7W7u8IzOuVl7ePceDe8+YxDwxHSEfXwU9j6NkbvrW1QV5pg7PCIiUQmCgJlLY/H257H1bur829UbRej5/DacvXxTtDFNiedCngttyfXUInR/bqsoTZ1/+/iHOEyYexBarfQmWrf1HAAwDxDZEjZ2bIC6XIXcU1chk8vh3tTf3OGYnJ2TC1xCOwOCAFXmNXOHQ0QkqoXfncOi788ZZezc/Ar0f2knrqfefa4eKeC5kOdCa5V9sxz9JuxESmapUcb/ftMVvPHpMaOMbUq2ngMA5gEia8bGjo1wC751AqvILzZzJOZx++SlcPM2cyREROI5ejYb7355Uq99Tvw6GCm7RuLEr4N12j47rwLPzTogyW/s/4vnQp4LrY0gCHhp/iEkpun+N61vDgBu3Z657cANQ0K0KLaeAwDmASJrxTl2rJCd0hGODdyq7ylu9Wx/eLdpjpzTCShOzDR3eEanVZVBXZQLQRCgzs9Ezs6vUX79NJxbdoJTUEtzh0dEJIoKlRrPz9G/4RLg44xG/vo91vzg6Sx88Us8XhvdWq/9zInnQp4LbcFvO67jj73Jeu1jSA4AgBfnHcL5jf7wcnfUe19zsPUcADAPENkSq2/s5ObmYuHChdi0aRPS0tLg6+uL4cOH44MPPsCECRMQHR2NVatWYfz48eYOVTTtpw1D+2nDaixL3nEMR2esMlNEppWxbj4y1s2vscyz81A0eWWFmSIiIktw8kIujp3LRmm5Gh5uDnioS0M0a+Rm7rAMtuTneFxKLDTZ8WYui8WogSHw8XIy2THrg+dCngv/q7SsCn/+cwPpOWWQy2Ro3sgNj3ZvDHt7aV7AXl6hxpRFR012vPTsMsz7+jSWvNXFZMesD1vPAQDzAJEtserGzrlz59C/f39kZWXBxcUFERERSEtLw+LFi5GUlIQbN25dUtquXTszRyquK9G7kbj5EOQKO3i2aoI2k4dC6eMJdUVl9Ta9v3kDgiDgn5cWVy9z8HTF0P1LEDvvJ1zfGGOO0EXh3W8cGvQYCUGrRnnyeWRuWIiqgizIHZTV2xTHx+DqvEdr7SuoKyFoNei4SWPKkInISARBwNqd17Hk53gcP19zokiZDHi0eyPMeL4tekrskb5qtRYr1l006THLKzT4ftMVvPVCW5Me11A8F/JceFt6dikWfn8OP25JQFFJVY11AT5KTHiyFaaPbQM3FwczRWiYtX9dR25+hUmP+f0fV/DBpI5wcbY36XENYes5AGAeILIl0vyKQge5ubkYNGgQsrKy8NZbbyEzMxOnTp1CVlYW5s+fj40bN+LMmTOQy+Vo3Vo6l5brojgpExkxcUjbdwbxX2/BnjEfwee+EHRdOKF6myMzVsK/cziaDe1WvazLgvHIPn5J8icxp8AQuLfvB4/7HkHA428iZPZWlCYcQ/LXr1Rv4xbZAx3WltT4F7n8ChRuPgh6Zv5dRiciqRAEAa9/fAxPz9hfq6lzaz2wPSYVfSfswHcbL5shQsNti0kx2kSpd/P175eg0WhNflxD8FzIcyEAXLxegM6j/sQXv1yo1dQBgMzccsxfeQY9n9+G7JvlZojQcMvXmra5CwBFJVX4Zbs0Jt219RwAMA8Q2RKrbexMmTIFKSkpmDZtGhYtWgRXV9fqdbNmzUJYWBjUajVCQ0OhVCrvMpL05ZxKwPUNMWj+RA/43nfrflpVfgkOTVuOzh+Oh9LfC00HdkHAA5E4MmOlmaMVn2urLmjQazTyD/yKkst1X7KsrVLh+sIn4BrRHYHD3jFxhERkDB98cwZLo+PvuZ1GI2DC3IPYvE+/eSrM6bcd181y3MS0Ypw4n2uWY9cXz4W2dy7MulmOR17ZidSsezdBz1zKw6DJf6NCpTZBZPWXmGq+9+JvO82Tf+rL1nMAYJt5gMhWWGVj58KFC1i7di38/Pwwf37tTrNMJkPHjh0B3P02rEcffRQymQzvv/++sUI1mbNL1kOr1qDDWyOrl6XtPY3krUfQ88sp6LJwAg6/sQKq/BIzRmk8gSNmA3I7pEfPrnP9jeUvQ1tVgeDXVps2MCIyitz8CnzwzRmdtxcEYPri45J58lPsBfM1V05elGZjB+C50NbOhUvXxONGhu5Xtp04n4u1OxONGJF4zJsDbkIQpJEr/8vWcwBge3mAyFZYZWMnOjoaWq0Wo0ePhrOzc53bODremtH/To2ddevW4cyZM8YK0eSKkzKRuPkQgnq1g1/n8OrlJ97/EW7NApC29zRS95wyY4TG5RQYggY9RqL47G4Ux9e8tDb7z2UojN2KFjP/gNyx7r8XIpKW7zddQWWVfrcMJSQXYe/xdCNFJJ7C4kpcvVFktuOfNOMHyvriudB2zoWqSg2+NeAWS3Pc3mQIc74PC4srcS1Fmo8Lt/UcANhWHiCyJVbZ2NmzZw8AoE+fPnfcJjU1FUDdjZ2ioiJMnToVn376qXECNJNzSzdAq9Ggw/QR1cvU5SqUJGcj/+INM0ZmGgHDZgFyOdJ/mVO9rPjcPqT+NAPNp6+Do3+w+YIjIlH9utOwOSB+NdMtTvq4kmy6J2HV5XKSeY9fXzwX2sa5cO+xdOQYMLHw8fM5uJ5qvsaprsz9PrycVGDW49eHrecAwHbyAJEtkQlSvZbyLgIDA5GZmYm4uLg6J0ZWqVTw9/dHYWEhUlNT0bBhwxrrJ0+ejLi4OOzfvx8ymQzvvfdevW7HioqKQmZmpl772AtyvKe93+Bj6uORDXORsusk4r/eYvAYc+XHUSUTb0JNmYMS/p8niDZeXVRZSbj0ZicEjnwPfgMn1Xu8rKktIVRKa+JFImuV4fkGtHJ3vfdzqrwM75JfjBCReFSKpsh1f6HOdSd+HYwAn7t/yxrgo4TCTg61RovM3DvnrMzcMnR6uvZ5wV6dAb+ir/UL2gCmPA8CPBeKcS60pPNgqUM7FLg+YdC+PkWr4KhOFTkiceW6jobKoWWd6+6VB3TNAcCd80CD4rVQVl3QL2gDSK0eBsTNA6bIAYD15gEiqQkICEBsbKxB+1rl485LS2/dT11RUfc3NWvWrEFhYSG8vb1rNXViY2OxatUqnDx5UrR4MjMzkZaWptc+DjI7wF+0EIwuPSMdlYJ4j0OUOzob9eVrVWW49tFQeNw/WJSmDgCkp6dDqyoTZSwiqifXKsCAJxdXlJfqna9NzlkJ3KFnFeDjjEb+LjoNo7CT67ztv1VVqkzyO5LaeRDgudCizoOejQDXe29Wl9zsTKDcwvNAcMUdc5yuecDQHAAAeXk3gSLmgbqImQeMnQMAK88DRDbEKhs7AQEBKC4uRmxsLKKiomqsS0lJwcyZMwEAbdu2rbFOo9Hg5ZdfxqRJkxAZGSlqPPqyF+SANJ4oCwAICgwS/VtKY8o/vAHliWdRkXYF+QfX1lof+eUFOPg20WvMoKAgfkNBZCFyZQVQwVvv/VwdyuDxn4a/pamyc0P2HdZl5t67mNbnip26ONhr4WuC35HUzoMAz4WWdB6stBOQA9yaGV0m031HQYMAbwfYCZadB/IcgDv9pu+VB/S9Yqcu3l4ucHJjHqiLmHnA2DkAsO48QCQ1hvQNbrPKW7EmTpyIFStWoHHjxti9ezdCQ0MBAEePHsWYMWOQnJyMqqoqTJ06FUuWLKneb+nSpfjkk09w6dKl6seji3ErliGqyioQ3WK0SY9ZH6OurYG9s5No45WrgR7bRRvOJGIGAEqrbJUSSc+6v65jxPR9eu935c+n0LKphxEiEk9llQZuXX7Se3Lo21J2jUQjfxekZpWi8UO/6b3/1NGRWPJWF4OOrQ+pnQcBngst6TwoCAI6DP8DZy/n6bXf8IebYe0nfY0UlXg++vYs3llm2OX69c0BAJC9/xn4NjB+08HW84DUcgBgWXmAyJZY5eTJb7/9Nry8vJCSkoLIyEi0adMGISEh6Nq1K8LDw9G9e3cANSdOzs3NxezZszFnzhyo1WoUFBSgoKAAwK1bugoKCqDVSuwrAyIiGzW0b1ME+Oj3oeOhrkEW39QBAAd7O7QNbWC243eM8DHbsYl0JZPJMHFE+L03/A9D9jGHjhH6X5EoliaBLiZp6hARke6ssrHTpEkTxMTEYMCAAXByckJycjJ8fX3x7bffYvPmzbh06RKAmo2d1NRUFBcX46WXXoKXl1f1PwBYtGgRvLy8cP265T8thYiIbjU/1izoDYWdbrdg+DVwwtezuxk5KvF0bedntmN3aWu+YxPp44WhoRjYs7HO2782KhK9ogKNGJF4oiJ9oVDocYuZiMyZf4iIqG5We6FcZGQktm3bVmt5Xl4eMjIyoFAoEBERUb08JCQE+/bVvmy/T58+eO655zB27NhaEy0TEZHlerBLELYsewjD3tyL0nL1HbdrEuiCHcsfRvNG+j9Fy1xeGBqKL34x/hNp/qtXVABCmkjn90S2TaGQ4/dP+2LUzP3YtCf5rttOe7Y1Pplmuqew1VcDD0c83jcYv/+daPJjj3u8lcmPSUREd2e1jZ07iYuLAwCEhYXB0dGxermrqyt69+5d5z7BwcF3XEdERJbr0R6NcXXbMHy38QpWrr+ElMzS6nVtQxtg4ohwjBrYAq7O9maMUn/tw7zxQHs/HD5zp2mUjUMqt6kQ3aZ0UmD9Zw9i7/F0LF97EZv33YBW+7/pJZ8f2hKvDA9Hp9a+ZozSMBNHhJu8sdOyqTse7Bxk0mMSEdG9WeWtWHdzu7Hz79uwiIjIegX4OGPWi+2RuGM4ArxvTWgZ4K3Emd+H4qVhYZJr6tw2fWwbkx6vRWM3PN432KTHJBKDXC5Dvy4NsXFJP+TFjIZ/g1t5INBXie/n9ZRkUwe4dQVdp9amnfPqjWfbQC43zy1gRER0Z2zs3IMgCCZ/IpahvCKaYuD2jzD0wOd46JdZcPR2R0DXSIy+Ho3Buz6Bk3fNy+dbDOuFsRnr0ahfx+plUbPH4KnYFei96g1Th2+QssSzuPjm/Yh/NRwJ7z+CqsIcFMftx6lhzrgwtT2qCm59m53x+wKcf6UVTg6Vo+DE1hpjpP08C/FT2uDC1Pa4MLU98mL+96jHrM1LcP6lEFycFmXKl0VERmBnJ4ednfz//78MMn0egWyBhvYNxlMPBZvseN/P7QF7e8srG3Q993VbPBGPxyzF4F2f4JFN89AgMrh6jPtmPoOhBz7H4N2fYuD2jxDQrXX1uogXB+GJw19g0M5Fpn5pOtH1PJi09HlcmNIWF6a2x8U3OqHo7J7qMXJ3f4/4KW1w8nEFsrd9WWN8azsPerg5QKG49Xcsl3gOkMlk+O79HrBXmOZ92btTICY8aXm3YYlR/9opHdBz+VQ8cfgLPH5wGZoM6Fy9ztJzAKB7Hrjt5t4fcXKIrEZNXJGegCtzHsKF19ohflIk62EiibHZW7Hatm1r5kjE1/3zSTj0+lfIi09CyMi+6DR7DK6u3Y+CK6nY+siMGts6BzZAqzH9kR17ucby2Pk/o+ByCho91BFSkLR0LIKn/ADn5u2Ru/t7pK2eDu++Y6FsHIHwxf97DKh72wfRoPsIJH05rtYY/o9Px/+1d9/hUVRtG8DvLSmb3htJCAFCElroXXqRJog0BcUXsKAgogiIiIglNhCUYgUFRJQiiIg0EaSHTkIN6SQhgfSyye7O9wcf0ZgAu8lsmez9uy4uzZQzz0LmObPPzJxTb/y7AICyWzcQ+0I4XFr1g9LJHb6PvAyH0FZIXfWqyT4TEZG+lr3eGftjMpCdU6r3PhnZxZX+q49pj0fiIQsdVFbfvi9553EcnrkSglaHwD5t0P2LGdjSdRoAIPPYRZxdvBHa0jK4R9bHgE0L8FPUZGjV5Yj7cjtun09A2/lPmusj3pe+/WDgxMVQOrkBAIqvn8GVN/ug5ZosyGQyODRsg9CZPyFj4/tV2mc/aNmah3lg/nOt8MbnJ/XepyY5wMFeiW8WdLXIp3XEuP5t9txQ6MrKsbnzVDgF+WDQ9neRcegCyvKKLD4HAPrnAQAoy05F1s4v4NikY5U2vPpNhlfvCSjPzcTFV9rBKbIrbD3rMQ8QSYDl3XozssOHD0MQBPTv39/coYjKo1kDaIpLcTs2EQAQ/9N+BPVrC9k97uJ0/ug5HJ+/Grqyew8oaumKr5+GQuUEh9AoAIBnz6eQe3wbBG3Vz+TYpAPs/BtW287dC10A0JUUADIZwKntiUgCfDxV2LyoN+ztFHrv027sNgT1/RHtxm7Ta/te7f3xwcvtahqiURnS96XsioGgvZPbs05dgVOg9518DyBt32loS8sAADkXkyFTyGHn4WyaD1ELhvSD/+7rtEW5lZ5Yc2jQEqqgCMjkVndZWCfMntjCoKf3DM0BCoUM6z/oYZEDzIt1/RvySBdcXLUTAFCYchMZRy8iuL9l5r3/MiQPAEDS8mcQNHExZDZ2lZaXJJ6Fa5uBAAAbN184NGiJnL83VNcEEVkgq3tip65yCvaBe3gwhu7+qGKZ0sEeKl/3Kts2ebIfci+nIPv0VVOGKDp1ZgJKks4jbnpUxTJdaRHKc9INbuvmr0txc8cylN9KRf2p30Lp4ilipERExtOtjR9+/awvHpm2B8Wl4hbre7bzx9alfWFvZ5mXC4b0ff8WOWkQUvedAgShyrrGY3qiIDETxem3RY9XbIb2g6nfzUbO4Z+hLcxBw9mbJf86It2hUMix9v0e0On2Y/PeRFHbtlHKsS66B4b2rC9qu2IR6/rXqZ4XilKzKn4uTLkJx3qmHb+opgzJA1m/r4AqqCkcm3Soss6hUTvc/mstfB+ZAXV6PAovHYatTwNjhk5EIrLMKzWqkcwTl7B3/D+PUY+58E2VbZyCfBA2rg92DH3DlKEZjWN4FzR+859p7c+Or9kAiD5DpsFnyDQUJ5xFwuJxcGnZh8UdIpKMPh3r4a9VAzFuzl+4nJgnSpvPjwrHopkdLLaoc5c+fd+/hY7ohpAhnfD7sHlV1vl3bY6Wr4zErtELRY/TWAzpBwOfikbgU9HIP7MHqatnokn0IchtbE0RJhmZna0CGz7qiTeXncIHq85VmvmrpoL8HPHdOw+hZ3vLngVLrOvff/+NSa3oqU8eUGcmIGvXVwiPPlRtGyEvrUbqqldx688o2PmGwqVFH8iU0pxcgMga8ZnbOqIwKRNO/7qzYOOkgkJlh5LMnErbebcNg4OvO4Yf+BSPHV8O79aN0WXR82j8RB9Th1xrdr6hKMtOrvhZW5wPnboYNu41HwfCoUFL2HrUQ8GF/SJESERkOm2beuP0T8Pw6lPNUZvvJPUDnLD3q4ex/I0uFl/U0bfvuytkaGdEzRiFXWMWovRWfqV1vp0i0eXTKdj7ZDTy428YNW6x1LQfdInqA21JAUqSzhs7RDIhpVKO915qiyNrhiAi1K1WbU0e0QQXNj9q8UUdsa5/C9Oy77ye+f8cA71RlJZtmg9RS/rmgaJLR1B++wZiX4zA+ckhKLp8FEmfTUTWrq/utONTHw1n/YzIT8+g4ZzN0BbnQRUUadLPQkQ1x8JOHXE7NhGCTqiYySN8Qn8kbj0MQVN5rJiELX9jQ8vJ2Nh+Cja2n4KsU1dxaMYKXF23xxxh14pDaBRkMjnyz+0DAGTtWA73rqMhUxj2RaQk5WLF/6vT41F8/TTs2ZERkQSp7JX46JX2uLztMcx4shncnPV/GqN9M2+sXvgQLv4yAr06WPaXubv07fsAIGRIJ7SeNRZ/jF5Q5Qubb8cIdPtsKvZN+BA5cUkmiV0M+vaDglaD0hv/vH5SdOU4NHk3YecXatJ4yTTaN/fGmZ+HYcNHPdG9rZ/e+zk52OC5keE4u3E4vpzfFS5Olv80l1jXv0m/HkHE0wMA3Hm6x69TJJJ3VR502FLpmwc8uj+Olt9loPlXiWj+VSIcm3RE/anfwLvfZABAeW4mhP9/PTXv1B8oSYmDx0OPm/bDEFGNWfatODLIwamfocunU2Dr7ICCxAwceHEp3JsEmzssowp5eQ2Slj6N5KI82Pk3QoMZa1GSdKHKdukb30fWjmXQ5GUhcckEyG3tEbH4NGxcvZH2/Wyo069BprSBTK5E8LOfQxUUYYZPQ0Qkjsb1XfHJqx2w8IU22Hf8Bk7GZeNk3C1cScpDiVoDpUIOdxc7RDXxQJtIL3Rp5YsWYR7mDrtG9O37Hlr2Ekpu5qL3qn9myflj1AKocwrR5ZMpUNjaoOviKRXrDkz9DLmXkqu0Y2n06QcFnRaJSyZAW5wHmVwBuZ0jQmdthNLpzjgkt/avRdr3s6EtzEHusa3I2BSNRm/8CofQVub4SCQCWxsFRvUPxaj+obickIuDpzIRE5eN0xdv4VZeKco1OtjbKhEa6Iw2kZ5oE+mF3h0CJFHM+S8xrn8vLN+KLoun4NHDn0HQCTg652uU5RYaKWLx6Xs9fD95x39FxqZoQKGEjbs/Gr+5A3I7lZEiJiKxsbBTh+RcTML2/rMevOG/7Bwx30jRmIZDSAtELHrwFJ/+j82B/2Nzql3XaO5WscMiIrIIDiolBncPxuDudbfIr2/f933wmHuu29xlqpghmZQ+/aDcxg7hH1Q/rgYAePYYB88e48QOjSxEkwZuaNLADZNGNDF3KEYhxvWvpkSNv55bLGZYJqXv9fC/NXl3f6WfvfpNgle/SSJGRUSmxFex6jhtuQYqL1cM3f0R7D0fPE1l23nj0XzqcJTlFZkgOuOQKW1RnpuJuOlRKM+9Wau2MrcuRvLKKVC6SGNmBCIiMrzvu5/IZwajY/QkqG/nP3hjC8F+kKydtecAgHmAyNrIBKGauT7J7MqLS7GuoXTunj0RvxY2DvaitVeiAbrtEK05kzg4EFDxGTgiixbYZz3Sbhajno8DUveMNXc4dB9S6wcB9oVS6QeZB6TD2vOA1HIAIJ08QFTX8IkdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIojrFjoQRBgKZEbe4w9KZU2UEmk4nWniAApVrRmjMJewUg4l8BERkBx9aQDqn1gwD7Qqn0g8wD0mHteUBqOQCQTh4gqms4tJWFkslkog7AKDUyGQdeIyKyZtbeDwLsC4msPQ8wBxCRvvgqFhERERERERGRRLGwQ0REREREREQkUSzsEBERERERERFJFAs7REREREREREQSxcIOEREREREREZFEsbBDRERERERERCRRLOwQEREREREREUkUCztERERERERERBLFwg4RERERERERkUSxsENEREREREREJFEs7BARERERERERSRQLO0REREREREREEsXCDhERERERERGRRLGwQ0REREREREQkUSzsEBERERERERFJFAs7REREREREREQSxcIOEREREREREZFEKc0dAFVPEARoStTmDkNvSpUdZDKZaO0JAlCqFa05k7BXACL+FRCRlZNaPwCI2xewHyBrZ+05AGAeICLSFws7FkpTosa6huPMHYbenohfCxsHe9HaK9UC3XaI1pxJHBwIqHhGEZFIpNYPAOL2BewHyNpZew4AmAeIiPTFV7GIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIpjthMRUZ11O0+Nw2cyERObjVMXb+Hm7VIAQNbtUoyeuQ9tIrzQtqkXOrX0gcqeXSJRXSMIAs5evo0TF7Jw8uItXLye+08eyCnFS9FH0LapFzq28EHj+q5mjpaIiKhmeBVLRER1iiAIOHYuC8s3XMRPuxKgLtNW2aZMo8NPfyTgpz8SAADuLrZ4elgYnhsZzi93RHVAfmEZ1my/huUbLiIuPrfabcrKdVj6Q1zFz11a+WLKqAiM6BsCO1uFiSIlIiKqPRZ26hC/Tk0xYPOCSsvKi0qQfz0d8RsP4OI3OyBodWaKzvgKzu/HlTd6Vlomt3eEXUAYPHuMh8/gqZAp+CtPVJelZBTimQWHsPNQqkH75eSXYdH3F7Do+wt4bmQ4PpzRDs6OtkaK0rjYF7AvsGaCIOD7bdcw/cOjyC0oM2jfQ6czceh0JuYsdcRX87uiX+dAI0VpXMwBzAFEZH2Y1eqghF8OIWVPDCCTQeXthkYju6P9gglwbVwPR2Z+Ye7wjM6962i4thsMCALKczJwa//3SP12BkpTL6L+C1+aOzwiMpJVv1zB9A+PIr+wvFbtrPz5En4/lIpVb3dDz/YBIkVneuwL2BdYm4zsYkx662/8diClVu0kpxeh/3N/YPKIJlg0swOcHGxEitC0mAOYA4jIenDw5DrodmwCrm86iOsbDyB2xTb8Nuh1FKZlIezx3rDzdDF3eEbn0CAKnj3GwbPnePg9OhPhHx6FjVcQsnd/jfK8LHOHR0QiEwQBc5fG4H9vHqx1UeeupBuF6PfcTqzfES9Ke+bAvoB9gTW5npqPzuO317qo829fbbqMPpN/x+08tWhtmhJzAHMAEVkPFnasgKZEjexT1yCTy+FS39fc4Zicwt4RjmEdAEGAOkO6X9KIqHpvLjuF974+K3q7Go2Aca//hY27EkRv2xzYF7AvqKtSMgrRc+IOJKQViN72sfNZGPD8ThQUGfZalyViDmAOIKK6i4UdK+EccqcDL80R/6JHCu524EpnTzNHQkRi+nlXAt758ozR2tfpBDwxZz/i4nOMdgxTYl/AvqCu0Wh0ePTlvUhOLzLaMU5cyMbkBX8brX1TYg5gDiCiuolj7NRBCpUd7DycK96pbvJkP3g2D0XW6asoSMgwd3hGp1MXQ5OfDUEQoMnJQNbOlSi5fhoOjdvBPqCxucMjIpFk3S7BlHcPG7TPifVD4eflgIzsYrQbu02vfcrKdZgw7wAOfz8ESqV07oewL2BfYA0+Wn0eMbHZBu1TkzywYWcCRvZNwIi+DWoSplkwBzAHEJH1sIrCTnZ2NqKjo7FlyxakpaXB29sbo0aNwjvvvIPJkydj3bp1+OqrrzBp0iRzhyqKqBkjETVjZKVlSb8fw9FZX5kpItNK/2kh0n9aWGmZW4dhCH5+hZkiIrIMOp2AgqJyCIIAFydbyOUyc4dUKy99cBTZOaUG7ePn5YBAX0eDj3XiQjYWr7mAmU+3MHhfc2FfwL6gOmXlWhQUlcNRpYS9nbQvAy8n5OKtFacM3q+meeD5dw6jV4cAuLvYGbyvOTAHMAcQkfWQdo+uh3PnzqFfv37IzMyEo6MjIiMjkZaWhkWLFiExMRHJyckAgJYtW5o5UvFcWbcHCVsPQa5UwK1JMJpPHQaVlxs0pf+8H97jy1cgCAL+enZRxTJbNycM278YMW9/j+ubD5ojdFF49pkIj25jIOg0KEm6gIxN0SjPzYTcVlWxTUHsQVx7++Eq+wqaMgg6Ldps0ZoyZCKjupyQixU/XcLqbVeR9//T/zo72mD84EaYMjoCTRu5mzlCwyWkFuDHnddNesxFay7gpXFNYWujMOlxa4p9AfuCu8rKtdi8JxHLN1zEwVOZFcvbRHphyugIjBkQCgeV9C4JF6+NRVm56abtzsopxeqtV/Hy+GYmO2ZtMAcwBxCR9ZDOM+U1kJ2djcGDByMzMxOvvfYaMjIycOrUKWRmZmLhwoXYvHkzzpw5A7lcjmbNpNFJ66MgMQPpB88j7c8ziF25DXvHvw+v1o3QKXpyxTZHZn0B3w4RaDCsS8Wyju9Nws3jlyTdiQOAvX8juET1gWvrAfAb/ioazduOoqvHkLTy+YptnJt2Q6sNhZX+NF1+BUpnLwQ8vvA+rRNJx93ZosIf2YQl62IrijoAUFBUjuUbLqLZo5sx46Oj0OkEM0ZquC82XoJg4pAzskvwy74k0x60FtgXsC8AgKtJeWg6fDPGztpfqagDACfjsjFx/kE0HvIzTl807HUmc8srKMPa7ddMftwVP12UTL5kDmAOICLrUacLO9OmTUNKSgpmzJiBDz74AE5OThXr5s6di/DwcGg0GoSFhUGlUt2nJWnLOnUV1zcdROij3eDd+s47xeqcQhyasRwd3p0Ela876g/qCL/OTXFk1hdmjlZ8Tk06wqP7OOQcWI/Cy0er3UZXrsb16EfhFNkV/iNfN3GERMYx85Pjes0WtXhNLF549zAEU1dKakir1eGbLVfMcuyvNl02y3HFwL7A+vqCxLQCPPT0b7iWnH/f7W7cLEaPiTtw/sptE0VWez/uvI6iEo3Jj3s1KR9/xaSb/LhiYA6wvhxARNajzhZ24uLisGHDBvj4+GDhwqoVd5lMhjZt2gCo/BrW/v37IZPJqvyJiooyVehGcXbxRug0WrR6bUzFsrR9p5G0/Qge+nwaOkZPxuFXVkCdU2jGKI3Hf/Q8QK7AjXXzql2fvPw56MpLEfLSatMGRmQke46m4ZPvL+i9/cqfL2Hb/mQjRiSeK0n5Bo+tI5YjZ29CqzXdqx9iY19gXX3B/948iIzsEr22zS8sx9hZf0qmwPv3afMN/nvoTOaDN7JQzAHWlQOIyHrU2cLOunXroNPpMG7cODg4OFS7jZ3dncHvqhtfZ9myZThy5EjFnzVr1hg1XmMrSMxAwtZDCOjeEj4dIiqWn3jrOzg38EPavtNI3Wv4AIRSYe/fCB7dxqDg7B4UxFZ+tPjmr0uRF7MdDef8Arld9b8rRFKz7MeLNdgnzgiRiO9knPleGSkq0eBK0v2ffrBk7Auspy+Ii8/BnycMe7IkNj4XB05KY7akk3G3rPLYtcUcYD05gIisS50t7OzduxcA0LNnz3tuk5qaCqD6wk5kZCQ6duxY8ad58+bGCdSEzi3ZBJ1Wi1YzR1cs05SoUZh0EzkXpXGnvjb8Rs4F5HLc+OHNimUF5/5E6vezEDrzJ9j5hpgvOCIR3bhZVKOnb3YfuYH4FMsvWpy5ZN4vVacvSvdLHcC+wFr6gi831uy1wZU/XRI5EvGVlGpwKSHXbMc3dw6qLeYA68gBRGRdZIJUnrk1kL+/PzIyMnD+/PlqB0ZWq9Xw9fVFXl4eUlNTUa9ePQB3XsXq2bMn/vzzT/To0UOUWNq2bYuMDMPugNkIcszXtRfl+A8yYNMCpOw+idiV22rcxgL5cZTLxHs9QWargu+nV0VrrzrqzERcerUd/MfMh8+gF2vdXub0xhDK9HvknciY1MoQZLs8XaN9PQrWQVVunvFr9JXjMBTF9m2qXXdi/VD4ed37TquflwpKhRware6Br6hkZBej3diqedG16Dc4qY8bFnQNmLIfACyvLzBFPwCI2xdYUj+Q7fwk1DYNDd5PqcmAb75lTwetlTkiw/21e64XKw/cKwfIdCUIyI02LOgasPYcAPB6kIisi5+fH2JiYmq0r/TmttRTUVERAKC0tPpxGNauXYu8vDx4enpWFHX+bfTo0cjOzoaXlxeGDh2KDz74AB4eHjWKJSMjA2lpaQbtYytTAL41OpxZ3Ei/gTJBvCkh5XYORv34OnUx4t8fBtf2Q0XpxAHgxo0b0KmLRWmLqFac3ACXmu16+3YekG9YvjK5wFLAvvpVfl4OCPR1fGATSoVcr+2qk5eXj7xbxv87klo/AIjbFxi7HwDE7wssqh8I1QI2hu+m0cLgaxaTU7oC7vdebew8IAgyk/wdWXsOAHg9SESkrzpb2PHz80NBQQFiYmLQtm3bSutSUlIwZ84cAECLFi0qrXN1dcWrr76K7t27w8nJCUeOHMH777+PY8eO4cSJExXj8hgai6FsBDkgofE5A/wDRL9DY0w5hzehJOEsStOuIOfvDVXWN/08DrbewQa1GRAQwDs0ZBHKFA7IquG+Xu72sHOuWuy2JLkqWxTdY11G9v0vpg19Yqc6bq6OcLQ3/t+R1PoBQNy+wNj9ACB+X2BJ/cAtGw1qMsS4rVwN72pueFkSnUyF+40eJFYeuFc7cpkW/ib4O7L2HADwepCIrEtN6gZ31dlXsaZMmYIVK1YgKCgIe/bsQVhYGADg6NGjGD9+PJKSklBeXo7p06dj8eLF923r119/xdChQ7Fq1SpMmDDBBNED5cWlWNdwnEmOJYYn4tfCxuEet9BroEQDdNshWnMmcXAgoKqzpVKSEq1Wh8aDNyIhrcCg/fy8VEj+YwxsbCx7+LWPV5/HzEU1exUqZfcYBPo6IjWzCEF9f6xRG/u+fhg92wfUaF9DSK0fAMTtC9gP1M4Pv8XjiTn7Dd5v8cwOmD6+6ivslkQQBHh2W4uc/LIa7V/bPNCxhTeOrB1ao2MbwtpzAMA8QESkL8u+eq+F2bNnw93dHSkpKWjatCmaN2+ORo0aoVOnToiIiEDXrl0BVD9w8n8NHjwYTk5OOHHihLHDJiKqNYVCjudGhhu83zMjwi2+qAMAbSI9zXr81hFeZj0+kT5G9A2Bt7thX7BV9go89UhjI0UkHplMhjaR5jsPzXlsIiKi6lj+FXwNBQcH4+DBgxg4cCDs7e2RlJQEb29vfP3119i6dSsuXboz64M+hR3gzt0hmUxmzJCJiEQz+bEmCA101nv7QF9HvDAm4sEbWgBzFlYaBbvA1dnWbMcn0pedrQJvv9DaoH1mPd0C7i6Gv3JuDm2bsrBDRER0V50t7ABA06ZN8dtvv6GgoAD5+fk4cuQIJk6ciJycHKSnp0OpVCIyMvKB7Wzbtg1FRUXo0KGDCaImIqo9dxc7/L68P4L8HjwwqJ+XCr8v7wcfT+OPaSIGV2db9OtsnjFARvZtYJbjEtXEc6MiMO/ZKP22HRmON59rZdyARPSYmc5FO1sFhnQ3bMwVIiIiY7PKN0DPnz8PAAgPD68yGPL48ePRuHFjREVFwcHBAUeOHMGHH36I1q1bY/To0eYIl4ioRsJCXHF07RDMX34K63bEo6S08kwldrYKjH04FAumtEawv5OZoqyZKaMjsOuwaWfukcmAZ0c2MekxiWrr7RfaoGlDd3zw7TmcvnSryvrwBq545cnmmPhomKSeTG4T6YX2zbxx/EJNh4qvmVH9GsDLwFfciIiIjM2qCzvVvYYVGRmJH374AR9//DFKS0sRGBiIZ599FvPnz4etLR+/JyJpCfBxxFdvdcNHM9rjp10JePXjYygo1sDVyQbxO0bB002aX1AGdQtC/QAnJN0oNNkxBz8UjPoB+r/eRmQpRg8Ixaj+DXD8fBb2HL2B6G/PorBYAy83O8T9MkJSBZ1/e2FMBI6/YdrCjlReWSUiIutSp1/Fupf7FXbmzJmD8+fPIz8/H2VlZbh+/To+/vhjODvzYp6IpMvNxQ7PPBYOF6c7BWonBxvJFnUAQKmU47PZnUx2PJW9Ap+82t5kxyMSm0wmQ4cWPpj7TBRc/z8P2NkqJFvUAYAnBjVE5ygfkx3vqaGN0aGF6Y5HRESkL6t+YqdFixZmjkRc7pH10fnj52DjpEJRahYOTP0M7mFB6LPudeTH38CuMQtReisfAzYtgGM9L5QXFAMArv38F+K+3A4AaDtvPEIe6Yzs09ewf/In5vw4eilOOIukZZOhKymArXd9hLy8BqXJsbj69kDY1wtD47d2wcbNB+k/v4db+76DOv0qGs7dBrd2g6u0VXB+P6682RtBk5bAZ9CLAIDMrYuRtWMZFI5uiFgUY+JPR0T3M6RHMMYNboi12+ONfqz3prZF4/quRj9OTeib+7ssmgKfdk2gLS1DWWEJjr/xLW7HJgIAWs0ei+B+bSFodQCAc59tQeK2wwCAyGcGI3xCf5TlF2P7gFnm+pj3pG8/kLjkaRTHnwTkcsgUNqj3ZDRcWvYGAKSteR25R7dAprSFzMbuzroWvQCwH7BkCoUcq95+CC1HbkGpWvvgHWohwMcBi1+zzLEW9c0BdzUc2R3dlk7FnvHvI3XPSQBAozG90PSZQXBtHIjjb67CpVU7K7avKzng8tweKMtKhsLBBQDg2fMp+D7yMgBApy5G4mcTUXz1BCCXo96T0XDv9CgA5gAikgarLOwcPnzY3CEYRddPX8Shl5fhdmwiGo3phXbzxuPahv3IvZJapSM+9sa3FZ35v8UsXIPcyykI7NvGVGHXSuKSCQiZtgoOoVHI3vMt0lbPhGevCVAFRVbqfF1a9IZH19FI/Hxite1oiwuQ+t0suLZ+uNJy30dehkNoK6SuetWon4OIambJrE44dj4LV5PyH7wxgIzs4kr/1ceALoGY9kTTGsVnCvrm/uSdx3F45koIWh0C+7RB9y9mYEvXaQCA2OVbcTp6PQBA5euO4QeX4MZfZ1GWV4S4L7fj9vkEtJ3/pFk+34Po2w8ETlwMpZMbAKD4+hlcebMPWq7Jgkwmg1NkN/iPmge5nQrFCWdxZW4PtFidDrmtPfsBCxcW4oolszri2bcP6b2PoXlAqZThu3cestgZwwy5/nPw90CT8f1wM+ZypeW3zsVj/7OL0Hzqo1Xarys5AACCJi+t9uZexpaPIbexQ7MvrkGdmYBLr3WCc/OeUDq5MwcQkSRY5atYdZFHswbQFJdW3H2N/2k/gvq1hUxZd/+Ji6+fhkLlBIfQKAB37rzkHt8GQaupsq1jkw6w8294z7ZSv50Bv+EzoXThFKZEUuLhaofdXwxA/QD9Bn9uN3Ybgvr+iHZjt+m1fbfWvtj4SS/I5Zb5uoohuT9lV0zFEzlZp67AKdD7zojQAMry//mCa+OkAgQBMgv9zP9mSD9wt6gDANqi3EqvILm2eRhyuzuzwqnqN4eg00KTn23U2Ek8zzwWjvemtdV7e0PygEIhw7r3e6BPR/PMxPcghl7/df7oORyfvxq6ssrnSE5cEvKupgE6nbFDFpUhOeB+cv7eAO+BLwAA7HwbwLnpQ8g9tlXscImIjMYqn9ipi5yCfeAeHoyhuz+qWKZ0sIfK173a7du+OR5tXn8cuVdScfK9dShMvmmqUEWjzkxASdJ5xE2PqlimKy1CeU66Qe3knfwdmqJcuHd5DHkx20WOkoiMrX6AM/5ePRiDp+7C2cu3RWv3kZ7B+CG6JxxUlttVGpr774qcNAip+04BglCxLGLiQIRP6A+HAE8cenk51DmmG5i6pgztB1K/m42cwz9DW5iDhrM3Vzu+zK29q2Dn1xC2XoHGCpuMYM6klnB2tMHLHx2FRiM8eAc9ODvaYO173TG0Z31R2jMGQ3JAkyf7IfdyCrJPXzVliEZlaA5IWz0TaWvmQBUYiXpPvg87v1AAQFlWMmy9//l3tvUJQVlWslFjJyISk+VerZLBMk9cwt7x71f8PObCN9Vud2DqUhTfuDPlafjTA9BnzRz80v1lk8QoNsfwLmj85m8VP58d723Q/prCXKR9PxuNF+wWOzQiMqFAP0cc/2EoFn5xBu9/cxZabc2/2Lk42eDT1zpiwiONJTGwrL65/67QEd0QMqQTfh82r9Lyi9/swMVvdsA9sj4e+nwa0g+ek0Rxx5B+IPCpaAQ+FY38M3uQunommkQfgtzmnxkv88/uxY0fFyCMfYIkvTg2El1b+WLCvAO1LvL27RSAr9/qhmB//Z4GNCd9coBTkA/CxvXBjqFvmDI0k9A3BzSYvga23kEQBAFZO5bh2sLBaLos7p8N/p3vBXGKg0REplJ339OxMoVJmXCq989rRDZOKihUdijJzKmy7d2iDgBcWrUTzsG+sHO3/AuX/7LzDUVZ9j93U7TF+dCpi2Hj7q93G6XJF1Cek45LM9vj/OQQ5BzeiBvr5+PGD/ONETIRGZGtjQILX2yD4+uG4tHeIVAoDCvKOKqUeOaxJojdPAJPDwuTRFHHkNwPACFDOyNqxqgqg6n+W05cEoozbsOvs+WOK3RXTfsBl6g+0JYUoCTpfMWyggt/IXHp02j0xq+wD2xitJjJuKLCPXH8h6H45NX2aFDP8BlNWzbxwPfvPoQ/Vg6QRFFH3xzg3TYMDr7uGH7gUzx2fDm8WzdGl0XPo/ETfUwdsqgMyQG23kEA7swQ5zPoRagzr0OTf+v/1wWj7GZixbZlWUmw9Q42bvBERCLiEzt1xO3YRAg6AX5dmiHj0AWET+iPxK2HIWgqvystU8hh5+6M0uw8AED9QR1Qkp0nibuy/+UQGgWZTI78c/vg0qIXsnYsh3vX0ZAp9P+1dorsipbf//MaWuKSCXBo1LZiViwikp7WkV7YtLg3UjOK8PXmy/jzRDpOXbyFwuLyKtt6u9ujTaQXBnYLxJNDGsPV2baaFi2XvrkfAEKGdELrWWPxx+gFKEqrPH6Ma+N6d8bXAOBc3xcezRog90qqST5DbejbDwhaDdSZCbAPaAwAKLpyHJq8mxWvYRTEHkDC4vFoNHcrHBq0NPnnIHHZ2igw48nmeOmJpvjjcBrW/RaPE7HVD7KuUMgQGeqGDs298fSwMHRq6SOJou5d+uaAhC1/I2HL3xU/D9i0ABdWbKt2Ig0pMSQHaApuwcbNFwCQc3gTbNx8oXTxBAC4dxmJrB3L4PjSaqgzE1Bw4S8EP7/S5J+HiKimWNipQw5O/QxdPp0CW2cHFCRm4MCLS+HepPLdBoWtDfqsfR0KWyUEnYDS2wXY+1S0mSKuvZCX1yBp6dNILsqDnX8jNJixFiVJF6psl77xfWTtWAZNXhYSl0yA3NYeEYtPw8bVsFe3iEg6Av0c8daU1ngLgE4n4FpyPm7eLkFZuQ72dgoE+Tki0NdRUl/iqqNP7geAh5a9hJKbuei96p9Zcv4YtQDqnEK0eX0cnBv4QSjXQKfV4ejr31QUeiydPv2AoNMicckEaIvzIJMrILdzROisjVA63RmHJOmziRDK1Uhc+nTFPg2mr4EqpLlJPwuJS6GQY2C3IAzsdudJjbyCMlxOzENxqQYyGeDsYIOIUDeo7KV9OaxvDrif0BHd0Ob1cbB1c0RQ/3Zo/uJw7H0qGrcvJBgpavHokwN05Wpce3sQdBo1ZDI5lC5eaDj3n8GzfYfPRNLS/+HCs40AuRzBzy6D0tnD1B+FiKjGpN2TUSU5F5Owvf+s+26jKVFXmfpSyhxCWiBi0YPvNvk/Ngf+j8154HYhL60WISoisjRyuQxhIa4IC3E1dyii0yf3A8D3wWPuuW7f0x+IGZJJ6dMPyG3sEP7BvafDbray7gwmS/fm6myL9s3r3g0dfXPAv+0cUfmV8+ubDuL6poNihmUy+uQAhb1jlanP/7s+9LUNYodGRGQyHGOnjtOWa6DycsXQ3R/B3tPlgdu3nTcezacOR1lekQmiMw6Z0hbluZmImx6F8tzazfaVuXUxkldO4TToRCQphub++4l8ZjA6Rk+C+nb1Y/JYIvYDZO2YA5gDiMi6yASBw75bovLiUqxrOM7cYejtifi1sHGwF629Eg3QbYdozZnEwYGABc+KTAQACOyzHmk3i1HPxwGpe8aaOxy6D6n1A4C4fQH7AeNhHpAGa88BAPMAEZG++MQOEREREREREZFEsbBDRERERERERCRRfBXLQgmCAE2J2txh6E2pshN1ZhlBAEq1ojVnEvYKQOKT65AV4CsY0iG1fgAQty9gP2A8zAPSYO05AGAeICLSF98AtVAymUzUd5SlRibj+8lEZN3YD7AfIOtm7TkAYB4gItIXX8UiIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIqFHSIiIiIiIiIiiWJhh4iIiIiIiIhIoljYISIiIiIiIiKSKBZ2iIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJEpp7gCoeoIgQFOiNncYelOq7CCTyURrTxCAUq1ozZmEvQIQ8a+AyKpJLQcCzIMA8yCJy9rzAHMAERHpi4UdC6UpUWNdw3HmDkNvT8SvhY2DvWjtlWqBbjtEa84kDg4EVDyjiEQhtRwIMA8CzIMkLmvPA8wBRESkL76KRUREREREREQkUSzsEBERERERERFJFAs7REREREREREQSxcIOEREREREREZFEcXgzIqI6rqxciwtXcxAbn4PC4nIAQHGJBjGxWWje2AN2tgozR0hExpZ5qwQn47KRlllUkQdK1BqkZRYhwMdB1BndiIiIyLRY2CEiqoMKisqwdns8vtt2Facv3UJZua7S+pyCMrQbuw02SjlaNvHA+MGN8OSQRnBzsTNTxEQktlNx2Vi+4SJ2HkpF2s3iKutv55UhsO+P8PVUoXeHADw/KhxdWvmyyENERCQxLOwQEdUh+YVlmL/8FL7efKXirvz9lGt0iInNRkxsNuYsicHTwxrjnRfbsMBDJGH7T6Rj9qcncOx8ll7bZ94qwQ874vHDjni0CPPAwhdaY2jP+kaOkoiIiMTCwk4d4tepKQZsXlBpWXlRCfKvpyN+4wFc/GYHBK3uHntLX8H5/bjyRs9Ky+T2jrALCINnj/HwGTwVMgV/5anu2nM0DRPnH0RyelGN9i8u1WDZjxexZV8SvprfFQO7BYkcofExDzIPWrPC4nLMWnwCyzdcrHEb567cxiMv7cG4wQ2xZFYneLhKr8jLPMA8QERkbZjV66CEXw4hZU8MIJNB5e2GRiO7o/2CCXBtXA9HZn5h7vCMzr3raLi2GwwIAspzMnBr//dI/XYGSlMvov4LX5o7PCLRCYKAt1eexlsrTovS3o2bxRj0wi689nRzRE9vJ8nXMpgHmQetTdKNAvR79g9cScoTpb212+Px5/F0/LFyAJo2chelTVNjHmAeICKyFpwVqw66HZuA65sO4vrGA4hdsQ2/DXodhWlZCHu8N+w8XcwdntE5NIiCZ49x8Ow5Hn6PzkT4h0dh4xWE7N1fozxPv8fSiaRkzpIY0Yo6//bhqvOYFn0UgiCI3raxMQ8yD1qTxLQCdH3qN9GKOnel3SzGQ0//hgtXb4varqkwDzAPEBFZCxZ2rICmRI3sU9cgk8vhUt/X3OGYnMLeEY5hHQBBgDoj3tzhEInqsx9i8cG354zW/ufr4xD9jfHaNxXmQebBuiq/sAz9ntuJ1MyavYL5ILfz1Oj33B/IyK46+LLUMA8wDxAR1VUs7FgJ55A7FzClOQVmjsQ87l7AKJ09zRwJkXguJeRi5qITBu1zYv1QpOwegxPrh+q9z5vLT+LMpVuGhmdxmAeZB+uiVz85jqtJ+QbtY2geSM8qxnMLD0ny6b3/Yh5gHiAiqousorCTnZ2NV199FQ0bNoS9vT2CgoLwyiuvoKSkBOPGjYNMJsPXX39t7jBFo1DZwc7DGXaeLnALD0aH9ybBs3kosk5fRUFChrnDMzqduhia/GyU52WhJPE8kle+gJLrp+HQuB3sAxqbOzwiUWi1Ojw97wDUZVqD9vPzckCgryP8vBz03kejETBh3gGUlRt2LHNiHmQetAa7Dqfiq02XDd6vJnlg65/JWL/jusHHMifmAeYBIiJrUecHTz537hz69euHzMxMODo6IjIyEmlpaVi0aBESExORnJwMAGjZsqWZIxVP1IyRiJoxstKypN+P4eisr8wUkWml/7QQ6T8trLTMrcMwBD+/wkwRkbmVlWuxeU8ifjuYgryCcjioFGgT4YWnh4XBy93e3OHVyMbdiTh6znRjJJy9fBtrt8fjf8PDTHbM2mAeZB78r+up+fh60xVcScqDRquDj4cKo/uHolcHf0kOEC4IgsFP7NXWa4uPY1T/BlAqpXFfkHmAeYCIyFrU6cJOdnY2Bg8ejMzMTLz22muYN28enJycAADvvPMO5s2bB6VSCblcjmbNmpk5WvFcWbcHCVsPQa5UwK1JMJpPHQaVlxs0pWUV2/T48hUIgoC/nl1UsczWzQnD9i9GzNvf4/rmg+YIXRSefSbCo9sYCDoNSpIuIGNTNMpzMyG3VVVsUxB7ENfefrjKvoKmDIJOizZbpPNkAt2bIAhYui4W739zDpm3Siqt27AzAfOWncK4QQ3x6ayOcHKwMVOUNVOb6YxratmPcXh6WGNJfAlmHmQevCvpRgGmvHsYv/+div++SfTVpstoEuKKD19uh6E965snwBo6fOYmzl0x7aDGaTeL8etfyRjeO8Skx60p5gHmASIiayGNWy41NG3aNKSkpGDGjBn44IMPKoo6ADB37lyEh4dDo9EgLCwMKpXqPi1JS0FiBtIPnkfan2cQu3Ib9o5/H16tG6FT9OSKbY7M+gK+HSLQYFiXimUd35uEm8cvSfoiBgDs/RvBJaoPXFsPgN/wV9Fo3nYUXT2GpJXPV2zj3LQbWm0orPSn6fIrUDp7IeDxhfdpnaRCEAS89MFRTP/wWJWizl3qMi2+2XIFPf63A3kFZdVuY4kuXL2NAydN/xrBqYu3cPy8NGZSYR5kHgSAK4l56DjuV+w4WLWoc9flxDwMm74H32w2/JUmczJHcRcAlv1onuPWBPMA8wARkbWos4WduLg4bNiwAT4+Pli4sGrHJJPJ0KZNGwDVv4a1ZcsWdO7cGY6OjnB1dUWXLl0QGxtr9LiNIevUVVzfdBChj3aDd+s771SrcwpxaMZydHh3ElS+7qg/qCP8OjfFkVlfmDla8Tk16QiP7uOQc2A9Ci8frXYbXbka16MfhVNkV/iPfN3EEZIxLPvxIj77IU6vbU/GZePx2X8aOSLx/HYwxSqPXRvMg9aXB4tLNHh4yh/IyK6+sPtvggA8u/AQDsSkmyCy2hMEwWzn4p8n0lFUXG6WY9cW84D15QEiImtRZws769atg06nw7hx4+DgUP3ggHZ2dgCqFnaWLl2KUaNGoWvXrti2bRvWr1+PPn36oKTkwReHlurs4o3QabRo9dqYimVp+04jafsRPPT5NHSMnozDr6yAOqfQjFEaj//oeYBcgRvr5lW7Pnn5c9CVlyLkpdWmDYyMorxch/e/OWvQPjsOpuL0xWwjRSSuk3Hmm6HqZJw0/o6qwzxoXXlw/e/xuJ6q/8xHWq2A6G/PGTEi8cSnFJjtKUOdTsBZE78CJibmAevKA0RE1qLOFnb27t0LAOjZs+c9t0lNTQVQubATHx+PmTNnYvHixfjwww/Ru3dvDBw4EAsWLEDbtm2NG7QRFSRmIGHrIQR0bwmfDhEVy0+89R2cG/ghbd9ppO49ZcYIjcvevxE8uo1Bwdk9KIit/Gj1zV+XIi9mOxrO+QVyO/1nCCHLtW1/Em7cLDZ4vxU/XTJCNOIzZ3HlZNwtyU55zDxoPXlQEIQavTK081Aq4lMMmzrcHMxdYDX38WuDecB68gARkTWps4WdpKQkAEBISEi169VqNY4dOwagcmHn22+/hY2NDSZPnlztflJ2bskm6LRatJo5umKZpkSNwqSbyLmYbMbITMNv5FxALseNH96sWFZw7k+kfj8LoTN/gp1viPmCI1HtPJRao/1+/7tm+5lacob57iRn3iqBRiPNwg7APGgteTA7pxSnLxn+ZJsgALsOpxkhInElp5v3aZLk9CKzHr+2mAesIw8QEVkTmSDVW68P4OLigoKCApw4caLaJ22++eYbTJo0CZ6ensjO/ufOU8+ePZGfn48pU6bgnXfeQUpKCho3bow333wTY8eOrVEsbdu2RUaGYQOd2ghyzNe1r9HxDDVg0wKk7D6J2JXbatzGAvlxlMt0osUks1XB99OrorVXHXVmIi692g7+Y+bDZ9CLtW4vc3pjCGXSfV2vLrntOBIldobPdCcT1AjIec8IEYlHgAw3PN665/oT64fCz+ved1r9vFRQKuTQaHX3HXskI7sY7cZWnxP8b78LOYz7GogpcyDAPFjX8qBG7oFMt5dqtK9L8W44l/4tckTiyrd/CAUOvatd96AcANQ+DziWHoNb8Q7Dgq4Ba88DpsgBgLh5wFJyABGRFPn5+SEmJqZG+9bZ6c79/PxQUFCAmJiYKoWdlJQUzJkzBwDQokWLSuvS09ORlpaGOXPm4IMPPkBQUBC++eYbPP744/D29kafPn0MjiUjIwNpaYbdAbSVKQBfgw9lNjfSb6BMEG9KTLmdg1E/vk5djPj3h8G1/VBRvswAwI0bN6BTG/76DxlBYB5gZ/hugqbE4HPVLNy1gExR7So/LwcE+jo+sAmlQq7XdtVJv5ECCJoa7asvqeVAgHkQsKA8qCwE3Gq2a35uFvJvWXge8MoB7lG70TcHADXPA0UFeSjKMP7fkbXnAWPnAED8PGAxOYCIyMrU2cJOnz59cPXqVbz33nvo1asXwsLCAABHjx7F+PHjkZubC6DqwMlarRaFhYVYs2YNhg0bBgDo3bs3YmNjsXDhwhoVdvz8/Azex0aQA+Ld+DW6AP8A0e9UG1PO4U0oSTiL0rQryPl7Q5X1TT+Pg613sEFtBgQE8C6VhSi0zUFeDfazFzLgWa+e6PGILV0ohk7mXO26jOz7X1Abcqe+OjKhFP4BvpDpH26NSC0HAsyDgOXkQQEyZGpzoVW4GbCTAMhk8HYshK29ZeeBIlslcu+x7kE5AKh9HnBxlMHZBLnS2vOAsXMAIH4esJQcQEQkRTWpG9xVZ1/FSk5ORlRUFHJycqBUKhEeHo6SkhLEx8djyJAhKCwsxJ9//olVq1ZhwoQJFft17NgRx44dQ0FBAZycnCqWT58+HWvWrMGtW6aZjaa8uBTrGo4zybHE8ET8Wtg42IvWXokG6Gb8p7xFdXAgoKqzpVJpyS8sQ70+61FYbNhTJb8v748BXQONFJV4Br3wB3YcrNl4QCm7xyDQ1xGpmUUI6vujwft3b+uH/d8OqtGxDSG1HAgwDwKWlQff++oM5n520qB9Wkd4IubHRyCTGbt0WTtnL99C1Mhfarx/bfPAri8GoG8n4xd2rD0PMAcQEZG+6uzgycHBwTh48CAGDhwIe3t7JCUlwdvbG19//TW2bt2KS5fuzH7z3yd2mjZtCgBVZn3R6XQoLS01TfBEVCsuTraYOLyJQftEhLqhX2fLvkt/V5tIL6s8NpEhJj7aBE4Ohn3DnD6uqcUXdQAgMtQddrbVv45pCq0jPM12bCIiIqqqzhZ2gDtFmt9++w0FBQXIz8/HkSNHMHHiROTk5CA9PR1KpRKRkZGV9nnkkUcAALt3765YptPpsG/fPrRr186k8RNRzUVPb4uH2uj3OKOXuz1++bQP5HLL/0IHAD3b+VvlsYkM4eupwk8f9YJSod95/fyocIwb3MjIUYnDxkaObq3NM/hMizAPeLqJ92QaERER1V6dLuzcy/nz5wEA4eHhsLOrPMLqkCFD0K1bNzzzzDP46quvsHPnTowePRoXL17Em2++WV1zRGSB7O2U+H15f4we0OC+2zVt6IZD3w1GWIiriSKrvR7t/NHEDPEG+zviYQm8qkZ018PdgrBzxQD4et57rBIbpRxvPBOFz1/vLImnde56bmS4VR2XiIiI7s2qCzv/fQ0LAGQyGbZt24ZHH30Uc+bMwSOPPILk5GTs2LEDvXr1MnWoRFQLDiolfvywFy5tHYHp45qicX0X3H0ox95OgT1fPozzmx+VVFEHuJOnpoyOMPlxnxsZAYXCKrsNkrDeHQOQvGs0fvywJ3q194fi/5OAUiHDu1PbIGX3GCx8sY1knti7a2iP+gjwuf+05mJzcrDBuMENTXpMIiIiejCrvEK/X2EHANzc3PDll18iOzsbarUax44dQ//+/U0ZIhGJqEkDNyx+rSOu/DoS/t53vgh5utqhd8cASd2h/7enhzVGkF/NpiuvCV9PFZ7lnXqSKFsbBUYPCMXerwfCz+vO0zu+niq8Pjnqvk/zWDIbGznemBxl0mO+8mQzODvamvSYRERE9GBWOW793cJOixYtzByJuNwj66Pzx8/BxkmFotQsHJj6GdzDgtBn3evIj7+BXWMWovRWPgAg6pVRaDC8K3TlGqhvF2DniPkAgLbzxiPkkc7IPn0N+yd/Ys6Po5fihLNIWjYZupIC2HrXR8jLa1CaHIurbw+Efb0wNH5rF2zcfJD+83u4te87qNOvouHcbXBrN7iijcQlE5B/dg+ULncGhXVp2ReBT38EAMjcuhhZO5ZB4eiGiEUxZvmMRNVxdrTF1291Rf/n/jDJ8VbO6wwPV7sHb2hi+ua9LoumwKddE2hLy1BWWILjb3yL27GJldry69QU/X5+E8fnrcKlVTsBAJHPDEb4hP4oyy/G9gGzzPAJH0zfPJi45GkUx58E5HLIFDao92Q0XFr2BgCkrZmL3BPbIJPfGZDXb8QceHQbDYB50JI9OzIcP+1KwP4T6UY/VssmHnh9cvU3xMzNkOsfAGg4sju6LZ2KPePfR+qeOzOntZ7zOIIfbg9dmQbasnKcfHcdMg5dAGD5eUDfHHB5bg+UZSVD4eACAPDs+RR8H3kZAJC951tkbluM0pSLCJr0KXwGvVjRPnMAEZHls8rCzuHDh80dglF0/fRFHHp5GW7HJqLRmF5oN288rm3Yj9wrqZUuRCImDYRbRDC29pwBXbkGKh+3inUxC9cg93IKAvu2McMnMFzikgkImbYKDqFRyN7zLdJWz4RnrwlQBUVWuvhwadEbHl1HI/HzidW24zdidqWLmLt8H3kZDqGtkLrqVaN9BqKa6tc5EM+ODMcXP1/Se5+M7OJK/9XHE4MaYlivEEPDMwl9817yzuM4PHMlBK0OgX3aoPsXM7Cl67SK9UpHe7R5YxzS9p2p1H7cl9tx+3wC2s5/0lQfyWD65sHAiYuhdHIDABRfP4Mrb/ZByzVZkMlk8B0+E/XGvwsAKLt1A7EvhMOlVT8ondyZBy2YXC7Dtwu6oeXILSgoKtd7P0PzgK2NHKsXPgRbG/PNxHU/+uYBAHDw90CT8f1wM+ZypeWZxy7i7OKN0JaWwT2yPgZsWoCfoiZDqy63+Dygbw4AgKDJSyvd3LrLoWEbhM78CRkb36+yjjmAiMjyWeWrWHWRR7MG0BSXVtyBjv9pP4L6tYVMWfWfuNnzj+DkO2uhK9cAAEpu5powUvEUXz8NhcoJDqFRAO7ceco9vg2CVlNlW8cmHWDnz3EBqO5ZOrsjBnTRf0DjdmO3Iajvj2g3dpte23dv64ev5netaXhGZUjeS9kVA0GrAwBknboCp0Bv4F+v4bVfMAEXlm+F+nZ+lX0tmSF58G5RBwC0RbmVXkP89zpdScGdvxudzlhhk4gaBDpj29K+Bk1/bkgekMtl+PHDnogKt8wpzg3JAwDQ+aPncHz+aujKKp8jaftOQ1taBgDIuZgMmUIOOw9no8YuBkNywP04NGgJVVAEZHJ+NSAikiKrfGKnLnIK9oF7eDCG7v6oYpnSwR4qX/dK29k4qWDv6YLgh9sjZFBHAEDsl9uRuE16TzGpMxNQknQecdOjKpbpSotQnmP4I+mZWxch+48vYesdjIAn3qm4QCKydLY2Cmxe3BujXt2H7QdSRG27b6cAbF7cByp7y+wq9M17/xU5aRBS950CBAEAUK9XK9i6OCLpt6MIksjTincZmgdTv5uNnMM/Q1uYg4azN1cq7tz8dSlu7liG8lupqD/1WyhdLPOLPFXVo50/dizrh6HTdqOoxLAv9PdjayPH+g96YnjvENHaFJsheaDJk/2QezkF2aev3rfNxmN6oiAxE8Xpt0WPV2yG5oC01TORtmYOVIGRqPfk+7DzCzVRpEREZEyWebVONZJ54hL2jv/nEdoxF76pso3cRgGFnQ2U9rb4bfDrcKznhYG/vovcyynIvSzul0JTcAzvgsZv/lbx89nx3ga3ETDuXdi4+0MmlyPnyBZcffthNFtxFQqVk5ihEhmNyl6JLZ/2wQffnsOCladRrqndkxYKhQyvT2qJN56JsthXL+7SJ+/9W+iIbggZ0gm/D5sHALB1cUCb15/ArtFvGzVOYzIkDwY+FY3Ap6KRf2YPUlfPRJPoQ5Db3BkM12fINPgMmYbihLNIWDwOLi37sLgjIb06BODE+kcw4Y0DOH4hq9btRTZ0w3fvPIS2TQ3vV01NnzzgFOSDsHF9sGPoG/dty79rc7R8ZSR2jV4oepzGom8OaDB9DWy9gyAIArJ2LMO1hYPRdFmcqcIkIiIj4vOWdURhUiac6nlV/GzjpIJCZYeSzJxK26lzClFeWIL4TQcAAEVp2bh54jK8WkrvNSU731CUZSdX/KwtzodOXQwbd3+D2rH1rFfx6LF7p+FQqFxQmnb5AXsRWRalUo65z0Th5I+PoGOLmn8Rax3hiePrhuLtF9pYfFFH37x3V8jQzoiaMarSQKpu4cFQ+bpj8O/ReOz4ctQf3BFRr45G1KujTPIZaqumedAlqg+0JQUoSTpfZZ1Dg5aw9aiHggv7xQ6XjCwi1A2Hvh+M6Olt4eRgU6M27O0UmDOxJU5tGCaJoo6+ecC7bRgcfN0x/MCneOz4cni3bowui55H4yf6VGzj2ykSXT6dgr1PRiM//obJPkNtGJIDbL2DAAAymQw+g16EOvM6NPm3TBYrEREZDws7dcTt2EQIOgF+XZoBAMIn9Efi1sMQqrlzf/2Xv1GvZysAgK2rI7xbNcLti0kmjVcMDqFRkMnkyD+3DwCQtWM53LuOhkxh2INoZdmpFf9fePkoNAW3YO/fSNRYiUyleZgHDq8ZgqNrh+DJIY30GnfDRinH4wMb4u/vBiPmx0fQOtLrgftYAkPyXsiQTmg9ayz+GL0ARWnZFctvHr+EDc0nYmP7KdjYfgqSth/FmY834MzHP5nsc9SGvnlQ0GpQeuOf10+KrhyHJu9mxWsYJSkXK9ap0+NRfP007IMiTfAJSGxKpRyz/tcSN/aOwfK5ndGs0f1fTbyrYZAzPn6lPVJ3j8F7L7U1aMwec9I3DyRs+RsbWk6uONezTl3FoRkrcHXdHgCAb8cIdPtsKvZN+BA5cdK5JjIkB5TnZlb8nHN4E2zcfPlUHhFRHcFXseqQg1M/Q5dPp8DW2QEFiRk48OJSuDcJrrLdqfd/QNfFLyB8Qn8AwLnPtuD2+QRThyuKkJfXIGnp00guyoOdfyM0mLEWJUkXqmyXvvF9ZO1YBk1eFhKXTIDc1h4Ri0/DxtUbiUsmoDwvEzK5AnJbFRq+9jMUjq5m+DRE4pDJZOjQwgcdWvjgize74NyVHJyMy8aFazkoLC6HIACOKiWaNXZHmwgvtGziYbHj6DyIvnnvoWUvoeRmLnqv+meGnD9GLYA6p9CU4RqFPnlQ0GmRuGQCtMV5d3KdnSNCZ22E0unOl/6072dDnX4NMqUNZHIlgp/9HKqgCHN8HBKJs6Mtnh8dgedGhePGzWLExGXjZFw20m4WQ12mha2NHH6eDmgT6Yk2kV6oH+BUacwlKdE3D9xPl0+mQGFrg66Lp1QsOzD1M+ReSr7PXpZBnxygK1fj2tuDoNOoIZPJoXTxQsO5/wyefWv/WqR9PxvawhzkHtuKjE3RaPTGr3AIbWXqj0NERDUgzSt5qlbOxSRs7z/rgdupbxdg71PRJojI+BxCWiBi0ckHbuf/2Bz4Pzan2nVhC/eIHRaRxbC3U6J9c2+0b275r1TUhL557/vgMXq19/f0ZbUNyeT0yYNyGzuEf3Donusbzd0qdlhkIWQyGer5OqKeryMe6Vnf3OEYhb554N92jphf6efNXaaKGZJJ6ZMDFPaOVaY+/zfPHuPg2WOc2KEREZGJ8FWsOk5broHKyxVDd38Ee0+XB27fdt54NJ86HGV5RSaIzjhkSluU52YibnoUynNv1qqtzK2LkbxyCpQu0ng1hYgMz3v3E/nMYHSMniS5adCZB8naWXseYA4gIrIuMkH4//leyaKUF5diXUPp3Dl5In4tbBzsRWuvRAN02yFacyZxcCCg4jNwFi+wz3qk3SxGPR8HpO4Za+5w6B6klgMB5kFAGnmQOUA6rD0PMAcQEZG++MQOEREREREREZFEsbBDRERERERERCRRLOwQEREREREREUkUx9ixUIIgQFOiNncYelOq7ESdJlUQgFKtaM2ZhL0CkOhMsVaF42tIg9RyIMA8CEgjDzIHSIe15wHmACIi0heHN7NQMplM1EE4pUYm4+B7RNbM2nMgwDxIZO15gDmAiIj0xVexiIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIqFHSIiIiIiIiIiiWJhh4iIiIiIiIhIoljYISIiIiIiIiKSKBZ2iIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCRKae4AqHqCIEBTojZ3GHpTquwgk8lEa08QgFKtaM2ZhL0CEPGvgKyc1HIAIG4eYA4gkl4e4LUA8wAREZkHCzsWSlOixrqG48wdht6eiF8LGwd70dor1QLddojWnEkcHAioeEaRSKSWAwBx8wBzAJH08gCvBZgHiIjIPPgqFhERERERERGRRLGwQ0REREREREQkUSzsEBERERERERFJFAs7REREREREREQSxcIOEVkNrVYHnSAAuDPbDBFZF0EQKs595gAiIiKqKzhuPxHVWWcu3cKvfyXjZFw2YmKzkXazuGJdenYJek7cgTYRnujRzh8Pdw2EQsFaN1FdUlyiwea9iTh8JhMn427h3NXbKFXfmT/7RlYJwob8jLaRXmjb1AuP9W2AYH8nM0dMREREZDgWdoioTtFodPhx53Us33ARR87evOd2ggDsP5GO/SfS8cn3FxDs74hnHwvHsyPD4ekm3nS9RGR611Pz8dkPcVi99SpyC8ruud3VpHxcTcrH+t+vY+aiExjULQgvjo1A3071IJPJTBgxERERUc2xsFOH+HVqigGbF1RaVl5Ugvzr6YjfeAAXv9kBQaszU3TGV3B+P6680bPSMrm9I+wCwuDZYzx8Bk+FTMFf+bosLj4HE+YdwIkL2Qbvm5xehLmfncSna2Ox4o3OGNG3gREiND7mAeYBa6bV6rB4TSze+Pwk1GVag/bV6QT8+lcyfv0rGSP6hGD53M7w8VQZKVLjsfYcADAPEBGR9WGvVgcl/HIIKXtiAJkMKm83NBrZHe0XTIBr43o4MvMLc4dndO5dR8O13WBAEFCek4Fb+79H6rczUJp6EfVf+NLc4ZGRLFl7Aa8tPoGy8tp9YcnKKcVjr+zD2IdD8fVb3eCgkmaaZB5gHrA2qRlFGDVz332f1NPXpj2J+OtkBr575yEM7BYkQnSmZ+05AGAeICIi68EBJeqg27EJuL7pIK5vPIDYFdvw26DXUZiWhbDHe8PO08Xc4RmdQ4MoePYYB8+e4+H36EyEf3gUNl5ByN79NcrzsswdHolMEATMWXIC0z88Vuuizr+t//06+j+3EwVF936Nw5IxDzAPWJP4lHx0nbBdlKLOXdk5pRg6bTd++C1etDZNydpzAMA8QERE1oOFHSugKVEj+9Q1yORyuNT3NXc4Jqewd4RjWAdAEKDOkOYFOt3bwi/OIPqbc0Zp++/TmRg6bQ9K1RqjtG9KzAPMA3XVjZtF6DP5dyTdKBS9ba1WwPi5f+GXfYmit21q1p4DAOYBIiKqu1jYsRLOIXcu4kpzCswciXncvYBTOnuaORIS075jNzB/+SmjHmP/iXS8teK0UY9hKswDzAN1jSAImDDvABKNUNS5S6cT8NQbB5CSYbxjmIq15wCAeYCIiOomaQ4eQfelUNnBzsO54r36Jk/2g2fzUGSdvoqChAxzh2d0OnUxNPnZEAQBmpwMZO1ciZLrp+HQuB3sAxqbOzwSSWFxOSbOP2jwfifWD4WflwMysovRbuw2vfb5aPV5DO9VHx1a+Bh8PHNhHmAesAZfb7qM3UduGLRPTXJAfmE5Jr/1N35f0V8ys2VZew4AmAeIiMh6WEVhJzs7G9HR0diyZQvS0tLg7e2NUaNG4Z133sHkyZOxbt06fPXVV5g0aZK5QxVF1IyRiJoxstKypN+P4eisr8wUkWml/7QQ6T8trLTMrcMwBD+/wkwRWYbk9ELExeeitEwDT1d7dGzhAxsb6T60t2DF6RrdpffzckCgr6NB++h0AiYv+BtnNw6XzJc65gHmgf/SaHQ4dj4L2bmlsFXKEd7ADQ0Cnc0dVo3dyi3FK58cN3i/muQAAPjjcBo27LyOMQ83NHhfc7D2HAAwDxARkfWo84Wdc+fOoV+/fsjMzISjoyMiIyORlpaGRYsWITExEcnJyQCAli1bmjlS8VxZtwcJWw9BrlTArUkwmk8dBpWXGzSl/wwC2+PLVyAIAv56dlHFMls3Jwzbvxgxb3+P65sNfxLCUnj2mQiPbmMg6DQoSbqAjE3RKM/NhNz2n2lrC2IP4trbD1fZV9CUQdBp0WaLYdPkWrKdf6fi8x/jsONgCgThn+X+3g6Y/GgTTBkTAV+JTelbVFyOrzZfNukxz1/NwZ/H09GrQ4BJj1tTzAPMA3dl55RixU8X8eXGy0jNLKq0rl/nenhhdASG9AiWTNHyrlW/XEVBUblJj7lkXaxkCjvWngMA5gEiIrIe0r1dr4fs7GwMHjwYmZmZeO2115CRkYFTp04hMzMTCxcuxObNm3HmzBnI5XI0a9bM3OGKpiAxA+kHzyPtzzOIXbkNe8e/D6/WjdApenLFNkdmfQHfDhFoMKxLxbKO703CzeOXJH8hZ+/fCC5RfeDaegD8hr+KRvO2o+jqMSStfL5iG+em3dBqQ2GlP02XX4HS2QsBjy+8T+vSIQgCZnx0FA9P+QO/Hahc1AGA9KxivP3FabQe/QvOXr5lniBr6Icd8cgrMP1sVcs3XDT5MWuKeYB5AAAuXs9FmzG/4M1lp6oUdQBg1+E0PPLSHkx55zC0WvFmlTM2nU7Aip9Mfz4ePZeFU3HZJj9uTVh7DgCYB4iIyHrU6cLOtGnTkJKSghkzZuCDDz6Ak5NTxbq5c+ciPDwcGo0GYWFhUKmk9cSCIbJOXcX1TQcR+mg3eLe+8065OqcQh2YsR4d3J0Hl6476gzrCr3NTHJn1hZmjFZ9Tk47w6D4OOQfWo/Dy0Wq30ZWrcT36UThFdoX/yNdNHKFxzPv8JBaviX3gdjduFqPfszuRkCqdwTTXbL9mluP+8mcS8gulOf0584D15YHUjCL0feZ3JKdXLej818qfL+GVjw1/rclcDp/JxHUz5azvfzVP/qkta88BgHXmASIisg51trATFxeHDRs2wMfHBwsXVr3jIpPJ0KZNGwCVX8Pq0aMHZDJZtX+ee+45k8UvtrOLN0Kn0aLVa2MqlqXtO42k7Ufw0OfT0DF6Mg6/sgLqHOnP+lEd/9HzALkCN9bNq3Z98vLnoCsvRchLq00bmJHEp+Tj3a/O6r39zdulmPtZjBEjEo9Wq8PJOPM8YaTVCjh9SVpPN/0b84B15YG3VpxC2s1ivbdfsi4W56/cNmJE4jl+Ictsxz4Ra75j15a15wDA+vIAERFZhzpb2Fm3bh10Oh3GjRsHBweHarexs7MDULmws3z5chw5cqTSnzfeeAMAMHjwYOMHbiQFiRlI2HoIAd1bwqdDRMXyE299B+cGfkjbdxqpe407bbQ52fs3gke3MSg4uwcFsZUfL7/561LkxWxHwzm/QG5X/e+K1Kz86ZLB+2zcnYjMWyVGiEZclxLyUFyqMdvxY2Kl8RpGdZgHrCcP5OSr8cPv8QbvZ47Xm2rCXMVdADh96RY0Gum8tvZv1p4DAOvKA0REZD3qbGFn7969AICePXvec5vU1FQAlQs7kZGR6NixY6U/Z86cgbe3NwYMGGDcoI3s3JJN0Gm1aDVzdMUyTYkahUk3kXMx2YyRmYbfyLmAXI4bP7xZsazg3J9I/X4WQmf+BDvfEPMFJ7LV264avE+5Rof1Owz/ImhqsfE5Vn382mIesI48sHFXAkpKDR/0dc32eEmMtWPO87CkVFujGfkshbXnAMB68gAREVkPmSD8d0jVusHf3x8ZGRk4f/58tQMjq9Vq+Pr6Ii8vD6mpqahXr1617WRlZSEgIABTpkzBkiVLahRL27ZtkZGRYdA+NoIc83Xta3Q8Qw3YtAApu08iduW2GrexQH4c5TLxvgzIbFXw/dTw4oQh1JmJuPRqO/iPmQ+fQS/Wur3M6Y0hlJn/iRcBStzwqP4R8wdxKjkM15I/RI5IXEW2rZDrNKzadSfWD4Wf1/3vsvp5qaBUyKHR6pCRfe9/r4zsYrQbW/WcUKkvwKPoZ4NirglT5gDA8vKAKXIAIG4esJQcAAD5qp4oUPWo0b5+OdFQCJbxOe4lw3UatArPatc9KA/omwOAe+cB77wVsNUa1q/XBK8FmAeIiMh6+Pn5ISamZsNj1NnpzouK7gwWWVpaWu36tWvXIi8vD56envcs6gDA+vXrodFoMH78+BrHkpGRgbS0NIP2sZUpAN8aH9LkbqTfQJkg3pSgcjsHo358nboY8e8Pg2v7oaIUdQDgxo0b0Kn1H8/CaGRKwKNmuxYWFqAww7DfVZNzDwGcql/l5+WAQF9HvZpRKuR6b/tvJSUlBp/PNSG1HACImweMnQMA8fOAxeQAAPApAGo4J0BGejqgffCAy2blqAEU1a/SNw/UNAcAQNbNTKCUeeC/pHYtANTxPEBERFajzhZ2/Pz8UFBQgJiYGLRt27bSupSUFMyZMwcA0KJFi/u2s2bNGkRERFRpw9BYDGUjyAHLfxq+QoB/gOh36Ywp5/AmlCScRWnaFeT8vaHK+qafx8HWO9igNgMCAizmLl26rhA6+T2qH/fh6qCD030KnZagxMYB9xreNSP7wRfThjyxUx0HlQLuJvg7kloOAMTNA8bOAYD4ecCSckCRnQ65NdhPJpTC388dMriJHJG4bip0KL/HugflAUOf2KmOr7c7lDrjP/AstTwgtWsBoG7nASIikpaa1A3uqrOvYk2ZMgUrVqxAUFAQ9uzZg7CwMADA0aNHMX78eCQlJaG8vBzTp0/H4sWLq23j0qVLiIiIwHvvvVdRCDKV8uJSrGs4zqTHrI0n4tfCxsFetPZKNEC3HaI1ZxIHBwIqCymVzlp8HB+uOm/QPrY2cqTsGgMfT+NfSNdGXHwOmg7fXOP9U3aPQaCvI1IzixDU90eD9//k1faY8WTzGh9fX1LLAYC4eYA5oHZy89Wo1+dHgwcaf2FMBD5/vbORohLPuDn7se63mo0JVtsc4GCvRP6R8VAojD9ModTyAK8FLCsPEBGR9aizgyfPnj0b7u7uSElJQdOmTdG8eXM0atQInTp1QkREBLp27Qqg8sDJ/7VmzRrIZDI88cQTpgqbSBTPjgyHTGbYPiP7NbD4og4ANAlxhaMZr5rbRHqZ7dhE+nJzscMTgxoavN/zoyIevJEFMOd5GBXuYZKiDhEREZG+6uyVSXBwMA4ePIiBAwfC3t4eSUlJ8Pb2xtdff42tW7fi0qU700Hfq7AjCALWrVuHHj16IDjYsFdyiMwtNNAFbz7bSu/t/bxUeHdqGyNGJB6FQm62L3VKpQytwqsfsJXI0rz1fCsE+ek/hswrTzZD00buRoxIPO2beVvlsYmIiIiqU2cLOwDQtGlT/PbbbygoKEB+fj6OHDmCiRMnIicnB+np6VAqlYiMjKx23wMHDiApKalWgyYTmdP851th1v/uP4YUAAT6OmL3FwNQP8DZBFGJ48khjcxy3OG9QuDiZGuWYxMZKsDHEXu+fBgN6j343J76eCQ+nGG6Wdhqq1NLHzQMMk/OempoY7Mcl4iIiOhe6nRh517On78z9kh4eDjs7Oyq3WbNmjVQqVR47LHHTBkakWhkMhmip7fDni8fxrBe9SGXV343K9DXEQtfbI1TGx5Bs8Y1nEbLTMY+3BBuzqYvsEwZLY3XVIjuCgtxRcyPjyB6elvUD6g6oPrgh4Lw+/L+WDKrY5UcYcnkcplZXhvr1NIHUXxqj4iIiCyMVRd27vUaVmlpKTZu3Ihhw4bB2Vk6TzEQVad3xwBs+bQPkv8YDU+3O4VMb3c7JPw+Cm880wreHpY/rs5/OaiUeHZkuEmP2bKJB7q3rflI9UTm4uFqh1n/a4n430bi2Lqh8HS9kwd8Pe3x6+f9MKBrIGSGDsplAZ4eFgYXJxuTHnP6uKYmPR4RERGRPljYqYa9vT1yc3Pxww8/mDIsIqOq5+sIe1sFAMDWRgGlUtqn/7xnohAaaJrCq0IhwzcLuknyyy/RXQqFHO2be8Pe7k4eUEp8AGAPVzssntnRZMcb9FAQRvZrYLLjEREREenLKidkvFvYadHiweOPSIl7ZH10/vg52DipUJSahQNTP4N7WBD6rHsd+fE3sGvMQpTeysfAbe9CqbrzGotMqYB7eDC29noFOReT0HbeeIQ80hnZp69h/+RPzPyJHqw44SySlk2GrqQAtt71EfLyGpQmx+Lq2wNhXy8Mjd/aBRs3H6T//B5u7fsO6vSraDh3G9zaDa7Uzs0dy5H122eAXAGZTI7wj49DbmuPzK2LkbVjGRSObohYFGOmT0nVcXSwwbdvd0OP/xk2F25GdnGl/+pj9v9aWOxsWPqe910WTYFPuybQlpahrLAEx9/4FrdjEwEAXT99Af7dWkB9Ox8AcOPAOcQsXAMAiHxmMMIn9EdZfjG2D5hlro95T/rmgMQlT6M4/iQgl0OmsEG9J6Ph0rJ3pbYKzu/HlTd7I2jSEvgMehEAmAMs3NPDGmPj7gT8/neq3vvUJAe4Otvii3ldLLK4q28OuKvhyO7otnQq9ox/H6l7TgIAWs0ei+B+bSFodQCAc59tQeK2wwAsPwcA+ueBy3N7oCwrGQoHFwCAZ8+n4PvIywCAtDWvI/foFsiUtpDZ2N3JES16AWAeICIiy2eVhZ3Dhw+bOwSj6Prpizj08jLcjk1EozG90G7eeFzbsB+5V1IrXYztGDq34v/rD+qIqFdGIudiEgAgZuEa5F5OQWBfacyQlLhkAkKmrYJDaBSy93yLtNUz4dlrAlRBkZUuvlxa9IZH19FI/HxilTZyj23F7b/WIfzDo1A4uqI89yZkijuP9/s+8jIcQlshddWrJvtMpL/ubf3x3rS2eH2p/hfa7cZuM+gYfToGYJ4BM4yZmr7nffLO4zg8cyUErQ6Bfdqg+xczsKXrtIr15z/fgkurdlZpP+7L7bh9PgFt5z9pks9jKH1zQODExVA6uQEAiq+fwZU3+6DlmqyKL+ra4gKkfjcLrq0frtQ+c4Blk8lk+Pbtbuj61HbEpxTotY+hOUChkGHte91Rz1f/GcZMSd8cAAAO/h5oMr4fbsZcrrQ8dvlWnI5eDwBQ+bpj+MEluPHXWZTlFVl8DgD0zwMAEDR5aZWbOwDgFNkN/qPmQW6nQnHCWVyZ2wMtVqdDbmvPPEBERBZP2s9hUwWPZg2gKS6tuAMf/9N+BPVrC9kDXrdpPLYXrq7fZ4IIxVd8/TQUKic4hEYBuHPnLff4NghaTZVtHZt0gJ1/w2rbydjyEQLGLoDC0RUAYOPmA5lCYbS4SVyzJ7bAG89EGaXt7m39sOXTPrCztczfB0PO+5RdMRV347NOXYFToDdggU8fGMKQHHC3qAMA2qLcKk9epH47A37DZ0LpYplPZtG9+Xk56D37l6GUShl+iO6Bwd2DRW9bDIb2/Z0/eg7H56+GrqzyOVKW/8/TSzZOKkAQIJPIYNqG5IH7cW3zMOR2d8acU9VvDkGnhSY/W+xwiYiIjMIqn9ipi5yCfeAeHoyhuz+qWKZ0sIfK1/2e+zj4ecCvc1McnPaZKUIUnTozASVJ5xE3Papima60COU56Qa1U5oSh+JrMbixfj6EcjU8ez4JnyHTHrwjWQSZTIaFL7aBn5cKr35yHKVqrSjtjh/cCF+82QUqe8tNkzU57wEgctIgpO47BQhCxbKmzw5B2Li+KErLxukP1ld8UbRkhuaA1O9mI+fwz9AW5qDh7M0VxZ28k79DU5QL9y6PIS9muylCJ5GF1HPG398NwpjX/sTBU5mitOnjYY/v3+2O/l0CRWnPGAzJAU2e7IfcyynIPn212rYiJg5E+IT+cAjwxKGXl0OdU2i0uMVkaB5IWz0TaWvmQBUYiXpPvg87v9Aq29zauwp2fg1h62W5//ZERET/ZrnfWMhgmScuYe/49yt+HnPhm/tu32h0D6TsPgn1bf0eX7dEjuFd0PjN3yp+Pjve2+A2BK0G6ox4NHnvALTFebg8tzvs/BvBte1AMUMlI3thTCT6dAjA028exJGzN2vcjp+XCl/M64KhPeuLGJ3xGHreh47ohpAhnfD7sHkVy05Fr0dxZg4gCAh+uD36rJuLzZ2nQlNcarS4xWJIDgh8KhqBT0Uj/8wepK6eiSbRh6BTFyPt+9lovGC3KcIlIwrwccT+bwfhsx9iMWdpDEpKa17kHftwKD6b0wmebvYiRmgc+uQApyAfhI3rgx1D37hnOxe/2YGL3+yAe2R9PPT5NKQfPCeZ4o6+eaDB9DWw9Q6CIAjI2rEM1xYORtNlcZW2yT+7Fzd+XIAw5gQiIpIQvopVRxQmZcKp3j+vENg4qaBQ2aEkM+ee+zQa3RNX1+81RXhGYecbirLs5IqftcX50KmLYePub1A7tt7BcH9oLGQKBZTOHnBt/TCKrh4XO1wygSYN3HBw9SD8+GFPPNTGsKnJG9Rzxocvt0PcLyMkU9Qx9LwPGdoZUTNGVRlMtTjjdsXTO8m/H0d5QTFcGwUYN3gR1DQHuET1gbakACVJ51GafAHlOem4NLM9zk8OQc7hjbixfj5u/DDf2OGTEcjlMrw0rhku/jICrz3dHJ5udnrvq1DIMKJPCP78ZiB++KCnJIo6+uYA77ZhcPB1x/ADn+Kx48vh3boxuix6Ho2f6FOlzZy4JBRn3IZfZ2lM7W5IHrD1DgJw50lPn0EvQp15HZr8WxXrCy78hcSlT6PRG7/CPrCJ8YMnIiISCZ/YqSNuxyZC0Anw69IMGYcuIHxCfyRuPQxBo6t2e79OTSG3VeLGX+dMHKl4HEKjIJPJkX9uH1xa9ELWjuVw7zoaMoVhv9YeDz2O/FM74dKiF3RlpSi48Bf8Rsw2UtRkbAqFHKMHhGL0gFBcuHob2w+k4GRcNk7G3UJSeiF0ujsFDC93e7SO8ETrCE/0aOuPvp3qQS6RMSXuMuS8DxnSCa1njcUfoxegKK3yuBEO/h4oTr8NAPBu3Rh2Hs7IT8gwyWeoDX1zgKDVQJ2ZAPuAxgCAoivHocm7CTu/UCid3NHy+3+e8EpcMgEOjdpWzIpF0lQ/wBkfvNweC6a0xtY/k3Hk7E2cjMvG2Su3UVBUDgCwUcoRVt8FbSK90CbSCyP6hFjsAMn3om8OSNjyNxK2/F3x84BNC3BhxbaKWbFcG9dD3tU0AIBzfV94NGuA3Cv6zzRmTobkAU3BLdi4+QIAcg5vgo2bL5QungCAgtgDSFg8Ho3mboVDg5Ym/xxERES1wcJOHXJw6mfo8ukU2Do7oCAxAwdeXAr3JtUP+Nj48V649uOflcbYkKKQl9cgaenTSC7Kg51/IzSYsRYlSReqbJe+8X1k7VgGTV4WEpdMgNzWHhGLT8PG1Ru+Q19G0vJnEftCJCCTwb3LSLh3Gm6GT0Nia9bYA80ae1T8LAgCNBoBSqXMIqctrgl9z/uHlr2Ekpu56L3qn1ly/hi1AOqcQnT99EWovF0haHXQlJZh/+RPUF6g/1TQ5qRPDhB0WiQumQBtcR5kcgXkdo4InbURSqf7j0VE0mdvp6wo9N6l0wnQ6QQoHzC5gFQY0vffS5vXx8G5gR+Ecg10Wh2Ovv5NRaFHCvTJA7pyNa69PQg6jRoymRxKFy80nPvPDGlJn02EUK5G4tKnK5Y1mL4GqpDmJvscRERENcXCTh2SczEJ2/vPevCGuHMhWBc4hLRAxKKTD9zO/7E58H9sTrXr5HYqNHj5e7FDIwskk8lgY1M3Cjp36Xvefx885p7rdo1+W8yQTEqfHCC3sUP4B4f0ai/kpdUiREWWTC6XSe7pvPsxpO+/a+eIyq8a7nv6AzFDMjl98oDC3rHK1Of/1mxl9YNKExERSUHduF1F96Qt10Dl5Yqhuz+CvafLA7dvO288mk8djrK8IhNEZxwypS3KczMRNz0K5bk1H0QXADK3LkbyyimcApkkxdDz/n4inxmMjtGToL6d/+CNLQRzAFk7a88BAPMAERFZF5kgSPxdnDqqvLgU6xqOM3cYensifi1sHMQbaLJEA3TbIVpzJnFwIKCy8GfgAvusR9rNYtTzcUDqnrHmDofuQ2o5ABA3DzAHGA/zgHRILQ/wWkA6eYCIiOoWPrFDRERERERERCRRLOwQEREREREREUkUX8WyUIIgQFOiNncYelOq7ESdZUgQgFKtaM2ZhL0CsPSJlvgKhnRILQcA4uYB5gDjYR6QDqnlAV4LSCcPEBFR3cK3gC2UTCYT9T11qZHJ+I46WTfmAOYAIuYB5gEiIiJ98FUsIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIqFHSIiIiIiIiIiiWJhh4iIiIiIiIhIoljYISIiIiIiIiKSKBZ2iIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIolSmjsAqp4gCNCUqM0dht6UKjvIZDLR2hMEoFQrWnMmYa8ARPwrsHpSOwcAcc8DngNk7aw9BwDSywPMAURERObBwo6F0pSosa7hOHOHobcn4tfCxsFetPZKtUC3HaI1ZxIHBwIqnlGikdo5AIh7HvAcIGtn7TkAkF4eYA4gIiIyD76KRUREREREREQkUSzsEBERERERERFJFAs7REREREREREQSxcIOEREREREREZFEsbBDRERERERERCRRnLuAqA4rL9ch7noOTsbdwulLt3A7787UwTn5aiz84jTaRHqhTaQXfD1VZo6UiIwl63YJTsbdwsm4bFxLya/IA7kFZfhq4yW0ifRCs8busLVRmDlSIiIiIqoJFnaI6qCrSXlY+fMlrPrlCnLyy6qsLy7V4s1lpyp+7tLKF1NGRWBE3xDY2fLLHZHUlZVr8cu+JCzfcBF/xWRUu01RiQbPvH0IAODiZIOnhjbG86MiEBHqZsJIiYiIiKi2WNipQ/w6NcWAzQsqLSsvKkH+9XTEbzyAi9/sgKDVmSk64ys4vx9X3uhZaZnc3hF2AWHw7DEePoOnQqao27/ymbdKMPX9I/h5V4JB+x06nYlDpzPx8kf2WDSzAx4f2BAymcxIURqPtZ8DAM8DAjbtTsC0D47ixs1ivffJLyzHZz/E4bMf4vBIz2Asn9sZAT6ORozSeKw9DzAHEBERWR/27HVQwi+HkLInBpDJoPJ2Q6OR3dF+wQS4Nq6HIzO/MHd4RufedTRc2w0GBAHlORm4tf97pH47A6WpF1H/hS/NHZ7RbNh5HS+8dxi3ctU1buPm7VKMm/MXft6VgJXzusDPy0HECE3H2s8BwHrPA2uWnVOKF947jJ/+MKyw+19b/0zGXzEZWDKrI8YPaSTJIi/APMAcQEREZD1Y2KmDbscm4PqmgxU/X179B4Yd/BRhj/fGqej1UN/KN2N0xufQIAqePcZV/Ow9cApiX4hA9u6vETDuXdi4epsxOvEJgoB5n5/Eu1+dFa3NrX8m49TFW9j9xQA0aeAmWrumYu3nAGB954G1S0gtQJ9nfsf11AJR2sstKMNTbxzAmcu38MmrHSRZ3LH2PMAcQEREZD04K5YV0JSokX3qGmRyOVzq+5o7HJNT2DvCMawDIAhQZ8SbOxzRvfGZuEWdu1IyitBj4g5cS5b+lx9rPweAun8eWLPk9EJ0/99vohV1/m3xmli8/OExCIIgetumZu15gDmAiIio7mJhx0o4h9y5iC3NEf/CXwruXsQqnT3NHIm4vtt6Fe99LX5R566M7BIMfOEPFJdojHYMU7H2cwCou+eBNVOXaTH4xV1IySgy2jGWrIvFig0Xjda+KVl7HmAOICIiqpv4KlYdpFDZwc7DuWJcgSZP9oNn81Bknb6KgoTqZ0epS3TqYmjysyEIAjQ5GcjauRIl10/DoXE72Ac0Nnd4oknLLMJLHx41aJ8T64fCz8sBGdnFaDd2m177XE3Kx+tLY/DprI41CdMsrP0cAKznPLB2b688jfNXcwzapyZ5YOaiExjQNRChgS41CdMsrD0PMAcQERFZD6so7GRnZyM6OhpbtmxBWloavL29MWrUKLzzzjuYPHky1q1bh6+++gqTJk0yd6iiiJoxElEzRlZalvT7MRyd9ZWZIjKt9J8WIv2nhZWWuXUYhuDnV5gpIuN4duEh5BVUncr8fvy8HBDoa/hMN0t/iMVjfUPQtbWfwfuag7WfA4D1nAfW7GRcNj5Ydc7g/WqSB4pLNZg4/2/s+/phyYy3Y+15gDmAiIjIetT5ws65c+fQr18/ZGZmwtHREZGRkUhLS8OiRYuQmJiI5ORkAEDLli3NHKl4rqzbg4SthyBXKuDWJBjNpw6DyssNmtJ/igA9vnwFgiDgr2cXVSyzdXPCsP2LEfP297i++WB1TUuCZ5+J8Og2BoJOg5KkC8jYFI3y3EzIbVUV2xTEHsS1tx+usq+gKYOg06LNFq0pQzZYTGwWfjuQYrLjCQLwzpdnsHPlAJMdszas/RwArOM8sHbvfnUGWq3pxr7ZfyIdB09m4KG2/iY7Zm1Yex5gDiAiIrIedXqMnezsbAwePBiZmZl47bXXkJGRgVOnTiEzMxMLFy7E5s2bcebMGcjlcjRr1szc4YqmIDED6QfPI+3PM4hduQ17x78Pr9aN0Cl6csU2R2Z9Ad8OEWgwrEvFso7vTcLN45ckfSELAPb+jeAS1QeurQfAb/iraDRvO4quHkPSyucrtnFu2g2tNhRW+tN0+RUonb0Q8PjC+7RuGVb8dMnkx/zjcJpkBlK29nMAsI7zwJqlZhRh65/JJj/u8p+kM9aOtecB5gAiIiLrUacLO9OmTUNKSgpmzJiBDz74AE5OThXr5s6di/DwcGg0GoSFhUGlUt2nJWnLOnUV1zcdROij3eDd+s579eqcQhyasRwd3p0Ela876g/qCL/OTXFk1hdmjlZ8Tk06wqP7OOQcWI/Cy9WPSaMrV+N69KNwiuwK/5GvmzhCw+QVlOGHHeaZ0eSLn01fUBKDtZ8DQN07D6zdN1suQ6cz/UxVm/Yk4uatEpMfVwzWngeYA4iIiOquOlvYiYuLw4YNG+Dj44OFC6vedZLJZGjTpg2Aqq9hHTx4EL1794aXlxfc3NzQsWNHbN682SRxG8vZxRuh02jR6rUxFcvS9p1G0vYjeOjzaegYPRmHX1kBdU6hGaM0Hv/R8wC5AjfWzat2ffLy56ArL0XIS6tNG1gNHDt/E6Vq8zwevz8m3SzHFYO1nwNA3ToPrN2fJ8xzLmo0Ag6dyTTLscVg7XmAOYCIiKhuqrOFnXXr1kGn02HcuHFwcHCodhs7OzsAlQs7Z8+eRd++faFQKLB69Wps2LABISEhGDFiBLZv326S2I2hIDEDCVsPIaB7S/h0iKhYfuKt7+DcwA9p+04jde8pM0ZoXPb+jeDRbQwKzu5BQWzlx+tv/roUeTHb0XDOL5DbVf+7YklOxt0y27HPXbmNsnJpjrlg7ecAULfOA2um0wk4ddF8eeBkXLbZjl1b1p4HmAOIiIjqpjpb2Nm7dy8AoGfPnvfcJjU1FUDlws6GDRsgk8mwdetWDB48GP3798cPP/yA+vXrY926dcYN2sjOLdkEnVaLVjNHVyzTlKhRmHQTORdNP1aDqfmNnAvI5bjxw5sVywrO/YnU72chdOZPsPMNMV9wBjh10XxfqsrKdYi9ZtjUypbE2s8BoO6cB9YsPiUfBUXlZju+OYtKYrD2PMAcQEREVPfU2VmxkpKSAAAhISHVrler1Th27BiAyoWdsrIy2Nrawt7evmKZXC6Hs7MzdDpdjWJp27YtMjIyDNrHRpBjPtobtE/GkVis9n/snuvzrqbh+8DR91xfG2GNw1Auq9nfT3Vktir4fnrVoH2cm/dAm633HnNCFRRRaYYPdWYirn80CoETPoJzi3sXAPUVFtYYQpnxx57Icp4A2DSodt2J9UPh53XvO61+XqqK/6bsHnPP7QAgI7sY7cZuq7K878OPwl5zXf+Aa0hq5wAg7nlQk3MAMO95YKpzwNqplUGAy6Rq1z0oBwD654F75YA9+48iMHCiARHXTE1yAMC+kDmAiIhImvz8/BATE1OjfetsYaeoqAgAUFpaWu36tWvXIi8vD56enqhXr17F8gkTJmD58uWYOXMmZs6cCRsbG3z77be4cuUKli9fXqNYMjIykJaWZtA+tjIF4Fujw5nFjfQbKBPEe0VHbudg1I+vUxcj/v1hcG0/FD6DXhSlzRs3bkCnLhalrfsK1QI21a/y83JAoK/jA5tQKuR6bVedW7dzgQLDfp9rQmrnACDueWDscwAQ/zww2Tlg7RwdAZfqV+mbA4Ca54Hycp3BfVpNWHsOAKTXFzIHEBERmUedLez4+fmhoKAAMTExaNu2baV1KSkpmDNnDgCgRYsWldY1a9YMf/75J4YPH45PPvkEAODo6Iiff/4Z3bp1q3EshrIR5IB4N/2MLsA/QPS7lMaUc3gTShLOojTtCnL+3lBlfdPP42DrHWxQmwEBASa5U5ltK4f6Husysu9/Qe3npYJSIYdGq0NG9v1jvVdbnh6usHepV+06MUntHADEPQ+MfQ4A4p8HpjoHrF2Zwh1Z91j3oBwA6J8H7tWWjY0MPvWYA6pj7X0hcwAREVHN1aRucJdMEATTz5dqAlOmTMGKFSsQFBSEPXv2ICwsDABw9OhRjB8/HklJSSgvL8f06dOxePHiiv2uXr2K3r17o1WrVnjuueegVCqxfv16/Pjjj9i+fTt69eplkvjLi0uxruE4kxxLDE/Er4WNg/2DN9RTiQbotkO05kzi4EBAZYJS6dPzDmD1VsNf0QGAlN1jEOjriNTMIgT1/bFGbVzdPhKNgu/xuICIpHYOAOKeBzwH6F5u3CxCvT41O3+B2ueBkf0a4KePjd8XWnsOAKSXB5gDiIiIzKPODp48e/ZsuLu7IyUlBU2bNkXz5s3RqFEjdOrUCREREejatSuAqlOdv/7663BwcMDmzZvx8MMPo2/fvvj222/RsWNHvPLKK+b4KESVtIn0MtuxXZ1t0TDI2WzHJyIgwMcR/t7mm7WoTaSn2Y5NRERERFXV2cJOcHAwDh48iIEDB8Le3h5JSUnw9vbG119/ja1bt+LSpUsAqhZ2zp8/jxYtWkChUFRa3qZNG1y8eNFk8RPdizm/VLUO94RMJjPb8YnojjYR5ssD5iwuExEREVFVdfqB2aZNm+K3336rsvz27dtIT0+HUqlEZGRkpXV+fn44c+YMNBoNlMp//npOnDhRaZBlInNpG+kNPy/VA8fIMYbB3YNMfkwiqmpIj2BsP5Bi8uO6u9iic0uJjWhMREREVMfV2Sd27uf8+fMAgPDwcNjZ2VVa98ILL+Dq1asYNmwYfv31V+zcuRNPPvkk/vrrL7z00kvmCJeoEhsbOSaPaGLy46rsFXh6WJjJj0tEVT0+sCGcHe8xPZ4RPT0sDA4cRIWIiIjIolh1Yee/r2EBwMiRI/Hrr78iLy8PEyZMwJgxY3Dp0iWsW7cO06ZNM3WoRNV6ZkQ4FArTvhI1ZkAo3F3sHrwhERmdk4MNnhra2OTHfW5kuMmPSURERET3Z5W33e5X2AGAwYMHY/DgwaYMicgggX6OmDG+GT5afd4kx3N2tMFbz7c2ybGISD9zJ7fEDzvicTtPbZLjTRkdgcb1XU1yLCIiIiLSn1UXdlq0aGHmSMTlHlkfnT9+DjZOKhSlZuHA1M/gHhaEPuteR378DewasxClt/Lh2bIhOrzzPyjsbKGwt8G1H//EheVbAQBt541HyCOdkX36GvZP/sTMn+jBihPOImnZZOhKCmDrXR8hL69BaXIsrr49EPb1wtD4rV2wcfNB+s/v4da+76BOv4qGc7fBrd0/hbv46MegzrhW8XNJ4jk0nPML3DoMRebWxcjasQwKRzdELIoxx0e8p7dfaI1f/0rGpYQ8ox9r0asdEOzvZPTjGErf3/kui6bAp10TaEvLUFZYguNvfIvbsYkV7TR5qj8iJj4MQauDoBPw28DZ0KrLEfnMYIRP6I+y/GJsHzDLfB/0PvQ9BxKXPI3i+JOAXA6Zwgb1noyGS8veAIDEJROQf3YPlC53BsV1adkXgU9/BAAWfQ5YOz8vB3w2uxOemLPf6McKCXDCBy+3M/pxakLfPHBXw5Hd0W3pVOwZ/z5S95ys1JZfp6bo9/ObOD5vFS6t2gkAFp8H9M0Bl+f2QFlWMhQOLgAAz55PwfeRlwEAaWvmIvfENsjkdyaO8BsxBx7dRgNgDiAiIpICqyzsHD582NwhGEXXT1/EoZeX4XZsIhqN6YV288bj2ob9yL2SWulitPNHz+HMxxuQsisGtm5OGH5wCVL2nETelVTELFyD3MspCOzbxoyfRH+JSyYgZNoqOIRGIXvPt0hbPROevSZAFRRZ6QLUpUVveHQdjcTPJ1Zpo+HsjRX/X3Q1BlcXDIBL6/4AAN9HXoZDaKWrwH8AAAttSURBVCukrnrV+B/GQPZ2Snz3Tnc89PRvUJdp9donI7u40n/1MaR7MCY+aplj6+j7O5+88zgOz1wJQatDYJ826P7FDGzpeufVyqD+7RD6aDf8Nuh1lBcUw97TBbryO3+fcV9ux+3zCWg7/0mzfD596HsOBE5cDKWTGwCg+PoZXHmzD1quyaqY5cxvxGz4DHqxSvuWfA4QMHZgKH79Kxk/7ryu9z6G5gEbpRyr33kITg6mH9NHH/rmAQBw8PdAk/H9cDPmcpV2lI72aPPGOKTtO1NpuaXnAX1zAAAETV5a6cbGXb7DZ6Le+HcBAGW3biD2hXC4tOoHpZM7cwAREZEEWOUYO3WRR7MG0BSXVjyFEP/TfgT1awuZsvp/YltXRwCA0sEOujINynIKTRWqaIqvn4ZC5QSH0CgAd+4+5h7fBkGrqbKtY5MOsPNv+MA2b+39Fp49xkFuI42xZNo398bGT3rB5h7/zv/Vbuw2BPX9Ee3GbtNr+26tffHjhz0tcopzQ37nU3bFQNDqAABZp67AKdAb+P/P1GzKUJz5eAPKC+58yS29lQ9BpzPNh6glQ86Bu0UdANAW5VrkvykZTiaTYfU7D6FfZ/1nbTQkDygUMqyL7oHubf1rE6bRGNr3df7oORyfvxq6sqrnSPsFE3Bh+Vaob+dXs6dlMiQH3M+/84OupOBOfpRIHiQiIiIrfWKnLnIK9oF7eDCG7v6oYpnSwR4qX/cq2x56eRl6rZ6FVrPGwN7DBYdf+wIlWbkmjFYc6swElCSdR9z0qIplutIilOek16g9XVkpbh9YjybvHRApQtMY3D0Yv37WFyNm7EVRiWEX8/fzcNdAbPykt8XOgGPI7/y/RU4ahNR9pwBBAAC4NQ6EV8uGiHp1NBR2Noj/+S9c/GaHUWMXi6HnQOp3s5Fz+GdoC3PQcPbmSsWdzK2LkP3Hl7D1DkbAE+9UfFEky2dnq8DWJX3w+Oz92LI3SbR27e0U+PHDnnikZ33R2hSbIXmgyZP9kHs5Bdmnr1ZZV69XK9i6OCLpt6MIksgTq4DhOSBt9UykrZkDVWAk6j35Puz8QivW3fx1KW7uWIbyW6moP/VbKF08jR0+ERERicQyv7FRjWSeuIS949+v+HnMhW+q3a7plEcQ8/b3SPz1CJyCfTBg8wJkn76G/PgbpgpVNI7hXdD4zd8qfj473rvGbeUc3gS7gMZQhTQXIzST6t8lEOc3PYr/zT+I/SdqVti6y8FeiejpbfHCmEjI5Zb9VIe+v/N3hY7ohpAhnfD7sHkVy2RKBZxD/LBz+JuwcXHAw5sXID8hHWn7ThstbjEZcg4EPhWNwKeikX9mD1JXz0ST6EOQ29giYNy7sHH3h0wuR86RLbj69sNotuIqFCrLG1eJqmdvp8SmRb3x1abLeOXj4ygsLq9Ve11a+WLV290kMViyPnnAKcgHYeP6YMfQN6qss3VxQJvXn8Cu0W8bNU5j0TcHNJi+BrbeQRAEAVk7luHawsFouiyuYr3PkGnwGTINxQlnkbB4HFxa9mFxh4iISCL4KlYdUZiUCad6XhU/2zipoFDZoSQzp9J2dh7OqP9weyT+euTOfsk3kX3qGnzaNjFpvGKw8w1FWXZyxc/a4nzo1MWwca/ZKwO39nwDrz5Vx+CRigaBztj71cNYOa8LQgIM/0KuUMjwaO8QnNs0HFMfb2rxRR19f+fvChnaGVEzRlUZSLUoLRvXt/wNQadDWW4hUvedhncr008jXRM1PQdcovpAW1KAkqQ7A8nbetaDTH6nO3DvNBwKlQtK06qOQUKWTSaT4ZnHwnFh83CMGRAKpdLwczjIzxFLZ3fEX98OlERRR9884N02DA6+7hh+4FM8dnw5vFs3RpdFz6PxE33gFh4Mla87Bv8ejceOL0f9wR0R9epoRL06ytQfx2CG5ABb7yAAd35PfAa9CHXmdWjyb1XZzqFBS9h61EPBhf1Gi5uIiIjExSd26ojbsYkQdAL8ujRDxqELCJ/QH4lbD0PQVH5Hviy3CNrSMvh2ikTmkTjYeTjDu3VjnF/2i3kCrwWH0CjIZHLkn9sHlxa9kLVjOdy7joZMYfivtTozAUVXj6Ph67+IH6gJyeUyPDsyHJMeDcMfh9Pw5cZLOHgq857TIcvlMkQ0cMWIPg0weUQTBPo5mjjimtP3dx4AQoZ0QutZY/HH6AUoSsuutO76lr9Rr2cUMg5dgMLOBn6dmuL851tM9TFqRd9zQNBqoM5MgH3AnYJV0ZXj0OTdrHgNoyw7FbZegQCAwstHoSm4BXv/Rqb9MCSa+gHOWP9hTyzK6oCvN1/Gz7sSEHc9F1qtUO32bs626NLKF5NHNMGgbkFQ6jlmlyXQNw8kbPkbCVv+rvh5wKYFuLBiW8WsWBua/1PU7/rpC8g+G18xK5YlMyQHaApuwcbNF8CdJ1Rt3HwrnsgpSbkIVVAEAECdHo/i66dhHxRp2g9DRERENcbCTh1ycOpn6PLpFNg6O6AgMQMHXlwK9ybBlbYRdDrsf3YR2r31FOQKBeQ2ClxYuQ23zsabKeraCXl5DZKWPo3kojzY+TdCgxlrUZJ0ocp26RvfR9aOZdDkZSFxyQTIbe0Rsfg0bFzvPLKevedbuHUaUTENrNQpFHIM7BaEgd3uPHafdKMQpy/dQm5BGco1OtjbKtAwyAVRTTzgaKEz3ehDn995AHho2UsouZmL3qv+mSHnj1ELoM4pRNyX29Hpw2cx7K/FEAQg6dcjSP79uCk/Rq3ocw4IOi0Sl0yAtjgPMrkCcjtHhM7aCKXTnXFIEpdMQHle5p11tio0fO1nKBwt/2kNuj9/bwfMe7YV5j3bCsUlGpy9cgvXkvNRotZCqZDDzdkWUeEeaFDPWdKDaeubB+oqfXKArlyNa28Pgk6jhkwmh9LFCw3n/jN4dtr3s6FOvwaZ0gYyuRLBz35eUeghIiIiy8fCTh2SczEJ2/vPeuB26QfP67WdFDiEtEDEopMP3M7/sTnwf2zOPdfXe2KhmGFZFJlMhpB6zgip52zuUESn7+/898Fj7rlOW1qGv6d9JmZYJqXPOSC3sUP4B4fuuT5s4R6xwyIL46BSolNLX3Rq6WvuUESnbx74t50j5t9z3d/Tl9U2JJPSJwco7B2rTH3+b43mbhU7LCIiIjIh6TxvTTWiLddA5eWKobs/gr3ng59GaTtvPJpPHY6yvCITRGccMqUtynMzETc9CuW5N2vVVubWxUheOQVKF68Hb0wWwdDf+fuJfGYwOkZPktT0xwDPASJrzwPMAURERNZFJghC9S/dk1mVF5diXcNx5g5Db0/Er4WNg71o7ZVogG7SmHG6wsGBgIXODC5JUjsHAHHPA54DZO2sPQcA0ssDzAFERETmwSd2iIiIiIiIiIgkioUdIiIiIiIiIiKJ4qtYFkoQBGhKqp+i2hIpVXaizqoiCECpVrTmTMJeAUh4YhmLI7VzABD3POA5QNbO2nMAIL08wBxARERkHizsEBERERERERFJFF/FIiIiIiIiIiKSKBZ2iIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIqFHSIiIiIiIiIiiWJhh4iIiIiIiIhIoljYISIiIiIiIiKSKBZ2iIiIiIiIiIgkioUdIiIiIiIiIiKJYmGHiIiIiIiIiEiiWNghIiIiIiIiIpIoFnaIiIiIiIiIiCSKhR0iIiIiIiIiIoliYYeIiIiIiIiISKJY2CEiIiIiIiIikigWdoiIiIiIiIiIJIqFHSIiIiIiIiIiiWJhh4iIiIiIiIhIoljYISIiIiIiIiKSqP8D/n5V2r0OBX0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1458.05x785.944 with 1 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Build the quantum circuit\n",
-    "qc = efficient_su2(num_qubits, [\"ry\", \"rz\"], reps=2)"
+    "qc = efficient_su2(num_qubits, su2_gates=[\"ry\", \"rz\"], reps=2)\n",
+    "qc.draw(\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "162f1384-98f5-406e-b5ae-0e12d0ad4b59",
    "metadata": {},
    "outputs": [],
@@ -357,12 +428,12 @@
     "\n",
     "$\\mathcal{L}^{(\\text{reg})}$ is defined as $\\mathcal{L}^{(\\text{reg})} \\coloneqq \\beta \\nu \\lbrack \\frac{1}{m} \\sum_{i \\in V} \\text{tanh} (\\alpha \\langle\\prod_i \\rangle)^2 \\rbrack ^2$\n",
     "\n",
-    "where $\\beta=1/2$, $\\nu = |E|/2 + (m -1) /4$, and $m$ is the number of nodes in the graph."
+    "where $\\beta=1/2$, $\\nu = |E|/2 + (m -1) /4$, $|E|$ is the number of edges, and $m$ is the number of nodes in the graph."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "fe608e6a-08ce-493d-9b0a-eb9d6e0028ff",
    "metadata": {},
    "outputs": [],
@@ -430,12 +501,12 @@
    "id": "583ba786-e2e9-4593-bf75-00723b589d78",
    "metadata": {},
    "source": [
-    "In this tutorial, we set `max_iter=50` for the optimization loop for demonstration purpose. If we increase the number of iterations, we can expect better results."
+    "In this tutorial, we set `max_iter=50` in the optimization loop for demonstration purposes. If we increase the number of iterations, we can expect better results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "8d203dd5-8b72-4b78-a36e-c10fdef3ebc3",
    "metadata": {},
    "outputs": [],
@@ -454,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "8d9d6313-9bcd-4ffb-b40c-361d18c68afe",
    "metadata": {},
    "outputs": [
@@ -462,68 +533,70 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 0: 16659.649201600296\n",
-      "Iter 1: 12104.242957555361\n",
-      "Iter 2: 6541.137221994661\n",
-      "Iter 3: 6650.6188244671985\n",
-      "Iter 4: 7033.193518185085\n",
-      "Iter 5: 6743.687931793412\n",
-      "Iter 6: 6223.574718684094\n",
-      "Iter 7: 6457.3302709535965\n",
-      "Iter 8: 6581.316449107595\n",
-      "Iter 9: 6365.761052029896\n",
-      "Iter 10: 6415.872673527322\n",
-      "Iter 11: 6421.996561600348\n",
-      "Iter 12: 6636.372822791712\n",
-      "Iter 13: 6965.174320702346\n",
-      "Iter 14: 6774.236562696287\n",
-      "Iter 15: 6393.837617108355\n",
-      "Iter 16: 6234.311401676519\n",
-      "Iter 17: 6518.192237615901\n",
-      "Iter 18: 6559.933925068997\n",
-      "Iter 19: 6646.157979243488\n",
-      "Iter 20: 6573.726111605048\n",
-      "Iter 21: 6190.642092901959\n",
-      "Iter 22: 6653.06500163594\n",
-      "Iter 23: 6545.713700369988\n",
-      "Iter 24: 6399.996441760465\n",
-      "Iter 25: 6115.959687941808\n",
-      "Iter 26: 6665.915093554849\n",
-      "Iter 27: 6832.882201259893\n",
-      "Iter 28: 6541.392749578919\n",
-      "Iter 29: 6813.3456910443165\n",
-      "Iter 30: 6460.800944368402\n",
-      "Iter 31: 6359.635437029245\n",
-      "Iter 32: 6040.891641882451\n",
-      "Iter 33: 6573.930674936448\n",
-      "Iter 34: 6668.031753293785\n",
-      "Iter 35: 6450.002712889748\n",
-      "Iter 36: 6519.8298811058075\n",
-      "Iter 37: 6467.134502398199\n",
-      "Iter 38: 6655.284651397334\n",
-      "Iter 39: 6371.168353987336\n",
-      "Iter 40: 6480.337259347923\n",
-      "Iter 41: 6339.256786764425\n",
-      "Iter 42: 6588.635046825541\n",
-      "Iter 43: 6617.677964971322\n",
-      "Iter 44: 6469.0441600679205\n",
-      "Iter 45: 6567.874244906106\n",
-      "Iter 46: 6217.899975264532\n",
-      "Iter 47: 6783.481394627947\n",
-      "Iter 48: 6813.371853626112\n",
-      "Iter 49: 6506.5871531488765\n",
-      " message: Maximum number of function evaluations has been exceeded.\n",
+      "Iter 0: 159.88755362682548\n",
+      "Iter 1: 113.46202580636677\n",
+      "Iter 2: 56.76494226400048\n",
+      "Iter 3: 32.63357946896002\n",
+      "Iter 4: 21.517837239610117\n",
+      "Iter 5: 30.96034960483569\n",
+      "Iter 6: 20.780475923938027\n",
+      "Iter 7: 24.54251816279811\n",
+      "Iter 8: 27.834486461763042\n",
+      "Iter 9: 16.705460776812693\n",
+      "Iter 10: 18.020587887236864\n",
+      "Iter 11: 12.252379762741352\n",
+      "Iter 12: 5.253885750886939\n",
+      "Iter 13: 6.985984759592262\n",
+      "Iter 14: 6.908717244584757\n",
+      "Iter 15: 12.915466016863858\n",
+      "Iter 16: 4.105776920457279\n",
+      "Iter 17: 11.707504530740305\n",
+      "Iter 18: 7.154360511076546\n",
+      "Iter 19: 10.3890865704735\n",
+      "Iter 20: 10.376147647857252\n",
+      "Iter 21: 2.533430195296697\n",
+      "Iter 22: 3.8612421907795462\n",
+      "Iter 23: 6.103735057461906\n",
+      "Iter 24: -1.1190368234312347\n",
+      "Iter 25: 6.125915279494738\n",
+      "Iter 26: 11.086280445482455\n",
+      "Iter 27: 10.102569882302827\n",
+      "Iter 28: -0.02664415648133822\n",
+      "Iter 29: 7.621887727398785\n",
+      "Iter 30: 5.967346615554497\n",
+      "Iter 31: 3.85345716014828\n",
+      "Iter 32: 4.5494846149011\n",
+      "Iter 33: 10.006668112637232\n",
+      "Iter 34: -3.1927138938527877\n",
+      "Iter 35: 2.8829882366285116\n",
+      "Iter 36: 3.3130087521654144\n",
+      "Iter 37: -4.907566569808272\n",
+      "Iter 38: -4.980134722109894\n",
+      "Iter 39: -2.990457463896541\n",
+      "Iter 40: -5.938401817344579\n",
+      "Iter 41: -2.1807712386469724\n",
+      "Iter 42: -1.0945774380342126\n",
+      "Iter 43: -4.7548102593556685\n",
+      "Iter 44: -3.8762362299208144\n",
+      "Iter 45: -4.9348321021624\n",
+      "Iter 46: -6.487722842864011\n",
+      "Iter 47: 0.7064210113389331\n",
+      "Iter 48: -2.3428323031772216\n",
+      "Iter 49: -2.626032270380895\n",
+      " message: Return from COBYLA because the objective function has been evaluated 50 times.\n",
       " success: False\n",
-      "  status: 2\n",
-      "     fun: 6040.891641882451\n",
-      "       x: [ 1.375e+00  1.951e+00 ...  1.923e-01  4.087e-02]\n",
-      "    nfev: 50\n",
-      "   maxcv: 0.0\n"
+      "  status: 3\n",
+      "     fun: -2.626032270380895\n",
+      "       x: [ 1.375e+00  1.951e+00 ...  9.395e-01  8.948e-01]\n",
+      "    nfev: 50\n"
      ]
     }
    ],
    "source": [
-    "# Run the optimization using Session\n",
+    "max_iter = 50\n",
+    "counter = {\"i\": 0}\n",
+    "last_x = {\"value\": None}\n",
+    "last_fun = {\"value\": None}\n",
     "\n",
     "with Session(backend=backend) as session:\n",
     "    estimator = Estimator(mode=session)\n",
@@ -531,15 +604,36 @@
     "    experiment_result = []\n",
     "\n",
     "    def loss_func(x):\n",
-    "        return loss_func_estimator(\n",
+    "        last_x[\"value\"] = x.copy()\n",
+    "        if counter[\"i\"] + 1 > max_iter:\n",
+    "            return last_fun[\"value\"]\n",
+    "        counter[\"i\"] += 1\n",
+    "        val = loss_func_estimator(\n",
     "            x, qc, [pce[0], pce[1], pce[2]], estimator, graph\n",
     "        )\n",
+    "        last_fun[\"value\"] = val\n",
+    "        return val\n",
     "\n",
-    "    np.random.seed(42)\n",
+    "    np.random.seed(seed)\n",
     "    initial_params = np.random.rand(qc.num_parameters)\n",
+    "\n",
     "    result = minimize(\n",
-    "        loss_func, initial_params, method=\"COBYLA\", options={\"maxiter\": 50}\n",
+    "        loss_func,\n",
+    "        initial_params,\n",
+    "        method = \"COBYLA\",\n",
+    "        options = {\"rhobeg\": 1.0}\n",
     "    )\n",
+    "    \n",
+    "    if counter[\"i\"] >= max_iter:\n",
+    "        result = OptimizeResult(\n",
+    "            message = f\"Return from COBYLA because the objective function has been evaluated {max_iter} times.\",\n",
+    "            success = False,\n",
+    "            status = 3,\n",
+    "            fun = last_fun[\"value\"],\n",
+    "            x = last_x[\"value\"],\n",
+    "            nfev = counter[\"i\"],\n",
+    "        )\n",
+    "\n",
     "print(result)"
    ]
   },
@@ -555,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "fa2db108-754b-4036-af98-87f5390a9c11",
    "metadata": {},
    "outputs": [
@@ -563,7 +657,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{0, 1, 4, 8, 9, 10, 12, 13, 14, 15, 16, 18, 25, 27, 31, 32, 34, 36, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 52, 57, 60, 61, 62, 63, 64, 65, 66, 68, 71, 79, 81, 82, 86, 88, 91, 92, 93, 94, 95, 96, 99, 100, 105, 106, 107, 112, 114, 115, 121, 123, 129, 133, 134, 145, 147, 161, 165, 166, 168, 171, 173, 184, 185, 187, 188, 192, 193, 194, 196, 197, 198, 202, 205, 206, 207, 208, 209, 210, 211, 215, 217, 218, 219, 220, 221, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 238, 241, 242, 243, 244, 246, 247, 248, 249, 251, 252, 253, 255, 256, 257, 258, 259, 261, 262, 264, 265, 266, 268, 269, 270, 272, 273, 275, 276, 277, 278, 279, 281, 283, 284, 285, 286, 288, 292, 293, 294, 299, 300, 303, 305, 306, 307, 308, 310, 312, 313, 314, 316, 317, 319, 321, 326, 327, 328, 333, 336, 338, 340, 341, 342, 344, 345, 346, 349, 351, 352, 353, 356, 357, 360, 361, 362, 363, 364, 366, 368, 370, 374, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 393, 394, 395, 396, 397, 398, 404, 405, 406, 409, 411, 413, 415, 416, 418, 421, 425, 426, 427, 428, 429, 433, 434, 435, 437, 444, 450, 456, 457, 458, 459, 462, 463, 465, 467, 469, 470, 472, 476, 479, 484, 487, 489, 492, 493, 497, 498, 499, 502, 506, 508, 513, 516, 517, 518, 519, 521, 523, 526, 527, 528, 531, 532, 533, 535, 536, 537, 539, 540, 541, 542, 543, 544, 545, 547, 549, 550, 552, 557, 562, 563, 564, 565, 567, 568, 569, 570, 571, 572, 573, 576, 578, 579, 580, 583, 585, 587, 588, 589, 591, 595, 596, 597, 600, 602, 603, 604, 605, 606, 607, 608, 609, 610, 612, 618, 619, 623, 624, 625, 626, 627, 628, 630, 632, 636, 637, 640, 644, 646, 649, 652, 656, 657, 658, 659, 661, 662, 663, 664, 667, 669, 670, 671, 672, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 692, 693, 694, 695, 696, 698, 700, 701, 703, 706, 707, 708, 709, 712, 713, 714, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 728, 730, 731, 733, 734, 735, 737, 739, 740, 741, 743, 744, 746, 748, 750, 751, 752, 753, 754, 758, 760, 761, 762, 763, 764, 765, 766, 774, 778, 780, 782, 787, 795, 800, 802, 803, 808, 809, 812, 818, 822, 825, 827, 834, 836, 840, 843, 845, 847, 850, 853, 854, 857, 858, 863, 864, 865, 866, 867, 868, 869, 870, 872, 873, 874, 875, 876, 878, 880, 881, 882, 883, 884, 885, 887, 888, 889, 890, 891, 893, 894, 895, 896, 898, 901, 902, 903, 904, 905, 907, 908, 910, 911, 912, 913, 914, 915, 916, 917, 918, 920, 921, 923, 925, 926, 928, 929, 930, 932, 934, 935, 936, 938, 939, 941, 943, 945, 946, 947, 948, 949, 953, 955, 956, 957, 958, 959, 961, 966, 975, 978, 980, 983, 988, 990, 996, 999} {2, 3, 5, 6, 7, 11, 17, 19, 20, 21, 22, 23, 24, 26, 28, 29, 30, 33, 35, 37, 42, 43, 45, 53, 54, 55, 56, 58, 59, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 80, 83, 84, 85, 87, 89, 90, 97, 98, 101, 102, 103, 104, 108, 109, 110, 111, 113, 116, 117, 118, 119, 120, 122, 124, 125, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, 163, 164, 167, 169, 170, 172, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 186, 189, 190, 191, 195, 199, 200, 201, 203, 204, 212, 213, 214, 216, 222, 223, 224, 237, 239, 240, 245, 250, 254, 260, 263, 267, 271, 274, 280, 282, 287, 289, 290, 291, 295, 296, 297, 298, 301, 302, 304, 309, 311, 315, 318, 320, 322, 323, 324, 325, 329, 330, 331, 332, 334, 335, 337, 339, 343, 347, 348, 350, 354, 355, 358, 359, 365, 367, 369, 371, 372, 373, 375, 376, 377, 385, 392, 399, 400, 401, 402, 403, 407, 408, 410, 412, 414, 417, 419, 420, 422, 423, 424, 430, 431, 432, 436, 438, 439, 440, 441, 442, 443, 445, 446, 447, 448, 449, 451, 452, 453, 454, 455, 460, 461, 464, 466, 468, 471, 473, 474, 475, 477, 478, 480, 481, 482, 483, 485, 486, 488, 490, 491, 494, 495, 496, 500, 501, 503, 504, 505, 507, 509, 510, 511, 512, 514, 515, 520, 522, 524, 525, 529, 530, 534, 538, 546, 548, 551, 553, 554, 555, 556, 558, 559, 560, 561, 566, 574, 575, 577, 581, 582, 584, 586, 590, 592, 593, 594, 598, 599, 601, 611, 613, 614, 615, 616, 617, 620, 621, 622, 629, 631, 633, 634, 635, 638, 639, 641, 642, 643, 645, 647, 648, 650, 651, 653, 654, 655, 660, 665, 666, 668, 673, 691, 697, 699, 702, 704, 705, 710, 711, 715, 720, 727, 729, 732, 736, 738, 742, 745, 747, 749, 755, 756, 757, 759, 767, 768, 769, 770, 771, 772, 773, 775, 776, 777, 779, 781, 783, 784, 785, 786, 788, 789, 790, 791, 792, 793, 794, 796, 797, 798, 799, 801, 804, 805, 806, 807, 810, 811, 813, 814, 815, 816, 817, 819, 820, 821, 823, 824, 826, 828, 829, 830, 831, 832, 833, 835, 837, 838, 839, 841, 842, 844, 846, 848, 849, 851, 852, 855, 856, 859, 860, 861, 862, 871, 877, 879, 886, 892, 897, 899, 900, 906, 909, 919, 922, 924, 927, 931, 933, 937, 940, 942, 944, 950, 951, 952, 954, 960, 962, 963, 964, 965, 967, 968, 969, 970, 971, 972, 973, 974, 976, 977, 979, 981, 982, 984, 985, 986, 987, 989, 991, 992, 993, 994, 995, 997, 998}\n"
+      "{0, 2, 3, 8, 9, 11, 12, 13, 17, 18, 20, 22, 23, 24, 25, 26, 27, 30, 35, 37, 38, 40, 43, 46, 48, 49, 50, 51, 53, 57, 61, 62, 63, 66, 67, 68, 70, 71, 74, 77, 81, 82, 83, 84, 87, 88, 94, 96, 99} {1, 4, 5, 6, 7, 10, 14, 15, 16, 19, 21, 28, 29, 31, 32, 33, 34, 36, 39, 41, 42, 44, 45, 47, 52, 54, 55, 56, 58, 59, 60, 64, 65, 69, 72, 73, 75, 76, 78, 79, 80, 85, 86, 89, 90, 91, 92, 93, 95, 97, 98}\n"
      ]
     }
    ],
@@ -571,14 +665,16 @@
     "# Calculate the partitions based on the final expectation values\n",
     "# If the expectation value is positive, the node belongs to partition 0 (par0)\n",
     "# Otherwise, the node belongs to partition 1 (par1)\n",
-    "\n",
-    "par0, par1 = set(), set()\n",
-    "\n",
-    "for i in experiment_result[-1][\"exp_map\"]:\n",
-    "    if experiment_result[-1][\"exp_map\"][i] >= 0:\n",
-    "        par0.add(i)\n",
-    "    else:\n",
-    "        par1.add(i)\n",
+    "def get_partitions(experiment_result):\n",
+    "    par0, par1 = set(), set()\n",
+    "    best_index = min(range(len(experiment_result)), key=lambda i: experiment_result[i][\"loss\"])\n",
+    "    for i in experiment_result[best_index][\"exp_map\"]:\n",
+    "        if experiment_result[best_index][\"exp_map\"][i] >= 0:\n",
+    "            par0.add(i)\n",
+    "        else:\n",
+    "            par1.add(i)\n",
+    "    return par0, par1, best_index\n",
+    "par0, par1, best_index = get_partitions(experiment_result)\n",
     "print(par0, par1)"
    ]
   },
@@ -592,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "bd85ceae-ef8b-4e21-b447-e92ca92e06eb",
    "metadata": {},
    "outputs": [
@@ -600,7 +696,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cut size: 24682\n"
+      "Cut size: 268\n"
      ]
     }
    ],
@@ -620,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "id": "b0df38ef-98d0-4d8c-bbdf-75d14e4680f7",
    "metadata": {},
    "outputs": [
@@ -628,16 +724,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1]\n"
+      "[1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1]\n"
      ]
     }
    ],
    "source": [
-    "best_bits = []\n",
     "cur_bits = []\n",
     "\n",
-    "for i in experiment_result[-1][\"exp_map\"]:\n",
-    "    if experiment_result[-1][\"exp_map\"][i] >= 0:\n",
+    "for i in experiment_result[best_index][\"exp_map\"]:\n",
+    "    if experiment_result[best_index][\"exp_map\"][i] >= 0:\n",
     "        cur_bits.append(1)\n",
     "    else:\n",
     "        cur_bits.append(0)\n",
@@ -646,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "id": "232658b0-a86b-4cb5-aff8-c8f2423491b6",
    "metadata": {},
    "outputs": [
@@ -654,34 +749,251 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "24733 [1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1]\n"
+      "279 [1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1]\n"
      ]
     }
    ],
    "source": [
     "# Swap the partitions and calculate the cut size\n",
-    "best_cut = 0\n",
-    "for edge0, edge1 in graph.edge_list():\n",
-    "    swapped_bits = cur_bits.copy()\n",
-    "    swapped_bits[edge0], swapped_bits[edge1] = (\n",
-    "        swapped_bits[edge1],\n",
-    "        swapped_bits[edge0],\n",
-    "    )\n",
     "\n",
-    "    cur_partition = [set(), set()]\n",
-    "    for i, bit in enumerate(swapped_bits):\n",
-    "        if bit > 0:\n",
-    "            cur_partition[0].add(i)\n",
-    "        else:\n",
-    "            cur_partition[1].add(i)\n",
-    "    cut_size = calc_cut_size(graph, cur_partition[0], cur_partition[1])\n",
-    "    if best_cut < cut_size:\n",
-    "        best_cut = cut_size\n",
-    "        best_bits = swapped_bits\n",
+    "def swap_partitions(graph, cur_bits):\n",
+    "    best_cut = 0\n",
+    "    best_bits = []\n",
+    "    for edge0, edge1 in graph.edge_list():\n",
+    "        swapped_bits = cur_bits.copy()\n",
+    "        swapped_bits[edge0], swapped_bits[edge1] = (\n",
+    "            swapped_bits[edge1],\n",
+    "            swapped_bits[edge0],\n",
+    "        )\n",
     "\n",
+    "        cur_partition = [set(), set()]\n",
+    "        for i, bit in enumerate(swapped_bits):\n",
+    "            if bit > 0:\n",
+    "                cur_partition[0].add(i)\n",
+    "            else:\n",
+    "                cur_partition[1].add(i)\n",
+    "        cut_size = calc_cut_size(graph, cur_partition[0], cur_partition[1])\n",
+    "        if best_cut < cut_size:\n",
+    "            best_cut = cut_size\n",
+    "            best_bits = swapped_bits\n",
+    "    return best_cut, best_bits\n",
+    "best_cut, best_bits = swap_partitions(graph, cur_bits)\n",
     "print(best_cut, best_bits)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Large Scale Hardware Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We are using 33 qubits\n",
+      "We are using the ibm_pittsburgh\n",
+      "Iter 0: 57399.57543902076\n",
+      "Iter 1: 56458.787143794\n",
+      "Iter 2: 40778.45608998947\n",
+      "Iter 3: 35571.58511146131\n",
+      "Iter 4: 33861.6835761173\n",
+      "Iter 5: 39697.22637736274\n",
+      "Iter 6: 34984.77893767163\n",
+      "Iter 7: 32051.882157096858\n",
+      "Iter 8: 26134.153216063707\n",
+      "Iter 9: 24914.322627065787\n",
+      "Iter 10: 24030.21227315425\n",
+      "Iter 11: 23047.463945514\n",
+      "Iter 12: 22629.42866110748\n",
+      "Iter 13: 17374.859132614685\n",
+      "Iter 14: 18020.11637762458\n",
+      "Iter 15: 17924.7066364044\n",
+      "Iter 16: 15825.1992250984\n",
+      "Iter 17: 16553.346711978447\n",
+      "Iter 18: 12393.565736512377\n",
+      "Iter 19: 11994.021456089155\n",
+      "Iter 20: 11199.994322735669\n",
+      "Iter 21: 9624.895532927634\n",
+      "Iter 22: 9073.811130188606\n",
+      "Iter 23: 9836.721241931278\n",
+      "Iter 24: 10555.925186133794\n",
+      "Iter 25: 9179.1179493286\n",
+      "Iter 26: 8495.394826965305\n",
+      "Iter 27: 8913.688189840399\n",
+      "Iter 28: 7830.448471810181\n",
+      "Iter 29: 7757.430542422075\n",
+      "Iter 30: 6796.187594518731\n",
+      "Iter 31: 7307.985913766867\n",
+      "Iter 32: 7340.225833330675\n",
+      "Iter 33: 7064.731899380469\n",
+      "Iter 34: 7632.270657372515\n",
+      "Iter 35: 7049.154710767935\n",
+      "Iter 36: 7486.118442084411\n",
+      "Iter 37: 6302.12602219333\n",
+      "Iter 38: 6244.934230209166\n",
+      "Iter 39: 7154.9748739261395\n",
+      "Iter 40: 6482.109600054041\n",
+      "Iter 41: 5718.475169152395\n",
+      "Iter 42: 5693.008457857462\n",
+      "Iter 43: 4869.782667921923\n",
+      "Iter 44: 4957.625304450959\n",
+      "Iter 45: 5582.240637063214\n",
+      "Iter 46: 4983.90082772116\n",
+      "Iter 47: 5416.268575648202\n",
+      "Iter 48: 4809.98398457807\n",
+      "Iter 49: 5092.527306646118\n",
+      " message: Return from COBYLA because the objective function has been evaluated 50 times.\n",
+      " success: False\n",
+      "  status: 3\n",
+      "     fun: 5092.527306646118\n",
+      "       x: [ 1.375e+00  1.951e+00 ...  7.259e-01  8.971e-01]\n",
+      "    nfev: 50\n",
+      "Cut size: 56152\n",
+      "The best Max-Cut value achieved for a graph with 1500 nodes on 33 qubits is 56219\n",
+      "and the specific partition we obtained is [1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -------------------------Step 1-------------------------\n",
+    "\n",
+    "num_nodes = 1500  # Number of nodes in graph\n",
+    "graph = rx.undirected_gnp_random_graph(num_nodes, 0.1, seed=seed)\n",
+    "nx_graph = nx.Graph()\n",
+    "nx_graph.add_nodes_from(range(num_nodes))\n",
+    "for edge in graph.edge_list():\n",
+    "    nx_graph.add_edge(edge[0], edge[1])\n",
+    "\n",
+    "num_qubits = int(np.ceil((1 + np.sqrt(1 + (8/3) * num_nodes)) / 2))\n",
+    "\n",
+    "list_size = num_nodes // 3\n",
+    "node_x = [i for i in range(list_size)]\n",
+    "node_y = [i for i in range(list_size, 2 * list_size)]\n",
+    "node_z = [i for i in range(2 * list_size, num_nodes)]\n",
+    "\n",
+    "pauli_correlation_encoding_x = build_pauli_correlation_encoding(\n",
+    "    \"X\", node_x, num_qubits\n",
+    ")\n",
+    "pauli_correlation_encoding_y = build_pauli_correlation_encoding(\n",
+    "    \"Y\", node_y, num_qubits\n",
+    ")\n",
+    "pauli_correlation_encoding_z = build_pauli_correlation_encoding(\n",
+    "    \"Z\", node_z, num_qubits\n",
+    ")\n",
+    "print(f\"We are using {num_qubits} qubits\")\n",
+    "\n",
+    "# -------------------------Step 2-------------------------\n",
+    "backend = real_backend\n",
+    "print(f\"We are using the {backend.name}\")\n",
+    "qc = efficient_su2(num_qubits, [\"ry\", \"rz\"], reps=2)\n",
+    "pm = generate_preset_pass_manager(optimization_level=3, backend=backend)\n",
+    "qc = pm.run(qc)\n",
+    "# -------------------------Step 3-------------------------\n",
+    "pce = []\n",
+    "pce.append(\n",
+    "    [op.apply_layout(qc.layout) for op in pauli_correlation_encoding_x]\n",
+    ")\n",
+    "pce.append(\n",
+    "    [op.apply_layout(qc.layout) for op in pauli_correlation_encoding_y]\n",
+    ")\n",
+    "pce.append(\n",
+    "    [op.apply_layout(qc.layout) for op in pauli_correlation_encoding_z]\n",
+    ")\n",
+    "\n",
+    "# Run the optimization using Session\n",
+    "max_iter = 50\n",
+    "counter = {\"i\": 0}\n",
+    "with Session(backend=backend) as session:\n",
+    "    estimator = Estimator(mode=session)\n",
+    "    estimator.options.environment.job_tags = [\"TUT-PCEFQ\"]\n",
+    "    experiment_result = []\n",
+    "    def loss_func(x):\n",
+    "        last_x[\"value\"] = x.copy()\n",
+    "        if counter[\"i\"] + 1 > max_iter:\n",
+    "            return last_fun[\"value\"]\n",
+    "        counter[\"i\"] += 1\n",
+    "        val = loss_func_estimator(\n",
+    "            x, qc, [pce[0], pce[1], pce[2]], estimator, graph\n",
+    "        )\n",
+    "        last_fun[\"value\"] = val\n",
+    "        return val\n",
+    "    np.random.seed(seed)\n",
+    "    initial_params = np.random.rand(qc.num_parameters)\n",
+    "    result = minimize(loss_func, initial_params, method=\"COBYLA\", options={\"rhobeg\": 1.0}\n",
+    "    )\n",
+    "    if counter[\"i\"] >= max_iter:\n",
+    "        result = OptimizeResult(\n",
+    "            message=f\"Return from COBYLA because the objective function has been evaluated {max_iter} times.\",\n",
+    "            success=False,\n",
+    "            status=3,\n",
+    "            fun=last_fun[\"value\"],\n",
+    "            x=last_x[\"value\"],\n",
+    "            nfev=counter[\"i\"],\n",
+    "        )\n",
+    "print(result)\n",
+    "\n",
+    "# -------------------------Step 4-------------------------\n",
+    "\n",
+    "par0, par1, best_index = get_partitions(experiment_result)\n",
+    "cut_size = calc_cut_size(graph, par0, par1)\n",
+    "print(f\"Cut size: {cut_size}\")\n",
+    "\n",
+    "best_bits = []\n",
+    "cur_bits = []\n",
+    "for i in experiment_result[best_index][\"exp_map\"]:\n",
+    "    if experiment_result[best_index][\"exp_map\"][i] >= 0:\n",
+    "        cur_bits.append(1)\n",
+    "    else:\n",
+    "        cur_bits.append(0)\n",
+    "best_cut, best_bits = swap_partitions(graph, cur_bits)\n",
+    "# Print final solution\n",
+    "\n",
+    "print(f\"The best Max-Cut value achieved for a graph with {num_nodes} nodes on {num_qubits} qubits is {best_cut}\")\n",
+    "print(f\"and the specific partition we obtained is {best_bits}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next cell to be deleted after peer-review!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial cut size: 62005\n"
+     ]
+    }
+   ],
+   "source": [
+    "large_scale_cut_size, large_scale_partition = nx.approximation.one_exchange(nx_graph, seed=1)\n",
+    "print(f\"Initial cut size: {large_scale_cut_size}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What to look into next\n",
+    "If you found this work interesting you may be interested in the following material:\n",
+    "- [Advanced Techniques for QAOA](/docs/tutorials/advanced-techniques-for-qaoa)\n",
+    "- [Combine error mitigation options with the Estimator primitive](/docs/tutorials/combine-error-mitigation-techniques)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "99f8259d-fb92-4fae-ab68-96b1e50929a0",
@@ -703,11 +1015,18 @@
     "\n",
     "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_8ANZAlsKSFf6DA2)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "© IBM Corp. 2024-2026"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "3139",
    "language": "python",
    "name": "python3"
   },
@@ -721,7 +1040,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,