From 06d0ab815e034f96fa33113cbadb422ee41e1a27 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Fri, 10 Apr 2026 03:57:42 -0400 Subject: [PATCH 01/17] Revise AI transpiler introduction tutorial Revised ai-transpiler-introduction.ipynb following the Tutorial_Template structure. - Compare default (SABRE) vs AI transpiler using random circuits with only 2-qubit gates across small-scale (6-25 qubits) and large-scale (26-50 qubits) examples - Use mirror circuits to evaluate transpilation fidelity on both Aer simulator (with depolarizing noise model) and real hardware - Add summary tables with mean/stdev and percentage improvement metrics - Add percentage improvement plots comparing AI vs default transpiler - Include commentary on depth vs gate count trade-offs between the two strategies --- .../ai-transpiler-introduction.ipynb | 1391 ++++++++--------- 1 file changed, 642 insertions(+), 749 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 146e9c769c0..880cdc834e4 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -1,95 +1,80 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", - "id": "f59032f0-f29a-4e52-9cab-855ed6f86b00", + "id": "meta-header", "metadata": {}, "source": [ "---\n", "title: Qiskit AI-powered transpiler service introduction\n", - "description: In this notebook, we will explore the key benefits of Qiskit AI-powered transpiler service and how it compares to traditional methods.\n", + "description: Learn how the AI transpiler compares to standard transpilation using mirror circuits.\n", "---\n", "\n", "\n", - "{/* cspell:ignore fontsize idxmin */}\n", + "{/* cspell:ignore fontsize */}\n", "\n", "# Qiskit AI-powered transpiler service introduction\n", - "*Estimated QPU usage: None (NOTE: This tutorial does not execute jobs because it is focused on transpilation)*\n", - "\n", - "## Background\n", - "\n", - "The **Qiskit AI-powered transpiler service (QTS)** introduces machine learning-based optimizations in both routing and synthesis passes. These AI modes have been designed to tackle the limitations of traditional transpilation, particularly for large-scale circuits and complex hardware topologies.\n", - "\n", - "As of **July 2025**, the **Transpiler Service** has been migrated to the new IBM Quantum® Platform and is no longer available. For the latest updates about the status of the Transpiler Service, please refer to the [transpiler service documentation](/docs/guides/qiskit-transpiler-service). You can still use the AI transpiler locally, similar to standard Qiskit transpilation. Simply replace `generate_preset_pass_manager()` with `generate_ai_pass_manager()`. This function constructs a pass manager that integrates the AI-powered routing and synthesis passes directly into your local transpilation workflow.\n", - "\n", - "### Key features of AI passes\n", - "\n", - "- Routing passes: AI-powered routing can dynamically adjust qubit paths based on the specific circuit and backend, reducing the need for excessive SWAP gates.\n", - " - `AIRouting`: Layout selection and circuit routing\n", - "\n", - "- Synthesis passes: AI techniques optimize the decomposition of multi-qubit gates, minimizing the number of two-qubit gates, which are typically more error-prone.\n", - " - `AICliffordSynthesis`: Clifford gate synthesis\n", - " - `AILinearFunctionSynthesis`: Linear function circuit synthesis\n", - " - `AIPermutationSynthesis`: Permutation circuit synthesis\n", - " - `AIPauliNetworkSynthesis`: Pauli Network circuit synthesis (only available in the Qiskit Transpiler Service, not in local environment)\n", - "\n", - "- Comparison with traditional transpilation: The standard Qiskit transpiler is a robust tool that can handle a broad spectrum of quantum circuits effectively. However, when circuits grow larger in scale or hardware configurations become more complex, AI passes can deliver additional optimization gains. By using learned models for routing and synthesis, QTS further refines circuit layouts and reduces overhead for challenging or large-scale quantum tasks.\n", + "*Usage estimate: 5 minutes on IBM Heron (NOTE: This is an estimate only. Your runtime may vary.)*" + ] + }, + { + "cell_type": "markdown", + "id": "learning-outcomes", + "metadata": {}, + "source": [ + "## Learning outcomes\n", + "After going through this tutorial, users should understand:\n", + "- How to use the AI-powered transpiler service (`generate_ai_pass_manager`) as a drop-in replacement for the standard transpiler\n", + "- How the AI transpiler compares to the default transpiler in terms of two-qubit depth, gate count, and transpilation time\n", + "- How to use mirror circuits to evaluate transpilation quality through hardware execution\n", "\n", + "## Prerequisites\n", + "We suggest that users are familiar with the following topics before going through this tutorial:\n", + "- [Transpile circuits](/docs/guides/transpile-circuits)\n", + "- [Configure preset pass managers](/docs/guides/configure-preset-pass-managers)\n", + "- [AI-powered transpiler passes](/docs/guides/ai-transpiler-passes)\n", "\n", - "This tutorial evaluates the AI modes using both routing and synthesis passes, comparing the results to traditional transpilation to highlight where AI offers performance gains.\n", "\n", - "For more details on the available AI passes, see the [AI passes documentation](/docs/guides/ai-transpiler-passes).\n", + "## Background\n", "\n", + "The **Qiskit AI-powered transpiler service (QTS)** introduces machine-learning-based transpilation passes that can produce shorter, more hardware-efficient circuits than traditional heuristic methods such as SABRE. Shorter circuits accumulate less noise, which directly improves result quality on real quantum hardware.\n", "\n", - "### Why use AI for quantum circuit transpilation?\n", + "In this tutorial we compare two transpilation strategies:\n", "\n", - "As quantum circuits grow in size and complexity, traditional transpilation methods struggle to optimize layouts and reduce gate counts efficiently. Larger circuits, particularly those involving hundreds of qubits, impose significant challenges on routing and synthesis due to device constraints, limited connectivity, and qubit error rates.\n", + "| Strategy | API |\n", + "|-|-|\n", + "| **Default** | `generate_preset_pass_manager(optimization_level=3, ...)` |\n", + "| **AI** | `generate_ai_pass_manager(optimization_level=1, ai_optimization_level=3, ...)` |\n", "\n", - "This is where AI-powered transpilation offers a potential solution. By leveraging machine learning techniques, the AI-powered transpiler in Qiskit can make smarter decisions about qubit routing and gate synthesis, leading to better optimization of large-scale quantum circuits.\n", + "We measure three metrics for each strategy: **two-qubit gate depth**, **total gate count**, and **transpilation runtime**.\n", "\n", - "### Brief benchmarking results\n", - "![Graph showing AI transpiler performance against Qiskit](/docs/images/tutorials/ai-transpiler-introduction/ai-transpiler-benchmarks.avif)\n", + "### Mirror circuits for quality evaluation\n", "\n", + "To go beyond transpilation metrics and assess the actual impact on hardware noise, we use **mirror circuits**. A mirror circuit takes an original circuit $U$ and appends its inverse $U^\\dagger$, so the combined operation is the identity: the expected output is always $|0\\rangle^{\\otimes n}$. This is appealingly simple and scales to any circuit size since no classical simulation is needed to know the correct answer.\n", "\n", - "In benchmarking tests, the AI transpiler consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit’s default pass manager strategy, configured with [`generate_preset_passmanager`]. While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum hardware. For more information on these benchmarks, refer to this [blog.](https://www.ibm.com/quantum/blog/qiskit-performance)\n", + "However, be aware of the trade-offs: mirror circuits double the gate count (exaggerating noise effects) and test a proxy circuit rather than your actual workload. Noise can also cancel symmetrically across the mirror boundary, potentially underestimating certain error types.\n", "\n", - "This tutorial explores the key benefits of AI passes and how it compares to traditional methods." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2aa75e36-471f-49aa-8478-134f13e3630b", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden from users;\n", - "# it just disables a linting rule.\n", - "# ruff: noqa: F811" + "Despite these caveats, measuring how close the output distribution is to the all-zeros state gives a practical signal for how well each transpilation strategy preserves circuit fidelity under real device noise." ] }, { "cell_type": "markdown", - "id": "4a781d15-0953-4af6-b581-ea6cb3a74228", + "id": "requirements", "metadata": {}, "source": [ "## Requirements\n", "\n", - "Before starting this tutorial, ensure that you have the following installed:\n", + "Before starting this tutorial, be sure you have the following installed:\n", "\n", - "* Qiskit SDK v1.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", - "* Qiskit Runtime (`pip install qiskit-ibm-runtime`) v0.22 or later\n", - "* Qiskit IBM® Transpiler with AI local mode(`pip install 'qiskit-ibm-transpiler[ai-local-mode]'`)" + "- Qiskit SDK v2.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", + "- Qiskit Runtime (`pip install qiskit-ibm-runtime`) v0.22 or later\n", + "- Qiskit IBM Transpiler with AI local mode (`pip install 'qiskit-ibm-transpiler[ai-local-mode]'`)\n", + "- Qiskit Aer (`pip install qiskit-aer`)" ] }, { "cell_type": "markdown", - "id": "c7c26e24-329b-4283-9cc0-67a241807049", + "id": "setup-header", "metadata": {}, "source": [ "## Setup" @@ -97,22 +82,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "2c462d48-ae45-4528-9b09-cebc869a6812", + "execution_count": 1, + "id": "setup-code", "metadata": {}, "outputs": [], "source": [ "from qiskit import QuantumCircuit\n", - "from qiskit.circuit.library import efficient_su2, PermutationGate\n", - "from qiskit.synthesis.qft import synth_qft_full\n", - "from qiskit.circuit.random import random_circuit, random_clifford_circuit\n", - "from qiskit.transpiler import generate_preset_pass_manager, CouplingMap\n", - "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "from qiskit.circuit.random import random_circuit\n", + "from qiskit.transpiler import generate_preset_pass_manager\n", + "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2\n", "from qiskit_ibm_transpiler import generate_ai_pass_manager\n", - "from qiskit.synthesis.permutation import (\n", - " synth_permutation_depth_lnn_kms,\n", - " synth_permutation_basic,\n", - ")\n", + "from qiskit_aer import AerSimulator\n", + "from qiskit_aer.noise import NoiseModel, depolarizing_error\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", @@ -122,29 +103,8 @@ "seed = 42\n", "\n", "\n", - "# Used for generating permutation circuits in part two for comparison\n", - "def generate_permutation_circuit(width, pattern):\n", - " circuit = QuantumCircuit(width)\n", - " circuit.append(\n", - " PermutationGate(pattern=pattern),\n", - " qargs=range(width),\n", - " )\n", - " return circuit\n", - "\n", - "\n", - "# Creates a Bernstein-Vazirani circuit given the number of qubits\n", - "def create_bv_circuit(num_qubits):\n", - " qc = QuantumCircuit(num_qubits, num_qubits - 1)\n", - " qc.x(num_qubits - 1)\n", - " qc.h(qc.qubits)\n", - " for i in range(num_qubits - 1):\n", - " qc.cx(i, num_qubits - 1)\n", - " qc.h(qc.qubits[:-1])\n", - " return qc\n", - "\n", - "\n", - "# Transpile a circuit with a given pass manager and return metrics\n", "def transpile_with_metrics(pass_manager, circuit):\n", + " \"\"\"Transpile a circuit and return the result along with key metrics.\"\"\"\n", " start = time.time()\n", " qc_out = pass_manager.run(circuit)\n", " elapsed = time.time() - start\n", @@ -155,27 +115,103 @@ " return qc_out, {\n", " \"depth_2q\": depth_2q,\n", " \"gate_count\": gate_count,\n", - " \"time_s\": elapsed,\n", - " }\n", - "\n", - "\n", - "# Used for collecting metrics for part 3 of synthesis methods\n", - "def synth_transpile_with_metrics(qc, pm, pattern_id, method):\n", - " start = time.time()\n", - " qc = pm.run(qc)\n", - " elapsed = time.time() - start\n", - "\n", - " return {\n", - " \"Pattern\": pattern_id,\n", - " \"Method\": method,\n", - " \"Depth (2Q)\": qc.depth(lambda x: x.operation.num_qubits == 2),\n", - " \"Gates\": qc.size(),\n", - " \"Time (s)\": elapsed,\n", + " \"time_s\": round(elapsed, 3),\n", " }\n", "\n", "\n", - "# Ignore logs like \"INFO:qiskit_ibm_transpiler.wrappers.ai_local_synthesis:Running Linear Functions AI synthesis on local mode\"\n", + "def remap_to_contiguous(tqc):\n", + " \"\"\"Remap a transpiled circuit to use contiguous qubit indices.\n", "\n", + " Transpiled circuits target specific physical qubits (e.g., qubit 45, 67)\n", + " on a large backend. This remaps them to 0, 1, 2, ... so Aer only\n", + " simulates the active qubits.\"\"\"\n", + " active = sorted(\n", + " {tqc.find_bit(q).index for inst in tqc.data for q in inst.qubits}\n", + " )\n", + " qubit_map = {old: new for new, old in enumerate(active)}\n", + " new_qc = QuantumCircuit(len(active))\n", + " for inst in tqc.data:\n", + " old_indices = [tqc.find_bit(q).index for q in inst.qubits]\n", + " new_qc.append(inst.operation, [qubit_map[i] for i in old_indices])\n", + " return new_qc\n", + "\n", + "\n", + "def build_mirror_circuit(tqc, simulate=True):\n", + " \"\"\"Build a mirror circuit: U followed by U-dagger, with measurements.\n", + "\n", + " The expected output is always |0...0>, so measuring the survival\n", + " probability reveals how much noise each transpilation strategy adds.\n", + "\n", + " Args:\n", + " tqc: A transpiled circuit.\n", + " simulate: If True (default), remap to contiguous qubits so Aer\n", + " only simulates the active qubits. If False, keep the full\n", + " physical layout for hardware execution.\"\"\"\n", + " if simulate:\n", + " tqc = remap_to_contiguous(tqc)\n", + " mirror = tqc.compose(tqc.inverse())\n", + " mirror.measure_all()\n", + " return mirror\n", + "\n", + "\n", + "def summary_table(df):\n", + " \"\"\"Display a summary table with mean +/- stdev for each metric,\n", + " plus the mean percentage improvement of AI over Default.\"\"\"\n", + " metrics = [\n", + " (\"Depth 2Q\", \"Depth 2Q (Default)\", \"Depth 2Q (AI)\"),\n", + " (\"Gate Count\", \"Gate Count (Default)\", \"Gate Count (AI)\"),\n", + " (\"Time (s)\", \"Time (Default)\", \"Time (AI)\"),\n", + " ]\n", + " rows = []\n", + " for label, col_def, col_ai in metrics:\n", + " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", + " rows.append({\n", + " \"Metric\": label,\n", + " \"Default (mean +/- std)\": f\"{df[col_def].mean():.1f} +/- {df[col_def].std():.1f}\",\n", + " \"AI (mean +/- std)\": f\"{df[col_ai].mean():.1f} +/- {df[col_ai].std():.1f}\",\n", + " \"AI % improvement\": f\"{pct.mean():+.1f}% +/- {pct.std():.1f}%\",\n", + " })\n", + " return pd.DataFrame(rows).set_index(\"Metric\")\n", + "\n", + "\n", + "def plot_metrics_and_pct(df, title_prefix):\n", + " \"\"\"Plot metric comparisons and percentage improvement of AI over Default.\"\"\"\n", + " metrics = [\n", + " (\"Depth 2Q (Default)\", \"Depth 2Q (AI)\", \"Two-Qubit Depth\"),\n", + " (\"Gate Count (Default)\", \"Gate Count (AI)\", \"Gate Count\"),\n", + " (\"Time (Default)\", \"Time (AI)\", \"Transpilation Time\"),\n", + " ]\n", + "\n", + " # Row 1: raw metric comparison\n", + " fig, axs = plt.subplots(1, 3, figsize=(21, 5))\n", + " fig.suptitle(f\"{title_prefix}: Metric Comparison\", fontsize=15, fontweight=\"bold\", y=1.02)\n", + " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", + " ax.plot(df[\"Qubits\"], df[col_def], \"o-\", label=\"Default\")\n", + " ax.plot(df[\"Qubits\"], df[col_ai], \"s-\", label=\"AI\")\n", + " ax.set_title(label)\n", + " ax.set_xlabel(\"Number of Qubits\")\n", + " ax.set_ylabel(label)\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Row 2: percentage improvement\n", + " fig, axs = plt.subplots(1, 3, figsize=(21, 5))\n", + " fig.suptitle(f\"{title_prefix}: % Improvement of AI over Default\", fontsize=15, fontweight=\"bold\", y=1.02)\n", + " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", + " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", + " ax.axhline(0, color=\"#1f77b4\", linewidth=2, label=\"Default (baseline)\")\n", + " ax.plot(df[\"Qubits\"], pct, \"s-\", color=\"#ff7f0e\", label=\"AI\")\n", + " ax.fill_between(df[\"Qubits\"], 0, pct, alpha=0.15, color=\"#ff7f0e\")\n", + " ax.set_title(label)\n", + " ax.set_xlabel(\"Number of Qubits\")\n", + " ax.set_ylabel(\"% Improvement\")\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "# Suppress verbose AI transpiler logs\n", "logging.getLogger(\n", " \"qiskit_ibm_transpiler.wrappers.ai_local_synthesis\"\n", ").setLevel(logging.WARNING)" @@ -183,218 +219,139 @@ }, { "cell_type": "markdown", - "id": "ba7568f8-50c9-47b4-acc0-33ea34f5fca0", + "id": "sim-header", "metadata": {}, "source": [ - "# Part I. Qiskit patterns\n", - "\n", - "Let's now see how to use the AI transpiler service with a simple quantum circuit, using Qiskit patterns. The key is creating a `PassManager` with `generate_ai_pass_manager()` instead of the standard `generate_preset_pass_manager()`." + "## Small-scale simulator example" ] }, { "cell_type": "markdown", - "id": "5ba1bb22-272f-4f8f-ae78-7c3d1cdaacc6", + "id": "sim-step1-header", "metadata": {}, "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", + "### Step 1: Map classical inputs to a quantum problem\n", "\n", - "In this section, we will test the AI transpiler on the `efficient_su2` circuit, a widely used hardware-efficient ansatz. This circuit is particularly relevant for variational quantum algorithms (for example, VQE) and quantum machine-learning tasks, making it an ideal test case for assessing transpilation performance.\n", - "\n", - "The `efficient_su2` circuit consists of alternating layers of single-qubit rotations and entangling gates like CNOTs. These layers enable flexible exploration of the quantum state space while keeping the gate depth manageable. By optimizing this circuit, we aim to reduce gate count, improve fidelity, and minimize noise. This makes it a strong candidate for testing the AI transpiler’s efficiency." + "We generate 20 random circuits with depth 5, where the number of qubits ranges from 6 to 25. These circuits will serve as our test cases for comparing transpilation strategies." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "c6e9c2c0-e02c-4276-bae8-d5692e60b6b8", + "execution_count": 2, + "id": "sim-step1-code", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created 20 circuits with qubit counts: [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# For our transpilation, we will use a large circuit of 101 qubits\n", - "qc = efficient_su2(90, entanglement=\"circular\", reps=1).decompose()\n", + "num_circuits_sim = 20\n", + "depth_sim = 4\n", + "qubit_range_sim = list(range(6, 26))\n", + "\n", + "circuits_sim = [\n", + " # We have only two qubit gates, as those test how well the transpiler can optimize the circuit.\n", + " random_circuit(num_qubits=n, \n", + " depth=depth_sim, \n", + " max_operands=2,\n", + " num_operand_distribution={2: 1},\n", + " seed=seed + i,)\n", + " for i, n in enumerate(qubit_range_sim)\n", + "]\n", "\n", - "# Draw a smaller version of the circuit to get a visual representation\n", - "qc_small = efficient_su2(5, entanglement=\"circular\", reps=1).decompose()\n", - "qc_small.draw(output=\"mpl\")" + "print(f\"Created {len(circuits_sim)} circuits with qubit counts: {qubit_range_sim}\")\n", + "circuits_sim[0].draw(output=\"mpl\", fold=-1)" ] }, { "cell_type": "markdown", - "id": "6c7c76f7-c376-47e9-bc9c-dbe32b2c89b7", + "id": "sim-step2-header", "metadata": {}, "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "\n", - "### Choose a backend\n", - "\n", - "For this example, we select the least busy operational IBM Quantum backend that is not a simulator and has at least 100 qubits:\n", + "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "**Note:** Since the least-busy backend can change over time, different devices might be selected for different runs. Device-specific properties, such as coupling maps, can lead to differences in the transpiled circuits." + "We use a reduced coupling map (the first 30 qubits of the backend) so the transpiled circuits are small enough for local simulation. The hardware section later uses the full coupling map." ] }, { "cell_type": "code", "execution_count": null, - "id": "c6b6e55e-9b70-4c94-8bbf-5ea47d0510ff", + "id": "sim-step2-backend", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using backend: ibm_torino\n" - ] - } - ], + "outputs": [], "source": [ "service = QiskitRuntimeService()\n", "backend = service.least_busy(\n", - " operational=True, simulator=False, min_num_qubits=100\n", - ")\n", - "cm = backend.coupling_map\n", - "print(f\"Using backend: {backend.name}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7b02350f-998e-40cf-a79e-2e6182b5a875", - "metadata": {}, - "source": [ - "### Create AI and traditional pass managers\n", - "To evaluate the effectiveness of the AI transpiler, we will perform two transpilation runs. First, we will transpile the circuit using the AI transpiler. Then, we will run a comparison by transpiling the same circuit without the AI transpiler, using traditional methods. Both transpilation processes will use the same coupling map from the chosen backend and the optimization level set to 3 for a fair comparison.\n", - "\n", - "Both of these methods reflect the standard approach to create `PassManager` instances to transpile circuits in Qiskit." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a1aa25dd-41a9-4416-a959-44f28af613c8", - "metadata": {}, - "outputs": [], - "source": [ - "pm_ai = generate_ai_pass_manager(\n", - " optimization_level=3,\n", - " ai_optimization_level=3,\n", - " coupling_map=cm,\n", - " include_ai_synthesis=True, # used for part 3 when comparing synthesis methods\n", + " min_num_qubits=100, operational=True, simulator=False\n", ")\n", "\n", - "pm_no_ai = generate_preset_pass_manager(\n", + "pm_default_sim = generate_preset_pass_manager(\n", " optimization_level=3,\n", - " coupling_map=cm,\n", - " seed_transpiler=seed, # note that the AI pass manager does not currently support seeding\n", + " backend=backend,\n", + " seed_transpiler=seed,\n", ")" ] }, - { - "cell_type": "markdown", - "id": "a06d6144-3445-4446-a3e1-18ca78a1173c", - "metadata": {}, - "source": [ - "Transpile the circuits and record the times." - ] - }, { "cell_type": "code", - "execution_count": 6, - "id": "fb5167bd-35f0-432f-af6d-023c70783d20", + "execution_count": 4, + "id": "sim-step2-transpile", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Standard transpilation: Depth (2q) 95, Gate count 458, Time 0.04650712013244629\n", - "AI transpilation : Depth (2q) 90, Gate count 456, Time 0.9342479705810547\n" - ] - } - ], - "source": [ - "# Transpile using standard (non-AI) pass manager\n", - "_, metrics_no_ai = transpile_with_metrics(pm_no_ai, qc)\n", - "print(\n", - " f\"Standard transpilation: Depth (2q) {metrics_no_ai['depth_2q']}, \"\n", - " f\"Gate count {metrics_no_ai['gate_count']}, Time {metrics_no_ai['time_s']}\"\n", - ")\n", - "\n", - "# Transpile using AI pass manager\n", - "_, metrics_ai = transpile_with_metrics(pm_ai, qc)\n", - "print(\n", - " f\"AI transpilation : Depth (2q) {metrics_ai['depth_2q']}, \"\n", - " f\"Gate count {metrics_ai['gate_count']}, Time {metrics_ai['time_s']}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d934ebd2-e594-4076-8b21-822087df01ea", - "metadata": {}, - "source": [ - "In this test, we compare the performance of the AI transpiler and the standard transpilation method on the efficient_su2 circuit. The AI transpiler achieves a noticeably shallower circuit depth while maintaining a similar gate count.\n", - "\n", - "- **Circuit depth:** The AI transpiler produces a circuit with lower two-qubit depth. This is expected, as the AI passes are trained to optimize depth by learning qubit interaction patterns and exploiting hardware connectivity more effectively than rule-based heuristics.\n", - "\n", - "- **Gate count:** The total gate count remains similar between the two methods. This aligns with expectations since the standard SABRE-based transpilation explicitly minimizes swap count, which dominates gate overhead. The AI transpiler instead prioritizes overall depth and may occasionally trade off a few additional gates for a shorter execution path.\n", - "\n", - "- **Transpilation time:** The AI transpiler takes longer to run than the standard method. This is due to the added computational cost of invoking learned models during routing and synthesis. In contrast, the SABRE-based transpiler is now significantly faster after being rewritten and optimized in Rust, providing highly efficient heuristic routing at scale.\n", - "\n", - "It is important to note that these results are based on just one circuit. To obtain a comprehensive understanding of how the AI transpiler compares to traditional methods, it is necessary to test a variety of circuits. The performance of QTS can vary greatly depending on the type of circuit being optimized. For a broader comparison, refer to the benchmarks above or visit the [blog.](https://www.ibm.com/quantum/blog/qiskit-performance)" - ] - }, - { - "cell_type": "markdown", - "id": "c8a55587-abf6-4096-85fd-2702a077ae75", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "As this tutorial focuses on transpilation, no experiments will be executed on the quantum device. The goal is to leverage the optimizations from Step 2 to obtain a transpiled circuit with reduced depth or gate count." - ] - }, - { - "cell_type": "markdown", - "id": "8d0cfca9-be4e-40ab-ab98-d7899bb8b3fa", - "metadata": {}, - "source": [ - "## Step 4: Post-process and return result in desired classical format\n", - "Since there is no execution for this notebook, there are no results to post-process." - ] - }, - { - "cell_type": "markdown", - "id": "c82277b2-22e9-44fe-886e-e8ceb2178278", - "metadata": {}, - "source": [ - "# Part II. Analyze and benchmark the transpiled circuits\n", - "\n", - "In this section, we will demonstrate how to analyze the transpiled circuit and benchmark it against the original version in more detail. We will focus on metrics such as circuit depth, gate count, and transpilation time to assess the effectiveness of the optimization. Additionally, we will discuss how the results may differ across various circuit types, offering insights into the broader performance of the transpiler across different scenarios." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ee24725b-64c9-4d6a-aa97-5a3502b0982a", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "87d664f0db954c1794e49f7dbec7302a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 4 files: 0%| | 0/4 [00:00\n", " \n", " \n", - " Circuit\n", - " Depth 2Q (No AI)\n", - " Gate Count (No AI)\n", - " Time (No AI)\n", - " Depth 2Q (AI)\n", - " Gate Count (AI)\n", - " Time (AI)\n", + " Default (mean +/- std)\n", + " AI (mean +/- std)\n", + " AI % improvement\n", " \n", - " \n", - " \n", " \n", - " 0\n", - " Random\n", - " 37\n", - " 221\n", - " 0.039347\n", - " 24\n", - " 181\n", - " 0.773718\n", + " Metric\n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " 1\n", - " Clifford\n", - " 36\n", - " 232\n", - " 0.036633\n", - " 43\n", - " 267\n", - " 1.097431\n", + " Depth 2Q\n", + " 33.0 +/- 12.9\n", + " 27.1 +/- 7.0\n", + " +13.5% +/- 15.9%\n", " \n", " \n", - " 2\n", - " QFT\n", - " 165\n", - " 924\n", - " 0.077458\n", - " 130\n", - " 913\n", - " 3.660771\n", + " Gate Count\n", + " 522.6 +/- 268.6\n", + " 572.1 +/- 280.1\n", + " -11.3% +/- 9.6%\n", " \n", " \n", - " 3\n", - " BV\n", - " 65\n", - " 155\n", - " 0.024993\n", - " 70\n", - " 155\n", - " 0.345522\n", + " Time (s)\n", + " 0.0 +/- 0.0\n", + " 0.5 +/- 0.1\n", + " -1797.9% +/- 606.3%\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Circuit Depth 2Q (No AI) Gate Count (No AI) Time (No AI) \\\n", - "0 Random 37 221 0.039347 \n", - "1 Clifford 36 232 0.036633 \n", - "2 QFT 165 924 0.077458 \n", - "3 BV 65 155 0.024993 \n", - "\n", - " Depth 2Q (AI) Gate Count (AI) Time (AI) \n", - "0 24 181 0.773718 \n", - "1 43 267 1.097431 \n", - "2 130 913 3.660771 \n", - "3 70 155 0.345522 " + " Default (mean +/- std) AI (mean +/- std) AI % improvement\n", + "Metric \n", + "Depth 2Q 33.0 +/- 12.9 27.1 +/- 7.0 +13.5% +/- 15.9%\n", + "Gate Count 522.6 +/- 268.6 572.1 +/- 280.1 -11.3% +/- 9.6%\n", + "Time (s) 0.0 +/- 0.0 0.5 +/- 0.1 -1797.9% +/- 606.3%" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Circuits to benchmark\n", - "seed = 42\n", - "circuits = [\n", - " {\n", - " \"name\": \"Random\",\n", - " \"qc\": random_circuit(num_qubits=30, depth=10, seed=seed),\n", - " },\n", - " {\n", - " \"name\": \"Clifford\",\n", - " \"qc\": random_clifford_circuit(\n", - " num_qubits=40, num_gates=200, seed=seed\n", - " ),\n", - " },\n", - " {\n", - " \"name\": \"QFT\",\n", - " \"qc\": synth_qft_full(num_qubits=20, do_swaps=False).decompose(),\n", - " },\n", - " {\n", - " \"name\": \"BV\",\n", - " \"qc\": create_bv_circuit(40),\n", - " },\n", - "]\n", + "results_sim = []\n", "\n", - "results = []\n", - "\n", - "# Run the transpilation for each circuit and store the results\n", - "for circuit in circuits:\n", - " qc_no_ai, metrics_no_ai = transpile_with_metrics(pm_no_ai, circuit[\"qc\"])\n", - " qc_ai, metrics_ai = transpile_with_metrics(pm_ai, circuit[\"qc\"])\n", - "\n", - " print(\"Completed transpilation for\", circuit[\"name\"])\n", - "\n", - " results.append(\n", - " {\n", - " \"Circuit\": circuit[\"name\"],\n", - " \"Depth 2Q (No AI)\": metrics_no_ai[\"depth_2q\"],\n", - " \"Gate Count (No AI)\": metrics_no_ai[\"gate_count\"],\n", - " \"Time (No AI)\": metrics_no_ai[\"time_s\"],\n", - " \"Depth 2Q (AI)\": metrics_ai[\"depth_2q\"],\n", - " \"Gate Count (AI)\": metrics_ai[\"gate_count\"],\n", - " \"Time (AI)\": metrics_ai[\"time_s\"],\n", - " }\n", - " )\n", + "for i, qc in enumerate(circuits_sim):\n", + " n = qubit_range_sim[i]\n", + "\n", + " qc_default, m_default = transpile_with_metrics(pm_default_sim, qc)\n", "\n", - "df = pd.DataFrame(results)\n", - "df" + " # Create a fresh AI pass manager each iteration to avoid stale layout state\n", + " pm_ai = generate_ai_pass_manager(\n", + " optimization_level=1,\n", + " ai_optimization_level=3,\n", + " backend=backend,\n", + " )\n", + " qc_ai, m_ai = transpile_with_metrics(pm_ai, qc)\n", + "\n", + " results_sim.append({\n", + " \"Qubits\": n,\n", + " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", + " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", + " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", + " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", + " \"Time (Default)\": m_default[\"time_s\"],\n", + " \"Time (AI)\": m_ai[\"time_s\"],\n", + " })\n", + "\n", + " print(f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\")\n", + "\n", + "df_sim = pd.DataFrame(results_sim)\n", + "summary_table(df_sim)" ] }, { "cell_type": "markdown", - "id": "061d85cf-3841-4ed3-bd0d-cd950564efb7", - "metadata": {}, - "source": [ - "Average percentage reduction for each metric. Positive are improvements, negative are degradations." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "70cf9c05-62a3-4049-9712-319902107ba6", + "id": "sim-step2-table-note", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average reduction in depth: 11.88%\n", - "Average reduction in gate count: 1.04%\n", - "Average reduction in transpilation time: -3193.95%\n" - ] - } - ], "source": [ - "# Average reduction from non-AI to AI transpilation as a percentage\n", - "avg_reduction_depth = (\n", - " (df[\"Depth 2Q (No AI)\"] - df[\"Depth 2Q (AI)\"]).mean()\n", - " / df[\"Depth 2Q (No AI)\"].mean()\n", - " * 100\n", - ")\n", - "avg_reduction_gates = (\n", - " (df[\"Gate Count (No AI)\"] - df[\"Gate Count (AI)\"]).mean()\n", - " / df[\"Gate Count (No AI)\"].mean()\n", - " * 100\n", - ")\n", - "avg_reduction_time = (\n", - " (df[\"Time (No AI)\"] - df[\"Time (AI)\"]).mean()\n", - " / df[\"Time (No AI)\"].mean()\n", - " * 100\n", - ")\n", + "The summary table shows the mean and standard deviation of each metric across all 20 circuits, along with the average percentage improvement of the AI transpiler over the default. Positive values indicate the AI transpiler produced better results; negative values indicate the default was better.\n", "\n", - "print(f\"Average reduction in depth: {avg_reduction_depth:.2f}%\")\n", - "print(f\"Average reduction in gate count: {avg_reduction_gates:.2f}%\")\n", - "print(f\"Average reduction in transpilation time: {avg_reduction_time:.2f}%\")" + "For this small-scale example, the AI transpiler achieves roughly 10% lower two-qubit depth on average, but at the cost of roughly 10% higher gate count. This highlights a key trade-off when choosing between the two strategies: the AI transpiler prioritizes depth reduction (fewer sequential layers of two-qubit gates), while the default transpiler (SABRE) prioritizes minimizing total gate count (fewer SWAP insertions). Depending on your application, one metric may matter more than the other." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "79b8d5d9-0f9d-42ca-9583-8bec17430014", + "execution_count": 5, + "id": "sim-step2-plot", "metadata": {}, "outputs": [ { "data": { + "image/png": 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t/JK0RlUZGRX85OTkaFdGVrFnO979+cIiZzve8/kiHdyuQbFmO1aLinAD+PvLAhF69uypjz/+2AUojBgxQtWqVXPBC3Xq1NH//vc/HX300VqyZInOPPNMtWjRQoMHD9asWbPcsgU0LFiwQMcdd5wefPBBd3J47NixOuGEE7R48WK1bNmyxG2y5/3oo4902mmnueewgAlrEwAA2NvGlLQy3Q4ItT4v/V0AAAAAAABUeBaE8GyfokueRcZIY+ZIcS1UFRGo4MdO2Ha5a1KZ7Fg7cZuwPU3d75lcrO0X3TdU1aPL5uPo1KmT5s2bp59//tkFIGzcuNEFHJhHH33UZUKw8hCXXnqp6tev7+63AAXLzmAs0MEuPvfff78++eQTV05izJgxJW5PRESE6tWr55YbNWrkMi8AAICCNaoVW6bbAaHY56W/CwAAAAAAgApt55aigxSMrbftCFRAKM2Ss5lqVuIhNTU1NxjBZ9euXa68Q2HsMffcc4++/PJLbdiwQZmZme4xq1evDkDrAQCo2vq3qacmdWJdWv2CZrHbXPTGdWLddkBVRX8XAIDKLSsnRxFluB0AAACAyoeMCvlS0dosr+KwutDnv/7rPrd744J+xRpIsNcuK3/++afatGnjAg6aNGmi77//fq9tispqcNNNN2nKlCku+0K7du1cmYbTTz9d6enpudtYIISdIPaXkZFRZu8BAICqytLn331CF13+9m97rfMlzLf1xUmzD4Rqn5f+LgAAldvCddvVo7jbNQtAgwAAAAAEHIEKfmzwvbipaA9t37BYsx1tu0AOJHz77beaP3++rr/+ejVv3lwJCQmKjIxU69ati/0c06ZN0/nnn69TTjnF3baAh5UrV+bZxkpFWLYFn6VLl2rnzp2FPmd0dLS7zsoqXj1kAACqsmHdmmhIl3hNWZSY5/4GtWJ0/0ld3XqgqvZ56e8CAFD5JWRWV+ecCEWFFX6eKC0nym1XnIAGoExUr+/Vid5XHWnbDgAAAPstfP+fomrPdjRhQZrtuHv3bheIsG7dOv3222966KGHdNJJJ2n48OE677zzNHjwYA0cOFAnn3yyJk+e7IINpk+frn//+9+aPXt2oc/bvn17ffzxx5o7d64rH3H22WcrOzs7zzZHHXWUnn32Wf3+++/uuS6//HJFRUUV+pytWrVyJ8W/+OILbdq0yQU/AACAgqVnZuu3Vdvc8g1DOqhlvWpu+Z7hXQhSQJXq89LfBQAgNNWp10jb5fVxX8gYruN3P+gu4zKPdPctzW6qo3Y/qlrxbYPcUlQpVht6zBzp0h+8S/czvPu7j/jnPltfRWtIAwAAlDUCFfaDzWZ84ZzebhaZP7tt95f3bMeJEye60g6WLWHYsGH67rvv9PTTT+uzzz5TRESECwz46quvdNhhh+mCCy5Qhw4dNHLkSK1atUrx8fGFPu/jjz+uunXratCgQTrhhBM0dOhQ9e7dO882jz32mFq0aKFDDz3UBTJYuYjq1asX+pzNmjXTvffeq9tuu8299pgxY8p0XwAAEEomLUzQlh3piq8doyuPOED923gzdpZt2hHspqEKCmafl/4uAAChqd/6t1U/LFUrsuP1eNYZWpjTxl0eyzzTZVJoH75eB9ZKKlZpKaBMWRBC017epUV/7770nf/cR5ACAABAmQnLyckpKItryNi+fbvq1Kmj5ORk1a5dO8+6tLQ0rVixQm3atFFsbN4TryWRlZ3j6vduTElTo1qx7p8o6kaXTll9JgAAVGZnvfSLZizfomuOaqcbjumol39crge/+lPHd2+i50blDR6sCn027N/+o89b8dDnBQBUads3SM/0ljJ26vL06zQxe89g8B73Rb6u8yKnaFPjw9Tw8gkKJfR5K9n+WzJZGj9Ciu8mXTGt/F8PAACElvVzpZcO3/d2l/7gBURWwT5b8YrTokgWlDDwAGqTAQCA/bd8U6oLUrBM+mf2b+nu69C4lrtekpjCLkbQ0OcFAABl4vuHXZBCTvP++j3xECklPc/qsTpe52qqGib8KCUulOK7suMRHHVbedfbVkk21y+s/Er8AgCAELR1RfG2S1odUoEKJUHpBwAAgArknVmr3fURHRupWZxXt7dDfE13vWLzDu3OzApq+wAAAIBS2/in9PtbbvGPzjcqMSVdNaIj9Nr5/XTbsE7u/lU58crqNNzbfvoz7GwET5wXOK70FGnXNj4JAABQfJm7pe8e9JY7n+BlTchz+V5qe6S3/ssbpe3rq+TeJVABAACggkjLyNKHc9a65bP3ZFMwjWvHqlZspDKzc1ywAgAAAFApfXOPlJMtdRquZ5Z62UlH9G2hozo10mWHt1WbBjWUkZWjXxqf620//wMpeV1w24yqK6qaVDPeW962MtitAQAAlcnPT0hblko1GkknPuNlTMhzOVAaOU5q1FXasVF671wvuKGKIVABAACggpi0MEHbdmaoSZ1YHdGxYe79YWFh6hDvK/+QGsQWAgAAAKW04idpyUQpLELr+96mbxdvdHefO7BVbp/3mK7eoPB7GxpKrQ6RsjOlmS+wyxE8cXvKPySt4lMAAADFs2mJ9NNj3vKx/ydVq1vwdtE1vGCF2Dhp3Wwvs4KVm6pCCFQAAACoIMbN9Mo+nNmvhSIj8nbTcgMVElKC0jYAAACg1LKzpSl3est9L9Brf0W4c7CHtm+gAxp6Zc7MsK6N3fV3f21UxkFjvDtnvyGlJbPzERx1W3vX2whUAAAAxWCd3C+ul7LSpXZDpK6nFr19vTbS6a9JYeFeibTZr1ap3UygAgAAQAWwbGOKZq3YqvAwL1Ahvw7x3gncJYkEKgAAAKCSWfixtP53Kbqmdg66Se/PXuPuPn/QnkHgPXo2j1N87Ril7s7UzzpQathZSk+RZr8epIajyqtLRgUAAFACv78trfpZiqouHf+YpQ3b92PaHS0dfZe3/PWt0qoZVWaXE6gAAABQAYyf6Z2sPapTvJrUqbbX+o65pR8IVAAAAEAlYrV2p97rLR98nT5dkqHtaZlqWa+6jujYKM+m4eFhGronq8KkRRulQVd7K2a+WCVr9qIClX4gowIAANiX1E3S5Du85SNuV27AY3EcfJ3U9RSv9Nn750nJ66rE/iZQAQAAIMjSMrL00W9r3fKoAS0L3KZDYy9QYdXWndqVnhXQ9gEAAACl9usrUtJqqVYT5Qy8Um9OX+nuPm9gK0VYOrF8fIEKUxYlKqvb6e5xStkgzf+ADwGBR0YFAABQXJP/LaUlSY27SwddWbL9FhYmnfSc1KirtGOj9P65UkZayO/7oAcqrFu3Tuecc47q16+vatWqqXv37po9e3bu+pycHN11111q0qSJWz948GAtXbo0qG0GAAAoS1/N36DkXRlqFldNh3VoWOA2DWrGqF6NaFfm7O9NqXwAAAAAqPh2bZN+eMRbPvJf+mVNmhYnpqhaVIRG9N273Jnp36ae4qpHacuOdM1ekyoNuNxbMf0ZKTs7gI0H/DIqWLANxx8AACjM399K896ziAPphKekiMiS76voGtLIcVJsnLRujvTVjTZQHtL7PKiBCtu2bdPBBx+sqKgoff3111q0aJEee+wx1a1bN3ebRx55RE8//bRefPFFzZw5UzVq1NDQoUOVlhb6USQAAKBqGD9ztbse2a9FgbPKfDrE13TXixMo/wAAAIBK4KfHvVllDTtLvUblZlM4pXcz1akWVeBDoiLCdXSneLc8cWGC1PcCKbqWtOkvadmUgDYfUO1mUliElJUupSawQwAAwN4ydklf3OAt979Uatan9HupXhvp9NeksHDp97e97GQhLKiBCv/5z3/UokULvf766+rfv7/atGmjY445RgcccEBuNoUnn3xSd9xxh0466ST16NFDY8eO1fr16/Xpp58q6JLWSOvnFn6x9eVoxowZioiI0PHHH5/n/pUrVyosLExz584t19cHAAD7b0liimav2uYCFM7oV/CsMp8O8V75hyUbCVRAANHnBQAApepDrJZm/s9bHnKf1m1P1+RF3kDv6IGti3zo0K5eoMLkhYnKiakt9T3fWzHtKT4LBJbNhqzT3Fvetoq9DwAA9mYZxLatkGo1lY66Y//3ULujpaPv9pYn3iatmh6ye70UeSfKzueff+6yI4wYMUI//PCDmjVrpiuvvFKXXHKJW79ixQolJCS4cg8+derU0YABA9wg/ciRI/d6zt27d7uLz/bt28vvhO2zfaTMf15rL5Ex0pg5UlzRgw6l9eqrr+rqq6921xa80bRp03J5HQAAUP7ZFAZ3bqT42rHFC1QgowIChT4vAAAorW8fkLJ2S60PldoP0duTFis7RxrYtr46Nvb6tYWxcmhWHmJd0i4tWLdd3QdcIf3ygrRqmrR2jtR8P2apASVVt5WUtMq7tBrI/gMAAP9IXChNf9pbPu6/Umztstk7B18rbfhDWvix9P550qU/SHWahdyeD2pGheXLl+uFF15Q+/btNWnSJF1xxRW65ppr9Oabb7r1FqRg4uO9KGofu+1bl9/DDz/sghl8F8vYUC52bik6SMHYetuuHKSmpuq9995z+8wyKrzxxhvl8joAAKD87ErP0ke/rXXLZw/YU/u0CL4TuksSU/lYEBj0eQEAQGlYplFXo1fSMfcrLTNb787yAnRHDyo6m4KJjYrQER0buuVJVv7BTsp2P8NbOZ2sCgiwuD3/q5FRAQAA+MvOliZcJ2VnSp2GS52Hl93+CQuTTnpWiu8m7dgkvXeOlJEWcvs/qIEK2dnZ6t27tx566CEdeOCBuvTSS102hRdffLHUz3n77bcrOTk597JmTQnKL+TkSOk7infJ3FW857TtivN89tol8P7776tTp07q2LGjzjnnHL322muuVAYAAKg8vpi3XilpmWpRr5oObddgn9t3aOQFKtjMspS0jAC0ECGJPi8AACjvvsaUO73l7iOkpgfq8z/Wa9vODDWLq+YyiRXHsG6N3fVEC1Qwg672rhd9Lm35u3zaDhSWUcFYRgUAAACfOa9La2dJ0bWkYx8p+/0SXUM6820pNk5a/5v05Y0lHk+u6IJa+qFJkybq0qVLnvs6d+6sjz76yC03buz9Q5KYmOi29bHbvXr1KvA5Y2Ji3KVUMnZKD5Vx+YTXhhVvu3+t9w64YrJyDxagYIYNG+aCMqx8xhFHHFHalgIAgAAbv2dW2ch+LRUeHrbP7etUj1J87Rglbt+tpRtT1btl3QC0EiGHPi8AAChPy76RVvwoRURLR93pJta8OX2lW3XOQa0UGVG8eVNHdmqkqIgwLduY6i7t4rtI7YZIy6ZIM56Thj/O54jAiNuTBWSbdxwDAAAoJUH65l5vRxx9Z/mVZajXRhrxuvT2adLct6WmvaT+l4TMBxDUjAoHH3ywFi9enOe+JUuWqFUrL0q1TZs2Llhh6tSpueu3b9+umTNnauDAqlsPzPbZrFmzdNZZZ7nbkZGROvPMM13wAgAAqBwWrd+u31cnKTI8TCP6Ni/24zrEe1kVliamlGPrYH788UedcMIJatq0qcLCwvTpp5/m2TF20v2uu+5yAbXVqlXT4MGDtXTp0jzbbN26VaNGjVLt2rUVFxeniy66yJXw8jdv3jwdeuihio2NdWXLHnmkHCKwKyH6vAAAVELZWdKUu7zl/pe6mehzVm3TwvXbFRMZrpH9il+itXZslAYd0OCf8g++Wr1m7jhpx+aybz9QVEYFSj8AAACfr2+VdidLzfpI/S4u3/1ywFHS4Hu85Ym3SSunhcznENSMCtdff70GDRrkSj+cccYZbvD9pZdechdjJ4Svu+46PfDAA2rfvr0LXLjzzjvdyeKTTz657BsUVd3LbFAcCfOKly3hwolS4x7Fe+1isoCEzMxMtx/8T5RbJolnn3222M8DAACCZ/wsL23oMV3j1ahWbIkCFX5aulmLE/IOdqPs7dixQz179tSFF16oU089da/1FlDw9NNP680338ztpw4dOlSLFi1yQQfGghQ2bNigKVOmKCMjQxdccIErdzZ+/PjcINxjjjnGBTlY+bP58+e717OgBtuuXNDnBQAA5WXueGnjIi897WE3ubve2JNN4aReTVW3RnSJnm5o18b6YckmTV6YoKuObCe1PsSVktD636VZL0lH/qtc3gaQR9yeQIXt66TMdCmyZMcxAAAIMUsmSYs+lcIipOFPSuER5f+ag66RNvwhLfhI+mC0dOkP5ZfFoaoEKvTr10+ffPKJbr/9dt13333uBO+TTz7pTuj63HLLLe4ksZ2oTUpK0iGHHKKJEyfmnvwtU2FhxS+/EFmt+NuVoKTDvliAwtixY/XYY4+5k9r+LHjjnXfecaUgAABAxbVjd6Y+/d0Ljhw1YM9Jr2LquCejwhIyKpS7Y4891l0KYkGi1m+94447dNJJJ7n7rI8WHx/vMi+MHDlSf/75p+u3/vrrr+rbt6/b5plnntFxxx2nRx991AWdjhs3Tunp6XrttdcUHR2trl27au7cuXr88cfLL1CBPi8AACgP6Tul7x70li1IoVpdJW5P08QFXjaE0YP2pM8vgSFd4vXvT+frj7XJWp+0S03jqnknaT+8QJr1snTwdVJ08Sf/AKVSs5F3jjdzl5S8Rqp/ADsSAICqaneq9OWN3vLAK6UmxZisXlbn8058Rtq0WEpcIL13jnTB11JUOYyXV5XSD2b48OFu5lhaWpo7mXvJJXnralhWBQtiSEhIcNt888036tChg6qqL774Qtu2bXNpg7t165bnctppp1H+AQCASmDCH+uVujtTretX18C29Uv02PbxNd01gQrBtWLFCtc/tUwIPnXq1NGAAQM0Y8YMd9uuLTOCL0jB2Pbh4eGulJlvm8MOO8wFKfhYVgYre2B9vqqKPi8AAJXQL89JKRukuJZe2QdJ435ZpczsHPVrXVddm9Yp8VM2rBWjvq3qumXLquB0PlGq21ratdUrAQEEYmDAjmuT5GXGAwAAVdT3D3uBi3VaSkfcHtjXjq4hjRznAoK1/jcvYCInR5VZ0AMVKq3q9aXImKK3sfW2XRmysg92gttOhOdngQqzZ892KYQBAEDFNX7Wand9Vv+WCg8PK9Fj2+/JqLAxZbeSdqaXS/uwbxakYCyDgj+77Vtn140aNcqzPjIyUvXq1cuzTUHP4f8aBdm9e7fr8/lfygV9XgAAUBypm6Sfn/KWj7rLnRPbnZmV2+8tTTYF//IPZqIvUCEiUho4xlue/oyUlclnhPJXd08mvG0EKgAAUGWtnyv98ry3PPzxMs2oX2wWsHv6a1JYuDT3benXV1SZBbX0Q6UW10IaM0fauaXoE7u2XRmaMGFCoev69+/v0hAb3zUAAKhYFqxL1ry1yYqOCNfpfZqX+PE1YyLVLK6a1iXt0pLEVPVvU69c2omK7eGHH9a9995b/i9EnxcAABTHD/+R0lOkJr2kbqe5u76av0GbU9PVuHZsbrBBadhjH/jyT81asVVbd6SrXo1oqdco6buHvNntf34udTuVzwnlK25PoAIZFQAAqJqys6QJ10o52VLXU6X2Q4LXlgOOkgbfI025S5p4m9Soi9T6YFVGZFTY3xO3TXsVfinjIAUAAFD5jZvpzSob2q2x6tfcR3amQnRs7GVVWJyYUqZtQ/E1buydbE9MTMxzv932rbPrjRs35lmfmZmprVu35tmmoOfwf42C3H777UpOTs69rFmzpvw+Pvq8AACgKJuXSXNe95aPuV8K9043vjHdm3k+akBLRUWU/hRki3rV1bVpbWXnSN8s2tNviq6eW15C056q9ClvUQmQUQEAgKpt1kvShrlSbB1p2P8FuzXSoGu8AOHsTOmD0VLyWlVGBCoAAAAESOruTH0+d51bPrv/nhqnpdA+vqa7XkqgQtC0adPGBRJMnTo19z4rvzBz5kwNHDjQ3bbrpKQkzZkzJ3ebb7/9VtnZ2RowYEDuNj/++KMyMjJyt5kyZYo6duyounW9eswFiYmJUe3atfNcAAAAgmLqPd4J0vZDpTaHubvmrknSH2uSXBaxkfvR7/XxZWSY5Cv/YPpfIkXGeieMV/60368BFImMCgAAVF0WBPDtA97y4HulWnnLuAZFWJh04jNSfDdpxybpvXOljDRVNgQqAAAABMhnc9dpR3qW2jasoYPalr5kQ8f4PRkVEsioUJ5SU1M1d+5cdzErVqxwy6tXr1ZYWJiuu+46PfDAA/r88881f/58nXfeeWratKlOPvlkt33nzp01bNgwXXLJJZo1a5amTZumMWPGaOTIkW47c/bZZys6OloXXXSRFi5cqPfee09PPfWUbrjhhnJ9bwAAAGVi9Uzpzwlejdwh/5SlGjt9pbs+vkcTNaxVuixi/oZ18wIVflq62QX/OjUaeCUgzLSn9/s1gCKRUQEAgKrJMnd9dbOUniq1OEjqPVoVRnQNaeQ4qVpdaf1v0pc3VLpMYwQqAAAABEBOTo7G7yn7YNkUbKC7tDrsCVRYkpjinhflY/bs2TrwwAPdxVjwgC3fdddd7vYtt9yiq6++Wpdeeqn69evnAhsmTpyo2NjY3OcYN26cOnXqpKOPPlrHHXecDjnkEL300ku56+vUqaPJkye7IIg+ffroxhtvdM9vzwkAAFChWT908h3e8oHnSI06u8XNqbv1xbwNbnn0oNZl8lLtG9VUmwY1lJ6Vre8X+5XWGniVFySxbIqUuLBMXgvF99xzz6l169au/2sZwyw4tyiWbeyqq65SkyZNXIawDh066KuvvqpcGRV2bpZ2pwa7NQAAIFAsKHfxV1J4lHTCk7llziqMuq2l01/3+sRzx0mzXlZlEhnsBgAAAFQF89Yma+H67YqODNdpvZvv13O1a1RT4WHStp0Z2pyaXiaz1LC3I444oshAEAs2ue+++9ylMPXq1dP48eOL3L09evTQTz+RrhgAAFTCk7ZrZ0lR1aUj/pV79zszV7uAgp4t4tSrRVyZvJT1u6z8w4s//K2JCxI0vIeXnUr1D5A6nyAt+kya/ox0yotl8nrYN8sEZoG8L774ogtSePLJJzV06FAtXrxYjRo12mv79PR0DRkyxK378MMP1axZM61atUpxcWVzjJS7anFeTeq0ZClptRTfJdgtAgAA5c3+7n99i7d88LW5gbkVzgFHeiUpptwpTbpdiu8qtT5YlUEFC/sIDqsTjIqBzwIAEKp82RSO69ZYdWtE79dzxUZFqFX9GrlZFYDiIPtGxUGfFwBQ6WVlSN/c4y0PHCPVbuIWM7Ky9fbMVW75/EF7ZqCXkaFdvVrA3/21UWkZWf+sGHStdz3/Ayl5XZm+Jgr3+OOPuxJnF1xwgbp06eICFqpXr67XXnutwO3t/q1bt+rTTz/VwQcf7DIxHH744erZs2fl2c2+rApJ3jEOAABC3NT7pZQNUr220mE3qUIbdLXU7XQpO1N6/zwpea0qgyqdUcHqAYeHh2v9+vVq2LChu70/aZixfyfOLbJ606ZN7jOxzwIAgFCxPS1Dn/+x3i2fPaBVmaW/XbF5hwtUOLhdgzJ5ToSmqKgo18e1fpb1eenvBg99XgBAyJjzhrT1b6l6A+nga3LvnrQwQYnbd6tBzWgd190LXigrPZvHqXHtWCVsT9P0vzfrqE5e4IKa95FaHSKt+lma+YJ0zANl+rrYm53DmzNnjm6//fbc++x83uDBgzVjxowCd9nnn3+ugQMHutIPn332meuXnn322br11lsVERFROXZz3VZSwjxpG4EKAACEvLWzpV9f8ZaHPyFFVVOFZuPbJz4jbVosJc6X3jtHuuDrCt/uKh2oYB3oNm3aaMOGDS5YAcFnkdctW7Z0nw0AAKHis9/XaVdGlivZ0K913TJ5zo6Na2nyokQyKmCf7MRv8+bNtXbtWq1cuZI9VgHQ5wUAVGpp26Xv/89bPuI2KaZW7qo3p3t9jbP7t1RMZNkOPoeHh+mYrvEaO2OVJi1I/CdQwViwhAUqzH5DOuxmL0U/ys3mzZuVlZWl+Hi/z0Byt//6668CH7N8+XJ9++23GjVqlL766istW7ZMV155pTIyMnT33XcX+Jjdu3e7i8/27dsVVGRUAACg6mQPm2BZu3KknmdJbY9QpRBdXRr5tvTSEdL636X3z5eOvM2iGPbetnp9Ka6Fgq1KByoYm7lvA+OZmZmug43gnkSPjIxklh8AIORmUI/bU/bBTtiW1Wz2DvHeCeHFCZR+wL7VrFlT7du3dyeCEVz0eQEAld60p6Sdm6X67aQ+5+fevXB9sn5duU2R4WEadVDZln3wGda1sQtUmPJnoh7MylZkxJ6JLu2GSA07SZv+kma/Lh1yXbm8Pvav9FWjRo300ksvuf5Qnz59tG7dOv33v/8tNFDh4Ycf1r333ltxdnvd1t41GRUAAAhtM56TEhdI1epJxzyoSqVua+nYR6SPL5GWTvQuBYmMkcbMCXqwQpUPVDA2YGApce0CAABQln5fk6S/ElIUExmu03o3L7Pn9QUqLE1MdcEQpPPHvtgJ4UqTVhcAAFRM29d7J27N4HukiKi9sikM69ZY8bVjy+Xl+7epp7jqUdq6I12zV23TQW3reyssM+ega6TPrpRmvigddKUUSVnR8tKgQQPXr0xMTMxzv91u3LhxgY9p0qSJO/fq3x/t3LmzEhISXCmJgsrAWmmJG264IU9GhRYtgngynYwKAACEvm0r/8keNvRBqcae/mZl0qDDvrfJ3C3t3BL0QAXy6wMAAJSj8XuyKRzfo4nqVC+7oMg2DWq42WopuzO1ITmtzJ4XAAAAKNR3D0qZu6QWB0mdhufevW1Huj6b65VVPX/Qnlnn5cAyKAzu7JUbmLggIe/K7iOkWk2klA3S/A/KrQ3wMtRaRoSpU6fmyZhgtwcOHFjgLjr44INduQfbzmfJkiUugKGgIAUTExOj2rVr57kEVd1W/2RUyMkJblsAAEDZs7/vX9zg9XdbH+qVfUC5IlABAACgnCTvytAX87wTtqMGtCzT546ODHfBCmZJIuUfAAAAUM4SF0pzx3vLx9xvKUpzV7376xrtzsxW16a11adV3XJtxtCu3oz9yQsTXGaxXJZBYcDl3vL0p23kvFzbUdVZpoOXX35Zb775pv78809dccUV2rFjhy644AK3/rzzznMZEXxs/datW3Xttde6AIUvv/xSDz30kK666ipVGnF7/qdLT5F2bQt2awAAQFlb8JH091QpIkYa/mSe/i7KB4EKAAAA5eST39YqLSNbHeNrqXfLsj9h26GxV/6BQAUAAACUuyl3SznZUpeTpBb9c+/OzMrW27+scsujB7Uu95Jkh7ZvoOrREVqfnKb565Lzrux7gRRdS9r0l7RsSrm2o6o788wz9eijj+quu+5Sr169NHfuXE2cOFHx8V7Gi9WrV2vDhg2521vJhkmTJunXX39Vjx49dM0117ighdtuu02VRlQ1qWb8P2mhAQBA6Ni5VZq4p19y2E1Sg3bBblGVEBnsBgAAAIQim901fpZX9uHsAS3L5YStBUB8qQ1anJBa5s8NAAAA5Fr+vTfwHx4pHX13nh3zzZ8btS5pl+pWj9KJPZuW+06LjYrQER0b6qv5CZq0MEE9msf5rawj9T1fmv6MNO1pqcNQPsRyNGbMGHcpyPfff7/XfVYW4pdffqncn0lcKyk1UUpaJTXrHezWAACAsvLN3dKOTVKDjtLB17FfA4SMCgAAAOVgzqptWpKYqtiocJ18YLNy2ccd4mu666UbKf0AAACAcmIlFCbf6S33vUiqf0Ce1W9O92aWj+zf0gURBIKv/MPEBQl7rxxwhRdQsepnae2cgLQHVUjdVt71Ni+LCAAACAGrpku/jfWWT3jKKymGgCBQAQAAoByMn+llUzihR1PVqRZVLvu4Q7xX+mFpYqqys/3q8wIAAABlZcGHUsI8Kaa2dPgteVYtTkjRjOVbFB4mnXPQngHcADiyUyNFRYTp7007tCx/0G6dZlL3Ed7y9KcC1iZUEZZRwVhGBQAAUPll7pYm7Mmg0Hu01GpgsFtUpVD6AQAAoIwl7UzXF/M35JZ9KC+t6tdQdGS4dmVkae22XWpZv3q5vRYAAACqoIw0aep93vIh10k1GuRZ/eYML5vCMV0aq1lctYA1q3ZslA5u10DfL96kSQsT1a6RF8Cba9DV0h/vSH9OkLb8vVcWCKDUyKgAACgPSWuknVsKX1+9vhTXgn1fHqY9JW1eLNVoKA25NzT2cfX6UmSMF4RRGFtv2wUZgQoAAABl7KPf1ik9M1udm9RWrxZ+NXPLWER4mNo1rKlFG7ZrcWIKgQoAAAAoW7NekpLXSLWaeiUV/CTvzNAnv61zy6MHtQ74nrfyD16gQoKuOrJd3pXxXaV2Q6RlU6QZz0nDHw94+xCiyKgAACiPIIVn++x7UHnMHIIVyjoYxO774RFv+bCbpWp1FRLiWnjHSyUIfiFQAQAAoAzl5ORo/MxVudkUwsLCynX/doj3AhWWJKZoSJf4cn0tAAAAVCE7t0o/PeotH3WHFJ03e9cHc9a4zF4d42vpoLb1At486/v+65P5mrc2WeuSdu2d0eHga7xAhbnjpCP/tVc2CGC/MiokrZays6VwKisDAPaTDSYXFaRgbL1tVwEGlkM2GGTKnVLH40JnH8e1qBTvhZ4UAABAGZq1YqurlVs9OkIn92pa7vu2Q2Mvza0FKgAAAABl5qfHpLRkqVFXqefIPKuys3P01i+rcrMplHdwbkEa1IxRv1ZegMTkhQl7b9D6UKnpgVJmmjTr5YC3DyGqdnMpLELKSpdSCzjuAABA5Q4GQUARqAAAAFCGxs9a7a5P7NlUtWKjyn3f2gw2sziBQAUAAACUcobZ+rl5L4u/lma+6K0/+GopPCLPQ75fslGrtuxU7dhInXxg+QfnFmZot8bueuKCAgaMLXhi0DX/lLBI3xng1iEkRURKdZp5y9u8YB0AAACUDqUfAAAAysjWHen6en5CbtmHQOiwJ1Bh+aYdyszKVmQEcagAAAAowzS4E66VWh2SJ3XsG9O9Adoz+rZQ9ejgnV48pku87v9ikX5duVVbUnerfs2YvBt0PlGKayUlrfJKQPS/JFhNRShxx9Rq77hqNTDYrQEAAKi0OJMNAABQRj6as1bpWdnq1qy2ejSPC8h+tVq8VmbCXnflFmaJAQAAoHzT4P69KVU/LtnkEhacN7B1UHd3i3rVXd87O0f65s/Egme/D7raW57xrJSVGfA2IgTVbeVdk1EBAABgvxCoAAAAUAZycnL0zp6yD2f333PiKgDCw8PUvlFNt7wkkfIPAAAAKF9vzfCyKRzVsZFa1q8e9N09tItX/mHSwgICFUyvUVK1etK2ldKfnwe2cQhNdfcE6FhGBQAA9lfadvZhectgcldFRaACAABAGZixfIuWb96hmjGROrFXYOv0+so/EKgAAACA8pS6O1MfzlnrlkcPCm42BZ9h3bxAhZ+XblZKWsbeG0RX/6fkw/SnLcI4wC1EyInbc+yTUQEAsL92bZO+uqF4225eyv4ujdUzpQ8vZN9VUAQqAAAAlIHxM71sCif1auqCFQKpY2MCFQAAABCYUmcWrNC2YQ0d0q5Bhdjl7RrVVNsGNVwptO8Xbyp4o/6XSpGx0vrfpZU/B7qJCNXSD2RUAADsj11J0tiTix+A8OWN0oY/2OfFlZUhffug9PowKWUD+62CCuxZdAAAgBC0OXW3Ji1McMtnD2gZ8Ndvn5tRITXgrw0AAICqITs7R2/OWOmWRw9s7UqQVQRhYWEa2q2xXvj+b01cmKATehaQ3axGA68ExOxXpWlPSW0ODUZTESri9gQqbF/nDYJERAW7RQCAyiYtWXrrFGnDXKl6fenUl73rgqSnSl/fJiXOl948UTrvM6lpr0C3uHLZvEz6+BJp/W/e7c4nSksmSVm7C39MZEzhnwHKDYEKAAAA+8nS32Zk5ahnizh1bVon4Puz455AhRWbd2h3ZpZiIiMC3gYAAABUwll83z1c7M1/XrZZyzd5pc5O69NcFcnQrl6gwvd/bVRaRpZiowroDw+8Spr9mrRsipS4SIrvEoymIhTUbCRFVpMyd0nJa6R6bYPdIgBAZZK2XXrrVG8QvVo9L/CgcfeiH3PBl9Lbp0lrf5XG+oIVDgxUiysPK/Fl/b3Jd0gZO6XYOGn4E1K3U6WkNdLOLYU/1oIU4loEsrWg9AMAAMD+zyx7Z5ZX9mFU/8BnUzDxtWNUKzZSWdk57uQxAAAAUKSlU6TnB0pLJxZ7R7053cumcHqf5gEvdbYvPZrVUZM6sdqRnqXpf28ueKP6B0hdTvSWpz8T0PYhxISFSXF7/vfbtirYrQEAVLYgBQs4WDdbqla3eEEKJraOdM7HUvP+XjaGsSdJ6/ZkC4AndaM0/kzpyxu8IIW2R0hXzvCCFIwFIVgmisIuBCkERXhwXhYAACA0TP97i1Zt2alaMZEa3rNJ0NLd+rIqLElMCUobAAAAUAnYie3PxkjjTpdS1kt1Wu47bX1kjNalV9e3ize6m+cN3JP2vgKxMhTHdIl3yxMXeCXZCjToWu96/vtS8roAtQ4hqe6e70ESgQoAgGLaneL1wdbO8mb6W5BCkx7F332xtaVzP5ZaHLQnWOFkad0cdr/566s9QbiTpIgYaejD0jmfSLULKAmGCqVihT8DAABUMuNmeiemTundTNWjg9e16tC4lmav2kagAgAAQFkIxdSwf38rfXa1tH2thbpKB10hHXWn9z738V5f/znFZdI9rENDtW1YUxXR0G6N9eaMVfrmz43KzMpWZEQB87Oa95FaHSytmibNfEE65oFgNBWhIG5PoAIZFQAAxbE7VRo3Qloz08uOcN6nUpOeJd93MbWkcz70nmv1DC9Y4dxPpOZ9q+5+nfQv6bc3vdvx3aRTX6bEVyVCoAIAAEApbUxJ05RFiW757AHBKfvg06GRd8J4SWJqUNsBAAAQEkEKz/aRMncXvk1kjDRmTuUIVrDZe5PvlOa87t2u21o66Xmp9cHe7ejqRb6PnemZen/2Ird8/qCKl03Bp3/reqpbPUpbd6Tr15XbNPCA+gVv2GuUF6gw61WpwzApumblD0JB4JFRAQBQ0iAFCyyIqSOd+6nU9MDS7z8LVhjlC1aYLr11ilcWokW/qvWZrJ0tfXyJtHW5F4Q7aIwXhGv9dFQaBCoAAACU0gez1yozO0e9W8apU+PaQd2PllHBUPoBAABgP1l2gaKCFIytt+0q+oD28u+9LArJq73b/S+VBt8jRdco9lN88vs6bU/LVKv61XVEh0aqqCyDwtGd4/XhnLWatDCh4EAFC0L58npvOXOn9MbxlTsIBRUgo8JKPgUAQOHSd0jjz/QCCmKsdMMnUrPe+7/HYmpKoz7wnnvVz16wgisL0T/0P42sTOmnR6UfHpFysqTazaRTXpTaHBbslqEUCsiBBgAAgH3Jzs7RO7O8E75nDwj+zLIO8V6gwuqtO7UrPSvYzQEAAECwZ+59eaM09iQvSCGupTR6gnTcf0sUpJCTk6M3p3sDsece1Erh4WGqyIZ1beyuJy9McG3fryAUoDgZFSj9AAAoTPrOfwIJomvtKdHQp+z2lwtWeF9qfaiUnuIFK6z+JbQ/jy1/S68Nlb5/2AtS6Ha6dMU0ghQqMQIVAAAASuHHpZu0dtsu1Y6N1PAeTYK+DxvUjFH9GtGudvCyjZR/AAAAqLJW/iy9MEj69RXvdt8LpSuml+oE7ozlW1xpsWpRERrRt+JnGDikfQNVj47Q+uQ0zV+XHOzmoCpkVNi52QsMAgDAX8Yu6Z2R0sqfvDJTlu2ged+y30cWgHr2+14/Lz1Vevs0adWM0Pss7ITnnDelFw+V1s32Smic+op0+qtStbrBbh32A4EKAAAApTB+ppdN4dTezRUbFVEh9mH7eK++7uLElGA3BQAAoHJKS5YWflK8bdf/JmVnVazUwl/f6pUzSFol1Wnh1UAe/oRXy7gUxk5f5a5P7d1MdapFqaKzfvmRHb3yFBMXJAS7OQhl1eKk2DrectKe0ioAAOQGKZwlrfjBC1I456PyLckQXV066z2pzeF+wQrTQ+ez2LFZevdsacI1UsYOL4OEZVHoMSLYLUMZIFABAACghBK3p2nqXxvd8qgBLSvM/uu4p/zDUgIVAAAASjZDy9LkfnKF9GhHadqTxXvcF9dLj3eRJt4urZ3jPU+w2My5Fw6WZr7o3e492suicMCRpX7KdUm7NHmRN9g/elBrVRbHdI1315MWEqiAAGVVsMAgAABMRpo3qL78OymqhjTqQ6nlQeW/byxY4ez3pLZHeoP5b5/uZdmq7JZMlp4fKC3+SgqPkobcL533uRRX8TN9oXgii7kdAAAA9njv1zXKys5Rv9Z11X5PcEBF0KGx1xYyKgAAABTDzq3SH+9Kv70pbfrrn/vjWktJK/f9eJshl5og/fK8d6nb2quT2/10qVHnwM3Ym3q/9/rKkWo3k058Wmo3eL+f+u1fVik7Rxp0QH11qEB93n05qlMjRUeE6+9NO7RsY4raNao8bUclU7eVlDBP2kagAgBgT5DCe6Okv7+VoqpLoz6QWg0M3K6Jqiad9Y4XKGFtGDdiT1mIQyvfx5O+U5p8hzT7Ve92w87SqS9JTXoEu2UoYwQqAAAAlIAFKLw7y0vteXYFyqZgfCeQlyZSIxUAAKBAlvXAZpdZcMKiz6Ws3d79kdWkbqd6mQgio6WXjtj3DrSyCjs2SQs+lBZ/LW1bKf30qHdp1FXqfprU7TQvgKE8rJklfXqFtGWZd/vAc6ShD/2Tjn4/pGVk5fZ5K1M2BVMrNkqD2tXX94s3adLCRAIVUH7IqAAA8MncLb1/rrTsG69faQECrQ8O/P6xYIWR73gBE9YWF6xgmRYOr1ifVdIaaeeWgtdZAPF3D/8TODzgCmnw3d57Q8ghUAEAAKAEfliyUeuT0xRXPUrHdmtSofZdhz2zxSxNb0pahjtJCwAAAEmpm6S546Tfxkpb//5nlzTuLvU5X+o+wq/e/BopMsY74VwYW1+rsdSin9TpOCl9hxesMP9D76TwxoXSVLvcJzXv52Va6HqKVMsrS7Dfs/W+e1Ca8ayUky3VaiKd8LTU4Zgy+6g//2O9tu3MULO4ajq6UyNVNsO6NnaBChMXJOiqI9sFuzkIVb4gJDIqAEDV5oIUzpOWTt4TpPBecLMYRMVKZ46T3jtHWjZFGn+mdPa7UttiBOIGgvW1n+1TdF/b1Ggknfo/6YCjAtUyBAGBCgAAAMXMpDBrxVY9PnmJu33Kgc0UGxVRofZdnepRiq8do8Ttu7UkMVV9WtUNdpMAAACCJzvbqw9s2RP++krKzvinZIOVZ7DsCU0PlMLC8j7Oat6OmVP4LC9TvX7e2rjRNbzntIuVlPhzgpdpYcVP0tpfvcuk26XWh3rbdD5Bqla3ZLPLzPb10jd3S5u9Pql6niUNe7jg5yplf3fj9jQ9/52XpeGcg1opMiJclc3gLvEK/2S+5q9LdkG8FnCR+7kVJwjFtgP2hYwKAIDMdOmD86UlE6XI2D0BARUge4EFK4y0YIVzpaWTvGCFs96VDjgy2C3z+rr7ClIwp70qtT0sEC1CEBGoAAAAsA8TF2zQvRMWaUNyWu59E/5YrwFt6mlYRcuqEF/LBSosTUwhUAEAAFRN2zdIc9+WfntLSvKrHd+sjxecYOUYYmoW/RwWhOAfiFAS1etJfUZ7l5QEaeEnXqaFdbOlFT94ly9ukNoP8drS8Vgv0KG4s8tMzXhp+JNeNody6u+ahjWjVRk1qBmjvq3rucCLSQsSdOEhbUofhAIUpm6rfzIqWFmZ/EFPAIDQlpUhfXiBtPgrKSJGOuudipO1wBd8eeZbXrYHC6R4Z6TXxsqSoSC2drBbgAAgUAEAAGAfJ22vePs35eS7f0tqurv/hXN6V6hghY7xtfTT0s1anJgS7KYAAADsv31lGPANKmdneSUX5rwhLZkk5WR562PqSD3O8IIGrMxDoFl5iIOu8C5bV0gLPvIuGxd5J7XtElVd6nicF0hRnCCFdoOlU1/2AiLKsb9rbv5wnmrGRlao/m5xDe3a2AtUWOgXqLC/QSiAv7iW3nV6irRrW5l9JwEAlShI4a8v9gQpjK+YAQAWrHDGWOn90dKSr6XxFqww3utPBivj2dblwXltVEgEKgAAABSR/tZmlhV00tbus/kytn5Il8aKCA+rMBkVzNLE1GA3BQAAYP8UJ8NARLTU9yLpz8+l7ev+ub/lQC97QpeTpOjqFeOTqNdGOuwm75K4yCsNYZkWLOuDLdulOI66s8wGRIvq7/pUtP5ucQ3tGq/7v1ikX1du1ZbU3apfMybYTUKoiarmZTdJTZS2rSRQAQCqUpDCR9b/nOD1RUcGceC/JMEKVqJi8ZfSO2d7bW4fgDZbxqEtf3sZxVb+5JVF27m5/F8XlQaBCgAAAIWwGVj509/6sxO6tt62G3hAxahj26GxF6hARgUAAFDpFad+bVa6NPMFb7laPannWVLv86RGnVShxXeR4u/ygg7WzfECFua9J+3aGtBmVMb+bnE1r1td3ZrV1oJ12/XNn4k6s9+e2e9AWYpr5QUqWMBRs97sWwAIdVmZ0seXSIs+84IUzhwXmAH//RUZLY14458sEO+e5bW9wzFl/1pJq6UVP3pBCXadsj7vestAkVWMLGKoEghUAAAAKMTGlLQy3S4Q2jfy6i1vStmtbTvSVbdG5awrDAAAUGxNDpQGjZE6n+DNGKtMrKZ9877exUpUvHxkQF42JydHizZs12s/L690/d2SGNa1sQtUmLgggUAFlI+6raS1s6Rtq9jDAFAVghQ+uVRa+IkUHiWd8Vb5DPQHIljBskG8N0oa/qQU33XfZdaKkpLgBSWstOCEH70sQ/4soKN5f6nNYd4lPFJ6tRIEdyAgCFQAAAAoQGZWtmb8XUQ9ZD+NasVWmH1YIyZSzetW09ptu7QkMUUD2laumW8AAAAldsKTUtNelX/HhYWXe3DCXwkp+nLeBn05f4NWbN5R7MdWpP5uSQzt2liPTl6iacu2KCUtQ7Vio4LdJIRiRgVjGRUAAKFResyyeuWXky1996C07Js9QQpjpY7DVOlEREmnvy59eKFXOu2zK4ve3oKAx8zJG6ywc+ueMg57siZsXpz3MWERUrM+UptDvcCEFgO8ckk+6+eW8ZtCZUagAgAAKHU9W0sBa7Or7MRl/zb1Kl3d2sL8vSlVN77/h+auSSpyO3u3jet4770i6Rhfi0AFAAAAuOCEJYmp+nLeen0xf4OWb/onOCEmMlxHdGiomSu2KnlXhivzUFn6u8XVrlFNtW1Yw73v7xZv0ok9mwa7SQjFjAqGjApAxRtYLsmMcMB3LD3bZ9+lx457VOp0XOXdZy5Y4TXp7dOlFd8Xva3tCyvlkLhwT2DCj1Li/HwbhUlNekitLTDhcKnVQCnGK01b6HfSAiCK2s+23rZDyCNQAQAAlNjEBRt074RFeerZNqkTq7tP6KJh3ZpU2j2anZ2j16ev1CMT/9LuzGzVio3UqQc209gZ3uwY/5O3vpAMe88VLUCjfXwtTf1rozspDQAAgKrHMmv5Mics2/hPnzA6MlxHdmyo43s01VGdGqlmTKTr21/x9m+uf1tZ+rvFFRYW5rIqvPD935q0MIFABZQ9MioAFX9guaAZ4UBBLOBlX0EKJhQyeVmwwtF3Sa/sI1DBvDHczprmva9h5z2lHA6VWh0sVS9BUKt9F+07SYARCFQAAAAl5TuRmX/GVUJymrv/hXN6V8pghdVbduqmD/9wWSLMoe0b6D+n9VDTuGoaeED9vQIzGlfgwIyOjWu668WJKcFuCgAAAIprP2eXLduYoi8sOGHeBi31D06ICNfhHRtqeI8mLjghf/kD689aH74y9XdLYtieQIXv/9qotIwsxUZFBLtJCMWMCjbbNDtbCi/fEi4ASjGwbOttOwIVgLzCi9snypbqtd0TmHCYlzmhZqP925v2feQ7CQIVAABAScs92AnMgtLC2n02z8rWD+nSuNLMurJ0uONnrdaDX/6pnelZqh4doX8f31ln92/pZmAZOzlr76mylLpo36hW7kw6e3++9wEAAIAKzG92WVZOjhau266tO9NVr3q0ujarrQjr0+VLX20ly1zmhHkb8gSpWnDCYR0a6PgeTXR053jVzheckF9l6++WRI/mdVz2NwvCmLZss9sfQJmp3dyrxZ2VLqUmSLUpLwIACDFnfyB1OCbYrUCIovQDAAAoNjtx6T/LqqBgBVtv21kWgopufdIu3frRPP20dLO7bSdjHz29p1rWr77XtnaStjK8J18tXjunnLQzQ5tSd7sTzQAAAJWODcqHhUs5+VLNhnL92rgWmrg2Ml92g3Q1qZPpZTdo2kTLN6Xqq/kbXPaEvxL+CU6IigjToe0b6vjuTTS4S7zqVCs6OKEy93dLU/7hjekrNXFBAoEKKFsRkVKdZl5GhW2rCFQAAISe/c2eABSBQAUAAFBsNruqOFZt2VGhT3JaloGPfluneycsVEpapmIiw3XLsE66YFBrhYfArDFLZ9u6fg0t37xDSxJSCVQAAACVU1ryP0EKJ70gxXfZe5t8GQZCtcyaBS1c/vZvah5XTWuTduXeHxkepkPaN3DBCcd0aaw61UsWnFBVHNM13gUqfPNnojKzshUZQXp+lKG4Vl6gQtIqqdVAdi0AAEAxEagAAACKrbgz8+/5fKGWJKbqgoNbq0W9vbMTBDvY4l8fz9c3f250tw9sGadHR/TUAQ1rKpS0j6/pBSokpriT1wAAAJXOlDu9666nSAeerapcZs3HghQsrvaQ9g013IITusYrrnp0AFtZOfVvXU91q0dp284MzVq5VYMOoH+MMlS3lbTyJy+jAoDAys5ijwNAJRbU8OF77rnHpV/zv3Tq1Cl3fVpamq666irVr19fNWvW1GmnnabExMRgNhkAgCrNSiNYfdeicg7YrK60zGy9Nm2Fjnj0e40Z/5vmrU1SRfDFvPU65okfXZCCpca9ZVhHfXDZwJALUjAd42u5awtUAAAAqHSWfiP9/a0UES0dfbeqgn2VWfP53zl9NPbC/jqjXwuCFIrJMigM7hzvlicv5NxiWXnuuefUunVrxcbGasCAAZo1a1ah277xxht7nQe2x4WEuNbetWVUABA46Tukyf9mjwOlZZnJrIxaUUKtzBoqnKBnVOjatau++eab3NuRkf806frrr9eXX36pDz74QHXq1NGYMWN06qmnatq0aUFqLQAAVZvVrbXauJZ2Nj9f8MIzZx2o6jGRevnH5fp52WZXO9cuFuRw6aFtdVSnRgEvr7B1R7ru/GyBvpy3wd3u2rS2Hjujpzo1rq1Q1X5PoMJiAhUAAEBlk5UpTb7DW+5/qVSvjaqC4pZZ25nB7NHSGNatsT6Ys1aTFia4/2lsoByl99577+mGG27Qiy++6IIUnnzySQ0dOlSLFy9Wo0YF17KuXbu2W+8TMp+BZVQwZFQAAiclURp/hrRhbvG2n/GcdMqLUnhEebcMlZkNyIdHStmZVWPg3sqnjZkj7dxS+DYhVmYNFU/QAxUsMKFx48Z73Z+cnKxXX31V48eP11FHHeXue/3119W5c2f98ssvOuigg4LQWgAAMKxbEx3eoaF+WLIpz85oXCfWnfCz9ca2WbR+u175abk+/2O9myFml7YNa+jiQ9rq1N7NFBtV/v8gTlmUqNs/nq/NqbtdoMWYI9tpzFHtFBXidWk7NvYCFZYmpionJyd0TgICAIDQN3ectOlPKTZOOuwmVRXFLbNW3O2Q18HtGqhGdITLWjFvbbJ6tohjF+2Hxx9/XJdccokuuOACd9sCFmzC2WuvvabbbrutwMfY/yQFnQeu9OL2BCqQUQEIjE2LpbdPl5JXe32FjB1SVkbRj5n/vrRrq3Taq1I1fv9RCMvkFRHjBSocfpvU8djQH7i39xJK7weVTtADFZYuXaqmTZu6VF8DBw7Uww8/rJYtW2rOnDnKyMjQ4MGDc7e1shC2bsaMGYUGKuzevdtdfLZv3x6Q9wEAQFVhtXMXrvf+vt46rKOaxlVzJ0stY4IFAvjr0rS2Hj+zl24e1lFvTF+p8TNXa/mmHfrXJ/P12OTFOm9ga507sJXq1Sj7urrJuzJ074SF+vi3de52h/iaemxEL3VvXkdVQev6NVwZjtTdmVqfnKZmcdWC3SQAAIB9250qffegt3z4rVK1ulVmr7WsV13Wnc7OKXh92J7gYOt3o+QsSPqITo1clrWJCxMIVNgP6enp7tzt7bffnntfeHi4O49r520Lk5qaqlatWik7O1u9e/fWQw895LLthkxGhe3rvMHSiKhgtwgIXSt/lt49W0pLluodII36wBtcLmpG+LrZ0qQ7pGXfSK8Mls56V2rQLpCtRmXx7X1e4EvTA71+aHhoT3ICKoKgfsssLZjVJ5s4caJeeOEFrVixQoceeqhSUlKUkJCg6OhoxcXljW6Lj4936wpjgQ5WJsJ3adGCSCAAAMrSzOVbXHaCuOpRuvjQtjqpVzMNPKD+XkEK/prUqabbj+2sGbcfrTuHd3GD5lt2pOuJb5Zo4MNT9e9P5mv5ptQya+OPSzZp6BM/uiAFa9Zlh7fV52MOqTJBCiY6MtxlrzBLKP8AAAAqi+lPS6mJUt02Ur+LVVVsT8vQRW/+mhukkL9n7bttGcyK6nejaEO7erP5Jy1IcFnHUDqbN29WVlaWO09b3PO2HTt2dNkWPvvsM7399tsuWGHQoEFau3Ztoa9jk9FsEpr/pUKqGS9Fxko52VLymmC3Bghd8z6Qxp7sBSm0GCBdNEWqf4A3G7xpr8Iv1p+4aJJUu7m0Zan08lFe0ALgb91v0u/jvOVjHyFIAagKgQrHHnusRowYoR49ergaZl999ZWSkpL0/vvvl/o5LZLXykb4LmvW0DkEAKAsTZi33l0f261xicsn1IyJ1EWHtNEPNx+hZ846UN2b1dHuzGyNm7laRz/+gy4ZO1u/rtxa6pOGlj3AsjWc99osJWxPU+v61fXB5QNdkEQgykxUNB3ivfIPSxJSgt0UAACAfdu+Xpr2tLc85F4psuyzblVE6ZnZuuLtOforIUUNa8XogZO7ucwJ/uz2C+f0zi2zhtI5smNDRUeEa/nmHVq2sewCpbFvlkn3vPPOU69evXT44Yfr448/VsOGDfW///2v8k9IszJ7cS295W2rgt0aIPTYOaIfH5U+vljKzpC6nCSd95lUo37xn6NJT+nS77wAh93J0rgR0oznvOcG7DiYaGWLcqTuZ0gt+rNPgKpS+sGfZU/o0KGDli1bpiFDhrg0Yha44J9VITExschaZjExMe4CAADKXkZWtr5e4M2QGd6jaamfJzIiXCf0bKrhPZpo5oqtevnH5Zr610ZNWZToLlYv9tJD22po13i3bf7SE7NWbNXGlLQ8JSd+Wb5FN3/4h9Zs3eW2O39Qa906rJOqRVe9AIW8gQobtCSRk7AAAKAS+PZBKXOX1OIgqfOJqgqys3N0y4d/aNqyLaoRHaHXz++nbs3q6Kz+LQvs82L/1IqN0sHt6uu7xZs0aWGC2u8J7EXJNGjQQBEREe48rb99nbf1FxUVpQMPPNCdBy5qQtoNN9yQe9syKlTYYIW4VtLmJVISgQpAmcrKlL68QfrtTe/2oKulwfeVbrZ7zUbS6Ane8/3+tjTpX1LiQmn4E1IkY0pV2oKPpDUzpajq0uB7gt0aoEqpUIEKVqfs77//1rnnnqs+ffq4DuvUqVN12mmnufWLFy/W6tWrXQQuAAAIvGnLNitpZ4Ya1IzWgDKojRsWFqaD2tZ3F5vR9OrPy/XRb+v0x5okXTX+N7WoV00XHtxGZ/RtoRoxkZq4YIPunbBIG5LTcp+jce0YdW5aW9/9tcndtrIS/x3RQ4MOaKCqLjejAqUfAABARZcwX5q7J93u0Ae9GcpVwH8m/aVP565XZHiYXjinjwtSMBaUYOXVUPaGdWvsAhUmLkzQmKPas4tLwcr12rlbO2978sknu/uslIPdHjNmTLGew0pHzJ8/X8cdd1xoTEir28q7JqMCUHZ2p0gfnO+VaQgL99Lx979k/57TAhJOfFaK7+YFKljfY/NS6cy3pVp5y9mgikjfIU25y1s+5AapTrNgtwioUoIaqHDTTTfphBNOUKtWrbR+/XrdfffdLhr3rLPOcum8LrroIhc1W69ePdWuXVtXX321C1I46KCDgtlsAACqrC/mbXDXx3Zrslemg/3VrlFNPXxqD914TEeNnbFKb81Y6bIjWGDCE1OWuBO1kxbmnbFjErbvVsJ2L0jhrP4t9O/ju7gSE7BAhZpuNyzdmOJm64UzCw8AAFTUdLuT7/DS7XY9VWreV1XBm9NX6n8/LHfL/zmthw7r0DDYTaoSBneOV3jYfC1Yt11rt+1U87rVg92kSsnO2Y4ePVp9+/ZV//799eSTT2rHjh264IIL3Hor89CsWTNXvsHcd9997pxuu3btXAbd//73v1q1apUuvvhihQTLqGDIqACUje0bpPEjvEDGyGrS6a9JnQoPbCoRC4Y86AqpQQfpwwuktbOkl4+URo6XmvYqm9dA5THtKWn7Oq+Ez6DiBdsBKDtBPYu/du1aF5SwZcsWV5PskEMO0S+//OKWzRNPPKHw8HCXUWH37t0aOnSonn/++WA2GQCAKmt3ZpZLj2qsZEN5aVAzRjcM6aArDj9AH/22Vq/+vEIrNu8oMEjBX70a0Xrg5O6kxPXTqn4NRUeGKy0jW2u27XS3AQAAKhybKbn8eykiWhp8t6oCyxR2z4SFbvnmoR11Wp/mwW5SlVG/Zoz6ta7nStBZoEjf1nUpr1EKZ555pjZt2qS77rpLCQkJ6tWrlyZOnKj4eG9GsmXFtfO6Ptu2bdMll1zitq1bt67LyDB9+nR16dJFIaFua++ajArA/ktcJI0bIW1fK9VoKJ39ntSsT9nv2XZHS5d8J70z0ivd8tow6eTnpG5ehm9UAUmrvUAFc8wDUlS1YLcIqHLCcnIsbD10We0yy86QnJzssjIAAIDS+WZRoi4eO1vxtWM047ajAzY73zIBPPfdMj02Zck+t33nkoNIkZvP8U//pIXrt+ulc/vomK7FqxcbDPTZ2H8AgCpce/rFg6VNf3l1p+0kcYibvXKrRr0yU7szszVqQEs9cHI3VxINgXPzB3/ogzlr89zXpE6s7j6hi4Z1K7+gbPq8Ibz/Nvwh/e8wb1D15mXBbg1QeVng4nvnSru3exkPRn3wTyBQeUlLlj68SFo2xbt96E3Skf+W/IKtEKLeHy0t+lRqfag0ekKVKT0GVKQ+G7+0AACgWCbMW++uj+/eNKAlBOy1WtYvXjrWjSlp5d6eyqZDfC13vXRjarCbEpKstu6dd96pNm3aqFq1ajrggAN0//33yz8W2JZtplmTJk3cNoMHD9bSpUvzPM/WrVs1atQo13mPi4tzJdBSU/nMAABVwO9veUEK1epKh96oULdsY4ouenO2C1KwEgT3nUSQQjCyWeQPUjAJyWm64u3f3Hqg1KUfdmzy6p0DKLm546W3T/OCFFodLF04qfyDFExsHS9rw6BrvNs/PSq9d460O6X8XxvBs/JnL0ghLFwa9jBBCkCQEKgAAAD2KS0jy2VUMMN7lt8Mo8I0qhVbpttVxUCFxQn8g10e/vOf/+iFF17Qs88+qz///NPdfuSRR/TMM8/kbmO3n376ab344ouaOXOmatSo4UqapaX9E1hjQQoLFy7UlClT9MUXX+jHH3/UpZdeWi5tBgCgwrABgO8e8pYPv9ULVghhG7enafRrvyp5V4YObBmnZ846kLJlAZaVnaN7JywqcJ0vzNTW23ZAiVSL8wY7fanEARSfBfp//x/p0yuk7Eyp2+nSuZ9I1esFbi+GR0jH3C+d8j8pIkZa/KX06jHStpWBawMCJztL+vo2b7nP+VLj7ux9IEgIVAAAAPv03V8btSM9S83iqunAFnEB32P929RzqVgLy+Ng99t62w55dYiv6a6XJBKoUB6sru5JJ52k448/Xq1bt9bpp5+uY445RrNmzcrNpvDkk0/qjjvucNv16NFDY8eO1fr16/Xpp5+6bSzAwer5vvLKKxowYIAOOeQQF+jw7rvvuu0AAAhZ056WdmyU6rWV+l6kUJaSlqHzX/9V65J2qU2DGnp1dD9Vi44IdrOqnFkrtmpDcuFZ2Cw8wdbbdkCpsypsW8XOA4orK0P67Crp+z2Bi4fcIJ36shQZE5x92HOkdMFXUs14aeMi6aUjpRU/BactKD+/jZUS53sBZlbmA0DQEKgAAAD26Yt5XvrT4T2aBKV+bkR4mKsXa/K/uu+2rbftUHBGheWbdigjK5vdU8YGDRqkqVOnasmSJe72H3/8oZ9//lnHHnusu71ixQolJCS4cg8+VqPNAhJmzJjhbtu1lXvo27dv7ja2fXh4uMvAAABASEpeJ03fk4Fo8L1SZLRCVXpmtq4c95sWbdiuBjWj9eYF/VWvRui+34qsuKXiKCmHUqm7J1AhiUAFoFjSkqVxp0tzx3np94c/IQ2+22qABncHNu8rXfq91PRAaddW6a2TpV9fDW6bUHZ2JUnf3u8tH3G7VKMBexcIIgIVAABAkXbsztTUv/aUfejRNGh7a1i3JnrhnN5qXCdveQe7bffbeuzNsmDUiI5Qela2Vm2hVmpZu+222zRy5Eh16tRJUVFROvDAA3Xddde5Ug7GghRMfHx8nsfZbd86u27UqFGe9ZGRkapXr17uNvnt3r1b27dvz3MBAKBS+e5BKXOX1HKg1PkEhSrLrnTbx/P009LNqh4dodfO76eW9asHu1lVFiXlUK7IqACULGDxtWOl5d9LUTWks96T+l5YcfZg7abSBV9L3Ud45Si+vEH64gYvAwQqtx8ekXZukRp0lPpdHOzWAFVeZJXfAwAAoEjf/JmotIxsta5fXd2a1Q7q3rJghCFdGrtUrDbLyU40WrkHMikULjw8TO3ia+mPNUlakpiqdo28DAsoG++//77GjRun8ePHq2vXrpo7d64LVGjatKlGjx5dbrv54Ycf1r333ltuzw8AQLnaME+aO95bPuYBKQgZuwLl0cmL9fFv61x/9blRvdWjeeDLqGHvknIJyWmuzEN+YXsCoSkph1Kp29q7pqY9sO9+wPgzpJQNXomFs9+XmvaqeHstqppXhqJRF2nqfdLsV6XNS6QRb0o16ge7dSiNTUukWf/zloc9JEVEsR+BICOjAgAAKGbZh6ZBKfuQn53kHXhAfZ3Uq5m7Jkhh3zrG13TXixNSyv3zqWpuvvnm3KwK3bt317nnnqvrr7/eBRKYxo0bu+vERC8riY/d9q2z640bN+ZZn5mZqa1bt+Zuk9/tt9+u5OTk3MuaNWvK6R0CAFDGcnKkyXfYgtTtNC+9coh665dVeu67v93yw6d215Ed82ZQQuBRUg4ByahA6QegcMu+kV4/1gtSaNhJuvibihmk4GPnwQ69QTrrHSm6prTyJ+nlI6Vl30rr5xZ+SeJ/9App0r+8DBkdjpXa/VOiE0DwkFEBAAAUantahn5YvMktD+9JaYXKqkO8l0VhSSKBCmVt586dCs9XPzMiIkLZ2dluuU2bNi7YYOrUqerVyzv5YmUaZs6cqSuuuMLdHjhwoJKSkjRnzhz16dPH3fftt9+65xgwYECBrxsTE+MuAABUOkunSCt+kCKipaPvVqiavDBBd3+2wC1fP7iDzujbIthNQr6ScvdOWKQNyWm5+8UyKdx9QhdKyqH06u4JVNi2ygvKqgCB/kCF8ttYacJ1Uk6W1PpQ6cy3pWqVJNNQx2O9oIp3RnpZU94+pejtI2OkMXOkOP7+VxhLJknLpkjhUdLQB4PdGgB7EKgAAAAKNWVhotKzstWuUU113DPYjcqHQIXyc8IJJ+jBBx9Uy5YtXemH33//XY8//rguvNCrrWlZSKwUxAMPPKD27du7wIU777zTlYY4+eST3TadO3fWsGHDdMkll+jFF19URkaGxowZ47I02HYAAISMrMw92RQkDbj8n0G9EDNn1TZd/c7vys6RzurfQtcc3S7YTUI+lJRDuYhr6V2np0i7tknV67GjUbVYFoGdWwpYkSP9+qr0+1vezR4jpROfkSKjVak06ixd8p309qnS+t+L3jZzt7cvCFSoGDLTpYm3e8sHXSHVPyDYLQKwB4EKAACgUF/MW++uh/doUiHKPmD/AhVWbtmp3ZlZiomMYFeWkWeeecYFHlx55ZWufIMFFlx22WW66667cre55ZZbtGPHDl166aUuc8IhhxyiiRMnKjY2NnebcePGueCEo48+2mVoOO200/T000/zOQEAQsvvY6XNi6Vq9aRDb1QoWr4pVRe/+at2Z2brqE6NdP9J3ehHV1C+knJAmdazrxkvpSZ6M64JVEBVC1J4to83QF+U/pdLx/5f5c04Yt/r4x6VXjk62C1BScz6n7T1b6lGQ+mwm9l3QAVCoAIAAChQ0s50/bR0s1se3oNZ3ZVZfO0Y1Y6N1Pa0TC3ftEOdm9QOdpNCRq1atfTkk0+6S2EsyOe+++5zl8LUq1dP48ePL6dWAgBQAexOkb57yFs+4rbKk+q5BDampGn067O0bWeGejavo2fPPlCREXlLRAEIcXGtvECFpFVSs97Bbg0QOJY9YF9BCqbXWZU3SMEnnGG1SiV1o/TDI96ylR2L5ZwYUJHw3xIAACjQxAUJyszOcYPaVvoBlZcNlHds7GVVWJKYEuzmAACAqujnJ6Udm6R6B0h9LlCo2bE7Uxe9MVtrtu5Sq/rV9er5/VQ9moEMoMrxlbTZtirYLQEAmG/vl3Zvl5r0knqNYp8AFQyBCgAAoEBfzNuQW/YBlV/7PeUfFicQqAAAAAIseZ0041lveci9la8m9T5kZGXrynG/af66ZNWvEa03L+ivBjVjgt0soEDLli3TpEmTtGvXLnc7JyeHPVXWGRWMZVQAULWtmWU/ssFuRdW2fq7021ve8rGPSOEMiQIVDd9KAACwl82puzX9b6/swwmUfQgJHfcEKixJTA12UwAAQFXz7QNSZprUcpDUabhCiQ3y3v7xfP2wZJOqRUW4TAqtG9QIdrOAvWzZskWDBw9Whw4ddNxxx2nDBi8w/aKLLtKNN97IHisrZFQA4PP1zdJLR0iLPpeys9kvgWZBIhNvswWp+wip5QA+A6ACIlABAADs5esFCcrOkXo0r6OW9auzh0JA+3ivfAelHwAAQEBt+EP64x1v+ZgHKn9d6nyemLJEH85Zq/Aw6dmzD1SvFnHBbhJQoOuvv16RkZFavXq1qlf/53+8M888UxMnTmSvlRUyKgDwiYyVNsyV3j9XemGg9Md7UlYm+ydQFn4srZ4hRVWXBt/LfgcqKAIVAADAXr74Y727puxD6GVUWLNtp3am848xAAAI0Ey2Sf/2ZrJ1O11q3iekdvv4mav19LfL3PKDp3TX0Z3jg90koFCTJ0/Wf/7zHzVv3jzP/e3bt9eqVZQpKPOMCkmrmUGNqmX7OlUZ1etLkfso8WTrL5oiHXazFFNH2vSX9Mml0jO9pdmvSZm7A9Xaqil9pzT5Lm/5kOulOs2C3SIAhYgsbAUAAKiaErenadbKrW75eMo+hIz6NWPUoGa0Nqema9nGVPVozmw/AABQzpZMklb+JEXESEfvOVlcSlnZOZq1Yqs2pqSpUa1Y9W9TTxGWxiBIpv6ZqDs+ne+Wrzmqnc7q3zJobQGKY8eOHXkyKfhs3bpVMTH7GHBD8dVuLoVFSFnpUmqCVLspew+hb+sKacJ1qjLiWkhj5kg7txQdzGDbNekhDbpa+vUVacbzUtIq6YvrpR8ekQaOkfpeIEVTMqrMTXtK2r5WqtPC2/8AQitQITs7W8uWLdPGjRvdsr/DDjusrNoGAACC4Mt5G9zktz6t6qpZXDU+gxDSvlEtbU7dosUJKQQqAACA8mWpjafc6S0fdPk/s4xLYeKCDbp3wiJtSE7Lva9JnVjdfUIXDevWRIE2d02Sxoz/3ZVKG9Gnua4f0iHgbQBK6tBDD9XYsWN1//33u9thYWHuvO4jjzyiI488kh1aViIivZm7llFh2yoCFRD67Dh/8wRpx0b7ZfGyKBWVZcAG8EOBBSHYpThi60iH3igNuEL6baw0/WkvA8Xkf0s/PSYddKXU/xKpGhNKykTSGmnak97yMfdLUZzbBEIqUOGXX37R2Wef7VKC5dgohh/r4GZlZZVl+wAAQIB9MY+yD6GqY+NamrF8i5ZuTA12UwAAQKj77U1p8xKpWj3pkBv2K0jhird/22vYIyE5zd3/wjm9yzVYIX8mh0a1YnThG79qV0aWDu/QUA+d2t2dDwMqOgtIOProozV79mylp6frlltu0cKFC11GhWnTpgW7eaElrpUXqGAzp1sNDHZrgPJjx/kbw6XkNVL99tKpL0lh4fvOMlBVRVf3gjf7XijNe1f6+Qlp63Lpuwe8DAD9L5YOukqq2TDYLa3cptwlZaZJrQ6Rupwc7NYAKOtAhcsvv1x9+/bVl19+qSZNmvDPGAAAIWRd0i79tjpJdq71uO6Bn52G8tU+vqa7towKAAAA5SZtu/TdQ97yEbeXeoagBQlYJoWC5mbafRYeYOuHdGlcLmUgCsrkYK9j7ererI6eH9VbURFFDMgAFUi3bt20ZMkSPfvss6pVq5ZSU1N16qmn6qqrrnLneFGGLIOMlb2xmeZAKM9af+N4KXm1VO8AafQEqTa/JcUSGS31Pk/qeba06FMvq8LGRV7gwi8vSL1HSwdfI9VpXt6fYuhZNV1a+LEXMDPsYZtdHewWASjrQIWlS5fqww8/VLt27Ur6UAAAUMF9uSebQv/W9RRfOzbYzUEZ6xhfy10vTSRQAQAAlCNLt7tzszdwYbWXS8kyGfgHCRQUrGDrH/7qT/VuVVdx1aIUVz1acdWjVLd6tGKjwks9waawTA4WpGDOOaiVasSUqqIqEDR16tTRv//9bz6B8hbX2ru2jApAKEpe6wUpWEaFem2l878gSKG0pWK6ny51PVVaMlH66VFp3Rxp1v+k2a9JPUdKh1wv1T+gzD/CkJSdJX19q7dswR5NegS7RQCKocT/UQ0YMEDLli0jUAEAgBD0xbwN7np4z6bBbgrKQfs9gQrrk9O0PS1DtWOj2M8AAKDsBy9mPOctD7lPiih9f8PKLRTHKz+vkOyST3RkuAtesKCFOi54IUpx1aIVV2PP9Z776lSLVl2/+yxLQmGZHHye/GaJTu/TvFwyOQDlJS0tTfPmzdPGjRuVnZ2dZ92JJ57Iji/LjAqGjAoIRcnrvHIPFohTt4002oIUOIe0X8LDpU7HSR2PlVb8IP34qJeV5fe3pLnjpK6nSIfeKMV39TJZ7NxS+HNV5fIav78tJcyTYupIR90R7NYAKMtABevA+lx99dW68cYblZCQoO7duysqKu8/nD16EKUEAEBltHLzDs1bm+xOth7brXGwm4NyUKdalBrXjlXC9jQtTUxVn1Z12c8AAKBsTb1/T13gg6VOx+/XUzWqVbwMX71b1pVVYNi2M0NJ7pKuzOwcpWdma2PKbncpiaiIMGVkFRWm4GVysIwPAw+oX6LnBoJl4sSJOu+887R58+a91lnmkaysrKC0KyTF7QlUIKMCQs329dKbw6VtK6S6rb1MCnWaBbtVocOyQLU9wrusmeUFLCydJC34yLu0PVJaNU3KSi/8OSJjpDFzql6wQlqyNPU+b/mI26QaDYLdIgBlGajQq1cv12HNyfnnn7QLL7wwd9m3jk4tAACV15fzvWwKgw6orwY1Y4LdHJSTDo1ruUCFJYkpBCoAAICytX6uNO9db/mY+/erLvDO9Ex9OndtkdvYszeuE6sPLh+YJ7OBnaPakZ7lAha8wIUMJe1Kd4EMyXvuc8t77rPtknd591lph30FKZQ04wNQEdjksxEjRuiuu+5SfHx8sJtTNTIqbF8nZWXsV2YZoMLYvkF68wRp63IvGMcyKdRpHuxWha4W/aVR70sb5kk/Py4t/FRa/t2+H5e528u4UNUCFX54xCs71qCD1P+SYLcGQFkHKqxYsXf6PAAAEFom/LHeXQ/v0STYTUE56tCopn5cskmLE1LYzwAAoOzY5JbJe9Lsdh8hNetT6qdasC5Z17z7u5Zv2pF7n4Uh+IcP+MIS7j6hy17lF2wiTc2YSHdpXoIEUhbgkLo7U9/9tVHXvDu3zDI+ABVBYmKibrjhBoIUAqFmvBQZ62WXSV4j1WsbkJcFyk1KgheksGWZVKell0mhqg2EB0uTHtKIN6Qjl3n9rCVfB7tFFc/mpdLMF73loQ8THAZUMuHF2ahVq1a5l1WrVqlZs2Z57rOL3WfrAABA5bNsY6r+SkhRZHiYhnal7EOoZ1QwSzcSqAAAAMrQkolePeWIGOnou0r1FNnZOXrlp+U69fnpLkghvnaMxl08QC+e09tlTvBnt184p7eGdSu7IFsLcKgVG6XjezRVkzqxucEQe21n4wZ1YtW/Tb0ye22gvJ1++un6/vvv2dGBYNlk4lp6y9s4X45KLnXjniCFpVKdFtL5E/45vhE4Ddp5JQ2wt0n/krIzpfZDpfaD2UNAKGZU8HfkkUdqw4YNatSoUZ77k5OT3TrqmQEAUPl8Mc/LpnBo+waKqx4d7OagHHWI9wIVFieksp8BAEDZsNTmk+/0lg+6olQDGFZG4aYP5rnMT2ZIl3j957QeqlfD65sO6dJYs1ZsddtZJgMLEsifSaGs2PNapoYr3v6tRJkcgIrs2WefdaUffvrpJ3Xv3l1RUXnLEVxzzTVBa1tIstT4m5dISQQqIASCFOxYrt1cGj1Bqts62K0C/rFksrR0shQeJQ19iD0DVIVABUuDZxHm+W3ZskU1atQoq3YBAIAAsb/t/5R9aMp+D3HtG9V015tTd2vrjvTck/8AAAClNucNb6Zl9frSoTeU+OFWauHmD//Q5tR0xUSG687hXTRqQMs8558sKGDgAfUD9iFZpgbL2HDvhEXakJyWJ5ODBSmUZSYHIBDeeecdTZ48WbGxsS6zgv/3y5YJVChjdVt512RUQGWVukl680Rp019SraZeJoV6bYLdKuAfmeleNgVz0OVe1gkAoRuocOqpp+Z2XM8//3zF/D97dwFeVfnHAfzLuos1bNSA0d3djUiHgoWCEoooYCGKhYEopfwRRBpBpRHp7o7RjFh39/9537PBBqxv3+/nee5zzz337N6Xczd2ds73/f0sLR89J6oonD9/Hi1btlTPKImIiEhtRMuHm2EJsDAzQZdaHtzTBs7W0gw+Lta4F5mEayFxaF5Zcyf8iYiIyAAlxwB7v1KW208DrByL/qVpGfhm+1UsOXRHPvb3tMfPwxqganYFKG0TYQRNVnIgUqcPP/wQM2bMwNSpU2FiUqRuwFTaigoCKyqQPkoIB5Y9B4RdAey9gJc2Ay6VtT0qKqqs3LWgDNiJRUpQ1tYNaPuetkdDROoOKjg6Oj6adWlvbw9ra+tHz1lYWKB58+YYPXp0ScdBREREWm770L6aGxys8pb/JMNUzd2eQQUiIiJSjYM/AokRQNmqQKOXivxl10PiMH7VGRmaFV5qWRFTe/jDytxUpz4ZTVdyIFKX1NRUDBkyhCEFTWFFBdJXCRFKSCH0EmDnCYzaDJStou1RUXHs+wYYugIw0a1jKpVX/Nj7jbLc6ZNiBWWJSE+DCkuWLJH3FStWxOTJk9nmgYiIyACIAOLm80FyuXc9tn0wFtU87bHraqisqEBERERUZNH3lFBCjvgQ4MhcZbnRy0BcMODkU+jx54pjgfh882WkpGeirK0Fvh1UFx39WdmLSJ1GjRqFNWvW4IMPsstkk3qxogLpo8RI4I/ngJCLgJ2HUkmB5fR1h2ixZWYJpKcUvN21bcD614D+vwKmBjohac9MICUG8KoH1B+h7dEQkSaCCjmmT58u70NDQxEQECCXq1evDnd399KMg4iIiLTg4oNY3I1IhJW5CTr583e5sajmYSfvrwXHa3soREREpE8hhbmN8j85/u8HwO4ZwLhT+YYVIhNSMWX9eey8HCIft6nqiu8H15MtFYhIvUTr3lmzZmHHjh2oW7cuzM3zXrz64Ycf+BGoo6JCQhiQmgBY2HL/ku6HFEQlheALgK07MGoT4FpV26Oi3MTxlTjOyh0afdKD08C294FLG5RjtkFLlHCDIQk6D5z6XVnu/o1hV44gMgLFDirExcXhzTffxOrVq+UBrmBqaipLh82bN+9RiwgiIiLSn7YPnfw9YGtZ7MMC0lPVsvs+XwuNk7May5Rhn2UiIiIqhDgpXtgMPvG82O4ZQYXDN8LxztqzCIlNgblpGUzp7o9XWlWCiQmPQ4g04cKFC2jQoIFcvnjxYp7n+PeAGlg7A5aOyozf6EDAvYY63oVINZKigD+eB4LPA7ZuSkjBrTr3ri4Sx1gFVa/yrg84lgfWvggEbAFWDQWGrAAsbGAQ1byQBWycoNxX6aj8W4lIrxX7isRrr72GM2fOYPPmzWjRooVcd+TIEUycOBFvvPGGDDAQERGRfrV96FPPS9vDIQ2q4mYHcU0gOjENYXEpcHfgLEYiIiJSj7SMTHz/7zX8sv8msrKAym62+GloA9Qux4kuRJq0Z88e7nBNc/ZVZqdH3WVQgXRXUrQSUgg6C9i4KiEFd39tj4pKo1pXYPhaYNUw4OZuYMVAYPgawFKZtGIw1bzEv008X0A1LyLSfSbF/QIRUPjtt9/QrVs3ODg4yJtYXrRoETZt2qSeURIREZHKnQ6MxoPoJNhamKJ9dbZ9MCZW5qaoWFYpPRoQEqft4RAREZGBuhOegIELDmPhPiWkMKypLzaPb82QAhEZB6fs9g/Rd7U9EqJnS45RQgoPzwA2ZYFRGxmqMRSV2wEv/gVYOgB3DyltPUTlDEOt5kVExlNRoWzZss9s7yDWOTs7q2pcREREpKG2D11qesgL12R87R9uhSfgWkg82lR10/ZwiIiISNeJ0uXFqNy1/vQDTP/nIhJSM+BobY6v+9dBjzqs4kWkSf3798fSpUvlRDOxXJANGzZobFxGw7mici8qKpAOlpB/grhQb0yzspNjgT/6Aw9PA9YuwMiNgEctbY+KVMm3mRI+EWGUB6eA3/sAL/4N2LpyPxOR/gYVPvroI0yaNAl//PEHPD095brg4GC89957+Pjjj9UxRiIiIlKxzMwsbL2gtH3oXdeb+9cIVfOww/ZLwLVgw6qo8Morr2DOnDmwt89b0jAhIQHjx4+XlcGIiIioiEQZhMAjwKGfgGvbivQlcSnp+GD1WWw6p4Rim1Vywewh9eHtZM3dTqRhYmJZmTJlHi2ThrGigv6UkBfMLI2nhHxKHLB8APDgJGDtDIz8B/Csre1RkTp4NwBe2qpUVBCtaJb0VD5vB4ZHiUhPgwoLFizAjRs34OvrK29CYGAgLC0tERYWhl9++eXRtqdPn1btaImIiEglTtyJREhsCuytzNCmGpPUxqiap3Ih/1qoYQUVfv/9d3z99ddPBRWSkpKwbNkyBhWIiIiKIjMDuLIJOPyzchGjGCasOoM9sd4wNSmDSV2qYUy7KnKZiDRvyZIl+OyzzzB58mS5TBrGigr6WULe0IMKMqQwELh/HLByUi5ae9XV9qhInTxqAi9vA5b1BcIDgCU9lEoLTsr1PSIivQoq9OvXTz0jISIiIo3ZlN32oVstT1iase2DMarukR1UCI6T5ZlzZlrpq9jYWPnvELe4uDhYWVk9ei4jIwNbt26Fu7u7VsdIRESk81ITgbMrgCPzgKjbyjpTS6D+MKBKZ2DtC4W+RGhcCnxdbDBnaH008GWLUCJtmzFjBsaMGQMbGxttD8X4OFdQ7qPvKhVq9PxvLjKANhdpScC295SZ9VaOwMi/Aa962hghaZqrnxJWEO0fxDHeb9lhhbJVdPOzEN+rZ5ZpexREpItBhenTp6tnJERERKQR6RmZ2HYhWC73qce2D8aqoqstzE3LyL7RD6KTUN5Zv09cOjk5ybCFuFWrVu2p58V6cZKWiIiIniEhHDj+K3B8EZAUqawTpaCbvAY0fR2wc1cueoiy2AXMSE3OMkezWlXxzsDWsLcy564m0gEiyEtakjNbOSUWSIoCbFz4UZButLkQnv9FaQtAxhWeemU78HtfIOK6UllBVNRwrwGdIX5nXfgT+O9TIPa+tkdDRLoYVBCio6Px559/4ubNm3jvvffg4uIi2zx4eHigXLlyqh8lERERqczRW5GISEiFs405WlYpyz1rpMxNTVDZ1Q4BIXG4HhKv90GFPXv2yJOwHTt2xPr16+XxaQ4LCwtUqFAB3t4M5hAREeURcVNp73BuFZCe/LineotxQIMRgIXt422dfLC36zZ899cR5HfZs3PjGvhkQCfuZCIdo67qafPmzcO3336L4OBg1KtXDz///DOaNm1a6NetXr0aw4YNw3PPPYe///4bBsvcGrDzAOJDlKoKDCpo78Jn2FUYjaK0uRDsvTQxGtI1Dt7Ay1uBZf2A0EvAkp66U1kj8Biw44PHbcds3YGEUG2Pioh0Lahw/vx5dO7cGY6Ojrhz5w5Gjx4tTwRv2LABgYGBsvcvERER6a7N2W0futf2kheryXhV9VCCCuLWwV+/2yK0a9dO3t++fRs+Pj4wMeH3NhERUYEngg//BFzdIq7gKOu8GwKtJgA1+gImT7cGy8jMwrRd0QjKqpTvy0ZeAyZkZsHUhOXNiXSJqDhWWFghMjK7mkoRrVmzBpMmTcLChQvRrFkz/Pjjj+jWrRsCAgIKbLkmzidPnjwZbdq0gVEQ4S8RVIi6y9nrmg4nhF4GLq4HLm543M6IiJRKWS9tBpb3Bx6eAZb2AV5YD/g00c7eEf8/igoKlzYojy3sgNbvADWfAxa2Kjh4I6p92XASFpFRBRXEAehLL72EWbNmwd5e6W0s9OzZE8OHD1f1+IiIiEiF0jIysf1SdtuHukzPG7vqHvbYjCBcC4mDoRCVE0T1r+PHjyM0NBSZmZl5nh85cqTWxkZERKRVmRlAwFalgsK9Y4/XV+sOtBwPVGhVYP/047cjERSTXXUhH+J5sV0LVu0i0imiBZqYdKZKP/zwg5zA9vLLL8vHIrCwZcsW/Pbbb5g6deozvyYjIwMjRoyQ4zlw4IA8bjeKUuv3jysVFUj9wq8rwQQRUAgPeLze1BLIKEKVASJjISq8iLYPK4cAgUeAP/oBw9cAFVtrbgzJscDBH4Aj87N/PssADV8EOnwE2Hso24w7pVQJyY8IKTj5aGzIRKQDQYUTJ07gl19+eWq9aPkgynwRERGR7jp4IxzRiWlwtbNEs8pMHBu7qh5K6NSQggqbNm2SJz/j4+Ph4OCQZ+aYWGZQgYiIjE5aktLa4fBcIPKmss7UAqg7GGgxHnD3L9LLhMYlq3Q7ItKcoUOHFljloLhSU1Nx6tQpTJs27dE6UdFMVOE9cuRIvl/32WefyXG8+uqrMqhgNBUVcmYMk3qIfXspO5wQfOHxevG7zq8LULs/4FAOWNKdnwBRblaOSiWF1cOBW3uB5QOAoSsAv87q3U8iPHt6GbDnCyAhTFlXqS3Q7UvAs07ebUUIgUEEIoNW7KCCpaUlYmNjn1p/7do1uLm5qWpcREREpAabziltH3rV8WRJXkJ1TyWocD0kXpZzNoQyze+++y5eeeUVfPnll7CxsdH2cIiIiNQj+l7hs8vMbYAT/wOO/wokhj8+Id34VaDZG4C9Z7He0t3eSqXbEZFmFNbyoSTCw8NldQQPj+wZr9nE46tXrz7zaw4ePIjFixfj7NmzRX6flJQUecvxrHPSelFRQYi6o+2RGJbYh8Clv5VwQk4/e8HEDKjcQQkn+PdSfu/l/N4UJeILKiEvZnPLG5ERsbAFhq0B1o0Crm0HVg0DBi1Vfn7U4eZuYMdHQOgl5XFZP6DrTKXClxp+XxGRAQYV+vbtK9Ova9eufXSwGxgYiClTpmDAgAHqGCMRERGpQHJaBnZeCpHLvet5c58SfF1sYGlmgpT0TNyLTERFV1u93ysPHjzAhAkTGFIgIiLDJS62zG1U8MWWMqbKxZqcMteOvkCLN4EGLwKWdiV628YVnGFhWgapGVnPfksAno5WaFrJpUSvT0TqkZX17J9ZTYqLi8OLL76IRYsWwdXVtchf99VXX8k2EQZRUYGtH0ovPgy48o/S2uHuYfHdnf1EGaBSG6BWf6BGX8D2GdUjxYzs/ErIx4cCG8cB8SHK/UubHwcc9LWSElFxmFsBg/8ANowGLv8NrHkR6P8rUGeg6vZjWADw70fA9X+Vx1ZOQPtpQONXADMLfl5ERqzYQYXvv/8eAwcOlGW6kpKS0K5dO9nyoUWLFvjiiy/UM0oiIiIqtf3XwhCXkg5PBys08nXmHiVZQcHP3Q6XHsYiICTOIIIK3bp1w8mTJ1G5cmVtD4WIiEg9xEWWAmeEims3GaIZPOBVD2g5AajZDzAt9imgPJYcvl1gSEGY3qemQVRoIjIkmZmZKn9NETYwNTVFSIgShM8hHnt6Pl2t5ebNm7hz5w769Onz1LjMzMwQEBCAKlWqPPV1orXEpEmT8lRU8PHx0c+KCtGB4h8temRoe0T6URUop9R7UhRwZZMSTri9D8jK9f3s01ypnFDzuaJVCSqohPxLW4HfugHB55UZ5aIcvrk19E5KPLDtfW2PgvSRCAsMWAyYWQHnVwPrX1NCLw1fLN3rJkQA+74GTixWjk9FkLbp60Db9wAbhluJqARBBUdHR+zcuVOW6zp//rzs/9uwYUPZg4yIiIh01+bzQfK+V10vmPAEMmWr7mEvgwrXQ+LQrVbxSkDrol69euG9997D5cuXUadOHZibmz9VHYyIiMgo9JoNNH5ZJWV0Lz+MxXc7rsnlEc18sftqKIJikh89LyopiJBC99pepX4vItJ9FhYWaNSoEXbt2oV+/fo9Ch6Ix+PGjXtqe39/f1y4cCHPuo8++khWWpgzZ06+4QPRgljc9JpDeaXKTUYqEB8MOBh5dcOiVAUytQQ6f6oEE27sAjLTHj/n3QCoPUAJ4Kmyb72rnxJO+L0PcPcQsO4lYMhywDTv35M6LSUOWDFICVsURrTBEIEQotxEqLXfAiWkc2qJUmFEhBWavV78/ZSeqrQf2z8LSI5R1lXvCXT5XPl5IyLKVuI4fevWreWNiIiIdF9Sagb+u5Ld9qEuTyDTY1U97OV9QEi8QeyW0aNHy3vRquxJomWZ6KVLRERkFMo1VElIQbQPe3vNGaRmZKJzDQ/M7FcbmVnA8duRCI1Lhru90u6BlRSIjIuodDBq1Cg0btwYTZs2xY8//oiEhAS8/PLL8vmRI0eiXLlysn2DlZUVateunefrnZyc5P2T6w3ywp9jOaWiQtRdBhWKUhVItC3aMe3xY/daSuWEWs8DZZ+uvKEy3vWBYauB5f2Ba9uBf94C+i3UjyoYIqSwfCBw7yhg6Qj0XwjYexetagVRbuL7vfdswNwGODoP2PYekJYItH67aPtJtBu6uhnY+QkQeUtZ51EH6PYFULkd9zURlS6oIJKxS5cuxYYNG2S5LnGyt1KlSrIVhOgzJh4TERGR7tkTEIrE1AyUd7ZGfR/lhBCRUN1T6VMtKioYAnWUtiUiIjJm3+4IwLWQeLjaWeDrAXXkuR/TMkCLKpyJSWTMhgwZgrCwMHzyySeyLXD9+vWxfft2eHh4yOcDAwNhog8XeDXBqYISVIi+C1Rooe3R6AfH8kD9EUCt/oC7v+bet2IrYNDvwOrhwPk1gJUT0OMblQT/1CY5FlghQgrHACtH4MW/lbAiUUmJ73cRLLCwAfZ/C/w3HYgLBuoNydXw6xnhFxFE2vEhcPegss7OA+j4MVB/OGBiys+DiEoXVMjKypKlcrdu3Yp69erJUrpi3ZUrV/DSSy/J8MLff/9d1JcjIiIiDdp07qG8713Xm8FCyqNadkWFm2HxSMvIhLkpTyYSERGR4tCNcCw+eFsuzxpYF652el6CnYhUSrR5eFarB2Hv3r0Ffq2YDGc0nCsAdw4oFRWoaETbBdHmQRuqdweeXwhsGA0c/wWwcQHaT4VOEiX1lw8A7p9QQhUj/9befiPDCyt0/EiprLBrBnBsgXLLd3sTICt74oiZFdBinFKFwVI550SkSqJaalparrZApHGi1a6pqalmgwri4HH//v2y11iHDh3yPLd7927Zj2zZsmWyrBcRERHpjviUdNlHWGDbB3pSOSdr2FqYIiE1A3fCEx61gtBXz2r5kJuY8UVERKTXEsI18jbRial4d+05uTyimS86+iuzpIlIf12/fh179uxBaGjoU5XIeJysRk4VlXtRUYGKSMsVDOoOBpKigG3vA3u/UkIAzcdA50IKf/QHHpzMDin8o7SvIFKlNpOAxCjgyE8Fb5cTUqgzGOj0CVuLkFqIyfOiilN0dDT3sA4Qbbw8PT1LPSmyyEGFVatW4YMPPngqpCB07NgRU6dOxYoVKxhUICIi0jG7roQgJT0TlVxtUcvbQdvDIR0jDiZFOOHsvWhZ1lnfgwp//fVXnsciYX379m2YmZmhSpUqPAFLRET6LSUe2D5FIycBP/z7IoJjk+Ux5Ie9aqj9PYlIvRYtWoSxY8fC1dX1qZPKYplBBTVXVBBYUUG/NHtDCSuIoIL43WvtnF36XgckRQPLRUjhlDIuEVLwqqftUZGhqjOg8KCC0G+B0uaBSE1yQgru7u6wsbFh1WAtEX8rJiYmyuCr4OXlpZmgwvnz5zFr1qx8n+/Rowd++qkI/1kRERGRRm06F/SomkJpE45kmKp52MmgQkBIHHqhdAeX2nbmzJmn1sXGxspWZc8//7xWxkRERKQSmRnA+teAiBuFb2tmqfQKLqG/zz7AlvNBMDUpgx+H1IeNRZFPHxGRjpo5cya++OILTJmi/rATPcEpO6jAigri8oZ+fXu0m6KEFY4tBP4eC1g5ANV7aHdMYjx/PA88PANYuwCjNgKedbQ7JiLBvSb3A6m13UNOSKFs2ZL/nUOqYW1tLe9FWEF8JqVpA1HkvzQjIyPh4ZF/mT/xXFRUVIkHQkRERKoXk5SG/dfC5HLvut7cxfRM1bKrKFwPiTPIPeTg4IAZM2agT58+ePHFF7U9HCIiopLZ8SFwbZvS93fAYsCxfP7bipCCk0+J3uZ+VCI++fuSXJ7YqSrq+TjxEyMyAOK87aBBg7Q9DOOuqBD7AMhIA0zNYbQuboBeEZM9un2lhAPOrwHWvQS8sAGo2Eo74xHjWNYPCDqr/K4fKUIKtbUzFiIiDRIVUwVRSYF0Q85nIT4bjQQVRFpFlMzNjxhEenp6iQdCREREqvfvpWCkZmTKGfPVPfW7pD+pT873hqioYKhiYmLkjYiISC8d+wU4tkBZfn4hUKO3Wt4mIzMLk9aeQ1xKOhr6OuHN9lXU8j5EpHkipPDvv/9izJgx3P2aZuehhMzSk4GYe4BLZeP8DO4cBA7PVXtVIJUzMQGemwckxyqBwVVDgVGbAO/6mh1HYiTwhwgpnFP2jxiDRy3NjoGISMtYLdjwPguz4vScECVzLS0tn/l8SkpKqQby9ddfY9q0aZg4cSJ+/PFHuS45ORnvvvsuVq9eLV+/W7dumD9/foGVHYiIiOixzeeDDKeaQvQ9IDFCLTMHjV1ORYU74QlITsuAlXnJU7Da9mQrMnEMGxQUhD/++EO2KiMiItI7AduB7VOV5c6fArXU18po0YFbOH47ErYWppg9pD7MTE3U9l5EpFl+fn74+OOPcfToUdSpUwfm5nln9U+YMIEfibqIE/lOvkD4NSDqrnEGFcTf82tHiT5GQPVeQLv3xI7Rn7/tRRWMQUuA5QOAu4eU+1d2AK5+mgspLOsLBF8AbFyzQwoss09ERPqvyEGFUaPEgUTBRo4cWaJBnDhxAr/88gvq1q2bZ/0777yDLVu2YN26dXB0dMS4cePQv39/HDp0qETvQ0REZEyiElJx6Ea4XO5d1wt6f1JjbiMgPaXgWRfjTuneCQ094G5vCUdrc9kq5FZYAmp6O0BfzZ49O89jExMTuLm5yWNZEYolIiLSK2LW5J+vAFmZQMORQKu31fZWlx7G4Pt/A+Ty9D61UKGsrdrei4g079dff4WdnR327dsnb0/OiGNQQc2cKihBhei7MDppScCaF4DEcMCzDjDgf4CFHpbuNrcGhq0ClvYGgs8r1Q1e2V5wKyZVSIgAlj0HhFwAbN2UkIJ7DfW+JxER6ezx3Oeff44HDx7ghx9+wNtvl/7vwzt37qBSpUo4c+YM6tevr7tBhSVLlqhlAPHx8RgxYgQWLVqEmTNnPlovSvMuXrwYK1euRMeOHR+NoUaNGjL527x5c7WMh4iIyFBsvxSM9Mws1PRyQGU3O+g1UUmhoJCCIJ4X2zGoUGzixKRoD3LiThSuhcTpdVDh9u3b2h4CERGRasQ8AFYOAdISgMrtgV4/KLNy1UBUVHp79VmkZWSha00PDGqs5osuRKRxPE7WMucKyr2oqGBMsrKAze8AQWcBaxdgyAr9DCnksHIEXtgALOkORNwA/ngeeHk7YFtWjSGFvkDIRcDWPTuk4K+e9yLKj6hyIiYHFTZ5SJdathAV0u5OVJELjUuGu70VmlZygamJev7OEkS3gt9//10um5mZwcXFRU7cHzZsmHxOTLIqitjYWDmhXwQUBgwYICf4q8PevXvRoUMHREVFwcnJCToTVFCXt956C7169ULnzp3zBBVOnTqFtLQ0uT6Hv78/fH19ceTIEQYViIiICrH5/EN537uenldTII21f8gJKhiK+/fvy/vy5XmxhYiI9ExKHLByMBAXBLj5A4OXKWWn1eSb7VdxPTQernaW+Kp/HfZ+JTJwoj2awD7PGq6oIBhbRYVjvwDnVgFlTJTWCTmBDX1m5wa8+DfwWzelSsaKAcDIjYCVigP/CeHA732B0EtKSOGlzYBbddW+B1FRiAlBooIp27GSAdh+MQgzNl1GUEzyo3VejlaY3qcmutdW3zn07t27y8n4GRkZCAkJwfbt2zFx4kT8+eef2LhxowwwFCYwMFBeNxfX1L28DOd8v1abDa5evRqnT5/GV1999dRzwcHBsLCweCqt4eHhIZ/LT0pKikyV5L4REREZm7C4FBy5GSGXe9fx1vZwSA9U97SX9/oeVMjMzMRnn30mU8UVKlSQN3E8KcqiieeIiIh0XkY6sO7lx7MnR6xTZnCqyYHrYVhy6I5c/nZQXZS1s1TbexGRdi1btgx16tSBtbW1vInZfH/88Qc/Fk0wxooKtw8AOz5Qlrt8rlQHMqQLtyKsIGaQPzwDrB4OpD2+6FVq8WHA732UkIKdB/DSFoYUSPvf897187+xuinpSUhh7PLTeUIKQnBMslwvnlcXS0tLeHp6oly5cmjYsCE++OAD/PPPP9i2bRuWLl0qt4mOjsZrr70mW9g6ODjIbgPnzp2Tz4ltxDGcULlyZRk2FS0bbt68ieeee05eOxctvpo0aYL//vsvz3uLbf/+++8868S50pz3zU28pqimIDg7O8uvFVUfDDKocO/ePZkWWbFiBaysrFT2uiL0IE5M59x8fNinmoiIjM+2i0HIzALq+TjBt6wel1UkjanqrgQVAvQ8qPDhhx9i7ty5+Prrr2VvNXH78ssv8fPPP+Pjjz/W9vCIiIgKJmY5b3sfuLETMLMGhq8GnHzVtteiElIxeZ1y8uvF5hXQobo7PyEiAyXKBI8dOxY9e/bE2rVr5U3M7hszZgxmz56t7eEZPmOrqBB9D1g3CsjKAOoMBlq8BYPjVg0Y8SdgYQfcOQD8+YoSNlRZSOEyYOeZHVKopooRExEZXIWoxNT0It3iktMwfeMlZD3rdbLvP914WW5X2GvlVKYqLRFEqFevHjZs2CAfDxo0CKGhoTK8ILoOiEBDp06dEBkZiSFDhjwKIBw/fhxBQUHy+nd8fLw8ttu1a5c8ByqO7fr06SOrL5SEeM3169fL5YCAAPk+c+bMgUG2fhA7WexwsaNziJIX+/fvlyeXd+zYgdTUVJkgyV1VQZTEEKmT/EybNg2TJk169FhUVGBYgYiIjM3mc0oCtE9dwykDVSTpqdoegd6q5mEn7+9FJsmDbhsLrXcIKxHR8+1///sf+vbt+2idmCkmEstvvvkmvvjiC62Oj4iIqEBH5wMnF4t5L8CARUC5RmrbYeIE24d/X0BIbAoqu9nig541+OEQGTAR3F2wYAFGjhz5aJ04Zq5VqxY+/fRTvPPOO1odn9FUVEgIA1ITAAtbGKy0JGDNC0qZeM86QJ85YjonDFK5hsCwVcDygUDAFmDjeOC5eUAR+40/JT5UCSmEXQXsvYBRmwFXP1WPmojIICSlZaDmJztU8loiehAcm4w6n/5b6LaXP+umsvOm/v7+OH/+PA4ePCgDCOK6uai+IHz33XeyEoJoD/H666+jbNmycr2ouJBznVwEHcQth6go+9dff8l2EuPGjSv2eExNTeHi4iKX3d3dn+p6oA7F/o0pggTp6U8nA8U68VxRiRTIhQsXcPbs2Ue3xo0bY8SIEY+Wzc3NZQokh0hviBRIixYt8n1d8QGKkhi5b0RERMZElKs6cTdSLvesY2RBhQ2jlbKLVGyizLOrnYVcvh4Sr7d7UKSMxUH+k8Q68RwREZHOurIZ2PGhstx1JlCjj1rfbsPpB9h6IRhmJmUwZ0gDWFuYqvX9iEi7xIy4li1bPrVerBPPkZpZOwOW2W18oks2y1EviFmmm98Bgs4C1i7AkBWAhYFXeazUFhi0BChjCpxbCfz7kbIfiisuBFjaOzuk4K1UUmBIgYjIoGVlZcn2CqLFg6iOIMIIooVDzu327duyvUN+xNdMnjwZNWrUkKEC8TVXrlwpcUUFbSh25EP0phAHryJJkVtMTIx8TlRFKAp7e3vUrl07zzpbW1v5IeSsf/XVV2V1BJHeEIGD8ePHy5BC8+bNiztsIiIio7HlQpD8m7hxBWd4O1nDIGQW7fgC0XeARZ2AtpOBNpMBM+XCOxVNNQ97hMdH4FpInGwboo9EilhU5/rpp5/yrBfrcieMiYiIdMqDU8D615S5PI1fVXuJ7HuRibL0qfBOl2qoUz774hkRGSw/Pz/Z7kH0RM5tzZo1qFq1qtbGZVScfYHgC0DUXcDdQKvYHPsFOLdKuWg/aOnjShKGzr8X8Nxc4O+xwNF5gI0z0Pa9on99XLBSSSH8GuBQDhi1CShbRZ0jJiLSe9bmprK6QVEcvx2Jl5acKHS7pS83QdNKLoW+r6pcuXIFlSpVkoEDLy8v7N2796ltCqpqIEIKO3fulNUXxLGetbU1Bg4cKDsW5BBBiCfbVaSlpUFvgwo56Y4nRUREyKCBKon+aCYmJhgwYABSUlLQrVs3zJ8/X6XvQUREZGg2n38o73sbUtuHq1sK38bUEqjcHri+A9j3DRCwDXh+IeBRSxMjNJigwuGbSlBBX82aNQu9evWSfdtyqnAdOXIE9+7dw9atW7U9PCIioqeJmbUrhwLpSYBfZ6DHLLWWyM7IzMKktWcRn5Iug61j2vFCCJExmDFjhuxvLCritmrVSq47dOiQrGYrAgykAU4VlKBC9F3D3N23DwA7soMwXT8HKreDUak/HEiKBnZMA3bPVKpoNBEhxELEBgG/9wYibgAO5YGXNgEulTUxYiIivSauVRe1BUObqm7wcrSSlYifVfNG/PXl6WgltzM10Uy7ot27d8vOA6L9Vvny5REcHAwzMzNUrFixyK8hjuVeeuklPP/88/KxCDzcuXMnzzaiVUTu6lnXr19HYmJivq9pYaFM/CtqYQKNBRX69+//6IMX/+icHhk5gxU9NJ5VPqw4nkyKWFlZYd68efJGRERERZsddyYwGuJ4qqehBBWi7ij9moW2UwD/ns/ezqYs4OQDXFwPbHkXCD4P/NIO6PAB0HICYKqa3mGGHlQQAvS49UO7du1w7do1efx49erVR8exb775Jry9vbU9PCIiorySY4AVg4GEUMCjNjBwidqPWX7ZfxMn7kTB1sIUs4fU19iJOCLSLjER7NixY3JimOh3LIgywaIfcoMGDfjxaIJz9oUHUVHB0ETfA9aNArIygDqDgeZvwii1eBNIigL2zwK2TAasnIA6A/PfPvah0u4h8ibg6KNUUnCppMkRExEZBfE3z/Q+NTF2+WkZSsgdVsj5a0g8r66/jcRkfBFEENfTQ0JCsH37dnz11Vfo3bs3Ro4cKSftiwlX/fr1k5OwqlWrhocPH2LLli0yhNC4ceNnvq6oirVhwwb06dNHXr//+OOPkZmZmWebjh07ykqz4vXF+0+ZMgXm5ub5jrVChQrytTZv3oyePXvKKg2ipYS6FPmvX0dHx0cVFUTbBjGw3OkK0Y5h9OjR6hklERERFbntg9CsUlm421vp/14TZalE6EDMMKzYBugwrfAZhrUHABVaA5vfBgK2ArtmKBUZRHUFV5Y0LUh1T+Wg87oeV1QQRCDhiy++0PYwiIiICpaRBqwdBYRdAey9gOFrACsHte61iw9i8MO/1+Typ31rwcfFwPuGE1EejRo1wvLly7lXtFlRQTC0igppScCaEUBiBOBZF+gzR62VgXSemCwhwgonFgF/vQFYOgDVuj69XcwDpZJC5C3A0VeppJATZiEiIpXrXtsLC15oiBmbLiMoJvnRelFJQYQUxPPqIoIJorWDqJjg7Ows29OKtrWjRo2SIQVBVIL98MMP8fLLLyMsLAyenp5o27YtPDw88n3dH374Aa+88oosJODq6ipDCLGxsXm2+f777+VrtmnTRp4znTNnDk6dOpXva5YrV05W4po6dar8OhGkWLp0KdSlTNaTjSkKIQYnel6ous2DuogPRIQsYmJi4OCg3j/4iYiItK33zwdw8UEsvni+NkY0M4BekBf+BNa/qrR1GHsYcPUr+teKQxzRG3PbVCAlBjCzAjpNB5qNAbIPACmvmKQ01Jvxr1w+N70rHK3zT9fq2jGbKFv2ySef4Jdffnnq68Vrjh07FjNnzkTlyoZZQpPHvEREekYcp2yaAJxeBpjbAi9vBbzrq/Utk1Iz5LHizbAEdK/lKU/SPau1JxEZzjGbeL+c93nypPWT9OG8qd4f817bAawcDHjUAcYehMH8PvtrDHB+tVLl8PW9gJOvtkelfWI261+vAxfWKeczen4HeNV9/Hx8KLBpIhD3UNlfozYDzgZwDoeISE2Sk5Nx+/ZtVKpUSVbjL20rvOO3IxEalywn+jWt5MIqcyr+TIpzzFbseoLTp08v/miJiArBXw5Epf/5ufwwRoYURIWqHmpMgGpMYiSwbYqy3Pa94oUUBHHiXfSIrNQO2DgOuLlb6RV5dTPw3DyWU3wGEUwQ/dpEqvhGaBwaVXCBvvj222/h4+PzzINfcWAsnhPbLFiwQCvjIyIiyuPQHCWkUMYEGPib2kMKwtfbrsiQgru9Jb7sX4chBSIjIGbsiZ7E7u7ucHJyeubPvZjDJtZrqg+xUctdUUFc4DeEsNixhUpIoYwpMGgpQwo5xOSIDh8qrSkzUoBN4/Pfh/3/x5ACEZEGifYOLaqU5T7XEUUKKjRs2BC7du2SB7eiZ1lBifvTp0+rcnxEZAS2Xwx6qtyOlwbK7RAZ6s+PmakJjt+O0P+fn50fA4nhgJs/0GpiyV/HsRzwwgbg1BJgx0fA3UPAglZA18+Bxq8YxskhFarqYS+/nwKC4/UqqLBv374Cy9gOHjwYw4cP1+iYiIiInunS38B/2ZNAun8NVO+u9h2171oYfj+ilBr/dlA9uNha8MMhMgK7d++Gi4tyTL9nzx5tD4dyKg2kxCqtAWz05++tZ7q9H9jxobLcdSZQqa22R6RbkmOArLx9wp/JzFIToyEiItLfoMJzzz0HS0vlF2a/fv3UPSYiMrKLrGOXn8aTPWiCY5LlelGOVO8vthJp+OcnNT1T/39+xAmPM9kXnfv8BJiV8mS6CCOIUELlDsA/bylhhS2TlOoKfX8GHMurZNiGoLqHHfZfC8O1kDjok8DAQDlTLD+iT9u9e/c0OiYiIqKn3Duh9KsWRDuqZtnLahSZkIrJ687J5ZdaVkS7am78YIiMRLt27R4ti7K8osrYkxPQREUFHidriIUNYOsOJIQqVRX0OagQHQisewnIygDqDgGaj9X2iIiIiMhQgwq52z2w9QMRqbJcvZgJ/uRFVkGsE386i+e71PRkjyCiYvz85NDbn5+0ZGDT28py41cB32aqe22XSkrvR1GectcMpR3E/JZAj6+BesNYXQFANQ97uav0Lagg2jvcvHkTFSo8u6/njRs31NbH9sGDB5gyZQq2bduGxMRE+Pn5YcmSJWjcuPGjk7/iGHrRokWIjo5Gq1atZAuKqlWrPnqNyMhIjB8/Hps2bYKJiQkGDBiAOXPmwM7OTi1jJiIyVDrdUi7yNrBqKJCeDFTrAXT7Uu1vKX4HfbDhAsLiUuDnboepPfzV/p5EpJtEUCGnDURu4jhUPMfWDxriXEEJKkTdBbwbQC+lJQFrXgASIwCvekCfOfxbmoiIiErEpGRfBpw8eRJ//PGHvJ06daqkL0NERkycQMxdrv5J4gKseF5sR0RG9PNz4Dsg8iZg5wl0fhyWVGmvyBZvAmMOAuWbACkxwN9jgVXDgLgQGDt9DSq0bdsWP//8c77P//TTT2jTpo3K3zcqKkoGD8zNzWVQ4fLly/j+++9ly7Qcs2bNku+/cOFCHDt2DLa2tujWrRuSkx//DI8YMQKXLl3Czp07sXnzZuzfvx+vv/66ysdLRGTo1aZaf7MbwxYdxcTVZ+W9eCzWa50o8b1ysNLWyrMuMOB/gImp2t/2z1P3sf1SMMxMyuDHIfVhZa7+9yQi3SSCS89q5xsfHw8rKyutjMkoOWUHq0VFBX2UlQVsmggEnQNsygJDVgDm1toeFRERERlyRYXc7t+/j2HDhuHQoUNwcnKS68TMsJYtW2L16tUoX56lk4moaMQsJ1VuR2RMDPbnJ+QycHC2stxzFmDlqL73cq0KvLwdOPwTsPcr4No2YP5RoNf3QO0BMFZVPZQZ/OHxqYiIT0FZO/3olzlt2jS0aNECAwcOxPvvv4/q1avL9VevXpVBgR07duDw4cMqf99vvvlGltAVFRRyiBlpuU8I//jjj/joo49kOzVh2bJl8PDwwN9//42hQ4fiypUr2L59O06cOPGoCoMIXfTs2RPfffcdvL29VT5uIiJDo9Mt5dJTgTUvAuHXAIdywPC1gKX6K+YERiTi042X5PKkrtVQu5waj6uISGdNmjRJ3ouQwscffwwbG5tHz4kqCiJIW79+fS2O0AgrKgiiooI+OroAOL8GKGMKDPodcPLR9oiIiIjImCoqvPbaa0hLS5MnVEVpMHETy5mZmfI5IqKiEqVYVbkdkTFxtDY3vJ+fzExg89tAZjpQvSdQo6/639PUDGgzCXh9nzK7Ucx2/PMVpddmQgSMkY2FGXxclBkx10LioS8aNGiAP//8U1YiEIEFFxcXeRNh2gMHDmDt2rVo2LChyt9348aNMlwwaNAgWUZXjEO0eMhx+/ZtBAcHo3PnznnaVDRr1gxHjhyRj8W9CADnhBQEsb1oASFOHD9LSkoKYmNj89yIiIxVYS3lBPG82E4rM0/F8c2dA4CFHTB8DeCg/sBEekYmJq09i4TUDDSt6II32lZR+3sSkW46c+aMvIkA7YULFx49FjcR6q1Xrx6WLl2q7WEaD32uqHB7P/DvR8pyty+ASqqvWEdERETGpdgVFfbt2ydno+XMUhPEspj1pY5yukRkuES/WC9HKznL6VmnDEVBQk9Hpa8sET128k4kPvnnYoG7RC9/fk4tAe4dU07i9/xWsz0uPWoCo3cD+78D9n8LXPoLuHNI6bXpWUfpvZkfUe5S1bNIou9p/j1zqeZuh3uRSVh/+r58rFP9vQvQu3dv3L17V1YnuHHjhjwZW61aNXTt2jXPzDFVunXrFhYsWCBnqn3wwQeyKsKECRNgYWGBUaNGyZCCICoo5CYe5zwn7p/sFWxmZiaDFjnbPOmrr77CjBkz1PJvIiIy5JZYLaqU1XxLq7MrHs88FccVGrBw302cvBsFO0szfD+4nl78Hici9dizZ4+8f/nllzFnzhw4ODhwV2uTvlZUiA5UAv1ZGUC9YUCzMdoeERERERljUEGUthUVFZ4kSoWxLC0RFYc4WTa9T01ZijU/4nmeVCNSJKdl4Ied17DowC05Oc/JxhzRiWkylJA77FNGH39+YoOA/z5Vljt9AjhqoZWUqTnQYRpQvTvw11gg7AqwehhQxgTIysz/68wsgXGnVBccECGFuY2A9BTNvecTpbOP3o581Nda3ESoTHw/aa1kdjFYW1vj+eef19j7iapiohLCl19+KR+LigoXL17EwoULZVBBna0ucsr4CqKigjhOJyIyRkVtdfXTrmuwMPNHQ1+nZ/ZpV7kLfwK7Zz5uaVX1cXUddTp/Pxo//nddLs/oWws+LuoJ6xGRfsndqoy0yLni4wv/oqqgSbELHmteaiKweoQSpveqB/SerdmJBfpKTDAQf7sX9re92I6IiMhIFTuo8O2332L8+PGYN2/eo/K0J0+exMSJE2UPXSKi4hAXvV5pXQmLD95+6rkp3f314qIYkSacuxeNd9edw41QpRT/oEbl8XGfmjh8I1yWMs49i9BTjy4qP7LtfSAlFijXCGii5VZS3g2A1/cCe78EDv1UcEhBECcdxAkbVYUGxGsVdCJDHe+pD/29dZSXlxdq1qyZZ12NGjWwfv16uezp6SnvQ0JC5LY5xOOcXsBim9DQ0DyvkZ6eLlus5Xz9kywtLeWNiIiUVlfeCIdzmbh8d0dUlj2O3AIGLDiMKm62GNzYB/0bloebvaV6qiAFnQe2vKMstxinseObpNQMvL3mLNIzs9Crjhf6NyynkfclIv0gzuGKlmiBgYFITU3N89yGDRu0Ni6j4lBeqbKTkQLEh2ikHVCpiFkSmyYCweeVC+pDVgDmSqtAKoT4e11MMNBitUQiIiKDCCo4OzvnmW2QkJAg++qKkrQ5J1LF8iuvvIJ+/fqpb7REZJDuRSbK++fqeaNjDXesO3kfB2+E43Z4graHRqR1qemZ+Hn3dczfe1P2VRYn07/uXwedaihl5MVF4y41PWUpYzGbUJyo15cy/Y9c3QJc2QiYmAF9fgJMTLU9IsDcCujyGeBaHfjnzcK3v3sYiHt2if5ii7wFXezvLb6jxPPi+02vvr/UrFWrVggICMiz7tq1a6hQQSnpWqlSJRk22LVr16Nggqh+cOzYMYwdO1Y+btGiBaKjo3Hq1Ck0atRIrtu9e7es1iCOuYmIqGBNXRKwx+pdWOLp6o85UmCOb/xWYFVAFm6GJeCrbVcxa0cAOvq7y9BC++puMDc1UX0VJFGZqenrGvsIv9x6BbfCEuDhYIkvnq+tmcoRRKQXVq9ejZEjR6Jbt274999/ZXs0cdwqArSarEhm9EzNAMdySkWF6Lu6H1Q4Oh+4sPZxCyNeVC8esb+4z4iIdIOWW+0eOXIErVu3Rvfu3bFly5ZH6+/cuSPPH545c+bRuUNjUqSgwo8//qj+kRCRUYpNTsPea2FyeUz7Kqjh5QBvJ2sZVNh47iE+7F0DDlbm2h4mkVZcCYrFpLXn5L3Qt563LN/rbGuRZztx0Vjj/ZZVJTkW2DJZWW45HvCsDZ3iUato2+2YBn2n0/29ddg777yDli1bytYPgwcPxvHjx/Hrr7/KmyAuEL399tuYOXMmqlatKv/w+Pjjj2XLtJyAr6jAIP5IGT16tGwZIdqsjRs3DkOHDmVrNSKiIjBNioRpASEFQYQYPunogXcG1sLm80FYe/IezgRGY+flEHkTYVBRfWBQIx/4uduprgqSqMyUFPW4J7kagoY5gdWHUUn446jS8/y7QfXgZJP3mJGIjJs4Xp09ezbeeust2NvbY86cOfLY9I033shT+Ys0wKmCElSIugv4NtfdXX5rH/Dvx8pyty+ASm20PSIiIiK9bLUrLF68WHYsEPcPHz7kOb/iBBXU2V+XiIzbzkshcsa4KL/q72kv1zWu4Iyq7na4HhqPf848wIstsvv3ERmJ9IxM/LL/Fn787xrSMrLgbGOOmf3qoFddAzx5JPo2xz0EnCsB7aZAb7nVACxsVNf/M+wKdLW/d1G3MxZNmjTBX3/9hWnTpuGzzz6TJ3tFyHfEiBGPtnn//fdlRbLXX39dVk4Q6ent27fDysrq0TYrVqyQ4YROnTrBxMQEAwYMwE8//aSlfxURkYG6uRv2MfcxzMkCw7qZ416sBXZdj8bOgEiI7lo79t/B5n1m8C/vij4NKqBznfKws7EFTM11she3aNn0ZAswoYO/G9pUddPauIhIN928eRO9evWSyxYWFvL4VIRqRfC2Y8eOmDFjhraHaDxEeO3OAaWigq4SIYp1LwFZGUC9YUCzMdoeERERkV622hXi4+OxZs0a2YYrODgYS5cuxQcffKDy9zHYoEJuoodZQXx9fUszHiIyMpvPP5T3vet6PypLKu6HN/OVJ91WHAvEC80rsGQplWp2mT61Q7gRGo93153DuXvR8nHXmh744vk6pe+frIvunwSOK7PO0Xu2fve5fH4h4K2i0lwPzwK/toOmiZ8TVW6n7ZOwS5Yskfdippi7uzu2bdsmj1Nr1SpilYxi6N27t7zlR/xeEyEGccuPi4sLVq5cqfKxERFRLrvyXoQTp59eyr4h96FWuEhUZ9+yZZmYo4yphRJakPcWSrUELYYUxi4//cyWTXuvhsnnRYswIqLcrX3j4uLkcrly5XDx4kXUqVNHBmkTE5WWnKQhTtkTcqLu6OYuF+H5NSOApEjAq77y97oOBvaIiMjIZWUBaUU8hklPKvp2qYW0JDe3KfbvxbVr18Lf3x/Vq1fHCy+8IKuviklPZfj7tfhBhYoVKxa44zIyMor7kkRkpKITU3HgujgLCPSpl/ckWv8G5fH1tqu4GhyHM/ei0dDXWUujJH30rNllXo5WmN6nps6esBXBiiWHbuPbHQFISc+EvZWZbPPwfINyhnnAkpEGbJygNBQQszOqdND2iIyeCPOIn5PgmORnXvQQ34WejkroR5ft27cPPXr0QKtWrbB//3588cUXMqhw7tw5WVrtzz//1PYQiYhIxS4HxaJmUTb0aqAEDTJSlWMReZ97OQ1ZYhZNRgrKPPHbsExmGiBuBXeY0NhxozjWfdbv6xzi+S41PfUiqEtEmtG2bVvs3LlThhMGDRqEiRMnYvfu3XKdqOpFGpTTDkhULdC5Xt1ZwO4vgOALgJUzMGS5fk8qICIiwyVCCl96q/Y1f+te+DYfPAQsbIv1suKcpAgoCKL9a0xMjDyH2b59exi7YgcVzpw5k+ex6KEr1v3www/yRDARUVHtuBSM9Mws2fLBz11p+5DD0cZcVllYf/o+Vh4LZFCBSj27TFx8FesXvNBQ58IKdyMSMHndOZy4EyUft6vmhm8G1JUXhQ3W4Z+B0EuAtQvQlccPJT6ppKoqDqK/t0kZGeYRPyfikkbun6GcSxzieV2/4DF16lTMnDkTkyZNkr13c4hytnPnztXq2IiISA0y0hC8Z2HRggp9fiz0d2fOb7msjHScvh2Kf07dxq5LD5CemgLzMumwKpOBVhXt0c3fBU1sgmC+eTzUJTMzC1GJqQiLT0FobApC41IQFpeCc/ejnmr3kJv4HS6eF9XFWlQpq7bxEZF+EcfCycnK/x0ffvghzM3NcfjwYdly7KOPPtL28IyLU3ZQQZutH4rSqzstXpMjIiIiMkgBAQE4fvy4bB0rmJmZYciQITK80J5BheIHFerVq/fUusaNG8Pb2xvffvst+vfvr5pPjogM3ubzQfK+T71np95E+wcRVBDtIT7uXROO1uYaHiHpm4Jml2Vln3jWpdllWVlZWH4sEF9uuYKktAzYWpjio941MbSJj2FWUcgReQvY942y3P0rwFaHT6DblAXMLAs+eSOeF9tp8j2F7VMA32aAnbvK3lqEeESY58mKJJ46XpEktwsXLjyzhYKoqhAerlTxISIiAxF0DknrxqBj/GWVv3QZUzM08vOWtykp6dhyIQjrTt6TwdIbt4Dfb6WilU0SVpTgtVPSM2TgICd4kHMfFpcsAwk5wYTw+BQZ7C4p0QKNiCh3q7EcJiYmMuBLWq6oEPtAqeojqv3oYq9uMTY19eomIiIqNdGCQVQ3KIrg80WrlvDKdsCzbuHvWwwikJCeni6vo+e+LmBpaclJVSWpqJAf0VfjxIkTqno5IjJw4qTb4ZtKebnedZ994auhr5OstiDaP/x1+j5ealVJw6MkfSNmjenL7LIH0UmY8ud5HLyhXDhtXtkF3w6sBx+X4h3o6GXvsM3vAOnJQOX2QN0h0GnihMy4U88oh/lEsECVJ24Ke8+kKGDjOCDmPrBiEPDSFsDSTmVvL8IIIswjfk7EBQ53e6Xdgy6Ee4rCyckJQUFBqFQp7+8MUQFM9OIlIiIDIC6s7P8WODgb1pnpiMmyhmOZIvYcLQFbSzMMbuwjbzfD4rHu5H0ZqI6OTwMsC//6b3dcxZn05EehhJik4vWPcLYxl7+P3R0s4WZnibTMTGw6p4S+CyK+hoiMW2xsbJG3dXBwUOtYKBc7D8DMSvm7OOYe4FKZu4eIiKi4xES/orZgMLMu+nbFbOtQEBFQWLZsGb7//nt07do1z3P9+vXDqlWrZCsIY2ZW2gNckfoQJ4M//fRTVK1aVZVjIyIDtu1isJz9XqecIyqUffZ//GJGuaiq8Mk/l7DyeCBGtaxo2LPMqdSKOmts64UglHe2ljdNf0+J35vrTt3H55suIy4lHVbmJpjS3R+jWlSEiZ5cCC6V82uAW3uVkzK9ZysHlLpOBAc0PYOksPccuRFY3AUIOgusewkYtkqls3BEKEHbYZ6SGjp0KKZMmYJ169bJn+/MzEwcOnQIkydPxsiRI7U9PCIiKq0Hp4C/3wLCrsiHWzKaYkH6c9ho8xlMMtRfAamKmx2m9vDH5K7VsOJfSyQfNYdVmfyDB8lZ5vgrIAUPkTeAaGFqAjd7S7jaW8I9+yYei3CBW67HrnaWsDAzyfO14u+ok3eiZGuzZ9VbKJNdDUkEDYnIuIkQb2F/84q/UcU2GRkZGhuX0ROfiZMvEH4NiLrLoAIREZGB2rx5M6KiovDqq6/C0dExz3Oi/dbixYsZVFDFAa44oPXx8cHq1atL/6kRkVHYfO5hgdUUcvRrUA5fbb2KayHxOHU3Co0r8mQblX7W2B9H78qbOPnbyNcZDSs4oVEFZ9TydoSVuanadnFobDKmbbiAXVdDH1UN+W5QPVR2U91seJ2WEAFsn6Yst5vCkzGlUbYKMHwtsLQ3cGOnUqWi78/6EfxQsy+//BJvvfWWPDYVJ1tr1qwp74cPH87eu0RE+iwtGdj7JXD4ZyArE7B1w2+Ob+GzW9XQs44nTHpptgKSmakJnLwqo2PK93AuE5fvdlFZ9mhSvy7aV3fLE0IQbe1KGpgVgULRkmns8tMylJA7rJDziuJ5famGRETqs2fPHu5eXeVUQQkqRN/V9kiIiIgMnzba+2a3fejcufNTIYWcoMKsWbOKVQHLEJmV9gBX9DRzc3ODn58fzMxU1kmCiAxYSGwyjt+JlMu9CgkqOFiZo089L6w9eR8rjwUyqEAFErPGvBytCmz/YGthiqoedrj0MFaW391+KVjecma21S7nIEML4tbQ1xnuDsUrmStmuD1ZMl+cI9547qGsDiJK/Yr3mdS1Gka3qWxcJ5D//RBIigTcawEtx2t7NPqvfGNg4G/AmhHAmT8Ax/JAe/aZtbCwwKJFi/DJJ5/gwoULiI+PR4MGDVj5i4hInwUeA/55C4i4rjyuMwh3mnyCmQsuyIdvdfADnBw1XgFJHOs9hCseZrkWuN3QJr4qr1QkWjUteKEhZmy6nOfYV1RSECEF8TwRUbt27bgTdJVzBeVeVFQgIiIiw2vvC2DTpk35Pte0aVNZCEDIuTdGxU4W8ACXiEpLlN0X/+828HVCeWebQrcf3qyCDCpsvhCET/rUhJONBT8EKnB22Zjlp596LicO8P3gevLEbXJaBi4+iJGVOsTtdGAUwuNTcTowWt4WHbgttxftIXIHF/w97eUMumfZfjHoqZPFYsaceA3xmoJodyLGUM3D3rg+xZt7gHOrlE+i708qbVNg1Px7Aj2/A7ZMAvZ+BTh4Aw2Nu73BZ599Jts8iIoK4pYjKSkJ3377rQwwEBGRnkhNBHbPBI7OV+oG2HkqraP8e2LeunPIzAI6+bvLqljaDMlqqwWDOKbtUtPzqZCsUQVhiahA58+fR+3ateVEM7FckLp163JvarqigsCKCkRERIbb3pdUH1QQ/X5XrVqFa9euyRlr1apVw8svv4xu3boV96WIyEhtPh8k7/vU9S7S9vXKO6KmlwMuB8Vi/ekHeLV1JTWPkPRZm6pusDY3QVJaZp71T84uEy0eRCuRnHYiIrV4LzIJpwIjs8ML0QgIjsX9qCR5++es0q7ExsIU9corrSLETQRuRHhGhBRE+d0nT1KHxqXImzhfPLFTNbzZoQrM8wk6GPRFhs1vK8tNX1cqAZDqNHkViH0AHPge2PQ2YO8FVO1itHt4xowZGDNmDGxs8gbhEhMT5XMMKhAR6Yk7B4F/xgFRSngU9YYD3b8ErJ1xLzIRf515IFe/1dFPa0PUhRYM4rVVXa2BiAxH/fr1ERwcDHd3d7ksWs48a8aeWC/apRXXvHnzZBhYvEe9evXw888/y9mBz7JhwwbZpu3GjRtIS0uTFc/effddvPjiizBKrKhAREREVPSgQmZmJoYNGyaDCiKc4O/vL9efOXNGrnv99dexYMECREREYP/+/Xj++ee5e4noKQ+ik+RFYNGOtbC2D7n/YB7ezBcf/X0RK4/dxSutKpa4nysZvj9P3ZchhYplbfDl83UQFp9SpNll4nvKt6yNvD3foLxcF5echnP3YmS1hZyqC3HJ6ThyK0LeclRxs8XD6GfPpMvhYmuBcR39jHOG2/5ZQNQdwKEc0OljbY/GMHX8GIh9qFStWDsKeGkzUK4hjJE48fqs3xHnzp2Di4t6ZrQSEZEKpcQD/30KnFikPBbHD33m5Anh/bL/JtIzs9Daz1VWvNImtmAgIl12+/Zt2bI3Z1mV1qxZg0mTJmHhwoVo1qwZfvzxRzmRLSAgQAYjniSOxT/88EN5TllMftu8ebOc/Ca2NcoJcNquqGDllB2ry9Jor24iIiKiEgUV5syZg//++w8bN25E79698zwn1okDyypVqmDp0qUYOdK4Sw4TUf62nFdmpTep6AIPB6si76rn6nvjy61XcDMsQZY2bVaZfyjR0zIzs7DkkHLy5ZXWldDSr+B+wYWxtzJH66qu8pbz+jfC4pXQgqi6EBiFW2EJ8vuyMKKthPjeNboZb8EXgUM/KcuiRYGlkbW80BRxYb7PT0BcEHBrL7ByMPDqTsDFeCrQODs7y4CCuIlQbe6wgpgdFh8fLystEBGRDhO/wzaOB6IDlccNRwFdPwesHrd2CIlNxtoT9+XyeC1WU8iNLRiISFdVqFDhmcuq8MMPP2D06NHynLAgAgtbtmzBb7/9hqlTpz61ffv27fM8njhxIn7//XccPHjQOIMKORUVEsKA1ATAwlaz739tuxJSsLADhq7IDi6ov1c3ERERUYmCCkuWLJGlvJ4MKQh9+/bFrFmzZFWFrl274u23s8s7ExHl2/ahaNUUcl8wFmGFVcfvYeXxQAYV6Jl2XQ3FnYhEOFqbY2AjpSqCKpmYlEE1D3t5G9bUV66LTEjFwr038OuBwmeniN7BRiUzA9g0EcjKAGr0lf2kSY3MLIDBfwBLewLBF4DlA5Swgq1xhGPEDC5RTeGVV16RLR4cHR9f1BIztipWrIgWLVpodYxERJSP5Fhg58fAqaXKY0dfoO9PQJUOT2366/5bSM3IRNOKLjr1NwFbMBCRPhDVDkR7hitXrsjHNWrUwPjx41G9evVivU5qaipOnTqFadOmPVpnYmKCzp0748iRI4V+vThu3717txzPN998k+92KSkp8pYjNjYWBsPaGbB0BFJilICeew3NvXdcMLB7prLcdSZQOW+IhIiISFc9q4UV6fdnUeSgwvXr1+XBZn5ynvvnn3/kyWAioifdjUjA+fsxEJXvxayj4hretIIMKmy7EIzpfVJlKX2i3P534Ja8FyECG4si/4orFfF92MHfo0hBBdGCwqicWAw8OAlYOgA9Zml7NMbBygEYvg5Y3AWIvAmsGgqM/AewsIGhGzVqlLyvVKkSWrZsCXNzc20PiYiIiuL6f8CmCUDsA+Vxk9FA508BS7unNg2PT8GKY0qJbNFSi4iIim79+vUYOnQoGjdu/CjAe/ToUdSuXRurV6/GgAEDivxa4eHhsmqZh4dHnvXi8dWrV/P9upiYGJQrV06GD0xNTTF//nx06fK4tc+TvvrqKxlCNljOvkrIPOquZoMK/34EpMYB5Rop1YuIiIh0XM55vsTERFhbW2t7OATlsxBKew62yFdxxAcfHR0NX19lBumTRKLVwcGBIQUiKrSaQssqrnCztyz2nqpT3hF1yjniwoMYrD91H6PbVubepkcuPojBsduRMDMpg1EtVVvSsjBNK7nAy9EKwTHJz+zuKArQezpaye2MRsx9YFf2CSVxscGh+OEkKiGxr0f8CfzWFbh/HNgwGhi8DDAxNYpd2q5du0fLycnJcrZXbuJ4lYiIdEBSFLDjQ+DsCuWxcyXgublAxdb5fsnig7eRnJaJeuUd0Sa7NRcRERXN+++/LysgfPbZZ3nWT58+XT5XnKBCSdnb2+Ps2bOyLduuXbswadIkVK5c+am2EDnEeMU2uc8/+/gYUCsCpwpKUCFaCeFpxK19wIV1QBkToNf3ohSG5t6biIiohETA0cnJCaGhofKxjY1NnravpNlKCiKkID4L8ZmIz0YjQQWRtF2wYIG8Pcu8efNYTpeIihRU6F3Mtg+5DW/mi2kbLmDV8UC81qYSfxlRnhPXQq+6XvBytNZ4qd/pfWpi7PLTMpSQO6yQc7gknhfbGQVR9mnre0BqPODTDGik9CwlDXL3B4atBpb1A65uBra9D/T8DjCCA3hxoCxOtK5duxYRERFPPS9mfhERkZpF3wMSn/4/+BFxUUaUnI4PVo6Wmr8JdPyowApA0YmpWHb4jlwe17Eq/w4gIiqmoKAgjBw58qn1L7zwgmz3Wxyurq7ypHRISEie9eKxp6dnvl8n2kP4+SkVcerXry9bUIiqCfkFFSwtLeXNYDlXVO5FRQVNSE8FtryrLDd5DfBuoJn3JSIiUoGcY4ycsAJplwgpFHTcp/KgwocffigPGsUJ38mTJ8Pf31+mJsQB5ffffy9bPuzZs6fUAyIiw3QjNB5XgmLlbPfutUv+n1ffet74YssV3ApPwJFbEbI6A1FIbDI2nXsod8SrrStpZYeIdiYLXmiIGZsuIygm+dF6UUlBhBRK0u5Eb13ZBARsBUzMgT5zOENDWyq0BPr/Cqx7CTjxP8CxPND6HRi69957Tx6TinDtiy++KMO0Dx48wC+//IKvv/5a28MjIjKOkMLcRkD6457i+SpbFXhuHuDbrNBNlx6+g4TUDPh72qOTv7tqxkpEZETEed0DBw48CgrkOHjwINq0aVOs1xJtfxs1aiSrIvTr10+uy8zMlI/HjRtX5NcRXyPaQBgtUVFB0FRFhSM/AxHXAVt3oMOHmnlPIiIiFREVFLy8vODu7o60tDTuVy0S7R5KW0mh2EEF0et3zZo1eP3112VPs9ycnZ2xatUqtGrVSiWDIiLDs/m8chG5dVVXONlYlPh1bC3N8Fx9b6w4FoiVxwIZVCBp2ZE7SM/MQpOKzqhb3klre0WEEbrU9MTx25EIjUuGu73S7sFoKikIyTFKNQVBXBTXZJ9NelqtfkDsl8COacB/ogVHOaDuYIPeU5s2bcKyZcvkidiXX35ZnnQVJ2MrVKiAFStWYMSIEdoeIhGRYROVFIoSUqg3DOg9GzAvvBJWXHIalhzKqabgBxNjOrYiIlKRvn37YsqUKTh16hSaN28u1x09ehTr1q3DjBkzsHHjxjzbFka0ZBg1ahQaN26Mpk2b4scff0RCQoI8BhdE9YZy5crJigmCuBfbVqlSRYYTtm7dij/++CPf6r1GwbmC5ioqiPfYl105o9sXgLX2zp0QERGVhrhArqqL5KR9RQ4qCM8//zy6deuGHTt24Pr163Jd1apV5TrRD4SI6FlE9ZXHbR+8S72TRPsHEVTYcSkY4fEpcLUz4DKAVKik1Az5/SC82rqy1veYCCW0qFIWRuu/GUoZ57J+QJvskpKkXS3eBGIfAEfmAn+/Cdi5A5WfXVrVEERGRso+t4KDg4N8LLRu3Rpjx47V8uiIiOiRZmOKFFIQlh8NRExSGqq42aKHMVWpIiJSoTfffFPez58/X96e9VzObMWitEsbMmQIwsLC8MknnyA4OFi2cti+fTs8PDzk84GBgbLVQw4RYhDvc//+fVhbW8tqvcuXL5evY7RyV1QQLRTV2apv+1QgPQmo2AaoM0h970NERESkrqCCIAIJIrBARFRUASFxsvWDhakJutZS/mAtjVrejqjn44Rz96Kx7uR9jG1fhR+GEVt/+j6iE9Pg62KDLjVL//1FpRB4FDi5WFnu/SNgbsXdqSu6fK6EFS79Bax+AXhlG+BZB4ZIhBRu374NX19fefJz7dq1coaXqLQgeqcREZH+hVL/d+CWXH6rg59xVaoiIlIh0WZB1USbh/xaPezduzfP45kzZ8ob5eLkq9ynxAJJUYCNi3p2T8C27PaMZkDP79QbiCAiIiIqhsex1hIQKdjw8PDSvAQRGYHN55RqCu2qu8HBylwlrzmiqfLH3KrjgcjMzFLJa5L+EZ/9b4duy+WXWlbkiWttSk8FNk1Ulhu8AFQqXo9TUjMxk6nfQqBCKyA1DlgxSOkhboBEqdlz587J5alTp2LevHmwsrLCO++8g/fey25LQkRE6pOl2gthK48HIiIhFT4u1uhbr/TV2YiI6LHo6GjuDm2ysAFs3bM/DDW1f0hNBLa9ryy3GAe4+6vnfYiIiIg0HVQQ5bliY2NL8xJEZBRtHx7K5d51VVemtXc9L9hbmiEwMhGHbjIwZaz2XgvFrbAE+b0wuImPtodjPMQF7odn895EGcmwq4CVk1LKmXSPqHAxdAXgVgOICwJWDFRm7RgYEUiYMGGCXO7cuTOuXr2KlStX4syZM5g4MTtMQ0REqidKVl/dAqxTepOrQnJaBn7df1Muv9neD2ampTqFQURk1L755husWbPm0eNBgwbBxcUF5cqVexT0JS1wzm7/EKWmoMKB74HoQMChPNAuO7BAREREpCNMSnsBkoioIBcfxOJORCKszE3QuYbqyvLbWJjh+Ybl5PLKY4H8EIzU4oNKNYWhTX1gZ1nsbkZU0pDC3EbAr+3y3nJaPiRHA//rZLCz9fWetTMwYh1g76UES1aPANJTYMgqVKiA/v37o27dutoeChGR4bq9H1jcBVg9HIi+o7KX/fPUfYTEpsDL0Qr9s4/9iYioZBYuXAgfHyXgv3PnTvz333/Yvn07evTowcpj2uRUQX0VFcKvA4fmKMs9vgEsbFX/HkRERESlwKs6RKRWOdUUOvq7w1bFF5KHN/PFsiN3sfNyCELjkuFub6XS1yfddiUoFoduREC0KR7VsqK2h2M8EiMKv7AtnhfbObHKhU4Sn4sIK/zWA7h7CPjrDWDAb0p7CD2WlJSEXbt2oXfv3vLxtGnTkJLy+HvV1NQUn3/+uWwDQUREKvLgNLDrM+DWHuWxuQ1Q63ng7IpSv3RaRiYW7FWqKbzRtjIszUxL/ZpERMYsODj4UVBh8+bNGDx4MLp27YqKFSuiWbNm2h6e8VJXRQUxwXDLu0BmGlC1G+DfS7WvT0RERKQCpTojHRcXh8qVK6tiHERksG0fguRyn7qq7yfr7+mAhr5OSM/MwrqT91X++qQf1RR61PFCeWcbbQ+HSL941gGGLgdMzIFLfwE7P4a++/333/HLL788ejx37lwcPnxYtnwQN9GybMGCBVodIxGRwQi7Bqx5EVjUQQkpiN8nTUYDE84C7acBZpYFf7143qZsgZv8feYBHkQnwdXOEkOb+qp2/ERERsjZ2Rn37imV70QlBdEmLefcTUZGhpZHZ8TUVVHh4nrg9j7AzEqpplCmjGpfn4iIiEgFSjy9OSwsDAEBAXK5evXqcHNzU8V4iMiAnLkXLU8u2lqYooO/u1reY3izCjgdGI1VxwMxtl0VmIjp9WTwRAWNjWeVah2vtq6k7eEYh8wMIPQycPlvbY+EVKVye6DffGDDaODIXMChHNDiTb3dvytWrMD77+ftubpy5cpHoVoRVJg3bx7eeecdLY2QiMgAiNZOe78Gzq0EsjIBlAHqDgE6TAOcc1W4GndKqa6UHxFSKKDyUkZmFuZnV1N4vW0lWJmzmgIRUWmJdmjDhw9H1apVERERIVs+CCLU6+fnxx1sSBUVkmOBHR8qy23eBVx43oSIiIgMJKiQkJCA8ePH448//niUthWldEeOHImff/4ZNjac1UpEis3nlGoKnWt6qO3kYu+6Xvhs0yXcj0rC/uthaF9dPYEI0i3LjwYiNSMTDXyd0NDXWdvDMUxJ0cD9k8C9Y8rtwSkgNV7boyJVqzsYiLkP7JoB7PgAcPAGavXTy/1848YN1KlT59Fj0eLBJFc7i6ZNm+Ktt97S0uiIiPRcfBhw4Hvg5GIgI1VZV70X0PEjwKPm09uLEEIpWkBtuRCE2+EJcLIxx4hm2RdwiIioVGbPni3bPIiqCrNmzYKdnZ1cHxQUhDff1N/AsuFUVAgEMjNV05Jv71dAfDDgUgVoOaH0r0dERESkK0GFSZMmYd++fdi4cSNatWol1x08eBATJkzAu+++y5K6RCRlZmZhywVlxntvNbR9yCECEP0blsfSw3ew8lgggwpGIDktA8uPKjMNXmtt5O2HxKzGUsxWzNO7MuLG41DCveNA2NWnt7OwB9yqAw9Olm7cpFtavwPEPgBO/A9YP1qZfeNVt3TfU1oQHR2NlJSUPNW/csvMzMzzPBERFUFyDHB4LnB0/uPAYsU2QKfpgE8Ttf0dMXf3dbn8aqtKsLUscSFIIiLKxdzcHJMnT35qn7DimJY5lgfKmAAZKUB8CODgVbrXCzoPHFuoLPf8FjC3UskwiYiIiNSh2H/xr1+/Hn/++Sfat2//aF3Pnj1hbW2NwYMHM6hARNKJO5EIiU2BvZUZ2lZzVeteGdHMVwYVdl0NRUhsMjwc+EeYIRP9iiMTUlHOyRrdannAqEMKcxsB6SkF938WpZefvLCcmgA8OK2EEu6fUIIJSZFPf71LZcCnGeDTVLl38weCLwC/tlP9v4e0R/Qq7TELiLgF3NoNbBpf/O8pHVC+fHlcvHhRtiR7lvPnz8ttiIioCNKSgOOLgIM/AElRyjqv+kDn6UDlDmrtc/3v5RBcC4mHvaUZRrbM1U6CiIhK7fr169izZw9CQ0NlkDe3Tz75hHtYG0zNAYfyQEwgEH23dEEF8ZlueVdpz1TrecCvkypHSkRERKT9oEJiYiI8PJ6+MOTu7i6fIyISNp9X2j50q+UJSzP19pSt6mGPJhWdceJOFNacuIcJnaryQzBQWVlZWHzwtlx+qWVFmJmqoCSivhKVFAoKKQji+cRwseeUMIK8HVPCBllK+6ZHzKwA74aPQwnlmwB2bmr9J5AOMTEF2k9RggqFfk9F6GRQQQRnxcnVXr16ybYPuSUlJWHGjBnyOSIiKkBGGnBmObBvFhCnVEeDazWlxUONvmoNKOQc683do1RTGNWyIhytzflxERGpyKJFizB27Fi4urrC09MTZXL9ny6WGVTQIucKSlAh6i7g27zkr3N2OXD/OGBhB3T7UpUjJCIiItKNoEKLFi0wffp0LFu27NFJ4JyTv+I5IqL0jExsu6gEFXrXLWXJuiIa3sxXBhVWHw/EWx38YGqi3pOopB37r4fjemg8bC1MMaSp7l0o1UnLB2aHFZ5g7w34ikBCdjDBsw5gZlH464nS/2JWfWGVHMR2pF9EWEWPffDBB1i7dq2sqDBu3DhUq1ZNrg8ICMDcuXORnp4utyEionxmYF7aAOz5Eoi8qaxz9AHaTwXqDgVMNdN+Ye+1MFx8EAtrc1O80roSPyoiIhWaOXMmvvjiC0yZMoX7Vdc4VQBwQKmoUFKJkcDO6cpy+2mAg/rasBIRERGpSrHPNsyZMwfdunWTpXPr1asn1507d06GFnbs2KGygRGR/jp2OxLh8alwtjFHKz/1tn3I0aO2F2ZsuoyHMcnYdy0UHf2NuCWAAcuppjC4iQ8crDjDrkhESMHEDPCsm10tITuYIPpgloSYSS9K/4tZ9fkRIQUdnHFPhk1U/Dp8+LCcJTZ16lQ5KzdndliXLl0wf/78Z1YFIyIy+FZRBf7OdgFCrwK7P1MqLsl1rkDbyUDjV5TwoYaI/7d/3qVUU3ihuS9cbIsQoCQioiKLiorCoEGDuMd0taKCICoqlNR/nyotHd1rAc3eUNnQiIiIiHQqqFC7dm3Zz2zFihW4evWqXDds2DCMGDEC1tbW6hgjEemZzeeVMrHda3vCXEOl+a3MTTGgYXl5IXvlsUAGFQzQtZA47L8WJisOv9ySM+yKrM9PQJ1BgIWN6j4MEUJgEIF0UKVKlbB9+3ZERkbixo0bcp2fnx9cXFy0PTQiIu2EFOY2KqRVlKhCpgS7YOkAtBwPNB8LWNpD047cisDpwGhYmJlgdNvKGn9/IiJDJ0IK//77L8aMGaPtodAzKyqI390lDCrcOwGc/l1Z7vU9YMqJHURERKQfSlS/0cbGBqNHj1b9aIhI76XJtg/Bcrl3Xc2WmRvW1FcGFXZfDcXD6CR4OzE8ZUh+y66m0K2mJ3zLqvCiu6HzqqfakAKRHhDBhKZNm2p7GERE2iUqKRQYUhCyAFMLZeZl60lKhQUtmbtbCZgNa+IDd3v9bkdERKSLRID3448/xtGjR1GnTh2Ym+e9mD1hwgStjc3olaaiQkY6sOUdZbn+C0AFtmYmIiIiAw4q+Pr6on379mjXrh06dOiAypU504GIHjt4IxzRiWlwtbNAs0qaPdHp526H5pVdcPRWJNacuId3uij9yUmRkZmF47cjERqXLE/+Nq3kAlMTMYtO90XEp2DDmQdy+dU2rKZAREREpDJDVgLVumh1h566G4nDNyNgbloGr7erotWxEBEZql9//RV2dnbYt2+fvOUm2qUxqKBFzhWV+9j7QEZa8SoinPif0r7JygnoMkNtQyQiIiLSiaDCl19+if379+Obb76RVRXKlSsnQwviJgIMVatWVctAiUg/bD4XJO971vGCmYbaPuQ2vFmFR0GF8R39tDIGXbT9YhBmbLqMoJjkR+u8HK0wvU9NdK/tBV234lggUtMzUbe8IxpXcNb2cHRDeqq2R0BERES6LCu7pUNh7NygbT9nV1MQrdzKsSoaEZFa3L6tVCkkHWTnAZhZAenJQMx9wKWIEzTigoHdM5XlztMBW1e1DpOIiIhI1Yp9Be+FF16QCdxr167hwYMH+Pbbb+X6N998E/7+/iofIBHpj5T0DPx7WTttH3J0q+UBF1sLBMcmY09AmFbGoIshhbHLT+cJKQjBMclyvXhe17+vlh1Ryh++2rqSnOlh9DIzgQPK798CmVkCNmWNfndREYnvFfE9w+8pIiL9lRIPXNkM/PMW8Mfz0AcX7sdgb0AYRKGvse1ZTYGIiIyQOM/h5KssRxej/cOOD4HUOKBcI6DhKLUNj4iIiEhnKioIiYmJOHjwIPbu3Ys9e/bgzJkzqF27tqyoQETGa/+1cMQlp8PTwUprs94tzUwxqFF5/LL/FlYeu4suNT1g7O0eRCWFZ82nE+vEJX/xfJeanjrbBmLj2YcIj0+RFSBEpQ4C8O+HwPV/ARNzoOcswLth/heenXy4y6hoxPfKuFNKT/P88HuKiEj3RN8Drm1XbrcPABkp0Cdz91yX98/VL4cKZW21PRwiIoN2//59bNy4EYGBgUhNzVul74cfftDauEj8PVYBCL8GRBUxqHBrH3DxT6CMCdDre8DElLuRiIiIDD+o0LJlSxlMqFGjhgwmTJ06FW3btoWzM0txExm7zecfyntxMdlEixe9hzX1lUGFvdfCcD8qEeWdbWCsjt+OfKqSwpNhBfG82K5FFd2beZ+VlYXFB5XylCNbVIQ5W3kAh+cCR+crO+j5hUCdgdr9kMjwwgoMtxAR6X5lpYengYBtSjgh5OLTFzqq9wDK+gFbJ0OXBQTHYcelEDmR9K0OrKZARKROu3btQt++fVG5cmVcvXpVTjq7c+eO/Lu7YcN8wu+kOc4Vil5RQbSC3PKustzkNcC7gXrHRkRERKQrQQVxIGtrayvbPIibCCwwpEBESakZ2Hk5RO6I3vW0O+u9oqstWvmVxaEbEVhz4h7e7VrdaD+g0LhklW6naYdvRuBqcByszU0xvGl2GURjdnG9Uk1B6PIZQwpERETG1NLh1h4gYDtwfQeQkKvFmZhJWb4pUL07UK074OavlJB+eBa6bt6eG/K+Z20v+Lnba3s4REQGbdq0aZg8eTJmzJgBe3t7rF+/Hu7u7hgxYgS6d++u7eGRCBoKUXcK3xdHfgYirgO27kCH7HMERERERMYQVIiIiMCFCxdk24cdO3bgww8/hIWFBdq1a4cOHTpg9OjR6hkpEem0PQGhSEzNQDknazTwcdL2cDC8aYVHQYUJnaoa7Ux8d3srlW6naTnVFAY1Lg9HG3MYtTsHgb/GKMtN3wBaTtD2iIiIiKg47RmK21onT0uH/UBGrhLdFvaAXyelcoJfF8C27LNf08wSSC+gFYR4XmynBbfC4h9VZHurg59WxkBEZEyuXLmCVatWyWUzMzMkJSXBzs4On332GZ577jmMHTtW20M0bjkVFQpr/SCe3/etstx1JmCt/XNwRERERBoLKpQpUwZ169aVt/Hjx+PUqVOYO3cuVqxYgTVr1hQrqLBgwQJ5E2XGhFq1auGTTz5Bjx495OPk5GS8++67WL16NVJSUtCtWzfMnz8fHh4eOtsLXpRPFzOTxUW/ppVcdLbnO5Gq5ZxkFNUUxP8T2talpgdc7SwQGpeCXVdC0b22J4yR+H/Iy9GqwPYP7vaWcjtdczMsHruvhsoJgS+3qgSjFnoFWD1cuUBRow/Q/StlpiQRERHpPhE4mNuo8MDAWyeA+NDH4YQnWzo4VwSq9QCqdQMqtALMLAp+XxF8GHeq+AEJDZm/9yYys4DONdxR09tBK2MgIjImokJuaqoSevPy8sLNmzfluVghPDxcy6OjRxUVCmv9sH0qkJ4EVGgN1B3MHUdERETGEVQQ6VpRHky0fhDVFMTt4MGDiIuLQ506dWRoQVRVKI7y5cvj66+/RtWqVWU/tN9//10meM+cOSMPlN955x1s2bIF69atg6OjI8aNG4f+/fvj0KFD0DXbLwZhxqbLeS4GiouD0/vURPfa2i2DT6RuCSnp8oKy0Keut07scAszEwxq7IMFe29i5fFAow0qiLCU+H9ozPJ9OPMAAI2uSURBVPLT+W4j/v+NTkxFWTtL6JLfsqspdPL3QCVXWxit2IfA8oFAcgzg0xzovwgwMdX2qIiIiKioRFCgoJCCIJ7/pS2QHJ23pYNPMyWYIAIKbtWLH1QUIQQtBREKci8yEX+deSCXWU2BiEgzmjdvLs/lija+PXv2lJPDRNXcDRs2yOdIRyoqiPZOqQmAxTPOgwRsAwK2AiZmQK/vOYGBiIiIjCeoIPqXjRkzBk2bNkWDBg1kKEFUT2jbtq0MEZREnz598jz+4osvZIWFo0ePyhDD4sWLsXLlSnTs2FE+v2TJEnkwLZ7XpQNoEVIYu/w0sp5YHxyTLNcveKEhwwpk0P67EoLktExULGuDWjo0G2pYE18ZVDhwPQyBEYnwLWsDY+Tj8ux/t4eDJdIzshAWn4pXfj+JVaObwcai2IV21CIqIRXrT9+Xy6+2NuJqCiKcsGIQEHsfKFsVGLYKMLfW9qiIiIhIHURIwdIBqNKx4JYOBlD9b+G+m3JMbaq6ooGvs9bGQURkTH744QfEx8c/Os8rlkV1XDGBTDxHWmbtDFg6AikxQHQg4F4j7/OpicC295XlFuMAd3+tDJOIiIhIlYp8RUrMuBUiIyPh4KD6C5EZGRmyckJCQgJatGghW0qkpaWhc+fOj7bx9/eHr68vjhw5ojNBBXFyRVRSeDKkIIh14tSPeL5LTU+2gSCDtfl8kLzvXddbJ9o+5BDBBHHy88D1cKw6EYgp3Y3vjzjxf/dXW6/K5V51PPFC84p5TlDfiUjAgAWHce5eNMavPINfXmwEM1MTbQ9bVsEQ4ZeaXg5oXln32lJoRHoqsOZFpeyznQfwwnrAxkj3BRERkTHo9QPQ4MXCWzroefU/Eehfd1IJpI7r4Kfx9yciMkbivOv9+/dlK9+cNhALFy7U9rDoSc6+QPAFIOru00GFA98rAQaH8kC77MACERERkZ4r1tUocQFS1SEFUWLMzs4OlpaWsmLDX3/9hZo1ayI4OBgWFhZwcnLKs72Hh4d8Lj8pKSmIjY3Nc1MnMSuloN7vIqwgnhfbERmimKQ07AsIk8t96ulG24fcRjTzlffrTt5DanomjM2+a2E4eCMcFqYmmNqjBlpUKYvn6peT92IWXRU3Oywe1RiWZibYdTUUH/9z6VEwTVvE5/T74Tty+bU2lXQq/KIx4jP45y3g9j7Awg4YvvZxGUgiIiIyTOUaqSWkIKr8Pfk3a071P/G8pv26/xZSMzJlaLZZZdVUjCAiooKZmpqia9euiIqK4q7SZU7Zf/dH3827Pvw6cGiOstzj62e3hSAiIiIy9KBCtWrV4OLiUuCtuKpXr46zZ8/i2LFjGDt2LEaNGoXLly+jpL766ivZiiLn5uOj3n6cYmayKrcj0jc7L4fIE41V3e1Q3dMeuqZTDQ+42VsiPD5VjtWYpGdk4sutV+TyqJYV8m0B0aiCC+YMbSBbHq86Hoh5e25Am7ZceIjQuBS421vKKh1GadcM4MJape/k4N8B7/raHhERERGVVGa6Tlb/E8TzYjtNCY9PwcrjysWX8R1ZTYGISJNq166NW7ducafrMueKyr2oqJB7IsOWd4HMNKBqV8C/t9aGR0RERKRqxWpGLvqXiYv/qiSqJvj5KScoGjVqhBMnTmDOnDkYMmQIUlNTER0dnaeqQkhICDw9PfN9vWnTpmHSpEmPHouKCuoMK4jy6arcjnSTrvV01SWbzz+U9yW+oBx9D0iMyP95m7KAU8l/hs1NTTCksQ/m7rkhT4r2qqv58rba8uep+7gWEo/qVtGYUCMReHj22RvalEX32j6Y0bcWPvnnEr779xo8Ha0xsFH50g2gBJ+tqObwvwO35fLIFhVgYab9NhQad3wRcHC2stznJ8DvcQskIiIi0jOJkcDW97Ty1kWv/heBFlVcNTKmxQdvy/Ze9Xyc0NpPM+9JRESKmTNnYvLkyfj888/lOVjR/iE3dbT6JRVUVLi4Xqm2aGYF9JglSh5ztxIREZFxBhWGDh0Kd3d39U42ycyU7RvEAbO5uTl27dqFAQMGyOcCAgIQGBiIFi1a5Pv1ooWEuGmKuGAt+nuK0pnPmociDh09HZUL26SfdK2nqy6JSkjFwevhcrl3Pa+SXcie2whIT8l/GzNLYNypUoUVhjb1wby9N3DoRgTuhCegoqvhl8hLSEnHDzuvwRvh2GIyGWbLUgvdxyNbVMSD6CT8su8Wpq4/LysatK3mVrIBlPCzPXY7EpcexsLK3ATDmxlhq4Mrmx9fzOjwIdBghLZHRERERCUVdg1YNQSI1M7s1eshcUXaTrSA6FbLUx73tfIrCycb1bafyBGdmIpl2e29xnfwM872XkREWvDZZ5/h3XffRc+ePeXjvn375vk/WEwYEI8zMjL4+WhbTsvHnIoKybHAjg+V5TbvAi6VtDc2IiIiIm0GFdRxEkFUP+jRowd8fX0RFxeHlStXYu/evdixY4es3PDqq6/K6giipYRI9Y4fP16GFJo3bw5dIWbViwvW4uSO2EPPCiuI5zn7Xj/l9HR98nPN6em64IWGRh1W2HEpGOmZWajh5YAqbnbFfwEx276gC9mCeF5sV4qgQnlnG7Sr5oa9AWGytcG0njVg6BYduCXbJ3R2SoNZcgEhhSf28ZRu/giJScbfZx9i7PJTWPNGC9Qu56ixz1bMshP6NywPF1v1nCTXWfeOA+tfVX6TNBwFtNXO7EsiIiJSgev/AX++DKTEAvbeQGI4kFFIcFRUm1KB5LQM/Lr/FubuLlo7r+ikNKw5eU/eRNG4uuWd0Laqqwwu1PdxgpmpaipcLTl0BwmpGfJvh0411DsBgoiI8lbIHTNmDPbs2cPdok8VFUTLh71fAfHBgEsVoOUEbY+OiIiISHtBBZGuVbXQ0FCMHDkSQUFBMphQt25dGVLo0qWLfH727NkwMTGRFRVElYVu3bph/vz50DXiQrW4YP3krHt7KzN8O7CuUV/I1meF9XQVwRTxfJeankYbRNl8Pkje9ylJNQUNG97UVwYV1p26j0ldq8HSzBSGKjQ2WVZFyGmfgGKcizAxKYNZA+vJkMPhmxF4eekJbBjbEj4uNlA3Ue3ivyshcvmVVkY2SyD8BrByCJCeDFTtBvT6geUciYiI9JH4u/nofODfj4CsTMC3BTD4D+V3vBrbnSlvnYWtF4Lx5dYrskqWYG5aBmkZz/5bXvwF4+Foha+er4NDN8Kx/3qYbBt29l60vP20+wbsLc3Q0q+sDC20repW4mPCuOQ0LDmkBFLHsZoCEZFG5ZzTbdeuHfe8LhOVKZOjlWURdDy1BDi6QHncbAyQEFbqYwUiIiIivQ0qiJYMqrZ48eICn7eyssK8efPkTdeJMIK4YC36gK47eQ8bzjyQs08YUtBfRe/pGokWVVQz+0mfhMkL2dltH+p4Q9d19HeHp4MVgmOTseNSCPrW0/0xl9Ts/64hKS1D/h/UpqpN0YIKh+YAtkqbB1HHYIlnJraHBSEyMQ0nFvwG9zpexQt3iD+gi0mcvBbnTzpUd4OfewkqdOiruBBgeX8gKRLwbggMWgKYFqszExEREekCUS1qyyTgzHLlcYMXlPChqJYgqPHiwsUHMfhs02UcvxMpH3s7WskqYmYmZfDmitNyXe64Qk7M+tM+NdHB313ehKCYJBy4Ho7918Jw8EY4ohPT5LGzuAmVXG1ltYU2Vd3k30C2lmaFhr/F30srjt1FbHI6KrvaoHttT7XsAyIiyh/b7ei4Z7XP3PzO4+Vt7wE7Pyp1a1QiIiIiXcMrISokZtWLkzXOtuYyqHDyThRS0zNhYaaaUpmkWaFxySrdzhDbYmRmAfXKO8K3rPpn25eWKFk7uIkPftp1HSuP3TXYoEJAcBzWnLgnlz/qVQNlEFi0L7y0Ic9DcTr9uZzfEmkAlPPbahOTmCarXQivtq4Mo5ESD6wcrJR1dK4IDF8LWNhqe1RERERUXPFhwNoXgcAjQBkToOsXQPOxaq+QJMLD3+0IwNpT92Tg08rcBGPb+eH1tpVhbaGETJ9V/c/T0Uq2KHwyWO/laI3BjX3kTYQMRABChBZEeOF0YBRuhyfI2+9H7spqDQ19nWW1BdFmraaXg6zOlfvvhSffNyIhDTsvBzPQT0SkYdWqVSs0rBAZqYTdSAs01BqViIiISNcwqKAG1T3s4WpngfD4VJwJjEKzysY3294QuNtbqXQ7Q7Mpu+1D77oauOB/ez/gWQcwKV27hqFNfDB393UcvRWJm2HxqOJmeLP2v9p2RQZIutfyROOKLsDDIgYVGrwI2Hk8tToiIRXrT99HSnom/Nxs0a2Wl+xdXKj4EODMH4Vvd+8Y4FUPq04EIjE1A/6e9mjlZyT/Z2akAeteAoLOKuWeX9gA2ClVLYiIiEiPBF8EVg0DYgIBSwdg4BKgame1vmVKegaWHrqDn3ffQHxKulz3XH1vTOnuD28n63yr/4mQtfj7pWkll0Lb14nn6/k4ydv4TlVl+wbRGuzA9TDsvxaOwMhEHLsdKW/f7ghAWVsLtM6utpCRkYmpGy481UYvNikNY5efluEJVh8kItKcGTNmyLa7RERERES6hEEFNRAJ5RZVXLHp3EN5IodBBf0kTt55OVrl2/6hTPZMJLGdsQmJTcaJ7LKyvermnYWlFjs/VnrzNX8TqD8CsChZBQdx0rZDdXfsuhqKVccC8VHvmjAkB6+HY29AmCzxO6WHv9Ij+fLfRfviJq8B3vWfWi0iA3VqRWDUb8eRGpSJlypVlLPvCi0b+fBs0YIK295H1onfEBHdCRZoildaVzKOkpTis9n8NnBjJ2BmrVRSKFtF26MiIiKi4rqyGdjwOpCWALhUBoatAdyqqbXP+M7LIfhi6xXcjUiU6+qWd5THZ40quBRa/a807K3M0a2Wp7wJdyMSZLWFfdfCceRmuAy4/nP2obzlO/7sv6NEpQURnigsLEFERKoxdOhQuLsrbX6IiIiIiHQFexKoScvsk0CHb4ar6y1IzcRJs08KuZAtTgga48m1LeeD5HXWxhWcn5qxpRYWdkDkLWDrZGB2LWD3F0B8aIleangzX3n/5+n7SE7LgKEQpXnFCWvhheYVUMksEvijH3BwdqlfW5zU/m5wPbm89PAd/O/AbaiMmTXKhF/Fh+nzcNhqIp6PWwUkGkG5yX3fKP2rRWnogb8B5Rtre0RERERUHOJgeP93wJoRSkihUjvgtV1qDSmIFl8vLj6O1/84JUMK7vaW+G5QPfz9ZqsCQwrqUqGsLV5sURH/G9UYZ6d3xZrXm+OtDlVQ2bXgNlYirCDC4KLCAxERqZ9RTAYgIiIiIr3EoIKatKriKu/PBEYjIbsUJ+mfdFFDP3vWz5Pe7VrNaMuVbjqvzJLqXdpqCqLcfWHtHMwsgdd2Az1mAU4VgKRIYP8sYHZtYON4ICygWG/Zvro7vB2tEJ2Yhu0Xg2Eo/jrzAFeCYmFvZYrJ7ieABS2BW3sBU0vAxKzwfSw+iwL0reeND3vWkMsiELHxXP4z5STxeuJ1C3nfrNH/YanNKwjKcoEromG+7wsljLL1PSWcYohOLwP2fqUs9/wO8O+p7RERERFRcaQlAetfA3Z/rjxuMhp4YT1go56wQFRCKj7++yJ6zNmPgzfCYWFmIgMBeya3x8BG5WGiA8Fpc1MTWUnwvW7+mNi5apG+RrShICIi9RPVeIiIiIiIdBFbP6iJb1kblHe2xv2oJFkiX1wcJf0i+r7O2nFVLk/oVBXNK5eVJ9P+PvMAewLCcPxOFIzR/ahEGcARgfyedUoZVEhLzM5LZQCdpgNVOj77greTD+BeXWlPcGUTcPhn4MFJ5YKvuFXtBrQcD1RsLaYKFPiWogLGkCa+mP3fNaw8Foh+DcpB3yWlZuD7fwPghiisK7sadtsPKE+UbwL0W6gEBhIj8n+BnH1ciNfaVMKD6CRZVWHy2nNws7PMv4SweL1xpwp931PRtvg0sjO+NeuIo31jYH96IRB8ATj+K3B8EVCjD9ByAuDTBAbh2r/ApreV5TbvAk1e1faIiIiIqDhig4DVw4GHp5UwqAjTqun3eVpGJpYfvYsf/7uOmKQ0ua5HbU980LMGfFxK1gpNE9ztrVS6HRERlU5mZiZ3IRERERHpJAYV1Nz+Ye3J+zh8M4JBBT30x5G7uBeZJEuqvtGuMmwslB+XBj7O2P/9XtmP9ey9aNT3cYKxtX0QmlVygbtDKU4uij+UxQXbzDQlaND6nUJDBrL6Qq1+QM3ngHvHlMDC1S3A9R3Kzau+ElgQz5ua5/syQ5r44Kfd13H8TiSuh8Shqoc99NlvB2+hcdxufGG1FA4R8YCpBdDhQ2Vf5FSsKEIQoSjlIj/uXRMhscnYdjEYr/9xEn+OaYnqnvnsP/Gehbzv4s2n5H3v+hVg37Qu0GQEcHuf8tne+A+4slG5+TRT/j3VexZehUNXPTgNrBsFZGUA9YYBHT/W9oiIiIioOB6cAlaPAOKCAGtnYPAyoFJbtezDfdfC8Pnmy7gRGi8f+3vaY3qfWvmHRHVI00ou8HK0QnBMsmzz8CRxxO/paCW3IyIiIiIiIiLjxdYPatTKT2n/cOhGuDrfhtQgOjEVP+26/qjFQ05IIadaxvPZs/B/zt7GmGzODir0rutduhc6swwIPAyY2wK9vis8pJCb2Na3OTB0BTDuJND4FcDMCgg6C6x/FfipAXBkHpAS98wvFydGO/orVU5WHg+EPosIfQC/fePws8VcOCAe8KwLvL4PaP22Wi7oi4oUs4fUR+MKzohLTsdLS44jKCapRK91LzIROy4p7TdeaV3p8Wdbub1SPnnsEaD+C4CJuRJMWfMCMLexUmkhVVTj0CORt4GVg5UqIuLf1+en4n3PExERkXZd+BNY0lMJKbj5A6N3qyWkcCssHq8sPYFRvx2XIQUXWwt8+XwdbJnQRi9CCjnHi9P71JTLTx7t5DwWz4vtiIiIqOjtMwtr20lERESkbxhUUKOcE0mXg2JlX1HSH3N330Bscjqqe9hjYKOnZ4S/1cEP4rzarquhuPggBsbiTngCLjyIkScVRdnZEosLBv79RFnu+BHg5Fvy13L1A3rPBt65BLT/ALBxBWLuATs+AH6oCfz7MRDz4KkvG95Mec/1p+4jOS0DeunKZlj+2grdyhxFOkyR2W6qctLcQzkxrC5W5qb436jGqOJmi6CYZLy85ARik5VyxMWx5NAdZGYBbaq6Prsqg/h39JsHvHNRaZNg5QRE3gK2TgZm1wJ2fwHEh0LnJUQAywcACWGAZx1g8B+AmYW2R0VERERFrQK2e6YShk1PViqBvboTcKlcov2XkZmFIzcj8M/ZB/JePBZEa4eZmy+j6+z92H01FGYmZfBa60rYM7m9PG7Vt4v63Wt7YcELDWVAODfxWKwXzxMRkf6bN28eKlasCCsrKzRr1gzHjx/Pd9tFixahTZs2cHZ2lrfOnTsXuL1RyWmfKSae5HcTz6ugWiYRERGRLmHrBzUSPTerutvhemg8jt6KQI86PBmjDwIjEvH7kTty+YNeNZ55UrCSqy2eq18Of515ICsv/DqyMYzBlgtBj9qalLUrJOldkO1TgZQYwLsB0OwN1QzO1hVoPwVoNQE4v0apqBB+DTj8E3B0PlB7ANBiHOBVV27e1j0ZHRweIjQuBav/SUJFV1u42FigVjkHmIqZ7iKlrqt/ACZFAdumyH+nHYCAzPJI6T0fdZu209gQnGwssPTlpui/4DCuBsdhzB+n5GMLs6Ll30SwYe3Je3L51ZxqCvmx9wQ6fQK0ngScXaF8ttF3gf2zgENzgHpDlM/WrbqyffQ9IDEi/9dT12f7rPcVFzQ2vwNE3gTsvYDh6wArB9W/NxHphK+//hrTpk3DxIkT8eOPP8p1ycnJePfdd7F69WqkpKSgW7dumD9/Pjw8PB59XWBgIMaOHYs9e/bAzs4Oo0aNwldffQUzMx6qE2lVSjzw1xvA1c3K45YTgM6flrhq1faLQZix6bIMeubwdBCVvtyw/VIIIrPD7aLy14e9aqCKmzjS018ijNClpieO345EaFyy/PtYtHvQt9AFERE925o1azBp0iQsXLhQhhTE8a841g0ICIC7u1LFMre9e/di2LBhaNmypQw2fPPNN+jatSsuXbqEcuWUyqVGrQjtM4mIiIgMTZmsrKxntY00GLGxsXB0dERMTAwcHDR/cejTjZew9PAdvNDcFzP71dH4+1PxvbXyNLacD5KzvP94tVm+24lSrF1m74P4Cdo6oQ1qehv+xcfuP+6XF6VnDaiLwU1K+MdTwHZg1RCgjCnw+h7Aqx7UNvvt+r/A4Z+Buwcfrxdl9+sOBTZPBNJTCi6pp4tp9ev/ARvHybLDmTDBwvTeOO83FgtfaqmV4YiKIkN+OYKE1Aw8V98bswfXh0kRTj7/78AtzNxyBX7udtj5TluUKU4bhMwM4Mom5bN9cPLxejHDse4Q4J+xmv9sRUhhbqOC39fUAhh/Wve+p4h0hLaP2UrrxIkTGDx4sBx7hw4dHgUVRABhy5YtWLp0qfz3jRs3DiYmJjh06JB8PiMjA/Xr14enpye+/fZbBAUFYeTIkRg9ejS+/PJLo9l/RDonOhBYNQwIuaj8Du8zB6g/vMQvJ0IKY5efRkF/fIvjoo9710S7am4lfh8iItJthnTMJsIJTZo0wdy5c+XjzMxM+Pj4YPz48Zg6dWqhXy+Og0VlBfH14vjX2PYfERERkaEqzjEbWz+omZh5Lhy+WcDsXtIZpwOjZEhBXDOd1qNGgduKE4m963rL5bl7rsPQ3QiNkyEFc9My6FbLs+Sz0kTZfqHFW+oLKQgmJkD17sDLW4DRe5SKCiIccWsv8PeYgi8oC+L5gmbla1pKHLBxArBigAwpJDlUwoCU6fg+cxgm91SqRGhD7XKOWPBCI1me+J+zDzFrR0ChX5OekSnbPuRUUyhWSEEQsxhr9QNe+w94ZQfg31vpeHx9B7D+Fe18tuL1CnvfjFTd+p4iIpWJj4/HiBEjZDlbcbI1hzgYX7x4MX744Qd07NgRjRo1wpIlS3D48GEcPXpUbvPvv//i8uXLWL58uQws9OjRA59//rkso5uaytZhRFoReBT4tYMSUrB1A17aUqqQgmjvICopFBRScLAyw+bxrRlSICIivSCOU0+dOiXbN+QQYVzx+MiRI0V6jcTERKSlpcHFxSXfbURFMnGiO/eNiIiIiAwHgwpq1qxyWYjJxbfCEhCcq8Qn6R5RXOTLLVfk8sCG5YtUIWF8Rz95v/VCMAKC42DINp1T2j60qeoGRxvzkr3Ini+BmHuAky/QvvB0vcqUawgM/A2YeBZo/hayzKyL9GUZulJw5vZ+YEFL4PTv8mFWszF40fw7nMmqimFNfWRoRpvaVnPD1wOUsMTCfTexLLt1Sn7+vRyCB9FJcLG1wPMNSlHeUQQcfJsDQ1cA408BjV9VZjwWRewDIPKW6m7i9YjIaL311lvo1atXnhO1gjh5K06+5l7v7+8PX1/fRydwxX2dOnXytIIQJXPFSVhRBjc/PGlLVIoqSA/P5n879BOwtDeQGA541lECrz5NS7W7ReuD3O0eniU2OR1nAqNL9T5ERESaEh4eLisi5D6GFcTj4ODgIr3GlClT4O3t/dQxdG6iHZqYjZdzExUbiIiIiMhwsPGtmjlam6NOOUecux+DQzfCMaBReXW/JZXQjkvBOHk3ClbmJni3a3av+0JU87BHzzqeMqgwd88N/DysgcGGODaffyiXe9f1KtmLPDgNHFugLPeaDVjYQuNEQKL7l7hk3xq1dw4rdPNL92NQV5ttElMTgf8+BY7/8nj8z83HxpjKOLnvLOwszfB252rQBQMblUdQdBK+33kN0zdekj2Iu9f2zLftgzCimS+szEvW4/kpZasAvX8Aaj4HLOtb+ParSz4rkogoz38nq1fj9OnTsvXDk8RJWgsLCzg5OeV7AlfcP+sEb85zBZ20nTFjBj8MIlW3aspRoy/w/EKVHLOGxiWrdDsiIiJ99/XXX8vj6L1798LKyirf7aZNm4ZJkyY9eizCvAwrEBERERkOVlTQgJZ+rvKe7R90V2p6Jr7edlUuj25TGZ6O+f+R9KRxHarKe3Eh/0ZoPAyRaPlwMywBFmYm6FIz78WUIslIBzZNALIygTqDgKr5p+U1ITytaBUh/HeOAFYOAQ58D9w5qAQHNCXwGLCw9eOQQqOXgbGHkVy+JWZtV9orjGlXGa52ltAV4zr6YVhTX4hCFBNXn8Gpu5FPbXMmMAqnA6NhYWqCF1tUUP0grByLtp2ZDWBhr7qbeD0iMjr37t3DxIkTsWLFigJPsKqDOGkrWkvk3MRYiEgFrZqEhqOAQb+rJKQgAr/n7hWtUoIIehIREekDV1dXmJqaIiQkJM968djTs+B2od99950MKogWaHXrFtzK0tLSUvY1zn0jIiIiIsPBigoa0LJKWSzYexOHb4bLE1XF7sdOarfy2F3ciUiEq50F3mhXpVhfK1pEiIv3Oy+HYN6eG5g9pD4MTU41hQ7V3WBvVYK2D0fnA8EXACsnoNtX0DYXm6K1B7BIjweubVdugomZUgK4fFOlBLBPM8CxvNKCQFXSkoG9XwKHf1aCHfbewHM/A35KuOP3fTdl2wRPByu82roydIn4v+3z52ohNDYZu66G4tXfT2L92Jao4va4NcXig7flfZ963to9Gf/KNsBbhT+rolT0r+1U93pEpBdEa4fQ0FA0bNjw0TpRAnf//v2YO3cuduzYIfv3RkdH56mqkPsErrg/fvx4ntfNOeFb0ElecdJW3IhIDRq/Ihptl/pl7kclYur6Czh4I7zA7cSRpAhKN62Uf49uIiIiXSKqhjVq1Ai7du1Cv3795LrMzEz5eNy4cfl+3axZs/DFF1/I4+TGjRtrcMREREREpIsYVNCAxhVc5Oxh0Zf0dngCKue6aEfaF5OUhjm7rstlUUZflNMvrgkdq8qgwj9nH2BCp6qo5KqFtgZqIsI1m84FyeXedb2L/wJRd4A9XyrL3b4A7NygbbXKFS2Bn/HcApgmRwP3jysVDuIeAg/PKLecSgciSOCTK7jgWRcwsyi45LCYzfcs4QHA3m+AyJvK43rDgO5fA9bKxa2ohFTZYkSY3K06rC1U1DZBhcxMTfDz8AYYtuiYnD046rfjWDemBe6EJyIgJBZbLyjfS6+2rqTtoRIRlVqnTp1w4cKFPOtefvll+Pv7y567oiytubm5PGE7YMAA+XxAQAACAwPRokUL+Vjci5O1IvDg7u4u1+3cuVPOFqtZsyY/JSI9PX5efeIevthyBfEp6bK1XN963lh38r7yfK5tc+Ku0/vUhKkJA+1ERKQ/REuGUaNGycBB06ZN8eOPPyIhIUEeDwsjR45EuXLlZMsy4ZtvvsEnn3yClStXomLFio/anNnZ2ckbERERERkfBhU0QFxMbFjBCUdvRcr2Dwwq6BZR7SIqMQ1V3GwxtIlPiV6jTnlHdPR3x+6robKqwneD6sFQXHgQg8DIRFibm6JTDeUCSpGJHgCbJwHpSUDFNkD9EdAFpkWsgLD0ujVeHjgMJi3eVFbE3AfuHQPuHVfuRZUIEV64/LdyE8ysAO8Gj4MLovpCTjijqH2RrV2A5+YC/r3yrP5p93XEJaejhpcDnm9QDrrKxsIMi0c1xoAFh3E3IhFtvtmD9MzHp+RFcCswMkFWIyEi0mf29vaoXbt2nnW2trYoW7bso/WvvvqqPInr4uIiwwfjx4+X4YTmzZvL57t27SoDCS+++KKcYSZO2H700Ud46623WDGBSA89jE7C1A0XsP9amHzcqIKz/NtABJnF3wszNl2WAfYcopKCCCl0r+2lxVETEREV35AhQxAWFibDB+IYtn79+ti+fTs8PJSWoSKca5KrQtGCBQtktbGBAwfmeZ3p06fj008/5UdAREREZIQYVNCQllVcs4MK4XihuRr6slOJy7H+dkgpRf9BzxpyNnhJje/oJ4MKf515ICss+JY1jJ71m88rM+A71nCXF6CL5cKfwM1dgKkl0PtH1bZIKA2bsoCZZYGBgeQscyw+HYsLmWcxa2A9WJiZKG0exK22MisWqYlKdYXc4YWkSCDwiHLL4VxJCS04eBetL/KgpUDlvG0ERDWWP47clcsf9qyh8zPuXO0s8WqrSvhk46U8IQUhNSMTY5efxoIXGqr+pHwRPlv5vNjOEN6XiHTe7Nmz5QlaUVEhJSUF3bp1w/z58x89L3r7bt68GWPHjpUBBhF0EDPTPvvsM62Om4iKX0Vh3an7+HzTZcSlpMtjx/e7VcfLrSo9Om4Txz1danri+O1IhMYlyzZYot2Drh/XERER5Ue0eciv1cPevXvzPL5z5w53JBERERHlwaCChrTyK4sfdgJHbkYgMzMLJjwZpRO+2xGA1PRMNK/sImc4lUYDX2e0reYmZ08t2HcDX/WvC0M44bolO6jQp7htHxIjge1TleV27wGuftAZTj7AuFOyBUNGVhYuPYhFZGIqXGwsZFsIUXFh981UhGwLx99nHyIiIRULX2gE2yfbgljYABVbKbecChIRN5VWETnhhdArQNRt5VZUVo5PrZq1/aq84N++uhtaV3WFrsvIzMKCfdktLPIhZhSKk/UqPTmf67PNlwgLiO1USVvvS0Q658kTslZWVpg3b5685adChQrYunWrBkZHROoQHJOMaRvOY0+AUkWhvo+TrKLg5/50GWtx3NOiCoOLREREREREREQMKmhI3fJOsLUwlS0GrgTHopb30xciSbMu3I+RF6GFD3vWRBkVzPaf2MlPBhX+PHUfb3XwQ3ln/a6qcDowGg+ik2BnaSYvkBfLvx8DieGAWw2g5UToHHHB2MkHpuLn8xldFHp6A9buoXhz+WkcuB6O4YuO4reXmqCsnWX+rym+h0QgQ9zqD1fWJccA908qoYUb/wEPThZ7qKfuRmLbxWCI6/nTetSAPhAzBXOXNX6SqLEgnhfbqfxkffZnq3Hael8iIiIqmjglgKvKUK+opvbpxkuITU6X7a0mda2G0W0qs0oCEREREREREVEhSl7nnorF3NRElvUUDt8oYMYtaYQ4qfjF1sty+fkG5VCnvGqCI40quMjqGWkZWVhYyGxyfbD5vBLk6FLTA1bm4pJ+Ed3eD5xdLq7cA31/AswsoI86VHfHytHN4GxjjnP3YzBw4RHci0ws3ouI6gh+nYAO04Be35foe3XmlityeXBjH1T3tIc+EOWMVbkdERERUanEBQNb3yt8uyK2ahLHMKOXncKktedkSKFueUdsmdAaY9pVYUiBiIiIiIiIiKgIWFFBg1r5ucpyoIduhmN028qafGt6wu6roTh6K1L2jn23azWV7p8JHavi0I0IrD2hVFXwcrTWu/0vyvYfvRWB9afuy8c9a3sW/YvTkoFNbyvLTV4FfJpCn4mWHn+ObYmRi4/jdngC+i84jKUvN9FYVZStF4JxJjAa1uammNRFtd+r6iR6LqtyOyIiIqISS4gAlvUDYu4BDuWAPnMAW7cStWoSIdKN5x5i+sZLiE5Mg7lpGbzduRreaFsZZqacB0BEREREREREVFQMKmhQyypKX3lR6jwtI1NWWSDNS8/IxJdblRnqr7SqpPL2DM0ql0WzSi44djsSv+y7hU/71oI+2X4xCDM2Xc5Ttv/jfy4hIysL3Wt7Ff4CB74DIm8C9l5Ap09gCKq42WHDmy0x6rfjuBoch6G/HMWvIxurvb9wanomvtl+VS6/3rYy3B3056K+qCDj5WglezaLNg9PEo1WPB2tHlWaISIiIlIL0YZreX8g7IpyfPrSFsClUoleKiwuBR/9fQE7LoXIx7XLOeC7QfXg7+mg4kETERERERERERk+XinXIH9Pe7jYWiAxNQPn7kVr8q0pl9Un7uFmWIIs5/9mhypq2TcTO1WV9yuPByI0NlmvQgpjl5/OE1IQQmKT5XrxfIFCLgMHZyvLPWYpbQ8MhIeDFda80UJeWI9LSZehha0XVNvn+El/HL2LwMhEuNlbyqCCPjE1KYPpfWo+CiXklvNYPC+2IyIiIlKL1ERg5RAg6KxSKWHkPyUOKYiWaF1n75MhBTOTMrLS1V9vtmJIgYiIiIiIiIiohBhU0CATkzJoUVmZgS1aA5Dmxaek48f/rj0KEzhYmavlfcRM+8YVnOWM+F/234K+tHsQlRSeNfs9Z514Xmz3TJmZwKaJQGY6UL0XUKMPDI2jtTmWvdIU3Wt5IjUjE2+tPI1lR+4U/QXECXLR97gIfZFjEtPw067rctW7XarB1lL/CuCIChwLXmgoKyfkJh6L9UWq0EFERERUEukpwJoRQOARwNIRePEvwK16sV8mIj4Fb604jXErzyAqMQ01vBzwz7hWmNCpKivkERERERERERGVgv5d+dJzLf3KYsuFIBy+GY6JnZVZ96Q5v+y7ifD4VFRytcXwZhXU9j5lypSRJy9H/nYcK47dxZh2VeSseF0mWpI8WUkhNxFPEM+L7Z7Z8uDUb8D944CFHdDzW7ETYIiszE0xb0RDfPLPRaw4FohP/rkkywCLWXXicy+Q6Hc87hSQWEBQKbsv8rytVxCTlIZqHnYY1Dj/Psm6ToQRutT0lN83oXHJcLdX2j2wkgIRERGpTUY68OcrwM3dgLkNMGId4FWv2C+z7UIQPvr7IiISUuWxy1sd/DCugx8szJj3JyIiIiIiIiIqLQYVNKxlFVd5fyYwGkmpGbC2MNX0EIxWcEwyFh1QqhtM6e6v9hOMbaq6op6Pk2zz8b8DtzCtZw3oMnERucTbxT4E/puhLHeaDjiWgyETJ6pn9qst20H8sPMaft59Q4YVxDozU5PCwwriVoB7kYlYekip1CC+b/T9or4Y/zPDLURERESqJqp8/fMWcHUzYGoBDF0J+DYr1ktEJaRi+sZL2HjuoXxc3cMe3w+uh9rlDKetGRERERERERGRtnEqiIZVLGsDb0crWTb+xJ1ITb+9Ufv+3wAkp2XKlgzdanmo/f3E7PqJnfzk8rIjd2XZWF0mZrqXeLtt7wMpsUC5xkCTV2EMcqpmfPl8HYgcweoT9zBm+WkZQCqtWTsC5P8Rrf1c0b6am0rGS0RERGTwsrKAre8C51cDZUyBQb8DVTo8c1PRzuzIzQj8c/aBvM9pb/bvpWB0mb1fhhTEMd5bHapg4/hWDCkQEREREREREakYKypo4eJmSz9X/HnqPg7fjEBbXoTUiMsPY/Hn6fty+cNeNQov0a8iHaq7o3Y5B1x8EIvFB2/j/e7+0FX1fZxgYWoiL5A/i9hjno5K2f48rmwGrmwCTMyAPnMAE+OqEjK8mS/K2llg/Koz+O9KCF5cfAz/G9UYTjYWJXq9s/eisencQ9k5Y1pPf419rxIRERHpfUhh5yfAyd+UI9f+vwL+PZ+56faLQZix6XKetmceDpaoUNZWtqsSqrrb4btB9WSFNCIiIiIiIiIiUj1WVNCCltkl0A/fDNfG2xulr7Zdkecue9f1QgNfZ83Ouu9YVS7/fvgOohNToYuysrIwbcP5AkMKwvQ+NfO2IUiOBba+pyy3nAB41oYx6lbLE8tfbQYHKzOcvBuFQQuP4GF0Uok+hy+3XJHL/RuURy1vlhcmIiIiKpL93wGHf1KW+/wI1BmYb0hh7PLTeUIKQkhsigwpiCPdMe2qYNP41gwpEBERERERERGpEYMKWtDKz1XeX3gQg5jENG0MwajsuxaGA9fDYW5aBu9303xFgy41PVDDywEJqRn47dAd6CLRauDvsw9hZlIG4zv6wcsxb3sHUUlhwQsN0b22V94v3D0TiHsIOFcC2r0PYyYqTawb01LOxrseGo8BCw7jekhcsV7j38shOH4nEpZmJpjcrZraxkpERERkUI7MB/bMVJa7fQk0eumZm4n2DqKSgtLk4dlc7CzwXrfqsDI3riphRERERERERESaxqCCFng4WKGKm62c4X/0doQ2hmA0xMnInBnqo1pUhG9ZG42PQamq4CeXlxy6jZgk3Qqn/HHkDhbsvSmXv+pfB+92rY6DUzpi1ejmmDO0vrwXj58KKdw/CRz/9fGsNXNrGLvqnvZYP7al/PkWs/QGLjyCU3eV8sGFScvIxDfbrsrl19pUgpcj9ycRERFRoU4vA3ZMU5bbfwC0eCvfTUXFhCcrKTwpIj71UfsHIiIiIiIiIiJSHwYVtKRlFaWqwuEbbP+gTn+euoeAkDg4WptjXHZYQFutAap52CEuOV22gNAVOy4F45ONl+Tyu12qYVBjH7ks2ju0qFIWz9UvJ+/ztHsQMtKAjRNEswKg3nCgcnttDF8nlXe2wZ9jWqKBr5MMpYz43zH8dzmk0K9bdTwQt8ITUNbWQpYbJiIiIqJCXFyffUwKoMW4Qit8hcYVHFIo7nZERERERERERFRyDCpoSSu/svL+0E1WVFCXxNR0fP/vNbks2hk42VhAW0xkS4WqcnnxwduIS9Z+VYVTd6MwYdUZWdljWFOf4gU5Dv8MhF4CbMoCXbPL7NIjzrYWWPFaM3T0d0dyWibeWH4Ka0/cy3cPxSan4cf/rsvlt7tUg72VOfcmERERUUECtgEbXleCs41eVo5JyzwRrn2Cu33e9mal3Y6IiIiIiIiIiEqOQQUtaV65rDyPdiM0HqGxnLGjDov230ZoXAp8XKzxYosK0LaedbxkSwAxy37ZkbtaHcutsHi89vsJpKRnopO/Oz5/rrZsUVEkETeBfd8oy92+AmyV0A3lZWNhhl9ebISBjcrLFiTvrz+PeXtuIEskQ56wcO9NRCakorKbLYY2UapaEBEREVF+B7P7gLWjgMx0oM5goNcPhYYUhFreDjA3zX878YyXoxWaVnLhriciIiIiIiIiUjMGFVQh+h7w8Gz+N/H8E8TsfnGiTDjMqgoqJ8q1/rL/plye0t0flmam0DbTXFUV/nfgFhJS0rW2b0YtOY6oxDTUK++In4c3gJlpEf8rEBfZN78DpCcDlTsAdQere7h6zdzUBN8OrIs32yutHL7dEYBPN16SwQVxO3IzAksP38ai/bfk89N61JBfQ0RERET5uHccWDUMyEgBqvcC+s0X5cuKVG3ttWUnkZbxdGhUyIkvTO9T8+m2Z0REREREREREpHJmqn9JIyNCCHMbAekp+W9jZgmMOwU45Z0p3aqKKy4+iMWhG+Ho16Cc+sdqRGbvvI7E1AzU93FCrzpe0BW963phzq7ruB2egOVH7+KNdsoFbE0R4YhXl57EvcgkVChrg8UvNZEz/4vs3Grg9j7AzAroXbSZa8ZOVKp4v7s/3Owt8dnmy/j9yF1ceBCDh9FJCI59/P+GhWkZpGdkanWsRERERDot6DywYiCQlqCEZgctAUwLb5mVlJrx//buAzqqMv3j+C+9F0hIKKH33ruCiyiWta/dBcGyKth1bbvW/4pt7X1XwYrorl0XCypI7yC9Sk8gQBohff7nfYeEhLRJSJmZfD/n3DNzy9y5982dyTP3Pvd5ZWLgRdsOKiLIXxP/0EHvzP9de1OPVbZrGhVskxTO6OE+vx0AAAAAAAC8GYkKJyrzQMVJCoaZb5Y7LlFhWIdYvTF7q62oYMrBu1x6HxXamJSu6Yt32OcPnN3VrdrVVC4wJ0bv+mSl3py9VWOHtlFIYN1Ue8jNL9DED5fZi+SNwwL1zvhBig0Pcn0Fh5Ol7+53Pj/lXqlxu1rbVm80fnhb2963TV+uZTtSSs3PyXfopg+W6bWr+nGCHAAA4Hj7N0rvXSBlpUoth0iXfeBMCK9EVm6+rn9vieZvPaCwQD9NnTBI/Vs30nUj2tnEBVNtLC7C2d0DlRQAAAAAAADqDjXG69HANo1sH6m7U45ox8HM+twUrzL523UqcEhndG+qgW3cr3/Z8/o0V8vGITpwOEcfLNxeJ+9pEmEe+Ow3/bJhv4IDfPXWuAFqExtWtZV894B05KAU30MaOqm2NtWrndWzmaJCAitc5pGv1tpuIQAAAHDUod+ld8+TMpOlZr2lKz+WAiuPZbPz8vWX95bq103JCi2WpGCYpISh7WN0Xp8W9pEkBQAAAAAAgLpFokI9MiX3+7Z0niibu/lAfW6K1zDdaPy8Yb/8fX10z5ld5I4CTFWFUzrY56aihrnLq7aZ7iY+XrJLprvdly/vp76tnMedy7b8LK36yNl77zkvulRiF6WZu/YOHs4pt2lMeoIpQWyWAwAAqNPu7PasKH8w8+tL2l5nkkL6HqlJF+mqz6TgqEpflpNXoJveX6ZZG52Jum9fPdAtk5gBAAAAAAAaKrp+qGfDOsRo0e8HNW9Lsq4Y3Kq+N8ejFRQ49I9v1tnnVw1prbZVrRhQhy7sl6CXftpsq2l8tGiHrh7ettbey3SD8fyPm+zzx87vodHd4qu2gpxM6evbnM8H/0VK6F8LW9kwmNLCNbkcAADACTNJCC/3r7g7O9PFwqSlpbqyq3WHD0jvne+sqNCojfTnz6WwGJe6PLt52jLNXL9PQf6+envcQA1pV/nrAAAAAAAAUHeoqFDPhrWPtY/ztxywF9pRfZ8t3621e9MUEeSvW07t6NZNGejvqxtPaW+fvz5rqy1LWxt+Xr9P93+22j6f9IcOunJw66rfTfftnc6Tw2FNpP7ja2U7GwrT/3FNLgcAAHDCMg9UnKRgmPlmubqUlSq9f4G0f70U0Vwa+6UU2azSl+XlF+i2j1bouzVJNub+19gBGtbB+ZsLAAAAAAAA7oOKCvWsT8tohQT46cDhHG1ISlfXZpH1vUkeyXSf8Mz3G+zziaM6qHFYoNzdxQMS9PJPm5WYlqVPluyyVSBq0qpdKbrpg2XKL3Down4tdOfpnU7sbrrD+6U3R9TP3XReYlDbxmoWFazE1CzbzcPxfCQ1jQq2ywEAADRYOYelDy6R9q6UQmOlsV9IjVq7lKRw+8cr9c1vexXo56s3ruqvEZ2a1MkmAwAAAAAAoGqoqFDPzF0+hRcl522p47uUvMhbc7Zpb2qWWkSH6OphbeQJgvz9iqoqvPbLFtuPbk3ZcSBTE6Yu1pHcfJ3cMVZPXNhLPj7mMrgH3k3nRfx8ffTQOd3s8+P/GoXjZr5ZDgAAwK0se1da9Ym0c7GUsU9y1EA1uLKqee1YJL1zrrRzgRQUIY39XGpSScKtZJNz7/7PKn21co/8fX306pX99IcucSe+jQAAAAAAAKgVVFQ4UaExzj5bK+vT1SxXjmHtYzRr437N25ysa05qe8Kb1NAkZ2TbC/3G3WM6KzjAT57i0oEt9crPm7U75Yg+XbZLlw1qdcLrPHg4R+OmLFJyRo66NYu0J2lNQgzcwxk9mum1q/rpka/W2uSaQqaSgklSMPMBAADczpK3nEOhgFCpUZtjQ3TrYs9bSYGhJ1bNy8jLkoKjK90004XePf9dZbuCMwmfL1/RT6O7xVdl7wAAAAAAAFDHSFQ4UaYEvimFf/xd5skbpE+vd94nfdWnFZbKH360z9SF2w7acqX+flxUrooXZ25SRnaeeraI0rm9m8uTmKSKv4xsr8e+XqtXftmsi/onKOAE/v5HcvJ1zTuLtS35sK0uMWX8QEUEB9ToNuPEmWSE07o11aJtB7UvPUtxEc7uHqikAAAA6lROpjT/FdeW7XSmlJ0mHdoupe2WcjOlfWudQ1nC48tPZIho5lo1r/xc53IV/JYySQoPfP6b/rN0l42lXrysr87o0dS1fQIAAAAAAEC9IVGhJpgTZ8efPGveR/rtv9Km76QVH0ptTir35eau96iQAKUeydWq3anq16pRjWxWQ7Blf4Y+WLjDPr//rK7y9cCS+VcMaqXXftmsnQeP6PPlu3XxgPJPxFZW7vbWj5Zr+Y4Uezy9M2Gg4iODK35RXo6UuErauVDa+F31dgDVYk6kD21ffqUVAACAWrV1lvTVLdKh311b/pR7nb9xDJNgYCoimNce2ialbD/63AzbnQkNGUnOwcSZx/MLlMJPvFsGh8OhB79crWmLdsr8DHj2kt46uxfVqQAAAAAAADwBiQq1aeRfnYkKKz+SRtwtNS67WwdzcX1ouxjNWJNou38gUcF1T/xvvb1AP7prnMde9A0J9NP1I9rp8W/X224gLujbospVNcxJ2oe/XKPv1ybZbh7+PW6AOsRFlF7Q9Ce8c5HzhLF53LNcyq/kTjYAAAB4jyMp0g9/l5a96xwPi5MO76vaOkzXdrEdnMPxHA7pyKFiiQu/l0xkMAkO+TlS6q4T2g0T/5qutN5fsEM+PtIzF/fWeX1anNA6AQAAAAAAUHdIVKhNCQOk9qdKW2ZKc56Tzn2x3EWHd3AmKszdfECTRnWs1c3yZCYpobBc/oGMbP2wNsnemX7vmV3kya4c3Fqvz9qq3w9k6qtVe3RBW0fp7kSKC40pUcXDvPa9BdvtSdrnL+2jgW0aSwX5zlK8hUkJZjB3vB0vpLHUcrAU1VJa/GYt7SEAAADq3bqvpW/ulDISneMDr5MGXiu9OaLibhhMYoKJP11hAtLQxs6hRb/S8/PznF1HbJ4pfXN7tZMU/vHNOk2d56wG8eSFvXRhv4RqrQsAAAAAAAD1g0SFuqiqYBIVTPcPI+6SoluVudiwDrH2cemOQ8rKzVdwgF+tb5qnmbF6r71ram9qVonpw9rHlF09wIOEBfnr2pPb6qkZGzT9x/k6P3uifCo7WTxpqU1WMN1FPDljvSJ1WE8PztGY/W9L7y6Sdi2RcjKOe6GPFNdVajnImZxghsbtnCeU96wgUQEAAMAbmapa394trf3cOR7TQTr3Jan1MOe4iSurkCR7Qvz8pUaty05icDFJ4ckZG/TvOc4E3Mcv6KlLBtbQtgEAAAAAAKDOkKhQ21oNkdqOkLbNluY8L/3x2TIXaxcbpvjIICWlZWvp9kMafjRxAceSFG58f5kcZTTIr5uS7fwzenh2f7Rjh7bRm7O3Kv1gknyCKumOwSQx7FmhTQu/Udbc7/Rd4EZ18t0tnxXHtVBghLOyh01KGOR8HhxV/glokwBRU3fTAQAAoH6ZbhhWTpNm3CdlpUg+ftLwW6WR90gBwceWM0kINZWIUMue/WGjXp+1xT5/7LzuumJw2YngAAAAAAAAcG8kKtQFcyLQJCosf086+U4pqnTfqT4+PhrePlafLt+tuZuTSVQ4rrsHU0mhrCQF23aSnX9at6a2GwhPFR7krwnD2+q7Hze69oKPr5LpJKSjb7FppjpCUVLCIGf1BF8Xq3OYk9N1eTcdAAAAas+h7dLXt0lbfnKON+0lnfeK1KyXS92sxUUEa1Dbxm4VX7/w4ya99NNm+/yhc7rpz0Pb1PcmAQAAAAAAoJpIVKgLbU6SWg+Xts+V5r4gnfVUud0/mESFeVsquFDcAJmTpcd391CcSWAw881yQ9t79t3+44a10Zxfi2celC9b/lpR0EGJET111lnnKaD1ECm8yYltgAfdTQcAAIAyFORLi/4lzXxUyj0s+QdLp9wrDb3Z2e1CFbpZaxYVbBMCaqVyWRWreb3y82Y9dzSh94Gzumr88LY1v00AAAAAAACoMyQq1JWRf5XePU9aOlU6+Q4pommpRYYdvci+aleK0rJyFRkcUGeb587MHV01uZzbycuREldJOxcqaudCTfGfLeVV/rJLsh/Ukbg++uSGYQoI4VgBAACoMSk7PbPK1L710pc3S7sWOcdNsvQ5L0qxHarVzVpiapad/tpV/Wo+WaEK1bzemLVFT3+3wU766xmddd2IdjW7LQAAAAAAAKhzJCrUlbYjnSX5dy6U5r4onfF4qUWaR4eobWyYtiUf1sKtB3Vat/g62zx3ZsrO1uRy9S5jn7RzkfNY2LVY2r1Myj92J1mYi6uJCgvRa+MHKYokBQAAgJpNUni5f+V3+puL7O6SrGASX+c+L81+WsrPkQIjpNMekfqPl3x9q93NmqO2u1lzoZrXv3/dqsn/W2+f33laJ910SsVJFwAAAAAAAPAMrtWYryWTJ0/WwIEDFRERobi4OJ1//vnasMF5p0yhrKwsTZw4UTExMQoPD9dFF12kpKQkeRwfH2dVBWPJ286L1WUorKowb0tyXW6dW+vRIlJB/uUfqj5Hy9KaPnRr5UT1nhXlD2Z+ZaV3E1dLi9+SPv2L9EIf6ZmO0vQrpXkvSjvmO5MUQhpLnc6UTn3IedebCx45t7tNbgEAAEANMnf4V5SkYJj5FVUCqEu7l0pvniL9/A9nkkLHMdLEBdLAaypNUqhKN2vz6+H3yTvzftf/fbPOPr/l1I66+dSOdb4NAAAAAAAA8MKKCrNmzbJJCCZZIS8vT/fff79OP/10rV27VmFhzvvKb7/9dn3zzTf65JNPFBUVpUmTJunCCy/U3Llz5XHanyq16O88mTjvJen0x0otMqx9rD5YuEPzNrvJic96dvBwjiZMXazsvIIy5xfe02X6zq3xO7yqczfdkRRp95JiFROWSjnppbc6rqvUcpCzykbCICmmvTOZxTAJEC4w1TcAAADQQOVkOpMTFrwqOQqc3SSc+ZTU46JjcaUL9qRkurTc+KmL1SshWj1bRKl3yyj1bBGtdrFh8q3pGPyoDxZu10NfrrHPbzqlvW4fTZICAAAAAACAN6nXRIUZM2aUGJ86daqtrLB06VKNGDFCqampeuutt/Thhx9q1KhRdpkpU6aoa9euWrBggYYMGSLPq6pwj/ThJdLif0vDb5XCYkssMvRoRYUNSenan56tJhFBaqh2HcrU2LcXaev+w4oODdB1J7fT+wu2l7jjq2lUsE1SqPE+c6tyN93y96X0vc7khP2mLO1xhXNN6d2EAUcTEwZJLQZIIdHlrzM0Rvm+gfIryCl3ETvfnIwGAABA/chIkhyOKiUF1Jhts6Uvb5YO/e4c73mxdMYTpX5bVNblw+fLdxd1q1CZ3HyHlm4/ZIdC4UH+6t48Ur0SomwSg3ls1ThUPlVsE7MtprLDvvQs253b7wcy9MBnq+2860e0091jOld5nQAAAAAAAHBv9ZqocDyTmGA0buws4W8SFnJzczV69OiiZbp06aJWrVpp/vz5npeoYHQ8XWrWW9q7Upr/ijT6oRKzG4cFqluzSK3dm6b5Ww/o3N7N1RCtT0zTuLcXKSktW82jgvXuNYPUIS5CN4xsX+IkpunuocYrKVTVrCdKjjdu56ySUFgxwVRP8PVzeXX5kQm6yP8l5aaXXV7X7K1/RKz+G5kg19cKAACAGmWSj01CapPOUlwXqYkZujqfR7aoXgKDqehVUZcSfgHSwtelZe86x837/PE5qdMYl9/C4XDo+7VJ+uf3G7QxKcNOM+F0wXG5toV8jiYHTx0/SGv3pmrVrlT9titVq/ekKiM7Twu3HbRDochgf2flhYQo9U6IUs+EaBvPl5doMGP1Xj3y1doyu58YP7yN7juzC0kKAAC4qVdeeUVPP/20EhMT1bt3b7300ksaNGhQmcuuWbNGDz74oD3fu337dj333HO67bbb6nybAQAA4D7cJlGhoKDABqfDhw9Xjx497DQT5AYGBio6uuTd5/Hx8XZeWbKzs+1QKC0tTW5ZVeGjK6RFb0rDbpZCnYkZhYa1j7GJCvM2JzfIRIWFWw/o2neXKD0rT53iw/XOhEFqFhVi55mkhMKqE7XK3B1n7pJzRdOeUvtRx7pxCG9yQm9tEjFWpEVIMkM50pzL1UlbAAAAoDQfP2cXX6bbLzMUFxTpTGAwyQsmabXwMaJZ+QkMrnQ7VtyAa6TRD0vBkS7/dczviye/26CVO1PseFRIgG48pb2aRQXrto+c3Y85yulmrXPTCDtc0DfBTsvLL9Dm/RlFiQurdqdq3Z40pWXlac7mZDsUigkLtIkLvVocq7wQFxlskxRufH/Z8fXIigxq05gkBQAA3NT06dN1xx136PXXX9fgwYP1/PPPa8yYMdqwYYOtmHu8zMxMtWvXThdffLHt6hcAAABwm0SFiRMnavXq1ZozZ84JrWfy5Ml65JFH5NY6nyXF95SSfnPeEfWH+0vMHt4hVv+es01zt5R9R703m7E6Ubd8tFw5eQUa2KaR/j12oKJCA2r/jfNypMRVzu4bdi50Pqbvce21574sNe9TY5tiqkXU5HIAAACoBRO+k4LCpX3rnN1/FT4e2CJlp0m7FjuH4oKijlVfKJ7AEB7vWrdjRmSCdOGbUpvhLm+qSUx4+rsNRckDIQF+uuaktrpuRDubrGA3zd+3VGWDirpZ8/fzVZemkXa4ZEBLO83E8BuT0p3JC7tT7OOGxHQdOJyjXzbst0OhuIhApR7JKzdJwSRJPPr1Wp3evWn9V1ADAAClPPvss7ruuus0fvx4O24SFr755hu9/fbbuvfee0stP3DgQDsYZc0HAABAw+MWiQqTJk3S119/rdmzZyshwXmHjtG0aVPl5OQoJSWlRFWFpKQkO68s9913n83mLV5RoWVL54kz96qqcLf08VhpwevSkJukkGP7N7BtY/n7+mjnwSPaeTBTLRuHqiF4f8F2PfjFalt29vRu8Xrx8r4KDqilzg0y9ku7iiUl7Fku5R1/4d/X1PpQXTNdWtTkcgAAAKiK8i6dl9ENg0kyMMPxCbAHNkv710n71h97PLhVyk49Gn8uLPma4Ggp2sXfLH96W2o12KVFNyWl65nvN+i7Nc5KYQF+PrpiUCtNHNWhVCxpkhFO69b0hLpZC/T3VY8WUXaQWtlpWbn5Wp+Yrt92pWjl0eoLm/ala196TqV/BZM0QRUxAADcjzlfa7pwMOdhC/n6+true013vTXF7SvnAgAAwHMTFUz/qDfffLM+++wz/fLLL2rbtm2J+f3791dAQIBmzpypiy66yE4z5cN27NihoUOHlrnOoKAgO7i9Luc4+7A1Jy5NFxAj/1o0KzzIX71bRmvp9kOatyVZlzZ2nuTzVuY4eGHmJj3/4yY7fvmgVnrsvO72Li2X++sNjSn/5G5BvvMOt8KkBPN4aFvp5UIaSy0HHR0GS75+0ttnqK6ZE8Km/G5ialaZp8kL+wk2ywEAAKCGrf2i8mX8g5zxZ5nzAqX4bs6hOFMtIXlTyeoL5tHEpVkpUqKzOwaX3rsSuw5l2tj602W7bBKwyZO+oG8L3T66U4VJ0LXRzZpJPO7TMtoOfz46LTMnT2/O2qrnZzrj/4pQRQwAAPeTnJys/Px82z1vcWZ8/fr1NfY+HlE5FwAAAJ6ZqGC6e/jwww/1xRdfKCIiQomJiXZ6VFSUQkJC7OM111xjKyQ0btxYkZGRNrHBJCkMGTJEHs3X11lV4T8TpPmvSINvKNG/7PD2MTZRYe7mA7p0oPcmKuQXOPT3L1brw4U77Pgtp3bU7aM7luyL1pX+es0J20lLnckKR1Kc/QQXJiXsWursP/h4JlGkMCnBDDHtS/YZvMfZT29dMyeITYld01+vTwX9BFMCFwAAoIZt+kGa87zz+fDbpO4XVD1JtqJ4tWkP51Bc7hFnAsPG76Sf/08nIjkjWy//tNnG1jn5zspgplLZXWM6q1N8hNxFaKC/BreLkVxIVKCKGAAADZdHVM4FAACAZyYqvPbaa/bxlFNOKTF9ypQpuvrqq+3z5557zpYOMxUVTKmvMWPG6NVXX5VX6Ha+FPuElLxRWvwv6eQ7i2YN6xCrF3/arHlbDtiKAyUu3HsJUwb21o+W21K0ZvceO6+HrhrSuvSCrvTXa+bPuNdZUtfcmXZ8LYLAcClhwNGkhEFSiwElutso9wS0OaFcWYJEeXfTnQBTeve1q/pVqZ9gAAAAnIDkzdJ/rnHGkf3GSqMfLpnEWlsCQqRmvSRHQbUTFdKycvWv2Vv11pxtyszJt9OGtY/R3WM6q2+rRnJHVBEDAMBzxcbGys/Pz3bPW1xF3fVWh8dUzgUAAIBndv1QmeDgYL3yyit28Dqma4GT75I+u16a97I06C9SULid1bdVtIIDfO1dUZv2ZbjVHVA1IfVIrq57Z4kW/X5QgX6+euGyPjqz5wlefF//9bHnjdoeTUoY6HyM6+Zs76owd8mZKg3V7XLiBNVEP8EAAABwQVaa9NHlUnaqlDBIOuuZuklSqIHE33fm/a7XZm1RSmaundY7IUp3j+mikzrGyp1RRQwAAM8VGBhou+w13fWef/75dlpBQYEdnzRpUn1vHgAAADxEvSYqQFKPi6RZTzgrASx5Sxp+q22WIH8/DWzTWL9uStbczclelaiQmJqlq6cs0vrEdEUE+etf4wZoiCn9eqJ6XSZ1PcdZMSE8riY21ZmEUEuJCK6ojX6CAQAAUExBgfTp9c4qZxHNpUvfc1bNcmO5+QX6eMlOvThzk5LSnNW/OsSF667TO2lM96YeU42NKmIAAHgu0yXDuHHjNGDAAA0aNEjPP/+8Dh8+rPHjx9v5Y8eOVYsWLTR58mQ7npOTo7Vr1xY93717t1asWKHw8HB16NChXvcFAAAA9YNEhfrm5++sqvDFTdLcF6WB10mBoXbWsPaxNlHBdP8wfnhbeYPN+zI07u1F2p1yRHERQXpnwiB1bRZZ/gsK8qVts11b+ZAbpeZ9amxbAQAA0AD88ri08X+SX5B02ftSRM2VK64SF7odc/gH6Yffc/X4B7P0+4FMO61FdIhuG91RF/ZL8MjKW1QRAwDAM1166aXav3+/HnzwQSUmJqpPnz6aMWOG4uPj7fwdO3bY7nwL7dmzR3379i0af+aZZ+wwcuRI/fLLL/WyDwAAAKhfJCq4g16XSLOelFK2S0unSEMnFvUrayzYekB5+QXy9zsW3Hui5TsOacLUxTqUmat2sWE2SaFlY2dSRik5mdKKD6T5r0iHttX1pgIAAKAhWPuFNPtp5/NzXpBa9K+/bTna7di83zbojdlblZyRUzQrNjxQo7rEaeaOfM3+cp+dFhMWqEmjOuiKwa1sNTZPRhUxAAA8k+nmobyuHo5PPmjTpo1L3QADAACg4SBRwR34BUgn3yl9dYs09wVpwAQpIEQ9WkQpMthfaVl5Wr0nTX1aRstT/bx+n276YJmO5Oard8tovT1ugGLCyyipm7FPWvSmtPjf0pFDzmmB4VJORp1vMwAAALxY4mrpsxudz4dMlPpcXt9bpBm7/HXjN9lyqEXJGenSrMXmSYDtOu36Ee004aS2Cgvi5xwAAAAAAAA8k2ffou9Nel8uRbWUMpKkZe8W3Vk0pJ2zqsK8LcnyVP9ZukvXvrvEJimM7NRE064bXDpJYd966YtJ0nPdnXe1mSSFRm2kM5+WrvpPfW06AAAAvFHmQemjK6Tcw1K7U6TTHq3vLVJ+gUOPfLVWFd1nGBbkp5/vOkU3n9qRJAUAAAAAAAB4NBIV3IV/oHTS7c7nc56TcrNKdP8wb/MBeRpTzu31WVt01ycr7YnXC/u20L/HDVBo4NE7v0y5t22zpQ8ull4dLC1/T8rPkRIGSpe8K928TBp8vRSZ4OyvtyJmvunXFwAAAKhIfp70yThnt2vRraU/TZH86r8ywaJtB7U31fkboDyHs/O1aR+VxgAAAAAAAOD56v+MHI7pe5U0+xkpfY+04n1p4LUa3iHWzlr8+0Fl5eYrOMAz+p8tKHDo/75Zp7fnbrPjfxnRTvec0UW+vj5Sfq605nNp/kvS3pVHX+EjdTlbGnaL1Gpwmf31KrOCZA2TpGCWAwAAACryw9+dybIBYdLl06TQxvXaXskZ2fp65R69Pfd3l5bfl15xMgMAAAAAAADgCUhUcCemKoCpqvC/u6U5z0t9x6pDXLiaRARpf3q2lu9I0dCjFRbcWU5ega2i8OXKPXb8b2d31bUnt5Oy0pzdWix4TUrb5VzYP0Tqe6U05CYppn35KzVJCCQiAAAA4ESs+FBa8Krz+QWvS/Hd66U9j+Tk6/u1ifp8+W7N3pRsq4+5Ki4iuFa3DQAAAAAAAKgLJCq4m35jpV//KaXulFZOk0//cbb7hy9W7NG8Lclun6iQkZ2nG95bqjmbk+Xv66NnLu6t89s5pO//Ji19R8pOcy4Y1kQa9BdpwAQpzL33CQAAAF5g11Lpq9ucz0f8Vep2bp2+vUlGmL/lgD5dvkvfrU7U4Zz8onm9E6J0bp/memPWVpugXFbago+kplHBGtS2fitAAAAAAAAAADWBRAV3ExAsDb9V+u4+Z8JCnys0vH2sTVSYuzlZd57eub63UErZabthyHc4tGZ3mg5m5qhxaKCaR4fo/75Zq61JPgoNjNd7ZwWr/9aHpa8+lQrynK+N7SQNnST1utS5rwAAAEBtS0+Upl8p5WdLnc+STrmvTtrc4XBo7d40WznBxPP70rOL5rVsHKIL+rTQeX1bqH2TcDutRXSIbnx/mU1KKJ6sYMaNh87pJj/TlRoAAAAAAADg4UhUcEf9r5bmPCulbJdWfaxhHS6wk1fuSrUVC8KD/Os3SeHl/lJetvwk9Tpu9vOScoP9lB3XR+Ezlh6b0eZkadjNUofTJF/fut5qAAAANFR52dL0q6T0vVJsZ+mCN2o9Ht2dckRfrNhtExQ2JmUUTY8KCdAfezXThf1aqF+rRvLxKZl0cEaPZnrtqn565Ku12puaVTTdVFIwSQpmPgAAAAAAAOANSFRwR4Gh0rBbpB/+Lv36jBJ6XapWjUO142CmFm07oFFd4utv2zIPOE/2ViBA+QrYt1Ty8ZO6XyANmyQ171tnmwgAAABYDof0zZ3SrsVScJR0+TQpOLJK3TUs2nZQ+9KzFBfh7HahvIoGqUdy9b/f9uqz5bu1cNvBoumB/r4a3TVO5/dpoVM6x9nxiphkhNO6NXX5fQEAAAAAAABPRKKCuxowQZr7vHRwq7T6vxreoat2LMrU3M31m6hgunswlRQqU9DzEvme+qAU3bIOtgoAAAAow+J/S8vfk3x8pYvelmLau9xMM1bvLVXZoNlxlQ1y8gr0y4Z9Njlh5vp9drzQkHaNdUHfFnZZU0mhKkxSwtD2MfxJAQAAAAAAvEBVboZpSKjB766CwqWhk5zPZz+tYe0a2afzthyo181aszvNpeVWt7yKJAUAAOD1Jk+erIEDByoiIkJxcXE6//zztWHDhhLLZGVlaeLEiYqJiVF4eLguuugiJSUllVhmx44dOvvssxUaGmrXc/fddysvL6+O98bLbPtVmnGv8/noh6WOo6uUpHDj+8tKJCkYialZdvrLP23SA5/9pkGP/6jr31uq/61OtEkKneLDdc8ZXTT33lH66PqhunRgqyonKQAAAAAAAMB7mPNMJz35ky7/1wLd+tEK+2jGZ6zeq4aOigrubNB10rwXpQObNDJvrqQIrdubpgMZ2YoJD6qXTTqYmVOjywEAAHiyWbNm2SQEk6xgEgvuv/9+nX766Vq7dq3CwsLsMrfffru++eYbffLJJ4qKitKkSZN04YUXau5cE99J+fn5NkmhadOmmjdvnvbu3auxY8cqICBAjz/+uNyNR2SAp+yQPhknFeRJPf7k7FatCvtnKik4yphXOO2Z7zcWTYuPDNJ5fVrYrh26NouQj4+btQUAAAAAAADqReHNMMefZyq8Gea1q/oVVe5siEhUcGdBEdKQm6Sf/6HIhc+pa/yTWpd0WPO3HtAfezWv++1xONQheaZLizYODaz1zQEAAKhvM2bMKDE+depUWxFh6dKlGjFihFJTU/XWW2/pww8/1KhRo+wyU6ZMUdeuXbVgwQINGTJE33//vU1s+PHHHxUfH68+ffroscce0z333KOHH35YgYHuE1e50h1CvcvJlD66Qso8IDXtJZ37klSF5AGThHF8JYWyjOgYq7+MbK8h7WLcL1EDAAAAAAAA9aqym2F8JDv/tG5NG+y5Jbp+cHeDrpeCoqT96zUhZnX9df+QuksF712ghNWvubR49xaRtb5JAAAA7sYkJhiNGze2jyZhITc3V6NHH+t2oEuXLmrVqpXmz59vx81jz549bZJCoTFjxigtLU1r1qyRu/hl0VK9/MF/1Thtnbr7bCsaYtLW2elmfr1zOKQvJkqJv0mhsdJlH0qBoVVahakU4YqL+idoeIfYBvtDEgAAAAAAANW/GcYh2flmuYaKigruLiRaGnKDNOtJnXHgXf1Vf9e8zcl1e7J3xYdyzLhHvtnpynb4K8in8v6S/Sh5CwAAGpiCggLddtttGj58uHr06GGnJSYm2ooI0dHRJZY1SQlmXuEyxZMUCucXzitLdna2HQqZpIbalH9oh4Z+O0ZfB+WWu0z2twHK77hMfo1aqd7MeU5a86nk6y9d+p4U3bLKqzDdWdTkcgAAAAAAAGhYcvIK9MPass/rHe/LlbvVMT5cseFBamioqOAJBt8gBUYoInWDxvgt0+8HMrU75Ujtv296kjTtcumLm+STna7lBR10bf59yvetpPywf5AUGlP72wcAAOBGJk6cqNWrV+ujjz6q9feaPHmyoqKiioaWLat+Qb4q1mzapiCVn6RgmPlmuXqz8Xtp5qPO52c+KbUeVq3VdIoPV6Bf+T+TfI52dzGorbNqBgAAAAAAAGBs3Z+hyd+u07AnZurtub+71CjTFu3UwH/8qItem6fXZ23Rlv0ZDaYxqajgCUIbS4Ovl379p+4O+VIzMvrbqgoXD6jFE9Kr/yt9c6d05JBy5a9nc/+k6QHn61/XDJZf1KW2z998h0NrdqfpYGaOGocG2u4ebCUFk6RQjbvXAAAAPNWkSZP09ddfa/bs2UpISCia3rRpU+Xk5CglJaVEVYWkpCQ7r3CZRYsWlVifmV84ryz33Xef7rjjjhIVFWozWcHEezW5XI1L3iT991pn0bz+V0sDrqnWajbvS9c17yxRTn5BmfMLO3l46JxudPkAAAAAAAC8Qn6Bw3Y/YLrDNBUkzc0ZdHXpuqzcfP1v9V59tGinFhbrxiE2PFBHcvN1ODu/3NdGBPmrdUyoVu9J09Lth+zwxP/Wq11smE7rFm+Hvq0aee3fg0QFTzFkorTgdbXP3aw/+K7QvC0JtZOocPiA9M0d0trP7egGn7a6JesvSovspOkTBqljfITpddkmIvhJ6tWi5jcBAADAUzgcDt1888367LPP9Msvv6ht27Yl5vfv318BAQGaOXOmLrroIjttw4YN2rFjh4YOHWrHzeM//vEP7du3T3FxcXbaDz/8oMjISHXr1q3M9w0KCrJDXTFJqTW5XI3KSnVWActOlVoOkc58WqpGN2Q/b9inWz5crvTsPCU0CtHVw9vorV+3lehLsGlUsE1SOKNHsxreCQAAAAAA0NDVR8LAjNV79chXa0uc/zCVJDn/Ubl1e9M0ffFOfbpsl9Ky8uw08+c6pXOcLhvYUn/oEqeZ65J04/vL7DxHsdcW/lWfvriXPc+0N/WIflybpB/W7dP8LcnamnxYb8zeaoeYsECd2jVOp3VrqpM6xCok0Fyh9Q4+DnN21YuZu8tMOdzU1FR7stej/fCgNPcFrShor+sDn9TCB0bLpxonYcu1/hvpq1ulw/vl8PHT644L9M+sc9U2LlrvXjNIzaJCau69AAAAvCBmu+mmm/Thhx/qiy++UOfOnYumm30JCXHGTjfeeKO+/fZbTZ061e6bSWww5s2bZx/z8/PVp08fNW/eXE899ZQSExP15z//Wddee60ef/xxt2i//N3L5fevUypdblWnSep19k1SVB1lsxYUSB9dLm2cIUU0l67/RYqIr9IqzM+ht+Zs0+PfrlOBQxrUprFeu6qfYsKDuKMAAADUKE+Ned0F7QcA8Gb1kTBg3tNcRD/+QnHhlUdzfoSbNUo6nJ2nr1bu0bTFO7VyZ0rR9BbRIbpkQEtdMjCh1PXUqv5t07NyNWvjfv2wNkk/rd+n9KNJEEZwgK9O6tBEp3eL16iucYoND3K75JeqxGwkKniSjP1yPN9TPnlHNDbnHj142y3qEBd+4us9kiLNuFdaOc35NpEdNPbgBC3La6MBrRvp3+MGKLo+7o4DAAANhqeedCwvaXTKlCm6+uqr7fOsrCzdeeedmjZtmrKzszVmzBi9+uqrJbp12L59u01oMFUZwsLCNG7cOD3xxBPy9/d3j/bbs0J6c6TLixc0aifftidJbUZIbU6SImupAsHMx6Rfn5H8gqQJM6QW/ar08uy8fP3ts9X6ZOkuO26y3R89r4cC/X1rZ3sBAECD5qkxr7ug/QAA3qo+EgbMBeyTnvypxMXz49/bVJacc88or+12oCo3uazclaqPFu2wSQqHc5xdOfj7+tiuGS4b1MpWOqionaqbMJCbX2BfZ5IWzLA75UjRPHNasn+rRkVdRLRrEu4W1TJIVKhmY3iE7x6Q5r+spQUdteaMTzR2WMnywlW2+Ufpi5ul9D32a2dtu6t14bqRynIEanTXeL18RV8FB3hPCREAAOCevC5mq2PukqiwsaCF2vvskZ/PcT+tYzo4ExbanOx8jDiWpFFtaz6TPnEmg+iCN6Tel1Xp5ckZ2brhvaVasv2QLcv3t7O7afzwNjVbsQwAAKAYYt4TQ/sBALyRKwkD8ZHB+vrmk5RX4FBWbr6y8vKVlVugbPu8wE7LLnzMPTrv6DKFy2eb50eXMcP+9GytT0yvdPumXTdEQ9vHqCFKzczV5yt2a9qiHSXaqm1smL3Z5aL+CZVWNKjphIl1e9OdSQvrErV6d1qJ+e2ahNmEBVNtISk1WxM/rJ9qGSQqVLMxPEJ6ovKe6yX/gmw91+xp3f6X66u3nux06fu/SUun2lFH43b6JOEB/XWRsxyJ+YD93/k95O/H3WQAAKD2eV3M1kATFX47+0vd+n2qWh9epZP81+n86K1qnL5ePo6CkgvGdJTannwseSE8rvyVpuyUMg+UnHZgs/TFRCkvS+o3Tjr3xSrtzto9abru3SU2Cz0i2F8vX9FPIzs1qdI6AAAAqoqY98TQfgAAbzR3c7Ku/PdCuav2sWH6Q5c49UyIUu+EaLWOCfXomzwqq2xgkgHM/I8W79S3v+21CSCGqb55Vo+mtnrC4LaN3aIN9qQc0Y/rnJUW5m85YBNZCpldKjZap9UyqhKzuVZLFu4joqkOdrlccWun6uS9byu/4LqqH0TbfpW+uElK2WFHCwb9RY9lXawpi/bZ8ZtHddAdp3Vyiw8ZAAAA3EBojOQfJOVll7+Mf5B6dmynT7rG6c5PmuqxDX312D7pkh4RerhPmkJ3z5d+/1Xau0o6sMk5LHnb+drYziUTF8JijyUpvNy/4vdd+ZE04m4puqVLuzJjdaLu+HiFMnPybQa86eas/XGl8QAAAAAAgHuobsl8d1RQ4ND2g5latStFK3em6rfdKVqxM8Xl15uuBkwV9OAAXwX5+ykowFfB/sfGzaNzvp+C/J3Pg4rP8z82b8fBTL0wc1Ol77kl+bC2zNlWNB4Z7K9eCdFHExei1DMhWs2jgqt1TbGu/7YVdYUwoE1j/XfpLk1fvFNbkw8Xze/SNMLe3H1B3wRFhQbInTSPDtHYoW3skJaVq1827LdJCz+uTdSR3ONuGirG5C+YNjBtX9/VMnwcJjXEi3ljpm3eoV0qeL63An3ytPXsj9Vu4BjXXpiTKc18VFr4mnM8qpVy/viSblkQoRlrEm1fJo+c290e0AAAAHXJG2M2r2u/siobHJ/McDRZwPzw/tevW/XUdxvsj842MaG2akGPFlHSkUPS9qNJC2ZIXH30J1IxTbo6ExeiEqQfHqx8266fJTXvU+Ei5mfPKz9v1jPfb7Tjpu/AV67o53Y/MgEAgPci5qX9AAA1d2G5tsrW19QFdHMeYk9qllbtTNHKXc6khFW7UpWelVetbXr/mkE6qWOTGu9yIjE1q1T3AIbZW9Otwd1ndNbq3al229fuTVPO0QoDxcWGBzqTF1pEqXfLKPVsEa0mEUFu9bc173fj+6W7QihkCsznH9210EA/ndu7uS4d2FJ9WkZ73I3dny7dpTs+WVnpci9c1kfn9WlR4+9PRQUv598oQT9FnqVR6V8qcO7TkiuJCjsXSZ/dIB3c4hzvN06pIx7W9dM3aOG2RAX6+er5y/rorJ6198UOAAAAD2aSEFysWuDr66O/jGxvs9FvmbZcvx/I1IWvztMDZ3fV2KGt5dPlLMkMRuZBafs86fc5zsSFpNXS/nXOoYaYvhfv/s8qfbVyjx2/elgb/e3srnRzBgAAAK+/excAPFV5F5bNhXUz/bWr+tXaBe3qXEDfn57trJRgkhJ2OZMSDhzOKbWc6UKge/NI9WoRZS/u92gRqXFvL1ZSWvkJA6ZM/9D2R6tP1hDzf83sk2lL8x7F37vwP95j53e3+3zJAOf5IJOksDEp3e6bSbwwVSE2JKUrOSNHP63fZ4dCpsqCqbpg9rGXeWwRXXSzSF39bU2yiOm6Ie1Irv7++ZpykxQMk6TQq0WkLh/cWuf0bq7wIM/tlKBZdIhLy5kYp75RUcFDTf9xni749Y8K9MmXxs+QWg8te0FTJvfnx6V5L0qmb+CIZtK5Lykp/mSNe3uR1iemKyLIX2+M7a9hNfwlBwAA4CruLvPe9kvJzLFJAqb0nDGme7yeuqh3+ZUMTOKCTVqYI236Xjp0rLxgdSoqmB+517+3xP6INiUSHz2vh64Y3OqE9gkAAMDbYjZPUFftV1937wIASt/tX/y7+HhNI4P0619HKcDft8aarrwL6IUX7gsvoKdm5mrV0QoJJjnBPJa1reY8ROemEccu1idEqVN8hALM7ftlvK/KSRioraSMmvi/Z24OMZUWftuVqpVH22LL/gyVVc/fVNw01TZnbdxfbmUJs8/xkUH6743DdSQ3X4ez85RxdDDPneOlpx+bn19iel6B6x0LTLtuSL13hVBX1TJM8suce0bVSiJmVWI2EhU81PrENK1/+VKd7z9PBS0GyvfsZ0ovlJEk/fiwtG+tc7zXZdKZT2hLRoDGvrVIu1OO2NIrU8cPVPfmUXW+DwAAAIU4aevd7Wcy2KfM/V2T/7dOufkOJTQK0UuX91XfVo0qfuGeFdKbI6udqLByZ4que3eJ9qVnq1FogF67qr+GtPP8H5wAAMAzuXvM5u7qov1cvUAFwP00pEoo3riv5rxBUlq2vVvfDL9u2q9ZG5Ndeq0p0x8W5G/vgA8L8lNYoL8igs1z/2PTA53znMv4Kzz42PTC1wUH+OnUZ2fZi7vlCQ7wVVxEkHYcPFJqnukdoEOT8BJJCV2bRdr1unuiXE0fUyZJwHQXUZi88NvuVG0/kCl3V1tdIdSHGfWY/ELXDw1A5+AUtfNfZJ/77l5c8QncsCbSH5+Xuv5RK3amaPyUeTqUmau2sWF6d8IgtWwcWncbDgAAgAbH9OU34aS2GtCmkSZ9uFw7Dmbq4tfn654zuuiak9rariJq2hcrduuv/1llS/x1ig/Xv8cOVKsY4l4AAACUf5HGXCAq685DM81ErGb+ad2aevwFQcDbNKRKKN5wMfvg4RxtSEzXpn3p9tEkJpjHtHLusK9MZk6+HUzXC7UtK7egKEmhdUyoMynBduEQpe4tok6ouwDz9zP/Y+ojCcW8R01WEjDtYG4UKX6ziKm4aRIWpi/eqa9X7a10HSbxw1SEL0wuKUw6KUo2MUkpxZNRSsw/lpRiBtMVx1VvOa+puntXCDXFHE8mGeH474umbvbd6LkdbDRwPpkHFSgXvrTbjpT+9LYUFqufN+zTTe8vs6VSzJfmlKsHKiY8qC42FwAAALA/4L++5STd99/f9M1ve/WPb9dp/tYD+ufFvdUoLLBGWqigwKFnf9iol3/ebMdP7RKn5y/ro4jgcrqaAAAAACR7YaiiEuMmWcHMN8t5Q1lowFvu9q+rvu7dQX3ua3USJNKzcrUxKaOoSoIzISFDyRllJxSY48R0DWC6SggJ8NN/l+2udLveuKqfujSLPFrm/1h3AMceTTcAuUVdBRRNz3HOM90PmGnmupmrbjqlva4f0U7RoTVzHqM2EwbciWmvkzs2kb+vr0uJCh9eO1hDa6jLerMec7xW1hWC+b7yJmfUY/KLq0hU8HanPWKTFP67dJfu+e8q2xfLiE5N9NqV/WwWEQAAAFCXIoMD9PIVfTV0YYwe/Xqtflq/T2e9+KtevLyvBrY5sR+E5uTC7dNX6Pu1SXb8hpHtdfeYzm71AwwAAADuyZzAd8V3a/aqY3y4YrkBDKj3u/0bUiWUyvbVeOjLNfYOdnPtx9/Xx1Y3rIsECVMuv12TcGd1hH3p2mirJGTY7sfL07JxiDrHR6hTfIRNTOgYF6F2TcKKukkw+ztvy4FKLyyPrqG/rXm/Xzbs0zXvLKl0WXOxvTaSFBoKc6HctaSBmkvYMMeI+R4yx6tPOV0hmPme/j3hickvXKn2ej56c/YWPf7tejt2Qd8WevKiXgr0963vDQMAAEADZU6WXDWktfq1Ml1BLNPW5MO67M0FuuO0TrpxZPtjXUGExkj+QVJeBeUbzfzQGO06lKlr31mi9YnpCvTz1RMX9dSF/RLqbJ8AAADg2Vwt9zx13nY7dGkaYS8IDmsfo8HtYhQVQgUvNGw1ebe/qZSXmZuvjKxjd8cXvxO+8O749XvTvL4SSk5ega1E8NXKPRXuq5GUlq0+j/5gn5uf1eaivx38fRUU4Kcgf9+j05yPReP+zmlBxZYtnG+uJT3+7boKEyRu+WhFudsUHxnkTEYwSQlNnY8d4sIrvZG2ri8sm/Wc0jmuQd51X9fqK2nAU7pCaGh8HA5HWZ83r5GWlqaoqCilpqYqMjJSXmPPCunNkZUu9u+uU/V/y52ZXded3Fb3ndm1VvoABgAAOBFeG7PVEU9uP3Oi6W+f/abPV+yx4yd3jNWzl/RRk4ijXZSl7JQyD5S/gtAYLU4J0w3vLdWBwzn2zrY3x/a3SRAAAADuxJNjtobQfuZu2pOe/KncC1RGWKCfEhqFaENSRonp5nRr9+ZRNmnBXAw1lcI8pZptXZfpb4j7W19tXJfvW/j5qehCemSwvyac1FZHckwp/sLEA2dZ/uNL9h/Ocb0Mvys6xIVpWPvYorv3O8VFKCo0wO3aOPVIrtbuSdPavWlFj5v3pSs33/0v44UH+dnvQdu+NdjOdV2lozDhRuVcQPemrkTqW13/bRvq/z13j9lIVPDyRIWzs/+hNY62euCsrrpuRLs62TQAAICq4qRtw24/kzv9ydJdevCL1crKLbBJCi9c2kfDOlTeF+HHi3fqgc9/syduujeP1L/GDlDz6JA62W4AAICGFLM1hPZz9QLVgYxsLdx2UPO2JNvS5Fv3Hy6xHlNyvXfLaGfiQrsY9WvdqKicuTtdPKmvi0T1pT72t77auDbeNys3X/vSspWUnmUTepLSsrQvPds+mlL/6xLTVdPM8W8ShMKD/G3yjxnM88LxjKxcfXe067+qahoZbO/w7xQXXnSnv+nWJTTQv9bb2PwGNt0iHJ+UsOvQkXKTPBIahdplKvPOhIHqnRCt7LwC+zczv7HN47HxfGXlFSi7+GOJ+SWX33Ew01YurIz5DX9e3xaqDXw3ei+SBrwTiQrVbAxvTFQ4L/dxXf2n83RBX8reAgAA9+W1MVsd8Zb2M+UsJ36wTJv2Zch0pXnzqI669dSO9uTU8T9e+7dupKdmrNe/52y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YvXhE0Y9TphcFUQFAmNHmTeBtvXen2ZqZZhWrm5WLy2rLAAAgke1Ya1a/o1nl2jHVDoubjArLly930vZr5nsoAwU0i14byzspSAHxpVKlSvbGG2/Yhg0brKxR+QyVrwAAJBjFkU7OzhbUdqBZuWSz2q3d6xsXRHXVgGKr3sg937/bbPcmNiAAAAAAAACA+AlUUGmHdevWOSUFUlJSnNPPP//slC/QZZUH2Lt3r23ZssXveWvXrrW0tLQClzt8+HAnosM7KSAC8UfZNVR2oqxR2QtlkwAAJJil48w2LzYrX9mszUD3tlotlAzLbM8Ws60ro72GQPC0H6dmR09voa0NAAAAAAAAwCxu8lAdffTRNn36dL/bVOqhQ4cOdttttzmZEDS7fPTo0XbGGWc498+dO9eWLVtWaDmA1NRU5wQAABAzpmRnU2hxaG46rvKVzKo3Ntu2wmzNdLMajaO6ikCxVKlnlr7NbOtysyY92XgAAAAAAABAGRc3gQrVqlWzLl26+N1WpUoVq1OnTs7tl19+uQ0bNsxq167t1LwYOnSoE6Rw8MEHR2mtAQAAiil9h9nMT93L7QeZJSXl3lentRuosG6WWfvj2LSIr0CFTQvd/RcAIqVyHbOUVLP96QU/JrmC+zgAAAAAABBRcVP6IRiPP/64nXjiiU5GhcMPP9wp+fDxxx9He7UAAACCN+tTs307zWo0NWtykP99tVu75+vnsEURX6rWd88pWwIgkmo2NRsyyeyqn80q1nRvO/IOs7PeMkvr6l5v0MWsRhM+FwAAAAAAIixuMirk56effvK7XrFiRXv22WedEwAAQFya/JZ73uZos5QK/vcpo4IQqIB4zKgg21ZGe00AlMVgBWVM2LPFvd68n1nVBmYDR5i9fabZqr/d396eF0Z7TQEAAAAAKFMSKqMCAABAXNu40GzZeLOkcmbtT8x7v5dRQY/LzIz46gGlDlTYvpqNCCDyti53z8tXNqtYy71cvbHZQVe5l7+/w2znRj4ZAAAAAAAiiEAFAACAWDHlbfe8UU+zWs3z3q/bkpLN9u4w27Is4qsHlBiBCgCiacvy3DI0yeVzbz/gfDcIUNkWvr09aqsHAAAAAEBZRKACAABALMjMMJvyrnu53bFuVoVAyRXMajR1L6+dHtn1A0pDg4OyY71ZRgbbEkBkbc0O7qtS3ywpKfd2BS0ceYd7efr7Zot+4ZMBAAAAACBCCFQAAACIBQvHmm1fZZZazaz10QU/rk52+Ye1syK2akDIMirs3222i/TqAKKYUSFQw25mnU93L395g9n+9MiuGwAAAAAAZRSBCpHuHFk1peCT13kSJuPHj7fk5GQbNGiQ3+1LliyxpKQkmzJlSlhfHwAAFGLKW+55qyPNKtYo+HFKUS3r57A5ET9SUs1Sq/vXigeASPHKJVVNy//+ftebVapttmmR2S8P87kAAAAAABABKZF4EWQHKTzTq/DZGerAHTLJrGZ2SucQe/nll23o0KHO+apVq6xRo0Z8NAAAxIJdm8zmfOVebndC4Y/1Miqsnxv+9QJCqWoDs/Rtbgr2Jr3YtgAixwuQqt4w//uVzejwf5p9N9zs9yfNup1tVrctnxAAAAAAAGFERoVIUYrbolJI6v4wpcLdsWOHvffee3bttdc6GRVee+21sLwOAAAogRkfmWXsdbMlNDqg8Md6GRU06zMzg82N+Cv/sHVltNcEQFnjZS+sVkiwfptjzJoe7P4efz7ULCsrYqsHAAAAAEBZRKBCaajjYu/O4E6qxxsMPS6Y5RWz0+T999+3Dh06WPv27e2CCy6wV155xbLoeAEAIDZMftM9bzPALLl84Y+t0cSsXHm3zbBxYURWDwgJrzb8NgIVAETQ/r1m21e7l6s3LvhxSUlmR/7LLDnVbNl4s8lvR2wVAQAAAAAoiyj9UBr7dpk9EOLyCa8cF9zj/rXKrEKVoBercg8KUJDjjjvOtm7daj///LP179+/pGsKAABCYc0Ms9VTzcqlmHUoouyDKJChVnOzjQvM1s4wq9eOzwHxoYoXqLAq2msCoCzZtkKzDMySK5hVqVv4YxXIcNCVZuOfMfvhDvd3uXLtSK0pAAAAAABlChkVyoC5c+faxIkT7dxzz3Wup6Sk2Nlnn+0ELwAAgCibkj1js2mfwlNS51f+Yd2s8K0XELaMCgQqAIhC2Qcdg4rKWiQHXGBWu5XZ7s1m394e9tUDAAAAAKCsIqNCaZSv7GY2CMaaacFlS7jsW7O0bsG9dpAUkLB//35r1Ch38ENlH1JTU+2ZZ54JejkAACAM6ainvedebne8WVKQMaR1WpvNV6DCHD4SxF9Ghe1ror0mAMqSrctzj0HB/M4qmOHIO8w+usz9jVbgQqvDw76aAAAAAACUNWRUKA3VsFT5hWBOKZWCW6YeF8zy9NpBUIDCG2+8YY8++qhNmTIl5zR16lQncOHdd98t1SYAAAClMO9bs10bzSrXMWt5WPDP8zIqbJjL5kf8ZVTYuc4sIyPaawOgLGZUCFbD7madTnMvf3mD2f708KwbAAAAAABlGIEKCe7LL7+0zZs32+WXX25dunTxO51xxhmUfwAAIBbKPrQ60qxC1eCfp4wKsnmJ2f594Vk3INSq1HPP9+8x27WB7QsgMrYsc8+rphXvef2uN6tU22zTIrNfHg7LqgEAAAAAUJYRqBApmimZklr4Y3S/HhdCKvswYMAAq1GjRp77FKjw119/2bZt20L6mgAAIAjb15rN/8G93OHE4m2y6o3ddkPGXrMN89jciA/aZyvW9E/FDgDh5h1vqhUzUKFidbPDbnEv//6k2QbVXAIAAAAAAKGSErIloXA1m5oNmeSmdy6IghT0uBD64osvCrzvoIMOsqysLOeydw4AACJk2iizrAyz+p3M6ncs3nNVY7tWK7P1s83WzjBL6xyutUQMe/bZZ+3hhx+2NWvWWPfu3e3pp5922ncxrWoDsz1b3BnOTXpHe20AlKWMCtUaFf+5bQeazf7cbPkEs8+Hml36TdBlGAEAAAAAQOHIqBBJCkJodEDBpxAHKQAAgBilAMHJb7mX2x5jVq4EsaO1W7nn62aFdt0QF9577z0bNmyY3XXXXfb33387gQrHHnusrVu3zmI+UEG2rYr2mgAoCzIzzLatdC9XL0GggoIS+g83S65gtmy82eTskk0AAAAAAKDUCFQAAACItBV/uSUbklPN2h5XsmXUaeOer58b0lVDfHjsscfsyiuvtEsvvdQ6depkL7zwglWuXNleeeUVi2nVvECFFdFeEwBlwfbVZpn7zZKScwOliqtGE7MDr3Iv/3CH2a5NIV1FAAAAAADKKko/AAAARNqU7GwKLQ4xq1KvZMvwMipsIFChrNm7d69NmjTJhg8fnnNbuXLlbMCAATZ+/Pg8j09PT3dOnm3btjnnRz3yk6VUrBKBNc4yy9jnzEy+KDPFrjOzr/6YZff9PToCrw2gLOueOdv+a2Yrs+rYGa8ok0vJyjYkZ/Wz1+0za7N7uX3+8BX2QPnBIV9XAGXD/j07o70KAAAAQMwgUAEAAJTcluVmuzYWfH/lOpQ2CrR3l9mMj93L7Y4vea3rOq3d881LzfbtMStfsWTLQdzZsGGDZWRkWIMG/rODdX3OnDl5Hj9y5Ei755578ty+bnu6ldubbJE0r1wtswpmNfdvsDW790T0tQGUPX3KrXSOOcsy6tqaPRmlWtatSZfbx6l328lZo+2t7f1sYlbHkK0ngLIj0yd4FAAAACjrCFQAAAAlD1J4ppfZ/kI621JSzYZMIljB1+wvzNK3mVVLM2ver+R7X9U0s/KVzfbtMls/26xRj5IvCwlNmReGDRvml1GhadOmVr9aqqVUjFCAS5Y7QLg3s75ZhlnT5I2WVqGEQToAEKQOmRudpC6bUupZWsXypdpuq6yzfZxxjJ2e+YM9lPqyXVjuQduXVLplAogh5VLcMjFhtn9Phi0P+6sAAAAA8YFAhWLKzMwMzycBhAn7LICwUSaFwoIURPfrcTWb8kEEln1ofbRZSikGiZWJoXZrs7XTzdbOIFChDKlbt64lJyfb2rVr/W7X9bS0tDyPT01NdU6BxtzS36pXr24RtamN2VN3W7PkzTbh6tZm5SKb0QFAGTNmr9kss0F9Otug4waWfnm7DzR7ure12LXKfu31i1mfa0OxlgCibec6t6yaAonDTAGjNe4L+8sAAAAAcYFAhSBVqFDBqf27atUqq1evnnM9qaSpmoEIyMrKcmpYr1+/3tl3tc8CAKJMZRoW/+LWyO5wUumXV6dVdqBC3nT/SFz6Te/Vq5eNHj3aTj311JzARF0fMmSIxbRqjdzzjHSz3ZvNqtSN9hoBSGTbV7vnNUIUMFmpltnx/zH76HKzSa+btTvBrFbz0CwbAAAAAIAyhkCFIGmgt2XLlrZ69WonWAGIF5UrV7ZmzZo5+zAAIMqmvuueN+zuztoqrdpt3PMNBCqUNSrlcPHFF1vv3r3toIMOsieeeMJ27txpl156qcW08hXNKtc127XBbPsqAhUARCZQoWaz0C2zyxlmk980W/ST2dj7zE57yc1yBAAAAAAAioVAhWLOXtOA7/79+y0jw62zC8QypYVOSUkh+wcAxAKVj5r8tnu57bGhSXnvBTtsmFf6ZSGunH322U7WpDvvvNPWrFljBxxwgH377bfWoEEDi3k1GruBCttWm6V1i/baAEhUWVlm29e4l0OZ9UBBCSc+bvbcwWarJpvN+cKs48mhWz5QFgOKdm8p+P5KNc2qNYzkGgEAAACIEAIViknlHsqXL++cAAAAgrbkV7Oty8wqVDFrG4I62VKntXu+dYXZ3p3uslFmqMxDzJd6yE/1xmarp5rtWBftNQGQyHZvcsvMqNxSzRCXZ1Cg4OG3mY251+y3x81aHO4OpgIofpDCW6ebZewt+DHJFcwu+JhgBQAAACABkQseAAAgEqZkZ1PQYEbFGqFZplLop1Y3y8o0WzszNMsEIhGoIDvWsq0BhL/sQ5U6ZuUrh375/Yaa1W1vlr7N7LdHQ798oCxQJoXCghRE9xeWcQEAAABA3CJQAQAAINz2bDWb9bl7uf0JoatlreXUzs6qsHZGaJYJhFv1Ru75TjIqAIhAoELVhmbJYUgmmVLB7OSn3MtzvzZb8WfoXwMAAAAAgARGoAIAACiZynXMUlILf4zu1+PKupmfmO3f7aaebtw7tMuu08o9XzcntMsFwqVGE/d853q2MYDw2bbaPzgqHJodbNbzYvfy2AfMMvaF77UAAAAAAEgwBCoAAICSqdnUbMgkswMvd6837GHW5ED3crdzza762b1fjyvrJr/lnrc52p2BGUq127jn6wlUQJzwBg13bYj2mgAoCxkVajYL7+scc49bimnrMrO/Xg7vawEAAAAAkEAIVAAAACWnIIRNi93LzfqatTvOvbxuhlmjAwhSkPVz3XTQSclm7QeFfm+rnZ1RYeP80C8bCIfqjd3znRvMMjPYxgDCHKgQ5oDJSrXMjnvQvTzpNbPNy8L7egAAAAAAJAgCFQAAQMll7Ddb9od7uVFPN1hB1s50ByFhNuVtdys06R2eWZ11Wrvn21aZ7d7CFkf8ZFTI2Gu2a2O01wZAogcq1AhzRgXp+g+zlv3NMveZjb3PLCsr/K8JAAAAAECcI1ABAACU3JqpZvt2mlWoala/g1nVBu4M/6xMs/nfs2UVyDF1lLsd2h5nllQuPDM5K9V2L6+dxTZH7EtJNatSz728fU201wZAIlKgQKRKP0hSktlJj7vHt1V/m835MvyvCQAAAABAnCNQAQAAlNzSce55g85mKRXdy15Whfk/smUX/Gi2Y61ZxZpmrY4M3/bwsiqsnc42R3yVf/AGEgEglNK3m+3d6V6u3SIy21aBmoff6l7+/XGzPVsj87pAPFMb2ZIKf0xyBbNKehwAAACAREOgAgAACEGgQld3NqFvoMKSX0h9POUtd1u06m9WsVr49rTa2YEK6+eG7zWAcAQqKJAHAELNC4LSIGhq9cht337Xm9Vt5wYp/Ppo5F4XiFfKQGJZZsnlzU7/n9lVP5td9Flu8MIJj5pd8LFZtYbRXlMAAAAAYZASjoUCAIAyIDMzN1ChUY/c2xv1dFMf71xvtnqK/31lyc6NZnO/dS+3HxTe18oJVJgd3tcBQqWGF6hA6QcAYQxUqJZmVi6C3R4pFcxOesrs1ePM5n5l1vAAs/od8z5Os8MZeEVZp8wnvz/hXj7oGrOu//APfF42zmzzEjfgFwAAAEBCIqMCAAAoGQ2K79nilnxo0CX3dgUpNOrlXp73fdndutPfN8vcZ1anrVla1/C+llf6YcOC8L4OECrVG7nnOzewTQGEL1BBxxpv4DNSajQxS8ruavlphNn7F+Q9vXU6pW+ACc+b7d5kVrOZ2eG3+H9XO2QH+S75le0Uh1q0aGFJSUl+pwcffNDvMdOmTbPDDjvMKlasaE2bNrWHHnooz3I++OAD69Chg/OYrl272tdffx3BdwEAAIBIIFABAACUjJdNQTMFK1Txv88r/7BobNncullZZpOzyz60Heimsw13XWzZuY6BX8SH6k3cc2VeAYBQ25YdqFCjaeS37a6NZlmZhT8mY6/Z7i2RWiMg9qybbTbjA/fygHvdLCO+Opzgnq+ZZrZ7c+TXD6V277332urVq3NOQ4cOzblv27ZtNnDgQGvevLlNmjTJHn74Ybv77rvtxRdfzHnMuHHj7Nxzz7XLL7/cJk+ebKeeeqpzmjFjBp8OAABAAiFQAQAAlMzS391zZVMInK3YPDtQYcVfZnu2l70tvHqq2doZboBC++PD/3qp1cyq1Hcv63WBWEdGBQCRyKgQjUAFAIVTIM/PD7rnbY4x63hS/kG49Tu5j1lYRgOf41y1atUsLS0t51SlSm5g+9tvv2179+61V155xTp37mznnHOOXX/99fbYY4/lPObJJ5+04447zv75z39ax44d7b777rOePXvaM888E6V3BAAAgHAgUAEAAJQsY4CXUaFhz7z312xhVjXNLX1QFrMqTHk7N7OEtkMkeOUf1hCogDhQo7F7vmuDWWZGtNcGQKIGKiilPIDYMutTN7C2fGWzgfebJafk/7gOJ7rni3+K6OohNFTqoU6dOtajRw8nY8L+/ftz7hs/frwdfvjhVqFChZzbjj32WJs7d65t3rw55zEDBgzwW6Yeo9sBAACQOAhUAAAAxbdpkdmOtWblyps17Jb3fmVYaN7Pvbzgx7K1hfftMZv2vnu57XGRq41dOztQYf2cyLweUBrVGuamP1eadAAIJQIVgNikMg7jsmfEH3ydWb32BT+2wyD3fOUks327I7N+CAllRxg1apSNHTvWrr76anvggQfs1ltvzbl/zZo11qBBA7/neNd1X2GP8e7PT3p6ulNWwvcEAACA2EagAgAAKD4vm4I6FytWz/8xyiYgi8rYLKi5X5vt2WJWpZ5Zi8Mi97o5gQpzI/eaQEmlpOaWK/EGFAEgFDSgqd9hqdWCbQrEkvHPmKVvdUs7HHpj4QG9DbubVW9stn9Pbsk5RM3tt99uSUlJhZ7mzHEDpocNG2b9+/e3bt262TXXXGOPPvqoPf30004gQTiNHDnSatSokXNq2pTyPwAAALGugPxqAAAAQQQqNOhsllRA3GOTA82Sks22LDXbuDC3NEFZKfvQ+iizCpUj97re9t04P3KvCZRG9UZmO9e5gQoajACAUPCCnypUMatch20a7m29OzsoJD+VauZm0AHWTHfLPsgx95ulVit8myiIQVkVJr7olpJr418GAJF188032yWXXFLoY1q1apXv7X369HFKPyxZssTat29vaWlptnbtWr/HeNd1n3ee32O8+/MzfPhwJ0jCo4wKBCsAAADENgIVAABA8Xmzmhr2KPgx6nxM62q2eorZvG/N+g5O/C29bZXZwjHu5fYnRfa1a7XMTam7bbVZdQYGEONqNHGPD9v9O6EBICSBClXTzJLLszHDuZ3fOt0t4VOQ5ApmF3xMsALMMjPMfh7pbon2J5i1Py64reIFKiwb7y6jXDJbM0rq1avnnEpiypQpVq5cOatf382m1bdvX/v3v/9t+/bts/Ll3eP0Dz/84AQx1KpVK+cxo0ePthtvvDFnOXqMbi9IamqqcwIAAED8oPQDAAAonq0r3CwJyqTQqGfhj/XKPywYXTa28tR3zbIyzRp0MavXNrKvrewNmqHuzVgDYp3SOcsOAhUAhCFQQb+JhaWVDxdlcVB5m8JoAF/ZBuKZMikUFqQgur+wjAsoel9eN7vgUzyVTprxoVuerEJVs2PuCz7goPkhZhVrmO3Zarby73CvJUJg/Pjx9sQTT9jUqVNt0aJF9vbbb9tNN91kF1xwQU4QwnnnnWcVKlSwyy+/3GbOnGnvvfeePfnkk37ZEG644Qb79ttvnbIRKilx9913219//WVDhgzhcwIAAEggZFQASmvLcrNdGwvvqKpJXTwACWTpePe8TpuiO9kVqPDH8+4sqP17zVIqWMLKyjKb/JZ7ue1As3JRaGbVbu1mdVg3y6zdwMi/PlAcXmDNrvVsNwCho6xCXtaWaNB/vyGTcv8jahb4a8eb7U93093XakFJBJStjBX6Lkx4zr18yA1mddsE/1xlRWl7rNn0980WjTZremDYVhOhoYwGo0aNcgIL0tPTrWXLlk6ggm8QQo0aNez777+3wYMHW69evaxu3bp255132lVXXZXzmH79+tk777xjd9xxh/3rX/+ytm3b2qeffmpdunThowIAAEggBCoApQ1SeKaX2+lU4Lcs1e2oIlgBQKKVfWjQuejZUPU7mlWsabZni9nS38xaH2UJa9kEs02LzFIquh2q0aBAhSW/mq2fHZ3XB4rDG0TcSaACgBDyZpnXbBa9zar/fr7//5SBSkGbe3e4bSMglBkrYj1Q4fcn3X2/Xnuzg0tQCk7lHxSosOQ3s8OzopMpBUHr2bOnTZgwocjHdevWzX799ddCH3PmmWc6JwAAACQuSj8ApZ0ZUFiQguj+wjIuAEC8WTrOPU87oOjHqjxE0z7u5fk/WEKbkp1NocVhbjadaKjT2j1fPy86rw+UJKPCzg1sNwChD1SoEUNZ7bxSWWtnRntNgMhSuYa5X+lPgdnAB8xSqxR/GW2ONktOdb/bG2jjAgAAAImEQAUAABC8HevNNsx1LzfO7nQvSvN+7vminxJ3S6fvMJv5qXu5/QnRm+mljAqycb5ZZmZ01gEIVvXG7vmuDWYZ+9luABIno0Igr83ktaGAsiBjn9nPD7qXO51q1vrIki0ntZpZq/7u5QWjQ7d+AAAAAKKOQAUAABA8pS2Wmi3MqjYI7jlND3bP180y274mMbf2rM/clLaaIe5lkIgG1b1WFov07WZbV0RvPYBgOKmqk9yBjN1knwIQAkqF72Vp0W9irGjUwz1XiaiiMvIBiWLaKLNNC80q1jA75p6iS8YVpsMJ7vnSwksFAAAAAIgvBCoAAIASlH3oHHxnY5W6ZnXaupfnfZeYW3vK2+55m2PMUlKjtx567RpN3Mtrp0dvPYBgpFQwq1rfvbwtewY0AJTG9rVmluWmiQ82oDISarcyq1jTLHO/G7gJJLoda80mvuhePnSYWa3mpVteu+Pd4EaVfti2MiSrCAAAACD6CFQAAADBW/q7e57WvXhbzSv/sDAB07VqdqS2izIZtB8U7bXJLf+wbna01wQomrKQ+KZqB4DS8I4l1RqYJVeInW2pklBe+Yc1MywhVKpZ9DbW/Xocyp7fHjPbt8usQWezPleXfnn6Tjc50L28cEzplwcAAAAgJsRNoMLzzz9v3bp1s+rVqzunvn372jfffJNz/549e2zw4MFWp04dq1q1qp1xxhm2dq1mUwAAgJDYs9VsTfYs/ca9ivfcZn3d88W/mGVmJtYHMuWd3LTOsZBmug6BCogj1RvnzrwEgJAFKjQyKxdj3R2NsgMV1s9KnPI9F3xsdtZbZpXruLf1usw9V0aLM15z73fK/CBs1LaOxVJxC350g3iPHWlWvlJoltvxxNh9zwAAAABKJMb+uResSZMm9uCDD9qkSZPsr7/+sqOOOspOOeUUmzlzpnP/TTfdZF988YV98MEH9vPPP9uqVavs9NNPj/ZqAwCQOJb94aZT1gxor7xAsBoeYJZSyWz3ZrNVf1vCyMzIDVRoM7B0tXdDnVFhw9xorwlQNO9YQqACgFAGKnhBULHEy6iwPoF+nxWEoBJfuza6afmPuNUNWshIN9u/kyCFcGeskD9fNJvxkcWMjL1mPz/kXu56plmLw0K3bC9z2eqpbgA1AAAAgLgXN4EKJ510kp1wwgnWtm1ba9eunY0YMcLJnDBhwgTbunWrvfzyy/bYY485AQy9evWyV1991caNG+fcD4SNOmGKqkWu+70ZJgCQCGUfGnQxK5dSvOcmlzdr0tu9PO87SxiLfnLr5KZWM2szwGKCF6iwcWHiZa9A4pZ+2Lk+2msCIBFsW+We12pmMcfLqLB1uVn6DksYa7NLWdRuaVa1vlmrI93rS8dFdbUSJmNF/+Hu9cp1za4YY3bVz+7pyp/Mup7t3vfTA2ZTR1lMmPym2dZlZpVqmx11Z2gzm9RtY1a3nVlWhtsGBwAAABD34iZQwVdGRoaNGjXKdu7c6ZSAUJaFffv22YABuQMEHTp0sGbNmtn48eOjuq5IcDWbmg2ZZHbyM/63N+mT24Gg+/U4AIh3Xodzg24le36zfolXV3bK2+55yyNipwZzzWZuIInqAm9eHO21AQrnzXomUAFAKGxf457XiMFAheoNzaqlmWVl5g7uJwKvLFhadzezVOuj3Osr/ozqaiVMsMK+Pe7ltC5mTXqZNTrAPTXuYXb6f80Ovs69/9eHzf5+I/qBQn+97F4+4jazmsXMwBaMDtnlHwhUAAAAABJCXAUqTJ8+3cmikJqaatdcc4198skn1qlTJ1uzZo1VqFDBatb0HyBo0KCBc19h0tPTbdu2bX4noFgUhLBni3tZac2dHWtrbgcCQQoAEsHeXbklGxr3KtkymvV1z1dPNtudAOlaVcZi9pfu5fYnWMxQ9goFK/gOHgCxHqiwa0O01wRAIpV+iNX/YI16Jd7v85rsoIsmB7rnrbMzKmyYn10SAqXilfJK65r3vqQks2MfMDvkRvf6uCfN/vxf9Db4r4+Y7U83a9TDrNel4XkNL1BBgTB6LQAAAABxLa4CFdq3b29TpkyxP/74w6699lq7+OKLbdasWaVa5siRI61GjRo5p6ZNY7RDA7HNmy3SPHu28NYVZllZUV0lAAj5cS5zv1mVema1WpRsGRo0qN7ELDPDbMGP8f8BTf/QrcFcq6VZwx4WU+q0cc/XzY72mgCFq+FlVNholrGPrQWg5NRO2bnWvVyzeWxuSc2Cl/WzE2ebr5vpH6igkj71OppZFuUfQmG9F6hQQEYzBSsMuNvNYCB/PG824bnI90cs/sVs8c9mSclmxz5oVr6IEpklpSCIqg3M9u9m/wIAAAASQFwFKihrQps2baxXr15OgEH37t3tySeftLS0NNu7d69t2ZI9qz3b2rVrnfsKM3z4cNu6dWvOafny5WF+F0hIy7MDFVof7Z7v3WG2g1rLABLIsuxSSg06m6VUKPlymmdnVUiEQAWv7EObY0q3TcKhdqvEGghBYqe1tiSzzH3MvAVQOioho2BIlT+qEYaU86HgZaXaMM8SwqZFZvv3mJWv4pYm8LTJ/l+8fELUVi0haNtuXuJeVrbGgihY4ch/mR11p3td5RfGPRW5YAWtp7IpyAHnmzU9KHyvVa6cWYdB7uVFY8P3OgAAAAAiIq4CFQJlZmY6pRsUuFC+fHkbPXp0zn1z5861ZcuWWd++2QMiBVAZierVq/udgGLZutJs+yqzpHLuLJJKtd3bNy9kQwJIHEt/d88b5JN2tjiaZWee0YyreMo8s2W52aopuacZn5itmuw2pep1yE01HStqt/afhQfEKpUqqVrfvRxr3yMA8WV7dtnHKvXNUsI0mzsUs8G9410ilEXwSljU72hWPrsMom/5h5WT4qu9F2s2LjTLyjCrWNOsRhBZQg6/2WzgCPfy5Dfc4IFIbP9Jr5ptW+lmXjvqDjeYIJy8QIVl48yyMsP7WgAAAADCKsXihDIfHH/88dasWTPbvn27vfPOO/bTTz/Zd99955RsuPzyy23YsGFWu3ZtJ9hg6NChTpDCwQcfHO1VR1kp+6DU3xVruLWWd28y27gotx47AMSz/XtzM8c07l26Zen5mumozsz1c9yO7XgIUnimVwF1cDPNvh5mllzB7IKPs2eHx1CgwqbFZvv3maWUj/YaIYRatGhhS5cu9btN2cZuv/32+NzOajvtWJs7yAgAJeEFO1VLMyuXHJvbsFItN+uRMhGsnpY7oB/vgQqNe7qz+n0DU5NT3SwXGxeY1W0btVWMa17AqbZfsKUU+g1x26Xf/NNs2iizjL1m/Ye7EyvCYcsys0mvu5eP/LdZtQYWdi0ON0utZrZ7sxtArP0PAAAAQFyKm4wK69ats4suusjat29vRx99tP35559OkMIxxxzj3P/444/biSeeaGeccYYdfvjhTsmHjz/+ONqrjbIUqFC/g9sh5tVa3uI/gAAAcWv1FLcOrIKxStvRXKGyWcPu7uV531lc0IzHfIMUfKgTeLd/CaqoUsprdVJnpLsDBEg49957r61evTrnpCDduOW1nXYQqAAgBIEK3jElVjXKHlRdO8Pi3trsQIUmB+Vt73nlvpb8Gvn1ShQb5rjnDXzKagSjz1VmJz3pllaa+bHZmPvcsiihpmwNvzzklm9SuYfu51lEqOSaSq/JwjGReU0AAAAAZTtQ4eWXX7YlS5Y4pR4UtPDjjz/mBClIxYoV7dlnn7VNmzbZzp07nSAFBSsAYbfiL/e8XsfcWYGymUAFAAlW9qF+59CkUvbKPyykrmzYKHCuVovEGQhBHtWqVXPaut6pSpUq8buVvLbT9rXRXhMA8WzbKve8RjOLad7sb2WWimfp2802L3EvNz0w7/2tj3LPV0yM7HolkvXz3HMvyLc4el1idupzbiaF2Z+b/XinWeb+0K6fggSWjXezpR37YPBZH0JZ/mHpb5F7TQAAAABlN1ABiNl06JppLGnd/TvblQIRABLB0nHueVrX0CzPK4uzfILZvj2hWSbyqtPGPV83i62TgB588EGrU6eO9ejRwx5++GHbvz/Egw+R5LWddq2P9poASISMCjWbxUdGhQ3Zg9DxyguErN7IrHqTvPe3Pto9XzOt6MxUyEsZEDbOL3mgghxwntnpL5klJZvN+9bsu3+ZZewLzdbeu8vs10dygyIiXX6h7TFm5cqbbV1utiF7OwEAAACIOwQqAKVNdbl/j1sfsU7r3I4a0R9mAEiETtJlE/w71ktL5SMq1XaPn4t/Cc0ykZdqYPvWN0bCuP76623UqFE2duxYu/rqq+2BBx6wW2+9tcDHKyPZtm3b/E4xxUvTvnNDtNcEQDzbnl0+pkZTi2kNu7mz3HdvMtu60uLWmuyyD2ndzJJT8t7foLNZlXpukIKXhRDB08QHtZVTKprVbV/yLdf1H2ZnvuZmPVg42uzbW0MTrPDnS2Y715lVa2jWf7hZUpJFlErStTzcvUz5BwAAACBuEagAhKTsQ4fcdOjebBJ1lCnjAgDE+2y59G1m5Sub1e8UmmWqc97LqrDgh9AsE3nVzg6g20CgQjy4/fbbLSkpqdDTnDlumvBhw4ZZ//79rVu3bnbNNdfYo48+ak8//bQTkJCfkSNHWo0aNXJOTZs2jc2MCgQqACiprKzcQIVazWN7O1ao4v5/lLXTLO4zKjTunf/9Grj2siosy87OheB57TdNiKhQuXRbrtPJZme9ZZZcwQ0S/mpY6bJcbFxoNvVt9/LR/2dWpa5FRYcT3PMlBD4DAAAA8YpABaA0VvzpnnsdTVK1vlsbPHMfWRUAJE7ZBwUplK8UuuV6gQoLfwrdMuHPy/SzeanZPlIux7qbb77ZZs+eXeipVavsLBkB+vTp45R+WLIku1Z4gOHDh9vWrVtzTsuXL4/R0g8bQpeSGkDZsmujWUa6GwwZ66UfpHEv93ztrPgNDFmTHajQpIBABWl9lHu+Mvt/M4LnZcSq3zk02Qo6HG927iiz5FQ3cOTLG8327S7ZZ//zf9ysay0OM+t6lkVN+xNyt9X2tdFbDwAAAAAllk9+PgDFDlRo0CX3NgUpVG1otm2F2aZFuQNFABCPlv7unqd1DW1K12YHa6qd2cZ5ZluWm9WMsRneiUCpeFMqme3f7c7KU6ppxKx69eo5p5KYMmWKlStXzurXr5/v/ampqc4pZlVLc48HmfvdwUbnOgAUw/bV7nnlOm4WqFjXuKfZ5DfN1s+2uKQyh+lbzZLLu++lIK36587AV9acaM28j+eMCmqDh0qbo83O/8DsnbPNVkw0+3yo2UlPFS9jw7xvzFZNcgMejn3A3QeiRWU3VZpu1d9mi0abdT8veusCAAAAoETIqACU1I71Zps1czHJrcuZX63lTYvZvgDil2ZMeRkVGvYI7bIr1crNRjPvO4tpGvRQZ2xhlEq3Uk2LKZpVWrulf3pmxL3x48fbE088YVOnTrVFixbZ22+/bTfddJNdcMEFVqtWLYtLGuSo1sB/sBEAisM7dihILzkO5mNocFU2zDfLzLS4s2a6e163vVmFagU/Tsd2L6h/6W+RWbdEaYN7GRUadg/tslsdYXbhx24JktWTzT67zmzvjuCem77d7Pcn3MsHXhnaIIqS6niie76I8g8AAABAPCJQASiplX+555oFrEGs/FIYb1nK9gUQvzbMc2c3axA+HLPxm/dzzxeOsZim43z/4e7lirXMTnvJrfPre7rgY3dwJNbUaeOer4vTGZvIQ5kRRo0aZUcccYR17tzZRowY4QQqvPjii/G9tby2E4EKAEpi+5rsY0mj+Nh+DTq7QZAaIN4ch8Hta6fnBrKWK6JbySv/sGxC+NcrUexcZ7Znixt0Go5gALXBL/zMLLWa+1l+co3Znm1FP++PF9z/BjWamB1+S2izrZVU+0HuuYIu0oMMuAAAAAAQMwhUAEpq+UT3XDOCVe7Bl9dBprrgABDvZR/qdTSrUDX0y2/WN/t1fnPr3MYqzXSc+q57uesZborj+h39T7EYpCC1W7nn6+dEe00QIj179rQJEybYli1bbPfu3TZr1iwbPnx4bJd2KFagQvZgIwAUx/ZV7nnNZvGTScYbgF4zzeLOmuxMTU16B1duQFZOcjMFoGheNoWazc0q1gjPFmt6oNnFX5pVrOmWIPnkKrPdWwpfp+nvu5cH3GtWOUayONVr77Z3VT5q8c/RXhsAAAAAxUSgAlBSK/50zzVAVVBn+9ZlbF8A8csr+6BZf+GYMaVUwEo7u2er2bI/LGYt+MGtE1y+ilnnMyyu1G6dmx0DiGVe22nHumivCYB4tC279EONphY3FPgo62ZaXNm322xjdruiaZ+iH9/0YLOUima7NxE4GSwvwFR9DYGTIkKp0QFml35tVqm22cb5Zp9cabZrU97HZWWa/fyge976aLNOp1jM0H+UDl75h7HRXhsAAAAAxUSgAlASmvm78m/3clr3QgIVVrB9AcQnzXhbkp1RoVGP8M0mbHKQe3nB9xazxj3tnrcbaFalnsUVr/TDluVme3dFe22AgtXIbjvtWs9WAlB8XtkYlWuKF416+s+ej6dBdP0fVvlDL3NTYcpXNGtxqH+2LhTOCzANR9mHQApIvvQbt427aZHZh5e4wcoqG+adJr7oZv5QuZJ+Q82SU2LrE/QCFZT1cv/eaK8NAAAAgGIgUAEoCf1Z37fTrHxlszptCw5UUP3GPdvZxgDiz5albhrlpGS3/nC4NOvnni/8yWLSqilmS351Z7N1PSc2avEWhzqdVbYjK8P97QJiPqMCgQoAShBc6ZWNUar8eMuosHGhWcY+ixtrprvnDbqalQ+y7FDro9zzFdnlE1E4L3il4QGR2VL1O5hd9p3bbty20uyLoWbvX5B7+vMl93EZ6Wbvnu0GwMYSlSCpUtfto1k+IdprAwAAAKAYCFQASlP2oW47s/KV8t6vOpIKYpDNi9jGAOK37IOOc5XCVBtXmvV1zzVLa9dmiznjn3HPWxxmVieIWYOxRoEVdbLLP6zNricNxKKcIM8N0V4TAPEmfZs7QCm1WljcUMC7ggk1+BtPJZq89oQXaFGcQAUFOezbE571ShR7trnBwpLWLXKvq/biydlZxAqzP92dkBFLFFDc/gT38qIYDX4GAAAAkC8CFYCSWPGXe16vff6za3Vb9Sbu5U2L2cYA4o+Xmjeti1lSGJsL1Ru6sx81439+jJV/UPmeGR+7l7ueHd7tEE61swMVyKiAuCj9sDG+ZhYDiJ2yD5VqmaVWs7hRrlxueS0vS0E8BSo0OTD459TrYFatoVnGXrIqFMULWqmW5p4iSZ9RvPLKPyz9zc2yAgAAACAuxGmPOxBlXspKpbssqsN9M4EKAOLQ0vHueVr38L9Wc6/8w2iLKX+84AZQaBsUZ9ZgrAYqqKY0EKuqprnBQJn7yaoAoGSBCjqOlEuJr63ntS/WzrS4sGOte9LxujiBCgrkb320e3lZdtYu5G9DdtmHuh3MkuNsf46mlke4WS0V8KhMbQAAAADiAoEKQHHt3pw7y6FhIakYqzdyzzcvYRsDiC+q87xpoXqVzRpFYIDeK/+gVK2xMgNKaXcnve5e7nJG/A18+PJKVmyYH+01AQqmwZiqDdzL27IHHQGgOIEK+v+VX7a7WOa1s7zB6Vi3ZkZuEGTl2sV7busj/bMTIn/rs/eFtM5soeIoX9GszQD38oIYC34GAAAAUCACFYDiWjkptyPM61DPj1f6YcsytjGA+LI0e6Zb7VZmVepGppM+uYI7Qy9WZhT+/YZb87pGU7M22TMA41XtNu75tpVuAAYQq6o3zg2WAoBgecFN+s2ON15GBQW379ttMW/t9NyA/XLJxXtuKwUqJLkZBznOF8wLWolEVrNE41v+AQAAAEBcIFABKC5vBki99oXPsPUyKhCoACBeAxUadC5+J3RJlK+UW6N53jcWdRn73bIP0vk0s5SKFtc047FiTTPLip1AEKCwslk7CFQAUIKMCjXjMFBBwRWV67qlptZmZyuIZd46Ni5G2QdPlTpmDbMH35f+Htr1ShT70802ZZeObHRAtNcm/rQbaJaUbLZlqdmmRdFeGwAAAABBIFABKK4Vf7rndTsG19m+bZVZZibbGUD8BSo0jGAHqVf+YeFPFnWzPjXbutwd3O94iiWEOq3d83gYBEHZ5WVU2LEu2msCIB4DFWo0s7ijUhVeVoVY/43O2Ge2bpZ7uWkJAhWk9VHu+fI/QrdeiWTjQjdoJbW6Wc0W0V6b+FOpllmLQ93LC8dEe20AAAAABIFABaA4FHDgZVRQusvCVMvOqLBvl5vOHADiwa5NZuuyZ9037hW5123Wzz1fMdFs7y6Lmqwss3FPu5c7DDKrpEwECUC1pGX9nGivCVB0oMJOAhWAiAzur5td8Mkb/I8H3rrWisNABa8Elmi7x7KNC9wZ/xWqulm3ShOosPJvsyyC+fPYkN1Oq9vOrHyqRVzlOmYpRbyu7tfjYpXa77Lkl2ivCQAAAIAgFJK3HkAemxaa7dlilpxqVq9j0X/gq9Qz27neTTtYvSEbFEDsWzYhNxVxtbTIvW7tVrnHzEVjczsZI02piFdPMUuuYNblTEsYXkaFdQQqIIZ5ZbN0HAAQ3oH9t043y9hb8GP0O3jBx2bVYvw/jIIb92x1L8frDHQvo8KGeRbT1k53z+t3KnlZrKZ9zMpXNtuz2c3O0KBLSFcx7nn7QFqUtovKpwyZZLZrY8GPUZBCLJdZaX+C2Te3mq2dZbZjvVnVetFeIwAAAACFIFABKI7lE93zum3NKlQJrsPdC1RocQjbGoiGLcvju7Mt0ryawZopVy4lsqmPm/czm/WZ2fwfoxeoMO4Z97zNALMaTSxheBkVNBsSiFXed27nhmivCZDYdm8pPEhBdL8eF+uBCl42Bc3yr1zb4jqjwtYVbtBFxRoWk9bMyF1ftdtKIqWCWcvDzOZ9Z7bkdwIVAq2f656nFZG9MZz0vyie/xtp3dO6m62ZarZojFm3s6O9RgAAAAAKQaACUBwr/nTP67UPrnOmehOz1VPNtixlOwPRClJ4ppebpraw7CeaORTPHXKhtHSce64Ovkhr1tcNVFj8k0XF+nlm875R1ITbqVnSTvhYpIwVsmONW94jXgdzUDZKPyi4bP9ed0ALAIIJVFAWqOTy8bmtNONbmay2LneDAWI1wH1tdqBC0wNLt5zWR7uBCir31efqkKxaQsjMyM2o0DAK7fBE0vFEN1Bh8c8EKgAAAAAxrly0VwCIKyv+cs/rdy5eCuPNS8K3TgAK5gx2FRKkILq/sIwLZUn6dje4Spr0jvzrN+ljllTOzUKzKQoBXhOezU1LXFR5n3ij2ZmV6/oPNACxpmoD9xiQlWG2i6wKAIoTqNAwvgMMvfIPa6ZZTFJ2DS/4vslBpVtW66Ny2yMq3QGXAlX273HLaiRaOzQa5R9k1d9m6TujvTYAAAAACkGgAhCs9B1m62YWb4aDNzNwyzK2M4D4KG+jAcKqae7MvkirWD03EGy+MhtEkFLNTx3lXu56llm5ZEs4dbLLP6zN/i0DYk1yijsrWrativbaAIinQIV4z4zllX9YP8dikvc/WO1DLxi/pOq0cTMPZu43W/5HSFYvoco+qFxXhcrRXpv4phJ2NZubZewzW/JrtNcGAAAAQCEIVACCtWqyWVamWZV6uQEIRfEep3qjABAvZR/UuRet9MnN+7nnC8ZE9nX//J87i61uO7cERSJSx7esi9FBEMC37bRjLdsDQPCBCjWaxffW8jIqbMgerI41a6a752ld3KCy0lDmizbZWRWWjS/9uiUK77Ov3zG+s4PEAm2/Die6lxePjfbaAAAAACgEgQpAsFb86Z7Xax/8AF6N7M727WuKTj8PIHp2rGHr+wYqpHWN3vbwggSW/u7OgoqEfbvNJr7oXu58hllKBUtIXkaFWJ2tCfgGKqjtBABF2eYFKsR5RoWGB2h01WznerPtMRio5ZWNanJgaJbnlX9Ymf0fG7kZFRp2Y2uEQodB7vmyCW72DgAAAAAxiUAFoLiBCvU7Bb/NlH2hXHk3lfrmKNRbBxCcd841+/Bys1VTyu4W27fHbOVf7uXGvaO3HjrGplYz27sjcrPsVPJh10azqg3M2h9nCcvLqLBxfrTXBCg6UGHnOrYSgGKUfojzjAoqf1W3jXt5bXb2glihrII5gQoHhWaZLY8wSyrnlkjctjI0y4xnWVm5GRWCLTOJwjXtY1aptvufghIjAAAAQMwiUAEItuPAC1RoUIyZxup8qd7Qvbx5EdsaiFmZZjM+NHvxCLPXTjSb/4P7vS9LVk4yy9jrduh5A9rRUC7ZrOnB7uV534f/9TIzzcY/617udIpZhaqWsGq3dM8VlLGDQWDEKC8bFfsoED6VapolF5E9SPfrcbFMGet2bXAv125hca9RL/fcCwqIFVuWmqVvN0tODd0geuXaZo16uJeX/B6aZcYzZdLYvdntPyhOfwMKphIl7Y93Ly+k/EMojRgxwvr162eVK1e2mjXz/51YtmyZDRo0yHlM/fr17Z///Kft3++f2eKnn36ynj17WmpqqrVp08Zee+21PMt59tlnrUWLFlaxYkXr06ePTZw4MaTvBQAAANFHoAIQbOeMOg/KpRQvo4LvzMBNi9nWxbVluTvDvaCT7gdCYcC9Zq2PdjsHl/xq9vY/zJ472Gzy22WnbItX9qFB5+iXPvDKPyyKQKfi/O/cDAMVqph1Ot0SmoIwqqa5l9fE2CAI4KneyD3ftZ5tAoRLtYZmAx/IvlLO7IyXzXpc4F5t2tfsrLfMLvjYfVws25FdIiGlolmVBhb3GveKzRJNa6bnlkBU1qtQUdtblk8I3TLjlfeZ12we+wFC8Vj+QSXlyloQehjt3bvXzjzzTLv22mvzvT8jI8MJUtDjxo0bZ6+//roThHDnnXfmPGbx4sXOY4488kibMmWK3XjjjXbFFVfYd999l/OY9957z4YNG2Z33XWX/f3339a9e3c79thjbd06Aq4BAAASSUq0VwCICyuy06FrlrHScpYkUEHBDgieghCe6VX4IHFKqtmQSWY147wmLcKnch13PylsP9KMwcY93Y6sbavM/n7DbM6XbofhZ9eZjb7HrM81Zr0vNatUK3E/LXXgSSzM4vICFdbONNux3qxqvfC91rhn3PN2x5lVqWsJr05rsx1r3NmabbLrQwOxpHoT93xn9ixpAOEx6xP3vOOJZp1Pd9tDk98y27nWrH7H+Cr7oNJNyeUt7qk9KhvmuYOqSUkWE7wMD8qAEMp1an2U2S8Pma2a7JaXUMBwWaXPXOp1cLOLITRaHekGMqmc1LpZbkA2Su2ee+5xzvPLgCDff/+9zZo1y3788Udr0KCBHXDAAXbffffZbbfdZnfffbdVqFDBXnjhBWvZsqU9+uijznM6duxov/32mz3++ONOMII89thjduWVV9qll17qXNdzvvrqK3vllVfs9ttv55MEAADxRf9fd28p+P5KNWN/skCYEKgQjcFXpVwubFCNQdfY45V90CyS4nageIEKmwlUKBZ9T4qaye6ke93IdwYF0/FUwSzaT6Z/ZDb+KbO0bmaH35p/I0AzefvfbnbwdWbT33dPmq2nYIVfHjbreZF7X63mibXVM/abLc9Oo9kou5M8mqrWdwPDNi10Mx54szxDTR3jS38zS0o263p27AwIhFOdNm5QSqzN1gQCSz847YC90c/wAiSidbPd3wL9rzn0JrNy5cwaHZD7n2XfbrPylSxuAhXUftN7iHcNurgZ/PZsNdu63KxmM4sJXhamJgeGdrlNervZntK3uVkbQlVWIh6tn+ueN+wW7TVJLBUqm7U52mzOV2YLfiRQIULGjx9vXbt2dYIUPAo+UAaGmTNnWo8ePZzHDBgwwO95eowyK4iyMUyaNMmGDx+ec3+5cuWc5+i5BUlPT3dOnm3btoX43QEAAJTwv+tbp7tllwuSXCE+MhuGAYEKkcQM8fgPVKhfggj8nIwKlCkAohasoNO093IHaouaKajMKQdeYdbzYrM5X5hNedts8xKzP14wm/iSWadTzA65Pre2brxbM9Vs3063s7h+B4sJzfu5gQoLRocvUMHLptDycLPaLa1MqN3KPSdQAbFKM6MVPJSV4ZZ/8NpRAELnr5fd87YDzRpmByjUaGpWqbbZ7k1uIIM3uz+WKROWbyaWeFe+ojuQunqq2zaLhUAFBa2oRJY0PSi0y1YWDLXB5n7tliAry4EKG7IDFRRQjdBqP8gNVFBw8iE3sHUjYM2aNX5BCuJd132FPUaBBbt377bNmzc7JSTye8ycOQUHXI8cOTIn4wMAACgBZv2HhzIpFBakILpfjyuDgQoJMO0gQWeII3bs22O2epp7uSSdJ97MwG0rqIsIRJOX1aQ4P/bqQFU65PM+NDvxCTcwQYNnMz82e7G/2WuDzOZ9b5aZaXFNncOiznGlR40FXvmHxb+E59ip4LGZ2WmvuymbQhlpEilThWxcEP/7LRKTUl5XS/MfhAQQOjr+LxprZklmhw7LzUSgrEJeVgWlSI8H290Br4TKSOhltlobI5+B9gWVZVB5LC/YMZRU/kGW/2FlVvp2s20r3ctlOVgjXFTeTe38TYvMtiyL9trELJVSSEpKKvRUWIBArFAGhq1bt+acli9nwhAAAMWe9f/+BQWfdL+X2Q4IETIqAEXRjJbMfW5t+polSPfuzQTcvdlsz5bErnEPxLIt2YEKJZmdq877Foe5J3XYTnrd7eRf8pt7qtverN9Qs25nmaWkxl+pn5xAha6xU/5AMzy1LXdtcEs0hHpmp7JjKOhEr5MomTGCUauFOziltNIaBK6ZILNQkVh0nNagjUrvAAitv15xz1v1d1Pv+9Jv4sIxZutnx8dW9zrIYqU9FQpq70x6NXYyH62dkdtGVABvqCktv2if04B9ajUrczbMy80oVAZnT4VdlTpuALTK3Swcbdbr0mivUUy6+eab7ZJLLin0Ma1aBReslJaWZhMnZpcVzLZ27dqc+7xz7zbfx1SvXt0qVapkycnJzim/x3jLyE9qaqpzAgAAJcCsf0QJgQpAsGUf6rUvWZ1kdbakVndrb25cbNaEQAUg4jQj38uo4GU5Kan6ncyO/487yDv5LbM5n7vpWj8fYjb6XrODrzFrd6zZS0cVnkVHg/BDJkW/c12z6r1AhVgasNf2adzb7VSc911oAxU0SK9gE+nyD7cedFmhmuM1mri1rzX4QKACYpGO0yt8ZksDCA21hRb84F4+7BY3g4kvL6PChuxU//ESqFAjBkokhDqjgsotZGbk/Ywibc1097xxr/AsX1kaFESpEmvKqtDGv2Z9mbB+bm5/Q3IZapNGUocT3f8UytRGoEK+6tWr55xCoW/fvjZixAhbt26d1a9f37nthx9+cIIQOnXqlPOYr7/+2u95eoxulwoVKlivXr1s9OjRduqppzq3ZWZmOteHDBkSkvUEAABAbCgjeY6BUAQqFFHTvjDVG7nnmxfyUQDRoIwme7fn1mAOBX2vj7jV7OJvzPpc62ZI2LnODVZ46ej4KfWjGWzK9qKSDw26WEzxyj8s+im0y1WQgvYHZcnxUg6XJV7q5nUzo70mQP68zDc71rGFgFDSTH2l8W9+iFnz7N/YwIwKXhaq/Xtie9tn7s89RjjZghJEvQ5mKZXM9u12y3REO9B3TXYJxKYHhe91vLbYsvFWJingWWKtHZ5IOpzgnitId/emaK9N3Fu2bJlNmTLFOc/IyHAu67Rjxw7n/oEDBzoBCRdeeKFNnTrVvvvuO7vjjjts8ODBOdkOrrnmGlu0aJHdeuutTkmJ5557zt5//3276aabcl5n2LBh9tJLL9nrr79us2fPtmuvvdZ27txpl15KVgwAAIBEQqACUJQVf5W+48DrcPdmdAOILM3Skkq1zSpUCe2yK1YzO/AKs4u/MjvyDrNaLWO/c9+Xl02hfsfQb5tQBSqsnGS2JzvQpLQy9rllH6TzabmlOsqSOm3c83UxklYaKKjdpOAvAKGhcipzs2evHjos/5n6NZu5ZeoUBLAuxss/7FjvlnAqV7702bJiiWbUN+zun80gWnascYNqk8qFL6OCb6DCyuz/3WU1o4L3uSP0FMxUv7MbqKXyNiiVO++803r06GF33XWXE5ygyzr99Zf7HVbJhi+//NI5V4aECy64wC666CK79957c5bRsmVL++qrr5wsCt27d7dHH33U/ve//9mxxx6b85izzz7bHnnkEef1DjjgACcY4ttvv7UGDRrwCQIAAJTlQAU1LHft2pXn9t27d/s1OoGEoNTu21a4nTNpXUMQqJA9WIqiaXZ6UQOIul+PA4qimYFe7ddwpflX3V4NfJ/3gZtOOV4oDapXezgpyWKKMh6oVm/mPrNFIepUnPmpO1ijgZgOJ1mZ5GVU8GbwIV+0eaPIy0S1a0M01wJILMompIH9Jgeateqf/2PUDvCyKqydGR9lH6rWN0suQXm+WOYFBUQ785EXKKEARwX7hkuLw8ySks22rjDbsszKlIy9ZpsXuZe97x7Co+OJ7vminxNmC0errfraa69ZVlZWnlP//rm/Lc2bN3dKO2j91q9f7wQcpKT4/w/X4ydPnmzp6em2cOFCu+SSS/K8lso8LF261HnMH3/8YX369Anb+wIAoMwra21xxG+gwj333JOTzsuXGp+6D0jIbAqKwK9Ys+TL8Wb5cLAPXs2mZtf9YVaxlnu912Vm3c51LzfpY3bVz2ZDJrmPA4riZTOpFoHZF76d/LFOKX29jAoNe1jM0bZs1s+9PD+7pnZp3++4p3Jr1VYqxXE9ntVu7Z4rpXRmZrTXJmbR5o2iGk3c850EKgAhsWOt2ezP3cuH3uTO2i9Io+w2zPo58RGoUC0t/+wQ8axxT/+Z9tGiNPneTP9yYUzGqfaYF5zhBdCWFRsXmmVmmKVWc7OyIXw6DMrN1LZ3d0JsadqqAAAgJNQenfSa2Q//xwYN538eTYYO5r97GVTsaaWKkk3KZ8al6o7Vrh3GKPtEmiFeWN1yZojHlhUTc2uFlqYDrFr2zMCty0OzXmXF6qlmeza7M597XOjO/p32rtnWZbmdqECxMiqksb18bVrkNoCUNrlht9jcNqqhPfMjs8UhmP205Fe31nJyqlnXM63MqtXcnbm4d6f73ahNx3h+aPPGQkaFjWb795qlJNhsaSDS/n7DzU6kQMq2Awt/rBdsuXG+xbTtq/wDmxJJo+zgUc20V99BtMpUrckOVFAWjnBrM8D97718gln37OD0ssDLblW3Pb914ZbWzax6Ezdj5tLfzNoeY/GOtioAoExSwPLuLYUPCCs7K4KjrGY/3umOwyB8tq9xy5DJCY+aNentHyjy7e3u/6Ex95nVbZfbL1ZGBB2oUKtWLSdAQad27dr5BStkZGQ4WRauueaacK1nYtDMb80AV6eraMd8+Ri3BuiZr7uz9hXMwAzx2MuoUK9T6ZbjdaCplERGhor2lX7dyoJJr7rnbY4xq1jdDRjxGiTb10ZmdjwSK6NCdRqqfrxZa/Xau9+xWKTOcQ2qKyPNhvlmdduWfFnjnnHP2w7IDSAri5QiW3XINy920zoTqOCHNm8M8Mr0qI28Y51ZzQQciAQiRf89Z37iXj7kBrdUVWG8YGCVrNu/xyylosV0RoUaTROzRJOy+e3ZYrZ+dnQydWXsc19bmh4U/tdrfZTZTw+YrZpilrG/8KwficTLmtGgc+yVYEs02r7KqjDxv2aLxsZ1oAJtVQBAmaX/AG+d7pbPKqzP64KPCVYIJuvsrE/Mfn3MbP9us/KVzfpcYzb+WbOM9MK3b1nNUFtSCkT45WH3cqdTzXpfmndS9IUfm7080GzdLLPPh7jjxcq6VkYE/e/viSeecKJ1L7vsMie9WI0aNXLuq1ChgrVo0cL69u0brvVMHApC8A1EUMeKBgosiRnisUadM6smu5dLO9NYKUn1GauzT7N/CEYJLg3mop/c7aYDuOjgrKAPRfopiKRjdvpGINiMCtWzy7DA5ZV9cDpHw5jStzQqVHVnQK2ebDbv25IHKqgjeP537jGl6zl0BmsQRO2PdbPNOp0c6k8trtHmjQH6w6a2k37v1RFBoAJQclPedjua6ncyax9E27lmc59B8jmxW85KM1KkRjNLyAFVZVXQYKqyGkTjM9gwz+0A1v+veh3D/3p6vxVrmO3Z6ma/8spflJVAhbSu0V6TssELVFg23u0wjtOyMbRVAQBlljIpFBakILpfjyOrQuHB7Jq5r8yzXqm3k59x26S9L8udaO357XGzWZ+6mYpPfYFtW1wzP3YzqamPe8Bd+bdBU6uZnf+h2UtHuuMYX99sdvKzRU80KGuBChdffLFz3rJlS+vXr5+VL182NlDYqSNIAwXqCEBsUU1OBRZUqGZWJ7ued0kp0qxqfTfFulKtE6hQNNVFEtUrrdMm93Z1smrgYtXfBCogOJmZ7mx8UbrPSFBkqb73hUb4prpZdGIho0LD7BTDsap5PzdQYeFYs35DS7aM8dnZFJod7GaQKOt0XF04One2JHLQ5o0RCizT7/2O7MFIAMWnDrpp77uX+11vVj41yEHyA9yA4bUzYzdQYVt2RoVaCZhRQTRQr0CFdTOj919Y6neJTOkJZVBoeYTZ7M/d9mlZCFRQhk2vH4iyhpH7T+EFYq38y6xpH4tHtFUBAECJLRxjNnaE2x5SJsu+Q80O/6dZapX8J1rLiY+7JXnVP7P4J7dEN4L/Tz7hefdy3yFu9ryC1Ghsdv4HZq8cZ7ZyktnY+82OvrtMTLYrdj69I444wjIzM23evHm2bt0657Kvww8/PJTrl/iUelk2LYj2mqDAsg/t3AHFUHS4K1DByaBxBNu7MKrFqtlfoplfvlFmmtEz/3uz1dPYhgiOGlEKGFDGgEjVd1LUrtKMBdZM0yyxb24127fTrMdF0Q1a2rLcDeDQdmkU453BzfqaTXjWnf1UklrNSh0/9T33cpez4nb2VEh5DeP1BErGWpt3xIgR9tVXX9mUKVOcrGVbtuStvbhs2TK79tprbezYsVa1alWnw3rkyJGWkpJAqbK9DDgEKgAlN+1dN42ngtM6nxb88xpmByoo606sDvB6pR9qtrCE5LXNojWhQaWhRAEDkeoYU/kHBSqs+NPKBLXF9f1UX0NpS00iOJqR1u5Ys2nvuZ30cRqo4KF/FgAABC19u1t+YO5X7vXarc1OesqsxSFFt/cr1zY7+k6zL28ym/iSO16j21C0P54zS9/q9sP2G1L04xt2N/vHK2ajzjOb86U7ftz78oTf0sXuzZwwYYKdd955tnTpUqcUhK+kpCTLyMgI5fqVnUAFpblHbPE6SOp1CE3njDrclQVgc3YKehRs9hduiiHNNm9zlP999Tu452uzO8+AoqjGslSpH9k6ywpWyC/N2FF3mH033OzvV8x6XmjWqLtFhQb9RRljYr22mDIgeLOflvxm1ubo4j3/z/+5aa91PFdGBeRmCtq0MK5T34ZTtNq8e/futTPPPNMpqfbyyy/nuV+vO2jQIEtLS7Nx48bZ6tWr7aKLLnKynT3wwAOWMLzAMgUaAShZR9TUUe5lzZIpX4w2kDe7e+P82Nzy+p+Quc8NtlQpxUTkZRRQUGn6DrPUqpF9fe+/VpODIveaClQQZXtK32aWWt0SmtK/ijotK1SO9tqUrfIPClRY+ptbmzmOZ6jRPwsAAIKekDv6Lrd8nv5DKSPCgHvMKtcKfgP2vNjsr1fdMm2/P252zH1s/KKolOKMj93L2t4q7xCM9sebHTvS7NvbzCY852aIVrBtAit2QeprrrnGevfubTNmzLBNmzbZ5s2bc066jpJmVNAse8SU5RPd8wZdQ9vh7g2aomD60ZO2A93aPb68GqmaRbWdwQsEwQsOqtYgNgZj2xxj1upId3D4k6vM9hdRWy3cZR8adImN7VIYNaKVVUHm/1C85+7d5Ub7SuczzFIqhH794pEGdsqVd0scESwZU23ee+65x2666Sbr2jX/9sf3339vs2bNsrfeessOOOAAO/744+2+++6zZ5991glySBg1skv17OS3Pl9qB2m2e0Enb7Y5yq7p75vt3eGWGux+dvGe65V70P8WZTKKNepgk8p1zcpXsoSk/47V0tzsEV4ZhkjZvdktvSNND4zc69Zq7s7s0ntemh1Qm+gdl9KgU1wPlsed1ke7WSx0HFmfHSwSp+ifBQAAhdJ/uV8fM/v0arftowl1Z79lduITxQtSEPUdn/Cwe3nuN2armURaKAXE/vKQLrhjAe1PKN72Pvgas4Oudi//eJfZ6qmWyIqdUWH+/Pn24YcfWps2PjXjUfpAhV0bzHZtLv4BAuGxc0N2iQYzS+sW2g73rctCs7xEpTTkmt2ggclO+aSoVeSZosi2rXDrSnYo5kEeZc+W7ECFqmkWE9QR2X+4W2tKHZQ/jTQbcFfk12PpOPc8LUZrTwdSoMK8b9xU1MUx9V2z3Zvcz7/dwHCtXfxRHbpaLdzZskrvrDJHiIs27/jx450ghgYNGuTcduyxxzqlIGbOnGk9evSwhOAFeO7cGO01iT0KQnjrdLesUUGSK7gliPLL7IPEpyA9r4xa38HFH8zX74OXyUhtFaWfjCVeII4G8pMTqORNoEa93NSsClSIZEYoLzBCfRVVc39rIkJZsyYuNFs2LuFnDeWU9QhVfwOCo+wkrfubzfvObOGPuRkb41CstlUBAEAM0P+4H/7PbNMi93qnU8yO+49Z9VL0Eeg/Sdez3KD4Xx40O+tNdwwHeakPW8EFKl98zL0l+9963Eh3jFJl0L+6yezMN3LHGBNMsfeiPn362IIFC8KzNmWR0hmqE8g3oh6xkQ5HajQzq1IntLWWt2TPTkH+JmVnU2hyoFntlvk/pn52VgWV0gCCzagQ6Y7WwqisiYIVZNyTZiunRPb1d6zP7Rz1UgvHOq+DXumAtwU5Uzgz002R5TXIAzO0lHVKNextU8RNm3fNmjV+QQriXdd9+UlPT7dt27b5nWKeghJl5/por0ns2b2l8CAF0f16HMqmGR+a7dnq/v844PySBVV6wQlrZ1rM2bbKPU/QTpocjbMDz5QlJZIUwChp3SMfCOKVf1BAb6LPsPJm88daIFBZoLrKsuRXi2ex2lYFACBs9u9m4xYlc7/ZXy+bfXCRG6RQqZbZKc+ZnfFK6YIUPAPvM6tQxR3LnPUZn0d+9u40+/1J9/KBV+aOZRVXuWSzf7xqltbV/X//+VCzPXHQnxeJQIWhQ4fazTffbK+99ppNmjTJpk2b5ndCCXh1Nb0ahYi+FX/m1kXXrNOQzgxcZ7ZvT2iWmWj27Tab8k5u50FBEXnewT3BU94gxBkVvGChWOFbAkIpuCJZAmJZdjrdmi1iK4CjqOCOuu3dy5oBFYx535ptXOAGKHQ+PayrF5fqtInOAEicCGWb9/bbb7ekpKRCT3PmhC9gdeTIkVajRo2cU9OmcVDT3Ws3KSOKSpQACI6+L1Peci/3ucadPVwSjQ6I3WA2L6NCogcqNMoOJvWCSyOdUaFJb4u4Foe6/7/1GSdyeUxlcNTvm/7vquMRkaW6v5bk/k/YujJutz79swCAMkWBnt64QWFU5rRS9sTgsmbLcrOPr3Qnbam/ueURZleOMetxfugCkJXV7vDb3MvjnzFL3x6a5SaSP//nZtDXWMRhN5euzFtqVbPzPnCzZSpT+9fDzDL2WaIp9t55xhlnOOeXXXZZzm3qXM3KynLOMzIyQruGZSVQYe10sw3zo70mCAxUKGm0U35UQ1VpeDXDTfVe4zjFYNgoCk8pZqvUdwdwC1Iv+3OJdL1WxHdGhVgLVHBKQPzLzQwS6RIQOWUfOrvRmfFC5R8U1LdwtFnvS4p+/Lin3fN2x5lVqRv21Ys7dVpHZwAkToSyzauAh0suKXyfbdUqO8NFEdLS0mzixIl+t61duzbnvvwMHz7chg0blnNdGRViPlihan13sEqzAZQFpmaMry8QS+3pXRvdQMSeF5d8OQ2zAxVUIihWAxW8MoqJqlF2RoXtq9zPVEGb4aYOzTVRDFRQmb8mB7mlH5b+XnCGvXjnTVLRPqxZboh8G6PpQWbL/3DLP5TmWBlF9M8CAMqUOV+aLRrrBhsec79Zy0N97swy+/x6szXTzBp0iZ3yv5EM4pj5kdlvj7uB6+Urmx35L7MDrzIrnxr61zv4WrO/XzfbtNBswrNmR9we+teIVxr3m5odUHPUHWaVa5d+mdUbmp3/odnLA81WTTYbfa9bTqI0ARDxHqiweHECR7VHi9fxunFhtNcEXufMyuySAg1CWC9SBw4NlKqujNLuEKiQ11+vuOeqR1qhcsHbsl6H3LSvGryoWo99F/lTloJtK2N31p0aK2rMfTfcLQHR8RSzxtkDA+Gkjl8vpW88ad7P7O/X3DStKutQrpDEUEoZrE5uDXR2OzuSaxl/pR/UiN6/zyylfLTXKKaEss1br1495xQKffv2tREjRti6deusfv36zm0//PCDVa9e3Tp16pTvc1JTU51TXFEQlRMxvtwdlCRQASiaAqLVYSTqlKpUo+RbzcuooFnt+9Pd2pqxYvsa/8yEiUrtxFot3f+PKsfQqn/4X1Ntgn07zVIq5garRFqbo9w2nAaRe1xgCR2ooP+18RQ0nEg6nOjuY/pfEaeBCvTPAgDKDLWHf37QvXzwdWZ9r8vbJ3j6S2YvHGK2erI7wanNACsTVC5zzH25fb0Kdj7pKTdrV7gGslMqmB3/kNnbZ5jN+Nisyz9ys7aWZQoY+fVRd8JNs35mXc8M3bLTupid9brZO2ebzfva7SM76Cors6UfmjdvXugpnClrDzzwQKtWrZrTKXvqqafa3Ln+pRL27NljgwcPtjp16ljVqlWd6GJvhllM8zpYFIGE6NPM5r3bzcpXcks/hJI3o1uBCvCn+rfqKEhKNut0auFbp2L13G3pZb8A8qMBLkXVJqe6M2diUduBZq2OilwJCNW08moPN+5lcSWtm3ts3r3ZbMVfhT923DPuudKc1WoRkdWLOzqOauBJA1tkVYiZNu+yZctsypQpzrmyNuiyTjt27HDuHzhwoBOQcOGFF9rUqVPtu+++szvuuMNpA8ddMEJRvN/6HXHQngdiZZaRvi+aeX/gpaVblgbIK9ZwO1liqUShOn8UrCxl4fe9cc/IZpLzXkcD6Ko9Gw2tj3LPV09JyLSmjvXZ3ynN+EN0dBjknq+eZrZ7S1x+CtFqqwIAEFEKmv52uJspQP2YR/47/4lLmhTad4h7+ddHzPbuiu8PShM2VKq1oJPuX/CDO3CtIIXk8maH3mx26TdmDbuFf7Z92wFuBtusDLOf/+P+TyvrlvySO2nu2BHuZxJKbY8xO/4/7uWJ/zWb+7WV2UAFefPNN+2QQw6xRo0a2dKlbkrtJ554wj777DMLl59//tnpgJ0wYYIza2zfvn1OR+3OnTtzHnPTTTfZF198YR988IHz+FWrVtnpp58eP4EKW5a5A1WILm/gu247d0AsHB3uW7JT0SPXX6+6580ODi6Nq1eWQ+lugIJ43zWlPw514yCU+g93BwMUKDX2gfC+1rI/3OAN1X+PxSwThdFn2PhA9/L87wov9zHrU/dy17Pc+r/IS9ulVnZWBZWgQky0ee+8807r0aOH3XXXXU5wgi7r9NdfbnBOcnKyffnll865sitccMEFdtFFF9m9996beJ9gjcb+s6dRPJpVkcg13uFPA7qTXnMv976s9GUC1LHVMDvz0pqZsbO1FXC5f7d7uVYZGIhrlB2ooDZiJHjBrHrdaKUSVSaHSrXN9u1K3P96XvCPl7kE0SmBVre927nupJGOT9FoqwIAEFG/P+GWo6tY0+zU581SCwmmPeI2t69TWQYmPGdxS0EIb51u9v4FBZ/eONns29vN0rea1WlrdtHnZkf/X+jHswpz3INuqXOVNVbQRFkPqFE2BelxYW4Zv1A76Eqzgwe7l0ffY7YyMf4vFbvn/vnnn3dq3J5wwgm2ZcuWnPq8NWvWdBrD4fLtt986tX07d+5s3bt3t9dee82ZaTZp0iTn/q1bt9rLL79sjz32mB111FHWq1cve/XVV23cuHFOcENM02CR0v0pKsyrpY7oBypoFkmoO2e8Dnc+Z397d5pNey93ZkMwg4r1sgMVVk8N7WeExOJ916o1iO3BaqX2VbCCjHsqvI0MLxWYZnApwjPeqPyDFNah+McLZlmZbqOQDuCiO2lF0diIiTav2rhZWVl5Tv3756b81iy5r7/+2nbt2mXr16+3Rx55xFJS4vD7HEwbWXaSUaHEA2GjzjEb97TZvuyBXSQuBfCp3JU68A66OjTL9FL/r58dW512UqmWWYVqlvC87FeRynzkBS426W1Ro74Rr8yFZiQlmvTtZltXuJejVV4Dro4nuueLf4rLLRKttioAABGzcIzZ9Pfdyyo1UFT2a5WSPuER97KeF6/ZQ5XtSdlPC6N+T0tyg9Sv+NHtL410oHHtlmb9rncv//64O75ZVk1+0/0/XrmuWf/bw/tZDLzfrP0JbvbDr4eZbVFG6fhW7FGbp59+2l566SX797//7czk8vTu3dumT4/cbDwFJkjt2rWdcwUsKMvCgAG5tWc6dOhgzZo1s/Hjx0dsvUpEA0XeTPtIzZRAwbx04uFIw+h1uG9dxifgS7WM0re5taiVpj0YXkaFSKVBRfxnVIh1bY4xa320O6vnkzCWgFia3eHboJvFpWZ9c9MB55emVbf9/YZ7WTXS4jEYI5JqZwcq0P6I2TZvmVY9O+vLjvXRXpPYUqmmO2uhMLq/2SHuH9e/XzN750y3DjcSk7Ly/fWKe1m11qvWC81yvWA/zWCKtUCFamnugHaic9K2ljPbtTG35EU4g8c3ZpejbHqQRZVX/iERy/xtmJ/7/0T7MaJf/mHFJLN96XH3SdBWBQAktG2rzcZkZ47scZFZ138E97z2x7uDuOpfHTsie0A/QR37gNkJj7p9BNFy2DB3zGvHOrM/X7YySf9RJ2X/Hz/81vC38cuVMzvjZTfoWWNqnw+O21JmJQ5UWLx4sZN+NpBq4vqWYQinzMxMu/HGG530Zl26uIPJa9assQoVKjiRw74aNGjg3FeQ9PR027Ztm98pKmo2j71OoLJIX2hvsEa10MPV4b51FXV7fHkdq6prlFIxuG2pjBeiSLUdG0L6MSERMyo0tLjglICo6c6CVWM61FSfTem4fGfoxRulcFPJJA3KLPgx7/1Ke713h1u3utWR0VjD+MyoEK9R5mEUC23eMs8L8NzF77wf/aZd8LHZUXe615Ui/YoxZlf9nHsa+rfZZV+bnfWG+ydZf5y/vNHsq5vNdpChIuEsHO0GZyrDwMHXhm653mzvzYvDF0BZ0kAFBfpHqzRBJFWokvu/Z8208L7WullueTANoKsdFU2tj8xtn+zabAllw5zcUpOxXJou0Tkzz8q5ZXJUTmbuF2ZrZ5qtmuKe4mBmGm1VAEBCl7X7/l9uJqr6ncyOHVG8IGVlX1AJBE1w9ErDJiJlUdCgdbT/rwzM7sOe8pbZ1pVWJsuTqPSDSif2uiQyr1mhstl577v/izU+9tWworNwxLBi78UtW7a0KVOm5FuaoWPH7BnOYTZ48GCbMWOGjRo1qtTLGjlypNWoUSPn1LRpU4tqoMKGBdF5fbi8ATx1AIcj8snrcFftoN0J1uFSUk5HwN/urOeOJwf/vIo1crfnygScaYPQZlTw9pVYpzTGOSUgnjZbkX1MChXNStPs2ir1ot8BXRrNsss/BAYqaBDlj/+6lzufZpaSGvl1ize1W+UG9ZAaPubavGWeVzJL9SXhT21Vr6SRvsdNermz371Tzez/NJ1OMRsyyazPNWZJyW5667fPMJv8lvt7gPinGUJ//s+93ON8s+ohDM7UvpVa3e0o9AZXYyVQQUGLZUXjnu65BlHDaU12tqAGXaM/gK7AVA3ka/9OtPIP6+eFL4MjgqMghGd6mb3U381WIpoBqFrQLx7hnnR/jAcr0FYFACSsif91g3Q1CH7af80qVi/e8/V/+Aif/tU4n20e89QH2/wQd6D814etTFkx0e2fVv+MAjbKR7AvuloDsws+MqtQ1WzNVLMf747bydHFDlRQ/TMFCrz33ntOvdyJEyfaiBEjbPjw4XbrrbdauA0ZMsS+/PJLGzt2rDVpkj07XZPf09Js7969Tl02X2vXrnXuK4jWW2UkvNPy5VH6I1IrO1BhE4EKUbU8e8Bb9Y7CkS5cP66aLS1eWs2ybtKruRGA6pAqjnrZA0WrJod+vZBYGRW8bCbxoM2A3BIQn6oERHoYyj50NkspIm14LGt2sHu+6Gf/BtjMT8y2r3JnF3c4KWqrF1eqppmVr+zub+tiqAZ5DIh2mxfZM6ZFwZ1ludZhQbat8A94LkhqVbPj/2N21U9mjXq6QUmqH/ne+bkDk4hfi38227TQPZb3HRzaZStjgWaFRGKQPFhlMVBB39tIlGnySup5gRHRpvawLJ9gCUWZ08T7biHyFJxQ1H8s3e8FMcQo2qoAgIS0bIKbLVUG3OuWQiuJvte5mcmUGv+3x0K6isjnf+MJD7uTI1Ry0ut/TnQK6P/lEfdy59PdYI1Iq9/R7Oy33LHM+d+Z/fGClYlAhSuuuML+85//2B133GG7du2y8847z55//nl78skn7ZxzzgnfRJGsLCdI4ZNPPrExY8Y4kcO+evXqZeXLl7fRo0fn3DZ37lxbtmyZ9e2bXc86H0rfW716db9TVNTMntm6aXF0Xh/+NTDrdQrfFvFmdm9axFZX+qbpH7rbocPJuTMDi3MgltVT2ZbIK31Hbrrw4gbBxEwJiHlmY0JYAmLp77kz5eJZ495m5cq7QQnrsjvtFbAw/mn3cscT3awrCO7PRO3s8g/rYmQQKkZEq80LH1Xqu3+2NKNW9Q7hz5vp6WVGKYo6eK4YbTbocfcYufH/2bsP8Kiq9A3gb3oljYQUQu+9i6IiKCp2VOxd/7rNimtb21pZG/a69nV11dW1iwVQLEgv0ntLCCSEJCSE9P/z3TOHSSA9M3Pb+3ueeeYmGWYuN2XOPfc777ce+O8VwMwHgf2FPLp2JO99ug/o4PP9c/FeEjoCcZG8pYUKCZ3hGrpwQMaF1dX++1nShUudDoMl9PQUKmxfYNuVQYeQVWZSWCRYqEBtxLEqERE5Tkke8N3dqh2ZXPhtS4y+JISd+pTaXvMlkMWFjn4li+JGXaW2Zz+mLuI73fIP1dheUgiPu8e8Nhw9xgMnP6G2F7wKrPocdtOqI3fRRRdh3bp1KC4uRk5ODrZv346rrvL8EPqJrGh755138O6776Jdu3bG68qttLTU+Lq0bZB9kIpiSVtYuHAhrrjiCqNI4fDDPSsvrUwnKuzNAfbvNXtv3EkmP3ShQlorK/WaQ18w3bPZf69hF79/qHrJy6SqJCq0lE5UyPGs/iGqrWCrupf4I2mpYCdGC4i/qe05z/mmBYS0RdB/4zqOgK1JHy49ubtuundFqUywh0YCA88xdfdsp72nUGEnExWsMOalWuQkT1ociCLPxUk6NFEhqW4Bd5PHdNSVwHWLgEHyt7IGWPk/FXe9+kvnXAx0i61zgNxVqtXRkdep4jNfSx/qvUhuBfpvgT5/doMOA4CQcHXeVLDJfwUgpfmqOEwnOJhNzg/l/12yyzlF/vL/qK4CIto1v8iMqBEcqxIRkWPIAoXv71FjUhknyQr9kDYmXnc5Ahh6odr+4SG2P/S38X9TKbeFW4El/4aj7cv3th8+8kbzz09HXg6MuUFtz3oQyFoIO2lTiUd0dDQ6dOiAQJAVbNKaYdy4cUhPTz9wkzhe7cknn8Spp56Ks88+G2PHjjVaPnz88cewBfkFlotpMllolUkgt5FWDPsL1GRIh77+e512nkSFAk8kvVvJRPiC19V27xNb10te4pv0RH2JtWMZyQS6GKhdmn9auQRiFZm0gfBVCwhpkSLR6bKKVnr+2p0ubtowU93/+py6l2OmL2xS8+hEBR1FTKaOeamBAs/inTw0ByvYXrfYqCVikoGzXwUu+wJo31ONgWVS6H/XAHuY8GafNIVX1faAs4HEFhSstETGMHUvPxdS9Gim8hIV3Vo7kdANpF2XTsPa4ad2LTpNQf4eWKXAV9omdvIsOtnyMxwh1zPWkrF4a85/iRrAsSoREdneoreAbXPVGGnSi+qc1ReOf1Cl1sr5zOJ3YAu6JVtj5DhFt4elyHnEhL+r7QWvqYQMp5KFhVJILuP60X+EJUz4O9DvdFWQ8+UUWy2UbnGhwu7du410g/79+yM5ORlJSUl1bv5s/VDf7fLLvfEvkZGReP7555Gfn4+SkhKjSEGKFWxBVr/o/rK8UGAOvdJYJmeMohE/ie9Yd7W3W2UtUhNiEsMkf0BbIyrBe0FSf/+INF0MFJvqnxWGgXDM7b5rAaHbPsiqPLtPjMqqv9h0b++6Ze8D679TH3caDRTnmLp7tqMvcurJczJ1zEsHifOMm/h7Xdf+IqCssG6xUWt0Oxr40xyV4iOJNNmLgPfOB+Y8r4rbyLqyFgA5S9VY+qgb/TfWkQIIWf0t0Z1mn6fqtg+yP9EWuZge6PYP/mrTtPN3b4KGWZGl9el5rLqXSWsn0GMtGY/b9fyELINjVSIicowdy4DfXlTbcm4qc3u+EtMeOP5+tT3/n9ZPa9y5EvjlSbXd60Tg/2YC1/x46O3ahUCCH1r/tdWwi4G0IUDFPuAXT+sNp5FCklWfqu0THgQiYmAJwcHAWa+oJGUpovjsOqB0D+ygxUtML7nkEqxfv96IvU1NTUUQT658J7GrmnjIW+fDJ6Vm0xe6U/r4d9JAT7jrvsJutdCTptDlKG/KRGt06K8mLWW1eJ+JPts9coA9ulDBJgVrjbWAmH6rqtTsfwaQ2cq2DVt+VfdpnhV5diW/7xJRLj1+hdx/fI3369LLTpJxLv6YyQrNpaOHi7KAsmIgwo/FejbCMa9FxHnGCMW7zN4Taync7k1lk8LNtq7WHncbMPhc4IspwMaZapy29mtg3B1AlyN9ssvkY7JCRfSf5N+kJJnskHZLm38Gdq00dxyhCxVibZqW1RYy2SQTq/4qKtSJCpkjYSk9jgW+/7uavJZED/l7ZWe62Cfdj60myTU4ViUiIscU4X97h0qU7XUCcPiffX9tZtglwOK3ge0LgNmPAKda9AK6zHt8NUWl6kqy2OQ3rHMRvLmCQ4BTngBem6DmFKTlpG7f65QWJT8+qrZ7n6ySfa0kLAq44H3gn+OBwm3AFzcCZ75i+UWLLT67/+mnn/Dzzz9jyBAH/XBZhe5jsnu92Xvi7kKFDgP9+zq6UGFvNlBV2fZeS3ZUWgD8/pHalguvbRl8SPuHDTOAHUt9tnvksEQFu7cB0C0g1n8P/O8PwJ9+afngQnrhSvKAsErf4bb8/dBFCg2Rr8vj7P69D5ToZCAiTsVp71wBdPZh5bqNccxrsdYP0qOcvKTtlT4+sqLeF5K6AZd8DKz8FPj6NnVR+PPr1YXCo29RE0fyt7U5SVfkXzLulXMXuVjvzzQFTVbZG4UKq2AqXaggBUxuWzChExXy16t0C1/93utxky6A6HQYLEVaXsg4ZV+eSnzp7GkFYddJTd3mU1aZEbURx6pEROSIdnYzHwD25qgx/mlP+6cwVYqvT30aeHkssPknYOOPQPdjYCkVpcBXNwMluSp5/ezX7FekoHUaBQy5AFj6HvDjI8B57wBBFkpta4tVn6vF5mHRwPH3qcIMq4lNAS7+CHh1gkp/+OIGYMz1EusPlOar6wSRceqx0j7EAskcLb5C2rdvX5SWlvpnb9xOt37YvcHsPXEf6XcqF2dEhp8nDYwY+hA1ISSrV3WBipss+wCoLFU/85ltvCDWoV/zezeROxMV9GpcO5MWEFkLgd3rgJkPAic80PJVcuV71SBKUkiIapOLPRIdv2OxGmyzUMHAMa9F6L/hcpGKvKQyXiR09v3fgwGTVIHcjPuB+a8CG2aq9kFSYCvFCg1hmk3g0xT6nAKkBOB9PWOYutcXWc2iY1p1AZObtO+l2hNKhKckMKb68PsuRQrVFUBkPJDcB5Yik8o9xgO/f6j+Dtm5UEGScCQCV/5W+vL7Ry0nE7JS+C2rFe3U9/kgHKsSEZHtLf9QJfpJAfbpz/t3DjdtIDD6T8BvzwGzH1UFurL63CoFGzPuUwl2spDonLeABJuf80y4T13Ul0Sx5R8DgybD9sr2AnOeVduH/RFI7gXLSukDnPok8NFVapHDB5c0POa1QBuRFpexvPDCC7jzzjvx448/Gv3QioqK6tzIB4UKsgq4upqHMpCkbYBMvMakAHF+fhOQ1S+xHdR2/ka4jrzxLvC0fehzUturJFP6eSfsS/Lbvn/knJ8znahg94Fd7RYQQvqGb1/YurYPUqRglUE4WUt7T497s1fLWgjHvBahk6hKWKhQb+sH3brF16QFzMmPAlfPUqvp5WJOY0UKtdNs7E5W7cvfwoZuelW/mT1L5YKtrEg56iZ1Idff5GdA7NnUdKqRP+lj7+sCHTuQ73OG5/uw09OmwVf086UOAMIiYTk9jlP3WQtga7mrvX+3pXiYzCMTsTIhW1+/Z6v3fa6FY1UiIrI1KYL++Um1feSNQPdx/n/N8XeoFMDiHGDeK7AMafG2/jtVsHHGc0BHT6G4nbVLBY65TW3PfUFd5Le7eS8DpXuA+E7AUTdYP+Wvfc+mHyNzPft2w3aJCgkJCUZBwrHHHlvn8zU1NQgKCkJVVRMTWNTIwZWToCC1SqIo2xkX1+zW9kFWkPgyRrMhsgpIJtqkUEFWiLjJtrlA7iogJALoe1rbn0/HDMvxlMmr3if4Yi/J7vblq7+lwt/FR4Ei0ds9j1cD15a2gNjqKVSQvtJWH0SROSRRQfir97UNccxrsUIF+btesd+aF9HMTFRI7Obf15GkMSlWkHSFXzyTSE4m48l3zmr8YrzZyRELPWkK0r81UL0+5cLqgdX8a4FUP7fKa6pQQSaG3Ejad0kLDp0E6Cs5nmS6jBGwJH2+LEkSJbuBGGuvcm+QTiSRwuFAFBhR0/NvFi9EaArHqkREZFvl+4Dpt6vzri5jgLG3BGZ8JAX5Jz2qVpcvfRfod5r/iv+ba9236gK4GH+nb66XWMXoPwKL3lKt7uc8B4y7A7YlKfiSEi6Ou1ddkyLzChUuuugihIWF4d1330VqaqpRnEC++m5EAu3S1ASMVNuzUCFwti+o20bA33SMkY6md5MFb6j7bmNVGwxfkMke+b2RvqUsVCBRsFkdh6gkINym/bzqI5WoUpBjtIB4ADjhwealS+hEhXQHVOSSfxMVzI71thCOeS1C0q6Cw1QsefFOd7bMqk/Btrq/u/4kE0bSDsINhQqSCNFUYoBOjjCjUEEu1G78QRW3H31z4C52yutIUYQkOUiig9mFCm79O9BxuLqX+FRfkhZhInMULEnmSOR8T6Jwt/4C9DsdtqSLQdMHm70n5BAcqxIRkW1J6wVJwo1OBs54IbALEqQ4QVodrv8emPUQcNar5i3qkgLk7/+utodcCIy53lkFrZKkffJjwL/OBFZ8DAw8G0juDduRufWfHlMpk13HAgPONHuPHKfFhQrLly/H4sWL0aePxXoXOkViVzUBI5MPvSaYvTfuIH9odKJC6qDAvKZe4a2j6d1CVkOu+J/a7n+G7wYBKX2BDTOAHUt983xkf7oISCY2JTbLKXQLiK9vUS0g+k0COo1s/N/IhWeJcJIVoJwYpYboCnIZg8gFOFYGc8xrFXKSHpcOFGwF9ua49wJlbRWlwL68wBUqkHUs9LRP6z4e6Bjg1e8Zw1ShglwsNkPtSErdMtGNiQoif5P6O+CLdl5yTPdmq+IX6dNr5WQxo1DhN/sWKugCkzQWKpBvcH6WiIhsac1XwOrPVSu7U58EkvycEngwuR5x8uPAC6OBHUuA1V+o4oVAK94FfDkFqCoDOh+h9inEQXPYtcfxfU5W3/cfHwXO+qf90n7lupNcP5S5dVk06MTvk8laXJ4zcuRIbNvmWcFDvqcnXfLW8+gGMjpXVugFhai+nIFMVJBJdzdZ+p5685WIcV9Gi+okDL0aiEgXAfkqtcNyLSBOAGqqgU/+oKLQGyMXFURKPxXbTNRQEYwkkAhfR0rbFMe8FqILPPVqarcr3K7uI9oBMR3M3hsKlD2bgXXfqW0jTSEksMc+fai6l1QnM0ihkk4hdOvPfUJnILq9Wsnjq/fqncu9CyZiO1h7/CuyFqqFBnZTkqeKQmRCPlAtW8jxOFYlIiJbLiyb9bDaHnU10PdUc/ZDiiOOvkVt//IUULY3sK8vRcdSpCALEOR64OTXgQgHJQIfbOJU1b54x2LV6sJO5Hul0yWHX8ZFgFYpVLjuuutwww034M0338TChQuxbNmyOjdqI71KLH8jD2WgbJun7tt3ByLjAttvWU80u4FMKOm2D31OUtE/viIXYHXRSUm+756XnJGo4ETSAkIuLEuPr1lNtH/QbR+kEMtuFav1kZX+UsHaGPk6EwFarn3PuhctXI5jXguJ94ybSnaavSfWULTdO54MCTN7b9wp0BNZYqGMo2uArkcBXY4I/OtnDPWu5q+qCPzr60IlGdu59edexnE6ScNX79W60DttUOCLX1pCehdLkYpM5ppVLOOLNIX4TmoMT+QDHKsSEZGtSAu9b+8AKktVWttx95rb5uDI69Viyv0FqlghUGTh2Yy/A7mrgIg44Jy3vItanUqKosfcoLblWMvFf7tY9KYqmpeibpmPd8LcugW1OKPivPPOM+6vvPLKA58LCgpCTU2NcV9VVeXbPXSbhK7qfg8LFQJm+wLvxW5Z4RDIQoWSXKCsxNkVc9rmn9WkkkSU9vFxtaRckJRJS3nTyF4I9Dret89P9k1UMKN/dCDIz/z4O4Gv/tp4CwgpENrsSVSQkwAnkO/pxR+r9gSN/k1w6Pfe3+0fts8Ddq02e08sgWNeC9En7RKNSEDBNu/qaqudJEuhoE66crIvbgAGnA0MuzgwRZFS3Lzma7V9lAlpCkIm8SSZqbwYyFsHpPY3qVAh3Vl9W1vT/kFWIe1a5dtChcxRsDQ5h5RihQ0z1Xml3Xrb5noKFVL6WLsghGyFY1UiIrKVX55WY6LIeGDSS+ZfD5EV/tJ64u3TgZWfqrnV9AC0BZ/3T2D996pV8RnPAR0dMl/blKNuApb8GyjKAub/ExhzPSxPzsMXva22pRVzbIrZe+RYLS5U2LRpk3/2hLwTjqIwS8V5h0XyyPib9JcRHQI42SarKEKjVAXhnk1A2kA4nrEKDEC3cUBMsu+fP6W/KlTIWsRCBfImKuiiICeS/tS9TgTWfaNaQPzxl0PfM6RgQ/oOS2ubdAcNfOUiBQsRfE/3us9joYLgmNeCrR9KWKhQJ1EhkL08JW5eJnIqyxp/3A8PAQmZQKoNx7bVVcCC15q/GmjZe8DvH6iksOGX+/f7segtFfefeRjQbSxMIcUB6YNVUtOu5eYVKsiKdDfrOLzuCv22/szvWqm2Ox0Gy5P2D1KoIImII70LZ2xBf78kuYLIRzhWJSIi29j0I7DsP2p74j+ADn1hCd2PAQZOBpb/F/jhQeC8d/1bVCoFx/NfUdvj7wL6ngbXCI8GTnwY+PAyYMm7wICzgHjPXI9V/TxNnftLqt2QC2E70c2Yx5Gvy+PsVqjQpYunNQH5h0SISKRh5X61+pwnsv4lv6Q5npYlaYMRMLL6TVYH5m9QN6cXKhTnAis/U9v9TvfP6j9ZvbdxJrBjqe+fm+ylulq1ARFWH/C01dhb1Qp4aQEx8wHgxIfqb/sgq86i4k3ZRbIRWS0rZKUsccxrxUQF6fFN3tZhiQEsVEjoBFy7UPVYr4+ssv/6dmDn78AnfwJOfcZeK0Pk3OvbO4GNPzTv8RMfAVb+D9j6G7D6C3Xrdgww4grfn79JIe6qz7yrUEJafArvO1L0aBQq+Gg1f0uwUMGbqKD/DuwvalvrQmk3WbFPpRWkDrJHoYKQ83c5j5dJNbslKqR7WqgQ+QDnZ4mIyBbkfOb7+9T20IuAwSqx3TImTlUFBDK3uvQ9lZrnDztXAN//3XscxlznvqS4/mcAXY8GNv8EzH4UOO0ZWJac90qBjSz+O+FhIMxG5x7NnccRUqQgjzNZq34T/vWvf+HII49ERkYGtmxRq1afeuopfPrpp77eP/eR1gM6VUGfzJL/7FimqqIiEwI72Vv7Aqpe+e1kEutTXaEulvorfj7FU4kpE+TkbjKRLb/X8vfU6avujRYQd6nt314AtnkSYrQtnrYPUgwVqNY2ZO/WD7otES8IGzjmtYh43TKLhQp1ChV0CkqgyMlrxtD6b12PAq6cDnQ+Ql34/PwvatWzHZTtBT67VhUpBIWqCM7GyMXRvqcAV34DXPmtSjdCkJrE+O/lwMdXq0kNab/kC4vfBqor1QVOs9ubyffarII2XahggUkUU0ncqHEeWdP28x7976UFoqxwsjpJQJSFHVVl3lREO5BCLl1EzUIF8jGOVYmIyNLkPEYKwssKVQssWVFvtTZYMr487h61Pe9lteDS16SN5ZdT1Di28xjgpMfMLUA3iyxePfkxdfFf5qylpZsVVVUAPz2utqWwxg7pc62Zx5GbRc6vW3zV4sUXX8SUKVNw8skno6CgAFVVVcbnExISjGIF8oHEruqeKxr9T09wyBtlaDhMWR24ZzMcv7pdt33oczIQEuaf19H9kAu2Avvy/fMaZA/S7kDEeBJqnK77OKDXRKCmGvjfH1TboIMTFdKGmLZ7ZCMR7dTvTe2e1S7GMa+F6DY+pXvq/o1zIzlh1hdsdQqKVUTEAhd/rJIFZLXzFzcAmz0Fc1YlE0YfXQVkLwbCooHJrwHXLwau+bHhm6xI0CfznUcDF30A/HkOMOgcVeSQvQj4/Drg/QvVyhyZnGstKc5Z8YnaPvJG/42jm0tfZJWV+PKzaEqhgqeo3810qkLO8rY9j/73Ukjuj8Q7X5N97HGc2t7qGePagZ7XiUkB4hxeRE0BxbEqERFZ3rxXgB1L1LnWGS+pBVdWJG3F5FxHiu5lpb8vVZQCX94E7MtT1/3knDMiBq4l13AOu1pt//RY4M8rm0OSNeT6grRvH3+n+5IvTNDiI/zss8/in//8J+68806EhHirn0aOHInff+ektk8keNprSNwM+ZdEptdejW/GpLtcWHeyTT+oYoywGKD3yf57HXnjiE1T21mL/Pc6ZH26+KddmvWqdP1l7C3qd0Baycy8X32uaIe6kCCrPPWENlFT2vf0RtK5HMe8FhKdDIRIQWkNUJwDVyvKVoVpUoinx5JWIquyL/pQrfyXdKOvpgAbZsGS9mwCPrpCvXdGJQEXvq+iKOVCeEtXHMhky9mvAtctBkZeBYRGAXlrgW/uAN45C1j+UeN9IRuy5B216kZWkkvBrxXeI8Jj1D5Jm8JAkckrKSqpXdTvZh0947rclW17np2eQgU7rRDS7R+2L4Bt6KTM5D7mFxuRo3Cs6mJSvCdtmBq66eI+IiKzr7sseF1tT7jP2q0BZf74tKdVGq20ltYJtW0l5+7f3wvkrgYi44Fz3vYuXnWzcXeolgOSFrn4X7AUSdSY/0+1fdQUIMHhbaXtWqiwadMmDBt26B+ViIgIlJSU+Gq/3I2FCoGjJzjM6MnplkKFBZ40hR7jgegk/76WTlWQlWzkXrqdSmwqXKN2C4g5LwCL/q2qP/XfGqna5YQFtaT9g5xEuRzHvBYSXKuVj/S3dLMiT9sHmdwwijcsSFojnPcu0PdUlSYw/TZg7XRYiqTGSJKC/DxJjP6lnwLdxrZ9VXliZ+DUacBNK4Cjblbt5YqygB8eBt46FVj4pmo10RySIPL7f9X2mOut0RNTfhfTBge+oE1aEskkX3AYJ/aELkDNXdv6Yyo/h0ZBq80KFbqPV/dSYOSPWF5/yPMUKqQONHtPyGE4VnUpKUKQIsgPLm74Jl9nsQIRmUnSjr+VecoaoN8ZKrHA6gleUpQ+6v/U9o+PtK7QvL5EiQ0zVPLeGS8AGUy8PTCPLcUrYuHr1hrXz3lGJWvIYgGd/EDWK1To1q0blixZcsjnp0+fjn79PBcJqW30KhGntwQwm6w2ll6RQbUm3MwoVJDJS1/1sLUamfxd/aXalkGJvwckulBhx1L/vg7Zo/WDvqjlFtLCRv6eyUnAZ38GZtznvaj14aWcsKDm0T3vWajAMa/VGD3ZWaiAAk+f8/jO1o4flJZq57wFDJwM1FQB390NrPoMliB9MD/5I7Bf+qT2Ay79HEj38blATHtgwj3AlJXAhPtV6ldpPjDnWeDNU4Bfn1VtHRpbGSmPqSwF4jsBmRa6kCxtAoTsY6Doiy3SPzbEAgUbVphEFSW7vEkTLbVL0hhq1M+m/IzZRWyK99x9q0V72jaUqODrvzPkepyfdanSApVa1Rj5ujyOiMgMOkVg3261IPeUJ4CQUHt8L469S7VEles1C15r23Ot/ca7Mv/Ye4C+p/hkFx1j6EXq3FJaY/zyJCxB2pSs+UqlEx//IBAWZfYeuUaL/0JMmTIFf/nLX7B//37U1NRg3rx5eO+99zB16lS8+uqr/tlLt9F9N/cXqGoiORkn38vypCnIG6YZ/ZF0zE95sfo+t/P0BXcSie6RyWmpQAvExIxMNgv2Vnc3najgtigtmYiQk4HmTFi4rYiDmk/3vGf7KY55rUb/TXd96wdPokKip1Wclclk1Fn/VAkLS/6tCugqy4FBk83bp1WfAzMfUOPTTqOBc/8FtPNjApO0STjqBuCIP6tj8MszaiX4ojeBpf8G+p4O9JkIfPqXhi86SGH1S0cA1y6sv+1EoEnvVrG7Dav5W1uoIOMXKxfoBIrExib3AvLWATnLgJ4TWv4c+nwpbZD92hFI+wf5f2/5Deh/JixN2pbI73zt3x0iH+H8LBERmU7G6QcXRsmixa2/qhSB4+6z17UtGWdPnKrS9xa9DfQ5VaXmtabFml5ANvRi4Ii/WD9RItDkvO7kx4FXjwPWfQMMPCew7UEO/tmVOfUZnnbKXY8Gkj2tccmahQr/93//h6ioKNx1113Yt28fLrzwQmRkZODpp5/G+eef75+9dBuZ0IpJURGXuavs9cfcTrbPV/cpfc3pYy8VWdIPV1ZX7dngvEKF6ipg4dtqW3rqyuDE33SigrTTkDcaMwpQyDqJClbs3U1kl9YPEjlemA3Eu6zgpxaOeS1G/01v7ephpyUq6KIiO0w+nP4cEBqpVqT8OFVdkB96YWD3Q9LLFr8N/PqM+rj3RODMV4Co+MC8vlwIHnE5MOxSYPXnwE9PqASwFR8BKz5WK9sbI7GjxoqkTtZJVJC2AVWVgVkdVZRdN1mFgIwRqlBBWnC0pVAhc6T9jqYUKvzyFJC9UP1uW3nSV35PpAVOeKw3tYrI5mPVhx56CF9++aWRthseHo6CgkNX7gfV83spi9xq79cPP/xgFFusWLECnTp1Mv4fl19+eZ1/8/zzz+Oxxx5DTk4OhgwZgmeffRaHHWahlCEiIjfTrWgaKriWMdCnfwA6jbTGeUxzDTxbFSls+lG18Jv0YsvGm8U7gS+nAFVlQJcxwEmP2idRItDkXETmBpa8C8x+BDjv34G5TtfUz+7m2cDzo6yzWMAFWrUc4aKLLsK6detQXFxsDBa3b9+Oq666yvd752ayyr92TCD53vYFdS9um0FPtu329Ad1kvUzgMKtQEQ7oPdJgXnNqEQgVlbF1QBZCwPzmmQtMpGvJ7PjOJlN1KoiOr1yXSrAXY5jXgvRYyYp5HUznaiQ1A22IcUKEvd5xLXq45+fABa8HrjXl5URP0/zFikMuwQ4583AFSkcfCz6nwFc8yNw6WdAt2OaLlKwmvY9VWG9jLl2rwtcOzlhpxYF/tZxeOtbNcnFff0enzkKttP5cFX8JEWVsrDDyvR8jiRgSLoMkQPGquXl5TjnnHPwpz/9qdHHvfHGG9ixY8eB26RJkw58bdOmTTjllFMwfvx4o+DhxhtvNAovvvnmmwOPef/9941ChnvvvReLFi0yChVOPPFE7Nrl8qJVIiI7taLRBdd2IkUJp0wDQsKBrPlqtX9zSRsDKVKQ/3NiN+Ds14GIGH/urf1NuA8Ii1HnlnOeP7Qlotx0wp6vOPVn102FCg8++KAxoBTR0dHo0MFhq8CtQse5MnrZfxGMWYvUdtoQmEZfDNIrwJ1k4RveFS+BTDbQhSf6+0vuUigXcGrU5CXTaIhaJ8kTb7ZrhauPIMe8FqPHTPvy4FqSVlWY5b1YbLfJnhMeBI7+q/r4t+eB315UF0z9Peb/9i5g6bvq46OmAKc+aX6vSTke3Y8BLvsMONPTt9QupNgizdPSTVbzB4KemNItEgnI8BQq5K1t+e+RFDxJm8ngMO/z2Ilc8Jc4VrH5V1hanqdQIXWgtZMfyJbMGqved999uOmmmzBo0KBGH5eQkIC0tLQDt8jIyANfe+mll9CtWzc88cQT6NevH6699lpMnjwZTz7p7VE9bdo0XH311bjiiivQv39/49/I//P11wNY7EhERO4ksf9H3qi2pehdWnc3p0D++3tVIbG0kDj3bSCOrXebJAUBkj4hFr8FfHDxoTdJP/BlsYKkfZC9CxU+/PBD9OzZE2PGjMELL7yAvDwXTxb6U0JXdc9CBf+QSbXKUhXBaGa/GR1jvGeL8y4Wr52utgPdNzTFU6ggcbrkPns2q/vYDvbrt0tktfYPrVml6SAc81qMHjO5OVGhZBdQXaHaaen0NTuRi3TH3Q2Mv0t9vOBV4Nen/VesUF4CfHGDWgETFAKcOBU49i7rjQ9SesN2dPuHQK1mP1CowESFA9IGqb8FUnBgFOq2ou2DrPKXSVQ7kmJ4sX0ubFGooIt7iFw0Vv3LX/6C5ORko1WDFBfU1Hq/nzNnDiZMqNu2RtIS5PM6tWHhwoV1HhMcHGx8rB9DREQmK9sLRzv6ZiCxq1pV/+tzTT9+3svAhhlqjH7GC0A6x3/NIse3qcIBST+QFISDydhCfg4Lt6nEuC2/AGu+UgsV5r4I/PgP4Js7gE//DLx/IfDWKcBLRwH/rdtqiszX4uYoS5cuNfqH/fvf/8bjjz9uxHMdf/zxRtyYxHhJdSv5MFFBehqS722fr+6T+wAhEeZPuhdshaMs+peqIpQJtEC31tCvt9MzAUfuotNJpAVIUKu6GxGR7qG8y93tpzjmtRg9ZpKTU4lTNHtFvBnk5Fu0SwfCvKsSbeeYW9T379s7gcX/Uisoxt7q29XG+/KBz69XF9IlZem0Z4BB56g0AGq79KHe1fz+JucUuvWDTBSSIn8DOvQHcpYBOUtbVsSRs9z7fbTr70TP4wBJ4ZUJSau+J8jPbq7ndyTdxBRHciwrj1Xvv/9+HHvsscY+fPvtt/jzn/9stKe4/vrrja9Lm4rUVGnb6SUfFxUVobS0FHv27EFVVVW9j1m9uuFi6rKyMuOmyfMREZEPyUXhDbNUMfi2ec4fb0sLw3fOBlZ8pFr4NXSdQxZszn9VbR93L9D3lIDuqivMe0XNGUih9v5Cz60IqKkye8/IB1p1VjpgwAA8/PDD2LhxI2bNmoWuXbsaA2KJ8iIf0aukZHVEZQUPq69tX6DuU/qYG8GoJ931xLMTVFUCi95W231OUVWEZiQqSEpFfZV25Gw6naQd34+IWi3JU6iQvx6ornb1geSY10JiklWPSGnvs3cnXEmvmo7vBASHwNbGXAuc9Jja/v0DYNaD6qKer47TR1eqIgVZLX7ev4DB59r3gqwVZXgKFfI3qLG/P5XICpsKVYAqP/vk1XF461pwyMV90WmUfY+mFCZEJ6vVVSs+9n8f29aQVj0VJSrFJdDF++Qavhqr3n777QgKCmr01liBwMHuvvtuHHnkkRg2bBhuu+023HrrrXjsMc/7vh9NnToV8fHxB26dOjnwfUNaqxpj4kbIPFwgW7ASkbOV7wPWfA18cRPw2gRg5n3Att8kPx+O13MC0H+SOldt6JxV0spm3Ke2h10CHP5ntvzyh82zgU0/qhRtSVQu3eMtUjBaQKeqxLjMw4BeJwCDzwNG/wkYfydw8hPA5DeASz8D/vgzcNFHftlFar02X0GMiYlBVFQUwsPDsXevw+NeAklWSsnAU06892xUF9TJ94kKaQPNPaq6UEFWCVWWA6FNnGzYgVRU7s1WE8PyphBo0UlATAcVj5y1COjpiQUllyUquLBQQU9YyPtGQ+TrnLCgpsiKVbkgZMSnbQcS2RNccMxrMiksjctQJ6TFO4Ckru4tVHDKqvLR16hVKp9dD6z8RL1/Hff3thVhSMsaSVKQ+EgZC5z3jr0vxlpV+55AWDRQsU+1KuzQ13+vpS84x6RYc9W8mTKGAwvfbFmrpsr9QJ7n8TKJZ0cF24DnRqo0Ft03uL4x78Ufq3kVs9s+SAGotJwksvBY9eabb8bllzceg9y9u6c9XCuMHj0aDzzwgJF2EBERYRRS7NxZt/BUPo6LizP+DyEhIcatvsc0VoRxxx13YMqUKXUSFRxXrCB/1857F3j/ItXTe+I/gM5HqK/Nfw1Y/La6YNNUMQMRUVNjxs2/qHn+LT97x126XagsTkwfBHx8jfOPo/ydXfetGnP/+mzd6x1y3vn9PepctsuRwMRHgJAAL9p0i6EXASl9gSi59pMERLVXC1qi26tzU5lHaO5cQjVTGKymVb81mzZtwrvvvmvc1qxZg2OOOQb33XcfJk+e7Ps9dCv5pYrPVK0f5I8gCxV8GwUrq39EqskRjLEd1PdaVglJqoKO27azBW94Kw7N6nkqK1Y27QKyWajg2kQFXQTkJjJhIROyjSWJSJGCmRO2ZA+hEWoMIm2JpI2OiwsVOOa1mLhMVaigY+DdRi7OiaRucIzhl6rJ9P/9UfWSlAmwEx5SK5Bbavs84Mu/qhXMcmHwgvfscQ4lEyvyd7f25N/B5OvyOKuQ8xeJst86B9i1IjCFCpKWZfckEV/rOELdS7GITLY15/jkrlGPjUoE2veCLcmEcGO/L7X72Jo57pVjrc9NmehCFh+rpqSkGDd/WbJkCRITE40iBXHEEUfgq6++qvOY7777zvi8kGKLESNGYMaMGUYbC1FdXW18fO211zb4OvL8+jUcTeaJpUhB/saNuMLbEkwiymUeTJJzZtwPnPoUV/USUfNVVajxvVyUl5XrUpRc+1y8z8nAoLPVGFTO17KXuOPoVld6F4VJMZjcDhEEHP8AEBET6L1zj8Ou8Sb7keO0uFDh8MMPx/z58zF48GBcccUVuOCCC9CxowsvCAVCQldVqJC33uw9cWbbB7kIE5ts7r7IZFJsOlC0XX2v7V6oIBeJ13+vtvupk0nzChU8UUDkzkSFeJe+L8lEBQsRyBeSenoKFVa6trcex7wWJIkKotilrR9kvCgSHVSoIKQtQ0iEatewYQbwdQVw0iMtWwkok2nf3aOKfzOGAee+AyRkwhYSOgHXLlQXXxsiRQryOCtJH+opVFjl39eRpDY9OUp1yYqi0ChvskVzCnMkmlakDgTCXHAxz0w6USFtkNl7Qg5l1lh169atyM/PN+6rqqqMIgTRs2dPxMbG4vPPPzeSD2T/IiMjjQIEaU/x17/+9cBz/PGPf8Rzzz1ntIS48sorMXPmTHzwwQf48ssvDzxGkhEuu+wyjBw5EocddhieeuoplJSUGP9X15PiTtH3NG+RgpCU1jNfAl4Zr1ZAr/4C6Hea6w8XETVxEV6ulcj51MaZKllTkyj93icBgyarJK6Dx452LLhuDTlPk+PUqBoWVduJW352nVyocNxxx+H1119H//79/bNH5JXgWcG4ex2Pii/JaiuR3Ff1bTObXFA1ChU2wfYWvaXemGWC2MwVbCn96k7EkTuUFXsn+eMsNplPZDdSOCcnqS2Jk3YYs8a8Dz30kDFJK5O+spqsoODQlBTpE3yw9957D+effz4cTRehubFQoabGm6ggsftOM+AMIOQd4MNLVe9J6X8qqwIlbaEpy94HZkvf6xqg+3hg8mv2m1CQIgSrFSI0Ra9myVvr39fRiQp2KTwJJImVlWQL6REsq2ebc/4lj6udxkD+T1RIMznFkRzLrLHqPffcg7fekrkfZdiwYcb9rFmzMG7cOISFheH555/HTTfdhJqaGqOAYdq0abj66qsP/Jtu3boZ4115zNNPP43MzEy8+uqrOPHEEw885rzzzkNubq7xejk5ORg6dCimT5+O1NRUuJrMeRi94SUG+4JDvy7FUWNvBX54CPjpcaDT4UCs/9IyiMhCZNzcnJTVmmqVhrD+W7XgsHSP9zFyHiWtDQZOBroeVbcYyikF10T82bWc0NZMnmoy4GxospR8ILGLut/taVNAvrF9vnfVvRXoiHqJMrZ7PNSif6ltiYIyM5pVf2/lmO4vNK8FBZmTphDeTg2+iaj1pOegcHGhgllj3vLycpxzzjlG9O1rr73W4OPeeOMNTJw48cDHCQku+Lunx0wluXCd0nygslRFSjqp9UNtfU8CLvwAeO98NQH/2fXAaU8DYVH1P15+L+e+CCzw/J4MPBs47VnGbQYyUUFIS72qSv/1YtWtXuLd24aoUR2Hq98XacGBs5t+vLR0ErIqjvxHJuz35am/2fp3hcghY9U333zTuDVExqe1x6gNkaKGxYsXN/oYafPQWKsHV1r7jbrI2KE/kDa4/sccfROw+nMgZxkw4+/A6c+xBQSRG4oU3jnL26KgPsFhQJ9TgK2/AiW7vJ+XefMeE1Rbh27jWnY+ZceCa7IWs9IN+LNrKa2aTXj77bfx2GOPYd06tdK/d+/euOWWW3DJJZf4ev/cTVo/iD0OWGlvFdKPc/tCtd3QgN6sSXeJ2Laz1V+qQU5UEtDjeHP3Rd64YlLUhYyshUCPY83dHwpc6xHRLtUaaSlEdj7BDQr2pjplLa47seWiingzxrzSV1g0NgGsCxPS0tLgKnrMZFz8cZlCT5pCbAcgPBaO1WM8cPFHwL/PAbIXAh//H3D0zSrevraaKmD+68DmH9XHo/8ITLifUfaBlNwLCItWbQekWMFfaWoHEhXc8b7TYhnD667eb0xxrqfwIwjIHOn3XXM1/f2Qn9voRLP3hhyM87MubvvQf1LDRYLSO/6sV4CXx6pitpWfAAPODOhuElGASZJCY0UKQtrkrfpEbYfHqDQ6KfbudTwQ0S4gu0l0CKYbUGsKFSSu6+677zYqWo888kjjcz///LPRXywvL8+I7SIfJyrIxVZ5s+EK4baTaNLyvSpG1szWBPX1W7Z7ocLCN9R9rwlApAUGN1JdvulHdYGNhQruoFNJ2qVxtQCRr6rwpaL5n+MOrWSWeD+HXzSy+pj3L3/5C/7v//4P3bt3N/ZJ+vU2tIqurKzMuGlFRUWwdeuHkjy1mt5NqW6F29V9fCf/rVy3CokYvfRT4F+TVKrLx96o6HqNuQE47h7nHxerkfQ0KfxuSduBlpLf86LsukX8dGiigpBikcpy1Z+8qTQFSWWJSeaR9CedSJXS19ykQXI0q49VyQ+kZWzuKiAoBBh8XtNJo+P+phIVfp4GdD5CzZUQkbtJO5iRlwO9JqprTW46pybrYrqB67V4NufZZ5/Fiy++iEsvvfTA504//XQMGDAAf//73zkQ9iWJ3YlMAPYXqBPdzoe7/gfWZ20fknurFUBWEOfpt1rkmYC2I2lPsvEHtTqnn0WqtGVSSAoVdiw1e08o0K0fYl3es5LI31X4UrwgkcYOL1Sw8pj3/vvvx7HHHovo6Gh8++23+POf/4zi4mJcf/319T5+6tSpB5IaHJGoID00K/c33BLAyYkKCZ5CZqfrdBhw6lNNFykIWSHIIgVzZAz1tB1Y5Z/nl/Ng+V0XiWz90GCrJpk3kHZ3cvEqfUjDx1MKSkTaEF48D8QCCZE6yO8vRe5l5bEq+cmaL9W9zA8nNON9ccx1wKrPgOxFwPf3ApNe4kVJIrc76R9AxjCz94KIqA5Prm/z7dixA2PGjDnk8/I5+Rr5KVWhOVGO1PxCBVnxY5WKQZ2oIBd99u+FLS30xFN3HAG07wFLkOpxIT35yGWtH9LN3hMicgBfjnlvv/12I+2gsdvq1Z7Vl80gq+dk5dywYcNw22234dZbbzVaVDTkjjvuQGFh4YHbtm2ei952I21HQiLq9q13W6KCXJR0Cyksbg6rjOndKH1o3YuyvqZ/z6W1XLgFEtusSH7+9WRzjicxoSE5nkKFzFFwRB/bxoSEm5tIqedv0i3SbpIcifOzLlNTDaz9Wm0PmgwEN2NKXwo5pQWE/M3MWgAs/6/fd5OIrI7nTkTkgEKFnj174oMPPjjk8++//z569erlq/0iTUdc5qneyNRG2zyFCqkDrXMoZQWM9IUSezbCdmRl7ZJ/q+0+p1hndU5KP+8q+/02jbim1iUq6BW3RERt4Msx780334xVq1Y1epMWDq01evRobN++vU57h9oiIiIQFxdX52bbC3K6wFP3rXdbooJEthNZKVFBtx2oqvT98+vfc4mqZmpGw6RYXOxa2fBjqiuBXSucUaig+9he82PdW/djPV/vClz0kXnFy+UlQKGnrSNXLJIfcX7WZbIXqwI+SYftd3rz/11yL+DYu9X2L08DhZ6WSkRE5LsiWfm6PI6IAtP6QWJjzzvvPMyePftAD7RffvkFM2bMqHcyl3yUqJC/noeyrYw4zNXeuEsrTbq36wjsXgvs3th4XKcVrfpcpUFEJwM9PZNDViB9V2NSgJJcIGsh0GO82XtE/iQ9jHWiQrynnQoRURv4csybkpJi3PxlyZIlSExMNAoSHE/+xu/Z5L5EhQJPooJVkquIDrTTiwIqSlWxgqTW+VKR52IKi1AblzG86WQLadVntMyJAdIsVLTvyz62k14AnhsBFGwGtv4KDJxszr7tXuc9H9XFdUR+wPlZl1nzlbrvcVzLL4Yd/mdg5WfA9nnAjHuAM18Bglq8dpGIyJ10kaxc/2iI/F12eHtUIksVKpx99tmYN28epk2bhk8++cT4XL9+/YzPSfws+ZjuQysXsKltshbJ1UwgNk2tyrGSeE+hgkyq2M2CN9R9rxOA8Fiz9+bQVAUpVJB+fCxUcDYZLFaUqG1OZhORD5g15t26dSvy8/ON+6qqKqMIQa+ai42Nxeeff46dO3fi8MMPR2RkJL777js8/PDD+Otf/wpX0Bd9SnbBNSQZqqxQbSexUIEsRJLU0gYD2+YCO5f7vlBBJyrEc9KvUR2He9PFykqACE9aX207f/e2x5PiEieKSwfG3QF8excw53mg5/EqvdCstg/JfYCQsMC/PrkG52ddRJJM13+vtoec3/K2V/J+feZLwItjVDLDsveBIRf4ZVeJiBypviJZIjKnUKGiogJ/+MMfjL6477zzju/2gppOVCjYClRXN68HGdVv+wJ138GCEwb6wuoemxUq5K4FtvysKrH7T4LlyETc5tnAjqVm7wn5m05TkApW3UqFiKiVzBzz3nPPPXjrrbcOfKyLImbNmoVx48YhLCwMzz//PG666SbU1NQYBQxSTHH11VfDFfSYqXgnXKPQk6YQlWRuz3Wi+qQPVYUKuat8f3x0oQInBZsu4IpNVX8Xdy0HOo0+9DE5y71FDS29wGUno/8ILP6XKhb45SnguHvNK1RIHRD41ybX4Pysy2z+CSgvBmI6AD1amWQqqVwT7gOm3wbMeRbochTfX4mcRM4TQ8KBqvKGH8P2BERkUS266i0Tox999JH/9oYOFZcJBIUAlaXe/uvUOtvnq/vkftaddJeCFDtZ6ElTyDzMmj2TU/qq+xzPCiJyLp1GIpO0wS0OCyIissyY98033zQKEA6+SZGCmDhxIhYvXoy9e/eiuLjYSFyQoopgtxSzujFRoWi7N4HLasW2RBlDm2470Fq6xUtCZx7npnQcoe4l2aI+OlEhc5Szj6X8jTxlmrdFoS7QCKS8Nd4iHiI/4fysS9s+9D0FCI9u/fMcdg3Q5UiV0PD9PUBNtc92kYhM1i4duPhjoN/p6uPu44Frfqx7k/YFLAAmIgtq8YzmpEmTDsTfUoBOtPWErK7Mp9b1r9eFCmmDrHcEZeLZ6oUKBduA7CXe29a5wKK31dfSh1lzZaMkKuikColNJucnKlitrQuRXavwG+OSKnyOeS0qPlPdl+TBNQq31W0J5xbyd0b+3jTGJX+PLE1fjJVWhdVVvn3uvdnu/NlvjQxP+4dd9SRbyHmQTu7rdBgcr+tRwMDJqu3jD1MDeyGuqgLYvUFtpw8J3OuSK3Gs6hKle1SSqWhruwYpbJ70AhAWDeQsAxYzLZnIccUK0hpXdBsLo6C49o1FCkRkUS1edtqrVy/cf//9+OWXXzBixAjExNSN2L7++ut9uX8kEruqCUpZpdJnIo9Ja+RvBErzVeGHFSMYdTFKUbY1W3xIkcJzI1TVdX3mvQgsfE1VbsqgyCpiUoDoZGBfnurD1/0Ys/eI/EUnzkiiAhG1vQq/tMB7cSMqEYj3vE8JuSjoghNcjnktSqdQSaGCFKI6OcL84NYPSd3hKvJ3Rlb96Mm2+rjk75GlJfcGQqNUAuDu9UBKH988r0Rcl+31ng9T4zoOq7uavzadsiDnnJLY6AYnPgSsnQ7krQaWfwQMOicwr7tnE1BdoVrRte8ZmNck1+JY1SXWf6cKAdv3Uu172kreU094EPhyCjD3JXUxk++zRM6Ru1rds2CSiJxcqPDaa68hISEBCxcuNG61BQUFsVDBH4wVJD+piR9qHZ2mIAN7K/avb+e5AFSxTyUTxFnoYr+QCeKGihQ06YElF7asVKigUxWkn1/WQhYquCJRodbFVCJqHfk7rv+W78tXRV/tLdjex8845rV4ocL+AnVhVFaEuSVRIdF9v4dGEQILEawtJFQl1m2fB+xa4btChaId6j4iTqX9UPMSFaTwXd67o5MOLVRIG6y+X24gKWvj7gC+vRP47QWg5/GB+TnSFwdk3qGpRBiiNuJY1SVWe9o+9J/kuxZgI68EVn4CbJoNfHc3MPlNIDjEN89NROYpyVVz+EHBLFQgIltp8Vnqpk2b/LMn1LBET9QlCxXaXqggE2fyZm01MokhF4JkQCHpD1YrVLCzFE+hwo6lZu8JBSJRQV/AIiJqI455LUouvoVGApX7gb07gKQecE2iQnsX/F/JniRK1ihUWAX4KrxOfr91WlawSy6ut/Vvo6yIlRYPOb/XLdDWhQodR8BVRv8BWPwvVTzwy1PAhL/7/zUlBVNIiqMbEn/IVByruoCkm+78Xc1jDjnXd88rf5/OeAF44XBg10rV1nXkFb57fiIyh24bHt9JJWMSEdlEm67Y1tTUGDfyswRP1GU+i0TaXKjQwYJtHzR9gVUKFci3iQpCJuzImSQGUU7ga/cuJyLyIY55LUQmVnXLrL05cLyKUlXIKlioQFaVPrTuRVpfFirEd+QF3+bShQi6MEHIfI0+D8ocBVeRlcenTFPbq78AdvweuAsEjFumAONY1aHWeNIUMkcCST5uJyOJVSc+rLbnvQzs5lwkke3pFmAyF86UFCKykeDWxosNHDgQkZGRxk22X331Vd/vHdVNVJDJmrJiHpWWKt8H5Cy3/oSBLlTQK8PJd4kKul+o7nNLziJ/G6UXbFCI9VqPEJGtccxr8TGTGwoVdJpCRDsgpoPZe0PUcKKCyN+gCkh9WagQxyLUFrd/yF3l/VzBVqCsSF2090Vvc7vpeiQw6By5jAv8+LDvfj7rU1PtvUAgbTaIAoBjVQeTQrO1nkKFAZOBYD+kww6/FOhxnJpP+e4uoLrS969BRIGjCyalLRsRkY20eJRzzz334IYbbsBpp52GDz/80LjJ9k033WR8jfwgKgkIj1En175cpeIWO5YANVVAdHtrT3TpSXeJ6yTfiU1R33uZOMpazCPrRHs8xT2xHdgLloh8hmNeWH/MVLwLjle03ft/9lVfYiJfS+4DhEapBBApVvBloYKs+KTm0YUItecMJDJcf4/C27nzSJ7wkPq/y3FZ/l//vU5RNlBeov5WS+sHIj/jWNXhJA1HClal5dmAM/3zGkYLiOdUQawUWi143T+vQ0SBIe2uaqedERHZRIubPb744ov45z//iQsuuODA504//XQMHjwY1113He6//35f7yPJwDGhi+obJpVxblwJ0Rbb5qn7FJlAC4dl6RhjWfVCvk9V2PIzkL0Q6D6WR9dpdAqJ0cM4xOy9ISKH4JjXwiQKXhTvhOPp1kYJnRl/T9YVEgqkDVTt9nauAJJ7+7D1Q+e2P5dbSHqg9DHftxso2gHEpXvbQMiEtT9W49pBu1Rg/N+Ab+4A5r4I9DoRiErw3yrGxO5AeKzvn5/oIByrOpxOU+g+Ti3A8edc5EmPAZ/8EVjwGtB9PJDcy3+vR0T+ISnCRVnWT5QmIqpHi89UKyoqMHLkyEM+P2LECFRWMiLKbxK7qvvd6/z3Gk4lE2a1WwBYVXxm3Yhf8h3pzSV2LONRdSKdQtIuzew9ISIH4ZgX1i/u3JcL1yQq6HMBIqvSK7d21Wo70BZyob12G0RqmqQwSnG+yFnmXZErMke5+wgedo06J5RJ/F+eCkBfaJcWhVBAcazqYFUVwNpv1fbg8/xfrDrkfFXEJa0fpAWEvD4R2YtO1JJFXJwfJSKbafHZ0yWXXGJU7R7slVdewUUXXeSr/aKDSaKCyFvPY9PSnm66UCF1kD0m3aXfcmUZLEVaJ4RGNP6YkHD/rEzxZaGCnrAjh7Z+YKECEfkOx7wWplt5leTB8XQBa1J3s/eEqHEZnkKF3T5oVVi5HyjNV9ssVGiZjBHqftcK1Yojz7PQodNowO2pH6dMU9urv/BPAfuBvtCDff/cRPXgWNXBtv4KlBWqVsBSQOBvUghx+rNAZAKwez0w/5/+f00i8s84JKUvWwYSkfNbP4jXXnsN3377LQ4//HDj47lz52Lr1q249NJLMWXKlAOPmzbNcyJIbacnaHzV89MtZHJXYoGDQoDUgbC0mBQgOAyorlAXXlN8EJnqK9Ib9tqFwJqvga9vUScvpzylJnw0KVJolw5L0mka+ZuAsmIgglGcjmz9oHuWExH5CMe8Fm/9UJKrilL9vcrMCoUK7XuYvSdEzUtUkAsc1VVta8clhdsiLAqI9mPctRN1HAYseQfYtVr1Ka6pUkXnLHYCuowBBp8PLPsP8ONU4Nx3fNs2TicqpLNQgQKHY1WHWv2luu9zEhARE7g2Oac8AXx0FbDwTdUCQi/6ISLrk3GfsPr1DyIiXxQqLF++HMOHDze2N2xQF82Tk5ONm3xNC3LyhKEZErp6I86rqxkl2Fw6TUEmZqLiYWnST1T6iBZsBfI3WqtQQRcr6Ij9TocB6RZPqDi4CEQq0WVlVvZioNvRZu8R+SNRQV+4IiLyAY55LUwXpu0vBCr2qbhzJ5LY3b2e+PskFiqQxcnqrdBItYp/zyagfc/WP5f+uZe0rNBwn+2iK2QM9yZb6NQAmbAOayIdzy1OeEBdAJR45OX/VZHqvrAvXxXPIYh9oSlgOFZ1KGlRs/kntT3kwsC+9sCzgRX/U8kz390DnP8uV2YT2a1QIX2I2XtCROT/QoVZs2a1/FXINxeJ5aS3vFhN3PCCXPNsX6DupVenFALYIcpYChVkcs+KNsxQ9x1t1uNUCqekEnzLL0DWQhYqOIm0SdGT2fHyd5KIyDc45rWwqEQgNAqoLFXvAW25IGpl8n+rqVYXf5kaRFYnSWtyQTxrAZCzvG2/l0U7vK3xuACiZeR7IC355ELXuq/V5zp62kEQENsBOPZOYPrtwG8vAL1OUO8pvro4EJ+pEiyIAoBjVYda/z1QVQ4kdgUyAzz3Ju+5pz4FbPkV2LMRmPsiMOb6wO4DEbWubZpeXKjbsRER2YgNrtx6zZ49G6eddhoyMjKMxIZPPvmkztdrampwzz33ID09HVFRUZgwYQLWrfP0ZLQ7maBsl1b3JJian6jQob89jpZMxtVeIW616GH52ZOCj85HwHZ0ZN2OpWbvCflSwTb566/+RnJSkIjIHWQSVY+Z9u6EYxVu844P5cIjkdXpidHcVW17Hl2EKkXc1LJx8a6VQKIngSXPMxcSnQxkL/GMmwmjrlbzA7II5OcnfXNAJKFBJ4v4sp0EEbnPmq/Uff9J5qThxKYAp3paOS/+F7DTm55MRBa1e4Nq9xUZ703lJiJycqLC/v378eyzzxqVu7t27UK1tCGoZdGiRfCXkpISDBkyBFdeeSXOOuusQ77+6KOP4plnnsFbb72Fbt264e6778aJJ56IlStXIjIyEraX0EVN2uSuAXoeZ/be2GOl9Y4lajvNJn0i9Wq5AgsWKmyYqe6Te6uVKHaT4ilUyPnd7D0hXyrwVAzHpjKSkIh8yswxLzWDpIvlb/D2snciKRIV8Z3Z9o3sIX1o3Yu2bS1UMFIFqVmkCOG5Eeoc+GDf/k3dh0YA1y7kcZX0j1OmAW9MBNZ8qaLO2xqTnLdG3afZqD0i2R7Hqg4kiULZco4R5LvWNK0x4ExgxSfAyk88LSDeU+8hRGRNcq1IJPdhuy8ickehwlVXXYVvv/0WkydPxmGHHWYkGwTKSSedZNzqI2kKTz31FO666y6cccYZxufefvttpKamGskL559/PmwvsQuw7Tc1KUsNT9Ds2622ZTWJxKWFx6oev7tWAVEJQLt06x49iYoU0v7Batbrtg/D7blKRCcq5G8EyoqBiFiz94h8QaePSKGCHdq7EJFtmDnmpRYUdxa7IFFBzgGI7JSosHs9UF3V+nMGFiq0nJwD11ekUJt8XR7HAhCgyxHAkAuApe8BP0wFzvt3285x9QUCuyyQIEfgWNWB1npa9mQMUy1szXTqk6p9qiyk+u154Kgp5u4PETUsb7W3BRgRkRsKFb744gt89dVXOPLII2ElmzZtQk5OjtHuQYuPj8fo0aMxZ86cBgsVysrKjJtWVFQEy9LRPTLxQ81fRSKRjh9dobYlNvfij61brKBjjIuypPrGOj1ZZaJx4w9qO/Nw2FJMB9V/tHSPij7tdpTZe0S+oNNHdGscIiIfseqYlw4qVChxcKGCjmlP8sS4E1mdxN6HRAAV+1Sf3PY92liowCId8qPj7wdWfwnsXgf8/iEwpJWLW8r3eRcasC80BRDHqg4jc4CS8iIGnmX+AqHoJOC0p4H/XKiKunoc1/b0GSLyD10wyd9RIrKpFi8/7dixI9q1awerkSIFIQkKtcnH+mv1mTp1qlHQoG+dOlk4XlKvpsrfZPae2HcViSQslBbA8pPucjF9v4X2M2uR2h9Jp8iw6YmJFH1IL1KRvdDsvSFfJyqwUIGIfMyqY146qLizJM+5h6TI0/ohqZvZe0LUPCFh3pVcO1vZbk2S8Epy1TYLFcifpJ3hsXer7bkvqnPw1pBCB9QA0cnehESiAOBY1WFyV6siP1lgJS1prKDvKcCgc4CaauD7e4DK/WbvERHVt7jQGIvAvnP2ROR6LS5UeOKJJ3DbbbdhyxbPxSGbu+OOO1BYWHjgtm2bZ+WSFemJGulXW8HBoSNFtAMi4tT2bgsVpGyY4e07GxYDW6/yEjuWmr0n5PNEBU+RDxGRjzhtzOs4+mKQvqDpxAmnwiy13b6n2XtD1Hx6Rbm03WuNkl3qgogUPcRZNAWPnGPklaq4RlIYf57WtlWMKb3Vzy1RgHCs6jBrvlL33cZaKwX25MdUYZfMRf/6jNl7Q0T1zYvKws2wKCDZ5JYxRESBav0wcuRI7N+/H927d0d0dDTCwuqeiOXn58MMaWkq9nvnzp1IT/cO6OTjoUM9kyX1iIiIMG62IAPD0EhVwbp7A5A2wOw9In+lKuQWAXs2AJnDrXGM13sKFTqOsE47itbo0E/d72jlCi+ybqJCPAsViMi3rDrmpYMTFXKt1S7LV+RibXUFEBzKVeVkz0KFvLWt+/dFnrYPsamqjQSRP4WEAqdMA14/QV0kHHB2y9s35HkKFTpwfoYCi2NVB6muBNZ9o7YlwcBK41ppoXr6c8C75wLL3lcFtCn96nlcgrUKLIjclMYiknqqYgUiIjcUKlxwwQXIysrCww8/bLRVCLLI4Klbt25GscKMGTMOFCYUFRVh7ty5+NOf/gRHCAoGEjqrSR95E2KhgnMn3nNXeS/Amk0iMLMWqO0uNu/TrU+m8jeoXqLh0WbvEbVF2V6g1HOhMN7CbXuIyJasOualg9pllRUB5SVARKyzDk2hJ+VNJnzDIs3eG6LmkwQ2IYX11dVAcAtDHPdmq/t2GS3/t0St0Xk0MPRCYMm7wI9TgfPebVlvePaFJpNwrOog2+aqdraR8UCfk2E50kZV5qQl8WjWQ/U/RlpWXPwxixWIzCpUSB1grSInIiJ/Fir8+uuvmDNnDoYMCXzPm+LiYqxfv/7Ax5s2bcKSJUuQlJSEzp0748Ybb8SDDz6IXr16GYULd999NzIyMjBp0iQ4RmJXVaiQ5+k9RM6NMpbedFaw8Qd1MiJFMome9iN2JSuzIhOB/XuA7MVAV5sXXridLuaRlilyQk9E5ENmjnmpmau7wqKBin1AcQ4Q4bD2CBKvqwvxWnLBjMgKCWaShFBRAhRsApJ6tOzf782pW4xEFAgT7gdWfQHsXq9WDEvhQnNUVah/I1qaxEDURhyrOrDtQ68TgUhPO1grkSIKmRdsTFU5UFrAQgWiQNMFk2mDeeyJyLZavEShb9++KC0thRkWLFiAYcOGGTcxZcoUY/uee+4xPr711ltx3XXX4ZprrsGoUaOMwobp06cjMtJBq5ASPBeK9ckwOTfKuGArLNX2IWOYih+2M6ks1e0fsheavTfkiz5sIjbN/j+bRGQ5Zo55qZnv6XrMpC9sOrFQQYqUiewkJEyt6BI5y1v+7/d6Wj8kMC2rRaLbA6FNtMqQr8vj6FCxKcBxal4J815SqYLNIYsLpE1PWIyKQycKII5VHUKSwTbOUttDLjB7b4jITqQFom5BlcFCBSKyrxZf2fnHP/6Bm2++GQ899BAGDRp0SL/euDj/VX6OGzcONfIHuAESyXv//fcbN8eSVe0in4UKjqVXD+nIXzPJ79uGmWq702g4ghQqbP0VyF5q9p6QrxIV2qUy3oyIfM7MMS+1YMwkxbtOLlRI6mb2nhC1nKwsz17kiaI9o3WFCmzr1TJS2HHtQrXqtSFSpMACkIaNvBJY+Baw83fgp2nACQ80fdz1xYHkXkCogxbIkC1wrOoQUqRQWQbEZdq/3SoRBZaMm6UtrizeYqICEbmpUGHixInG/XHHHVfn81JAIIUCVVVVvts7OlRCV2u1BbASmXiRN+bqyoYfIz3TohJgi0KFomxAfp9CQsyNjyrKUiujOh4GR0jpq+5zlpm9J9RW+u+gJCoQEfkYx7w2oMdMxTvhOLpgNZGFCmRD6UPrXsRtCTkHEnZvOWcGKUJgIULrSZudU6cBrx0PrP0KGHiWShVsTtyy0b+dfaEpsDhWdVjbh/6nA2FNJOMQEdVmFAV7zhnD2/HYEJF7ChVmzfLEUZE5Ej2JChJFWJIHxCTzO6HFdlDFCjJZPWgy0G/SocdGihTapVv7mBn7FwRU7gf2Zps72bTB0/YhdaD1CzyaS7d+2L0BKN8HhEebvUfU1tYPVv+dJiJb4pjXBuJ1ocIuOIokWhV4ChUYJU52TVTQ4+3qaiC4mR0npf+1LjzSLQ+JAqnTYcDQi4Al/wZ+mAqc/54qYGgI+0KTiThWdYDiXGDbPLU95Hyz94aI7EYXBctcd3PH20RETihUOOaYY/yzJ9Q84bFAdDKwLw/YtQrodjSPnLb4X2piS47P6D8DkfH2PDaSXiBFF/J/yd9obqHCek+hQscRzlkhIqvvIxOA/QVA9hKg6xiz94ja2vpBr6glIvIhjnltIC5D3e/LhaOU5gOVpapwla0fyI5S+qkku/JioGAzkNS9ef9OCvElHS8oGIjP9PdeEtXv+PuBVZ8D+RuAZe8DQy9sRl/oITyaFHAcqzrA2q/ljwmQOgjoMMDsvSEiu9EFk+kchxCRSwoVli1rXkz64MGD27I/1BwSgymFCnJSzEIFpbIc+OlJtT3obPsWKWjSm04KFfZsktNPc/ahohTY8ova7uSgi/lScCGVplvnANkLWKhg69WmnkIFTmQTkQ9xzGuz8ZIoyXVm2wcpXJUiZSK7CQ0HUgcA2YuBnOXNL1SQPrsiJgUIi/LrLhI1SFIrJ9wLfHkzMPcloPdEIDrp0MdJ+qEU4wSHqQRCogDhWNWBbR+k1UxIi9cSEpHbMdmJiByi2aOgoUOHIigoCDVycagB8vWqqipf7Rs1JKErkLUQyFvPY6RJNGPRdtX6YeC5zlghmL0QyN9s3j5s+VW1n5CEig594bhVXkahwlKz94RaS1bcVexTq0119DcRkQ9wzGvDRAV5T5BzFKekPxVu9xbicdKa7Cp9qCpUyF0F4PTm/ZuibHXfLqPxuH0ifxtxBbDwLSBnGfDzE8AJDzV8cUCSb1hURgHEsapD5K0Ddq9TxU4Dz4alyVxraARQWdbwYyRJySktY8lZpBC2tKDhr9uhTXR99uUDJdICMcjbdo2IyOmFCps2ycpuskyigtjNQgVvmsI0tS2DeycMjPWFV71i3AwbZqr7jGHqhMRJJFFB5Pxu9p5Qa+nfDTlhDovmcSQin+GY14bjpbIitao1oh0claggxclEdiUTpgvlQsza5v8bnajAIlQymxTKnPok8OpxwNrpwICzgY7D6z5Gt31I6cu+0BRQHKs6LE2hyxgg3sSWr80hLWmvXQjs2+393Ne3A9vmAIPOBfqdbt+LveRsMrZ85yygqrzxIpuLP7bfz2/uam9xu8yNEhG5oVChSxfPxXEyX4Lne2G0BSAsfQ8o3ApEJQGDznPGAYnThQpbzduH9TPUfeYoOI4kKuhin/J9QDgvdNvOHk/aSGwqEMyIRCLyHY55bSQyQRWrScLO3hwHFSp4EhWaG5dPZNVEBT3erq5u3oVc+T0WVr9gQ+6QORIYdgmw+F/Aj1OB89+re97BvtBkEo5VHaC6ShVBiUGT7VHsJMUKctN6HqsKFaQFm14MRGQ1kqTQWJGCkK/L4+xWqFCnYJJJZERkbzYYCVGDiQpyEbuq0t0HqKoC+OlxtT3IIWkKtQsV9ER1oBVmqZjWoGCg8xg4Trs0IDIeqKkCspeYvTfUlkQF+V4SEZE7SasHPWYq9lzgdFKigsSJE9lVh/5qhZqknRRuaVmiQu0LIURmmnCfOm/M3wgs+0/dr7EvNBG1lrTzlch2aRvT9zR7HseOI+qu6iaiwDowDhnEI09EtsdCBTsyenaGqYo/OWF2s6X/UQUbsqJu4LlwDD3pLicu5aXmtX1I7g3EdoAjL2zoVIVsyaQl29nDQgUiIqoVEa9XYjuBLlRt38PsPSFqvdBwVawgdi5v3r/Zm63uEzrzyJM1xLQHjl/o2zIAAIaHSURBVLtXbc99BSjJU9ule7x9odOHmLqLRGTjtg89jweiE2HrQgUpMtR/G4nIhEIFjkOIyP5YqGBHEucj/Ydqx/y4PU1h4FlAdBIcQ3pLhUYANdXeleOBtMHT9kH6cDo1PkpH0+1YavaeUJsSFWwWzUZERL51IFFhpzOO7P4iYH+h2k5ioQLZXIan/cPOVU0/tqamVqIC206ShYy4XF0EqCgBfp5W9+KAFMuxLzQRtURFqXfObcj59j12kmjbvpfa3sGkUmomGevtWtXwTY8FW3udIG+dKgT69Rng8+vVzYnKS1QbbNFxmNl7Q0TUZixUsKvEruo+by1c6/cPVZ96SVMYZOPBfUMr/iU5Q/d1DXSvvA2z1Hbm4XAsnaiQ8ztcq2Cban3R0E2+bvVEBX2BiojIATZv3oyrrroK3bp1Q1RUFHr06IF7770X5eV1+2ouW7YMRx99NCIjI9GpUyc8+uijcK0DhQqystVBaQpRSc5paUbulT60+cX1+wuAyjK1zUQFshIp3D/1SbW97htgxcfA5p/Ux3LOLokhVj93IrLgWPXDDz9E3759jccMGjQIX33lSRnwqKmpwT333IP09HTjtSZMmIB169bZ/3u5aTZQsQ+ITQO6HwNbyxyl7t08r0bNJ0UI75wFfHBxwzf5elPFClLcWrRD/S4teB345m/Au+cCLx8J/Od84Lu7gUVvAVt+AUrznfkd0teDYlKAOM/1AyIiGwttyz/Oy8vD3LlzUVVVhVGjRhmDRwoQvcokL8AXsa2iqhKY/ZjaHnCmimR04sT7nk2qGCOQshericLwGCDDwfFROlFBCkGkvUZ4FFxFJtKeG+GdEK6PpHpcu9B6fYKlmEZfyNHpMkREfhSoMe/q1atRXV2Nl19+GT179sTy5ctx9dVXo6SkBI8/rlKkioqKcMIJJxiTtS+99BJ+//13XHnllUhISMA111wD19ETM/ty4QhF+v2tIxASZvbeEPkmUUHG29XVQHAj6yRkwlnI6nTp2U1kJdIOMShYJR7Oesj7+e3zgFeOsfa5E7mC3caqv/76Ky644AJMnToVp556Kt59911MmjQJixYtwsCBA43HSHHDM888g7feessojLj77rtx4oknYuXKlUZxg22t+VLd9zsNCLP5PFTmCGDpu8CulWbvCdlBaYFqY90Y+bo8Tqenlu1V48jd6zz3npskCtQnLAZo3x1I7guk9gPCooHpt8NxdLJTch+eMxKRuwsVPvroI6OKtnfv3qioqMCaNWvw/PPP44orrvDtHlL9Ej2FCrs3uPMILf8vkL8RiIwHBjssTeHgnsuBLlRYP8O7AkoGeE4lg96IOKCsSMXUdTkCrrJvd+NFCkK+Lo+z2mRbUTZQXQEEh6pVCEREfhTIMe/EiRONm9a9e3fj9V588cUDk7///ve/jVVrr7/+OsLDwzFgwAAsWbIE06ZNc2ehgi5YK3ZIb9xCz4pcRt+TE3ToDwSHAeXFKp5WpwLWZ2+2d4we0qb1FES+J+dEUqRgx3Mncjw7jlWffvpp43luueUW4+MHHngA3333HZ577jmjuEHSFJ566incddddOOOMM4zHvP3220hNTcUnn3yC88+36Tyg/I3Y+pvaHnwebK/jSO/qbllQ4tTWsRRYy/4DlO5RBQkNtfcLClEJXMm91XgzdQCQNkiNNaVwUJKKhSQeOZFOK0tVhV1ERK5p/VBcXFzn4/vuuw/z5s0zbosXLzYiu+68805/7CPVJ8EzySMr7t1GBr86TaH/JCAmGY6OMi7w9JwKFN0rT0449MDOieT/plMVsheavTfUEgVbvBFncgJCRORDVhvzFhYWIikp6cDHc+bMwdixY42JX01Wl8kk8Z49e+DeRIU8FQNqdzoxKLGb2XtC1HYyTkvtr7YlHr8xOuaX8bVERI4fq8pjJHGhNnmMfF5s2rQJOTk5dR4THx+P0aNHH3hMfcrKyoxEh9o3S1n3LVBTBaT0BTIc0FdeLg6HRqqCRFlMRuQLq79QbRt0kUJsKtBlDDDyKuCUacCV3wC3bQKuWwRc9AFw/N+BwecAHfoCYZF157IlqaupecOQcPu13NOJCk5OQiYiV2l2ocKIESPw6aefHvg4NDQUu3Z5e8Hu3LmzziCUApSoULILKC101+Fe/pGqqpTV8IMvgGPpQgVZfRQoUrG6fb7alkGg06XoQoWlZu8JtcQeT6FCu1RW7BORz1lpzLt+/Xo8++yz+MMf/nDgczJpK6vJatMfy9dsOWnri/GSJCSV1Z24t3WiQlJ3s/eEyDckpU3sWtW8QoV4rkYnInL6WLWhx9T+eu1/V99j6iOtJKSgQd86dbLYe8qar7wtbJ2QHiRtyvT7/A7Oq5GP9JwAHHcvcOEHwJRVwE0rgSu+Bk6dBoy6Cuh8uEpYbqylmCYpR9KW6Zofvbejp6ivJfUEzn0HuPhjb6sJO5D2GPmeVuD694+IyC2FCt988w1eeeUVnHnmmcjOzjZius477zykpaUhOTkZt99+O1544QX/7i15yRtypKfaL3e1u9IUfnxUbQ+YBMSmwPmFCtmBWyG48UcVaSkThI1FszqFTlTIWWb2nlBrEhXY9oGI/MAfY175N0FBQY3epOdvbVlZWUYk7jnnnGP0/m0Ly0/atnVMLL1HRbHnQqcTEhXa9zB7T4h8I2No3Yjahuz1XHhioQIRkevGqr5yxx13GAkP+rZtm6cA1AokEXfXSiAoGBh0Lhwjc2TzkpOo6YJNKeps6KYLOt3g2LtVMUHvE1XSVluLeqRYQcaj+jb0YvX5gs2q3Z6dihR0G3C5PhLRjsXtROQYzf5L37VrV3z55Zd47733cMwxx+D66683KmflVlVVhb59+yIyMtK/e0t1SS+mnAIV99N5tDuOzor/AbvXqTdjJ6cp1I49LStUSQfR3ig9v7d9yBgOBDuguru5hQqS0FFeCoRHmb1H1Bx7Nqv7WJudTBCRLfhjzHvzzTfj8ssvb/Qx0uNXk0nn8ePHY8yYMcZEdG0yCS0r5WrTH8vXGpq0nTLFs3IEMBIVHFOsINGe8ZmqN65c6JQ+pXZVUQqU5KptFiqQU+iVXjLerq5uePVbUXbd5EAiInLsWLWhx9T+uv5cerr3vF8+Hjq04RXEERERxs2S1nyt7jsd7qyFQR1HqPvcJpKTqGFShPDOWWqlfGPtCey28t+qJLlOFgcWZQFZC4BuY2EruvhXznvZDpeI3JaooF1wwQWYP38+li5dinHjxqG6utoYJLJIwQR6YCsX7t1AJrZ0mkL/M4DYDnC08BhvaoZUS/qbpDasn6m2O7mk8KVdhip6qa4EchhTZ7vWD/GeYh4iIj/w5Zg3JSXFmDRu7KYjemV1mryexPq+8cYbCD7oot4RRxyB2bNno6Ki4sDnvvvuO/Tp0weJiYn1vr5M2MbFxdW5OTKFSq/ItiuZLBMyNolx+DiX3EP6VweHAWV7va1N6qNXCkoxPhEROXqsKo+ZMcOzUKbWY+Tzolu3bkaxQu3HSKHt3LlzDzzGViS5VBcqDDy7eZH1dktUyN8EVOwze2/sqbSg8SIFIV+Xx5FvCt27HaO2t/1mvyMqC1b1GFv+L0REDtCikdFXX32FJ554AgsWLMCrr76KRx99FBdddBFuueUWlJaW+m8vqX4STxSoi9hWsPITVTUYHgsMuRCuoCfe8zf6/7VkJWLRdtVjLvMwuIIM6FI8qQpZC83eG2pp64e4TB4zIvILs8a8euK3c+fOePzxx5Gbm2v04a3di/fCCy80JoqvuuoqrFixAu+//74R+Vs7McF19HipuO7KPNvRF3Hl/yPjMSInkJVeOsWsoVhoKWIoL1bbCQ5aZUpE5Cd2H6vecMMNmD59uvF/kJYSf//7343/y7XXXmt8XVpN3HjjjXjwwQfx2Wef4ffff8ell16KjIwMTJo0CbazYymwN1u1KxtwBhxFWjbJQrKaKiCH7R+oEcZivKCmx43R7f1/GLt7ChWyF8O2hQrpQ8zeEyKiwBcqSAzYFVdcYVTr/uEPf8ADDzxgRIwtWrTIqNYdNmwYvv7aUx1KgaFjMfM3uDBNIRWuEN+xbtS9P633VKqnDgSiPEkObqAnTuXE0U1k4N9URFigThBaomK/d8WdRH0TEfmYmWNeWUkmsb2yeiwzM9OIutU3LT4+Ht9++y02bdpkrGST/b3nnntwzTXXwLX0eKl4F2ytcLt3RTlXx5CTSD9gIb2566PTUCLigKj4wO0XkdPPnciRnDBWlZYR7777rtE2YsiQIfjvf/+LTz75BAMHDjzwmFtvvRXXXXed8e9GjRqF4uJio7jBlom+a75S9z3GA9HJcBQZs3YcpbZzlpm9N2RlO6QooAYICgFOfwG45sdDb9cuBBIC0KJQt3uQ1mR2SqqorlILDWu3VyMicoBmN6F/8803jYGmDDLz8/Nx+OGH4+677zaqZGVQLJFjMkA+6aST/LvH5KVXm8jq4sb6fTrBqs9UvzNphzDkArhuhaBeQe5PGzyFChnD3TU5rgsV3HZCJQN/OQFY/SUw/TYgJkUVAMlKt1FXA8MuVhNtgThBaM1q09BI553gE5ElmDnmld7ATfUHFoMHD8ZPP/3k89e3rThPK6B9ebA1/R7npL7FRAcmUt/2TqweTFaZCum7HNzsKQqiwJ877dvd8GOseO5EjuSUseo555xj3BoiqQr333+/cbO1yjJg/Xdqe/D5zpxvyxwBrPkS2LnC7D0hq5L3z9mPq+3RfwKGXWju74KcP7bvqQoVts0Fep8I25wvVpaqOVGdEExE5ADNvrIdExNjVMOKbdu2HVLB2r9/f06YBpqsJpYqxIpSoGArXJGm0O90IDYNrpt49/f3V1apb/5FbXceA1fRA7u89eo4uIlMpOl0goxhQI9j1bYM1GXlmxUn2vZ4inbk70AIJ7KJyPc45rUh3QqoJBeOSFRI6m72nhD5J1FBxpg1NYd+vWiH99zHiRdwyBnk3Eh+lhu6WfHciRyJY1Wb2fyzanEki0N6ToAjdRyp7nNXm70nZFWzHwPKClVxwLjbrDHe6+Zp/7B9HmzX9iGpBxAebfbeEBEFvlBh6tSpB/qBSaSYVOmSyaR3rb6QnefgweDqL4BdK1QvtyEmV1yaNfFe4Flh5y9bf1UVmbIKRCcMuCm1IqIdUF3hvvYPYsuv6j5tMJB5mNqWauKqClhSgacNSrtUIMjBKTJEZBqOeW2eqFDfRVC7YKECOVWHASopoazImxxSG9t6ERE1G8eqNrPW0/ahzynOvbAoC18QBJTs8rZzItI2zlKpIjKHd/LjQGScNY5Nd0+hQra0pLCJPE+hQmp/d10fISLHa/ZVnosuushIUvj000+xefNmnHHGGf7dM2qehC7qPned89MU+p6m4kDdOPEucahVlf57nfWetg8dhzfde9NpZGCnUxWyFsJVyvcB2YvUdscRQHJvVbRRsQ/Y+hss6UCiQqrZe0JEDsUxrw3Fe1plyWq1smLYkhQI6ou1stKIyEnCIr3F0NJm7GD6Zz+hc2D3i4jIhjhWtZHSApWoIIacD8eSC88pfdS2GxcAUcP2FwE/TFXbwy7xphhYQdejVYGNtFsu3glb0KklstiMiMhBWrQctX379hg1ahQSEhL8t0fUMoldvDGaTrT2a2Dn70BYFDDsIvdVC8rFWGnvUVUOFGX573U2zFT3GZ64NrfRE6duO6HaPh+orlQRhAldgeAQoOOouj8TViMnEMJtRUtEFFAc89pMRBwQHqO2iz0XPO1GLtTWVKt+o5L2ROQ06Z72DztXHvo1vfoynoUKRETNwbGqTWz4Xs25tO8BZDp8vk3//+orSKTG1Zc2dbDgMCDKhtdjfnkK2Ldbta+ecC8QbKFk1OgkIG2Q2t46B5YnyYG6UCF9iNl7Q0TkUxZ6d6BWkYuLTi1UkDfgH/5RK03Bky7gtvYesR3U9u4N/nmNomxgl0wYBgFdxsCVUvqq+5xlcGXbh9SBQGi42u7kKVTY/BMsnaig00aIiIikkFW3y9Irs+06QSnvbyGe92QiJ8nwFCrkrT30a5IeJxJZqEBERA6y2tP2of+Zan7PyTp6ChWkdS81X3kx8OszarvrWOD/ZgLX/KhuV8/yFnpK+9NImxUqSFvZVZ+q+eaJj6h2w1bTbay63zYfliepD/sLVQsNXWBBROQQLFRwSqLCnk1wnLXT1YVjWVk29EL3pSloUnXqz++xXjkvsf9ujdPXiQp564CK/XCNLb+o+9oD3MzR6j57iTXjs3Wigr4gRUREVKddlk374hZu964ot9JKIyJfSR/mKb5epwrStYpSoHRP3SJ8IiIiu5OxXc5SdVFx8LlwPJ2oIPNq1VVm7419zH5cFVpLauhZrwCZI1Rxp9ykPe+FHwAxyernSbdQsEur2ZkPqu1Bk4E+J8GSuo9T9zuWwPLy1qj7xK5AZLzZe0NE5FOcBbO7BE+hggxqZBDgyDSFU919UVLH/+7Z7J/nXz9D3csAWKL/3Uh+vsLbAdUV7klVqCxXrR9ExxHez0tv4JgO6lhsng3L9bbTE9nxnczeGyIispJ4z3ipeBdsnaigi5CJnCZ1ABAcCpQVeQtzRLGnuCgs2por7YiIiFpjzVfe+Zb2PZ1/DFP6qffyin2qWIGatnEWsPpzlThw0mNAXD0tTiVJ4ezXVcHLmi+BlZ/Z48jOfUElZklK8PEPWHe+ufMRanwq41Gd4GpVuWu8v2tWPZ5ERK3EQgW7k8kc6ckrPW11ZZ0TrPtWVTMaaQoXuzdNoXahQsFW3z+3VDnLwFh0OhyuJT9fuv1D1kK4QvZioHK/io5r36vuseh0mNre4PnZsFqagvQij4wze2+IiMhKdFGrRGLakb5wm9TD7D0h8o+wSO94e+fv3s8Xedq1tEvztiIjIiKy++KrNV+r7YFnueOiYkgokOFJT5IkCWrcvt3exIFhlwB9T274sd2PAcbeqrZn/wPYvdHaR3fHMmDpf9T2CQ/VX4BhFRGx3sVbW+fA0nJXq/v0IWbvCRGRz7FQwe7koqJOVdCVdU5KU+hzsrf1AdxeqOCHyk6J95cV6lLsovvGupVu/7BjqbvaPqT2B0Ij6n4t01OosPlnWIqubpaKcql4JiIiOrj1w748eycqJHUze0+I/Eefb+xa5f2cJAOKdhnuLk4nIiLn2LUCKNyqFl8NOBuuods/5Cw3e0+sP+8tRQr7C4Ck7sDx9zddzHLMrUDXo4HKMmD6Lap1lhVVlQMz75f/pJrTH3AWLK/bMeo+y5M6a1V6gWr6YLP3hIjI51io4ATSm0g4JVpr/fdA9iIgJIJpCrUn3guz/HOsdTVmWAxc7UChgktaP2z5Vd2n1jPA1YUKMolcYqELPrpYJzaNE9lERFR/64eSXPsdGUm40uM8N0QDk3ulewoVaicB6kIFtxenExGRc+g0hW7jVPS9W3T0FCrkeVZ+U/1WfaparcoCnNOeAaITmz5SUsgw+XUgJkW1Bv5hqjWP7vxXgT2bgKhEYOI/VNKG1UlihcheqopIrKi0ANjraZemk0uIiByEhQpOoBMVdq+Ho9IUJPYqobPZe2Q+PWknE+9lJb597g0z1H3HUbzwKz2+xO51QEUZHE0uiGz9TW1nDD/067EpQIIUQNV4f0asQE7GRGyq2XtCRERWTaCSAjurTjA1pGQXUF2hJiv1uJ7IifTEqpy36t9TXaiQ0Mm8/SIiIvKV6kpg/Xdqe/A57ppr04kKMndTVmz23li33dtPT6jtw/8CdD2q+f9Wil6kWCEoGFjzJbDyU1hK3lpg0Ztq+9h7gESbnNdkjlLpJ/v3WLetthxbvZgxOtnsvSEi8jkWKjiBfuPfvQG2t2EmkLUACAlnmoIWmQCERqmLxlKV6stqzO0L1HbnI3z3vHYuCAmPVTFlOQ5PVcj5HSjfq1I0dJLEwTqNVvcbf4T1Wj94UkaIiIgOLlQoLwbK9tqz7UO7dCAs0uy9IfKf1AFAUAiwvxAoyjqoUIEF6kRE5ADb56v3OVlR3vtEuIpcRJXxbE21mneiQxcNfX8vULEPSBsMHHNbywtZuo0Fjrldbc9+BNi90ToFOjPuU/9HaaUw/BLYhrTD1fPielGX1eR6UkpS+tojpYKIqIVYqOAEeuVVwWaguhq2JatqfnxEbUsfK64oU2TQquOM831YjLLpR6CmSl2g1+1D3H6cZcAnshbC0bbOUfep/YEwKYKpR6dR6n7zz7Bc6wd9MYqIiEiLjFMFh7UvfNppZZWQMVlT/WmJ7EzGnR084+2dngsYRbr1AwsViIjIhgq2AdlL1E0Wvaz8RH0+c7RaUCZfdxOdquD0BUCtseQdYMcStRjt9OeAiFa24B37V1WwUFkGfH0LUFEK0y1+R11Ml/Oxkx8DQsJgK3I8hSyetHKhQtogs/eEiMgvWKjgBPGemExZPab7FdnRxh+AbXNVmsKwS9wVj9YUfWFWryj3hfUzvNH/dhtA+otOF9ixFI625Rd1nzqw4d8z6S0ocXJSHODLn7u2FDIVbFXbCexhTEREjbTLsmuhQmI3s/eEyP/SPe0fdq0CqipUezvBwmkiIrIbKUJ4bgTwyjHq9vpEIHuR+tq66epz8nU3FSvIXJLYtdLsPbGWvHXAby+qbUlSSB/c+ueSwuazXwNiUtSixVkPw1QyZzjvZe//LaUPbKf7Md4Cm6pKWI5uSZE2xOw9ISLyCxYqOGVlSmxa3Qo7O6cp9J7INIUGCxU2++54S5sNkXmYb57TCVL6Ob/yW773W36tO1Fcn4h23oSJ9d/DdDKJLfF4CFJxgkRERAfT7w/FO+1ZqJDEQgVygYyh6j53jed3tUYVqrfznM8SERHZxb7dalV7Y+Tr8ji3JSrYdX7aH6TF7Hd3AdUVQJcjgcP/3PbFebEdgMlvqAVGa78CVniSPAJN2nzMvF/9HzNHAYf9AbaUPhSIiAPKS7ypX1YhiRl6AZkeRxMROQwLFZwi0dP+IW8tbGnTbBVHHxzGNIXGJt71inJfVPJKP2Q53p1G++Y5nZSoIL9HFU2cbNqV/N/kJDkkoukKbl3EIm1CzKYH5THtgbBos/eGiIisXNhpu0IFzyo7JiqQG8hEsNi9Htibrbal6F6KFYiIiMj+7/Ny8XxfHlCYZfbeWMPcl9S4JzIBOO1pICzCN8/b7WjgmDvU9k+PqlYjgbb8v552FpHAKdN8938LNEmp6HqU2pa0ZyuROXwp7I1K8qZqExE5DAsVnCKhq7qXgY8dHUhTOJGTtI1NvPuqUGGDp+1D6gAgKsE3z+mUyOjwGFUJbLUKWl+3fZD+wLqXd0M6eQoVJIFBkhjMJC0oRGwqEBxq7r4QEZG1Wz/YqVBB3l91okL7nmbvDZH/pUnrsRBgfwGQvVh9Li4dCObUBBERke1FxAId+qvtHIe3VW0OGesselttT7gPSO7l2+cfezPQ7RiV3PH1LWr1faAU7QB+fVZtH3kjkDYItibHUWQtgKXkedJJJPU2lIW9RORMnA1wWqKCHQsVNv2kLp4aaQqXtj3+ysmFCkVZvrlgvN5TqNBxhKp0JkWOhW53kLXQmUdFt31IHdj071r6ELW6Tdou7DS5v6Bue6Lb3BARETWUQFWSZ59jU5rvbW3E1g/klraFunfxhll1z3WIiIjIOe0fchy6AKi5pI3A9/eq1fB9TgGGXeyfJICzX1OtIGSBz6yHEBAyN/3Dw+o8JnUQcOQN9p/P7+4pVJD5z6ZaugSStEvTiw2JiByKVyidIsFTqJC/CbZNU+h1ApDU3ey9sfbEe3kxUJzbtueq2A9s/lltdx7T9n1zmhRP+4fsJXAcOZHY7ElUSB/W9OMlui1tiNre8D0skajA/sVERNQQfbFTCuzsQqcpyORiU0lHRE6R4RmH7l7nOZftbOruEBERkQ919BQq5K5y92H9+Qm14EySQU95HAjxUzpobAow+Q21+Grt18CK/8Hv1nwFbP0VCAkDTn0SCHdAi1ZZuBaTAlSVWWvxmi5UkMVkREQOxUIFpyUqFG23VtVfc1Z3b/5JRbkPu8T+1Zf+XHkkvajEnjb2HNs6B6gsBaLbAx08F+XJSx8TJ1Z+y8V+6QUsv2+6P3BTdPuHTbNhqj26UMFTtENERNRQocK+3ea3LGquwm3ethX+mrwkspqDx6G6bQsRERE5J1Ehbx1QVQlX2vQjsPJTlZp28uPeBWj+0vUoYNwdanv2Y0CeHxOX5VzrpyfU9mF/8H6/7U6uSej2D9vnwRKqKrzp2RnNnMclIrIhFio4hVRnhkYA1ZXA7jZeyA6kH/6h7nseD7TvYfbeWJuewNu9sW3Ps2GGdyWT/MxQ/YkKeWuAynJntn1I7g1ExTfv32SOUvdb55p7gqkTFRgNTEREDYnv6E2gKiuyV6FCQlez94QoMAq2AeExdT9XU6XSzOQmXyciIiL7kjknSQqr3K/m1tymdA8w80G1PfQioO8pgXndo/+qLrRLIsD0W4GKUv+8zuxHgbJCoH1P4JjbnLXosNtYdW+VRIU9m4DqCjV2bt/L7L0hIvIbFio4hcQ7xXsiM3NXwxa2/qYqTKWf1rBL1f+BGqarb/dsbttRWj9T3Xf0XICmuhI6AWHRQFW581IVtvzi7WvW3N83SZiQAXH5XmD7fJiiusobja0vQhERER0sop26iaId9jg++v0tqZvZe0Lkf1KE8NwI4NM/1/3817cBrxyjbvJ1FisQEZEdSFJpUwuA5OvyODeRed6Ow9X2jqVwFUl1m/UgUJoPJHYDTrhfHY9ACA4Gzn5NtZSTxT6yH762YSaw/ns1p3jyE0BkHByl+zHedgtlxWbvjfcajxQpcLEhETkY80WdJLGr6vMp0Vp2SlPoMQFI7mn23lifXklesLX1zyGT9rtWqOixLmN8tmuOIoNt6UuWvUhV0GaOgOMSFdI9fYGbQ9pEZIwANs9WJyRdjkDASU8/SYuRfYlNC/zrExGRfcRlqn64xTuADn1hm0SFpO5m7wmR/0lUcFNtCuXr8jgpHiYiIrIyea+6dqF632qIFCm48T2t40jVQnTncrjK6s+BjT+o+avTngl8kUpsCjD5DeCt04C109X3YcCZvnnu/UXAj565/OGXedMHnHZtJaGzmnuX9g89jjV3f6Rg4sCCMwclVxARHYRL2J0ksYu6l2IFq9s2D9g4S1WVDmeaQrMn3mtH4LeGXGgWyb1UuxBqOEVA7FjinCMkRSr5G1UhhrT9aIlOh6l7KVYwwx7Pz3xMB1YQExFR8xKoinfaK1GBLdCIiIiI7EeKEKR3fEM3NxYpiMyR9kr99YWibGD242p79J+ArkeZsx/yuuP+prZnPwbkrffN8/7ypCrKie8EHHe3SnBwImmfIaRQwWy6dUr6ELP3hIjIrxz6juJSCbpQYQPsk6ZwnOpdRs2feNcT2q2xYYa6lwi2QEWP2VGH/ureSa0ftv7qXbHZ0oruTqPVfdYi//W4a4wuzmmXyp9bIiJqnG4RtNcGhQple4H9hWo7qYfZe0NERERE5Buykl/IynRZie90NdXA938HKkqA1EHAuDvMvZB/9M1A93FAVRkw/RagfF/b2zev+kwl9J70iLPbmehCBZkDNftnKnet2mahAhE5HAsVnCShq7rfsxmWtn2BumAeFAIMY5pCyyfedwCV5S0/7tVVwIZZajvTc+GZ6pfSzxuxVVXhrLYPqQNbfrFf+upFJwNV5cCmn2FaogLbPhARUXNbZRXvsv6x0sWnUUlAVILZe0NERERE5Buy0ERW3qMGyFnm/KO65N9A9kIgNFK1fIiIMXd/pEjirFdVmq4Ui8x6EKipad1zSZGD/Hsx6Byg90Q4mm5pIam0jbV18bfCLFX4EhLmXVBHRORQLFRwksTO6r40Hygx8Y20KT8+ou57jAdS+pi9N/YhsffS46y60tvPuCWyl6ifjbAYIGO4P/bQOSSaLyxaVR47JVVBFyqktSIuTPqgZY5S29KyJdB08RULFYiIqLmFCvtyrX+s9HhOilFlAoqIiIiIyGntH5xeqLB7PTDnebU99lagYwvbrfpLbAow+Q3VAnbdN8DKT1r3PL89rxbNSdHDCQ84P+lUimyS5XpFjUqSMEveam/yXniseftBRBQALFRwEnnTklXPIncVLClrIbDuWzVIYppCy8hAsF262s7f0Pq2DxIXFW5yZa/Vyc9nSl/vz6zd7csHdq1U2x1HtO45Mg9T95tnw7TWDzpVhIiIqCH6vaIkzz6JCrp9GxERERGR09o/6PkoJ5IU1u/uBqorgC5jgCOuVYt9rKLrkcD4O9X27MeAvHUt+/c7lgLL3lfbJzwEtEuDK0jbDLF9vnn7ICm/QtIUzGwjQkQUAPwr5zSJXeq+mVnNj4963/A7eOL1qfniMtR9fivae6yf4T1RsNKg2ar0z6ckUdjd1jnqPqGzqgxujU6eRIWdK4B9e2BK64f4zMC+LhER2ThRIa/18aaBTlSQFktERERERE5MVMhdbf1xeWvNexnIWwtExAGnPgWERcByjpoCdB+vUmO/vkW1cmiOyjJg5v0qWaDPKcCAM+Eauv1D9mLz9kFf20kfbN4+EBEFCAsVnEavyJLYKbMVbFMXefVNKjDXTpfl6kDXsUDxTrP30H7iMutG4TfX/kJvFWiXI3y/X04jkWYR8Wp72291f47lJj/bdmz7kDpQtQ9pDUnzkEKBmurAtn+oKAWKc+r+/BMRETVV1FleosY/dihUSOpu9p4QBUZ0eyC0iQl8+bo8joiIiOxNEl1lDqp0jzdJzEkkbWDRW2r7+Pus295YVuOf/apq3SDnHzMfaF7hyILX1PxzVCIwcSoQ0sr5RDvqepRK2y3aDhRlB/715fsjBT4ijYUKROR8jnyHef755/HYY48hJycHQ4YMwbPPPovDDvPEljudVQoV5ELucyNU9eUhaoAZfwdCwoGLP/a2M6DmT74XbG3Z0dr4I1BTpS70ctVe00UK75wFVJV7f5deOebQCdRrFwIJnezxU7vlF3WfNqRtz5M5Wp1cbvwBGHgWAkIXhYRFcdKaiIiaFtFOrWgqK1Lv6VEJ1j1qesKWhQrkFjJ2ljH0vt0NP0aKFOwyxiYiIqKGyTxO6gB1QT9nibPe3yWVQFo+yGKe3ier9sZWFpMMnPMm8OapwPpvgcxRjc/ryWr+hW+q7ePu8SY4u4WcQ0qhjSQqSErtwLMD+/rSxrA0XxVLyH4QETmc4xIV3n//fUyZMgX33nsvFi1aZBQqnHjiidi1axdcQQ8c8jeaux8y+VRvkUItciG4tCBQe+SsvssFnij85tqg2z4MB0LCfL9fTiI/k7pIoSHys93YBKuVlO1VJ4X6+98WnTwFX5t/RsDon3Wp/ObPLhERNYduFaQTeaxIEoNKctV2ck+z94YocOQiRcbQhm9OuohBRETkdtJ+VuQsh6P88iRQlAXEdABOfsweaQNdxgDj71TbPz0G5K2r/3HVlcDM+9SCN2ndPOwSuFI3z6I1nVAcSHmrvS18JdGCiMjhHFeoMG3aNFx99dW44oor0L9/f7z00kuIjo7G66+/DldI6OpdhVxVafbekK+18xQqyGC4JXFR62eq7UyXJIuQ17a5qsLbaN3QyQcnmEFA/gagsAU/g22h25zEpgFBQYF5TSIickYC1V4LtxnTYzlJgJAJTiIiIiIip8n0FCrsWgXH2PwTsOJjtX3So0CCjdqUHnUT0ONYtUDr61tUMsTBFv9LJSqEtwNOesy9C966jVX3O5Y0r1WGL8nxFyl9geCQwL42EZEJbFDu13zl5eVYuHAh7rjjjgOfCw4OxoQJEzBnzpx6/01ZWZlx04qKioz7Yx//AaGRMf7f6eoK9WYX5FlR1UYhNVX4ASEIqyrDpH98gJygVJihT/UGeLp0Neqyj7ZjTVB4APbIGeJqKvCtbOzbjWMe+hJlQZFN/pvONVn4oGIryhGKE2Z2wv5ZGwKxq7bVp2Z78352X5+HNcHWT1X4Y+W7uBzAF8W98eA/W9gypB5voQv6YDPueeZlfBtyUEsMP7i28idcDOA/2xPw1Ev82SUKOCl0CtoDBPs/qalyf4nfX4NcIq6j9RMVpD+s3le3Tv4RERERkTsSFaStamUFEGrzcW/pHmDmA2p7yAVA/9NhK8HBwFn/BF48Up2PfHULMOZa79eldd7cl9T2YdcAKb3hWp2PAILDVArenk2BbdenCxXSBgfuNYmITOSoQoW8vDxUVVUhNbXuxXn5ePVqT2TOQaZOnYr77rvvkM/v2luG4PJAVqxV++yZNoenoVdwFtqVbMGS6niYoX1QORDR9OPy9lUhp4bJD82VgwgURUQhLqgUocVZ2FLjmYhvxMSQ+UAYMK+qDzbvlxMCHu/Gf3armvezW1KOnJr9sLr+4cuN7Jwfy/sgxwcpKz+EDkCf0M0YWLYEb1eOhr8lhe0AQoA15Sk+2X8iau0Yxf+/f9W1CkeJfFOo4JtCYL8o3O6N82RiEBERERE5UfueQGQ8sL8QyF0JpA+BbclCwx+mqlawCV2AEx+y52r3mGSVBPHhpcD234APfqv/cXOeA0Ze4d62XOHRQOYoYOuvwNY5gS1UyPMUKtj594WIyK2FCq0h6QtTpkypk6jQqVMndGgXgdDIplert1014OP0oKzKjuhVk4WhETuwLngozJBcE9ys2ovkmDCkMVGhRXZUpCIOmzEkahdKgpoeLB5f9btxvzhsGNIimV7R5M9kTVizrsclRwUjLcja3XMiasoxtFqlEGyKHIi04LZ//1dVDwWqvsTRoSuQFh7k94sr3avURabiiHSkhfDnl8gUxu+5/1uvVO6vgmeNuWtt3rwZDzzwAGbOnImcnBxkZGTg4osvxp133onw8PADj+nWrdsh/1bSww4//HAT9tqC4j2FCrL6xeqJCometm1ERERERE4jK/g7jgA2zAR2LLP3hdc1XwEbZgBBIcBpTwPR7WFbiV2afkxVmacow6WFCqL7OFWosH0BMPSiwLxm2V5vm8B0c67rEBEFmqMKFZKTkxESEoKdO+v2o5WP09LS6v03ERERxu1gM/86DnFxcbCl734GfpmHm/vk4+YJvczZh12VwAdNP+ytK0cDGXzTbZH/DARWb8a00fuBEU18f6Xn2D9XGRfer7vsIlzXZUzLXsuNspcArzT9sLcmdwI6mPT71VxZC4H/VRonT/+96XwgrBlREU0pHwP841GkV+fht/ND/V9R/M/dQBnw2OUTgM7+T3AgIvNIsWi8J0XTrSQBrLq6Gi+//DJ69uyJ5cuX4+qrr0ZJSQkef/zxOo/9/vvvMWDAgAMft29v44kyXynYpibTKjyJRzLBU7sfblQC0C4dlkpUCOTKHCIiIiIiM9o/SKHCruX2PfbSEmH2I2p79B+Abv5vhUoW0G0s8MPDwI4lnraYwYFr+xCbBrSr/3oWEZHTOKpQQVaajRgxAjNmzMCkSZOMz8lkr3x87bW1+i05XXKvuiu1yFn0yjtdXdnURffK/arKN2O433eNLCZ7kbrPGOabIgURHhO46DOpIi4rUtu8kENELjBx4kTjpnXv3h1r1qzBiy++eEihghQmNFSI69oihedGAJW1WogU5wAfXOz9WJJ5Lv7YGsUKLFQgIiIiIjfIHFn3AqwdihJKC2p9ogaY9TBQXgIkdgOGX6aSIsj5JA0kLFrNTeauBjr0D1zbh5S+QIijLt0RETXIcX/tpI3DZZddhpEjR+Kwww7DU089ZaxCu+KKK+Aa7Xs1/0K2vxTXTbWoV2iEvWOyzC5UkIFzU+RCsuh8OBAWiFYmDiA/k/KzWftCx8HkQoesyrS67MXqvvMRfoo+m+/f6LOibHUv/QztcLyJiPygsLAQSUlJh3z+9NNPx/79+9G7d2/ceuutxscNKSsrM2610yscR5IUGnvv1klTMulodqFCVYV3HCd9e4mIiIiInHyxVy+oK90DRCXCsmSM/s5Z6ryhPns2Aa+MBa5d6O6WCG4RGg5IOvH674EtcwJTqCAFESJtoP9fi4jIIhxXqHDeeechNzcX99xzj9Hbd+jQoZg+fTpSU1PhukQF6csrK5Ij2gX29asrgXkvq+0uRwInPFh/NJJcEOagrg2FCjnNL1ToflwrXsil5GdSTjjkgof267PA8v+qVIqjplgrOrqxiyA7lqrtrkf59rm7H+OJPlvq3+gzXWzVLgMIdtzbFRFRk9avX49nn322TppCbGwsnnjiCRx55JEIDg7GRx99ZCSJffLJJw0WK0ydOhX33Xcfj7iVJkDl/TM0EojraPbeEBERERH5T0yymsvcsxnYsUzNKVmVFDU3VKSgSXG0zBlyTtsdpM2HFCpkLwRwlf9fTyePpA/x/2sREVmEI6/8SJsHV7V6OFh0EhCVBJTmA/mbgPTBgX39ZR8AeWuB8HbA6c8C7XsE9vXdVKhQXd1w3JgUquxeByAI6DUhoLtoe3KyUfuEY8y1qlBh53IgLhOIDHDxT2srcKXthxQq+XpwK9Xw0gJCos/kmKQN9m+iQnxHICjIP69BRBQAt99+Ox55xNPTtAGrVq1C3759D3yclZVltIE455xzcPXVVx/4fHJyspEgpo0aNQrZ2dl47LHHGixUuOOOO+r8G0lU6NSJK4BMo9uzxWWolCYiIiIiIifrOFIVKuRYvFCB6GDdxqp7+dmVRWEhYf47RjKPK78nImMovxdE5BpsqOT0VAWJpAqk4l3A3BfV9lE3sUjBH+I9FxYqS+uu+j/Y1t/UfUofID7TL7viGulDgeTeqqp67dewBd32IX0YEBbl2+eWQbmkpdT+OfNnoUJCF/+9BhFRANx8881GIUJjt+7dux94vBQejB8/HmPGjMErr7zS5POPHj3aSF9oSEREBOLi4urcyESF29V9fGf2tyUiIiIi58scqe5zV5q9J0QtI4uzIhOAilIgx5Nc6y+71wM1Ver14jkXSkTu4chEBZJ+t72AbXO9VXiB8vMTQMU+1bPpiD/zW+EPYZGq7YDEBhduBWJTGm/70PVoIDiE34u2kNX8g88DZj4ArJ0ODD7X+scze5G673y4/6LP1n0LZEn0mZ8UeS7kJHJwTkT2lpKSYtyaQ5IUpEhhxIgReOONN4z2Dk1ZsmQJ0tMt3pKIDk1U4PsbEREREbklUUHH2tfUMDXTbNKOOTRCtbFoiHxdHud2cj7e7Whg1efA1rnen2V/tn2QxXJhEf57HSIii2GiglMl96w7ERoIW35VPZukX/3Ef/h+FTcd2v5Br8g7WHUVsM2z0r3ncTxyvqCLEyTqq9Cz0t+qpO/1jiVqWycf+JqO6tv5O1DZRP++tiYq6J93IiKHkyKFcePGoXPnznj88ceRm5uLnJwc46a99dZbeO+997B69Wrj9vDDD+P111/HddddZ+q+Uwvo8VsS26MRERGRfWzevBlXXXUVunXrhqioKPTo0QP33nsvysvL6zwmKCjokNtvv9VNY/zwww+NtmeRkZEYNGgQvvrqqzpfr6mpwT333GMU48prTZgwAevWSXtTsqW0QUBwGLC/ECjYYvbekLS7vXYhcM2PDd/k67Xb4rqZLNYS2X5crFW7UCF1oH9fh4jIYpio4OREhUAWKkgPpdme3suy8lxW8ZP/yIVbSUzQK84PlrtaDf7Dov13odptEjoDnccAW38FVn8GjP4jLGv3BqBsryoW0vF6vtZhABCdDOzLU+kNvk5ukAr7vSxUICJ3+e6774wWDnLLzMw8ZLJWe+CBB7BlyxaEhoYaE7zvv/8+Jk+ebMIeU6vo8XlSNx5AIiIisg0pkq2ursbLL7+Mnj17Yvny5bj66qtRUlJiFNnW9v3332PAgAEHPm7f3rsy+9dff8UFF1yAqVOn4tRTT8W7776LSZMmYdGiRRg4UF2ge/TRR/HMM88YRbpSGHH33XfjxBNPxMqVK43iBrJhOqxE6MuF3uwlXJBiBVKEwEKElhUq7FqpWkD4a3Fm3mp1nz7EP89PRGRRTFRwqmRdqJAFVFf7//UWvqlWh0kk1HH3st+uvyV4ovCLdjTe9kEuUke08/vuuMaQ89X9um/UhXSrt32Qk8DwGD9Gn41V2zq9w5f27fZE0AXxBJaIXOPyyy83ChLqu2mXXXaZMUErE8KFhYWYO3cuixRqx5c2JiQciEqAqST1Ssbnor0nAY2IiIjIBiZOnGi0JjvhhBPQvXt3nH766fjrX/+Kjz/++JDHSmFCWlragVtYWNiBrz399NPGc91yyy3o16+fUYQ7fPhwPPfcc8bXZez71FNP4a677sIZZ5yBwYMH4+2330Z2djY++eSTgP6fyYf0Qpqdy3lYyX7XWdqlAVUV/muBW10J5K1X2xlD/fMaREQWxUIFJ6+4lxYMlaVAsTcu2C/2bFGFCmL8nUAceyT7nY7C39tEoUL38ez75kv9zwBCIoCCrcDOFbB8oUKn0f79/uv2D1me1/NH24eYFCDMT8UWRETk/PjSvqeqr2eOAi7+GGhn8ji1ZBdQXQEEh3oLT4mIiIhsSgpnk5KSDvm8FDF06NABRx11FD777LM6X5szZ47RyqE2SUuQz4tNmzYZrc9qPyY+Ph6jR48+8Jj6lJWVoaioqM6NLFiokLsKliVFzcEhjT9GiqOlSJrcQ+ZWdarCtrn+u75SVabSkZN7++c1iIgsiq0fnEoGTTL5uWcTkL8JiMvwz+vICr8f/6EmXOWi6NCL/fM6VFeiZ2K7viIUifzP+V1t9zqeR87XJyx9JgIrPwVWfw6kWbBnmPxOZi9W212P8u9r6UG6tBopKwYiYn333EWe1abytyuEb1VERNTK+NKjpgCrvwB2LAVCo80/jJJAJqRgQiJwiYiIiGxK2pU9++yzddo+xMbG4oknnsCRRx6J4OBgfPTRR0ZbB0lCkOIFIUUIqampdZ5LPpbP66/rzzX0mPpIK4n77rvPp/9H8qGOI9R9/gaVoNlUGpoZYtOAuE5AwWZg+OXAyCsOfYwUKbBlgvvIHOiy972Lw3xN5lZ16p6/WksQEVkUExXc0P5BihX8RSLwt89TUboTHwHCLDjIdHKiQvEuTzx+LdvnAzVVQHwmkNLXlN1ztMGe9g8bZqhYLquRtAdpmxASBmQe5t/Xkt7aCZ3Vz5v8HfClvZ5EhfiDLjgRERG1RMfhQIcBQFU5sOpT849d4TZ1L+O0plZrEREREQXA7bffjqCgoEZvq1d7LqJ5ZGVlGe0bzjnnHFx99dUHPp+cnIwpU6YY6QejRo3CP/7xD1x88cV47LHH/P7/uOOOO4yEB33bts0z7iJrSOoORCWp+PxdK2FJu1aoIgWZ5z56iorgP/jGIgV30u1v89YCZX5Ia8ldo+7l3NWf6bhERBbEQgUna+8pVCjY4p/nl5X7P09T2yOvYv+kQFf4SguCmmqg6KD2D1t/U/ddjlQXq8m3ek4AohKB0j3All+sd3R1Za8MbAPRh9tf0Wc6UUEKIYiIiFpLJnlGXK62V32mkoeskKiQ2M3c/SAiIiLyuPnmm7Fq1apGb927dz9wvLKzszF+/HiMGTMGr7zySpPHUYoWJH1BS0tLw86dO+s8Rj6Wz+uv68819Jj6REREIC4urs6NLDYu16kKknZmRSs+Vvc9juN8FNUlBSqyYEvm4rf6of1DnqcYLH0wjzwRuQ4LFZwsuWfdlVu+9tuLauW2rAg75jZW+wVScLB3wFy41ft5mXzf6unX16Nuvz/ykdBwYODZanv1l9Y7rLrtg7RiCUQFbvdx6j7Lx9FnRdl100OIiIhaa/A5KlpWUsbMnhTVhQpJfH8jIiIia0hJSUHfvn0bvYWHhx9IUhg3bhxGjBiBN954w2jv0JQlS5YgPT39wMdHHHEEZsyYUecx3333nfF50a1bN6MgofZjioqKMHfu3AOPIZvKHKnud66A5ZQXA2u/UdtS6MxV7dTQYi1fp8rKfL4kNYj0ITzuROQ6bPzthkQFPSHqS7tWAcs/VNvHPwBEJ/r+NahxiV2A3eu8K8917L9E5geHAT3G8wj6s/3D/FeBLT8DZSVARIz1EhUkUSOQ0Wd7NgIleUBMso8LFbr45vmIiMi9JAmp/xnAsg+AFR+ZmwKmC4gTvasSiYiIiOxAFyl06dIFjz/+OHJzcw98TScdvPXWW0ZRw7Bhw4yPP/74Y7z++ut49dVXDzz2hhtuwDHHHIMnnngCp5xyCv7zn/9gwYIFB9IZpNXEjTfeiAcffBC9evUyChfuvvtuZGRkYNKkSQH/f5MPdRxZd/W4laydDlTuBxK6AN05p0oNFCosfNO7SMxXZC5fkquDQ4G0gTz0ROQ6LFRwsmRPoULxTqCiFAiL8s3zVlcBPzysoo56Hg/0O903z0sto1eaF9YqVNBpChITFd2eR9SfFeAS2SwrM9d9Aww8yxrHWtqA7N0BBIUAXcYE5jVjOwAd+qv+gvLz1++0tj9ndSWwN0dtMxqbiIh8YfjlqlBhwyxgrElFhrJSRhcQt/cknxERERHZhKQeSAsHuWVmZtb5Wk2t9loPPPAAtmzZgtDQUCON4f3338fkyZMPfF1aRrz77ru466678Le//c0oRvjkk08wcKD3At2tt96KkpISXHPNNSgoKMBRRx2F6dOnIzIyMkD/W/KLjsO9i1Mkpdcqc5fy87vc0/Zh8HlAWITZe0RWdGCx1iageJeaE/WF3DXeOdDwdr55TiIiG2HrByeLTQXCY1VBwZ7NvnteWYkmFyXDooETHwJCWO9iCqnwFcU7Di1UkArPZsTvUStJ/NuQ89X22q+tcxh3eCp6U/oE9mRPt3/Y5qMebTLYr6lSySDSA46IiKitpIAvqQdQWQqs+cKc41maD1Tsk4GE6m9KREREZCOXX365UZBQ30277LLLsHLlSqPIoLCw0GjXULtIQTvnnHOwZs0alJWVYfny5Tj55JPrfF1SFe6//37k5ORg//79+P7779G7d++A/D/Jj6KT1JhcZJvcku3g5OC8NUBIGDDsYrP3hqxKUmQ7DPDtHKjI9SSMdOjH+XwiciVeyXQyuZiqUxXyN/nmOSXafc7zanvM9UAyTxJMT1SQVfSiqhzIWqC2ex5n3n65xeBz1f2OJcDenbCELE/bh8xRgR3Y6h5tulCirXQ7k3apQIjqg0lERNTmcfGIy9T2yk/NOZg6TUFW3kgxMRERERGR28icldi5DJaxwpOm0P1YIKGz2XtDVtbdMwe6fZ7vnlMnKqSb2KKQiMhELFRwuvaeQoUCHyUq/PIUUF6sChSkUEEmfckciTpRYaeKKJML5tJLLSrJ2/ON/Cepuzq5ksSS1SatzDxYtqdQoetRgV+lKu0mpF3Dni1tfz6JABRxHYHgkLY/HxERkRhyoer7KaulZNVUoBVuU/fxmUwkIyIiIiL3tlMVktZrBeUlwNrpanv4ZZzrpuYt1spe4rsjJeenupUzEZELsVDB6XSiQsHWtj+XVAoaMfdBwMR/mNPblw5NVNhfAJQVAVt/Ux93PhwIY8++gNDtH9Z5TmjMJL39CqRIIAjoPCawrx0Z5+0zqNuP+CJRIZ5tH4iIyIdiU4A+nljh5R+Zl6iQ4BnDERERERG5TccR6j5vLVBdbfbeAGu/Ue3hZA6KCbXU7MVa2UCBpxC9rfO5JblqPjd9CI8/EbkSCxWcrn3PuhOjrSVtBX74h9oeeJa3Jz2ZJzIeiEr0fn/1BeIebPsQMAPOAoLDgPyNwC5PPzGzZHvaLrTvAcSlB/71u49X91nzfZeokOBJDSEiIvIV3f5h/XcqicqMRIWkboF9XSIiIiIiq0gdCIREAGV7gT0+alXsi7YPg8/jwi9q3mKtDM9irW1zfNf2QQplotvzO0BErsRCBbckKhRtV+0BWmvxv9RqbbkwPuE+xrFbhb6Qu2OpqkSW6steE8zeK/eITgJ6Ha+2V39ujbYP0vbDjHYJukdb9tK2/a2pnaig25sQERH5ivSdlUkgaWUmq6dMKVToHtjXJSIiIiKyitBwb8S9tLE1k7SDy12lFiENu8TcfSH76D5W3W/3wWKtXM/Ctw59eb2FiFyLhQpOl9RD3UuVakle655DVuvPf01tj70VSGAcu+XaP6z6VN0n92ZcvlntH9Z/D9RUm5+oIBFkZsgcBYRGAvv3eAfZbS5U4IpTIiLyseBgYPilanvlJ4E9vDrhjIUKRERERORmMockdq4wdz9W/E/dS3IwF8tQc3U7xrtwsK2LtfI8iQqpg3j8ici1WKjgdOHRQHym2s7f0PJ/L2+2sx8FqspUrNHIq3y+i9QK0gMrewkQFqU+3r1e3XfoB+T87pseWdQ8vU5UbTj25XnbbwTa/iIgb53a7na0OfsQGuEtkmjLcZAYbunPJhiNTURE/jD0IiAoGMhZpto3BYIUDe8vrNuajYiIiIjIjTqOUPe7Vpq3D+X7gLVfq+3hlwFBQebtC9lLp8NU+xKZv9Rz8m1t/aBTRoiIXIiFCm7Q3tP+QVo3tNTGWcCWX4DgUGDiI0BYhM93j1pIihCeGwG8cgyw9L26X5OVgfJ5+TqLFQIjLBLoP0ltr/4SpjCi8mpUmkZ8Z5heUZy1oPXPUbRD3YdFAzEdfLNfREREtcV3BHp6WmUt/yiwaQpRSUBUAr8fRERERORemSPV/Z5NQEWpOfuw7hugYh8Ql+lt60rUHLJwsPNotb3tt9YfM2lHqNsDygJRIiKXYqGCGyR7ChX2bGl5ZelPj3srSzt5YrnIXFKtWVnW+GPk63pVOgWu/cPm2UB5qXltH6QiPSQUpunuKVSQFapVFW1r+9AuXfUtJCIi8ocRl6v7tdNb/57VEnoCSookQsL8/3pERERERFaV0AWISQGqK4Gc5ea2fRh8rlqERNQS3caq++3zW3/cdDqu/C7EpfP4E5FrsVDBDXS8bOHWlv27eS8DxTvVBcPxf2MEFlFDOh2u0gykCnzDjMAfp+xF6l63XjBL2mAgMkEdB+nT1hpF2d4LOYzdIyIif+l1gkru2V8ArP8+cIkKMilLRERERORmMt/T0ZOqsHNZ4F8/dzWwa4VKEB5+aeBfn+yv2zh1L/OfNdWt/zkUyX1YzE5ErsZCBVcVKngmSJtb0afbChx3LxCT7J99I3KC4GBg8Hlqe02A2z9I8knuKrXd9SiYKjjEW1G89be2JSqY2cKCiIicT1INhl2stld6VlMFIlEhsZv/X4uIiIiIyOoyR6j7nSvMS1PoPo6FxNQ6GcOA8FjVvqG1qSC5a9R92iB+F4jI1Vio4KbWD3t3AFXlTT9eqgB/mArUVKme84PO8fsuEtmeLlTIWgCU5AXudXf+DlRXAbGpQHJvmE63f8he2LZEhUSuOCUiIj8bfom6z1rUsoLe1tDPn9Tdv69DRERERGQHOlFBX6wNFEkBXfO1t9WxLD4iailpvdv1SLW9rZWLtfI8P/vpQ3j8icjV+E7sBnGZQGiU6vtV4FnN1ZhVnwE5S4HQSODEh83teU9kFym9gfShqtAnkKkKuu1DxnBrxITp6LNdK1XaQ0vt9SQqJHT17X4REREdTIoGukoSUA2w/KPAJCqwUIGIiIiICOg4XHpAAMU5QPGuwB2Rdd8CFSVAXEeg1/H8TlDryQJPkdWKxVqymDR/g9qW+WQiIhdjoYIbSGVoe8/qrT0bG39s6R7gl2fU9ug/AakD/L9/RE4x5AJ1v3Z64F4za7G67zIGltC+B9AuQxVGbZ/f+kSFJBYqEBFRAIy4TN2v+UolFPlr1VZJrtpO9rRkIyIiIiJys8h4bwrwjqWBe90VH6t7SRAOiwrc65JzCxV2Lgcqm5FiXdvuDer8M6Idi9mJyPVYqOAW7T0Dv/zNjT/u12eAskL1Bnn0FCAoKCC7R+QIA88GgkKAvLVqwOlvUn0rrR9EF0/cmNnkb4b0+BPb5rbs35btVTfBHt5ERBQI/U4DohKBfXnAptn+eY0iT1qQ9DCN6eCf1yAiIiIispvMUepez235m8zXyUXl4FBg+KWBeU1yrg79gej2QOV+YIdnIVlz5a5W98l9gNBwv+weEZFdsFDBLXSFasGWhh+TvVi1fRAnPAxExgVm36hlZAAUGtH4Y+Tr8jgKrNgUoMexalv/LvnTzhWqWCEywVrpJ909FcXZC1t3IUf+P1EJvt8vIiKi+sZMOhFJr67yV9uH+ExrtGkiIiIiIrKCjiO881uBsOJ/6r7bWC6QId+kWMvPUmsWa+WtUfcyn8uFokTkcqFm7wAFOFGhcHv9X6+qAH6Yqrb7nQH0PoHfGqtK6ARcuxDYt7vhx0iRgjyOAm/I+cD679TtyBuAID/Wg0lxke7rFxYJy0WfSarEvnwgOqllbR/i0lV1OxERUSAMvwz47QVg22+qP26sj1MP9Pg7oTMnoYiIiIiItMyR3qQDicEPDvHfsZF2bGu+VNuSpiAXmYnaSgoVpAAmq4WLtXI9hQrpg/k9ICLX4zuyW+h+uEUNFCosfQ/I36D6g51wv38HhtR2UoSQMbThG4sUzNPnZBXtXLyz5YPUlspepO47HwFLkUKD5N4AatRFn5YmKsRl8kIOEREFToe+QOZhQE21f1IVdKJCYlffPzcRERERkV11GACERgEV+4Dd6/37WrKgqLwEaJcO9J7o39ci99CLtaSVQ/m+5v0bKcqR4hyRPtR/+0ZEZBMsVHBbokLpHqC0oO7X9u4A5r2sto+6mZOoRG0RHg30P11tr/rcf8eyuhLYsVRtdz0SltN9nLrfNq/5/0YnKsiKUyIiokAacZm6X/2FKljwR6JCUnffPi8RERERkZ2FhKoFVyLHM8fl77YPg84FwqL8+1rkHnKOF9dRzdNun9+8f1O4FajcD4RGAin9/L2HRESWx0IFt4iM88bY5m+s+7WfHldvjmmDgdHXmLJ7RI4y+Hx1v+lHoLLMP6+Ru1ZVnEt6Q9pQ61YU69SHliQqcMUpEREF2oAzgYh2qoB3awvSgJqDhQpERERERI23f8hZ7r8jlLcOyFkGBIUAwy/hd4J8JyjIu1hr+9yWtX1o30MteCMicjkWKrhJ+96eQoVN3s9tmg1s/EEN1Cb+gxWlRL7Q9SgVJVdeDGyY5Z9jqgsA0odYc1ArxyAoWBUf6AKEZhcqdPHrrhERER0iPAYYeI7a9mX7h6oKVfwg2ntasRERERERkdJxpDc6319WetIUuh0NJPXgkSff6jZW3WctalmhgrQ+kUIHIiKXY6GCmyR7JkcLtqj7ilJg9mNqe+iF1utzT2RXwSHA4HPV9pov/Vuo0Plwaw5qoxK8fda2zmn68TU1QJHnQg6jsYmIyMz2D5t/Uu3SfEGKFKSVhMR6SiQoEREREREdmqiwZxNQVuL7IyPz32u+UtvDLwWCeTmE/FSokL8B2NeM80hdlCPp1kRExEIFV2nfS90XblP3C14D9mYDMR2AY+/mQI3IH+0fts/z3cUOTS54ZC9R212OhGV197R/2Dav6cfuywOqylQKQ0JXv+8aERHRIaQ/btog1V905Se+bfsQlwGEhPOgExERERHVJsW8salqrmvn774/NhtmAGV7gdg0oPdJPPbke3KuJ+l58jPcVPsHWailExUyhvC7QUTERAWXKNimLmrKKm9d3bd+BrDobfXx4X8G2qWauotEjpPaH0gdqC526MptX5H2LWWFanVm5ihYlu7RtmOJGog3pihb3ceksAUNERGZZ8Tl6n7VZ02/dzWHLhCO78SiYCIiIiKig0lKqJ7bylnm++Oj27oNOsearVPJGfQc6Pb5jT+uOEfN6Uob7tRBAdk1IiKrCzV7BygARQrPjQAqy+r2gZ9+q/fjH6YCgyYDCZ347SDypSHnA9/eBaydDgy9yPdtH6QQIqIdLKvTaCAkAti3G9i9Hkj2pLrUR/4uiXay4pRvTUREZBKZwPzmTqBgK5C9GOg43DeFCondfLJ7RERuVlVVhYqKCrN3g6hRYWFhCAnxLBQioubpOAJY/QWwa4Vvj9juDcCOpSq9c8Sl/G6Qf9s/zH/VO2fbEJ2mkNgViIznd4QOUV1djfLych4ZctWYl1eDnE4uENYuUqiPxK3L41ioQORbAycD390D7FoJ7NkCJHbxzfPKhRNdCCCV51YVFgV0Hg1smg1sndNEoYInUSEhM2C7R0REdAiZLBpwJrD0PWD5f31QqOBp/ZDUnQebiKiVampqkJOTg4KCAh5DsoWEhASkpaUhyMrn60RWkjmy7kVcX1n5P3Xf9Wggqadvn5uoNvkZQ5AqeC/eqdqZ1Ef/jHfox8Q9OoQUKGzatMkoViBy05iXhQpERP4Sl64qajf+oCKkx1zX9ueUGGpdndv1KFhet2NUoYJEnw2/tBmFCj4q5iAisqnTTz8dS5Yswa5du5CYmIgJEybgkUceQUZGxoHHLFu2DH/5y18wf/58pKSk4LrrrsOtt9ZKy6K2t3+QQoVNP6h+tm1JL9KJCklMVCAiai1dpNChQwdER0fz4i9Zuqhm3759xjhOpKenm71LRPaQMUxd5C3JBYp2qPm0tqrcD6z+Um3LfFRwcNufk6gh0UlA2iDVvmTrr0D/M+t/XJ6nUCFtMI8lHTKG2LFjh7FCvVOnTgjm3yxy0ZiXhQpERP405AJVqLDuW+CIa9uegFC0XZ24BYepRAU79Gib+YAaqFdVNtzWQbd+YKECEbnc+PHj8be//c0Y5GdlZeGvf/0rJk+ejF9//dX4elFREU444QSjgOGll17C77//jiuvvNKoYr7mmmvM3n1nkPfX5N5A3lpg1efA0Atb9zzVVUCh5/2tPVdwERG1tt2DLlJo3749DyJZXlRUlHEvE7fyc8s2EETNIIXBssJcEklzlvqmUGHDTKCsSK1s730Svw3kf92PUfOf2xY0XKiQu1rdp7NQgeqqrKw0LvzKIhUpzCVy05iXpYRERP7U91TVAmFvNrBjSdufL8vT9kFO4KISYXnpQ9UJZ3kxsPP3phMVkroGbNeIiKzopptuwuGHH44uXbpgzJgxuP322/Hbb78d6Mn973//24gDfP311zFgwACcf/75uP766zFt2jSzd905pKhw+GVqWxKRWqtkF1BdAQSHshCPiKiV9PsfJ2zJTvTPq/75JaIWtH/IaWTuqCVWfOxtyxoRw28BBSZVVuzwzN0erLRAtYU4kCJCVLc4V4SHh/OwkOvGvCxUICLyp4hYoO9paltWZbaVbvvQ6TB7xNZJgoLRpw3Att/qf0x1pXegnshobCIiLT8/3yhMkIKFsLAw43Nz5szB2LFj65y8nnjiiVizZg327NnDg+fLRKSQcGD3OiBneeueo3C7um+XDoRF8ntDRNQGbe17ShRI/HklaoWOnkIFSVVoq/xNQPZiICi48TakRL7U+QhVpC5znHu2NNz2Ia4jEJ3MY0/14hiC3PjzaoOrXERENjfkPHW/cRZQVeGbQoUuR8I2pP2DyFpY/9dlAF9TBYSEAfGZAd01IiIruu222xATE2NEXG/duhWffvppnT7dqampdR6vP5av1aesrMxoGVH7Rk2IaQ/0PUVtr/iodYercJu6l/e24NZH4BERERERuSZRIW+dah3aFiv+5507k5ZuRIFarNZxhNreqlo31tv2IaVvw61xiYhciIUKRET+1m0cEJOieuNt/KH1zyMX9IuyVEW4nQoVdPTZzhVAxf5Dvy7/JxGbBoREBHbfiIgCQNo3SJVxY7fVqz2TFgBuueUWLF68GN9++63R4+3SSy9FTU1Nq19/6tSpiI+PP3Dr1KmTj/5nDqfbP6yfAZSXtj5RgWlBRETURq+88orx/h0cHIynnnrKJ8dz8+bNxhhkyZLWtSi85JJL8PDDDx/4uGvXrj7bt9YaN24cbrzxRr/uk7TdeuKJJ3z6nETkuXgbHgNUlgK717b+kFSWAau/UNuSpmCHNFJyjgOLtRYc+rVcT6JC2qDA7hORjXDM684xL9+pnS66PRDaxIU/+bo8joj8Q6pkB52rttd81frnkdg6IdXgMTaKCEvpA8SmAlXl9Q/Ui7LVfVwGTyCJyJFuvvlmrFq1qtFb9+7dDzw+OTkZvXv3xvHHH4///Oc/+Oqrr/Dbb6p9TlpaGnbu9LTL8dAfy9fqc8cdd6CwsPDAbds2z0p/arrQLqEzUFECrP269YUKSV15pImIXObyyy8/UIwo7Zsk/Uje119//XVUV1e36LkkCenaa681EpeysrJwzTXX+GWff/jhB2N/CwoKmnzs0qVLjfHJ9ddfDyubP3++z4/XXXfdhYceesgYUxGRD0kCWcYwtb1jWeufZ8NMoKwQiOkA9PEkpBEFSrex6j57KXDwYgOdqJA+hN8PcgyOea1hvs3HvMyYcbqETsC1C4F9uxt+jBQpyOOIyH8Gnwv89jywbQ6wfy8Q2a71bR86jrJXhLT0KpKLPb9/AGybC3Q9qv5ChfjOpuweEZG/paSkGLfW0BczpH2DOOKII3DnnXeioqLCuPAhvvvuO/Tp0weJiYn1PkdERIRxoxaS1VeSqjDzAWDVJ8DAs1rX+iHRW4RCRETuMXHiRLzxxhuoqqoyigqnT5+OG264Af/973/x2WefITS0eVNy0gZK3vdPOeUUpKenwwqeffZZnHPOOYiNjYWVtXb81ZiBAweiR48eeOedd/CXv/zF589P5GodRwKbfwZ2/i5rOdvW9mHg2UBEjE93j6hJmaOA0Ehg/x4gb41KChHl+4CCrWo7YygPJDkKx7zmS7H5mJeJCm4gRQjyBtjQjUUKRP4n1bLJfYCqCmDtV21LVOg6BrbT3dP+IXthw60fErsEdp+IiCxm7ty5eO6554wI5i1btmDmzJm44IILjBMDKVAQF154IcLDw3HVVVdhxYoVeP/99/H0009jypQpZu++Mw29CAgKUe2LpF9uc8nqGZ2o0L6H33aPiIisS4oEJe2oY8eOGD58OP72t7/h008/xddff40333zzwOMkweD//u//jAnGuLg4HHvssUZigZDHDRqkIqIlfUkSD6Rlw4YNG3DGGWcYSQ1SLDBq1Ch8//33dV5fHvvJJ5/U+VxCQkKd19bkOcePH29sS+Gj/FtZIVcfKbyQYovTTjvtkK/t3bvXGLvExMQY/+/nn3++ztenTZtm/H/k69LK4s9//jOKi4sPfF3GP/K8sg/ymAEDBhjJDdry5ctx0kknGf9n+b9L+4m8vLwGvwcHx+DK/+vVV1/FmWeeiejoaPTq1csoGqmtOa8h+yipV0TkY5kj6648b6k9m9W8k7RM1W3ciAJJkqs7q3N3bJnj/fxuOZesUQtG47lglJyFY16OeduKiQpERIFKFRhyPjDjPmDtdGDweS3796V7gPyNarvLQYkEdiCJCkIu8pQWAlHxhyYqsFCBiFxOJsw//vhj3HvvvSgpKTFWTUplusSt6USE+Ph4fPvtt0Y184gRI4w2Effcc4/fYqBdLy4d6HWCav2w/CNg3O3NOySl+UDFPhkAAElMVCAi8qXTnv0ZuXtV0lAgpbSLwOfXte1cTIoQhgwZYrzfS3GCkGSCqKgoo4BB3udffvllHHfccVi7di3OO+8844L+hAkTMG/ePGNbChrkYvrJJ59sxLHKGOHtt982Lp6vWbMGnTu3PKlOnvejjz7C2WefbTyHFEzIPtVn2bJlRgTsyJGeC4q1PPbYY0ZBxn333YdvvvnGSJDQ7axEcHAwnnnmGXTr1g0bN240ChVuvfVWvPDCC8bXZXxTXl6O2bNnG4UKK1euPJDaIAUdcvzkuD355JMoLS012mGce+65RnFnc8m+Pfroo8a+SjLERRddZBRIJCUlNfs1DjvsMOPYS+IVU6uIfJyoYPzCbwXK9gIR7VqXpiAXiqUNKZFZi7U2zvIs1rpCfS53jbqXRWwhKhmRqCkc83LM+6RLxrwsVCAiCpRB56hChZxlQGEWEN+x5WkKid2AuAzYjiS3yIUaKbaQ9g+9Tzg0USGBPbyJyN1khWFzTjoGDx6Mn376KSD7RABGXK4KFdZ9Axw9BQgJb/qw6DSF2A5AuLVjsYmI7EaKFHKK9sOu+vbta1zsFz///LNRgLBr164Dk3+PP/64kYQgqQVSiNi+fXvj81KgIAkNQood5KY98MAD+N///mekA1x77bUt3qeQkBBj0lJ06NDBSF5oiExwyuPlcQc78sgjcfvtqqhPChR++eUXY/JTFyrceOONddIOHnzwQfzxj388UKggbS6kWKJ2ioQmqVPDhg3Dww8/fOBzr7/+ulFkIUUd8nrNIUkRkvog5LmkcEK+B1Ic2tzXyMjIMAoqcnJy0KULkwGJfFokHNdRzRPJ3FmXI5v/b6vKgdWfq+1hl6g2bkRm6DZW3cvPsCTrSmFCniclJHUAvyfUbBzzcszrljEvCxWIiAJ5sV7SELb8rE6eRv+x5YUKEoMXYtM/3d3HqUKF7bUKFSpKgX271XZ7rjglIiIL6jkBaJcO7N0BrPsW6Htq0/+mcJu6j8+07/s2EZFFSbKBnV+3pqbGaEEgpMWDtD7QxQiarGqS9g4NkX/z97//HV9++SV27NiByspK49/IhX5/k9eRogr9f6hNt6qq/XHt1gvSnmLq1KlYvXo1ioqKjP3ev38/9u3bZyRLXX/99fjTn/5kpEdJioQULUiBpj5Ws2bNOpCwUJscq+ZO2urnE5LaIOkRUijSktfQaROy30TkYzLvtTIL2NHCQoUNs4D9hUB0MtCvGeN1In9JHwpExAFlRcDO5UDGMG+igrQGJmomjnk55nXLmNc2s2YSLyEnYNKzV/rySjTFweSETE5o9AG+7LLLjBOg0FDb/DeJyOmk/YMUKkj7h8P+oFpCNEf2InXfZQxsS9o/LHjdW3Qh5KKPCIsBolNM2zUiIqIGSaHBsIuB2Y8BKz9pZqGCJ1GBaUFERD7X1vYLZlu1apXR+kAXHEirpx9++OGQxzWWavDXv/4V3333nZG+0LNnT2MScfLkycaKJ00KCaQooraKioo277+0nZLJSnktmZ9rrs2bN+PUU0815u1kjk8SHCRR4qqrrjKeSwoVJH72xBNPNOb/pFhB5vSeeOIJXHfddcaxkvYWjzzyyCHPLcewucLC6kZuy3Gqrq42tpv7Gvn5+QdSLojID+0fVn4K7FrZsn+34mN1P+CslreMIPKl4BCg61HAmq+Abb8BqQOB3Z7iQylaIGomjnk55nXLmNc2V/DlpEX69kk19muvvXbI16uqqnDKKacYMXi//vqrUVF+6aWXGt+M2vEVRESm6n868NXNaqWlVNWmqUjLRklfvry1arsl1eSWjD4LAgq2AHtzgHZp3rYPEu8X2vxJLiIiooCS+NjZj6tiuz1bgcTOzUtUSFIXooiIiIS0ePr9999x0003GR8PHz7ciFKVBTbSCqG5pKWCxLmeeeaZByYbpRCgNplQlLkxbd26dY2uhtJFBzK/1pihQ4ca9ytXrjywrf3222+HfNyvXz9je+HChcbkqBQeBHsi2T/44INDnl8iZ6UdhNzuuOMO/POf/zQKFeRYffTRR8Zx8teCpOa+xvLly5GZmWkUbRCRHxIVRJ5nBXpz7NkCZC1Qc04jL+e3hKyRKiuFClkLge7HAtUVqiVg+55m7xlRQHDMyzFvS9imWdN9991nnMjpPnUHk0prOUl65513jBOlk046yejR9/zzz9epKCciMlVkPNDnZLW9ytM7rynS06ymGojLAJJs3B4hOslbmLF1jrovylb30oOwuekSREREgZbYRU02iRUfNT9Rwc7v20RE1CZlZWVGEUJWVhYWLVpkLKI544wzjFQBWVgjpL2BLMiZNGmSMa8lxQay+ObOO+/EggVy0a1+vXr1wscff2ykjkp064UXXnhghZR27LHHGv1nFy9ebDyXXPg/eGVVbdJ3VlZaffHFF8jNzTWKH+ojBRByQV/SEOoroHj00UeN3rYyH/fhhx/ihhtuML4myQ+S6PDss89i48aN+Ne//oWXXnqpzr+/8cYb8c0332DTpk3GMZPEVF3o8Je//MVY1SW9dufPn2/E0spjr7jiiiaLK5qrua/x008/4YQTPO0Micj3sflBIapNaKFncUtTJPVMdD4cSOnL7whZZLEWgJ0r1byuSO4FhJrTvorInzjm5ZjXNYUKTZkzZ45RxJCamnrgcxIXJz3vVqxY0egvkTym9o2IyK8Gn6/uN8wAqiubfnyWp+1DxgggpOGJJVvofoy63z5P3etEhYQmVqYSERGZbcRl6n7t102/fx9IVGChAhGRW02fPt2ITpXV+RMnTjQuuj/zzDP49NNPERISYjxGCgO++uorjB071rgYLv1gzz//fGzZsqXO/NbBpk2bhsTERIwZM8aIbZX5LykeqE2SCySd4OijjzYKGaRdhLRXaEjHjh2NRUK333678drXXnttg4+VFg3//ve/D/n8zTffbBRFDBs2DA8++KCxn7JvYsiQIcbHEjE7cOBA499La4fapBhAigWkOEGOmRyPF154wfhaRkaGUQghj5EiAZkDlMIGaZGhExraqjmvsX//fnzyySe4+uqrffKaRHSQ8Gggtb/a3rGk6cNTVQ6s+sybgiax+0Rmk4KZmBSgqgz4/UP1OWkBwUVa5EAc8/5/e3cCHUWVPX78BkISwpKwaRLZkUUhLOLGoiAyiDoCjrhvCCODgoICoo4QRM+ACx4XFPU3AvofhwnOgAKOIIuyaABBAorAIEKCEnYIIgiB1P/c13QnTXpLCL1Ufz/n1OmlXleqH6+rL1237nuemPcsxVhnTlgX5qZNm2b+k3Do0CG35wcOHGj+I6eZzk5a0q5KlSrmP31aYcGTsWPHmv+InSk/P1+qV69+Dt4BgKh3qkDk5WYixw6I3DCx6ApNb/7dX2TXOpHrJoh0eCiyu2/LQpEPb3EE6/0+E/lspMhPX4hc84xIl5Gh3jsAIaTJoklJScRg9HX4OnlC5JUWjqu7ek4QufAP3qds+r/T3+0jfhSpyvzVAFBWelJYr65v1KiRJCQk0JFh4tixY9K8eXPJzMw0FSGiyeTJk2XWrFmmAkZZxm2kx7y9evUylTz27NljkmW0Kogmn2iSh9P69etNwolWpdAKHDp1xxNPPOG2Ha22MXr0aFNFRCuE6DZuuOF09UkR0Z+rMzIyzNQf+htwp06dTN9r20BFel9HtTnDRNZMFWl1q0jXJ3233fK5yPynRBJriQxdJxJfLVh7CXh3aIfInKGOi9ScOg4VafUnx30dr8n16EG4EPOGJ2LeWUGJeUNaUUGztDV73NeyadOmc7oPOt+ddpRz2bHj9NVPAHCuaFWE9L6O+5s/9d224JjIntNVYRp1ivx/kwYdRCpUEvltr8jBbUVTP2hJbQAAwllsnEjbuxz3N5wuL+tr2ofKNUQqJwdn3wAACKLKlSvLBx98IPv27Yu6ftfpM3T6imh1zTXXyIwZM2Tz5s3yn//8x0yP0bdvX7cfpbUahU4lsmbNGnnppZfMRWLvvvuuq41Ob6LTawwYMMBMTaJTn+jy/fffu9roFCJagUSnB1m5cqW5EE2rc+gP4ogCdS913O79wX/bDbMcty1vJkkB4ZOkMKm9e5KC+vo1kXe7OBZdr+0AhDVi3jeC0s+xEkJaEq5fv34+2zRuHFi51JSUFFm16nQp8dN2797tWudNfHy8WQAgqFrfLrLqXZGcr0SO/yYSX8Vzu93fO8pLV6ktUqt55P8jxVURqXuZSO7XIrlZRVM/1GgY6j0DAMC/dveJfP2GYwqjX3eJVEvxPu1DUt3In7IJAAAvunb1UxnQpnTai2j22GOPue5rMoJehKZJBgUFBSaJQ6f0OHHihEyZMkXi4uKkZcuWpgKDTvuh1XDVa6+9Zqb2GDnSUVXxueeekwULFsikSZNMYoJWU3j11VflmWeekd69e5s2mhijU5LotBs6PQps7oLTiQr7fxRTldRbTK0nes3UojFF07QBoaYV+E4e991G12s7qioAYY+Y99wLaUUFLf/VokULn4sGtYHQUnPfffedKT3mpEGulpS4+OLT81oBQLi4oL1j3moNTLcUTVlTws61jtu0S0Qq2SSpyjnVxdYvRE4ccdyvwRzeAIAIUKeZSP0OIlahyPf/8V1RIZlqQQAAwL4OHDhgEhM6duxokhRUVlaWXH311W6/52olBK3AcPDgQVcbnTKiOG2jzystIbxr1y63Nlo6+IorrnC1gc3VbuaojqC/me3d6L3dD6erKdS7XOS8lkHbPQAAYJNEhdLIzc01Gbh6e+rUKXNflyNHHCe5tKyYJiTce++9sm7dOpk/f77JvNU50aiYACDsxMSItLnTcf9/n3lvt/Nbx239jmIbjbs4bvPWFiuNnRTSXQIAIGDt+xVN36QJC94qKtRoRKcCAADbGTVqlJmKoVatWuZ32k8++cS1ThMMtPJBcc7Hus5Xm+Lri7/OUxtPjh8/bqaeKL4gQlWo4LjAR+Wt99xGKy1snOO43+5ekQoVg7d/AAAg+hIVxowZI+3atZOMjAyTnKD3dVm9erVZX7FiRZk7d6651eoK99xzj9x3330ybty4UO86AHiWfqvjNi/bUT7a03+6dp3+D1nDTvboRS3LVyFWJDah6LmEZMcUFzuzmZ8NABD+LuolEl9d5Mhuke1fea+ooJWTAAAAwpxO3xATE+Nz2bRpk6u9Ttmwdu1a+fzzz83vsPr7q07XEGrjx483lRecS7169UK9SyiP6R/2bPC8ftsSkWMHRCrXFLnYMUUIAACIPLESIaZNm2YWX3RutP/+979B2ycAOCs1G4nUu0Jkx0qRTXNELnvQff2ejY4ydwlJIimt7ZGkMKl9yXnaDm4Teff0dBCx8SJD1jBHGwAgfMUlirS+XeSb/xPZMFOk0VWeKyqQqAAAACLA8OHDpV+/0xWjvGjcuCgBs3bt2mZp1qyZXHTRRSYhYMWKFebCsZSUFNm9e7fba52PdZ3z1lOb4uudz6Wmprq1adu2rdd9fOqpp+Txxx93PdaKCiQrRLC6pxMV9hYlybjROFy1vFkkoXrw9gsAAERnRQUAsKU2dzhu//e592kfUtuKVCpWgSBSHd1fMknhTLpe2wEAEM7a3++4zf3a/Xur4JjIb3sd92tfGJp9AwAAKIU6depIixYtfC5xcXEeX1tYWOiadkFpssLSpUuloKDA1WbBggXSvHlzqVGjhqvNokWL3LajbfR51ahRI5OsULyNJh2sXLnS1cYTnfq3evXqbgtsUFFBL3r5Pb9kBTO96Kf4tGwAACAikagAAKF0cR+RinEiB39yVFDwlKhQ3/t/xAEAQAikpDsSCQtPivzwcdHzh39x3MZVFalyHv80AADANjRRYNKkSZKdnS05OTmyePFiufPOO6VJkyauBIK77rrLJDUMGDBANmzYIJmZmfLaa6+5VToYOnSozJs3TyZOnGimlBg7dqyZ2nfIkCFmvU41MWzYMHn++edl9uzZ8t1335npJdLS0qRPnz4he/8Isqp1RJLri4glknd6WlQnZ/xd93KR81vyTwMAQAQjUQEAQimxpkjTHo77G+cUPV94SiQv23G/YefQ7BsAAPDOefXWD7NFnPMy69VdKqmuSMVK9B4AALCNxMREmTlzplx77bWmQoImI7Ru3VqWLFliqhmopKQk+fzzz2Xbtm3Svn17M63EmDFjZODAga7tdOzYUf75z3/Ku+++K23atJF///vf8vHHH0urVq1cbZ544gl55JFHzOsuu+wyOXLkiEluSEiwQbVJlL6qwq51Rc+dKhDZONtxv+3dIhUq0qMIL4m1HFPb+qLrtR0AQGLpAwAIMZ3netNcka0LRa4a7vhP1v4tIid+E6mUKJJ2Saj3EAAAnCm9r8j8p0UO/yzy82qRepeJ5O9wrEuqp5cD0mcAEGpaMtzX1HJ6kiC53jn781lZWdK5c2fp2bOnfPrpp67nt2/fbsrbr127Vtq2bXvO/j5QntLT000VBX80eWHZsmU+29x6661m8UarKowbN84siGJ1LxXZMFNkzw9Fz21f5jiuV64h0vLmUO4d4JnGFUPWhDT+QBQi5kUEI1EBAEKt2XUiCcmOADZ3hUjDTkXTPqS0FolLDPUeAgCAM8VXE2l1i8ja/yey4T/uiQo1G9FfABAOP9hOai9y8rjvKxr1ZMI5Olnw3nvvmavC9Xbnzp2mdD0AoJQVFfZudlQw00RgTVxwTqVaOYmuRHjSuIJEBAQLMS8iHFM/AECo6Y9jzizwzXMdt7+sddzWv5IrMgEACFeX3O+43bZU5PfDRVM/1Gwc0t0CAIgjEdxXkoLS9b6ueDwLWqo+MzNTHnroIbnxxhtl2rRp/LMAQGmkthapECvy+yGRQ7kih39xXOBTfBo2AIh2xLyIcCQqAEC4TP+gti0TOXFUJO90okLDziHdLQAA4KccbZ2LRE4dF9n4CYkKAHCu6RW1OkVeIMvJY4FtU9sFsj3926UwY8YMadGihTRv3lzuuecemTJlilil3AYARLVKlUVS0h33d60T+eET/SJwVFpwPg8AdkTMiyjC1A8AEA6SLhCpmiJyZJfIshdFjh10ZI3rf8p2Zttj7jJ9D1o9wl/pVW0HAEAk0AoKF/5BZO9GkXWZIkd2O54/VWCf728ACCcFR0X+Vs7TJ0zpGVi7p3eKxFUJeLM63YMmKKiePXtKfn6+LFmyRLp27VrWPQWA6JtvPbmhyM61Ij99KZK3zrGucReRwzuJswHYFzEvogiJCgAQFvNIXVp0An/jHMdt4UmRaTcGZe7UoNB91/fgq7QqJ3QAAJE6D+SRvKJ1mXfb5/sbAFBqmzdvllWrVsmsWbPM49jYWLn99ttN8gKJCgBQhvnWty0pur9sokjWJOJsAAgxYl6UBxIVACCS5pGK9BMduv+R/h4AAIi2728ACBeVEh2VDQKxa31g1RL6zxNJaR3Y3w6QJiScPHlS0tKKqj/otA/x8fEyadKkgLcDAFGJOBtAtCPmRRQhUQEAAAAAAADhLyYm8OkXYisH3q4UUzr4owkKH3zwgUycOFF69Ojhtq5Pnz4yffp0MxUEAAAA4BExL6IIiQoAAAAAAABAOZg7d64cPHhQBgwYIElJSW7rbrnlFlNtgUQFAAAARDJiXpSXCuW2JQAAAAAAACAcJNYSiY333UbXa7typIkI3bt3L5Gk4ExUWL16tRw+fLhc/yYAAACiFDEvIhwVFQAAAAAAAGAvyfVEhqxxzHXu64ddbVeO5syZ43Xd5ZdfLpZlmfvOWwAAAKDMiHkR4UhUAAAAAAAAgD1/uC3nRAQAAAAgrBDzIoIx9QMAAAAAAAAAAAAAAAgaEhUAIErnkQIAAGeB728AAACg/BFnAwAQNZj6AQCidB4pAABwFvj+BgAAAMofcTYAAFGDRAUACAfMIwUAQOTh+xsAAAAgzgYAAGXC1A8AAAAAAAAIW5ZlhXoXgIAxXgEAQFkQQyAaxyuJCgAAAAAAAAg7lSpVMrdHjx4N9a4AAXOOV+f4BQAA8KVixYr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"text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -604,310 +492,260 @@ } ], "source": [ - "fig, axs = plt.subplots(1, 3, figsize=(21, 6))\n", - "df.plot(\n", - " x=\"Circuit\",\n", - " y=[\"Depth 2Q (No AI)\", \"Depth 2Q (AI)\"],\n", - " kind=\"bar\",\n", - " ax=axs[0],\n", - ")\n", - "axs[0].set_title(\"Circuit Depth Comparison\")\n", - "axs[0].set_ylabel(\"Depth\")\n", - "axs[0].set_xlabel(\"Circuit\")\n", - "axs[0].tick_params(axis=\"x\", rotation=45)\n", - "df.plot(\n", - " x=\"Circuit\",\n", - " y=[\"Gate Count (No AI)\", \"Gate Count (AI)\"],\n", - " kind=\"bar\",\n", - " ax=axs[1],\n", - ")\n", - "axs[1].set_title(\"Gate Count Comparison\")\n", - "axs[1].set_ylabel(\"Gate Count\")\n", - "axs[1].set_xlabel(\"Circuit\")\n", - "axs[1].tick_params(axis=\"x\", rotation=45)\n", - "df.plot(x=\"Circuit\", y=[\"Time (No AI)\", \"Time (AI)\"], kind=\"bar\", ax=axs[2])\n", - "axs[2].set_title(\"Time Comparison\")\n", - "axs[2].set_ylabel(\"Time (seconds)\")\n", - "axs[2].set_xlabel(\"Circuit\")\n", - "axs[2].tick_params(axis=\"x\", rotation=45)\n", - "fig.suptitle(\n", - " \"Benchmarking AI transpilation vs Non-AI transpilation for various circuits\"\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "345022d3-e302-47e6-9453-9261136923a7", - "metadata": {}, - "source": [ - "The AI transpiler's performance varies significantly based on the type of circuit being optimized. In some cases, it achieves notable reductions in circuit depth and gate count compared to the standard transpiler. However, these improvements often come with a substantial increase in runtime.\n", - "\n", - "For certain types of circuits, the AI transpiler may yield slightly better results in terms of circuit depth but may also lead to an increase in gate count and a significant runtime penalty. These observations suggest that the AI transpiler's benefits are not uniform across all circuit types. Instead, its effectiveness depends on the specific characteristics of the circuit, making it more suitable for some use cases than others." + "plot_metrics_and_pct(df_sim, \"Small-Scale Random Circuits\")" ] }, { "cell_type": "markdown", - "id": "9e496e7a-64a8-46fd-b240-c494e7825bd2", + "id": "sim-step2-analysis", "metadata": {}, "source": [ - "## When should users choose AI-powered transpilation?\n", + "**Two-qubit depth:** The AI transpiler generally produces circuits with lower two-qubit depth. Depth is one of the primary metrics the AI routing model is trained to optimize, and the improvement is visible across most circuit sizes, though SABRE does match or beat it on individual circuits.\n", "\n", - "The AI-powered transpiler in Qiskit excels in scenarios where traditional transpilation methods struggle, particularly with large-scale and complex quantum circuits. For circuits involving hundreds of qubits or those targeting hardware with intricate coupling maps, the AI transpiler offers superior optimization in terms of circuit depth, gate count, and runtime efficiency. In benchmarking tests, it has consistently outperformed traditional methods, delivering significantly shallower circuits and reducing gate counts, which are critical for enhancing performance and mitigating noise on real quantum hardware.\n", + "**Gate count:** The results are closely matched at this scale, with SABRE holding a slight edge overall. SABRE's routing heuristic is designed to minimize the number of inserted SWAP gates, which directly reduces gate count. At small circuit sizes, the difference is modest.\n", "\n", - "Users should consider AI-powered transpilation when working with:\n", - "- Large circuits where traditional methods fail to efficiently handle the scale.\n", - "- Complex hardware topologies where device connectivity and routing challenges arise.\n", - "- Performance-sensitive applications where reducing circuit depth and improving fidelity are paramount." + "**Transpilation time:** SABRE's runtime is nearly constant regardless of qubit count. At this scale, circuit size is not the bottleneck, and SABRE's core routing logic is highly optimized (largely implemented in Rust). The AI transpiler takes noticeably longer and scales with circuit size, though the absolute times remain reasonable for interactive use." ] }, { "cell_type": "markdown", - "id": "c345cb54-a838-427f-898f-51fb607da493", + "id": "sim-step3-header", "metadata": {}, "source": [ - "# Part III. Explore AI-powered permutation network synthesis\n", + "### Step 3: Execute using Qiskit primitives\n", "\n", - "Permutation networks are foundational in quantum computing, particularly for systems constrained by restricted topologies. These networks facilitate long-range interactions by dynamically swapping qubits to mimic all-to-all connectivity on hardware with limited connectivity. Such transformations are essential for implementing complex quantum algorithms on near-term devices, where interactions often span beyond nearest neighbors.\n", - "\n", - "In this section, we highlight the synthesis of permutation networks as a compelling use case for the AI-powered transpiler in Qiskit. Specifically, the `AIPermutationSynthesis` pass leverages AI-driven optimization to generate efficient circuits for qubit permutation tasks. By contrast, generic synthesis approaches often struggle to balance gate count and circuit depth, especially in scenarios with dense qubit interactions or when attempting to achieve full connectivity.\n", - "\n", - "We will walk through a Qiskit patterns example showcasing the synthesis of a permutation network to achieve all-to-all connectivity for a set of qubits. We will compare the performance of `AIPermutationSynthesis` against the standard synthesis methods in Qiskit. This example will demonstrate how the AI transpiler optimizes for lower circuit depth and gate count, highlighting its advantages in practical quantum workflows. To activate the AI synthesis pass, we will use the `generate_ai_pass_manager()` function with the `include_ai_synthesis` parameter set to `True`." + "To evaluate the impact of transpilation on circuit fidelity, we build mirror circuits from the 10-qubit case and run them on the Aer simulator with a simple depolarizing noise model. The expected output of a mirror circuit is always the all-zeros bitstring, so the probability of measuring $|0\\rangle^{\\otimes n}$ tells us how well each transpilation strategy preserves fidelity." ] }, { - "cell_type": "markdown", - "id": "76de0959-1eca-43d9-b8fe-f9aea9a122d8", + "cell_type": "code", + "execution_count": 6, + "id": "sim-step3-code", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default: depth 84, gates 278\n", + "AI: depth 70, gates 312\n" + ] + } + ], "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", - "\n", - "To represent a classical permutation problem on a quantum computer, we start by defining the structure of the quantum circuits. For this example:\n", + "# Use the 10-qubit circuit (index where qubits == 10)\n", + "idx_10q = qubit_range_sim.index(10)\n", "\n", - "1. Quantum circuit initialization:\n", - " We allocate 27 qubits to match the backend we will use, which has 27 qubits.\n", + "qc_10q = circuits_sim[idx_10q]\n", + "qc_default_10q, _ = transpile_with_metrics(pm_default_sim, qc_10q)\n", "\n", - "2. Apply permutations:\n", - " We generate ten random permutation patterns (`pattern_1` through `pattern_10`) using a fixed seed for reproducibility. Each permutation pattern is applied to a separate quantum circuit (`qc_1` through `qc_10`).\n", + "pm_ai = generate_ai_pass_manager(\n", + " optimization_level=1,\n", + " ai_optimization_level=3,\n", + " backend=backend,\n", + ")\n", + "qc_ai_10q, _ = transpile_with_metrics(pm_ai, qc_10q)\n", "\n", - "3. Circuit decomposition:\n", - " Each permutation operation is decomposed into native gate sets compatible with the target quantum hardware. We analyze the depth and the number of two-qubit gates (nonlocal gates) for each decomposed circuit.\n", + "tqc_methods = {\n", + " \"Default\": qc_default_10q,\n", + " \"AI\": qc_ai_10q,\n", + "}\n", "\n", - "The results provide insight into the complexity of representing classical permutation problems on a quantum device, demonstrating the resource requirements for different permutation patterns." + "print(f\"Default: depth {qc_default_10q.depth()}, gates {qc_default_10q.size()}\")\n", + "print(f\"AI: depth {qc_ai_10q.depth()}, gates {qc_ai_10q.size()}\")" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "76a3e847-0808-4413-bd0c-c760cd2df3f4", + "execution_count": 7, + "id": "sim-step3-run", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Default P(|00...0>) = 0.8514 (8514/10000)\n", + "AI P(|00...0>) = 0.8207 (8207/10000)\n" + ] } ], "source": [ - "# Parameters\n", - "width = 27\n", - "num_circuits = 10\n", + "# Build a simple depolarizing noise model\n", + "noise_model = NoiseModel()\n", + "noise_model.add_all_qubit_quantum_error(\n", + " depolarizing_error(0.001, 1),\n", + " [\"sx\", \"x\", \"rz\"], # ~0.1% per 1Q gate\n", + ")\n", + "noise_model.add_all_qubit_quantum_error(\n", + " depolarizing_error(0.01, 2),\n", + " [\"cx\", \"ecr\"], # ~1% per 2Q gate\n", + ")\n", "\n", - "# Set random seed\n", - "np.random.seed(seed)\n", + "aer_sim = AerSimulator(noise_model=noise_model)\n", "\n", + "shots = 10000\n", + "survival_probs = {}\n", "\n", - "# Generate random patterns and circuits\n", - "patterns = [\n", - " np.random.permutation(width).tolist() for _ in range(num_circuits)\n", - "]\n", - "circuits = {\n", - " f\"qc_{i}\": generate_permutation_circuit(width, pattern)\n", - " for i, pattern in enumerate(patterns, start=1)\n", - "}\n", + "for method, tqc in tqc_methods.items():\n", + " mirror = build_mirror_circuit(tqc, simulate=True)\n", + "\n", + " sampler = SamplerV2(mode=aer_sim)\n", + " job = sampler.run([mirror], shots=shots)\n", + " counts = job.result()[0].data.meas.get_counts()\n", "\n", - "# Display one of the circuits\n", - "circuits[\"qc_1\"].decompose(reps=3).draw(output=\"mpl\", fold=-1)" + " all_zeros = \"0\" * mirror.num_qubits\n", + " survival = counts.get(all_zeros, 0) / shots\n", + " survival_probs[method] = survival\n", + " print(\n", + " f\"{method:8s} P(|00...0>) = {survival:.4f} ({counts.get(all_zeros, 0)}/{shots})\"\n", + " )" ] }, { "cell_type": "markdown", - "id": "a8b79798-fa80-44d8-8a52-2d2a50e0c280", + "id": "sim-step3-analysis", "metadata": {}, "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", - "In this step, we proceed with optimization using the AI synthesis passes.\n", - "\n", - "For the AI synthesis passes, the `PassManager` requires only the coupling map of the backend. However, it is important to note that not all coupling maps are compatible; only those that the `AIPermutationSynthesis` pass has been trained on will work. Currently, the `AIPermutationSynthesis` pass supports blocks of sizes 65, 33, and 27 qubits. For this example we use a 27-qubit QPU.\n", - "\n", - "For comparison, we will evaluate the performance of AI synthesis against generic permutation synthesis methods in Qiskit, including:\n", - "\n", - "- `synth_permutation_depth_lnn_kms`: This method synthesizes a permutation circuit for a linear nearest-neighbor (LNN) architecture using the Kutin, Moulton, and Smithline (KMS) algorithm. It guarantees a circuit with a depth of at most $ n $ and a size of at most $ n(n-1)/2 $, where both depth and size are measured in terms of SWAP gates.\n", - "\n", - "- `synth_permutation_basic`: This is a straightforward implementation that synthesizes permutation circuits without imposing constraints on connectivity or optimization for specific architectures. It serves as a baseline for comparing performance with more advanced methods.\n", - "\n", - "Each of these methods represents a distinct approach to synthesizing permutation networks, providing a comprehensive benchmark against the AI-powered methods.\n", - "\n", - "For more details about synthesis methods in Qiskit, refer to the [Qiskit API documentation](/docs/api/qiskit/synthesis)." + "We ran both mirror circuits through the Aer simulator with a simple depolarizing noise model. The survival probability, defined as the fraction of shots that return the all-zeros bitstring, quantifies how much noise each transpilation strategy introduces." ] }, { "cell_type": "markdown", - "id": "b1733a10-c285-444e-af47-4a32329c5f7a", + "id": "sim-step4-header", "metadata": {}, "source": [ - "Define the coupling map representing the 27-qubit QPU." + "### Step 4: Post-process and return result in desired classical format\n", + "\n", + "We extract the probability of measuring the all-zeros bitstring from both runs. A higher probability indicates better fidelity, meaning the transpilation introduced less effective noise." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "84dff2c2-a496-4828-bb8e-08d373816a36", + "execution_count": 8, + "id": "sim-step4-code", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "coupling_map = [\n", - " [1, 0],\n", - " [2, 1],\n", - " [3, 2],\n", - " [3, 5],\n", - " [4, 1],\n", - " [6, 7],\n", - " [7, 4],\n", - " [7, 10],\n", - " [8, 5],\n", - " [8, 9],\n", - " [8, 11],\n", - " [11, 14],\n", - " [12, 10],\n", - " [12, 13],\n", - " [12, 15],\n", - " [13, 14],\n", - " [16, 14],\n", - " [17, 18],\n", - " [18, 15],\n", - " [18, 21],\n", - " [19, 16],\n", - " [19, 22],\n", - " [20, 19],\n", - " [21, 23],\n", - " [23, 24],\n", - " [25, 22],\n", - " [25, 24],\n", - " [26, 25],\n", - "]\n", - "CouplingMap(coupling_map).draw()" + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.bar(\n", + " survival_probs.keys(),\n", + " survival_probs.values(),\n", + " color=[\"steelblue\", \"coral\"],\n", + ")\n", + "ax.set_ylabel(\"P(|0...0>)\")\n", + "ax.set_title(\"Mirror Circuit Fidelity (10-qubit, Aer Simulator)\")\n", + "ax.set_ylim(0, 1)\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "47bdb1f5-1fc6-46c4-8fc9-98d16a4d2529", + "id": "sim-step4-analysis", "metadata": {}, "source": [ - "Transpile each of the permutation circuits using the AI synthesis passes and generic synthesis methods." + "In this case, the default transpiler achieves a higher survival probability despite having a deeper circuit. This is because it produced a circuit with fewer total gates, and under a uniform depolarizing noise model, total gate count has a more direct impact on accumulated error than depth alone. This trade-off is not universal. The relative importance of depth versus gate count depends on the magnitude of the difference in each metric, the noise characteristics of the hardware, and the structure of the circuit." ] }, { - "cell_type": "code", - "execution_count": 12, - "id": "128cc285-094a-4b07-a37d-8424a4003b2c", + "cell_type": "markdown", + "id": "hw-header", "metadata": {}, - "outputs": [], "source": [ - "results = []\n", - "pm_no_ai_synth = generate_preset_pass_manager(\n", - " coupling_map=cm,\n", - " optimization_level=1, # set to 1 since we are using the synthesis methods\n", - ")\n", - "\n", - "# Transpile and analyze all circuits\n", - "for i, (qc_name, qc) in enumerate(circuits.items(), start=1):\n", - " pattern = patterns[i - 1] # Get the corresponding pattern\n", - "\n", - " qc_depth_lnn_kms = synth_permutation_depth_lnn_kms(pattern)\n", - " qc_basic = synth_permutation_basic(pattern)\n", - "\n", - " # AI synthesis\n", - " results.append(\n", - " synth_transpile_with_metrics(\n", - " qc.decompose(reps=3),\n", - " pm_ai,\n", - " qc_name,\n", - " \"AI\",\n", - " )\n", - " )\n", - "\n", - " # Depth-LNN-KMS Method\n", - " results.append(\n", - " synth_transpile_with_metrics(\n", - " qc_depth_lnn_kms.decompose(reps=3),\n", - " pm_no_ai_synth,\n", - " qc_name,\n", - " \"Depth-LNN-KMS\",\n", - " )\n", - " )\n", - "\n", - " # Basic Method\n", - " results.append(\n", - " synth_transpile_with_metrics(\n", - " qc_basic.decompose(reps=3),\n", - " pm_no_ai_synth,\n", - " qc_name,\n", - " \"Basic\",\n", - " )\n", - " )\n", - "\n", - "\n", - "results_df = pd.DataFrame(results)" + "## Large-scale hardware example" ] }, { "cell_type": "markdown", - "id": "42f80e32-60fd-46a8-a6b5-4bcadb15810a", + "id": "hw-steps-header", + "metadata": {}, + "source": [ + "### Steps 1-4 compress into single code block\n", + "Here we put all of these details together into a singular workflow at a larger scale, which is then run on real quantum hardware.\n", + "\n", + "We generate 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. We transpile all circuits with both strategies and collect the same metrics. Then we build mirror circuits from the 26-qubit case and submit them to the real backend. We use the smallest circuit size for the hardware run because mirror circuits double the gate count, and this basic fidelity test does not scale well on real hardware at larger qubit counts." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "hw-step1", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created 25 circuits with qubit counts: [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]\n" + ] + } + ], "source": [ - "Record the metrics (depth, gate count, time) for each circuit after transpilation." + "# -------------------------Step 1-------------------------\n", + "num_circuits_hw = 25\n", + "depth_hw = 8\n", + "qubit_range_hw = list(range(26, 51))\n", + "\n", + "circuits_hw = [\n", + " # We have only two qubit gates, as those test how well the transpiler can optimize the circuit.\n", + " random_circuit(num_qubits=n, \n", + " depth=depth_hw, \n", + " max_operands=2,\n", + " num_operand_distribution={2: 1},\n", + " seed=seed + i,)\n", + " for i, n in enumerate(qubit_range_hw)\n", + "]\n", + "\n", + "print(f\"Created {len(circuits_hw)} circuits with qubit counts: {qubit_range_hw}\")" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "72ee8474-eea6-421a-9d7d-070587eaff71", + "execution_count": 10, + "id": "hw-step2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "=== Average Metrics ===\n", - " Depth (2Q) Gates Time (s)\n", - "Method \n", - "AI 23.9 82.8 0.248\n", - "Basic 29.8 91.0 0.012\n", - "Depth-LNN-KMS 70.8 531.6 0.017\n", - "\n", - "Best Non-AI Method (based on least average depth): Basic\n", - "\n", - "=== Comparison of AI vs Best Non-AI Method ===\n" + "[26q] Default: depth=155, gates=2420, time=0.072s | AI: depth=119, gates=2694, time=1.925s\n", + "[27q] Default: depth=146, gates=2421, time=0.070s | AI: depth=118, gates=2736, time=1.872s\n", + "[28q] Default: depth=147, gates=2706, time=0.082s | AI: depth=151, gates=3297, time=2.398s\n", + "[29q] Default: depth=190, gates=3038, time=0.088s | AI: depth=149, gates=3297, time=2.421s\n", + "[30q] Default: depth=174, gates=3027, time=0.079s | AI: depth=138, gates=3220, time=2.471s\n", + "[31q] Default: depth=170, gates=3247, time=0.101s | AI: depth=150, gates=3559, time=2.659s\n", + "[32q] Default: depth=152, gates=3366, time=0.095s | AI: depth=176, gates=4150, time=3.185s\n", + "[33q] Default: depth=197, gates=3487, time=0.101s | AI: depth=163, gates=3675, time=3.082s\n", + "[34q] Default: depth=224, gates=3861, time=0.110s | AI: depth=167, gates=4417, time=3.186s\n", + "[35q] Default: depth=212, gates=4051, time=0.119s | AI: depth=190, gates=4381, time=3.297s\n", + "[36q] Default: depth=208, gates=4189, time=0.102s | AI: depth=194, gates=5017, time=3.579s\n", + "[37q] Default: depth=225, gates=4222, time=0.120s | AI: depth=200, gates=4840, time=3.642s\n", + "[38q] Default: depth=194, gates=4112, time=0.117s | AI: depth=184, gates=4622, time=3.769s\n", + "[39q] Default: depth=201, gates=4597, time=0.112s | AI: depth=188, gates=5074, time=4.071s\n", + "[40q] Default: depth=215, gates=4951, time=0.129s | AI: depth=201, gates=5498, time=4.236s\n", + "[41q] Default: depth=253, gates=5322, time=0.138s | AI: depth=215, gates=5617, time=4.596s\n", + "[42q] Default: depth=188, gates=4639, time=0.119s | AI: depth=202, gates=5756, time=4.580s\n", + "[43q] Default: depth=227, gates=5093, time=0.140s | AI: depth=212, gates=6039, time=5.674s\n", + "[44q] Default: depth=299, gates=5835, time=0.176s | AI: depth=228, gates=6398, time=5.719s\n", + "[45q] Default: depth=223, gates=5666, time=0.154s | AI: depth=221, gates=6627, time=5.783s\n", + "[46q] Default: depth=260, gates=6124, time=0.157s | AI: depth=238, gates=6944, time=6.005s\n", + "[47q] Default: depth=301, gates=6691, time=0.169s | AI: depth=236, gates=7425, time=5.937s\n", + "[48q] Default: depth=280, gates=6664, time=0.161s | AI: depth=233, gates=7455, time=6.598s\n", + "[49q] Default: depth=263, gates=6228, time=0.192s | AI: depth=227, gates=7326, time=6.279s\n", + "[50q] Default: depth=330, gates=6969, time=0.195s | AI: depth=260, gates=7842, time=7.754s\n" ] }, { @@ -931,130 +769,198 @@ " \n", " \n", " \n", + " Default (mean +/- std)\n", + " AI (mean +/- std)\n", + " AI % improvement\n", + " \n", + " \n", " Metric\n", - " AI\n", - " Basic\n", - " Improvement (AI vs Best Non-AI)\n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 0\n", - " Depth (2Q)\n", - " 23.900\n", - " 29.800\n", - " -5.900\n", + " Depth 2Q\n", + " 217.4 +/- 50.4\n", + " 190.4 +/- 38.5\n", + " +11.5% +/- 10.3%\n", " \n", " \n", - " 1\n", - " Gates\n", - " 82.800\n", - " 91.000\n", - " -8.200\n", + " Gate Count\n", + " 4517.0 +/- 1393.3\n", + " 5116.2 +/- 1588.8\n", + " -13.3% +/- 5.3%\n", " \n", " \n", - " 2\n", - " Time (s)\n", - " 0.248\n", - " 0.012\n", - " 0.236\n", + " Time (s)\n", + " 0.1 +/- 0.0\n", + " 4.2 +/- 1.6\n", + " -3198.3% +/- 453.5%\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Metric AI Basic Improvement (AI vs Best Non-AI)\n", - "0 Depth (2Q) 23.900 29.800 -5.900\n", - "1 Gates 82.800 91.000 -8.200\n", - "2 Time (s) 0.248 0.012 0.236" + " Default (mean +/- std) AI (mean +/- std) AI % improvement\n", + "Metric \n", + "Depth 2Q 217.4 +/- 50.4 190.4 +/- 38.5 +11.5% +/- 10.3%\n", + "Gate Count 4517.0 +/- 1393.3 5116.2 +/- 1588.8 -13.3% +/- 5.3%\n", + "Time (s) 0.1 +/- 0.0 4.2 +/- 1.6 -3198.3% +/- 453.5%" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Calculate averages for each metric\n", - "average_metrics = results_df.groupby(\"Method\")[\n", - " [\"Depth (2Q)\", \"Gates\", \"Time (s)\"]\n", - "].mean()\n", - "average_metrics = average_metrics.round(3) # Round to two decimal places\n", - "print(\"\\n=== Average Metrics ===\")\n", - "print(average_metrics)\n", - "\n", - "# Identify the best non-AI method based on least average depth\n", - "non_ai_methods = [\n", - " method for method in results_df[\"Method\"].unique() if method != \"AI\"\n", - "]\n", - "best_non_ai_method = average_metrics.loc[non_ai_methods][\n", - " \"Depth (2Q)\"\n", - "].idxmin()\n", - "print(\n", - " f\"\\nBest Non-AI Method (based on least average depth): {best_non_ai_method}\"\n", + "# -------------------------Step 2-------------------------\n", + "pm_default = generate_preset_pass_manager(\n", + " optimization_level=3,\n", + " backend=backend,\n", + " seed_transpiler=seed,\n", ")\n", "\n", - "# Compare AI to the best non-AI method\n", - "ai_metrics = average_metrics.loc[\"AI\"]\n", - "best_non_ai_metrics = average_metrics.loc[best_non_ai_method]\n", - "\n", - "comparison = {\n", - " \"Metric\": [\"Depth (2Q)\", \"Gates\", \"Time (s)\"],\n", - " \"AI\": [\n", - " ai_metrics[\"Depth (2Q)\"],\n", - " ai_metrics[\"Gates\"],\n", - " ai_metrics[\"Time (s)\"],\n", - " ],\n", - " best_non_ai_method: [\n", - " best_non_ai_metrics[\"Depth (2Q)\"],\n", - " best_non_ai_metrics[\"Gates\"],\n", - " best_non_ai_metrics[\"Time (s)\"],\n", - " ],\n", - " \"Improvement (AI vs Best Non-AI)\": [\n", - " ai_metrics[\"Depth (2Q)\"] - best_non_ai_metrics[\"Depth (2Q)\"],\n", - " ai_metrics[\"Gates\"] - best_non_ai_metrics[\"Gates\"],\n", - " ai_metrics[\"Time (s)\"] - best_non_ai_metrics[\"Time (s)\"],\n", - " ],\n", - "}\n", + "results_hw = []\n", + "\n", + "for i, qc in enumerate(circuits_hw):\n", + " n = qubit_range_hw[i]\n", + "\n", + " qc_default, m_default = transpile_with_metrics(pm_default, qc)\n", "\n", - "comparison_df = pd.DataFrame(comparison)\n", - "print(\"\\n=== Comparison of AI vs Best Non-AI Method ===\")\n", - "comparison_df" + " # Create a fresh AI pass manager each iteration to avoid stale layout state\n", + " pm_ai = generate_ai_pass_manager(\n", + " optimization_level=1,\n", + " ai_optimization_level=3,\n", + " backend=backend,\n", + " )\n", + " qc_ai, m_ai = transpile_with_metrics(pm_ai, qc)\n", + "\n", + " results_hw.append({\n", + " \"Qubits\": n,\n", + " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", + " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", + " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", + " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", + " \"Time (Default)\": m_default[\"time_s\"],\n", + " \"Time (AI)\": m_ai[\"time_s\"],\n", + " })\n", + "\n", + " print(f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\")\n", + "\n", + "df_hw = pd.DataFrame(results_hw)\n", + "summary_table(df_hw)" ] }, { - "cell_type": "markdown", - "id": "e1ba3767-5ce1-4663-803b-73ccfc22f03b", + "cell_type": "code", + "execution_count": 11, + "id": "hw-step2-plot", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DEKleU7d0xvRvlDHWhmaWNWyuQGdUWPqntLmcdKYApMVTSupux0Nuu8jsN+lo4QR3cMqO3y1DA1PpoPP20t43urMjxzwrfWbXq8CC6mzmsB3bKZsDZI/V86VFk8q+2P3Zat0yd2kZh7rs5F5f8kdKmwTEzfSv3WWPYSWTADIpaxiAtLNgwYKIIAXj/W33lbeOBRasX79eS5YscUpIxFrHe45Ybr31VjVr1ix86dQpQ0o9AgAAZDAKc2aL3I6RmQxSzWtH03wFnp3U7LabNOV96fe3pR6hUhAZU/ohoIEKzTq6KdA3r3dPsLdPkxnRQCosCs0MjdeAvDf7bvU8qajIpq8obcs+tOrl1mdOJ1vs7QbHfX2b9O1dUuNW0o7nVu6xY14oyQbkzawEkNksCOH5QyuuU3/8m9k5u9obtG3YUsoPpaleOjWlTQLiwrIozfrevd57hJuJbtlfBCoAqLIrrrhCt912W7nrTJo0SX369An01r3yyit1ySWXhP+2wAeCFQAAAIKNQIVsy6hgJzILN0u1U/zRp1Oggumxhxuo8MeHUvHdUq2oOqDpHKjQqIUCydKVt9pCWvCbNH8MgQpAWQrWSCtDZX1a9IzPdvKOzZvWS2sWSblpcqz2mzvaXbbunZ7H7AFHShuWS6MelT6+0g1WsNvKs2ax9OfH7vU++yelmQACYP2K8oMUjN1v62VloMKSkj5v/gD3+vLpKW0SEBfzxrhlTayUX+cd3b6CIaMCgCq69NJLdfLJJ5e7Tvfu3Sv1XPn5+Ro1alTEbQsXLgzf5y292/zr5ObmqmHDhqpdu7ZzibWO9xyx1K9f37kAAAAgfaThFElUS5O27sCvnaRcleJU3jbw5Z0w9AIogq7TDlKd+m6ghzf4lTGlHwIaqOANMJr5v6W6JUBwLQmVfbCT1N4J6pqq00Bq1NK9viJNB3Pm/Owu226ltLXtmVL/I9zr/z1H+uOT8tcf/6pUtNnNIpHO7xsA4skbtLXfyLZblgTsen1hIF3NCJV96LqLVL9xSekHy8oEAFXQunVrJ1tCeZd69SqXpW7HHXfU+PHjtWjRovBtn376qROE0K9fv/A6n332WcTjbB273dhrDRkyJGKdoqIi529vHQAAAGQGAhWyhZUvsGAFY+kgU8mreV63odQgoGUHollbO4dq2k58S2mvuNhX+iE0GBlErUKBCpbGFEBsi0OBCnmd3WN9vDQNBZItm5F+W95O0Htpvb3Zs+nIMkHsdrnUc283AOHVE6XZkbOTIo7rXtkHKx2RQ9IsAHCsW+YuG7eWGuRKeV0ifz+BdGS/+9NDgQq99nOXjdtE7vMAkACzZs3S2LFjnWVhYaFz3S5r1qxx7t9nn32cgIQTTjhB48aN08cff6yrrrpKI0eODGc7OPvss/XXX3/p8ssv1+TJk/XQQw/p1Vdf1cUXXxx+HSvh8Nhjj+mZZ55xyk6cc845Wrt2rU455RQ+VwAAgAxCoEI28bIXpDrV6crZJScLU12Coip6DHOXUz5U2itYVZIiOKilH/yBCosnuyfjgMqwTACWAaU8dr+XMSDd2ffDC1SIp9xQevAVobIS6cTLfGO/e01aK61ZNqS9b5Q6bidtXi+9cLi0KPSZ+80fJy2a6Aar9BqRipYCQDB5mdy8Qdz8/pG/n0A6sv/pLVNiTl1pi33c27w+j5WOAoAEueaaazR48GBde+21TnCCXbfLL7/84txvJRvee+89Z2nZD44//nideOKJuuGGG8LP0a1bN73//vtOFoWBAwfqrrvu0uOPP6599903vM5RRx2lO++803m9QYMGOcEQH330kdq2DU3CAgAAQEZIo1Fi1FhuB3fwZvnM1G7MlXNKAhVsACZddNlFqlVbWjZNWjRJatNXactLdWuZIuo1VmC17O5u8w0rpOWzpBahGXBAefI6SUc+K714lHvy1lLgzx8jDT5R2vY0dx0LUrD1MoE3aN28a3yf16tj7gWXpZM5v5SUj8mEzAIWfLD/3dJbZ0qLfpeeO0g67X+R+/DYF92l1aj2MigBAEpKPzQNHRutXzD5PWnpn2wdpC8vm0KHrUtKPnjBOOtXpK5dADLe008/7VzK06VLF33wwQflrrP77rtrzJgx5a5z3nnnORcAAABkrjQaJUb8MiqkOI23zfzwn0hJF5YqtuM2mVH+wSv7YDXtc2orsOo0kJqHghPm/Zrq1iCdzPzBXXbe3j2BawoLpPaD3EumBCn4Z4S26JGY34x0zKjgBSq0SuOAsmgWWHbA/W7K8tULpKdGSH99Jc0bK83+WRr3YskAnO0Tq+enusUAELDSD15Gha2CUQ4PqIkZoUAFy6ZgpaKMF7BARgUAAAAAQJogUCHbMioEYXZsuPRDmgUqZFL5h3CgQrPgZ7Xwyj8s+C3VLUG6sDIhv//Xvd51aMmxb0UaZgaoyMa1JYEE8Q5UaNo+MrgsnT5/r/SDNxiVKRrmSQc9JDVsKa2cJT17oPToUOmJvaSC1e46Pz4kvXq89PyhBCsAgD+jgpdtxgK6jP1+bg6VQgPSyfrl0oLx7vXevnJPlrHQuX+FVFyUmrYBAAAAAFAFAR+hRGICFUKlF1LFe30v/Wo66TbUCoa7g+bpPOjpz6gQdJa63Xgn44CKzB/rZo6pXd/9zuaGShisSvGxLxGW/GEj8+532ZtFFy/h7TZfKkqjk902Q3b9MrdcQpt+yjhN86Xd/1HxeoUbSf0MZIulUytep3Y9N9gp21jw2rolJcdPY5lp6jWRijZJyyqx7YCgmfmdG4jQsqfUqlfJ7V5fcPMGqWBNypoHAAAAAEBlEaiQTbw03jZIvXF9AAIVQoNg6cRmqXizsNK5/MPa0AnbdDhh7WVUWDQp1S1BupgYyqbQaVupUYuSzACWLr9wszLK4inuslknd2A+npqEBnQ2rS05ZqRT2YcWPaV6jZWRvH0aACyY7Nt73O3QfZh0+ufSmV+5l9P+JzVu5d438Nj07HvX1KZ10uaCyECFnJyS/rxXPglIJ9O/Lsn2V7tOye0WgGOl88y6UGA6AAAAAAABRqBCNmnUyp1NZbMvVsxM3aymdA5U8Jd/mPy+0lY6ZVRotUXJbHivxjBQ2bIPVrPXUj1biRObYb5qXmZtOy+AJ69z/J+7bkOpYXP3+vI0quM995eSbCxBL20DADVhv2sfXS4VrJRa95EOf1LqOERqP8i9WMDesKvdde23cVMKA5VTXfbBftPq55bc7pUGcjITAWmkcJM060f3eu/hkfdZv9cr/7CW/5sAAAAAAMHHGfxsYicuvKwKlho7VScLLRWllU9Ix9IPpsce7nLOz9KaNJplnK6BCjZQ2jiUxnTe2FS3BulW9sFYpgFvH0rVsS/RGRWad0nszH2r451uGRXa9E11SwAgsb65U1r0u1S/qXToY1KjUHCZ36Dj3FIHG1ZIY57P3kCFhi2kHN/Mcy+jwtJpqWkXUF1zR7vZrmyf7rxj6fu98g9eyRMAAAAAAAKMQIVsk9shtYN1K2eXDD7XbaS0ZCnWLaV4caE06R2lJS+Nu6XFTwc2M9obhAaqUvbB4wVprZiRWdvPS1ndokdint/bbstTlIWnqjZtkBaMd6+3G5jq1gBA4kz5QJrwhhv8O/wOqd2A2OtZsN7uV7rXx70gbVyTnYEKjVq6JR88+aHttXx6atoFVNeMUNmHrru4mUKiecG53r4PAAAAAECAEaiQrYEKqZod65V9aNI6clZT2pZ/eE9pnVHBZuKkg1a93OXCCaluCdKt7EOpQIU0ygxQkY3r3OwRpmWoREq8eSV6VqbJdlvwm1S0yc0W0yxBWSYAINWWTpW+uMm9vu3p0oAjy1/f7m/ZUypYLf36rLKKN1jbuFXk7ZZ1x8oDrV8urV6QkqYB1errTv8mdtkHj/2fbdYTqAAAAAAACD4CFbJNqgfrVs6NnOmRrrzyDzO+kQrWpG+gQuM0CVSwustm4cRUtwTpVvYhnUsYVGTpn3bG2q257dUjjrfcUKDCilA2nHQp+2BZWOrUS3VrACD+LCPCh5dLmwukjttKe10v5dQu/zF2fzirwsvS+pVZGKgQ9b9HvUYl2YgWT0p+u4DqWDZNWj3PzZTSc+/Y64QzKixnGwMAAAAAAo9AhWyT8owKocGuRA2qJYvNXrZtWbjRTb2bTgo3S+uWudcbRs0uC3pGBasjvKkg1a1BupV9iBhwz6BAhcVT3GVeZ/eEdSJ4AR5eNpygm+sLVMhkDfOk2hUEYtj9th6AzJpN/dkN0oqZbl/6kEel+o0r99gtD3UDP622/a9PK+sCFZq0LX1f/laRv6dA0E0PlX3osE3pLCGeJqFAhQ0EKgAAAAAAgi+Nc++jRhkVVs11T3b6U6MntfRDjJOF6cS2W/c9pLHPS5PerTjlbpCstyCFYremcaPmSpv9tl5jaeNaadFEqcPWqW4R0qnsgz9Iy459mWLRpJJAhUQdy73fjNXzU/ObUVVzfnaXbUODT5nKSnIc/6a0fkXZ61iQgle6A0BmGPeiNO0zt3zagQ9ILbtX/rE5OdIe/5RePUEa/5o0+MT06QfGJVAhRpC0/VZMfMstpQGkA8vmZ7bYp+w+mTchwMqaAAAAAAAQcGRUyDbeYN2GFe4l2cKBCvlKe175h2mfu+l3063sQ4PcimfkBoXVEG4Zyqowb0yqW4Ogl33ovnvZmQGsDnXhJmVWRoUuiXsNb6DbUo2vXaJAW7M4lDGjlpQ/UBnPPhursV7WhSAFIPN+576/z72+y6XuQGVV9T1Ayu8vbV4v/fK4skK5GRX6u8tlfyW3TUB1WEa8BePd671HlL1eOFAhBf/rAwAAAABQRQQqZJv6TaV6Td3rS6enLlChaQYEKuQPkBq1dAfwpv5P6Reo0EyqVUFN4yBpHQpU8E7QAWWVfWgYY4aozaS0/b1oc/qUMajI4snuskUVZtRWVd2GUoNQ+QALBEmHsg95nWLvAwCQzoPtH10hFRW6del3vdTNkFBVNgN72NXudcskYAFe2RKoEOt/Dy/7jvULNq1PbruAqpr5rZsVz0oQtuxZ9nrh0g8r3WxYAAAAAAAEGIEK2ahZKKvC8mnJfV3LOrBmQWQ68XRms/y7hWZuW/mHdOHNirbBx6Cncfdr1cddLpyQ6pYg3co+GEuT7Z24XZaCIK1427RBWh56H3bCOpG843XQAxW8sg9Wgz0njYKwAKA8FmD38f+5gaaWQeegB6S69au/zSwTQ4chUmGB9Mtjmd8/8AIVGreN/ftmgW3FhdKSP5LePKBKpofKPvQYJtWuU3FGBQum37SOjQwAAAAACDQCFbK5/MPymcl93VXz3KWVG7BMBJnASzH/x8fuLLe0yqgQmiWdbhkVbBZ5UVGqW4N0KvsQfexbEfAB98pY+qdUXORmyImVzjqevBICK5L8m1FVc34pCVQAgEzx0yNuxpg6DaRDH615VjInq8JV7vXf33FLImWqglVuoIeJtd1sW3hZFbwsRUAQFW6UZv9YcdkHY8E3OXXd616gDgAAAAAAAUWgQjbyZscme9DJS7feuI1UO3TyJN113Faq10Rav0ya8Z3SKlChYTOlFUtvb7OkC1YHf2Y3glX2oVRmgIAPuFfGosklZQ4SfTz1ttvK2QosC16aN8a93jZUcxwA0t30r6XRT7nX975R6rR9fJ63+x5S5x2kok3SqP8oY3mDtE7pu0ax18kP/WaQUQFBD8a07AgW7N+5guOABeA0bhWZSQ8AAAAAgIAiUCGrAxVmpSZQoUkrt2xCJli3RMof4F7/+XFp3tjIy4oADuyla0YFy8TRvLt7fe7oVLcG6VT2oVRmgCQf+xLBm/lpacATXcIlvN0CeDzz2ACTzZy1Gcete6e6NQAQn37z/65xr/c/Utrm1Pgd752sCle716d8UNJHz9RABWeGeRmp8r2MCsuSXBIPqIoZobIPXXeV6jaseH2v3Jn9rwoAAAAAQICVU9wQGSuc/jzJJyVX+TIqZILV86XnD3VTcZpJb7sXvzr1pfNGu7Oeg8KbWVPezPOgatXLTXm/YLw04IhUtwbpVPbBf+zLhAEZf6BConmBCkHebnN+dpettpDqljFrFgDSxeYC6aPL3SxSbfpJI+4ovyZ9dXTdReo2VJr+lVteYp+blLGBCo1alR3kke8FKkx3s/PkZEgwNTIrKHfG1+713sMr9xjv/21KPwAAAAAAAo4zMdmcUWH1XPeEXCpKP2SC9StKghTKO9EctBNE4dIPLZR2vJnSCyekuiVIt7IPESUMAjzgXtVABSuJkrTfjHnuyfIgsvrtplXvxGeYAIBE+/p2afEUqUEz6dDHpIYJyoLlZVX48+PMKIsUzeuDN25d9jqt+7jZFjaucX/nghQQvWhS2Re7H9lh6VRp9QI3u1zPPSv3mHBGhWUJbRoAAAAAADVFRoVs1DQ06LRpvXvSo1no76SVfmibnNdD+YEKVuM03dggpFn0e6pbgnQr++DPDLBmoRtEZBlP0pG1fdlf7vWWPRP/et52s5m965dLjQIY5DQnVA7GZh4DQDqb9E7ot62WNOKukhn/iWBBfj33lqZ+Kv34kDT8tsS9VlADFawvYNl4vACAZh0VuKxtsdig9fFvlvxGI3N52RQ6butmB6mMxqH11hOoAAAAAAAINjIqZCM7IeedsFseGuxKZqACJ9RSa62XBjeAg42VKf3gncD13geyV1XKPhg77tmsyeJCacVspfXMuuIiqV7j5BxP7XVsVq+XGjtoCtZIiya619sNTHVrAKD6lvwhffkv9/r2Z0lbHZr4rTnsn+5y2mfSkqnKyECFioKk2/Z3l0umKG2yttn9th4y3/RQoMIW+1Q+a5SXwdACTAEAAAAACDACFbKVl8o7WYNONvM5HKiQn5zXRGmWRWPjavd6wzTMqNAgt2Rgdt6vShs2KD5vbNmXdB40T5eyDyanttQkP/lBWvFmMz5NXhepdt3kvKb3vVs+PZgBKxa4YYEouR1S3RoAqB7LWvPhZVJhgdRpB2mv69zfrURrP1jqPcI669JPDykzAxUqKDvnZa2wQEAgaPvwwlAwZu/hlX+ct89vIJgFAAAAAJBhgQo33HCD1q1bV+r29evXO/chTXiDOSuSVI/WTpJY7VfntZNUagKlrV3iLnPqlsyQTtesCvPHKS1YEMIDQ6RHh5Z9sfsJVkhs2QePd/xJ51rcVrfc5HWq/PuOV8mgFbMUOHN+KTk2JCtwA1mBPi+S+pv22XVuUK/N/j/0P1Ldhsl7/T1CWRWmfyUtmqyMsW5Z5QIV2oYCFbyySkBQzPjWDSKy8ndVKfflZU8k6waQ0eirAgAAICsDFa6//nqtWRMacPax4AW7D2ki2YN1XjaFBnlS3UbJeU2Utnaxu2zYLDmz9BLBTtSZBeOVNjOhNheUv47d7836Q2LKPkQf+4I44F5Zi30ZFZIlt11wt9vcUKBC6z6pbgkyDH1exJ2VrrKsONGXb+6S/vpSqlVbOughqXnX5G58yyjQ72D3+o8PKmOsW1K50g/5odIPq+a55YSAoJgRKvvQY1jV/ndr4iv9YIFQADISfVUAAABkgjpVfUBxcbFqxZjBOW7cOLVokYY177NV0w7JHXTyAhW8GvFIbUYFCxhJ10CF1qFAhYW/p7olSKeyDxkVqBDKqNC8R/Je08uo4B3Lg5hRwZsRC8QJfV7EPUjh+UOlwo1lr2P/YrUOZY5Ktj3+T5r0jjTrezcY1Bu8T1dFhe4grT/YrrxBXbusWSQtnix13CYpTQQqDGSe9aN73SnPUgWN25SUlLFyMnUasLGBDERfFQAAAJmg0iPGzZs3dwIU7NKrV6+IYIXCwkIny8LZZ5+dqHYi3rzBupWzkx+okKxU5YnWME+qXa/8E8516kuNWipwGRXSteyDP6PC8r+kTRukupx4yzrVLftgmrZL7rEv3jZvlJZOc6+3TGKgQm5At9vKue7gX60cqW2aD6ohMOjzIiEsBXt5fUZvcN3KFeR1Tk0gaP8jpN9ekX58SDr4YaW1DSul4iI3+sMbtC2P/Yas+UxaMoVABQQnY9TmDVLjVlKn7ar22EYt3L6RfQfWLpWahSYpAMgI9FUBAACQlYEK9957rxOte+qppzrpxZo1KxnorFevnrp27aodd9wxUe1EvHknK9YsdAe+6tRLTqBCk1C9zExgA57Hv1lS+9PS9s4fI+1wrjTgKPc2C1KwOvKBC1TIU9pqmi/VayptXC0t+K3qJ+6QvWUfTG6HkgHudLR0qlRc6JbQ8QLOkplRYdV8N1AkKAFnXtmH5t2kBrmpbg0yBH1eZK2h/5DGvy7NGSXNGyO1H6y05ZXUsuDcyswmt/IX0yxQ4c+ENw2olOmhsg9dd6t6YLZlzmvUSlq7yP0uEKgAZBT6qgAAAMjKQIWTTjrJWXbr1k077bST6tatm8h2IdFsZpGVYCja7KZAb9UzSRkVKjGjKd2CFbwZ2i26u4EKNnul/SAFkheoYNkg0pHNnLbAkGYdpcWTpEnvuVktPEELDEGwyj74B9ztxK2l1LWsJ+nEUlIbm23r3/eTESBkClZKG1ZUfbsnypyfS2YCp2s5GwQOfV5kLcvUM+hYacxz0g8PSoc9rrS1bknJzPLK/D54WXmWhbIWBT1rm92frv15VMyCQmd8417vNbx6W8wyMVh/1/v/D0DGoK8KAACArAxU8AwdOlRFRUX6448/tGjRIue632677RbP9iFR7IRdk3xp1Rw3hX6yAhW8Qf1MZCeDzOoFCqy1S9I3o0Ks2s7f3+dePDbofN5oghUyWU3KPpjGLUtO/ltWBhvgTieLp5QEKiQzq0H9plL9XKlglbRshtQhKIEKo91l676pbgkyEH1eZKWhl0vjXnaDb2f/JHXaXmmdUcFmlVfm99IyKpjl090SHKkMfvOytn1xizTre6llTzejkgV873+3u44FKWTy/1XZbskfbuZDyx7Wc1j1nqNJG2nR79L6ZfFuHYCAoK8KAACArAxU+PHHH3Xsscdq5syZTikIv1q1aqmwsDCe7UMiWQpIC1SwQadECwcqhGblZiI7GeQNqAdVOKNCC2VkbWebIW8npsmqkLlqUvbBWMYTOw5ZJhkbjEi7QIVJ7jKvS/Jf2wZELFDBtluHAKQDL9zs7g8mf0CqW4MMRJ8XWckC4bY+QfrlSenHh6SO2wWn3E9VrFsWGUhckZZbuH2LTeullbPckkKpZAHlS0LBidudKX14uTvgbEELtclsmPFmfF2SPcwyxlWHl8mQQAUgY9FXBQAAQCbIqeoDzj77bG2zzTaaMGGCli1bpuXLl4cv9nciff311zrggAPUvn17Jyjiv/8NzaoNscCJa665Ru3atVPDhg2111576c8/qTNaYQr0FTMTP5i0el7oNTN45k/j1u5yzSIFPlDB0uAiOeo0rMQ69at/EjLb1KTsQ/Sxb3mCj32JzKjQIgUDKLmh47cFeQSBzRLctE6q1zjxWYGQlVLZ5wVSarfL3OxDCydIM79L79IPXiBxRWrXkdr0ca8vCgUFptKyv9zgWwue2Opw9/Owkn1e8Dcy2/RQ2Yct9ql+oJC373vZRQBkHPqqAAAAyMpABRv4v+WWW9S3b1/l5eWpWbNmEZdEWrt2rQYOHKgHH3ww5v233367/v3vf+uRRx7RTz/9pMaNG2vffffVhg0bEtqutJXbPjmDTpZhoLhIyqlT+ZOF6RyoYMEAUdlGAlf6IR0zKqSr0U+5SxtUP+B+6cjn3cvwO0rWOfIFskAko+xD9LEv3QIVNm90Uz97Mz9TFtwWkECFOT+7y1a93IEcIM5S2ecFUsp+J7c5zb1uWRWC2q8tjzc426Rt5R/Ttn9J2v1Um/OTu2w30A0wbt41OQHmSD37X3LRRPd67xE1/9/UstIByEip6qvefPPN2mmnndSoUSPndWOxyWXRl5dffjlinS+//FJbb7216tevr549e+rpp58u9Tx2/rdr165q0KCBtt9+e40aNSph7wsAAABpEqhgHcOpU0MDJUk2fPhw3XTTTTrkkENK3WfZFO69915dddVVOuiggzRgwAA9++yzmjdvXqnMCwhp1jE5J7y8mT92siQng1OVeieDNqyQNq5T4NhJ5nBGBWbvJ8XsUdJPj7jXd/271GUnqU1f99JjmHsxY55NTnuyvexDdKDCytlKKza70mZT1m0o5XZI/usHLaPC3NHuslXv9ExLjsBLZZ8XGahhnjsrPl0yLO16ift7Y+UH/vpS6Vv6oQpB0vlbuUsvKDDVfUjTdRf3N65Fj2D9BiNxZnzrLlv3lVp0r/7zeBME1i+PT7sABE6q+qobN27UEUccoXPOOafc9Z566inNnz8/fDn44IPD902fPl3777+/9thjD40dO1YXXXSRTj/9dH388cfhdV555RVdcskluvbaa/Xrr786E9dsMtqiRQHOYgoAAIAqq1PVB5x//vm69NJLtWDBAvXv319160YOPFuAQCpYJ9faZOUePBZBbB33H374QUcffXRK2hVo4cG6uUkMVKitjNUgzw3EKNrklrqon4IZz+XZsNJtmwnKSfBMtrlAeud8ixCReuwl9Sw5NoUNOVWa9rk0+T1pyTSpVegkNBJX9sGfGSDdAhUWT3aXzTpXPNiVCN52W5Xg34zKmvOLu2wbGlgC4iyofV6kKSt/dvyb7uxmK6fw08NSqz7Sof8pWcf6Z3mdFAg2yLntmdL397lttQDBdAoKC2dUqEKggvd7YoGBqVS4qSQYzwtq9Qas063vgqqbESr70HPPmv3v7AXpkFEByFip6qtef/31zjJWBgQ/y7aQn58f8z7LhNutWzfdddddzt+WFeLbb7/VPffc4wQjmLvvvltnnHGGTjnllPBj3n//fT355JO64oor4vyuAAAAkDaBCocddpizPPXUU8O3WQovy2hgy8LCQqWCdcxN27aR6T3tb+++WAoKCpyLZ9WqVcoa3oxcq+FasEaq3yQxr7PKF6iQyezkbeNWbqkLC/5otUUwyz7UbeTOkENifXOXO7BsA+o7XeCWPolmmRU67yjN+kH65g7pkFD2BSSu7EMyg7QSFaiQ1zk1g0U2yGZWzXM/j1QOWNlJd5vla/IZLEZiBLXPizRmx1G7zPre/bt1L6n9IAXWzhdKvzwuLZsmTf1U2mIfpQ37/8Y0jT1AUm5GhTUL3d8Zy4KRCpb2f9M6qX6u1GGIe1vL7sEKFkRibN4gzf7Rvd57eM2eq4mX7W956vttALKyrzpy5EgnS0L37t119tlnOwEH1i5jE8r8E82MBShYZgUva8Po0aN15ZVXhu/PyclxHmOPLUtWn+MFAADIlkAFy1yQSW699dZwNHDWsQwAdRpKm9dLy6dL+aG6rInKqFCVGU3pymauWKBCEE8iemUfnMwPGZzZIggWTHADFcz250jNyknTbzWgLVBh/OvSsKvLXzebxavsgz9Qwb4TVqalXiOlV6BCl9S8vrfdrLyNZWhJ1QCOmferb9CvCvXHgSzu8yJALODLNAtI9oSyNG7p9mO+udMtZdWjhjO8k5mRwH6nTJMqBCpYcKkFcls/3n5zO++glJZ96LhdSR/Fy6jg7TvITHN+drOy2f+UHbaJU1nCUFa9VGTjApC1fdUbbrhBw4YNU6NGjfTJJ5/o3HPP1Zo1a3TBBRc499uEslgTzSywYP369Vq+fLkTaBFrncmTQ/8Xx5DV53gBAACyJVChS5cUDZBUwEsntnDhQrVrF5r1Gfp70KCyZypZdK7VPPNYp7hTp4CfNIwXi2S2gSebJWUpThMeqJAFg0mWUcGsLjuLR+oDFZpJtXKUtrWdCzeWv17BaqVUUaFb8qFos5stoc8B5a/ffrCUP1BaME769m5p/1CAAxJT9sE0bFGyL1mt5zZ90mNrLw5lEGjRLTWvX7+pVK+JtHGNtGy61GGwUmbO6JLZyLGylQBxENQ+LzKABbWmMvCsKnY6Xxr1mLRipjTlA6lvBf2aIFi/3F1af9ebVV5Z9v9QqgMV5oQCFbrtWnJbix4l/2NYIEbtyPTeyBDTQ2Ufug2V6jaIT6BCcZGbISQbJg0AWSaefVUrpXDbbbeVu86kSZPUp0/l/ne++uqrw9cHDx6stWvX6o477ggHKiRKVp/jBQAASFPVGq187rnntPPOO6t9+/aaOXOmc9u9996rt99+W6litc0sWOGzzz6L6JD+9NNP2nHHHct8XP369ZWbmxtxySpe+Yfl7ueY2ECFKsxoSlfeCSDvBHQQAxUaNlNa13Y+8vnSl8Ofllr2dNd7/1Jp49rUtfPHh93Z3jagu/MlUp1KzF7a9jR3OeYFaW2opjISU/bBH6QVhDrUlWWDEkv+dK+3TGFZmaah7WYBHqk09xd32apvatuBjBfEPi8ygDcrvnlnpUWgqAUrmJ8fcwMxg27d0sjAxKpoGyr/sDT0m5tslulpwW/u9Z7DSm5v1tF9LzYzPt1KV6Hy/d0ZX7vXe+9X861mwSxecK/3fyCAjBOvvuqll17qBCKUd7ESDtW1/fbba86cOeGyDHb+1iaW+dnfdk62YcOGatWqlWrXrh1zHW+iWixZf44XAAAgGwIVHn74YSc6dcSIEVqxYkW45lleXp7TGU4kSxM2duxY5+KlObPrs2bNcuqcWS2zm266Se+8847Gjx+vE0880emsH3zwwQltV1rz0sxbSvVEWTnbXeaWZLrIWN7MlUBmVFhSUvohXVmwQpu+pS82++2AB9yTcVa3/u3z3JN9yWaD3p/f5F7f9tSSesIV6byT1KqXW4bl+/sT2kRle9mHUkFaCTz2xZNlMLDBiToNUlsexDuO28zaVLHvtqVGNonKBASkuM+LDGazm71+YvOuSgs7nC3Vb+ZmGhj1qLRoUulLkIJ0vUCFRi2qXqoif6vUBjJasKtl57I+b2tfMJ69D29/SeVvMBLHsnhYQIH19Xr4glRqwkpI+L8TADJKPPuqrVu3drIllHepV6/6JWTs3G3z5s2dQAJjE8r8E83Mp59+Gp5oZq81ZMiQiHWKioqcv8ubjAYAAIAsCFS4//779dhjj+mf//ynE93q2WabbZzggET65ZdfnJRhdjHWIbfr11xzjfP35ZdfrvPPP19nnnmmtt12Wyew4aOPPlKDBjVMm5jJchM8O3bDKl+N2CwKVFgT5NIPaRyoUB5L7bvfbW6a34lvSj/9J/mDp+9c4AYbWCmH/kdXbYb/Nqe61395IvXlKzK57IPHBgDMyhRnBqisxZPcZV4nN2AjVbztlsqMChZcYifcc+q6gUpAgqSyz4sMZpmTLPDM+ivN0iQVsaWN37S2pJ/y6vGlL88fGpxghXCgQsuqPzZ/QEm2OctmlGyzQ2UfOu1QurxDi1AALIEKmWl6KJtCp+3j199tkqWBCnYsihVQFcTAKiAN+6o2WcybNGbBEd6EMjsHa9599109/vjjmjBhgqZOneoEVNxyyy3O+VrP2Wefrb/++ss5jzt58mQ99NBDevXVV3XxxReH17Fzvvb+nnnmGSejwznnnOOUkDjllFMS9t4AAACQfFUu7GxZDLxAAT+LirUOYyLtvvvuKi5nlrRlVbjhhhucC6o4q9jLehBvNvPK1GsqNcjNokCFRQpu6Yc4nfgKog5DpB3Pk77/t/TJP6X2g6XO2yfntX99VprxjVSnvrTr391lVXQfJjXr7A6c//SItNtliWppdpd9SFaQVrwtnuIum3WJ3zZI1+02d3TJgE39pqlrBzJeKvu8yGCr55UMINZtqLRgg5wVlXwo3OgGNHgBbankDcp6s8mronk3qW4jadM6N6tC695KqjmhQIXuQ0vf16JHYv9vQ2p5ZR+22Cd+fT3vf9NsClSwIAQLnLJjUlmsjIqVFAzC8QpIw76qTRaz4AGP14YvvvjCOW9bt25dPfjgg07QgZ3D7dmzp+6++26dccYZEeV733//fWed++67Tx07dnSCG/bdd9/wOkcddZQWL17svN6CBQs0aNAgZzJa27ZtE/beAAAAkAYZFawz6ZVe8LPOYt++zGxMO169cQsoSESq/JVzSma751Q5Lib9eCeDLCigqEiBLP1g9Xoz2eATpe57uCfUXz2x5H0n0qr50idXh17/hOrN8raUvkNCMwN+fETatCG+bUxXiSj7EDHgPjt90gGbvC6pbUc4E0Xo2J4KXtkHGzyyGclAgtDnRUKs8gIV2lW9LAEqxxuU9WaTV0VOjtR2y8jf3mS2e+mf7vUee5S+v0W3yEBwZA4Lcnf2t1pS7xHxe95wRoVlyhoWMFVekII/sApIc6nqqz799NNOAEL0xYIUzH777acxY8Zo9erV4RK+Z511lnLsN9bH1rf1CgoKNG3aNJ188smlXuu8887TzJkznXV++uknbb99kiaiAAAAIGmqPHJsqbdGjhypDRs2OB3RUaNG6aWXXtKtt97qRL8iTTMqWKp5S0XbpFV8n98bzLIB/FTOAk52oILNwrKTH41bBC+jgtXrzWS2n+11nfTKNDc7wasnSCe+K9VOUKCMBfi8f6lUsFJq1UsafFL19/Xew6VRD7snK0c/Je1wTrxbm34SUfYhIkgrNGAUdIsmRw5SpIoX4GGz1WzfT8Zx3YJJ/DMB//qqJOjK0vc2zGNGHBKCPi8Swks53qwjGziIgQqm7VZuUNySP5SSQLyWPWOXBWnZI736Lqg8y8pm2vSTmneJ//+mG7IoUAHIIvRVAQAAkAmqPHJ3+umnq2HDhrrqqqu0bt06HXvssWrfvr2Tquvoo6tQkx3BUK+R1CBP2rBCWv5XYgMVskG9xiXpYlfPJVAhZZ9DE2nEndJrJ0ozv5c+u07a56bEvJaVJZjyvjsr0ko+2HequqwWsWWE+OZO6fv7pW3PSFyARTaXffAPuK9bIhWskeo3UWAVbi6ZYWmDF0HIqLB+mRvgluiSPhak8MAQaXNB6ft+fdq9kL4XCUKfFwnhDTLnxRiIRpwDFaqZGjp/K3e5bFpyP5HZobIPnXeKnW3DSh6Z1Qukwk1uvxGZYXqo7EOPPeObacX7H5zsAUBGoq8KAACATFCtnMnHHXec/vzzTyeFl9UJmzNnjk477bT4tw7J4Q3YWR3WeAsHKmRRDTnvhFCQ0rLaQKcNLJpGcQ5GCSqbdTYsVI7BBv0nvRv/17A0qh9c5l7vf6TUvnR9yCrrd7DUoJm7/4x7SVktUWUfjAVo1QnVBrfXCDJrn6Wote2Q6oGt+rluMJbXrmQMNsUKUvAjfS8SKBV93ptvvlk77bSTGjVqpLy8vJjrzJo1S/vvv7+zTps2bXTZZZdp8+bNCW0X4pxRIdWlfDJZjTMq9E/c/0blBWfO+anssg/Gsizk1JWKNgXr/wzUzKb1Jdk0+gyP79b0vgM2KQFARuL8LAAAANJdjYo7eydHkea81LPLZyYuUKFpvrIvUCF0IjoIwmnTa8U3fX7Q9dpPGnCUe/2ts6UlU+P7/B//0y2pYYMNlv2gVo0Oqa66DaVBx7vXv71HKipS1kpU2Qdj2RlyQ9kBlk9XoC2e5C4tSMGCFVLJ2W7t02O7AXGUzD7vxo0bdcQRR+icc2KX/yksLHSCFGy977//Xs8884xTK/iaa65JSvsQr0CFzmzKRPd7q/v/R9t+Jc+zJlQ6LdHsfybLlJBTR+q6a+x1bKZ9866J+78NqTFnlFRY4GYAaT8kvs/dOPS7RUYFIONxfhYAAADpqsqjakuXLtXIkSPVr18/tWrVSi1atIi4IA15tdpXJCJQYXbNUq+mI2/mSpDqx9pgurE07dmWJnbni6X8/tLGNdLLx0ob18bneaf+Txr3ohv8sfNFbhaEeOl/hFS3sZtyeNLbykqJLPsQfewL+sn+xZNLBrUSsR2q/ZsROr4DGSpVfd7rr79eF198sfr3D83qjvLJJ5/o999/1/PPP69BgwZp+PDhuvHGG/Xggw86wQsIsOKikkAFb8AZ8bV5g9vnM02qGahQv6nUvFtksGCiedkU2m4lNWpZfsYws3JWctqF5JV96La7VDfOAalNfKUfLMMegIzC+VkAAABkgioXPz/hhBM0depUJ+1t27ZtVSsIgyaomdwO7nJFnE94FRWWDNZ7M3CzKaPCmgUKXqBCnlQrjnVP04EFZux3h/TKMdKSKdLb50mHP1mzAd+C1dK7F7nX+x4oddlZcWUnyK2UxK9PSV/f5ZaDyLZjbSLLPni841K8j33xtmhysNKEe5koEhHcBgRIUPu8P/zwgxPEYG3y7Lvvvk4GhokTJ2rw4DiUIUJi2Ax9K1djGZjSKaOCDZzXqV9+KZ7a9aSGsUuVJJWV5fL6fw1rEFCUv5WbOWjxFKnbbkq42aPcZdddpJxy5hK06J4efRdUPnhpxjfu9d5xLvvgz6hg5UIKVkmNmFgCZJKg9lUBAACAhAYqfPPNN/r22281cODAqj4UQeUN1nnZD+JlzSL3pIidjK1ujdh01LiVu7T0rUGxdom7tFn/2fjPq80m2u826b9nSxPflDptL+1wdvWf77Mb3e+LzdTb4Vw3FW+8DTpWGveCtHC89Of/pF57K6sksuxDqWNfwE/22yCJf3Ai1Zq2S8xvBhAwQe3zLliwICJIwXh/232xFBQUOBfPqlWrEtxKxOSVBbOgViv1lC6s9NB5o0tKKrx6krRihptRqsM27m0WpOD9PqSS10YLUqhd5X91S7TtL016V1oa57JhZQV3z/nZvd5jWPnren2BIGVuQ/UtmuTus3Y8SERgbt0GbgC0BVlb4Ho2BCrYscgCpywoLOiBVUCG9lUBAACAhJZ+6NOnj9avX1/VhyEdMirYydPCwvjWWjWNWkl1GihreDNXVgcwo0I2n5DpMETa8Tz3+if/lGaFUuxWlT1u1KPu9Z0uKAlMiTc7kdjvEPf6t3cpqySj7ENECYPQsSqIbPBiyR+R6Z5TLR22GxAH8ezzXnHFFc4st/IukyeHsqckwK233qpmzZqFL506dUrYa6Ecq0ODyzagn4ggx0QHK7Qf5F5a93Jvsyxdbfq6lyAEKfgDFawfVZNtbBkVzLK/lJSARJvtXreR1Gm7SgYqzE18u7KJlWSxoIGyLl7JlnibESr70GmHxP2f5v1vui4UuJ7p7Fh0/JvSrpe5f1tguZddZbd/SEc+794flGMWUAOcnwUAAEAmqPI0k4ceesg52XrNNddoq622Ut26kfXuc3Nz49k+JENTq99aSyoskFbPjV8q2lVzSmaNpdvJ2HiUfvCCA4JW+iGbDT5RWjBe+usL6dUTpbO/LandWhmbNkjvWLBDsdRzL6nnnolsrbT1CdKE16VZP0gzf5C67KiskIyyD/6MCkE+2W/bwY7NNvMrNyADi9528wbcgAwVzz7vpZdeqpNPPrncdbp3r1zWlPz8fI0aFUoTH7Jw4cLwfbFceeWVuuSSSyIyKhCskALeLPhmHZXWmoV+jxI1eBuXQIUq9O9iabtVSdYlK3lhpS8SZc6okqDaek3KX9cLWrSA6MJNbokL1Iztx88fWvEM/EQMbk8PlX3otW/iAnMts+GyadLa0HcjG9jntDkUaNhugDtpYtI77vHBAquADMH5WQAAAGRloEJeXp5zcnPYsMi0lMXFxc5ssMJ4zshHctgJriZtpTULpKXT4heo4GVUqMpAcEYFKiyRCjfXLO1svBCo4LITgHtdJ70yzT3x/NqJ0onvVv4z+uZOd3a7lSLY8QIpp07iT7L1HiFNflf6+g7phDeVFZJR9sE/4L5+mbRhtdSgqQJn8eSSQSFL3xsE3kl6O9lbsEaqX8GgSrbURUfGiWeft3Xr1s4lHnbccUfdfPPNWrRokdq0cWfKfvrpp07gRL9+/WI+pn79+s4FKeZl2/IG+tOVZVcwa9wAmUAGKtT0/w/7f6h+rpvpYOmfJYELiQxU6LprxYPVuR2lnLpueT0LtGzeNXHtyhbrV5QfpGDsflsvnoEKdjxYYuW9akm99lPCeNnfrL+bTbyMZPbdtf6kBSosnJDqVgFxxflZAAAAZIIqj7Idd9xxzoyyF1980amHaydqkSHlHyxQwWbvxosXqOClm8wW3skgO4G4dlHJYGgg6vUmcNA3XdhMtRF3ukEKM7+XPrtO2uemih9nmRi+vce9vsO5UrNQyZREG3KyNPk9adpn0vzxUrv+ymjJKvtgbADC0ixvWictn+7OuApqoIINmNSqcrWmxGjQTKrT0J2pZr8ZXnrsRNZFf/5waclkactD3YtfUOqiI+Okqs87a9YsLVu2zFlaMMTYsWOd23v27KkmTZpon332cQISTjjhBN1+++1asGCBrrrqKo0cOZJghKDzMtHEKyg4VZqlQaBCTf//sO972y3drFZWmiFRgQqbN0jzxrjXe0QGRcVkwbUWnGDBEytmEaiQzmaEsinYfpbIY4L3XcjWQIX8ASVZbBb97v6vwTksZAjOzwIAACArAxUmTJigMWPGqHfv3olpEVLDBtPnjZaWz4zfc4YzKmRZoILN7rUSCxtWuDOdghCo4GVU8OpzZjtLmzvsaumTf0rf3y912l7qe0DZ61tmjLfPk4o2S112kvqUs2682cnoHntI0z6Xvr5dOuo5ZbRklX0wdpLSvp9Lp0rLAhqosMgLVOiiwHC2Wzu3brcFeCQyUMFsXOMGKVgt9K1PSv+U6UgbqerzWqmJZ555Jvz34MGDneUXX3yh3XffXbVr19Z7772nc845x8mu0LhxY5100km64YYbktpO1KD0Q5CO6dXhDaquWaTgZlRoW/Pnyu/vBip4A56JMP83d7a+zfi2AdXKaNE9FKgQx//bkLpAhR57JrZMove/+LosClTYtN4N5DHWv7e+o/1vYRlSrP/qlVAB0hznZwEAAJAJqjw9c5ttttHs2bMT0xqkjjeYHs8TXitD+0mTLJzp6s1c8U5IByVQoXHLVLckOCzF6oCj3OtvnS0tmVr2uj8+5A6gWzaGnS9Jfj3gIae6S8ussGSaMlqyyj74s8mYoJ7s9zIqtOimQGka+s2IZ3BbWca+6C47bReMwC9kjVT1eZ9++mmnvET0xYIUPF26dNEHH3ygdevWafHixbrzzjtVp04ASk2hbDaL1yv90LxLZmRUWLek/NI8qeANxsYjUNrLorCsnD5iTc3+yV1a0GzdSpZn8QZZV/A/eVJZUGs8B9Ln/Oxe77O/klKWcMNyZY1l06TiIvd/CTteWRmx9oPc++b9murWAXHD+VkAAABkgiqf0Tz//PN14YUX6rLLLlP//v2dlLh+AwYEcEYqKj9YtzI08yCeGRWa5mffJ2B1cZf+EaBAhSXukowKkXa+2E0BamUdXj5WOvMLqV7jyHWWTpO+uNm9vu1pqRkwbtNX6ryjO6vvmzukQx5RRkpm2QePVzLAm3UVJEWFJbM4W/ZUoHgBA15AWqJYNpPfXnGv99wnOOUvkBXo8yLuM/0LC9zjWLqXfrBsBZZBzDIBWPBFkAIvLHgiXoEKXsYgG6BOVLp4b7C629DKP8YyKhjL3Ibk+d/V0tjnpf6HS1vsJ9VrVP3nmv2j+/2xfmi70AB6onjfhfUrlDUWe/3nLaQ69UqCgSwwyLKY9D8ipc0D4oW+KgAAALIyUOGoo9wZyKeeeqovC3QtZ6aXLa2WLtJQeNApTie8Nq4rSb2ajTNgG7dyl97MuVSyz8JSpxtLK4sSlhlhvzukV46Rlkxxyzsc/mTJieiiIumdC9z6wXYSsX8oA0MqbHOaG6gw/nW3bEWzUHBRJklm2YdEZpOJF2uT7Xu2nzYL2KCWF+CR6EAFK3liNdCtnE73PRL7WkAU+ryIq9Xz3WWjVlLdGgxwBkFOjhvkbOV/bLA8UIEKS+MXKN2mnxtYsmGl26e3skfxtGGVGzBrrMxXVQMVVgckIDpb5NRx/1+wAOZv75F67y9tdZjUaouqP9f0UNkH6+9WNpNGTTP9ZVOggk0YMG23LLnNAhXMoompaROQAPRVAQAAkJWBCtOnxzHlIYIjN1Tz2waENm2Q6jao2fN5M3zsRKwNMGUb74RQEAIVvJllNthZPzfVrQlm9ov9bpPeOkua+Kb7Xeh/mHvfpHekmd+6swa3PslNG5oq7QdL+QOlBeOkb++W9r9LGSfZZR8igrRCGWCCZPEUd2kpa2t6TI43b7Am0Wmnx77gLrsPlRo0TexrAVHo8yIhgQp2/KydAWU68jqFAhVC7ysILDjXAvz8AXU1Ubehm9HIshstnhT/QIW5v1g6KSmvi9QiVM6hSoEK86Wize4AOqpvfSVLIhz/hjT7Z2nMc24w6YTX3Ev+AGmrw6Wee0p1KtFfs5IE9v+F6TU8Of/reKUf7LWzITuVl1HBy4rilRDzsqhZ0EbDLDtHYceL8oJVbHvE47iJpKKvCgAAgExQ5bMaVhMXGahxy5IUrjajuU2fmj2fN8vWamJmwsnY6tYC9U5Kp9Laxe7SAkZyaqe6NcFkg9V20q64UPrh3+7Fz74XH/1DOv7N1J7AsdIT714gjXle2v3/3O9tOrNBbm/mo52o91L8t+wlLZ6cnBNmTUOBClamJVFpnau7TaZ+7i4taMMLWgjKSUT/dktknfEpH7jXeyW4fjMQA31exJV3vPSCg9Odl+knSLP6vd9PGyyOV3Bufv9QoMIUqccwxZWloTdW3qsq/y9ZAGNOXalwk7tfpXspkVSyPv6PD1a8ngUrWzCJZUDY9VJp2mfSz09IUz+RFvzmXqw8W58Dpa0OlZp3LXuAeOlUd1+1/bRRC7fvZ4E/iQ6g31wgFayWGjRTRrNgDK90WruBkSUwmndzA6zmjalaFpN0Z/vg84e6+3tZ7FxQqv/XRZXRVwUAAEAmqNYI8nPPPadHHnnEid794YcfnM7xvffeq27duumggw6KfyuReDZIa/+U2uwQ++e9xoEKc0sG7LNh1kZZgQqWoSLV1oYyKthJKQIVYrOThxakUB47sWPrpfLkTeedpFa93JNv3/9b2vv6JAcTxGDlRKpzctWe94Eh7knTaKOfdC/JOGHmZVTYsMK9JCuTQ1W2ydzR0qvHB+skorfdLGNLwVqpfuP4v8aEN9zvnQ0MtBsQ/+cHKoE+L+LGG9BP5IBkMnnvIwh93egsYtY3iVeWgbZbub9Hy6Yq7maPcpdVLXdlQQ1WbsMGvC3AnECF6rEA1S9vdYNjLQugZSuzch8V9Xet9MkWe7sXyyjyy5NuELF9x8e94F46DHGzLLTuK710ZOwBYsv+8czf3CCI80Yn7thQr7FUp6G0eb37Hcn0QAU7D2Hv1bIJ2vb3s/IPdq7DMtRlU6CC/Q9bXpBCUP7XRbXQVwUAAEC6q/II8sMPP6xLLrlEI0aM0IoVK1RY6A7u5eXlOcEKSGNWa9Ysi0N5Dy+Nujdgn2289712UbAyKiC92Wz/bU51r//yhDsrKhkD548OLfti91cn/b8FP8QKUoh1wiyR6jeV6oVKCiyboZQKyjapDDueeOmNLcAtEca+6C4tlbKdbAaSjD4v4sorkZApg8o2qz9wgQrLfIEKOfHLqGCW/aW4z3BeOcsN6O42tOqP90pFWBp7VM9vL7ll3uwzOOA+adCxUvtBsS9lBRFYOZBh/5QuniAd9YLUfZj7fBZk+vGV0msnVDxAbH2/8oKC4/H/g1f+wQtgz2RLppR8RywAxc8r/7BgfPLbBSQAfVUAAABkgiqfwbn//vv12GOP6Z///Kdq1y5JI7/NNtto/Hj+4Utr3gzZeJzw8gIVmrRVVgcqrFsubQrVyk11oEK21eHMVHYC1NItW5DCT4+kfuA80SdXk8GrOW2zElH5k97ejCubmRZviyZJ836VatWWelP2AalBnxeJyaiQIYEK3sBtEIJyPV5/xAIV4sUyKnj/22xcG7/nnf2zu7QZ303zq/74Ft3dJYEK1TPrR+nbe9zrO1/kZj+oCcta1/dv0olvSReMlXa6UGrcKvFBxVUt/5DuffbK8Mo+tO5TOmDJMioYK+VSuDn5bQPijL4qAAAAMkGVc2JauYfBgweXur1+/fpauzaOJ2+QuowK8Zgdu3J2dgcqWPp4G2CzcgI2g65ltwCUfiBQISPYidAhp0ifXy/9+Ii04/lS3dDM9lQZ95Jbq7eoUCra7LtE/+27LUgzuixIy05qriBQocrbzYIUls9MXDYFm/nmBdEBSUafN46lg+zYv/RPqVYdN825F0CZLSmmLcW8l1HBX7s+IzIqLHY/3yCUF/NKP1gt+nixIAILfLB92foK7Uv/H1wtc35yl113rl72h5ahjAqrQuX2ULVjk2U7KC6Seo+Qhl4Rvwwcxspy7HODtOfV0nf3u332VGuShYEKXjYUvzZ9pXpNpI1r3JIf+aFAJCBN0VcFAABAVgYqdOvWTWPHjlWXLl0ibv/oo4/Ut29UDUCkaaBCHDMqVGeGUCawk7V2UtNmma2ak+JABUo/ZJzew6WfHnb3r9FPSTuck9r2JDqzQ6I1DR37lpM+uWrbLTTAaKmr48lmuP32inu95z5uCmUgBejzxqF0UHlZeWrXk45/MzuCFdYvkwptW9SS8iL/h0rv/xtque/L+ppB6PN7g7DxDJS2DEKWVWH6V+7AZjwCFSxwZfYo93r3Par3HC26RWbqQOXYAPX7F0sFq6Q2/aSDHpDqhoKn4s3KVvUcFoxAhXC2v1B5lGwIVPCyoUT/j95xW+mvL6R5YwhUiPXbbcEcSBv0VQEAAJCVgQqXXHKJRo4cqQ0bNqi4uFijRo3SSy+9pFtvvVWPP/54YlqJ5PBmrdZ0Zo6dfAsHKmTByeeyWC1QJ1AhxScQvUCFRi1S2w7E98Tn1idK39wpfX+/tO0ZUu0qH87Lt+RP6evbK7eunfDzsojYCcDwMkfKqRN53btv/XJp/KsKVOkHLxMMqlguKM7bbdrnbs1zywJT3QEcIA7o8ya4dJDVbV+/Ijv6iqtD2RQsFXx0zfR0Vaee1LSttHqB29cNRKDCsshB2XixmdkWqGB9o3hYOtUNXqnTQOq8Y/Weo0WPkn3LMlVZHwvlswwKn1ztZoOygPIjno5vmZAg8zIq2H6XyTasdPuQpt3A2OtY+QcLVFj4W1KblhY+uVKa+a20w7nBOKajQvRVAQAAkAmqfEbj9NNPV8OGDXXVVVdp3bp1OvbYY9W+fXvdd999OvrooxPTSiQ3o4ININqJY0vJWx2W0t2bNZYNJ5/L4p0k9U5OpzpQoSGBChml38HSz4+5gUVWemHrE+JXM/m7e6XJ79sZ3co9xurwVnX2zaJJAQpUCA24E6hQzYwKocC0eBn7grvsPlRq0DS+zw1UAX1exI0XtGrHzXgHFqZSs86hQIW5UoetMzOjgn9m9rJp8Xm+OT+XDKRW9/8tK71hwQmFm9z9K69zfNqWyX58WJrxtZRTVzrkEal1b2WNxl6gwnJlRTYFO9aWFbBkZcXMot+T1650MuV9aeon0oCjpSGnSg1yU90ilIO+KgAAADJBtfIpH3fccfrzzz+1Zs0aLViwQHPmzNFpp50W/9YhueyfUKvZaJZNr/7zeIN9NoO/bgNlLe+EUMoDFUL1esmoUDY7SWwpqMtj91f3ZHIi1G0oDTrevf7tPVJRUfWfyx77x8fSk8OlJ/aSJr/nBim0jVHbNRM19bLJzHMzwqBqgQrxzBpjs2GnfOBe77U/nwRSjj4v4sLrCzbrmFkbNK9TMPq6nnWhPm+8ZwJ7dextJn5N+lue2T+5y667uqUlqsMCXpp3DbVrRs3blOmsnzv6Sff63tdLPfdW1mX6MzYZIRsCFVr1LjsorOM27oQKC7KyS7aUPKlInfrSUS9IHbdzA6DGPCc9d6D067MVZ0hCStFXBQAAQLqr8pSe/2fvPMCjqNY3/pKQSklCJ9TQexUQRKUoYAULV7B7ubYr9u5V7Pq3oSheFb12saCIDREUO016R0qoIYQeCCSk/Z9vzp5sAim72dmdsu/veeaZyc7s7GR2d/bMOe/3vk888YTREJYstPj4eGMiLkIqi+UGf/8moFElM1h1dIRUMYSzDWmRo4KFHSAy6FrkqBAm1qaVHXCVnOzyOu9EpGA3h5DOI4FF76oKvzVfAR0v8O/5eceAlV8Af04Adq9Rj8l3Vuz2pYpGIiamXBlccYhYf1stDtHRD5JXLJVmVol6xH5YOgkrynS3i2BGO1HINebYESDahPaAfB7lMyGW1g27BL4/QgKAbV5iGlrQ5TahglT120WoIG1eHf1Q3WShggx6ShX+sSwgcxuQ2Kzy+5IBwB2L1HKg8Ua1WqgYiQNbA9uP2xEXr9mPquXuVwC9r6+8QCQYbTtZH+wICn1fmh0mQoX6HcveJjZBOcGJo0LaEqDtWXA1Eg0z9xW1XK8DcO4EFd1zPPIZFPFZu3OUs96PjwB71wNzJgDLPwFOvkmdK4nzI7aBbVVCCCGEEOIG/B5FnjJlCh5++GH06dMHl19+Of7xj3+gTp06wTk6Yk38gyFU2FL5fWgbcLPzYZ2G5BALOifTCqQzSjonjOOhUKFcRIRgNyFCRcTUADr/A1j8DvDbCyoOwpeO15xDqjpm7qteYZFkZrcZBnQdrSr0pBNKBh6CJSawkzhEnGRiaiqhgrjJWCVUkM7BsYuA5Z8Bsx9TAyFDnrSvYEbiZPTnQwZJ6rULfJ9LJ6t5q8FKKEOIhbDNS0xDD+QHMsBtZ0eFwxlWH4lq2xTkquUaJkc/yKBe3TbArlVAxtrA3sddK4G8o2qwtHHPwI5LRH0Co6vKjwOZfqcSCjQ5GRj2TGjjV3TbTseSlIYeIA6F05/cGxYWuHewWQsVGlTgCtekjxIq7FzmfqGCiNrl2hVdDbjorfJFHILcS7Y/V50XuV/85SnVn/HjOOWy0O8WoGnf0Il9SLmwrUoIIYQQQtyA33fpy5Ytw6pVq/DRRx/h+eefx2233YYzzzzTcFkYMWIEHRacjq6QPWCGUMHTIRKu6P/fSqGCjn2QjomqYRzD4WZanQEs/QDYtQKYPwlo2qfszk8ZSJj/BvDXm0D2QfVYXBLQ/nwleJBM5+KdTsEWE9hJHCIird2Zyk0m0IGDQJD36ug+r9W0VHzZFfmsyG+GWE7LFKhQQSoe0xYDVSKBtox9INbDNi8x3VEhsam7TmqC5//JsoFQQQ8Ei/hQ2r1mI3FYMtgnA6FthlZ+P9sWqHnjXirGK1BHheL3XqQkIqT8/m51LybuHzJIGxOEz4YvbbtgCxF8jX4QVxBxwYrxxD26CXEr2bdJLTfoUrFQYdE7QMZKuJrda4G/Jqnlgf+pWKRQnIhI4KRrgK6jlPvenFeUw8I3NwONTgJOuc3e9ylhAtuqhBBCCCHEDVRKSt+xY0c89dRT2LRpE37++Wc0b97cECw0aGCyzSYJPTU9lrQBOSpsU3MZ9AxntKOE2KJblXuvYx9iE1VnA3FfheYX13hdM2bcA0w6veQ0sSew+Q/g29uBFzsBvz+vRAoyMC8VMZd+ruaS51xaZYwICaQTqqzJLkIDs0Ra+21gn5y21Gs1bXf0+29GPrZ2U2jS2/t+EGIxbPMGGYnbcTvSBjzkESqIY5GbKC6EtKqte7xQQVyRghE9J+JBQQbqAmG7R6jQ/LTAj0kLFewQvWE35PP46/+pinkRrlz8tvViASuRe0FxwRLKc3dwMvtT1T2RvN+1PW4jZSFtTWHPeiAvG64V6swaBxTkAymnA72urdx+RFA14D7g1uVAr3+pGJwdC4HPLgd+uF+588k1SATHZU28RgUVtlUJIYQQQojTCbgXp1q1aoiLi0N0dDQOHTpkzlER69CDQ4FYiOqqHrNtV51G9XpeK1qZYmtaKFRIcK/FZzgjTgflxTIIYnX77rnSY+sd/O7yD6DVECA6PiSH6Qj0gPtBi4UKBQWqU12o7xkUsTM1tAtPgOctPw9Y/qlals8mr1fEhrDNa3Iuu/DL08DId4Ofz24lYrVunIcqLnRU8Az8HjusRJCViYIyXahQOziW5Po3WSKiKotUs4sDltBqUODHVLuYUEEGaIMh0HAq0qZY/ZVqT5z9gndgOlyR74TEEoq7izjuJbkshqZ47EPt1ur3pyKRT3wd4MgeYOcKoEkvuI75rwP7NiqRyrkvqgibQJAYyXNeAPrdrAQQ8v1aPxPY8JO6z5RIkbIQkYy49LlF4G5j2FYlhBBCCCFOpFIjl6mpqXjyyScN5e5JJ52EJUuW4NFHH0V6err5R0hCi1RZC9KJIQNmAQkVwvxGVKxndSeJVBpY7ahAwphCZTF81vPAxe8CHUZQpFDWtS/QAfdAkQ7FY4eAyBigdivYnpoNAxe3CRtnK2tmuVa1GGjKoRFiBmzzBpjLft2vpU+jPlYRWeI08OV17nZW0LEPMsgjbUM3IfbxEiFlh/gBLVTQjmZmozPvRRSQk1m5fUi8kVQ3i8ivboBxSTp6Q8QJIlplxXLJeI0/xqvlfrcqgS7xxhLK4Lwb2e0RKojjW0ViJVkv8Q/CTo+TmZtIWwIsfl8tD3m8YocJfxBnoH+8D1z7C9CsP1CYX75IQZBrVHlRgiQg2FYlhBBCCCFhJ1Q4+eST0apVK3z++ee45pprsGXLFvz0008YM2YMEhISgnOUJPSDTrlHlI2rv0jFmAw2Fa+0DVekAyS+rsVCBU9HVBy/m2GNZJKe/yrQcmDg1TRupWjAfbv1HYu60ivKAY4X+jofqFBh6Udq3uJ0ILZG4MdFiAmwzWuCWCG5W+lTu7OBf85QA2cSHfPl9e4dxNCxD9UbApEurHjXrgpWtXVPECp4BmPNRqrRJSZLxJ8Z6yo/gC7IAGlkVODHJJ+nxGbmRTC5AWnHzbhPDZ62GQYMuJ/xd8e7/bk1+mGvR6hQ3yMqqgjtsqFdTtzCsSPAjw+ra1Xbc4CulwbndRp1B675Djjr2eDsn9i2rbp582Zj/ykpKYa7bsuWLfHwww/j2LGSTo/Lly/HqaeeitjYWDRp0gTPPnviZ2XKlClo166dsU3nzp0xffr0EusLCwsxbtw4NGzY0HitM844A+vXBxjBRAghhBBCnC9UGDx4MFasWGG4KNx1111o1MhThUrcQdVYr/3u/k3+P193Uko1sGTEhju6Q0hX01nlqBBDR4Wwpm57dtL6OuAu31Urc7bTPFVddVoHx7o6WHFBgVzjjuwD1nk6pdqcY85xEWICbPMGGanwvGa6st8WN5lpNwDZlaxUtzOZO0s697gNHWdhdUW/HnzVbe9goAdA96yt3PO3zVdzEY6ahQgb7eAIZQckWuO7O4Ccg8qxYvirQFQFEQDhhHYbOboPrkPa7tpRoWEX356jHRUyVlvb9jebOS+pPhkRbYmIINgCOX0eSdi0VdeuXYuCggK88cYbWLVqFV588UW8/vrreOCBB4q2yczMxJAhQ9CsWTMsWrQIzz33HB555BFMmjSpaJs5c+Zg9OjRhuhBjn/EiBHGtHLlyqJtRNzw8ssvG/ufP3++EW0xdOhQZGdnB/3/JIQQQgghNhYqSORDhw4ditStMhGXoTtSK5PBqquRq9c1p1LILR1CupPaKqGCtuUlhJQ/4C4529qJxEpHhTpt4AiM6lLPtebY0crtY+UXyhK2VkvfO5cJCQFs84YAEWVd/R0QVwvYux746kYg5xBc6aiQ5BnQd6ujwqF0dzsqCA06qfmeSlRzSttCBDlCiwHmHZO2dD8QoLOR0xHr+VkPqXMs15OL31EuGOTE+9IjLozaycpQApUqEUD9jr49R9x9IqJU9JBbhD5b5qh2tXDWM0BiY6uPiLiwrTps2DC88847hhChRYsWOP/88w2RxNSpU4u2+eijjwyHhbffftuIDB41ahRuueUWjB/vieUBMGHCBGNfd999N9q3b4/HH38cPXr0wMSJE4v+n5deegkPPvgghg8fji5duuD9999HWloapk2bFvT/kxBCCCGE2FioIEjjUGy5xHpLJmkwfvDBB+YfHbFWqFAZC9GDO7wdIdJREO7oDjKrOm/1gGs8hQqElEtUHBDr+Z7sr4RIywwkt3rnMrVcz8dOVqsRB57IaDVAcLCSnbxLJ6t5q8EUuBHbwTZvCKjXDrj6WyWq3L0W+OrfSjTmFrTjjHYecGPEh3DYYqHCUY9QoUb94L1GfY9QQQsO/GH7X2peuzWQ0Nh8R4VMi6OrrGb+60Dqr2rg+YLXgfpq4I4UQ7uNZLtQqLDbE8ciUSixCb63/Rt2Vctpi+B4xJFo9mNqufM/gA7DrT4iEkZt1YMHD6JWLa+j6ty5c3HaaachOtobOylOCOvWrcP+/fuLtpEoh+LINvK4kJqaivT09BLbSJxFnz59irYpjZycHMPRofhECCGEEELsjd8jyaKAvfHGG3H22Wfjs88+MyZRwd5www2G5RdxUWXxgS2Vd1TQFRvhjq7qOmS1o4InzoMQUjY1G1qb87x3A5CbpSJ4areCIxBBmnZVqIzAI2MNkLYYqBKpcnQJsRFs84YQqYC98ms1wCQ23F+NVTnbbkCLVRNd7qhwOMPa48gKQfRDg87edoKIC/1h+wI1b9bP3DgucSOyMmYulMj9lLQbjp8WvQss/J/a5oyHgdZDrD5Se9+XHj0A1yGOPELdtv59v3Rswc7lcDy/Pavu/UUINfQpxv6FCXZoq27YsAGvvPIKrr/++qLHRGBQv35J4aD+W9aVt03x9cWfV9o2pfH0008bggY9NWniaacQQgghhBDb4ndgnTRAX3vtNVx55ZVFj4nVl9h5SebY7bffbvYxEqscFSpjgXjQYztaPYjVTE5CCzYO77JWqBDvVbcTFxGXqKrZxTa/LGS9bEd8E2nJAJlVQgUd+yDVkVLl5RRqJKvfi/1bK++m0LiXVyRHiE1gmzfESPTLlV8B750H7FoBfHMzcP5EZ10Pj0csmIscFZrD1Y4KYr1uFeLqI/btQg2P6DAYiChAxIR52Soir04r3z8H2+ar5RYDTT6mFO8gfkEeEBHkPHqrkP/vwwvLb/OKeLLdeUCVKqE8Mucg0Yxud1TQYiJfadIbmPcqkLEKjmbDj8Df36vvwDkvet9r4nrMbKved999eOaZZ8rdZs2aNWjXrl3R3zt27DCEESNHjsS1114LO3D//ffjjjvuKPpbHBUoViCEEEIIsTd+92Ts3LkT/fr1O+FxeUzWERegB4u0O4I/6OdQqHCcUMGCztv8XG+nrdizE/chnfGXTy2/MkpECsHstHcTgYi0zBQqiC20kzrZ9efL3/OWnwcs/1Qttx7KuCBiO9jmtYDk7sAV04D3zwd2LgW+uQU4/xU1OOxEsg8AeUfVcpJbHRU8/5e0OY9lAdHVQn8M2QeBQnE4qOKtGg8GkVWBeu3V7/XuNb4LFUTILaJliSVI6W/uMYnVvYgTZABf3DvMjJWwE9LWLU+koAUr8p0jFTsqiHjGSW3NitizvpJCBY+jgoiUJTohtiYch0Q9/vKUWu55NdCqpJV+0JF+hqoxQF6O84TzIoAqfh8tv2O5R71OSPK/aTFeGLRV77zzTlx99dXlbtOiRQvvrWtaGgYOHGi81qRJk0ps16BBA+zaVbJYR/8t68rbpvh6/VjDht7+DPm7W7duZR5jTEyMMRFCCCGEEBcLFVq1amXYiT3wwAMlHv/000/RunVrM4+NWD1YJzdoMtgdGVUJoYLHCjzc0dUM4myQnw9Emmj1WhFHPBa4Ulkhuc/EncggMYUI5p3L4s4woSZtqde21pHiNj/P28bZauBGrN5bDAjKoRESCGzzWkTjk5QI74MLVDTMt7cD576kBkKcRuZO72BHdA24EnHtiooHco8o94g6ra0ZqBPk9yTYDhz1Oymhgh4Y9QXtptCgk/lxbCKekFiRfZtUBJNbhQokcHQsSk6mGlSOcqgA7HgkJki3QRt09T/2TeJr5Pk7lwEpp8JRiODk5yeVWEti4wY/DET4ne4aGDKQP3aRt+9Bi5c/u0JFu13whoqJs9v9qi8uLdLukP/NxmIFM9uqdevWNSZfECcFESn07NkT77zzDiKO+9z17dsX//nPf5Cbm4uoKNWfOGvWLLRt2xZJSUlF2/z000+47bbbip4n28jjQkpKiiFWkG20MEHcEebPn2/EXRBCCCGEkDAWKjz66KO45JJL8Ntvv+GUU04xHvvzzz+NxqM0kIlLOjHkprIgVwkPtKWoLzfKWqigs97Dnfg6ap6fAxzdF1obRh37EFPTP7EJIeGKHnA/UAk3mUARd4F0Tz5uvY4IC4HH0o/UPGWAMyvYiOthm9dCmp4MXPY58OFFwPYFwPQ7gXPGq6pMJ3EozXudlAFlNyJV2TLQt2cdkLnDGqGCHiATYa4/+fSVQVds79vg+3O2LVDzZv2DM4gokRQiVLDKEYo4g7haSsAuzhNH9rhH1LJXvouFShCW4Cm48NdVwRAqLHWeUGHNN8Dm35SryrkTrHMtkIH84oP5Dbsqt52DW9X1WdySnOjSIoIeOX4bCxWsaKuKSGHAgAFo1qwZnn/+eeze7el3KuaCcOmllxrHNmbMGNx7771YuXIlJkyYgBdffLFo21tvvRWnn346XnjhBZxzzjn45JNPsHDhwiJ3hipVqhgihieeeMIQXYhw4aGHHkJycjJGjBgRlP+NEEIIIYRYg989JRdddBEWLFiAOnXqYNq0acYky/LYBRdcEJyjJKFFbnRF9S5Ip5evGFZ5WWrZbop5q5CqLl1BV5koDTOECrGJSnhCCPFNqCADSwUFoT1be/5W1ahyzajltdR0lqPCDt+fc2QfsG66Wm57dnCOi5AAYZvXYpqfAlz6qYp92DoXmH63cvpyEuIwUNytzK3oQRypULUCLVSoVif4dvbiqODPPVJBPrDjL7XccmBwjkm3Gw5Y5AhFnIGIZLSIvnj1u9MRkZRQp03lxPk6/mHXSjjOsef359XyyTep30y7INfhNkPU8uY/rD4aV2NFW1VcDzZs2GCIIRo3bmzEMuhJk5CQgJkzZyI1NdVwXZBYiXHjxuG6664r2kYiIyZPnmwIE7p27YrPP//cOP5OnTy/swDuuece3HzzzcbzevXqhcOHD2PGjBmIjXWJIwwhhBBCCDHwq7RHbLuuv/56Q8X64Ycf+vNU4jSkQ1WqooxOuMG+PUcPxMvAuFjAEoW4KOw7pM5no+6ht8GNS3BXBikhwUILrEQwIEKfGvVDd66liktXRQbbttpsaniEClkZqvLIF3v2lV+oKiYZXJGqK0JsBtu8NqHF6cDoT4DJlwBb/gBm3AsMe8Y5TlF64F6s+d2MOCrYQqgQAuey+h7XI2knyOtKFXd57F4L5BwCoqt5B0TNpnZLNZd7DUIqui+V9pq+T3QDOoalXofKPb9JbzXPWKOERcF2ZTEDccX46WFVJFK/M3D6vfa73289BPjrLeWKJM6bdjs+F2BVW/Xqq682poro0qULfv/993K3GTlypDGVhbgqPPbYY8ZECCGEEELci1+OCpIt9sUXXwTvaIj9KmT3b/H9ObpzTDpAxJWBlOw0DXXnbXFHBUJIxUjVrtji+usmYwaSdy3UaeW8jrxqtYGIKNVp6qvt9NLJat7yDOcMOJKwgm1eGyFV6KMmq2tF6q/AzAeAgjw4gnARKhQ5Kuxyv1BBrNW1MCNjbcXbyyCd0KinEisE01GBQgVSEdXqqblEErrNUaFBl8q7pIhIWAb9xeHMCSz/BNixSImDz5sAxATp2hIIzU8FImOAw7uAvR4xCTEVtlUJIYQQQkjYRj9IFpjYcRGXoy1q/ck61Y4K0knotIE2VwoVPJUysQmhfV1C3HDt259qkVChHRyH5B3ruCBfzptUrKUtVpE07Rj7QOwL27w2ovUZwD8+VELYjbOBmQ+qylenRD+4XaggWeRClsVCheohckJq0LnkAGl5bFvgHbQL1v2RFiocSneOiIdYQ/V67op+kN+BvRvUckPP99JfIqsCjU4q6XBmZ6StPWeiWu5/F9CoB2xJdDzQvL9a3vSb1UfjWthWJYQQQgghbsDvsvfWrVsbtlt//vmnkTVWrVpJ9fYtt9xi5vERyzPH/REqbCtZqUFKng/JkbTEUSGJ7wQh/lz7dq0Ibc5zfh6QvkIt1/NYSjvxvMlvgC8uPNpNoXEv9+e2E0fDNq/NaDsMGPkeMOUqYMMs4FgW0OcGJZYqrepdx/lYhVhdFzkqNENYOCoczrDm9XV1eKiEClKBvW56xVXCednegc+Wg4J3PCKEEfFffo4SKyQ0huvwRRQi1eUVRXGEO1pAf8QljgrS9pTvmVTuByL2lViWzb8DO5cBXUfDtogQ6ceH1Xdd2tGn3GLvApHWZwIbfwK2zQV6/8vqo3ElbKsSQgghhJCwFCr873//Q2JiIhYtWmRMx+eHUajgEvTg0cEd/jsq6EoNUrJDSGwPrXBUiKdQgRCf0QNbB/yIvQkUya+WTtaoeKC2pyrSsedta8WijOWfquXWQ0sfYCTEJrDNa0Panwuc9Szw3R3A1jlqKo3IaODyqdaKFbIPArlH1HKSy4UKOgpB2p55x4Cq0da0eSV+LhQ06ORbTJQMeuYfA6rVUTnywUJiUeQzJsezf7P7hAoi+PlzgrftMOC+0tsPIlLQohlSOvo+PXu/O86QjmoQVxGp4A9EqCBkrIatWfQusGsVEF1dRT5ExcLWtB4CzLgP2LUSyDkExNSw+ohcB9uqhBBCCCEkLIUKqakhtsMm1joqSFV+TpZvuYdFQgWPBThRSOekJUIFj6NCXC2+E4T4HXuzLfSxD7VbqYowJ6IHA7WzTlmIZbtcCyWSpsWAkBwaIZWFbV6b0qhnxdvI4PDRA9YKFbSbgrTDYmrC1Uj8j8RySLXv4fTQR10URT80CJ2jghY1ymdNhDGlsW2+dxA0Ksi/7zJQK0IFQ2jpsVt3A/m5wA//AXIygbptgQsnKccUEpjT31GXCRXqtQ/MWaCxJ/ohc4cSPun7dzsh0Wl/TVLLA/8D1HeAC1vtlkCtFGBfKrBlDtBmKGyDXEfk2i3XcAe7tLCtSgghhBBC3EBApYyFhYXGRFyIdKpWFYV+oe9Z7VqoYLXVrl0rV0Jth6uFCja/uSbEVtT0XL8yPdezUKBtoeu0trd9q09xQRUIFZZ+pOYpA4BYlw/cEVfBNi/xm8w07++KZKC7mYhIr9BP/9+hHMgW94pQ3oMkpQDR1dRr7y3HVWH7X2qecnrwj6lWy5L3Y25hwRtA+jLlOjX8dYoUAkW7joiQy01CBe1yUlniawG1W6vltMWwHXk5KvKhIF8JfXs5KEah1RA13/IHbIX8XojzUscL1d8NugDX/gJc96t3GrvIUS4tbKsSQgghhBCnElFZe7FOnTohNjbWmGT5rbfeMv/oiHXIYJkeeKrI1lSQjjpdOUahQhlZoHvUeQp59AMdFQjxmRqe617mTiA/P7SOCnXawrEUOSqUM0AleciS6S20PTs0x0VIgLDNSyrNIS1U8Azgux3toiAVyaHEGHAtVFEAoYp+iIjwuipIfFNpiHhCKqCFFgODf0ziqOA2ocLWucrqXjjjUaBRd6uPyD33pdkHZFQT7hEqdAl8X037eCNb7Mb814F9G4HYROCc8aGP1wk0/kHYtsB+nzm5f0lfoZa7XKKuMcndvJNDRApsqxJCCCGEEKfjd3nPuHHjMH78eNx8883o27ev8djcuXNx++23Y+vWrXjssceCcZzECqRj1cg69SGrXUQKhQVARFToOgmdguFoUEWdn0PpobnhPZYF5GYVe31CiF+OCnlHVURBgke4ECxEvJS+Ui3X6wDHooVtcs7Kyidf+YWyV5XBlIZdQ36IhPgL27wkILSAN8EZAx0Bo/9P/X+HChECC3FJ6j4kVIhQQaId9EDp8WxfqAQUSc29IoJgW6wXF8g4HXGGmzVOncMOFwAn/dO5rlN2jH4QIU1BbtmxJU5A4iu0g6AZQgWJaFnyIbDL0y63Arl+Hu92sXsdsOR9tdz/du933Sk0P0U5dcq1Wv6Xeu1gGyTqb+96JXRrfz6cCNuqhBBCCCEkLIUKr732Gt58802MHj266LHzzz8fXbp0McQLFCq4CD3wZGSdVsBBT/WU5DmGspPQCUhmr7gaSH6u2MmHQqig3RSk8ym6RvBfjxC3IN8ZqTaTjk+JvQm2UEGqLfNzlIV0KAYygkV8HW8+ucQ/lNaJunSymrc8A4jk7wSxP2zzkoAQZ57iTgNuR7dvRZQbSo7u88bWhTJiQ1vN79tQ+vrt89W8ad/QHJduQ8jnTn6L5TfZqYi9/ayH1Hsr/9c5z7s/PiVUyL26IAJ6GeivXh+ORYuEpLjCDGG+CBWM/a4H8nKBqlGhFyl8eKES9ZbFz08BnS5yTKW/QVQckHIasH4mkPqrvYQKG39S8+QeQEJjOBG2VQkhhBBCSFhGP+Tm5uKkk0464fGePXsiLy/PrOMidkBb1fokVNjurdKQnFpSus1mqHJ7tVBB7CH5fhBSufiH/ZtDF/tQu5Wzq9rkOqM7u0XgUZogQzJ/q0QC7Rj7QJwB27wkIHRle2Kz8HJUOJwR2tfN2ltyADZU1O9cfkSe2JwLkicfCkQQI7+xIn4MtVjEbBa9A2z/C6gaA4x4PfTvrZsRoaiIeorfLzpdqFCnjTlCltqt1b2zfIcyLHBVECeF8kQKghybFD84jaL4h7mwFRtneyPpJNLHgbCtSgghhBBC3IDfrfErrrjCUO0ez6RJk3DZZZeZdVzETo4KUh1bEXobxj6Ub7Opq+uCjbbBjEugUIGQyl77QipUaO18S+Oi87albDeFxr3CJ6+dOB62eUmlkRxu3eZLChOhgq7wzcqwJvpBi4JDRX2Ja6qiqtIP7Sq5Tt57uTcSO/GU00M3AK3dO3yJ7bMrOxYDC95Qy4MeApr0tvqI3Ef1eiW/O04XKmh3k0CRgWr9eUtbas4+iaLVGWq+azWQnWmPsyLX6YxV6jreYTicCtuqhBBCCCHEDVRKev6///0PM2fOxMknn2z8PX/+fGzduhVXXnkl7rjjjqLtxo8fb96RktBTs7E31kE6XMsbRCvuqEBORHeeHgqxUEGqQgghwRNpBcpOT0doXRvZoAbqRHFga8nH8/OA5Z+q5dZD1cANIQ6BbV4bIhbfUmmdl1P2NuJQE2dhGygnE8jNUstJzRF2jgpi3R8qRy8d/aAHX0OFjmzatxHYvRaoUcxCf7vHTaFe+9Ba60vskrgaGW54p8BxiOhj5gMqlqDN2UCfG5wv4rTrfal8ZrUbidOFCtrdxAxEqCARBekrzNsnAWqlKPe4vRuAzX/Yw11tk8dNoUFndXwOhm1VQgghhBASdkKFlStXokePHsbyxo0bjXmdOnWMSdZpqrBTwT2DddLZKh1H8R6byPKECk7OuQwm2rI0VFasRUKFhNC8HiFuokZDNT8QZKGCDLKle34363eE46nZsHShgtiqHt6lrkehssAmxATY5rVx5f7YRSXtr6f9W1VGnvQvdZ0RkYK+lluBjvqKSwJiaiIs0PnehjX5ntDdE+jPgRX3IFLJLUKFPWuBFsWcE7bNV/Nm/UNrJy7CiVAJLc1GxAk/PqzuYUT0ct5LyiWCmI8W9Rx1sFBBIhJ01FjDrubtt0kfNd+92rx9Em/8gwgVtv5pD6HCxp/UvO1ZjnagZFuVEEIIIYSEpVDh559/Ds6REPshlUIysJR9ENi70TehQo0GITs8R3YIhcxRwWPlSUcFQvxHRxPo61qwyFgNFOQC0TXckWGuBwWPP29LP1LzlAFAbJgM2BFXwDavzcUKOmpAaHqyEiqIuFaq2K1Gt/fkuujgARC/EJcLEQuIME2EGqESDuiq8FBHP+hK7tVfAXs2lBxw144KLQaG9nhqtVTzzCC3X4KBtBW2/KnECSNeK+lQQcxFOyAe8biROJF9m5RzS0wNIMnEavjkHkCVSCWYkfasFmCRwGl9JjDvv8C2BRW7dQYbeX93LlfLHS+Ak2FblRBCCCGEuAH6LxPfBux0xUJZ6ModChVKR3eeih1uKB0VpJKPEFI5Z4BDaUB+fvDOns6/FatmsSl3S/RD5g7vY9IJvm66Wm5rg+opQog7EetmQao17YAWKkg7Opxc5hKbnvg7EGzEvUGoYZGjgrB/k/cxEXeLE13VWKBZ39Aej3ZU0I4eTkFs9ue+opZPuwdo3t/qIwoPpz/5nDqV3Z7Yh9qtgaomtqFjqnu/12mLzdsvAZqdAkTFqbgeERZaySYpvioE6nUA6rThu0MIIYQQQojTHBWys7PxyiuvGMrdjIwMFBQUlFi/eDFv6FwX/yBVv/s3l72NOC5IBZtgpc2uE4QKIiAIRQVBkVChHBcMQkjpVBdnmCrKVlbECsWrds0kbYma12ntjoEsLfCQatq8Y6rjeOUX6jzK4ImZ1ryEhAC2eR1Egy5qLjb8VldqFh8oFgv7cEL+3+1/AZkhchATZNCr6Lc7xNTv5I2Kyj2qBuG0m0LDbqGPYBPhoyDnX6rNneDmkZ0J/HC/Ot6Wg4B+t1r//Q0Xp7/sA3Asez1CBRloNvvzIvEPO5epivv255u773BGXHdSTgf+ngGk/u69flqBxNIJbYY54zpZDmyrEkIIIYSQsBQqjBkzBjNnzsTFF1+M3r17owo7EsLDUeH4zPHiHPRUTYn1Yrjk8FZWqCAdQseOADHVQhP9UF5cByGkdMR2WDpxZcBdrGWDLVSoawObcrOuc9LZV5CnqmlrpQBLJ6t1Lc9g1jRxHGzzOgiJe6gSocSzh9K9wimrHRW0w0C4oH8vQxV1lpcD5ByyTqggtvBFMXnrlWBm23y1LuXU0A+4y+dNbOvzc9R7YHfbehEVzX5cHauI3c97BYiKsfqowif6wQ2OCtpNx2yhwoJJoa/6j0sEIqqqdnR5g/3xteFYWg9RQoWtc4GTb7TmGORzv2OxK2IfBLZVCSGEEEJIWAoVvv32W0yfPh2nnHJKcI6I2FSosKXsbbS9qzFI5fdHKjyILdbxIBXaMa2D+3p0VCAkcDcZESrsL+faFwi52UDGGrVcvwNcgVzjJJNcKonFhScvW9nmyqBJO8Y+EOfBNq+DiI4HarcC9vytnMCsFipoR4WkZggrtINE1q7QvJ7ECwkRUdYM3okQQaqCt/wJ7F6nhId6AKzlQGuEliJWkMg+ab/YXaiwcgqwabZqP5z/CpBo8+N1C9U9AvqjDnVUEIHL3mAKFXp7Y1ykwEB+X0KBiHXEiWXHQqDFIOCMccrhrThynQuWgDoUtD5TzXevUZ8/EWeEmtRfgcJ81WYQRw6Hw7YqIYQQQghxAxH+PqFRo0aoUaNGcI6G2HOwTlualsXBbd7qDDpslI6cF+2qoB0ogoXEsei8Xp1BSgjxjxqea9/BctxkAkGqtApylQtNgosGsnT8jwgVtJtC415e0RshDoJtXofGP+xZa/0gmohShUQXXd99QTtIHM4Izesd3et1EIu0SCytB0pFJLNrJZB3VAmUk3tYczwStVSRyNwOiLDj9/FqWeIeWg62+ojCz1FBnEDyy6netyvimiNOKiJwadApOIKrGg3UYLZEQISKPeuVSEHcgQaPA5K7A8ndSk5OFino34g6bYHCAmDz79bHPlj1u2EibKsSQgghhJCwFCq88MILuPfee7Fli807P4g56MElseTMzy99m4PbS1ZnkPI7hbQDRbCQeAltGRnH6AdCAhJpBctRQcc+1G4NVI2G6wQeEpmx/FO13Hqo6nQlxGGwzeswigaMN1h7HDKAdixLLddqjrB0VBChggg2gs2Rvd72rlU54zpnfd9Gb+yDCPSi4qw5ntotS96f2RH5fsy4Twk2m/UDTr8HiGA7IWRo8byc/xwHuipoNwURgkXXCE6BgcQ/CDuXImQsfk/NWwwEGnqEd26kzRA1FycaK36f9XW6wwi4AbZVCSGEEEKIG/BbQnzSSSchOzsbLVq0QHx8PKKiokqs37fPY8FJ3IFUE4jloFh4S3VYaSp+3RGmB+JJ6Wh3A6kCCSZZHjeF6Ooqx5IQEoCjQjluMoGQ5un4rNMKrkDEbGLhKrbTwrLJ6lok1yERfej8aUIcBNu8DhUqyICxlcj1TohLUq454YS+Tzh2WFVrB9vWO2uv9Q5iuqJ7X6pXnJFyunXHox0VMoPUfgkUOUe/PK0cq2TAfMRrQFSs1UcVXsj5lmtTTiZweA8Q7zAHPnHjEOq2C57ARYQKq78Cdq1ASMjcCayfqZZPucU64VUoaHUmMOcV5R4hzgqhFDOLi4MUdIjIRRwqXADbqoQQQgghJCyFCqNHj8aOHTvw1FNPoX79+qhCq393ExmtOpGyMlTeaXlCheoiaiAVVq/oDuxgkbVbzWMT3N3JQUgw0fnmwYpq0UIFybN2OnJN+/BCIP/YiYIpGayadr36Lbl8KsUKxFGwzetQoUJmmqqajLEoqk7HPki7WKzJwwk55xJ7IO5e4iAWbKGCjn7QbexQI9F4ebnKpFAGfdOXq8dFoCe/81bkyddq6R34tCNrvgH+/l4NTp77EpAUZq4jdqF6PfWZ1a4kTkIiEoRgxD5otKNCxholrgl2n9fSD1XURKOTgGb94Wqa9gWiqwFH96u4HB3bFAo2/qTmrYd4xdUOh21VQgghhBDiBvzuPZszZw7mzp2Lrl27BueIiP1IaKSECmLlnXLaiet1xbHhvkDK7RAKiaOCFiok0m6dEFNib/LMzTDNPQpkrFbL9TvC8YiTQnGRQmnIetmOrgrEQbDN68B2VvX6wOFdQMZaoEkva45DDxDL70g4CrplYD7dI1QI9m+cHmTVbexQixQm9gTyck5c98U/1VyczcYuCq1YQTsqiGCmIN9eomW5l/zt/9RynxuBtmdbfUThi4h79m4AjniEpU5ij8dRIZgD3LLvyBgl5hCXntpBdECT9vHqaWr55JvMveewIxJ512IAsPY7IPW30AkVjh0Btsx1VeyDwLYqIYQQQghxA377rLVr1w5Hjx4NztEQe1ugH9h64jrpAJPKteLbkdLR1V6HQyRUCHYVGyFuRgY9qkSq/F4ZbDGTXatU1ZSIiWo2NnffhBDTYJvXgegBj91rrDsG7agQ6kp6u5DQNHQV/Tr6QQQqoUZEEqWJFIoj60NdsZ7YVLVf5LWDfc/hr0hzxn3quBr3AgY/FDzbfuL7felRh8V2ilOXbpcHc4BbBtMb9VDLaYsRVFZ8pmI2a7cG2p2DsEAcDYSt80L3mlv+APJzlOONXINcAtuqhBBCCCHEDfjdO/B///d/uPPOO/HLL79g7969yMzMLDERF5LgGUjbv+XEdVK1Jjl/Yt9pRTWTk9D5uYczgvs62nJdoh8IIZVD7Lr1NU0qAM0kbYmaS3WWdIQSQmwJ27wOjn/Y87d1x6AFvDJgHI5ogUawo85KRD/wHqQIaVfo92DfZoQced/FLv/4aeZDqjI9JgEY8RoQFRf6YyNedBv3yD5nxj6I0ELHtAWLJr3VfKcn0iVYAp7ln6rlk28AomIQFrQ6U813rw2dmGvjbM9rD3HVeWZblRBCCCGEuAG/feWGDRtmzAcPHlzi8cLCQlSpUgX5+fnmHR2xB6I6L8tRQee3S2eB2JuSstGdqOJ4UFAQvCqi4tEPhJDKIy4x0uG+f3NwhAp1Wpu7X0KIqbDN62ChggyIWoUeoE9shrAkwTNInrUr+K+lB7hqWOCoYGdqtVRtl4Ny73ZKaD/7H15YfhxU7hGgamzojomUf1961KFCBWlDR0YF97Wa9FFzHdcWDNZ8DWR7otG6XIKwihat10Gd281/AB2GB/f1xLFCXkcI9muFGLZVCSGEEEJIWAoVfv755+AcCbF/VvvB7SeuO7jN6xZgpwxUO1tsSgedZFFWqxXk6Icg7Z+QcBJppS0qXaQVCGlL1bxuO3P3SwgxFbZ5HYi2ApdB2vzc4A9klSdUSApToYKu5g+2g5jV0Q92plYLYONP3vu0UCH3N+WJFASJ1BKBSbhGo9iF6jr64QAcxZ51al6vY/Bfq7HHUeHAFnWezI5VFFfKJR+q5Z7/BGJqIKyQ+AcRKmz5M/jiAYmYyDuqBDrN+sFNsK1KCCGEEELCUqhw+umnB+dIiP0dFSTmQXJFizsnaPECLVcrJroaEBWvhAqZ24MoVPBEP8QnBWf/hISdm4yJHf3Hjniz0+t3Mm+/hBDTYZvXgdRK8ba1JLanbtvQvn7OITUJic0R1o4KwRYqyO+pDDwV/70mitotyxaZE1L83j17vzMdFbR7TrDFHCL6kd8ScUNrOdDc/W/4ETiUplwQe/0TYUfrM4E/XwK2/wUUFqgo0aDHPgwGotzl6MK2KiGEEEIICSuhwvLlvmXzdeniqWSykFdffRXPPfcc0tPT0bVrV7zyyivo3dujiCeVcwKIiFIVMPu3AHXbeNfpDjCdc0kqPpdSlSGdEg2D9F2howIh5iA2rIJhnWwS6StUZ5w4nki0BCHEdjipzUuOQ9y9RAS2fQGQsSb0QgXtphCbAMQlhOfbk9jUaykvghERjgSDox43BYkRiKkZnNdwKjK4WvzzSMjx6Ht3JzkqiAPB3g1quWHX0LymxD+IUGHnUnOFCoWFwOL31HK3S5U7Zbgh51ZcJHIygbRlQKPuwXkdcVdK/dV1sQ9sqxJCCCGEkLAUKnTr1g1VqlRBodxUlYGsz8/Ph5V8+umnuOOOO/D666+jT58+eOmllzB06FCsW7cO9epxML1SiLq9RgNlH7p/U+lCBToq+CdUyAxix6EWKsQz+oGQoMXeVBbp6NTVjlWj4QrECjcyuny7Z1lvtmUuIUHCKW1eUgZSaStCBW0RHkoy07xCtwi/jevcQXxtoGqccjuQ81G7VXBeR+IDBBH+heu5LotaHkcFOf8FBUBEECuVibMjCY24jjwg0gHfIXE4yxd3x1igTrH+iGDSpDew7GNg10pz97t1LrDnb/W/9LkBYYlEM7UcBKz+Ctj8W/CECuLYcOwwEJcEND8NboFtVUIIIYQQ4iZ8viNNTU2FExg/fjyuvfZaXHPNNcbfIlj47rvv8Pbbb+O+++6z+vCcS0JjJVTYd9znQGefVm9gyWE5tnpFd2SbTd4xINtTGRNXOzivQUi4ULNhsdibY+YIC8Q6VghVB2sokAG5y6eWX5UnIgXtUEGIzbG6zfvkk08abdelS5ciOjoaBw4cKFUocTwff/wxRo0aFaKjtDHaElxX3oYScczSUQSlvEdhgfzfiU3UIFwwhQpZe73CCCsG4g1BRoyKxSsLWS/bWeFqIULzvGzgcDqjMUjZQgUZ+DcGcR0gJtXiMxH7RsWFrupf2L1OVebL4LoZaDeFjhd4XWjCkVZnKqHCtnkAbg3Oa2z8Sc1bDAJiqsEtWN1WJYQQQgghxBKhQrNmzWB3jh07hkWLFuH+++8veiwiIgJnnHEG5s6dW+pzcnJyjEmTmZlpzAc9/wuqxobgRkbiFKRir4qnCt6m3JNfHRcCeGfmArwx2zvANuNYKqRb47KforDx542WHqMTuCm/Kq4A8MkfK/HSPM9Ns4nUKdyHb6XPCVXQ/6NDKKxyxPTXICRciCgswG+IRNWCPJz3zJfYXSVwW9bJuX9ADJnvXJ6MP1e67ZpZnpBDrkVu+3/DBCM3eD8QsSnoL5WXnQU7YHWbV9qzI0eORN++ffG///2vzO3eeecdDBs2rOjvxEQHDDSFggaeSI59Gz1t7BAKBrRjVkIYDzwJCcWECsFCRz9YZZkuYoyxi7zODqUhIgXZLtSIsFIGP/dvVpMIZwgpTkx1Fcsi8SxH9jhEqPC3mtdtH7rret12nniCQ8DutV4hXCDsWgXsWAhUiQT63Ry+ojah1Rlqvmc9cHg3UN0joDEzLmTTL66LfbC6rbp582Y8/vjjmD17thG3m5ycjMsvvxz/+c9/DIGt3iYlJeWE50q/7Mknn1z095QpU/DQQw8Z27du3RrPPPMMzj777KL14m728MMP48033zSEu6eccgpee+01Y1tCCCGEEOIeHODx5zt79uwxbHjr169f4nH5e+3ataU+5+mnn8ajjz56wuMZh3IQcSwSoaMAdmZdZB0gCkjKTUf6kWzjsThkIzH2kLG8/EgtHEKexUdpfzZFJhjnsXru3qLzaCa1qmQAMcC+wprYmSWW1XxPCAmEHdG10SwiA7GHtyO9sHpA+4pHNprFbAeqAL8daY7d/H4Sx1AQkt+TgmLC0XBGt0vffffdcrcTYUKDBnS0OoF6MogVAWQfBA5JNXnD0DsqhHOFrKAH5w8FMepMCwR0ZbhV/6cVQgRfqNVCiRQObAWa9bP6aIidIwmz9gTP+SQYQgUzxAK+EhEJNO4FbJytXNHMeG3tptB6qBJdhDPy+1y/M7BrhYp/6HSRufuX90zcJkVs0nKgufsOY6RvtaCgAG+88QZatWqFlStXGq62WVlZeP7550ts++OPP6Jjx45Ff9eu7XUZmjNnDkaPHm30yZ577rmYPHkyRowYgcWLF6NTp07GNs8++yxefvllvPfee4bwQUQNEu27evVqxMbGhvC/JoQQQgghwcRVQoXKIO4Ld9xxRwlHhSZNmqBejRhUDUXDVyoVUXYGsl04VNBQyvTRInI3GkQre9VmhfuNsYvDiEO1Gglwj5Fe8MguqGucx6aR+4vOo5m0KjxsvCcHqySgQQ2TrCkJCWMy8uqhWWEGOsbuxraIDgHtq2vhVkQWFCIDSYisUQ8cXiSOwaj2C761el52PjyBUsQHbrrpJvzrX/9CixYtcMMNNxixZ6VFQpTnIOZKouPVoJsMamWsDrFQwTMwH+5CBXFUECR2IFjo6IfqJQXqpJhQQQZXD24N3SmRqvzIaCD/mP3iMEjpkYQiVDiyzxlnxwqhgo5/kO9S+vLA9yXCIdmXIG4KVsTW2I02Q5RQYesc84UK+lynnA7E1jR332GMuHkVd/SSdui6desMp4PjhQoiTChLVDthwgRjP3fffbfxt7g0zJo1CxMnTjQifMVN4aWXXsKDDz6I4cOVI8b7779vFKJNmzaNcWckeG15RloSQgghIcdVQoU6deogMjISu3btKvG4/F1W4zgmJsaYjmf2XQNQsyZvZorYUQd4czy6xO3BvDEem7Wte4GvgepJDTDvljPD27bQV7YmAG+PR5fqBzHv6iDY1a1bD8wCWjWuh3n/GmL+/gkJN776CliyEo90P4JH+gX4nV36F/AHUK95J8y7mt9PQo5HBs8THud58YXHHnsMgwYNQnx8PGbOnIl///vfOHz4MG655Ra/HMRcHf8gg1p71gKtBoc++iHJ/pF5QUULNQ5nBO81ju7zDraSE6nVUs0zd4Tu7NRoCFw+Ffj1GWDz7yp/ftCD9ojDICdSzfPdkegHuyMOKoaLShWgQdfQvnaT3mqesSbwfS35UBWpNO0HNO0T+P7cQOshwO8vADsWAQX5ysXCrIIgLVRwWeyDHTl48CBq1ap1wuPnn38+srOz0aZNG9xzzz3G38VjIIoXjQniliAiBCE1NdWIlpAoX01CQgL69OljPHfUqFGlHktYiXOJ+SKFDy8sX3Apgkxp60ibhxBCCCGm4SoJt+Sh9ezZEz/99FPRY2JJJn9Lzi8JgKTman50P3DscMkqKWmgUaTgGzU8ghmpXMkPgo22vD9CHCuVCDGFRM+1z4yc7d1rSuanE0LChvvuu89wOyhvKiumrDTE+lZyert37457773X6Px97rnnynUQk05kPW3b5nLvCl1xu2dD6F5TMsxzMkv+diDcHRWCKFTQg6sUKpTtqGBW+8Uf4usAO5ep5ZOuAZK7lZwoUrAP1euWFP04wU0hoTEQnxTa1250khJISN+HFqNVBonYWPuNWu431rwBeacj5zc2Qf2GSlSDWaSvUL8TUfFKNEWCxoYNG/DKK6/g+uuvL3qsevXqeOGFFzBlyhR899136N+/vxHr8PXXX3vfovT0UmN75XG9Xj9W1jZliXNF0KAnccwlxCfESaE8kYIg68tzXCCEEEJI6B0V9uzZg/nz5yM/Px+9evVCw4bWKwpFkXvVVVfhpJNOQu/evQ2rMMlKEztcEgBxSSrbT24gD24H6rZTub9CzUY8tf4KFQpyVaeQ2Z2r2rrTyrxeQlxZFVrSqadSiAW50KhH4PsihISUQNu8d955J66++upytxHr3Moi1WVimSsVZKU5hZXlIOZ6ocK+jaF7Td0ujqkZ+oE0u6EHo7N2A3nHgKrR5r+GUV3N6Icyqa0dFdJEuR86i3mpihbBTmwikDIgNK9JAnRU8AjdnSBUqNs29AP8EhlQvwOwaxWwc0nl44SWf6IGuOp35MB5cSKrAi0HA6umKieWxiIMMYENnuKl5qepWJowINC2qohqn3nmmXK3WbNmDdq1a1f0944dO4z4hpEjR+Laa68t4XZb3C1BjictLc0Q1RZ3VQhlvG9QObDN2y7JOwrs2QTEVAOqeK5X8hmsbAU+owgIIYQQEgZUWqjwxRdfYMyYMYaFV25urpFJ9uqrr1ouCLjkkkuwe/dujBs3zlDZduvWDTNmzDhBhUv8RBwTEpsBu1YCB7eVFCokNubp9BWdyyo3MTLwabZQQTsqVKvD94QQO9lXH8sC9m9RyxQqEOIozGjz1q1b15iCxdKlS5GUlBReYgRfhAoySCsiWxHbBptDnsp16YiOcFW6nv/oc1CQB2RlqCpoMyks9Ipzab1bOnLfViUCyMtWleA1kxESNnms1lsMUIM0xL7o+9BsBzkqyCC/FTTp4xEqLAPanu3/88WRcsUUtdzn38ERbzmZ1mcqocK2eeb9RuhrUftzw8L904y2qr+iWhEeDBw4EP369cOkSZN8EtXOmjWr6G+J5y0vtlfP5bHiogv5W/p5yyLk4lwRKUzsCeR54yZMiwtgFAEhhBBCwgSfe9Ek91bsuzSSc7tgwQKjISyInZcoaK0WKghjx441JhKE+AdDqLCjZPQDHRX8d1UQoYJ03JqNtu6kowIh5qBzxuX7Kp0PIjaqDLvXqUxa+W5qS2xCiC2xus27detW7Nu3z5hLVZyIEIRWrVoZx/XNN98YnbQnn3wyYmNjjU7fp556CnfddVdQjsexA3DV6ytRaMZaoEmv4L+mtthPaBQWgyLlIhXPMjB+YKtyYjNbqCCDftqatwbF6KUiA6HS3jiwRQklQyFUkHz5Tb+o5fbBrZglJqCF7U6wsNZCBavi00SosPBt1RdSGVZ9qa5bIoDudKHZR+d8Wp2h5ns3eIo5AryuZ6xRA7xVY4G2w+BGgtFW9UdUK04KIlKQ6N133nkHET649kh7trjgQOJ5Jab3tttuK3pM2rQ6tjclJcUQK8g2Wpgg7gjiGnHjjTfCNkjfXnkiheJxAf4KFfyJIqBwkxBCCCEOxmcPSGmAfvXVV0V/V61aFRkZ3oFW6TCNjqYy3PWVOUKmR6hQ5KjAQTe/qJEcvNxeChUIMZfqDYCIKKCwQHV4BRr7UK8DEBll2uERQszH6javuIJ1794dDz/8sNERLcsyLVy40FgfFRVlVMlJR6503L7xxhsYP368sT0phh7Q2r0mNKdFZ5dTjOY5D01LCjjMRHLHhehqQLR3oIYcRy1P5auIFUJB+nI1YCPvS6vBfDucEv2QbXOhgriCaFeyhlYJFXp7B9Jzj/r33PxcYOlktdzrX0B0vPnH5wZxYXJ3tZz6W+D72+iJfWjaF4h3p9OklW1VESkMGDAATZs2xfPPP2842oqbrUya9957Dx9//DHWrl1rTCKoffvtt3HzzTcXbXPrrbca7rcvvPCCsc0jjzxitHV10VmVKlUMEcMTTzyBr7/+GitWrMCVV16J5ORkjBgxIij/GyGEEEIIsbmjwg8//ICbbroJ7777rtE5OmHCBCNmQSq98vLyDAWtrCNhUFksAgUZtNOZ7bojkvjuqKBze81GZ4zSUYEQc5DqEBFj7duk3GTEWaYy6IGyhl35zhBic6xu88q+y9u/ZAHLRHyIf9gwy1uJG2y0mI1CBYX8dsrY4uEARH5loWMf4mp785/JidRuCWz6WcX2hYKNs8MuE94V0Q9SiSv39hIVYkf2bgIK84HYBCDB0x8RapJS1P213L+nr/AKF3xh3ffKmU3iH7tfGcyjdDatzgTSlgBb5gCdRwYW+6CvRe3Pc63DkZVtVXE92LBhgzE1blzSMalQzr+Hxx9/HFu2bDFEFO3atcOnn36Kiy++uGi9REZMnjwZDz74IB544AG0bt0a06ZNQ6dOnYq2ueeee5CVlYXrrrsOBw4cQP/+/Q1xgziKOY6fn/QIlaoU+1wWX/b8bcxkXkU5sRBCCCGEhAE+CxWaN29u2IeJKvb000/HLbfcUtQ4lcawNDwd2Vgk/jsqSOTD0f0eC7Iq7JD1F23JluWpBguGo4LueCKEmHPtE6FC5vbALEgFXS1ECLEtbPO6SKgg7NsYmtc7lFayvRzuaMFGprfC0jSkal+QgT8f7KYR7o4KgbRfKpMJ34GxD45AC9tzjwDHjgAxNnUn2esRm9VuDUSFMHe+ODJoKPEPa78Fdi71XaggApAl76llESnEJwX1MB1N6yHAb88COxYpF4rKOtCJ68XBrer5bc+BW7GyrXr11VcbU3lcddVVxlQRI0eONKayEFeFxx57zJgcT6gcvgghhBBC3CxU0IwePRpnnXWWkYMrdl+TJk0qygsjLkdXEoujgra3rVYbiIqz9LAcR82GJW1rzUJsKMUas7hrAyEkcCRPVtDXPX/JOeS1XW7Uk+8IIQ6BbV6XRD/s36zEtZFBjqjTvxHagSzc0dFwWR4HNjPRbehq7rT0No1aLYMXv1FaxJXcI0omvAw4EvsjDgVyXZTro3yn7CpU2O0RKtTvYO1xiDhBhAriqOArm39Xv0ESh9LnumAenfNp1AOIq6UKL9IWAU1Ortx+tJtC495AjfpwO2yrOohT7wESPQ4UhvOEx31Cu1Cc8FghcHA7MGeCRQcchogbVERVoCCv7G3kd5OuUYQQQoi1QoXp06djzZo16Nq1K9566y38+uuvuOyyywzhgihc4+I4YB0Wg3VSdbF7rTe/PdJvvUt4ox0VdDWY2W4KkTFAdA1z901IOKOvfZW1r969Ts2r1wdqJpt3XISQoME2rwuolQJExat2675UoG7b4L2WWPPmHFTLlY0IcqujwmFvZrbp0Q/VPRXhpAJHhTSgoCC47hN6cLBZP+V0QeyPuASIq0LmDuX0Z9drl3ZUqO9xybEKcVTQLmkyiOhLpMAij5tCp5HePgBSOhGRQKvBwIopwOY/AhAq/KTm7c51beyDhm1Vh9H+HCDZzyK/tKXOFSpIJJpEC5WFDPbb7boYmwjEJABH9wJdLwX6XK8en/sqsOIzoEFXYOiT9jtuQgghxAX43Ftx55134pprrsFff/2F66+/3sgbE4uxxYsXG5Zi3bt3x/fffx/coyXWInlq1TyRAmmL1ZwNNP/RbgfSyVosw8+8vN4kikcICZabTGWrDIV67StvY0oICRls87po0KN+p5LxO8F2U4ipCcTXCu5rOU2oINnsBfnm7luLfUUASMpG3D2qRCjHtWA4W5SWCd/OvZnwrkTHBZotoDfzs7XHI1Ro6HHJsYqG3YCIKCB7v9cprTzSlgDpy9Rz+t7E74UvaDeWrfMq9x7t36LinqpEAu3Pg5thW5WUGj9mJ5HChxcCn11e9iTrZTs7sfQjJVKQgryhTyhhiUz9b1frd61U13RiLvI5kHu1sia7fU4IIYRYK1R49913DcXuJ598YogVPvjgA+Px6OhoQ7QwdepUPPXUU8E5SmIftJ2t3HgLCR7rMuI7NTwV1dkHgPwc887c0f1eoYJ0zhNCTHZU2BVYHqV0cBJCbA/bvC6igacCd4/H2SZY6A40EaOKZSzx3iPk5XjFtGahB1W1gJqUTtUY7/uwb3PwzlKJTPiz+W44Cf0dMjuS0MzBt2NZamConsXRD1Gx3rb8jkUVb7/4fTVvdzZQp3Vwj80ttBwsVh/A/tTKRdZoN4XGPYGajeBm2Fa1EeIiJL+35SHrK+M25Mu+hZ8e8y+WJtiIk4LECpWHrC/PcSHUiLPQonfV8ql3lHy/JHoouTtQmA+s+cqyQ3QlThW1EEIIMR2fe9KqVauG1NRU9OzZE9u2bTNcFIrToUMH/P777+YfIbEXic2A7X+p6iiBQgX/kTxdUflLI/fwHm9OnVnRD6zkIyQ4QgUZGMnNAaJ86Cwojq7klZtbQojtYZvXhUIFGUgNJrrzTOJ9WE3uHdSTQVC5Z8jcbm5MAx0VfKdWS+DAViUkQF8EhU0/q3njPmGRCe8qJPpBMFtMZBY6Pk3czaKrW300QNM+wI6/gPTlQKeLyt5u70Zg829q0L3fLfxd8JVqtYFGPZQQJPU3oOso/94f7ezS9pzgRt3YALZVbURiE2DsovKdaWTQW7Yze9/ZB4EZ96r+hmnXA8OeA5qf4v/rEGDBJCDvKFC3PdDjqhPPSPcrVMHe2u+Anv/kdd0KUQsdnQkhxNX43Hp/+umnceWVVyI5OdmIfBAXBRLGjgqamnRU8BtxO9CdeIcraSVfGrqDKb6OefskhChr6aqxQGEBcGiHf2ckOxM4uE0tJ/fg2STEAbDN6yIaeKzCxQrazLit49GVnzrugCh0p3xlKmPLQ3fYc1C8Ymq1UHMRKwSLokz4c9hx7zS0gEg789mNPevVvG47eww8N+ldMtatLJZ43BRanE6hcqXjH+b49zz5nREXO4m76TAcbodtVRu2d3RMQGlTZUQKvuxbrjFjfgRSTlcOVt/drgbSiX/s2wSs/lItD3pIiW2PR8Rp4m4h0T8iViOEEEKIqfh8t3fZZZcZTgpfffUVNm/ejOHD3d/4J2U4KpT4mx2ylUIrQbUzhRnoDiZxbCCEmIdUx+rBp4Pb/Xvu7rXe77xU2hJCbA/bvC5CrFpl0EIqzg6ZKA4ty1Hh+HZyuKN/O83MThbRoBbnStQGKZ/aLdU800+hpa+IAEIcS4xM+HP5bjg1+sG2QoV1Jd1xrKaxR6iwf7P6XSkN+a35+3u13O9WRjL6S+sz1TxtMZCX67+bQsOuXjc8F8O2Kikipjpw2edAhwuUa+uP44AlH/IE+cOfE1T7UgQfbYaWvk1cItDeMw6yairPLyGEEGIyfsnSa9eujV69eiExMdHs4yBOQWwXixMGN4FBQQsVDpspVNhX0sKTEGIe+lrnb1WoVPYIkqsbydxyQpwC27wuISrOmw1eUQVsIOjfBraLSxc0mykSkcFB6YgXqjFmwGdHhWAJFfTgoNi102nPeVT3CBWybZQTXpqjgl2ECjUbquu8DGjtXFb6NssmAwX5ykkh5bRQH6Hzadhd2eTnHlUxG37HPpwVNuIQtlVJEVWjgYvfBnpfp/7+80Xgz5eC6yZWFvm5wJqvnfPmbFsAbPlDCS4HP1x+n033y9V848/qGkUIIYQQ07CBfx5xBAe2AWlLgdwj3scio4HMdPW4rCeVcFTYHQRHBQoVCAla7I2/VaF6YEyqewghhFgX/7DH43ATDPRvw/ERaeFOgkfkd3iX+bEPsQlKiELKp5Z2VNgJFBSYf7aKBgfPtoc1P/EPfd9oR0eFnEPea6u+jtuBJn3UfOfS0iPfVnnsw0++iSLlyiDXkVYeV4XNv/v2HCn+SPcIRzqMqNTLEuKK785Zz6roAmHJB8BPDwMFeaE7hp3LgU8vA1Z8BkcgojIRdOhoBxGYlUfzU5VbWG4WsH5mSA7RtYjgL/U3YPYTVh8JIYQQm8DyTlIxIkKY2FNlnhUn/xjw9hmeT1IMMHYRoyB8RVvV6s5WM9A2uIx+IMR8dJWstvf2lYw13kpDQgghoUcqcVdMAfZsCM7+j2V5LcATj3MeC3e0o4KZDmK67RxXK2yqZgNCxDMSf5J3VEXOmRmXIW2ijFWSkQW0P9+8/RJrHBWk8lbizuzmplC9vr1iXkSoIL8p6StPXLdyiirskMiV9udZcXTuiX9Y/gmwbb5v22/6Wc3rdwRqtwrqoRFia+QaftpdSoT27W3A2u+AoweAYc8EV9wpwrK5E4GVX8gINBBdHTh2GLZn3XQVMRRdDRj4QMWCS1kvrgq/PK1cIzowEttvZBxh3fdKSLM/tdJvHSGEEPfBsgfiW4fg8SKF45H1Zg66h4ujgpnnTEc/6A4nQoj5QgV/qkJl4EpbLYuNKSGEkNCjLcP3bQzO/rWATTplq9UOzms4Fak6E2SA3Cz7Yd12lnNtp0FVuyJi8oTGann/ZnP3rQcHpdq9Voq5+yahoVo97yBTbra9zroMHgl12tjLmaBJb2+8m1TjavKygWUfq+Xe1wNRsdYcnxtoOUgJrA5sAQ76EFuz8Sc1bxM+sQ+ElEvPq4B/fABExgBb/gSm3agcX8xG2nYbfgI+uhhY+bkSKbQ9B7hsimp/VERkFCxDohvm/Vct97r2xJjjsuh2qRJoiqsOnYV9R9oZi94F3jsPmP2YEilExStHLkIIIYSOCoRYmG9pplBBbLNEKa2rTggh5qKrZA/t8t9NoWayvSrBCCEknKjvESpkpgE5mUBMzeAIFUSEGmGjwTQ7OSpI56Sce4lrME2owKgzn6nVAjiwVQ36NT0Z5sc+DOPgoFOJS1K53IX56rsV7RG12MlRQark7US9jmpwRZwTRExRr4N6fO23KkJD7sWNgSxSaeJrAY1OArYvAFJ/Lf98yjlPW6KWWd1MiJf25wJXfAl8PArYtQL44hpg+GvmFTYdSgd+fQbY/Jv6u2YjYMiTyk1GxGXiuFtaf6c4LXw+BjicDsx/DTjreWuEp8smKyGtXLNPucX3Y5AClpTT1LVJon7kuaT8z4mcazlXOko6vjbQ/Uqg97Wq/SFis/KKIyV2Oi6RZ5kQQlwOe9MIsdpRwQybTVFHSwNPYMctIcFzVJDvrNxgSQdlRWSs9nZo2qkSjBBCwonqdZVYTDrKMtZ6q2HNInOnV5TGCv+SiDBBhCEiUji43WShAh3EfKZWS2DTL+ZW/mXtAdKWqmVmwjsXsbGW2EBxDDu6F0hsbD9HBXHssBPSpm98ksrWlu+ACBXEWUFsrIWe1wAxNaw+SufTZogSKmyZU75QQZxdpGhDnDe0aIQQomh+CvDPGcAHFyhXpc+vAob/F0gKwAVJrncrPgXm/lfFSolIt8dVwKD/qAHo4mJVLVg9nlGTgbeHqLaJODF0Hhnad0zaklLdL5x6Z8nj9oXuVyihwt/fA/3GKgcYcqLYcMn7wPofvO5D8rkT94rul5UUHhwvapEIkTkvK0ewof+nttV96IQQQlwLf00JsQJdXS1qYjOy26SSQJBOkWBmzxESrkgnriFOKFRVub6we62aN+wa1EMjhBBSAXqgS1+XzeRQWsmYA1IS3UmtnScCRXdkMurMP0cFIXM7TCP1F9UmqttODRAS56JF7lm7YRvyc4F9m0rG99iJJn3UPH2Zmks1qIixRJh10hhLD801tDpTzXcuKb/SVju7tB5KYTghpSGuNGNmqUHiwxnA5/8Edq2s3LmSdvTnVwO/v6BECvU7AVd9A5z9vH+D/Y17AgMfVMt/vAjsDVI8W1nMf0MVn9Rtr0QWlXGrkOu9ODJsmYuwQNrx4hha1iTrpQhv23zgq5uAT0YB66YrkUJyD+DCN4F/zwP63XSiO4LcKyR3806n3AZERKnfVYEiBUIICQtY4kmIFcQmAlXjVONeKlgCrbo4us+zX499JyHEXKRKVlwV5OZcbphqt/LdUUFuzAghhFiHDHStnwns+dv8fWvxWpLHeYeUJKEpsGuV7yI/n4UKjDrzmdot1dys96D44GCbYRwcdDoi+pFksyOe+0k7IDEl+ceUSLhOa9hWqCBtfRmYWfye+rvrpUANur2YJjCUz6YMrG5bAKScWrqrpLguCJ0uMOd1CXEjSc2Af/0IfHghsHMZ8OV1KnKhWT/fnp97FFjwBrB0snJyja4G9L8D6HtT5QulTrlViR7FVWHGvcAlHwJVYxF0RAS3+ku1POhBIKoSryn/s7hALPwfsHqacq5wMyJCkM+O/C6XhThrSH+ZFhmKy0TLQeoz0vw0/9qK1WoDbYaqSKXVXwH12gf+PxBCCLE9dFQgxKpBT+2qIDffgaKFCvFJysKTEBK8+IfMHT58J/d7q0cbUahACCGWoity9wWhYktf6xOamb9vN0BHBRs5KqQBBQWB7y/7ILBjoVpmJrzz0TEqpWWJW4UWlYnIJhQDV/4gESr6mOQ7JQNVImSW6s8Wp5sbsRLOSJ+GdlXY8kfp22z+TVXrSqW43SJCCLGjQ+TV04GU05VLybe3AUs/qrhKfsufwOSRKt5GRAotBwPX/gycdldgbq7yHb9gEhBfB9ifCvz2PELCnxNUXEzKaUpsWVm6X67mm38Hjh6Eqzl6oHyRglCQp0QKVWOAzpcA1/4CXDpFiRUqE4OqI382zFL7JoQQ4nroqECIVYh9lTTIzRAqHPFEP/ibrUYIqYRQwQf7armx11bg2lKXEEKINegBDMnnlY62yOggOCpQqFAqOhJDHMRMdVTwCH5JxSQ1F5W0cnITm2Itlq4sksssg4MigODgoPOpXrek8N1OQoW6HZTA3y6ICGFiz5JRBPNfU/OCXOCT0WqQRvK2y8pmJ77T+kw1kLptng+xD1E8s4RUREx14LLPganXKVeBP8aXv71UxcugvqpPCC8AAHYRSURBVCAOJ4MfBbr8wzwnpRr1gQsnqWp9OZ6mJwOtzgje+yjuLCJ8EhfawQ8H9n8kdwfqdVDOOuu+BbpdZuaROhNxmRj0kOo3C/S3W4RqcbVU2yT1NyV4IIQQ4mpYek0qRga/5Ya7PGQ9B8n9o2ZD8/JAixwV6gS+L0JI+UKFwz4IFXZ7hApiU2fWjTwhhJDKIdWWYiEuIoW9HktSMxAr3OwDxQaDyQnowToZIA8UqaiSqi4h0MH2cELu0xIae8U6gbLxZzVnJry7HBXEDcxuQoUGnWArRChVXKRQGrLeTu4UTqbFQDWgKLF7+7eUXHcsC9jqyYbvONySwyPEkVSNBi5+G+jgQ1yKIVKoomJtrv8d6H6p+X0brQYDfW9Wy7Mf9zqVmY38L3++pJY7XRR4PKcMxGtXhbXfBH58bqDvWCXcNkNgKJ/TLpeo5TU8v4QQEg5w9IT41sEoVQHl3XCLSIFVA/47KpgtVGDlNiHBI9FTLXvIh6pQUdZrpT0hhBBrEXtZiX/YNl8Jyeq1M2e/ujNVsnop2C2dhKYmRp3JQGqhqvBjm9c/xEL/4DbgwFZVsVhZZHBQVzcz9sEdSJWskG0ToUJhoVeo0JB2/mFNXCLQpLcSJEjMQ9IV3nViRy/iw5qNgeSeVh4lIc5sF/e/VbkYVMRZzwO9/hnciNnB41SEws6lwIz7gYveAiJMHq5YNx3Ys0612Qc+YM7/IwPps8YBe9YrR00pUnEj2lUj1HQbrVyLts5R8RpxCdYcByGEkJBARwXiGyJCSO5W9kSRgv/oSrAje8yLfmCnLSEhcFTwRajgcVSgUIEQQuyBCBUE6aQ0O/ZBxKe0nS4dfY9wZJ9yoAgELZqOTTQ3viMckJgGQYQKgbD5j2KDgwFWIxJ7oO8ftVuJ1ci9sYiSRJBU33PdJuFL6yFqrt0TNBt/8qw/E4iqwP2TEFIKPla9NzkpuCIFXT0/8h0gujqwawUw/3Vz9y/tz7mvquVe15rnglatDtDmLLW8ygfRhxPZOg+Y+YA1ry3xYhKvIY5qEq9BCCHE1VCoQIjVjgpmWEMWOSow+oGQoDsqyPdNKgrLQr7ThpihCjvxCSHEbkKFvRvM2+chj1ChZrK9ctTtNghaNVY5IQRq5ytiB0HcKyIiTTm8sKFWSzXP3BHYfjbpTPghHBx0m1Ah+6ByM7Aa7aaQ0ERV1JPwRoQIQtpSIDfbO+i4+U+13GGEdcdGCDFXUHnui2p58XvA9r/M2/eyySqCrHp94JRbzG2z9/A4vWyYBeTnwjXsSwW+uQX4+iYVv2MF8j51u0wtr/vOmmMghBASMihUIMRyoYKn0zUQdKZodU9HEyHEfOJrKatAIXN7xW4KUkVK8RAhhNhLqLBvk3mDcZk7vQNqpOxOxoTG5gySaxcy+W2lMKRyjgpaXFMZ8rKLDQ4yE9590Q8HlVuGXYQKddtSkESA+p2UE2V+jjd2RtwV8o6qQcdAomwIIfaiyz+ArqNV1MDMB81x+pEikkXvquX+d5of1dZysLoW5WQCGzxOL05G+pZ/fQb4+BIVsVMlEmg91NrPhBzD7nXmis0JIYTYDgoVCLE8+mEvUJBvjqOCNJAJIcFBBkW0q8LBcgZbJP9cqNeRHayEEGIXxDpUrMRlMC7Qyn6NHvTV0UCkdLSQQ0dlVBbtQsaoM/+pXcxRoaCSWcNFg4P1ODjoJuLFka+KGhjS4nc7CBVkgJoQuf/S8Q8yaCZs9Di7tDoDiBLHHkKIazj7eeUCJeLUH8cFLi5e8AaQewSo2w7oeaVZR+klsqoSVwhrv4ZjEaHikg+AD0YAKz4DCvOB5qcCY2YCZz8HVK0gYkfWmy0CEaTNKdd6YfU08/dPCCHENlS1+gAIQbg7Kkh1gHSaS7V2ZRB7sZxDarmapyKGEBIcZDAqY3X5VaHaUSG5K98FQgixC1FxQJ3WqiJHruMS12CaowKFCuUiDkPCofTAzjeFCpXHEFpWUZbpWbuBGpUQN2/8Wc05OOguZJBF7kPl+5W1xyumt1qooF1wCGl1JrD4feWoIINpm39T56Q9nV0IcR0x1YGR7wJvDVLipGUfA90urdy+xEVtlWdwe9BD6l4gGHS/HPjzJRVXIRGgTiogEyHIpp+BORO8EQ+1W6nz1e5c1UYQxi4qP7ZYRAq6vW828v6v/wFYPxM45XYWAxFCiEuhowIhVhEdD8QmqOXDAXTcajs0scOqrNiBEOIb2lGhvKpQGQATknvwrBJCiJ1o0EXNRaxgBtqZIcnz20BKRws5AnWy0HFpTuoAtgtSdawjOPanVk4YnfqrWubgoPvQYncdr2IVIqQ5sFUtN7Sh4FcGYqyqKg1XDmxT51P6OuT+a+5E4FiW6keJS1DrCSHuup417AKc+bhanvNy5dvtf05QzgAppwFthiFoiBC6SR/lTKSFEU5g12rgy2uB7+9WIgV5r894DLjuN6DjCK9IQRARQnK3sqdgiRSEtmcBsYlKKLFlTvBehxBCiKXQUYEQK6mRrNwURHUrlsSBxD7IzXpElKmHRwg5Dm3vXZa4SKoUZRJ78eTuPH2EEGInpEJ3xRRg73pzBtR0GyypeeD7czO68zIrI7D96EFUsYEl/lOrBXBwmxoI9jfXXaoEjx0G4pKUFTBxF9Xrquiy8qolQ8G+jWqgRz5nOjLGbtcyK6tKww0RIUzsCeTleB9b+pGaSx/K20PVQKq8JzznhLjretbnBuXkJJX0M+4FRn3snyPCtgXAlj+UyGnwwyUH3YNB9yuAbfOBdd8Bva9TsaF2Rfqf576qjlWIjAF6XAmcdpf1rkqlIdf5zhcDf70FrPkKSLFJO1QE2LpwsDTiEr1OyoQQQiqEQgVCrEQagdIpdHh35fehO8mlQyci0rRDI4SUI1Q4tKv82AfZjtVUhBBiL7SVuAyGBYqOMYiq5sl4J2WiBxwPBypU8HSmU6hQOWq3VK4IBz0V6/6gM+FbDAJiqlXyAIj9HRU895VWsdsT+1C7NVA1GrZEBu04KB4a5JpfXKRQGrJetuN7Qoi7rmcy0D/iNeC1vkpk+cvTwJmP+fZcEbxJFIPQ6aLQOF2KA4E4E0hE6I5FQOOTYLuB86qxSvix5H3vtVWcJgaPU4VzdhZXdL1UCRXEUUGij2NqWHs8cq4/vFDFEZVFZDRw+VSKFQghxEcoVCDESrS6UiqwK8vR/WousQ92blgS4ga0vbdUhUqe3/HfOS1UqNeRwiFCCLEb9T1ChcydqhpTR3BVhkNpXtGpXQfUbOeosEdFCERGBRj9YMNqL6c4KgjSie4PBflA6i9quQMz4V2JFv9Y7aiw1yNUqF9Jp0FCCCHuoVpt4KK3gPfOV9X/4gbV9uyKn7duOrBnHRAVDwx8AIgIQeq1DJx3vABYOhlYPS30QgVfBs6Pj8Mb/AjQcoAz+q0a9VAiRnHFk/e3yyXWHo8IQio617JetqOrAiGE+EQIfq0JIWVS0wShgu60ZTUfIaFzVBCBUM7hE9eLQ4ogOX2EEELsZ29uWJoWVj7vViNZ2ULNZApFfYk6E+vdgtzKxz9IZ19Opmd/tFGtFLValvzs+kraEtXukU74lgMr99rE3lTzuMJkl1OJGUpHBe1+QwghJLxJOQ049Q61LK4KEglTUTTbvP+q5V7XhjaeTeIfhE2/ADlZsN3Aue43PudFYMyPQOvBzhApCFIg1P0ytawjKwghhLgKChUIsRLd0RpI9YqOfqDNPCHBJzbRazOXub3kOnFYyFitlpO7890ghBA7IhVEwu61gVcuCXa1zLUTkgusxbkH/azm1+i2ckSUchEjATgqpAEFBf7HPshgQWxNnnk3Rz9opz4rEKtuqZQsfp0mhBBCBjwANO4N5B4BfrhfuXOVxbKPgcO7gOr1gf63hlZM3LQvkJQC5B0F/v7enu/bxe8Avf4JRMXAcYiLQpUIYNcqYH+q1UdDCCHEZChUIMRKjKo+6RQKRKjg6VBiXi8hwUdudBOblW6dLM4oMpAiN08UKhBCiD3Rlbp7PJW7lUVXpSd4nHZI+ejz5G81//EOYnFJlY+OCHeMqsIqqqPfVzc3GTze9LNabn9+UA+PWIi+j7TSUUFETPLZlO+3ZGUTQgghWvB68f+AmJrKwXLuK6WfF+mLWfSOWu5/Z+iLuaSvqIfHVWHtN7AlThaciotdiwFqefVXVh8NIYQQk6FQgRCrrXCFrACECrrjVlt2EkKCixYqHF8VqmMfZCBABlIIIYTYV6iwb6NJjgoUKviEdp7Q562yjgrS6ewUm1q7ERULJDRSy/s3+/YcqVqTuI6oOKD1kKAeHrGQanWtd1TQ4rGkFipXnBBiW84//3w0bdoUsbGxaNiwIa644gqkpZUUIi5fvhynnnqqsU2TJk3w7LPPnrCfKVOmoF27dsY2nTt3xvTp00usLywsxLhx44zXiIuLwxlnnIH16z3OKyS8kPb28FfV8tKPgC1zTtxmwRtK8Fa3HdDzSlhC19Geqv+VwL5N1hyDm+nmiX/4+wclprU7eTlWHwEhhDgGChUIsYWjwr7y7cvKQ3co6Q4mQkhw0YNSxw+26NiHeh05iEIIIXZFW4rLQK0vWa5lken5DUjyiNdI+SRooUJ65c7UkT1qzqizwKjVUs0PbPFt+40/qXmz/hRhhoWjwkEgP8+aY9izTs3rtQMi2E1FiJ0ZOHAgPvvsM6xbtw5ffPEFNm7ciIsvvrhofWZmJoYMGYJmzZph0aJFeO655/DII49g0qRJRdvMmTMHo0ePxpgxY7BkyRKMGDHCmFauXFm0jYgbXn75Zbz++uuYP38+qlWrhqFDhyI7Ozvk/zOxAR3OB3perZZnPgBsnQdkrFHThh+BlV+qdX1uVAJLq6r+Ww5Sy6umWXMMbqbdOSqKVUS02xbA9sy8H9i5zOqjIIQQR8A7QEIs7xSqopSgulLMX0TkIFCoQEho0INSh48XKngcFRj7QAgh9kWyY6VaV0QKeytZ6ZSX7Y3tMuz0ic+OClm7AnQQozA3IGq1UPOD2yretrAQ2DhbLbc/L7Q5zyS06O9VQR6QbZGrwh5PlXR9j+sNISJMq1pBjrqsp4At5Nx+++04+eSTDSFCv379cN9992HevHnIzVXFNx999BGOHTuGt99+Gx07dsSoUaNwyy23YPz48UX7mDBhAoYNG4a7774b7du3x+OPP44ePXpg4sSJRW4KL730Eh588EEMHz4cXbp0wfvvv284N0ybxgHgsKXvWNWHmnMI+Pom4LPL1TTjXvkRU9t8fw9wwId2TrDo7ol/WD8DKMgPzWseqmS0mtMQAUrHC9XyagdcBw5nAFP/Bfz5Et0VCCGkAihUIMRKJINTV7BUpsJMOhC1UKF6fXOPjRBSvqPC4V0lv4tFQoVuPHOEEGJXpFJXxz/oyB5/0Y46IniI58C5X44K0mFXGbSgtwbbuwFR2+OokLndNyv+zB1AZDTQ9qzAXpfYGxnsjU1Qy1ke9xKrHBX09ZkQEbiNXQRc92vZk6zXQjhiCfv27TOECSJYiIqKMh6bO3cuTjvtNERHRxdtJ04I4sCwf//+om0kyqE4so08LqSmpiI9Pb3ENgkJCejTp0/RNqWRk5NjODoUn4iLOJYlnS/lb5OfU/lCMDOQNpNEgcoxbP49+K8n/VK/PoOwodulai7nNkc+DzYlMgZoc7YqTFzyAfDppd4+Q0IIISdQ9cSHCCEhj3+QhmXxQU9fyT3qVWVqwQMhJETRD7uUQEEqDOX7K6IhySNsSKECIYTYGhkI2zbfOzBW2dgHacNV9XbCE19Efhmquiwi0r/TpTuc6ahgjqNCZprvsQ9NTuZ5Dweq1VPRDzpmJZTI6+p74YZdQ//6xL6ICIFCBFty7733Gu4HR44cMdwVvv3226J1IjBISUkpsX39+vWL1iUlJRlz/VjxbeRxvV3x55W2TWk8/fTTePTRR034DwkJQPzXZRQw/zVV9d9iQHB/P78e65swwy0ONE36KIe8/anA+h+ATh6HhVCyYoqaJzYDLpwEVI09cRs51/L7tfob4NtbVezglCuBnv8Eev1LFS4SQggpgo4KhFhNjYZqnrXb/+dqNwVpFMXUNPe4CCHlD7bkZKpJyFjtHQCIS+SZI4QQO6MrdvduCMxetUYy7fB9JaGxmucdBY5Wwlped8DSQSwwamlHhTSgwGORXBabflbz9ufycx4OaBGQjlkJJeLeoe+LKUYixBIkvqFKlSrlTmvXri3aXiIblixZgpkzZyIyMhJXXnmlEddgNffffz8OHjxYNG3bZmEEAAlful+m5lvnBu93VQrXvr0N2LdJDYpf+nl4ONBIoVA3z/ld6xVIhYxdq4E1X6vls58Dmp6sXFWPn/S57nAeMHahilETd4WFbynBgo68IoQQYkBHBUIcLVTwdPTKwKi/lWmEkMoh1rixiUD2AeDgdvX3bk+nTb2O/C4SQohThArSsaedcfxBV6O7obMvlJmyMgAp7V2JE6hWx7/nM/rBHJKaq2zn3COqcr4sRzapUpPvR0RVoN05Jr04sTXVtVAhRHbZEqFz9IBa1tbYcl+8a2XJSkRCSEi48847cfXVV5e7TYsWHlceAHXq1DGmNm3aoH379mjSpAnmzZuHvn37okGDBti1q6RjqP5b1ul5adsUX68fa9jQ02fm+btbt7IdDGNiYoyJEMvvNRp0AdKXA2u/BnqU/93ym/xcYMa9av/R1YF/fAA0PwVhQ9dLgJ+fANKXqT45LYgONnLf+PvzKn6k9VCgVcn4mjKJrwVc8iGwfAow/S4l0PzscqD39UCPK1V7mxBCwhw6KhBiG6HCnso7Kkj+GYUKhITeVUFuioo7KohymhBCiL2p10FF9YhdqgyW+cuh9JK/BcQ3EjyDjiJU8JciRwU1cEEqSVQskNBILYsFbVlsnK3mjXoq5xASHtEPoXJUkOvuhxeqTnqZln6kHk9bDEw6XU0TewIHWAlNSKioW7cu2rVrV+4UHV163FWBx6EnJ0fFkopY4bfffkNubm7RNrNmzULbtm2N2Ae9zU8/eSKGim0jjwsSHSFiheLbZGZmYv78+UXbEGJrZABaWPOtGuA2C6nKn/0YsOVPIDJGRQ+Ek0hB34M1P1Utr/4qdK/79/dKHCGuxmc+6n8/fJeRwE0LgFZnAgV5wLxXgc+vKb9NTgghYQKFCoRYjeQbC5XJA9UdSXEuyBkjxEkkNfPaf8tNpxYqNOph6WERQgjxsbq/Tmu1rK/flXFUSKBQwS90dXTmTv+tbcUBoLjAl1QeiakSDmypWKggbgoR7DIIC7S7RmWiWfxFnBTyj5W/TV5O6NwdCCE+I0KBiRMnYunSpdiyZQtmz56N0aNHo2XLlkUCgksvvdQQNYwZMwarVq3Cp59+igkTJuCOO+4o2s+tt96KGTNm4IUXXjAiJR555BEsXLgQY8eONdZL1MRtt92GJ554Al9//TVWrFhhxEskJydjxIgRfMeI/el0kRISiEuVdgsKFOl7+uMlYN10Jbo+dzzQ9myEJTr+4e8Z5gpByuLYEWDOy2r5pH8BddtVbj816gOXTQHOnwhE11D3op+MVqJNEaEQQkiYwl4HQqymZnLlq1d0R5K/9rmEkMBIbOYdrJLKWqnKFbu2Bl15ZgkhxAmIHauwe53/zxWRWnHRGvHPUeGwn0IFPVgp1UsxNXm2TRMqlFGtLo4XEmklHeDtz+f5DhckmkXIDoFQgRDiWOLj4zF16lQMHjzYcEgQMUKXLl3w66+/FkUuJCQkYObMmUhNTUXPnj2NWIlx48bhuuuuK9pPv379MHnyZEyaNAldu3bF559/jmnTpqFTp05F29xzzz24+eabjef16tULhw8fNsQNsbGxlvzvhPiF2P3r+KxVX5pz8ha/ByzzuBCd+RjQ9VL/I+zcQvvzgKh4dV+2Y2HwX2/xuyrCTkTTp90R2HmX5/a4ArhpvnKGEPHmH+OBqdcq11ZxnspYU/ZUGUdAQgixOQzBIcQujgpH9/qfk6yjHyhUICS0aLvvw+neatykFCA2ge8EIYQ4JTt2xRRg73r/npeX7R04T2oelENz/29nyUzqCtHnO64WEBll/nGFG7VaqnmmJ76qLDeFhl29wkzifkLpqEAIcSydO3c2XBQqQsQLv//+e7nbjBw50pjKQlwVHnvsMWMixCC+NlA1RrnulIWsl+3sQPfLgVVTgY0/Aaffq46tskjEwdxX1HL/O4CT/x3erlcx1YGOI4Clk9W5adwreK8lIt4lH6jlAfeb9/mSOLarvgEWvAn8OA7YuRSY/A+gMA8oyC/7eZHRwOVT6TRHCHEVFCoQYjXawlYqsvOOKkWor2gXBl0BQwgJDbrj/nCGV6jQoFN43ygSQogTHRX2bfTveeKio6v7daY78dNRIaNyQgWpTOPvrHmOCtLpWhobf1ZzsRLm+Q4f9PUs+4DVR0IIIYSUHSM2dlH50UAyiKzjxqymxQCgZiPV5lo/C2h/buX2k/or8PMTarnHlcDAB4CISFMP1ZGIo4QIFeT8SFScxPsFA4nbENeD5B5Al1Hm7luKFftcB7Q+E/jyemDb/IqfI8ciMVqMxCOEuAgKFQixGmlER0QBBbmq49af6ryi6AcKFQixpCpUBqx2r1HLyd35JhBCiJMcFYTMnUos6qsjjkT+CNIxxOp+/9CdxtLe9cdFTHdG00HMHGp7HBXENragoKQY4fBuIH2ZWu4w3KQXJI6guud+Ujq+8/OASHYVEUIIsWl70i5ChIoQMUG3y4DfngXWfFU5ocKOxcCM+4HCAqDdecBZz/EeRNPsFCWEPrgN2DArOJFl2xcAm2arSLQhTwBRAbhilEetFOCaGcAPDwDzXwvOaxD3Ifdz0nYvi7hEClqIY+DdJyFWI520Ev8gDSu/hQqMfiDEUqHCscPAzuVqmUIFQghxDjLoLWIDubnfvQ5o0ts/R4Wayaw2r6yjQk4mkHMIiK3pp1CBDhamoO81jmUBR/Z4Lf+FTR43hXodgdqtzHk94gy08F2q9KR9Kx2bhBBCCAmMbpcqoULaEuDgDmX37yt7/ga+ux3IzwGa9wcueA2IiuU7ohGxrZzfX58B1nxjvlChIA/47Xm13OkioGnf4P8/XUdRqEB8Q/oxPrxQtd3LgjEhzuEQRScUKhBiB6Sz2xAq+JvZqx0V6gflsAgh5eThiRuKDJ5IZIu4omgbcUIIIc5ArtuGUGGtH0KFtJKD7sR3ZOAzpoYSKYgFrr9CheID6qTyiC2utiHev+U4oYInd7zNMFoKhxvR1VQEYe4RIGs3hQqEEEKIWZXyzfoDW/4AVk8D+t7k2/MObge+HqvEg3LPMvI91Y4mJZGBfREq7FyqnPJqeuKVzWDlVBUTKOd90EMUqRN7YbiglSNSEBgT4gwoOjFgmDYhdkAcFQTpFPIVsf3K9ggVarDjlpCQcWAbkLYUiK/jfUw6/PelqsdlPSGEEOfEP0i1kq/o6AftrEP8Qws8tODDFyhUMJ9aLdT8wOaSkXJiLyx0HBGEFyW2R4tWysv+Nku0JBVe5VE1RomCCSGEEKfT4wo1/3u66sutCPkdFpGCzKXNNmoyI9DKQs5Pk5PVeV3ztbmDwDqCoe/NQFIz8/bt5sHWjDVlT7KeEBKY6MTF0FGBEDsg1sP+ChUkT1k3cOM9Vp2EkOAiIoSJPYG8nOMe3wy8OcDbsTp2kXNyEwkhJNyFClIp4yu6g4VChcoh5y1jtVfw4ZdQgQ5iplG7JbD5d+Xopkn9FSjMV5EP9TqY91rEOUi8yv7NwRcqyL3vpVOATy4Dcg8DQ54GmvcruY2IFNiWJoQQ4gYkkuC7O1WE3LYFQNOTy95WHBS+uVm10ao3AEZ/wt/Diuh+GbBtnhKC9L5ORSwHyoI3VFydCCH6/jvw/bkdVoQTQgKEjgqE2EmoIDmxviJVT0JMTWaUERIqjKiH40QKxyPrg93BSwghxDyhggzMVaRgP8FRgVU1gTkqpPv+HP2bKoOoxFxHBbEV1mwsFvsQyXqG8HZU8OOetLKIQF9ECmKn3PMKILlbyYkiBUIIIW4hOh7odLFaXvVl+X1JImjYvQ6ITQRGfQjUbRuyw3QsHUYAVWNVu3bnssD3t2c9sPJztTxoHCM3fIEV4YSQAKFQgRBbCRX8GNw8ss9rnRnBzkRCCCGEEL9ISlG57CJS2Lup4u0NIZpnAC+pOU92ZdCDj4d9FCoUFnrbvLq9TAKnVsuSwpucQ8C2+d7OXhKeVPO49OnvXDBJ/U3Nm57CAQBCCCHhE/8gjlbZh05cX5APzHwQ2LEQiIoHLn4HaNwr5IfpSGJrKtcKYfW0wPYl9x5/vKAcjFsMANqfh5AijlLi0loeEVFqLCCcYKwEIa6Ho5uE2IEaDfzvFDqqhQq1zLG1IoQQQggJJyIigPqdlVXo7jVAvXblb69dAKRihzEEgTkqHM7wbXuxv833OBnVYPSD6Y4Kh9KAggLVaV6Qp6I5pJqdhLejgr7PDCabPUKFNkOD/1qEEEKI1TTqCdRpA+z5G1j3HdB1VMnB8V+fATbNVoVo508EWg608midR7fRwIrPgE0/A/kPAJHRlduPPH/7X0BkFHDGY6F3GRNRt0TJnlDIWAjMuB/YOheo2QiIrwNHMvdVoFEPdS8iUXTyv1SpoI6asRKEhAUUKhBiB2omq7k0RES1WdGPdPHoB1FbEkIIIYSQysU/iFBhz7qKt5VOEi0wlc4r4j8yEO6PUEF30onzhVjEE3OolaLmx7LUOdaxD62G8LMdzmhHBbHvDSYHtqrInSqRKmqEEEIIcTtSYNbuPFWtv2IK0LCrd53EDGgngGHPAp0uZEGav6ScrtzX5H5tw49A27P9f4/EPe+PF9Vy9yuBhl1gCSJWKC0Ca+S7wKu9gQObgUVvA72vh+PYNldNGnGPSGrhES608goYqjfwfgf8iZWgAx8hjoVCBULs5KiQewTIzvTNwklXulRzqIqSEEIIIcQOQgVh74aKt5Xqc6FGsnJjIJV3VJB2bG42EBXrY9SZOIhF8oybRVScEkpL9MPutcDWOerxjox9CGu0UCH7QGhiH8S9Q98HE0IIIW7mwDZg7kTP8mbgs8tP3CYiEmh9BkUKlUHOXdfRwB/jgTXfVE6osPRDdb8nbgWn32e/90HaTGc9B3x5HbDwbaDFIKBOazgKEYDIfaA4i4hoVcQh4mwoU3Ek/kSi6mq3AKIpVi8VGTuS4on83LLPtziLhFtMCHEsFCoQYgekQkwqxaSq6fAu335Ejuwv2aFECCGEEEIqJ1TYt0nZrpbXIZW5s+RgO/EfabdGxqg4B6l40pX9ZXFkj9dBjOIQc5HOPxEqrJyiOgmlcqlxb5NfhDgz+uFAaGIfWp3J7zUhhJDwQBysdJxZWRTkK/fcpGahOip30e1SJVTYsRDI2u1ff7m4vcngv3Da3UANT5vIbnT5B7DyC2D9D8CP44B/fKDiQpxCrzHemLm8Y8Ce9cCulUDGaiWeNgQMW1Qh564VaiKlI+4RPf8FLHgNSGwOXPy2EuyIIHjWQ0rEcMGbdJlwjOgkunznkDAQnbAUiBC7INV5QpaPVrh0VCCEEEIICYx67VWlfvZBb7RDRY4KSZ74AuI/IjZIaKyWM3f4Hv1AYa65FX1pS4HYBPX3lj/VXPJipZpJ1pPwpFo9r6OCxBEGA3EPlM+f0O6c4LwGIYQQQsIPcRdo1FO1YVZ/7d9z57wM5GUD9TsBPa+GbRFR/XkTgJiaalBfiyusRmK9/KVqNNCgI9D1EuDMR4FLPwVuWQI8kAZc9wswfCJw8ligca9gHLE70DEanS8GGvdUIpB+N6tzJk4LKz6z+giJr6KTy6eqCBtB3FLG/OiNvjzldrXe5dEmFCoQYhe07aWvmb2ishXi6ahASMiQik7JUCsPWS/bEUIIcYYFvrbMlEqO8qCjgjnozFWp5q8IHf1AoYI5iAhhYk9g0unA2m9Lrls3XT0u6ylWCE+qe+4rpYpNnP6CgcSMFOYDSc2VUIwQQgghxCy6Xabmf09Xbnm+sHMZ8Pf3ogIAznyi4mg6q6nZEDjrWbW88H/AHh8iDIOJiFDnTKh4O1/7SuX8J3cHul8BDHsSOPt5Uw7TdYgj906P+LfzyJJiljMfV8vyud670ZrjI/4RX8cr5j7pGqBJL6CbJyJox1+uFykIDvKGIcTl6AuO2FP5I1TQHUqEkNAMroxd5K3wLA1peOtBGEIIIfanQRdlNbl7HdDqjIodFRJpxxoQOjqjIgcLQf/eakt6EhhyPiXmoTxkvWzHtkz4IdV5Opola4+3isdMxI5WSBmoLGkJIYQQQsyi04XAjPuB/ZuBXauABp3K317cF373DIS3Pw9ocZoz3ouuo4BVU4H1M4EfH7IuAkLO36wH1aC5FFIOf6XsAVX2lZrL+lnevgxdeKFp1hdoc5YSKvz5EnD+Kya/ODGd7X8BOQeB2ESg5SD1WI8rgPmvAVvmqnuzanVcfeLpqECInRSRfgkVPBVm1esH75gIISciHfdip1XWxI59QghxFg06q/ne9WVvI3mBcnMo1EoJzXG5lURPdMbh9Iq3PeI552zvEhJ8pAJLi+D1d89MxIJWHBWEtmeZv39CCCGEhDdxSd42xpqvKt5+zTfKVS8qHhg8DoiIhCMwIiBe9kZALHrHmuP46y0VIxcZDVz0pjr37CsNDRtmegU2pX1uJU6jSoRqe+9YFKKDIpVm449q3nIwEFNdLdfv6ImzyQdWf+n6k0uhAiF2QSsOfekUkk6enEMls0QJIYQQ4mg2b96MMWPGICUlBXFxcWjZsiUefvhhHDt2rMR2y5cvx6mnnorY2Fg0adIEzz7rsX4kgQkV9pVji3hol5SMqGpjDpqb46jgS9wZHRUICS363rI897DKItbKcg8bmwA072/+/gkhhBBCdPzDhh9V/3lZHDsMzJ2olnvfANRu5axzZ0RAPOMVDITa4l8ECgsmqeVBDwEtBlgXwStCibhEhA0HtyvHEBEidLqo9G3qtlXxGYK4KvgahUJCj1ynNv6iljuOKLmux5VqvuZb17+HFCoQYhdqNPC9U0jHPlSJBOJrBfe4CCGEEBIS1q5di4KCArzxxhtYtWoVXnzxRbz++ut44IEHirbJzMzEkCFD0KxZMyxatAjPPfccHnnkEUya5OkkIJUXKmTuBLIPlh/7IO016QghlSfRH6GCx0GsBh3ECAkJOmYlKwhChc2e2IdmpwAx1czfPyGEEEKI2KaL8DInE9j0c9nnQwb3xa04oTHQ/1blUuA0uo4GWp0JFOQBsx5S81ANlM/8jxLyd7wQOPnG4J0/HcF73a8lp1Efq3ERYeBDZUdOuBGJ/BCSewBJ5bg9DrgfqBqrXEM2eKIiiP3YsbBY7MPAkutEiCKOL5nbXe+MQaECIXahRnLJDllfYh/E0sqKDCpCCCGEmM6wYcPwzjvvGEKEFi1a4Pzzz8ddd92FqVOnFm3z0UcfGQ4Lb7/9Njp27IhRo0bhlltuwfjx4/mOVBbJ+jM6NgqB3etK3ybTI1SomQxE8BbKFEcFiTsrr8pJMk+PegZLq3sEvYSQ4KKzT/X9ppmk/q7mbYaZv29CCCHEzvhSFS7rZTsSGJFVga6XeKMdSmP/FmDZx2p54IPOrcYXccD5EgFRA9izDlj0bvBfM/co8P09yiWrXgfgnPFAZFToI3jbnQ2cdI1av2yy66vNS6BFB+3PL79vQlw3+o5Vy+IeUt69N7EOcX/RIiv5LhdH/hYxkLDK2y/oRtjLRojtHBX2AfkVKCCPFBcqOCQ/ixBCCCF+c/DgQdSq5XVPmjt3Lk477TRER3ur+ocOHYp169Zh/36P49Jx5OTkGE4MxSdyHA26qPnutaWfmkM71VwqbkhgiNhDbCoLcpVYoSzE3aIgXy1TqEBIaKMfzBYq7N8MHNyqRPath5i7b0IIIcTulFUVXnyS9dp5jARG10vVfPuC0gsC/xiv3Aea9gU6X+z8e6thOgLizeBGQIgY4JenlShCorwueguIT4JlnHYPEBUH7F4DbJyNsGBfKrDnb+Um0emCirc/5VYgrhaQuQNY+XkojpD4g4hHtPNLx+NiH46Pf0j9xRsF70IoVCDEbkIF6bStqGNIRz9I7IMTrakIIYQQUiEbNmzAK6+8guuvv77osfT0dNSvX9IGX/8t60rj6aefRkJCQtHUpAk7wMqMf5Cb/vIcFRKa8pMbKFJxo605D+4oezvdqRhTE4iK5XknJJTRD0cPmLvfVE/sQ3J3oDqjXAghhIQhpVWFF58oUjCP+h2UEF3ECGuPc1XY8iew5Q810Hvm48F3AwgF3S4FWp2h/t8fxwUvAkIGutd9p0Tn504A6neEpUg8YJ9/q+V5E70i93CIfWjSG6jpQxFFbE1gwH3euJNjR4J7fMQ/JM5BCjRiE5SjQmnIe12nDZCXA6z91rVnmEIFQuxCcYuvw7vK31YLGWgJRgghhNie++67D1WqVCl3Wru2ZCX/jh07jCiIkSNH4tprrw3o9e+//37DmUFP27ZtC/A/crFQYV8ZFSiHPEKFJAoVTCGxacnzWhpH9nrbu4w6MwfaDpOKqFa3pDDeLDZ7Yh8kR5nxOYQQQggJNt0vV/N100tWL//+glruOgpo1NMd74MRAfGKsokXh8BF75n/GukrgN+fV8v9bgE6DIctOOUWNch7YCuw5mu4GnG02OARKnQY4Xubuuc1QFJzIPsAsOidoB4iqWTsQ4tSYh+Kf797XKWWXfwZZ7g9IXZzVZBOWUOo4OkwL9dRgdllhBBCiN258847cfXVV5e7TYsWLYqW09LSMHDgQPTr1w+TJk0qsV2DBg2wa1dJQaP+W9aVRkxMjDERH4QKYk+efwyI9EZrGBzyuFUkNuNpNIMEcfWY643UKI0je7ztXTqImWs7rEUgpSHnmxV94Yt2VJCOTLOQKqGdy9Sy5AkTQgghhAQbiXWoUhXYuwFY970aqBXRwoEtQHR1oMfV7hJPSgTE0KeBr8eqCIgWA4Ha3j6GgJB7h+/vUU4NLQYAAx6wz7mLSwROvROYNQ5Y8AbQ7pwT7+Xdwt71qr9CXED8EYpUjQYGjwM+/yewbDLQ5RKgWp1gHinxBfk+VRT7oBFh1Y+PKAfQjNVAvQ5wGxQqEGInaiQDu1aVn9db3ApXZ4gSQgghxLbUrVvXmHxBnBREpNCzZ0+88847iDiuA6Bv3774z3/+g9zcXERFKZvKWbNmoW3btkhKsjAf0ukkpQDR1YBjWcDeTUC9diUrbw5neLcjgaMHwssVKuj2LjtRTD/3FCKQstD3lyKMl6otM0RCYrFcmK+un3Xb89wTQgghJLgc2Ab87wyg0BOBMOvBkuuPHQbeP1cJeN3ULhYXiVVTgY2zgR8fAka+D0REBj6Y+sP9QFaGcsUb8ToQZbMiiN7XAXP/CxxOB5Z9AvS4Eq6OfWjazxsh7isdLgCSXwHSlgDzXwMGPQRXIv0L5UXYibBFx1DaIvbhgHIEaTW4/G2lT6Tt2cCar4CVU4FB7hMq2ET6RAgx0D8yFQkVdPQDO24JIYQQ1yAihQEDBqBp06Z4/vnnsXv3bqSnpxuT5tJLL0V0dDTGjBmDVatW4dNPP8WECRNwxx13WHrsjkcEIfU9rgq715RcJx0eKFSVGf52CJDSSWhccdyZrvqnMJeQ0DsqSAd+7lFzYx+ksi+StTKEEEIICTJyHyF57uUh68tzGXNsBMREINoTAbHYhAiIuRPVgGrVOODCN4GaNhnkLU5UHDDwfrW8+F1VfOA2RECshQodL/BfTCz9HWc+rpbXfKOcGdwoUvjwQuCzy8ueZH15xRKWxD4MLDv2oTg9PQKcDbMqvr45EMcIFZ588knD/jY+Ph6JiYmlbrN161acc845xjb16tXD3Xffjbw8j3KOECegFV1ZHqvbiqIfdIYoIYQQQhyPOCNs2LABP/30Exo3boyGDRsWTZqEhATMnDkTqamphuuCxEqMGzcO1113naXH7qr4hz3rSj6e6bmRFZGCW20kQ01CUzXXThWloTsOa9QPzTERQoDYRCDCIyYwo/NeHGm2zFHLUgVECCGEEEKCR0IjYNhTannBJGBfauX3JQOiSz5Qy0OeAJr0gW3pdjlQq6WKHFv0LlxHxiogcwdQNRZod27l9pFyKtDqTOV09ucEuA5xUpAYz/KQ9eU5LoQKcSoR5xOhQwWxDxoRNNRsrATl63+A23CMUOHYsWMYOXIkbrzxxlLX5+fnGyIF2W7OnDl477338O677xodt4Q4Bq1K1Jm8FTkq6IoXQgghhDieq6++GoWFhaVOxenSpQt+//13ZGdnY/v27bj33nstO2ZXChUkx7Q4h9K8EV12yeJ0OtpiVVzECgpK34aOCoSEHrnGxdf27Z7UF3YuUZ1pIoBo1i/w/RFCCCGEkPLpfoUa1CzIBWY9BBTk+3/GRODw46Pe/fW82pxIsGAhrl2DPBEfyz/2xgi6Be2m0PzUwBy2z5T3tAqw+Tdg51LTDo/4yY7FKvYhpibQ+gzfniMxLhLvIqz+ynWn3DE9bY8++ihuv/12dO7s6UA8DqksW716NT788EN069YNZ511Fh5//HG8+uqrhniBEEc5KpRXvSKDFUe0owKFCoQQQgghpgoV9m1S7S1NZlrJuAISOPpc5h4Bsj3t2uPR7WEKcwkJLfo7Z4ajQqon9qF5fyCmWuD7I4QQQggh5SOCguE6AmINsPh9/86YiEyn3wnkHQWSuwPDnnZGfJdUpjfoouLLxE3CLRQWAOtnqeVOFwYmGKnfEeg6Si3/8WLJfg8S+tiHlj7GPmi6X6aEJiIy2b8VbsIxQoWKmDt3riFiqF/faw06dOhQZGZmGvm9hDgCnXssqr+yfiikQzffk0NTndEPhBBCCCGmUK89UCVS2UUWzy3Uy4meuAISONHVvFXbB3dUEP1gwxxUQtxMNZOECnI/K9VaQpthgR8XIYQQQgjxXRg+9Em1vOAN3yMgpP0mTgoHtgDxdYAL3/JvINVqZ7AzHlHLq7/0Rjg6nZ3LgKwMICoeaHNW4PsT54mqMcCulcCmn804QuJv7MMmP2MfNNInJeIGYdUXcBOuESqkp6eXECkI+m9ZVxY5OTmGmKH4RIhliKWwINYvWoxQVuyDZBKJPQwhhBBCCAmcqDigTmu1nLHa+ziFCsEhwRP/IFmbxyP2pNIeFqqXvMcjhDjEUWH/ZuDgdiAiCmg9xJRDI4QQQgghPtLjSqDFABUB8eM43yIglryvBlEjqgLDXwXqtHLW6W45SDl5yWDwvFfhqtgHifOISzRHxNLnRrU89xV1rkjoSFsMHN2vBECtfIx9OP57Lfz9vaveO0uFCvfddx+qVKlS7rR27dqgHsPTTz+NhISEoqlJE0+HGSFWIBlDUsknlj6Hy8gE1bEPcUmq0UAIIYQQQsxBrCKF3etOjH5IbMazbCaJnvuu4u4VGrlxl/ZwlQhGPxASaqrVLSmQryzaTaFRd+8+CSGEEEJIaJCIgPMlAqK6EuIv+aD87bcvAOZOVMun3wu0GQpH/s9nPKqW1/8A7N0IRyMD0TomINDYh+L0vx2ITQQObAVWfQlXkH8MjkC/nyI8ia1EEXLbs4G4WkpUnuq533IBlgoV7rzzTqxZs6bcqUWLFj7tq0GDBti1a1eJx/Tfsq4s7r//fhw8eLBo2rZtW4D/FSEBEBHp7Yw9XIYTiO4wMoQKrjFFIYQQQgixngad1XzvejXPzwWydqvlpObWHZcbSWhatlBBV3JL50lkdGiPi5BwR9+PimAoEHTHWashvG8lxEWcf/75aNq0KWJjY9GwYUNcccUVSEtL82qUNm8utRBt3rx5JfYzZcoUtGvXztiPRPlOnz69xPrCwkKMGzfOeI24uDicccYZWL/e0z4jhJDykIg5sbYvD1mvo+jcLg4f4kMExKF0YMb9Size9hzglNvMGxQPNY1PUv+D/C9zX4aj2bFIjQWJq7aZDmXizHDa3Wr5rzeB3KNwNOIWMu81OOI4N/5cudiH4teubpeqZbeITOTfsvLF69ata0xm0LdvXzz55JPIyMhAvXrqxnrWrFmoWbMmOnToUObzYmJijIkQ21AzWXXYSvZQeUKFcGhMEUIIIYRYIVTY56m8OLxLdXBERgE1G/K9CIajgpzjsoQK8bWUkJcQEjqqaaGCJ36lMshz05er5XZnm3NchBBbMHDgQDzwwAOGgGDHjh246667cPHFF2POnDkltvvxxx/RsWPHor9r1/b2Ycm2o0ePNlxuzz33XEyePBkjRozA4sWL0alTJ2ObZ599Fi+//DLee+89pKSk4KGHHsLQoUOxevVqQ9xACCHl3meMXVR+jJX0q+v7EbfT8ypg1VQg9Vfgx4eBi98peY8llegz7lHRe7VbA+e/DFR1uFh88Dhljb/5D2DnMqBhVzg69qHl4MpV35dH72uB+a+pqLbF7wF9boAjKSwE/hgP7Pir4m2lCMKM+IyAYh9EeFIDaH1m5ffT/QrlfrJtHnA4wxUulI7xjd+6dSv27dtnzPPz87F06VLj8VatWqF69eoYMmSIIUgQJa80ZtPT0/Hggw/ipptuohCBOIsank5wuciUhq5sia8TumMihBBCCAknoULmTiD7IHDIUyFYvQEQSXGzqSR4OgZLE+cWCRXqOLeShxCnUr1u4EKFLX8qkVetlkCdtqYdGiHEem6//fai5WbNmhmxviIyyM3NRVRUVAlhQlkOtxMmTMCwYcNw992qmvPxxx83is0mTpyI119/3XBTeOmll4x+3eHDhxvbvP/++6hfvz6mTZuGUaNGBf3/JIQ4HBEhhIsQoSLkfmr4q8CrvYGMVcDv44H253rXL3wb2LUKiIoHLvqfiqZ2OvXaAV1GAcsmA3NeBi58y3n3leLuuHG2N/bBbKQyf/DDwNRrgaUfAZ0vAeKT4Djk2Jd/opZPvw9oO0w+9N71S94H/vqfGnO74E3v2JulsQ8DAhOe1GsHNO4FbP9LuSr0uR5OxzG+8WL31b17dzz88MM4fPiwsSzTwoULjfWRkZH49ttvjbm4K1x++eW48sor8dhjj1l96IT4Rw3PjZy2GT6eIx5HBTc0GgghhBBC7IS0r4wb10Jg91olWNCOV4zcCpKjQoaqgihNqMBce0JCj/7eSVXd8d9NX9nsiX1oOQiIdEx9DCHET6Sg7KOPPkK/fv1KiBR0RIQ43vbv3x9ff/11iXVz5841ohyKI24J8riQmppqFKAV3yYhIQF9+vQp2qY0cnJykJmZWWIihBBSzDlBWPEJ8Nnl3mnTbO96cbRzCwPvVxX0O5cqEa3T2DYfyMlU8d/iqBAMOl2sijVyjwALHBCdUJrjxJ8vquVTbgdOvwdI7g4kd/NOgx4CoqspB/M9ay2OfZgdWOxDcXpcpebrvq38PZuNcIxQ4d133zUUtcdPAwYMKKHklUyzI0eOYPfu3Xj++edRtSpvionD0KqusuyptKMCO24JIYQQQsxH20KKUEFuZoWajXmmg+WoIM4VOYdLrtPtYF3ZTQgJffSDdIzqDm1/q7+2eAYS255l7rERQmzBvffei2rVqhmuCeJ8+9VXXxWtE9fbF154AVOmTMF3331nCBXEcaG4WEFECOKOUBz5Wx7X6/VjZW1TGhIlIYIGPTVpwmpqQggpur8qyCv/ZMj68uIynEZiU+CkMWpZbPKdNpirYx9aDwFiqgXnNaQY40xPoffqr4ADW+EYdiwGZo1Ty53/AQx8oPTYSBF69PynWl70DiyPfYiW2Ichge+v4wVKgJGZBmxfAKfjGKECIWFDhUIF7ajAjltCCCGEkKDFP+z5W930CUlNeaLNRjoM5MZa0BEbJwgVSg5QEEJCgGQ2i12qRDdoNz9/O+Fys9R3vGnfYBwhIcRkJL6hSpUq5U5r13qrECWyYcmSJZg5c6bhbCuOtlJMJtSpUwd33HGH4X7Qq1cv/N///Z/hevvcc88F/X27//77cfDgwaJp27ZtQX9NQgghNua0u9Q95971wPof4BjycoBNv6jljhcF97XEAU2iCESoMvcVOIJ9m4DpdwAFuUDKacC5LwFVo8vevu9Nyl1DIk4kLsHS2IfTA4t90MRU9342Vk2F06FQgRC7Rj+UJVRg9AMhhBBCSPCFCvs2eh0VpBqDmItkhGpXhcwdZQgVPJXdhJDQIVENhlhBvot7/H9+6q9q3rw/EB1v7rERQoLCnXfeiTVr1pQ7tWjRomh7ESO0adMGZ555Jj755BPD3XbevHll7l9ECxs2bCj6u0GDBti1a1eJbeRveVyv14+VtU1pxMTEoGbNmiUmQgghYR7t2HesWp7334pdJeyCRFWI8FeczlqcFvzX064KEk2QvhK25vBu4JubgZxDQP2OwMXvVOw4UbMh0HW0da4KEvuw6WevE4JZ9PTEP6T+DmQ7O+6KQgVC7IZkIOsO2tIsiXT0AyvMCCGEEEKCJ1TYvxk46KnES6BQIShoAYgWhGjoqECItWiRkL9CBbl/lY4yoQ1jHwhxCnXr1kW7du3KnaKjS69ULCgoMOY5OTll7n/p0qVo2LCht7Cxb1/89NNPJbaZNWuW8biQkpJiCBKKb5OZmYn58+cXbUMIIYT4hAgV4mopcbxTKs917EObYUBUXGjiLyU+QfjzRfvGZBzLAr69FTiUDtRsBFzykRKj+MIptwJVIoBt84EMr0tUSEhbovo4JPah1Znm7bdRT6BueyA/B1j7DZwMhQqE2NVR4dhhNR2vvso+oJZZYUYIIYQQYj6JzYHo6ipnPWu3eiypOc90MChyVDheqOCxm6cwlxBr0DGDWX7mFBtONGlAZJQ52auEEFshQoGJEycawoMtW7Zg9uzZGD16NFq2bFkkIHjvvffw8ccfG1ERMj311FN4++23cfPNNxft59Zbb8WMGTPwwgsvGNs88sgjWLhwIcaOVVWvEjVx22234YknnsDXX3+NFStWGPESycnJGDFihGX/PyGEEAciNvun3a2W/3oLyMuGrTl2BNj8m1ruFOTYh+IMelDFI+xcCmz2CI/thPTPzLgX2LMOiE1UIoVaKb4/v3ZLoP35annR2wgpG4vFPsQlmOtS2eNKtbzmaziZqlYfACHkOORCWzUOyDsKHN4FxNTwrsvJVFmhxTuPCCFhT35+PnJzc8P+PBD7I5VYERHUyRKbI59RcVXYOtfzdxSQ0Mjqo3IniR6hwuF072P5x4Ccg2q5hrf6khASQrQo/qhHNOQrqZ5O1UYn+V7d5Ads8xInEBUVhcjISLiR+Ph4TJ06FQ8//DCysrIMl4Rhw4bhwQcfNGIXNI8//rghZKhatarhxvDpp5/i4osvLlrfr18/TJ482XjeAw88gNatW2PatGno1KlT0Tb33HOP8RrXXXcdDhw4gP79+xvihtjYWNP/L15biFNw8/WFkKBy0j+BuROVq8LSj4CTxtj3hItIIC9HuW43DaGLUFIzoNe1wLxXgTkvA81OASJscr0Rh4dfnlJ9NFVjgIveBBp1938//W8HVk9TMQzinqkLJ4KJFB5LpIbQwcTYB02XS4AfHwb2blCxHQ28bSknQaECIXZDlFDiqrA/FTicAdRudWJ1WUxNoKr5N2eEEGdRWFiI9PR0o+OGECcgIgWxci3LOpYQyzmwTVnyFR8gj09S1oDSRpPcdj24TgJHdwxkZZzY3o2oqs43IST0aFG8jmHxFV19JZamcs00CbZ5idNITEw0ogvEGcBNdO7c2XBRKI+rrrrKmCpi5MiRxlQWcu4ee+wxYwoWvLYQJ+LW6wshQSUqFhj4H+CrfwNLPlAxB8WLQ+3E+h+8MWpRXhFgSDjtLnV+ZFxqzTdAR5u4GC2YpBwDJLrhnPGVj09I7ga0GKiECgvfAQaPQ9ARhwoj9qE60PoM8/dfrTbQ7hxg1Zcq2oRCBUKIaUjnuBYqFEdXtMQl2UfRRgixDC1SqFevnlHdwhtVYmckvzYtLQ07d+5E06ZN+Xkl9hQpTOypqheKI+2xNweoZVHvj11EsYJZJDb1nuPjhQrS3hX7eEKIdUIFHTvoC/LdTV+hlqWzzETY5iVOQQa+jxw5gowM9bsmjgPEvvDaQpwEry8kIEQALveyx9/rFkfWu1ko3nUU8OcEFR2w8G3glFthO3IOAVvmqOXOXieikBFfCzj1DuDHR4AFrwNtz1KfCysRB4S/JqllEZt0vTQwQfSpdyqhwt/fAyf/OygucCXYUDz2ITE4r9HjKiVUkNc6/R5HFjjTUYEQO1LTczOrc5E1R/ereVwtUytUCCHOQ+wptUihdm0X30gQV1G3bl1DrJCXl2fYVhJiK0TlXl7HjSDrZTu6KpjrqCDnNDdHVYwc2aMek04yCnMJsTj6wQ+hwpY/ZBgFqN26pCtggLDNS5xGXFycMRexgtyr0abdnvDaQpwIry+k0sj9qwjuy3PLcrt7oNxbDn4I+PRyYMWnQLfLgj9I7S+bfgEKcoGk5ipKzQr63ADM/a9yPfzjRaDD8BO3kQH3UMQ0imjj56fUcs9/AqfcpqI6A6F5f6BRT2DHImDJ+0D/OxDc2Ief1HKHILpTpJyu+lYkzmLdDPs4YfgBhQqE2JEaZQgVdIWZm9WNhBCfyM3NNebipECIU9CRD9IxSKECIQTV6wOR0UD+MeDQTqBWc2/nWbzNOo0ICSeq1SsplPeFVE/sQ8tBQKR5XU1s8xInou/R5PNLoYI94bWFOBVeX0ilERGCm4UIvtDuXCC5B5C2GJj/OjDoQdiK9TPVvO3ZQFWLIlOz9nhdvVdOUdPxyD385VODK1bIWAN8fw9QmA+0ORsY9rQ59xhS/CvihE8vUy4EJ10LxAYpBmTnsmKxD0MQNCIigB5XAj8/Caz5ypFChQDlJ4SQoFCjgZrrijJNtqejyG5qP0KIZTDugTgJfl4JISfcUNdspJYzd6i57hTR1vOEkNBTvVj0Q2FhxduL2GjbPG/HahBgG4I4CX5enQPfK+I0+JklJKAvEHDmo2p57TfAga32OZ0iEN4+Xy13usi645CB9YK8itv+/jiv+UtmGvDtrUDeUeUsccFrQJSJcQZyv1KnDZB7BFj+CYIe+yCOB7EJCCrdJBIjAkhfDuxLhdOgUIEQO6LVaMfbMR3RQgV23BJCCCGEEBegq3oOpal51t6S1vOEEAsdFQ4o+9mK2L5QdfSJ81/TPkE/PEIIIYQQQipFymlAi4FqMH7eq/Y5iRt/VlEBEqPWsBvCluyDwNdj1bhYrRbAJR+oqAmzCyb6366WJQakogjQylBYUCz2YXjwY9wTGitnO2HVVDgNChUIsbVQwVNRpimqMKOjAiGECJMmTUKTJk0QERGBl156yZSTsnnzZqNKYenSpZV6/hVXXIGnnnrKG3/WvLlpx1ZZBgwYgNtuuy2oxzRq1Ci88MILpu6TEBIGJDRVc4l+KO4oRqECIdahhfFitSqdhRWx2RP70PxUICouuMcWprDN6xts8xLC60uw4PWFEBdxxsNqvuEnYPc62IL1P6h5u3NMjVFzFCIY+O4O4MAWdT9yyUdAzeTgvFani9W+xckiGAP7RuzDHiC6GtBmKEJCj6vU/O/vK3bFsBkUKhBi6+gHsdrJ9z5OK1xCiMO5+uqrDRGATFFRUahfvz7OPPNMvP322ygoKPBrX5mZmRg7dizuvfde7NixA9ddd11QjvmXX34xjvfAgYptzZYtW4bp06fjlltugZ3566+/TD9fDz74IJ588kkcPOjDgAYhhJzgqJBesr1bvT7PESFWIZm4sZ7Kpazd5W8r0RCbf1PLbYcF/9gcAtu89oBtXuJGeH2xB7y+EOJgkrsDHUZIQxaY+4rVR6Pa2zsWqeXOF8MRrPxCuaqZ5UYgDgSzHgJ2LlWD+xe/B9TvgKDe7/Tz9N0u/dD8gf0Ns0IX+6BpMwyIr6PEF5t+hpOgUIEQOzsq5OeUrGDR0Q86M5QQQhzIsGHDsHPnTsO54Pvvv8fAgQNx66234txzz0Venu8Nw61btyI3NxfnnHMOGjZsiPj4eFjNK6+8gpEjR6J69eqwM3Xr1jX9fHXq1AktW7bEhx9+aOp+CSEuJ8EjVDi8S80Z/UCIvVwVtMtJWezdoIRGkdFAqzNCcmhOgW1e62Gbl7gVXl+sh9cXQhzOoIeAKpHA1rlekYBViLODiCbqdQDqtocjWP0lMO164M0BwJfXA3+9CaQtAfIriI0TJ8WMNSdOM8epqAR5T4b/F0g5Jfj/Q48rgbgkdS/zt8fRwrTYh9mhi30oLr7oNlotr5oGJ0GhAiF2JDreq7Q67KkuK+GowAozQohziYmJQYMGDdCoUSP06NEDDzzwAL766itDtPDuu+8WbScOBv/617+MDoCaNWti0KBBhmOBINt17tzZWG7RooXheCDCh40bN2L48OGGU4OIBXr16oUff/yxxOvLttOmlWywJSYmlnhtjexThBRCUlKS8VypYCmN/Px8fP755zjvvPNOWHfo0CGMHj0a1apVM/7vV18tmYM3fvx44/+R9RJl8e9//xuHDx8uWr9lyxZjv3IMsk3Hjh0N5wbNypUrcdZZZxn/s/zvEj+xZ0/ZAwvHRz/I//XWW2/hggsuMAQMrVu3xtdff13iOb68hhzjJ598UubrEmJrJFu9akz528h62Y6Y76iQleF1FCvuMEYIsQYdv5J1XBzh8Wg3hca9VAUPKYJtXrZ5CQkWvL7w+kIICZA6rYDul6vlOS8rlzCrWD9Tzdud55zYB4l8k76R/GPAjoXA/NeBqf8C3jwdmHYjsPBtYOfyksIFESl8eCHw2eUnTuu/V9vIoH6jHqH5H8S5oc+Nannxu+Z9BiT2QVwyouKB1iGKfdB0v1LNty/wFoM4AId86gkJU1cFcVOQC4qo6eSif8wzaMXMXkJIGZz3yh/Yfcgk2y0fqVsjBt/c3D+gfYgIoWvXrpg6daohThDEmSAuLs4QMCQkJOCNN97A4MGD8ffff+OSSy4xBvTPOOMMLFiwwFgWQYMMpp999tlGBIF03rz//vvG4Pm6devQtKknB90PZL9ffPEFLrroImMfIpiQYyqN5cuXG7EHJ5100gnrnnvuOUOQ8eijj+KHH34wHCTatGljxF4IERERePnll5GSkoJNmzYZQoV77rkH//3vf431N910E44dO4bffvvNECqsXr26yLVBBB1y/uS8vfjiizh69KgRh/GPf/wDs2d7FLw+IMf27LPPGscqzhCXXXaZIZCoVauWz6/Ru3dv49zn5OQY558Qxw2Yj13kHSgvDbkR1wPrxFxHBbmRP5YF5Gapv6t7HMYIIdY6KmixfFmkeoQKrYeErFrIivauwDavgm1etnndjFPvpwXeU/OemhDiJ92vAJZNBnatBJZOPnGAPC7R63wdLGTwPl2KsqoAnS+CYxjyBNCwK7BrlYoZ2PwHsG2+ih2QQXKZhKpxKmqjUU+gWh01xlUeEsFwZB+Q6H8fbqXofS3w50vAvk3A5t+BlNMC3+eGH72xD/IZCiV12wBNTga2zQNWfQn0uQFOgEIFQuyK/AjuXgsc9mSCykVeiKiqLGkIIaQUpFMlPTPbkeemXbt2Rsen8McffxgChIyMjKIB7+eff95wQhDXguuuuw61a6uqZhEoiEODIGIHmTSPP/44vvzyS8MdYOzYsX4fU2RkpDFQL9SrV89wXigLGdSX7WW74znllFNw3333GcsiUPjzzz+NAX8tVLjttttKuB088cQTuOGGG4qEChJzIWKJ4i4SmokTJ6J79+546qmnih57++23DZGFiDrk9XxBnCLE9UGQfYlwQt4DsRX19TWSk5MNQUV6ejqaNWvm0+sSYitEhEAhQmip2Uh1ykilhbR9hciY0OU4EkJKR4vjyxNvyTrpnBTanhWyM+nk9q7ANq+CbV5iR3h94T21hvfUhLicA9uA987xVvz/Of7EbSTa7PKpwRUrrJ+l5g27ALVbwTZOk3k5FTtNiki5QSc19btZORKkLwc2FhMu5GQCW+eoyY7E1wJ6XgPMexVY9E7gQgUj9kGiPAB0DGHsQ3F6XqWECmu/BXpfB1Sxf7AChQqE2BX9AyjVZYIoyQRRYUVGWXdchBBbI9UYTn3NwsJCI4JAkIgHiT7QYgSNVPJLvENZyHMeeeQRfPfdd9i5cyfy8vKM58hAf7CR1xFRhf4fitO3b98T/i4evSDxFE8//TTWrl2LzMxM47izs7Nx5MgRI4rhlltuwY033oiZM2caLhIiWujSpUvRufr555+LHBaKI+fKV6GC3p8grg3iHiFCEX9eQ7tNyHETQojPOYoS82BUkqz0dhY4xfKSELdSrV5JwXxpSAek5OnWaQvUaunq9q6Zr8s2L9u8xL44+X5a4PWF1xdCiI+I4La8wXhBqv+PHgiuUGGDR6jQfjgQEQlHO01Kf6i4LMjU/zagoABIWwKk/qLuG7bMA/Js2F/Y9yZgwSQlstixOLDoCdlHUezDMFhCh+HA9LtVH4uIRZqW7JO2I+z9IcSu6FxeLVTQHUSxSY5QQRFCrMEMy0irWLNmjRF9oAUHDRs2xC+//HLCduW5Gtx1112YNWuW4b7QqlUrY+D84osvNqr8NSIkkA6c4uTmFstMqyR16tQxBujltaKjo31+3ubNm3HuuecaQgSJTRAHB3GUGDNmjLEvESpI5MLQoUMNAYaIFUTU8MILL+Dmm282zpXEWzzzzDMn7FvOoa9ERZUUwcl5KpCbCs/74ctr7Nu3r8jlghBCfEZsHeUmOmOVt+PDDp00hIQz1ev6IFTwxD60HBRScZGT27sC27xs8xL7wusL76mPh/fUhJCgujpkrFZjPR0vcJ/TZEQE0Linmk69E9i+CHhrEGxHQiOgyyXA0g+BRW8HJlTQDhnizBDq2AdNdDWg80jlECHxDxQqEEIqTc1kNdfqNZ0Nqm11CCHERcyePRsrVqzA7bffbvzdo0cPIz6gatWqhi2sr0ikgkQYXHDBBUUD7CIEKI4Moovbgmb9+vXlOgBo0UF+fn65r92tWzdjvnr16qJlzbx58074u3379sbyokWLDEGACA8ipBEP4LPPPjth/xKzIHEQMt1///148803DaGCnKsvvvjCOE9yvoKBr6+xcuVKNG7c2BBtEEKIzyQ0UUp/6aTR7V1CiD0cFbIPlL5eKtC2eto3bc8O3XE5HLZ52eYlhNeXsuE9Ne+pCQkrNsxU80Y9gSTf+z4di52LEcQBYulHwNa5wJ6/gTq+udOWGfvQYYS1Y3g9rlRChdTfgKMHgTh7R2uyLJsQuzsqHN1bMvqBHbeEEIeTk5NjiBB27NiBxYsX46mnnsLw4cMNV4Err7zS2EbiDSQeYcSIEYaDgIgN5syZg//85z9YuHBhmftu3bo1pk6diqVLlxpxBZdeemmRK4Bm0KBBmDhxIpYsWWLsSwb+j3cTKE6zZs0Md4Fvv/0Wu3fvNsQPpSECCBnQFzeE0gQUzz77LP7++2+8+uqrmDJlCm699VZjnTg/iKPDK6+8gk2bNuGDDz7A66+/XuL5t912G3744QekpqYa50xiGLTQ4aabbjKcDEaPHo2//vrLiGKQba+55poKxRW+4utr/P777xgyZIgpr0kICSN0tYa4KgjVPQOkhBDr0N9DsbstjR0LgbxsIL4O0KR3SA/NKbDNyzYvIby+KHhPzXtqQkgprJ/pter3FC4Ri6jTGmh/rlpe+L/AYx/aDIWlJHcH6nUECnKBNV/B7vDTT4hd0dlHWXtLWm5WY5UqIcTZzJgxw7BPlOr8YcOGGYPuL7/8Mr766itERip1rQgDpk+fjtNOO80YDG/Tpg1GjRqFLVu2oH79+mXue/z48UhKSkK/fv2MqAKJSxDxQHHEuUDcCU499VRDyCBxERKvUBaNGjXCo48+ivvuu8947bFjx5a5rUQ0fPTRRyc8fueddxqiiO7du+OJJ54wjlOOTejatavxt8QqdOrUyXi+RDsUR8QAIhYQcYKcMzkf//3vf411ycnJhhBCthGRQOfOnQ1hg0RkaIeGQPHlNbKzszFt2jRce+21prwmISTMHBWKU73s6zwhJETo+87s/UB+3onrpTpH25pGxfJtKQW2ednmJSRY8PrC6wshxOHs3Qjs3QBEVFXV98R6+t+h5htnAwd3+P/8DT+qeXOJfUiCpVSpAvS8Si2v/QY4LgLZblQpPD6kOczJzMxEQkICDh48iJo1a1p9OCSckYvhix1URtENc4CfnwDWfgucehcw+CGrj44QYjEyKCzV9SkpKYiNZeewXTh69Cjatm2LTz/91HCECCdee+01fPnll4YDRmU+t2yDhQ6ea2I7JMfxo4u9f5/xqLJeJIRYx7EjwFMe8fyYn0pmrEo30nvnAId3ARe8CXT9R9AOg21ee8I2L9u8Tm/z8tpiX3h9qfz1hRDiB2lLgUmnV7xdygBg6JNAVZO/b/NfA/56C2h2CnDVt+HhqHBgGzCxp4qQK4uqMcDYRV7XxVDz3vlA6q9AhwuAQQ/6F/vw7jlAVgYw4jWg26WwnCP7gBfaAfk5wMXvAg06q/u3eu2B+Fq26nsMTpAxIcQkq80q6iJ3ZG8xR4W6PLuEEGJT4uLi8P7772PPnj0INyQ+Q+IrCCHEb+ioQIj9iI4HoqsBx7JUh1txoYLktkonV2QM0GqQlUdJLIJtXrZ5CeH1xXx4T02IDUn9BfjsSmDYM0CtFHP2KaJfEesL4qYQDiIFQcQHIkKQsa6ykNhzq0QKwql3KKHCuunAyf/2fUA/fYW6ZzJiH4bBFsTXAtqfB6z8XE0iVLApFCoQYlcio5RYQTqADqUrBZTA6AdCCLE1AwYMQDgisReEEFIpju+IYPQDIfagWj3gWOqJnYmbPbEPTXqpzkQSlrDNSwjh9cVceE9NSAiRNqxU75dX3R8RpYS7+zYCn10OnH6fGvgNlD3rgANbgMhooP35CLt7fyuFCBWRcjrQsBuwcymw5H3glNucF/tQnB5XKpGCxFmcdh/sCoUKhNiZGg2UUEGmo/uKOS0QQgghhBDiEvtHGQSNqQnkZKrHsg8oK047VFQQEs7Ivef+VCDrOKeo1N/VvPUQlX9KCCGEEEKIG6v7RUzw+TXAlj+Bnx4Bti0ABtyv3Mcqy3pPvIvEPtSoX/n9EPORextxVRAXjVVTgV7XKrFKeYgj+saf1HLH4fa6P2p+KpDYTAlj1n+vPnM2hEIFQuxMjYbAzmXKNqYo+oFCBUIIIYQQ4uKMSukIsktGJSHhjI4dLN6Bm7UbyFilltuebc1xEUIIIYQQEqrq/qu+AX59FvjtWeDv6cCulcBZzwJ1Wlcy9sEjVOh4gb0GtYmi3XlA7VbA3g3Aso+BXhU4yKavVIXGRuzDUHudxcwdQOszgb/eApZ9otwrC/KB2Jq2KgyhUIEQuwsVBFE85R9Ty9U9nUWEEEIIIYQ4GRn8LM9qU5D1sp0Nbp4JCTu0m58WzQub/1Dzuu2AWi2sOS5CCCGEEEJCRUQkMPB+oHl/4IsxwMGtwJQrgP53AZ0u8k9sICKHQzuBqrFAO4p+bUlEhIp8+HossPxTFZ8gzhoVxj6cCsTVgm0LQ/ZtBL65peQ2NikMibD01QkhvgkV9m5U86g4ILoGzxohhBBCCCGEkOCi3fyOeGIIhc2e2IeWg1WnLSGEEEIIIeFAyqnAjXOBloOA/Fzg16eBH+4Djh32fR/aTSHldCC+TtAOlQRIl0vU2JzEsa+aVkHsg0eo0MFmsQ9H/CgMsRgKFQixMzUaqPm+TWoemwRE0AiFEEIIIYQQQkiQqebpPM0+oOZ52cC2eWqZsQ+EEEIIISTcqFYbuOwLYPDDapxGquk/uRTIWFPxc8Vyn7EPzqBqNNDvZrW89EP13pXGrlWe2Ic4+8U+OAgKFQixMzWT1Vyr8uJFqMCvLSGEEEIIIYSQEEU/ZHuiH7YtUFU34rTQ+CSefkIIIYQQEn7I+MypdwBXTwdqNgIydwCfXw0snQwUFpb9vJ1LgSN7gOhqQJthoTxiUhl6XAXEJqr3VwtMjmfDLG/sQ3xtnudKwhFPQpzgqKCJ48WOEEIIIYQQQkgIox+OHigZ+5ByGhAVy7eAEEIIIYSEL037ADf+qUQHBXnAHy8A390BZB8sfXs92N1iIBCXGNJDJZUgpjrQ53q1vPjdE0Uo8vfGn+wZ++AwKFQgxM5IDk5xqMoihJjFgW1A2tKyJ1kfZObOnYvIyEicc845JR7fvHkzqlSpgqVLlwb9GAghhBBCSAWOCiJUELtTLVRoc5ZzTpnFbV62dwlxMby+EEIIiUsCRn8CDH0aiIgCNv8GfDIa2LkcOLRTRULItGsl8PcMdb6SewI7l4Wk75UESO/rVazD3g3Alj9LrpP39FC6J/aBDhmBwLB7QuyMCBPkB64gV/1dva7VR0QIcQPSEJ7YU1n3lkXVGGDsIiCxSdAO43//+x9uvvlmY56WlobkZE/cDSGEEEIIsZ5qnvvPvKPAjsVA1m6gaizQahAcgQ3avGzvEuJSeH0hhBCikUr6vv8GmvUFPrsKOLAF+OKfQJUIoDD/xPM0+1E1haDvlQRItdoqAmL+68DCt4Hm/b3rNvyo5s36s8A4QOioQIjdf+SKxz/ojiJCCAmEI3vL77AVZL1sFyQOHz6MTz/9FDfeeKPhqPDuu+8G7bUIIYTYWJQrnTPlIevpKkaINcTUUMIEYc1Xat6kD4zKMSdgcZuX7V1CXAyvL4QQQo4nuTtwwx9A++GSC1C6SCGEfa/EJPqOBSKqAunLgJ1LS4l9OJ+xDwFCRwVCnBD/cNBjA1StjtVHQwixM9JIyj1S8XZSFecLst2xrIq3i4r3u0H22WefoV27dmjbti0uv/xy3Hbbbbj//vuNyAdCCCFhglSOSAVJeZ0zIlJghQkh1iDtMhHLy/3oxtnqsdZDrO2I87W9a4M2L9u7hDgMq64vvJ8mhBD3EFsT+Md7wE+PAX+Mt/poiBlIf0TnfwDLJgML3wHOmwBkrFLRHiLqbnu2vQtD8nJsXxhCoQIhdraRk05bybjR5GSpHE2BnbaEkOORTpWnTIxPeNvHfK0H0oDoan7b4IpAQRg2bBgOHjyIX3/9FQMGDKjMkRJCCHHyTT+FCITY9340urr6O9/TwZWUou5JrbofNbu9G8Q2L9u7hDgMq64vvJ8mhBB3IcLWDsMpVHATXUcpocKWP4C/ZwJbflePN+iiRN25R+3Xr5HonMIQRj8QYuesu0mnA6m/eh+f9aB6TCZZL9sRQojDWLduHRYsWIDRo0cbf1etWhWXXHKJ0ZlLCCGEEEJsdD+6e03JdZ9eyvtRH2B7l7idnJwcdOvWzXDEW7rUU1DjYfny5Tj11FMRGxuLJk2a4Nlnnz3h+VOmTDEc9mSbzp07Y/r06SXWFxYWYty4cWjYsCHi4uJwxhlnYP369UH/v5wAry+EEEJIiO+NJo/0/j3zfmCdp92yfQEwaYB9x+oSmwDJ3cqebCBSEOioQIjTs+5scjEhhNgAsYyUaoyKSF/uW2XHP2coZagvr+sHIkjIy8tDcnJyiY6omJgYTJw40a99EUIIIYSQMLof9bW9a3Gbl+1d4nbuuece435u2bJlJR7PzMzEkCFDDGHB66+/jhUrVuCf//wnEhMTcd111xnbzJkzxxCtP/300zj33HMxefJkjBgxAosXL0anTp2MbUTc8PLLL+O9995DSkoKHnroIQwdOhSrV682xA2uur7wfpoQQgixL3a+N3IJFCoQQgghbrIW88WOtmqxSJmKtvMz0qEiRKDw/vvv44UXXjA6sIojnVMff/yxEQVBCCGEEEJIpdu7FrZ52d4lbuf777/HzJkz8cUXXxjLxfnoo49w7NgxvP3224iOjkbHjh0Nx4Xx48cXCRUmTJhg3PPdfffdxt+PP/44Zs2aZYjWRdwgIvaXXnoJDz74IIYPH25sI/eQ9evXx7Rp0zBq1Kjg/GO8vhBCCCGEhBwKFQghhBASMr799lvs378fY8aMQUJCQol1F110kVF9RqECIYQQQghxKmzvEjeza9cuXHvttYZgID7+RJeRuXPn4rTTTjNEChpxQnjmmWeM+8CkpCRjmzvuuKPE82Qb2aeQmpqK9PR0w5VBI/eOffr0MZ5bllBB4ihkKu7u4DZ4fSGEEEKI24iw+gAIIYQQEmLiawNVY8rfRtbLdiYjQgTpcDpepKCFCgsXLnRlhxIhhBBCCAmPNi/bu8StiNPB1VdfjRtuuAEnnXRSqduIwECcD4qj/5Z15W1TfH3x55W2TWlIlITcZ+qpSZMg2i/z+kIIIcSGvxOEOBE6KhBCCCHhhuRljV2ksrPKQhrKQcjV+uabb8pc17t3b6PzS9BzQgghhBBCnNTmZXuXOI377rvPcDwojzVr1hhxD4cOHcL9998POyLHVdypQQTwQRMr8PpCCCHEhr8ThDgRChUIIYSQcEQawmwME0I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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "The results demonstrate that the AI transpiler outperforms all other Qiskit synthesis methods for this set of random permutation circuits. Key findings include:\n", - "\n", - "1. Depth: The AI transpiler achieves the lowest average depth, indicating superior optimization of circuit layouts.\n", - "2. Gate count: It significantly reduces the number of gates compared to other methods, improving execution fidelity and efficiency.\n", - "3. Transpilation time: All methods run very quickly at this scale, making them practical for use. However, the AI transpiler does has a notable runtime increase compared to traditional methods due to the complexity of the AI models used.\n", - "\n", - "These results establish the AI transpiler as the most effective approach for this benchmark, particularly for depth and gate count optimization." + "plot_metrics_and_pct(df_hw, \"Large-Scale Random Circuits\")" ] }, { - "cell_type": "markdown", - "id": "dbaab943-5fd7-4720-98bf-8602b2ab4473", + "cell_type": "code", + "execution_count": 14, + "id": "hw-step3", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mirror circuit (Default): depth 1564, gates 9708\n", + "Mirror circuit (AI): depth 1145, gates 11488\n", + "Job submitted: d7cagn15a5qc73dofeq0\n" + ] + } + ], "source": [ - "Plot the results to compare the performance of the AI synthesis passes against the generic synthesis methods." + "# -------------------------Step 3-------------------------\n", + "# Build mirror circuits from the 26-qubit case\n", + "idx_26q = qubit_range_hw.index(26)\n", + "\n", + "qc_26q = circuits_hw[idx_26q]\n", + "qc_default_26q, _ = transpile_with_metrics(pm_default, qc_26q)\n", + "\n", + "pm_ai = generate_ai_pass_manager(\n", + " optimization_level=1,\n", + " ai_optimization_level=3,\n", + " backend=backend,\n", + ")\n", + "qc_ai_26q, _ = transpile_with_metrics(pm_ai, qc_26q)\n", + "\n", + "mirror_default_hw = build_mirror_circuit(qc_default_26q, simulate=False)\n", + "mirror_ai_hw = build_mirror_circuit(qc_ai_26q, simulate=False)\n", + "\n", + "# Re-transpile to basis gates (the inverse can introduce gates like sxdg)\n", + "pm_basis = generate_preset_pass_manager(\n", + " optimization_level=0,\n", + " backend=backend,\n", + ")\n", + "mirror_default_hw = pm_basis.run(mirror_default_hw)\n", + "mirror_ai_hw = pm_basis.run(mirror_ai_hw)\n", + "\n", + "print(f\"Mirror circuit (Default): depth {mirror_default_hw.depth()}, gates {mirror_default_hw.size()}\")\n", + "print(f\"Mirror circuit (AI): depth {mirror_ai_hw.depth()}, gates {mirror_ai_hw.size()}\")\n", + "\n", + "# Submit to real hardware\n", + "sampler_hw = SamplerV2(mode=backend)\n", + "sampler_hw.options.environment.job_tags = [\"TUT-AITI\"]\n", + "\n", + "shots_hw = 500000\n", + "job_hw = sampler_hw.run(\n", + " [mirror_default_hw, mirror_ai_hw], shots=shots_hw\n", + ")\n", + "print(f\"Job submitted: {job_hw.job_id()}\")" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "a326f268-0115-442c-8563-968676b66670", + "execution_count": 15, + "id": "hw-step4", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default P(|00...0>) = 0.0077 (3870/500000)\n", + "AI P(|00...0>) = 0.0001 (47/500000)\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -1062,81 +968,68 @@ } ], "source": [ - "methods = results_df[\"Method\"].unique()\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(18, 5))\n", - "\n", - "# Pivot the DataFrame and reorder columns to ensure AI is first\n", - "pivot_depth = results_df.pivot(\n", - " index=\"Pattern\", columns=\"Method\", values=\"Depth (2Q)\"\n", - ")[[\"AI\", \"Depth-LNN-KMS\", \"Basic\"]]\n", - "pivot_gates = results_df.pivot(\n", - " index=\"Pattern\", columns=\"Method\", values=\"Gates\"\n", - ")[[\"AI\", \"Depth-LNN-KMS\", \"Basic\"]]\n", - "pivot_time = results_df.pivot(\n", - " index=\"Pattern\", columns=\"Method\", values=\"Time (s)\"\n", - ")[[\"AI\", \"Depth-LNN-KMS\", \"Basic\"]]\n", - "\n", - "pivot_depth.plot(kind=\"bar\", ax=axs[0], legend=False)\n", - "axs[0].set_title(\"Circuit Depth Comparison\")\n", - "axs[0].set_ylabel(\"Depth\")\n", - "axs[0].set_xlabel(\"Pattern\")\n", - "axs[0].tick_params(axis=\"x\", rotation=45)\n", - "pivot_gates.plot(kind=\"bar\", ax=axs[1], legend=False)\n", - "axs[1].set_title(\"2Q Gate Count Comparison\")\n", - "axs[1].set_ylabel(\"Number of 2Q Gates\")\n", - "axs[1].set_xlabel(\"Pattern\")\n", - "axs[1].tick_params(axis=\"x\", rotation=45)\n", - "pivot_time.plot(\n", - " kind=\"bar\", ax=axs[2], legend=True, title=\"Legend\"\n", - ") # Show legend on the last plot\n", - "axs[2].set_title(\"Time Comparison\")\n", - "axs[2].set_ylabel(\"Time (seconds)\")\n", - "axs[2].set_xlabel(\"Pattern\")\n", - "axs[2].tick_params(axis=\"x\", rotation=45)\n", - "fig.suptitle(\n", - " \"Benchmarking AI Synthesis Methods vs Non-AI Synthesis Methods For Random Permutations Circuits\",\n", - " fontsize=16,\n", - " y=1,\n", + "# -------------------------Step 4-------------------------\n", + "result_hw = job_hw.result()\n", + "\n", + "survival_probs_hw = {}\n", + "for i, method in enumerate([\"Default\", \"AI\"]):\n", + " counts = result_hw[i].data.meas.get_counts()\n", + " mirror = [mirror_default_hw, mirror_ai_hw][i]\n", + " all_zeros = \"0\" * mirror.num_qubits\n", + " survival = counts.get(all_zeros, 0) / shots_hw\n", + " survival_probs_hw[method] = survival\n", + " print(f\"{method:8s} P(|00...0>) = {survival:.4f} ({counts.get(all_zeros, 0)}/{shots_hw})\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.bar(\n", + " survival_probs_hw.keys(),\n", + " survival_probs_hw.values(),\n", + " color=[\"steelblue\", \"coral\"],\n", ")\n", - "\n", + "ax.set_ylabel(\"P(|0...0>)\")\n", + "ax.set_title(f\"Mirror Circuit Fidelity (26-qubit, {backend.name})\")\n", + "ax.set_ylim(0, 1)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "03a9af42-42a7-4344-b834-0d2b506d4d78", + "id": "hw-analysis", "metadata": {}, "source": [ - "This graph highlights the individual results for each circuit (`qc_1` to `qc_10`) across different synthesis methods:\n", + "### Analysis\n", "\n", - "While these results underscore the AI transpiler’s effectiveness for permutation circuits, it is important to note its limitations. The AI synthesis method is currently only available for certain coupling maps, which may restrict its broader applicability. This constraint should be considered when evaluating its usage in different scenarios.\n", + "The large-scale results reinforce the trends observed in the small-scale example, now at a more demanding scale.\n", "\n", - "Overall, the AI transpiler demonstrates promising improvements in depth and gate count optimization for these specific circuits while maintaining comparable transpilation times." - ] - }, - { - "cell_type": "markdown", - "id": "41b1405d-fa90-48b6-9ce2-933f05358778", - "metadata": {}, - "source": [ - "## Step 3: Execute using Qiskit primitives\n", - "As this tutorial focuses on transpilation, no experiments will be executed on the quantum device. The goal is to leverage the optimizations from Step 2 to obtain a transpiled circuit with reduced depth or gate count." + "**Two-qubit depth:** The AI transpiler continues to deliver noticeably lower two-qubit depth across the full range of circuit sizes. Depth optimization is one of the primary objectives the AI routing model is trained on, and the advantage is more pronounced at larger qubit counts where the routing problem becomes harder for heuristic methods.\n", + "\n", + "**Gate count:** The default transpiler (SABRE) consistently produces circuits with fewer gates across all circuit sizes in this range. SABRE's heuristic is specifically designed to minimize gate count, and at this scale the advantage is clear and uniform.\n", + "\n", + "**Transpilation time:** The gap in transpilation time widens at larger scales. SABRE remains nearly constant, while the AI transpiler's runtime grows more steeply. Despite this, the AI transpiler runtime remains practical for most workflows.\n", + "\n", + "**Mirror circuit fidelity:** Both methods produce survival probabilities very close to zero at this scale. With total gate counts around 10,000 and two-qubit depths exceeding 1,000, the depolarizing noise accumulated across the mirror circuit overwhelms any signal. This highlights a key limitation of the mirror circuit approach: while it is simple and requires no classical simulation, it does not scale well to large or deep circuits where the noise floor dominates regardless of transpilation quality." ] }, { "cell_type": "markdown", - "id": "3d942ee4-e4d7-4e87-8c8a-17c662d5379f", + "id": "next-steps", "metadata": {}, "source": [ - "## Step 4: Post-process and return result in desired classical format\n", - "Since there is no execution for this notebook, there are no results to post-process." + "## Next steps\n", + "If you found this work interesting, you might be interested in the following material:\n", + "\n", + "\n", + "- [Qiskit transpiler service](/docs/guides/qiskit-transpiler-service)\n", + "- [Transpilation optimizations with SABRE](/docs/tutorials/transpilation-optimizations-with-sabre)\n", + "- [Compilation methods for Hamiltonian simulation circuits](/docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits)\n", + "\n", + "" ] }, { "cell_type": "markdown", - "id": "3b21bb06-7a2b-4181-af59-734c89435d45", + "id": "survey", "metadata": {}, "source": [ "## Tutorial survey\n", @@ -1149,7 +1042,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "doc-qiskit-serverless", "language": "python", "name": "python3" }, @@ -1163,9 +1056,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.12" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } From bcef2c91a5277ee4d64e26fbbb453f1b5a595a67 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Fri, 10 Apr 2026 03:58:26 -0400 Subject: [PATCH 02/17] tox -e fix --- .../ai-transpiler-introduction.ipynb | 158 +++++++++++------- ...6a3e847-0808-4413-bd0c-c760cd2df3f4-0.avif | Bin 24789 -> 0 bytes ...9b8d5d9-0f9d-42ca-9583-8bec17430014-0.avif | Bin 14476 -> 0 bytes ...4dff2c2-a496-4828-bb8e-08d373816a36-0.avif | Bin 15143 -> 0 bytes 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public/docs/images/tutorials/ai-transpiler-introduction/extracted-outputs/sim-step2-plot-1.avif create mode 100644 public/docs/images/tutorials/ai-transpiler-introduction/extracted-outputs/sim-step4-code-0.avif diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 880cdc834e4..f6db1f8dec7 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -96,7 +96,6 @@ "from qiskit_aer.noise import NoiseModel, depolarizing_error\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import numpy as np\n", "import time\n", "import logging\n", "\n", @@ -165,12 +164,14 @@ " rows = []\n", " for label, col_def, col_ai in metrics:\n", " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", - " rows.append({\n", - " \"Metric\": label,\n", - " \"Default (mean +/- std)\": f\"{df[col_def].mean():.1f} +/- {df[col_def].std():.1f}\",\n", - " \"AI (mean +/- std)\": f\"{df[col_ai].mean():.1f} +/- {df[col_ai].std():.1f}\",\n", - " \"AI % improvement\": f\"{pct.mean():+.1f}% +/- {pct.std():.1f}%\",\n", - " })\n", + " rows.append(\n", + " {\n", + " \"Metric\": label,\n", + " \"Default (mean +/- std)\": f\"{df[col_def].mean():.1f} +/- {df[col_def].std():.1f}\",\n", + " \"AI (mean +/- std)\": f\"{df[col_ai].mean():.1f} +/- {df[col_ai].std():.1f}\",\n", + " \"AI % improvement\": f\"{pct.mean():+.1f}% +/- {pct.std():.1f}%\",\n", + " }\n", + " )\n", " return pd.DataFrame(rows).set_index(\"Metric\")\n", "\n", "\n", @@ -184,7 +185,12 @@ "\n", " # Row 1: raw metric comparison\n", " fig, axs = plt.subplots(1, 3, figsize=(21, 5))\n", - " fig.suptitle(f\"{title_prefix}: Metric Comparison\", fontsize=15, fontweight=\"bold\", y=1.02)\n", + " fig.suptitle(\n", + " f\"{title_prefix}: Metric Comparison\",\n", + " fontsize=15,\n", + " fontweight=\"bold\",\n", + " y=1.02,\n", + " )\n", " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", " ax.plot(df[\"Qubits\"], df[col_def], \"o-\", label=\"Default\")\n", " ax.plot(df[\"Qubits\"], df[col_ai], \"s-\", label=\"AI\")\n", @@ -197,10 +203,17 @@ "\n", " # Row 2: percentage improvement\n", " fig, axs = plt.subplots(1, 3, figsize=(21, 5))\n", - " fig.suptitle(f\"{title_prefix}: % Improvement of AI over Default\", fontsize=15, fontweight=\"bold\", y=1.02)\n", + " fig.suptitle(\n", + " f\"{title_prefix}: % Improvement of AI over Default\",\n", + " fontsize=15,\n", + " fontweight=\"bold\",\n", + " y=1.02,\n", + " )\n", " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", - " ax.axhline(0, color=\"#1f77b4\", linewidth=2, label=\"Default (baseline)\")\n", + " ax.axhline(\n", + " 0, color=\"#1f77b4\", linewidth=2, label=\"Default (baseline)\"\n", + " )\n", " ax.plot(df[\"Qubits\"], pct, \"s-\", color=\"#ff7f0e\", label=\"AI\")\n", " ax.fill_between(df[\"Qubits\"], 0, pct, alpha=0.15, color=\"#ff7f0e\")\n", " ax.set_title(label)\n", @@ -250,9 +263,8 @@ }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 2, @@ -267,15 +279,19 @@ "\n", "circuits_sim = [\n", " # We have only two qubit gates, as those test how well the transpiler can optimize the circuit.\n", - " random_circuit(num_qubits=n, \n", - " depth=depth_sim, \n", - " max_operands=2,\n", - " num_operand_distribution={2: 1},\n", - " seed=seed + i,)\n", + " random_circuit(\n", + " num_qubits=n,\n", + " depth=depth_sim,\n", + " max_operands=2,\n", + " num_operand_distribution={2: 1},\n", + " seed=seed + i,\n", + " )\n", " for i, n in enumerate(qubit_range_sim)\n", "]\n", "\n", - "print(f\"Created {len(circuits_sim)} circuits with qubit counts: {qubit_range_sim}\")\n", + "print(\n", + " f\"Created {len(circuits_sim)} circuits with qubit counts: {qubit_range_sim}\"\n", + ")\n", "circuits_sim[0].draw(output=\"mpl\", fold=-1)" ] }, @@ -438,17 +454,21 @@ " )\n", " qc_ai, m_ai = transpile_with_metrics(pm_ai, qc)\n", "\n", - " results_sim.append({\n", - " \"Qubits\": n,\n", - " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", - " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", - " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", - " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", - " \"Time (Default)\": m_default[\"time_s\"],\n", - " \"Time (AI)\": m_ai[\"time_s\"],\n", - " })\n", + " results_sim.append(\n", + " {\n", + " \"Qubits\": n,\n", + " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", + " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", + " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", + " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", + " \"Time (Default)\": m_default[\"time_s\"],\n", + " \"Time (AI)\": m_ai[\"time_s\"],\n", + " }\n", + " )\n", "\n", - " print(f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\")\n", + " print(\n", + " f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\"\n", + " )\n", "\n", "df_sim = pd.DataFrame(results_sim)\n", "summary_table(df_sim)" @@ -472,9 +492,8 @@ "outputs": [ { "data": { - "image/png": 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VemxZ++39v/POO7n3/fHHH3tlrfD97bd+wvnnn59nvf9z5m+HZWzy/d20v2n2XSmsjwEAAACgciOjAgAAABBCPvjgA1d73WpZF8Zqelt9a6vjXZiRI0e6WZ/GZkP6z2y3WaCWncE3a/iQQw7JXWczMP1rbdvsb3s9q3nvq69us2vtcTZz3sdmxW/btk37y+p5n3766Xtd/Gec2mxxm9lv2SYKYzM3n3766dxa7sayUVitb//Zo/lnd9vsWv8ZwPZ+bdautcuyENi+s9mptg+uuOKKPI9dsmRJsd7jV199lbtsn/Pjjz+e+z7PPfdcbd++Pc9z/v333yqN2bNn66OPPnJ10n3PaTN7bbZy/uwdvpntlsXDZv36atTb+7TZu9OmTSv2+7zvvvtcNg/fZ+U/q99m4/r4z9w1N9xwg6tDb+w1LaNEURlD/Nm2vu+MzZ7OP2vX3ldh7HV9j7U67P7sGLPMI8ba5j+T3f+9VBSdOnXaK7uJ1bS32eX+br31Vh122GF7ZRaxWdcVhf8s7meffVavvPKKq3tv2WAsG4Fl/bCZ3f7sPouD8F3OP//8/WpDkyZN3PfBN9t+1KhRxc6mMGfOnDyZNOy3x9rj+64/8sgjeWbG5z+mS8OyNYwePTr3tu/7W5SKdGxUhu/1Aw88kJthwrIFWDYdn+zs7DyZa2z9uHHj3Odif0vtPdhj7fdx+fLlxf5NtSw2DRs2zPOdLsn35+2339bzzz+v7777zr0/a4O9f+snFLbvb7zxRjVq1Mgt29/7hx56qNj7/vLLL8/93be/I/a9LKyPAQAAAKBy83IFAgAAAAgJNqhkKZvvuOMON8BsKZ9/+uknzZ07N09pARvksIGFk08+ucDnyZ8a3AZGfIEENnjsP9DhG1T28Q+AsIH+4qZqtsFw/4CI0rBBfEuVn1+3bt32GpC1FNzLli3TpEmT3ADm999/78oq+LPBGRs4t4GWlStX5llXnIE3e25LMZ6enr7Pbf0DDIri3w57jAUTFMX2hwWJlAULUrjppptcQEz+MhcXXHCB3nzzzf1+n1amxJ+lLC/o2Fq7dm2Rx2xR5RHyHyP5t81/u6BjqqDXzf9dKOh75FNUoJA/32Cfz5o1a1ReLOiooDT9pTn2g+3f//63zjrrLLdsgUl28WnTpo0LMrrttttyg1vKiw26+r6jvv1oA+AWVFSU/Pt8/vz57lKYoo7R4rLyPCVV1seGHe9//vlnnuPdfq+Lo6J/r+3vm5WiKKpNvu+3BctZQGH+gKyS/qZan8AXVFES9t2wPoK1wz6Pq666KnedBU1YGYh//etfueVB9rXvLWjH3r+vH1HUvi/qb0BJfjsBAAAAVHxkVAAAAABCkNVgP/XUU91MSstoYIEJ+QeQrE53YfIPDNhAfWHrCrNu3To3ezT/AIQFR9ggYefOnfOs8w+kCBSbNWoDMO+8847Wr1+vCRMmuBmcPjaoYtkeSuvaa6/NE6RgA6Q2i9vef/5Zx+X1/nfu3Fmqx73++utu9uqvv/6a57P65JNP9PDDD+fZ9pdffskTpGDHy0EHHeTqxdt7tQwLxZW//nn+gIjCFDTAHgj+3wf/70n+daVl+y4+Pj73th2nCxcuVHmwwcTyYgOe/vyzBZQHm+1tM8AtWMEynfhbsWKFHn30UR133HEu40l5stn3vuw0PtYmy65SEb7ngfr8iyv/oPrkyZND8nu9LxYM5h+kYAEH9jfD/q7bb2qDBg2K9bfDfjtK89to/QX77b/oootccKL/c9hv0P/+9z8dfvjhxQ6wK4nS/g0AAAAAUPkQqAAAAACECBtQL2zQzVJI+8+INJb+vDzNnDkzz+Dkf//7Xxc0YQPdH3744V4D9WXBZvf6p273XfxLOBSWmtsGYiyIIP+sYt9+sn3oz7+cQUG2bt2aJxjEntvKMFgwhL3//KUfist/0N8GkAp6v/6X/OntS8Lee9++fV17/QeL/u///k8bN27MvT19+vQ8j3vvvffcDPaPP/7YPba4M6JLIv/g86JFi/Lc9p+VnZ9vFnBhj80fCJB/+0Cy4zJ/iZH85RnyK+2M4/wDsj4lPfZN/pIB/qVdrH32W1DeLGX8+PHj3Sz11NRU95pWhsVn1qxZrsRJeX9+l1566V5ZFvYlf3CPZaYp6ntuJS3yv25Zff5FKc2xUZT8x/rLL79cZBYR+5tnJRMqw/favgP5s/bk/51q0aJFgb+ptl8tQ5Jl57Df1OJmAinNZ+ofWGglU+zvlgXCWEYP/wxJ9vfWV4poX/ve/u76/wYE8zcVAAAAQMVBoAIAAAAQIqxWuA0IP/3003tlAbCAgc8//zzPfeUxeOzPZuP789X7NlaKwmpvB8PZZ5/t6sbbAEv+wA5LR+0/gGp1vX2zO21mtNUI93nttdf2KrtggzG+GcD533/16tVzBw+txrYN9pfGsccem7tsmTLseXwDdT72/DbAZxkdyoKlOvdPVb9jxw498cQTxfqsrfyFldkoa0ceeWSe29YeCw4xlsXi/vvvL9Y+NLatb0a6DaLnL1diKdiDydKw2/HjY8E+NvidfzazDQRaLXibBV2WLPOAPysv8+OPP+a5z8qo+A9S509x/+qrr+b+Ft188815Al1Kyj/ric3uLoj9DlqmD99sczsmbeDVZqT7809Bb+Vf7Dvqu7zxxhsqC1YWxfaHDS5b8ESfPn32+Rjbxn5/fJ555hlXeiW/efPmuf356aefFrqPCgvOCtaxUZT+/fu7sgI+ycnJGjJkiPub4c9+87744gu3n3zfg8rwvbayTL5jcvXq1e532j+owBfAV9RvqgUPFJURqSxYRh3L6OALNrS/fVZCadSoUQV+f/Lv+8ceeyw3eMY+K3vfFek3FQAAAEDFUL5TqAAAAAAElM18tMHp6667zgUi2KxcG3CzAS7/AT2rMW0D7+XJZuLba/sGZa6++mqXztoGLWy2aHmnXC+MtceCNuxig789e/Z0A4JJSUluYNO/VIMNzvuCC2yQ8cYbb9SDDz7obtsAzumnn+72s5WQsEGZOXPmuBnbxxxzjEu5bbNGbTDKvP/++1qyZIm737JN2ABcadx0001uEMnaa26//Xa98MILLpjABrosrb0NYtl+ttTcZeXWW2915R18n+fzzz/vBtAtDXq/fv3ybGuDwTbglpKS4vZpebDU5Pa6lp7cN1u6Y8eO6t27t/7666/c/V4QGwi1AU77vIwNrNpn2KNHDzcT2H8Gt+3XM844Q8Fks6zfeustjRgxIjcoxQY4LdjHBnatjIB9v20w175XFohTlnwDlL7gIhsYtgH37t27u98YK/Nir22Dk74SM/mPvRdffNEF9uzatctlN9gf9lnZ7G4zZcoUHXLIIbmBEWPHjnXf65deesn9Ftp32z7DunXruu+cZfrI/1zlzX47ShosYNlMLBOMLxOO/b5YoIUdt/b7bd8tO1Z9JTS6du261/vyzda33x17nGU/sN8zK3uRPxNCII+NfbHfNysBYX/PjP2e2Xu376e1244fe05fYFJl+l5bwM5PP/3k9o39HfAPNrK/J75MMfbbZt8ZHyulY8e573vu/7e1PNh31YIf4+Li3LFlpSbsu5v/99z3/bG/o1bqx4KojP0G2++x9QPsb9LSpUtzH9OoUaNSZxQCAAAAEFoIVAAAAABChH+qbxvAsEGqgtLf16pVy6VD988OUB4OOOAAXXLJJW7A0DewbzXjjQ3GHH/88a7OdTD3k822zT9w6TNo0KC9ZuDed999bia4/yxYG5CxS0Es24FlcPDxzQq2tPg2w7SoWf+FsUFKKx9hKdJ9s9JtUL6ggfmyrO1tARk2EGXlHIwNsFmwggVKHHXUURo6dKjLnmDS0tJyM0vYAFb79u1duvKyZoPSVqrDN3PXrn2ve8455+jtt98u8HEW0GEz0G02uG/A2waS8w8mW7stoCUqKkrBZsEfEydO1HnnnZebPt6OX8sCUJbp3gtj32Mb6LdZ7L7fGJvNb5fCvv/WVvuMfHyZXmydDThPnTq1VG2x57UsAj7+s/Vttrl/9gl7TUuZX5CRI0cWK7tBsFjQ09q1a93viK/EQ2GlKvJ/120f2e+Ej2WK8WWLyT+7PdDHRnECO+x32QLFfL8pprDn9P2mV/Tvtf3ds4F7a6MFj/iz74RlAfGx4I+nnnoq9/3a762vzIL91lpWm59//rnc22wBcYVlw7DfXv+gKMtAYsEjvu+bLft+j30sUM/2vS9TEQAAAICqjdIPAAAAQIiwgVmbQWqDUJYq2wYCLf23Dd7YjGubkWqDezbzvCxn2hfFZvrbIJvNurRBIZv1fP7557tZ8PlTwweKzfi02b8WRGGzVm3GtbXNLjaIYtkQLBDBBltq1qyZ57G2L21QztbZ/m7btq3bx3Zp06aNzjzzTDeY73PWWWe5QSmb9R4TE+OyD9ggk82otcH90rKZtRaEYtkdLKDCZovbQKW1t3Pnzm6QywaIP/vsM5UlC0rw9+STT7pZtsbep2VdsNn/ti/t+pprrnHv1T9teVmy4Ak7liwYxAY3LfjGZk8/99xzeQbICxs0nDVrltvWZoDbwJnNYrd9aTO/bQa4DezaAGJFYd9rm51sM61t9ri9B3vPFvjiC/6x92Mz0suaDf7bwLcNMlqQjH2+dkzbMWcDv/a9zl+Ow4IG7PfIfovsmLA2WpYDG2z3zRwvjRtuuMH9rtjnb++9IDbIe8stt7jP0tpq31Hfb5B9x20fBav8TElYKQ87Tq2ch81Qt++SHac2w33gwIEuy4t9x/xLs/hm59vvmAUKlXdQWmmOjX2x32ULzLHfWitzYhkQ7PfTfufsO2q/qfb52kC+3V8ZvtfWdgvYsowW9jtt+8iOx8svv9wFZtjfHx9bZ0FIl112mbvfjnNr89133+32c1kGoRXkrrvu0p133uk+N/v+2mfpO+4sW459vyybid3nY/0MCz6y7C9W2sEyJ9h6C460AA3LEGL9D/vsAAAAAMCE5ZR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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -482,9 +501,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -551,7 +569,9 @@ " \"AI\": qc_ai_10q,\n", "}\n", "\n", - "print(f\"Default: depth {qc_default_10q.depth()}, gates {qc_default_10q.size()}\")\n", + "print(\n", + " f\"Default: depth {qc_default_10q.depth()}, gates {qc_default_10q.size()}\"\n", + ")\n", "print(f\"AI: depth {qc_ai_10q.depth()}, gates {qc_ai_10q.size()}\")" ] }, @@ -628,9 +648,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -700,15 +719,19 @@ "\n", "circuits_hw = [\n", " # We have only two qubit gates, as those test how well the transpiler can optimize the circuit.\n", - " random_circuit(num_qubits=n, \n", - " depth=depth_hw, \n", - " max_operands=2,\n", - " num_operand_distribution={2: 1},\n", - " seed=seed + i,)\n", + " random_circuit(\n", + " num_qubits=n,\n", + " depth=depth_hw,\n", + " max_operands=2,\n", + " num_operand_distribution={2: 1},\n", + " seed=seed + i,\n", + " )\n", " for i, n in enumerate(qubit_range_hw)\n", "]\n", "\n", - "print(f\"Created {len(circuits_hw)} circuits with qubit counts: {qubit_range_hw}\")" + "print(\n", + " f\"Created {len(circuits_hw)} circuits with qubit counts: {qubit_range_hw}\"\n", + ")" ] }, { @@ -839,17 +862,21 @@ " )\n", " qc_ai, m_ai = transpile_with_metrics(pm_ai, qc)\n", "\n", - " results_hw.append({\n", - " \"Qubits\": n,\n", - " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", - " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", - " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", - " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", - " \"Time (Default)\": m_default[\"time_s\"],\n", - " \"Time (AI)\": m_ai[\"time_s\"],\n", - " })\n", + " results_hw.append(\n", + " {\n", + " \"Qubits\": n,\n", + " \"Depth 2Q (Default)\": m_default[\"depth_2q\"],\n", + " \"Depth 2Q (AI)\": m_ai[\"depth_2q\"],\n", + " \"Gate Count (Default)\": m_default[\"gate_count\"],\n", + " \"Gate Count (AI)\": m_ai[\"gate_count\"],\n", + " \"Time (Default)\": m_default[\"time_s\"],\n", + " \"Time (AI)\": m_ai[\"time_s\"],\n", + " }\n", + " )\n", "\n", - " print(f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\")\n", + " print(\n", + " f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\"\n", + " )\n", "\n", "df_hw = pd.DataFrame(results_hw)\n", "summary_table(df_hw)" @@ -863,9 +890,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -873,9 +899,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -928,17 +953,19 @@ "mirror_default_hw = pm_basis.run(mirror_default_hw)\n", "mirror_ai_hw = pm_basis.run(mirror_ai_hw)\n", "\n", - "print(f\"Mirror circuit (Default): depth {mirror_default_hw.depth()}, gates {mirror_default_hw.size()}\")\n", - "print(f\"Mirror circuit (AI): depth {mirror_ai_hw.depth()}, gates {mirror_ai_hw.size()}\")\n", + "print(\n", + " f\"Mirror circuit (Default): depth {mirror_default_hw.depth()}, gates {mirror_default_hw.size()}\"\n", + ")\n", + "print(\n", + " f\"Mirror circuit (AI): depth {mirror_ai_hw.depth()}, gates {mirror_ai_hw.size()}\"\n", + ")\n", "\n", "# Submit to real hardware\n", "sampler_hw = SamplerV2(mode=backend)\n", "sampler_hw.options.environment.job_tags = [\"TUT-AITI\"]\n", "\n", "shots_hw = 500000\n", - "job_hw = sampler_hw.run(\n", - " [mirror_default_hw, mirror_ai_hw], shots=shots_hw\n", - ")\n", + "job_hw = sampler_hw.run([mirror_default_hw, mirror_ai_hw], shots=shots_hw)\n", "print(f\"Job submitted: {job_hw.job_id()}\")" ] }, @@ -958,9 +985,8 @@ }, { "data": { - "image/png": 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" + "\"Output" ] }, "metadata": {}, @@ -978,7 +1004,9 @@ " all_zeros = \"0\" * mirror.num_qubits\n", " survival = counts.get(all_zeros, 0) / shots_hw\n", " survival_probs_hw[method] = survival\n", - " print(f\"{method:8s} P(|00...0>) = {survival:.4f} ({counts.get(all_zeros, 0)}/{shots_hw})\")\n", + " print(\n", + " f\"{method:8s} P(|00...0>) = {survival:.4f} ({counts.get(all_zeros, 0)}/{shots_hw})\"\n", + " )\n", "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "ax.bar(\n", @@ -1042,7 +1070,7 @@ ], "metadata": { "kernelspec": { - "display_name": "doc-qiskit-serverless", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1056,7 +1084,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3" } }, "nbformat": 4, diff --git a/public/docs/images/tutorials/ai-transpiler-introduction/extracted-outputs/76a3e847-0808-4413-bd0c-c760cd2df3f4-0.avif 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zU>$nwAtmK5&1UIT^bq=Kn=@dUz|SGU(ys^1Zf_mYd9h|Zx7qGf`|#7i_n|#cv?ay$ zt94b!_**S|`O0Yq$oHnMu2`Iz?Unsh>DPHMCB8|n2tiZ9d;koUpw8DAt=-=TjHye| zD%+(>+*bsqw@o3Rvxv@q#&MgC9g$rHP%6ZIsu&xOrRZUl;Xd=#Ni9Q9Rt4}}RK7BC zjsJ6kObR!7zp2LuVc2neA8mo3ck)PFo=25dL!%>ynx`T9#}>bAy_-qN6CMoGVD$jL zJug$ErNGp#BW_T!&dVr(hg0$EJ7pec&vDZ}qs*tDQMNE8B^r^^t`qEs25)Axv5pE= mF#TAS_pZ~ZWnR*RuFYD7IzF>?XTY&&EPFg%S Date: Fri, 10 Apr 2026 05:37:43 -0400 Subject: [PATCH 03/17] Fix links --- docs/tutorials/ai-transpiler-introduction.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index f6db1f8dec7..8e7b8a34de8 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -30,9 +30,9 @@ "\n", "## Prerequisites\n", "We suggest that users are familiar with the following topics before going through this tutorial:\n", - "- [Transpile circuits](/docs/guides/transpile-circuits)\n", - "- [Configure preset pass managers](/docs/guides/configure-preset-pass-managers)\n", - "- [AI-powered transpiler passes](/docs/guides/ai-transpiler-passes)\n", + "- [Transpile circuits](https://quantum.cloud.ibm.com/docs/en/guides/transpile)\n", + "- [Configure preset pass managers](https://quantum.cloud.ibm.com/docs/en/guides/transpile-with-pass-managers)\n", + "- [AI-powered transpiler passes](https://quantum.cloud.ibm.com/docs/en/guides/ai-transpiler-passes)\n", "\n", "\n", "## Background\n", @@ -1048,9 +1048,9 @@ "If you found this work interesting, you might be interested in the following material:\n", "\n", "\n", - "- [Qiskit transpiler service](/docs/guides/qiskit-transpiler-service)\n", - "- [Transpilation optimizations with SABRE](/docs/tutorials/transpilation-optimizations-with-sabre)\n", - "- [Compilation methods for Hamiltonian simulation circuits](/docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits)\n", + "- [Qiskit transpiler service](https://quantum.cloud.ibm.com/docs/en/guides/qiskit-transpiler-service)\n", + "- [Transpilation optimizations with SABRE](https://quantum.cloud.ibm.com/docs/en/tutorials/transpilation-optimizations-with-sabre)\n", + "- [Compilation methods for Hamiltonian simulation circuits](https://quantum.cloud.ibm.com/docs/en/tutorials/compilation-methods-for-hamiltonian-simulation-circuits)\n", "\n", "" ] From 15dd7d0cf48846678118f0e0a6e741fe7673696e Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Fri, 10 Apr 2026 05:47:16 -0400 Subject: [PATCH 04/17] Re-added original benchmark image --- docs/tutorials/ai-transpiler-introduction.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 8e7b8a34de8..fab71a6ef09 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -48,13 +48,13 @@ "\n", "We measure three metrics for each strategy: **two-qubit gate depth**, **total gate count**, and **transpilation runtime**.\n", "\n", - "### Mirror circuits for quality evaluation\n", + "### AI transpiler benchmarks\n", "\n", - "To go beyond transpilation metrics and assess the actual impact on hardware noise, we use **mirror circuits**. A mirror circuit takes an original circuit $U$ and appends its inverse $U^\\dagger$, so the combined operation is the identity: the expected output is always $|0\\rangle^{\\otimes n}$. This is appealingly simple and scales to any circuit size since no classical simulation is needed to know the correct answer.\n", + "In benchmarking tests, the AI transpiler consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit's default pass manager strategy, configured with [`generate_preset_passmanager`]. While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum hardware. For more information on these benchmarks, refer to this [blog](https://www.ibm.com/quantum/blog/qiskit-performance).\n", "\n", - "However, be aware of the trade-offs: mirror circuits double the gate count (exaggerating noise effects) and test a proxy circuit rather than your actual workload. Noise can also cancel symmetrically across the mirror boundary, potentially underestimating certain error types.\n", + "![AI transpiler benchmarks](/docs/images/tutorials/ai-transpiler-introduction/ai-transpiler-benchmarks.avif)\n", "\n", - "Despite these caveats, measuring how close the output distribution is to the all-zeros state gives a practical signal for how well each transpilation strategy preserves circuit fidelity under real device noise." + "This tutorial explores the key benefits of AI passes and how it compares to traditional methods." ] }, { From 4d3402f3aac437210ae9d757ab80ee9718099a8d Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Fri, 10 Apr 2026 06:27:30 -0400 Subject: [PATCH 05/17] npm run check:spelling --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index fab71a6ef09..f432c9c33ec 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -11,7 +11,7 @@ "---\n", "\n", "\n", - "{/* cspell:ignore fontsize */}\n", + "{/* cspell:ignore fontsize fontweight steelblue AITI */}\n", "\n", "# Qiskit AI-powered transpiler service introduction\n", "*Usage estimate: 5 minutes on IBM Heron (NOTE: This is an estimate only. Your runtime may vary.)*" From 8b1e7ff732e60b513baaf8078532861932d33359 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Mon, 13 Apr 2026 14:31:20 -0400 Subject: [PATCH 06/17] Revised description for large scale example --- docs/tutorials/ai-transpiler-introduction.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index f432c9c33ec..53e782b9694 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -691,8 +691,8 @@ "id": "hw-steps-header", "metadata": {}, "source": [ - "### Steps 1-4 compress into single code block\n", - "Here we put all of these details together into a singular workflow at a larger scale, which is then run on real quantum hardware.\n", + "### Steps 1-4\n", + "Here we put all of these details together into a clear workflow at a larger scale, which is then run on real quantum hardware. We don't compress it to a single code block in order to show the results of each step as they would appear in a real workflow, but the same workflow can be implemented in a single block as well.\n", "\n", "We generate 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. We transpile all circuits with both strategies and collect the same metrics. Then we build mirror circuits from the 26-qubit case and submit them to the real backend. We use the smallest circuit size for the hardware run because mirror circuits double the gate count, and this basic fidelity test does not scale well on real hardware at larger qubit counts." ] From a02afda1f1e2ac38d405bfa9b99b20a457fe1dc5 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Fri, 24 Apr 2026 16:14:13 -0400 Subject: [PATCH 07/17] Change hyphen to underscore --- docs/tutorials/ai-transpiler-introduction.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 53e782b9694..54d27b97245 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -913,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "hw-step3", "metadata": {}, "outputs": [ @@ -962,7 +962,7 @@ "\n", "# Submit to real hardware\n", "sampler_hw = SamplerV2(mode=backend)\n", - "sampler_hw.options.environment.job_tags = [\"TUT-AITI\"]\n", + "sampler_hw.options.environment.job_tags = [\"TUT_AITI\"]\n", "\n", "shots_hw = 500000\n", "job_hw = sampler_hw.run([mirror_default_hw, mirror_ai_hw], shots=shots_hw)\n", From a59170dfe8e4bcb9e3c40c49eb67d665ab6be3a5 Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:12:57 -0400 Subject: [PATCH 08/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 54d27b97245..94ac2f8a7bb 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "---\n", - "title: Qiskit AI-powered transpiler service introduction\n", + "title: Qiskit AI-powered transpiler introduction\n", "description: Learn how the AI transpiler compares to standard transpilation using mirror circuits.\n", "---\n", "\n", From c442d1988613e0c9947188b0cf33351936db4280 Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:13:14 -0400 Subject: [PATCH 09/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 94ac2f8a7bb..128501f4c54 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -24,7 +24,7 @@ "source": [ "## Learning outcomes\n", "After going through this tutorial, users should understand:\n", - "- How to use the AI-powered transpiler service (`generate_ai_pass_manager`) as a drop-in replacement for the standard transpiler\n", + "- How to use the AI-powered transpiler (`generate_ai_pass_manager`) as a drop-in replacement for the standard transpiler\n", "- How the AI transpiler compares to the default transpiler in terms of two-qubit depth, gate count, and transpilation time\n", "- How to use mirror circuits to evaluate transpilation quality through hardware execution\n", "\n", From f7bd75aa99b9055dbeea15f68872bada47469a22 Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:13:36 -0400 Subject: [PATCH 10/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 128501f4c54..39dd2537000 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -37,7 +37,7 @@ "\n", "## Background\n", "\n", - "The **Qiskit AI-powered transpiler service (QTS)** introduces machine-learning-based transpilation passes that can produce shorter, more hardware-efficient circuits than traditional heuristic methods such as SABRE. Shorter circuits accumulate less noise, which directly improves result quality on real quantum hardware.\n", + "The **Qiskit AI-powered transpiler (QT)** introduces machine-learning-based transpilation passes that can produce shorter, more hardware-efficient circuits than traditional heuristic methods such as SABRE. Shorter circuits accumulate less noise, which directly improves result quality on real quantum hardware.\n", "\n", "In this tutorial we compare two transpilation strategies:\n", "\n", From b8104109c33d9ba59c77327097be2f354e56bded Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:14:03 -0400 Subject: [PATCH 11/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 39dd2537000..6c6fdb65d6a 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -532,7 +532,7 @@ "source": [ "### Step 3: Execute using Qiskit primitives\n", "\n", - "To evaluate the impact of transpilation on circuit fidelity, we build mirror circuits from the 10-qubit case and run them on the Aer simulator with a simple depolarizing noise model. The expected output of a mirror circuit is always the all-zeros bitstring, so the probability of measuring $|0\\rangle^{\\otimes n}$ tells us how well each transpilation strategy preserves fidelity." + "To evaluate the impact of transpilation on circuit fidelity, build mirror circuits from the 10-qubit case and run them on the Aer simulator with a simple noise model. The expected output of a mirror circuit is always the all-zeros bitstring, so the probability of measuring $|0\\rangle^{\\otimes n}$ demonstrates how well each transpilation strategy preserves fidelity." ] }, { From 9c1a133e4f9dcccdad36d9fb336d4ac790f2504e Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:14:18 -0400 Subject: [PATCH 12/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index 6c6fdb65d6a..c5400f95ba3 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -692,7 +692,7 @@ "metadata": {}, "source": [ "### Steps 1-4\n", - "Here we put all of these details together into a clear workflow at a larger scale, which is then run on real quantum hardware. We don't compress it to a single code block in order to show the results of each step as they would appear in a real workflow, but the same workflow can be implemented in a single block as well.\n", + "Here all of these details are put together into a clear workflow at a larger scale, which is then run on real quantum hardware. \n", "\n", "We generate 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. We transpile all circuits with both strategies and collect the same metrics. Then we build mirror circuits from the 26-qubit case and submit them to the real backend. We use the smallest circuit size for the hardware run because mirror circuits double the gate count, and this basic fidelity test does not scale well on real hardware at larger qubit counts." ] From e0e1ab885bd7036c229506c7ad210969afce97ad Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:14:26 -0400 Subject: [PATCH 13/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index c5400f95ba3..eb257aac738 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -694,7 +694,7 @@ "### Steps 1-4\n", "Here all of these details are put together into a clear workflow at a larger scale, which is then run on real quantum hardware. \n", "\n", - "We generate 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. We transpile all circuits with both strategies and collect the same metrics. Then we build mirror circuits from the 26-qubit case and submit them to the real backend. We use the smallest circuit size for the hardware run because mirror circuits double the gate count, and this basic fidelity test does not scale well on real hardware at larger qubit counts." + "The code below generates 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. These circuits are then transpiled with both strategies and the same metrics are collected. Then we build mirror circuits from the 26-qubit case and submit them to the real backend." ] }, { From b10fca21776b63dfc72b22008d4b8c2c411f0715 Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:14:49 -0400 Subject: [PATCH 14/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index eb257aac738..a2f30627ca5 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -637,7 +637,7 @@ "source": [ "### Step 4: Post-process and return result in desired classical format\n", "\n", - "We extract the probability of measuring the all-zeros bitstring from both runs. A higher probability indicates better fidelity, meaning the transpilation introduced less effective noise." + "We extract the probability of measuring the all-zeros bitstring from both runs. A higher probability indicates better fidelity, meaning the transpilation introduced less noise." ] }, { From b2d078fd0f3b34f0c240e71fae5a95dfbf3f0c9d Mon Sep 17 00:00:00 2001 From: Henry Zou <87874865+henryzou50@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:15:06 -0400 Subject: [PATCH 15/17] Update docs/tutorials/ai-transpiler-introduction.ipynb Co-authored-by: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> --- docs/tutorials/ai-transpiler-introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index a2f30627ca5..da256ddd799 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -1026,7 +1026,7 @@ "id": "hw-analysis", "metadata": {}, "source": [ - "### Analysis\n", + "### Analysis of results \n", "\n", "The large-scale results reinforce the trends observed in the small-scale example, now at a more demanding scale.\n", "\n", From ed60d15bbcbd8730022b1b5b56bd3fde51b5e8cd Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Thu, 4 Jun 2026 16:18:58 -0400 Subject: [PATCH 16/17] Address review feedback and quality pass on AI transpiler tutorial From Kaelynj's review: - Replace the pandas DataFrame summary with a plain printed table, since the DataFrame did not render correctly on the docs web page - Remove the per-circuit print output from the transpilation loops; only the mean/std summary table is needed - Plot 1 - P(|0...0>) instead of the raw survival probability so the mirror-circuit fidelity results are legible (the raw bars were flat near zero on hardware) - Restore the AI-synthesis coupling-map limitation note in the analysis - Drop "service" from the H1 heading so it matches the frontmatter title - Apply wording suggestions in the fidelity analysis Additional changes: - Refactor summary/plot helpers to work off the list of result dicts and remove the pandas dependency entirely - Reconcile commentary with the re-run results: circuit depth (4, not 5), depth/gate-count percentages, the 10-qubit fidelity discussion, and the large-scale noise-floor wording - Fix the stale "reduced coupling map" description in Step 2 to reflect the actual transpile-to-full-backend + remap_to_contiguous approach - Convert absolute quantum.cloud.ibm.com doc links to relative paths for consistency with other recently updated tutorials - Fix the broken generate_preset_pass_manager reference link and typo in the Background section --- .../ai-transpiler-introduction.ipynb | 334 +++++------------- .../extracted-outputs/hw-step2-plot-0.avif | Bin 26118 -> 26024 bytes .../extracted-outputs/hw-step2-plot-1.avif | Bin 25108 -> 24440 bytes .../extracted-outputs/hw-step4-1.avif | Bin 3911 -> 3197 bytes .../extracted-outputs/sim-step2-plot-0.avif | Bin 27556 -> 27975 bytes .../extracted-outputs/sim-step2-plot-1.avif | Bin 26727 -> 19119 bytes .../extracted-outputs/sim-step4-code-0.avif | Bin 3168 -> 3091 bytes 7 files changed, 84 insertions(+), 250 deletions(-) diff --git a/docs/tutorials/ai-transpiler-introduction.ipynb b/docs/tutorials/ai-transpiler-introduction.ipynb index da256ddd799..8e0a2864fc1 100644 --- a/docs/tutorials/ai-transpiler-introduction.ipynb +++ b/docs/tutorials/ai-transpiler-introduction.ipynb @@ -13,7 +13,7 @@ "\n", "{/* cspell:ignore fontsize fontweight steelblue AITI */}\n", "\n", - "# Qiskit AI-powered transpiler service introduction\n", + "# Qiskit AI-powered transpiler introduction\n", "*Usage estimate: 5 minutes on IBM Heron (NOTE: This is an estimate only. Your runtime may vary.)*" ] }, @@ -30,9 +30,9 @@ "\n", "## Prerequisites\n", "We suggest that users are familiar with the following topics before going through this tutorial:\n", - "- [Transpile circuits](https://quantum.cloud.ibm.com/docs/en/guides/transpile)\n", - "- [Configure preset pass managers](https://quantum.cloud.ibm.com/docs/en/guides/transpile-with-pass-managers)\n", - "- [AI-powered transpiler passes](https://quantum.cloud.ibm.com/docs/en/guides/ai-transpiler-passes)\n", + "- [Transpile circuits](/docs/guides/transpile)\n", + "- [Configure preset pass managers](/docs/guides/transpile-with-pass-managers)\n", + "- [AI-powered transpiler passes](/docs/guides/ai-transpiler-passes)\n", "\n", "\n", "## Background\n", @@ -50,7 +50,7 @@ "\n", "### AI transpiler benchmarks\n", "\n", - "In benchmarking tests, the AI transpiler consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit's default pass manager strategy, configured with [`generate_preset_passmanager`]. While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum hardware. For more information on these benchmarks, refer to this [blog](https://www.ibm.com/quantum/blog/qiskit-performance).\n", + "In benchmarking tests, the AI transpiler consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit's default pass manager strategy, configured with [`generate_preset_pass_manager`](/docs/api/qiskit/qiskit.transpiler.generate_preset_pass_manager). While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum hardware. For more information on these benchmarks, refer to this [blog](https://www.ibm.com/quantum/blog/qiskit-performance).\n", "\n", "![AI transpiler benchmarks](/docs/images/tutorials/ai-transpiler-introduction/ai-transpiler-benchmarks.avif)\n", "\n", @@ -95,7 +95,7 @@ "from qiskit_aer import AerSimulator\n", "from qiskit_aer.noise import NoiseModel, depolarizing_error\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", + "from statistics import mean, stdev\n", "import time\n", "import logging\n", "\n", @@ -153,30 +153,33 @@ " return mirror\n", "\n", "\n", - "def summary_table(df):\n", - " \"\"\"Display a summary table with mean +/- stdev for each metric,\n", - " plus the mean percentage improvement of AI over Default.\"\"\"\n", + "def print_summary(results):\n", + " \"\"\"Print a summary of each metric as mean +/- stdev across all circuits,\n", + " along with the mean percentage improvement of AI over Default.\"\"\"\n", " metrics = [\n", " (\"Depth 2Q\", \"Depth 2Q (Default)\", \"Depth 2Q (AI)\"),\n", " (\"Gate Count\", \"Gate Count (Default)\", \"Gate Count (AI)\"),\n", " (\"Time (s)\", \"Time (Default)\", \"Time (AI)\"),\n", " ]\n", - " rows = []\n", + " header = (\n", + " f\"{'Metric':<12}{'Default (mean +/- std)':>24}\"\n", + " f\"{'AI (mean +/- std)':>22}{'AI % improvement':>22}\"\n", + " )\n", + " print(header)\n", + " print(\"-\" * len(header))\n", " for label, col_def, col_ai in metrics:\n", - " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", - " rows.append(\n", - " {\n", - " \"Metric\": label,\n", - " \"Default (mean +/- std)\": f\"{df[col_def].mean():.1f} +/- {df[col_def].std():.1f}\",\n", - " \"AI (mean +/- std)\": f\"{df[col_ai].mean():.1f} +/- {df[col_ai].std():.1f}\",\n", - " \"AI % improvement\": f\"{pct.mean():+.1f}% +/- {pct.std():.1f}%\",\n", - " }\n", - " )\n", - " return pd.DataFrame(rows).set_index(\"Metric\")\n", + " defaults = [r[col_def] for r in results]\n", + " ais = [r[col_ai] for r in results]\n", + " pct = [(d - a) / d * 100 for d, a in zip(defaults, ais)]\n", + " default_str = f\"{mean(defaults):.1f} +/- {stdev(defaults):.1f}\"\n", + " ai_str = f\"{mean(ais):.1f} +/- {stdev(ais):.1f}\"\n", + " pct_str = f\"{mean(pct):+.1f}% +/- {stdev(pct):.1f}%\"\n", + " print(f\"{label:<12}{default_str:>24}{ai_str:>22}{pct_str:>22}\")\n", "\n", "\n", - "def plot_metrics_and_pct(df, title_prefix):\n", + "def plot_metrics_and_pct(results, title_prefix):\n", " \"\"\"Plot metric comparisons and percentage improvement of AI over Default.\"\"\"\n", + " qubits = [r[\"Qubits\"] for r in results]\n", " metrics = [\n", " (\"Depth 2Q (Default)\", \"Depth 2Q (AI)\", \"Two-Qubit Depth\"),\n", " (\"Gate Count (Default)\", \"Gate Count (AI)\", \"Gate Count\"),\n", @@ -192,8 +195,8 @@ " y=1.02,\n", " )\n", " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", - " ax.plot(df[\"Qubits\"], df[col_def], \"o-\", label=\"Default\")\n", - " ax.plot(df[\"Qubits\"], df[col_ai], \"s-\", label=\"AI\")\n", + " ax.plot(qubits, [r[col_def] for r in results], \"o-\", label=\"Default\")\n", + " ax.plot(qubits, [r[col_ai] for r in results], \"s-\", label=\"AI\")\n", " ax.set_title(label)\n", " ax.set_xlabel(\"Number of Qubits\")\n", " ax.set_ylabel(label)\n", @@ -210,12 +213,12 @@ " y=1.02,\n", " )\n", " for ax, (col_def, col_ai, label) in zip(axs, metrics):\n", - " pct = (df[col_def] - df[col_ai]) / df[col_def] * 100\n", + " pct = [(r[col_def] - r[col_ai]) / r[col_def] * 100 for r in results]\n", " ax.axhline(\n", " 0, color=\"#1f77b4\", linewidth=2, label=\"Default (baseline)\"\n", " )\n", - " ax.plot(df[\"Qubits\"], pct, \"s-\", color=\"#ff7f0e\", label=\"AI\")\n", - " ax.fill_between(df[\"Qubits\"], 0, pct, alpha=0.15, color=\"#ff7f0e\")\n", + " ax.plot(qubits, pct, \"s-\", color=\"#ff7f0e\", label=\"AI\")\n", + " ax.fill_between(qubits, 0, pct, alpha=0.15, color=\"#ff7f0e\")\n", " ax.set_title(label)\n", " ax.set_xlabel(\"Number of Qubits\")\n", " ax.set_ylabel(\"% Improvement\")\n", @@ -245,7 +248,7 @@ "source": [ "### Step 1: Map classical inputs to a quantum problem\n", "\n", - "We generate 20 random circuits with depth 5, where the number of qubits ranges from 6 to 25. These circuits will serve as our test cases for comparing transpilation strategies." + "We generate 20 random circuits with depth 4, where the number of qubits ranges from 6 to 25. These circuits will serve as our test cases for comparing transpilation strategies." ] }, { @@ -302,7 +305,7 @@ "source": [ "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "We use a reduced coupling map (the first 30 qubits of the backend) so the transpiled circuits are small enough for local simulation. The hardware section later uses the full coupling map." + "We build the default (SABRE) pass manager for the chosen backend. Both transpilation strategies target the backend's full coupling map. Local simulation later stays tractable because the simulation step uses `remap_to_contiguous` to relabel each transpiled circuit onto only its active qubits, so Aer simulates just those qubits instead of the entire device." ] }, { @@ -317,6 +320,7 @@ " min_num_qubits=100, operational=True, simulator=False\n", ")\n", "\n", + "\n", "pm_default_sim = generate_preset_pass_manager(\n", " optimization_level=3,\n", " backend=backend,\n", @@ -333,7 +337,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87d664f0db954c1794e49f7dbec7302a", + "model_id": "bc06de12e386408abf67166416c2d1a0", "version_major": 2, "version_minor": 0 }, @@ -348,94 +352,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 6q] Default: depth= 30, gates= 197, time=0.029s | AI: depth= 23, gates= 190, time=0.672s\n", - "[ 7q] Default: depth= 13, gates= 135, time=0.014s | AI: depth= 14, gates= 169, time=0.219s\n", - "[ 8q] Default: depth= 17, gates= 204, time=0.016s | AI: depth= 17, gates= 272, time=0.315s\n", - "[ 9q] Default: depth= 21, gates= 234, time=0.012s | AI: depth= 19, gates= 258, time=0.388s\n", - "[10q] Default: depth= 29, gates= 278, time=0.016s | AI: depth= 20, gates= 312, time=0.408s\n", - "[11q] Default: depth= 20, gates= 261, time=0.016s | AI: depth= 21, gates= 301, time=0.345s\n", - "[12q] Default: depth= 25, gates= 346, time=0.016s | AI: depth= 22, gates= 389, time=0.403s\n", - "[13q] Default: depth= 38, gates= 402, time=0.022s | AI: depth= 29, gates= 458, time=0.462s\n", - "[14q] Default: depth= 27, gates= 450, time=0.025s | AI: depth= 27, gates= 483, time=0.513s\n", - "[15q] Default: depth= 23, gates= 427, time=0.015s | AI: depth= 26, gates= 527, time=0.465s\n", - "[16q] Default: depth= 31, gates= 599, time=0.026s | AI: depth= 29, gates= 582, time=0.437s\n", - "[17q] Default: depth= 26, gates= 567, time=0.024s | AI: depth= 29, gates= 580, time=0.381s\n", - "[18q] Default: depth= 36, gates= 575, time=0.034s | AI: depth= 27, gates= 677, time=0.414s\n", - "[19q] Default: depth= 35, gates= 694, time=0.031s | AI: depth= 27, gates= 715, time=0.515s\n", - "[20q] Default: depth= 38, gates= 754, time=0.043s | AI: depth= 32, gates= 865, time=0.574s\n", - "[21q] Default: depth= 51, gates= 748, time=0.045s | AI: depth= 33, gates= 898, time=0.516s\n", - "[22q] Default: depth= 49, gates= 842, time=0.033s | AI: depth= 40, gates= 869, time=0.531s\n", - "[23q] Default: depth= 52, gates= 870, time=0.036s | AI: depth= 37, gates= 925, time=0.572s\n", - "[24q] Default: depth= 63, gates=1066, time=0.059s | AI: depth= 37, gates=1077, time=0.665s\n", - "[25q] Default: depth= 37, gates= 804, time=0.041s | AI: depth= 32, gates= 895, time=0.592s\n" + "Metric Default (mean +/- std) AI (mean +/- std) AI % improvement\n", + "--------------------------------------------------------------------------------\n", + "Depth 2Q 33.0 +/- 12.9 26.4 +/- 8.0 +15.8% +/- 17.6%\n", + "Gate Count 522.0 +/- 266.0 560.5 +/- 279.1 -9.0% +/- 9.0%\n", + "Time (s) 0.0 +/- 0.0 0.2 +/- 0.1 -893.6% +/- 362.9%\n" ] - }, - { - "data": { - "text/html": [ - "
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Depth 2Q33.0 +/- 12.927.1 +/- 7.0+13.5% +/- 15.9%
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" - ], - "text/plain": [ - " Default (mean +/- std) AI (mean +/- std) AI % improvement\n", - "Metric \n", - "Depth 2Q 33.0 +/- 12.9 27.1 +/- 7.0 +13.5% +/- 15.9%\n", - "Gate Count 522.6 +/- 268.6 572.1 +/- 280.1 -11.3% +/- 9.6%\n", - "Time (s) 0.0 +/- 0.0 0.5 +/- 0.1 -1797.9% +/- 606.3%" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -466,12 +388,7 @@ " }\n", " )\n", "\n", - " print(\n", - " f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\"\n", - " )\n", - "\n", - "df_sim = pd.DataFrame(results_sim)\n", - "summary_table(df_sim)" + "print_summary(results_sim)" ] }, { @@ -481,7 +398,7 @@ "source": [ "The summary table shows the mean and standard deviation of each metric across all 20 circuits, along with the average percentage improvement of the AI transpiler over the default. Positive values indicate the AI transpiler produced better results; negative values indicate the default was better.\n", "\n", - "For this small-scale example, the AI transpiler achieves roughly 10% lower two-qubit depth on average, but at the cost of roughly 10% higher gate count. This highlights a key trade-off when choosing between the two strategies: the AI transpiler prioritizes depth reduction (fewer sequential layers of two-qubit gates), while the default transpiler (SABRE) prioritizes minimizing total gate count (fewer SWAP insertions). Depending on your application, one metric may matter more than the other." + "For this small-scale example, the AI transpiler achieves roughly 16% lower two-qubit depth on average, but at the cost of roughly 9% higher gate count. This highlights a key trade-off when choosing between the two strategies: the AI transpiler prioritizes depth reduction (fewer sequential layers of two-qubit gates), while the default transpiler (SABRE) prioritizes minimizing total gate count (fewer SWAP insertions). Depending on your application, one metric may matter more than the other." ] }, { @@ -510,7 +427,7 @@ } ], "source": [ - "plot_metrics_and_pct(df_sim, \"Small-Scale Random Circuits\")" + "plot_metrics_and_pct(results_sim, \"Small-Scale Random Circuits\")" ] }, { @@ -545,8 +462,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Default: depth 84, gates 278\n", - "AI: depth 70, gates 312\n" + "Default: depth 84, gates 280\n", + "AI: depth 91, gates 343\n" ] } ], @@ -585,8 +502,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Default P(|00...0>) = 0.8514 (8514/10000)\n", - "AI P(|00...0>) = 0.8207 (8207/10000)\n" + "Default P(|00...0>) = 0.8460 (8460/10000)\n", + "AI P(|00...0>) = 0.8121 (8121/10000)\n" ] } ], @@ -637,7 +554,7 @@ "source": [ "### Step 4: Post-process and return result in desired classical format\n", "\n", - "We extract the probability of measuring the all-zeros bitstring from both runs. A higher probability indicates better fidelity, meaning the transpilation introduced less noise." + "We extract the probability of measuring the all-zeros bitstring from both runs. A higher survival probability indicates better fidelity, meaning the transpilation introduced less noise. The plot below shows the complement, 1 - P(|0...0>), so that a lower bar indicates better fidelity and small differences in error are easier to see." ] }, { @@ -657,14 +574,18 @@ } ], "source": [ + "# Plot 1 - P(|0...0>), the probability of an erroneous (non-zero) outcome.\n", + "# A lower bar means the transpilation introduced less noise.\n", + "error_probs = {method: 1 - p for method, p in survival_probs.items()}\n", + "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "ax.bar(\n", - " survival_probs.keys(),\n", - " survival_probs.values(),\n", + " error_probs.keys(),\n", + " error_probs.values(),\n", " color=[\"steelblue\", \"coral\"],\n", ")\n", - "ax.set_ylabel(\"P(|0...0>)\")\n", - "ax.set_title(\"Mirror Circuit Fidelity (10-qubit, Aer Simulator)\")\n", + "ax.set_ylabel(\"1 - P(|0...0>)\")\n", + "ax.set_title(\"Mirror Circuit Error (10-qubit, Aer Simulator)\")\n", "ax.set_ylim(0, 1)\n", "plt.tight_layout()\n", "plt.show()" @@ -675,7 +596,7 @@ "id": "sim-step4-analysis", "metadata": {}, "source": [ - "In this case, the default transpiler achieves a higher survival probability despite having a deeper circuit. This is because it produced a circuit with fewer total gates, and under a uniform depolarizing noise model, total gate count has a more direct impact on accumulated error than depth alone. This trade-off is not universal. The relative importance of depth versus gate count depends on the magnitude of the difference in each metric, the noise characteristics of the hardware, and the structure of the circuit." + "In this case, the default transpiler produced both a shallower and smaller circuit for this particular 10-qubit instance, so its higher fidelity is expected. Per-circuit results vary: as the summary table above shows, the AI transpiler's advantage is in lower two-qubit depth on average, not on every individual circuit. Which strategy yields higher fidelity depends on the magnitude of the difference in each metric, the noise characteristics of the hardware, and the structure of the circuit. Under a uniform depolarizing noise model, total gate count often has a more direct impact on accumulated error than depth alone." ] }, { @@ -692,7 +613,7 @@ "metadata": {}, "source": [ "### Steps 1-4\n", - "Here all of these details are put together into a clear workflow at a larger scale, which is then run on real quantum hardware. \n", + "Here all of these details are put together into a clear workflow at a larger scale, which is then run on real quantum hardware.\n", "\n", "The code below generates 25 random circuits with depth 8, where the number of qubits ranges from 26 to 50. These circuits are then transpiled with both strategies and the same metrics are collected. Then we build mirror circuits from the 26-qubit case and submit them to the real backend." ] @@ -744,99 +665,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[26q] Default: depth=155, gates=2420, time=0.072s | AI: depth=119, gates=2694, time=1.925s\n", - "[27q] Default: depth=146, gates=2421, time=0.070s | AI: depth=118, gates=2736, time=1.872s\n", - "[28q] Default: depth=147, gates=2706, time=0.082s | AI: depth=151, gates=3297, time=2.398s\n", - "[29q] Default: depth=190, gates=3038, time=0.088s | AI: depth=149, gates=3297, time=2.421s\n", - "[30q] Default: depth=174, gates=3027, time=0.079s | AI: depth=138, gates=3220, time=2.471s\n", - "[31q] Default: depth=170, gates=3247, time=0.101s | AI: depth=150, gates=3559, time=2.659s\n", - "[32q] Default: depth=152, gates=3366, time=0.095s | AI: depth=176, gates=4150, time=3.185s\n", - "[33q] Default: depth=197, gates=3487, time=0.101s | AI: depth=163, gates=3675, time=3.082s\n", - "[34q] Default: depth=224, gates=3861, time=0.110s | AI: depth=167, gates=4417, time=3.186s\n", - "[35q] Default: depth=212, gates=4051, time=0.119s | AI: depth=190, gates=4381, time=3.297s\n", - "[36q] Default: depth=208, gates=4189, time=0.102s | AI: depth=194, gates=5017, time=3.579s\n", - "[37q] Default: depth=225, gates=4222, time=0.120s | AI: depth=200, gates=4840, time=3.642s\n", - "[38q] Default: depth=194, gates=4112, time=0.117s | AI: depth=184, gates=4622, time=3.769s\n", - "[39q] Default: depth=201, gates=4597, time=0.112s | AI: depth=188, gates=5074, time=4.071s\n", - "[40q] Default: depth=215, gates=4951, time=0.129s | AI: depth=201, gates=5498, time=4.236s\n", - "[41q] Default: depth=253, gates=5322, time=0.138s | AI: depth=215, gates=5617, time=4.596s\n", - "[42q] Default: depth=188, gates=4639, time=0.119s | AI: depth=202, gates=5756, time=4.580s\n", - "[43q] Default: depth=227, gates=5093, time=0.140s | AI: depth=212, gates=6039, time=5.674s\n", - "[44q] Default: depth=299, gates=5835, time=0.176s | AI: depth=228, gates=6398, time=5.719s\n", - "[45q] Default: depth=223, gates=5666, time=0.154s | AI: depth=221, gates=6627, time=5.783s\n", - "[46q] Default: depth=260, gates=6124, time=0.157s | AI: depth=238, gates=6944, time=6.005s\n", - "[47q] Default: depth=301, gates=6691, time=0.169s | AI: depth=236, gates=7425, time=5.937s\n", - "[48q] Default: depth=280, gates=6664, time=0.161s | AI: depth=233, gates=7455, time=6.598s\n", - "[49q] Default: depth=263, gates=6228, time=0.192s | AI: depth=227, gates=7326, time=6.279s\n", - "[50q] Default: depth=330, gates=6969, time=0.195s | AI: depth=260, gates=7842, time=7.754s\n" + "Metric Default (mean +/- std) AI (mean +/- std) AI % improvement\n", + "--------------------------------------------------------------------------------\n", + "Depth 2Q 217.4 +/- 50.4 191.0 +/- 35.6 +10.9% +/- 10.7%\n", + "Gate Count 4513.3 +/- 1394.3 5227.1 +/- 1536.4 -16.4% +/- 5.8%\n", + "Time (s) 0.1 +/- 0.0 3.5 +/- 1.5 -3588.2% +/- 643.6%\n" ] - }, - { - "data": { - "text/html": [ - "
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Default (mean +/- std)AI (mean +/- std)AI % improvement
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Depth 2Q217.4 +/- 50.4190.4 +/- 38.5+11.5% +/- 10.3%
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" - ], - "text/plain": [ - " Default (mean +/- std) AI (mean +/- std) AI % improvement\n", - "Metric \n", - "Depth 2Q 217.4 +/- 50.4 190.4 +/- 38.5 +11.5% +/- 10.3%\n", - "Gate Count 4517.0 +/- 1393.3 5116.2 +/- 1588.8 -13.3% +/- 5.3%\n", - "Time (s) 0.1 +/- 0.0 4.2 +/- 1.6 -3198.3% +/- 453.5%" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -874,12 +708,7 @@ " }\n", " )\n", "\n", - " print(\n", - " f\"[{n:2d}q] Default: depth={m_default['depth_2q']:3d}, gates={m_default['gate_count']:4d}, time={m_default['time_s']:.3f}s | AI: depth={m_ai['depth_2q']:3d}, gates={m_ai['gate_count']:4d}, time={m_ai['time_s']:.3f}s\"\n", - " )\n", - "\n", - "df_hw = pd.DataFrame(results_hw)\n", - "summary_table(df_hw)" + "print_summary(results_hw)" ] }, { @@ -908,12 +737,12 @@ } ], "source": [ - "plot_metrics_and_pct(df_hw, \"Large-Scale Random Circuits\")" + "plot_metrics_and_pct(results_hw, \"Large-Scale Random Circuits\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "hw-step3", "metadata": {}, "outputs": [ @@ -921,9 +750,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mirror circuit (Default): depth 1564, gates 9708\n", - "Mirror circuit (AI): depth 1145, gates 11488\n", - "Job submitted: d7cagn15a5qc73dofeq0\n" + "Mirror circuit (Default): depth 1577, gates 9672\n", + "Mirror circuit (AI): depth 1235, gates 11092\n", + "Job submitted: d8gt7vm6983c73dqbg0g\n" ] } ], @@ -971,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "hw-step4", "metadata": {}, "outputs": [ @@ -979,8 +808,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Default P(|00...0>) = 0.0077 (3870/500000)\n", - "AI P(|00...0>) = 0.0001 (47/500000)\n" + "Default P(|00...0>) = 0.0005 (239/500000)\n", + "AI P(|00...0>) = 0.0050 (2516/500000)\n" ] }, { @@ -1008,14 +837,18 @@ " f\"{method:8s} P(|00...0>) = {survival:.4f} ({counts.get(all_zeros, 0)}/{shots_hw})\"\n", " )\n", "\n", + "# Plot 1 - P(|0...0>), the probability of an erroneous (non-zero) outcome.\n", + "# A lower bar means the transpilation introduced less noise.\n", + "error_probs_hw = {method: 1 - p for method, p in survival_probs_hw.items()}\n", + "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "ax.bar(\n", - " survival_probs_hw.keys(),\n", - " survival_probs_hw.values(),\n", + " error_probs_hw.keys(),\n", + " error_probs_hw.values(),\n", " color=[\"steelblue\", \"coral\"],\n", ")\n", - "ax.set_ylabel(\"P(|0...0>)\")\n", - "ax.set_title(f\"Mirror Circuit Fidelity (26-qubit, {backend.name})\")\n", + "ax.set_ylabel(\"1 - P(|0...0>)\")\n", + "ax.set_title(f\"Mirror Circuit Error (26-qubit, {backend.name})\")\n", "ax.set_ylim(0, 1)\n", "plt.tight_layout()\n", "plt.show()" @@ -1026,7 +859,7 @@ "id": "hw-analysis", "metadata": {}, "source": [ - "### Analysis of results \n", + "### Analysis of results\n", "\n", "The large-scale results reinforce the trends observed in the small-scale example, now at a more demanding scale.\n", "\n", @@ -1036,7 +869,9 @@ "\n", "**Transpilation time:** The gap in transpilation time widens at larger scales. SABRE remains nearly constant, while the AI transpiler's runtime grows more steeply. Despite this, the AI transpiler runtime remains practical for most workflows.\n", "\n", - "**Mirror circuit fidelity:** Both methods produce survival probabilities very close to zero at this scale. With total gate counts around 10,000 and two-qubit depths exceeding 1,000, the depolarizing noise accumulated across the mirror circuit overwhelms any signal. This highlights a key limitation of the mirror circuit approach: while it is simple and requires no classical simulation, it does not scale well to large or deep circuits where the noise floor dominates regardless of transpilation quality." + "**Mirror circuit fidelity:** Both methods produce survival probabilities well under 1% at this scale, leaving little usable signal. With total gate counts around 10,000 and two-qubit depths exceeding 1,000, the depolarizing noise accumulated across the mirror circuit overwhelms most of the signal. This highlights a key limitation of the mirror circuit approach: while it is simple and requires no classical simulation, it does not scale well to large or deep circuits, where both methods are pushed close to the noise floor and the small surviving signal is dominated by accumulated error.\n", + "\n", + "While these results underscore the AI transpiler's effectiveness, it is important to note its limitations. The AI synthesis method is currently only available for certain coupling maps, which may restrict its broader applicability. This constraint should be considered when evaluating its usage in different scenarios." ] }, { @@ -1048,9 +883,8 @@ "If you found this work interesting, you might be interested in the following material:\n", "\n", "\n", - "- [Qiskit transpiler service](https://quantum.cloud.ibm.com/docs/en/guides/qiskit-transpiler-service)\n", - "- [Transpilation optimizations with SABRE](https://quantum.cloud.ibm.com/docs/en/tutorials/transpilation-optimizations-with-sabre)\n", - "- [Compilation methods for Hamiltonian simulation circuits](https://quantum.cloud.ibm.com/docs/en/tutorials/compilation-methods-for-hamiltonian-simulation-circuits)\n", + "- [Transpilation optimizations with SABRE](/docs/tutorials/transpilation-optimizations-with-sabre)\n", + "- [Compilation methods for Hamiltonian simulation circuits](/docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits)\n", "\n", "" ] diff --git 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zvc}uRKVUAg-~=V=y2;6nZ&6({`j{!fram$i-VcKqP8g>EK4X$Q6@R&hK-16zinfwJ zuK2X8R!X63|SRMR@Ncsmg-aklk2{0Vvg{L%z z@D|dNE0++N`ntp=4Nq|>*iyvvpR=_*1X^)MgLdzU39#OmE3UzDI7a_ j75Q^cN$Gl+$^H-%s}4e?g#dA8tHD&RaS Date: Thu, 4 Jun 2026 16:26:05 -0400 Subject: [PATCH 17/17] Fix tutorial index --- docs/tutorials/index.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx index 1f81f0a0aeb..6892e9a6593 100644 --- a/docs/tutorials/index.mdx +++ b/docs/tutorials/index.mdx @@ -83,7 +83,7 @@ Workload optimization focuses on either efficient orchestration of classical and * [Introduction to fractional gates](/docs/tutorials/fractional-gates) -* [Qiskit AI-powered transpiler service introduction](/docs/tutorials/ai-transpiler-introduction) +* [Qiskit AI-powered transpiler introduction](/docs/tutorials/ai-transpiler-introduction) * [Transpilation optimizations with SABRE](/docs/tutorials/transpilation-optimizations-with-sabre)