{ "cells": [ { "cell_type": "markdown", "id": "083f9cb1-82a6-4147-8594-b32ff4dc9666", "metadata": {}, "source": [ "Fe4N2 - 3 - calculations on Quantinuum hardware \n", "===============================================" ] }, { "cell_type": "markdown", "id": "60c1176b-3c18-414b-a031-420dd2526699", "metadata": {}, "source": [ "This part marks the conclusion of the three-part series tutorial, and the user will learn how to conduct both emulator and hardware calculations using Quantinuum resources on a system more complex than [H2](InQ_htut_qsys_H2.ipynb). It is important to note that one should run the [first part](InQ_tut_fe4n2_1.ipynb) and [second part](InQ_tut_fe4n2_2.ipynb) before proceeding to this tutorial. Additional details regarding the chemical systems, methodologies, and outcomes presented in this tutorial series are available in the associated [research paper](https://doi.org/10.1039/D3CP05167F). \n", "\n", "Let us move beyond a simple single-point energy calculation that is neither optimized nor variationally solved. Instead, we build upon the results obtained in the second part of this tutorial series, where the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) algorithm was used to compute the total energy of the Fe3N2 molecule. This tutorial is also an extension of the initial part of the series, which provides detailed insights into the classical workflow necessary for defining the system.\n", "\n", "To carry out these calculations, access to the Quantinuum Systems is essential, which can be obtained by contacting Quantinuum support. Additionally, the user will require Hardware Quantum Credits (HQCs) to run jobs on Quantinuum Systems. Users with credentails and HQCs can access hardware and emulators through [pytket-quantinuum](https://docs.quantinuum.com/tket/extensions/pytket-quantinuum/) or via [Quantinuum Nexus](https://docs.quantinuum.com/nexus/trainings/notebooks/basics/getting_started.html).\n", "\n", "\n", "Here are the steps outlined:\n", "\n", "* Import the parameters characterizing the ground-state wavefunction.\n", "\n", "* Conduct calculations on the Quantinuum emulator utilizing the PMSV error mitigation method.\n", "\n", "* Execute calculations on the Quantinuum hardware using the PMSV error mitigation method." ] }, { "cell_type": "markdown", "id": "814ffcc8-65e2-4cc8-8714-290aac3c6431", "metadata": {}, "source": [ "To begin with, let us start by importing the qubit Hamiltonian, the fermionic state and space from the first part, along with the ground state parameters and the list of fermionic pool operators from the [second part](InQ_tut_fe4n2_2.ipynb) of this tutorial." ] }, { "cell_type": "code", "execution_count": null, "id": "c903f62b-6088-4747-ad06-ec1634f1b593", "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", "with open('InQ_tut_fe4n2_qubit_hamiltonian.pickle', 'rb') as handle:\n", " qubit_hamiltonian = pickle.load(handle)\n", "\n", "with open('InQ_tut_fe4n2_state.pickle', 'rb') as handle:\n", " state = pickle.load(handle) \n", "\n", "with open('InQ_tut_fe4n2_space.pickle', 'rb') as handle:\n", " space = pickle.load(handle) \n", "\n", "with open('InQ_tut_fe4n2_gs_parameters.pickle', 'rb') as handle:\n", " gs_parameters = pickle.load(handle) \n", " \n", "with open('InQ_tut_fe4n2_exponents_with_symbols.pickle', 'rb') as handle:\n", " exponents_with_symbols = pickle.load(handle)\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "96d66b6d-93ed-4fb5-b413-1ad51fa8a6f2", "metadata": {}, "source": [ "InQuanto employs an efficient ansatz circuit compilation approach, provided by the `FermionSpaceStateExpChemicallyAware` class. This method is designed to minimize the computational resources required.\n", "\n", "Parameters used:\n", "\n", "- `fermion_operator_exponents` – Contains exponents and symbols. Assuming input exponents are ordered as single exponents first, followed by double exponents.\n", "- `fermion_state` – Initial fermionic reference state." ] }, { "cell_type": "code", "execution_count": null, "id": "98608a33-1007-4a6f-a8ed-f9273b5be7e5", "metadata": {}, "outputs": [], "source": [ "from inquanto.ansatzes import FermionSpaceStateExpChemicallyAware\n", "\n", "ansatz=FermionSpaceStateExpChemicallyAware(fermion_operator_exponents=exponents_with_symbols, fermion_state=state)" ] }, { "cell_type": "markdown", "id": "e7dc5130-db75-40ad-bf5a-3bcab2ba21fe", "metadata": {}, "source": [ "The `state_circuit` attribute within InQuanto represents the symbolic state circuit, complete with a default compilation. In a Jupyter environment, one can visualize this symbolic state circuit by making use of the `render_circuit_jupyter` function provided by the pytket library." ] }, { "cell_type": "code", "execution_count": null, "id": "782eeefe-314d-45c9-a174-0164b9e21971", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
\n", " \n", "
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pytket.circuit.display import render_circuit_jupyter\n", "\n", "render_circuit_jupyter(ansatz.state_circuit)" ] }, { "cell_type": "markdown", "id": "7f6a7b57-a941-4858-97c3-8ff1e8b8a750", "metadata": {}, "source": [ "To obtain an overview of the quantum resource costs associated with creating the Ansatz, one can utilize the `generate_report` and `circuit_resources` methods of the Ansatz object. The report typically includes information such as the count of Ansatz parameters, and the number of required qubits, while the resources method holds gate metrics such as 2-qubit gate counts. In this circuit there are 6 qubits, 7 parameters, and 59 2-qubit gates. These metrics provide an overview of the complexity and requirements of the quantum circuit. \n", "The circuit depth is the length of the longest path from the input (or from a preparation) to the output (or a measurement gate), moving forward in time along qubit wires.\n", "\n", "InQuanto offers the `generate_report()` and `circuit_resources()` methods for concise circuit analysis. However, for more in-depth diagnostics and analysis of the circuit, the user can examine the tket circuit object directly, which is usually accessed through `ansatz.state_circuit`. The [tket documentation](https://docs.quantinuum.com/tket/user-guide/examples/circuit_construction/circuit_analysis_example.html) provides comprehensive information and guidance on this topic." ] }, { "cell_type": "code", "execution_count": null, "id": "5b01d814-562a-412c-a0a1-544e07d34ec4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ANSATZ REPORT:\n", "{'n_parameters': 7, 'n_qubits': 6}\n", "\n", "\n", "2-qubit GATES: 59\n" ] } ], "source": [ "from pytket.circuit import OpType\n", "\n", "print('ANSATZ REPORT:')\n", "print(ansatz.generate_report())\n", "print(ansatz.get_circuit())\n", "print('\\n2-qubit GATES: {}'.format(ansatz.circuit_resources()['gates_2q']))" ] }, { "cell_type": "markdown", "id": "f4a45a45-e4db-4c1f-a72a-23325a662f01", "metadata": {}, "source": [ "Conducting emulator experiments prior to hardware experiments is a pivotal phase in the development and optimization of quantum algorithms and applications. Emulators offer a controlled setting with the possibility to refine algorithms, explore quantum error correction techniques, and gain valuable insights into the performance of quantum circuits, all without being restricted by the limitations of physical hardware.\n", " \n", "To simulate the specific noise profiles of machines, one can load and apply them to the simulations using the `QuantinuumBackend`, which retrieves information from the user's Quantinuum account. The `QuantinuumBackend` offers a range of available emulators, such as H2-1E and H2-2E. These emulators are designed for specific devices and they run remotely on a server.\n", "\n", "Parameters used:\n", "\n", "- `device_name` – Name of device, e.g. “H2-1”\n", "- `label` – Job labels used if Circuits have no name, defaults to “job”\n", "- `group` – String identifier of a collection of jobs, can be used for usage tracking." ] }, { "cell_type": "code", "execution_count": null, "id": "8806527c-17ed-4ea9-b744-f0d57becee0e", "metadata": {}, "outputs": [], "source": [ "from pytket.extensions.quantinuum import QuantinuumBackend\n", "\n", "# While H1-1 and H1-1E backends were used to produce the results below, they have now been sunset\n", "# As such, the H2-1 and H2-2E backends should be used\n", "backend = QuantinuumBackend(device_name=\"\", label = \"\")" ] }, { "cell_type": "markdown", "id": "54b15d3e-a4f9-4950-9b51-8ca2daee1c32", "metadata": {}, "source": [ "To reduce errors and inaccuracies caused by quantum noise and imperfections in the Quantinuum device, one can employ noise mitigation techniques. In this case, let us define the Qubit Operator symmetries within the system, which enables to utilize [PMSV](https://doi.org/10.1103/PhysRevResearch.4.033110) (Partition Measurement Symmetry Verification). PMSV is an efficient technique for symmetry-verified quantum calculations. It represents molecular symmetries using Pauli strings, including mirror planes (Z2) and electron-number conservation (U1). For systems with Abelian point group symmetry, qubit tapering methods can be applied. PMSV uses commutation between Pauli symmetries and Hamiltonian terms for symmetry verification. It groups them into sets of commuting Pauli strings. If each string in a set commutes with the symmetry operator, measurement circuits for that set can be verified for symmetry without additional quantum resources, discarding measurements violating system point group symmetries.\n", "\n", "Parameters used:\n", "\n", "`stabilizers` – List of state stabilzers as QubitOperators with only a single pauli strings in them.\n", "\n", "The InQuanto `symmetry_operators_z2_in_sector` function is employed to retrieve a list of symmetry operators applicable to the system under consideration. These symmetry operators are associated with the point group, spin parity, and particle number parity Z2 symmetries that uphold a specific symmetry sector. The users can find additional details in the linked [page](https://docs.quantinuum.com/inquanto/api/inquanto/spaces.html#inquanto.spaces.FermionSpace.symmetry_operators_z2_in_sector)." ] }, { "cell_type": "code", "execution_count": null, "id": "1323fbf1-0eb8-45dd-a212-2d98c0a1fc11", "metadata": {}, "outputs": [], "source": [ "from inquanto.protocols.averaging._mitigation import PMSV\n", "from inquanto.mappings import QubitMappingJordanWigner\n", "\n", "stabilizers = QubitMappingJordanWigner().operator_map(\n", " space.symmetry_operators_z2_in_sector(\n", " state\n", " )\n", ")\n", "\n", "mitms_pmsv = PMSV(stabilizers)" ] }, { "cell_type": "markdown", "id": "5c7c11f6-3901-43cc-9fa7-f1f2d1c4c888", "metadata": {}, "source": [ "Here, a technique known as \"batching\" is employed, wherein each experiment is iterated a certain number of times with a specific shot count as the target. For instance, if one aims for 10,000 shots, one conducts the experiment ten times, resulting in a total of 100,000 shots for that experiment. This limitation exists because each experiment can only accommodate a maximum of 10,000 shots and this implementation helps to avoid a single task from monopolizing the system's resources or a user accidentally using all their credits in a single instance.\n", "\n", "To compute the expectation value of a Hermitian operator through operator averaging on the system register, the `PauliAveraging` protocol is employed. This protocol effectively implements the procedure outlined in ['Hamiltonian Averaging'](https://arxiv.org/abs/1407.7863). To launch the circuits to the backend the `launch` function is used. This method processes all the circuits associated with the expectation value calculations and returns a list of `ResultHandle` objects representing the handles for the results. One can pickle these `ResultHandle` objects so that the results can be easily retrieved. The user can monitor the progress of the experiments on the [Quantinuum page](https://um.qapi.quantinuum.com/user) by using the same credentials used to execute the experiments." ] }, { "cell_type": "code", "execution_count": null, "id": "bc3f3e72-df8a-4313-b373-c507b9f0ecde", "metadata": {}, "outputs": [], "source": [ "from inquanto.protocols import PauliAveraging\n", "from pytket.partition import PauliPartitionStrat\n", "\n", "\n", "set_shots_10k = [10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000]\n", "repeats = 10\n", "\n", "#build and compile the circuits once\n", "protocol_template = PauliAveraging(\n", " backend,\n", " shots_per_circuit=10000,\n", " pauli_partition_strategy=PauliPartitionStrat.CommutingSets\n", ") \n", "protocol_template.build(gs_parameters, ansatz, qubit_hamiltonian, noise_mitigation=mitms_pmsv).compile_circuits()\n", "protocol_pickle=protocol_template.dumps()\n", "protocol_template.n_circuit\n", "\n", "# launch 10 repeats of these circuits \n", "for i in range(repeats):\n", " protocol= PauliAveraging.loads(protocol_pickle, backend)\n", " handles = protocol.launch()\n", " with open( \"handles_\" + str(i) + \".pickle\", 'wb') as handle:\n", " pickle.dump(handles, handle, protocol=pickle.HIGHEST_PROTOCOL)" ] }, { "cell_type": "markdown", "id": "73972a27-a123-47f9-8290-c0d1ef96f8c6", "metadata": {}, "source": [ "After the experiments have finished, one can obtain the results by utilizing the `retrieve` function, which retrieves distributions from the backend for the specified source. The expectation value of a kernel for a specified quantum state is calculated by using the `evaluate_expectation_value` function. In addition, the `evaluate_expectation_uvalue` function can be used to calculate the expectation value of the Hermitian kernel while considering linear error propagation theory. The `std_dev` property returns the standard deviation which is used as the error associated with the calculation." ] }, { "cell_type": "code", "execution_count": null, "id": "c9dae4d6-258b-45ed-ac14-4748bc4ff79a", "metadata": {}, "outputs": [], "source": [ "repeats = 10\n", "emulator_energies_10k =[]\n", "emulator_10k = []\n", "\n", "for i in range(repeats):\n", " with open(\"handles_\" + str(i) + \".pickle\", 'rb') as handle:\n", " handles = pickle.load(handle)\n", " #only need 1 copy of protocol to eval sets of results\n", " protocol.retrieve(handles) \n", " energy_value = protocol.evaluate_expectation_value(ansatz, qubit_hamiltonian)\n", " emulator_energies_10k.append(energy_value)\n", " error= protocol.evaluate_expectation_uvalue(state, qubit_hamiltonian)\n", " emulator_10k.append(error)\n", " " ] }, { "cell_type": "markdown", "id": "3e141002-e45f-4c96-9dd3-15b0f20e65d2", "metadata": {}, "source": [ "Additionally, one can showcase circuit statistics after employing the `get_compiled_circuit` function to compile the sequence of circuits. In this case, both the circuit depth for each circuit measurement and the circuit depth associated with 2-qubit gates are visualized." ] }, { "cell_type": "code", "execution_count": null, "id": "ebc63a2b-8e54-47bc-9c96-b634f7a9a301", "metadata": {}, "outputs": [], "source": [ "for meas_circ in protocol.get_circuits():\n", " print(\"Circuit depth =\", backend.get_compiled_circuit(meas_circ).depth())\n", " print(\"2qb gate depth =\", backend.get_compiled_circuit(meas_circ).depth_by_type({OpType.CX, OpType.CZ}))" ] }, { "cell_type": "markdown", "id": "e93cf6e3-430e-421c-9a38-6d98994f3ff3", "metadata": {}, "source": [ "After completing the 10 repetitions, the mean value is computed. The mean value is used to determine the energy values for shot counts such as 20k, 30k, and so forth. Meanwhile, the standard deviation is used to calculate the error bars." ] }, { "cell_type": "code", "execution_count": null, "id": "20f6e0d3-6017-4130-884e-1d25e0b723b4", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "emulator_10k_mean = [] \n", "for i in range (1,11):\n", " emulator_10k_mean.append(np.mean(emulator_energies_10k[:i]))\n", "\n", "emulator_10k_std= [] \n", "for i in range (1,11):\n", " emulator_10k_std.append(np.std(emulator_10k[:i]))" ] }, { "cell_type": "markdown", "id": "c3f9a513-89c5-4c7a-aee5-02f5274a6c7e", "metadata": {}, "source": [ "This procedure can be reiterated for various sets of shots to analyze how the number of shots impacts the energy and error estimations." ] }, { "cell_type": "code", "execution_count": null, "id": "15b387eb-9afe-4030-9b60-f8ab3e207dc2", "metadata": {}, "outputs": [], "source": [ "#target:5k\n", "set_shots_5k = [5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000]\n", "\n", "emulator_5k_mean = [-598.5233976942287, -598.528748007567, -598.5254365672856, -598.5266267836435,-598.5252272612659, \n", " -598.5241590945266, -598.5255975795899, -598.525113940237,-598.5249076903503, -598.5239581110884]\n", "\n", "emulator_5k_std =[ 0,0.0016,0.0015,0.0014,0.0013,0.0012,0.0011, 0.0010,0.00095, 0.0009]\n", "\n", "\n", "\n", "#target:4k\n", "set_shots_4k = [4000, 8000, 12000, 16000, 20000, 24000, 28000, 32000, 36000, 40000]\n", "\n", "\n", "emulator_4k_mean = [-598.5261956279538, -598.5239748592571, -598.5250271719032, -598.5236355685819, -598.5232948710519, \n", " -598.5243534836842, -598.5244650328887, -598.5251092284366, -598.524141170795, -598.5235882519817]\n", "\n", "emulator_4k_std =[0,0.00163,0.00154,0.0015,0.0014,0.0013,0.0012,0.00115,0.0011, 0.0010]\n", " \n", "\n", "\n", "#target:2.5k\n", "set_shots_2_5k = [2500, 5000, 7500, 10000, 12500, 15000, 17500, 20000, 22500, 25000]\n", "\n", "emulator_2_5k_mean = [-598.5386462829877, -598.528495041739, -598.5223780085176, -598.5221927569415, -598.5220793440851,\n", " -598.522950988168, -598.5245581694165, -598.5236303692766, -598.5243398619134, -598.5237327682132]\n", "\n", "emulator_2_5k_std =[0,0.0017,0.00165,0.0016,0.00155,0.0015,0.00145,0.0014,0.00135,0.0013]" ] }, { "cell_type": "markdown", "id": "51081333-e8b2-41a0-b956-1e6def9d3e48", "metadata": {}, "source": [ "Once collected all the results, one can proceed to visualize and analyze the data. By running the same set of instructions for different shot configurations, one can observe that the error bars notably decrease when a sufficiently large number of samples is used." ] }, { "cell_type": "code", "execution_count": null, "id": "e434ac54-9fec-4aa4-9fb2-2d9614b7e684", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:37: SyntaxWarning: invalid escape sequence '\\D'\n", "<>:37: SyntaxWarning: invalid escape sequence '\\D'\n", "/var/folders/cw/3wy7pvf5797c290hwkms0m2r0000gp/T/ipykernel_65343/1243066924.py:37: SyntaxWarning: invalid escape sequence '\\D'\n", " plt.ylabel('$\\Delta E \\; [Ha]$',fontsize=15)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set_style(\"whitegrid\")\n", "\n", "#plt.plot(set_shots_10k, emulator_10k_mean, label='emulator_10k',color='red')\n", "plt.plot(set_shots_5k, emulator_5k_mean, label='emulator_5k',color='black')\n", "plt.plot(set_shots_4k, emulator_4k_mean, label='emulator_4k',color='teal')\n", "plt.plot(set_shots_2_5k, emulator_2_5k_mean, label='emulator_2.5k',color='orange')\n", "\n", "plt.rcParams[\"figure.figsize\"] = (10,6)\n", "plt.xticks(fontsize=15 )\n", "plt.yticks(fontsize=15 )\n", "\n", "#y_error_10k = emulator_10k_std\n", "y_error_5k = emulator_5k_std\n", "y_error_4k = emulator_4k_std\n", "y_error_2_5k = emulator_2_5k_std\n", "\n", "#plt.errorbar(set_shots_10k,emulator_10k_mean,\n", "# yerr = y_error_10k, fmt ='s', color='red',\n", "# elinewidth=1,capsize=2)\n", "\n", "plt.errorbar(set_shots_5k, emulator_5k_mean,\n", " yerr = y_error_5k, fmt ='s', color='black',\n", " elinewidth=1,capsize=2)\n", "\n", "plt.errorbar(set_shots_4k, emulator_4k_mean,\n", " yerr = y_error_4k, fmt ='s', color='teal',\n", " elinewidth=1,capsize=2)\n", "\n", "plt.errorbar(set_shots_2_5k, emulator_2_5k_mean,\n", " yerr = y_error_2_5k, fmt ='s', color='orange',\n", " elinewidth=1,capsize=2)\n", "\n", "plt.xlabel('Number of shots',fontsize=15)\n", "plt.ylabel('$\\Delta E \\; [Ha]$',fontsize=15)\n", "plt.title(\"Convergence behavior of the expectation value by varying the number of shots when running computations with the Quantinuum H1-1E noisy emulator backend.\")\n", "plt.legend(fontsize=15)" ] }, { "cell_type": "markdown", "id": "92dbea74-9a12-49b1-8231-395bfa411c84", "metadata": {}, "source": [ "With InQuanto, it is extremely easy to go from hardware emulation to hardware experiment. This is because `pytket.extensions.quantinuum` requires the same details for emulating the hardware as for sending the experiment to the physical quantum circuit, the only difference being the substitution of the name of the quantum device. For example, from 'H2-1E' to 'H2-1'. \n", "\n", "Here, only one series of experiments was performed on the hardware device. To limit the effects of noise in estimating expectation values, the PMSV method was used.\n", "\n", "In this tutorial, three hardware experiments for three different structures of Fe4N2 cluster were performed in order to calculate the activation and dissociation energies of nitrogen on iron cluster.\n", "More specifically, in this tutorial, the emulator calculations with 4000 shots were used. In addition, a hardware experiment was conducted simply by changing 'H1-1E' to 'H1-1', and these results served as the starting points in the figure below. Note that as of 15 October 2025, H1 backends have been sunset. Therefore, if a user wishes to run this tutorial, they should use 'H2-1' and 'H2-1E' backends.\n", "Subsequently, one can re-run the calculations described in this tutorial, altering only the initial geometry in the first part. Namely, one can use the geometry where the N-N bond is stretched, representing the transition state and denoted as 'ts' in the figure. Finally, one can repeat the three tutorials once again, using a geometry where the N-N bond breaks, denoted as the 'end' in the figure. Notice that in these six experiments (two for each geometry), the single experiments with both the emulator and hardware were used instead of the batching method described above.\n", "The primary goal of this tutorial is to offer a deeper understanding of how quantum computers can contribute to the comprehension of dissociation processes.\n", "\n", "The electronic activation energy with respect to the kinetic constant of the process is calculated as $E_{Fe/N_2}^{tot}-E_{Fe/N_2^*}^{tot}$, while the activation energy is $E_{Fe/N_2}^{tot}-E_{Fe/2N}^{tot}$.\n", " \n", "The data points computed on the hardware are in excellent agreement with the emulation results." ] }, { "cell_type": "code", "execution_count": null, "id": "e583f8eb-3ee0-49f2-b526-9e17fe80e4d2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:31: SyntaxWarning: invalid escape sequence '\\;'\n", "<>:32: SyntaxWarning: invalid escape sequence '\\;'\n", "<>:31: SyntaxWarning: invalid escape sequence '\\;'\n", "<>:32: SyntaxWarning: invalid escape sequence '\\;'\n", "/var/folders/cw/3wy7pvf5797c290hwkms0m2r0000gp/T/ipykernel_65343/3447554942.py:31: SyntaxWarning: invalid escape sequence '\\;'\n", " plt.ylabel('$Energy\\; [Ha]$', fontsize=15)\n", "/var/folders/cw/3wy7pvf5797c290hwkms0m2r0000gp/T/ipykernel_65343/3447554942.py:32: SyntaxWarning: invalid escape sequence '\\;'\n", " plt.xlabel('$NEB \\; Images\\;$', fontsize=15)\n" ] }, { "data": { "image/png": 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8mTNnNtcU5zxd7o53e7ip3eYJb8ghr488x/h5WefEixfPnB/OD1C8he0Lbm+ef9wPPNd5zLH9zPObNxI8/tmeigiPFU+OIQaMuP0ce+7wms3615NexbxGsmdQZFKHeNLO8OS6wzYlh/3a13C6dOmSOR/t9pur8obXZrRvRtn2Z13Gh1McQcJrKOt2nk/h4Tp4TPImncca9xeDcjxvHG8iWVdye3N9fE/2EOX1xm7rM3jDaxxv0rkOXqt43EaU3oHblFz1InW8z+J+dn4Q544nbRQb60Wum20QtgFYT/N84Y0r6xwe08R9yetNeCLbRokOnhNsm/J+i+/Be1R+TgYTWGYeI6wfuI84fNqT9bn6cmzfsL5mm9B5XzFgT2w/uMPgG/c3tyfrpKiUJ7xrSWTac9w/rJMYCGGdxLqZxwCDRq7OlYja4VFtN/Eaz/3HaxIf2rMnMANavK92vk6xvDz32TZkoJfHGs9FvjfLw8AO74/sYGxUeBI/sAN3rH/snyMTC+C6eEyy/cngTnjYgYRtOdZl3PasSxzjIZ7UW7t37zZ1Eutk7je2lXlt5LnpPPSVv7O+5v95PbMD7J60xT255jKQxrJwuDzbwSwnP0N4ATafDD9ko4UbI6Lu/Y43aKzEWNHYyZHZQGLFwhOLFyw2RsjeWU899ZT5nRcfBlQYabWHKPBk4YHOi13KlCnN37hBuGF5oJGdtJ434dzhPCh5sedGs580sgysSBgpZbTRxgsyGx6u8IRiEMG+MbXfiwek3ejnjQOfQPMCZ1dQvBHm0wFWFAwI2NhoZsVB/HysBNnQ40HvCt+HDUh+LnvcOitqNvJ4cHI7s0LiMuwxF15XVTZu7Nc7L8MKwb5AMYLLMvHk5IHHymbq1KmmIrH3JT8bL6w8yPnE9aGHHnL51IkNGO43NlrtdbPhat+kc3/yc/F97AYR9xkbDWyY8AS2u72G97nY8GHQgo1r+1jgjSpvunnh4D7iMcaLNBsExKcWvHH99NNPzUXbsXtteO/DIAdveljh82JNPB5Y8XE/cH/YxxlzxzjmNWHlypxV4fFkW9l4/LMCs48zPjXhjQHPUe4HViI8Pnnckz0W3t3NrbvPz23Iz2cfryyT/VSGF3lWRnwPfrc98cQTZrtwOVcBHO53VtSM1Nvj0bkO3rD98ssvUT4Pua14jtvnNp8y8EJqP332ZB+y7uL/2RBmg9z+3OwVwHqDyzhimdmI4sXJPv74vtwv9oWON+M8Nx2HVzn3rPLkPLexzHYgk2VjY5V1COtbV9jAYH3I85gNDmKvATZcuN/soF9Ex25k9jcvjI7bhNuDgRL+zc7Dw/Xw/OMNnyus4+2LNc9fu+5hQ4O/cz1syHC/s061G5g8v7lMRPkmeA7xom0f+6wzGHRhA4x1Eq8JbDCxcWWfl/wfj6/oDNNheXktZaOAdQbLy+M0ohttd9cO3qDw+GRdYDccuY95/Dg3ppzxSSb3u93Tg8FVHm88D9gbwVMMUvJmmg0q/kw8trgv7J4W7NHBMnL9vOlw5tjAYh3NgA6PR24vV3VJZDGYwkYag7OO13y7x6C9DfgZeN2w64nw8LrP84JBXMfroKsHS8R9bveycsbPxwAx6xS2lbhPuR7e9PMpK+sV1o889pyH/PFYtxufrG8chwTxRoTXBbuNxYdBPF+5Trt3l6PofnZ3WL/yGHBMYcEGPT8bj1+7foroeOfni6jNY9chPL7tgCjPM57b9sQ63sa6mzdB8ePHN/uIdR4b67wR4UgH4j7cvHlzhOti3cevqOD1n+0KT5IMRyUJtCftDE+uO6yreIPF4J+9r9kDjEEU52utp21GloE3u2yf2u0kvj/rbb6n80QQNp53vJlz7B3Ec4U3ZJwwwT7OeKPIm277/oTXP/Y24PWP5xj3Oc8JfiY7qGj3FnKHryF391l8OMSvyEyG4EkbxbH+5Q2p3QZhPcDXsRMBe13Y51FEw70j20aJDtZvvJbYDyR4jHA/sv1mtzV47rG+5w13REPpwqu7yTGHnzNuT7bhuW3sNmp42KvZU1G5lnjSnmN7g8Ea9jZzXA9fw3W7Olc8aYdHpd3E1/MY5X2/fezxuGGbjoEaxwf3XL+9X1mfs/wsA4MjxOA86xDWsfZw7MjyJH5g1wc8nuyfIxML4L2zuyGqNtYprG9ZDm5z3nszYMagOfeVJ/XWsGHDTDuM7V17uCvbGDwfHXuR8VxisJafkfdL9rHnaVvck2su97Hdu8xOF2AP242xoJa9cyIaJmezb8QdA0f273xqzw1mnxCsoO3GFtmNYcfGDTccOQa1WKHaFbS9g9g44oWNeHPCLwbjGJhhYIwVMz8DG96OXHX1dSwPL0yMRvLmizuSF2a7Gy5vTtnw5s25Y8Sd5bQvdI6cI7I8IdwNHeK25HocEzGywcRtyQOdF51kyZIhOhy3I08YNma5rYkHIhuuvClxvOHg76wQGUxy9WSJ23TixInmJGHjitufDVpWiPZ62Kjmz87Re/uk8QQvMo8++qgJZPFzcH+z8WsH4NjwtYegsXHJY4Hl4RML8rQ3AvcD951zQ59PGnks8HPZDRjnRjb3sbtZazzZVjbedDkeZ/YFmuvnRYE3QHbl7hi09aTHhit8Uu6I5519bLB8fPLFhqRjOdldl8crGzKubkS533kO8rh2Lmd4Qa2IzkO7ocaAOMvE+oVfdkPZ031oD/dzPibDuzlg44SNbJ4jbGhy33E9vKBGpqeLJ+e5zdXx5W5oFm+O2auBFyjH/cT3Y8CMjRvWwxEdu5Hd3471Kut8biPnizhvXMILatk9VVzVPcT344WU+537h0+jeHPl2KB2h92wHRu4LC8bYbyG8CLMbc8GIW/OWL9zn/L8ZDA8Ohiw5s0Eg0esO1leBo8iypHi7trBepq5pxzrYjaCGMh3d2PLIC/rQt4U2Oc1tynPEZYxMkEt+0m2/Tl4jtv5q7gf+cVrFesU3uiyjnYc2sZj2FUDnm0B9oyITCDRud7k+9jvxUYxG4W8HvD8ZRCH24AP3CLL/qzObSPHILQjd8O7WKexbmcyZrZduE95vvDc5zHJ45wPBHn8RGZbMODk2Mayb0bZ+IyO8D67O/bMzPxMvBbzRpDtJ3KuLyPbVnKFjWbHHn6O10tf4E0U623H6xYb9XZAy27P8oYkImzku2ro8xof3mQlPAd5I8YesXwKb1/H2LZw7NnBfRdRD5Hw8Dz2pJ3h6XWH9QOHQNnXarbleEPr6sY/ojYj9ysfvPGzc1vY78t6necArxnhBbXssrNO5LWO1/Lw2omO10n7mLKPTfYE5zHn2EuOQ3W9lRuJ9ZgnvXSi0kZxfPhtvzYq50tk2yjR5VhX2HVsePeQEQmv7nY3QRAfHPGY47nP4IV9rXG+DvGci2wutKhcS2zu2nM8l7j/nYPHvI/i9Z9tUudzxZN2eFTaTbzW8V6O1zV7m/E4ZFl4PYzMvrYfsETn+haZ+IEtsrEAd3EHR2wDOLaTWPex/cI2AYNantRbmzZtMu1Mx/xt3I5271B7OC8fNjDwy7rYcTi7p21xT665LAuPS8f8l9x/vI+PsaAWLzxsjNpPEsLbodzZXNZ+4uw8GxFPeB5wjgdbeLNmhNft3ebqgscDnBdb4g5gNzyeWNwJbMhxJ7IMzsOi3L0XKyBGNxnAWbp0qYk42zcPfMLE9+H67GCcI/7N+cRybozyYHU3TIvbMrx183V8mh3doJbzkFLHMrHB7ypAaXPXZZCRXkaI7Sf3fNLD97K3ib3u6CR35f7hjRefhLJhw5OZx6LjzRgDJawEeMJzWzGSb+9zd9veeT/YTx0c2fvG8WLpbntGdVu5WzexkcMy8n2cn5pHZzYo53PD1bHB84BfzuyhwM7s+sG5nO5mL4voPGS9w6f/rEB5I81zn1885zl8lvvck31of6bI5Bbh+9nDpNl44sUqsj15PDnPo3p88TPxSXZ4T/z4Pzuo5Uld4On+dqyX7HwmztvV3Xa2388OUIf3fuwZwXOH3cLZu4hltofkuOtt4mp7szz2ucxrExsU3L9sqPCY4xMld13FPcFAE89XBrLtIVgsJ3sY271JXXF37WBPTR57zvmPIjqO+dl43WYZnINfvAlhY90xIOIOrwUso30DwSfzfLLLRrmrhjnrZccn2jz/HYeo8IEEz2f72IwM52Od+49PLonXBt5U80aaTxh5E836xTHBPT8Dj9+IekTY/3c+zpzzG3mCn5PHFoMBrNfYQOZTUz7x5g0GG9T8n6u8ppGpw+2bqvBujKP72d1hPcCbQz54Yjn4VNt+qOZch0W2reSKc30W0WePLlft2YjasuHhddvVcRReYI83MDxeeDzzBtsxSMuex455IbnP7BuayPK0neHpdYe97VkPsXcE62TeYIWXriGiNiPrbu5bDovhlzN3Sbl5vvG6xu88bnjzbt9oOR93jseVXefay3D7uOq5yPrN3bBr+714XoVX57IdwG0Q2RtAT9so7tqXkRGVNrC3z7vwUuVEJLy6O7z7LB6vvLbwPGcw2b6hd8xzaGOwx1UOuaiUJ7r7wW6He3rv6mk7PCrtJh7TvB7zy5nzue7NfR2eyMQPbNxekYkFeHpdcL4vstt0djvVk3rrwoULHt3TMIDHh9Q8jtkry46zeNoW9+Say+POVS/PiGav9vrsh4zI8uRl5NLVhYFPXtn1ko1XuxHKi5Zjg4eNZ3uYVHS5yuPAC4a949g9mDc4HNrIGxz7AHI161JEuGN5Y8zGGC+87L7JCyY/B7tAc6e5uljx89sN/Kjitgxv3eSNbemO3TOOB7mrSj28iyuHdzAyz+3DStyumPh0z34ya6+bDV07wSIxeMonuM69hMLDRhG7mvP4ZKXIk9I+7rgee2gSl2FQg/uLCRTD6xUU3n6wt7m394Mn28oT9k2t8/FiV0jeZu8/Np5ddcsO72bU3la84XXc7xGV0915yL/xJphj+/nFY4gBTgYN2MWWDX1P9qHjMenYi4c39yyf8zHJp7LsFcSuv7yRsC8CvGl2TPLujfM8vCBhRNhLgEMkHIcMOvJ0WHlU9zfZ24Wf0bHOcJe8134/lpvld2Y3Hvj5eO7wi4Fr5gThfueF3s6z4oqr4X7c3o6NL/bq4c0gu+3zesJ97A7rFueeK65uQvl0lF9s6PCJJ49jlp/HV2SGJtj4Gl4T2XBwDGy5y5tEHGJgJ5V3LjOHGDDXjic9Z9nwY/3L3m/2U0qum/Utr8WO2NBiY5e9Kh2DWjx/o9OAd+QcRHO84WY9yesB610Gtdhw5fAA52s1n66yZ5Rjb2gGDtjw45At7mcGZ3jTHpkguDvcHswxynqF24MPN3hzxBsEBrZ4zDr3cPWFqH72iI59Bm55jnIIAo87fkY+wWX70R8ce5vZx61jr1jHZZxv7L3RS95bWI/whoO9LxyHztt4vjn2uohoaLY32hmeXnfYJucNDR9K8DvvL8LLvRNRm5HnC/cXh9e6ehAb3s0vg0XsRchj205fwc/I3hXO+fciwmu1Y44wT9s37PXAeyi+n2O+KrY9eM1k2RlI4HHI3jERHZvebqN4myfXSU+W8ScOMWUgmUEbDpNzvHbzmuN8HfK0rRUT7PYaz2PHc8luB7l6AOxJOzwq7SbWFbxPt4cVOnLs+RoVnpwjzqISP+Bn8EUswLnesO8JeN31tN5KkSKFy7Y2l3PsMcZAHtv27HXL9rM9/N3TtrgneJy42kbeuE+N1OyHzITPN+VOdsaNzOgtI4Rs5Ng3PM6zTfB3VlKeBivcYXdGx1whfErMLnL2QcfKmt317dmciN3quGMj89SB6+RBzZOTBywPAN4AsNsfL6RcNy+kvCA7VsBsZPBJdXQ/KwM0vDl37KnB9+G25A1AZBonrmYxioj9FJU3THw/+4vbkeODwzsQuf15IvCEs4M0rET4d3v7s6s+o/x2V0kbjyU+jWYj05My86kWjztuE56kjk/cuc8ZiGWDj09Q7ArODmjZkeyI3of7gceCcxdq3mywIcYnzlHlybbyBBuDvFHgeHLHpwmePJGNyrHBCpQVK2+6HI8NXvTYJZbDwVxhGfmU0DnPmPNxEJnzkE9WOF6bxw6xEchGPBu2dg9TT/ahfb46bzNW5s4353a5uI/4pM5u0PD8tLtM2/vPk+PLW+e5M9bHfELLfeW4n9hlmA0xT4ehRHV/E7cr34eNckc8VsPDLsmsH1i3O74fGzl86sxycF8yEGAfSywjJ/3gseKuZzHx/HJ8esZhK1wfh3c57hdexDmkh8uGl7zYxptcOwel4/s4euONN8KG2rCxwQYEb2IYGIpq4JL7mK93PG5ZB9iJa11hwJzDoBhI57XS8YtBDW4HBnzcJVe2cagi2wF2klT+zDqW55/zurlePsllXe2N5KCuOB4v9jHqiI1u9upmoIj73Z4owxGH2fKzs3Fnn8e8nnBYAv/Hp+72QxNv4fBdOwcTA4Q81rnNWA72pGMuqfCCnlEdTuZKVD47n6A7J2J2Pvb5OwMW/Ex2nWbPjBTZ3iBRuWY5s5/6O5bbucyulmFwMaJcdTGF5z2HD/N8Zq9V54CWfTPteD5EJY9WZNsZnl53+J29J3n9Yz3u2GZ3FlGbka/jOcLAqeN7cmgMzx9XM4MTl2dblvlleR9jH1tROTZZv/Ha5PhAku1lBhzd4XWG24EBAsdrKc81e0Y3XmfZ1rUD266OTfsBXGTbKDF1ztnldq77nfPMeVKf+BOvX3zAx3OBD3+c62X7IY3jl687IUQG21cso/MESAyCsu3E609k2+FRbTexrmDKFa7P3la8r+Yx79xmjCxPzhFnnsYPHM8HX8UCnGcOtHuWc5t5Wm8VKVLE1LuOw41Zx/C+2B7dZgeneC9kT2hiJ8X3pC0emfqRx5Hj+c997zjTZVRFKvzJMZDsOcKgFg8INm54gjLJGHMWsRFvB7y4cZkMlGOL+RSOBzjHozIZHA8UO6lkdPBiygs5TyheFLluRp7tZGm8+HGHsLJh0IMRZXaZ5MEQmfHhvEDyBpyVFy8K3Om8GPDz8EAi9gRhJJoHCJO/sUcaewjwAIpug5dP2HiA8r24fh5YfJLLA4ANg8hg4IQHE4cwOCaSdYeNHwaJOIaaN3w8afm0lo0nNpRcRW3t7c9tzx5IvADzZo3HCSO09hMCBnD4uVhxsXLlScobDL6O25snqB0hZsXLEyu8pwesNPmUiyeZY94eNgD4N1auDMxynzCJHSsZxyc/Eb0PnyAw+MEngNwnjLqzWy3HgrOrfHQu9p5sK0+xMmKjlmVk91HuKw7Nioin29kRzzuef0wyyZ9ZdnaJZXSfFVZ4ww5448+beNYXfPrISo4NBHdBrYjOQ/6P78d6gOcIj1t+dj4tshMCe7IPOUyRxw+PF97U8SLL849lc5xRzGYnouRQN94o82aHvQDt6c15fPGiyu3L/en8ZMQX57kzBi24Ln5+1pnM78Ftx8Yzc0fxvTwR1f1NPJ64fXgBZP3I7czGir3PXZ0/vL4w0MvgOYN9vHbwffg763Gug0Eh9qjjLDNchoFrNj64nXnz7Q4v+NzW3CYMRPGGgY0zx6A4sdz8H5+OR9SLituEkyBwKCwDJQwacWixY8CBxzufaLK+4jq5DXlssS6NzAxkjniNZSJTvq/dG45PiZkoNLwcHuxJxX0fXq8I1qk8Tuwpv4nb2L454/bjdmNPMwa1uN3sdfG84s12eMPW2X5g4IY9dOxhgTGJDXNuI17XeB1z9QSWxwLrZOYLYQCHCcq5LIOSPAbZcOXr7LwSjtzdwPKpfnj1Ot+T5WIw0k6qzEYm2zBsaLubcczO28TrO5d3zDMSWVH57Dz22RuaX3xvBjmcZyBlfckecqwreN7yRpZtpci2y4h1KhvmDExGNSEwgwUMGrBOYxuOQRjmMHTsgcVrCetM/p11uT1JjreHvEQVrze8EeX1ntvU+djzVi6nyLYzInPdYV3DwBSvA66GDdo8aTPakxrx/GGdxGOV6+Zy4Z0/PCe5b/kZ2F7kF3s62D1tInNs8rMwhxW3Da+XXC/vPTwJHvG6wJtvPpDjvYTde4UP1eyE32x723U6r4ls+/Bc5f0ZH4byvsuxV4inbRRP2O1EXrt57fJ0aLqr4D1vzllP8GEi2+TOvdtYn7AO4edmPcNjnNeVQMD7XV5rWU/w2OZNeXiTL0VXVK8lEeExwvOE9RrPRW5vBidYv9v38FG5H45Ku4nnJa8xbLPxwRgD52xT8DroyUyx7nhyjjjzNH7A84HXMOa3YuDIF7EABsd5rLFnP39m2dm2ZHuRATNP6q2OHTuaeprb154Flvdf/JxsNzrPzMp9wXOND/JZB3nSFvcUrxssH7eTPbMq6zTn+xC2OxhEjMz1K9J9+jishwc1K0TeBLJy5IWKFZR90bJxY7CyYsOZFyl2xbRn2fNGtJ8NPwYpWA7uOG54HnT2gcouoTyguON4QLFBxvKz8mFF6WlyU55cvCDy5ORn4g0IDyZeIOyGPht3vHHhwcYLKi+2PMB50xLR1K4R4euZe4U3g2xg8gDigciLpmOCd0/wQskbPvZk4IXC01xLXJaNODsJN5+8MfcLG7jhPR1mhcgKkvuf5WelxgYkT3TeSNi5Wjjkhuvjunnzzv3E/9uzQfImib0FuD95IrPbqyv2LHGsmB2TsvIY5L7jTSP3Py8APEl448kAKC+UbLRG9D68sWAFx3XxBtq+MecNvasZpCLD023lCR4TPN94vLBRxe3Jc8TVTGOOPN3OzjjjDC/s3He8CPFpBZ/wsBHmLjDGytXOQcAvPu1iF/nw3teT85A/83zncuwpwuOKn8VOaOvpPmRAi8cLy8Wbdm57ntuuJkRg5c4bIp7/fMrMizz/xtfzImYnaGQZGWjh3zjVrXPuJG+e5864nVln83PbT844PJcXYNahkRHV/U08lrk89w8vjKw3eU6yURXek3nWMdxv3DZ8T56/fB3rWfs857bmduMFlvuL1yEe++GN/7dxf/I6wjqIARjWHWw8OA+v5/7jtvMkFwYbCDyOWb+wcWEHWh1nt+XPPPZY5/FzscHFz8RyeBpgdIUNAzbcWFZ+Hh7TbCC6uhFgo5w3FSxveI07e2YyltP+7AwisHFEPEZ5LDAAwvPWnn2KeJPCY5r/c4VPLVk3MbDlLlDjK2yDsN7lscfzMbzAH4Ny/Ay8iWYdYA8j4LHDJ5U8Jtl4Z33i+KDI3kau8D1d1SWOxxvrKcchvqxTeB1wN/SQjVveAPO8ZF3DJ7PREdnPzjqdjVA+kOHxzXYh62qe4zYen3a+Q2IdzmOMDxt4LY4Mu/3HxjHr36jgTSHbaWw7sr5gXe9YPmIbh5+f78V6h3U8G+Z8Ss5gjr/ZvV25351nsyMGtr3Nk3ZGZK47vA7zWGP9HVGKkIjajEyVwmOQ9S7PbdaprId5jIR3g8RrCdsBbEOyvcB6jQ+fGJRje5nHpqvgtSts/7PtwO3B4591CxP+8/oY0XBwloPnG+tF1tvcnwyG8bW8AWTwgDeXvJfiduRNNXugcRuzbcHta+cximwbxRN8He+1+H4Mnrsb3u8O2zi8RtkPotke4udxHOrOusWeNZn7mg9ueB7avYH9iYEMexiYqzaUYw7H6IrOtSQidmCKxzmPNdbvfKjLdperNpkn7fCotJt4/rOuYBuGATN2WmF9wM8X3fsrT84RZ57ED3hdYH3HeoN1BB/++SIWwDLzvp3vxfqB5789a7yn9VaePHlMm5TbgPuWbQXuH6YDcDUKhO0jOwjOsrMu86Qt7gkGyNi+4fHD7cwy8zM551Pj5+K5H5nrV5z7vszc50PcEHw6F9VElyIiEnPYRZo90dhL17ELPi+YDICENywkELDhzhsN9u6MzjBQX2IvWj7RZQPQMQEwb+rY288xSbR4D4M4bCyy8RdI+VJiQih/dhF/4bBuBlRdDTMVCRTB0G6S2CV62ddEYgCj6eytwieivFljzwJ26fZ0xiWO82bUnE+VeGPNpwrsseZqqmzicBhGotloYCSewy0Z8bYTc4pI5HGoDp/M8CkSG+N8CsggDJ8oRTRM0F8YCOLwQdYH7E0UyA0zPlnjwx4Gtdg7kXUXb37Yi8MetiLex+FQdk/QUBPKn13EX/hgyBspXERCvd0ksYt6aklAY1dUjhXm+G4OZWC+C3afZndSds+OaLgTey+wSzq7tLN7M7vZcow2x+5zmAaPI0fsYslu4+xiyTxM7HbKHFMc3sobcj6RFpGo4dAJdudmMIvnFPNf8fxkkDm84V/+xGAQhz1wZjz+HJ2hgTGB+YvYXZ/bmUM7OJSK9RyHZouIiIj4UrC1myT2CNqglsR+TMzHhJvMvWInBbUTVHI8MXOLRJQAnctxeXaDtXMGMEEee4owaSiT1bEnFrEnGGchY44dDoey88xwDDV7PrAnBHOUBEpyWBEREREREZFQ5p25WUV8gEMO7SR5jt1XGf1nwkiO03Y3HTx7abFXFhOQOybB5BBGDl9kPNcxqSqnqOXfuH7HxMnsJcaEvZwxgzN9ioiIiIiIiIj/KaglAYtDaTgrCgNYzp599lkTgHKeLtwRh6dyGVcz6bD3F7vEOr7eTl7NYJizU6dOPbCMiIiIiIiIiPiXgloSkJjLigneM2TI4DLJoJ1Li8ncw3P48GHzPUuWLP/5HwNaGTNmxPHjx817EYNfWbNmNb27OB00k8qfOXPG5Nlijhrm2Iooh5eIiIiIiIiIxAzNfigB6eLFi6aXVapUqVz+P0WKFOb75cuXw10Hk8OTu3Xcu3cPV65cMbM4MdDFmdh69+6Nzz//3HzZmMjaOam8O1wvEzUzD1cgJsAWEREREZGYwfsa3h9wFArvD0TEexTUkoB0+/Zt8z28qWDtv9+8eTPa67B7avFiwx5aq1evxpNPPomSJUuaGdqYu2vq1KlInz492rdv71H5GdDasWOHR8uKiIiIiEjsly9fvnDvTUQkahTUkoDEZO6OgSlndiAqadKkXlvHqFGjTFCLwwy//PLLsAsOe3y1bdsWAwYMMEMZq1atGmH57ScwefLkQbx48SJcXmKfu3fvYvfu3ToGREKc6gIRUT0g9jGgXloi3qeglgSk5MmTm0o/vOGF9t/tYYiu2MMOL126FO46ODSQ70X2TIjvv//+A09QmBz+3XffNUMQp02b5lFQyx5yyPWo8RK6jRfSMSAS2lQXiIjqAbGPAaUlEfE+hYolIPGiz6TsTBbvqqfVsWPHzPds2bKFuw4mfaejR4/+539c54kTJ8wwQ/uJCd8rderUSJs27X+Wz5EjR7gzI4qIiIiIiIhIzFNQSwJWsWLFTPBp8+bN//nf2rVrzZOOQoUKuX09l/n999//87+NGzeadRcuXDjsb4888ojp1cUZD8ObSZHLiIiIiIiIiIj/KaglAatBgwbm+8CBA3Hjxo2wvy9dutQEpcqXL48MGTKE+3r+j8ne169fj2XLloX9nesaNGiQ+ZlDCm01a9Y0s5L079/fJHq3Xb16FZ999pn5uXbt2l7+lCIiIiIiIiISFcqpJQHrmWeeMUGnCRMmoE6dOqhQoQJOnjyJRYsWmSGC77zzTtiy7I3F4FXu3LlRsWLFsL/36tULjRs3xuuvv45q1aqZGQyXL19uel4x+TuXt3Xo0MGsY/78+SaRY5kyZczsh5wNkUMVmUvLDrSJiIiIiIiIiH8pqCUBrXfv3iY3FpO4jxs3zuS8ql69Orp06WJybtkYjBoyZAjq1av3QFCLObP4WvbMWrNmDW7evIknnngC/fr1wwsvvPDAeyVJkgRjx441syAysDVx4kSTzJN5uxjwatiwoZI7ioiIiIiIiASIOPfv37/v70KIxMYZTrZu3YqCBQtqlpsQpWNAREh1gYioHhAdAyK+o5xaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIiIiIiISdBTUEhERERERERGRoKOgloiIiIiIiIiIBB0FtUREREREREREJOgoqCUiIiIiIiIiIkFHQS0REREREREREQk6CmqJiIiIiIiIiEjQUVBLRERERERERESCjoJaIiIiIj507949fxdBREREJFZSUEtERETEizZv3ozOnTujYMGCSJIkCYoVK2a+83f+nf8XERERkeiL74V1iIiIiIS8gwcPom3btli9ejXix4+PO3fuhP3v9u3b2LZtG3bt2oUhQ4agTJkyGDlyJLJly+bXMouIiIgEMwW1RERERDxw4sQJ8+XKokWL0KdPn7Chho4BLUf233/99VfkyZMHH374IapWrepy2YwZM5ovEREREXFNQS0RERERDwwfPtwErrzh7t275uu9994zX6588MEHJuglIiIiIq4pqCUiIiLigfbt26N27doP/O3o0aNo2LChGV4YVQkSJMC0adOQOXPmB/6uXloiIiIi7imoJSIiIuIBV8MBu3btivv370drvXz9gAEDsGrVqmiWUERERCS0aPZDERERkSjYtGmTSQofXv4sT/H1XI9mRRQRERGJHAW1RERERKJg9OjRZpZDb+B6Ro0a5ZV1iYiIiIQKBbVEREREouCXX36Jdi8tG9ezZs0ar6xLREREJFQoqCUiIiISBbt37/bq+nbt2uXV9YmIiIjEdgpqiYiIiETSvXv3ojXjoStcH9crIiIiIp5RUEtEREQkkuLGjYsECRJ4dZ1cH9crIiIiIp5Ry0kC3uzZs1G/fn0888wzKFGiBLp3746//vrL49f//fff6NmzJ55//nkUKFAAderUwdSpU8Ndftu2bXj11VdRvHhx855169bF5MmTcffuXS99IhERiQ3y5Mnj1fXlzZvXq+sTERERie0U1JKANnDgQPTo0QM3b95E06ZNTVBr4cKFaNCgAY4dOxbh6xn8atSoEebPn2+CVM2aNcP169fRu3dvfPrpp/9ZftGiRWjSpAk2bNiASpUqmfe5dOkSPvjgA3zyySc++pQiIhKMSpcu7dXZD0uVKuWVdYmIiIiEijj379+/7+9CiLiyd+9e06uqcOHCZtr0hAkTmr8vXboUnTp1Qrly5TBs2DC36+ByXP777783PbXoxo0baNWqlemRNX36dDz99NPm76dPn0bVqlWRJEkSjB8/Hk888YT5+7Vr19CwYUMcOHDABNSeeuqpCMvOXl1bt25FwYIFES9ePC9sDQk2OgZEYr/Nmzeba5S3bNq0CYUKFfLa+kQkMKhNIDoGRHzHO48XRXxg7Nix5vtrr70WFtAi9qAqWrQofv75Z5w8eRLp06cPt5fWsmXLzBBCO6BFiRMnRteuXU1ga8qUKWFBLQa4rly5Ynpx2QEtSpo0qVl+xYoVuHz5sg8/sYiIBLITJ06YL+chg9GdtZA3OPnz5w8LlNkyZsxovkRERETENQW1JGCtW7fODMdgAMvZs88+a4YIchn25nJl/fr1YEdEDll0xifrTMjL19tWrVpl/sagmbMKFSqYLxERCV3Dhw9Hnz59fPIEf8uWLf/p9cWh7x9++KHX309EREQktlBQSwLSrVu3TIL3TJkyPdBLy5Y5c2bz/dChQ+Gu4/Dhw+Z7lixZ/vM/Bq/49Pv48ePmvfj7vn37zPtxOnXm21q8eDHOnj1rem21aNHCDEEUEZHQ1b59e9SuXdv8vHv3bjPE/eLFi3jkkUfMEPao+vjjj83wd2fqpSUiIiLinoJaEpB4k8BeVqlSpXL5/xQpUpjv7oYDnj9/3nx3tw4GsDjkkEEt5s6ixo0bm/cvX768+f/y5cvNkEQG0DiLYmRoxsTQZe97HQMisUe6dOnM18qVK9GhQwdz/ShSpIiZjIT5G9u1a2fO+Tt37kS4rrhx45prDD3++ONmdl5XVIeIBD+1CUT7XsR3FNSSgHT79m3z3VUvLce/c1bE6K6DPbXsZdm7K3v27Jg7dy7SpElj/vb666+bQNeoUaNQuXLlSCXx3bFjh8fLSuykY0AkdmE+x3fffddcOzg8/ssvvzS9fnPnzo3Jkyejb9++Zigh82S5uomx/85kwexJPGvWLLRt29b8zc7xKCKxk9oEIiLep6CWBCQmcyc72OSMNxN2EndvrMMxOPbWW2+FBbSIT+VfffVV01trwYIFkQpq5cuXTzOchCjeoLLxqmNAJHZNYNKjRw9zfjOf44QJE8KuNcRAVa1atUyy9zFjxmDNmjVmmCKvQ+wRnCdPHpQqVcpMVMJrCdfToEED09PrnXfeMXkeOQxeRGIXtQnEPgZExPsU1JKAlDx5cjM0I7zhhfbf7WGIrtjDDi9duhTuOuLEiWPey7E3FxsczngjQkeOHInU52DDRY2X0KZjQCR2GDx4MN544w3zc+vWrTFixAgzmYkr7MFlT3LCGxkGuRjEcq4L+PvEiRPx3HPPYefOnSbAxUlL3D2wEZHgpTaBiIj3xfXBOkWijUEmJoNnsnhXPa2OHTtmvmfLli3cdWTNmtV8P3r06H/+x3VyWvYnn3zSBM/4pN1OyOvq/ey/JUmSJBqfSkREgg3zO77//vthAa2uXbti5MiR4Qa0XOF1Jjx8OMMh7w8//DA2btyINm3amPcUERERkYgpqCUBq1ixYiaYxCfcztauXWt6WbkbCsjXc5nff//9P//jjQPX7Th9OpenX3/99T/L292Fc+bMGeXPIyIiwYWJ3Dt37oyPPvrI/N6vXz989dVXboNUUcEHLDNnzjSBsilTppjZEEVEREQkYgpqScDiMAwaOHAgbty4EfZ3zjDFoBRnJ8yQIUO4r+f/SpYsifXr12PZsmVhf+e6Bg0aZH5u1qxZ2N+bNGligmDffPMNTp48GfZ39uj6/vvvTe8x5lAREZHYjw8+WrRogW+//dZcG7777ju899575mdfKFOmDIYOHWp+Zg5HBrlERERExD3l1JKA9cwzz5igExPxMphUoUIFE2xatGgR0qZNa5Lq2tgbi8Erzj5VsWLFsL/36tXLzFzIGQyrVauG9OnTY/ny5WaWQ842xeUd369Tp04mqMVEv1WrVjV/X7JkCS5cuGBuMjgkUkREYrfr16/jxRdfNJODsPcUE8TzwYevtWvXzuTWYv4uBtQ4jJ7J50VERETENQW1JKAxkMRGPYdjjBs3DqlTp0b16tXRpUuXBwJMDGgNGTIE9erVeyCoxSEdfC17ZnEWKs5y+MQTT5ghJC+88MJ/3o9BLQa6Ro8ejXnz5pkhJvydNxply5aNsc8tIiL+cfHiRfNg45dffjH5FmfMmGGuOzHlyy+/xJ49e/DTTz+hdu3a2LBhg3kgIyIiIiL/Fee+spGKeB1nu9q6dat5wq5ZbkKTjgGR4HPq1CnTS3fLli1ImTIl5s+fj9KlS0drnVGpC9g7uHjx4ti/fz9KlCiBlStXIlGiRNEqh4j4j9oEomNAxHeUU0tERERC3pEjR0wAiwGtdOnS4eeff452QCuq2CuZvYX5nROjvPLKK5oRUURERMQFBbVEREQkpO3duxelSpUyPaOyZMlihqszz6I/5ciRA1OnTjVP9JnTi7MuioiIiMiDFNQSERGRkMXZdNkj6/jx4yaHIgNa2bNnRyCoVKlS2Gy9b7/9tklcLyIiIiL/p6CWiIiIhCQOMSxXrhzOnDmDIkWKYPXq1XjssccQSF577TW0b9/eDD/kDIy7du3yd5FEREREAoaCWiIiIhJy5s6da5LCX7lyxQS2VqxYgbRp0yLQxIkTB998842Zgffy5ctmRkQG4UREREREQS0REREJMcxRVb9+fdy8eRN16tTBwoULkSJFCgSqBAkSYPr06ciaNSsOHTqEF154Abdu3fJ3sURERET8TkEtERERCRmDBw9Gq1atzPTqrVu3NsGixIkTI9A9/PDDpncZg2+rVq1C586dNSOiiIiIhDwFtURERCTWYwDogw8+wBtvvGF+5/eRI0cifvz4CBZ58+bFpEmTzJDE77//Ht9++62/iyQiIiLiVwpqiYiISKx27949dOnSBX379jW/f/TRRxgwYADixg2+ZlCNGjXw+eefhwXmli5d6u8iiYiIiPhN8LXmRERERDx0+/ZtM9yQydZpyJAh6NWrl+ntFKy6desWNoSyYcOG2L9/v7+LJCIiIuIXCmqJiIhIrHT9+nWTEH78+PFmmOGECRPw2muvIdgxIDd8+HCUKFECFy5cQK1atXD+/Hl/F0tEREQkximoJSIiIrHOxYsXUbVqVcyfP98kgp89ezaaNm2K2CJRokSYNWsWMmfObHpqNW7cGHfu3PF3sURERERilIJaIiIiEqucOnUK5cqVw+rVq5EyZUosWbLE5KKKbdKnT29mREyaNCl++ukndO/e3d9FEhEREYlRCmqJiIhIrHH06FGULl0aW7ZswSOPPIKff/4ZZcqUQWxVsGBBjBs3zvw8ePBgjBgxwt9FEhEREYkxCmqJiIhIrLB3716ULFnSDMd7/PHHsWbNGjzzzDOI7Zg3jDM6UseOHbFq1Sp/F0lEREQkRiioJSIiIkFv06ZNpofW8ePHkStXLhPQypEjB0LFe++9F5ZXq0GDBvjzzz/9XSQRERERn1NQS0RERIIaeyYxh9aZM2dQuHBh/PLLLyaBeijhjIg//vgjihQpgrNnz5oZES9duuTvYomIiIj4lIJaIiIiErSYKL1KlSq4fPkyypYtixUrViBt2rQIRUmSJDGzPGbMmBG7du1Cs2bNcPfuXX8XS0RERMRnFNQSERGRoMQE6cwndfPmTdSuXRuLFi0ysx2GskyZMpnAVuLEiTF//nwzLFFEREQktlJQS0RERILO119/jZYtW5qeSPw+Y8YME8gRoFixYmYoIn322WdhsyOKiIiIxDYKaomIiEjQuH//Pvr06YMuXbqY3/l91KhRiB8/vr+LFlCaNGkS1kurXbt2WLdunb+LJCIiIuJ1CmqJiIhIULh37x7eeOMNfPjhh+b3vn37YuDAgYgbV80ZV7h96tati1u3bpnvx44d83eRRERERLxKrUAREREJeLdv30br1q3NsEP65ptv0Lt3bzPrn7jGYB+HHubPnx8nT55EnTp1cPXqVX8XS0RERMRrFNQSERGRgHb9+nU0aNDABGjixYuH8ePHo1OnTv4uVlBInjy5mSHykUcewZYtW9CqVSvT401EREQkNlBQS0RERALWpUuXUK1aNcybN88kgufMfs2aNfN3sYJKlixZMGvWLCRIkMAk1OewRBEREZHYQEEtERERCUinT59GuXLlsGrVKqRMmRJLlixBzZo1/V2soFSyZEl8//335mcm2p86daq/iyQiIiISbQpqiYiISMBhUvPSpUtj8+bNZujcypUrUaZMGX8XK6gxJ1m3bt3Cft60aZO/iyQiIiISLQpqiYiISEDZt2+f6VnE75kzZ8Yvv/yCQoUK+btYscJnn31mhnMyTxkTx584ccLfRRIRERGJMgW1REREJGCwZxZ7aLGnVq5cufDrr78iZ86c/i5WrMFE+5MmTULu3Lnx119/oW7duibAJSIiIhKMFNQSERGRgMDcWWXLljW5tAoXLozVq1ebnlriXalSpTIzIqZJkwbr16/Hyy+/jPv37/u7WCIiIiKRpqCWiIiI+N38+fNRtWpVXL58Gc8//zxWrFhhcmnFBvfuIeBky5YN06dPR/z48TFhwgQzLFFEREQk2CioJSIiIn7FoAqHwd24cQO1atXCokWLzGyHwWrzZqBzZ6BgQSBJkrgoVqyw+c7f+Xf+PxBwZslvvvnG/Pzuu+9izpw5/i6SiIiISKQoqCUiIiJ+M2TIEDRv3hx3795FixYtMGPGDCRJkgTB6OBB4PnngcKFgWHDgG3bgNu345j/8Tt/59/5fy7H5f3t1VdfxWuvvWaGHzZr1gzbt2/3d5FEREREPKagloiIiMQ4BlH69u2Lzuy6BOD111/H6NGjkSBBAn8XLUomTgSefhr47Tfr9zt3XC9n/53LcflJk+B3AwcORIUKFXD16lXUrl3b5DQTERERCQYKaomIiEiMunfvHt544w188MEH5vc+ffpg0KBBiBs3btAGtJo3B27eDD+Y5YzLcflmzazX+xMDiVOnTjV5to4cOYL69evj1q1b/i2UiIiIiAeCs/UoIiIiQenOnTt46aWX8PXXX5vf+f39999HnDjWML1gc+AA0KYNe55F7fV8HV/v76GInAlx3rx5ZmbENWvWoEOHDpoRUURERAKegloiIiISI5gIvkGDBhg7dizixYuHcePGhQ0/DFbt2gF370ZvHXx927bwu1y5cmHy5Mmmx9yPP/6IwYMH+7tIIiIiIm7Fd/9vERERkei7dOkS6tSpg59//hmJEiXCtGnTzEyHweTECevLtmcPsHp19NfLoYhcz4QJQO7c//97xozWV0yqWrUqvvrqK3Tt2hXdunUzgS7+TURERCQQqaeWiIiI+BQTj5cvX94EtFKkSIElS5YEXUCLhg+3Zi60v5hHy5u4Psf18/38oUuXLmjTpo3JfdaoUSPs3bvXPwURERERiYB6aomIiIjPHDt2DJUrVzaBkbRp05qAVqFChRCM2rcHatf+/++NG1s5tbwlR44HZ0OM6V5aNuY3++6777B//36TX4sByN9//93k3RIREREJJApqiYiIiE8wKFKxYkUT2MqcOTN++uknM5wtWDkPBzx82Lvr//NPIFDifRwiOmPGDBQrVgwHDx5Ew4YNsWjRIjNTooiIiEig0PBDERER8brNmzejVKlSJqCVI0cO0+MnmANazu7dA27f9u46uT6uN1CkS5cOc+fORbJkybB8+XKTZ0tEREQkkCioJSIiIl61evVqlCtXzuTS4lDDX375BY8//jhik7hxAW93WuL6uN5Akj9/fkyYMMEMSfz2228xdOhQfxdJREREJEyANZ1EREQkmC1YsABVqlQxsx2WKVMGK1euND1+YqM8eby7vrx5EZA4a+XHH39sfu7cuTNWrFjh7yKJiIiIGApqiYiIiFdMnDgRdevWxY0bN1CzZk0sXrwYKVOmRGxx4gSHVf7/K2dOIF4876yb62GieMf18/0CRc+ePdG0aVPcvXsXL7zwgsmzJSIiIuJvCmqJiIhItHFoWvPmzXHnzh3zfebMmUiSJAlik+HDgcKF//81dSpw96531s31cH2O6+f7BQoOP/zhhx9M4vjz58+jdu3auHjxor+LJSIiIiFOQS0RERGJsvv37+Ojjz5Cp06dzM8cnjZmzJhYOUte+/bApk3W18aNwEcfeScHFntpPfPM/9dtf/H9AgmDlLNnz0amTJmwZ88eNGnSxPTcEhEREfGX+H57ZxEREQlq9+7dQ7du3TBo0CDz+wcffGC+2KsnNsqY0fo6dw7o0MHqWUX8uPfvR3298eNb68qWDQEvY8aMmDNnDkqXLo1FixahR48e+PLLL/1dLBEREQlR6qklIiIikcZhhm3atAkLaPH7hx9+GGsDWralS4F8+awgFHtY9e0LjB1rBbai6p13giOgZStcuDBGjx5tfv7qq68watQofxdJREREQpSCWiIiIhIpTATPZOEcZhgvXjzzvUuXLojNrl8H+BErVwb+/ttKEr92LdC7N9C8OTB+PJAokdXryhMMiNmBsK++AtatQ1Bp2LAh3n//ffNz+/bt8euvv/q7SCIiIhKCFNQSERERj12+fBnVq1c3Q9ASJUpkEsK3bNkSsRlnImTi9q+/tn7v2NH6W9Gi/1+maVNg507guees38MLbtl/L1UK2LYNKFuW2xSoUgVYvx5BhUNNGzRogNu3b6NevXo4cuSIv4skIiIiIUZBLREREfHImTNnUL58eaxcuRIpUqTA4sWLzSx4sRVzoPfvDxQvDuzZA2TIACxcyJkegaRJ/7s8hxCuWmUleX/1VaBgQSBBAivZFr/zd/6d///5Z2sY4/z5QJkywKVLVi8wJqAPFnHjxjW99AoWLIjTp0+bY+HKlSv+LpaIiIiEECWKFxERkQgdP34clSpVwt69e5E2bVoT0GJupdjqzz+BFi0Ae1Rd/frA8OFA2rQRv7ZQIeuL7t69h82bt6JQoYJmqKazZMmABQuA6tWBX34BKlUCli///+sDXbJkyUyvvWLFimH79u1o0aIFZsyYYQJeIiIiIr6mFoeIiIi4tX//fpQsWdIEtB577DH88ssvsTagxVkMmfc8f34roJUiBcCc6NOnexbQciWi+E7y5FZgq2RJ4MIFoGJFYMsWBI3HH38cs2bNQsKECTF79uywXFsiIiIivqagloiIiIRr69atKF26NI4ePYocOXKYhOC5cuVCbHT6NNCgAdCmDcBRdHbeq1atoje7oScYPOPQxhIlgPPnrcAW3ztYlChRAiNGjDA/f/zxx5g0aZK/iyQiIiIhQEEtERERcYk9sp5//nmcOnUKzzzzjPmdvXJiIwaUmONq1izmvwI+/dTKe/XkkzFXhpQpgUWLgGLFgHPngAoVgB07EDQ4YcDbb79tfm7Tpg02bNjg7yKJiIhILKegloiIiPzHggULULlyZVy6dAllypQxyeHTpUuH2ObqVaBDB6BGDeDkSSBPHuD334EePQAXKbB8LlUqYMkSoEgR4OxZK7C1axeCxieffIKaNWvixo0bqFOnDv766y9/F0lERERiMQW1RERE5AEcOla3bl0TmGCAgknhUzHaEsusXw888wwwbJj1+xtvWDMT8m/+lDo18NNPANOWcUhk+fLA7t0ICkyGP2HCBOTNmxcnTpwwx9H169f9XSwRERGJpRTUEhERkTDfffcdmjVrhjt37pjvM2fORJIkSRCb3LkD9OkDPPcccOAAkCkTsHQpMHAgkDgxAsJDD1mBLQbYTp2yAlt79yIopEyZEnPnzsXDDz+MjRs3mqGI95mBX0RERMTLFNQSERERE3To168fXnvtNfNzp06dMHbsWCRggqlYhEEszjL44YfA3btAo0bA9u1WYvZAkyaNFWwrUMAaGlmuHLBvH4JC1qxZMWPGDMSPHx+TJ082wxJFREREvE1BLRERkRB37949dOvWDb179za/v//++/j6668RN27saSawo9Dw4UDBgtawQ46mnDABmDzZCh4FqocfBpYts5LY//OPFdhiYC4YcJIB9vyjXr16YRaz8IuIiIh4UexprUqsNXv2bNSvX9/MvMUpw7t37x6pxLN///03evbsaRrXBQoUMIlrp06d6tFr//jjD+TPnx8tWrSIxicQEQlcHGbYtm1bDOTYOwCDBg1Cnz59ECdOHMQW7OVUuzbw6qvAtWtWYIizCjZtiqCQNi2wfDnw9NPAiRNW+Q8eRFB4+eWX8frrr5ufmzdvjq1bt/q7SCIiIhKLKKglAY03WT169MDNmzfRtGlTE9RauHAhGjRogGPHjkX4ega/GjVqhPnz56N48eImPwwT1rI3wqecrz2CGz1OTc73FhGJjZgI/sUXX8To0aNNgu8xY8agS5cuiE3mzLGCQfPnA4kSAQMGWD2fMmdGUHnkESuwxdkZ+VyHga1DhxAUvvrqK1SqVAnXrl1D7dq1cZJRRhEREREvUFBLAtbevXsxbNgwFC5c2AxZeOuttzBgwAAMHjwY58+fx8cffxzhOvr3749Tp07h22+/xeeff26CVExeW7BgQXMTt3PnznBfy/d2938RkWB2+fJl1KhRw/SGTZQokcl/1LJlS8QWly8D7doBdesCZ84A+fMDGzcCXbsCwTqqMl06YMUKIHdu4PhxK7D1558IeMyrNWXKFOTIkcM8kGLvaz0wEhEREW8I0madhAImKCYmLU6YMGHY3/m0t2jRovj555/dPu1lL61ly5aZYYscemhLnDgxunbtahIhs5Htyq5du0xQqzynmxIRiWXOnj2LChUqYMWKFUiePDkWLVpkhmbHFr/9ZuXOGjkS4CjKt9+28mixx1awS5/eCmzlzAkcPWoFtg4fRsB76KGHMG/ePKROnRq//fYbXn31Vc2IKCIiItGmoJYErHXr1pmnuwxgOXv22WdNY5jLhGf9+vVmGQ5ZdMbeX5zRy9Xrb926ZYY8ZsqUCW+88YYXPomISOA4fvw4SpcujQ0bNuDhhx/GypUrUY6RkVjg1i3gvfeA0qWtoXmPPw6sXAl89pk19DC2yJDBCmxlzw4cOWIFthjgCnTsqcWclhzqyt7S7H0tIiIiEh0KaklAYmCJCd4zZMjwQC8tW+Z/k6EccpNQ5PC/j66zZMnyn/8xoJUxY0Zzc8f3cs7jxQTxzLnFXl0iIrHFgQMHUKpUKezZswePPfYYfvnlFxQpUgSxwZ49AJ9hfPIJZ3MEOL/H9u2cgQ+x0qOPWgG7bNmsnloMbHmQatLv2NvanpSAaQWYJ1NEREQkquJH+ZUiPnTx4kXTyyoV51x3IUWKFGE5YcLDvFvkbh2cxv7KlStI8+987hs3bjRPj1966SUzbPEIH4FHw927d6P1egle9r7XMSCBgrPOVa9e3eQZzJ49OxYvXmyC/sF+jDKA9d13cdCzZxzcuBEHadLcx3ff3cMLL1j/9/fH82VdwB5bTHpfvnxcHDoUB+XK3ceKFfeQKRMCWocOHbBjxw6MGDECjRs3xq+//oo8zIAvEkupTSDa9yK+o6CWBKTbt2+b7656aTn+3V2iWU/XYffUunr1Knr27Iknn3zSa8MO2WiX0KZjQAIloMV6jUF8DgEbMmSICfzbwf9gdfp0AvTpkwXr1lkPL5599iI++OAIHnnkNrZuRcjUBV9/nQDt2+fEH38kQunStzF8+H6zDQJZmzZtsGnTJmzevBnVqlUzM28y35ZIbKY2gYiI9ymoJQHJHvZnB6ac2YGopEmTem0dn332mRnyOHny5HADYZGVL18+kztEQvOJHBuvOgbE3zi8q3Pnzrh+/boZejhnzpxwe7AGkxkz2OMnLs6di4PEie/js8/uo2PH5IgTJy9CsS5YvRqoUOE+Dh9OjDfeyIdly+4hY0YE/LHJvJd//vknPvroI9N7kOkBRGIbtQnEPgZExPsU1JKAxNm44saNG+7wQvvv9jBEV+ybtkuXLoW7jjhx4pj3WrVqlZkJsX379sjPed+9hA0XNV5Cm44B8adJkyahZcuWuHPnjhl6OG3aNLcPA4LBxYtA587AuHHW74ULA+PHx0GuXHEQynVB1qxWji3mENu3Lw4qVYpnfucQxUCVPn16MyMiJ3/hdZi9CYcOHWquzSKxkdoEIiLep0TxEpDYU4rJ4NlzylVPq2P/ZsPNxgy54cjKFj44I9R/p4TiOk+cOGGGGjJ4xunsafjw4ciZM2fYV+XKlcNmUuTvLZh5WEQkCDA40KxZMxPQatq0KWbPnh30Aa1VqwA+d2BAK25ca6bD334DcuXyd8kCwxNPWIEtzqWydy97bgGnTiGg5c2b1wRfGcjiNfi7777zd5FEREQkiKinlgSsYsWKmV4FzLdRvHjxB/63du1a0wAuVKiQ29dzmd9//x2dOnV64H9MCM/AVmE+4gdQsWJFZHKRWZcJ68eNG2f+V69ePZfLiIgEEk6y0b9/f7zHiA+A1157DV9//bUJ4Acrpk/s3Rv48kt+PqtXEgNbzz3n75IFHsceW7t3M4m89fsjjyBg1axZ06QAePvtt9GlSxfzEInXZREREZGIKKglAatBgwYmqMWpvzkjoZ0ja+nSpSYoVaFCBWRwM66C/ytZsiTWrFmDZcuWhTWQb9y4gUGDBpmf2YuB+D9XDWjOfmgHtZiTRkQk0ANa3bt3x4ABA8zvvXv3Rp8+fYJ6OBdTkDRvDmzfbv3erh3Aj+dm9HnIe+opK5BVtiywa5fVY2vFCiBtWgQsHrc7d+7E2LFj8eKLL5oe0pylU0RERMQdBbUkYD3zzDMm6DRhwgTUqVPHBLFOnjxphgqmTZsW77zzTtiy7I3FBnDu3LkfCE716tXLTBf++uuvm9mVmL9j+fLlOHz4MNq2bWuWFxGJDTjM8JVXXsGoUaPM73wg4K2ZXP3h3j2Azx9Y1XNeDwZkfvgBqFPH3yULDowHMZDFwBYDg7w0Ll8OPPwwApI9/PDAgQOmN3atWrWwbt06zYgoIiIibgXvWAQJCexlwC/m2GKPKQaumOyYMxQy55aNf+cU9eyR5Yg5s5gAnrmx2GNr4sSJSJIkCfr164e33nrLD59IRMT72AO1YcOGJqDFYYb8HswBLaZCZBCmWzcroFWjBrBzpwJakZUzp9VjK316YNs2oFIl4Nw5BCz2yJ45c6a5vu/bt888lGKwVkRERCQ8ce5zrIKIeH3a3q1bt6JgwYKa5SZE6RiQmMKZXJnzj71Q+QCAgfy6desiWE2cCHTsaM1yyLz2AwcCL7/MnjwISoFQF+zZY/XYYtJ4pqLk85+HHkLA2rJlC0qVKoVr166Z4Cx7HYoEs0CoB8S/dAyI+I56aomIiASps2fPmiHXDGglT57cDM8O1oDW+fNAkybMdWgFtDg/yNatwCuvBG9AK1BwpD2HIjJZ/ObNACf2vXABAZ1+gLm1iDkwf+C4UxEREREXFNQSEREJQn/99RfKlCljhl8//PDDWLFiBcpzqrsgxJ5D+fIBkycDfIDdpw+wZo2VF0q8I29eK6cWc5Nt3AhUqWIFDwN5spi+ffuanzt27IjVq1f7u0giIiISgBTUEhERCTIHDx40s7vu3r3bzM7KG/6iRYsi2Fy/DjD1F3M9/fWXFcT67Tfg/feB+JrKxusYOGQAMU0a5qIEqlYFLl1CwOJkL40aNcLt27dNkOvPP//0d5FEREQkwCioJSIiEkS2bdtm8g0dOXIE2bJlw6+//oo8efIg2GzZAhQpAgwebP3eoYP1t2LF/F2y2K1AAavHFnNqrVsHVKvGvGwI2BkRf/zxRxQuXBhnzpxB7dq1TQ45EREREZuCWiIiIkGCAaznn38eJ0+eNMlmOatrlixZEEzu3gU+/dTKmbV7N5AhA7BwIfDdd0CyZP4uXWgoWNDqsZU6tdUzrnp14MoVBKSkSZNizpw5yJgxI3bu3IlmzZqZhMsiIiIipKCWiIhIEFi8eDEqVaqEixcvmp5aK1euRPr06RFMOHqMs/C98w5w+zZQrx6wY4fVW0hilj0LYqpUVv6yGjWAq1cRkDjEdvbs2UiUKBHmzZtnhiWKiIiIkIJaIiIiAW7KlCmoVasWrl+/jmrVqmHJkiVIzW42QeL+fWD0aGvoGwMoyZMDo0YBM2ZYicvFPwoXBpYuBVKmBJiHvWZN4No1BKRixYqZoYj06aefYvz48f4ukoiIiAQABbVEREQC2PDhw9GkSRPcuXPHfGePFQ7JChZnznAmO+Cll6zcTSVLAtu3A61bM2eSv0snnF/gp5+AFCmAn38GatUK3MBW06ZN8e6775qf27Vrh3VMCiYiIiIhTXMLhRjeDHlL3bp1vbYuERF50P37902PFPsmvkOHDhgyZAjixg2e51GLFgFt2gD//AMkSAD07Qu89RYQL56/SyaOmN9s8WKgShVgxQqgTh1g7lwgSRIEnI8++gi7du0yebbYDtmwYQMyZ87s72KJiIiInyioFWJ69uxpZhPyBgW1RER8F9B6++238eWXX5rfmUOob9++Xqu/fY09fRi8YvJ3yp0bmDABeOYZf5dMwvPcc1YQsmpVK9cWL/Fz5gCJEyOgMKjLoYclS5bE9u3bUadOHfzyyy9IplkGREREQpKCWiEoV65cqFChQpRfv2zZMuzbt8+rZRIREQuHGbZv3z4sf9CAAQPQtWtXBIsNG4DmzYH9+63fu3QB+vcPzF4/8qBSpazAFhP3c0giE/nPmhV4ga3kyZNj7ty5KFq0KLZs2YLWrVubvHPB1ItRREREvENBrRANanXq1CnKr//rr78U1BIR8YGbN2+avEEzZ840N+g//PADXmIyqiBw5w7wySfWEMO7d4FHH7WSw1eq5O+SSWSULg0sWABUr24NSWQ+tJkzgUSJEFCyZMlizpPy5ctj+vTpZljiBx984O9iiYiISAzTI60Qw6ebSaL5uJxTanM9IiLiPVeuXEHNmjXNjXrChAnNjXqwBLQOHLB6+TCmwIBWo0bAjh0KaAWr558H5s+3etctXAi8+CJw6xYCTqlSpTBs2DDz84cffohp06b5u0giIiISwxTUCjEbN27E+++/H611sOHIxKwiIuIdZ8+eRcWKFc3wbuYGWrhwIepx7FeAu38f+P57oGBB4PffgVSprNxZkyYBadL4u3QSHeXKAfPmWUMP+b1hw8AMbLVp0wZvvvmm+blVq1bYvHmzv4skIiIiMUhBLRERET/6+++/8fzzz+P3339HmjRpsGLFimjlPYwpJ08CtWsD7dtbieHLlgW2bweaNgWCJJ+9RICHIZPFc+ghvzduDNy+jYDz+eefo2rVqrh+/bpJHH/ixAl/F0lERERiiIJaIiIifnLw4EEzi9uuXbuQKVMmM4tbsWLFEOjmzgXy5bOGqCVMCHCSxuXLgccf93fJxNsqVwZmz7b2M5PGM2gZaIGtePHiYfLkySZn6PHjx00vxxs3bvi7WCIiIhIDFNQS3L1719xIMRfFTz/9hP379+PevXv+LpaISKy2fft2kxPo8OHDyJYtG9asWYM8efIgkF25ArRrB9SpA5w+bQW2Nm4EunUDNPFc7FW1qhXQYmBr+nRrdktODBBIUqVKhXnz5uGhhx4yvR5ffvll3Of4WBEREYnVNPthiLt48SJatmxpAlmOmKQ4e/bs5gYrd+7c5nvOnDmRONDm9RYRCUK//fYbatSogQsXLqBAgQJYsmQJ0qdPj0C2dq0VzDh0yBpeyEBWv36BNyue+AZnQ5wxA6hfH5g61QpijhsHxA+gliSDw5xgoXLlyhg/fjyefvpp9OjRw9/FEhERER8KoKaI+MPw4cOxb98+FC5c2OSjmDFjBvbu3YukSZNi586dZkiMY/d+/k1ERKJu8eLFqF+/vsn/w6GH8+fPR+rUqRGoONSsb1/gk08AduLlEMMxY6wcWhJaata0emq98AIwebIV2Bo7lu0DBIzy5cvjm2++QceOHfHOO++YB3O1mfxNREREYiUNFghxK1euNDdTP/74I1q0aGEaf3HixDG9CEaOHIl8+fKZ7vtFixY1+V5ERCTqpkyZYm6wGdCqVq2aGfIdyAGtvXuBEiWsHlkMaLVoYSWDV0ArdDE+xJ5a7KE1cSLw0ktMY4CA0qFDBxPUYvulWbNm2LFjh7+LJCIiIj6ioFaI4wxBhQoVQiKn8SMMbLEHwcSJE80QmZs3b2LBggV+K6eISLD7/vvv0aRJE9y+fRuNGjXC7NmzTa/YQMRUREOGAM88A2zaBDz0kBXIYK+cVKn8XTrxt7p1rZ5a7KHFIYjMsxZoqTgHDRpkem1duXLFBJJPMwmciIiIxDoKaoU43lA5BrQSJEhgvt+6dct8jx8/Pvr06WMSGTM/hYiIRN6nn36K9u3bm54jr776KiZMmGByFwaiv/8GqlUDOncGOIEcZ79jR5cXX/R3ySSQNGgATJpkBbZGjwZefjmwAltsz3ACnKeeesq0YRo0aBDWthEREZHYQ0GtEJchQwbTW8tmD4M5depU2N+SJ09uhh/O4tRHIiLiMQax3n77bZPbh9577z189913JkdhIGK+JM5ouGQJwHlBvv4aWLQI0OhzcYWBzgkTrNxaP/4ItG8fWIGtNGnSmBkRU6ZMaWZ5tockioiISOyhoFaIY4L4gwcPhj295AyHtInjTZwcPXo0xssnIhKs7t69i5dffhlffPGF+f3LL79Ev379zPDuQHPxItCqlRWkOHfu/8MO2VuLAQuR8DRqBLAjN4+TH34AOnYMrMAWc4VOnjwZcePGNblCv2akVkRERGINNVVDHKe9Zk+s1atXm9+ff/55JEmSxMyKeI53NoDpts/E8Q8xqYqIiESIeQgbN25sbqLtm+lu3bohELH6L1DAypfFwMS77wLr1gF58vi7ZBIsmjSxZsRkvHb4cKBTJysvW6DgpAwMKtObb76JJeyKKCIiIrGCglohjsMKf/75Z5QrV878zgDXG2+8gUOHDqFChQqoVavWAzN1iYiIe0xMzbpz+vTpJm8W8/q0adMGgebmTaBHD2smwyNHgCeftAJcH38MBGi6LwlgzZtbubUY2Bo6FHj99cAKbLFtw/Pw3r17ZqKGvZzaU0RERIKeglpiOOZ3admypcn/kjhxYhw4cMA0AOvVq4cuXbr4tYwiIoGOPVwrVqyIpUuXIlmyZGbW2Pr16yPQ7NwJFC8OfP65FXhgzG3bNqBkSX+XTIJZy5bAyJFWYIuzZ3btGjiBLQ77ZT67UqVK4eLFiybwbPdIFxERkeAV398FkMDUqlUrtGjRwkyBzWGHgTpLl4hIoPj7779RpUoV7Ny50ySoXrhwIYozchRAmOto8GCAeevZUyttWmDECKBuXX+XTGKLl16yjrN27axjjc/MOPIvEFLJcbbnGTNmoFixYiafaMOGDbFo0aKwmZ9FREQk+KinVoi5du2ax8syD0z69OkV0BIRicAff/xheoAwoPXoo4+aPIWBFtA6dgyoVIk5hayAVo0awI4dCmiJ97Vta+XWogEDgLffDpweW+nSpcPcuXNNT8rly5ebHFsiIiISvNRTK8QUKVIEmTNnNrMcckagXLlyme8ZMmTwd9FERILSjh07zKQb//zzD5566ikz9PBJJqgKIJMmWbPSXbgAJE1qBRpeeSUwes9I7MTjiz22OnSwemqxx1b//oFxzOXPnx8TJkwwqRWGDBmCvHnz4tVXX/V3sURERCQKFNQKMffv38eRI0fMF2+8bClTpjQBLjvIxe/ZsmVD/Pg6REREwsOZYWvUqIELFy6YG2XOqhZIDwnOn7eCWZMnW78XKwaMGwfkyOHvkkkoYJzo7l1rNsTPPrMCW/36BUZgq06dOvj444/x7rvvonPnzuZhnz1pjoiIiAQPRSxCzLZt20weif3795sk8PbXiRMnsH79evNlY0Ara9asYUEufj377LN+Lb+ISKBgAItJ4Dms+7nnnsP8+fNNDsJAsXw58yMCf/1lBRN69wbefRdQ+iCJSa+9ZvXY4myIn3xiHYt9+yIg9OzZ0wwZnjhxIl544QXTBmJvSxEREQkeCmqFGObHypMnj/lynoKeQxPZw6BEiRLYtWsXDh06hH379pkvO8fW7t27/VRyEZHAMW3aNDRr1gy3b99G1apVMX36dJOjJxBcv24FrwYNsn7Pnt3qnRVgKb4khHTubPXY4myIH31kBbY++CAwZkT84YcfzMM+BrQ4I+K6detM73UREREJDgpqiZE8eXLznUGt/kx6AeDWrVvYu3evCWQxyLVnzx4/l1JExP9GjBiB9u3bm+HcjRo1wtixYwNmQo2tW4FmzQD7+QOHfzGfUYDE2ySEvfGGFdjq3h348EMrsNWrl79LBSRJkgSzZ89G0aJFTTunSZMmJpF8PBZQREREAp6CWhIu3qQxRwy/RESEeYE+M0OWiIGtb7/9NiBufhksYPCKQwxv3wbSpwdGjrRmOBQJFN26WUMRORsij9W4ca1ehf6WMWNGzJkzB6VLl8bChQvNOf7FF1/4u1giIiLigbieLCQiIhLK2CurR48eYQGtd955B0OHDg2IgNbhwwDzW7NoDGjVrcsZGRXQksD01lvWLIj03ntWAvlAULhwYYwePdr8/OWXX4b9LCIiIoFNQS0RERE37t69i1deeQWff/65+Z09OD755BOTj8ef7t8HxowB2Jn2l184jBz48Udg5kzgkUf8WjQRtxiA5SyI9s/sZRgIGjZsiPfffz+sJ+avv/7q7yKJiIhIBBTUEhERCcfNmzdNjh0mk+ZkGfzenUmB/OzMGeDFF4HWrYHLl4GSJTm7LfDSS0x+7e/SiUSMvbT69Pl/762BAxEQPvjgAzRo0MDkFeXspkeOHPF3kURERMQNBbVCzDfffIPFixfjjz/+wD0mthAREZeuXr2K2rVrm5kOmWNw6tSpaNu2rb+LhcWLgXz5gBkzgPjxgY8/BlatArJm9XfJRCKHnaL+7RiFN98EBg/2d4msmZ7HjBmDggUL4tSpU6hTp46ZIVpEREQCkxLFhxgmNbaHzCRIkABPPvkksmfPjhw5coTljRERCXXnzp1DzZo1sXbtWiRLlszMjlaxYkW/lunaNatHy3ffWb/nzg2MHw8UKuTXYolEC2dC5EQHDM5yhkSmqevUyb9l4jnPxPHFihXDtm3b0LJlS0yfPt0EvERERCSwKKgVYlq0aIF9+/aZr4sXL4b9zEAXv7Zv325m/8mbN6/5ypMnD55++mmk51RaIiIh4MSJE6hcuTJ27tyJhx56CIsWLULx4sX9WqYNG4DmzYH9+63fO3e2EmwnSeLXYolEG5+zffSRNSsiE8jz2GbsqGNH/5br8ccfx6xZs1C2bFnzncMSP2JBRUREJKAoqBVi3mMSC4cbtz179mDv3r1hX8eOHcPp06fx888/YxXHs/wrTZo0JsA1YsQIP5VcRMT3Dh06hEqVKpnvGTNmxE8//WQC+/5y5451o9+3r/Xzo48Co0YBlSv7rUgiPglssacWe2xxPobXXrN6bLVv799ylShRwrR7WrVqhX79+pl2EHPsiYiISOBQUCuE8YaNX+XLl38ghwx7bjHYZX8/cOAAzp49izVr1vi1vCIivrRjxw5UqVLFBPyfeuopLF261AzR9pc//mDvWmDtWut3JoYfNowPGfxWJBGfBrY+/dQKbH31FfDqq1aPrZdf9m+5OPSQvTY562mbNm2QLVs2FC1a1L+FEhERkTAKasl/8kgUKlTIfNmYUP7PP/80QS4Rkdho3bp1qF69Os6fP498+fJhyZIlJujvD0xt+MMPQNeufNAApEzJfIhAs2aa2VBiNx7fX3xhBbYGDQJeecXqsdWmjX/L1b9/f+zevRsLFiwwieM3bNiATJky+bdQIiIiYijjZYi5cOECrl+/HqnXMDEqey3who/4eq5HRCQ2YI+sChUqmIAWhxtx6LW/AlqnTgF161o38wxolS3LHmRWPi0FtCQU8DgfMAB4/XXr93btgDFj/FumePHiYeLEiWb4IXty1q1bN9JtKREREfENBbVCDG/YopvotG/fvnjuuee8ViYREX/hjGY1atTAtWvXzNBDBriYHN4f5s0DmL5r7lwgYUKrx8ry5UxY7ZfiiPg1sMWeWsytxZ6LL70EjBvn3zKlTJkS8+bNw8MPP4yNGzeaoYiaMVpERMT/FNQKMWyAeaMRpoaciAS7H374AY0aNcLt27fRsGFDzJ071wzBjmlXrlg9s2rXBk6ftgJbnO2we3crp5BIqAa2vvkG6NDBCmy1bg1MmODfMmXNmtUEwuPHj4/Jkyfjk08+8W+BRERERDm1QtHmzZvxzjvvRPn1mzZt8mp5RERi2ueff44ePXqYn1955RV89913ZohRTGMSeCaDZ1J43sS/+SbQrx+QOHGMF0Uk4PCcGDLEyrH1/fdM2m4Fev05AWHZsmXx7bffon379ujVq5cZklivXj3/FUhERCTEKagVgo4cOWK+oiOOkruISBBiL1MG9T/77DPze8+ePU1vi5iu027f5lBugB097t0DMme28gaVKxejxRAJeAxiDR1qnSecQIH55Rh/btjQf2ViIJwzIn7zzTdo3rw5fv31VxQsWNB/BRIREQlhCmqFmLFjx/q7CCIifnH37l107NgR37PLx7+9td56660YLwcnkuWN+caN1u+c1ZC9UVKnjvGiiARNYGv4cKvH1qhRQNOm1t9eeMF/ZRowYAD27NmDZcuWoXbt2mZGxPTp0/uvQCIiIiFKQa0QU6xYMX8XQUQkxt26dcv0qJg2bZqZ0XX48OFox2nVYhDzArHHCXNlceI05qPn740axWgxRIISg1gjRliBLT6f4xBE9tjy18g/5tWaOnUqihcvjgMHDqB+/fpYsWIFEiVK5J8CiYiIhCiloBURkVjt6tWrpicFA1oJEiTAlClTYjygdeIEUL26NZsbA1oVKwI7diigJRIZDGL9+KPV0/HOHWsI4pw5/isPZ0rljIipUqXCb7/9hldffVUT6YiIiMQwBbVERCTWOn/+PCpVqoQlS5YgadKkWLBgAV6I4TFLM2cC+fIBixdbCeAHDwaWLAEyZYrRYojEmsDW6NHWEEQGtl58EZg3z3/lyZkzp+mxxR6go0ePNsMSRUREJOYoqCUiIrHSiRMn8Pzzz2Pt2rWmRwVz3zDAFVMuXQJatwYaNADOngWeeYazxwKvv24NpRKRqAe2OLFC48bWpAs8xxYs8F95KleujIEDB5qfmadv4cKF/iuMiIhIiFGzWkREYp1Dhw6hVKlS2LFjBzJmzIjVq1ejRIkSMfb+v/wC5M9v3XhzYsV33gHWrQPy5ImxIojEavHjA+PGWT21GNiqXx9YtMh/5encuTNefvllM/ywcePG2L17t/8KIyIiEkIU1BIRkVhl586dJqDFwFbWrFmxZs0aPP300zHy3jdvAj17As8/Dxw5AjzxBLB6NfDJJ0DChDFSBJGQCmxNmGD11Lp1y0oaz6G9/hAnThwMGTIEZcqUweXLl1GrVi2cZRdNERER8SkFtULczz//7O8iiIh4zbp168xNJYceMpDFgBYDWzFh1y6geHHgs8+smQ5fegnYtg0oVSpG3l4kJCVIAEyaBNStawWV+X3ZMv+UJWHChJgxYwaeeOIJE1Rn/r7b7EYmIiIiPqOgVojjTD3t27fHEXYpEBEJYkuXLkXFihVNcvhnn30Wq1atMkMPfe3ePWDQIKBwYSuI9fDDwIwZ1ixtKVP6/O1FQh4DW1OmALVrAzduALVqAStW+KcsadOmNTMiJk+e3Dw45LBEzYgoIiLiOwpqhTgmN+WNX82aNfHll1/i2rVr/i6SiEiksXdEjRo1cPXqVVOvMSl8mjRpfP6+x4+zHgW6drV6iVSrxuGPVn4fEYk5HN47dSpQs6YV2OJ3f3VGZy/RiRMnmiGJw4cPx3fffeefgoiIiIQABbVC3Ndff41Ro0YhS5Ys+OGHH1C1alXMmTPH38USEfHYyJEj0bBhQzPM58UXX8TcuXORLFkyn7/v5MlAvnzA8uVAkiTA0KHWDGwZMvj8rUXEhUSJgOnTgerVgevXgRo1gFWr/FMW5tT69NNPzc9dunQxgXYRERHxPgW1xMwIxkBWz549cf36dfO9SZMm2MUEMSIiAYw9TNu1a4d79+6ZmccmTZqERLyz9aHz54FmzYAmTYALF4CiRYGtWzmc25rpUET8h6c/h/9WrQqw8zkDW5yN1B/eeusttGjRAnfv3jUB9wMHDvinICIiIrGYglpixIsXD61bt8aSJUtQt25dbN261TTAevfujXPnzvm1bLNnz0b9+vXxzDPPmABc9+7d8ddff3n8+r///tsE6p5//nkUKFAAderUwVSOUXCBPT1GjBhhnrDmz58fBQsWNIlep02b5sVPJCLRxRw17777rrlppB49ephhPqzLfIl5evLnByZOZL0JfPAB8OuvQI4cPn1bEYmExImBWbOASpWAq1etnls8T2Mahx9+//33JsffhQsXTNuC30VERMR7FNSSBzAHTf/+/TFlyhTkyZPHBHM4JHHs2LGmJ0RMGzhwoLlZvXnzJpo2bWqCWgsXLkSDBg1w7NixCF/P4FejRo0wf/58FC9eHM2aNTO90Riss4cF2Pgk9ZVXXjE9P+xhTLVr18Y///yDXr164f333/fhJxURT/Fc7dChg6mriOcyv3gD6SvM0fPmm0CFClYerWzZrJvkDz+0klSLSOAFtphNgefslStWvrt16/xRjsSYNWsWHnvsMezbtw+NGzfGnTt3Yr4gIiIisZSCWuISeykNHToUFSpUwKVLl8zNI3s4rV27NsbKsHfvXgwbNgyFCxc2DUL2yBgwYAAGDx5sZjf7+OOPI1wHy33q1Cl8++23+Pzzz/H222+bfDvsgTV69GjsZEbnf82cORO//fYbypUrZ5Zh4Ktv375YtGgRcuTIYQJ96/zRIhaRMLdu3TLBafbKsntBMPDtS5zRsEgRBtmt3195BdiyBShe3KdvKyLRxFx3c+cC5coBly8DVaoA69fHfDkyZMhg2hVJkyY1PeLtHqYiIiISfQpqSdiN4pYtW0yg58033zTBrDJlymDFv3Nic6gPc0G0adMG3bp1i5Hu8+wdRq+99hoSclqjf1WqVAlFixY1U2WfPHnSbS8tJmblsEUOPXR8atq1a1fzmRiosrEHGL3xxhsPvF+KFCnQtm1b8/PKlSu9/ClFxFOc2ZDBdZ63CRIkMN+ZR8tX7t4FPv/cypnFFIPp0gHz5gHDhwPJk/vsbUXEi5Imtc5bNgMuXbJmK92wIebLwbbImDFjzM+DBg0yk/OIiIhI9MX3wjokiPXp0wfbt283XeI5pIcY7IkfPz5y586NQoUKmYYYvzhsj72dFixYYHJuMQCWOXNmn5WNvaJYDgawnDE/xYYNG8wyvMl1Zf369eazcMiiM/b+4k2xY88r5hJjzq2sWbP+Z3k7yMWbahGJeeydWbNmTdObkr0d2HuzMu9OfeTwYaBVK2D1auv32rWBESOswJaIBBdOhsqZSTkEkUnjWXVwMsLChWO2HMzRyXbXBx98gI4dO5pe4HyAKCIiIlGnoFaI40xhlCpVKjMkzw5gcfhhEvbbd8IhP9OnTzdD8z777DMMGTLEZz3HmOA9U6ZMD/SastnBtEOHDoW7jsO8KwWQJUuW//yPAa2MGTPi+PHj5r34HuEFx4jDBShnzpxR+jwiEnXMa1elShUTgE+dOrXpVekqWO0N9+8D48YBnTpZw5V4Mzx4MNCmjWY2FAlmPJfZIZuzIjIfHpPIL1/OHlQxWw62nzi7NCesYX5QPoB78sknY7YQIiIisYiCWiGuX79+Joj11FNPRepJI/NMsSHmKxcvXjS9rBhsc4VDAuky7zrd9Owgd+tg8vsrV66YBPnhWbVqFRYvXmyWZ0+RyLB7v0nosfe9joHo+fPPP81kFX/88YfJS8O6J1++fD7ZrmfPAh07xsWMGVb0qkSJ+xg9+h5YPfphngyJJVQXBA4+q5s/nz224mLdujioWPE+li69hwIFYrYcHHp48OBBbN682UxI88svv4S1ayR2Uj0g2vcivqOgVohjgCoqeHPJYUC+wtkHyVUvLce/c1bE6K6DPbXCwyGOzLFFHDLw0EMPITJ27NgRqeUl9tExEHUMZHXq1AmnT582vTY54QMbhRz+7G1r16ZEnz5ZcOZMPMSLdx+vvPI3WrX6x/TW8sHbSQhSXRA4Pv00Ljp1yo6dO5OjfPn7GDZsP7Jnvx6jZfjoo4/QsmVLM2ENe4pz5uW4cZXqNrZTPSAi4n0KakmUMGF8njx5fLZ+JnN3DEw5swNRzK3jq3UwyTyT4t+4cQPdu3dHjRo1IvkpYHqUxIsXL9Kvk+DH4AsbrzoGoub3339Hhw4dcO7cOTz99NNmyOGjjz7q9fe5dg145504+PZb62YyV677GDPmHgoXzsDwvdffT0KP6oLAtGoVZ0O8j40b46Nz59xYtuwenn46ZsvAGRHLly+P1atXY8aMGR7N6izBSfWA2MeAiHifglohbv78+SY45So5ujscrhiZIYuRlTx5cvPEMrzhhfbf3XXXt4cdXuJ0R+GsI06cOOa9nI0aNcokxbfzXzRv3jxKn4MNFzVeQpuOgchjQJkTN3BiBk4Kwckp3A0RjqpNmwCe2nv3Wr937sweHHGQNKn2l3if6oLAwipl6VIrt9bGjXFQqVI8cILjvHljrgzPPfccRo4cadoYzFPKgEezZs1irgAS41QPiIh4n4JaIY49kBjYYa8mJkFngMv+yp49u0mo7g8cGshk8EwWz55WzuU4duyY+Z4tW7Zw12EH6o4ePfqf/3GdJ06cMMlZHbv7M48Xn5SOGzcOiRIlMsMBfDnDmog8aObMmWjSpInpSVmpUiXzu6vAc3TcucPgFYcUWz9nzMhANnttePVtRCTApU4N/PQTULEisHkzUL488PPPQO7cMVcGBrGYOL5///5o27atadcUL1485gogIiIS5DR4P8S1b98e5cqVM0PsmKdm4sSJpmcSZ+RhAnn2lnj33XcxefJkl8EhXypWrJgJPjGRqrO1a9eaYFyhQoXcvp7LcBiTs40bN5p1F3aaz5s5LhjQ4gxrY8aMUUBLJAb9+OOPePHFF01Ai/n+5s2b5/WA1h9/AGXKsAemFdBiWkGOBlBASyQ0MVUme2wVLAicOmUFtvbti/lJe5hXi3lC2e7izMwiIiLiGQW1QlyrVq1MrycO1eMTQiZD79mzp7mhZGBn7969mDVrlvl7lSpV0KJFC2zfvj1GysbAGg0cONAE3WxLly41QSnmoWDC+vDwfyVLljSzNHI4k43rGjRokPnZsZs/c1tMmDDBDGkcP368CeqJSMz46quvTB3EGUn5nYF09pb0lvv3OeMYzCxna9cCKVMCY8cCU6cCDz/stbcRkSAdishmQv78wD//AOXKAfv3x9z7s8c4H6hx+OE///xjAlzXmPBPREREIqThhyGO3d3ZgJozZ85/kjAzoSF7Kw0YMABly5Y1N5jLly9H06ZNzVNFPk30JQaVGHRioIkNvAoVKuDkyZNYtGgR0qZNi3feeSdsWfbGYvAqd+7cqMhxBP/q1asXGjdujNdffx3VqlVD+vTpzWc4fPiwuXHm8nTnzh3zOYl/W7x4sflyxiGa6r0l4j0c8svz9JNPPjG/v/322/j0009NL0tvYe+Ll19m4Nr6nT21GNDKksVrbyEiQY7BbQa22FNr504rsMVk8m6yHHgVH6jx4VrRokVND/XWrVtjypQpXq0LRUREYiMFtUIcZ9xhA8rVrGJMZMlZDhkI6tGjh+mx1alTJ7z88st4//33zRNFXyaLJw6FZG4sNuzsYYHVq1dHly5dTM4tGwNaQ4YMQb169R4IajFnFl/Lnllr1qwxXfufeOIJE5RjbzTbwYMHTY4te138cqVWrVoKaol4CQPnrFOGDRtmfmcwi3WNN82fD7RtawW2mJqPk4u9+SbrN6++jYjEAo88AixfbgW0du+2vjPHlo+bOmHYPmEeQT7EmzZtmpn5le0tERERCV+c+3xMLiGLOamYe8q+qQwPe2UxeSkTp/MJIntrNWzYEH379o2xsgbbzTpzlBUsWFCz3IQoHQPuMW9Wy5Ytw3oisA565ZVXvLb+K1eAbt2A77+3fn/6aWD8eGv4oUhMUl0QfE6etAJae/YAfH7GwFYkJ4mOFs6I2K5dO/Mzg1uOD+EkOKkeEB0DIr6jnFohLm/evGbo3pkzZ9wux95S69atCwuE5ciRI+x3EZHIYK4YBsoZ0OLMpsyf5c2AFqsmpsSzA1rsmbVhgwJaIuKZ9OmBFSuAXLk427IV4Dp8OOben+kRunbtan5m8H/Lli0x9+YiIiJBRkGtEMdE8devX8drr72G06dPh7sch+ddunTpgS7yzG8lIhIZFy5cMEN4mRsvadKkZoZD9vr0htu3gQ8+AEqVYp1l9bDgUKKvvgISJ/bKW4hIiOA8NAxs5cgBcPJnBraOHIm59//8889RtWpV00arXbu2yX8qIiIi/6WgVohj/ikGtrZt24aaNWuavFR/cM77fzGQxQTO+/fvfyB/1u3bt81sPSIinuJNGSed+PXXX01+PM5kyllVvWHfPqBkSYAjou/eBZo2BThRK5M+i4hERcaMVmCLyeLZU4uBLfbcignx48c3vVhz5cqF48ePm5yhjjNBi4iIiEVRCTGzCDJxOtOrMajF4BaTwBcvXtx8MUE7A1icQdB25MgRPMypgkREPMAZR0uXLm0C6Jx8YtWqVXjuueeivV5mhRw61BpuyCGGqVMDkyYBEyZYP4uIREemTMDKlVay+D//tAJbx4/HzHunSpXKzIj40EMPmZQPHKatVLgiIiIPUlArxLEXFof/MAnpihUr0L17dxQuXBgJEybExYsXTYOqZMmSmDhxIsqxJQdg7969OHTokJk1UUQkIrt37zb1CIcxc0ZS9tTKnz9/tNfLCUtr1AA6dgSuXwcqVAB27AAaN/ZKsUVEjMceswJbTz4JsDM7e4D+/XfMvHf27NlNsngmluZDxi+++CJm3lhERCRIxPd3AcS/JkyYYG42a9WqheTJk5vZduwZd+7cuWO6vzvLmTMnFi5ciESJEvmhxCISTNavX49q1arh3LlzZmKKn376CY8++mi01ztzJsDc8mfPAqyKPvsM6NwZ0KhoEfEF5uhjYKtsWeDAAavHFmdF5BBFX6tQoQK+/vprk/+0Z8+eyJ07t2m3iYiIiHpqhTwOA0qWLJnL/7kKaFGcOHHMbIiZ2CdfRCQcy5cvR/ny5U1Ai0OZV69eHe2AFuereOkloEEDK6BVsCCwaRPQpYsCWiLiW1myWIGtxx8H9u+3Alsxlb+9Y8eO6NChgxl+2LRpU+zcuTNm3lhERCTA6RYgxFWqVAkbNmzArVu3/F0UEYlFZs2aherVq+Pq1atmQoply5YhTZo00VrnmjVAgQLA6NEMrgM9ewK//w7kzeu1YouIuPXEE1YPLfbc4gQVHIoYU5NBDx482KSCuHLliump5W7WahERkVChoFaI45M/5mn4+OOP/V0UEYklRo0aZfL0MVhev359zJ8/3wxvjirG3N95ByhTxpqBjDeVq1YB/fsDCRN6tegiIhFibi322GKurT17rHx+p075/n0TJEhg8mtxNmpOvmHXsyIiIqFMQa0Q16lTJzNd9NSpU9GsWTMzM5mISFQNGDAAbdq0wb1799C2bVtMmTIlWvn3du0CihcHPv3UmumwdWuA1VTp0l4ttohIpHA2RAa2OKKa9VTFisCZM75/X848zQl+UqZMaYZ0M8+WZkQUEZFQpqBWiOPQw1WrVpkG0aZNm9C4cWOULl0ab775JkaMGGFmKWM+HBERd1iH9OrVC926dTO/cyZV1iHh5eaLyL17HGoDFC4MbN3KGzlgxgz2AgNSpvRy4UVEoiBbNiuwxWTxnHmVPbaY68/XmCh+8uTJiBs3Ln744Qd88803vn9TERGRAKXZD0McEznv2bMHe/fuNV/8+a+//jKzG/KLSeEpXbp0yJMnD4YOHervIotIgGGvLPb6tOuH/v37o0ePHmH1R2QdP24lg1+2zPq9alXgxx9jZpYxEZHIyJEDWLHCShq/fbvVY2v5ciCaKQQjxFllv/jiC/MgoWvXrqbXfeXKlX37piIiIgFIQa0QxxkM+cVEzjYmILUDXPb3gwcP4mdmRhURcXD79m20atUKkyZNMkEsBrbat28f5fVNmQK8+ipw4QKQJAnw5ZdAhw5WYngRkUCUK9f/A1vsWWoHth56yLfvy2AWZ0FkHsOGDRvi999/R86cOX37piIiIgFGQS35DyZ0LlKkiPmy3b17F4cOHfJruUQksFy7dg0vvvii6dXJYYbjx49Ho0aNorQuBrFeew2YONH6ndXP+PGA7s9EJBjkzm0FtsqWBbZs4ezSVm/T1Kl99572g4T9+/ebdBGcEZGBrYd8HU0TEREJIMqpJR7hDInZs2f3dzFEJEBcuHABVapUMQGtJEmSmMTFUQ1oMSdN/vxWQCtuXKB3b+C33xTQEpHgkiePFdhKmxbYtAngaMCLF337npyIY+bMmXj88cdx4MAB02Przp07vn1TERGRAKKglphE8MyBwyd8TBJfs2ZNjB492t/FEpEAdfLkSZQtWxZr1qxBqlSpsHTpUlRl4qtIunGDCeWt5MrHjlmzif36K9C3L6eu90nRRUR86umnraGHnNxiwwYrJ+ClS759T+Y9nTt3LpIlS4Zly5aZyX5ERERChYJaIY43p/Xr18fYsWPxxx9/mGGG/L5v376wZTZu3GhmSLx586Zfyyoi/nf48GGUKlUK27ZtQ/r06U3dULJkyUivhwmVixUDvvqKMycCr7xi5aJ59lmfFFtEJMaw5ymHHjJZ/Lp1TOoOXL7s2/csUKCAGQJOnA1x+PDhvn1DERGRAKGgVogbMGAA/vnnHzRp0gSbNm3Cb7/9hvu8w3TAbuyvvvqqGV4kIqFr9+7dJqDFiSOeeOIJ01OLN1KRcfcu8MUXQNGiwI4d7GEAzJ0L8P4reXKfFV1EJEYVLGgFtpjeisOpYyKwVbduXXz88cfmZ85Iqwl+REQkFCioFeJ++eUXc3Pau3dvkxfHlWeffRZp06ZV40gkhG3YsAFlypTBX3/9hTx58piAVrZs2SK1jiNHrKGGb78N3LoF1KplBbb4XUQktnnmGWDpUitZPIdW16jBGaZ9+57vvPOOeVDJB5INGjTQJD8iIhLrKagV4i5fvoxcuXKZGXTc4TKcXUdEQs+KFStQvnx5nD17FsWKFcPq1auRKVMmj1/Pzp/jxllDclatApIlA0aMAObMsXpqiYjEVoULAz/9BKRKxQeJQM2awNWrvns/tudGjhyJokWLmpypzJd6yddJvURERPxIQa0Qx+SizKsVkdSpU5sbWhEJLbNnz0a1atVw5coVVKhQAcuXL8fDzIDsIVYbnBSxZUsrWTJzZjF3Vrt2vPnyadFFRAICh1svWQKkTGkF9tk79do1370fe96z7n700UfNsPGmTZuanKkiIiKxkYJaIe65557D9u3bzTTQ7ly8eBG3b9+OsXKJiP9xFlQOX7l165aZUGLBggVIHonEV+ydkC8fMG0aED8+8NFHVk+FSI5aFBEJesWLA4sXW7kDV64E6tQBrl/33fsxoDVnzhwkTpzY1N0cligiIhIbKagV4lq3bm26qnfp0gV///23y2Vu3LiBnTt3mpnORCQ0DBo0CC+99BLu3btnvk+ZMgWJEiXy6LXsgdC5M1ClCnDiBJAzJ7B2LdCrlxXcEhEJRSVK/D+wxSTydeuyjeW79ytSpIh5OEFffPEFxowZ47s3ExER8RMFtULcU089hV69euHPP/9EnTp18NVXXz3w/+vXr5v/nz9/HiVLlvRbOUUkZnD2U04c0bVrV/N7t27dTH6W+B5GozZvtnLIDBli/f7aa9bfihTxZalFRIIDm1KLFlm5BdmbtV493wa2GjVqZOp0euWVV8ws1yIiIrGJglqCxo0b45tvvkGCBAkwgtmbASxcuBCVKlUyiUbnz5+PVKlS4dVXX/V3UUXEh9grq3PnzujXr5/5/ZNPPjFP9yOaSIKYruWTT6whNnv3AhkzWjduDG4lTRoDhRcRCRKlSrGdZdWN7LnVoAFw86bv3u/DDz80Q8g5lLxevXo4evSo795MREQkhimoJUbFihWxbNky9OjRw3RXZ4Dr2LFjpncGZz2bOHEiMmTI4O9iioiPMGdeixYt8O2335og1tChQ00OFk8CWpwxvkwZ4L33gDt3rBu0HTuAqlVjpOgiIkGHdeaCBUzqbgW4XnjBd4GtuHHjYuzYsShQoABOnTqF2rVrm8k/REREYgNlN5EwSZMmNblz+GXf5DK4JSKx27Vr19CwYUOTTJiB7HHjxpkenBG5fx8YNQro0gXg/VGKFFbPrBYtNLOhiEhEypYF5s0DatYE5s8HGja0JtZImND775UsWTLMnTvX9MDftm0bWrVqhWnTppmAl4iISDDTlUzCpYCWSOzHmU2rVq1qAlqcBp43PZ4EtE6fBurXB9q2tQJapUsD27cDLVsqoCUi4qkKFYC5cwHOw8HvrH59Ndn0448/jlmzZiFhwoSYOXOmGZYoIiIS7NRTS0zD5qeffsK5c+eQLl06ZM+eHblz5zZfmTNn9nfxRMRHTp48aQJaW7duNXnzmD+vFJO9RIBDZtq0AU6dYvAbYAqubt2AePFipNgiIrFKpUrAnDlAnTrArFlAkybApElW/eptzz33HL7//nsz+/VHH32EvHnzmmTyIiIiwUpBrRD3ww8/mBkPOeOZjbm17Dw67K6eM2fOsCBXAybLEZGgd+TIETMZxIEDB0wwm4Ft5ltx5+pVK3g1fLj1e968wPjxQMGCMVNmEZHYqkoVYPZsK7A1YwbQrBkwcSLg4cSzkcKhh7t27TITgTC4xZmwmU9VREQkGCmoFeKmTJli8ilwtrPSpUvj/Pnz2LdvH3bv3o09e/aY75s2bTJfDHQpqCUS/HhuV65cGcePH0eWLFmwdOlS00PTnd9/B5o3Bw4etH7v2tWa7TBx4pgps4hIbMfJNWbOBOrVs3JrMd0VHxz4IrDVv39/08bj0PM6depgw4YNePTRR73/RiIiIj6moFaIO336NJ599lkzxTOlTZvW3NzWZNbSf504cSIsyCUiwW3jxo1myOHZs2eRJ08e00MrU6ZM4S7P3C4ff2wNMbx7F3jsMWD0aCsPjIiIeFeNGlZPLT5DnDLFGtY9dqz3h3fHixfPzGxdokQJ08arW7cuVq1aZXIrioiIBBMlig9xfCqXMmVKt8tkzJgRFSpUQKdOnWKsXCLifStXrkS5cuVMQIszYK1evdptQGv/foAptvr0sQJazPPCZPAKaImI+E6tWlZPLfbQ4hDE1q2tOtjb2P6bN28eHn74YdNTq23btg+koxAREQkGCmqFuOrVq5sk0ffu3fN3UUTEh+bMmYNq1arhypUrKF++PJYvX25uZFzhPc2wYcAzzwDr1wOpU1s3Vvx66KEYL7qISMhhbi27pxaHIHKmWV8EtrJmzYrp06cjfvz4mDRpkhmWKCIiEkwU1ApxfCqXIEECfPPNN/4uioj4yNixY00+vJs3b5ohJsyhkiJFCpfL/vMPwNHHHToA164B5ctbvbPYS0tERGJO/frA5MlWYGvMGODllwFfPIMsW7Ysvv32W/Pze++9h1mcglFERCRIKKgV4jik8Omnn8awYcPwxhtvmJnQRCT2GDx4sJnp6u7du2aWq2nTpiFxONndOfNWvnzAwoVAokTAgAHA0qVA5swxXmwREQHwwgtWL1kGtkaNAtq3901g65VXXkHnzp3Nzy1atMC2bdu8/yYiIiI+oETxIe7XX381sxoyh8LixYuxZMkSPPbYY8ifPz9y585tEknz+0MacyQSVHhOf/DBB/joo4/M72+++aaZvp2znTq7fBno0sW6YaICBazhLk8/HdOlFhERZw0bWoGsZs2AH36wZkUcOtT67k0DBgwwkwItW7YMtWvXNnm20qVL5903ERER8TIFtUIcZz7bu3ev+WJDZt++fTh27Jj54hAlBrwoffr0Jrg1lK0oEQlozJHXpUsXDBkyxPzer18/vPvuu2Hns6M1a4CWLYE//wT477feAvr2tXpqiYhIYGjc2Mqpxfr6+++tnlscMeiiWo8y5tWaOnUqihcvbnru169f3+RfTKQLgoiIBDAFtULc448/br4qV64c9rfLly+bAJcd7OLXwYMH8fPPP/u1rCISsdu3b+Oll17ChAkTTBCLeVI6MEGWk1u3gA8/BD77zOoBkCWLNW18mTJ+KbaIiESAPbVYX7dq9f+eWkyJ6s3AFnvmc0ZEBrbYm5/Xj5EjR7p8KCIiIhIIFNQKMVevXkWyZMncLsME0sWKFTNfNubjOXToUAyUUESi6vr163jxxRdNL0s+cWeC+CYuMrzv3g00bw5s2WL9zhukwYOBVKlivswiIuK5Fi2sHltt2lg9tdhja9Ag7wa2cubMaXpsccbcUaNGmdyrHMIuIiISiJQoPsQwUMW8OpEVL148ZM+e3SdlEpHou3jxIqpWrWoCWkwEP2fOnP8EtPiE/+uvgcKFrYBWmjTA9OnA6NEKaImIBIvWra3cWsQ6vVs35lH07nuwB//AgQPNz2+99RYWcgYRERGRAKSgVohhj6tz5865/B8bL5rtRiT4nDp1CuXKlcPq1auRKlUqLF26FNWrV39gmb/+AqpWtRLC37hh/bxzJ9Cggd+KLSIiUcSeWiNGWD8z9sR8iN4ObHE2xJdfftnkaeRDkt3s5isiIhJgFNSSMMOHD8fkyZNd/m/KlCnm/yISWI4ePYrSpUtjy5YtZpYq5r4rVarUA8tMnQrkywcsXQokTgwwfzwfumfM6Ldii4hINLVrBwwbZv381VdAz57eDWwxjxYnHClTpgwuXbpkZkQ8e/as995ARETECxTUEo9s3boVg5i0QUQCBidxKFmyJPbv348sWbJgzZo1KFiwYNj/L1yw8q80agScP///YYevvebd/CsiIuIf7dtbubXo88+B997zbmArYcKEmDFjBp544gn88ccfJm8jJyQREREJFApqiYgEoU2bNpkeWsePH0fu3LlNQMsx7x0nK82fHxg/3pohq1cvYO1aIFcuvxZbRES8rGNHaxZE6t8feP997wa20qZNa2ZETJ48OVauXIkuHMcuIiISIBTUEhEJMhxiyBxaZ86cQZEiRUwurccee8z87+ZNoHt3oHx54Ngx4KmngDVrgI8+AhIk8HfJRUTEFzp1smZBpH79gD59vLt+zoA4ceJEMyRx6NCh+O6777z7BiIiIlGkoJaISBCZO3eumeXw8uXLKF++PFasWGGeotP27UDRolZuFT6lZ76VrVuBEiX8XWoREfE1dqAaMMD6mUEtPszwplq1aqE/u4IBeP3117F8+XLvvoGIiEgUKKgVgi5cuBDuDIgiErjGjRuH+vXr4+bNm6hbty4WLFiAFClS4N494MsvrYDWjh3AI48Ac+ZYM2MlT+7vUouISEzp2hX44gvrZw5D/Phj767/7bffRvPmzc1s2syvdeDAAe++gYiISCTFj+wLJHYMXWJy6VSpUuGpp54yX9myZfN3sUTEjcGDB+ONN94wP7du3RojRoxA/PjxcfQo0KqVlUOLatYEfvgBSJ/ev+UVERH/4BD0u3et2RCZTzFePOtnb+DwQ15/GMz6/fffTe+tdevWIXXq1N55AxERkUhSUCvEtGjRAvv27TNf7LHFZNP8YiOFX3PmzMHGjRtN4umcOXMiV65c5ktE/OP+/fvo06eP+SIGtr766ivEiRMXEyZYMxlevAgkSwYMHGgNOdTMhiIioa1HDyuwxdkQ33nHCmy99ZZ31p04cWLMnj0bRYsWNe3Jxo0bY/78+eZBi4iISEzT1SfEvMfWzb9OnDiBPXv2YO/evWFfx44dC/taunSpX8sqEuru3btngljf/Dut1UcffWTO4fPn46BDB2DqVGu54sWtWQ7V4VJERGzvvmsFtjgM8e23rcDWm296Z90ZMmQwD0JLlSqFJUuWmGGJA+yEXiIiIjFIQa0QljFjRvPFZNO2q1evmqduDHbZ39nF/MaNG6Ynl4jEjNu3b6NNmzYYP368OfeGDBmCjh07grHm1q2Bv/+2blA++MB6Cq8H5CIi4qx3byuwxc6+3boBceOyx6931l2oUCGMGTMGDRs2xMCBA5E3b160bdvWOysXERHxkG6D5AHJkiUzjRR+OfYW+fPPP02QS0R87/r162jUqBHmzZtnhnPwpqFevaZmZquvv7aWyZHD6p3F5PAiIiLh4cMPTijC2RCZSJ4PRDp39s66mSz+ww8/NF8dOnRAjhw5ULp0ae+sXERExAOa/VAiFDduXJNMvnr16v4uikisd/HiRVStWtUEtOy8JblyNUXhwv8PaHXsCGzZooCWiIhEjB3t2VOLwxHp9deBb7/13vp79+5tglvsYcwZeg8fPuy9lYuIiERAQS0RkQBx+vRpMxx49erVSJkyJRYt+gnbt9cwObP27GEOE2DhQutmJGlSf5dWRESCKbDVr5+VQJ46dQKGDfPew8/Ro0ebXv5nzpxB7dq1cfnyZe+sXEREJAIKaomIBICjR4+aIRubN2/GI488gvHjf0OvXqXNk/U7d4D69YEdO4Bq1fxdUhERCdbAVv/+QPfu1u+ccOT7772z7qRJk5rE8Uwgv2PHDjRv3tykrxAREfE1BbVERPyM+eo4gxS/Z878OLp23Y6mTfPi11+BFCmA0aOB6dOBtGn9XVIREQn2wNbnn1u5tah9e2DkSO+s+7HHHjND5hMlSoS5c+eiV69e3lmxiIiIGwpqiYj4yJ07ET+lZs8sBrSOHTuGbNlKIG/ePXj33Qy4cgUoVQrYtg1o1cq6EREREYkuXk+++gpm8hF6+WXr4Yk3FC9eHD/88IP5uX///pgwYYJ3ViwiIhIOzX4oIhKBEyesr4gsXPgnxow5h6NHH8atW5kAMJP7bSRMeAyPP34WrVqlQfXqT4Ytv2nTJrzxxhu4di0zHnvsbZw50w0HD8ZF/PhW7hMOEeEsVSIiIt4ObA0cCNy9CwwZArRpw9xYQMuW0V83hx7u2rULn376Kdq2bYts2bKZYJeIiIgvxLl///59n6xZJITdvXsXW7duRcGCBRFPUYmg163bZQwYkCLG3q9586sYNy5ZjL2fiPiOrgcSyHgXwKTx331nBbrGjuU1KPrrZT6tunXrmpl8mWdrw4YNZnhiqFI9IDoGRHxHww9FRCI0HEChcL6uRGF9V/59bQsARxz+zmEaJZAu3VAvll1ERMQ1BrK++cbKrcUAF4e7T5zonRkROfTw6aefxj///IM6derg2rVr3iiyiIjIAzT8UAIek46OHTsWf/75JxInToySJUuia9euyJSJw7si9vfff+Prr7/G2rVrceHCBTzxxBNo1qwZGjZs6HL5n3/+GcOHD8f+/fvNk5TChQujS5cuyJUrl5c/mQSL7t2boVmz8v/5+6efbsW0aVGZ3YmvqQWgt6mG06W7hQ8/PILixXMD+BYZM2b0SrlFREQiwmGH7KnFoYhMh9WihTX0vVGj6K03RYoUJmF8sWLFTP7I1q1bY8qUKYijJJEiIuJFCmpJQBs4cCCGDRtm8jE0bdoUJ06cwMKFC7FmzRpMmzYNmTNndvv6v/76C40bN8b58+dRvXp1pE2bFsuWLUPv3r1x6NAh9OzZ84Hlp06dav7HgBmDXhcvXsSCBQvw22+/Ydy4ccifP7+PP7EEIgaZnANNS5cexrRpTQHcisIaObSwj/mpbNmrmDEjGdKkye6l0oqIiEQ+sDV8OIcNAj/+CDRrZv3txRejt94nn3wSM2bMQMWKFU27jT233n//fW8VW0RERDm1JHDt3bvXdFdnT6nRo0cjYcKE5u9Lly5Fp06dUK5cORPwcofLcfnvv/8ezz//vPnbjRs30KpVK2zbtg3Tp083DSw6c+YMypcvb4IX/DufMBKXY8+urFmzYs6cOR49YdS4+dgvVaqtuHQpL4DrAFI6/fcugB0AONQiKYB8AJyPA1a9d5Ey5U5cvFgwBksuIjFJ1wMJJgxqMWn8mDFWb62pU4H69aO/3pEjR6Jdu3bmZwa3XnjhBYQS1QOiY0DEd5RTSwIWhxzSa6+9FhbQokqVKqFo0aJmmODJkyfd9tJir6xnnnkmLKBFHMLI4YuM57IbvI0/37x508zUYwe0qECBAqhRowb27duHLVu2+OCTSrAZP34PLl1iICqBi/+uA8Djkv9/7t/vJ//9u+0OnymYzrJcz4QJe2Kw9CIiIq6xd9bIkdYQRA5H5BDE2bOjv162rTjbL7Vs2VLtKRER8RoFtSRgrVu3DvHjxzcBLGfPPvusCUpxmfCsX7/eLFOiRIn//I+9vxIkSPDA6+2fXS1v/83d+0no+PzzUwBuu/gPj49iADI4/T3Dv3+3jx/HZLm38dlnXJ+IiIj/sRPJqFHWEMQ7d6whiHPnRn+9X3zxBapUqYLr16+jdu3aJoG8iIhIdCmnlgSkW7dumQTvzG3l2EvLZufSYl6s8Bw+fNh8z5Ily3/+x4AWhxkeP37cvBffg8sziPboo4/+Z3l7Gmp37xfR57l921UQ5P+9x+yuyJFZlstx+fAkSpTIfKbILnvnzh3Tay083F7chpFdll2vOfwzPFzO3t+RWZZTh7OR7I1luQ24LYhBUVezNe3fz+GG3Jb3nIYcPh7O84K4/y6b+d/l6GrYfw8cSIGrV///O/cv97PN8X+uZphKkiRJlJblZwtvBDqH2SZNmjRKy3L7cjuHJ1myZFFalscDjwtvLMvy2kOJefzyOPbGsty+3M6enMuRWVZ1RHDVEY7L2tuMy7o7PyNz3quOcL2s6gjv1hHffsvzNwmmTIkLjhacOvUOKlW6Ga06gsMQmT7iwIEDqFevHlauXGleE9vrCJaFr+H55zj0LDL1ieqI4K4jRMSHmFNLJNCcOnXqfo4cOe7Xq1fP5f+XL19u/t+nT59w19G7d2+zzIoVK1z+n+vm/8+ePWt+f/rpp+8XK1bM5bJ79+41y77yyiself/OnTv3N27ceP/mzZvmZ5bl3yRKLr/Wrl1rluPXp59+6nbZZcuWhS379ddfu112zpw5YcuOHDnS7bKTJ08OW5Y/u1uW67KX5Xu4W5ZltJdl2d0ty89uL8tt4m5ZblN72W3btrld9s033wxb9uDBg26X7dChQ9iyJ06ccLss0Oo+cPG+NRH6ln+/R/S15d/XhL/eatWqhZWBX0mTJg132TJlyjywbNq0acNdtkiRIg8smyVLlnCXzZMnzwPL8vfwluV6HJfl+4S3LMvnuCzLH96y/NyOy3K7uNtujss2aNDA7bIXL14MW7Zly5Zul+VxYC/L48Pdsjy+7GV53LlblsetvazqiNhZR/DY4nWA14Nz5865XZbHrOMx7G5Z1RHWl+oI39cRM2fOvf/ii3fN9St+fB6X1bxSR9jHbPPmze/Pnj07pOsIe1kec+6WVR0R3HWEfS3g8iLiXeqpJQHJfsLoqpeW49/dPdnzdB32E0ouH533c2XHDiYLR4Rd7Pfv3x/2FJI91Nw5ePAgUqdObX5mTzN3/vzzT5OUko4ePep2WfZUs5e1e7mFh+uyl+V7uMMy2suy7O7ws9vLcpu4w21qL/vHH3+4Xfb06dNhy0a0fTlhgL0sZ8303DWvLXfp0qWwMpC7J5BXrlx5YFl3Twr5lNRxWXe9cviE0nFZd0+7uR7HZd09aWb5HJdl+cPDz+24LLeLO47LXrhwwe2y27dvD3vafO7cObfL7ty5Ew899FDY8eHO7t27w96bx11Ek2HY9ZTqiNhZR/DYsq8Du3btcrssjxvHY9gd1REW1RG+ryOOHj2Ebt224OzZrFixgp9xJoA6AH6KVh3RoEEDTJw4EePHjzczTYdyHWEv6673F6mOiB11hIh4n2Y/lIDEiwPzWHFmQk4F7WzFihXo0KGDSTb63nvvuVxHnz59TINp+PDhKFu27H/+X79+fXOTsWHDBqRMmdLMRsKu2q7yZjFJPPM/VKhQAd99953HM5zky5fPdAHX0KLgHjbgLFUqdkfn+ri9+Nk5HJENIU9mMeRyWZ1mQ7yFixf//7uGDbheVkOLVEcESx3huCy/GNji9czddtDQov9THRF4dQRXx6Txc+fGQ6JE9zF58jWUL383WnXEiBEj8Prrr5ttN2nSJFSrVi3W1hEsC9ucefPm1fDDEK0jWEZeCzT7oYj3qaeWBKTkyZObC+fly5dd/t/+u+Mshc5SpUrl9qkM18GLEd/LXv7s2bPmgmtfpCLzfq7wosUvNgAcGwHuRGZZ50aLN5e1G1neXja83nDRXdZu8HpzWWLA01nixPtw40bOf3+zG+35+Pz236TwrubgYIPrxL/LscH4/4ZTkiTHkDJlrkiVwRvLRuZ4jsyy9jnl7WUdG5veXDYyuS4is6yvznvVEYFfRziyb4ocb169sd6YWFZ1hEV1hJU8fto0oGFDYM6cOGjSJBnmzwcqVAh/vREd7506dTI914YNG4Z27dph7dq1JvgbG+sI1gPcJ/zZXUAjEM571RGRX9aT895dgExEokezH0pAYiOEyeDZxdvV08Zjx46Z79myZQt3HVmzZg13SA3XeeLECTz55JNhT0G5vP338N7vqaeeisanktgie3YOA3E+LtlItY815yeG9u88jpwbs7eRLdtJH5VURETEOxgfmjoVqFWLvVis7ytWRH19fID49ddfm970HEJWq1atCIdkioiIOFNQSwJWsWLFTJBp8+bN//kfn+axMVSoUCG3r+cyv//++3/+t3HjRrPuwoULP7A8uRp+yPcjx+UldPXokY6DEVz851kA65mpw+nvJ/79O//vLMG/6xMREQn8wBZ7bNWowWFfQM2awKpVUV8fezxNnz7dPFhkHq4XXnjB7XBIERERZwpqScBiElEaOHDgAzkRli5daoJS5cuXR4YMHOrlGv9XsmRJrF+/HsuWLQv7O9c1aNAg83OzZs3C/s6cWewhNnTo0AcSe27btg0LFy5E7ty5FdQSo1mz3EiZkrmxXOUsYeAq/b+5s37793uGcAJat816uD4REZFgwFGF06cDVataga3q1YFffon6+h5++GHMmzfPDE9bvXo1XnvttXBzL4mIiDhTongJaH379sWECRPwxBNPmCTtJ0+exKJFi8wMIpMnTzZDFIm9sRi8YuCpYsWKYa/nrFuNGzc2ObGYgDR9+vRYvny5eRrYtm1bvP322w+838iRI/H5558jXbp0qF69uukOP3/+fDNEccyYMcifP79H5bYTxSsZZOzAIanOw1LXrz+BDh3SAMj7b6L4yGCeN87KdBtDh55DsWIZH/hvxowZzZeIBD9dDyS24vPGOnWAn35i/iFgyRKgZMmor48PEGvWrGkCWoMHDzZJ5GML1QOiY0DEdxTUkoDGw5NBrSlTpphAFKegLl68OLp06RIW0KJvvvkGQ4YMQb169fDpp58+sA6+jj2zOISQs5MwQMYeWuzi7pwQnhjEGjVqFA4cOGCSTxYoUMC8X65c4SfydqYLV+zSrduXGDBgYjj/Xc00pZFcI6efLhPuf998sym++qp7JNcpIoFI1wOJzdhTq3ZtgB3ima+bAa4SJaK+vq+++grdu3c3DxP5ELNy5cqIDVQPiI4BEd9RUEvEB3Thil26dbuMAQMiN/NldLz55mV89VXMvZ+I+I6uBxLbXbv2/6TxnOCOga1nXY249wBvS9q0aYPRo0ebWanZEz9nTnu24eClekB0DIj4TnwfrltEJFbo3j0FHNKv/QeHInbvfglXr+b8N8+WqyTy1t+TJ9+HL75I+Z8hh44yZlRAS0REgkPSpMC8eVby+J9/BqpUYf5TTsAT+XWxB/2wYcOwf/9+/Pbbb2ZGRAa2mHZCRETEFQW1REQiwPRW7lJcFSqUEa++mhETJuzBZ5+dwsGD6XH9elbOEwXgFpIkOYRs2U6aWQ6VFF5ERGJjYGv+fCtp/OrVAEcNckhikSKRX1eiRIkwc+ZMMys1U0E0bNjQDEWMH1+3LSIi8l+a/VBExEsYsNq+/Xlcu5YLd+7Ew7p1G8x3/s6/K6AlIiKxFZPFL1gAlCoFXLwIVKoEbN4ctXVxYp+5c+ciadKkZgbrN99809vFFRGRWEJBLRERH4kfX1WsiIiEDiaLX7gQeO454MIFgBNSb90atXVxop7x48eHTQg0fPhw7xZWRERiBd1xiYiIiIiIVzBZ/KJFVrL48+etwNb27VFbF2e17tevn/m5U6dO+JlJu0RERBwoqCUiIiIiIl6TMiWweLGVLP7sWaBCBWDHjqit691330Xjxo1x584dNGjQAIcOHfJ2cUVEJIgpqCUiIiIiIl6VKhWwZImVLP7MGSuwtWtX1GZE/PHHH1GkSBGcO3fOzIh46dIlXxRZRESCkIJaIiIiIiLidalTAz/9xFmCgdOngfLlgd27I7+eJEmSYM6cOXj00Uexe/duNG3aFHfv3vVFkUVEJMgoqCUiIiIiIj7x0EPA0qVAwYLAqVNWYGvv3sivhwGt2bNnI3HixFiwYIEZligiIqKgloiIiIiI+EyaNMCyZZzREDh50gps7d8f+fUULVoUo0aNMj9//vnnGDt2rPcLKyIiQUVBLRERERER8amHH7YCW/nyASdOAOXKAQcORH49TBrfq1cv8/PLL7+MtWvXer+wIiISNBTUEhERERERn0ubFli+HHj6aeDvv63A1sGDkV9Pnz59UK9ePdy6dQt169bF0aNHfVFcEREJAgpqiYiIiIhIjHjkESuwlScP8NdfVmDr0KHIrSNu3Lhm6GGBAgVw6tQp1KlTB1evXvVVkUVEJIApqCUiIiIiIjEmXTpgxQogVy7g+HErsHX4cOTWkTx5cjMjYrp06bB161a0atUK9+7d81WRRUQkQCmoJSIiIiIiMSp9eiuwlSMHwNGDZcsCR45Ebh1ZsmTBrFmzkDBhQsyYMcMMSxQRkdCioJaIiIiIiMS4jBmBlSuB7NmtgBZ7bEU2PdZzzz2H4cOHm5/79u2LKVOm+KawIiISkBTUEhERERERv3j0USuw9dRTwJ9/WoEtDkmMjNatW6N79+5hP2/cuNE3hRURkYCjoJaIiIiIiPhNpkxWYCtrVitpPANbTCIfGZ9++imqV6+OGzdumMTxf3N6RRERifUU1BIREREREb/KnNkKbD35JHDwoBXYikxcKl68eJg0aRLy5MljAlp169bF9evXfVlkEREJAApqiYiIiIiI3z3+uBXYypIFOHAAKF8eOHHC89enTJkSc+fORZo0abBhwwa0a9cO9+/f92WRRUTEzxTUEhERERGRgMCAFgNbDHDt22cFtk6e9Pz1Tz31FKZPn4748eNj4sSJZliiiIjEXgpqiYiIiIhIwOAQRAa2HnsM2LvXCmydOuX568uVK4chQ4aYn999913MmTPHd4UVERG/UlBLREREREQCCpPGM7DFJPK7dwMVKgCnT3v++vbt26NTp07m52bNmmH79u2+K6yIiPiNgloiIiIiIhJwsmWzAlsZMwI7dwIVKwJnznj++oEDB6JixYq4evUqateujVOR6e4lIiJBQUEtEREREREJSNmzW4GtDBkAdrZiYOvsWc9ey7xaU6dORfbs2XHkyBE0aNAAN2/e9HWRRUQkBimoJSIiIiIiAStnTiuwlT49sG0bUKkScO6cZ6996KGHzIyIqVKlwpo1a9ChQwfNiCgiEosoqCUiIiIiIgEtVy5gxQogXTpgyxagcmXg/HlPX5sLU6ZMQdy4cTFq1CgMGjTI18UVEZEYoqCWiIiIiIgEvDx5gOXLgbRpgU2bgCpVgAsXPHttlSpVMGDAAPNz9+7dsWjRIt8WVkREYoSCWiIiIiIiEhSeftrqsfXww8CGDUDVqsClS5699vXXX0e7du1w7949NG7cGHv27PF1cUVExMcU1BIRERERkaCRL5/VYytNGuD33z0PbMWJEwfffvstSpcujUuXLqFWrVo462nWeRERCUgKaomIiIiISFApUMAKbD30ELB2LVCtGnD5csSvS5gwIWbMmIEnnngCf/zxBxo2bIjbt2/HRJFFRMQHFNQSEREREZGgU7AgsGwZkDo18NtvQPXqwJUrEb/ukUceMTMiJk+eHCtWrMAbb7wRE8UVEREfUFBLRERERESCUqFCwNKlQKpUwJo1QI0awNWrEb8uX758mDBhghmS+N1335kvEREJPgpqiYiIiIhI0CpSBPjpJyBlSmD1aqBWLeDatYhfV7t2bfTv3z8siTx7bYmISHBRUEtERERERIJasWLAkiVAihTAypUMWAHXr0f8urfffhvNmzfH3bt38cILL+DgwYMxUVwREfESBbVERERERCToPfsssHgxkDy5lUS+Tp2IA1scfjhixAgUL14c58+fNzMiXrx4MaaKLCIi0aSgloiIiIiIxArPPQcsWgQkS2bl2qpbF7hxw/1rEidOjNmzZ+Oxxx7D3r170bhxY9NzS0REAp+CWiIiIiIiEmuUKgUsXAgkTWrl2qpfH7h50/1rMmTIgDlz5iBJkiRYvHixGZYoIiKBT0EtERERERGJVcqUARYsAJIksXpuNWgQcWCrUKFCGDNmjPl5wIAB+PHHH2OmsCIiEmUKaomIiIiISKzzv/buBMzG+v3j+G3su0ohWyTys2TLkkIUUiTyt2SPKLSh0E+0KP3ao5IS2bKFsrRJWbKVrRKhKJSy74Txvz7f6TmdOXNmM2PmnJn367rmOjPPeZ7nPGeMmTOfue/7W6+e2dy5ai+MCrhatTL7+++4j2nVqpUNHTrUvd+zZ09bunRpylwsAOC8EGoBAAAASJPq1zebMycq2NJt69Zmp0/HfczgwYNduHX69Glr0aKFbd++PaUuFwCQSIRaAAAAANKsm24y+/BDs6xZzWbPNmvTJu5gKyIiwsaNG+faEffs2WPNmjWzo0ePpuQlAwASiFALAAAAQJrWsGFUoJUli9nMmWbt2sUdbOXIkcMNjtcA+e+//97at29vkZGRKXnJAIAEINQCAAAAkOY1bmw2a1ZUsDVjhln79mZnzsS+f5EiRWz27NmWNWtWF3CpLREAEFoItQAAAACkC02amH3wgVnmzGbTppl17Bh3sFWjRg1755133PvPPPOMTZ48OeUuFgAQL0ItAAAAAOnGbbeZTZ9ulimT2fvvm3XubHb2bOz7q/VwwIAB7v2uXbvaqlWrUu5iAQBxItQCAAAAkK7cfntUpZaCrUmTFFbFHWwNGzbMmjZtaqdOnbLbb7/ddu7cmZKXCwCIBaEWAAAAgHTnjjvMpkwxy5jRbPx4s27dzGKbBa8VESdNmmTly5e33bt3W/Pmze348eMpfckAgACEWgAAAADSpZYto1oQFWyNG2fWvXvswVbu3Lnto48+svz589vq1autS5cudu7cuZS+ZACAH0ItAAAAAOlWq1ZRLYgREWbvvmvWs2fswVaJEiXsgw8+sMyZM9u0adPs6aefTunLBQD4IdQCAAAAkK61bm02YUJUsPX222a9epnFVoRVp04de+ONN9z7jz/+uAu5AACpg1ALAAAAQLrXrp3Ze++ZZchgNmqUWe/esQdb3bp1swceeMC937FjR1u7dm3KXiwAwCHUAgAAAAAza9/ebOzYqGBLxVjKrWILtl544QVr2LChGxivFRE1QD42kbH1MwIAkoRQCwAAAAD+0amT2ZgxUcHWiBFmDz0UPNjKlCmTTZ061UqXLm07duywO+64w06ePOnuW7NmjfXp08cqVapk2bNnt+rVq7tbfaztuh8AkHSZUvsCAAAAACCUdOkSNSy+WzezV1+NWh3xhReigi5/+fLlszlz5liNGjVsxYoV1q5dO9u7d68tWbLEhV5nzpzx7Xv69Glbv369bdiwwUaOHOlmc40ZM8ZKlSqV8k8QANIIKrUAAAAAIMDdd5u99VbU+y+9ZPboo8ErtlSppZUQIyIibNasWfb111+77f6Blj9v+7Jly6x8+fL2/vvvX8BnAQBpG6EWAAAAAARxzz1Rs7Xk+efNBg0KHmzt2bPHNzcrofOzFG6dOnXK7rrrLps8eXKyXjcApBeEWgAAAAAQi3vvNRs5Mur94cPNBg+OHmxt2bLFunbtet7nP3funDt+69atyXC1AJC+EGoBAAAAQBx69YqarSXDhpkNHfrvfd26dbOzZ88m6fw6/m71OwIAEoVB8QAAAAAQj/vvNzt48JANGZLXnnzS7M8/f7cbbvjSFi9enORzqxVR55k0aZKVLVvWt71QoULuDQAQHJVaAAAAAJAAkZEvm1lf9/5bb11u7dtvTNbzt2/f3qpWrep7e8ubVA8ACIpKLYSsNWvWuOWOteyxlkCuUKGC9erVy6pXr57gcxw/ftzeeecdmzdvnv3xxx+WP39+u/XWW+2+++6z7Nmzx9j/008/tYkTJ7rH/Pvvv61w4cLWuHFj69Gjh+XIkSOZnyEAAADCiV4TNmv2h7333i577bXCZva0mgc1bStZzq+VFP1XQ6RKCwDiRqiFkLRo0SIXPOXJk8eaNm3q5gzMnTvXOnXqZCNGjLCbbrop3nMolOrZs6etXLnSrr/+emvYsKGtW7fORo8ebd98842NHz/esmTJ4ttfAZrOnS9fPhdk5cqVyx07atQoW7JkiQu7CLYAAADSL68dsEoVs4IFo1ZDNHv2n2Dr+SSff9u2bVZFJwcAJAihFkKOwqjHHnvMhUozZ870/YWqS5cu1qpVKxs6dKjVrl07aKWVvylTprhQSsM7+/fv79s+bNgwF2hpZoHOKb/88ou98cYb7rFmzJjhKrq8JZl1LboOVXzdr2EKAAAASPcefTTSBg16/J9qrf/9E2y9lMizZND6h76P1J2g158REUyJAYCE4LslQs78+fNtz5491qZNm2gl18WKFbO77rrL3bdgwYJ4z+NVYt2rdZj9PPjggy4QU+jl33aoajAtp+wFWqIXFF6Q9eWXXybTMwQAAEC40+vEzJkVZinYkhf1SjORZ8llZm18H2XOnJlACwASge+YCDmqrpKaNWvGuK9WrVrudsWKFXGeY9euXbZjxw6rWLGiq/jylzNnTrd9+/bttnv3brdNgzgVXqkCLJDXonjs2LEkPCsAAACEO81o1dxX7+2KK64ws6fMbOI/e2iQfK9EnPHsP4PnG7mPSpQoEe38ejwAQOxoP0TI0SwBKV68eIz7ihYt6msXjIsCK6+6KxidR+GZzlOwYEE3fD62AfSffPKJuy1TpkwinwkAAADSEq1G+MQTTwS5p4NegZrZfzWp1cx+1qvIBJzxuJld6/to8+bN7o+tniFDhrjRGwCA4Ai1EHIOHjzobvPmzRvjPq/q6siRIwk6h4a+B5M7d253e/jw4TjP8/vvv7vh8dKuXTtLLLU0In3y/u35GgDSN74XAGmLZrVqJW3Ppk2brGPHk2Z2zT+/WqkLoKCZfawIzMwUSOkPoxqpoaqrzWb2sJnVDzjzGTNbb+PHZ7Orr77at1WjOPj+Ef74NwQuHEItpIibb77Zfvvtt3j3++mnn9yATPFfmdDjbTt16lSc54nrHAk9z19//eVmbB04cMBat27ta31MjO+//z7RxyBt4WsAgPC9AEg7/GdeZchQzczKxrJnDzO7559h8J5zAR/7q24RET9aRMQJ35Y///zTvQEAgiPUQopQG2DGjBkTtG+2bNl8wZSGZQaujCg5cuSI8xxZs2aNtn8gb7vmawWjtsTu3bvbzp07rX79+jZ48GA7HxUqVEjw81b12b59++IN7BA+gn0Nhzr937nkkkt81YwAkv7XeQVaifl5ACB0acSV/5irOXMyWETEOYuMjC2oCtyeIY6w7Jx99NHVVrr0v6shas0kv3WTEOY/CwAkP0ItpIgxY8YkeF+v7VAhT2B4dfToUXcb3y/cXtthbG2K3vbAIfLeEPo+ffq41sTbbrvNhg8fft7BhH6BScgvMXosDQLV9Vx66aXu8TJkiP1FD0LfuXPn7MSJE26lzXD4t9T1KoQ7dOiQ+1rUX6Hz5MmT2pcFpBkJ/XkAILS9845Z0JFayUDB2LRpevt325AhZozUAoDYEWoh5JQsWdJWr17t2hULFCgQ7T6vhfHKK6+M9xz++wfytpcqVSra9jlz5tjAgQPdL/eamdCvX78UCST27t3rAq0iRYqERQCChIVEkZGRrvIwXP5NFcApMFaFor4mCbUAAIiuRw+zZs3+/bhGDbMzGoeVTDJl0krg/35MlRYAxI1QCyFHqxBOnz7dVUxde+2/q8HI8uXL3a3/qjDBKAzT6onfffedHT9+PFrF17Fjx1z5r+7Pnz+/b/v8+fPtkUcece9rlZm2bdtaSlCAppZDXUu4hB9Iu/Q1qGrJXbt2hWX7JAAAF5J/O2BkZPIGWqLzVaqkVsTkPS8ApFV8u0TIadCggWsfnDhxou3YsSNaddXkyZNde16jRo3iPc+dd97p2r9eeeWVaNv1sbb7r2a4bds2GzRokKusUbthSgVa/quhEB4gVHhfi6zUAwBAdJqntWZN1Nu6dVGVVclJ59N5vcfwn98FAIiJSi2EHA1vHzJkiPXt29datmzpWzZ53rx5bqbWiBEjfIPgRa1Ss2bNcm1TnTt39m3X+5988om99957tnHjRqtUqZKtW7fOVq1aZdWqVYsWao0cOdIFXYULF3bhmR4jkNoDu3TpcsGeN1VaCBV8LQIAENxbb124mVpepZZ/QwIztQAgboRaCElNmjRxLVBvvvmmzZ4921WOlC1b1nr16uXaE/2pTUqhlAIp/1ArS5YsNn78eHefwi0FWoUKFbKePXu6lQ11v2fx4sXRzhVbS+OFDLUAAAAQXjO1nnvO7IMPVN2c9HNrLYmWLc0effTfbczUAoC4ZTinacYAkpXathSiqTosvtWuTp486dofS5Qo4YaKpxS1WmqFO1wY+tbqzXMLt8qn1PqaBNL7zwMA4UctgvGMek2U1avNqlRJvvMhNPCzALhw+I0WSCfWrFljffr0cT9MVaWmH6i61cfarvsBAACQcAqg6tRJ+mwtHa/zEGgBQOIQagFp3NatW61u3bpuxchRo0bZ+vXr3ap2olt9rO26X/tp/9Q2c+ZMK1OmjA0YMCDO/XS/9tP+KeXXX391j9mhQ4cUe0wAABC6xoyJah1MCh2v8wAAEodQC0jDtFpk+fLlbdmyZe7jM7GsO+1t137a//3330/R6wQAAAhXpUqZjR2rhVbO73gdp+N1HgBA4jAoHkjDgVb79u3dbKeEUrilt7vuussd579CJAAAAIJr21bzNM26do0aGh/L3xFjtByqQkuBlo4HACQelVpAGrRlyxbr2rVrogItfzpOx4dCKyIAAEA40N8Cf/jB7Lrroj6Obc6Wt7127aj9CbQA4PwRagFpULdu3dwqK0mh4++++24LN5p59fjjj9vNN99sFStWtGuuucaaNGlir7zyilvVz59mY/Xo0cOmTp1qtWvXdvs+/PDDvvuXL19unTp1cvPGatSoYQMHDrQDBw5EO8fzzz/vzjN//vxo25cuXWpVqlSxhg0bxrjGNm3auMc6ceKE+/jUqVM2duxYa926tVWrVs21gF5//fX24IMP2ubNm4POEVu9erXdeeedbt/69evbjh07fOd666237LbbbnPP/9prr3VfD998800yfHYBAEBc1EK4aFHUKoY9e5pVqmSWOXPUHxl1q4+1Xfd/9RUthwCQVLQfAmmMwo7Fixcn+TxqQ9R5tCqiwplwsGnTJtc6qWu/6aab7PLLL7f9+/fbggUL7M0337Rt27bZq6++Gu2Y77//3oVXt99+u/u4bNmy7nbevHnWv39/y5o1qwumsmXLZgsXLrQlS5ZEO/7GG2+0d955x77++msXnnm8OWa//fab/fHHH1aoUCH38cGDB91w/nr16ln27NktMjLSunfvbitXrnSf51atWrnr1+f9448/do+n28suuyza495///1WokQJN7B+165dVrRoUReSde7c2S0ZXa5cOWvbtq0dP37cPv30U+vYsaMNGzbMWrRocYE++wAAwKOXTt7Lp7NnI23NmnVWpUolt/o0ACD5EGoBacy4ceMsU6ZMsQ6FTwydRxVEqRVqbdy40UaMGBHn/f5UjXX06FF77733rGbNmr7tqr5S5dZnn33m7s+VK5fvvn379tkTTzzhqqc82ufJJ5+0HDlyuKH5V111ldv+wAMPuMqtPXv2+PatXLmy5cuXzxdieRSU5cyZ044dO+YCq+bNm7vtCqkUZKm6Sj7//HPf/c8991y0czz00EOuAkz7KKzzpxBr/PjxFhHxb8GtAjsFWgrJ+vbtaxn+mVjbu3dv+7//+z8bMmSIXXfddVawYMF4PvMAACA5+f24BgAkI0ItII1RaJIcgZboPGqjS83KK70llKqWVFXlH2jJJZdc4oIpBT6qlPIPteSWW26J9vGiRYvcfpor5gVacvHFF7uWwPvuu8+3TX9xrVu3rn344Yf2888/25VXXumqw3Tdup6JEyfaihUrfKGWzq0gygu11Er4zDPPxLhmUcujQi2dL1Djxo2jBVpqF50+fbq7RoVhXqAlBQoUcK2kqtTSdarlEgAAAADCHaEWkMb8+OOPyXq+DRs2WGq54447bPjw4bHer/lSs2bN8n2suViiQEqhkuZMqf1Pz8F7HqqS8pc3b173FuxzqJlUgTRfK5BaEBUWqQVRoZaqtDRsX9tVhaU377EVEmqeloI2ueKKK9zb6dOn3eNu377ddu7c6WZpKQyTYPPRVKnlT62VqjBTFdYbb7wRY3+1KKb2vycAAAAAJCdCLSANUWiicCQ56Xw6r39VUKj666+/XAimGVJetdqll17q2idVraSwKHBFSM3KCnT48GF3G1jRJYEBmNxwww2WOXNmF2ppdpVaEdV6qLlWqsBSO6QCtr1797pB86oA8+h61OI5ZswYd79o1tZ//vMfN99LrY7BVrHUPv4OHTrkbnfv3m0jR46M9XPk7QcAAAAA4Y5QC0hDFDwpXEnOYEvnC4dAS8HPPffc4+ZsaUB606ZNrVSpUr4QSjOlFGolhGZkyZEjR2Lcp8HrgRR+aZXBVatW2d9//+0qtbSKoWaSeaGWqq68aqkGDRr4jtV9mqWlAEuzva6++mo34F6fc83zSujQf4VoXsCmwfUAAAAAkNaF/m+qABJFFT7JSdVG4eCnn35ygdb1119vQ4cOdW2CXqClkE9tfRKs6ilQhQoV3O23334b477vvvsu6DFqNVTgNXfuXBdeaR6WKOzS3C2FWpqnpVZDtSh6Zs+e7W5Hjx7tVmwsUqSIL0TcunVrgp9/yZIlXdWZ2i4VrAX65ptv7IUXXogx0B4AAAAAwhWhFpDGqFJHFULJQedRSBQOsmbN6mtB9B+Ur3lUzz77rK/tLiFD9OvUqePaFT/44AM3XN6jmVVaYTC2UEu81j9v8LuquBSSKdDSzCxvQHxg++Pvv/8ebbvmcE2bNs29n5DKuyxZslizZs1cu+Lzzz8fbXaYWh4HDx5sb7/9tp06dSrecwEAAABAOKD9EEhjunTpEudMpcRQAKTzhQNVQGl21po1a+zOO++0WrVquTBIq0GqSkuD2fft2+eGyMdHQZOCsHvvvdfat29vjRo1ci2JX375ZayBoQa3a6XELVu2uH3VTnjixAlfwOWFY/6th9KiRQtbu3atde/e3a3CmCdPHldtpYqqiy66yM3ZSsg1S//+/d3jjB8/3oVi1atXd/+Gn332mXvu+rzUq1cvQecCAAAAgFBHpRaQxijYUaVRUqu1dLzOo/OFgwwZMtjrr7/u5mlp0PvEiRPtiy++cGGTWvseffRRt5+CqYTQSoqTJk1ylWoKxrTKYvny5e3dd9+N9RivWkthkq7Ho4BNFFJVrlw52jGa9TVs2DA3R2vOnDk2ffp0279/v/Xu3ds+/vhjNxBec7WCrYAYSIHYlClTrE+fPm7/qVOnunMUK1bMze166qmnol0XAAAAAISzDOcSMmAGQKIoUFDFTKVKldw8pbicPHnStm3bZiVKlAi6Et/50CwmBTBJaTVTO98PP/zghq0j8fStVTO2cuTIEXZB0oX4mgTSq8T8PACQNvF9AHwNABcOlVpAGqQgauzYsecdpug4HU+gBQAAAAAIVYRaQBqlNjy14KniKqGtiNpP+6vtTscDAAAAABCqCLWANKxdu3auhfC6665zH8cWbnnbNUdK+xNoAQAAAABCHasfAmmcWggXLVrkVgVUS+HSpUttw4YNbmXAzJkzW7ly5dwwdK1yGC5D4QEAAAAAINQC0gkFVv6hVWRkpEVEUKwJAAAAAAhP/EYLpFMEWgAAAACAcMZvtQAAAAAAAAg7hFoAAAAAAAAIO4RaAAAAAAAACDuEWgAAAAAAAAg7hFoAAAAAAAAIO4RaAAAAAAAACDuEWkA6FRmZ2lcAAAAAAMD5I9QC0ok1a8z69DGrVMksSxazjBmjbvWxtut+AAAAAADCBaEWkMZt3WpWt65Z1apmo0aZrV9vdvp01H261cfarvu1n/ZPbTNnzrQyZcok6G3cuHGpdp0DBgxw17Bs2bLzPsfff/9to0ePtrNnzybrtQEAAABAWpcptS8AwIUzebJZ165mXl5y5kzw/bztymbKlzcbO9asbVtLdVdffbXddNNNce5TSaVmYax9+/a2fv1666p/KAAAAABAghFqAWk40Grf3uzcuYQfo3BLb3fdFXVcu3aWqsqWLWt91BuZhu3duze1LwEAAAAAwhLth0AatGVLVIVWYgItfzpOx4dCKyIAAAAAAMEQagFpULdu/7Ycni8df/fdFjY6dOhg1apVs/3799vgwYOtVq1arjWxTZs2tuafKfgffPCBNW3a1CpWrGg333yzjRw50k57A8bMbOXKlW5GVr9+/WKcf/r06e6+ESNGxHstK1ascBVmDRs2tAoVKljVqlWtdevWblZY4GPt2rXLfVyuXDn3HDyasTVhwgS744477JprrrHKlSu75zJ79uwYj1e/fn277bbb7IsvvnDv6zHbtm1r58431QQAAACAMED7IZDGrF5ttnhx0s+jNkSdR3lQlSoWFs6cOWN3qXfSzJo3b26//fabLViwwO6++25r0aKFzZgxw2655RarXbu2zZ071wVUERERdt999yXbNegx/vvf/1r+/Pmtbt26dvHFF7vrWLhwoQ0cONBOnDjhrrFw4cLWu3dve++99+zIkSPuGooWLeobHn/vvffa0qVL3X56LgrfFi1aZI8++qitWrXKnnnmmWiP++eff9rDDz/sZpDly5fP8ubNaxkyZEi25wUAAAAAoYZQC0hjtBhgpkyxD4VPDJ1HQ+NTK9TauHFjnJVRuXPnts6dO/s+VmCkMGf8+PGWJUsWt+2hhx6y+fPn29SpU13gpOHzosqpxo0bu8qn5Aq1FDw9//zzdtFFF7nzZs+e3XLkyOHCpcWLF1v37t1t1qxZLtQqUqSIq+bSxwq1evXqZZn0CXf/huNcoFWvXj17+eWX3TlEVWhdunRxFWeqRFPVmefw4cN2zz33WN++fZPluQAAAABAqCPUAtKYJUuSJ9ASnWfpUks1mzZtcm+xURWTf6glHTt29AVaotY/hVqqmvICLSlRooSrolL7n9r0kqOqSS2DTzzxhOXMmdMuueQSO378uO++mjVr+oKp+Ci0UgXZk08+6Qu0RNf72GOPuTZFhXT+oZY0adIkyc8BAAAAAMIFoRaQxvz4Y/Keb8MGSzWaJzV8+PBEHaOwyp8XChUrVizGvqqkUsikMMqrkkqKbNmyueov2blzp23YsMH27Nlj27dvt7Vr17rteqy4HDt2zO2v51GgQIEY91epUsUyZsxoPwb5h/baFwEAAAAgPSDUAtKQyEi1wCXvOXU+nTciTJaV8K9s8udfvXUhKbz63//+5xtOrwqw4sWLW40aNeyHH36Id3j70aNHfa2VwSh8U8XWvn37goZqAAAAAJBeEGoBaYiCp8yZkzfY0vnCJdBKKq8FMVjw5N9KGJs//vjDunbt6o5/5JFHXFWVWh5VEXbq1CnXMhgftS56g9+DiYyMdMGXhsEDAAAAQHqWTn5VBdKP//wnec9XrpylG5mV4P3TAhjo119/jff4zz77zIVfWrlQ4Vbp0qV91VNbtmxxt/FVauXKlctVdv3111+uDTHQd9995wbi69wAAAAAkJ4RagFpzA03RK1amBx0nuuvt3RDYZLa+9Q6ePDgwWiB1pw5c+I93guwNHze36FDh+zpp592758JmOLvBWlaOdHTsmVLF35pULx/hdiBAwds2LBhvnljAAAAAJCe0X4IpDFdupiNHJk851L+ovOllo0bN9qIESPi3EcrILZo0SJZHk+zqjTofe7cuS5YatiwoQukPvnkEytXrpytWrUqzuNvvPFG1xaoNsPdu3dbyZIl3fELFy501VWqwjpy5IgLtrzB9IUKFXIVWf369bPq1atbp06dXJXX8uXL7euvv7bbbrvN6tSp44756quv3OB5Pd/mzZsny3MGAAAAgHBFqAWkMVWqmNWpY7ZsWVQodb6UuVx3XdT5UsumTZvcW1wUBCVXqCWqqNKqg/Pnz7cJEya4FQUffPBBq1q1aryPc9lll9n48ePt5Zdfdm2CK1eutIIFC7pQqkePHjZ27FibMWOGC6vq1q3rjlGYNWjQIFu0aJFt3rzZhVqq3nr77bdt0qRJNnv2bJs1a5bbpvlcAwYMcEEXAAAAAKR3Gc7FN+AFQKKdPXvW1q1bZ5UqVbKMGTPGue/Jkydt27ZtVqJEiWRbvW7rVrPy5c1OnTr/c2TNavbDD2alSiXLJaU7+taq1kGtxugNoA8XF+JrEkivEvPzAEDaxPcB8DUAXDjM1ALSIAVRY8dqNb/zO17H6XgCLQAAAABAqCLUAtKotm3NJk6MqrhK6OB47af9J02KOh4AAAAAgFBFqAWkYe3aRbUQajaWxBZuedtr147an0ALAAAAABDqGBQPpHFqIVy0yGzNmqiWwqVLzTZsMDt92ixzZrNy5cyuvz5qlcPUHAoPAAAAAEBiEGoB6YQCK//QKjLSLIJaTQAAAABAmOJXWiCdItACAAAAAIQzfq0FQsS5c+dS+xIAh69FAAAAAOGAUAtIZRkzZnS3pzXkCggB3tei97UJAAAAAKGIUAtIZZkzZ7asWbPaoUOHqJBBqtPXoL4W9TWpr00AAAAACFUMigdCQP78+W3Xrl22c+dOy5s3rwsTMmTIkNqXhSSGQ6dOnbKIiIiw+LfU9apCS4HW0aN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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# these are example solved energies from hardware and emulator\n", "\n", "sns.set_style(\"whitegrid\")\n", "\n", "steps=[1,2,3]\n", "emulator=[0,0.04681999999991149,-0.021029999999996107 ]\n", "hardware=[0,0.06232999999997446,-0.018699999999967076]\n", "\n", "\n", "plt.hlines(y=0, xmin=0, xmax=12, ls='--', colors='black')\n", "\n", "plt.scatter(x=steps, y=hardware, s=200, c=\"black\", label=\"Hardware\") \n", "plt.plot(steps, hardware, c=\"black\")\n", "\n", "y_error_hard = [0.00196006200237305,0.00171393177265877,0.000768517902650537]\n", "plt.errorbar(steps, hardware,\n", " yerr = y_error_hard, fmt ='o', color='black',\n", " elinewidth=10,capsize=10)\n", "\n", "\n", "plt.scatter(x=steps, y=emulator, s=200, c=\"blue\", label=\"Emulator\") \n", "plt.plot(steps, emulator, c=\"blue\")\n", "\n", "y_error_em = [0.00222116960181585,0.000835916404261285,0.000489635318758811]\n", "\n", "plt.errorbar(steps, emulator,\n", " yerr = y_error_em, fmt ='o', color='blue',\n", " elinewidth=10,capsize=10)\n", "\n", "\n", "plt.ylabel('$Energy\\; [Ha]$', fontsize=15)\n", "plt.xlabel('$NEB \\; Images\\;$', fontsize=15)\n", "plt.xticks(fontsize=15 )\n", "plt.yticks(fontsize=15 )\n", "plt.xlim([0.5, 3.5])\n", "plt.ylim([-0.04, 0.08])\n", "x=[1,2,3]\n", "my_xticks = ['start','ts','end']\n", "plt.xticks(x, my_xticks)\n", "plt.legend(fontsize=15, loc='lower left')\n", "plt.title(\"Comparison of the activation and the dissociation energies by using ADAPT-VQE with Quantinuum H2-1 device and Quantinuum H2-1E noisy emulator backend.\")\n", "plt.show()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }