{ "cells": [ { "cell_type": "markdown", "id": "846d1463", "metadata": {}, "source": [ "\n", " \"image0\"\n", "" ] }, { "cell_type": "markdown", "id": "a675d59e", "metadata": {}, "source": [ "# Rabi experiment\n", "In this tutorial we will combine the techniques explained in the other tutorials and show how to perform a Rabi experiment. For this tutorial we will need one QCM to generate the Rabi pulses and one QRM to perform the readout, although the QCM could be replaced by another QRM if needed.\n", "\n", "Ports $\\text{O}^{[1-2]}$ of the QCM are used for the driving pulse, while $\\text{O}^{[1-2]}$ of the QRM are used for the readout pulse. Finally, ports $\\text{I}^{[1-2]}$ are used for the acquisition of the readout tone. In this tutorial it is assumed $\\text{O}^{1}$ of the QRM is connected to $\\text{I}^{1}$ of the QRM for time of flight calibration.\n", "Furthermore we assume that $\\text{O}^{1}$ of the QCM and $\\text{O}^{2}$ of the QRM are connected to an external oscilloscope to view the Rabi experiment pattern. The scope can be triggered of marker 1 of the QCM.\n", "\n", "The SYNQ technology synchronizes the programs in the two modules." ] }, { "cell_type": "markdown", "id": "23deb312", "metadata": {}, "source": [ "## Setup\n", "\n", "First, we are going to import the required packages and connect to the instrument." ] }, { "cell_type": "code", "execution_count": 1, "id": "de6b7363", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:30.009507Z", "iopub.status.busy": "2025-05-07T16:47:30.009310Z", "iopub.status.idle": "2025-05-07T16:47:30.622143Z", "shell.execute_reply": "2025-05-07T16:47:30.620769Z" } }, "outputs": [], "source": [ "from __future__ import annotations\n", "\n", "import json\n", "from typing import TYPE_CHECKING, Callable\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from qcodes.instrument import find_or_create_instrument\n", "\n", "from qblox_instruments import Cluster, ClusterType\n", "\n", "if TYPE_CHECKING:\n", " from qblox_instruments.qcodes_drivers.module import Module" ] }, { "cell_type": "markdown", "id": "47f8a159", "metadata": {}, "source": [ "### Scan For Clusters\n", "\n", "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)." ] }, { "cell_type": "markdown", "id": "46ea436b", "metadata": {}, "source": [ "`!qblox-pnp list`" ] }, { "cell_type": "code", "execution_count": 2, "id": "70fa5380", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:30.625319Z", "iopub.status.busy": "2025-05-07T16:47:30.625106Z", "iopub.status.idle": "2025-05-07T16:47:30.628979Z", "shell.execute_reply": "2025-05-07T16:47:30.628055Z" } }, "outputs": [], "source": [ "cluster_ip = \"10.10.200.42\"\n", "cluster_name = \"cluster0\"" ] }, { "cell_type": "markdown", "id": "55db176b", "metadata": {}, "source": [ "### Connect to Cluster\n", "\n", "We now make a connection with the Cluster." ] }, { "cell_type": "code", "execution_count": 3, "id": "16ef7779", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:30.630944Z", "iopub.status.busy": "2025-05-07T16:47:30.630782Z", "iopub.status.idle": "2025-05-07T16:47:33.986294Z", "shell.execute_reply": "2025-05-07T16:47:33.984761Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "cluster = find_or_create_instrument(\n", " Cluster,\n", " recreate=True,\n", " name=cluster_name,\n", " identifier=cluster_ip,\n", " dummy_cfg=(\n", " {\n", " 2: ClusterType.CLUSTER_QCM,\n", " 4: ClusterType.CLUSTER_QRM,\n", " 6: ClusterType.CLUSTER_QCM_RF,\n", " 8: ClusterType.CLUSTER_QRM_RF,\n", " }\n", " if cluster_ip is None\n", " else None\n", " ),\n", ")\n", "cluster.reset()" ] }, { "cell_type": "markdown", "id": "5df1889b", "metadata": { "lines_to_next_cell": 2 }, "source": [ "#### Get connected modules" ] }, { "cell_type": "code", "execution_count": 4, "id": "6ff68f45", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:33.990867Z", "iopub.status.busy": "2025-05-07T16:47:33.990449Z", "iopub.status.idle": "2025-05-07T16:47:33.998639Z", "shell.execute_reply": "2025-05-07T16:47:33.997305Z" } }, "outputs": [], "source": [ "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n", " def checked_filter_fn(mod: ClusterType) -> bool:\n", " if filter_fn is not None:\n", " return filter_fn(mod)\n", " return True\n", "\n", " return {\n", " mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n", " }" ] }, { "cell_type": "code", "execution_count": 5, "id": "808b3503", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.002270Z", "iopub.status.busy": "2025-05-07T16:47:34.001879Z", "iopub.status.idle": "2025-05-07T16:47:34.033440Z", "shell.execute_reply": "2025-05-07T16:47:34.032123Z" } }, "outputs": [ { "data": { "text/plain": [ "{4: }" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# QRM baseband modules\n", "readout_modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n", "readout_modules" ] }, { "cell_type": "code", "execution_count": 6, "id": "33d072ab", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.037705Z", "iopub.status.busy": "2025-05-07T16:47:34.036950Z", "iopub.status.idle": "2025-05-07T16:47:34.042408Z", "shell.execute_reply": "2025-05-07T16:47:34.041118Z" } }, "outputs": [], "source": [ "readout_module = readout_modules[4]" ] }, { "cell_type": "code", "execution_count": 7, "id": "fe956c11", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.046424Z", "iopub.status.busy": "2025-05-07T16:47:34.045460Z", "iopub.status.idle": "2025-05-07T16:47:34.071828Z", "shell.execute_reply": "2025-05-07T16:47:34.070566Z" } }, "outputs": [ { "data": { "text/plain": [ "{2: ,\n", " 10: }" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# QCM baseband modules\n", "control_modules = get_connected_modules(\n", " cluster, lambda mod: not mod.is_qrm_type and not mod.is_rf_type\n", ")\n", "control_modules" ] }, { "cell_type": "code", "execution_count": 8, "id": "7590a91c", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.076158Z", "iopub.status.busy": "2025-05-07T16:47:34.075147Z", "iopub.status.idle": "2025-05-07T16:47:34.080636Z", "shell.execute_reply": "2025-05-07T16:47:34.079293Z" } }, "outputs": [], "source": [ "control_module = control_modules[2]" ] }, { "cell_type": "markdown", "id": "1595701d", "metadata": {}, "source": [ "## Sequencer Setup " ] }, { "cell_type": "markdown", "id": "de165394", "metadata": {}, "source": [ "Set `sync_en` to synchronize across modules." ] }, { "cell_type": "code", "execution_count": 9, "id": "b0e55ebf", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.084837Z", "iopub.status.busy": "2025-05-07T16:47:34.083904Z", "iopub.status.idle": "2025-05-07T16:47:34.108132Z", "shell.execute_reply": "2025-05-07T16:47:34.106852Z" } }, "outputs": [], "source": [ "# Set sync_en\n", "readout_module.sequencer0.sync_en(True)\n", "control_module.sequencer0.sync_en(True)" ] }, { "cell_type": "markdown", "id": "1f4d7754", "metadata": {}, "source": [ "Configure the `NCO` of both the QRM and QCM to 100 MHz and enable the up- and down-conversion in the sequencers" ] }, { "cell_type": "code", "execution_count": 10, "id": "01ac38be", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.111745Z", "iopub.status.busy": "2025-05-07T16:47:34.111322Z", "iopub.status.idle": "2025-05-07T16:47:34.159624Z", "shell.execute_reply": "2025-05-07T16:47:34.158266Z" } }, "outputs": [], "source": [ "readout_module.sequencer0.nco_freq(100e6)\n", "readout_module.sequencer0.mod_en_awg(True)\n", "readout_module.sequencer0.demod_en_acq(True)\n", "\n", "control_module.sequencer0.nco_freq(100e6)\n", "control_module.sequencer0.mod_en_awg(True)" ] }, { "cell_type": "markdown", "id": "01151725", "metadata": {}, "source": [ "Configure the outputs of the QRM and QCM such that sequencer0 is the only enabled sequencer and maps to $\\text{O}^{[1-2]}$" ] }, { "cell_type": "code", "execution_count": 11, "id": "063d9fc5", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.164230Z", "iopub.status.busy": "2025-05-07T16:47:34.163090Z", "iopub.status.idle": "2025-05-07T16:47:34.231411Z", "shell.execute_reply": "2025-05-07T16:47:34.229936Z" } }, "outputs": [], "source": [ "# Map sequencer of the QCM to specific outputs (but first disable all sequencer connections)\n", "control_module.disconnect_outputs()\n", "\n", "control_module.sequencer0.connect_sequencer(\"out0_1\")\n", "\n", "# Map sequencer of the QRM to specific outputs (but first disable all sequencer connections)\n", "readout_module.disconnect_outputs()\n", "readout_module.disconnect_inputs()\n", "\n", "readout_module.sequencer0.connect_sequencer(\"io0_1\")" ] }, { "cell_type": "markdown", "id": "9858cc5f", "metadata": {}, "source": [ "## Define waveforms" ] }, { "cell_type": "markdown", "id": "4539c40e", "metadata": {}, "source": [ "To readout the systems we define constant pulses `one` and `zero` which will be up converted by the `NCO` to create the appropriate tones for an IQ mixer.\n", "Similarly for driving the qubit, we define a Gaussian pulse, together with a zero pulse of equal length to serve as inputs for an IQ mixer. In this tutorial we do not assume mixers to be connected to the inputs and outputs of the QCM/QRM." ] }, { "cell_type": "code", "execution_count": 12, "id": "5b27d1d0", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.236017Z", "iopub.status.busy": "2025-05-07T16:47:34.235276Z", "iopub.status.idle": "2025-05-07T16:47:34.243749Z", "shell.execute_reply": "2025-05-07T16:47:34.242423Z" } }, "outputs": [], "source": [ "t = np.arange(-80, 81, 1)\n", "sigma = 20\n", "wfs = {\n", " \"zero\": {\"index\": 0, \"data\": [0.0] * 1024},\n", " \"one\": {\"index\": 1, \"data\": [1.0] * 1024},\n", " \"gauss\": {\"index\": 2, \"data\": list(np.exp(-(0.5 * t**2 / sigma**2)))},\n", " \"empty\": {\"index\": 3, \"data\": list(0.0 * t)},\n", "}" ] }, { "cell_type": "markdown", "id": "2bfefb10", "metadata": {}, "source": [ "Hence we obtain the following waveforms for readout:" ] }, { "cell_type": "code", "execution_count": 13, "id": "c74cdbe8", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.248247Z", "iopub.status.busy": "2025-05-07T16:47:34.247259Z", "iopub.status.idle": "2025-05-07T16:47:34.404132Z", "shell.execute_reply": "2025-05-07T16:47:34.403720Z" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wfs[\"zero\"][\"data\"])\n", "plt.plot(wfs[\"one\"][\"data\"])" ] }, { "cell_type": "markdown", "id": "61dbfb88", "metadata": {}, "source": [ "And for drive:" ] }, { "cell_type": "code", "execution_count": 14, "id": "a64bef94", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.405593Z", "iopub.status.busy": "2025-05-07T16:47:34.405443Z", "iopub.status.idle": "2025-05-07T16:47:34.505950Z", "shell.execute_reply": "2025-05-07T16:47:34.505509Z" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wfs[\"gauss\"][\"data\"])\n", "plt.plot(wfs[\"empty\"][\"data\"])" ] }, { "cell_type": "markdown", "id": "3fc5f40e", "metadata": {}, "source": [ "Finally, we define two acquisitions. A single readout to perform the calibration measurements. Secondly we create a `rabi` readout sequence that contains 50 different bins for saving the results, one for each of the different amplitudes used for the drive tone." ] }, { "cell_type": "code", "execution_count": 15, "id": "47a48c6b", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.507505Z", "iopub.status.busy": "2025-05-07T16:47:34.507350Z", "iopub.status.idle": "2025-05-07T16:47:34.510075Z", "shell.execute_reply": "2025-05-07T16:47:34.509672Z" } }, "outputs": [], "source": [ "num_bins = 50 # Number of amplitudes to be measured\n", "acquisitions = {\n", " \"single\": {\"num_bins\": 1, \"index\": 0},\n", " \"rabi\": {\"num_bins\": num_bins, \"index\": 1},\n", "}" ] }, { "cell_type": "markdown", "id": "07a9b103", "metadata": {}, "source": [ "## Calibration experiments" ] }, { "cell_type": "markdown", "id": "f273c28c", "metadata": {}, "source": [ "### TOF calibration" ] }, { "cell_type": "markdown", "id": "01e1d98d", "metadata": {}, "source": [ "As a first step, we calibrate the time of flight (tof) for the QRM module. In order to do so, we play a readout pulse and analyze the obtained signal on the oscilloscope to find the travel time of the pulse through the system." ] }, { "cell_type": "code", "execution_count": 16, "id": "0535ac75", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.511487Z", "iopub.status.busy": "2025-05-07T16:47:34.511339Z", "iopub.status.idle": "2025-05-07T16:47:34.513734Z", "shell.execute_reply": "2025-05-07T16:47:34.513328Z" } }, "outputs": [], "source": [ "qrm_prog = \"\"\"\n", "play 1, 0, 4 # start readout pulse\n", "acquire 0, 0, 16384 # start the 'single' acquisition sequence and wait for the length of the scope acquisition window\n", "stop\n", "\"\"\"" ] }, { "cell_type": "markdown", "id": "d6669b55", "metadata": {}, "source": [ "Upload the program, together with the waveforms and acquisitions to the QRM" ] }, { "cell_type": "code", "execution_count": 17, "id": "eefc0d09", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.515112Z", "iopub.status.busy": "2025-05-07T16:47:34.514972Z", "iopub.status.idle": "2025-05-07T16:47:34.570101Z", "shell.execute_reply": "2025-05-07T16:47:34.569630Z" } }, "outputs": [], "source": [ "sequence = {\n", " \"waveforms\": wfs,\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": qrm_prog,\n", "}\n", "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)\n", " file.close()\n", "# Upload sequence.\n", "readout_module.sequencer0.sequence(\"sequence.json\")" ] }, { "cell_type": "markdown", "id": "6886dd83", "metadata": {}, "source": [ "Perform the calibration experiment" ] }, { "cell_type": "code", "execution_count": 18, "id": "6063e30f", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.571541Z", "iopub.status.busy": "2025-05-07T16:47:34.571395Z", "iopub.status.idle": "2025-05-07T16:47:34.589433Z", "shell.execute_reply": "2025-05-07T16:47:34.589012Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n" ] } ], "source": [ "# Arm and start sequencer.\n", "readout_module.arm_sequencer(0)\n", "readout_module.start_sequencer()\n", "\n", "# Wait for the sequencer and acquisition to finish with a timeout period of one minute.\n", "readout_module.get_acquisition_status(0, 1)\n", "readout_module.store_scope_acquisition(0, \"single\")\n", "# Print status of sequencer.\n", "print(readout_module.get_sequencer_status(0))" ] }, { "cell_type": "markdown", "id": "5dfb4008", "metadata": {}, "source": [ "Analyze the resulting signal on the scope to find the tof" ] }, { "cell_type": "code", "execution_count": 19, "id": "4de2bb7b", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.591195Z", "iopub.status.busy": "2025-05-07T16:47:34.591053Z", "iopub.status.idle": "2025-05-07T16:47:34.642157Z", "shell.execute_reply": "2025-05-07T16:47:34.641760Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "147.16666666666666\n" ] } ], "source": [ "p0 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n", "p1 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n", "\n", "# Determine when the signal crosses half-max for the first time (in ns)\n", "t_halfmax = np.where(np.abs(p0) > np.max(p0) / 2)[0][0] if np.any(p0) else 16\n", "\n", "# The time it takes for a sine wave to reach its half-max value is (in ns)\n", "correction = 1 / readout_module.sequencer0.nco_freq() * 1e9 / 12\n", "\n", "tof_measured = t_halfmax - correction\n", "print(tof_measured)" ] }, { "cell_type": "markdown", "id": "0ef176ca", "metadata": {}, "source": [ "Plot the signal on the scope, around the rising and falling edge of the acquisition signal, as determined by the tof analysis above:" ] }, { "cell_type": "code", "execution_count": 20, "id": "a9c0194a", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.643890Z", "iopub.status.busy": "2025-05-07T16:47:34.643751Z", "iopub.status.idle": "2025-05-07T16:47:34.865353Z", "shell.execute_reply": "2025-05-07T16:47:34.864852Z" } }, "outputs": [ { "data": { "image/png": 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", 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KPFHXLDx6BOHBidi2M6J7ukhsS+/nxtPg1Wql9Mjk97VyAtq/1zyjYRgKZbV9mSdHtu05NJmuV4JOGOK+Q5M9r4lCFum6Oc5cm9/PM+F5XikHL2Tb+oh6t6SQM08MBoPBcA8mTxJ0/5BHX8uDDAkJcNi3QA7OhP6YHtscZgP2HJpE0Pdav23how9HMtG1Ovz1m3ttW+iANpkf87QxeMtX9trmiHTS72c3oO23a8F97cF+X4sPERaaqD9xMBL78H34G0+PbXPra5tXj8D3vJ7XKp6HTat7M0k9QhaDUUYqJQKOnlHTvaO/1BHHjiAM+p/wcm3rCEp7kW2Lqx+LwWAwGE9tMHmSgPyHXMg8AUJJkMO+hc2rR+D1vdYfnEX9Md2SIAClZCootnW+fVtkz/gp8GLZaNcEhRrQdvoyKACc97FFtvW+prSt/346nFlEuZ/AwmSe9hyaRD9l7H9GE1/D2g3wGgO9tjnytfGRYVy/7dye135587qMkr0+IQuURex6XzO5nwhD4NgRZ7aRfK0/87SIRjAwGAwG46kPJk8SkAPae78PAOnAUgwMArU6AHcB0PjIMN7+zM09r1370uf1BGfhoccQHn4C8DxgaFlk4wr3ZXvjI8N4wfgJPa/93bZzU9vmdu7A7AfeHtnx4E8wt3NHZJvjpu+sgPa1T98wL6Dt3P3NyJ6169PXXJdijo8M46MXPbfntT980dnzbfvpXZE9x41GLwwOO59ZlOVrH37xs3t97dhkWuroLVsR/b+EgFYXbM/t3IHZP3xT9I1HHuz6WgmiLtu3norVA4306+88+jO0+zI2ia/5o+vS11zPLBofGcbVFzy757X3Pe+M+b62+/uRPfG+5tUb0d6Ghd3XgF6ZcsD9mjEYDAaDIYLJkwTjI8P40wue1fPaL528tucP+dyn/xfw+H4AwOzvvxZzO3f09sg4JClb1x7X83WjWun5eu4f/yz6Rxhi+uLTI9uEWS0upZBHlw32fH3yykhkITg4geYHL41Or2M0P3gZgoMTpWQqtm89FaPDA+nXX9v3GJrtTvr13M4daO/8OACg/a//VyB27m375c3rer7+8c8O93w99y8fR7jnhwCA5od+q+trJRCBfl/bf2y617Z/+Ej67+nXPyOyrYRnYHxkGOeOP63ntasviIjdQvsaAEy1WgCARsXHxNFp/Nvufen35nbuQPvGTwAAWp/936mvldFbtG3TeM/X+/pKkec+93cI990PAJj9o7eWSjqfvfb4nq/797VwZhrhxAPRF3FZ7WIac8BgMBiMpz6YPCnwslN6g4yv73scT840AcRE4Jr3dL8ZBKUGZ5PNKDCrV6Jb+LFv/TglRMHBCbRu+It5toWzcdDbmgNmZ5zbdkJMVD727ajMJti7B+jvlwg6CPbdV5rs9lQrIkuD1Qr2H53GP/4wkplPg+0EYdi9nyXIbh9pzgEABuJg8fM/eRg/iQlUZNtvCbZ1fa2MwDG5nxtWRAcH131vN342PZvaNvd/ru6+OfW12L+aswibs85sWzMcEfUkA/XQkUhOnuRrDtes2e6g2Yl+/m/G2ZS/+U6kEqf0tRJUJ/t97V9+uhcHj810bfvT3+m+ueR9LbGt5s/f14Beoj7zxmdFRD3JDB89jLDTPQxhMBgMBsMFmDwpkASNm1Ytx5YTVuHoXAv/KwmA9u4BQklwVsIJ7ZHZKMj4tadvxFCtih8+9iT+6+EDgm19maWgg+CJA0AlCpjKIAL/7XlRb86/796H+w9NRuIQfp/L+RX46zaVcqrdCQIcnYvu6fti2z5yxz1oB4Ey2C6TDJ+0chl+5dR1CAF85I5oFpbK18rI8CS+9qpT1+PZa4/DdKuND3z1+7jtoUdx8Cc/lPtafK/LWLe3x3LWf//93ZiYnFL7WtK/43DNErsA4PeffxZ8z8NXHnoU//f7e6I1WwS+9vTjV+CcsePRCgL83x9EM50W2tcS237tjIx97eAE5j7+J4JdMVEX+/0c9WMxGAwGg5GAyZMCCQlYOVDH//fCaB7RX3zzR7hpzz4cXHVi1E8kIgnOVrhXf5qMScDGlcvw1i2nAIiC7dseelRqW2X95p7SPWe2xQHQOWPH42WnnIgQwB985bvYP7gKjSuvF2zy0bjyOvhrxkuReD82107//d+efyaOH2rggcNHcdWuu6I10wTbZZy4r2jU8YcvegYA4BM/vB//fM/9al9zPLMI6PraikYNf/jCswEA137np3jxDV/GOf95L8L+ziO/gsqGU9OyqjLW7VWnrsMvrl+DuU6Ad33pG2pfK4GoT8Z2LatXcdKq5Tj7hJUAgN/84tejNfMkvuZYbS+yLbmfdfzWs04DEGXFbn1wv9LXyjgUSnxtw4pleMuWkwEI+5qEqIcH9kb9f2DRCAaDwWC4B5MnBZIgY6RRx+uevhEnDDVweHYOr/jMrVj/mTtx/y/+WvfNfqUbnKWqdg5Lb2a7wfa7nvt0AMCXHtiPF9/wZaz/zJ04eNo5mbaVUuY111230+Lm/c/99GFsuPaz+MTGXwBGIzGGgY/+C+oXbwcg9hW568dKAu16xcfqwQZ+Yd0aAMCf3nE31n/mTnzvkiu6b864n2VkA0YaNTx37HiccfwKBADe+IWvYf1n7sSe89+wYLaJvvbsvv6nicFV+K8Tnq6xzT0RGKnXcM5Y1C/zhd37ur42thEAMHDNZzN8zZ3E+xFh75iYnMIPD3bvz8TgKlxx9iXdN2etWQmZ4ZFGDa8/8yQMViuYODqNiz75H1j/mTtx33mvV9tWAulc0ajh3c+NBCGSfU1G1HsPrJg8MRgMBsMtmDwpcET4Q/7osRk8Pt1MvxeEIf7yWHTa6W95PoZveqgbnJV4QjvSqGGkUev5XhCGuGUmeq36mnf02lZCViwJtqdbLVwblzlGdgGX3XQngrmoB8Y/8aT0eynh7HSA6WNO7BIJysTkFP51917BthBvORwTA7/Ss2Yopeepmw2YmJzCT3/WLT8KwhAfPRYpJvpPf7bkfjokKIKv3f/kfPn9h4eidau+enuvbSPus4mJbbPtDv7imz9OX+/6WvTMZvpauwXM9opfWLMrISj1Wubss39fuzX6R62WuWZlZJ5G6jUcmmliVhBNCcIQf5742pnP7bPNfZbzSFO+r00MrsIuKVHnWU8MBoPBKAdMnhQQg+2smTID7TgwG10XZXVilJ0N2NM3ewoABmPbKpu29Nq23G1WrBMEmGpF5XGPTzczZ2WFM1HA6g0Mdb8xMARUo2DJ1bqJ5UrRHK/e7zfiNcPQsuz76TCgTe5n4mv9wXZim79mPPN+luVrWfLgw51oXSubzlqw5+Bn07OZvhZk+drQsm7vnyPbUjI8UMucMbYsiHuihpaXv2bJodBAXb2vrenf10og6kLGLmtfe3goyi5WX/2bmcSOy/YYDAaD4RpMnhToDxrnBUCd6PtJvX2CUspbhGxAdkAb2eaVbFsiyAAAZ5+wav5gWgB+Mz7tF2zzPM95VkwsV1LdT9maldNXtLh9bXxkGL///O5Q14rn4dzjhrJtc5yxa3UCzMRZky1ZvuZ5qCS+JpCnMiTeu9md+rwZY74HfPB5kcDFfF8rkaDUa5nz7BbU1/qe0f59LbFt3qFQsm6O1ToZDAaDwWDypEB/0CgGQBXPw+tOjgdIiqfacB80AsCR2W6wPT4yjN8/tzeg3boyHtDZb5trgjLbnW1zyuoRXL/t3DQA8gB8/CXPhhcrjc1bN8c9MmLmKSugveqceECnzK6jDntkZrslolm+9oZTJL5WSnanu24A8PozoxK44wcbeOhdr8X6uqe2zVE2QCTqpx+/cp6vXf+S58BrR+/pJymue/8m57okAIhmjF24MRpufPUFz8arNkTzqfrXLLELU0cRtlpwATHzND4yjL/b9oL0exXPw+sX0Nf6n9H+fS0l6iX7GoPBYDAYCZg8KSBmnoAoAFo5EP37P9/0EjxzRTxwtf+EthS1vd7g7JI4oD0uDmhHq7EtsmyAK4IiiEUA0Zr9z198JgDglZvH8bbTTuy+uT9wdJwNEDNPiW2v3BydXv/PX3wmXrku6t2RZp5K6sdKbFs9EBHgL73hl/DsVdnZnTLmY/X72nA9cq52GGJ8ZLg7P6zkTEXyfA5WK6hVfGzfeir+5Pyol+hlp5yI33i6MHi4ZCLQTzgB4GlD0UyqesUHEnntec/nyvTf4dHDTmzr97V3bD0Vg/HMp/9668vwrJWD2baV4mu9tvXvaylR738OSthzGQwGg8EAmDwpcaQvyACiUhcAWF6vpQGQ7PTYFUEJw3BecDZciwLaVhBgfGRYHpyVFNCKa3bi8mh9ghDdmSzVGrxab0O4a9vEU+0ETxuKCHDN97skoP9+ltCPJUqVJ1gxEP3MZfVqum5yMuzGrixfWxb72rG5VpSJk9m2wm0pVVrqONBds3UjkQ1BGHZ9zfeBeqPXNseznvoJJxDdRwCYarVTX5uX3alWgWUjbm3LeA6WN7r7WmZPIsrtr0v22f59TU7UWW2PwWAwGOWgFPJ0+PBhXH755diyZQvWrl2L0dFRjI6O4owzzsA73/lOHDx4UPn5druNP/7jP8bmzZvTz46OjmJ8fBznnXcedu3a5cTuyYyANjl1PzbX6gZAJROUZqeDVlz6lgRny+Jg49hcG2EYyoMzxye0Yu9Ogq5tgrpZ/8mxYJvroLE3oO2um5QEeF5pxC7LtqlWu7tukgyKq34sla+1gxBznUBOOkvrK5LcT8Eur68fqrSeJ42vZT4HjhX3+jOwALCsJuwfsxLbHD8DYg+bbF+TPqPJc8BlewwGg8FwDOfkqdPp4MILL8Tw8DB27dqF/fv348CBAzhw4ADuvPNOnH766XjhC1+I6Wm5ZPBf/MVf4K677sIdd9yRfvbAgQPYt28fPvKRj+Btb3sbHnzwQeu2i4pZCdI/5i0hAJKc0OLoYYRBv26aBbtmu70Qyxvd7AQQnbg3Ox15cOa6bC8rMBNP3GXZOsD5AN8jzd5TbUDIorRaUsIJuC9Zyso8DacZnrY88+S4HyvL15IDBCAmdjrbSiQByZr1+FomQSm/bG9Z1sGLytdKJZ3ddZNl1FOCcuyIk31N7GFLyn7797WU2JV8KMRgMBgMRgLn5Gnnzp3YuHEjrrrqKhx33HE9J8ArVqzAe97zHmzbtg3XXXed9Bpf/vKXceWVV+JpT3taz+ue5+H5z38+3vKWt+CLX/yiddtFxawEPSVLmswTggCYmj8XpyiSoHF5vZYqjCVBY2SboiyopKAxK6AV7co+cS+pbG8gKyumzgaUlkXJIJ09GTtZQNsW7LeILF+r+j4aFT+1TedrZZafUdZMtK0swQig9zkgZZ4cHXCIghEJxOywrocNYQgcOwLbSA5ekh42YP6+JiXqjg+FGAwGg8FI4Jw83X333bjggguU77noootw9913S7//vve9D2effbb0+yeeeKK29C8PukGGpvSmP2gcGEx7LFxkKrpBY9euiu9jIG76Ptack/c8rXA7uFRbtreQ2QBFVuzYXEudFXO8buqAVpFFGRwGqtHv4GLdsnyt3zZIyrxcDy7Nvp/dzDAp81QqsUtsW7yZpx6C0r+v1RvAwKAz27J6THv2tR6iXr5ID4PBYDAYQAnkaWJiAmvXrlW+Z2xsDHv37pV+/5WvfCXqQvYn62c861nPym1jFsIwzMw89ZQFSTJPgNvAMSvQBroB0PTUseh0GIrMk6MyL23ZnjIb4FhgQBHQ9vQVlUzsen1NXYI2j6g7nlkk87XUtunpKOsFdZbTja9lZOtqtMxT2cqOgPAc9By8yHv/XJQ7in1F4j0dzipfzXoOHK6bOCBXRHfdWtpDIc48MRgMBsM1Sul5qlQqyvdUq1W04wDMBO12G1//+tfx5S9/WZndajabmJyc7PlPh9l2t1G+N/MkBGfK0psygoy+bEDc9D19VCgVlPVjdTqOSgrnE5Th2vxsnTIbsAA9Mj3ZHVU/lgNid2yujYRa9Gae5hOBrHVz2cgv9bWY5M0cE54lWZlXa65LZBzYliXoMjXXRjATycovRJZTJxjRJSjl+lpyuAHEiqEZtsky6oDrfa0760xEsq9NTU9H+xbkZXtoziKcnbFuG4PBYDAYCX7upMo/9rGPpWp7w8PDOP/883H11Vdj1apV0s9cffXVWLFiRfrfunXrpO9NkAQ/HrqBBSAJgFRBhgMikCUuAHQDx5mjcUBbq0fSxyLEkkKHAVDWiXsrCNCO5yRlrZnrmUXqciWNEmAJQWPF89J5O5Ft80vQys7YyXxtWeprMQGvVFI59xRDy6LX4TYrlkVQQgBzU/FMLsXzuSD9WK2WMvMEh5mnxC6xr0i0bUrRywm4LXeUZZ6SfW16Uuiz6r+ny0ac+hqDwWAwGAlKJ0/33nsvTj75ZOzevTvX59/znvekantHjx7FHXfcgT/8wz/EnXfeKf3MFVdcgSNHjqT/7du3T/tz0kb5RrdRHqBnnlwSAXk2ILJt9thRqV29stvugrOe7I6gztZU2ea8iV8tGLFQ6mxHBCELUVAlLY0TZLdVtrnox5L5WmJbcyom6oPD8+XAPc9p+WpWsD1Um+9rZd/PMAwlWU5i5smpr8lKfjNURBeoHFm6ryVEPWtGnOelA4a5dI/BYDAYLlE6eTrttNPwwAMP4NRTTy18rXq9juc+97m45ppr8KEPfUj6vkajgZGRkZ7/dMjKUgCSnidVmZfLYLs/GxAHFHNT8qBxIWyrVyqo+X5smzzzlARm7iTeM6TKF4E62xFZuZKogLZA5Y5SX4ttmzuW3M9sX3NbUjh/3XzPS59RZeZJeAZs92PNtjtoB9E1deIkmT1PyXPg8nCjb1/LVBFVZYddkuF5tkVfN+OsdeZhFQA4FihhMBgMBgMogTxR+pna7Taq/eVlBnjGM56BH/7wh7k/nwXdCe1Uc45U5uUmG6A+oVUFjYBbZSqdbe0pfeYpknjX96WZQNYoT1VnczkfS5dJPKYYkgu4LqXS+VpyPyW+5jDLmaXOBnQznYmvqcpqo34suz0yyd4hK/kVxUmUmSeXZLhfACTxtSatl9MpsZP5WtxfJz0UcljuyGAwGAxGAufkaXx8HAcOHFC+Z//+/Vi/fn3m9374wx/iN3/zN5Wf930fvm/3V9H9IW9Od2fqqKSty8w8JWVBrSS7Iz2hLYEISDJ2LZUoQ2MAaAzEttldN2mjfEZp3MJlnrIziTMzM0A77sErWQFN7msxQZlWZ57SRv4y1dnidWtPy0kAhpd3e2Qsk87ErnklvxniJJm+tsJdD1tKhiXZndmZ6ejwAuVn1LPGHADCvnZMnXnq+hqTJwaDwWC4g3PytGXLFtx6663K99x6663YsmVL5vdWrVqFb37zm8rSmh/96Ec45ZRTCtnZD3mjfByYTQkDSUtWZ9NmdxQEpce2Beip6EzRAiD75Cm7UX5YELMIpvWlcaX2FSX3c1rjayWUxsnU9lLbFjDLOb/cUf8c9PbIuPG1eQQl/joIQwSqMszl3cyT7ZLCrL6/yLZozToL6GtpWa3sfqrEZsCznhgMBoNRDpyTp1e/+tWYmJjAVVddhSeeeKInGJicnMS1116LG2+8EZdeemnm58fHx3Hcccfhox/9KJrNZs/3wjDEnj178L73vQ/vete7rNrdPXHPzqAkwQ8aA/Aysl7l9KFIgjNNb4DLga/ywDEe5Krqp4C7AEgWNA4LjefBrCIb4HA+ljzzFAe0seQ2KhWgNn/eWSliFpKANvE1aSlVCbbNnwvU+xxIM7COiLrsAGGo1lVSDAiZJyclhRl9f0AGGc4QZQAcE/U5tSx+h+xrnHliMBgMhjs4J0+VSgW33HILZmZmcN5552FsbCyVGj/nnHNwzz334Pbbb8fwcPQHcdeuXdi0aVM6i8nzPHzmM5/BT3/6U5x22mnpZ0dHR3HiiSfikksuwbvf/W78yq/8ilW75QMb48BMMUMGKGvOU3YpVaA7oXUUZIh9RTJ1tkClUOjQNlnQWKv4aMSZqJCSeep0gIScWoJullIoZHf6Fe2iDy6AfHR8P1XlZ4CYSXRRIpqdeUptUw3Jhbtsoux+Vnw/laJXyYH3SrzbXTddOXKyr2mfzwXwtY527+DME4PBYDDcI79KgwFWrFiBa665Btdcc432veeffz7uu+++ntfWrl2LHTt2uDIvE3IFNFpg5jKg1do2IxeyAFyWxnX7imSk09OUFLrKBsgyT4ltzZkmwqbing4MRXOM2i2Ek0/CG15uzTbd3C5loI2SyjBlxE5nm6NDhGa7g2Yn6s2REQHVQGaXtmXNeOraVsNMu6N8DhKJ9/DJxyPb1oxbs03XwxbMaDLDLnuedPua4nADYMEIBoPBYJSDn7shuWVBl3nSBmYuA9pZdT+WaiYQ4LA0TtJXFNkWB7RNDbFzFNDKMk+ibd6MfN1652OVk6lIyvaQZANKzqAAKl9LbFugMsx4zYBeAZDItpioN+OSNx0RsHzAIZtXFNlW7bVNJ+pimQjoetgwq5MDX7gyTMxqMk8Os5wMBoPBYCRg8iSB7MQ9OaH1ZjWBWdK34GBmkbw3IAnMaOVK1kvjJL0eQLe3yI/XTRdsWy+l0mSeKkEHfntObZujbKJullLia9oMioN+LJmvJffT05BhOCIoCQlYVq+i4mcTdX+BsmKyvr/IthpqQRteJxrfIPc1N4p7MkW7lAzrns+EqB87Yn9f0wjheLMaIRwWjGAwGAxGCWDyJIGuN2CgPQtAIdGcBBlhaHVmURCGOKopvdEGjY4V7VTZncqc+sTd1emxKvM0XKtiqNMtOdQSAUfljjJfG+zEQik6X2u3uzN6LEDla+n9bBKJneUMiixLAQjPwZzmgMMVeVIQ9eFaFUNtva+l99T2ukkV7aKvq5qMWM++duyIVdt0Yw78tFS6/BEMDAaDwWAkYPIkgbQPJf5DPpwEtLLhoPUGMDAIwG5wdmyuhSS3IAuAKpqgEY4V7bLKlZJ1S4IzvZhFOaVxQLRugwl58jyg3si2zVFWTJZ5GozXLCF2WQNVozcOA/GQ6dJ9TVca50yUQVWG2UsEKBk7m9CViA4le0elEvXRZdnmXHUym6invia7n472NdkQ68i2OGs9Fx9YsWAEg8FgMBYQTJ4k0ClmDaYlXpJTUPTOa7GFI7ORXTXfx0C10vO9JACiBo04dgRhp2PRtmzCGdkWB7RzxIDWVdCYaVsVw+2EDA9nK9o5tK27br2+5ntenKnQZJ5igQHbtql8LSXD1PtpuaRQRgKA7nNQi4Nt6SGC6wyshKgPUXzNWQY2ye70PgeNSgW+56W2SeXde2yzt25H51Q9bMm+pumXFA43bJcUMhgMBoORgMmTBCoiMCyeHsuyAXATbE/Odctu+gOvJKDVBY2uSm9UmackAKprbXPT6yErVwKAZbVaWhonVQGEO4EB3brpsgFA+b5GJSjJ/bQt8S4jAUB0PwGg3tJlKhaAqNeI99NZ5in7OfA8LzpESEtEy7UteT6zxGa6+xqxH8tBSSGDwWAwGAmYPEmgDGhrNSEAUpzQOigLSoJGVXZHGzTW6unprdVgW3HiPlyrohq0UQ3aattcqYxpM09x2Z7qxN155ilbaIOSDXAhzGDD1zAwmA72tXlPZSQAiCXewxCNFvEQwZWyo7REtJt5ksHFcxCGofaeDqYlooTMk0VfUx8gRK+lRF22d9Qb6b3m0j0Gg8FguAKTpwyoGuWBOBvQ1p/QuigLUgWNSTZAFzQCAOI5RZ0Hf2rNNl3ZHkmUIQkaDz2O4OCEddukpVSUzJMD8qQaLBzZRswGOCACSoKS9GNpngPP84DhEQBA5yF7vqaepVTt9rChfPXELhHIzloPa1QdI9ui7E4wcb+156DZ6aAVZM/GAuKsGGFfc5Ll1NxPoLuvKQ8REl974CfWbGMwGAwGQwSTpwyoGuWBXiKgPKGNy1s6P/qOtQCoe3IsP6HVnR7P7dwB/OwAAGD2va+KvrYAXflZ0icG30+zEf1of/3m6B+zU5jatqEU24Zrgm2EbECw7z5r91M1WBiIswFtgq/FtnXu+VYpvjbcpwSo9LXDPwMAzP7uNmv3UzlLqVbrIU9atb3DTzgh6pnPaK2qVU8EgM6Pvw0ACH78HWvPQc9srAL7WnLAYXNfkylOJnYBSFUKlb72hP19jcFgMBgMEUyeMqBqlAfQ28SvCDLCxx4BALR3ftx6AJQVaA8lSoBJmVdGcBYcnEDzg5cKRgZofvAyK0GQ6vR4uCZkUCSN8sHBCcz95X8XXrBnm7pcqWubKhvQ/tF3IrN+9G3r9zOr1yOxbYiQeQp/9igAoPW5vyvF16rxs6EqdyzD16TZuuT5rDfgVeY/wwDQ/q9/j//RKo2oL6vXus+npF8yODiB1mf+VnjBzrolqo7L6zX4Gc/fMDHz5GJfU0nPD6Wqk/I916WvMRgMBoMhgslTBrrDXuc3ygO0Jv7g4AQ637pVeMFWACQ/1fY9D0M1oWQpIzgL9u4B+pWogg6CffcVsku0TScHLl0zh7apyx31fSjBwQm0P/u/hRcs38+MmUBA7wwq2Yl7cHACne/scmdbxpoltqmENpzeTxVRF6XnFWvW/Oh7hRfsrFkYhvSSQknmKdi7Bwjtr5squ5PYRvI1B/uayrZkX0t7/0r2NQaDwWAwRDB5ykB3WGN2QNsvN5yFKADqk2a28Me8S1BktolBxnzb/PWbo7K5nhcr8NdtKmQXYJANkKxZGbbpBCOUxM5JQCsfLBzZtvC+JiN2OqENl/dTXbZHONxwFGxPtdoI4nuhzTxJbPPXbwY8++umyr4mti2Ur3WfTzmxG1YccLj0NQaDwWAwRDB5yoCYecqCeHqsDoD6slYW/pjrggwxU5GVefLXjKNx5fVd2zwPjSuvg79mvJBdgHywcGSXPvOU2pa+4FuxbbbdxlxH0ShPyDw5C2iT+6kgKDoi4NrXVMROlXnq3k/7vkaepSTJ7rgKthO7KnG2pB/DQmZYlt3x14yj/q4/7bHLxrqpsq/AIvE1CbET+xKz1s3lvsZgMBgMhggmTxnQBY3DtVq3R0YRAFV/+deFF+wEQLpswMoKUAs7StvqF29H9aWXAABqb/491C/eXsimBLrMUxLQhop+ivrF24G1GwAAA3/2eSu2aRvla0LmSRnQfkh4wVJAm2RQpL6m76/z14yj+sq3WrdN52vLKx4GNNLzka+9HgBQe+t/t+5rUjXMmASEjcHMz7si6mL5WXbJb42knlh71dvSfw/d+KCVdVMJgCS2qfqKAPf7mpSo9xwKlbuvMRgMBoMhgslTBijlSqk6myIAqp77UgCAf9ozMXzTQ1aJgOz0+Hiv0/1CJTe86mnR/+uNwjbNt002WDhas049O6BN4A8ti2yL5dSLQtsoXxcV0BQB7S+/Lf23tYBWm3miyahXn/9LAAD/9GeV5mvHUX1t5fHR/2v2fE1VtjdQraQEpaMh6t74ydFnPvwZq/dTld0hqSfWB9J/+/GzWhQqCXWgL7uzyPa14/wO/FgDVbluyb4mUfNkMBgMBqMomDxlgFKupMs8AQAacQBUH7BWPnJEceIOAKsRBbRBpaoMILzYtnB2xopdkW1ygYF6pYLlQUyeGor5UwCQZAuSoZgFoW+Ur2llkIHumgGAv/oEu7ZJfa2a2qZSdkzXrFGir8XkKfS8rq9n2hb7WtOer6kyT57nYVX8HLTrCrsAePFcIFtEXVd+1pPdUc1hGxAOGCw/B3n7ilI42NdUQ6wB4DgIRF2STQQAL/5e2LSzZgwGg8Fg9IPJUwZImaeOPvPkWSYBgJ4IrEIUhOiCRtsEpdnuKPuKItuiEq+WLqC1TOxUgTaQlATpszs9QZul4KybeVL1sBEG+C6Ar60Mu76WVaI2zzZL5CkMQ2XmCeg+By1NlhN1u8ROS1AI6okAgGot7cmy9Ryo5MABmiIm4MrX1JmnZO9o1+TS8wC6xM4iUWcwGAwGQwSTpwzoe54ERTtVAFS3/4dclw1YmRKUsoNGoa9Ism4rgiSgLZfY6QJtcTioqh8LtXrakG5r3VQiG/22kbIBVjOJOl+LsgFaX0vJk537OdvuoB3IFe0AYEUYPwc1DVEfsGubjqAM9/T+KTIonmd9/9BmYBd0X9MQ9YQ8EX0ttEjsGAwGg8EQweQpA9rMk9i8rDqhjYMjm6VxugAoCRrnSg8aI7uW1auo9KuYpbZFgWWzqu596Zbe2M48qUrjoqBRFWx7YnmaZduUCmgaaWvA/ppFtukyT9H3mzpfs1y2l6yZh4hcZmEk8TUqUbdNUFTS8/HeMafLwA7YLUGjCUYs1L6mJp1JllO7Zg4OERgMBoPBEMHkKQO6gHa5F6Aaz/xR9zzZL285oiECadCoa863fqqtDn4AYHlqmy6gtRsA6bIBgwIZntXaZjmg1WWefD9VtCP1PJXoa8n91BF12wQlWbPljWwBEAAYCYhE3VEGVlaGOVitpGR4tlLyM6oTjKgADY16IgAnvqYj6t19jZrlZPLEYDAYDDdg8pQBXbnS8ji7A0B9Qms5MJvrdDDbjkqlZAHQCJEEeJbLW3S9HgCwPLZtRhPQ2i690QVmvudhWSxmMVtVq3TZ7t/REvWwWw5JEbOwlQ2g+NrymKDo7qftJn5dWW1kW+xrOoKSZGBLIuqe52FZum5Eol7SM5oQTgCl7muRbZpDoXjvIGetuWyPwWAwGI7A5CkDKtU4AFgWN/DP+RV4NXkAB8ulcWJfkbTMKw62tafazoJG+XoME4NGz/KJu44MA8CyODib1q2b5RI0beYpvp8BdIp2bkgdoCB2RPLkqtRRVlYLdH1tmkiGrRN1BbEbDhJfU+wdsF+CpiN2qRqm5wGqEQaW97VWJ8CMlqjHZFiXUeeyPQaDwWA4BpOnDGjlhju0U+00S9FuIex0lO81sWuoVkVV0leUSA1PU8uVrAWNSTZAEdAmtmmCRvukU515ApCW7U1VqJkny+smI8NxiddMta5UtEvJU2sOYRBYs0vla0n5mY5w2u7H0okLAMBwaptm3o8roq4gdon0/JSWqDvqx5IeCnX3NZJ6orV9bS79t0xsZllKhjnzxGAwGIyFBZOnDOgyT2KgHYSh/EJipsBCAKSzCwCG28mptiZotB2YzSUiGwqCEtt2TBdsu+pDUZGnONg+VvK66bJiQ50uCVD5micqt1kgdiRfWzAyrC/bG+wQCYpl8QNK5ikZyEwl6raIgK6HbajTiu0iZncAK89Bcj8HqxXUKmqirrXNgRIgg8FgMBgimDxlQJcNSP6Qz1TqmGm1M98DoPuHHHb6PY7Mqku8gG7QqCcBdntkEiUvVeZpMCEovibYtpzd0ZUrAcBAbNtRXxPQWiR2YRhqswFDAgmYJvqaFaJO8bU20ddsk+E5fXanez91pXGWBzKntinIU4tmm80StMjX1M/BUDtag+lKHXOqjJLlfY1yP7vkSeNrA3aznAwGg8Fg9IPJUx/ERnlZ4FhP/pBXG5hSBLRepRINuwTsnNDO6fuKBltRMFN+0KgvpWpQCYrlAEhHUMIgIJMnm1mUGWFekdTX4vszU6nj2Fwr8z0A4FWrQLUKwE6mguJrA6mvlSuykRA7VXaHapttYqc7RAiDAI2YEE96tGfUxv2carXTzKUs8zSYlvzWMTVX4r5GySQSD15sl9UyGAwGg9EPJk996Bn2KgkyvNlpAElAq8gGAEKwbSMboBc+SAjKpEfLPNkLaPWlcY04oD2iCRrTk23r5UqSNRHWYNKrKK9lM/OUkDoP0fDUzJ8X/5zpakPva0ngaCFTYeRrugyK7bI9jeS2aNsRL3tdE3iWhVO0/XXx3gEAR3TrZrEELdnXKp6HoVr2mlSbCVEn+JrNfY3Qw5bsHZMlk2EGg8FgMPrB5KkPyan2sKJRPgmApqoNZTYAsCvTTMnu1BOC4muCRlfy0YqAtp4GQGrbUonmkgLacKYb0B4GtaTQRtDYze7I5hWFM1MA9FlOwDKxo2QSiWTYfg+bvh8rydhpCYqzsj1J5kkkT6GaqMPikFzxGZCJQYTpvlbHsVaJ+xph72i0ib5mmagzGAwGg9EPJk99oJQrJQEtKfNk8fSYkg1ICYqOBAh2hSrRCyIowXZtLlqDJzW2ea6kraXZgPh++jUca2vUw6wGtPr7iZ4spzqgtUrsCL5WiwnHYVTVPiTYZcPX0tI4la/Fz8HhUHOIYJHYBWGIo7oStIQMV+qYaqtVEW2WO1JGCXT3NULmyWpWjE6GD1Oz1pZ8jcFgMBiMfjB56gOlUT4JaKcp2QCbwTaFoMTBzJNelabOFgRAWxMoEUAJtqtxAPSk7sTdYjaA0iifZJ4opXE2Z1ClvTuEgHaKENCW7WvVmAxPVRpoKgQGbPtamt1R+Vp8fw5Bk+W0WLZ3bK6F5ImTZjlFMqzJ7rgo21NldyBmnnQZdQeHCJSDlyPUfS0Mgdac9H0MBoPBYOQFk6c+mGSeooB2IbIBctsq8c+ZKVmdTRdsh0GASkyGDun6UCwqAU632uhoGuWTzNN02dkdzYBcQAi2CQGtmyynwtfigHZal4F1pASoekb92LZDGqJuU5QhIQE138dAVfJzhTLMMskwRXpezDzpDoXclK/Kn4NkX9OqTiZ2AVy6x2AwGAwnYPLUB0oGBSaCEVYFBvRBRhI0TmkUs8RZLTYDR2lwJvz+TwTlZZ6SwMxXNMqLmadSM4mEE/ck2J42CGjL8jWvJ2OnIHair1lVnZRkElst+O3oPYdA9DXLwge6vqIZysHLAmWeSIcIDoRTVGp7vpjtV+1rtToQrz2LRjAYDAbDBZg89YEy7FVs4tc2VltsYKacHqOnb0Ehbe15QD0eOGlDnU1bGjeV/vtQqHY7F4p2qoAWJplEm9kdSuZpRsyK0YhdWb4m9v4pJfs9T1B3tCd+IM8kdkUZfqYl6vaynEZkuEq/nzYFIyj30yQrZud+6uc8UYldj69ZEgFhMBgMBkMEk6c+UIa9ipkn5SkoUPrpcSgqAVJLbwoGGZRhr2Lwc7RFE2WwQwL05We9pXGLLPM0S8zuAAvYI0MXGLBBiHXiBwkJ6MDDobZaNMDmvDOKAEiYPgeETKJVok73NZI4iU1fSzKJisxTUlpL2tfq9ggxg8FgMBj9YPLUh/TEnZB5ogS0nsVSKrPME6Hp25Jts4RhryYZlLLLz8TSOG0Tv4NeD9WJu7hu2pLCkn0tzOFrRdeNIgAikrpmEKIdKFTtHJXtSdHzHNB8zW5Zrd7XSJknm75mIJxSpq8xGAwGg5EFJk99oEy77ykh0Z2CllzekmaeKpreAMBaKdURwrBXMYPSCgLMqdTZLJbdkMqVekqCyuxh0/d69GaeFpeviSRFm0WxZNt0q52qrcnuqRhoA1A+BzbFSYwyTyalcVbKagnETujHKut+AoYiPbqeJ8u2MRgMBoPRDyZPfehmngjZAErmyWKw/eRsM/q5qsBG7KkoKVORBI3LCcNepytRn5VazKIbNBad1UJR8krV9ij300VJIcHXKP1YNrMBFF8LZ4WMHVHivahtFAEQkXAC0IhZ2JtB9fCRYwCAqi/prQPMMk8li82EPf1Y5e1rpHlnPdnh8oZFMxgMBoPRDyZPfTg4Ff3BbXcUpT5CpkKf3bHTU/H3d+1OA8eLPvll7Lhr97z3hO12OtuEpOZlLfMU/ZxGpYKJyansN8XBz2w1CpBUwbYnSlvPNQvZtjcJaGViERDU9gilcTZLCh8z8DVKP5atPhSSrwVBmhEpU+KdIgCSZHeaCXlSiVmIc4Hamt9BgR137caHbv8hAOAL9+7NXDPRNhIJsNiPlexrHVUJo2hbSfsaAG2/ZNjppPvAFKX3z6JtDAaDwWD0g8mTgB137cZXHjoAAHj/V74rD4BMTkETgYECpTcTk1O47KY70q+DELjspjvnExVBZYxSSmUrU/GZHz0AAHh8ehYbrv1cdrAd2zZbiwL8KVVvURLQAoUCoB137cYHvvYDAMC/79knvZ+9madyhuTuuGs3/mvvQQDAf7vl2yRfU64ZkK5bETELsq8Jv3+ZEu+TKVH3tUS9GfuaMtgWiXrOZ3RicgqX3nRnOiA3hGTN0JvdmaIebhQs29tx1258Nfa13/tPiq/pB/ja2NcSHI57nqT+LexrJF+zWIrJYDAYDEY/mDzFSAKgBLQAqBwFtD2HJhH0VRR1whD3HZrMtCuAh6ZfLSVTMTE5hb/45o/Tr4MwzF632LZWGtAqbKvWurNaSglouypjkfiF4nTeQtmeia/BRKrcQnbH1NcAYKZSKy0r9ukfPwgAODilJ+pz8c9UZofrjcJzgaI16120rDUD0NNX1OwEaCmyjjYG+Br5GnWWEmDtfl73vZ+iGa/Bi/7x5uz7Kexrsz7B11gwgsFgMBgOweQpRp4AyEg5rkAAtHn1CPrbKCqeh02rRzLtatUHAc/TEzsL2YA9hybR3ymSGWz3B7TauUDFSm9M7mcoyCADOoGB4tk6M9sEgQFd5slCr4epr83VGgg9n64cV5Co/xWBqCfBdqse/Uxl2Z6FuUDRmvUuWuaaCbZNxeWrymyiNTJM9LU8ipgFid1v3/yN9GtpljO9nwOkfc0G6WQwGAwGQwYmTzHyBECUWS2ehdKb8ZFh/PEvPrPHruu2nYvxkeFMu1p1QnYHdkrQNq8eQX/nSea6JbN3koCWWLKUN/Nkcj8x09cjQynzKkxQqLYJUuUlDC419bV2fD+pZXtFbKMS9YTYdeqEsj2g8Fyg8ZFhXL/t3PQ58IHMNRNt6/qaWyVAI18Ty1d1ZXsW9jVyllM8FIJ+X7NV7shgMBgMRhaYPMVIAqAEviRoBCCUt5TXKP+KzesAAMcNNvDQu16L7VtPldrVaSQBrfvG6vGRYbzhzJPSr6XBdmxbu0ELgIo2y/cHtB7kAW2Seeo0hiLbCAIDRbJ1Jr5mIm29IL5GJcOWsmIUop4Su+R+llDuuH3rqXjxxlEAwIdf/OzsNRNs6wwkthEyTwWfT2NfMxGMKCPLmayZ4b7GmScGg8FguACTJwHbt56KwWoFAPDVt74sMwCar2hHFGUo+Id8th3NRVrRqGcTOgiBGfGE1lZj9dbR4wAAv3TSmDTYTmwLG7RMhY0yr+1bT8WFG9cCUAe0SXAWJLYpe2Ts9Hps33oqGpXo8fv6r788e82CoG+mWDny0Ua+ZkqGC9g2PjKMSwhEPVkzsq9Zkp9P1P/Glg/J35TYRsnYpeWEzcgXcoLua3T1RBv72vjIMP7HLzwz/VqX5exQybClZ5TBYDAYjCyUQp4OHz6Myy+/HFu2bMHatWsxOjqK0dFRnHHGGXjnO9+JgwcPaj//u7/7uzjppJPSz46OjmJ8fBwvfvGLcccddyg/b4K5uHn55JXLs9/Qp2hHlgMvSFCSgHYgJncq2wLKqTZgTdI3se2klcukwbapbTbKggBaQJucuMMkG9CaiySUcyIMw7RR/iSZrwnBPKmJ39JQVZKvxaWOYbxmZRGUZ65ZDQB46ckEok72NTuqk5R1S20bHI5t0/fXASgk2U/zNTP1RFv72rZN4wCAE4YG5FnOvsMNai8nl+0xGAwGwwUkUybtodPp4MILL8SrXvUq7Nq1C6tXr04D2iNHjuAf/uEf8MIXvhA//OEPMTSUHeC+733vw8qVK3HPPfdgeLgbnAdBgDvuuANvetOb8PWvfx1jY2OFbG0HATpxc7UsAEqDHy9StEO7g04QoOJn81DbmSdSYEYMaG1lnkxsA/H02DaxUxOB2LZBetleZFsz/Ywp5gSVNZ2vAVE2IFxMvjZrSFAsZ8U2Eoh6lwyXoxxncsDhUdZNlFFvzvRK+BvA1NdmKjV4c22EYSidpWXb11YN6rOc9H2Ny/YYDAaD4Q7OM087d+7Exo0bcdVVV+G4447r+WO8YsUKvOc978G2bdtw3XXXSa/xpS99CX/6p3/aQ5wAwPd9vOhFL8Ill1yCf/3Xfy1s60yrm0mQBkBJ8DM4nEocT7cUGQhLfSj5ArNyCMqMiW3xibs2ALIVbHcoRCBZtyQbYBDQ5rWrTfc1NAYQetGjSirzKsPXZnL6WonZHRJBgZh5Ku+Aw6Nknmo1oFIpbJuZrw0i9HwEYdjzuXko83727R1lkWEGg8FgMLLgnDzdfffduOCCC5Tvueiii3D33XdLv3/55ZdjcFB+6rpu3Trs27cvt40JxGChoTmh9QaGUhUrdZlX+dmdbpDhXj7a1DZ/aBnNNktlXoltg4TMU2WIENBWq0A1StgWOdkWfa1eyX4M02zA4LDgayryZGdwKWXNkswTmQxbGpJLIcMJEfAHl5Fs6/paeQccFeK62SApRr420M2kqn2tvH0NhvuaLV9jMBgMBiMLzsnTxMQE1q5dq3zP2NgY9u7dK/3++973PuXn77nnHmzatCmXfSKSP+T1ij9P3rf7pviEdnAYy+pREK0s8yqx/CwlKMQT2rSvqIQT925Aa9b0XZTYzcT3hnKyXaESu0bxngpxzWSlUYl0tDcwlPqacj7WApQ6dgmn+2HRPbZV9M9BZWhhSgopzyh13WzYRvO1bnYnIc0q22z52kw7fj4J95O6d/CQXAaDwWC4hHPy1Ol0UFH8YQSAarWKdlvzB1GCf/u3f8OXvvQlXHzxxbk+L4JU4iWcgg7X4oCWkHkqHDQanLgnJKCMU23ALGisprZRJd7dEoEwDFMiUCVmKqwEtCRf6wa0y+o1ALQsZ9H7SSnDnE843asnAmZEvTq83Mi2wr6muadhGKa21YaJ62YhA2u2rw2lvrZoSkRnE8K5XG8XYEUWn8FgMBgMGZwLRrjCk08+ife973342te+hi984QtYtWqV9L3NZhPNZletanJyMvN9JoEZBpIgY4akmGWtvIVwQlsbGgbaBqfaRTMVBsQuIk8hOWh03vPUmgNiGejasmUAniylV8xIyGJgCMtiol6qr1HI8OAw0IkCWqXAgKUyzKQvcbAm37oSYlellq/a8jXdMzrX7Pra8HJEvuZ+PpZJDxsGooz649O0zFOZvlYbXgbM0cv2ivoag8FgMBhZKH3O07333ouTTz4Zu3fvzvX5MAzx6U9/Gs94xjOwatUqfO9738PZZ5+t/MzVV1+NFStWpP+tW7cu830mBMXrKdujDboMYyW/PMhz4j4Tq7NJYVvamrBu9WW00+N0VoutEjSZbYLKWGOYVraX9lTYKNtTrZkgZDGclu0tLl9L7qdWYMBW2V5Hv27I62uusyjCmINakrEjKsdZIepKXxP3tSTLqe+vW5T7GgtGMBgMBsMhSs88nXbaaXjggQdyfXbfvn1429vehjAMcfPNN+PMM88kfe6KK67A7/3e76VfT05OZhIo48xTTR9kpH1FQQC0W0CtTrI5j20JQWksWw4ciV6bbnWwvCGRti6x1yNZt4igTJbSV0SxLZ3xVK1haJA28NVGcGYqoU7ytYQMl+hrtaFlqa8dm2tLM0KlzlJKfW05gOlSemTaQYB2QBtzgFodw4ORD2lnd1mYp2SWeRoiHQotyL42vAx4MnpNua9ZUk9kMBgMBiMLzjNPlH6mdruNalXN4771rW/hF3/xF/H6178et956K5k4AUCj0cDIyEjPf1kwVbRLswGUE1qgWN+CAUGpDQ0TlQDLF7OoE/tQ0hlUjgNaCIp26Yk7sR/Lhnw0VUI9DWipMupFgm0TRbuhZanAgDIrZlk9kUI6B5YnvuZena1pIgc+MET2NRszi0zmdom9nKTME+B+XxPK9kj7miX1RAaDwWAwsuA88zQ+Po4DBw4o37N//36sX79e+v3Dhw/jNa95DT796U/jRS96kW0TU5gEtNQTWtQb3c82Z+AtyyZuVmxLid0yLKtXMdlsqcuCSpIbFkUZBpcTm74tEDtKQNslKLS+oui99uSjB2v6RnkIRF1pW70RzR4LQ4Rzs/CwopBt9PLVGmbaHXVWrExFu/ieDiREnSgwYEsOXDvmYHCY7Gs2lR3Vvjaf2CkPhRZsX/O1+5otX2MwGAwGIwvOM09btmzBrbfeqnzPrbfeii1btki//+d//ud44xvf6JQ4AWYzgaIAiFBK5XlWlKlMy7y6p8fu5Ya16zbXBOK+iKHlI1q7ItuKEzvKcFBxllI3k+hedptGUERip1dAK9XXhGCb4mtlzVIKgyC1bSjOMOtK4zwLghGJXVXfQ9WXzFLKyjxRnwPnvpZB7FRle7Z8zUAJED1ZMfdZTgaDwWAwsuCcPL361a/GxMQErrrqKjzxxBM9zcWTk5O49tprceONN+LSSy+VXuO73/0uXvGKV7g2tTtzxDTzRB5GW2aZF+H02Jq0tWbdBFGGoWVRNqRMRbua76MiCWghZp7SUipaE3+R+0nxNTHzRPa1uj3SSS1fNZlBVXhIro6oC9cfjol6Kwgw11GIWViQKm92IgED6rBXUiZRtK3Ac0DztSxiR5WfdytmIc6gomXFOPPEYDAYDHdwTp4qlQpuueUWzMzM4LzzzsPY2BhGR0cxOjqKc845B/fccw9uv/12DA9HssK7du3Cpk2beuTEDx06hNe+9rXp57L+e9Ob3lTYVtNp98OEoBGwo+ZF60MRBAZITd92A1qtKEOtjmVD0VpoA1oLARBpXpHY80Q51QZKy+6kBMUgoLWqzkaVUafYZkkBTUcEQpGoL++WLZIk3l3fzx6xGYJ6ImCltDYvGaaOOijrOQBR4dRWRp3BYDAYjCyUora3YsUKXHPNNbjmmmu07z3//PNx33339bz2jW98w5VpPTAOaAlle9EFLQy6NCnzGhzGcG1ab5sQ/Khm9JBt0wW0A0Mp4QSi0+P6YPZnbMxqMSk/y0NQSgu2xYHMuv4dC/LzlPLVcFb0tcgmUinVXBNhEMCTZQKJtmlFGRoDqNeqqFd8zHUCHJtrYfVgI/szNskwQUKdLAeO8shwmFmGWd6+pprbBWHPHa5N6m0T7CqyrzEYDAaDkYXS5zwtZhj1epic0JYUbKeZJ2LTd6+0tSZQKmKbEGjXKxXU4sCZpOZVVnbHpDTOQpmXUZbToETURrO8eeaJXrYHIOqBc2SbSNQBdJ8DpXBK2WW1eUp+y7mfPaVx5PLVEvuxKPuaqDrJfU8MBoPBsAwmTwJclVKVNRco7OkNMBjgW8C2UBiOSg9o9SVLXRJQfvnZ0WYL+44ck3/GZhM/MfNk2odSxNdMev+Mh6oWtM2EqAMgqdp5JRH1LFl8na95Jc15CjPIMLW/zr04iVnZXo+vcekeg8FgMCyDyZMA08xTUt4yMTmFickp6UdszJGhqYzFQcwATQmwR244Z5DRCgIkEiDUgJZUFmRRDpxKAj7/04cBAAGAjf/rc9hx1+7Mz1gppaL0sPWUK0Vrtm/ymNrXSsiiiNLzYpnX3Y8dktrmVatAnF3ISzrDMNQKM4iHGwDQiH/mg08elV+4LNU4IfNE9TUrwikGc7uMSgot7Gu6vsSw1Uqz4lSFU1SrQJzdLjqGgcFgMBiMfjB5EmAim+sNDOGOiccBAHcdPIQN1xICIJdEQLi2N9jtLbrnsSflAa0oN5wzyDCSA+8PaA8rTtxtlJ8ZEJRjfg3v/Y9vpS8HIXDZTXdmr53F2TvKciUhoE187fsHn9T4WgnZAEF63hscTu/jP/zwfuJzkC/Ybnb0viYebuy4azf2PBn1yFyy87+0ZLisLOexSp3sa1bLMCmlcULm6ZGj6kOhUva1WeHnCyI9+n2NRSMYDAaD4QZMngSYyOY+Hvj42+/8JH05CEN5sF1CH4qoMobGIO6Pg8Yb7nlAHdAmpTc5g4ye4aCydRNIwI67duP+w1EW4PX/sktuV1kiG7Fth1BFIMjoA0AnDHHfocl5n7E6e4cQbD8e0n2tlP46wdcemQtxy4P7S7FN9LXBarbAQPIczFUbuPSmO7uvg0CGS8pyHgorZF+z+hwQM+pf3XsQAPDjnx0h7R2l7GuVClCtkfc1G88Bg8FgMBhZYPIkwKQ3YF8zRND3PWmwXUYAlGZ3BvHIsRl8+f5yA9pGxZeqWiVrNmsS0FoMzAZreoKyauVK+H32VzwPm1aPzP9MyQpoJr5WhpiFKD2/Z3IaYd/35bYVy4rNtCK7fM9D1ZcoqMW2TVUbi5YMr1q1iuxrZQmAJM/o4x0PH/vWj9PXlXtHmfva4DAeOTpN3teKZtQZDAaDwZCByZMAkmxuHJyNr3ka+uM3abBdQgmaOCB3z6FJ84C2YNkeZc0m/ZpBQGthOGiLTlBWrFyF67edm77uex6u23YuxkeGpba5nr2TBI7ja06g+9qAhcyTzteEgHbz6hH00xgd6cxbHieumZSox/dzaNlyczJcktreihUryb5WFlFPntGH50IEfZuHdO8oI/OUe1+LfY3L9hgMBoNhGUyeBNDKvKLAcfT44/GB87amr1cIwXY5macho4DWKxhkUAKz4PHotHh5rUoPaPtmteSyzaDnCQND2L71VIwti37uv/7ai7F966nZnyl5SO7occfRfc2mAprsORD6Y8ZHhvHms06m2VawPM7kfg6OrMD1285NnwMP0BOU1hxC1eBmlW0Gc54wOIztW0/Ficujn/uF110g97WBcoh64mvrT6AfClnZ14hEPfQ8nNo+arCv2RnKzGAwGAxGP5g8CdAqPwmKdsGxI3jzWacAAGq+j4fe9VptAOQyoE1LqQCMzTyJt245Jf1aFdAWzTzNaOya27kDrU/8OQCgfuvn8J/LJkgBbc+slpxzgSgBbXDkUM/PSJTGVjTq0s8UJZyibdLeHUHRzsjXSizbCz0PwcEJXHjSGADguWPHa2yLMxUFs5xKov6zR+N/BNi+9VT82tM3AgAuP/csqV1poA0U7v1T2pb4WnxvhmPluJUDkuG9sEuGKb52Alr40PnPSr+n3DtKUMRs3/L56B+P78fI65+Ov211e//U+1rx54DBYDAYjCwweRKg+0Pe+tzfpf+eeeOzMfylT0SvBwFOXD4kvW7RvoUgDDGnkWhuf2Vn9DMO7MXUtg34zX23AwCePXqcMqB1mXkKDk6g+cFLU2U2IMRz/+kqXHpiFOy8XxHQ2pgLpLufczt3ILjra9GP+Oh7MbdzR/peUZxgHpKg0WE2oPW569J1m3nLOXRfK1i21w4CtOO6LVkpZvvWf4n+cXACU9s2YNPtnwMQSdBnBrKJbQVL0HRkeG7nDrRu+IvIxpv/GXM7d+C4oejZa6iyVYKvuSJ2uX2thLK9fl/bvvfrAKI/Dsq9w7ESYHBwAq1P/ZXwQoA3fPGjGJs+RN/XOPPEYDAYDMtg8iRASwQ+8i7hhQD1P3sXxqaj0+RW0N/SL6CoRLNGDjw4OIHWp6/tse2Z//ePMDZ9CEOagLbooEvlmu3dA/SvS9DBpunHAGgCWnFWi4UemXm2JcQuQRii+cHLcOLskwB6ZbHnwXEPW3BwAs0P/47wgoGvFbyfJF/754/12Lbl/1yJselD6jUDCqvaqQRAsoh684OX4WnHIol3lW1erOQGID+x093PnL7mFczWkWzr87XGRyNfCwCMLhuc95kUBfc13YDtYO8e4X5G8IMAJ089pt/XWDCCwWAwGI7A5EmAKRHwgg5Onnqs57NZKJoN0M1SygoyEttmdQFtwUGXqjXz129OCVD3xQqOnbAeANKBp1nomdVSIrE7afJgz2czbXMsZmHH12xIz8/fHpS+psqgoDgRUJXVyu7n6JNRv53ONlvCKbZ9zUbmyb2v5VuzOeH5l+4ffT2SoV/BA8MnaPc1G6W1DAaDwWBkgcmTAC0R8OYTgQeGT+j5bCYsZXdkEs3KIEMX0DrMPPlrxtG48nog6XLyPDSuvA7N48Z6Piu1raCEdB5i98TxJwLozcDMg+OyvUK+VpBwJgSl5vuo9K8PivlaUSKgKvGS3c/p0Y09n5XBlmS/ma+Na21zPbdrMexrQHY20V8zjurL39hj14OXfQT7h1arn08LtjEYDAaDIQOTJwE6IlC/7H8IL1TQuPI6PLH8+J7PZsKxRLO/ZhzVl17SY9vEb/8ZLciwFdBKSvDqF29H5fkXRf9+94dRv3g7rddDtM1FViwldskLPhpXXoepVaNa21zP3vHXjKP+W1cJL0S+9rPY15ptfdmeCxKQ2FZ92Rt6bNv/O5GvLShBmUfUo/vZPj4i6so1A5w+B3JfWxPbpto7EhLgpudJ5muPLytvX/MQkfUsVJ7+7Miscy7E8E0PYfJlb9bbBRTOqDMYDAaDIQOTJwE62dzqi7ZF/1h1AoZveqiHCChL0ArK5lIkmitPjxSy/Of/EoZveghTL39L9FldQFtQCpk0QyYmfP7xa3veqw2ALBGBQQWxw9Dy6D3X39ZL7FRlQTaa+HW+9guvjP6x8vh5vqayrejgUsr9TAPa51+E4Zsewuy2t0Y/UvEMAHBaGgdE99N/3oXRv999NeoXb0/76sp6DhoSMYvCvlZERp3qa6u6vtaoRn8aVMSu8L5GmdsV+3Fl7Qb4a8bJe0dRWXwGg8FgMGRg8iRAK9Ecy1l7y1fAXxOV3JCCM8fZHUAIMsY29gYZuoArKW8p2iivmm+TSI3HwVaXcNIC2rxEYKbd7vl5mWjPAQD8sY2RifHvocpUpEFjp4Ow1cplm/aexr+z6Gsm6myuMk9At4+kMhoFtFSCUnRwKcW2JAj3nxZlnBK/JD8HDtetkK8B7vaPxNeWZfiaw0ME0vPZyt47qPeT1fYYDAaDYRtMngRo5wIlQYYQ0KTBGaVvoeiJu5KgxAFMvS/IoJZSOcw8JcGyV4vm2SRCBPSyPTe2hWEoJXYkMgw4k91O16xu6GtlkICEzCZrRrALsDAk18Q2w+fANbHL7WvivLOi2UTbvlbC3pH+zvHekR68EDOJPOeJwWAwGLbB5CmGKJub1bwMYB5BAajBtvuANsmKoR4HGcSANrWtYNAoXTNgfrBNJXbJ6bGrE/fWXPdnpcSOkBUTyHOek23K3C703U+gm+UkiVmUkHkyJyh2yvZkZZgAEMaZCi9eN0r5GSAE266IQIavUdbNq1Yj2X449LXmYt7Xeg+syAcvLBjBYDAYDEdg8hRDJ5sLCGV7QkBLOQl1OYg2RbP39Dh571wniE69ZSihzCstvTEIGqM3Fgxodb1i4qm0AbHzPK9LanLYppulBMwnKGTbijbxE/rr0PccJO8NwhBtxQyqos8BqcxLRtRdl+3l8DUSUQcKCaeQfK01n6h39zV395O0d/QfCgk9pqp9jaXKGQwGg+EKTJ5iJIEZoMoGzA9oG5TgzPWpNtAlKH0n7gBtCGfhviJFSWHYV+5IEdkA3Gee0qARAGr1nve6lN3Wze0CkF0iatKHUpQMG5SIisOOXcqou8yKWSMCmtI4AMa+VkSlMK+vlbKvEYi67H4CGtJpYZwAg8FgMBhZYPIUgyKbG/adagPE8jjH8tGibf0ERWtbGX0o/cE2uaTQMRFIy5UaqdBAUhakFbOwENDK5nYBmJet67GNoIBWWKFQUYbZ72viMF11Viy2zeUhQl9WjCLKEF3U8QGHkEEx9bUiWTGSr2VlORfJvtZP7HrIkyorxoIRDAaDwXAEJk8xKLK5SUDr1eaXt1BKqVyq7fUHQDXfTybeOJ1ZlKsfy7BHxlmwnbeHTbAtT7At9u7oJJozM08kMlxGKVVkW8X30wOHhVadzC2c4vo5KOBrXoGZRSRfU5Qjl7KvqbKcfYcI4sGWS19jMBgMBkMGJk8x8qiMie+nDrpU9h/JbDPoQ0kIiud5pL4FlDAXCHPZp8dkdbbCYhbVzO+nQWNGuZIuU1GkzIt0P1u9BEV8PylozDkXaMaIoOTs/SuBqM/zNWpfkasS0bxkGCiWeSLtHXJi53Rfy5FRF/c1iphF3oMXBoPBYDBkYPIUw0j5qafnKT5xV/YVidLWTen7tLap+ooybdMHjq5P3HskmvuyAS7LlSi2pUGjmEmkzgVq5C/zMgkaswiK2te6979IP5a5r+mfg8Jqe50ki5JNhgHMu6em0tbOspyZZZi03j8UEAEx8jXDnidr+1reQwSKbZx5YjAYDIZlMHmKkUf5CSD2BohBRoG+BZJtecu8XJVStdtArMDW34fislwJELIosiZ+RSZRnxXLTzpnWgbZOpGgUPp3enzNTbCdmYE1eA5yq+1p1i0Mw3m2kfvrHBP1Qr5WQH6e5GupvHu++5nXtqKHCOqsWLF+SQaDwWAwZGDyFCNPP4X4fmWQUa0Cca1+npPtPINoAWoAVFJfEZB7cGmeAEic26Vr4vd6ZinR5gLZUNszJuqUPpRKBajWABQTsyD1sOXt/XOVSWy3uj/LuL8uf0lhJwjQCnRzu+aTYeOyPVe+pspylrCvUTKJvYcI+v66ouIkDAaDwWDIwOQpBkk2V9G3oFR+8jz3wXbOk+3CfUWadQvFch7DgLab3TG3jTK3S0mGiWV7uYJGE19bIDELkx428f3qrFjBsj2dbU05Ue/oZlAV6P1rknwta+ixobKjK19TEDtlD1sZ+1rOQwQWjGAwGAyGKzB5ikE7BZ1ff0+ahwI70tYmg2jF97ssb9GuWxK8VGvw4lNqakBbxDbKfJssgkKVtu7OxypwPymzlBoZtlF9zVlAO590mvTI5J6lpCXqInlKSkSFeWeOnoO8vmZMhp352vySX1IPGwruawWJHSnzxGV7DAaDwbAMJk8xzAZwGpbGAc6zAVlZMZqYRf7sDsm2rGZ0akBbQMxCvB/1isTNWzlPtYHyTtwNS+N6bHNctufl7HlyNrcrXbN6d5YScd5ZEeGU5LoVz0NVMiNOSYZdKjua7GumJb+A+yynSuFUJbRRULKfwWAwGAwZmDzFIDVWFzo9Lj8bYBrQ5pEb1jbxZ8zGIge0BUqpKHO7iglGFCilMulhKyJm4TqgNZ4LFP8unQ7CVkv6vty2ZdzPqu+nw2GV62bJ12Ton3Umvn+xEJRcMuoF9jWacEpGuaPJvpZTsp/BYDAYDBmYPMUwmr2TOReIFtC6mtWSNzhL7QqCSBnPtm1FAtoi2QCD+TaZZXvanicLpVQkgpI/oM2VFUvlwCkZWMPnoEfaOj9JGaypy/bE+wnQJMGdl9Wqys+o/XVlHrwYEnV3Wc5iZXsAuO+JwWAwGFbB5CkGab5NckKbVUpFnQtU5GSb0CNjfHrsWG44K9AW308JaAsF2i6a0VFCKVUqHy2euNMEBoooxxUVjCDNecppm3aAb8b97LGNQNSdk+HMsj2arzkvw6zPzw7rSgoL7WsmA3xN9zVxv+HSPQaDwWBYBJOnGEYntKa9HnB7st0ziDajPE7ZGyAETIWIgKYPxesLaEmlN0kplevSuAwZZHLmaQF6PcjZAAe2yXyN8hx4ntf1tyK2yeZ2aYg6xdecDaLNKF81F4xYgJLfMoRwZPez0+lmw7OEcFS9nNVqJKWe0zYGg8FgMGRg8hTDrG+hgGKWi2BbHERrSOwiueF8tgVhmEqCa8v2cgS0aRN/rn6Kttou0baSez1cl1IVEWaYaWvWTeZrjksKSXO7Mnp3AFoppuvsjko4haye6HoQba776XBfE8YceBkZO/pzwJknBoPBYNgDk6cYZn0L5kNVnZZ5ZQyiFd9PHcJpGpyJv7NOMCJXKVWRgNakhy0jW9cOQnRUc4Es2KbuK8oq2yNkErEwvmb8HBiuWzsIEcSCJlLbMu6n+P6F7K/LyooNCL6mlOwvUlJI8LU0O5wjK+bU11pd8pRHdbII6WQwGAwGQwYmTzEKl1KRe55c9BXNH0TbYxu1p8IwAKLMt5FlA0ilNwVk1PPKu4vvV0shO1a0K5J5slHuKC2N0/iao3JHo1lKEl9zruxImKUk9TWCba6eA1VfEbW/zsm+lvy+vp+W4Invpx4K5ZXGZzAYDAYjC0yeYuRWfjKch+Ik2M4YRCu+31X/TmKX73mpep7UNokCmrPMk4HIRhZB0dlmQ8yCVCKap+fJwswiY18z7f1zQdTTLKc5US8i2W9EUCSS/bSZRSX2/iUz4hbDvlYf6Bk3QN3XipBOBoPBYDBkYPIUoyx1NifBtkRljNobUDSgHVTNUpIIRtDK9uJgbq6JUFXWpLDNVN696vvw49+FJLudp6+I0o+VBLSGjfI9trnwtYzsq/h+ch9KTqLeqPhSX8uaP5V8RmdbmhEKQ6A1l8s20yxn1fdR8Uwk+x31/mURdcO9w4WvZT2fAO1+Rp/jzBODwWAw7IPJU4y8fQtkSV+HPRWy+Taum76NpIbzCEb0zAVqSt+XaZvJcNBcthU4caesm0JgQBvQJifuLtT2ZLOUiM9BtxTTzDatkIXCNnPJ/nyHCLRhr7J1o5QUOva1PAN8LWQ5pQdWhfc1zjwxGAwGwz6YPMUwCrZz9DxZ6alwlA0omnkyVfISP6PMovTMasmXqTDt9QCIKmg2TtxVM8WKSFu7nFlUZJYSkHvdzAhKDttqdSDOApkSAbMeNolthOfAhRw4oB5EqxUnKbKv5RiwLb6feojAmScGg8Fg2ASTpxgzrXzqbGXIR1NnKRUOaE2zAQZr5tVylFLVakD8O5ueHndPtavS94QZKmOAWVbM2SDaBZBRp8iBS2cpEecCpbblLNsjlcZJ+uuUA5k9L/dzkLevSPyMczELw0OERpn7mqZsT1byqyN2XLbHYDAYDBdg8hRD+4e83QbiwNB0lhKQv4mfMktJF5hRBSPyEhRTEiB+xlVwViTzZNSP5aI0Lgi6fTc9A3wNswGGtrWCAIlUgpR0FlC06/ncYrufwudcELusviJAyHISep6cEBTZIFrH+xrFtkJiMxAPOLhsj8FgMBj2wOQphraEhDDfRqnSlfP0mDJLSdpPQe2RyUtQSH1i+bM7QP5BuTMmJYU5MhVOh+QKggWepA9F6Ws5g22Sop20d4c256mUsr28vpbXNspzkPhakSynS7EZ5Bx6nHNf6wSBfsB24Yw6Z54YDAaDYR9MnmKQS+OAzMbqENHJvQx5y7zErJG+lCpnkJEEwnkDWoocuLTnSVN6kzOLQgq2W+qyIJKYhcsTdyCzbC8IQ7QDOXnKG2wnZZgAUK9kbw3dLGeO3h3RtpziJOoyzOysmCmxy93zRHgO5PPOFM9BEcEIE1/L6HmKfM3Fvta9pishnCKkk8FgMBgMGRaMPB0+fBiXX345tmzZgrVr12J0dBSjo6M444wz8M53vhMHDx4kXefOO+/Ehg0bMDU1Vcgecq9HtQpPCJKoc4GKEhTlLKWipVQ51dlMej3ylt4gZ+apSElhGmwrBSOSU+1Z63OB0iyF5wHVWvp6z1BVlW0WCKdUDryAeiKA3NkAUn+dJJNIJ3b5SjHzDj0GaM9BSh7a7ah82KJtqRy478MTiGmjpH1NZZv0+Uwzw8QsZ47SWgaDwWAwZFgQ8tTpdHDhhRdieHgYu3btwv79+3HgwAEcOHAAd955J04//XS88IUvxPT0tPI6X/3qV3HJJZfgySefRKvVKmQTWdFOEvyI18iEBYIiDWgzhCySzwAGjdUOS6lyzXlCgZLCIk38lIDWpYy6UK4k3nNqQJu3DyWvhDpg0CPjUtmxJSnzIg+ydldSWGzemehrlm2TkrrunwaX+1rF81D1JVlOidiMcQ8bl+0xGAwGwyIWhDzt3LkTGzduxFVXXYXjjjuuJ0BcsWIF3vOe92Dbtm247rrrpNf4+te/jje/+c34whe+gNWrVxe2Ka/yk+d5aYmTasaNV7APxXQAJ5CjsdrFibsuG7CgM6gK9FT0zAWybJskoPU9DzWf0MdWBgnoV08kznkqOiTXdJSA+Bn9DKqCZXt5spwkWfzuZ/KWO2qznH12VXyf5Gsu9zVbIxjyjBNgMBgMBkOGBSFPd999Ny644ALley666CLcfffd0u/X63XcfPPN2Lp1qxWb8p7Qip9RlgU5bZRfmFIqo+xOnqARFjIVhPk28sGlimC7WgXi4NK2jLqMDAPEe1o0y1mgh00vVV4wK0axTXI/nRH1AuWrpP4634/mUAG59w9pr1ja95fT1xb1vsZlewwGg8GwjwUhTxMTE1i7dq3yPWNjY9i7d6/0+8997nNx5plnWrNJO+1ecgoKmM1qcRHQhpbmPOUlKNI1A+wpZi1ApkJFBJzOBZKUYQK0fiynpY6SWUrOFe1slMa5JuoOy1ed+VqaGc7pa077JbPXjH7wkq9fksFgMBgMFRas56miOkEGUK1W0TZsjs5tTxCkSnnS8hZJ/T1A7KlwWd5SWJWq/EZ514pZKbGr6dX2+klK2pBOXDeTLEo7CFKlPF0pVWbmycDXnJSfJWuWs+ep6JBc5f3UDfBdIIIShqFWMEI7uyvHLDaSr0n2DmDx7GuFM0/c88RgMBgMi5Dr/j6F0Gw20Wx2m/onJyd7v0+QzVVlnrplXvpsgDEJsNC7Q1alcjHfZpGeuMuGHuezjb5upLldqfBBsSyn6ewdoz4Uac8Tkag7KRGVDKIl2pZ3SG5T9xy020B8OFPc1+i2Gc2Iy5tRdykAUjBr7eW8nwwGg8FgqLAo5jzde++9OPnkk7F7924n17/66quxYsWK9L9169b1fH+m1c1wNTQntHkDWpfzimSlVMmp9lwnQKCQ087bKJ+sWxGJZq0SYI4Td0AYkiud2yVcr+DJtsm6zQjXbEhmKalKqUjiB0KgbSKjTinDlPWwJXZ1NHOB8hL1mTgLTZqlVPaQXJ2vtboHN0VJp8lzQPE12fBewPG+1tH7GjQlos1OoPZvFoxgMBgMhgMsCvJ02mmn4YEHHsCpp57q5PpXXHEFjhw5kv63b9++nu8nwUHVl8vmynoWAGJPRTqrpYVQlwnKsI1EUCQBLaAJzoSZRSYwIXaFFbNsZyokQ48B854KE9sSu2q+j4pMollRStUg+FpqVxgCrTlj24qoJ4rXybatBGXH3GIWjrKc4nOVsx8rj20UX5MN7xVtUx5wLMC+Jo6HmFPYlvdQiMFgMBgMFRakbI/Sz9Rut1GVKUQZotFooNGYT3oSFKm/B4hzgZIT2uRag8MKi01tU895Sq4zWMteT6cSzUVntTiaQSUbemxkW46yPaP7mUXUKX0oPTLqs5nXyWubbJZS/1ygZfUaMuGybE/iaykZdlTmRVbqrNbm+5rpsOgc5CkPGQaIA3wd7mu6kt/kOrJqgbyHQgwGg8FgqLAgmafx8XEcOHBA+Z79+/dj/fr1pdhD6t1RNPGnqlSUxmqYBWdF5MCrvgc/nqFFavp20Y9VNPPkSs2LEDTqm/jNSadRti5viWitDsT33aykkFCGKXkOKr6Pqh/9TCe9fyXMBXLW+6cgw6S9A/n62IwIpyqjvkD7muzAqk4d4MuZJwaDwWA4wIKQpy1btuDWW29VvufWW2/Fli1bSrGHNN+maBN/pQJU49N4g+CMNnsnOzjzPI9WguZURl3dj6UVs3Ck5iU71RY/40JG3Ug9Ma84ieflyvAUzsAucO+fNWVH676mULRzKBhRNPPkcl+jEHXZIYLneUQxi3x7B4PBYDAYKiwIeXr1q1+NiYkJXHXVVXjiiSd6mn4nJydx7bXX4sYbb8Sll15aij1m2YCcPU9ArsDRKPOUNwByVBoXXbOoYpajTEVREpDTtsJle4Y9MiYCA2alVIp1I/T+5SUo0mGvkBNisjiJK2KnUuqkZjmTdbPsa6Gkr0j8nPaAo8i+RjiwyjrgIB0K5SirZTAYDAZDhwUhT5VKBbfccgtmZmZw3nnnYWxsDKOjoxgdHcU555yDe+65B7fffjuGh6P6+V27dmHTpk3zJMYTnHDCCajX67ntKTJLCaD3LXiu+xZyyg07a5QPgq5gQV5FuxyBmTi3K8/QY3JWzFE2oPDsHeQT2qDJ4isOESg9MrnV9ooLp7h4PoMwTEUL8pTtuXwOigzvBWg9T9FnF+e+xoIRDAaDwXCBBZvztGLFClxzzTW45pprtO89//zzcd9990m//61vfauQLd0BnIrlUMxD6ZZS6WS3zQNHUkArGfYqfo5S3mIqB65dN0HpLe98mzwn7pS5Xcqhx4Zle0a9HiZ9YorsDnlmUZ6AVlWGSZp3pngOhN6dMAyjEkMT22T3s9OJ5ikh/7DoPM8nZZaSjbK9XATFiAxbKCm0nRVrEbKcFBVRlipnMBgMhkUsCqnyhYZJ0Fgk2M4jfmA05ymv3HDBuUC0WUr55MCLZHcA+dwu9dBjqjpbgaBR5WsSRTvRNnrmyXZJoaUSUdu2KaTnk8+0AvW8szy9f7ME8kQTJ3FIUCj7Wt6sNdzta5R5Z6QsZ7sdDcVmMBgMBsMCmDyh2MwR8XPaPpQcvUUmcuC55YaT3ykI0tN7G7alWQrP6zaVx0g+0w50Q1XNs2LJ8F7V3C7S0GNyX5F50DhYM1e0y2ObfcGIgv1Yooy6zedAMfRYnAukFNrIIU6S2FXxFL6mIMNOs5wUXyMI4ZBFXaz3/lFIJ2EGFcCiEQwGg8GwBiZPoPZTKIIM6qwW59LWOYOznrlAJtLWdInm/vIs6gDfXL07BXs96H1FSbBdXtBo2vNk3dcK9mN5tRoQv89mpiJ9BioVeH2iEtQBvq7Kz1RkmD6Q2a1tmVnOCi3L6WxfK6qIKay3aUkyg8FgMBgyMHmCadCYVUpF60PxXEtb5+0NEH4nqwGQ6uSYHNDmICgFez2Me9hKDBrptuUndipFO3W5I7EfK0+GR3dPFX1/1HlnucrPSu4rKpUMk1Un3e5ruWXUfb+7t3HmicFgMBiWwOQJ5cxDAeCsb6GomlfvXCB7J9uqfoqq76MSB7Sq0ptCGRTXvR6ObLMSbMetPcHj+81ty1kiSratFiljdh59yNg2mXqiSsiCPO8sEWU4dgTBwQkjuwrLuxMJSrD/YXPbVL5WVKkTcCaEY+MZRTXytWD/Q2TbGAwGg8FQgckTDE9oi/ShxM3qtgNayumxtm8hPrHvPPKgPdsU2QDxc+qsmJuAVmUbXcwiDmgf3Uu2zahENGfQOLdzBzrf+I/o39degbmdO0i2GYmT5Ox5mtu5A5h8Mnrfb72EbNtMi+ZrWc9Aj22KdWt/9YvRP44extS2DSTbzDIoikyiZs5T5yffAwAE3/4K2TaKr4UFlTqji+Tf15RZTpVwCkFoY27nDmD6KABg5h0XkH2NwWAwGAwVmDzBQqM8oddjbucOdL5+c/Tvv/0jqwGtUkad0Lcwt3MHcOxI9L7LLiTZ1g4CdOKgKU/mSfycMqD9WhzQTj5pNaBVZSqoQWPnR98GAAR3fc2ubQkZzgpoNUFjcHACzQ8Kw6XDEM0PXkYid0azlHL0PM23LSDZJs7t0iva5SPqwcEJzP3tHwkv0Gwrw9eCgxNo33SDE9so4iQqYudqXwvDsFC2P6+vMRgMBoOhA5Mn2OiRUUtbFwlodbaFYViobC9vkEGSaFaUeFFtm/vb/yG8YGYbZW5XVqaCouQVHJxA+9//MbdteYeD6qTKg717ItXEnhc7CPbJ56RRbQvbbSD2x0zxA91zkNM20twuRfYV0N/TYO8eIDS3zSTLmVdkI7KtT2Ldlm0ERTv63mGwr+lsa7fT3zlP2V6R54DBYDAYDBWYPMGwRybPKajDgFacb1OmbT2zlCoSN9JkA3SlN4UDWlJfkZpwyuZeuQxoi/TI+Os3A/2S2X4F/rpNSrtItrW6vpaHqOe1jUTUFdnXMmwj9RUp5nY1Fb7mr98cyf3ntc2Rop3bfU3on8pRUljkOWAwGAwGQwUmTzBtlDfv9XAa0CqGg4qfk524Fw0aa76PimaWUt4+lLy2kXo9CGQ4CEO0g/IDWhKxk/namnE0rry+a5vnoXHldfDXjCvtItnWJAa0OtsSeD7JNtIspcS2nP11/ppxNK7438ILFSPbivYkhkBamphlW+2N73FiWxFxEiv7moR09kiLK2TUZQcveX2NwWAwGAwdmDxBLPPK2begKW9xGdCGPSe09Xnf15XeFA1olQM4FeWEADWg/VvhBYtBY0vRJybOoFIEZ7W3/Dc3tlGGgyqkyusXb0ftTe8FAFRf/ibUL96utCm1TVcimthVrc6bpQTQStDqF2+H/8wXAQAav/+XJNtovkbrr1MJDNR+9R3pMzr4iW8a2Za/J1HwNcU9rZ7/agCAN7oOwzc9ZM02Sj+WlqDk2Ne0BxzJ81mtRZLjEtu0vnb28wEAjT+4lvwcMBgMBoOhApMnFJ+lRJm9U794expsV196CT2gpWYDGgPzBtGKn9MGGc84N7rM+//afmBWQAGt9qvdnorBG75lL6BVZCrEMkSlbRdcHP3jaWP0gJYi0Vx0OCgAb/Wa6B8qNbN+26hZTllfEXEGlTeyMvrHwLAdu3psk5WI6oVTIsn+SHbbX7GaZhvpfip8jTjvLCWFlSo5e0Lr5VT1/unXrIx9LQvk52DZyuj/g8tIdjEYDAaDoQOTJ5QzSwkA/OPigLZiMaC1IQcOMcgwDGgJfUWybABFEtzzPGAgCWiPM7Mtp/R8xfdR9eMZVKp1iz/reR49oC1p9k5KvMSyzoK2qWTKjWxLfLVFs22m3VbaBRQvEU0R/24hcd2KKtr5noc6gaR0h73anhFH6/1TwcW+Zut+eun9pK8bg8FgMBgqMHkCsUeG0LegnfOUfJYYNAIGAW3RE1rDoNFoXlHBYDslhsQAyCxTUWDdks/mGXpMyVQUmdtlaFsQhpiL++Kk5XEKu0Tb9ENV488TiYBRJlFjmzYrZrhuiW0N0sGLTglQ72smJMBWP5aecBbY17RZTp3YjPp+prYZkE4Gg8FgMFRg8oTi8tHdwIx2qk39Qx6GYRocFM082Q62iwZm4me1xC5e89BisN21TUPsVFkxQ8Ip2kbpFcuyTdfDltpmuGai7+YupaKQAHT9wWp2RyHoAnTL47QHHMnvZtE2sq9RyvZykCdSX2LOOU8AimXFdP11toi6AbFjMBgMBkMFJk8g/CEPAqDdir5Q9Mjosztmp8diEKobRCsLaKnBNhwQFCvZHfHzVGJH6fUgrpsyU1EgoFXP7cqvgJbCMBtAkQMPk3lFEqKum0HVtc1+JlGbgaWSzoQQW30O1ESAUr7azb42pZLmprbpZsSRibrhvkYZsJ0eINjKqHPmicFgMBiWwOQJhrOUNH0oysAm56m22jZbvQHuTtyLlt6YZnjSE3dSpiL/uqVr1m4j1GU0+myT9qG02+nsnDzDQVMYBo1JGaZKDtxWE7+LLKe1kkIXGVgL65b6QhBEPmJim8zXDGbElb6vFZSeT5GjtJbBYDAYDBWYPMFwYGPOWS1A98SeekKb2OV7XipgMN82S31FDrIBqkZ5M9vcBbSyUipaH4rwWVukU8wUKbKcnTBEW+VrDsswtQRFW7aXlwzLBQlCXY8MubTW/iFCmkUpQgTENbd1T8XrKIi6at4ZkH9fU9mmEnQBiM+n8HmT0loGg8FgMFRg8gTKfJv4D6/nZUo/i58jlXnlKAnKkiEH1MNeRdv0Aa2Dsj1LSoCm6zbTKilTkSeg1fmaZjhor6/plQCdkADdLCWNKINpEz9NnMTWc5CPCJB8TUYEKL1Fgi+QSSd1XwOAam3e93t8TbVuOfe1esWHL9nXFkpshsFgMBgMHZg8weCEVjJLqS6UxZCCbcM+lEKN8kk/heXGatJ8G1tKgMnJtk3bFI3yVNu8ahWI7z25PK7VVtsmDgfNKLcizwXKmQ0gkQBZz5Npf50L9cSCPU/GRMBgzpOU2BFs83y/S3CI66b1Nc2MOKqvOdnXFkjZkcFgMBgMHZg8Qd8joxv2Sp3V4qIZ3UY/BYBuQGxom7KvyEajPJA/Y0eZQSUlAkTbbAeOqa9l21UVZ1CpMhUO5MBty+I7UbTTiFnoDhGMiTrJ1yyVFNq+p5rn0/c81PyF3dd0mUSysiOr7TEYDAbDEpY8eQrDkCwYIeuPET+rLAvK24dCIQG25MCdzFIqOFTVUM2rqPS8iW0mwgwUX9NlKQBihsdJ746dnicnghG27qfLGVQlZmBpvmawr1mcd0ba14gZdXIvJ2eeGAwGg2EJS548tYMQgVY2Vx00ip9VnR6bNi9bJSjUgNbJLCU7BMVqpiKR3ZZlxYj9OybCDK0gQNJ2r1V2lATa4mdJ2QCL8u46UQbqvLO8ghE02xYfsSM/BxYzsDRfo+9rFCVAq/fTsrKjyXBhBoPBYDBUWPLkiaT8pClXAqjZAENFO4v9FHoSkLNcqUgfCqVRHnCbqZARgYphQEsIHI0kmotmAwxnUNnIcpJ7nnLO7aKUiEoH0Zo+ByUecJD7Eg2IgK19jVQeZ7ivkQRANIcbrkpEGQwGg8HQYcmTpxlhZkpDFjhqZgIBhuUtzVnSoEsbBKVBPaE1VbSL100VbKenxwUFBtKTbUv9WGEQAK256IuiwZmBMIN4LZmvmZTtqQJa0xlU6ZrVyiuNs6rsqOlLJD8Hlol62Ol0B2yXSAQovkbZ10jlcS72Nd3hhunBC5ftMRgMBsMSljx5Sv6QNyq+VA7cVjYgPeENw25ARbCtiAJa8tlWEKCjmAvkcnBp4TlPNctlewlxUthGFRgwIZ0kX9NkKQAqUTebQWVSGqfLBmhnUCX3k5jlpEjPd8sw7cjiWyN2mgHb4mdtEoGf931Nd4ggHryoCJtpLyeDwWAwGDowebLwh1z8vLK8peYgoCUqoEW2qYaqll+uZBzQ2ip37Bl6XHS4ML3fw8YgWrJthjOozAQj1GsGEGdQLYQog06dzTpRVw89BnJkYC3dT5N9TWmbg32NuncAwBxlPhaX7TEYDAbDEpg8WSiNA6jlLcKgS4PTY5JtmsyT3raFa5Snyg3Th+Sq59uka+95mcNBAbE0jtYjY0JQBmvzBy2nsNSHYjqDilSGSZSeB4gzqKzO7dLZZigHbnvose9nDtgWP2tTOIXkawbiJOQBvpb2Nd39NN3XOPPEYDAYDFtg8mRhhoz4ee2gy1o9+sLk9JgS0EoCoKrvoxKX7VCyAU76UGSlcaaDS22JWQin2rKSJtPMk0kpFUmiWeFrdJlm+rqZ3E8ZsasIM6hIYhZW53bZKduzLosvkACZrxnPFLO1d2h6EiPbHOxrBDKs87W6QNSVhwg8JJfBYDAYlsHkyQIJED/v4vSYFGSomr5J0taWCQqEzIImK0ZtlLc1S8mWRDMglFJZIigmCmg2Z1DZyHKSbXPYX1dUlCG3LL5UAMRSDxsEYmftftr3NaN9jTJYWOJrnufRDhEM++sYDAaDwdCByZOBbC5pzpO2zMtu34JumKT4efUAX7NhkjZOj42zO5ZmKZlIz2uzATX6utkgAeLnqcG2rZ6n0CDYdtFfp1IC1N1T0+wOhaAEYZj22+iIeuG+IsAoA7tQvma9H6vovDPDjDqDwWAwGDoweUpnyMh7A9LATEFQjEvQKME2pdeDkHmiDVXNV66kWjcd6aT2PHXJk6VZSoTSOBdiFjYG0QL0fqw8M6iKBtuk58Cwryjtx6IcIhTNchoQlCbF1whZ6wbx4MUok2jga6p9jfyM5lCdpAlGFCR2hllOBoPBYDB0YPJU8gktDJrlrWWeKM3yBoE2xbaw3QbigEsWnFF7d7qZCnpgFl2/SOaJlqkwEbOgSG7bzFTkmUFVVDjFhKibzqCSlsaFob0ZVAYZWNIgWo28O9D9vWxmEkm+ppmNBRgcCuXY15QzxTTKjoChmMVckzSDisFgMBgMHZg8Gcy3sdHzZNLAbNIjoz7ZNukNmIuGyBa1rUcOvGBAm3PN5LOULJLhOr2novT+Oke+VlQ4xfoMKnFulzbLGajnAuUoP/M9LxXKmAcCCUgzT+TyVcuy+Au1rxHELCjDoklEHejxEwaDwWAw8mLJkyeTE1pK0OhEMctWpoKiSgWQgrMZg+Ggulkt7SBUDvBNA1prvR70PrFmW9PDZtBTYTSIluJr5BlU9DIvZRmmQbCtfA5sz6Ai+JqYhbQ1F4hC1ENCdsdYOMVyJtFKP5btfc2kl5OSUQdYcY/BYDAYVrDkyVPaG6BUfiJkd8ilN/RmedtEoMyhqmmAV63Ck6xtz1BVSkBreYYMSaHQJhm25WtJJlFbUmhX2ZFSgkbKBlSr0dwj2HkOesgEYaiqrR4ZkmhKTAJIwgc2+4pMfM1Cz5PtfY2SUSfta4mEOuhzxRgMBoPBUIHJk6Vg2+kJLWHOU+Geimo1GhoLwwBIZhuln8JBQDtDWDNSaRy118O29DylUZ5sm+0mfnqw7eI5kGbFCHO7TOcCWZMDb+rXLO2vo2YSS5TFN54pRnpGCQIgBnuu8n56nrGSKIPBYDAYKjB5KjO7AzhUpSpWguZ5HlkFjTJLiaLkJQ7wJckNW86gKIeDEkvjnEk025gLZKIEuED9WLp7SprbRSAoLuYCWTvcMCzbs7Z3GIxgWIz7GlnMghX3GAwGg2ERTJ5MSkgKnoICYrBdony0YTZAd+ou9owUGUQrfp4U0NruE1uEogwmGTtdP1Z33Sz0sMGMCGifA+I9nesQ5nYR7BI/b2sukK1MIll63nZfEYF0ms95sitmYZN0UueKMRgMBoOhApMnS8E2vbzFgAiYzHmilKBRZbc1QQZplhIhSyF+vtRSKgO1PeqQXGulVC19xs60R8Yo86TskdGrTpKfAyLpJPkaIUshft5az1PJpY72M7A2y5Ht7mtWs5yceWIwGAyGRTB5KrlszyNmd8i22VKlEq+hCTKS63jo7SURkZIARWkcQMyKJcGPLYJCKCkkz6DKkXkaLBo0ms4FsuBrYacDtFvRFxaGC1OzAeJ1pL5GzHKSMjwGc4Fs9RWZzogz6xOzQ1B0WTGb+1oYBEa+Rh3gS7GNESE4OIH2t29DcHBioU1hMJYE+Jn7+QKTJ0KQYfWEtmGPCEQBbZtsm7Z/hxhkkGYpUTNPFCIg9HrYCGgppXELJtFskhWz6GtTc1GweqQpmYUjkBwrRIC4bia+prKLalt6jTDsBvAE26Qg9BV1SZ09EmCU5VT1/lH7ivLsa7IsJ9HXTIdsUweALxR0wZP4fdNAy+T9czt3YGrbBsxc+mJMbduAuZ07rNltG/3X1n1d5Pqm13a5LkV/zyK2ufS9PO83+exC/t6qz+ueOdu+ZGKb6+fb5f12CcVQl6WBQzPRH1TlH2CLJ7TpSSohADpmENBaLW/RBEBJ2U3F8zAxOYXxkeF570nL2DSZp1wBrSA/PM+2+DrH5lpy2wyk55OhqrLA3aSfguJrpKyYaXZH42s77tqNA1PRe175mVtw/bYXYPvWU3vfJPoEqdyR1r+jJeoEX4NFX+v53ZqzJF+bUvmaQV/RXCdAEIbwpb5GJ+q29zVyVswGsevxtXKVHU0QHJxAsHcP/PWb4a8Zn/e1yfvbd3wZzQ9eCgQB4PtoXHk9qi94aff7u/4VzY+8K9oD4UVp/zDMfK/ptcX3Y3AZmn9yKRDGz28QoPnBy+BvPhuYOQZ//eboZardBj+b8rXqZ1d/6dfQ/s//F33t+ag8/5fQ+cZ/kNdp3rp95V/Q/LP3Rp9Pnsn4WtVXvAXtGz+R+/fOsy4J5nbu6H7W81F96SVo/8enydcyta3+7g+jcsZzctld9P3iz9Y9Yz3rYuGe2Py9Vf4SVmto/sk74mc7fub+5FJ4w8tRecYL0L7jS2j+yWXRM2nBl+bZ9vI3o33zDZLfW/jZno/GH12H6gteZu35Nr3fPZ+9/SY0P/RO6brk2VvaP/4+ef9f0uTpn35wH/5tzz4AwP/86vdx4vKh+UEjQArOyHOeiH0oO+7ajUeOzgAAfuX/3Zod0IrXUCrH0eYCUSV9//meBwAAx1ptbLj2c7h+27nzbTPMBpCHqmoC2l0PHwAA3L7vMbltBIIiBnZznaBHVj3TNsL9JPmaQebJxgyqickpXHrTnenXQQhcdtOdeOkpJ/aQgZQE+H40p0kC03lnOqKe+NqUwtcopXGAILShWjdxLtBcE5JcFwDg1oceBQB8c//PFL5Gz1oDwFyngwHZ+hIzKE58zaKy49RclC2XHQqlvuZ5QLVW3DaDkkIq5gewr0f7Pz4jDSJa//lZzP3Ff0sDcf/Z5yH47le7JEVEEKD5J+9AE1729xEiVVEJAjQ/8HY0PS87sPe8+L1h9rX735+FoIOZt5wjvOB1r9dvt2iL7vfyfVRf9ga0b/7nODjz4J91DoJ7vpWuk7f5bIR7fph9vb5rt7/8aWGJAnTu/HKfbe+IbYt+duXcl6Jzx5fin+WjcuGvovOVz2evg/jzgwDtf/9H+bV1v3fmuvXZdv6r0bntC+nXSVAZtttofuAdSNc/DND+0qf6rvX26GeBsIbpz4b0+3N/dfn818Wfldxvz4N/9rkIfngn7Wf5PqovfxPaN38ye83Fn+158J/xQgQ/+Hp6v8RAPuy05xMQ3T0R10nzs+c9Q4a/d7a/QO7XYYDZ979ebrfJM6Z6voMA7Rv/Kfv3zrCp+YF3QPrXJ2tdznoegnu+KTxjr0HnK/9Cu99bX4Tgrq+nxK3yopejc/vN6vvp+en7oz12V2pL5TkXoPOdXd295sxzEPzoW8I6epjpaPYZAUuaPL37P77VLYVBdtAIWO5bIIgyGAe0lYoyoDXtkVEFQBOTU/iT238g2BZmrxu1D6VKkY+mBbQTk1P4zI8ftGJbYhcQBdtS8kQopeq/nxRfszODSu9rew5NIujbhDthiPsOTea6nzZVCm37GmXdPN+P/K01pyWdn4qJndI2wv0UfW22LSdP1OeT7GsGhwjakkKi2MyOu3bjsenI/m2flmQ5hcywtFQTJllOuvy8DsHBCXR+cEdvoBYGaH/pn4U3aQKcMETwnV3qHxSGyAzqlO/H/EBN8vPFQLDn/bQfRrNF+j3hZ9/0yZ7vBXd/s+frcPcPYA9hzzp1vn6z8K0AnVs+a+famd/W3c8+277yL91vqQJa1fXin6tfw9DI1eZ/vPuzgh/cQf9ZQRAF9cSfEXz/duFrTSCf/izdL0b4xaUEx+T3zvi5NtZc+r0iz3dBiOty9zeE1wN0bvkc+RrB977W+9mv3Uj4XJD+P/jObT3X63z7K73Xv+ebvZ81vCHOe54OHz6Myy+/HFu2bMHatWsxOjqK0dFRnHHGGXjnO9+JgwcPaq/x3e9+F6961atw8sknp58/8cQTcc455+Dv//7v0dH9YZegv38mCRrnweIJLdIyL3kApApoe2CzXAkgBbSRbb2vZdlGCcwAw4AWUAa0ew5NznP/TNtIw0G7ZElpGyEbQL6fQHpPlX0oVdpQVY/ga5tXj8wrE6t4HjatHul5jULqAINsAEGlkOprVtX2AGHemdo2kq8R7mfN99MDAaVt5OfT3r5mqiJqQuySQ6GJyak+u2j7mmk/Vp7Mk1hTn/QlzL7/9YSgDLT3LFb4cWjgLfm2aAajHPj8rP08wend6nQ6uPDCCzE8PIxdu3Zh//79OHDgAA4cOIA777wTp59+Ol74whdienpaeo3vfve7eN3rXoff+q3fwk9+8pP08/v27cMnPvEJfPGLX8T73//+XPb1n2pmBY2A3RNaijqbq4BWNxeIEtBuXj0yL/OTuW4LkKkg20YgneShqoTSOOr9BOBkEK1qzcZHhvHXL+2W5FQ8D9dtO3d+/04qoW6hDJNoG/V+ms550pLOGo10knyNcD89z6PN7iIQdRNfs5lRp+xrVGJHLcM0Ldsz7XnqaeJ+2To0P/B2eWlbUXge4McHNp7f7bHJ/L7XJTVZ7zW9dj/8Cgb/8RsY/PhtGPynb6iDOiO7CT/bBOK1/Qqqv/zr8q/z/GzZmlOu7XpdfANblL+XxjbTz9p8fx74iudioX5vyj2Kn7mBj/y/+QcWtn1JZZvqvS78uMj9drm3EOCUPO3cuRMbN27EVVddheOOO66HrKxYsQLvec97sG3bNlx33XXSa1x11VX42Mc+hm3btqHR6Aa7vu/jtNNOwyc/+Ul89rOfJWWw+vHXL+kGjb6H7KARsHxCG19DQVDGR4bxNy97Xvp10YC2Qe2RIQbbv/2c07W2LQSxGx8ZxoUnrdXaRlUCbBDKgiilVOMjw7h+27np1ypfM1J2JA9kVgeNl5xxUvrv+3/nNdn9MQThAyDHc6BZN4qv0e8nsfeP+By89JQxrW1UGXXKPbXtaxQlQJtiMxGx632tlIOXHGp7wcGJbk+TDqbBdEag3/ijj2P4pocw+PHbMHzzw2j80ccV39+L4Zsflr7X9Nrz3n/ldaie9VxUn3M+qmc9F40rr5eSCDO79T9bTYAUP/umhzD4gX+Qf53jZ0vXnHBtq+uS4W8Juc3ze5rYVn/vnxXzpQLv7//ZVAJi455Y/b0p/hI/c7WXvA6NP7o+t91FbTP9vYs+3+T7bcG3yHsLEU57nu6++25ccMEFyvdcdNFF+MIXvpD7GsPDw3j2s5+Ne++9F2vWrDGy763P2IS/+sGDuP/wUXz64vPxujM2zntPGIZWswFUBbRLzjwJ77w5qhfd/dsX4+RViiyFhdK46Dq0E9pfWLcGf/Odn+IZa1bhi6+/SEI4iXOeiAN8qUNVN68ewS0PPopfP/sUfPD8Z+UmKEB0TyebLVoplSYw2771VFz99buVvtZzHVWWM1EC1BBOatCY/H5V38OGlcsz32Oa3bE15+kX1+t9LST6mulzoBt8fNpxK/Cl+/fjjWeehI9c+Bz1c2CjpLBBez63bz0VH77jbtz3JHFfI2XUdXOe9Pva+MgwPvaS5+FdX45qzeWE02zNtNl+4iGCiGDvHj1x8nwMfPjTqDzj3Ei56Xc+iGDfffDXbYpVpC4Dgk43OHrBS9Pv978/UQ5L/l+/ePu894vf173X9NpZ70/Q/34Aue2m/GzV16qfnfxb9nWeny1bc8q1ba1L50ffwdxf/0GvL5313J57ZPp7Um2rPud81F52SWFfyvP+rJ+tfcaEdSlyT2z/3lR/ce1LOtvy/N6mPzvv/bbhW7q9pfWTHwAXvBIUOCVPExMTOPvss5XvGRsbw969e6XfP3LkCJYtW1boGioEcefC+hUZgQ/QM+uFUrY32+4opa1N59t4AE5aZAFtEqysXTaUHTACPU3fNmwDMQBKrnP6cSsUttF6ZFK5csoMqnioqqrBXedrYRBEQgWARszCLhk2GhRt+X5SbRsdHizN17x6I7pTxHlnp6l8jTBTDOhm7JS+lpDDdhthpwNPNicJUTkcAKwbGcp+Q7uV9uRQev9s7WuvP+OklDw9+Lu/inUrMvZ2w6HHNspX++Gv3xydRoplhsnppBCs1V7yuu5nDILp/vdn2qD5vuq9ptfO8/68dut+NuXrvDD9WUWuXfT7uoDWxJYitpXhS9SfTSUgeW0p8llT37H5s23a5vpn573fLn52/9fVwYwkhQROyVOn00FF8QceAKrVKtrxoNe80F2j2Wyi2ewSgsnJbo198oc3CV7mf1j4g0sobwkBtIMQtYokyEglfTUEJc4qKIeD2m6UNwxopWsG8fTYckBLzKIoh4NaJJ0915hrKgN47bq1utLNlD6UVqCZC0QdetwhrFlyDaI4iVYBjfgcGBE7YvmqPmNHew7EZ1SG1NdsDvAFot95UEKMQFg3cd0J+1oQhlb2tcSuesXPJk6ibbaIeuKzBmp7/ppxVC54NTpf2Rm/kJ090l2jSKDPYCRgX8oGrwtjobAk5D2uvvpqrFixIv1v3bp16fd0QUZPpkM1uJSqzpb8IdcRlDiglUpkQwhoLTfxa4NtUkBLLCkkNvGTA9qOPqCl9sgYlVIBxYkd0dfEz6uzYvo+lOga0Zo1VAcdRBJAnvNEHKpqRoaJhwjaMi9aBjYlwyRiR8xyqkhnD1Evtm49z7hibpr4eZv7Gul+WlIRpR4i9KMSD06sXPSrGL7pIdQv3p5mAjhgYzAYjKWLUsnTvffei5NPPhm7d+/OfY0Pf/jDeNWrXmX0mSuuuAJHjhxJ/9u3b1/6PfIJbb2hLMlqEIMMagmJCUGxFtASZ7WQbKOWKxGb+MlZlDS7o7CNKPGellKpbBOGeFJLCrUBrWY4KJWog9hfZ/N+msuBWyTDFuY89VyHSgQUvmYsGKEiKNUqEjUpfWmtZt0Su2r1aBSABOK+pnwObO5rpmIzFoZFZyE8egQAUDnlLCZLDAaDwUhR6pDc0047DQ888ID+jQr8wR/8gfFnGo1Gj1JfgjAM0yBDGmwTRRl8z0O94mOuE5DKvMiBtjIbYLtsj3Z6rF0zCPNtbIlZEEtvSJkKi1LInudFQV5zVhk4knxNKFdSEfWq78H3PARhSCsptNDzlN5Pi31FgEF2h+BrZPVESz0yJkTA1rqhPgDMTivvaSj4hXZf09jlex5qvo9WYGdfaxL2tdQniIcb9BlUZkNyw2MRefKWrTD6HIPBYDCe2nCaeaL0M7XbbVSrxThc3mu0gzCdO6Ir29P1x4jXUJ6EEhXQmpSA1rpghCGxK6s0TryO1ZJCWsZO279DKI+j+Fo3G6AOGj3PM1RnowmAkO4nueepRDGLhKBYOkToznkq/ozSs5z2SmtJ+xpheG8C0roZKjuqSx3tEs50fzQs22PyxGAwGIwsOCVP4+PjOHDggPI9+/fvx/r166XfX7lyJY4dO1boGjKIf3T1ZXt68kQqjzMVZagqbpFh5ok8uHQBRBm0c56oJ9uUXjHLGTuKTDPF16gZMfEaqntKGfYq2qbM7hAV7cgKaDZLRC3OUgJALins9iUqhFNsD3wlZGBJ+xrxcINsm1Vlx4UpEe1HQp7A5InBYDAYApySpy1btuDWW29VvufWW2/Fli1bcl9jamoK3/3ud3HaaacZ2yeW78iC7W7Pgp0TWpsKaPQMCrG8pWaxR4baKE8NaA1PttXljg5KqYTrquwCFMSOmKUAiPfUarbOvnoiQCfDaqJuViKqK9tLiXqZWc4qob8OIN1T8Rr6fc0OebKq7Gg854k4YNuwbA9J5mn5SrPP/RxgYnIKtz30KCYmpwp/tsi1nspwuS6Lec0Xs21FYPp7uX7/zwts7heLae9x2vP06le/Gh/+8Idx1VVX4d3vfjdWr16d9nJMTk7iH//xH3HjjTfiBz/4gfQaH/jAB/Ca17wG1WoVF110Udq7FAQBHnjgAfz+7/8+Xvva1xoPyAW6QUbN96Vyz9SeBcCwvIXau6PKBtg+1SY38RNEGainx4ZiFkUD2t7hoNSyPf26hVAHZxRfy5N5ImUDWnMIg0AqDOBCPdF2GaYNX0vFSai9fwWf0TAM6dlhg+cg8jU9UVfua3O0HjbAsBxZq+xIONwwfD5dZ55kZXsTk1PYc2gSm1ePYHxkeN7XOug+r/oaQO7Pfvn+R3DpTXemYw6u33YuXnrKiaTPf/fRJ/D+r3w3/exbzjoZn7jngVzXon5ta83K/NlF1lh3v21f2+Y6uPy9y7jftta86Ps/fMGz8Jyx463crzzPja1nrv/30u0Xqvtteq089/v7D6sr5UQ4JU+VSgW33HILPvShD+G8887DE088EQUUiMrxzjvvPNx+++0YHo5u1q5du/D2t78d3/ve9zAyEv0yW7duxb/8y7/gqquuwrve9S5MT08DAHzfx4knnoh3vOMd2L59ey77SEFji162RyqlSq7TbiNstyMFraK2EftQ2kGIdhCgKlPXIvYG0Mr27AoMkNXZdOWO7XZ3OKgtIkCQabaZpRCvozp17/n9WnPSEi1SdseQqHdCta+l2QCLAiDWB/hqngPtnCdxbpf1EjR92R7J1yxlOU33NWWpI1EAxHQwualUuYo87bhrd6E/5P2BwJvOOgmfvOcBBGE0GP3c8RNw58RjCBF9fdFJa3HrQ4+m3weimYK+B7zxzJPxqR89GF8LeOnJJ+JL9z+SfvaCDWuxa2/02X4EYYi333gHPOF6r3v6Rnz2Jw9n/m79n/3Hu++fd60E0c8exa69BxCE0bVfsWkcN943IflavY79xO21p2/AZ3/yUPp7nr9hDf5r78HMa3sAXjh+Ar4urOnWNatx18FDmWus+9n9dopfi/cnWZd33HgHPA+S3xP41dM34PM/fTi1/c1nnYIbMq7nCdeV3T/VGnsAXjB+Au6I10F3v/sD+Rvv24ffvukbCGJbfuXUdfi3PfvSa/fb9o7YH7Js8wCce+LTcOcjj6fff8tZp2Suo+95uOSMjfj0jx+U/l4vWncCbt/Xvb8v3rgWtz38aOaa+h5w/vpR3PbwgfRnv/6Mk/CZHz+UeQ9Vz0z/z6a8X/S9rPdf/pXvAhlr4nse3nzWybjhnvvT3+tNZ52MT97zYOYzs3n1CD7/k4fwvv/8dvp79q/DL500hv94YH+6zuL97t2ngJeePIYv3b8/c2+Jrn0ybohtyfq9svaLfr9BfO3zNqzBVx8+iKwIJ+ta/c/Ya5++AZ/7yUPSveUNZ56Ef/7RA7374uxMxk/LhnO1vRUrVuCaa67BNddco33v+eefj/vuu2/e61u3bsW//du/WbfNKGgklO0ZlVIBEfmRBBmkAZyGQWN03Q6qdUlAW7cX0FKDM/M5TxrbdPe0Z5YSsZTKQhbFZqkjQDx175lBNSv1E8rQY9MSUUDta1ZLConiB+Qyr6QETfcc6PrrxN+NrASoESchlNbaVJwUr2NjX3MhAAIAc51Aeh88YlZMRNjpAFNHoy+W95KnicmpNKAAiH/IT9+Az/30ISmB+cTdXRXaEMAdE4/1fP2fDz7a83X3s8AN9zzQ8/XN9z/S896vPPwodOgG+sBnfvxQj23i72aC6Gd3T3KDEPj3PROKr7PXUbRP/N7/+0nXzhDAbQ8flF47BHB735p+7+Chnq/FNVYFdll2il9nBcQh0jO7jM8Dn/3Jwz1f/5PkelnXFl/XrXEI4OvCOujutxjIZ/3ML+zep7St31f7bbnjkcd7vi9bxyAM8akfPai81tf29d7fWx96tOf9/9S35v2++c/C9WXrnPW79f9syvtF31Ohf02CMJz3e4h7R7r3ABLSMX8dvvzA/p7PJ/d7/iEAcPP93ff27y3Rtc3VtGV+s0t4nqnXEZ+x/6fx60/eU0z5e0kMyZWBNEPGcm9AD5lQnR4ThA+ofSi9s1oUwZnFgJZK7Kg9T9QeGW0plfi7WRuqOqC1zabIRo9tKl+r1qKZUSgebJv2sGltIw89JhA7qq+RxSzsDPDtIV+KQbTiNajzzlTrRlqzHBl1m/uaTcEIwM4Mqh5MH03/KWaeJian8H++vyfzZFVECKREKQn0s4gTQ40QtEDW5c9nMH6eECKbOOW5Dvu/HEubPJFO3GklJIDBoMs4iCMFtKReD3VAW/V9VOIgmhLQkhXtCOtmbc6TtVKq+H5Wa8rhoKJt2mwAQczCJgkADGZQEWyzSeyqvo+qr/c10zlPZUr2U58D7TMqzIhTze0Sr2EjA2s29JguhKMsESXua+nzSZjzpNs76sKzRJpBZZJ5Ono4+n+9gV37D2Ficgp/9c0fYf1ffxZXffX75OssRnhA+vfAR/eE2RQVz8OvbznFyrWearC1xmVfuygWs21FYPp7FXn/Uwni71V0v7B5LRtY0uTJKDAzmIdCngu0yDIVHqGfgmqbK7lhVQDUCQK0gngQrQWJ5oZpsF20lIo4HFS8jo15SqQMbA5ip3wOrEpbW5aep87H0s0sMshaU32NkoG1vXeYy8/rbaPMedKtm+d5tFJpYiZRRHg06nd63GvgxTd8Gev++rNp30A/8vwhVwUCuq89RP0FeT5b8Tx8/BUvwEPvei1ue/NL8fC7X4ePv+IFRp+/5sXPxm1vfikeetdr8Q+/8guFrqX6WrWORa/t+mcXXWPV/bZ5bd3X/dD5rcvf2+X9tr3mRd7/Zxc+x6rfmuw1pvfb5DnQ7Re6+21yrSL3mwrnPU+LGUbDQSnBtklZ0MyUWp3Nco/MQLWCqVabVnpjIaA1n/NECxrVEs3dYF07HNRWuRJopVQmhJPSh0KVn/caAwiP0oidcs6Twbo1KhUcg8bXiJknm6W1yZppCSfhOWgHATraAdvminY2MrCuyLCNfc3mgO3kOs1OYCWjLuKxxw5gGYDJ2qDyfX950XPx2jM2YnxkGB+84Fm479AkNsWN1pfddCc6YYiKlzR5P5B+fV3c1J28v//zuq8B5P5soqaV/H/71lONbRExPjJc6Fqqr/vX8eoLnoXnjh1v5dquf3bRNVbdb5vXVn39nUefwB985btGfuvy93Z5v22ved73n79xLS458yRrfmuy1+S53ybPgW6/0N1vk2vlud8/ePgRvPJDyi0/xRInTwRRBtt9KOjKDSuDbcqwV6Kkr2ibUgmQOKuFFtBSe2RiEmAjg2IyHNRAAESf3aGU7ZmsmYNgmzBU1XaZFy3LSSPq6gG+dpUdKc8BbcB2/HkDRTvynCfC/SStmUGW0+a+ptxziQIgyXWONFvqkkJiJlHEowcOYDPU5KnieSlxAsz/kCefSSB+nvp13s/2o+jni1xL9XXWOrr6WbZ/tot1cXntrK+zAvl+W8r8vV3eb93PNv29irzfhd+6ut9F9grZ521ey+TrkQ2j5J+1pMkTbYaMWXZHvK4UJupsKttalk+2CYG2eA3ZuvXMt9EFtKZzngin2r7nSSWybQ8H7bWtYHYnJer2SkS9eiNq/CwsGGF53YQMikpmekFmUCVBO4GgAPI+Nqpoioltadmerftpa86TeC1lzxNhX7Od7Sdm1EWc6LUBAEeqQ+lrUWmH13MqazO4YmRjIdeN7xmvwc8jitwzvt80LGnyRJEDzzd7RxMA1eKAtsRsACnIEErjCgW0Lubb1PUBkEl/jNlwUGIPW9GA1iCTaLMPxeacJ/E66nln8f0Mw2j2Vq2W+T6tol2nE30eBiWinUA9F4iQeUpIQNX3UJEJjxDVMPttU8JSv6RRPxb1gMNgX1Nm1HOsGynL2ekoZ1CJWNWZRRPdzJOshIXBYDAYSw9LmjzZnodiPPGeckJbpmCEeJ3WnJSUadcthxx4OwjRCQJ5IEoppTLpYSOVUpkRu6JkmDocVLyOzSwnSdra1rqJvjY3KyVPWmInZiKJUuVBGKIdhKhVJE2y1gmKPV9LSwoJyo6kIdYLlVG3tG5GWU5AOYNKRDIgd7I2iL/8pefitU/vLc9jMBgMxtLFklbbmwsMRBks9gZQGpi7PU8EGXVbQYYYfCob0jXZAPH30sy3oc6g8gglhRTp+dBAlMFYMKLAmomftxnQJn0jhYmd7RI04XeklGJqFe0A8kBmsm0F5xV1B2xbJMOEEjRSOXKOfkmb+1qp2X7xeSIq7gWxVPlkbQivOX0DEyYGg8FgpFjS5MmkD8Vq3wKhgdlszpOlIEMgOqTSG5ltAqnTzrcxDWitDXslZBItyoHT+uvsl4jazgZYy3L6fjTEV7i20jbd0ONKRVuO1UvU9RlYkhw4oSeRJotvKBhRWBbffs8TZV+jHXDQs2KUjJ1XqaTZJqri3tShJwAAx+pDOHH5kObdDAaDwVhKWNLkiSQHnuP0mCyFXDQAMiop1Etb9wxVldgWhCHmdMpxBiSAOsDXKKC1VRJEFrPQy26bzHmyFTRG19Kvm5ESYIkCAyRfMygn9D0PNZ8g8U4qq9XME4OohmnT12w9B/Yl+23ta25k1PXETsTU4UMAgOryFfJSYgaDwWAsSSzpvwqkP+QtemlcQlAePnIME5NT0vd1+xb0wRnJNkLgGMYzaR49Oq1+oyaLQpEDNyF14nWUs55IpXEE6fk8ZNjicNBSFe0AWgmaiRIgwdeCeFCxztd0JYVzAvmSla+alBMChiWFRecVORhzQJPFp5T8mvRj2dvXjLJiVhUx9SqFIpoxeRpYeRzp/QwGg8FYOljS5Ml2sP2dR38GANj18EFsuPZz2HHX7uw3Ek5BTeY86UjKjrt240sP7AcA/OGuu+R2QZ9FEbMJNrIBAFCLg7OHjhxTvIkuH60sjctRSmVFAMTyQObkOvsmp5QBrTViR7TNxNd062YyS4nyfAJIM08PK3yNlN0h9e6Y+JqZ2l7RksKF3tdoWTECUY8PhQ5MFTsUmnfdo5FgxPLVq0nvZzAYDMbSwRInTwSVMWJvwMTkFG64+4H06yAMcdlNd2YHtilBsdRToQgyJiancOlNd6Zfh4DcLkAbZCR2eYhkmrNgkg3YcdduHJ6NpM0v+uSXpYGZyeBSkvCBgSjD5FxLfeJuIMqg7PUwGA76rUceBwB8da86oLWxbmEQdOXnFcF2fl/LXjfR12oaOXDK/dxx124cmo3e/5JP/YecBBAG+JIIikG2Lln7o02Nr9nqYSMeItje19JyRwu9fzvu2o0b75sAAPyPXd+nEXVi2Z43NQkAWHn8CaT3MxgMBmPpYEmTJ5M+FF1wtufQJMK+1zphiPsOTc57b3raWySgDUOSYtaeQ5Pp6azOLgBaSXDRLqkYBLGfoj/YDkJFsG1pXpFJpuLf7t0HAJhsttQn7rYCWiLpnJicwj/dfX/6tTqgJTTx69atZ26X/Dkw9TVPE2yLpasyX6PK9Rv5WkJ22u1ojlSmbfrMsEkm8V/v3QsAODqn8bUaoWyPUPJLPUQofV9rt4G47FOVUTcl6ukBBzHzVJs5CgA47gQmTwwGg8HoxZImTyTBCGJwtnn1CPrDu4rnYdPqkflvToKCIvLR7VY0YBTqIGPz6hH4fYGn1C6IM27U2QAbogwmwXYatMeDLlW2qTOJNLW9ickpXPlf30u/VhEUW038VCJgEtCaZDnl6onCZy36mm64MKV0lUpQjHxNvFaB54DauzMxOYU/3EX0NYO+IuW6LdZ9jSg9n/dQiCpVPtiM1n7tmlHS+xkMBoOxdLC0yRNFZYwYnI2PDOO955yRfp1MpM+aD0JpXjYaDqoIMsZHhnH9tnPTAMgDpHYBEBrSJdkAi31iRsG2uP6S4MyMoFCIXe9rOoKizu4YKNppbDMJaK1kA5LPel5XXjwDeX1NVu5olBnWlMYZ+Vr/UNUs2yz27pj5GqGksKMvKVyofU23bj2fVaxbXqKuKq1NMD3bxLLWDABgfGyt9v0MBoPBWFpY2uSJojJm0Ify6tPWAwDWjQzhoXe9Ftu3npr9RpO5QAWDDADYvvVUvO95UQD0xrNOktsFIYuiKdsjiTJo7EqC7QS+pwi2xfUnlBRKQQwao+Cs9zVtsF3SnKfxkWH83vNoAa2dbEC3P0Y3t8vI14iCEZRZZzrRFBNf86pVIO6xkj0HTQPbdPfTzNcsyeIv0L6mnfOU+Gm1Gs1nksCUqKcElpB52nvgQPrvVdzzxGAwGIw+LGnyRDk9Dqej8o3g2BHt9ZLr+J6nnkhPUaXSBWepol09GjiqwZrhQQDRXCUlNMG2iShDOHMMwcEJ5Y/bvvVUvGA8ClA+9pLnSQMzr1oF4rWQKwHqm9HTk2cCQfnfL+8G28oTd1tiFkmmgtAj85rTNwIAxpYNKgNaXTagHQToxOVP2lIqz9feT8DA14iy+LaGHm/feip+Yd0aAMBf/tI5SmJHFU5RyYFT1TDHR4bxd9tekH5N8TX1DKqfg31NZls69LhK2jv++/PPBABccsbGQvdTxCOPPhqZUqnBJ0rgMxgMBmPpYGmTJ01j9dzOHcChg9F73/WK6GsFjGeOSP6Qk4aDpkFGhRTQms6RoQhGyND59lcAAMEPv4GpbRu067ZqoA4AGK5VNbbRAlpl0Hgkmt+C5oz6ZwG49FmnoRIfbX/jN15BOHEvmA1IAtqjh7W2JdfxyAGtmqAA8mxA6+Z/jv4xM0W6n+bPgZqoKwlK/HuFU5Ok52Al2ddotinv5+ST0f9n9b72jq2nohann+5427ZiWWvNnKeF2tfCMNSuW+vmT0b/aM6SfG102RAAaAfZUgZZJzhwMMo8zTYUzxWDwWAwliyWNHmaU2SegoMTaH7w0u4LYYDmBy9TBmhkgqI5PZ4jzFJq3fyp6B+zM5YDWk2wrcnWBQcn0P7SPwsv0NdtRmebgRJgFuZ27kBn179G/77+A9o1A4DBOMhOCF4mKBLNmnWb27kDePIxAMDs726zdj9BLMMEsktEg4MTmPv4nwgvWHwOdGSYkBnufPu2yKwf3GH0HGh9raBtczt3oPPVf4/+fd3/NPO1QXkpHYUEqJ6DhdzXWkGQCp3IbJv7uz8WXjCwTaKKmMIg8/TEY9Fz2Bparn0vg8FgMJYeljR5UqmzBXv3pJK53Rc7CPbdJ72eKUGRBdu9AW2GbQcnMHddziCjYACkU7QL9u5JVQC7L9pZN92g3O7cLkrQGGrXrMc2RXCWllKRep6y76ergDbt9dCUYVZ9L7PMLrqfZs/BYLVKso2c3ZGUYQYHJ9D+j88IL5T3HKRy4C58TWWbydDjLNsWcF9L1gyQPKM5fM30fqpKaxMc/llEnjC8QvteBoPBYCw9LGnypOoN8NdvTpvGuy9W4K/bJL1eEjQ2O0E0h0kGjTqbbjioy4BWN/BVl93x12+OVNl6XlSvm21il3mqnSNoBLoBKC2gbUrvu23brJEATXmoy/upzXImA1VlWU6HRJ1eUliyrzX0vqZatzz7WrJmtvY1pW1ePttmWrR9jTIk99ihqLS3MsLkicFgMBjzsaTJk6r0xl8zjsbvf0x4oYLGldfBXzMuvZ54naaqjMSgdydL3axIkKEtbylAUIBo3SrPf0mPXdR1s5Wxs0WGqbal2Z0gADQzqGzZllynE4Zo9wfqPbbR1kwmsuGvGUf1db/dYxf1fupK4wr7mkuiXqC/Lq+vDdYIviaq4wnDi3tss7yvJQcvgJ19rV7x58mMJ7bVXv87RrYZHyJoyvbCMMRs3BfZWLFafU0Gg8FgLEksafKkE2WovuTX0n8P3fgg6hdvV15PvI4yANKUkGizAWvGUXt9voCW3CNTYM6Tvz4KEquveAuGb3pIu26D5L4F9emxqq/IXzOOxpXXCy/42jUDun0opMwTkGs+lr9mHI3//lfCC5SAluZr1GyA6n5Wn/nCyKzTt5LuJ5kM1zTZHU1fkb9mHJVzLhReMHgOiD0yefrr8voaad1E9becpLP6S69L/13uvqb3tcqzfhEA4G/aYuZrmvupG8GQ4MnZOdRnjgEAhlcxeWIwGAzGfCxp8pSo7UmlrROVrMYAKqPrtNer+l56oqosI9H07uhmPAFCkLH5bKMgQ1veQgy2lfOK4gDFP+l0bcDYa1t21iYFOYuS7db1i7fDGz85+pkf/ox2zXpsoxAUEMq8JOtmGtCKvqG8pxYUCtNZSsevNbqfRbMBlFlK/vgpAIDqL/+6GVEv2CvW1AyirV+8HRjbGL3nms8a+priOah1hUvkvUWadRNmnZnua8p1o+5ryr0j2nO9pxF9jVLqCHSfUc2Q3AcPH8VIaxoAUB1Zpf35DAaDwVh6WOLkSR04homcNWGGDBDJRpucHmtPtVVBxqxhkFFCuVKCZN28xqDWLhPbyENVVUQgDnr9tetpthGCM8/3gWot+iJj3SgSzal0+sAgKaD1PQ/1mCS6zga4up/Q9KFQDhESAuGffIYbYqcTTlHIqHudiAT5azdo7QKIvuZ55GyifMB2cihEu5/W9zXC/fSIe67xvqbJPD14+BhGWrG/L+OeJwaDwWDMx5ImTyFxOCg1aBSvpVRns0FQ8gYZuvIWCwEQDEmnObGTZAM0c7t6bLNNBBTBdjsIEWh8LV1vy76WrlmRbICQgbVmFwRfK5IVy0ns9BlYe7ZZ9zXF/mEy9Ji6d4jXUmVgbexrXV9zc/CiGicAAA88eRQrmDwxGAwGQ4ElTZ4SSP+Yz3azAabXKtKHolIB7L7JdWBm3leUICECHnHd6H0L6nlK3eGg9rIolCb+yDZ5cNYkzO0yJQHitShiFlayAdQ1s5QNoMx5EkvQKKD3PGmyOybPgaltBeadiZ9ddPuaQZaTahv1+dTZliDKPEVle0yeGAwGg5EFJk9QlLfkOaGllN7oTrVTEiC/PaFpNsC0NyCnOhsAY2JHnwtEPNkm9GOZBtta5ThFcKaTaAbMS6kAUxn1xZdJTImdrK+IcD+NSwrJzwFNRl2aSQxD989Bxro1RV+T9P6la1b2vqbpSQRgnBWj3k+dbQkePHwUI+34vjF5YjAYDEYGljx5qvnZsrkAjEtIAEPFrCI9T4bZgMSuuU6QlpBlwUa5Unoibhhs60qp0p6KnMpxYacDtFsA3AXbWeuWfFbpa4ZZCoB46i74WtaMHpNMIjUbQPU1G31Fzogd0dekhwjtVjrryTQDSyXqqsxT1fdQyZgRB0A4QFigfW0ByvZ0tiW499Ak9zwxGAwGQ4klT55ojfKWA9pU+WkuM6B10U+R2AX0nk7Pg40AKFk307I9orS1dOCrTmAgWTPAvMyrgJiFi/spXk+lUugJSoAJccyyTZkNyNlXJF4/E8R+LJslouQyrxotAysvwxQ+Zz1jJ8+KGQm6uCrbK7Cvme654ryzVkc+70ynBAgA//cHe/CQULb3b/uPkGxgMBgMxtICkyeCRLPtUqqekhRFmZeLfgqdbdDM3qFkKtJ1syxmoZO21vWh9JzUW+55UinHuVC0E69HEowAChC7fPdTvH4WtP1YpJ4nt5mKvLalZZiiOp5l2zL3DgfPJ9U23b5GmRFnuufamkEFAO/+8jcBIM08/cG37sPE5BTJDgaDwWAsHSx58qQ6cTftKwKIpTfi9TIFBuyrxlV9H5VkBhUhyND3POnXjZwNMDxxz1u2l65ZrR7JixNAmr0DtZoXKbuTx9dIPU/iDKpyMhWirxXpx0r7ilQ9Tzl7ZLT3UyOjrhV1aXbvpycr1ey3zTADm0UESGvmuhwZUGbFVP2SpntHg3oopPE1AAhDwA8DjLSj9zxZHcR9hyZJdjAYDAZj6WDJkyfScFDbQUYyEwjZRIAUZOQoKaSV3lgQGMiZedL2PGnELJo6ktJ0RFAA5bq5ULQTr6edC5QMVi2aDbCcqUh7dwrMeTJVjrM1wFf3jJr2/QEGKoUK20hrlqcc2eK+ZlOchDrvTOdrAOABWN7qrulUfQibVo+Q7GAwGAzG0gGTJ0d9KNqAtnCPjJumb48Y0DoZqqqVj5avWRiGWiLQLXW0TIYBktqebV8brBHV2Qi+RsoGWPe14kTddJC1aV9RbuGUAmWYuqwYFBlYo8MN2/PrLOxroSvbCJmnt2/dnPY7zfpVXPvLv4jxkWGyHQwGg8FYGmDyZLGvSLye80yFo7lAOsEIs5JCRwpoWRLNBrOUrJcrQQi2s0qpCGuWq0TUgkyzGbGznKnQiZMY9O/YFidRkWHSIFpXs5QgiIBk3U9Kf50jMgxAua+R+iUNy/bIthHU9s5Z+7RUpry+YhW2bz2VbAODwWAwlg6WPHmyOa8IsCOFTAoyDOcVkW0jz2qRZHfabSA+ObcvBy5X20t6PQB9E79ZuZJhdidnr0eRElG9tLVCYGAhSwqT3h3NnCepr4WhO2lrFRlu64l6rhlxFmS3dWsGQMg85ejl1JTWqvY1k3Jk2+WOOl8DgGPtVioWUVm+kvzzGQwGg7G0sOTJEym74yIAUkghGwUZtgNahZKX+Fnpuom/Tw5FuyyJ49Q2QgYFiOYpZaJAqSN59k6JZXumWZSsdTPJBuQTTlGUoBWVxRfWmvqMimRY6WsEMgxQ+uscZjkXW9YaIO1rC9JnSsg8Tc21sSIu2+MZTwwGg8GQgckTJbtjUEKSnoJq+ndU0rlGs5TynNCqbBOCnzyzWnrlwM3K9oIwRCtQzGoh9lPI1M1cEU7AwuydWfOsmM0ZVEr1xCJ9KC7nAolrbVi2FwJqXyP0sKkG0eYpjSNnOYv21xUgw873tQLljqp5Z92ZYnMIJff92FybB+QyGAwGQwsmT5RmdJe9ARllJEaCEZZ7A9JAOwjS8rueH6vLVBSQA9fZphp0aTITyGkPW2bPk+Mmfue+5lgwAsg1HyvtE/O8HqU3il2AugStcJ9YkdI4aklhzkxiLjJsqjqp6Et0lu1XCkYIs7Zac5nvOTbXYvLEYDAYDC2WPHlS9zw57FtQ9e+4HqpKkSoHlMG2VAo5Ry+WeA/yDrrUypT32JYjk0hs4s/qqTCTnndoW2FlR8vPAXkukOSeCiSAOkupLlwrb5kXSQDEYWlc0UyiKzIMQLmvkWxzRew0vgYAx1rdsj0sZ/LEYDAYjGwsefJE6kOxXUIC2sk2KcjIQezU5S3CUFVVsC2db2MemHmel/6ueWdQue710MpHF5WeL1JKVUgBbQGJQMG5QKYDVYHI10i2qbKcFFEGV4NoibYtCBmGhX4sV2p71VqUoUT2vgYAU3OtVKqcM08MBoPBkIHJE0VlzHIJCQBBnS2nAlqRIENV3uL73aC2LwAKwhBzumA7h1oWQJxZVFiUwV3QqCqlcq1oRyV2KjJsW50t7d/JOReIMrcrr6+RnoOCvTthgbI9slJnXttcZp4K7muuBjJ7nqfsFQO454nBYDAYNCwIeZqZmcHVV1+NrVu3YmxsDKOjoxgdHcWpp56KN73pTbj//vvJ19q9ezdOP/103HvvvblsoWUDHPQG1IqWUpkHjmTbJAHtHGGWUjoby4DUiddT9XuoRBmSQNt2aZz57J280vMOxSwKZCrySM+b2CbLoiQkXWlbjhIvsm0L0CdGFZspmknMs3dQS0RV+5p2iHUQdJ+fkmdQAcBUq40VTJ4YDAaDoUF1IX7oG97wBqxduxZf/OIXMTY2lvYrTE1N4fOf/zwuuOACfOMb38DY2JjyOj/+8Y/xile8AjMzM5iZmclli1JlzKHAgEoKmRRkJE3PjoLtEEfn9e/0SDRXNRLNptkAk76FMgmnhcCMpGjncKhqoXVrCs+Vg3XzGgMIjx2ZF2z3yoFLbMuhUAgQ5wIp+sScy7trZilR1PbUhwjmBxw29jWtbcJau9pzw6Pysr1jrTaX7TEYDAZDi9IzT9/5zncwMTGBv/mbv8GJJ57Y0+g9PDyMt771rXjve9+Lj3zkI8rr3HvvvXj5y1+Ov/u7v8Ppp5+e254FV0DLEwCJQUaJwXbyOQ/yWUouswGqQZe0ciXzoDEpP9MpoC2E9LyNPhStol0O6XnxenoikB1si79TnSAYYQIjaeu8c9hczSuCBSXAMlRE85Tt5ZgRBxhIvCsysEDc89SO14bJE4PBYDAkKJ083X333Tj//PPhK2SsL7roItx9993K6/i+j0996lN46UtfWsieBVPMUgwuNQoyHGZR+m2jzFJCjl4ssm1KBTS3QeNcJ0CgGKpauMwrBxGg9BVFthXIVKTZugZZeh4w6P2T9LGZzO0yeT7Jtgm9O/0zqFwTlFYQoGNh3pkMLns5i+xr6WiIahVelV4UYaNXDOCeJwaDwWDQUHrZ3sTEBNauXat8z9jYGPbu3at8z+bNm7F58+bC9tgWGKAPuswfAOUNMqi2efUGQmBesE3qp8jR8A0AgzWTPpRo0KUYzJvIgechnEAkh54IW8y3TVHm1SYo2hUQAMmb3QEMiLqLMkxEAXyYYVtChpWKkzlK4wDqDKqYcIYh0G4BtXr3x5oIuuTI1gHRszYkI6u1bMIZfc5g3RyoiBaadzZr/nz22Fa450lQ22OpcgaDwWBIUDp56nQ6qKiUvQBUq1W0dQpiBmg2m2g2u4HG5ORk+m9SsO1QlSpX5qlgkJGX2GlnPKFANqCiV46bN+hSCExN1PbyEJTkZ8jIk0rMYrajJ3Yu+7GKlBTmIXUmtsmyYq4UCgEisRPvQ3O2hzy5Hnqc2DYk8zVCXxElo+4ka03Y12TELvf9LOhrCY7NdYTM00ojGxgMBoOxdGClbO+GG27AypUrlf/dcMMNNn5ULlx99dVYsWJF+t+6devS79EUs1z0oeRXZ3MdZHiSAEg34wlArmwd2TbFoEtXZXu1io9KXDZGsi2v9LzDHhlVH4p23fJmnkz7dyRE3bYACNk2gSzJSgopYjMmvlb1fVR9gq8VnHeWfM6JYAShRFQrTuJqX1P0igFAp9PGSDv+HmeeGAwGgyGBFfL05je/GYcPH1b+9+Y3vznzs0eOHMGmTZuwa9cuG6Zk4oorrsCRI0fS//bt25d+T0pQwlAgAnlm79hQZ3MTZNBty5ENKNqHknPQpUlAa9LrIdqmlFFPS6lyBrRF1PbIfUW9trWDAO0g6ueRZgNy3k9SGSYgkM5+MkwZ3ht/xjArRpkp5vl+l0Apev+kyKkESCmP8xTzzrRKnWGYbwSDBRl1apbTycGLeF1Jz9PyVvf1cOqokQ0MBoPBWDpYEKlyEStWrMB9993n9Gc0Gg00Go3M70kDoNZc1O8At1Ll/SSAMog2dJndAYTylr6A1mE2gNKPlQ66bM7mInbIGWwPVCuYarWdiVmEnU7UVwO4KduTlGE2hc9pS0RNfY06U0wjGEEpdXRStgdEv3NrTlq+artsL7nmsbk2Tcwiz9DjZMQBDAUjqD1skn2NNPS4aBmmjtgpesUA4M0Pfy399/SrT0XjyutRv3i7kS0MBoPBeOqjdLU9Sj9Tu91G1UAIoQjk84ryyeYWHQ5KCmhdZnegKKVKendka4b82YCiGbsFz4pRpOdl6yb8LmYy6sWyO71zu9Qloq6ynLJesTLK9rS2SaTxaVkxh8SuiFS5OLfLxZgDyb7WDsJUrVJG7MIcGTET21S9YgDw//1oZ/eLIEDzg5chODhhZAuDwWAwnvoonTyNj4/jwIEDyvfs378f69evL8UeWf9O2rPgeT39D9rrxX/IO2GIdg654WZHT55KK2/J1fNUrDQu78l2EtDaFmUADIeqqgJaXdBoaJsoo66StvYkJ+7JmlV9D1WZsluOvj/RNrqv5SkRNe/dyWWbtESUUIbpUgkwR19R6mueF5XBGtrVDvLta7NGh0KGzye1RFSjtjfPqqCDYJ/bqggGg8Fg/PyhdPK0ZcsW3HbbbQgUf4BvvfVWbNmypRR7tI3yjQH5TCPN9dQBkFqUwQPS5nGZbU7m2wCk2TtS5OgT67GNGgC18gfb+bNiioxpEQW0VHq+Bk+jRJllF9AlQia20Urj3N5PWa+YkQCIY9VJ1bwzGfL3ihHGCQgkoH8GFVkApDFotK+JKpPNAvsaoFDrdDhgO7JNXbY37wnyK/DXbTKyhcFgMBhPfZROnp797Gdjw4YNeOc734lHHnmk54//9PQ0brjhBvzlX/4l3v/+95dijz67k+8POaALgPQSzdLgJq9ghMHsnci2/h4Zg3IlR9kATzJPyaTMy0XZXrpm7XbUwyT+WF2ZV0FSp7MNBRTtnJY6AoWyO64zT7IMj+ueJ0AjTpKsWRAAfYRet255pedFQZE8+1pC6uoVH75s6PFsTl8j99epBSP+4aTzul/4FTSuvA7+mnEjWxgMBoPx1Efp5AkAPvWpT2Hjxo14xStegbGxMYyOjmJ0dBTPeMYzcNNNN+G2227D2NhY+v57770XJ598Mnbv3p15vVWrVmFoaCiXLdJT0JzlZ77noR4HGkUyT6RyJdflLdKeJ0LQ6KA0rue6sqGqlufbANSeJ0GQRCZ+IBUAyRc0Vn2ajHpKAnJk65xKzwPSHhmjnifXWc4cWbE8Q3IBs54nAObrlvP5rPg+an7xfY1UVptXoVA3G1AxgwoAvn3cKQAA//StGL7pIRaLYDAYDEYmFkRtb2BgAFdccQWuuOIK0vtPO+00PPDAA9Lv79y5U/o9rS1SOfB82QAg+mM+1wlyqbNRmtHdz0PJbpSn9DzlPj2mCkboemQIJWhOMhViMDo3Cwx2ybzWtpykLrEtrxIgiQQUzTxpSkRlPWyuhr0CdKLenXdmPufJ6TMqEPWwOQtvaNk82+RKnfnsSq7ZmgvUz6gmk+hk6LGFGVQAMNCOlAi9E07kjBODwWAwpFiQzNNigi7IMP1DLl5TPYRTfeKuLldyK8pQJAASe8Vc2CbrkdEGjWFYWEZdWUpVrQLxPTMlAnlLHQFaNrFQNmChM4lKglKMDGuJevKMmvpaEHQlwV2UiPp+V+zBdN1yZsSothXKJLoWwtEIRgx1onEB3kC+KgYGg8FgLA0webJc3gJ0MwyUvoV8vTs5e2QIdgGQNspT5jwtVI+M1rZ2qzu3y5U6W0bg2A6CVKJZ2/OUh6in95QiZpFDZCNnj4zpTLE84iROyzCB/IcIwvudldZm3FNx6DFFMMIUJGInVcOkZ55cS5X325ZeJ8hHeBkMBoOxtLDkyZO0DyVn+RlgOheoQLlSgSCjX6VLhCcLtik9T45Pj7tZFJlt6hP36E1uiZ0YnJFmKRUspdLaljNbB6D4LKVWW+1rsv46gyyn83lnEol30iylnOIH5BlUwj2lzIhznlHX7GuUUkdnBy+SrFiCwU5ctseZJwaDwWAowORJ9sc8Z/MyYNa3MD8wo2ee8pbtBWGYnlBnQleupCopzHl6TC/zymdb3rldgHmwLdrWQ540vuaqlMor0vNUMBsQAmip5gIVEbNICLHjrJipxHu6ZtVqVM7pwraMe0qSA88pAEK2rciYg4IHL81OoCTqskxigoQ8gckTg8FgMBRY0uSp6vuoSIaD5m1eBgwD2gIntK6krYuVFBYbqqpVzMqbscs5twvIQ+y6gWPymZrC1wqV7ZlkA6T9dQpfy6saZzrvLMfQ48K9fzoxi7wS77P5snU9tpH7d7r3NFmziicfemwl86RYN92+5kIwYlAgqE2VbZJy5PQ6bc48MRgMBkOPJU2eBmQlXkDuU9DougYBbbN30KXR7B3D4KxBDGhlwTal58n5UFWJAloyg0pXhlmEoGhLqTKCM5d9YuJ1SVLlnQ5CgZy6nFckkh6SEmCeksKyspyGWTHnpXEQB77Ozzy5KMMEDGXU+/a1MlRE9bap1fY488RgMBgMCpY0eaoryFPeciXAMPMUhj2DLs1m75jZRp1BBdkgWl1fEUoQjEhKCiW26fpQTAmnkW1ZpVSENStSIpqcuhvJqCe2OSR2nuelGS1XZV65ZymZZndMyx0L7B3GKoUZZXuUGXF5RzDobNPta8pMYs77WfW9dPBuHtXJBEPc88RgMBgMApY0eVLPBMp/ekwKgBISAOQPaPNIW1NKb3SlcRRpa1cBrWTQJVkBzVHQCEAQZphftqfsE0uHHjvKivXNBUpAywbk75EZrOmJnczXSL1/BfuxZlrqElFP42vSnicLWWtd+WqWbbR+yQL7GkUJsMC+lvd+ep4n3FNKllMy54kzTwwGg8EggMmTDDYaq1U9FYUCWsc9FTklmsN2Oz1tztu3QO1DkUmVy/p3ipRSmQ5VNQ4aHavteZUKkPSFtOYTO1JfkbN+rOxSKi1BCYLuZxwN8NXZps1yOizbU/XXKed2Jc+Nq71Dsq+5zHL22Ka6pxIhnASDyZynQSZPDAaDwZBjSZMnleS2jT/kqpNtz/e7qm/GAW3xkkKlbUkGZV7Pk4FEc25pa/M+lDAMyQFtkaCRLGYhkmHdmiF/uZJ4XVdlXoUOESjzzjQDfF3MUiL17gDSeWf6nifHAiCCbWVmrSnrJtvXSFkxx32mujlPg53YXs48MRgMBkMBJk8yuM7uAF0iYBzQOg62ZZknzZynnqDE1ZynjACoFQRIWtN1AW0pTfwiGabMxnLcXxfZNp8IuJwpRrYt7yBakdA4FicRbWsHATraocfuyXCWEqBzwQiicIpqX1MeChV5DkzELDSCEdzzxGAwGAwVljZ5ovShOOyRyRvQOg+28/Y8JYFZrR6dQOewqxOGaCvnAs23LcnuiNeR2uay50mV3SGUxrkVGJhf5mXUI+PqEEEyuFRnW7pmlUruWUrtQONrGbaJg2j1M+JKKNszzO4s6n2tpBJRuWBEK74YkycGg8FgyLGkyZNSqtxG5knXU5HRwEwKgGYLlN6QBCNyKqBZIHXiz8mEotcDAOqaIbmFSADxfhoHjTbK9qi+lpkNUD0H5aizyWYpyQlKAUW7mjAXyNA2yiDaIlnOpPdPm91R2EaaEeeS2GXsay6H5FJtk82gSq/BmScGg8FgELCkyZOzviJqT0Vt/kkorenb8awWSZChm1lko3cHUPc9qU616xU/lSyehyLBdhLQavqxvKzsTkmZRL1txcr2nGWeNAN8XcxSEsmFWqVQ3ldU9eWDaItIz5tmd4x7npL3uyxHztjXSIdCBQaTkzKwQtmeOIMqvQar7TEYDAaDgKVNniglJC7LWxTZAFdBBm2oamxXu41QyGho+3cKkADyDKqMNaOVK5VYticGtISepzKGqoZetLbhwUe6ti2CEtGUcLbbZgN8CxCUiu+j5uebQUVRwyzjfia2BQ/vRnBwouczpCznAu1rC5kVS30tCHpmUKXXSDJPOX4+g8FgMJYOljR5osx5cnlCmzugdX2yLVw3mHiAbFuRoLHHNlVJYXyqHTz52LygkZKtczkkNwkaO1kBrco2x+VKczt3ILzv7uh9V/065nbuANAlAlI58FYLiO9FoZliVF97pOtrqW0OFO0As0OE8MnHDX3NvQBI8NC9AID2l/4ZU9s2YG7nDu39BIRDhEW2r/X4WqH5WITME4Bg4v553x7sxISKM08MBoPBUGBJkyd1NqB434I2oN3zw+h9//NtQkCrCTIKzFKi2ta66ZPpv6dfc3pqm14wIr+sNUALztrf2QUACB/8aRo0mkluO5ohA6DzwE+i/9/yOSPbXM63CQ5OoPnBS4UfFqL5wcsQHJwwU7RzJPHe62tPN/C1/GRYtE3pa9/+CgAg3Lsn1/10NSMuODiBzh1fEl4I0PzgZag8/ojWNtjIWitsk+1rLtUTe2xT7ms3pP+efu2ZqW394DlPDAaDwVBhSZOnhcruUAJaaXmL4yAjODiB5p++U3ghCsw6B/ZhTlOyVEQ1DtBnKoKDE2h96mOZtqnsEm3Lc6pNyaAEByfQ+doX59lW+9l+rW1FSuO0a7Z3T1Sm1PNiB8G++8iZxMg2R8+BxNfIc55yZMSotrU+8RfzbGsTfK2IahzJ1/buAfp7doIOBg88rLdtNn/5KuX5lO1runLHHl8TBu1SQfK1D/2W8EIQ2/bI/Ddz2R6DwWAwFFjS5KlekYgLwE5fkayEpFBAO2snoDW1rfnw7u6PlakUFiAoPbZJBvhGQeN827xHohIcZT+FFVEGeQZFFtAOHYwCWumawe1AZn/9ZqBf2MCvwF+3SU9QEl+rN+DJhDgItpkSu5bga85LRFW2ZfiaH/ua6yynytf89ZuB/vvhV/DYqjGtbem+5qBsj7KvSUsKkzWrN4zHHBSy7ZG+8r3GQK6fz2AwGIylgyX9V4LUh+KgsbpQQJuQgByzlIrYNrd247xrzIPjPpQoaJxv25GnrVfbBTvy7s1OkKnS1bVtfkB7YGUc0LrueZKUUvlrxtG48vqubZ6HxpXXwV8znpaISgPaAoSTZJvM18ZOSr/UEfU85YSAPoui8zVKX1GR3h2lr60ZR/WVb+2xq3HldXh8+fGRbZQRDA6y1oX2tYJZ69y2nXhK34W4ZI/BYDAYaixp8qTseXIo6asKaF2qjBnZlr7go3HldZhdvTYyFUiVyvpRRNa6xzYFEai/60PCC1HQOLnihJ7PZ8JCozzQ7UnLsq366u3zbHts2XFa20LHinb1i7ej+oq3AABqb3gP6hdv7/mMPruT835qZPF1vgYoxCwKDHsF9BlYf8046u/9iPBCDl8rOO9M5msAUH3ByyKzTnsmhm96CPWLt9MGMlsYJyDrYSuyrxX2NeohQvqCH9t2Ys/7eMYTg8FgMHRY0uRpIafd1y/enp4e1y55VxrQahXQCghZmNjmbTwtev+HPon6xdvRbHfVz6QlXEX7UAgzqGqv+o3030NffCCyjaIyVqivqDtUVWVb9UXbAADeKWemAa24blIUkcUnzhTzjlsT/UNYI3JfkWtf23Bq9P7Y18S+P7mvFcs80Wx7e/rvoX/d3UtQHInNUIdFp0F+rQ5/zXjP+51JzxN8rXdfe7ewrzn2NaJt3unPjH7MldentvVeiMkTg8FgMNRY0uRJSlDCsJByHFXS108D2vnB+UKVtyTwlq2I/h8HE7MGs5TyErvBml4J0BscTv/trzy+5/2u5ttUfS8dvqsc4Bvb5lWq5IA2DEOht8ihjHoSFM5Op6+RswF5S+MI9xMAvOUro//H60ebpeT+EEEMpP2RVbFtbkcJ1PzuoGelbclzINzPhKhL72cQdAc4O5RR9xVEXdaXWHzvINo2clz8c7LvDWeeGAwGg6EDk6csCEMxXUn6Rm+MA6CZqfSl0spbdAFtHJyFcXBmdKpdMBugIihiQBvG62YkH53DNs/zjILtUCQoSbAt87XWXPfn5MmKUQlKcj8FX+v2PMn6itwq2slsSwNtCkFxaJtXqwNx1rH/OVCJkxQhAqKvKYl64mvi3qEbyCwMrS1Stqcd4JuDqBfp+zOxzRteDgAIp45KLsTkicFgMBhqLGnyNKBrRgecZp6SeSKhSZBhq7xFR+wkAS2pn6Jo2Z5qSK7vd+/JbGQbKRtQQD1RvLbStkFzMtzjawXELJTDQYFuUJiLqBe7n2Tb+om6o2GvgMkBR++6Gc0UK0rsVLZlZJ6061bCLCUgm6hrfc3S86maKQYAGIrIE6azyRNnnhgMBoOhw5ImT3UJeUpKguD7QLVmfF3KrBYAmZkn7ZDcoipjxPIWry9obKbZAIKSl8M+FEAMzvqzAW7UE6m2eRmZJ926pdLznpfL18g9T32ZxHYQoB1Eam5aZcfS7qc5Qcld5kUudzTPwFojdhRfy0GGUanAE/r4bNoVvXF+5klbilm0HJn6HGgyT0yeGAwGg6HDkiZPlD/kRefbyOSGATHYjgKgIAzTQbS6eShFVca0AVCOsr0iogxGtqXBmXnZXu7+HZOSwoxSKi1Bcexr/bY1hTXWzhRz7Wt9RKBLON30FYm2qeYpAZiXgaX0YxURmwGIREDIPCX3XbtuFgVASPtavGZhGGoPOMKyyvbizFMoyTzxgFwGg8Fg6LCkyZNc0c5OXxGglhtOA6A4g0IJaG0JRujKW/ozTxTBCBSQQQa6WTGtbZLMkyuVMfHaJDGLdgthq0WyrXCpo3DdhHirbEvIcFN4rzzYtiQ9T8w8pWV7FHESW2WYhhlYUpazYNkeJTuc2iWIQOjWzdZg4RBAq3/grIi++9kOQgQhLcvp+n5iaFn0/+lj2d8f5MwTg8FgMNRY0uRJ2htg6YQWoJZ5Tc17rysFtLxlXpQ+lMIEhWhbf7mjVvgA9kiKsg9FLPmhrpulEi/xZ2Wirx8reW/F81CVDVsuOZNo0l/XFRhweD+BfBlYS6RT2StWxNfyHm4QJfv7M0+Ufa00FVEu22MwGAxGQSxp8iQ7cS/as1DzfSQFWCZywwkJ8D0PVd/RLKWc/ViUeUXWhuQSswH9pNPVkFyASOzqjahPDkKGR7NuYcGgsV6h+Vp/PxaJBBTMJObOPJkQFEcDfFPbDAUjojEH7vt3vFot7ZGj9oqFBQ+F6sLhRJ59DVA8B2X5mq5sj8kTg8FgMDRg8pSFgn1FVGnrVG1vXkmQfDhoaQRlMA9BKYnY9ZU7aoPGIOhKghecI6PqkfE8b746G3E4qA1fU5Y7SjJPLksd6aIMsp4nxfZUUlZsfrmjZt3a7aiUDuX1JaYkRbduBfcO8r4myTyJM6zmoawsZ5x5AmeeGAwGg5ETS5o8aaXKcwYZ0bUJf8wHzE/cyyul6iMoJQzJzZt50mbFCs63MbLNtMyrJF+bt2a6mUCwlw3QSpXn6XmyVbZHJurzDzgyIUrPl5WBNSTqeQ83xGsr72mRTGJuQRfivDPOPDEYDAajIJY0eWr4kkxFweZlwExgwGjYq63eHTIJMOhDScuCigVAuYNtXa8H4L5HxnBmUVHC2WMbKaCdQRgE5fSwmcqBm/Q8pc+o42DbsNwx7PE1t+RJ2o+1wL6Wp0TU2igB3fOZ9DxJBCM488RgMBgMHZY2edJNu7dwQqvOPMV/qJuzCIPASAbZ9am2TNqa0vNUWikVtdcjIQHVaq75NkAOoQ3qulnMBpDU2QCgOWM2r8h5dqeXcCaZRNJzUFh1Ml/mST/EeiCX9DwgyOJTFTGp62Yjy2kqox4EpCHWtsYcKEcJAPAGE7W9KPPU6VMNZPLEYDAYDB2YPGWgaAkJQDvZThvlAWB2miSDXDTISOxqdgL1rJYcKmNlSVvL5gJJbZstHjQO1miZCjHz1A4CdDQSzaVlnoSgMJyZNsoGFC3bawXBvCBVhCzzVEZJoXE/lmYOW1GxGRPbxBlUnSBI5cPl+1qx/jqqbb1EfVZf6giUJ4vfp7Y31d/DyOSJwWAwGBosafJEGVxa9NrKk20h8AtnpkoNaAHNDKo8c55KEoyQzQWSNcrbLMPUn2x3A1ojiWYbRF1xPz3f7/rb7JTQ8+RelAHonSs1D2nmycDXyur9M+3fKYsMozfzJK7vQvfX9RJ14r5mae/ohCHaqhlUcc8TZqcRdjqYmuslT5x5YjAYDIYOS5o8yU5Ci067B4gntL7fk6koUzACUBOBPHOeijd958s8kUUZbAS0Bj1P4u8h9TWbTfxkYjddyv0kzzuTDT2m2FaWOIlp2Z7rkl+YE/U08+S656mHqNP2NVv3U2tborYHANPHcGyu1XchJk8MBoPBUGNJk6eKZjhomafH1BPaokFG1fdSuWBK38JCyEfr+lD6M09pr4eslMoGGc7R85SsWdX35L5WYqaiq+5YTjag6vvpvDJSpmKW3l9XdOCrq8yTjZJf477E2el0zXzV0ONmSb2c6HsONKWOPbblXLcGdQZVvQHEmdpw+iiOtXrJE2eeGAwGg6HDkiZPUtjMVBgEZ2kplao3oCARMJ3VQh4O2moBsf2lyYFTe2RKJMMQlMaMRBlKCLa7c8UMswGuZdQN++vCdjuap4T895Q8g6r/ECF9Rt1lOamqk1mZp8Xia6YZ9aL9WBXfRy0mjUpf8zwgFo0Ip4/h2FzfeweZPDEYDAZDDSZPGeiWt1gIMjRlXsYBkE0ioLItIXVzTYSdjr4PJck6ASXMoOqTA9fYVnQmECAOySVmxWamjPrErDTxa0sKu7aRsgFWe/8UynFpRiyWUTfytaIzqNSKdqYlomWJMoi2YXbazNcKETs3GfUyD6zS0r3po5jqK9srsjYMBoPBWBpg8pQFG3/IiWVexj1PBcuVxOvnUQLUzZApYpsrqfJuaZx7MpyZeaKUK5Xga+KgXJOZYq7LHT3xtF+UUZf6WnlEfZ44CbW/rpRMotnBi9W9Q/ccCOtWVpYzOeAgD8qdOopjc6y2x2AwGAwzMHnKQGgz2LZeemMhG0AJthsDQNwbFfXvqGWQ0/6YeqPwfJvcM6hkJMVGBsWQoICyZig3aOzOLDITJ3Fe7ihcX+wVk/bXJSSgVo/ECVzZBeTIPJVY8pvR86Qq+bWrOkmbQRX1PGmeT8CO0Ab1wEqQK5/inicGg8FgGILJUxYsEBRyQJshN0yapeTYNs/zzLJiFoNG7Vwg0x6Z2fJEGUTbTKTnS1HbM8w8lVVKFalOxj/DyNeKH25Q552l4iS6vsQSy/aMM08Wylfz9HKWMZBZvL62VyyRK58+imM854nBYDAYhmDylIFym/jnB0AkVaoysmJi34Kur8hitk5rm9BXRBlEa2PN0p4nYo8MteeptB42IFucRKGeWHQQbY9tWl8TngOtr9lbM0A970x8PttBgHZAG3pcbG6Xm54nqxl1XS9nzp6nUnxtqCsY0T/nqcgewWAwGIylAefkaWZmBldffTW2bt2KsbExjI6OYnR0FKeeeire9KY34f7779deo9ls4o/+6I+wadOm9POjo6MYGxvD85//fHzxi1+0a7SN0+MK7RQUxlLlFgJHqm1Zp8dSlbHi2boGkTx5Ql9RU3ifdJZSmdLzWZknhxLN4vVzNfHL+orC0KpghEkppk563mZfkda2Hru67ytjSK6J2l66Zg5nxAEGpXEGmacwDK3sH2RfEzNPInkaGMxdcsxgMBiMpYOq6x/whje8AWvXrsUXv/hFjI2NpX+cpqam8PnPfx4XXHABvvGNb2BsbEx6jQ9/+MPYs2cPvvWtb2H16tXp62EY4p577sHrXvc6jI+P45nPfKYVm8vsW/CMg4xys2Ih+vtQNJmnAnYlc4HaQUjOPM0KZTfS3qIS5zxl9qFQRBnKUGf7/9s78/ioynv/f85smWSyAIokISERAogsCkpbq73FpVihSsGtClVb3LByFb0WqVX6a70VW7lci1q1l3pFXFoVhCraBUG9KigIyiI7CCEQ2bJNklnOOb8/zjLnzJxtZs4SyPf9evmSzJyZfPM8z5n5fp7v8mSTSpVMAGLqZD7RAKnttuW1prBNf63lv7kR9PngYxhwvPFak+/PZAKdnalGFaa1f25327MQtXbr8G8gbYPDLNUxHku9zo3PNWXNU1mq5onqnQiCIAgrOCqe1q5di/r6eixevBi+tMLuSCSCG264AUeOHMGjjz6Kxx9/XPd9li9fjoULF6qEEyDU5QwfPhwzZszAyy+/bJt4gg1CIJeC9JhZ6k0ymXJo3XTOrETFbHAaAcHZbo0nDHfdZSeH59HZLhTyB3z6h4N6IYYtpyt5ERXLZj4BV+pQsltr+c+ndN5ZeyJpXCumcKhj0TYAgN/gIFo3D8ntymtN2Rbf8nwCrohOudteeuSJ2pQTBEEQFnA0bW/jxo0YM2ZMhnBScskll2Djxo2G7zN58mQMGDBA9/nq6mrs378/ZzszcHH3WCvyZHoAJ+CuELBQU2FHrYdl2xQObVx0aLuO05jt2Ts21MhkWY/FK5qTmEUSBdsKcrbthFhrRvU7oQJA/PyytNbsFOq5tMXvAg1AAMWBzFYi6tLnrc8HBIKO2yZ120Natz2GDsglCIIgLOCoeKqvr0dFRYXhNZWVldi3b5/hNf/+7/+OQEA/SLZp0ybU1dXlZKMWbu4eI4vdY7cdWlV6nJlzZoNAsWobEwgAwZDwa6OtAEyabNiQrlQYFNZfNnUoVmqe5KYMLrRoZrTm07SGLZxXHUguay1mMSqWz5hZtY1hGNm2eFursV2wJ6XQ8kG0yjFjs1hrLqTt5RR5Ctm01syaWSgaRqTXPBEEQRCEGY6m7bEsC7+RUwsgEAggabZjbsDq1asxf/58vPfee7rXxGIxxGKpvPqWlhbjN3WxUJ7Jqh24uw6tujGDsFR0mzLYULuTjW0ojACJOOLRqOp1mrbZUcSfV82TwR6FB00ZshLqLohhQL3WOpPCa/TbgdsUecqi0QYfbUW8PYvIkw0bL2ZnKWlFnpxea9Zr2DSinCYHbOcrhi2LTp20PaaAIk8EQRCEOVlHnhYtWoQePXoY/rdo0SInbFXR0dGB++67DxMnTsQLL7yA/v376177yCOPoKysTP6vurra8L1dbdGs7JjVlVLjAEVUrM38DCobonXZ2CaNWyJqHg2wW6BYORfIevdE98+gQmc7WjrjAIC2eEL7YimS6PZaa28zF3YuRjmVtiXbzaOcdtfXWTqDKtu15kotZw71dTbNp6noVKTttSkPyaXIE0EQBGGBrMXTlClT0NTUZPjflClTNF/b3NyMuro6rFq1Ki+j//GPf+Dss89GY2Mj1q9fjzFjxhheP2vWLDQ3N8v/mdZH2bJDm13dAjrb0Sw5tLG4oV35Oo1WbZOiAUmx1gMAjnbEtC+2YcyUtlltMMCK0YAky6G+Jap5qZ1iGADirP4Bvsr5/Doq/N6E0fUenNv19ZGjWHvoKADgtuUfY8H67ZkX2x1JtLjW2PY2SHJBd625HOVMvw9YTn+t2XlILg/hwGgzu5CI40iLYJvR9bbWcprNZ6H1iLrbm0LKyFNUFXki8UQQBEGY4+ohuWVlZdi5c6ep2NHj+PHjuPbaazFz5kz8+c9/xsKFC1FeXm76uoKCApSWlqr+A4CFn+/MuJbnuFTrXBejAV8fPYpPDwoO7e1vr3bHobW44/7FvgPyQ8OeeUPTNjsESja2SeO2ZtdXAICvWqKomf+a9rjZIYaDKcfPqDGDHA0A8PwnQiOUBRt2aI9ZMil0UER+jpvVVCppPg8dPZayAYKAShcDdkQpgCzSHUXbPv8qtdZGPLtUe9xsss3yYbSibWt3C7WZhmvNzTOolGvtU2GtPbdhp/aYsazQfh42nUFl1KEQUEWemmMmUU67a9jMNqwiSvGksInS9giCIAgLOCqerNQzJZNJw2YQEjt27MD555+PYcOGYe3atTj//PPztu+uf3ySuYOsaNFsR6TCat3C10ePyg857tBatC1doAAAx2vbZlfkKdtowL++TAlgjueNxy0P24I+H6QqM0PbFL8jzApOo958ymOW9rpsyfZQ1aKkOqLD8jx2HkurA7QplUoSnVbn8+Nde+XH3FprVsdtxdZdCtucW2vKtEDDcQsVgBdrH03XmqpTpxvpyMJ8Hjt+HOsPCWL9lrc+Mhac+c6n5cYpQsMIIW1PEXmitD2CIAjCAo6Kp6qqKhw6dMjwmoaGBvTr18/wmkQigYkTJ2L27Nl48MEHTZtQWIXTchptdmitRlAKk+pUPScd2mwFihVn2+3IE7IZNxu67UnnApnZxvj94IJCJ8SIYtyMxixf27JtGFHEqufTzzCo61Wqti1m73yaRiqymE87DntV2mZ13LK6R/NwxJVrzWjcGIYBJ0ZLTNea8twuF8+vO3b8eMoGuLMpZPmQ3PY2tCvHlw7JJQiCICzgqHgaPnw4Vq5cCc4gD3/FihUYPny44fssXLgQZ555Jq699lpb7fMZOI3w+4WW2DmS7VktEQ8cWlPbpEgFq3YatWyzu+bJvKZCsM3SuLkcFZOFgGLcDMcsGAJjcBaaXXZJY9YLqd12P8PgmXHnoao0or7Y7jGzWPNkZT7dbIsv2JbFWrNbCFisLSpU2KY5ZtLnWiAIJo/NJ+t2Zd4DgHOCU2Wb2f0p1jyhsx0Mn/puopongiAIwgqOiqdzzjkHNTU1mDZtGg4cOKDqHNXe3o5FixZh3rx5mDlzpuH7rFu3DuPHj7fdvsfHfiPTabQrupPl2Ts9kLrO57RDa7lFs/D7h5WE5Mf0nG27i76tpjued0rKDl0hYEMqFWD9rCd/oVoI+BgY2uWaqBPHLJjoBMR7cd3UyzF15KCMS22L7mR5HwwvTZ1fZrrWXBPDwrh9q1cqMmG21twSAn7xzKKIGBXTX2vuChS9qLXxppBLtknd9gAUJ1MRuQ3NHVqXEwRBEIQKR895AoCXXnoJ8+bNw/jx49HY2CgLqJKSEowePRorV65EZWWlfP22bdtw2WWX4Z133sGgQYJTd+zYMcyYMcNQZA0aNAjvv/9+VrbdcFbmwbpuf5FLUYpgvENwaBkGa3/6A4ysOCXTtk5vnMbePuG6AT2LserHl2U6ZoCrB5cCKWe7OiTUfIyvq8LTWk4jYEvaHpCdEOABnFUWxnoIzqyRQHG7yQY4DgVcEjF/EINOKdW+2KN24KeJa622LIIPbhxnOJ+ujZu4idDXwlqTD8l1bYOjCDyAkT3C+BTAU9//lvZas+tzLcsGICV8qiGDqeDMuwGIxcYpoQLw/gAYNolIohOt4sOv7z6MQS1R7TVHEARBECKOi6dwOIxZs2Zh1qxZlq4fPHgwdu/erXrslVdeccI0bWxoHQ2kHLMkxyPJcQjopGXJ7YZ5HmEugU5/SN+htTttz6LTiI52AEB5pEjfsfAoisJ3tAGFwMBepbq2uX4ukBThiXcCYaCmrET7OpvGTEp1ZHnjtaas6YgkY4j7g6bndrldXycJoz7Fhfprze4UUauisyMKFAODTtFfa7Yd4JvL5kshUNOj2BW74iwHjufh0zmsW5pPH5tEgEsi6Qtg/S1XYPhpPTOu5d2OijEM2HAEgWgzihWRsag/iJ3HWkg8EQRBEIa42qr8RIC3uSkDAMSMvswVDm1RMgYGqfSwDFyv3RFsYzqFAu9ISF9ru1/rIR34KvzeYgPb7BbE1p3tdkPb7BZ1gEmDgUAACAopmIVsDMWhIBgd59erBiDoENZacTCoe6ntXSdNOoIqz+4ysk11zEGeQkDqUmhmmyRSpPnStc3myDBg8rmmaKMupRQONtsUsm0+TdYaAJ+YuhdhU2l7sUAos1aMIAiCINIg8ZSOzV/kgHl3NoRS3dkioYDubq5dDq3V9BZJBDAxSaDoO7R21VRYTr0Ro2KM5NDq2MYnEoAoxNyOPDExY9vsSicssHoukMK2omTcWHC6Hd2RIhXimBkJddfrdwrT15re5kYqiuF2uqPpuNmVGhe0uNYCQUBM8StkYwj6fAjpNKpwvYYNQKBYEEnKmqcfjTqTok4EQRCEKSSe0rHJMfP7fAiK6VPZdGczFChuR55EgeKXxJNeRAxOnEFlrcuY7DSaResA24SA1XOBzGyzy2n0MQxCfmtrTT7rSYw86eF6dKcwfa1ZsM1lMew3E8PKs5RcOlxYFp1mmwg2fXYEfD74xc0dw00hhpHnVNoU0sXleknBqBLZNokxgwfk9fsJgiCI7gGJpzTschqBLA7hVLRptiRQXDscVLArELcQefJa2Omlxqkc2gLNa+y3TXK2Ow1ts6spg8q2LNrP6wpOwLPubGbzKdjmTRqm5bXm94MxEH922pYpOnXGTYpa23AQbLafa4Vs3PBzzfU6MQCM2K68RBF5ynetEwRBEN0DEk/p2FTzBOR2UK7hDq3rh4OKxeiJGBieM655srujncXzbUyFnWRXqCCvs5SA7OdTalWuGw2wacyysk3RQtpIoNjdet5y4wOxjbpxVMzdM6gkMRwQxa6u6PTis0MWKMJaczptLxvbkG2U0635BMBILd4TKfHEFNAhuQRBEIQ5jnfbO9GQv8ht3KG16mRE2JjhjrXbh70yiqLvIrPdY9c72kldxqxFd9x0aKFwaBmk0v0ysMlpBLLvHFfExhC0UMPm2tk7Ul2R2HXSOCpmU0phlmdQBRPSWtNrymBf1Fqq/TNtfiDNp5h+ppsi6kSU02Ib9aJkHFwXqmEDIB+UG1Ee4kuH5BIEQRAWIPGUjgc7tMr0Fp+FdCXbOtqZORmKiEhh0qQey6ZUKrnLmMkhuZJDG0oIvzei22XMniYbgLLmyZptETGSqNfRzglhZ/Vw4SI2Dtbl1Die5/XHQtV10qSZhV3NSYIWm5NIay1uklJo0wG5QA6RRDaOwoAffr3oqgdRTmmDo4iNgXejXtLqGVRIHZSrbBhhx4YZQRAEcfJDaXtp2HXIJZBD5Clpkt7SaY9zJtklnQukB+PzyU5thDVL87I5UmGWtifaFUpIqXHGtR5eRZ6szKebUU5GEakwiu7Y3UadB5AwWmuKrpNup3mZNicR5zNsGnnyLuW3yKQpgyeRp8KUsLO28WJPe3dJqBvaVpQpnpQCniAIgiD0IPGUjk077kD2QkDotudeahyQXY2MbkoQz9t3qKrV3WPRaTR1aD1KVwLEyJMLhfIq2yzW7xSZRRJtEp3ZrDVZdBqMm2qtuXSmmLTWCsTzinTTVx1oNmNdqMcNOxTadX+qbLO81qxtvNi1KQQIh/gaIkaeesSj8kMUeSIIgiCsQOIpDbvqioDshUCRSeTJbqcxK9uMdo+TCUCMKrh1qKosOE1qPTxJV5Jbz7sTrQOyr98pMrXNnnTHAn8WQl1R+6drWzKZWmv5OttZtgMvSpoIdZsiw0AOzUnMBIqd94HlcbPw2QE4craeaWqt2DCid6xV8QYUeSIIgiDMIfGUjk0H0QLqNBIjlK3KDVOpbDoPJadzgYycs5gi9cXlw0GL2LjYnc35QnmrRfyMMrpjJRrgZnMSuUYmZmybTZsIDMNYTo9TRypcOEspy7UW5pLw8ZwrreetNgBRRp4MO3V6EIFFoXnUWmlbvmst6PNBqqgz/VwT0/ZOibco3iCU1+8nCIIgugckntJxIL3F3GlMtSp3I/Kkss2icxYxqEOx8ywlqYjf8hlUPIcQlzRoH+1BzZMiumNYh2LjfFptfpASw8bONu9F+qoqymkshu2wLZeuk5FkTB7rDDy4P1XROgsHC7vbCMda5Mmu2j+lUDe9R8W0vVNjbarXEwRBEIQZJJ7ScKRGxuKZReZNGexrbZ31GVRGh6rKhfLhvB2QbDt5AUAvhtXtMublWUqFVtOVbEzbs9za2izy5EHjFEYRFbOShpnvWrMaGUZBGLz4u3r5OPh0fq8XDUCUdWJWOhTaYZvliHqhxVpOL4SdGHk6tbPF8DqCIAiCSIfEUzoe1AbIkSfTom97Cquzss1C2p6tu9pW6ymCQfABYaxOZQzqGzxJV1LUoRg2jLBxPnNobe3GIblALmtNX3Q6YZdpdIdhwIsHqPa2sNY86bbHxnTb9QOwNwKb5eeaWS2nF01dpMhTiDdpLEEQBEEQaZB4SsOblr4Wi7492KGVnC3D1BsHxizOcuBM2g1z4u87lTH4Gzw+t8tKapyr3dksNyexL1KRy7lAbtQVpZ9BZQQnbqacYrDWPJnPLMWwnbaZn3eWss2w5smL6HBhcd6/iyAIguiekHhKxwuBYjXNK+6+Q5sssNBu2EbnR0oJAoBdx4xTaljRtl5GDq2NqVSFWdahmJ5X1Nok/L+jPW/bpPncdrQZ9S1R3eustMW3sx04ADnNbb+BXUBKoAitynXGzQFHm+N5JDlj8ZS0staONgr/58wPabVqW3YNXYzWWrPw/0771lp2kSedtcayQrdOAFzLcddskw7JJQiCIIhsIfGUBic7GcaOnhWkL/LtR1uMHVoLB5fyPC87jlzzsbxt84llG0Z2AUBCdJ6L2biq7bTKNsnR5jhwjfV52fXSpt3yv894egkWrN+ue21StK0HErrX8Me/Fv7PGu+SW0Gaz8PRTuNxU3Xb057P+JIF4DZ/CgCIPTIN8SUL8rJt21Fh3b68eQ9q5r+mP26KKKduNCCZAMRIDN+S31pbsH47thxpAgDcsPQDw/lMhFK1f0VBnbUmpcbZsNaUra13HW81uBJIhgTx3ZPXXkfxJQuQeGW+cO2bC/OeT0moN0Y7LK21CBtHJKA9ZvElC8BtXA0AiM35Wd62Wf5cU0XFtIVd4vVn5H93XDcyb9v8klBvNfn8LqLIE0EQBJEbJJ4UxJcsAL/1MwBA7De35v1F/qXoNL62da+xQ2sh9SbxmsLJmDI6L9sWrN+ODY3CLu/UNz80cWgFgVKGhG6BfnLVUgAAf/ArRMfV5GxbfUsU095eLf/M8cBtyz/WddASokPbg9feZY4vWYDEX54SbFz6XN7z+e7egwCAHcdbDOdTEsMhnkWxPzOiwTXWI/bwrakHeB6xh2/LWQzUt0Txzq4Dqffned1xs+TQKtZa+zUj8prPW5d/LP/Mw2Q+g0KnxlI+odsAJLnyDeG98lxrALBIIdSHPmMs1OPB1H2QjjyfUupfnvMJACvFtbb9mLW1BgA9NOqx7F5rAPDlEUGo//VLk881VXOSzM81rrEesUenKx7g8rJtwfrt2HS4CQBw49L/M5zPjhCd6UQQBEHkBoknEScc2jd3pF5r6NCapN5wjfWIzfmZ4oHcnYxsHdqY6DSWctrRHa6xXt5xz9e2HcdaMuqcWJ7HTp30vbgUeeINHFrY49DWt0TxX2s2p97fYD6Vh2320Ejh4vbtkA96TT3Igtu/MyfbdhxrQbpE0x03kwYgXGM9Yo/drXjAvfmMiWK4TGM+JdvsWmv1LVHcrrgPzIR6aq1pCBSb57O+JYp5n2xR2Gaw1hS1X2UamwhO2LZ8p8XPNUUnQK3aPztty/ZzLQofEox2pI4gCIIgjCDxJOKlQ8uozlJy1snI1qHtDIjRAD3xtG9Hasc9T9sG9irNaAPtZxjU9SrVvD4m2qblbDsxn+llMbrjFgyBZYRbq5SPZzzt6zcQSI+s+PzwVdflZNvAXqVIjwnqjRtn0gDEznHLfj6FQ0pLucwxk22zaa1lLezk+8D5+cxmrTF+P+LiuJW4ZFu2Ql3vjDhfv4EAY49t2c5nW4JFWyD/ujmCIAii+0HiScRLhzYpOrR6DSPsdDKydWg7RKexWMehFWxL+0tztK2qNIJnx52XehsGeGbceagqjWhe3ylGxYo1hJ0T8+lL+zP1xo1hGFl0lmgIO1+fKhT84o8quwp++Qx8fapysq2qNIKbzx6osktv3Dr8gqNdqHOWkp1rTZpPadgYGM9nR0CaT+26IjvXWrb3geFa61OFgl8+m3qA8eU1n9msNUCxwaG31h54WvFAfmstm881qQW/cASDxlrrU4Xg5LttsS3b+WyLJ9AWyO9Ab4IgCKJ7QuJJxNenCgU//4Pigfwd2jvOPUP+2dChFXeO9VKpfH2qELzxPltsy9ahbRcd2girI576VMF/wThbbAOAqSMH4ezyngCAZ8d9G1NHDtK9VhYoGrbZ7dBWlUbw32O/Kf9sNJ8A0CE621q2AUDgoknyv4uW7kBo4tSc7JKYMLgfAGBQr1LsnX6V7rhF/cKYRZJxhPyZt7+vTxWC196heCD/+bxVtOXWkYMM51O6D4rZmObzwlobb4tt2Qr1dmkTQWc+QxOnghFFXHjOy3nNZ1VpBP91yWj5Z9O1ZmJb8HtXy/8uemNr3rbdPmqwJdsS4j0QMegE6B/xLQCAr24YIsv35mxbtp9rbfEkRZ4IgiCInCDxpCDw3SuEf/j8KHprT94O7ZVn1AAA+pVGLDm0RWwcQZ1C+cBZ3wYAMLWD83IyAMGhvesbQwAAPx4+wNChjfqDsm16+CprBRvHTcnbNgDoXSjsWBfodA+TaJeEgE5ULDRxKlBeDQAIz12ct113nJNyGtdO/YGxEBAjPHq2QWrLXFQMf9XpedkFAKUFIfnfeg4jALSJdgV5FtA5p8d/puC4+84815b57CvaY9wMXH0f6OGrFO4pO9ba1JGD8M3KUwEAT176LcP5bDebTwBoFzr2+Wr038cq0xQbL+tvuSIv23hprYUL4c8x8qpkovi5dnqPYsPPNUlwGp13JtnGVJ6es0CXmDpykCzsbj7bWKi3xRNoC5J4IgiCILKHxJMCXmwBzvQ8FX7R6c6H0gJBeLA8b+jQRkXnJ8BzQMLYAfJV1ubtZAApB9vsINo2n+jQJrWjAUBq3PxDRtlimzRuLTH9FuRAatwiBrahTah58NUO1r/GIn6fT44MGp7HhZRDW5TUmc9W0Wks7Zm3XUBqzJpjBs49gFafwm6ddvxSe3JfdZ3L8ymKYZ3IE6BYa2eMtMW23hHBgQ5qROFUtvlS0WFNu3g+JQRKe+VtV8jvl1uClxgcfgsoo4k6tkmfayX2rjXO5HNNvgfYOPS2QeQxK8t/zACgb6lQP8qbSPW2RBKtFHkiCIIgcoDEkwL7HVrBeTBzGtUOrfYhlrJtdjlAIdG2uLFtbT7BcStMdupeIznbdjiNgNLZNhYCUbl+R0egsCzQJrRVtss5szqnbWbiqcXu+bQmUNo4Bkmxponv0BFPkrNt15hJtpmtNb+w1sI6YwbY72yXWLZNuK5QT6h3tssbH3YLYnPRKdqmdx+0OCPUTdeaLxUNlQ9dTsd226x9rrXGEpS2RxAEQeQEiScFktMIm0RAmehktMYThhGeNo5BXGyba+rQuuwAtYgOUEHCPBpgn0CxZpskOnUd2tYm+Z92iZQyC8KO5Ti0ibaFdaIoTjm0HUkWCZbTvS6aZOVIBTp0hLpHYlhaa+GEF0Ld2n0Q1ovuSKlxgYBtB7BaEcQ8z6PVZNxSm0I2i+FYQji8W4dWJhUx483WmtubCIkEoiSeCIIgiBwg8aTAKYcWEHLs9WiLJ9AuFsvrRp5s3nG36jQ2M5J4Mndo4XI0oIUJGtomi+FICZigcZqdVeRxM7CtPcHK9Vi6ttm81koUa63VZK1JjRl43bQ9byKwLRDn00KKKNy2TVxrugJFkRqnd5h0tpSFzaMonUlWTo/Ti9jZPZ+SXSzPoyOpfUA1ALQlOdk2/RRRj6Ji1DCCIAiCyBEST0ps3tUu8PvlBhBGX+Zt8aTsbOtGnuxO27MYDWgWI2LBuIF4arY7GmDNoW0WozshnaiY3bvagCLd0cC2aCIlhoMm0QC7RICyRsZoTpVrTTfy5FEkUZ5Pg7WGVps3EUJW7wOztWavXVZta4sn5cYMwbh2apzdgjMSDMhd7QxtSyTkxin6kSdvav/a4gmqeSIIgiBygsSTAru/yBmGseQ4tsWVToZbaXvWagOOi9GAYKITfPrhqRDrisT0OLsdWqMICqBwaPWcRiccWkvzaUGgSGNmp7CzuNaiorOtH3lyKDXOZD6bIKR5BeMdmulgPMfZ2pQhK9vEFLSA7loTBYoH8ylFdxi9uiKbazmtfq5F40l5relGnjyqM22LJ6nbHkEQBJETJJ4U2L3jDljbCY0mlE6G8Q6tXalxVmsDjkPR6UvLOWtrBkRH1+3UG9nZ1k2N82Y+lWJYbz7tLpQHgDILjmM0kTSPBjTbm4apjKAY1chIa83HsUBS42+ItgCigHd7rR3nxbVm1vjA1rVmdT4loW6WGueubW1xC2vNKTFsIfJEaXsEQRBELpB4UmB3ahyQRaQiYJy2B4fS9jqTLOKsft3CMT61RLQcIFnUFUbABEMZz+djm5kDdEx0tv06Dq3dLZrVthk7tFGT+bQ7yqmyzbDmKZlqGGEaebI3GpDkeHQa1Mgc5RVCXWPclOcVMQX2OL5WIxVHxWbbfpPIkxPzaSbU2+UaNndS4yzblkgompMY3we21bBZbGYh1DwV2PI7CYIgiO4FiScFjkSeLNTIKFNvdCNPNqftqRoMGNjWalL07Ux0x3zMeJ7HMV50aGPuNNkArM5n0jTylBLqPWy0zUqNjKJhhJYY5rhUGqZN0YBISFkjoz9uLRyQkLpOaoyb3bV1gLUxS7Cc3DDCZypQnLDNaoqoO2IYsL4p1GEg7FQpvzZHEnkImxi6tiUSaAsU2vI7CYIgiO4FiScFjkYDDHdojRtG8DyfcrZtEgIBnw9FwYBoW+71WE44tNKhoEYRlI4kK0d3fHqRJ5trdwCL86msKzKLPHkR5TSKPDmQGudjGFmsG0fFEohKXSc1I0/OiQCj+rpoIjWfenVFdm9uKG0zi3JKmxumkSdbG6dYsS1hLOzEM9gA+8atKBiAX+x2aHYfUNoeQRAEkQsknhQ44WyXWWjMoIwGaEYqOqJAMina5m6al7ro2yAa4HLkSYjWmey429xlDLA6n4o6FFdTqbLrBGgkhhEusi01DrDeOU4aN81IhSO1OzYITjgU5bT42WGW8uudbcbCLpWGWWRbyq+6mYXxBgd12yMIgiBygcSTArvz7wHrzlnUKPIkObSBIBAuss82K2lBZlExB6M77YkkWI0Of4DaMXPrbCylbabRAAOBorTNi7XWbiDsnBDDgm0WhZ2VKKdDQl2vRkadVtuh3XXSgbb4Vg5kzuo+sHUTwZpAaTfotueEXYD1++DCrzerHosve95WOwiCIIiTExJPIk603AasO9tGNTJKEWDXAZxq20wcIL9BKpUDDq10SC6gn04lCBTRMUsmwCc0rnM0bc9iVEyv1kNMWbI18iQfLmxtPjUFis2to2XbsmycYpS254TgNDrwVWUXoNl10tHojuHmRsJ4c0Ox1uwdN2u1f+0G3facW2vmthUdP4SZXy5TPRZ7dDq4xnpbbSEIgiBOPkg8SSjz7x2pQ8m9RsYrJyPOsoizXCotSFPY2V/rURDwo8BvfLiwStQB2jvbjtahmKWfGUSeHKj1UNtmEkm0koZpo+C0YhvH86b1O06k7Vk58FXY3EgJeqMIrNuRxGjceD6lDSHAm66THYbNZuyfT0C5iaBv22nH6uFHWqSR48Dt32mrLQRBEMTJB4knETk1rqgYTDBofHEWSAKl2Wp3NqO0PRudH8E2YwcoGhfqrKxEnmC7s20s7NriScR9ASQZYQkb7mw7EA0wmk+zc7ucqPWwbJvFFFH70/aMo2LtCWmtGdSxOWCblQNf2+IJ8IwPnQZ1iY7WYxnVFakEp0F798KIzZ9rVtuoS2vNSKg79bmmbRvP89hccCpYpEXxfT74qutstYUgCII4+SDxJOJYdMdi+2jj6I79IkBlm0FqHAB0emGbmbBLJACGQSyo7WzzPO9Za2tVh0JDR9tdp1G2zVAEOBR5MmnxLgt1o9bWTtlmQagDQCwoNBhIF53q9u72p2E2d1qsK3JpA0Fpm9W0PcNNBJdTRGMsi/2FPfHvo24C70t9BRbMnA9fnypbbSEIgiBOPkg8iTiermSye2x4mKTjdSjazlmbaHM8VKhvm0MObYlJ/Y7k0MaDgm0ZznZnO5CIi7bZn64UYznEDGpkoka1Ow442krb8msA4o1Da2WtOR+pMLFNFE8ZQqCtGRCbTTjRPTFq0DglqkwRNUpdtT1qbbEjppV0ZJdtkz47Xjj931D4eqppROiKG221gyAIgjg5IfEk4m3np4TxYZIeOUCSk5EMae+4q2xzKPLUGtM+6DLlbIsObXo0QO5QGACKim2zS3W4sG7EziTy5JTTaCkakGpV7mrkyUygiFHOhBTdMRo3l1MKZdtEYZcReVK2dw8VwC5KLay1Nospoo59rhk0JzE9g8px24zFcGHAj2B5ta2/myAIgjj5IfEk4pTTWGYiUHieN29V7pGwk5yMZIFOdAduONvGkaek7GynObSKGhQ7OxQGfD5ETA4XVs4nOtszWlvL0Z2SHrbZBZifvRNnWSQ53jDy5ERdkWCb8XxKaXvSWjOOPLmbgibdB6yeUHcodbUg4EfIQuMUuV4ymQSfUI+v1+3APWlVbpJaK312FCu6ehIEQRCEVUg8iXhVhxJjWXA8b7lVuSO2mdQ8sQXi2VJGdUW211QYCwHJNlnYpdV7yKLOZrsAC8IuoagrAoBYZ5ptXqVhqhuAuBpJNJlPSaBwIStC3ZsIrHQfpNvmlF2A+eaL6hBrIOPzI1XL6cWmUMJaq3KX6yWltVYcCtj6ewmCIIjuAYknETn1xsEvcq1DOCXHzLBVuQMHcALWd2j5Au10JXREgaTgiHjlAHFSpCI98uRQh0IrtkWVhfLQGDeHxVNbXLtGRhqzREindgfep+2xYR2hHusEOoXzlbxaa7q2OdRy25JtiQQSjB+8zy/YopdS6GDkSetzrSPJgoeie6IH9Vh6XScp8kQQBEHkg+PiqaOjA4888ghGjhyJyspKlJeXo7y8HIMGDcLkyZOxa9cu0/fgeR5PPPEEhg4dioqKCvk9Kisr8Y1vfAOLFy/O206ni5d5pL60laTvuLtbt2C24y4+XlikaVuqrigISI6lbbaZObTCWHJSNCAj8uTMrrZgm1kUJQme8YHTESlOH0Qr2aBlFwCgMCLY0QVTRPWEupzq6PMBkVJbbTNrTiJFOXmd9FWnGlkAFuqx4kmAYcDJwi7t88Ph+UxwHGJsZuMUa90TPYrAJijyRBAEQeSO498e1113HSoqKvDmm2+isrJSrj+JRqN4/fXXceGFF2L16tWorKzUfY9XX30VL730Et5++23069dPfpzneWzZsgVXX301KioqcN555+VuqEPpSoUBP/wMA5bn0RKLqxoOABYdWtHZdjIqpoUUDeDD2rYpU+PsrCsCFA6twcGlAMCL45axs+1QBAUwbyEtOWd8uAiId+oKAbuFejgQQMjvQ5zl0BJLoCysPkNKGjMmLI2ZQeTJ7ZqnhPo+yBCc0iG0JT3A+Ozd87Eq1GXb0ufTodQ4wLweKyXsioD21szaP4eEujJq09yZQLhY/VWSigxrR+uctM28hk2MPNl47hVBEATRfXA08rR27VrU19fjySefRN++fVUOdiQSwQ033IC7774bjz76qOH7/P3vf8c999yjEk6AcMDl0KFDce+99+K1117Ly1andkFVh3BqRCokJ4PRSQlS2ebUIbl6dUWik+HTdWidT1fS7TImPR7WqXlyJRpgPG5yNE533NyNVEhj5isS5zMeA6+IGvCdHXJ9lmNnKRlE64DUfaArOL1IjZPGTc82h1LjBNvEFLROi9FhvQiszePmYxhFxE7jcy2RNp/p9wDLAm0twg8uRzlbqeaJIAiCyANHxdPGjRsxZswY+Ax2ii+55BJs3LjR8H2mTJmCiy66SPf5vn37orGxMWc7AedqPQDjL3PJaZQd2mQCfCJ1Hc9xzqXemNY8qZ1tPafR2aYMxs62r1BsQ67Xbc9l21iOQ4d4/hOjE010VDwZHEYrjVlAWmuAStjJ0R2/H4iU2GyXNYHC6Ah1pyLDgm3WGkYw0n2Qbpsba01DDPM8n9rgkEWKzj3qpFDX+PzImM+OqLo2SjzrDHDwCAYToV5CNU8EQRBEDjgqnurr61FRUWF4TWVlJfbt22d4zYUXXohevfQdk/r6eowaNSonGyWcjQboO2dS2k2gSOGsKp2zaIsjB3Aq7dJtMCDZppeuJDnbjghOE4dWFJi+Qr1IhZNi2Hw+lbbp1Tw5E6kwEurCY8FwESBGgZXjpoxw2p2GKdnVmWQR16qREcfNHynOsEuwzRsRAKTWml86L0zPNpebk0hNGQDAVySNmzv1dWa2SaLOLwl1lpWbywCKGrbCCBib0+fKFPOp3aRHijyReCIIgiCyx1HxxLIs/H6/4TWBQADJpPZBqGZwHIcvvvgCf/rTn/DDH/5Q97pYLIaWlhbVf+k42dq6zMIObUFhWCiGh3pnW3YywoVgCsK22mXeYECwLSA6tFzzUXCNyoA9JwAANLdJREFU9SnbHI0GWEuNkx1aF4v4jeZTjgQwDHy6tWLOpGECJg6tKFCKC0KAuJa4+lTDFqc6TgLqw4UNhV2hdnTHlbQ9k0iFtInA1u9S3wceRTnbFPaapta6vNYkwamKcqqEupNRa2FzI8nx6ExmCvVUtz1K2yMIgiCyJ2vxtGjRIvTo0cPwv0WLFjlhKwBg8eLFcre94uJinHXWWbjjjjvQv39/3dc88sgjKCsrk/+rrlafKq9qg+xy5En+Ii8IAWI3L27/zpRtDrXzBcwP4ZSc7bpta4QHjjYiOq4G8SULhJ9dSXU0bqMeENPLMh1abyJPUrSuOBSQ07xUqXEcB7Q1i7Z5k0p16ea/y+u946f/Js+nk2MW8PlQZHC4sDSfcuSp+Zh7Qt1izVPJvq0AAO6jv6vuA6/OeZLGrCgYkDdeuMb98vN8IgG0twm2ObIpZG5bQbgQCAjzrrUp5MTnWiQUgBQ3NRKdFHkiCIIgciFr8TRlyhQ0NTUZ/jdlyhTN1zY3N6Ourg6rVq3K2eBJkybh0KFDOHToEFpaWrBlyxa88MILeP3113VfM2vWLDQ3N8v/7d8vOBjxvy0E4GwbZMA4iiLt0I7d+Hd5Z7bj5jEKx8y5XW3ArMFAEpXtx3D2m0+lHuQ4xB6+DVxjvaMObYmZQyu1G96zSTBrzYo0h9bBaIDRfIqPRYIBQHThuK8PpC6ItgJiiiRT0sMB2/TPuJHmc8qbc1MP8qn5dLKuCLAm7Oq+/Eh4oOmItkBxJIJiXvNU2X4Mxe8pjkRQ3geuRMU0opyiUP/JVx+AW/MvAED8D/enxkxKDwUAJ9aaheYkxaEgILbs5/bvlp93Mk3axzCpzw/Ne5QiTwRBEETuuHpIbllZGXbu3IkxY8bY8n6BQABDhgzBggULMHv2bN3rCgoKUFpaqvoPAGK/v0vl/DjRBhlIORlara0lx+z6Nx9LPahwaJ06f0q2zbDBQAID2hrB8Gn1UBwLbv9OZ4vRQ6lue1p1C1HJoV2p6LKocmjdqJHRr/WYvPs9sB+9AwCIP/lApkMbKgAjdgp0xjaNtZYQ5tOnN59OC3WDphHRhDCfI5bOV9ilIVCcFMMaY8bzPKKJpHgfpK1DV+4D4/uzsv0YHl79J6XBKTEsfa4Vl4IxSZ+22zZJ2I3d+Hc5+tVxi8amkANjJthmfB8A1KqcIAiCyA1HxZOVeqZkMolAIL8dwJqaGjQ0NCCR0N451oXjRKfRuXQlwLimQhIoug6tg46ZyjadSMWu4j7gmbRl4vPDV13nSmqcVt0CZ+LQsnu3KdogOxcNaNZxzCrbj+GB/3s69aDCoXXcaTRYa1FxPjmz+XRKqJtEAwyFugspojGWQyxtrXUmWXA8j13FfQCNcWP6VMtpme7XPAlj5tcTdQ6mxilt07wPLG4KOdE0RbDNQqo0RZ4IgiCIHHBUPFVVVeHQoUOG1zQ0NGSc36Tk2LFjGDt2rOnvCuayi+jzwVdd51grcAmz7mzGDq0zB+SmbDOKBiTQUNQL++74fepBxoeCXz4DX58qR6MBRnUL7eKutq5D27N3ylwPati6gkOrZ1tDUS+8d/1Dcrc9gEnNp+Npe8ZRFEOh7kKKKJB5rpjkaDcU9UJw1pMquwp++UyqFTfDAMVltttmJFCiCWHM2PTOiGmfHc6nYeawKeTpfUA1TwRBEETuOCqehg8fjpUrV4LTaIMtsWLFCgwfPlz3+dLSUmzYsMEwgnXw4EEUFxdnLaAK7nvcJafRqNZDcGjfv362pkMLp9P2TGwDgPj4GxGY8BMAQGDizQhNnArA2Y5ZRnULkvNzsKgXCh58RvEiUdhJXQkjJWDyjGpqYTRm5tEdD3fcxcjs/jHXIPyfLwoP9q3NnE+nI7CaQkC4DxrufAxSrRgYhbCT2+LbP25GzSyktVYUDCB89e1gas8AAIRn/w9CE6e6lvJrJIafGXu33DACAAp++bRqzLyMWuvdB+7V11HkiSAIgrAXR8XTOeecg5qaGkybNg0HDhxQ1a60t7dj0aJFmDdvHmbOnKn7HoFAAJdeein+4z/+A62trRnPHzhwALfeeiumT5+etX2B88cBgAt1Rea7oAcuvAbhXz8v2NGvLuXQOp22J9UtGDQ/KA4F4B9xnmDPwb3y8ynbnK6RUTvbUl1RJBRAaOLN8I38DgAg9O9zBIfWNbu0BUpDUS88P+4ewJeqMymY9aQ6WudFrYc0bsEA/KMvFB48+BV4sfOe45sIhveBYFvyBzchOGUGAMB/yVUKYedcUwYg5WxnRJ4SakfbP0Q4T447fFC0y2mBYhytA4DV5/wARW9slwVU4PzLRNu8q5eUxLCwKaQUdqIYdvD8KSCLZhYEQRAEkSWON4x46aWXUFtbi/Hjx6OyslJuM37WWWdh+fLlWLlyJSorK+Xrt23bhv79+2P79u3yY0888QT8fj/OPvts+fXl5eWoqKjApZdeirFjx+YknrjdmwG44DRa2gUNwn/OdwV7GvYIbYbhYre9NNt4nlc723VCdJDbKXS34+MxuTugU7bpddxLObTC8/6howWbxNbWbo1ZR5JFglVHVaUx++wbV6DorT1AWDgo1z/yAuEC15xG/WYWxaEgmFP6gOlxqlD3t3sLABeK+C2kUkWCAfjPPl+wp17ozsZzXCq11mVhJzvaYlTbJ98HG4ULnBZ1isYpXFoqqHxuVzAAf/UA+GoGCbbtEj/XXJtPfYFy4MJrEJ73hvBgz96ZYtiLRjgJijwRBEEQueP4t0c4HMasWbMwa9YsS9cPHjwYu3fvVj1WVlaGuXPnYu7cuTqvyg1u9xbgossddzLk81AMWpVHggEw5dVAUTHQ3gZu/074+w9xb4c2zcmIsxxY0VkrDgXhGzAUAMAfbgDffAx8QnSYHKr1UNqWWYeibAcO+AeOQAIAt+MLwUaXogGSbb0KC+SflQLFX14N38AR4DauBrdrM/wDhnpb65FIRRIZhoGvbhjYtavA7dwE/5nnuJC2p30fsByHDrFRQ3EomBLqe7aAZ1kg2gKIa9FtIZCe4uUfKNq2S9xEcDgyXBZWHmSdUK095cYLAPgGDAO3Z6uwwXHeWMc3ESTbjNuBBxEYfaHwOXH8MLhjh+Hr1dtTod4ao257BEEQRO642qq8q5HaofWu1kPpZDAMA9/pQwTbpGiAg4fkGtnWpnCIIqEAmEgJmMpaAAC7Y6Nsl1O1HoD+7nG6Q+sbNEK06wvwPO942l7Q70NhwC/aljZuCoECAH5RdLoXDbDQZSwtisKKURSvav/aE6kOd8WhAJiq/kC4EOjsAF+/O1VXFC4CEyqAE+iNW3qKl2/AMAAAt2cr+ERCsbnhzJgV+P0I+rQPspY3EaT7IGOtSfeoF9EdxaZQUbEwp0hF7NwST+mNNuIsi4RYg0uRJ4IgCCIXurV4Yne7m96idXBpNF0I9D8TADJTqZzugKbTZSwc8CMgOm9yytKuTY42i0jZpr17nEpXEh3a04cAfj/QfAz84YOOj5mRbalUR8nZdlc8lVlIpZKdbUUUhWdZoK1ZuNDxTQRtEeBjGIQDfjB+P3ynC/cBu2uT46LO2LZU6ioAMBX9gEgJkEyA27fd8cgTwzAW7gNJPAnCjhWjYs53ETVuVQ4oUmvT0h29iqhLdiltIwiCIIhs6Nbiidv9JXiOU3Tycr5Fc/qBr6lIhUIIAOD2fAnABSdDr9ZDsXMs4a8Td913bEx1y3JozADzqJgkOJmCMHz9xHqPHV84HkkUbBPmNF0QZ4hhSTxJQr2tSbCtpIdDdqXmM32tRdNrxQYo5rO1Sb7O/bWWEiiM2HHSp1hrTqdhqmyLpwuUtLXGMKno046NLgt1vZTCNKG+e4v4ueZdalzGfSCJpx0bhXrOqNj8x7FaMeNIYoHfh6C/W3/9EQRBEDnSvb89Yuq0IKedDI7n5TOKJFKpVGKalyLyxCeTqcNeXa6RSXd+gPTIUxeI7ih2jn0DFal7Ukqhk862nkMrOdvpkad9O8DHY4rW1s6m7fFIiSVA3QAkFalI1bFx+3cKFzrU3l1pW4ZASRPDgDpS4XSnPcE2awIFUNQ97XRJ2OlErjMiidV1QDAEtLeBP7TP8ZRCya64xuHCGZtCA1MporxSqDu+iWA+nwRBEASRDd1bPEGs93A4LSgS1D7wleU4WUzJToYknvZuBd98VL7Wu0J5hUARowHszk2ybU46tCW60QC1CABSdU+uRZ50u7Ol2qgDANO7UmiowbLgvtruuFAvDPjhF6M3Sts6k6zcrU2aU6a4FExFDQCA/ex90a6uIVBUKYWupO2Z1Twp1poUedq5yR1hpxNFSU9fZYJB+GoHC7bt2ux4SmGJYr7M0h1TGy+bU59rDgp1vWYWWvNJEARBENnQ7cUTt+MLx1PjVHULii9zZaG87GxX1gIFYSAeA/flZ+KTzkcDMna1ExpOY+1gIBAA2prBbv9csNcFh7bVpIgfEDruAV6kUhk7Z0Kal6LuyWHxpK6RSYkUZa1HUTB1/pQkUpLr3hNe78WYaaSIygJl3w7whxsE2xyK1hnbpi/s2J0bvY1yakWHByg2OBz+XPP7fPKcKW1TRtiLlVGxgjDQEQW3Vfhcc3Q+TZvNUOSJIAiCyI1uL57Yzz8CWEHEuFEjo/wylxxtBpC7tzF+P3y1Zwi2bfg/4TFXnEZtxyyiaOfLBEMp29yIVOhEntKjO4BiZ3vPl+CPHhJtc3LcdFLQ0qIBgLrjntMOrWBbZpqXVLtTGPDDr+iOKNU9sevFteZhtE4pApjeFUBZL4BlwX7xsfCgg8JOL8qZOucpU6DwB/aAb9wv2OtGxM6kZb9gm7jWtn4GiIcfu7HW1JtCmU0ZmEBAjqqz66TPDi/EMEWeCIIgiPwg8bThQ+EfoQIw4ULHfo9WFzRlAb9UKA+kUvck2xyNBigcWmWDAa2aJ0DhODbsdd42s+iOwmlkKvoBxaVAMiEfruqkQ6vX1S69DgVQzOfmTxRC3QVhpxLq2jvuUhRF6rTnhl3RRBIslzpcOKphG8MwKWH3xUeibV0kpbDnqWBOLQcA8F8fEG1zXwikNwABUuKJ/ewD4QEHz2HTs01rUwhQtMb/TIxyehmtozOeCIIgiBzp9uIJHVEAzjpmgHHkKUOgKJ1tuONkpDcYSG98INsmOdsiXkTFtJxGhmHk1D35MU9qZLRSqUSH9nNBBCAQBMJFztkWyhw33bVWlzafLohhQH3wsV40QF5rbkZQLNQ8Ae6OW1kW9ViS4OSPHBQecPAcNj3blJFh5aZQ6vDjrQDcmc9YWjMLqnkiCIIg8qVbiyemLPXl7eQXOaB9JopuNEAUT7LT6KBAKQoG4NNoMKAlAoCUcybRVYr4gVTHPdk2L6MBCtHp6y+IJ7S3yXYpnUo3bEsv4Jdtk+rYRJzcRCgI+BHyZx74qmubm8LOrAFIum3p94GHURSlbUzf04XaIhfsUtrWrCnUTTZeXGg2A6QJdap5IgiCIPKkW4sn2amFs44ZoOPQ6u24i2c9uWGbfoOBzPQzINMBcrYORSxG16t5SndoleIpEAQKI47ZpuU0JjkOneIudyS9fkfRktkth1Y5blrROkCsY6sZnPrZg/tAK20PSHV3lG1zoyZRr4YtzTa/8j4IhpyNJGrYxvG8dgTW709tvsDZjReVbTGNtWYihp1ssqFuZpGyrZUiTwRBEESedG/xNCAlnpw6d0dCa2dbVwRUDRCcf/nF7tumFUEBILS2VogSNxza9G57ug6twjljyno5G93RGjNFRztV8wOGkZtGAHB+rWWRIgqkRVGcTl/V6IKmt4ngbpTT2oHMEkph53gkUVxrzZ0p2zoS2msNcHlTSDNFVEeon1oOpscpqZ/d2kSwEBUjCIIgCKt0b/HkauQpc/dY74ucCQbhqxmU+tlxZ1u/6DvdMWN8PrXj6IJDm95gwKpD6yRa8ymJOj/DoMDvV12vFOreOI366UqqaKJinB21LZ5pWyRdqJeUgSmvTv3sQmpcWzx9reml1g4VmjHAjXrJzEiisvV8YUaEx837QP+zI31TiGEYVXTYm8817agYQRAEQVilW4snVTTAoXOUJLQc2mjaWShKVKk3ngg7fWdbGeHhxYYbTmBWt5DhnBWXCjUfAHifH1xjvWO2maU6pkciVEJdkcLniG1aUU6Ns5QkuMMH5X/H/vN2xJcscM62LIQ6oE714sUaQCftEuzRaJySvolQWASmaoBgl8/n0lrTFii+tLWm/FxzTaBofnYYz6cXGxzSuJUUUOSJIAiCyI1uLZ5Y8bBGAEi+8WcPnEb9trkq8eRheouWs82LjQ8AoH3CQMfGrSDgR4FWg4GEdsQOABixLTO/azOi42ocs81wPrWiO55EnrTqitTzyTXWI/nqH1MP8BxiD9/mmBjQtE0nRVQwMBUFar9muHNrze9H0KffzELLNqaoGIAba82grsgsDTMtAuqubRr3gbLuiU1mPG+rbZqbCNSqnCAIgsiPbi2eYo//PPUDzzvqNGq39DXYcVeIJ8edDCOHVsvZ/sdfFQ847WxrOGcGQoDb/rkrtmnNp16hPJBWX+eWQxvXcBrTHFpu3w6AT0vV41hw+3c6Y5tWzZPBfLIf/13xgHPzqWqcIqYUKhuAeLnWjNIw01MdASC55p/yvxOv/tHZTaEs2uIDAN+wR/5354M3urRhZc02giAIgrBCtxZP4Pi0nx10GrNoVQ6kzkIBgM4HfuySA2QeRXHd2U4TdizHoUPqaBfUss2dOdWqx9LrUAgAyQ/elP+deOUJ153GQ23tAAAurabJ128gkH4OkM8PX3Wda7bpjZub86m2TbBH3QDEu7VWprBLOshaTwRwjfWIPXxb6gGnN4XC0ueaVlpt2pg11iP+3ByFbe5EObVso4YRBEEQRK50b/HkS+uQ5aTTqCFQGkWHNpnm0HKN9Yg/+/9SDzjuZGRGKpo6YwCA9oS6053bzrZU9yTVPEWNuoy5aJtWPZZeipfrDm1aVGzB+u14YeNuAMB/f/IlFqzfLl/r61OFgl8+C/jEaJjPj4JfPgNfnypHbDNso55+ILNnwk6aT+H/PgBfR9X1Vm7aJt2fyoOs9VLjuH07Mpt+OCo4rXdP7BK2UeSJIAiCyJNuLZ4K7vuD+06jwqF9ecteAMDc1ZtVDq37ToY6GrBg/XbsbRYaQVz1+qqu4WynRQMYAEfaO1XXummbsh6ruVMSdsL/YyyL+pZUIw3v5jOB+pYobl3+sfwcD+C25R+r7AtNnIrI8r0o/NNKRJbvRWjiVEfsSrdNQhLq0XSh7vZaS0sp/N8vhPnhANQ+8bpn90FhwA9/2kHWklCPJdVrzXXBqZG219gmCE02LTLXFWxrEtu9tys2YQiCIAgiG7q1eApdfoOLTmMqumPm0Hq5455uG8d3FWdbcHqe+1xwaHlkOrTu26aO2P1zdwMA4JOGI6iZ/5psm2fzGU9gx7EWcGlOLMvz2HmsRW1OnyoEzh3jmDBJ2ZYZFTvQKjjbP3z1XY/nM7XW6luieOi99fJzHM97dh8o67GktN+/7z4AAFh36Kh6rXkcSVywfjte2izUNf1X2qaQ1xsvC9ZvR32rEO3XWmsEQRAEYYVuLZ4AN53GlGO2/WizoUPr/o57ysnocs52SC06H3wv1SFRy6F11bY0Z/vlzalieKVtXs1nc2ccdT1LMrJT/QyDul6ljvxuM5TRAEGofyQ/pyXUAQ/mUxad6ue9FZ3qDY6XNu2Wn0u/D7yKJHa1KGdZxoZV5lo7kLbWCIIgCMIMSvx2CcnJSHI8qksjYCA4FxLpDm1o4lQEvn0puP074auuc9Q5U0YDBvYqNbXNTZTOmZFDW1Ua8dS2zYePI800lW1ezCfL8zilKIwxNRV4d69wlpOfYfDMuPM8GS/BNmsCxcv5bI0lUNezJON5b++DEIAoWmIJROPNhmsNEESd04IuZRfQmWSx5UiT7saLcj7ds838s2PX8VbH7SAIgiBOLkg8uURxKCiLktKCEGrKirG3WTgvSc+hdd3JiMdxSmEBCgJ+uUVzl3G2YwkM6GoOrSJi1yCmAylJt82t+YyEAvJaO9Leic8bjwEAHr1oFK4fNsCzuQTU89k1BYog7A63x1TPeX4fKCJ2XLoKgHfjpmycUhEp7KIbL3EM1LDBzzCanykEQRAEYQSJJ5fwMQxKCoJoiSXwwf5G7G1uQ4Bh8OqVY3Bu5aldxqF9Y9s+dCZZVJUU4fkJ38GgXqWe2qbstrcjLWXKa4e2LJyK2L28RUjZk5xHL21TrrVXNu/B0Y4YKooLce+3hsGfXnvlMsoo5+6mNtVzXs+nUgy/sHEXAODygVW455tDUefxfaC8R5fvEro0doW1FvT7UBjwoyPJIhIKory4EAfFhhGez6dirUmNIiQk2/p6OKcEQRDEiQmJJxcpDQkO7dzVmwEAE8+owQ/PqPHYKrVj9rzYYeynZw/ERbUVXpoFQL17/PRn2wAAPx7eHz89a6D3Dq3obK/Y24BPG44g5PdhzU/Go6kz3iVsa4kl8ORa4bywKcMHeC6cgLT5XCfM53VDT8etIwd5P2aibcc6Yli2fR8A4NZRgzGmC9wHUv3Ol0ebsWSrYNvfr/segn6f5+NWFg6ho60DS7Z9hYNtHSgK+vGXiWNwdnmvLjGfLfGE3Gjm0v6VuP/bw+Uxi0ap5okgCILIDhJPLlJaEAJa27H6wGEAwC0jB3lskYDUlOF4Zxz/3CPUxtwwfICXJslIDtC2oy3YdPg4AODebw3DWX16eWkWgNTO9qtffgUAuGZILc4uP8VLk2SktbZPLIi/cUQXmU9FdOf1rcK43XfeMIzsAuMmrbV/7G5AazyB3kVhXNq/r8dWCUi2PfvZNrA8j+9U98H3BnQR20JBHEIHfv/xJgDAT88aiB8MqvbYqtRaa+qMy5HEn507pEuIYYIgCOLEhcSTi0gOEADUlhXj4tO7xpe4ZJdU7P2d6j4Y4FGdQjqSQNkg1u2cV9W7SwgnIHPcfnbuGV6ao0K51s6tOAVDe/f00JoUkl08gATH4RuVp3YJ4QRkHsj8o6GnI+j3PloHpMatWWy73RXXWmNUOHdt+ughXpojI9nVmWTRmWRRXlyIy+q6huAkCIIgTly6hmfQTQgpHLGpZw+Ej2EMrnaP4pBaQ3eVKAWQ2j2WuH3UYI8syUQpUIb17oFv9u3toTVqlGvtxhHOnCeVC0VBoZmFRNeaz5Dq5x93kegroLatd1EYE8/o56E1akJ+v/zvcXVVGHRKmYfWpCgpUH923DhiAAJdIHWVIAiCOLGhbxKXWLB+O97f1yj/HA74Da52l/8V6wEkOsROe10BpUApKwji6iG13hmThpRGCACbDzfhzxt2eGhNivS1xnKch9ao+fOGHapubJ1ddK2d3qMY51Z0jYgYAGw53CT/+0h7J174Ypd3xihYsH47Pqr/Wv55UBeJWAOCqCtQbCL85KyBHlpDEARBnCyQeHKB9MMjAeD+d9dlHAbqBVq23f2PT7qEbQDwj90H5H+3xBKqw0G9pL4lipc3pQ7F1ToQ1Au05vPef6313C5A27bpf1/TJWwDgH8q1treprYuI4brW6L4y5YTY63N//RLz+2SWLB+O2JsauPg/xQbCgRBEASRKySeXEA4oFH78Eiv6cq21bdEMfv9DfLPXcVpBIRx0zuo1Eu68nx2ZdtorWVPV5/PdGHXVeaTIAiCOLEh8eQCA3uVZtQ3eXl4pJKubJvgnKkf6yrOWVcdt65qF9C1baO1lj1d1S6gaws7giAI4sSGxJMLVJVG8Oy48+AXHQ2vD49U0pVt68rOWVcdt65qF9C1baO1dvLYBXTt+SQIgiBObBie59MzQk56WlpaUFZWhubmZpSWuvdlWt8Sxc5jLZ4faqlFV7VtwfrtuG35x2B5XnbOpnaR87GArjtuXdUuoOvaRmstN7qqXVbmMxqNori4GADQ1taGSKTr2E8QBEG4RzbagMSTi+KJyI2u6pwRJx+01k4uzOaTxBNBEAQBZKcN6JBcostTVRohR5ZwBVprJxc0nwRBEITdUM0TQRAEQRAEQRCEBUg8EQRBEARBEARBWIDEE0EQBEEQBEEQhAVIPBEEQRAEQRAEQViAxBNBEARBEARBEIQFSDwRBEEQBEEQBEFYgMQTQRAEQRAEQRCEBUg8EQRBEARBEARBWMAV8dTR0YFHHnkEI0eORGVlJcrLy1FeXo5BgwZh8uTJ2LVrV87vfejQIQwdOhSHDh2y0WKCIAiCIAiCIAg1ATd+yXXXXYeKigq8+eabqKysBMMwAIBoNIrXX38dF154IVavXo3Kysqs3jeRSODaa6/Fjh070NnZ6YTpBEEQBEEQBEEQAFwQT2vXrkV9fT0WL14Mn08d6IpEIrjhhhtw5MgRPProo3j88cezeu/7778fQ4YMQXNzs50mEwRBEARBEARBZOB42t7GjRsxZsyYDOGk5JJLLsHGjRuzet+//vWveP/99/Hf//3feVpIEARBEARBEARhjuORp/r6elRUVBheU1lZiX379ll+zy1btuC+++7De++9h3A4nK+JBEEQBEEQBEEQpjgeeWJZFn6/3/CaQCCAZDJp6f1aWlpw1VVX4Y9//CNqa2ttsJAgCIIgCIIgCMKcnMTTokWL0KNHD8P/Fi1aZLet4HkeP/nJT3D11Vdj3Lhxll8Xi8XQ0tKi+o8gCIIgCIIgCCIbchJPU6ZMQVNTk+F/U6ZM0Xxtc3Mz6urqsGrVqqx/72OPPYa2tjY89NBDWb3ukUceQVlZmfxfdXV11r+bIAiCIAiCIIjujSutypWUlZVh586dWb9u5cqVeOqpp/Dpp5+apgGmM2vWLNxzzz3yz83NzejXrx9FoAiCILox0WhU/ndLSwtYlvXQGoIgCMIrJE3A87zptY6LJyv1TMlkEoGAvin19fW44YYb8Nprr+HUU0/N2oaCggIUFBTIP0sDRBEogiAIAkDW5wwSBEEQJx+tra0oKyszvMZx8VRVVYVNmzYZXtPQ0IB+/frpPv/888+jubkZEyZM0Hz+8OHDGD16NGbMmIFf/OIXpjZVVlZi//79KCkpkQ/stUpLSwuqq6uxf/9+lJaWZvVawh5oDryH5sB7aA68hcbfe2gOvIfmwHtoDuyB53m0trZa2khzXDwNHz4c8+fPB8dxumc9rVixAsOHD9d9jwceeAAPPPCA7vO1tbXYsGEDevToYckmn8+HqqoqS9fqUVpaSovUY2gOvIfmwHtoDryFxt97aA68h+bAe2gO8scs4iTheKvyc845BzU1NZg2bRoOHDigyiVsb2/HokWLMG/ePMycOdNpUwiCIAiCIAiCIHLGcfEEAC+99BJqa2sxfvx4VFZWory8HOXl5TjrrLOwfPlyrFy5UhUm27ZtG/r374/t27dbev/evXsjGAw6ZT5BEARBEARBEIQ73fbC4TBmzZqFWbNmWbp+8ODB2L17t+X3//TTT3M1LWsKCgowe/ZsVQMKwl1oDryH5sB7aA68hcbfe2gOvIfmwHtoDtyH4a305CMIgiAIgiAIgujmuJK2RxAEQRAEQRAEcaJD4okgCIIgCIIgCMICJJ4IgiAIgiAIgiAsQOKJIAiCIAiCIAjCAt1KPLW1tWH8+PH405/+pHp83bp1uOKKK9C/f3+5jXrfvn3xjW98A//zP/8DlmXla3/729/K12j9V1hYKHcKfO6551BcXGx4/cCBA5FIJFwdB6/RmwcA+Pjjj1FTU4NoNKp6PJlM4tlnn8Xo0aPRt29fefz69++PCRMmYMOGDRnvxfM8XnvtNVxwwQXo16+f/Jqamhp873vfw6pVqxz6C7s+uczBmjVrcPHFF6Nv377o06ePPJ4DBgzAHXfcgWPHjqmunz17NsrKygzX/7/92785+nd2ZbKdg/b2dtPPk/Lycixbtkz1XgcPHsRtt92GIUOGoKKiAuXl5aioqMCIESNw//33o6WlxfG/tSuTzTw0Nzfj9NNP1x37nj174ic/+Yn8+pqaGtP5evLJJ137W7sKWmPe1NSEO++8M2N8q6qqcNFFF+Gjjz7KeJ+Ojg488sgjGDlypOoYlEGDBmHy5MnYtWtXxmty+S45GbFrDt5++22cd955qKyslL8XKioqMGjQIDzwwANob29XXX/jjTeiV69ehvfE5MmTHf/7uwJ2zMGOHTtQWFhoOJ4VFRVYt25dxuuuv/56DBw4UL6usrISo0aNwqOPPopYLObKGJyw8N2E5uZm/oILLuBramr4efPmyY+vXbuWP/300/m33nqL7+zslB9nWZbfunUrP2HCBP7ee++1/HtGjRrF79+/n+d5np83bx4/e/Zsu/6EkwK9eeB5nn/vvff4fv368SUlJfzx48dVz9111138pEmT+B07dvAsy8qPd3R08MuWLeNra2v59evXq14zb948/rvf/S6/YcMGPplMyo/HYjF+1apV/NChQ/lly5bZ/Sd2eXKZgyNHjvDV1dX8v/71L57jONVrOjo6+NmzZ/OTJk1SPX7XXXfxzz33nEN/xYlNLnNw/PhxvqamJqvf09rayvfv35+fP38+39LSIj/OcRz/9ddf87/85S/50aNHq+6p7kSun0d6LFu2jJ86dar8c1lZmX3GniTojflNN93E33333XxbW5vqepZl+Q8++ICvra3lDxw4oHpuwoQJ/O23387X19erPpfa2tr4559/nq+urs54TS7fJScbds3Btm3b+H79+vHr1q3L+F5obm7mb7nlFv6ee+5RPT5hwgR+5cqVtv9NJxp2zcH69ev57373u1n97v379/PV1dX8iy++yEejUflxjuP4ffv28bfccgt/5ZVX5vR3dRdcOefJazo6OjB27FiMGzcOHMepnps9ezYef/xxjBs3TvW4z+fD4MGD8eKLL+LMM8/Efffdhz59+hj+Hp7ncfjwYfTu3dv2v+FkwGgePvzwQ0yZMgVLly7FxIkTVc8dPHgQb7zxBr788ksUFhaqnguHw7j88suRSCQwe/ZsLF26FICwu/i73/0On332GcrLy1WvCYVC+O53v4uFCxfipz/9KS6//HIH/tquSa5z8Mknn+D888/HxRdfnPGe4XAYDz30EKqrq9He3o6ioiJH/4YTnVznIBeefvpp/PCHP8Sdd96pepxhGPTu3Ru/+c1v8Pnnn9v2+04knJiHffv2oaqqym5TTxqMxvydd97B7t27Mz7jfT4fLrjgAvzoRz/C0qVLMW3aNADA2rVrUV9fj8WLF8PnUyfRRCIR3HDDDThy5AgeffRRPP744wBy+y452bBzDlasWIHrr78eo0aNyvg9paWlmDdvHgYOHIi5c+c69wedgNg5B7kwZ84c3Hfffbj++utVjzMMg+rqajz99NMYNWoU1q9fj5EjR+b8e05mukXaHs/zuOeee/DQQw9lPLdx40ZceOGFuq+NRCI455xzsG3bNtPf8+GHH+LMM8+kg8p0MJqHUCiEt99+W/NG3bp1K84999yMDxMlF198MTZu3Cj/vH//fjmNQ49Ro0bh4MGDSCaTWf4lJy65zkG/fv1U6Ujp+Hw+9O3bF/X19bbaezKS6xzkgtnnGwBccsklqnunu+DEPCxZsgTf//737TLxpMNozH/+858bfsZXV1dj//798s8bN27EmDFjMoSTkvS1nct3ycmGnXMwdOhQXHnllbrXRyIRsCxLKWBp2DkHuWD2veDz+XDRRRed1PdBvnSLyFNRURGuueYazeeam5tRXFxs+PrKykrs27fP8JqWlhbceeedmD9/fs52nuwYzcPo0aN1X1dfX4+KigrD9y4rK1PV3Fh5DQD07t0bhw4d6ja7xbnOwdChQzF06FDd5zs7O/HVV1+hX79+edt4spPrHOSClfugsrISX3zxha2/90TA7nl47rnn4Pf78c1vfjNf005ajMZ8xowZhq/dtGkTzj33XPlnq2tb+d2dy3fJyYadc2BWs9rY2IhwOEwbymnYOQe5kMu9Q6jpFpGnfAkEAobRid27d+OCCy7AzTffjO985zuq5zZs2IDrrrsOZ5xxhqp4b9SoUfjNb36TUUxJZMKyLPx+vyOvMZtbwpzOzk7cdtttuPrqqxEOh1XPrVq1ChMmTMDAgQPlYmKpGcuTTz5JY58l7e3tePDBBzFq1ChV445BgwbhRz/6EbZu3aq63sp9QPdAfvA8jzlz5mD+/Pn43//9XzAMo3p+zpw5+Na3voXq6mp5vgYMGIDLL78cn3zyiUdWn1gsW7YM77zzjiqFMpe1nct3CSGgNQdGNDU14cc//jGmT5+e8dySJUswduxY9O/fX74nqqurccEFF+Dll18Gz/N2m39SoDcHhw8fxowZMzBixAhV45QzzzwTN998c0ZGCH0v5A+JpzxZu3Ytxo4di0ceeSSjrqCurg4HDx7EVVddhQ8++AAHDx7EoUOH0NDQgL/97W/Yu3ev6S4DQXRlPv74Y5x99tngOA6PPfaY6rnBgwdj3759+NnPfoZPPvkEhw4dwqFDh7Bv3z68/PLLeOedd/D73//eI8tPPMLhMKqrq8HzPBYvXow9e/agsbERhw4dwvr16zFp0iR8//vfR1NTk9emdhuSySSmTZuGDz/8EO+//37Gbu7o0aNRX1+P559/Hjt27JDna9OmTbjzzjtxzTXXyN1ZiUyOHz+Om266CTNmzMAbb7yBnj17em1StyOXOXjzzTcxbNgwnH322bjnnntUz5155pnYu3cvfvWrX2HDhg3yPbFnzx489dRT+MMf/oBXXnnFqT/nhMRoDk477TT4/X5UVVVh+fLl2Ldvn/xd+/HHH2PkyJG47LLLul1XZ6ch8ZTGnDlzcMUVV1i6trm5GVdffTVeffVVjB8/PuP5H/zgB1izZg2uvPJK9O7dW96RZBgGffv2xVNPPYUlS5YgHo/b+jd0B0aOHIkXX3wxq9dcccUVmDNnjkMWdS+ampowffp0XHfddfj1r3+NhQsXZkSdpk2bhnfffRdjx45Fz5495fXv9/sxYMAAPPfcc/jjH//ohfknJOFwGOvWrcPDDz+M2tpahEIh+blIJIJrrrkGl19+OV5//XXd91i1ahXq6uq6fXtyu3jsscfQ1taGN954QzP9+5///CeeeOIJDB48WHV/FBYW4tJLL8X06dPx3HPPuWnyCQHP83jllVdw1llnoWfPnvjss88wYsQIw9c0Nzejrq4u6yMocvku6Q7kMgcNDQ247rrr8POf/xzPP/88fve732XUpP32t7/F0qVL8e1vfxulpaXy44FAACNGjMATTzyBp556ypG/6UTDyhxIadf33nsvqqqqEAikqnHKysrws5/9DLW1tXj//fd1f8+iRYuoMUSWdIuap2y4//77LV87Z84c3HzzzTkvuoKCAtTW1mLv3r0YNGhQTu/RXVm/fn3Wr0k//4bIjcWLF2PGjBmYPHkyNm/ejEgkktP7nHrqqYjH4+js7MwQXkRuDBkyRPNsG4kxY8Zg586dLlp08nLkyBH86U9/wtq1a3NOBRsyZAgWLVpks2UnNvv378dNN90Enufx9ttvG9ZaKikrK8tpbefyXXKyk+0c8DyPZ555Bg8//DBmzJiBhQsXIhgM5vS7zT7Dugu53gdaSGOq1S0XAKZMmYIpU6bk/P7dERJPFkgmkyo1L/HGG29gxYoVeb13UVER1T2ZkEvurdXX6M0tkQnP8/jNb36Dv/71r3jnnXcwZMiQvN8zHA6TeLKR9M8TK/cB3QO5sWLFClx88cV5pZLR57+aTz75BNdeey1mzZqFW265JaN+TEkua5vqOMzJZg4AIJFI4M4778SWLVuwZs0a9O3bN6/fT/dE9nNgBn0v2E+3T9vr0aMH2traDK9paGjI6CLW1NSEjo4OVFZW6r5uzpw5OHjwoOF7d3R0GLalJICqqiocOnTI8JqWlhb06tUrq9cAQqGlUTtzIsVf/vIXLFu2DGvWrLEknGbOnGnaojYWi5FwssiaNWvw0ksvGV6T/nli5T7Q+nwjzFm9ejXOO+883ef37Nkjny+kB33+p2hqasKkSZPwwgsv4NZbbzV1GHNZ27l8l3Qnsp0DAJg7dy4aGhrw7rvvWhJOd999t+Hz3f2eyHYO/va3v5lu4tP3gv10e/E0fPhww4UXjUaxbt06DB48WPV4Q0ODoXACgM2bN2Pt2rW6zycSCXz11Veora3NyubuxhlnnIG1a9ca7katWLECw4cPl3+uqqrCwYMHDcXr+vXrUV5eTrsrFuB5Hvfddx+ef/55y2l6H330EbZv3677/PHjxxEIBEg8WaStrc2wngkAtm/fjv79+8s/m32+AZn3DmGNAwcOGDqLHMdh4cKFhu+RPl/dmblz5+L666/HBRdcYOn64cOHY+XKlRmHjCpJX9u5fJd0J7Kdg5aWFsydOxd//vOfLafpvf7664Y1l939nsh2Dr7++mssX77c8Jpsvxc4jsPKlSu77X1ghW4vnn7961/j7rvvxltvvaXaJec4Djt37sSUKVNw1VVXoU+fPqrXtbW1mTqREydOxK9//WvNPOzm5mbce++9uOyyy+gMBBPKy8sxadIkTJ48GTt37lR9WcZiMbz55pu455578Ktf/Up+PBAIYObMmbj22mvx+eefg2VZ+blEIoEPPvgAP/7xj/Hwww+7+aecsBw+fBiBQCCrvOuJEyfi/vvvR0NDQ8ZzjY2NuO2223DzzTfbaeZJzejRo7F+/XosXrw4w2FkWRbLly/HkiVLMGnSJPnx22+/HcuWLcP8+fPR2toqP87zPI4ePYqHHnoIDQ0NmDBhgmt/x8mC2XdAbW0tOI7DU089ldHpiuM4fPTRR3j88cdx4403Om3qCcG6des0Gy/pcc4556CmpgbTpk3DgQMHVO2t29vbsWjRIsybNw8zZ86UH8/lu6Q7ke0cbN26FcOGDUPv3r0tv2bixIm4++67cfz4cdXjPM9jz549uPvuu3Hrrbdafr+TjWzn4JJLLsGrr76K9957L6PFeyKRwMKFC7Ft2zbVmVz3338/5s6dixdffFG1kcDzPA4cOIDbb78d/fv3pyYSBnS7LffS0lKUlJTIP48cORKLFy/G7NmzMX36dHkh+Xw+9O3bF7fccgumTp2a8T5+v9808jRp0iQ0NTXhyiuvxOHDh+UPaoZhUFxcjCuvvBL/+Z//aeNfd+KQPg9KTjvtNFUnMQD4/e9/jwULFuC6667DgQMH5LEsKirCsGHDsHjx4owbffr06aisrMQdd9yBr776Ss7xLSgowMCBAzF//nzDU7ZPdrKZg8OHD+PgwYOmKY7PPvus3K3yrrvuQjKZxEUXXYTm5mb5g51hGPTs2RM33XSTaQrHyU42c1BaWoqlS5fiF7/4Be666y6VQx4MBnHWWWdh6dKlOOWUU+THI5EIPvzwQ8yePRujR4+W54FhGJxyyikYP348Vqx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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "r = readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"]\n", "plt.plot(r[\"path0\"][\"data\"], \".-\")\n", "plt.plot(r[\"path1\"][\"data\"], \".-\")\n", "plt.axvline(tof_measured, c=\"k\")\n", "plt.xlim(\n", " tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n", " tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n", ")\n", "plt.show()\n", "\n", "plt.plot(r[\"path0\"][\"data\"], \".-\")\n", "plt.plot(r[\"path1\"][\"data\"], \".-\")\n", "plt.axvline(1024 + tof_measured, c=\"k\")\n", "plt.xlim(\n", " 1024 + tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n", " 1024 + tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d21dcff5", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "markdown", "id": "374cc753", "metadata": {}, "source": [ "Set the parameters for the Rabi experiment" ] }, { "cell_type": "code", "execution_count": 21, "id": "545b1adc", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.867334Z", "iopub.status.busy": "2025-05-07T16:47:34.867189Z", "iopub.status.idle": "2025-05-07T16:47:34.869937Z", "shell.execute_reply": "2025-05-07T16:47:34.869545Z" } }, "outputs": [], "source": [ "# all times must be divisible by 4\n", "reset_time = 200 # reset time for the qubit in microseconds\n", "tof = int(tof_measured / 4) * 4 # time of flight must be divisible by 4\n", "readout_delay = 164 # time to delay the readout pulse after the start of the rotation pulse\n", "\n", "navg = 1000 # number of averages\n", "stepsize = int(65535 / 100)" ] }, { "cell_type": "markdown", "id": "492581ce", "metadata": {}, "source": [ "## Rabi" ] }, { "cell_type": "markdown", "id": "d81429b5", "metadata": {}, "source": [ "Normally, a Rabi experiment would be performed by changing the amplitude in the inner loop, and averaging in the outer loop. To make the resulting experiment visible on an oscilloscope however, in this tutorial we swapped these two loops" ] }, { "cell_type": "code", "execution_count": 22, "id": "6802ba32", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.871744Z", "iopub.status.busy": "2025-05-07T16:47:34.871608Z", "iopub.status.idle": "2025-05-07T16:47:34.875126Z", "shell.execute_reply": "2025-05-07T16:47:34.874736Z" } }, "outputs": [], "source": [ "# QCM sequence program.\n", "qcm_seq_prog = f\"\"\"\n", "# Registers used:\n", "# R0 loops over the different awg amplitudes used for the rabi driving pulse\n", "# R2 is used to count the averages needed for a single amplitude\n", "# R3 contains the qubit reset time in microseconds\n", "\n", " move 0, R0 # start with awg amplitude 0\n", " wait_sync 4 # Synchronize the QRM with the QCM\n", "\n", "\n", "ampl_loop: add R0, {stepsize}, R0 # increase the pulse amplitude by the stepsize\n", " move {navg}, R2 # reset the number of averages and save in the R2 register\n", "\n", " # let the qubit relax to its groundstate\n", "navg_loop: move {reset_time}, R3 # reset the number of microseconds to wait and save in the R3 register\n", "rst_loop: wait 1000 # wait 1 microsecond\n", " loop R3,@rst_loop # repeat the 1 microsecond wait as much as needed to let the qubit relax\n", "\n", " set_awg_gain R0, R0 # Set the new amplitude used for the drive pulse\n", " set_mrk 1 # Set marker 1 high for to enable synchronization with external oscilloscope\n", " wait_sync 4 # Synchronize with the qrm to signify a measurement is coming\n", " play 2,3,16384 # Play waveforms and wait remaining duration of scope acquisition\n", "\n", " set_mrk 0 # Reset marker 1\n", " upd_param 4\n", "\n", " loop R2,@navg_loop # Repeat the experiment to average, until R2 becomes 0\n", " jlt R0,{num_bins * stepsize},@ampl_loop # Repeat the experiment for different pulse amplitudes from 0 to num_bins\n", " stop # Stop.\n", "\n", "\"\"\"\n", "\n", "# QRM sequence program.\n", "qrm_seq_prog = f\"\"\"\n", "# Registers used:\n", "# R0 counts which bin to acquire into, a new bin for every new amplitude\n", "# R2 is used to count the averages needed for a single amplitude\n", "\n", " wait_sync 4 # Synchronize the QRM with the QCM.\n", " move 0, R0 # the first acquisition uses bin 0\n", "ampl_loop: move {navg}, R2 # reset the amount of averages to be taken to the initial value\n", "\n", "navg_loop: wait_sync {readout_delay} # wait for the QCM to signal a pulse is coming and wait the readout_delay\n", " play 1,0,{tof} # play readout pulse and wait for the tof\n", " acquire 1,R0,16384 # Acquire waveforms and wait remaining duration of scope acquisition.\n", "\n", " loop R2, @navg_loop # Repeat this measurement for every average\n", " add R0,1,R0 # Increment the bin into which we are measuring\n", " jmp @ampl_loop # repeat\n", "\"\"\"" ] }, { "cell_type": "markdown", "id": "2e5e103e", "metadata": {}, "source": [ "Upload programs and waveforms to QRM and QCM" ] }, { "cell_type": "code", "execution_count": 23, "id": "25f81e86", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.876914Z", "iopub.status.busy": "2025-05-07T16:47:34.876777Z", "iopub.status.idle": "2025-05-07T16:47:34.978698Z", "shell.execute_reply": "2025-05-07T16:47:34.978222Z" } }, "outputs": [], "source": [ "# Add QCM sequence to single dictionary and write to JSON file.\n", "sequence = {\n", " \"waveforms\": wfs,\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": qcm_seq_prog,\n", "}\n", "with open(\"qcm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)\n", " file.close()\n", "\n", "# Add QRM sequence to single dictionary and write to JSON file.\n", "sequence = {\n", " \"waveforms\": wfs,\n", " \"weights\": {},\n", " \"acquisitions\": acquisitions,\n", " \"program\": qrm_seq_prog,\n", "}\n", "with open(\"qrm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n", " json.dump(sequence, file, indent=4)\n", " file.close()\n", "\n", "# Upload sequence to QCM.\n", "control_module.sequencer0.sequence(\"qcm_sequence.json\")\n", "\n", "# Upload sequence to QRM.\n", "readout_module.sequencer0.sequence(\"qrm_sequence.json\")" ] }, { "cell_type": "markdown", "id": "57e77ab1", "metadata": {}, "source": [ "Arm and start sequencer0 of both the QCM and QRM. The `wait_sync` command together with the SYNQ technology ensures both modules start simultaneously." ] }, { "cell_type": "code", "execution_count": 24, "id": "c2f2a59e", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.980686Z", "iopub.status.busy": "2025-05-07T16:47:34.980542Z", "iopub.status.idle": "2025-05-07T16:47:34.995195Z", "shell.execute_reply": "2025-05-07T16:47:34.994795Z" } }, "outputs": [], "source": [ "# Arm and start sequencer of the QCM (only sequencer 0).\n", "control_module.arm_sequencer(0)\n", "control_module.start_sequencer(0)\n", "\n", "# Arm and start sequencer of the QRM (only sequencer 0).\n", "readout_module.arm_sequencer(0)\n", "readout_module.start_sequencer(0)\n", "\n", "# We didn't specify how many amplitudes would be measured, so we stop manually here.\n", "cluster.stop_sequencer()" ] }, { "cell_type": "markdown", "id": "ab6b3c18", "metadata": {}, "source": [ "## Stop\n", "\n", "Finally, let's stop the sequencers if they haven't already and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before\n", "closing the connection by uncommenting the corresponding lines." ] }, { "cell_type": "code", "execution_count": 25, "id": "0ed16a33", "metadata": { "execution": { "iopub.execute_input": "2025-05-07T16:47:34.996950Z", "iopub.status.busy": "2025-05-07T16:47:34.996810Z", "iopub.status.idle": "2025-05-07T16:47:41.327628Z", "shell.execute_reply": "2025-05-07T16:47:41.326514Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "QCM :\n", "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n", "\n", "QRM :\n", "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n", "\n", "QCM snapshot:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module2:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connected :\tTrue \n", "marker0_exp0_config :\tbypassed \n", "marker0_exp1_config :\tbypassed \n", "marker0_exp2_config :\tbypassed \n", "marker0_exp3_config :\tbypassed \n", "marker0_fir_config :\tbypassed \n", "marker0_inv_en :\tFalse \n", "marker1_exp0_config :\tbypassed \n", "marker1_exp1_config :\tbypassed \n", "marker1_exp2_config :\tbypassed \n", "marker1_exp3_config :\tbypassed \n", "marker1_fir_config :\tbypassed \n", "marker1_inv_en :\tFalse \n", "marker2_exp0_config :\tbypassed \n", "marker2_exp1_config :\tbypassed \n", "marker2_exp2_config :\tbypassed \n", "marker2_exp3_config :\tbypassed \n", "marker2_fir_config :\tbypassed \n", "marker2_inv_en :\tFalse \n", "marker3_exp0_config :\tbypassed \n", "marker3_exp1_config :\tbypassed \n", "marker3_exp2_config :\tbypassed \n", "marker3_exp3_config :\tbypassed \n", "marker3_fir_config :\tbypassed \n", "marker3_inv_en :\tFalse \n", "out0_exp0_amplitude :\t0 \n", "out0_exp0_config :\tbypassed \n", "out0_exp0_time_constant :\t0 \n", "out0_exp1_amplitude :\t0 \n", "out0_exp1_config :\tbypassed \n", "out0_exp1_time_constant :\t0 \n", "out0_exp2_amplitude :\t0 \n", "out0_exp2_config :\tbypassed \n", "out0_exp2_time_constant :\t0 \n", "out0_exp3_amplitude :\t0 \n", "out0_exp3_config :\tbypassed \n", "out0_exp3_time_constant :\t0 \n", "out0_fir_coeffs :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n", "out0_fir_config :\tbypassed \n", "out0_latency :\t0 (s)\n", "out0_offset :\t0 (V)\n", "out1_exp0_amplitude :\t0 \n", "out1_exp0_config :\tbypassed \n", "out1_exp0_time_constant :\t0 \n", "out1_exp1_amplitude :\t0 \n", "out1_exp1_config :\tbypassed \n", "out1_exp1_time_constant :\t0 \n", "out1_exp2_amplitude :\t0 \n", "out1_exp2_config :\tbypassed \n", "out1_exp2_time_constant :\t0 \n", "out1_exp3_amplitude :\t0 \n", "out1_exp3_config :\tbypassed \n", "out1_exp3_time_constant :\t0 \n", "out1_fir_coeffs :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n", "out1_fir_config :\tbypassed \n", "out1_latency :\t0 (s)\n", "out1_offset :\t0 (V)\n", "out2_exp0_amplitude :\t0 \n", "out2_exp0_config :\tbypassed \n", "out2_exp0_time_constant :\t0 \n", "out2_exp1_amplitude :\t0 \n", "out2_exp1_config :\tbypassed \n", "out2_exp1_time_constant :\t0 \n", "out2_exp2_amplitude :\t0 \n", "out2_exp2_config :\tbypassed \n", "out2_exp2_time_constant :\t0 \n", "out2_exp3_amplitude :\t0 \n", "out2_exp3_config :\tbypassed \n", "out2_exp3_time_constant :\t0 \n", "out2_fir_coeffs :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n", "out2_fir_config :\tbypassed \n", "out2_latency :\t0 (s)\n", "out2_offset :\t0 (V)\n", "out3_exp0_amplitude :\t0 \n", "out3_exp0_config :\tbypassed \n", "out3_exp0_time_constant :\t0 \n", "out3_exp1_amplitude :\t0 \n", "out3_exp1_config :\tbypassed \n", "out3_exp1_time_constant :\t0 \n", "out3_exp2_amplitude :\t0 \n", "out3_exp2_config :\tbypassed \n", "out3_exp2_time_constant :\t0 \n", "out3_exp3_amplitude :\t0 \n", "out3_exp3_config :\tbypassed \n", "out3_exp3_time_constant :\t0 \n", "out3_fir_coeffs :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n", "out3_fir_config :\tbypassed \n", "out3_latency :\t0 (s)\n", "out3_offset :\t0 (V)\n", "present :\tTrue \n", "cluster0_module2_sequencer0:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\tI \n", "connect_out1 :\tQ \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tTrue \n", "nco_freq :\t1e+08 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tTrue \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module2_sequencer1:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n", "cluster0_module2_sequencer2:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module2_sequencer3:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n", "cluster0_module2_sequencer4:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module2_sequencer5:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "connect_out2 :\toff \n", "connect_out3 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n", "\n", "QRM snapshot:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connected :\tTrue \n", "in0_gain :\t-6 (dB)\n", "in0_offset :\t0 (V)\n", "in1_gain :\t-6 (dB)\n", "in1_offset :\t0 (V)\n", "marker0_exp0_config :\tbypassed \n", "marker0_exp1_config :\tbypassed \n", "marker0_exp2_config :\tbypassed \n", "marker0_exp3_config :\tbypassed \n", "marker0_fir_config :\tbypassed \n", "marker0_inv_en :\tFalse \n", "marker1_exp0_config :\tbypassed \n", "marker1_exp1_config :\tbypassed \n", "marker1_exp2_config :\tbypassed \n", "marker1_exp3_config :\tbypassed \n", "marker1_fir_config :\tbypassed \n", "marker1_inv_en :\tFalse \n", "marker2_exp0_config :\tbypassed \n", "marker2_exp1_config :\tbypassed \n", "marker2_exp2_config :\tbypassed \n", "marker2_exp3_config :\tbypassed \n", "marker2_fir_config :\tbypassed \n", "marker2_inv_en :\tFalse \n", "marker3_exp0_config :\tbypassed \n", "marker3_exp1_config :\tbypassed \n", "marker3_exp2_config :\tbypassed \n", "marker3_exp3_config :\tbypassed \n", "marker3_fir_config :\tbypassed \n", "marker3_inv_en :\tFalse \n", "out0_exp0_config :\tbypassed \n", "out0_exp1_config :\tbypassed \n", "out0_exp2_config :\tbypassed \n", "out0_exp3_config :\tbypassed \n", "out0_fir_config :\tbypassed \n", "out0_latency :\t0 (s)\n", "out0_offset :\t0 (V)\n", "out1_exp0_config :\tbypassed \n", "out1_exp1_config :\tbypassed \n", "out1_exp2_config :\tbypassed \n", "out1_exp3_config :\tbypassed \n", "out1_fir_config :\tbypassed \n", "out1_latency :\t0 (s)\n", "out1_offset :\t0 (V)\n", "present :\tTrue \n", "scope_acq_avg_mode_en_path0 :\tFalse \n", "scope_acq_avg_mode_en_path1 :\tFalse \n", "scope_acq_sequencer_select :\t0 \n", "scope_acq_trigger_level_path0 :\t0 \n", "scope_acq_trigger_level_path1 :\t0 \n", "scope_acq_trigger_mode_path0 :\tsequencer \n", "scope_acq_trigger_mode_path1 :\tsequencer \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer0:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\tin0 \n", "connect_acq_Q :\tin1 \n", "connect_out0 :\tI \n", "connect_out1 :\tQ \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tTrue \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tTrue \n", "nco_freq :\t1e+08 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tTrue \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer1:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer2:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer3:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer4:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cluster0_module4_sequencer5:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "connect_acq_I :\toff \n", "connect_acq_Q :\toff \n", "connect_out0 :\toff \n", "connect_out1 :\toff \n", "cont_mode_en_awg_path0 :\tFalse \n", "cont_mode_en_awg_path1 :\tFalse \n", "cont_mode_waveform_idx_awg_path0 :\t0 \n", "cont_mode_waveform_idx_awg_path1 :\t0 \n", "demod_en_acq :\tFalse \n", "gain_awg_path0 :\t1 \n", "gain_awg_path1 :\t1 \n", "integration_length_acq :\t1024 \n", "marker_ovr_en :\tFalse \n", "marker_ovr_value :\t0 \n", "mixer_corr_gain_ratio :\t1 \n", "mixer_corr_phase_offset_degree :\t-0 \n", "mod_en_awg :\tFalse \n", "nco_freq :\t0 (Hz)\n", "nco_freq_cal_type_default :\toff (Hz)\n", "nco_phase_offs :\t0 (Degrees)\n", "nco_prop_delay_comp :\t0 (ns)\n", "nco_prop_delay_comp_en :\tFalse (ns)\n", "offset_awg_path0 :\t0 \n", "offset_awg_path1 :\t0 \n", "sync_en :\tFalse \n", "thresholded_acq_marker_address :\t1 \n", "thresholded_acq_marker_en :\tFalse \n", "thresholded_acq_marker_invert :\tFalse \n", "thresholded_acq_rotation :\t0 (Degrees)\n", "thresholded_acq_threshold :\t0 \n", "thresholded_acq_trigger_address :\t1 \n", "thresholded_acq_trigger_en :\tFalse \n", "thresholded_acq_trigger_invert :\tFalse \n", "trigger10_count_threshold :\t1 \n", "trigger10_threshold_invert :\tFalse \n", "trigger11_count_threshold :\t1 \n", "trigger11_threshold_invert :\tFalse \n", "trigger12_count_threshold :\t1 \n", "trigger12_threshold_invert :\tFalse \n", "trigger13_count_threshold :\t1 \n", "trigger13_threshold_invert :\tFalse \n", "trigger14_count_threshold :\t1 \n", "trigger14_threshold_invert :\tFalse \n", "trigger15_count_threshold :\t1 \n", "trigger15_threshold_invert :\tFalse \n", "trigger1_count_threshold :\t1 \n", "trigger1_threshold_invert :\tFalse \n", "trigger2_count_threshold :\t1 \n", "trigger2_threshold_invert :\tFalse \n", "trigger3_count_threshold :\t1 \n", "trigger3_threshold_invert :\tFalse \n", "trigger4_count_threshold :\t1 \n", "trigger4_threshold_invert :\tFalse \n", "trigger5_count_threshold :\t1 \n", "trigger5_threshold_invert :\tFalse \n", "trigger6_count_threshold :\t1 \n", "trigger6_threshold_invert :\tFalse \n", "trigger7_count_threshold :\t1 \n", "trigger7_threshold_invert :\tFalse \n", "trigger8_count_threshold :\t1 \n", "trigger8_threshold_invert :\tFalse \n", "trigger9_count_threshold :\t1 \n", "trigger9_threshold_invert :\tFalse \n", "ttl_acq_auto_bin_incr_en :\tFalse \n", "ttl_acq_input_select :\t0 \n", "ttl_acq_threshold :\t0 \n", "upsample_rate_awg_path0 :\t0 \n", "upsample_rate_awg_path1 :\t0 \n" ] } ], "source": [ "# Print status of sequencers.\n", "print(\"QCM :\")\n", "print(control_module.get_sequencer_status(0))\n", "print()\n", "\n", "print(\"QRM :\")\n", "print(readout_module.get_sequencer_status(0))\n", "print()\n", "\n", "# Uncomment the following to print an overview of the instrument parameters.\n", "# Print an overview of instrument parameters.\n", "print(\"QCM snapshot:\")\n", "control_module.print_readable_snapshot(update=True)\n", "print()\n", "\n", "print(\"QRM snapshot:\")\n", "readout_module.print_readable_snapshot(update=True)\n", "\n", "# Close the instrument connections.\n", "cluster.close()" ] } ], "metadata": { "files_to_bundle_in_zip_file": [ "figures/Banner.jpeg" ], "jupytext": { "notebook_metadata_filter": "files_to_bundle_in_zip_file,variants" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.20" }, "variants": [ "Any" ] }, "nbformat": 4, "nbformat_minor": 5 }