diff --git a/tutorials/custom_botorch_model_in_ax.ipynb b/tutorials/custom_botorch_model_in_ax.ipynb index 4f687d77a1..0a6b23a04f 100644 --- a/tutorials/custom_botorch_model_in_ax.ipynb +++ b/tutorials/custom_botorch_model_in_ax.ipynb @@ -1,1633 +1,2488 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "hidden_ranges": [], - "originalKey": "8760cbbb-0419-4ddd-b16d-360a0f8efb23", - "showInput": true - }, - "source": [ - "## Using a custom BoTorch model with Ax\n", - "\n", - "In this tutorial, we illustrate how to use a custom BoTorch model within Ax's `botorch_modular` API. This allows us to harness the convenience of Ax for running Bayesian Optimization loops while maintaining full flexibility in modeling.\n", - "\n", - "Acquisition functions and their optimizers can be swapped out in much the same fashion. See for example the tutorial for [Implementing a custom acquisition function](./custom_acquisition).\n", - "\n", - "If you want to do something non-standard, or would like to have full insight into every aspect of the implementation, please see [this tutorial](./closed_loop_botorch_only) for how to write your own full optimization loop in BoTorch.\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "8760cbbb-0419-4ddd-b16d-360a0f8efb23", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "## Using a custom BoTorch model with Ax\n", + "\n", + "In this tutorial, we illustrate how to use a custom BoTorch model within Ax's `botorch_modular` API. This allows us to harness the convenience of Ax for running Bayesian Optimization loops while maintaining full flexibility in modeling.\n", + "\n", + "Acquisition functions and their optimizers can be swapped out in much the same fashion. See for example the tutorial for [Implementing a custom acquisition function](./custom_acquisition).\n", + "\n", + "If you want to do something non-standard, or would like to have full insight into every aspect of the implementation, please see [this tutorial](./closed_loop_botorch_only) for how to write your own full optimization loop in BoTorch.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996758749, + "executionStopTime": 1730996764518, + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "e93e2f29-61fe-4f9f-b7bd-b742e2fe9344", + "output": { + "id": "469571788973641" + }, + "outputsInitialized": true, + "requestMsgId": "e93e2f29-61fe-4f9f-b7bd-b742e2fe9344", + "serverExecutionDuration": 3997.7363101207, + "showInput": true + }, + "outputs": [], + "source": [ + "import os\n", + "from contextlib import contextmanager, nullcontext\n", + "\n", + "import plotly.io as pio\n", + "\n", + "from ax.utils.testing.mock import mock_botorch_optimize_context_manager\n", + "\n", + "# Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n", + "# though they also lead to large file sizes, which is not ideal for files living in GH.\n", + "# Changing the default to `png` strips the interactive components to get around this.\n", + "pio.renderers.default = \"png\"\n", + "\n", + "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n", + "NUM_EVALS = 10 if SMOKE_TEST else 30" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "007adb71-ee1b-407c-9a61-ff948c104fce", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Implementing the custom model\n", + "\n", + "For this tutorial, we implement a very simple GPyTorch `ExactGP` model that uses an RBF kernel (with ARD) and infers a homoskedastic noise level.\n", + "\n", + "Model definition is straightforward. Here we implement a GPyTorch `ExactGP` that inherits from `GPyTorchModel`; together these two superclasses add all the API calls that BoTorch expects in its various modules. \n", + "\n", + "*Note:* BoTorch allows implementing any custom model that follows the `Model` API. For more information, please see the [Model Documentation](../docs/models)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "code_folding": [], + "collapsed": false, + "executionStartTime": 1730996759581, + "executionStopTime": 1730996764532, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "5aa02924-4c3d-4654-a81e-ef58084337d3", + "outputsInitialized": true, + "requestMsgId": "5aa02924-4c3d-4654-a81e-ef58084337d3", + "serverExecutionDuration": 2.7337353676558 + }, + "outputs": [], + "source": [ + "from typing import Optional\n", + "\n", + "from botorch.models.gpytorch import GPyTorchModel\n", + "from gpytorch.distributions import MultivariateNormal\n", + "from gpytorch.kernels import RBFKernel, ScaleKernel\n", + "from gpytorch.likelihoods import GaussianLikelihood\n", + "from gpytorch.means import ConstantMean\n", + "from gpytorch.models import ExactGP\n", + "from torch import Tensor\n", + "\n", + "\n", + "class SimpleCustomGP(ExactGP, GPyTorchModel):\n", + "\n", + " _num_outputs = 1 # to inform GPyTorchModel API\n", + "\n", + " def __init__(self, train_X, train_Y, train_Yvar: Optional[Tensor] = None):\n", + " # NOTE: This ignores train_Yvar and uses inferred noise instead.\n", + " # squeeze output dim before passing train_Y to ExactGP\n", + " super().__init__(train_X, train_Y.squeeze(-1), GaussianLikelihood())\n", + " self.mean_module = ConstantMean()\n", + " self.covar_module = ScaleKernel(\n", + " base_kernel=RBFKernel(ard_num_dims=train_X.shape[-1]),\n", + " )\n", + " self.to(train_X) # make sure we're on the right device/dtype\n", + "\n", + " def forward(self, x):\n", + " mean_x = self.mean_module(x)\n", + " covar_x = self.covar_module(x)\n", + " return MultivariateNormal(mean_x, covar_x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "0f22b707-cec8-43a1-9152-503a1904fbd5", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Instantiate a `BoTorchModel` in Ax\n", + "\n", + "A `BoTorchModel` in Ax encapsulates both the surrogate -- which `Ax` calls a `Surrogate` and BoTorch calls a `Model` -- and an acquisition function. Here, we will only specify the custom surrogate and let Ax choose the default acquisition function.\n", + "\n", + "Most models should work with the base `Surrogate` in Ax, except for BoTorch `ModelListGP`, which works with `ListSurrogate`.\n", + "Note that the `Model` (e.g., the `SimpleCustomGP`) must implement `construct_inputs`, as this is used to construct the inputs required for instantiating a `Model` instance from the experiment data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996760524, + "executionStopTime": 1730996764544, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "14a8c659-c142-4bd6-9a8e-29cfa78d7b98", + "outputsInitialized": true, + "requestMsgId": "14a8c659-c142-4bd6-9a8e-29cfa78d7b98", + "serverExecutionDuration": 2.4179229512811, + "showInput": true + }, + "outputs": [], + "source": [ + "from ax.models.torch.botorch_modular.model import BoTorchModel\n", + "from ax.models.torch.botorch_modular.surrogate import Surrogate, SurrogateSpec\n", + "from ax.models.torch.botorch_modular.utils import ModelConfig\n", + "\n", + "ax_model = BoTorchModel(\n", + " surrogate=Surrogate(\n", + " surrogate_spec=SurrogateSpec(\n", + " model_configs=[\n", + " ModelConfig(\n", + " # The model class to use\n", + " botorch_model_class=SimpleCustomGP,\n", + " # Optional, MLL class with which to optimize model parameters\n", + " # mll_class=ExactMarginalLogLikelihood,\n", + " # Optional, dictionary of keyword arguments to model constructor\n", + " # model_options={}\n", + " )\n", + " ]\n", + " )\n", + " ),\n", + " # Optional, acquisition function class to use - see custom acquisition tutorial\n", + " # botorch_acqf_class=qExpectedImprovement,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "4254a19d-cf71-4a88-a00f-608331cd9f54", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Combine with a `ModelBridge`\n", + "\n", + "`Model`s in Ax require a `ModelBridge` to interface with `Experiment`s. A `ModelBridge` takes the inputs supplied by the `Experiment` and converts them to the inputs expected by the `Model`. For a `BoTorchModel`, we use `TorchModelBridge`. The Modular BoTorch interface creates the `BoTorchModel` and the `TorchModelBridge` in a single step, as follows:\n", + "\n", + "```\n", + "from ax.modelbridge.registry import Models\n", + "model_bridge = Models.BOTORCH_MODULAR(\n", + " experiment=experiment,\n", + " data=data,\n", + " surrogate=Surrogate(SimpleCustomGP),\n", + " # Optional, will use default if unspecified\n", + " # botorch_acqf_class=qLogNoisyExpectedImprovement, \n", + ")\n", + "# To generate a trial\n", + "trial = model_bridge.gen(1)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "40d38579-279d-49fd-b2aa-e8c1fcab2a61", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "# Using the custom model in Ax to optimize the Branin function\n", + "\n", + "We will demonstrate this with both the Service API (simpler, easier to use) and the Developer API (advanced, more customizable)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "f4553323-645d-40f9-b1d5-b549e5265eb9", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "## Optimization with Ax's Service API\n", + "\n", + "A detailed tutorial on the Service API can be found [here](https://ax.dev/tutorials/gpei_hartmann_service.html).\n", + "\n", + "In order to customize the way the candidates are created in the Service API, we need to construct a new `GenerationStrategy` and pass it into `AxClient`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996762310, + "executionStopTime": 1730996764558, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "4d444eb9-21a3-43fc-a855-8793290d60dc", + "outputsInitialized": true, + "requestMsgId": "4d444eb9-21a3-43fc-a855-8793290d60dc", + "serverExecutionDuration": 2.1021906286478, + "showInput": true + }, + "outputs": [], + "source": [ + "from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n", + "from ax.modelbridge.registry import Models\n", + "\n", + "\n", + "gs = GenerationStrategy(\n", + " steps=[\n", + " # Quasi-random initialization step\n", + " GenerationStep(\n", + " model=Models.SOBOL,\n", + " num_trials=5, # How many trials should be produced from this generation step\n", + " ),\n", + " # Bayesian optimization step using the custom acquisition function\n", + " GenerationStep(\n", + " model=Models.BOTORCH_MODULAR,\n", + " num_trials=-1, # No limitation on how many trials should be produced from this step\n", + " # For `BOTORCH_MODULAR`, we pass in kwargs to specify what surrogate or acquisition function to use.\n", + " model_kwargs={\n", + " \"surrogate_spec\": SurrogateSpec(model_configs=[ModelConfig(botorch_model_class=SimpleCustomGP)]),\n", + " },\n", + " ),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "24e0fd52-cf2f-4c5a-8130-82e3b133c7df", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Setting up the experiment\n", + "\n", + "In order to use the `GenerationStrategy` we just created, we will pass it into the `AxClient`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996763198, + "executionStopTime": 1730996765492, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "4ee5509a-7cd5-4e0f-adcc-43961562f5f3", + "output": { + "id": "8782682878442163" }, + "outputsInitialized": true, + "requestMsgId": "4ee5509a-7cd5-4e0f-adcc-43961562f5f3", + "serverExecutionDuration": 701.53528917581, + "showInput": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "customInput": null, - "executionStartTime": 1646802598883, - "executionStopTime": 1646802598888, - "originalKey": "0b364288-7551-4ebb-9f96-719361878af3", - "requestMsgId": "4c7bff19-2eca-4be8-8d92-f4f3b9f23d85", - "showInput": true - }, - "outputs": [], - "source": [ - "import os\n", - "from contextlib import contextmanager, nullcontext\n", - "\n", - "from ax.utils.testing.mock import mock_botorch_optimize_context_manager\n", - "import plotly.io as pio\n", - "\n", - "# Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n", - "# though they also lead to large file sizes, which is not ideal for files living in GH.\n", - "# Changing the default to `png` strips the interactive components to get around this.\n", - "pio.renderers.default = \"png\"\n", - "\n", - "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n", - "NUM_EVALS = 10 if SMOKE_TEST else 30" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 12:52:13] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n", + "[INFO 11-07 12:52:13] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 11-07 12:52:13] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 11-07 12:52:13] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "originalKey": "007adb71-ee1b-407c-9a61-ff948c104fce", - "showInput": true - }, - "source": [ - "### Implementing the custom model\n", - "\n", - "For this tutorial, we implement a very simple GPyTorch `ExactGP` model that uses an RBF kernel (with ARD) and infers a homoskedastic noise level.\n", - "\n", - "Model definition is straightforward. Here we implement a GPyTorch `ExactGP` that inherits from `GPyTorchModel`; together these two superclasses add all the API calls that BoTorch expects in its various modules. \n", - "\n", - "*Note:* BoTorch allows implementing any custom model that follows the `Model` API. For more information, please see the [Model Documentation](../docs/models)." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" + ] }, { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "code_folding": [], - "executionStartTime": 1646802598911, - "executionStopTime": 1646802598919, - "hidden_ranges": [], - "originalKey": "63288450-0e06-4487-857e-9e1e9d85a343", - "requestMsgId": "d9929043-08ac-41b5-9268-2a1bef0676c8" - }, - "outputs": [], - "source": [ - "from typing import Optional\n", - "\n", - "from botorch.models.gpytorch import GPyTorchModel\n", - "from gpytorch.distributions import MultivariateNormal\n", - "from gpytorch.kernels import RBFKernel, ScaleKernel\n", - "from gpytorch.likelihoods import GaussianLikelihood\n", - "from gpytorch.means import ConstantMean\n", - "from gpytorch.models import ExactGP\n", - "from torch import Tensor\n", - "\n", - "\n", - "class SimpleCustomGP(ExactGP, GPyTorchModel):\n", - "\n", - " _num_outputs = 1 # to inform GPyTorchModel API\n", - "\n", - " def __init__(self, train_X, train_Y, train_Yvar: Optional[Tensor] = None):\n", - " # NOTE: This ignores train_Yvar and uses inferred noise instead.\n", - " # squeeze output dim before passing train_Y to ExactGP\n", - " super().__init__(train_X, train_Y.squeeze(-1), GaussianLikelihood())\n", - " self.mean_module = ConstantMean()\n", - " self.covar_module = ScaleKernel(\n", - " base_kernel=RBFKernel(ard_num_dims=train_X.shape[-1]),\n", - " )\n", - " self.to(train_X) # make sure we're on the right device/dtype\n", - "\n", - " def forward(self, x):\n", - " mean_x = self.mean_module(x)\n", - " covar_x = self.covar_module(x)\n", - " return MultivariateNormal(mean_x, covar_x)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "0f22b707-cec8-43a1-9152-503a1904fbd5", - "showInput": false - }, - "source": [ - "### Instantiate a `BoTorchModel` in Ax\n", - "\n", - "A `BoTorchModel` in Ax encapsulates both the surrogate -- which `Ax` calls a `Surrogate` and BoTorch calls a `Model` -- and an acquisition function. Here, we will only specify the custom surrogate and let Ax choose the default acquisition function.\n", - "\n", - "Most models should work with the base `Surrogate` in Ax, except for BoTorch `ModelListGP`, which works with `ListSurrogate`.\n", - "Note that the `Model` (e.g., the `SimpleCustomGP`) must implement `construct_inputs`, as this is used to construct the inputs required for instantiating a `Model` instance from the experiment data." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n" + ] + } + ], + "source": [ + "import torch\n", + "from ax.service.ax_client import AxClient\n", + "from ax.service.utils.instantiation import ObjectiveProperties\n", + "from botorch.test_functions import Branin\n", + "\n", + "\n", + "# Initialize the client - AxClient offers a convenient API to control the experiment\n", + "ax_client = AxClient(generation_strategy=gs)\n", + "# Setup the experiment\n", + "ax_client.create_experiment(\n", + " name=\"branin_test_experiment\",\n", + " parameters=[\n", + " {\n", + " \"name\": \"x1\",\n", + " \"type\": \"range\",\n", + " # It is crucial to use floats for the bounds, i.e., 0.0 rather than 0.\n", + " # Otherwise, the parameter would be inferred as an integer range.\n", + " \"bounds\": [-5.0, 10.0],\n", + " },\n", + " {\n", + " \"name\": \"x2\",\n", + " \"type\": \"range\",\n", + " \"bounds\": [0.0, 15.0],\n", + " },\n", + " ],\n", + " objectives={\n", + " \"branin\": ObjectiveProperties(minimize=True),\n", + " },\n", + ")\n", + "# Setup a function to evaluate the trials\n", + "branin = Branin()\n", + "\n", + "\n", + "def evaluate(parameters):\n", + " x = torch.tensor([[parameters.get(f\"x{i+1}\") for i in range(2)]])\n", + " # The GaussianLikelihood used by our model infers an observation noise level,\n", + " # so we pass an sem value of NaN to indicate that observation noise is unknown\n", + " return {\"branin\": (branin(x).item(), float(\"nan\"))}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "customInput": null, + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "25ac5ba7-291b-466f-bed0-f8cb5f842605", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Running the BO loop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "173daaa5-4a91-4294-aab8-305cada3efb4", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "The next cell sets up a decorator solely to speed up the testing of the notebook in `SMOKE_TEST` mode. You can safely ignore this cell and the use of the decorator throughout the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "executionStartTime": 1730996764634, + "executionStopTime": 1730996765512, + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "4cc92119-f4be-42d3-8a3c-c1518bd860dc", + "output": { + "id": "905203617909706" }, + "outputsInitialized": true, + "requestMsgId": "4cc92119-f4be-42d3-8a3c-c1518bd860dc", + "serverExecutionDuration": 5.7004098780453 + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802598937, - "executionStopTime": 1646802598969, - "hidden_ranges": [], - "originalKey": "e848d43f-cee5-4d6f-a930-2559554582d6", - "requestMsgId": "f55bab2a-3462-4377-82d3-8ea107d2551b", - "showInput": true - }, - "outputs": [], - "source": [ - "from ax.models.torch.botorch_modular.model import BoTorchModel\n", - "from ax.models.torch.botorch_modular.surrogate import Surrogate\n", - "\n", - "\n", - "ax_model = BoTorchModel(\n", - " surrogate=Surrogate(\n", - " # The model class to use\n", - " botorch_model_class=SimpleCustomGP,\n", - " # Optional, MLL class with which to optimize model parameters\n", - " # mll_class=ExactMarginalLogLikelihood,\n", - " # Optional, dictionary of keyword arguments to model constructor\n", - " # model_options={}\n", - " ),\n", - " # Optional, acquisition function class to use - see custom acquisition tutorial\n", - " # botorch_acqf_class=qExpectedImprovement,\n", - ")" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if SMOKE_TEST:\n", + " fast_smoke_test = mock_botorch_optimize_context_manager\n", + "else:\n", + " fast_smoke_test = nullcontext\n", + "\n", + "# Set a seed for reproducible tutorial output\n", + "torch.manual_seed(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996765169, + "executionStopTime": 1730996789818, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "1f85e052-7e4f-4d0a-907d-e259fa70f902", + "output": { + "id": "1297036191745441" }, + "outputsInitialized": true, + "requestMsgId": "1f85e052-7e4f-4d0a-907d-e259fa70f902", + "serverExecutionDuration": 24149.213106837, + "showInput": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "4254a19d-cf71-4a88-a00f-608331cd9f54", - "showInput": false - }, - "source": [ - "### Combine with a `ModelBridge`\n", - "\n", - "`Model`s in Ax require a `ModelBridge` to interface with `Experiment`s. A `ModelBridge` takes the inputs supplied by the `Experiment` and converts them to the inputs expected by the `Model`. For a `BoTorchModel`, we use `TorchModelBridge`. The Modular BoTorch interface creates the `BoTorchModel` and the `TorchModelBridge` in a single step, as follows:\n", - "\n", - "```\n", - "from ax.modelbridge.registry import Models\n", - "model_bridge = Models.BOTORCH_MODULAR(\n", - " experiment=experiment,\n", - " data=data,\n", - " surrogate=Surrogate(SimpleCustomGP),\n", - " # Optional, will use default if unspecified\n", - " # botorch_acqf_class=qLogNoisyExpectedImprovement, \n", - ")\n", - "# To generate a trial\n", - "trial = model_bridge.gen(1)\n", - "```\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/ax/modelbridge/cross_validation.py:464: UserWarning:\n", + "\n", + "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", + "\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.62583, 'x2': 14.359564} using model Sobol.\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Completed trial 0 with data: {'branin': (104.365417, nan)}.\n", + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/ax/modelbridge/cross_validation.py:464: UserWarning:\n", + "\n", + "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", + "\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 3.166217, 'x2': 3.867106} using model Sobol.\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Completed trial 1 with data: {'branin': (2.996862, nan)}.\n", + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/ax/modelbridge/cross_validation.py:464: UserWarning:\n", + "\n", + "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", + "\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 9.560105, 'x2': 10.718323} using model Sobol.\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Completed trial 2 with data: {'branin': (66.530632, nan)}.\n", + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/ax/modelbridge/cross_validation.py:464: UserWarning:\n", + "\n", + "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", + "\n", + "[INFO 11-07 12:52:17] ax.service.ax_client: Generated new trial 3 with parameters {'x1': -3.878664, 'x2': 0.117947} using model Sobol.\n", + "[INFO 11-07 12:52:18] ax.service.ax_client: Completed trial 3 with data: {'branin': (198.850861, nan)}.\n", + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/ax/modelbridge/cross_validation.py:464: UserWarning:\n", + "\n", + "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", + "\n", + "[INFO 11-07 12:52:18] ax.service.ax_client: Generated new trial 4 with parameters {'x1': -2.362858, 'x2': 8.855021} using model Sobol.\n", + "[INFO 11-07 12:52:18] ax.service.ax_client: Completed trial 4 with data: {'branin': (5.811776, nan)}.\n", + "[INFO 11-07 12:52:21] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 2.562464, 'x2': 4.925756} using model BoTorch.\n", + "[INFO 11-07 12:52:21] ax.service.ax_client: Completed trial 5 with data: {'branin': (6.61104, nan)}.\n", + "[INFO 11-07 12:52:21] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 5.503428, 'x2': 4.951339} using model BoTorch.\n", + "[INFO 11-07 12:52:21] ax.service.ax_client: Completed trial 6 with data: {'branin': (31.249773, nan)}.\n", + "[INFO 11-07 12:52:22] ax.service.ax_client: Generated new trial 7 with parameters {'x1': -2.306809, 'x2': 4.436082} using model BoTorch.\n", + "[INFO 11-07 12:52:22] ax.service.ax_client: Completed trial 7 with data: {'branin': (38.632786, nan)}.\n", + "[INFO 11-07 12:52:22] ax.service.ax_client: Generated new trial 8 with parameters {'x1': -1.582296, 'x2': 7.318848} using model BoTorch.\n", + "[INFO 11-07 12:52:22] ax.service.ax_client: Completed trial 8 with data: {'branin': (12.208769, nan)}.\n", + "[INFO 11-07 12:52:23] ax.service.ax_client: Generated new trial 9 with parameters {'x1': -5.0, 'x2': 9.065641} using model BoTorch.\n", + "[INFO 11-07 12:52:23] ax.service.ax_client: Completed trial 9 with data: {'branin': (78.686066, nan)}.\n", + "[INFO 11-07 12:52:23] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.779998, 'x2': 6.842907} using model BoTorch.\n", + "[INFO 11-07 12:52:23] ax.service.ax_client: Completed trial 10 with data: {'branin': (20.849186, nan)}.\n", + "[INFO 11-07 12:52:24] ax.service.ax_client: Generated new trial 11 with parameters {'x1': -0.959171, 'x2': 9.756062} using model BoTorch.\n", + "[INFO 11-07 12:52:24] ax.service.ax_client: Completed trial 11 with data: {'branin': (19.968334, nan)}.\n", + "[INFO 11-07 12:52:25] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 1.759405, 'x2': 0.0} using model BoTorch.\n", + "[INFO 11-07 12:52:25] ax.service.ax_client: Completed trial 12 with data: {'branin': (21.157597, nan)}.\n", + "[INFO 11-07 12:52:25] ax.service.ax_client: Generated new trial 13 with parameters {'x1': -3.67521, 'x2': 15.0} using model BoTorch.\n", + "[INFO 11-07 12:52:25] ax.service.ax_client: Completed trial 13 with data: {'branin': (3.70913, nan)}.\n", + "[INFO 11-07 12:52:26] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 10.0, 'x2': 0.0} using model BoTorch.\n", + "[INFO 11-07 12:52:26] ax.service.ax_client: Completed trial 14 with data: {'branin': (10.960894, nan)}.\n", + "/Users/sdaulton/miniconda3/envs/botorch_tut/lib/python3.11/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning:\n", + "\n", + "A not p.d., added jitter of 1.0e-08 to the diagonal\n", + "\n", + "[INFO 11-07 12:52:26] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 4.693345, 'x2': 0.0} using model BoTorch.\n", + "[INFO 11-07 12:52:26] ax.service.ax_client: Completed trial 15 with data: {'branin': (11.7103, nan)}.\n", + "[INFO 11-07 12:52:27] ax.service.ax_client: Generated new trial 16 with parameters {'x1': -3.16039, 'x2': 12.343285} using model BoTorch.\n", + "[INFO 11-07 12:52:27] ax.service.ax_client: Completed trial 16 with data: {'branin': (0.400116, nan)}.\n", + "[INFO 11-07 12:52:27] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 10.0, 'x2': 3.798226} using model BoTorch.\n", + "[INFO 11-07 12:52:27] ax.service.ax_client: Completed trial 17 with data: {'branin': (2.575594, nan)}.\n", + "[INFO 11-07 12:52:28] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 3.304444, 'x2': 2.327283} using model BoTorch.\n", + "[INFO 11-07 12:52:28] ax.service.ax_client: Completed trial 18 with data: {'branin': (0.555859, nan)}.\n", + "[INFO 11-07 12:52:29] ax.service.ax_client: Generated new trial 19 with parameters {'x1': -3.375582, 'x2': 12.520736} using model BoTorch.\n", + "[INFO 11-07 12:52:29] ax.service.ax_client: Completed trial 19 with data: {'branin': (0.764316, nan)}.\n", + "[INFO 11-07 12:52:30] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 9.267105, 'x2': 2.183014} using model BoTorch.\n", + "[INFO 11-07 12:52:30] ax.service.ax_client: Completed trial 20 with data: {'branin': (0.543305, nan)}.\n", + "[INFO 11-07 12:52:30] ax.service.ax_client: Generated new trial 21 with parameters {'x1': 9.536612, 'x2': 2.744301} using model BoTorch.\n", + "[INFO 11-07 12:52:30] ax.service.ax_client: Completed trial 21 with data: {'branin': (0.487921, nan)}.\n", + "[INFO 11-07 12:52:31] ax.service.ax_client: Generated new trial 22 with parameters {'x1': -3.055135, 'x2': 12.529729} using model BoTorch.\n", + "[INFO 11-07 12:52:31] ax.service.ax_client: Completed trial 22 with data: {'branin': (0.646773, nan)}.\n", + "[INFO 11-07 12:52:32] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 3.099745, 'x2': 2.457142} using model BoTorch.\n", + "[INFO 11-07 12:52:32] ax.service.ax_client: Completed trial 23 with data: {'branin': (0.428578, nan)}.\n", + "[INFO 11-07 12:52:33] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 8.94462, 'x2': 0.943412} using model BoTorch.\n", + "[INFO 11-07 12:52:33] ax.service.ax_client: Completed trial 24 with data: {'branin': (2.820818, nan)}.\n", + "[INFO 11-07 12:52:35] ax.service.ax_client: Generated new trial 25 with parameters {'x1': 9.510065, 'x2': 2.361432} using model BoTorch.\n", + "[INFO 11-07 12:52:35] ax.service.ax_client: Completed trial 25 with data: {'branin': (0.467552, nan)}.\n", + "[INFO 11-07 12:52:36] ax.service.ax_client: Generated new trial 26 with parameters {'x1': 9.425844, 'x2': 2.589096} using model BoTorch.\n", + "[INFO 11-07 12:52:36] ax.service.ax_client: Completed trial 26 with data: {'branin': (0.410706, nan)}.\n", + "[INFO 11-07 12:52:37] ax.service.ax_client: Generated new trial 27 with parameters {'x1': -3.091638, 'x2': 12.315311} using model BoTorch.\n", + "[INFO 11-07 12:52:37] ax.service.ax_client: Completed trial 27 with data: {'branin': (0.435478, nan)}.\n", + "[INFO 11-07 12:52:38] ax.service.ax_client: Generated new trial 28 with parameters {'x1': -3.221389, 'x2': 12.345989} using model BoTorch.\n", + "[INFO 11-07 12:52:38] ax.service.ax_client: Completed trial 28 with data: {'branin': (0.443229, nan)}.\n", + "/Users/sdaulton/botorch_2024_11_07/botorch/botorch/optim/optimize.py:576: RuntimeWarning:\n", + "\n", + "Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + "[OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + "Trying again with a new set of initial conditions.\n", + "\n", + "/Users/sdaulton/botorch_2024_11_07/botorch/botorch/optim/optimize.py:576: RuntimeWarning:\n", + "\n", + "Optimization failed on the second try, after generating a new set of initial conditions.\n", + "\n", + "[INFO 11-07 12:52:42] ax.service.ax_client: Generated new trial 29 with parameters {'x1': 3.182468, 'x2': 2.521964} using model BoTorch.\n", + "[INFO 11-07 12:52:42] ax.service.ax_client: Completed trial 29 with data: {'branin': (0.48354, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "40d38579-279d-49fd-b2aa-e8c1fcab2a61", - "showInput": false - }, - "source": [ - "# Using the custom model in Ax to optimize the Branin function\n", - "\n", - "We will demonstrate this with both the Service API (simpler, easier to use) and the Developer API (advanced, more customizable)." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 3.166217, 'x2': 3.867106} using model Sobol.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "f4553323-645d-40f9-b1d5-b549e5265eb9", - "showInput": true - }, - "source": [ - "## Optimization with Ax's Service API\n", - "\n", - "A detailed tutorial on the Service API can be found [here](https://ax.dev/tutorials/gpei_hartmann_service.html).\n", - "\n", - "In order to customize the way the candidates are created in the Service API, we need to construct a new `GenerationStrategy` and pass it into `AxClient`." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Completed trial 1 with data: {'branin': (2.996862, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802598995, - "executionStopTime": 1646802599053, - "hidden_ranges": [], - "originalKey": "906806a1-8b77-4719-82cb-4b86cc696271", - "requestMsgId": "5d74fa60-f595-4e7a-bbbc-e91f42b934d1", - "showInput": true - }, - "outputs": [], - "source": [ - "from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n", - "from ax.modelbridge.registry import Models\n", - "\n", - "\n", - "gs = GenerationStrategy(\n", - " steps=[\n", - " # Quasi-random initialization step\n", - " GenerationStep(\n", - " model=Models.SOBOL,\n", - " num_trials=5, # How many trials should be produced from this generation step\n", - " ),\n", - " # Bayesian optimization step using the custom acquisition function\n", - " GenerationStep(\n", - " model=Models.BOTORCH_MODULAR,\n", - " num_trials=-1, # No limitation on how many trials should be produced from this step\n", - " # For `BOTORCH_MODULAR`, we pass in kwargs to specify what surrogate or acquisition function to use.\n", - " model_kwargs={\n", - " \"surrogate\": Surrogate(SimpleCustomGP),\n", - " },\n", - " ),\n", - " ]\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 9.560105, 'x2': 10.718323} using model Sobol.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "24e0fd52-cf2f-4c5a-8130-82e3b133c7df", - "showInput": true - }, - "source": [ - "### Setting up the experiment\n", - "\n", - "In order to use the `GenerationStrategy` we just created, we will pass it into the `AxClient`." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Completed trial 2 with data: {'branin': (66.530624, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802599081, - "executionStopTime": 1646802599411, - "hidden_ranges": [], - "originalKey": "84dad757-0494-4fc3-9acd-7262367b2671", - "requestMsgId": "a3e2cc43-6913-4c70-bd5c-9d867c95b8ed", - "showInput": true - }, - "outputs": [], - "source": [ - "import torch\n", - "from ax.service.ax_client import AxClient\n", - "from ax.service.utils.instantiation import ObjectiveProperties\n", - "from botorch.test_functions import Branin\n", - "\n", - "\n", - "# Initialize the client - AxClient offers a convenient API to control the experiment\n", - "ax_client = AxClient(generation_strategy=gs)\n", - "# Setup the experiment\n", - "ax_client.create_experiment(\n", - " name=\"branin_test_experiment\",\n", - " parameters=[\n", - " {\n", - " \"name\": \"x1\",\n", - " \"type\": \"range\",\n", - " # It is crucial to use floats for the bounds, i.e., 0.0 rather than 0.\n", - " # Otherwise, the parameter would be inferred as an integer range.\n", - " \"bounds\": [-5.0, 10.0],\n", - " },\n", - " {\n", - " \"name\": \"x2\",\n", - " \"type\": \"range\",\n", - " \"bounds\": [0.0, 15.0],\n", - " },\n", - " ],\n", - " objectives={\n", - " \"branin\": ObjectiveProperties(minimize=True),\n", - " },\n", - ")\n", - "# Setup a function to evaluate the trials\n", - "branin = Branin()\n", - "\n", - "\n", - "def evaluate(parameters):\n", - " x = torch.tensor([[parameters.get(f\"x{i+1}\") for i in range(2)]])\n", - " # The GaussianLikelihood used by our model infers an observation noise level,\n", - " # so we pass an sem value of NaN to indicate that observation noise is unknown\n", - " return {\"branin\": (branin(x).item(), float(\"nan\"))}" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Generated new trial 3 with parameters {'x1': -3.878664, 'x2': 0.117947} using model Sobol.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "customInput": null, - "originalKey": "25ac5ba7-291b-466f-bed0-f8cb5f842605", - "showInput": true - }, - "source": [ - "### Running the BO loop" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Completed trial 3 with data: {'branin': (198.850861, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell sets up a decorator solely to speed up the testing of the notebook in `SMOKE_TEST` mode. You can safely ignore this cell and the use of the decorator throughout the tutorial." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Generated new trial 4 with parameters {'x1': -2.362858, 'x2': 8.855021} using model Sobol.\n" + ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if SMOKE_TEST:\n", - " fast_smoke_test = mock_botorch_optimize_context_manager\n", - "else:\n", - " fast_smoke_test = nullcontext\n", - "\n", - "# Set a seed for reproducible tutorial output\n", - "torch.manual_seed(0)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:05] ax.service.ax_client: Completed trial 4 with data: {'branin': (5.811776, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802599439, - "executionStopTime": 1646802636908, - "hidden_ranges": [], - "originalKey": "84f831d3-a148-4872-8ca6-2413d3c75b9c", - "requestMsgId": "10bccad3-09b6-4711-8221-5fa0f75e7bdc", - "showInput": true - }, - "outputs": [], - "source": [ - "with fast_smoke_test():\n", - " for i in range(NUM_EVALS):\n", - " parameters, trial_index = ax_client.get_next_trial()\n", - " # Local evaluation here can be replaced with deployment to external system.\n", - " ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:07] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 2.562432, 'x2': 4.925782} using model BoTorch.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "2794d041-6a39-483d-a603-cc5088bcd1b2", - "showInput": true - }, - "source": [ - "### Viewing the evaluated trials" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:07] ax.service.ax_client: Completed trial 5 with data: {'branin': (6.611189, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802636942, - "executionStopTime": 1646802637202, - "hidden_ranges": [], - "originalKey": "db21dcbb-b74b-4a91-89c3-5ffbc8020220", - "requestMsgId": "71578ba1-7826-4c60-84a5-a14d5a10608c", - "showInput": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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trial_indexarm_nametrial_statusgeneration_methodbraninx1x2
000_0COMPLETEDSobol104.3654170.62583014.359564
111_0COMPLETEDSobol122.9305881.45845314.529696
222_0COMPLETEDSobol7.3907749.1405714.816245
333_0COMPLETEDSobol61.2718478.2154639.037606
444_0COMPLETEDSobol145.0156719.93431714.913237
555_0COMPLETEDBoTorch16.6653337.9243354.209484
666_0COMPLETEDBoTorch36.188915-2.8605205.658734
777_0COMPLETEDBoTorch28.414114-1.55239713.052576
888_0COMPLETEDBoTorch106.3994988.00599311.430362
999_0COMPLETEDBoTorch27.1055240.3260892.664659
101010_0COMPLETEDBoTorch22.409479-4.67447212.688425
111111_0COMPLETEDBoTorch271.716095-5.0000001.094121
121212_0COMPLETEDBoTorch155.017776-4.3714023.250981
131313_0COMPLETEDBoTorch17.016251-0.8029427.950013
141414_0COMPLETEDBoTorch3.365896-2.90585910.071743
151515_0COMPLETEDBoTorch10.96089410.0000000.000000
161616_0COMPLETEDBoTorch2.05919510.0000003.343624
171717_0COMPLETEDBoTorch20.7281006.1542400.000000
181818_0COMPLETEDBoTorch0.6015679.2255432.194878
191919_0COMPLETEDBoTorch0.5419029.5965652.675480
202020_0COMPLETEDBoTorch3.603829-3.83035315.000000
212121_0COMPLETEDBoTorch0.518112-3.27904012.435520
222222_0COMPLETEDBoTorch0.4009179.4494602.485637
232323_0COMPLETEDBoTorch2.3051503.4757023.202017
242424_0COMPLETEDBoTorch0.9619233.3959991.580014
252525_0COMPLETEDBoTorch0.439405-3.16733312.141147
262626_0COMPLETEDBoTorch1.4026263.5988322.080916
272727_0COMPLETEDBoTorch0.4536659.5173192.675371
282828_0COMPLETEDBoTorch0.4130403.1977752.229931
292929_0COMPLETEDBoTorch0.5719669.5874012.833282
\n", - "
" - ], - "text/plain": [ - " trial_index arm_name trial_status ... branin x1 x2\n", - "0 0 0_0 COMPLETED ... 104.365417 0.625830 14.359564\n", - "1 1 1_0 COMPLETED ... 122.930588 1.458453 14.529696\n", - "2 2 2_0 COMPLETED ... 7.390774 9.140571 4.816245\n", - "3 3 3_0 COMPLETED ... 61.271847 8.215463 9.037606\n", - "4 4 4_0 COMPLETED ... 145.015671 9.934317 14.913237\n", - "5 5 5_0 COMPLETED ... 16.665333 7.924335 4.209484\n", - "6 6 6_0 COMPLETED ... 36.188915 -2.860520 5.658734\n", - "7 7 7_0 COMPLETED ... 28.414114 -1.552397 13.052576\n", - "8 8 8_0 COMPLETED ... 106.399498 8.005993 11.430362\n", - "9 9 9_0 COMPLETED ... 27.105524 0.326089 2.664659\n", - "10 10 10_0 COMPLETED ... 22.409479 -4.674472 12.688425\n", - "11 11 11_0 COMPLETED ... 271.716095 -5.000000 1.094121\n", - "12 12 12_0 COMPLETED ... 155.017776 -4.371402 3.250981\n", - "13 13 13_0 COMPLETED ... 17.016251 -0.802942 7.950013\n", - "14 14 14_0 COMPLETED ... 3.365896 -2.905859 10.071743\n", - "15 15 15_0 COMPLETED ... 10.960894 10.000000 0.000000\n", - "16 16 16_0 COMPLETED ... 2.059195 10.000000 3.343624\n", - "17 17 17_0 COMPLETED ... 20.728100 6.154240 0.000000\n", - "18 18 18_0 COMPLETED ... 0.601567 9.225543 2.194878\n", - "19 19 19_0 COMPLETED ... 0.541902 9.596565 2.675480\n", - "20 20 20_0 COMPLETED ... 3.603829 -3.830353 15.000000\n", - "21 21 21_0 COMPLETED ... 0.518112 -3.279040 12.435520\n", - "22 22 22_0 COMPLETED ... 0.400917 9.449460 2.485637\n", - "23 23 23_0 COMPLETED ... 2.305150 3.475702 3.202017\n", - "24 24 24_0 COMPLETED ... 0.961923 3.395999 1.580014\n", - "25 25 25_0 COMPLETED ... 0.439405 -3.167333 12.141147\n", - "26 26 26_0 COMPLETED ... 1.402626 3.598832 2.080916\n", - "27 27 27_0 COMPLETED ... 0.453665 9.517319 2.675371\n", - "28 28 28_0 COMPLETED ... 0.413040 3.197775 2.229931\n", - "29 29 29_0 COMPLETED ... 0.571966 9.587401 2.833282\n", - "\n", - "[30 rows x 7 columns]" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax_client.get_trials_data_frame()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:07] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 5.50005, 'x2': 4.949873} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802637270, - "executionStopTime": 1646802637381, - "hidden_ranges": [], - "originalKey": "521e7082-5a4d-4110-b6ac-dd94fd674d3d", - "requestMsgId": "034baf37-374d-4281-8943-9356ee6f3e84", - "showInput": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters: {'x1': 9.517319461327038, 'x2': 2.675371257532727}\n", - "Corresponding mean: {'branin': 0.3263206610090492}, covariance: {'branin': {'branin': 0.06906659192184665}}\n" - ] - } - ], - "source": [ - "parameters, values = ax_client.get_best_parameters()\n", - "print(f\"Best parameters: {parameters}\")\n", - "print(f\"Corresponding mean: {values[0]}, covariance: {values[1]}\")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:07] ax.service.ax_client: Completed trial 6 with data: {'branin': (31.211433, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "10562d0a-5fc3-4771-91c3-f881bd211174", - "showInput": true - }, - "source": [ - "### Plotting the response surface and optimization progress" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:08] ax.service.ax_client: Generated new trial 7 with parameters {'x1': -2.300231, 'x2': 4.436402} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802637385, - "executionStopTime": 1646802638565, - "hidden_ranges": [], - "originalKey": "24bbc6f8-d9b7-45bc-ba2b-b92bbc9a3712", - "requestMsgId": "d0b78eb5-687f-48ac-b482-e5bc2a1203b3", - "scrolled": false, - "showInput": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 11-22 08:58:03] ax.service.ax_client: Retrieving contour plot with parameter 'x1' on X-axis and 'x2' on Y-axis, for metric 'branin'. Remaining parameters are affixed to the middle of their range.\n" - ] - }, - { - "data": { - "image/png": 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UFmrBlgDnr1/7SOWKTDykYhNTf3KwfeYPH1INWFdkEoo0pH8PvPz0Qg62/58PMz/XfacAB9v77pbxAj/oCrBgS+b5W/bcP9DF0Q4HtwjwylpsSfIokin56UdmYvmCyTQR1At/+gQTRw2kCS9WvPhPGqf79h+fRFZOAd7/4lucv3QVn/7jBYwa2teggeSuyA/608WvjyvAFeAK/LQKtLS0Yuajr1EvIZLIMMDXEw2NTTh5LgEfrfoe777+NKaOG4JX3voCZNu3frcSFhZmGD7zObz+wjLMmz4Kx8/GY83WA7h6PYtmKCbJDknyqI4stsTV+bGX3qXeSYNp9uKz+GTNDkwbN4SC7Z3aQi3YErfmsfN/g4fnTKAJpZKvZ+H1f64GCRG6ncX2P2+/iJCgrgYCO9jZ0Ol+tNdgzGJ74mw8Xn9nNb5877fo2d0fB07E0Hhl4mlFMkBzi+1P+/zyo9/fCnCwvb/vHy/9A6gAC7bk8siUPGOGR+DZFbMMwJZaYeNS8P4X25CRXUBHxx+aNJwmnCLz8CWlksZ4FQpLytGjuz/+/ocn8NWmPTh2Jg5fvPMyjSHiFtsH8CHil8QV4ApwBX5GBUjyKJLQ6czFJDrgamtjRZNIkSmAyMAr+Zy+kEgzAhO3YxInu+/Yefx71Q/U5XZcZATNlvzob96hSQ8JTJKpcjoCW3I84iq84btDqKltwMxJw9He3o6W1jb8/Q+P37EtjIpOULki07Y1PgVvf7wZuYWlNCZ2XGR/bPz+MA5vfd9AWSnGtiPJn185l4b8dAZsyTFIYshNPxyhUxAF+XvThJJDBwjT9HGw/RkfbH7qX7wCHGx/8beIF5ArwBXgCnAFuAJcAa4AV4ArwBXgCnAFbqcAB1v+fHAFuAJcAa4AV4ArwBXgCnAFuAJcAa7Afa0AB9v7+vbxwnMFuAJcAa4AV4ArwBXgCnAFuAJcAa4AB1v+DHAFuAJcAa4AV4ArwBXgCnAFuAJcAa7Afa0AB9v7+vbxwnMFuAJcAa4AV4ArwBXgCnAFuAJcAa4AB9t7eAbWbPoGw0eNhKeXF3Ti/sK/OujEBdJyYamyEbucWSxvte2bb5CZkW5QqsUPL0VQcHAHpdVDb2QNXSauYNdL3/XMBuTrpYsxuHXrFsZNmCQfT9hWD2Fb4XDSbvJxyHoAVxMTkZuTg0kzZojbk+XCvuI3tOsNl+3etg39hgyBb4A/yCTtwn+Qv0vnl9aQ46VcvoyyklIMmzAO7dCjXd9Oj03/6DmV79Jysqy+vh4HNm3FrCcfFfcTt6XHEMpJy8geR7OsuaERp7btwJgVi4QyimVWrk0pv1QWlQ7yNTLa0GVAa3MLzm/egW4jB8ItOEDUTzwe2YLRUtZV3NeYTsIVSVqK90G8mXQNo3djVQ1S95yAV0QPuIYHMfddumfy06DaTzoGe37hu3QnhfPLT4H4AAmaCMuLLl1HU2UN3Pp3h6Wzneo6heNLz6F0HPUxWV1U2zOa0SNID7JmObuP8jIx75UeqL6ej5baRrQ3tqCtqRX6tja0N7cJ96hNDxNTE5hamQMmOphamtM/c0dr2HfzNKgApPpCqhx05ItYeejov+IW4nfyD91G/H+yrfSf8J3sLq0n9ZCwlqzTt7Sh4HQSnMP8Ye/rpmwnHYP+ayLvw+5Lj6IzEc7FbE80urH3FLpNjISFrbVY94klYrdr0yN+0y6xrEJt+Lelv8bEsaPuoeblu9zPCtTWN2Prlp0YMWIMTE1McKuqAoeP7MXyZSthaqKDiYkOra3NWLXmS7zw3EvkNaLPnAl5nnRAeUU5du3ZiadWPkVfD/pOiM9+aVkJVq1ZLcjDNLJurm54+omn5PZLqg/Fqk+oHYX/4eLFGDQ1NyNy+AihtpHqKantk+sh9bqKinKcPhOFkSPHwMnJWT6eVCcpx1LuHtv2Ms10p2+vYT+CVhDspQtSaDYU6gRFpsKiQuTmZSM/Pw/V1dUICAjE2NHj5HIo+8s1lmp/RW+hDpJ+K6dV+kTZOTdxI+0GEq9cRrfg7ugZ1hNhYWGqa2aLa6xP85/PP8X4cRMQGhIm9kPEOpo2ZUJ7WlxSjP0H92H5ssfEfgCEfkG7Hl+v+gyLFy+HlZWNvOzQ4X3w9w9CUHAo2tr19G/t2s+wcPET0Jmbo7Vdj/PRJ2Fr74ig0D70d05OJjKuX8WA0dPQ0q5HZVUFYk7ux5BpS9BCMjG36xH1wyoMmvkYWqBDc1s7rp7eB7fQCJg7eaC5TY+qohyUZV6F68AJ9DfZpyo9Ea2tbbAK7IPWNj09V9OtctRdi4ZVxFRaNrJMKmdLThK9LviEk4sE9O3CX3ub/F1qY4WOmPwwM+8K04ulX4WXi9T7MDEByL/0ZSP/mqL98j5Y9JkEEysbmOp09N0lf6gqQFtxOuz7jIWZqQ5mdLkJKk5ugvfkFfS3uYkJWspy0Zifjq6DJ8KCLtOhMP4kXH2C4OYXTH9bmJggYe8GDJ6xFFYWlvT4ZjodijKuoqGmEr0GjhLqDHJ+HWg5yPe8jBRkXI2DraMzLC0s4erhjYDu4VJrKj9r5KlprK/DtcQYNDU1orqqnF63s7s3XNy94OjsBntHJ+GaNR8iUVNjA2oqS5F5IxkePgEIDgmn5ydb0391wr9Rh35AxMBIeHn50OtPTY5Da1MThg4dieLCHKQkx2PWzAWIjT0HW2srDBk8FBcvnoOFuQWtD8+eO4W6+jpkZKSjd99+eOedv3e6jvhf3pCD7T3c/V0Hj6KLkyP69u9/X4Ot0OBKyADU1dZi3epV+PWLL2nAVr2d1BCzsCtBzYf//AdeevV1AyCTgVUFZgL4ZNxIQ2pyMqbMmaUGYbEjIQGQFng3ffYF5j62HBZWVjLQshArAK8h7F6LT0B9TS36jBxuAMESEBNVJFBlQVf6XlFcghuxCRgwfcId4VaGZQ3kC5ooAM8OAKSejEZbaxt6jI/UgL4Cp0K/SzsQIHbdjACwdL9ZHdllEowWXL6G6txiuPYIhFOw7z0CrhpG5V8y7EpQq1xPfUklSuJvwMLeBm79u8GEQCLzDHQGcOXOqDgEo/wWn1pRF6nnKj/97HKmJ6q8HcogEdM9FQ5jpOFjeg3KWqbnp3yVh8TEDqMAkEL3QuqsisAqdjpkwBVhVwu0MgLrdKi9WYziuDT4juoLGw8nAWBFSBUG4gxhVAuz2u0ay2uQdvAMwmaOg3UXexX0kg4QPYN4XH27HvEbCdiKV6QD3nqYg+09NDv3/S41dU24lpwLBwcH2iklnTkzMzMMGzqcdoBJRy7h8iXU1FRj/NgJItSK8KoDzp0/R/vXIyNHKgM5IuCWlpZi1ZpVBmQngS3zSouvPgNEDNzu3LUT/SP6o2tXP3k7aV/tIK8ExKReIoB44uQxuLl7YNyY8dARIBCBmQVc6SbK42uauyrXJcygM0usxgbGpUPcDmKFtw+oulWFvIJcZGVmoKy8jHagg4OCERgQDC8vL+EtZQlVGifQDtjLdZF4dg1US3UYgeUrSYm4lnoVDg6O6NcnAt27d6f3XXMapc4Th0BV0jAD6zt374SDvT3GjhmvupcCtwlwe/zEUTg6dkG/fgMEuG0X+gGxsTGoq6vD8Mgx8rKb2VmIj7+E6dPnCsCo1yP6/CnYOzghqHs4WvV6FBUVIjbmNMZNnU/Bkvxt3/IFJs1biTYTU7S06XHq4HfoPmAkrLu4obW9HSmxp2Dl6ArngDAKrkXZN1BRmAOviNEUYpvb9Ug79i3cB0+F3tJGBuK8QxvgOv4RtLa1C+dq06Mu7RLazW1g6q3AtwS3rTcvo93UAvAKNQ630vMtDiILcMu2WtLNUwBXR0eVJKhV4FZfkApTUzOY+fagzCtBLQHL5qSjsA4ZBEtHVwpx5K8xKwFmllZwDOpNfxOQLb2wH159hsPOyY2CbntdFQrjoxA+YR4FWgK2xdfjKawG9RoEMx3ovq2N9Yg/tgNjZ6+g68gyUkwKtQBio/bD1tYOPfsNgZW1rcDiyuOp/GCeJbn91gM11VUoL8lHWWkRqivLUFVRBmtbe7i4eaKlpRmmZuaouVVB27vG+lq4evnCw8sPAd16yINvLNxWVZTgasIFTJg8WwBzEx2+WfcpHl35PCzMTHHxQhRcnJzRr19//PD9JkydMgOeHh7Ytm0jFsxfgKtXk3Hm7Ck0Nzch+WoyIgYM5GDbyVaQg20nhWI3O3vpMvLzcjBp2jQN2EpWE3X7anwkU9vfFbbSWmylfRfd1mIrvJ7aDra6kVRXZbK1VbaeCeXZunkTIkeNpg27vI3QfIiWV2E7qd1VthHWH9yzG/5BQQjr1UtlqZUhjrHkscvWfvofLHxsBaxtbY1abZWRdcl6CCTHx6OyohJDx40xsNpKMClZcLVwemjztxg0aRwcXF0MrLOstZdcE2sBZo9bkpuHrMvJGDBjYgdwy0KoUu7bWXGFTpSwbWlmLjIvJiBgcF+4BHRVQb/aOstatzu2zrIWXumJYWGahcjWxibkxSahsaoa3gPCYevpIj4DitVWscJ20nqqglrZlswMrgiwW32zEKWJGbD1dYN7RDD05CW4a8Bln0xlf+U9Uazf0hOtsqLIjR/zVhnpjRqzKkidSIOqhel5spYT1gor7CuOmEtdPQZ0DWFXsswK+yn76lB47ipMzUzhNTRcGIHvAGTlPY1YZpV9hFH7yvRclN/IRuj00RooZi2+whFJI08s2XEbd8reLGT5mw8/yy2299Du3O+71DW0ICerggIs6eht3rIWc2bNh4uzM11G/jZ/sx7TJk+nHTzSuZassuRZWr12FebMngMCq5IHg7AeKNGArdRuuooWW0k7ZTBWsdTK4CmC6KeffYJHlz9GO8lSfSz8K9cUGmhV2sfLiQm4FHcRISE9EDksUg24ciGMtdbiSgmGjdxsLbiym6jrE6X/QSyJuXm5KMjPQ0VlBfVYImDp4+2LAP9AWFhY3BZkVXXZHWBW8hwh+yRfTULqjWsoKy1D7959ENE3AnZ2dgYwy9h5VZZgbd9FkU7oZ8TGXkRKagoeWbpCvkcs2JL286OPP8Czv3oBpiamonVWGKz+6OP38PxzrwhgK4LsunVfYu7chykQEWAsLCpAfEIsxk6YIYPsD9+uxdgpc2FpY0+XXYw+AfsubvAO7iFAaspl1NRWI6jvcLTo9agoKURa4gX0GP0QBVuyzaXd6xA6aQlaTczQ3N6O4huJaG5uhn33CAq71Pp7IwHt0MHSv5dstSWgXHXmO1gPmol2E3PZYkvK2t4OtGRcAqwdoHcPZuCWWG1FK60GahXPJaXPqgygSFDLwK2JCfSlWdBV5sO852gBJsX3mLzL+oo8tJdlw67XKMVa296Cygt74DVukWytbS0vQEPuNXQdMlm21hZcOg73gBC4+gTSZSbtbUg8sBkj5j4uALJoFY47sh29h4yBk4s7hV0K1WTwtrIMZw5+h7HTFsLF1YPCruKYID5hmoEXbf9V6k9JfVvp+aupvoXa6gqQAVpTc3NYWlrDxsaOGlPYd0Ox0opeJgB2b1uNyTMWwKmLE72O5MSLMAUwcOAw+vvbbWswf+5imJuZYceOb/DkE79CUWE+HdybP2cBLsZewJlzUWhra0NcXCwGDRnGwbaTjSAH204KxW52PacAZ06dwLxFizsEW/ahvxuw1RbnTq45yvaGYCu9pBKhsp1wLdhK2yYlXkZpaRnGjp9gFGy1L76qsQeQm52NhLg4TJ8z14g1UQA22WWXgbj4mBg6mXr/oUNEsNNCIWPZZPb7Yd0GTF+8EGaWFiqrrdYVWXItliy4VeXlOH/4GCYunq+4JEvuyBo3ZOVYQhlYS27RzWwU3MhAn4mjFSCXt2HLLLnmshZajcWWtegyIJdy9CxMzEwRMmow9AQW5MGBjq2zKmsuY43UDjCIXTIDzaWKvqHqFvJjk0GGRH0G9oKFg60MuIoFQxgBpv/PuBwbwqhkJZFtxuPifXsAACAASURBVPLghzHX5crUbJSn5sAx2BsuvQS37NsBrlgClRu8qmMqOTQz7sgdWmwZ93upOyu/ayzrCt0qzWur6q5prEii7VJy+RL3NABWWoFIx9FYcWVAFWoZretwQ1EVCqOT4TEoDA7+HgbAa9S12Cj0ql2QC+NS0NbcCr/ICJXlVwvMgsVWaOAVsJXxHW89/CwmcFfke2h57u9dCNjm3qykHeLKilKcv3AGc2cvkN2QKyrLcOz4ESxb8ojiZiy69VVWVuDg4QN45OHlihsy85yJr4sskGQxVLw8mJZSAlTGW0kOZdADTU1N2LB5PZ5cqbgwy55NrLXHiGuyUN8AF2KiKdz5+QWgf78BcHYhA4NKVWEwIKZZwLbmxuCPfRKkPkJbextKy0pRkJ+LvPw86l5sZWWNrr5d4evTFT4+vnB0dBT6LLexyqpXS74WckWldneW6yjQ811JTkR29k06ME6ss127dqU7sufUukOrnmqpXEZGDBUYEeCWXOP+A/vw5ONPC9KK1lqhjQauX09F5s1MTJowRXY7JiB7Lvo0rG3s0KtXXwqFBA5jL12AiYkJevUeIEPj5s1fY9rMxbC0tqUgm5AQA52JKbr1iECrvh3FhflISU7AgFFTKbTW1tch+tguDJ68UIbUuOO7EDxwLHTWdhRks5MvARaWcPDvQbdpaGrAzTP74DlyjmyxJa7IhWd2wWn4bMVq265HY2kemkqyYdF9qAZshettSYsBHN2hd/ZTW261llrWJVkeLdFYbWU3ZAFu9bdKoCtJg1mY4AIshQ5IrsiNCQdg3288zKxsBBg11aExPQ4Wjq6w9w2WrbUV8SfgFtoP9s7ugrW2thKlV2MQNmo6/U3ANj/pAnUF9g4MlcE2LzWBfg8O709Bl7o5k4HW4nxkpiQgcsJM0R2ZeE8rb4z03IlDvqrWWjKWsC24gReitnlXeS4o7waRkXVBPrJnK0ZPmAF7OwfBsgw99u3YhAWLHqO/ievzmVOHsGjRCqSnpdD6cOzYCTh7NgpOXRwxeOBgXLx4AafPnaLvDqlPBg8dzsG2k00gB9tOCsVuVlbXjI/e/ydeee11Q7DVxLBI+ykvmOEJNU2H0jgbKVtHFiKp1TRqtWUbY/GYKshlGvimxkZ88Z9P8eJvf2fojsy4bMnvuzjCrEAO8NWnH2PpysdhQ62vcmQlA37aZUBDQz02f70KK194Tu2ea+C+K5xZgDsgKS4et6qqMGTsaBlQWfCUXZMZt2QJrhPORsPc0hKhA/oZscoKZ7lTvC0pQ0F6JgozstB30hgV3EplvG2crYE+xq2vJRnZIO7J3UYOhnt3EfJkwFcGAeQGXlJJ7A1IZZGQkgVEQ9hVYn8l4KzOK0ZhYiqsnBzgOzgcMCVjj3cTRytBuHjv5GdJLJEmjpZ1XS5LykB5yk249euGLiG+HQCu+ARqrMIdWWjljiNzXqUNU8wmHQ4XGXmnDDqbxnztxG6etnMp1QEqN2RV3KzQPZRiCqXvirVV6DSXxqfRuF+vYeHUWqSFTmV7QwsuGb3oyDU54/B5OAX4wK1HkMoqLG1P4yBVcEyPRo937eBpOTqYdADemLkSgwf0u4eal+9yPytAwDYvWwDb89HE1dMeA/sPokBBOspRp4/DuYsTBvQfaOiGHH2WxrCPGD5ChiTpXZDfGbbTyZo3GQCV9JNDFGTIZWoKPVBUXIQDh/bjseUrmbBEeRiMsdgyNYRcbUh1EWg86fUbqTQ+ODioG/3z9vKGpWjxMRgT68wN1oG605aUFqO0pATEDZt8J/kxfHx94O7qTiG2q68frK2tNXG2TG+DGTVXV1WGA3PsADtrmSVu49dSr4FYqt3c3NC9Wwj69O6j7sPI+QFYSxoLyqqjG1VAqa9FI6TY/peUlmDv/j14bPnjSl+DAdyvVn2BhQuWUMCQ4myLiotx5NghLF74CF1GwLa4uAjHTxzBvHlLqSsyWXb+/BlqwQ3p2Ze6I5eWFuPcmWOYOGOxbMXdsfVrjJu1DDC1oHAbfXQn/MMHws7Vi4JretJFan31CI2g62uqynEj5jiCx8yloEu2yY49AWsPf1h4+MtAXH7tInQWNrDwDZOttqRMNVdOwtSzO3RdvGS4ldysKdymnAI8ukHv6KnArTggLDy0Upyt1AOUXhrpHoiAK7sh66AvToeutgxmIZFCTKsMtoLVlLhCm9t1gZW3ALAkrEDXUoeahGPwGDVXtta2VZWgNiMB/sOny9baoviTcOkaDHffIOqC3N5Yj+tn9mLo9KXifjo0195C0tlDGDXjYZUFt725geo9aeYyASqJa7QYaysNphjLeSPXAVJfWNOWs+F5Qn+KfVbVj6ekmpQHgLgwx5w5gsjRU+Ds7ErLRTRJjDsHGytr9Os3kP5OunwRen07hg0bgWPH9iO0eyh6hPXAzl3fYcrkKXB1dsG11BQkJSXS9zfqdBQGDByCV373YmdqiP/5bTjY3sMjUF7fgg1rV2HGrFlwIy5TynN/+wRSRpI7SC+gQXCQOgeGqpQdw+2PY7X9futWDBgyBIGBQXfvjgzg7MmTFBgHDRsmW88UuNNYYhmwO7RrN4LDQhEUGqKyzMnxuWw8KvN9wyefYeFTK2FmoVhtjVpsjSSE2vn1Okx6eCEsbKw7cElmAFcDumwirPy0DBRn5aDPxFEauFVbaAUdWA00wGckNpYdHMg8H4+qohJ0HzUYNk5d5PhbOdZW3l/tvivBq9aF2TBGV53sS4kPEyC29MZNlF5Nh3M3P7j3CjZipVUGLbQxsaxFVfkuD4kw1l6p7BL0Au3tbShLykTljVwKuI7dvO8IuCzUyh1Z+iZJydCUc8sDQ/LLpbxlrFXXWHXBdHeV5E7aDZmeo9KNYzuZinVWgFYRZDuAWzaOlnyvTi9AZUoOXPp2g2OgpwySxlyQVQBrxFKr7GOClrpGmiQqaNwQ2Hu6dujOLEEtG18ruYuqoFenw596TYODjcU91Lx8l/tZAQK2+TlVFGzXrv0cjyxbCXtbW9ml8ZP/fIDnn3sJFuZmwgCLlBxKp8Oq1V9i4fxFcHZ2VrkhC+PISoIi+tYY9dlVEiAKNYDwf5LVxtjv1OupSLmWgjmz5twebjUDvnJsLVPX1NTUIDMrg1ozc3KzYWpqCjc3D7i7udN4U+KmSyx15mam1FPDzNSM6lJbV4uGxka6rry8jMYfk2OREAMPdw+4ubnTY7i7e8DZyVljjdX0Lm4DssqWSsejI5gl2yYkxCE9MwP1DXUI6RaK8J7h6NKlC71iA+usuFDuJ6lH9QwSXGmfceneiDW3wmei0FevpdCkVDNnzFJZbUmdH5cQR2OJx4+dqLLart+0BtOmzoKjo5O8fP2GrzFr1kJY29hTaCSxyJdiz2PStHlqd+Sp82BpbUeXxZw9DgcXD3gHhVFwzUy7iuqqSgT2HUoh9VZVJZLOHUbviQsEkG3TI+nkLnj2GgozR1f6u6okH6Vpl+E2aLJstW1sqENJ9D44j5gvW21JmVoa6lETdxDWg2fL8cDE6szCbXPiEegC+0Nv49ShK7IhrWkAVwLbwlSS0RJmgf1ki6TkAkyeT1QVoq0oDXZ9xspxpKamOtSnnIONe1fY+gTJgFoZfxzOgT3RxbMrtc6ioRaFl0+j55iHZGttdvwpuHh0hXdgCAVdYp2NP74DvQaNgrOLh2ipBarLi5Ead5a6HwuwLVlMhbhbcjVSG1pfW00T1TU31tMko8RaT/5sbB1gbiG0Q6p6QBV2INcWqh65diiGnDPjehKy0q5i4vT5QvInGo4DoK0V27etxorHfi3HBu/d8y1Noufj7YN16z7Dykefpvpt3LwaLzz7ojA4wNR/e/btQnBIGIYN738/NwH/b2XnYHsPUpfVt+DY4QPw7eqH8D69f3qwJS9HR645BuU3hFt1w6DswPbhZQsZyW6clIScnGxMmTbD0GrLum+JxdK6I1eUl2Pfrl1YtvJxtdusKhswyV4sQYYAMPm5ubgUfR4zFs6/K6stibWtqa7BwFEjjGQ6VrsOa625Rbl5SE9KxtApE0WrLRNTK1oklX2Mx9tK1tD8tHSU5RYgfGykgXW6qb4BiUejZJdfCfrMra3Qa8JIA/diNrGUVPFKy6pLy5F5Pg62bs4IGhIhaMW4J2vhVRsLy1rXZfupBrbVx1BcgCUrbVHSDVRk5MC9V3c4BxMr6n/vhsxeJ2uVl76Tk7S1tFDrbV1hORxDfCnEGXdRViy4csPFlFFprtTbsdsKj6caW4W3p+OhJePViQZlVbG2TOdTzmosLFPgVgRgZr2UMKqxrAYlF1Nh5+sGt37BgjVVA6sdWWmNx90qVt6q7EKUXElH96kjYGZhLgKEoRWYJO4wZukVgFZZJwHua72mcrC9h3bnft+FgG1BThXy8rJx9WoiZj40R3ZpTE29isKifEyeOFUFtaRjSBL3RJ0+iaWLlxlxQ2bfFQmqlAEjgzfVwH349nAbExuDhoYGjB5JQk3Et1/2gtG4FmvrClW8LFMSkqimthYkk3NlZSU9fmtrC1paWijkNjQ20LAc0jkmAEysrna2pCNuC0cHRzg4OtJ1Gj407Hhret93ZZXVDBaQK29sakBsbCyiL5zD8KGRCA0JhaenkPH9TjArDUBIz7BBoivDi5HFvhh3iboVSzWvBCEDIgYgNDSMVsfbd21H3959ERgYLGdCFlyT9fjq6y/wxONP0/snWWjPRZ+BmZkFIiIGyVAYdeo4nJ1dEBLaW7aGbtjwORY+/CTaYSKA7Pko2Do4IbB7L/o7OzsdNzNvoG/kJLS0taOq+hYuHN9NLY4kLpbE2l48sh1BA8fC3NaRwm1BxlXU1VTDrecgORty5tl9cO47ErAU3J4JFJcmnoa5iy/M3PxUmZDrMi5Db2IGU58etJwC1GrgNn4/TPpMQjuJ6jTqiizeCfahYO8BianNviwkZwrsr3KzlS22Oh0aYnbAcdgcOtBCEySZmkDXVIeapCh4RM6EmYkJhVFdQzUqr5xGwKjZsrW2LCUGNnaO8Onei0Ismhpw/ex+DJ66WIBaEx1KslLRWF2BngNHyi7ILQ11SDh7GOOmLjCAWpIhmWZHzslE7s0buFVRira2drh5eFHPEEsLK9yqrkRDXS3VjVhN3T194ObpCw9PX1hYWSt9XiNtv4FvgTiwlpoUh/bWZvQdMJzJ5g6Yglhro+n726dPBL2G9rZm7Nv7PRYtWo6qylKcOXMCSxYtQ25uFq5dS8acWXMFd2pmcO/GjVRk3LyJRUvm3u/NwP9L+TnY3oPMBGwT4mNxq7IS4ydP7jzYii2A9uVg7Daq0qgbos7CrWFHXGuEkn6zjT5r0SKN6ofvvoPfvvqa4nIlQWwn3ZG3bliP0RMmwtPHu9PuyKTB2rV1K0ZOnIguLs4qC94drbaffoaFTypWW8mN2Nj0P1IMjmRFjj15Ci6eHuga2l0VA0yz598h3lZ2VxatgPk30lGSnYveE1jLLRmcrMWpjd8xAwWC+pa2NohcNs8AhI3BKetOTNYXpqQj7fwldB81BG7B/mLDz8Tyqtx9WfBUJ5hS4stkO6oq5rajuNbW5mbkXUxCU00dnSLI1t1JdhFXwJSxijIZnCVLsQTKCmzLztKqeF3l+RR6iy2NTahIycKtm0VwDg+EU4jPbWNwBUbtyELLDuswlhzV22jcG0LeROr13ibDi/Y9V8eaKX4fHWZFZqy3ZAqfkrgbaGtogcfAEFg4ksQs6lhc1fQ8UqSt7DIsAGpHSaWKElLRXFOPwDGDDUC509ZZsTws+FZk5+OtMUvg6eZ0DzUv3+V+VoCAbWHuLZyMOoLAgACEhpDMqoJr49ZtGzFl0jR4eLjLFlipY3ci6gRd3qdXH+NuyGz2U8bTQdKqU3BrJN5WsrwePnIYbm6uiOjXn0kgpRn0YiHWCOAaDofdZnBMu8qgN60Mhqm+3QFk1XsZcQumG2jckAHculWJ8zHncf3GdQq0gwcNZjK3qwFJ3pv1TmNdkVVZl9UDEcafbT2OHD2KmEsXRdAVlCQSTRg3AYMGDqbtwbXrqbh5MwuTJk6RrbZCGBGwact6jBs7ER7unnISqfTMdFy5chkzps+hrscEclKuJSMnJwdjx06WraH79m1HeO8BcPfqSuEyLe0acvNuYsiISfR3XUMDDuzYiInzHxczHbfjxO7N6EtiRm0d6LK0KzGAiSncQ/rR3/X1dUg6sQOhE5dQKy+ZHqgo7QqamxrhENJfBtu6skJUpV2GQ/9JaGtvV8XVVp3fBZtBM0WwVaYzooBLrqetHc2XdsMkJBJ6e1eRbYUhcBl0lSEJJhW2cOP018/A1M0fJm4BMtSS91FIGiW6IGfFw8zaHtZdw5TsyKY6NFyPgWUXVzj4hdLlBFJr0xOoldQlsIcItia4vm8dImY/BktTMzm21tbOAX6hfWSwPbN9NcbPWwlLM3PZInzhyA70GRgJd3dvAbyZaX9qblUiOmo/3Ny94entBz+/IJiamTHJpJjHiDzbVZUoKcpHRVkxcrMz6JRO3r7+8PD2g6u7l9h3FfYx9hqS5v7sif1wcnJFn/5D5Wl+aMwtqcna27B312YsJLG1oht3etpVVJSXYvTo8biafBl1ddU0w7cwzY8ZRgwbIYOt0EQLb1VC8hWMGj30fm4C/t/KzsH2HqQmYFtQkIezUSexZPnynwRsDaCWLad68NfIFdy91VYCB6kB3r19O0J79kRoWA+j7sjSdooLl1RhCOe+Eh+P4qIijJ86TU4ipXLDZeIzWWtf8uXLKCkqwujJZC5dtVusOl5XauCEbVISE1F96xYGjIg0Oo8tmzxKC6ukITi4eRtGz50FCyvLu4q3lcrEuiWX5Rcg9Xwshs0j8/kKepDphaI2fSe7WEult7SzwYil85l4VUM3ZdmN2GD6IzJK246Mc5dQXVaOoGEDYO/uIjb7HcAr44Yr6a61+Bqz8Bq3igplbay8hYKEFJpkwmdgOMxsScZAxd1XsiZLHRJ1gqk7uS6rLcFawCXZm8tTslCdUwzHbr5w6elHsygrWR/ZcijPjPRQK2+K2j1Z6YgqLxv7jtxdtaFkZpSaSHUjqXY9VuJnZYdjlfW28noOypNuwnNgGByCyFza0vGVOFdjVlqVJddI0imhATVB5rELcPByh0fv7gwsC9CsdjUWzqe4GktuodrlQvkyz16i8zSvf+EtbrG9uwfogdiaJo/KKsXadV/guWdfEjIh63QoKyvB8ROHsXzZoxoXZKFT98FH7+H3L/9BZa2VUllI8CsJpAwKiT1RycqqUVCbDEqpr4xnP/72+2/Rr09fhISEKl6cqsEyjfWW8Uc2BGt15/pubu7t+gWqOoWxvmmXG1qdDGGWdMwTkxKRlHSF3oPevfqgb5++clG1BlajQCvSgLF1yv7amtBQmyNHj2jAVmgzCNgOHigkmywoKsShwwex4hFh7lohlFSwZApT/zjSqX8kt13i3r1m3Vd46snnhMzIJBtxZSX27P0Bi5eslJeRpFKtbW0UWgjIEovs4f3bMX3eCroPAdWDu7ag/8hJsLRzor+vxJ6CpV0XeAYLUwWVlxbhRkI0eo6ZRSGWwOzVk7upxdaii7ucRCr9+A/oOmGJDLZk2+ILh2DXYyj0VoJ7tPRXn30Vba0tMOva26jFVrDitqP1+jnA1By6QJJ4UmJaEXAlXNM8K+0ZF2Dq5AMTNz8RaoX2SUqOREBSV1+F5rQLcBg0XYm7NdXBtL0VVdE74DV+iQi1gsW2+MxO+I94CNZWVgLo5t5AS3U5giJGwtxUmLc2ducqjF7wtOy6nHctgc5RG9J7oGytzbmehKb6akQMHiXH00puyMX52bhy6SzGTp4NOzsyDZ3wAGqn/dEadlijDgHcvNyb9J4V5GfDxdUTnl5dKRxbWllTzcmB62pqqHtzbc0tRAwaAT//YDkvgDz/NoCkhBhqxR4wYKjshrxr5xZMmDAdrq4uOHJkD8J79EJISAj27NlOk0YFBwWJCbCYfoEOaAHg7GJzN9XF/+y2HGzv4dYTsG1ta8EXH3+E3/z+D4YJpAxcDdVJCDtjsb0t2Cq9blp6w7HfDqy2RoBYbbVV9ku7fh3JSUmYPU9wC2bPYxBcL65Uqks9df/4+L138Js/vnZbd2QDqIIen7//Pp586TcwMTWVLZn0FAZztooWRnH5xv98jvmPP0rje1mQVbsfC42i1iW5sqwM8afOYvTch4R5zkVLLZmvtqW5Wdxe2dfRzQUm5uZq+Kb7CPtWFpcg5ewFDJ07nTayxGJ7cuO3oo7KVBMEbEcum3/ba+vIeqtoAtSUlSM9Oh6WNlYIGhYBM2viUiPeT9VUOQpISlCu0tZIjK8x7SVEZGN0b2UXoCD+Ghz9PeDZjwyIiHdXkxjqxwdcPZ3zlwDurcxC2Pm6wjnMD6a2lqLHgdpqLJddeq7l7BDsgJCknVRBqN+yjn8Z76gp1YzY3Gp6hpL1VnYlpm2yEndLvlWmEqDNgkvPALj0JkmcpIZbQlsluZR2blvFmsvOXSu6FYuW25aaBqQdOIOA0QPh4C1ZzhRYlo6hhlllvZwNWeMK3VRVg6uHouDXvxe8wrrh9z0ncbC9h3bnft+FgO2xQ1Gob6hH5LCRshty9PkzsLWxppCijq0FSJxrWvoNzJ45W7HWSm6ymvha4ZVi/CKkV9EY3HbCJVmp44Q2dtfunQgKCkbvXr2Nwq3cRmrPJ9cv6nb0v72fnQVZoZbQfIxYZol0mVmZSE29RqG2X59+6NOnL3x9yFzmwqezQNvU3ITi4mJlP7EAVpZWdConbWCtMWuYVMceOXYEF2MvKv0csY1iwbauoR7fbN2ClY8+YRBnS7JTFxTmY9zYSUosarseGzavpRZbOzGxFIHG9eu/wsKFK+icpeQ3iYdOvBKP8ZNmynG2321djYkzFsHCisw9q0dsdBSsHZzg2z2c/s7PyUR2+jX0GkFiZgX4vXjoOzrtj54mmWpH/o0k1NfXwq3nYDS3CUmkcmKPw9o7CJZuXakLM1lWlZmClvoaWHdXsjVLcFt5ahtsh5FZHUxU0xkJCTMl12SgrSQT7bnJ0Ln6Q+cVCr2Zhfz8SvdTX1cBVBVBn5cM0+7DYOJqHGql5FFNySdg2y0CZg4u8rQ7JLa2KTuZutM7BPWSAbWlNAeNhTfhO3AcLESIvXl6F4IGjIaDkxtdVpGVgtbGOnTvK0yFY67TIXrPBoybswLmpqYUbHXtrThz4FtMmbNczopMXY9NdKi7VYn4mChMnDqPgrY88CU+tMrgr+SRxfQCGG8LxVAj9AXKSopQXlaMhvo6+ly1tbXSMAACuU4ubtSNmXyktk+Cf8li++3mL/HIil/J5SWwnJGeiokTp4HotW7tF3hsxROwtbXBmjWf0+fXztaGuk0z1RmtF5sBuHCw7VS1ycG2UzKpNyJga2Kqw6rPP8OCJUtoQgu28WCTV0gVNguqPwrYSkXqcN67e7faSg3KB+/8Ey+88juayIKFWynGRQW8qspBOPfB3beb07Zja+z5U6dga2ePXv373ZXV9tqVRFRVVtFYW6PJo4xlOWbmqU1NuIyG+nr0HjZEjrc99u12lBcVM8kFhGsbOX8Wuni6G2Z6ZuG2qBjJp89jyOwpaGpsRtTGbxXYpPWqHpZ2tgLYSv/JCU2MuwWzFkvFgq0kiirNykbWxStw8fdBwJC+jBu4YTIpqWqXj2Nkrli1+zObCEvCUzaLs9gYpGYi50IifAf3hmtY4B0A15hlV4p7k1RRkkkJssnDIXJyMtaiXHE9BzU3i6AzM0WX7j6w7eomu2AxiKuJn2XhV3nfO3iL5A2khpCtIQy9kQ3ss8rmjJuejLFiLC3dqE2Pyuu5qLqRT+OJXfsE0+ywrPVVGZUm52HBVfNbdlVWb0POeyunEIVxqQh9aDRNwkZts0bmvmWn8jEOuIrVlmxbmJIG4n4cNnY4LG2saafjlR4TOwW2uolKh/oequl73kV/NO+e9+U7dqwAAdsP3vsIM2fMgYuLqwy2a9d/hUULHqbTXMhgK8Lr4aOHqdsyyRhKn2a20yr2yuXMp8aglmknhfqOebcZ4FTliZDaMqkuFnck+27f8QPc3N0xMnKkQZgOS7tsvWB0EEy98O4eG82otyG0CoczqHWM0COR7GZ2FrKysijMenv50ERQvcJ7yWXSDrKr7LvahJji79y8PGzcvNGgIH6+XbHiEWHuWYMysuVj+hN3BFvoaYKttevW4JmnnjUAWzKf7vXr1zF92kxVAqnvt2/DwIFD4ePTVY5V/f77LRgeORaubh50WW1dHb7/fhONs23Tg8LtyeMH4OMfDG+/bjSONivzBnJupqNf5GQKrU2trTj83WqMWfAkhVOyT9L5o3D09IOjTzBdVldXi6tRuxEyYZHsjlxVlIvyrBS4DpggWm3b0UKm/jm7G87DZ6kstqRsDXk30FpXDfPACAFkRZdq5buUE0Ro6tryU9BeepN6VsHeVfAea6qFrrmBukqbuPrDxDtUfs8USBMTMlFXZNBj6KuLYdtjuOKCLIYUVJzaBs+xC2Fubk7ja4l1tiLhBJwDeqCLhy9NEtVaXYrSlIvoOXqW4JZsqsPV49sRPmwiHB2dKQQWpl1Be1MjevQfTuesJcuuJ5yDna0DQsP7MWCrQ2lRHhIvncH02UtlV2mttVYCePKv8q4Lz6ACs8Iv2rtgBqfkKCOjD62wkFhpFbgV6qnkyxdpEqiIiMGyG/LBA9sxbNhIeHh4UYvvkSN78ejyx9HU1IBvtq7Hc888T6GWJuZSufPrqMW2s2BrNutPd1en/Ehbt+7++490pP/uMBxs70E/CWz37NxBkxeEhfdUWW0l6wtbcf+UYKttsJW23EiTamT0Wt6KacgpmO7bB29fX/TpSwCTqQRkN1OhUpDXyccWvmRn3URMy2kcsAAAIABJREFU9DnMf3ipDIDG3JGl6kSyyNZUV+PI3n2Y/fBisZIxBByh8lGWS993btqCcTOnw9benlpPbzfdj2H8LBC1ay+69e0ND/+utIo7um07KijYKq695IoJ2Dp5ussWWpXbNAO3jfX1iNryPfpOHINL+49oLIhCjO2oRxao40M7sEyzsa7G3ZOVijn3yjVkxiQgePgAePXoJtlOxUrcEJpZy6t0bMWFWHKPVv6V7b7yM2M4RVD+pauoLSmDR+8QOPi6yxpK+8qxvWLcj4KWQpywfDVGY3PvDLh1ReWoSs9HXUE5HII80SXYB+ZdbOSWTHk7RHxVxcaxa5mWzmh9oe2lGrM/SDFt7Dqmi6ixzjbXNaAqNRfVGYVw7uEPl/AAOpex1k1Zgk816KqTORlzQWaXlVxJQ2NVLQLHDOrkVD4Eew0TRmldklOPnYWVvT26DevPZFMGfttjQufAdtLPBLZHONjeQ7N4x11q65ux6os1mDd3kRxbSxIoHTt2iLqQSlNmyO7FOh3+9eG7eOWl38OUJKWRBoHkrKes94OSDVnqxJKMxt2Cu8FCzHyq9GLZNot5z6X2y1i8rdjIkk3OnjuD4pISzJ0tJHJhDbJCJ1lbdxhKcy9ca7RWERcaX2foYkz7JBRmbyLl2lXkFxTA3s4ePXv0RGhoKIhFle23SB0bLcyqthF/sNvk5uVi4xYRbMXLJ2Ukc+oSsDW0NgsbZWVnwc/XjybNkry5JbCV+zlaiy30uHWrGpu/2aQGW7GfkpWViYuXLmLenIUM/JHY3UNw9/BCzx6COy+BxWPHD8LT0xfdQ3rKyzZu/Aoz5y6FmaU13YYkPquuuYXeAyIpgBL4PbTnG0yc+5gMqeeP7qLT/tiL0/7kZaWiorgAAQNGy+7IySeY7MiiZffGka3wHD4DsLCWrb2V6YnQ60xg2ZUkixLm3ZX+apNPw8w7BDp7N4MEUjQ5kvg4yv0TAsDEOltXRdfpLK2hs3WCzsxKBVJaqKXvpjidTn3093AYPg9m5iTuVYxxNdGhpSgD7dVlcO4dKVtr0ViLyqRz8I8Upvghf0WJp+Hs0RXu/t2pC3JjVQnyk2PQf9xsCr4EYi/s3YTIqQtga2NLoVDf2oSoPVswc9GTCtSSqYaaGnBozxbMX/IUhW5h2h/J70nJjCwMiinvoRpupfaf6c+KAyuSEUfe09jgkNgWSm0iYdKcmxlIu56EKVPnyOUtLMjBlcRLmDVLSHp17RoxxFRgwviJyM/PQVz8RSxesEQI0WDm4pXO3QIdXFw754psNvvPd6yPf4oNWnf97ac47F0fk4PtXUsGSGAbG3MBLU3NiBw9qvNxtkam/JFGl9iiSI1zp4rXSastC7ByI2Ewii3jBHKys3HpYgzmzF94T+7IpPLYvG4NZs5bADsHeyUhkSo7MusaqwDX4d17ENKzB/y7CVkOBYAW/9O4y7KQlZ2RgfRrqRg9XZyUnU3+JJ5XctXRuiRLoLtr1TrMWLmc1oRHicW2sEhy6pWTCYwSwVYokQDQWrhl4Ttm72E4urrAuauQkEBSWWdqAkcPN0PLr0GMqhoujbknS8dUXIz1yLqYiOK0LAQM6Qe3ID9FR3GYsqMBAqmDJl2fEpOmhmLtOaWGQNqqubYO+QnX0d7SDM9+obDqYt95wJXnpL1XC67oOt7aiqqMAtTkFFMrroO/B3VX1pkTUNQmi2JstKJGyrtyL91R6Q3WdDJVroDy+DKqbxZRmCV6OwZ4oktIVymCVragCp1T0RqryZ4sL5esrbex0pKeTMbhaHTx84J7eLe7nMpHuB7D+Wt1aKiqRuqRMwgeNoB6DqhdmIGXwsZ3Dmwnd+1U9fdjb6Q/nPtjH5IfD0BNXRMunrtM48mk+NoLF87C2tqKxpbpaCZQyeIPZGSmU7fYmQ/Nums35MPHDlP4rK+rQ8+e4Rg1YqTqHijsqUwDxLzuzMCapn4Q29r0jHREnTqJxx97QqhCNFWDFnDZ9laqc+76oWA61XcFsrTCEAYCL8XF4mrKVTo1EJnOj1jCiXVN3EQokgzLhic0Zh3W4jO5hwRsN4gWW3YfAraPEottB2N/ew/sxbDBw+DqSiyKAmhUVFbQKY7kgV2x/Xfq4kTnpyU1dkFBAU6fPY0F8xYpXkpirG1ubg4uxF7A3FnzVa7IF2Iu0H379x8iA29cXAy1kkZECMsIQB44sBM9ew+Aq4cPTTSVn5+LywkxGD1pjhwPe2TPVvQfNRXm1nYUSK8nx6G1vQ1de/QXE0bV4nLUfvSeMF9OMpV34wqam5vhHBIhT/NTlBqPdhNT2AX0kpcReM4/8S1cRy0wsNqS8lVf2APriMnQm5oLMcTUFVl0RxY6A0YBV2hHmFsu900VCy21RIrvJanrW3OvUpi19u/NzGcrJICrSTgKxx6DYO3oIs9dW309Ftb2XeAaEEattyR4K+PE9+g7damcHTn/ygU4OrvBJyiUblNVlIOy3HT0j5woZFXW6ZCWeJ4OwHQP66MC2yN7vsH4yXNga2tLrbVScis5O4V4TSR0sLK8DE1NjdRV2snJBZZWAiSyhhl5OF3rnmzkZVUNUTMW29ybGci+mYax46bK89iSch09uhd9+0Sga1fBzTsq6jCCgrqhR2gYkpIuo76uBmNHj5XBVntKarF1te1UtWE256+d2u7H3qh155s/9iHv6XgcbO9BNglsb2ZmgsDtwocf/tnB1qDhFF9ZbVdc1Xgz186+3NKrTpat+epLLFq6DLa2dkattvJ+YgGU3wIgRJ86RWNlB0eSKXCMQ6xkbRU6CAJKkal/LsfGYsqc2XdttT28czf6Dh4EVy9Pg+l/WBdllcVWLBtpEIrz83ElOgZj58+iFtvyQsEVma30Ri2YTS22rNXYEG4lGBX2vnz8NGwc7BE8kLgIG1pN2WMpjbiE8+qsvuz+iuXT+DbN9fXIuJCA+qpqBA6NgKOnmwpw5ZIYia+9k+uzZNVVNwiiWkxsbU1hKXJjrsDe240CLrE+SgMBysCEfBQ54RgL2MIjJmR1lO+HDL+GFmN5e7HnSY7VUFZFE03V5pXC3NYKdn7usPVyod8lKJfckZTXQyqXkRdGfivYisS4GUU7gCV1CGvzS1GbW4b64grYerjQOXpJhmmpcZYyFwsdEQlotXBrZDkDt9rsxw3lVcg6HouAsYNg7yHNT9uJqXwMEkipXY9LbmSiIDkNEbMn0jg1w4RTwG9Cx3UObKf+TGB7kIPtPTSLd9yFuCKXF9XIUEs6zKvXfomlSx6Bo4ODMOejNMUFdNi9dye1JIaFhhlYawU3PWX+Wjb8p6y8FLt278JTjz9J304CMCSjL7Gw2tvbC+XUZDFWt39izSEPQBqH27KyMuzeuwsL5i2Uj9sZwJWE6uwwWQcMKBKJcassCy1k7twryVdA3INDu4di0MBBNJmS9JGPr+LYe4Na6bws2NJl4smMWmzZDZiOjKIPM/gg3ROpbRHzYZCszSRz8LAhw9WuyADSM9KQeCURsx6aq7LYXrx4AU3NzRgyZIS8PCkpEcUlRRg5coIMtkePHYC3bwACgkMpyJKpmvbv3oaHFjwmuxqfPr4PPsFhcPUOoMsK8m7i5o1k9BoxlQIqSSIVe3QHQoZOolZSOsdtZTkyLp1E0KjZsqW3rqoc+Qmn4TlCiOmVYnSr0i9DrzODZdcwA6ttS00lGtLjYN17nGo+W3kAX2rXZRd7Zk5nNhSGabZovS1m5CXhnpI3RWPsPjgMnQETnTC9j/BHGtZq1N24CNfBU2RrLYHUwqjtCBq/ABamgltyfUEGGksLEDx4rJwdOX7POgybuRxWFhZ039Tzx+AT0B0+fsF0HxO04/B3qzB36bMC1Irz256POoDg7mHwD+gmJLASXaVZa2fqtSu4mXEd1bcq0cXZFeZmZjA1N0dxQR4Nd3N2cYdv10D4+gUKD6lsqWW/G39TVWArzlmbmZGKkuJ8jBgxQU62RcC8srIMp6IO0yl+JKsy8QQg0/yQd/H06eNwdXHBwP4DDcBWOjsBW9dOg+0bd6yPf4oNWnf+POfVXgsH23u4u6VijC1xM13z1Rd4/uXf3hXYauvx/9piq22oVdfEWKBY66zGysu+umxyqFMnjsPKxgaDhwwzCrZMOySPCrJAUVlRgR3ffYvHnv4VA4HCaKICVEYgD3p8v3EjxkyZAhc3kq6e2Ua0Zkp4o4UvklX5zJFjmPXIEgNXZAFmFQurMCUAWSb9K8Bhcuwl2inPTctAWVGx7EJMLZzQY/R8EWw1LtEdwq1Y5usxl2iG5D7jR2pAn4U2jeuvWNuy1lMZs1WArChvOFgA1JRXIPtSMtrbWhEwuC9snLuIfTz1FEHG3ZwV6DSM7RXvTQcxuqwLdUVGLnLPX4Zn3zC4hQcxz4BU9k66IXcScCXoZ59J6fmuLy5HbWEFteSaW1vCoos9bLycYOVsDzNrS3koQ2FX9bvU2apD9X6T6fqq6tBYUoXGihpUZxXC1scNDl3d4RDoqbLESp13NdBKsTdiOgxprlgxAZSYykk5jhG4LU3JoGAfPJnMuWdidMofdfZjASKMWWfZhFEZZy/RepBMP9WRNZcc58XQMZ0D22l+nZX4R91OfyDnRz0eP5igAAHbiuIauWNXWJgPMp/ow4uXCs8WeRRFWG1ra8O/P/3QIBuyQTyt1Bln4mtTrl/DzawsTCcZ+cU2j2S83b5rB8aNGYugwCA12Mpx+0I5tfG2chtnJOaWJMJat34tJk2YhG7dusu3Wgu47DEUG9HdPhmauDsju0v6lJaVUstsdnY2LC0tacKr28XNCn0S4yZhY1Zag+3FskjnvxPYdu7KGQATd6Btj+QqzgDb5199hqefeAY6nYlBjO3Va8nIyMzAtCkkMaQ076secXGXUF1TjcjIMfLy9PQ0XEtNxqTJM2VX5Ojo07CwtkaPXsJUPMRKunnDZ5iz5CnqItzSpkdi3HnoTUwRFC5YaOvq63Fq/1aMmP2oDL8psVGwd/OFo08A3YdsF39gC4JHz4Le3EqG2PQT38N9wATA2l6AW70ezSQZ1+kdcBm10MBqS1yOG7Kv0nlUzf37GVhs5eeZBVyxw3c3FtsWaq01gbV/uACRImQSkCRgbW5tC4fAcBlsmwoy0VJdAu8+I+RY2oLY4/AIJAMA/hR260ryUZmXhvChE+h+Ju1tiN69HpMXPiUfJ/NqHHT6dvSJGCpDbRZJvlVzC4OGjpbnslUstjokxl/A5fgL6N1vEPz9AuHhSTyHlA+5fDLtUk52JoiLMKmLyDbB3cLg6xck9PJYyGX2NTbQRJZdu3oZFeUlGD1mijgoANmKHBV1EP5+AQgLDafgWltbjd27v8MTK5+hSaR2796OQQMHIjgoWK4f1XWGkBXZ1a2TFtu5P4/ltHXHz2Mp1tYnHGw7V8OqtpLAljzMX3z6MR5Z8RgcHB3oNtJDfzcJpH4ssJVfBFVpDTvjxqy2arAVjkSWlZWW4vjRI1i4ZKncSVBGt9WWLOm4AvyJx9AD6jlttRCrSSIldzL0SElKQkFuLsZOnXJXVlty9pMHj8AnwA+BoSFGpu9hQJax1JKSsWB67IedMlCrKzo9+o4eATvnLh1aXtWxxOqkS0VZ2Ug8fgpDZk+HvQtzDIPYWklDVjPp3rC6Cb0xCboV0GcTOykxwhV5BciKSYS9uzN8I8JhYaPNoHy7Y6st1yxsy0MVtwVcYf+ixOsovZYBnyG90cXfWzXIIV+hnCRKuj7x3J1yUTYyzQ/z4MtvhVjW5uo6kJjchvJqajklhbT1dIGpjSUs7K1h4WADc3trmFoIbnvKG9JRBaJDe2MzWhua0VRVK/+11jVRl0sbDyfYeDnDzttVhlBaf8gddNECSysHKfOw8l3aVojnl3FWjJNioVedKCrnbALMra3ptEysizA5Rmen8tFu197Sgit7jqNr7zB4hgWr4mmlGGAWip8PGd05sJ3+M4Htfg6299As3nEXAWxr5aRRUaeOw9PdA3369BGnylDALTHpMioqyjFh3ESDpFHqAR8pU6/SipJ4ysqqSkyZOFnTEgLbd+1EWEgITZCkstoyeSPouy1ba5URYKV9U0y5Ult44NB+VJRXYNy48fDy9FLqiDuYZW+3+raWWvEMbNxgWXkZ0tLTKNCS941cY3h4L+rCyfZNtG7Ad4yfla5G5YhivHQyWJeWgiT+0n7c3dwwedKUOzwrxoFWrnMFpx3Zw+bMuTO0TYkcNkLpJ4gATLaLT4hDTU01RkSOVoFtwuV4VNfUYNjQkfJyMo3jpbiLmDxllgy2iYlxqK6tQf/BIylUEuvr3h1bMHjERNh1caFAmpVxHbnZmegbOVEAWb0ex3ZuxIDxs2FqZUsBNSfjKqoryuDfb4QcZ5t5+RwsHF1h5y0klSL7FV2Lo5mO7YL7CNZe0XJbkXwOpg5usPAKFuFWiLcVpvbRo+bKCVj4hsPEgYm3NRJjK/VlpIdCSVoo3BbBcULtjtxekYfW0puwCx+lzBvLWGzLTmyG9ySSrViIkSWW1tKYA3DvORj2Lh40ltYM7bhxaBP6z3pciLc11SE7/jRc3H3gHSi4IZfevI6GqjL0GjxacF020eH0vq0YN30hrC0sKCiSuNrTR3fhobnL5LhaCtmkHdS3Y9vmL9F/UCR6hUco2ZE1IYCGHhpAZmYa0tNSUFiYh5DQcPj4BsLLh7RBd/atuBRzhgLr4CFCWKJk7SbBTs3NDSDzIS9ZtEK2xpL5kPPzsjFl8nSYmeqwfuNq6vnh4uwsJI8ioQPCAy/3NVqh6zzYzvt5Yl1bt/88sb3aCoWDLZlr6lomfvvm55g7bRSeWT7ToNJ9/Z3VKCqpwJoPf0/XsWC78/vvaHKlbqEhQoUgNThMSjNlmbxS1bb8KGArvgSGr2DnwJZ5h+RMs9Ky9atXYdrMmXBz87it1ZYFW6kRIssS4+IoII+dPLlDd2RjiZFIybesWo35yx+BGQGK21htFXdcoVGsr6vF/u+2Y86jy1RT+5B1BDoNLbXCfpLlVrLi7lmzCdNXLlNbdaVyMJZeBSo11ksjsEqO3dzUhEsHj8HNzweBEcL0EYYW6NtnIRb07gBoO4jRFXYR9inJzEbWhQQ4+/vCf1BvmJiR7Ndal16pXNpzGQNcNUxqk3sp5xbUamlsRmF8Chqra+AZEQpbN2kOXsWCLDTCkrodAa4ms7Ksizi8IptP1GVmhl/E1kN5V1obmtBYUS0AaXU9CPiSZeTPzMoClo52aG9tg85UB525GfStbfS3vqWNxu42llfDzNIcFg62MLOxhKWTHSy72MPa1RGm5maqnKVqmL093Cqdeg3sinCrTRQlQW99aSVunoxFwMj+sPfx7CCethOWWSZTMgHcW/nFSD0ejX6zJsDWqYsqk7KYu9kgu/JzFGyVAYKOerm6Gf536AD/NKv1+7J/mgM/YEdtaWnFB199h292HsOV42sNri4rpxBzn/gL1nzwO/TvHUIttpUltULiKJ0On/znA7zw3Es0xlPqCNIYW50Oa9avplP8uLm6GUkaJXS/Feut1B0X2t/oC9F0Ds+Rw0cYVXzTN5sxXgLQDlySxSpE7lQqHUx1Z1PcgK4mVsrjJ47B08OLwjoLuOzx/pvHQJtxPS8vFxmZmUi9kUrdLEkCqNCQMKqboorS75DO3WnrLHOQjp2ehaMaZoPv+Eq1WNwROqgTcynxolJ739zcQrMhP/Xk00IbKsbV0hZD/L5n3250C+qG0NAejKuuHtHRZ6GHDoMGDZPBtrCwAKfPnMCcOUtky2ha+nVkZKVh1JipgsVWD5w8th++Ad3g5RdMl5VXlOH8qcMYOX2xDKOxpw/C3S8ETv/H3nuAV3Gk6cKvJIRyQBKSkEBZCIRIEiBENjlnbGPA2BhwnJkdz/yzu3fv/nf/OxvuTrI9M/Y424BNztFkkRFCCCSEMhKSEMo5p3Ofquqqru5zjnQQeNj9h/M8Njp9uqu7q7vqq/d73+/7/IPoPnV1NbiXcA7Dpi5h5Xw6DSh9kIXKkkL4jWZJpSjbW1OJouQL8JtE6t4yZpcc31JXg5r063CLnqVhbRmwBVVi1V4/BKfYZWr5H8lhI/pHctxYwtgSmXFr7k04jyLyWpYsSmZsmx+k0rATma1FUx2q711D4IQFFJwSINtIZMgVjxA6dpqSSMoaqad2YeycF9HXtg9NHJV64QjCho+Bjy/JoGyF8oJclD3Mw/gpcyhgJrLexEsnERgcjqDgcOlagM62NhzcuxUvvvw6TRjHALo6V5h85yQVBntnQMs75t7PxP3cLJSXlSBscCRcXd3Qz8MTjo7OtNQP+ZDnSeJ2r189j7hJ0zFkyHA2j/HcE0rc7Y2Ei3B0ckLM6LEC2F6IPw1f3wG0NjRhwT/842/wq1/8AwW1tE6wBGx5iFSH1WMA25XPJjtxx95nk41ZP9P8zQPbgoel+PUH2xA0yAee/dyMgG381dv44vujsLfraxLYXr10kfbpxCk9J5ASA8tkAintsFPG4+PZQNWRrDvOPLiVDYrmby73AZCUmEg9nlNfmKEDtnJMp4INNN5uas7R3sZq/r73y19p5Mh6MCcDOw7Xbly5Qo8ZM3GC5aytIlNNuHAJfR3sETU2RpT/4awsY2Z1kmQuR5ZY3JqqKiSdu4CpK5YYtcFBoADDCs8t7ksCwEbAVzE4GdcTUV1ShjELZ4MkktImaVJBMutJLbutgnl9bK0aJaqJ29WAVhXgFqdn4f71ZPhFRSAwZrjE/D4uO6sF5+JoXWItlf1mvdJUWY2iG3fRx9EevqOHgNT2FXBditO1FOCqUF99GdX2WE9q/i9LDIVWQRlC4vzq9w7CxLa0wtDRBUNXJwxkVUE83H1s2H+2fSiYJbI4owlXcngpJlesOoURVpakGsCry5qs7ssYWb7Q5/kg5VI9xTfvobmqDqEz42i2UaMEUxq2Vs12bFp6rP5enJpJgW3U/BcUqbIab6uywYr3XypD9E74FMuA7aJnBGyPMGCbmpGHr3ccg5eHG1ycHfHTN1bgky2H0NDQhOraBvx88yq0tLbh428PwMPdFb7eHli/SssSPt4E/t9r719/sBVRQ4Lx//1hC26f/lJz8aSO+Ru/+A3qG5rwTz9bK4BtTXkjZRNzcjNBAMOShUuFDJnWn4QViooKcPnqJaxZvVZNGiUlWxPjQidD5hjsxKkf0N/LC2NjxpplWz7+7C/Y+PobtCalBlRJ490UqBLzhszoKht5O+kZ6biRmECnmdGjo6kM2NyHn8NSe19UVITyynLk5+ejoqKcZo0NHzwY4aHh6NePxOXr8jJJy4regVmjFo1vxcR65knfZK2U2zjBF7eFxAEye+YcWmtXA2wl2/vFV59hxfIX4erqTudqLkc+F38Gbm79MHw4KZfDmM+yslKcPXcSK1asYYytwYCi4iIkXL+MuQtfpN8JyExKvAIrmz6IiBojgOzuLX/CwjXvCiCamZZEJcSBUeME63rl8FYMn7EcVkrWYyKHvXtuPyJmrxYglgDZ3HP70D9mOqwcXERNW8LOlieegkNQFGzcWTkikiWZM7YkcVRraR7aKopgN2Qiu1eLGFvFEkmMPGds0d6C5lsn4DpxBQVbHNDyerbWhk5UXdqNATPXMJkwZVmt0ZCTTLMme4ePpDVpSUmf4sQz8AkeCi//IApsm8qLUVWQhWFxMymI7Wprwc2TuzFz+esKW2uNG+cOIXLEWAzwG0RBbWtzAy78sA/LXtqgXIsq9z20bytmzV4EDw9PwToLsonH5MtqQv6SGsXV8nUX0N7ejvz8HFRWlKGxqQE1VZXo6uqCwdAFe3tH+PoNQnTMePShdlUt+SP//flnf8A77/xSKFVI323f/jUWL1pGk6Q11NfiwME92PzGW2pGZAXYMp8+ux4CbPtbKkVe9W9POgR7dXzHnn/q1XFP+6C/eWDb3NJGvUUEvJKPzNjW1jXizb//PX751kv4y5ZDRsCW7J+Xk4P0tLtYuFQpIC8Mi+RRFktX9Q+998gos6AF9sTky2AS3PaetSXNtba24vst3+L1zW9q5FuCR9NnkNN8Z+c+efQIQsIjEDo43LR8VzBzHMCx49rb27Dz62/wymaSCIQnDjIGUNyrpQfHOz77Aqs2baBPRcTRamJqldhaEX9iHG/76MED5KalY/y8WSw2WMPUqmCTg2UNANUxqnpwSiauyuJHuHP2IiInx6F/oL+GvdWDVw7JLEse9XgA90HyXRTeSUdATBT8hg0WEK/7c6ngW30ftCwyu2dx5Sbvj1xpTcEjlKZlw6GfG/zHDmOZPOlL/iQMrnK8ojtUR4Isd1MBPN2bUeGa4WU8gh5/KtaOcXV+UJYVEgvFNRxcZixyPKoeaCFBVn6TmFQuXW6rb0TeuRvoHxGE/pE9Zz1W42i1pXyMJcpA+qlLNAlZwChZ0mzqOGOw+3bYZMuA7eKgx+/kp3CE4XA+bSU7rwjeXv3g6uyIN97/DT743+/h3z7cht/881sU9F68dhsNTS1YtWgaQgIG4Gf//Cf6m50kV38Kl/NftonGphY4Odpj1KyNRsCWO4NvJKfj9ZfnCWBbW8GA7YmTRxEVGYXQkBAhQ+Zs7dHjhxE5JJLGrHJZpPAHmUp2I8XXkv0OHz2MIYOHIGJwhNm+I3JlkgSIxNxqJcnqQlKdC1gz3MZoZgedU0xmdh+VPKIy2JycHAwY4Efj58JCw+HqqiSwsuDJlpSW0Iy/efmk1ux9msnY09MTfgP8EBwULEoZyU5zudnHBbOm1GNmL/MpA1rjuGStJJlPy/w5HDx6CDGjYuDvz22m4uCVmNvaulocOLQf69asFywmtd9dBpw6cwIBAcEICRksgC1JCBZ/4QwWL14lgC2Jzz57/iTmL3pRxNhmZaehvKwMI8dNodtIPduzJ/Yhatw02Du7M0kM6JOpAAAgAElEQVTxo0I8yErD0LhZ9DvZJ+1GPNy8B7E4W4WNvRt/BH4jJ8Ha0YXuR7aXZN2GAdZwChomQDH5ramsCA0Pc+ESNUlIpQVjq4D2xoxrsPEcCOt+A5U1j/L+athstq0nxrbpxiG4xi6hyiReAkjD1ubdRh97R7gEDqXAjcqHraxQeu0oBo6dAQdHF7qNyJDvx+/DyDmrGYNrY4WiO1fRr/8ADAgMZ+xsXjraGusRGT2Bfm9vasDtq6cwY/4qUQc2IyURDvZ2iBw2WiSSIteTkXYLne3tGDMmTi33w8uC6dQdWocVM/XqOoP9zdlbdQWlGwW6dbZQnEgyZDKW0tJuo6W5EePHTxYSbrKm3bdvO9a/+gbdVlxciJTU21i6eJlIrEcZWw64ledGgK23t4Uxti/+hwWzy9PfpWP3Pz79RnvR4t88sOV99unWw/RPGdj+6tefYtm8yejn7oLffrJTANuqlnYYFBdrQ10dvtvyDd756c90CaR0dfWUE3HPrDGwZctb/rHUg2v0zC1kbeW1uynWVl7ck9+PHDyAqBEjERRMEv5wQ6+Ye+mc4jedRzs3Oxt3km9hyaoXjeTIfNFgjsGN/+EkvHy8ETlqpMWsLWcF05KTaexT3AyWIIKDUp68Si9JJpfN42zleNuslFQ0NTRg2PhxdIEjQKyOjeTtasGtKSAuSXuVNm6dOgdbOztETiHstA7Ey5IZSd7cfWyt+nxkxpKBf+1vImYJBuTdTEVFXiH8h0fAe3CwurcmdrY7dlZ1TrDFoQx0TbHA6r2SI8vT76MsPRceoYPgPZwkZFGOke/bUokyuwAtRyuBbD5+1EWrzOZqlrBSSQ8t6O1+3pWWitLqUzfSdbG1Ojmyjq1l8mIdoFVW/pwprcjIR0V6HkJnxcHexVkTh6uyuealx0JOJTGt5LiOphakn7qIwLEj4DnI3wLpsTED/E7EZDjZETl2Dz33X6COLQlTISxtXEwkJo4bjn1HL+BX765GeWUN/vjVfhBw9+tfbaAA719+9y3eWr8Yvv09erq1/1/9rge2WfeL8JtPduCL3/4SP/2ffxTAtqW1g0qRScmNLVu/wnvv/FQTy0dizwsLH+DK1ctY8/JaFh9nJvuxdkGuHWNfffs15s2eA3+/7uogG2g5mnlz5zHZrj6ZorT67Za5laYIjfNL1x4pXUQkw0TqWllZCS9PLwpQPTy8QEqRkI+tTR/U1tWhvqGOlrchiY1cXVxpeZCAQQEUGNP6rvJawuSXx0gEJS07HgvU/ohvsDkHAl8nkGd15vwZGjs8dsw4NR5aADfF2WwATp85CU/P/hg5YpRQXPGSONu++wZzZi+kElPOepLnc+HiWSxf/ooG2B47fgDLX3xNANv7edm4n5OBuGnzBUN74fQhBEYMh4dvAN1GVG5XTx/EhEVrBDjNz7iN5uYmDIqKVYBtF3JuXoCdhw9cBoZT8EuObagqR/HdqxgQt1C0z8CxAY/i98Bj7DwY+rK6uqLEjyJJ7mhuQGPaFTiMmiWVPlLBmty/qoBIefqKYSIgtjXzGvr6BKKvp7/IikyAGGdrSd3YivPfw2/2OiEJJoxtV30lau8lIGjyYhEn21JeiLqCbAyeMFvIkO+e2YvoGcuoGpKwvGmXTyAwfBj8BgXT4/Lu3qQxtFGjxjMZsrUVju7+EguXrYWTo6Mo+9PW2oSjB7Zjzbo3WcytqGOrDVdg+Si0QFZZBqlrWhESJsfZaxa8Ri5vOoSo6kmZr7h9trLC7t3fYu6cxfD09FJAK3DvXgoqK8sxc/ps2m9Z2RkoKMjDwvmLjIGtpDw49sNRrFu/2qKR12fRP1i039PeqePI/3naTfaqvefAVuk2PbA9deEmEm7dwz///FVk5BRogG1lSzvPWEHHyccf/gFvbH4Ljk6sLhZftJpNICVJPrR26SkAW42hld8Jy1hbDciV5Mjp99JQVFiEmbPnWCRH5gsFGUB98uEf8Nrmt2DvwJIVGbGXAgQpRylAhmRWPrp3L17ZxFhbDfvXgzyX7H94+05MnTcXTm6s5h2VHQnZMesXFcTy3xickkFu4tkLcPXyROjwyG7BrbY9DgDlbI4mGE3lPgrTM5GbnIrBsdHwCQlUk2RIfcOcElIb+u+K14J5HWUgaQpU6trispe2Vty/cQc1D0toiaB+g3iCJy1QVRllOTbXODZYvWbluk1clwzmSa89vHUPNQ8eon9kKDzDiSy1Z4CrypV5PJbqhpH7TfLRCnJWO0L0CUz0gFc/3/LzaF1WJr9J9QLV8a+ys2JZKuJmZRDLZhgGTuW/GdTtau+gLK2LrxcGjB5qlCCKA1+Vhe2OndUCXyI7zr54AzEr58PWrq/JLMmcYdOcRzH8fNtb4ZPgbG8BsI1hcYJ/7Y8hqZyekoDa0CA/ODrY451//AD/6xev4/d/2UlZ2ZR7ubiceBeNjc1YvmAKQgP98NN//iN+989vo+/fCGPLn4sMbEkN0A0//0/8+z9uxCA/b/zknz4SwLa5tQO15Y24eTMBbW0tmDblBaV2rZqo5putX9EFno+3j2VsLVtVqnbXCvjwTx9h42sb4OLCEjpqPnxAGgwgjOqR48ewacNGaQ7ge+vYQskwdge8mPnVepf1bjBSu5QkeiLsIAGwRNZIPtbWNrCzt6Olj8i1e3p40pqb8kczn+i840YxsCbWGaYaezaAVpptTfgJtY53tT9ramuxd/8exERH05qg3OkgnKfM6LFYyY52/Onjj/Czn7zP8mcoTC3/+8M//hbvvv1zkHTcHNiSmrc3blzFIomxJec8fHgPVr68QQDbh8WFuKOrZZt49Ryc+3nBPzRSYXENOL7rM0xd/gY6YcViccsfITclAUOnLFLZ2bwM1FWVwycqjsqOebKorFPb4TdxMbps7RXGl8X31uamUHbSIWQUA7b0vrT1a1vLCtBamA6HkbME+8jfW6WLxKtgirXtqCiEoboEjkNihfNJMLZK0qjmB3dBpMhug6OFRJkA0vqsJBqH2j9suJAmV6Rdh5OLG/zComg25JbqMjxMu4Ho6UspG0uTRO39DHNeekspDWSNy8d3YvzUufDo50kBYNnDfDzIvocXZi0SsmfC1p47eRDDR0QjcFCQYEXl+HsOaOtqa1BXX0PHupeXNxwcSMiT8pGSkdH+kZRbRgyvCYPEHXCMuWV2ubS0GHduJ2L+/KWaEmcHDuzChLhJCBg0iMbXJiffRFNTA6a/MMMksCVkyf28XCSnJGPT5tcsMoc2sS9ZtN/T3qkzYdfTbrJX7T0Htkq36YHt+//yCUj8bZ8+NmhpaUNxaQVmTRmDf/uHjShvamNJY5QE+Xt37sCYcbEIDg3RJJDSJLhQziORNt0mkOo1Y6sZqPI7YRmwZYaZfThrS76TWIOvP/8Mm999T+Pd1jJ/ynFKIzKkIH9fPHsWDo5OiBkfa1aObC6J1OmjRzE4MhKDgoMem7UtzM9HamISZq9cKphWc8mjZDBL2V1d1uSLh48hfPRI9Pf3UxNNyaBT4/HTg3c9e2ka9HZ2diD5dDyttzZkYixs7Wy7lSfzxZTKunLGVF1mWQJwTZUIaqqtQ/7NVLQ2NFCA6ywSPBkzsfrrUN8fbZZlOVZWK3NWwbqIK2lrR3HSXdQ9LIVfTCTcAv0eC+CKHtAkieB0io7JVV969v7rfLOyPFleeJmceTUyPT28lb7LsYN89pDlliYlx8xwcuaVzzMV9/JQmfkAAZNGwdnbywRLqwWxKvg03q7G1zJw+zAlA42V1YicMclk1mO1Xq35OFt+vk0hEyyTIi8P7pVRe9KDDPvzaBPXbqZh1+Hz6OfGGO//9f56GrJSXVOPqtp6GqLS2taOP399AK4ujhTIrV0x60lP/9/ueBnYEsD/9//2Gdxcnel9PCgsQX9Pd/zLL19HRFgQGqqasGfvDsyZNQ+enh4aGTKJTSVZaWcr2YzNsbVymLqR89gK+PW//yv+3/9BkpjI40zXrcoAPhN/Dq4ubhgbE2MMbnXx9eaYW2Ez9eBXa36NnqsJPGe0jw676gJodcwsmxbEx+hY6fe/DpjVzaDd3LDmJ4FjVUDb0tqKW8m3cPtOMpYvWwHv/qSGPF+ncIenZJMMAKlTS2rbjhs7XmFrlTAigwHV1TU4dfoEli9/SYm7ZbGq+Q/uIz3jHmbMmCcYW5I5+eCBnVi5+g0BbEmt5EsXTmL2otUCxKbeSaRgOmy4ElNrMODi8V0YOm467F09GJDt7MS1Yzsweh47jjK7tdXIun4KYS+sEOws2f4oLQE2zu5w8Auj23l8L1mLlV8/hn7jF2kZWwXQc5DeWl6ItpI89A0dDSs7Nh7l5FridZAcrZTQ7GxDU8IhuE5+kSZDYjVsmfNJsLXWQPW1Q/COWwgb275SfK0VHl3Yh0Fx8+Do5CoY2/vn92LwxLlwcXGn20oybsHO1hZBQ0fT73XlD/Ew4zZiZyxRZMj1SL1+Fi/MXUGBL2Frb145haCQCAQGhohtRQX38agoH1OmzqJsLWeU+dxBxkNhQT7u3ElEc3MznJ1dqPPoYVEhdR75+vojJCQMPj7Ecc/fJ8X6i/dL2a4ZndqXmdk2rcJk67bPKVtLwhBo35GySE2N2L3nO2x64x2WRM/aCleuXoCjgwPGxzIZNa8ZzMPeyDP78usvsHjZSgwZYlmlgD4v//aZ2JGOnf/PMzmv/qTPga3SI6akyLyz9IwtBbY0cxkzD5fjz9NYl/ETJ+rkyJbH2eoNzdMHtspgld4ArVdU/UE/wLnh3rNjB2JiY2mcjwpadZMAnxwEMFa5sYryChw9uB/rNm4yA9SMS/8wgGNAUUEBbly+jCWrXxbAlt+RzPxyCTI7irOkBpw/fgLBERHwDwnSJYFSZbP6WrbaWFqV1T3x/S5MXDgHDi6MAVa9xTIbqWUmTclx+XVzCKXdByh9UIDbp+MxeFwMAoYPNSlPlo8V/aEDcbKDwhR4VT25xkCcA8zasnI8uHWXZu0jANfO2Unh3I0ZX2FAFXCoBdVaCbPpexfuEoWZNqC1oQnFSWloa2iCd1QYXAf5CuipYWmlZ87eb4mlVr5rR4LKAvP33BjAdrcENYa/3WUPlRfifBXKxrrEwEqyYrb40LG5PFmUwoQ2VdSi8OptuPr7mCzjI5f1IecylRTK/HYrZJ65DGcvDwRGR2kYYJYBUltOSM/Ysrhd7X5vhMRZBmxXkHqCf/2PYd/9v/5J/xuf0VSMLb8dmbElWZGz03Jw4dJ5rF29TizguC398ycfYdOGzXB0dDROGmUitpYNG63lrG+oB5Ei//wnP1NRnkmExz24Bvz+ow/w5sbNcHJyMpIkq/OHsrjVg1fNd8n4aeysmfmju2nF3DXz+UB+X3oCs2wSEQqPH+dV43O26T6Qz2kKxPLf9Wx3VXUVridcB3F6xMbGYcL4OBXQyvO7yJHBHPI1tdXYvWcnNm5g2ZKZI5uBV2Ivbt1OoiWhpkyartZ97QLupt1BaWkJJk+ZSQEvYUTJO7Vv/3a8+MomAWzJtuNHdmHhytcFsCXS5OLiAowYP10wrEmXT8HDLwheg1j2ZAJQE07sxOC4ObB1chVxtklHtmDwjFUw2PYV4La6OB/VhTnwin5BnIOD28rbF2DrHYC+/QOlTM9SIinlnttqK9CSfhV9vAPRN3CEholkfa7YHeUd6qx8iOaMy3Cb/JIRoCVjjUt920vz0FFdAo/hk5R6tqBgs6uhGtV3ryJ4CgOoNClUSyMKr53AyDkv0yRSpPRP1pXjCBs5Hu4e/ek++SnX4eTojLCho+j3gqw76GptxYiYCay+LbpwcPuneOW1n4h4W7J9384vsWTpari6uGgSW/H3/dy5E2hpacHEiVNpUinh+SHlkRrqkZubhaKiB2hoaEBISDiGRg6HowNZ23DJsnHpKVPvsrB7yji7ePE0goPDERgYrGFrbycnoqWlCVMmvyCALYn1DhwUSJPMaYGtAUTMkXYvFYVFRZg+ey58fCyLz++z+nc/zjDvodWOHb98JufVn/RvHtj+cP4GjaGqrqmjfdPP3RX/8N5qTBgTJfqqJ2CblZ6OrIx0LF6+/L8OsBUDUzsM9bbUFLiV91Flv0DK7dt00p85m9WVVU7B/pVky2K7nG1Okdp+/83XmDF3Hrx9fQUw0oBDo9hSlcXb/e0WTJs7h8bbctDKASwHwHS7ci4BoQxAa0szDu/chWXrXzVK/qTKjfWJoUxLlAkA3vvx51i8cT2sbW0tB7ca2bQECE0kmGJ9yCbVzBtJeJRzHyNemEwT9siAzRw4lgE+fz68b9TvymLE1Pl12zgArcgvRMHtNLj69kfQmBHEDWmyv7XPRXUeCMiqK28k4KcURytLzvnzba6pQ/GtdLQ1NGJAdCRc/IhcVevEkIE670flJRUsrFiGaZK/SCBVGhjqeOhuJdrTfK5zXWkW5Pw3XseTmkllcc91IXwRr8bXkuyMD6+loqOlBf5jh8PB3VXNeCzJleUMycKzrPyuTQylAlCyvbOtHfdOXsCgkZHwChqkxBF1D2Rpezrpsf6cG4LHWwZsVz4jYLv3ObDt6W0mv6//2X/Q7NC5+Q8RGuSPwIE++NO//lRzqB7YHth9gCY+GjVyJFMcKExQQmICWlqaMX3adBXUCgePCmA1bC1dl0vjygp4WPwQP5w6SaXIEpozfTvKGKdleuLPY/3aV4VR04IvrfPKKGOvbAglxZPGQEpXYOwK6763TTrK/kuAWc3sKvsDNTdkDsga9YOyY1V1Ne6lp1Ew6+DgQOvxkhhZZreURySvN6QkOzw0ZvuO7zB58gv0XeNrBQZqmU0/dHg/IocOR3BwqFTbFkhIuEptRHT0eLG9vqkRe3Zvw8trN4s6tq2tbdi9/QusWPOWAKIlJUW4m5yA2JlLBYjNSLlBmdbAyBhRp/bu1TNwGxCAfgNZ7VryX3ZiPFx8g+DoM0hprwvtnR3I/OF7BM5eKzG2oO01lj9EfV4a3EbPUIAtB7VaSTJnaFsK09FWnA27oOHo05+p3uinow2dNaXobKhCe0kubFw84Rg5iZb+49JjuaYtZ0VrEo/DPTIO9u5ejK0loJfIkLNvUZKHZEPmGZLrCzLRUV+F4NGTKKi16mpH6smdmLjkNRpbS9nYk7sQM3ku3N09KLBNPHsIw0aNx4AB/vT3wrwMVJY8xMQps0W8bWZaMlqaGhE3YSoFtX2smdOWgENSi/b8+ZOYPHk6ggJDmONYDbE1Umc0Ntbj3r1U5ORkwsvLB9ExsXB36ycALu8uc2sBLneur6vByZNHEBU1CsOG8frcjK0l17Z79zbMnDkPPj7e9JrJ9oOH9mJMzBiEBodSwoxmhyfJo5T39fOvP8PyJSvg6uEJH18Lge0rv7dkCn/q+3Rs/8VTb7M3Df7NA9vedJrK2FLLirqaahw7fBhr1q/XAVtjj/JfTYqsGFvjpbjOSEtGWQNolY6Rk0g1NTdj1/fbsH7j5l7LkVOTb6O6qhKTpk/XyZG5kTTP2mZnZqIwLw9T58zugbWVYjyV1QW5j3u3b6O+rg4xkydqkj9pk0UZZz3Wy5FJW0TidGzL95i//hUWo2uKuZWBrBmgqGdpVZCmypSJNa6vrkb6tZuw6WODyMnjYWtvp42/1YB5fWytKqvVSH/Nxe1akJzqUWYuCm+n0eRSBPjIDgXhitDJsk2xs/rkV0YA10hCzPZoqqpBaWo2mqtr4TM8HO7B/iYALh8Exqyt3kBxMKxbqvFRYGTgxArY7ASijHSjBai6QSxYJTZKDV8wA2gVtrYqKx/l9/LgM3IwPEMCJNkxZ38lACoBTb3EWAa9LPkFO29tcSke3LiDYXNegL2Tg5TERwnBECWCLGRtpbZfC461DNi+GNqb6fmJjzHszn3iNp43YNwDhLH96Hcf4M1Nb2uTRlkBn335Kd7Z/A7Donw8cJbRLFvL7K/4vxWQlZ2N9Mx0LF1I6tFbkLZXAbeXrlyGTZ8+iIsdL4a25eBWmdlM+L3Epifxiam3KTrVLKH7uMysrotkR3f377DeCajd2yIwy0EqgILCAuTk5iA3NwfWNjYIDwvH0CFDaZIt4yy2XGXDHxXPp8CAb0rqHVRWVWLK5GnMRvKkUjS0iLFg32z9AmtfeZ3G1/IsyeS3i5fOw9PTG+HhQ+m+hLFtamnG0SN7sWjZGgFsyfb9u7/FvGVr0aXEz9bW1+J6/ElMmKNKiosKclBWXIjwmCmCxX2QcQet7W3wGxKtAOAulORnor6mEt7DYtUsyF0GPLhxhtWHdfdWaugaaDvk/KXXj8FtxFTA1tEozpaz03K5H3R1oinrBqxs7GDt4YuOyofoqi2Dlb0zbN19YesTCGtbe9qpXA7LAJmSHEmRInc1VKHlfgo8Y2YItpYBSyuUJ5zAgFFT4eisypAfJcfDa1AY+itlfupL8lFfUoQh46YxRre1CfeunsLEOSso0CWu8svHdmDusnUso7JSuzY8IgoDBwYI2fOB3d/g5dVviCRSXIackHCR1pidM2cxVZgxR6usilJwrY6M4XlhsrLuIS0thYJsks24b187zcttaiiT5oseFiDp5jVMnToTnh5KKJDSZ6Q/6+qqce78KaxauVoAcLL94ME9mDZ1Gnx8fERoBl2HGAzIyc1GRlYm5syaj05rK/haCmzXfPBMTFDH9z9/JufVn/Q5sO3FY+DAltlNtjz96He/wds/+Rns7dkg4IZHL5X67wxsyYDe8d02TJg8BQEBgRaxtsLDquzd3t6Bj//wO/zkV38vATNjMKstD6NlbeevXEETdXG20lLWlux3fO9+xEyaAE/C+nJDR5NE6SXJ7LsmqZQu3rarsxN7Pv4cK997k9WL6xHcCmgnSbHlWFW9rFcCpApwLc65j7sXryJ4VBRCRo9gZ5XYTzWZkwLRJHApg0h5P3N9rZcwC9eDBNLzE1NQWVBEa+B6h5OYSJU9FQyxCXaWLaDYk1OvX9omQLf+PuSkUAY0VdehLDUL9SUV8B4eBq8InsWZS4hUUMvvWQNeNXF0gk/W4NaeGRbZ1JnXEMpAls0ROpAr2CdlsS5JjnlMbX1RKcrScuHk7Qm/aDU5FJ+J+HyjlRvrwaf2ux7sFt1JR3NVLYbOIKEVaqIqDnqN2pZKDZlkhHUM7rqAMXB31i4WTE3DVi89I2C76zmw7YVZ7PGQhqY2HNpzAHPnzNfIkElMY15eLhYvXGKGrVVGi25omYqvvXsvDVlZWVixdNljAFs2Ixw9fgwhISEUUHHjpnH2aqlaDeDSzindgDz+U09A18Q00o06+fGlxsZ+NzPOO/1jtRDQSvenmT+V7SRmlgBZsnAn//r6DFDKIYXRbNGKedACChMsrSwVJz+XlZbgxKkTtLwPA7WSXe9itjYtPQ0FBfmUORMAUEkstWv3NkydOouCWw5sq2qq8MOJQ1i2ar0G2H7/zZ+xfC2x/dYUdDa3tuLY3m8xZ9VGBk4NBpSXPkL6rSuInrlMANbSh3l4mJuBwXGzRQKp+toq5CVfQdCEeZryPiUZydSJ7ho2SgBaCmwNBtRk3Qb62MF+YIQip+YJsrj0mq9rpHwpRL1WkouOymL08fRHXx/CZsrrVdbl+rhaWpdVUVc0ZibAzs0TLgERmmzIhuZ6lN88jdDpqxggVaTIGce3YPSCV2lMLS3zk3Idzq5uCAgfTverLMxBTUkBoifOpt9rSguRn3EHU2YROTNjdPdt+zOVIZPvBMBm3r2F9rYWVkZHia0l4Pru3VtUVjx50jQNS6uvfa0byspY5msX1gcE3CYlXaPloIKCwuCnZFkX77P0jhOm98GD+5g1a76qtFL6lWZptrLC9euX4GBvj7FjYjXy5K3bvsKSRcvQv7+XEbA9cvwwAgOCMWRIJDqtrTHAUmC79sMe5+MfY4eO7/7ux2j2sdt8Dmwfu8vAkkcRN5ZYsAE7v9uGSVOmYlBgAE8Bw36XZg0Bdvk5dd5SveSIZ3brxSWqh+jKDnCLbcTOSt5TZqSlJiSDknzzJsoryjFrzjyLgC33ggnIYACOHz6I4NAwREQOMylHZmBNBbMycEtJSkJTYyPGTZ70WLG2HADWVlfjxL79WPnGa8yoKeeSGVeeWIoBWz3g5UXPGSjr7OjAnk++oOBWZS2NwR2/B2awTf8uwV4tG2uizE7urRRkJ93G0AnjMDBycI/yZL7wUgGlXrKtZbkF2DUqsaNlkkl7HW1tyLmahIaKKhp/6+bno4u/NQVeH1eezFkR9b2Q42fbGptRkpqFqpwCGoPrMzwMBjqAVIArLz75/WkXpPISzJjTFSOip0WpNL7lsasd39I3k4CWA18moWoorULpnSzK2PsR2TFJzmOm7I+adIezqwL2imzK5uJssy8mwMHJCUFjRxqBWlP1bPUyZ7NxtwpAzk64hf+5eD28PU1krNVNdFYvhz3R1Nfbgw07c3p76PPjuumB+sZWpNxMwdAhQzQyZJblNgZhoWHGbK0cR6tXP0gGkv5kBdxOuYOCggIsWbjIMmArjB0b1HsP7EPM6BgEBQVpVEnstozj7fQLZOGK7GaOsHT6MDONaHtYANRuYa84prs1hfG96B+mlinlv1rKznZ0diLt3l0Q5wOpARsSHEIT94SFhop6vMw+apYvmg2cUZbtmHqMAX/57BNs3vi28rT0bC37vnvPdoyPnUSBioi9VYDtnz/+Hd566xeCxSUAsqT0Ea5cicf8RS8JcEm2f//tx1i6ehNgbSNiYPds+TMWvPI2zV5MAGhDQz0un9yHiYvXiX3q62tw58IxjJr7sgCxRI6cdORbRMx6GQabPkLeXFdZivKMW/AeN1tlbLuYHLm1vhrV967DPXo2XaeIerbcYS/FFqv9xm0x62OVgGHf+DjSs7QqsLVCxfnvMGDGGppYlde0JaxqY2EmDI21GDA8TsTXtlaXofM4RToAACAASURBVCLjJiKnLhbxtffO7cfQcS/AzcOL7nf/1mW49fNCyOAo+j0n5Trs+9ohcsRYBnxLi5CVloRZc5eL2rWH9nyDRYtfgquziwDXVZVlSEy8jMWLV4p1t3C0SioQ2cEt3mG+/uVrXZFf0oD0jDRkZNylZZq8vX3h6zuQlu8hpbc6OzuRlZVOm5kyZbrR/MXCLZhT4Otv/oK1r7wGJ2cnxUnApMhff/sZXly5Gh793MW8SJ4DeTd//8Fv8d47P4OVtQ26CLAdYKEUed1Hz8QWdWwjuQ2e/ec5sO3FM9ADW9IESSBFas6NGhPzBHG2WuPEJxlzl8j37tZQGgFbxUBLjfJJT9h45TferszcEVBJsiO/+/P3eydHNgAF+flIuHoFK1555THlyGzZ8Mlvf4vN779PJ4XesLZ3biSis7MLI8ePlWrbqsBOrlGrAl7jDMkcrJLJ7eg332Huq6tpbEp3zK0pRtMk6JUWUfI98qdHjulob8O9KzdQW16J0JiR8Ake9ATyZBVsq+eQQaQKaLUMrwrHG6pqkX/zDgiTHRw7msZ8avpC8XIoPK1Z8K4F1SoAlu9d5WHVGrjUEdHeQQFudX4RnH280H9oCOzcSUZIxbWikR9pt5kcGZrx87hLUu3SVFVryEyturxgXmUpiZQVUF9cQQGttV0f+I0YAielRqq6L09QZ1p6zMsCadlWY/lwR0sr0k9eRMCoYfAKHmRBkijFYPeYPIrJ2OrLq3D7VDwGDR2Mf1n+hmVS5NXPCNjueA5se2EWezyESJFbaluUxRtb7LW3t2Hb9q00aRT1FZuRIesBGS+pIS/OybFJt5JQWlaGBXPnWQ5sheFj4/t///u/4p9pVmWeQEY2lsb6DdOA0DQI7LGTLNlBw7Y+OaDV3b7JK6B3Y2L6E5u6YWjz8vOoPPxu2l2MGjGayowDA0n5NvVjxKDRi9I5F4XzXZUeq5dlwAcf/R4/effnlHTQSpA5wDWgupbEQB7DyhWrmdOaVz7oMqCsvBRnzvyAlavWst+UTMT5D/KQknoLM+cso4CSyIDJvzu2foJFL26AlY2tAK2Hdn6BaYvWwNrWTkiPj2//BNNfekuwuOT4+D2fI3bZBnQarEScbVbCGbgHRsDe04/Ws+Wsb87ZPRg4ZTk6SN6DLlWKTIBzZXI87HyC0Ld/gBFry5JfquCedylfT9Cxoywy+VqTv01CikyAGY9dtQLaygvQWV2CfpFxRjLkiqSz6Bc0BO6+AZRZJaC0KusW+tjYYNDQaBpfa93VgdvHv8Pk5URCzPZJvXgEw8ZMQT8lvvbWheMYNmocvPv7UiCbdusKHB2dMHx4DJM7PypEbg7JXD1fI0M+f/4EhkZEITAwSCM91mRXZ75uPrSlMc7XAsomruSSXvuW5mYUFOahtPQRqqoq0draShPOEdl6eLjirFMmJDqVKZMWWacWFeYj+fZNLF9GMk0r8m4lnvbzLz/B+rWvw8XFSQK2BuTm5eHu3VTMm7eQOS0eC9j+yZKZ5Knv07HtJ0+9zd40+BzY9qLXynhWZGliSEtJQdGDB5i3eNETAFuNmRbeM3OX+NcGttwAbt+2FZOmTqOF4wX4VaYKVXqsTh6yHJmD6C8/+TNWrV0LZxc3Ll7VynMVJpWdU8vgJickUHlONCkbJMWJsolbAmN6mav0fdeXX2H28mVwcXeX4mMfT5IssiYT71pXF/Z98gXmrlsNBxdnk+BWKxvWXqte9qs6FeT7MS1bbqitRfqVBLQ1tyAibgzcB/iIbMB8QcLApNKbctyqydjf7hhW/kRUR4C+36sLHiH3ehLcBw1A4JgRsLIh0Zv6++ULP5mZN75G/nz1DgG5L039TbaVZ+WhJu8hOjs64TU4CP3CBipDSc/i8jdbTYImLbl06zm9VTQzOs14pRSdhzhIlVNqAW1N/iOUpmTBzs0ZPpFhVHrMmlRYWM3fPDGGmqndXJ1avVSYxtM+LEF2/HWMXjoH9i7OTxHUsuvKSbyNioIijJ49HU6uLlgbOMYyYPtKeC9m5yc/xLA9+8kbed6CUQ9wYMsXzeTdz8zKRGZWOpYvXf5kbK1iOhNuJKCurhazZ87uBbBl80B+fj5uJidhxbIVJiXJsvyV36Q5tlPA4N76xORefFxAa0GIcU/Xb46B7hbQKj/eSErEvXtpsLOzx+iRozF4cIRGC2aqz7gdVGdkHRCRQ0cU0EHenwsX4/Ha+o2URaNt8NhaCdgRh/XJU8fh7z8IQ4YMEzJkDm7vpaei+GERpk6bLerCEgCblZ2B/PxcTJ42TwNsd277FAtWrod1n74C2J448B3GTp0Pexc3wbqeO7QN0dOXoI+9kwC7iaf2IGTMNNiRMkBKAqnCjGR0dHbBM5zJjvn2oqTzsPceBHvfYFHyh2ZHJuWDmhpRdTse/cbOM8vaMhsq237VZ6AHtNyBJEuR5VI/tUk/wHVwDBw8fDQyZCIHLji5FYPnvQpbmz6CsX1w+TACR02Em4c3rU/bWFaE8vv3MHIKA6XWXZ24dmgLZr+4STnGGie2f4wVrxLZMZMznz26A5OmzqUsKU00dS0e3t4+GDqE1MllJX5o0qYfDmHtmg2U8BBqJiU+mAFNeX2tdTzJ6y22VlGGvgRwNe+kspYSiwNe1UAZc7L0mfTlqdPHEBIciqFDIpncWIpd/sunf8TmjW/BwcFeAFty9vPnz1H1VPTosXR9SYCtn1/PSidyaX1e/fMzsUAdW997JufVn/Q5sO3FYzAFbCvLy3H6hxN45dVX/2oJpCwCtspo1NtVvd/ZFGsrHyNnPb6VmEiTMzxJduTrVy5TcBo3aXI3cmTOEspsIvv74//8T7z9q18xuCaDM8WQ6WW9oiXlRstKSnHtXDwWrF6lTSQlgWhT8bUCzMreUCmu9MjX2xA3fzb6+bDsxbLsWDEtJuNrjUCvMrsyG62NF9WAYOm36pIy5CanoKO9AxHjo+HS38sswDXNupqRI0sOARWgmgCm8jUTCVfWfWRfTkRA9DAMpAmmVAeFKZBvis3uPrmUcoRGqi2DY/YGN1ZUoep+EcrT78MzLAD9Qvzh5OslsbiqyeLgX0wLmqzJ+smiu9WqaSZFX+5H5m4bSspRX1SG8vQ89B8SDK+IINiTTMdKRh0Oijk45XG3PM5fyII1Ma3m42sJ+C24dRdNlTUYNmeKDtDqZMxSHC0DzXp5sxZck6Obampx68RZBA4fipARUeKYVwJiLAO2a54RsP3+ObDthVns8RACbFvrVMaWvK+nTv8AvwEDMGrk6F4ljZKXqmSYXLl6BW1trTS7srnkUWbtpoTWzsafg11fO0ycMNFicMsXxMYdYTSrmGQ/jY6TphC9Q8xcZ/cmfEm2/dp2TbPOelCrWUsYgOs3ExAffx5xsXEYNWo0XJzJYlxiX3XTJju/9LsOQYjek9gzsntNTTXOx5+jCYIWLVzCoIYmrlYrRa6vr8eOXduw4fW31fwaQq5rwMWL5+Di6k4z2nJZLwGQqam3UVldgdi46Rpgu+u7z2jyKJu+9gLYnj2+F5HRE+Di6SO2XT1zACFR4+Ds5cuyG3cZcPf6Gbj5qpmRCYitLClCcdYdBMbNFceS/Wse5aNGKfujMrZM6ky+12YkwqqvIxwCWMIrKkum0mp17WAK3Mpjh783PHROliJzYNvVWIP6tMvwnrBYgFqeEbmtugQN91MRMH6uAKTWne3IPbsboxaup9v6kmzF6Uk01jY4MpqC1IbyYhRlJCN2xmLK8jZUliLz9lVMn7eS/t7Z2oyzx3ZhxUsbRTbk/Tu/xLLla+Di7Cyu4/q1eFrOZ8TwUQzYKkBWVSwxBpXIicn84OzsSpOUqa+lJNEW4WdsNGjDlpQRovNv84zLavJHNQke4c0//fRD/PS9Xwr2WzgLrK3wxz8xpUHfvn2EmoWc5dut32D2rLnw8vKmz9nwOMB2/cc9zsc/xg4dW979MZp97DafA9vH7jJABrZyAqkvPvkzNr3zLkvX/SMnkNIvm7t1CP8IcuTdO3dg/YaNbOBLY11lCJVJQdlBcaKKOKWG+noc2rcHq1973UzyIxXMcsMms7E3r12DtZU1RsWO7TVre+PSZTg5OyNiJKnvxhNFPa4k2RgMntqxF0PGRsM/lCUyMgUi1bhhyZsqgVgBzwRoY1abA11hxCWJLf+tvPAhcm4mw97ZBeFjRsDBjbPipkCyFgiaBtKm97EkARXZJy/xNqoKiym49QwZJIyFei7xhE0nkjLDMHOjw4G/dJWir1TGgf1alVuAuoelaCyvplJlFz8vuAUMgBVxOUuebXla0DuBNFOGfpXXbSZWbboo0k5bXQNq8otBGFpbR3v0CxkIz9BBgpk1V99WZl65MTXFxnJJFE8CJWc+JtJjdz9fDBxJklApmS+7kRZ3l1FZD64LUu+h6F4WYubNgLO7EjukCK1XB0RbBmzXPSNgu+05sO2FWezxEBnYctb2u+3bsGTxEri7uYsyF9y26RO+CJuqQ28yEXP1+lUYurowacKkxwe2/A4UpLd9905MmTQF/n5+jwduZaNosldMAF1pP2OXWM9y494AWt3tGl0pt+XauVCDUYUOiEyuJLPxufhzCAoMwpTJU6mjTJ47zSXsMXUd6rSq6ytlLXPl6iXcz8/DhPETEBxEksypwETOgswBHdl28dIFuLu7IzJyuJAqCzlylwHHjx/CyFEx8Pb2ExJlAihup9xEW3s7ho+M1QDbowe20wRHNnYqsE24dAoDAgfDY8AgBk4NBqTcuABXrwGili3Zlp+Rgo6OdvhGjBaZkck57p47gPDpK8SxBNiSdnLP7oX/lKXotGKJqrgkmrRFAGzZpf3wjFuMLitrTaytWGcIBlJeA1goRSYxotZWaMq+CVvnfnAeGK7IkEnGYoUxzUqGrb09vEJYnCz5r6m0EE3lhQiJnkJBLalhm3v9DAKHjoKnF5MZP8pOgVVXJyKGs3jahzl30dHahBGj46hU+dGDbFSXP0LshBfo/k31Nbhx9RwWLlolSgwRcL1zx1dYt26Tpg6sHIZD+vry5fN4VFJMHSFESuzr60cTM5GyPPb29mpolNJXakiUOgJMOmH4Wp/nvODKCmVQZmWmo7y8BNOmzVCzTVOJN5Mkf/X1Z9i86W2RfZoc3tbWhq3fb8Frr77BpO8E2NpYw99Cxtb2tb/0OB//GDu0f8vi25/15zmw7cUT0ABbKYHU/t27MGHiJPgNJKVHzGdGVg20uo+6v8zhSI3orvNZAVtus0l25IkkWdYTyJH379qB0ePGISCI1KtkYllmiCQAZ7LGqwEkEcXnH3yAN3/xvnHiJD3DacR4qvDw+0+/wLL1a9DXzk6NR9EljWKgl12hhsXVMbLy9V89cQquHv0wdNwYhVWW2Gc+cZq4LnFlUjZj0wwtb08HeKW2y/ILkXvrDpWXDo6Ngb0zKTyuLBTMSbaNQKQMak2fU70+VcLMQKL6HNtbWpBzJQktDY0IGR8Np/79pEyEj5tcit+zKeeH9CYpC1QVNqvMADmytrAENflFqHlQAld/Hzh6ucF5QH84eLrp2NweV6oWziJsqVf3sAwNJZVorqpDe1MzPMMD4REeCFu7vsq8oUqp1IQeegmyzMRq/yZgWCNF1oBVoKG8CimHz2DU4tlw9fGioNZcMilz240zJKsMbtKx03Dr74mI8WNMZlV+edBoy4Dtq4Mt7Nenu5tha9bTbfB5a7QHGLBtFQu4+vo6fL/zO7z31nuMrVUSrXDjKcT5sgRXJ/EXdlD54+q1q7Qe7owXZvQIbM2OaonC/I/f/id+8Xfvw9bW9rHALZv/ftwH/yRgVgNWTV6nMVsrdQs9XIBWA3Dq7GmUlZdh0fyFcBXhRewsxoBWx85KD0JmZzXPR7Frufezcf36NQQHhyBu/ESTYETP2pKdqqursf/gHqx/dZMaVyuVASJZmr/d8ine2PAThfVUmM8uIgk9Ce8B/ggJi+w2xpaAj+uXTsN7UDD6+wcLcJqWdAV9HJzgHz4cHUrip0f52Sgrzkfo2OkiMzKJq005tQcB42bCxslVSJkJkC1MOgdH3xDY+wRoEliR38japD4vFZ2tbXAKG61hbbmkVuTxEKCNPZvupchqduSuxmrUp11B/7hFAtTyEj/k30cX98E/do5S5ofFzpbdvQZnNw/4hkYyYGttjaRDX2Pi0tdhZ9uHAtWM62fgHxCGgYFhLN6WfB8YjODQCPr7nYR4eHl6Y8hQIju2Qk5mChpqazBpEgO6BNTm3c9EwYM8zJm9gAFbJQsxVxXV19fi8JF9VNIbFUUqSTDzTsAtSQxFMhqTMkJjx8bBydmFOcV1jgA+XuR3WR4PgvGWQC3v24MHd2PcuDgEDgpUZMi8fBJTOX3w4W/wi/f/nt4Ld07fz7uPm7cSsWzpKgpquzoNOHbyKDZtWmvRpGL7+qcW7fe0d2r/5q2n3WSv2nsObHvRbaaALWnm0vnzcO/nhpGjo3sVZ6uXG+lsuOZKTflwH4+1Neah5IEqs7DqoFa9p8lJSSgvL+tFdmQOrIDMjHRkZaRjwdJlFiWRYkZSBUtXz8fD3tEBo8b1xNpqWVVZBltcWIikK9cw/6WVZiXJsvyYXD3PmkwTS3UDblOv3UBDTS1i584UcmI9eDctyWVrKM5E6iW6fFFhHLssOwRUEPowMwe5yalw8+mPweNGo6+Dgw7gqvchty1ak0C2XvatlwrLkm9+r2IfgwF1ZeXIuXYLLp79EDBmBGzsbMUztUSerN+HtW0e4IoFmEZSzBdtKuRtKKlAXXEZZXIbHlXAsX8/uAzwoiwqiXO1d3NBHwdSosby1Wp7UyvaG5tp6Zymqlo0V9Sgq6MTdq5OcPbtD1f//nDoR0A0W2Kw8S7F2irsL2dD5fha7o1W5chqDVotS6uVIhenZKCutAJRs6eImrWmE0wZl/lRwaxx8ilyzupHpbh5+CTGLJiN/gH+GmmznKn5pYGjLAO2658RsN3yHNj2wiz2eAgBtm11rVSOR96le+lpePAgD4sWLFaBLR8NXPUky3FNGEQ9sE28mUgXrHNnz1W9wjpjaZFTWDGGtbW12LbjO7z7liKxU4a/ZhbQlAwz7oanCnAfI262xwdiBnz3xNTKgLa4pBi79+7BzBkzMYzErXLUoIAHuZ+MJJ3SjzKg1fYtm3ELiwpwMymRxhrOmjGbSkllllasDSQpshxnS2JrQ0PDERQUqkksxbMikzhdUnKKJCSi9p4CRsKMAocO7ULM2Inw9B6Azi5m/wljuu3rP2LVunfQqdSx5cDW03cgBgQNFsA2624SWtvaEDw8VsTOVleWISvpEqJeWCqALWFncxLPw8nbHy5+oQzYKrG21YU5qC8rgueIySzOlsqQmRyZy6ZLzu2A16TlMNjYPnUpcs3NH+AWMQZ27v11sbVW6KyvQlXaNQRNXkxZVM7Y3o/fj7DYGRTcksRRHY01yL1xBuPmvCTK+Nw6tQejJ82Bu3s/etzlYzsRN20+PPp5iPjaiVPmwMurP/1+Nf4EQkMHI4wAXysGbE+fOkxZVxLDqgJbZqOaW5qwZ8/3eH39ZiWhlDIqdOM4KzMDl67E07I6ceMnGzlM+GttToGgl3LzqaqluQV79m7D6+vfNJIhMzmyFT7842/xdz/7JWWoqUqFhlRcotcwPnaiwth24aOP/4Df/OevLRnWsN3wmUX7Pe2d2r9+82k32av2ngPbXnQbB7ZsOarKObLS05GZfg9LVqx4JsBWsSWm76iXcmStYRIwgWaE+37Lt3h9E3uRZSDMDaMGHAsmUWlD+f7x73+LTT/5KfrY9uXwxCiJlBY8qQwike7s2bIVL7/xugnWVsfqMd2sAEEyQEq4cBHOrq6IGDVCAVnaJFIymFVZW2MJstiPw0YqOcpC3r0MTFw8H9Y2pBi8GdZTksHKYFkjsdKxrVqAy8Gd+ozU58B+K8rIRub1m/AJCULY2JGwtbNXeXINQyyztKbBcvexrypwlAG6DJTLcx8g++pNDIgMR0B0lCb+Vn/dbEHUHTvL42BM76NyC9rrYgNFD3LZNgJwCSBtrq5Fa209WmobacZl67590MfeDnauzujq6GAeWBtrWvapq72TAleSLKupvJoCYRIja+fsCAcvdzh59YNDP578QVf8R5cRWQ5x0ANaDoO1ca7mGFw19jXz3FU4uLogeJxcyocBWCpRNpIhm8t8bBxPm5t0hwLmMQtm0hAB4xhcFXivGjjSMmD7Gkk289f/GL7N/Ouf9G/gjBTY1rcqtRqBH06dgL+/P0aNGCVKYtB3WwKw6oJR/KDpKT2wvZN6B2WlZZg9c5bFwNak3RSGj2QnvY+r169h7eo1fMow4d7i9qX7B9lbkPu02Fn56kwmb1LsJN9P6gZ5tqS2NDXtLnVOLJy/EI4OqhqI9qcMWiWPuSnnOd/ZeK0Bmrgp4cZ1ONg7YtzYWAwcFCDsuLhUKSZSw0wqDoeMzHTk5mZj7pxFJtlaAlTj48/Az38grVlKpL0sVpWtA7Zt/QyLl69BX3sHCiYpsOzsxPdbPsaL694V4JMA21sJF+Dczwt+wUPE9vycdFSVl2DwmKmCbW1pa8H1o9sxZvF6TTxtcXYqmhvr4TMsltbBpXLmLgPaOtqRf/EwkyNTwKtchwLAyXU2FmWjvaEWTmHRIs7WMimyWpaSjyc5K3JraT46akrhPmw8BWJqiR8mQ67LTIKtvQO8QlldWpoUqqMVuef3YdSCVwXQrS3KoTG1kbHT6TZrGJD0wy5MXbSGfreBAaf3fIlla96m360MnThzZAeWrFxPQR9p98ShHZi/YDmcHR3pd0NXJ7Zt/RSbN/+UXosAtrBCY2M99u3fgddfe1OE2pBxZKQgEGtDIDXlDm7dvoGlS1bBhSgPuENc79DS5d7gsclqGAWz0vfupaK6pgJTJk/XypCVEkDE9n757afY+PqbsFGALbnG3Xt3YWzMOPq+d3YakHM/Fylpd/DuOxssshS2b3xh0X5Pe6f2rzY97SZ71d5zYNuLbtMDW74IbW5sxJ6dO/Daxo1PJYHU4zK2RsZIY8WETZa2allb2YhpjIxyhD5j4e4d2zEmdjyVBWlArJDY6gCvmBw4mADOnzkNVzc3jB5DWFcVRHFmVsvQGQOmy2fPwcnFGSPHEsmvLlOvGamvDJL434e+34mJs6fD3dNLkhz3FG9rGtzqa/DWVFTg1M69mLRoPrwDBuqkySoLbcRGGsmyVSaXLR7U50f7Tlk1sH5QQZseKBamZyHjWiIGDh2M0OgRsLElJYq6Y4i1WanNx9aKntU6JxRLwq5RBs3Ag1upKMm8j0HRUfAZHGRCii44YN39adth746+DzgPoL5v2h7jHhm1LbHIlVdgym4GQxc6mlvR3tyCzrZ2GDq7lPhpA12N29jawrqPDfrY2cLWyVGk+1dL+CgDSbCyzPiJ/ysJmth35rmVY1f5dj2g5e2L5FH8OKW92oelSD91EUNnTYFngJ8iUzbNujKQK5UOEnFDXCal/C4lqLp59BQ8BvgifOwo0yytnGzKCljpbyGwff0ZAdtvngPbXpjFHg/hwJYvmj//6jO8tPIleHp4qDJkXu5Hh2O1SdfYqTTMq/IlOycHSbduYvWLLz8WsDVpO6U5IFmpj7uY1sdlE4KGWRQNaG1qT50iFtlPmYnt8bxiotMuEoykltJ+4s4MwJVrV2gCyUXzF6uSZD2gZUaKd5foMLHFJFhgdi35zi0kJFxDYGAwRo0cReMhud3RA1p+GjVRoxqeQ8r6bd32FV59dSOzPZTRVRzX0ve/fPoh3tjwLq0Xyn8nwLalrY0C27WvvcuYWgXYEunyvp1fYfkrb2qA7Z2bV2Dn7IaBoUOV7UBxUR4Ksu9h+KS5Yl/CtF4+8A1Gz1sNkHJBJMMxSRRV/giFaYkImrhAgFrO2j64dhzuYaNh289HI0cmrDJnbUvjd8MjdgHQ10HU6BVyZAnA8efcvRSZxYCWn90G31mvijI1PGEU//dh/B4ETFgAOydn2CqMbVNpAeqL7yM8dqYAtg9TE6jUNzBiBN3WWFWK/LsJiJu5jCWOqipF5q0rmD5/Ff29pqIEabeuYc7CVRTEtjTV4+yJ/Xh59QaWSMrKClmZd1FRUYrpL8ymoJaBWxJn3IHPPv8T3nv3fQ2oJferH7ey8o38WF1TjRM/HMHIkdEYEsGyZ/NBb9Ixw826Milxi07s9PYd32Lu7IXo37+/RobMk0eRf7ds+wqLFyyFl5eXmAc//Oj3ePOtd2FjbUsdGWfPnYa33wDMmUVyB/T8sX3jy553+hH2aP+K5d151p/nwLYXT6BULvfDX2plAfjFJx9j1erV8PDwoL+oHhy90Ng4vkHdX17smr5AU1JkdfiZuSkjY2xshE0NXK0nVTVUqXfuoLCwEPMWLNQBW2Y8qior0NDQKAwbnyA8vDzh6OREJ4zy8nKcOHQQazcSw6OCGBU8qSwcs7FacESC7L/5+GNs/Lu/UwCjBI4ViyfxfVrGVAFC5AjCQO/47Eus+wnJmAhNCSD9dx5vK2c8FqCcS6VNxAaf338YXn4DMCyWxd2aZm8VcKhjUPn6QgWpEg/52ACXHVt4LxP3riQgcEQkwsYQ1oREgKoAV8A9Re7LulPt354Arh5Qa5Noqe10trYj51oSWhqbMHDEELj5+7B3xuS5jMGreL7S+yMDe7ktlaFV+4+/VwrtL95XNorUt19jEHXA12jESRSLdqzK84C6mtUIkJXVhiopZjMDB7T0m1T2xwjQSvvmJ9xGc3Udhs2dAmtrG5MxryqYNc3a0gyZJsBufUUVru8/hrEL58BroJ+6gBD78uvUsskr/EdYxthuGNKL2fnJDzF8nfHkjTxvwagHCLBtVxjbxsYGbPnuW/z0nZ+qMmQiUZbgqsVsrWRoS0pLcOToEWx+g6ATgwAAIABJREFUgzAHysjrQYqsv1Bz4/z6jQQ0t7Rg2pSpmqnBGOBaxt5294o8KHhg9DMpZTPQn5cse7IXrCe2VnZyi/lRmQ5PnDxB7feUiVPMg1rpBHyqlAGtvo/Jd/JOJN68QSXHsePiaKZsJydSg1y2iUrXc8DMwbGwuyqoJTb9u++/wWuvvamRH+sBbl5eDtLupWDu3KVqtmQFwJZXlCE+/iQWLVvDYh0VeXJDYyOOHdxO69hSBldJ8JOafJ1mSQ4YPJyyqmR7VUUJ7t68jJiZy4Q8mbSVeHofgkdPgoO7l4inbWvvwK1jWzFsgcLkSqxteW4q2lua4R4xhgFbCWhzdrmxMAtttRVwHjJe5AURwJY7u6V1oJZhVB2qPCtyw72rsPPwhaN/qDGwtbJCZ10FqtNvIHDSIpD8i7x+bXnadTi6uGFAGEsmRWJsMy8eRciIWCrpJgC4JPcu2hrrEDVmCt2nODcNTbVViBk/jTK0+VkpaK6vw7i4qRTIFuZn4VFhPmbMmCcyJB8/tg9jxoxHwKBACdhaYceOLViydCVcnJwFWFTDd3RWXeoP7vggXUVqGre2tWLe3MVisJkDuWrOSF5XHigseIB76SmYN2+xYGtlQMvzChw5dhBDIyIRMTiCzoWVFRU4fOwQ1q/bwNj5TgM+/fzPeGXdBgyN8LNo4Nu+/BuL9nvaO7Xv/NXTbrJX7T0Htr3oNhnYcpvKB83ZUyfh5++PyKgoDWurlyoIW6zz1OqWvTq3tHqx3QFbvpeRwdV7SFWToQ5cxXhJ/yh7cYOimrnOzk589fln2PT2OxoPNjdgxw4dQlpqquZ48mXOwsUYNoJlJiSf/bt2Im7KFHj7DlAlpybYPcHK0UtQgdH1S5fg6OiIqOjRgvHjV2nE4nUjSS64n4+c9HRMnT9HE29L2pDBLI2tFfInFXiroFAC1zqAmpaQiOqyCsTOm8WkyUpHy8BYvXYd8yp5v/WgkXWJlo/UsrbaPuNeb3JEfspd3E++C7+IUISPi9HF3+oBtPJ0LQK45kGwFjSzczRW1SL3WhJs+trS+rf2bsqiptcAV71nLUvPDZvK5GphrvRN7619zPnCCNSK8a7+Is8NasIcCepKzK2lcbat9Q3IuXADXiEB8I+K0IBOkRBKBsiinA9b3GiTRklAWtmvICUNlUWPEDNvJq0dKeJ0jUAtuw85jneZ33DLgO3GZwRsv3wObB/zNbdodwpsG5gUOSsrAwUF+Zg7Z74AtqxKh2IBpYFjKVtLjm5sbMT2XTuwWcnYr8geNNf32LZTcmJdS7hOM+ROmTRZMYiaf3T90DuA29TUhD/88QOjPnVydMTPf/pzi/q6u51M+eS4PdGsHcR6QV04XEu4RuNcJ5DETSI0Rj0ba1t1fnO/oNF2YfeAzs4OnL9wDvn5eYgePQajR0Vr1DkyO67PVKs6S9lp+fe6ujqaLGrd2g1sbcJZWm67Jbb2woWz8PH1Q1hYBLPzSkImAhbv5+Ug70EuJk6axYCk0k5dbS0uXviBlqWRgW1a6k0KRkIio5lcuMuA+oY6JF85hegZyxQZMQPCmUkX4NJ/INz8lERTXV1Ufpx+8Rj8RkygCaS4FJm01Vxfi0cpl+EbO1cjR+bXROOFu4Dy68fgNnIq0NdRWqeo6zd1TdR9VuT2mhK0FmWh38ipgm0kdoHGtlqBgsu67GT0dXCCR9BQwaISkFp4/QcEjoiDs7sHBbsE2Kae3oWxc15E3z6szu3921fg0X8AAoIG0+9ZyVfg4emN0HDWVkriRfj6DEBo2BD2Pekq3FzdEBU5kn7vaGvB8eP78NJLrwoZMmFsz5w9gYjwoVRJyBPSiX+VV9XYcSOtjyRnSVZOJi5disfkydMQFjJYS+BoBpIKaMkpOjvbKRO78fW3NYnx5BreHNheS7gC+752GBMzlu5LJP4VFWWYMvkFKkN+VFJCr2HpypcRMIjn5Oh+Gugz7dkwpx3xz4Yp1vfGc2DbCzNhGtgyo5yWcgdFDwowb/Eik3G2slHVe8sksy6uylxszWMbZ8liaQGvCdbWBADmx+jlyPt378aI0aMRGhZuJEeWga1ix+hVzF24GJEE2CobU24no+TRI8ycN888S6cANwH6JEkr8cx++8knKmsrAU8Z8OnBlDHYMYDE29o5OCBqbIymBi0DsyqI1X9XQa2WVabbNVJcoOzhQ8QfOIJxs2dgUHioMXurAalSGQOxkDAPYOX7VfuK/6UAOqMSQqy97Ju3KYs7cEg4QseM6hHg8sWKfO9yn5oCr1q5NIfzWna2qqgE+Ym34eTpTjMoW/Wxoe9NdwyuqXMJ6ZpGZq0F6pqFnIi35YtVvWtIWrBZMG8YazQk4bHGoaWCP3kOkCXH5mXJyrESS1uanoOHqVkYPn8ajalV2VY9QFW/my3lowBVuUxQ2oWr6GNri8hJsRZJj3k+Z34/S/2iLAO2q8Ms6OWnv4thR87Tb/R5izQrcntDG5U3Xr5yCTZ9bGhZHvJesAWfCVBLB4QpzYPUoTpj+KdP/oxXX1lLS7v0FtjK9opNPupMcTb+PJydnDBu7Dg+UWh30T9rPXvZw7vQE7DtyfYbObR159OztZaC2qMnjsK7vw/GxrCwITYnq43r1wYqINUBfMGQGZCYdAOXLl3E1CkvIHo0sbmqreoe0Brvx+1sRWUFDh7aS2MrRXsKuKXqKwnUktN98snv8dab74vtPDkkkfgmJl4ByfozfFSsSBpF5MhlZY9w41o8Zix4UQtsUxIpGA0dNkZsb25twflD2zB1+QYNY5uTkgD0scWAwaOkzMjaBFK8ZBAv8ZN7bg98xs6BtYOzkCMTMC6D24b8NHS0NMEpnPUnd8Lz58XWJOy5dSdFrr5xDO7DJqCvq6cYo2Ts0mzECsB9eGEPBsXNh72Ti0gcZYMuZP7wHaIXbxAxt13N9ci6cgKx81aLxFHJZ/ZheOw0eHp60/1unDmI4dFx8PX1p+eIP7EH48ZPg48PKw1EZMgxY+Iw0H8Q3T8vNxNFRfmYPWu+YGtzczPps5k6ZYYJUKsFn7KzRds36rvN1xykFJRdX3tMnzGHrSbMDDIyVRUVFdIszG+/9TMqizYFZlUVFFBbX4vdu3fgzU0MBF+4GA8HBwdER49DZ1cXSEI8ksRu6vSZCBhE5rSeP7abv+15px9hj/bPX/sRWn38Jp8D28fvM5Q2ttEaVFx+wBgJNk3U1lRjz/ffY/N77z2dBFJmrFhPxs3IMKt2WWeETQBb6WABaKV+kr2kJFnWg/wHmDV3npEc+dihg4Kxla+HA1t5MvnDv/8rfv4//kmahLVMKJtMJCZUOZiDqqvx8TQBVNRoAsh0YFLsa5pBVCcq9vvB77Yj9oVp6O/nayaZlBSjw+OCTUiPtQbFmMW99sNpWFlbY9ys6UqEKI/d4kwiP8ZYnsxuqTcAl8u9tQyuvKDIunELRZk5CIiKQPBI4oDQX4/S6+YSYUmLE3aNxser5zNf6udReg4KU9LhHRpIY3AtbUfbN7oSRNJ7Iy2N2JgwWoDqgKyRMTMGujIPK4aMkSLSVOIoNZ6WZ0Z+3DjbjpY25F5JhL2TM0InsMzsqmRZlTL3WI+WAA0T0uOOtnZc23MEQyeOg29IkIkyQaalx1o2F1g8wEJg+4JlsqteTOHdHmI4X/y0m3zenlLup6OhjdrKw0cPYUjEEAwdMlSz8KNWtBu21qTd0208cOggwsPCMHxYlEk5siW206T9lBia/YcOIkycQwW+3YNKbSyQuX1NAVtyzUSV9L4FjG131yDdgrIOMHFNSgPCuhiAS1cuwd7BAWNGK+XrjECtelY+jYrZkZ9CArQFBQ9o8rCoyOGIi2Nle7RhImzIGDG08vwttccBXP6DPFy9egEvv7xeB2oVe6lJNGVAVlY6rW06ceJ0VYasgF8CGE+cOIiIocPhNzBYAY8szragIA8Z6XcwecYiTV3Z1NsJlE0NHz5OE097YvsnmP7SWwKMEga2MDsN9TUVCBw1Sd23y4CHWXfQ2tIM76FjNW0QcFty9zpsHF3hHMCSU1FAS2TTiqqMs7YlF/fCa9IKyWkuqb8skCK3lxeirbwA7sMnaZIeEaaWg9rOxmpUplxG0JSljMFV4mtbq0pRmX0bQyYvEMC2vuQBqguyaZwxTzB1cc/nmP3iZvTtY0O3ndv/LWYveQVODo70+8lD32HBkldg37cv/b53++dY+eJ6kTjq8sXT8PcfiMjIKJp4ibC1x44fxLix4+Hv568wpTwMRldOzESMvKkYbbGGMwDZOVn44eRRzJk9H2FhRE2kAlwyZzU1NVIZfVVlOZYte1EThsjZWdWJp9pKsuOJH45h8OAIhIeG4/iJIwgPj0BISDhIjPihowdpJujBQyMRaCmwfXPLM7E57Z+tfybn1Z/0ObDtxWMwBWyZQWYL1m1ff4XlL74EFxcmpVSZWS2HIwyskRxZtzw2YYktMc5mDVwPsbay8ZPbECBXMkJElvT7//Mf+OX/+CcjOfLxwwdxN4VJkeWFAgO2pJ6YOjGcOn4MPn5+GD6SMYUcsGoAkIWsLYduGmApztU9uOXGfOcXX2HVxtcF0Gb1a00kkzIlS+6GMRYSZsVAF+bcx5VjJzF+7kwMGhym3rsOHPL+4951let8+gCXtm0w0ARTxdn3ERozAoMih+iApQmwKi06WBuCvzUTTyzFS+scA/IzfJB8F6XZeRgwNAx+URFmAC5fRhnX0jXVd+r7KLMJWiePzECYXOhq3mp5IjEJb9lcYBR7q7JUcpxtT4mj1PmGHfXoXjaKku9h6IyJcPfzEfXwtLGxHDzLyaHU+FdN7VshTWZ8a1VRMdIvX8e4RXPh6OKikRbrGVmVaTYNdBcNGGYZY/tmZC9m5yc/xPDZvSdv5HkLRj1AGFsObHfu3oE5s+agv5QwhcsF+YHMV2x+LIkT6IzhreRkWvJn1oyZTwRsTY55yTju3rcH48aMQ2BgoNbAaSLzzb0I5rJPkQWyaSkyBbY/00mRTRj5boGt5kc+h0uXbwLUptxNRUHhAyyYS3JpKPOs1I7s6NZLj8VuEgg9cfI4GuobMGvWHFrz1hJAy0yL6kgU4JlvMwDJt5OQn38fS5asZKBWirsV9khmaw3Art1bMX36XPTr56VJLMWzIu/dsw2z5i2DnYMjlSnzONvs7HQUPyzAuEmzBLAlwDPj7i20d3RQxpYnfSLg8/S+rzF+/mpY2/ZVWFugvLgAhVkpGDJxngbAVpc+xMPM2wiKmysYXsbcAnWlhajOuwfvMTOV8/LyQzyTM/u3qTgXTQ9z4R49SwNuRV4M5cGocewqUUP6mpb3iZqIvk5uGrZWzohcl3OHgsn+g0cLFpcA0OrcFFh1diAgaqxIJlWSeRs2VgaERo2l+7Y31iE7KR7jSeIochEd7bhw5DssfnkTBbEdbc24fPIgFq1Yx2THrc04fvB7rFn3poivPXRgOxYsWAYXZxd6HW1trdi79zu8/hop7aNk9Ff+lSXJ6juqHSlcys6HuPrO8PFhQGdHJ86cO4m6uloM9A/AAL+BVDZcU1NFy0WNj52E4bxWLp2/eI14FVhr5NHKPtXVVTh+4ijWrVmP/Qf2YHzcBHj3J+WlSHztx3j5pXVwcnO1HNi+te2ZWKD2T9c9k/PqT/oc2PbiMZQojC1bwLGPyo4ARw4eQHj4YAyNGsa283241Eo6p14KwtuSRIsm42wtAbYmDbOyUTekpRQ5XK5qZKtVRlYy7uTPwwcOYGhUFMJ0cuTugO2wESM0LOWj4mKcP3USL7/2mllAxECdedZWH2vb3b7cUKpwSMuKlj0qwZUz57B47cs9JJPi0mO9BNl8kiPVo62CsJvnLqCxrg7jZs1AX0cHAfrFQsIkCy2xr0/K4AoQqpX8EpYu/eoNVD58hPBxo+EbGqw8Nw4IVcDPXi1jYGmOaRVPshvmV+zT2YXcG7dRU1xKAa7PEEXCzR6k5AjhCy+155iTRN1Pu5CSmAYRfy1PClqwa2q64G2zdbj5kSm7tfi4l9PlMBDKZgA+n/Bvxomj2F5N1bXIv54MR49+CI0zz9LqE0wZsbYahlYbZ3s/KQV1FZWImTfDbG1aNs9JZYB0mZDl7M4LfSMtA7ZvPSNg++lzYNsLs9jjITKw/ePHH+HNTW/BycHBSDLIG5KBbbf2TvdjRUUF9uzfg3c2v/1/2fsOsCqSdO0XlSCK5CQgSZCoSBZzzo5hDKNjGHUcJ2++u3vv/e9/7+7evXd3diePM6OTDTNmzBEToCBiAFRAQUABEUVEkij8T1V3dVf16XM4oM78967neWaku6uru6u76qu33vf7PtU6c2XMtZ3sPgyAImf/Plz1MRbMmw9HR0cDg2kKYOo3lnQGBbbvG/rY2tr20ABbBYUK1Rm7rrkSZJ6prbx5E0ePH6XRqx8X1FberMCmzT9Q2XFEOFnY5m2u3Hwc3cueQwuc+bGcvYrjJ46gtfURhg0b3T6olW1lWVkJcnPPY9w4KQ0QHy2ZgNj79XXYunU95sx/WYmIzHLY5uVko76hHpExcp5ROcjTxZwsmrO2X/9ECazSAE9tOLJjLaKGTYZVT+I3K0mH79XWIDdtPwaMm03LsPQ+TU1NyDm4EaETFnDAVjqH/Je/80v4T1qi+PCywFFa1vb+9QK01NfBLihascuyGVTepUGaGgBNFVfRQtL7RAwW2FoiUqTAVpYhV6TvgOeAobB1cJb9bqUctjdOH4KrXz+4ePkpwaSKTh+Gu3cAPPv0pcD0bsU1VJUUIHbYJHoOjYh8Np36LJPtOzfLkZ97GuMmzqLlqyuvg7Tt5MnSdnNjPZK3f48li19B165Smp8rVy6jtKQIkyZyebEVNwfJNjc1NtL3TBaJNGs8mgUAzgWMWzxh32z1rSpcuXoFd2puo2ePnvD07I2gvlwUf010d97HVw/YkveQmn6CpigizDBJ/2NpZY07NXexcfMGLFr4MrpYdoVvHzOlyK+ubXc8fhoFWla9+DSq7XCdz4Bth5sMMA5spWno+ewzqK6qwtiJE59aAClzjbN5Rs5w8q6uWqkNJK6+qqunly9eREnJNYybMEmQI2ekp6H46lXZgKn1xA0aDL8AkiJIXP39ds1qTJg2Dc6ubsoqLmMPFdBkgrUltX3zySosfHWler60dKvDAMs18oCOY5DJGRfPncftqltIGjvKZDApdgWFjTUCEvUYZL4Nyq+VIOvIcQSGhyIkTjJGvCEyNPTqirspBldZpZVfgckgU0ZAZtP9euRnnMHdqmqEDo6Ds3dvpU35+rX+xHrgteNlVJD6oLEZxVnnUVd1m0ZQdgn05Yy21Aradma+Wmp7qt8um43yiw2GE9rOBYGRRgN1bi3GfOW25AUyU4GjaF1cJGRydvHJbNyrvIXgYQmwc3Uyk6U1DATFQLOeRDknJZUytMHxKmjWZXY1AaIMgC5LGwQLTPIINQ/YvhbeidH58U9p+yTv8St5VoNBCzBgS8bjd979C377q99JckGSs1KegPInGaobdBrViCF8/6MPsHTREvTqRfJGi5Ioc20nfzVT4Pa///ZX/PJnv6BB1JSfPuY066sgwGbT1s0GZW2srTF71mzNfsML6dl8DovL9rgdCXIbKPP45TdfYsXSFR0AteJYycZVwqYWXinE1MnTYG2tLtxKVlh1wWH2TvqXG6cVObM6VyGH8wsu4ciRQxg6ZDhCQ2W3GdYkzA5zfqYKK9cGbNmyHmPGTkHPnj2loFEym8uAYlFxIZUqjxw9RYmGzIBt9uk0dOlmhX6RMRSUsgBSl3LOoLmlGcGRErBl+1P3b0bwwCHo4eRKpcw0vc/Dh0jd/hUSpqu+t8yn9vzeDQgYMgUWNj0Ev1xSX1nmAdj5hMLK1UvwraVpf+TgVsxPuK7kEpqrSuEYPQ5t5MOX25F9IzwxQ41Vaxuq07bBfcgMWHTtSv3hSf8kMRakHLaS5LituQE3M/YiYPQcZR9hTQkoLTywAWGjZ9Hcw2Sb/Jd7aBPCB4+Hvb0jBaY3Lp8lEZYQGpVIj1dey0ftrXLEDybpgbqg9OpFNNy7i9iEoVKdl86jsf4ekpJGSMdLClFcVEAjFrM0P6knjqB3b08qTdaCR3LvF3LOIu3kCeorSwKWOTu7IMA/EBER/WHZzVJV5PHfmpHvjs1HBXTMgVnF7DPbLjPHipsR2c/S/nEBJfcd2A27nr2QlDSEBo7KvZiD4uJijB49CV2susLPbGC7zqyx5kkXalkl5/p+0hV3sL5nwLaDDUaKK8BWnrkyVpaxLXeqb+Hgvr14YeEis/1sBb8iek8cv6Njic01zkZXjZ+gHLmlpQVfr1mN5UaiI0tAQ/rxgJmZLbbvwrls3K25iyEjRxpPK9MOa5uTnY37dXVIGDZUYOl4UMNfV2KBeeZTZFpPHj0GJxcXCjbNDR6lAGkdv1tmsHkwzAZJBsgunzmHKxdyEZ4YB98QEonPOIBVzuUmBzwAVttdnXAYtoUxplVkOsl5dXdqcDU7B49aWuAf0x/2Lk5GAa7Szkr7mmJ51cUH40BZBbjN9Y0gEuX7t2vg1tcPHqGyjJuXPxu0iSp9V2tSncWE71H5WBV+1+RIoZ0o6BVWPGs1kZEZM8XAIO39woovE/tK40JVYTGupmYhcNBA9A4L1uS85VhTbQ5ajkVtL+pxS1MzsncfRHBcFNz8fHWiHvPXUf2DDVMPaeXIwER3M4Ht6z8RsP34GbDthFls9xQGbElaly3bN2Hp4mVCRGQDINueDNmEEdyzbw+8e3tjAHV5eXxgyw0HnCGT9pLghV98/RVefYUsqHI/zvgatcPttpo5BVQDa+w6IlsrBr/hQa9qD4ENP6zH6JFjaA5OyW6p96IvP9YDtW1ISTmMR62PMGb0OHXRUQEM6viqyouZVVOBGO+2RMo1NjbiwOG9sOpmiTFjJqBrF5KLnRvfmc2RDK264CmD14zMdFhbWyMykkRgls5jQaUYOMzKSqesWUj4QBkwEqAqlctIPwYHZ1f0CQwRga0sRe4rS5EZsM1OPQBP/xD0cveW0/RIbO7pfRsRNmwKLKys5QjIEui9cjoFvbwCYevqLUiaCYiuKclHU30t7PvFytJoFdBqWVsS5PLBnZuoOZsClxFzaSAsvi2ZrSF95H5BFhorrsBt6PPo0s2SgloW+Ij8yxjbbhZAfWk+2h40wC0kVmJwaUApAM31uHHmCEJHTFdALQGm5/aux6ApL9JyBJheyT4BVw8v+PgFUSlyUe5pdLe2RkhENC2TdzYdjvaO6BcSQbfPZh6Hm4s7QkLCpe2sdLogMaD/QHTtKvnXbt68DlMmPwd7e3uDgE2FBZeRezEXs2fNoR/xw4cPUXa9DDeulyErOwtRUQMxbMgIAdzyczW2IMDUYdr+IPRSrWshk0OzxV8lfZ58lmY+0PLwAbp1lfPXHj0MJ0cX9OsXBgurbvA3F9i+tsGcgeOJl2n55IUnXmdnKnwGbDvRalpgu2f7NlzKkyZDzBZnpKfj3/7wJ0TFREv75f+rPg3ShXlGR1g9e0LAVjIROr8nBGxZ/Rs3rEdc4iD4+RGpqvQzZBhFYMuZL2pYSAS4D//633jrn34rBJMwYG0NWFZRdvrNqlWYPn8+7HrZKQMVu5ZkxDkQJUh4uXpka0/+2btpK8JiBsLL31cJJkUNoeyLSm0nF6WZ96PVj+SriVRoIN8lq+UtyDp8FA+aminAdXRzfSyAy94HP3HRAlxt24jbYrvdKa/E1ewLVB4TOiQR1t1tdFlxsV2Mg1fDe2kfBNNJZX0jrudcQmVBMfrERFKZsvi9iBJz9bvkJ3c8C6D2Fu1fxvqRQe8yKnvUDxoljQPyMY7Z5P1sycByt6wcxRnn4ejljoDEGLpSrZvPlmd3teBWSR1k3M+2uuwG8tNPI+G5ibCxtVWvIeSz5QNMcavPghyZZ4fVZ5zgHmIeY/sGCf7z4//aPsr98S/6D3BFCmzrH9AInxs3bcCL8xfio1Ufqos4cr+x69kTP3vzZ0qLGOBXM1Z1iV9ocVERpk97zgDYCna3g+0ujAHKRhvu3LmD9Rs34PWVr4s16gwaTx7kmga2piTI3CMorCwZyE9lnkJ9QwNGDh+pPI+BXytn5Hm7IptYOgNIT09Dj549ERk5QLK7/CK3gcSTcxcxUe5C7jmcTE/FlCkz4OkhKYcYWFNslgY48/6TlZUVSE09ghkzXjAAvAzUEru1c+cmREUPgpuHlxIRmfnZphxMRkBwJDy8/ZQUPgR05tLgUUBQ/zjB9zY77QCcvfzg6t2XCyDVhqyDWxAwcChsHFwEZrbsYhYF0S7BA0Vg2wY01deh9OQeeI8gqYZElpbfVqTVcvtUn9oNh6hR6GLVXX3XsMCDW9dRV3AaPfzCYesdLCw2KcCWpoBTA0dVZe2HS98B6OnaW5EhE3DZUHkN98uLEZQwRgaxFnh4/y6uZB5G/IS5SoCp7MNbaf5aF1d3KZVP2gH08QuCn38Q3U4/vAOhEQPRx8eP5rQ9vHcLjYjs4+VDj+/bQ/LXJtDj5LrNzU3Y8P1XWLniDRqJWBukafWaT7B0yXK6mKHtD6R5zuecQ1bWaSxZtIxO4vmgZcocxsQ3qeeBxNyNFLm3Yot5e6kNAyANblQZ8KgVazd8S+X1Ls7u6EKAra+ZUuTXv+/gyPZkird8PO/JVPSYtTwDtp1oQAZsqYG0APSALQG6819chHGTJ3XSz1a13kY7jZn33hlgq9gsDQDWM4Rk39kzZ3DrVhXGjjeMjmxg9LjVX94okWvu370Lnt7eiBgwoNOsbVFBAQouXsK456ZqjJ7KyUmDmwhk1aO8oZT2Jq/bgMHjxsDB2VkBTq0GPqV8fluVBRakv0ZAtT7gBm5XVuLMkROUGY1ISqBA43EYXPZeTQNckd01BXBJcKnL6ZnoHdIXQfHdBrhBAAAgAElEQVTRGn9XtY3bj46sBuYyANjcu2L1SJMnFSiTiMBl5/Nw80oJZW+9I0OIFRZYez3fWvbOlRVZzuSz7sW+FWPdje9fxufcYqoBHuIKfrUyu8QD3ftVt3H93CWSYBH+idHo4dDLQHYsTdYZWNWPhmyOn23x+TzU3KhE3JSxSn3mSY8N89UayJHlAXO8Wz/zgO2bPxGw/fAZsDXTtHSoGAO2d+/exeYtP2DBCy92Dtiyq5oAuAQ8f7P2W7z9+puPHUBK+5DGwO3169dxLPUE5s/TsBZPHdyKTKnB/WpuWGBeFUOvLgDWNzZg9Rer8fbrbxtIkPmxma1cGwO1W7dtRp8+voiOjhNBLb8wLd+bObJjwrLt3LUNdna9MGrUOIGJleyBFuDySiRZ2twGfPfd53h+9iJYWRGQI9tslueW/CsDi3VrP8ecF5bL20ArlRxLoOPw/u3oH5MEOwJIZT9aws5SYNsGBEWKwPZC5lH0dHSFh3+omh6orQ056Qfg7N0XDr39hMjI1TeKcKv0CvrEjeYAr+pney19N5wjBtMIybz8mN4fHyFZA/CrUrfDMXIoutja42HdHdRdPYtu1rboFZaELnJaPRYrgbG07F9iP4jYnqTzKT24HsGTFgsyZMLc3r6cRdvVOyRKYWfvlV9DzfUriBg8XomInLbtS4ycthA2NjZ036kDWxCdMAKuMtA9mLwWI8dOgyPNg2uB7Ru/xJRpc+HQqxcsu1pg/drVNPI1ye1MfGzLSouRm3sOM6fPlhd8JRtI7v1UZjrNvTx08FAuBgbXKeQ2IgHnvlv3NV57laTp6aoBt9x8UUeezPc5psDihykWFZlhBkWGrBnL2JBGIl0/bG3Fu+/9Fa+u/BlaYYGuVl3h7yv78rcz8lq+8UOHxuYnVbjlo7lPqqrHqucZsO1E81Ww4FEmgO2NsjLEJSRi4dKlBgGmpEmo+lNXdjT7GGv7GFJkzm4ZPKnhypXKXCnnMcPDnc2GBB4ckoAXG9evx+LlyxU/DvXampVa+QCpRwCTbaD5bFP279MEkWIGSgtEeaAqsra7Nm9G7KBBcOvt2WFJMrtvrUx5/arPMXXBPNj27EkNvhIpWSNn5gGX4O+pG/lXn71lwyibSFy7XICzJ9LRp19fRCYlUn+u9gGuyFaq702Bc9LEhZNi8+9UkHYpcmh91rX4Qh4upWYgdGgC+hDZNvdm1TYwntrH3DKizFt+Dq5dWx88ROn5i7hVVAp7T1d4RgSjuwPxs+MmPtKWgW8X/72qQJd9+GLfMHfY4BwK6Cmsr+v522oDRxH/WRLp+NHDh/CNHQDH3m4KoFXiG2tYVCGAkxLZWCltGM2YK5Nz+AR6OjogKCZKYdF0oyoLrK82aBQnPeZ8gvmUP2Pdgs0Dtm9FmtvMT7Rc2wdqJPcnWvE/eGVMiny3toZGAJ3x3EwTwPZt0R2nvbbTsZEffPwhlry4iEoTn5QcWdeeyoCKHLtaVITTZ7Iwd7YkeRR+TwXgPiZbq9h3NpYC23dux4DIAfDz9ePGTdUtRR1LRVcVfozds3c3AgP70rQlAlPLS4QV1yR+LJbHZgXwSi148XIuTp5MxahR4+Hr4ycsWAtjuw5TKx2XbM+RowfQu7cPTdfCuxYxRpcwssSul1dcR07uOYwcPZkCWm2u2M0b1mDs1LmwsumhSIsJsM3JPoW2Ll3QNyJWSAOUl52Obtbd4d1vgBIEivjTFp47ia42tvDoGykwufV195CfuhvBY1ieXAnUsny2VRezACtr2PmF03tjYJbPZ0v9hhU1mCrJvnPmEB423IOlnRNs+4TC2sldtk2ytaL+n5IMmcqRZbaW+dg23SxF060y9I4aJsuQ1aBS1zP2wzO4PxzdvZXAUVUF59D26CH69k+gILWtpRmZ+77HmFlL5ajJXXB4y5cYP30BTfVDGdlt32DGnKWKxHnD1x9h2ctv0+1HLc3Y9MPXWLb0dQpqCWOblXWS2rbBSUMosGVMM3nH33y7BsuXviK49xj2Y1XB9cmnH2EFzSnbRQS38ofGujubm2kMu9Dl+bk9H/XdANgqkwPpdPLNVd26hZ27k/HCvCWUvSWMbYCfmcD2zU3tjZhP5XjLh9oYAE/lMu1W+gzYtttEhgUq6pulgBeyfHB38jZc1kiR6+7dg4ODI371+9/rBpDiv+POAFtNP2j3KXRsqgBApQo6B2zZILH2668wcuw49O7t1Wk5Mqlr7ZdfYMykSXBz91DAmwEAa8fXtqqyEsf2H8CsRST8uAzGlGAUzJByII2VMpA5qy1D2vDr9z7EC6+uQDfLbnQlVy8QEgXsRkGsIUusleGyN6FIvzhQmX/uAi6knUJofAxCYgbSwbyjAJe9L36VXAWxrK0EPluR76jXMgS4ZEJwKS0TNRU34R8dAXd/MgExBJ/6waPMZGwVPyrpTowtHJDLVly+gptXrqGttRUeIX3hFiTeD2/c2Pdl2At4Kb/OKo9G6s+7FqidUgtvZVgrF1bcFGQJ8e3iMtwqKsPDhib0iYmAo5eHBtBqAknpAljzGFwyaXnQ0IiTW3YiYngS3BV/WmMRjqVAImJuWg1bq2GdlbIWFig8ewEvDZkEdxcCNkz/urwtRU79sX+t71/4sS/5D3E9Bmzv19/HuvXfYtGLi58qsN2+IxmBAQHoH0EWSJ6Mny17UQb2lAO353MuoPjaNTw3dZrhe9U1xEbchcz6Kh6frVWsfhtwpegKzmRnq0Gq9OSX8sApsLfcwmHKkcNwdnJG//5Rapo3eRVbWCxlDKuJ+kggrT17d8DJ0QnDho82ZGmVczmVkVbiLINaEmiqpKSIBuJR5hPkNMbWckxnetoR2PVyQEhYlBCgSQoeBaz/+iPMXvQalR3TaMVyAKkL2Sdh0dUSgeHRArDNzzlNy/hpfG9LC3LQUH8PfSIHKcCVMcDZO79GyLj5aO3aTZU7y9evqyrH7Svn4R4/TgK28jMowFZ+FsUtilsooO/gUSssunSRu4VqiJj90rK1dJuwtRYWuJObih7OHnD0CaagkgSTIv8SH9uCfWsROW4erAkTK+e1vZZ9DE7uXvDy60eBaUNNFYrOn0TSuFkU6KL1IVK2foXp81dKqX6aGpCyZyNmzVtOyzfdr8WRgzswZ+4Sevz2rUqczkzFzJnzpOt2scDevcnoHzkAfQP7Ki465J7TT56gc7WkhCSK1nWTFnBmnXwLRBnw4cfv4+23fqGkgKKfGacuEMCt3E91BSRs2OFlyIp9NN7ByfvMzctDUfFVjBkzCQ8ftaKwKB+TJgwxa1SwfMsw+JxZJz5moZYPnn/MGp7M6c+AbSfakQe25PQ9HLDl7eetqiq8877kQ0R+7fnZPq0AUvLYr2tkRTurA2y5kxXGj7PuKkwDTqWn0UAaw0aMMglsWZUqcJP2sMHi/NlsEGA6euJEMfWPEV9WdrbgzwkgZd8++Pj6IjCE5D7V8a3VsJVSEY4Fps8pBhwid7nhsy8wd8VShbEV083oSZBFplLLijJ20BA06vkDS/dEAkydpwA3GuEJcexODVhIBrKVNleeWW3zJwVw2XM11jfgYuopNN6vR/iwRNgx+bbyDnQWGvigTwKDzL0PbiIkAnHTAPde1S1U5hehurgMzr7ecAv2Ry8PyWdZNVjsOurHrfQNDavRkSFD8KrVCxoljwyNNbWoLi5FxcUrcPLxgGdoMOw9XI0CWjqecP64PHjkU+voypM5xvVmcSny0zIxaNYUdO/Rg/On5QGsnsyYB76GAaKE68ICtdXVOHssDU7ubvjlzMVmMbZdf/bTANtH7z0Dth35xs0ty4Dtg5YHWPXZR1ix7JWnCmyzz50FSSM3ZdLkJw5sdW0qB24zTmegsbEZw4cN02+eJwZwjQPbdn1r+XFNBrCfrfkUc2bOhYOj5MunTuZFG60FtcyGkejHJN0SHyiK1qFlallMChN+i4QxPZWRhrFjJ6OPN4l+r7HHOsBY8I3kbD7xwdy27QfMmbNIASvKAjQFtyo4JHbh+w1fYspz82Bl3d0gcNT9+nocO7QTY6bMMQS2Z9LRxdIaAWEDVWDb1oarly7g/v1aBA0czO0HbpYVoeJaPoIHjVPAK8tZe/nYTriFxsDa0Z2TO6ty5KKUTfAeNkuVSssyap61lZ5RZW7ZO2Wfn5ZUYXNQAdjKEZGZj23Z4e/hN3wGrG26c4GjLPCo8T71/e0//gXFv5YA0YtHk+lzO7q4U2BaXVKAe7cqEDVotARc793B+fRDGDftBXq89nYVcrJOYOLUufR4VUUpCvLOYeKkGXS7pCgflZXlGDVyrARsu3bBV1+twoIFS9DLrif1saXRnC0s8Mmq9/Hm6z9ToyRreqPQDeVvkXxDxF1iz75dmDdngTCnMugPQkMadnUlbg5L9cmCQmoCTGnHE8LYHj2WQvMnD+gfQyMk7z24E2+9sdSs4dbyrS1mlXvShVo+mPWkq+xUfc+AbSeazSSw5QJIWVvbYM4LL6C3tzfnZ6uuGikfPbsHTV5ccyIj5+bkIHn7NoOnCI+IwPQZM5X9unZU4z8rFW6fteXr4kEaCaCxe8d2vLhkaYfkyPxVpUEY+PKTj/HSa68ZBJFSZbNGWFeOKSWrvZu//Q7zX16uy9qy65rrb8vapqm5Cdu/XYfnl7+kz9jqAjNeFsQAldrWeoypwjRzBloLxnIzs5CXkYXQ2IEIjYtFl65dDBhcNmgKfky8RIljtPnJgwop25FLGwGcdypvojDzLLrb96LpYrpZW8n3ppU/a1lsGeKbALjcGUYDd6kRDNVFk4r8q6gpLUfDvTr0cnWGvZc7XPx9pFQI8k/rU6t+74a9SK9fcd7xQr/k9xM79+jBQ9wqKsGtwmt09dzV34eyyt2srDoAaMklON/aDvjZXsnIRlNDIwaMGsaxsDp+umZJj40D3Zz0U6gqvY6E8aOpj/pIl75mAdtuPx/QidH58U95+O75x6/kWQ0GLaDmsbXAh5+8h2VLlnPAVjWcUvCox5ciE3u0fcd2LCP2SIexXfPVFyivqDC4z5eXLoOnh6dZb1A7MWY2lPybvGsH+gYGISw0VL8uI+CWjdfm3YBor/lzRGDLL9CygY6z9m1A9vls1NyuwciRo6SZgAJ81Q1eRaS4a8g2u7ziBs3FOXvmXIPox0wKLNWrjsdagMxA2J7dO9DL3gGDBw8TfGeVtmELoRrpMbtvvl5SZ3LyRiQlDYOzszsnQVYXjvmgUdevlyEv7xxGjp1CywoBmohEtOomzp5Jw/Bx05U0QIyxzT17Cl2tbODbr78AbMuu5uN2dSWCY4Zy7Ctw785tFJw9gbDhU7kgVFJk5JKcDHTr3hMOviFy6iCRHb5x9hhsPf3R3dXbJGurtgnHasufgAq8pB1sIZZFRBb+hQUe3ruN2oIz8E6cIDO1UvofwuTW3yxBw81SBMSMEIBt7qEtGDhqOqytrag8ueziGVhZWaJv6EApZ21lKW5eK0DisPE0UNTN60W4eb0Yg4eNo8eLC/JQV1eDwUlSvRfOZaBb166IjUmgoLapsR47d27GooVL0ZWAWjl92Pnz2bjfcB8jhoxQgK0hZat+5Gxdii14FJcU4ULOBUyd9JxJcNteP2XR3hWCi4FctRuKVciLLNuSt6B/ZDQ8entTYPvt2tX44x9+397l6HHLtw0xgVknPmahlvdnPGYNT+b0Z8C2E+3IgC0bBmiQFI6VZR9y6tEjsLKyQuLgwY8VQEr1yxNvluw3F9gaNZYG4LZ9YMvXxYMAMiCs+XQVps+eDWcnlw7KkaVameFMObAfjk5OGBAb20HWVq5HhrKnjh9Ht27dED0o0XDFV+M3qgJJfhXcEHSRKzTWN2LnDxsx66WF6oooJ41lK6WiT6hxn1dpUZs3PAz8is/Ds7uqkW8DA7j9BvZHaHwsulmSvGz8BILBVE1+QM09CxMPXfCrJ1FmDKzhMXKk4koR8jOy0TsoAIFxUe0EmGLfgVynjv8yaxFDGbKxxQ61Ddk3RtIVkSBJxBe3rvoObHrawsHbE44+nrB1IDJZwwmjFvCaHjqkVSqtPOl+9R3cLatE3a07aKqrg7OvF9yCApSAUCwSsjTJUCXHdFs3YrJh2h0J6PJSZJVxZYGgMrbuhldwIHwjwwz9boXzNdGPDYCzxseWA8El+QW4cOIkIhLj0Ld/hDwGWmCES6BZwNby51GdGJ0f/5SWd889fiXPajBoARXYAsSP7aXFS2Fn11MO8iJ93+zH/M2VbXPaU0cL+Pf338XK5SvQo0dPqQauzBemgK2np1n6YANsquwgtqwNf/yv/8S//O6fTd/94wBcJRq/eInOsLUffPw+XWywtbWllfHSS9XWaPxqlflDGz74+D2sWLYSVlYkQr4oDTYm4+QZVnINkupk3bpvaHAob68+Jn1pxXsSU/2wcZ407dnsTApABw6MV9xXeAaZsbWkLCmXcngvvH384B8YIgRmYn62pSVFKCu9itik0bIMWWVRz2WegHUPO/j16y/Ih8tLi3G9+DINoKTkvW0DmpuakLFnPeKmLcZDmhlCSgNE/r1JcrtWV8AraqgMktXrEAn0nZLLaLp/D04hsWIAKfk5tD62gmuT/M2pjK3qX0vaVQG0HFtLWNy6qxdg0fYIbv1iZPkxkSKD/n3r8hlYWVrCO2SgEjiqrakBeUe3I3HqQipNJsA0P+MQPL394e0fTPddv5KLxns1iEkcSYHs1Uvn0NJcj7iEYbR8TvZJWFtZISY6gW6nHT8Ar94+CA+PoEC2/EYpzp7LxPOyNJkC2y4W+G7tV5g0YTLc3T3kSM/ayBbaPiPOl0h7nTyVBktLSzkAmvTtG6gC2hmbDBYPWP5a+TxuyJD6HZGRt7ZRVcu8eYtgbW2L6tu3sXf/Dvzff/u1OSMhLH+23axyT7pQy3vTn3SVnarvGbDtRLMRYEt6Pp14Kmk65ImoAnItUHzlCs5ln8GsuXPNy2erzX/Fp/zRGGW2+aMAW8WCqLae74y8HPnEsaPo2q0bBiUNUYAtO11kDJmBlAcLpUtLsqXbt29j59bNWPTyCp3UP6z7m2ZtGThZ8/4HWPzqSnS1JLnuTEmSRWDGjJ/6+KLktam5Gdu/WYvnFs2n+e4YkOT9a1WG2VDmLEiSFQm0cYDLs5TGAO7FrLPIy8xCQEQowuJiYWUj3RebqPDvSpVQmwbc/CRBrcs02ywyqlL9hafPUpAbEN0fnsGBBgBXrNvw3eq3F/9ONMGpNDJyqQ14wKoaspobFbhTVoG6qtsU6Nq5OcHB0w1WPXqgu70dDUBlaWMtmCJj81EyLjxsbkHjvTo036/H/eoaNNbeR03pDTh6e6CnixP1myVyaKkfMxDM1stVYEqPK4bQkBVVFtcMctbyqXZUYHuvqhrnDxzHgLHD4OThbpDKR8pva8Q/VwGtpgNEtT5qRdbBFBpZMmHcSPov/xwjnANhZ2vZ7shr9YuB7ZZ5GgUe/P3s06j2H75OHtiu+34tRg0fBR+iZpIZFvLtsZ804TamfTDSlDrAdsu2rQgNCUF4aJgBa/skgC1nGtWb4oxj+qmTqG+ox5hRY0y/fyODiQnMy/S9gp1lF+GBLQOZyjH5ppWRsA3Iu3SR5vUcN2acYiuUZ2uHYSWj+74De2nqncgIorJQg0UKIEBTjxbU1tbV4vvvv8NLS15Bt66avLT8PZuQL2tBx/lzWai+fQsjR04Q/Wo190hBIPGvfNSKr778EEuWvUVZUAPGtq0NeTnZNL1M+MBEA2CbfeooevRyQp/gCAHY3qq8gcKcLESNlJhZwgJT39y2Nhzf8gVip7wIyLlLCbAl/92/exslOafglzhBKi/78UrnAU0NdSg/cwS9B02RgC2kOnmfW7ZgzpRwKivJviyWZk5d8yHdyMDHVg4gdTNjH1z6DURPF0+FqWU5bG9kH4ObXzCc3KWUPASk3q+6jqqreeg/bJIUEdnCAmcPb0U4l+rn6oUMWFtZI7x/LD3vYnYaXYiKiIym52SkHkLv3t4Ik3PaHtyfjIEDY5RUP5cuXcDt27cwdvR4GkyKANvauzUgjOfypSuEhTMypBhbjBLUA5x8e/PWTRgyeAjc3DzExR5dxaN+N1eJL3HBW28eTWogasPPVq/CipffpIsaBYX5uFpciDdfX2KWHbH6ebJZ5Z50oQfvkvRqwP6jmcjOKaR/p2bmYPuXf4SlZTe6/c//tQbdbawp254QHYZRg5+8nX8GbDvxZivuy8CW6eVlGyxNCJk9tkBLczM+/egDvP3LX5vnZ2sAbKl1p3eox9o+HcbWkKtigyFvxPnBgQccVTcrsWfXLixe1rHoyFLdPEsJbFq/DgmDh8Dbt49xuak2gJACZlTG71JODm6UlmLUpEkdkCSLYI89u7oSLdVPItauW/UZpsybA3vOl1Tw9+UktbQePuUP8+tQnl0EjAYAVvFFNQKApQsg/+x5KlH2Dw9B0MAB1H/SGMDV+tgKYFyuzzijbAhwtdfhDUZLywPkHk1DS/MD9I2LQi9Xwuyz986xtFom2UQwLvbt6C0oKO9NWThQmWtmpdi3rLzb1lbU3qxGw527uH+nBg1376Gxto6mDCCLNgTgErD78MEDyWh260qBLNl+9KCFbj9oaIJNr57o4eQA2152sHNzQS93Fyo55vuyCmWlgYOBStrnjQBavpzWz1Y9j/eLlQBxac5lVBQWIWnWVDOlx/p5aPm0QZQBli5K/71xtRhnj5zAoIlj4e7DXDBEQD7MOcAsYGv9yydv8MwZ7pv/9gzYmtNOHS3DA9u9+/cgwD+AAk4WLVQKyKj+eNZWB7MamUGKu89kn0FVVRUmTZjYcWDLqjKNLmkp/cmytHf1l2swc/pMODk5GW+yzgBbI2ytPGQr1+KBrWjLVVb1+40bMGzIcHgSplqPrZXvj2dimW26dq0IOXk5mDJpmpo+R24UutiryIX1AS8pSrIqbNu+ES+8sNhQeizftArKVJ9d7b3yZc6ezUC3btYIJ2BbBqnUjsl/s3NZdgPyb0H+RVTdqkRC0kjB71aSKkug8VTaYTi5esI3MEQEtq1tyDqZAnsnN3gHhknBoGTwWnO7GnmnjyFmzAyZkVWBbdaBzQiKGw1LO3slEBUFt6S+basROW25Ug8Bt4w5JvWXZR5Erz4hsHH1FkGtzNqqgJZre/6D1bi/sT4nRUPWAFy04caxrfAfNVtI88OkyPkH1iNs1Ex0t+2hBI66WXgebQ8foG//RAXspiV/jWGT58O2e3cKXHNPpcCzdx8E9A2hZbJSD8CnTwD69pWCTR09uINKcn37kJy2Fti9axNGjRxHA5QRIHvy5HH07NED8XEJlDkmwPb06VN00pyUmCQqkkwMJMJ3qvQBqd0+X7MKc+fMp6mm6FRC47fc3ljYHrBlczD2TZZXlCPlyEHMmrWARkTOzMpAV6sumPs8mcO2/7Nevqb9Qk+hRPMa4van/tJO56KqugYzJg5Vdr7++/cQ5O+N2rp6LJgxBn39vZ74nTwDtp1oUh7YqoyJytiSKtmE86vPP8O0GTPg4kZSdXCrYhqdvdLfOuhnm2emj61RG60YLL4heFZLZVa1BlwBBJy1JH9++tGHmPfiQtjbO2jkyKKfj3g+mxyoLFr+5UsouHQRk2fMfGzWdtv6DRgyehSc3UjQIOlJBGmOPKIox+TmaC+YlHRaG3Zv3IzwmIHwCQjgJMAqUGMGlcF3U2CS1ckDLsG3iQsIobwTbpLDmGPyoFdzLyL3VCbcfX0QGheDnva9jD6/9lkFhpRrH35yo8vgtpurtw13q6qRcywN9q4u6DcoBl0tJf9b9dnltuMAKQ+Q2YSJb0/1vfJ8sU6aH36Sprxn+ZsQxgOuHxBD1tqKB41NeNDUTAFs66NHcuVtsOjalfrGdrO2hKWNDZWCKz+5c3NLVNwEnve5F0GiICmWBhWNSkSOzK7rAysyr9l7D8POwR4hSfHtSo9Z4Cca/dhA1mx834XUk2ioq8PgSePVexUYYOncoU7+ZgFbm19Fd2J0fvxTmt7JfvxKntVg0AIE2Lbcf0BljiczTuLhwxaMGDZCSc3Bp8Ogn7v8zZtqSoN5qmYHCWS0aesmvLbi1c4DW9E06t6OPrCVxhQSUHHV55/ibeo3bOKnA25NYmqzZMiqnVNtBbd43QaUlJbgRHoq5s+dL4+/0j3y4y0DsWrcAnVc/fTzT/DC3BelSb8pCTIXnEc2J/Q6JEr2xk3rsWTRy8alx/INqcCVdxcy9Ns9nZmG5gfNSEoaydkUzhZQcCu1gyRFllLm7Nj+PeITh8PF1UPIB8uzoCRCb2hkHBxd3ZXctiwycmbaITi5ecKL5auVgS3JkpGZkoykKQsUMMxA74UTe+DuH45eHj5CSh9ynARe8uo/BN3sHGWGV/SzvV9djrslBXCNGq6wtnxeWwXIy0v6CnjjPkNVjmzEx1ZmaxsrS9BQWQyvmFEGaX7Q0oTitD2IHDtb8K8tzjoKZw8fePoFUxBr0foQJ3d+h7Gzlyupfki7hPWPo2mYCHBNPbANA6ITKUtLfG73bl+HkaMnwtXZleaw/fbrj7Fk8auwsbGiQHbfvh0ID4tAcFCwzNh2wYbvv8OokWPg1dtLliHLi6t8hFZ+lUf53sXvXpkDtQFrvvoci198CV2pmkBcXNH2al4xoQW1ytgmXJPNfaSacvNyUFJSglGjJ1AVwZ69O9EvLBQTxg4yy7JYTfyZWeWedKEHe99TqiSA/J/+9Bn++q8rBTeT0htV8PJwQX1jE377p8/wyZ9//qRvA8+AbSealAFb5g8nMrWqbxyp+tDePXD38MCA6GgR2GpCfv9kwJY3GEpbdAzYaiW7Rw8forle4+KZXysnYeYkt6KhZcZU7OCr3n8XC5e/DFtbiXGUFsvE4EOq9EmFpVoQVHP7Ng7v3oOZC19UuWEtAB1eaYEAACAASURBVJOtbafALdpwYGsyvAL8EDKgv05QKelpFWMqt7UhsyuX004sOMOklhBBm3HADhRdvISLmWfg6O6GsLho9HJ2EiYRKhTk5boMXOrfkymAawBCubblQemNwiJcPnka3iFBlMFV28cwwBS7i/YBLg+Que9KYLvV98E+e/6r1/rTmpxgCnyN/pIwL6oUo5/LcFeeXSiMLSf71Qe0hnJhUZYsGfKaGzeRtesAoieMhrufj4H0mDLOutJjPTmymGaILd7V363F8e27EJEQD//wfrJ7hr6cmgDbIU5+ZgHb7r+O6cTo/PinNP71zONX8qwGgxaQgG0z/QaLi4tAIujOeX6uAGzppI/rQlpfW2PNKvQ6TRc05mdLgkdVGAseJTOXBtczMRDog1tpb05eLq5cuYLpz7Xjg6apxPjlRGDH36cxGTI/j+fH7q3JWxE7MBY+Pj60GgPfWn4hUBOsKfN0BuobGjB8KAGQpiXIEtPF2xSgpuY2Dhzah9mz5otAml+8NSU95m22XC4t9QisrK0REyOBAMXecEBWuhX1fgkYvHO7GilH9mL6zBcVOS/zv+Vzxe7avg6jJ8xCF0srKg9mKYDI3xmpB+Dq2QeevsFqvloiG25uxLHkdRg28yVVhixLiy+fSYVNLwe4+Ussr5SvVgKw186lw9rBBb28Aw0kzEyefO34NrgNGEbBrwTA5QjPgjqMAXl1/sS+GSF/Ou2AOmytBXAnLwM9nN1h7xUgAVsuzU9TdQVqSvMRFC9FOqay4y4WyEvZipD4kbB3dKFAlkRAzs88giGT5tBAUqSO47vWYcioqXB0cKTn7N/+HUaNmw4neXvzhs8xe85i9CS+360PsX3rOiyYv0zJYfvDxm8xacJUuLm6Unnro9aHWLXqQ/z87V8aLJrxfvzavi0GNZMXhDi1Qe29e9iwcR31I1eYcL6ztTPmM5cjuYlV0ofNiZV+Bhw/cRTdulli4MAEytiuXf8VJk+bgfiYILMsi+0vd5hV7kkXavibmuLs2MnzqKi6jXnPScHo2C8l7SyVHz989Ahv/P49fPrfv3zSt/EM2HamRcuZFFmRIIt+CrwcmeS3LSkuwsSpUx/Lz5Z1Bm00Gt6Gr/r4Y9y5c9vgkVa+9jqcnZ2V/XoGWNwnAlvJOKh6K1aWP4dn90iKhaMphylrq6z0yqcLUibtKplyCXWV+VRaKr3v+MGDH5u1PZ2WRuuKSUpSwC3PUjIjqN4zx/xxQTL0ACQ7N+PYCepTGD1kkCpXkQ2MYmR5mYtwTJQXq2ykcmeioVYhuqZt5PL8hES+ZklBIS6dPkPlsWHxsXDUMNgK28sZRb37ENlcvaBX2kUIeYJBb41jsuUPqyAzG9dyLiFkcDy8+gXqyLV1GFz5XLbYwUAxP8FSuAkNo234jtX7Yh1F2wuEPqI/i1VYIWOKJ15syU8opP6t8bXVZWi15bSBolRAevXMOdy5UYn4KRPQtVvXDvjO6kU4VvepeWwtUJx3EUU5FzF4ykT06GXHXUPD7NIHlJ5+sKN5wNb2Nz8NsG34yzNg2xm72N45FNjWScCW+J1u+GEtVr78KgWy1LebS4nB6jKHtVVso3CSejckn21IcD/qa0tn7TxwBlBQWIDvN21UT5CP9wsKxtzZcwwfywjaNDokyFNY4u8bHh6OfsH9jDdVB4CtsTm1ur99tvbmrSrs278XCxcskmwLN7lW2FleSizfOSlHGPdPV6/C6yvfNC5BZouJHFvLrnHvXi22JW/GwgVLBVDL5hrCXIFflGT3yI3/bDzPyTlHpaihoVKqMNHnlgfeKuCl4BVtOEUkrXa9EBI6QMlRzzO1BMAS9n3XtrWYNmepkL+WyoNb25CZegBuPv5w9wpUQCoBoIR9PbBxNUbMWmYAbEvzL1Dg6xMWJ8uXJVBLpMiVRRfRWHcX7uEJGj9bVcpce6MI9bfK4RI52IC1VQA8m39x8xjpVaoWSQo4KP0UH1uZrW1tbkBVdgp8h0yjMmQKbGnuWum/mqIcKjnuEx4nAVt6vAuydnyFITOW0n3kv7vlJaguu4roIeMUAHxoy1eY9PwS2Fh2k6TGG7/EjLlLYW3ZDV3Rhs0bVmPR4lfpscb793Do4E7Mm7dYTvVjga++XIVly1bCxsqSBpMqKS1Gbt4FTJ82Q3Vz4HK9mxqnBHAr+IRLDXet5BrOnM3CjOdmqRJ7abql/Axnz6rN5oco4TvXLCjt2r0DfYNC4OsbgIeP2vDZZ+9j5Ru/QFCACXcG7sF6/OqnAbb176jA9r3VmzFy8EAMCAukd/avf/kSf/jNUqzfdhh5+cX03UwenYhBseHtmY4OH3/G2Ha4yQA9YEuHCHlgUKUdFrhXexc/rF2LV954438QsJV6qgBcuc5rsJ8DaqzYJ++/h0XLltMgACIQ7pgcubGxEV99tgorf/ZzESjqsbY6AFKVTknPs271akyaORP2To6S4dOcozy5BsiKgFwEvHxrkfounbuAihvXMWLSBA1Dy/kbGfgGi9GK9a7Hg0ntfSp3xBt7EyxveXEx8rPPA126ICw+Bi69PQ18cJX3poBCHQaXY+C1TLp0K1oQy00oVM6YtlPLgweUvb17swphQwfRPK56IFj7zlRAy9hm9ds1WLjQaR8e5GpslMJCdGKYEE5hYwI/kdBOKnjGlU052MTekMkVwacEGqV9bY9acTp5Pzz7+sN/QLghS6uJeiyfKQGMDkiPMw8cRs9evRBJ5M3yBEkBJwYpiJirhgUGOfqaxdjavvrT5LFtWPUsj+3jfu965/PAlnxrqz77GIsXLkEvsiBClAMGUZGlWkyxLPx1jLG2pvxsyTlPCtgajh38hLcNN8pvYN+B/VhK0w+Z+AmTZCPlzJAhs/FYuC8N0Nx/cD9cXVwxMEryZzeHrWV26NiJY7CxtkZcLFFmcYubMhMq1SeyuMpkvq0NH3z0Dl5/9Rc07oDI5Mrjt4YdFhbJdfLgHj16gCq7YmPJwrXGnmr8askVpKjB6j1+seZ9LHrpDcCiC71vEoxJYGzb2nDzZgWyT6di9MTnZaZWYlgZsE0/uge9/YLgThhWmX1lgaIIsB363CK0kSBR7BiJflxWjJulhQhKHKPmxZWBbe3tm7h+KQv+iRO5+kjUZBXYkmtfPfQ9vIdMg4WVjcDaSou+fFwPaQc/h+MBLbM3kn+tCnCrz6eih4snHHyCJDDbRU3zQ+TAledTYe/uDRfvAMrEEsa2pb4WhacOIn7CXAqGyf7ywgt42NSA8OjBtAweteBI8rd4bt4Kabv1EXZv+hJzF75Ktx801mPfrh8wf8HLdPt2VSVOnz6BWTNfoMCWxOxYu24NXlv5FgW1xOc2/eQJGp15UOJgRYbM8toKSuS2NmFsUWTa3Ddr8F22AYdTDsLR0RlRpM9o2lKZVHIzaJ6pZb2ZtDMPbPl+R/Z/8+0XGD9uKuwdnHCnpgbbkzdhybKVZgPbnr/e+TSG8HbrvP/Xqe2W+TEKPAO2RCZ0qQi//PdPMHPSMKxcpK44rF63Czv2SyzfwMgg/NsvllCpw9Z9h5A4bJgQKIqxtEyWzAwy2f7kg/ewZOly9LSTUg4wA6xOAmUDLr9xUaqonqFOjtVP44kwtrLlExeLOwZstXLkwwf2w8HRCdGxcRpgKxocxejyciMGeOQb2rNjO/wD+6JfeLgCFKXzGEjm2UH2t1yCAzLkSHVVFdJSjmDqXLISbwqgij647Hra52TsoHJcHrBKrxYj4+gxDJs4Di6eHhxA45+fN+B6YMx8BpcNsOoTiQykMPHg2reytIxGUSZBkfpFR8HNx4vzEeZY1nYZXOllSYZU814070Bge4WVeOnua6urUZCRTXPyhg5NhJVt9w4CXM0CgjxjY0aemXXdhQJuFqg+kdTfxP7R/vBsyNqqyg6pVwsCZS7AnOhrq2VypXGDDw6lsrS3rpXh/KFjiJ82gcrO1UBPavnHlR7fv3sXJ/ccxICkQegd4GsWS8v76iY69DEL2NpMCWi/kZ9CiaZdRU+h1v99Vba0PMTfPtuI9dsO4cLhL5UHLL1xk67O191voN/f7996ETH9g0GA7YO6ZkUeuHP3TvQN7IvI8HAlMjLrEwY2kN9hoimVHsV1PlN+tj8msCW3/cEnH2Hh/Bfh4OBg/CmeFrCV6+Vtwd/f+xtef/VNmle0I2wtGQ3/8s5/4de//K0BeJWHW918sRQ8tLVh+44tSIhPgrs7CValCTDFz0e0jC83lvOgIjn5B0REDIS/vyTVFGJYMPuj8auVAJ9kr/LzL+L27SrEJQ5XwCw5zue3JQCyMD8XVVWViEsapeS3pcBWBpppKbvg3TcUbr39DYBtyvZvETt6Oiy79+SALVBbcwuXTx9F/9EzFVaW+d+2PHqE83vXI3S85JuryJ4VYCyxzXeK8vCg4T6cQuMF1laRzAr2WwNsdeakUvAostgEtDU3ovL0AfgOnwES354ASkmKrP595cgWBA0aD9ue9ooM+V55MWpuFCE8aRyVHBPGtehcGux6OSAwpD/d13z/LrKP78X46S9S4NtUX4u0QzswffYSWv5eTTVOpZIgSi/S7RtlxSgsyMPkSdMpiCXRj/fv34HFi5YpwHbb9k2IjYlDgH8gvX/ed/946jGUlFzD7Tu3aSTm1rZWuLu5w9/PH3ExJB2U1l9bP43Uqs8/xsqXX1e+M935AevHOvItBmwNUgfJCzYffvQuli0japZuKLpWhOzsLMycPRdBAary0pRF6fWbnwbY3vvLM2D7/4WlJ0b4D+9+Bz8fdzg72ivAloSq/o+/f4MfPvs3mgx62S//gvkzxmDc8Fi889EqzF+6TIluyqaZqgSZ+dlK09cdW7cgNDSMOn8LwLeDfrZSbdwoJP4Jc6XIxjqhKWCrnKNZoeLP4Y1JWWkpUo8f05cjcyyfXr0MULAVrfIbN3D00EHMW8xFTGSwlPnNyNBDy+YxI8fXSXLbWlpZYWBCggputawkb0A59ppnJdV25CVfKrBuedCM3T9sgX9IMCJjo02zt8pquQhwWfvqMrjCPRoBwSbK8Cxv1Y1yGmSKGM7w+FjzAa6W2dYwuCKI5Z5Ny1jz75G+izaUXy3GlaxzcPbqjZDB8gKJiejIyjvWlR0bLhxI708+iwPf/MAkLO/oIFs9sGuMORKBrNyRFfkXA54q5DUX0DJf/wuHjlOAEDV6uE6AqPYjHPOsKw9EeelxaX4BLp8+izFzZ8HK2kpe3JOeWAg0xQXQ0z5HgpnA1u638T+Jjaj7r8yf5Lr/0y76h3e/RUSIP/7979/g3EE1CiexlSQK5pQxg3Aq+yL+/OE6JH/1JwXYSuwscO78WdyqrsKEcRMVsMu+Zfo98ZLhjgJbjZ0U/GwfV4qsa0DVtyeMCcqG9MexE1Je9aRBEquo++sksBWlye3LkAuuFOLixYuYNlVazDdga+XnlIZGcRE243QGHjQ/wOCkoSpbq/FhpefoSJDPnstCY1MzBbbCwrAGxCqLyKwOg/uQbnD//l0IDx9Agw9JRbhFVh7UckBWe1+bN32LkaMnwd7BWZAhM+aWAdwzJE+tTXf0C48WgC1hUEmZ1JSd8A2KgIunrwGwzTicjKCBQ9C9FwsEJaXtIUqlrIObETV+nsDkMnB78UgyPCMHUV9bdh2eDWb3Vrj3G/iPX0jvn/naSpaU968VF7xZN9GSLDxbS3xruzu6wt67ryJDVqXIQGtTA4rT9yBy3FzVv9bCApWXs6ksPCBcSuNDgGxe2j706RuG3j7+FADfvXkDRRfPYMS4GVS6XFtdibzsdIyfIgWhulVRhku5ZzB58iwKfK8U5uFWVQVGj5JS+1SUX8eZ7FOYPWseJZ3IOZ+v+QRLFi1DD1tbIXBU8q5k9Oxui8SERNjYdIeVlRUaGupBfGcLrxTQ72bokOHKN8TazUCSjzZkZGbg4aOHSEyQ+7E5fVbu7Gz4Eeaq8oIP2dfY0IivvlmN5ctfx6NHbXScrLpVhTHjJiAo0Dxga/9Pu34Sc1L731N+kutqL/oPz9g2Nj2AlWU3EHaW/Bhj2/ygBY2NzXCwl1hWEt1rUEw4pk8Ygk+/XU8ZWzcW6VgI9MIkd2ranzOZGbh/7x5Gjh3bYTkymzKyF2eKtf2xgC1v10VgKx1h+z78+9+wbOWr6N7d1qQcWanPBGv7w3ffYPjY8XDzcDeDtRW4WFnywbG6AL799FM8N3cujRTLrm/ob8sbAT1GVTWgyhU1AImclX3yFK4XXcP4WdPRTc53a3hNue0MfHn5YE764JUZc1Mg2LCM+mwCwC0vR96p0+hqaYmgqEi4eZvJ4OoAXH4xwVAuzKQ46nvhJ1D8AkLRuVxcSs9EyKA4+EW1w9rzCxQ8G8xJ5QSZvRYEyx2NLaoo7O5jDNc8L8u7+In+tgpXxS2YqSyrNAHh/BA5yfDNomvIO5qO8GFJ6B0U0CHpMQOjkgRZvp4ROfKZlKOwtLTCwOGDDQJESYt68milVw93PN7exyzGttfvfhpge+/Pz4CtOZ97fUMTetjaIGrscgHYVt+phaO9HZ1okgiYBOge/P4dCmyb7xHGVvpWbtfcRvKObVixjOSalGSP9DvnjJwaUZSfFepQINwN67G2pvLZFnbUx5Y3gDoNZbDYxYHb4mvFSE1Pw4vzSRBDI792J8kiaGW18MCWjZ/CrWqA4649O+Hr44eIiAjJBsrX1eaXVRU4rEwbSCTk+fMWomfPngaBdBgrqwUGZH9t7V1s2fYDlixawSl7VOZUYWHNYmrbsHfvTgQF9UNAQLD8DB0EtQDKb5QhN+csRo2bKj2LRoasyJEBpBxMRmBwJDy9/ZS0O5RFlYHtiUM74BcSCRcPHWCbsgO+IdGwd+ttAHrTk79B1Ng56GJlraYJkvPdluWdhoWlNZwCwhX/3FYuFy7zA76Vn01l1PZ9ByisLbO7PLhlcwHW2USVofQ1UR9bApCbG1CRvgv+Y+bRbeZfq7K2FrhXfhVNd6rgGyXJi1ngqKsZh+Depy88fAIpy0v2k9RGUYNGw9HJhYLQqtIrqC4vReLQcfR4ZVkRrl/Lx4jRU+h22bUClJcVY8yYybR87oUsEMJgcNIwOr5cuXIZZWXFmDxhKgW6D5qb8PW3a/Dma2+rKhALC5SX30D6qVTMm/2C0W63Z+9uGosmLjZBjVmiyOE16ro24L0P/obXXn0Llt3kKMlcZ9Nb8GYX1gJbrQy5srISh1MOYPbzC6h/bWraUVha2SAucRCCzQS2Dj8RsL37DNiaYzp/vDKffis5W/NSZHb1qyXleOP37+P7T/8P7O16YFfKMTxsa0X8IDLBY761qlEWBwoLVJaX49D+fVi0dGmHgS2b0AqdQmPX2ebjAltDey3wVYpPgLacAqhUNEDLHti7B65u7oiKjumEHFm6CqvyYm4OSoqLMX6qZHzYMR4A8UCK981kAzkP/G7drMKpY0cxZfZsVXorrPZyoFXD5vKGQQGFklkVJwhsG8DN6zdw4uAhRMQMRFBkhOybwRth9e7ZM2vrVnxABLaaayftarURNlJsP32AW11ZiQtpp9DVshtCYqOpnJqt/rKVdOWOeVArMOg8KOfz6LF24hYblFV59s45wCs3CPG/Lb1YgH6DYuEdGqS7wKGCdLX1eMCstJbOAgLPHiiTRR0BsvGJqyqh0Jt6GzC2QsovTRRh2vF5oMmxojJIJKmHclJOwKZHD/QfOUQDTPWlygyAKioTdg3Ntfhyba1tOJG8C77BQQiMDDcpPebBsRA1nqs/0akPulsTUZvpn/3viaLix//V/mfGj3/R/8FX1AJb/lGIJLmPlxteXjAFTQ8eoqGmiQsWBbz/0Xt49ZVXaU5LJW+zJj88X58IWvUBrp5iwpSf7Y8JbFtaWvDOe3/Hb3/9T8bf+NMAtgy0cgt8733wLl5ZsRI21jbmyZDlMZNIOU9lnqQsmWJ/zWBrSdnt2zcjPi4JHp6eHJDWLNiaA2rb2rBp81okJY2Ap6e3bHf1Qa1kr1UXGRXkSTbpSMpemkPVi4BVJkWlAaVkkMuiDLcByZu/xvCxz6GHnb0KbGWQSQDusYPb0TdsIJzcpdQ90n+SP+yFjBQ4efjBxctPTN1Djh3fA6/QGNg6ugoyZcLa3r15HRUF5+E3iPezlRhi5t9L/370CFf2r4P/hIWqhJp/dn7xgvv6lPkqpx6SGFug5lIWbOwdYe8dRLelYFHsX0leXHE+DfbuXnCm/rUSM0v+zTm0CeFJ42Fn76hIkVO3fYmRzy1Cd2trem5J/nk8am5EVOwQabswF/dr7yAxaSTdvnL5Ahrq7mLw4FF0+0xmKmVio2PiqPQ458IZNDXWY/jwURQ8V92swNHjKTQYmiRBltQhuXm5qKi4gQnjST5r9cePFS0PH+KTTz/GG6+9pXxP/FxMBaDSLCIn9wL1z5/x3PNyhep8VZ0N6l9L/5uUOlh+wWUUFOZj3LgpNCLy7j074B8YjAEDIuHbx4QLA/dcjr/9aRjbmv96xtj+f2XKjQHb3Pxi/O5Pn+PP/7wCEf386T1nFxbj+NEUzFmw0KifLZ2XcnK8d/78J/zyn35HV5nYMfqvkXy2eilBlPN07LnpNWzDptZdUdLIjAVmS+m6En7jzxf+5mRA14qLkXEyHXPmL1DOUUEwBxwVSMitGktDizzASBf/+O/vYNnrb1IJpMAAanw6VZ8dLVgUwU760aOw6d4dUfGEGeKO8ZMAoyBSzweXtbOhNFl6mjYc23sAjx4+ROLoEbDuTnxHZYMrP6/q5ySuEPIDpX4OXHYF6R74MuwIXzcbWNUFAhHgsgG9qrwcJD+pdXcbhCpRlDWrl9y900mDMnkSWW7xGPfcJgJM8QsY5P4fNDXi8sks3L5RgdDB8XD18xG/BZ0cunzL8KBe2c+BXL6d5ZZUJmCPM2AJLJRSkepzy/O1fN47NXKyyN6W5eWjLO8yQpPi4dpHmtiJUmA+WrJ8TBmPTEc95setO5VVOLZlB8a98DwcXJxlNlkE3CpI1pcji/cGxNn7oEf3bu02p8M/J7Zb5mkUuPunU0+j2v+1deoBWzIhI1JlMln8w2+WUUliQ9NDNNU2ChPObclbERnRHyHBwRLDwlrJBLiVvif5pydhMjgOtOdnK7wcc4yprgFVaxEOKxvSH5+t+RzPTXuO+vbp/toDtjqBo0QZsjr+K+OZBtiWXi/DsWNHsUBmjo3LkKXBkYFB8tfO3TsQ6B+IkBCinmFSV9VWq/s4m0In7Jdw5WohJoyfahgsSmHGRJArLEpy7NmOnZuRkDAErq4eBvZOGePZM3M2VpAgAyD5ZXfv2oTn5y1V7AgNKiWfI/2tBoda//WHmLfkTQU4Sv61EnilwPbANgRFxsLR1csA2OZlnUAPB2d4+IUYANvLWcdg5+QJ5z59lWNUiiynBDq9bTUGTFtuUKeas1a6ftXFTHSxsUUvv3C6LdlcDtjLHwT/fYrBoyR0q8fWqsCWBI+SAC4BsZf3r0PY6OepRJum8JGjImdsXY1hs1co0ZMtHrXg5K61GDd7uQx+u6Dg3EnY2toiNCKaguH8nNO03qjoRCld0LkMKlGOjR1Ej6ceP4jevb0QER5Jge3JU8fRs0cPxMcl0HvKL7iIsrISTJ44RQlIR57vRCpxP7PEkEGDxS7H9XXyzkmEcA8PD/SPjFKVfrqsrfRd5F3MAYk1MKC/FHyNJ2PaG/CFxRYuDdbp06fQ2NSExMShNIfthu+/xbBhY+Hj64NAfynoaXs/59/tbq/IUzl++8+Tn0q9Ha30H16KzBpMD9iezS2kQTA++tPb8PORBlDyu17XhI/+9he88avf0M7F5IWqj5pUjrG55K/kzZsRFx8Pb98+Amur+p+p57CTWZ/jmR5WL5+yQNnXgbf/uMBWHh/lrqxeWAsc1qxahYXLlsHK0uqx5cjpJ46jS9euiE9K6iRrqw48DHJ9RyTJL7xAo7uqgxIHyBTWVTXwWrZYbQserGtX79RjN4qvIWXPXkTGxqB/fKzmWeR75CYi/Kqh0O7cIoJyT3JhdQFBfWblL44dpfu4cwTZGceYV1wrQc7JTNja2yFiUDzsHBz0GVzemJoFcNnESHw3wj1pJMXkWH1tLa5mX8Cdikr0jR0IzyB/0YdZy7Jz9yUumBi+a/ahiksv4mKOOV3NcH6sAlmpzzIWVt6SJ+kMWLIy/HbdrTsoPH0GvVxc0C8hRkd2zC+WcfJiHXmwGAHZEBhfOX8BldeuY/iMyYbSY879QmHaeAmzCR/b6F5eZkmRHf/lpwG2NX98BmzN+b5ZGS2wJRP9X/zfjylT+4tX1HQ5RIrcVNsk+dMSs2lhgbNnz1B56tjRY6VFXi7liF76H/6+TIFb8Zh01rsfvIeVy1fQyLm8/TTop+YAW34g1mksA/vKgdttydsRFBSE8DAjaS4eG9iKmQeYkVbVLEBq2gm6e3DSEOmwAgI5gCof0NqSd/7+F/ziZ7+R7KUCejUBoAz2t+Grrz/HrBnz0NPOTjlPUVbJCIy/R2OgljC1BOj4+kppRIz51LLnYguqWlBLtrOy0tGtazdERsUJQaOY/Jicy8Dj3ZrbSD26F+OnLaC2jw8aRaMit7Xh+IGtCIqMMwC2pI788xlA127oEzpQyElLjhHmsrmpET4R8Zq0PlLan4L0fXAOjEAPFyJjllIL8Wyt5FfbRhd+S1N3oM+oORKwVWy9uDghAFtmi4R0PyRv7SnY9HKCg2+IEiGZDx5F2NuWuhrcOHsMYaNmUjaXyZAf3L+LoswUxI6fLQHgLiSHbQ3yM1IwZNJchdnNOXUYvb184R/Yj5a5cPo4nBydERrWX2JoM47D2ckZkeED6HbKoV0IDQ1HYEAQBbIkEraPtw8iIiLp8awzmWh5+ADDh44QAkcRl4fIyP4IDjKRB7YNqLl7F99v+h6vLH9FkaWrgWYFPQAAIABJREFU8y9xoYZ9/3v37aYEyYhhLF+rOPfTG0v5OR19R9y7SjkiRV0OCxtAGdvVX3yMuXNfQk+HnuhnphTZ5fc/DbCt/s9nwLYjtvOpl9UC23v3GzD3lX/H6nd+BW9PV+H6BNgmb9mEATHRCAgMEuTIbAFZlHdY4PSpk2giicxHj+6wHJmf/kqTXU3UKMNNs9rLmPFV96uTflYh65Bau641fuz4ruRk+AUEIDwishPAVgU75GSS+mfTuu/w4vIVnFFl8EOMxKvH2jIjqNwr2nDr5k0cP3gQMxdIxkp5Lh3fS5WJ5Llsnr1UW0WAuNykhr82yXlbXlKK+BHD4OnjrWGh5WfXBbhcuxiws/pMrzBJ0LDQ4mRC2548swpcLyrG5ayzcHBzRnhCPKxsrHUBrj6rrFndl+9DBLEi0BSYeR2Ae7+mBoVZ53D35i0ExkbJOXAF/l0A7tqJkAJyNZM49Xtvb5YpAl49GSSrSy94FM/O0m6tKDgYCJb+fdjcgkupp9BQW4eI4YNg7+qiSIJFybLpIE6KVLgdf9qsQ0epMmLg8CEmpMf6rLDu/ciNQK4/0K63WcDW6V8HmTWOPelCd/5w8klX+b+6Pi2w/XztThA/WxINmf/V1TejMLcAfn7+yqTzzt072LZ9C51ISriWA7ftsLZqv9JEmmLfmlAA2Lx1C8JDwxAWGipZTLmz6uJYc8CtgQFVn9YUsE05mgIrK2sMTtKwR4KRlTYML8EvsHLXE4YpVVIl2mv13E1bNmJgVDQCA0jOcO5a2jQ6mu3CwnzkXczDtKkzVB9ZXubLMU88aL10KRclZSUYO2aSQW5ZlRFWQbVgqznG7MBBAmwi4eXlK7UPv7jL3QdrO60EWVFIyXWuX/c5ZsxaSH0YKQCW2Vm2wEsBrpwaqKS4EMVX8zF45CQ5QJMaDZkB22P7JWDr5O6lgFcmGb5WmIf6uloEDEiUZMRcyp7qyuuoKL6MoHgp2jKVMLe20fy3ZLui4AKV3HqEJ3BphqRyDMAyX9vy7COw9fCFrYc/vU9hAUJuGNXK8kELufWetkcoT90BvxGzKHvLB5MiLK2U8seCRmNubapHn8hEus3Y2rs3rqK2shRhiWNkGTJwt7IMFVcvIn6E5C9LQPDplB3oFxEDL68+dN/pE/vg5x+MgIAgytSePL4fvn380S84lB7fu2cL4uMS4ePtS31q9+1LRnhYJEjOabKdlnYctrbdkRA/SAW2JKDU6lWYP28BHOSYKlqyiM2oyPvenrwNwcH9ENIv1CzWlpy7/+A+CrBDQ9hilXFwq86V+YjL8iJEWxuSd25FeFh/+PTxx4MHD/Hp5x9g2fK3YNXd0mxg6/rPe34Sm3PrT5N+kutqL/oPz9juO5KJT75JRs3de7RtHB164bdvvIDi0kq8v2YzPNzUKGTTxiVh+fzJuF7XiLPZWairrcWIMePM8rO9fasKO7dtw9JXXtEAWyNyZH7lmt6ZammNqK+0JG67H5iuXW5HjtwesGWGgxmW/EuXQP6bOmOmeX62ysDLjK1o7PbuSIZfYKCS+kfLnvJiYz0wJ+6TSp/LzKSJ1+OHDDECblVJDzMSbCBU5yIioytOSrQRKlXYW3vnDk4cOARrGxskjhoBW5ISSgBwqmFSBl+hjUS5r3pdQ8AtDKjyjeuBdSVJuQA8RYBbfOkyzh5LpQGmwhJYSicd4KpMPHSYWc3igQhijciYOVkVb4zq792jEZSry25QgOsdFqxpR0PGnZ8YGb5PNqvklyja7VIGBcT5sQQE2c908ChV7luQkYXKqyUIiouCdz9pxdlYBGJ6RGFTTcmRuSjJHLtK/GkPrt+EsLho+IX2E9wpWPAqXqpsbiRkntWNsvM0C9g6/x8TkWM7/irMPuP2f6SbXfYfueDit/+Mmtr7uHrtBgL9vODr7Y4P//gWkqa9TmNRWFpaKs2z4ZN/RRu6YOfWHRg/drwgE1z95ed4cd4C2JF8tkzFoMqVDPfpNLpkE7W9TS4o7z6ddRp37tzGhHETBGArWlfxHJPvt5PANiv7DKqqqjBxgujvp1zL5FpaJwJHcYujDES+9+F7WLHsZSmwIzsuG3dm4wXbIO8kAaf8fQNolgctW8vGU7393639ElMmz0Ave3uZCVPtGn89o8wx2pCVdRJdu1piwIBY2lR6oFYBybwSiWctOeCdm3sW9+7dRcKgEQZsLXl2Jklmkt6c86fx4EEL+kcTYKoyuXwaHi2wZYCXlLlRchVVN64hNGGkAF7JscaG+7hwfC8GjFXz4xKmloHc+nt3cDXjMIJGztIBttK9kHYk/zbW3satS1nwiB9H75O3k+ocQP3OFRKG60J11y7iUWM93MITVFArB5Xi0/yUZhyAW0AYnDz7SCmAZMBafjGLRv/2D4tRpMg3r+ah6f5dRMQOU5jdzJQdGBg3jLKy5NwTB7djwMB49O4tAd1jB3cgIiIKfr5SFOXt29Zh5Mhx8HD3pEA2OXkjkhIHw7ePBHRTUg7A07M3ovpHSXawiwUetrSApOf5+Vu/EHJlC32bNILMnN6qrgaJoLx00VJlsUP9RvVZW1LX1999iTnPz0N3G1vOtc1wdUoLbNmCCmNu1677GmPGTISTkwvNYbtt20bMe2GZBGz7mhcV2e0nArZVz4Dt/9wpQVldI2pq7mD31i1YvGKlwqDyUmTOLisszGcffYAXFy2BnX0vmQWR2qA9P1sNpJXPMWw/cxaZ+bM6DWylkdIsP1sS0OC9v/4Fv/jt74RzGFwQVos5LKF0fmVYlg5W3byJY4cPYhbx2zUCckS+jjeAPEjmITCwa/NmhA+Igm9fIm/SA4scwNEJJsWMhypjVVuXh0bseUXOF7hWUIj0w0cQGBqCgUmJNGiTaYCrTgwUwyW/XD3/WvV96QPhzgLc/LPncPZ4OqJHDkFgZIQq3NaAcxUsGw8iJa4ss3fFLSoY+M/yYFsq13j/PgpPn8XdW9Vw9+uDPhEhsLSxEd+pIpFW3hprOcGfVg/SGuszSr8ywQKJQFadTjNFhsJYycD1yplzuJ6XD9/IMPSNGSD1ey5/LRs7pImJnt+sup+dya5BI9MKuXAtUFNF/Gl3Yuy8WbB3duJAram6RWbZ4D6kmxRY3/49zQO2Lv/HCKP1lE1H9X9Iucuf/Z5sCxAp8nvv/B0rX35NkCOnHDkERwcHxMfGy0SqviRZ/pSM3hTvx84K8YvAJGXG7j27sXTxS+0DW7V7Gm+ETgLb/IIC5OTmYNbMWfp1mwK25vjXcsZZAKmy3b5bW4v136/DyhWvKjZUsqW8fZTHVg3o/XjVh1j60ss0B6hpGTJjoICi4iu4kHMeU6fMNM3Wanx5eTBRVFSI/Pw8jB//nDojMOmXq9oGPQkyAYBrv/kECxa/Jj6HnCqH9xNmUuT04/vh5ukDv8AQo8A29fAOBJDgUW4SY8sD2+qb5SjIyUT0yGlqdGOOuT2x9QvETVlI5cpK4KlWyKxtG3L2bUDg0KnUh5blzVXkyLIsnAHuivMnYGXvAjvfEGXexVho+TMQTJaqNJSsUXnaTvSOHQWr7j2lCMk0kjnQFRJ4ZQGk8nZ9haipL6EbSbUjA1ty/GrGQXj49oO7t7/E5FpY4FpOBmxsbBAUHq0EmDq45UuMn74APWxsab2Hd23AkOET4eIsRU3ev2sjBiUNh5dHb3TraoFNP3yFaVNn0zzQBMhu3vQdxo2dBA93d7pNAi2FhoQitF+o4u5QWVmBwykHsWThEmWurefKx7P7G37YgMSEBMoWS9+hsVzLap8pKytF+qk0zHmeRV7mFI86fVqRH3OLLWTf199+gednvgBLa2tcv34daenHMXXqnA4BW49/+WkY28o/PmNsn6zV/BFrI8CW9PQvPvoILyxZAjs7Gai2k/bn4J7dNBrggOhoXTkyD4blWaxBOT2DzT96R8BtZ4CtMigqK4Hq1cXVQBUWbNywHrEJifD3DzBPjsz5/EjXEw0uyQs8MC4WXj59jPrasvO0hl2tS5S8kvJff/wx5r70khTUyWQwKXXVTnp6TV1C22jhkT57y9o170w2Mo+nYkBiAvrHx8CC+HCbZHC56xuU0/gfGQF0+gytIWOqtKnyfDLobG2l/rfFl/MRPXwIegfIxoDLVSusUrI244J6sIkVXb3k7pMaY2ZYjEqoteC3DU2NjSjNvYyS3EtwcHdDn4h+cPbxEtpSaTllkUYfyqqLEZ0fZMwNHtV4rw4lOZdw61oZegcHUj9a8mMg1KjMVwGPpvLVagAwBziLcvNo3uDhM6aa8KdthwFWZKRqoCsGuqVD0v7Inh5mMbau//bTANtb//4M2Hb+Szd+JgG2X3y6BsOHjaCyPZb2p/KmNPFcTCae0sduVJKstYsGtq8d1vavf38Hb732Bs1jadLPVr4Pk+3QSWDbbsqfxwW23IClB2zzLl0EiQQ9baoMEhXwqtpZfsxlOPlW1U3s2rsLixcSJku2Y2bIkJN3bEFkZBR8feU4CPJN8YBTHuKFsZ5dl+Qa3bVrC55/fqFqbQVQK4/kGqBLbYmRfSeOH4SLqweCQyJ0g0ax83iZ775dPyA6fhgcXaSUg5LEmDG30t/HSVTk8BgqRRZT8rSh7t5dXMg4guhRzwmAl7GyOSQyclgsepB8tXLdfACpknNpsKY+r/2481XmmGdtybVL0nbCtf8QWNk5cr624lyKfua85B/A/fIiNNdUwj1ysMDWEgKGAVoCVJtqKlF1+Qz6DZ0qS5MhBY+ysMCFAz8gcuhE9OzlQLcJuL186hA8vf3h7R8MS7muYzvXYuLMxQrTu2vTl5gyfQENCEXO27ntO4wbPw0ujk60zNpvP8WC+UvRo4ctrXPt2jV4ftY8ODo60Hg3W7f9gEEJSfDz81MiIl+8lAfS556bMo0yuMIcmuvg0vcifTD5hQW4kHMBM6dLi0/KHEkrd2cdTP7Wjhw5BDd3D4SFkhRaHQC28nfa2tqKDz78G954/Zc0cNTly5dwpagQI0dNhHV3q//H3nuAZXGs7eM3KAqIXSwoIkoRBUVU7Apq7L1FY9eYmJ6ckpzyld9XznfOycnJSTVGo7Fr1Nh7pShFUKoURbogVaWKjf81Mzu7M/vuC69o4jn/+F4XF++7Ozu7O7M7z9xz38/zWMzYdvr34z/FEF5vnfn/Y0aFUu+Rz7bAL16K3JDmZMAWOHPsGAVX3n361u1nq7CyaampuJoQj5lz5/4kfraW2GLxfs3ZZXkiL7ycysHCu2zI2nKfSWZugJjoaBSXFNME0yrA4XWJPjK6+rWyAuCoBU37Ex0ZgZnz5yuDjo6jlQCTHhipcEaNzMiPrq6qws4NG7H87bc05pGbUgHM8fviPkTsssV20gfv0FpahE5iO8tHA/GXohEVegG+g/3Rd7C/GYCrj34syo9lkCazuHI5U9CpW61XFxZkAK+x7Wx7VVkZYi9G4P69GvgMHYRWju3qTRGkgVgFnPJJjwRizbC2qsTMFPyK11aQnoXMxGRU3S2Ds3dPdOnpxlhc4VxSD0pqBH3fWT5imC4yGQePIjXeSstETlIKiBS4U49ucO3LckuaAlp9cCg986ocZcDIahJlDn7Z//jQcDx68BADxoxqoPRY89PSpKQySytu927WwSJg2/7/scA2P/en8P9d+LlP+Ys4HwG2oedCaSTaMaPHSHLkzVs3Ye7sOWyR2MC3lquaxIbSYVj2vuj8dNR3UPnyw97d6NfHF54enqyqOhQW9fr21AFsVRshXrAy3uTl5+HYieNYuXylcb8/FbDV2R693aoFzp4/i+YOzTFwAAuYxD/y5F1YvFXsKZFyl5eXIWDUGCkasigf1rO45RVl2PXDNqxc/oYKhuWoyXpmWLRN7Br2/rgdw4ePQfv2LOWcSkgLC5LmACzvB/GcRUUFCLtwFlNmLDBhazlrZxQZ+YdtazFt7nI0tmmiyJRNgW3o6YPo7uWLtp2cJT9aAjpr7t/H+YNbEDBrhSZF5hGXSXCpy6Gwb9kWjq49Jf9b5m8LlOZnoSgzBS7+L6m+tRqwZr7ADHArYJcEOo08CdvW7dHanUX5Fe0ifwFUKbIVUHv/HrKD96H7uIWKby1ja7nCRwW21lYoSo2h4LKzl5+SBoj5zTayqkXUgY0YMec1UNmywuTGnD2AXn5D0a59JwpSH9+rRnTIMYyZPE+RJltj39avsWDpW7Bp3IiW+XHHesyeuxjNCdC1tsLG777A66+9BxubRvTcGzZ+jRXLXoe9vS31yd2+czMmjp+Ijh07qcD2YtgFGpV9xPARWkoxMyOuyNp++fUXWLZkOZo1czDL2ooKPFKo+l41vtu4Dm+9+Z7J/EJT8ykzRs4CC//LK8qx84etWL5sNR49qqXBsCoqKjBw0Ag0sWuCnhZKkZ3+4/kA27z/fgFs/2WNeXZ5NZU5XE9OQWb6DUwgK0H6lS/FR1ZlYa2s8PjRI3zzxed454Nfqe5A4n7Bzgor11ozmQSfMaBnn5qxVSyBbLNlcFsfsKVVCIC1orwcu7Zvw8rVbzyVHJnVy9pj19bNCBw3AY4d2puytrrzaxJXDvbEVUuVw6XjTk5mJhKuXMHEmSQ4BmsMdr+mDK9kZMWySpfx/dokRwabXMxtJE/mxyREX0ZcZBR69euLPoMGwtra2jQYktH9Sv0os6/i9fC+VJlRcXVSuXu9MazPB7ck/xbiLkbArrkD/EaNROMmJIG5KZhueIAprV8sZXdJW1eVV1AG92ZqGpq1aolObt3h5OGKRjY2Jm2qTvbEpRvdRNbcvFY/meZ16d/fe5VVyE2+jvy0dLTt3AlObt3pfzYO6CW+poGlpEBTBlGKxZyypqCWsa9ndu5FTz9fuPT0kKXHYlTjOnLcyj69AmA2YW+1KNBedo5o6dC03vG/w3+NqLfMT1Gg4D9ZxNgXn2fbAgTYFuQUYffeHVi14nU62eR5JlOvpSIlNRmzZsyyjLVVjKXe3jFcK28VsW7kpUhUVlZiTCCJYPo8AkjV0tRD+w8dwKsrXjVu4J8K2CpAcO++3RjQfyBcunarI3CUBiD5QvXRY0fg5uYOdzcPQxkyZ7tEEElSlzx8/IjmrpVSA6myY80W8/OoIBW1iIuLQVVVOQYNGlknqOVzA25nNJulsW283qOH9mDQ0JFo05bNHTj7zPLW6sszOTEBLMcP7sTUucvVAFNaqh0WwImAzPCg43B264V2HNgSkKlEMCb7z+3fhKFTFgLWjdRjuOw4Lz0FZbeL4eo71MQHl7O6MYc2wXvKUknmzCMic0BLWlS9NgB3Mq7SZ71FNx4Mic88NNECeWXul99BTsh+uE1aRucZasAoxbeWSpEF1jYz7BicvQeheRtHCdjWlJciM+YC/MbMVEAtO+7S8Z0YNGYGHJo5UCBbcacIqVfCETB+JgWlePwQJw9sw+z5K1Uf3D071mHhotfQpHEjNMJjbN/2HVaufJOlGyLAdsPXWP36O7BpbE0Z263bv8fsmXOoVJlGX7cCTpw+jm5dXeDd29vUx1ZY2JIUBLXAhbALdHwaPIjFejBkbfVqr1rg9NlTNGWQd68+hqytRCTw501ZjCC+9+eDTmP27AV4+KgWF8OC0aSJPXr39kUTe8uBbef/PPFsB28La7v5XyR+wfP/vGBsG9AHBNiS5ax7VdXYum4d3iBAVTW0InvBKmegl71Bu7ZuwYhRAXDWp/1Ro6Fqxwh2V61HCiJlBsVaCm7NLjjr/GdlNlIbFE1WoIS21LO2m75bj4lTpyqrrqygChzVVWVxOyugDgI6OXIqkVOlpmDSjJnKoGM5a8vOzQ2qCDbZ1ujwcBpwYNCIERZEStbQtgyb+b1oN2EMa4WrUTvEdCEhJiwcV8Ij0dd/ALz8fGFnLwT9UFtSD8DlujUIb8z0qhML3jcGwZ30bSf2j8Tg1gIkwBSJoOzs6Q6vgX5Kn3NmVmsNDeAasLaG12DM7krXryB26fkRGN6S3DzkpWWgNO8WjezcrmsXOHbtjBaObTmRqzzNcl80YLiQwr6VlZSgMCMbBRnZsLKyRoduznDp7YWmzYj83RjQSvJjMlEpKkLSxUvSuEKObN2hHXyGDTFgXbkEWWNRy4pLcGrnHoxfOA+t25EIy0+a91aWNdNrN4i0LG6vqb6HS8GhWDppNtq3Jem16v50/O/nA2xv/ccLYFtf3zRkPwG2FaVV2PXDdgQGjEaXzl1UOTIBubt278AwEgiGyAhFeyr46LCvgpzQaHG3Dtb2Zt5NnDx1EiuXragf2MqnMr5ls0ZUsJP8SGVMun37Nrbt2oG333ir3jr1C8yiTVSrlYCwzt6otlUDqt9+txZzZs5FmzZt6gwcpTGxbNTf8P13mD51Jlq3acNssxkZsuirSxi0MYHj4dheAZGilNNE1imztY8ePcT69V/itdfeV2y8uECrWCJh7sCvh7cR2aW3Cenp15CZcR2jRpPozKyPRCDOQS4Dh9q+4uICRF48h3FT5tM5gRg8ioFIBmwjgo+jk4s7Ojp3V+XEomQ55OgueA95CfYtWuv21+Lu7WKkRgWhzxgxQBRJ98Nlz7VIizyLlp27o3knF1WOLF2LyNoq10laqij5Eu6X3UFH/5fU+RIbm9lTVJoSjZq7Jeg8mAETCmohs7ViHls8eoBrp3fBd8pSVYbMmdk7OWkoK7qJXv6jVWBLIisH712HiQveUH1xi/MyUHwzCwOHjqVA9l5lGS6cOYgZc5dRdtaq9hF2b1uL5Svfpfsf3r+HvXu2YMUKBmxR+xjrv/sK7779K+p/S4Dtug1rsGzxcjg4MN9gcn8/7t+DAf0H0AjgQcHnQRQTfBjh48mYwDHo2IEoAvi8sxa3Cgpw9PgRLCdBpJRnge5W5e06dYHy4BHZc9TlS5gz62XlFdXKKdVrz7NSL3/WSOTwhPhYmuuZBA87dfoYOnbsgh5uXmj6BMC20/tbGjJEP/Ux+Z8teeo6nkUFL4BtA1oxu4wBWzKR27FxA8ZPmUp19SJrqxlmDmyZlYy8eIGCphGBgRb52cpRVHVG3Yzh/amBrfpy6gCp/NJqv0ixCyHBdBQdNpysvLKPuHLFu0HaJwFbxZAJB3635ivMXbgIzZVIizpoK0dNNPHVFOszZWPPHD2KHp6eNAKzCAiNmVt1NBT4PT04rQ/gCrC3DoCbHBOLy2ER6Obuhp6+fdCGyn21wVhsdXHVWmOexVZS+kEXDOtJAC47twI0lV5VJxiKEYgPi0BGUgr8AkcY+t/ya7Y4wJTJhEXuP+n8Jm0jSNmU6y29VYCirFwUZuXgwf0HaNayBVp1cETLju3RrGVz2LdoLj2v6pBhblKrvIA1FZVU/nz7ViFK8wtwO78AbTp1ROuO7dHBtStatGMRDrlx5WyTJg0T5LxKQbKlKDsX4YeOa8cp5+vg3AXDZ05R6jQT8MnKChkJSchNS0fALEVpYghqGwZ0RV9aUVJ6OfQiUhMSMThgFKYOHGWRFLnT/4xswOj89Ifk/3vI01fyogaTFiDAtrykCjGx0SAqHr0cmeSz3bt/D15b+Tp7hvW+tuo7oL01vJx4Mj1rq1dQfP7V53j3zXdgbd3o6fxs6wC1on2Tx4talJWVYeOWTXjv7XeNnxIDu6qM1NJiLz9YU/woY7t4vAIe+QBGdv3t7x/j/Xc/oFFrNfCq2B9+rLigSCffj/H3f/yN5q/l4I/N47U0JRxI8qA4RLa8e892KquUyhmxtdyOCKAhKPg0HB070vQ+kjqK35O6UGkKYEWbqF4XIRa2r8fUGfNhZ+9QtwyZA16a7gfIJql+0q9hWABJ9cMlvyLAZcD2SsR5tOnQBZ269lDzzPIAT+R/VNBRdO5Bogi7SPt5meC932LIzFfxGFYq8OWpfwj4Kc5Jw538LHTpTyIrM0BNga3A0tKURfy3av+A+1XluHXlPNp6DUTTlu3woLoCFTdvoLIgGy2c3dG6u486JzViawlQ5Hlsy/MyUJ6fCTf/MYw9VZhcAkhvJl5CU1s7uPT0VSMiP6gsQ1zIUQRMW6SmBMpNS8S9qnL06z+cbisrLUTspWBMmjaf/n5YU42jB7Zj0eLX6e/qygocO7oXixe/Ss/54H4Ntu/YgDdXv8uuoZE1vl7zGd5c/RaaNrVVge3mbd9j0oTJcOrYCT/s2YUbGTcku0t+LHh5Abq7aj7g/Hlds/ZrmiaoRYuWZuXI2mKT9g59ueYzrFq5Gk2bCIErde81f0/UfMNEjp6agpzcLASMeokytgcP/wgvrz7o3KUbmtrboKdbO4ssS9uZv7Go3LMuVLL/k2ddZYPqewFsG9Bs2WVVqLUmq1pWuHj+POzsm2HgYM6UaJI7Jd+8BmCtrFCQn49Tx49h6cqVT+xnK06C1e9PIUc2a5frYWxVo10HsOVGj5ctuJWPE8eOYcnylSbAlkuFxcmABnq1DtJYVrYtPjaG5qENHD+e2W3BiGuGkANGWQrLziUacw2+8nNvX78ek2bNQkuyQi0FAjAtq8JSqe3Mg1vtXgVAy2cfKmOqTl1EQSz9npacjPjIaDRr0Ry9+vVD527OZgBuXX64UitoK5Fq0Cdtvwhg+VZzAFgGu+z+qiuqEH0+GFaNrOE9eCAcWrWSGHtqSJT7V1fSFYuh7RPkQMI1av0oruibLlaI1yu1vyAnul9TgzuFxbhzqxCVt+/iTmERqsrK0axVC9g1b47GTWzQxNYWTZvZU9cCcs2EbXpQcx/3q+/hYU0NnWzcziuAjW1TtHRsi+ZtWqN1pw5UZtyocWPJqIrMlPZOa4CWbhOD0hFgm5OD8IME2MpSyvZdnTFixmQz7CsDqrGhYdSfduCYADr+0FVtKTqynnnVAkGx66s7+rLexzYlNh6RQcHoP2wI+vr702vwtHO0CNg6/e+oBozOT39I3r8FP30lL2owaQECbMuKK0GCAe3ctRVvvP6WEh1TJ7qeAAAgAElEQVSZR121QlDIedjb2WHwIGZP1YUS0c6pj7155tbQ11Ypvm3ndgwbPATdXbs/HWvbQGBLfObWbfwOH7zLmEiTTx3AVgSx6hgmXAe3e+I+dSSsBSqrKrF+43d49y0Gqo2ArTTWK2NjYcEtHD91HIsXLjMBqaweIdq98j0+IQZ3y8owdMhIQcZpWk49Xrlocv47d0px+vQxzJ7Nsx+YsmMio8xthJEEmQOPpKtxID6/A/yHm9g6czJkUh8BjkmJl1FVWQnfgcMpaGTbTYHt5fBzaNGmPbq69TIEtlejQ2Dr0Aqd3b01xlVI6xNzbj/NCevQpoPkh8uB78NHjxBzeBN8pi5X5cg8zy69HqUvGIvL/G55P5Nv9+4UoTg5ioJcGzsHNHPsjJauvdGoMUvNxd85ka1lNoLZCu5jmxcbiuZtO6C9a08WHEoFtta4FnYcXdx94OikpACyssLdojzkJF/B4DHTKUglf2lxETS6dq8+A+jvoptZSL+WgDHjWJnq8jsIOnsE8+ax4FJ3b5cgOOgE5s9fSoFsdXUF9u3bRQEkYWtJVOTPvvgbfv3+b9GoUSMF2Frhm2+/ooHpWrZooQJbvfKDAdvuJnL5s0Hn0MzeHgMHDFLaUZ5Hqu+jMIcg246fPEql/iyvrQFjK6gBRGAbEsrwhG/fAXj06DF+2L0dQ4YGoG27DmhibwMvC4Gty3+dfC4WKOs/2Vz8eX9eANsG9AAHtoSxzcnIwJXIS5i94BWVseXQlgNbPmBwqd6Xn36C1954C7b2duwY5RoM0/4I+WzVgUflaiRVlnQnT8XamgBbZnFEO85XRFUDqpxdKiMUIl/XfP4ZFq9YCQeH5jpwqw+2xCrTVl2F37pB4utPP8HKN9+mclIZuCgsolKJBm/1cl0+8IsAkx9bi63ffovFr7+ugS5+n8IMQ2wZaWVZuwtd28kNLB/Pu1G4Hv3ERWjrvOxsJMXE4XZJCbz9+sGzr49qyMQ+MwKmosHjZU2CTIkDtnDv6sKDAjDV45VKyX6xLfjEoyA7F9HnQ9CpW1f4jhjKpGAG/rciCywuUqjnFVlmQWLMTq8Dtfr9wsOl3Yey0WBRh+ypuHMXxCeWgNcH92rw6OEDPHr4iLUIMeqNG1HA28TOFk3t7agPrwxihbdXeTlln1stx60GDoVFMhVQAqQNIw6x4BAi29u+axeMmDnFFKiC+fcH7zsEN59ecO3lVb/02KyPrV6CrAe6bH9+Ti7CzpxDF5euGDI6kMmUlQmSh107i4Bt5/8NaMDo/PSH3Py3oKev5EUNJi1AgO3d4ko6Cd61eztGB45BZ6fOTI5sTcAte5q/XPM5Vq14Dfb29sasrfLgS++PzuDVxdqGhYfhwYMHCBhJFk7q8bPVXjLTHm0gsK2ursZXa9fgNx/82vgpeRJgq7uG+oBt3q18nDx5AkuXLBPshN5/ULCRymJx4tVEZGVnYuL4yRb71x4+sg89e3qje3c3RcqpMbwS28tthsDWnj5zDC4u3eHm1lNSXqk2XmgjSyTIpPiW77/G/IWr0FiJqUBtj5hqRVwsVUAhk6ACUZFBaObQEh5efc0CWwIkE2LC0dS2Gbp6EOCqBZfiQZ5uJMeiuroKbn0HqxGVeQRkUv56TBia2Dugg5sGfEnaIH48KXs9/BTauHiiWQcSoEr2p9WiOHPWltk1Zme5sosc85i6wqhzT/WdYo8kTfHD/9NUP+w3B7Ypp3bCK3AmbO3sFSkyS+lDAGjMse3oN2Ym7Js5qBGRCzNTUV58C30Hj1b9Z5OjQ+HYwQmu3T3ocbkZKSi+dRPDR42jx90pKUB0RDBmzFxAfXBLivJxKTIUc2YvoIvJZWW3ceL4QSxbShhca1hZ1eLLrz/Fbz74UAW1ZFz55NO/4jcf/BY2jRvjh727kJ5OGFt5wFCBLV9MV577nNxcnDt/FosXLjGUI/P5ksTa1gKx8bEoLCzA2DEE6JkHtqrcXXkODx3Zj36+A9CxY2caFXnz5vWYNHk2mjm0eCJg2+2/nw+wzfyPF8D2X9b0Z5VVMSkynWxa4fO//Bkf/P6PghRZ9JVTpMhCxMajBw/QIAxe3r2lgeVJga0ypzZsx6cCtsJKrla5AbBVAYLsT6SBD60A2UaYaiLZ7tvPz1SOrI0M8j4d0JBZVuBSRDhN7TI8MFAw1Hwg57JTU9BqKClWwKoIggmDt3frVixYyZlmmRXk7WMKTk0lr/IcxGCwk4TMWs1qK0oVyP1xu7QUiVGXkZ56HV6+fdB7QD/Y2tlZLlOW2G4DlvdJAa6woECBqzoRYdedFpeI5OgYeA3qD9dePVnfGQDcugJMsfmQaV9rRtwY4GqmnrWxnsk1aHlDCWBdA5j0/glaSGkiwWGpAHSNGFwOCHmgpsKsXIQdOiZLmAF0cOmCkTOm0stS/V2JSiQzBzHBoRg+ZQJamfWnbZj0WI5gy6DGpaBQVJaXYcCI4WjVurVJOgl3C4Ftlz89H2Cb+8cXwPanMM4U2BZV0klpTEw0KisrMDpwtBQdmTy36Rk3cCXmMubNeVlibQUMyi5PiJ5sZAvFSMqiHJn42J09dxZLFpL0MT8/sL1//z4+/eIzfPSbD42b2RywrTeHrZkJNLcrtUDKtVRcvZqImTNmqWOatOgpgEsNCNXifPB5ylwN6O9vCmx1eTj5mLrmm8+wYvkbsGnCgvNxP0UOKHk5fTTmO3du0/Q+r7xCbK5gR+uSIAuyZBU0C/OY+PhokAUFI7aWXQ+zPxRoCL/5dZ8/cwjd3XvDyVmRq4oBmgRJcHJCNGqtrGlkZJ7DVgS4ednpyMu6Bu+h46SUP7zsrazrKMnLgpv/aHU/SfnD89aSckVZ11BWlIfO/UYKUmSNPSbXL0ZGVttZx94qr5D4CvCQhVrgKOprqwBbJY/tg4rbyLl8Hr3HzJH8a8mClfWjB7hyfAeGz1yhpQaytkLW1WgKTj37DFQBcNS5Q/DyGYBOTs4U2KanxKGmmkQAHskY3LwcJCdewaTJs2gaoVt52UhMvIJpU2dTxra0tAhBQaex8JUlFNg+fHgfGzauxXvv/Er13X/w8D6++fZr/Pq9X9NxhgHbdPm9swIWzGOMLVcP8ueB/P9qzZdYvnQ57OyaKfMFg5y20pyZBYg7dISA7pXKe6ZNZNX3Snlu1PeiFvh67edYsWw1XXwhwHbtt19g4cJVsG5kQ6XIXu6WSZFd/+fUTzGE11tnxr+Pq7fMz1HgBWPbgFYmwJZKkZUX/vCPP6K/vz+cu7KgF5J0UIiOzOVRyYkJyM3NwfiJk57Yz5YNRoIESx2dTN5Vi+7M7KLzTyBHzkhPR3xcDKbNmG0CbOuSIwuYV2ZOlYv/9ssvsOrtd+gAbSo31cCtCFi58RNBDjfwbJu258H9+9i18Xssev01E+bWCCCLxwtzCqFWsWssBbhCrXUA3IcPHyIhOhpJMfHo0r0bPPv4wLFjBzMAVwawIjPKz2aUJsicBFk1CjqZt7oYoQe4tUBMyAXcLS1F70H+aNOxvdrqsqxcvE6tvTgQrh/8ir0sM8msJ0QArHaTiUrB3AsldkddC0oquyoUEhlXcaLOxhFtLx9XyENOfIHDVB9bbYLfoWsXjKR5aDVWNeFiJO5VVGLwhLH08uUAT5rMWDyGi5PVcazeiMusnsK8fJw7dAQDRgyDp7e3Akq00Yp/c7draxFj6/ThEIvGsGddKO/j8Gdd5Yv6ABBge6ewggJbIkfef3APli9ZKcmReaA0Min08fahAV/ExRM9uJUkx3p1E18lUlpf/PnF11/izddWw8amSf1+tmZsrCTDMehhE9uqbHj8+BHI+d975z3j5+JpgK14rLCYyG1oTFwMSktK6YKCKNtV7ZgePCqzcNIfvXr5wLWbK5uoK+VksKpJkguLCnDxQjCmT59rkhpIPJaOvgIwJhWHhJ6jQSY9PHrpItEqY7UKvrXF0LokyOQWftixAbPmLUGjRsyvmINrDcTWFUSqFmeO74PfwBFo0aYdkyFLkl/N1zX1agxN6+PRZ6AEbDlwJXLapOgQmsuWRzoWAXB1RTkSw0+jz+iZio+tzNaSskRtkHRuLzzHvqyTQ2u+tuwaGdjVbLlm6/izqS20KrZG2SAHj+JSZBbd+HZGEh4/uIcuvQao0ZA5k1t9uxB5qTHoM2KSwtaC/r9+ORRtOzjBWclhS4Br+Kl9GDhsLFq1ak2BbGpCFJra2MC7T3/6Oy8nHTez0zE6cAJjdLNvICvzBl4aO5EC28LCfMTEXMKM6QToWqOmphp79+3CymWrVBVI2d27OHTkAJYvWU5VIcTHVgK2yv0SYNujO5Eii88Be1COHj8Kp86d0ce7L2tLvsAklOWNrM4ba4HPv/qU+v82Js+cnrhRjhVlyHfLy3Ho0F4smL+MysyJ7Hzj999i0aJVFOSSqMi9LAS2PZ4TsL3xAtj+69r6rLJKCmw5YxsdFgYCKIaPYgGh9Hn42KSRfcjEsrK8HFu+34C33n1fl/ZHlCMq5RWjKk6Y+XRXtbdmZtOWsLZPAmz5AMl7jq88GW5XCumB5id//j+8/+FHaGTdyJS1lRhTbUA2ArZsgGFlwi+wKKaDhrO8lzLwUoYaAza2LnCrtQsDPYS5/XHbdsxfsbxOcKuZDuH6xVVn1czoWW55NsMHSPEtEa9JvH+tP+QSGdeu4VpCImru1aCP/wB0deth0j687/SRieW2qZ/BNWnzeuTBInC9U1yCS2fO07y3viOHwboRfzb4yqjWGlKAKX1fC32sl0Grz6iO9dArAPT9Z/SsyyOX/g2SUav+HTT37qrpfRTJMXu3tQjrmnsD8+e9W1QsyYlJ+aZ2tmjVrq26/fyeA3BydUGvgf3N+8bWkcpHjpSsCqSlhTsOlGPCIlGQm4uAyRPRzMHBhKVVRdNWgJutZcC27dxeclP/TL9K9iT9TGf6ZZ2GANvbBNgSZsfaCjt3bcG4l8ajU0cndSLKF3SI7DU8IowGbtGrGCRwWwdry44zXgQ+dPQIerp7wNPDo+GsrVnjKY79Qh8r5Um037/+/W/4/Ye/M34AfkJgG3kpApWVVQgMCHwiYLt1+2aMGT0OHUjkWIoGNVmxoX9tYiwKSfyLwHGmaX7EYxVDJoLNTZvWYulSFnBKtf3CMZrN0ru7GAeRupZ6FbdLizFwMPH1FRatJXa2bmC7/4eNGDN5LuyVoFPmgG369STcvVNK87WK8mHOoD549Aind6/H6HmvG+aiJVLjiCPb4B04Aza29gL41cuRT6JNNy80a8/y5ZIuEX1+2W+hj4T1WtH26hdb+dtC31GFreXRhXnwqKzIU3Dq6YsWbTtqwJYGkAKKblyl/rse/YZR8Mn8b4HY84fg2XcQlR5TH1srK5zcvQ5T5q2ErU0Tui028jzateuAnl4+9HdGWhJKCm8hIGCc8jsFeXnZKrC9eTMbcXHRmD1rHgW2lVXl2L17h+K7z9wbSktLcPDwAby2YhX9XVxURFM36YYGtHdsDzuicBPBqvI9MekqbqSnYerkaab7TZ5RZQZRC2zfuQWjA8eiYwcnlQkW51siU0veoZzcHIRHXMCsmfMpsK2qqsamLeuwbNmbTwxs3f73+TC2af/2grH9l7XqmWWVkhS5IC8fwadP4ZVlK0z9bAXbyo02+f/9um8xbdYstHN0fCo/WzYBNva1fbbA1nTKLwJbFTiIhJcAPPj+Q/v2wcPLC549lZxq4oCrX9XSDcZ6ACee/9M//4nKwTXZq5EMVYSLnPnTlTOQpvISBNxuWrMGq95/Xwh1pFyVGcCktYtOisIgpsmiv7pFmDTpS5kAXLHxDeolz2dybBxuZmVTgNvLz1cpJUxQ+HHCKj9vX41xfTqAy+rj0mHxOztpZsp1XDpzDn2GD4F7H2+2qi7Jp4S+MvCxVX2JzPjYSsys8PDw9tS3vTxvNe2rugYwEcCK5fg820R1YQBoOYPLxw3+mvOgUSZBn5Q6CnPzEHrgCAJmT0d7p071+9MaMbKKm4U4ZukDRxHQQNj2cwcPw6tvX3j39zPL0mr4wgputm0sYmxd/kJyjf78n6zfnfv5T/oLOCMBtqUFjLElk96o6Eg8fPgAo0aMlOTIXHK/aetGTBo/meaENHlvBHbWUtZWtYdWwJXYGOTn5WHKpMm/KGB74WIoBTwjhrEASqrpEFkoUVmjjJNr163B/JcXoXnz5pqvoch86r6fO38Kbds6wtvbVwgspUVQVkyBCSN77VoyMigrN1mO0aBchwpMOWhTZcPKvUj3wUDsof07MHzkS2ilsK3UruijPpvzr1XK/rDlG8xYsIoGJTJmbBmwzMm8jpysNPQfPl4FtnpJcvDhbeg3aiqaNmtuEmCK1JF06Rxatu+Cdl3dpHy2Wj1AUfZ13C3MRZd+o4QozRpLSyMjC4vL7J61/ubftfeK7ROJGTUysmIfyG+S5if11E74TllmIkMmbG7mlWC0cuyEzq491WjJja2AsMNbMWT8bDg0a059bEk9Z/dvxvQFLOIxAbqXQk7AtYcHXF3d6bZrSXE0avLQoQH09/WUBCo/Dgx4iQaKys7OQHJyAmZMm8WAbcVd/Lh/N1579Q11oayg4BZOnT6BFUtXSH789F7lNWiFxRcWS5RnjAR727hpI95+8x1tYUQIliap9rRJE06ePoGOHTvBhzK9chwZdTFIqYf0+9WrCci9mYsxY8bT3Md37tzF3n07seCVV2kgqSdhbN3/dPq5WJTrf2TppJ7354UUuQE9kHlXYWypQz3zsyX5bF9ZsQJNbJoobEXdfrZBZ06jRYsWGDBokOxnqyxHaxIRye6qV/us5MhmF51NpMjMBIrl1e8GK8wqWNABiJTkZGRmpGP8RGK4BMOqmFjJ2Ir7heuR4KlSycWQYGp0/IcNYzVxQ8jrNbNNZCbFO9QzzRrAqsX6zz4zBrfC4GXaWtpKschA8xbQ94MxwFWnIdqERP9NqkjuL5JiIyH6MlITrqJnXx/0GTgQTe2aSjJl3rZaP8jSX34FxvJf7a71AZwkRlfoG20hQpOVxV8IR9a1NPQfPRIdujqrPSgCbfE65e8y+NVaTLwPuXc44JZb16BnDF4W/SZx8iwPLaIkl+/RKzSUMlIUZFk+LIFdUo0ud2xKVAwKsnMwes4MCWRydQlTjzTAn9YgR21KTDySYmMxftZMtGjVUsfSKi2hi2BLtvawENh2++uYBozOT39I5kdnn76SFzWYtAABtiW3yumElwZ/Kb+Dk6eOY+H8RfS35hfOnvmU1GRkZmZg0kQS6VtbvNUWdpRTNIC1LS8vx+ZtW/AOzSVrgZ+tcH7dkGu2p02GC2XD82Rsg0KCaCTawYMGa0BHtJV6ZlTZR1L9kHyh1o2sNdZKlQRryhrGQgEHD/4I/0FD0b59B82/loNHZTLP7LSy0Kkgr7PnTlBg061bDx3o1QMDy9ja7Ox0pCTFY+z46Tpgpy2wcgZTY41lppOo8fZsW4u5S95SQbopY8uAbcGtXKQkRGPw6GkmeWZJqEFSJjr4GJy6e6GNk5iLlgFRwtjmpafgbsktdPcbpQSgMpUjk3JxR7ag16TFar5dnuKHA2/O5Kr3JbCL/Nk0nWdqKiEtgJQWPKoiLwOVJfno5jtcSvNDoyJbWyHx7D54DgxAyzbt1GjJBMdGHt2BgOmLVP/a6vLbiAk9ifHTFzJm19oKoSf3o6/fYDgpPrdX4yIpeB4wYAgFw1cTrqC6uhIjhgfQ1D4k/VJ6+nVMmTyN/i67U4rDRw9g5fLXVGB7MzcHoRdDsPiVxTKwNXFbsDKVGAvvwroN32LW9Nlo04al6BNVCiKw1eZ2tbh85TKIv3hgAHEDEvxyxeMFYBsRGU7Hvf79/SmwLSgswsmTRzB7zmI8evxkwNbj/54PsL32hxfA9l/W9GferWBSZCVVBhkADu/dC59+/dDDzaNuObJihDNv3MDlqEuYO39BnXJkcaJsTo6s2lwDirY+1vZpgK0KBOoAtnwQ4GWJf8hX//gU7//2I0OQrEaZk5hczixqj4zIIpKKHj16hC8/+RjvffR7YaXXUtZWYAaFWYsROCa7a+7dUwNKsc4TobbGRPKrNWVctQaT29+IFdQAmVafKQTT6lGuRdex+prjoqKQdjUFbTu0Rx9/f7RsI+RpE+6ooQBXXAjQFjD45ESbzGj9KAeYqrhbjpjQC7C2skbvIf5waNXSPMAVWGBan7Car3/+VEgrPbO6BRthMcYI7D7pwCW9gwpAlOfJppJjhln18l8hSrIOaN6rqsaFQ8fg7N4DvQYQ5tR8HltTcKsK0gxkxsb1BB05jhatW2Lg8OH1sLR67toKPWxbW8TYun7M/IJ/7k/Gh2d+7lP+Is5XXlmD+CvJcOnqosqRN2/5DrNmzEbbtu3UgC8cZ5Ln/+tvvqRsC5W3Ky+NukQkTk4FcCsyMfw94g0sAuTvt2zC9MlT6bn56nKd9lLcadZwijZK163/BMD29NnTaN2qFfr7DbAY2BLfxbXr1+KdN5lSSZJQKpN0OuYKIPXbdV9h0aIVsLW11YCtul8XfEdge7dsWYfFi0ksC02GrNphPmYbsbW6bXzcPnHsRxqMqLXI1urqZpjaDNAlKZIqy3Hi8G5Mf3lFvcD29u1ixEeF0rQ2XH7MAS0Hrimx4Whk0xRde/oKrK4GXivL7yI+5Cj8JiwwkSuL8uas2Ito2qINWrv0VANfcfkxPze9N4mtFRYSBLWf5grDXemUyMhUjsxsAAGoudFBaO3kgnbOPWT/WiWXbfiP6zBq3moQlpaAUgJYayruIj70OEZNXcgiIhM/3YJcZKTEYuRYktoHNLDU6UM7MHL0JLRt3ZZFV44KhYODA/r1ZemA4mIigdrHGDyEgGprXL+ejJs3s2ik7saNrCmbe+LkUSxf+qoKbLOyMhF9OQrz572sqkLYmKC9m2xMYMCWzRdM/WxPnD6Jdm3bop9vf6WMbsFfXeTRZoPZOTm4GBaKl+e+YhGwPXX6ODp26oxeXt4U2ObczMWF0CBMmz4fDx8/RkJiNJYsnGaRrfD88/OxYam/fz42W98oLxhbix4TuVCGAmzVHJBWVoiNikJlWTlGjX3JVI4sBJBSDW1tLT7585/w4R/+TV0wVlfP1KAx7LyaQde9jBYEkZIn0KY3+/MAW2Zm+Ll2bd+GwcOGw8Wlm0WsrXqN9bC2oUHnYWtrh/6DBwvSDxE6adJbbvjU69LLV5WmMgqeRHYRgL5r40aMnzYd7Tp2qBfcGrPdRvJkua3EaZIOb+nhNBtw1QMEsC11sgziUhOvIjUhEU1tbeE7xB/tOrAVdlXWq9TaMIBbt3S4LvDL993KykF0UCg6OHdGv1HDiOMMvyITprk+P2FmtORFCJEZF/cYvRe8DcQ+MX2j9EylXEKEefJ7bQpiuSRTg50i2NW+X49NRHZqKgYEjkKbDu11AaJEX10eFkqOmmwEdE22Kca/uqISB7fuwIjxY+HSQw7sYwo4RF2JBsq7Wwhsu3/8fFZ/0z98PqvdDTBF/1KHVFTdx77dB6iUkMuRw8JC0bRpEwwheeCFlFA0ToWVFSKjIlBdVUVTA8m2kBlVeeFXm61KE1cBzYrlT54+idatWmPQQH/LWNv/HwDb4yeOoVOnTujbh0iE2eMjTuj5b25ryP+SkmLsP7gPK5atqjMQFK+npqYG3234Bqtff88UCEu+uTLrmpWVjvj4GEyePEsGGeyiFNcUQY2lSm2NfWvrY2uVarVrVGXNYq5d4HZpES6GnMKE6a/Q69IiJ4t5bBkbW1VVhbNHdmL87OUMtCoBnHgeWipXTk9F8a0ceA1ikY+N0gJFHNkKn8CZaKz42TL/WTl9UOWdYmRcDoZ74Ewmj9bnsdUBdu1+tdmIZnU0sEfHfjXdD891zv4nn9wOn3ELaOocGjBKAbQE9N4rK0V6dBAGjJurgFoGbu8U5OLmtXj4B05VZcf5GSm4U3wL/kPHqIztkT0bMGXGQjg0a8akyWFnqX8qAXoE2F6Oughb26YYMGAQBbYpyQkoLi7AS2PHU2BbXFyIM2dP0Zy1PIVYWtp1JF5NwNzZc8wDW3Ww4Is2pgGiSDRxkvJq1gzybOr2C88nb2PSwvfu3cO6DWvx9hskX7W8oCBKkXnKn/0H9qCv7wB0dXahwPZGRjpiYi5j4sSZFNhejgnDq8vmWDTmez0nYJv8Atha1D//lIU4sCWDAge3JUVFCD13FnMWiMEu9AFgNB8G8u3Qj3vhN2AAunZToikrd9ugtD+8pcwsOZtbia4L2MpASTGCOq9QEWyJdUnfhULka+yVK7h79y5GkgAWQg9zYGGJHFmDXgqYIwP748f4/OO/4P3f/UEOPMEHFcFfUwghoQAlmWmVGWEZ8vBrJoPTkb174ermjl6+zI9CbjNt1Znfpil7qxzBJxnSE6+vT9cHQuMZnNm0ZesAuBnX05ASF0+Zb5+B/dHFtdsTAVyxTXgb6NlT9hjwCYqwIq/2izZ5kcqR9EAJSbh6KQruffvAs39fQVZmHGDK9BrEJ0Y3oVNbSugdk/6Q3xSz743k7i6/dSJbJE4oyOnld56zpHSPslAmSpnZ/ocPHuLCwaNo59QRfYcNMQkmJUdA1kmQVUbYDLNrIFfOSL2OxOjLGDdzOuybNRMANLtOdh/Cd7VdBYBrBXRvahlj2+NvzwfY3vjtC2D7UxheIkX+5C9/x/Jlr6nAtqiokAZMmTNrLpMj83eBLwZbWeGbdV9j9atv0FzRJu+QBawtZ2QkE2kFpN1IQ0JiAmZNn2kZsOWPdl0vv8moa7qBSJG/WbcWb1EZtMHH0BaYKoGU4VQ6gew3K9gLBQSePHUCzs7O8OpJIg7rxkHhtwhsc2/m4NKlSBp9VmNlBeZWZLlIOpriQoSEnMfMGfOMgbDI3HI2sbYWUdHhaNKkKXx8/MzLkJ+ArQqo1/kAACAASURBVD194gD6+g1CO0cW8EqcX5DvhhJdEXgrZW4V3ETK1RgMD2B+v3UBW7Lv+L5NeGnmUpVt1YNXEhk5+cpF9AuYYuCHywBq6pVQtHB0QpvOrkIQKh4gSgO4NKetay80b99ZDR7FpMiij628oKu1hTgfFQKccmCrsLUUJFoB1cX5KM1MQQ9/BkbJdipBVv5Kc9JQebsAnn4jVP9bAlBvpafgXsUd9PIbpuawzUqJhVXtY/QS0v8c37cZ0+cug00jawpkIy+cgWu37uiu+NwSYNuyZUv4+PSlwDY5OR4VFWUYRaXJVigqKkBExEXMmT1PBbbXr19DRmY6Jk+cpOa21ewUJ400O61JjJX5ifI8VFffw7YdW7Fy+auqAkGSF+t8tvm+zdu+x+xZc9HMrpnMnHMfXb5QUluLPXt3YtTIsVTuTIJHXUsjUus0jBw5jgLbS1EheP3V+RYNzb3+8nwY26TfvWBsLeqgf8ZC6XfLpeBR3M92+8YNmPfKIpq0mhnouv1soyLCqax1ZCCLpixOCdmx4jZTX1uR/VGPrUNLpd9Vr31+Rn62oh8rOScJwb59y2asfvtdYzmy4ISqAib+INTD2kaGh4Gk5xk6iuTAlKWvdCIgSFXZb3WripC1cwpwUWL6ZCYy7Px5miNvzKRJErTV2leT2Wq3YQSUzDO42rWKb0QDZcq6ykSwnZeTg9iIKNoyPX37wMWN+DppbWS6+CBLckSAK7at7GOrtSuXtkkMsRnwS46KCw1HfnY2evbvh66ebgYTFrlvjPpYWrRQmlPP5GqtbAp2LRqT9FInvbpCedvl4B1GgJa92ZxB5SxVZlIqUi/HYOCYADh2dqqHpVVqUMejunxsNRcLDjLI/8hzwbhXXYXRU5i/o5iKiA9cIrPMRy4jRs21aSuLpMhunzyfZO9pv3k+ye0teq7+hQsRYLtx3WY6MXXr7kaBLPlbs/ZzvPHam2ja1JbZTP6cKqxtUvJVmtt22pTpmo00kh6LvrYGPnS86Tg4Jot4f/3kY/zxo9//jMC2lmZQ+PjTT55LVORjSuqSvj7C4qAgwaTjpS4AU3Z2FsIiLmLenPkaGBQm5ipIVLZl52QiKipSArZiWhMRUPNjScUHD+2Bn98gOHV2VtxJDNhZbovqYWvLyu7g5PH9mD1vGbPwgtyZ2wQR2Ir+tSJgIdvz83OQGHsJgeNn1QtsSfkTB7ZQKbJds+YmKX8463py11oEzl2FWlgxJlaIakzY3YKsNBTlZcLDf4zZ6MkEMFdV3EVa+An0HDOXsb/KnEf8TttYaQT1uzCOSPNM1cWFSY95dGRigfKvRsKhZVs4urjT7QzQaqxtVuwFOLRsgy5ujGHl+zPiI2Braw+33v1UH9vES0E0ArKbpzfdVvvwPo7t24yXF61mx1pbIfjMYfj49EPXLi5028XQM+jUyQnevfvQcSM+7jKq71UgcOQYCmwLbuUh9GIwFi5YrALblJRkCg6nT5uuAVv93FqQd3Bgqz4DwkLHV998iaWLlqFZMwetPYUFE3XaqhxDfm/Zvpkyyh2Ir7koCTcAtus3rMG8OYto/QTYknEvMysDAQETKLANvXAa77611CIL0PuvzydOxNWPnk9cDH2jvJAiW/SYyIUIsGV5bHnAC/b/7LFjcO3hBo+eXhbJkQtv5ePk0aNYuvLVZ+dny+bBZj9PpKZ6VsDWQAK6a/tWBI4Zh/ZE+srBhXrVcqAIDQxyKbEp7OArzGTP159+gpVvvo0mtk1VoyZ60Wqr0QJw5VJpPiip11Q/uCVFk+PjkZyYiGlz58K6cWMD9tbMirsRA863mazci9crd7EoqZXay0ScrNsr4GsR4BYXFOLyxTBUllfAb/gQOHdniemNJMr6xQEKVnn7GUQvrkuCLEp2RBG5KIWurqxCTPAFEMmbp58vlSkzgy2DbG0CY9DPyk7p2oUmFX29eT1POlQYvYYcEMqvqeWAtqaqGrEhYWjcuDH8XyILYhpIpXUagVfBX9eQxVUONPLLJZ1+aNtOeHr3Rm+/foYBooxYWiNZMr/nbhYCW/e/T3jSJn8m5a//+sQzqedFJXILEGAbcj6CRjOdNGEKrK2t6eT3zLmT6OHaA56eHkIAKS41Zs/3zh+2Y8SwkejatSutVIqSbCZCcp1yZOXlJAGkAkeOgkvXbpb52T5Bp8pLlyq6oG4sn3z2KX7324+MazMZ99XBVGcttEi3rITObqr1aOoYwtg6Ojqiny9hRZWj6gG2JIDXpehIzJlFGFgGkqSorjrGNvV6MtKuX8OECSQ9Cpdgyr659GqFyLLk+3fffYnFi1+HTRObOoGttjgtXIswnpPri7p0gQby9PEdaBg0ip1fPl4F6Lqo0Pn52UiMjULgBCaRNsfY0n0AQk4fgFsvP7Tt2EWVGouRkcn38LP70cNnEFq262gYPZk8I+GHt2LQ9OWqxFiUM/Pv5H92XBiaOrSkzC2XtaqBpIT71NtD/vBxRYMIcJlPLQmOyv4/rKlEVuQZeAXMUECtBmh5DtvE8wfg4TccLdu2Z1JkJaBUcuRZdO7qhk7OriqwjTp/GJ69+sGpiwsFsfcq7uLi2cOYPmcpkytbW+Hkkd0YMjQATh060d9BQcfRnYwTHr0osCU5bB89vI+RCmN7K/8mLoaH4hUSjI6MCdZWSE5JQkb6DfPAVlnlIv/4c60+G8KzS3b+sHc3/Pr50WsQn1/lzVOfWY2XqcW+Az/Cx7sPenR31/mNa+8D77Ovvv4HXl35Jho3tqHANuFqPPLybmLEiJdoup+gkBP44N0VFo1A3h8/H2Cb+OELYGtRB/0zFuLAlkcapQAXVki5ehU5GRmYOG265BfLHfNFdoZPcDetX4dXFi+BrZ2twsqwOzYnR+aTQ+2/ebmjubbjR5gYXv0BFgBbPljqsJhiZrUKRVBEtl4MDaGHDBs+sk45slq/ChVlYy5BFuWG4mNjcCsvD2NpKgczrK1qCJ8c3GrtJguLS4qLsW/bNoybOhXO3bsLV8yZYc366tverERZaAD5GGOQawnAFdtU5azNAdzCQsSER6KirBwDRg6DU1dniwBuXQwtZ/AbDn4ZiC0tLEZc6EVq+PsMG4zWHdrz6Z2yoiouZ+gmccITqi10GLWM6TNc75gkBIgSy2pvqizNFZlODhL1DC054mpkNG4kXMWgcaPRyaWrWZZWHylZG3805leLQCtuk/1xSwoKcXLffkyYPRuOku+uMPoIEwNl5DLv98hGNnRr2tIixtbj04n1NvVPUeDar47/FNX+4uskwDYv+zY2b/4Wr616S5UjX09LxY0b1ykjy/xsTVlbEl306PEjWPzKEs23ljO0TyFHvnDxAk05FDgq0HLW1oKeNLStykBz//59fPrFZ/joNx8a12QO2Oqi7tPRSjqRuLCnmR8RCJLgUa1atsSA/gLgqwfYkhyeMbFXMGvGHIuAbWz8FeqXGzDqJcNUP6oUVgC2d+/ewalTRzB79kKLZcj8/vmCpijD3r93G8ZPnAFb+2bGMmTNFJsPHKW0b35eFq7GX0bgeOLLagxsRR/Xy+Hn0LJNe3R16yWBVtHfNvlKGGya2sG5Z1+T6MkcBCdePAEnT184tGlvGmFZ9c1l15Nwcid6jZuv5q5l8mdhsVeSXgsPjkEwQwpqhYBR5HtuTAhaOXVDm04u7L3VyZAJkCSBowLmrdb2KYGiok/vRZ8hY9CqFQsKRQBvyJEdGBI4Ga1btaHbiL9twuWLmDBlngpszxzfixEjX0Lb1qzM6VOH0LuXDwWJBAxfjg6nITeGDRlBGdu8vBxERIZhwcsLNWBLWM/MDEybOk1lbOUFL033aA7Y0vlKbS2CQoNh09gGQwYN0RZ3hOeIu0+JwPb0mVNo184Rffv004Ct5Geu+E4/eowvvvo73nnrN2wh43EtfeeKS4oxdFggHj2qRciFU3jvbaZAqO/T52/PJ2Vd/G+fT4o+fXu8YGzre0IM9t+4W4ZaZUWLJvxR0v4QCezubVux5NXXVJBanxz5yIH9cHf3gJd3b0l6bAhslfmkCGXl8CxaoKkG3JbpIT8BsOXQgSRvP33yBF5ZvPTZyZEFXLLx228wc97LaNm6Nb0vPbAWWUPtMAEs1sPcmgO33O+2Xfv2GDySg3axNG9mU/9bdh2mUyL1qgwnPMYAV6pJAq1CIxlAb22iJF9JYX4+4i5dRlVlBQaMGI6OXZyUCYPYZoKPrBreXttvnCJIOMYC/1veRmxyxOq+lZ2L+LAINLW3g9cAP7ShwbwEUCv6V6nNL7DK6pNvKjs217riyyL2mClLq3MYkCSSgt+sABDpVrWcFXKv30BK9BV07u4K78ED62BpzaUGqkd6LPnTsmtKS0xCUkwsZi5ZZCg91iYHsu8vbRdREqp53/JdcLEQ2Hr+gyxM/fyf1A+O/vwn/QWckQDb/Ny7OH7sAJUTuru5K361tdi8dQNWrXxdAbWCXznTvdNn8HzQWZpH1X/gIPVZUp81ibVV3kIL5Mh5+Xk4duI4Vi1f+YsAtueCzsHezg6D/MV0P8oox+2LTop8Pe0aEhMTMH0aC1LEAQD9rmNdybaISLbYOMh/mMXAVh84SltsFCMoa0DNRDosALeSkiKEBp/CtJkkGm1dvrSKlVDvSbk3ITULlSLnZSEp4QoCxs0wBLYc7HLgStL9kMWLnr6DDSMjk7bJy05HXtY1+AwdJ4NfQZacnRqHmqpKdO0zRPWfNQkipUiYS7LTcLfwJpz9RrJAUpIvsGYNRfDPhxyVqdVeGyV2DGNrK4tyUXwjEW5DJ1LQKsmQFVa2igSyiglF/7GzFbZWY3TP/7AW4xesho01850lfyd2rcPUl19FUxsbClILbmYg+0YKAsdMUYNJ7dv1HWbOWojmDg70mOPHfkR/P3+4dHVlebCjLqKJjQ0GDxpKge3Nm9m4FBWB+fNegTVRVFpZUTkviYw8dcpUi4CtGtuDP9vKdInMDJKSk5GSmoIZ08hzYFkAqfCIMDx8+AjDho6oE9hWVVZj09bv8Nqrb6vANvryJZRXVMB/0Ag8fFSLM+cO48NfvW6Rpej7yfMBtnG/eQFsLeqgf8ZCHNhyQCum/dmzbSuGB4xGF+eukp8tne8ZREdOSohHfn4eXho/4Zn42SrzyjrlyBa36ZMAWwEviRN9jZXTCvBtW77fiBmz5qB5ixZPKUcW6lYqJ1Koq/FxmDZ7rmEgKRX2CABJBDHMsMrgTgPDImxSkZIESeOvXEFSXBwGDR+Obm5uZnxv1VHTVGJWF8A109Yq+2rSwdpKvna1QiU6gMsnFays3Aa3bt5EXGQUbJo2gd/QIWjeqmWDAC5vazZJEg2vAIR1bLvYJ/J3dkxeRjYSIy/BpkkT9B48UAC4wn2Y9Lc2uZHbxvR5NegS41dJQLd6PYW0KMWd/RTwpwe0Jfm3EBcaBnsHB/QPGAE7GrCJnVIvG5ZYWjPSYw4CGFYQg1LJAaounQ+hfuojJ4yrU3psnoHWgXnFR1i5cLg0sYyx7fnZFIuHqmdZMOX9I8+yuhd1KS1AgW3OHVy7nozcXJamg8qRra1AoiO3aNGcSmQ11pY/l8rzagX87dOP8at3f43GNo0FVZT5CMmWyJE//vvf8P7b71IfX9GNx8iVwNLONF2e1KTIxIXis6++wIe//q1xdSYLkYKN0VVsjrFVzJc2giu2PDg0mLoxDB081EIpci1Sr6WC+CpOncJywfJFRXPANij4DFq0aIW+ffsbSJbloFNcvRMbexkkt/DQYaOeWoZ85XI4vW9fPwG8c+mz0pQqa6wDsdQe6bbl3cxCcqIGbBlwZMBGL/0lv7MzruFm9g30Hz5e9XvVB5CqrqpE2Mm9GD5tiQJaZT9bGmG5ohxxwYdp2h8OVMXIyJq8mV3ztYvH4ejmAwdHEkhKuT7tsdNcdXRMv1GcByJBpvNbKyD1zB64D5+Cpvb2lMkV/Wo50C1KT0Z1WQk8+4+UAkeRoFHJkecwdPxcha0FHj+oQfDhHRTYcgY3Ky0Jd0oKMHTEWFWuvGfneix4ZSUFv6TckUM/YNiwQDh1cqJjRkRkKJrZ28OfREluZEXHlMuXozBv7nwV2F5NSgTxEbcY2PK+F/xk+fNeUlKKvfv2YNXK1ywGtiQ4XU5uNiaMY4HHGPsrPGNKtOvbpbex78AeLF2ySgW2kVHhqKm5j/4DhlJge+r0Qfzut29YNAT1+uNOi8o960JJf1rwrKtsUH0vGNsGNJsIbMXIyGQgiI+OpmG+h44YZdbPlk1M2cBRXlaGw/v2YdGyZbq0P4xl4WXF/xqbo8Yile5CmC834O6EQ54hsGWGVua/gs+fQ4uWreDbz8+YtRWstgqQNRuvA8Mic8gKHdizB/5Dh6Bj5y6GkuQnBbcS8BXQnzbXkEEgSVMRfPoMHj18gLGTJ8OmKfH5FUuL3fOkDC5tUbU6eb5jjmd8GoArQ9zczExcCYtAq7Zt4R8wkvpFiSwqX9VnVyn7vmqLHYIXLV8150sAuvRLMvjltSq3LzC9pG6SIuh6XDyqK6rg3q8PXDyJf4sG+0XgLvaY1I/6CWQD3iSjibFeicHea5mhvV1QiGtX4lBVUYF+I4ahXaeOwtigMKRCkA/TfLcyUJVAsC7/rT7q8ul9B9HF1QXefgrAEBlXgUVWV/k5EqiHpeUohBzXtUkLi6TIPT+3LGdfA7qmzkNS3jv0rKt8UR8AJkW+g9rax/jhh81YuuRVFl3V2oqOkTt2kaijqwxZW/7eJCUlIv9WPl4aO47ZRmGhR7WLRoGl+HOsS6VHjjl89Ag83NzR09NTSnD5UwLb77d8j9dXrTZ+LuoAtsKQT4+Vga0yRivH83FOlCJHXorAo8ePmZySl6tTilyL1NRUGsSGBMGxBNieO3cSnbu4wM3NQytv5GvLr7+2FmHhIWjVqg08e/auA9hyf13WbCpAENqB3NKJY/swwH8E2rRtJ8iQBYZNx+Jq7WDECNciPy8HN1KvYkjABJM8tqIEmQfIKi0pRGJMBAYHTqEAWPSNFQFu2Mkf0XfEeNhIKX145GN2XFzQYbj6DoVti9YmqYFUYKvMre7X3MP1sBPwGMVy6JLWIv+VmYLCtGu2U55byotD1EcVVihOi6fvWEf3vmowKSMZcmbsRbRs6winbj3VFECkXEnuDdwpyofPwFEqiK26W4qU2ItKDlsWZCo9JQ4kWrhvP39azrq2Fof3bcW8+cvVSMqEsR0xYgzatW1Hz3Hp0gW0bNECvn39VGBLgCxxaVAZ25Qk6pY2btw4E8aWzZOVRVjyT6dGoC2oA6Jr163FqpVkjLJW3J1Ev3bhu/LyZWVlIS4+FlMmT68T2BbcuoUL4aGYPnU26+fHtSDAtrbWCj59+lNgez7oOH79PlGW1P9xWfC7+gv9BCWydv7lJ6j1yat8AWyfvM1AgO1j1Q+BebDxtD+lJSU4vn8flr7GjJbG0mqrz3xA4f50u7ZuxuSp06hslgJe5ZoslyOLQ9QzlCObAbZ8oBSbTgYL2h4RH+iBbVZGBmJjYzCN5AYTKjMCGCb7ddfGoZx4HUTufOroESxcsdIMayvLVWXGUwSBomxVuDoLwC25LZJKJ+jUSfQdMAD9/P0l9takLXXgnzeLkURZO1ZuDD101mE0xdCJsx9TrlcC8dJFyhLelPh4xEVGw62XF/oNHST5tqp9IkqBdbLgOuXJSvsyUKoBf1FGroJkKXAKMzC3CwuRGhuPmzcy4eHXB+59fGDDA4qJvaA2hb7lxIfSXA8YDyDa4pLIa2pljYIr3b5ViKsRl3C/poZKjjt1czErO2ZvvD5VD98qS5Lr86clg1RNdTX2bdqKMVMnw8nZ2TSVj4Wgtj4Wl1yhpcDW6wsWBffn/iS/e/DnPuUv4nwE2N7MvkOB7MGDuzHIfwi6de2m+toGh5xDhw4d4dPbm8WYUKIiM59bTfL+xdef4fVXV8POzs4saysFaRMVFAKFy9/RyzFXQCaWJCWI4ANQVwzGevvLaMzlg2NlZSXWfrcOv3rvA+N6zAJbIxWRtFaqjpPqkC0qVGqBK7FXUFRYhHEvjROALSstAl0NFANEipyQGI8Z03gOT5lx0suTj584DFfXHnB372kxsD11+ii6dXNDDzcPgdXS7lcPYkXQTu2DYldIyr9NG77AslffY3bOwPaI98bZWa2sBp55vSSPbZiQx1Y9lyLDVmW/CoNLgMmPW77E9MWKrJTnmK3lIJcFmYqPOIeW7Tqho6unQZApJifOSo6hDF5nz36qHPmRAlgZe8tZY1Y+L/kKBd/te7LgYGSb2A7SYofJoqo29yTz2dpHD5B8cgf6TllG57GErdVS/LBUP9zf9vKRbfB7aTbs7JtJgaPSSUTkpnbo0aufKkMuzLmBguwbGDJqIhpbs3oSoolioyV6e/ejoPXhvXs4d/oAZs1aqB63+4fvMXXqXLRu1YqWCb1wFu3bOcLX14/+JkHpiF/qvDkvWyRFflJgS5py3YZ1mDF9Jtq2aacBW+Vlk+YoSkMXFhbS2ABLF6+oE9jm5eUhKPgs5s1dqALbsPALNGo2AfsE2J44uR9/+MhMijDdSNL/0/P1jlE/RYHLvyKxCp7/5wWwbUAfMGBLHjkN0GqyZGt8/80azF24CC1btpLkyNyQquBVWRULCwmBrZ0dBvgT/zn24Ywu/y7+Fxlbbbte9Fh3dGSLbtsQ2CpGUFeBZcDW9Ni//d+f8KuPfk8laRpI4ZXXER1ZHUxYWXNA7OTRI+js7IxefVieWc78icfoB3ujfSJfKbF8FoJbYmzCg4KRlpqCgHHj0MXVVblJM2DKDMDVS4O1ltI1iDzfMZEUCy0sksiS8NgEIEuzNRngXrkYjvjoyxgyOhDu3l4/H3urWG3z4BcgK9nXYuNxLSYenVxd4OHbB63aO9b53IjPk9zGFr05hpNiUzDL3t6s5FRcj0uAnb09vbaOLs4Si8uDP2lyY83v0ESSbJB/VpIem4BhK+Tn5OL8kWOYvWwJ7O3tTUCtpf60ogBZBPbiyESu17lJc4sY215fzrCssZ9xqaR3DjzjGl9UR1qAANvcrNt0EhoffwWVleUIGKXkxLS2wu3bJTh67CCWLyVZAoyDSJF6wiPDaL7tkcNHysBWZHAF2ZJRsBjVbloBpaWl2Ll7J95eTSaNsmNuQ1lbE2CrbqhFWVkZNm7ZhPfeftf4wXgCYKsMf2o9fHKt2jAB2BIjmZyagqTkJMyczvxlteMFVY3Ox5YE34mMisDc2S9bxNgeO34I3Xu4w93N02Jge+jwXvj6DkTnzizqtQpIVabXiE1ldykC24JbNxEddRETp8yV6pEXRgV2TgL0xueoqq7E8QM7MGM+yWEqnI8DWe7PKgDYs0d3oe/g0WjRup0EWsUAUjczrqEwLxu9B4+Wg0MJwLWyogzxwUdUOTJpFzk6MmN4SRsw/1sg6exe9Bg2CY2a2FEAzVtJA/mayRfnm7Sg8vgTEHsz/iLsSXofVy8t9Q+U3LVKACkCgKtuFyIzLhx+Y2bKQaXIex58BK5evujg1FWVGGcmx+LR/Xvo23+Y4k8LRIacQLdu7ujew4MC2erKMoSHnMbUaVowqe3b1uHleUvg4ODA0gEFn4KTU2f0oXltGbCNjYvBXJLHlvvYGkRFlpSQdTK2GstPF+Brgb3799Iox2493JXnS1SjCQtPyntXWVmF7zd/hzdXk9SWwuK8lO6nFrm5ubgYFoLZs1gAMMLYXggLgbVVY/TtN5AC25OnDuD3H75pkTEZ8I8gi8o960LRH5A0m8//8wLYNqAP0mjwKAZsjfxsLwYFoWWr1ujbz88iP9viokKcO3US8xctrtPPVjTGeoMrTirFcg24Pe2QZwBsVQOryoVkyHDk4AF49uoNNzcmF+UfXkqUWpkAXxPWVqhbKUyCOHz7xWd4+zcfqqBNlLVqppEbOxOILLGQ2vUJsM8MuBXvnR939/ZtRASH0Fy+/iOGw5kCXHkaJONHI4glXbXUxVJpPnExX0Iwe9pl6M9YF8AVQX7N/RpcCgpBUX4BBo8JQPvOToq8x1RyrMp8hCUJPXtrXn78ZOwtbS2ljzJSriE75TrulpbSPLgunh5o0baNYvzlHhO6VdlvMl01fL30Ad0Eta5S3gqEnc3LzMK1K7Fw9ugBtz4+aEsiD9OXV4tUrPxUvVQ5kGXvuJ6x5aOA6Xa2mGYaROrq5SvIzcjExLlz1MU0TTXC3SFkyKopStSRRrlFoZwCLPSglhxhKbDt/dWspxq+Gnrw1bf3NfTQF8fV0QIE2OZkMmBbUXEXhw79iBXLVimMrTX9f+jIPgzsPxDONEYFB7fKc6i8FzU197B1xxa8tpIFUnkqObLygH7x9ZdYtmgJXYx+Fn62dQHb27dvY9uuHXj7DTPsy08IbLNyshF6gaREeUUDtsoAycc7lcVU7GvuzVyEhJzH/JdJxGJmraR0P7ptZHHCzd0Tbj0slyLv3rMNAQHj0Lato6z6EcA3vy62iKmPzcC2JSXE4F7NPfj21/nXSoqfJwO25Lw7vv8C85e9awpsFUDJJMkayBQjI/PtelnyvepqRAUdhv84koOWsbgcnDLwyoBqbNAhOPfqj+btnIyDSCngnB9fdacUmVeC4BEwU/EFZi+lxE6rlkj7olkP4FFNFTIjT6NnwAyJrdUYW429zbkaBRsbG7h4+ZkEjrp0cg/8x0yDva0d22dthaToYLRu7Qj3nj5qSqCQU/vRt99gClRplOSSAkRHhGD6jPkKY2uNzZu+xrKlq9G0aRM6hpw7f4IqPnr39qa/SQAyIvvlwJbMzVNTU5CWdg0zprM0RXzBTLVcXAlCbl54jvmcQUxXRfafDTpH/XoHDmAB7PQ5n9W4LHxuWluLj//+F/zmV7+rE9hmZ2Uh4lI4Zs2cpwJbkpPXpnFT+PRlUuQnAbYDGZcwqgAAIABJREFUP3s+wDbq/RfA9l92EkCALWFsxTy2XIpMXhyS8udK5CXMXvCKRX62pCE2fLMGc+bPR+s2bQQ5spjfkjWXOlHUR3yU96or2U/VyA0EtmwQ1T4qIDUAgPFxcbhFg2dNND6GjzZGdZqRI6vnV04cGR5Gg+EMHUVeOo0FNmJhjRhdrT5Zjmrin2nIsppKWMlgWJCfj0sXLsDayhq9fX3h4kZyo9UBcAXDrO9TI5FsQwCuWo8EiPWwlptI8XJl9pakibl4lqQ8aINBASNg06SptqigGAPV0CqNS42ucmOqMbFYfqysogsTNHF1VLhiFeCS9EXZqdeRn5WNirIymj6nI/1zRiOah9hUnq1/rs29W0Ysz+NHtSjKvYnC3Fxkp6bBrpk9unp6oLu3F50UsHdb9Ks39aXVB4hiq+um/rTmtqsR2pXjIs4GwbqRNYYEBugAtXI1IuulXiFf1dfuUpwQsWIytNcDfUuBrffXs59q+GrowYlv/djQQ18cV0cLcGBL5YvWVtix83tMnTIDjm3bUVBL/tLT05B6LQVTJ0+Tg0jxwIsKiiX5Ift494GHh0f9cmQxYrLyfOpt6aEjh+HStSt8+/iaBF58UtbWcPlL3ViL4uJi7N63B2+8ZiYQzE8IbIuKi7H/4D68umKVxcC24FY+Tpw+gcULl1oEbI8cPQAPDy/06OEuM7YKi8X8FmXmc+vW9Zgx42U0a9b8CYCtzJaRZrsQcgbt2rWHh5ePdH/Soqlg3yyRIpMyP+5Yj0kzF6FJU1uNIVbAqOpnq9wXAZg3riXiTkkRfPxHmQSQEoM+hZ3Yg57+o+DQijG7Jv64qEVhbgZK8rPQw4/UpQHgRwJDTK5RTPFTmpuBwvSr6DGcBC0SgbxiDfkzZuhiAqSHH0dHD1+0dHRiwFbIact9bOk2KyDu1G70HjYBDi1aqYGjCIh9WF2Jy2f2I3DmEhXUklQ/UUFH4Only3LYKlGVTx3chsCxU6mfNQG2Rfk5SL+ehNFjiFyZRVP+bv0/8MbqX6FRIxZw7vSZozSyulfPXmaB7bVrqXQ8mTVjZp3A9s7dO7C3s6eBJ9VnRXhO6VSkthaXY6+gsLAA418i/tYWAFvU4utvvsSSRStg38zexI+XBSCrpSmJoi9HYcb0Oew5IIzthSA0aWILb8XH9kmArf9zAraXXgDbf905QMT1NLRp3446kJsDt19+/FfKEjZu1MgiP9vw0BCaI4sEO9IYEzP5bBWrLLMhkqlWpqFPKUd+UmArzPyNgC3DKTIfWFFejs0bN+DNd983BLaS74/wyAjzBN1xQv2CAVv35efU19a+WTMGWpTRXoRt4oo1PxWvTTufZeBWBkHyPat1KwD3cng4CJPbb9AgeHp7/wQAV7BsZmqX+8vIZ1cPcJV7kiZhcmump1zHhdOn4T2gP/oO9n927K3SgeRsssSMX5MMtNVVVEGQra2x1KKyvBz5mdnIz8rB7cIiGnG1bccOaOfkBPsWDmjeuhWNSCx+DCevCmivLq9AdWUFykpuo6q8AgU5ubTe9l2c4OTqis5urmjm4KC9ozpAy+feHBCaY2n5G6/60Kr11BU8ioHhQ9t2wru/L9x7sTRjmj+/AlNNGFcOtHm4KTlwXV3SY95unI3uYuNgkRTZZw2TE/7cn4Q39/zcp/xFnK+8sgapSblo0bw5dT2JiAih0ncS1ZQDW/L/m7Vf4NUVr8HOjsjilTgVKrPCnsMbGWlISkrC9Klizng5AM6T+NnGJyYgIz0dM6ZNlwJImVrV+ruqPmBbWFSIfQf24/VVZlJ3PAWwNbGXOilyZXUV1m9Yj3ffelebZAvjKbfRokyZ5KQ9cGg/lYiLDJY6sdcxtoeP7IenZ68nArbr13+BZcveQKNGjU2ALR3n+X3wcxmwZaTcsSN74es3CB07dakT2Kp2XTB1Rn68fNvR/dsxdNQEtGjVpm5gqwDMkuICxEQEYeREkpVBz8QqvwFcT4ii6X5ce/eXJcsKgOVAN+LwFvQZMxs2tnamQaSU9uGgl0drvp2fiYK0RHRw74vmHboILLe4pK8tO/IxvDQjGferK9Cltz/LB6v41pJ3sRGJikwWoZQ8tjUVd5AafhoDJ7yMRkSmTCXKLHJyaV4WbmWmov/IiWhMyis5bIOPbMewQAJiW6vbDu5ahxnzlsO+qS2TFWekIj83E2PGTKKsLmofYfOmNVj9+vvs/I2scPLEIXh59YKnBwtYRVNGJcRhzqy5VIpM3v/r16/RlD9zZs1WojxrY4SojvrDf/wR48aOw6iRo8wDW9QiIyMD4ZEkpdB8Fdjy+SSbU+ikyajFpi3f0wjwjo6OhgtDpN/S0wnbHIMpU5iLAAG2ISHnYGfXDL19/J44KvLgz4PrH6h+ghIR7436CWp98ipfSJGfvM2wZtt2GmSFs7QiW0uhrpUVzhw7ih4ennBzZyvK5vLZskmsFUqLi3H21Am8vJDljRQnrXrjygcg/UqyKBhUj3nS5WaD2bupoTZgIflxApjUbaI/9QGkyLbdO3diVOBotO/QQWPt1OsQGVaN0zQPbOlZlHNpN3Mj7TquJSdh/FQSkEaTshr6zoqTAYG5E8GQCRBUTiWW0a5EawmjSQ9pE9L/MZGRNIiJe8+e8O7fH7ZKoCOjdpTvQX6IjRhcoVXURpSvRX+UNkAbXb0JzBU6hH/lk4eokIsgUZT9hg2hvsXi4ob2PIgGoS7pMrsTNtnRFgy0lXeNCZD7VjlOfT51d6B0HPlXVnobJbcKUFZSitLCIpTfucveX2trtGjdGo8fP4KdgwMePXhIg1E9vH8f96qqca+yCjX3qunkrJVjW9ja21PWunXH9nAkkY2F95o7M2nvuimgFA2v5mersbSmKX4E0GkmnU91RSWO/bAXo6dMhGPHjs8U1IrMrDgWiaCXNIGlwNbrT88n3U/yH1+k+2mAWaz3EAJsDx04jQH9/SmQLSrMR0xMFKZNnaVGRybvWUTERepvPsCvv5yWSnlZOGDduPk7LFqwGLa2toasLQ/OyGysdnkS4FUMJQnotPOHnVi14tWn9rOtD9iSqM7ngoLwynwzqTGeMbBVR0wFxH3z7RqsVthivS+rEbAtL7uLg4cP4JUFSywCtidPHaVS5G4u3etmbJXrIf7SJEr2/PnLddJOLYetCjqF+YXI+nL7dujADox5aRoNYiT5lIpR9lUwK9SvVMCVQ5rkmlmR0PPHqXTWsUNnDdhyCTCXICvBmjiDe2LfFoydydP5aOCWBn1SwO6d0mJci4uA78hJZnPeEgYv91o87j+oQRevAWqaITGIlBgFmQeNItvuVZQhNz4MjZvYwrn/KIm9leyRwtzeuHgMbbu6o01XD+ZXqyyW8vQ/Wh5bxuIWXE9A7eOHcOnVX/KvJUA2OyUWjRtZo4dXP7qP57ANOrQd42csUplYq9panD60HdPmLFUjJ6dfv4rK8rsYPHgE3UZSBB0+vBfzX15CIyCT8ePs2ePw8uqNHq7d6fiRk51B88xOmTxVBbbpGTdoVO9pU6bUCWw//MNHmDxpMvXb1zO2bN7KnoO7d8uw/+B+LFlE3gVhDsKfS91zRo46dOQgfHz6wsXZxTywvXEDqddTMHbsRBXYkhRoBNh6evlQYBscchIfvLu83nGWFBjyxfMBtuHvvgC2FnXQP2OhzzZsxMjxL8GhRQuVsdUzt0lx8cjPu4lxk6ZY5GdL7O53ihy5TRvm88eYW12OPilqstw6hsCWV9SQhjQAqawaY+ikgTq9qFYApEIhbr8vhATTmccwMqgI16kBJG1Z1Wi/oFaW+GD99Rw7uB9uHp5w9/Jid2FwLaIcV2aXNRDFrYOl4JYbXd528m/thsmAWl1djYTLl5FwJQbO3brB268fOnXpLPWeycTJUAJdTz8ZSryFe1TPKANcfQkRHortrV8wqCyvQOjJ0xTs+QeOpDkjNfmxNsHgjIMx+BUZWguly0pH68G27Lst3IXUuNo5Hj18hOrKSty/dw/379XQ4DWPHjxgslsrK5rKyaFVCzomGH3Ud9PEhUB4a4UUPvTNF2ViYgoTZTFMlQDrJMnslTf1s83PzkF08AVMWzQfjay5kkQWEnMwoOEAS5ha89JjDs75MESUCT1atke71oyxruvjvIz5Mf3cn5xNkT/3KX8R5yNS5C8//xYzps9Tgey3336ON1a/gyY2NpTFJUxLYVEBzpw5hSWLlqq5NNUoyYK64HzwORoV1cenj7Ec2VzaH22lRXLZ+XLNV1jyyiLKJOmjvz3J+nB9wPZG+g1EXIqkfq6Gn7qAreRKo1gU/Zgl2m1xIU8xo2vWfo2FCxahBckfr5+QC3JhbiMfPLiPr9Z8gffe+bVFwPbEySPo+v+x9x1gVSRpuy8YUEERI+aACUUFFUQUjCjmnLPOGMbJuzM7m+69/93/3zQ748zoqGPOOQcMiEgWBQEFQYIJFQxIFEFR7lPVXd1V3X0OB8X17q7MM3LoU11dXfmt9/2+r2VrytpKm3/OJlcJoaLO+STMy7p1K7Bo0eeWA1tWTgV0SHWxZdNKTJu5iJp3vCmwZe9PHD7FRIWglm1tdOjsygFb41i2zN429OwRODm7okGTFioby7G3zIY26MhW9Bo8HtVr2epZWyY3BhCy7xd4TVwkxLTl7XcZoCZNyvImn8n/ufdu4F5CFFp7DEHNurJHX7nzEdD4KD0RefduoEHbLmjYtjPt/pStVSTI0pqiAltZhhywD537+MLOvp7OcVR88HG0dXZD4yYtFGa2pKgAUWcPY/jEuYp9bfHTAoQGHMa4yfOUdInxURQ49+rVR3ImVViA4yf2Yc7sD5W549jR/XB390Sb1q3ptVu30qn3bp6xvXnzBvWUPHXyFBXYctFK2OHwF1//CmNHjUZ/nwEK6yqoBOT+Rpw6/e3bv+I3X32j9G1lr2Mwdsh3/qdOoHnzlnDp7GIS2Kalp+Pq1TiBsQ0NDUJ1m5pKuJ+z547h6y9NqDw0E0nfn0LeyZoS/qnPO3mu9qHvGdvXaIYdJ/xhU6smuvUkzqFUz8g8c1tEToA3bsLiTz8TFl2VvZUezP4mn0KDzsHGxgaeffvq7GxZWv63jHmFN6hUcPtPArZZmZk4c+okZs8jLtHVHwE0GSz2yiULbW1J+uV/+R988dvfm3QkJYB2nXTacnBLF0XxH+HdjC042UZFek5acjKuXr6MkpISdO3RE527d3sLMmW1kfn6VoquNEcFAK7QViL0vZN2E+dPnoRbH0907ummLiIGDKzCHshlUBw1cJ4wVfZWKrG0kJtg5OUG0YJc8WBEvy0VAbvRhMHfw1ND6hjn71IlxuoplcLIGtrZGrG0vE2uaekxnS9IOIWL0Xh0PxO+48foQwgx10/yht8Ui6zOPSrYVTli09Jkct+D+/cRF3UR2Y8f48tFH8OhTs1yZ97uv7ybYO/xi99NcPtyK+RfPAEBtv/3//wFkyfNpKCKbEZPnjwCly5dqa0cYWHoRtrKCus3rcHUydPhUNdBE9dWHlNWVrh7NwPRly9hwjjJFpupovh10UiOzB+2KH3aCjh89Aic2raltrvK4izXeWUC24RriUhJScH4ceONW7QcYMvPV3TW00xZgirKANju2L0D3l79qIMuY2CrSirZ4e/yn77H0sUfU3MpOsdyXl2lv9Vr54LOUCdQLi7dLQS2L7F27Y9YvPiLNwK2pEyb1v9oEOrHOIateojK1l1p7WDvw+qWXElPuYaHD+7Do+8QxQmTCCC5sDty3Vy/dhnPip6ic4++hna2Urge4Hp8JKrXqo1mTp0N5cgMpN64EgXratXQlAv9I8iPKaCVwbbcRtQrsrw/el5chBuRp1HVxgY16zaksaOfPs6ide7QrA0atesG66pVlT0pC+/DQv1QKbJsV0t+P8vLRnr0efTwnSTLjyXgy+TIwQc3YNC4uahevboSAijnQQZuJsfDZ8gYJV3u4yxcjQmH3yjJAzK5P+ZCMOo51IOLiyu9lp+bjXOB/pg+XQK/ZK44fHgP+nn5oEULCTgTYJuQeBUTx0+SQDn1lHwLUeQQaep0+jeZW9hcwdZGcmXZ5x9jwtjxGDRwsFlgS/r5ylUrMHf2PNja2sl7PFFtxuqbjZ2Q0BBUr14NHu6SQzPt+CFtSCTOl2OjMWbMRIWxJV6Sq1hXQzfXXpSxPR98El9+Zlkc234r3g2wDfvkPbD9l12mgxMSEHc5GsPHEo9x0paUOAHiQS65vnPjBgwbNRqNGjPJH79BZYwsk0lZ4WFWJs74+2POwoVm7Wz5xbg8ObK6GX0Ne9sK2NgqQMgEGFbABIcS+PV4xfLvseDDxdQGVgQebNHRwi75OutFvK0MvaYFodKVlOQkZNy6hUF+w01Kkt8E3EpPFnca0isbl98SgJv96BES4+IoyO3WsyecOnVCk+bNBZBrCMdMtJ9h+SqVwZXr3gDgsrqIiYhEevJ1+AwfRiWxlSlPVvuPZQBX6C0W1pklk5fWaRI/bnlAKF03chzFzRcmGFz+YIwpPLROooKOnkAdh7pw9+mneHKX0solMhOjVhtLWyk3x4ax8rM64YEDkaDHXbyE5yXP0cOzN9q2b4+m1WwtsrF1XWuC0bKk8t8gTdyinW9w9/tbTdUAAbY7th1A/foN4NKlG92MJiVfRV5uDgb4DJKArWy/Fx5OQuDVgKeHpyBHZqCV9ct1G9di0rhJqN+gvniArMiW1RWwPDlyXHwcHmc/hu+gITo5srCOmmli/TwsJ1a+KMPF6Et48iQbw4b6mc7JxDoqLW0GpkDauZbNYwbAlsTVbNG8Jbq6cA6WtLaBnHMnskatXb8GkyZOg30d+3KBbVh4MKrb2KBnDw+LgC2JPbtmzXIsWfLlGwFb4i3b/9g+jJ04i1WTupcoR4rMlmgKOriDUcbaPcl+iNjoCAwYOk5wfEVD7QgSZBVY5uVmIyr4NAaMmkbLwdhVxemTzKzmZD/AtUuhcPedoGN2eQdRL0tLcTnwIFx9J+tYWwayeRmyIt+WdwpsC5L/6D6e5T5Gleo2qFW3IWraSw5L+fVJK0GWbG1FYHvnahRsatREi06uQnxbYhNbnJ+LhIgz8B41TQG9BKBmpCagqCAXPTz6KzFs799Jx/3bqfAZNFKRK18MP4cWLVpRz9rkvsePshB1IQQTJ05XgO2B/TswcKAvmjVtahLY3rubgdDwEMyeMdsssP3qd7/BiOEjMMC7v1lgSxpy646tGDhgEJo2kWTpOrtazdiJuRyD3NwcDBww2CSwvX3rFi5GR2HcuMkKsI3UxLG9EHUeHy22bE30WflugG3Ix++B7b/sDuB6bi7W/rAcCz/9FNXoCZdx2J+I4GB6WtXbi2wouRNlTgqhTCiyDPGXlSswYw4JO2CvTDQ6m6AKyJFJJru2b1PsBvhKnzlnjvk2eANgq4IF6RHcuq5zIEW+9z9+HM2aN0e37pLUh/3wcFA5XTb6vgKs7fFDB9DBuTO1ZWULGiul8Dy2J9GBcS1oNthoGIJbsVaMwKipjRE7hU9OSEDSlSt4+eoVlSo7d+uG2nVqC+2og9UWgjXlrXSbKu59uZbhqoU/RuDeXAtwOfkwkUc9eYKooGDY2tdBn8ED5ZPl17OvNc3uqgcNav/hWlnXtnx/FSvC5KbVYBQpB04CgcsLfNlNpgEt22gw51AMVPJyYd5xlNYbcsmzZzh76Bi69uqBth07SPOJgdxZy7UypabALKsw2KTXYx7Q3r9zB1EhodTLZC8vLzSlBzHSj6XA1m2dtEH9Z//Efrj9n/3I/4jnEWAbHBSDxIRYjB49kW5GCwpycfzEQcyfS8L+WCvyx0ePHyAg4BTmzp6vyJEVm3J5MSW/SHiPvPw8DPBhnr015jsm5ciasWgF5ObmYtvO7fj0o4/pCpuUnIRLl6OVtmF3dO7kjF49exm2meEcIVwsQ3Ao2XSWwYdsok39VAKwVVYbXnlUBoSFh9I5up9XP4GxZWshv1lnrNP2nVsxoP8QNGnSRJQWc/JLxuJGXYxE6ctSePYm+cuSY2Wzz/3NsVerVn2HpUtVqbMynytpZDaVA+xae9jnz0uwd9cGzJgjeZsuX4qs5smnF4AtY6cB7Nm6ChNmLKb9lAJggR3l5b+MvS2D//7N8B4+CTY1NTJjLlYtAbwXAg6ii+dg2NjW1ntH5qTFabHhsLGrA0cnFzX0j2LrK62dgr0tZx/K+oOW4ZfWBakjsj4uqQ8lO1qJseU8I5PDJwCXT2yHm+9E1KhpywFbibV9cPs68h9loZvnIMlxlOz9OPlyOGrXroOOnV0VD8o3k6+gqDAfHn36K96Tz50+BFdXD3oAQ4Bt5v07uHolBmPGTJKYXmsr7Nm7FX5DR6Fx40YSsL2ZhoRrCQJjSyJuBAadxbzZ88plbCeOm0ABKwOqrO/zLCup4MPHDtM4tsQbs9RveMZWC3TLqN0vCTs0etRYk8A2484dREaFY8L4qYpX5KioCJS+fAm3Hp54+bIMkVFB+GjxTIvWigErQy1KV9mJzn/sXdlZvlZ+76XIAK4m3cCv/msVJozwwZI5Y5SK3Hf8PHYfPofikufw9emFzz+cRL+7npuDk0eOoEPnzmjXsaNJz8hZd+8h/HwQps6eK0iOVZCrTiZswxl0NgC1a9eGh6enMtEwO1t+4uE3zuWxtn/57z+pjCGX+Ld/+KPiDdXUKm28mTcCOrz0VsrNGKAa27amXL+OxKtXMW7ipHKBrcm8deBWC2DUcv3w1z/js9/8VpnJjextRdsJ9an82/OAXc/VmmJvxTcwArjad2TtwwBuQV4ericm4s6Nm6huUx3tnJ3fwKOyCMzfBsAVQbB4fHD9aiJCT51BXz9fdHDpIsiTlUVG7lGK3YtyCq22Bt3oyA9iT9A6KxNlymL7sA2dUtcGg8IScGtKtqhnSFVIaQQmebZUtbXnvFhy8W4VGaYMXu/dvIUQ/9MYN2cGatexV2WazG5f2cyILp9MgVp2XZQec+BATYCQMwEoLipCd3d3NGmm2oez92lStZZFjG2P9bNfa1F705suf7DtTbP4j7j/xYtSfPfLXuw8dBZXAjcq75ydk4/f/3UdHj7ORdWqVfCX334Ip9bNQIBteupjrCNhO5Z+SSMGkA3p5s1rqGMisu5R78iyHHnD5l8wZdI0VY7M7Gu50D9lZa/wj+Xf4je//kZaLxmQ5UP8cFStsIFnskRWcitg9drVmDJhMho0aIALFy8iIDBA15aevXtj6GBf3XWTc4O4SODUmVOoV68+3Hu5m+4nXGa6fC1kbJU1RANsr1y9gjsZdzBy+EiLgS0Jr0RYdienduUC29i4aJDwKT7ehKEyA2w5cLh69fcWM7ZsLdECWxLSb9eOdZg17yNpteBVXIaMrRE7K9WaAmY44BJ0muz5XOHYrKVJYCsBS5nFLQNiIgPh0MARLZycTcqRCRBNTYyhnbdlJ1dRjqwBwM9LihF9eg/cR81RWFuRpZXCAfH2tupaqEqtlU2WNhSWLImQnEVJzqO0trZkfBY8vo97SbHoPmC0AnpZGCAyplOig1G3XiO0at9FkScTJjc6+ATadeyKZi3aKDa2iZcjUMPGBl1d3ZVrJw7vwMBBw9GwfkMKbDNupyMtLQkjho+V415bYefOTRgzZjwa1G+gANvEpERqmsCkyCQ0z6kz/lg4d6FZYPvrb76i42EACQkpMK4qcKV7iTLgXHAQatjUQG8PaY8uOJuSOx7fR+/euwcpDvQsQxt10l/u3r0LonSYNHG6AmwvXopESclz9OzlRYHtpZgwLFo4xaK1ZeDP7wbYBi17D2wtaqC3nejOvQf40/JtaN2iMeo72CvA9m7mIyz66h/Yv+6/YFO9OuZ+9hd8uXgKenRtj+ScHFy/loCHWVnoP8TXbNifbevWYvq8BTQP5gxG9HAq2tkSh1NRYWGYMHWqWTtbuojL/1gCbMng07JIZoGtuRNjgZ9TW0izflcI2JLyrV29CovkoPVGoJiBEi3o45+rv4+Dodw73UxPQ3JiIvxIeAf2Phz6EqGXaqciSoc1AF8Dqvi+ayz/FSXKejCryV8zGNiEmnXvHlKuXUNqUhJaOzmhg4sLmrdqZV6qbLA50sLwigBc3umWvDVQSivUpbBh49+vDBFnzyM/NxceA71h70DCKnCgVT5mVhxOcc5D2OLCUpPfFgFcucJ1bc3Vs9olzENa9q04FtVjcP66AnCFMaxerQigFVlaCXYmXIrB/TsZ8Js0vsL2tCw/ZX6R4+Ty16XvuLeQAcKt1DRcCA6Ge79+ihpCAhtirVgKbHtuKEdR8pYWh5iFW99Szv9e2f5p+Va4dGqD//p+C+IC1isv99s/r4ObSztMGTMQoVFXsXnvSWz47msKbNNSHyP4/Cl0aO+MNrI305CQQLRs2RKdOjorNrbEidSli5GoWo3YpnkoSgOetWX96uy5ADR1bAoXFxeL5ci8uoBfS4NDg1GrRk36zLcFbE+c8kfrlq3QuXOX1wO25TiQ0q2THLAlUyoBtZdiLmH82AkcsBUBnSivLENgUCAaNWxMy8zb00pzrcjCJiUnICsrCz4+lgPb7dvXYeZMEluXt3E19opsCtiWlr7A4YM7MGHy3HKBLVtr9eysunqpTJ20MiYlxKG09Dm6dPNQ7GwlEKkytFpQefd2GjLv3kZ3z4Ey4JTsapndLElPPj8tLEBc+Bm4DyFyZDVWreIcSpY7k79vxEWgRp16aNi6owSieTDNPnPMLa90M1ItKXM5x9rydrXMIzLzkkx+346PRO16DeHYuoPqUIoL9xMXfAyde/SDvUN9JfwPAbaRZw/Bva8vlbQzFjcu6jwcmzSDk1NHxXmU/9Fd8Bs+Hna1bCmwvXXjOh48uE9VAywO9oEDOzFyxFjUrWsvhfu5cxMpKckYNWK0AmxznjzBmcDTmDXhrGZgAAAgAElEQVRtZvlSZL/hVPmhHGyw/SLPyJaVITY+Dk9ynmBg/4HKWJDxrKE0OS8vF8dOHMWMabNV+2xuzJA2zMrMQsSFMOohnsWxjY2NQdGzIomxfUWeewEL5kywaBEZ/I6AbeB7YGtR+7z1RM+Kn6N6tapYt0MK8cAY252HApH5IBu/WiKdkOw4GICshzn0bwJsi4qLsGfDRiz45BMTYX8keTJhdtt37IiOzixeJLcpZCfP8lsy1vbnH77HgkWLYWtrq9o+8KfOLD2rHY2nVX5TSj4TxrZyga0WukgFMQds+e+N2VFgz66dcO/tSTc8RsCWTTjstXVpDMA4Dyh5CQ5JeubEcTRr0QKdu3V/A3Crvpn4/np5snGt8Uw3D7G0Xd8ykEtY3OsJCcjPz0cHZ2cqVZY89ZrKm7dDVWvW1AGBbH4kJ9SXid9Uad9XgLFcZbGP5PfjzCwEnTyFNh3bo4dXH/nk3FKAy5+0m7CvZSuQpkbUzRJf7+br3JLJSSN4FLywasPjiPasGq/IHECkig/O6zEd7zJ7G3YqALZ1aqNXXy9aPK30WHWsw8cvlEGqwGCJwJW9hx6UkxiDZTh74gRKn7/AkNGjqYdbZWrSzVtWcKxa0yLGttfGeZZUcaWniV6wudLz/HfM8GlRMWxr1YCr7wcCsO039hOc2f0P1KppQ1+b/V0Ga6SmPELStXjk5+diQP/BdJOanJSAnNxsDKR/M/mjFTIz7+FCVDhlbanTFxbTlo/XTMKK3LmFiAuR1MswrzhQul4F5MiFhYVYt3E9vvz0c+q5OCDwrK7p3pSx3bB5I/yGDkPTpqK3e+FBwgGgvvewQ01lxhbO3dQQeerBnMpOPi16St+RxLIVzHvkzbY0RYoOcSIiw1H68hX6yvJlBcwaANsbN9NoPNHRowhwtoyx3bRpNaZNmyd7y+cOJisgRSbl3rzhJ8rYWltXMcvYslVbC2x5kxaF8ZUPBvLzchEUcASjJs5V3osBW52dLce0HtmxGsOnLqLh4oyAKgO5l4KOoXWXHrBv0KRc1vbSqd3wGD2XsrbSszmArQG67MDaCOAKdudyZ5IUhTJTy+TIspdkSU0BROxfC5/Ji2V1hTRmGWNbVvoCEce2w3fyQs6pFLnvFU7u+gUTZi9TJMcE3IYEHEI3Vw80bap6T96xaQXmf/ApqlWxpixu0rU4FBTkwcdbssUnQHbT5tWYNXM+atvZ0b9v3khD0vVr9MCGMba5OTnYf2gfln64RLHfp6snA/Hyh29+/w2G+Q7DwAEDywW2KWmpiL8Sj4njJUdPvGRZGDvyATuxjf5x5XJ8/umvTQLbR48e4+SpY5gxfa4CbOPiLiP7STb6eA2gwLagKBc+fST5c3k/Q1aFlZfkrXx/9qN+byXfimb6Xoos19iarUfpJwZsf1i3H/Ud6mD2pKH0+unzl3Au7DL+9ofFSM3PwYtXZTiwbTv6+/rCsWlTw7A/ZHIgQCPr/n0MHupnsRw54KQ/Gjs6wrVHD/pstpnU2kFYKkdmwFbNS8r09RlbaVkw4rDEhVTsjjzw49k4lirm0iXk5DzBYN9hrwds5dXKFCjjy8YWtq3r11KpOJHz0h9FviSCGp6RFACa8ooaEGQA3FhSU3BJLZ9Ys/p6Lh/kPi0sxLX4eNxOv0HfrVO3bnKYI1N56wGuMcss3y/Ivs0DXFP1JbYHb39bhoSYWMReiMKAkcPRtFXL17a/NZKTK0CaK4DYZ/S7SqO+Xt5kK/CUymm4FupKmi8JrEojVNpY8J85NldGpjy4JfcU5uXDf/deeA0ZiNbt2st58DmqeQpzivyHJfa0AhCXy5eenIyAY8fhN24cdQzFfoz8ArA3bFHDFjWqVymv+tBrk2Ux+8rNqIIJoudvquAd/9nJeWD7ovQlfMZ9gsjjq5RKGTPv91j5P5+iccMGSLz2AIUFuThz+ghmzVxIN6T5eTk4eeoI5sxZqMiQJQ/JwI8r/oHPP/kVqhJfFpwEmXciRa7/9POP+HDBh6hVy9asHFl0IMWPBNXA8MChg3Du1AkFBQUmga2vgRTZsBdoACdJ8/fv/0HteG1q1DDdccoBtgqjyuUgHNwqChf1BI8HcT+s+AGLPliEmjVqqgBQw0zxh32JiQm4efsGRviNUjbzKruqMpbknqysTAQFBWDKlNkqsNWG+eGYWZLP7t2bMXToaNStS5Q6bB2uGGNLbtu9cz1GjpkqeazVSJHZms+/l1Z2zNZ/AfBy6qCTR3bBve9gONRrqMiRVdZUZa8VwFkGxF48j9r29akslwJbjbyYMbTEgVLmnRvo6uUrpxNj3zJ2l7xXWlw49aTs2K4r3YlJ3pClvHmgS95VlVWrazzrXsKBJbdGMbtaFu6H/i2vU9m3U6hHZCe3vkqsWwIkKQNrBTzJvI2sG8no2X+EEAKoKO8J4iMCMGTMdEVyTO45eWgrhviNh32dutShVOmLEhzdvxWz5yxRGNy42CgQs4M+nv0UYLt+/UosXLAEtWrWoNdupKfSWLDjxoxXgG1hQQF27t6OZUuWmQW26zetR88ePeHW3c08sEUZMrOycPLUScybM8947HCKCgZ6V/z8IxbMW0Qd4gmKB8r2k/i4edi7fxfmz10ktf2rMiQkXsHde3fh7eNLga193Zpo28rBosVm6Op3A2zPLH0PbC1qoH9WIi2w/XH9ATjY22HO5GG0CKeCLuJ8ZBz++rtFSM59AhIc+3LEBTpxe/bzViRUgiMpWKH42TPs2rwJHy77RJAOm5Mj375xA1fjYjFm4kRuw6tK+vSyRr0TR55Z4YGtspT/E4AtW0hEQCf9xZ84s0k2Py8PB/fvw9wFH0hp+EVb+ayeRhumUXEXd4eaUgtu79+7h/NnAzB97jwVqnPg1hDwaMCvWA4jgGsaiBodD3CY2LycmKslI+DF6vju7dsgcZVvpaeha48elMW1dyATpHqX8EnnbEpfSmNZuPY91VN/TQsITxaZdDUPEioh4mwQjRnbd+hg1KhZS3Q8xjZHcj0w+1sVuIqQWruh4dtN+s6oPuT+aniMY3p24scfSyWMWwMmU2Cb5JsEx1GKwkNlWMmntIRrNPbx8EkTYGtX+63a00pllJ4ffOo0nj9/jmFjVL8E0tda8C7WhmO1mrCrqbK6pmrRfbNloQ0qe424NG9DZWf5b52fFth6j/sEFzhgO3rOb7Hqr1+gfj0HJCU/otLCnTvWYdLEGbC3J2F/rLF9xwZMHD8F9vZ1ZTtb0N8nThxB9+6uaNO6jRrTnR0EyXE2SeWGhAWjbm17uLq5mQC2ysonOsrR9lUr4N79+wgND0WbVq1lG1vhiAqEsX1dYFtcXIxN27Zg6YeLzc8obwpsOWkNDxTZ5R27tsO7r7eZkD8c61sGZNzNQFhYMKZOmaECWxNgNb+gAEePHsD0aRKzyZwZaTf0DHSRDA8d3gN3975ooniaFZ1FKXM7W985sMkD2JP+B+Du4Y169RsKbDTva8EUsGVLAFs1VEDIwGEZkhPjQJxUuXTvzQFK1a6Vvq/GxjX78QPERQXD20/yHyKwthrZcdjJ3eg5cByqVK9ugrVlYPcVIo9shseY+aoUmT1X40yK1rO82EmfuQVec+DKejpvV8vLksn3CecPo72bN+rUaygBWxYCiMS5tbZCelwEatayozF8+fA/D26n4eG9m/D08UMVawkIk7lg/9afMX3ex6hmbU3vL8x/gpDAE5g8Za4EbK2tcCkqFLa1bNGjh7vsFdkaa375AcuWfkYVl2SuSE9PQWpaCsaOHqcA22dFRdi8dSNVJ9C11FoTgYCplOQXV1htzSGP2jfK8PSpqHhgewejccaA7ZZtmzHMdzgaNmok9UvmeEweH0W0nOux6IOPJSn6K+J0Kglp6akYNGgESl+9Qg1bGzi3q2/RWjJsdbhF6So70emlfSs7y9fK7z1jK1ebFtjuPnIOGfcf4qul02iKzXtPITevkDqQSsp5gldWZch++AhB/icxbf58To4shf1RJwMr7Nq0Cb4jRqKxYxPz3pFlyRQZY8v/9hd8+uuvZK/LUiFf14nUnTu3BSDJJq9WrVqzjMXOYwAO9b2rfMZWnkuVWwWQyCFM/vr6X9Zg7ISJaNBAOhFlPzzk0EqKdWnMeEhmExR/T/SFSLx4/gJ9fHw08W2lNxDLoVaOHsYZlVa+ZuJ91Tv4p2jem7tX/wS+ZYzbRHrnMhDnGklXr9KwQXa1a6Nrz55oQ1k2I0BXAYmyrr/ouV7+FQR5OC2+XJNKMXj2FriVkopLoRFw6twBrp69KyhP5pw7KE1h3Ia6/sZ3NM1Bi7nZVtwGs5Sq/Ekay5p/ubFPv5E372zciwyuMhsg6Jg/atnZwmsQkVDp75OuqemFT7xEUy4PD665kqultbICOQU/vGMnvAYOpM7zlHTlAFopnRUaV61hkRTZY4t0wPXP/rk4V7UX/Wc/+1/xeVopcv8Jn+Hkjr9RJoVs0PqN+xjn9i3Hy1fE0/BDVLW2RkjIGTRv1gJdOrtIcStjLsDGpjoNEcNkhGTDHH/lMp49ewafvt6SsoFjbZV+bQVkZNxGWEQ4Zk6fqR4gc2NIsB3nBqjuEEb+bsv2rXDv0RN2dnacr1ipdYgXehJj06Ifbk4j6VNSU3E57jKmTp5a/u3m1uHXdSAlr42nA06jYYMGcHPtwdnZivJjhb0sAwry87Fj9zYs+mBpucD25ctXWPHzd/hk2a8VSTNzqKSXJkto7OSpo2hP7a7blcvYkoqTQDFvjytdO3/uJJq3aI227TqJwFZ+GdYcApvLM2zK+aaat/Qs6aklxcU4emAL9Y7M3kWUAauOo1QmtQwBR3ehl88w2NZx0IX04YFuasIl+qw2XXpq0smAlpMZZ1yPR3FxEVp39VSYWlJMgUHmSAT1PUTSQGFtuXGhekWW9p0s3M/T3GzcjA2B25CJIJobBmp5KXJ0wH64eAyAQz3JWzGTKKclXKJjn9goM2BLYusGntiLidMk9QaRHT96cA8JcRcxctREJa5teFggHBs7oktnEibMmgLZVau/w+effqWE/0lLu4709DSMGT1WmUNICCgSqurLT7+sMLClLc7s02VGnLQpufb3f/wNv/ri19S/jpSOHzvSjXy/Ic7Xurp0R9u2TjKoZdJxKb8XpaX4edUPWPbRlwqwTbuRioSEKxg6dCwFtja1qqNz+wblzxsABvz5gEXpKjvR+d9JMcXf9c97YCu3gBbYZj58goVf/g371v4XDa48c9mf8L+/nIcuHVsjKScbL62IxZA1tq1eg8lz5lCgIMX+Iq7Qpd+MvSUxO62rVIG7Zx+L5cjHjxxGu/bt0bmL5GBCERUqJ2xSwYVNtMbWVsub8J3NePMtZ/gOgW1w0Dkq0ert6WUC2Ionjobg16D8PAjV4kTy97nTp6i9bQfq0MM00JKWN/EBxpJdtvxKtS7ub7SQWG0Z098Y2+KKT2H5mMtFmlAJi5t6LQlEHeDm4QGXnj3oYiHmIOdeDoOrPK0cgKtnebXlFBle/lCBfBMXdQlXL0Wj31BftGwvLxAch6tuVngbM/EZWi/JRlJlfR3op2m+3/EdVeMnSbmRH4vKZ6P4sZydq+oJmbfLlwTBD+/dR/jZc+jRx5OG8pE2/Bq4bDY+LT+rSJ+1oFbITf4yLSkJEUHnMWHWLNShttumGVpxflKBfaMqlgHb3ls/fCfrY9Scde/kuf+qD9UC2//17UZ07tAa08YOQkBINA6dDMWqv3xBnUddo8DWCjfTr+Pe3dvw9R2BKlWskJuTjTMB/pg9c57qGZmEA8rPw6kzJzBj2iypjzOwagByl//0PZYt+Zh6WFWALBsDJu1sNeNAHpzpN9IRdTEKM6eRuJGaRdfShjKYJM6dP4fq1W3Q18sCZsPsOiwql5R1QLlHnbT59Y5tzGMuRyM7Oxu+Q4YKwJbmw27VbM6//Y5s5n9j6NmVZ1TJ51/WrsTMmfMlqTMHxlRgqzKy5IHBIWdRv34jODsTaa1pKbIqFTYGtvGxF/HixXP0cO9bLrBldSbIjtUlzzCeLSkbYRNbOXVE85ZOFsuRU66RA5oidHbzMpYjy3VUUlKC8JN70G/0bM7rscR6qxJmqe7ItbhzR9Culw9s7Oqq9ps6G1v1IEB5Z7mfmLOxNZIj3yJOoxwawLF1RyXEDwsFRAAsXr1E6OHN8J3yIWdfK7GzsWGn0apNB7Ro3U4J9ZP7KAtXYsLhN2qKBGKtrHD3Vioy7qRjyOCRFACT64GB/jTETocOzhTIlr16ifUbfsYny75Qwv+kpF7HzZtpNKwOOxwrffECK1f/hK+++KpSge3qtaswfcoM1KkjheU0BWyl/lqGgLNnUL9BQ7h2cxWk/KoH7TL88NO3WLb0S3qARw4Eb96+iZiYixgxYiIN71i9AsC2+/w/WDpLVWq6+E3/Xan5vW5m//HAlkiMV205gpzcfFqHDnXr4JuPp8OrlwuIA6lt+8/QBXXssH5YPHs0TZP0RAa2VlaIOHce9Rs2RJdu3QU5snTiJf1HbGyjIyIwbork6VixDxI2k6J35LSU60i7fh0jZZmfeqrGbzelZtdvlNXuoE+tucdMzxHWZV06M4ytssqakhOrJ6BcUvqE+3fvIjIiHBPkE21D4KrxCmkK3GrLr2MKuQTk44lDB9GtR0/Bo7BK2plgbrmF0Jj5FN9QBLjSd6bq+W2DXDIZFz19isS4OERHRqKrWw90d+8FOy4urlq212Vw9W9RUYDL11Bx8TNcCArB85Ln6D3QhzpKYouHtCGST0ql5UbYJPEtYSSFF9rCAKRXfJLVjD5hfyx+Z+wJWQ9oSRniIqJA4sQOmzAO1W1szEqP1fmhHOBKExo4lJKvk18Xzp9HQX6BID0uT3bMO+pg9WcpsPXctrjiVV4Jd1yY/Usl5PLvnwWJFJCTV4j0W/doOJ9WzRtjxX9/ikfZufjqT2uQ/SQPdna18LffL0bLZo1QWPQcV69lwaZqVZQ+L8Hx43sxY/o8ysCQzSqRow4aNJSG75DC/khy5GPHD8HDvTeaN2tuVo4ccPY0WjRvQT33Kgc9QtgfaTToNvPcYRJ/Ukxi2g4dNASOjo56Wx9LmtcA2O7etwd9Pb3QvEWL8nPQrE/aG7QOpOTpT05m3oHUbeIZOfoSJozjPSPLc7UJYLt1+xaMGTWehmUiKbXekPlrBw7tgXe/gahfv4FiUyjZgTJnUuoBNbkWHXMBVapUQ7fuMoPMpMZsHub+ps8xABPk2p3bN3Aj/Tp8BvoJ8lv6NN7mVrNui/a4ytE2Z5+qsndZ9zKQnnoNfXyGKrJjXoJsJEcm69aZI9sxfPIHqkdkWYaqtblNjA5BnfqN0bhVe4G1pbazitRZArYE8BBHUj2HT1fYRVM2tmz9U0AYt3eU9qXqmkQ+MTky+4xXrxB7ei/cR0wXmFpmX0sAbu7Du3hw8zpc+w4V7GsJQL0QcBg9vQahrr2D4hE5804aHmZmwLPvYIWdTU9OQNHTfHh6estyZSDonD86d+6Gli1b07nixYsSHDy4G3NnL1CAbWp6Cu7evU0lvwzYkrKvWbcKHy/52ASwFecEXorMxpNy8MGxt3sP7kOf3n3QtInkAE45MNeMHbY3uRh9EaWlpfD0kB1iclJk1q5btq7H5EkzUa26DQW29zPvI+piBIb5jQNRQVQE2I76JaL8+eUtpDi+WHJa+a5//uOB7es0wLUn2VSKTDZ1d9JvIOnKFYyaMFFma60FtlZib4Gta3/BrIUfolrVamblyExeRTyprfjuW3z5zW8FO1vBo5tceB7YqhtZ6UtzrK02rW7hNFs5FQe2bGKVZgIR0vF7gOV//xs++uwLVK9e/fXlyMLCJb2IFmLxJ9ns+02/rMb4KVNpuBmLmVvuxUyzt8Lbaxhco9KJlW8a/lYOk0smZiJRTkm8hlp2dujh2RuNmpANHV93aqWKhwkiNFfq2ayDKZGdrag8OSsjAxdDwtGwaRP0HuBtRp4slVllAfR1bQxyDdrE/EmP2mCcHEI7/nilhBYU8gytkROpx5kPcO74CXTt1RMuPdzkQzLR8RQd0/LpmY4lNiM9ViG0hvm1sqKL9oGt29C5e3e4uLoq76n14qybb3Te2qWyNqxiY5EUuc/2JWZnoLf1ZeSsNW8r6//ofAueluD4yQi0d+qIalWssHfPZuo5t149BwpuL12KQBXrKvD09FJALeljideu4tHDB5RdpIytVo4sH8gkXkvArVs3MXrUGENgW1E5Mgnrce/uXYweOariwNYA1BLbvDXr1+JXn31h8jBT6CDlAFt2iMffI5jpyH/w6xzbpD8rKcbqNavw+adfyGux3hOxkr+8Vu87sBc9e7iDmDCZspdlwPXY8YPo1MkFbduKcW8VSTIDA3LeiYlX8eBBJvoP8FWlmhonPIxtNgdsiQ3zgb2bMH22NHeIgFXdAWjXA2PQK8qR+QPUg7s3wG/MdNjUqCnbEPNeiY3kyMDF0NNo0qodHJu3MS1HBlCQl4PLoafQZ/g0wdGUBFhFR1EE3D7JykDG9Th08ZEde3FpaJ2xnYy8HrMDYFpB/KEOD3Q5Z1HMrO7RzWQ8e5qHtt08BadRio2tlWn7WmuUwX/XGsUjsiRRtkJKYjRelZaiRy8vxeY2LiYCtWxqwNVNjWt75PAuGj6qiWMTethVVFQIEu7nw4VLFWBLPCITYDtyuBruh5Ttr9/+Gb/7ze8lc0HOLp/NI8zEgfUX6bd6yKMcinDA9uiJo2jTui06O0uKyvKA7bWka0hLT6OhiBTbbdmWlgHbTZvXYszoSdTPAHEW9egx8ZR8FJMmz6kwsB2z9t0A26OL3gPbf9lFngDbl1avYE309UR284/vsOzrrxU7W60cmQyos/7+aNuuHdqTWH06r6cyWyvXCJNbHdi7Bx69e6NVa8kWlp2nsRNnYZPMatNk6B9x26mtfD4vdUtvqolMc4nKWqxjvLQAyTRre/LECbRt54QOHTqZlCOzyYaVUARa8lWdhNYA3BpsHn7465/x2W9+K0/6/AQn1YwWxmn3MEa1YwT9dPXM7UDMYajKArlGzyATdHpKCj2sefXyJXr17YsmzdWwFOo9egbXGNRr0xkw30Jf0R8/iAcQ6v3kekLMZUQGBsHLdxCcXburp9asC/CbO43EXA+mRadmRn2ropOWdlyZ8hasmxPkTQe9DuBiSBiyMu5i8OhRqG1fx5ClZUoQI5AqbOrl2URVj+ggsAKaH2dlYf+27Zg4ezb11C4VyxiuqwdsvP9kad7h68FSYOu146OKVnelpI+YqXr0rZQM32dCa4AwtmvW7kL//kNQzdoKIcEBaEHCrTlLdraPHz/E+eAAzJg2R5Ajk9ih69avxqcff6H0e2WDyoHcp08LsXnrJnxCHMXw/fQ15cikzP/z1z/jd19/A2sSh0gdYOZbVDexShcuXrqIp0XPMLB//7cHbLlFRaeM4e0FAaz+ZRWmTZmOunXr6uTHFMRpQv6cCwqk0ks3t54aYKvKihmwDY8MhZ2tHVxc5DlZiNvJDmNVFpUAkujoKIweM0kE2rz3ZAbSGECTFwYK3Li1/uC+rRgweATqOtTXsLTiWqSwc2xVF4CfzArL2mctmI6LjkSVqlXRuVsv5RnMtpUBFXKPwlIDeJCZgeQr0egzZKzgYMrIU3J8ZAAat2yPek1aKiCY5cfHrpUccwE3Ey5Rc7dmHd1U6bfCbKv7FhHQS93YSPHHCBk2jl49L0Fc4AF4jJihY2tJWsbaXjy9F937DEHdeg0oUGXXC3Me4+rF8xgycorC1pLv4y4GU4luh44uiqfkyJDTaNGiNTp26Czb6JJDsE0YPXoSHOpKzuUK8nNxwv8Q5s35QAG215ITce9+Bkb6jRLs9L/9/m/46suvUbVKFdPAVq4II8aWZ/spgC0DgkLOo3q16ujjyUISqmox5QBE7jTk1/3Mewg8F4CZ0+VQUTIYJsws6y+7925Hv74D4Ni4KQW2efn52LdvB2bM+pACW2Jj62yhje3YdwRsj7wHtv+6q30iZWxfyUb1Vjh96Aj1NtuyTRvBvlaRI1tZIS05GalJyRhNmF15EEmbVj68B5MjS9diY6Lx5PFj+Pr5CRNQeU6khMlKrmZVZGK+3kk6c6BKuttyYMuts9ydci4mgNyV+HjczcjAcHpSbkrOXI53ZPaaGnCrh03iA8j3T4lXxwP7MX2e5HGwPOZWXTbkhxoANa44XAMIMFG9rlw2Xc9qfqZbS61eE88x05bMDjfmwgXaQ3t69UHTFs2VMloKcJU3MAteVVmatnfxz+EBrghKyxAVHIobSdfhMdAHrTu0VyTJSn6csxVWJjG/cvo1T4VYOHXxAJDdwjYRCtQzYWfL0mWk30RU0Hk4u3VHt169BJZWGufyyGaTiiAvM/WdXpZslFdKQgKSExIxbprkQI+mEUCtdlYRZZ58nvz7N7CQse27c5mFNV25ycJn/Fy5Gb7PjdYAsbH9w//6C6ZPX0hlh2kpCch+/JDKjwmwJRvWPXu2Yfy4SbC1s5M20LIk+eTp4+japSv1jsxUS0qMZo6FWbdxLSZPmIz69erp7Wy5/mupHPkMsY2rVx+9evR8TWCrzmArVv+MWdNmoC71SG/BDze1G87yBg6k6CymzLV6O1venvTw0UPo0L4jnDs5C3a2KviR11gZEMfGxeLRo4cYMnioCtgUkMnFqy0D4q/G4vHjRxjQf4geBCtgVQW2JEbpoUN7MHPWB8YMMpMiCzbAMqDWANsLkcFwcKiH9h1dBDvbCsmR5eVAyxIz0FJQkI/IkDMYPHyisRxZw5qycDwBR3egl89w2NapqwGssh2tLDXOyX6Ia5dC4O5L8lfDKUkhf1R2mA/vkxB6Ai2ce8KuXmMlri3rNxRjyQ0r4y1pPue6IT+bM2dRbIzFnNqFbj4jUY8MQ5EAACAASURBVNPO3pCtJWP1VUkxok7vw6Dxc+S4tqrjqPs3ryP/yUP06D1AcRxFxvzFkNNo19GFOpJjzqPOnjoIt56eaMHFtd2yZTVmzfpACe3zJPshgoLOYOYMyXMy+T8xKQFZD+5j+NARArD97odv8fknX8CmenXzwJbbCoqMLW/PLXX46NgYPH70CENJaEoT8mMVJAPFz4pA5ibixVl1ekb6P/GALEnMjx6TVQ5t2uFlWRmKi59jw4afMX/hJxTo2tSqBud2ljmPGr8u0oIJpvKTHPqwT+Vn+ho5vpciv0alJT55LDuPkpxEJV6ORV5ODvoP8aVe0rTOo9jfG39eiQ8+lk+TZdbWXNifZ0+fYveObVi4eIkyAb0ua6tOYloO6TUq4C0DW+KifcumjVj80ce0cPyiLn4WUathOktYW81DSD4PMjMREngWk2fNthjc6thcMwBXfKS4bRH+qgSQK2ZhCuQag2gy8d67cweXwsNRrVp1uHn2NsngarGfobxYI0/Wvrlp8KrWmCC341rnaX4+YiIu4GFWJjz6e6MZUTpwkncF0Oo2jFxJ+U0hNzQMN5Zmho7RKNODWRH68Z6QS56VIPT0GTruvYf5omatWhpPydyIlkGtjnc1cijFNjMG9rQ0R3nHTxzekTltyMiRylualR7zHmh1HK2qNiEv1MC6ukVS5L67pPH/z/4Jn77yn/3I/4jnEWD7f/9nOYYNHQ37OvYozHuCwMATmDF9vgJszwWdpptcly5dBTvbq1fj8ejxQwwdMkyVIzNP4hywJXa2DRo2RC+3niaArTzmuAEqDR9uW899l/UgC8eOH8OihcSRmfYmg2YznryRmpaK+CtXMGnCRAsOjuV8ywO2Gl8TrDSCXwh+DeLnwjLgQlQkip4RBnmgeWArgyISXSE8MgxTJ8v2nAxsMnZXtgElz7+TcQuXL0dj9OiJBra4HAhmoLSsDKvXLMfiRZ8ra74CxHjwzAN3DdhlK0TGnZu4lhgHX79xUl7cmqN1HihKkFVGVwX36qErA4QMmASdOYoOzt3h2KylxLoxu1/+s8aRU3ryFeTlPkFXdx+antrM0vrTx7eNCfFH8/YucGjcXJE7K+mVWLhS+RhzG3ZgHfqMW4AyKyL+1ZrgyCw0v8gqOkC1e0uEizQkyJqVeukcGrVoh/pNW9HrfIgfxtYSYPvoTiqeZGagu9cQnX3ttehQ1LF3oPXF4t0SMHrOfz+8B/hRJQCJYUu+O3JgK4b5jUW9uvUUu9t1a5fjo6W/kuYEaytkZd3DxYvhmDJpugJsE6jJQhb8hg6Xyk4PxazwwwrJqVzNGjXeGNgyJUNqeirIQc+kCZNNjB2VwWX9+MeVP2DxB0tRjZjYKWNGArakTwUEnkbDRsTzc1cKZF++LMPPq77DBx98hpdlqBCwnbD+3QDbgx+8B7b/sot5QvZjibGlUgtrFOTm4fjefZi9eLFZOfLJw4fRwdkZHTp1tliOfGjfXrh7eqJly5a0vhTGR2MbwX9nSjHFb3zfrPKNQZAA1kwsygL4FCZYEcDu270L/foPgKNjE91GgIdmIsgR34oHdFpgon0DTVHoMzNu36bekucuIjEHuaeWCzY1olyLAa4Id3V/lftc9f1NyZXV96w4wL2fkYGo0FDUsrWFu7c3HOqpzINaNPNSbaXezQBcnYTO2DpaWVD4AwWSf/bDh7hyKZr+JuGB2nTqaABwpdrV9x9NzzC7uTSCu1oZrrhhkFpITMPbxBYXPUN81EVk3b2Lnl5eaNXOSZAd83errBOf35s7iTpz5CgaN20KN3d3qbRmpcdallbzbmzWkicf8qu+hcC23+5P32yaes27w6b9RO9MvXkXP286DPs6trCvbYsvF0+hjgYLC4uo06QvFk1Gcclz/Lz5EOrVrQPHRvUwV457/pqP/re+jQDbFau2oWnTZujcyQXVqlhj86afMW/uYtSsWUOKRZl2HTdupGLkyDEcY2sF4nxn376dWDD/Q8E7MrO5ZSwu8YyalHQN48dNEOX6qlRCo3KQR6S2j3MYdu36tRg3ZiwaN2qs4bjk5mJpddOBemHX3t00bmyzZqpZh0WNXR64NRv2R+NASl5Q2Px66/YtRFyIxLQp04TNuQAGOSnw08JCbNm+CUsWLZM35noJMgN9+fkFOHHyCKZMmimFn+GkzYLTKQ7Y7ty5ESNGTKAgRwWWKqsrgVARfKrsl1SbrLq2bFyBGXOWoEqVqgJry9+vpFfWIr2DKR245urw/t07SE6MhY/vGMmOlTGpBp8Zsyr5K1iJsbPleKUGDqGYgyjC2qZeuQhXn5FKOB/VeRTvSEoCxeT5z0ueIS7wEHUmRcCuvMpx9SnWk7SeqD/q/lJape4kXoJNzVpo2s4FshjfQIoshf1JigpEgyYt0aJNRyF+LQGrEQEH0dWtDxo7NpMkyjKIPbF/M8ZPXaDIkImTqd1bV2Hm7CWoUa0aBa2lL0qwZ88mLJy/jM4R5FpGxi0kJMRi3NhJCrC9mhCP7OxHGObrJwDbFat+xIcLF8Gulq0O2LL1TTuETTG27KD8wcOHOH7iGObPXWAxsNXGsmXjgI6PV2UIjwijcvJevXorwHbd+pWYNm0+qlazocC2k4WM7aT1FyyaXio70f4PPCs7y9fK7z1j+xrVRoCtZGMr8a3k94FtOzBs1CjUrV/fpBz5ZkoKtV/0Gz3WYjlySnIy0tNSMHL0GLZFVFbTitraqpPYm7K2lQNseTaNX5TI5wsREXj16iX6kPiFFsqRTaYTgKXa4DpGUbMxIX8SyVHACX9MmDbNJHPLchRvN4CW5QBcbfm1onAh/7cOco3bmEzGGbdugTB6DR0bw8PbG1WrVdW1keJ4RK4cU+yt+s76owb+sIH/lofkYhrxmyePs5EUG0/NANx6e6CLe08B4Ir1bSJ8hv7NzDIu+pGllevKGwnNwVRxURHiLlzEreupcO3jiS5u3U3KjqXFWD+aRUaYf64Kds05iSJ3HN2zF11cXZX4tGalx2ZYWuXpHKBl48RSYOvx84LXmJ3f/JaLyzbSTAiwdbCvjQb17LHwV3/H9/9nGf7nh234+x+X4GryTYRExqGwqBiTRw9A25ZN8NkfV9DvbKpXe/NC/BvmQIDt/sPBePjgLgb095XCeAQcR7durmjdsjW1Y3358jl27dqKBfMXCXa2ZEO7/8AeDPAhh52OSqx4bWzbkufF2LRlEz5a/JHi/VhwylbBsD+kGaIvxyA3Jwe+g4cYA1vDtlLnops3b+LS5WhMmTjZcrbWYFExOkbTzrN0ThPAsGk5cnFJCTZu3oAli5ZKs5wMzJTP8gcG2sjk+cu61Zg7ZyF1gildFyXIinS3DFi7biUWLpDs5CVGUZOWDyckx7Lt0qU7mhEGVJ6cBSmwfJGxrMrzlbTqihkWchZNmjZHG6eOGjky5+iHrU08o8sBZ74e9IBaep9TR3fDy8cPtnXsDZxIGTuUuhIdSuPZtmzXWQjjw2wteSaXhMhp2tYZDo7NFVZYCRHDMbW8fW9JcRGuBB2Bm980VdYtr2NqvaqdVlmzeA/isEJmegJKnxWhdbfeEhHDs7WaGLaExY04vhNefpNQw6aGwspK8W2tEHhoC4aOmwUbAlbJNWsrPHuaj+iIQAwdPlGRIb8qfYHjh7dj2nQpri1J+7QwD+cC/TF58ixJ4mxthVu30mhoH79hJCSQFQ0ZRmK+5uU9waABQwRgu2HTWkyfNhN17GrLwFZaO5X9M7eOsaFTHrB9VlyMTZs30rFjZJcr1bO062GHIyf8j6N9+w5wciKmUpycXAa2cfFxyMvPhZeXD/WKXPqyDLt2b8HQoaNRy7ZOhYDt5A3vBtjuW/ge2P7LLt1Xsx/JjK0qO448F4Ta9vZw6+Whi2XL29r+8Jc/48vf/UGZKJjcg+1TeRkI24T+9N23WPLxJ6hRowatM7ZpfH1gq+byeo1gGtjKa4yAybQLMv83G/TKfXKBHmRlgjiRmrvgA3lKVkuqA5DCQi6+EY//jMvBgyG9gTH59smjR9i3awdmLlgIW7vaBja3aun1mw8TdSUnNFeT2vfk30z3HO5FDTdAbBE32Fqp4FBTF1zN6+qurAzXExJw/swZ9O7nje4ehN3Tvo16uq7u07TP0KYxcC7FdQ4jh0965pV/RhlKnhXjSvRlpCcnw6lTR7j06onqNWzk4orOqPheVhGzWvZEU0dGRkCUPIswtAnRMUhLTKLhi9o5S87lxNA73KjndiG8AoOfPzjhr8oOcxt6eVmX5hL5xpJnz7Dtl18wYcZMNGjUSP5OBMfCrGFCyszPT3rliJRffetqFkmROy4b9nrT0xvedf3n00oOZIOzfucJGq6mXZvmOHA8GF8vm05D2vy04SCeFhXjT18vgG2tGvg//9iMJXPHwLEh8aj+/kdbAwTYBkVcx9mTBzFjhmRnGx97gXpE7t3bSwGye/ZsxdChIyhDyqSHZOMcGRmGqlWroo+nFydHVn1UsEMYYss2afwkNKhf//XtbLnOnpOTgx27d+KTpczmu7yDYXG23LJtK2V869hLMS8r9GNmbZPy0R/I0avK+mIa2JIkv6xbQ+WU9erVE20FNXaDbJ3esWsbfLwHoWnTphrbWZ5ZlT5v37EJw4aOQr169enKIDC3svxWAYxlZYiIDEbNmrbo1l0+gOTAMHsnJq9VwK28eKkAQprWSYzkq1eiMXT4BAO2Ulxz2PrHVi9Rnqz3Fq2mL8NNEmIm4yYN/cPKYOREioFWkubp0wKc99+PoRPniaF/5IMC6kxKtrXNy8nG9bhIytoyybHqPEquU3nl5Z9bVJCLmDP74Dp4Amra1zMGuLpjGlVtk//oHjKSLqNb/9GKCQzzjiyFAZJYWhbDtjDnIVJiwuA1bJIiQ5ZALfCsIA+Xgk9g2LhZCjNLvsu6m4476dfRf/AoJYZtQd4ThJ33x8RJcxSw+/BhJi5GhWLixOkSY2tlhevXE6kcmYb2ocDWGvHxMZSIGDxQBLZr1q7CrBmzUdfevtKALWnP73/4DsuWfkxNtKT+KfcgzdhhBE5oWCgtp2dvL5XdZwD3VRmup1xHSmoy/PxGU2BL5Mi792xHXxo2qzF1HtWpXX2Lpo+pG98NsN2z4D2wtaiB/n9MRIAtZWzpqY8kR7536w7ioy9hzOQphnJkltb/0CF07NxZkCPzDmAYsJU2ndI3YeeDUKNWTXj0ljqNKhfht7ZqTRmdwPH1qOeQKlrLpoSubKkVAaIxoJTTqqhKB7lWLP+e2hfXrFnrzeXI0h7ARD4asKUpMPnz5cuX2LBqJXX+5diMOFFSkalpOMjXqxb0yd+p2ehRNXe7WCRzf/HZlNdOBvlw7SFXmVAKI4AbExmJ+OgY+PgOgVOnToYAVwSJWvCq7sRESKo+2pQ8WS2juqgoGz5N/REvz/EXLyE96Trs7OugfZfOaNOxvbLx0dlI61pE+/ZiG6qDU9z4arfBZFRnZtzFrdRUytD26NsHzt27SbebAKAqSBRHrzmWVpkrygG1DzMz4b//ADWlIPY/bO5R5xoN+8u9kCiClu8QPLPrZ5t6FgLb/vu+rOjEVCnpgyd/T/Mpef4Cf/5pOyaNGoCundogv7AI/718K2Vlr1xLR9ilBDx9+gwTRvrAqVVTfPrHn/CPPy5F9feMrWE7EGB78UomDu5ejwkTZ6GOnR0e3L+D1NQkDBs6UgGx4WFBcKjrQL3vEskicyKVQWKvXorElMnTzdvZBp6h8Wy7OHeuFGBLXmbFqpWYM2MW9SAs/mhHtzhH7N67B709eqNN69YVZ2s1E7Dx7KM/PKS3KYn18Wx59vHEyeNo3rwFurl0M5ZUajwjnwk4jYYNG6NbN723YwbsGIt78tQxtGnthPYkugHHUCksr1xQBiQJWCF2vIMG+6lATM5UyZvzjMzekwe7fJXt3rkeI8dMha2tnUWsLbtXJ1dmNr4mZNCBpw6ip8cA2NV1UA4HqFMgzjaYZ6zJ50vhAWjg2BzN23TUs7aK/ayUR8Kl86hT3xFNWnc06UiK1I/yDLn9yZ4lMcwftRs0QcvOPTn2UN0H6dcmoORpPq6Fn0LPYVMEUCupExlzK8eZlj0f30q8TJ1Cte/Si4JZxqySz5m3UvHo/m309h6mOo4i4DQhGsTA1LWnp8LiZt29hdTkq9TGlhx8Udnx7RtISUnEiBHjFFv8q1diQRyODRowGNZVSDprXL58EcUlxRjoM1BgbNeuX0O9f9dzcHgjYCv0tzJg995dFESTuNvqwQo/3njna8C1pESk30ij4Yi044EcSmTez8SFixEYPWq8JEV+VYZjxw6ho7MLmjdvUyFgO21jVKWshRXNZPeC3hW95a2kfy9Ffo1qZcCWyZDZ7zXffoePvvqKSqp4B1I8Y0tYo1tp6Rg2egwnR+ZOneXyUIArA9vcJ09w8vhRzJg7rxKdSKnb1opVgQmAxmWiwUZmQKkY8kezjuP0SX+0aNkSzp1daO78wi5+FhGrSSAtgEi1wNo30paffzbxltyqTRt0o14yuSdpbEZZ7qY2I4bXKwHkautJLaJpkKu3imWvZgTZpSdoy1/09ClCzwai+Nkz9PMdYpH9rSl5svapOrBrUE9CGqFwBiAaZbiTfhMpCYnIeZyN+o6N0MrJCa3atYVVlSqaZjUBZiswaBj4e/zgIe7fvo2kuHjUcaiLTi5d4dSZHAToY88KgNEcSysPY+VwTMpN/bccJ1FpSUm4GnMZE2fNUt5I6yRKfbzWflbD6MoJdUyypq4sBbYD9v+6ArVceUnPT/oHzYzY0yYk30CbFk3o359/OAmb955CTm4BnuQV4NdLplLwu3LjIdSpXQstmjbCrIm+lVeQf7OcCLCNupKJ8CB/tGvXCe2d2uNFyTMcPLAdCxcsldZNKyvcuJGC9PQUjBwxRrGpI9dJt17+49/x1ZffmLWzjY2LQfaTJxg2ZKgKbFX/UKKMnz+k0dmSq6fI586fozZ6BKQa/5CMxLniyLGjaN26Nbp37fZ6oFY3mesPZqWFUT8fGwFbOakye5M0sfGxyMoiDnf8TNgKipvzy7ExePIkG4MGDinXM3JMzEW8KC2Fu7unwO4y5lYqj8r0EvvIM2dOYOpUPiSKvN4oING0na36flILxV2WNvfd3DxYNUm/5X9VVlsR7yjtpJUeM9ZNBTDMKVUZsjLv4UpsJAb5SY7BeHZW+5kCXgD5+bmICDyGwWNnCaytBFBFD8kEsJ7atQq+05YqrDeTK9P8WT1qJd/yu96+FoO8h/fQpF1X1G8mHbCwd2dKH1ovr17ifsoVPLh1Hb38pmlArbQfpXa2GraWgN3os4fQvc9g1K5TV/aILDmCIv8nxUbA1rY2OnYWHUfFRJ5Ds+at0bpNO4XJTU26gvy8J/D2HqzY6aZeT8SjR1kYOFD1nh4TcwFlr16hX19veY4gcbAvUPM1n34+ArBlCo6GDRpIY59zOkdXSwulyML4KQP2H9qPri7d0L5de1HtIHcyJqNn9z18kAX/0/6YM2u+3O85b9dlZSgoKMCu3duwYP4SBdieJQ6lGjqifccusKlpOWM7Y9O7AbY7578Htv+yy/aV7IcyY2styI7PHj2Oji5d0NapvUk5MpnYVn77d3z+zW+FUD/mvCOTsbh721Z4DxxI4/7RwSj/qwtbINcqP2EZCaeMuV5LmqTiwFa7PgugVIMi+e+Srl1D6vXrGD1uPLcgSWUUtxCmQ//o0lrA2ir3aDAN+5M4lCI/A4eRMEyakmhAFatRUwBX/y7yHa8Fco1qhmtTrq6NyvOmAJf07ax793AhJAQNGzvCa9DAdyRPlvtouQAXKH3xHDdT03EnLR23UtPQvE0r1G/UCI2bNYVj82Y0VqGmi1oySOgAzc/JpTL2ezdvUSBtW9sObTp0gJNzJ9jVqa0cbIl2r+roNsXSKum5UEHCnKD8oQJRAfjKkwZxsPUgMwvDxoyWZhMDJ1FGoPZ1WFr+HR2sq1okRR544CvL6rqSUwVN/LaSc3yfHakBAmwvxN/H9cQ4PC9+ir5e/Wk82z27N2HSxOmwIyF+rImjqCLs2b0Viz6UnMUwxpZ0z737dsHbuz9aNmsuyJHZWCF9ODPrPs4Hn8fM6TPfzM6WG4qFhYX4/sfl+N+//6NFjRkUfB61CBB2d399UGuwOBgfiJoDtioQ5Nc1BtwePX6Mo8eOYP48y5zg3Mm4g/DwEEydMsPQgZQSt7UMuHEzDVeuxlMGijFUym8ZXPHOn8jn1au/x+LFX6hrPG+bqwAyTvbMX+MAGwWIL19hy6YVmLtQckLHA1mpLsqRJGvuYXJsBgyl31LdR4YGoFnzNmjayskka8tAqwR2Jda2UdOWaNqqveIhmX3HQvwwkHvvdgqePLiPTr1IDGQV+Ep5SrsQBpolBlyso2cFubh59SI9SLJzaEgZYOuqVfGipBjPnxUi58FdPH/2FA2bt0UrF8mcTiJXmIdk+W+tbS0x1crKwP20BPTsP0KOXauG+SFjN/z0fnR390ajRk1kJlcCvWeO7IT3oBGo51BPsaeNj4lATZsacHVzp2mIp+T4+EsoffECXn0kEEv+v3AhFLVq1YJHLw96IEaY3aiocPpdPy9v0cZ283qMGzMOjRo2pM83ArZsqLPxpbOxZX2Fc2BGD7tq26FXD3eLHEi9fFmKH376Hl989pXaPq9IW6oAd+XPy7F0yec03A+JXRsRGQpr62ro7toL1SsAbGe9I2C7/T2wtWh9+P8yEQW2kL0iUzmydNJ8/WoCCCvT39dXcCxFQSs97ZJ+H9u/H13d3ODUroPgVl3aXMoTiTzSGOBNTkzAvXt34UvBVMXkyHx6VqF6sGsEf42qv3KBLTsJZU/iF+7nz5/j5x9/wOdffW1GRqwsUZbZ9QqAUX0/o7cyAjSsfIlX4pGUcBX9B/uiQWPiLZMruQ48q98ZA1x1B2O8cTFfTr6VtIDf8DslkXFbGnG7vC20uN/S50EWhfjoaMRfiob3kMFo3b59ufJkPajW2IbJWx3+/UzJk7W1rW1HQwAv53//zl0qE35w/z4K8/Lo4mJfz4H+X7tOHVSrXg3VbWqgRs2aIAsVkXuR9332tAhFhYV4WlCIF89LcO/2Heo9unGzZhQkt3RqK4XsUQajCe/ByrhXR60hA6oL5SOlN8rfCNSGnT2LGjVt4dHXS5pPBFCrkTsbfKc8rVyWli+T1BsdrCwEtge/fifzf9CEv7+T5/67P5QA24j4+8jLfoCYCyEYP346qlWxQsj50+jQwRlt27RVgOy27RtoPFu26SUbVtINL1wIp321r1c/k3a2ZWWvsHbDWixdtFRQRSkHRaYcSLEFmLc95JbFzKxMbNuxHWNGjIKzs7PJ5goND6PSSK8+Xm8GasWJVp6hTKzJmjWHn/N08yS3OSfpiK3gR0uXwaaajQKQ6KMN7GxLSp5hzbo1+OSjzzm21dgpVH5+Pvbu24F585ZYHPJn377t6N/fFw0aNpLAggakaZlT9p5MjizjD6XeL0dH0E2Vaw/JjEsHbpldpFyt4vcaL8lGkmQ5TM+LF6U4cXAbRk+eZ8jaMjDKM7iFhfkIOX0QvuPnmmZtOdvky+Gn0dypCxwaNdNJkhmYVUGuDG7lPiRXJQjAzb5/G0/znlBvw1WqVkOt2g6o09ARdRs2VTqXKVBrxNbGBh2Dk0tPNGzcnEqVGVNLpMlkX+y/ey3GzVjMXZeA7/EDmzF+ygIFwJL7woJPoXUrJ3Ro30kGu0BUVAhq29WGm2svBdiGhJylNvjdu7nSsUbmh4iIENSoYYM+vYkNvhruZ/O2jRg5fBQcGzeuOLBV6k8+BOHGTmx8HHJyc6hDO6XfCNJ9VU3B9k/rNvyCiROmwt7eXhpfMrBl/WL3nm0YNmw07OzqoPTlK1yOjUZeXj48+/hQxrajhTa2szdffCfLybZ5kjriXf+8lyK/RgvEZz/AS5TpwGvp81LsXr8e8z/+2KwDKQKA72VkYOjIURbLkUkxf/j7X/HFb74xKUdWt8KiC3dT4X/49FI1WAJuKxnYcieuBus4dm3fBp+BxFmFFCZBBDZ845nzaqvhVSsJ3D7Jzsb5gNOo36ARfIYQj5kaWGoG4Bq9q/o2ZupYxM/6Z3JVUlGQqwXVhgCX3x0IT9enfl5SgrDAcygtLaXsbS3bWkrpVGytlQobwFIDmTefSreJY5sUzdP0BxXG8my+9gvzCkBMAXKf5OBZYSGePXsG8l5kqJQ+f0Hd8xPnEQTw1rKzo6xs7br2lPUlAeHFISWCWXXEcdDVUHbM8Z0mWFoSc7aooFCQXpL869arB1s7O8EhVeCJE2jUpBm69XCTRr3goVmUF4uKEL302JzsmHmjZc3A7q5rVcUixnbQoW9eY3Z+81vOjf/rm2fyPgddDRBgGx53n7K0JKTHgoWf0M/xcVGoYmUND48+6sY1OABNmzRDF008W3K4eyEqnMZSZaF+FAeMXDzbzVs3YOzo8eU6kOLXRulQyEDLxC2LJcXFCAwKxNXERPTx6I3+Pv2F9wyLCKeHXf29fd4c1JpYJIwPPy2TI/NAkIHAA4f2w7W7Gz1YEMCsCQdSZHM+dfJM2NraKkBYYGQV78fEOdVKzJ39IapWq6aytrwEmXcQVVaGkNBANG7cFO3adzJjZ6sykqyKGHPKL08UzJUBe3etx6RpC2h/EdcK6W492JWaVAKDKqurBdGsrtizr1+LR2FBPlzd+wksql4uLD2TgNDYqPOwr98QLdo6y6wt5y1Xw8wSM5+I0/vgPWa2yvIx4KscRPC2varREKsLc/sOfvdnxNSycabEsQVQmPMIqbER6O07TpYgi2wtYXNvpVxF34EjFTtaAnYLch4jOvIcRoyZprC1BAgHnDiA3p7ecGzcRLkeEnwGLVu2QseOzortbuC5U2jTpg2cO3ZWU3zQMgAAIABJREFUgG14eDBq164N957uArDdvnMrhvoOQxNHR0NgS1QIbC3mx1bLFlKITXZooByayA2fdiMdxJPxhHETBGCr3qMHtoePHqJzWts2MrNP2NpXrD+U4cDBPfDw8IKjYzN6qJ6ckowbN9MxYMCwCgHbuVveDbDdMvc9sP2XXfolYMsYW1WOTAbs0d174d7XCy1atjYJbl88f44NP6/Esl/92gI5sroZPnX8OJq1aI7urq7KQOQ3pupGWapabo8s/q2peRHOmgO35YNaYeLUgTB1weCLYJoNBCLCw/CytBT9fAYoi41wr/KHvLCbeKZuQjeQJKtpDCChHrMqT05KSMCpY0fgN3oMtZs0soRii6dadnOwU/+GhpsZzUuZtqLVS7fZE1SAqV7RA1z9cigt8loQKqXT3n/7xg1EBoegQ2dnuHqQiU/bj7SHEsZ2sWxhYb2If44p9lYsOVdeXYWaqz0Ttm2GW1eR7TSa5IxSqPtp7bccGNY5gZJGObkjLDAQV2NidUdT/QYPRncS5khOd2T3bnR390AbJydpTjAFak3Z5rKZxBBgs3lHpHG1b2QpsB18+LdG1ffWrwWO+8tbf8Z/4gMIsA2LvUfj1545vhdeXv3RrEkz3LmVils3U+DnN1ZyDmNlhcRrcXiS/RiDBw+V415KaidiV7dy1XJ8+flXxna2cr897n8UHTt0QqcOHc06kJLGgNoaejm+8XkvWcPjrsQh7ko8JowZjwYNGuCY/3HY17GHTz/vygG1+mnXcA2ULlomRxaArTyFX4iKxLPiEgzw6W+BnW0ZCBDu1tUVbduqm3NDiTGxQzywGx7ufdCsWQvjkD+s7DLATU5OxN27GRg0iNj8Su+kSIA5O1sGSCXwyaWR64xdJ3/evJGK9LRkDPIdxapKqMdyJcncXsFUeRgjG3b+JDo4d0f9Rk10trY8a8s+v3jxHP77NmHEtEUcWOVsbDkbWgKEbyTHobjoKdq7EjWAnI6TROuYW6U+1DVXvxcx3isqcmTOqaHkGVl26GYFXAk9hRZOzmjcvA3H1srOo6yscC0mlNrdtu/UXXAcdedGEh4/uAcvb8lutioN4wPs27mOOpazs7WjzqPIXHDq5EG4ubmjJQkJJqc7fuIg3Lr3QNu27RSHUueDz1JHTj3deuqA7eBBQ9C8WTMdsCWHUH/9Vn+QSeTNv/1aWn+0wJbuQ8rKkJObi0OHD2LeXGIzK+9KmAKA+5sfc2HhYfTwjnlGZjFsmSfsMwEn4dikGZw7ueDlq1fIyMigB3kjR02qELCdt+XSO1liNs8l0THe/c97xvY12uB4zAU0ad2K84pM3UdJQarjrqAwPw99+g8wkCMzp1LAoV274OHVFy1btzErR2bhOMjaezcjg3pInj5njnSPUnYuJhf3PgJEFTyV6l/aNLjVQyqTIEvOlg1k/ikiCBGfbw7YkrA/p/z9MWf+QsNFXQsPtZO2MUhTsafRu2hhl7K/MANuSZrA0yfx4H4m+g/xRVNqC22Qezksrom9DFdhRqXjK16eYE1vgTSl0uTHIV2LAC7X2Lq24EtNwjkEncfjR4/gM2Qw7OuRkCjaZ1vG3op1ZHAPW1Q0daAtn6l2Lb8GKzZpGHBAUgZmvQdrAa18g3bcy5fDzp5DQsxlXcGIFLxbr150dd65bj38xo0zGc5HPQjT2+ZKj1ELzMCqdt6QWC/l9XSogHxlbyFj63v4dxWr6EpKHTDuz5WU0/ts+BogwDaUAFtrK8REBaNh/Qbo2qU7igrzcTbgGKZPm6swto8fZSE0NAjTps7iQv5I69zWbRsxetRYajMnmfnIBzScU5iIyDBUqVJFDg3E+iMfu5IbTyaArZLCzFlvQX4+Dh05REGbp4cHnDs5v1VQa3p9EO1FaTpuwhMO/pT5UbonMysLIWHBmDxxikXANjwijL5jH8++GttZzrZTdgoVGnYeNWrURA/ZDlEb8kcqpyr5ffq0EHv3bsPcubJ8WX5h3ma0XDmyVnJcBlyIPI/6DRvBqZ0kIdextBwzy68LbIVSmGwOSAt2rDIIf1pYgODAYxg2errI2jJbWDmdGosWSLkWi9IXpWjvInku5sP9KOlkp1OkHIS17dTLB7UdGqmSZA24JTa62jKzejPqQ9oubgrUknQszE9R3hMkXjiLviOm0r0wL0Omn62tEHZyH3oP8ENtO3slti25HncxBPb2deHcxVVhcstevsS+nb9g3vyPORbXCvv3kfBfo9CgQUPl4OvAwV3o792fHpjQwzASEzvwNJo1a4buXbvDylp2dmVlBRKiauCAQWjRnEil5ZVLpqRJpARLgC1/kMLL41euWkFD/ggSeI0ncdrX5H1faloKriZcwbgxEsurtC9lbctwKVry7Ny7dz/qQIqEGjt0eC+mz1iI6jWroaOTZeF+piw/9k4Wn71fSP463vXPe2D7Gi3wXyt+wMjpJKyPBGaZV2QyuIuLS3Bk1y7MXPiBwNjyXpLJpEE8keZkZ2PQMD+TcmQ22fBe29b9vBJTZs6Eg4MD2xtLv40kVBVgbbn9aLk1Yg7YcrhIyMckwOROW9kN2rR82B/tpPzawJbL6E3ArbY8D7OyEBRwBnUdHNDNrQccmxEJtXGNlcfimt7EiDVlsj1MADy+YdR7DT7JoLU8gKtiWy3o1oPwRw8e4PSRI3Du1g1uvYkHPYN7dPZi2rJpN3Gmwa1ah0aw1qAezXVui7esZnbCBmBWO/YEwbIJlla5R/4+7CxhbFVgKwFMK/QbPAitnZywc/0GzPvoI2r3y88XOoBqkqllTjdYnRl4SOYBgoY75mvEUmA79Mgfyp2L3kaCM2P/+21k+x+fZ8HTEhwOiEUTxya4lUo8kj/EgAG+FOju3LEes2YtoPJ9afNZhjW//IjPPvmVYndrRRkc4MyZk1Sa6NLFRQ63J/V1Xj5JNpAJiVcxcfwkQztbdmAsDxOlbfjrwrg0J2QiDnSys1G/vhSvtdJ+zGRm+JWGtVXnZWkWFMCcxs52zdrVWDj/A1SpUpUW31RMTpJHWnoq4q/EYfzYiaoDKQ1gY2D1ekoSTe83bJR5EMyYsbIykDjGQ4aMgEO9+hIg4OXKGmDJAwqF2ZUnfRlPKO9z5NBO9PMegnoNpDjdegmyuBax+jMEt1oHTdzfly+GwqaWLTp2cVPKLjnVUoE/C83Drp/z3wP3/iOo3wMKbBUZsnQPA7ukLIUFeYgLP4PeQydJwEjjQIq+t5yHALjYasvaXu6oOgMZzs+LIPOX95MU2FoBiZEBcGzRDk1aOtG/Wdxa9ru4IA+xFwIx0I/EtpU8JJPwXeT3Of996OXZH40aOco2tlbIz8lGWMhpTJw4SwC2W7asxqyZC1GrZk3lkGvnzk0YPXIcVUowYHv6zAk4tWmLLp1dBGC7c/d29PcZgJYtWojAltTbqzcDtms3/IIJ/4+97wCr4trafgF7b1hRRAEV7KJSVGzYe2/YWzQmucnNbd93v+/e7/a0m8RYYsUuKoIIqFgRQUBBBRREKSJVsCt2+Z+9Z/bMnjlzzpmDEHL/yPMknnNmz549u6317rXWu8ZPFta+ynWfB8Bsvj15/Bjbd/lh+dIPBbIv8VCHWW7TbqSB7F0+PqMosCWs4uvXf4fFSz62CNh6L/tTuW1DllQU8WPlPFfdxvfA1pJRE8v+c+OP6O7ZF02bNxPBq9IdOWjXHngOGoiWrVqLglcGv8InK5SUPIW/3zYsJvG4opugtIlw7lHUMsu5C8ZEncPb0rfo13+AoKDKIpljgJRfSm21Vd5j+PJm5LdZwV0WYMufJIoySdGw0OBgmmLHlbr5GsLEdwW3xvQHQ1gmPtvIDfzPGTduIO58NN2ICcDt6NrZAMIpXlLTNdrwQaYVJ60Wi08xA3LLBnA1wCKnTfF1qtsdH30e11OuYcT48WjQmJxCqtpuoKQZjLI0EYw9R4K7ut/dcHYplUMLNwulR66kRvC1aFo8xUWqyT6sOKySS/DAloFaUk0HV1cU5OXBd+lSYb+wkCSKAV+1dVmx73CbilTe4G3lO+pbWeuKsR1+SB8DrYWjYrb4sfF/MVvmfQHLe+DR0xfYtCsMXbr0xKP7RYiLOkmVWEIgdexIIHoyd0Mxf+WevX4YPXI8VV4FdmTQfy9fScDDB/cxZLCPUQKpkmdPsWfvHixZtKTMBFIKWWlOMFreHabvMIOQtS+bstrKoToyYJPL79q7C/09+6F16zYSWFVaqWS3YGJV3bZjq6Sc84BNsr6K+/eDB/dxMHA/5s5drAvYkkJR0RGUUbprt17acbYq12MZUJiOKSVt27trI6bOXESZdOlur5ANHKDnxaYGaRS7l1lthZQ7MnA9sOtHTJq1VASYMnuyZKVj4FPsp5zsdNy6mYo+A0eZJJJi+XGzrl/B0yeP4Nyjn8CwawTcCuBeFSes0qG0pragdwpeOjyhKSVBtQJKHt5H8vnj6MdZa5XA1gqZKZfw8sUzdOvVT7DmknVNAS5wYOcazPBdiSo2NpKLcs6tdGRlXMcwn7HSbwTib926BsuXfiJ7blhbYefOzTTOu26d2lJKsJCwQ3Dp5IKOzh0VwHaP/y707zcA9m3avAOwlcdQ0ldLgeCww+jo5AxHR2cj3g7CQLPDBjIaa9f/AN/ZCyirs5TvWAS5OTmEQyAa48dPwZs3Qi7bjZvWYOq0uajfqL5ui+2SHRfLe0fSVd9GXzdd5Sq60HtgW4Ye3nn8KJ6WPIFbPy+FOzJ1NCZxBZcu41lJCfr2669yR2YAWFAw/bdvwyAS1N6ylW535IcPH8B/xw4sW7VKRSKlw2orKcymX9qUDDcjb2Xgqyqovk/xXQJ1WvAKuJqcjIybNzFm/ARBGKmabymwNajDSLytXE4DwGk1hBeG4ufC/HxcuhCHjJs3aO5bAnAb29oateKyFzSquJh8d/5i2UBu+QFcrecrLat3i4oQeeIEddt28/JS9InUDhMAV3qCilzKcH6otBcNOzHrOcN+NzfjzW0ghqtJ6xfpN1Xsqqxcc5BRQK7c+rcCAbaJ8fGKHJ1FBQVoYWeH5Z99RhtpHNTKLpp8zYag1riVVg+gZZi6HvQB2xHB/2Oucyvk+tFx/1ch9f7SK31c8hJffL8N/b2HUSvtzi3fYemyTyUCKaIAurv3k9wNT5wMg4O9A1xcOkvAlszhgvw8RJw7jTkzfY0SSJG5tnb9GsydMw/16tY1jLNVe0Lwh1B80C07SPqpgK3O7cZoMfV+yRVUxJJyVjsCtiLORaBWjVpw6+WmbXkypZzz7sSSWzFjSibj8B3mz1uGatWqyeBW5YJMLcRim7Kzs3DlSgJGjybumjwg56zODESqwKnCaqsBXIlB4UjoAUycMldajpa4JatBogLUc+7AN64n4/HDh+jq5in1JwP9LO8ss9oKYBeIjghF+w7d0ahpC6NEUgKAFfoh/mwYWrbrhCYtSW5a0+BWUFcMAa6loJYB3sRzx2DXriOai7G1zD2ZWmVFZuQLZ4+gY+eeaNK0hZCTVrTaPn54D7GRxzBmwmwpZpbE2KYkJ9D0Q4Q8ipa3tgJx7Q4N2Q9f3yVS2i9yuLV+/bdYsfxjVKtaRQK2h4ID0L17D5pXloUnkHZRYOvVH/b29uUObCOjIqk+2s+zn2acrXCIpAS2B4MC0LlzV7Rv5yiyIsuEYSRHdNChAPj6LpKArf++7ejXbyhatbGDs05X5KU7KwfYbpjzHtj+x8r5UzdSEB4UhCkL5hm6I1tZ48nDRwje64+5y5ebdEdOiInBi+cv0H/QYAnYMoWVPyUTrCWyUqmd01ZUSTWtRPqJpN51UGRAoqzJNBiVyR+0AAZhA/xx7Rp89NmvNax0wh2Wgls1sDaBU41aWoVNy3iP8ZcI2Uhy4hUkXb6E2rXroI19Wzg4OlL2XK03UNRaTtZczaZKmM8IeOdeUDm2GmQl6lHgOogHzOp2JMTGIvPGDfQfOlSROklxj2rA+K96Aa50nq9qgBYENw50LVshxvVhQ4Zkts7ZE3iwKGJZBaDlQWhSQgIy09Ikey7pT8LePHXePLR3dtbFfGxgIVYRS/Hvooy35e23Gq3nbkxLuYYu7ZzQpGEdsx05Mrhy3JqOjKuc55rtkP/wAk+evcKnv/srJk1fgGo21jgavAfe3sPQvGkzFBXmICE+BhMnTJcU1StX4lHy7AkGDhgsHBCLZDLEfXDN2m+NEkixAxzKQOriig7OHSTrk4IFmfdeMAdshcVWcX86Aa35fUmV0129b7L9nt/3S4Hka1dxM/0mxo0ZpyvO1hICKcL02qtXX9jZMWswU/R5oMrlXRXz2S5f/qlxYMtbbSWLqihJGYGPKJ9ZF7B/798rxpHQAMyYI3ix0GJSfzBprLR+89cVANGES3Jo0C54DBiOeg1FN1UuTpbPOcvciR8+uIvYiKMYNHaWBpGUoUsyAcMRwTvgOXoWTTlJXZYlyzFnYeR+E7pEpVCo5rXSg1CVx9YKeHyvCNfjI+ExfLIUb0vWHAGjzGr79tVLnDy0E2OnL5bYkpnVNjtdSRzFrLix0afQ1LYZXFy6UjIpYlQvKsxHbGwkJk+eKYUklL59jU2b1+LDFb+S3JAJCA4I9Id7Hw/KlswD2737dsPLwwtt2wrkU3QpS/8CO3fvVDLIiXHEs2bMUs4NNq84N/7UtOu4du0qJowXcjULc0nUKIy4Jkefj6JMyJ4eXnj7loFaYf4/e/4cm7esx9Klq6i1llhtQ0ID4eTsgg6dOukGtssrCdiufw9sK05GVHTNF+7k4YCfH4ZPnIAGjRpK7sh8vO2B7TsxdOQoNGnWVIrBFdw6xHy2sMLDBw9w+MB+zFu6zCJ35KQrl6l74fBRo6RXZUqwguGR6wjF3mWGSOpd+q8igC1pzw6/rfAZMQLNmrUQNg9VIy0FtgZ1qASbug+MgR9j78vfr25rcdEdZKanIzszEw/u34NzJxe0d3ZCi1aEcErr7Qxf1lAP0vOLJMYN8XgFAVwZ26p7UAmMHz58iJMhIWjdti3cPFn+R9U9Oq23yh40BOCyw5rhRDIFct9lXQj3aoNZ9RU1TDQHaoXrMgw+cywcjZo0QbdehA25LMzHpuJpxXeQgIAJUCteIm75qdeu4vrVq+jg4oKpYyegQd0aZrtz1OE/my1TEQXCxv5vRVT7i6+TANuPP/8Txk32RZ2aNakrsl0rO3Tq4EqV4m1+a7B06ceSspqXdxuXEi5g0sSpBnG227YLeW4bE/nLEUjRZSDGicfEnaepxgb0GyADWz7MR15YRpmRhbUp/lUEsLUQ0Mo7uPHpxBRrqSwnpIzF2d67fx8HDu7H4oVLdAFbqpyXluoikDp//hxNi+bWq6/oMivnvaV7NY0/la1a5PORI0FwcelGs0tIcaKiMFG7HpOftd5LArRcH7OPr1+/wvGjQRg+eorUkZaCW9YOQ6utIGGIbI8+e5QSSQkxr/J7CrG1zMoqpnsBkHghEjVr10PbDl0k92LSz0orr2C1Jb89e/IYMSeC0H/cHGU8ryjaJJDFRJ0J7zR+rqvdj5mllvx7OTIM9k5d0bQlce0VwCz5XQa2VridnoJH9+6gl/sgOf5WjK+9HBuBho1s0aFTZ0XO2xNHAtGjZ1/YiYRQpL4sypiejmHDRgsHXlYkjO8JAgP9sWDBUokxnQDbfQf2YKD3IMp+zAPbfQf84dHXnQJbiRSLA7a8/JTXjDxptAGr0JGEGZnUv2SR4HYu6R4mCKQyMzNo/P/oUWMFgjFVLltyaLd48UqSiJeC28jIM6hZqzbc+vTVDWxX7IqvFHmzdragc1T233tX5DKMQNydXMRGRqJmzRqUAIcRQ1EyKRG8JkTH0I2rbz8SX8BibOlVSciS37dv2IDxU6ehUaPGut2RibBe/fWX+PR3vzdwR6bKcyVabSsK2J47G0EVFq9+QmyxOWDLBCY/vFo6hOK3MoJbRXvMKCrqy48fPcTNtDTcvpVFgW47J2fYtWmDNm3bon5Dwhys9bbcWxltsxrqG5voKuBXTgDXEMbK2ofBIQTXtISYWOq2PXjECO3YW86dSlbyOCHE+stAgBs/mjB1OKGuWf92wdtbVXcp1qdSW7bESispIhyoDQsIRAdXFzh27Egfqk7nIyvpppmPebjKPmuxIvPKEIMApDnEy+LSxQtITEhAu/aOlMCKHOCQv7qw0hVjOzqkclyCQ8dUjgu0/rn1n1mSANv/+fsP6Ny1F9q0tsf15Hi8efUSHu79aZxtYMAuDB06ErZNmlJw++LlcxwK2gffOQuoUsvi+8hn4q5Hcq86tncU5CsDtFxcoDECKUk+VjawLSOoNSMRJIuRtD/ywM7AmilbJr/59mus+GAlqlWtLuyiRixP5GeSWzM+4SImT5wqxeSqgRpzvc3IuInEpCsYO1awaslxuHJMqiK1T2kprqUkIz8/F4MGDddM+8P6gIFK3ggpA11ZtjHQwffd3btFOHn8MIb4jEXDRk3Ed1ZKXIX7toHbswjGJYuo0upMnpl85QI9XOnc013hkqx2Q2YEUeSe44d3oT8hXLKpqiCSIu/Fsymz+MziO7lIT7qIXoMFa7tUF6cnaQFcxS6iMnYIy0mQETyofXT3DtIuRcF9GLHWyql/yJqy4ZiRY08fhrNrDzQXwa9EHmUFHA/eAy/v4WjUuInEiEyu79+9AZOm+KJOrdpSPG5SUjyePyuBl5e3dOBFLO5R0Wfp3KOx9yLg3bnbD6NGjEbz5s0VwNZ//x54unuWHdiKk0aaC5zFlozJV//+Eh+t/BhVWa5mUfkUz2EEN3GmV5UCDx7ex/4D/li0QIzBJiCYglthbWzx24jx46agTt16FNhevnIJ9+4VY/DQ4XDS6Yq8cnflANs1s94D2/9M6QwgrjAX+QV5uHDmLCbOJoniBXZkHuDeL7qLEyGHMXPhIpPuyHHnzqFG9Zro2aePaXdklZIadigITp06okMHQYGVFUwdwJbTRsv7ELq8gK1aeOfl5uLUieOYPW9+ubkjayoIusCtcdWCbWbmJreWTkPclW9lZYLkfSX/1qxZCw0bNaJAt7V9W9Rr0ECs1oRGpMNtWfvudwS43Ivz9avtpSwGStmDylL3i4sRFhgIl+7d0d3NTeEKLs8v5T38NwnCao6lMYArtMgUyDU3pmava8TPqtcutzRFnKgR18rWu6iYk39Knj6F/xY/jJwwAS1a22mwpHP16GI+VgJz06CWK2sFREWcQVZ6OhydO6Bbz16oWauWomv0AtsxIZXDThwypnLYmM3On//wAgTYfrPeH7Vq1kC3br1RmJuF9OvJGDlqAo2zjT53knrkdHbtKrkjb9y0GgsXLEfNGjUUcbYkjQwhjunt1ldJIMUYXa2sQIiL9gfsw7Ilyy0jkOIAr3J9lpM78jsAWtYek1WUMc7Wf/9e9O3jDvs29so4W5VLJdnDnz8vAWGE/fCDT7h0PbIllgewJc+fYdv2TViyeKUS2BqNswVevHiB7Ts2YNFCgT2WBwmSBZcDHDKYlQ+9maxhfaUFbt++fYtjRwNhZ2cP1y6CUm5YznjqHAOrrWSBlkH7iSMH0a23Jxo2bia9P3MZZm7I1KLLEUllp19Hb++RJl2SeXfm7PRrKLydgR7eoxXglslZuQ+05Z9aD9QCtUQGXDwVhA49PdCwUXPJEktouCiwpfG1Vnj76gVOBO3AuBliXCznplz6+hWC92/GdN8VIqgV7nv98gWC9vvBd94HEqgldcXERKB+vXro3q2XBGLzcrORnHwFY8dMkPYJAm63+m3E5ElT0aRxY2lPIHXwrshaFlsqc1Vx9VpeD1ox6mTcdu7eQVmXifeJOXdkNie/+uYLfPLRZxQ7qJmRCRB3d/dC8+atKLDNzMrA5csJmDh5mm5gu2q3Yeq/n0J8rJ7V86d4jNlnvLfYmu0iwwKxhTl4jbcI2LQVE+bMQp06dQRwS0+sZAbk3Rs2Ydy0aWjQqJFRd2TiUnz25AnMnLdAdTomnpZxJ2ksFQHZhIgr65VLCZg4daohiZRBvkn5HbTcqsoL3CqErUryqgWxwXcNRKEus37ND1iwZCmqVa0mCCDV0Ci/ixu4mXZUGLjVpYHIL6ClqBBr7u1bt5CTnY2CvFw8f/4crVq3Riu71rBrY1/G+FytXuI7smwAVzl8puNvBSVFS8Aq77sQHY2827fh7eODejS9lcY9etyTJQVI+Z7mh0jDjdmcUqqxmIzZbo3aajnAKivWvA1V6Xqck3ULx0NCMWvRQtSoWdMk8zFz0xQAtMo+zAFvvm2m42nFOqyIdeIyTh07Bo/+A9DHw9Ng42R11tFpsR0b+rcy7M7vfsvh0f/17pW8r8GgBwiw3bzvJApzMjFw8Cg8f/oIp44FYubMRdRim5aahPv3ijBw4DCJQCok9CC8PPpTKwyJuROstlZITk7EnaJ8DPcZaZJA6ouv/oXf/Pq3QmwdI4xi85wjkFKE8FQksDW3f+icNxYBW07IqZVz4ZKAXE+fPUNlq4e7hy4CqY2bf8TkSdNRv159kfVVBWxZLtVSwG/bRowZOwkN6jfUJJASRILS4hkcvB89e/al2SW03JEl0KYiwpJel/9d7FctcEt+uxB7lhIVeQ+Rw7uUrsmG4FYCzizGVxVvy6yyz56VIDRwFybOEJihJZdkA/dk0SWZtCcqHC3bOKJpK0IMJee2lT6LfcsD43tFBbh9Mwmu7kMlcKsG/GqAK8gB8U/BmK+01JJ1l591HY+KC+HS29vAWktja8XY1Ns3r+LRvSL08iCx8WLeW5EMqijvNtKuJWDI8IniGheA7d0iQrIZhbHjpilS/RwPP4yOHV3RXvTMIAD2xs1UZN/KwKgRYxXAduOmdfCdPQ/16tU1ALaE3ImQR1UEsD1+8jgaNWyEnj16mgW2jCBti99mjB09nnpqqpmRw46EwN4nwHmTAAAgAElEQVTeAY5OnfDmzVuQMIHDhwMwb+FSOLXTl8f2oz2VA2y/n/ke2Orcwn9+xWIosH2DuFMRaGJrC9ce3TXdkWPORNC8kb36iKfKYHlvmZuHIKR3b9mCyTNmolZtovJxNOvirsPcQehXztqy4YfVmOHriwaiJU/hPshppgancbK2LHVueYDbdwG2siuRXItaeAcFBKCjiws6dBSTrGtMDTVs4wWZKSBjoCiIPxhTIIy3UtkoU31iamZrPZekWcjNzkZeTg7uFhdTsGtnb482bR1g17qNSLzEaTHqBxhxz9W+o/wBrhqW6rHekry3R4OC4Nq9O3r06aNtveVcfSRFjXt3xXM1x1ULZFuy7/CjZX4lqSAqfRCvXBiuV+4OTukmH+PPn8fdomIMHzdOqOed0/koKaTY41QQmGlCtN2EIT704EE0b9kKXt4DUaWKkAtTY5uhP+kFtuNC/27JIJRb2eDRfyi3ut5XJPcAAbYHwq8g9uwRTJjsi6o21ti77QfMX7gSNapUxf27dxAVdQpTp8yWFNYzZ8LRpnVbdOrkQq1CQtofK+Tl5iAqJhKzps82CWw3btmIyRMmUyuOKWArrB2hrcKc1zp2egeLbTkBWn4+Ga1SddhH90Tu5FEZCygDW0KEk3w1GZMmEDZi4UmarMQigDscGoz2Do5UHitdjJXxsuRaeHgYWrVqjQ4dXWXQRVGXcXfkpOTLICyx/fsP0W21Vbsks/p5GacFbsn13JxbOHbkIIaNmoSWLduI7y/2g9jxCpdeibSKB+Tc+3D9l552DYWFuejrNVRwORWvGXNJfv36DY4GbcPwSQsE4MPS+RiJt2UANistCQ+KC9DZg4Fb2Q+JH3c2HYxJK4UeagVkX7+C4rxs9B48VjTSiIBVlDkCgBVAaszJYHTs0gvNWpC8sTKhFLl27UocjcXt1tNdIpUi39NvXMW9okIM8PaRGJTJWg88uBve3kPRvHkLkRUZSLmWiMePH6F/P28FsF27/nssW7wcNcXDXbpfiBZbS4EtYzJm683A5Z1zR76SlIicnNsYOWKUwuWYJ6tUriPgUHAgOnToBCeaJkh2Qybz4dy5CFSpWpUe6rx5+5YSSP2w5mt89KvfwqkdC08zLVk+2Vs5wPbbGe+B7X+szI8uuI03eIuC7NtIib+E0VMna7ojF+bmgYDbyXPmSJZciTxKtOyS77FnIykA7uHW2yJ35MTLl1BYUIBhI0eWzWorSXFOoL/DqFQ0sL1y6RLy8vIwYvRo3e7ISqEuv5yWUmApuOWFpUl6ZCYUpV3S8k7Wai9hvc25nY38vFxk3UzHq1evaG5lh/ZOsLNvQ2M0lGKZe25ZYlC5FzZwMZaq5kAi/ajfeqvuT/6dCYBLTU7GiAkT0LAJiYfSOFowm/tW8QKC4mIwFOqjEcvHSn2HERVZSbJqoZWWPCP8cAjq168P9wFiTmsVi7G8vE2k8zHKfKwOaTCEtuTWK/HxiI2OwpgJk6g3Af9nTGmqY2WFujWrmu3YYX6/NVumIgqEz/9XRVT7i6+TANtjcdkI3LEWsxd8KDAjH96LgSIzMlFwN2/6Dh988KkUT5eQEAvCguzlKaTOY+zIz0qeYucuP6z8YJUUTyco4yKHhfhvQGAAunXtBmdHJ0Ngqz4IMgFspbVk/tzKcJwrANRq712ygFEf6CrAHO8twynojx4/xvad27BiueAyTJ/BynJgTPgduHAxjgKMgd6DBUukyuLKg93EpMu4c+cOBg0UANdbxpWgAWwZOCWpefz9t2PevOUqYCvKFamNsi+PKHKkfZ0BPgNvH07+8cND3vfU8cOoU7c+eruLfB6qw1BNcMvIr9Txttz7nY88jlZtHNCyTXu5v8TyWi7JGWnJuFdciG7ug7nYWhEEacTbsnfNzUpDXuZ19Bg4hnPhNgS4Rjck5s4v8rVkXL0Iq9JSOHYh+qmQzocsA2E9il4U4vc3L5/jVPAuiQ2ZlWFpgM4eD0Lnrm5o2aqN5LpsYw0kxEWibp266Nq1l5Tqh9y7Y/t6zJgxH7VrC3G35Le4uHP08NS9r6e0TxAQ/N3qr/CrVZ+hStUqCostn+5Hr8VWC9jSHpTc8uUDocKiOwgJPYwF8xaKa0acbSZSVZ2LOgsbGxv06eOBUhUz8qUrCSguLsKAAUMFZuS3b7Ft20ZMnjYLvbo76JIjv9p7SVe58i707xk9yrvKMtX33hW5DN0WRYHtG9hYWcN//SbMWb6ELjQtd+RdGzZh6ty5NBkzi8FVsyPfu3sXJ8JCMHPeQkWgPp8cmwlW3h2Z/LZhzQ+Yt2gRPaWSFVnhkzGGZGU55Sl0WeS2LE7FTxqCXP2TJrBkUslINY8fPcK+vbuxcOlyncBWFIAaWoAxXaMs4FZZvT4txtQhgN4pqX7Si+fPkZWRjvzcXNy4nooaNWqinZMT2rZrT0EHBzmVj1CAXA3AKGzXShAoCXstIKiqQwPgqtsiFDH8la/9/t27OH30KFo7OMDNw0OX9Vaj5YaQWKW4GPa/vjHVHjfDFSVbZ9kd6jha1UpWWWnJ1YO7dqNn3740ZZTSSivfy6CovA/whFEye6zQChWZFK/kq12WRYNWoP9eNGpii4FDhipeXXMP4eqrTVyRdQDbXp9N17sUyrVc/Nf+5Vrf+8qEHiDA9mhsNs4eC4CH12DYNrFF3LkTaGvfDs5OzqhqbY3AwN3wGTIKjRs3pkprZuYN3LqVieHDRnIEUkIqkB83rsHSxR/QXJZqZmQGcM+cJbG4deDWq7eRlD/ceuEmLpOz/NgZrlsdI/suW4eZ6k1WbSrOVgvYitvvjxvXY86sOahVs7ZoVRR3UI04W0LuFB0TjYnjJ8sEUpL7seyWTKxQxXeLcPz4UUybNttknC0FFByIJi6Ybm4eaNa8heSOLFlhJYspZ/lluoN6T9dwS+ZEjijh5A5PTkpAakoifIZPQF3iaq0GtxyJId9eBi4ZYRD/nfTDHr/VmOa7gjLemmVJLgViIsLg4NwFjZq1klySGTkUs/byFmD2vDv5t5EaH4nu3qNRq049TYBranqx9ZR2KRo1atSCg0t3aY0JMbUCqBV0WsFaSz7npF/D4wf30KPPAIUbMksDFOy/CRNnkNADGzGHrcB0HHHiMFw7d0drO3uR6ZiM6Vvs2rURCxeskHPYWlkh8txJNLVtii6du0nAlkyODRvXYNWKj2laMNY+8u/+AH+a7qd169acK7Io85hcUnloaANbQw8Gdgjz3Q/f4sMPVsHa2tqIt4OwwNj4pF6/hsxMYV9TMyOTmNorSVcwcsQ4CdiGhgZRw9egAfqA42f+lQNsv56ur306ds53KvIe2Jah+wiwfY3XFNhGhh6Dg7MjnDp1EmNsrRVkUdEnTsK2eQu4dOkiLio5Dlc4PRK+H9yzB4N8hqGJLUkPpHRHVrMc80ynhHm0pKQE/by9Da22xtyptDyqFIqs5Z1iDqiVBdgKIlX5t/nH9ZgyYybq1atvIIxYSU2opaEFlDe4NWyvfq3GXP/pGRH108iBSXZWJtJSUnC3uAjOHTvBsUMH2Du00wa5FQRwpXZpAlw1CNYC1kpQfTE6mr4Tsd6S+HU91ltRPTPoRoOncZ2of/RMj44C6Km0YzUINJ1LFsjPycWJkFCMmz4NDRoKqcbYn4FN1ShJlF5Qy9coKgIACvLzsG/nDoyfMpUemiifr+oLjX1FL7CdeOQLPdO+3MsEjvxNudf5vkIB2B6JzcbFyKNwcHBGe0dnXL0Ug+pVq9JUMATYnjwRgg7OneDo6EyV1uLiO4iJicTkSdMEuUqtNoJLst+2TZhEUv40FjgsWIoP/l9iVSSWyKGDh1oGbOl0N+ZrIY6mqVNgnZsHX8zSQ2VLgC0P4ozF2RKARlL+dOvanbJNy9Yp3lLFuxm/xdf//hKffvIbjrlYm0CKWGjXrf8eC+YvQ9Wq1RTuyNQCyoFZpvyTQlevJaKo6A61XqndOZkFjcldKeZVHB41GFWHO/Fyie9L9vnhg/s4dTIE7R07USZvZR9yKYrEBmiBWdaHDITmZGfi5vVk9B8yRqdL8iucCPHHkHHkQEDOU8ssvKbA7dOnj3AlKhy2Le3h4Oom60uaBJNCp7E5SKb+jcvnUb1WLbTtQEAt00nl9D58qh9mmY0OP4jOPT1g26yVwg2ZXH9QXIjLFyIxYsw0EdTKhFP792zApElzUKd2HSnG9tGjeyAxtjNnLpCBrbUVjhw5BJdOrnB2dJYIpV6+eA6/7ZuwauXHorVWtibv3L0dQwYPFdMAydw1igwiFgBbNg+YVkLGeNfeXejn4YU2IvGaUEaYSYp1JM6VouI7CA4Jxvy5CxXeDiS/7d17xTh0OBBzZi+kbsjEansu6gwa2zbB+LGDdImSX1cSsP3qPbDVNT4/y0LnCrLxqvQNqlhZ41baDeRlZGHImNESsQXPjnwrPQOpiYkYPUlIZs2stQzQsu+JF+Px5NEjDBgy1Kg7Mtt41Fbbb//1D3zy29/Jyac5VVO31Zbb1d5ZwOoAkVpCmQkmftDV5Y6EhqJlq1bo2q27VMw8aJZZFdUTylJwy4SouYmpBa7N3WPqvTV8Zs1Wx7eBpF8haYXSUq6hsCAfnVw7o2PnzmjeoqWhs3AZAa64jWuMC3dFE9wqe5WfBzzw5N/nXnExIo4fpyf5HgO9jVpv+Zgr1jANSihZ6Ks+laXfDQbGDJgVlp5Kidaw0l6IEsi0JsyYISghJuJptUiipOcoUjsYs9RqgForIOFCHNKuXcPU2b6KWFqDPcPEQZleYDvp6Jdm53hFFDg44vOKqPYXXycBtmExt5CWdIGSRfXo6YHs9BTcvZOPwYOGUWbkhPjzdF716e1Blda3b15hq996rPjgE85qI8z9gEB/uPf2kNN4iG6RErAFkHbjOlJSrmHihEkKZmRp6RgjkBIWmGLMTHkiKAqWAdTqmRz8880+wkScLQO20q7LuSOfiz6H12/eYkC//tqWJ5V7JXFd9hk6HE2byoy/inhbjkDqUHAAXF27oW3bdmbjbNm+/fLlS2zbth6LFq2SgYLYcB6gi2JFkgPK78oDcm35osWGLIxKXMxZFBUVwHvwSNSqVUdhvZXSFHFtki24gss1/520mbgk2zZtibZOLpL1mrEka4HX3OwMZN28hj4DCeOxVryt4E+lZbkl/XAjMQ73inLR2rEzmrVxVEs4aeqx+fX00X1cPhsGB5cesGvvqvIiFIGtaKEVXJEFa+2LZ09w7mgARk2ZL4UN8DG2JL3X29ev0N3NU8xfK9T16vkzBAfuxBzfZQpG5JycTBpPO3r0RCm2nuwJAQF74D1gEFq1bCntCU8eP0RA4D4sXbzcANhu3+mH4cNGoGWLFuVisdVaN2ciI+i+RdIKyW78QknerZ+fl19+/U98+gmRNVYKZuQ3b95g9Q/fYMWKTwWL7ZtSEFf+B4/uYfFCfV5Mv9l3Wc+WUu5lvpgm6+XlXrkFFb632FrQWaxoZP4tvIIAbEtfv4X/ug1Y+KuPxMUs5Krl2ZF3/rgB85Z/YGix5cqReMmt69dhxa8+M+uOzJRathGdO3MaVatXg7unl8pqKyqtvIuV6n2NKaSCEmz+z0DAGpG45sEnEyymIWFqagpuZWZi2MhRRt2RpY1HpW2o4460y8k3mXo3s4oF92zDspbcra895kdKFu4E5KZeTUZWRgaePH6MLj160LQsamCqBIU8xOSfZuiirB1/q6pdE+CqnmHgmiwKCu7x8TExuBx3AT5jx8Kurb229ZZzG1P3kzGQazg3LB0zHUqxFqAVFjdtJquBpIEirMdNmzVDby8v4ZrReFqlNdaACuodQC1JMda4iS3cvfoZKEOKflXlRJSvCW9Um+Sy1eGKPPno13qmdbmXCRjxWbnX+b5CwWIbGnMLBdk3cSc3C96DRuDenXwkXzqPceOnU2CbcfM6cnOy4OMzCjbW1rC2scLevX6YOGEq6tWtJ8T0ibF2x08cRWs7O3Tp3NUw5Y/orUDS8p04cRzz5s6vGGCrFpQ6tgkdRUxOF7KKzNZRRgKpjMwMxF6Iw/Sp040o5KKiLoLh4yfCaT5SklNYAWjVsbM0JjcGz589h4fnANE11jDvqyAWlBbi4yfC4OTUCa3btJXdkaXIFQ44iJs2kzRqcKvY0znXZPVez+sJrJ/vFObTnLc9ernDuWMXOj4GLsjq+FrN70J7A/dvxajxs2Ej5j7VirHlc9YmxUehRq26cOjQRRFvK8Qrs7ZogFvxWsnjB0hLjMPzkseo37g5GrWwQ81a9ShJEQmhe/bkEZ49fYQ7ORl4+ugBevQfgdp1G3BrhrEkk7SWIphlMbZi3G1maiLevHwB1+59DdyQybqNPH4Irl17KeNrrYDC/Nu4mhiPUaMng8Tb0nhcayukpiTj6ZOH8PDorwC2u3dvwcTxUylpKsthW1RcCLIfzJ+7wADYbt2+GWNGjUXzZs3eHdjyugR3IHQz4yYSLiVgyqSpsvcBm8vSvFTms/XbvgUjho2CrW1TAwIpwvA8ZcpM1KxVB8SKm5uXg6TkS1j1oRDHa+7vt5UEbP/1HtiaG5qf7/WI/Cy8JhZba2vqjnziwCH08OhDGWqllD9i+h+igB4LDEKnLl3R3snJpDvyscOHqSXNob2jLnZkJlNJGpi9O7ZhwdJlBu7I5mJt1XJZ0JoN+14L5GoKV40ftcpp/ib9aAxECfnttm7aiGUrP7Qc2KolmPiappQEU+9oVrnQmMLa95SlJqFyxZ0WVMOKFt25g8SEeFxJiKfgtmuPniK7MleZpRZc8VbD3LJyi5nSoY7dVYNrPdbbZyUl1Hr79vUbeA8fhpq1CXTSmkOyy5vh0JiCuMb3InWX6zkMUttCpSWnYaXNSLuBk6GhGDt1KlrYaeenVSxZnTlq+XaydD58uxSfrYDtmzbA3bMfOri4Sp2hdSim/f7KNyZZbfUA2ynHvqkUIXBg+KeV8tz/3x9KgG1IzC2UPChGwvnTGDthJl49L0F4iD9mz1lKge2De0U4e/Y4pk/zlRhPQ0MD0aN7L7R1cFDE2sXERNEyXp79DIGt6JpMCIi2btuCVSs/emdga0Q06h42C7Zn3XWa2pk0D3LZ3qwVZ1sKPHvxHOt/XIePV5H8tKKMMeJSSX5OvpqEW9lZGDlczJ9KtlgRVEtkUiJQzcvPxdmzpzF1yiwp1lCyeIplqITgYhHJPp6efgNpaSkYPnyclJpICVrLCG45cMz6kR8jLYAbd/4sHj1+gME+Y5Vxq8bIo4yQSeXn5yDpUiwGjxAYqFm8rTGWZFLmVOhedHcfgrqNmshASGGp1bbcMqlL6iAAtiAngzInEyD78vkzVKteE9Y2NqjXyBaNmrZCs9btZNd+jiWcuiNTkiglYZRgsQWOH9yGQaOmoHbtuhrA1goHtv+AGXNXogqNr4WU7ic1+RJePCuBu8cABSPy+egzqF+/Abp36ym5HBOD0br132LF8o9RtWoVaY/Izb2N2LhozJg2U/KcZJ4bm/02YeK4ibC1bVJhwJZfN8IclteOYq5yczvsaAiNKSZu1exQiK0Z/wN70LePB5o3t6NW26clJfDftx3/+Psfde0Nv9tfORbbf04VLLZBR8/hZGQ8WjZvghbNGmP+tBG62l1ehd5bbMvQk2fysmi6HwJqidX2+pVEPH3wCP2GkLxdYuA6FazEcmuN60lXUZCbgyEjR5l0R05PTUVaSirGTpqsALZUoPL5bCWjjmyPORoSglat7dCtu+wKICuZFhBJmZDerD6TArqCgS1p3q7t2+A9eAhatbKTRk8feJYFoHrYywpuDcClhfOpQoGuzsaxNiReSkBiQgJsqlShRAUdXAmIUbZQFvbGDh84gCgBYnVZFXwVd36FUqEGppyWwdfG35OeloaIY+Ho3KM73ETLpimAa6p7ygZzjQ++FphVA1L+O1Huwg8dRvUa1TFohCAU9JNEWZ6jVgvUkt/u37uLbRt+xNwly9CYslELfwoAa8JCqwV09QLbacf+beFqKp/i+4b/qnwqel+LogcIsD18/hZs8BYh/hsxa95K6pK8128NFi5aiepVqgKlb7DNbx2WLftYcEu0tkL0+bOUFbV3rz4Ky01y8hXcKSowzGUrHu4wAql/fvlP/O5zMVRHdD3W54psKAz1HFxpDftPC2qFnc0ksFVZnsQ76D3rN67DtMnT0bChmHOWXtSOsyXxgIFBB7Fw/hIuD61hnC0FbKWl+P6Hr7ByBTk4stLtjkzu27RpNebOXU4Zb3lPInbwaQBCOdkj9b0qttTwdw2ZpsGenJGehqzMNAwcIoB5Jkd4QE5+Vn5XuiQTF+WLMRGoXac+nFy6SX0h9BMHUEUGafLb61evEbJ/E8bMWCZYbVl+Ww13ZwaWhaGTpZnQLvMbE9M3KZjlD0s1CKNIWeKBkX3zGjwHjdG01hLPjKSEaAwfPVW0srI0QFaIPnsMdnZt4OzsKrgoWwvXjh4NQpfO3eHg0F6KqyfeSzt2bsTyZYKHJGVKJp4e6TdwLfUqTVXFcl0zYLtxywZMnTRViMWX9gZhbUv8NeoYW4MUgjJBmTT/OIst6dKNmzdg4vhJlPjO3KEQqSPuYiweP3mCgQMGGRBIHTsehmbNW9EcvsQVmVhtt+/cgL/99b/NDx6AFWuP6ipX3oXWrhB0lS17w5CWkYN6dWqhT49OGNpfiFH/qf7eA9sy9PSZvEwaY0tcpQiwffHkKU4dCsHkeb4UyPJuyAR6EjfjQ7v9MWvRQpPuyGTRbVmzBgs+WEFPtYTNRQx2F2UsOzWjX7kN5+7duzgWEozZ8xeUj9XWUKab7ykjG6Y+0Mk2DiYm5Mep74+LjcWrFy/gSdKccIJF3UDD54qAyoJ2sjo1b9EJ4s13nLKEcbmjQyJpPIwX4KbawsoV5OUh5WoybqSmoK+nF7r2IsQTpgGu6qqBJblcrLeigqWcIYYQNO7cOdxISUEvDw84U3CuvoPvBVNWXPW4mO9/YwBW3e+ycZY/fhJKXb18hean7TdkCNo7O5sBtGoiKL2gVg5TMAZqSa7Qc6dPYbrvPKn5+qy0pntBL7CdHv6dpUunXMr7D/u4XOp5X4myBx6XvERQVAZqVq2K06F7MWT4eNSvUxfhofsxZMgING7QCFUIkeLBnRg7dhJN/0GU1hs3SA7QAgwZ5CMBW+KOfPt2FpKTrmD8uIlK4igVkdQWvy3UtZawI5vMZas6oDHJjKxzcM3vGDorKkuxMsbZhh4NhUNbB3TsIOSnFbZdmadCbVHdsPlHzJ2zAFWrVBUtsRoEUmIdhw4fRI8evdGyhZ0E4ChQ4F2XxX2eufqSi1HREahfvyFcXLqatdpKu70OcKuQDEYPYg3jb0tKnuCAvx+mzFiA6tXJjiZISL3gllnmjh72h+fAkahVp65MIiSCTwVBlNj/BXnZyLxxFW79RyjIpJQxtgIwllylmfRWAVxTU4rpmRIQJIX5ND8cMzIpGx8ZjvYdO6NpczsZ2HIpgdKSSV7VUnTt3ltKEySkALJCeNgBePUbjCaNmkhWXPJ7UNAeDPMZjQYNGko5bB8+uo/Tp8IxbeosBbC9fj0F+QW5GDZ0mGRcYsB2286tmDR+Mho0qF9mYCurHhopf8TxOnHyOGxtm6Frly7G42w5wHz7djbiLsZhwrhJArAVD4/IuF+4GIsXL1+ht5uHkMv2bSlOnD6Klcvn6toJxq36q65y5V0oeLUAvIvvPUSN6tVQu1YNLPn8K6z5+yeoXs18ir/yas97YGukJ8kk+3LtXiSlZlAK75bNmuDPv56PatWq4nRuBl6KMbZVrG2o5TZkxx4MGj0CTZo2FRYhFa7WUoqfIwGB6OPlScl6eDZkNZnUySNH0KKVHbp045no1Eooi3cQdhumbO7fvQt9PD3h4CDnutJrtRX3Le3e0HNMbUKClzewLcjPx7EjYZi7cJECb+l7jnEiKYWQ0+gJo69YQQBX3QTt51umOukBuazM06dPEHvuHI3H9RzgjW5uvZUAV8NF2SjA/Ymtt4QpPPrUKdwtKqaeFC2kHKtyCzUPPkwwRr7rpqsFZvl1d//uPZwKO4ImTW0xcPhw+jjTVlpT7MayJsJgptZeYAzUpt9MQ3xMLKb7yoLUPKjVB+v1AtsZ4d+/a5eX6f69wz4q033vbxJ6IPl6Jr5Ys4e6Cj5//hIr5k+AV+/OePT0BfwOx6JF81Y4F34QPXp5wq6VHaJOhVLLTJvW9jS853CwP7w8vdGyZSuq6BYU5CI2LgrTpohuhiIz8r17xQg7chgL5y82CWw3bRVdEYnHASOL4j2guLQD/BwX1qty1usRhWweWLYzV8DsKSOwJcr2w4ePMGTwEF1xtvsO+MOtVx/Y27c1G2cbGxuNV2/ewL2vwBXAlHkJFGoBaQB37hQgLi4ao0ZNlO6T5DWfyofrdPpRJXd4+aceH8V3I/fx1s4XL18g7PB+9HDzgF3rdkK7zFhqGeBkrsfPnj7FifAgjBw/S+GSzLsnkzr5HMEkJt3augqcuriZB7e8W6wGwNWadWzey+BW1DdFZEvcjgXjjeCWXJibhds3U+AxSPBIZAzJ/L8nQv3R12MQmtg2k/PX0ge9xZ5t67BgEUmVI6T+EWJsgd27NsN37hIFaRw5zEpKuoQJ46dIsbjkvoRLF1Dy9CkGDxoiuUozYLtm3WqaY7Zu3ToWAVs18aTaO0C21guTJeV6KiWqmzCezVFRPVURrrF6X716hdVrvsPHqz6VDzVEgHsj/QaSkxMxcuQEKeVPZnY6Rvh46Noo/vvAFV3lyrvQX6d0o1XGJ6bB0aEV6tetjVX/9R2++OMHqFmjWnk/zmh974Gtka6JSbiGNVuDsGP1H2iJT/+0BgPcu2HCiH44RYAtF2NLrLZJMXGwsbaBm6eHJjtyUnwCnjx8iP6E9VgCvrC4LQYAACAASURBVHK6HwZw87KzEXsuClNnzzHrjswrvmSPSL9xA1cuJWDy9OkaVlsRAouS2ZiANim4tS6akd7GLmv+rgF+JOGlGqdvv/oSy1d9hOrVqktX9AJbQaAaX2PmFBKT138ikGtaeTL3BsLd5kAuu07y454/F4nr167Cy3sgXCkjtVKDUMNFvgUKm6pR0g7hDqaMlEfsLamvqLAQsWcj8ebNa/Tp1w/NWrUSu04NwdXzgWlYqr7SuTVLS8XwA62BX0rEvSr6TASKCgowwMcHzUT2Rv5RhgDUFEmUmoBKRSGl2AOUNRNl4GpiItLTrmP8lGlSExRLX9P1WM3rrOwo/n6ScVtPjO3M46t19nb5Ftvjs6p8K/yF1ea76u9YuWAC3Hu6ID0rF8t/928c3/sVBbardx2Dk5MLLp8/RUNJnDu44mLUCWrBc+3UGVWsrRBx5ijs7duhYwcXCmyfPXuKfft3YsliPp8l8OrVS2zash4ff/grGdgyTyaOIXnn7h0YNHAwWpPQFVPAVoVj3wXY6tuBK3hilJFA6nZuDs6cOY3Zs+aYBraiHD0XFUn738Pdi4sV5EAeZ43Ky8vB2cgzmDpVAHIKSy0fyysKAsnqWFqKsLAg9OrljqZNmxu12qpluxLcylLPMoDLySYmPTjxcPzYITRqbIvuvTxk4iAullKdBkhiShZlYWZ6GnKyM+DhPdxsCiAGeM+fDkV7lx5oZNtCcktWuDBzsb3srRVu06qpp1bt2HfZJVl221Xmr7VCeIAfBpLY2lpasbXAqxfPcSRoJ6bMWiqCWjm+trgwH5cuRmHsuGkKRuQnTx7gzOlwTJo0QxFXf+3aFZoCbOiQ4YqwhPMx5+hBmqe7lwGw/X7Nt1i2ZDlq1axpAbAV3e+5fjJwLxY7lrFzkzjbdevX4pOPhFAWPXG2W7dtxqgRY9Ckia2CQOruvbsIPBSAOXMW4a2Y8qdK9Spo17ahrk3jjwGJusqVd6G/TO5Kq0xIuoHt+4+haZOGaElibKe/j7Et774uU32pN7PxX//chD1r/0hpvJf/9mv4ThmO/n274EROBl7hNXVDZu7ID+4UI+bkKUzynSO6QgjWWuaWTMDBod17MXsxOV0WAa0Yh6tO/bNp9WrMWbSI5vRigpidnjF3EHnjUVpJNq75AdNmz6bxMexPy1KjVq4NlWgj3UYqs0BqvyuwvRQfj6clTw0aU1BQgC5du8GpQweT7siyKOOrEAVVRYFb7YdKDbCg+8o0dw3rN/9EUyCXXSMnooSB+25xMXUDb+NATqk5SKuqRA0dDZUJbTisLGe0FolZ07C7Dd2TszMzcSEqirJA9vHysgDgGs4b84OifTxk+KsViNt04sWL8Bw4EK5ifLxZKy0XGySsY36Fm7LiyoiU7RrqveFiTAzu372LYaPHaOwfsjua3Af6AS1rZs1SfcB21ok15ru6AkrsHrqyAmr95VT50R+/x6jB7hgxqA8uJd/Av9bswd51/wPiivyPtf4UAKQlX0QVK6BXLw+kJl9EtSpV0LO7GwW28RejUY2sUzHlDwG323ZswDzfxfR3Ppft6jX/xsoPyAFnVaO5bElu1p49esKxneNPAmzN77YVPxfuP7iPpKQkgwfVb9AAXVwZq6/yMJEp6K/fvMHX//4Kn38m5HNmAJTF7QpgUc5nm0EYYS8n0JzCWmCVgjEWo1taitVrvhFiJK1tzLsjSwChFLm5OYiLi8L48ULKEylPqCgAePhpEHOrKCNLDLXcU4+dlixiv/FAkaSpenD/HgYOGWXgVq12s5bcTpnLMUoRH3MW9Rs1gYNjJ8ktVSsFkORyXAoE7/0RwycvgDVxAdcA0vR+6b3l3tEVY8sd8kg5osUDWSGGVTiczbqehFcvn8OlG2NCFqy5PMFUdnoqigpz4Nl/mMLVmFhmU69ewrOnT+Dh6U2Jo+i91la4lZWOGzeuYtTICQoAGxcbRQEsIVYS9gGh/Okzx2HbxBY9e/QyALbffPcVPlr5MapXr1ahwJb0sN/2rRjmMxwtmrfQjLNl85aUJeMQHn6Epizs3LkrSt8Kc5qsFfLfv7/9AitXfkbnA3FFrlJNP7D9n0oCtv8nAtuK3+FMP+G9xdZE/3zz4z6EnDhPhWaXju3wxR+X09LHc9LxqvS1BGqZO/KBDVswyXcWatetK7kg05hbEcCG7juAHn37oE1bgd2RWWnZdQZwz506hVq166C3u4dZq606p238hTg8evAAg4cNM2u1FZRiwz+TVlsLZqwpAa95TcNiu2XDBty5U2jwVLe+7nj9+pUi7Y8hwBFu026HaZdk4/cpm6JbidFRUEcRC3rfVDuNP0kPwKWJ648dRdXq1eE9ZCjq1K2nG+BWpvWW9AgBuEkJCdRtybmTCzp160rBrumZUrZuN7aOHty7j+RLl3DrZjo6dHY1SOHDnmbOSiusXwU0leKghDosy1FLDi1K35ZiwOAh0gubttQaB7VGDNW0Xr3AdvaJtWXr+He8a9fQFe9Ywy/79pz8Isz96O+wbdQABUX38MPfPkaXTu0osP39P9ah/+DRyMu6jrsFuRgwcBhu3byG+8WFGOg9jALbG9eTcbe4EIMGE0WYHB5bYdfuLRg3dhIaN2osEckQBXvb9k2YOH4ymjRurJ3yxwo4HBqM9u0c4eriKvFWsANiukoE06ywYrgJr2WxlUtqj3FF7uGWzKr0jHTs8d9jIPvaOThg5rRZolyU4y54AEQU7s1bN2HM6LFoSlORcC6V/GdROX/+vAQkzvbDDwiTMgO8GnG2IhFS0KEAdOvaE21I+h4L3JFJ3SfE1D9t2jgorbaiwDYGbhXy3FwIjTiI/FgaA7g8uM25nYWLF6IwdqJgjeYPAnhwy9yQWb+yWNqjwXvRb/Bo1BBz5WqlABIstkLfPnnyGBcij6H/8MkikZR82CAAJ/G7CuDqmaOCIUVAr8yownO+EOD6vOQJosIPYsSkeaIXosiWzACq+G9MxBG0aesEh3ZOQlysCHzJ56gIwTvD0bGjgjgqKTEeb9++gZubuwLAnjx5hIYsuLp0lpiSSX1hRw/DydGJMgwzUM1ckb/85l/4/NPfwMbGunyArYp4jek0ZEwiIiNorLmHO2e9Fya58pBB9FZITL6CnNwcSoCniLN9Wwq/HZsxYvhYNGjQiAJbGwuA7Z8OVo7F9k+TBIttZf+9B7ZGRoCcNH+1zh+bv/kNqlSxwWd/WovB/Xpi/HAvnM7LQMmbVwIrspjyh/wbf/osGjVpQi0vjJmNgVaJHTknB0NGjTLpjny38A7CQ0Pgu2iJBGyZkspksGTB5eLwmEz+8m9/wW/++48K0GrMamtKUL8rwK1IYDtp6jScPnkCS1asNBtnqxBqivEuH3BrvH4zy1uPhBGrsKCo2T1FWZcx2G/6weSurPR0nDgaBpcuXWkMriFolXtG/UyloqDsQYVywt5G5cIsqg3Suwp6BH+nfKPWG94tKsK1K4m4nZWJxra2aN+xIyVq0tM3ZjtYo8Dr12+QkpiEtKtXQVyPyR5B2JtJ/D5dg5xGrQloOSWDVc9gpVT+HXLUnjgShvoNGqKPh6fUen79G5BGKiC1/MKmAC3ba+pZW6N6NRuz3Tjn5DqzZSqiwM4hH1REtb+YOpd+/hWmjRtEmTBJvO1v//ojDm/7B16+fovlv/kbRoybhUd383HtUgxGjp2K4rxspKclY8Tw8RTY5udmIykpHuPHTYGNDWE+tUbw4QPo1bMP2rZtq3BNDAj0h3tvD+l3pszy/4YfP4Ymtrbo1aNXuQBbef0ZDml57tPvMmEYsFXLJj3AlmyCYUfD0KJlK3Tr0tUkgZSg45eCMM9OmiDkFhWstipgy8iMSktxMeEinj8roflJTboji3s6s26RwiXPSrB37zbMny+sUWZlNiZPtKyThmW15I8s/0wBXKltInjMz7uN+PjzGDl6qhBvy4EghaswR5bFgC6x+EaeDsPICbPNxNvKoJV4Ap4K84fPhLna4JYjKxLaI/zPHOs/b6UV5BNHZCoC1sijAejq5oUmti3EUADR4sqRRhHwGrxvM8ZNmY/qJD2PGIPLmI8D9/thxMiJaFi/ocKae+Y0AbwO6NChk2K9Bwb50/jsNm3aKMijDgbuQx+3vnBwaGcAbP/11T/w+9/8QRWuIEgj46zIUkdJOoF8iMEdCEmHIMJvWbeyEHU+GjOnzxTmJ7uuALYy0C0uLkJQcBAWzFukiLMlhxqHQw+hfTsntGvnTIFt3Xo10Lx5XV3bwp8rCdj+73tgq2t8Kq3Qhp2H8ez5S3y8eDJtw4GQCEok9edfL8C3QfvQvmdXCmp5d+SinFwkxcVjzLQpmu7IROnbvXETfJctA7Xjmoi1Ddi1Cz4jR6ERPYkWukFSZM2k/ok4eQINGjZC9149NcEtO4njO9cYiC0ruDUn4DWvW2CxXbBkKY6GhWDi5KmoU69eGdyRZWHGNntjk83cu6jvs7S85nMtqMSComYeZVgTrwQYe8/YqHNIvnIZ3kN94OjcQSE0ZcXCEHT+dNZbSQXSfP+smzeRevUq7hcXo7WDA1q1aUNzxlavXt2E172xXpdXDFHu8m5lI+NGGrLSM9DWsT2cXVzQvGVLYT0bokVmNxLXO2uuOl2XyvXYAPRaZqkNOxSENvZt0aV7D9VztV2Phb1I+ae27JraW2rodEX2Pfljpez/O4Ysq5Tn/v/y0O5DF+HC0Q2oWkU4vBg05RP4r/8TatWujQ8+/wtGTZqLty+f4fypEIyZMBNPH93D5bhIjBkzhQLbp48fIvJsOCZNnCGm87DGuajTaNa0GVxdOysU3dNnTsDOzg4uHV3Ew2QxW4AYY0ueHx0TherVqqG3W58yAFut2W44UmT+v+s+XJ7jrwfYMouiJAkZ4CkFEpMScae4CIMHDtZFIEUOD+xat0EH545KYMtbZEWwS5R5Qvo1e/Z8BbBVsyPTdqkAAfl++fJFypnQs6c77TKFS7L4MgppY4QQUC3b5PHT+KSqgweI8mdhDhCyM2JxHDxUyHXLYmyFtopu2czFW5GHthTXUxJBci936eGuEW/L5wiWPz9++ADxMafQz2eiJrhlMNZUjK167imttMIaoL+JuidJ7fPs6WN07uEhuSUzF2WeNKogJxPp15MxyGecCESZq7IV3rx8gSOh+zFliq8IamVrbmhoALy8BsHW1lby0CD1BgTsxsgRY0Bc6gWSKeG/wKD9GNDPm7oA84dab9++xeatG7F86QfvDmy5hcL3pXRQIM6RdRvWYtni5XQ/MojL1fB42Lx1A2bPnIeqVQWXcmK5JcA2NvY8SmGF7j3caLofYrF1sNcXY/t/gYZhCOW5vxir638mCmEOlf333mJrZATCIy5i3+HT2Pjlr+kE/dNXfnCwb4F5U4dj5V//F8PnzuBArY1kufVftwHTFy9EzZo1Nd2Rjx8Kpspte+cOJt2RL0RH482bN/AaMNCsOzLddkQlmWw+r1+/xg/ffIVf/fZ3uq225sS3JQBXj4C3BNgW3Sk0UBoIsL0QG4s2bduic9eu72S1ZQLH3GLU815adZT1PqPt0VGhjiIG1WsJdlbIHMB9VlKCM8fDKTX98DFjRfde6TiTj8Q1sIrydavhL/9dPoAwPG82SCfEHdUr+8L4WTU5/b6ZmoqC3DxkpKWhSbOmqN+wIZq1bEkFab369VGtRg0KePk/IoyIa/Pjhw9xr7gYDx88QO6tbDx7VgLbZs3Q1skJHaW0Q+YBrWItcim9ZPVCeLqhpZa39arJ4pTX2Gn1hZjzqF+vPjq4CGmR1ABVue61XY+NWWmN7Rl6ge3cUxvMLckKub598NIKqfeXUun4Bf+F//10Hnp2cUZ+4V1MW/YnnAn4Ds9evsGij/8bIyfORp2atRG48wf4LvwYb16W4MyxQ5g8ZS6qWlvh7euX2LNnM5Ys/lBgSrW2oqzIBJz26a10TTxz9iQaNWiIXj3djALbyKizqFqlCjw9vCoM2P7cxvZdgW1+YQGOHj2CeXPnKwiRmKwU9mLZ9fXy5QQUFRdhyGAfTSss72ZJ9sstfj/Sg4u6detJXAnMNVcCqqK7piK2V7RA7tu3HcOHj0e9evUlqyhtEiewDOSZOYCrqIDVpZJIskijT6LAlU+jIz7j8aOHCD8WhAmTBUsqb7nVirHlLdenjwejvXMXtGzdVgS3AthRuDOrLL4lTx8j5swReI8ULcUEIIl9IbmSi0fOemJsBZ1SttLyLslvXr3EuWMHMXScyFLOlaXEUryr8elQtHPsBPu2JBetEHvL0vzcykxDzq10DB46msbXst+JE9OGH/+NFSs+kz0fxdy2a9Z+jY8+/DV1K6bPEoHtps3rMWvGHDSoXx9W1mK7ibv082fUm+CTVWqCOVGaMqORxiGzMMBKfcEAqIqTgB/fQ4eD4NjeGS6d+HRZQkHFgYg4poFBAXB17ULDJZi3AwG2aTdvIPlqIo0zJnqVJcD2L5UEbP/4HtiaFgXkhOLh4ydoWF+f6b28BQvZrAjpReK1dBqs3qJZY/zfrxfQdD9/9tsAW3s7tHFsT8Etb7m9cCqCuiN36dFD0x05M+0GbqakYuSECSYttk8ePkLAnl1Y9MGHOt2RlWpvbHQUXr58gQGDBusGt+bAq7nrKrlgdEiMgi4jFltjwJYQSOXcvo2RY8a8A7CVW61nwy8LYNTqiPKqx6BuIxVb8jxTAFfqLY0KyU+Z6ek4e/I4evV1R+du3cvRequlYKiEjgI+c35XTMArOksNoQ1HibgrF+TmUrKse0VFePrkKQWwL188R+OmTfHw/n28ef2agviqVatRIFynbl0KZps0a0bZjdmflnVWWLFSCfpB8V2dV1ODJEq+Rwk6ea8MpcuyLNAPBxxA02bN4d6vv+rZ+kmiaHtVG4O5fUIvsJ13amN5b+u66ts2eImucu8LafdAUqqQ7qcaSffzQk738+TZK3z4h3+ht+dgNLVthiMHtmDMxNmoX7s2AnZvgO/c5ahqI8jTzZu/w7KlH1FASoAtSfHx+MkjDBwwWBFzR9LHEJ3Uy7OfFGNL+Ss4i23U+XN0ivbz6v8zBLZsIzW3aiybbfqArWztFHV4BQD755f/wG9//Tv6YAa8jBFIkXQ8xH2Z5LM1FWfLQB05kKhfryG6de0hgkO1dVZwZRaezQMCoTGFdwoQGXkSkybNFsqILr+yNJdRrtZOb0rWK0SbiXhchQVURCwC0BXUkXt3ixAXGwmfEYIllfUd7QPuvYTvzHVbiKH1374OE2cshnWVKjLYUVt5+fsgxNxGHNlPCaX4+gVgzVyQzbshq2UKs9LS362scCEiDG2dXNCylYPsgiy69cqgFnj96hVC9m/F1DnLFYCWgltrK8REnaL7QKdOXRTEUffvFeP0qSOYMWOe5J1B2vD06WMcPLgXixaS+kRrrQhuv1/9NU2bQ/YLCmzFA+HHjx9j154dWEn0aMaUzhuCzAFbzpWcrQN+vvFAVRxipKZdx7VrVxVpf7TmMT3AKQUuJlzAgwcPMGjgEJk4rLQUDx8+xN59uzFv7lJqsbWmFtsGujaCvwVVjsX2vyb8wi229x8+xj++34WLidfR3LYRPlo8maYHYH+EdGLI1E9x9YyfroH8KQttPBOOWxk30XfoYAN35OK8fFyOisG4mdM13ZHJgty1YSPmLCXU58wdmQW1yyzKZGHu274NA4cOQ0uSokQK4hctLyp3ZLbp8Hrmuu+/xbxFi1GHJKYX/3jFuSxKqSnxqxc8WQJsCSsycc1R39OjZy+QPGB7du7Asg9X6QK2CqGnmDAclNP5EjqLlXlalmv9RkCoucaZArjSNSN1Hw8LpbGkhF3XhpIzMS3FyEm4rJ6I2oxSAKvbwgSKWkyrlRi+HHtfZZPNA1y+n5g14eXz57C2sYENEaR8fKyxk1+uEjWYNVibFllphRXPr0vjoFbeO44EH0LHTq5o5+REH2+ppdZSKy3/jtV1uiLPP73Z3BStkOt+gxZVSL2/9EoJsP3872vh0K4DHByccCbMH579hqJ502YIPrCNuiI3rF+fuiPv2b0ZkyfPoN4ERAm+efM6bt1Kx8gRYxXA9tKli3jy5BEGDxLS6JHlpwa2xBWZrNv+Pztgy6Emk7jWctBLWZGTkwz8oxs0bIDOLrLyqQCE0hYtgKxtO/zgM3SYxPAq7KUiAGOfpTjCUnzz7Vf4cMUnsKkish2r42w5Yqlb2bcQHx+H8eMNmZQlECABW0Fyy5ZHAezGxESibt36cHERCGvMg1sNCWDEiqvY87nbeDmkBIsMOCoJnAoL8xETfRpjxrOYS8GSqrC+SgcHwr3EWne3+A4ux0fD22e8BPyNkUlJoJh46716jRMhuzF0vK9oKVa6avNtNrUfMd2Q/Mv+I4srJ+M6igtuw83Lx8AFmZRja4/8ezP1Cl48K0EPN08hQ4g1s8oK5QL3bcXIUZPRoH4DhZtyRnoa8gtuY6C3j7zWrayQny+wYk+ZMlMRjvD69Qts2LQOn6z6lJZn+wCRg/fuk7Q5B7F00dIKAbbiMlDMP9LH//ji7/jd578XfpfmsahrcN/JtaLiOwgOCcb8uQu5ONtSGle7Zt33mDd3CapUrSYA2zb6gO3fg5IrRdz8YULnSnmu+qGV5or8m7+sx82sXPhOGYbCovvYsjcMf/xkLsYOE8hLfs7ANiQzBXvWrsf0VcsN3JGJBXff+k2YvGAuateuremOfCr0CNo4tEVHMV7IGDvylfh4ahUa5DO8TFbbtJQUXE9NwbhJk4xbbQ20Wm2m5PKcrZYAWw1RpGjK+h9WY6bvXOomyqNfU8BQ+9rPD9ya6/Mygd8ygFyzANdIndlZmTi0fx+GjR6LDi4uOq23nBVWw4LPw1AluDZuvRWUHnMWXKlAhcXKmQW0Jgii5GUqM1UK80Mb1PKOx4rPhCX2YABcOneh8dAGy98gR62h+7EWqDWlequv6QW2C85sMbcEKuT61oELK6TeX3qlBNj+z7c7ULdOHbh27om4iDB06NgZDm3bI+L4IfTq1RetWthRYBsctAfeA4aiRYsWVFnNy7uNhPhYTJ40XQFsU1KuIic3G6NGjDYKbM/HRtPwHO/+3j8ji60ORMUmjNYJtK7JJDxDvT3Le6EGGOTibI8S0q3GTWiqJG3rqWhVFe/Zu28PzWVrZ9fakECKB28iudTqH77G8uUfC2l/RASg6Y7MrolbNJMBpKyf33pMmzYXNWvWUoALA72B6wRT8ow7fzXoYaW8Ea2sXJvYdWXqHSA35xaysm7C3UuIV1an5lFYcCnrsVAm9epl6qXQo7dAsiWxKYt9yQNdHtw+efwQZ8MD4T1yCqrVqC32iwrgmlEe1FZaMgUfFBfiUsxJDB07i2NBlt1++RQ/BLiGHPCDz+gpqFuH5LcV3JAZK3LJk0cIPxKA6TMWyiRQ4vXYmLOoW7cuunfrBSsupc/11KvIy7+N4T4i+arohvz44X0cDNqPJYuWK4GtlRUKCwtwLPwoFsxbIB16UZnHQvfMWmyV7siaBFLSHJDXGwHTnTq6ogMhpFQf0LDynCv/6jXfYuH8pahRo4YEbon78f4De+Hm5oGWLe0osG2rE9j+41DlANvfj/+FA1uv8R9i01efo5OTPV14JG/s4s++xBd/XAZPt84/b2B76zpOkljZrq5o3d7QHfni6bNo0LAhuvYiLIxiri0x5Q9hR85Oz8TVy5cwdspUk+7IL589h9+P67HiV58JwLQMVts927fBe8gQtLKzk8CtUtFUEtMYKLq6BKhlhcoT2IYdPozW9va642xl+KLVZpXIswA5WlDUss4qY2nd7dEU+NoPLSvAPXk0DM+fP8foCZMklywmbTWBqhyNIjSEKVtis9Tt4EEr/9583QpFh9fsZFuyxkubVIFMjowh0FOvOtXtKiutIWCVNwADrwtalQhAJWCqAW2tgOOhIWjn7AxHp3cAtaqXMwZqjf2uF9hO8/+qjLP/3W7bN/3X71bB+7s1e4AA239sDMLLp4/g5u6NpLgI2No2pRa32LPhsG/TFh07uFBgeyI8GK6u3dDOoT1lRr5//y7Cw0PgO0dQhFlsXWbGDVxNScak8ZMV6X6Ykk0aEhsXgxcvX2Ag4asQ4waZLBWUXFnqGab7qQiJaAGoZT1ZJnCrDWzplsqUbbULLwdsryQlIifnNkaOEHKzCvcZxgmy+s6SVCdijlEpZlQsz+exZeAuNOwQnJw7wbGdk0XuyJIMLy1FfkEuEhIuYOTICdKc4y23Ull+RhqVedpSU8ttmYkQCWQr+lQNXoFbWTeReOUiRo2drnQTFvubj6HlP0eePgI7e0e0buuoIJPSAro8uCV8EZHHg9DSvj2cO7tJhxukvXxMsLGtilppOZmUmXoF2RmpGDRa9ESk1lkZ1LLyDLwW5GYh/XoSBg0dJ8W+82l+CAs6SenVf4CP5IYskEEBISEH0NvNA3Z2hPlYtAJbW+FCXDSsbazg0ddLcbhVkJ+HyKgzmD1jDgXCksWWWJhvZyMyKhJzZs2RrMnCmpe9l/jv2v3Bu8XLmgTviq7QUUqBqynXcPPmDYwdM05cN8bXD6kn+HAQnJyc4SySr9Fctm9LcTriJGrVqo1u3dxgXVU/sP1nJQHb3/3SgW3f0R9g349/gr1dM2kuxSRcw6d/WgO/b3+PenVr/WxdkQ/fSqUkM/fyC9FnsLfkjsxS/9wvvIPE2AsYOWmiNjsycTPe7Iep83xRraqQNJqxJLNctsyKGxZ4kMYqOrR31EkixSm5AIoKCxFx6hSmzpppxGorltfQQi13gDKv0ZkEXBoWOnkb0a776tVk5OXkYujw4QZH06aeZfxa2cGtEdlpvlN+ohJmwa5OkGspwCXl01JTcObEcYydOBktWrWqQOutFrGUoXrDg2H942a2BzX4gpWDawBv2YkxPNj2PgAAIABJREFUV0yZm1ZUrBXleNCqXL9allqmE58KPwb7tu3g6OxMn6ZoixlLrSVWWnP7hl5g6/2HyiFxivh75ZBW/UTbQKU9hgDb7/aeRtHtdHj090FmymXYWJWie/c+SEm8gBrVqlErDQG2cbERNIeqC4m/s7bCy5fPEXw4ALNmzlUA2/x8AmziaC5bXqHlgW3CpYsoefYMA/oNqGRgK6FJDoSZHg71GrXMn8o8sGUuvjJYlK24xXeLcSz8GGbOmCVZfhXETvQm2fU2MysdV69dxagRY43G2UoAF6XUmywvLwfeA4casiOLAEx8hGzRFRvKdmLSnvPnz6JOnbro3FlgdReK6OxrjS1d+ZPqm6q8ABS5J7L4WQ5AMit0bk42UlMSMXDIaANwy3LTCoBVGW979LA/+g8egxo1a5lkSubdm1m70lIuIT31Clx7eKKVvXiAwDrV1NQTQe2rF88RExGKxrYt0KWnJz0+FWJVBVArgFsZBDMgGnv2GNp3cEWLlq2F9UqAMCGCY7ltz51E69b2NJWNRChFrbPAgf07aBhCtWo1FIzIEZEnYdfSDh1JCiDOkpt1KwM3b1zHqJHMa0OOsc++lUUZvieMGy/tD1T2WQRsDWPRhS7kjs5VB0Vknq/fsBbLlwo50bXckdkhA5kMSVcTUVRUBO8BgziLbSlSU1NwOycbAwYMocDWXqfF9ovgq5Wyz/9mnEBCWdl/leaKvPIP36J6tar470/molEDmSAqODwKX6/fh+Vzx+Gv3+74WcbYBmel4unLEoRu3oFpK5eKBFI2Ul5b6o7842ZMnjcHtevUEeILaPwPha90UUUcDadpP1y7ddNkR2blsjOzcOlCHCZNF4ApO12mn5U6r2yR5RiSSZHDgQfh3LEjOrq4GLfaGmi6wtQ0p6RaOoFNgk3pomEpY/fdu3cPAf57sfgDYQPR646sKqp6jfIBt+q+0QOLLO3PspY3d8DA6jV/OGBkrDQUAOIOeHDvbtg7tENfr34VCG61CTK0+JCNAVw976+37zWttxqAVlhvvD1WXuAav4qP53LxcStWAXCtAJKnlqT0KSv7sdpgZKmVVuorK6D6W6BuTRJ3bfpvccR2c0Uq5Pom77kVUu8vvVICbNcGRuN6/Dn4jJmO2zeS8fTRfXh6DabK97Mnj9DPaxAFthfjImkoD8ldS4Dt27evsWnzWny48leK+DqSWuXsuTOYM9PXKLA1H2PLrS5uYgtzvjwloNJSa4k8UB4s6W3TuwFb0r4vvv4XPv34M9jYsLhZQXJK8a4csCUs8Ju2bMDKDz42SSDF7n3x4gU2b1mPZctWKetj3cSRI9GnqoAEgxXk5yNHgtCpU1fY27dTLDO19VbXvq4huwyUC5WFllnsmDyhoEUD3KZdv4qiokK4ew6SCKOkmFvJus2n9SnF82fPcPjgDkycuURh2ZZjdQ3dmyWXbrGdcWePwKZKVTi69ECd+o10bUX52elIuRyDPt4jUL9BE2kpEJAqx7EqLbdEz33y6D7OnQzF2Mm+ksVVZksWwO2ubWsx23cZqlWtStmQWV7bR48e4kjYQcyZs0jQi0WwSz4fCNiNgd5DhPAE5glpbYXEpEu4f+8efIb6SB6SjCgq9XoK0tKuY+L4ieUPbE14O5CxDwg8gM6uXeHs5MR5SCgPg1jM+pOnT7Btx1YsX/qhmPJHsNjevXcPBwP3w9d3Mayr2ugGtl9WErD9/JcObG/n3cEHv/s3HNu2wrf/96FioZ06l4A/f7MNxfce/kyBbQqevX2F6OAjcOnZA3YO9op8tsRye+H0WTRs1AhdevbUdEfOzcrGpbg4TJg+3ag7Ml3YsILfj+swecYsNGjQUKc7slJFJmlI/HfvwpIVKyy22oqqta6NUE+h8ga25Jmr//0NFi1bjpq1apUTsBWEt0IIWqKF6OkIE2V+wkfRVmg+j/vRPLg1rEUGjMoXJb9Hn41AcdEdjJtMUhNw/aw62OBHQNEGTddkpuaIzxMboG67qpRyjKXXMP7G5sbGtMqpjo/l+0YdKyuuPIUlVcNSKxfjEwCJFQug93R4OFq0bIlOnQXSGEsstVJ5tcKvmr8m31tlDa6mE9guObvjHVdS2W7fOMC3bDe+v8tkDxBgu+nIZVw4EYSxUxbgTk468m/dxKCho5F36yZyb2VgqM9omvIn8XIcSkvfwMO9v5jL1gpr132DVR9+Bhtra9FiA9wpKsTp08fhO3ueUWB79lwETRXk4e5pxGL7UwDbsoNa1qmWg1s9wFa20MqgTNwlS4Edu7ZjoPcg2LWyMwCfdLtUuSZv3EzS+ExDgwYNDONsVey/5N4DB/eiT29PtGrVWpBFGnlrhd+1LaPiTVSKhIQEUCtg165uinmoZb1VT1ST+7qmLJT7iBcbAqCV+10L3F6IO0dZ9Lt06y2AGPZ+4oEBz5LM4mrz827jWnICvIeO0yST4t2QDSy3YgNzb2fi2qVo1G3QCA4duqJh42b0wEL9V1yYi6wb11Clqg16ug8Wjlu5PdyYCzKZn0RnvXj+FBo3aQbnjp1FQw6LrRX+vVOQgyuXYilZHLO8MktuRvp13MrOwDCf0QpGZFJu06YfsGDeMtSsWUOIlxWttudjzonpvMj6ZkYkAXwnJl1Gfn4+Ro0Q4nLZOVW5WGyNAVuxv5OvXUV6+k1Nd2R5TsseD9t3+mHokP/H3neAVZVkWy/AnBUDigFzJghKMgfMWTHn2Grnnp70z5s38+ZN6uncapuzYgIVEVSCgoqKShAJKgoqBsAEiCgi/1d1Up1077mExjfj/b5uL+fWqVNnV9qr9t5r+6BZUztKIEaALSGQWrvuR8yatRC169VBm1bGyKP+FVg5Ftsvxv6HW2yFRezx01w0blRfNbleFBTiUnwqBng6vXPqwuF0DtjeSUpBXvYTuA8ZyOex5fLZEott9v0HiD93HmOn+4LE1Qoglf1335atmDRzJmrUqGUS3MbGxCA/Lw8Dhgw1ZrVl84/x0os4eYKmIenp5FRp4NYcKJBiWdQlTd3rv38/jbHt2LmzRe7I4oakO8IU4Nb8DZUyVs3J1Wijyg5wdfpNcZn8eTcjHf5+ezBt7jw0a95CgtcKd3T2VpnF1UTcrRwfa0NZswCX1VaMClCznKQR6Fs55b+I8FUBapWl5B4bGqDXCtT9u2nTZujuKK2jrIKsWSfzHkYstbqgVsO9mVTNAdsqZqW6LGqX2TIVUWBdPy6FyPtP+Uogv6AIO0/fwMl9GzBlzko8z7mP1IQYjBw9mRLTpCbGwmf4WApsU5PjkZv7FAP6DxGB7IaNP2Lh/OVUseWUWuDxk2wcP36MMorquSKHR4RSoOXm2lsNbIWYW/KqqvRa5WWxLTuoFXpCDm75Rpvav3TCedm4UVPMyCfDTlLWWjdXN0MEUkePBcKhTTt07drNPIEUShAbexnEStevH+eCKYAyATOwrs+CzsguzcJaL5SLjj6NgoKXGDx4hEoqyv3DyOhW7WgqkCvFXwrtYoGldE3Olhx5ioSFdIR967baZFIasbfJ1+IoV0VPF3d5vK0CGHNgmie3YvpfuJ6ZcQMZt1KR+zQHxcVv0KBRE7wqfImq1aoj59F9NLGzR4duzrBr4aDgdhHmiNoFmWMjB94WF+PEkd0YO5mk6uHAJbXW8m7LxPviMu+NQQ4gpLy2HOgViKOcnFxljMiFr15i967N1LovEFEJxFInQ0PQqlUrOPboqQK2MZcuID//BYYOHlImYCu43HNjUBg5TOwt39HsIQohf/rq63/iyy9+TW/QdkeW3M6jo8/gTfFbmrqMAFsCagm45fLcOlJuDKPA9pujSUaGd7mX+WyMlNmm3Cu3oMJKc0UW2khcj8f5eKua/Pp1EdZsO4xPlkyx4HV+maIBt5NQWFKE4sLXOLlrL6YsW0w3X1lOWytr+K1dj2mLFqBWLQG4Cul9OHfkqBOhaNy0Kc15qwS+rNvy2+K3WPP1V/j4179lgK06eTa7zYknUrxISHqSdWt+wkeff2Ea2JrYK406QOn1grlTUQ0YKVZl6t4L56NpftGBQ4aWM7CVFjDZt/JCkhU8XEvbzNICXL3+o9c1wG1xcTFILlWHdu3h5EZO2flCJsCtzMnYCKkUowVpvZcewFX3vJ5pW6MTzVg3hTvUfMOCNdU087FpoihuAhNFI/x4CBo3aQqnXq5iI9k5LPey1GE/LqWlVqpbvWoYBbbLo3ZX8AzRrv7nfjMr5bn/7g+lwDYyDeEHN2PUpDkoeV2Ii5EhmDBlDl7mPkNURBB8fedTV+TbN1NwPzMDPj6jRGC7bft6TJs6C/Xq1ROBLSGVOnrsMBbNX6ILbI+fDIadXXO4OJHc8hLhDZU3A2yVXsfl5orMoMjSrsns2DAMblnXXcXgMgJsyXKckHgVGRnpGD1qjAFgC1yJvYwnTx7T9EssgZTI6CuwAvNrd15uLvYf2IP585fKrbJ67sg8QmAPL7n9RYKt6elpOHbsEHyGj0H7dhxJntZHFZpipnO0QC67f7BkQmbBbQmJJd2KwUPHol6DRiK4FfL8CiBUluIHJTgfRXTG5jSPrOhuzLiDs5ZbUpe4XzKxvyxT9qvCAhS+fEG3XsJOXad+fRoyp7TSSoeokvVWy3KbFH+B6rKOzu4SqOXjagV35EMHtsNn+Dg0bNBInt/WGjh8eC88PfrD3t5e8nS0tsKjh5k4ey4S03xn8ZZcDgiTA66AQ/vpoRUhmlOm+xJyWHOpvkpvsdUGtqbjbIn4OXfknujUkWFHFsar0Cd8RxLvk6PHjmLenAUyi+35C9F4XVQEr34DDAPb/9ocWinbyZ8XDq2U5yofWunAtu/4D9G9swP++/P5aN7MlrbvcsJ1/NdXm2mC94DNf3knBMU2wv/2NRS+LUIVaxtEHQyEc18P2Nnb89ZaG1ShMQPWuBB2irI+dndx5mNshTy1nAX3/p27uBJ9HhOmT5dZbIX4WpZIisTINW9hD0dnjiBBZHdkDpXJ4iNsemwsrtD26KhIVKtZA269+5TKasvrAaXqD7MbuuxkWV3a1P2ZmZmICD2JWfPmc21TnaqabrLZtikrFR5h7MZSyasibrK0uaYArqm6tMCiHrgl7xkVEU49EkaMG19KcCt1uhJcswqQMDj02q4Vg1sR/WAO0MrnmUY6H6aA8CvjTEnXhqiICJCclY7OvcRX+EVArYbrtFKGRoHtB2f2VIT4zda5ti+Xc/L9p3wlQIDtjsg0RB/zg9fAkahbpzbCDu/GtFlLUVL0Ckf9d2L2vGWoam0N4jaZmnIVY8ZMpHspsfTs3rMFY0ZNQOPGjUVgm5v7DAGHD2DJwmW6wDYoOBBtHdqhZ/ceIpAVQWyFA1tpY7N0/TUlfWU4gZoNw/RzdYEtv5QKVtzsnByaB3TxwiU6wFYeM/jo4QOEnAzBnFnzObAmcz8u4SyNrLtxSQl27dmGoUNGonHjJtxOKxxYsoq/lsVLbKsgKbnrdXh4CJ49ewoPj/5o3tze8GA2CnjZPU24xxC45d+LgFa/3RvgK8bO8vIRXJIF12w21U8JEH7iEHq4eKBR42Yy+SpdmoX2CfuxGBdtIPxIysDBejJwu43glizmjWbIow7uWotJM5ZS3V0gjRKstiSWNj/vGcKOH8LUaQvE1D8U8PIeGJs2/oDFiz9ElSrkft7iSzw4+FQ/PsNGMTH23H07dpJ1YSz1TBLuEWJsI06F0dRB7r05oF1aV2QW2ApjlBt+innGHrYbYEem44WZJ+s2rKXgnaRI4iy2b5GekY4LF6Mx2XcmWht0RZ712d8Mj/fyLLjrGy53b2V/Kh3Y5uYX4KfN/jhy4hxWzBuP9HuPEBQajZXzJ2DWpGGwIVHl79jn4K1r1GJLLLQZ15LxKr8ALn29RIutjRVnvc3KvI8bcVcxbPwYjjiKj5ll/92/ZRsmzpiBGsSqK5BMafybk52NqPBwTJ5eehIpsiitX/0Tlq5apSKFkim9Sp8shfxLY7k1u6mXAdiSuul7reRjtRUPM/tsC4xxStOjGji9Y4PVRHPKJBf+Zn2QyGsdzPO1ZCVcu512E2dPRWDcFF/UrV9PvEvm9iN7F+P5bmUtYbQRS9peHr2qBWhJvazrsfQ3983UvNQDtdFRp1GzZh24UCs491EqwjIgrDW/S2GpNWWlZR9hFNiuPONXHmK3uI7VfadbfM/7G8xLQAC2V8+eQKeujmjZohWOB2zD5GkLUc3GGof3b8GMGYtQ1cYaj7MfIj4uBmPGTBAttkFB/vB078vntuUsNi9e5CMo+IhJ8qjg48fQsWNHdO7QyTCwlVzwS7PbsYue1jGfeVkZKWEa3BoHtqaYkUnr1/y8GksXL6MWPbqWCpZg0aoqudmSujZsWo8F8xZTIMHp7YK7JfOvGDdbgktXLoJ4pbm5ukvkSEycqkjMxO45mkRS/ErP6BKPHj1ATMw5akUm6WPs7VujWbMWlJisShXzBHZCP+i5MbN7GgsghT1HDdI5MCTkrn327AnORoVixOipqnhbvRRApE5CJjVi7HRY2VSR57dVuCFz/cW9Be0LVq5mBhlrpRX2EBmoZUAumYu3b15Dfu5zuLh5S2l6BGstH397PSUBhQX56N27L03tw+W15f4l3hfR505h/Hg+DSYPeMk4unzpPKpVrwpXl95Sblv+9527tmLa1BnUM1IJbMMiQmFnZwfH7pybcqmBLX/YII4HUaamgS05vFi34WcsW7Kcnzt8XygOd3h8i1Onw1GvXn04ObqgmI+zJd6r5B3nLliK1q3UYZta3fh9UOW4In88+r0rsqw/TkZewif/9RNq1ayOXav/gE7tWhpZ2yulDAG2L9++phba1/kvcfrgYUxYNE+MrxXckgnA3bP6Z8xctoQmXmbdiwWrLHFHprGvzs7cxCOJfxQAmLtuBb9tWzDYZwQlgilNTlsirEsXLqD4bTE8vbxkstOMtTOwp5srYgQ8sYuvDpWRNsER/wbkGTu3bsEQn+GcbEoBbIWF39iA0n8r2S9GX97YQyuslJFmapYxBG7VkmWwpfhO5BqJHzq8by9c+vRBxy5dpN8UiFhqixFwKz1fDayNKZ2ls+TqwVipG0VAy2sNLNgUFAmhNK2NL8Dep7TURoaHo2aNGujjJYV3WAxqmYdrzW/VNUXuXK2Byt5T1WCM7aqzeytszJuq+CfvaZXy3H/3hxJgu/30TVw9dwIObTvRVB9H/dZh6owlqFWjOvbtWItZs5agZo3qeP40B2eiQuE7dTY93CaWGX9/P/Tz7o+WLUkKEc5aY85iS8bd7r270K9vf7Rp1foXBrYVY61lx4k2uDUf08tabJXuqgKQFIxRm7duoq7IJP2SjMRJB9ju8dsFb+8B1J2UK68Atkw6G7K25uTkIDg4ELOolZffvhn3Wk4/4NdqYc/hNxF2L5HvvfJVOz8/D5mZd2he3idPsvH06RPYVKmCxrZNUVT0mloIq1eviZKSt5RQiYD4GjVqombNmqhVqw7q1KmDRo0ao3btOsyepQY13G4jj71UgluWHIq0OevRfcTFXcSQYeOpC6oAcpRuyGwM8vNnz3D+7EkMGTlFJ95W7iYryEkJvnXXHGZNlwAuV1rLBZmUORawHYOHT0TduvVkpFGcCzIHXkOO7kfvPt5o3rylKr42OSkBT55mY+CAYSpG5KBjhyjDcIf2Hei8F6zAb98WY83a7/HZJ78Sr7Gx9oeOBKB7t+7o0qmzBGw1vDSEnL2mdCFhDCr1VfbgQIqmkrwHSNx561Zt0LNHTzU7ssyjAUhNTUZKagrGjBkvkkeRONtdu7dh8LCR6O2m71rP9uWPx5IrZTv5cFTXSnmu8qGVbrEtePkK63Ycwd4jEVgyawzu3c/C8dMx+GTJVEwdM0DMN/VOSItvxIFbiSggwNaKI4s6tdcf7kMHUYBK3JOpxZa4T1lZ41LEKTSxa44uPQWGOLk78oM795Bw6RJGT55sMu0PUWyvJSTgwb178Bk9Rp3TVqEca522Cfqq384dGDaSuP40lolVCW5peXPIla+BLWYEKMkebMZaK0ET/VEQeuI4GjZshF69e2tiY6NtMlpOaonld5TXWDYFNkv7DHNvU3qAK79TD9ySdgcd8kfDRrbw7D/AkGuyKVIpaeywcJiRDoN2zb17aWWqWnTpBRlK1UryI95mFNSeOnkCbdp1QLv27Zl7maebJKNirLrypmnXJcx7ceKrFwq9pcMosP3o3L7yErlF9fzg5WtR+feFjUlAALaplyLRpEkzdO7SEyEHt2D0+BloULcODu3bgnHjp6NB3bo0ZUhYaBBmzpgPGxsSS2eNQ4f2wtPdG61btxGBLbHyEIvtQt5CqIyxI39v2LwBkydMRmNbWwuBrcHNT/f15a6xxqRkeSk1uJUtm5oVyoGtDjMyvy8fDjyEDu07optACEUXVX7VVboWowRRZ6JQvUZ1uPUirL/gwBpTjgW7XFUl2LZjE7XON+BT0ZhzRxbuE9d3zUNW0/J/9aqQWvxJGjrC90DcPouKikDAEiFVItfy8nJpGRLj+OD+PdrWxo2bomXLNujQoTNq1a7NUkNwsla6UotWU04OYryxaF0FUlMSkfXoATz7kdhkTras27Yq9hYlePQgE5cvRMJn3HSdeFuuMSoXZAP6lprHgU0vqSaPunM7FQ/upcN7wAhN0igCHIsKC3DYfydN8yMQQIlpewjRYUQI2rRpi06dpDy1wnzesXMTJk2YSsNrhHsIedTzZ09w6MhBMRRBabH127cb3p7ecGjjUEHA1rQ7MpH9nbt3EBUViZkzOFJCEQiLLsj8IUQJ8Dz3OXb77cTSxR+I5FEE2J6KikCtunUxcfwwQwvET5UEbFe9B7Zc/wya8gkcWtnhT18sRGv7pvQaYUMmMbb169XBnjV/MNSRv2Shfbeu4mUxibHlXI7T4hJgVVwCR48+YtofwR350d27uBF/DT4Txuq6I+/bvA2TZuq7IwsWW6Lgbl67Gos+WMnH7JK3lixDLCW7sNkJsbZcSe7zIi8Pu3dux9IVK026JItOkmXd3811joGF1hzoSElKoiQXPiNHiZsL+1hz95e2rP6rWfJEcwIq/e9qK6Wxusy1XvU7c0HrXi1YaQrcxkRHIzvrIUaOn6gBbvkNW/YqSuutBpAWtA7+PvkJv1CZcKZtTE5GSymtsQpcy1ejtPLyfxuw1F48dw6vX79Gv4GDxCbpW2qVTs6S0iLcrJzympZb4fRMQwimlgyjwPbjc/uNirdcy33vNbVc63tfGScBAmy3nb6JW1cvoHb1Gujp5IbQI7sxeNhYNG7UCMcCdmHIkFFo2rgJCl7kITjoIObOWSwC28AjB+Dm5k6VVMFim/M4GydPBlPCFTHmj3c5FP7+/qfvqCtt7Zq1VMBWtj8qD3SMnurqdTDrLvsLDALlnDNtfZI3SOaiysQJklc4e+4MVbL7kdzjAoBUpPnhYgU5jHcz7QaSkq5hzOhxJgmk6CrOA14SQ2htY0NdTWlV/OYgWXAl2kC5hUyxXmsBXAWKN7e3meoqkqs3K+shHj68j+vXk9GgQSP06OGCNg5t5QBX1GnYnL+SNVcL3J47E4Z6DWzRpZsTn8uUdx8Wga5Ul2DNzcl6iJSkeHj29xEBrGjp5bZJ3oioALgGhCC67Qq7E3+Bgk0mrpb8HXJ4NwaPmIRaNWsy1lrOSsuVt8LN1ETk5T6Dh0c/KQ+t4KpsDezZvQkTJkyj5HBC2h5y/9uSYqxf/yM+XPkZb62ViKPu3s3ApcsX4DtluvgsAnjJXCB1bN2xGaNHjoFds2ZUU6avoLDYctuYtPfqioYhKBPGrqiJsONO/C4drJC0WSQOWEiDxd3PuPAz339a8z3mz12EGjVriuA27VYariUn4oMPeB4ZM+vJ6uDKsdiuHPneYku7Zv/RU5gyWm2ZffW6CGvfUVbkvWlXOVdk3jJb8DQXF4JPYszcmZruyLt+XIu5Kz9AtWpVeZIoOTvy+VOnUbtOHfQipE6iGzJ1VpZZcckEJFYZW9vGcHZ1U7sj0wnKEEgpFyRmMiQmxOPe3bsYOYaz/uoptBUObg2AWn59NjmVs7OzcCQgAAuXLuPKaaxOBtZy8RmWlP0FdJYyPEL9JlqAUlc3M/NkPYCrJz8t2KgHcO+k30b48eMYP9UXDRo1lPpGgdZZ0Kz4SZYnVz4s9ICvcuxoQXLTQtHyfKB3MD9oekfI5qHSoquCxiJzJckLTFznPPv2057HqjQmOi202FJr3ErLSswosP00+kAZxn3pb/3W891j4i/927w7dxJgu/X0TWSmxlOyqF69vREZfAAeXgPR3K45QoMOUKXXvrk9Xr0swOFDu7Fg/nIR2B496g9np15o366dSCBDQMap02GYPXOuLrD9+1d/w2+//J2UuoRRYnWBrYlDG2MS/WWstcq2CDPS3P6lXHOVwJZbK7l3SE5NQXJyEiaMn2iIQIqAv42b12PlBx9rEkiprLcoQVZWFj2gmDFjLvMMuSVZyxWUew9tcGtab9BCwLw02WWNEaSWTO/eTce1awl4/Dgbbm5e6NipiwQmFaBSaCobY8uCG/Iax44dgGvvvrBtzLl9a+W0FcC+YAlPv3UdD+/fRW/vwbIYZY4VuYwxtqLXnsKAwoNVMn9izoTBzr4VHNp1VllrBYsrOYg6ccwfLq7usG/BhxIw8bUFBfk4FLAH8+ZxJHAscVRODpereuaMuRJQ5nPYXrt2FQ8f3ccIn5G8+zIHaAVg+/P6NZg1YzYaNKhfYcBWK0adnT+kG2IuXcTz57kYMniI2CeywxsG2PoH7IejoxMcHNpTd2Ty36uiIvy8YTX+8fc/GVp+1gSnGCpX3oVWjJRCyMq7bkvqq3RXZEsa+66U9UtLQEExF2MruCOf2O6HwRPHokHDhmLqH8El+WL4adi3aoUOXbvwp1kHkHr4AAAgAElEQVQkklaIE7DG05wniAgOhu9cbnMWf+PLcCdNXJzt4+wchBw5jLmLl6hy2lLdWRPYSlq1aMkFcCL4GNo4OKBLN3nAt566WtYDbM3+KydgSxaPr/73L/jid7/n3NfLCGyFtppTEN6VMWlZOzRAnZkXNScHSwCuJeD29atX8NuxjR76dHdyLqVrslrF0YO1mu9p7uX1hK9httQDvfLDJe14WuUspuzH4WGoXqMm3A3F1OpYaqWKzXhwyBky2dc26tRBylUxGGP72fmDlg3rcir9jcfkcqrpfTWsBCiwPXUTj26n4MXTLHh4D8G5sCNwdHJDm1ZtcDo0ED16OKFtm3YoefMae/22YPGilSKwJSlcSJxdxw4dRGD74EEmzkZHYea0WZrA9uXLF9i0dRM+WvmxDrBlDozYg52ybna/sLXW0pGmB2zlQIjbnAkzMolVXLRgsQzYSkBNck0WlPVde3Zi9MixqFuvnk6crRR7y9VTgq3bNmD8+CmoX6+BWautErCJDWPPJU1gV0vlpa27SF5Cz58/Q0LCFdy7dwdDh46mxgdx12HiKIV2v2VchOXgtgSbN36PuQs/ovezTMcsmZTMpZm4u2ak4dbNJPQbPEYGbmVuyIyuxR4S6G5f/OGOqDMy80MAj2mpCch7/hRuHgNFyywHZuXW2qLXhQjYvw2z5y7XdEO+fes60tNvwsdnjAy8kuekpiYhMzMDw31Gi6l+BIvu+Qtn6bW+Xn0pqZTAhixYZ7/78RusXL4KNWpUVwNbfptVWmzV2oKkDbLGf9l3M+zI5BDin//6h2ZOW6GPaJ+UAOfOn0FxcQk8Pbw5iy1PIuV3YDe++HyloaH7c0jlANvlI94DW0Md9C4W2nMzXhZjSyy3qTFXUKtmLXR1cZLF2JLf7qdn4M71Gxg8aqSuO/KxAwHo4+1F0wZpsSNL7sjA4QP74Ondj6b/UZFI8UoqC2DpJcGNhBEoKbN5wzrMnDuPEs6YVlS5Gsu638seIue40Daz8jeYwxbk9/17dmPg4CE01lnaVaQnmqtDb6yV9r53cexqt4mBmmUAuJaDW/kWolS22F9PBB1FterVMGCoj+Xglq9IF1CLQlG/fFn7Xhvw8XNJ1RnGXI+FeXjrxnVk3M7AYB8iE+4je57CUqtmWZbfpGyr6m/xgh48V48urfc3Cmy/OO9fKVPoXx6TKuW5/+4PJcB2y6mbePYgHY8zb8Oznw/izoejnUN7tG3bAZfPRaBl69bo1L4zrEvewv/gDsyetUgEthHhIWjXvhM6McD2/v17iI2/jInjJmkCW8KGG3LyOGbPmCVacUSvJsYlUebpVGZrLROD+o52qnqtlRMeKeNE1/y8Bh8s+0C0RqpdhZmYW5TgxMnjaNmqNTp36mIS2NKlmXdHPn/hHKpVqw5n5166VluxXYwlUqiDPc2WrdtlXcQVfaje5ySAm5v7HFFR4WjTph26dXdUWW9ZEiwVuOX1IWL9vUBiZ0dMkpFJiZZeHigLzMoCME67ngRi+ezmKLlzk7aKzxR2TpXeJbwg+2ZyCy27twig9knOQyTFx2DgsHES5wtvbeWMM7zLsBVw8/o1Gg9LctSycbVCrO2Fi2fQoH4DdO/uKB5aCffHXIpGjerV0cvFjTP88KmByHfirdGihT16dOvGz38W3AI/r1+NFctXyfNXK3JZawFbuWbC6JBKt3bx8ESfSEzAvGERYWhs2xiOPaVxoeWOnJGRgfiEOIwaNVa02BKAe+vObfTvJ6XvM7W0rKskYLvsPbB9R1d8A83afSMOBTSPLRdjS/59/jAbiWfPw2faFE13ZP9NWzFj6WIpny2d+NaidTY1IRGPHtzHkBEE/MrZkSX3ZE41vRafgAeZchIpQWkV9mQ1sJWWJva3jPR0XIg+B9+ZM81ba6TEJOq0eQbkpipSTtZaYREK2L8fPRwd0bEzzxxXTlZbtt3lvEcalppRi1jZ2ifdrQUyjcrBcoCrYT9lLglfE+PjcDUuDjPmzZe5GEt7jZK/WPE3uwlpSF6jFYb7x3xBxjKkUZgliFLOVPZOYX4f2rcXze1bwsO7r1hbxYPa0gHaV69egbCS5ufl40V+Prp27IxG9WqaFdmvLgSYLVMRBb5yJ3Hd7z/lLYHc/FfYFJ6Cl0+zcCclHoN8JiD2XChat3ZAp45dEXOWU1K7dSFEi2+xdQtRSj+lOWwJeVRwyGF069oDnTp2FJXftLQbSLmepAtsSbxnwtV4TJlEUojwu6Rw4KOTw5Z1Ty6dDCrHDdnStsp1dNPAdt3GnzF1ki8aNmyoYi6WpePhQVdcfBwlwiEs1gJwlQExDnHJrIsPHj7A6VOh8PWdLQO2SgIkXXArohHFPmapYIyWVwJEJgYzMjIMhYUvMXTYaAW4lWJdOQMd+7dELnU9NRHEG6Fvfx9dpmQtl+bryQnIuH2DxrvyIhad12QA14CioBtjyxs49m9fjWnzuNSRLFOy0lpL6tFyQxbKkX/379uO4cPHwtbWVoyvFYDtkcADcHV1hwMhjWMYkcn3ffv3oF/fAWjdqqUIbAU35oIC4q2xER+v+qRUwFYYBvJDErlOIdM9lDqLIlY9Pz8fW7ZtxqoVH9KqWZd8dg69ev0aq9f+iI9XfSoRSJGxZWODlvZSKkRTw3T98VSjo7hcyy0dboy1uVwfqqVPlRjxSajoVvwfq/9/D/uhSZd2IqiV3JH3YPj0qahdq5bKHTn6RBjadmyPth07abgjc0B27T+/wqpf/1qTHZl1Rybf1//wHZZ//KnkXsXQSJkDt6x6Sr4HBwaiZZvW6OnkZBjcisq3UcSlgYqMAgkDazBdvMNPnkCjRrZwdnXlnqZzo5H6jAzJ0tZTGpEZaY+5Mpa1lytdWoBb3uBWaMuDzEwEHzmMidOmo0GjRmInsxuMuuv1AK7+IFHLyhLpmbLTKntJbqXl5pUWCJbKXTh7Bs2at0DbdkbYj5U1Ws5+rKXo641h4Xri1QTcTc9AZuZdSgpT8vYt6tStizp16mKkz0jY1q9lbrji1xcOmS1TEQX+4T6hIqr9j6/zeV4h1gRdhvWbItyIi8bQ0VNx9cIp2DVrjq5de+By9Ck0adIEPbs7wRol2LL5R6z44DNU4YFt0LEAmt+RjbFNSiIxdg9ojB0bmye4JF66fBF5efk0ts04sC3jCl0GN2RxlWGWm3L1kmJGoSFgyyvg+w/ug4sziW9ub5pAii+f9eghQsNPYvq0WRKBlJjmRw3mOEW/BFu2rcfkSTNoeh2RJErBMixsTMKqrjCiMXuW6TWb/bU0Pa7sK+lvrmXp6WmIjAzHkCEj0by5PddsFZjVB7cxF8+gVu066NyVI5MidWqxI7OurKRMXu5zXIyOwMBh4xmXbkkdEuoytyCJBhDGg4F8JeMxIsQfPV080MzOXpxXHKDkiKJYa+3rV4UICdyHyb7zFG7InEWXsFPv27sVCxeuUMXXkno2bVxNSeRq1a4pMSJTyy2wfsMazJ09H3Xr1lEB28ePcxBwJABLFy1lXJQFr0Mmpy3dIk2n51P2rSA7c+7InIYhHXQFhxyjebidHJ3lB0TCIQnPlLxl2yaMHEFc2pvQgw1isbUE2G6oJGC75D2wNTet3t3fF/zp9/CYNo6x2NpQkJt07iJVtjv27C6CXoEdOfN2OtISkzB84njeaitZa8lCQCb4mbBw2DVvgS49emi7I/MLBpmC4cdD0LSZHZxcONcEQfmkCw+ny4rqMetBKExfJbhdv/onLFnJsS2rVW+tvlAo30Z3Bk03GOMbkN6oIDVcjonBq8JCePWTSHTKK9b23R2NZWuZebhWjgCXf5jWM5mniC+kBarJNZKGYcemDeg7cCDaE7IOoZMZsg5tyGo5wBU3MAvFbG46iNsoU5D7qgNq+cvHAwOpq71rH3exRe+MpbakBDEXzlMPkG49eqKZnR1atLSnh03sp0oxULdmFbMS/c3Fw2bLVESBv/cZXxHV/sfXSSy23x08h5rVq+Ha+TCMGD8T1y5FolHDRujZwwWxFyLRsH4DODn1go0VaKzhihWfi8A2kFpu+qCtQ1uq1JK96vKVGBQWFmDQgMGawPZk6HE0btIEri6ucqsNs2dK+yfXRULYTqk7TMGeaqoecweH7CQ3t6ZY0l7NtZUF5Iy1iSyvoRFhqFO7Dvr07mM4zvbb77/Gig8+QhWbKjzAUuazleenJYDrwsVzqFKlKlxc3DSttuy6rp1XVJKC9I7mdzlt2ZmSuFSnJsAVgOjbYhw5cgCtWjnApVcfznprCtwq3IyDAvfBxdULTe1ITmCdNEAa8boZt27gdloK3DwHoWat2iqAa8SeJSNW4wVEriUlxNC8v12795LNKcFyyzIhk/LJiVdQ+LIA7sQNmVp3OXIo4d/bt2/izt1bGDKYpAriQTFvmSXs6PsP7MbiRR9Iv5Ey1lZ4W/wGa37+AZ99/IVmGAKJd446ewazSBgCBd0CqRS3z8os0kaBLZOjWNQNmAEgHsYwigMLbInnkt++vVi0YJH2ARHfxydCj8O2oS0cnVxEd+SSKjawb2HMYrvpROVYbBf5vLfYWrIOv1NlP9/wAxo62MOudSuat1ZwR36a+QgpMZcxdMpEMY8t+c2Gd1k+tHUHJs2ZhWrVq8vIo4SY2of3MnEx6gwmzZzJTXpSiieNEk6bJRKpbESGhWIKHztkPNaWn9QKiV5PSUH67TQMHzVaZbWVq9vKrmAUcXM7bylALbuRmRsEt2/fxqUL5zFl+gzF7qa+s7Rbnbk2/F/+3cjxgilFTO9+1XWdGB8NVUHTYiyUO3EsiM6twSNG6oJb9fhRuivLbjUZ510efasFaKX5pQS1PNTlLx/1P4j2HTuhG4nRERQNtlEVElMrn9RaU5xci4+9gqjTp9GjZ0+4e3mhZi21RVa418YgsP1dzJHyELnFdfy19ziL73l/g3kJEGD7r72nKXi9HBGIMZPnITnuHOrWrg0nJzdcvXyOpuQhoKaqtRU2rv8WK1d+IQLbw0f2wb2PJ9q0dhCBbXR0FKpVqwZPDy8p3QeT7mf/wb3o49YH7dq2k6f6YeNrGe4I7lDY3EZm6l0tc0NWWhvNSbFMTWMqNwJsubWTe58r8bF49PAhhvuMMAxs/Q8dhIuTK1q3aaOOsxViZBXuyAUvCrBn73bKhi1jm2WstuKazoJvRcwtK0dL9npLe16INWZwDJNiRwLusbGXkJv7DP36D+HkZwbcivG3JcDB/dswZsJ02FSpqmJKFq24GuCW5MU9GxGMbj1d0aGrI+OSrOvMJht+MuMH/0filfN0Hjn18hSJSlnAqLTWktuOBx2EV7/BaNTQVgZoBWAbFRmKpk3txPhaAloF4JuefgtJyQkYN3aSCIS5360oE/WxkEDdHNap11OQkpKMiRNI/L2cLZmd5qxRSG/+ycaQTtofdr5ojlFe7zl6LBBtWrdFj+7dxXlByzNj4lpSIkiKn1EjuDjbYuqKbG0Y2G6uJGC78D2wNbeEv7u//+3YAZBUJC6DB3BWW4Yd+fjWXRg+wxe1a9eWYm35OFzCjmzbuDF6uLhIcQRMSh9rK2vs3rAR4319KbuyOt0Px4wsLCRBAf5wceuDVq1bU2EZstqaIJI6ERyENu3ao3OXLqUGt8peExZH7c3F/JZjvoT0xNzcXOzcugUffPSxdNFEBZbU/e6OxvJv2S8CcMvJeht/5TKuxFzE1FmzacosUcFQHKOr30kD4Iq7EVeLlhXZcmkrXJwUmpMpKy27+ZL81cNHj6PkOsLHEkstrUv8nzpEXqnQKd2P9RS+gvx8BB4KQMNGjeAzapTM2qV3j1Fguypwk+XiLoc7fhq7qBxqeV+FUgIE2P5tx3E0b94K54L3Yvy0xbh59SKqVbVBr14euBZ7HtWrVqXxdATYbtrwncxiG3BoL/p69UPLlq1EAplTp0Kp+zJxkxWsPVz+Ws5Vcd2GnzFt6nQ0athQF9iyYNEUsCW/md8zjANbS0EtO/EtBWDKvrAU2GbcyaDWr5nTZ+rHvypy216IuYC3b9+iT28PBbBVp/GR3I5LEHz8KCUUI6lzWHdk/i4ZcGTjbdVr/y83B1nrMLv1cNe5vSYxMQ4EqI0azadN0omx5cCqFH+cl5eLsLAgjBk3nR40aLMja7g082Ap7tJZPMjMgJvnYNg2aSaz3pqTEGvVTE2MxbPnj+HZd5gmqOX0Vbk7cvaj+9RiO2TYWE02ZDJP/fZsxtixU7iUPNSaK7kyE+Io8mZeHn3FOS2UuX3rJhKTr2LS+Mma3hpx8VeQnZ2DET7DubnPpAFi99aKArZ81/O6hLQuPHnyBMEhwZg5g5tL4hhhvpPUQLv8dmDJ4g8ki62NNVoYtNhuOXndXNdWyO8LhnWqkHotrfR9uh9LJUaYhJMuIHinH4bOnS7F2fKW2+TzMTQ+pIuLE00FJKT8IZbbZ9nZuHLmHEZPnaLpjkwmXsLFGOpq2cerr0g0JY+vFSaoFYiF99L5aJrnU1Bayb+iuzG/+wmboPgvo+HKTuUAqqQ69epFT1n1rDP6IjO63ZpXD8RNyoL+IbWuX70aC5YuRdWqVVl0oluL8ZZY0JBfuCgr9fJ8H0MAV6eQ3r3yk0/9LtIClZrKGICnT57g4J5d6D94CDp2lRKEK2PI6Eaj6hsT8FVR2KhsVbNAY1ooAS2DOaUZbAUUvnyJ7Rs2YMa8eahXvwGr20pvYsZSK9atWA+ECkoLagmZV9TpUxgzYSJdL/TqU4rcKLAd96fPfuGZxD3uyB+/qZTn/rs/NO/Fa/x5y1E4tO2Ec8f2UottRkosrEpK0MvVHdcTr8Cq5C3c3DwosN2zawPNa1nVxpqSRx0N8oenuzdatGjBA1srhIWFUNfkLl26SsCWIZj5cc0Pmql+ZG6WzAQwRRxl+pCW7z2D8bWlBrXMICmL9VZ7LWVAuXjwyF3Lf5EPv31+WDifO/SRKeT8wioQIfFGXty5k47LsZcxfiwH5FiyHOpWyyvyLFMw+Z7zJAenIkIxadJ00WqrAo6s14/CcivqDkYXbMXEYw8Azc1JrX5kSZoEWQjW54yMW7hxIxWDh/CWb4Pg9s6d27h5Ixn9B42kLsmqNECa8bsSU/XTJzm4cDaMujQ7uXkbOKDh3pzI4lr8RaQkxqJTNye5pVYMjRPSTDL5Z/k9KYKk8OrpihYtWkrWWsYdmbA4h4Qchq/vHHl8LXU3BkJDg9GxQ2d0aN9BRhxFdOW4hCsoLHhByaO04uvPX4ymY9fb08sMsNXajdU9Lw0niWhNa6wJXg6ivqFw6yf1bN2xFRPHTUTdupxrsRj3TOcJV/+W7ZsxacIU1KpVm7PYWhsHtlsrCdjOfw9szS0Z7+7vm1NjEBl4DG27d0WLdg7UHbkqb5XNy3qCuMgzGDF9qqY7csCW7Rg5eRIaNGrIuCNLLMgv8vIRsHMXFqzgA+lFd2QJ0Eqpf6ywb8c29B88FPYtW3ILEb8qcwBXHmsrKriKOCIluCUpgMZPnkxzsVkObtl+U95t2U5jWWkOtPz84w80fVG9+vWlhpioyNJn/JKj0ugxgaVtsvSdzQFcLSVJXPA1GlcR4JY8JuiQPxraNoZnv/4yCKsklhI3HFXbdKy4pl5GWYeZTtPbQtnrwrx9/uwZdm/ZjBWffi57iuwRlQBqyfNJDmwbGxsMGT5cv206A9MosP2vy0GWDu1yKf9n19HlUs/7SuQSIBbbP64PQKeuTji5dx2mzl2J9JQ4FL9+BXf3fki+GgOrt2/h7u6t6Yrst3cbRg4fg6ZNm1Ill7AlHzjgh77e/dCqVSsO2Fpz+yT5np2ThaPHjmLR/EWqVD9sHK3MYmsCLRoFtubWV1PrpaVjprTgVhOQSShMDAFh4wO//u5f+HDFR/TQmLWwCu/DliW/Fxe/wXc/fINPP/6VqKyLLMCMRVLJjkweHnD4AO/G7KCw2op2W7OWW1GWQodU1IbKP0hOJCRZahkKCCqHGzdSKOgfNGi4TM4yEqgSIYetxJR85XI0vd+5l6d2GiAdcipBYqT+m6lXcSM1EZ27ucC+VVtUrVZNd8ilp11H/OUz6NC5B7o7usHa2kZK66MBajl3X4k8KuvhfcRficaoMZwhh42rFYBoauo1EJfpQYN8VPG1pPyWreswY9oc1KlbR8ooQp5hbYXQsOOUeM7ZyVkzDIHE1zdp2hSuziQWmNsoqV7MTxrRGi38bWbyyfUWPXZkyRtB1DMUh0Tkz/BT4ahTuzZ6u3Ex64JLOzsGjgUfRcuWrdG1a3fOamsBsN0eWjkW27lD31tsLV3D35nym1NikJp0Dc/uP4Lr0EEqd+QT2/Zg8OTxqN+ggcodOeH8RQpoe3t76eS0tcaRvfvQ29MTrR0cGNdjwQWZxN7yubpghfRbaUiMi8e4yVPERYcDsIyqrEEkpUUMwALfr/76F3zxu9+L7hsW6u/l0lfmFATlQ0j53du2YfioUbBt0sQQsNUHOeXyCoYrqeA912Q7jMpZvxxv9dQpoHVZdU1c/OVNZU9JZfhSUYHwZ9ylS0i+lgjfOXPp5scqN7p1qaSjVdJwV5oAeupelq7w85W/cP/eHYQfP4E5i5aI9anuthDUsvNb+Z3+rWGqUFlzAezYspkyqIvs48r8uWZEZRTY/vFycOmEXsa7/uRKYrbff8pbAgTY/r81+9HV0Q0n/X7G5DkrcCc1AW9eFcDDY4AM2NoQVuRNP2LFis9QxYZL97Nzx0ZMnjQNDRo0EIHtlq3rMWXyNNg2aigSyAjANuFqHB48eICRI4iLvDzVjxaw5Ya/9krMXjW5DurwB7CyLA9rLVtfacCtRcCWB0wkVQmRZbOmzXSArQTmBEV9x65tGDLYB82a2ikIpEy7I9+9dwcXL0Zj4sRpqlhb1iVZtn8zHWPmiNLs0JYFkJjanFnQwtcqA7isBZ+xUBNARxjjBw7UALcKAinBgkestGEnA9GhY3fYt24rWvmE2EwpnZLklizIRwROlDU5F8mJl5H1KBMlb0tg16I1qteogVq166KIpGbLe47HOY/QqEkzOLt6oWrVaiqPQB4nqsijRMspgJMhAXB2cafWWiJCLXAbFXmSEmt17NCJEkKx4QQvC15g3/6dauIoPsxgPznU8uqH1q1ayeY+F4oAkBhvkjO2c8dOpQO2iklvCthSOSvHH6vTKKy2mQ/uIzT0JObMnqtLInX1WiJup9+ih3mEFfktcUVuXtfs2CUFvt112lC58i706awB5V1lqep774pcCrFtTLmI568KcH7vYU135BuXY1HFpip69O5FXZU5d2SOZOplfh5OBwZj4pxZTE5bjiSKA6xWSEtKxr07dzB05Gi5OzIDaFmrrf+eXRg0fARN/swqqKzVllVkhflqCtw+ffIYwUFHqfVTSwkWxFZRgMwo2BLaIZTfvX0bhvgMp4ys4sdAZQaKlGKkaN9SFpm9efMGz54+oTkCX+S/wMuXBXj96jWKil7zp+IlqFa1GmyqVkHNGjVRq3Yt1K1XHw0bNESt2rXNvoNROWiX0we4evXKNwuuefpAWP4L/Uu2mXD3k5RAIUeP0ByvhGGc/Wi5J+s9U28AmZKRvG/NK8ncM+SgNjoqEk+yczBm0mSxCe8CqH318iU2b1yPiZOnorl9C/226Ywy4R2sDZJH/elKiNnxWhEF/thrREVU+x9fJ3FF/u0Pe9DdxR1h+9Zj/PQlyLyVjNcv8uDpNVAGbK1LirF921osX/aJCGw3b1qNeXOXoFatmnxuWyv8uPobfLjiE1SrVkWV7uNEaAiaNbODixPhtOCArcpKw0ysXwLYljeoFfdhCzeV0gDbQ0cC0KljF3Ttwsa+Sms+tx5L7q/kG7FM1a1TF716cSzHrAsyLavljsxbjg8G7EM/7wFo3KSppPyLi7X0HM31WwNw6k1AwyBWcYCnfzgr3wfVcuHkkJqahOzsR/DyHqgmlGLBrQLoHjq4C8NHTUTV6jXNglvBCC/sldRizguM/Jv7/CmePM5CXu4zvHn9mv5at15DCpxr1SIpdNgdSppDnOVT+29ynpxFY2tjMWTYGFnMrGi15S2nu3dvwvQZ86jOQq29THztrds3cf16EsaMniCvg4YaAOvWr8b8uYtQp04tNbAlB7C7tmHYUB/YN28hAVuN3NWiBVecTBojRePASsksbQ7YcmKXKtq6cxsmjJuAunW03ZHz8vOxdftmLF+6ilroicW2uUFgu/zX/6yU/ebnf3xZKc9VPvQ9sC1FN2xIvoD84leID45A257d0KJtG5k7cv6TZ7h0IhyjZ88QGZFZduRje/bBa/BA2Nnba7ojk4X256/+hVVffsmwIkvEURQAMyD3enISbt+8iZHjJlhkteVcM5Q2I2khu52WhkuEmGfGDE2XZFPrQCnEKt5iFFzJAAv/B7EmDR0+As1bSIq34YASQ+Qglr+ZhTqH+ID8vDxkZt7Do0cPkfXwEbKzHuFlQQFNKWXX3A5WVjZUySO5QYUOEuKxSXLyly9fUvKO7KwsGodKQHEL+5awbWxL/21hb4/6TNymljxNva2l4FZTAVHKnD3lVDycVxdkV5Xgln1GSOARFBa+xKgJk1C1KpNeRrVJSW9SmrFndESYtNdaAW+KiuDvtwcO7drD3buvWG15g1pSMVunEUtt7vNn2LZpIz789HN6sq6sQ0sGeuPeKLD985XjRkVbruX+q5fcvbpcK/8ProwA2y+/3QFHVy9E+G/G6Mnz8DDjBgrznsPLe5AM2KL4DY2xXbrkQ9jwFtu1a7/Fyg8+oXOZuCGTw7zNW9bhw1Wf8KQ1PGkUTxSzdcdmjBoxGs2b2RkjjtLwWNAa57prhJk0P1prVXkOB0sst5YCW7KHnj4TScMPvDy8DBJIAYSZNjk5CePGTpBysAprvI47Ml3Dicvuzeu4fj0ZI0eS9FtSXKMkR6KC8fIAACAASURBVDPgVm/DMbB4mdyzhR8VA0H8k7HOKa23LKgk70gIpZ49eyqCW1qekQvLjix8J3v7If9d8J25RLKCM27IrOVWkJvQNr56AWLJhx/7PjJAK8+1bg7UknEYErgfvd37wo7kuaXWWrnFlpS5f/8u4mJjMG7cFM342vPRUahevTolHxPI4ARvjKKiV9iw6Wd8vOpTifGYAcXkxdauW405s+aifr16IvBldV5NV2S9jtfSS5TsyIrxZi7W9lTUaVStUhWeHp5qEin+0GfT1o0YO3ocGja0RbGNNZrbGbPY7gq7UZ5Li+G6Zg3paLhsRRZ8D2xLId11SefxovgVHt24jecPstBryECVO3Lorn3oN2YkGtnaclZbPuUP+fd6XALynz6H99DBIEzI9JRKwY4cERxCGVC79ewpd0fWsdru3rKJkkjV44PRRdcr/v3UJ2/cD3pWW2HtT4iLBWFxGzhkiElwa2CvMCTp0gIL4b4Dfn7oN3Cg3GJraoPTaVVp21FaEEuaQXKcZaSnU4tj2s0bqFmzFmXHJgCU5C61tbVFnbrGFjat13r16hWePn2CzLt38SDzPp4/fwZyrUvXbujcrStsG3EWf/ZjRA56AFdPkdMqL7tWjuD2dtpNhIWEoI+XF3rSnM/yp+tZcAUZGHl/UwPbJJhlJg05nAoNDsFE32lowcfLa86pMrofq+oU69M+4CLlc7IeIWD/fixdtUp8VVPj3NwcMAps/xJ7wtCaUd6F/p+LT3lX+b4+4gL54jW++NcWOPfpj9OHtmH4+JnIvncLBc+fwKvvEKQwMbYlb15jr98WLFm0kgJbovmtW/8DPlr1uWitffIkB8Ek3cf8xaJyK1h8iMD/+fXf8dsvf8e4UfIWW4VbstA5yvy1ZnRcdZ+aA7ZlXUwMjCKj4NYosOW2Tu4kMCHxKjIy0jF61BiFBVVupZXib0EPYjdt3YAVyz+UCKSE1DQy665kzWWfuXXbBowfN4WGdclBorRCmzrcNCAyWkSzrxUXzY4H5b6lAXBlJEE8gL98+QI9dO7d20tkLBbKiVZuvqxAGpV5LwMpqYkYMGiUSXArsEbLADWvD2kd5wrXhKNL4axHeRAqGVYYyy0PYB88uIcbKQkYPETwOOTibjnGZCnWNjr6NBo2bCSl+eFdjIUyAYf2waOPF1q1aq0ijiKH/RGnTmLOrHm6wParb/6BX332JWxseFMQReQa5KpsjK0lwNZkTlt+FLNjQvzOfcnJyUHw8WDMmjFb5Y4seDaEhYeiXr16cHZ2RTGx2BoEtnvCbxod+uVabsbgDuVaX2krew9sSyG5dUnRyHvzCtYlwJnd/hi+YJaKHfnGlThYlQBO7n0kq601YUm2QnHRGwRu34WZy5aIRBdCzlrBHZkwHsdduIixU6cK9lkmMJ+32DKpfxLjYvH0yVMMGDLUMqutAXB78Xw07mfew4QppC3mP0bKKGsp654v3L91w3pM9PVVWyLL8AD5Ym/+/S0pQU5rU5KTkJKURJWAdh06oE1bBxqXIiPAYiotjXzZNrGiyMnOws0bN3EtIZ7mHu3QqRO6dOmmerY58emBW7q8a/xYdnDL7878i5lTbsJCgkEs4MPHjkP1GtVVXaRuo7k3tqSX5aqT0jAUfiIEhS8LMXr8RFmlFW2pFcg0WLVO+cyi16+xdeMGLF25Umyb3vgzOi6NAtv/jTtpqZDLpfzvnYeVSz3vK5FLgADbz77aBBf3gYgO3osBw8bjeVYmXjx9DA/vgbh+7TKsit+idx9PFL8uRODhvZgzexEFtmQc7j+wEwvmL6V7KImfv3s3A1evxWHiOJKnUkoRQvTUBw8fIDTsJObPna9BHMXNRxYEcnPSmCeC7lpnIr7Wchdk5VOMzi7zaXj1DxylF2DLCMA2834mIqMiMc2XsBVL67ocrEnMykJc57YdWzFm9Hg0qN9AHmfLV6Lljsz9VIKk5ESaP3fAwKEyq618X2Eias3sNYYssSqga0T2CpjIjAXWZZUFqqKbcEkJLl48S11/u3Xj8s0qAa0gIzYN0NWEK3j58gV69e6rC24FOXGtk1IIyXdPaRMVdR2ZxZb7XQK5AseLHNRyrsRA8NEDNLSgceOmorWWBbQCwN21awOmT5uP6tWJG7I8zQ/5++d132HZklWoWq2qjDiK/EYs+XfvZWCED5dijhpyeNBM/i189RLbtm/FB8sICSsThmAK2JrrZq35rXGYZYlL8o49OzF08FAxbl2MhSbkYSUlSLt1E1evXqXzp9jaCnYGga1fJQHb6e+B7f/dbf/na+eQV/yKuh/HHwtHO6ceaO7QWnRHJlbZl89ycT7oOMbMI6DXRiKRsuZibsMCjsCxlwtat2vHxNqSCSpZcHes/RlT58xBXeJKIYJYiUSKHkDx1l7y+0//+idWfPIZl+qGscQI81XPasvt6foWG/LLvbt3ER93BaPHjTcEboXeNbdWaC6wpRgaZEF+/fo1tqxfh2WrPlTXUN44pRRtlHYH4Gp8PFKTk/Dk8WN069ETnbp0RlPiMqf4GJFfaZvC3kfE8/DBA9qmtJs3ab5lJ2cXOLRtJ6venBjVv/NbqkFwqxoPOsqi7mmziec8yLyHk8HBcO3TB90cnbQdz3WVU3Nvbr7nlIA29/lzHD8aCEcXV3Tp1k1WgazfVVZaXjHXGysaSomomDATU6nGK8da9qOHOOLvj8UrVohP0hqPxsaoVMq6uAR1azKu4ToD+G9xoeUxtC2u47fORIl+/ylvCRBg+/Hf1sHNeyiijuzE4JGT8CwrE/lPsuHdbyiSEy6iipUVevf2xKuCfBwN3If5c5dSYEsIbQKP+mP+vCWcxdbKCinJibj/IJMnh5IsQmQru5oYj5ycxxhKDnp1iKNkwJaZnObGc8UBW61VzdSxk4ke0lwzuPJ6IFvLdZJbj7lFsaDwJdat/xkff/iJrB6lFZJ1pSU3hpwIRnM7e/To0VMWZ8u1hQFb7Hfmues3rMasWQtQs2ZNVfuld9GgizK3ZCs6WlcDMiFLoQfkhwUaLtIK662W5TYoyB89erhQMiUO3MpjkMm7chZb6fq5cxGoW68BunZz1gS34qEBv7HSevn+NHW4LOqMzMYh7BcicZSofwoAF7ibcQtpN5MwlM9bS8Euf4gkuCMTXfPu3XRcS4zFmDHcoZQAdoU4W+JZFh4Rgmm+s2XxtYJL8pmzkahRvTo83D1Vh1qkDGFEDwwKxOIFvDeHcJBVRmCr0k8stdqK/cDNqctxV5CdlQ2fYT5yqy0fj1789i2+/vYryixuCbDdG1E5Fttpg95bbMt73/zF6luTeFYEto9SbyEvKwcug/tzBFG8yzH5N2z3QXiNGIbGTZvI3ZGtrJGekooHGXcxePRIni2Oc5eQ3JKtcSX6PAWpLn36iMCWK6WV+gc4fyaKujZ79O1nyGrLKrumXJKFcvGxVyjomeTrS2VtTgH4pTpEWKAJWMx69JCSR6k+5ja5im6sFZD77BliLl5AakoK2rZrD+deveSxwHwbKlOugpju3snAxehovHjxAn0HDEC7dvIFy5Q49RQ/fUuBme7iK9TfiNWn5coa2XtPh53Ew/sP4ObhgXYdCT29ztsYYDg1N2yUYFYof+70adzNSMfw0WPR0NZWVk2Fg1oNBmTlmHv25AlCgo5ixty5YtssB7XaI9kosP17fJg58VbI779xGlIh9f6nV0qA7Ud/XYvefX1EYPv04V3RFflabDRqVKsOV9c+eJH7FOGhRzFzxgLqSkjIaM5FR2K67ywuh621FS7FnMfbt8Xo17e/SjkOCz9JQzdce7lpEkcpXXZNHeyaWkvE30y4IZu21mqDWa2xon/0bGJkaYCy0gJb0tLvfvwOSxctoWEyksuxdHjJrfFy1+TEa4nIuMtZ1wRLJF9Mnr+TB23Cb9y/JYi/Gov8XEIwxqdxU6zLLLgVJGFku5evToq/yrAJS/ucnKhJeB/p3dXM0Hv37sCoUeNRu3ZdPu6SB7FMvlsW3JLvkadPoHWbdmjVur1JcEufKx5sKA4CFHussG+xY469JumgEqglcyg4cB/69R+GBg0bwVogmBLcj0WAa4WIiBCayqZLl+4yJmQyr+nB1NVYGns8eCA5mGKALz3UAo4FB6JH955o17adZg7bW7fTcCX2Cnyn+MpJrviXkB9q8ZDdXJ/r6SFlsNqSND7/+Nff8esvfiOmrxIPLvgDjL0H/ODm2hutHNqhmUGL7b5KAra+74Ht/91tfnXiGeQVF1JLLN6U4OyeAAxfNFvMZUsALrHK3rgUR/PyOXt5yGJsScwtAajbv/sRS7/4TGRE5uKDuN/IZM598hQnA49i+oIFHFBVWG0FEikhbuDN6yKs//F7fPjFlzJ6dpYiSrlgsXPZXLwt6bG0Gzdw7mwU5ixY+M6AW2ETO7h3L1xcXak7r+bHyG5XnsOSFy6Jab0QHU0ZjIllrpdbb5mFnDzS3Jpans0yWhcRF7HiXow+h9zcXAwYNBitWreR3W4ZwJWfZLMVmTo9puXKAdwy1YAAt9PhoTTdgUvv3mhNLdNmBojR8WOiM+OvXEHE8RAMHOYDF7fe+oCWHxTqqmQcnvI5yBdW3qP6W4G2lb8TEqufvv0an/76N2L7LAO1jDqkcaPVG2MW238mhBsdquVa7kvHweVa3/vKOAkQYPvh/65Bn37DRWD75MEdvMx7Bi/vwUi4FEUZdF2c3fD8STbOnQmDr+9sundmZKQhNeUaxo2dKKb6iTgViiaNG6OXi6s8j6W1Ffz27kI/735oQ9YrDVdEPTdkc2ux3qEdQ3aq6m5zwFZ3WRF+UMyh0gBc9n0NA1t+wRQgELlv5+4dGNB/IFratzQIbIGnz56CpGZZvHCZOs6WR3m67si8YLdsW495c5bwftaSu7MgbO13MrVgay1M+vuwYsnU1S/YJ4rAnxkcHLBkDgIESy4P1gnZoZ/fdsyZu8QipuTgoINwdO4Nu+atNMCtAkCL/aregeVDjtlrFCkjOU9BOai9nZaKrKz78PIi3DF8Tlu+nGSt5YwyO3asw7y5y8TYWRa8UnfmkEB06tQFnTt2VhFHkbG8x28Hxo+dKCOGYl2S4xNi8ejRI4wYPrL8gK2uHiIRm2mPR/nvwsGHYDk/FnIMzZu3gJOjk2xOUbfzt8ClyzF0Dg0YMgzNmhnjWNl/Kq1Stp2pA9tXynOVD30fY1uKbvgpMQq5bwo512NrG8QeDUNHV2fYtW4ppvUh4LUwNw/njgRj7ILZojsyAbwkRohYdM8En0Dbjh3RoTM/eZmUP8TySpaOQJLT1ssLLdu0kVl0pRMzuWty+PEQ6tLq6NLLYqst3dSZFVxPQSZgZ9+eXZg2axaa2TWvVFDGbiQ/ffsNVn5CWPJ0kIVRYFKKMaGFAMghwPlzZ2nsqqOTM41fVU3AsjzrF7qXiO1+Zia14BKW5aHDh6Nevfri0y0Ft7wuo1YANd5HVrcF4FbcgzQax156+OA+oiNP0wOlLt27o3N3IT1Q+Q6WhCuxOHsqHE6ubvDqP8D8wUZFuB8bsNSSmbN+zU+YM38hatauRXvEOKhVqNw609AosP0qIeIXGuHyx/zKcVClPPff/aEE2K76y2q49x8hA7av8nNpTF7sxdNo1KARnBxdkPMoE1cuR2PSpOl0r0xOvors7IcYNnSESB4VGBRArTZE+aWHwtZCnJ0VflzzPZYsWoraNblUIEJojrC/lasbchmstapVxgIsxtuZDA8b8s6mQLbKFVkD2AYTJbyFPZx6OhomkCIF16z7CbNnzkPt2nVkbrbcXqB2Rxb2CKFNFy+dp4eQfXp78sePGuBWRBUGRaJzECjcrVYjrFDwsgCPHj5AXl4uSkrewsamCmrVqk05PWxpukUJtMpBjtx6qwS3EntxCW7fvoVr1+IxctQE8SDAHFMyeeqxowfQ270fGtk2pe7G0mGB2CqJfZc9xmXGnAzYKuQjLOdqUMvtEgf8NmPchOlUHhTY8rlrhVAAwd349q1U3L6dBh+f0So3Ys64Y4WNm1ZjzuyFqF27liq+trj4DdZvXIMPVzJs6IIXIx9re+ZcFD0Q6+vVV3awJab2Yd9NiK03Yl3Q00FKYbUVxvbDrEc4FhyE+XMXqNyRCbh99uwZDhzch7kLlxgGtgcqCdhOeQ9sDS4+lVjsUnwq/ue77bQF3Ts54M9fLkQVGxv8cDUSeTywJeD2QfJNFDx5DueBfSVgy8fSRuz1h8fQwWhi10x0R+bArTXu376Nm4lJGDFRyNPFWWtZd+Q7t24hLSUVw8aM4UmkJDdkoRw9O+NPpZ8/fYZD+/Zg4fKVokIqbua8LAXsys5jadGSq7J64LaoqAh7dm5Htx494NbHXVcBrujuExbiyzEX8ezpU203ZEs3vNI0mhHUlZgYpKYk003czd2dshqzHyPrZ2maoAdCyuv1iawJWD8bGYku3bqij4eXrJl6Opn6unrzN9XGigC3vM4mtv/Rg/uIjYmhseTkAKJFq1Zo49AW1WrU4MtYBnRJbmHCyEzieuMuXUIfr750LFQX65NEpxoPlQhq/ffthbu3N+x5ZmZjoNYYoBUVRoMW26+vnirtNCjTfZ/3HFim+//Tb35TXIw/fb0NsYk3ULWKDX730Wz0du5CLbYr//wjPAaOEoFtTmY6il7mw9NzIC5Hh8OuWXN079YTD+9nICkxDuPGTUYVG2vExl5EcVERvL37i8DWb+8ODBs6HC2aN5flsCVWr41bNuDjVZ+Yja/l9kLjNlDNVaBUwNY04ZHJMcRMSkvBral6WWArro+iIs8ByfMXz6Og4CUGDuDmiACelO7HEjESV9ORo0fQsX1HaoVTWy31QR93N/fsjZvXYu7sRahStZq0Jpsi7FJuKmaArLh/yhY9K2Rm3sWN6ylIz7hF2YvJYX6jRrZ02Lx69Rpv3hQhOzsLxGOO8KF07+5I35O14iutdCYttyUliLl0HlWqVIWjYy/DTMmkzsAjfujbnxw8E6IuBtzyHUqfa0GMrUynEDwfZJZarsTV+IsgbrWurh4ywigO3DLETrBCSMghdO/uBAcHgV9GOpAiMn32/CmOHQvA3DmL5W7KfD2EETnyTDhmTpvNMCIzOXWJS/TxILRq2ZoewFDdWLQw8zOmtMCWkaN8LqmttsL84MqZttruIJ4Q/QagZctWsjh0arUtKcGWbZsxYvQ4dO/e1tD2cvB05VhsJw94b7E11EGVVai4+C3GzP0NfvrrJ2jfpgX+9PVWTBo9AD27tMX3VyOR++YltcISi+3b10U4fyAIPvNnUndkIbUPAb23Yq/SxNcu3p4SgRRTxm/NesxcvhTVCPObAGoZd2Rybdf6DZg0axZN9SK4KYu8yMyE5a4BR/0PoltPR3To1FnTJVkAtuyiJQO5bAENiw1bNiLsJM2XOmSYD1XaKxK0KccCq2Ss/eF7zF+8hFpHdT+WYRNjQ4954UsXL+DM6dPo4egI19690bCRidhJY7Ub1W/KVJulYiHliSWa5E4eM2Ei6vIppkRlSKc1WgC3YhiTdaC0zouyl8mBzY2UZApKSRxuvfr10Kx5C7Rt3wFNmjZFDZ7ARKkzvXiRj8yMDNzJyEDm3Tt0HpAUTQ7tOtCUXVofzblSSlArU0D4hyn0M35u6qvCJ44F0fzGPZ0JsZZRSy1Tn8HJb9Ri+03i6TKN69Le/FmPAaW99f19APYdiUBs4k387XdLkHLzDnYePIm//HqRDNieDdqDQcMmIOdBOt4UFsDdvR9izoaiVas26NKpK+7dSUP6rRsYMWIMBbYkNUj9evXRy9lVjLElOWxnz5yLunXryIDtvXt3EX3hHGb4zpC7ITNWHaGjWA8lccyTcWxgrRAUVj03ZH3raBlArdhwaaiVH7jVVr55tZzK5OatNMTFx2HShEmcui4AX0XqHiWBVGxcLB4/foxBg4aoXWz5ilQgWVH/i4IXOBkajPFjpzDdo7bclmYSylUeTqLkMCUhIY7qXT17OKNFC3u615la5giZ2bWkqyh6XQQPj75o0KCBHOAqXI/lllu55fpokD+cnNzQooXgXsxatyXQyjIlk+7w27UB4ybNQvXqNeWHCOxBBM+QzHWiJDGZxZafEOz4It8F8ijBmELi35MSYzFkKGeB5ay1QlysRB5F7iMEn/v2bsX8+cs12YxJmeSkq8h5nC3G1wqEUgJxVHLKNWRlPcKQQTwxHMOKLKQh2u+/Fx69PeDQxsEQsFWovKaHUDlYbYW5IxwyJKem4Pr1VIwdM04GbIV426izkbCqUgVjxxlLRedfScB20ntgW5rl55e7JyYuBVv2BmPN3z5VPfS7hNMcsKVkUTaoamWDy4En0NWjD5raN+dT/9jQf1/lv8CZgKMYt3COikCKAODzJ8Nh17w5ujk5Mal/hETznDtyfEwMCl+8hPfgQSZT/wgLTc7DR4iMCMPUmbMVVlsmh5ds0eJesbTg9uaNGzgWeBi9PTzg6d1XVykuz95j9Y7tmzehj4enillW9TxLEZxegxU7W15uLgIO7EfHTp3g7uUNa2tCm6BUPMr+9gZxQ5keZImISAzLwb1+cPfyQi9XebyoJdZb4TSbbbjW/aprJhmTLQO3iv1dbMrTx49xP/MubqWl0fzCVatUoWmZCOHTi/x81KpdB/m5z6lu4NCuPVq3bcud6CsIoQyNh18I1CrnOvk75nw0TaniPYADdVpjTX1NfvptdOAZBbbfJkYarbJcy33ag5DUvP+UVgLzP/k7fvXBdHTv7CCrglhsV/zpB3gOGk0ttkNHTcHDOzeAN0Xo06cvzp06ho4du6Bj+064dTMZ2Y/uY+iQ4ZQ86uTJIHRo3wFdu3TjgC2s8N2PX+FXn/0GZLll04VcvnKJcgIMGzLUZHwtp8xKo1r8JnzRWITUa5AGGy//1iZjWfUWHEuEzkzI8gG35oHt02fP4LdvD5YtWU5bapRAKjs7G4FBRzBv7kIO6DFkSFw9ilQ0TBwqJypusU9KSaJs2BMn+JYZ3KrBDCdFklf2/PkzcPfoi04dO1M3Y9Xap7VAMoMjPSMdp0+H0rR9ffsORPXq1RlZqUmjBFkq89du3fYzZs0kKa+qmCeT4vuDeEz4H9iGyb6cayvXLG3rrTTc5ONYCWaFTYECW36DILrp86ePcSr8GCZNmSNaRUULLUMeRe4hHoZXr16mhJTe3lw4DsuGLMzhkOOB6NihEzp36iKV4ZnQyXQ9dTocDRvUh2svjquEAm0ZuAXWb1oH38m+sG3USBGKIN+zhDWgNMBWPYXNWW2lfhfvZebB2vVrMH3qDHoYQhmwaYwtVyexUgedDMbnn2tk/NBYMwIib1mykpRb2Yn95Vk0yq1iCyt6H2OrI7BDIWdw4Uoy3UTvZD5Cjy7t8NkyX+pa9aewgyhpUItaawVwm5mYiqIXL9GzL7HMcqBWsNye2hcA9yGDqDuy4IZcxdqKfs/JvI+U2AT4TBjHu12o3ZHfFhdj0/c/YPnnn4tuymKaHzHdD5v6BwgJPIL2HTqic7fu+kRS/OIsA7SCPJh0QcwllbTYe89EnkJCXBwGDh2KbnysYnmDMVaxIOlSAvbvw/jJU9CgYUPzQ98S1KZXm+KFCBNz3JUrGDZiBD2gYD/l8e7lUYd5wWiXMCIuUiYi9CSys7IwZfoMysotfCof3GptPXzrTLycqfcmMcYEABKCpbclb+lBU7Xq1el/5j4m+1IFarVVVZXybcajguogil1b2Y4bqalIu3EdI8eOFfQXk/Oc+7F0oJbcaf2mBHUMpPv5/lqUOZFWyO8fd+9XIfX+p1Q6bPoXWDBtJKIuJKDw1St8vHgKnLt3oBbbFX/+EZ4DR+H0oW0YPm4GMtOSUNXaCq5unjh94jCcnd3g0KoNkhNjUfiqAP28B9J0PwEBfujfdyDs7VtSV+S8vOfwD9iHJYuX81kFJOU2KPgo2jm0Q8+ePTgfJn5uKeNrdeeFDrDVXBd03JArHNRqbMplB7fmgS2Rwb++/Yq6eVepwoEtusoqgajCgkt+/2nN91i0YCmqV69h1h1ZdG3WAM80t23WQwzsP1QObtkJpmFV01o72bUs8Wo8zpw7BceezvD07Ef1N+WGrqbuU89q1qU7MekqLl+6QN2Te/XqbRrc8iCUTefz9OljhIcfx4SJ01UHAsLhwFv2Pl5eRW/e4MDeLfCduVgWCy2mGRIMtRrWWuXQUhOt8QaSkhLs27MRM2YtYVx95RZaIdWPAD7379+OYcNGo3GjxgqLrQRQN2z8EQvmL0ONGtXF+FrJagscOnwQfdzc0bp1axWw5WJ0gX9+/Q/86rMvaR/KY+wtB7aFhYWooQwhKqXVlnVJFg70hfFyLfka0tPTafoygT2c9q3gjrxjCz7/4iND28ihSgK2E94DW0P9U2mFiDvV9gMnsGfNH1CrZg18/qc16OPSFTMnDsGs//0NOozg0/vw7sjFL18hNigUA2dO5S221uK/6fFJKH5TBEf3PtQdWQK33PegnXswcupk1K5dW3Q1JgBBcDsm/0aFhqGZnR26Ojrqp/5hYm0LXrzA7i2bsezDj81abSU1VWGpUYBbPcWcvZ6fn0fdcW/euE4tuO6eXBxmWQGaUqkgREZXLsVgyvTpaNykqbFxYgSp6dWk8QKhISEgrqsCIGBvLev7lvV+YwIxVsqI2MiCfGDPbkydOQutGebkdxHcUkVMeHUDL2egiKYgDfWhxuGSCBoVtVYEqM19/gx+O3Zg2YfcSbBWm9XXePXOzAvq/VwVQI2qNmYH3w/XzpgtUxEFPurOeZ28/5ROAgMmfYyV8yfAd9wgJKbexoe//x6he7/B66K3WPbfP8BjwEic8t+CUZNmIyM1ATWrVYdLrz4IDdoPb6+BaGHXHLGXo1G9WlX06UMOiq2wbfsGTJs6E/Xr16fA9u69DFy57ddCDQAAIABJREFUchG+U6YrLD9W2LxtIyaOm4gmTRorgK2k1HJnPfIRqppfiolv1Fqr5YHCrTl62nDp5CzeJVtDDK06Og80Bmy37dhKY5ub29nJgJr4hip3Ww5tHQ48hM6du1JrHKvUy1yQGS8cFVhmfL7PXzhH3VVHjxyvD25NilU6BriZdh1RURFo36ETPD28UU2M4ZUWRC1AK2Oalm0q3INZgHvm7GlkZKRjGo0L5QCzAEyleGTJqkqBPS+La9cSkJX1EP0HDFW7cdPctop8t3zdxO034OBODB0+DvUb2srSyWg0V+wTYSNgRxI7N4SQt0P+OzB06FjUb9CQIypl0/sovpO59jjnESIjQzF1KpGBBGRZq21OThYiIk5gxvS5mvG1BLhu3b4Rs2bOpXlsBSDL1kfCgsgYXbXiQ/Mx9vyhr+LsVxw5xP19w6aNGO4zHF07d5FGlIkDAfZQRlQzZOWlQc4yZ5PvP639EfNmz+dI1oQxUFJCrbe3796CqxsXJmTucziqciy24/u9t9ia65tK/f3UuTgEhUXjqz98QNsREByF+Gtp+O8v5mPOP/8I257t0bBZU8lqa2WDSwHBcBzQF7Z2TUVQa2Nlg1d5+Th7KIi6IxMgy1pzyd/x0RdQ1aYK3Lw8uHQ/GuzIj7OzER4UjBkL5vOnYwwbso7V9kxEOGrVqkPzdQoTV1jOhYWKndCya4L0mYTWVBfQ6RXl9YKCF4i5cB6pycmwa94CXbt3R8fOnZV7sdk+ZpWJnOwsXLt6lf5HUuZ4eHubvV8sUBp0ovOy5J0CA/wxZsIEdOnWXdaGsqgVpuRr/kXL8mRjwjFXivzuv28fWju0gVtvjlBMawPVv66dDsgQODZLIqJdiyUA13wfWFBCE9BKI0DZmxUBakmdm9evw4w5c2hsenmBWrMj0SB51E9JZy0QaPkVXdXNgnWl/B77b1PT1KX/ja/+sBwOrezoOw31/Qx71v4XatSohZX/8xMFthEHN2PM1PlIS7yEenXqwtnJFUEBOzHcZywa29riwrlTNJWPo6MLBbZr1nyDjz/6lUgcRVhjs7MfYbgPlwdeYEQuefsW3/7wNb78/Nfy/LVsyh8+1lYpcJlyaw7U0oVN2w1ZL/WMHuDV63ihCWbnE7NxSHCtNMNJA9jyCzhLOEQYXEksPiXm0SWQkuTDyaOE5hUlcbaDB6sBGlePIu8rI1+t1Dk3025QoiFXlz56IdGKYGm5dAigTUpKRJUqNtRdmMRwix+F3iNc10u4oJQ2OwYE2REm5a3bNmD58o9gbV2FkZ0UJ8xaaymw5w8JiFuznZ09OnQQyLfk7ttyFmTJrZuI/uD+bXDu5Y627TqrXJPZdit3SBmwVbjsHw/2h5Nzb9jbtxZBLfnCEZryqX74vLWCtTb63Ck0bGhLrddagJQA3MuXL6Ko6DX6enO5qVlLLfmbgFa/vTuxfOkKRRofCSgTsq/TkacxexZxj+aty6LXBj8GhP3XDLANDDqKhg0aIPN+JqZNnSbvZt1zKh3Gbg1wq7TaxsbH4enTpxjYfyDniixabIEiKys0bVrb0MQ+EnXbULnyLjSunzFyq/J+rmotLxGOxSr6Sf/H6i94WYgpS/6InT/9PzRqUBe//esG9OjigFmThuHTveuQ/TgHHdx7iSl/iPvxvaspePvqDbp59uaJooirMgdkzx4Ogmu/vrBt1oQDtoyrMonDDT8ShMlzZ+u4I3NnZId274HngAGUsZULzhcopFg3ZG6C8xxx+P7vf8Wnv/09lb6w4Ai/ios1/6NsIWP6y5wro6weDW0hJSkJ5D9CyNOmbTu0aGlPN0bCvGpjo2+5ITGMJLUQAfVJSdfoxte9Z08KaokblEUfc6hMo91a9YedOE4JEIaPGl2usbSGlBdVg/TvMlefvjjMC8pcifDQkygsfIWRY8ZoElOY2kzZU3+VsqDRIbK2mAW3VH3SHDa/GMDVBbTKWSk1s6JA7aGDB+h8Eg6cdMG02BTTllpzY06sxiCw/Z8wf4umeHkV/sMQjhjn/ad0Eli7/TBVyFbMG4/bdx5gwaf/QPj+b/HiZZGY7if8wEaMn7YY1+OiYdvIFj16OOPQ3k2YOGkm6teti8jwYLRv3xFdOndFYeELHDiwG0sWfcDF11pZ4Vx0JGrWqAEPd08xFyZRjB88uI/wU2GYO3seLSe4IQv8E4J+rkwJp5pjpiwydBmpGFCrt7YamluytcXQHYoONgNs+eXz4qWLyM3Nw+BBXL5nowRSOTk5OHL08P9n7zvAqkiytl8QHXMWM4iKmAVUEMw5AYqDOec4OW349vu/sLvfbJhszhED5gSoqAioKEkRFVBAjAQVFVFM/E9Vp+p0uy+izOxy95n10re6uvpUOm+dc94jxdkqXJiVFkyubk4iWkCRXI+IPEnZg7t56h9GsQcEJF3P1StJuHT5AurUrosuXTxRvz6TslAL0GpwH5g9BNFq95p1yzGZsDuTuFml5ZaJuVSmQdq8ZQ18fAIogRV3nzKlj7bllpSNCD9K9ULvnvyhgmonVMeHsnofO5oO7A2EZ7feaNioicwbkJQRAatAHiUcItkAK1f8iLlzP6E6nwA4OdAr3GeDPXu3w9PDCw5NHWXxtUI8bsaNdBDyKN9hflJsrSLVT9LlS9Q67jPcV364xRxoKdcBWX/ys+L169dYvGwJPl30MX5c/BMWzF2A8uWJvxHz0QO3Bul/lGNbWE2ePMnH9p3bMGPqTCnOls9n+8rGBvVMAtsDkaUDbH17lAHb4u2a7/GuiOhE/LJ2F169eg2XFg747y+nUfbiP585iPCgvfCaOIoSRwlxtm+ev0DM3hD0nzJWzFtLQCwBtzcSr6Cw4BllR6ZWW94l2c7GhsYBhO3ej45d3OHQvLmmOzJRKa8nJyMj5RoGj/DjT8mMrbYkhUn+kyfo3X+AwiVZcq5hJ/XbgltOPdf42IDmQE27dg1379zG7Vu3aLvy8/NRtWpV1KxVGyQ/GQGNxL2XLH4E2BIinoaNGsHZpRXq2dcvXu8bITFlrTo6wd6dO+Hg6Aj3rnKiJN13Ntla61QQdWkt4GPy0VIxTVBYfPh7ISEBSYkXMX7ylGKBW6UyIzRUq0VmwS2vl+mCW+l3/mnWjhtLQrcIaLkRpDUOigNqRWWeqVFZd+y5aBDSs74DB9JWmwK1OgPVuvELwCSwnfrtf1o9jEvihg2/+5+SqObfto5nz1/gv/65Duk374JkF/hy3lh4dWlHY2w/+vMSePQagmPbV+DDSfOQdD4CDRs0Qps27RG0eRkmT56HSh9UwJGQ3XB364pmjk7IybmLqKhwjB87iYJY4op8OHg/JZdp07qNDNjGxcfi4cMHGDxwsOXYOivckDWXAdPAVtsLRUM3NjVeDOfaW4Fbc8A2PSMdZ6LPYtyYcbTNZgmkqKvl0p8wY9psVKzIMfYKFkkVI7KwH2lYbbnbpA0rLj4GFy/Gw7t7Lzi3lLzC2DPMnNxsnI2OQnb2XXTs4I627TqgamWF9UvpnaYhbE5XMuwF1cGH0uL808//wEcffSWWk5Fp6YBbEua1c+cWTJkylwG15sFtakoSzp4+iZ59hsDBsbnisEA9/GRvaQM8ffIYxFLbu88Q2DOHAfz5kYo8itwvWGuTrybh3r1b6NdviKYlVhDpkiXfUc8MFuyyVtvoc2do/uAe3j11U/1EneFCWHp258vwlmPhcIvtQjGvrcYmeOHiBdy8dRO+w3xwODQY9vXs0dm9s1pQxQS3srRLjPv+8tXLMXrUGNSsWUvsZ2K9pcC2njmL7dqdpRPGMyPg1xHGU0YeZWo7kRf6Nv4otcA27+qK2g0byNyREw6HwaWLG+ybNBYBLEkLVPTqFY5u3IZRc2eoYmwJ+L2Vlo4bV1Mw0M9H4Y7MBb+TqAyi+m5cugzjZs5E5UqVxFhbIeZBJJQSXEB4+Lt26WJUqVqVnmwqT6bGTpwsKbaKk0mVsqtxrKW3xOsu/YofCgsLqXvJ0/yndBKTE7Hy5e1QqVJl2ua3+hQHmOg0/ODevahrXw/dvOUnwya2ON1XsO5eeekSAbNaLdNcpIsHcO/evYsdgVsw76OPZXFLerXJr6tP6q0BtyqQyryr9BzLA0QJmK0ai0x3Gfdz8UGtaj82QfqWk5WF0OBDmDx9xvsHteSJJoHt8qvRVom8pArPay250ZdUnWX1gALbT75diS7e/RG2fSUCpizAhTNhcHRwQssWrbAzcCWmz1iI8ra2OLh/K/r2GYT69vWRnpaKzMw0DBk8nMlhu5mC1wYNGsgYkQ+HHEJzp+ZoT0gTbZnDW15zFpRw3dy1wmTlJ39ySjKiY84ruq8ILs4u6NpFwQSvsZwoc8Mqx0FxtigNHVxebbHBrRqEC+CTBZNPnxVgzbrVWDSfi8vXJZAScncy5EYHD++n4NPZuZUqXpSrS+GOrGO1ZdtDvj/Me4gzZyOpxb5ChQpo0KAhiMWNeHa9KCykB+bErd3JqYUmw7FyBVZiVyWgNVrTJQ8guWWftUr/vPg7LFr4hSa4VaUB4mWYnHwZt25lok/fQWoyKUZ+nAsrdwAgWIWJnIgcTkcdx4MHuXDt3A2OjlzeUT2yMyKH588KkHQpAZkZ19B/kC9q1qytstRKeWLlqX6E9Dv79u+At1cvmqdai8mYeFtkZmbgUtIF+Pr4a8bXkrYcPLQP7dq2h3NLZ6YeeQ7bkNDDaOrggI7tOmiyoksHv5ynozifFJ26c/cuuHbsSJ/1JP8JNmzaiIXzF6qXcj1gywtWOcclWUsHSeK4ABB69Ajq1qsL1w5usjhb4opsFth+/h/flcqW8/2fvyiV5yofWgZsi9EN/xd3BFcSEvAy/zlcuhO3Yy6fLfk3KyUdBQ/y0KGnt8odOe7oCTRzcUHT5s1ExmRirRXibgOXLMfEueQ0syIHbll3Y97tOCE6mgLazl5estQ/3AKi5ZoMXE26hBU//QRHJydpUecn8Wff/EF0BxZwKzu/3wbcmt2Ai9EF2rcUV0tga9PYsUhMLfnPb5TaRdFog9N7N/P3aQBa8ze/nWhVVlxtAVsSO2EUXPbLz5g0bRrq1pVIvkob3HKKkfAxHjjGJTR1SwP5M5uqoqTWwYVWtxvNT9XvAH767h+Yt+gjzbzT6mfoux8XexiaBLYrk8+93fgt5t1zXDyKeWfZbZYkQIDtZ39bhU5deyF873p8OHEeYsIPo3WbDmhYvyFC92/FxMlzKLAluTjHjp1Mc9TGxZylVlnvbiSVmg39b/GSH/DRwk9RoYKdjDl1xaplmDh+EmrWrCFakCy5IavGsALYnos5j9CjoarX6uzeBUMGDZZWED1QK19ozK04WouNxmSzOP+KBW7NAVvSPEJyM2XiNNo/ErDlXlayxKrjbC8lXaI5SHv37qcmM+LBmF4dWgBM6+Dg/v1cPHqUhxcvX1A9ihgBSH5k2UeUj2J/VQhVF9BqleMfID8MEC5KAFcAMYRZf+26lZg1ayEDbhXuxKzlmv9+8uQRNGnSDE7NW6rJpJQuyuxhARMPnZubgytJ8biRcQ2t23ZC/QaNYV+/kehq+6zgKe7czqQW7oz0VLi5e6JtWzfJtZ/JZyuCWkV6HwHA5j28j4iIMIwcOVaVloeNtSVxxHXr1EWnTm6aVl1SduXqpZg0YSqTu5pJ9cO7Gm8O3Ih+ffujaeMmFtN9Uf3UArD9y9/+ij9+/XuxTOTp0/SwzLubl3qZ0wW3llMAiQcY/DpBRklqaiqItXjUyA9lcbbEYlvXpMX2cFRGqWxGw7rLU7yVSiNIv5bF2Fov+r/GhSL78UNc2nsE3pMCZGl/yhUBkVt2YdD0SRywtS0nWm6z0m/gXtoNeA8eoCKQImVjT0WherVq6Nili7hZE3ZkNp6WnJ5tX7MOMz76SJ36R4g1ECAvj1TJ/dNHj4JL27bUBUgAsOTNP/3mD7Dj41zZ67rgVsMiRBcIUyq8CVkbVWQei5h4mB6akF9fsWQxZs6dp4rrNdtUZUPM3VeKgFbZ4LcEuGTNJyRFA4cMRdOmDobK3fuy3KqHkrXw1fohxt2hD2hlc4kZAiUFaoMPHoBzaxe0dG5lgizq7UCtVpuphE0C21WlBGxnlwHb4g5si/cRYPvFP9ehXScPnA7ejpFjZ+Fs2F5KbFO9ShWcCQ/F6NFTUL6cDdau+hHz53+O8na2CAsLRovmLfgctrZ4+vQJgnaSXKrzpVQ/tjYgmQA2bpbYUGmMrYW4Os19i9zCKKnnNYAt+bkLA2y1rF2WGJBLyhPEcN+1GtzqAFtG6RY8gHfuDqJpcZxbtpQAKi1nGdg+evQYew/sxuQJUyUiI9b1VqsOxmrL/ywbZ0ZWcVlhHUDLjROppBGg1d/DpQGkBXBlllgU4fmzZ1i7fiXmzf2EiSfWBrcimVRREQK3roO//wRqnZYssgLZEG+pFSzlokVWTipF3raw8Dmup17Fzcw0PHr8kIaHkQ8JDatdux4cm7WAU/NWomDI2wl6Iv0ukEXxg1GW3of3HDwVQQCrPdq376RJGiW4K2/YsBKjA8ajevXqKqZzYtEtKCjApi3rsHAex3ZM7bQaOWx/WfoTZs+cgyqVKhcb2Kalp+FsdDQmjhsvGz4rVq+E/8hRlNhOMQj19RqteFuNOSWsO69ev8JPv/yETz/+TBZn+wrmgW3w6dIBtkO9y4DtO9k830elf44NQd7LAlwNjUBz9w6o07gRZ7Xl420Tj4TDoY0LGjd34sGtkPqnHPYvXwf/2dNowm6BREqIt32al4fj+w5h7MxpNB8oy47MWmRPBgejRSsXODk7m7ba/vfvvkZudjZasAqtDQG2v6fJv4WF+l2CW8ON+H10nqVnaOxWhNmZxPr26T9Aa3+0usXWglq2P6x+WEnfIAO41ltvAzdthHfPXnB0lBa/X4Pllt9jGGm9K4BrGdDK5sdbglqtuXYxPo6SsQ0ePpy+q3IsahylaIaTmRnDFsuYzGO7JkXpAlrSA1q7vpmt1DH07+fJ/9pPIcD2qx82olXrTog9vg++AdMQEboTXt59YPvmFS5fjIGv72i8fvkcu4M2YuaMBShXzha7SMqSfkOo2zGJr83MTEfChViM/nAsz4hMCKWAq8lXkXotBSP9RooKtGFcnQWRk1VACWyFlcEysNWPq30rUCu0VTG5dOfaOwS2Z6LPoOhNEbx465URgRQbS7tqzXKMDhiH6tVr6LgjcyuyluVXEIHyMEGbykvduUZpe+i6yGy6omxlsuQKWVrjBCZocW9hDkuU4PZZwTME7dqKSROn8yy4UgwxG3vLkkllZd1FZORxCm6VJFMC0JUxJTNASgLCnHzYMfnm9RuOFFNn/7EEajkDjAB8OdD55vUrbN26HlOm8LluGXZiAZSSe0hcfEjIfkyaNEMFfgWrblr6dSQmJuBD/9G6jMiFLwqxbMVSfP7J5xIAZ5iaBTAsTiUdi+3xkycoOZ3SOktc3vceOIBphDtE+WEEqdIgLJBJaVltt2wLhLenFxwcmonuyC8B0xbbkFICtkPKgO1vdxP/35gQPHz5FPeT01H4KB+te3iKBFLEJfnBjdu4n3kHbv16STltiauyjS0unz2P6jVqoFWH9gyBlK3omhy8LQhefXqjYZPGfGwtF2PLWm1v3biBizGx8AsIUKf+0bHa/vL3v+FCbAwFtpWrVBEnPQdsy8moa1Qnl3xXqRRhDdRlRunVUqp/FaNBo/Grli7BzHmEjVOerN3se7LvZe4eqVRJglpSV4nBNQOAa+k527ZshoeXN5yaN5dZRrT6v6Qtt2oAa3Fv4n98W6lpwUXt0a5UovTmiWocmYirfZyXhz07gzBt9mwToJZ7shZHitEYNvqdPtwksF2bElMqy8KMVl1K5bn/6g8lwPabn7bAqYULEqOOYNjISTh+IBD9B/ri6aNc3MlMw6CBPnjy6D5OHg/GhAnTYFfOFqtW/YI5sxZweSttbZCQEIv8/Mfo32+AyJJMxuqx40dpqiACOqU4PvlY5tZUy6CEXSdYYMuuBAKwVVtrTaYse9tlRbE4lAy4NWGx5YVDLFrETXtMwBgOHAnATcFirIyZJX8fDQuFfb0G6EDSBTHxt0I9HKDVj7UV5oleXKjZeWQpjlYX0Oqsi3rP1AK4HLDkBCYA11s3MxETGw0/vwAZuKXFlGRSvMzOnydESkCXLt0sg1u2fxSuyWzfseOeHU9KPYT8rbTUCrG03G/S7+fORdIDjLZtOyqssPK4WJKT+sWLQlmaH4EJWZjLZ89G0Tq6e/fQZUS+l3UXR46EYtrU6XwWEIkZnbXyCv2l54q8ccsm9OnZG44OkoeZcE/ChQTcunMbw4dyB8SyjxXgVkkoxvZF9PloPCbkjn36FwvYHjlTOhbbQV5lFluz68+vrtx/xwQj7+VTFL14hcRdoeg+JQB2tnYyl+SwVZsxbM40lLezkxiSbW3xJPs+Ek5GYMiEMeqctra2SE+6ipw7d9FnyGDGHVmIneXiaIm7x5ZVqzFizBjUqEWSY/P/E3P1qWNtCbB9kJtLGYnbtGsvHsh98rvf867IxizJir2U9gt7usl2lCkF14QL83vtfEWjjx85gnr17dGhk6usGWbfzTp5lBygLU772E3NrMxZEgSte/R0tx1bt2LgkCGoWavW+wO3zAua1SnV5czeaR7Mihus6ou2e39xQC25Z8uG9fD196eHaqoDKlXnWQdqrR5vJoHt+tRYs0OxRMtNc9ZgvizRJ/x7VvY4vxB/XLKdxgcmx0ZgkO9YHN29HsP8xiH7djqePnqIXr36I/vOTSRejMHIkaPxovAZgoI2Yc6shSIjctjxI9R669bJTQZs129cC9/hfqhvb88p3tTwxI1OzfQeOt3AznIB2CpnPgG2hLxKqdUKVkb2enGstKwF0iIMZyafMbg1mqkWQLnA2sqvo88Kn2PZ8qXUXZJVyLWsT7IY3KIiXEu7juTkqxg6ZJimO7JUnxLcGucHNTOzdA/sWOsdr+zIdmUluaYlcSrCdwSAy1qhleD2zNkoEGaVrh4EqPKglwf4QmofTjaSq/KxY4fg7NwWTUgMMXNIILFMqwmkWGCt3PeFvtICs+IWpXA/VoFanp3p0aOHCAneiwkTZuhaWAUQvHv3NnT36onGjZuoATDReW1IKqAguLl1pmEJ4sGVItXPlauXcf36Nfj5jjDOYcssDMr3/evf/w+///IbxqAhNw0QluQPPqiIfn36qoecLrjVGL/KAyG+Qx48eIDtO7djzsy5eMPPPWqxrWuOFfnomRtmpkKJlxnopYhlL/EnmKuwLMbWnJxkpf77/CE8eFlAgWzKsdNo1qmtyh05OSIa9Ro3hoOLs5jLVnA5Phm0B96DB6FWnVoqd2RSZuvSlZi6aIHojiy4IXPxCxxovRQfj5fPnqNrjx7q1D8Kqy0pcCfzJj36Ox4ago5unWnqHDJVmzqSfGGSVi18ZSe66vROIbO3BbcaOn0xeqUEbmFelGwei3/4AR99/rmqYiP1QHmDcfm3B7XGz7BOPmYhHJN1QdMmrFfP2lUracxtk6ZNSwXcKjd0I+mYlgdfkTX9wUw/sRla92teU+zIWqA1PCwM1WvVgFvnLubiajUepPc+hu8p1we49zMJbDekxhl1yzv5faqz+zup99+9UmKx/dOK3bCvWx83r8ajz0A/HN29ASMCpiDt6gWUL2eLrp09kZGWjOx7t9G/3yBkZ99DTMwZBIwaIwJbouD26N4TTRrzXk02Nnj56iXWrV+NBfMWitYc4uJo0fVQp0PYuU7YUB/cv6/ydqlWtTpqkYM58WPCUquxiJh1oeXfRLvFRuCW/52H+BaGoWVgy62ZknK+YdN6+AzzQ+3aJC0JV62mtVUEZ3wNRcCSZT9j/txF9MRBaZVU1cNf4G2cIoBjX8Ss9VbTSkuFy0hHJi+1hV8LGLP9I+tTFcCVg3Ul+/GuPRx7sD2f2lCeq1Yee0u7oqgI27ZvwLBho7gsEiwbsgiauH5h+04ciuyBBbODy9d1OdcC+U3YdlhQy4mRj3sl7L6h+9GhgyuaNOFy0oquxzIwCrx8+QKBW9djxvR5qjQ/rNV2zVqS/3caKhGeGLE+ueX3bPQZvKHpgHirLg+yaZvFmHtu5HDvoO53km/5YPAhTJ88VbGbyyfw6TNnqLejh4cG2SA7H2QzTpFSSywnDRTh4GHdhvXw8/ET0/4QYFvHJLA9drZ0gO2AbmXA9je7z/+/8wfx8EUBdT8W3JFdenigvK2d6JKcd/MebiUlo5vvENEdmQBhwoCcGpNAkrrC1bubPKctz5Acc+IU6jdsAJf27UR2ZLoo8MCWAFwSC7FtzVpMnjuXYUMWFhV9huSce1k4dSIMoydMYia3ZB1iT4dLAtyKC0gxettQaS5GnRZvYR54KzMTMeeiMTJgtOwWa9tkXP7tQK1x/W8nJFOArpiuyVs2bkCvvv0Mwa2e5VSbtEX9vqr7LbkLvZ24rLpb1ncGymlxQC1pzK3MG4g7fx4jAgKk+c600pICIxQrFqi1NDBfFqFqJTtDWW28Fm9Y5l0UmNLS7V1U+29fJ7HY/s/qA6hapTJybqSgZ7/hOBC4DJNnfIy4s8fRqH4jtGvbAZcunEdR0Wt4e/XE9dQruH07U5bqhzAiL1r4CT6oUF6MsU1JScbV5CsYNXKUTDlmdFe1IqvoEc21zkJsnHC7HnkRCx6UnW8NoFXeq2nBVYIx9U38/Lc0Ma0DtifCj6N2zTro2LEjrVuyElomkCLljhwNofGDrVq5aLojC/WpYm25J2mCW2snGAtsRD1FcQigAsKydVphwmUboACM0vuoUxqx4Jak49m6fZMUb8vfKFpw+RRKbFzt8+cF2LcviBKvCXG1rPsyLzH+8EECx8J1cRzL3A3UxIHCq3MgkZMYp5dyH0lHBTJuXMfVK5cwbNhrsiJLAAAgAElEQVRIyW1ZSfTE67RXriTi7r07GDRAynGrdEPOy3uIPft2YvYMou8yz2aANLknOPQQmjZxQCfi5i7kJWbiei2uB/yLpF5LRVx8HMaNHiu8GdOz8lUiPCIcRTY26N2jl3r4WQtuBfd03ip/8lQ4ytuVRzdPL3qAYQ2wDSslYNu/DNhauwz9esr/6fwBPHzxlLofo/AVLu4ORQ8Nd+STa7dhwNTxqFiRI4oiQJj8W/i0AKd27sOIWVNlFltKJmVriwd37yH21GmMmDBOSmWgkfrn3KlTqFajJjq4uWlYbSWQKy1A3GIVefIEatSoiU7unWXpf4SzK3brsx7ciluErMNKGoCVdH1iY/mKL8TFIScnGwMGD3mr9zBupyB16/yyjest2fliBuBack3Wu3/9mtUY6uMD+/oNLFpuzYJbvedoK62SjMy8X0lKVA/Uas8eDaIndmJqbL9CPT9//0/MXbjIRGofbQZkq0GtmYFpEthuvl46wHZSizJgW5JjXaiLANu/bAxBBQBPcu+gs0cPHD+wFeMmzkFk2AFq3Wnu2Bxnoo6jQf36lHU3Lu4sVWS7e/WgboGPHz3E/gO7MWvGHM5l0RZ0jww9GoKGxD3Z1V1GPmNRkeUbpjv3iwlqZfUpKn8bQMv2iQrcGhyMcciDXUe0Jqp1wJaQdV25chkjR/jT6rXibNUWXA5YEZIvcr/PMF99d2SmUgFzSeIsPriVlk5JBsI19oqMa8QCoNWWpBwLCf2uR7LFuhqnpF5FSspVDB3qp4q31ctvm55+HZcvX8DQof6KXMCSnDhLIBWqnJjLwjxQ6YIWQC0LdDdvXo1Ro8aCeDbQmHYm7pYljSJz+MCBXejU0R1OTs010/yQ8pcvX8Kt2zcxbMhwGYAW6+Ytslu3b0HPHr3gSLIviMYg7gXNWmxJ7Hjew4cYPHCQzs4qn9Rnz0XjwcOHGKrQFblJwVWhpb/IXPT5Uuy1O3fvIORICKZMmmY1sD0enfkulnDDOvt5qmOSDW96BwXKXJGLIdQ/nTuA+y/y+ZhaO1w/fhYOHdugTqMGFOza8ezIKZHnUKteXTRv31YEtQS4EgAbsfsAOnl7omGTJrKcthQA29giZMdO9Bk2lLohCHG1EokUZ7198fw59m3bjnHTSQwDG4crWXe5q5K7Bv8NgevW4MOx41FJIJJi45AYh0VxYeO/KBdx1d/KGxTyNaP3FqNLhH27uLdK9/ENPHXiOAUDnl7esjqtab9xWetBrXGdby8CSzUYAkDReqsHRdW1b95A4uxGcK59uhuB9ubAKlRCzZbaWNoA1xKgpZuvhvBV10yQRZF7jgQHo2HjhjRG3GjeWkMWpTkGNS6yl2RyNwlst1xPeLeDWaf2iS3kMfWl0oh/wYcSYPv3wDC8epaP1wVPaP7M8+GH4T96Co7u34o+fYegob09joXuQ8eO7mju1Bzh4SRnZ1N0aNeRMiITpT89/Rr8fEbIGJGJW+zoUaNRvXo1ea5MFpBIiEacD+8M1JoFtIYLqo5uze/pepuT3hyVX1eWMmBzVsTZ5j/Nx9oNa7Fo/kecWi6s3YzlSWU55NEV+eef3/8NX3z2jdxFVkU+xcEC1pgoPziw7qiAGwIKQMsvvJLqIj82kJNpSqcDFtdyrX2MIYHi5KV2SxbIpMLDj8O+Xn24tG6rG2/LiVICrufOnaausW5uHhbBrdAnIuCysOcyU4bRsdSWWhbUJibG0TRCHh7d9a21PNB88+Y1Vqz8GYsWfs54WgjkU9xzyMHVsbAQNGjQCJ06dlIRR1EAy8f9LluxBFMmTaVpM4sLbI8cO4qaNWrAs6vgYmx4dIGY2Bhk5+Zg6OCh6pVbR77CIYVs7gjeCLyb+S9Lf8HUydNQpUpVvCCuyHUqm9oZTpQSsO1bBmxN9c+vstB/RO/DfWqxLUfdj3OT0/HiUT5k7sg25fD4XjbSYy+iu7+PRCDFW2VvXk7Go6wceA7sx+S0LScyJV+JicPrl6/g0VNYHOTMyALYjQo7jtr16qG9K6e8Cu7KSkIpbj2XSKVu37yJmOiz8OfdLYQFTJjCZlyShc4xo3izHfk+wFmxn8HfeDQ4GE0cHNCmXbtiAVvj5//2QC0rCIv6WDHA7eIff8CsefPpYYK14JZVekoC3IobfgmuPlpzRFl9SYLaGxkZSIiNMe+CbABMdee6Bho3GvtFJoHt1rQLJdgD5qsa37yT+cJlJU1LgADb73dG4mnuXVSwAZo6NMPl2Cj4jBiLvdtWU1fK6lWrYs+uTRgy2Af16tXDgQM74eXhDUcHRxpjG3U6HJUrVaLueeRvovTm3M/BocP7MWs6SSkixfkJeWzFsasANCUJavWstLqwyyygNdg4rbbc8pNTA9px4FROmiA+XbKYSlo6+UYOFIj7d9Wq1SzE2TJWQ8FiWASEHCH5iVuiRQsmF64C2AoA0CII07CsayxLUmAoe4ColAfDmM2COipnVVnlQaQN3rzh0+UIYJzZTFgZaltumTy0RUVYuXopZQYnMaWCHAR3bzH2VgaOi3Do0F56KNS4sYMKONPeFfZmxR4tDkfhC3sgJI4CXmdRuB+zoJbEum/auAKzZnG5ZgW5ibG4Cnfk1JQryMhIw9AhPqoDKSHND9Fb129cjRE+/qhbt64ucRSR/T9/+Dt+99XvVTHAgv7Lei2whxxsX+/YuQNunVzRylnI32tpR5MmctLly0hMuoSxozmmcNlHE9xK8bastxvbx6FHj6B2rVpwd+tiFbA9ea50LLZ9PMostqY3xF9bwT9G70Vu4VORBRkvXsvckQkBlJDX9tSmIPQeywX2C67G5F/bImD/inUYu2ielPaHj7Elv78k1tgNWzBl4XyeJU5gPpYDXJKUfueGjZi+kJBm6FltBTutwHbKlTt2+CAaNWmK9oxFR4tIStwkVJui1DOaSrl0BKrZhUYKcEn0u9XP4G/Yt2sXBbWtWrdWLOvmWmX5uYxaYbKBJotZbJxeHcXRsZj9WvuZFuJutZ73+vVr/PD3v+GL3/9BZprVKqu+ZoK4RXuf0d2A2B+KKx9Neet0QkmBWmGu/vjPv2PBJ5+iQoUKBtZa8y7IRu9jdoyaBbbb0i6am2wlXGpccy5msOxTshJ49OQ5ftl/HvczU1G9SlXUqV0bmamXMHDwCOzashwzZn6ECra22LRhCaZOnYfKlT4AcWkcHTAetWrWpBZbQhzl2dUTTk5O4r54Jvo0iOWnd8/eDHGUnNmbVWKFOaI5rw3djy2nxJGvGyqzrZ6+a926rZhoeuDW/HyVSopAXKvponIuAdXde3ehbZv2cGnVymoCqZTUZFy5eoVa35UsvnR/YcEA2y86IEFbiGopKNUS6W9JkhattIzHDAFyWVlZ1HX4zr3buJ+bi6pVq6JzZw+4diQhDczRhgxUsqCGK8ORaEnf7927ixPhxzBm9ETjFEDM/StW/oTp0xegnJ2dFIvMAFm5XDmpcSLVmhHM6GL0PwEISsCVq+f48RCap97ZuY02ABXicnmAGxyyHy6t2sC5ZStdN2RCLrVy9TJ8sugzMU6eO8CSE0c9fPAAQXuCMG/2PAnYCgcVCrdkugYwaJYFtivXrIK/30jY16vHDykGyWsvGuLQI2mwjoYdw4xpM2BH5K+hSLBV6FltuZjqImRkZCDqTBTGjZlgFbANLyVg27sM2Jbspvk+a5u/ayle16vOW2yJldUO18LOwKFTG9Rt1FB0RSbgNv18AqpUrYbmHUneWi7GVmBHjg8LR9PmTnB0bikDtxQAE3flw6Fo3aE9HJyaiaCVnmKJ6X241D+njh5DoyZN4NKunS5DspD4gLXakmtrlvyCWQvJ6ZrWoq5wyRE0Ag2XST2llgXKWn1kVhl+2/41/Ry+4MF9e9C2XQc0b9lSfLTZOozLcSXYxdTS+xnXZ3ZLNydFa4GcpfJ6cbda95C4lgP79mLStOklBm71tmtBEhbf1VpBFKMTdeeNRl1a7OPK+8nfYUdDacxyh06diuWCbApkW1gLjEaZWWC7Iz3RqKp38vsYpw7vpN5/90oJsF0efAF3UhNRv159VCxvh7zcu+js5omw4CBMnDgLePMa+/ZsweRJM2mIDrH+zJ61gIJaYqHduGkNJk2YgkqVKoquyNuDtqF/v35oYN9AFl8r7D1KUCsMXdX0LiFQq7LSaqwj1i4tqjlpCdwyv1m6T/qt+MCWuGAWPCtAz+496fA2G2crGIaXr1qKOTPnc5shk66Gq0zaBmRphJhFvdhyVPI+aeo/rMVRjnHiE+JonPDdu3fQrFlzODR1oNbnGjVqoKCgANHnziIm9hzate+ATu1d0ahRYw7kmgC3bLwtyW1LWuHm1kUz3lZp+SV/52Rn4czZU/DxCZC5eUvyVDDzMpuhymDLgFmhmB6ozcnJwvnzp+HjQwjc+BA4Upj5LrEmcwdPGzasxPTpHCGUaKEVwS9XJjMzA0lJifD1UaTwUdSbkZmOixcvYqQfIaxS9J0VwHbpimWYPX0mPRRWm+l5KVgAuNk5Odi9bw86u7vTnNqyj+pQRoclWUjpVFSEFauWY+rk6UCFCqht0hX51HmSBeX9f3p1bfr+H6qlKxWxRwa/iib9+hsx7A8L0GhwNzF3LWE7zk3OEN2ROQBL3JTLIT/nAVLPxKBnwAjRHZmAVgJeszMykXk5Gb1HDBfZkYUY23K25XD7ejpup6Wj99DBFMqKoJbPZSuA1EcPH+Lwrt2YNJu4YrGWXdY1WctqC1y5lIibGTcwxNePCl5atKRljN0cWTCmpVRr9p5GTKBWueICOLMjxnT9NsDh/fvh6OSEdh0kJdfs/ZbLvXtQa7adRnKzRmkoKXCbnpaG89HRGD1+fPHArU5DLLavJAXB1mXQEe8C1N65fQunIyMQMG68NJ+ZNsmfaT5frZ6CXJyxZhbYBqVfMuqZd/L7aKf276Tef/dK8wteYt3Ja7gUGQLnVu3w4ukjvHnxHM7OLog7G44PR01A3oNsSh41Zswk5D95hODgvZg2dRYFuU8e52EvYUadyWUCIMRRubk5OBxyEDOnzpK5IQsKtLSnWRqpcuVSxE3MoqHlokt/VpZhO1n2m0K/LSpCdk42CNvr8+eFeP3mNc0nX6lSZdSoXl1M96IcM7K3MAFuNd9aBXzZUiYJpPh3v3n7Fk6cPI5JEybTppqOs+VBXsiRw2jcqCnatWuvsC7yNkSF1VYmcgvy1V2GNYCaALzoWJHJhlNcxEs2QH7+E+wI2gbnls5o7dIG9Qnpocb6KjQt6fIlJKdcxcuXL+HZtRu1ZloEt6pctEUICtqK/v2HoGaNmrxlV572R6pPuk5I1wBbuLp25ftFOm4RCKTY/hJeQQVsFQNQD9SS+Xb48B506dINDeo3lAijZCmABBIpLlwg6fJFZGfdw4D+g1VuyCIrMjHcRJxAlSpV4NHV0yJxVGzceTx69BgD+g8wCWwVRzv8n3/521/xx69/z5NNaQ0YhVBkyoX0x8lTJ3EvKxujRvqjfPny0k1KcMscqMkOKnir7ZGwI6hbpx7adnQ1DWwjSgnY9iwDtr/dbX7Ut1+hvFMD1GxIyKI4AIsXb3Dl8Al4jvEVU/4IADcqcDe8/X1QvUYNDsAyVtsDK9bBf/Y0fPABYU6WYmyFMnvWbYL/5An0d4k8Su6WTP46sncfWrVrh5YuLppWW27xFk7RpO9k2d65ZRO8e/WhMaXsIi58V8XbitqCSbIbvrxiGdEdAMVRlq0ZTYb12wDHQkNQu3YduHflNgbmlQ0fZQrYGjZCW7aWHm6iSsO2axUwC3BLCtzGx8Xifk4u+g8e/N7ArUxhslZK7Iub6ARLRbR+0/J60CwHYNXSJQgYPx61atd+dy7IJvZ6SyI0C2x3ZZQOsP2wWRmwtXYKmCkvANu4sD1w79oTubfTUa1KFdStXRs3rl3GsKEjcSM9Bbcy02iMbWZmGpKvJmHkiFEU2F69moRbtzIxfJiPGF8bERmOGjWqw92ts4pUhk1FIkMsbGNLwEpryUIrLA3EfkDSiKSnpyHzZiYePHwA+3r2qG9fH7blytG9nQDbh3kP8fr1G9y8lYm2bdpSMFSnTh2VeNl9Wg6sVKhVk5BOFS/K0GkpAbsM9CgIpEjZb//xf/jmy9/xAIorzdXBkiNpx9lmZt4AcSUfEzBOAnwCKRL/YDl7rLTYit/MblCqdUsOXOUsyFxhVs452dk4ciwEPsP9UKumlMNYAHuqTmIszuQ94xLi0KRxE3R2562vvKCEFD3kNUQyKMYlmWRnIOBu5MjRBi7J8ly10dGR1Hrcu/dAvm/4wwLmAIIltNASo9a7Kd2PKUhNuoAHD3LRq1d/y9ZaBugG7dyC3r37o2H9hvI8t4o43C2BGzBwwGA0bMCUE1iOmVQ+R4+Fop69PTq7uovEUeLBhKbFltFI+a+vXr3CP3/8Dr//8hu51me0r6uEx124eesmtmzfipF+I+Di7CINEUWoluTZxqfNEvu/CDcyMxEReQoBYyeaBraRMaVjse3Rpcxia2Yv/FWWmbnle9y6fQsO3dzE3LUE3CaHRMCpc0fUIe7IPOAl/6ZFx6Nipcpo1dlVFmdLXJIvhp9G7Xp14dKxg+SOzADfi2ejqWXY3bsbdTtmLbLs37k5OYg+GQ6/MWO0rbZizjEe3goTHcCjvEfYsz0QM+YtlC3mqtNL+VTX9dKQbQiKHjRyTVZ2uNF6UpwBYlinDXAmKpKml2BZkQ3vM8zYw2yWBpWZeZbw7taULY68ZIqNiQr09Axr3JKPsgcLBifz8ufJT/qVzTXSgYx+N/H6ukWM+knzdxMMyMJ8Ox0ZSfV3rx493p0LslpvtlokZoHt7htJVtddEjeMcuQI457kF2B14CEkX7+J5X/7nF5bumEf8vML8PBRPj6bMxrPC19gyfo9qF2zOhrY18bU0YNLogn/knUQYLv+5DWcORSI3gNHIC0pBk2bOqLcm9d4kpeLPr0HIjHhHMnSDi+vnrh4MQYvC5+jV68+dG88cfIYJZRyJyl9eOKonxf/gPnzFlCCHZY4iiNXZDcsjdllBtRqrT16VlqlJQagKW1I/OWV5Kto2aIl2rRuQ9lu69apy4ENpqfZFhILX8q1FMTHx6Fho0bo37e/akxogVtZ8JClAyjVPBYuyGNC2YeygFewYJNrQbt2wNPDC02bNFG5Dwv3qIEu9/ZkT1i2YjEmT5yOylUqm7DayqWmC251FltJZpbCrNSW2sLnz7F+4xrMn7tIFAmrH2kfQzMgnP964mQYtWi2bt1G03LL5qdlWZPPRkehfPkP4OrqrsmSTKXCW/g4gMz9X+KlBLx+/QodO3IusTKXbtl4VQxemfzUIF/4mcyzrKw7OHMmHP7+41WWUpnnBJP258H9XISGHsSkSdMlN2TWusuXffXqJZavWIxPP/5CHl/LlBXIq7btCER3r+5o5thMBWyFMB6twwu6f/Iv9LTgKVasWokvPvmMXTzMWxl0AO7+Qwdp7HXf3n2kKcWCW4XVVjzs4F2S165fC/+AsWjSlFs3jD5RMbeMiryT37t3afJO6rW20rJ0P9ZKDMCXpwJxcvNuuE70E92RCYC9z7oj8yl/yPWC+3m4euoseo/xF1P7kOvEevsoKxtJUdEYOPZD0ZIrxNiSMq9fvMShwG0YO5MsABywVQJc6qYMG+zfvgNdvLzQtFkzldWWm7xKqy29SsuePhWOChU+gIeXt0wptkgmxd1OP1r7iK4ib5hPT90pRqDA2m60WJ8NkJSYiIy0NAwfMULayEw8xHI7+Q2iNEGt3rOtQHRmipYEuN26eRO69+qNpg4OVlpu3w7cCt1s5j1NDAlti4niRrOgVmuukXsJacnR0GCMm8S5BCrrk/+tdkE2NX9LANRS5cokK/Kq08fMiLfEy8z2HkDrfPjoCcrb2eGr/12OZd9+hkdPnuIvP27C3/80D4lX03HqTALyC55jtG8fNHdoiE/+9Av97YMKjNtZibfut1shAbYbTl7Dyd1r4Tt6GuIiQtGhY2c8fpCFCjY28PT0RtSpo2jaxAHt2rbHqfCjFNS5dnSlMbZbAtfTPJYNGnCxtGkZ15Gckgy/Yb6abshaMemS9DTcj5WAVQlqTQLavLw8JFyIR1x8HCW5IiRCTs2cGBBrzcpig5iYc7iUdAm+Pn6UFZb9lAS4lcMWK4AtncxA5OlIamXu2cOaOFvuZgLAwiNO0oOJrl1ImhpmqWesw2rCIwY0mpgScpymotsSgY3aGgncunULUWciKIGP8DHjRaN34Bq0czsFqHps0CLjsZASiJfD2nUrMDpgIqpUrqLrkkylylh7iTRjYs5Sd3dv7960+dbEK2vgW3FvIfpkWloK0tKuYeDAYZrAk+5Fshy23N5zKuI4da12cyWeFkyeW6YsnePp13AxMQEf+o+Wg2bWAsvH2i5dvpimxmFT/QgWW0vAlr4j/6LEYyJw21YsmreA72plMLaJwcbPC3atId8jT0chJzeHxgBzHSE/2FISSbHM18dOhKFilaoYNJgBxhaasvsgic1+/59RPp7v/6EaTywDtsXohi9Pb8f54DDYu7RAbYfGIri1fV2ExN1H0G2iv2Sx5QHume374OkzGNVr1lIRRR3dtB39PhyB6jWqcxZd3iVZcEeODDmCJo6OaNW+nQzUsjG3ZEG+lZGBC+fPw2/MWG2GZAtWWzKzV/78I6bMmoPKTG5bWYwJD4LFxV3xxZRyzMjbWuut6rnF6DtNpUCjHkIKEXYklCMykpY4wyfqY1ZGfSghYGsa7JsuyLyege5lRjUrCXD78/ffYfb8BahYqZLVaYC4TVy/y8y8g3C3NWWZfdJwvOiOLZ24dL05tmbFcowcHYA6deoag1qNBmrVu3blCmRnZ6veYdacObCvX9/w3YQ6lbIzC2w//eFbw2e8iwI/fsa5VQqf+b/7gQLb6zfuYNfBcHy9cDxy7ufh5zW78bTgOf736xmoUrki/uuf6zFvqh8a1Kv9Lpr1m6+Ts9im4PiOVRgzZSHCD29Hr96DkZF6ibrkEpKdwwd2oHv33mjcuDH279uBHt17UmIeMpGXLPsRn3/yFW+tBc6cjeJyjXf1UDGkCsq0ntCU1CIyV0CFxqmyVCoWBWF8Z2Sk43zseeTez4VHFw+0b9eBHhZrs81a151PCwqwa/dOtHZxgadHN9nNZsCt3sGZtBRIs1U2X3XAPcuefOvObRw/EaaOs+UXYMlqq0xvwmn2JNb4UPBBSpAjlRUUf+1DSq6cGtwabnUMSRR9d9lhndpSe+fuHZw9G4WAUVIKFxWbrqUFX2EIFcD8jp3b4NGlGxwcHWWkWap4WQakEhKl8zHRKpdkOlyZHLmCbKRDgiJcupQAwrLcrx+XZ5WNGdfrb+VGwo4zom8SsqjIyDCMGjVBArWCTFliJ16JZFmMly3/EXPnLKKxp9TcwgNaW4Ub8qlTJ6ilkxx6yAihFMRRxMPh5yU/4qvPv2Z4YqQDXAKeVf0tXGP6715WFg4cOoA5M2bxc8x6YHs97ToFx/JPEc3L7dXNC1GnT2PShIncz5astgKBFIpA2hUadgzzFwjtsrx+/Od//2jdAlNCpf/n/31aQjW9XTVlwLYY8vsiaitSk1PwPDsPjt7uMnfk9IhYNGjuCPtmDrJY25txSdRa69LVXUYURa6lno+nblOdvLrJSaT49D+P7z/A+fBTGD5mtJinVm615eyu5NqBHUHo0a8f6trbq2JquYnNWG2FBYcHrLcybuBCQhx8/T/kFgFBNmzeMsVqp9xU9TYW3etaR4Im+sRwAzNRh6X96HnhcwRu2IAZc+eKNZl5piGwfV+g1kxjzcjoLQGuZXCr/lV5Jf/JE2zesB5zF30kbgT8fqBqvV5tJQVuzYjL2jKW54X8Vz1Qe+7sabwpeoNu3iXrgrxGA9iSNsw0ALZ67yT0j1lgeyDzqrXiLJHyvg5Sii9SoQBsH+cX4M8/bKRW2YuXryPy/CU8ffoMo4b3QgvHRvj4Tz/jn3+ajwplFlvNfiDAdlXIRVyMCMZg33E4FbwDg4b6IzE2Ci4tXdDCqQX27d6MkSPHoGqVyggK2oSAD8ejapVKuHf3DuITYuA/YpTIhrz/wB7KOuro6KgdXyv3FeXbJMV6Co3UA7XieiKCEzlKIX+RNGWEIfdi4kVUqlwZXd27oEUL5xIBs2ohEiKdcNy+cwsTxvGKsUZOVm5fkyE2+X7OVqw6QOOBpCgcqTArDxbYkusrVy3H9GmzYGdXzlQ+WyVDLyFk6td3IGrXqS0/wNSw2sqaxp46GM1+K0Ht/dwcnDx1AqM/HCvpAFSs+ofUeod58tzsHMDftHk9/P0DULlyFTW4ZQCtaGEtAo6FhcKhqSNatOTSK7GAlpMpkwuXF7LQb2fOnEKVKlXRvj1JQyRsp3KzIbuH6h2GkLGVm5uNEydCMGbMFBmolSykclBJ6hLAa+q1q7hz6yb69RvEWGsFolMpDzXRVXft2Y4+vfpS932JUZlnWmbia7Nzs3DixAmMH8u4QzMHF2aB7e07t3H6zGmM+XA0L6GSAragwHbiuPGIv5CABw8eoF/ffqJOI80tKd2T0h1507ZAzJs/02iU09/PxpWOK3I39zJXZFMd9Gss9EVkILKeP8blwFC4TxWss3YUyD6+cRcPM26j/YBesjjbVwXPEbs/FP0nj1NZbF8/L8SxwJ0YNWcG/Y0llxJSAwVv2wmPXj3QqGkT3dQ/ZMFJvXwZN9MzMNCHT3itIoziT8eExVlc7DnAGxV+Ah9UrASPbl7yzdAI3Kr3UVXXWcJaxvEq+iPhbTCc7r02wMolizF24iTUqFlTXzFgmmW5HeoTQ603MvMuFsu8BXC2iGEt/GiAfTWz47GKDSsHrbrSrl3D5ctJGO43wkqXZFKz9om/UvZG7/Au1qGSALVPHj/GzvJ6v1IAACAASURBVB1bMX02dwCjrFMJjbV0fb17lMBWKKcHbM2MXdLGNyZdkQ/eTH4XYjes06cpR/KReCUNISfO4VhELAb07IyPZ32IjUGheJj3BA8ePcGX88ai8MVLLF67B9WrVUbTRvaY9CFH1FL2UUuAANvFe84gJTYCg3zGYP+WpZg68xMcPbgNfXsPQq2aNbAraANmzVyIly+eY9u2DZg3dxHsytni9OlTqFixIrw8vcT42lVrV2D2jNnUXVyM5VPktmQ1R605zsb9ywGuhE2VuV1JPcTKmJAQjwuJF+DWyR1uru6oXVsiFNLsf4Xrod4Y0cTjTOHc3Fxs2LQeX37+lXhVnHuyfVi+KesBFW7dYH9V0GExgmNxpFCKyG3fgb1wbtEKbdq0oW1SpqER13sZoRS3PpOyxBJZUPAMvXr2llttWSIpDflJ7VH2rhqQsG+oZakV108boLCwEFt3bMGMKRKQUFlqTeg8qjGnyCf7/Y//wCcffQEbWxs+LpYFp2r242fPn2HDxtWYPWuR5FIsWvYYN2Mm3pa1Ct65cwvnzp2Gn58A3IQZojM4NcZTevo1ENblgIBJuqCWylLmLiwB1507A9GzZx80athY1w2Z3PvyRSECt23GzOmztd2cGWBL2KczbmTAdzgfliAabbj3UwJb7nxCfUBx+/ZtHAk7ihlTpvFzq6SBLXFnL0LQ7p003r5d23aaLskyqzvfl2m3bqF9B3JoZvyJjrttXOgdlPB0J2mtSv9TZrEtRh98HrkF2YVPcPNUAuo2d0Qdp6bUaksIpAi4PbdhN3pNH4vyduXFnLbkt/iDR9GqqxvsmzRhACwXa3vu8BG0aNsGji1byOJwOddkG9xIvYbM1GsY4OerSv0junLwVttta9dh5PjxlCKdnpTxpFPCBmbJaktKb12/FgOHDod9A47KXljbVJsBIzvmIFO92SrLWZB5cQGuWaVa69F69+7bvQtt2rVDq9acBcfoGfq/qxdQa9rBli0OqDVqt153aII9HQRoBAz16lLYB2hTtMpGhJ+kCee9uvf4zYNbS/2h556vdQ+5tn3LZnTr0QOOfFy9/ljhx6CiIr16ST0ssGXLKYGt9vsor0q9ahbYHiolYDucB7bF2BrKbrEgAQJsvws8hqz0q/D07ovI0F0IGDcTe7etwrhx0/D08UMkxJ2Fv/9YZN27jdjYs5QtlwDbrds2YvCgIZQZlYTgZOdk4cjREEyfMgM2tnJrD12reZdGQWtUrikqgMYaY8Xv7EVu2UlLv47o8+fwrKCAph+hiqnOx5KniDUDRQvoPnyYB+LOOm/OfPV+qwVu+WuquSpelwNb1TpsIJ+kK5dx7VoqjQOm9wrl+S8yF1u+xWLamSIg/+lTbNi0FvPnfqR2k2WZmHXCSzRgra6IjUAtkcTylUtpvuRq1apxez+j4LC6jkyeKiVIxxrKgFvCWkxIj6ZNmcXExsoBLedqLFnyiGs0eV8PDy+Z1ZbNA6x7sAAgKeki0tNT0bNnf1SrVkMmJ/ZIQx2JDKSkXAYBtkOG+KlALacjSSl9OLkJmTi478TSe+xYCCZOmMqBTR43ijlsmXjb5OQruH79Gh1Tcjdk/iCGAbY0TvuDiujWzUs31Y/Uj7wupwFs7969i8OhwZg1bQYvlxIGtuMn0IWEuE5/99N3+EZgX9ZgSRbJwPjxX/gGqFW7kqml41x86QBbD7cyYGuqg36NhT6N2Iycwsd4kpmN/Js5aNGH5LQlaX84cJt59gJqNaiHhq1a8GDXjgLc7NR05N3JgvsAjuWRjaXNvXkLaReT0Hekr5j2R7Dc2vLgdv/GQPiMDaAuT6rUPwxjclJ8PIgLp3fvPhqEUVLKH+GcViJM4H57UfgC2zdvwNRZCiuQYo4rFz5xkZdtrNo9aATQ5L+bh2bmS8rbpXXfmdORePXyJXr26atWHjRe610DWyOZKZtUXFko6ykpgPu24Hbvrl1w69IZDoT1UGlFUDRa/axfh+XWqA95+Cl7Gz3wefVKEm5l3sSAIUM0D12Uc0ipIFsCtaQBa1atQHZWtqpuy8DW0htyfWAW2AbfSimV5X9ok1al8tx/9YcSYPvXNftQ+PgBnFu1xeXYKAz1CcDuwBWYPetjXEtJxKO8B+jbZyAuJyXgyZNH6Ne3P169fIH161fho0WfUlBL8tcmJMThyZMn1J2PXNNzgRRTzjDCtRbUkvJ3793FyfATKFfOjsbPEvdnBSLQ9kwpoU5VYSYA19PScOXKZfj6+Kr3JyVg1QO2HBphnWtlR4viOirH+Iy7MKeRv6Axjj/h80++oG2Rkz1pp/1RuiMfOLQPHdu7wsHBQd9qy79pcQ8NzIDa/Qf2omOHTnByak6fZghqlaBXaKPYK5LwWHkKh7qZN28i6nQExo4eL5JnscRBpJwS5KxYSZikZ1IvBvF33vrNuiRzfcHKn2sUsdxGR0egRo1a8PTsjkqVqlocqXl5uYiPj0VR0Rv07z9U01LLyYrfwRgSKG58cYA3IvI47Os1QNs27eTWWpZkir+XuF03atQYHdp10I2vFXIQ79oTBDdXN7Rs4fxWwJbEsh48fBCzpwuWemNgq9zxSIztFlWMLe+KTIAt/4mLj0Xeo0fo27uvij9E3f9FsAbYni8lYNu1DNiW0IpfCtV8cmojcgqfoJytHS5tDkbnacQdmbgic8D26d1c3EtMgevw/mKcLQG9BPweW7ERw+bP4ONyObdjEmdL/g1dH0jZkatX50mk+OvEoktYIROjY1D0+g269uxuaLVd98tiTJwzB5UqVRKttsIiIFhwyYQUwKkAlLl9zgbp11ORGB+PkaO5+BIWtEoTWYNdUOiPtwW3sg1Xo1KDfrcW1GmVT0+7jphz5zB6/Hi14qDxfENga6FRZtqrW0bjBzP1WTN13iW4Zd0B2TZpPfP7v32Lj774EnZ2dlaSSdEtXqZ0Gb2/5jsb3aTzu2F/WEkWRR7z/d/+Dx9/+RWVhbJ+FaiVTWKukZbvMQds2bXArGjevHyDqpXsDIuH3E41LPMuCgxpbM7d6108+1+5TgJs/2vxVnxgVw72derhbmYqPDx6ICLsACaMn47zZ07SnK2undwRceoYZT927eSG69eTKfOqn88IMb720OH9NM9rK+dW3DVuMxMVaDq+bdTkcRbjaVnLIAPkjh47gjt37mDY0OEqVmIzAMviOiL8aAl4MoNCeTh1KjIC5e3Kwduru3yPku2/UuWa65Dm2iO1mj0IEFdRhVWbyCFweyC6e3pzhEhm3JHFMhw4vp5+HfEX4jFqRIBAbyS5aSqsttwDrKPlsghq+fESfe4stab17NGLWyMZxYc9XJC+80DOYOJKgFayibIWa5LL99Wr1/D27iGCWEGGWizJySlXcO16KoYM9jV0SebqUYNb8qArV5OQmBhPAbKzcxvUrl2Xxvy+efMaDx7cx8OHuUhJuUKvtWnTAS1buuiCWlG/5OchnY/MnHzz+hWWr/wFixZ+rkv2RqexDeic3rR5HcaMHodKlXhDjjBOFVZg8n4rVi2jcec1a9QQ1wHxwEZoDxtzy3Qu269Z2dnYd2Af5s6czfeoZWCrNZ/MAlsyfr//6XvMnT0XlSszqa4UbNh0nBQBhW+KUNOkxTY2oXQstp1dyyy2v9k9/ONTG5Bd+Bh2Nna4HZWI2o6NUae5o2idpW7H2w6iy4fD6IJAAC33nx2uhp9B3YYN0KxdGxVRVGrMBdi8eYNO3sQCLMTaEldlG1r29cuX2LFiDaZ9sojfzLm4WJEdmbHapiQlIevOPfQZNFDXasstRLwFV4xJEKEuIk8ep/l3LcXbKqNzZApzSYBbvkKlom5m8BiCCUUlyvIFT59i7aoVWPQZl8NS9m4aDbAEbNnFU6vtRm01C2qN6jEjN0tlVEqajtamp8zpXleaBvhGKMtnZ93DkZAQTJzKx8AoLQlM47WfJZyTm+d3KS7AtaYvWKuApbEm1BkeFobqtWrArXMXQ4BK56iiMVptk10zoWgXB9SSdzMLbI/cvva2w7VY9w9q3LJY95XdZFkC+c9e4vf/WIP69g1QHkUofPoYzZ1aIPlSLEb4BiA0eDc8unpRi93ePdvQu1cfNG3SFMePH0GTxo3RqUMnGotI9rvFy37GnJmzaeoTck1QiNmxTq6xc9caUEvuS0lNobGjgwYOQcf2HaSXswCo5MvYW6wcFvRp5VwO2hVEmaEdiSeLBpmUePysN6d1DtW4F1YfBiqXaoFhlzBC5+U9Qv9+XM5dM+7ISqvtqjXLMTpgPGpUr66y2gqWSOUos3S4oLXvCh5qSlkRN9STp8IwcRyfMs0EqNVbM3W2M/6dtMHtpsCNGDbUBzVr1OKtsOoYW5ZIakfQFnh79ULDRo1Fl2QhZpmz4sr7jv1bNjKLinDrdibS06/j3r3beP68ABUrVqZEUw0aNEKzZi1Qu1YdRgHiRxQ7RhmwSeUq+5vbf+LizqGw8Dl6dO8tI4JSpfsh5FT3sxERdQoBfJofYV5z81zuoaHHiFwcYJuTm4vde3dj3qw5ksans5Fbs78LY1a5IpA81bfv3cXwIcPE6absO+FgwxpgG5dwp1S2I3fXRqXyXOVDy2Jsi9ENH4evRxYBtrZ2eHr3PnVJbt7DQ3RHJiD2TvxlmjanafvW3HU+7U9+9n3cuHgFHkMHquJs37x8icjdBzBs0jieQKocn/6HuF8R12VbnDt+Eg0bN0bLtm0089kKienJ0rN/+3b0HjgItfncd0pLreAeIroky4ikuGV//84d8Ozegy6eyo2AVWyVk5zZE0QJ6y0EZheI4sTfmq1b9m7MmNi0fg38A8agqhBrY2G8aD+Lu2oJ2Bq18dcCatlXl2+M2kIpHriV36VVR3xsDPLz89GDJDtnCmiVtQRuWeXLmmXAkrpq1Jeq51hQKvXA58MHDxBy6CDGT+EZKRWVyu+TlBC2mO58ZSaCpXcpLqgl1ZsFtkfvXLemW0qs7MBGLUqsrrKKJAkQYPvNtyvR0rkN8h9koWrFSqhWpTLNY9u7Zz/s3xOIESNGU0bk7ds3YvzYSahc6QOsXb+Sgo0aNapRUPvocR6CQw5h8oQpNL5WckXm11oGwAlz1RSo5QErSdsTERWBalWrwWe4L8qVK0dfQg9A0WeYMd0WazCoD6XoFFVMzjVrV2PK5KmoUKGC5n7DglvVvLYIbLlGa6aHEQ8VOcE9evII+/bvpbGprEgEyyQnJ6ku6W/+CUXAxYsJyHuUh549SL5OLSujeFUmTUH8lvZZbmmT0Biro5Dva9atwoxps6iuJdUjXz9V14VWWDiIUB8EcONFPjaLQFyS4xNi4Tt8hIrxmI2xFYBOTm42wiNOwH8ESUWkwYaswSitAruyTV1qk1x/U+4orPu65H4sjEsB1LIeFKSG7Ts2w9fXn6bvkWJxBTJTORvy+dho4A3QzbObYXxtVvY9nIo4hbGjx/IHXIp1gO80ucVemzyKsBUTV+Spk7gxzPjpa23dxZrRwk3C+F+3aT38R4xEjep8vLOS3ZofKxTY1jIXYxt/oXSArVunMmD7VoOiNG/+KHwdsp4/oq7IdrblkRQYArdJfihvV0F0Ry588BgZp+PQxX+ozB2ZgNPTW/fAa+RwVCMux7YceZRgoaUkUu3aUhIpZYwtKfcwKwdnj5+A/6SJ/KmXYLXl3bAYqy1hR06Mi4VvAEluzVhnme+se7J0KiaAMRsUPnuG9SuXY/6nnNVStsTJFvO3c0tW1a3XwaoNxBhKGJeQHqYsSwikWrdtCxee7dGcss82/v0AW2vesaTmjhG4tQQANX9jSDVk+61Gg/fv2YOObq5wbOZUKuC2RGSomD/qUSN/itDH+/fsRid3N/ruFgEqP2NL2lr7NqCWNMkssA27k1YiYra2kv6NuNi6sk/JSoAA2wW/+yu8ew3G9aRYtGjujILHD1CxvB06tO2IA/u3Yfq0uXia/xgHD+7CjOlzUPD0CXbt3oZ5sxfQPYzE1164EI+c3BwMHTxUdFuUxUFqLIZSzCeT7kcBzMifxA3x5YsX8PTshiaNudQVWpjVCMwWx1ZrcQ1XpKqh+yVzw+PHTyiD7Py5C8ROY0GeRautlcCWykT8PzlQ3bRlI/r16Y9GjTgFV0UaJazxGsBOsN6uXrsCo/zHoFZNYrmUzOOalmIrhqglUEuIyJo5OsHFhSeKZIQnyFkCe4yuwxyiGDVFT2as1XrP3t1o27Y9WrZ0VsTV8hZYBZHUyfBjqF69Jjp1cpczUYtylg4CtAi8BHRtZrwqDwK47UWShYzgSRif/O9paalITr4Mn+EjJQZzwbLLxOPSOmxssHtvEHr16E3zW8uAsircwAYXLsbTPK90PVBYdKV5Ij8g4rpXrZs9e/YMS1YsxVefcrHi7xzYEob1rCwcPhKMqZOmco/k+04ZZ2sNsE0oJWDrWgZsjZaBX+/vi8LXMsDWDnfOXkaNhvVRr2UzWU7bi7tC0WFIH1SvWVN0RSYA9kZsIsrZlkNrj84qd+ScjFvISLqCviN8RIIpEl9L42x5l+SDgdvRY0B/1G/YgAGsZOIqXJNtgH1bt8Oje3c0Ibn++IkquonwjsiCUkAXBdFtUXJJvpp0CTdv3MDg4T7CVJc2TwXS1VW0mR+sB4eKsfAOwa2ybefPnaVEXH0HcGk8rG+7/PRQa1RbX6e6IaUBbBndRlyQle9nLbg1G29LFv3lixdj3kfG+W1V7RQbKbXunRlc9JaxYoLatOvXcCnxIvz8R5kjjNIYtBbBsIGi9ragljTHLLA9frd0gG2/hmXA9l3svgTYTv/oD/ANmIroEwfg7d0P169eoPlrq1T8AKnJl+Dr44+09FRkpF+D73A/XL+eAqIUj/AdSdP8kL3rwMG9NFUG+Y+G4fCsyEKblQc5ZkDtrTu3KagdPGAwmjeX+l9u6dVGuUYHfKZlabRHGoDby1dI3GUKlZU47cX5LJ/Yqv2CiUPUbi9nERQ+LMhk0yHFJcTj/v37mu7IMgstA2yV7siEeTrhQjz8tWJtFQ1QJCbSFbUlUJuZmYmz505T8iYqNwbByQ8HpDhuVhmQq0Cc3iSBWA0PJA1wL1iyyQHFlq0bMXf2Am2rrQboWbLsR8yeuQB25ctrsiSryKTohsi1S9OazEtRa4zIthMFqOVkx7opS9bYvft3wqMrOSxqajl3LWyQn08OaTZh/tyFUjyvRnytAKqPHAuheW7d3dzVxFFif5oDtuQd/vztX/Af3/yBJ8LSNsWXhL7Fyv702dN49vw5+vFEpUqyMNJRz62w2F64eNf0slOSBTt1bFiS1RW7rjJX5GKIbsHJ1ch+/pi32Nrh2b2HeJR2F837eHLAlnc7vnfhKgWkzbt0YtiRbfHy6TPEHjiK/pPHytyRBQvtie270dffl8Y4cOl+JFBLQG7a5avIunkbfYYRNlRusyfOM1Jcg2CdtUFudjbOnYqE7+gATastuxiJ0bZssD2/gh8/EkItRM7CiaaoRSjB3nuy3PIrrDWKtpnFSFnmzu3bOBF2VIzptB6EvgNgq2iEmfcqxjA3fYuRYqcHbnWvs0fbfCu0yt7IyMCZqEiaa9jIJVm2icverBTArQVQK1McmHYKffzTd//AvEUf4YOKFX+T1lrySmaB7cm76abHYEkW7NPQqSSrK6uLlwABtvO//jOG+09GyE4S4jEFkWEH0KtHP2TdzUQ5W8CrWw+ci45AlcqV4enRDVFRJ1G9ejV4dvXk9jkbG/yy9CdKuFKFkMrwMbeskFlgawbUxibE4tKlS5g8UXA/lFtptayzSkCg1cmWDvUM12w9kGsAbo8fD0P9+vXRno8JlgMzC+DWDLBVLKJaFkjCjrxyzQosmLuQA06CVZyJ9xQhlcyqyFUulCfMxJ1c3eHQhDAka1htmbbogVuVJsK+PvN90+b1GOE3CjUI8RALhJjFWJKj3FqrBWrZsSCNE2af4dsujCvhF8FlO+pMFCqUrwB39y6aVluOTEhK/5OSehWpqck0BY/kaizF53L9wEtd7A9JgBbHsp6uoQVqRZ4W/nCAL5OTk4UTJ45g/LgpMoI3MbZWYbnl3NEfom/vfiIjNR9WK1qIBcsueYuNm9dj0IDB1EtAaTUW9FupX7ne4fqT180Um+4PP/+IubO4+H09i63h/DW56rOy37l7J7p06QpHwgquzENcVFQGbE3KlHZnkTDirbjp373oh8v+ADjV5oCtTXn67+WtR9BpvA8qVPhAzGf7Kv8ZUo5EwnOsn+iizDEgl0PcgSNo060r6jVqxIBbDsRejY5F+fJ26ORJ4nYFYMuAW8IY9/NSTFk4H+UrlFdZbSWQy51She7Zh1bt2qGlC8dox51eijCWYbmTrgnxEdyc5+rZvmkj+vQfiAa8mxFzsKlQst8e3CrWGv0hZ6X11syCpCzzj7/+GV/98T+kBVGnNdp1v1tga+Z93sd8LVFwa4VLcvSZM3hTVIRu3t4WWZIFGegrm/KN/13JzChOXKs/hWsnw46hZq1acO3c+TdhrRXarZS5WWAbfi/jXXWDxXp7N+BIeMo+JSsBwRV5sM8YhO5ch4lTF9ActuPHT8fZiGPUBdO5ZSvqhkxyxDo1c8LOXVvRp1cfODR1gK0NkHM/B8HBBzFj2kyRIZnG2AofxX4gxnfKFHrJfTY4NBgVK1VC7569aQ1Kzw1WPdIDAOo1RXFF+FNzsZZf1CtiBKBYEXz/43f44rMvGQWeE46xO7Ll3YQFmFRWzKIq/EauEcIt5xat0IaE7wgWRl64AhjWBnVcraQPSConznJJALIUayt7rvL57Dsy44EdxUqdJSY2Bq9evYRXN29ORkwB+aGA3O2WCdXlnmpiI2YPSPQOXMibkt++++Hv+PTjL2BjY8u7cwsxtBqEUijC3n274NrRnabC40CRIpZZC9yKwhSPGjQnvOrVDECtMrY2OPQgXFq5oGWLVirSKE4V5fVOxg3Zo0s3CvDU7s1y4ijS4H98/zd89fnXXGy0cEDDrANsCiJxWDCdy44J8vuK1SsxaoQ/7OvVe+fAVt4FRVi8fCkWzuOs9ezhBVmHrLHYXkwsHYttxw5lFtuS3TXfY239vpmOmn3aihbbcrblce/8FVSzrwf7lk48iRTHhJx04DhcenigVn170ZJLwOq95Ot4kv0Abv16yVL+kHtePC1A+M598J81ld4juCATUEy+k7y28ZGnKUW7q6eHxdQ/ZMl4nJeHg0E7MXnOHJNWW8alREoIRBeNlb/8jEkzZlJrsnyzZS23zEkY0y/KBYTbbC1/jH4Xdm8T6oG00Vv5zM0b1qH/oMFoqAT0inqKA2wtvZ8JPchQfnqvaiRXS9YGvTotgVtL9Wn9ZtYlmbRl25bN8OrREw4OUm5JvedZfi9mgy+OAIzGlXLCmBo/3By5d/cuDuzbg4f376ueQojNFn3yqaq2koytlZpuNHK05zQrTrPANiLrxntc1aVH9ayvyFFaKq3413vok4IX+PovS+DexRvxUUcx3G8MDgStw4zpC7Fz+1oEfDieputYu3YpZkyfiyqVK2Lxku/x+cdfcCmtbG0QG3eehoYM6D+Aia9lRiczPFXWQl6LFEDYtqBtcGnVGp06dqLClrnaCghMw11TVEa5u7iOetv1QrE5qsGEkt9CfnjMzvVz588h/2k++vfl2InlAI2vWXkgbGix5V5SBfxZmfLfb9zMRGxsDEaO8JfJVZVyhnWHZQ4zhWecORuF12+K0N2rh8xqqyluVv46S5QS1D58+BChR0MwfiyXW1SSkwFhlJJbxHhJFCezeNAiyk36wmMZKmcCuPPy8tC3b39dqy1rhSX9vSVwA2bNXCAHRPzJgnSgoD7AZQG35VVHApVUXuJQYgi5+K8CmCTsxpGnTmLUh2PVKX74OiTLrQ1evizEilVL8clHPKcL69qsAK3kvtz7ufQgZfaM2WL/CW7nQn+Sv8NOhOHsubPi6wldNqDfAHh5duOu8xc3B25Brx494Ej1iXfniiw0hj0gSryUiDv37mJQ/4FyZmwrgW1iKQHbDmXA9re7cft+/zmeV7FBDcfG1BJLLLbPsx4hL+0OWvQmVlYuZy1hQs5NycDrgkK09HCT0v7Y2FLX4TM79qPfxDFyd2Q+zc/50DC06tAejR0dZK7IBNQScFtYUIDj+w/Bf9IEiWFOJ/UPWYCiwk7AvkEDtG7fvphWW4lRcO2ypZg5n3M1Ytd0pTVKa73/LYLbiPATqFqtOtw6d1a9MzuK3zewtWI/tdhuSzPRWn2tOOBW8xlWWG1J+1cuWYLZCxea0jPNgFtRebJWAFrCNOlVYGm+BG7cgF79+iJwwwbVE9TAVq6YCTco69fC2XpjygywtTQeiwNsI7MyS2WT6FHfoVSe+6/+UAJs/7xkK5o0bIJ7mdfg6toFF2OiMHjQcIQe2oXx46ei4Gk+wsIOYfy4ScjLy0VExEmMGz2extcSy+zBw/vRoX0HtGjeQsxfS37T+qisVowVa8euHfDo7KHOuUonvjRaGXzLYFd1vp+3WSa0QKywsyrnqPxvfXC7fuN6jAkYgypVqpQssFWgSrl8JGKujZs3ws/XjzK9arkjc9UwRF6i2OXAK3DrJgwf5ofqTPoftq/Nyl0JagsLX2B7UCCmTp4ugR3mBMASYZTMqqvoPL34blmbtQC9LM8sd4CwOZDIcCSqVqlmymobFxdDc/B27dpNtNqqmKU1mJJFgMV2pqAxSOcgsimmCWoV8azk1j37gtCnT3/UqVVHmzRK4YacnHwFt+/cwsD+gyy6IXPA2gZXk6/g5s1MDB5EwvKYGGgxJpfTW60BtgcPH4Jzi5Zo7eLy9sDW5KYojOMnj58gaHcQpk+Zrkj5xFtsa5pjRU68dK9UtpMO7RuUynOVDy1zRS5GN4zf8mdcS7wMhz6dRWBLXJJTD5xCW7/+KF9eAdh/XwAAIABJREFUckfG8xe4HBwOzzEjRBdlwR05KSwSjVo4oalzS5k7Mk0XlJqGrPQb6DlsMAdsbW3pRs7F23LgNjLkKJo2c4Rzu7aauWxZkqhnBQXYsW4DZixaqG+15V1qJNoo4YSOuWID3M/JwfHQEIzhKdGVm4YkUrVLsgwMM5PeDEgzLGMSQCgBuQ4WES9fT03FhYR4jBpDaPX1rcx6wJbZL1WPsvRO2goPK13zg9ey7MivZuGe8TPZE0hlaWssqdZYbdOuX0fs+fMIGDeuRMGtqMeZ1aCEF9ZRCPRGjyVAGXMuGk+ePEYXz25Y9tOPqg6oVq0aFsostiWbt9YI1Gq2nb2oMGqZtdiezr5pPNjeQQlv+6bvoNayKh8/LcR36/bjAxsbvHnxDA3sGyDrdjpcO7oh7nwUAgLGIz0tBZmZaRg+1BeXki7g4YNcDBk4RCSO+vt3/4fff80RuwjEUawrslI3l+fu5IDUyjUraUweyZdL57cwPpVWWtm4lYNZU8uBspDhBqbOOsA4EHMDSLHHCZYxcdnhn3EpKQk3MtPhM8xXOnxmrbLK9Ums13Ijle7I7PrIAtWECwl4/OgRevbsZQHYytPesCRSwneSeikuIVZOJCU+lHtrvb7Q2j+5azZYt2E1xgSMo6lnxD2d36jZ/Zr7rh1XqyX7G5k3QPLhksMWe/v61OonlJN7BMgBvGA1Jf8KVt1LSZdw40YGhgwZLrfaytLBSPWQsb5y1RJMnDgDlSpW5IERJyFhaEv/yp8vjB9Blhb1ElZvU8SpSuSjwKVLF5CX9xC9evXljClEjlwUnERSqnRD3rMDnd27orlTc8vAlr/v6LFQ1KtnzxFHifOD2f/4Z1kDbE9FRtC6evXoKWl7CoGYmMrG7ojMwGV1pnUb12OErx9qElZwcuDB/0hckWvUrGhqI7lUSsC2PQ9sU9NvYcm6vahRvQpqVKuCz+dyujP5/PHb1ahU8QOUK2cLT/e26NfdzdQ7WVOoDNiakBbpiHvZD7Dm+69p6TlhSxGxcR9aj+4Hu/IkxQ8XZ5sVm4yq9erCvkUzmTvyleBwtOjqijqNGkr5aW3L4eHNu7idlIJuvkMYdmTiwsyl/zmwcj1GzZ6BCuXtZIBWALr5eXk4tmc/xs6aIZJIkQlHN3yR8Zj/ZgOcPn4CtevURTtXV36zk37jvgmLuLSQiyx3CpfkK5cSkZmegSG+ftLGoLHxqjZmXt7iwmDlgmFmQTGKYxQ3Mgt9zz6HnIIu+fF7fPr1N/J3Vdyv3TZt65lwqyVAo2oeu6GYGLeWn2FGknKVwZQyxz+0ZMGt+slabQkNPkyVCVd3zrJOPpbabPw+Jt+fLabtuWTR6d7SGHj9+jWW/fwjFn3+BY07Mwa2vOpmMK/0lT71wBKUQb0hJ6tL62UUG7hZYHumlICtVxmwtWJ10S+annkXo2b9J9Z89xXcO7QCAbardkXgwZ0bsK9nT8FthXI2qFW9Oh7kZmPAgME4ffoE6tSqDXf3zjh2LJjG2XZs34HGz6WlXcPFxAsIGBXAxNdKeTT14mNZy+3GLRvRp3c/NGncWOF6rFb0+StinKjqTRUHNtYKzWjeyPdIfdIiFmCxoGzt+jXw8x2BenXrmbLaCtkRLL+HPN5VXGMZSyBZdF+/eYPlq5Zh/hwu/RDXBwyQZT1yNEikhHvIv7v2BMHNtTMdC7I1XQMYWNzV+LV5156dFAg1d5LyVbOZIYSTAEmW0h4u9YnaWk5c24vevEHDho1Rvnw53LiRSXPyNnNshi7uneneJMXWMszE4sEKC0A5QLN1+xYMGzIc1apxlm8hBldMBcPHzgq/Xb2ShIwbGRg0aJjqwIZ9tpJaRzl3tMaAPJSdcT/mFSphnyDl3rx5jeUrfsaihZ+LoJYWE2JqGZdlAegSN+SVq5fh40WfcXqWhuuxcLAjjPl1G9fAZ6gvJUwTga3qIMI6i+2Vq1dw+cplBPh/WHxga0a9UigownA+HBKM+vb2cHMlKZykdckaYHv0aIy1y1GJlB84sAuthwDbWjWqoW7tGpj5xd+x5jsOO5HPwj/8CGenJnj05Ckm+g9AS6fGJfJstpIyYGsg0pOnE7Bqy0FU/KCCCGxnhy1FYlQ0KtepjdotmkruyNmPkHMpDS5DenFkUTw78oPUG3jx5CladetCLa8CazKxzIav245+k8agUuVKEujl89pePHUaNWvXRhvXjvQ+GTsyb7U9eTAYLVxaoXlrF0Or7f9n7z2gssiyteGHbAIEASMCCggmzDnnnHMObWw7h5m54ftn3bl3ZjpnbXNocxbFBOaIqCgqYEJFMhIFFBT41zlVp+pUeN+3QLpx7oW1uoWqU+mEvfdznh1Ifb5NK37Fwg8/1LC2gmySga4MSOWU7epjF04TF11HtGnfURBErC91mFO9df6vBG5/27AOAwYPoXG2ZQKj0oaB/kQr073KAWy19zcqcfn3NQjwuEsUV6gQpClAqXu8DC7J5Prf1q3F2EmTUaumsBOv0h2aQbAMbi3dwZJMNt/flsY/7PhR1KtXHy2DgnSBLbmerEGZsa0kttbStOI6uvhVCWpVt7XUcbicnmCxze/RoIu7UL+06qf8PVBcXEINmud5Bfj3D2ZIwHZ72G3cvnIKbdt2QvzDGDTxaoK87Aw4OTqiQ/uO2LNnCwb0H4QG9Rvgt9/WUhDrVseNMmAnToWirkc9tAkKkgxkuolrpayrqmCoOPfjvQf2oHWrtmji46M1+sVlzoNZxcrnCNvXr18jOSUZJNsrAS452dkoelVEYyJfvSpCSWkJSopLYW9nBzt7e9SuXRvVq1WHq6srXFxc6XomNVqFH3nhmNWHotVuSscqwK3YiLCHN6NuaMr/8Emk1ODa0iaW0CdKRC8AViZrZffikCMhlBVv2bzlG7G2OTk5CDlyEFMnz1BIdHObp6qulf4kZVUc7O3pXGM/egmj9ACsKRdkcpwk7Jo2dRrq120gD6n4gpE3IxETG00ToHXv1sP83OOyHZNONcLaqpMMEfffHt37oE4dN11wy/ebOqM0D3BNJcRiMazS7OXYfnbNmTMnaGkff/9mopeBbEfSuaqTNIrUp36WkU69KYTpztmeOiCXrMMffv4On330uXg/OVxOuU7MA9sunbsoNn6eZWRg955dWLJwsbxGVfrNkrrTMxLV1/Drhs1Fciz27l3ce3API4aS8posaZhQ7scoY/uPv/9UfuH9Blf+5d/E8ovihsuarSFo3NADg/t0ku4an5iGhvXckP/iJf78Pyux/B/CRkZF/lQBWzO9mZObj0V/+gafLp6MFRsPSMD2nbCfEfc4DjmxifAe0BEkeZStlRBrG3fkEvwGdkO1GjWlTMh4XYzYkDPoOGE4F2crJJd6cOkaajo5wa9NK+GcCGoJY5ufmYNroScxdPoUKYEUi7FlQDfnWQbOHjmGCXNmWWZtAVy9cBH2Dg5o20mYaIypZTVwBXmicEYWsyjzsFa80go4tHcP2nbsiEaerE6u3KF6zKl6cZtS5kqVrz9IRoSLjtmguJm5e/Dnzp0+BTsHe3Tp1l1PZ5o8JvWaiQdZAjay9uX61aAEUN7bYm8ZuKuMTowAwooCt2VxSU5KSsKJ48cwY7YcO0U+zNz7GvkWoXOMtjTW15bm3rP0NATv34d5CxfRp5OkOctVrshKYCuapQaUsL5Bq50C5gxdc2tXdzKJ3WcU2AZHhhuYkxXfZFTbzhV/0/9jd2SbwVciYzB3ylAKbElW5IOXHiN070YMHzUFl06HoE/vgbh14zKCRMC5atUP+OD9z/D6VSE2blqND5Z9JMTXWlvhhx+/xbKl78HBwUGROIoCW85a1zBTpcDho4dRv34DmijKdFIplWusuOTJrVNTUxB7LxaPH8fROq0NGjSkTKiTkzMFri61XWFrZ0vLtNjReqKlNN6xsKgQr4oK6TVZ2VkUCCclJ4FsMpNkd75NfBEQEMjNDtWKExeZHtAy55bMdO/2ndvRrUtXeHl5W2ZtDbojC92i45qtYm1JEqlz585i2tTphjYSmKHPaxk2VhcunYOtrT06dWBrU9qCUIpmHaHKDt27f4/GZLI6v5KNIaAoVf9w8ZpiQ3Yf9SbC199+Recl2bwwuetdCoSdDMOLFwUYMXyk6TmoE2u7ZesmjBwxGrVqOVlkbcmrJqck4fTpMEyaNFOR8EsTc85mHT9uFuSUEVCblPQUly8LoQWkz/isxOoMyFKsrpUVdu7ehh5de8LTk9S6FcdEipUVxodvHx//GJfCL8nJv1Tgl95BvI85V2Q1sCXXWapla1bDcyfNtdPbmCHHSCKwVWtW46P3PlTE2ZYF2EZHp1aKxmnevC59bmHRK/z9x82YMKIPWgUoy+edvBBJ3Y9fFxdj2b99j1+/+KTC37UK2Jrp0s//9ivGDu0Jl9qO+Gr5dg7Y/oSkF1l4sv8SfEf2hH316iJra4f0G/dhX6066rcKkGvXWtsg7swVNAz0g1tDUt5HALWEuX2RmYPoMxfRe/JYkckljC5xRRZcki8EH0ZQl87wqF8fpIYtH2PLGNyw/QfRPKg1vHzFhBo6bsi8a/K21WsxcdZMqRamIKxEwaFwSVZwtPLOm9Rnwo7a9o0bMXj4CLi6udEz/GIuE7hVX2zWgVN4CYsQQpXBUD3clq5n5x8/eoQrly9i0rTpJp+rfy/TrsiWgI3iXQ0KS3ZNxYNaTguKvxqBeRULbpVPNPX80ydP0BIencUSDto311/0Rr6noiSwkbE/sHcP2nXoAE8v7aaR/tzXLzuhfpYa1JpaR3I77duWGdRyg1DyugQ1q1lmbP/6yw8V1d1lus9f3/2gTO2rGit74F5cAr5cvg2rv/oU7//HjxKwzX/5CvvPxeHIztWYOXcZ9mz5FbNmLcLBfVswdswkFBYW4MqV85g8cRqexj/CrVs3MHH8JKr3Hjy8jzvRtzF+7HgxaRSL1xMZWx3GkGKv0lKQuqBFr16jtxjvKQAoVdwhn/GXw2yRN65RMFRU9AptgtqgXt361KVUms5lFRqiPsrLy8PTp08Qn/AUpA4pAWxdOnVVuj0x/WYY3CrXP9G9qWmpOHwkhJZHkta5KtaWX8vmNrKUo6xTgkdnDH7bsgnDh42gDLWeOzIdCn48xAM8bGXg9njYUbi5e6Bt63bcq6gArq5NYIXc57kIPrgPM6fPlq4VMY/4t9x38nHzLsikf9dvWoeJ4ybBsZajNHbkelPTgmSsLigooOWlWBtlHLg8N1msbXT0HSQmJaFfvwEmY20VrC1KERp6FA0aNEJgYAtVySXlpoRaP/MMrsLZWqUCWB+x+SIDTmDbdiHhV23n2hq2VmZhlfG2BQX5+G3rBixdtEyYp2bckJmtevHyBZSUlKBXj176iaM4YMvuKdlHysFXbGqQNqvWrsbYUWNMlvypCGArzX3FL8LcWbFyBSZNnEQ3zFg28aJSwNHJwZC6iYmpHGAbGCjIxuUbD+B2bBx8PIXyPx8umID/+m4T/vb5PGzddwJ37j6iGx7D+3dB1w4tDH1TWRpVAVsTvXX8zFWEX4/Gf340C7EP4hXAdsHJn5CQl4HsW0/h4FgTbv5eAmtrbYvXuS+RfDUGzQYTd2QBwNpZ2yL3aSpyE5IR2LurwhWZgNfrB8MQ1KcbnF3raNyR057EI+nhY3QbNEDpisyV/slMTcONi5cxdMI4ibUVatny7sUM2gLxjx7hfnQMBo4YYdYlWdgRthBvK2rLbRvXY8KU6ZQNlhQoUxkaKWA8qZT6XnrDZRGcWrDCjYAM8tyVv/yERcsEVwtT1+hBAJUMVSpWnQ/SvbfauDGzyn8/UMs/VGtQmHolU+DWlPLXPV5Gl2SSRXj4qFFwdmYuf8b41rLaqWURtpJSNTB2jx89xO1bURghlszQmxN646x2H7N4ncmYYDbH9Wd6uYEt+fZioLq9tcWuu5ZRObX42td5O2rxWeygt7DBq9fFmPfRF/j7X96BZwMPvPfvP0jA9kVRMTaHXEX01XPo028Yzp84iAnjp2Pvrg2YM2sB7kRH4vXrIlre5fq1K7CztQFhUwiwPXr8CPz8/NDMr5m2bIjE2Mo1PgVCsZS69T2Me4ihg4eajvNkIFcMeySxvDF3oxH36BEC/ANorBvv1il0u3HGkB8mvc2iwqKXiIy8hmuR19AisAV9nouL0lVZGd9oOqERn+iIXXP0+FF4e3sjsFmglrXl17+kYyxpVPZF+uCWd1WOuHYVBS9eoGd33gWX6zsu9lbuVSUDz5PDx44fQVFRIUaMGMMJdD2prfyGDZvWYuqU6ajmICTf0YBaTqkL5/Rimjnwa0WyFv+GIYOGUuZeE5ereiX+z02bN4pg31WeSTpMLZvDQobkjRg1cqzFDMm0D0mMc3ExNm1eh9kz35HicqX+5dzz5ZEUf2MvamIK6G2C8HMzMjKCPrtTp64SW8uzrOR6FmcrdjO1QW/cvI6C/Hz0JCBVHBzRY1kCx+r42uBDBxDUug18vGVvBMYos/fUc9Fnz9WEAXDfTNzom/o0RWBAAKPuFdLW5ArhThhfRdqdkGOhofCo647WLYmHiTgoNlawr2FvSOrHxqQZalfRjQICPSr6luW6XxWwNdFtH/91OeITU2Fra4OXL4uQlPoMA3t1wP/8+R3MCf0eKS+y8CqzAJk3H6HJ4C6wsSIlfoQkUg9DLsCnV0fUquMqsLZWAriN3BGCzpNHwt7eQVHTNunOPbzMyUPr3t3F7MhiAikra3r9sd+2of/4MXB0ctLE2QqZkq1wdNdetOvaGY0Iu2MieRTP2h7btx8BLVuhib+fKOiZq4cMZelvTMgwZx0+nkK6Ulj761eswLQ5cylbxukJ4fc3BLfq+6mHzZIQ4eNj9IbcCFDduW0LOnXtBm8fn7cW2P4xoFajDi066eq53fCGjHpM9EyVsrgkZ2ZmYvf2bViwVC4BZO55/PN/T3BrcZ6KL7J3105069ED9UzEdOtuoOjcXH1I8beZzRI9A5z10RuBWoJri4zF2FYB23Lp9Eq9KCr6If70Pyvh7CTEuD95mgL3OrXx10/nwt/XG6u2hyE98RECA1vh/p1IdOvaE5fPh2HKlFk4cmQf2rftQEv57N23Hb2694JX48ZIS0tF6IljmDNrLpdZVdBNLCOyws1SNNzjHj/CzZtCjCnvfiy7KnPlZkqBuw/u4eKFc9S9OKBZAAICmnNbmNqkSUbliWbdcMpMsc6sAFKy5f79u6hRoyb69ukPJydH8XI9V1n9hFJqYz4/Px/rN67De+++b9Ad2ZKUUkpLZUIk4RxzVSbuht989zU+++Rzk+7IfHteT8hyWFk/lzDdZ88TF+eZlncrrYBde3agZYtWaE7HU/hR2wMS+JLGRsvW8v26becWDOw3iCZB0yabUi1BTqGQX8l8Dj4UjPlz3zEdA6uKtY24GoHc3Fz06dNPl7XlQTBbC4/iHiLq9k2MGjFOCW4FilwcJyWoUus+3ZmgE1NLblZY+BKbNq3BokXvaV2QxU5nYJWvXUuObdy8DiOGjRISnWmArdYNmdzuq2+/oPG11jaExpFdx6Wx5T0TVDYoe4ZibXIfS+oIk/CBIYMG6yaQejNgy67mZ7hyHG5GReFx/GOa1ZzF2b4sLoWTwazIsbGVBGwDqoBtpSrgsjxczdjOOf4dkl9kUhAbfzACPoM7o1qtWvRvAm4zo5+gtKgYnh1bc9mRbZEQHoWazk5o3CqQA7A2sCF1ttZswYgl8yUXZN4d+cH1KBS9eIH2vXrIwJZLJkXibtMTExF9PQqDxpD0/kTpy4mgeEBLeBLyd+HLl9i6ei3mv/+eFFNrziWZOSUrd8S4WFxxrf7wz3/g/c//TLNYcvpbpUxkcaInIMwZzpZUrrnz5sCt2evE140Iv4z8/Dz06T+gTMBWqUjlmWcETKs7sWzfb6l1WVaBqbbGmFtTrK2sYpX3N8nmckqZXaELggFci7iC7Kws9CfKSWVcGPnyiga4lkaDnY+5cwfxTx5R9371GlIoYukjhCv/CLb2TUEteU+jwPZ6RuXU4mtX5+2oxWdkjr7tbXjGlsTYfrNyFxxsrVGzWjW8fpmH+h51kZaShEGDhmLVqu/x3rsfw97OBj/+9DU+//hP1GjdsXMrdSMmMalk/jGjWPIoEhlb5rJH/k1NTaUxjVMnT9OAWkGEyEA1Lz8fh48eRI3qNdGzR2/UIm6l9Eftriwd5s8aHgI+PlGzjlXxrcQ9+fyFs+jQrhN1gWaWOzPgJblgoRQNa09KmNSqWRPt27VXum2KN+LXtShNlN9FDpoQiHy8LRPPjLUlIOvY8aNwd3cXs7yKPcuyvYoKgI2JQh+ovHT4xEak/NnW7ZsxYvhoGu+seDfxY65eDUdk5HWMGDGaJiOT+lwlxAz1KdfPJ0+foK7V7du21wW1zCpSJ2YSJw191XPnz9G53a1LN+X8lPpDBPNcJuSvvvknPv34TyKwVWVIZpmnuSy61A3/4lnUqFkLQdR9W94gUMSkm1OkOgpIsvrEfmT9d/JUKF2j/r7KhFGCbpLBqTrmlsSvnzh5XHITl3K8cJmTZf0mbGiR2rXnL57D9CkzREa3YoEtAbUhRw5j9gwSp6xeJGZC4Dglr6/v1Uf1PT+eZWZg+84dWLpwiRRnWxZge7eSgG2zKmBrWB9UekM1sJ197BskvcikIDYr6gmNsfVo0VRyRy4tLMaTk1dpTVviniy4I9ugMDsPCRG3EDSsnxxnKzK6sWcvo079evBpEUjdke2k7MnWsCoFQrfsxMg50zUxtlIyKSsrHN6xG9379YF7vXo6rK3oViwBXitERVyjSSw6du9mxiVZdsvhYKzsHiLtZwkChyTM2LZ+HWZLGeXKHnOrkaUqWWAUIOhNnPKAW/a8Z8/SEbxvr5TMxywol9WoIBZ1Glc0sFXez1IvVeSyKiO4NeOmxb+Vrh1VRpfk4L170aZdWzT2UiYwMApajbYz15tGR4K1+/J//obP/+M/mTrV3Fp7v3+B2FruK4wC28hKArZtq4BthQkHNbD97+/Wo4l3U6QnPUHD+g3w4nk2zYjcsGEDGl87ZdJ0pKYm4dLFs5g+dSaSkhJw9foVGscouDAyPSYbsgIW4IBAaSm++f4bvLtkGezsBNc9dXkU4RgQcTUcN6Nuon/fAfDy8lEkRVKyh0pJYKQ8iroTtRtPPDJQhtcycHn2whlaD3bk8FEK7yklS2jZbZZ8//c/foePPxQStTAwonbdlG14lTZRJOhSfpkesGVjQqxykmV2z/49WDBvQdlYW7kijoCqlfmq6Jhu27EZdT3qo0WLlkKyrpcvEHM3FikpSfDza0bd2kkyL/ajYWo5Y0MGuKbZ2syMZ/RbFr6zWE5kpLiHvrTnQS6bOxs2raeJpFxd6ohzVOw1VWkkFmtLwHDNWjVpojUyGzVzWrxOPi4c2B+8Bx3bd0L9BqQ+NwduhceJP7owXEcHiTNTBWpJGa7EhCcYNmy0TsIooYP02FrSOOzkMXi41UXbNm0tuiEzF/FzF87CxsYGPbr1kOwrzVw2FUuuir3lhk9hrP684hfMmz0HNWvUNO6OrFzSKhGgNzdUrK04JuTo1999jXcXvUtz4ZAxLQuwvXe3chhb/2ZVjG2FKc8/+kazjn6NpJcE2NridfZLPIt4gCbDuokgVnBHfnwiAvVbB8CF1K4VgS35907wCTTr1QW13d3E5FJCHG7+syzcPReOXpPGatyRSQbkm6fPo3adOkLpH1Lqh4uxJa7IBODG33+AJ/ceYMCoEYoMyVrXZBnkblm5CqMnT4azi4uoK5VMryiSlEpV4ZbM7+8Kwisv7zm2b9iABcvelxUKN0hKBa+7P6y9rgzAUCGoNNaFJrJC0cIS2Fzx0w+YMWcudQv/o4GtJYBUXmBr6r5lA3V/HLhlip4fOFPvSuopfvfFP/HJX/5NwziU5fvK0lYyosogmNgYhAQfQFN/PwQECm5zRudYedha8/fXg8/iB1maiBa+2yiwvZFZOQkw2rjKyYHKMIRVTS30AGFsP/n3f6Bv/xG4fukEenTvi1uRl9G+XQdkPEuFjY0VLYdy7Wo4UCokhbkUfgF2trbo3q27DMYYe8YZrrwxH3L0MJp4N4G/XzMRMMixtzRerRR4WfgSBw7ug7t7XfTu1VdgcHngxGWKVYNYHvCa94U1AVzZMlKtI4nJ4gQIaRIbG4PrN65h4oTJtFQN854yBW5lV0w+Pwao+66tjQ3tYwWwFQWB9DoqF056WlNSSTnYfF1UBWsr9inJzty5Uyd4k80DsQOlpEmcMc9OKjcV2LNEHaMSxrduR+He/bsU2BImlWSAbuYfIGXjVchjGb1qEgYJp5R9Jghh+dje/bvRqWNnNG7UWGYKpf7TQTWaTVwZoBOm8tDhQzSxl8JVnvWHKu42KzsbO3dvxzvzFgntxeRoEshlGwGcGzPbdFix8kfMnb1IzIGidO1Wzm+1ptPRAypQS9jNc+dOYezYiaZdkE3E1pJbfffjV/jo/U+plx/buGLxtFLfq9jb37ZsxID+A9GwQUPTmzTcPNbVj9xBfu6z+ULkSIO69WjNYyU1L7TQVYNlBrbiYIsPldcOqWG8DR3ak9AM33IA2/RK0Uf+zdwr5bnqh1bF2JZjGGYc/VJ0RRYSRj0NuYrG/dqjhrOTVPon51EyXqRlw6dnRxpfy8Dts9jHeJVXAL+u7VVJpGxwZe9hBPXuAbd6HorSPwTIvszNw+UjoRg6fRLH2pLatgKoJf+S/0K27US/kcPh7OxsgLUFkp4mIOrqdQwbO0bB2jKBwrO0fLytIO8ZKNWC26zMTBzevw8z5r2jKwSMxtwqBEiFg1szxrtqXrCWR0NC0KBhA7Ru29Yw6GCGgWbxmZh7mrcyKyzlm8jXGUcellqWDdBZBreK+xlgbiuCtb0XG4skp0Z4AAAgAElEQVTo27cxZsKENwK3/HCZ6hdL/ak35OyaB/fv4f7duxg6ksTVlAHU6jRWv4fibzPzydQc0lP85RCd9JIqYFvenvvXvo4A24XLPses+R9g75ZfMXv2YuzbtRFTJs/EyRMh6NGtJ3VlPHBgFzp36oymPk2oIU8SD3l7eWuSyDBXZMZCEQOdZE++Fnkd48dOpJ2lx2oRUPvjL99j5rTZ8PCoJ7G0UpyugilkiYxMMFplEASKtaVK2sbrQ20CHCAnNxe/bVmHMaMmoLEnYd7MxNxy9+bdPkndz7Xr12DxwiWCfJHkgFhXlE0vnbI/rC0zvNUz0Rxry8Yl8kYkJoybaIi1VZj7el46KvbWyMrg2Vq1PDMH9Fkfxj99gvMXz1P3V3VMrgR9ub43rbtkcHvm7BnUqFGDAhg6/8QOljZZ+Fjb0lLs3rcLbYLawce7iYa15Tdn2LwX+rEU+Xn5CD60F1MmzZTnu6rTTHoh6CQYZP2VQtyIw45ixoy5unGuem7FPHiNib2DJ48fYfgwEkLHbSLouiELNmdRURF++fUnfPLBpxo3ZAkQq1jZ8gDbuEdxiLoVRbMjvzmwNWcZ6LO2JHyAxKiTDNpkbF+UIcb2/r3KAbZ+/lXA1ogseivbTD/yBZJeZFBXZAJss2MSYWdrB/cWvlLZH1vYIHbfabSeNFSqZ0vArXVJKW7tC0XnKaPlrMlWAmubdv8x8p5loHWvHgJra20NOysbqczPlaNh8G3RHA29SRZmAmaVwJYA3Ie37yAnIxNd+/U1zNqePnoUXj5N4EszwIlsrgq4KnczlbG1Sngr3sEKNL5x/47tmLt4qdZQ183GyrlUmQCWeha/OZFh6lx5XZIf3LuHe3djMGyU4HKj/jEFlSvCFdnYdxqDVsZayV9nHOCWAdwaALYKA4frbL1EUqbekRwPO3YU9Rs0QIuWrTVjZvzbKl4c8eOwasUvmLtgIXWdMzlvdWackblVNmBrahab2qYuW78YBbY3MyvHnSrI9e1wpypbr779rQmw/Y+/fY/e/Ybi0skQjBwxAUdDdmL69LnYtnUdFsxfTFnb9etXYsG8hbB3sMfqtb9SF1YHB3uunrr4rSIjw8fWrly9EjNnzIKDveC+xwACY2ofPLqPmOhoDBs6SnLLZIyXAITl8jP64EqvnzmUxSx+c7pBrfv0WEKFV5Tsdr1j5xaa2Kotie80BG6VmXxPnzkF59q10a5NO7PuyGwTm32GWsboAVxLrC1xvR0zaixNzsV+dFlbXbdj6QpFz1pyCdcOB7NWlLJMad9woUPc2OzYJWyyNGpENhZkd25mM/GgysQsEd2plczzyjUrMWPqDFSvXoNexuYzz8iy5FCPSUK0qJs0blgAwvK95HWgLhkktHsS/xjRMbcxZNAIFj2ude+Wl5ZJgcLmAimBtXnzWsyfv0TSV4r4WdoxsguyRJbQkALB1tu1Zzu6dumOxp6NDbshP4i7j3v37mLE0JEVBmyl8VT8Avy8/BcsWbgItrakPJ1y4erqaN1NYyPWlgrclgKPnzzB5YgrmDRuAt3EeFFcAidnIau3pZ8HlQRsfauAraWheXvPD/p6CYp8akkgtuR5IdIu3UXTESSrseCKTP5LPH8Trl6NUKeJF2VtaYZkaxs8OHkZ9fx8UK+Jt6KmLTkXtnITRiydz7kpywA2/WkiHty4hf5jR4kMrTXH1pLfhTq3O1auwaT5c6lhQI7xyaPIElMklwJxyyrEtlVrFImkpGRRVIqJgf/iwpaPiGc0mZJFUW8FmsJ999YtmL1gkawMJG2px0pVPrg1ByrIrveP33yFj//8F+33mFQKxmIguW5RTn5dYanbxBDy0HyfqQ/WQXzGQGDFgts3ZW2psQDgy/8WY1fL/V0VK5P4br9w9gxJ8YruPXuVbV6pFLHOnwbXnWkXfek9jehnrotIc72xMwpsoyoJ2LauArYVO9HFuz0vKMI/vlsHP99mePowBkGt2yL61lV07doL4ZdO0yy3qanJuHDhNGZMm4WsrAwEH9qPRfMXCskINYBQ0D8MwJ46cwoO9g7o1KmLBA4El00B4KampeBgyEHMnc2VQOGS8whgTaeEjQg23rRTeLCoXlMmgZW4wcwAGvneQyH74e8fQAEuD25lECcn6hGwhQjmrIAXLwpojc4P3/tQ446sAK9mMsqq+0Fyo+WCYBnwZUwu+fva9asg2eqJC6nseitIZx4os3Fgcps+T8PQcpJFdc703oI5UCv1pGKesb57mhBPXblnkkzMbB5KelnFeJuZKHr9cv/hfcTExGDk8JFasMrNSTaXV65ZgcmTpsGxlpNF1la8XOjC0lKEhpEN3oZoHthKfEslyDY3x/l+zc7KxMGQ/Zg1c74uqGXzTp63ctI35pGQmfkMBw8dwLw5olcfm3NS/+rP49CwYzQZWTu6uWMiXlzldWCJsVXoTU7PnTpzGjWqV0eXTp0rFtiyZygUJAduS4EXL1/ipxU/49MPPikzsH14/9mbiqtyXd/Uz61c11X0RVWuyOXo0Q6LR6LmAH/Y2AiMLQGzCUeuoVGvtqjp4kxL/5C6tvmJGciJS4Rv/25SyR8CXnMTUvDs/hO0HtSHq3VLGFpb3D13BS7ubmjSqrngvkxZWTmm9syeYHQbPABOzrUlJpd3Ryas7e0rV1FaXIIOPbqJIJaxsHy2ZBmskkV//XI4fU7bzp0NuiTLikB2neIUhyhyyL0znj3DgZ07MH+pWHyb73MTzK0pA92UADLUXj3WOi5XrImeDc+OHdi7B+06dIAnKa2kM3+0x94A2HI3M4cr5HPm0YdhUMt/l0FmVb6kYoGtwsDh3qusrG3UjRvISE9H34EDK8wluRziQ7qEjQVxrVr+w3f48PM/yYq6AueVYszNbJKYmkOmDUX9r+ef90bANqty3Klau7wd7lRvMrfexmsJsF2zOQQofgV7GytUt7dDyetXqO3sjLzn2RTwXIm4SDdte3bviVt3opCWloJhQ4bB2trEhqe4e5KVm419+/Zh1ozZFByUMLZWpGCfJsbj9JnTmDZlhiarLDP6RQwsdB1X2/ZZxjOkJCchIzODgu28vDyaHb+kpASvi1/T5k6OzvR34lZaq6YjZSXd3T3g4V6X1sCl+kkty9UxfvwGMVun0r98QiMrRN68hoKCAtpPeuBWk7mWA7fHQo/Cw6Oueda2HMCW9SObe3og7vufv8f7S0nuDbkzzLG2CtlvDtxanPBmQK04OOyN9GKUSckgAqT8fFl5RJlFZ0Jbfb0JzCKBeB7079i1Az179ES9ukINbd7bgM1F5qYccfUKCFvaRcyobIm1Faez5IK8YdNqDB44HPXqNVBWYza4QZCbk409+3Zi3txFZQK1bA0wtvbo8cPwbNQYrVq2UrC1UjuW9YVbA+TcqrUrMWn8ZLi6CrWeeZd6eey4jQrV2qPXqBckPyO5dUrWGPEC+fiDj8QZZoG11dWvKqtLvIWe14NUhkkcix9//hGzZ86Go6NTmRjbhw8qCdj6VgFbi6LobW3Q97/mILdmMZx8SGIogaHNjU6k7GndoGYCk0vBrS1id4Wh1cShQu1aAoKp27E1rm8JRuepo1CtWnVFrG1BRjZIhuTeNImU0FZwObam2ZIf3bqD/OwcdOzbW+OKTNow1nbDdz9i3kfv08xxjKEl1b5YfAM7JixyAfBu+PkXTJk7FzVrCTUIBaZXjntQ/82LDmVcEG+ECM8kmR23bViPxaKA0ANYymNqkKycDVJbrczQnTam4J4pl2ST7QFcvRKO3Nwc9Bs4yDiwlaSp/HrmniG1UhtDOl/3u4JahZUiP9wyc1ux4LaiWNtN69Zi8LBhqFuvfqWCW37sQ48egbuHB9q053agVeOsnSvi+jAw/xVNdBWv8DBTK06QAealsbnT6rEzytjeyqoc5dzK5e1Qzm+r/ivve+XmF2JXyGU8uX8HLQJb4Onj+/Br6ofkpKfwbNQIbYKCsG/fLvTo3gNeno1x4mQoGnk2ROsWrSiwNfVDDP6du3cgKKitlGiFgVtiuT9+8giRN69j1IixCtBL7qfnvkkA6917sbSebEJiPC3/QzLvEqaLZEitWbMW/c/BwUF0ZyYz3AoFBfkoeFGAly9eUABMsuinp6fh1asi1K7tiiZNfOHb1I9eq2afFYmjTABcSceKoPPCxXOwt7dD505ddMAtD4RlHU6+Ob8gDzt378T8OSLbxjFl/FJXMMxmFjjvDmyy9I9oqF+4eB62dnbo1KGTZdZW7ZIsChKtLjCljfQsClmW8TaEXowsG5NnGek4cjQEs2fMlUMtJTmqZGvVLtzCnOV0oeTqLvLU4t8kAdbjx49prCk/L/n6tOz3/PwCbNi0FksWLdOW/hEuVrrgczHjbHy2bd+Edu06wd9PCD8zr63lfkxMiMex0BDMnye6H+tsgChsRlF5ULuTm2cvX77A+g2rsWzpB4Lu4dtxNqc6iRSp5btl2294d/EyrRsyD2AtbMzIOs38HCGvsj/4AJr5+aE5TehYccBW6nedHRAWEkGSrpHkVURulMUVOa6SgG2TKmBbXvVY+deNWv8JHty5C4+uAVKJn5K8V0i9EAPfkT3EerYCa5scHg1H9zrwaNZUkQX56ZUo1HB0hFdrgZklgJe5KofvCUGbfr3g5kGSSPGMrfD7vl9WYcp7S2icLQOyyiRS1oiJvIHCFy/RqSfJgKgFtJpjABKexOP29esYNm6cFAOhdEnWgl0FuGW7wqKZzAMucupFQQHWLf8Fyz79nDOkufEsb9ytAePenG1eFnBLHpWTk429u3bSeEg9fa8HQvTAQVmArTngYATYaq63AFRMW5HyGSPg1lwbszvaOi+gd6+ysLbklunp6TgZehyTpk3nbQ7paZa/6c3lD9/12dlZ2LllCxYu0/FmUC4N1YNlw5U/YXE+6q6xN3NDtjSVSJ9ev3YNpD4v+QkKaoeO7bSxzuqevV1JwLZlFbB980mucwfC2J65Go9jBzZj9KjJOHF0L0aOHI8TJ0IwbPAIuLu7YuXKn/HxhyRDqhXWrl+FSeMnwc2tDtVzej9kbj16/AjhVy5j4vjJKCkRjXrRuM/JzcHR40cwYdwkDZMrM4oCwCX1bC9dOo/omFvUXZr85+XdBDbWNgaMf+0qZG9M7h3/9DESEuIRG3sHrq5u1JW4ZYvWCjdhwZ7ntpd0Euiowe2x0MNo1NCTsl5a5lYJbnmX5MNHQ+h1Qa2DypVVVj0WJsGtBEaFcSHeKWvWr8ESUp+T9yZmsaKStW8i1ll9XiO5TUsjtQ7mwQ27So+tPXLsMK2DS8rRSHaE2m1WPGE6kEpUN6rv5FnbVWtWYvbMObRElTA3uUzdfBIplGLPvt00iRTNMs25yrO4W977gGd0he4T0PSpMydAyhcNHDgEjrWcdcNGWL+QzZ6IiEtISHqK8eOmaNzYhbkrfKO5OrTs3Nnzp1G9WnW6KcMDYTWQ5e9J7h11+yYSExMwfMgILbDldZsBjwO9TQh+s4NNradPn+Li5YuYMnGyZldEd7ZpdKzWQJVkAzef5TUljzvxMgk9eQL+fv54VVKCRYsFt21LP48eZlhq8ruc92kqlK6q7J8qV+RyjMDk4P/Cle3H0GhkR9hVI0yswNomHotEo25BqOFK3ISJi7ItXqTlIP3WQwQM6SXVtCVAtij7OeLOXkXH8cM0NW1TYh8gPyMbbfr2pIDXxtqKsrWMuY2+HIFajrXQLEgs/aPIjCyD3W0rVmHC3FmoUbOmIq6WbH5LjsiscLbI2l48dQp16rgjsDVxD5EcmhTgWBbu/HnhKO8WIrUT1QE59/rVK/z89Vd477M/0YB8PcBlEhjqjJWeIFI+V3mReUGkPGsOJGzbvAmDhw6Dq5ub5hveemBrCYkYWRMmd8/5i99O1vZ2VBRSU1LQf9Ag3SDQ3xPcqrt+/57daNGqFfyaCaVJDG12cOtJPVTq6xV/M8PD5DrSm7lvxtYyvX0iLBThly7RJ/fpMwD9+wj1B8393MmqHOXcQqwraen9qs6XrQfyX7zCyfA4HN6zETNnLsKOLSuxaNH7WL3qe3z0weeIi7uHR48eYNSI0XhV9BJr1q/CR+99RONrSYgt77onrdFS4LctmzBk8DC4uLhSgErdkEuApJREHD4SIsTUqtyT+aRS5F4XL57DnTtR6NWrPwW0bN7q1U8ty1crwZSgL5NTEmnym5tR19CuXUd07dKDJp+k658DB0we0GNMT/OASvz9UMgBBLVuAy8vL5UOVsbXCvcXHkA8jrZu34Kli9/9fYGt2JE8gAs7EQYPdze0ahVkmLWVx0PsfUP6RyVPOfEm2w3Cb2ycZIAr/FZcXIxvf/gan3/yZ25shHdQeKlxQIp0semkVlwdWVVJqbATobSMIKk5S7+XL/ejKv1z78F9PIx7gEEDhohlfwTAqsimzGWTVsY0y0mnntJMz2fh7OSMoKD2aFC/oTS9ySZEanoKYmJuIzHxKVq1aoMO7TtrQS0H5NSgVnfeAvjm+y/x2cdi6I3Y+QKjy7l4q+5LTh0MCYZvU1+0aN5CNW5K5lwAyPKA6+2LCc8yY/Nxp5avXIGpkybD1cXVEGtrzpJUv4ueSzJbL9ExMfivv/8NgQGBeFVSir//42+GxE8VsOVT2RnqsqpGk/b/FXeuXIeDsyOc/RqKINYOefdTgGIr1G3tS12RCbBNunQHCRduon6rZrB1cJDAabM+XfHgdDiade8IZ3c3Ra1bAnxPrduGYQtm0+Ms+zHLhFyYm4dLh49j2IzJ9Jwp1vZx7D0kP01A7yEDVQmjWKyt7IbMxDsRTDvWrsOIiRPh5CxkL9RzSdYIdkmMmAK34hPEFf/Tl19g4XsfoFr16m8FuDXK2jKBde7MaVSrVg0du4g7jqploSfY1ALNEIgxA0bYIznxrbs49cDNG69ifrfd7M3Mg9uKY22VcNQSU3wqLBS1XV3Rtl17k6UoKxrgqsc7MSEBp0+GYfrsObKS1ulLPbipNJiZoaW9WG/s9eYdZ94pbqL3HL6BqTmsfpOw8gDb7EoCtrXfjl3nN16jb9kNCLDddyQC96Ovo1OHLoiKDEeP7r0QHn4WkydOw+nTx+HVuDFatmiJS5cv0uQ0JK7RmuggaytquDdq0Ah9evWRQO71G9fxLDMD/fv0V7C1pAxJ2MkwTJk0TQNqS7hssrdvR1HDvmOHLmgT1F63FEpZvEL0ZLFG7jNDHsCd6ChERFym7Fvbdh1kLcrkPgdozYHbLdsEcO/uRtzo+Uy0agAmJ+Q5cvQwvL19qIul5CYqAQtRpjAG2cBCN8fa8sD2ed5z7DuwDzOnzTTG2upkSZamtg7A1X1V7qD0KzcOCozDZUIm8azExbxv735mGUIjbtvqJFuyx4CAQNOfpdO6toS11WQ8FpCuyM4Kv/+84icsWrAE1tY2JllbKekWFzOul+zr3v1YREffwZP4ODg714a9nQOyc7IoI9zEpykCAgQgyRSVruu22ImkW7WuxTKTey3yKk1i1qtHb7NsrZq9JbdfuXoFZs2cg5o1hAzSCiKlrIytYtAVn6dRqFeuRiA/Lw/9+vR9M2BrwmNKDW7ZesnIzMK8RfNoPHxZgO3juMrRnd5N3g7dWcXYlkP5T9r3//Aw4TGeRyejwYAgyR259GUJkk7chP/Y3lJm5IfB55F1Lx42dvZw9HCjcbgEiLadMAzPE9PwuuAlmnXroIizJS7JMWcuwb1hA3gHBnBxtkLWYwJwLx08hoB2QWjQ2NNsrO3+jb+h/8jhcHV3V4JbcUdL66YMZKVnIPTQIRpvq2Rt9WrXyjtl8g6ZaXDLG8obVv6KcVOmUkGqUUa/o1uySUCpY8WbAgGpyck4f+4Mxk8W3XJU88gIsBVtCM0MNApGOD1jllZTKvKyTfj9e/dSga7+GT1uHGqRWDHxxzQIfPtYW2ojAFj76wqMmThRSO5i4gMqCtzqzaOd27aiT79+8KhXj9kLuoOjC2x1bmgasPIGifYRpjZGjMwbA/YufWB5gG10dmbZJmsFtW5em+zKV/1UdA8QYLt280GUFL1EbSdHFL0ogKuLC57nZmHggEH4+Zdv8MkHn8HW1ga79+zA2Qtn4NXYSwG6SPKeCeMn0vVaXFKMNevW0HJADKwytvab77/Ce+9+SI1+PpEUaxcTG42r18Lh4V4PPbr3gUM1BxV7KEgJXZbYQseYlt+qeExJAVjh8uXzuB4ZgR7deqFN2/aSh5RgeysTR6njFRko3bBpDSaMmwwnJ0cVmyiDWXY/8i8Ng9i9g9a1ZapPmStDy/ia+3QlSyn3nRrAkb8PHgqmoJpsYqgzJDP5zGQyc8uVjqtfwpSQNicjVaCWD51krDZ5zJp1qzB29DiahVfoO+Hh5thacv5pwlOQGrXqH0/PxujVo5dcpkf8KMa2rl2/FsOHDqfJvegpM+7IxEW6YQNPtGjR0jRrK92fA8ZcR0rVmbk+JGw++U5HR7ksk/jR8reryE6hO027vvMu9MtX/Ih35i2k+WV4AMz6V/ID5DZ3yLnklCQQVnv2jDm68bUyENbmizDN2EqLUBoqvfVLTv7tH/+N//eX/xDbyQaqZdtVuatiSmcqwa3MvvcfOhC9e/QqE7C9eO6GBSn1+5zu1rPN73PjMt61CtiWscNI84l7/xMJBelIDY1Gg/6tYV+rpgRuU07fhntrPzjVc6Pg9uHBi3iRlo38lAzUadpYBLbWaDNhKN11un3wJDpPHqWpaZuXloEH4dfRczwp7SPH2dqKJX3SniTgSXQseo0caoK1FQB00qPHuHvzFoaMH2OWtZV2g0WX5Bvh4SgtKUX7rl11XZIl4c4YXbWiMOmWzMcGWmHzmtUYMnIU3OsKglwtVPSMeq0oYspGljn8sFo0+FljSVbpwVL5juzsml+XY/L0GXByctLMoooEI6a+l3vt3w3YLv/pJ2oAqX+Wvvc+ateuLQFC8wDw7WRtszMzEXIwWGJMfw9wa0qJXbp4nrrl96Q7wKr5y3W2yflvANga3SAR2pl4khnkahTUkruXB9jGVBKwDawCtuXQipYvIcD2xxVbaGKopKdx8Gvqi6TEeHh7eaN6dQeQxDRDBg2h8bWrVi+nsbNuJNRDcisshZ+vPyaMm0gh56GQQ2ji0wQBAYESW0tA7N79e9CieUs0aeInAQMe3B45epDO9969+sHegdS7Fd6dgQj6O/sczl3U8hcqWzBjXrG+VTpGdj0W3F6vXQ3HrTs30ad3fymxj+yiKTOxFOzquCWvWrscc2e9o637yzO/jNG1Ao4cO4ImPj4IaBZoGLSZ6wc91lYP2JKyP2Sc5s8V3MSl7jYVa6uq91PeDUcG4JWOqjwJJ2/Kp6WlIuTIIcwn5WgMuMny4Cn2bix279uj6aoA/2YYP5bUJRVYWnXfXAy/RNnMPr36yXNSnJAM/LIkUnGP4mgJpfFjJ+omi1K4Motvoo63lae5JuW0RimZcu2VwKmoRniQy7vWk+O370TR7OKEAdcDw2pwLP0N4PTZU0Lpnc5dZY3Fuxwrxkip0/SArdRC56Tepu7J06dQvVo1dOtCnm8miZS0+aGj2U0wtpLM4QQPcysfMXYUWrdsBRt7B8OuyN99s7ys4qpC2n/0ydIKuc+b3qQK2JajByfs+Q8KbPNiUmBb3QEuzb3E0j92KHicjqLMfDTo3Iq6Ij88eAGv814i6/5TuHg3gp2DAwW3bSYMgaNLbTw6cxX1/JvCo3FDrvSPkEzqWvBxtOnXE84uLpSllQCulRVlcY//tgO9Rg5D7TquJllbAm6P7dyDjj17oF6jhhy45d2Q2Y6u4HTMBM6OdesweNRoIY5UqocriAN+51IhoLjIBl6xy9G44vWyVMHWdWvRZ+AgNPTkCp9zMuGPBLe8S5GOWJIFKoATx4+hjrsb2rQjO+xqo0Y9sYyX/DEKSFSiW3cm6wnoskx5i8BWksgmSU+LORe5WxjKVKxn1Oi5Cyruq/PR5D5XLl/Cyxcv0atvX3MfIBgZZek4M1sNmZkZ2LdrJ+YvXiLd0RRI1N0gUQ68Yk7yr2h0Hr29wFa7oVLGIShX88DaQhmJqp+K7QECbP/yn3/H6JETcfzIXowdMwlHj+zDmDHjcenCGbRvR7J/+tIN2S++/geKigpRnYSqiMYrMez9RWD7JP4JrSs6bcp02dW4pBSRNyORlp6Ofn0GSEytwOIKDMjW7ZtonGCTpgT0Ct+nZMUknlY8J/eBkfWv0QOqA0ayH+fn5+Ps2ROUARs+dJSgeyWj3Ty4zc7Kwt4Du7Fg7kKBQWNGtirZEXsPkgiRuL/OnD7TImtLxY6F3Sx1bKle4iLW5vDhEPj5+cHXlx8LWdJq2XIl+DIyHrrykLlXs5M6sZzk1LkLZ2kW7A7tO8gyVtWPfCwnD+Ji7sZgz14B2PLvyYAtm3fCv/I8zH3+HFu2b8GidxZJGY/pjJRAsNId+dc1KzB9CsmjUkO4j5m6zGx7WXZF1io1icFlrLSOCODngALUqlyQ2Xzh2doVv/5EY95JWSwNWyteQKYY35dM3f26egUNLXBxqW16PMTGGp1mAlDyGx3qT1XbToWFhSCxth+//6HY1Chryy0aM8BWmivixGdjPnHqJHh6eqKmo7NhYBv/qHK8nRr7vB3eTlXAthy6e9zuf6PAlmRCzo58ioaD28CW1rS1gzVs8HjPRQRMHUAZ20fBF/EqrxAv0rOo0HGqX5cq7qDxArDNjU9GbmIqAnt1pUCYZUYmsbUJt2LwuqAQLXt0kcr9yADXCkn34/AsMRmdB/TRsLYs9paA6PSkJFw7dxGjpk6mz5ZBKnONEgGtyj05OzMLxw8ESy7JgsyQgS8THlQQcUJJkSmZ29nSgFtOchzatwfNW7ZCUz9/WWhxiocJN3m4TJnjHPeklCe6I63R02VgbR/HxVbhQ80AACAASURBVOHWrZsYOWasbtIf5b15plr9FcpXMwpIlH2ib3H8kcBWrcSVX2WQtVVZK6aMF81xLlEG/1xLxg85T0oADRo6DPXqa0sA6U0aS/e0YPth3eqVGDVuHNzc5HqpZQK2Oo31rjc6j/RWkpF5Y+k7+b7LysoCyVBLfpxr1UZ9D8sK8OTtqHJI5ze/pF9Lyxmb3/wp//fuQIDtf/x/X2Dc2KnYt2sjZs9egK1b1mDhO0uwe89WzJ+zgLK1hYUvKLAloIJ3uyVGt8DYTsCmzZswZNBQGkYgANNSPM/Lx4ZN67B44bsoLilVxNwSF+TNW9ajf/8htLZsCRVW5sqiyIiDrPdXr14hOyuTZvUtLn6N4uIS2NhYwd6hOs3uWqNGddjZOUjKR14bKvdjBjQVelFOmMMD0UeP4nA8NASTJkyjskLBfol6So+5vX3nFp6lp6F/vwGqeFt9l+SQIwdpmSQaaysBAyUolpw7DSx6o6xtckoyjh47gjmz5uqytkpDnytJwzO8BpaRVg7y3KOSfOMzR//4yw+YN/sdODrW0iRM0gAvaVwFVBZzNxZ79u7WvF0z/2Z0/gqgRcvakuMbf9tAx44lctIrSSX0cSmuXL2Cly9foke3ngrWVu3eLTPDyhrN4jIw0IsaklIGppwhIoBSUaOoNgGibt9AZkYm+vXtb4KtVbl4c/dKf5aGQ4cPYv5sJXvObFIFgWrKhjMxd5ntqu4EPf15LPQ4DZ/o1KGjokNM25Eqy9UCsJXnvCyb1m9aDw93DzT1D0SLVkJiO0s/Tx9XDrD19Las1y29e0WcrwK25ejFsbv+goT8NNjY2iH9ZCw8egSiem0nKYlU+uV7cG5cF7W9G+HRwUuUsUVxKbLjEuEe0ARWhLEdNxi1XGpTlvbGzsPoPGUU7G3tYEuTTgmlf6xKShG++xD6TZ+oYGxphmTK4FrhyIatGDBhLBydncyytmH7gtGybRs0btrEBGsrM7hMWBABdTMiAsWvXqNDt26aeFvefVkrYBQRt5znBm9G8yymFYJ370Rgi5bwp/XCdJwjNULhzcGtLiDQserV7djfK3/5GYuWLfu/DWwlaSwsJtPAj98P1i483v2PnTUMbCU7VHuFOSBKzhFX68MHgjFt9mxLH1AOaaG85MK5M9Tw6d6zl3SiLKCWrotyAlvz4Fd51hKwNWDfavqKjYPROrZf/Lrijfu7PDf4E8ekl+f6qmv0eyCvoAjf/bQOLQNb4W70TbRv3wG3b5FEUp1x7+4djBo5mgJbUj/2zLnTtKyaYCgL2oAC26b+NENoUlIi+vcbqIif3bJ9M7p364kGDRpRUFtSIrCxBNT+9PM3mD9vKewdSCytPqCVwAaAlOQkWqInNTUZmZnPUPjyJRo09MTr169p3C6JAyb6MT8/j9attbW1Q2HhS9Rxc4ebmwc8POqjsWdjepwpB2ZAs7VjLl6TnSOs9eHDB9CyZWsENGsu6VGexdUDt8EH91OgSoCUMpkUB25F92TSz7v37aaxyjyw1bjfisNqkbVVKAGebVSBOQD7DuxF27bt4OXpJTOX9HpuI5QxmtK00rrNqmW8rnwSP04NWGRZJ9ssTxPiEXE1AhPGTjDthsy5waqBrhFgK3ym+J0c0L105TKdV3179xf0qSojsszMgs7HH376Fh998JmUFZlnbRXPEMdFALlaXaxm203pGvm4crOen99qF2Qylzb+to4yrmQjyBxbq5c06vzFczQ7eo9uQjZ9xTwVD/A6S88yNO+OLN1VIbzUepBsbH3/8w/408efsdWgs5ml3QTgGxnRnbyMunDxAgqLitCley84OlczpF4SnlSOt1Mjr7fD26kK2BqaJspGY3f+CU9FYFvw4BkFqq6tfETW1haFyc+R9ygFnr3boSgrH9YlBIja0Jq2dbwbwalhfTjXdYO9rT0FsUnXo1HTyRGNmvuLjK0Ibq1scPdCBNwa1IWnv59Q8odzSSZ/P4m5i6yUNHQZ2FfF2rJsyVaUpc3JyMS5I8cxbtYM0bWJuR2ra9wyVpbtullhz6bf0HfIEFpXtyzJpPSYWyWsFYQJL3DCjhxG+05d4FpHyK5mfidMbmHWaOdOmjfu1bLKhLHPvdeBvXtouRZff38L7sisP5VzyeL7iA0sgx/9FpYAiqXpb8gVmd1EY4Co715xrK0uWH0D1vbiubN0VLv17Cm8tCVa1lLH6ZwnLAqJ6Z09X1mLzvLYsptVPOuvZwDwhoOpzzSinPVHHzAKbO/lZJejl9/8En9nwdWt6qdie4AA201bgmFvYw1bayvY2dkI/9paw8bGGj2796RlfU6eCoNjrVpo1LAR1TcE7JL5RlyKq1ergR27d2DWjNmoUaOmVN7nxo0bSE1PQ7++A0RQKzC2Obm5WLd+JZYu+RBWJHusCGrJvegypxmSBcBFSp88eHAX9+/FwLWOOxo39kb9+g3h7OyCmjUdLXZGXt5zZGY9Q1bmM6SkJCE+/jHc3Tzg7eNL431diIu7CbdX5m7NzvOGP/n96NFDNONxp07dBL1I7sO5KPPglrHcPy3/DksXvQ87O1JWT2SOTbgkh544Du/GXmjWLEAZZsT0D6ekLQFb1q+SWlCBVJ6pfExqEEdcwaQJQp1h+RpZCDO2USmWLSob7XjpuCBLso5nGgEcPhaCJj6+CAwIEPpb/B+1mqQ+EQ7KGxTCwJDTBNju1mFsA5oJMbbS3FOztqWlyMrJxrYd27B4wWLL7sgopQmVSG3kNkFt9Flb8WEK1pY/ZnFmKxuo3enlPjSdjft65FXk5eWhT+++5tlaPTdkK2DthjUYM5J4OXF2oV58LTdO6s8yN2/Z+OpYnLJdJ477qTOkBm81dO3cxTRrq7Hb5ANGdCcPbEm26rv372PwsJFVwNbgXK0CtgY7im82ZsdnArC1sQPJhJwZHodGw9pTYGtjTVySbfF43yX4jeoJu2rV6d/ELTkvIQM5j5Lg16+ryO4KzOyrnHzcP3UZnSeO4OJsBXD7IjsXt0LPou/UCZp6toy1PbRmE4ZNn4IaJIkVTTQlZE8mgJaVAiL/nj54GL4BzdAkoJmiLq0grGVHYfY7czvOycnBqcNHMG7aNK6AvGwSy4KOHePdr0wzt/ICV4LbA7t2wi+gOQWMvOKRxqCszK1KklgEkwpWjH9LlYAHcCvqJhLi4zF05MhyxdlafJeKALZGJKmJdUBK0rwufq0527BhI1qHWPFjyEXs7WNtmcH0688/Yt7CxbC3t+csrHIICBOX/LZhHQYOGSq4PIs/xkGt0ojiH/Emc6g8wLa806msjO29HMF1+Y/+8RfLnP3Rz/3f/rzn+YUIPXkdd+9cR9ug9oiOvoEO7Trg1p0b6Ni+A00mRfTUNz98hU8++AR2pM65taDDiFFKgOrtO7fxIO4hRgwbKbkaP3/+HBu3bMCid5ZS8MvckJ88eYLjoYcxZw6LVxTYW8loFAFt5PUI3L5zE05OzmjRsg0aNfSCnZ1QV5aBMCYjLK1bprCY1iMANyUlAbGxd2BjYwt//0AEBrakrJWamdJjuRiQIuduRF7D04THGD16gqy/zYDbpOREnD13CtOnzJTibQUDXp+13b5zG5YsWqrL2vKZgo0AW9pfEvbUT5TE5MHqtaswZvRYuFG3cqGHGauo6Hdd/WIi6ZHuQJl2QeaTFL1+/Qo//Pw9Pvvo8zIljeJdcAsKCpCenq5Z0tVr1ISbuGnPx9ay/mIJolatXolxY8dRwCqc47JMqxjcjIwMynzPm7PANGsrPkADbvnjZgSQGvjJf6vD0MRNF7aRIqgt/PDjt3jv3Q+ozVBWttaiG7L4DH4TXzXSwrK0oLjY2tDrBv57yfm/f/kP/PmTz+maVnhk8Bcr7NSyAltZ9jyJf4qTZ05i0pQZhoFtYnzlMLYNG1cxtv+yenz09k/xNC+VuiKT/zIvx6FOmyao5upM42wJiM268RjVnBzhFuAt1rQVjsfsDEXLCUPg4FBNBLECgL0fegHebVvBtX5dibVl8bY3j52CT1BL1GvUSACt1tYCeyv+9/DmLbx4no8OfXpIxwRAS4wCmbnNzczG5ROnMGLyRDnOlik5DtwyoCvICwH0Rl29SpPsdOnZUwFuJSgrrds3ALeigCL/nAkLo1ljBwwdxmwF5Xx5A3BrEQiIT2IC2JQxQ+5DEgoQQPTBp58Zc0cuK8h+A2DLC/o/bLFZ3Ej/fVnb8iSRYsZT1I1IZGZkoE9/Epsm/lQQcxsbE417sbE0ttbUfFLpRNWQietKZ/JanM9m5pAesLU0b/4oYHs/N/cPm7b8g/x0spxXyov8L3soYWxvx6Ri++aVmDt7EbZsWY1FC5Ziw4aVWLp4KU0UFX7lMl4VFVFmx5rIeBHYCvZ3KTZu3kTr2BJ3Y+pmXFJKYxKHDRuJ2s4uEqjNzM7C/v27MWPGPC65FHNNFkBCZGQELl06i7ZtOyMgoCUFtpJrKLf+zUss5WpWrx3+78yMZ4iLe4A7dyLh6emN1q3boW7d+rK7tSJzsVzmR2JnacmTZBwI3oX58xajmkM108yteK+r166gpKQYXbsQptd0EiryFSdPhaJOHXe0bdNWBreqUkPS1xoUAiwWVBnzqXRJjrwRiZTUZBozrYzP1XdJ1opmFbhVvJv8h/ow/zcP3KNu3URaeioGDRgs2x4mkkbpsbW6hoAKsJM/ZZApAn/OHTn0RCgcnZzQsX0n+g7m3JFJp23buRXduvRAI5KAU+wOTeIuDXPOCxhu84H3lFMjOo69lsw1ru6v3ubM5fCL1Gbs2pmEszGrkiM7zbh0k/anz55GbSdn6rIuP1N4d2WCUvF7eCZXrUXLCW7V6zrqVhRIArtRw0eaZW31Zp8lgC2Mt7xGnmVkYMfunZgzb6FhYJsUXzneTg0avx3eTlWMbTmMh/7fzUdmrUIB2NrYoeBRBvAKqNO2icjO2uF1VgEyIuPQZIjAzpL/CHObFnkfDrVqoV5z4lpMXLEE1jbnSTJyk9LQvDdJImUDO3pOAL1ZCclIvheHDoP6iTVt5Xq2jKHdv3wNxi9eQN2O2DECagVwKwBc8vv5Y6Fo5O0N/+aBokJlvKxll+Swg4fQrEULeDUhccIygGU7XWwRc9yvIrZWcZ4XZpypz8vR6KgolBQXoxUv0FTWv1JO6ZnpnHMJ19giGDDA2rJ77Ni6GV179ERjLy+L7sh6Qk39Lpp3M5NwwITalhWApAnKMdHLe4nZTfTfF9jKVSG0iNQSRiXnT4QeR9269dCyNZdAyNKFlvrJCli9YjmmTJ9BjRWlKax/sXZ+6rshmxpec0acaglpHP7fFmD7oJKArW8VsLU0o8t1niSPungpBmdOHsawoaNw8uQRjBo+GidOHcXsGbPphu3X332JTz78VHCfVbnbpqamY//B/Zg3Z77I1pbi1JlTtB5m+/adJBfk7Jwc7Ni5BXNmL+IYWpmtjY8nTO4hBAS0QOfOvQQFwbkkC0hCGY3Au8MqhavcFUqgJK9yptMk/WdF4ohjcPt2JH33jh26woMAXCbn9QAu6wvikl1SgrXrlmPO7AVSvKKuW7LI5gYH70e7du2EmsBmXJIJAF61hmwyvFshsbbMQBf+5eS+OnESgC+/+RIfvf8RZfSULsmy8NWMgSJihBfS+uhFLdf0gC0Zg517dqJr5640RpoHUko3ZNHWEG/C7CFSv5aUCCJjZGdrh/oNGtB+D2wWQL0A9FhaZdZjoa8ePopD+JVwTJk0xZA78t17d3Hv3l0MHzZSU/qHn8vqcRCnOreeWT/qaSAeOLLfOY881hccW/vy5Qts27EF8+cuEPqSAVuprexBILvpK929v/zmC8qeW9tYa8aDHdDqOxNzQP+wQp4xe1Yt5NTzh2yyDejbH40aNpToYPV7KPWzcs6YE6I8sC0oeIGfV/6Cd5d9ZBzYPq0kYOtZBWzLpRzfhouC3hkAq04eFNQScIvXoKxtw8FtxbI/BFzaIuHwNXj2bYcaJLGT6KL8KqcACRei0Hx0fwp2BQAr/HdtSzC6TB1NlRU7ZkdAsZU1Lmzbh84jh8Cpdm2OsRUBrpU17l69DpLqMahbJwVry7siW8MKrwuLsGfdRsxatoRjbeW4Wt4NmU8OxRb7rg0bMXrSJFSj6doVEFYu0K1T8odBTulfXggqTGulAX/2xAk4OzujDc1CZyzm1ixo5U6abSfLbdXbKWcgucf1qxHIyspE/0GDLQNbnY9Qv4fmvf5gYMs/v1yYzgBra+q+iuMGXJv17sNYArWssPQt7Pzhg8HwbxZA46alH0sX6wkmsSPDL12kyWd69RVqE+rOY+56U2YZM5D5R1mcw7wBoXpH+VrlXfSeY+mZRuQy60KjMbYPcp8buW2Ft/F1shxPWeEP/T9wQwJsDxw4jdzsZ/Bwc0N2TgYa1K2HjMw0DBsyFFciwmkt1149BK8gnqkk3RMaFgoXV1cEtW5Ljf+EhERcDL+AMaPGK1yQt2zdiGHDxqBGjVoyWyu6IEdcvUwTQvXpMxgO1cQatpoSKVwGXgWzprN2VQtQoef4eFpx0UvAUvw7NSUFEREXULNmLfTvP5jm62AAV5Fdlqtby/plxa/f492lH5p3Sxav++6Hr/HR+5/QWGYZ3Gpdkglj/rKwEH1799Gythwwoa9vACSYdUcmNxE3EM6cPY3qNWqgY/uO5llb8Rp+uZgTzbqvqNanHONIkjat27gWH7z7oexiqmZr+XhcJl+trPDsWTr27N+LiWPHw8XFBekZz5CdlU3jbQnT3r5tO3Tq0Elys6asLdOVOgmi/v7F/+Avn/+bptSPsAnDuSaL5YB+Wv493pm7iM5rk6wt7T9VOJAZfW3WFtFjalUuyLt370DnTl3g5eVdZraWvOqdO7eQnJqCQQMGKfWmqfhaaZ2Z1rJG5q0RcEvq8QYfDMa82XPLxNoaeb4ia3Yp8Ld//jc++fQvhoFtciUB2/pVwPZfV5N3/mwMcl2KUd2jtsTaZoU/gktLL1T3cJFY25yYRNjAGh6t/aU4WwJm4w5fhFe3dnB0d5MBrJUNUm7ehb29A7yCmktMLmVtrayRfO8hXmTmomVPofQP+Y/UspWAK8kw+MtqTHl/qXRM7YrMWNs7BASXlqBNl85i+R45xpYvBUQWt4KZtQLyn+chZPceVQkgQYio28uiRVn3lpkHshsPMwdks583rkNDQtCuUye4uXvogwIdt+TyglvTglz9jrKR8zw3F5s3rseS9z/4w+NsTYEThVg3YIDIPa9dl2XGdAaArY59Ij1Yep7qwbogVkeMmAK25p7JbsOeQerMtm3fAd5NmshPKEtHiH3+oqAAa35dgfc+/kTxpuaGRHtOnHs6F705sDXxNBMvyB8myUAyROONGITsp0bNmtSwI6VYatWqpRlXo8D24fPKAbZNHauA7e+hnQmwXfnrb/Dx9kFyUjya+jRBYlI8mvj4oE3r1vj62y/w58/+IrvMisYr0xP//PoLfPjeR7CxsaHs7PZdO2gW5Loe9VAsuiWHhh2j865FiyCaFZnWsC0V/j158hiqV6+Bjh27K9xABSNSVc9W7ADGEnJ8I9c1XCQftzCoHlQZ2IJuFHWGeJJ3Y713LxrXrl1Ghw5dKZPMX68A+VyfkOME3C5csBT2dg4qhpttDAg6PDk5EZcuncfECVM0SafkhFXCC5KSSZMnTZHLLanAirSdbUCvqIGtiKvoc/gkUpmZmdi1dxcWzl8onOM3Nbk/jOgGs6+lZytwF0TeuI7nec+pu7ukQw26IZN6wD7e3mjVoqXu8iHz1bORp+gWrp8RmQc023ZuQ8cOneDjLeggs+7IKMW58+dgY2uDLp26cmBYrstsNH7ZnKKSukoH1NL+4lzXb98moDQJgwcMEfpSZ95Lsc06SdVI+02bN2L4kBFwcxdijXm7UFp9mo0KtidheiYYAZfCs8zrxwMHD9DM44SRZ20VV5iIs01OTqalwxISExXdXa9uXZrjg8wTfn188/23mD33HdStLyTPsvSTklA5+SnqNXK29Gp/yPkqV+RydHPvL2fi8aNHcG3nJbG2L55kouRFMdza+0rsbMmL10g5fRu+o3sKwNZKjL+9l4DXeQXw6txWYG1FxrakoBAxh8+gy7QxEpNLzpGMygTIhq3ejKELZtOkGgTU8omiyO+3L4bTXc8WHdoqMiRL8baQgfCuNeswYvIkONV21k0kJQgR3j1ZPGJlhYexsYi79wCDR43UibdVgmFZGKnYXU18hBo4Kpnbg3v2UFffCmFuVUYIyYh78SzJiqv86d6rF7r17CUJZHaWF1zsdz4xkFoUKv9WJsqS+kf1bLVwtNzOFBzSo7n1J70lO6UsuI5lFda/Rt9MZG9lxHjRtOU/yUR2ZNLE0jfw59evXoWRY8ZIGyqaXjN1M64jD+7bh8CWLeHr5yddbqmfTY2kETd2zTxhxrTOkKtXnGJ+m3hJ/nDUzZs4GHwA9+/dA1HU7Kdu3bpoFhCAocOGo1379tLxsjK2cc9lsFwOMV3uS5o4ymC83DepulDTAwTY/tdf/4nx46fg0MHdmDh+MoKDd2HK5KmIjr5FY0a7dumqMI6ZDrodfQcPHtzHiGGjKEh9GBeHiGtXMG7MRCmuliSVunkzEsOHj6VtCPgtFcHtseMH4eXVFL6+AaJrp8BaMSBBwS/nMpuXn4fcnGzk5z9H3vPnyM7JQkH+c7x6VUR1vqOjE2zt7CgrTLIzE2OUxKc6OdcWNzeV2XJloCrHufLHKHa0As6eCUNOThb69BmI2rVdpfhbc+D2p5+/wvvvfUrDjRTu2zQESdbjl8Ivokb1amjXtr3GJZkHHXfvxSImNgbjxoyzyNp+9e2XKHr1SjPWn3/8mZSET+N+Kwpixhuy81u2bkavnr3QiBj1am8dA+DW4pLTk4UqgLZ522b06tEbXo21bsjyBgCzhYQnss3/H37+AUsWLEK16tV1FQ2ZO19//y3+/OmfpI0U2hUq1pa6C5eCZosmtb8H9BsgzVnWXthwkecwuSA39zm2bNuERQve1a2Rq3yWUhtqdK7KRlL0rRlQy/QPKX21dv0qLFv6gdhHYp+JDRRzn8eP3L2fxD/C5fDLmDppmjJBk4qt1dN5pnQb/x3lBbcyuAfVe0eOH8V8U6ytYs7Jf6xcvQpJyUk4f+G8omvbt2uPOnXq4E+ffK4Yw19Xr8SwEWPg07SRxWlOGqRWErCtWwVsDY3PW9lo2MZluL7/DOoObSlkQra1g1WxFdLP3IfnyA5cTK0dkk/fQt22/qhF2Vkh1pa0vbf/DFpPGSbVrWWxtvdPXETjls3h5tlAArwkFpfEHz28coO6Kfu2ay0wtmL5H8balhQVIXTLLox5Z44mcZQ63jY1IRG3I65i6PhxZhNJyQknhFhctot14cQJuJKd8TZt/jBwezYsDK5ubmjVpq0gLNWzoyzMLXfxJUPAVn6iHnC9cvkSrWPYs4+Yzp57t38FYGsJbLHPsQQMpc82wNqaupfiuNrA0ZEImvu8AbDl1T2pT7lhzWosfu/9cskh4oJMGFs+GZWRftYDtswQ0Jnymncr26aIenaKt3sLgO2jSgK2PlXAtlzz3dJFBNj+/b+/xoTxU7F/3zZMmzoLe/ZsweKFS7By9S9Ytvg9GkfHz3XmNrttx1bKdDVq6EmZ2K07tqJH956oW7cBjWcsLHqF1WuW4513llFAyzIjkxrVe/duR+8+g2n5HpnBFZgsnq198CAWjx49QGJiPOzt7OHiUgf2DtXgXNsFrnU8UMvRCaUlJRTckv/y856jID8fxSXFKCp8iYxnaXielwtPTx9ay9azoRdq16mjALrk2yRXY1XpGMZ2JSU/xbWr4WjUqDE6dBC9qvh4Y5VbMun3lat+xNIlxGNIduGmoFbl0r1+4xpMmjAFTk6O3AaC7JLMQNqO3dvQs1tPCjKF9xWUqxqQlAnYqkrbCLJWTlp0M+omUlKSMWigkLRJydpaAGKWJh8PqFhbDkSRQ2RMV6xajg+XfaT7rXxiJHo7hlOsrJCYmEDjvWdNFzJQm/r5jYD3Hj3h2aixCphqGdnUtDQcPhKC2TPnKN2RmX5TuCML/bN3/160CWoLby9v+Ro+xtnMhgHfw/pfwM0BFZmpLgG0Z+9OdOncjTKPCs88HugpEpMJPca73+8L3ouO7TrBkyTEMsPWypaZrLuMAFt+DM1NH2bz8m14GbVtx3Z079qVxlIbZW1XrrEMbPnY681bN6NTt54IbO5rZKYjNbFyGNu6DasYW0MD9DY2GrrxXdw+dxU1GrqihqebBG5JnK1LCy/UqOcisbZ5D1JRnFeE+h2JezFhbAlItUXCuZtw9fFEHZ/GNIkUiaUl4Pb50xRkPIxH60F9JHdkGxHYFhe8xOV9hzFw9lSOsZXL+xCwG3nyDNwa1IdfyxZS4iip9A+UZYBI+Z+mAc3QNIDUYBXckaVoW7bTKzGrovLjwO2WVasxauJEOLu4aONtFfXdmJjhBSOXdEDOGiBF58qCVcnchh0+DC8fH/gHNpeFnaSo9KuQ6QFR/mLC2F4yw9jKApB/K3lmkqMkxnbPju14Z4lYLoGbuBpgq5HE2vcuGzjRuSEP/A2gKQNNpC8qC7g13bbsrK0hICy+pansyLzyNidb2LMu0Pq2QPeevcokii6eO0eThXTsQmrdKeeKRSWqafBmiaP0Z4e+u5Y0D94KYJtfpj6vqMY+jjUr6lZV9+F6gGRF3rxpNxo2aIjExMdo5uuPR4/vo13bdoiIuIxpU6bLm5WSAWwFUs6EJI2aM2suBa0PHj7E1esRGDtmogRiN/22DoMGjYCLiysFteS/hw8f4saNCHrcoVoNwZVTZHAFdhZ4lpGGO7dvIDb2Npr6BsC3WXO4u9WDrZ1c8ovJAsYw6hnNbLkUFRUiPT0FyQlPaD3b3NxsNPENQNOmqmnp3wAAIABJREFUzaiLtKRhVSCRgFBBz8iM7vXr4UhPT8XQoaOlkkdqoMr+JkbwL8u/pcwtA7esTBJ/TVbWM5CMu4QFUyec4l2SyXNPnjmFqZOmmk0k9eW3X+KVUcaWy2Iku3jLwDa/IB87du7AXMp+qYCt+gDvfWNJIXGyTKlXOVdyAHfvxYhM9XjZPDAD6vlkX6SWrKOjI006pd1xlxdBxNUI5DzPRb/eQr4FPnOxBPK5mG+ycfDBsg9hS3K5mHJHlvqqFLF3Y2kSqRHDR1tkbdlbMfZXX1gpwSxrw2N3Nai9efMGnj1Lw0CWVVpEgbzrPV9eSboXt9GQlJSAy1cuY8K4idJraTa8RMWmUVV67r8WJLEl9tYcuE1MTEToyTDMnTlbP9ZW533KCmy3bNuKNu07onUbwe619JNWScDWowrYWhqat/f8kA1L8ODBAxSl5MG1UxOp7E/h02y8zi2Ee0d/Wt+KJIyyfg3EH4pAs0n9aEIplh25IDkTmXefwm9gd03t2utbD6LTxJGoUaOGdI66HVtbI+rYGXgG+KJh0yZSyR9r6pYsgNYXubm4FnYGAyeOE2Nt5YzIfOkf8vurwkLsWWsukZQgORhTq0gWZQU8z8nF8eBgTJw5S1bKokqQoCxnoLC7KbLficPMdoSlNqqUTbxQ27ttGzp26YrGPj6yApIkbtnBrRFgK7yzPrAV5SuI6+qIMaPh4VFXMXm1GMGyO/IfCWzLAmoVytDSEjXL2pYd2FLFrvNM3WOm/JkNuCOrv3Hj2tWYOHU6XY9Gfu7fvYs7t25hzIQJmuaW+lr/vHa+sDmnfoB5Ja9sbc5AN2WcsfuHX76MmJhoXLpwAQlPn1LwQX6cnJxond6KcEV+nFc5wNa7VhWwNTLPy9qG1LENO34R6enJaFCvHnKyM+HuVocyn7VrO6Fzp866tww+FEzjcpsHthDK+2zeiP79BsLdvS6Ki0tx8nQY3Nzrwt+vuQR009JSERZ2BBMmzpTcOEkNW+qiXFqK1LQUEOBIXI2D2nSCl4+flEFZvf4ZCNNbzLqbptymMPm2hKdxuBt7m7pa+zVrAT+/QIkFlRNFATStk+iSzDyl0lKTsX//DkycOA3u7vXoeQmwcuysYJiXUtZ68aJlVFdpGFuRwb12LZzG9/XsQTbruFhcaTNaOBZ2MhT1POqhdevWMmurihX+8tuv3hzYciD2t82b0LdvP7r5Qb+IZxj5P8TBMCnmdQSpOVBLvjfkSAgaNWyENkFtZLvCUnytaJj8vOJnzJw2g+YWUD+a10/ZWVnYtmsHFr+zSOOOrAa2pAHxTCDJl7y9BFtHCYS5RFBcpunvfvoWSxe/R0PWlHG12nhbfk4r5rlkt2mXpDlQS5Ikrlm3Eu+R5Ft8XK2osGQ7Tt6s1diKVsChwwcR2CwQvr5CCI/Up5bckLnGpq0105LLHMBVg1v+nbbv3E5rcfs29TXL2qalpyMmNhbBIQeRmZmBqFu36MuQTfCaNWqAd0XmGdtde3bBL7AF2ncIMiR205Mqp1SeewO56oOhF/2dGlXF2JajY4esX4wnz5ORdTIOHoNbwNbensbdWJNyPsduw3NMZwpsWU3btHMxcA3wgnPDugpw++DQBfgP6QmH6gKAZaxtys1Y2NvZwbtNK+qOLIBaGwpkc1Of4cnNaHQeNgg21nJWZAZsSdvww8fhE9gMjf2aSmV+iNJkzK3E4FpZ4XbENVpSp303FtvEJZISWVxe8HAOyXTHNzYqCsmJSeg/dCjnkixIFyYImABQRNlKu1jlY263b9yAgcNIUgF3peDjBag0tsqdWXaYCVkWY6tWSCzGVmrPoWs9gyb84gWUlJbQ0j+6Bg//PqoGZtubUTKyHNeDz+IDLaApS2BLb4lY2iQXtLBwpX7b3xfYvknZH/a97L1JgqT9u3dj/qLFFqXFrZs3kRD/BENHjtK0NdLPpkaxXPG1umtBeC1TSp9X1nofy87/+N13NObx7t27tPbvczHRE6mj2KBBgwoBtk/yCiz29+/RwKuWsQ2M3+PZ/5vvSRjbuPtpOHRoF42VPXrsIEYOH4GTJ0MxYtgIeIiyXMrZWvr/s/cecHFc2Z7/r8k5ZxAIJUCAhJCQBEI5opyjZVvBcrbHM2/mvXm7+9/d/3sz88Zjj8M4y7ayZCsHK6IISCgjBBJCgMg55wz7ubfSrerqppGw5dml/ZGB7qrqqlu37rnfe875HaC6pgYnTh3HC2vWU2jNyMhAUnISFi1aRqE29WEKsrOzMXPmXBFqCwoLkJgYj4ULV8rEowSwvXIlFlVVFQgLHwdvH39JOErMuZVGAMmzKL8zckhinykprFd4zoT81erKcjxIuYPSkkKEhI5GSOgomQIyOSabEyuGAKMbP/10CEOHBlBRLCGPVr4tB6MNDXU4dPgHbNzwKrXHpECKMj+XgPG+H3djyuRptFSJGGbMlBki+5AQb1J+iQh6CaZPFpIM4G8f/Q3tbXyOLdMoqjm2ujy2vJEgbX0v+R7Ky8swY/pMznb0EDqr275oP0nyeyafE5C22/rdN1i5fBUcHLiSJWrXzOYi0200GhDhqx8O7MNbr73JVIZQf5LJ9Xz+9edYtWwVnJydtfJk2dB40lxxCVzEEAm7p5/xHVLYTlQ4ZtJvrsRdod7jsJHhcq+t2M687TXIiEvXwUx/xD4jbyfgwKEfERUZTRcIhLbi2ol5RvSEIJOtGhrrcPjIYby8foNWbi33ffIJkZbNZN7QZef0jbNPA7el5WUgC3BbNm7W67VNSU3FkeNHcfvObdQ3NCAvL4+eirW1NVxdXHSC7dHjR+HtNwjjIyXNCn3XUF78nMDWsx9s/2lt+Ozvt1CwbUqrhLmzDWwGuole29o7ebD2dYWtn4eojtycX4mWklr4TBjJ5d/yntuyexkwt7CEZ2igzGvb1dyKhycvIXLtEgq7nAIyB7jkZ+KBExg1cwrNNxWBllFIrq+oRuq1RExbwoUwsSV/WKiln0GDH776FsteXk8fLjFHRwia0hOSLAzs8bGxcPfyRmBwsEFwy4KyMiCyN57brz7+CBtefZ0Ta1Dm3BqQbysMetcSJPEodpCMmjRJFoLKDtTK7yP7Ebn/Y4cPUQB6VrDt+XoUBkfFxdYTpAhHMAS41B7WHu2iXrDlrKxB4cXKyY3KyWgdR1aHUvtbejx3/juE7cpKS7B/717EzF+AAX5+ojAKeyrlZaW4feMmYhaQou3aL0PauU/BVs+CiF6w1XGi7NsEbPPy85Cfl4fqqioQhWTyCg4Jhbe3NxXLGhc5HkOHSiWThLY0VBW5H2z/aU2k6omTHNtHD/Nx5NAebNywBd9v+wrvvPkOPv/yU/zhd7+nEUmsKmxnVxe++OpzvPn6W9RTRcB2285tmD0zhgo11dTW4fCxQ1izer2YU0tK1fzwwy6sXsOFLVNRqC6uhm1TSxOOH9+PkaPGYeCgAFFlVqaKrAhxlcYJaaxSPjvsOCv8Tn6KEMTm1Wo0aG1pxsMHd/EwJQljxk5ESAjRqeAsIfXGCtvzvwse2ri48zAzM8WECVMluFUB17y8bCTfT8LiRct47y4jAsl7fNvaW7F3325s2rBZb0hyUvJd+nwT0FQDPRnYMgZFDWwFDxQ38rMLn1I4Mnn7661fYfPGV0Ai0ehmzwC3qkOZIreWfEdTUyO+3bZVK7+Ws8NsXVV+9iKMrRoN7qfcR05uDhaTxUyVY7MPA7mU8xfOU8/uqLBwzgaKObOKPFuVerZcezClfpjSPQL01tbW0YWLLZvfkGwsYw8Fr69wXiqOcNnzqwQ9+RxN4rjE69fouU2Iimby5BWeWYXHletTjHNDA1yKuwRbGzvqAZXNg0R7ZjjYas9xDLHCMjbVGstU54EagMAn8dhSVWy+0WTfpgFSebBNf/wYTc1NKCgooMcn/cHfbyDCwkbBwd4ea1aulvUNEk3g6OKOiZMjDTIK9249MGi7vt4oLCK4rw/5VMfr99g+RbPN/O4V5NUVoauuHS1PauA8YYhY9qe9ohlNuZXwmBQs1bQ1MkH2jwkIXDtTVvano64FeXFJCFkyk8KuoI5MYDY9NgF+oUFw9R3A59OSWrck5NgYxemZ1HM7avpkCWxpCSDJg3v3wmV4D/KHH/HagvtMVtOW/s2pJOdlZiH7UTpm8irHQr4tNbCiaIQ8JFkc8PmB6fShQxgVMRZevr4yMSlxYBIMtiilIR/Q2EBfQ+G2o6MDX330d7z9+38V76JyIJEPY9qeWyX86QdSabIiG3D5byf7fvfVl1i8cgVcnDl5eu2BVWg5OYmrDbf6r0V5bF1IpCR+7Q5v4FCvtaNBcNjHXluDQJg/02fNsxUNP/8LAddbN26gMD+fKqKSUkAE4Ab4+uF+UhJSU+5j7Yskz+bp2lj9Pvyc+bXavViYvPZ0DQRsSVkM5evd934rK/GjbEPyt6Fgm9fQ/BSj87Pv4mvDLZT1v/q2BQjYXoy9iqLCPAQPD0Zq6j1ERUbSMjTr162ntkj0kHZ345vvvsHiRUvg7ORCJ8zpj9NxPzUFCxcsod7a4z8dQ3BwKLy8fEVv7Y/7dyE6ehqcXdzFcj8k9Li4pAix545j0dL1dDFZyLEliECgl3wvC9XCONPR3o62jnZ0tLeho60dxqQigakpTM3MqMCU1hjPAyrr2WSBkA09JpFSKUk3UVJSgMioqVRwioYaM+HB0u9caDHJBc7MTMeiRSskBWQBiBmxqKR7t9Hc1IhJEyUIloUxk2ir9IfIzs7Cgrk8kPFh0CLI8Tb74OH9mDJpKtzc3GRwK3rhVFxc7FvsOKwNqXIBKdKeZ8+dod9FRJDISwleXB+RWwLhL4NsmQp8ZmQ+xv2UZKxYtpJ+pwziZeHX2mB76vQpeHh6YAxRm+7hBMh5pqSmICMzE4sXLuavT/KgKnNuW1vb8Olnn+B37/2Lzpq3oiOc8doePXYEgUHDMWxIgDbcCmHLKo+30Ky6rkMX1BYVF+PSlfN4YY2Qlsa1ImtPZBDLOB6EHF1qGzo78NGnf8cffvuvT+etFa5Jx32Q3jaop6jeT+E6lM9+XUMdtu3Yjt+8RcQmVXKTGbBVNj2B4aWLl4hvKz3z52LPwdzGFtOmG6b18fmnX/ft4G3g0d5851UDt/x5N+sH26do3xlbNyG3vogm9Ndey4dz1GCYWluJpX/KzqfBa+ZImFpa8Dm1pqi6nQUbL1c4+HnL4Db3/G14jwmFnasTfZ+DWxPUF5SgOqcQIdMmijVrCdQKcBu36yCmvbCCGlfWayvUrm1rbMLV46cQ88Jq9dI/PNQSw0kmFOcPH0PY+LHwGuDDwywDsjpCkoWhS1jB2vftt1iylstFZOvfCoZC7T1ptY43GPz96BFu+XGJrCafPXEca3jBCdEoMQOcQXDLbKQPbmXhNYq+Q/ZLTIiHkbExxkVFPbPXth9s+QZW0Kwa3Kq+J76pjsMGgbn6KYBAbmFBIXKyn9ByF6bGJliyQhK60JrwGjDO/PJgq2MxpMdJAfBLgG1+Y4sBrdb3mwywtuj7g/YfEQRs9+4+CF9vHxoya2VlAVNjI7S2tWDalCl0oZV6o7qBxOuJIAuXUZETxDI8u/bsxMwZc+Do4IT8wkJcS7yKBQuWit7aq9fiYGlpjeHBYSDeXqKeTOrbJl67gtr6WkybsYDPseXr2zIwS76TiCDl5j5BZXkRzf+tqa6kNr6jswOmJmawtrVDU2MD2tva0NHRRj2KVtY2sLN3gqOTC5xd3eHu7k3Hf/II0X/8zF74nfvJeGUB1NfV4kbiRdhY2yIqejpMyf48pAqhxGy+bElxAUg49bp1m/TCbVzcRZDSW8HDQ+Xhy4xS8vnzZ+Dn54egwOE6Q5JJPu5327/FmyTUVvQ+SxN3pQdLNL0auWdWjNBhFztZyOLBLL8gHzduXMeypbxGgSz6hj1mb0ZwYWKhsritARKuJcBIY4wJUVHcNfL/k4VeM95GYYGCNOzuPbsxY8Z0eHl6yUCOHkjlFKuqqrH/yEFs2bDZoDzbb7d9i4XzF8LF2VVleybPlhGRKi4twdWEeCwl4ktM/q14SuI9MKwNVcN/mXSyL7/+B14nud1839ILtcxCgdJbGxd/hdaaHhsxVroPzKSuxzBktXHWAHumT/FLZxoQ84HQXy5cugB7O3vO26z02j4D2F6Jj0OXxhgzZnH1lXt6VZY8nxrwzh6/jhrw/WDbUw9R+Xz61o3IrSukINuaWQ0TK0vYDvUQw5HrHxTDzNYKDoEDRHXktopG1D7Mh9+MCFmebW1WEToaWuEzJlQEWwFu7+w5hvGrF8HCwpKGaQllfwjgZt9JhoWlFQaPDNHptU1JuA5HF2cMDg4Sy/8I4CvWtuU9ubWV1bh56QrmrVzOlP/hRhTuP8YgK7y4nLHToKWxCT8dPIiVL3FeKxZkhbFFG26VK1tMWIqkMqWulswfNPNxOh6lpmIBbwi1wFRBiDrBlf9AH9jqE5Ei+/VlOLISbJnxXdYr5UsC0kfi/j0sTpKJGhEDUr5IbcZxClVf5TY9msU+9tjqmCuohzTrKfuj6zi6hgN91/kkKxODiGiEyquHple5j8qDqAtH6e8L/DF09Gdp31832BY8J7D16Qfbp7CKPe9CwPY///df8NL6jThz9himTJ6KlPtJCBgWgBEhIdRecGBLBKJ20s+9vXzo32npacjJycG0qbMoyB4+cgCjx0TC1c0DXZ1dePDwAcrKSxEZOVlURW5uacGpk4fhPzgAgcFh1DPLiUeRmrVCmHIXHqenIC8nE5XlpfAbHAg7Byc4ODrD1t6J2l7hpfQKdnV1oq6mBvV11WhsrENJYR7KSgpp3WsPbz8M9B8Ge74WLWcPGW8sA4gCJGVlPERK8i2EhIZj+HA+l5YXgSJgy6ockxq7R4/uw8aNb8qEqJRKyIeP/Iio8RNo6SBZbi4PIGR7UvuUQKS1lbW8BJDgfdZo8CQ7iyoGL5i3QGe+rdI2atsKOaGK3nlGjEFo46++/gKvbnldanu1xc2eYmhVB2R1sD16/AgChgViOFttQZcisuiVB57kZOPTzz9FzKw5sLYhqVzca9CgwTR/Wavz8PD5n//1J/zx9/8GDQm3ZurRqglInTj1Ey2bMyJ0hHrZH0UZJeEYVxKuwMLcHBFjiDK/FL4ss316AFcJkcK1SNDPWZLPvvgEWza/CnNzC8bDKa/jLHQa+b7sHJFLFdjzwx5s2bBFv7eWMX69sa9a3UFlZ11zKeH5ZY/BXYt0EOHa/vTXP+O//eGP0GiMBMetuFvKg1Qasqx89eSxJXnTrV3diJnL5Z739Kp6TmDr1A+2Pd2aX+/n077ZgNzaAgqy3Y1daE6rgMvkYWI4cmd9G+ruF1KvLYFfTgnZFPknb8NvxhhY2NqIcGvUBTw6eAlh6xZQsJW8tsYoTkqDuYUF/EYG8zVrpTzbjuZW3DtzEZNWLObEpYQwZEYhubOtDae378Wy1zerCkdxObZcODLZ/9alK3DxcMcwPleWzbdVC0kWVtuEAZBMTHIzs/AgORnzl/GS+WLYj7gVIyQgDCPyMkDMuKUQKVAOO9LgeSsxka6kT5jMrWgpoVA+hukISWY20ge3PXlt9+/bg2kzZ8GVF0ORGQSxW/e1MrL6EC8Mtvqepob6enz68Udam9ja2OKd997T+yA+G9hyJrbH8OKn9NgyWiWqS+c9njtz5b3ZVsv4GTiU6bqDusPCtA/cJ4shihM5uH8/iNol+yKlVCZOmghTRTgmWQghCyLKF9t+hoYiFzS2GthyfbuZj7V53x6w/2i0BQjYfvnZN1i1ci0+/cffqCjR99u+oflkLs7O1A4R8CRlX77a+g3effNdXvypG99v/w5LlqygHtlHj9Px8GEqLYPT0dlNa4jevnMDs+csEkOSW9rasX3bZ1iy/CVY29rLoba7m4bRp6XeQfrD+wgMCYeHly/cPH3oduyL/VMIE2VDJ7XshAaorihFUUEO8rIzqFd30JAgDAsMpc8KB5dS3ibZnwVeArDXr16kpYJmzl5E69YLUCt4bYWfbW0t2Pb9F3jttd/AxNhE8t6KEMzZmB9/3IVFC5fCxobUrpUDMmnz2toanDx9HOvXvaQj35Y7zuUrF2FlZY3x48arhiQLhlcdDuRgxY383P9YsTChvcn9nhszD+58hQH1cGTxKIY/YSphyOTayPfNj5kPdw8Paf7QkyIyyZe9dAHvf/g+JkVPEhfySePMnD4DkePkpd7ogXm2/+bbb+gigbu7R48CUneS7qK0rBRzZs3pVTgyaTOiULx82SrY2xFBLCY3V63FlEZO0bnZhXLhHn/1zWfYsvl1GPNRCroAVvTM6ghBJvfg2IljCBgagKCgINk9EE5VNmvT0lAxvAvo3ZLxQqttp7TFanB77/49VFRWYLD/ICRcuyaD27qGetpEocGh0uE1gLurG4ICA8X3lKHI8QnxaG7vQMy8WQZdaFXp8/HYOrn3e2wNukG/xo2mfk3ANp8r6WNihvpbhXAc7QczW2sRbiviM+A6PgAWDjai17Y2rQiaLg08woZxIlK8kFTR1ftw8PWGy2A/URmZeG07m1qRcSEREUvnUo8tDUVmFJIfXr4GF28v+AUF6FRIfpB4ExYWFhg+JlzhtZVybAWBKRKWvOuTz7H+rTdgamYqyEdJoVN8SDK5J6wXl/2bDGB3yMMMDSKEsB4l3LJiAWLOrRxu2THV0LDki2fOwHuALxWx4s6JeRkqJqXDyyU7HwUtys8VuH7tKkg9w0lTp+kJR9bOnVQDG9aYaF0Tf3mKNUPZI/PcwZYx5urPsgFgq5i/9AjCzBf1VZ7tU0yhtO59T2OZ9v3npw/qxKt6/J8DbD/64AM0N2srFL/3u38xuATS04DtjbSMnprsZ/l8XBBXYqL/1bctQFSRfzp2BoP8/XE9MR6rV67Bth3f4q3X34SJsRG1M0ToiXhfM7MyMTeGCx2+m3QXZeVlmDZlJjq6urD1u6+xZPFKWFvboqGpCXt2f48XX3pN9NQ2NjVh//4dWLXuVbo/CUdmvbW3b8QhNzsDASHhGBY0UpZfKwr58JeulRPK2hWFTRGePTbUuK66kpb7Sbt/h5YUCggOg5OTK69+LIdcQeWY/KwoL0HcpTOInDAFfr6DxNI9SsglE+09u7/F2jUvM+DMpRcJHtrOznZs/fZzvPXGe5xSspiLqwFxGJL3kpPv0vIjM6fP4sR8lADOz+h/PLAPkydOocrngm2R1TEVugwrEiQgFTMIsFkiamB76vRJ+PkNFD2oas5ZDox7s+Soo2yaBvji68+xZeOrdN4j2lkDwPZM7Fl88fWXGBcRIfUMA8D2+IljtJRNYACBOF5ASvCeKsShSP7qmXOnaR1n4XJlAlJ8OwgtwUJReUU5XZAgcCusJAgLCsKt0teCWqaHAb9de7Zh6eIVVFNBmmfoyavVE4JcVFSIxBukbq1UIo+duyihVtdcqM9GLD2Aqw9uhXP+auvXGB4YhLiEeK1TChs5Aovmk6oJzMxN0dBKsL167Srqmpoxf2GMQZdY/ZzA1rEfbA26P7/KjUa+Oxu1Xh10lZR4bVuza2FkYgL7QC8KugR4GzMrqDfUcYQ/t52RKTRtXSg4m4ShSyfL8mxbK+pR9TAHg6dHycr+ELhNP3cVA0IC4ernI4NaUv6nvb4R9y/EY+LyRapeWyoOBVCv7aJNL0qlf3jvrqCKLIKtxghFObl4kvYI0+bN7TEkWayBx8CpsIIVe/w4AkNC4TdokF6lZHbAkuWzyKrGSiJW3ICmXBOWDNa+bdswe8FCOLtw4k29gdueIFIX3CoH//raWpw4egTrXn75F8mz/SXAtrW1FTdv3NB6Hs3MzTF2nHr9SXHjPg5H7iuw7S2s9m4K1fuhqzdgq4N1tfq7rgmA8gli5qNa6UbPC2w/2fp97xuxD/Z495WNfXCU/kMoW4DUsU1/kI2Cghx0dbZT9dBTp0/glY2vwNiYKzNHQPTsubNwdHSiqrEEdIl4zsYNr8DE1BzJyfdQUVWFyMhJFGS3b/8aixavhoWlNc2rJe/98MN2zF+8FhojEx5suRDk+vo6nD9zGMOGj8LgwFAxrFOABEE8Sg1m1e6mmj0QYU/MsZUsXE7mQ2RnPoSZmTnCRkfB0cmV99bygMuU5+EgF7hw7hjc3DwQPjpS5rnlbLbk+f3++8/x4vpX6CK2FHIswW1VZTnOXzyDtWvIPIAv6afw3p45+xOtFTzIf5DOfFvSVp/84yP89t3fcZ4oFmCVi8f830qQEsddZRgsE6lMJvLECyjUNu4TsNXhrSXX9NcP/gt//P2/SyGw/M1Vy7EVwIb8PHc+Fh9+/HdMio7WCbaZWVkg4MaCZGJiIkaMHInZM2bTt2WiUSzY8tD757/+CX/8w7/LwJblejGEmW9crmk5YL6XnMSVUCLfxd8MtXvC2kP9C6QafPLZ32n4sZUlH37Nd3w5jAoaSmJjivZJFvWgARWKW7FkBZycnaR5G3sP2AdQOtzPO8jKvkducbXglrlw8mtufh72HzpAFdCVBnXkyBFYrABbJuuO7w/S/SP3imgOVNXVY+HieQZdc3UZV6Xgl345utn80l+p+n39ObZ6bsPWPT/h+NmrdItRoUPxP3/7MoyNjRD40kQ0DzOBiRkHtmjuQkNKGdymBsDY2IwqJ6KtGxUJmfCZP0asaUvCjEtJTdsAX9h5C+WAOM9t5okEDJ4yFtZOTtRrKygkNxVXoPhBBkbGTJPl2Qr5tinn4+E1yB8+wwYrcm0lJeT0W0mk5gFGRo0XvbZSGSDGc8sLSSWcjaUiUgF83pPukGTSMkwpAQE4+Yd815dfYem6dbC1szNITIo/GhO6IQ8ZVoIvt73w4s6DqFh+/cnHeOtf/sB8wtxkmfHtXUiyoWBLtvtx7x5MmzEDru7uMthQDI+TCPkoAAAgAElEQVRaqntKYNEP5tJ19QZUlF3ekFDkuro6/OOTj7WeFjs7e7z17rv6B7M+BlvWALNfrAqefZhnK3yXIYCrCzz1NdT/rWCrbC9DQ5GLmtqei5H0stIOpX4uJ/JP+qV5haX4H+9/j/qGJjru//s7L2D0iGE0FJmEyJ08eQTh4eHQdHfjYVoqVixbQUURyfhNvKvfbvsOMbPnwtXFDUn3klBVU40JUZPR2dmFjz79AK9teRfdGg0SEuJgY2uHgIAQ0Vt7/MQBhI4aB1d3b05UinpsQT20d24lYNqcpbCwsuVCnBXiUdLf0pNOyuKQHF6iYEzqupJwYjNzC5mNYu2QkC8rpKuQZ1p6j7M3xYU5SL2bCDt7R4RFRIOkfIgeVj6nlg1Pvp90A5XlJZgds0QbbgXtCxJOqwtuea9s+qNUVFRUYPLkqSLcsqJU5Bw+/vRDvPPWb2BqSkKbJW0NYeJN3isoyMeV+MtYv+5Feb4t3xBaUKQyYLJgpeaxJfVbSRj3hMgJ3M1QCEjJxmJDvbY6wLapqYmC1XtvvyfdVwPB9vzFC/jrB+9jyiSiViuREBuKfDb2HG7cuileB/nlSfYTRI6PxOYNpO6pNthy73HBw+Tn9l3bqDedCFRJ5YG4z0R7qFL6RzjO6bMn4TvAD8ODQvjtFTdFzahpASSXA//5V5/g1VfeoMKlEs/xMxB2HwXsiv1CUd7nxs3raG1rw+SJk+VzNjWwZTpXb2ysLptt0DF0ALY23LKzUeDDTz5ESWkpFZNiX6pgK0/X1apXfPPWTZRWVmHxUvVSgkpTUfOcwNahH2x/3Vb7bkoG/v+/78CPX/9PmBgbY9Pv3sfaJTMwa/IYhP12DkraK2E5wF702tbfKYZ96ABYONrQvFriwa2+lQuHAG9YeTiLNW1bi2vQVFgN76hQmmdLYJeAbe2TYnTUNWFARJjotRVEpB6evoyACeNg5+zAhSOTsGSinqwxQnN1LR5dvYWoxfO0vLZCeR+SP0u8tvNfWktVHoXSP8ocW8lzq8GBb7djxYaXaGkDapwZZWR5vq3gTeWNIAO3pHD74d27sWbjJnqztcWkWIEBGaI+BdxKA2vukyd4mJKCuYs4OX39cKgfbnWCpiLUSga9AG7duI729jZETZzUp2CrdT38Y9QPtqoClMyESN20GQKpaiNVr8K3DBjq1A2sfLLAHkZte633tCYm0hF601+e1WP7tGBb/JzA1rMfbA3osbo3IbZyScxEzJ8Riet3H+Iv/9iDY9v+RMG2prwBO3ZuxSsbtiD1QTIIVBBFZLJgTCaKLa1t2Pr9VmzZxIUW79i1HfPnLaJhx0S51sTEDCNGhKOuvg7Hjh/CsmWkhi3nqb1+IwHmFtYYGjQCHV3d9L2Ojk7EXToFUwtLjImcxohHSfVABYAoLy1CWXEBqqvK0VhfS/9ZWNnwCsjGsLSyQUNdNdpaW2BmbglbOwdY2zlQgSgHJ1e4enhTGJPSdHjbw3hWBZVj8vwV5GYi/cFdWpooInKKWOaHDUkWQoeLC3Jx7eoFzJmzGM7OLnx9ekmMSgDj/ft30bBTMzPiuZVCjYXc2vj4i3BzU1dKJscoKS3C9RvXsGwJKSckLFrzwpHCeELSje7epvdu8qTJDNxIkVU9AUNPYHuZ1jK1R/goruSPAGjKXtebcGTWSygch7RLDRHiOn4UG17cIM0XVMBWXKwQ2wEgYPuPL/6B8LBwmBCHBm+gewJbovwcPiocr27aYpAyMsnlpbVvR44yKBxZWgiQagQTkbD5cxfCjubb0haV2lblcZZjGlBfX4/9B/di80aupIsW1PJv6gJYbidGHJS0fU0Njv90HC+tl8rkSceVovPE09Nj01T7xlOMZDr7rmLOx1+O/NR42iXHOH3uDHbu2YlgKkgmHXXkiBFYtGChVtwh67VVhiKTdIzi8kosXDzXoCvqB1tltWaDmu3//o1a29rR3NwKB3vOtf6vf/oakaODsXhONMb9eTkyU9JgO9IDJibGXDhyTj2MjIxhF+Qllv1pKahBR00LXMcOk2raakyQczgRQ5dMhKk5KQdkChMNB7epu09j9MtLZCJSBG4rMnLRWFaJoMlcqDIRixJ+ktzbe6cvYWBwIDwHDVRVSCYG60nyA7S3tmBk5HhRLEqoYyvl2HJeXmIAaG3btHTMXLRAJSRZAl1uLGNWdtlQYY0GVWXlOH/yJ6zewIX3CavA4pClmm8rjZDCBFyBvTpWzKX8joTLl6iiZcR4rqB1b+CWHViV+8oAltlQORjWkwH7+BG88NKGXoUj6wWWpwUVrQaQP7+GqCL/83ts5YZcaIGnBVv1o3FH7WlSpzZ66gJbZV8U9tX1Hcp+rut8+sFWtw3rB9tns+8VVbVwtLelsJpXWEYXhWN/+ICC7Z0b95D+6AGWLVmGa4kJsLK0oCI7xI6RB+fBw4dIS3+EBfMWIuNJFh48eIDZs+aBjD+Hjx3GyhXraA3bc+dPwc9vCAYMGETB9m7SLTQ0NyE8IppCLfmXl5eNW4lXMDJiArwGDJaV+uFq13ajKD8b+dkZKMx7AntnF3j6DIKVnQNsbO1hZWNPS/eIL6bIZ1trM1qaGlFbXYGGmio0NdWjOC8Lzq6ecPccAC/fQbB3cOYjcjhAZKFWyGElGJzxKBkpdxIxevxkDBk2nPOm8rVpuZxaTvCpo60Nly+exNChQQgIGE5BmFVLFuD2668/xuuv/YaDbBW4PXb8ICZGT4abq7uqmNTNW9fR3tEuCiIp822FKK3YC+dgamKCaVOna8OtZMJlnUkcb1XCkNkx9dDhgxg9JgJ+A3y5/X9Gj21jYyO+3bYVv3mrlx5b4nnNyca2HdsQGhoKL09PcexnVZHVPLY5udmIGB2BVze/ahDY3rpzG5VVlZg1Y5Yq2HJNJEGsDGz59iP5tnt/2I133npPJTVZv8s26d5dJCcn4eUXNzIlfSTrwnovOZvFOAz0CEZ9v+M7rFm5VtRqkKBYsKWMRVP/Vb1/KYcwQw19T9/RE9wyBvt+yn3819/+Cz4+PrC3sxNnBnKwlVtooR2VYHv79i0UlJVj2fJFBg3OZAHxebwcXPtDkZ9Huz/Vd2blFuGtf/8EP3z1/8He1hoTP38R909ehd1YH5ham1GvLdqAptRyOE8cKgpIGRmZoOTkffgtH8+DLaeQXHUvB+Y21nAN8hdFpAjcFt98CBtXF7gHDBbr2RKwJd7ZWzuPIGrdEpCcRiXc1pdV4nHibUQvWyh5banYlKB4zP089tX3WLz5JZibm9N8W2IYJe8ttw0rJHXh2AkEhIbAf8gQGdyyvlkZqDIPPauUnJORiUcpqZi7lCtA3SdwK4M8dpouwe0PO7ZjyoxZ8ORl93XDreFeW11gqwQIst3u7dswbdYseHl796nXVjf8an+iNBSGQ5V8y36wVR86lPbyaaBWF3xKiz/a3903YKv9zWr9pSA/H51dneJJCNsQcRdDXso26u7ohpU5Aww6DlLynDy2Hv0eW0Nuq0HbkJBkX283vLJuPppbOnDwh4MYMmgIAgMCEXvhLLw9PTFq1Cgqfkgmw0ePHYWfnz/N9fzx4H6EjRwF3wH+OHriMA059vX1R05uDm7fuYm5c7katukZj1BYmIexUdNEqC0pLcbVuFjELH5BC2hJGDJRRM56dB/2Tq7wGzIcLh4DoDE25iCDJSz1UqRMCK4wAed+VpUVo6K0gEIusXO+/gHwGzocFuYWspxYNtRYgNg71y+jrqYSEybNho0tUTCWwJUCLA+yiQkXaGmeMRFRkueWEYUi+ct79nyHjRte52ytESsYxUHyZ59/iLff/C2nZivk2jLbHTr8I8aOGQd/f38m31bK6RXAhUAwmZ9EsHVHFXm3qh1FBWzZceIvf/0z/u0PfxR31RVt3BuPrQy2+COTayd1ev/x+af4l/d+z0YT84vnjKglO78RYE2jwYWL52FtbY2o8VFaqUXka86eVwtFzqJlmDZv2GQQ2Gbn5uDatatYs3qtnjxbtZq2UmcmbUjs+PkL57B0yXKtfq51nzRAcXERYs+fxWD/wYiOJuHWbIUbHQKYBkJt7PlzNPpgdPhouW0R53a6KZO1fzqZ1VCYNWBSpD13lFtgGdjzfzQ0NiAlJQU3bt3AssVk7svtY2NjDRdnF0VfkSa05Dcl2N65cxtF5eVYvJSITvX8qi1v7Hmjn2ELe1frn+GovT9kf45tD22Wmp6NP/7pG/zlv21BSIA/3Xri5+vx8HYyTCxMYTXQkYagEK9t3Y1COIwcAHNnslptSv+Rsj9WXs6w9XMT1ZE76lpRfvMxBsVEMiJSpmivaULpnXQEzJnIlP0h4cpGKLr3iAL0wFEhFGwFdWRa6sfICMlnL2PIyBFw8fHkPLrU0Al5ttzP7PsP0dzYgPDoCXKIFbbjc2wFuO1oa8epH/Zj2csvSmCrJySZzbelA6Ag86TR4EFSEhobGjF+4kS9YlLCftxP/v+MQp00nCjqpMkCO7jPSE7Utq++xOY33pIPnIxRk26/Mp9X2Ij7KQNaps/oKv1Atk+6cwc1NVWYOmPmLwS2yjNlzr0H4uoJyP5pwVYmnNm34chCN2CP2lM76hpu1PfTnjgI+/+SYKs8595co2qLd3bD0swAsG1+Pjm2Hpb9Oba9n0rI9yD5sP/x0U60d3TgP/6wCUZGGrS0duA//9d/4r13f0f/Phd7BkMHE8gNoH+TMfvrb7/B2tXr0dLSQmtabnx5C7KeZOHm7ZtYuGA5Bdc9e7dj5swFNL+2qKgICdcuI2bBKnR0d9HPMx6noaAgF2OjZymgtpvWq72beAl+w0IwcFgIzC2txVxbobYtuRKtxRjmTVE8iDURvI2S9CiA+ppKFGSno7w4H3aOzggIGgUHZ0EwSlIepsHLPFxWlBYh+XYCfP2HIDh0tMwryyoip9y7ieamBkRPnMHk3UoA29RYj9jYn7B82VotuCXHISV+Ll46ixXL1khKyfQeSMegpVw2vQYzWiGBBzwx71byyiVev4rSsjIsXbxUspVqcKLoVGxuLdvm5H4TZWxSDkccY/Xm6RpIMDpybMl3fP7VZ3jrtbf1gy2bTiVcn0aDh2kPkJuXh3lzYuSeSv7kKdje5HNs+fdIju2USZNpnjK9dp7chbB4weNK3+8G6hsaaEmit998hwFbrtWEhRiuFdS8tlLrksNlZmUgPv4KRo4Ig4/PABqaruzz2dlZNNy8u6sLU6dMh6urG3dvmZVP+quSPXWJiSlCkAuLCpFwNQGrV64W7zF7PNlsTMXgCG+p3nkDuwPTesw56P4ybbhlGoD1SjMNRfbZt/8HREVGYqCvn05PNtvx1MD27t07KCorxaKlhnlsayueE9i69IPts9rOn33/pNQMKoLx2Z/excABXH0z8or89AVkFWaiPbMG9hHedNWT5K62FTYA7YB9sLfotW0rawQJSXaPDuLe4/NqC2OT4TU+GFZOjiLcknzbJ2euwzdyFOxcXajXlghJEVBFRxeS9p9E9EsrxDxbQUCKfF5dUIzc+2mIXBgjy7Vl82xJaNL5vfsxfcUyWFqY80ZT8tRy3lvub/If+fk4OQU1FZWInjldhFvOxjF+W/FvaSIuemwZuL164SLs7R0wYsxoVTEpWf6FAlSl/AMmR4NJSpC+T7hL3GQhK+Mxrau7aPlK+QDKj0lyYFWswsnHLe39xYFeRHBZn2xpbsaeHdux+fXX9YYjC5MG6czlXVs5oCrsieK8tAdmpQFSe3B6ghVdqsjE+x/xTKrIauZFOkNl6JrwiZrt0mXP9JX8URr0px1UhO/uqR11Hf+fCWx19T+1a1O7J4aKR5U+J7B17wfbp30M6H5Eyfi3/+tz6qn97avSuEvK/Zw6ehLTp82gHtoTJ49Rzy3JQSNgm5efh8vxcbQMUHx8PLWXo0aNwc7d2zFj+lzYOzjS0j9E3XjM2Gg0NNSDhNUuXLZe9NTmF+Th8aMUjJs0G51dnBoyCTsmolQ3E2IpaIdPmAEYGVNAEGCWemrJtswAowq3zFxeDrhCOg43ReUEo6QQZAK4WWn3YGNjh2HBo+Hi5sGHJ8sBVxBzSrlzFTVVFZg2e7EIrpx95jyu5GdGeirKSgoxdVqMlqgUAdTKilLEx1/AiuXreK+tpJJM9s/KeozMzHTMm7uIX1iQAJucP4Hfw0f245VNr8muR7DTXIgyN3I9zkjHpcsXKaiR8i90jFBlBO5NXVBLPjtw6ABVQx7gM0Dsh7pVkXsxgusB2++2fYuF8xbCzZ2DPMFmSnoi3KmIOiEM2JIFdBJy+t//jagqa0d/ZT5RqCJ3A2fOncGiBYsQMWYs1x4M2Irlf/hLEwSk/v7Jh3h9yxtU+VomIMU0gSwcWdxfbjXJvg0NdUhOSUZmZiY6Ozvo8Rzs7SlAk7JPgwcPQdiIUbTkkva91OWp5VpOajv+exVQS979xxefYvPLm2FlzYGQNiSLKyOqYxGFP7VPdEwCdBcVVD28oiaHcB26Utr4+Z8W5HNvkMU3Ioa2dhWBeCYCgL9waTfpOFo5tnfvIKeoGCtWclGPPb3qnhPY2vWDbU+35vl+XtfQhFWv/m9s/eBf4OPpKjuZ8Z+sQ3ZNLlrvlMEm1A1m9hYctHYboe5mEVynEXVkUxFuS07dx4CFY2Fibi7Wrm3ILEVnYxs8I4I4sKV5tqaoe1KM5opaDIqOkEr/8LVrn8TfgpO3J7wDiAIyEZHi8m0Fr+31gycxcupEOLu7ysKQBWAlP/PSM1BfUYXwScRrKyknc2FOinBkWn5Ag9M/HkTExAnw9PExON9WNH78sCUIL5w5fARBoSPgP1QIbxaHNX5wU4CrOOix8Ng7uL149gxcXFwxcvQYaRAV7qgelWRxwFEZY9nPWFMmB2Vg/769iBg/Hv6k7BHTi5QIrZWjouj++s6FuRTVDM++AFt9T2OPC6Ri2JmuoxhQy1bxJf1gq8sga3cyNWhWGlN5H+o5UdhQgO8H2+drx57Ht3+z+wRIni1RQ2ZfpNxPeWEFnUCTlNrLly9SQZwxo8dQsDp/IRa2tnYUZj/+9CPqrc3OyUF2XjYmTZwBsrj2zbefY9Omt9HR2Y1Tp48heMRoOLp4ULCtqa/FuZOHMHfZS6J4FAHbiooyXDl7GGMmzoGrl69Y15aFWQEU2ttaUVdThZameppD29HZTnNbqZUyMqaiTEQwinhgLa1tVWwWlxPLem4FyCXvV5UVIS3pOoWTkPAoKjzFhiVLZXyAyrIi3Ew4jykzFsLRyUmMsjJm4JZ4oDPTUzFn7lJVuC0syEFaWipiYjitDCEPVyj5c/v2dZB183FjSTkhuceWnFdmRjoeZz7GwvmLZGJSanBLFhoOHT2EqLGRCAgM5NpM30ChYhcI8JF5yMwZM6Wu0xf5tfzJKE9HsI3HThzFsCHDMHx4sDRHUIQec4eQQ4zw94HDBzAiZASCAgP1aiwI3tW/fvg+3n3rHVr6ibwMAdudu3dg6pRp8PH20Q5HpgcRwugV9lTWfrxflxmYSSg2CZttbWmBqakpnSuJ9kDWYMwcTAviuHmc8LbMs8tuS0okxZ6lHmAiniXO/mTHUx5cPooJn8psSx8BrXK81Fqm0FHSSowvlF2r1F779u/D2IixGDJocI9eW9J2SrBNSrqL7MKifrA10KD1hyLraKg9h8/jk28PwsPNWdxi4awobF47D+M+Xosn1bnoLmmCURdgPcSJlvgxMTZF04MKWPk6w9LDQYTbhoelMHeygd0gIizFCUVpuoxQHJuEQfOj6d+COjIB3IyjcQhZNgumxqZc2DENPTZGS00d8m+lYOTcafRvQRmZhCITuK3ILURFTj5GTZ8sem0FeGXzba8eO4mw6Cg4uZFC8UKerXaOrRCS3NzQiNgjx7DsJSEkmTXc5OEVVqtZP60UqiS+y6/cHf9hP6KnT4OzKwnLkvtaOUOjG27ZCbk4iOr03EpG6MddOxGzYCFd9RcHU/7Oyo2vSkiyPrDtwWubnvYA+Xn5mDlnjsFgqzw/2d8q56IFJQrTqoRiXWODobCi3P8XAVvecAvfrdM7q3Jxv4TH1sDxVudm6m3/y4ciq0001E7a0L7yLGBb9pw8tm79Httn6s5RC9+kWhRkkiy89n3xPwCNMVrrWkTAepzxGDm5WZg7Zx61Q6SkycJFS1BUWIzHjx9j5owYHDpyAOHh4+Di6oGLl87B3cMH/v7DkJWdicwnGYiMnoF2Xizq0I/bMI+HWkFA6u6NK2ioq8WYSXMAY6GuLeeZFWC2OD8LlaVFqCotREdnB4VXW0cXujBNVJG7urtgRHQ0ADQ31KGlqQEtjXWorSyFg7MHVUi2c3KFs7sXnN05PQXBrikhVwDX0oIcPE69BUdHF4wcO5mKMAneWiIyxQJoXOxR+PoORsDwEUwKEbcNgdyiglw8TLmNufNXqNS61SArKx3FJYWYMnmGTCxKANkrcbHw9R2IYUMCOM8sE45Mtrlx8xr1+EaOIzmkklIytdNCCLYAfABOnT1F6xBHqQk3CgOMCtTeuHmDluuLiiI1YaWRo0/yaxXzCraDk1O/dfsmXTiZKOSRMkZXWKTgD8HNdwQ7zJPxo/RHyMzKxEISPq2EH+bLyFXV1tTiwJGD2PTyRh35slwZHynEmPsj9kIs3Nw9aP1nscyP4Onl7aNWODJjMJkW5d5lBmflOK29GG441LLXr6xXS/Lj76ckY9ECpmKFgVCrtDn6wFZ1qbzHiQpzo1QMnDgzVIVbZmbKztH4jlJaUoKLcZewbtUacbWHnZcxrcsxMn/zhX6QfP8+8oqKDC73U1/Z9Exj+NPubOts9bS79ul+/WD7FM0Z8dEaZFfnwrijG633K2AfPYCWBDI2MUNnRQs6yprgGOHPQayJKTpr29CYWQ73icOpgeQg1hRlielw8PWEw0AvHm45canS249gaW8Hz+EBNByZAiyFV2M8PHkZQ8aNgqOnuzzPls+1jd9zGBMWz4ONvR1v+OR5tgRwa8sqkHbrDiYv5CYU2kJS3HvUwAkhyfdTUV1WjomziHFkAFbIuRFDLORwy5b44QwD9/nur7/B+ldfVc+3VRghGfrKQn20PbfsACH8Tn5WlJfhzIkTWL+JqxvH2C2FIdIOJWIHeQmmpY4jeKO1jss7vt7/03/gD//9f/zs4cgs9Cu7tbah0u74hsIKu6dBtqIvPLY9GGLGfmtdWD/Yqg9yMmOqtOkGdAZ9m+jsFyQstL0LNpZ8aQw9429Zc/tTjM7PvoubpQRkz360/iMILUBUkdvqW0V4IrZgz95dNCSTlEYjE/eVy1dj7w97MW5sFExMzUDKvsyfvwzZOdm4dfsG5s1bRgWj9u7bhrkLV0NjbIr2ri7s2fEFFq3ajG4jY+qtJe8d3v0FwsZPg5d/AFfPlg9NJmHJ1VVlKMhMQ/6TNHj6DYONowscXL1g4+AkC3EU+zHbocV8WlCvbmNNJZoaa1FekI3ailK4DxgEdx9/ePkN0YZcalMlcC3IfIj0lFsYFjwKQwPDJLgV1JCJ/TXSIOnGZXR3dSMyeppCMIoLS66qKEXcpdNYuXojhV0BoIXF6bS0FNTV1WDChMnacEvCwk8cQlTkRHi48yHSArTyXt7YC2fg5emNEaEjZHBL7TurgcEPCtdvJiIlNQVhI0ZiyOChcHaWHATsE0HCeNPS06gA05jwMYgcHyV7YPokBJk9oo5w5KrqKuw/+CNef+UN+XxA8NqKYlGSX1KCW27O8PFnn2DzyxthSxVw1e0r6UYknL6svByzicIxv1lPHluyIQlnJd8ePSFaJ9iKZpKpaUu/QssGG2S5WaUTxaSJDR9m5kx6FJAbGhqwfec2micsmyuJICgjXPkcTVfosda8QOW69AC8rvske18NvFl4JRvryC3m5l3cxvt+3Ifx48ZhsP+gHr223GVJ+dK99dj2g21/uZ9ezzzG/H01B7bGxmhLLod1gDPMnazE0OPqC0/gNicYJmZmItyWX3wEj8khMLO1Fj20LSV1aMgqge+UcAq6gue2va4FhQnJGL54Bs2xFXJpyc/q3ELU5hYhePpEKhpFQ5J5qCXQWpSWhebaWoRER+r12t6OvQRPvwEYFBQoE5Iix2CBllVJvnjsJwwLDZapJOvOt+WediEMi/9LDN2qqazElXOxWLKGE6/gxwZFOAuLauyKoTz4V4JNuZGVHVMDXIu7QnOhx0VxRd9lE/OeQpKVA5lifxZu2eOS3386dhSDhgzB8JAQg722SmhQnqvW+fO9uK9gJS3tISrKK7SejaCgILi4SqFKBpnHvgBbxRf9v+KxZeyiLlur+32VPsturLev6Opgih6h7Kc99odegG15y/MBW1eLfrDttVE0YAcCtu0NrVKYrkaDisoKnDl7CgMH+tPJd2DgcBw4tB8bXnoFJ04eh4+PHwYPCcLefTswJ2YxzC2sEJ9wiXpShw0fRb21Rw/vxpRZi2BsZkmhtrG5CWeO7Mb0xS9BY2zChx53o7MbtEbto6REWovWa3AQPP2DqDdS8IwQAGZfuvoz6ffCpJVd7Ozu6kR1SQFK8jJQlv8EPoMD4TMoCA4u7jKgFRSOhTDkh3cSUFtVhrCxk+HoTCKpJBVkAVILczORcvca5ixYBUtLK7oNm3fb2FCH2NOHdcJtcvJtdHa2Y+xYkoYkeGb5vFojDV0sWLF8LSwtLFXhlygljxo5GkOHDtWGWybsWLDnzc2NSL6fjNSHD2BubgEnBwd4eHBaJS2traiqqsKjR2kYFTYKUZETqLKwrO11MYouF64BfZD1tsrGQg1AQn1nTZ8NTy9PaX6gBFsmQkvptc3JycHluMsYMniwdGi+c5ibmdO8YdLHd+7dheioCfD38+d5k/PQUpjhy1BJYcUS4Ny9dxclJSWYMztG2l7YiVHz1ue1pZsb3MO5DdUWxSB24AoAACAASURBVKX31KFWFnXHt8HW77Zi/br1sLS01DquzJ2gzbdc26jdXxm06p4ksJ90dHSgpLQEVZUVqKuvR2dnJ4xNTeBg50AXdlxcXMRvUp93yQ2ruI1K6Dpnv7ktCgrycSnuMl5at15Prq30jSzYEvGo3uTYpiU/Muhp6OuNgkZyKQjP+9XvsX2KOzD6g9V4UpNDvbQkHFnT1gnrQFeqWkw8tC0Z1TBzsIb1QFcxHLnxcRn9zDHETyrxY2SC3KM3MGjBBJhbWvLCUhzg5l++C6+w4bBzc6XeWjb0+P6RcwibPwOWVlZ8fq081/bSd/sw4+U1tDQQMXxsGLKQb4vOTpzZsQ+LXnmZ8+zqybFlywDt+PgzvPzOmxTYOe+rEJbDhyMr/laFW35UzMt6gtSkJMxfvtwAuNXtndXCXz0qytu//hJLVq6Gg6OTNLjyfaC3IckygFWM/uxnOVlZuHP7FpavXq3HayuFNwldUifc6gGWZ4UV4TuPHD6Ehw8eaD0dS5Yuw/BgLg9Jp7FR7vX/ANgKhlN5zwwdXtT34++mjoOqvW3oAog4YVHJCGMNtSHnT7bvEWiZzmKox7YfbA1p/X+ebQjYdjS2MXl4XE9raWnGseNHsGrlWly6chnWVjYYMiQAO/Zsx4svbMbVxAR0dHYhPDwSFZXluBJ/ETNjllGv7LHDezE6airsHF0p1Da3tuKng9sxZ8VmCr2CcBQJM31wJ57WnQ0Ij4adqyf9jPRbsrbP/eTEpNiBTSfYspNY/mESYFeoQ9vd3YUSoopclIOu9jb4Dw+Hu7efKuCSfZrqqnH3aiw8vf0QPIrkvEo1bAWAbWqow6UzhzBx6ly4u3vRXGUBfMk2rc1NOHJgB17c8Kaq5zYh4QLNoQwJYb3DpCQQ0NnRjn37dmDDy1tEcBXzbkmZYY0Gp04dR1jYaAwQ9DZET6ZCNEghGFRTUwOihEuEidra2incuLu5UZEirZeunFp6M9TvSK/GXx1e29QHqfQc58yaI80NRFvLLJrryb198iQLb733NvVs2/IiWmQOZGdnh3fffIcKn/1w8EcqnCReCeNd1Qe2jzMzcO9eEpYvW8F1USanVmwZsftKjShrsR5tsXQ3dNmBp4Ha3Xt3YcqkqRgwgBMEY6dLhkCt6p3XBbU6PLQZmY9x++4d1NfVwsrKGh4eniALDmZmRAW/G+Xl5airr0NtXS1Cg0MREhwKBwcHsUGUgK8amtwD3O7auwtTJ02BL2kHHWWRBJcLC7ak3E9eSSmWr+BCuHt6ffPF1p42+Vk+3/LGKz/LcXt70H6w7W2LAQj/YBWeVHNga9SlQevtEjhM8ePDkU3RVd+BlowqOEdLNW3R3o2a27nwnD6C5uNSD63GBDX3c2FmaQmX4EEwYby2tVnFaKmow6DoMXwuLR+ObGSM4uR0aLq6MTgiTNVrm5f8EF3tnQgaP4Zf0eUFpmQlgDTITLqPzrZ2jIgaJxeSYkKQlSHJpQWFuHftOuavXikpJco8s1K+LTeACQZPAl/6Pv9Qp6WkorqiAhOmTtUBt2xZHwXC6gFY4bvFQZSfTlXSkOTjWL9JegDlK25Ch+i7XFty/E///gG2vPGmuFopfQtrSOTJGzrBlplIKbsv20LsZ72BFbJtP9j2bmDo1cRK5dDPD2ylJ0SrvzwtpetqOn6u1WVgKHLFc/LYuvR7bHvX+Q3cmoBtZ1O7TnEd4i19/8O/4u033sO169eg0Rhj2LDhOHT0AJYuXUdDkA8c3I3xE6ZTkCWQZu/kBt/BQRRyCdge3vMlYla/KubdEi9tYW4GMlPvwDdgJNz9hnFKyUS9WQG23N/daGmsR1tLE4VEUhOWvmhorjHMLa1gYWNHPcfUQjBhyfxmNH2HzT0loce1FcXIfZhEFWj9g0bCw8efAq6sVi04D2xeRgoyUu8iavo8ODq5MYDK5dQS2Lx4+iCCQ8LhO3CIVl5te2sTjh3ejXXrX1OF2wsXTiIwMITWCxY9t3y5pcqKcly4eBbr1nB6GvRzRRmg7TuJgvBiuAoaGcowXZXQZNHe6RtT9IpEkSPoh1pd3VD1K3XA7SeffYxXNmyBtY1crZe918L8Rbjf0k8N6uvq8Kf3/0xFzzo62uHt5Q17OztaUuft19/Et9u/x9w5MfD29NYKQ6ZXqMdjS7yMP506iY0vb6SXqivPVmwpxrOtBrc9PraKhpPPIQzw1PKm5dz5c/R6Q0JCuEeJMTmGQq3q3WchXrgYFXBvbGrEiZ9OwNzMjEYHDBQ95WpH1aC2pgZZTzJx6+4teLh50BrN5D7KLCW74MF/ILSPLHpP2I6/6OzsJ7h+8wbWre4p11ZexomAbX5pGZYtN6zcT2PV88mxtXbqz7Ht8bn6tW4Q+JuZKLKshYmJMYXZ9tQKWA50hIWbtRh6XJuQD6dIfy70mCgmG5ui+kY27AO9Ye3pIopIdda3oTwxHYPmRYm5t4KQVPqhSxixaq7MW0uEpDSdXUg5dh7jVi1kVJElry3ZJn7PQUx9YRWvnKyhACwr/cMrIJ/ethszVi6jBeFZISlBQVHIsWVDku8kXIOFhTnCxo5VzbdlxaS4Z14/3F6/EgdzCwuEjxvXJ3ArGCHhu6UBiTuPa/G6Q5IN8dqy473sd2bEVhrTi+fOUmXL8IiI5xuOLBud1Z8wQ8HWIC8dYzt0b983qsi6pj6/RI7trxJsZRMI7XutaxFEzYD3yVjcD7Z90oz/rAchYNvVpB1ezjniunHv/j0UFBZi+rTZ+OiTD7F50xsUcK2tbTE0IATJyUmorKnEmHFTUFBUgPvJdxA1bZ4Iscf3f4eJMSthbG4pvpd8/RIFjODIGaL3luTYSlALdHS0oSQ3A5VFuWioroCRsTFMLSxhamEFU1MLdHWRcijdMDY1Q1NtFRWO6mhrhZ2zO6zsnGDv7AZ7Fw8KvHKo5cKJBcilXtn6ajxJvoGOthYEjJoAJ1cpRJkt5UPA+m7cWfgNDsLgYcHUM8t5bSW4vXbpJLwH+NO2UebVElXnMycPYtWaTapwe+DgTsyaOQ/OfNgztfc83OZkZ+FJdgZmz5zLwS3vrWVFpXbs+g4L5i2CqwsvAClO9LWVg9VK4EjKr/p7s9A31LZShTU94Kz1kQJuyTkl3buLqupqzJg2Qx3Cesi1JfXeP/3iU7pvfX09SktLQfJ3iYd6wvgozJ83H4P9B8vzuGWlfuS5lVxIMjdwNjQ24tvvt+Kdt96lx2fDlwXoF5zaXNvIVwp6hFsdbSd7m5/j6HQGMBvT9kxOQl1NLaZMmaobDHUBtOKm6zp/faWj0h6lIS7+CmbNnI2BfOkiw8dPDfLycnDz9i34+PggclxkL+CWsa4i3HK/fL/je8ydHQMvT08dXluynRxsb9++hYKycsPBtrrZ8Mvswy2tHbkw8+f96vfYPsUd8F01FtX+3RRqCdx2V7QAjR2wCXLhvLbGpmjLr4eRkTFsh3mKYNte1oi28gY4RwylYCt4aEvjHsI9bCisnJ2YmramKEt6DCsHB7gNI95cI5nnNvd6MhzcXeE5dJCq17bwwWO0t7QiaNwYGdByYclSmZ+yvHwUpGchcg4p9M6U/OFFo1igFcOSoMG5g0cwftoUOLu46AxJZvNve4Ldy2fPwXfgQAwOIMqM3AAgE41ilQh5NBS2kolX8PdTvsIqbinm/R7YvQuz5i2Ag6NCJflZcm1ZAQFFDm5ZcTES4q9g2apffzgyacKjBoQi9w3YiiZY9UmUwrXkH+v6brX3+8FWkU8uPiNCm2rPaOSr808xSCp3YVbSDfXYVj4nj61zv8e2D2649iFIuZ+W2gaaw6nlbeoGFe+JnjAJRcUlKCkrRWTkZHy//RusWb0Rza0tOHBoHxYtXY+2ri6cOnEQoyOnwsLGnkLsycO7EDVzCYzNLejfrR0duHbuMLwGB8PTP5Dm2RLvLRWR6u6m+awEZCuL8tDV2QFbV084eg6Eg7s3nWzqiXqlD1N3ZydaGupQX1GChupSNNfXor21GS5eA+Hs5QdHd28F1PLeWV44qq6iGJnJ12FpY4eg8GgKPhRc+c8FyE27HU9DhCMmEPsMWv9X+Ixsf/fGJVhaWGFk+HgRfIXPG+urcS3hAuYvWCnz+gqf7z+wg+bUmpmaczm1jGc2NTUZTU2NiIqMltW4ZeF21+5tmDxpKvwH+ksL3IrQZNlCsYqXVFdHk9pfe0TXekefIdILTfL0H2Fdevfe3Zg7Z64oeCW836PXFqA5m59+zoEtS0FWllZ45823OaVwljcVYchkH1ktWoUH96tvvsCWzaSuMJcXzm0vUS7bb3uEWwOecrkdYOdRYpqoNONSQG1BQQFVm166ZBnbFKIxYkN5ufme/pca2OqC2qamJpw+ewpmpmaYT5Sqn/GVeCMRZaVlWLxICgVm/Biy6A3hYsX5K7PoQx40Ujs4Lf0hFs1fqJVryz4v7CIFERwrrazC/IUxBl1J03MCW6t+sDXo/vwqNxq8ZRKKu6pgRjy0xGtrZISW60VwmDJQBFtNO1B/uwiuM4JELy7x3JacJDVtI2BsZi5CbGNuJdrK6+EdFSoLR26vaULp3ccYNnsi9czSurW09I8R2uoakXf9HkbOm857bclnXMixUN82cf8xjF88XzSarNeW9c5eO34aw8eMgruPj0xIikKtIveWPKxcPlA9Tu8/hNVbNkpgq5JvqwW3PPyJ6MqsAP64bQdmzJsLFzc3dbh9yjJAckzmhtDy0hLEXbyAFWulWos6VyGZXigZOS3bxY1nzGjHDtTk9++++hJLVq2Ek5OzDq+tlKeksItKOykaTTVjwKwVyp6f3sDKPyPY6oRdXXTMt47BgK5jNGL378k46xvQ1PfVzr3W1TdUJwesUVX5csVURbufPcsFsd/HTOYMBtvW5yMe5WzeLx71cxjexqY2XDl/EZOiJ3Ehl8KkvhvIL8jH1WvxWLJ4FbZ+/zXmz1uKjKxMNDY1Y1T4eBw/cRDDQ8Lh6jkASUm30NrRjsDQsTQEOeHyGQwJjYClrQP9m+TJXTqxFxPmr4OxuZUIs8RLW1aYjZyHd9ENDVx8h8DWyQPWjs58fi1zTooGYIGBDTMWHg/ys7WpDtVFeaguyUVLXTXc/IbBwz8QNnYOdOLLemTJ9sSOVhQ+Qca9RAwYFIghIRFa3lXiiS188ggZqbcxbc4KTleDgVtyjLTkG2hpbsL4CdNkcEvAt76uCpcunMSKFS/JgFgIMf7m64/w+uu/5coL8ccVorWu30iAlaUlwsO5xXGh1I9Qioj8TQSl/P0HIWI0id7iRiChffTlEAoTeHVIfUaY1dV5VcdCBdxqQMv+7Nq7k4Yki4vm4r76c22Jl1YGtvy52NvZ45233lY4USXRKLKZ7hI+kjruBx/9DW+/Qerfmonbc/syC8Qs8NLv1+O51fOgPwvUklziA4cOYNMGrgqFcu6hmp+q51wM8dYK25DSS4nXr2FC1EQMHaKSw63dJFrfLI/c4z5Of5yOi5cuUFD2HeArXZda3+AvWgtu+QN/t/07zIuZBy8ipqYj15ZduLh56yZKKquwZKlhkN5U83w8tlYO/R7bn8N2/iLHDPzXOchJewyzYBcxHLkjrQoWPrawcLcVQbb+VhHsgr1g4WIvem3rHxTBzN4G9sO8qWeXhh1rTJBz6BqGLZ8KU1Mzmn8rhCNnn72BARGhsHN35RSQmdI/WZevwytoGFy8PSnwEqhllZKLH2ehtqQcYdMmqeTact5Zavwqq3Ar9hJi1q2SwBbqdW3ZEOW0e8kgNW7HTZqoCEkW/bNiuQPe5MmUJKWHXppe7936LVZv3AgTE64ciBaUqsKtouQA3wv0hiRrgEvnzlLIDBsToTX4snCoBFRhlGYnNSJs6ABb8vmNa1dpWYuJU6b+6sORH/WgitwrGNQytAri0SM+1BuP7fMAW7XvfFoW1Am2spmB1Ha6vkf2/lOCrdpk5JkG16cA26rnBLZO/WD7TLda185Nze346P2/4d23fyOBLQ+4h48eQlBQCAXOlJQUzJo1H19+/SlWr9lI64MWlxZjzPjJqK6txemThzB36YvUM3vq2F4EjYqCk7sP/ZtA7a24Mxgzcyn9W/DS1lVX4PHdBJhZ28BraBgs7B25cGTeIyaEJguqyD2Nb+SxEnQiuN85W8VVFOCEmMqfpKEsN52GKHsPDoaLl6/4uawcD4Ds1JuoKMxFyNjJcHL1kAShQNKIQPN+kxLOISA4HAMGDtGC2/ysR8jNTsf02YvlcGukQV1NFa5cPInlPNwKIc0kxLitrRX79n6PzZve5MGWV0nmQfby5XMwNTfDlInTmDJNUrkicr1x8ZfQ3NxM6xGrwa1yLOnN+NjDmqTY1QxeXNQxHirrrZaVleH8pQtYt3qtDGCUoCIs3NNr5KGYhK4qX+bm5hgfMVY1BFnYlgMZRiFZBDAJbEkO8KaXN1MFaVYcmi1uou211SY5Xf1b694IixXsBcmlQEQnhNAG5OdfP/gv/OG3/woj0nl15dX2YJu0ZwnK2rvSxEK4npycbFyJu4wX12/Q1n/u6aHWMXCxkEsUlY//dBxOTk6YOpkJr9YBt+zzILUDqS2diTtJd7B6xSq515ZpWxZsSY3n8uoaLFoy36Cxufk5ga1lP9gadH9+lRuF/GUZ0s/fgsVoN5iYmVAvraaqFd21rbAOdhNFpNqLm9Dd3AX7EG8RbLuaOkDg1n1yiCwcuTo5B+bWVnANGiyraduQV466/FIMnjKOg1oN55klP5vKa1B47wFGziUrtUKtW64EkOC9vXXkNMJmTIato4OokCwoI4teWxjh3uV4uHp6YtBwefkfeY6tUAqIN2zQ4Mz+QxgTHQlPnwGi55b10krhO1Jgsa4SQGRsqK+txbEfftRb41YZqswNGPrhVkJnBpY1wHdffI6XNm+BKVXGk68sqsEtw6064VSX17axvh67tn+P199596nVkQ2FFvZ6FTaJ+9OA2YW+TQy2EcyG6vswK80qT/uvGWwNnhwYOIo9P7BV7xDKVXYDL0N7M8XChqEe2+SMrKf+ymfZceRQplzHsxyof19ZCxCwPfLjfgQGBGHYsEDqZSIgWVlZSUv+rFyxDgcOH0B4+FhUVlahuKQYEWMnYefurVi+ehPaO7tx9uxR6qm1d3bF1ctn4eztBw/fIWL4cezhHZi4+CX6t6CKnH43HvW11fANHgNrJ3cRdjkVZM7bJQhHNVRXUs9rR2sLDS0mYcoaUgKPr1NvZmkNC1t7WFrb01xcwR5QqGXCiAUPLbEFjVXlKEi7g/bmRvgNHw23AYM4cOVBWMiPJd/7IPECnN08ERgWJZbzoZUN+BzbO/FnYWtrjxGjo2iIsJRbq0F5cT5SkxJpOSAxH5dXTW6oq0bC5bNYtGStVCKI99A2NtQjNpZ4dddpl/khHuG0+5TaRoSM1Am3Dx6mID39EebFzIe1NRGQYUUfJbLp1Ziix8jo9unqGkWZrqgGVCr5tlevJaCrswuTJk2WwZkhIcncDrotqLLKpgQxjJqxOG5KYLtj13YsWrAY9vb2CrAlX6jPa8t/bqjh1rVArw9qeTNClH8Xzl0IB0dOUVg+Z2LaRHEsfcOl2jxACNkWrpyAIilROGvmLNmhnqk6FNttmFO/fuM6cvNysHrlGu2+wV+0NDbwszGh3/H9YtvObZgzcza8vbxkXlsxmoGxm7dv30ZlfT3mzpNfm642a65peS7Wx9LB4rl8r/JL+3Nsn+I2BP9lKTJu34eJrTnMvG24XFtjYzTfKIHDpAFiOLKRxhg1V3LgHhMigi3Jra2Ie0zzbC2d7cXatQR4S66kYsiiiTQcmfPYcj/Tj11BwJyJsLC2EUv/cOHGxnh46hIGjRkJJ+K1pR5YKRSZ/F6enYey7DyMnkXgV8itlXJsBa9tZ3sHLh84irnrV/NGlws5Jp+zebasx5b8TvY7sn0n1rz6CrOCrV0CSDfsMgjGG5f87BykP3iImfPJCrAcSYW/hIdflocrM06S3h4L19IUnvv8SWYGMh+nYzafh8GaIskuqSsky7bVGgC10ZK8s3/vHoybMAEDBw6U9TwZRCvsodI8KuFWzXyyx9N66NU55imeBAN20eutVRhjlcP93GBrqJ1XnlpP+xmwbqB1tb82sFVOSgy42+qbKNRODQXbL7/b9tRf+Sw7vr6JrPb3v/q6BQjYPkxKRuaTLMyLWcABZVc39h/6EePGRREsROz5s1i+bB2I8u6cmCVIvn+Ppu0EBofjfkoSamprMCJiIrKfZKIgPxsh46bS8OP6xgbEnT6I6IXreajtQkVZEdJvx8F90HB4DA7h82y5HFsCsuRfY005qovzUVdehPqqElg7ecDY1Bwm5hYwNbeEiZkFOtrbKOASUGmpq0JrYx3aGmthaecES1tH2Di7w87Fg/4uhOkK4CqqDgNorq1CYXoSmutrMGRkJJw8hNQfXhiKB2NSIijrfiLGTF0ARycSqUUAVoLbnEfJKC/Kw8SZizj9DLHkjwaNdVWIO38CS1Zs0MrJra4ow41rF7F4yVrZfsTGVlaUIj7+PFasWC+vccsfO/b8KQwY4IeQ4aEi3ArXRheqNRqUlpbgyNEDNE86bEQYryQt9SJuoi+3pYb2Md0gqxLuozWQ6rCGSrBSlCgi50byvidNmExr22oBmh4hKeG6JJCXzkEX1HLWUIJYqfKU9N7fP/kQr295AxYWFnKwpTtrg61kYYUz0iE9zd4IPVArmzqoLAb8sH8fosZFwY+f3/ysUMtfHLnq9Ix0ZGVmImbOXFmX0ge18rkF/5dsvqndO9m1itLyMhw7fhSvbn6VbigDWaZv6fLaKhWSpb7Cfy8zd4qLj0NLZxdi5s406JFpqX0+YGth3w+2Bt2gX+NGwX9egozsx9AUNsEijNSq5cC2I7OGKiNbeEjhyI3JpbDydYGVt5MIt815Vehq7IDz6MGycGQiIuUcNBD23h4i8BLIrUzLRWdzK3zHhilEpIxRX1yOskdZCJ05WR6KTFWQOdBNPHACo2ZMhqOriygkJSgks17brORUdLS1YcT4sfJcWz6vVqmUzIVdaVCUk4ustEeYNo9TUWThk/uL/z8jCkXfVYpE8SMD+fEw+T7qamsROWnSU+bbaoMtN/goQFkDnD5+DMNDQjFwEOepYQcYGXDynVE+WCsMN38A1nyzdvbB/fvIy83B3IUL+ywcWRdEaaM1d65aA+jP+ZA9b7CV2XG90yODW6EnqJW1scFH1eVE5++ijpusf1GD7bC6nfS6+kmf9ZWnBNua1o5etF7fbepgzqVB9L/6tgUI2NaXV2H3vt14dfPr1FtL8tZSUlMxf/5inD5zEr6+/mjv6ERa+iNMnRqD77d9iVUvvIq6hgacOnkIsxetQ3tnF37Y8Rnmr3mNVNFDU2sL4s4cwviYVaKntqqyFJnJNxAQOQswNuEUkbu60dHdjZamBpTlPkZlfhY0Rkaw9/aHpYMb9eZ2a4x6rMnM2S6gvakBjZUlaKwuQ1tDLdpbGuHqOwwufsNoWSAhLFkZdtxcV4Xc+4lUXHJIWCRs7JxE76yQh9vZ2oyUa+fg7TcEA4eFasFtZUkB0pNvYNrcFZwHlgdQ8ntTQy2unDuKxQRuFWrKJUX5eHD/NubOXyZ5bnm15cKiXCTfu4OFC5drlQEi13Al7gKcHBwxatQYnXBL2ub8xbMg6sCLFy7lxJIY56Ua6Am9jB3LdI7Uhgy+at1WD6yx36sMSe7q6sLf/v4+/u33f5RsJysSaQDcstenPH36N0NYckEoURtXTJP981//hD/+4d/pIWWhyIo35MdVFkpiBuQ+WAQQ7u+Zs6fh7e1Da/gqbYdsOaMXnlpF83DXzdSaJtdZW1uLPT/sxutb3pStciihVhVk9Q5xbBSgtCE7/yM1b48fP4YX17+ke+GDnevy7S30s517dmL6lGkY4EPq2sq922xqdOz5WJhaWWP6DC56oKdXP9gql496arH+z+GzagxKasuhKWqExXRfmFibcTVt69rRVd4E61B3MRy5s7IVHRXNcBg1kAdbLqe27FIavOeOloUjt5bUoqmgGj5RoaK31kRjSg1g+pGLCF0eI3pk2bDkR2fjMSgiDA5uLjzcSqV/SEhydWExitKfYPTMqXq9tsR4xR89ifDJ0XB0ceaFoxjPLaOULECtYLTv37hNAX9kBDF6ZBgTBgUGbVWEoxgM5g2gNM1OvHQZTq6uCAoNZQZKBljF5TM17yw3tArjtnqoMvdpc2MDjh48gHV8fTi5oZPMktr7sveYZ0OfiNQ/PvoQb//2dwaDrcxIKKOIhYFS5bnsB1ulbsYvB7bKe2bIsKnOrv/kYKviIDDUY1vT9pzA1qwfbA3pr73dpqS0Aj8dOYq4hHisWrGaevZ27t6BeXMXorOzCz+dPIGlS9fg4GHiwZ2InNxcNLe2ImhEBM6eOYbAERGwc3LDzeuXYefkDne/oWhqaUXcucMYN3u5CLXpSYlo7WjDwJHRjJcWaKivRsGDuxT8bFy94egzGGa2DrxwFJfbSACYfalxlJhPK+bWchFKbfU1qCvJQ0V2GixtbOHiOxTOAwbDxNiUEW7i0ngIwNaWFKDo8T3YOrhgyKgoLeEnsk1W8nUQyB0ZSUQiiXeWCz8mv5O821uXT2HKnKXUg8eqJTc31iHxyhnELFgtenS58GSgMD8b+bmZmDJltngu9JyMNMjNyaQlTqZNnaWllMzl016EjZU1xo6NlIUtC8JS1PJrgNy8bFy8dB5DhwYgOiqaznN0LQiL7a0cAHuAWH0fq46lBsGttphUZVUljhw9jFc2bZHmIQLcqnjm6Niva7WZ6Vza8MmEIXMEJ4O41rY2bN+5TTwPhSaUKCDF76pa9ldqM+E3fVaHP1k9nm3hWkkOaGdXFyZEksgL7iU0d49QSzbUczOVmhLl5AAAIABJREFUUVtKsCVCTGtXroWFpeQtVIXaZ4pJ1hZxFO5xRlYGVTomStpa16xY+BAjDfkN8/LzcOvOTaxYslxLREq8v91A7IVY2Dm5ICp6rEHDbmvd8/HYmtv1e2wNukG/xo3cZg5HZUMVNHXtMBnhCotB9mI4cmtSGWzDPWBqaUa9sUQJuer8/2HvPaDjOM583/8MZpBzzjlngiBBAExgzgqUqERKtrK9ttdpvc/3vvPeuefeDX5re+11UrBEWVYgRYkixZwTSIIgARAkQOSccw6T36nqPNMzGJDUcq0FjqgBejpWd9dXv/p/oYm6I6scyTIVXT5a3g63CH94REnV2dZDJYjfVghHV1feJVmpUKGvopbOAodkJAl1bdmEUWPt3Rhs6kDG+pV8/C0XY8uV9yn98jjSli+Df0gwhVvOvZj7nSk1oMTE8AgqLl7GuqefFNyQzUv/sOBq7qJ86sBBLMpfhtDIcLZ2raVLMu+WYVbblsNQ5n0XcPer/Z8hb3khQsLDhc5SpLva75IsjvkRIx+zvOTKFZppcHHeMtFx+Lh+dplgBIQOTHhCLcHXEi3JklPHjyE4JATZOTmSx1u8d3PDaG5++L/nBFt5xDLf/mt7z+xQbG0NUB7YFZkfK8gfZb4CwHzWt2NsY+X+ixf/Z4Gt/HMiWTrfC+IHZ9Kny16wHXtEYOu1ALZfS3fQ1t6NP7/1J1y/UYJvv/gyFmXl4NzFc3h2126cPX8GAf7BcFA5oqKyHBs3PY633v4Nntv9JiqrKingJmXkoqWlES1NdchZsRHjk1O4cvYQ8qlSa6Rg23SvApMTY4hetIJJHkVibfU6tFRew+TYMPxiUuEVFkfjbMkAmYNZ8jtf25Y+t9bfdN4riUsYJalVy4Dr9GAPJvs7KOT6hscjODYF7j7ErZgDWyFLcl/jXbRX30RS7ioERyUw6i2bRZnY5aGuFtSWXUH++ifg6enNwi0DuEa9Dme+/AuKNj4JH1qTVoi71UxP4PTRz/D086+JFGHm+5amWvR0taNozSYBqNl6tfV199DT04F1azfzmZLFWZFLS69Bp9Vg1aq1vLLLuSNz+TOYv4Gbt26AxKrm5xUgLS0dnp5eEsC17Omkj958+ltbD62k62KNt/kywSaahR4pQOvRnj57Ci/utk+ZE9OdXLcp93hJMhvzfadgQNs62mlN1heeEyo5SBNIsRvxJYDkW0R2skZuVdmxhSX4k3ABEg+/ecMWUSkfYQZemsTT7EDixrFys8UTAOblfT4hrs9L8xEZFcXv2AJqzWcQ2DXnPTEiUl65g3HjtJtltzAzM4VVK1bbcEmW2nKuH3n7vXew8/EnERAQYJH6hDv1z7/8HHFJKchdkm1X37wAtguKrV0PinilABZslXoTlN5OcFkSzMbVOsDQNgGVswpuMT58duSZ2iE4+XnALSqAV231QzOYbh9GYCFTDogosw5KFcaqO+nfQZmJfJwtyXhsnNGi8cQ1ZO7aYpFEikDs7S9OInPjarh7e0mSR3FxtyPdvWitqEL+Y1skqq247A/3+92rJXB1c0NKziLZ8j/WXJLJ8r2/+i1e/emPLOJtxcqs2AVZDLIcBprD7Z9/8xu89J3vwMmZmQ2SrGetvq3YJZq9edIO1hJu//CrX+L17/0Ajk5OUjVVRIHcrxzYmuOAFG7NQJg9j76eHpw+eRwvvvyK3aqtVbBlT8Aac1iiNXMS/ylgawfUCqZ4DiMsskLWDJLs8ocItnL7v1N5G71dXQgIDMKi3FyLi5gvC8qvz749Ml/avu/8gy+951bHFtaOLtpgPhdk5f4vgO28Tc43YgMebEuu4/XX3gTJQeHt7UtVvX2f7cOzz7yITz/7CMuWrUJHZyetCRqbmIaLF89QVXJWp8OBj97Ctue+Q2vZnjuyD1mrt0LpyNSurb9TCr3RhLDUXGgNDOh2N1aho+omwrIK4BUeL6lny4GsTqfB7OgQpkcHoZkYhXZ6AgadBkadFgqlA0wmI5SkgoErKe/nCEc3Tzi5e8LZyw/OHj5ssA2bEZmAK2uTOAV1orcN/bUVcHRxRWhyDrz8giSuxwReYTSgqewSTHo9UvPX0vqbHKSS7w2aWVRdO4WIuBRExaVIYm7J99fOHUJCUhbCo0jySQaeyXLt7DQunv4S25/cw0M1p9w2N9ZgeLAPy5ev4dfnygC1tjSiuroCjz/2jFkZIOY6K++UY6C/F5s2bpMklOIzRbNxtxzoEsC9XVlOwTY9LR2JCUlwcnKWDuTn6lvmS7lW9ie1fSKINa9hL9qeXEdnVydOnzmFPS+8SCfBuTGAZTIp81HBXK8vi2vi/pL/XYivLb11AzPTs1i5YiWzQzNvGC6rMnc0KzzHbWrTJnA3Rgr/lsDf2NSAmtoa7Nj6mHWotTZGEYGztQlsfnwgbg924cXLF+Hu7o6cRcTuMivM6Z4taZw57ovZ8yM8N5btQPZ07vw5eHl5YknuUvlng/NY5K6bfYDKK8pA4nW3btxsEYPO3cN9B/Zh0ZJlSE1LmOthot9rxzV2rfewV3L0dHrYu7yv/S0kj7qPZiO1aaliS+JYXVRwSvOH2tOJcUfWGKFvHIHHkjA+iZRxTIfZ1lH4FcSzKq6KGsqB87UIKUqH2o3UplNRhdakMaCv+B7iNhcw7sgKkkTKAUS17bp+Fz6RYfCLDueTSHFlfoabOjA1OIykwqVMHC7JjMyW/+FibW8dPonkvFwEhIfyiaEYpZYYQU7FZRJLnf30cxQ9uQMuri4il2Q2KzJf35apbcdkTmYM3mB3L+6U3sTGJx43i7e1jL2VA1lJDCwLpxPjEzh9+Cs8tWf3POJt7cuSLD6Hupp76O7owJqNm6SdvtjdSGSKBcMmPERSsBUMnGQ5gA/+/C62PrYDgYFBkidQjMK2VFupwWF2IWfHrYGtZP25BhT38Y6Iraf18QhrjKzsX7LdfYKtYOgeXLEV76G7qwvHvzqMhKRk+Pr5oeZeNcIjIlCwfIXs1djbxF8L2Ipu9nwnQLiLsdjO1gXZvFcmGHUmuLvM7e47/ogUW88FxfZ+33ib2xGw/e2vfgUyGN7zwksUXnc99RxKSm9QgAwICEFJ6TVs3foUPv70A2zZvgvnzp+icaZBEXG4dP4oohOz4BkQglvXzsPNNwCB0ckUcqtvXoKjuw8C49KgNZoo2FZdOgI3v2AEJuXQ2Fqi0pLEUUTFJW684z0tGO9pg14zA5WLOxw9/eDkHQCloxOUaic4ELh0UMOg11JllMCndmoc+ukJ6KbG6D/99BRc/YLgFhAKj+AoODq7SerVcraRqrgDnehvuENL2UVm5MFNFFvLweZEfxfqSs4gPrsAobEEYEEVWhWrxNaUXoSDgxJZeSSsiHFJ5lyUb10+gcDgcCSmZDEQS92WFfT8D+9/D8+9+HcWbskNtXcxOjqEFSzc8vHASgW6u9qpQr561ToJ3BK7RNarr69BY1M9dmx7QoBbzj2blgsS5dFgbSiBQ5JBubmpCa5urggPj6A1QT09PeHj7Qtn54dfB1MKsjK2mjXk4vW4381jbodHhrH3g/ex6+ln6Hlbg1uxQbaWLstcfWR5jXfLNXe5JYmZiPIdGcnUULUAW7pM6Hyt2c85X24RcPLryiSK6uzuRHFxMZ4jmYHF2/C/Cwut2Q9uuTWwtabW1tbXofpeFZ54bKc81Mq1g8wQwG71mrWh1kCfG6udOHUC8bHxSExMlH02LBJJsRuS2sc//N4PrILtXz76C1av3YjY+Ig5bx9ZQTvxiMDWYwFs7bpB/xVXClibgsHJYWo4lOEeUKmUcIrx5mvaair64JYaCEdvF2rEiDvy2OU2+K1MgJqAInVRVmGyth8qRzV80qMkJX76rtTANzES3hEhVLUlUEsAdnZgHH2365C8eRX9m8mMzJT+IartrU+OYMnOLXAhoCwq+cOptmN9A2gsrUDhE9v4urYWSaRYwO1v70RDRSWKnthhkRmZc0EmHbZcxuTKklKYDAbkLi98wHhbpjchnUlzfQPqq+9hMwvMwjdcLIdZPC23JQ+lcvG2YuxjDPG+v3yAovUbEUxSsItgUYBM8X7E7jbyBtNaEqnbZbcwNDSIdRs3WVFtLVU6c+NgDrfWwVYee60Z/If2zj2gYmvT2MmcpJyRsgW28xEAxOuW37yJ1pYmrF63ntZB5n6qq+6it6cHa83KDViFQ5lrsAa29A5agUnb9509iNxgRXR88YSKtfs/J9zaNXBYANuH9n79je2IgO3//n//H0xOTWHl8lV0DP70U8/j3fffxrPPvISDh0ipn2Xo7e/D8MgIYhPSceXaBaxc/ziqq25jZHQYqYtXoKOzFQ33KpG1ags0BhPam2owMzuNoIQsvuzPjcMfIKZgM5y9A/k4W6LmTo0Moq+uDJrJMbgFRcGNwKh3ANV7mLq2PE5YbV2xu61Jp4FmqBfaiWGMdzTA0cUdnmEx8I5MhEpFVFdOPRU+J/s7Mdh4B04u7ojOKoCjk7MAqWwJnpbyyzDodEgvWM9CrRBf29dSh87GaqzYTBJHieBWAdRUlkDt4ID07GWCKkxOwmjA8YMf4slnXrGA2/qaOxgfH8HywiKRhxYDxjX3hO84oFUohVq33d0dOPjlfuza9QJCg8Osq7dczg2RakXsX0dnBwYG+tHX34uR0RGoVWqacIooouTT3d0DWg0zSCeeVOR3eo9MJqZEnwlQq1VwcXWDl4cnAgIDERgQYAbIZr2bWUfGwak4JwefyIfteAXQZR6L/Qf2IzQkBCtXrLIeVyl+gkT9rzUl1RrEkeU6nQ6/+d2/46c/+pksyJFDMdtbAVt+BSuPtahNLMYZZoaH/Dk8PAziHvvGK288ANSKjyRVrbmztGgTkixudgYffvQhPTb3tkqTbpm1gdWJVisDFJmBkdWxlkwm7Y8/+QhFRWsQFhomeTaE+Fp23MnH3ypAgDgoMBC5Obl8Eilh/GPCP//bv+L7P/gJvH3d7Or1F8B2wRXZrgdFvFLAGgK2Q1SxVWX7A43jcCbuyComO7KpZwrQmuCe7A8HukyN2dYxGmPrkRDMgK2DCtCYaKxtyNoMRsllVdvZ3jFMNvUhqmgxVW0ZVZaB24ajxYhZkQsPP18ebDnVtr+mEbrJGSTk5/LfUbWWKrjMv/KjZxCfk4XAiHBRsgq5MkAKlJ+/hOCICEQnJVq4JFuDWm75hSPHkbYoC6GRETbibTmwFL/olss4g3Pr2nXavovy8qRqMDd2Z3sOieorclUW50SWjc2FAgN9vbh45gye2fMi3au4j+ON20NQbY0GA377y/8PP/6/fv7I3JHNr2/eL4KtDeaEWsbS2oLL+YCt7H4eYkZkbv8Xzp6BwWCgExJyP+dOnaLx4KlpaRZfW+FSO9YT3g+5Y1rbr7kxltxvmR2Jp3ms3Vp7r8GWPm6vYjuhezTJozzUc6vJD/Vd+W+yMwK2P/vJj+Hj7QN//wAsX74KjiontLa3IzklHZevXMT2Hbvw7p9/T8vVXLp8FiERsfAKDMWxLz/Ghp3fpmrs6UN/Rd6mZ6CHAndKLwEOakSkL6XfaXR6lJ/ch4Q1O6FXOAhQqzeiq7IYMxMj8E1aDCe/EB5kmVq2TG9knjxKfGu4Z5qpP8tYE+ZT+Fs72o/p3lZMdDTAPSgSvtHJcKOux6yyKgLdsc4mDDRUIjg+HUExnHuxkBxqrK+TZk/OLNwIdy8fVp0FVEoFpkYGcO/GRRRu3AlHR7Wg3CqAxqpbMOh1yM4tFFyM2aRTX322FzuftYTblsYaNDfVYetWkluDm7Bm4Lah/h76+rpRtJpJKMWFItHfWRA/evRLek8LC0gVA2YdoW1E9p0DATO3X87YajWzmJqehk6rhVang0GvZ2OhTXS8ZTIa6UQ7VVJJwi6tFjqtDkaYMNDfh77+PgwM9sPLyxshQSGIjopGTEwsn51ZCGSSujgJdt6Ka7IMwFy9dhUtrc3UNZmDY35rG6Ao97qb2zpztbaq6i66e3qwfh2pY8qsbU+CJGt2kT8Hm+cpnzBJo9GA1Kp97duv2Q+1FscxtyS2wVbcHl8c+gKLc3KpYi5uBzHYW7aniOzNGsW8jaxP4FpzW5d6JpAj/fZ3v8Ebr78JF2cXvq/gJ0ssXJIV6O3txfFTx/EqSWDKPmsc1A8MDeDEqZN4atfz8LCznI5u8tEotmr3BcX2b9achz62CD2eU8RHCCofZygaxuAY4wW1pzOFWweTArO3++FdEEHdhohiC50S03d64bciQciO7KDGyM1WeKdGwiXAmwIwV7+263QFIlcvhrObGwVeosAqlSqMt/RgdngMUXk5DLwSuKbQyvxeceA48p7eBpWKGDsCtUKGZGIQJgeH0XyrEnnbmIyIcomkmBp8DOwe2/sRHnvlRau1bLmkEozLleCaTAzPgXffxzOvvQKlknwrHghwf0lL/pgbBx5QeWAFTh06jPTsbETExIjAU+rmzAzgRcN0G/G2ZhhNO6FzJ04gIioaSampErh9UNVW3GGS38+cJMeJQnJqqnXV1qyXNe90zScX5cBDWGb5rfn2D+2lnBNsWeNs44APD2xtYdbcV8xtTZRakvlxSV6ezY1uXL+G3u4ePLaTuEkJP/ZCofx6DyfO1hbc2npOzC/Y2rXYmqjgJjJMdroiT+gMc9+cr2END7XD17DXhV2OjU/j17/4BS0Dc+rsaTz91LNUqcjPX4WrV68gJTUTw6OjGBoaRmxyJs6dO46iTU/h1ImDSMxaCnefQNy6foGCrm9YHGrvlAIqRwTEpVGldnxiFNVXTiBxzVOMOzJJHmUyoa+2HMOttfBLWwqXoBihji2bPIrW0wWgn52BZnIURu0MTAY9THotRQjiJq0k6qvaEQ7EZdnNmwIdY8VYiKM1aFnoY+vRTnc3Y7y9FkoHB/hGJsE7LJZ3D+aSSJHPvns3oZsaR2TGMrh6eFK7y7geK6Aw6XH3wmFExKcjLD6Vd0km7skw6FFy4jPkrtzE1LtlXY9pcqia2zS+NmvJch56qYcZTDhygMDtqxbKbW9XO3p7O7Asb4UF3La1NqGurhpbtjwurXMrgtiyshvo7GrH9q2Pw4nNU8EBKN9ObMchrU9v7vlkb09p9k6JYJmUgCHQ2dbWStVg4lmTkpyK1JRUOh6xCbjsQIQ7C4nd586f7UiJ4kxqmRLgTElKFoGeJTGKr8qin5SoioLx5Bbv/cv72L5lO/z8/fmLlgdbEcCJmsdWv2zV3piPPdi/333/Hbz0wrfg5MxADDduE4+7JIo3721k+75yNXy505ZTaxsa61HX2ICtG7daqrW26vjaUrLn6JolZ21eh1mSFZsdTSqA8clxfPnll/jWi9+yW7X9dN8nWLtmLYKDggVFxQScu3AOXt7eSM3IngfYah+JwVG7Oz6S41qMT0wLiu28b0Twlkz0hekAZwda4kY5MAsHnQmO8T6saquErnoIzpHetK4tAVuiyE5U9MAzIQjOgd483Gq6xqCb0MI/hykLwKm2Y/c6aXKN4KwkHmoZeFWh+tOTyHpmC9SOThLVlgBu1+17dLu4xZm0A2eUWiHelvxddb4YQZERiEiKZ2vdEihlYmyZWB8ha3JrdS1G+wewdO1qui9riaOkgMuA5UBXN6orbmP9ju2y8bZC0gVxailxVmQRoIo6lPd++zvsefMNWuJAGKjbgtu54m1FEAwFNLMz2PvWn/DdH/1EArbizloKzswjZA6u3IPFxVWYr9PV0YErly7guT3sjC+7gXQ/0lz7VsGW3bk10yG+QquQcp/jCYsXaE6oFYzvf45i++Bge/XKZYwOD2PrY4/b1V9cv1qM2upqbN6+g2bA5p8FO7a2NtCgt9jKPZJbbLFMPCiTOQ/p+nM/DOZrzAW13F23F2wnHxHYui+ArR1P6fxXIXVsP/3wr1hesBKnzpzCmjXrcfzkcaxbuwVfHTuEp59+iaq1T+5i1NrAsBho9Hp0drYhY9laNNRXY6C/G0m5q9HV2YrW+rtIKthEAXawrxsNty4gcd0uaA1MjG1/UzUGm6vgHp4Ar/gsvo4tgV2izGrGRzDT3w7N6AA0IwNEnoTaKwAKBwcoSHkaBzUUKjWM2lmYSIwtqV07NgTD9DgFXBKPS//5BdP4Xuqqy0It54JM+XNyDKONt6EdG4R/QjZ8IxMlgEtsLjmHjrKLCIhORHhyjqS0D4HcloorUBiNSM1bQ4GUA18CszdOfYaEtFwai8zE2zLuyV3NNRjs6cSyVRslcEvAd/9ffofnX/oe1CqSv4Oz+wCJuSVZpfPzV1rAbV9fF86fO4k9e5gsy5wqK86YPDQ8gOPHv8KS3Dykp2VaxN5yapS4H+PGAeb20Z5O01ZfyRllsk5rawvu1VajpuYeMtIzsSh7EZvfQmQZzaDVmmuyedwtOQ4pB6RSq6hrso+3t+TlkMTYkmPY6CjN427Jqgd5hZJk/hU2ls+sLNhWa2+o+PDzhdqDhw5iSe4SXi19WFDLnLU0GxYHtuI2+XeihL4qjP14F2QZqOWnzm2NR8y+k0KsZQsK1yuv3vITNgqgrPwWRkZGsX7dertU29LSG9DqtFhJ8nSQF8QEkARZU9NTtJTQjN5oN9g21zTOv4N+CFvEpsQ/hL08+C4WkkfdRxtG7s5Hx1QvEOoKBXE1JtO9VcNwXhbKlP1RKWEa0sA0PAuPzCA+O7K+bwb6kVn45EQJSaSUavSeuIPIJ/LoMpJAiqi2Jq0JXafKkbSziCq1DNQyLsn9lQ1QOTohNDOZj6/lVFuFEbh76DSWPrOddz9mXJGJAWNA16jT48rHX2Djq7t5xVYo+8NCrSh+9urhE8hangffgECJS/Jc7sjkJa8qvQm93oAlhQV8vC0fQC+Jg7UNt0yHwqwzOjyC4198gRdee02UTIoxi+L1REtEdXXNlvLGTDBwZC/Xiy/T9s4rXM7ZR+aT7/nsj7W1BrZkV++99Sc8vutp+PsJM7HmgCEdBEgfWLmO2DbkWH47V2c+71fkIYDtfNRaa6b8YcTXkvPo6uzExXNn8cJL35pXU8zOzuKjD97HjieeRCCZhWV/5kZGuURg7PM5D7C1GCjOAbbS9e05y/k0h+B2vgC282m3b866k1MaVNy4ReNn3T080dPTBx9fP/T2E9dRX8xqdDQTckRsMs6dPY71O57HgU/ewaqtz2FKo8GVM4dQsO15zGi0OP/lB1j2+MvQGIzobmtEX1sDIvPW079JMqmWkjNQuHrAJ3kJBVoCsySBlF6nx2hLFaY6Gym8OgZGwtE/DCpXT8DBcY7gCOZekJ7fODMB/eQIdCN90I700hfWyScIrsHRcPELZuykCHIJaJpmpzDWdAczg10ISllioeASWB1uqcZQUxVilxTB2z+ETxxF3I9HO1vQcucqFq99grYfnzhKoUBt6QW4eXgiMWOJRNXtIW3T2YJ8Fm75TMtKBQ789Y94+vnX4UTKELJqLwHW5oZ7GBzow4qVTO1cAdIVmKRK1Cd46qndNOETA7Ui12PWRp8/f4qWP1leuBr+rMoozpgslPRj21TU3ViA4ByvgGxPJdmfqOclccg11bhVdpPG8eYtWYbY2FhZBdcCYsRzzDKuyQ2Njbhw6TyCAoOwNHcpwtg8HeIZb/G1yQbiiGxnW3sbzp0/i9ycJcjMyJQ+mzL1wQWF0/YUo83mNFckRWOea9evUXjPW8J4LNkDtYwYYI8tmTsz9KXiy1CrVFiWl0+Pbx5XK1V4hRXMxxKWrWO+xHwEJjXcwhBQOinCLRfD7Sf7PsaKwhWIjIyygFvxemRb4mp+ufgSnn/mOdq6ZRXlGBwcwAY2Z8d8wHbvO+8/EsPx7ddffiTHNT/oAtjex22I//46NN25B6R6A2oSQ6uEom4M6ihPOPq6UvdjArgzxZ3wXhPNJJBia9qOFrcjcE0Sr+IqVSqMV3bBJdAbnnEkWRRRbRm47b9WB5/4CHiFB7FgSxJJOcCo0aP9wi2kbCeGR2mh2rbdqISblyci0pMl8bVi1+TmsjvU8CYtXSyCW6laS40glJgcGUXJiTPYsudZxpCZ17WlsUZsXI7od269c4eOIjE9BTGJCZJ4W86Zi+sgmT1zQwcBZOWWkURSJINx0aZNNjMlS5RV2dJAcmouY6z3/umP2PPq6zQ+R8Sz0nPk+jwZaBB3j9bgtuLWLUxMjmNV0Rrr7shmhGJuJszB1JoZEXXDFk/9Q4PbhwC1ElA1sznWTLaca5dobtvieu01/WS9t373W7z23e9R74z7+fm3f/o/+On/+J9mkzC29yR/Dx/QHVn0HNkaagjf2TMgsadF+LlzZkBipyvy1CNSbN0WFFt7buq815ma1sI4rcPv/vg7vLjnZez98H3s3v0q/uN3v8Krr/09Pvzrn7HjyT04f/EUQqMSMDQ8jKmZWcRmLMHZY/uRnr8BDq7uKDl7GNHZBVC7+6C7rYGCW/jiIh5q6859Aa+kxXD0D2eBFtDOTGC0sQqTXY1wi8mAU3AMlE5uMLLJopiERFbK15q9BuRPSYwtEVf0Omj6WqDpa4VhagxuYQlwi0ikpYFoXVo2qzEFXM00RupuQT85huD0fLj7BfFgSeDWoJlEZ9lFePoF0+zJZBkBW/LPqJlB9eUjiM/IQ1BErETZba26Cb1Wg8ylqyTJqIb7ulB3+zrWbtkldYVWKnD08w+wdcezcHEhlRkYiCXn2NJYh66OZqxdv1UCtwycmvDxx+9h7ZoNiIiMZm2/KNSIhV2SWOpG6TW4uriisGAVfHwYJZNTeiVmzQZQzfWg2QJhedvILO3s6sCdu5WYnJzA2tVraeIpYXJcOCozPrGt0LGr0I2IKlxaVkpHMyQWND4uHi6sd5lNxmON0vDoCG6UlmBoaAg7tj0GD3cPiwkXWbWWN5z2WjezlrVxD0hJmsHBIWzcsJEdoXEDHxG7p2lVAAAgAElEQVS4Sp3L2CRI9toQ5v3jfiRqrQl0woso1y/uJpPLQgkkZj0BisUqraQVzPJtTE1NobWtBV3dXRgaHIRWq4NWr4WXpxdcXV3o5ERYWARCQ5gkonKu67YmPjho1em02PvhXrz52ptWXZLFSaXefvdtZGVl0czx4WHhWLOqiNfo5wO2hqlH44rs4LbgijxXf/Vf9vvkf9yCustlQIoP4KqCUqWEw5AWyhkDnBJ9KeiSQbC+YRSOvi5wifCCyoHJjkxq2jp6u8E9NpBPImWc0GKirg/Bq9KZmrYUbNWY7RnFZHM/IlfnUBdkArVcvG3XtTvwjYmET2SohWpr1OhQfewC8kSqrTSJFFF+FTj/wT6sef5pOLm48C7IzAwt909wSa4pvQVnJ2ckLcoSxdsy30uhlqvjJwXdQ3/5CJueehLuHh6ieFspyIqVXNpdimb7+DXZ3oR8XDx1GoFBQUjLzrY/mZTNeFsx+ilQfacSPV1d2LBlq6QzN088xXd7ZnBrD9jqdTr84Te/xg9/9o8PLYmUZMAgeovmApYHhltzw2T1DZaCjtxq81FsZc24jfq19pp9st6Jo0dpGZ+MrKz77o8a6uvQVN+ATdu28fuYy9xbA1vxAMr8hOS2sVgmMwFjez9znak9zSLVJuwGW/2jibF1U93fBIY9LfHfeZ2pGR0aq+toeZ+EhGS0trXBx8cfQyMjcHRygUZvREBQOK4Un8eaLU/jo72/w9bn3kRZ6RXq+hsan4F7lSUwKVQISsrCYG8nmqtuIbpwKwO1BgNqz32BgJwiwNWLxt0SlXakrhzTg91wCYmFU3gijEaSAZkZSIszIRO41c9MwqTTAkYSY6tnBucqR+qSrFA50Zq50sRIQowtdUUm4KrXQNvbipmOWnreHtGpcAsMZ1x+2SROxM7qJ0Yw1lABlZMLwrK5WFgmORT5fqTlHkY7GpCycjtVVclyNbuPhpIz8PILRHTKIjYmlykL1N/WiN62euSt2Q6VKGPyxMgAqm5ewpotuyTqLDnOoX3vYseTu+FK6vQS28+6Mre3NqK9tQFr125lKkCw18e5IR8/dhCxsXHISF9kqdqK4o0bmupx904FXFxcsCyvEAEBAbxnlTXbyVKRFKKsvDwCYFiuIHZzlthFUbWE9o42XLh4jpYeKlpZRFVJc8C1BTFi6BV7V3V1d+Pu3UrU1NUgMCAQyUnJCA0ORXBwsGSSlLNHDY0NaG5uQnNLM1atXIXU5DQuTZTkwuSglvIdC3n31cfYgNqGpgbU19dj2xYz+yVWYx8IaqVuyFLllTHk+w7sx5LFuTQRmPgyuUhKJvmbvEorHm3crqwAidMdGBigz25IcCh8fHwp0BqMBurRMTg0ABKj3dTcAALApOZyYX4hzco958SHuAoHazrLym9idHSMcUlmJ3a4sDZ+zMtuR8713IWzyEzLQFCw4OVFLm1eYDv9iMDWdQFs7+v9+6+wUfLPNqPuThXgqATC3aFQMwmcUD4I58IwXrFVTOigb5+AZ24ok1TKQQ3TtBEz9UPwLYjjVVsCs4OX6+G/JBEu/l58AikCt52nyhBZlAsnWsKHyYxM4HZ2YAwD1c1IWFcoq9p2lFXBzdMTYSkJfD1bLjMyUxdXgb7GVgy2dWHxBibNv2wiKZE6e+ngESxZuxrePj483Ap1bNnkUZIkUlwyDVIoXoNTnx/Eky8ytWg5g2Ou0kqTSswNt/ve34sN27fDn862CvNqHKJaZj+2rtDy27B7In9//ulHWLthM3z9/R+CaiuPlqQeakxcHFLT07/WJFJS4LWFTmYBw3O9dHaqqsJubIOt+Uwrt53daq0kY6TlVvaALVmHuCBfOn8Oz7/4ku0WMN+hTNPu//gjLCtcjqjoaLsGa9J7JT78A6q2onOzhazS7x4Ebi3vtb1gO/2IwNZ1AWzneuPv63sCtscPHaVlMErLbiIvbwVOnDqGTZufxKHDn9FMyEePfI6E9Bx093TTOFkP/1DU3LuNtIINNL62pvw60lbvwMjIMBorriIqfxNmDUbMaLWoPv4hIje8AD0c6Lbj/V0YvlsMl/AkuMRkwEhcko0kvtbEwK1BB81AJ3RjA9CPDVClVUlckskPibFVqqBQO8E4y8AuSSJFfndw94bK3QcqT3+ofYPh4OQqijdl4JaDXP1IL2Z7m2icrXfSYrj4h0pUVgKS050NGGu+i+C0PHgGRzKgygKwdnwYnWXnEZ29HL7B4RRsSZ16lRLoqCqFUadFyhKi0ArZlEf6OtBadYtmTOZcjInqq5mexM3LJ7B+2zMWcEsU8eWrN8Pby5sFcOY6+ro7UFNdgU2bSNIoFm5ZyCW2+tbNqxgfH8WGDSSPhnDt1K5LXJSBxsZ6VFffgdFkRGZGNgUGib0XJYk07yQteyCBpKz1TnLAK5e0irP7tXU1OHnqGFauWI3cxbnsMy7tb5l92q/eci9KR0cHGpsb0dHRDhKiMjM7Q8OPSEylZlZDFdkA/wCkpaYjNTlVFmhtcaswEWyPdTN7feWgljVAo6OjIPVzv/PGd/mNxO0qjONE+5RxP+bGYWJoFZ+FOL7WXK1taGpETe09bNuy3SrU0rZhk8Hx+xVNbpO684ePHUZcTByys7IREBDE7Iv/n3mbMGNJrVaD+oZ6CpvLC1bY91zIwO0Hf30fWzdvo0owL9jwGdWF7NOcu76csj8fsCWeMY/iR+mqfhSHtTjmgivyfdyG5J9uRl1nPdAzDaT6kClWWtZH2TwBdbA71P4u1BWZqLaasl545oRC5epIXZJJqZ/Jsm54ZoTDydudTyI12z0O/dgsAnITeFdkArYTTX0wzegQtIi4FTMxtkr2s/1iGUIXpcDd30+k2jLZkZUGIyq+OI1lzz8mxNqalf4hKi4p/5O0LBd+QSR+Vij7I6i2QkKpmfEJ6pK88bmnGZXWzCWZd1GWcUcmL3N3Sxua7tVgzbatc8bbMv2qYFTMnJR5hZaUBdi/9wPseeONecXb2gu8rU2NqKupoUmAxLDxMFXb7s4OlFy7ip3PPPvIVVsJUNnDMzJ21LZpfbhqrTW79KDxteQsP/rLXmzetgN+fkKtWkl3YetCzdpueHCQqr8vfEuI052reeer2toc4IlP3A7VVvIc2BUjZa0jtYwkWwDb+zA634BNCNj+5t9+SWvXfnXsKyxZUgiioISGRWJgeASBIZGoqLiJZUVb8cWn72Hdzm/h8pkjiEhdDCdvf1w++jEy1jwBo1KNsjOfI2LJOhid3DA6PIiWkpOIWLOL1rWliaOqS6GZGIFnWgFMahceaAnYznQ2QDPQAf1YP1S+oXDwi4DCxQNKF0+YlHOo9SRT8vQYjNPjME4MgIArjdX1j4A6IAyO3qS0Dwd4gsppnBzGVPNtOKgc4RGdDBcftgQQC7AqkwkDFRfh6OqGkPR8ODgImZEJ5LaXnIR3YBjCk7J5t2QCqwNttRjv60RGwUY+aRRZPjkygMbKayhY94QEbnWz0yg5/xU27HieVWcFID5/6gvkLl2BwAAmRphmbiaxvUP9uH71PLZt3wVHNcn3IZQDItmhu7racOrkV9iwYRtiouPk1VsedIHOznbcu1eFnp4uBAWHIDwsAokJiVQx5vsdGSqV9G82J+jEk9fCi2MzYZUIRi5eOoeOznasWlGEaDoRyaKvuN9kz487Tf50zCDR8jKYNaenpzE9NQWj0UCVQDKhQMZUtqyjNaVWCmjzBFsZqKX3gL2gP739R7z28utQOzLA8iBQy90JOZjkwFZOrX37vXew+7ndVPG3Fldrvr24HUtKS9De3oZtW3bA2cXFLHmXpX2Slx+Aq9cuY2h4CMuW5lPV3dw9WTLpwT1PbPsSFfj0mdPY/fxuC5dkiWrLPVcyxnwBbO03ggtga39b8Wsm/2Qz6gaagKZxIM4T8FRDSRTZUR2Uo1o4pfix9WsdYGgbh4OjCm6xvnQZUW21nZOAxgivjHCJatt9pBJRTy6DSk3cjphat+Sz8dNLSN29QQK1BHKnu4Yw3NSJ+DX5EtWWlgFSKNFZXgW12gnRi9ItVFsukdTE4BBqi0ux/CkCwEyMLW+4eHAVMibXl1dCNzuL7OUFkhJA5qV+zGNxOegtL75GkzZkL8sTuSRzGMs6enB1vtiu1HyWVABeBndbG5twr7ISW3fulI23FWZpublZ8ayrMPsqtx759tO/7MWaDZsQHBpqU7WVdvoig8r9KnGtln7/3ttv4fGnn5IkkTKHC/OMuOZ9n2BcmX3PDTrW0Ur2G/OF84ZaAUNtmV9h9ln6cs5fsb1/tfbksWMIDQtDZna2ZQ8xn7GDqM1IuaDh4SFJDdz5w610kGV+cnPfc+mDYf/x51rT/EysD9HsBduZR6TYuiwotvdhFefeZHJai9NHT0BvMMLJyZW6IickZ6C4+CJWr9uOM2ePI3vpKjQ01lH3XAdXDzQ11CC1YCNKLx2Df3QyrQ1bc+M8nPxD4BYSh6G+TnTVlCF42RbWHdmIzqtH4BSaAHVIPFVnCczqZqYx3V4NTW8bHHxDoQqMgdIzgHFH5sv+kHA99uWWedy5ReIkSBRGZiZgHOuDrr8VpplxOAXHwikoGmpPPz7+lHFDBvSkzi0BXCcXeMVmwNnbXwKeM73NGK0tQ0ByDnwiE/n4WrVSgaGGSmhG+pFUsBFqkseDxN0qFJga7kVdyVla25eEC3EqrXZqHKWnD2Dtk9+GE6nMwLo4kyzPpw/uxWPPvgFHtZp3PSZ2/8yx/cjJLaSTDZwKTOBWOzuDrw7+FRs3PUGzCUtq3bIK9cmTh2lCqRUr1kqyJgvqrShvBjvg7+xuR2NDHXp6e2jbx0THIiQkjLphurOgKzKfUrJiv5DcKtEf0uWSSFxJdnmJ6sgCyfDwMM5dOEPFiaJVa9jJTfvVW/PkWGJYnPtNMVtDJlGUeA2pvZyfcTIfU4jP8/SZU7Q28eKcxfJQyw4yhPfCctQht386CjA7Td6lmB0icLh54dJF6oZvkTDKPK5WJs62q7MLx04eQVpqBvKXFbJNJgJZe5qKvThu5NjR1YGLF88hKCgYa4vWsvWRxQKMaGwpUmTJwS9evgA3F1fk5S2bW7V9QLAlYtij+FG4LCi2j6LdH8oxk3+0iQHbEQ0z+xPtQVVb4m6MmwNwzg+hcRrkb8WsAbraYXjlCzVtlSYHjF5uRdDmND6pFIHYiepeOHq6wjs5glVtmSRSQ7db4OThBv+kGF61pbG2Sgc0HL2CmBVL4O5HCrhzdW2ZTwK31977DKtfe0GaRIr9joPbO2cvIzQ2GmEJcRJ3ZIl7skidLT58DJkFy+AfxBg4ucRRRM2lrkkyiaVOHzyExfn5CA4joCgM1CWq7DzhtrT4KlxdXZGRkyOBW/FghOl2uS7K0iVZ6lYjrNnV0Y4bxVfw1PO7JcD4MFXbils3qXvd2g0braq2VuOFbBh426BjG1bmgzL22Ai+sLyNt1CyH3PjJ7Od7HEfIL6W7K/sZinGx8dRtHadfWRtT6/CNubZUydphmQxMNtqZ/nvHtAdWTT+sOceS9eZawvpHZG7Pwtga88D881bZ2JKg/7Ofvz5/Xexa9dufH7wMyxfsY6WxYhJSENzWwtSM5fhzOlDKNz0NE4c+giZK7eiraWeuhqHJi9GW/1djI+NIDB9GUaHB9BafhnBBduhMZowOdyPrqtH4b10C+DmC73RCIPegMmGW9CNDcIhIBqq0CSmjq1RcF1kkkYxA16ixJL6tTBRX2VmFojE2KqdoFA7AzSMh00excIZ73JLbpleA8NwJwy9jbQfdw5LpJBNkzJRF14mhtUw2o+pxjJaJsgnfRn15uJK+BBgHb57FTDoEJ67BgRqifsxcUMmJYNarh5Dyqpt8PILotuQ7wmsVpzaj6wVm+ETEMK7M5OkVjfPfI6lRdvh4eHFZ1lWwogTn/0Zmx7fDTc3D17tJed37cIxRMXGIy4uRZI4ioD50cOfIiMzFwnxpAyhoEhzbVBTcxc3S69i48ZtCA+LlM2YzK3Ld0O0S1FgaGgAHR2t6OrqRF9fD216sg9PTy/4+fkjwD+QxkOS5JxyP8xuhP7JHHj5v81VVR5eRGMCFkpaWltw8dJ5xETH0DhLJ5oEyjbgWkxAW6M7zm6bdam2lFkLoLXqS2tP/yG4wHJrc6dKwP7AwQM06ZEEV63F1dpwP5Y7E2miKIHcBUXWhJGRERw9fhQvPLeHcTRmjYlcXC3XDNx0KimTMzI8hKKidfD08JJsz7Kzddtudj+E50bQaMnk2/ETR1G0ei11bRY/E9xzyD7WQlwtgD+9/Qe8uPsleHp48Mm1mPeBHZ1y7vsyjTZDQjO8mBKXc/0sgO1CHdu5nhGL75N/uBF1/U3M8uYJINsPUDMJo5TtU1B5OEId5sGX/tGW98M1LRBO3s586Z+pqgG4hvnANcyXSSKlUsM0a8BYRQdC12bxai3JkGyc0aH/ei1iNuYJcbbUJVmFyY4BTHYPILqQyW7MqbXcZ29VPYgVj8nJkqi2HNSST/3MLK5/fgTrv/28oNhSkBWSR4nr2+qmZ3Husy+x/eXdbFkDIeEUp8yKXZWZ31n3LBaQ3//lb/DaP/xYiLflsiVynT2fwdjMCZkbTEjcg5h1Pv/wIxRt2oiAICZ+QoSwTMcjNmCcq7PMMiawn9ua2dOJw4eQuWgRIqKiefAU7JVI9RVZAXH/KO0cxftmL9hkwq9/8a/4yc9/brc7ssTgyCm05gkdRE+yFfMvedbnQhhZA2HzbbLlaMVs+PDUWvmzmwvA29vbUXzpIq0tLPmZa0N7ehG2QY8dPoS0jExE0zITzI+1trYGtnQbKxvZvS/+2Z/75G2dh9md43dmVWG3Myvy3Ge1sMbfUgsQV+TykjK0tnVQ912V2gm9vX0Ij07A9ZJiFK7ZhtLSYvgER2BGZwCpieoTHofayhtIKNiE4cE+NN0tRdSyTRgdHUFjyWmErXyCxtiO9XbQZEue2WugMxihM5gw2XoPs131UIUmQhWSyAItF2cLGCdHYRjphnFqBMRVmJTwgbs/816RnBnkn4Ma0JAYWw2NsTUZDVC6eUPp5gOlhx9U3oFMcin2feQneUmm5OkRGHoaYRjtgVNwHFxjM/kEUhzg6gc6MF5dDK/kXHhGpfClegjcaga7MVpzA6GLVsLDN4hJHsVCbsuVrxAYk4LgmGReuSXf11w9jpCoBIRGJzH7Yl2drx/7BIsK18PPP5ivdUu+v3h0H5auWA8/v0De9ZjY+vKSCxQo0zNz+UzJXIztjWsXqUvysmUrhaRS3EQ2ieXVzODMmWPw8fGh6zg7OzFhRTITAlySKK5/4Qf4AKamJjEwNIDBwX4MDw1iYmIC3T2d8PLyhp+vH/z8AuDr6wd/9lOpVFr0ibKwK7aLMmV75BTc6nvVOH/xLBZl5WDlipWiUQrTeXMQI4wcRMtFvfwcjGvX6yyw7IMYJdvu2qQub86ixawr9twuyOLavrZsE7XKFhPWQpZjJuEx8/dHn36EFYUrERERIYJaxq6LIVcCtSYTPvzoAyQnpyB3MSlLZJ4xWRhkyJZbkrkD/OhOdJ+5EdyVq5doCaKCfKIIyyu3/LiT1Jbu6sDV61fx7K7nBOC1EmtrPjaYD9ja9SB9g1dacEW+j5ub/Pcs2JK0g22TQIQb4O3EuCPPGqFon4RTdiAbZ6uEqWcKCj3gnuRH4Ze4IxtGtNB2TsBnWQyj2rJwO3ytCd7pUXAP9qNJopjyPyr0FlfDNy4CXpEk/oVNIsV+1n55AYkblsPVi9S2c6Czu1S9ZWNqSz74HMtfehpqtaMk3pbLlEwMVmNJOZxcnJGQk826GZm7JAuQS9ZvvlOFyZEx5BYJhdz5GFtRSSC5ZWT7ge5e3Cm9iY1PPs67JHNAKQbS+SSTmp2exr739uLl73/PZrytBFrlsiTzSSyENQf7+3Dq6BHseeU1AUREBlLs6CQYOQFYJJArWkG8/OzJEwgKCaFqniwUi+DcGhBJAGQOcLEHbs1fD/H+52dW54bahwu283dD1up0eOcPv8ff/fBHDx9qzW7Y/o//irUbNsE/IMAm2FqH3oen2toCa3FD2DPRIbmHMn0rvSsLYHsfVudvfxMCtu/88R0UFKzEgc/347HHn8VXRw8hY1EemlqbkZCWi2tXzmLZhifx5Sd/wvIdL+HaucOIzCyA2ssfV754F+lbXoLGBFSfPYCQwu3QmJQY7m7BWFu9ALV6E0bKz8Lk5g3H6GxJfK1eMwt9Vw30fS0AUWC9gqDwDILJ2R0mJ3fJoFs8Gcm3vmYamBkDpsegmB2DcaQbSmcP6t7s4B8JpZuX1E2XTbSka78LXXsVXKKz4BqbIQFcIkBO1ZfSOrc+aflwdHXnMyOT2Nv+0hPwCo1FQHw6D7cEcLtvX4Gzixsi05natWQZAdmmmxfg6e2P6JRs3p2YQO+dKycQFZ+KkIhYFnqZ0j6Xj+/HoqWrEBQcJkCsUoG7t4rpoD17cQFVdMXuxzVV5ejubMOWrTuFpFLi5FEKBRoaanDt6gVkZizC0qWF7IQB02/JqbZi7y2+uzS7CeTP0bERDA0N0rCO4eFBGvdIcm2oHR0RGRFFXaXDwyPh7uYuTq7M9LPmtldswzm7z9tNSwW35MZ1XC+5itWr1rAuuuxoRWxr2WOYPz9WJwetTEBbWLAHUmfN+g9+jCMs5wCMlBk6ePggXn/ldclYh7ETwoWKr08y/pnDUFh1Q+agFsC9mntoaW3F5o2b6TmIsx4LICsGXMby/Pt//BrPP7ubqvuSuFu6D0sXsLnGMNbUd+4xIp+Xiy8jKjIKMWw8tvg7ziWdmSxjWokkzCNx5VmZWZKyYfz4l1VtzXt8MoFnr2L7t28tHuwKFsD2Ptov+fsbGMVWrQTGdIDWwMTaEndkBwco7g7BMd0fKjcnpvSPEZgt64P3qigWdpkkUqOXWuG/OhFqV6LkMqqtpmcC2r4JBBakQOXAuCKThFG6oSmM1HYypX+4sj/ULUqF8eZuzI5MIDKPGDJOtRXckXurGmCY1SB+Gbct46bMZUFmPpU4885fseX1lxjl2YpayxSeZxTaa0dPITV3EQJCQ4T1RTO3cm7I3PYEWOsq72JyfBx5q1Zaj7dllVxu8C3MporxVyi50FBTg97Obqxcv87OeFv7XZLPnDiG2Lh4JCQlP5Bqa2Fc2WdwaHAAJ44cwZ6XX7ZbtTW3IRY2xYZqKwCNvchyHy8LY5b4DW0Zkkfthnz8yBHExMcjOSVFeqFzWb/5Ngvb3G///vd47bvfBVEabMGl1QHRf7JqOxfkztVM/PcLYDvfJ+YbsT4B29//5vdIz8xBW1sb1I5OcHTxRH1jHdJyClFRXoqwuFT09vdAawSULh7o6mhC9OIiVFw8Ct+4dDj6haLu2km4hidB5R+GobZ6jPW1wyN9JaPU6k0YvnEUDtHZgGeQoNJqNdC234W+vw2KsFTANwJGFXHrY4vX8kVs53qKedmRleQUwPQIFJODMPU1MUp0cBxUwfEWWYSJ7TN01ULbfgdu8blwDicuyqyqqlTANDWM8TuX4RGVDM+YdN49mADrZGMlDDMTiFi8mlFtWZAdbavFZH8Hkgs2CYquQoGO6lIoTSYkZBfQDMo0XpbA6pWTFGyj4ombMXhVt/zKKUTFJiM8KoaFYcZluqnmDsZHh5G/nImb5WKFacbknk7cuHoeK1dtQHBIqHC9ojEAsXXl5TdQX38PyclpyF3MxheaQ6QYtESJnAQYtQQwZomAVCRBT/9AHzo72kBcaTWaWURFRSMmJo4mtTLfl1gd5lVX0XiDW99cwTUZjbhy9TJqaqqxamUR0lLTrLgnS89P3Ml/3RbXdodhW609d/4sLX+zZMlS4ZTlYJ81WvOBWjoaEL1ilkmfGLX2z3v/jOefeQHOLs421VmyK2Z/Juz9y3t4ZtcLcHZy4rMdiyGYFYIlTWOpfgsDJtmxFdcOZs/r0WOHaX6UwvzlQtZscYyt6Hej0Yg/vPV7/P33/p5X+oW4fa4SiKVxJ4qtp52uyN8Ig/EAF7EAtvfReHEvrkDzTBcDtkS1rRsHFonckftmqHFwjPYWatrWjcA53BNO/m58EqnZplGoXJzgER8kJJFSqdF3qhrhW3KgpokgiGLLZEPuOncbIUvT4ObrzcMtU9vWAbUHLyB1exEcaU1aS9W2bN9RLH1qKxydnej3XJIoDmpJTG53TSNmxyeQUkBcnoU6tBzkimvcks7MoNXhzCcHsOOVF3nYtYi35cv/MPG23Kwvp+QWnz6L8MhIxKcyMMG5LHEGSw5khRkxAW6ZZUwXe/boMcQlJiI2MVGmvq0tl2SxSzHX+QvHmJwYw5f79+Ol196QdPhcB/gwVNsvP9uP3Lw8REYLLs9S6JGJizF7hiUdsngmWeZZF9b9Ok3tf7Zayxg68c9cw9WR0RF8+dln+PbrzL3lf+ba8D76D+7hqaqsxMDgAB/La+sOWINb7l0wPw1r+5Kb+OC2/TqfAMkdWQDb+31q/qa3IzG2l85fxb1797B02QocPLgfazY+gZIbxYhPX4y7d8qRWbAeJw99hILte6ibbFLhJrS31GFGo4Fv4mJ0NVZhenICHomLaZmZ3tuX4ZW7iULt7PgYhm4chWveY9DDkYmxnZ2BtqcB+u46KKIWweQfDQImNMhWArUGxuV4dorGtjISEVmH0J8TE2fr6AQ4ugpAS92VqRTDSZAwTY9CMdwO9NTBISyFqrgqDz/qssvViFWajNC1lMM4PgC3xKVw8g1ivmfXmWkqh2FiCH7Zq6F2dmHdjxXQDXXT8kWReZvg7u3Hg+x0fyf668qQunIHnBzVfMKpvoa70EyNISV3FYVkLoa39uYF+PgGIiY5k691SxXdGxfg5xeE+GSSbJKx1QR+ezqb0VhzB2s3kQzLnCcXA7mkZL+8pbkAACAASURBVNKViyfh4+OHpUuXW1VvjQY9ystLUVZWgry85UhJSYOHu4c0REgOGkTGj7PErKmXvAvSfpCxxGNjo+joakd3dyd6e7sRFxOPyKhoREXGMF2wqMOTlh3iXG9F4wEuPEnkjjo9NY2r16+gs7MDhQXLkZJMxjBSTxr+EOwJPsr+V2gwS7AVt99//OE/8Hdv/h2N+xYvl1NrJZP0dhoQccUCwaWYOTsCutdLrkOn12N5wXLmFRWprebKLQe1n+z7mMa7ErVeDLDieFzeBpkp33ImXu5S+GdEDvIVwLXrVzE5OYHNGzZL4dbs2SH7uXa9mFY3KcgvkKi24lhb4RllDrgAtvabvwWwtb+t+DVDilLR6zvDgC351zkFBLoA/s5QqByg0puA2lE4Lw3ha9piSAPjyCw8MoLoMqLOYtaEyco+BBQl8bG3ZPlkbR9Uzk7wSY3iXZEJjE61DkAzOIGw/AwaX8sot4xqO1zTCsOsDhFLMvk4W8YVmXFJ7q9pgmZiCokFuaybMlu31iyR1PXPvsKi9avg5e9vUduWUVulLsmtVTWYHp9A9vJ8ixJA1tyQzZcfeG8vtux8El6+PkJcrCTOVYBMATMFkJVb9vavfo3XfvhD2jkLtpEzOqw7lDiO1sIlWTRzJoq4PXviGEJCw5CRvehrUW1bGhtRXnYLTz17/6V/RGMB5pm1S7W12Oo+3gy5Tb4etVYCSqLDcobO/Ezm4tODBw4gIysL8YmJwqZzbSQ+Lvu7nbadvy+f/vVDrFhdhPCICNFzatmO1sDWfIAm3vK/EtzyTUl+0Zvg7sK8lws//31agGRFvnu7DucvnENMbCKGRkYxOjGBwPBYVFdXIj1vNe5UlMIjIAyTszM0jtYrKgm1ZZcRlb8VQ/096KwuRWDeZpBarUONVXBftBY6vRETXc2YbL4Dl5ytNL6WgO5MQymME8Mw+UUBQQkwcQmhSFKo2QmYxvuZfyS2lrgXO7pA4eLJjKSJXKlwABwcAM00THoNtQIk6zFcvaB09aYJqhSegYAzcXnl4JaNzVUoYRpsgWKkGwqTgSq46oBICeAqZsehbS6nGaDdkvKgUqt5ZZWot2OVF+CVuBge4fE83DqY9Bi6fQlewZHwj0ll4JYA5uwUmq8eRcKydfAWxeOOdLWgr/EuctY+Lrg3KxRoqb4JhdGI1EX5/HIC1vW3S6AwGZGVu5w/VwK3k2MjOHPsM2zYuhP+/kxJI3HZn8a6alRWlGDjpsfh5x8gAlzO9ViIsb1zp5xOYpBEUKkpGYgn9WzZjoyfJBZDp8iAiUHRfFLW0m2Ue7cUMOj1qKuvoRm3R0eGERoaRhNgxcUlMP2uHFTzECu4TzNWUuqiPDY2huKrl2lMcHp6BjLSMiwAl7euUtK1mlvB+hf29Re27ZAM2LIb9Pb14tSpk/jWS98W7JFoLCYZS4jHaHYaPjFTyqm1pKbvH976A374gx9bj6WVZEA2Yf+BfbQET0REpMhlWVrXljmu2QS7RDkW2tXa5AM/zuSfFc6Ms2KDAqisvE37GSaTtDDJIc6kzgkwv/jlv+DnP/sfVlVb/ullZxcWwNa+Z5++a6aF5FH2txa7ZvimLHRhGPB0ZMB2Sg9MaYF4L0DtQFVa1I7BMdoLal9nvqbtbHEnvFZHQ6UmcbYqCrcTpaSmbRic/TxY1dYRJq0RQ1cbEbFtCeOKzP4j6mzz58VI3LmaunERFZcBWwZw7350HIv2PM4cj82QTF2T2d9vfnwYebu201haLnkUr9iyrskTA8OoKS7Biqces8sdmcDu9WOnqUuyfwhT/85aqR/z5ZyrsmZmFgc/+BB7vvumzWRScjG4fAwDB6lsJ9DR3II7ZWXY/jRTc5c3SAznySwTOns+1oc3YML2M9NT+Oi9P+ONH/zQrOMXOjnBnLIHEz6kLsYiS8wbdADvvfUnPL7raZoJUtzJig25hRE3e4olnTM/w2j9UTcfJMz7pZDdwD6opTaH294MJq2xpezy+8yG3NzUhLKbN+lkAv9jB9TOtYo9tn56egrvv/M2vvcjYsitJ5Gy/t1DiLU127k95z2f58Pi3i6A7Xya7xuzLnFF/vjD/QgJi8KVKxdRuHozSImY9NwVaG6uR0RyNipvXUP6ym04se+PyHv8Vdw4/QWCswphcHRH1elPEbX+BarUdt44DZ+Cx6HVGzBaVwadZhbquKWMcjs6hJk7Z6CIzoHJL4YmfILJQLMcm3obYBpoZUDVwx9w84PCw49RZQn4iv0k5TL9EKWXKLsEcGfHYRobINIlFF4hUPiE0HhdmnSK1MPlElBpJoGeWiimhqGOyoTKP0ICuKahDmjrb8AlPgeu4YmMcsv+I7G3CoMW/tmroFYyJX6IK/LovRKqpoZlFvJ1bcny1usnERCZgGCSPIqF3umRftSXnEbhtt00zIgsJ1DaWVcJzfQEMpaslEJv7W1Mjg1haeF6XmXmIPbM0X1ITVuE2ATGlVkcd0ts47XLp2kiqrxlK3jXZSaLNJNAkg7w2b87O9tQW1tFMyAHB4ciPi4RsXEJgs0TJ3YS9VFi5VDSdUnsnHV4MxlNaGispZA7ODhAldzMzEUUtHnApTsWgYn4bxaEzQF3dGQEt8pKUVtfi8XZi7F4cS5cXIjCL02+ZNGXs2MWybjhft96mc5btj+3khm6prYGLS0t2Lplq9n4Rmhc6eQCOzay02iIsx5zRp9XZE3A4aOH6f1ITU214oLMxtWytv7AF/uxOCcX0VExDNSK4nTlEkxJxhr0CkWJq9g2F26H9KK45cynFTVfAXx56Assyl6EuJg4Zj3xBInIJZnU1yXu8kWrimyqtuzhMGMwLbgi2/leLICtjYY6cPQi9h06j1mNFutX5uKHrz1F147dlY+W9mYmaRQJYCFw2zAOZPnSv5Xk37CWJpJyjPOmZX8IbBpaxqHycIJrhCefRErXMwXjlAHeWaQcEBNnS/6N3mqDZ0Io3EL8hZhahQqjNR20zm1ACin9o+Khlqi2Q1XNcFCrEZyWaKbaMsmkBhtaoB2fQlweibVl4mr5WFtWuSV/110rhaefH6JSk0Wzsko4iEv3iFyVYQQOvfUedn3/DbPyPqxBE8XlirMlc6BLlnU1t6KpphZrtm9h3JFFUCnGUklcDJdpUdB5mc6Y7XSKz52nyXlSMjNlSgDZ6ZIsk0jq6sXz8Pbx/dpU2+o7d2jx+vWbNt93rK288bQXnOy0UjY7mfuAWkurY+ZQLBxQDirFLk7iU7MFoOS7ve+8jaefex7uJAW/zDnYuy9bzWGrRe9UVqCvt5feb4v7ZrZTy/3YHlhYO678YEc42MN4Ari9LYCtndb4G7La0Mg4/ue/vov+wVFq+/7l568hLjoMBGz/z//6ZxSuWI+6+lqoXN0BB0e0tDYhadFylN0sRlT6UrS3NgJOztAYTBgdH4dPUg6qzn6OwMXroXd0QdPZffBdvhNagwnDNaUwOjhDGZrMqLSdDdD2NALJRcQsMSV7tDMw9dTA2NsAhX8MFL5hgJMbOwqmw2rB9VhSx1buLRDF2NIRqxLQzADTwzANd8Fk0EHhFQRlYCzg7Mkqvyzozk4CvXVQzIxCHZ0DlQ/rgszGv+pbb1MF2T2tEGo3Tx5u9YPtmG4sh//iNXD1Ii7ISgq3Mx11mOnvQNSyjZK42+7bl+Hq6YOIpGwehI2aaVRf/AqL1jwGVzcPPrZ2sL0R3S01yF/L1LHnoLe/owmN98qxduszknq2xGbfuXUFRr0BeYVFEtWWy5p8r6oc1XfKsKpoM1XRGDtP+Z8tDyhVPUnMYUtLA5qbG2i5H1LHliR/igiPgn9AoCzocoN98SvDjRkkfag4XlfkucRBCrndlZVluHv3Nry9fZCZmY3YmHh2HMHsnRtPCGqlaOxg4WYKaLValFfcQnlFGeJi45CakkZjfc0zJkvP3VpHL98T290/W53UloY0cUB/q+wmxsbGsY4tdSfAnCXYztcNWdYFmbW3RL3t6u5G8dViPL3zabtckI8ePYyk5BTExcbzSi0t28UaHM57i1OJOdXW5tjBhnebLXd1Bl7ZyRQFUHLjGh1f5+flS+CW7kMEt39463d489XvwNFRzUY0CB4O5s/4rHEBbO01kQtga6WlOnsG8Po//BKfv/u/aIHol/7+X/DjN3YhJyMBya+tQV1xBZDqzcbZKoHuacDXCQhygYK4GhNXprJBOBeGsmV/HKCY0UNXPwrPZWF8EikCp8MXWxC0KR0qFmoJ4Gr6JqDpHkfQ8lRGsWUTRkFrQvuJm0h6ihgVxg2ZU20VRhNqDpxD9gs72FgYIYEU55Zc9slXWLJzC5xcXfmsyeLSP1xCqbPvfoSNr+6hrrziUj/8DK1ZOaDe1na019SjcOtGvgSQON6Wr3crE2dL3nWi3t68XAwXV1dkLV3CqKpWSv6IFVjzjpdFYn5Gbe/v/oDnXnmZ7pc3ULyLyNwuyWLAZgymAlrNLN774x/wdz/+qcjoijMgC2ZHMsvHPmtioyROk88tJ5+//dW/4c3vfR/OtG6eHHA83Fhb7ghmZ25vP2K2nv1Qy9o1ZvsHUWtFSSTMT9oW2F6+eIG6AOYXLqebffjen9Hb02Nx3S++/CrNWP0gP7YGI19+fgDpmZlISGLd8qwcyNowR5hllm5o65gW35ktsHvwZKNRuLY/f+YsbpRcp2uuKVqHtauZ9l74+ea1wM//+V0sSo/Hrh1FuHLjLj747ATe+9XPQGJsvzp0Gs0tzUjLzsORIwdQuPYxVFaWIigqBd09HQhJzMKN80eQseEZXPjsT0jb8QrqSs5SN151YBTar5+AU0w6TQw1VFkMo6MrlOHp1B15urEMepMCxrB0mAx6wKCHseMuTANNUAQnQeEXyYAuVXA5ddYEzIwzCqxmCiA1bLkYW9IhKdWMOzKtZesMOHsALl6s6zEbY8u5IRNN0qiHaaQLGO0B1I5AQByUvuEiFdeBuj2bemuhNOqgjs2FylWAWMXUCHQtZXD0D4drNEkgpYDKQQEHox7jFWfgHp4Ir+hk3jXZMD6I/ptnELt6J1xc3Xj1dpgkm5qeQNziVZKkUhUnPkF64UZ40/I+DMiO9XehtfoW8tc/wcfhEqezyeEBlJw/gs1Pvggnku+DSx6lAFob76G+phKbtjxFa7ty0Mp9zs5MofTaReh0WizLX0UT64hVW64WMLWoYuBUKNDe3oLu7nZ0dLRjamoC4eFRFHSjIqPh7uFJXxi+b7KiPDLrCDsW1hfwUqrOAt1dnSi/fYvWP83JWYLMDFKfVMZNWQTLgguzFNY56GtsakDF7TLqVp+TnUNji93d3fkrsNZvS65R3EVIOmVpD221v7YGtzKljkpvlkKv16OwoNAyvtYM+sRjl7b2Nlqex/wnKjISe54ndWgFZwhrCaPeee8dPPHYk/Dz85NmQeY8sVgXZAKsx08eQWRENFJT0vka1BzUSgDWbFvu/Dj4Je7jff19VLnX6jRQq9RUZff28kZAQBBcXFz4S7Llrs55IjDPC/NMHzt2BPHx8UhNSeVjyTkAZkLzFSgvv4WRkWFsWL/RTLUV7uY//eKf6DnojSb887/872+ewfgarmgBbK006idfnkNP3xB+8uYuusbHB8+gt3+E/k3BtvwO4OPEwCxRbKcNwJgWSPKiqi3NLNw8AVWgK9SBbnxNW01FP9xSA+Do5cInkZqpG4KTvyfcIv1pJmROte05dgcR23OZJFIcwCpV6L9eB8+IQPhEh4tckRn47S69R5NLBSbHSeracqV/hhrbMTM8joSCxfLuyLQ8gRL9ze3ob27F4g0kGyITVytOHsVnTRaB6u1LV+Hp442kbEYhlUsWJc6KLHZb5oze0U8/Q/7qVQgKC2UNGAdwIgg1i79ljIPINUQErn1dXbh24RJ27n5BtgSQpZuybZdkprtR4Mr5s3B398TivLyvJda25Gox9Hodjb+UgLDkeTWbdZV5luXs4FzA8mBwe59QSy2f9AIehhuyLaglZSIO7t+PV7/zXebAJuDD9y3BluyDgG3wA4Itd3Wy7a8AfvUv/4wf/uwfad8xLyCVuOFbPgTW9mXtPKyOo+7DAHGz5efPLoDtfTTf3+Qmyx/7Pk7v+yVcXZzo+XN/G00K3LpZi9NnTyAqNgVDY6MYHB5GWHwaym8WI6voMZQVn0FAXDp6ulphcnaDDmoMDffDI2kp+urKqUrrGJWOkbpy6FVOUAQmQEugtvoSjG4BMATGw6TX0/hWY1MpzYCs8IsQgJa4EhOwJOBJPicGASd3KJzcYFI7Q6F2gklB6qHSkSf9j9SwJcBLYmVN02M0Plfh7ge4eDPuzO6+rNsxq+BSV2QSojQK0yBxe9ZCGRQPBYn1JcuJmzKpiTveD3Tfg8o3BOoIBmK5f4b2O/TcPLLW0Ik3AqAEcGebb1MAJ67JnFuy0qBHz5UvEZK9At5B4Yyiq1BgvKMeEz0tSF6+RaLo1l49QbNPB3FlfxSAbnoClZeOIn/DTjqw5zImG/VaXDtzCDl5qxAYzGQ+ZsYBwMToMK5fOon07FzExaVYwC1pQpI5uaqylILx4twC+Pr4SlRbDmrFSXP4CV5aE3cWHR1tFHT7+/swOzuD0NBwREREIzgohKqskrhPs5J4Ym9yieswY8aF7Bkc/CmA0dFRVFTcRHtHG7IzF1HIZYcY7Cc3CuByh5nF4PJeXkJ+i7HxcVRX38Xdqkp6zkTFTUpMhLMzASdhf+zwwup7b7vPtmG55ZRIGbC9cOk8vUfZWWwOER6KzVy7xXG3AOYC27nUWgLURCleu2atvAuyCGqvFF+iQkVOdi5NNsW4IAuf1JSLwpLME1SRUlG19ffQ0tKM2ZlpCrDBIWEULFVKB5BEkuS5I/efAC7xOkhNzYQPedbkJjo413QWaJl1mLHZ+3vfxQvP7YGrq4tI0RWeF7Le7//0H3jlpVeYCQ/WTZ/hXqbxF8B2/mZwAWyttNlv3v0cfj6e2PPUBrrGqYs3cb64HL/4v99A+uvrUF1XzcBslLuQROreKJMd2UkJJQHbcT2UQ7NwSiPASuJqlTD1zQAzerinBPBJpEyTBkzXDcF/RQKFWqrcOqgxUdsLtYsLvFMiWNWWUWd1I9MYLG9C7KZlFHi5GFvyaZjRovlUCTKe2iSotmw9W8bt2AFVR88htagQbl6eAtwqOXgVXJQrTpxDVHoKQqKjRLE0JH2UkN1YquAqcOqv+7BixxZ4+fjIxtty24rBl1FSmX0SuNj769/itZ/+SKqwslObzHCD62Ql0bA83PJL2W2uX7gIVzc3LFq6dE6XZCkki/ZvBtMGgx5/+vdf4wf/8I8PptpK9is8jEaDAb/5t1/gJz9nkwuwX5nP0c4r1pY15qIPOw3oXChsiY9zxZ9yB55vbC3LnxbnfT9Jo/76wV6sWb8B/3979x6fc/nHcfy9qYUcshzKoXQgic4OKUIocpZDIaeinM/l1MyGOUXRHKIM6aCckuRQ/fRzCFE55JCIqJwXZmyz3+P7nd1ts7n3dW/3fc3vdf9T7Lqv67Pnx737fu97uAoXKZIwXyrBNrG+jAy2afn//vt+fb92jZq90PKKn29S78alD0dptMrb4TZZ/+OlpMG2Vo0aerIyR2ydv12b/4yY2DhVadhN65aEu4qt33aQJg3vrkIF8mvKlNkKLHir1m9Yp0cee0pr13ytYqUe0LETx5Uj8Fbt27NdRcpWsLf3ubN6U21aOENFa7bUySN/6tien5T7oRo6/ddBnfl9p64vXUXnYy/q7E9fK77A3YrLe0tCqLW23rGuaS1SxnVtbeKR2vhDv0hRpxR/Yz753Zgv4Qhs4qnIrv8mlu76VJ/8p6/1Qzf6jOKjTlqHNS8F3fzyK1bGPrXadX2tfQMqfykmOuG63qhT8i9dXX7+1yUPuH//Kr8jexXwSF1XsLVCq3X09vyOb5W3YgN7j1Z760B/P8UdP6Dzf+xWwfJJtvjx99PRTStUqHQ55bJPV064FvfcscM68du2hHB7aYsg62t71i3XnfdXUJ68ga47Jls3jVq7ZI6q1mtt313ZCreJ2wT9sPpLlbrvYeUvaG3tZwXwS1v/+Plpy4bV9hHwCpWqJbtplOvUY0kH9+/Vls1rVfyOEipXrtKlS5YS73eR9Oysf/eavfT27TpyaL0bW3edPXz4oI4fP6L9+3+zT/stUqSoqlSpaZ/dlOw5l36KJgu3aQS1pME6MbzExMTYAXfjpu/VolkrFShonRad/DTkZEecLwXFf48EJwmDSY7y/nHooPbs2aUdv2xX/vwFVLdOfeXObR3FvfydwfVzO70/1xMBUrtfQ8pwm0qwnb9wvsqWKauSJUpe+YjtVQbb1I7WxlyI0YRJE9SnZ9+Et+HEoHrpPTkxvFovz337f9PGHzaqSaNmqYTahBtGpXYnZWuqP/86rJUrl9mfAUuWLGVvP5XLujO3KwSnuFTLT/b1/FZo37VrhypVfEJFihZLdvOwpJ8XE07cSHJdtnUd+x8H7NOSmzd9/t/ryxND66V+bN32sw4d/kPP1q6b6r62w8OGJwRefyk4ZJj5bwIGVEiwTaMJb03/TPny5tKLTZ+2Ryz7ZoO+XfejwgYmbFrNAwEEEEAAAQT+FbCCbeWG3bQ+SbCt9+IAhYf1UrHCBaFCAAEEEEAgUwUItmnwfrToax08fET9Xk24U+rMT5bpVOQZ1w2kMrUrTI4AAggggEAWFHiycQ99+cEo5cyRXRcvxuuJhl319bzxyn5DQBb8bigZAQQQQCArCRBs0+jWn0dOqEPvUZo3Ldi+Y1nLLiEK6t1W991j3d2OBwIIIIAAAgikFHhjzHsqXbK4WjSorhWrN2nBl98pfGQvoBBAAAEEEMh0AYLtFYitG0jN/nS5fd57g6efUKfW9TK9ISyAAAIIIIBAVhU4evyU+oVM0fETkcqVK6dGDeqk24pwGnJW7Sd1I4AAAllJgGB7ld2q0byPAq6/znUzorbNn1HTulWvcjae5g2BtPYl9sbarOG5AK85zw29PUNMTKzGTf1Ecxes1M+r3nMtn9Zep96uj/W8K2AdvR0T/pHy3ZSwZ7T1HrrgvVDvFsFqjgRi4+IUNnGutmzbo/MXYtS9QxPVevJRR3Mw2HcCvOZ8Z+/JysdOROq10Kn2FDPe7O+ais+x7lUJtu6NUh1RqV4Xfbdwon1nYx7mC1xpX2Lzq6dCS4DXXNb7dxAyfpbKlLpDwW9G6McV013fQFp7nWa975CKnQjM+WyFTkaeVrf2jZ08jbE+FLCC0dpN2zRmyKuyjsY/3zlEn0eMVI7sXDPtw7ake2lec+mmMmpg255hqlrpQX23/mdXsOVzbPpaRLBNn9Nlo8rXeUUblk65ymfzNG8LXGlfYm/XwnpXJ8Br7urcfPmss1HRujFndj1Y86VkwTatvU4T9z71Zc2snXkCU2YtVkDAdWrfok7mLcLMGSrQK2iSGteposoV7rfn7TFkop6r+6Trzxm6GJNluACvuQwn9cqE1nvnrr0H9M77C13Bls+x6aMn2KbPKdmoqHPnVaVRNz1R/n79+fdx3Va0oF7r8oLyB+a9itl4ijcErrQvsTfWZw3PBHjNeebn62cnDbZX2uv0tiKFfF0q62eiwJjJH2nH7v2KiYlTbGysWjauqXq1KmXiikztqUDLLqEa0utFlbr7Nnuq0Amz7f+3wi0P8wV4zZnfo7Qq3Lx1d7Jgy+fY9PWSYHsFJ+sf0frNO5KNqPhwab3apoHmL12tmlUetcOs9Rsx6zcr44O7pk+dUV4XYF9ir5Nn6ILWtV285jKU1KuTpQy27HXqVX6vLmZvkxdy+dlMY4a8or+PntSZs+fsU+yOHDulll1DNWl4D91zVzGv1shi6Rdo1XW4Bvds7Qq2w6zLC+4pbh/F5WG+wKafdvGaM79NqVaYMtjyOTZ9jSTYps/piqMOHDqijv3Gatnc0RkwG1NkhgD7EmeGqu/m5DXnO/urWTnlqcjsdXo1itfec14bPlVPlCvLUVuDW9t7aLgaPP24nnzsAbvKLgMn6IVGNfR4uTIGV01paQnwmss6/zZSBls+x6avdwTb9DklG2X9RnrgyHc1dXRfWdeEWRfn//DzLo7YXoWlt57CvsTeks6cdXjNZY6rt2ZNGWzZ69Rb8matM3LiB/aRv0a1K+v0mSg17ThUb4d2V8k7i5pVKNW4BJasWKdv1m7RuKDO9qVXrbuP0NLZYQoIuB6lLCDAay4LNCmNElMGWz7Hpq+XBNv0OV02avrcL7R4+Vp7u4L8gXkU3Le9ChXId5Wz8TRvCLAvsTeUM28NXnOZZ5tZM7fpMVInI89o7/5Duqt4Ed1etJAmhna3767KXqeZpW7uvNaZFoNHTZd1zXxcXJyeb/iUmtWvZm7BVCZru5/gcRH2L++z+furX+cWqlIx4egtD/MFeM2Z36OUFVp3P+48YIKio8/rxKl/VPiWAqpbo6I6tqonPse67yfB1r0RIxBAAAEEEEAAAQQQQAABBAwWINga3BxKQwABBBBAAAEEEEAAAQQQcC9AsHVvxAgEEEAAAQQQQAABBBBAAAGDBQi2BjeH0hBAAAEEEEAAAQQQQAABBNwLEGzdGzECAQQQQAABBBBAAAEEEEDAYAGCrcHNoTQEEEAAAQQQQAABBBBAAAH3AgRb90aMQAABBBBAAAEEEEAAAQQQMFiAYGtwcygNAQQQQAABBBBAAAEEEEDAvQDB1r0RIxBAAAEEEEAAAQQQQAABBAwWINga3BxKQwABBBBAAAEEEEAAAQQQcC9AsHVvxAgEEEAAAQQQQAABBBBAAAGDBQi2BjeH0hBAAAEEEEAAAQQQQAABBNwLEGzdGzECAQQQQAABBBBAAAEEEEDAYAGCrcHNoTQEEEAAAQQQQAABBBBAAAH3AgRb90aMQAABBBBAanz9ZgAADn9JREFUAAEEEEAAAQQQMFiAYGtwcygNAQQQQAABBBBAAAEEEEDAvQDB1r0RIxDIkgILvvxOI96eo1ZNaqnHS02y5PdA0QgggAACCHhL4MKFGE1491NFzPtK08f202OP3uetpVkHAQQyQIBgmwGITIGAaQKvj5img4eOKOpctKpWeohga1qDqAcBBBBAwCiByNNn1a5nmMree6fmL12taaP7EmyN6hDFIOBegGDr3ogRCBgn0C9ksqLPx2hiaHe7ts1bd+ulPmO04L1Q3V60kGZ+skytmtRUp37jdH/puwi2xnWQghBAAAEEvCkQff6CGrYbrJaNa6j1c7XspafMWqwvVq7T/BkhOnYiUj9u/1W1q1dQ2ertCLbebA5rIZBBAgTbDIJkGgS8KXAy8rTqvjhAowZ1UqVHy6hpxyD7zfilF55NVkaH3qMJtt5sDGshgAACCBgr8P2WX9RjyER9MTtMF2JiVb/NQE0b00cPlSmRrGaCrbEtpDAErihAsOUfCAJZVGDJynUKn7lQzzd8SguX/VcfTw3SddmyEWyzaD8pGwEEEEAg8wWCxr6v2Ng4nYu+oPyBeTSwe6vLFiXYZn4fWAGBzBAg2GaGKnMi4CWBV14bpzUbt+njKUEqXbL4ZatyxNZLjWAZBBBAAIEsIXD6TJTqtRkof38/LZk1UjlzZCfYZonOUSQC7gUItu6NGIGAkQIxsXFq+nKQDv11VCH9O+iZauUJtkZ2iqIQQAABBEwROHj4iJp1HKqL8fH6bPowFb21AMHWlOZQBwIeChBsPQTk6Qj4SmDyrEVat2m72reooyGjZ2jJrDDlzXNjsnI4Yuur7rAuAggggICJAtb74oNl7pZ1M6ldew/a2/qkfHAqsomdoyYE3AsQbN0bMQIB4wR+O/Cnmncaqg/D39DddxRRt0FvKXeunBox4GVZR3KPn4y0a+43bLLuLVFc7Z+vbV9/mz8wr3HfCwUhgAACCCDgDQFrf3frTsiLI0YoLi5Oz7Z+Xd07NFGj2pXt7fH+ORNll1GzeR+NGvSKHr6/hHJkv0F5cyf/pbE3amUNBBBwLkCwdW7GMxDwqUB8fLxadxth/8a57yvN7VoO/3XMvmbo7dDuujlfHjV56Y3LaixWuKCWzR3t09pZHAEEEEAAAV8IWNv51LN2ExjcSVUqPmCX8NW3GxQ8LsIOuktXrdeodz68rLTGdaoopH97X5TMmggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLA1qx9UgwACCCCAAAIIIIAAAggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLA1qx9UgwACCCCAAAIIIIAAAggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLA1qx9UgwACCCCAAAIIIIAAAggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLA1qx9UgwACCCCAAAIIIIAAAggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLA1qx9UgwACCCCAAAIIIIAAAggg4FCAYOsQjOEIIIAAAggggAACCCCAAAJmCRBszeoH1SCAAAIIIIAAAggggAACCDgUINg6BGM4AggggAACCCCAAAIIIICAWQIEW7P6QTUIIIAAAggggAACCCCAAAIOBQi2DsEYjgACCCCAAAIIIIAAAgggYJYAwdasflANAggggAACCCCAAAIIIICAQwGCrUMwhiOAAAIIIIAAAggggAACCJglQLB12I9qz/XUuKDOerhsSYfP9P7wCs++qkUzh+uWAoHeX5wVEUAAAQQQQAABBBBAAAEvCVxzwTYiIiJVujZt2mQIKcE2QxiZBAEEEEAAAQQQQAABBBDIMIFrLtgGBwenihMUFJQhaEmDbcW6ndW7Y1MtX71Jfxw+qsZ1Kqtjq3qXrRMTE6vBo2fox22/Ku7iRT1cpoSG9W+v7DcEaNFXazR19mLFxsbp1kI3K2xQJ91aMFCzP12unb8e0IWYGO357ZBuKRioFg2q68OFq7T/4F/q0q6h6td6XDM/Xqbtu/fp/IUY/X3kpAICrteYN16xj9ImPWL7n3U/afy0eYqJjVWxwgUU0r+DCtx8U4aYMAkCCCCAAAIIIIAAAggg4EsBgq1D/aTBtlL9LmpWr5p6vvyc/j56Uk8/31frlkxWjuwByWb96tuN+uTzbzR9bD/Fx0vjpnyspyo/ortuL6yqz/XU4pnDVaxwQQ0dO1N+flJQn7b6YP5KTZvzuT6fNVI35siumi366PFyZRXSv702/bRLQWPf1xezwzRr3leaHLFIX8wZpcCbcits0lxFnYvWsH7tXcH2umzZVPfFAZo9caBK3FHUDsObt+3W2yHdHX73DEcAAQQQQAABBBBAAAEEzBMg2DrsScpgO2Ncf91b4nZ7lsfqdta8d4NV9NYCyWbdvHW3+gSHa2ifdqr4SGndEHC96+tno6J1Y87s9p+/WLVeC778zg7AVrDd9NNOjQ/uan+tdbfhatm4pp6pVt4O0fXaDNCGpVPsYLthy05NGtHDHrf+hx0aMfEDOywnHrH9fvMvWrpqvaaO7mOPsYKvdbR5y/LpypbN36EAwxFAAAEEEEAAAQQQQAABswQItg77kTLYfjT5Dd1WpJA9i3UE1/rz6vU/Kzxiof13nds0VKsmNWUdtbVOI/5lz+92OH29a0s74L4zc4HWbNhqj408HaXChW7WjDf728F2+659GjHgZftrbXqMVLvmtVW10oM6diJSz7zQT5uWTbODrXXKcuK4rTv3qdugt/TtZxNcwfbLVd/b69yUN7fruz1z9pyWzBqp/IF5HQowHAEEEEAAAQQQQAABBBAwS4Bg67Af6Qm2+QNvso+KWo+cObIrZ44bXKv8cyZKfYaG6/HyZexrXK3TjWdPHKQ8uXLq8+VrtXDZfx0H2/Wbdyh8ZC97jTUbt2lM+Eda+H6oK9hu3LJTy/+zUROHJxzV5YEAAggggAACCCCAAAIIXEsC11yw3b9/f6r9KV68eIb0LT3BNvEIbuKCcz5boch/zqhz24b2Xw0Z/Z59fa11o6c1G7faoTTy9Fn1DnpHZ89F20d9nRyxDY9YpE8vnQJtzW0dCR7cs3Wya2wbthusD94ZrNuLFpJ1VPfz5Ws0sHurDDFhEgQQQAABBBBAAAEEEEDAlwLXXLDNbMyrCbYnI09rUNh07d57UH7+/ipb6g6FvtZB0edj1HnAeEX+c1a3FMynXi83VbfBb6tR7cr20dz0nor84/a9Ohd9Xr/uP2Sfymzts2udYpzaXZGjz1+wr+kd1KNVltiLN7P7yfwIIIAAAggggAACCCCQ9QUItlm8h9Y1tlagte6CzAMBBBBAAAEEEEAAAQQQ+H8UINhm8a5bwXbPvkP2NkA8EEAAAQQQQAABBBBAAIH/RwGCbRbvOsE2izeQ8hFAAAEEEEAAAQQQQMBjAYKtx4RMgAACCCCAAAIIIIAAAggg4EsBgq0v9VkbAQQQQAABBBBAAAEEEEDAYwGCrceETIAAAggggAACCCCAAAIIIOBLAYKtL/VZGwEEEEAAAQQQQAABBBBAwGMBgq3HhEyAAAIIIIAAAggggAACCCDgSwGCrS/1WRsBBBBAAAEEEEAAAQQQQMBjAYKtx4RMgAACCCCAAAIIIIAAAggg4EsBgq0v9VkbAQQQQAABBBBAAAEEEEDAYwGCrceETIAAAggggAACCCCAAAIIIOBLAYKtL/VZGwEEEEAAAQQQQAABBBBAwGMBgq3HhEyAAAIIIIAAAggggAACCCDgSwGCrS/1WRsBBBBAAAEEEEAAAQQQQMBjAYKtx4RMgAACCCCAAAIIIIAAAggg4EsBgq0v9VkbAQQQQAABBBBAAAEEEEDAYwGCrceETIAAAggggAACCCCAAAIIIOBLAYKtL/VZGwEEEEAAAQQQQAABBBBAwGMBgq3HhEyAAAIIIIAAAggggAACCCDgSwGCrS/1WRsBBBBAAAEEEEAAAQQQQMBjAYKtx4RMgAACCCCAAAIIIIAAAggg4EuB/wFzBfjXPUWe8gAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ax.utils.notebook.plotting import render\n", - "\n", - "render(ax_client.get_contour_plot())" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:08] ax.service.ax_client: Completed trial 7 with data: {'branin': (38.505764, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802638584, - "executionStopTime": 1646802638597, - "hidden_ranges": [], - "originalKey": "143fe15b-4d45-47ed-88cb-39595bdb5033", - "requestMsgId": "f3e3f04c-14ca-44df-adfa-0e30f46d2866", - "showInput": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'x1': 9.517319461327038, 'x2': 2.675371257532727},\n", - " {'branin': 0.3263206610090492})" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "best_parameters, values = ax_client.get_best_parameters()\n", - "best_parameters, values[0]" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:08] ax.service.ax_client: Generated new trial 8 with parameters {'x1': -1.583362, 'x2': 7.318469} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802638636, - "executionStopTime": 1646802638751, - "hidden_ranges": [], - "originalKey": "53e7063d-b1e2-42db-9629-98467c0f69d7", - "requestMsgId": "236623d1-ba20-47b5-83ba-670831a6706c", - "showInput": true - }, - "outputs": [ - { - "data": { - "image/png": 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UwqsWFqlSCjUja/fEIi5Vf1m7WlktqgkV3qIFc2Hm6P+flVLvq27/K/FTs84f9JmIY6cuPrVNVUx0DNR1/fD1BH17X8mimimcM+7xrdriC29COcRKzPB+H+jyiCeFd9OyCcifO9tjwqvqfWNv86sXtPA2rK5n7tTMuxJVJRodW9XTgq4kTy0urFq+2GPC++Q1xApv7My54qFm2dd/+fgYxfYxoePzrM+rkjFVyjG0Tzu0a1oLSjBV/aqaTY7d3UIJ/vK127F733Hc9LmjS0y6tKmvZ7j/i/iqmthZX67XM9eqqVv967bsfmyG8sk+xtbnHty26LEdDFRuqrsQqj5WlfNICG9srqgaaYd/7z7E9keV3qR0c8XeLZ/HCe+TuaJKXZTwqtKUWm+V1f1V7NSXx/h3CF4kvJkypEnQ5/Z5+fqk8Kr+//jLHmzesQdHTlzQJUSqZlzdmciZ7fVtHZhEf3fwskiABEggURGwaeFVJJUAqRo8tRpdLWRSsznq9uWTM7zqF2ud6uXj5Cp2FFRdn6r3VecpkfW97f/U7KG61atEWAnv6fNeaNVjjJ65VLWmTzZ1y17duk+o8Kq+fzVzyGNhlLyo/XJVycRHn87GRc8bj5UWxD9Y1W6qEoaECK86LyEcpIVXlUGcOHM5bvst1Q8lVBXq99Sz7vFneF8mvGrG+5Kn92Ox4vNI6Pg871OoZi+Dgh9ixdxPtZDny5VV17U+q6kvXEpS1QK7hO5n63XdR+fZw4ehesZT1a2qpkpnVKGNm1tyVCpTRMd7ssXuL/3kHrUXrnjrLcVUXW+rxu+ICK967/L1e6BaxZLP3OHD3s5O3514Xq48KvspocsvYpv64qY+rwkVXpXbJvn6LOGN7Yu6Y7PvyGn9M0N9gVJlKv/lC0ui+inOzpIACZAACbyUgM0Lr1p0pnZVULWLWTKlx8j+HfRFPSm8qh5R7c2r6hFjt9eKva1cvFAeXQYxef5qrNn0G/7ePD9ORNQv9Hea90eenFm08Krb8VWb9EXT+m9BzWrFb2r3ACVs6hdnQoU3IPABdseru41dca4W8ajtxFQd4ufLftCCp27LxjY1q+fi4oRUKR/tQ5xQ4U0IB2nhVYKnVsWrBX2xLZaP2mkiVoqedQ1PzvB+sWKz5hH/gR6qXlQtrlJ1z2p3i4SMz/MyX5VtqNxRi8bUjP+SGYNQpVwxfbiqcw0Kehi3yC42hirpeKtiCXw24H8v/UDFHvDppCV6cZWa4VZN3UXo3akpihfO89wYqlZY1RQ/KdeqFEPVrKvFWaoW1WSGN359bfdBM3D3XoAet/gyqD5HHhnS6LreZ+WKKlcp/W43tG1aG0P/rc9WFxX70JgnhbfVe+/E3Xl5sobXJF/jC6+qzf5j/3G9IC/+jgxqtnf45CV6Fj/+7hwJHkgeSAIkQAIkkCQI2LzwqpkxdRs6PCIC337xGYoXelRb+qTwqlvGSryaNaimb6tHRkZh2drt2LpzH1bMHY4yxfPHzd5Wr1wSvTq+r+VWydW1m7d1rV/8XRrUivNPerRC9cql9Hur+lS1tZgqh1Ab/SdUeENCw1CuZEH06NBYb/ukZpwOHDuLLSsm6YVysbs0qD9/0qOlnj0+d/m6lnN1rbEL5xIqvAnhIC28iuGiVT9i7ri+KFYoD/YcOqlvLasFUGpnjYVTBmiZT4jwqi8EjTsORza1fduHLfQOBwtXbMbpC17Y9PUEXWKgFvq9bHye9+lUY65KY7JlzqBlb9e62XFfkNQM64JlP2iJU/Wfqqlt2NRYqBpjNVutvvQMm/il3hUj/lZqT76fWqCmciWFW3JER8fo3UV2rpsZt3jxef1Ti7g8r93C2MGdUTBPdpw674Vxs1ZAlcbELjB8FeFVW9+pmvL2zd7VnxG184laMKgWyTVvUB1tm9bSrNUODWpx55CP2uit756XK6qfajuyBZP662tUn4+g4BCs3/K7XqSmdvRQnylVx5w5Uzr9RVWVMPx94ORjuzSY5Gt84XVzTa4XuaovBD3/1wQZ06fWZUpqu7/AoGBdj8xGAiRAAiTw5hKweeFVQzN0wmItPD/FW8j0pPCq4/7Yd1yL0Pkr3lC3ZIsUyIXend/Xt5Bj208792nJvelzV/8C7ta+EdQODMdPX3rsl2L8fV5dXJz1Ii61dZOSV9USKrwli+bV/VC7Q/jduY+c2T0wuFdrfTs4tikZmf3lel3nqXZ1yJg+jZYptagsdkeGhApvQjhIC6/qs148tPcfRMfEoGr54hjx8Qd6X+ExM5chU/o0uowkIcKr+q8ePDJz0Vpdh6n2HVaz+4N6tdG1zLHtZePzoo+0qutWdw5UPayqi41tauZSfUnatP1vXY+q3lvVfqo659jH88budvBkTWv891Nbq3UfND3uC5TavWDKgtVPLTh8Vh/V4rbZSzZg119HtGyqLwqqVEft6BG7A8arCK96rxFTlmL7bwf0DOgPX03QW5OpnPt8+aN9eFVTZQxt3q8Zt2DzebmiSjcUx1PnPLXwvl/vLf2gmOlfrNFs1ey2Wui47sff9dZ2jo4OmDqih17U9uQ+vC/73Ca0hlc9gW7O0o3459RFvUgwTeqUqFSmqN5NREk3GwmQAAmQwJtLwOaENykNhZpRU7L2vBrRpHStvBYSIAESIAESIAESsFUCFF4LjgyF14JwGZoESIAESIAESIAEEkiAwptAUK9yGIX3VajxHBIgARIgARIgARKQJUDhleXJaCRAAiRAAiRAAiRAAjZGgMJrYwPC7pAACZAACZAACZAACcgSoPDK8mQ0EiABEiABEiABEiABGyNA4bWxAWF3SIAESIAESIAESIAEZAlQeGV5MhoJkAAJkAAJkAAJkICNEaDw2tiAsDskQAIkQAIkQAIkQAKyBCi8sjwZjQRIgARIgARIgARIwMYIUHhtbEDYHRIgARIgARIgARIgAVkCFF5ZnoxGAiRAAiRAAiRAAiRgYwQovDY2IOwOCZAACZAACZAACZCALAEKryxPRiMBEiABEiABEiABErAxAhReGxsQdocESIAESIAESIAESECWAIVXliejkQAJkAAJkAAJkAAJ2BgBCq+NDQi7QwIkQAIkQAIkQAIkIEuAwivLk9FIgARIgARIgARIgARsjACF18YGhN0hARIgARIgARIgARKQJUDhleXJaCRAAiRAAiRAAiRAAjZGgMJrYwPC7pAACZAACZAACZAACcgSoPDK8mQ0EiABEiABEiABEiABGyNA4bWxAWF3SIAESIAESIAESIAEZAlQeGV5MhoJkAAJkAAJkAAJkICNEaDw2tiAsDskQAIkQAIkQAIkQAKyBCi8sjwZjQRIgARIgARIgARIwMYIUHhtbEDYHRIgARIgARIgARIgAVkCFF5ZnoxGAiRAAiRAAiRAAiRgYwQovDY2IOwOCZAACZAACZAACZCALAEKryxPRiMBEiABEiABEiABErAxAhReGxsQdocESIAESIAESIAESECWAIVXliejkQAJkAAJkAAJkAAJ2BgBCq+NDQi7QwIkQAIkQAIkQAIkIEuAwivLk9FIgARIgARIgARIgARsjACF18YGhN0hARIgARIgARIgARKQJUDhleXJaCRAAiRAAiRAAiRAAjZGgMJrYwPC7pAACZAACZAACZAACcgSoPDK8mQ0EiABEiABEiABEiABGyNA4bWxAWF3SIAESIAESIAESIAEZAlQeGV5MhoJkAAJkAAJkAAJkICNEaDw2tiAsDskQAIkQAIkQAIkQAKyBCi8sjwZjQRIgARIgARIgARIwMYIUHhtbEDYHRIgARIgARIgARIgAVkCFF5ZnoxGAiRAAiRAAiRAAiRgYwQovDY2IOwOCZAACZAACZAACZCALAEKryxPRiMBEiABEiABEiABErAxAhReGxsQdocESIAESIAESIAESECWAIVXliejkQAJkAAJkAAJkAAJ2BgBCq+NDQi7QwIkQAIkQAIkQAIkIEuAwivLk9FIgARIgARIgARIgARsjACF13BAbt4NMYyQuE93sLdD+lQu8L0Xmrgv5DX33jWZI5wd7HA/OOI19yRxv33alM54GBaF0PCoxH0hr7n3mdMmh49/CGJecz8S89s7O9rD3c0JdwLCEvNl2ETfs6RLbhP9YCcSNwEKr+H4UXgpvIYppE+n8EpQBCi8MhwpvOYcKbzmDGMjUHjlWL7JkSi8hqNP4aXwGqYQhVcC4L8xKLwyMCm85hwpvOYMKbxyDBkJoPAaZgGFl8JrmEIUXgmAFF5BigCF1xwnhdecIYVXjiEjUXiNc4DCS+E1TiKWNEgg1DE4wyuDksJrzpHCa86QwivHkJEovMY5QOGl8BonEYVXAiGFV4wiZ3glUFJ4JSg+isEaXjmWb3IkljQYjj6Fl8JrmEL6dC5ak6DIGV4ZihReCY4UXgmKFF45ioxE4TXMAQovhdcwhSi8EgD/jcGSBhmYLGkw50jhNWcYG4EzvHIs3+RIFF7D0afwUngNU4jCKwGQwitIkTO8EjApvBIUOcMrR5GRKLyGOUDhpfAaphCFVwIghVeQIoVXAiaFV4IihVeOIiNReA1zgMJL4TVMIQqvBEAKryBFCq8ETAqvBMU3R3grNuyFzcsnwiND2sfALVuzHVeu3cL4IV1eCei+w6eRO2dmHbfXsNloULMi3qtT5ZVimZy0cv0OXPS88crXYfLesedSeA0pUngpvIYpROGVAEjhFaRI4ZWASeGVoGgmvJ53gnH1brBcRxIYKU/6FMiRzjWBRz867HnCGxIajqioKKRwe7XHK/cZPhfdP2iEkkXyIvDBQ7g4O+n/rN0ovNYmboH3o/BSeCXSirs0SFDkLg0yFCm8EhwpvBIUzYR3xykf7L9yR64jCYxUrUAG1CyU6amjAwKDMXbWCpy9eBX29nZoWLsyPurYJE54e3ZojHVbduNhSCg6t6mPTq3qIf4Mb/DDUIyfsxLHT1+Gk6MD2jerjdZNaurzL3newKjpX8Pn9l1ky5wRk4d3x8+/H8SCZT8gU/o0GNSrNb7f9pee4T13+RoiIiIxvN8H+tz7AQ9Qq9Un2L1xDu7eC8ToGctwxz8ArsmTYXi/9ihdLP9j1zJ94Zrnnu/l7Ytxs1YgMChYi/WI/h1QsXRhxBfeDwfPwPv1qqFBrYo6bvy/e133een7J3AYnjqMM7yvSu7f8yi8FF7DFNKnU3glKFJ4ZShSeCU4UnglKJoJ7z/X7+Py7SC5jiQwUiGPVCia1f2po8fOXI6YGGDMoE54EByC1j3HYlifdqhWsYSe4a1bozzGDuqMazdu4/0uI7Ft1RQtrbElDVMWfIt7AUGYMvxDKHlu2WMM5k/oh4J5s6Npl1Ho06Upalcri6++24YDR8/gy+mD0LjTCF1GoGZ4Y0sacmbLhE/GfoGda2fqPv6w/S/s/OsIPp/UH827fYY279dEy0Y1cPKcJ/qOmItf18yAk5Nj3PWcOHP5uee36D4aHVrUQZO6VbF11358sXwTtq6akmDhTcj7J3AYKLyvCup551F4KbwSOUXhlaBI4ZWhSOGV4EjhlaBoJrx3gsLgHxwu15EERkqf0gVp3ZyfOrpmywGYN6EfihXMrV+b/eV6hIVHaOlVwrtk+iCUKJJXv9buo/Ho2KoebvrciRPeOm0GYcZnveKOUTOtbsmT4f16b6Fp11E4sHWhPjciMgoRERF6hvZZwqtqeGu3+gQLJvVHoXw50Hv4HNSrUQHlShbEex0/xcFti/UMtGqteozB4F5tUL5Uoceu51nnq7ihYeFwcnSEg4M9/O7eR+1WA3F811cJEt7SxfIl+P0TOBSPHcYZ3lehFu+csT+eMowA1CmcGTnT/rd6H+M3FQrgYE/hlUBJ4ZWgSOGVoUjhleBI4ZWgmLSEt1Ttrvhp1RRky5xBX9jXa7bh/KXrmDqyhxbejUvHxb3Wc+gs1KpWBg8ehMQJb7l6HyJlClc4ODg8EtuISCYaIzQAACAASURBVNR7pwIavVsFAz6bj53rZj0F/XnCq2aLU7m7oWPLeqjVcoA+1/P6LbTtNQ6Z4i2cCwkNw2cDOurZ5/jtWee7uSbDtl0H8N2mnVq6o6Kice7SVZz8bVmChDdHtowJfv9XyS4K76tQExbe6vkzokDGlIY9eT2nU3hluFN4ZTjywRMyHPngCXOOFF5zhrERXvXBE7Y4wzt3XF8UL5xHX9rMRev0grQhvdtq4V02eyiKFMilX2vfewI6ta4H75t+ccJbr90QzBnXR8/Kxm9qFrhJ5xE4sHWRnplVInz9lh/y5Mj83BneoycvYsqC1ejWrhG2/LpXl0b4+PmjSScV59FM8Yvas8739buHeu2HYMOSscibM4uO927rgU8Jr5J5NRvcsFaluGtt3+xdlCmRP8Hv/7L+Pet1Cu+rUIt3jklJw74rd3DqVgCq5EmPoplTGfbk9ZxO4ZXhTuGV4UjhleFI4TXnSOE1Z5jUhHfc7JWIjorWNbwBQcFo3WOs/nOlMkW08DatX02XN3jf8tPi9/O30/DTr/vihHfq598hVM24ftIRkVHRmLV4HRrVrowiBXKiSeeR6Ny6no6xasMv+OvACV3Dq2pi+3dviWoViz+2LVlMTIwuNyicPyfq16oYJ5+qBlctmFMy6n8/CJPnf6PrilV5RPz2rPMvenqjU/8p+H39bD0LPXvJer3o7siOL7Hux9/jtiVTi+LSpnbHx92a49oNX11/PH5IV72ILaHv/yrZReF9FWpCwnv4mj+OXb+HcjnSoHT2x/feM+yW1U6n8MqgpvDKcKTwynCk8JpzpPCaMzQV3ofhUQgJj5LrSAIjuTo7ILnzo7KD+E1J7tiZ/79Lg1oYpmZxVStfvwf6dW2OjVv/RNCDh+jWviHavl/rqV0aJsxZhWOnLiIyKgo1KpfC0D7t9I4NF65447NpX+Gm713kzOahd2lQpRNfrNiM5Wu3a7n8++Cpx/bhnTTvG2z46Q/8vXl+nNCqXRLGzFwOn9v+sLOzQ6dWdeN2gnjyep51/qeTluDQ8XNIldINgz9qg8+XbUJ0dLQuiYjdh1ftKDFo3EK4JndB3lxZ9Y4O775dDo3erYz/8v4JHI64wyi8/5XYE8ebzPCevHEf+73uonjW1KiUK51hT17P6RReGe4UXhmOFF4ZjhRec44UXnOGpsIr14PXF8n0wROvr+e2984UXsMxMRHe876B+POSHwpmcsfb+R4VsSe2RuGVGTEKrwxHCq8MRwqvOUcKrzlDCi8w7fPvEBUdjU/7tpcD+oZGovAaDryJ8HreDcbOcz7Inc4NtQt5GPbk9ZxO4ZXhTuGV4UjhleFI4TXnSOE1Z/imC+/ClZuxav0v+Hxy/6ce/iBH982JROE1HGsT4b0ZEIKtp24ia6rkaFAsi2FPXs/pFF4Z7hReGY4UXhmOFF5zjhRec4ZvuvDKEWQkReCNE15V6D1nyQZdCK4KtdOk+v/twJas/gkr1u3QxeANalXCiH4f6M2T1VNPhk9eoh8HmNUjPcYN6YJSRfPpDDIRXr8HYdh03BsZUrjg/ZLZEmVGUnhlho3CK8ORwivDkcJrzpHCa86QwivHkJHeQOFVj8lTe9gtWvUj/vxhXpzw7j9yBiOnfYUVcz/Vqwv1I/hqVdSrJDv0nYiq5Yuja7uG+GPfP1ArE3d8N0OvjDQR3oCQCKw7eg2pkjuhVZnH99VLLMlJ4ZUZKQqvDEcKrwxHCq85RwqvOUMKrxxDRnoDhffcpWtaeIvX7PyY8Kr98TJnTIvu7RvpvPh97zE92ztz9Eeo124w9v30BRz/fbqJ2iduaO+2+lF7JsIbGhGFVQe9kMzJAR0qPNpsOrG1gwHXceWhPyqkyo48rolzazVbYE7hlRkFCq8MRwqvOUcKrzlDCq8cQ0Z6A4U3dtCfFN6uA6ehTZOaei841Tyv3ULnAVMxa0xvjJu1ApuWTYjLF7V/XMUyhaH20DMRXrVx89K9V3Tc7lUfPT87sTUKr8yIUXhlOFJ4ZThSeM05UnjNGVJ45RgyEoU3rqRBPcavR4f38Halkjov1KP63u8yUj/Gb+6SjVi7eHRcvoyYshQF8mZHx5Z1ERAcYZRHn+++gIioGPSpUQCODnZGsV7HyX/duYoLQXdQLX0uFEiZOPcSfh3cnnxPZycHONoDD8Osv0m6LVy/VB/ckjkiPDIaEZHRUiHfyDip3JwQGByBmDfy6mUuWv08T+bsgAchkTIB3+AoKh/ZSMCUwBu3aC0W2JMzvN0GTUez+m/rul3Vzl++jh5DZmL22N4YOfUrbF01JY51v1HzUK1iCT3DGxxq9sNs4R+XEBwWiV5v54P6ZZ3Y2m5fL5wPuoPqGXOhkHv6xNZ9m+mv+uVob2enZY3t1Qm4ONkjMioGUdFUtVenCLi6OOJhmNnPNpP3Twrnqs+zs5M9Ql/Dk76SAr/415AYfzcmtTFICtdD4f13l4aJc1chtXsK9O7cVI/rtl0HsHHrH5gxupd+3vSeHxcgmYuzfq1hh2EYP6QLyhQvYFTSoGKtP3od90PC0bJMdqRO/ih+YmosaZAZLZY0yHBkSYMMR5Y0mHNkSYM5w9gIWdIllwvGSG8sAQrvv8J79OQFDBm/CCvnDYebW3J8OGgGWjV+B80bvg1V31u2REG9oG3H7oOYu3Qjtq+eqhexmdTwqqzbfMIbt4PC0LhEVmRKmSzRJSKFV2bIKLwyHCm8MhwpvOYcKbzmDCm8cgwZ6Q2r4b0f8AA1WvTX4x4REQknp0clBDvXzkT6tKmwYv0OLF39EyIio/B+vbf0Tgx2dna45XsXQycuxunzXsieJSMmDuuGogUf7apgKrw/n7mF6/ceol6RzMiexjXR5SSFV2bIKLwyHCm8MhwpvOYcKbzmDCm8cgwZ6Q0TXksMuKnw7jrviyt3HqBWgUzIkyGFJbpo0ZgUXhm8FF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFT4f37kh/O+gbirbwZUNjD3bg/1g5A4ZUhTuGV4UjhleFI4TXnSOE1Z0jhlWPISBRe4xwwFd6DXndx/MZ9VMiVDiWzpjbuj7UD8ElrMsQpvDIcKbwyHCm85hwpvOYMKbxyDBmJwmucA6bC+4/3PRy66o9S2dKgfM7E96QyCq9xCukAFF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFT4T3rE4i/L/vpcgZV1pDYGoVXZsQovDIcKbwyHCm85hwpvOYMKbxyDBmJwmucA6bCe8nvAX6/4It86VPgnYKZjPtj7QAUXhniFF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFT4b3mH4wdZ32QI40b6hbxMO6PtQNQeGWIU3hlOFJ4ZThSeM05UnjNGVJ45RgyEoXXOAdMhdc3MBQ/nryBTO7J0Lh4VuP+WDsAhVeGOIVXhiOFV4YjhdecI4XXnGFSFN5LnjcwZ8kGHDp+DpGRUciXKyu6tG2AujXKvxTYtRu+uOMfoJ/yumzNdly5dks/9ZUtYQTe2CetJQzPy48yFd57D8Ox4dh1pHF1RovS2V/+hjZ2BIVXZkAovDIcKbwyHCm85hwpvOYMk5rwXr95Gy0/HIM2TWqi1Xs19FNd9x46hfGzV2JI77b6gVcvaurhWOHhEfqpryGh4YiKikIKNz52OaGZRuFNKKnnHGcqvA/CI/Hdoatwc3ZAu/KPnt6WmBqFV2a0KLwyHCm8MhwpvOYcKbzmDJOa8I6a9jUehoRi5uiPHoOzddd+TPv8O/y+YQ5Wrt+B0xc8ERYeAd/b9+Ds7ITpn/WE13UffDL6czg6OqBp/WpI7Z4iboa378h5KJwvB06e88Qlrxv69WQuTvhz/wn43b2v369Qvhz4cPAMvF+vGhrUqqjfP/7fKzX6CP26Nseuv47g6g1ffNqnPXbv+wcXPb3h5OiAxdMGwjV5MrlBfQ2RKLyG0E2FNyIyGssPeMLJ3h6dKuc27I31T6fwyjCn8MpwpPDKcKTwmnOk8JozNBbeu+cQ43/hhR2xS1sASFfoxZ0VilO//RAM+LAV6lQv99j7RURGoUydbtj09QTsOXQKC1dsxtZvpiJt6pSYsuBbLcnjBnfBhDmrkClDGj3DG7+kof9nCxAaFo6FUwbgqrcvGncajrGDOmvx/WLFZvjduYfRAzu9UHirNO6Nrm0bomvbBli3ZTcmz1+NzcsmIEfWTOg8YApaNnonTpTlRta6kSi8hrxNhVe9/ZI9l3UvulXJAzs7O8MeWfd0PmlNhjeFV4YjhVeGI4XXnCOF15yhqfDGXNiMmIs/vlh48zeGXYEmLzxGKk7VJn0wf0I/XYP7ZHu7aT/MHtsHp8974uCxc1gw6WN9yP4jZzBp/mr8uHziC4W3QunCaNe0FqKiolGiVhf8+cM8pEvjjp9+3Yeffz+o471ohlcJ78q5w5Evd1b9nhPnrsKWlZN1Hz6b/jXy5sqKji3ryg3qa4hE4TWELiG8Kw94ISwyCh0q5kIyRwfDHln3dAqvDG8KrwxHCq8MRwqvOUcKrzlDU+GFmpm9e/7FwpuuYMJmeAXiNO44HL06NkH9mo9KCmJbZFQUSr/bDZuXT8LfB07g3KVrmPRpd/2yKlPoO2Iudm+c80LhrV65pJ7RVa1ojU44tH2RLkHYtusAtvy6V8/+vkx41385Flk90mvhnr5wDdZ/OUbHGztz+aOZ3jb15Qb1NUSi8BpClxDetUeuIjA0Eq3L5oB7MifDHln3dAqvDG8KrwxHCq8MRwqvOUcKrzlDY+GV64JIJDVr6nPbH/MnPpq9jW1qBnb6F2uwc91MrNrwC/YfPYMvJg/QL6sSB/XapmUTjIW359BZeK9OFTSsVUnHbt97Ato3e1eXKqgZXgqvyDAn3SASwvv9cW/cfRCGZiWzIV0Kl0QFi8IrM1wUXhmOFF4ZjhRec44UXnOGSU14ffz80azLKDRr+DY6NK+jd1jYd+S0nkEd3u8DPfOrFq2putsNS8YiW+YMUAvdXJydMLJ/B13P6+zkiE96tHqqhjchM7yjZyxD2tTu+Lhbc6gtzpp2GYXxQ7pSeOVSNWlHkhDeradu4mZACBoWy4IsqRLXFiMUXpn8pvDKcKTwynCk8JpzpPCaM0xqwquuR+22MOvLdTj8z3mER0Qgf57sehFazaql9eUq4f3n9GWEhIbpHReyZEqnd1lInzYV9h4+hY9HLcA7VUqhcP6ccbs0qEVrCRFetQfwoHEL4ZrcRdfkBgYF4923y6HRu5U5wyuXrkk3koTw/nrWB17+wXi3sAdypXVLVLAovDLDReGV4UjhleFI4TXnSOE1Z5gUhfdlVJTwKtFVuzKwyRJgDa8hTwnh/ePibVy4HYTq+TOgQEZ3wx5Z93QKrwxvCq8MRwqvDEcKrzlHCq85wzdVeC963uAT1OTSJy4ShdcQqoTw7ve8g5M3A1Apd3oUz5LKsEfWPZ3CK8ObwivDkcIrw5HCa86RwmvOkMIrx5CRAAqvYRZICO+R6/dw9Jo/ymZPgzI50hr2yLqnU3hleFN4ZThSeGU4UnjNOVJ4zRm+icIrR42RniRA4TXMCQnhVbO7apZXze6qWd7E1PikNZnRovDKcKTwynCk8JpzpPCaM6TwyjFkJM7wGueAhPBe8A3EH5f8dP2uquNNTI3CKzNaFF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgEJ4fW6G4xfz/noHRrUTg2JqVF4ZUaLwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOSAhvDcCQrDt1E29B6/aizcxNQqvzGhReGU4UnhlOFJ4zTlSeM0ZUnjlGDIShdc4BySE986DMPxw3BvpU7igaclsxn2yZgAKrwxtCq8MRwqvDEcKrzlHCq85QwqvHENGovAa54CE8AaGRmDtkWtwT+aI1mVzGvfJmgEovDK0KbwyHCm8MhwpvOYcKbzmDCm8cgwZicJrnAMSwhsWEYWVB73g4uiA/1XMZdwnawag8MrQpvDKcKTwynCk8JpzpPCaM6TwyjFkJAqvcQ5ICG9MTAyW7r2i+9K9al7jPlkzAIVXhjaFV4YjhVeGI4XXnCOF15whhVeOISNReI1zQEJ4VSeW7b+CyKgYdK6UG44O9sb9slYACq8MaQqvDEcKrwxHCq85RwqvOUMKrxxDRqLwGueAlPB+e8gLweFRaFc+F9ycHYz7Za0AfNKaDGkKrwxHCq8MRwqvOUcKrzlDCq8cQ0ai8BrngJTwbjh6HfdCwtGidHakcXU27pe1AlB4ZUhTeGU4UnhlOFJ4zTlSeM0ZUnjlGDIShdc4B6SE98cTN+AbFIrGxbMik3sy435ZKwCFV4Y0hVeGI4VXhiOF15wjhdecIYVXjiEjUXiNc0BKeH8+cwvX7z1E3cIeyJHWzbhf1gpA4ZUhTeGV4UjhleFI4TXnSOE1Z0jhlWPISBRe4xyQEt7fzvvi8p0HqFkgE/JmSGHcL2sFoPDKkKbwynCk8MpwpPCac6TwmjOk8MoxZCQKr3EOSAnv35f9cNYnEG/lzYDCHu7G/bJWAAqvDGkKrwxHCq8MRwqvOUcKrzlDCq8cQ0ai8BrngJTwHrrqj3+876F8zrQolS2Ncb+sFYDCK0OawivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOSAlvMe97+HgVX+UzJoaFXKlM+6XtQJQeGVIU3hlOFJ4ZThSeM05UnjNGVJ4X86wYsNe2Lx8IjwypH3uwX1HzEWd6uXxXp0qLw8IoP9nC/BWheJo0ah6go5PLAfZxajHfLG9MgEp4T3jE4g9l/1QOJM73sqX4ZX7Y+0TKbwyxCm8MhwpvDIcKbzmHCm85gwpvC9nSOF9OaPYIyi8CWf1zCOlhPey3wP8dsFXL1hTC9cSS+OT1mRGisIrw5HCK8ORwmvOkcJrzjCpCW/fkfNQOF8OnDzniUteN9C0fjUkc3HCn/tPwO/ufcwc/REK5cuBgMBgjJ21AmcvXoW9vR0a1q6Mjzo20Tj+PngSE+eugr29PRq9Wxkr1u3ApmUT9Azv4lVb8OMve2BnZ4dKZYpgaO+2cHJyxPNmeAMfPMSYGctw/PRluCZ3waf92qNKuWJ6hjdfrqzYe/gUrt7wRZniBTBnbB84ONjrvo+btQKBQcFwcXbCiP4dULF0Yaza8AvOX76O8PAIXLt5G1FR0Zg7rg+yeKTHDZ87GDxuIfzvB6FsiQIICQ1HtYrF9fV7XffB6BnLcMc/AK7Jk2F4v/YoXSy/XPLEi0ThNcQqJbzX7wXj5zM+yJ7GFfWKZDbslfVOp/DKsKbwynCk8MpwpPCac6TwmjM0Fd7rwYHwDg58YUeyubkju9uLF4pLxVEiGRoWjoVTBuCqty8adxqOsYM6a/H7YsVm+N25h9EDO2HszOVQ997HDOqEB8EhaN1zLIb1aadltFarTzBhaFddcrBm82+YMGcVdq6biTPnvTBnyQas/nykFsf+oxdoEf2g+bvPFd5Ycf60b3ucPHsF3QfPwB/fz8XQCYvhe+celkwfBEdHBzTpNAKjB3bU79+i+2h0aFEHTepWxdZd+/HF8k3YumoKVn+/E58v/wE/Lp+E9GlT6WtI5Z4C/bu3wMCxXyBb5gwY8GFL7D9yBj2HzcL4wV10iUXzbp+hzfs10bJRDS3TSs5/XTNDi7p0o/AaEpUS3ttBodh84gYypkyGJiWyGvbKeqdTeGVYU3hlOFJ4ZThSeM05UnjNGZoK777b3lD/vahVzpgN6r8XNak4SngrlC6Mdk1r6RnQErW64M8f5iFdGnf89Os+/Pz7QSyY9DFqthyAeRP6oVjB3Lpbs79cj7DwCLRq/A7a9hqHA1sX6n9X/1amTnfsWj8LC77+Abmye6Bbu4b6tT/2HceytduxfM6w5wrvu20GYd74viicP6c+JyAoGKlSuukZ3lLF8qFTq3r63/uNmod3qpTWYq6E3cnRUc/2qlnp2q0G4viur7Tw7jt8WvdftW82/orT570weXh3VG/2Mb6cPggF82bXrzXsMAw9OzRGuZIF8V7HT3Fw22I9k61aqx5jMLhXG5QvVUgugf6NROE1RColvPdDwrH+6HWkTu6MlmUeJUViaBRemVGi8MpwpPDKcKTwmnOk8JozjI2QJV3yVwomNTMrFUeJZPXKJbU4qla0Ricc2r5Iz8hu23UAW37dq2d/S9Xuip9WTdGzoqp9vWYbzl+6rmdCB49fhJ1rZ8bxKFv3Q2z9ZgrGzlyB42cu6ViqRUdHI12aVFj/5ZjnCq86Vy14i32f2KBPLlqL/3fVz+827UREZJSW9nOXruLkb8u08J44cxlTR/bQYeL/vWStrtixZnrcwrpug6ajSZ2qyJ0zsxb4TPEW3IWEhuGzAR1Rt0b5VxrzF51E4TVEKiW8weFR+PaQF1ydHNC+Qi7DXlnvdAqvDGsKrwxHCq8MRwqvOUcKrzlDU+GV64FMpIQKr5rhnTuuL4oXzqPfeOaidYiKikLzRtXR7qPxcTO8D0NCUb5+Tz3D+/myTcifOyv+17LuU519Xg2vmuGdNfqjuPfxvHYLWT3SY8iExY/t0hArvNUqlkC99kOwYclY5M2ZBT5+/ni39cCXCm/VJn2wYs6nyJf70d3rRv/7FD0+eA/lSxfS5RKxM9YylJ8fhcJrSFhKeCOjYrBs/xU4Otihc6VHSZ4YGoVXZpQovDIcKbwyHCm85hwpvOYM31ThHTd7JaKjonUNryozaN1jrP5z2RIF8U7z/pg8/EO96GvZmu2Y9eU6/Lp2Js5cuIqFKzbrEgY312RYt2U3nBwd9Gzy84R3/OyVuixi3OAueqa26yfTsPvfGt7425LFCm/JonnRqf8U/L5+NhwcHDB7yXrdhyM7vsTGrX8+d4a359BZKFEkr1549+f+47pkQtUuqxpeVRPcuU19NKxVSS9qmzz/G/1a7Ey1XBZxH15jllLCqzqyZM9l3Z9uVfLoVZaJoVF4ZUaJwivDkcIrw5HCa86RwmvO8E0VXiW5qkQhdpcGtaCrU+tH9bS//X0Ukxd8C7WjbIuG1fXCNbVQTc3MfvnNFmz6+W9ERkbpet4JQ7shY/rUzxVe9T6jpy/DsVMXkcItOYb3+wBVyz/apeFZwqv25f100hIcOn5O1/oO/qiNnllW5RMNalV6rvBe9PTG4HGLtFxXKlsEt+/cQ/13KupdJtQuDWNmLofPbX/tPZ1a1UXrJjXlkideJM7wGmKVFN5VB70QGhGFDhVyIZmTg2HPrHM6hVeGM4VXhiOFV4YjhdecI4XXnGFSE145IokvUnR0TNzCtI4fT0bXtg3wdqWSVr0QCq8hbknhXXvkGgJDI9CqTA6kSu5k2DPrnE7hleFM4ZXhSOGV4UjhNedI4TVnSOGVY/g6I03/Yo3e0UEtaFMzum16jdNbmanty6zZKLzxaKutP3bsPqT/RW3JMfqTTkiezBnXbtzG8MlL9O0Fddtg3JAuKFU0nz5OUnh/+Oc67gSH4/2S2ZAhhYs18+CV34tPWntldI+dSOGV4UjhleFI4TXnSOE1Z0jhlWP4OiOph0oMnbgYalGcelhF785N0ah2Zat3icL7L3IluktW/4RV80fA2ckRg8Z9oZ80ogamQ9+JqFq+OLq2a4g/9v2DSfO+wY7vZuiCcEnh3XrqJm4GhKBhsSzIkurVtmGxdgZReGWIU3hlOFJ4ZThSeM05UnjNGVJ45RgyEhetxeWAesqJ/71AjOzfQf+b2kPuyIkLGPHxB6jXbjD2/fQFHB0e1dWqVYXqkX1qY2RJ4d15zgeed4NRu5AHcqdzSxT5SeGVGSYKrwxHCq8MRwqvOUcKrzlDCq8cQ0ai8MblwOHj5/XzodUMr3qmdL9R81HzrdLImzOr/nf1rOrYNmjcQlQsU1g/Ck9SeP+85IfzvoF4O18GFMz04kcd2kryUnhlRoLCK8ORwivDkcJrzpHCa86QwivHkJEovI/lwOgZy7B5xx792LzC+XNg6YzBOHziPOYu2Yi1i0fHHTtiylIUyJsdHZ+xwbNJUu04dQv7r9xFnaIeqJw3vUkoq527w/syTt/3Q52seVEszaOnwrCRAAmQAAmQAAmQgC0RYA3vv6Px3aZd+O3vY5g7vg+cnZ0w7fM1UE8xad7wbYyc+pVeURjb1HOl1RNHpGd4j17zx5Hr91Ame1qUzZHGlvLkuX3hDK/MMHGGV4YjZ3hlOHKG15wjZ3jNGXKGV44hI3GGNy4H+gyfi7crl0Sr92rof1MbMQ+b+CXWLPoMtVsNxJ4fFyCZi7N+rWGHYRg/pAvKFC8gWtJw6mYA9nneQbHMqVA5T+KY4aXwyvwYofDKcKTwynCk8JpzpPCaM6TwyjFkJApvXA7MWbIBl6/exKwxvfXuC3OXbsQlT2/Mn/gxug6cph/p1719I+zYfVC/tn31VL2ITbKG9+LtIOy+eBv5M6ZEjfwZE0V+UnhlhonCK8ORwivDkcJrzpHCa86QwivHkJEovHE58DAkDBPmrIRavGZvb68fyzd6YCdkzpgWt3zv6j3kTp/3QvYsGTFxWDcULZhLnyspvF7+wfj1rA9ypnVFncKZE0V+UnhlhonCK8ORwivDkcJrzpHCa86QwivHkJEovMY5ICm8ag9etRdv5lTJ0ahYFuO+WSMAn7QmQ5nCK8ORwivDkcJrzpHCa86QwivHkJEovMY5ICm8dx+E4fvj3kjn5oJmpbIZ980aASi8MpQpvDIcKbwyHCm85hwpvOYMKbxyDBmJwmucA5LCGxQaiTVHriKliyPalMtp3DdrBKDwylCm8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDkgKb1hkFFYe8IKLgz3+Vym3cd+sEYDCK0OZwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOSApvDExMVi694ruU/eqeY37Zo0AFF4ZyhReGY4UXhmOzbn9bAAAIABJREFUFF5zjhRec4YUXjmGjEThNc4BSeFVnVm+3xMRUdHoWCk3nB3sjftn6QAUXhnCFF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFp4f32kBeCw6PQrlxOuLk4GvfP0gEovDKEKbwyHCm8MhwpvOYcKbzmDCm8cgwZicJrnAPSwrvx2HX4PwxH81LZkNbNxbh/lg5A4ZUhTOGV4UjhleFI4TXnSOE1Z0jhlWPISBRe4xyQFt4tJ2/AJzAU7xXPCg/3ZMb9s3QACq8MYQqvDEcKrwxHCq85RwqvOUMKrxxDRqLwGueAtPD+fNYH1/2DUbewB3KkdTPun6UD8ElrMoQpvDIcKbwyHCm85hwpvOYMKbxyDBmJwmucA9LC+/sFX1zye4AaBTIif4aUxv2zdAAKrwxhCq8MRwqvDEcKrzlHCq85QwqvHENGovAa54C08O657IczPoGomicDimR2N+6fpQNQeGUIU3hlOFJ4ZThSeM05UnjNGVJ45RgyEoXXOAekhffwVX8c876HcjnSonT2NMb9s3QACq8MYQqvDEcKrwxHCq85RwqvOUMKrxxDRqLwGueAtPAev3EfB73uokTW1KiYK51x/ywdgMIrQ5jCK8ORwivDkcJrzpHCa86QwivHkJEovMY5IC28Z30C8PflOyjk4Y5qeTMY98/SASi8MoQpvDIcKbwyHCm85hwpvOYMKbxyDBmJwmucA9LCe+XOA+w674vc6d1Qu6CHcf8sHYDCK0OYwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOSAtvN73HmL7mVvIltoV9YtmNu6fpQNQeGUIU3hlOFJ4ZThSeM05UnjNGVJ45RgyEoXXOAekhfd2UBg2n/BGxhQuaFIym3H/LB2AwitDmMIrw5HCK8ORwmvOkcJrzpDCK8eQkSi8xjkgLbz3Q8Kx/uh1pEruhFZlchj3z9IB+KQ1GcIUXhmOFF4ZjhRec44UXnOGFF45hoxE4TXOAWnhfRgeidWHriK5syM+KJ/TuH+WDkDhlSFM4ZXhSOGV4UjhNedI4TVnSOGVY8hIFF7jHJAW3sioGCzbfwWO9nboXDmPcf8sHYDCK0OYwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOSAtvKpDS/Zc1v3qViUP7OzsjPtoyQAUXhm6FF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFLCO83B70QEhGlSxpUaYMtNwqvzOhQeGU4UnhlOFJ4zTlSeM0ZUnjlGDIShdc4BywhvGuPXkNgSARalc6BVK5Oxn20ZAAKrwxdCq8MRwqvDEcKrzlHCq85QwqvHENGovAa54AlhHfTcW/4PQjT25Kp7clsuVF4ZUaHwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOWAJ4d126iZuBISgQdHMyJra1biPlgxA4ZWhS+GV4UjhleFI4TXnSOE1Z0jhlWPISBRe4xywhPDuOueDK3eDUatgJuRJn8K4j5YMQOGVoUvhleFI4ZXhSOE150jhNWdI4ZVjyEgUXuMcsITw/nnJD+d9A1EtbwYU8nA37qMlA/BJazJ0KbwyHCm8MhwpvOYcKbzmDCm8cgwZicJrnAOWEN79nndx8uZ9VMyVDiWypjbuoyUDUHhl6FJ4ZThSeGU4UnjNOVJ4zRlSeOUYMhKF1zgHLCG8x6774/C1eyiVLQ3K50xr3EdLBqDwytCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhCeE/fCsDeK3dQNLM7quTJYNxHSwag8MrQpfDKcKTwynCk8JpzpPCaM6TwyjFkJAqvcQ5YQngv3g7C7ou3kT9DStQokNG4j5YMQOGVoUvhleFI4ZXhSOE150jhNWdI4ZVjyEgUXuMcsITwXvV/iF/O3kKONK6oWySzcR8tGYDCK0OXwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOWAJ4b0VEIKfTt2Eh3syvFc8q3EfLRmAwitDl8Irw5HCK8ORwmvOkcJrzpDCK8eQkSi8xjlgCeG9GxyG7//xRlpXZzQvnd24j5YMQOGVoUvhleFI4ZXhSOE150jhNWdI4ZVjyEgUXuMcsITwPgiLxHeHryKFiyPalstp3EdLBqDwytCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhCeMMio7HygCecHezRsVJu4z5aMgCftCZDl8Irw5HCK8ORwmvOkcJrzpDCK8eQkSi8xjlgCeGNiYnB0r1XdN+6VckDOzs7435aKgCFV4YshVeGI4VXhiOF15wjhdecIYVXjiEjUXiNc8ASwqs6tWK/J8KjovG/irnh4mhv3E9LBaDwypCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhKeFUNr6rlVTW8qpbXVhuFV2ZkKLwyHCm8MhwpvOYcKbzmDCm8cgwZicJrnAOWEl61S4ParaFZqWxI5+Zi3E9LBaDwypCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhKeLecvAGfwFA0KpYFmVMlN+6npQJQeGXIUnhlOFJ4ZThSeM05UnjNGVJ45RgyEoXXOAcsJbw7ztzCtXsPUadwZuRM62rcT0sFoPDKkKXwynCk8MpwpPCac6TwmjOk8MoxZCQKr3EOWEp4d1+4jYt+QaiePyMKZExp3E9LBaDwypCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhKePde8cPpW4Gokic9imZOZdxPSwWg8MqQpfDKcKTwynCk8JpzpPCaM6TwyjFkJAqvcQ5YSngPX/PHsev3UC5HGpTOnta4n5YKwCetyZCl8MpwpPDKcKTwmnOk8JozpPDKMWQkCu9jOXDg2FmMnbkcfncDUKZ4fkwb2ROp3N1w7cZtDJ+8BGcvXkVWj/QYN6QLShXNp8+1lPCeuHEfB7zuonjW1KiUK53N5iqFV2ZoKLwyHCm8MhwpvOYcKbzmDCm8cgwZicIblwMBQcFo3HE4po/qhZJF82Li3FUonD8n2r5fCx36TkTV8sXRtV1D/LHvH0ya9w12fDcDTo4OFhPecz6B+OuyHwpmcsfb+TLYbK5SeGWGhsIrw5HCK8ORwmvOkcJrzpDCK8eQkSi8cTnw/bY/se/IaS288dvde4Go124w9v30BRwdHPRLLbqPxtDebVG+VCGLCe+VOw+w67wvcqdzQ+1CHjabqxRemaGh8MpwpPDKcKTwmnOk8JozpPDKMWQkCm9cDkyevxqRkVHw8vbBVW9flC1RAKP6/w8Xrnhj3KwV2LRsQtyxg8YtRMUyhdGyUQ2LCe+N+w+x7fQtZE2VHA2KZbHZXKXwygwNhVeGI4VXhiOF15wjhdecIYVXjiEjUXjjckDV6B47dRFfzx6GdKlTYtikJUifNhVqVCmJuUs2Yu3i0XHHjpiyFAXyZkfHlnURHBppkTy6FRCK1Qe84JEqGT6omMsi7yER9A9fL5wLuoPqGXOhkHt6iZBvZAxHB3s42AFhkdFv5PVLXbSLkz2iomMQGRUjFfKNjOPq4oiQsEiQ4qsPv9rBxsnRHqHhUa8ehGdqAm7JHEmCBIwJ2MXExPBnGgA1w2tvb69LFVQ7evICxsxcgbGDOmHk1K+wddWUONj9Rs1DtYol9AxvQHCE8SA8K8C94HCs2O+J1K7O6FQ5t0XeQyLoX35XceHBHVRLnwsFUtru4jqJa7VkDGcne6hfkCFh/OVowln9YgyLiEZkFL84mHBM5eZksZ9tJv1KTOeqz3NyFwc8CLHMpEhiYmHaV5WPbCRgSsDqwnvu0jV4XruF0LDwp/retH410+t55fO/2fgrTp/3wuTh3XWMIycu6MVpS2cORu1WA7HnxwVI5uKsX2vYYRjGD+mCMsULWKykISQ8Et8cuopkTg7oUMF2Z3hZ0vDKKffYiSxpkOHIkgYZjixpMOfIkgZzhrERsqRLLheMkd5YAlYV3hmL1mL52p+RKX0aODk9fYvi52+nvbaBuOMfoHdp+Hr2UOTNmQVDJixClkzpMfijNug6cBrKliiI7u0bYcfug5i7dCO2r56qF7FZalsyNfG+dO8VzaN71byvjcvL3pjC+zJCCXudwpswTi87isL7MkIJe53CmzBOLzqKwmvOkMIrx5CRrFzDW73Zx3rGNH/ubDbJ/uffD2LGwjUICQtH5bJFMWZgJ6RwS45bvncxdOJiPQOcPUtGTBzWDUULPpp1tZTwqtjL9l1BZHQMOlfKA0dV4GmDjU9akxkUCq8MRwqvDEcKrzlHCq85QwqvHENGsrLwqlKA+LWwSWEALCm8qqRBlTa0L5cTahGJLTYKr8yoUHhlOFJ4ZThSeM05UnjNGVJ45RgykpWFd+rn36F0sfyoU71ckmFvSeFdd/QaAkIi0LJMdqRO/qh+2NYahVdmRCi8MhwpvDIcKbzmHCm85gwpvHIMGcnKwqu28/rlj8PI4pEOmTOmg53d47fpF04ZkOjGxJLCu/m4N24/CEPjElmRKWUym2RD4ZUZFgqvDEcKrwxHCq85RwqvOUMKrxxDRrKy8E5fuAYO9vbP5f5Jj1aJbkwsKbzbT9+C9/2HqFckM7KncbVJNhRemWGh8MpwpPDKcKTwmnOk8JozpPDKMWQkKwtvUgRuSeHded4HnneCUatAJuTJkMIm8VF4ZYaFwivDkcIrw5HCa86RwmvOkMIrx5CRrCC8q7/fidrVyiJThjRQf35Ra9+sdqIbE0sK71+X/XDOJxBv5c2Awh7uNsmGwiszLBReGY4UXhmOFF5zjhRec4YUXjmGjGQF4W3WdRTGDemCYgVzQ/35Re37r8YnujGxpPAe8LqLEzfuo0KudCiZNbVNsqHwygwLhVeGI4VXhiOF15wjhdecIYVXjiEjWUF4kzpkSwrvsev3cPiaP0plS4PyOdPaJEoKr8ywUHhlOFJ4ZThSeM05UnjNGVJ45Rgy0msQXlt9tPCrJoMlhffMrUDsueKHIh7uqJo3w6t20aLn8UlrMngpvDIcKbwyHCm85hwpvOYMKbxyDBnJysJry48WftVksKTwXvQLwu4Lt5EvfQq8UzDTq3bRoudReGXwUnhlOFJ4ZThSeM05UnjNGVJ45RgykpWF19YfLfwqCWFJ4b3mH4wdZ32QI40b6hbxeJXuWfwcCq8MYgqvDEcKrwxHCq85RwqvOUMKrxxDRrKy8PLRwv8t5XwCQrHl1A1kck+GxsWz/reTrXQ0hVcGNIVXhiOFV4YjhdecI4XXnCGFV44hI1lZePlo4f+Wcv7BYdj4jzfSuDqjRens/+1kKx1N4ZUBTeGV4UjhleFI4TXnSOE1Z0jhlWPISFYWXj5a+L+lXHBYJL49fBVuzg5oVz7XfzvZSkdTeGVAU3hlOFJ4ZThSeM05UnjNGVJ45RgykpWFl48W/m8pFx4VjRX7PeFkb49OlXP/t5OtdDSFVwY0hVeGI4VXhiOF15wjhdecIYVXjiEjWVl4XwR8+bqf0alVvUQ3JpZctKZgLNlzWTPpViUP7OzsbI4PhVdmSCi8MhwpvDIcKbzmHCm85gwpvHIMGek1CO/Js1dw5oIXwsIj4vjfvnsfazf/hkPbFye6MbG08K7c74mwqGh0qJgLyRwdbI4PhVdmSCi8MhwpvDIcKbzmHCm85gwpvHIMGcnKwrti/Q7MWrQOuXJ44Op1H+TNlRXXbvgiY/o06Nq2AZo1eDvRjYmlhXfN4asICotE67I54J7Myeb48ElrMkNC4ZXhSOGV4UjhNedI4TVnSOGVY8hIVhbe2q0+weThH6J8qUKo3Xogdq6diQfBIfh00pdo1fgdVKtYItGNiaWFV+3SoHZraFYyG9KlcLE5PhRemSGh8MpwpPDKcKTwmnOk8JozpPDKMWQkKwtvqXe74dC2RXBycoSS353rZukx8L8fhI79JmHLysmJbkwsLbw/nbqJWwEhaFgsC7KkSm5zfCi8MkNC4ZXhSOGV4UjhNedI4TVnSOGVY8hIVhZe9eCJgT1bo2bV0mjaZSQmDuuGIgVyIejBQ9RsOYA1vM/IyF/O3sJV/4d4t7AHcqV1s7mcpfDKDAmFV4YjhVeGI4XXnCOF15whhVeOISNZWXh//GUPPp20BLs3zsEP2/+C2pmhUpkiuHDFG5kzpsOSGYMS3ZhYeoZ398XbuHg7CNXzZ0CBjO42x4fCKzMkFF4ZjhReGY4UXnOOFF5zhhReOYaMZGXhVcC9rvsge5aMsLe3w/fb/sKxUxeROWNafNC8DlK5294M5suSxNLCu+/KHZy6FYBKudOjeJZUL+uO1V+n8Mogp/DKcKTwynCk8JpzpPCaM6TwyjFkJCsKb2RUFL79fidaNKoO1+TJkgx7Swvv4Wv+OHb9HspmT4MyOdLaHDcKr8yQUHhlOFJ4ZThSeM05UnjNGVJ45RgykhWFV8Gu2qQPVi8YiVzZPZIMe0sL78kb97Hf666e3VWzvLbWKLwyI0LhleFI4ZXhSOE150jhNWdI4ZVjyEhWFt7NO/Zg2679aFCrki5rcHZ2fGwMihW0zcfnvihRLC28530D8eclP12/q+p4ba1ReGVGhMIrw5HCK8ORwmvOkcJrzpDCK8eQkawsvEVrdHoh89O7lye6MbG08HreDcbOcz56hwa1U4OtNT5pTWZEKLwyHCm8MhwpvOYcKbzmDCm8cgwZycrCqx4y4ejoADs7u2eyd3G2vSeJvSxJLC28NwNCsPXUTb0Hr9qL19YahVdmRCi8MhwpvDIcKbzmHCm85gwpvHIMGcnKwhsfeHR0DI6evIDIyCgUL5wHbq6JcyGbpYXX70EYNh33RvoULmhaMpvN5SyFV2ZIKLwyHCm8MhwpvOYcKbzmDCm8cgwZyUrCq2Z2Zy5eB89rt/RDJ9o1q43ug6bj4LFzegw8MqTFkpmDkSdH5kQ3JpYW3oCQCKw7eg3uyRzRumxOm+ND4ZUZEgqvDEcKrwxHCq85RwqvOUMKrxxDRrKS8I6esQx/HTiB2tXK6f+XLpZPP0546sgeegxGTlkK2Nlh/oR+iW5MLC28oRFRWHXQCy6ODvhfxVw2x4fCKzMkFF4ZjhReGY4UXnOOFF5zhhReOYaMZCXhfadFf4wb3BXVKhbHtRu3Ub/9EKyaPxxlihfQY3DynCd6fzobf/4wL9GNiaWFNyYmBkv3XtFculfNa3N8KLwyQ0LhleFI4ZXhSOE150jhNWdI4ZVjyEhWEt4Stbpg++ppyOrxaB/ZcvU+xKZlE5Et86Nttm7d9kftVp+AuzQ8OyW/3ncFUdEx6FwpNxwd7G0qbym8MsNB4ZXhSOGV4UjhNedI4TVnSOGVY8hIVhJetR3ZrvWzdK2uahUb9sL3X42PE2AfP3/UaknhfV5Crj7ohYcRUWhXPhfcnB1sKm8pvDLDQeGV4UjhleFI4TXnSOE1Z0jhlWPISFYU3oVTBiBtGnfNvHP/KZg2qicypEut/+5/LxC9hs3mDO9zMnL90eu4HxKOFqWzI42rs03lLYVXZjgovDIcKbwyHCm85hwpvOYMKbxyDBnJisKbENgsaXg2pc0nvHE7KAyNi2dFJnfb2r6NT1pLSGa//BgK78sZJeQICm9CKL38GArvyxm97AgK78sIJfz1LOmSJ/xgHkkCzyFgF6NWRVm43fEPSNA7pE+bKkHH2dJBll60pq715zO3cP3eQ9Qt7IEcad1s6fJB4ZUZDgqvDEcKrwxHCq85RwqvOUPO8MoxZCQrzfAmZdDWEN5d531x5c4D1CyQCXkzpLApnBRemeGg8MpwpPDKcKTwmnOk8JozpPDKMWQkCq9xDlhDeP++5IezvoF4K28GFPZ4VAdtK43CKzMSFF4ZjhReGY4UXnOOFF5zhhReOYaMROE1zgFrCO9Br7s4fuM+yudMi1LZ0hj3WTIAhVeGJoVXhiOFV4YjhdecI4XXnCGFV44hI1F4jXPAGsL7j/c9HLrqj5JZU6NCrnTGfZYMQOGVoUnhleFI4ZXhSOE150jhNWdI4ZVjyEgUXuMcsIbwnrkViD1X/FA4kzveyvfoYR220ii8MiNB4ZXhSOGV4UjhNedI4TVnSOGVY8hIFF7jHLCG8F7ye4DfL/jqBWtq4ZotNQqvzGhQeGU4UnhlOFJ4zTlSeM0ZUnjlGDIShdc4B6whvNf8g7HjrA+yp3FFvSKZjfssGYDCK0OTwivDkcIrw5HCa86RwmvOkMIrx5CRKLzGOWAN4fUNDMWPJ28gY8pkaFIiq3GfJQPwSWsyNCm8MhwpvDIcKbzmHCm85gwpvHIMGYnCa5wD1hDeew/DseHYdaRO7oyWZbIb91kyAIVXhiaFV4YjhVeGI4XXnCOF15whhVeOISNReI1zwBrC+yA8Et8dugpXJwe0r5DLuM+SASi8MjQpvDIcKbwyHCm85hwpvOYMKbxyDBmJwvvMHPh82Q9Y++Pv+POHefr1azduY/jkJTh78SqyeqTHuCFdUKpoPv2aNYQ3IjIayw94wtHBDp0r5bGpvKXwygwHhVeGI4VXhiOF15wjhdecIYVXjiEjUXifygGv6z7oPXwOgh48jBPeDn0nomr54ujariH+2PcPJs37Bju+mwEnRwerCK/q5JI9l3Vfu1XJAzs7O5vJXQqvzFBQeGU4UnhlOFJ4zTlSeM0ZUnjlGDIShfepHOg8YApaN66ppVbN8N69F4h67QZj309fwNHBQR/fovtoDO3dFuVLFbKa8K484IWwyCh0qJALyZwe9cMWGoVXZhQovDIcKbwyHCm85hwpvOYMKbxyDBmJwvtYDmz6+W8cOHoWQ3q3QZNOI7TwHj15EeNmrcCmZRPijh00biEqlimMlo1qwC8gzCp59M0BTwSGRuoa3lTJnazyngl5k33+13Ap+C4qp82JfG5pE3IKj3kGgWTODnBysENQSCT5GBBI5eqE0IgohEVEG0ThqendXXA3MAwxRPHKBJwc7OGW3BH3H4S/cgye+IhAhlQuREECxgTsYmJi+DMNwP2AB/ig70Ssmj9cQ40V3r2HT2Huko1Yu3h0HOwRU5aiQN7s6NiyLlR9rTXaV3uuwDcgFF2q5IZH6uTWeMsEvcfOW1dwNuAOannkQZHU6RN0Dg96moC9vR1UoUpUND+OJvnh4GCH6GiAP9ZMKAJOjvZW+9lm1lPbPVtVnql9yiOj+Jk2HSWVj2wkYEqAwvsvQSWxFUoXRpO6VXEvIChOeI+duoiRU7/C1lVT4lj3GzUP1SqW0DO81li0pt5466mbuBkQgobFsiBLKtsRXpY0mH4EH53PkgYZjixpkOHIkgZzjixpMGcYGyFLOtv5nSd3VYxkbQIU3n+JV2ncO65GV80O3Qt4gLSpU+oZ32ZdP8OeHxcgmYuzPrphh2EYP6QLyhQvYDXh/fWsD7z8g1G7kAdyp3Ozdp489/34pDWZoaDwynCk8MpwpPCac6TwmjOk8MoxZCTW8D4zB+LP8KoDug6chrIlCqJ7+0bYsfsg5i7diO2rp2pBttYM7x8Xb+PC7SC8nS8DCmZyt5ncpfDKDAWFV4YjhVeGI4XXnCOF15whhVeOISNReBMkvLd872LoxMU4fd4L2bNkxMRh3f6vvfuArqpM9zD+Ty9AQif0piAqVRDFPqJYcBydARU79j42RNFBUVQcBcSCKAyKDQuOYtcZFUcBAUEFFUF6DQFCEgLpuWtvIJQrJJz3S07Ifs6dte4aOOfN/n77k3nY7nOODmu77QsgKip4py1Zr3mrM3RUizpq37hmpdm7BK+bU0HwunEkeN04Erx2R4LXbkjwujNkEsFr3gMVFbzfr0jX7OUb1aVpbR3RrJb5uF0NIHjdSBK8bhwJXjeOBK/dkeC1GxK87gyZRPCa90BFBe/c1RmavmS9Dm+YrKNbVZ5PQyB4zVvIH0DwunEkeN04Erx2R4LXbkjwujNkEsFr3gMVFbwLUjM15fc0HVy/hk48uL75uF0NIHjdSBK8bhwJXjeOBK/dkeC1GxK87gyZRPCa90BFBe/SDdn6fP5aNa+dqFPbNTQft6sBBK8bSYLXjSPB68aR4LU7Erx2Q4LXnSGTCF7zHqio4F2VsVUfzVuthskJ6n14I/NxuxpA8LqRJHjdOBK8bhwJXrsjwWs3JHjdGTKJ4DXvgYoK3vWbc/XvH1eqTrU4ndupifm4XQ0geN1IErxuHAleN44Er92R4LUbErzuDJlE8Jr3QEUFb+bWfL0xe7lqxEXr/K7NzcftagDftOZGkuB140jwunEkeO2OBK/dkOB1Z8gkgte8ByoqeHPzCzVhxlLFRUfqku4tzcftagDB60aS4HXjSPC6cSR47Y4Er92Q4HVnyCSC17wHKip4va87Hjt1sX+8Vx3T2nzcrgYQvG4kCV43jgSvG0eC1+5I8NoNCV53hkwieM17oKKC1zvQ8dMXq6CwWJce1VKxUZHmY3cxgOB1ocjn8LpRlAheN5IEr92R4LUbErzuDJlE8Jr3QEUG72szlyo7r1D9ujZXtbho87G7GEDwulAkeN0oEryuHAleuyTBazckeN0ZMongNe+Bigzet2evUPrWPP21UxPVrhZnPnYXAwheF4oErxtFgteVI8FrlyR47YYErztDJhG85j1QkcE7+adVSs3K0VntGyslKd587C4GELwuFAleN4oErytHgtcuSfDaDQled4ZMInjNe6Aig/eTX9ZoRfoW9WqXoma1q5mP3cUAgteFIsHrRpHgdeVI8NolCV67IcHrzpBJBK95D1Rk8H7xW6oWrd+sE9vU18H1apiP3cUAgteFIsHrRpHgdeVI8NolCV67IcHrzpBJBK95D1Rk8H6zKE2/rs3UMa3q6dCGSeZjdzGAb1pzoUjwulEkeF05Erx2SYLXbkjwujNkEsFr3gMVGbwzl23UDyvT1bVZbXVuWst87C4GELwuFAleN4oErytHgtcuSfDaDQled4ZMInjNe6Aig/fHlemasWwxfmG4AAAgAElEQVSjOjSuqe4t6piP3cUAgteFIsHrRpHgdeVI8NolCV67IcHrzpBJBK95D1Rk8P6yNlPfLkrTISlJOq51PfOxuxhA8LpQJHjdKBK8rhwJXrskwWs3JHjdGTKJ4DXvgYoM3kVpm/XFglS1qltdJ7dtYD52FwMIXheKBK8bRYLXlSPBa5ckeO2GBK87QyYRvOY9UJHBuyI9W5/8slZNaibq9MMamo/dxQCC14UiwetGkeB15Ujw2iUJXrshwevOkEkEr3kPVGTwrsvK0Xs/rVL96nE6u2MT87G7GEDwulAkeN0oEryuHAleuyTBazckeN0ZMongNe+Bigze9C15envOCiUnxKhvl2bmY3cxgOB1oUjwulEkeF05Erx2SYLXbkjwujNkEsFr3gMVGbzZeYV6beZSJcRG66Juzc3H7mIAwetCkeB1o0jwunIkeO2SBK/dkOB1Z8gkgte8ByoyeAsKizV++mJFR0bo8qNbmY/dxQC+ac2FIsHrRpHgdeVI8NolCV67IcHrzpBJBK95D1Rk8HoH+8K3i/xjvrJHK0VERJiP3zqA4LUKbnt9Yny0YqMitCk7383AgE6pXSNWW3ILlZNXGFABN8smeO2OBK/dkOB1Z8gkgte8Byo6eF+esVQ5+YX+LQ3erQ3hfhC8bs4AwevGkeB140jw2h0JXrshwevOkEkEr3kPVHTwvvH9cmXm5Ktv52ZKTowxH791AMFrFeQKrxvBbVMIXjeaBK/dkeC1GxK87gyZRPCa90BFB++/f1ih9dl5/seSeR9PFu4HwevmDHCF140jwevGkeC1OxK8dkOC150hkwhe8x6o6OD9cN5qrc7YqjMOa6jGNRPNx28dQPBaBbnC60aQK7wuHQleuybBazckeN0ZMongNe+Big7e/8xfqyUbsv2vFva+YjjcD4LXzRngCq8bR67wunEkeO2OBK/dkOB1Z8gkgte8Byo6eKcsTNOCdZk6rnU9HZKSZD5+6wCC1yrIFV43glzhdelI8No1CV67IcHrzpBJBK95D1R08E5bsl7zVmeoe4s66tC4pvn4rQMIXqsgwetGkOB16Ujw2jUJXrshwevOkEkEr3kPVHTwzl6+Ud+vSFenJrXUrXlt8/FbB/BNa1ZBgteNIMHr0pHgtWsSvHZDgtedIZMIXvMeqOjg9a7ueld5D2uYpB6t6pmP3zqA4LUKErxuBAlel44Er12T4LUbErzuDJlE8Jr3QEUH78J1Wfpq4TodXK+GTmxT33z81gEEr1WQ4HUjSPC6dCR47ZoEr92Q4HVnyCSC17wHKjp4l27M1ue/rlWzWonqdWhD8/FbBxC8VkGC140gwevSkeC1axK8dkOC150hkwhe8x6o6OD1PoPX+yzelKR4ndW+sfn4rQMIXqsgwetGkOB16Ujw2jUJXrshwevOkEkEr3kPVHTwbticq3d+XKnaibH6a+em5uO3DiB4rYIErxtBgtelI8Fr1yR47YYErztDJhG85j1Q0cGblVOgid8vU/W4aF3Qtbn5+K0DCF6rIMHrRpDgdelI8No1CV67IcHrzpBJBK95D1R08OYWFGrCd0sVGxWpS49qaT5+6wCC1ypI8LoRJHhdOhK8dk2C125I8LozZBLBa94DFR28xcXFGjt1sX/cV/ZopYiICPMaLAMIXoveztfy1cJuHPlqYTeOBK/dkeC1GxK87gyZRPCa90BFB693wC9OX6L8wiJd0r2l4qIjzWuwDOCb1ix6BK8bvZ1TCF43ogSv3ZHgtRsSvO4MmUTwmvdAOIL3tZlLlZ1X6N/D693LG84HwetGnyu8bhwJXjeOBK/dkeC1GxK87gyZRPCa90A4gnfSnBXauCVP53ZqojrV4sxrsAwgeC16XOF1o8cVXteOBK9dlOC1GxK87gyZRPCa90A4gnfy3FVKzcxR78MbqWFygnkNlgEEr0WP4HWjR/C6diR47aIEr92Q4HVnyCSCd7c9MHrCe5r47hfKzy9Qj26Ha8idlysxIV7LV63TPY+8oF8XLlPjlLoaMqC/Oh12kP/acATvJ7+u1YqN2Tq1XUM1r50Y1n1M8Lrh55YGN47c0uDGkeC1OxK8dkOC150hkwjekj3w2ZRZGjVukv41/C5Vrxavm+4dpSM6tNX1l56ti28aqmO6tdcV/c7UlGk/6OFRr+jT1x9XTHRUWIL3ywWp+j1ts044uL7a1K8R1n1M8LrhJ3jdOBK8bhwJXrsjwWs3JHjdGTKJ4C3ZA/N+W+Jf2e18+MH+r7301qf6ZcFSDbj+Ap3W705N++BZRUdF+b/3t6sG664bLlC3ToeEJXi/XZSmX9ZmqkerujqsYXJY9zHB64af4HXjSPC6cSR47Y4Er92Q4HVnyCSCd6974Nq7huvk47qodfPGGjL8Jb07/qGS594xZLS6d2mnPr1PDEvwzlq2UXNWpqtrs1rq3LR2WPcxweuGn+B140jwunEkeO2OBK/dkOB1Z8gkgvcP98CzL76r739aoBcev1PTZ/+sJ1+YpDfGDC557qBHx6pN66a6tE8v5RcUVfg+mrZ4g76cn6ruLevo5HYNKvzn7/oD/7tmsX7JWK+TU1rp0Jp1w3osB/IPj4yMkPcVIoVFxQfyMsJ+7FFRESoqkrwvaOERukBMdGRY/mwL/Ygr3yu9LwWKipQKCtmL1rPj7UceCFgFIor5X4YSQ4/ikade1bKVqRrxwI1KTIjTnHkLde+wcfrw5UdLnnfzfaN0XPcO/hXetIxc6znY79f/vCZDUxasU7uUJJ3UNrzBGxURoZo1YrQhM2+/18ELdgrEx0YpJipCWVsLYDEIJCfGKCe/ULn5Ff8XUcNhV7qX1k2K04bMXJFqoZ+amKhIVUuI1qbN/NkYuuK2V9ZLDu/Hb1qPn9dXDgGCd5fz8Ngzryt1fboeHXSN/4Y075GekaWefW/Xt5OfVnxcrP9rZ148UA8O6K8u7duE5ZaGxWmb9d8FqWpZp5p6HpIS1p3EVwu74eeWBjeO3NLgxpFbGuyO3NJgN9wxoVGd8H78pruVMCmcAgTvdv2ZP8zX0Cdf0dtjHyh5c9qOE3PF7Y/5n9hw1YW99elXM/Tk2En6+NVh/vPC8bFkK9O36ONf1qhxcoLOOLxROPePCF43/ASvG0eC140jwWt3JHjthgSvO0MmcQ9vyR64++EX9MF/pipq+ycxeL9xUIvGevuFB7QmdYPuGjpGP/+2VE0b1dfQgVfqsLYt/NeGI3jXZeXqvZ9Wql71OP2lY5Ow7mOC1w0/wevGkeB140jw2h0JXrshwevOkEkEr3kPhCN4N23N01uzVygpIUbndWlmXoNlAMFr0dv5WoLXjSPB68aR4LU7Erx2Q4LXnSGTCF7zHghH8G7JK9CrM5cpPiZKFx+57UpzuB4Erxt5gteNI8HrxpHgtTsSvHZDgtedIZMIXvMeCEfweh9zM376Yv/YrzqmtXkNlgEEr0WPK7xu9HZOIXjdiBK8dkeC125I8LozZBLBa94D4Qhe76Bf+HaRf+yXH9VK0VHeJ7iG50HwunHnCq8bR4LXjSPBa3ckeO2GBK87QyYRvOY9EK7gfXnGUv/zRi/s2lyJcdHmdYQ6gOANVW731xG8bhwJXjeOBK/dkeC1GxK87gyZRPCa90C4gveN2cuVuTVffbo0Vc2EbZ8PHI4HXy3sRp3gdeNI8LpxJHjtjgSv3ZDgdWfIJILXvAfCFbzv/rhSaZtz9ecOjdWgRrx5HaEOIHhDleMKrxu53acQvG5UCV67I8FrNyR43RkyieA174FwBe9H81ZrVcZWnX5oQzWplWheR6gDCN5Q5QheN3IEb3k4Erx2VYLXbkjwujNkEsFr3gPhCt7/zl+rxRuydXKbBmpVr7p5HaEOIHhDlSN43cgRvOXhSPDaVQleuyHB686QSQSveQ+EK3i//j1Nv6Vm6tjW9dQuJcm8jlAHELyhyhG8buQI3vJwJHjtqgSv3ZDgdWfIJILXvAfCFbzTl2zQ3NWbdGSLOurYuKZ5HaEOIHhDlSN43cgRvOXhSPDaVQleuyHB686QSQSveQ+EK3jnrNioWcvT1alJLXVrXtu8jlAHELyhyhG8buQI3vJwJHjtqgSv3ZDgdWfIJILXvAfCFbw/r8nQ1MXrdWhKko5pXc+8jlAHELyhyhG8buQI3vJwJHjtqgSv3ZDgdWfIJILXvAfCFbwL1mVpysJ1OqhudZ3UtoF5HaEOIHhDlSN43cgRvOXhSPDaVQleuyHB686QSQSveQ+EK3iXbdyiz35do2a1qqnXoSnmdYQ6gG9aC1WO4HUjR/CWhyPBa1cleO2GBK87QyYRvOY9EK7gXZOxVR/MW60GSfH6c/vG5nWEOoDgDVWO4HUjR/CWhyPBa1cleO2GBK87QyYRvOY9EK7g3ZCdq3d+WKlaibH6W+em5nWEOoDgDVWO4HUjR/CWhyPBa1cleO2GBK87QyYRvOY9EK7g3ZxboNdnLVO12Cj169bCvI5QBxC8ocoRvG7kCN7ycCR47aoEr92Q4HVnyCSC17wHwhW8uQVFmvDdEsVERuqyo1ua1xHqAII3VDmC140cwVsejgSvXZXgtRsSvO4MmUTwmvdAuIK3uLhYY6cu9o//yh6tFBERYV5LKAMI3lDU/v9rEuOjFRsVoU3Z+W4GBnRK7Rqx2pJbqJy8woAKuFk2wWt3JHjthgSvO0MmEbzmPRCu4PUO/KXpS5RXWKSLu7dQfHSUeS2hDCB4Q1EjeN2o/f8pBK8bWYLX7kjw2g0JXneGTCJ4zXsgnMHr3cPr3ct73hHNlBQfY15LKAMI3lDUCF43agRveTkSvHZZgtduSPC6M2QSwWveA+EMXu9TGrxPazi3YxPVqR5nXksoAwjeUNQIXjdqBG95ORK8dlmC125I8LozZBLBa94D4Qze9+eu0trMHJ15eCM1Sk4wryWUAXzTWihqBK8bNYK3vBwJXrsswWs3JHjdGTKJ4DXvgXAG76e/rNHy9C06pV2KWtSuZl5LKAMI3lDUCF43agRveTkSvHZZgtduSPC6M2QSwWveA+EM3q8WrNPCtCydcHA9tamfZF5LKAMI3lDUCF43agRveTkSvHZZgtduSPC6M2QSwWveA+EM3qmL0/Tzmkwd1bKu2jdKNq8llAEEbyhqBK8bNYK3vBwJXrsswWs3JHjdGTKJ4DXvgXAG78xlG/XDynQd0bSWujSrbV5LKAMI3lDUCF43agRveTkSvHZZgtduSPC6M2QSwWveA+EM3p9WbdJ3Szf4V3e9q7zheBC8btT54gk3jnwOrxtHgtfuSPDaDQled4ZMInjNeyCcwfvbuix9vXCdmtZMVMcmtcxrCWXAgry1WlOQoTaxKWoYHZ7bKkI57sr2mrjYKMVESptzgv0NYRERxUpJCv0TRwheNzub4LU7Erx2Q4LXnSGTCF7zHghn8C7dkK3P5681r8EyIL5ejmJqFGjrungVbI62jOK1CPgCKTXidWLbBqoRt//7ieB1s4kIXrsjwWs3JHjdGTKJ4DXvgXAGb9rmXE1fssG8BsuA3OpZKojPUWxWDcXkxltGBfq13hd4RERIBYXFgXbIyslXdl6BoiIi1KNVXR2Ssn+fPkLwutk+BK/dkeC1GxK87gyZRPCa90A4g9d88A4G8E1rDhAlcQ/vNsfComJ9t3S9/+kj3qNhUrxOattA1WLLdrWX4HWzHwleuyPBazckeN0ZMongNe8BgjdCdZPjlJqeY7YM8gCCd/ezvzZzq774LVXZeYWKiYpQj9b11KZejVK3CMFbKlGZnkDwlolpn08ieO2GBK87QyYRvOY9QPASvOZNxBXePyQsKCrS1EXr5b0503s0qZmgE9s0UEJM1F7JCV4Xu1EieO2OBK/dkOB1Z8gkgte8Bwhegte8iQjefRKu2rRFXy5Yp635hYqLjtSxreupVd3qf/gagtfFbiR4XSgSvC4Ut81oVCf0T25xdxRMOtAFIoqLi4P9LhnjGSR4CV7jFvJfzi0N+1bMKyjWN4tTtSgt239is1qJOuHg+orf42ovwetiNxK8LhQJXheKBK87RSYRvMY9QPASvMYtRPDuB+Cyjdn+Z0/nFBQpPjrSv8Whaa3EkgkE735g7uOp3NJgdyR47YY7JnCF151lkCcRvMazT/ASvMYtRPDuJ2BOfqGmLFyn5elb/FceXL+6jmlV339zG8G7n5h7eTrBa3ckeO2GBK87QyZxD695DxC8BK95E3FLQ0iEi9I265vF6+Td7pAYE+V/fNnhTZK1JbdQOXnB/sa6kEB3eRHBaxWUCF67IcHrzpBJBK95DxC8BK95ExG8IRNuzSvUVwtTtXLTVn9GhybJOqhekvLyi0KeGRMdqbrVYkN+fVV4IcFrP4sEr92Q4HVnyCSC17wHgh68MzJWaPGWjToyualaJdY2ewZ1AG9as51576PLpi1OU76jb6rzvumtfo14NagRp5TkBKUkJfi3TATlQfDazzTBazckeN0ZMongNe8BgpfgNW8irvC6INTm3ALNXrFRWbkFKgr9Aq825+b7s/Z81EqMUf3q8X4AezFcMyHGyXFXxiEEr/2sELx2Q4LXnSGTCF7zHiB4CV7zJiJ4XRD6M1y9ac37zN+1mTlam7FVqVk5Wp+dqz0/wNH7lIgGSfFqkJSoBklxql89TpERVeMqMMFr35IEr92Q4HVnyCSC17wHCF6C17yJCF4XhE6Dd88DKiwuVlpWjlIzc+V97fHarK3+m+X2fNSrHqcGNeL9NyxZHjUSYtSgerySw3QVmeC1nL1tryV47YYErztDJhG85j1A8BK85k1E8LogLNfg/aMDTN+Sr3WbvQjeqtTMHG3amu9sHTsGeV+jnJIUryY1E/1bKSrqNgqC134qCV67IcHrzpBJBG+Z9sDyVet0zyMv6NeFy9Q4pa6GDOivTocd5L+W4CV4y7SJSnkSb1pzoejuloZQjiavoKjk9oeiItsXWHqv3xbTufKuLu94eF+t7L2BrnGtBKXUSFCdcvo0CYI3lB2w+2sIXrshwevOkEkEb5n2wMU3DdUx3drrin5nasq0H/TwqFf06euPKyY6iuDlUxrKtIdKexLBW5pQ2X7f1T28Zftp5f+souJiP3pXbcrW6owcP4J3vZc4NirCD+CGyYlqmBSvutXj5OI2YoLXfm4JXrshwevOkEkEb6l7YEN6pk7rd6emffCsoqOi/Of/7arBuuuGC9St0yEEL8Fb6h4qyxMI3rIolf6cqha8e664oKjIfzPdqk1btHrTVq3PztvtKdGR3sepxalN/WRVj4suHWwvz6iTFKuNmXmyXacO+cc7fWFExI5VREjb/uM/dv7/nb/uPdP/9YiInb+//Ynb/t+2X/eft9uv7/HfI6TYqChVT4xWeta2c7Rt7o4JuxzDLu9zjNg+1D/kkmON8H9WybGpePvP3n6MEZL3MXpV+cFXC1fls1txa+OrhUuxnj13oYYMf0nvjn+o5Jl3DBmt7l3aqU/vE1X04RWlnq21Rz1d6nNSpt9Y6nMq45zSPof3QF1XaSfDzbqKlTL9ptJ+lEo/767mSG7WFb45ewveyrUuV+erbHMeLLy1lD1WrPuiRpa6D5mzb6Lg+kj3RY1wsH/2PifyzHGlzucJCJQmQPCWIjR11jw9+cIkvTFmcMkzBz06Vm1aN9WlfXqVKXjL8g9rWcKZOfs+WQeeT7GKPryytH9GVfq6XM0R+7mUs+Hmn1NX56tsc75q9bC/qh3XOYu33xPh//di7z/F6rn03lL34cdNH9w2Z/sLS67++jO2Deu9auefk3sb+O+U+0tuy/B+9vb/7Da7T9qQUo/ntdr3bfu5xf6UnWssOZ4iXbJp29r39RhX457tP3vHKna4bJ9ZXKRrtgwrbYyejrvLf+FO1x2v32bj/d/f8x8vdc7jkXdsm1Kyju2+28+V998GykVguvqLDsFb6knlCZVCgOAt5TTMmbdQ9w4bpw9ffrTkmTffN0rHde/gX+HlgQACCCCAAAIIIFC5BQjeUs5PekaWeva9Xd9OflrxcbH+s8+8eKAeHNBfXdq3Cfw9vFGREaqbHKfU9JzKvdMr+dFxD6+bE1TV7+F1o1T6FN60VrpRac/gTWulCZX997mHt+xWPHPvAgRvGXbHFbc/piM6tNVVF/bWp1/N0JNjJ+njV4f5b2IL+seSEbxl2EBleArBWwakMjyF4C0DUhmeQvCWAamUpxC8dsMdEwhed5ZBnkTwluHsr0ndoLuGjtHPvy1V00b1NXTglTqsbQv/lQQvV3jLsIVKfQrBWypRmZ5A8JaJqdQnEbylEpX6BIK3VKIyP4HgLTMVT9yHAMFr3B4EL8Fr3EL+ywleF4rh/eIJNyuoHFMIXvt5IHjthlzhdWfIJD6H17wHCF6C17yJCF4XhP4MrvC6oSR47Y4Er92Q4HVnyCSC17wHCF6C17yJCF4XhASvM0WJ4LVjErx2Q4LXnSGTCF7zHiB4CV7zJiJ4XRASvM4UCV4XlASvC8VtM7iH151lkCdxD6/x7BO8BK9xC/kv5x5eF4rc0uBGkeB14UjwulAkeN0pMongNe4BgpfgNW4hgtcF4PYZ3MPrBpNbGuyOBK/dcMcErvC6swzyJILXePYJXoLXuIUIXheABK9DRa7wusAkeF0ocoXXnSKTCF7jHiB4CV7jFiJ4XQASvA4VCV4XmASvC0WC150ikwhe4x4geAle4xYieF0AErwOFQleF5gErwtFgtedIpMIXuMeIHgJXuMWInhdABK8DhUJXheYBK8LRYLXnSKTCF7jHiB4CV7jFiJ4XQASvA4VCV4XmASvC0WC150ikwhe9gACCCCAAAIIIIBAlRYgeKv06WVxCCCAAAIIIIAAAgQvewABBBBAAAEEEECgSgsQvFX69LI4BBBAAAEEEEAAAYKXPRCSQF5evjqfepViYqJLXv+nYzpr+P03hDQvaC/6deEy3Tr4GR17ZHvd+/eLS5b/v+9+0sOjXlHahk3qeNhBGjboGtWtnRw0njKv94P/TNMDT7yoh+66Ur1O7Oa/7n/fzdV1A4crOjqqZM6d152vC8/tWea5QXriF9/M1hNj3vT3XNvWTfXAnf3VqllD5eTmafA/x+vLqXOUEB+nG/ufoz69TwwSTZnXujl7qx4Y/qK+nTHP/zPx/L/8Sdddcrb/+vOvG6L5C5dJERH+f0+qnqiv/z2qzLOD9MS585fooRETtHj5GqXUq6Xbrz1PJ/botP2fa/5sDNJeKI+1ErzloRqAmes3Zujsywfp2/eeDsBq3S5xzryFemjkyzqoZWPVqJZYEryZm7fotAvu1OODr1O3Tu008vm3tGbdBv4SsRf+F9/8RN//+Jsfapeff0ZJ8H703+/0+dczNeKBG92euCo4LTUtXX++7B6Neex2dWjXWk/96x398PNCjR8xUKPGTdKvC5fricHXyXvepbc8onHDB+jglk2qoIRtSUOffFkbN2Vp6MArtTE9Uxdc/6CeGHy9unZsqzMvHqgnh9zk//POY+8CxcXFOrnvbbr1qj7qfcrR+mraD7pzyGh9O/kZ5ebl82cjm8csQPCaCYM5YMnyNbpu4Ah98tpjwQQwrHr5qlT/qu2Etz6T9xeHHVd4P/lyht756Gs9/887/OlZm7fohHNv0fQPnlVsbIzhJ1bNl87/fbl/RfLK2/+pvn8+qSR435z8pbwrRQ8O6F81F+5wVV7I/vTrIp1yfFd/qvdvHm64Z6S+eGuEzrrkbj008Ep1PLS1/3uPPfO6qldL0PWX/cXhEVSNUV98O8ffi41T6voLuvGeJ3XSMZ311zOP9/8ZfmPMYKXUq101FltOq/D+jcKnX83U2b2OKfkJXU69SpNfeljz5i/hz8Zycg/SWII3SGfb4Vp/+mWRbrp3lFo2a6iFS1aqbaum+sdtl6pF0xSHP6Vqj3puwuTdgnfMy+9rQ3qG7rn5opKFe/9jOWHUPWrepEHVxjCs7orbHtsteMe+9qE+nzJLW3PztCkjy79tZNAtF6taYrzhpwTjpeNe/0i//b5cj913rTqefIX/r96Tk6r5i/f+IjHrx9/83+OxdwHv9obel9ztXw1v3byRf+vX8d07aPbcBapdK0m3Xd1XJxzdEcJ9COTnF/iB+/q7X2jS2CHy/pnmz0a2jFWA4LUKBvT1i5au0oS3P1O/c3qqZdMUjZ4wWV9+O0fvjn8ooCL7v+w9g3fkC2+roLBQd1x7XsmwU86/Q6MevEntDm6+/z8gIK/YM3g//3qWfvxlkS4/73RFR0XpjiGj1aJpAz96eexd4JsZc/XgiAl6+alBqlWzhjr1vEKzPnleCfGx/ove/eQb/efr7/X0w7fAuBcB7yrl3//xlH97iHclvKioWPc9Nk49jz9Cxx7ZQd/M+EkDHnxOk196RA3rc8X3jxi9e8ZvGjRKDerW0sgHb1L7Q1qKPxv5R86FAMHrQpEZfqh17XW1Pn39cTWoVwuRMgjsGbzPv/K+1qRu0ODbLyt59dG9r9fE5wZzhXcfnnsG755P/f6nBbp32Fh9/Cq33+yN0Xvz3+iX3tPoR29Vs8bb/m2Cd4X3v28NL3nT5CuTPpf3b3a4wvvHit49+DfcPUJHdm6nm/qfu9cd2//WYTr3jOP9+1R5/LGA978nM+fM111Dx2ji6H/I25/82chusQoQvFbBgL7ee6NQRmZ2yRsxvH8FdcRpV2vKO0+qVnKNgKrs37L3DN7PpszSq+98rpeevNsf5Bmf1m+Afw/vrp+GsX8/peo/e8/gXbRstf9mwPp1a/qL/27Orxr65Cua/OLQqo8Rwgq9T2kYNe4djX3izt0+EcR7U+qgmy/WkZ0P8ad6n4bRoF5tXXvJn0P4KVX7Jd6bqrx9eNpJR+qiv55SstgtW3O1YPEKdTrsoJJfu+Tmh3XhuaeU3HNetWXKvroN6ZmaNuvn3Wwbdi4AAArYSURBVP4icNnfH1Xfs07yP3GFPxvLbskz/1iA4GVnhCQwddY83TtsnP+vP703Y3hXh76ZOdf/2ziPsgnsGbzZW3J0Wr87Nezea9St4yF69OnXtHnLVv+jyXjsXWDP4PX+9ecvC5Zq5JAbVVws3Xb/M/4bim67pi+MewhkZGXrnP73+veJN2lYb7ff9e4pnzNvgYbff6NWrknT5bc+qleeGuTft89jd4FnX3xXaRszNPi2S3f7De+q78l9bvU/McS7l9y7bcT75IEPXn5UdWolwbiLgLcXe/a9zf9UmuO6d9Bvi1bI+8vBK08PUqMGdfmzkd1iFiB4zYTBHTB+4sd6edJnysnJU/t2rfw3re14l3JwVUpfuReyE9/7QkVFRfI+iicqKkp9ep/g32M6ffYveuCJl5S2IV1dvei952rVTK5e+tAAPuNvVw3W70tXqaCgUFGRkYqIjNCwQVfrhKM7+feieh9r5N3D+6dju2jA9ReU3IsaQKq9LvnfH//P/4vrnv8G4au3R/pv8rv/iRfl3ROdmBCvW6/us9s76HHcKeCFWtqGDH8P7nicf/afNPDGfv7nQv9z9ESlpm30/1Ix4IYL1L1zO/j+QMD7HPLhY97U6tQNqplUXVdfdJb/SRfegz8b2TJWAYLXKsjrEUAAAQQQQAABBCq1AMFbqU8PB4cAAggggAACCCBgFSB4rYK8HgEEEEAAAQQQQKBSCxC8lfr0cHAIIIAAAggggAACVgGC1yrI6xFAAAEEEEAAAQQqtQDBW6lPDweHAAIIIIAAAgggYBUgeK2CvB4BBBBAAAEEEECgUgsQvJX69HBwCCCAAAIIIIAAAlYBgtcqyOsRQAABBBBAAAEEKrUAwVupTw8HhwACCCCAAAIIIGAVIHitgrweAQQQQAABBBBAoFILELyV+vRwcAgggAACCCCAAAJWAYLXKsjrEUAAAQQQQAABBCq1AMFbqU8PB4cAAggggAACCCBgFSB4rYK8HgEEEEAAAQQQQKBSCxC8lfr0cHAIIIAAAggggAACVgGC1yrI6xFAoMoIPPLUq0pNS9fIITdWmTWxEAQQQAABieBlFyCAQKUS6Hra1Rpy5xU64+Tu2pqTp/c/n6q+Z51Ybsf4+rv/1d/OPEExMdFavipVeXkFOqhl43L7eQxGAAEEEKh4AYK34s35iQggsA+BXYN32qyfNfz5t/TW8/fvt1lBYaGio6L2+TovqI868zpN++AZJSbE7/fP4AUIIIAAAgeGAMF7YJwnjhKBwAjsCN5aydV13cDhKigsUnxcjN4Yc79aN2+kV9/5j8a/8bE2ZWSpeZMU3XLlX3X8UR19n79e+Q/17nm03vnoa7Vo1lBPPXSz5s5fomFPv6b5vy9TfFycTj6uiwbdfJH//KN6X6/cvHwlxMdqwA39tGjpqt1uaZj5w3w98dwbWrRsjerWTtK5ZxyvKy44U5GREfrn6InKyMxWclI1TZn2o7I2b9FZp/bQHdeeF5hzxUIRQACBA0WA4D1QzhTHiUBABHa9wjvhrU/1/ufTSq7wfj39R9332L/07CO3qu1BTfW/737Sbfc/q/fGP6RmjRvo/GsfUObmLRp8+2U65KBmSqqeqJ59b9fpJ3fXDZedow3pGbrqjn+q71kn6fLzT9f835f7kTzz4+f8K7y73sPr3cvbq9+dGnTLRTr71GO0ePkaXTPgCV1xwRm6pE8vDR/zpl5/9ws9dFd/9TrxSP22aIU/6+0XHvB/Ng8EEEAAgcojQPBWnnPBkSCAgKR9Be+1dw1X+0Na6obLzymx8iK0Q7tW/q+df90Q/yrw0IFXlvz+xk1Zqp4Yr9jYGP/Xhj75srxfe2Lw9fsM3rGvfagP/zNN//7XQyWzRr7wtqbOmqc3x9zvB++U6T/qvfFDS37/5D636Y7rztPpf+rOuUQAAQQQqEQCBG8lOhkcCgII7Dt4z7joLi1bmfr/mM7udYwevvsqP3hP6tFZ11x8Vslzvvh2jsZP/Ehr1m30fy0zK1vdO7fTU0Nv2Wfw3v/4i8rI2qwRD+z8xIZJH37t3+Iw9f1n/OBdsHilnht2W8nPOq3fAP9nn3P6cZxKBBBAAIFKJEDwVqKTwaEggMC+g7f3JXf7n9jg3VLwRw8veHud0M2/XcF7LFm+Rn+5/F4NGdBfZ53Sw7/3dtgzr2vl6nVlCt7Mzdkafv8NJT/q7Q+maPjzb2rq5G3Bu3DJKo1+9FaCl42LAAIIVHIBgreSnyAOD4GgCezrlobr7x6h2jWT9NBdV5SwrEndoAb1avsxu2fwTv7sW414/i19+fbIkudfcvPDSq5RrdTg/dfEj/T+Z1N3u6Xh8efe0Kwff9PE0f8geIO2MVkvAggc0AIE7wF9+jh4BKqewK7B++bkLzV6wnt6Z9yD/pvKvpv9i24d/IxGDrlJPboeph9+/l1eBHtXWbu0b/P/gnfGnPn+m9TeGTdETRvV17MvvadvZsxVdFSkJj432L8C7F01fmPMYLVs2lCjxk0q+ZSG9RszdOr5d+jev1/sXx323uB27cDhurn/uTrv7D8RvFVv67EiBBCowgIEbxU+uSwNgQNRYNfg9a7eXvb3R7Upc7P/yQxHdGijVyZ9rhff/ERekDZqUEdXX3SW/nLasf5S97zC6/3akBET/DefJSbE6cJzT9GxR7ZX/9uGqWvHthr5wE26esDjmjN3oW64/C9+7O76TWv/+26uHn9uopavWqeG9Wur3zk9deG5PRUREUHwHoibi2NGAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAoQvIE99SwcAQQQQAABBBAIhgDBG4zzzCoRQAABBBBAAIHAChC8gT31LBwBBBBAAAEEEAiGAMEbjPPMKhFAAAEEEEAAgcAKELyBPfUsHAEEEEAAAQQQCIYAwRuM88wqEUAAAQQQQACBwAr8H3EE+2wY0/heAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "render(ax_client.get_optimization_trace(objective_optimum=0.397887))" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:08] ax.service.ax_client: Completed trial 8 with data: {'branin': (12.206194, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "hidden_ranges": [], - "originalKey": "59a5ea1e-1b70-433b-bd3d-e3708d270769", - "showInput": true - }, - "source": [ - "## Optimization with the Developer API\n", - "\n", - "A detailed tutorial on the Service API can be found [here](https://ax.dev/tutorials/gpei_hartmann_developer.html).\n", - "\n", - "### Set up the Experiment in Ax\n", - "\n", - "We need 3 inputs for an Ax `Experiment`:\n", - "- A search space to optimize over;\n", - "- An optimization config specifiying the objective / metrics to optimize, and optional outcome constraints;\n", - "- A runner that handles the deployment of trials. For a synthetic optimization problem, such as here, this only returns simple metadata about the trial." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:09] ax.service.ax_client: Generated new trial 9 with parameters {'x1': -5.0, 'x2': 9.066302} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802638781, - "executionStopTime": 1646802638852, - "hidden_ranges": [], - "originalKey": "16ddad39-c4ba-48d0-a67d-e42de25e7236", - "requestMsgId": "9063b301-cf83-423c-9d3c-809ec8107cc6", - "showInput": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import torch\n", - "from ax import (\n", - " Data,\n", - " Experiment,\n", - " Metric,\n", - " Objective,\n", - " OptimizationConfig,\n", - " ParameterType,\n", - " RangeParameter,\n", - " Runner,\n", - " SearchSpace,\n", - ")\n", - "from ax.utils.common.result import Ok\n", - "from botorch.test_functions import Branin\n", - "\n", - "\n", - "branin_func = Branin()\n", - "\n", - "# For our purposes, the metric is a wrapper that structures the function output.\n", - "class BraninMetric(Metric):\n", - " def fetch_trial_data(self, trial):\n", - " records = []\n", - " for arm_name, arm in trial.arms_by_name.items():\n", - " params = arm.parameters\n", - " tensor_params = torch.tensor([params[\"x1\"], params[\"x2\"]])\n", - " records.append(\n", - " {\n", - " \"arm_name\": arm_name,\n", - " \"metric_name\": self.name,\n", - " \"trial_index\": trial.index,\n", - " \"mean\": branin_func(tensor_params),\n", - " \"sem\": float(\n", - " \"nan\"\n", - " ), # SEM (observation noise) - NaN indicates unknown\n", - " }\n", - " )\n", - " return Ok(value=Data(df=pd.DataFrame.from_records(records)))\n", - "\n", - "\n", - "# Search space defines the parameters, their types, and acceptable values.\n", - "search_space = SearchSpace(\n", - " parameters=[\n", - " RangeParameter(\n", - " name=\"x1\", parameter_type=ParameterType.FLOAT, lower=-5, upper=10\n", - " ),\n", - " RangeParameter(\n", - " name=\"x2\", parameter_type=ParameterType.FLOAT, lower=0, upper=15\n", - " ),\n", - " ]\n", - ")\n", - "\n", - "optimization_config = OptimizationConfig(\n", - " objective=Objective(\n", - " metric=BraninMetric(name=\"branin_metric\", lower_is_better=True),\n", - " minimize=True, # This is optional since we specified `lower_is_better=True`\n", - " )\n", - ")\n", - "\n", - "\n", - "class MyRunner(Runner):\n", - " def run(self, trial):\n", - " trial_metadata = {\"name\": str(trial.index)}\n", - " return trial_metadata\n", - "\n", - "\n", - "exp = Experiment(\n", - " name=\"branin_experiment\",\n", - " search_space=search_space,\n", - " optimization_config=optimization_config,\n", - " runner=MyRunner(),\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:09] ax.service.ax_client: Completed trial 9 with data: {'branin': (78.675331, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "hidden_ranges": [], - "originalKey": "a5cb8ffb-3af8-4fdd-85b4-a10917177dc7", - "showInput": false - }, - "source": [ - "### Run the BO loop\n", - "\n", - "First, we use the Sobol generator to create 5 (quasi-) random initial point in the search space. Ax controls objective evaluations via `Trial`s. \n", - "- We generate a `Trial` using a generator run, e.g., `Sobol` below. A `Trial` specifies relevant metadata as well as the parameters to be evaluated. At this point, the `Trial` is at the `CANDIDATE` stage.\n", - "- We run the `Trial` using `Trial.run()`. In our example, this serves to mark the `Trial` as `RUNNING`. In an advanced application, this can be used to dispatch the `Trial` for evaluation on a remote server.\n", - "- Once the `Trial` is done running, we mark it as `COMPLETED`. This tells the `Experiment` that it can fetch the `Trial` data. \n", - "\n", - "A `Trial` supports evaluation of a single parameterization. For parallel evaluations, see [`BatchTrial`](https://ax.dev/docs/core.html#trial-vs-batch-trial)." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:09] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.787884, 'x2': 6.879815} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "code_folding": [], - "executionStartTime": 1646802638888, - "executionStopTime": 1646802639051, - "hidden_ranges": [], - "originalKey": "13b3c924-8bf6-4af3-ab0e-1f8abe6580f0", - "requestMsgId": "33b6b5d0-ffe2-429c-9891-df71eef48da9" - }, - "outputs": [], - "source": [ - "from ax.modelbridge.registry import Models\n", - "\n", - "\n", - "sobol = Models.SOBOL(exp.search_space)\n", - "\n", - "for i in range(5):\n", - " trial = exp.new_trial(generator_run=sobol.gen(1))\n", - " trial.run()\n", - " trial.mark_completed()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:09] ax.service.ax_client: Completed trial 10 with data: {'branin': (20.990005, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "33fef7a2-67db-43cd-bc84-4d5736c582e7", - "showInput": false - }, - "source": [ - "Once the initial (quasi-) random stage is completed, we can use our `SimpleCustomGP` with the default acquisition function chosen by `Ax` to run the BO loop." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:10] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 1.60023, 'x2': 0.584966} using model BoTorch.\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802639078, - "executionStopTime": 1646802679659, - "hidden_ranges": [], - "originalKey": "bbdd71d2-e2c9-40a1-8d86-c085a4789689", - "requestMsgId": "83733f3f-531b-4847-99da-468cc9bac3f8", - "scrolled": false, - "showInput": true - }, - "outputs": [], - "source": [ - "with fast_smoke_test():\n", - " for i in range(NUM_EVALS - 5):\n", - " model_bridge = Models.BOTORCH_MODULAR(\n", - " experiment=exp,\n", - " data=exp.fetch_data(),\n", - " surrogate=Surrogate(SimpleCustomGP),\n", - " )\n", - " trial = exp.new_trial(generator_run=model_bridge.gen(1))\n", - " trial.run()\n", - " trial.mark_completed()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:10] ax.service.ax_client: Completed trial 11 with data: {'branin': (19.951, nan)}.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "cb16843a-0e12-47fc-860f-eafb74f1bd3b", - "showInput": false - }, - "source": [ - "View the trials attached to the `Experiment`." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:10] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 10.0, 'x2': 0.0} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:10] ax.service.ax_client: Completed trial 12 with data: {'branin': (10.960894, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:11] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 7.38266, 'x2': 0.0} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:11] ax.service.ax_client: Completed trial 13 with data: {'branin': (16.027073, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:11] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 4.173322, 'x2': 0.0} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:11] ax.service.ax_client: Completed trial 14 with data: {'branin': (7.656268, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:12] ax.service.ax_client: Generated new trial 15 with parameters {'x1': -3.935855, 'x2': 15.0} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:12] ax.service.ax_client: Completed trial 15 with data: {'branin': (3.810518, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:12] ax.service.ax_client: Generated new trial 16 with parameters {'x1': -3.321259, 'x2': 12.38287} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:12] ax.service.ax_client: Completed trial 16 with data: {'branin': (0.660087, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:13] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 10.0, 'x2': 3.666754} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:13] ax.service.ax_client: Completed trial 17 with data: {'branin': (2.383767, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:14] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 9.34166, 'x2': 2.5446} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:14] ax.service.ax_client: Completed trial 18 with data: {'branin': (0.450308, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:14] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 3.076019, 'x2': 2.418569} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:14] ax.service.ax_client: Completed trial 19 with data: {'branin': (0.426966, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:15] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 9.537424, 'x2': 2.493842} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:15] ax.service.ax_client: Completed trial 20 with data: {'branin': (0.4648, nan)}.\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802679684, - "executionStopTime": 1646802679777, - "hidden_ranges": [], - "originalKey": "28a95fc8-848c-4cf0-a31d-20bde4fc95ff", - "requestMsgId": "cd1182e4-2326-48f2-bb18-6be5b16c7a10", - "showInput": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: Trial(experiment_name='branin_experiment', index=0, status=TrialStatus.COMPLETED, arm=Arm(name='0_0', parameters={'x1': -0.4329736530780792, 'x2': 12.617264986038208})),\n", - " 1: Trial(experiment_name='branin_experiment', index=1, status=TrialStatus.COMPLETED, arm=Arm(name='1_0', parameters={'x1': 2.9818817181512713, 'x2': 2.856269543990493})),\n", - " 2: Trial(experiment_name='branin_experiment', index=2, status=TrialStatus.COMPLETED, arm=Arm(name='2_0', parameters={'x1': 6.599703226238489, 'x2': 8.393928492441773})),\n", - " 3: Trial(experiment_name='branin_experiment', index=3, status=TrialStatus.COMPLETED, arm=Arm(name='3_0', parameters={'x1': -4.985555941238999, 'x2': 6.132936486974359})),\n", - " 4: Trial(experiment_name='branin_experiment', index=4, status=TrialStatus.COMPLETED, arm=Arm(name='4_0', parameters={'x1': -1.767810033634305, 'x2': 10.938136987388134})),\n", - " 5: Trial(experiment_name='branin_experiment', index=5, status=TrialStatus.COMPLETED, arm=Arm(name='5_0', parameters={'x1': 10.0, 'x2': 0.0})),\n", - " 6: Trial(experiment_name='branin_experiment', index=6, status=TrialStatus.COMPLETED, arm=Arm(name='6_0', parameters={'x1': 4.826118509694027, 'x2': 15.0})),\n", - " 7: Trial(experiment_name='branin_experiment', index=7, status=TrialStatus.COMPLETED, arm=Arm(name='7_0', parameters={'x1': 7.881232756494741, 'x2': 1.667689434902604})),\n", - " 8: Trial(experiment_name='branin_experiment', index=8, status=TrialStatus.COMPLETED, arm=Arm(name='8_0', parameters={'x1': -5.0, 'x2': 2.2314214877327094})),\n", - " 9: Trial(experiment_name='branin_experiment', index=9, status=TrialStatus.COMPLETED, arm=Arm(name='9_0', parameters={'x1': 1.524281688383832, 'x2': 6.8288515949282935})),\n", - " 10: Trial(experiment_name='branin_experiment', index=10, status=TrialStatus.COMPLETED, arm=Arm(name='10_0', parameters={'x1': -5.0, 'x2': 14.195328959491395})),\n", - " 11: Trial(experiment_name='branin_experiment', index=11, status=TrialStatus.COMPLETED, arm=Arm(name='11_0', parameters={'x1': 4.954093857651461, 'x2': 0.0})),\n", - " 12: Trial(experiment_name='branin_experiment', index=12, status=TrialStatus.COMPLETED, arm=Arm(name='12_0', parameters={'x1': 10.0, 'x2': 4.292254247708062})),\n", - " 13: Trial(experiment_name='branin_experiment', index=13, status=TrialStatus.COMPLETED, arm=Arm(name='13_0', parameters={'x1': 1.784995401645844, 'x2': 2.48105215133849})),\n", - " 14: Trial(experiment_name='branin_experiment', index=14, status=TrialStatus.COMPLETED, arm=Arm(name='14_0', parameters={'x1': 10.0, 'x2': 2.636078735605977})),\n", - " 15: Trial(experiment_name='branin_experiment', index=15, status=TrialStatus.COMPLETED, arm=Arm(name='15_0', parameters={'x1': 3.5206582595405216, 'x2': 2.734373821999437})),\n", - " 16: Trial(experiment_name='branin_experiment', index=16, status=TrialStatus.COMPLETED, arm=Arm(name='16_0', parameters={'x1': 10.0, 'x2': 15.0})),\n", - " 17: Trial(experiment_name='branin_experiment', index=17, status=TrialStatus.COMPLETED, arm=Arm(name='17_0', parameters={'x1': -3.432540455981013, 'x2': 11.981022952446304})),\n", - " 18: Trial(experiment_name='branin_experiment', index=18, status=TrialStatus.COMPLETED, arm=Arm(name='18_0', parameters={'x1': -3.889312984711564, 'x2': 11.70726330370977})),\n", - " 19: Trial(experiment_name='branin_experiment', index=19, status=TrialStatus.COMPLETED, arm=Arm(name='19_0', parameters={'x1': -3.3829822918223034, 'x2': 13.579969027820502})),\n", - " 20: Trial(experiment_name='branin_experiment', index=20, status=TrialStatus.COMPLETED, arm=Arm(name='20_0', parameters={'x1': -3.298671582798891, 'x2': 12.874831586512578})),\n", - " 21: Trial(experiment_name='branin_experiment', index=21, status=TrialStatus.COMPLETED, arm=Arm(name='21_0', parameters={'x1': 9.50823416510418, 'x2': 2.8940046336419702})),\n", - " 22: Trial(experiment_name='branin_experiment', index=22, status=TrialStatus.COMPLETED, arm=Arm(name='22_0', parameters={'x1': 3.074374349475095, 'x2': 2.3894152080730544})),\n", - " 23: Trial(experiment_name='branin_experiment', index=23, status=TrialStatus.COMPLETED, arm=Arm(name='23_0', parameters={'x1': 9.588438538916012, 'x2': 2.6513939215449205})),\n", - " 24: Trial(experiment_name='branin_experiment', index=24, status=TrialStatus.COMPLETED, arm=Arm(name='24_0', parameters={'x1': -3.229034722434058, 'x2': 12.655684947578434})),\n", - " 25: Trial(experiment_name='branin_experiment', index=25, status=TrialStatus.COMPLETED, arm=Arm(name='25_0', parameters={'x1': 3.105126694005836, 'x2': 2.6126209051274367})),\n", - " 26: Trial(experiment_name='branin_experiment', index=26, status=TrialStatus.COMPLETED, arm=Arm(name='26_0', parameters={'x1': -3.3716682849456543, 'x2': 13.126453746040617})),\n", - " 27: Trial(experiment_name='branin_experiment', index=27, status=TrialStatus.COMPLETED, arm=Arm(name='27_0', parameters={'x1': 3.0652809834567183, 'x2': 1.994860653228331})),\n", - " 28: Trial(experiment_name='branin_experiment', index=28, status=TrialStatus.COMPLETED, arm=Arm(name='28_0', parameters={'x1': 9.400941016992647, 'x2': 3.04534723557459})),\n", - " 29: Trial(experiment_name='branin_experiment', index=29, status=TrialStatus.COMPLETED, arm=Arm(name='29_0', parameters={'x1': -3.8481443576407575, 'x2': 15.0}))}" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:16] ax.service.ax_client: Generated new trial 21 with parameters {'x1': -3.360749, 'x2': 15.0} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:16] ax.service.ax_client: Completed trial 21 with data: {'branin': (5.432912, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:17] ax.service.ax_client: Generated new trial 22 with parameters {'x1': 9.516079, 'x2': 2.791557} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:17] ax.service.ax_client: Completed trial 22 with data: {'branin': (0.494746, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:19] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 3.202976, 'x2': 2.439512} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:19] ax.service.ax_client: Completed trial 23 with data: {'branin': (0.460872, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:20] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 9.625609, 'x2': 2.470825} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:20] ax.service.ax_client: Completed trial 24 with data: {'branin': (0.622846, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:21] ax.service.ax_client: Generated new trial 25 with parameters {'x1': -3.235781, 'x2': 12.32664} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:21] ax.service.ax_client: Completed trial 25 with data: {'branin': (0.471375, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:22] ax.service.ax_client: Generated new trial 26 with parameters {'x1': 9.466124, 'x2': 2.301119} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:22] ax.service.ax_client: Completed trial 26 with data: {'branin': (0.449765, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[W 241107 08:26:24 optimize:576] Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + " [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + " Trying again with a new set of initial conditions.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:25] ax.service.ax_client: Generated new trial 27 with parameters {'x1': 2.97826, 'x2': 2.43746} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:25] ax.service.ax_client: Completed trial 27 with data: {'branin': (0.526684, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:27] ax.service.ax_client: Generated new trial 28 with parameters {'x1': -3.286554, 'x2': 12.040548} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:27] ax.service.ax_client: Completed trial 28 with data: {'branin': (0.84146, nan)}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[W 241107 08:26:28 optimize:576] Optimization failed in `gen_candidates_scipy` with the following warning(s):\n", + " [OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n", + " Trying again with a new set of initial conditions.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:29] ax.service.ax_client: Generated new trial 29 with parameters {'x1': 9.459437, 'x2': 2.554713} using model BoTorch.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 08:26:29] ax.service.ax_client: Completed trial 29 with data: {'branin': (0.406186, nan)}.\n" + ] + } + ], + "source": [ + "with fast_smoke_test():\n", + " for i in range(NUM_EVALS):\n", + " parameters, trial_index = ax_client.get_next_trial()\n", + " # Local evaluation here can be replaced with deployment to external system.\n", + " ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "2794d041-6a39-483d-a603-cc5088bcd1b2", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Viewing the evaluated trials" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996766045, + "executionStopTime": 1730996789881, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "26f42620-0f39-4836-aa09-42d1718ee6e6", + "output": { + "id": "2736915803137170" + }, + "outputsInitialized": true, + "requestMsgId": "26f42620-0f39-4836-aa09-42d1718ee6e6", + "serverExecutionDuration": 53.62950079143, + "showInput": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING 11-07 12:53:20] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" + ] + }, + { + "data": { + "text/html": [ + "
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trial_indexarm_nametrial_statusgeneration_methodbraninx1x2
000_0COMPLETEDSobol104.3654170.62583014.359564
111_0COMPLETEDSobol2.9968623.1662173.867106
222_0COMPLETEDSobol66.5306329.56010510.718323
333_0COMPLETEDSobol198.850861-3.8786640.117947
444_0COMPLETEDSobol5.811776-2.3628588.855021
555_0COMPLETEDBoTorch6.6110402.5624644.925756
666_0COMPLETEDBoTorch31.2497735.5034284.951339
777_0COMPLETEDBoTorch38.632786-2.3068094.436082
888_0COMPLETEDBoTorch12.208769-1.5822967.318848
999_0COMPLETEDBoTorch78.686066-5.0000009.065641
101010_0COMPLETEDBoTorch20.8491860.7799986.842907
111111_0COMPLETEDBoTorch19.968334-0.9591719.756062
121212_0COMPLETEDBoTorch21.1575971.7594050.000000
131313_0COMPLETEDBoTorch3.709130-3.67521015.000000
141414_0COMPLETEDBoTorch10.96089410.0000000.000000
151515_0COMPLETEDBoTorch11.7103004.6933450.000000
161616_0COMPLETEDBoTorch0.400116-3.16039012.343285
171717_0COMPLETEDBoTorch2.57559410.0000003.798226
181818_0COMPLETEDBoTorch0.5558593.3044442.327283
191919_0COMPLETEDBoTorch0.764316-3.37558212.520736
202020_0COMPLETEDBoTorch0.5433059.2671052.183014
212121_0COMPLETEDBoTorch0.4879219.5366122.744301
222222_0COMPLETEDBoTorch0.646773-3.05513512.529729
232323_0COMPLETEDBoTorch0.4285783.0997452.457142
242424_0COMPLETEDBoTorch2.8208188.9446200.943412
252525_0COMPLETEDBoTorch0.4675529.5100652.361432
262626_0COMPLETEDBoTorch0.4107069.4258442.589096
272727_0COMPLETEDBoTorch0.435478-3.09163812.315311
282828_0COMPLETEDBoTorch0.443229-3.22138912.345989
292929_0COMPLETEDBoTorch0.4835403.1824682.521964
\n", + "
" ], - "source": [ - "exp.trials" + "text/plain": [ + " trial_index arm_name trial_status generation_method branin \\\n", + "0 0 0_0 COMPLETED Sobol 104.365417 \n", + "1 1 1_0 COMPLETED Sobol 2.996862 \n", + "2 2 2_0 COMPLETED Sobol 66.530632 \n", + "3 3 3_0 COMPLETED Sobol 198.850861 \n", + "4 4 4_0 COMPLETED Sobol 5.811776 \n", + "5 5 5_0 COMPLETED BoTorch 6.611040 \n", + "6 6 6_0 COMPLETED BoTorch 31.249773 \n", + "7 7 7_0 COMPLETED BoTorch 38.632786 \n", + "8 8 8_0 COMPLETED BoTorch 12.208769 \n", + "9 9 9_0 COMPLETED BoTorch 78.686066 \n", + "10 10 10_0 COMPLETED BoTorch 20.849186 \n", + "11 11 11_0 COMPLETED BoTorch 19.968334 \n", + "12 12 12_0 COMPLETED BoTorch 21.157597 \n", + "13 13 13_0 COMPLETED BoTorch 3.709130 \n", + "14 14 14_0 COMPLETED BoTorch 10.960894 \n", + "15 15 15_0 COMPLETED BoTorch 11.710300 \n", + "16 16 16_0 COMPLETED BoTorch 0.400116 \n", + "17 17 17_0 COMPLETED BoTorch 2.575594 \n", + "18 18 18_0 COMPLETED BoTorch 0.555859 \n", + "19 19 19_0 COMPLETED BoTorch 0.764316 \n", + "20 20 20_0 COMPLETED BoTorch 0.543305 \n", + "21 21 21_0 COMPLETED BoTorch 0.487921 \n", + "22 22 22_0 COMPLETED BoTorch 0.646773 \n", + "23 23 23_0 COMPLETED BoTorch 0.428578 \n", + "24 24 24_0 COMPLETED BoTorch 2.820818 \n", + "25 25 25_0 COMPLETED BoTorch 0.467552 \n", + "26 26 26_0 COMPLETED BoTorch 0.410706 \n", + "27 27 27_0 COMPLETED BoTorch 0.435478 \n", + "28 28 28_0 COMPLETED BoTorch 0.443229 \n", + "29 29 29_0 COMPLETED BoTorch 0.483540 \n", + "\n", + " x1 x2 \n", + "0 0.625830 14.359564 \n", + "1 3.166217 3.867106 \n", + "2 9.560105 10.718323 \n", + "3 -3.878664 0.117947 \n", + "4 -2.362858 8.855021 \n", + "5 2.562464 4.925756 \n", + "6 5.503428 4.951339 \n", + "7 -2.306809 4.436082 \n", + "8 -1.582296 7.318848 \n", + "9 -5.000000 9.065641 \n", + "10 0.779998 6.842907 \n", + "11 -0.959171 9.756062 \n", + "12 1.759405 0.000000 \n", + "13 -3.675210 15.000000 \n", + "14 10.000000 0.000000 \n", + "15 4.693345 0.000000 \n", + "16 -3.160390 12.343285 \n", + "17 10.000000 3.798226 \n", + "18 3.304444 2.327283 \n", + "19 -3.375582 12.520736 \n", + "20 9.267105 2.183014 \n", + "21 9.536612 2.744301 \n", + "22 -3.055135 12.529729 \n", + "23 3.099745 2.457142 \n", + "24 8.944620 0.943412 \n", + "25 9.510065 2.361432 \n", + "26 9.425844 2.589096 \n", + "27 -3.091638 12.315311 \n", + "28 -3.221389 12.345989 \n", + "29 3.182468 2.521964 " ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax_client.get_trials_data_frame()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996766786, + "executionStopTime": 1730996790153, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "89ba4880-e577-40c0-a9f7-5da1df38cb50", + "output": { + "id": "2997713397043895" }, + "outputsInitialized": true, + "requestMsgId": "89ba4880-e577-40c0-a9f7-5da1df38cb50", + "serverExecutionDuration": 252.73659080267, + "showInput": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "f96540ca-1043-4803-ad8c-d143d98373a2", - "showInput": false - }, - "source": [ - "View the evaluation data about these trials." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'x1': 9.510065129079985, 'x2': 2.361432108875333}\n", + "Corresponding mean: {'branin': np.float64(0.372037358815291)}, covariance: {'branin': {'branin': np.float64(0.04886421886415146)}}\n" + ] + } + ], + "source": [ + "parameters, values = ax_client.get_best_parameters()\n", + "print(f\"Best parameters: {parameters}\")\n", + "print(f\"Corresponding mean: {values[0]}, covariance: {values[1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "10562d0a-5fc3-4771-91c3-f881bd211174", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Plotting the response surface and optimization progress" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996819269, + "executionStopTime": 1730996821837, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false }, + "language": "python", + "originalKey": "9848063a-11f7-41b7-abff-44d6a3d7eb9f", + "output": { + "id": "1070981858093152" + }, + "outputsInitialized": true, + "requestMsgId": "9848063a-11f7-41b7-abff-44d6a3d7eb9f", + "serverExecutionDuration": 2040.9845691174, + "showInput": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802679808, - "executionStopTime": 1646802680139, - "hidden_ranges": [], - "originalKey": "a0a7e159-ce6b-43d0-97e3-4d68f9779659", - "requestMsgId": "a00d8d83-c18a-46d2-8179-c180709a8c81", - "showInput": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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arm_namemetric_namemeansemtrial_index
00_0branin_metric53.572651NaN0
11_0branin_metric0.725682NaN1
22_0branin_metric71.991264NaN2
33_0branin_metric133.872314NaN3
44_0branin_metric11.081824NaN4
55_0branin_metric10.960894NaN5
66_0branin_metric198.016434NaN6
77_0branin_metric9.773301NaN7
88_0branin_metric236.403839NaN8
99_0branin_metric19.176552NaN9
1010_0branin_metric21.676010NaN10
1111_0branin_metric13.951876NaN11
1212_0branin_metric3.605428NaN12
1313_0branin_metric9.146261NaN13
1414_0branin_metric2.077741NaN14
1515_0branin_metric1.621861NaN15
1616_0branin_metric145.872208NaN16
1717_0branin_metric1.809717NaN17
1818_0branin_metric8.897885NaN18
1919_0branin_metric1.190839NaN19
2020_0branin_metric0.564129NaN20
2121_0branin_metric0.552207NaN21
2222_0branin_metric0.423343NaN22
2323_0branin_metric0.527413NaN23
2424_0branin_metric0.463321NaN24
2525_0branin_metric0.499759NaN25
2626_0branin_metric0.735993NaN26
2727_0branin_metric0.541708NaN27
2828_0branin_metric0.749163NaN28
2929_0branin_metric3.622983NaN29
\n", - "
" - ], - "text/plain": [ - " arm_name metric_name mean sem trial_index\n", - "0 0_0 branin_metric 53.572651 NaN 0\n", - "1 1_0 branin_metric 0.725682 NaN 1\n", - "2 2_0 branin_metric 71.991264 NaN 2\n", - "3 3_0 branin_metric 133.872314 NaN 3\n", - "4 4_0 branin_metric 11.081824 NaN 4\n", - "5 5_0 branin_metric 10.960894 NaN 5\n", - "6 6_0 branin_metric 198.016434 NaN 6\n", - "7 7_0 branin_metric 9.773301 NaN 7\n", - "8 8_0 branin_metric 236.403839 NaN 8\n", - "9 9_0 branin_metric 19.176552 NaN 9\n", - "10 10_0 branin_metric 21.676010 NaN 10\n", - "11 11_0 branin_metric 13.951876 NaN 11\n", - "12 12_0 branin_metric 3.605428 NaN 12\n", - "13 13_0 branin_metric 9.146261 NaN 13\n", - "14 14_0 branin_metric 2.077741 NaN 14\n", - "15 15_0 branin_metric 1.621861 NaN 15\n", - "16 16_0 branin_metric 145.872208 NaN 16\n", - "17 17_0 branin_metric 1.809717 NaN 17\n", - "18 18_0 branin_metric 8.897885 NaN 18\n", - "19 19_0 branin_metric 1.190839 NaN 19\n", - "20 20_0 branin_metric 0.564129 NaN 20\n", - "21 21_0 branin_metric 0.552207 NaN 21\n", - "22 22_0 branin_metric 0.423343 NaN 22\n", - "23 23_0 branin_metric 0.527413 NaN 23\n", - "24 24_0 branin_metric 0.463321 NaN 24\n", - "25 25_0 branin_metric 0.499759 NaN 25\n", - "26 26_0 branin_metric 0.735993 NaN 26\n", - "27 27_0 branin_metric 0.541708 NaN 27\n", - "28 28_0 branin_metric 0.749163 NaN 28\n", - "29 29_0 branin_metric 3.622983 NaN 29" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp.fetch_data().df" + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 11-07 12:53:22] ax.service.ax_client: Retrieving contour plot with parameter 'x1' on X-axis and 'x2' on Y-axis, for metric 'branin'. Remaining parameters are affixed to the middle of their range.\n" + ] + }, + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ax.utils.notebook.plotting import render\n", + "\n", + "render(ax_client.get_contour_plot())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996822382, + "executionStopTime": 1730996823213, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "2b8c5ca1-db2e-4aa7-8f53-e1d5ea6406e4", + "output": { + "id": "555876497377688" + }, + "outputsInitialized": true, + "requestMsgId": "2b8c5ca1-db2e-4aa7-8f53-e1d5ea6406e4", + "serverExecutionDuration": 255.65920583904, + "showInput": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "({'x1': 9.510065129079985, 'x2': 2.361432108875333},\n", + " {'branin': np.float64(0.372037358815291)})" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_parameters, values = ax_client.get_best_parameters()\n", + "best_parameters, values[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996822759, + "executionStopTime": 1730996823380, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "998452cf-6251-4f0b-ad9b-ac35b484b634", + "output": { + "id": "444230641668471" + }, + "outputsInitialized": true, + "requestMsgId": "998452cf-6251-4f0b-ad9b-ac35b484b634", + "serverExecutionDuration": 118.52293275297, + "showInput": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "render(ax_client.get_optimization_trace(objective_optimum=0.397887))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "59a5ea1e-1b70-433b-bd3d-e3708d270769", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "## Optimization with the Developer API\n", + "\n", + "A detailed tutorial on the Service API can be found [here](https://ax.dev/tutorials/gpei_hartmann_developer.html).\n", + "\n", + "### Set up the Experiment in Ax\n", + "\n", + "We need 3 inputs for an Ax `Experiment`:\n", + "- A search space to optimize over;\n", + "- An optimization config specifiying the objective / metrics to optimize, and optional outcome constraints;\n", + "- A runner that handles the deployment of trials. For a synthetic optimization problem, such as here, this only returns simple metadata about the trial." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996823528, + "executionStopTime": 1730996823772, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false }, + "language": "python", + "originalKey": "7af04314-588b-44c9-b041-12795d720597", + "outputsInitialized": true, + "requestMsgId": "7af04314-588b-44c9-b041-12795d720597", + "serverExecutionDuration": 4.5209536328912, + "showInput": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import torch\n", + "from ax import (\n", + " Data,\n", + " Experiment,\n", + " Metric,\n", + " Objective,\n", + " OptimizationConfig,\n", + " ParameterType,\n", + " RangeParameter,\n", + " Runner,\n", + " SearchSpace,\n", + ")\n", + "from ax.utils.common.result import Ok\n", + "from botorch.test_functions import Branin\n", + "\n", + "\n", + "branin_func = Branin()\n", + "\n", + "# For our purposes, the metric is a wrapper that structures the function output.\n", + "class BraninMetric(Metric):\n", + " def fetch_trial_data(self, trial):\n", + " records = []\n", + " for arm_name, arm in trial.arms_by_name.items():\n", + " params = arm.parameters\n", + " tensor_params = torch.tensor([params[\"x1\"], params[\"x2\"]])\n", + " records.append(\n", + " {\n", + " \"arm_name\": arm_name,\n", + " \"metric_name\": self.name,\n", + " \"trial_index\": trial.index,\n", + " \"mean\": branin_func(tensor_params),\n", + " \"sem\": float(\n", + " \"nan\"\n", + " ), # SEM (observation noise) - NaN indicates unknown\n", + " }\n", + " )\n", + " return Ok(value=Data(df=pd.DataFrame.from_records(records)))\n", + "\n", + "\n", + "# Search space defines the parameters, their types, and acceptable values.\n", + "search_space = SearchSpace(\n", + " parameters=[\n", + " RangeParameter(\n", + " name=\"x1\", parameter_type=ParameterType.FLOAT, lower=-5, upper=10\n", + " ),\n", + " RangeParameter(\n", + " name=\"x2\", parameter_type=ParameterType.FLOAT, lower=0, upper=15\n", + " ),\n", + " ]\n", + ")\n", + "\n", + "optimization_config = OptimizationConfig(\n", + " objective=Objective(\n", + " metric=BraninMetric(name=\"branin_metric\", lower_is_better=True),\n", + " minimize=True, # This is optional since we specified `lower_is_better=True`\n", + " )\n", + ")\n", + "\n", + "\n", + "class MyRunner(Runner):\n", + " def run(self, trial):\n", + " trial_metadata = {\"name\": str(trial.index)}\n", + " return trial_metadata\n", + "\n", + "\n", + "exp = Experiment(\n", + " name=\"branin_experiment\",\n", + " search_space=search_space,\n", + " optimization_config=optimization_config,\n", + " runner=MyRunner(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "a5cb8ffb-3af8-4fdd-85b4-a10917177dc7", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Run the BO loop\n", + "\n", + "First, we use the Sobol generator to create 5 (quasi-) random initial point in the search space. Ax controls objective evaluations via `Trial`s. \n", + "- We generate a `Trial` using a generator run, e.g., `Sobol` below. A `Trial` specifies relevant metadata as well as the parameters to be evaluated. At this point, the `Trial` is at the `CANDIDATE` stage.\n", + "- We run the `Trial` using `Trial.run()`. In our example, this serves to mark the `Trial` as `RUNNING`. In an advanced application, this can be used to dispatch the `Trial` for evaluation on a remote server.\n", + "- Once the `Trial` is done running, we mark it as `COMPLETED`. This tells the `Experiment` that it can fetch the `Trial` data. \n", + "\n", + "A `Trial` supports evaluation of a single parameterization. For parallel evaluations, see [`BatchTrial`](https://ax.dev/docs/core.html#trial-vs-batch-trial)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "code_folding": [], + "collapsed": false, + "executionStartTime": 1730996824494, + "executionStopTime": 1730996824758, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "e7c73fd6-9664-4524-b0b1-91e127e26b67", + "outputsInitialized": true, + "requestMsgId": "e7c73fd6-9664-4524-b0b1-91e127e26b67", + "serverExecutionDuration": 11.998974718153 + }, + "outputs": [], + "source": [ + "from ax.modelbridge.registry import Models\n", + "\n", + "\n", + "sobol = Models.SOBOL(exp.search_space)\n", + "\n", + "for i in range(5):\n", + " trial = exp.new_trial(generator_run=sobol.gen(1))\n", + " trial.run()\n", + " trial.mark_completed()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "33fef7a2-67db-43cd-bc84-4d5736c582e7", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "Once the initial (quasi-) random stage is completed, we can use our `SimpleCustomGP` with the default acquisition function chosen by `Ax` to run the BO loop." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996825586, + "executionStopTime": 1730996847043, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "abc178e3-3e93-4855-b03b-c9acaf3fc7a5", + "outputsInitialized": true, + "requestMsgId": "abc178e3-3e93-4855-b03b-c9acaf3fc7a5", + "serverExecutionDuration": 21109.300409909, + "showInput": true + }, + "outputs": [], + "source": [ + "with fast_smoke_test():\n", + " for i in range(NUM_EVALS - 5):\n", + " model_bridge = Models.BOTORCH_MODULAR(\n", + " experiment=exp,\n", + " data=exp.fetch_data(),\n", + " surrogate_spec=SurrogateSpec(model_configs=[ModelConfig(SimpleCustomGP)]),\n", + " )\n", + " trial = exp.new_trial(generator_run=model_bridge.gen(1))\n", + " trial.run()\n", + " trial.mark_completed()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "cb16843a-0e12-47fc-860f-eafb74f1bd3b", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "View the trials attached to the `Experiment`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996826387, + "executionStopTime": 1730996847147, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "a96bc2d0-161f-4d04-b007-d4dee67495b7", + "output": { + "id": "594908669713548" + }, + "outputsInitialized": true, + "requestMsgId": "a96bc2d0-161f-4d04-b007-d4dee67495b7", + "serverExecutionDuration": 3.2807770185173, + "showInput": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "code_folding": [], - "customInput": null, - "hidden_ranges": [], - "originalKey": "c3ed0ec0-5002-4e7c-9e57-915e2a671abc", - "showInput": false - }, - "source": [ - "### Plot results\n", - "\n", - "We can use convenient Ax utilities for plotting the results." + "data": { + "text/plain": [ + "{0: Trial(experiment_name='branin_experiment', index=0, status=TrialStatus.COMPLETED, arm=Arm(name='0_0', parameters={'x1': 8.268271088600159, 'x2': 13.676363825798035})),\n", + " 1: Trial(experiment_name='branin_experiment', index=1, status=TrialStatus.COMPLETED, arm=Arm(name='1_0', parameters={'x1': -3.0115276388823986, 'x2': 0.19308556336909533})),\n", + " 2: Trial(experiment_name='branin_experiment', index=2, status=TrialStatus.COMPLETED, arm=Arm(name='2_0', parameters={'x1': -0.794604872353375, 'x2': 10.19062165170908})),\n", + " 3: Trial(experiment_name='branin_experiment', index=3, status=TrialStatus.COMPLETED, arm=Arm(name='3_0', parameters={'x1': 2.7553387405350804, 'x2': 4.206141787581146})),\n", + " 4: Trial(experiment_name='branin_experiment', index=4, status=TrialStatus.COMPLETED, arm=Arm(name='4_0', parameters={'x1': 5.150513867847621, 'x2': 9.072991241700947})),\n", + " 5: Trial(experiment_name='branin_experiment', index=5, status=TrialStatus.COMPLETED, arm=Arm(name='5_0', parameters={'x1': 1.5497872135088082, 'x2': 9.19017678783918})),\n", + " 6: Trial(experiment_name='branin_experiment', index=6, status=TrialStatus.COMPLETED, arm=Arm(name='6_0', parameters={'x1': 7.35880350484438, 'x2': 3.182282876550653})),\n", + " 7: Trial(experiment_name='branin_experiment', index=7, status=TrialStatus.COMPLETED, arm=Arm(name='7_0', parameters={'x1': -5.0, 'x2': 8.206550963671184})),\n", + " 8: Trial(experiment_name='branin_experiment', index=8, status=TrialStatus.COMPLETED, arm=Arm(name='8_0', parameters={'x1': 4.765065782500189, 'x2': 1.252289390966608})),\n", + " 9: Trial(experiment_name='branin_experiment', index=9, status=TrialStatus.COMPLETED, arm=Arm(name='9_0', parameters={'x1': 4.505201068669873, 'x2': 3.3644975986881467})),\n", + " 10: Trial(experiment_name='branin_experiment', index=10, status=TrialStatus.COMPLETED, arm=Arm(name='10_0', parameters={'x1': -3.1164341906014323, 'x2': 15.0})),\n", + " 11: Trial(experiment_name='branin_experiment', index=11, status=TrialStatus.COMPLETED, arm=Arm(name='11_0', parameters={'x1': 10.0, 'x2': 0.0})),\n", + " 12: Trial(experiment_name='branin_experiment', index=12, status=TrialStatus.COMPLETED, arm=Arm(name='12_0', parameters={'x1': -5.0, 'x2': 15.0})),\n", + " 13: Trial(experiment_name='branin_experiment', index=13, status=TrialStatus.COMPLETED, arm=Arm(name='13_0', parameters={'x1': 10.0, 'x2': 2.8078129263430163})),\n", + " 14: Trial(experiment_name='branin_experiment', index=14, status=TrialStatus.COMPLETED, arm=Arm(name='14_0', parameters={'x1': 2.445554777112278, 'x2': 2.2462261858873593})),\n", + " 15: Trial(experiment_name='branin_experiment', index=15, status=TrialStatus.COMPLETED, arm=Arm(name='15_0', parameters={'x1': 2.480451132691808, 'x2': 3.056693071733582})),\n", + " 16: Trial(experiment_name='branin_experiment', index=16, status=TrialStatus.COMPLETED, arm=Arm(name='16_0', parameters={'x1': 10.0, 'x2': 3.9059386481288194})),\n", + " 17: Trial(experiment_name='branin_experiment', index=17, status=TrialStatus.COMPLETED, arm=Arm(name='17_0', parameters={'x1': 2.0237354666184544, 'x2': 3.511643776100397})),\n", + " 18: Trial(experiment_name='branin_experiment', index=18, status=TrialStatus.COMPLETED, arm=Arm(name='18_0', parameters={'x1': 2.8769752653087997, 'x2': 2.2483802909633863})),\n", + " 19: Trial(experiment_name='branin_experiment', index=19, status=TrialStatus.COMPLETED, arm=Arm(name='19_0', parameters={'x1': 3.0536513312697213, 'x2': 2.4346471208663614})),\n", + " 20: Trial(experiment_name='branin_experiment', index=20, status=TrialStatus.COMPLETED, arm=Arm(name='20_0', parameters={'x1': 9.427576408084287, 'x2': 2.557223349069929})),\n", + " 21: Trial(experiment_name='branin_experiment', index=21, status=TrialStatus.COMPLETED, arm=Arm(name='21_0', parameters={'x1': 8.84736166287066, 'x2': 0.8696191586858866})),\n", + " 22: Trial(experiment_name='branin_experiment', index=22, status=TrialStatus.COMPLETED, arm=Arm(name='22_0', parameters={'x1': -1.5039526251440347, 'x2': 15.0})),\n", + " 23: Trial(experiment_name='branin_experiment', index=23, status=TrialStatus.COMPLETED, arm=Arm(name='23_0', parameters={'x1': -3.335556146334603, 'x2': 12.910932366431291})),\n", + " 24: Trial(experiment_name='branin_experiment', index=24, status=TrialStatus.COMPLETED, arm=Arm(name='24_0', parameters={'x1': -3.491879380808762, 'x2': 13.514831783855984})),\n", + " 25: Trial(experiment_name='branin_experiment', index=25, status=TrialStatus.COMPLETED, arm=Arm(name='25_0', parameters={'x1': -3.031782920987203, 'x2': 11.976525526649187})),\n", + " 26: Trial(experiment_name='branin_experiment', index=26, status=TrialStatus.COMPLETED, arm=Arm(name='26_0', parameters={'x1': -3.1412934283043814, 'x2': 12.616052311698178})),\n", + " 27: Trial(experiment_name='branin_experiment', index=27, status=TrialStatus.COMPLETED, arm=Arm(name='27_0', parameters={'x1': 9.455852758717114, 'x2': 2.3674336079024183})),\n", + " 28: Trial(experiment_name='branin_experiment', index=28, status=TrialStatus.COMPLETED, arm=Arm(name='28_0', parameters={'x1': 3.1364699781417986, 'x2': 2.025242611585696})),\n", + " 29: Trial(experiment_name='branin_experiment', index=29, status=TrialStatus.COMPLETED, arm=Arm(name='29_0', parameters={'x1': -2.936274156572808, 'x2': 11.259953484546505}))}" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.trials" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "f96540ca-1043-4803-ad8c-d143d98373a2", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "View the evaluation data about these trials." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996827087, + "executionStopTime": 1730996847292, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "fa05b927-d262-46fd-bb82-e7f89f8bbc3d", + "output": { + "id": "483337300710020" }, + "outputsInitialized": true, + "requestMsgId": "fa05b927-d262-46fd-bb82-e7f89f8bbc3d", + "serverExecutionDuration": 168.97921916097, + "showInput": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "code_folding": [], - "customInput": null, - "executionStartTime": 1646802680167, - "executionStopTime": 1646802680358, - "hidden_ranges": [], - "originalKey": "261d0e3b-8f3c-4a46-93be-bd1664682527", - "requestMsgId": "75429ef3-ab3b-43bf-87ea-a5fa1a4a9d24", - "showInput": true - }, - "outputs": [ - { - "data": { - "image/png": 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arm_namemetric_namemeansemtrial_index
00_0branin_metric150.233566NaN0
11_0branin_metric139.047729NaN1
22_0branin_metric24.817539NaN2
33_0branin_metric3.699482NaN3
44_0branin_metric75.591110NaN4
55_0branin_metric38.786335NaN5
66_0branin_metric18.167435NaN6
77_0branin_metric93.378693NaN7
88_0branin_metric10.515005NaN8
99_0branin_metric11.683224NaN9
1010_0branin_metric8.159270NaN10
1111_0branin_metric10.960894NaN11
1212_0branin_metric17.508297NaN12
1313_0branin_metric1.981222NaN13
1414_0branin_metric3.033621NaN14
1515_0branin_metric2.465075NaN15
1616_0branin_metric2.758517NaN16
1717_0branin_metric5.839406NaN17
1818_0branin_metric0.790689NaN18
1919_0branin_metric0.443105NaN19
2020_0branin_metric0.404304NaN20
2121_0branin_metric3.303451NaN21
2222_0branin_metric50.510307NaN22
2323_0branin_metric0.605148NaN23
2424_0branin_metric1.127027NaN24
2525_0branin_metric0.457026NaN25
2626_0branin_metric0.514696NaN26
2727_0branin_metric0.420453NaN27
2828_0branin_metric0.462405NaN28
2929_0branin_metric0.877364NaN29
\n", + "
" ], - "source": [ - "import numpy as np\n", - "from ax.plot.trace import optimization_trace_single_method\n", - "\n", - "\n", - "# `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple\n", - "# optimization runs, so we wrap out best objectives array in another array.\n", - "objective_means = np.array([[trial.objective_mean for trial in exp.trials.values()]])\n", - "best_objective_plot = optimization_trace_single_method(\n", - " y=np.minimum.accumulate(objective_means, axis=1),\n", - " optimum=0.397887, # Known minimum objective for Branin function.\n", - ")\n", - "render(best_objective_plot)" + "text/plain": [ + " arm_name metric_name mean sem trial_index\n", + "0 0_0 branin_metric 150.233566 NaN 0\n", + "1 1_0 branin_metric 139.047729 NaN 1\n", + "2 2_0 branin_metric 24.817539 NaN 2\n", + "3 3_0 branin_metric 3.699482 NaN 3\n", + "4 4_0 branin_metric 75.591110 NaN 4\n", + "5 5_0 branin_metric 38.786335 NaN 5\n", + "6 6_0 branin_metric 18.167435 NaN 6\n", + "7 7_0 branin_metric 93.378693 NaN 7\n", + "8 8_0 branin_metric 10.515005 NaN 8\n", + "9 9_0 branin_metric 11.683224 NaN 9\n", + "10 10_0 branin_metric 8.159270 NaN 10\n", + "11 11_0 branin_metric 10.960894 NaN 11\n", + "12 12_0 branin_metric 17.508297 NaN 12\n", + "13 13_0 branin_metric 1.981222 NaN 13\n", + "14 14_0 branin_metric 3.033621 NaN 14\n", + "15 15_0 branin_metric 2.465075 NaN 15\n", + "16 16_0 branin_metric 2.758517 NaN 16\n", + "17 17_0 branin_metric 5.839406 NaN 17\n", + "18 18_0 branin_metric 0.790689 NaN 18\n", + "19 19_0 branin_metric 0.443105 NaN 19\n", + "20 20_0 branin_metric 0.404304 NaN 20\n", + "21 21_0 branin_metric 3.303451 NaN 21\n", + "22 22_0 branin_metric 50.510307 NaN 22\n", + "23 23_0 branin_metric 0.605148 NaN 23\n", + "24 24_0 branin_metric 1.127027 NaN 24\n", + "25 25_0 branin_metric 0.457026 NaN 25\n", + "26 26_0 branin_metric 0.514696 NaN 26\n", + "27 27_0 branin_metric 0.420453 NaN 27\n", + "28 28_0 branin_metric 0.462405 NaN 28\n", + "29 29_0 branin_metric 0.877364 NaN 29" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "custom": { - "cells": [], - "metadata": { - "fileHeader": "", - "isAdHoc": false, - "kernelspec": { - "display_name": "python3", - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 - }, - "indentAmount": 2, + ], + "source": [ + "exp.fetch_data().df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "code_folding": [], + "customInput": null, + "hidden_ranges": [], + "isAgentGenerated": false, + "language": "markdown", + "originalKey": "c3ed0ec0-5002-4e7c-9e57-915e2a671abc", + "outputsInitialized": false, + "showInput": false + }, + "source": [ + "### Plot results\n", + "\n", + "We can use convenient Ax utilities for plotting the results." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "code_folding": [], + "collapsed": false, + "customInput": null, + "executionStartTime": 1730996827721, + "executionStopTime": 1730996847354, + "hidden_ranges": [], + "isAgentGenerated": false, + "jupyter": { + "outputs_hidden": false + }, + "language": "python", + "originalKey": "1546c0cf-94f1-4573-acd2-fde9e38d105e", + "output": { + "id": "957716379715459" + }, + "outputsInitialized": true, + "requestMsgId": "1546c0cf-94f1-4573-acd2-fde9e38d105e", + "serverExecutionDuration": 78.405936714262, + "showInput": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from ax.plot.trace import optimization_trace_single_method\n", + "\n", + "\n", + "# `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple\n", + "# optimization runs, so we wrap out best objectives array in another array.\n", + "objective_means = np.array([[trial.objective_mean for trial in exp.trials.values()]])\n", + "best_objective_plot = optimization_trace_single_method(\n", + " y=np.minimum.accumulate(objective_means, axis=1),\n", + " optimum=0.397887, # Known minimum objective for Branin function.\n", + ")\n", + "render(best_objective_plot)" + ] + } + ], + "metadata": { + "custom": { + "cells": [], + "metadata": { + "fileHeader": "", + "isAdHoc": false, "kernelspec": { - "name": "python3", - "display_name": "python3" + "display_name": "python3", + "language": "python", + "name": "python3" + }, + "language_info": { + 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