From d25c733d4509335cae24d0a1f697273ebed9fbda Mon Sep 17 00:00:00 2001 From: BoTorch website deployment script Date: Fri, 30 Aug 2024 11:40:56 +0000 Subject: [PATCH] Update latest version of site --- v/latest/en/index.html | 4 +- v/latest/files/closed_loop_botorch_only.ipynb | 1159 +---------------- v/latest/files/closed_loop_botorch_only.py | 77 +- .../compare_mc_analytic_acquisition.ipynb | 187 +-- .../files/compare_mc_analytic_acquisition.py | 64 +- v/latest/files/ibnn_bo.ipynb | 151 ++- v/latest/files/ibnn_bo.py | 85 +- v/latest/files/meta_learning_with_rgpe.ipynb | 360 +++-- v/latest/files/meta_learning_with_rgpe.py | 60 +- v/latest/index.html | 4 +- .../tutorials/closed_loop_botorch_only.html | 1088 +--------------- .../closed_loop_botorch_only/index.html | 1088 +--------------- .../compare_mc_analytic_acquisition.html | 135 +- .../index.html | 135 +- v/latest/tutorials/ibnn_bo.html | 116 +- v/latest/tutorials/ibnn_bo/index.html | 116 +- .../tutorials/meta_learning_with_rgpe.html | 138 +- .../meta_learning_with_rgpe/index.html | 138 +- 18 files changed, 1075 insertions(+), 4030 deletions(-) diff --git a/v/latest/en/index.html b/v/latest/en/index.html index 9e6f01e5fe..50e84206d6 100644 --- a/v/latest/en/index.html +++ b/v/latest/en/index.html @@ -48,13 +48,13 @@

  • Construct an acquisition function:

    from botorch.acquisition import LogExpectedImprovement
 
-logNEI = LogExpectedImprovement(model=gp, best_f=Y.max())
+logEI = LogExpectedImprovement(model=gp, best_f=Y.max())

  • Optimize the acquisition function:

    from botorch.optim import optimize_acqf
 
 bounds = torch.stack([torch.zeros(2), torch.ones(2)]).to(torch.double)
 candidate, acq_value = optimize_acqf(
-    logNEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
+    logEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
 )
 candidate  # tensor([[0.2981, 0.2401]], dtype=torch.float64)
    diff --git a/v/latest/files/closed_loop_botorch_only.ipynb b/v/latest/files/closed_loop_botorch_only.ipynb index 5faaae04ab..19378c7330 100644 --- a/v/latest/files/closed_loop_botorch_only.ipynb +++ b/v/latest/files/closed_loop_botorch_only.ipynb @@ -7,7 +7,7 @@ "showInput": false }, "source": [ - "## Closed-loop batch, constrained BO in BoTorch with qEI and qNEI\n", + "## Closed-loop batch, constrained BO in BoTorch with qLogEI and qLogNEI\n", "\n", "In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.\n", "\n", @@ -16,7 +16,7 @@ "However, you may want to do things that are not easily supported in Ax at this time (like running high-dimensional BO using a VAE+GP model that you jointly train on high-dimensional input data). If you find yourself in such a situation, you will need to write your own optimization loop, as we do in this tutorial.\n", "\n", "\n", - "We use the batch Expected Improvement (qEI) and batch Noisy Expected Improvement (qNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is\n", + "We use the batch Log Expected Improvement (`qLogEI`) and batch Noisy Expected Improvement (`qLogNEI`) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is\n", "\n", "$$f(x) = -\\sum_{i=1}^4 \\alpha_i \\exp \\left( -\\sum_{j=1}^6 A_{ij} (x_j - P_{ij})^2 \\right)$$\n", "\n", @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": { "collapsed": false, "customOutput": null, @@ -38,22 +38,7 @@ "originalKey": "2c0bfbc7-7e42-4601-83ed-4a77270803a8", "requestMsgId": "18ccce84-9f39-4c3d-89b1-1e9ed2540859" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0214 132746.769 _utils_internal.py:247] NCCL_DEBUG env var is set to None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0214 132746.770 _utils_internal.py:265] NCCL_DEBUG is forced to WARN from None\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "from typing import Optional\n", @@ -79,13 +64,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649988205, "executionStopTime": 1668649988602, "originalKey": "b1c9de4d-a7ba-4782-ab68-2def8b562f7b", + "output": { + "id": 364616190032149, + "loadingStatus": "loaded" + }, "requestMsgId": "96673081-cc25-4ca0-a40d-48756fde8647" }, "outputs": [], @@ -122,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": { "collapsed": false, "customOutput": null, @@ -133,10 +122,11 @@ }, "outputs": [], "source": [ + "from botorch.models.transforms.input import Normalize\n", "from botorch.models import FixedNoiseGP, ModelListGP\n", "from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood\n", "\n", - "NOISE_SE = 0.5\n", + "NOISE_SE = 0.25\n", "train_yvar = torch.tensor(NOISE_SE**2, device=device, dtype=dtype)\n", "\n", "\n", @@ -153,12 +143,18 @@ "\n", "def initialize_model(train_x, train_obj, train_con, state_dict=None):\n", " # define models for objective and constraint\n", - " model_obj = FixedNoiseGP(train_x, train_obj, train_yvar.expand_as(train_obj)).to(\n", - " train_x\n", - " )\n", - " model_con = FixedNoiseGP(train_x, train_con, train_yvar.expand_as(train_con)).to(\n", - " train_x\n", - " )\n", + " model_obj = FixedNoiseGP(\n", + " train_x,\n", + " train_obj,\n", + " train_yvar.expand_as(train_obj),\n", + " input_transform=Normalize(d=train_x.shape[-1]),\n", + " ).to(train_x)\n", + " model_con = FixedNoiseGP(\n", + " train_x,\n", + " train_con,\n", + " train_yvar.expand_as(train_con),\n", + " input_transform=Normalize(d=train_x.shape[-1]),\n", + " ).to(train_x)\n", " # combine into a multi-output GP model\n", " model = ModelListGP(model_obj, model_con)\n", " mll = SumMarginalLogLikelihood(model.likelihood, model)\n", @@ -181,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": { "collapsed": false, "customOutput": null, @@ -192,8 +188,7 @@ }, "outputs": [], "source": [ - "from botorch.acquisition.objective import ConstrainedMCObjective\n", - "\n", + "from botorch.acquisition.objective import GenericMCObjective\n", "\n", "def obj_callable(Z: torch.Tensor, X: Optional[torch.Tensor] = None):\n", " return Z[..., 0]\n", @@ -203,11 +198,7 @@ " return Z[..., 1]\n", "\n", "\n", - "# define a feasibility-weighted objective for optimization\n", - "constrained_obj = ConstrainedMCObjective(\n", - " objective=obj_callable,\n", - " constraints=[constraint_callable],\n", - ")" + "objective = GenericMCObjective(objective=obj_callable)" ] }, { @@ -223,13 +214,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649993442, "executionStopTime": 1668649993515, "originalKey": "f450c171-6984-4114-bf99-99c3a4e68eb2", + "output": { + "id": 385726674337920, + "loadingStatus": "loaded" + }, "requestMsgId": "57d29886-0a14-410b-aaba-596c8559f5a0" }, "outputs": [], @@ -281,7 +276,7 @@ "showInput": false }, "source": [ - "### Perform Bayesian Optimization loop with qNEI\n", + "### Perform Bayesian Optimization loop with qLogNEI\n", "The Bayesian optimization \"loop\" for a batch size of $q$ simply iterates the following steps:\n", "1. given a surrogate model, choose a batch of points $\\{x_1, x_2, \\ldots x_q\\}$\n", "2. observe $f(x)$ for each $x$ in the batch \n", @@ -295,17 +290,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649993811, "executionStopTime": 1668650936026, "originalKey": "f137bf2a-5d39-4c8c-bb24-84326d4ab5d7", - "output": { - "id": 3649554978648837, - "loadingStatus": "loaded" - }, "requestMsgId": "0b4d1d37-a9cf-4f69-a896-0836506ee521" }, "outputs": [ @@ -314,1048 +305,9 @@ "output_type": "stream", "text": [ "\n", - "Trial 1 of 3 " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([0.2733], dtype=torch.float64), std = tensor([0.4715], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([-0.4174], dtype=torch.float64), std = tensor([0.7068], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([0.2733], dtype=torch.float64), std = tensor([0.4715], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([0.3525], dtype=torch.float64), std = tensor([0.4876], dtype=torch.float64)). 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Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([0.3792], dtype=torch.float64), std = tensor([0.4548], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([-0.0395], dtype=torch.float64), std = tensor([0.8258], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([0.4358], dtype=torch.float64), std = tensor([0.5339], dtype=torch.float64)). 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Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.6458], dtype=torch.float64), std = tensor([0.7703], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([-0.2558], dtype=torch.float64), std = tensor([0.7850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.7303], dtype=torch.float64), std = tensor([0.6473], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.0330], dtype=torch.float64), std = tensor([0.7920], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.6421], dtype=torch.float64), std = tensor([0.7931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([-0.2332], dtype=torch.float64), std = tensor([0.7745], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.7330], dtype=torch.float64), std = tensor([0.6406], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.0029], dtype=torch.float64), std = tensor([0.8178], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.6627], dtype=torch.float64), std = tensor([0.8222], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([-0.2511], dtype=torch.float64), std = tensor([0.7655], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.7176], dtype=torch.float64), std = tensor([0.6282], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.0095], dtype=torch.float64), std = tensor([0.8024], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.6257], dtype=torch.float64), std = tensor([0.8225], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.2089], dtype=torch.float64), std = tensor([0.7925], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.7042], dtype=torch.float64), std = tensor([0.6213], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.0026], dtype=torch.float64), std = tensor([0.7856], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.5965], dtype=torch.float64), std = tensor([0.8144], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.1965], dtype=torch.float64), std = tensor([0.7931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.6865], dtype=torch.float64), std = tensor([0.6127], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.0005], dtype=torch.float64), std = tensor([0.7878], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.5893], dtype=torch.float64), std = tensor([0.8044], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([-0.2281], dtype=torch.float64), std = tensor([0.8216], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.6766], dtype=torch.float64), std = tensor([0.6235], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.0016], dtype=torch.float64), std = tensor([0.7704], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5766], dtype=torch.float64), std = tensor([0.7922], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([-0.1866], dtype=torch.float64), std = tensor([0.8565], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.6880], dtype=torch.float64), std = tensor([0.6248], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.0048], dtype=torch.float64), std = tensor([0.7548], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.1254], dtype=torch.float64), std = tensor([0.6022], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5027], dtype=torch.float64), std = tensor([1.2975], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".\n", - "Trial 2 of 3 " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.0739], dtype=torch.float64), std = tensor([0.5614], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.3059], dtype=torch.float64), std = tensor([1.2141], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.2051], dtype=torch.float64), std = tensor([0.5907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.4776], dtype=torch.float64), std = tensor([1.1252], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2421], dtype=torch.float64), std = tensor([0.7397], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.3858], dtype=torch.float64), std = tensor([1.1179], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.1629], dtype=torch.float64), std = tensor([0.5363], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2150], dtype=torch.float64), std = tensor([1.2032], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.4086], dtype=torch.float64), std = tensor([0.7939], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1987], dtype=torch.float64), std = tensor([1.1134], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1204], dtype=torch.float64), std = tensor([0.5100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.2172], dtype=torch.float64), std = tensor([1.1218], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.5324], dtype=torch.float64), std = tensor([0.9596], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.1259], dtype=torch.float64), std = tensor([1.1775], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.2220], dtype=torch.float64), std = tensor([0.5402], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.0006], dtype=torch.float64), std = tensor([1.2874], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.6056], dtype=torch.float64), std = tensor([1.0648], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.0996], dtype=torch.float64), std = tensor([1.1398], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.2459], dtype=torch.float64), std = tensor([0.6041], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([-0.0298], dtype=torch.float64), std = tensor([1.2304], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.6541], dtype=torch.float64), std = tensor([1.0531], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0220], dtype=torch.float64), std = tensor([1.1516], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.3704], dtype=torch.float64), std = tensor([0.7145], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0785], dtype=torch.float64), std = tensor([1.1710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.6305], dtype=torch.float64), std = tensor([1.0355], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.0104], dtype=torch.float64), std = tensor([1.0959], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.5133], dtype=torch.float64), std = tensor([0.9123], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.1256], dtype=torch.float64), std = tensor([1.1236], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6377], dtype=torch.float64), std = tensor([1.0196], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.0550], dtype=torch.float64), std = tensor([1.0710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6463], dtype=torch.float64), std = tensor([0.9966], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.1539], dtype=torch.float64), std = tensor([1.0888], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.5757], dtype=torch.float64), std = tensor([1.0170], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.0256], dtype=torch.float64), std = tensor([1.0642], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.6964], dtype=torch.float64), std = tensor([1.0204], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.2024], dtype=torch.float64), std = tensor([1.0637], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.5354], dtype=torch.float64), std = tensor([0.9934], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.0055], dtype=torch.float64), std = tensor([1.0357], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.7830], dtype=torch.float64), std = tensor([1.0484], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([-0.2246], dtype=torch.float64), std = tensor([1.0465], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.5546], dtype=torch.float64), std = tensor([0.9647], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.0322], dtype=torch.float64), std = tensor([1.0190], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.8242], dtype=torch.float64), std = tensor([1.0807], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.2051], dtype=torch.float64), std = tensor([1.0388], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.5227], dtype=torch.float64), std = tensor([0.9521], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.0076], dtype=torch.float64), std = tensor([1.0119], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.7937], dtype=torch.float64), std = tensor([1.0924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.2510], dtype=torch.float64), std = tensor([1.0452], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.5390], dtype=torch.float64), std = tensor([0.9474], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.0277], dtype=torch.float64), std = tensor([0.9907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.8655], dtype=torch.float64), std = tensor([1.1495], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.2606], dtype=torch.float64), std = tensor([1.0160], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.5780], dtype=torch.float64), std = tensor([0.9779], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.0618], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.8900], dtype=torch.float64), std = tensor([1.1546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.2629], dtype=torch.float64), std = tensor([1.0080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.6193], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.1043], dtype=torch.float64), std = tensor([0.9695], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.9010], dtype=torch.float64), std = tensor([1.1830], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.2370], dtype=torch.float64), std = tensor([1.0052], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.5845], dtype=torch.float64), std = tensor([0.9868], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.0820], dtype=torch.float64), std = tensor([0.9546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.9435], dtype=torch.float64), std = tensor([1.1793], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.2761], dtype=torch.float64), std = tensor([1.0077], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.6265], dtype=torch.float64), std = tensor([0.9853], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.0924], dtype=torch.float64), std = tensor([0.9369], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.9787], dtype=torch.float64), std = tensor([1.1862], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.2420], dtype=torch.float64), std = tensor([0.9960], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([0.6123], dtype=torch.float64), std = tensor([0.9678], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.0746], dtype=torch.float64), std = tensor([0.9263], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([1.2043], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.2621], dtype=torch.float64), std = tensor([0.9816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([0.5825], dtype=torch.float64), std = tensor([0.9687], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.0862], dtype=torch.float64), std = tensor([0.9230], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([1.0427], dtype=torch.float64), std = tensor([1.2031], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.2598], dtype=torch.float64), std = tensor([0.9646], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.5553], dtype=torch.float64), std = tensor([0.9707], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.1290], dtype=torch.float64), std = tensor([0.9302], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([1.0351], dtype=torch.float64), std = tensor([1.2100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2435], dtype=torch.float64), std = tensor([0.9776], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.3545], dtype=torch.float64), std = tensor([0.3441], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2680], dtype=torch.float64), std = tensor([0.7962], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".\n", - "Trial 3 of 3 " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.4261], dtype=torch.float64), std = tensor([0.5340], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.6275], dtype=torch.float64), std = tensor([0.9777], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.3743], dtype=torch.float64), std = tensor([0.3378], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.4575], dtype=torch.float64), std = tensor([0.8915], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.6648], dtype=torch.float64), std = tensor([0.7171], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.7409], dtype=torch.float64), std = tensor([0.9093], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.3962], dtype=torch.float64), std = tensor([0.3885], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.4020], dtype=torch.float64), std = tensor([0.9841], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.7743], dtype=torch.float64), std = tensor([0.7166], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.7568], dtype=torch.float64), std = tensor([0.8463], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.3175], dtype=torch.float64), std = tensor([0.4189], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.4621], dtype=torch.float64), std = tensor([0.9163], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.9228], dtype=torch.float64), std = tensor([0.7735], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.7598], dtype=torch.float64), std = tensor([0.8169], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.3610], dtype=torch.float64), std = tensor([0.5045], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.5577], dtype=torch.float64), std = tensor([0.8854], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([1.0540], dtype=torch.float64), std = tensor([0.8249], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.8083], dtype=torch.float64), std = tensor([0.7994], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([0.4168], dtype=torch.float64), std = tensor([0.5080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.6147], dtype=torch.float64), std = tensor([0.8524], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([0.8136], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.7390], dtype=torch.float64), std = tensor([0.9137], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([0.5018], dtype=torch.float64), std = tensor([0.5476], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.6296], dtype=torch.float64), std = tensor([0.8479], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([1.0463], dtype=torch.float64), std = tensor([0.8390], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.7581], dtype=torch.float64), std = tensor([0.8850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([0.5743], dtype=torch.float64), std = tensor([0.5818], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.6208], dtype=torch.float64), std = tensor([0.8064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([1.1631], dtype=torch.float64), std = tensor([0.8880], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.7808], dtype=torch.float64), std = tensor([0.8668], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([0.6677], dtype=torch.float64), std = tensor([0.7461], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.6186], dtype=torch.float64), std = tensor([0.8060], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([1.2158], dtype=torch.float64), std = tensor([0.9105], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.7430], dtype=torch.float64), std = tensor([0.8541], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([0.7965], dtype=torch.float64), std = tensor([0.8424], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.6100], dtype=torch.float64), std = tensor([0.7746], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([1.2303], dtype=torch.float64), std = tensor([0.8849], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.7314], dtype=torch.float64), std = tensor([0.8314], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([0.8024], dtype=torch.float64), std = tensor([0.8711], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.5337], dtype=torch.float64), std = tensor([0.7928], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([1.1456], dtype=torch.float64), std = tensor([0.9094], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.7164], dtype=torch.float64), std = tensor([0.8053], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([0.8021], dtype=torch.float64), std = tensor([0.8747], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.5722], dtype=torch.float64), std = tensor([0.7890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([1.1029], dtype=torch.float64), std = tensor([0.8993], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.7169], dtype=torch.float64), std = tensor([0.7839], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([0.8495], dtype=torch.float64), std = tensor([0.8904], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.5345], dtype=torch.float64), std = tensor([0.7764], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([1.1081], dtype=torch.float64), std = tensor([0.9595], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.6900], dtype=torch.float64), std = tensor([0.8444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([0.8781], dtype=torch.float64), std = tensor([0.9012], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.5189], dtype=torch.float64), std = tensor([0.7619], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([1.0653], dtype=torch.float64), std = tensor([0.9486], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.6231], dtype=torch.float64), std = tensor([0.8772], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([0.8641], dtype=torch.float64), std = tensor([0.8816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.5111], dtype=torch.float64), std = tensor([0.7444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([1.0327], dtype=torch.float64), std = tensor([0.9331], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5712], dtype=torch.float64), std = tensor([0.9219], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([0.9016], dtype=torch.float64), std = tensor([0.9030], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5222], dtype=torch.float64), std = tensor([0.7306], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([1.0067], dtype=torch.float64), std = tensor([0.9164], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.5624], dtype=torch.float64), std = tensor([0.9069], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([0.9085], dtype=torch.float64), std = tensor([0.8961], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.4901], dtype=torch.float64), std = tensor([0.7506], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.9833], dtype=torch.float64), std = tensor([0.9078], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.5367], dtype=torch.float64), std = tensor([0.9050], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.8983], dtype=torch.float64), std = tensor([0.8931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.4601], dtype=torch.float64), std = tensor([0.7749], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9557], dtype=torch.float64), std = tensor([0.8952], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.5011], dtype=torch.float64), std = tensor([0.9009], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9365], dtype=torch.float64), std = tensor([0.8924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.4550], dtype=torch.float64), std = tensor([0.7684], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9053], dtype=torch.float64), std = tensor([0.9064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.5207], dtype=torch.float64), std = tensor([0.8890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9151], dtype=torch.float64), std = tensor([0.9004], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.4307], dtype=torch.float64), std = tensor([0.7606], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.8586], dtype=torch.float64), std = tensor([0.9161], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.5021], dtype=torch.float64), std = tensor([0.8843], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.9372], dtype=torch.float64), std = tensor([0.9005], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n", - "[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.4370], dtype=torch.float64), std = tensor([0.7482], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.\n" + "Trial 1 of 3 ....................\n", + "Trial 2 of 3 ....................\n", + "Trial 3 of 3 ...................." ] } ], @@ -1364,9 +316,9 @@ "import warnings\n", "\n", "from botorch import fit_gpytorch_mll\n", - "from botorch.acquisition.monte_carlo import (\n", - " qExpectedImprovement,\n", - " qNoisyExpectedImprovement,\n", + "from botorch.acquisition import (\n", + " qLogExpectedImprovement,\n", + " qLogNoisyExpectedImprovement,\n", ")\n", "from botorch.exceptions import BadInitialCandidatesWarning\n", "from botorch.sampling.normal import SobolQMCNormalSampler\n", @@ -1384,7 +336,6 @@ "\n", "best_observed_all_ei, best_observed_all_nei, best_random_all = [], [], []\n", "\n", - "\n", "# average over multiple trials\n", "for trial in range(1, N_TRIALS + 1):\n", "\n", @@ -1421,23 +372,25 @@ " qmc_sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n", "\n", " # for best_f, we use the best observed noisy values as an approximation\n", - " qEI = qExpectedImprovement(\n", + " qLogEI = qLogExpectedImprovement(\n", " model=model_ei,\n", " best_f=(train_obj_ei * (train_con_ei <= 0).to(train_obj_ei)).max(),\n", " sampler=qmc_sampler,\n", - " objective=constrained_obj,\n", + " objective=objective,\n", + " constraints=[constraint_callable],\n", " )\n", "\n", - " qNEI = qNoisyExpectedImprovement(\n", + " qLogNEI = qLogNoisyExpectedImprovement(\n", " model=model_nei,\n", " X_baseline=train_x_nei,\n", " sampler=qmc_sampler,\n", - " objective=constrained_obj,\n", + " objective=objective,\n", + " constraints=[constraint_callable],\n", " )\n", "\n", " # optimize and get new observation\n", - " new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qEI)\n", - " new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qNEI)\n", + " new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qLogEI)\n", + " new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qLogNEI)\n", "\n", " # update training points\n", " train_x_ei = torch.cat([train_x_ei, new_x_ei])\n", @@ -1500,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": { "collapsed": false, "customOutput": null, @@ -1508,7 +461,7 @@ "executionStopTime": 1668650937028, "originalKey": "8729310f-7438-4d16-a2d5-5c46e5ef1c03", "output": { - "id": 338045315894746, + "id": 804722568408483, "loadingStatus": "loaded" }, "requestMsgId": "3e10cd44-d4fa-4efc-941c-07dabdd6689c" @@ -1517,16 +470,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1556,13 +509,13 @@ "\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "ax.errorbar(iters, y_rnd.mean(axis=0), yerr=ci(y_rnd), label=\"random\", linewidth=1.5)\n", - "ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label=\"qEI\", linewidth=1.5)\n", - "ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label=\"qNEI\", linewidth=1.5)\n", + "ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label=\"qLogEI\", linewidth=1.5)\n", + "ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label=\"qLogNEI\", linewidth=1.5)\n", "plt.plot(\n", " [0, N_BATCH * BATCH_SIZE],\n", " [GLOBAL_MAXIMUM] * 2,\n", " \"k\",\n", - " label=\"true best objective\",\n", + " label=\"true best feasible objective\",\n", " linewidth=2,\n", ")\n", "ax.set_ylim(bottom=0.5)\n", diff --git a/v/latest/files/closed_loop_botorch_only.py b/v/latest/files/closed_loop_botorch_only.py index e109011f3a..d04448640f 100644 --- a/v/latest/files/closed_loop_botorch_only.py +++ b/v/latest/files/closed_loop_botorch_only.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # coding: utf-8 -# ## Closed-loop batch, constrained BO in BoTorch with qEI and qNEI +# ## Closed-loop batch, constrained BO in BoTorch with qLogEI and qLogNEI # # In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch. # @@ -10,7 +10,7 @@ # However, you may want to do things that are not easily supported in Ax at this time (like running high-dimensional BO using a VAE+GP model that you jointly train on high-dimensional input data). If you find yourself in such a situation, you will need to write your own optimization loop, as we do in this tutorial. # # -# We use the batch Expected Improvement (qEI) and batch Noisy Expected Improvement (qNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is +# We use the batch Log Expected Improvement (`qLogEI`) and batch Noisy Expected Improvement (`qLogNEI`) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is # # $$f(x) = -\sum_{i=1}^4 \alpha_i \exp \left( -\sum_{j=1}^6 A_{ij} (x_j - P_{ij})^2 \right)$$ # @@ -20,7 +20,7 @@ # # Since botorch assumes a maximization problem, we will attempt to maximize $-f(x)$ to achieve $\max_{x} -f(x) = 3.32237$. -# In[1]: +# In[14]: import os @@ -37,7 +37,7 @@ # # First, we define the constraint used in the example in `outcome_constraint`. The second function `weighted_obj` is a "feasibility-weighted objective," which returns zero when not feasible. -# In[2]: +# In[15]: from botorch.test_functions import Hartmann @@ -62,13 +62,14 @@ def weighted_obj(X): # # Each component is a `FixedNoiseGP`. The models are initialized with 10 points drawn randomly from $[0,1]^6$. -# In[3]: +# In[16]: +from botorch.models.transforms.input import Normalize from botorch.models import FixedNoiseGP, ModelListGP from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood -NOISE_SE = 0.5 +NOISE_SE = 0.25 train_yvar = torch.tensor(NOISE_SE**2, device=device, dtype=dtype) @@ -85,12 +86,18 @@ def generate_initial_data(n=10): def initialize_model(train_x, train_obj, train_con, state_dict=None): # define models for objective and constraint - model_obj = FixedNoiseGP(train_x, train_obj, train_yvar.expand_as(train_obj)).to( - train_x - ) - model_con = FixedNoiseGP(train_x, train_con, train_yvar.expand_as(train_con)).to( - train_x - ) + model_obj = FixedNoiseGP( + train_x, + train_obj, + train_yvar.expand_as(train_obj), + input_transform=Normalize(d=train_x.shape[-1]), + ).to(train_x) + model_con = FixedNoiseGP( + train_x, + train_con, + train_yvar.expand_as(train_con), + input_transform=Normalize(d=train_x.shape[-1]), + ).to(train_x) # combine into a multi-output GP model model = ModelListGP(model_obj, model_con) mll = SumMarginalLogLikelihood(model.likelihood, model) @@ -103,11 +110,10 @@ def initialize_model(train_x, train_obj, train_con, state_dict=None): # #### Define a construct to extract the objective and constraint from the GP # The methods below take the outputs of the GP and return the objective and the constraint. In general, these can be any `Callable`, but here we simply need to index the correct output. -# In[4]: - +# In[17]: -from botorch.acquisition.objective import ConstrainedMCObjective +from botorch.acquisition.objective import GenericMCObjective def obj_callable(Z: torch.Tensor, X: Optional[torch.Tensor] = None): return Z[..., 0] @@ -117,17 +123,13 @@ def constraint_callable(Z): return Z[..., 1] -# define a feasibility-weighted objective for optimization -constrained_obj = ConstrainedMCObjective( - objective=obj_callable, - constraints=[constraint_callable], -) +objective = GenericMCObjective(objective=obj_callable) # #### Define a helper function that performs the essential BO step # The helper function below takes an acquisition function as an argument, optimizes it, and returns the batch $\{x_1, x_2, \ldots x_q\}$ along with the observed function values. For this example, we'll use a small batch of $q=3$. The function `optimize_acqf` optimizes the $q$ points jointly. A simple initialization heuristic is used to select the 10 restart initial locations from a set of 50 random points. -# In[5]: +# In[18]: from botorch.optim import optimize_acqf @@ -170,7 +172,7 @@ def update_random_observations(best_random): return best_random -# ### Perform Bayesian Optimization loop with qNEI +# ### Perform Bayesian Optimization loop with qLogNEI # The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps: # 1. given a surrogate model, choose a batch of points $\{x_1, x_2, \ldots x_q\}$ # 2. observe $f(x)$ for each $x$ in the batch @@ -181,16 +183,16 @@ def update_random_observations(best_random): # # *Note*: Running this may take a little while. -# In[6]: +# In[19]: import time import warnings from botorch import fit_gpytorch_mll -from botorch.acquisition.monte_carlo import ( - qExpectedImprovement, - qNoisyExpectedImprovement, +from botorch.acquisition import ( + qLogExpectedImprovement, + qLogNoisyExpectedImprovement, ) from botorch.exceptions import BadInitialCandidatesWarning from botorch.sampling.normal import SobolQMCNormalSampler @@ -208,7 +210,6 @@ def update_random_observations(best_random): best_observed_all_ei, best_observed_all_nei, best_random_all = [], [], [] - # average over multiple trials for trial in range(1, N_TRIALS + 1): @@ -245,23 +246,25 @@ def update_random_observations(best_random): qmc_sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES])) # for best_f, we use the best observed noisy values as an approximation - qEI = qExpectedImprovement( + qLogEI = qLogExpectedImprovement( model=model_ei, best_f=(train_obj_ei * (train_con_ei <= 0).to(train_obj_ei)).max(), sampler=qmc_sampler, - objective=constrained_obj, + objective=objective, + constraints=[constraint_callable], ) - qNEI = qNoisyExpectedImprovement( + qLogNEI = qLogNoisyExpectedImprovement( model=model_nei, X_baseline=train_x_nei, sampler=qmc_sampler, - objective=constrained_obj, + objective=objective, + constraints=[constraint_callable], ) # optimize and get new observation - new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qEI) - new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qNEI) + new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qLogEI) + new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qLogNEI) # update training points train_x_ei = torch.cat([train_x_ei, new_x_ei]) @@ -314,7 +317,7 @@ def update_random_observations(best_random): # #### Plot the results # The plot below shows the best objective value observed at each step of the optimization for each of the algorithms. The confidence intervals represent the variance at that step in the optimization across the trial runs. The variance across optimization runs is quite high, so in order to get a better estimate of the average performance one would have to run a much larger number of trials `N_TRIALS` (we avoid this here to limit the runtime of this tutorial). -# In[7]: +# In[20]: import numpy as np @@ -337,13 +340,13 @@ def ci(y): fig, ax = plt.subplots(1, 1, figsize=(8, 6)) ax.errorbar(iters, y_rnd.mean(axis=0), yerr=ci(y_rnd), label="random", linewidth=1.5) -ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qEI", linewidth=1.5) -ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qNEI", linewidth=1.5) +ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qLogEI", linewidth=1.5) +ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qLogNEI", linewidth=1.5) plt.plot( [0, N_BATCH * BATCH_SIZE], [GLOBAL_MAXIMUM] * 2, "k", - label="true best objective", + label="true best feasible objective", linewidth=2, ) ax.set_ylim(bottom=0.5) diff --git a/v/latest/files/compare_mc_analytic_acquisition.ipynb b/v/latest/files/compare_mc_analytic_acquisition.ipynb index c74c7e7334..c61f34689c 100644 --- a/v/latest/files/compare_mc_analytic_acquisition.ipynb +++ b/v/latest/files/compare_mc_analytic_acquisition.ipynb @@ -19,18 +19,23 @@ "showInput": false }, "source": [ - "### Comparison of analytic and MC-based EI" + "### Comparison of analytic and MC-based EI\n", + "Note that we use the analytic and MC variants of the LogEI family of acquisition functions, which remedy numerical issues encountered in the naive implementations. See https://arxiv.org/pdf/2310.20708 for more details." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649205799, "executionStopTime": 1668649205822, "originalKey": "f678d607-be4c-4f37-aed5-3597158432ce", + "output": { + "id": 8143993305683446, + "loadingStatus": "loaded" + }, "requestMsgId": "0aae9d3f-d796-4a18-a4aa-b015b5b582ac" }, "outputs": [], @@ -57,18 +62,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649205895, "executionStopTime": 1668649206067, "originalKey": "a7724f86-8b67-4f70-bf57-f0da79b88f52", + "output": { + "id": 1605553740344114, + "loadingStatus": "loaded" + }, "requestMsgId": "25794582-0506-4e89-a112-ba362b7c7e59" }, "outputs": [], "source": [ - "train_x = torch.rand(10, 6)\n", + "torch.manual_seed(seed=12345) # to keep the data conditions the same\n", + "dtype = torch.float64\n", + "train_x = torch.rand(10, 6, dtype=dtype)\n", "train_obj = neg_hartmann6(train_x).unsqueeze(-1)\n", "model = SingleTaskGP(train_X=train_x, train_Y=train_obj)\n", "mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", @@ -87,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -98,10 +109,10 @@ }, "outputs": [], "source": [ - "from botorch.acquisition import ExpectedImprovement\n", + "from botorch.acquisition.analytic import LogExpectedImprovement\n", "\n", "best_value = train_obj.max()\n", - "EI = ExpectedImprovement(model=model, best_f=best_value)" + "LogEI = LogExpectedImprovement(model=model, best_f=best_value)" ] }, { @@ -116,13 +127,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649206218, "executionStopTime": 1668649206938, "originalKey": "dc5613c6-2f99-4193-8956-6e710fee5fa2", + "output": { + "id": 422599616946465, + "loadingStatus": "loaded" + }, "requestMsgId": "3df2fc12-7f4c-4abb-b1d2-90bb3b8bf05c" }, "outputs": [], @@ -130,7 +145,7 @@ "from botorch.optim import optimize_acqf\n", "\n", "new_point_analytic, _ = optimize_acqf(\n", - " acq_function=EI,\n", + " acq_function=LogEI,\n", " bounds=torch.tensor([[0.0] * 6, [1.0] * 6]),\n", " q=1,\n", " num_restarts=20,\n", @@ -141,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -150,22 +165,10 @@ "originalKey": "76fb19a3-c2c2-451a-8c0b-50cb14c55460", "requestMsgId": "a5cbada9-0b7c-41a2-934f-10d9bbe2e316" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.4730, 0.0836, 0.8247, 0.5628, 0.2964, 0.6131]])" - ] - }, - "execution_count": 20, - "metadata": { - "bento_obj_id": "140510701845616" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "new_point_analytic" + "# NOTE: The acquisition value here is the log of the expected improvement.\n", + "LogEI(new_point_analytic), new_point_analytic" ] }, { @@ -180,26 +183,30 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, "executionStartTime": 1668649207083, "executionStopTime": 1668649207929, "originalKey": "aaf04cba-3716-4fbd-8baa-2c75dd068860", + "output": { + "id": 495747073400348, + "loadingStatus": "loaded" + }, "requestMsgId": "0e7691f2-34c7-43df-a247-7f7ba95220f1" }, "outputs": [], "source": [ - "from botorch.acquisition import qExpectedImprovement\n", + "from botorch.acquisition.logei import qLogExpectedImprovement\n", "from botorch.sampling import SobolQMCNormalSampler\n", "\n", "\n", "sampler = SobolQMCNormalSampler(sample_shape=torch.Size([512]), seed=0)\n", - "MC_EI = qExpectedImprovement(model, best_f=best_value, sampler=sampler)\n", + "MC_LogEI = qLogExpectedImprovement(model, best_f=best_value, sampler=sampler, fat=False)\n", "torch.manual_seed(seed=0) # to keep the restart conditions the same\n", "new_point_mc, _ = optimize_acqf(\n", - " acq_function=MC_EI,\n", + " acq_function=MC_LogEI,\n", " bounds=torch.tensor([[0.0] * 6, [1.0] * 6]),\n", " q=1,\n", " num_restarts=20,\n", @@ -210,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -219,22 +226,10 @@ "originalKey": "73ffa9ea-3cff-46eb-91ea-b2f75fdb07f2", "requestMsgId": "b780cff4-6e90-4e39-8558-b04136e71e94" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.4730, 0.0835, 0.8248, 0.5627, 0.2963, 0.6130]])" - ] - }, - "execution_count": 22, - "metadata": { - "bento_obj_id": "140510701845696" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "new_point_mc" + "# NOTE: The acquisition value here is the log of the expected improvement.\n", + "MC_LogEI(new_point_mc), new_point_mc" ] }, { @@ -249,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -258,20 +253,7 @@ "originalKey": "c5c20ba9-82af-4d07-832f-86ede74f8959", "requestMsgId": "0b3db1ad-6ddb-4f86-9767-e0a486914b33" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.0002)" - ] - }, - "execution_count": 23, - "metadata": { - "bento_obj_id": "140510702063760" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "torch.linalg.norm(new_point_mc - new_point_analytic)" ] @@ -292,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -308,11 +290,11 @@ "from botorch.optim import gen_batch_initial_conditions\n", "\n", "resampler = StochasticSampler(sample_shape=torch.Size([512]))\n", - "MC_EI_resample = qExpectedImprovement(model, best_f=best_value, sampler=resampler)\n", + "MC_LogEI_resample = qLogExpectedImprovement(model, best_f=best_value, sampler=resampler)\n", "bounds = torch.tensor([[0.0] * 6, [1.0] * 6])\n", "\n", "batch_initial_conditions = gen_batch_initial_conditions(\n", - " acq_function=MC_EI_resample,\n", + " acq_function=MC_LogEI_resample,\n", " bounds=bounds,\n", " q=1,\n", " num_restarts=20,\n", @@ -320,7 +302,7 @@ ")\n", "batch_candidates, batch_acq_values = gen_candidates_torch(\n", " initial_conditions=batch_initial_conditions,\n", - " acquisition_function=MC_EI_resample,\n", + " acquisition_function=MC_LogEI_resample,\n", " lower_bounds=bounds[0],\n", " upper_bounds=bounds[1],\n", " optimizer=torch.optim.Adam,\n", @@ -333,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -342,27 +324,15 @@ "originalKey": "81c29b36-c663-47e1-8155-ad034c214f53", "requestMsgId": "aac6f703-e046-448a-8abe-1742befb9bf9" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.4527, 0.1183, 0.8902, 0.5630, 0.3151, 0.5804]])" - ] - }, - "execution_count": 25, - "metadata": { - "bento_obj_id": "140510701998384" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "new_point_torch_Adam" + "# NOTE: The acquisition value here is the log of the expected improvement.\n", + "MC_LogEI_resample(new_point_torch_Adam), new_point_torch_Adam" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -371,20 +341,7 @@ "originalKey": "17fb0de0-3c5a-414e-9aba-b82710d166c0", "requestMsgId": "a13ce358-3ee6-43ad-9e3a-16181a8cdc1e" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.0855)" - ] - }, - "execution_count": 26, - "metadata": { - "bento_obj_id": "140510701610704" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "torch.linalg.norm(new_point_torch_Adam - new_point_analytic)" ] @@ -401,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -414,7 +371,7 @@ "source": [ "batch_candidates, batch_acq_values = gen_candidates_torch(\n", " initial_conditions=batch_initial_conditions,\n", - " acquisition_function=MC_EI_resample,\n", + " acquisition_function=MC_LogEI_resample,\n", " lower_bounds=bounds[0],\n", " upper_bounds=bounds[1],\n", " optimizer=torch.optim.SGD,\n", @@ -427,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -436,27 +393,14 @@ "originalKey": "350e456d-0d1c-46dc-a618-0fbba9e0a158", "requestMsgId": "aa33d42e-c526-4117-88a7-aa3034d82886" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.3566, 0.0410, 0.7926, 0.3118, 0.3758, 0.6110]])" - ] - }, - "execution_count": 28, - "metadata": { - "bento_obj_id": "140510702066640" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "new_point_torch_SGD" + "MC_LogEI_resample(new_point_torch_SGD), new_point_torch_SGD" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "collapsed": false, "customOutput": null, @@ -465,20 +409,7 @@ "originalKey": "e263cfc7-47a0-4b81-ab33-3aa16320c87e", "requestMsgId": "3c654fc0-ce64-43c7-a8bf-42935257008a" }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.2928)" - ] - }, - "execution_count": 29, - "metadata": { - "bento_obj_id": "140510701611584" - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "torch.linalg.norm(new_point_torch_SGD - new_point_analytic)" ] diff --git a/v/latest/files/compare_mc_analytic_acquisition.py b/v/latest/files/compare_mc_analytic_acquisition.py index d0d1aa1926..261af4b72d 100644 --- a/v/latest/files/compare_mc_analytic_acquisition.py +++ b/v/latest/files/compare_mc_analytic_acquisition.py @@ -6,8 +6,9 @@ # In this tutorial, we compare the analytic and MC-based EI acquisition functions and show both `scipy`- and `torch`-based optimizers for optimizing the acquisition. This tutorial highlights the modularity of botorch and the ability to easily try different acquisition functions and accompanying optimization algorithms on the same fitted model. # ### Comparison of analytic and MC-based EI +# Note that we use the analytic and MC variants of the LogEI family of acquisition functions, which remedy numerical issues encountered in the naive implementations. See https://arxiv.org/pdf/2310.20708 for more details. -# In[16]: +# In[ ]: import torch @@ -22,10 +23,12 @@ # First, we generate some random data and fit a SingleTaskGP for a 6-dimensional synthetic test function 'Hartmann6'. -# In[17]: +# In[ ]: -train_x = torch.rand(10, 6) +torch.manual_seed(seed=12345) # to keep the data conditions the same +dtype = torch.float64 +train_x = torch.rand(10, 6, dtype=dtype) train_obj = neg_hartmann6(train_x).unsqueeze(-1) model = SingleTaskGP(train_X=train_x, train_Y=train_obj) mll = ExactMarginalLogLikelihood(model.likelihood, model) @@ -35,24 +38,24 @@ # Initialize an analytic EI acquisition function on the fitted model. # -# In[18]: +# In[ ]: -from botorch.acquisition import ExpectedImprovement +from botorch.acquisition.analytic import LogExpectedImprovement best_value = train_obj.max() -EI = ExpectedImprovement(model=model, best_f=best_value) +LogEI = LogExpectedImprovement(model=model, best_f=best_value) # Next, we optimize the analytic EI acquisition function using 50 random restarts chosen from 100 initial raw samples. -# In[19]: +# In[ ]: from botorch.optim import optimize_acqf new_point_analytic, _ = optimize_acqf( - acq_function=EI, + acq_function=LogEI, bounds=torch.tensor([[0.0] * 6, [1.0] * 6]), q=1, num_restarts=20, @@ -61,26 +64,27 @@ ) -# In[20]: +# In[ ]: -new_point_analytic +# NOTE: The acquisition value here is the log of the expected improvement. +LogEI(new_point_analytic), new_point_analytic # Now, let's swap out the analytic acquisition function and replace it with an MC version. Note that we are in the `q = 1` case; for `q > 1`, an analytic version does not exist. -# In[21]: +# In[ ]: -from botorch.acquisition import qExpectedImprovement +from botorch.acquisition.logei import qLogExpectedImprovement from botorch.sampling import SobolQMCNormalSampler sampler = SobolQMCNormalSampler(sample_shape=torch.Size([512]), seed=0) -MC_EI = qExpectedImprovement(model, best_f=best_value, sampler=sampler) +MC_LogEI = qLogExpectedImprovement(model, best_f=best_value, sampler=sampler, fat=False) torch.manual_seed(seed=0) # to keep the restart conditions the same new_point_mc, _ = optimize_acqf( - acq_function=MC_EI, + acq_function=MC_LogEI, bounds=torch.tensor([[0.0] * 6, [1.0] * 6]), q=1, num_restarts=20, @@ -89,15 +93,16 @@ ) -# In[22]: +# In[ ]: -new_point_mc +# NOTE: The acquisition value here is the log of the expected improvement. +MC_LogEI(new_point_mc), new_point_mc # Check that the two generated points are close. -# In[23]: +# In[ ]: torch.linalg.norm(new_point_mc - new_point_analytic) @@ -109,7 +114,7 @@ # # Under the hood, `gen_candidates_torch` uses a convergence criterion based on exponential moving averages of the loss. -# In[24]: +# In[ ]: from botorch.sampling.stochastic_samplers import StochasticSampler @@ -117,11 +122,11 @@ from botorch.optim import gen_batch_initial_conditions resampler = StochasticSampler(sample_shape=torch.Size([512])) -MC_EI_resample = qExpectedImprovement(model, best_f=best_value, sampler=resampler) +MC_LogEI_resample = qLogExpectedImprovement(model, best_f=best_value, sampler=resampler) bounds = torch.tensor([[0.0] * 6, [1.0] * 6]) batch_initial_conditions = gen_batch_initial_conditions( - acq_function=MC_EI_resample, + acq_function=MC_LogEI_resample, bounds=bounds, q=1, num_restarts=20, @@ -129,7 +134,7 @@ ) batch_candidates, batch_acq_values = gen_candidates_torch( initial_conditions=batch_initial_conditions, - acquisition_function=MC_EI_resample, + acquisition_function=MC_LogEI_resample, lower_bounds=bounds[0], upper_bounds=bounds[1], optimizer=torch.optim.Adam, @@ -140,13 +145,14 @@ ).detach() -# In[25]: +# In[ ]: -new_point_torch_Adam +# NOTE: The acquisition value here is the log of the expected improvement. +MC_LogEI_resample(new_point_torch_Adam), new_point_torch_Adam -# In[26]: +# In[ ]: torch.linalg.norm(new_point_torch_Adam - new_point_analytic) @@ -154,12 +160,12 @@ # By changing the `optimizer` parameter to `gen_candidates_torch`, we can also try `torch.optim.SGD`. Note that without the adaptive step size selection of Adam, basic SGD does worse job at optimizing without further manual tuning of the optimization parameters. -# In[27]: +# In[ ]: batch_candidates, batch_acq_values = gen_candidates_torch( initial_conditions=batch_initial_conditions, - acquisition_function=MC_EI_resample, + acquisition_function=MC_LogEI_resample, lower_bounds=bounds[0], upper_bounds=bounds[1], optimizer=torch.optim.SGD, @@ -170,13 +176,13 @@ ).detach() -# In[28]: +# In[ ]: -new_point_torch_SGD +MC_LogEI_resample(new_point_torch_SGD), new_point_torch_SGD -# In[29]: +# In[ ]: torch.linalg.norm(new_point_torch_SGD - new_point_analytic) diff --git a/v/latest/files/ibnn_bo.ipynb b/v/latest/files/ibnn_bo.ipynb index 371b34245c..8d6928d6af 100644 --- a/v/latest/files/ibnn_bo.ipynb +++ b/v/latest/files/ibnn_bo.ipynb @@ -24,18 +24,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0617 091042.635 _utils_internal.py:282] NCCL_DEBUG env var is set to None\n", - "I0617 091042.636 _utils_internal.py:300] NCCL_DEBUG is forced to WARN from None\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import warnings\n", @@ -48,7 +39,7 @@ "from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood\n", "\n", "from botorch import manual_seed\n", - "from botorch.acquisition import ExpectedImprovement\n", + "from botorch.acquisition import LogExpectedImprovement\n", "from botorch.fit import fit_gpytorch_mll\n", "from botorch.models.gp_regression import SingleTaskGP\n", "from botorch.models.kernels import InfiniteWidthBNNKernel\n", @@ -78,8 +69,13 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 14, + "metadata": { + "output": { + "id": 1056366939451157, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { @@ -120,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -181,12 +177,17 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 17, + "metadata": { + "output": { + "id": 1453096472021894, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { - "image/png": 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P6W2sqrqabzZ+T1DwdQDs7ezw8vKkoLCQpOQUkpJTOHPuIj976tE6AfOq6mo++PgLovUz1OzsbHFzcyU3N5/tO/cQFh5htk9uKwMH9MPDw528vHwuXg6sN0Bt6Gv79+uj/BatrW1AX7hz647dnDpzHjs7W9zd3FCVqMjOyeXUmfMEh9zg5ReerTMjsrS0jE+++EZJF+Po6IC3lyd5+fnKMXvm/CWef/YJOvl4m2yXVqvjo8+/JjMzGx9vLxyNapfE30zk/Y8/p7y85vfj5uaKu70bxcUl3ExI4mZCEsdOnOHF55+my20DdysrK8aNHc2RYye5fCWI+5YvblYBNSFEXRUVlZy9cBmA4cMGN6pmQkvWObCzs8XGxprKyioq9P2tpS5cqknDMXzoIKWfNMfJ0ZH+ff2JiIrh/MUAVi67t57tBgIwetRwYmJvmn1dYlIy73/8hdKPOTs74ePqRX5BgZKz+1JAIC/94lnlZpyTkyPdu3UlKzuHyspKbG1t8dGfn11dXZRtN6cPNPbNt98THhmNj7cXDg72qNQ11zE2+j4EfZD8o0+/QqPR4uXpgYurC3l5+YRHRhMRFcMTj66vd0JUY+zY9SOnz15ApVKxdtVypkwa3+B7jp44za49+9HpdFhZWeHt5UlpWRmZWdlkZmVz4dIV1ty3jGlTJprdxpFjpzhw6Bhuri507uSDjY21fj/cujYqKi7m0y82kJWdg5enB+5uruTk5pGYlExiUjIZmVmsNlHwsSXaZ4qMlzseCVCLZguPjKKgsAjAojyQM6dP4fKVIJKSU9i89QdefL55RQtaenu3C7keik6nA2D40MEtuu2GqNVqHlq/hv/7x39ISk7h5OlztfKMXbgUQExsvFKR19b2VkeYnZPLJ59/Q3lFBf379WHdmpXKAKiiopJDR49z+OhJLgUE0snHu1aBrrKyMr79bitarRZvby+efvwhZQBcWlrKzj372bBpS5vuC1PGjBrB9p17qays5PzFK2YHw1eCrgHg4+1FX/+aqtjGAflNm7fTrWsXHly3Gm998a6CwiK+/nYzUdGxHDl2kmFDBtHH/9bsv/ibCXyzcQtarZbRI4ezasUS5a5rcUkJu/ce4PzFAA4dOUGXzp1N5/zS6Thw5BiLF87jnnmza7XpWkgogUEhqFQqVq1cytRJ47HWd/Bl5eUcO3GaA4eOcerMeUYOH1qniJeDvT39+vUhPCKK4Gs3pMMVogOS/hPCwmsC2N26dsG3R/dGbX/i+LHKDO3roWG1AtQq/fn0/MUAioqLeebJRxg2ZBAqlQqNRsPJ0+f4Yfc+0tIz2P3jAR5YW7Ok+rlnHq81K3n92vvqnF/NUesHpRUVFWz6fjsL7pnD/DkzldnSGzZtIejada5cDaa8ooLqag1/fP1XSt8cFR3LR599TWVlJcdOnObh9Wsb/Mz6iicWFBbx1jv/Bv3+HTWidmGrH3b9SFDwdWxsrFm1YimTJoxVgpRx8Ql8t2UHaekZfPLFBn7/m5drzSI7evyUEpxeMH82C+bPUQahEVExfLPxe1LS0i3ab61FrVYzcdwYDhw+RnhEFPkFhSZzTGZl55CYlAzARKMl0Ia/Z2RUNLl5+ay/fxUTx49BrVaj0+mUm9QFBYVs3raTF372ZK3tfrNpCzGx8Tg6OrD+/lUMHzpYeW9oeCTfb/2BnJxcPv1iA6/96he1BvEGwSHXqa7W8IffvlIniL156w+Ul1fg26M7Tzy6XgnAANxMSGLDpi1kZGaxedtOXnrh2TrbHjFsMEeOnaSsrIzIqBgGDxrQlN0shDAjLv4mlZWVAIwfM7rd2lFQWERlZRVAo/LspqVnkJGZBcDwYZbNuh47ZiQRUTFcDrjKssULTN74ysjMIi7+JjY2NowaMcxsgNpQ4K+kpBQXF2eeeeKRWv38xcuBbPp+OxmZWezas5+HHlhT09ahgxk+dLAyy7mnbw+TRRKb0wcaJCQmkZdXwKsvP19nHGoIVJeVl/P1hs1MGDeGpYsXKIVpE5OSef+jLygpLWXvvkMtEqDes++gkjLN0oDt1eAQdu7eB8CMaZNZvHA+jo4OoC/uuXXHbkJuhLFl+y66dulM3z6962yjqqqaYydP8+C6VXVma98+5nZxceG5Zx5XbuoWFRXz0Wdfk5CYxOmzF5g3e0atYp4t0T5zZLzc8UgOatEkGo2G7Owcjp88wxdfbQJg9oypJtMN3E6tVvPA2pWo1WqiY+IsylPVltu7nWHmi4+3V7ss++jcyYdli2tm7+7df1hZelxcUsLOPfsBuGferDqd4o8HDlNeUYG3txc/e+rRWgMbOztblt67gFnTayrWHj52QpnBhj4XquFO9UMPrK41O8vR0ZH196+qNRBqL/b2doweWdOZB14Npqqqqs5rqqqquH69Jv+pudxbpWVlPPvko0pwGv3S+yceWY+tfubc6XMXar1n5579aLVa/Hv34rGH19U6NpydnFh//yplCd2+A4eVII2xao0GJ0cnFt4zt84M9sjoGAC6dunMzGmTleA0+s508cL5TJ08gRHDhlBaVmbyexkC6glJycoFshCifUn/WVtCYhJArUGnpTp39lFmdCUl1c5DraJmYJiVncP9q1cwfOhgZXa2lZUVc2ZNZ8yompsCl69cVWahNk/N9ouKivHr0Z2F8+cog11bWxvuW74Y9OmbboSG8+hD99fqm/v366O0KTbO/IwyS+h0OjZs2kJxcQk2NtY89vA6bGxu3cTOzMrmzLmLAKxasZSpkyfUCiT49+7JCz97EltbW4qKijl15rzynFarVf49oH9fliy6p1ZwdWD/vjz64P0UFNSftqYtTJowFpVKhVar5XJAoMnXBOpvYtvb2TFy+K0gvvExtGjBXCZPHKf01SqVinFjRimTBiIio8nMylbeGxEVw43QcAAef/gBRg4fWuu9QwcP5JknH0WlUpGWnkHg1Wsm25aUnMoDRhMMDEpKSklJTQP9NeDt12S9evryyINr6d+vD95eXlRXV9fZtp9vD+WYiImToqBCtLQE/Y0v9L/J9nLsxCnlv4cOsXyylXGxYMMEn4aMHjkCezs7ioqLuX4j3ORrLupnZY8cMRQHBwez20pOSSUnt2bcu2De7DrXCRPHj2HcmJEABF273qiCiM3pA40lJaeybMlCk5OkDH1Ifn4BXl5erF21XAlOoz8HGwLIGZlZze4zDxw6yqEjJwCU9GYN0Wq17NxdE08YO3oka+5bpgR/0a+2e+rxh/Dz7YFOp+NHM7mac/PyGDJoYIOpRNIzMnn2yYdrrTgyFCw0tMf4uGup9tVHxssdi8ygFg0KuRGm5G0yxbdHdxYvnMfQIYMs3qafbw9mTJ3EidPn2LVnP8OGDDKbv689tmfMUHzJz7dxM7ta0szpkwm5EUpUdCxbtu/mZ089ys7d+ygpKcXPt3ud3KFV1dUEX6tZrjR18gQlyHq7GdMmc/zUWSoqKgkLj1Rm8IXpl2R7eLibvSCZNnlirQ6kKTKzsnjrnfcsfq0pkyaO4+LlQMrKywkOucG4MbVnKoeGRVJeUYFKpWLCONOzFyaOH1MrB6mBs7MTgwb241pIKJGRMcrjObl5SvBg1owpdYLLBjOmTuZaSCjZObkkJqXQ069uDnHDhZU5JaWlVFVV1QosGKxbs7Le9xpylmu1WlJS05sUABJCNJ30n/X3n2Xl5UrgzN3NzeRrGuLu5kZJSSn5ZgZ2Ls7OjBk1wuRzY8eMIjAohKqqamLj4uutydBYM/U3gI15eLjjYG9PWXk5ffx7mazlYHjMMLO+qY6eOE1EZDQAK5ctrvNZAYFB6HQ6HOztTealRp8yYuTwoVy+cpWg4Ovcu6Amb2dySipFRcVQTx82oH9fOvl41wraNsWOXXvZd/BIg68zpMq5nZeXJ/379SEyKoYLl64wf+6sOq8xBIdHjxpu8npJrVYzw8xAf9yYkRzRz+KPiIxWAskBV66CPmXMoIGmc4v69uiGf+9exMbFExQcUmv2toGLizMD+vet55tjNqjR08+XXz73tNn3WVlZ0b1bF24mJNW5wSOEaL7i4hLQ35QyTi3RFsorKkhLy+Ds+YtcvFxzc27KpPH49uhm8TYMfbiLi3OtGa31sbOzZfSo4Zy/GMC5i5cZedvKHa1WyyX9+XFSA6kz/Xx78J9//o2S0lJsbUyPZf38fLkUcJWKikqKiktws3A/N6cPNKZWqy1aATd/jumZub5G10d5+QVNngx39PgpfjxwRGn3+LGWzdiPjbupTH6bPXOqydeo1WqmTZnIpu+3ExMbT1FxscnaBmMbGNMCTJ40HkfHuqli/Grth/xWaZ85Ml7uWCRALRpkb2+Hl6dnncfLy8vJyy+oWRq8bSfTUtKYN2eGxTnsFt97D0EhN8jPL2D7zr089vC6ZrWzpbeH/kSVq79za2ofmGLpYAowuyz3diqVigfXreatt//NjdBwduzcy8XLgdjYWPPw+rV19nlycipVVTWD/vqKGXp7e+Hs5ERxSQlJSSlKB5uWngFAj+7mL2L8TARbG6uqqlqZAdRUff17K4Pgi5eu1AlQG2ZG9evrj6eHh8lt+PcyX/iye9euXAsJpai4mJKSUpycHLl5M1F5vr7929NotkRiUrLJALW53KGDB/bn1JnzFBQU8u//fcw9c2czaGA/szcbTPHyvPV9c3JypcMVoo1J/1l//2kcVLS1s/zcZsxQXKnSxAoa9LPWzN1E7G50/k7PyGzRALW54lD29naUlZebzfdtr59dZWpFkKUSk5LZu+8QAMOGDjY5i8rQj3Xu7FPvcderpy+Xr1wlPSNTyeWZlpahPF/fdUJPP99mB6gNs+eaY9KEsURGxZCZlU1s3M1a6brS0zNI1acimTDO9CqrLp07mZ3l16VzJ9RqNVqtlvSMTOXxeP3+7d5AQelePX2JjYsnMdl0gLiHmWsEJydH/Hx7kJiUzM49+yksKmL82NEWFWAz5uXpwc2EJLJzchv1PiFEwwz5nm1tbeqtr7D/4FGCQ26YfX7alIlmUzXs2PUjO3b9WG87VCoVM6ZNrjcntCk5+vOCt4VjYIPJE8dx/mIA4RFR5OXn4+F+q8ZGWEQUBQWFeHp4WJQ+S6VSmS3UC9SqEVHdiH6zOX2gsZoaEvUXeQTo1s10X2A8o7qp/X7IjTBycvNQqVSo1WoKCgr5/OuNPPfM4w1eV8ZbOKY1rADQ6XQkJaWYTAllrr8yZq5PtDfaD8bXcy3ZPnNkvNyxSIBaNKh/3z488+QjJp+rrKwkMCiEXXv2s3f/IWJi4/jZ049ZNMi2t7NjzX3L+PSLDVy5Gsz4caMZ0oz8dy29PfSD52qNBm4rqlCflhhMmeLl6cF9Kxaz6fsdHD91FoAli+4xWbHWeDbNh/oK9g0xnq1lmBnlWs8susbkMDOne7euFgfp33rnPbPB7EkTxrH7xwNERseSk5undDQVFZXcCKtZXlZfgUs3N/N/W+OZhCWlNQHq/IIC5bG//N+/LGp/oZnZcM7Opi+6hg4ZxLzZMzhy/BQJicl8+uUGrK2t6d3TjwED+jJ86OAGC2gZzyIoLGrebDwhRONJ/1l//2k8I8rcDFhLPofbBqnG6puN5OJyq12GQrotxdx3VqlqguWuLuaet6xIpDnlFRV8tWEzGo0GNzdXHly3yuTrDDPObyYk1TvL30Cn01FUVIyXlydFxcXK4+a+Bw3se0s9/fjDSrqs+lwLCeXTLzeYfG7k8KE4OjpQWlrGhUsBtQLUhhoV3t5eZvNW1vc9rKyscHJ0pKi4uNYxZLgOCwwKUQow1qeoqBitVlvnZoqTmWsEgEcfvJ//ffQZefkFHDpygkNHTuDh4c6Afn0YNLA/Q4cMwt6u/sCJq6ur8vlCiJZlWP1YWVll8vdtkJefX++EHXNjCPTnJ1MBXBsba5ydnfHz7c64MaPMFmKtT5F+BnhjZ3/37tWTLl06kZ6eycVLV1h4z1zluQuXalKJTRw/xqL+TqfTcTUohMDgaySnpFFUVNwiaRia0wcaMzeOu525VWLG+8BUOkhL5OTm4eLszKMPryMvL4+Nm7cTGRXD5q0/8OC61fW+13hM++Kvf2/R55lb4WXJvjA3E7/Wb8NoN7Rk+8yR8XLHIgHqu0x+QaHZgOXwoYMbXTHY1taWSRPG0rVLZ/753geER0Zz9vwlZkybbNH7RwwbwohhQ7h2PZQt23by+9+83KgZoq29PUNBCfR3vy1h6WCqKYYPG8LWHXuoqqpCpVKZLVhRWXWr4+7k420yPcTtjCszG+7gWpso2GNQ33NtbcL4Mezdf6hm2djlQBYtqLkQuh4aRmVlFfZ2dnWKQxmzq2cAZ/w9DUvRje/sduvaxaILLDszd9etzFysAixfuojBgwZw6ux5wsIjqaysIjo2jujYOH7cfxj/3j1Zc99ys8v1jI99c7MLhRCWkf6zcSzpP52cHLG3s6O8oqJJMzh1Op0yeDGeAWOs/vP7rZsBVdUte440F4gwaG4g2pxtO3aTmZWNSqXi4fVrzc48M/QJ5mb5m6LR5/c0nuVV/3WCZSsCWpuNjQ1jR4/k9NkLXA0OYfXKZdjpZ+xf1QePJ5pJAYb+Bk59DN/T+Bgy7F9nJyeLA/VVVdVKuwys1Ob3YefOPrz+6kucOnuegCtBZGRmkZeXz8XLgVy8HIi9nR2zZ01jwbzZZm98GX6bco0gRMvz1PdLOp2OnNw8s/V71t+/ivX3172Z+Ic3/q6kNzBn7qzpSi78lmYIBNtYOAY2NnnCOH7YvY8Ll66wYP4cVCoVxSUl3LgRXpN20UxdIGNVVVV88vk3hOvTVaGfOOTp4a6c00pLS8nLL6hnK6Y1pw80Vt852lhD1wTN4dujGz976jGlr0lLz+DYiTNcuHQFH29v7plXN7WVgaE/V6lU9c5QNmaqoC/6G7YNaey1T0u2zxwZL3csHSfCJNqEptp8SoX6lmk2pFdPX7p360pySipB165bPMAGWH3fUiKjYsjJzePHA4dZuWwxzRm3tfT2bmmdwWRjbNm2i6qqKmU56cbN2/jlc0/X6fTsbG8Nph5ev4bevXo26nNsbKyprKxCo5/9ZkpHOoG7ubowZNAAroeGc/nKVSVAbcgrOWrksHoDLRqN+aIaxoWFbPWBfuOZes//7EmL8501Rf9+fejfrw9V1dXExd0kIjKa66HhpKVnEBefwL//9xGv//rFWsUmhBAtT/rP5jC/ET+/HkRFxxIb2/iigBkZmcqs1V49TS/J1NbTj9U+vzc9GN9RXLkarOQanTt7BgPryV1s6Md69+rJ888+0ajPsTa66V1dz/5tTpqSljZpwjhOn71ARUUl166HMn7sKJKSU8nIzEKlUjHeTHoP9IVN61OlP46MjyE7W1vKyssZOWJog/UimsPR0YGF8+ewcP4ccnJyiYiKJjwimtDwmvob+w8epaiomPtXrzD5/iZO2BNCWMA4tV9MbFyHKDDfFKomjIHHjxvN7h8PkpObR2RUDAMH9CPgShDVGo2+eGvDQeFDR08owekxo0awfOnCOukaL1y6wsbN2xrdvub0gR1NX//etW6ELl+yiIyMLG6ERbB3/yG8vT3N1uIw7AcrKyuLVzW3pY7ePtHyWu9WjuiQvLw8+eC9t03+7+H1a5q1bcOJsbEVaD3c3Vm8qKZy64lT50hMSmnWXcaW3J7xrC/j2WDtITDoGleDQ7CxseaFnz2Jg4MDMbHxSroPYx4et5bP5Obl13m+IU764gXGy3hvl9eE7bamSRNrqgZnZeeQmJRMWVmZUuyxobv0BYXmj1njZXXO+oILHh63cqm11X6wsbZmQP++LFuykN//5mWee+ZxbG1tqKioZM++gybfY7wEztaCWfRCCPOk/2wcS/vP4UNrVgLl5uUpRf0sZQjGqlQqRg43vUom39LzewsVhmwvOTm5fL91J+iDIksamNFv6Mea0oc5GRU4Kq7nOqEp1x+txc+3u3IjyVCbwrhGhbkZ+DRwjaDRaJSbJC4ut2arG/ZvW+4DLy9PpkyawJOPPchbb7yurOY7e/4S6ekZJt9juIkg1whCtDz/Xj2VYm3nLlxu7+Y0mmFyj/HKXEu5ODszbGhNAejLV4LAqM+uL+2isSuBwaDP9f/oQ/ebrCVUUlLS6LbRzD6wo1Or1Tz28AN06dIJnU7Ht99tJf5mgsnXuuv3Q3V1dYdM9dQW7ZPxcsciAWrRYvL1y2uMU0VYasa0Sfj59kCr1bJ56w5srJt3cmip7Tk4OCjLV9szJ1FhURFbtu8CYOH8ufTv14cVSxcCsHffIaWooUG3rl2UtB7RMXH1btt4BpmBochOamq62ffFxZvu6NrL0MEDlRxpgUEhBF27TnV1Nd5envT1N51X0iAhIcnsc8n6GZOeHh5KEYxefreKH0bHxNa77YZmXjX1vYMHDWD82JrAe3KK6Vmdxjm46ssTKoRoX3dz/zlx/Ggl4PnD7n3KbNSGZGfncPrcBdCf/zt3Nl10MCEh2ew2ko1mxHczUc/hTqHVavl64/eUlZdjZ2fLow+ta3CpraGgUEZmVr03Ruq7RgBIqec6wbi4UUdguJEdERFFSUkpV67WBD8m1jN7GiA1Ld3sTZbUtHS0+mXfXbvcWn5s2L/x8Qkm96FBfc9Zwtx1goODgzJrWqfT1TrWjRXqg+8ud/gNGiE6IisrK2bOmAL686EhUGsprc78Cs+2YDgv1JcDuz6TJ9Scc0PDIkhPzyA5JbXBtIvGDDf4+vj3Mnuz/EZYRJPa1pw+8E5gb2/Hz558FCcnR6qqqvn4829MplIz7AeAqHrGtFqtVunr2lJbtE/Gyx2LBKhFi0hITFKqoFtSkfd2arWaB9auRK1Wk5iUohRQaKqW2p5KpVJm1eTk5DSrTc3x3ZYfKCkppUf3bsydPR2AyRPH06+PP9XV1WzYtKXWIMXa2ppRI4YBcPnKVfLyTd8dTkxK5tev/5kPPv6i1l3JAfplwTWzketWltdqtZw9f6nFv2dzWFlZMX5sTQ7Jq0EhykXghHENF+G4FBBoMihSVFxMZFTNjL7Bg/orj3t4uNNHH/Q+eeY85eUVJrcbfO0Gr/7uDT7/eqPFQRf0+dTeefd/vPzaH+u9EVCl5E+zN/l8jtGFiJeX+dlhQoj2c7f3nw4ODqxYtgiAlNQ0vtrwXYPny/yCQj75YgMVFZU42NuzauVSs6/NzcsjLDzS5HOG2Vn29nb4976VCkulvtVnNObc3V72Hzyq9BVr7ltuUTGssaNHolKp0Ol0HDl20uzrPv96E3/66zucPntBeaynXw8c9P3OVf0s5NuF3AhTbrx0FOPHjMTa2ppqjYYdu34kLy8fOztbRuqvl8ypqqrmUkCgyecC9MeQSqVi0MBb1wljR48EoKy8vNa+M6bRaPjnex/yt7f/TfC1G436LsdPnuF3f/obH336lfl2GwXVHcxcJ2Tn1OS3vb3wlxCiZcyZNV3Jnbt56w+EmumPjBmK3RrOoa1Vs6AhhjQcTakRATBoYH/c3d0oLilh38GjAIweNdzi+haGnPzFZmZJBwQGERMbr/z79v7a0JebqjHRnD7wTuHt7cVTjz2ElZUVxcUlfPTpV3UKQvv36qlcqx09fsrsTc8Tp87y2z/8lc1bf2iTtrdl+2S83LFIgFo0i0aj4WpwCJ98/g06nQ5nZydmz2haoQbfHt2ZOb3mLvMJE2kr2mt7vj26A5CUnNrsNjXFxcuBXL8RhlqtZv39q5RZUSqVinVrV2JjY01iUgoHDh+r9b57F8zFzs6WiopK3v/4CxISb80S1mq1XLseyoeffFWTa1qrrTV7Zvy4McrFwzebvic9I1N5rrikhG+/20ZhYaEyO669LpxuZ7hTn5uXR3RMXE0RjnoKHxlUVVfz+Vcbaw2m8wsK+eKrTVRW1uT8vj0v7PIlC1Gr1eTnF/DBJ1+SkZGlPKfRaLhw6QrfbNpCeXkFDvb2jSrY4OjoWDOIrq7mi683EhYeWeuusEaj4VLAVQL1s7/MLW1PTK65uaBWq+neravFny+EaH3Sf94yacI45s+ZCcC1kFD+7533uHI1uM7Nv/yCQo6dOM3/vfMeqWnp2NnZ8tTjD9Wby9LV1YUNm7YSHhmlPKbRaDhy7CRB164DMG3yxFrF/txcb+VyDL52HY1GQ1V1dYcMVkfHxnHo6AnQD7gnWlB4CsDH24tpUyYCcOL0OfYdOEJFxa1lrvn5BXz73Vau3wgjOzunVnEiKysrJk+q6W+vh4Zz+OgJZdCo0+kIDYtg4+ZtSm2EDnKJgKOjIyP0xaUNAedRI4bXKUx4O1dXF37YvY/Aq9eUvlir1XIp4Kry+xg1YhjuRjlAB/Tvy5DBAwHYtfcAp89eqDUTLzMrm0++2EBScgqZmVl07964Prpbt67kFxQSHhnNlu276swAzM7O4Tv9QN3JyZG+ffzrbEOj0ZCaVjOz2vBbFUK0LBtra5596lG8vTyprKzk48++ZuPmbSQmpaC7LQl8alo6Bw4f409vvq2s8Bg2dDCz9LOw25rhvFBUVEx+I1OQoR9/GPokQ387aYJl6T3Q51YGuH4jvNaN5pKSUvYdPMK3321j6b33KI/HxsXXer9hZW1KarqS5qisvBya2QfeSfr19WftquUApGdk8tlX39YK8qrVapYvrZkkkJiUwhdfb6o1Hq6oqOToidPs2nuA4pIS3N3dTHxK62mL9sl4uWORIomiQVExsbz1znt1Hq+qqiI3L1+54Pb29uLpxx/C2dl0xXhLLF44n6Br11ssH1RLbK+Pfy+uXA0mKzuHwqKiNl36kZefz/ade0F/B97Pt/YAonMnHxbMn8PefYc4dOQEw4YMVgpyeHt78fQTj/DZlxtIT8/knXffx83NFXs7OwoKCimvqBn49/TrwSMP3l9ru26uLty/ajnfbt5Genomf/m/f+Ht5YlarSY7Jxe1Ws2zTz7C199+T3F1NVpt09NYtKTOnX3w792LuPiaYlt9+/S2aFbQ2vuWs3HzNn7/xv/h5emhfE/DQHTZ4gV1Lkz8e/fkoQfWsGnzNuLib/Lm//0TTw8PrK2tyMsvUGY3Dxk8kPuWL2n0d3lw3Wr++8Gn5OkD4PZ2dri6uqDV6SgsLFSWGw8dMojZM6ea3EasflaBb4/uFs9WEEK0HOk/Le8/ly1ZSJcunflh149kZGbx1YbNqNVq3NxcsbOzpbS0rNYy4969/Fh//yq6NpCaY/KEccTdTOD9j77A2ckJZxcn8vMLlOB3Tz9fFt4zt9Z7vL086dzJh4zMLM5fDODi5UB0Oh2/+PlTTZrl3poOHjqm9FWJSckmjzeD4UMHs9goN/XKZfdSWFRE8LUb7D90lCPHT+Lp4UFFZSUFBYXodDrUajVr7ltG3z61U2UtvGcukVGxJKeksvvHgxw6eqJmplxRCcUlJQzo35dhQwazfeceNO2wLNicyRPHKbmnAYsC+oMG9Een0/Hlhu9w2OaAm5sLRUXFlJSUgv73a2oW/6MPruXjz78hNu4mW7bvYtfe/Xi4u1NaVkZRUTE6nQ5bW1see3hdo4unDezfl/lzZnL42ElOn73AmXMXcXN1wd7BntLSW9u3s7PlsYfWmQzCJyQmU1VVcw7q69+rUZ8vhLCcl6cHv37pebb9sIfAoGtcuHSFC5euYG9vh6uLCzqdjqKiYmVsBtDJx5v5c2cycfzYdpsIZHxeiI2LN1torz6TJozj0JET6HQ6Ovl449/b8nPNogXzCI+Mpqqqig8++RJ3N1esra3Jyy9Aq9WybPECpk+dzKGjJygvr+D7bbs4evw0Dz2wBv/ePRkyaABXAoOpqqriL39/FysrK/x79eTFF56BZvaBd5Ipk8aTlp7BiVNniYqOZfPWH3hw3Wrl+dEjh5OzJJfdPx7k2vVQQm6EKePn/Lx8pRjypAljlckEbam12yfj5Y5FAtSiQeXlFaSYyF1nY2ONs7MTPbp3Y/jQwYwfO0rJe9xUdna2rF21nI8/+7pZ22nJ7Y0YNoStO3aj0+kIuR7G1MkTWqRtltj0/Q7Kysro5OPNvQvmmnzNvNkzuBoUQkpqGhs2beE3v/qF8ncY2L8vf/rdrzl5+hxh4ZFkZedQUlKKi4szvXr6MnbMKMaOHmHy7zZh/Bi8vDw5cvwUN28mkpdfgLOzEyOHD2XenJn4+XbHzt6O4pISZYDTEUyaMFYJUFtahMO3Rzde+9UvOHLsJOER0eQX5GNvb0dP3x7MmjmNIYMGmHzf+LGj8O/dkxOnzhIRFU1eXj5arRZXV1e6denM+HGjGTl8aJOKjHXy8eb1V1/i7PmL3AiLICMzq+bmgEqFs4szA/t3Z9zYUYwaMczkhWtZWRlR+vzj5mZYCyFal/Sfjes/J4wbzagRQwkMusaNsAiSU9IoLCykoKAQO1tbenTvRu9efoweOdziQLFareb5Z5/gxOlzXAkMJis7G41GS9cunRkzegRzZ02vs+/VajVPPLqeLdt3k6SfWePt5dmsGwitRaO5FfzNzMqu97WGIoEGNjY2PPXYQ4TcCOPi5SvcTEgiJycXWztbunbpTP9+fZgyabzJmWMO9va8/IufceT4SYKCr5OTm0d+XgHe3l7MmTWdObOmKSlUqjvQNcKA/n3x9PAgNy+vpkaFhUGHh9evoV/f3ly4dIX0jEwqK6vw8fZi+LAhLJg3G0dHhzrvcXR05MXnnyEgMJgrV4NJSk4hKzsHB3t7unfrypDBA5gycXyT02ssW7KQQQP7c/5SAAkJSeQXFFBQWISdXc1vZeCAfsyYNgkPd3eT7w+5Hgr6v6UhtZsQonU4Ozvx2MPrWDh/NgFXg4mOiSczM4uc3DzUajXOzk507dqZPv69GTigLwP69W1W0eOW0KVLZ+Vmbcj1sCYFqL29POnX15+o6NhGzZ5GPz575Zc/58Cho8TExVNUXIKLsxPDhgxi5vQp9OtbszLkwXWr2bX3gDKz1lAzaOzokWRmZXPh0hWKiopxcnKkR49b/WBz+sA7zcpl95KRkUlYRBQXLl3B29uLBfNmK8/PmzOTQQP7c+rMeaJj4sgvKESlUuHh4Y6fb3cmTRhXK41VW2ut9sl4ueNRRUZG6ix4nRB3tQ8//YrQsAh69/TjVy89197NEfW4HhrOx599jb29HW+98XuzS3ejomP5zwefAvDmH177yeVfPHXmPFt37MbKyoo3//Bamy/JEkII2rH//MMbfyc3L49F98zl3oXz2uxzRcf39r/+S2JSCosXzWfh/DlmX/fe/z4hOjaOCePG8PD6NW3axtam0Wj4w5t/p6CgkGlTJioFFYUQwtiR46fYtWc/trY2/O3Pr+Po2PhizkJ0VDJe7ngkB7UQFpg3ewYA8QmJxN80X7ROtL9jx08DMH7s6AbzSv5UabVaTp05D8C4MSOlsxVCtBvpP0VHEhUdS2JSSk0ebX3dirtRYFAIBQWFqNVqZs9sWu57IcRP39RJ43Gwt6eysoqz5y+1d3OEaDEyXu6YJEAthAX69fVn2NDBoC92IzqmayGhRMfGoVarmXMXD7guXAogIzMLW1ubWvlGhRCirUn/KTqK6upq5RgcP3YUbkZFDe8m1dXV7DtwGIDpUyfRyce7vZskhOigHBwcWKRPM3n42Ckl974QdzoZL3dMEqAWwkKrVy7Fwd6emNh4Ll4ObO/miNtcvxHGt99tBWDWjKl4N7Lg0E9FUVExe/YdAmDJonvM5p4UQoi2Iv2naG+lpaV8tWEzCYlJONjbs3jh3TsYPXTkBNk5uXh6eLBY0t8IIRowY9pk/Hx7UFZWxg+7f2zv5gjRbDJe7rikSKIQFvLy9GDdmpV8ueE7tv+wh359ev/k8hbfafLy8/nfh59TWlZTsR6gf78+LFu8oL2b1i50Oh2btuyguLiEIYMHMmvG1PZukhBCSP8p2s0Pu/dxIzSc7JxcNBoN1lZWPPrwurt2KW/8zUQOHjmOlZUVjzy0FgeHusUdhRDCmJWVFY89vI63//VfLl4OZNjQwVJQTtyxZLzcsckMaiEaYczoESycP4ey8nI+/vwbyisq2rtJdzUVKgoLiygpKcXLy5NF98zluWcex8rKqr2b1i72HzrK9RthdOvahcceXodKpWrvJgkhBEj/KdqJVqMhOycXGxtrBg3ox0u/+BlDBw9s72a1i4KCQj776lu0Wi33r15BX//e7d0kIcQdopOPN08++iBqtZpvN20lNS29vZskRJPIeLljU0VGRurauxFCCCGEEEIIIYQQQggh7j4yg1oIIYQQQgghhBBCCCFEu5AAtRBCCCGEEEIIIYQQQoh2IQFqIYQQQgghhBBCCCGEEO3Cur0b8FP33IuvAbDonrncu3Bei2zzwqUrbNy8DYC3//pHnJ2dWmS7QnR0xsf+m394DS8vz/ZukhDiLiH9uRC1/eGNv5Obl8eEcWN4eP2a9m6OEEKIO5ShP5k+dRJrVy1v7+Z0KK1x/SlER3VHBqj/9+FnRETFYG9nxztv/QkrKyuzr42KjuU/H3wKwIL5s1my6J56t/3+x18QHhGFnZ0t7/ztT1hbN28Xde/WFQBXV5dmbae9FZeU8Ps/v0VVVTWjRg7jyUcftPi9Wq2WP/3lHXLz8ujWtQu/e+2lVm1rS9LpdJw6e4Fde/ZTVVVl8SBMq9Vy7sJlAoOukZaeQXlZOc7OTvTs6cuUSRMYMmhAs9p15WowlwKukpySSmlJKY5OjnTv1pWJ48cwdvTIet+bl5/P8ZNnCQuPJD+/AK1Oh4e7G0MGDWD2rGl4uLs3q22m7DtwhP2Hjpp8TqVSYWdri5ubKz26d2XkiGGMHD4UtbruAg8n/fcEsGrmb7M1ffbVtwRfu4GNjTV/+/PvcHJytPi9e/cf4uDh4wC89soL+Pn2aMWWNk9cfAL/+s+HFr12+NDBPPPkI43+jOb+ltLTMzhx+jyRUdEUFBaiVqvx8vRk2NBBzJk5DUdHy/82ouVJf9727sb+PDjkBpcuB5KYlExxcQlqKzXubm749+7FtCkT6dXTt8nbjoqO5dyFy8TfTKCgsAiVClxdXOjV05dJE8cxaED/et9fWlrKiVPnuHY9lJycXFCBh7s7gwb0Y/q0yfh4ezW5beYYBrumWFtb4+Bgj4+3N/37+TN54ni8PD1MvrZLl044ONjj6dHy1w0tJTUtnb+9/W8A5s6ewYqliyx+b3l5Bb/941+prKxk2JBBPPvUo63Y0pYTHHKDi5cDSUxMorikFGtrKzw9PBjQvy8zp09p1jHVnD41Ly+fk2fOEx4RRU5uLtXVGtxcXejfrw+zZ06jW9cuTW6XEHeS9/73CdGxcXTv1pXXX32xvZtzVwmPiOJqcAgJicnk5uVTUVGBWq3W93te9PHvzcTxY+jSuVN7N/WO0BbjrMCga2zZtouS0lL69fHnxReeqff1v3zldao1mga362Bvzz///kaz2ydaVseN8NRj6JBBRETFUF5RQWzcTfr362P2tWHhkbX+u74BbVVVFTGx8QAMHNCv2YNZoEN2OoaL9cbMeHF2cmLUiOFcvnKV69fDKCouxsXZ2aL3RkRGk5uXB8C0KROb1fa2lF9QyMbvthIeGd2o95WVlfG/j74gITEJACdHR7y8PMkvKOBaSCjXQkKZMW0ya+5b1ug2aTQaPvvyW66HhgNgb2+Hl5cnhUXFhEdEKZ3uE4+sNxnoCY+I4vOvNlJeUYFKpcLTwx2VSkVmVjYZmVlcuHSFp594uN7fVHN169oFlUql/Fur1VJUVKy0ITAohN69/PjZU4/VCewOHzqY4UMHt1rbWsr0KZMIvnaDqqpqLl+5yqwZUy16n1ar5eLlQAB6+vl26OA0QGlZmfLfhuCdOU2Z7d7c39LFy4Fs3rKDao0GtVqNp6cHWo2WlNQ0UlLTuHAxgOd/9qQMituR9OfNI/15/Sorq/jym01Kn2llZYWHhztVVVVkZeeQmZXNpYBA5s+dydJ7FzRq21qtli3bd3H2/CUA1OqaoLcOHXn5BeTk5hEYFMKEcWN4cN0qkzddk5JT+Pizr8kvqBnUeXq4o9FqSc/IJC09g3MXL/P0E48wsH/fFtojtTk7OeHm5lrrsfKKCgoLC4mLv0lc/E2OHjvFurX3MXH8mDrvf+6Zx1ulXS2pW9cu9PHvTWxcPJcuB7L03nvqvRFmLCAwiMrKSrhDjveKikq++GYToWERoD/ePT3cKSktJS09o+aYunCZRx+6n5HDhzZ6+83pU0NuhPH1t5upqKjZnx4e7thYW5Odk8uFS1cIuBLEA2vvY4KJ40wI8dPw9r/+S2JSCh+893abf3ZRcTFffL2J6Jg40E+QcnN1wd3NlfKKCoqKiomLTyAuPoFjJ05z74J5LJg/u83beSdp7XFWWVkZW7bvJiAwyOL3VFZWKsFpby9P7OzszL7W3t78c6L93JEB6mFDBrF9517QD1LrHdBGRCn/nZScWu9ALComjqqqKgCGDh7U4u3uKAyD9saaNmUil69cpVqj4eLlQObNnmHR+85dvAyAnZ0t48eObtJntzXjO3U9/XzJzsmhpKTUovd+s3ELCYlJODg48OD9qxg+bDBqtZqq6mpOnT7Hrr0HOHXmPJ07+TBj2uRGtWvHrh+5HhqOtbU1a+5bysTxY7GyskKr1RJwJYjN237gWkgou388yMpl99Z6b05uHp999S0VFZX06+PPg+tW4a2fRZOXl8/GzduIiIrh0y828PvfvIy7u1uj2mapl154FkdHhzqP5+Xlc+zkGU6cOkv8zUS279zLIw+ubZU2tLYB/fvSuZMPGZlZnLtw2eIAdah+Vjt3yGC4tLQmQK1Wq1sleNec39LNhCQ2fb8drVbLqBHDWH3fMtz0M1/T0zP46tvvSU5J5aNPv+YPv30ZW1vbFm+/aJj0580j/Xn9du7ZpwSnFy+az5yZ07G1tQGgoLCIHTv3EBgUwqEjJ+japTPjxoyyeNvHTp5RgtPTp05iyaJ7lL6trKyMvfsPc+rMeS4FBNKpkzcL5tUe6BYUFvHRp19RUFjEyOFDWX3fMtz1weLs7By+/vZ74hMS+fKbTbz5h9+0ykBq3JiRrFq5tM7jWq2WG2ER/LDrR7Kyc/huyw569/Sjc2efFm9DW5g+ZSKxcfEUFRdz7Xooo0cOt+h95/XHu5eXJ4MG1j8TviPY9P02QsMiUKlULL13AbNmTMHGpuZ4j4mL59tNW8nOyeXrbzfzx9d/haeH6ZnxpjSnT01JTePLbzZRVVWNn28PHl6/hq5dOgNQVFTM99t3EnztBhu/306nTj707uXX4vtGCNG+yssrSE5Ja5fP1mq1fPrFBuLiE7CxsebehfOZNH5srbRqGo2GsIgodu3dT3p6Jnv3H8LDw50J4zr+tU57aO1xVmRUDN9+t5W8/AI6+Xij1enIzs5p8H2GsSnAQ+vX0Ne/dxO+nWhPd2SRRG9vLzp3qrlINh6w3i6/oJCU1DSsra3p26c3Op2O8Hpeb3hOpVIxZPDAVmh5x2C4c9hY/r17KrMkz1+4bNF7ioqKuX6jZnA4bszoO+JOVWpaOl9+8x2lZWXMnzOTV375M+xsLWt3TFy8Mhh+8P5VjBxxK1WFjbU1c2fPUAJp+w8eVWaSWCI7J5cz5y4CsPTee5gyaYIyC0itVjNh/BhlBtip0+fIy8uv9f6Dh49RUVGJu5srzzz1iBKcRj+T5anHH8bdzZWy8nIOHjlucbtaioeHO6tWLGHokJpg0tXgEKqrq9u8HS1l6uQJAKSlZxAXf9Oi95zT/64cHR0YM2pEq7avJZSW1ty0cXSoe8OhuZr7W9qz7yBarRbfHt157OF1ykUTQJcunXn2qUewtbUlNy+PU2fOt3j7hWWkP28e6c/Nq6io5MKlAABGjhjKwvlzlOA0gJurCw+tX6sEhc+cu9So7Z86fQ4AP98erLlvWa0brw4ODqxeuZSefjWrYM7q+25j+w4cpqCwiH59/Xni0fVKO9D/Lp58/CH6+Pdm2JDB5OTmNvr7N4darWb40ME8/sgDoB+4BwZda9M2tKSRI4YqN7POWXi8JyWnkJiUAsDUSRNMzoDvSFLT0gkMCgFgwbzZzJ87UwlOA/T1781jD9f8Pauqqrlw8Uqjtt+cPnX/oaNUVVXj5OjIc888rgSnAVxcnHnikfX07umHVqtl1979Td4HQoiOKzYuHq1W2y6fHR0TR1x8AgCrVy5l3uwZdWp+WFlZMWzIIF75xc+U/niv/rwn6mrNcVZZeTn/++hz8vILmDh+LL/51S/xcLNs4lxJ6a0Jha0xPhWt746cQY1+WXBGZhYpqWnk5xeYnO0ZFl6zxM23Rzf69fUnJjaesPAos7N+DMuH/Xy71/qRAVRVV3PuwmWCg6+Tlp5BWXk5Dvb2dOvWhdEjhzN54jiTSwbrS2qv0Wg4deY8AYFBZGZmA9C5sw+TJoxj6uQJnDp7gW07duPs5MTbf/uj2X1RWlrKkWOnCA65QV5+PlZWVnTr2pUZ0ybVykd8ex7gSwGBXAqoSSfw4LrVTJow1uxnGEybMpHvt+0kMyub6Jg4+vX1r/f1Fy9fQaNfZjFtyoRa+/PCxQCCrl0nNS2d0tKyWjny5sycZjIlwIZNW7kUEMiggf15+vGH2blnH8EhNygqKuYXP39KmX1nyO3l6eHBX/70mwa/lzGNRou7uxuPrF/b6FQXlwOugn5wOWL4EJOvmT1zGidPn6O4pITQ8AiLZ/IEXr2GVqvF3s7O7OzaKZMm8OOBw1RUVBIYdI25+llx1dXVXNUPXKZOnoCDvX2d99rb2zF50nj2HzzKlavXWL1yqcXLYFtSn969uBEaTnV1NaVlZbi63PotNlQksay8nDNnL3I9NIyMjCzKysuxsbHG28uLIYMHMmfmNLNFyNLTMzh55jzRsXHk5uaj1WhwcXHGy8uTMaNGMH5s4wIyE8ePYc++Q1RVVXHuwmX8e/eq9/UFBYXKstyJ48bUCqREx8bpc5wmUlBQiFarxdnJET8/X6ZOGq8E9Y0Z5+t9683fExMTx8Ejx8nMzGLM6JFKOgDjffrL555u1DFvuEttakZ8czXnt5RfUEhUdCwAs2ZMNXkce7i7M3rkMC5eDuRSwFXmzZnZ4t9BWEb681ukP2+5/jwvP5+qqpqbnOZm0NhYW+Pn24P8gjCysrMt3nZVdTV5+tUuffx71UpbZaBSqfDv1ZOExGTy8guoqq7GRp9qpqysTEnntHzJIpPBT3c3V17+xbMWt6k1+Pn2wMbGmqqqagoKi+o831CRxKb0Xeh/TxcvX+FqUAgpqemUlpVha2uLm6sLffx7MXni+EblDbe2tmbShLEcPnaSyKgYsnNy8W4g7ZQhkG1tZcWkibd+T029zjAcx1MnT2Dxovns2Pkj4RFRFJeU1LqeMexTS/JsGktKSsHZyYmS0lImTxpn8jW9evri7OREcUkJySmpFm+7OX2qVqtVbhiOGT3C5L5Rq9XMnzeLTz7/hpjYeHJycqUIthANyM7J5fjJM0RGxZCXn49Wq8XVxQV//15MnzIJ/94967zn5JnzbNuxG1tbW/79zl/Izy/g0NEThIZHUlBQiI21Nd26dWHG1MmMGW16okxRcTGHj5zgemg4+QUF2NnZ4dejO3NmTWfggH5KHZ5RI4bx5GMPkpOTyx//Ujulh+F6qr7zXGh4JCdOniEpJZXysnJcXV0ZPKg/i+6ZWyc1VUOSjM53Da2qc3R0ZNXKpaSlZdCtaxc0Wm2dPrqqupqz5y5yNTiE9IxMKioqcXFxpl8ff2bPnIafb3eT287OzuHkmfNERceSnZNDVVU19vZ2dO3ShTGjhjN18oQmjb2bcizQjOur1h5n6bQ6HBzsWbdmpcVxEgPj9JNSZ+jOdOcGqAcP5NiJ0wCERUQyeeL4Oq8xzMby792LPv41waHwyCh0Ol2dwURObh4ZmVkAdWZb5eXl88EnX5KWngGAi7MzXp4e5OblExUdqxTIee6Zx3FxsSyPY1VVFR9++pXy47a1tcHD3Z30jEy+37aTyKgY5WRibWP+z1RYVMTnX20kKzsHby9PXF1dyc3NU3IHFhQUMmfWdNAXdurerSvpGZloNBocHR2UgniWFnEbN3YUu/bsp7yigrMXLjU4oD1/sWb2kn/vnvTo3g30A/D/fPCZcnFsb2eHj7cXJSWlpGdkkp6RyaXLgTz37OP1BvW279zDuQuX8fTwoJOPd4sFUz093Hn91y82qrCdQURUDAAD+vUxOWAF8PL0wMfbi6zsHMIjoiw+8UZE1eTC7t3Lz+wyGTs7W3r39CMiKobwiCglQB2fkEh5RUVN2/r3M/sZA/v3Y//Bo5SVlXEzIUn53bSlsvJyAGxsrHF2Mh1MNiUnJ5f/fPApObk1+VEdHR3w8faisKhYyYV16fIVXnzhWTr5eNd677XroXz59SaqNZqanGRurtjb2VFQWERMbDwxsfGcOHWWl37xbK2AeX0cHR0ZM2pEzSA7OIRVK5bgUM+d3AuXryh36adOvnUDwjgQZWVlhZurKyq1iry8fK7fCOP6jTDmzJrGymWLzW47Lv4mX327GTs7W3x8vFssoGy4CKjvezVVc35LkZHR6HQ6gHpztw7o34+LlwNJS88wGxgVrU/68xrSn7dsf258Y8LQ/5lSqU8FY+m5HX1g29HRgdLSMios2LaDg4MSnAa4HhqBRqPB08OjWQUaW1tlZRXV1TU3JRpbCLGpfVdVdTXvf/S5kr7G3t4Oby9PqqqqyczKJj0jk/MXA1i+dBFz9b8HS0ydPIEjx0+h0+k4f/FyvTnHKyuruBIYDMDIkcOU2dfNuc4w9s233xMeGY2PtxcODvao1Kb7uMaYMH4ME8aPQWsimGLM3sGe4pISNI2YFdicPrWkpFRZ4VRfHlLjm0gRUTFMmVS3HxBC1LhyNZhvv9tGdXU1KpWq5hxZXU1uXj45V4IIuBLEPfNm1TnPGfdD6ekZvPfBp5SUlOLt5Ym7mys5uXnExt0kNu4mBYWFzJ45rdb7s3Nyefe/H1FQUAj66w1HBwcio2MJj4zmvuVL0Gpqzi2G6x0ra2u6d+tKYVERRUXFYFS3xsenbsFWlUrF8ZNn+GH3PhwdHXBxdkZTrSE3L4+z5y8RGh7J67/+ZZODj7l5+Q0GuEeNGMaoEcNMPldQWMQHH39BSmpNuhJ3N1ecHB3JzcsnIDCIwKBrrFqxpE7qwRthEXz+1UaqqqpQqVS4uDjj5upKXn4BsXHxxMbFExgUwvPPPlFrklJDmnosNEdrj7NsbW14/dcv4tGEAszGKT5kBvWd6Y4NUPfx74WDgwNlZWWEhUfVGdBqtVoi9QGOgf374t+rF1ZWVhQXl5CYlKIsuzQwLr40zGhGh0aj4ZMvviEtPQMPD3cefmCNMqtHq9Vy5Wow237YQ1JyCl9v/J4XfvakRe0/evyUMpidM2saixfeg62tDRqNhuOnzrJ77wFlgK1Wmb/Q/H7bTnx8vPnlc08rJ9vMrGw++ORLsrNzOHDoGNOnTsLGxoZpUyYybcpEZXbGsCGDLS6qZGBvZ8f4caM5ffYCwdduULKy1OxgODomjsysmhlJxjN+d+7ZT3JKKiqVivX3r2LCuNHKxXRcfAJffrOJvPwCvtm0lT+9/iuTF9oFBTUFfH7+9GMml297eLjTuZNPo++w0ojB/e0qKyvJ1Q9aunapvxhAt65dyMrOUYIklkhLy9Bvu3O9r+vatQsRUTG1tm14b83z5t/fzei5tPSMNg9QV1ZWERRcM9N75IhhjVpSu2nLDnJy87C1teGJRx9kqNFxcSM0nK+//Z6CwiK+27KDF5+/dbdeo9Hwnb7Aw+BBA1h//yplaZdOpyMiMpoN320lMyubXXv28/B6y/NiT586kYuXr1BZWUVAYDDTp04y+TqdTscFffBnQP++Sp7PmNh4ZYA/asQw1q5ergyUS0pK+X7bTq4Gh3DsxBmGDRlsNsB06MhxJk8cx+r7ltW6OAVwcLBXUiw0Nj+YkuLD0YG09AwuXwkiKSmZktIy7O3t6N6tK2NHj2x0AKa5vyXDfzs6OtR7Drj9eJcAdfuQ/ryG9Oct2587ODjQt09vYmLjuRYSyvw5M+sEvmuOoWTQ3yhpjGFDBnEp4CphEVHKLHxjVVVVynFx+7YNhV99fWsC/YlJKVwKqBnEVVRU4O7uxpBBAxg/dnSLFPhsqoDAq+h0OtRqtdmVLKY0p+86c/YCMbHxWFtb88iDaxk5/FZ6p6LiYvbuO8S5C5fZvfcAw4cOrjcQbMzLy5PBgwYQGhbBxUtXuHfBPLM3Qq4Ghyg3y42P96ZeZxhLSEwiL6+AV19+np5+dftGb29PbGysmzQwRz8b2Zzi4hIl/Vu3Bq4ljTWnT9WhUx4zBDRMcXR0QKVSodPpGnVtLMTdJv5mAt9s3IJWq2X0yOGsWrFE+V0Wl5Swe+8Bzl8M4NCRE3Tp3JnxY2/VVlDrb4bpdFq+3LCZPr17sXb1cuUGbVZ2Du9/9DnZObnsP3SMaVMm1koVtOn77RQUFGJtZcWDD6xmzKgRqNVqiktK2PbDHnbu2aesTjFc77i7ufL6qy/WumlZX92axMRkLgdc5bGH1zFKPxbUaDQcPnaSH/cfJi8vnzPnLnHPvFkW77NeRufazVt/4OknHm5wFY0pOp2Or775jpTUNLy8PHnikQeU83hJSSmbt/1AUPB1tv2wB98e3ZQb82Xl5Xyz8Xuqqqro3MmHpx9/iC76c7BGo+HYiTPs/vEAsXHxHDl+knsXzKu3HQbNORZoxvVVa4+zrK2b3gcaxqbW1tZUVFZw+twFYmPjKSgswtraGh9vT4YNGcyI4UPMToAS7atjJ1Srh5WVFYMH1swEjYiMVpadGtxMSNQvM63JV2lnZ6sU3QiLiKyzvfDImtlZbq4u+Pa4tSzjUkAgScmpqNVqnnvm8VrL39VqNePHjuahB9Yo7bAkH6RWq+XU2QugX9qyctli5U6ZlZUV82bP4J55s0hNS29wW2Vl5Tz9+EO1Tg6dfG4V4ykrLycpOaXB7TSG4WK9urpaWVJsimF5pJOTI6P0Mxu1Wi0hN8IAGDFsCJMmjK11Me3fu6eydDo7O0fJ/3e71LR0Zs+YZja36CMPruWPr/+KXz73dJO/Z2Pl5uUrF9/u7vWf6A1/L8MsnIZUVVVRVFxz19mtgZO7IbhaUFhElT6Hc65+QGJnZ2syvYeBg4ODEqTMyWmbnJdarZbikhJuhEXw3vufkJWdQ9cunVmxZJHF2ygqLlbyPE+dPLFOMGDokEHMnDEF9IGWwqJbS5VTUtMpLi4BYMmi+bXygKpUKgYN7M/9q1cwcEC/Rt+J7ennqyzzqi/vpWHJMbcNhoNDbqBSqVCr1TywdmWtgnBOTo48uG61cu6oLz9oaVk5a1ctrxOcBhg5fCh/fP1X/PH1XzU6kGy4S30zIYm/vf1vDh89QXhkNIlJyURFx3Li1Fn+8e/32fT99kblE2/ubyk3r+a/3RvIV2b8fHYbHe+iLunPa0h/3vL9+eqVS3FydCQpOYVPvthA/M0EyssrKCkpJSIymg8//YrS0jK6de3C3DmWFYo0WHLvPXh6eJCfX8AHH31BZFQMpaWllJWXExefwCdfbCArOwd3dzeW3HtPrfemZ2SCfgnsjwcO8867/+Pk6XNERsVwMyGJ4Gs32PT9Dt565z2LigK1pOrqatIzMtl38Ahbt+9GpVKxYukiunTuZPE2mtN3GYL6Awf0Y/TI4bWOKRdnZ9atWcmI4UMYMWwIBYWFjfpuhuO9oLCIG6ERZl9nON67de2izOxtznWGsaTkVJYtWWgyOI0+1dYfX/9VqxSJPnzsJBqNRqlbYqnm9KnOTk7K37q+wHOeUb9vKBYthKhr5579aLVa/Hv3qsn9a3TN4OzkxPr7Vyk3FPcdOHzbjaGagFxVVTUVFRU8/sgDtVYP+Xh7MX9uTeC3rKyMpORbqTFS09KV8/O8uTMZN2aUcn52dnLikfVr8e/VU7kh31TxCYncv2aFEvxGfz21YN5svDxrCrtGxcQ2apt9+/RWCt2mpKbxl//7F59/vZELl640arwbFh5JdGzNteGjD66tdR53cnLkkfVrcXNzRafTcVS/MhD9OK+8vGa11Yqli5TgtOG7zZ87k759avoaQ0pOSzTvWGj69VVHHmcZxqZarZY///Uddu7eR8iNMBISk4iNi+fi5UA+++pb/vneB+QXNO4aQrSNO3YGNfqLwcCgmlkO8QmJtZaHhYUblgP3VAJugwb0V/JWLpw/R3mtRqMhKqrmRDdk8MBad1MuXwmqee/A/mZnrg4fOhgPD3fy8vK5GhzS4DLZ5JRUZYnL+HGmK8bPmz2T4yfPUFlZVe+2Zs2YYnIGiGH5LaDkSWwp3bp2oY9/L2LjbnLu4uU6y3/Q370KDrkOwKQJ45TAmFqt5u2//pHy8gp0OtPLC41P9tk5OWaDZmPHjDT5eHsxXuZr18BMVMPzhs6qIeWN2LbxLNiK8gpsnK2VttnZNZxD2c7WlsrKynqXRDfHr1//s9nnHOztWbxoPrOmT21UvmcXZ2fe+8ffKC0tQ21l+r6b8XGVk5Nrcjl3QUEhmDjcRgyrGQw3xbQpE9n0/Q6SU1JJSEwyOSg1DIbdXF0YPnSw8viqFUtYsXQRJaWlJpey2dnZ0qVzJxKTUurt+EePHN4q+cQNKT7Kysro49+Le+bOwt+/F1ZqK1JSUzlw+DihYRFKaoD196+yaLvN/S2V65cTN+q30krHu7CM9OfSn7eGHt278auXnuPAoWNcDQ5R8vwbODs7MXf2DBbMm1XvzVtTPNzd+dVLz3Hw8DEuB1zlvx9+Vut5Q72IBfNm15k1VFJSc1M0PCKKzKxsZk2fwrSpk/Dy9KCouITAq8Hs3X+IjMwsPvz0K37zq180eoWLJU6cPscJfbFHU/r368PihfMbvZqqJfquwsJCkyl8VCoVTz/+cKPaYzBk0AA8PTzIzcvj3MXLJmeFpxsVNja+YdxS1xlqtbrROTVbQnDIDY6fPAPAzGmTG1yNZ6w5faparWZAv75cDw0n8Oo17l0wz2QeakPbkP5YCLMMKTjQXzOYWzExY+pkroWEkp2Ta3KlGfo6LqauOYzzJ+fl5wM1KcqMC1lPGFf3BpdarWbhPXOI/rBpxZsNPDzcTRaKV6lUdO/WlZzcvCbdxHri0fVs3vIDgUHXqK6uJij4OkHBNdc2bm6u9O7lR78+/gwdPBBv77qpRzC6oerl5WkybZmNjQ3r1qykqKhISbuGfjLQf/75N0pKSs2mWezp14OY2HiLA7kteSw0VkceZxnGplqtFkcHR1atWMLgQQNxcnQgJyePcxcvcfzkWW4mJPHhJ1/yqxefa1RKFdH67tgZ1ACDBw1QLlyNl/RidBIdNKC/8pjhzplhNpZBfEKispTPuGCLVqslIbFm6Wd9edMwWjqSZGaGkLG09Ezlv3t0N51E397ejj4NFFXjtothYw5GwT1DkaCWZLhoT0/PVC7kjV0KCKKqqiYX0tTJE+o8b29vZzZvrfHJzlzbbWyslbQEHYVxWxtakmt43tJZpdW1tl1/oNH4s6uqq/Rtq/l/S4KUhu03ZsZrY3Tr2oXu3brW+l/nTj7Y29lRVl7OoSPH+W7LdrKaMGvM0dEBezNBeHPHVbeunZVZ099s3MKRYyctntluibGjRyrHuqlZ1MXFJYRcDwVg8qTxdf5GVlZW9eZGtbOt+b7V9fzOe+hzvbW0CePGsHjRfO5fvYIXn3+GIYMH4mBvj62tDb179eRnTz3KqJE1OdzOXwzgZkKSRdtt7m9JOd4b/K3cer6qlY53YRnpz6U/by3JKalkZmVRXV2NWq3Gy8sTNzdXVCoVpaVlJCQmKcdGY6WnZ5CekUmlPqekh7sbHh7uqNVqyisqSExKISYuvs77DDl5MzKzWHrvAu5bsUTJve3u5sqcWdN57KEHlNecPX+pmXvBNGcnpzr9cbeuXfBwd0OlUhEVHcvGzds4ffZCvekZTGlq32X4bScmpfD+x18QHhHVYtcjarWaKZNrUgiFhUcq6S6MGfppOztbxo2te9OpKdcZxry9PBt1A74lXLocyJfffIdOp2Ng/74sX2r5CjVaoE+dP3cWKpWKktJSPvjkS9KNZlKXlpaye+8BTp45rwSutY081oS4W9y8maj8d33XMj2NbgYb0ljdrls30+83vllrfB4z/G4NtSZM6dund7Nvpvr5djebesFe3zbDOakxHOztefyRB3jtlReYOnlirRvHBQWFBF+7wbYf9vCnv77Dfz/8zOT1kOFaob5x1bAhg5g8cbzSlxmo1WpcXJzNjsWVPtHC/q4lj4XG6sjjrL59erN40XyWLV7Ab3/9CyZNGIebqwvW1tZ07uzDymWLWbVyKehn0586e75N2iUsd0fPoHZ2cqJ3Lz/i4hMIC49Ukr+XlJQqP0Djk4Nvj25K9eqIqGhlBoOhurS1tTUDjQrIlZWVU1lZM4g4cuwkR46dbLBNpqqc386QqoHbivjczsfHm/DI6Hq3Ze4i2fjE3thBhSVGjRjG9p17KS4u4ez5y3XuIp6/WHOBP3BAP5OdWFx8AucvXuZmQhJ5+flUVFQ2qp2Ojo4dLm+QjY1xYLj+k7Ch87Gpp2CWMePCWoaCRQ1tG/2dXOP/t6TTM2zfOOdYS3rphWdN3j3W6XQkJCaz+8cDBAaFEBYRzUsvPKMU0rBEaHgklwOukpiUTEFhIZWVVQ0eV9bW1jz+yHo+/vxrSkvL2LX3ALv2HqCTjzf9+/VlyOABDBrY32R6DEvY2toyYdxoTp4+x5WrwaxcvrjW7/ZSQCDV+iW3pooCFRUXc/bcRcIiosnMyqK8rJxqTf3HwO2cnFuninFDRYxUKhWrli8h+NoNdDodVwKDLEoj0tzfkqXHu/FvqbWOd2EZ6c+lP28N+w4eYf/Bo9jYWLN8yUJmTp+i/NZLSko5cvwUR4+f4oO4L3lg7UomTRhn8bbPXwxg89Yf0Ol0zJ01nXvmzVIC9ZWVlZw6c54fDxzhqw2byczMZtGCuXW2YW9vx+yZU01uf8TwIXTv1pWU1DSCQ26YnN3eXOPGjFQGarcrLS3lUsBV9uw7yJbtu0hMSubBdast3nZT+65pUyYSF5/AlavBRERGExEZja2tLX38ezFwQL9G5Z02ZfLEcew/eBSNRsP5iwFKGhr0fYZhpcW4MaNMzqpvynWGMVOzh1vTjwcOc+DQMdDfCHzqsQcbvaKquX2qf++erLlvGVt37CYxKZm//P1dvDw9sLKyIic3D61Wy8pl9xIQGExxcYnZc6EQd7v8glszh//yf/+y6D2FZq5lPMykZzB3zVGkT4foWs+1jpWVFV6eHs3KI29vZ341k6FtzbkU8vPtgZ9vD2AFWdk5xMUnEBdfUxjS0O7IqBiiY+JYu2p5rRvzhv1vaRFtYxqNhouXrxAcEkpqWjolJaVNCrTf3hZa4FhorI48zho0oH+tCS2mzJg6idNnzpORmUXAlSDmzW5cijfRuu7oADXA0MGDiItPIDkljYLCItxcXYjQVxZ1cXamR/dbwS21Ws2A/n0IDAohPCKqzoC2X19/7Oxu3fWrrKpU/tvNzRVnp4YvKh0cGl4iWlV5a7v1zQ60ZFvtxdramkkTxnHk2EmCroWweuUSZWAWfzNRybdpvDzSYO/+Qxw8fFz5t4O9PT7eXsqJS6PRKPkZzbFSt3y6guYy7lAbWsZiSJ9hb+GSYuPXNbRt4+cNF/mG/zfM2rKsbW07QFCpVPTq6cvzzz7BO+++T3JKKhs3b+e1V15o8L1arZaNm7fXyqHq5OSIu5ub8hurqKw0m8uzj38vfvfay5w8dZbAoBBy8/LIzMomMyubs+cv4uzkxL0L55ktctiQaZMncPL0OSoqKgm8GsyUSbcudgzpL4YNGVRrORj6YkoffPwlJfqCD2q1Gg8Pd+xsbZWLtKzsHCXwZk57/l7c3d3o3NmH9PRMUizIw0sL/JYsPd6Nt+3Qxse7qEv68/bxU+3Pk1NSlcDcymWL65y/nZwcWb5kIZrqao6fOsvWHXsYMWyIyZQUtyssKmLrjl1otVrmzZ5RZ0aqra0t8+bMxNrahu0797D/0FHGjBqhFMA19K9dOneq97jp49+LlNS0dika5+joyKwZU3Fzc+WLrzdx4dIVRg4fWmtlgjnN6bvUarVSHOvchUtERsdSWVlJeEQU4RFR7Ny9jyGDB7LmvmVNKnLl6uLCyOFDCAwK4cKlABbeM0dZGn0tJJRiffqV24/35l5nGLRVf1xVXc3G77Zx5Wow6G8or121vEnpvlqiT50+dRK+Pbpz7MRpYuLiyS8oxMXFmVEjhjFz+hR69/Lj5JmamWzmlsALcberNApoduvaxaKbu3Zmrm8be2PYcL5uaGVjR77euZ2Ptxc+3l5MGDca9LmVT5+9yImTZ6jWaNi+cw8D+/dVUn4YUrU19jxaXFLC+x99Xiunt7ubK/YOHkqfUFhUpKSMs0RLHguNdaePs1QqFQP69yUjM4u09AyT6cRE+7nzA9RDBrJn30F0Oh1R0TGMGzNKKZA0cGC/OgfboIH9CQwKITIqBpTZWSn6bdW+6DYstUCfr81QNKC5jE/sGq352SSW5iduL1MnT+Do8VNUVlYREBisDP4Ms63c3d0Ydts+jYqOVQazPbp3Y92alfT061Hr75STk8sf//J2m36XluDp5YFarUar1TaYG8uQR9TcEqnb2Vhb4+bmSkFBYYM5SA3Pe3i4K8eat3fNIK6yspJSMzkh0Xeghru5lratpVlZWTFx/Bi270wlMSmZnNw8pSiGOZcCApVB44D+fVmzcmmtAhToj73/fPCp2W24u7myfOkili9dRHpGJhGR0YRFRBEZGU1xSQlbtu+ioqKCeXNmNvo7denSmX59/YmOiePchctKgDomLl4J3pgaDH/5zXeUlJZibW3NmvuWMm7M6Dp5st773ydKwY6OytGh5nizNP9Yc39LhuO9Jndefe+99byPd9Nn5ImWIf15+/kp9ufBITeUQYepfJkGY0aP5Pips1RWVhIZHcuoEcMa3HZoWISy9HliPcXmxo4ewfade9DpdFwPC1cC1B7u7maLRhozFOa15OZyaxk1YhhOjo6UlJYSdO16gwHqluq7Ro4YysgRQymvqCAmJo6IqBhCboSRk5NLaFgE/05J5XevvWTRDYXbTZsyicCgEPLyCwiLiFIKHp7TH++9e/nVyv1OC11ntJWysjI+/PQr4uITUKvV3Ld8MTOnT2ny9lqqT+3dy48nH3vQ5Hurq6uVlCuNyY8txN3EOI3Q8z97st6VWy3NcL1zeyHr23X06536eHp4sHzJQrp27sSG77ZSVVVNQGAQC++pWQFla2tDeXlFo2c+/7DrRyU4PXP6FBbMn12reDDAvgNH2H/oqMXbbM9j4acwzjJcX2m1Wqqqqlqlzodomjs6BzVA925d8fSoCV7FxNbk+TNUmB08sO70fsOU/5zcPHJy84iMjlGWrwy7rSK3g4O9coco10SeuqYynhlQ352ytqp22lTeXp7KkmvDLNDKykqlgMDUSePrJOwPCKxZOqlWq3n2yUfo1dO3TtChuKS0jb5By7KxtlaCZCmp9c8UTU6p6aQak77CkF8qtYFZqIYO0Hjbxrmp6pvFmmx0Z7cxbWtpxlWICwoaLoRxJbBmhpCToyPPPPFInUEj+uC7pbp07sTM6VP4+dOP8eYff6Msef/xwJFa+W4bwxCATkhMJiU1DYALF68A4O3txcAB/Wq9Pv5monIOuHfBXKZMmmCyiENjvld7KdanQXCyMJDQ3N+S4XgvL69QKk2bYvitqFSqBvMSi9Yn/Xn7+Sn25wX66uxWVla1ZtPfztK/Ye1t31omay7/dn3bNuT+zMmpv96BIZ96e85IU6lUuLjWDKQtqXjf0n2XvZ0dQ4cMYtWKJbz5h9d46IE1qFQq8gsKOXzsVKO/D/oVFl26dALggv54z8nNU84306bUXS3V0tcZraWyspIPP/2auPgE7O3t+PnTjzUrOE0b9anxNxPRamuKrZrLxy/E3c7D49ZKS1M59FuToT8rLjZ/ntNqtfWeI9qTTqdTzjENGTd2lDJL2nhimLs+LUpjClZrNBqu6osxDhrYn9Url9YJTtOE/qM9j4WfwjjLkKLPxsZagtMdzB0foEY/6wr9gDYvP5+c3DxUKlWdgA/6WUCGi9LomDhlENy1S2e8blsqqFKplMTyhotWcxpTwKWTz61iQOaCjRUVlcR08FmRANP1Qbek5BQys7K5HhpORUUlarWayRPr5qc1BAa8vTxrnViN3QgLb+VWt57BgwYAEBkVbbYTTE1LV2aFDr0tiFKfIfptx9+8VQTsdsUlJSQmJtXZdk8/X2VJe0SE+TyoYRE1xcnc3Fzx7WG64FdbMJ4162TBUnzDceXr291sICI0LKLebZibEeDm5srKZfeC/neekVn/cnVzRg4fquRtM1SQvqYvjjht8oQ6gR3jDr9vn94mt5mdndPg8vnWkpScyieff8Nb77xnspCIQUFBoVLwsibnm2Wa81saNKC/EkwLt+B47+Xn2+Z5QYVp0p+3n59af26YWVtdXV3vSowCo6CrpakFjF9X380H44Cuk9F7DP15UXFxvUWLDIVl2/OGsVarVQLylqTGaYm+q74ZehPHj2GQ/nxguEHZFNMm1xzvoeERlJdXcDXoGjqdDicnR0aPrDuLviWuM1qbRqPhk8+/IS7+Jk6Ojrz4/DN1CnU1RXP71KqqKpKSU4mNM3+tYEhF4ubqQu9efs1usxA/Rb2Mbt5Ex9R/LdPQTOfGMuT+LyktNXuzMjb+ZoebQZ2cksq7//2IX/3mTyaL1ZtSXa1Rxh7G49CefjXjmMTEZLNjkyuBwWz6fjs7du4Ffe5uw4xrc32iVqtVCoJbqj2PhY46zqqsrOLLb77jH/9+n+Mnz9T7WsOYwbeH5WNT0TZ+EgHqIfrARHpGJtdCagI+3bt1NVs9fLB+1lVc/E3l4DQXKBw7eiToq6gbtn278ooK/vTXd3jn3feV7dWnp18PJT/j1aAQk685euJUqy3pVKlrAmHV1U1PzG8wZPBAZWAaGHRN+T4jhg2pNQvWwHBRX1JaarKoTHpGZq0TSktVbm8rE8ePQaVSkZdfwJWr10y+5vDRmuJc3l6eDOjf1+Jtjx41AltbG6qqqjh12nTF2RMnz1Kt0eBgb6/kZEU/w228Pr/WuQuXKTExq62oqJgLl2pm9E6eMK7dcjFpNBouBVwFfQDKkmJIynFl5u5zdEwcAfrZT9xWeG/nnv289vs32bJ9l9ntGy/lMlU0yRJWVlZM1hfgMuTNLSsrw9ramokTxpr4TrdSEpiahajVamu1uaoFfs+N4e7mSlh4JCmpaRw6etJskaiDR44ry+zHjB5h8fab81tycnJk+NDBABw/dcbkeSQ9PYOQ62EATG6g2KNoO9KfN4705+b19b9V7PHi5UCzr7saXPM9VSoVfW4rEGlOn1rbvmL2dUHBt46JPv63Bqa9e/VUZhPt3XfI5P6LjbtJgv6G88jhQy1qV2u4dj2UsrKalUP9+/k3+Prm9F3pGZm89c57vPKbP9U7K8uQXqWp/THAhHGjsbW1paqqmpAboQTqj/eJ48eaLObUnOuMtnLg0DEiomKwtrbm58883mITDZrbp+7ee4C///M/fPjpl8qxZCw1LV1JnzJz+tQ6qzWEEDU8PNyVvuTkmfNmg8HB127w6u/e4POvN7bYuahfn1vn/6tBda/LdTod+w+aT1FhPK5sTnHAxvL28iItPYPyigoOHT1h0Qzv0+cuKP2y8Qq+0aNqxjElpaUmr/m0Wi0HDh/j/MUAMvWTc+yMVhCVmJl9fvjYSXKMbnZbsn/a81joqOMsW1sbMjKzuJmQxLGTZygtNb2K72pwCBmZWaAvFi06lp/EFcCAfn2U5YOGwVB9MwYG6p8Li4hSZjyZy6k3fuwoJQ/dhu+2EBh0rdYds8SkFP734Wfk5xeQnZNj0fIFW1tbxuhPcCE3wjh89KRyZ0uj0XD85BkOHj5O/359LN4HjWHIURQbd1OZVdTUu51qtZqp+ny6lwOuEhpeM3vEVDElgL76E2lJSSn7Dx5V9qWhcvq///sx06dOVgYdMXENBwhM+WbjFt58659tnguwR/duSqGFLdt3cjU4ROngKioq2bPvoLIsesWye+sUWbh0OZAXXv4tL7z8W6Jjas+4c3N1Ya6+yuz+Q0c5c+5irePm5OlzHD5WE7BbtGAuTk610yncM3cWzs5OFBUX8/Hn3yizWgEys7L56LOvKC0tw8PdTfkcYxs3b1Pa1lpS09L55IsNyqyoJYvusShQbjiukpJTleXp6Pf5yTPn+fDTL1m8cL7yeKzRcdW5kzfFxSWcvxjA/oNH63RmKalp7Nj1I+hTf5ha1mupKZNrlslnZ+ewd/9hAEaPHG5yVlrvnn7K8XHg0DEKi24tKU9KTuV/H31Odk4uk/TB7bT0DJM3HhoSHHKDN9/6J2++9U9ltp4lXFycmTF9MgA3QsP59rttFBhVhy4tLWPHrh85ffYC6M8Jt58f9x88qhxTObm1Lxib+1tacu892NjYkJ6eyZfffFerbQmJyXz8+TdotVr8fLvXm0NWtC3pzxtH+nPzhgweqPy9Dxw+xtETp2sNziorqzh6/BRnzl0E/fFx+0zwP/31HV54+bd1Prt7t67KcXbh0hV2/3iw1somjUbDhUtX+PHAEdDPmjIOagOsWrEElUpFWEQU3363rVbfExEVw2dffQtA504+TBxf+yZmTm6ecu6sLyDQHJWVlZy7cJmN321T2jG+nlzeBs3pu3y8vSivqMnv+eEnNTmUb2/TUX2RPZoZuHdwcGCs/qbp0eOnSUpOQaVSMXXyBJOvb851RmP854NPefOtf/LNxi2Nel92do5yDbhs8QJ69Wx8mgzDMbVx87Y6zzWnT50+bTJWVlaUl1fw6Zff1spLGhMXz4effkVVVTVdu3Rm1ozmpSMR4qdu+ZKFqNVq8vML+OCTL8nIyFKeM/Q932zaQnl5BQ729tg0UNTQUn379FbS7+07cKTWipGSklK+/W4raWkZZq+d3NxuTTQwBHfNrQhuSfb2dqxasRT0qTD++e8PuHQ50OTNsvSMTLbu2M2uPftBXwuhX99bgfkhgwYos6C/376L0PBI5bnS0lI2bt5GekYmKpWKeXNqxtIODg7KKqgLl67Uqj+RX1DI1h27OXj4GAvvmaM8bun1UnOPhebES5o7zjJ3fdVcixfOA/1q7A8++bJWYUqNRsP5iwFs2LQVAN8e3Zg0cVyLfr5ovju+SCKAjY0NA/r15XpouBLkMJWv0qBfH3+sra2VfD1Ojo749+5p8rXW1tY8/cTDfPjJl6Rn1PwAHRwccHN1obikRMnD5OLszNNPPGzx8tClixcQHRtHTk4uu388wKEjx3FzcyW/oICKikoWL5qPVqNtcClyUwweNIC4+ATyCwr53Z/fwsrKirGjR/Lw+jVN2t7kSePYf+gomVnZoB/EmJsZPHnieM6cu0hmVjb7Dx3l5JlzuDg7k5efT2VlFSOGD2HxwnncTEgkKjqWK4HBxN9MZOa0ycyeOc3iNuXl5ZORmaXMsmmMTd9vJyGx9pLbgsKapUzXQ8N46533aj23/v5VypIfgPtXryC/oJCIyGi++HoTTk6OODs5Kd9RrVazbMlCkwMrrVF+LFMzqhbdM5ecnFwuBVzl+2072b33AK6uLhQUFipBiVnTp5jcV87OTjz75KN89NlXxMXf5I2//QMvTw90Oh25efnodDrc3Vz5+TOPY2+i0q5Wa3nurvr8+38f1wk6a7VaCouKlEGqtZUVy5YstDhwOGf2dAICgykuKWHT99vZs+8gDvb25OblU11dzfSpk5g/dybnLlwiOyeXw0dPcjUohKWLFzBx/FiiouMICAxi38EjHDh8DDdXF+zs7Or8xh99aF2zvrunhwdDBg3gemi4kofaXPDHxcWZOTOncfjYSRKTkvnDn/8PT08PysrKKSouxs3NleeeeZzklDTOXwygoqKSP/31HXp078qLzz9jcZvKysqVu8iGCt2WWnrvAgoKirhyNZhLAYFcvnIVdzdXbGxsyMnNUwJ1E8aNYdWKJXXeXysfnInjvTm/pS6dO/H4Iw/w1YbvuHY9lOuh4Xh5elBltNy/S+dOPPvkozJbqwOR/rxxpD83z5Ab+8NPvyI1LZ2du/exe+8BPNzdsLa2Jicnl2r9OWrwoAHcv3pFnW1oNVq0Wi06bd3z06MPruWTzzcQHRvH4aMnOHLsJG5urtjb2ZGbl0dlZc0MKD/fHiYLww3o35cH1t7H99t2cikgkMCga3h5elBaVqbkq/by8uSZJx+pm8O5gWsFSwUEBhMVUzf9TEVlJbm5ecpn+PbozjNPPGxRoKO5fddjD93Ph59+RVp6Bv/6z4c4ODjg6uJMdXU1BYVFykytKZPGN2pVjinTpkzi/MUApT8e2L+v2VVbzbnOMF7R1pDs7Fxy8/LMrhox5+SZ80qfe/5iQL2rBgxef/XFWv82/L21Jo735vSpnXy8WXPfMr7ftpOo6Fj++ObbeHq4U1VVpQQ1unTpxHPPPG5y9roQP1WZWVl1xpWmTJsyURkv+PfuyUMPrGHT5m3Exd/kzf/7J54eHlhbW5GXX6DMvh0yeCD3La977d1UarWa9etW8f5HX1BeUcGHn36Fi7MzDg725Obmobay4qnHHlRWN95uQP9+WFlZodFo2PDdVjZt2YFGo+GD91q/kPKEcaNRqVRs3bGbgsIiNny3FZVKhZubK44ODvpUVoVKwFylUjFtysQ6YxeVSsXjj6zn/Y8+JzUtnQ8/+RJXVxfs7eyUcY9arWbViiXKTU3014kff/Y1ZeXlvPPu//D0cEer05GfX4CVlRXr719Fn949OXj4OFqtlo8/+wZPD3d+8dxTeLibTqFGCxwLzYmXNHecVd/11Y/7DxNyI6zWY4aJdQlJyXV+M4sXzVdmdA8bOpj7li9m55793ExI4u///A/OTk44O9eMHw0rGv18e/DMk4+02A0c0XJ+Mn+RoUMGcT20Jtehra2t2QEq+un/ff17EREVA8CgQf3rDVB4eXrwm1//kvMXLhN07TppaRlkZefg6OhAT78ejBg2lEkTxzbqYtLN1YVXX3qeg0eOc/1GGPn5BZSUltKndy9mTp/CkMED2aefeWNYwttS5s6aTmFhEUHXrlNWWoaLizNduzZ9Vqiriwsjhg1Rlsmam32C/i7my7/8GT/uP0JoeASFhUWoVaX4+fZg4vgxTBw/FpVKxeqVS9n0/XZSUtOprqrC2UQxgdaSlZWjDFZuV1paVqdIXkVF7dlqNjY2PP/sE1y8XFPxPS0tg5ycXFxcnBkx3J+Z06Y0aWYL+o7x4fVrGTFsKOcuXiYxKZmcnFycnBwZ0L8v06ZMVAqHmdK7lx9/+M0rHDtxmuuh4eTm5aNS1RQ7GD50MLNnTmswKNPcYJ6pPK1qtRoHe3t69/SjX78+TJk4Dm/9XXpLeLi78+uXnuPHg0eIjIqhuLgElUpF/77+TJ08kRHDhwDw4AOr2bp9N5lZ2Wi1WpwcHVGr1Tz60P2MGjGMy4FXSUpKobCoiILCIuzs7Ojdy48hgwcyY+okJadpc0ybMlE5V3Xv1rXec9WyJQvx8vLk3IVLpKVnkl9QiIe7GxPGj2HOrGm4urjQpXMnYuPiCb52g2pNtUU5QluKtbU1jz28jgnjRnPh0hXiExIpKixCpVbh7uZK7949mTJxfJNnjzb3tzR86GB+9+pLHDt5hrCIKPILCrC2sqanXw9GjxzOjGmTZTDcAUl/bjnpz+vn4eHOa6+8wOUrQQRdu05KSir5BYWo1SpcXV3x8+vO+DGjlT6iMRwcHPjl808TdO06V64Gk5iYTFFxzY0OJydH+vftw+iRwxk7ZmSdFR4GkyeOo3cvP46fPENkdCw5uXlYW1nh59udEcOGMmP65AbTWKitmt4nF5eUmCzMZGNjg4eHO349ujNqxDBGjRzWqL6/OX1X7149+f1rL3P67AXCI6PJys4mKzsHKys1bm6u9PLzZfLEcSbz0jeWn293evr5KqlUTBVHNGjOdUZbML42TUvPaJXPaE6fOnXyBHr69eDEqbPE30wkv6AAKytrevX0ZcyoEUybOkkCBeKuU1VVbXbMaazQaHYq+hU//r17cuLUWSKiosnLy0er1eLq6kq3Lp0ZP240I4cPbfEJGP36+PPqy89z8PBxomPjKCstw8raijGjRzJvzgy6dumsBKhvn4zk7eXJ4w8/wN79h8jOzsHWzpbOnXzMfFLLGz92FMOHDiYgMIjwyCjS0jIoKCykoKAQa2srHB0c8PXtTh//XowbPYrOnU23zc3VhVdfeYEzZy9wNTiE9IwsSkpKcXF2om9ff+bMnFan5s7QwQP5xc+f4tDREyQkJFFQUIiLqwvjxoxi9sxp+PaoWe21asUSjhw7SVFxCTY2NlhbcE5sr2OBVhxn5eblm/1dVFZW1nnu9hXEs2dOY9DA/pw+e4GomFhycvIoyy7H0dGBPv69GTtqRL3XZqJ9qSIjI5s+9UK0qh27fuT4yTN079a1ziwHIdrDgUNHOXHqHO+89af2booQQtwxpD8XLU2r1fLLX/2O+5YvZuZ0SYsghBCi/f39n/8lKTmFmdOnsHrl0vZujhDiDiPrmtuRTqczmf/IIF0/A8LT06MNWyWEeTcTkpqVg1kIIX6KpD8XbS0pORWtVktX6ZOFEEK0EY1GY7bws0ajITOrJm2gl1zvCCGaoE3WUuUXFHLk2EkiIqPJzcvD3t6ezj4+TJ82iVEjhllUBO2n5rstOwgIDMLdzY0//PaVOssu8vLylXyV/fs2XDVdiNaWnZ1DWEQUK5Yuau+mCCFakfTZjSP9uWgPp89ewNnZSSnYJIS4+0h/LdqKRqPhH/9+n5TUdCaMG82D61bXeU1wyA0leN2vb+sUhxZC/LS1+gzqjMws3nr735y7cJlBA/tz/6oV3DNvNpVVVXzx9Sa+37artZvQIfXr609lZRWZWdls3LytVg7AjIwsPv1yA9UaDc5OTnWqtwvRHrKyc1gwfzZTJpnPSSqEuLNJn9140p+LtlZVVYW3lwcPPbBGcigKcZeS/lq0JSsrK/x8e6DVarl4OZDjJ88ohVkBwiOi2LK95pgb2L+vkldZCCEao9VzUG/YtIVLAVe5f/UKpfos+ovrv/zfv8jJzeON37/aqIJoPxVbtu/i9NkLoD/pe3q4o9VqycnNA8DB3p6nn3i4yQXGhBBCiMaQPrtppD8XQgjRlqS/Fm2tvLyC9z/6nPiERNAXsvZwd6O0tIyi4mIAunTuxAs/fwp3N9d2bq0Q4k7U6ik+snNyAejj36vW4zY2Nvj59iAnN4/s3Ly7svNcu2o5w4YM4sy5i6SmZ5BfUIBaraZb1y4MHNCP2TOm4uHh3t7NFEIIcZeQPrtppD8XQgjRlqS/Fm3N3t6OXz7/NAGBQVwKuEp2Ti7ZObnY2dni37sXI4YPYdrkidjZ2bZ3U4UQd6hWD1B37dKF2LibZGZm061rl1rP5eTmolar6dLJp7Wb0WENHjSAwYMGtHczhBBCCOmzm0H6cyGEEG1F+mvRHmxsbJg8cTyTJ45v76YIIX6CWj1APX/uTK7fCGX7zj2oVCp69vSloryCcxcvkZiUwrw5M3F3d2vtZgghhBCiAdJnCyGEEB2f9NdCCCF+alo9BzVAXn4+X3/7PTGx8cpjNjbWLFu8kFkzpjZqWy4OsmRECCFE6ykqq2zvJrSrluqzpb8WQgjRmqS/lv5aCCHEncGSPrv1c1Bn5/DRZ19TVFTMkkX30KN7V8rLK7h2PZTtO/eSnZPL6pVLLd5e5+5+zW6TVqslKy0Zn649UKvVzd7eT5nsK8vIfrKc7CvLyb6yTEvvp6KYmBZp152oJfvsluivkd+BxWQ/WU72lWVkP1lO9pVlpL9uOdJf37lkP1lO9pXlZF9ZRvaT5dqjz271APXGzdvJyMzi1y89R08/X+XxsWNGYvudLSdPn6NfH39Gjhhq0fZa8iBSq9VyUFpI9pVlZD9ZTvaV5WRfWUb2U/O1ZJ/d0n8L+ftaRvaT5WRfWUb2k+VkX1lG9lPzSX9955P9ZDnZV5aTfWUZ2U+Wa8t91aqfUl5RQUxcPF6eHrU6ToPhwwYDEB4Z1ZrNEEIIIUQDpM8WQgghOj7pr4UQQvwUtWqAuqqqCp1OR3V1tennK6sAzD4vhBBCiLYhfbYQQgjR8Ul/LYQQ4qeoVQPULs7OdPLxJr+gkKjo2DrPXw0OAaCPf6/WbIYQQgghGiB9thBCCNHxSX8thBDip6jVc1CvWrmUTz7/ho8++5qpkyfQvVtXKioqCLkRRkRkNP69ezJh3JjWboYQQgghGiB9thBCCNHxSX8thBDip6bVA9RDBg3g1y89x9Hjp7kadI2Tp89hbW1NJx9vli1ewKwZU7GysmrtZgghhBCiAdJnCyGEEB2f9NdCCCF+alo9QA3g26M7jz28ri0+SgjRQZSXl5OVlVXncZ1OR2VFJcnJyahUqnZp251C9pVlLN1PPj4+2Nvbt2nb7kTSZwshhBAdn/TXQgghfkraJEAthLi7lJeXk5mZSffu3evM3tDpdFRVVWJjYytB1wbIvrKMJftJo9GQkpJCp06dJEgthBBCCCGEEEJ0IK1aJFEIcXfKysoyGZwWor1YWVnRvXt3k7P6hRBCCCGEEEII0X4kQC2EaBUSnBYdjRyTQgghhBBCCCFExyMBaiGEEEIIIYQQQgghhBDtQgLUQgghhBBCCCGEEEIIIdqFBKiFEEIIIYQQQgghhBBCtAvr9m6AEELcCd55978cOXqi3tcMHzaEf7391zZr0+1iYuPZ/sNuQm6Ekp+Xj5OzE716+rFg/hzmzJpR67WvvPZ7gHrbu2Hj93z73Rb27dqCra1tq7W7rT5HCCGEEEIIIYQQHY8EqIUQwgLPPfMETz76kPLv/7z/MdExsbz/3j+Ux6xt2u+UeuTYCf713gdMnTyRV1/+Bd26dqGkpJQLlwP4z/sfc+bsBf7w+q8bVShw9X3LWLzonhYPGv/7vx/i4uLMk4893KqfI4QQQgghhBBCiI5PAtRCCGEBJycnnJyclH/b2tqiVqvx9PSo933V1dVYW7fuqTYhMYl//+dDli+9l2efeqzWc71792Tk8KG8/Orv+W7Ldh56YK3F23VwcMDBwaHF2xsWHsmE8WNa/XOEEEIIIYQQQgjR8UkOaiGEaEGHjhxn3qIVBFy5ysOPP8vzL74K+pQaL7z0Wq3XBly5yrxFK7gWckN5LDomlt/8/g2W3reOJSvv57XfvUFoWES9n7lj5x7s7e157OEHTD4/eNBA5s+dxa49+6iurq713PETp3nkyZ+zaOlqHn/meS5eClCe27Dxe+YtWkFlZaXy2MlTZ3n2+ZdZtGwNK9c8xN/+/i+ys3NqbTM0LIKXf/07Fq9Yy+p1j/D3f7xHXl4+APMWreBmQiJbtu1k3qIVpGdk1vqcv//jPdaufxytVltrm6fPnmfeohWERUQCkJqWxht/fZsVax7k3uVr+eUrr3Px8pV695MQQgghhBBCCCE6HglQCyFEK/h+2w+88uLz/PXPv7P4PSmpabz86u/RaDT84//+wn/ffRsPdzd+8/s/k5ScYvZ910JuMHLEMOzs7My+ZuKEcRQWFhEVE6s8lpycypHjJ3n91Zf433vv0MnHhzff+geZWdkmt3H85Bn+9va/GDF8KB/971/8+Q+/ISExiVd/9ycqq6oASExK5tXX/0SPHt14/71/8Oaffkf8zQR+96e/otPp2LLxSwCWLVnElo1f4uPtVesz5syaTm5eHjdCw2s9fvL0Wbp368rggQMoLCripV//jtT0dN784+t89N9/MXhQf/78l78TfO26hXtbCCGEEEIIIYQQHYEEqIUQbebdd9/F19cXf/8++Pr60qNHD5P/W7p0aZ33Ll261Ozrjf/37rvv1npfUVFRg69pDdOmTmbE8KF43xaArc+OnXsA+MPrv2ZA/7707tWTl3/5cxwdHPlh116z78vOyaWTj3e92+7cyafmtUaznQsKC3n15V8woH8/+vj35uVf/JyqqirOnD1vchvffb+NwYMG8LOnH6enny/Dhw3h1Vd+QVJSCufPXwJg5+4fcXFx5sUXfkavnn4MGtifF37+ND39fMnLy1dSotjb2+Hp6VEnJ/boUSNwd3fjtFEbysrLuRwQyNzZMwE4eOgoeXn5/O61Vxg2dDB+fj14+olH6NXTj63bdza4n4UQQgghhBBCCNFxSA5qIUSbKSwsJCXF/ExgA19f3zqPZWVlWfTewsLCWv/W6XR13nf7a1pD/359Gv2eiMgo/Hv3wtXFRXnM1taWwYMG1Jvmw9rKCq1OV++2DSkz1Opb9yW7deuCh4e78u9OnXxwcXY2OVu7pLSUhMQk1q29r9bjffv44+LiTGh4BDNnTCUyKpo+/r1rfc7QIYMYOmRQg98fwMrKihnTpnD2/EWee/ZJVCoVFy4GUFlZxdw5NQHq8IgoPD098PPtobxPpVIxcsQwDh46atHnCCGEEEIIIYQQomOQALUQos24urrSvXv3Bl/n4+Nj8jFL3uvq6lrr3yqVqs77bn9Na3A2KqhoqZKSUtIzbrJk5TqjR3VUVVXj4mx+e97eXqSlZ9S77YzMLAB8vG/NtHZxdqnzOjs7O8rLy+s8XlpaCsD2HbvZuXtfrecqKirIzc1TvkOXzp3rbUtDZs+azu69+wkNC2fokMGcOn2WoUMG0aVzp5rPKC0lLy+/zn7SVGuoqq6mvLwCe3vz6U6EEEIIIYQQQgjRcUiAWgjRZl5++WVeeuklqqoqsbGxRaVSWfzePXv2NOkzXVxcSE5ObtJ7W5Kp71phVHwQwNnZmaHeXrz8y+dAP/u7uroKa2ubOqkwjI0bM4r9h45QUlqKk6OjyddcuhyIp4cH/fr6K4+VlJTUeV1xcTEODg51HjcE3FcsX8KiBfPqPO9gbw+Ak5MjhUVFZttqicEDB9C1S2dOnTlP7969CAgM4vmfPXWrLc5OdOncibf+8ke4bT+pVCpsbW2a9flCCCGEEEIIIYRoO5KDWggh2oCzs5MyC9kgJjau1r8HDexPckoqPj7edO/Wle7dutKtaxfQ6fDS5242ZemShei0Oj79/GuTz0dERnH0+EnWrFpeK/VGSmoa+QUFyr+TklMor6igZ0+/OttwcHCgV08/UlJTlbYZ/ldVVYW7uxsAA/r3Izo6lvLyCuW9kVHRvPir35Kckqo81kBGEmbPnM7Fy1e4eCkAlUrFjGmTjfbTALKyc3B0dKi1n6ysrPBwd6/1HYUQQgghhBBCCNGxySheCCHawIB+/UhOSeXa9RtotVqCgkO4cDGg1mtWLFtMaWkZ//f2u0RFx5KWnsHBw8d49oVXOFBPbuVuXbvyq5de4OjxU/zxzbcICg4hMzOL+JsJfL91B6/97g1mTp/KyuVLlPfodDpcXVz4x7v/Iyo6lti4eP734afY29szfcokk5+zbu0qzl+4zLebtpCYlExCYhKffP41zz7/MvE3EwBYvvReqqqr+ce7/yU+PoHIqGj++8GnVFRU1gTb9cH6iMgoYmLjKS6uO4sbfZqP9PQMduzcw+SJ43EySplyz7zZuLg485e3/kFoWATpGZmcOXeRF156lY2btzTyLyOEEEIIIYQQQoj2JCk+hBCiDSxfuoiExETe+OvbaDRaRo0cxlNPPMJvf/8GGo0GgO7duvKvt//Kl99s5Fe/+T1arZZuXbvwzJOPsuTeBfVuf8b0Kfj59uCH3Xt5978fkpOTi7OzE7179eSVF59j+tTJtV6v0Wjo29efmdOn8re//5PMrGx6dO/KG3/8LZ5mZmvPnjkNtUrFlu072bx1BzY2NvTr68/f//on+vj3BqCnny/vvPVnvvh6I8+/9CoO9vaMHTOSp554RJnZ/MD9q9m4aQu//cMbvPHH35r8LD/fHvTr6090TByPPPRAredcXVz49z/+xudffsvv//xXKioq6eTjzYqli7l/zcpG/FWEEEIIIYQQQgjR3lSRkZENLLTuWPr379/sbURnR3Pw+l6em/GiLAVvgFarJSMlkc7d/WRf1UP2U21JSUn4+vqafE6n0zUpB/XdqL331dfffsemzdvYv3srNjYdN69zY/ZTfcemQVRUVAu38O7UEv01cn61mOwny8m+sozsJ8vJvrJMS+8n6a9bhvTXbUv2k+VkX1lO9pVlZD9Zrj367LvyL+Lh4MHJlNPt3QwhhOiwMjIyCQuPxM3VtUMHp4UQQgghhBBCCHFnuytTfHg6eFJQUdjezRBCiA5Jo9Hw2NPP4+Bgz5OPPdzezRFCCCGEEEIIIcRP2F0ZoAZQqVRotBqZ1i+EELexsrJi/+6t7d0MIYQQQgghhBBC3AXu2uisr3MPEgoS2rsZQgghhBBCCCGEEEIIcde6awPU/d37EZYV1t7NEEIIIYQQQgghhBBCiLvWXRyg7kt4dnh7N0MIIYQQQgghhBBCCCHuWndxgFpmUAshhBBCCCGEEEIIIUR7umsD1F4OXmSVZrV3M4QQQgghhBBCCCGEEOKuddcGqA10Ol17N0EIIYQQQgghhBBCCCHuSnd1gNrPzY+kwqT2boYQQgghhBBCCCGEEELcle7qAPVg78GSh1oIIYQQQgghhBBCCCHayd0doPaRALUQdxKtVktxcTFarba9m8K585d4/Q9vsur+R1i0dDUPPPIUf/v7v7gRGl7rdfMWrWDDxu/brZ2tJT0jk3mLVrB338H2booQQgghhBBCCCHuYBKglgC1EB2aVqvlwIEDTJkyBT8/PwYMGICfnx9TpkzhwIED7RKsfu9/H/G3v/+TPv69+dubf+Drzz/kd6+9gpOTI6+89nu2bt/Z5m1qbaFhEaxZ/5jybx9vL7Zs/JL5c2e1a7uEEEIIIYQQQghxZ7Nu7wa0p85OnUkvTm/vZgghzCgtLWXRokUEBQVRWFhY67mUlBTWrVvHyJEj2b9/P46Ojm3Spv0Hj7DvwGHe+ONvmTxxvPJ4p04+DBk8kG5du/D5V98yYEA/Rgwb2iZtMkWj0aBWq1GpVC2yvbCIyFr/trKywtPTo0W2LYQQQgghhBBCiLvXXR2gNgRudDpdiwVxhBAtQ6vVsmjRIs6dO0d1dbXJ1xQUFHDu3DkWLVrE8ePHUatbf1HIlu07GTdmVK3gtLHV9y1n/8Ej7Ni5RwlQa3VavvjqWw4eOUZpaRkDB/TjpRd+Ro8e3QHIyMjksy83EHIjlOLiEry9PJk7eyYP3L9K2W5qWhqffbGB4JDrVFRU0qunHw+vX8vECeNAn3Ljocee4eVfPsfBw8eIiIziyccf5tPPv+bzj/9LTz9fZVt5efnc/9ATPPbwA9y/5j5SUtP4/MsN3AgLp6SkFB9vL+bPm826NfehVqt5593/cuToCdCnLHnogbXMnzebhx57hl889wxL7l0AQEJiEp9/9S3Xb4RSWVFJ165dWLZkEUsXLwSgurqahUtX8+xTj1FaVsaP+w9RWlpGvz7+/PKFZ5U2mtsf69etxsrKqpX+skIIIYQQQgghhGgPd3WKD4BuLt1IK05r72YIIW5z6NAhgoKCzAanDaqrqwkODubw4cOt3qas7GxSU9MYP36s2deoVComjh9L4NVr6HQ6AA7/P3t3HR7F1QVw+De72bgrIQnB3d0pToEKVKDUv7oL1JAa1A1oqbtSpQVaSnH34q4JIe7ZrO/M98dCIMU2xJPzPk+fktmZO2dvNnt3z9w5d9FS7A4Hb746lZdemExaWjrPT3ut6PFX35xOXl4eL784hS8/mcWd/7uFOXPn88tvfwCQX1DAY09MIjk1lRefncgH775Fq5bNeW7qq2zbvrPY+X/57Q8uHzqQLz99n+FDB2EwGFi5em2xfU79PKB/PzRNY9KzU0lLz2Da85P54uP3uPXmcXz7/U/Mnb8AgAfuuYN+fXoRHBTIj99+znXXXHXW887JzWX8k5PJy8tj2vOT+XDWOwzo35f3PvikqB0PD9c10b/+XkRhoYk3X53Kay89x4mUFN59/+Oits7XHz+f7A8hhBBCCCGEEELUHLV6BjVn1KGuG1C3skMRQpxh2rRpZ5X1OJ+8vDymTp3KsGHDyjWmzMxsACIjwi+4X2RkBDabjfz8AgD8/Py49y5X/eb4enHcevM4Xn9rBoePHKVxo4YcPHSYm8aNoXGjhkXHx9eLw8vLE4C//1lCTk4ub7zyIvXiYgG4/5472LFrNz/9Mof27doUnTu+XhzDhgwq+rlrl46sXLWWm8eNKdq2YuUa2rZpRWREOJqm8earU/H0NBAYGAhAVFQkc36fx6YtW7n6yhH4+fnh6eWJotMVlfXIO/ncTln4zxIKjEaeefJxoutEAXDj2OvYuWsPv86ZWzSLGsDLy6uoPwD69OrBwn+WFP18vv7w9vJy8zclhBBCCCGEEEKI6qJCE9RHjh5jwcIlHEs8jsPhJCI8jB7dunBZ356VVmLjVIJ6UMNBbuwthKgIqqqSkJBQomMSEhJQVbVcy3x4eLjKS2iqdsH9Ti3ceCqW1q1aFHu8UcP6ACQeP0HjRg3p1aM733z3I1lZ2XTp1IG2bVoTXy8OTdOw223s23eA0NCQouQ0J2dqd2jbhgULFxdru2mTRsV+HnhZP158+XWOJ50gLjaGzMwsdu3Zy4THHixqJy09g+9//IVDh49gNpvRNLDZbDRt0tjtvtl34CAR4WFFyelTWrVozpZ/t1FoMuF3sk54yxbNiu0TEOCPxWrFZrfjaTCctz9ExamK47UQQgghipPxWgghRE1RYQnqbTt28ekX31I3ug5XDB+Kh4cHGzf/yy9z5pKZlcV1o6+sqFCKaRnRkt/2/lYp5xZCnJvJZCoqf+EuTdMwm834+fmVW1zh4WEApKReeHHVtPQMvL29CQjwh5MJ2DN5nZwJbLFYAHhy/MO0aN6UZStWMXf+AvR6PQP79+WeO2/D09NAYaGJnJxcrhh9Q7F2nA4HdocDi8VatO2/z79bt874+fmyctVabrzhOlauXouXpyd9evWAk2VLJj47lciIcJ54/GGiIiPQ6XS8+vo7Jeobk8lMQEDAWdv9/V3xmE3mogS1t3fxmdAKJ79Anfydn68/7r3r9nL9/QqXqjpeCyGEEOI0Ga+FEELUJBWSoDaZTHw3+xdi6kYz4dH7MRgMAHTr0pG3ZnzAkaPHsFisZyUtKkJMQAxJ+UkVfl4hxPn5+vqWeNaHoij4+PiUW0wAIcHBNG7UkLXrNnLt6LPrMHMyUb5p879069KpaJvRWFhsH6PRCIDvyXj1ej1XXzmCq68cQUGBkdVr1/PJZ1+hqiqPPHgP/v5+1ImK5OWpz57znJ6ehvPG7Gkw0Ltnd1audiWol69cTa+e3Yr6atPmrZhMJp6d9CRxJxdtBDBbLCXqTz9fX5KTz67nn19wqsyJr9ttnb8/NJ54/CG32xElV5XHayGEEEK4yHgthBCipqmQRRI3bPoXk8nMyMsHFw2enExCPPn4gzw1/uFKGzwVRUFDK/FsTSFE+dHpdMTHx5fomPj4+HIt73HKdddczc7de1iybMU5H//t93mkpKZx7RmzVvbs3V9sn4OHjgAQHx9HfkEBi5cux+l0wsnZ1pcPHcSQQf2L9mvRvCkZmVn4+voQUze66D+9Xk9IcPBFn/eA/v04cvQYu/fsY9/+gwwa2L/oMZPJBEBQUGDRtv0HDpKQeLxoRvMpF3qfbNG8KekZmaSmpRfbvnPXHurFxbqd7L5wfxx2qw1x6aryeC2EEEIIFxmvhRBC1DQVkqDes3c/Op2O5s2awMkkh81mr4hTn9eZeZYovygyTBmVGY4Q4j8mT55ctGjfxQQFBTFlypRyjwlgwGV9uObqK3jj7Xf58JMv2H/gEOkZmezZt58Z733Ip198w4P33UXzZk2LErp5efl88tlXJCQeZ+u2HXz/4y80adyIBvXj0VSNGe99xDsz3+fwkaOkZ2Ty77YdrFm/kbatWwIwZPAAAgL8mfryG+zes4/UtHRWrFrDQ48+wbc//HjRmNu3bU1YWCizPvyE0NAQOrZvW/RYi+auetA//vQbKalprFqzjvfe/4Se3buSkprG8aQTOJ1OAvz9yc8vYNv2nZw4x0zpoYMHEhwcxCuvv8OevftISDzOF199x/Yduxh7/Wi3+/eC/dGmldvtiEtTFcdrIYQQQhQn47UQQoiapkJKfKSkphESHER2Ti5z/viTvfsP4nA4CPD3p2uXDoy8fOgFb1E/06nFx0pDVVXM9tNttQhvwc60nfSv3/+ix9Y2p/qoLPq9JpN+Kk7T3Lsr4UL7DBkyhPbt27N27VocDsd59/Pw8KB9+/YMHjy4wu6EuOeu22nfrg3zF/zDsy++TEGBkZDgIFq3asn0N1+maZPGJxc4dH1RuHLk5RQYjYx/ajImk5nWrVrw2MP3o2kagYEBvPbSc3z17WyeeOZZLBYr4WGh9O3Vg1tuGgtAYEAAb7/+Ep9+8TWTn5uG1WYjKjKCq68cyZjrRp3V3//tB0VRuKxvL36dM4/rrrnadefIyX1atmjGHbfdxO/z/uKP+Qto0bwpEx5/iPz8Avbs288TzzzLR7PeYdiQgazfsIkpL7zMyOFDuXLk5cXOFxgYwJuvTuWTz77imSlTsdtsxMXF8OT4hxk04LLiMWnFY9TQirVzof642O/Yncfl7/T8qtp4jby/uk36yX3SV+6RfnKf9JV7pJ/KjozX1Zf0k/ukr9wnfeUe6Sf3VUZfKfv37y/3jM6jT0zG19cHBWjfrg1NmzTCYrGyZt0GDh85RvOmjXnwvjvdqjkb4ONZJjFlFiqE+7me+ooTq9ifc4C7W99RJm0LUdsVWmzUK2GJjnMxmUxceeVVbN++nfz8/LMeDwoKpG3bdsyd+we+vu7XOBa1V2JCAn7eFx5HCsy2CounqqmK47UQQghxLjJey3gthBCienBnzK6QGdROp5O8vHyuuXokAy7rU7S9S6f2vP72u+w7cIjde/bRulWLi7YVFVOv1PGoqkri3mQiomPQ6XQMCBnKn//8UyZt1zSqqpKRkkREdGyF1PetrqSfiktKSsJgOP+HXbvddsHHTwkK8mTZsmUsWrSIqVOnkpCQgKZpKIpCfHw8U6ZMYfDgwTW6z93tq9rO3X7y9PImKib2gvsUHDpUhpFVL1VtvEbeX90m/eQ+6Sv3SD+5T/rKPWXdTzJey3hdHUk/uU/6yn3SV+6RfnJfZYzZFZKg9vL0xGyx0KVzh2LbdTod3bt25njSXA4cOuzWAFpWLyK7U8GpKXjodET6R5JtyZYX6AXodDrpHzdIP7koinLeGRtnlmBwZ1aHXq9n2LBhDBs2zFWex2zGx8enVvRzSfuqtipJPymKUiteO5eqKo7XZ7Ynv7uLk35yn/SVe6Sf3Cd95R7pp9KT8br6k35yn/SV+6Sv3CP95L6K7KsKOUtYWCgA+nM8qcDAAAAsFmtFhFJEA4xnnNJL74XZbq7QGIQQJaPT6fDz85PBRIhyUhXHayGEEEIUJ+O1EEKImqZCsjyNGtYHIDEp+azHsrJzAAgOCqqIUIox2U7PumsT2YbdGbsrPAYhhBCiqqiq47UQQgghTpPxWgghRE1TIQnqnt27oCgKCxYuLrYCpM1mZ83aDQC0aX3x24/K2pkzqDtEd2BrytYKj0EIIYSoKqrqeC2EEEKI02S8FkIIUdNUSA3q2Ji6DBs8gAX/LGHmrE/o1rUjZrOFdRs2k5GZRb8+PYmLjamIUIqxOjQcTg0PvUKHOh14bc1rFR6DEEIIUVVU1fFaCCGEEKfJeC2EEKKmqZAENcDI4UOIjIxgxao1/PTrXDRNI7pOFOPGXEOvHl0rKoyzFNogyAfqBdUjIS+h0uIQQgghqoKqOl4LIYQQ4jQZr4UQQtQkFZagBujauQNd/7PScGUzWjWCfBQURUGn6HCqTvQ6fWWHJYQQQlSaqjheCyGEEKI4Ga+FEELUFBVSg7oqKzxjocSmoU05kHWgUuMRQgghhBBCCCGEEEKI2qLWJ6gtdnCqriR1h+gObE2VhRKFEEIIIYQQQgghhBCiItTKBLWmaVidlpP/dtWhBuhQpwNbUyRBLYQ4t9ffnsng4aN48513z7vP89NeZfDwUXz97ewKjU0IIYQQQgghhBCiOqqVCep9mfv4ZO8bRT8XWl0zqJuFN2N/1v5KjEwIUdV5e3uzavU6rFbrWY8ZjYVs3PQv3l5elRKbEEIIIYQQQgghRHVTKxPUzcKbkVBwqOjnUzOoPXQeODUnmqad/2AhRK3WuFED9B561q7feNZjK1evJaZuHYKCAislNiFqIrNdxmQhhBBCCCGEqMk8KjuAyqBTdHjpvTHbC/H1DMBk01BVDZ1OIS4wjuP5x6kXVK+ywxRCVEE6nY7u3bqweOkK+vfrU+yxJctW0KN7V5YuW1m0zel08t0PP7No6XIyM7MICQ6if78+3HbLOAwGQ9E+3/7wE8uWryIjMws/P19at2zBXXfcSlhoMAB//f0P78z8gE8+mMGnn3/N9h278PH1oXfP7jxw753o9foK7gkhKkah1VWOSwghhBBCCCFEzVQrE9QALUPasytzE13rDkDTwGQHf6/TdaglQS1E2Rr6biEZBa4sk4aGgr1S44kIUFj4kN8lHXtZn148++Ir5OTmEhLsSiBnZGayc9ceHrz3rmIJ6nff/5hFS5Zz393/o0P7thw8eJiZsz4iLz+fCY89BMDsn37jhx9/5ekJj9KyRTNycnOZ8d6HvPjS68x462UA9HrX2/WM9z5k1FUjefD+u1m1Zh0ff/olzZs1YcigAWXQK0JUPU5Vw1y5bxdCCCGEEEIIIcpRrU1Qtwnrwra0tXSt60rqGK0a/l4KHaI78NfBv7iq+VWVHaIQNUpGgUZK/pnTIKvvlMhOHdsTGBjAsuWrGH31FQAsXbaK+vH1aNAgvmi/7OwcFixczPXXjmLk8KEAxNSNJis7m48/+4rbbrmR8LBQrrpiOIMG9CMqKhKAyMgILh862JXIzssnPDy8qM1+fXvTt3dPAK4ddSXffvcj+/YflAS1qNGMtlpZkUwIIYQQQgghaoVam6BuFtSGnxO+KPq58OR6Z20i2/DamtcqLzAhaqiIAKXo364Z1MoF96/IeEpKr9dzWd/eLF66/HSCevlKBvTvW2y//QcPoaoq7du2Lra9Q/u2qKrK3n376dOrBwC/zJnLps3/kpuXh9Op4nQ6AcgvKCiWoG7ZvFnRvxVFwd/fnwKj8ZKfixDVgdFWue8XQgghhBBCCCHKT61NUHvqvXCoDpyqE71Oj8mmoWkaPgYfLA5LZYcnRI1zqpyGpmnY7TYMBk8UpfomnQb278ecP+aTkHgcVVU5eiyBqf0mFtunsLAQgOemvoKinDED9GRB3ZycXADemv4eGzf/y52330z7tm3w9PJk9Zr1fPrF12ed19vbq9jPilKtJ6ML4RaLAxxODU+ZSC2EEEIIIYQQNU6tTVADNAltzcGcnTQPa4+qgckGfl4Q7htORmEGEX4RlR2iEKKKata0MXFxMSxbvgqn00nrVi2IjCz+nhHg7w/AUxMepWGD+me1ERwUiM1uZ+36jVx/zdWMumpk0WPVOHcvRLkosEKYobKjEEIIIYQQQghR1mr1XKQOUb3Ylra26OdCm2saYreYbqxPWl+JkQkhqoOB/fuxcfMW1m/cxMD+/c56vGmTxuh0OjIzs4ipG130X2hoCDqdDj8/PywWC6qqEhQUWHSc0+lkyXLXQouazI4WAoACi/wxCCGEEEIIIURNVKsT1O0iexRLUBtP1qHuEduDdUnrKi8wIUS1MLB/Pw4fOcaJEyn06d3jrMdDQoK5fOggvvnuRxYtWU5KSir79h9g6kuvM+GpKVgsVgIDAoiNqcs/i5dx9GgCBw4e5oWXXqN1yxYA7N6zt6hUiBC1WYFVEtRCCCGEEEIIURPV6hIfgV4hFNhy0DQNRVGK6lC3iWrDs8ufrezwhBBVXJ2oSFq1aI5/gB+BAQHn3Oeh++8mLDSUr7+bTWZmFv5+vrRv15a3X59WVE/66SceZcZ7H/HgY08SHh7G2OtGM3jgZRw5msBnX36HweCJXq+v4GcnRNXiVMFk0/D1lPo3QgghhBBCCFGT1OoENUC0fzwpxkTqBsTjVMFsB19PV7c4VAceulrfRUKIk558/OGztr39xktnbfv2y4+L/q3X67n5xjHcfOOY87bbrGkT3p/55tltvz6t2IKSQwcPuOC5hKjpCiySoBZCCCGEEEKImqZWl/jgZB3qrWlrin4uPHkLceuI1uxO312JkQkhhBDiTAXWyo5ACCGEEEIIIURZq/UJ6vb/WSjxVB3q7rHdpQ61EEIIUYWYbBpOVWpRCyGEEEIIIURNUusT1HX940k2Hi36udDm+uIrCWohhBCiatG00xeShRBCCCGEEELUDLU+Qa0oCgGeweRbc+DkIkxmu0aUfxTphemVHZ4QQgghzpBvkRnUQgghhBBCCFGT1PoENUD7qJ78m7q66OdTdajDfMLIMmVVYmRCCCGEOJPRKglqIYQQQgghhKhJJEEN9IodxuqkBUU/F55Rh3p90vrKC0wIIYQQxdgcYLFLkloIIYQQQgghagpJUANxgY04UXAUTXN94T01O0sS1EIIIUTVUyBlPoQQQgghhBCixpAE9UlNQlpzMGcnAA7VNTurXVQ7tqdtr+zQhBBCCHGGAlkoUQghhBBCCCFqDElQn9Sn3ghWHf+r6GejVcOgN+DUnDhVZ6XGJoQQQojTCq0aqiqzqIUQQgghhBCiJpAE9Unto3qxNXVN0c8mm+v/rSJasSdjT+UFJoQQQohiVA2MtsqOQgghhBBCCCFEWZAE9UkGnQF/z0ByLVlwRh3qHrE9WJe0rpKjE0IIIcSZpA61EEIIIYQQQtQMkqA+Q6/YYaxJWgiA3QlWh0b32O6SoBZCCCGqmFMXkoUQQgghhBBCVG8elR1AVdIrdhhvbhjPiMbj4OSX3+iAaFKNqZUdmhA1wvYkJ6DhcKh4eDgBpVLiaBerr5TzltbWbTtYsmwFe/buJyMjEz9/P5o2acRNN4yhaZNGlR2eEBXKYgebQ8PTo3LeR4QQQgghhBBClA2ZQX2GUJ8I8qzZOFQHACbrqe2hZJmyKjc4IUStN++vv0lLS2fUVSOZ9uJk7r/nDnJz83j48afYum1HZYcnRIXLlzIfQgghhBBCCFHtyQzq/2gX2Z0d6evoWKcPRpvri2/3mO6sT1rPiKYjKjs8IUQNMv6pyURFRfLk4w+7tf9D999NSHBwsW1dOnXg1jvv54effqVD+7blFKkQVZPRCuH+lR2FEEIIIYQQQojSkAT1f/SNG8HCoz/RsU4fbA7X7cM943oyZ98cSVALIQC4+fZ7aNO6JU+Of6TY9ieenoJTVXn79ZfK5bz/TU4D+Pj4EB8XS0ZGZrHtTqeTX+fMZfHSFZxITsFmsxV7PCIinO+/+qRc4hSiohitGpqmoShS5kMIIYQQQgghqisp8fEfzcLasz9re9HPRiu0jWrL9rTtFzxOCFE75Ofnk5qWTtMmjYtt1zSNg4eP0LTxuWtBa5qG0+ks9p+maXCO7SVRWFjIwcNHiI+PK7b9renv8dmX39KxfVuen/wUTzz+EMHBQQBcc/UV3HLj2BI/dyGqGqcKhTY3dhRCCCGEEEIIUWXJDOr/UBSFugH1OZ5/mLjARhRaNUL9DGiahkN14KGTLhOiNtt/8DDAWYsSnjiRTGGhiSbnWaxwx87dTHh6ylnbd+7aw6Ily4tt++aLj6gTFelWPO++/zEWi5VxY64r2rZ02UoWLVnOYw/fx/BhQ4q2axq8+c67tG/Xhu7durjVvhBVXYFFw99LZlALIYQQQgghRHUl2dZzGFR/NIuP/srt7Z6k8GQd6rZRbdmZtpMO0R0qOzwhRCU6cOAQer2eRg0bFN9+6AgATc4zg7pJ40a8N/2NYttmvPcBYaGh3DRuTLHtYaEhbsXy5dffs2TZSh64765iCfP5CxZSLy62WHIaoF5cLAAFRqNb7QtRHRRYNaIrOwghhBBCCCGEEJdMEtTn0Dn6Mr7e+Ta3t3sS6xl1qNceXysJaiFquQMHDxFfLw4vL6+ztvv4eBMbU/ecx/n6+tCsafGyID4+PgQEBpy13R3ffPcj383+mdtvvZGrrxhetL2gwMiu3Xu5ZtSVZx2TmZkFQHhYWInPJ0RVZbaB3alh0MssaiGEEEIIIYSojqQG9Tl46Dyo41+PpHzXjMhCG3SP7c66pHWVHZoQopIdOHiYxo0anrV9x85dNG7YEJ2u/N9Wv/nuR77+bja33DiWcWOuLfZYRmYmmqadcxb2mnUbCAoMpG2bVuUeoxAVqcBS2REIIYQQQgghhLhUlZKg3rv/AA88+hQPPPpUZZzeLYMbXMOio78AYLRqhPuGk2XOquywhBCVKDs7h8ysLPT64m+d23fu4uChI+etP30+b702jScff7hEx3z7/U98/d1sbhx7HTffOOasx/39/ABIPJ5UbPvuPftYtmIVV185Ar1eX6JzitqtOozZRqtW2SEIIYQQlao6jNdCCCHE+VR4iQ+Lxcp3s3+t6NOWWJfo/ny7a7qrDvXJL751/OuQakyljn+dyg5PCFEJ9h88BMDK1WuJrxdH3brRHD58lLl/LgAgNzePo8cSaFA//qxjC00mEhKPu3Wexo0a4mkwnLX959/+4Ktvf6BLpw507dqJPfv2F3u8ZfNmREZG0KZ1S/5ZvIw6daJo3rQJ+w4c5Icff6VTx/aMG3vtWe0KcT7VZcwusEiCWgghRO1VXcZrIYQQ4nwqPEE9Z+6fGI2FREVGkJaeUdGnd5uHzoM6fq4yH7GBDbE7NXrE9mDd8XWMajGqssMTQlSCAwddCyQ+/sgDfPLZV2Tn5NKieVNefPYZXnljOtt27OSqKy4/57GHDh1hwtNT3DrPN198RJ2oyLO2r9+wCYBNW7ayacvWsx5f9NccAKZMfIIPPvqcn3/9HavFSkxMXW65aSyjrxpZISVIRM1RXcZshwomm4avp9ShFkIIUftUl/FaCCGEOJ8KTVDvP3CINes2cuWIYezZu7/KD56DGoxm0dFfuL3dkxit0DOuJ19t+0oS1EJconaxejRNw253YjDoUZTqlUzaf+AQDerH07d3T/r27lnssa8+ff+Cx7Zr27oogXyp3nptmlv7hQQHM/Gpx0t1LiGq25hdYJEEtRBCiNqnuo3XQgghxLlU2FQ6i9XKd7N/IS42hkED+lbUaUulS3R/NqeuAKDQptEyoiV7MvdUdlhCiEpy8NBhmjVtXNlhCFHuquOYXWCt7AiEEEKIilUdx2shhBDiXCpsBvUf8xaQl5fPvXfdVqpbzFVVLXUsp9pQVQ2d7vzt6RUdUb6xHM87hKe+EQTq0Ct6LHYLnnrPUsdRHZzuq9L3e00m/VScpmlo2sVrwrqzT1WRnp5Bbm4ezZo2qZS4q1NfVaaL9ZOmafJ36oayGLPLqp/Vk7/Ti43ZhRawOzT0uto5i1rGIfdJX7lH+sl90lfukX4qe1VqvJbfr1ukn9wnfeU+6Sv3SD+5rzL6qkIS1AcOHmbVmvWMGDaIutGlW2Aw7URiGUWlJycz7aJ7dQvpz9xdXzGm8X34WZ009WvEkh0L6BjZoYziqB4yUpIqO4RqQfrJxWa1YbfbLrjPxR6vakJCgvjz99lQCbFXt76qLO70k81qKcNxpGYqqzG7rPo5q1ABdG6N2UcsKoHetftijoxD7pO+co/0k/ukr9wj/VQ2qtp4fYr8ft0j/eQ+6Sv3SV+5R/rJfRXZV+WeoLbZbHw3+xfqRtdhyKD+pW4vKqZeqdtQVZVDWSmEhEehu8hMqwHho/lj0deERdbBP1TH4JbD2J2+m8tjrip1HNWBqqpkpCQRER0ri6tdgPRTcUlJSRgM57/LwG63XfBxcZr0lXvc7SdPL2+iYmIvuE/BoUNlGFn1UpZjdlmM1wBqnpOMI6lujdk+vgpRIbXzPVjGIfdJX7lH+sl90lfuKet+kvG6io3X8nfgFukn90lfuU/6yj3ST+6rjDG73BPUv89bQHZOLk8+/iB6vb7U7ZXli0inU1CUC7dn0HtSN6A+ifmHCQ9oSo+4Hny+7fNa92LW6XS17jlfCuknF0VRzrsA4pklGKrbIokVTfrKPSXpJ0VR5G/0AspyzC6rftYp6sn2Lj5mF9rL9nNCdSTjkPukr9wj/eQ+6Sv3SD+VXlUcr89sT36/Fyf95D7pK/dJX7lH+sl9FdlX5ZqgPnTkKCtXr6Nv7x74+/uRk5tb9JjD4QAo2hYSHFyeoZTKsIZj+PvIbJqFTyE2OJB8az6apknCSAghRI1RE8ZsmwPMdg0fg4zPQgghaqaaMF4LIYQQ/1WuCer9+w+haRorVq1lxaq159xn8vOvADBr+mvlGUqpdKzTh0+2vYzZpuFwajQIbsCx3GM0CGlQ2aEJIYQQZaKmjNlGiySohRBC1Fw1ZbwWQgghzlSuCerOndpTr965a33Onf83ySmp3HvXbeUZQpnQKTqahrZhf/Z26od1pFdcL9YcXyMJaiGEEDVGTRmz8y0QEVDZUQghhBDlo6aM10IIIcSZyjVBHRUZQVRkxDkfW7J0JQBtWrUozxDKzLCGY/n78Gx6xXegV71evLb6NW5qe1NlhyWEEEKUiZoyZhfaNFRVu+iCikIIIUR1VFPGayGEEOJMUhXcTS3DO7E3aysFFifxQfEcyztW2SEJIYQQ4j80DYy2yo5CCCGEEEIIIYS7ynUG9YU8+tA9lXXqS6IoCu0iu7PhxDqaRPQh0CuQXEsuwd6y8IQQQoiarbqN2fkWjUBvmUEthBCidqlu47UQQghxisygLoFhjcby9+EfMVqhR2wPNiRtqOyQhBBCCPEfRotW2SEIIYQQQgghhHCTJKhLoGFwCxLzD5JnsRctlCiEEEKIqsXqAKtDktRCCCGEEEIIUR1UWomP6qprdH8WH1nCLR0GMXXl1MoORwhRgV5/eyaLFi877+M3jxvDLTeNrdCY/mv7jl1MeHoKL784hS6dO5b7+QYPH1XsZ18fH+rUiaJzx/aMumok4eFh5R6DEOdSYNHw8pcyH0IIIYQQQghR1UmCuoSGNhzDx9umcWuHYWhoOFQHHjrpRiFqi+CgQD6aNf2cj/n4eFd4PEuXrWTO3D95953XAGjZohk/fvs5AQH+FRbDVVcMZ9yYawEoNJk4cPAQv/w2lwULF/Pc5Kdo17Z1hcUixCkFFgivuD8DIYQQQgghhBCXSEp8lFDdgHjSC0+QZ3bSNrIt21O3V3ZIQogKpOh0hIaGnPM/Hx+fCo9nz779xX42GAyEhoZgMBgqLAZvb6+iPoiLjWFg/37MfPtVmjdvygvTXiMvL/+cxzkcjgqLUdQ+RquGpkmZDyGEEEIIIYSo6iRBfQnaR/VkRcIaetWTOtRCiLONf2oyDz32VLFtmzb/y+Dho9i+YxcAn3/1LVdfdyMJicd57ImJjBw1hhtuvpNvvv+x2HGZWdm89NpbjLr+Jq66ZhxPPPMs+w8cKjrPH/P+Yt/+AwwePoqFi5ayfccuBg8fxabN/xa1sWfvPp545lmuvOYGRlw9hvsfHs+KVaffu5JOJDN4+ChWrFzDrA8/5Zqxt3DVtTcy6blpZGZlX1IfGAwGHn3wXgpNJub/tRBOlh8ZPHwUq1av4677HuH6G28v2n/OH/O5676HuWLUWEaPuZmnJ7/AkaPHANjy7zYGDx9FQuLxov1Pbfvg48+LnfeW/9171jZRO6kaGK2VHYUQQgghhBBCiIuRBPUl6B9/FXMPzKFHbA/WJa2r7HCEENWQh94Dp8PJu+9/zI03XM+nH75Lv769+Prb2Wzf6Upi2+x2Jj07jbS0dF56cQrvzXiD4KAgnpr0HOkZmTw3+SlatmhGk8YN+fHbz7msb6+zzpOQeJwnnnkOb29v3njlRWbNeINWLVsw7ZU32bBxsysWD1eZom9/+JHwsDDefec1pkx8gh07dvHFV99d8nOMjIygUcP6bNuxs9j2H376hdtvGccHM98CYPHS5bz/0WeMuHwon3w4k7denYaiKEx+bhpWq5U2rVvi5eXJjp27i9rYtn0nkRERxbZlZGaSkppG504dLjlmUbMUWGUGtRBCCCGEEEJUdVI8+RI0DmnNvsxd+BuCyLXkVnY4QlQb982/jxMFJ1BVFZ2u8q6PxQTE8MHIDyrt/KdYrFauHXUlnTu2B2DcmGv4dc5c9u8/SLs2rVm7bgNJJ5L59MOZxNeLA+DRh+5l+nsfkpKaSrs2rfHw8EBVNUJDQ855jjl/zMdDr2fik48VlSB54N472fLvNn79fR7dunYu2jcuNpYx17kWPawbHU37dm3Yf+BgqZ5jZEQEiUlJxba1a9uGnj26Ff3cq0c3vvrsfepGRxdtG3XlCCY9N41jCYk0a9qE1q1asmPnbq4YMQyAbTt2ccXIYXzx1XcYjYV4eRnYvmMXXl6etGvTqlQxi5qjwKJBUGVHIYQQQgghhBDiQiRBfQkURaFpaBs2JO2kXmA9EvMSqRdUr7LDEqLK+2DkB2iaht1uw2DwRFGUyg6pxHJz87hi9A3nfGzSU4/TvVuXErXXskWzon8HBAQAUFBgBGD/gYN4eXlRLy62aB8/Pz8mPTXe7fb3HzhE48YNz6qP3aJ5M9Zt2Fh82xmxuOLx58DBwyV6Pv9ls9vxNHgW29a0SaNiP+s9PPhn0TJWr11PVnY2DocTVVUByM8vAKBTx/b88tsfAJhMZg4eOszEJx9jwcLF7Ny9h84d27F9x27atm6Fp2fx84nay2IHm0PD06P6vdcIIYQQQgghRG0hCepLNCD+an7f/zs943qyJnEN9dpIglqI2iAwIIAZb796zsfON4v5Qry9vYv+fSphr+EqS1BYaMKrlMlWk8lEVFTEWdsD/P0wmczFY/HyKr6TohTFcqmSk1OIOyPBDuDv51fs58+//JY5f8xn3Nhr6dWjOz4+3uzbf4BX35hetE/nju35+NMvSTqRzIkTyYSGBBMdXYe2rVuyc+duOndsx46du7j6yhGlilfUPAVWjTBJUAshhBBCCCFElSU1qC9R28gebDixll71erH2+NrKDkcIUUF0eh0xdaPP+Z/PyWTzuWaGW222Ep/Lz88XY2Fh0WziS+Hn51s0I/tM+QUF+Pn5XnK77jhy9BgnklPofkYZkXNZsXINffv05NabbqBxowbE1I0uqot9SoP68YSFhbJj5262bt9J2zatAWjbphXbd+4mMzOLlNQ0unTuWK7PSVQ/RktlRyCEEEIIIYQQ4kIkQX2J9Do9ET6xoOo5lHOossMRQlQh/v5+mEymYtsOHT5S4naaNW2Cqqrs2bu/aJvNZmP8U5NZsXLN6R21889ybt6sKYcOHcFsNp+xu8au3Xtp1rRJiWNyl9VqZeasj4iKjKB/vz4X3NdkMhEcVLxQ8KLFy4piPaVjh3bs2rOX7Tt20a6tK0HdpnUrDh85yvqNW4iKiiQuNqZcno+ovgqsWrHXkRBCCCGEEEKIqkUS1KXQP/5Kft07Fz+DH0bb2TMUhRC1U7MmTUg6kcz2nbtQVZWt23awbv2mErfTs3tX6tatw4xZH7Jr9x6Skk4w/b0POXT4KM2buZLLAf7+JKemsnffAdLTM85qY/RVI3GqTl59YzqHDh/hyNFjvDPzfdLSM7j+mqvL5PlaLFays3PIzs7hRHIKy1es5pHxz5CUlMykpyfg6+tzweObN2/K6jXr2bvvAMcSEnlr+iyioiIB2L1nH/kFrjrUnTu0Z/v2XRw5eox2bV0LIdaJiiQ8LJTf5/5Jl04dyuT5iJrFqYKp5DcwCCGEEEIIIYSoIFKDuhS61h3I5JU3MKRRXzae2MiABgMqOyQhRBVw9ZXDSUhM5IVpr+F0qnRo34a77riVZya/gNPpdLsdT09PXn5hMp9/9T2Tn38JTdVo0qQRr7/8fFECd9RVI9m7/wDPTHmBW24cS6OGDYq1ERsbwxuvTOXzL7/hsQkTUTWNRg3rM/W5ibRv16ZMnu8f8/7ij3l/AWAwGIiICKd7185cO/pKIsLDL3r8ww/cw9szZvHkM88SEODPFSMuZ+z1o8nNzeO3P+bj4eHBzTeOoWPHdrz65nTCw0KpGx1ddHyb1q1YsmwFnTtKglqcW4FVw89L6lALIYQQQgghRFWk7N+/v1rd99q0adNSt6GqKmt2nCAgrA6KUrpJ5BOXj+PBbrexOXkzU/pNKXVsVY2qqqSdSCQqph46nUy4Px/pp+KOHz9OXFzcOR/TNA273YbB4HnOWs3iNOkr95Skny702jzlwIEDZRxh7VQW4zXAvXMf45q6jxIRVfeSx2wfT2gaqS+TeKoqGYfcJ33lHukn90lfuaes+0nG67JRVuO1/B24R/rJfdJX7pO+co/0k/sqY8yulb+R9MJ09uRsK5O2uscMI6UgjS0pW8qkPSGEEEKcFuwdypaMVaVqw2wDh7NaXY8XQgghhBBCiFqjViao/Qx+fLr3DVRNLXVbbSK68m/KDuyqvUzaE0IIIcRpN7W+kz8Tvy91OwXWMglHCCGEEEIIIUQZq50Jak8/OkX0YkXivFK3VS+wCfuzDtAyvCV7MvaUSXxCCCGEcAnzjSDAEMSxvP2laqfAIjOohRBCCCGEEKIqqpUJaoBRDW7lx70fomml+8Kq1+mxOzW6x3Vn7fG1ZRafEEIIIVxGNbid2XveL1UbkqAWQgghhBBCiKqp1iaofT386Brdn+VlMIs6JqAxEb7RkqAWQgghykHDwBakGBPJt+ZechsOFUw2SVILIYQQ5cGpajik4qUQQohLVGsT1ABjWt7Hj3veL/Us6hZhHTiQmUBaYVqZxSZEdVfavyshypq8Jqu3q5vexu8HPi9VGzKLWgghhCgfDhUScvTYHDLWCiGEKLlanaD2MwTQObofK4/PL1U7LcI7sjn5XyL9IkkvTC+z+ISorry8vDCbzZUdhqglNMCd3LPZbMbLy6siQhLloG/cSFYfX4BTdV5yG7JQohBCCFF+rE44nKlhsUuSWgghRMnU6gQ1wNiWD/DD7lmlmllXP6gZB7L20TO2J+uOryvT+ISojsLCwsjMzMRut1d2KKIWcLiRr7Tb7WRmZhIWFlYRIYlyoNfp6RM3vFQLHJtsGk5VvjQLIYQQ5cXu1DicoUpZLSGEECXiUdkBVDZ/z0A61enDquN/0rfeyEtqw0Pngd3ppEvd7vy4+3uuan5VmccpRHWi1+uJiIggPT0dVS1ejE7TNGxWC55e3iiKUmkxVgfSVxenaVBoU/FQrXh5n7+fdDodERER6PX6Co9RlJ2rm/2PSctvYUD9qy/peE2DAgsE+5Z5aEIIIYQ4yaHCkUyVhuE6fD3lM6wQQoiLq/UJaoCxrR7kqaU30CduxCUngWIDG+J0erM7Y3eZxydEdeTj40NMTMxZ21VVJe1EIlExseh0tf4mjguSvrq41HyV7Dwnvs4TxMZKP9V0AZ5B1A2oz76sbTQPa39JbRRYNIJ95cuyEEIIUZ6cKhzNVGkUocPbIOOuEEJUJ5WxfpN8kz/5hbd1RBc2JC+55Daah3Vgc8o2PHQeWB1S5FIIIcqbU9XINMrto7XNDS0fYPaeWZd8fIFVXjNCCCFERTg1k9oqCycKIUS1kpovCepKM67Vw3y3e+YlH98yvCP/pmyhY3RH/k35t0xjE0IIcbYMo4ZTdWNHUaPEBzWlwJZLtvnSFiW2O8EsizcJIYQQFcLudM2ktjtl7BVCiOogu1Ajz1zx55UE9UnB3mE0Cm7JlpSVl3R8w+CWHMzeS7eYXqxOXF3m8QkhhDjN4ZTZ07XZtc3v5pd9H1/y8QUWee0IIYQQFcXqgKNZKqosVCyEEFWazaGRnFc5s8AkQX2GW9o8zje7pl/SsR46DxyqgxZhXdlwYkOZxyaEEOK0zEKZPV1bfLH9fZyas9i27nUHsSV1FXan7ZLaLLCUUXBCCCFKpBJKWooqwmyDxBx5AQghRFWWlFt537MlQX2GUJ9I6gbUZ2f6pSWY6/rXJykvA5PdVCkFxYUQojZwODUyCuQ9trYosObz2d5Xi21TFIXBDa7hn6M/X1KbhTYNp8ziEkKICuVwauRaZLG82izPrJGaLzMMhBCiKso0qpV6p6kkqP/j1jbj+WrnW5d0bIvwDmxK3kLTsKYcyDpQ5rEJIYSAdKOG5BZrDx+DD6tSFnDj3B58u2sGifmHABjR+Cb+PPTdJV0Q1jQwynrGQghRoZLzNBySm6z10vI1ckzyQU4IIaoSm0MjJe/0e7OmaRU+8VYS1P8R5RdDsHc4B7N3lvjYlmEd2Zn+L11jerLm+JpyiU8IIWozu1MjS2pP1yp3dXiELwesINwnGgX4fPtrTFhyPbvSN9A0tB3b0i5tvM2XOtRCCFFh8i0auWZ5363Jcsw5HCvYz470Daw7sYiVifMxO0zn3DcpR8Vkk9eDEGXF5tAosCqk5KkkZqsczVQ5lKFyIN3J4QyVhCyVE7kqGQUqhVZN6sGLs5zIdU0Cs6t2/jr8A3cvGMFxY1KFxlArE9QnclWO5ujP+/iNrR7m+93vlbjdRiGtOJyzm7aRPWWhRCGEKAfpBTJ7ujbSK3qm9f2c5YnzeKDTizzdYyYbU5axM30DL6198LxfgC/EaJUXkhBCVASnqpGUI1Ona7pNyRtYlPQbG5OXcjR3H0kFR3l00Sg+2jqVXEtWsX1VDRKzVSm3JUQpmGwaSbkqe1Kc7EtTOZ6nI8PoukMh36JRaNUw21yfeXPNrgXmk/M0DmWo7EpROZjuJDVfxWKXv8PaLs+skVZg5PPtr/HA38MpsOYyY/DP1AuIq9A4PCr0bFVAjknjpi8tJGb786yXk471zs7RNwhuTr4tmyxzGmE+UW63bdB7YlftBHhEk2JMKePIhRCidrM7NbILz/4AVWgvICPvAHH16lVKXKL8WB0ac3c4aOQLAV7BTOo5i0nLb+Gly77mgU4vYnaYeGXtQ1z3W3vGtLiPUc3uwN8z0K22bQ6w2DW8DVIPVQghylNyrobd6caOolob0mgY/qa2hEXWQVFc37FvaPkga08s5NmV/6NJaBvu7/gCep1ropjVAUk5GvFhMg4L4S6nqpFV6EpCW+yX3o6mgcnmSnKn5Wv4ekKIr0KIr4JeJ3+TtYmqahzNMvHUsnFc2eRWbmv7BDpFh0GngTOnQmOpdTOo31ps5UC6hsWh8Ox8O8v2O8653/Ut7uWnvR+WuP26/vXZm3mYCN8I0gvTyyBiIYQQAGnnmD2taiovrLqbfHt+ZYUlyommaTw9x8Lzf9n5YKMvVodG/eBmPNPzXSYuv5nj+Yfx8fDlhT6f0iSkDQadJ+OXXMfq4wvcPkdlLgIihBC1Qb5FI1vqDddaiqLQK3YYM4f8Qf2gZjy36g7s6umsWq5ZI6tQZtcLcTGappFpVNmXqpKSV7rk9LmYbK4SD3tTVdLy5e6G2uR4ro0nl97G2JYPMKjBaHQnLzAeyN5Job2wQmOpdQnqSZd7MaSF66qtQ4XXFtn4devZf91dowewLW0tVoelRO33iRvG0qN/0rVuT9YeX1tmcQshRG1mc5x79vR7W6bQM3YonSM7V0pcovxsS1KZvcV1EXltoidPzbGRVajRILg50/p9ydTV97IvcyuKovBkj3dYnfQ3Mwb/zq/7P2VTynK3zpFfsiFeCCFECUhpD3GmK5rczID4UTyz7EYsDnPR9uRcDXM1LzFgsmlkFKgcz1E5luWq/bs/zcnBdCfHslzb0/JVjNaKX3RMVH+5Jo19aSoncst/oVmnCqn5pxPVUqu6Ziu0OXn477sZ2uB6esYOAeBQzm6eWX4zn21/E6PdWKHx1LoEtY9B4ZNxXgxqZC3a9skaO5+stqGeMVgoisLwRuNYcOSHErXfte5ANiQvoX1UL6lDLYQQZSStQOO/n+f/OvwDdqeVq5veVllhiXLUIU7PZzd542Nw/bw/XeORnywcTFeJ8ovljYE/MnPzJDanrCDavx594obzx4Evefmyb/hqx1vsSF9/0XMU2mSRGCGEKC9S2kP814D6V3Ft87t5culYCm0FcEY96uo2HueZNY5lqexKdnIwXSU5zzWZIs/sqv1rsbtmpeaZXdtT8zUOZ6jsTlFJyNWRadRklqq4IKeqkZitkpCtYjv3jf/leG5XovpAuioLi9dQmqZx3/xH6FinL4MajCbVeJyJy2/h213TGRB/NemFycVypBWhwmpQm0xmli5fxfadu8nMykZRILpOFD27d6Vn9y4oSsXVufHQK9zV2UydUD++3eT6S/91m4Ncs8ZjAzzx0LtiubzRWB7650quanKb2/F5e/jg5eGDn0cUezP3luvzEEKI2sBid9VZO9POjI0sOvoLbwz4kc0JTjYc9OODmzV0te6ya9mrSuP18NYGvjBoPDDbTJZZR2ahxoTfLDwx2JPejUJ5a+DPPL38Ruyqjeta3MPD/1xFn7jhvNr/O55cOpaHu7xM87D2521f08Bog0DvCntKQghRK5yrtMfGE1t4beUHrLjrM2JDZMAurao0XpdE95hB+Hj48fTyG3l74C8Y9J5Y7JBaoFE3qGrGfIpTdSWbswo1rJeYMFQ1KLQpJOeppBVAsK9CmJ+Cr2fVfu6iYhVaNRJzKj4x/V9WBxzNVAn2UagbrGDQy+u0pvhky3f4eYRxddPb2JWxiZmbJvJMz3fZnr6OhUd+ZOaQX4jWF1RoTBWSoM7Ny+fN6bPIy8unW5eODLisD2azmdVrN/D9j7+Slp7O6KtGVkQoRRQFbuzqQaifjvdW2FA1WLLfSYHFysRhXngbFDz1XnSq04f1yYvpETPY7bZ7x17OP0cW4qX3wmw342PwKdfnIoQQNVn6f2ZP51gymblpIm8N/IUFuzU+WGlH1bypv8jOpMv1lRlqtVcVx+umUTpeHFTArE3B7El1fSGctsDGHT01ru3gy2v9f2Di8puxO2082eMdXl33MNMHz+GV/t/y5NIbeKzLqzQP73De9vMtGoHe8mFbCCHKyn9Le6iayqxNM/h111r0pme49Wsrf9yrx99L3nsvVVUcr0uiXVQPRjW9g1fWPcyUXh+gKAoZBRpB3hp+VfR1kWlUSc3XcJZhiQVVg+xCV9I70FshOkiRxZsF6QWuOtMlYbIbWZE8n3/3rsHkMFLHrx51A+KJC2hEz9gheOq9ShVTrlmjwKoRF6IjyEdeo9VdSkEKX277kplD5rPk2BzmHvyKV/t/z8fbphHoGczrA2bjpVfAWbEJ6gq5dD13/t/k5ORyzdUjuemG6+jRrTMDLuvDhEcfICDAn6XLV5NfULFP/JThrT2YNMwTw8mcxsYElYlzrUULJ13b/B5m75lVolpRfeuNYHnCn7SL6sym5E3lFboQQtR45nPMnn5j/eM80vlVvt/oy6wV9qKFE49kyoIepVVVx+sgb41XrvZkULPTFyA+W2tn5jIbHoo3r/b/jnmHvuFQ9i761hvBd7tmEOQVyusDfmDm5klsTVtz3raNctuiEEKUqRNnlPbINqdz9/zrmbO9EIvdH7PH12QaNU7kSm3q0qiq43VJDKh/FXGBjfhm1ztF247nVL1SHyabxsF0JztT0vlyx3Te2jCBZ5bfzEP/XMmji0bzydaX2Jyyolhd7UuRb3GVUziRK59na7Ok3JIlp7ekrGTCkuuZvOI2Cux5PNrlFV7v/wM3tnqIZqFtSS1M5MGFI5l/8FucaulqLjlVOJbleo1KLfXq7f4/H+ORzq/x3a4ZrE36h2n9vuKltffTuU4/7u/0QtFCiRWtQs4aEhJE+3at6dm9S7Htvr4+NGpQH03TSE5Jq4hQAHCqTgpsuUU/92rkwbQrvPA9WedyT4rKE3MsZBdqhPpE0Cy0HcsS5rrdfoh3OIX2AlqFd2FN4vm/FAshhLiw1P98QPvn6C9E+Tbg542t+X276543q24pXRrO5qMbvNDr5Ip+aVS18fpMnnqF8YM8uaWboWjbgj1Onp1vxe7w5OXLvmHJsTnoFD3b0tdyKGc3QV6hvDHgR77Y/jrrTiw6Z7tWh6uMjBBCiNLLM5++sHwoZzd3zR/L/hONMWor8XP8j3YBLzL/Pm+aRckdT6VRlcfrkritzQSO5u5nReI8ODkmp+RXjTFZ0zRS8lS2n8jj7fUv8uzK/1E/qCnXNr+HST1nMXPwH7zW/3s6RfdlW9panl42jieWjmVl4nwc6qXVZdA0yDRq7EtVyTNXjX4QFUNVXTXNs4zu/d4zTalMWfk/Fh/7jSm9P+TtQb8wMv5Gwn3rYNB7EhvYkK51BzC25QO8N3Q++bYc7l94OWuT/il1rJlGjYMZKlaHvEarox92/kQdvyZsSl2OyWHk0S6vMnH5zYxpcR+DGoyu1NgqpMTHFcOHnvcxk9l1pdHXp+LKYBzPP87MXS/wavTpBRDbxep5bZQ3k+dZyDPDsSyN8b9ZeOUqL/7X7mke+ucKuscMxNfg79Y5ukRfRrbJWKkzqO1O1+wFqwMcTg1FAZ2ioNeBQQ8+BqpsbTIhhDDZtGKLcmSZ05i9+xM8C37iWJaKhobFMIsGUTt5vNtd6CQ5XWpVbbz+L0VRGNfFQHSQwtuLbdhV+Pe4yoTfLEy9woup/b7g3c2TCPOO4vV1jzFr6Hz8PAN4Y8CPTFpxKxaHmf7xV57VrtGqyS21QghRSg7n6dIeG5KX8dLKV0jNN+CtxRJim0SXegYm9s4gJjioskOt9qr6eO0uRVGY2PNdHl9yLXX969MktA2ZRo1gn8ot9eFwaiTmaHy57RP+OfoTN7Z6hHs6TDnru7OXhzcd6/ShY50+cPKOgfmHvuW73e/SJqIrA+uPollY+xLPRnScnKka6qcQE6TIZ9wazqlqHMlUMdkuvq+qqfy09wNWJv7JQ52n0SK8IwCadv67Ujz1Xoxr9RBXNrmV19c/RpY5jSua3FyqmM02OJSuUj9MV2XL8oizZZoymbXpY65sfAcbkpfyYKepPLF0DA90eoE2kd0qO7yKWyTxXE4kp3Do8FEiI8KJi63r1jGqWvrbweoF1sPkKCTLlEGYb0TR9sYR8NZoTybOtZFeACl5GuN/tfDSlZ7c2mY8H219kUe7vOrWOfrGjeD73TOxOCw4nI4KmSKvaRpGq2vmQp7l4vWxFMDHoODrBYHeyjnrwJ3q77Lo95pM+sl90lfuq+19debtY5qmMXn5I+Slv0ihyYBNWYfR62kCfDLZm5vJtM3p/N2qT2WHXGNV1ngNFK0eraoaOp2rzcua6Ijw9+TFv2zkW+Bolsajv1iYNtKThzu/xB8HvmJ/9nbe3TyJx7q+hqfek5cv+5qH/rmSrtGXnXWxOdekEOpbJuFWmtr+flES0lfukX5yn/SVy/EcFbtTY+6BH/hk8xzSC73wc9yPl9qPK9rouazFHl7d+iq/tpxd2aHWWJU6Xhf9HZwer93hofPg+d6f8PTyG3lr4M8EeAaRkKXRLEqplMlUZrtGQpbGB/++itluZNaQP9Hr9IB20bIGId7h3Nz6UW5q9Qjb09fzz9GfeXfzZCJ8o+kddzm9YofhpXNdOHCnn7KMYLQo1AtR8KmFiyjWhvdWVdU4kqVhsl18NnK6KZmX1jxAr9hhvDtkLjpFV5SYPlUa50KvKz+DP8/2+pCpa+5D09RSJ6ntTjicoRITrCPEt3q8PmvDa+pCHl0wnsHxN7Dg8A8802MmTy8bx4Rub9IktM1ZFzlOfweruL5S9u/fXynz8nNycpn+3kfk5Obx0H130qRxQ7eOC/DxLJPzf7V7KUfyDzOm0b1nPZZlUnhtpT/JBa5bz/w9VZ7sU8jsYw8wtvH9NAxs7tY5nlp/Ix0jWzCm6dW0DG1RJnGfi0OFLJOOPIuCoxSvHU89BHmrBHtrRTW5hRCiMhTaICH39BvRJzv/4O/DafjYn8Sk/4ZCz8mEewcT5h1Cx4g+TO3xCF4eZfPBqMDsxvSFWqSyx+uMQoWMwnNf5E0p0PH6Kj8yCl2vFV+DxmO9jLSIcLItcy2vbXuM25pNYGjcdQCsTvmbE4VHGdP4vmLtKAo0C3ciE5SEEOLS5FkUkvIUvj7wEYuPpJFtzcHHORYfdSg3tTcTFryYX498yhu93qZpaEiZnFPG6+Iqe7y2OeFQ1qV/idyTs4U5R79gYod3URSFCD+VCL+KTVUUWBVO5Ov4/uD7mByF3N5sQpkkyTPMKaxLW8zG9KX4GwLpGz2CzhH93F64TqdA3QCVQG8pqVCTaBocz9NhtF38NbY29R9+P/YlD7R6gfiAJqU6r1Nz8s6Op2kX1p3BsdeUqq1TKuPvtaY6dR2srK/PbcvYzoxtX5JuzuCpdtN5Y8d47m0xhQaBzc65v6ceGoeVrm75mdwZsyslQZ2QmMRHn35JYaGJW28eS8f2bd0+tnHjxqU+v6qqrN6ZxJObbufDYX+fc3ZzrlljyjwbhzJc3eNjgEcHZfDToQeYOfh3t2ZEz9w8iXrBEUT4+XJf5/suun/Jn4dGhtH1X1muo6AAIb4KUYEKekUjIyWJiOhYdLrKKZReHaiqKv3kJukr99XmvjqUrmI6WRf4xaXfsODwXIJsX2BTdpLvPZKu0X0I9wunVXgnrmt+L2FaMjGxZdNPhw4dKoNnUDNU9ngNkJLnZN+RVELCo855i2uOSeO5+TYOnhyvPXTw9BADvRrp2ZWxiYcXXc3UPp/TK24oqqZy/8IRvD3w57NmUdcP0xHoXX0z1LX5/aKkpK/cI/3kvtreV3anxr40B1OWP8Gmo6Hk2g7hpQ4mRBnNHX2Oc6TwS/ZmbeXOdhMJQ2Vkx34yXpexqjBem21O1u8+/3jtju93v4tTc3Jz60fRKdAkUldmExAuJs+skZit8uXOt8i35vBgp6nlMoM7zXiCubu+ZGP2Cm5p8yh94oa7fWydQIXIgNrzHlOT31s1TSMhu3g5w3Nxqk7e2fQUoPBw52nnvaihqho5mWlu//05VSdT19xH95iBDGs45pKfx5nC/BRigqv276kqvqY0TSPf4ipvabKD2ebK7+kU0OsUPHQQ4O3K0ZXm/XDE91eSWpDL1H5fMGPT04xpcR+do/udd38PnUaYmlxmfeXOmF3hJT42b9nGt7N/wdPTwAP33kHTJo1KdHxZdMzxvOMsSPyZrtH92ZC8lJ6xQ87aJ8QXXhvlzfPzrexMVjHb4e1FEfRsPYh5h77l6qa3XfQ8/eOvYsXx3ziYk8oDXR8oddxnyrdoJOVo2J0KoJT51ZUcM+RZINQXNNXV71XlD7gqk35yn/SV+2pbX+VbNMwOBYeqctcfkzmQbibI8SUqWRh9rqRDVBdCfILpHjOQEY1vdN2O5Kx9/VTeqsJ4DaBT1JPtKSjnuDgc6gevj/Lm5YVWNiWoOFR4eaGdB/spDG/djTcGzGb8kut5f+iftIrozPUt7uXnfR9zW9sJxdoptCkE+1b/14/8HbhP+so90k/uq619dSzDwv0L7uRgUneM6hY81V7U9RxFw4aT+XD3POr4xdEjZhCbU5cTiMKVuv61sp/KS5UZr3Xayf+fe7x2x7hWDzNxxS1sS1tLhzq9Sc1XaBBe/q+VXJPG8VyN2Xs/JNeSxSNdXim38iJR/jGMbngHN3V+jE+2vcRfh2czvtsbRPhGX/TYtAJXWYXYkMopf1JZauJ76/FslQLrhdcEs6t2Xlh9N93qDrhoOY5TZT3c/fvz0Ot4tvdHPL7kGpqEtqVxSKtLeBbFZZtcsx3jQqr+76oqvKY0TSPbpJFeADYHJ6eKUpTf005WS3CoYDFChhH8vCDUVyHUr2R//0uOLCGtMJubWz/O1zvfYlD9a+hStz8ABbY8NiYvZW3SP2SaU1BOxqEoMK3L40TF1KuwvqrQBPXipSuYM/cv6kbX4Z47byU8LLQiT18kNjCWhIJD+PoF8/uBL86ZoAbw81Rciy4tsLIlUcXqgFU7/se/dUYzsvFNeOgu3H2tI7ry4dYXCfBS0DStTAYRTdNIydfIKCj/ie+qBhlGjbxsPb7hGqF+5X5KIYQgNV8lu9DIzXPupKCgL4HO21AxYfTvTlxADDEB9egZO5ShDa+r7FBrrKoyXrvLx1PhueFeTF9qY/F+J6oGM5fbyLNojO3Ulxf6fMr9C4fz7ZXr6R9/Fff9PYwxLe7Dx3B6YCu4yAwWIYQQZ0vKLWT0j2NJTR+DSZmHQe2Ij+9WUvTPEWRpy2v9v6d1RBc4uYiXYjxR2SHXKNVtvL4YRVGY3PN9Hls8mtcHzAbCyDVpBJdjfdsck8bxHJVNySvYmb6Baf2+rJDkr4+HL490eYUD2Tt4buUdXNfi3nMu5Pxf2SYNhwrxocjiidVURoFKtunCnzttTiuTV9zGkAbXMajB6HKJQ6/TM6XXh0xcfjMzhvyBj0fpF2TJLtRAU2vdRZSSyjVppOa7cowlUWjVKLRq5JgUYkPcm1GtaRoTlzxLqFc0R/P2ERvYkKENr2fJsTn8fuALvD186VZ3AP9r9xQxAfWLjjPoNEKcSZfy9C5ZhSWoV65ex5y5f9GiWRPuvP1mvL3dq7dUHhRFIWJHHTboVnLYew+fnXidJnVb0bRFW6KiY4v9IXkbFJ4b4cWrC22sPeLEoXqQmj6SGetmM77XTRc8j07REekbg5fBzrHcYzQIaVCquK0O121H7qzuWpYcKiRmqxRYFGKCFTz08kYjhCgbqqpiMpnw9fVFp9ORa9I4kmlh3G9jwPQgXmpzzB6fo/q+QqDBmy7RlxGu1cHnqC9zt3xNXk4WuTmZPPHAxe9qEe6pSuM1wLo1K/n11zmERdbBzz+Q4JAwGjVtSf1GzTB4nq6b6aFXeHyQJ0E+dn7d5vq099V6O3lmjbt7j+SJbm/zv/n9WDg2geua38tP+z7k1jbji463OlzjbEXdSiyEENXNf8dsm0Pl6u8mkpx6PWaPn9GrDdD7/kxIkMLzvRdQ1yOevXv+5bOVr7E7bxNHbfuID49jSbO/K/up1AhVbbwuKCjg4xkvFY3X/gFBxMU3pEnzNgQGu1933M8zgIc6T2P6pqd5vs8npOSrBHrryiUZe6qsR1phEp9se4npg+ZUeFKtaWhbZgz5gykrbkPTVAbUv/qix+RbNI5lQ31JUlc7BRbXhMMLMTtMTFx+M6Ob3VGiEjCXIty3Dv9r9xRvrH+cZ3t/WCZtZps0NCAu5MIzxGsjTdNIytVcifxSMFo1DqRpRAUqRPiffTHgzPH6172/kmZMZ1ib29iTuYW+9YZz39+X0z1mEK/3/wEfgx92m43DB3az9Pg2cnOzyM3OJCgwkPtuGlnKZ1wyFZKgPnL0GD//NpdGDetzz123YfCo8MoiZ5n7y/ckHj0Ew+DdehNhEqBBQFAwTZu3oWO3Plw25Epatu2Ep15h0jBP3lxsY9kBJ96Om/hx9w20DR3D4BaGC56nd+zlbElbwKrEVaVKUJtsGkcz1VItglhauWaNQptGbEj1rtMphKhcqqqycOFCpk2bRkJCQtEdJvHx8Qy9+03eSZiBzdYMu8d0FCUPb+9EQryD6BN3OQEeIXxwzfNnrSb8v7FXULrlQgRVdLzesG4133/x3lnbPQwGGjRqTqt2nek7aCQ9+g3Gx8eXu3p7Euyr8NlaOwC/b3dgsmk80v9G5h/6lrc2PsH4bm9w39/DuL75vWfNovbyl/FNCCFOOd+YXa9+Y5wjH2J3YSqqxzeAAX//DUQF6An0DuWDrc+ze/tmMtNTIR04BiTCFQ9dfIaouLiqOF5nZmTw0zcfnfOxOnXjaNqyLT37DaHjZX0o9M7H5DBSP6g50f71zlrfqU1kNxYc+ZH1JxbTPWYQaQUa0UFlOz6bbK7ktM1p5YVVdzOx56xinwkqkkFnYGq/L5mywjXhwp0kdYFF42gWNAiTJHV1cWrCoXaB3KSqqUxZcRvjWj5YVIKhvHWPGcTmlBX8eeh7RjQeVyZt5pg0FCAuVF6bp9gcGgllOOFU1SAlT8NodV2sAu2s8RodZI3LpX/caP4+8gOeHj7sz9rGWwN/xs8zoKitVUv+4vG7ry3WfpPmrWpmgvrn3+ahqiptWrVg564959wnuk4U0XWiKiIcAMyFRtc//gbuB0YDv0NBXi5bNqxiy4ZVfDLzZe5+dDL3j38evU5hwiBPvDxs/L3HF0+1Fy8v+xNVG8nQlufvxm4xA5l3+AtWJazilna3XFKshVaNo1kqzkpMTp9id8LRTJU6gQpRgVW/tpAQomoxmUwMHz6crVu3kp+fX+yxTN+ObNv/NXZ9LpruAKpygmDNi4e7vkSGKZl8Ww73dJrM4ia/cmj/rmLH5uTkVPAzqZmq4nhtLCg453aH3c7BfTs5uG8nv//4BXHxjZi7ah+KonBdRwNB3grTl9lQNfhnrxOz3ca0ft9zzW/NuaXN41zX/F6+3/Mud7R7uqjNfAuE+5/zdEIIUeucb8xWvEMpHPAEBeYnUBRfwIHemkXd6Hj61buS29o+gaIofLb/Vd79YXLxNvOzK+GZ1DxVcbz+7cAvMAYoPPmfEUgAnJDa4DipEcdZufdP+Beuu/weGtZrwYrEP0k1JuLUnHSPGcTYlg9g0LkmgD3Y6UUeWXQ17aJ6kmH0JdSv7O5ysjpck79UDd5Y/zhjWt5HfFDlTnW4lCS10SpJ6upCVTWOZV18wuGH/75A77jhFZacPuXejs/x6KJRtI7oUmZ/C9kmDb1epW6Q5I0Kre79/i9FgUVj13EzD916Bdu2bin+HbsXYPbknwO/4evhx7jge9j2zVpa3dOX0MY9OZGnUWDRSPboTcDgt1A8vMBpQ3M6yPM28O46X+7sr9IwogbVoE487qpb8vu8BefdZ/jQQYy4fHBFhAPAi298iAMdpsJCVuTM44j/HjLuTUH/hwfpSadro3XvPbDo33qdwv19dOgVjXm77yTP817eWToEu1NjZJtzz6QO8AxCU3Uk5CVeUpxG6+nBsypJzdcw21XiQhT0MhgKUeM5VSdrjq9hTeIaWka0pEdcDyL9IkvUhqqqDB8+nDVr1uBwFC+45dP2FpQREZgN34JSiGIPRj2Rg8nDg23pa+lUpy93NH4GRVG4euzt5OVkERZRh+CQMIJCQomPjynjZ1w7VcXx+pb/3U18sw54GAyYCgvJSEvm4L5dHNy7k2OH9xW9lrr1GVjs9rYhLT3wUOy8vcxVq3HVIScWuy9XNL6d8Uuu4+uRq3lsyWhOFBwrqrdWaNVQVa3cvuQ5VQ2TzfXFWNVA01wLoOgU8PJQ8PQAL718yRRCVL7zjdn64IYEjplFftTDKFo4KpmoOWk4TPkMaTOem9o/hI+ngp8nXHX5QJzGFBo1bEh4eDghISGEBQdc8LzCPVVxvL6n9/14ptTHqjOTWHCYNca/SbAdwO6w4Uxxom5wwgEIDAzhqbdn4HHGrG+L2cyipF944O/h3NV+Il3q9sfX4M+tbcbzwb/P83jX10nN04gPK/346HCeThT9fuBLQrzD6VfvilK3WxZOJamfWXYT4b51aBvZ/aLHGK0aCdlQP0zKKVRlSbkaFvuF91l67A/yrDnc3+mFigqriIfOg0m9ZjFtzX3MHDwXvU5fJu1mFGjoFbVWT2402TSOlGNOT9NU7rn/Ybbty8RaaHFt1BlQ/ILRemeDSUUt1OHYeTk/xTTFo8k1vLi1AWyzntGKL34d7zqr7S/+hREdNRpGlE/s/1UhCepZ01+riNOUSKu2HQkIq4Oi6BikjuaxxaO5of6DLO74G5+0Wsym5cvZtGYZ7Tr3LHbcP3N/5M83nqXDXb+yyt4Ym7KR91Z0xaHC1e3OnaTuFN2Xf9P+IaMwgwg/93+zBRbX4FnVktOn5Jk1rA6N+mE6qdkpRA2haioZhRlkmjLJNGWSakxl8ZHFHM8/Tq+4XvSN78vezL08segJMgozaBjSkPE9xrtVwmjhwoVs3br1rOS0f59nsffbglX/EzotDKWgPs5t+2GtHZ3ei3vvfpY6deOK9r/pzkeKHa9pKgFOWXSpLFTF8bpefAM6ObwJi6xz1qrkVouFTeuWsWzhXIZeeX2xx/Lzcpk6tjldrnuWvWG3YncqbEpQaWV/hmzbryw48gOPd32dtzc+wZsDfkJRFFQNjDYI9C67+AutrhW6TbaLfTE5Pdj7ekKgt0Kgj4KPQcZXIUTFO9eYbYjpit/1j5Dv/yQqmUAB2iEreOTDfIhso6d1XV1Rkiy6X3cG9judYFNVlbQTlzZpRxRXFcdrf39/GrdtxYK0H9in284jvV6ma90B6BQdJ/KP8t2Gd1l5ZB4t7J1Q/nMh9rnxd5CZkcrdT01mReJ85hz4gie7v0PvuMv5+8hP7MncQsvwToRbNfy8Ln1c1DSNhGzXeLw3819WJM7jzQE/lcGzLzsGnYFne3/EhCXXMWPw726VHcm3aCTlQlyIfGaoinJMGjkXWRTxSO5eft3/CdMH/VZhcf1XtH89BtYfzXe7Z3BLm8fLrN3UfA29TiXcv/Ylqc328k1OA6xbt459+/bjRI9HaFM0pwXF4I+zTwYY9Hh69ybE9zeUAe73v0oudt0ODGo7PPRl+MXoIpT9+/dX0fTnuTVt2rTUbaiqypodJ4oS1ABphUk8v+ou7mj3NJ9vf403BvxYrCYLJwe0cSO6sXfnvwDEjHmJrCZbCLZ/CcA9vQ2Man92kvpwzh7e3vQo43s8yKgWo9yK0WzXOJxR+WU9NE0lKz31nImBUzx00CBch69n7R0QT33gjoqph05X+954S0L6yn0V3VeJeYncPe9uYgJiiPCLIMI3ggi/CPrG96V+cP1zHrM9dTuvr30db703T/V+iqZh53+P7tWrF2vXrj29QWcgaPj7mNv/gk2/BL3WGC3fivrTMZSsABq27Mz3P83Fy/vCg6KmqYQ4TxBXr2z66cCBA6VuQ5TNeA2Qkutgz+GUC45D5/L1R2/z9rQnAQhqPpSAq77DgWtRxeCQrzB7vscXI5fx094PaRTSisENrgEgzF8hNrh0ryOn6voiklV48dkyF+PlAWF+CqF+F75jSd5b3Sd95R7pJ/fVxL7675jt3XwUnlcNpNDzQ5y6fegtbXBm7IHNVtgFnTt2ZtOmTRdss6z7ScbrslFW4/UPO35m+poPuaPjU3StO+Ccs3nNDhO/7PuYVYl/clPrR+hbbySpyccZ0bMxTqcTgMuvvoHh993A18feZny3N4n0jeGZ5Tcxa+ifBHh70DTy0md2JuepZBRo5FmzGb/kOt4c8BPB3mGlet4l5c73a4D1JxazNOEPJvZ81+22IwMUomtQOYWa8N5qc2gcSL9wXqfAlsf4xdfy8mXfEO5b55LO4+7r6uLtaDy+5Foe6fwy9YObXXI751I/TEeQT+XmjCryNWU5mdMr73Xk7rjjf2zfvsP1g6LDUKcDns2Gktd8HHpnL0Ltn6MqyTiVZFQlDU0rJDJQR6ifSqCPEw+9FZ3OgqJYyLOlYLIb8fUIpkFgO4bWGc7ILq0I9i39jHp3xuzKX02hiojyi+WaZnexNmkhd3WYxLOr/sfr/WcXu7XBVGgkNOz0DOgTP07C4652OML24aE156PVdjRg9H+S1A2DW2C2m1mVuMqtBPWp244qOzntLocKRzJV6ofp8C/FFW0hROVZeGghb657kw9HfEij0EZuH9euTju+G/0dezL28PKql8kwZdAguAHtotrRrk47fDx8MDvM5JmN7FOOQQ8vCFEhRI++bkPyfB8ErChqGziqR/01CZ1XfXTBgZgdejy9PMv1eYuaS9VUvH18sZhN5O1biClvKGFj54JnADk5N2Ly+5Lxi8cyofvrzNj0DD1iBuPvGYjRUrrr9jkmjZQ8FbuzbJ6H1QHJeRppBRohvq6Vuj3lriUhRDlSVZWEhISin72GXoOzaxb5ugmAFZ25Ic6F+2CHtejmj5SUFFRVrbZJJFF6QxtdToStG+FR0ectNeHj4cvNrR/luub3MGPTM/ybuprLPK4kNr4RCUdcyYsFv//AkgW/Mfb+B5ilf5bLG9/AiMY38v3umdzS5nGyCzVC/Uo+DuaZNTIKNFRN5YVVd/NYl9cqPDldEt1jBrEi8U/WJP1Nr9hhbh2TXqBh0NfOmapVVWKOdtG8ztsbnuC+js9dcnK6LCmKwtM9ZvDi6nvKtNQHQGK2SqOI2jGx0eZwrSNXlslpu9PGsbz9JOQdxOq04NQcOFQ7h3w2onbMRx8Zhz6qPqpXFnm6WwHQK74UGJ5DKdCjZhlxpqQSqFiYMHUaHjoP9DoPPPXeeOt98PLwIdQ7smiirkGnEeJMItC74n5fkqA+w6AGo1mTtBCH08bA+qN5a+MTPNn97aLH/fwDmPXNn2xau4w3X3yC/bu34fh5Ozk3jSXcbzMKHny82o6mwTUdTiepFUWhUXA7NidvuWgMmqZxLFvD5rjorlWK82SSul6IjmDfmv+GI0RNoWoqLyx/gRRjCnPHzsXH4HNJ7bSMaMmXV3+JpmkczTnKhqTt/LjzL0x2KwbFB1QPHGGB6JK90Z+oi6FZZ0w+XwIehBfspmD+Y1gPzHPFZDaiAppXI0K9rCgGX8x210zUC616LcSZbrt3AsOvHsf7bz7HHz99iT1lC5nfDyd0zB/ofELRmx8jLWcdH219lQbBzXlv87M83XM6VodrxoN3CUtrWOwaJ3I1jNbyeZE6Vcg0amQXaoT7K0QFKFKrWghRLkwmE5qmgaLHa8wt2JsuRCUTRQvF599HMf01GdTiX1Y0TcNsNuPnd/FyBKJm+nnvjySlWLkm6uw6pv/l7eHDUz2ms+DwbD47/CofzlvIit/m8eHbL5Cbk4XNauXrd96maet2rHt4EaFhERzN3cfA+qMx6OsT7KMr0RhodWgcz3Flij7Z9hK9YofRKqJzqZ5vRXi4y0s88s9VtI7oSpBXqFvHnMjV8PLQCKjApJI4t/QClcKLfC5cmfgn/p6BdKjTu8Liupgov1gGN7iOb3a9w21tJ5RZu6oGx7JUGkfoavRkC1cpIbXUOT1N09iWvpa5B74iw5SCQe9J/aCmxAc1w8fDFy/FG4fdic6jLQGxw/EIaIFOC6RAnQ46K8FpK3DsWYIlYQVYcora9YqIoEN4b7y9L+17f3mSS2v/8UT3t/h420v0j7+KAM8gftzzwVn7dOnZn+//3MALb31GuGcdnKuOkJ10VdHjn6yx89u24vf09om7nBxzHkab8YLnT8rRLvomVlVpGiRkq2QXVs/4RdmzOVx1Vwssp253V8k2aeRbFPItrsecVbXIei2gaRqPL3ycOv51+PiKjy85OX2qrVyTq65foTmelqFXckvrKdzbYRp3tJ/EyOhx+B2JwVDYEY8hvTAFfoyiQFjOcvJmjytKTp9J57QQG+5LXIiOppF6WkfraBiuIypQwc9LQdaBERcTWacuz7/5CT8s2ETnHv1wpG0n+4eRqKZMvNQryDEXkpl+I3X9m7E8cS4rEufDyQWHSiLTqHIgXS235PSZVM01Q2pfmoy3QoiyZ7fb+Wv3QnIvc6J/shHWZt+jKpl4qYPwWTQO0/ynz0pOc3JCjo9P1fuyKyrOuNY3sSplAWuSFrp9zOWNxvJol1d5bvX/aHF5B+au2s8t9zxetIDigV3bWfDADyT8exB/QxBvbZiAzaGRYXR//FPV03cnr0z8k7TCE4xudsclPceK5uPhy8NdXuaN9SWrB5yQrWKxy2eEymSyaaTmX/h3kG/N5Ztd7/BAp6kVFpe7rmpyKzszNnAkd2+Ztmt3wtEsFbUG5wBO5LkWRb9U+dYcvts1k3v/Hsaa439zZ/tneG/oPN4Z9CuPdHmFq5vextCG19M06Gr+3DIYr8af4uM/GoPWAovubyz6z9AdrU/BnIcx7/6+WHIaQFHAy8ur9E+0HEiC+j98Df6MaHwTi47+wr0dnmVnxgbWJP191n56vZ6rrr+VXxZvZ2j89djNq8jddl/R4x+vtvP79tNJ6i51+2Nz2lmftP685840upJ31d3xHPWiiwCImkdVNfLMGsl5KoczVHYlO9mbqnIwXeVIpkpitkpSjkZSjkpSvo5jWa7HdiWr7E52cihD5USuSq5Jw+GU109FeGf9O0T4RnBfl/vc2PvcbA6N1HyVvakqCdkqeWat2CIQmqbx52/fcc2g9hDSGG2UE7PvG3io7QhJXUju12OwJ288Z9vx8fHFbhXW6RQCvBXqBOpoHKGjVbSO+FAdIb4KkqsW/7Uvaxu3zuvD+/8+T2yThnzy42ImvvQeHsYjZP8wHGfhMfztz3DI9AGr9/Tn8S7v8erahzmcs4d8i3vn0DSNpFyVE7lahc/utztd4+3hDBWbQ94zhRClt3n7LuJurs/dnz6CEtsfp/cJFHzwt7yC8ps/pnVvn/fY/47Zovbx0HnwVPu3+WnvB2xNW+P2cY1CWvLOoN/4audbrMv6h8cnv843c9fSqGkrABx2O8tfmov5UCFZ5lQWHvmZDKP73xeS81134R3J3cuPe9/n6R4zzluCpCpqHdEFf88gdmac+/PyuThVV5JaJgJVjlOfDy/22fCdjU9yf6cX8Paoehf3FEXhye7TeXP9eBznuChZGha7q/RJTZRj0sgqwQW0M2Wb05m5aSITl99CtH893hs6nwc7TyUmoEGx/exOja/WmrjnOyMHs06XwzQa36VAewqsGixQUbP348w6gFqYVuz46OjoUtUpL09VM6pKNrTh9fxz5GcURWFKrw/558gvPLV0HJtSlrtudztDcEgYr836nucGfIIj9kcsm14qeuzDVXb+OJmk9vHwJcgrjCVHlpzznBa7RkpezfkjTcyWJHVtoKqu282PZansTlE5luVaeMRovXitrTM5VCi0amQaXbfD7E5R2Z/mJCVPxWST11F5+Gn3TxzMOsjEPhMv6Xi703Wr5L40lbR87Zz1dnNzshh///VMeutWLMPqkdZ/HjbDXAJs0wk6/gY5X1+OM/fIOdsPCgpiypQpF4xBr1MI9lWIC9FhKLvyaKIGWH18Ae9tnsLMIX/QNqIb45dcx+w9sxg2ZgwPfT0N/9EZZOb3osDzHgzOrmzPf4i5W7rRILg1L6y6myPZJy46s8Ph1DiSeekfQsuK0epafKcmXOAWQlSOfLOTx1+ZRrc32pGeqlEYWQeT9xoUPAk0zsT6/W9Y9vx03uPdGbNF7eCp9+bly77h463T2J+13e3j/DwDeH3AbP5NXc2n216heesO/PDXRm67bwKKotC2Yzdeu+V7+sdfzYxNT5NtyiHdjfE33+Iap/OtObyy9iFe6PMZnvqqOXPwQu5o9zSfb3+tRMdY7JCYLZ8NKkOmUcN8kRm0RaU9onpVVFglFuUXw4jGN/LFjtfLvO08s0ZafjVZdM1NZrtrMl5J5VgyeWP947y4+l56xQ7jvaHzGFD/agw6w1n77k9zctdXOfzwL2iK604TpTCJgv03YVReBKsKW0HNOIZmNwGgGlPQTlZy8Pf358477yz1cy0vkqA+Bx8PX2ICGnAoexdeHt680PdTHu/2BpuSl3Hv38NYeOTns44ZddXtvHTdN7S/ai83djld2vuDVXbm7XQlqS+rN4pFRxajqipGoxFVdb14NU0jMUelpl3glJnUNZfN4ZopvSdV5XjO2bNmy4LF7rqN/WC6yr5UJ6n5MkuwrKxMWMlPu3/i3eHvlngGicPp+t3vS3WVFzjfzIB1qxZx+bMNWRowB8++fdA1rYeqZBBqXYnhWEOyf7oGzZx9zmM9PDxo3749Q4YMuZSnJ2q5X/Z9wl+Hf+CtgT8T5BVK77jL+WDYAgK9Qnhh9d14B/vyy+M7eLPX53w3agEBnvGo5LExdwzWnGcJ8Ylm8orbSSkoOGu8PsXu1DiUUTElPdzhVCEpR+V4nk5mSwkh3Gayafy1ZRtt72rJO/umoNvbCX2fuqgeRlDyCDO+i+WbV7EfP/9sWBmzxX/5GQJ45bJveWvDBBLyDrp9nIfOg6d7zMDbw5cXV9+Lptd4dOKrfP7LMt748Cc8vby4re0EesUN4+4Fg8koKP7d4L9jtsPpShY5VAfPrryDR7u8WiUWobsUEb7RxAU05N/UVSU6Lt/iWrhZVBzX3aWlL+2haRoHsnfwxfbXeXTRKJ5aOo4Zm57hl30fsyF5CfnWnPMeW5aGNxrHoexdJbrg5K7UfI38Ui5OXlWoqkZidslzev8c/YWnl41jcINrmT74NzpF9z13+5rGT1tsPPqziVSTNwCa6sCyaSadQ99EH78ZRdWDTYOVZ88cc+YeRa+oNGvWlO7du1/ak6wAskjieYxudge/7v+Mp3q8AyevHt3f6QVsTiufbHuJjauX8kT3t4vdjjG01XXsN29D5/0BN3S+jx82u26FmLXCjt1iJKCwLjuP7iG2YQMUuwNFUYiPj+eBJ16kZae+VXaa/aXSNFeSWq/TVejKn6L8nBpwc80Vezu71QFp+RrpBRqB3gphfoos/HGJkvKTmLpyKnPGzMFDV7IhILvQ9SH3QqsRO51OPnn3JT7c/SJk6AiMnIWj405M+s8Js6xnVKsm7Np6L3s8zOSf4/igoCDat2/PX3/9JbcKC7dZHGY2Ji9h4ZGfifSry7R+X6I7Y0zVKTpGNL6REY1vLNo2aPhoAD646g6e+P0KEtTr2FnwFIGmSIbV6UHryc3xnaOARtF4PXnyZAYOGsLRbNf7UlVTYFU4mK7RIELDp4SLPAohag+LXeWjTd/z5foP2btlC9bNFrz8x+EcsQ+VXDQli9a+X/PL3QO5e/MnbLUEkp9/9qgtY7Y4n2DvMKb2+5JJK27hpX5fE+UX49ZxiqJwU+tH+OPAl9zxZ39uazuBNq264WcIKNpncq/3ueGXLoz5oAsLHt3Avs1LmDZtGgkJCWiaVjRm3z1+Gm079+KdjU8ypOF11WJRxAu5re2TPLfqDjpE9S7RBJP0Ag0/L02+j1eQE7kXn7j17uZJ3N/x+XOW9rA6LHy6/WV2ZWyiSUhresddzk2tH8Wu2kguOMYJ41H2Z21nzv4vKLDlEugVQlxAQwDMhUZ8kwLQ6zzQKXr0Og8MOk8ah7SiTUQ3/DwDzhHNhSmKwpM93mHi8lt4b8g8DHpPN45yX2K2a9HEki5OXtWknCwl5K4scxpvrH+cekFNeG/I/Av2a65J45UFRran6AHXbcP21K2EHprFdY/2ZaMxl/qF9Th4zIJjnwO75ewEdVCgP21b1OHNmTOrdN5REtTn0SysHYn5B7A4zMXeODz1XjzQ6UXWJP3NI4uuZlKvWdQLbFz0+EOdpjFj0zP4e7/NdR0e5cet2egI5qP1Bmzrt2Pr6CAtVIe2z4pmziI5PZc9E56jWbNmzJgxE1XvIDHvIMfyDpBhTmF00zsu6Y2kqtA0SMhSaRiuw8+rer/p1GaqqpFu1MgoKPuZ0iWhaa7bgfLMGt4GiAzQEexDtaojV5k0TePBvx7k3cvfxd/T3+3jrA6NE7muxS4vJDsznYkP38J6/WJwehHSaDbWJosweXxOiPUfHujdktHtDTS+7mtWLV/E1KlTz/oyMWXKFIYMGSJfdMVFqZrK8oR5/HX4exyqjS7R/bmv43PEBjYsUTsNwnW8fnUkj/00lxOGgeQ74adDyTgO68mPsqFuzQLNyYkTJ7hh3I206Dac6e99hLe3d7k9t9KwOTUOpavEhegI9pX3RiHEaaqqkVag8c66GSxb8wc73lqHatbwu+xpLH3no5KOgp7Bkav5ZGwL6oXqWbp0Kf/884+M2aLEovximNLrQ6asuI03Bv5IkFfoBfdPyDvIoqO/sCV1JXX969Muqifv//sCnnpPYgMaYXGY6FL3MjoG9aXg3XySrjpM6xti0G+BgtzMYm0lZ5vZ/eQUvAfncP3wmxje6IZyfrblL9QngqahbdmQvITuMYNKdOzxbJUmkTo8PeRzQXnKNV18RvCWlJWAQoc6vc96bFfGJmZumsgNrR7igU4vFnvMoPekcWhrGoe2LrY9z5pNSkECGiq52ZkEhoShoaFqTpyqA5tq5UDWDn7b/xmF9nzqBzVjXKuHiQmo7/bzCvOJ4trmd/PxtmlnxVVap+qlN47QoddVz9fnqVKl7tqXuZW3Nj7B+G5v0jys/QX33XnCyYvzjRTYXeU+NE3FtP4drmvnoNsbd/LDvveI9q9Hni2bLi07cHef23k/+/3zjtfZJtdFlKpKEtQXMLjBtSw++isjm9x01mO9YofRKLgVU9fcS73ApsQGNCAmoAF1/ONoGtqOr3e9Ta7pY7KtPnj5XYG/4xk8uz2JPn0uzubJeCTHo/mEoug8MBoL2bp3C5e/15IuHbsRH9SE+kHNiPKL5bHFo5nU633ig5pUSh+UBVVzrdTaKEInM7qqoVyTRkq+iq2KzRZ01VVTSfNwJapDfCVRfTEfb/mYy+pfRvPw5m4fk2lUScm7+IWJfbu38dCtV5LRIBl8/QmNmYu57peY9T8R5pjNc0O707exB6F+Cn7eOoYNG8awYcNQVRWz2YyPj498wRVucapOFh39hTkHPqN7zGCe7f0RgV7BpWqzYbiO2AOvYqz3OXn+t6P65uETfSOm6A/QH2+OmncczVaAURfG1p37eOSRh/nwww+r7AwEVXN92DfZFeoGVc0YhRAVK8fkugtqzbFlfDXvTdLfT4YoA4bb+2AK/xINMx5aA25ttoCnB0dQL9Q1S0unkzFbXLr4oCY83vV1nll2E28N/Bkfg99Z+1gcZqauvhcfgx9DG1zH7W2fRK87vbjI8oR5fL97JqOa3UmAZxCzVk8hqfth+ANM12bCQU/IBRQd6Awoei90QdHkd91D4b5ANiYe4n8fqlV2zC6JW9uM55nlN9Gt7sASfe9xqK5F6RqFy/el8uJUXWUQL8TqsPDR1qm8NeiXYtvtThsfbZ1KauFxXh8wm2DvMLfPG+QVSpBXKJqmkuVMJSy8zlmv9W51B8LJyUr7s7bx7uZJ+BkC+V+7p9xOVA9ucA0rEuexK2MTrSO6uB2fOyx2SMrRiA+rfq9NVXWty+SufZlbeWfT07w58KeLXrSbv9POrBVWNFzJaacxDeeqJ3jj6XsJbhHGzM2TuK3tE3y5403qBsQR6e/NjVfcyI1X3nje8TrcHwosapUtrVL936XL0dAG1/P3kR/P+3gd/zhmDp7LDa0eoGFICzLNqSw6+gsOzc6MwXMY2/p+OkU1wHn0BDme1+HUHyUo6jOIc1XMVwx+oPdCCzVjH3QYx5pQRvm4rpaNaHwjQxpcy9R+X/LaukdYmTi/Ap952XOqcDRTaghXJ6qqcTxbJSG76iWnz2R1uErJHEh31cIW53Yk5wjzDszj4W4Pu7W/U9VIyFLduk0NICwiCnNHI0p0CIGtXiQv9i7M+jmEa+8w48oR9G3sgV4HdQKLf/DQ6XT4+fnJF11xUTmWTL7dNYP7/h5GjiWD6YN/5/a2T5Q6OX3K2NHDMc2bhJ9lAh5qa0xxn+Dh3RstzoI+pCEe4c1RDP44nU727z/A+vXry+S85SmjwPV3/N8FnoUQtYfNoXE0UyUxWyW5IIVPd75Mm+zucJsH+lubYI/YDNjx0nrxdJcljB8YQf2wc4/JMmaLS9E8vAN3tn+Gp5aNo8CWBycTZUarxt60NO756xqaBNxIr/CZZOX24c9dKr/8a+fXrXZ+324nP28YI2J/ZUPicT7Y8jb9Wt/DtXWfhTbAMWCsDQK80Ie3xCO8BbqIeNQhSShHgmFvSLUZs90R6BVC28jurDr+V4mPLbRqpFykNrK4dGkF5140/kyfbHuJG1o9RIBnUNG2AlseE5ZeT9Owdkzr92WJktMlpSgKzcM78Gr/77i+xb3M3DSRtzc8gV11rzbFhG5vMXPTRKwOS5nHlmvWyDRWv3rpKfma22X/9mVt451NT/P6gB8umJy2OzVmLrPx3go72smUrfXYUupue4zZn80kum0872x6ipcu+5pPt72C2WHEoFeZ0HNCURsXGq/jQhQ8qugwLjOoL8DH4Ee0fzyHc/bQKKTlOffR6/TUC2xcrMzHKbe1ncC8r//G4j8Xz7Se5Ec9iafaFTx00DoY7XgqWogFrWUWukXxmM3w6aef0qNHz6I2ovximD54Dq+te5R9Wdu4q/2kanvV0+50zaSuzrdv1BYmm6vIf1WssXo+Fjscy1Lx81KoG6Tg6ymvsVNUTeXhBQ/z7uXvFqvLez4l/f2nFSbx8vYHaTfkWjZk7caoexUIpLnHct64qg1xIa5zRgYoGPTyexElsyJhMR//+yF6Tz0jGt/ErGF/nXNV69L68quvsOWn4vzjN/TX1kVHf2zhm1D6+aN9p6HovQDQvBzkNzzCs0vu4rUGH9I+qpdbf1eVJdesYc+EBmHI2CtE6eZxUAABAABJREFULZNpVEnN13CqYFftvLDqbiZ0f5tH8q/FkNscu8cRfBy3EMYkJg+NoGdDD+JDdXjIWC3KgMWukZyncSJX5URud/zsE7h69jVEK7PIN8ZhUg+Sb3iMAPvrzE5sCdgu0JoHcD8qtzAr9RNsPivxbzER8n0o1L2L9qAR3ZxGaIYU1K570G2pg5LiKmdnNBrP+o5dnd3Y6hGeWjaOvvVGlPjYjAINf6lHXeasjouXeDiQvYOkgiM82Pn0wojphclMWXk7D3aeSpuIrhUQ6WnNwtrx2oDvWXz0Nx5bNJrn+3xy0UVEg73DGNfqYT7493ke7fpqmceUnKfh66lVm+/xJSntsS9rG+9sfOqiyek8s8bUBVZ2JZ9O1jdTtlEvZDWPvPoLObYMpi2/j5f6fc3KxD+xqVZuaHkX2zIW0jSsqVuxeOgV4kJ1HM2sehcEJEF9Edc0v5Nf93/Kk93fLvGxmqZi3mNAn94YR/wBlN7+WP3Wgqbg6LUDrIWgOuFACHi4XhwpKSloWvFbkDz1Xkzp/QGz97zPs6vuYEqvD/A8+UW5unElETUayu1FVVamUSU5r2IXQSxLhVaNg+kaob4K0UGKfMkCpq+fzlXNrqJBSIOL7ptdqJGUq17095+fl4tOp2N95mI+3f4KVpsfSXnJOPWJ+NkfoUPYY7w40ouQkzVwvQ0Q4S+/C1FyOZYs7m05mab12pXb7bmappKSkoKiKKh5CSjzvHBelYFOC8MZcAzPQYOw7/oXrUUOeDlR9ofAzjA2nFjCR1un0SqiM6Ob3lHi+tcVpdCqcShDo0GY1J8UojawOzWO57jWjtA0jaOJ+/j2+AyGNLyOR/4ZS7rRhKa3EGZZSZRvM14Y6UWTSB11g+UCv7g0B9OdbE5wsGy3N2kWG4nZroXVi+uEoszkgOF+fLRbMRu+Icj2KXounBQ7kw5//B2PofEwNt0KLKG/YFDaYlc24bh+JQatA4H5f6M1zcTmvwn7iY2opoxzfseurvw9A2kY3JydGRsvKal5PFulWZRciCpLybkX/u7sVJ1M3/g0L/T9rGjbkdy9vLL2ISb3ep/4IPcSi+VhUIPRNAhuzjPLb+KhztNoG9n9gvtfFn8FyxL+YHvaOtpF9SjTWDTNVcKzSWTVn9Coaa7vzO7IMKXw1oYJFy3rkZyr8vScQtILXSWODHp4pL8ng5r3BHpishuZsuJ2JvWcha/Bn+92TadTdD8O523h8R6Plyj+QG+FcH+lRLWzK4IkqC+ieVh7Zu+Zxew9sxjT4v4SJVUtFguaBgoKSkIITv8MlPYGPH37Y9dtxk+dSGHBKyjpvqid0sDLie2QN1arFW/vs1d0HdvyflYfX8Bji69hWr8vCfEOL+NnWzGMVo2kHIgLrdpvOrXRiVy1yr1JXarsk4tURAUqhPtX/w+jl2pn2k7WHF/DL9f9ctF9U/JU0gsu/vtPSjjCA3eOxDggF88GPsT4jOJQ7no0pYAw61/0im/FM0M9i63GHBeik4tS4pJc3WwMew6nlOs5To3Xmp8NLT4fZ9xRyPRHFxGEphixNvkdD89OqKsN6ApciyN6RPhxe6un8Orozc6MDbz/7/PoFB23thlPk9A25RrvpbDY4XCmSoOw6r9SuhDi/PLMGkk5Kg4VCsx53D9jOHvMW7jpskd4fd3TGG2FeDp7E2L/gQZhBqaO9CIiQEeon0KYX+39vCRKZ/pSG79tcwDewPmTNh5afSK1z0n16kYT3ydoFxpNkI+eQB8FP0/w8lDw9ADPkyWonaqrfrLd6foOmW/RyDY6WLJ6Ox5eTQgMmImqyybH8y4culXY2Y4x8AV0AQHomzTFR7sBJdcXR0Y6+5PNNI85uwZ2dTS25QN88O8LtOlX8gS1Q4XjORoNwuWzQFkosFx8YcTZe2YxqME1RPhGA3AoZzdvrH+cV/t/R5hPVAVFen6NQlry9qBfeWHV3fSPv4oRjcddcP/x3d5g/JLrmDlkLj4evmUai6t8p0b9Kl6POtOoYXGjMopDdfDi6nt4pue7F0xO70lxMun3AsxOTwBCfRWeG+FJsyh9UTtTVt7OPR0mUz+4Ga+uexhF0XFX+/FM3/wInet2LvFziA5UMFrdex4VRRLUbniu98d8uv0VXl77IE/2mO72rcXe3t6cyseojXIg3IL6vQ7/vreQ12I1frr/4eXVi+x2V6BbGgk2PcbBh5l/9BuuaX7XOZM5veMuJ9IvhieWjmFKrw8q9WpbaWSbNDw9VKIC5YNwVaCqGok5Wo2r4exQXavU5picxIbUvkU6LQ4Ljy18jO9Gf3fB5LBrcQeNXDd+/9s2r+WhiVdQMCAPrNAkbSZbfd9DTxTB1iVc2Sac+/oYil31DvOXGVmi6nKoDlYkzyW797+oORpKQiC6pfVQ7Ho8GwzE+7IPyPYaiqPeQXxix2LfuwYARQEvLy8URaFtZHfaRnYnMf8QX+14izxrNre1nVDmi8iUls0BRzJVGoTXvvdDIWo6VdU4kaeRXajhVJ18u20Gny15hfzlORACX9edBbogfNRrCbTPoHM9DyYO88LPU8HXE2KD5T1BXLrmdYp/p/MxQHyojtgQhdhg1+z8ukE6ogIUZu/9jkj/18m3ZrPm+ChuazSB7jGD3D6XpnmwbPpLZGSlobXKRmtoxnCkF77OCZh6vQMOTwLVd3EqB3HoDmAP2YwlNIlb//wLHwNE+nsxoGFvBjQYSJOQNtVyAkVMQANsTivphclE+tUt8fH5Fo2sQlUuSpWSpl18YcTkggTWJy9m5uA/AMgyp/Haukd4rf/3hPpEVlCkFxfgGcRr/b/nlXUPYbTlMablfefdN9ArhFvbjGfW5ilM6P5WmceSd7IedVWdZGZ3aqS5MakL4L3NkxnR6EYaBrc47z6rDzt4ZYEJJ67ktCNjD7cPsNEsylWWSNM0Xlv3CEMaXE+HOr1JyDvI0mN/8O6Qufxx6IMSz54+RadTiAvRcSjj4ndPV5Sq+RuvYhRF4a72E+kRM5jHF19DjiXTzeN0REdHo8bmozXMQ7cyFp1qp2DVqyg2f6y6fzB4tyLUcz5q/wzwctIyYRj5thwmrbgVoy3/nO02DW3Ly/2+Ydqa+8+7T3WQmq+Ra6oifwm1mMOpcSizZi8waLLBwXSVtPzatVjYpCWTeLT7o0T5n//KvFPVOJypupWcXvD7bP43sz8F/fPghAE//9s44vsOXs6BhNrmck+vCB7oWzw5bdC7rs4KUdXkWrL4cseb3P/35eRYMmlxbAj65fXQHQ1GsetRC9OwHVmEbd1yAu3voqBi7vsjHo1cMxSio6PPulW4XmBjpvT+gCe6v8X8Q98yYcn17MncUknP8NzsTjiSoWKy1Z73QiFqOotd42CGSnahxs6Mjdw1dyDffPA2+fNyoLOCvkd90EXipQ4g0D6Tka09eXGkKznt6QH1w+QuJ1E6lzXx4KnBBib0NvLlzV78drcP06/zZsIgL8Z2NtC3sQeNI3SkmnZxIGcHVza+mZtbP8rrA2azMXkpjy4azcrE+Zjsxouey2gvwNDOiDriCJj16H6rg3PLHqzbf4eP7NiUP8lI6Y1t6zY8Ulrjb5tEsP1jQmw/4V34E7lpH/LTxkZMXPgVN/0xnLc2PEG+NbdC+qksXd/iXn7e9+ElH5+cq2F1yGeB0sgqvPDsU03TeHPDeCZ0exNFUTA7TExZcTvP9Hy3SiWnT9Hr9EzqOYtk4zE+237hGtO94y6n0G5ka+rqcoklOU/DbK+ar8+UPNfaDhezLGEuVqeFYY3GnHefeTusTPvLgvPk3GFrwgo65H5M324di/b5dPsrxAY2ZGjD6wB4ZtlNjGg8jnpBMRzO3UO/+v0u+bn4eipEBVSd8V9mUJfAgPpXExPQgKeXjePNgT8XW331fAaO68n21fPQ/qoHdguOvEQ8wpqi7rFR2Hw63gzH4NOSUOdc8gZcxeheVzCy3Wi2pq3hscWjeaHPZ9QNiD+r3Ui/utzf8XleWvsAL/f7utp+qDyeo+LpoZPZlZXE4XQlJ6vSbR3lRdNOXhQxa8SF1PzX3KLDi7A4LIxsOvK8+7j7+9c0jS8+eJ2ZOydBU+CEP16tW+LQZeHreJQQbufJYZ70bnz2kBITXPVriInaw+60ser4Av4+MhtVUxnZ+CZubv0Yep2e+Ns78MyBiRQWFqJZ81CNKaDaseybg6/fveg7x6KooVgH/UGgMpw777zjvOeJ8ovl6R4zSDUe5/Mdr/Ptrhk82/sjvD3OLt9VGRyqayZ1w/Ca/14oRE2Xa3LVwcyz5DNj0zNkZqZy+L095LXKhlbe+AaPxuzzF97qSILt73NnLwOj23ugKAo6xTXLVRYwFqXVJkZP4whYu9NBWKByzu+mdtXOWxueYGq/L4oeD/QK5uEuL5NWeIKlx+bwx8HbsTtttInoSqBXKBoaoGFxmDmQvYM8azb+hkB69+7NvDfBVGDGWZCIZslG8QxA5xuPusqO1iuBAvsL6P+uR0BIPUbd/xoJjnrsSVHR4YencxDGrEEYgXW5q1iTeB1jWl3Ptc3/h16nr4QeLLlOdfry6bZXsDoseHl4l/h49WS938YRcoHqUjhVjbSz6qwXt+DIbFpFdCY+qCmqpvLCqru5re2EC86mrWyKovBol1f5dPsrzNj0DA93fvm8r4/Hur7G+CXXlkupj6J61BE6dFXou2ShVSPHjUmWx/MP8/PeD5kx+PdzPq5pGl+vLeSHrTo4OeHFvGs21zc9wf2PfVTU59/smk6hLZ87u7wCwNwDX5Nvy+ahzi/xze4pjO8xvtTPKTJAIc+iYb7QOrUVRGZQl1CzsHY82GkqU1bchl29cFZnY/JSVtl/pV3acDwwoBYkg8OMWpiG7lAAdvs27Dn7ATD4tyHM6yv+KfwBgA5RvXi+z6c8t+oOUo3Hz9l+hzq9aR7Wnh/3flAOz7RiqBokZKs4nFXz6lhNVpuS02ey2OFQRs2eTZ1lyuLVNa/yxpA3zruP3c3fv8Ph4KVJDzJzzySoB2j+BLS6Gx/1IXRaJHUNt/Pq1V7nTE4H+SgE+VSdDxSidtuXtY17/x5KamEiT/eYwZsDf+Sy+CuKvoj26NGD5s2boVdUnHmJAKimTDSbEdOWj/A8PgQUB57qZRQM/JsCz4t/GazjH8fEnu9yRZObmbziNhyqo9yfp7ucJ5PUMpNaiOrp1K3lCdkqdqfKc6vuIDy7Dqt+/ou8TtnodtfHr+XNmIPn4eW8hkjtfaYM9+SaDoaiL7614YK9qDq+3PEG/2fvvMOjqLo4/M729N5DAknoEHrvvSoCYgEEFRtFpKMIgtIUEEEsCCpFQEFRQUF67y30TiAhlfS6feb7YyEYk0BCNkW/vM/Do9mZuTOze+eeueee8ztPVR2So8P7d7zs/Hix9ig+7fQzn3b6mca+7fFzqEyAYzABjlWp49GEyS0W8WW3P/mk4zom9JxNzWo1kYlaJF0yAJIhAzH1NsKJTIRYe3DPRux+G39fGSN7VWFhfw2rhmgY1ESBm93Dfp+Z2Yas+I18d1RL/597cjXhZql+L0+KIAh0D36Bv8J/fOI2sg0Uqv5MBXm5lyFhekQUbYoukU3XVzCkrsWBuOTU+zT360xT346ld5FPyAMFAS87f+YcGYVZNOe7n6PaOUfqoyTQGSE6rfz0z8IWRtSaspl1eATTWi9FKVfl2S5KEp/tSLU4px8cc2oJE7uoGTluWo6N/unyV8Rn3uWdJnMRBIF0XQqfnpjA5102Y5BSuJ58kfaV2xf7vgRBIMBFRnlYp6pwUD8BdT2b8XTVl5l9eESBDq5fri7n12vfs7DzRr5c+A0N61bFQWP5xcXMOKR4CcwSaRdft0RpAYK6DadvKtl1bRsAfg6Vmd56GdMOvEJ8VnS+5xlSZxyn4w5wMeFkid1vSWMwwZ1k6T/rLCyP/L86px/wIJr6ZoL4n0xtm7xrMh93+hhbZf4r2XqTxK2Ex//+2uwsxr7ej1+yvwF/EEyeuLn+gFp8mWzFCmrZfcyiARpq+eSNNFHIKvQsKyg/xGREsPD4RBZ23sjA2m/nm1YpCDIWL/6cJrV8cXR4WETJnBYBkgn99j1Iej0IOlRiNyaf6c603wqn+dbCrwvdgp5j5qG3EKXCVfwuDSqc1BVU8O/ELEqEJ0ok3HcsLTk1lcTIeL4+/iGmZCOKox1QPhuKVrMdpbk1QaqFLOinoWXQw8Vkb0cBZ9sKO11B6RCXeZfLiafpGfziY/dVylU08GpF60o9aOnfjZb+XWns0y5XgTFBkPH555/TrHYlHB0dcx0v6VMRtroiUwo4i65cCdjC1JmvIEkSXo4yXmqmYvVQDR/0VNGgksUdIqBApn2NrMQlvLzpDb49EvevsI09gl7gr1s/FWseHZ9RfqUUyitGs0Ri5qO/s0+PT2RU41koZUr23Pkdo9nAM9VeLrVrtAYv1BpJfa+WTDtgyWzID4vUR0aJSX0kZ5UfWdjHSbpw34k998jbvFpvEj72AXm2m8wSM35NYMcNdc5nhqOz+fTN5vTuPyjns41Xv+VO2jXG35eHESWR0Tv70DGwL0EuNfnl6qIn1p7OD41SwLscyHJWOKifkI6V+1DdtR7fhM3M9blJNDH/2Djis+4yp/1qbBS2aDQadm5aw/r162nZsiV+fn542ZhQ6+1xrJ/GmOYpyA2WlV872SdM3f0hGVlaAPwdg5jWeilT9w8lITs2z3UIgsC0Vl+z+OR7JGvvldLdW58svURUavkYeP7rPNAc/n91Tv+dbANcjxdJzCw/DqPicj7+PKIk0sQv/+JsBpNEeKKIvoBATrNoJk2fTEJ2LAu+mMBBr63gBUpdfRzcp6KQWpGufIfmrktY9KwzPk75mxF/FxmKipThCsoB6foUPjz0OtPbLH9k9WwAf3cb9u/ckste+3p74GknUr9OXVr4BuAvvYIoROJk/JatcdvouNqfg5FbH3sdXar0J9SzOQuPTyxXC7JmEW5XOKn/LzCYJLINEhk6S3pqUpZIUpZFtzglWyJdZ9luMFUEDZRndEaJG/dE0nVmdt7eyKt/tufPa2s49+sR+BE0zgORDdJgkl9BwJ6GTiv5/Dkbqno+tNeudkJFofIKSpWVFxbwev0pVpWSCPCwYd/u7blttq8vfn5+NK1ehberzSXVPQLzbhPb9D/xwbKH0lxymUDLIAVz+2j44nkN7arKkQkgl/yx089h2cVXGLI6hV/OGMt1MItaoaGeZ3NOxe574jYkySK7WTHuF564dAnxEV/Xrtu/4mnnS12PpkSlh/PL1WW8c1+i4d9Gr5BBdAsawOS9A9GasvPdZ2zTeXx1ZkaB24tLVKqIoYyfw8JIugCsv/I1VZyq08y3U55tBpPErL/0nIi1B0ASTXBkKss+GEjjFg91pP+48QNXks4wqflnOWPmsrBZpOmTGd9sPgYpmcuJ5+hQuYNV79HTQYZt3oDvUqVCg7oYvFh7FItPvsfbO55GJsiQJAm9WUufaq/kWh12tROwU8vo3r073bt3RxRFtFotC45/zc9X1tOxdW1cXOOYsSMZmcYNpTCAAcvn8ufbM1DIZQQ4hjC11de8v28IH3dYmyfyy1HtwuQWi5m8dyAjGn1IA69WZfBtFJ/kLAkbZfmt1vpfQJIk7iQ9fuXv/wlRguhUiUy9iL+z8K93qs7YN4PF3Rfnu81otjinDQU4p+9lxTDj4Gu42nghSiJnKh3AUe+GydAFwdsLG9NQ0pWjaePzNh/3rFagZqWrbYW0RwXlA71Jx5R9Qxjb5BP8HCo/cl+N0lLQUyYT8thrGxsbIpLh6N2TbLr2E9knppJkPxNXw58Yo//gw4Nv0sCnFfM6/PjIyXf/Gq+x4tw8vj07h9cbvF8Cd/xkmO47qYM8ZNgoK57d/wJao0SmTkJrBJ1JQm/kEZPp/DcoZBKZqTLM9iK2KgFblSXCpoKyI10nEZkskqJN5cNDrxPgGIJcULDlxRtMyhrMwfaXER0SEYlBJnnS2WcV07u75vrdHDVCRYZTBaVKQnYs97KiqeXeyGptapTg5WDRus7PZstkMm4nilz4dj97ev8O38AfXVeT/VMm85//CdnfihyHeMh4r5uaoc1FNpw2suNKKKJpOFHyUSw/soxN50282lJJ+6rycqnV/GyNN1l4YhJNfJ/cWaW9L/XhVQ4iKMs7OuOjNYiTtPH8fHUpS7r+gcGsZ/aRkUxrlb/Uw7+FdgFPYat0YMLu55jdbhXOGrdc2x9IfXx9Zgbjms6z+vnNIkSmSIR4lF3/TMh8tKQLwNn4I4TFHeLjDmvzbNMaJD7cqudslKURyazH7syHLF04CU9v35z9dt3+leMxe/io7Xc549SByC2ciTtI/xqvY69yZO2VDxjXfFyJjEeVXGTcuCc+cgGmJKnwBBaTd5rMZUnXzSzu8jufd93ENz125HJOywTyhMrLZDLs7Ox4plYXHNXOHLr7Fy1CqzCqsUXr0tY8jGTbI7z55UHE+yuZgU5VmdRiEe/te4l0fUqe6whxqc1nnX/l5yvfsOL8/HKVQlwUYtIksvQVq7clRVSKRGbF95svaVqJ6/fEf/X3syt8F9XdqlPJqVKebSbzoyOnz8UfZer+oUxusYghdceSmB3Dxx3+pHLQ94jeBuxM75GmfIN2lRuzoPdTBTqnVQrwrZj4VlDGmEUzJ2P3MXHP8wysPYoa7g0eub8gWF7I/lmE5YG9lslkuNoJ1HJvRETGBb58sQFOmdNIU72OyqEv6ivLCE++ypt/dSUxO+6R53ql3iTS9Mn8fn2lVe7VWjxwUpfnSLEKCkaSLCmwkckil2PNXI8XiUmzTKK1hkc5pwvGaJbINAgkZkrcTRG5Fi9yKcbM7USRhAyxIiW8lEnMFLmTJHIt8SLjdz/LgBpvcTnxNDPafMv8o+8RaV8DJ81STMJlVGJ73qyzhdm9A3M5p21UEOiafwG7CiooKVadX8DQusUv5PV3/JzzFvb7u80G8HIUWDBoA+6uPtAe2Ar7NmzmjY1dyDJk5GnT10nGmI5qlg/S0D2kG2qxDZmKGSRkSnyyw8DYX/Rcictfi7cs8bTzRZTM+WZbF4UKqY/CEZcuUVCwuSRJfHz0HcY3W4BKruazE5N5odYIfB0CS/syrU4Tn/aMbjybiXueJyLtRp7trSv1IFmbwJXEMyVy/iy9xL2MsvFxGc0PJbUKIjE7jq9OT2da66V5xqYMncR7mx46pzVKGNEgnh++nJnLOX0kajvbwtczvc2yHOf05cTT/HT5K8ySiQE13sQg3eNywjk6VikZLXONUijThaoKB3UJ4+kgFOjICfUKRRDM7Lz9CwBPta/PgEqXwCRiZxrNVdVW3lt5ISfdJsSlNmOazOW9fYPzNar2Kkdmt1uFndKBSXteKFAnqDwj3S+aaKwommh14tJFksuJflN5xWiGWwkisWn/vjQ3URKZd3ge77Z+N882sygRnlSwrMtv177nh4uLeF4zgou3T/HFqWmMqLeWqX8d4VrWcuyNM0hXD2ZYw8F83G34Iye2/s4y5OWo0nIF/1/cTL7IJ0fHMnJ7L8LiDjG+2QJa+nd77HEe9sJjC4U5qEEmE2ju15kY0xHm9KyJg3Y6qaphGL1D4dJ4sowZvLt3MHvu5F+x+wHjms3nRMweDt39q8j3WJIYzRYndYUN/veQbZCIThW5HGsplpeSLWEsQf+JSbRE8cakSVyPtzjD76aIpGkrZEFKkpg0kehUiW23fmbxqSkMCR7Hgj3jeavBB8w6PInjN+uQktqWFHVfXMwzmdlhAa+1tEf2N3utVkAVt7wLcRVUUJIka+8RlRFOPa8WVmvT1VbAXv34fmyrEnCxlfPzS2dRNleBB5hPmri6PIwRf/YkJiMi3+P8nGVM7qpm5YA38XDMRi/bD8DVeJGxv+hZsEtPqrZ8jXd9q7/K79dXFKuNCqmPx5NtkEh7xG+/+cZqaro1oJprKLtu/4pSrqJdwFOleo0lSXW3esxqt4o5R0blqzk9ruknLD45pcSKgselS2UiSfc4SRej2cD0g68xucVi7FW5NfETM828uSKWq/EW57S9Gub2UdOnbQ3s7B1y9guLO8T6K18zu90qlDIlAJHpN1l04l2a+3XmqapD0Sg1LAubwfR200t0odkyLyqx5h9JhYO6BFHILD9uQQiCQIBTJbKMGaTrUwF4bUAH2qj2oDJ1wCzc5lS2nk83heccU9O9Ia/Xf5/39g1GZ9Lm2+ZzNd+iXcBT/HT5yxK6s5LFaIaIiqKJViU5q3CaSRVYuJchcStRKnOtq6Kw7sI6elXthZPGKdfnD2RdtPmsV4mSyMITk4jOuE3Le12ZOPp5Zv75Fl3cl/D21umk6G9hb3wPrc0o5nScz+tNejzyGjwdhJxisBVUUJpIksTai5+zNOwj+lcfxtIe23ijwVQCnao+9liNMm+mU37IZAIOaoFewYPYcmsdTWp4MaaJH/b6qaSqXiXDrSvCrd74O1bhYuJJ3t07mPisqPzbEmRMb72M9Ve+5lLCqSe655JCb7IUTjRVOKnLNVl6S7HbG/dEEguRdlpSGM2Wd4w7SSKXYkUikyuc1dZEkiQikkSuxMfy3r6XuJ58ji6u/Zmw/jluLb3EBzsmEh75Kgn6Q6SphlBJWMuSPgPpVD23iqNSDlXcZQUGzVRQQUnxw8XPGFxnjNXaU8jAx6nw/djTQcDFxo0pbb9A9aoGVJB5OZ3E7+KZtu8VzsYfKfDYKu4y1jz7MV7en+Lv8vBFetdVM6+t0bLlojEn27msaebbiVOx+zCLxVuh1BosUgYV5E9sWsHfTUxGBDtub2Bo3QlkGtLZcOVr3m48u1SvrzTwsvPjs84bWXvpc7bczC1l4WrjSY/gF1l7KX+5yeIiSRCZLCKWov7E4yRdAOYdG8ezNd4g2KVWrs+jk/UMWxZJstkZABcbmNdXQ01vea79riefZ/nZOcxtvwa1QgP3I7I/OvQmU1t9zaG7W+kdMph47UWMor7AWlPWQhAES2ZpGbwyVDioSxCv+1qWj6JHSGd87APYE/FbzmfvD+9HtbSfcTB+QoZyCjujXPnhwMMCiPW9WjKozmhmHHytwAlAr5BBHIvZRZI23op3VHpk6SWiH2EAKig82QaJqNR/p+RLWZKlt0h+PGqVvLygM+n4Pux7hjcZnmdbZHL+si46k5Yp+4bga1+Z+KNRfLT0TcT+ImJiG2aeegOZ0RLpItjPZ1WfdXQMrvvIa7BTl4/KvxX8/5GqS2LSXou01vyO6wlxrVPoYx9IexQ2CsFBY0ml1ZmySden8FTLYF4IdMbONJIsxackqsZx/YaWGm4NeK3eu8w8NJzVFxZiFPOmL6gVGma3W83np6aw4vx8ph94jVHbn+LNv7qy4Nj4Ms2C0hkhPKl0JwAVFI4HjumbCeVPksosQkr2Q2d1VKpYIdtWDMyi5bdeee57ph14haF1xiMlS8z8eTj6wzqEpwPJyHyXBNkoTMItmtqf47vnulDLJ/fEVyGDIHcZakWFja6gdEnVJXEj+SKNfdoVYu/C4e1UtHoxtipLXZS+NV+lhn99bIbagwDR58OR/6Jg9YXP2HZrfYHH26scGdZgOI1qLmFEWyX2asvnmXpYss/I2F/0hCeW/TxLJsho4deVw1Hbit1WfLpUIfeVD5n6gqUyzaKZOUdGMan5IuQyOcvOzuKVepNyImH/a9gq7fm4wzpupV5m9uGRuQInn646hDNxB4lKD39kG0+K3kSp+oli0wqWdAH45epy3Gw86RD4dK7Pr0el88aKOPQqLwDMaZG8XvM6Qe65XbAJ2bHMPzaO2e1XY6u0FE/MNKTz/v6hTGn5BfsiN9O/xhsoZXKWnJzOzA4zS+I281BWUh8VDuoSQqUAN7vH/6BdQ7qgNaezN2JTzmeCILDw3ZdwC/8TW9MwMhVzWXvenr3XHqZKNPPtRAOv1nx//pN825UJMoY3nMGXp6db6Y5Kn6RMieSsCuNYHExmiYhk8ZGDagUFYxbhTpJIdGr5TndbcnwJbzZ6E9U/im/EpIn5piCm6BIZs/MZVDI13+/6hD8O/gAt5Cjsm6Kq0Rq1uStaxUoa+bRgy6CfCXH3fuT5FbIKTcsKSp+E7Fh+vfYdk/a8wCuhExlUZ3SR+2BhpD3+zoPin92qPMeO2z8D8MbT9WildMUku4KZBBIzF7EybDlqhQ1Lum7GRePB29ufIkWXmKc9R7Uzc9r/QG33RoxuMpslXTfzTY8dNPRuw9s7nuZu+q0i3Y810RrgTkU2U7nBaLboS5dHx3R+mEXLe9zNBJGrcWbuZVRE5RcFk1niZoKZ8buGkWFIY2rLr5m7421WrV2AMVlC1bEbDg7TSVO9hp3pbfoH7mDxAA88HHJP7eQyCPKQVRS3rKBMWHfpcwbVGW219uzUAm52RXdfeDpY+v8HbZdSuVY11E9ZIhRP7NlDH2EIJ2P3sf7y1wUe37lyP24kn6Ne4B2WD7KhU/WHi0DX4kXe3qDjh+MGDGU8xj1ddSibb6wqdjuiZKlbVEFu4h6Rjfz9uY/pUuVZAp2qcjv1KnGZd2nh16VUr6+0UcgUjG48mw6BT/POzme4k3oN7vuyxjdbwILjE0rsHTI569FSK9YiSy+Rriv4PGfjj3AiZg9vNJia6/NT1+J5Z306ZrUHAOaUW4yse4eOLevn2k9ryuaDA68ypeUXuGjcAUjXpzBpzwuMbPQhXrb+HI7aTpcq/bmQtI363qH51poqKR7Me0qTCgd1CeHlULiILG97b7IMSdgo7HMVNlAqlSydNhDbC/cQhQQMsqMs2G3gxJ2HaTvP1XyLqPTwAjUs63g0QZJELieettJdlT7RqRVFeJ4USZKISJYwlIwE1P8ViZkSNxLKZ/GwpOwkdt/ezXO1n8v1eWKmmG8xh8sJp3lpc0sydOlc//089+ZHI0v1ReXbBTebnYjEIwkpvNNgE4uffrpQEVcBrhVpwxWUDjqTlqVnPmLk9l4sPjkFO6UDi7r8Ri33RkVuq7DSHn9HKRewUUH7wKfZF/FHzuczX2tFYGYXspQLkclsyUxYwpS9b5FtzOSpqi8xvtl83t07KEfO6++42XjR1LcjbjZeOe8NHSs/wwetv+Hjo++wPXxDke/NWmToJCKTy9+49/9GYqalQOHjUkxLC5NoKlC+Jj/0JksE0uU4kYikf4eDvSwxmCyO/R8vriDAMQSVTM3bvzzNucVHMVfSYFd9PHJndzKUM3DR/8zbTUcwuasqj72W34+ctqlwTldQBmQa0rmQcILmvp2t0p4ggN8TFuF+EEUd4lqHOj5NaNGpC8ouKuZ8/gPtOj3F1FZfEZcVyTdhM/N1qAmCwLim8/jsxCScbWBiFzXz+qoJcLFcj1mEtSdNjFqvK9Miiq42HqjlNgVqaxeFTL1EUlbZR4aXFzJ0UoEZQefvHeN22jWerjoESZL4/OQU+lR7mV+vfcf35z5hWdgsvjz9AUtOTeWny19x8O5WwlOvoDfpSv0+SoKW/t34qO33fHpiIptvrEaSJAIcQ2jo3Zpfri4rsfNGpZR8zZTYRyxK3E2/xVenp/NB629yChoCbD92g6lbJCS1KwBi0hU+6KTlmae75zpelEQ+PPg6r4ROpIpzDbiv2T9xz/OMajyTUM/mrLv0OQNrj0IQzHx/9jMmtZpUYvdaXqhwUJcAGiW4FiJ6+gH1vEMJ9WyaZxLq7OLGutmD6R8yj0zFbIxiBrP+0nMxxmL4BEHg3Rafs/bS50Sm38y37ZGNPuLL09P/tRFQogQRSSLmijTjIhOXXnAaUgVFR2uAG/dEUsuJg+ABsw7MYkqbKbkWxNJ1EtGpua8zTZ/M5D2DGLG9J89VGY5pmZGrv4ShrNkWeb/KOMuWo5f9gVIOn3abwaAmDoVaZPNyrNCdrqB0sFRGH02QSy0+77KZWe1W0C3ouZx0uKLi71x4aY+/46QR0Chs8LD1zYlwFgSB1SPfwtE2AjMxGIz+ZCS9y5R9ryNKIlVd6/J241m8t28Q2cbMQp3H1yGQRV1+41j0bnbcL6ZcFqRqLUX4Kih9jGaLozI6VcJcyj+BzqRFb9LlvD+KkkhY3CHmHhnN6B1P8+Xp6Qzf1oPvzn1c6Eh/SbL0p1sJItfizSRmVsjI/BOd0fKbX0+6zo7bv3Dh3nF2b/uN8I8uI/TwRO3bHZPjJZRiCxyFp5nXuw3PNVLmGcseOKeLkiFSQQXW5JeryxhQ402rZdd52AvFWmx5kKr+VsMPEO1FXhw2ivhgy0KbIAi802QuarkN846NQ5TyDrj+jkHU8Xg4Xw/1k/PFCxoGNlEgv+9RiUyWGL9Rz/dHyi6aun+N1/j12rdWaSs2Taoomnyf2PT8jXCmIZ3PT73P6MZz2HhtOUP/aM3NlEscj96Fo8qFBt6taenfjc6V+9O1ygD8HCoTnXGH3699z7v7BjFq+1N8fnIKh6O2kaSNz7fv/RvwsvNjUeffiM+K4r19L5GqS2JwnTEcjd7J1aSzJXJOkwh3SzDS/1GLEtEZd/jo0JvMbLciV1HENVvPsPCoKzz4LPEcnz3vQJuWTfO08cWpqTTz7URT344AxGdFMXnvQCY2/4xa7o1I16dw9t4R2lbqzY473zIodBD2qieb8/ybUBRinwqKiI9T0fz+vap1Y8vVE5yI2c/gOu/k2ubs6srYDhLRWVM4GjsRwfg10//UM6+vhmAPGWqFhumtlzH94DA+6/xrnom6u603TXza81f4T/QMftEq91fa6E0Wg1/FveIlu7Ck6yTu5RM9a00kSeJedgwp2nu42njhYuPxn9XZeoBZhIhkkUyDgJ9T2UtahKeEE50RTdvAtjmf6YyWNPAHiJLIN2dm8lf4TwQ6VeOrhtsY/2p/YqPvouk+BHOT2zgaP0MUkhFtVrLy6d8JcpMXcMbcOGoEvB0r1jkrKB02XFlKgGMIXas8W+y2XO0E7NRP9vw62gjEpUv0ChnInzfXMLyhRUpLo1Yxp/MUpu5ciJi+AF12CyLjrvLFyZmMbjqdOh5NeK3ee7y7dxDzOv6ERmHz2HMpZUqmtPqCibufx8euEnU9mz3RNReXxEwJhUzEq+J5LzXSdRJ3k8USKX6YZchgadiH3Ey5hEpuEVQVEJCQcv5fJdcgSSKZ2jSUKjWiJFLbozEv1BqRE+ljEk2cjtvPivPzMYlGprVeWuj3AJ0RolMl4tIlXO0E3O0EVP/nGslao0R4gojWaODDg29gEk2YD9hyYsM+5COrIbgosTENQG3ojs6uH9/0/pHqHnntteK+rEdF5HQFZYXepONI1HaG9BhnlfZUCvByKF5/tlEK99PVvQhxrUMzn44cid7BuktLGFj7bQCG1h3P79dXMPPQW0xt9TVyWe7na2jd8YzY3pMOgX1QKzSo5AJDmqloHazgs90GbiSIiBJsOGPiRISZiZ3VBHuUrt1s4NWab8JmYhSNxZ6XmUWISZUIdPv/HkvStPkXmweYduAVnFRuzDo8nK5VBqCUq9nQLwwbhW2++1d3q5frb7No5kbyeU7FHWDn7Y2k6hKRsMirPWqeKSDDz6EyVZxrUNmpOtXd6mOvdCjejRYTuUzO6/WncCnhlEV+r94kprb6msl7X2RR59+wU1n/+jJ0EgkZYh55K2sQV8CiRGxmJDMOvsZHbb/Hy84/5/OFa/axPakBgtLy3CkSTrL0zer4++aVyvzu3Mco5Cr6Vn8VgIi0G8w6PJwPWn9DJcdgAFZeWMCQuuNI0t9hb8QW/nzxT6vfY3mkwkFtZezUAo5FjCZsHdCaTw4txsc+kMj0mwQ4huTaLpcJzOvViZd/ucidlBkIhg9597csFj1nh5+zDG/7Sgyr9y4LT0xiaquv8rQ/qPZoRmzvSfuAp5440qyssThcRTxLYPD5r2E0Wya11kKSJBK1cURn3CYmM4LItBtcTz6P3qzD09YPD1tvknUJJGnjMZoNeNtX4s0G0/Cw9Sn0ObKNmdxMuUgV55o4qJysdu0lRVKmRLZBItC1bIsOfbD3Az7q8FHO3yazpTjVg0g7o9nAlH1DiM68zZC643i2xuusXLqAuKRMbF8bgdknEhfDGgRskZyeZ0WvZQS5agp1bo3SojtdQQWlwYXkE5y9d4S57dcUuy2lHHydnrzv2igFVApo6N2G5Wdn55pENPRpSjWfhSRwl6T0SmjTX2FT2JtUd/+NbkF9aeDdGr1Zx7t7BzK73epCvawrZUpmtlvB+F3PMqPNt/g6BD7xtReHuHQJhVx8Iv3PCgqPJEnEpkv5SjRZo+3t4Rv49dq3vFpvMuObLXjM/iJJ9+Jw8/RGEPL+7gqZgma+nWjm24ndd35jyr6XmN1uVY7TuzCYRUjIkEjMlHDSCHg4FE0X/r9CtkEiPNFiv2cdHkGqPpkJVRcw/sMRyCdWR1Db4Gr4BRnuBPqup2VIL6p7uOVpRym3RE5XaE5XUJb8eXMNvUIG50p7Lw5+zjJksuL3aS9HgTStxJA643h//1CWdNnM/OPj+PHSFzhcdWbThpV8uXoLKrmaGYdeZ3rrZShkD90lSrmKZ6q9ysZry3Oc2mB55hYNUPPzGRNrThgxiXAnSeKdn3UMbqpkQEMFcitcf2EQBIEOgX3Yc+d3ugUNKHZ7qVoJF51UZP/Gf4n8HJV6k45RO54iXZ/CR22/o7pbPVaeX8CAGm8W6JzOD7lMTg33BtRwb1CkazKJJqIzbnMn7SqXEk+x7tIS1HINbd170cX9WRTysntXq+3RmMVdfmfhiUmcjT/CGw2mMffoaGa2/b5Egrti0yXsNZJVF2XTtBLZ+SxKxGdF8cGBV5nR5lt87ANyPv81zMCOlMYI9793h+RjfDehGY4Odnna+O7cx+hM2Yxo+CEAYXGHWBo2k1ntVuFl5wf3pT5uJl9kRIMPef9gP77q9WWeBbP/KhUOaitTVD1LAI1Cg1Iup21AT3bf+Y1XQifm2UcpFxgd2Iyxkd+S5bAYDO/w7m9aFg6wwcNeRlPfjhy8u5UDkX/SNqD3P45V8UroRJaFzWJM04+LdX9lSVy6hK1Kwv4Jo97+X7ibIlkl4ioi7Qa/XP2G68kX8LWvjJ9DZfwcqtCqUndeDp1YYPTfpYRTzDz0FrXcGzG07nhslA8HZlESiUq/RVjcIWKj73Ir9TJaYxY2SjsqOQSx4co3zGq3ovgXXwpoDXA9XsTfRYaLben3yRPRJ3BUO1LD3RLJ9kBzXH9fczzLkMGoHb3JNKQzo81yans0BqBOryHYSZeQBBucDauRC3K8/SYxpMFogl0rF+rcSjlUcbPOZKGCCh5HXGYMq68tZEnPP6zyYuvjJCv2RNFRI2Awyajr0Yxz945S36tlzrYRjaaw/PR8khM+QlI7ozAu4bP9rxCZfpVXQifS3K8zaoUN43cPYFa7lbjbProIKYCDyonpbZbz4aHX+bTTL7nSCUuT6FQJpfz/e6JakphFyzie8YiCPEXhQabTtaQwriad5Wz8ERr5tOWLbn8WyYlcGDpV7otKrmbSnheZ22FNkSbo/E3+I1UrYasCD3sZzmVgW8uCLL3E7fuLyyvOzeNo9E5+euYU+27dwDDchNJcHSfDKhQygZdbmNgZu46BtbfkaUetgCruZbtwXkEFZtHE9tsb+KrbVqu052xT9OCvgrBRCjjbCIAbtT0acyxmJ5Oaf8brSztz6uR+OAqfTB/DtI+/RiFT8sGBV/mw7Xe5IpG7Bz3PiO096Ft9WK5xTi4TeKGxkqaV5czfqed2kmU+tvKYkVORZiZ1UZVaoFWvkMG8v2+IVRzU3K8JZe/5//nen5otoTPm/iw89QpT9r6EncqRH585gUyQka5P5Vj0Lr7q/mT9XpIkRAlkAoV611XIFAQ6VSXQqSrtAOpCfOZd1p/7mt+2f0/Hyn0ZUOPNMnNq2ijteL/Vl/xydRkbry4n0DGE365/T7/qw6x+LkmCyGSRqh7W66P5LUqEp15h7pG3+aD1N/g5WObMoiTx3REjG8NMcH9BzjvrOMvea4tKmdfV+nfntCAI/HXrJ3bd2chnnTfmCiRdfnYur9abzMbrixhQqx9BLkFWua9/AxUOaitirxae2HnaoXIbBEHOqdh9+TqoAUJDQ/H56DrRXXRka1aRkDWUKb/rmd9fg7ONwKhGs3h7x1PU8WiGq41HrmNb+ndj0/VV3E69mpOa+W9Duq9HXc2roiBbQSRmisWe2IbFHeL7s/OQ6eRUyw6lVkRDkhMTOJ98jIOqrTwz7hVkyKjmGopSrmLbpvVs2/QTrh5euLl74uMfyKtVJxNnc5cJe55DKVMB5KQO+9gH4iOvRIvALgypOy7XYPzJ0TGciTtIQ+82xfwmSgfxvkHM1An4OQul9uImSRIf7f+I757+LuezmLSHmuPxmdG89ldH/B2CWdL1D+xVjtxNu8VH+z/jQlwENtJw1Ob22GkSqFRpLjU8fGkf2PsRZ3yITIDKbrL/+1TsCkqPD/aPY3Td2dhZIXXRUSNYZUHJyUYgMVOid8hg1l76PJeDupprKDZqiRD5J1w3TkNQ2iJlreFC+CrejnuK8c0W0MCrFZNbLOK9fYN5v+WXVHau/thz+jlUZmSjj3hv32Dmtl9TJk7qB3Y42KNC29baGM2WCNp/ToT/TljcIf68uZbnar6VkyZsMpmIirhF2JXD/BK1jGTDPYwGI0ajgcrB1Qj2rU1113o08+1Eb7+XmDn2LW57XMXV3RMPL1+CQmoQVK0W3r6Vir0A1KZSTzRyGybufp7nar6FRmGDWm6DndKBYJfahW4/22CR04pLB3d7AVfb0rOvpU2mXuJ2okUWYOmZj/jl6jKW99jLK5sGcTfzIg6GWWjE5/GwkzGlu4rD8Yt4vtYIlHJVrnbs1AKVXQUUFe/HFZQxB+K20j7g6Tx99EmQy8D3CQsjFoSno0CqVmJw7TG8u3cgLfy6Mr71fF7a1RLTMyY2/rScmnUb8uyg11HIlEzdN5RZ7Vbm3I9cJmdAjbdYf/krXg6dkKf9IHcZnz+nYe0JIxvOmBAluBgjMvxHHaM7qGhXteTdLw4qJzzt/LiVcplgl1rFbs9ggrgMqVjZZ/9W4jNyOyp/u/Y9W2+tw07lyJfdtuRkCay6L8dQUNZApl7ixj0z16PSuH43mbtJRlJ0CgySCqOgQVLYIQkPnckCIBP1yEyZqAQ9tgoTbnYyAj3tCKnkip+rigAXATe7h5KTnnZ+DAwZhYuHJ79dX8HI7b0Y2fgj6nrk1T8uLZ6t8QaBTtVYHjYHTZINLhoPOgQ+bfXz6IwQnSZRyaX4fTQln0WJI1HbWXNxMXPbr8kJLDGaJRbuNrD3+sPCqP3rmBjWth0yWe5+IEkS356dg0HU50ROLz87hyRtPJ90+DFXpkZk+k2StfHYq+y4nnKa2Z3XF/ue/k0I165d+1cp31erVq3YbYiiyOHz0Ti45Z+y+KQEe8ie2EF98d5FFh75joSsJN5uPCsnvP+f3L1zi4EDemEYUhuFsioqsTnVXOrzab8A7NUyriaGseL8fD7usDbPRCA64w7zj43js84bc7ZpjVksPzeHyLSbVHOtSzXXelR1rYuHrQ8qufqx6Z1lgZ1aINi97PV//44oisRHR+LlF5BnQCottEaJm/csk5yikpqSxJF9O/j54jdc1YWh/TkLtPetoydQE6gEJMDHk9cSnnaZ68nnMYkmUq8lcfGXExAJ/KNwtYeXL7XrNaZ+k5Y0ataWug2aPrJPpetTmLjnBb7qtvVfl8aiUUKAq3U1HwvqV1tvbOVE9AlmtJ8BQHKWxKW4JPZEbGL37V+5mhxG/+pv8ELlEUwb/zKmnnKiDA5kpw5HKdVHQo+d8ze4uR5idJMPCv3iIggQ4FL+otqs/fxdv37dKtf1/4417DVAeGIGtyNSi22HZAJU97LO4ookSVyKtUQ8jtzei886/5orIlVn0jJpzwvE7HMhw3sJwv3x7MXGsVzIeo+mvh15ruZbJGTHMnX/ywyqPZo2lXoWyq6dv3eML09Pv6999/BdoTTttUJmeef5t8oIlAeb/Xe0Ros8k8GU//bYzEi+ODUNR7ULT4cM5cv904iLv4v8kILws1cwNjKAH7AXiH143OzFq+jVb1DO39cun+P5bo3yPYetnT1Va9alfuOWNGjSmmZtOqHRaJ6oT11PPs+5+KPozFoMZh2pukSuJ1+gd8hgugc9X2THlUIGbvaWiXh5DVB4kj71wDltFiXmHxvH5kur8U6tToTrTZSGQdibZiIgUNXnCiH+W7mWcpSqLnUY23RerrHC2Uagksu/w4lfYa/LJ9ay19l6E22+7cAXPf7A1gp6s37OAu721h+jI5JEUrUSy8/OoZprXdoFPMWfG9cw9YuXoSnI/1Sw4od9hDZszr6IP9h6ax2z263KGbtESWTEtp4s6LThkYvFF2PMzNtpyFUXqEsNOcPbKMhOjS9Re30t6Ry/Xf+ed1sstkp7ggAhZbA4XZb2OiU7d12fdZeWcCf1KuGpV/m4w9ocR2VidhwfHXqTxV1+zxmbU7IlTt4xc/x6MhejjaRJLiVyjSrBQICLQHVfG6p5gpcimTrBHijkcpK1CSw5NRWFTMG4ZvOLnNlkTaLSw/nw0BsoZEq6BT3HM9VeKZHzBLo+fp76qD4lSRLX7z0MFpAkiXWXlnA1+SxTW36FWmGRwjx5OoxZW3XoXSzBAjIBRrZT0qtOXt13g1nP3CNvU9m5OkPqjCNFl8icIyNp7NOe52sOz/Pu/+7ewQyrN5nPT0/gl+fW4WnnWdyv5YkpC5td4aC2klGwVwvFKoIgSRIdVnajT9W3uJcdzQu1RhS47+G923j7nZE4DpmD2S4Wk+w8dpp4angrkAmWVRcbhS1NfDsQ4lKHEJfaVHGuiY3ClmVhswh2qU2nyn05dPcvVl34lCF1x9HIuy03ki9wPfk815MvkKK7h1E0IEkS2bpMRjSZQUOf8hPV6m4v4Odc9pPKB5T1ZPefg2lR2b/zD95Z3RdUwHGgOhCI5e8E4AoQBUiw52wsrm6WCH1REhkzpS8H7myxOLBF4BZwFcjMfY6GTVvz/cZ9j3WibLz6LSbRyPO1hj/ZzZQhgmDRtrXWy3R+/UqURLqt6cbG5zbiqHZEa5RYdPgHtt76ifpeLTkStZ2JzReiTFQx5rV+RLfKwMHrfWxVliIMJuEWatcxjGjyBj2DnyvSQk8lFxmuduVvAlwx4S2fWGvCG5tq4vKt2GJP5LwdBasW+YtMFknJlvjp8pd42PrSqXLfXNu1pmzG73iWa9vcUVZbavlQEpnaQ82++Hdo4deFTpX7km3MZOX5+VxPPs+bDaZR073hY8/9oJjK5BaLCXGpbWm6lBeU1QqLk7q8OgwfRVnb7L/zd+3hfxKVHs6v174lPPUqoxrPJMSlNmazmfb1vMhQpkI7wO6+3b6W9/iJMxYyaNjonL+P7N/BiME9C3Vd247fxsvHz2p9Sm/S8cfNH9gRvoFOlfvxXM23ihxoIAjgYivgYS+Uu8WRovapB85po9nMlL0vceDaFrSJGmRedrgatyMIZnTyn3B1OULrwLp0rtyXUM8WeRbvPR2EIhdnL0sq7HX5xFr2etOVLfx+di/vtJ5b7DHDVgVVPUsmWEVnlLgWL5JlyGDc7mf5uvtfyAQZ82aMY92Wz6EbuJzzYOOKc7i6e+brpD4QuYWrSWG80WDqI8+VqZf4Yp+BfTceRvH4OwsMb5JGw2qeJWqvR21/ik86rLNacbqS/E0Koizt9bV4c87c+o8bP3Am7hDxWVGMaTqXaq6hOft9fPQdegUPxEPThMO3zBwON3MlVuRxTjZJNCPp0xF1qahlRqpXr4F0P1tNlODqtWtISkdkGmcEReFluWyUloCMur5y6vrJSBf3sfbSAma2XVEoWbmSItuYyaxDI4jNiqBtpd68Ui9/1YDiIJdBNc9HB6M8qk8lZ0ncTbG8kJlFM/OOjcXTzpdXQyfnvLOs2bCJVdeCkLtYihmq5PBeNxUtgvJmRyRrE/jgwKsMqPkG7QKe4mTMXpafncO4ZvOp4VY/z/7n7x1j0/VVZJtSGdl0KL2q9Sr2d1IcKhzUhaC8OqhDPGTYFVMb+aVfX2FA9XdZeGICi7v8/sh9l38+h6Xfr8Vt4DZktpYiKU0CZXzQU40gmHl7x1MMqTuOdH0qt1IuEp56BZ1Ji1ym4HrSOep4NMXPoTJvNpiWSyP4n0iSSET0db4Pn4e3XSVebzC12BWBrUVhVshKi7Ke7Mali8SnP/5R1mqz2bJxDVVr1qVeoxZwv5DejP2vs+WrtWACoYaMwKSq1HFtQvWQ+gRXrYmntx/Oru44ObuiVOWOfDIajaQkJZCcGE90/G0ORvzFmbSDpOqS0F3RYjiqAx28OnIyo9+dneNEcfXw4p1X+1KnfhO6P/08gUGWZ9ssmhm5vRdzO6zBReNeQt9YyeKgsUQzFddxk1+/2nBpA3fT7jK+5XjMosS2a5eYd/Rduge9wPbb65neejlh+w7x/rjXMD9fDRvPIdiKQwHQaG6hdh3L591WFZilURAlFcViDSomvOWT8uSgVissL+vWzLxJ01qiXlN0icw98jbzOv6YZx+tMYuRf/Tm+r4a2Ff7FAA5Jub2VfHtped5q+EH1HK3RLTey4rhm7CZ6M06hjf8AD+HKo88f7L2Hu/vH8pr9d6jkU/bMsl4slFBsHvxNb1Lm7K22Q/Izzmdbcxkx+1f2H3nV6QMicoZ1Rk/dH6uvjv2tf7s3b4JgMCgalStWZfgarUIqloL/4AquLh54OLqjsbGNtdxkiSRnppCUmI8SYnxxEZFEn7jMuE3rnDz2iVi7t4BwMcvgL+OhefqU2uWL+bW9ct0e/o5mrTsgELxZGnqoiTy/blPyDKmM7rxnCd+Jh3vF1QsL3VJitKnHjinDSYTr//RiQsRpzDjjtzGHVvTW+gVm7CRuzG86UsMqNMx35RxuQz8ncvPe3BhqbDX5RNr2esjkSeJj9ZQxb9GseyQIEBVT+tmJf6TB4vMK87PJ8AxhE6V+2I0Gnnj+c6EnT8MvaGSOpiNsy6gUqryOKklSWLUjt7Mbb8GR/Wjo2MlSWL3NTNf7jegve/wVMok3mitpHddZYllBf916ycyDWkMqPmm1dos7flAWdnrv0dP77nzOztv/0KmMZ0hdcfRxKd9zn4RabeYeWAqLrqvuRCntHTefyCZjZjuXUBIOo+XjY4AdzXVA1ypU9UfD08vnF3ccXByznN/Wm02yQnxJCbEExN3jyvhsdyMTiEmxUiKXoPMtRoK95rIHf0feS9KOQR4XCfKPJ4xjRfRLqgOsjLKRH8Qkfz79RXU9WjKmKaf4Kh2tuo5HpdtX1CfkiTLwpXeZFlUn35wGG0DetMz+EW4L6n2wbxlnFL1QaaxPPOCIZUZvTQ0q+6a5zw3ky/yybExTGr+GZWda/DNmY9I0sYzsfnCXBKnfz//61u7ICEyq+MMulftYMVv5cmocFAXgvLooHbQCAS5F7+dDZc2cDEuiePRe5nS8gucNXmrcz9AFEXGDuvH4YvRuL7wBzK1Jb2obYicyV1VxGXdYfbhkSzuuimXQ9lg1rPx6nJOxx1gbvs1j02z/PvkZFv4Bv64sZr3Wi6hkmNwse+3uMjuv7yUhyiaspzsZhskbiaISI94kuNi7rJ+1VdsXPst6Wkp9O4/mFmLVpJtzOT9/UNpF/AUPx5aQqhHc8Z2moetbcGLFoXFaDZwOGo7K0/NxynTjaH1xtGwfpucPpWenkq/jg9Xn2vUaUD3Ps/Tu99goqXb/HlzDe+3+rLY11FWKGRQyVVWrMIu/+xXJtFEtzXd+PPFP7FR2nA1Poshm3oT4FgVO6U9w+t/xNIFM1jz85+oXmqKwrYmduZRAAR73SZL8w6fdCy6c9rXScCjlAq7PAkVE97ySXlyUAe5y3CwcmE/UbTIfIgSTNj9PFNbfZWv3c42ZvLyurZEn++JQ5UpANgqTMzua2Th6edzVe0GuJVymWVhs3Cx8eD1+lNws/Eq8Bq0xiym7B9C32qv0qZSjzKR5HLQCFRxK1+yW4+jPDio/+mcNotmNt9YxZYba/FPDebiuhPcvHARWzt7dofFYGPzMDX3+KHdJCXco2mrDrh7Wi8aKikhnnOnjqLVZtGr36Bc74ADezXnyoUzALi4edCl17M89exL1Knf5Il++x8uLiI+8y7jmy0oVt+xUYG7nQwX28IVliopCtunsvQS1+8Z2HrzJ+YdHktWnAnBIQRJZUQmuaIW29HWdySTu7jhZJP//WiUliCN8vD+W1Qq7HX5xFr2Wmswc+RCTLHtUGlkBuiMluzTLEMm43ZZoqgFQSAhPpYXezYh8V4cNIHKnauxethhHNUu7Iv4gy231jKr7UrUCg1Ho3cSFn+YEQ1nFOqcUSkic3fouZXwcNLWOljO2I6qYge65YfBrOftHU+ztPs2q42Pcpllwb+0sqfKwl7/PTP5eMxufrr0FWbJzOA6o2nq2xHu958/LhhZcvpVVIZxKKTcNb4quQi0DJJTwyWLqwfW0LJ1W6rWqGu1e9DrdFw+f4qwk4d55qXR3E1XcS3ezIXIbC5FG8gmrwPUTAJpqjfwlL9D+4BONKsip2GAHLsyqCkSFneIj4++g0quoWuVZ3mu5vAc+Qxr8KgxpKA+9SB6OtOQzpR9L/F8reG08u8OwL24WEbPXUdK8OsI9/WibYyRDO8Vh5YI7qbf5G76LbSmLBQyFQnZMSRp4+lf/TWqutblx0tf8FS1ITnO7vxYef5Tfr6ylPXPbqNVoHXG5OLyn3VQm81m9h44zImTZ7iXkIhcLsPfz5dOHdoSWqdowv3l0UFd1dM6ekwZ+gwGbHiJtpX6YxKN9Kk29JH7p6elMqhXM+JEb1wH/IqgtAGga005Yzqq2Hn7Z64ln2N049l5jt11+1d+u/4d01otxdu+UoHn+GdEVkxGBLOPjKBPtVfoWuXZYt9zcdEosWrF1ielrCa7kiRxI0FEa8h/e/TdO3zz2Uy2/LoGs/lhapmtnT0bj55n8sGBBLvUIiYzgonNPiXEtU6JXOPBu1tZd2kJoZ7NCHKuicZgQ9TF2yyYMMEiC/I35HI5bTr2RNslmzfaTaWRb/mRlnkSXO0EfJ2EJ4ow/Ge/WhG2AoPZwJuN3+RehsiwTUO5nXaVl0MnEqppxsQRg7gpa4isozuSPBsH0/soZNC7fiSnUkYxu/1/zzlNxYTX6ljLZpcXB7WTjUBlt5Lpww90LLeH/0yGIZVna7ye734ZhjQGLGtAVuxY7HwsFcw97EQm9Ijhq7C3WdT51zzZTBfuHefbc3Op5d6IN+pPLXByaTDr+eDAMNpW6klTh/ZlUjPC1Vagkmv5Hif+Tlk7qP/pnA6LO8TXpz/CIdaJM18dIiUhIdf+85eup0uv/qV+nQ/eAVUaG3o0DyIrMwNkQBXAH8gAH4dAurUdwAt9RuLtVfD7ZH78dPkrwlOv8G6LxQUWliosSrnF3paVTnVh+lS2QeJEZAwTdg3kTtR1UuO9kXk7YJJdQSP2wMU8nZFt/ehZW1Hg8+5qW7pFma1Nhb22LuXNXlvDQa1RWlL0S2PB6W6ySHK2RYu6ums92gZY0unDThzi9ec7YzKZwBdCRtXh495rCXGpzeGobWy4spQ57X7AVmnP8G09WNTlNzQKm0Kd02CW+O6wgU3nH87LfJ0E3u+uLpZcaEEsOTWV1v7daeDd2mptluR71T8pC3v9IHo6OuMOHx16A43clhdqj6SFXxcMJomtl0ysOZpFsvk8WsUqnIxLADCl3sFHe5I5E16ikktZSH5abPansyaxY88RlAGtUVVqhSqgDQrnypZ90JKqehMb04toxB4oZFDHV0bLIDktguR4lGJ0fIoukVmHRqBR2hCfFU2v4IH0ChmUq6ZLcajinn+gWH596kH0dGzGPabsG8LIRh9S17MZAAf272Xm7/HIg59GwoBBthe5ch1V/bU09G5BFeeaVHIMppJjMEbRwOzDIwhyrkXvqi/x85Vv2HV7Iz4OAdgrHfFzqEIlxxACHENw1XgQkX6TWykXuZlymbD4Q/zW/xRtgiqVGxv/n3VQL/t+NefOX6JO7ZrUD62DyWTi8NHj3I2K4YUBfWnTqnmh2ypvDmpHjUAVK0RPP6D32r683egzFhwfz4JOGx67/7XL5xjapzWCfztc+v+IiEUXqk+ogrfaKJl5eDjdggbQzLdTnmMj0m4w58hIhtYdT0v/bvm2n1/KsFE0suTU+xjNBsY2/cRqg8iT4mwjEFhKRrIgymqyey9DJDYt7yOcEB/Lt0vmsnHdckzGh8LUSpWKdv2eQtsqiwsZx+kQ2Ic+1YYS6tkiV/XYkkCSJM7eO0J0ejjh966QSTp3kq6RHp9K5qk0onaHg/5vB2hAOVjFYPsxjB43+18VnfdPVAqLfnNRU5H/3q+MopGe63qybdA2jGYFY//6gD9urubrbn8RHXabqTNmIbSag+h/F718H47GRQS4yHix+Q1+ujaeme1WFsk5LQiW9OHyqDn9TyomvNbFWja7PDiorVkYMT9SsyUiksWcbJTPOm8scN/EjDie+SoUuWExNi59AAh2FxjY6iTrLn/CzLYrcbXxyHWMJEl8HfYhVV3q0qVKwQ5Ks2hm1uHhVFIF80rTiWVS1PjfpIVblg5qvclS0NhgFjkStZ1friwjOyaT299dI+FOTK5969RvwuDXxtCpR9888lqlwd8luQ7e+Ys1xxYRHneV1FOJmG+bLPrXTpZ/gpeAj3Mgb3aZRs/6AwstB7fx6rfsjdhEn2ov0y6gd7HfKQXB8l7oZieUSERiQTyuT2UbJK7Ga3ljcw+uXbuJQRWKye4ikmDCRb+O6i4NmdLDtkCnhlIO/i7Fy8oqD1TYa+tS3uy1NRzUwR5Ff19+UvQmi1MqXZ/Gu3sH8kXXPx/qzH67mAUfjsfd05spi7/g16zv6Fv9VTpV7ktY/GGWhc1ibvs1HLi7BZNopF/1YYU+rySJ7AhLZPlpezLvz32UchjeRkWP2nKrznliMiL48vQHzG6/ymptAlR2kxWY5WFNStteP4ieztAZGL2jD3KZghdrj6SlXzd2XzOz/EAWaQYlElpSVM/hpF+JMfwU8ls/0799DZ4b/IZVM5uKdu0PbfbVi2fZtnk92zdvID42CrlzEKoqHVFX7ogysAUZ9uPRmAegEXNrHFf1lNEqSE7rYDn+peBklySJ9Ve+4lj0bhr7tONA5J+0qtSdZ6u/UWztdIXMcj//nAPk16eSskRuJWgZu6sf45rOI8S1DmazmU8/X8qOtBbI3auila9EJ/+NmnatmP3Ua/g7BuZq92j0Tr4/9wmjm8yhqktd5h0bi6vGgzcbfoBSpsRoNhCTaYm2jky/SZI2nkCnaoS41OZI1A487fwY2/LlciNdRhnZ7JL1SAFnz1/k3PlLNG5Yn1eGPAxpb9akIXPmLeLXTVuoH1oHB4e8aQj/BrwcrduBelXrxsXEE5hEE9nGzHz1af5O9Vr1+OizFbi5e5LtbsPc7QZECTadN2GjhInNPuWdnc9Q1SU0z8Q30Kkqi7ts4tPjE/jp8lc5xlBARtuAXvQMHog6n4mCUqZkXNN57I/8g3d2PsO0VkvxdQjMs19pkaqVsMsUy60+bkmhM0rE/UN3Wq/TsWrpAr7/8hN0Om3O5w5Ozrz4yig8O/mxKXIlomTmp2dOlervJggCDbxaUd+zBUmODxc9krTxHGi7hZ29fuFudDjxMVEYUnWQCcYEAz87LsP1kidOald6BL9YbjTQi4LBBLcSRNztBXwcCx/5JIoiWVlZiKLIstPLeLX+q8gEBTP3fcav177l1/4XwaDktdU/o+r7B0bVWbSKn3ExrKRvPSWNQk7z7dkP+bjDujzP/6OQCZYXT2tLIlRQ/vmv2WxPB6HEnNMAjhrL82KrtEcuKEjXpxaopefu4M13L+xi7PbncVDVJi0zhFuJEn+cbsrI1nOZtPcFprT8giDnmjnHCILAa/XeY9T23rTw64K9yjHftuUyOdNafc2He95g5+2NdA0aUGL3XBD3MiSU8v8/W1wUTGaJ8AQzP11ezs7bv+Clq8TNry8TdeVWrv069+rPkDfGEtqw8AEcxSE64w5fn/kQnSmL/jVep7lv55x3wispYWw49w213Rvz0dPf42MfQFZmBju3bGTT+hWEnTgMgIREvFsUMe3uMGbnM8gFBW42XnjZ+eNjH0htj8YEOdfMEyndv8ZrdK7Sj2231jNmZ1/8HYN5ruZbOcU/i4okWaLfUrIlbFSWiGMX2yfLYioKf7fXeXREjZbffdq+N7kSE4PByQ+T/ABqcx9cjF/yYmM1A5uoCoz8drUV8HUu+Xuo4N/Ff81ecz/rsDSdM2qFZXyQJCdquTfieMxumvt1BmDQsNHotNn0ee5lPLx8aCP2YuHxiVxPPs9bDT5gTJOPmbz3Raa2Wsqsw2/Rt9qrRXIsN/IzERqsYs52EzfuiRjN8Pk+A5dj5Yxqr7KahI+vQyAGUU9Cdiwetj5WaRMgJk3EQV32WczWJiUbdEZYdPJdFDIl3YNfIMCuK2PXp3M1UQlY5qEZilko7zTE9spwhg18ju4fr0ClLtugvQcIgkCt0EbUCm3EmCkfc+b4QTZtWMXOLetIDfsW5CqUAS1QvfQ9aoUZfdbTOcfeuCdy457IymNGKrsJtAlW0DpETmAJZckJgsALtUbS0LsNnx6fwICabyEX5Ezc8zw13BvwYq1RT9xvTSJEJIuEeOTNyBBFkczMTOzt7REEgfh0kVlHRjC4zjuEuNZBkiSWb73GLmkAZs/TpCsmoTH2ZEbz9fRskHtOfS8rhkUn38XdxotFnX8jSRfPOzv75AkCVcpVBDpVJdCpaq7j76bf4mLCCVa3+q1cOafLihKPoP7m21Wcv3iZyeNHE1Apd/Terj37+W3zVgb070P7Ni0L1V55iqC2VwtWT8WJyYhh1JZ3qe3eEke1S5FlNHZcMbFw90O9h6HNlTSocolvz85hfsf1hTKcBrOebeHr2XpzHbXdG9HFoz/VA+vn+11FZ9xmxsHXmd56Gf6OQUW6VmsiCJZCTaUZLfN3yiIa62aCSJY+9+ObnZVJ3w51iI+NAsDG1o5Br42heb9hLL/yAWm6TDINOmrYLENvdCVTL5GpkzCYc7etUYCtWsBOZennrnYC7nYC7vYCng4Cfs4yfBwFFE+QRvu4Ql4mk4nd+37l1z++4+TJvdQb1YLWtXvgpHZj1+2NTGi6gIs7T9L1qQHY2v17XrofoFJYIpMLcv6Kosj27duZNWsWERERmEwm5Eo5WX2zWNN9DRfsopl/bBq/9DtHRKIbX+03EJUqYRJuka4cR031aiZ39SRN3MOPl75gTvsfCnRq5YdSbnFOW0O2qLSoiMiyHta02WUdQa1WWNKES3rydCdJJE0rsfnGauSCgl4hAx+5/72sGCbsHkJG/DK0ekuUTbuqcoa1Tmb6wVd5OXRCnqynkzF72ROxmcktPiuwXUkSiY2NYMbZN5jfaT1O6rwFW0qD8lTAuCDKwmaLosStRJHvwr4gVZ/Ea/WnsOCD8axf9VXOPu0692bExA+pXqteqVxTuj6Fb8/NJSbjDm81nI6rxoMNV5YSFn+ITpX7ERZ3CJVZzeiWs3GzzT8i7M6ta/y+fgWb1q+kWZvOfPzFGsv9SiLJ2nv8tfdH5O4K4ogkPPUqaoWGEBeLnJhJNGISLVleGoUtGoUtmYZ0otJvIQgCL4dOzLfKfVGRPYiqthesatvys9cKhYLAwECmTp1Kt27dMIoCtxJEPtgzm803l2ES0wElTsbvCFS3ZOpTLlT1zL8PapTg6/TfWiyusNfWozza6+JEUCvlloyn0l6IMZgkrsaLJGsT+eDAqyzpuvmR+6+5uDhHGvFuxi1mHRqOj30g3YIGFJiR/E/+PhcyigLfHjay+bwpZ3tlN4FpPdT4OVvHPp2I2cOpuAOF1souLO72gtWusSBK014/kHnYevM3frz8BY28OmBrnMTPZ0yY7ktyiWSQnjoRk91mKrtWxs+nCghSzvESEvW9WtLKrxvV3OoVW76qaNf/6Dl2ZkY62zav59d133H75hX+OhHOJ2feoaZrb+T6/uy7msXd9PyztQJcBdoEy2kToigxeReDWc83YTO5lx3DhKYLuJlykR8vf4GDyoXBdd4h2KVo0sAPeNBPH9jsmTNncvt2ODKZJVvBP7guXs/7UyO4NgNrv01SpsiivQaOR6STrpyATHLF1zSMzwbWpJLrw/jebGMmv177jmPRu3inyRyqutZlb8Rmfr6ylGmtl+JjH/DYa5MkibG7+jGl1ad0DAkpdwvR/0mJj8lTP0KvN7Dwk4/y3FT47Tt8uvhrGjWox6tDHz2he0B5clCXVApSp1Xdmdz8GxafnMKc9quLfPzm80a+OvBQ1uH1VkqyFV+gUdgWqYqvJEmcjjvAurNLMMkNtPTvRofAZ/LIBMRl3mXagVf4oPU3ZVo8USm3pHGUV+1Ba5KUJRKVkv+ju/3PX5n+yRc06D0Gu6B2nE/5nRRWopAqI6DGwfgJAoWLQpYwYRZuIpdCEP6RcCGXgbejQICLjCB3y2JNkLsML4dHF8uSJJHzt4/xU8RXJOvu4Wtfhc5V+tLUp2Oeop3xsdHYuzoxdndfPun4Iwazngm/D+DCbyewu+zAM8+9wouvjMI/sOwWR54UF1uLNvXfnfzZ2dn07NmTsLAw0tPTH+4cBPiCjeSOsX0Gi9rs52BUXfbfsKwsmIkjTfUag6t/x5stA/nr9moOR23LKeJSWBw0ApVcyka/szhUTHithzVtdlk7qEvLUfqgqEqaPpnZh0cyr+OPjz0mIu067+8dRVrcaowmywJSn1AFQ1sY+ejQG7Sp1JNeIYNyHTP9wGs8V/Mtans0zrfNBxOTKO6w6fpKprdZZqU7LBqCYClKWZ6jQMrCQX0nSeR4VBjLz85mQccNCIJAemoKfdrXonJQNcZO/YTQhs0RJYlsA2ToJDJ0EpkGS1SwcP+7lcssC8f2anBQC2iUlgiks/FHWHvpc/QmbY4NliQplz3+59+iJDKkzlia+OauFK836dgT8Ts13OrhoHcq1LNn0OvJzEjD1d0z5zOdVkv3ZpVJTUmiVYfuDHxlFPVbteZO2lUEQUAhU6KUqZCQ0Ju06EzZZBjSWHNxEYPrjOHg3a2k6ZP5sM23eTTanxSN0uKsdrYVUBcju6JAe30fJycn6jVoxKgZy5h/djMnU94FZCjNzXEzreCZWo682tYRVT72ViGzZGi62f27io8Whgp7bT3Ko70ujoO6IL3Y0iAqVSQpU+KzE5NpF9Cbht75178RRZFrl85ySXmKq0lnmdLyC3SmbKYffJ1bKRf5pd+5Qjkk83MkHrhhYuEeA7r7U3hbFYzvpKJVcPET3iVJ4q1t3VjS9Q+rSnMKAoR4lGxQS2na66QskaMRN5m4+3l87UPJTviCO8mWbSbhGlrZpxiS92HvbWZxz9+p6900zxhtNBs4d+8oR6K2cznxDLXcG/JCrVF42vmW6LVTCAf134mLuYu3byVMoom5R94mwCmEO2uus3nLLryav4Jbw0EkiPkX6S5pZ/WFe8dZcmoqT1UdQvfgF7ibfpMfL31BVEY4lZ2q08y3Ew292xSYsZgfbhodg5/tlddmy1XIGnuhCDJSM74dL05YzrfHINVwjXTlBOxN79HKvTrv9/VHoxTQGrM4cHcre+78ht6so2vQALoHPY/BrGfJqfcRJbFIEribrq8iXZ/EzM4TsCmHhY//cxIfOp2ezMwsPNzd8r0hFxdLp0pITCp0m6IoFmKvwrUhihIy2ZO1Z6sSsFVKiKL1/fttA1oTkXaddH0KBpMuj9PuUYiiSPy+eWQcSMKh/SwAlh828lab4ey5+zxNfNoR6FT4l5AGnq2p3LAqNs6OHI/dyZJT72MSjXzQamnOZMHLzo+Zbb9n2oFXmdrqKwIcQ57grouPwQS3EySCPUr/hf5hnyp+/8yv7ezsbGxtbZHJZJhFiZhUEVGU+Ov3H2naqiPObl6cixY5dEvkSFx3nJ/vzjXhBBkpzyCXfBAEBxRiPezMbwBgEsIxCecQVOcRZVcRBBNKwRONLAg1VcgyR5JuPoFJNCKXgjAJV1GIdbExD0QpWVYvzSJEp0pEp5o5evvh9dqpJbxdw9DLfydLvIC7nR2NfVrSxKcDHrY+fHt2LvfSo3m72WyquFQnKj2cPRGbWHfpC9xtvOkdMoiG3m2QCTI8vS0pPWOafMzHR0Yzt/0PaP6wAzNkPZPB2lOfs3b157Rr35sXXxlF01Yd/jWTueQsSM0GH0cZrnaWl8cePXpw5MgRS1GWv1MPiJajbZ+Czam3mKmrgqAwIyGhk29EZvsD89ssIcjNzOR9/Qn1bM6cdqtRyBRI0uP7pHBfCsEiWVQy41pJUpLP3/8T1rbZ1vo9REm6317hbbadWsBRUzp92UEtgSTiqHJGb9aSbczERmH7yGMCHEOY1OIjPtw1EH3SBmQyWzadN+Fko2RW25V8fOwdUnVJDKw9KueY0Y1n8/7+oSzpujnfmgEP7rWuezO2h2/gaNROmvvlrT9R0kgShCeIBLvLsCmnmRglNWb8014/ICZN5GjYaaaffp2lfbdZBDEkCdSOjPn6JGmCB3/ek/j6Ry3RqZaspgeFeHTybcilQFTmViil+rkWmCW0iIqDGNTLcVXXoKn7xwS7+RLoKhDgasl4khXCJv7TTqjkKroHPYcoSqTo4wv17ClVSlzc3HO19demH0lNsYwXh/du4/DebVQJqc4LL4+kd//BBWZB1fNsztQDrzCgxpuAxHfn5jKy0UePvY/CoDVY/sWmWd7lnW0EHDUUSQpIFMWC7fV90jL1nDTV4cVdvTHLr4Nkj7Pxcxp69uft9sr7k/r7/eA+CpmAm50l2ksuux+NJ/277PHjqLDX1qHc2usnnGM72wjYq8ru/dPDTiI5U2Rw7XeYc2QUDbxa5dknPTWFqWNe4dihXazYuB+lh4oZB19jWqulfNJ+Da9u7cjwv7rzScd1j81genCff/+e2oTICHRVMWubkbsploXKmX8ZGNDQzNBmimJHVnatMoDt4evpHTK4WO38HUmCu8kSISU49y6tObYgCEQm63hv72D0BkeuXpuHCBhkh8hWfE2wmxuj67/FtkhbulTtT13vJnnGcACFTEEj7zY08m6DJEmExR9m/rGx2CrtGVR7NFVd61r9Ph7eT+Hfl718/JAkEbkg4/2WX/D1iY/YnLESc4aZmO0ziNk+A3uvatTvPwMpoAu3Uh46XCOTJdYmm1h70kQlF4HWwTLaBMup7GadflDHowlLum5m843VjNzWk85V+jO+6XxUcjWR6Tc4HrOHOUdGkm3M4MG376rxpJJjMAGOwQQ4hlDFuWaOk1gUJfoPeZszJ85i0v7NOS1TIK/ij7lmIuL+BkQ2H8Ki/aCVb0Sr/Alv+bt0rHMXUb6DKftvYTAbUMlVtPTrxqTmn+XIZ15JDGPRyXcZXGcMbSr1gHzeq/IjSRvPX7d+5Lfn/kQtL5/z77Kw2SUaQZ2als7702fj7+fLexPfybM9O1vLxCkz8PRwZ/r7EwvVpoONdYrE3EyS55E1KAoBziL2qpL56q6lXOfLc+txUXsT4liLRh5tC33s1YtnefuVPkiShH3LSdi3mZqzbUDoDY6kjmV2kxUoiqHjey7pGD/d/IopDT7HQfVw5SpRG8fHZ8fwTt3ZVLIvu0hqFxsJH4d/94uvKIrs23+ARZ9/QVR0lCV6SgB/P3+GDp+Ip18wiz9+n2OnzlLtmTnogpuSbDqASXYZo3AKo+w8AraozR1xlnegjksLqrn4ki0c4nTK1/jZ+VDHtQFVnWsTYB+CQlCSakgiJusO0dl38ND4UNOlIWqZDVoTpGQLnEu6xMG4DcRl30aQfMEUik5XD7PZBbNwC5PsJmbhBmYhCqXYCI25DwopFNDiaH8EpWY/KMJ5MWQYLXwa5nvfsdmR7I76jXPJx6jp3IDBVUejklsigH+4vgh/uyACskL4ff1Kdu3+FWNVA9QCUoAjEOhalT7PvUyXnv2wsbVOtFVpoFHAtbB9jBk9koyMjNwb1cBLArhrcNb+gEbTFQCRRLLVk2joVZkJDV/jz8gfuJB8nLdqfYCfXeVCn1stBx8HM7alX3+r3JKhNRRir/8e1rbZ1rLXCVkCCVmFX7UXBKjiYkZT4lU2HhKZKiPTILD5zg+4qj1o7dP9sceYzWYGj29JcogGd68wBCz3+GqjbDpU0fP9tXkoBCVDqo3NeeHfGvkjWlMW/YNee2TbWlMW004OY2aT7x/rLC8pFDIIdDajLsXfoSx4lL0eM3oU9Zq05otlq1h2ew7q6xomvb+XAynnSE6vzb30muhkO9HJNyHDGZnkgiA5Y5KdxyzEoTJ3QCU1uO+sPopJdg4QARkSEqKQiCCpcDJ8g4K8NSXUcolKTmaquJqp4mKmiosJP0eR0sogTU9L5a9NP7FpwyruxUXn2mZr50D3pwfw9ICh+AdUyXOswaxn4fnJ1HZtxMl7B3ir1lR87UquboaNUsJBJWGnkrB5zCvynr37GD5qdF57fR9VSE/kTwWgdVwOCCjEVvhJqxkUqqZ9FUOe718tB1dbEWeNxL9kjb3cUGGvy5e9Npgtc+yioJBBkKsZRRmXL7iXKSMxW+DLi9Pp4NeHWi655yqrvlnID8sXAeDl489XP2zhTPZBTiccZHzoPCIzb7Di2gIyjem8VvM9ajg/mVSTzgTfnrLl2N2Hv0ktTyMjm2XjpHly34POrGX6qdf5uOkPVncme9mLuNmWP+dafhRks30q14ZnZFzX3cVOuwOzEEGm8hOclUG82/hVank4883lWTipXBlU9e0inzcqM5w1NxbjZ1eFgVXfRi4U7TkpacxmM8v3zGXnnV9IW5EM/xhaG7TvR2Dn17ircCUyxR0Zrnmyq73szTTxM9LYz0iQq9kq7xom0ciemE3sjv4dR6ULIY61CHGqQ5BjDeyVTpYsLEkiRZ9ATHYEMVl3iMq6w+2MK8gFJQ3cW2ET5843C1eTlZGOKfk6mA0gyJC5hSA9HY9dwljsaryDUXaJdPVoJFJwU1WhmW8Qoe71CXKogY9tYJ6oaJNoZP2tpdzJuMaoOh/hpCq8tJ4kScwNe4dXa71Bx4CahTjiv0FhbHYZO6izmTjlQ7w8PfhgyoRCtRkSUvzoXFEUOXoxFjsXryfSprRVCoQUoBdnDSRJosOqboxpvJCfry1jcvOCNSfzY+nCj/hmkSV62qPbXOT1R+Zsa1/3VxztbzC84fRCtSWKEimJ8bi45/6uLieeuS9Bsgo3m4fpHwnZsUzd/wpjm86lhluDIl23NfF3luFqV7rV2xNio/Dw8S92+kN2dja9evXi7NmzedNGFTbYuVfBhBJ1wzeR1wlAq1oBKFCINdAq1oGQRQ3H18lmN838WtMusB3Zxgx+u76Caq51GVxnDC4a92JdY2J2HFeTznI16SxxmamoCMFkCCYtI5g7SR6kax/93XvYmannryTUX06oryxPsVFJktgWvp4rSWGMa/oJAEbRyNs7nmZW2xW423qTkpzIbz9+z4bVS4k3R0E3YB9wF+wdHJmxYDmdevQt1n2WJsOGDePs8X2IGdHwYNVVrkI+uArmwEhcDVtQSo0wCWFoZT8jGg8ysfN4bqWdIjz1Cr1CBtIreFChXzplgiVq2sP+359CbM3nD+DmzZtWua5/G9a22daw1wCxaWauhsflsUMF4WZX8pqI/yQ5SyIqVSQhO5bPT73PzLbfF+q4a5fOMnh6S6hWH3e/PXD/2Xyvm5LWwXJWX1hIfFY0E5otQBAERElk/O4BjGz0YY6O7wP+aa8P3d3Gidi9OWNoWaCUC4R4lD/ZIGuNGY+014CDsxsICjKrmlHUqIat02tonVeiMT+DSbiJTv4nAgIqsR0yyQ2ZYI+TxgYHlRwtFzBJcTip/cg2xaGU2VPJrj3+tl3QGhQcSZiERqiF3NSUO/oF2BhHIJM8MMpOYJZFYBaikMjC1vQKGvGZnGuyUUINbxm1vAVqesuo5V1wpHtB74BFxWQysX/nH/y44ktOHzuQZ/vTA4bw4aff5j2/JLLm4mKORG/nVsoVark3JMi5JsMbTrdqmvo/UcgEHDSWTAw7FXmkQFq3bs3Ro0dzHyQHRWgDlK3roXX5HYQ0kNxx0a/DJiKJNdOfwcXuYV9TygWcbCyRo/+mmg/FpcJeW4fyaq+1BjPHLhXeXnNfjsvJpuyfAZPZokV9LyuOmYffYlHn33K9HxsNBoY914kLZ44D0LJ9N5as3MTay59jFk28HDqBcbueZUyTuSw5PY3G3u14ruZb+b5jP25slSSJzefNLD9iwnx/SuBuD+93U1HD+8mfm8Unp9Au4Cnqe7V44jbyQyZYZDaLI5tUEKUzxxaQd6qD1CIRJ/NytPLVCNhjczmEX2eNRK2BGQdfp6V/V56uOqRY17D5xmp23/mNaa2+xr2A2g5PijVs9tn4Iyw4NAGbq3acPXkEU5CRnMQtPdjLnGjSuTsx2iySskxk6M3IpUCUYjOUYlPkUmUEBFxsoXllOc2ryAj1kz1x0U9JkkjSxhOeeoVUfRIxGXeIyggnPsuy6C2XyXP2EwQhJ6JdIVPiqHJBZ87mYNhesjKzkJ33gFsqxORwFF4NkHfxQendErnck0zlPCRSsckYil+EHesXvfvIazpwdwvrLi3hmWov0z3ohSLPpX++uoxMQzKfdptSrguNloXNLtG4FhuNJfJRp9fnu/3B5xpN4TVSrak9JJMJT6RB7e1c8kWX6nmHYhSN3Em9BhTNgfTGmGlcCDvBkf07SNj+HkEunmQHDgBg/4V++FR6iwuVThDq+fjq8A/SQ/75XdX2aMy7LRfz3r6X+LDNd/g6WKJaPO38mN/pJ97bO5jX6r9XoIZXSROTbinyV9ov/TKZrFh9VBRFevfuXWDaqNyxErL6b6FqoCFL+RlyyQe5WBmj/BCoDtDRbyA66TqBTqlUcR7Mz1e/wUQyVV3qMrv9KqsVzfKw88XDzpc2AT3zbJMkifgMS5GJK3Eil2JFbiWI/D1rJSFLzq5rIruuWfqXj6NAqL+Men5y6vnJcLOX0zNkIKfjDnI67iCNfdqhkqsZ32w+846PY16HH3F182TYqHcZ8uZ49m7fxNrVizjX4hg4QebFdIKq1iqWxnxpYjDriUq7jeBni1yqglmfhrpKW2QN3dDarcDBMA+9/A8yZR8ipZsxJdxB6ZRFePoZ+lR7mepuhY/SEARwsRHwdip/TqPiUtzn7/8da9tsa/0WMiF/O5Qfchn4OJV+VXlnW4mYdIsNTNUlYRJNhZLnqlGnIdOe/4bpO4eRqn8N56BvESWYt8OIXW8ZQ0MnsPrCQlZdXMgroRORCzKmtvqaKfteYnHXTbmio/9pr9sE9GR3xG85Y2hZYBLhTjIEuz9ZUd2SpjhjxuPsNXLIqm2PrKkrGrt6gBoDh3A2rMMsRKBTbqKGzSLaBHTG1zUKlSoGnRTB9eQwVDI1lZ3bolHYYjDrUciUSJLEnbRrnIodxp20q3Su3I++1XvjrHFn8/V2/H5jMkrBjrquzyOam6HVDiA+TU2E9AV62WHsTOORoSLTaOBkVDYnorQI2KMikKqeMur4ygj1lVPHT4bd/Xengt4Bi4pSqaJzz/507tmfa5fP8dOKL9n62zr0eh0A3r6V8m1fLsgYGjqeoaHjWXTiXRr7tMcoGnhv30vMaf8DGoXNE1/TozBLkKq1/ANQyiXsVBa9b7VCIiIqNtf+ss6VERrYINqkoRXWAjJsjR+giWhF5qmvsFFl4unYH3u1DFuVxfH9/+SUzo8Ke108yq29lkn3/1u4McPFVsi1cFOWqO5rv0v4Ut2tPkdjdtLK/2E2lEqtYcHS9bzQvTEpyYkc2bedb5fM5Y13pjLz8HD2R26hX43X2BOxifkd17PqwqdMO/Aqk1sszqOV+7ixVRDgmfpyQjzlzN6mJyUbEjNh0m8GRrRT0aP2k7lwnq81giWnptLAO6+ESXGQgNh0gSD3kvstS3KOrWzYGXOrs9iYh5Gl+Az7jCm4X/6DT957GlGRzYQ9LzOk7rg8BayfhD7VXqaOR1OmHniZYfXepalvx2K3+YDi2mxJksgwpOHh5ENEjRsE1axJ44S27Fv9JzF37wDgEujOp5/+kPNbJGaKbLlyk123jnIrbQkm4RYKsS4G7TNsvdyIvy4LKOVQz09Gk0A5DQPk+DsX7NuSJIlbKZfYcftnriadRZRE3G29CXauhUZhi63SgRCXOvg7BHEn7TrJunsA+DsEEerZnFDP5vjaB2ISjSRp40nIjuH42itk2UcjNo+FdqDMaoKtzSQybcdjJAwBJba60YgnMtBf20ymuzPwbr7f4bWkc3x9ZgbV3eqzuMvvT1QfIyzuEGfiDrDx+XUoFOUrkr4gStNml6iDWq1W4eToQGpqGqIo5rmppKQUADw9ixfNWZpolJRKAYdna/Vhw8U/qOpah5spF4ukVySXy5n9+WoG9mxKbHQk4T8No+loHyJtWiMB0Xfn8qFhCGue2VysojNBzjWZ0eZbZhx8jfmd1uc4P53UrizotIEp+14iy5hBm0p5nZgljSRZChKFeMiKpCtY1mzfvp2wsLC8k12FDbZN30bRqjqZNrORhARkUiAm4QqCOozWvl2QyQ242+l5td5XeNhatJt7hwxm8t6BdAt6zmrO6cchCALejgLejjLaVbV8pjVYohIuxpg5H23mapyIUXz4u8SmS8ReNrP9skV3x89ZoJ6fnKbec/ji1AC+6NYAe5Uj1VxDqeZSl623fqRXiKXoi1KppGvvZ+na+1kuXzrNtL9ewdBIj9pbw53Ua5hEI0q5ikO/bCP8xhWeHjCE0IbNyzRqODz1CltvruNq0llLMRVJILP6HdBpkDtXQ+kWiEH1G6KQCCjIVn4DOgExNQUhSkAW6YgrNXh73Cw0msJN0BUycLUTcLf/7zmmK7AO/wWb7eVQNo5QhVzAXi2QoZNo5tuRE7F7ck1sH0Wf54YSdvowv4vfk3ErGIfg9zCK8OFWPXP7qHmpzlg+OvQW+yL+oH3gU7jbevNy6EQWHp/E+62+eGTbE5stZMyuvgS71C525syTojPC7SSLJnV5jhIpKgXaa0Do64ZQzQaNogciOnTyTSjEYGR4gNMbBDm7837rdfg4OHE9+Rxn7x3l3N2jaE1ZBDvXxMfBUvldQsJO6YBJNKITtbjbetOvxjC6B73A1aQz7L7zK8m6BHoGv8ibDadxI/kCYfGH0ZvvoTdFEGLWUl/vxbn4MGKzuuAoa0qGzh6DSYMg2SIKyaQLd0hLqs+FhJ5sCAtAksXi5RyHp1M8NT29aOxciWx1Fsdid6OSq3HVeOBi44m7jTeetn450Uvcj3i+nXqViwknqe/VkkCnqnm+m+q16jF9/jLGTPmY39Z/z8Y1y+n74rBc+9y9c4vZU0by1IAhdOz+DDY2trxe/33G7urHl923Yqe0Z+Ke5/m4/VpslfYlbs+NZkjVSqAFrTYb0TUIma+I0tAaWQsn9O5bMBOPgByZWA3H+FloD64l5eYCJNGE5O1BZSc9dnb/HumxCso3/wV7rZSDr1P5sgke9gKJmRJD605g/O4BtPDrmqvooZePPx9/uZbhg3ogiiJLF35E3fpNea/N54zb/SwjG37ED9GfMTR0Ai+HTuBs/BHG736WV+tNpoVflyJfTx1fOV88b8OcbXouxYoYRVi818DVeDMj26qKPMf1sQ9AlMzEZ0XhZedf5Ot5FBk6ieQsqVQzmItCQTbbpsUodF02oZCCkcjEPno6ut2T6TfjFTYkfEPkreuMafKxVbWjg11qsajL70zb/zJG0VDo98WS5GrS2RzH60dtV+CodiYi7QafnZhM3y+HEXSvBhtWLaVF2y65xht3exlXVn1E26o1eb/3FCJ0Pvx14zRn7v1GJjNRSMGozJ04EdmGU5GOgBE3O4F69wPTavnIcLfXcy05jKPROzkXf5Rgl1p0qfIsbzb4INc7RkGIkkhURjjn44+x8vx8YjIjUMqUeNsH4GMTgCR3QqOogwxPRFUiepstpNEXJAfs9RPhgiu6S78i6SwVMSUJ9Hp9zhxbZ9Ky+86vbAtfj7ddJd5rueSJn5/4rCi+DvuQzS/8jq3q3+GcLm1KVOID4PtVazkddp4JY0ZQpXJu7bit23axZdtOBr3Qn5bNmxaqPWtUGRZFkcPno3FwK3qF4QBXGS62JT/wmkQTHVf2ZEjdyZyNP8Lr9acUuY2LZ0/ySv92GA0WrZces84SlhEEgEG2j+oBf/J1768e2UZhqsFeTTrLklNTWdjpF9SKhyv1BrOeyXsH8mq9ydT1KNzva21sVBBSCpNia1U4bdWqFUeOHMn1mdK/FTZPv0GGx0gkTIASuTkEMtOolBDITzM3czP1Ih62PvkOllpTNnOOjCJNl0Qz3060C+iNv2PQE19jcZEkkdjYOBJETy7ESJyLeuCwzn9/g+wwMpv1PF35K+r4yqjhbeajI334qO33BVZE/v3aCq4ln0MpU6KQq7iRfIEr34eReNgS9RRQpSrdn36Ozr36U7VG3RKZ3EqSxKoLn6I363BWuyEhkWFI5UzcQQIcq9Iz+EXqejRDaxI4fNPIwg1nkNzropX9QIZqKjLRE4VUH+WdWuj278GUcAGZyh6ZygHkKjw9PdiyZcsjxzCVAuxVAvYaAScN/ynn0N8piwrD/1WsabOtYa8BYlNNXL4V+9iq5GoFVPeSldniU3KWxN0UkeiM23x37hM+aL200MfqdToGv9CSG3XO45j9FbZBliJGdiqY11dDJVcjY3f1Z0yTuTmTpM9OTKauRzM6V+kHj7DXVxPD+PbcXOZ3XF+mC3P2aoEqbkK5GIesMWbkZ6/ROKJ4PRDRJQUBZ0SiADMycwAKcxa+XvaoFUpkCMgEOW423tR0b0Bdz6bU92yFncrBOjeYD2fjj/DV6eloFHYYTBKZeoE0nZF0rQy9wQNJyAaMFqkR3BBwwigcxyg7g1Iu4KLxwt+xEoHO3jiqHMg0ZnAvOwpREnO0NE2iiSDnGtR0b8TO279Qx6MJL9Udh/IRdU8epOX+na8WTGfZ4tkA2Nk70LF7X7r06keyXwLR2bfxcwhi9+1fuZBwnACnEF6sNYpQzxZkGtJQyzV42Vd6rPa6waznWtJZark3fuQEWJIkItNvcjHhBCEudcgymHnrp4Eo6E22/RoQkhEkbxRSFSSjAfk2X/Tn/ngo1QX4+fkRGRlZETFcYa+tSnm011qDmSMXYh5rrwGquMtKJeirqCRkiMSkSfxwcRFuNl70DH4xzz7fffExSz6x1Hlycnblx79OYuNux6Q9LxDq2ZyG3q1znI5aYxafn3ofs2RmTJO52CrtCzW//jsms8Tyw0Y2nX/oXK3qKWNaDxWeDkV7js7EHeRQ1DZGN55dpOMKg0JmeQ+zZqBAic2xBRkOXReia7oWk3AHR+NizJF3yE78HMkvCw+FP1+OXFeiRQ0NZj3v7h3EgJpvPtECxj8par8CuJcVw9dnZiAhMbzhDLzs/P7RpsRf4T+x6foKRjT8kFDP3EFet29epW+Hh5JzjVu0o0uvZ2nVpS+RWjd237rJkahdJJv2I5GFgA0yyQEBB0DEJNxGKVPja1ef+p7taeHfkspuCvxdBFRP0I8kSSIxS+JirIFjEZGcjblObNZtQIVJuIRRdhKZFIjCUAtTbBh6j51gkiBNjZCiQUi0wdnOhRET3yA+O4qYjAhSdAl0rtKf7kHPY6vMv7hzYdCbdIzZ1Zeven1BfR/rjLklTVnY7BJ3UF+9fpMlXy2nfmgdXn/1pZzPs7O1zPpkISaTiQ+nTsLGpnBRgGXpoC7tye+wTcPpWvl1lpx6ny+7/flEbfz8wzfMnmLRoLaxtaPP/Gvsi3QEIF05mQG1uzG2de8Cjy/sQHckagebb6xmTvvVuVaa0/WpjN/9LPM6/lRm0VsutgIBriU7KbDGwyuKIgEBAUTHxIJMiaC0xb7Vu5ibaMlWz0ImVgHBiDI2FOO5O5Cux93Jgx9W/oBSrsZR7YpCVnBShM6k5UTMHvZH/klc1l1UcjX+DlUIdKpGfc+WhLjWKfBYa5Jfn9KbJK7EiZyLMnMuWuRavJijuQaQrpiKSmyJRrRE4zvaXyFDOY1Xa62lvp8TVdweHTV589YlnlvZAPFXEf4hExoYVI1OPZ6hVfvuhDZqgVL55AVEHxCeeoXJewYiCAKuGg+yjZk09e1IY+92qBQa9kds52jkce6lGdGKGgTJEYVUA71sL6IQjWAOQHmrJjq7tUjfZeU9gVxFs1Yd2PznVkwimO8XfJXLQSmzRHPaKHliva9/GxUTXuthTZtd2g7qQFcZzqWwgFwQZlHicqxFzmjEtp4s6fpHoSI/HhAdeZvnXmpEVosMnI0b0FTpBoCTDSzop8FOk8SkvS9YJI5sPDGaDYze2YdprZbi6xD4SHv946UvMEtmBtfJq1VamjhqBKtVeS8OxR0zcux19MPCf8q63RD73MQsj0ImeSJItshNlZAiFJgTruCodGDW1DnIBAV6s5bwlKtcTDyB0WygXUBv+td4Pdf7U2lhNBs4HXuO3beOcSMpmnsZMtK0MgQUKMUmqMTWCCiRMGESLiEpT2BjewqVMoHmfh0YWm8I7nYeSJKYS9ZGkiS23lrH5hurGdV4JgGOIehM2WiNWajkmhxpuPx444UunDi8N8/ndg4OVBoYTINqrenX+jVMKgPLzs7mbvpNDGYDrfy7oZQpic26i96kxSyZae7XmWdrvJHLYX0u/ihfnv6AGu4NuJZ0jppuDegdMphA5+rEZ94lKiOcqIxwzt07RlJ2HP72QRjuebA7YQtZ8rvIzTUxyo8BEhrTi5iNkYgJEZjXRpJfBfaWLVty+PBhq/xe/3Yq7LX1KI/2urAOajd7Af9SrhVRWCTJkvWZqdczantvvuy2JY9klyiKjB3Wj/27LPPzWqGNWbFxH3ezb7Lw5GRsFfYs6LQ+1zEnYvaw/Owceoe8RM/gF0lLTCySIxFgzzUTi/ca0N/3Uztp4N1uahpUKvy7hiRJDN/Wg0VdfisRmSRnG4FAN+v9tladYz+w2UpbnJ5eRnaNVRgV+1Cam2FOi0O6k4YQ7YgQa4eXu9djA4Gsgc6k5d29AxlUezRNfDsUq62iOKizjZmsvLCA60nneKuBxR4+ijR9MotOvIdKrmZU45k4qJwA2LjuW2a9OzxH+/kBgiDQsGlrOnR/hhZtuyK4VuN0pEjYXS3nY9PQmTMAKUez+p/IBHC3F3C3E3C7/19blaV+g0oBcsFSUFRrkMg2QIpWIjrVsrikM/7tOyEbrfxHdPLf0ZifR4h2IsNuCvziiEwSkWQKpKopiDWSEG66IIgy/Cp7M+HN9/Cyq4SXnb9VfFhm0cz0g68xpN4LDKhT+uoCT8p/0kENsObHnzl6/BR1atWgQf1Q9Ho9+w8e4V5CIsNeHkSDeoVfmSpLB7Wfs4C7fekZ0zMxZ1lw+DvS9SmMbfrJEwnpS5LEtLGv8OfGNQDUqteYNpP2s+m8GQktKarnGNtoDS828ing+MIPdJuur+JmygXGN1uQ6/OrSWdZeuYjPu30c5Em69bEx0ko8gpzUSjuw5ttkLgdl0mXXv1ISEhE5uyH1CcDo80JQI8guSHX+yGdEJAMKWBvQLIxYWOnoVuPLoiCSKo+EZNoIsAxhFrujWjl3w1XG88Cz2k0G4jOuE1E+nWORe8mIu06HQL70C3o+TxaadakMH1KZ5S4FCtyPtrMhWiRq/eySVS8iJ1pJGqxM9yPrM5UfIqzYQU2Cieqesqo6iEj5P5//ZwF5H+L1LsWd473tw3F8S8Xwo4eymNIAWzt7Fn9+yFCahTOWZ+qS+JCwnGa+3VBKVOSZcjgm7MzORa9izruTZjWeimCIJCiTWbVuTXsufkHuswamE39UdAYAcv9i6STqViM0XwYc2Yy4sFkSL8HlYC89aRwcnLip59+onv3sk8JKw9UTHiti7Vsdmk6qG1VUNWz7FPlIpJEUrUS3537mDoeTYqsV3hwz1be/vhpqKLA1XUrKj9LrQh3O4H5/dRkmS/z2cnJLOy0EbVCw+3Uq6y68Ckz2ix/5NgqSRKT9r7IK6ETqeXeyKr3XFScbAQCXcvWSV3cMSMzM5Nq1asTl5CK4OGFsl8t9G67ASOCFIjSXAPZSReMZ84gyFWAgIeHB7/99mseWSaTaOL369+zN2ITY5vOI8SlthXv9MlIzZY4F23m7F0zZyINxGfmfbYkzBhku9Erf8RRY6apT396hHSlob9rrkJZydp7LD87hwxDGjYKW2wUtqQbUknVJdKv+mu0rtQjj2NekiTCThxi88+r2bnlF7IyM/KcXxAERk78iNfefg+Am8kX+ezkuzT17cCLtUahkqsRJZFdd35l49VltPbvQc+QgSw/O8cSxdR0Lk5qVyRJ4lLiKbbcXEtsZiQ+9gH4OVTBwexE5EUTJ6/ZE6P2IsNjCSpzS0REspQfojA3xDFyNllHFqC/+dd9Fda8VNjr3FTYa+tS3ux1YRzUGiVU9Sjfkk8PMqL+uPEDWcZ0Xqg1Ms8+6WmpDOzZlKjIcADeGvcBb439gN+vr2Dtpc9Z0mUz3vaVch1jNBv49dp37L7zK119nqVvvWHIHxFclB/hiSIzt+qJTb+v9y3AKy2UPNtAUWi7uun6KiREnqn2SpHOXVisGTBgjTEjMzOTqrVrEOecihAqRwh0RFQkAVqU5g6IETGYuYsgV4BOgRBlj2dGCL//sqnQUorFQWvKZvKeFxladzyNfNo+cTuFmWObRTO/X1/BjtsbGFxnDK39exTpfexk7D6Whc3ixVqj6FjZUoA5NjqSPzeu4Y9ffiDy9o18j6taoy4bdpxBEASMZonr90Qu3A9KuxpvJiX7CW+6AMzEkK1YjkkWho/8GdJPQebVbRgbHUE6kAGxDxaTBQSNMzI3X8Qm91A4Sczo+jW92va12rUYRSMfHnyd3tW68maTlwpxRPnhP+ugFkWRA4ePceToCe4lJCCXy6kcGED3rh2pGlw0qYGyclAr5VDDq/SNabsV3elS+QVMopG+1V99oja02myGPtMGk8nIwmU/ExhUje+OGPklzIRRCCNL8TkTm/5A/wZ5CzoVNVVkzcXFnIrdx9DQCTTweliAYdP1VURnhDOi0YdPdA/WoCSj65704dUaJeLTJdK0EpIk0qtXL5KcPDG1v4RZdgM5XijMjVDsCyHryDwQBGT2PshsXAEhj8yDJEnczbjFhXsn2B/5B2bJRNcqA2gX8NRjV8n1Jh37IjezLdyy4t/Quw3NfDsS4lLHqlFdT5J+pDNKnI3O4qsz48jUemDKeBeDSY5BOEmmcjbOhu+RkVtjW62AABcZld0EKrvJ8HeREZ6xkeisUwypPIY9235n19aNnDl+EFG0hGvb2tmz//w9lKqHz8Lv61cSduIQAVVCCKhSlUqBQTi7umNQ6pl1cgRNfNtzKnY/rjaeRKffxtPOH2/bOtS1GcX+09cJT9WQJvdDKuD7N2VF4eg7m5p+Hhw+/RuZh42YTsdAf2AXkJZ7f4VCQatWrdizZ09FuvB9Kia81sVaNrs0HdRVPWXlouhYarZERLJF5uPbs3OZ3mZZkdv4euGHfHt5Dr3av06SYj63Ei2vaZ4OAvP7qrmRtp1t4euZ2XYFgiAwbtezfND6G5zULo8cW9P0yUzc/TzvtVxCFecaVrnfJ8XFVqCSS9k5qYszZkiSRHhiGvXeCSXbKw3JUQRBByhwNHyOcNOVjG1jEDOi7h8hIKgd8alck607D0ABfTg+K5pPj0+gsnN1Xq83pVBFNkuaB/babOPFuWiJ81FmzkaLJGbmnjqIJKKTb8Eg2wOCFl/bVjTz6UOLgGpU95Lh6ZD3t07VJbHx2nKOR++mbUBvulYZkK9sl16n4+iBnezaupF9OzaTmfEwDerTZT/TqcfDSeTdiFu8981LJFWKo6VDd3pVfREPL19Udho23V3Brju/8nLoBHrer2Hxd/bv/IML58M4HZHGHbmA0dsJpUMdRFLQKtZib5xDlmI+evlfNHOfiWFPOhf++hyTLj1PWw+osNd5qbDX1qW82evHOagFAUI8yoe9fhzX4s1k6c2M2N6ThZ1+yVeC6drlcwzt05r23fowfd432NjaIUkSI7f3xtXGg1ntVubbdrYxk1UnFxCWeoQW/l3oFTyoQNnC/MjQSczbqedkxMN009bBcsZ1UhXqu9WbdLyz8xm+7v5Xidhha0p9FHfMiM1IYdy2yfy2+zcMWXLklfyQywPQy7fgmrGbzN9nYwjfbtlZkIOrA7JaGpS1TPRo9xS9QwbTyLttib+vaI1ZTNr7Iq/Wm5zLh1IUHjfHDos7xDdhs+hUuS99qw97ZOb1o9CbdHx3bi530q4zvtn8HIlRSZK4dvkcu7ZsZOeWjUSEPxyfO/Xoy6fLfs7VztypoxEEgUqVQ3Dyr43RPogUkyPx2Rqi0gUSMiTSdYW/LtGsR6f7Ga1iHZI+Bbsbnvz51V/Ya2S89dZbnIk9jDHgLmzKxxuusEHtHkK1liE49dTRNqAXz9Z4o9g+EINZz7QDL/NSvQEMCh1QrLbKgv+sg9qalJWDuqQjcAvii+PfE52WxanY/XlShYpCXMxdHBydsbO3GFdJklh13MhPp0xkKj5BLlXlzcbP82Lj3KuvT+JMTNLGs/L8fO6mh/NqvUmEeloiwGYdHkHbSr1oG9Drie+jOMgECHKXYae2voF5koc3MVMkOvXh4ydJEl2njybF/yJm2SVkog+C0R7WiZgiT+Y+WK5G7uhP/Sat+O677ws8R5o+mR3hP7PrzkbaVurNc7WGP1IL8gFaYxZh8Yc4FrObWymXkQtyvOz8CXSqhpedP+n6FFJ0CaToElDIlNT1aEZdz2Z5dKvy40n61N/ZfGM1O2//ysDqS4hP9eZI5BmOJLyPTfa3yCk4YvwBGcoJuKvaUM2pL+72AnaybFKjrxJ5/Qw2MiNvjngbjdJScVgph+ljh3Dy6AEEQQFypSUazlWDuUMEmgstUauCyap5HFs3OxRqV0R9S9TGYY+8BlNKOJL+W8y+WzDbpRDoUo3KTtXpHjiADdMWc/ruMbJqZsE/lH2cnJyoX78+W7duxdb20fqa/09UTHjLJ6XloLZ2OmlxEEWJy3EWiaKR23vzWeeNqOTqIrYhcvvWFT6/8z5Dan/IN/uqEZlssRU+jgLz+qnZe3cZidmxjGj0IQfvbuVm8kVeDp3w2LE1MTuO9/cPZXTj2dT2aGyVe35SytJJ/aRjRrpOYuvVw4zc3p/MFBUmlYQgOCFIbthnTUS3bQ26yxvyPbZly5bsO3CI5CyJpCwJY14lCAD+uPEDlxPPMLnFZ096e1YjP3stSRIxaRLnox9mOSVm/e1dBj0G2RF08s2YhduoxFZ4Kp+htmd1gj1khLjLCPKQ4e0oIBMEjKKRQ3f/YtftjaQbUmhTqSfOGnciUq9xJ+06KboEGvm0ZUCNt9Bgw+njBzi6fwfHDu3h2w27cXR6mPG15de1vP/OUEsJ+KZAMGAGRCAWVBlqzFXMaNxsmfT8Z9T2aMGeWyfZF/En1xL3I8pMCNihEGuhklqil21FFFIQJD9EziGT61jaZRdNApviaaPjhX69CAsLIz09r5O6wl7nT4W9Lp+UloPa21HAy7F82OvHkaGTCE8UORy1jbPxRxjZ6KN897t98yqVg6vnsmVZhgx6rA9iQ78wvPOpD/RgbHVyd+dE7B623FyH1pRF20q9aO7XGR/7gMdenyhJrD1hZO1Ji96HhAFPp0j6NrqLXrpNtikTSZKQ7md32CsdcVS74qR2wdXGk+3hP9OmUg8a+7QrxrdUMNZ6N3vSMcMsSvx59TCzD02jV/Ag5m6cgsJmGGbbSHTy33DIWIL2p2WYYk7ke3yztt35cNnnbLy6kksJpxhY+21a+Xcv0XeWLEMGk/a+yJsNpub4T4pCQXPs+Kxolpyair3KkeENp+Okdn1kO4XlZsolFhwbT/8ar9OlSv9/XIvEreuXObJvO0f276DHMy/S57mhOdv1Oh2tarlgMhrzaRnUag0KpRKTKGPhugM4+dRAbwKDSWLPzm388euPSMYsJEMGoiIFc/BdJO9UhDtQS2xE5zb96Nl3EF4+Ft9EVnYWPZbWRFyXSWZ8/ja7XoNGfLFqMyZBxYYrSzkctY0JzT7Nt+BzYdCaspm2/yVGNn2NZ2oWLKlbnqlwUBeCsnBQy2VQ01uWSy6gtEjXZdFzbX8UMiWfdPjR6lpRa08aWX08gxTVAFwMP/NsfXtea6XMGXyL40xMzI7j81PvE+JSm5fqjMVg1jN659PMbrf6ieRKrIFCBiGeslzpp9agqA9vbJrIvYyHj162wcyAVSNIFhXoZduRyEKu9cC0MgoS8o/OcXJy4tsffqFGow659JrzvT5J5M+ba/jzxg8MDR1f5GrBoiQSnxXFnbRr3MuKwUntiovGHRcbD/QmLRcSTnDh3gnuZUfhpHajqW+H/7F33tFRVF0A/81sSe+dhJ7Qe++9NwWliIDSFOmC0gSkCVKkI/qBqCiIKEUFQXrvvfcWQnrvW2a+PxajkYRskk0B5ncOh5PZN2/uzL59d+59991L3SItM3zByq2Dmqdbeb+5NJckvWnbb4IujtDEYHr4L0EjNed2mMSDSIngWPmZTbcyqcRrpmEQrqGV6mFtfAO1nPG8IpFAknoZyI4I2KAXT2AUHmEQb6CRKiNga6r4bJiIVmqUqbzG+CDUUZfx1oRTz98ObflgjkbtRBREJjdaSTFHfwD8XARcbKDZimYkbk0k5EYIktGIqFJRokQJpkyZQps2bZRIrP+gGLyFk/xwUAuCKUrH0nN6bgiMkohKktlwbQWetn60KPFajvqJSg5n/P63mFL/F6Zvt05b0PR1FpjX1ZrvrnxMgGsVOvn3YciOtnzZ9k9iIsKznFvjUmP45GA/+lYaTZ0iLXJ8n5agoJzU2Z0zJEnmUZTE50ens+7aUlRGX1JSyyJgh4webVQtEn9Y8q+o6fT8N82DLJuc1OEJMjrDs+2/OPkR1b0apW2fLSjM0deyLBMcJ3P1icTVYCNXgyUCo01j1ZQG5Cgpqp+R0eFgmIJKNr0XWKlNOq+Yi0hRFxEfJwFXu2Tux/+FICZQwqksJZzL4qh14XDgdn6+/hVlXCvzdsVRmb5Dzv30Q35as+yfA+qnDmpBjVjWDbmeAVF0x8aqIo6+PkSl3MEghKKSSmJvGINGroqRMPTicXTicdwMNanuY8cp3QSKOPjxZdsd2GhtKO4q4mQjIEkSu3btYubMmTx48EDR12ag6OvCSX44qO2tBEp7vFi/h7vhEvEpEh/ueYOJDZaZFYjzN4tPTeR08H7Wdj76TIrLjObWuNQYTjzZw4mgPQQnPKSYYwDl3WtQ2qUCpZ0rpBVnM0pGEvSxhCQEciX8NPvuneD8kyCMkjUquRjWQil6Vi1DvRLOCIKAgICMTIIujrjUaOJ00UQkh3Az4gJXI85S0aMmpV0q0tivPZU96+U4qjYjLLF7OSdzRkySgSn7P+NBzB38HSvx04E16FKXkOg6H71wBtvE4aR8txlj5I0Mz/9bZ7dp05aIRJlb4dH8eGUJV8JP0a/ymGynb8sOCbo4xu3rxdCa06nkUTtb52Y0rg4++oOfrq5gTN15lHGtYnF59UYdK85OJUEfx9g687HR2Jl13uXzJ+nbxbxI8XXbTlCx6j9BFbu2/cq4D3pBcRBqC9g7OlFFqE/nmn1p0Lg1js4uz/Tx641V2GkNVEvyZ+bMmdy7exdRpUIQBIoXL56mswVBIDReJjxe5nHcQxaeGoe3nR99K48x+/dvkAzsvLeBP+9+z7Rmk2hdupVZ5xVGFAe1GRSEg9rTQcDHqeAU6jubR4KspZZPE4tFH6ckJ7Pos/H0HTyak1FFWXpiA5IQhJ1hJO0qqBjRTItKFHLtTJRlmTWX5hKeFMzHdRdyN+Yq/zs/i/ktfi6wrb3WGijtXjAVhmVZ5nG0TFTSPz+7kNhEuv/UB+SGJKvXI8lRkKRH+l8sxGe8r+Xf20aNskBQjClNSFYk6RP45uLn3Iu5zruVP6KqV/0c3nHmRCWHczr4ACef7CUo/j7dyg6iTck3LbLokRmyLLP6why23/2RJkU7M6L2LDSihhS9TGC0zINIiaBYk8M6KEYiJE4mPlVCLx4nRbUJg/AQW8MgVHJ5EtWzkYUYJGIxCg/RSPUxCncQsEPAGhBx0i9FJfs9jUqQ03JJq0XwchQo6SZSwk0k+Opeqpawp261iji5uCDLMl+dn86e+5tp4NeO4TVnYKW2hn9FGpx5cobvLnzH8g7LMRgMPLx7i+Kly6BWW+6F8WVDMXgLJ/nhoHa3F/AtZIWW4lJk7kdIRCaHMv/EWD5v/mOO+7ocfoo1F+dSK6Y7v4U1J04y7YLycxaY3UXNgjP9aFOyO0/iH+DnWIoqNnXMmltTDMlMPvgur5d5l0ZF2+dYPkvgbCNQLJ9zUmdnzjAYZW6FpdJrSzPuxdzAWt8SY2ovUlXbEGRPjI/uk7p+Oxiz1tf/vZYsm94HwuLTO6r/LoA5ueFKfB1KWOamc0BO9XVssqnI2M1QIzdCJG6FSUTrrpOgmYVaKoOd4UNEHDM9X6syLV642Aq42gnYW4GdViBKf5TLMV9jlJLxd2pORbfOuFgVR280RaSn6iXCImMIiUwgMl5HXIpMnBBPtM0SRFyw108EZAziTQzCDWSSsTH2RMQdiXi0YhQ1vD1oWsGJsj6JrL40juOPdzGw2kR6lH8fQYASbiKO1s+OVUVfm4eirwsnee2gVotQxktEY0HbKz9I0sncDpO4EXmBn699aVbarscP7/H9V18w5JNPGbq7A82Kd+aDGp+ma5PV3Pp3ysabkRe5G32VezHXSTYkIgoiAiL2Wic8bX2o5FGHSh51MOi9mbkjlfuR/9iCXauqGdhA81x7d9KBfgytMY3Y1CiOPN7BpbCTOFm50q3sQItEVlvie8/unBEer6PPlj7UK9KSc0FHObX/EnqnhSQ4TcQg3MY2fpDJOR19N2OZM9DZyXrTInVYQjSrL84hKjmcMXXm4WrjkeP7eh7xuljG7evFyFqfUd69htnn/XtcGWWJL89OJVGfwNi687O9my+7nAjaw5qLcxlTZ16WBRf5+/0nIoxH92/z6MFdHt2/TVjIE+Jjo4mLjSYuNgZJMqJWa5i15DvKlDc516OSw9hw7kv23N5EFc96DKw3kaKuz09nFJsaxfj9b3Ho3Z1o1GokSSI48AGOrh7Y2dllOK50BpmQOJnoJJlLYSdYe3kRTlau9Kv8IcUcAzJ8Z03QxbH/4W9sv7uWHhW78UHtQVg/tfNfVBQHtRnkt4NaEEzR0wWpUI8+vMLsw3OwUdsxpdHKXPcXHhrMqAFduXbpDAHlKrP2tyPsv6dl+rFOOOlWI+JGY38VH7fWohFlizgT/7z7E/sebGVmkzVsuP4lzlZuOc6pbQlstSYntaVyipv7430QKaVzJF8MDOT97e+ikd4iWf01BuE+1pKKKsfrc+P0oWxtG41KNDlfJTN+0eFJwXx/aQGP4+/Rp9LoPNvepTOm8t2l+dyKusTYugvwsS+WJw7qvzkSuIOlZz7BUevCzKbfPneLXIpeJiLBFMH2MCaYtdf7E5x0marOo4nWPUCWRUrZDibFmIKLphJatRaVaHrZsrcSnv4zFfzysBfwchBwthUQBXgUd4dLYSe4En4aa7UNRexL4GbjxdZb3/Ig9ibTGq+m1r+KYFhrTDn5VKLAGxvfYGm7pfg6+lpcKbysKAZv4SSvHdQq0VQbwpKLjZZAlmWuBUsYJBi9uxufNf0+w9yV5vY1fsFb7Ir5FfVJbwJ6XCZaZzIyfJ0EpneW+P3uPK5FnCPFkMTntX4wa241SAa+v7SAH68uYWaTNXm+bTUrnGwEirkI+Vbnw9w5Q2eQuROup+/vrbgRcRHP5B9JMTQjWtsTja4RYuQWyl614ebVi7lK8yBJJl0UFi+n6fDHcfeYfWw4S9r8ZlZqrrzAUvpalmVC42Xuhkvsvf8Xx0JWkawzkGLAlKNbdkYjVUYtV0EjVQa0SMITjEIQkhCEUQjEKAQiCRHI6LEytkeUvdGp/kISQgER00b2SGSMiLgiYIUg2yILidjrp6KRK6STSQDcnYKxsd9BgrwXR2sRbwdXwpOCiUuNIiI5FCcrV+Y0+4GSzuUQBSieiXOaPNBDLyuKvi6c5LWDuqR75r+dws6jKInoJJlphwfzVoXhlHWrmmnbU0f38dH7PYmLjeatASNIbpVAkj6B18u8SwO/tmnt8sIWStHLLD2gY9/Nf/JHVfAWmdhOi4d9xte4GHqcfQ+38mGduWnHIpJCWHtlIYFxdxlcbVKuCyvnNnI+O3NGaJyeAb+/S22fZvxx7Qce7tQhlRtHgt1cDMI9vOThFD/zkOtHN2VbZ/+d3ioiQeZq+BmWnpnE62UG0K5Uzzx5f4pLjebjfb0YW3e+2ZHPf48r2R5mHv2AdqV70aH0WxaXLTOiksOZe3wU/q6V6F9lnMWi8SOSQjgTfJA9DzahEtR08H+LBn7tzH43mnt8NG9V7k7ncibbOztjKkln2vEWnyJzLfwyP19fSVhiUNrnWpU1OmMKIONs7UjzUk35oFZ/rNR5uyCQXygOajPIbwe1q61AUdeCfdHUGWRare1EqiGZle12PrNNKLvEx8XSt0sDHty9CUDrTm8y78uf+O7sIVaf/RUHvUlJVfUVmdJeQ0psqEUU6Jngg/zv/CwGVp3I2isLmVh/KX6O2SuSaUkcrAVKulkmasucH29InERo3D8/t5/P7mbe2fcR8MAo3gZJg7+qHENbDWdEve7s3r2bmTNn8vDhQ2RZfmYLSkbXSdGbCnSlZJzO6RmiksNYe3khYUlPmNLoK2zUeZMn8UHMTb449THl3WvgauVBfHwMtvb2+NgXp2mxzrke0//mYewtphzsz6O4u5R0LoeD1gmdMZVeFYbRrHjnZ9rvf/g7668upXfFkdQt0pI/bq+ltEsFs532eqOO65HnOB96lMthJ0k2JFLMMYCqnvWp5FGbZEMSm26s4njQbqp41mNC/SXpnFWiYEo7Y6MROB54nI1XN7KonSn3qGLwmodi8BZO8tpBXZhzWT6OkYhMkPn99lpERDoF9MlxX1/M+IgfflwMHcBG703pGleISDYV0PNxFJjb1Qqj8Jj3d7ShtH0FulYcgIPWGTutI45aF7zs/NLpubvR1/ji5Ee0LdUTg6xn1z1T0ZqO/m/TvvRbBeYMtbcSKOEm5Es6NXPmjBS9zN1wI+P3vcu+B7/jmLAOgWakiodIkr/GLukWpyecoISXY1qah+zo64zQG00RO1FPczrvuv8rV8JPMabOPIvev7nk5YIyT99vH8fI3AwL5XLYRe5EX+Rx4kVSDHpkgy8GvS8q2ReVXBRRLoaIO6AnWfUzKaqNWBk7oJUakaLahF48h1ZqgiDbo1MdAmQ0xlo4aqrgaeuGt4MrXvbWGNVnCU89SIzuLl72RWharBP1fVtjq7EnPCmYL05+hI99cRoX7UB59xrYqG0RBZODzf45NUwUfW0eir4unOSlg9rDQaBIAe5Ezi06g8zNUInghMfMOTaSxa03Z9r24tnjDOzeIi2v7uB5n5BUMp470VeZ1nhVWuBMXs2tsiyz7YqBrw/rMTxN/+hkDePbWFGj2LO2lqmgY0e+aPnLM6kZQhODWH1hDvG6GMbWnY+HrU+O5crN+5q5c0ZwrIFhfw6hjGsVfr/yHUEb7aBpb5Ktv8MgPKCoaji/9JtIdT+BvXuyb2P/TVyKKZo6xaDnu0vzuRdznamNvs4TGzo6JYIJ+3szrt5iSrtUyLK9LEucuL2HNXfmMaH+UrPOsTSyLLP9zjr+vLue8fUXU9wp+3NLRFIIx4J2cSb4IJHJIbjZeFPdqyHNinfBxdo9W33di7nOmotz+O2tH9OCIHKqh5J0MgmpclpqVUmW0UspeNjZYqulQAM98grFQW0G+e2gLuslYq0p+MH2xZEf+f3WBt6t8hE1vBvnur/7d27Qt0uDtGroIyd8xoBh4xm87S2CHk9EMphy4pZ0ExhTPwb/4l4WUaBxqTGsujCLB7G3SNQnsKr9bos6J7OLpbYWZ/XjjU02pZng6cQ9c+cXbH28GCvja+hUBxFT3BlRaiDHVevZ1XcH9laqdH0nJydjY2Njdq7MoJj0aUSy4tSTfXx7aT7TG3+TrSrS2UGSJc6FHCbVkExCbAxOLu7cjr7C4Ufb6eD/Nh1L90aj0lrkWkbJSGxqFNMOD6Jb2UHU923NynPTiEmNZGzdBThonXgSb8orVcq5HAOqTjA7v7ssy9yJvsKxoF2cDzkCQHn3GlT3akgljzppOeIkWeLAw9/ZcG0FbUr14PUy/TNcSS7qIuJqZxp/r214ja87fY23vSm/pmLwmodi8BZO8tJBrVGZoqfzK+I2uySmytwJl0jQxTHlUH8WtdqU474MBgPD+nXk5OG9UBXs63tS0vci4QkmY9LLQeDz162IN1xm2YkpNC7VgURdHPH6WGJToghNfIxKVFHapSIiIg9ib/JxvUV42fkiyzLDd3ViasOvORi4jWOPdzG72dq0eSy/sdFCKbe8j4rPas7QG2VuhxmZc+xjtt/+hdSE9jgaFppidMUWOEkRHBh+gUp+zs/0mx19nRlJOpnHMRLJOvjm4udcDD1O30qjqeXTLF8NoLx2UGeF3igTkyyTmAqJOpkknenZGCXQG41ciNjEo/jT1PDsjr9zbbRqARuNgKO1gFadyK2YQ4QlPTRFRCeFcD70KM7WbpR0KkcRhxJoVVbEpkYRmxpFTEoEcanRfFhnLgGuldNkUIsm57St9vnPXdHX5qHo68JJXjmobbWmHYIvuuPm79pBy85MpqZ343TR0P9l80/fMGPc+wBorLSUnV2VGa3XsODkWJa03opGpc3zufVmqJHPdurS6h0JQO/aanrX1jyzCLz9zjqSDYm8We69DPu6E32V+Sc+5J3KY597389DEEzjIKt5NCPMmTPC442M2jkaH/vi/H7hewJ/cUDdthmpmm0YhXDKW3/GNz37UdZbhcu/cmLnVGen6E12faoBTj/Zz5pL85jRZE2unPiZEZUcxoT9b/NJwxVZOnv33N/Er1f+x+et1+GcTUeupQlJCGTeiQ8p6liatyuOeq5/QZIlLoWd4FDgdm5EnMfVxosGfm2o49M813XLPtrbg3mt51O9yD9BkYq+Nh/FQW0G+emgdrQWKOleOAZtYHQyHdY3pYJHLSY1WGbGGVlzaM82Rg3omrZyuGTNForXLsvcY58SEbSauKfpFN1sJWa/Zk1xN8vl1HsYe4uJ+/tgrbFjdfu9BTo5uNoJFHXJ3fWf9+NN0Zvyl0myKTfRe5ve4XZsNLKYjCxEYR3dlMUth/J74nLeqfoub1Rsnss7MhGRIPEkVkY28xf+MPYWs44OZUydednKd5Vd/vtSpjOm8sftH9h5bwMdSvemS8A7Flu00Bt1fHZsGAEuleldcQQXwo7x5dlPqeBek/sxN/iwzlxKOpczq6+HsbfYcnMN1yPPU9a1CvX92lDdq9Ezju0UQzJ/3F7LngebqO/bhh7lh2Tq7Pn32Nt3fx877+xkXut/IuUUBWoeisFbOMlLB7Wvs4B7JltWCws3QoykGmD8vt6Mr78kV7kKY6IjebtTPYIe3Yfi4P5mcYq6niI4zjT/uNoKzO6iYc7xDsxv8zOOVukLxOglPXejrhCTGkndIi3TOQvOhxxh94PNjKu3kHMhh1l9YQ6zm/2As7VbLu4+51hrTHl+87Lw5fPmDEmSuRshsez052y7+QeBMbG46Y4hoCY+egGiz3K297tAwxJ+eb5AEpFgqpkQnhjOj1cXcyvyEu9UHkPtIpZ5T8iKgnZQW4qg+PvMOPI+A6tOwNehFAm6WBJ0seikVJysXHG2csfF2h1rtW2634ZWDSXdzAtUUfS1eSj6unCSFw5qjUokwFNEW4iKGOcUSTLl1Y9OjmPs3jdZ0fbP56Yv+GzScH754SsA7Js7MWLULDzdfTn1ZB/j6y/Ol7k1Lllm/p5UTj+U0o5V8RUZ31qL27/en3TGVEbs6sJX7XZmupCQYkjmi5MfY6exZ1itmTnaaaVVQxlPMdu7pLKaM+JTZMb+NRmtaMUf534keLsnqtZF0KlPAVY0dP6JBV2r4GYnWrRIp8Eo8zDKFFF7P+YGs48NZ1y9RekWOC1FeFIwkw70ZVrj1RnWpZBlmdUX5xCWGMTAUuPw8i5aaHT2+dCjrLuyFAetM29XHIGjlSspxiRSDEmEJj7m4KNtPIl/QCXPOjQr1plybtURLST78aDdnAvZx/9eS78LTdHX5lMQOlv5Rp6Dh0PhUage9tZ8UGMahx5tQzbX45gFTVp1YujYafB0YpswvA+6JykUdXJnaIvreD29/8gkkbGbdZwPNGbRo/kUdyrDutdO4mTlRtfNlUjWJ1ms7+wSlWiKVMoLjJIp5YYkQ1jiE3ptbsOt+CcY1BcBDS5PhrDhrU85KW+huldtupZvZrFru9uLpmKQZv7KizuVYUHLjaw4+ylXwk9bTI6s0KqseKPcIFa224nOmMqwvzpyNviQRfrWqLR82uh/pBiT+PTwIMq5VWdp69+o59uKJa23muWc3vfgN0bs6sLay4toWaIrX7XbyZi686nv2zqdc1qWZTZe/4rRu7tiq7FneZttvFvlo0yd07ZaU6EzgBRDCp8d/oxPGn9ikftWUHiZsVKDm13h0c+Z8XeUTptS3dl9/9dc9eXs4sai1ZuwtrGFhxCx6SGRyc3xdU4GICpJ5uMtOuq7v8fKc9OfOV8jaijnXp16vq2eMUCrezciIimYwLi71PBuzOg6nzNuXy9CEgJzJXNOSdHDnTCJxNSCiZ8IjJbZevNX9t49zsPYxzjr1yGgJuX+dvBdzPIuv1KveN47p3mqx8t5iZR282Rkrc/4rNn3bLuzjo3Xv8rza78snA4+wIwjQ5jWeDV1irTA16EEZd2qUtOnCfV9W1PBvSZFHIpjo7FL99v4u6B2YdhFqaDwoiFgytn+MjinAURRwMtRwF7rSIfSb7Px+vNrQo2btpDqtRsCkHA0lmU7JlPPsxVWKmt23v05X2R2tBGY3smKd+tp+FtdXQqSGLohhdMP/7HptSorKnnU5kLo0Uz7slbb8EnD5VT0qM2Hu7sRr4vNtjw6g0m/WpIUvcycw8tI1idxNuQwtk/aILdLQac+iEYqT0fvw3zRrQrWGgFfZ8uORbVKoJS7qZBvSedyzG3+E4tPT+B40G6LXgfAw9aH6Y2/YfrhwUQlh6X7LC41hokH+uCgdWJi/aWoCyhNW2ZU92rIgpY/83alkWy6+Q1fn5/Jz9e+5K97G3kUe4d+lcfwZbs/GVpjGhXca1rMOW2UjHx3aT6fNlNs6xcNxUGdCTZanptrLr+x1gi0LNUWJytXfrmRdQVhcxk4YiJtOnUHICkxgZEDXqezb1/2BC5n0ZvWlHY3PYOEVJj8Ryo7rhqy6NF8BEFgUatfaV6sC51/KUNIwmOL9Z1dIhNkAqMs76QOipFJ0UOyPpG3t3TkSXwwBvEOTrqvKfpwLL+MHMjJxJ0YZT0TmwyzuMFrZyUQ4CliY2bmDCcrV2Y1/Y5lZz5Bb9RZVJasUItqelb4gHktfmLfw61M2N+HiKSQXPcrCAIDq06go39vRu9+nbCkIOr7tjZru+H3l7/gdPAB5jX/iSmNVlLZs26mVXsnHexHkj6Ble120NH/7eemK9GoTBGCf/c169AsRtYZiZO1Uy7vVkHh5cfb8cXYLvy3g7qRX3sOB/6Z6/7KlK/CZ0u+N/3xAB6tu0YinSnlkQpAfCpsvdieoNhEzj/H0MyIoTWm8eU504J1GdcqTG30NVMO9Scs8Umu5c4JBgnuRkhpuZjzi5A4iaOPTrP81FJuRt7G1vABajkAOXADXrXm0qRkB7pXbpavhbPVKoFiriKl3EU87VyZ1ngVQfH3WXX+M4sFLLys/HpjFZturGZxq83PLZr8XxysBfw9Xh7nmoJCfuPt+Pyc7S8ibnYme6pzQF+OPt75jKPw32i0Wr743y/4+BUHHSQExTJm0psMrTGD7XfXcS/mer7ILAoCvWppmNfVCnd70/cRmwJT/kjl68M6dAaTDnmz3Htm+Rdal3yD96tPZvy+t0jQPVtkMCtik2VC4ixjbxuMMitP/czlsNPcib5CEe1b3PL+A6MQiJ3hQ7qW+JVpHV3RqgS8HYU8WWwUBNNOWC9HAVcbD75o+Subbqxi34OtFr9WEYfijK+/hEkH+qUtEFyLOMtHe7vzdqVR9KowrFC/G/u7VGR8/UVMabSSj+stZEStWbxdaSTFHP3z5Hpbb31Lp4A38XVSbOsXDcVBnQmZVbstSJxsBD6qu5A1F+eSpE+wSJ+iKDJj4RoqVq0FQPDjh8wb9SGp+mT0BDGvq5ZqPqZCD0YJluzXseqIDqNkOaNodJ3P+aDGNHpsqc796PxR2BkRlWQqemApgy8+RSY6ScZgNPDGL52ITI5AkN1xSf2Vt6u8zs+fduRy/FHOhxzhi7af51mUjlYt4O8u4mRjXv/O1m50KzuYNZfmmtHa8jhaufBxvYUMqjqBTw724+STvRbpt26RlsxosoZ5Jz5k881vCEkIzPS7lmWZL89+SqI+nnH1Fj5TOOTf3Iq6xId7utGj3Pu8W+WjLF8ORMHknP7bwXEp9BL3ou/xWrnXcnmHCgovPzZacLYtvC/g/0arFrC3ErBSW+Nk5WqRBbeW7bsyYvws0x/34e4PF7G1GUEVX9M7S4pB4M696cw+PJVUQ4rZ/ZZwLouj1iVtvvVzLMUnDVYw48j7GCTLLUxnB1mGwGiJ4Ni82eH0X+JSZC4FP2bM7iEEx+pQyWWwll7H3XsSJZtuBGuZ/3VZiU0BRdQ6WAuU8RTxchQZU3cuapWWBSfHIsn583xeJCRZYtGp8YQkPGJ2s7XP1eH/xc3eVEA7P4p1Kii8jDhZy4VqF7Il8XUSEQWRoTWms+zMlOe2dXX3ZOmardja2cNZOB6/i9WLP2Nqo6+Ze3w0yYbEfJO7UhEVX/a0pm6Jf1IobrloYOTGFO5FSPjYF0Mv6cx6T6nsWZf3q09h/P7eJOrisy1LaJxMTDbqJGXG+ou7+f3WT0QkBSPrqrPl/keAGhfdr/SqMIpxbaxQiaaaBJ4OeevX8XYU8XUWsFbbMKf5OvY+2ML2O+ssfp3SLhUYVmsGE/f34ccrS1hzcR7zW/5MZY86Fr/Wi0yCLo5d9zcyqv6gghZFIQcUPi9sIUCrBmfz6qXlK842ArV8muBs7caS05Ms1q+1jQ2LVm/G09sXAHt7R94oPYj1V5dhqxUY0zCR16v+o9A2XTAwbXsq8SmWc1K/UW4QY+rOY/SebgUaERSdJPMoSs61DKZChRIpqTpafNWW4MRryAg4GCbwft1GDGqo4WrUKX658TUrOqzCxTZvC0WKokAJN9HsF8Y2Jd/kbvQ17kRdyVO5noe/ayUWt97Kzrsb+fLspxZxknjZ+bG41RZEQcWqC7MZsasLY/a8yVfnZrD/4e8EJzxCkiXmnxyLvdaJoTWmPeNwDoq/z+77m1hxdipj93Zn1YXZzG2+nurejcySwc/lnyIhRsnIx7s/ZmHbhbm+NwWFVwGfHFaBLyj+jqJuWrwzBx79bpE+BwwbT8dubwPgGO5MrD6U4S3CqFvC9GwkyZnwsCF8uHN6tnTZ6Npz+O7SAu5EX4WnTutO/n1Ycfb5RnheExYvczdcQm/Mu3cDg1HmRkgcPTa0IyrBFrBCLZXE1XM6H9RtByKs6PA//Jyczegt7xBFAR8nU17XYbU+xt+lEhP2v01MSmSBylWYSDEk88nBdyjpXI7htWZma7twEScBP+cXY4eGgkJhRC2Cj8PLu2hmZyXgbCNQ0aMWoiByOfzUc9sHlK/M58t/RAgRwAMErYiHrQ+Dq01i6ZXJ+WrzOtoITOuoZUhjDZqnZueDKJlRG1PYdF5P1zID2XxztVl9VfWqz+Bqkxi3/60cBc0FRksk6XJ+7/vvnWfJsZmcOXeIe2F6ToWvQm2shnvqQfrVrMXQJhpEQUCrhqIu+TOfu9uLFHcV0ao0zGiyhvOhR/n52vNTweQIWSYqJYzfb3/PrCbf4WTlavlrvOD87/xMRtT+GAerwpXuRME8XixLL59wtxMK5cuptUbA1kqglncTolPCOR18wGJ9e3oXYcmaLfR7fwxLv/udeiVbcSf6Kon6eEQB3m+kYUSzf3JYnX4oMfKXFB5EWu4l5PUy/VGJatZeLlhnXUyyzN0IGUMujOHQeJmQyDhaffUacVYnEHDGI/UUHzd5nV61NBwL+ovvLi9gRbufKO6af6shRZxE/FwEshregiDwUd0vWHhqHEbJcrnHs4uN2pZPG39NMccAxux5g9jUqFz3qVFpeb3Mu0xptJLlbf9gdrMfaFi0HZHJoay5OJdhf3XE36Ui/SqPSXdeiiGZ+SfG8OXZaSTq42he/DVmN/2B+S024Grjada1PRyEdNWjl55cSs+KPfG2z12FYgWFVwFbrYCDdeHTzc/D2ca0a6KRXzuOBO60SJ+CIDB17td0fWsgP/5xnLFN57H2yudMbqehaUlTug9rqSPngh4yY/dps3c82Wjs+Kzp98w9PorQxCAA2pXuSZI+kUOPtllE9pySkCpzK1Sy6ML4v3kYZaTdt21JMjog4IRWakwNP3uWtp/H91e+YGGbRbQsXXgilGw0AgGeKobXHciAqmP5eF9Pi6SRedF5FHeHsXu70yWgH6+X6W/2eWoRSrmLeORxlJ2CwsuOShR42Tcf+DiZ7nFYzRl8efbTLHexNGnViXHTF9G9xvv4tS8FQC2fphS3D7Bo2k5zEASB16tqWNbDmpJupi9KL8Gqo3p+OdGQo4GHzA4IqubVgEFVJzBh/9vojKnZkkOS4UFkzhae70Q+Zvhvg7h45QxJjlZE6G5jY+iLq34L7zVw5916WgTBZOsWdxVR52NKLmdb4ekuWRWfNFhBWFIQnx0dTrIh97W2QhODmHpoIL/fXsvyNtsYXftzxu3vlaMo9peZ6xHniEoJp3ultgUtikIOUd7E/oNKBNdCXHzJ2UagZcluFHEobnFHbvnKNRgzeR5qtakq8etl+rP11rdpn3espOGzLlY4Wpv+Do6VGfVLCoduW27778f1FrL55up8y82VGYmpMrfDJZL1WStOSZJISEhAkkwvKEk6iRWHvqXlT+VIsDuCWqqIW9Juunjeo2MlDX/d28hvt75nWdsNVPB2yIe7SY+bnUhJNxFVFr9+T7sitC75JuuvLcsv0TKlU0AfhtaYxsd7e/Ik/qFF+7ZW21DZow5vlhvMJw1XsLLdDrqVHZiuzbWIs4zc1YV6vq34rNn3vF6mPxXca2Kltjb7Os42AkWc/nnoQXFB7Huwj/7VzDeiFRReZXwcC69uzgxRNEVb2WrsUYmqHBUWyggra2s+nfc1Pr7FKOdenQRdHCGJ9xlUM5m+dUw63EE/h+0PPmH69hQSzYxUcrXxZEqjr5h6qH+arGPrzmf91eUExT+wiOw5xSDBvQiJJ7ESUi7SjP1XZz+J0dNgUR/iiEEjNUJtrIhWt4tR9Xsx5VB//tdpFfWLVbLgnVgOd3uRrhXrsPa1bZwI2sNnR4dxL+Y6salRr0R+6hRDMgcf/cGcYyP5YGd7fri8iA/rzKW+b2uz+7DRQoCn+MItfikoKBQMWrWQlnO4abFObL75TZbnvNV/OGNen8eOuz+lHetReggngnZzO+pyHkv8LCXcRJb0sOaN6uq0Y9dD4GFwG+Yc2GL2wnZ170a8WW4w0w4Pzna6Kb3RpNOf56T+r76OS4mny7eduX7nCpKnFkHlhJ1hHK7SLCa0sebNGv9EzPo4Cmk7VvMTR2uBku4iKlFgRK1ZtCjxGiN3deFq+Jkc9ReW+IR5J8Yw9/go+lQaxScNV+Bq40EDvzYMqDqej/b1UHZRPcUgGVhyehJzWsxTaki8wAg3b958od5gy5Qpk+s+JEni6KUgHNy8Ef6z9c/dXsDXufD67XUGmavBBobu7IC/S0Xalu6ZZ3mHjJKRAb81o5dqKO0690x7ViFxEjN3pHI3/J+h06WymkENNbmeDGRZZvCOViDDl+3+RKuyyvV95AaVCMVcRRz/Y7hIksRff/3FrFmzePjwIbIsI0lGPKp6EtvQwGNdDIjWqOQiOCbPY2BZK7q3rsCWW2u4FnGWOS2+IsBDU6A5DpP1Mg8iJXTPWV+QZZnZx4YTkhhIGdcq1PRuTDWvhthq7HN8XVmWiAwLwc3z2d9fVgTFP2D64cGMrbuAsm5VzTonJCGQby7OJSzpcZrBbpD0NPBrS5eAfjhauaS1jUoO5+jjnYQkBiLLEkbZSHRKBHGp0YyvvxgXa/ds3q0JOyuBUm5CuiKYo3eOpnfl3tTxzfz3K0kSoUGP8PIthigW3nmpoLH0c7p165ZF5HrVsYS+BgiOMfAo8Am1K/q9kL+DhFRTioqtt77FSmVD+9K9LH6Nu9HX+O7SAircrU7Pd4ey96bM4v06YsUv0EiVCXBuy7SOVukWyZ7H5fBTfH1uJm+UG4yPXVFkZJacnsTi1luwVhdcDrQkfQK/3fqOUi6laV6qBv5uPmbteMtMZ/uVqsS9OhWIcfwFe/0MdOIebLnCd29tYebR9/ix6zrKePjmy73llthkma3X97H/wQ4ik8OI10WjM6ZS1bM+71Qem608zH+TG32dV0iyxLmQw2y/s56wpCAa+rWloV87ijn6Z3v3o6utgK+zYJEC1Yq+Ng9FXxdOLKWvX5XfgSzL3AqTSEw1MnJ3FyY3XGlWIdaFp8bRsnhXqnjW5a/fN1KiahkWXZ/Akta/FZhuvRRkZNE+HcGxMhIxxGrfp47TLwxvpqGsl3kpKLfe+pa70VcZW3dBtq9vpTbtYPnbh5CZvi5RqiShzaO4nxSI4KBCJZfCwTAVN3VrpnWyplKRf2R1tTMVLyxIknQy9yIkjBLEpETy+fGRlHAqS4/yH+Bq45Hl+UHx9/nhymLCk57wbuWPqOxZN8N2t6Iu8cXJj5jZ5Ds8bL0Lnc7OT368sgQnawc+aTroue8Dr8o8ZQkKQmcrDur//HjLeYtYFfIVl9thRiYfGEGr4t3YcnsNc5r9kCfXuXntIgPmN0cXkcyaGQeoXP2fiTHVILN0v469N/9J/1DaXWBiWyv8cqkQDj3axo67P+Nh58OYOvNy1ZelcLMXKOJoMmKSkpLo0KED58+fJy7uXxWMtcB7NoiOfqhULsiosNLVoWKRJ2js4tGIWmp4N2JIzQ8p7a6yiEGUW/RGmfuREsm657czSkZuRV3kbMhh9j/8jeVt/siRsYsFDN6YlEg+OdiPRn7tSTWmEJr4mLCkIFysPWhXqic1vBsjCiI6Yyo/XFnExdDjjKg1C3+XSmnKSi/pOfRoG3/cXouLtQd+jqU4F3IEB60zjYt2oJhjaQRBRCWo0KqsKeVcPsdpf6zU4O+RfotZaEIo7297n629nl/lWVGg5qEYvIUTSzqok6MeU6LEi/s7uB5iJCQ+nHknRvN5c8sXzklOTuL1xRUI3fiYQW9NYPi4WZwLNDJz5xOeyCNx0a3D3gomtbOiRlHzDM7bUZe5FHaSkMRHBCc84mbkBTxsfVnRdluBpEGLSAph8sF36RzQj3hdNDcjLxKTGoyPgxsj6w6nrl/GxltmOluwcsK2/wcke36DtfFdJPkxLpobLOn6DSvPT2VOq5nU9auWj3eYeyRJJiReJiJBRpZNTpSDj7ax/upS2pXuxWsB76ISza95UZgc1HpJz++3vuevez9T06cJHUr3pqhj6Rz1pRLB11lMl3Irtyj62jwUfV04URzU2Sc+xeSAfBh7i0WnJrCo1aYsdWNIQiCLT0+gTkgLZk0cSukyFXh/+VRORexjUoOC27GaopdZe1LPlgsGYjSjsDMMRS2XpW0FFf3ra3G2yXqu/N/5WVirbZ9JkWgO2qdOaqMuOWMbG6CjCirZgrWMSvLHWb+cYvaVmfWaTbrgQg8HwezF+LwmSSdzP0LCIP2jj3fc/YlUYwrNi79GQ7+22Gkc0KqsUYmqtDpHp57sw8O2CD3KD6GiR60sr/Mw9jazjw2jb6UPKaetWih0dn4TnPCI2ceG88dbf+Bk8/x7f5XmqdyiOKjNIC8d1E42prxBhZ2IBInN1/ZwOewU92NuMLTmNHwdSlr8OtM/fo8tm9dAD3Da7so3v+zHv2zFtM9lWebPqwa+OqxH/9RPba2B4U21tCyryrEBK8syH+xsT1XPetyMukS/yh9Sw7uxpW4rx1ipwc8ZOrVrydGjRzEY0oceC72KIASoEPHBKDxCZfBlQOW+vFG9K562RRAEUyXh4q6WidaxFJIk8zBKJs7M3J7HHu/ieNCuHK2SYyGDN8WQzKHA7bhZe+Jl54ennS/BCY/YeW8D50KOUMq5AneiL/NmufdoU7L7c8fiw9hbhCY+prpXIzQqbY7kyQy1CP6ezy56jds9js5lOtO4+PPHtaJAzUMxeAsnljJ4E1ONxIUFvtC/g5A4idA4mRG7urCgxcZspQcyhxOH9zBkRDtoCmyGjz79gj6DRhEUI/HO1oGQOBq1HIAowID6Gt6ors62jk7QxTFge3OKOpTmi1YbLSp/VtyLuc7sY8P5pMEKSjqXS/dZSMJDttxewYPYawysPoBu5bulOWElSaJFixbP6GzRwQ+7vmNJcl+KSi6LSl8KEncwu+cCrkUeoVmJ2vSu8la+3qMlSdHLBMXIJKT+vWvIwJab37DnwWY+rDOXcm7mOd4Lg4PaKBnZee9nfrv1Le1K96JzQD80Ys4LH9lqTTvjLB2Mouhr81D0deFEcVDnjIeREjHJMj9eWYK12oY3y72X5TkT9vbhwvyjhFwNBKBGnUaUHVOd2r7NaFHitXyQ2oRBMhCviyEuNQqVoKaIQwluhMjM2XuOu4lrcNR/AYC9FbxdW0PHSurn7pSWZZk5x0dS2rkCPSt8kG151KLMB306cvzwvmdsbKq5Q6dkBJWASqqCi24ttYt6MqGNDY7/cp57OQp4F7Ji2sl6k5Na/6+STgm6OA4++oPTwQdIMSSRakxBko242XjTqkRXahdpkW09l2JIZtGpcRhS9IxrsgirAtztlt/IsszH+3oxocFMWgaUz7L9qzZP5QbFQW0Geemg9vcQsbMqPI7DzNAbZS4/0THiry4MqzmD7XfXM66e5QsL6lJT+aBPe87aHoJ74JHkw5pNByhaIn3Eyr0IiTl/pRIY/c9QalBKxchmWpxzGJ2y6/6vhCUG0b50L9ZeXsj9mBv0qzyGWj5Nc31fueH4saNMGP0esSF3QP5H01i16oauwUkgGbDBPvxDxP37+HzGBOrXb2Cqau0kFur85kExEhEJ5k0Hnx4aRPfy71PJo3a2r5PXBq8sy9yOvoyfQ6lcpSLJLaqnRZf+m/8sIimCd7e+y7beWRcdUxSoeSgGb+FEMXj/QWeQuREq8cPlJRRz9KdJsY4Wv8ZP3y5n7unRcBoIganzvqbbWwM5H3KOmfu+JSlqblrbuiVUfNRKm+28uwajgT6/18PL3o+FLTdlKxo3p5wOPsCai3OZ1fQ73Gy8Mm2XrI/nz/urORdyiG9f+wYPOw927NhBr1690kViqb1r4Nh9HrEuw5AwYJVal9So3dif9Kb/mPZYFZGZ17pw7N7KLdFJMiFx/6TyikoOY87xkVR0r0W/ymMQs9DBBe2gDk54xMwjQ2hctANvlBucq7RvggCeDgJeDnlTCP1lmKfyA0VfF04UfZ0z9EaZGyESBkli5K7XmNhgaZZBY9cizrLuzBIOf7SDuNhoAOo1a4XUy8jMZt/iZedncTl1xlSuhJ/ibPAhrkacwSAZ0Ki0OGpdcLJyQSfpCE54iCRL2GucORd8BauETeh0/+hcD3uB3rU1tC6nyrTwoCzLrDw/nSRdPB/WmZetd4Rjx44yceJE4sPuISWEmA4KKqzrvUNK6/UgqLAydsApdQnGKxuZ+W4NGjRoYGommHJOF9ZCtyl6+Wm+7by9jixL/HFpLduf/MTH9Rbi71LRjLNefLbe+o6IpCcsaDfZrMXnV22eyg2Kg9oM8spBbaOFMp55b2hZirvhEh/u7s/wmjOYdXQonzX9Pl0uXUsRHxfDO32acK/sNdgARYqW4NtNB/DySa88U/QyXx7Ssev6PzOvkzUMa6alib86g56fj1Ey8sHOdkxutJJijv5Ep0TwzYU5RKdEMLrO53jY+ljk/rLLwIEDuHjxEshGpMRQpJRYHNp9QUKNpcjiQ1RyMezujyFp3/dgSKFq1Sps/flbvB2EfK0inFMiEiSexJq2Bj+PmJRIxu9/i+Vtt2d7hbegDd78QBRMzumMFrym7JtCsxLNaFmqZZb9KArUPBSDt3CiGLzpuR8hcT38PqsvzOHTxl9bvH9ZlpgzZwQb47+GdSAg8PnydbTt0oNhf3Wmqv13bD7/T0SNp4PApLZaynln791Hb9TR5/f6aFXWaXkUJVnCXuvE1IZf5Tj903+5HnGOby/Nx8nKjTF15prd783Iiyw8NZbpzT5nev/xHD96MO0zbZU22LR9mzibj5GFaATJFjlGDw/0kKjHtZwrYcvC8sXxnl9Ikkx4gkxYvIz0NO3H5pvfcPDRH3xYZy7Fncpk6qguSH19Jvggqy58xpSGX+HnWCpXfdlqwc9FxEaTd+9hL8s8ldco+rpwoujrnBORIBEUIxMYd5f5J8awuPWWLBf/Ruzqwmuqfnw6fDCJCfEA1O/WCvsuzsxvucFisiXpE1h3dSlngg9Sy6cptbybUNGj9nMX+2JSIllxdiqnnhxCY6xLVNTbaKQ6CJjmzyJOAm9W19CirArrTObUbbd/5GDgNqY3Xm12sFCajQ3IhmREWw8cWy8gyq81spCEvX421rFdSTg0C0PYJapWrcI336zByUbA21HIVJbCQqrB5KR+Xu2n3PK3zjbaS3xx8mNKu1RgQJXxFt8hXJi4HXWZFWen8mPXTfg6m+eTeBXnqZxSEDpb+Uae4mH/Yj0KZ1toXuw19j/8nTfLDWbTjVV5ch17B0fmz/8JJ8EVisCTwAe8/1ZboiLC0rWz1giMaWnF5HZanJ7uXI5Ngdk7dczakUp4QvYq+6pEFdObfMPc46PZfmc9LtbufFTvC/pVHsO0w4PYcG0FRimPlyH/gyxLBAcHm/4QVKi9a+LWbz/6aueRxduopcrYHB9EwraFSHGPMMY+JPj6IYo48kI4pwHc7UVKuIlklYHE2dqN18sM4PtL/6T5kGWZh7G3SDWk5L2ghRhBMFXHzsg5HZMSw5ngM7Qo2aJAZFNQUCg43OwFfB1KEJoYmGf6a/CASdR1bwm1THPyJ6P6cXD3H3QJ6Iujy8/M7GyF41MdHRYvM3ZzKj+d0WOUzI9V0Ki0fNX+L6zVNkysv4wlrbeyrM3vdCs7kNF7uhGc8ChX93Aj8gIf7e3Bllvf8mGduXzScHm2nN5l3aoyv8UvzDv6Odc8QhAb+SJ0d0A1wh+xixtxNmORhUhUiVWQv1XBV0lwSA83wGq7VZoR/rIgigJejiLlvEVcbU3Rw2+UG8TYugv46doKPtzTjeF/dWbMnjc5E3zQjB7zFlmW2XBtBb/eWMWiVptz5ZwWBZMzxd8jb53TCgoKry7u9iK2WijqWJomxTryzYU5WZ7zRtlB3LC+wNJvf8Pa2rRwfHzzHu6fusH+B7/lWiadMZWfri5n9O6ulHauyFftdjK42iSqezfKcieKs7Ub4+ovxtvehwXtx9G4wg4Ep7fRC1cBeBIrs/SAjj7fJbPqiI6QuGdt/E4BfXirwjA+3PMGNyLOZynvv21sjXcNnNouwaX3eiL92iALibimXEZ1xZvYPwZjCLsEkoHgR3co7W6yuQq7cxrASi1Q2l3EKvtxe9nG07YIc5uvp4RTOYbv6sSV8NN5f9ECIFEXz/wTY/is+df4OObDg1XIF14sr2weoVGB8wuWpsfZRqBB0dacCNpN46IdOR60G70xi0p3OcTJ2YUVH2zDppVpBfTB3ZsM6tmK8NDgZ9o28lfzdW8bGpb6J/royF0jg9el8Os5PQaj+Uawj30xlrTeysPYm0w7PJgkfQJl3aqyrM0fqEUtkw70RZKz5/jODSkpKWmRxVal2uD82rfofI6Rol6DylgJ9bYqJOyZhBR9Gyn+CXJKNBh1JCcn55uMlsDRWsDfU0SbxTzfrlRPrkee47db3/PZ0eEM/asD31ycyxenPs4vUQsdggDFXMRMt80vO7mMEXVGFEiBMQUFhYLF0VpAq4aqXvW5GHYsT64hCAIrRm/Dq5kfOIDBYOCjIT1R3VKz/+FWahUTWdHLmgo+ptc/owTfn9Dz4a+pBEabr09drN35tNH/mHpoAHGppi3KtX2aMaXhSqYdHsT50KPZlj3FkMyS0xP58cpiPq63kEkNluFjXyzb/QA4Wrkws8F3YBRB5YCTzde42v5FivgnMklY3xuEcdFleBwFeiAWeAKCXnjhdLa5aFQCRV1FynqJONkIFHcKYFID0wLD8rZ/MLPJt/zv/CxiUiILTEZJlph7YjTxuljmNPshV2m6XGwFynqJeDiIis5VUFDIU4q6iAgCvFF2MOHJIey6/+tz2zcu2oGz4YeoXKsOC1f9ikZrinC9878rzPptGAkpcc89/3k8jL3FiF2dsdc6sbLdTlqUeC3bc6BG1FDLuynBCQ+Z3mwe33VdRsXSK1E7j8JIKAAJqbDpgoF316Yw+pcUNp7V8/hf7xE1vBszo8kafrnxPyYffJfAuLuZXu9JZAqSX0ucO6/God0UUkruI8yqIggJOAftJWHzx8TtHIb+8TEMoRcwhF+BuAcIxhcrKEqrFijtIWKd8zIKZiMIAm1LdWdu85/46epyVl+Yk69+k7zm75zn71efQrUi3oWqvpdC7lAc1ICbXd7ko8tLVKKAl70dGpUV8boY2pTqzs57P+fZ9SqWqEXjxu1xK2/KRXXv1jUGdm9BclLiM22dbQUmt9cyvo0Wp6eO/xQ9rD6mZ9jPKZx7ZH7kmFpUM7TmdDr6v82o3a9z7PEuREHkzXKDqe7dKF0Eb15jbW0NVo44NJ2GQ5PJJFv/RLxmAkiO8D+BlPNr0uWl5qlysLF5wVY/ABuNKeLI9jk7ggRBYHy9JaQak+lbeTQr2+1gRpNvkGW5UERh5Td/O6czy7semxLLkcAjtPdvn++yKSgoFA5cbQVaFH+dvQ+25Nk1VKKKJW9tpUj/4gDodTomfNAbz1RfTjzZg4e9yLzXrehVS522W+ZWmMSwDSlsOm9+NLWfYyl6lP+Avr83YMiOdgzd2YEtt9YwoMp4NlxdzqoLs81eOD8XcpgRuzpTzasBs5p+Z5EcnLY2tlhHt8Ct0u+IRbyJsK6KiCOaG3VJ+eFryEC2F1VnZwdrjakgeICniOO/FlPttA6Mqj2Hz4+PQs4qz1ceYJAMTD/8HuXcqjG42qQcv5fbaiHAU6SYq/jcgl4KCgoKlsJaI+D5NL/9x/UW8ueddc+NWhUFkaZFOvHX/Y00aNaWucvXo1KpwACRW0MYsKxZjuTY92Arc4+PZkaTNXQO6JurdFXdyg1i043VAHjbF2VpuzXMbfs+3kWHUL74HjT/8iLdCJVYc1zPoHUp9P8hmc92pvLzWT2BEd70Lb+C10qPY9HJKXx6aAgbr/zCvjv32XtDz6ojOoasT6b/hhj0VeuS5PU9MdohpIp/gZCE9thrxHzTCt3t7ci6BJD0add8UfW1RmWKpM4PJzVPI+JnNf0OZ2s3Pt7Xk9jUqPy5cB6z6eYqSjmXo12Zps/Ue1J4sXnlY+EFweSgfhFxsRVoUrQjhx/9SSf/voza/Tqd/PvkmbN9aL1pLB89haszzxL8+CGv9XgHG9uMt90KgkDzMmpqFVPx/Qk9268YkIGHUTKTfk+lelGRAfW1BHiat0ZS26cZFd1r8fX5mWy78yOjas+hZ/kPmHJoAKef7Kd2keYWvtv0GCWZP68akZssQat1IFY9jmT1ajCoYYEeY+qlDM8rXrz4C5vbSKMyOakDo2WikzI2Vj3titCj/JB0x0bVns2He7pR0aM2NmrbfJK2YBEEKO5qikrLjC+Of8GYemNeuMUwBQUFy+FqJxDgWok70VeRZCnLPJU5pZxHNVo2foMbMec5vXY/FarU5L1Gk5l1eijVPBtgo7Hj3Xpa6pVQsWCPjscxMjojrDqqZ99NAz1qP+F67DpCE4PQGVNINSajl9I7dFMNKRRzCuD1Mv25HnmB2c3WciPyPIcCt5Ogj2fv/c38fO1LGvm1p5p3A5ys3HDUOuNo5YKMzI3IC1yLOMvjuHv42Bfji5a/4mjlbJH7TzXILNsRDPVnkaj9jGT11yBp4KqMfvP+TM97kXV2drHVCpR0F0jSyYTHy8Qky1T0qEUp53L8eXc9Hf3fzjdZ9EYdUw71p3nx12hbqkeO+rDWgJdD5ovECgoKCnmJl4NAbLIMeg0zmnzL2L1vMrPJt3jbF82wfWu/N5l+4X3alX6LFu1fZ97KDYwf1hvNEy2upTy5HXWZANfKZl3bIBlYcXYKKYYUlrTeapGcw242XlirbQmKv59W+LGiRy1Wd/yNz4+Poo33FdwZy8E7Eg8i/7ETg2NlgmONHL7z76CtEsBqDMJt9osn0IuzMQqPAVO9DEHjjFoujZXUCZ1uKkbtZTgroNud+WL+i6yv1U+d1PciJZLzZgN8OgRB4M1y71HBvSYf7e3BqNpzqORRO+8vnEccD9rNqSf7WdR6Hd4Ois5/2XjliyS62ako6vpiTm6yLHP8fgTTjwxjXouf+PLcNGp6N6ZukayLr5l/jfQFcibsf5tefsO4eugMbw8caXY/N0ONLD+g53Z4+q0lTQNU9KypoZS7+d/BnagrLD49kbaletCqRDdG7X6dmU2/w8vON1v3Zg4PY2/x48VNXHhQlJi4egh4EK19Db14AlIFWCJDcsbVDpycnNiwYQPt2rWzuFz5TXi8RHBc1sUT/+Zw4J+cDj7AmDrznvnsZSuS+HfOacdM0noAhCWGMeC3Afzx1h/ZclArRRzMQym6VDhRii5lzMNIidlHptLAry3VvBpYrN//zq3J+kSG7+pC+fvVea/vZBydnDkTfJDVF+Yws+m3acWGUw0y3x3Xs+ViKiniLpLV6xBke5r6vct79cvjaW+LlcoajajNdP7afmc9f937maE1p1POrdpTeWQexd1m0akJpBpTqOPTDEEQidNFgyxT1q065d2qE5UchrXGNu283HLhsZE5f0QSbRSJsmqLkTBkKR5+s4bLEZme9zLp7JyQajAVU4xI0DP8r9eY3HAl3nZ+eaqvJVniUextlp75hNfKvEPTYp2z3YeN1uSYft4CcX7wss1TeYWirwsnir62DEk6mdthJls3MO4us44OZXaztbjZeKVr97e+Phj9JzIyb5YbDMChPduwsbOnWJUAZhx5n2Vtfs9yITs44RGfHR1GB//edCj9lkXv53LYSXY/2PSMPSfLMr/c+JozwQeZ2uhr4pLtOXbXyPH7Rm6HSeiyUWZDQEaIukbsjd9JCliB7BQHegFWyJCJ3fmy6GujJPMgUiYh1TLuOHNs7LjUaKYfeY8WxV/P14VoS3E+9CjfXpzHvBYbqORjl2HNp6x41eep7FAQOvuVd1CX8VK/0NsCgmIk3t7yGp83X0+yPoFZR4eysNXz815lh/9OdNcizvLDlcV81vT7ZxRmZHgobh5emfYlyTIHbxn5/qSekLj0w65WMZHuNTRU8TUvV6AkS8w8MoQGfm0JcKnE/JNjWdx6Cxox9/tldMZU9j3YysZr64iM9yIupitG4TEp4jZ0qj0gSfDQCu5r4HBMhn2o1WoaNmzIvn37XpqJLz5F5lGUhMHM9FVTDg2gZ/kPnlmhfZkc1OJT53RmOaf/ZsxfY+hWvhuNijXKVv+KAjUPxeAtnCgGb8YkpMr8dfMSm25+w/j6iyzWb0Zza0hCILOPDad58dd4vUx/BEHgQcxNph18j4kNl1LWvSoAyYYkRux8m/Do2iTH9kXEDQBbDXSrrqFrNTV2WbwrhSYG8dW5GUiykaE1p6VL03E57CTrri5DJah4u9JI/F0qsfv+r2y/s44yrlV4FHeHliW6Zmgs6YypWRZ1AohKlPnmaAp7b8kYeEikVSsE2RpVpC3GSyFIR6IzPfdl1Nk5RZJkTgfdYuLej1jQ4leiw8Msqq9TDSl8f3kB1yLOYpAMFHMKoF2pHlTxrGd2H4IATtYCbvYC9jkwTvOCl22eyisUfV04UfS15QiOlQiLN9m5d6KuMO/EhwyuNindbt+/9bWTuzvD/urIkja/PbPr9Icri3G2cqNDqd7ExUbj4ur+zLX23N/MppurmNhgGcUc/fPkfob91ZG5zX/CXuv4zGfnQ4/y5dlPmVB/KaVdKsBTp2tgtMydcIn7kRIJqTLJOkjWy+gMplSg7nam+Tvszil+mNWHxNRA6A7YAA4CfClDfMbyvGz6WpJkHkbJxKXk3iVnro1tlIwsPj0ercqaYTVn5NluPktzPeIcy85MZn6Lnynu6oivc87kVuYp81Ec1GZgSQe1l48PZbxe7CwnyXqZGfuX4WVXlObFuzDjyBDerjgyTUnklowmus03v+FaxFkmNVieNqEd2rudcUN6MWXuV3Ts9vzVOL1R5s8rBtaf0RP7n1pEpT0E2lVQ0yxAnaXTzyAZmLC/Nz3KDyFJn8j2u+uY0WRNjtNKpBiS2XRjFdtu/46Q8joRkW8i4oiMTLz6I5LFnxEioHupPux4sgX5+1Tio57N4+Tk5ES1atX4888/sbV9uVJc6AwyD6LM244UkxLJuH29noluf1kc1BqVyTmd1QLX47jHjNo5ik09NmX7GooCNQ/F4C2cKAZv5twIMdL/j3Z82e5PixkGmc2tRsnI2isLuRFxnokNliEnyfTpWZ/U9ilM6LKE+sVaM+HA2/SrPIYaXk3ZfsXA9yf0JP5rnne0hp41NXSspMZa8/w572bkRVZdmI0kG6niWY+aPk0o71YDtagmKP4+664u4170NVqVfIOOpXtjo7HDKBlZcmYiAgIja81GJaq4F3OdtZcX8jj+HkXsSzCi1qy0qO9/k5gq88t5PZvP69AZRVLE/cRq30GVUJbSqXa4V9Egrtdx8dxZ4uKeLTz1Muvs3LDx6kbWX/qZsRWm4eJeHqOce0dwkj6BiQf60CXgHZoU7ZjtbejWGnCyEXCzE9CoCodj+m9exnkqL1D0deFE0deWQ5ZlboVJpDxNl5ykT2Du8dEUcSjOoKqTUImqdPp674OtBMbdoX/Vcen60Rt1DPurI0VP+HNs9y5WrN1GqYDyAMSlxrDszCfYaOwYUXOWRVJ6ZMbRxzu5EHqMYTVnZPh5RFII04+8Ryf/vrQt1d2sPvU6HcvmTWbt1wuhFNAMtLbWOBRxxuWyK2HbH79S+lqW5eem1DS/n+zZ2JtvfsPJoL1Ma7wKG03GaVsLC/dirjP3+Gjmt9iAp70LAR5ijgsjKvOU+SgOajOwpIO6YkARXO1yXjygsHDw3gMWnZjO9CaruRdznR+vLGFqo68s0ndmE93WW99yKewkkxt+ycO7t+jdsW5awcShY6cxaOSkLAdxil5m13UDm84bCI1PPww1KmhYSkWzMiqq+akyNYiTDUl8tLcHw2vOIF4XwzcXP6dViTe5GHYcLztfano3oZpXA2w19iTq4rkYdpzzoUcIin+An0NJijr6U8zRn+OPT7P91lb0KY1ISLFDFmIBCZkUUsRNyDEGtCmJTGi3mBB1IHV8a1Ak3JqZM2fy8OFDZFlGMhopVbo0U6ZMoU2bNi/thCdJMkGxMlGJWU8dd6KusOzsZMq5Vefdyh9ho7F7KRzU1hoo6WZeAaYh24bwfs33qe5TPdvXURSoeSgGb+FEMXgzJyJBYur+GdTybkpNnyYW6TOrufXvyJO7h64R+vtjSAT3t72hvMD0JqtoWPSfrbLRSTI/ntKz85oB4792zThYQYdKajpXVuNu//zvItmQxOWwE5wJOcT5kCN09H+b1wLefe4uqd9vr2X/w9/QiFrsNA70qzyGks7luBFxnmVnp1CnSHN6VxiBjMz9qMdsu/6IPbeCSdInIhGBTjyNTjyE9kpFyleXsfW2Z3HbFdT0LcWuXbteSZ2dG04+PsnYPz/kyy5fUcqlMnEpMvEpcprjJTvEpUYz8UAfBlWdSHVv83cTWalNTmknG6FQ73h8GeepvEDR14UTRV9blmS9KdXHv1Mj/nbre3bd38iAKuOp7tWQqPBQ3Dy9AYFhf3Xk8+brcLRySdfP52tHsWHvCtgHDk7OzFyxhluOl7gQeoz+VT7O8xpMPHWejt7TlckNV2a4SAygl/QsOz0JSZYYVefz5+5oDnxwl3FD3+L69XPQGHACl9IeVChbE0Gl5+C7u15ZfR0UIxGRkHPXXE5s7NPBB1h9YTbj6y+hlHP5HF87LzkfepSvzk1ndrMf8LD1IsBTzDJY4nko85T5KA5qM7CUg/rU1cfUruBrqpj7ghORIPH6zx1Y0vo31KKasXu7M6nB8mfyXeWE5010W299x8XQ40yst4x5U0fz64//S/usaatOzFz0LY7OLhn0mh6jJHPojpHNFwxpebv+jVYF1YuK1CmholIRFUVdBMR/GbhxqdEM3N4CR60LWrU1IQmPmN7kGzSihjMhh7gYeoxkQyI2ajuqetankkdtUvQ6Dj48zcWQSzyOv0OyQY9K9kMjVUMj1UaUPRB1j4jQjUI+Eol7c09mdfsOf89KzDjyPrv7/Y5WbXoekiSRmJhIXFQ4PkVLvDITXXSSTFCMlM55kRGyLHPw0TbWXV1C17IDaV+q5wvtoLazEijpJqAyY9X2TtQdpu6fyvo31ufoWooCNQ/F4C2cKAZv5kiSzLYb11h/5UsmNlhqkT7NMUxkWWbtn1+wZM8nSIIRrMHhkRN+nUvzfr0pNCuePgfwk1iJH0/p2X/TmC4VpEqEJv4qWpdXU9VXzHI+NEgG1l1dwvmQI3xcbxG+DiUybXsn+ioOWqd0KUL+ln3HvQ38eu1HIhOsiYr3RpKcMQp3MAh3EWQbeGKFy8UwPN62xcPZh3erjqRf9ZbpnOKvqs7OCZIkcfXOOSaemUbfKn3pWaknPN1NFZ8qk6Qz5VxNNfDcGhWRyaFMOtCX0bU/p7x7jede00oNdloBOysBeyvMWgguDLyM81ReoOjrwomiry1PeLzEk9j0E2NEUgjrry7lRuRF2vl0p2PlPqhENaee7ON40G5G1Z6Tvo/QYLp9XYn4v2KhuKnWYCffPkwf8k2++jCuRZxl881vmNzwy+e223N/Mxuvr6Sub0u6lR2Ei/WzaUmu3j/LgDlNSXVPQbwkUqJrWTrV6sNP15Zzd+RtHKz+ieR9FfV1WLxEcGzO3HM5DQILTwpm1tEPaF78tSwDCfKb32+v5dCj7UxvvBo7rQPFXEVcclkMWZmnzEdxUJuBpRzUt+8GElC66EsxKA1GmTE7P6OyR13qFGnBySd7ORtymKE1puW676wmuu131vH77bX4u1Qk4UIcv83/Dp5uCy5StATzV26gYtVaZl/vXoTErusG9t00EJeScRtbDZTxEvH3EHGzT2J/8HjstRqiUx/RpGgnmhRrx6xj72OvccHZyhdHrS/IjlwLv8Dj+DvoDFakplRANpRAJZdAJRdHlIsgICIA1YqKNAmI55fbPXnwww2cO7kzs+MaKnrUYu7xD+lduRedyqUvbPWqTnSpBpmHZqb80Bt1rDw/Hb1RR9/io/DwKvLCOajd7QWKOAlmKW5Zlunxaw9mNp9JOfdyObreqzqusoti8BZOFIP3+TyJlXjzl3Ysb7MNlZh7QzM7hsm1S2cZPrQTUbHhEAMqKxXVxjegfu22DKo24Zn2DyIlfjmn5+Bt4zN1CFxsoVmAmiYBKsp4Pt9Z/TD2NgtOjqWcWzXKulajqGNpijqWxlZjjyRL6Iyp6IypOGid0s2zIXESR+8aOXLXyPUQCaPwmET1EoxCELaGd2nr34Inf0zEQZ1AdOsINGoNr5Xpz+DaXTPMUfyyjilL8/dzcvMpwuT9k0nUJbKgzQJsNDb/aSeTYgCdAXRGk8Nab5A5H3qaP279xK2oq0xquBh/l/IIgimHtEYUUKtMO+Y0KrDRCFiryfGW3YJGGVPmoejrwomir/OGO+ESiRkUwItLjeaHMwu5En8GNxtvano3Zue9n5lYfxnWahvidTHEpEZyOvgAxwN3czvwMim/JsFd0/mNWrRn1uLvcHZxy7d7+eTAOwysNiHLKFtJljj2+C823/wGB60zHrY+pBqTSTGkEJ0ShlrUUDq2ItuW/EjRUaVpV6kn846PZk/fI9T2q/hsf6/gmIpKlHkcIz134TcjcrNL2SgZ+fbSPO7H3ODjegtxts6/sZWZPMvOfALAiFqfoRJVuNoJFHXJ/Rh4FcdUTlEc1GZgKQd1yONHePu9PINy350bfH1uKZMaLEOWZT7Y2Z5FrTblOp+QuRFZ92KuczxoN39eWs+9v66Tsi8JAI1Wy+hJn9Pr3WHZWunVGWXOPTJy4r6Rkw+MRCc920YvXCJe8wl2hjFYSc2RkUhUL8EgXMbZsBBJVmMUnmAUrmMQHmEltUQtl0fgWePHwzqZys6h9G9bDrUmign732ZI9Sn87/xnjKg9iwruNQlJCGTByTHs7LvpGQP8VZ7oZFkmNF4mLF42S5FuubmGQ/e2MbvVD4U+39XfiAL4uWRvxXbR8UWIgsioeqNyfN1XeVxlB8XgLZwoBu/z0Rlkxu6cRVXP+hbZpptdwyQ8NJgJw9/m7IlDacf83i9F344f0rP6BxmeE5kg8ccVA39eyXgR2U4LVXxVVC8qUt7btOPpv9swJVniYugxHsbdITDuDo/j7pFsSEQUVGhVVqhFDWGJ4diIJXES2hMd05jbEfEYxDsYhDvoxePIJODKSDqVq0PnKmqKuogkpySx8OzHPIy7TYfSvRlQrT9FXTN+Di/rmLI0/31OO+/sZMGxBSxpt4SKns86EgASdYksPbmUvff3UrtIbd6q/BaVPSsXqoisvEAZU+ah6OvCiaKv8wadwZSP+r+7Tf+tr6NSwjkbcpi9D7ZwKfQ4jYp2wMHKCUetC9W9GlLZsy7zj48h6WwCv8//HvmpseXjW4xZi7+jZj3LpAnLisdx91hyZhLzW2zIxjn32bplDY2bdMDLww97rRP2WkdkWWbusdH4u1Xiu0vzGVRtBJ+2GJ1hH6/qmIpPMQWBZbVT+d9YIo3mxdDjrL2yEI1oRSf/t6nv28YiQRTZITI5lFlHh9KsWBdeK/MOPE2vmZu80//mVR1TOUFxUJuBpRzUL9ugjE2Wab+ubVok1p77mwlKuM87lcfmqt/sTnSyLLPy2HR+2f0V0Wsj4Gmewio16zFn2Y/4Fs18W29mSLLMnTCJ848lboZKXA9JIVC3CINwDUf9F4ikX+HTCadJ0ExDwFQ8QZDdEGV3jOJdZFJRyyVwUJelqLMWbzsjjy7s4tLFnVgVs6Z6k8bYWNkyqOpEVl34zFQ0yrsxAJ8dHcZ7NQfRyv/ZiPCXcUxllySdzKMoiVTD89vJssSuq7/w2+PvmdX0O1xtPPNLxBxhpYbibiI22ch1dfTRUVacXsG6butyZZQr48o8FIO3cKIYvFlz8N5Nlp9emOW2WXPIiWFiMBj4csFU1qyYZzoggOYtLQPqjueDbp9mel6qQebUAyP7bxk5/cCIPhMDSgB8nASKuYq42Qk4WIOjtSmXsFGSMRjBIEFcikxonGmhMyROJipJxiDcJVXcgU51FEF2RC37o5YCEKPtSTm9i0/f70Db9h3gaTTa1EMDsdc64u9SiUHVPqKsl4g6k0J6L/OYsiQZPaeQhBCGbBtCPb96tC3dlspelVGLavRGPavOrWLT9U2MqDOC18q+9tI7pf+NMqbMQ9HXhRNFX+cdsckyDyLTK8nM9PWX56YR4FKZ1iXfSN9HahTj9/Wmr+1oJo98l+ioiLTP3uzzHuNnLEGjyTzvs6WYd2IMrUt0M6uOwPXL55gzZSSXzp6gbecezP3yn3SHK89Nx0Ztx9WI08SlRnN04J5Md1+9ymMqWW8aO7osbOu/sWSdp/CkYLbd+ZGTQXsp5hRA46Ltqe3THGu1jRln55xjj3fx/eUFfFT3CwJcKwOgFiHA07zaT+bwKo+p7FIQOlud66soFAocraGGdwMuhB6lpk8TWpR4nQ92tqd7ufex1djnmxyCIDC04TSq+NXjM4+hBK98BLEQ9Og+9vaOzz3XIBkQBRHxPxOqKAiU8VJRxkvF+ZAjPD43ndElBlHSbjJhCRAaLxEaJ5OYKqMzgs5YF71xB9ZqsH+aw9DBWsDXWaCYi4BG+4jguCvs/WsLu//8ldTUZEiG1DMp1CnWjL4fjGHSgb70KD8kzTkdFH+fBF00LUrVzNPn9yJjqxUo4ykSEi8TkfD8aOqaHo0pXaQCE/a/zcBqE6hbpGV+imo2bvYCRRyFbK3WhiWGMWX/FLb22vpKGecKCgrZp7ZfGZ7se4BBMqAW8/+VTK1WM3LCbKrVasCUDwcQGxOF/hcd+yv9TuuoN/B3rZTheVZqgcb+ahr7q4lPkTl2z8jZR0YuPDami6yWgSexMk9ijdmXTS6N2jgcO+NwfKzjiTy3nqCjMzBG3QFg9cKjtGnXngexN5l9bDh9Ko5m081VvFN5LN5OQqbOaYXc4W3vzeaem9lxewe/XvuVTw98iiRLJOmT6FulL3/1+atAxrKCgoJCYcTJRsDDQSA8PuuYwMFVJzF8Vydq+zRLl2LBycqVViW7ESQ/YMPOs0wc/jbnTh0BICoiDLU6f+bcwdUmMW5fLxa12oy9NmO7PjjoEcvnTWH75nVpx/76YyMDR0ygTPkq/HhlCQbJFMF2Jew0pwdfMauuz6uIjUYgwEPkfqREkhnpNC2Jh60P/at8TP8qH/Mw9jaHA/9k041ViIKK6l4NqeXTlDKuVS0WXZ1qSGHFuakk65NY3HorNmpToKEoQEl3yzmnFQo/yhvkS4IgCPSo2J2vznxFTZ8miIJIz/JD2XBtBQOqjs93eRoVb8fqfnuZ5N6PW1cvUaNiYy7GHydAUxkXaw+s1TbIskyqMYXjQbvZ93ArUcmhaEQtkmxaZXax9sBW44CVyupp8cNArFTWLGi58Zkqx+aiS01l+5YDfLVwBqHBj9OO29jaMWLiLHq+M5S5J0bTtmQPGvi1Tft89YU5TGr8yQubGzG/EEVTjmYXW5mgGDnDvGt/U9qlIktab2XhqXEcDdzJ8Fqz0Kqs8lXezNCqwc9ZxME6e9+3UTIy6PdBLGm3BEer5y/IKCgoKNhqBRoUbc6pJ/to4NemwORo0qoTW/ZfYe7U0eh0qUx+YyUTDvRmaqOvKepYGlmWM11wc7AWaFtBTdsKaiRZ5l6EzKUgI/cjJB5GmbaoZrWz5t8420AxV5Hy3iI2ibfZu3Y6p/dvTtembecejJu+iDvRV1h4ahxzmv3I3BOjGVNnHnZWAm52SkRMXiIKIh3LdKRjmY7wVPdJsoRGlfcRfAoKCgovGj6OAolPC8s+D41Ky/Bas1h4ahwzmnyT7rOuZQYyfFcnWpV4g1Ub97Jx7Uq+/XI+E2YuTaefn6evc4uLtTsjas1i/P7ezG+xIV0QXGR4KD+sWsT6NcvQpaamHS9WMoApc1dSpnwVtt76jsC4u1Rwr8mai3NZ1u5HfJ0c8kTWlwW1SsDfQyQwWiY6qWASHxR3CqC40yj6VBpFsj6RC2HH2PNgMyvOfopGpaWsa1UqetSionttXG08stV3aOJjNl7/iqvhZ+hZYSjNi3dJ93lRFxFbreJ/eZVQHNQvEfWKVmD83htIsoQoiDQv3oUPdq6kR/kPMl3lzEt8HUryfc/DxCXG8CDhBtciz3E4cAexqZFEx0dw7eIZipcqQ6dqfRlecyZedr5p58qyTHRKBMmGBFKNKegMKdho7CjulLMtaHEx0fzy49f89O1yIsJC0o6LosjrvQbwwZhP8fDy4UjgDrSillYlu6W1eRh7C6OcSsPilXP5RF4dbDQC/h4CkYmm6HZ9JsFzNho7Pmm4gv0Pf2fErs5MarCC4k4B+S1uGoIArnYCPo5CtlfzZVnmw78+5M0Kb1LZSxkrCgoK5jGwej9G7hxVoA5qAFd3T+Z+uR5daipaKytmNf2OqYcGMKrWHJaNnkzl6nXoPXDkc4syiYJp7vf3+MdBLMmmXTWxyaZUHnEpMsk6UKtM2zbVKrDVCHg5CnjYC2hVMgd2/cHa+V9w4fSxdP1XrFqLDz+ZS636TZFlmRl73md642+4HH6Kkk5lKelSFj8LFNBRyB4qUYWK/M1RqaCgoPCiIAgCxV3FDPNR/5fKHnXYff9XDgf+SeOiHdKOq0QVQ2tOZ/nZKUxt9BVv9R/OG70Ho7VKH9zz8/crOXFoN0PGTKVcpeoWv5cqnvUYVHUCE/a/zdwWPxF8/yE/rFrM9s0/pnNMOzq58N7oyfToOwRBLbDw1Dj0Rh21fJqy7upS2pfuzmvls04VomAaP8VcBWy1Ek9izav5lFfYaOyo79ua+r6t4Wnk862oi1yNOMNf934hJiUCjUpLKefylHGtgoetD3YaR+y1jlirbQlPfMLj+Ps8jr/HjcjzgECP8u8zvObMZxZWfJwEnLNR/0nh5UBxUL9EaNUCdYrU51LYCap5NUAQBHpXHM5PV5cxuPonBSaXo50zVezqUcWrXtqxCcP7kPhbPNc4S4R3CDG9IujaawA+vsXg6URsWoHL3ipcZmz84SuWz5uS7ljjlh0YNXEO/mVNhX7iUqNZe3khS9v8nq7dqgtz+LTpZCVdQw5wsxNxsZEJTzD9y+ylrHnxLlRwr8mUg+8yscEySjqXy29RsbcSKOIsZCvX9N/IssyonaMo61aWflX75Yl8CgoKLydlPXyw19jxKO4OxRz9C1qcNGPXy86P+S1+Zsivbbn2+BzHD+3mu68W0LJ9N97oPYia9ZqYpRdFQcDTQcDTzCCpxw/vM/a9N9MKQQEUKVqCEeNm0bZLj7QceIcCt1HBvSb2WkfWX13KsjZ/4GGfszlcQUFBQUEhL9GqTfUY7kdkXfVuaI3pjNzVhaqe9dPtGq7sUYetN7/lcthJKnvWfcY5nZyUyKqlnxEZHsqB3X9QuXod3ug9mLZdemBja7nC9NW9G6GXdEzc34eIlSFcOX0q7TONVstb/YczaPhEHJ1deBx3j8/2D6Nb2UHYaRxYd3UpNmpbZrf6VEnFlU3c7UVsNKadaZkFf+U3VmprKnvWpbJn3bRjOmMq92KuczvqMpfCTpKojyNBF0+KIREPWx98HUpR2aMOXQL6ZVqLysNBwNNBCTh4FVEc1C8ZvSt3Z9nJ1VTzagBA46Id+enaCuJSY3C0ci5o8QDQ63SE/Su9RlhIEP9bPItVSz6jdoNm1G/ahvpNWlOmfJVsJ2OPigznwuljlChdhlIB5dOOv9nnPVYvm0NqSjIt23el3/tjqFKjXrpz558cy4has9Il/78TdQVrtZpafvnvMH1ZEEVTZJybnUxYgkxEfMbtvOx8md3sByYd6MuEBksp5Vw+44YWxloD3o4iTjY5e0mSZZmRO0ZSwaMCH9T+wOLyKSgovNwIgsDIuiP539nljK+/uKDFSYejlQst47txvdZ5ZHsZ3aVUdmz9iR1bf6JYyQCatOxAvSatqFm3SbaNX11qKlcvnSE+NpomrTqlHfcrXooW7V5n744tlCpTgX7vjaHD62+lM8T1Rh3rrixlSeutrDw/nf5VPsbJ2gYvB8XYVVBQUFAonDhaC/g4CTyJeX47a7UNo+t8zrTDg5nf4ud0eX6H15rJpAN9Wd5m2zP5fx/ev41K9Y975/L5U1w+f4r508fQuEUH6jVpTf0mrfDy8cuW3LIs8+j+bc6fOkqHrr3RWllRp0gLDJKBL7p+DFqwvWdP99fep/eAEXh6+xIYf5cd1zZw8NEfTG64koexN9lw7Usk2ciC1l/iYqukhMoJdlYCAZ4ij6JkEp6TSrMg0aqsKOdWjXJu1XJ0vqeDgI+T4px+VRFu3rxZOEd2JliiyvDLXLlTlmUaftOaFW13pBUbPBK4g0vhJxlaY1oO+rNcNdj/ynn84C42rv2KQ3u3I0nPria7uHmwaPUmqtVqkHbsyeOHPLh7E5VKRWpKMiFPHhPyJJCQJ4Fcu3SWB3dvAvDG24OZ8vnKdP399ccvlK9UnWIln41Q2/dgK1ciTjOy1mfpjo/b9xaL2s6lglfJ597PyzymLI3eYOTGnccIDr4Y5WedCZHJoUzc34dx9Rfj71Ixz+Sw1YKXo4hjNvNM/xtJlhi5YySVPCsxpNYQi8qHMq7MpiAqDCtkjSX0Na/I70CWZVp815kZTb7P8WJyXulrgEcPbjN180CuxJzCcMUAF4F/5dLUaLX0e28MI8bPSjum1+s5fXQfokqFLMuEhwYT+iSQkODH3Lt1jauXzqBLTcXRyYW954LQaLVp5966fonQ4Mc0bNYuw+98/dVlOGidKetWle8uLWB2s7WU9hCxtzJvPn8VxpQlUJ6T+SjPyjwUfV04UfR1/vIgwsDdh8FZ6uvfbn1PYNwdhteame74L9e/BqB7+fefOSc1JYXfNn7Hr+tWcevaxQz7LRVQnl92X0Cl+sfBfe3SWWJjolCpVMRER5n09ZNAgh7d5+K5E0RHhgOw+JstNGvT+Z/r6VNZuO5jYv0iSZIS0Ki0pBiSKeboTzWvBrQo0ZXDj/7kz7vrKOlcHn/XAD5q2M/suk7KmMqcsHiJkLh/Un7k5XtgfpEfzmllTJlPQehsJYL6JUMQBOr51eVq+Om0rRYN/dqx4dqXhCcF42HrU9AiwlM5GzRrS4NmbQkNfszWn7/lt43f8yTwQVqb6Mhw7B2c0p13YNfvzPv0wyz737N9ExNmLEln8Lbt3D3DtlHJ4fx8/UuWtfkj3fHL4afwtvfI0jmtkD1UooC7nYynt0B8qkh0kkx86j/K1c3Gi8+br2PigT4MqjaJ2j7NLHZtUQBnGwFXOwE7Mx0ZmXEz4iZjd42le4XuvFPtHYvJqKCg8OohCAIDqg9k66019Ks8pqDFeYZiJQL4bswhEhLjWPbHZHbU+Ym429FwBEgy7YyS/5MUMSE+lqF9O2bZd1xsNMcO/kXT1v8YvGXKV6FM+SoZto9NjeLQo+0sb7uNUbtf59NG/8PVVjDbOa2goKCgoFCQ+DkLPHmSdYzga2Xe4fPjo9j3YCstSryedrxb2UGM3N2FZsW7PGPbW1lb06PfELr3fZ8rF06zef1qdm37hcSEf7awJicnpXNOAyz9/BNOHN6TpUw7f9uQzkFtpbFi4rtLTf3qE+FpnuK/2XF3A4cCt9OxdG8OBm5nUbsZZjunFZ6Pp4OIvZVMYLREir6gpck9Xo4C3o6Kw/hVJ98c1ElJyew7cJiLl68SERmFIICPtxcN6tWhQb3aSn5fC/J2le4sPbE2zUEtCALDak5nxdmpTGu8qqDFewYvHz/eHz2F90ZN5uG9W5w4vIcTh/Zw7tQR/IqVStfWaDBk2o9ao6FClZpUr92Q6rUbPWMsZ8ai0+MZVXsOWlX6PF5rLn7OmtdWZnqeQu4QBFPhA2dbAYNRJiZZJi4FElNlXG08WdhyEwtOjuV40G4+qDENjZizrWCiYMov7WQj4GxDrl+KdEYdc4/M5WLoRb7s+CXFnIrlqj8FhcKGoq8Lhrcqd2bF6Tb0rjgStVg44wfs7RyZ2GspE1nKodt/8uXJTxGeCMT+GUWxkukL3D5PXwMULeFv0td1GmarkNOq858xuNoktt35gQa+bfB1LIKPkzImFRQUXj0Uff1iIooCfo4ScaKAMQtzdWyd+Xy45w1KOJdNS3+oElWMrv05X5z8iM+br8vwPEEQqFy9DpWr12HS7BVcuXDKZGMf3vNMABiAJGWe1Nje0YlqtRpQvXZDajfIPHDo345pgE03VnM5/CT9Kn3IirOf8lO3TdhZKQ5IS2KrFQjwEAmOyzyNZmFHEMDXWcDNThkbCvnkoI6JjWPB4hXExsZRt3YNWjRrTHJyMkeOnWT9z5sIDQuj22udzOhJwRxq+FTlXswlJFlKS/NR3r0GAiJXwk9TyaN2QYuYIYIgUKJ0WUqULkuvd4chy/IzL1aVa9TlvVGfYDQa0Wi1ePn44eXjh3eRohTxK4G1jU2m/WfEySd7cdQ6U8G9Zrrjp4MPUNa9DCVciljk3hSej1ol4G4v4G5v2uqeqIOEVEe+aL2KLTc3MnJXFyY2WGZWATGtGqzUArZacLAy/W+pF/SwxDDe3vw2g6oPYnITpXCmwsuHoq8LDrVKTZeyr7Hv4VbalHyzoMXJkiYBHWjs355jQX/xQ+XFJBSNQS/p0xYTbWzteG/0ZCSjEVmWcff0xsvbD68ifhQpWgIXV/dsXU+WZX6+/iUpxmRKu1Tkfxc+Y0Xb7RRxEpVCSwoKCq8cir5+sdGooKS7wP1IMi0ib2qnZVrjVUw80Ie5zX/C1cYDgADXyhR1LP1MdHWGfWg0TwO4GvLBmE8zDOLq2K0PVWvWx2g0Yu/giFeRong/tbF9/Ipna3u/JEssO/MJsizzQY1pTD74Dl932IifS/bsdAXzEEUBX2cBZ2uZhKiCliZ7qEUo4SbmemezwstDvjiof9+2k+joGLp360KzJg3TjterU4sZcxaw78ARWrVoiqODmSXeFZ6LIAh0r/AGG69/Ra8KQ9OOD681g6mHBrC8zbYXwrGWkYzVajVIl5M6N6QaUlh9YQ6LW21Jd1yWZb6/vIBfu/9gkesoZA9BELC3Im279hj3XnSrUJ93fuvP/zp+j7e9H0YJJFlGFARUoilKWq0CK1XuI6QzI1GXSL8t/VjcdjEVPfMuN7aCQkGi6OuCZVjt/nT+qecL4aDm6Xzd0K8d9X3bsP3OOobt7ED/quOo79saO3sHho7Nfu2LjEjWJzL7+AhKO1fgkwYrmHV0KMNqTsfNToOzbeF/n1FQUFCwNIq+fvGx0QiUdBO4FyEhPSeS2t3Wm/H1FjPpQF++aPkLdlrTdzqo6iRG7OpMLZ9m2apfkZGN/VoPy6QrTDYkMf3we9Qp0pz2pXoxZu+bTG+6gipFsrcorZB9bLQCJV2MqBxFwhOfv/BRGLDRQglXEa1aeY9T+Id8iaN3cXGiWtVKNKiXPnLX1taG0iVLIMsyT4JD80OUV4bhdd/neNAOghMepR1zs/Gitk9zdt77uUBlKyx8e2kePcsPTVPyf3Pk8Q7q+9XFy0FRpIWFEi7F+brTl4zbOxwHaxlXOwF3exFXO1PqDgdrARuNkGfOaYNkoN/WfkxqPElxTiu81Cj6umBxsnGkdpGa/HXvl4IWJVuIgkjngL4sbLWJE0F7GLfvLR7G3rZI3w9jbzN6T1c6+/fh3SofcTHsOKIgUsO7Hn7OilGjoKDwaqLo65cDOyuB4m4iWcWO+btW4oManzLpYF9SDSkAWKmtGVJjKsvOfJI/wmZBeFIwY/a8Qdey/ekS8A5TDw9kYNVxNCtVHpWSdzpfEATwcBAo5yXiZi9kOa4KAkEwFUMM8FCc0wrPki8O6s4d2jK4f1+0/ypY9zdJyckA2GYzNYPC8xEFkS/aLGTBybHptvH0rjiCzTdXk2xIKlD5CpqHsbe4E32FliW6PvPZrze+YlLj0QUil0LmlHMvR6uSrfjy9Jf5el1Zlhm2fRi9KvaiSfEm+XptBYX8RtHXBc/sllPZeW8dNyIvFLQo2cZe68iHdeYyrOYMvjo/g9nHRhCRFJLj/m5HXWb2sWHMaLKGOkVacDPyIivOTmV4rZn4OiupPRQUFF5dFH398uBobUrRkBVVverTo/wQph4agEEy1Xmo4d0YnqanLEj2P/ydyQffYVy9RdTxacHsY8NpVaIb3Sq2xEaj6Or8Rq0S8HMWKeMp4mxTeJ6/jRb8PUR8nMQXYke/Qv5ToFV4gp4Ec+fufTw93CnqZ16uX0nK/V6Fv/uwRF+Fmeo+ZajoXp0dd9fTvvRbAGhEDb0rjmD5mSl8VHd+ln1IT/cbSZKMKL4cz0uWZb44+THj6y0C5HQO/JuRFynjVhJHK8dsjY9XZUxZgtw8qw9qfUC3jd1oWaIlZd3L5oF06QmMDWTB8QWUdy/PG+XfyPfvVxlX5qE8p7ynoPQ1r+D3q1Gp+arjN/TZ0pPPm/2Iq42nWecVJn1dzLE0s5t+z5Xw08w48h6lnCtQw7sRJZzKUsS+BCpRlWUf92NuMP/EGD5tvIrjj3ez6/6vFHPyZ1KDZRR1csHRWk675+zyqo2pnKI8J/NRnpV5KM8p71H0deEno+fkYgOSEYJin//sGvi2IS41mplHhjCl4UpUoopRtWczZs+blHQqi5uNV57L/2+S9AksPDUOO40jS1v/hlZlxbIznxDgUom3K3fHKRe6GmVMZYuMnpVWBUVdwMMeIhJkopNkcv5t5ByVCJ72Iu72IAi5GxO5RRlT5lMQz0q4efNmtkfHjl17zWrXrHEDbDJZuY2OjmHx8q+JjollxAeDCPAvZVafDjbPrhIrZE5YooH+e99lco0VOGld045/f3Mh1iobevp/UKDyFQQb7nyJlcqariUHPPPZ8isT+LDGAAKcSxeIbApZ8yQxmLFHxrO29TdpxbgsSWxqHFvu/cbewP242bjRtVQXmvo2tvh1FAov8cm6ghbBYij6+sVlz4M7LL8yh+m1/odGfHGfpSzLXI85x53YawQm3CEkORC1oOHDKnNx1GacMzMo8QGLLk3g3TJjWXNzPm2LdqdpkU5Yq2xQi1DK1YhaKfauoPDKo+jr9Cj6+sUnOlkgOD5rBbf78SYuRBxjTNV5qAQVD+Jvsvr650yvtQqVmD8xiOcijrD+9nLeDhhBdXdTHvRN91aToI9lZNUx+DoqDsDCht4IMSkCMSkiemPeX08tgputhIuNjJLlRcEcnZ2j2Wvbn7vMalenZvUMFejDR4/5evV3JCYm8W7fXmYrTwAv32LZkjUjJEkiPPgxHpoqaqEAAC8qSURBVD5+2apI+yLiKcuMT1nAd1e/YEaTb9KOf+gxl4WnxrM3cis9yg/J9HxJkomOCMXF3SvP8vvmJwce/UGUFM7k+l8+s60kPjUavTqeRhWbZ7vfV2lM5ZbcPisvijEwdTCfX1nEvFbzsNXYWky2RF0ifTcO4r0a7zGy6RhsNAW7NVIZV+Zh6ecUf+eOReQqDLzo+ppX+HfQxa0oUWI8q+/O5ZMGy7PcClmY9XVjr440pmPa3zciz/P5qVFMbfQVvg4l07V9kvCQ5WenMqzOdFZdmM38NhvwtP0nCrC4q4hTLrervqpjKrsoz8l8lGdlHoq+zhxFX786PO85eQGeSTJB0dJzI117eQ7D6a4bS29MZlrjVdT09CaUIDY8XsnIWp/lqfyP4u6w/MwUijgUZ2n733DQOgGw7c6PREphzGq5lFLulknhoIwp8zH3Wfk9/T8hVSYmSSYmWX5ukc7sIgC2VgIuNgLONhS6d1JlTJlPQejsHDmoVyyem5PTADhz9gI/bvgVrVbDsCEDKROQvUhVSw4iURRfiUHZvHQd/ry7kWNBu2jo1w6eJqcfW3c+nx0bxvY76+kU0CfDc//eJiyKAoLwYj+rW1GX2HRjNYtabULMYHvxnkc/MLjmwFyNiVdlTFmC3DyrvlX7Yq+1p8uGLrxb7V36VOmDmMvxKckS721/j8lNJtOsRLNc9WVplHFlHspzepaXRV/zCn6/TrbQuexr3Im+ysYbX9OrwtDntn+R9HV595pMb/INnx4ayKjac6joUQuA2NQoph9+jwFVx7PqwmxmN/sBd1vvtPNc7QRc7JT3wPxGeU7mozwr81Ce07Mo+vrVI7Pn5G4PalHkUbSE/BzHYQf/3mhUVkw5NICZTb+lo38fZh8bwcFH22lWvLPF5Y1KDuP7y1/wJOEhw2vNorhTQNpnu+7/yqngA8xtsYrSHmqLOyWVMWU+5j4rRxvTv6KyTLIe4lNkElIhSZd9h7VGBbZaAXsrcLIR0LwANUKUMWU++fms8jUH9Z59B9ny+58U8fHm/UHv4O7masZZCrnFzU5gaM1pjNj1GtW9GmGrsQdAEAQm1l/Gp4cH4m7rTT3fVgUtap4RlRzG/BNjmNdiAxrVs9vYREHicOCfTG1mXvSCQsHTtXxXOpbpyFdnvqLdj+2Y03IONYvUzHF/U/dPpUWJFoXOOa2gUBAo+rpw4O0oMLDqBCYd7EdplwrU9nl55icf+2IsaLmRTw6+Qynn8jhZubHj7k+0Kdmd7y7N5/Pm69Ll37bWgK9T4Td4FBQUFPITRV+/nDjbCmhUIg8iJQzPyZTRuuQbaEQtH+/twbTGq/mo7gJG7X4dX4cSBLhWtogsj+PusfbKIsKTntC74ohn3kUOPdrGvgdbWNDqewI8NIUuYlbh+QiCgK3W5GD+O4O5ziCTaoBUg4xBAkkGowTIIIqm1B0qETQq07kvgkNa4cUg3xzUh44cZ8vvf1K+bACD+vfF2toqvy79yqMSBfyc7RlQdTwrzk7l43oL//WZikkNljNq9+tU92qEldq6QGXNC6JTIvjk4DuMr78EF2v3DNtcidxJ29JtzCrcpFB40Kq0jKw7kj5V+vDO1nf4oNYHdAjokO1+1l9eT6IukcE1B+eJnAoKLxKKvi482GoF3OxEpjb8itF7ulHEvvgzKTFeZBytXFjYahN3o6+y5PREmhbrjIedD583X59OXwsCFHURFaNXQUFB4V8o+vrlxs5KwN/T5KRO0Wferlnxznjb+fHxvp6Mq7eIz5quZdKBvgypMZVqXg1ydG1ZlrkQdoyN179CJajpW2k0Zd2qPtPuRNAefrv9PYvbrKOspxa14qh8KdCqBbRqcED5PhXyl3yJ0753/wG/bP6d0qVK8P7gdxXlWQB4Ogg09GtNgi6Oy2En031mq7GnZ/mhfH95QYHJl1fcjLzIuH29GFV7Dv4uFTNsY6uFX65/y6Aag/JdPgXL4Grjyq/df2X95fV8e/5bs86RZInTQaeZdmAav177lflt5ue5nAoKhR1FXxc+vJ0E7K3s+LTR/5hx5H2S9AkFLZJF0Yga9tzfRMsSXRleawavl+n/zGKyj6OArVYxkhQUFBT+RtHXrwZWagF/DxF7q+frwHLu1Znb/CeWnvmE86FH+KLlL3x7aR5HAndk63qphhR+u/U9H+xsz+HAPxlVazazmn6boXP6SOAOfrq2nMVtfqC8l7XinFZQUMg1+RJB/cvmP5AkicoVy3P5yrUM2/h4e+Hj7ZXhZwq5R6MScLMX+LDOXMbvf4vlbbalS3XRskRXtu9dR1D8/ZcmOmvn3Z/Zce8n5rXYkGnktCBAsnQXF2sX3Gzd8l1GBcthpbZibde1fLTrI2YdmsUnjT/JsDjH6aDTrDyzkifxT6jpU5OOZToypckUJXpeQUHR14USjUrA21HAKBVncLVPGLfvLaY0WomXnZ8ZZxduYlOj+OrcDGw19rxZ7r0M2zhYC3g4KDkCFRQUFP6Noq9fHVSiQCl3CIuH0Hg507zUrjYeLGy1iUWnxnP08V9MarCCRafGEZsaRYfSvZ9btDAw7i6/3vgfNyMv0rZUDxa33oK1OuNi8bIss/7qMm5GXeSr9j9TxtNG2eGkoKBgEfLFQf0o8DEAW//IfAWvQ9tWdGzfOj/EeWXxtBeITHCja5mBfH/5CwZVm5j2mSAIjK79OYtOTWBBy58LVM7cIskSy89MxiDp+aLlr6jFzIe5u73AzMOLGFl3ZL7KqJA3iILIwrYL+fL0l7T5sQ3VvavTo2IPavjUYNfdXaw4vQJ/F3+mNJlCSZeXYyFGQcGSKPq6cOJuLxKdZKSWT1NcrD2YeeQDGvi1oWf5oS/k4ppe0rPx2kqOPN7B4GqTqOHdOMN2GhUUc1GMXgUFBYX/oujrVwtBEPByFLC3knkULaEzZNxOI2oYV28hl8NO8umhgbQo0ZVHcXcYsrMdZd2q0qpENyq61yYsKYj7Mde5F3ODcyGHsdc60b3ce4yu/flzHdl6Sc+84x/iZefLyg7f4OMkPre9goKCQnbIFwd1bqoSK1gOtUrA3V6gbakejNr9OmGJT/C0K5L2eXGnAEo4leHgoz9oWszylX/zgxRDMtOPvEfdIi14vUz/57bVqkEimPCkcKp6P7ttSeHFZWjtoXxQ6wPOh5xn49WNjN8znuYlmvP969/jaqMUj1FQyAxFXxdefJ1F7oRLlHapwNI2v7H11rcM39WJUbXnUM6tWkGLZxaJunj+vLueXfd/oUvAO6xoux1RyDg6WhCghJuobBlWUFBQyABFX7+a2FkJlPEUeRIjE5WUSSg1UNmzLivabmfTzVVcDjvJyFqfoRJU7H24hVUXZuNtV5SSzuUo7VKBzgF9cbLK2j4KTQzis6ND6Vr2HQbWfBMHa0U/KygoWJZ8K5KoUDjwsBeITBQYVnMGK85OZXqT1ek+H1htIiN3daFOkZbYqG0LTM7nEZcazbY7PxIYd4+2pbpT1bM+giAQnRLB5IPv0qfSKOr7Zh0tUMRJZObhRYytPzZf5FbIXwRBoIZPDWr41ChoURQUFBRyja3WtMgcHi8jCiLdyg6kabFOzD8xhtLOFXi3yscFLWKm3Iq6xM/XVhKe9IT2pd9iRdvtaFXPz5daxEnJO62goKCgoPBfVKJAUVcBVzuZoFiJZF1m7VT0KD+EViW6seT0JGw0dgytMR1HK+dsXU+WZTbf/IZ9D7cwpdEXtPCvgEZZPFZQUMgDFAf1K8bfUdRlpapoVFquhJ+mkkfttM9t1La8W+Ujlp2ZzLh6CwtMzvsxN1h3dSlqUUNRh9IUdSyFVmXN9rvrSdDF0sm/D3V8mrPr/i+sPDedCu41uBp+ho/rLSTAtXKW/TvZCBjkKG5G3qR+0fr5ck8KCgoKCgq5wdtBIDZZTtva62bjxZxmP/Ln3Z8Ytft1BgVMwM3Tu6DFTMejuDssOjWecfUWUdK5nFnnuNgKuNsreacVFBQUFBQywxRNrSIyUSI0TkZvzLidq40n05us5mzwIT7a2512pXvRoXTvTHNM/5vHcfeYf3IM9X2b81vPbbjZK+4jBQWFvEOZYV5BPOwFIhJkhtaYxpRD/VneZlu63FEN/dqx/+HvnH6yn1o+TfNVtsC4u6y6MBsZmQFVxqERtQTG3+Vx3D0S9HEMqT4lXRFHf9dKyLLMlYjTvF1xFO62WRvmKtEUmTX7yDJG1lFyTysoKCgovBiIokBRF5G74VLaMUEQ6Ojfm1rejZl2YDBva0fRqGj7ApXzb2RZZsGJsUxuuBJfhxJmnWOtAT9nJTJLQUFBQUHBHNzsRFxtZaKTIDxBIkWfcbuaPk1Y4fUn2+/8yNi9b1LMMYBuZQc+E9wVlxrNngebOfjoDxy0TsxrtZDafv5KrmkFBYU8R3FQv4KoRAEvBwGj5Eltn+b8dW8j7Ur3hKfG5O3oy3Qv9z7zT4ylvHv+pEdI0MWx4uxUYlIjGVxtEqWcy6d95udYCnwzP1cQBCp71DH7Wj5OAqnGBI4/Ps6nTT/NregKCgoKCgr5hr2VgKeDQFh8+tyTnna+TKnxJTMvDaOooz/FnQIKTMa/2XzzG+oWaWm2c1qjgpJuIqKoGMEKCgoKCgrmIggCrnbgaqciPkUmJlkmLlnGIKVvpxE1vF6mP6+X6c+tqEtsurmaoPj7afUgZFlGq7KiU0A3fuy6niKO9opOVlBQyDcUB/Uriru9QGSiTO+KIxj2V0ecrF059Gg792NuUMa1MnrJgF5K4c3N1ajkXJsmpTrSqGgHXG08LC7LgYd/sO7qEt6rPpnaPs0s3v+/cbQWcLMTWXDsa4bUHKKsBCsoKCgovHB4OwrEp8rP5J3Uqqz5tNHXTD00kIWtNmGvdSwoEQlNDGLfwy0sbf27We3Fp0URtWpFLysoKCgoKOQUB2vBVMDQBZJ0MnEpMil6SDWYUoRJT9e3y7hWYUL9JahEsNYI2GjARiPgYI2SY1pBQaFAUBzUryiCIFDEWeR+hBVDa0zjbMhhupd/P13kMsC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    " ] @@ -199,21 +200,21 @@ "fig, axs = plt.subplots(1, 3, figsize=(18, 5))\n", "\n", "plot_posterior(axs[0], model)\n", - "axs[0].set_title(\"I-BNN (Fixed Hypers)\\nWeight Var: %.2f, Bias Var: %.2f\" % \n", - " (model.covar_module.weight_var.item(), model.covar_module.bias_var.item()), \n", + "axs[0].set_title(\"I-BNN (Fixed Hypers)\\nWeight Var: %.2f, Bias Var: %.2f\" %\n", + " (model.covar_module.weight_var.item(), model.covar_module.bias_var.item()),\n", " fontsize=20)\n", "axs[0].set_ylim(-7, 8)\n", "axs[0].legend()\n", "\n", "plot_posterior(axs[1], model_optimize)\n", - "axs[1].set_title(\"I-BNN (Optimized Hypers)\\nWeight Var: %.2f, Bias Var: %.2f\" % \n", + "axs[1].set_title(\"I-BNN (Optimized Hypers)\\nWeight Var: %.2f, Bias Var: %.2f\" %\n", " (model_optimize.covar_module.weight_var.item(), model_optimize.covar_module.bias_var.item()),\n", " fontsize=20)\n", "axs[1].set_ylim(-7, 8)\n", "\n", "plot_posterior(axs[2], model_matern)\n", - "axs[2].set_title(\"GP (Matern Kernel)\\nLength Scale: %.2f\" % \n", - " model_matern.covar_module.lengthscale.item(), \n", + "axs[2].set_title(\"GP (Matern Kernel)\\nLength Scale: %.2f\" %\n", + " model_matern.covar_module.lengthscale.item(),\n", " fontsize=20)\n", "axs[2].set_ylim(-7, 8)\n", "\n", @@ -238,12 +239,17 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 18, + "metadata": { + "output": { + "id": 375446892116177, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -270,12 +276,17 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 19, + "metadata": { + "output": { + "id": 1155028772461291, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -302,12 +313,17 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 20, + "metadata": { + "output": { + "id": 395095789997175, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { - "image/png": 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    " ] @@ -350,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -399,10 +415,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ + "from botorch.acquisition.analytic import ExpectedImprovement, LogExpectedImprovement\n", + "\n", "def generate_initial_data(f, bounds, n, input_dims):\n", " train_x = draw_sobol_samples(bounds=bounds, n=n, q=1).to(**tkwargs)\n", " train_x = train_x.squeeze(-2) # remove batch dimension\n", @@ -410,12 +428,12 @@ " return train_x, train_y\n", "\n", "\n", - "def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, optimize_hypers=False):\n", + "def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, acqf_class, optimize_hypers=False):\n", " train_x = init_x.clone()\n", " train_y = init_y.clone()\n", "\n", " for iteration in range(n_iterations):\n", - " \n", + "\n", " # fit model to data\n", " model = SingleTaskGP(train_x, train_y, outcome_transform=Standardize(m=1), covar_module=kernel)\n", " if optimize_hypers:\n", @@ -425,7 +443,7 @@ "\n", " # optimize acquisition function\n", " candidate_x, acq_value = optimize_acqf(\n", - " acq_function=ExpectedImprovement(model, train_y.max()),\n", + " acq_function=acqf_class(model, train_y.max()),\n", " bounds=bounds,\n", " q=1,\n", " num_restarts=10,\n", @@ -437,8 +455,7 @@ " train_x = torch.cat([train_x, candidate_x])\n", " train_y = torch.cat([train_y, f(candidate_x)])\n", "\n", - " return train_x, train_y\n", - " " + " return train_x, train_y\n" ] }, { @@ -450,10 +467,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ + "from functools import partial\n", "# define kernels\n", "ibnn_kernel = InfiniteWidthBNNKernel(2, device=device)\n", "ibnn_kernel.weight_var = 10.0\n", @@ -467,19 +485,33 @@ "train_x, train_y = generate_initial_data(f, bounds, n=N_INIT, input_dims=INPUT_DIMS)\n", "\n", "# run BO loop\n", - "ibnn_x, ibnn_y = gp_bo_loop(f, bounds, train_x, train_y, ibnn_kernel, n_iterations=N_ITERATIONS, optimize_hypers=False)\n", - "matern_x, matern_y = gp_bo_loop(f, bounds, train_x, train_y, matern_kernel, n_iterations=N_ITERATIONS, optimize_hypers=True)\n", - "rbf_x, rbf_y = gp_bo_loop(f, bounds, train_x, train_y, rbf_kernel, n_iterations=N_ITERATIONS, optimize_hypers=True)" + "acqf_classes = {\"LogEI\": LogExpectedImprovement}\n", + "results = {}\n", + "for acq_name, acqf_class in acqf_classes.items():\n", + " run_bo_with_acqf = partial(gp_bo_loop, f=f, bounds=bounds, init_x=train_x, init_y=train_y, acqf_class=acqf_class, n_iterations=N_ITERATIONS)\n", + " ibnn_x, ibnn_y = run_bo_with_acqf(kernel=ibnn_kernel, optimize_hypers=False)\n", + " matern_x, matern_y = run_bo_with_acqf(kernel=matern_kernel, optimize_hypers=True)\n", + " rbf_x, rbf_y = run_bo_with_acqf(kernel=rbf_kernel, optimize_hypers=True)\n", + " results[acq_name] = {\n", + " \"BNN\": (ibnn_x, ibnn_y),\n", + " \"Matern\": (matern_x, matern_y),\n", + " \"RBF\": (rbf_x, rbf_y),\n", + " }" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 24, + "metadata": { + "output": { + "id": 361920396970842, + "loadingStatus": "loaded" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -489,15 +521,23 @@ } ], "source": [ - "def plot_cum_max(y, label):\n", + "import matplotlib\n", + "def plot_cum_max(y, **kwargs):\n", " cum_max = (torch.cummax(y, dim=0)[0]).cpu()\n", - " plt.plot(range(len(cum_max)), cum_max, label=label)\n", + " plt.plot(range(len(cum_max)), cum_max, **kwargs)\n", "\n", "plt.figure(figsize=(8, 6))\n", "\n", - "plot_cum_max(ibnn_y[N_INIT-1:], \"I-BNN\")\n", - "plot_cum_max(matern_y[N_INIT-1:], \"Matern\")\n", - "plot_cum_max(rbf_y[N_INIT-1:], \"RBF\")\n", + "colors = matplotlib.cm.get_cmap(\"tab10\").colors\n", + "linestyles = {\"LogEI\": \"-\"}\n", + "for acq_name, res in results.items():\n", + " ls = linestyles[acq_name]\n", + " ibnn_y = res[\"BNN\"][-1]\n", + " matern_y = res[\"Matern\"][-1]\n", + " rbf_y = res[\"RBF\"][-1]\n", + " plot_cum_max(ibnn_y[N_INIT-1:], label=f\"I-BNN ({acq_name})\", color=colors[0], ls=ls)\n", + " plot_cum_max(matern_y[N_INIT-1:], label=f\"Matern ({acq_name})\", color=colors[1], ls=ls)\n", + " plot_cum_max(rbf_y[N_INIT-1:], label=f\"RBF ({acq_name})\", color=colors[2], ls=ls)\n", "\n", "plt.xlabel(\"BO Iterations\")\n", "plt.ylabel(\"Max Value\")\n", @@ -505,13 +545,6 @@ "plt.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/v/latest/files/ibnn_bo.py b/v/latest/files/ibnn_bo.py index 85d6a5848e..4796d546e0 100644 --- a/v/latest/files/ibnn_bo.py +++ b/v/latest/files/ibnn_bo.py @@ -18,7 +18,7 @@ # [2] [J. Lee, Y. Bahri, R. Novak, S. Schoenholz, J. Pennington, and J. Dickstein. Deep Neural Networks as Gaussian Processes. International Conference on Learning Representations 2018.](https://arxiv.org/abs/1711.00165) # [3] [Y.L. Li, T.G.J. Rudner, A.G. Wilson. A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. International Conference on Learning Representations 2024.](https://arxiv.org/abs/2305.20028) -# In[1]: +# In[13]: import os @@ -32,7 +32,7 @@ from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood from botorch import manual_seed -from botorch.acquisition import ExpectedImprovement +from botorch.acquisition import LogExpectedImprovement from botorch.fit import fit_gpytorch_mll from botorch.models.gp_regression import SingleTaskGP from botorch.models.kernels import InfiniteWidthBNNKernel @@ -55,7 +55,7 @@ # # We start by visualizing the posteriors of an I-BNN. Here, we define a toy function and draw five initial function evaluations. -# In[2]: +# In[14]: torch.manual_seed(1111) @@ -78,7 +78,7 @@ def f(x): # **Initializing the Model**: We now define two versions of the I-BNN, constructed using a GP with an `InfiniteWidthBNNKernel`. One version has fixed user-specified values for $\sigma^2_w$ and $\sigma^2_b$, and the other uses the marginal log likelihood to optimize these hyperparameters. -# In[3]: +# In[15]: # Function queries are not noisy @@ -106,7 +106,7 @@ def f(x): # **Visualizating the Posterior**: -# In[4]: +# In[16]: def plot_posterior(ax, model, n_draws=5): @@ -128,27 +128,27 @@ def plot_posterior(ax, model, n_draws=5): ax.plot(test_x.cpu(), pred_f.sample().cpu(), color="green", linewidth=0.5) -# In[5]: +# In[17]: fig, axs = plt.subplots(1, 3, figsize=(18, 5)) plot_posterior(axs[0], model) -axs[0].set_title("I-BNN (Fixed Hypers)\nWeight Var: %.2f, Bias Var: %.2f" % - (model.covar_module.weight_var.item(), model.covar_module.bias_var.item()), +axs[0].set_title("I-BNN (Fixed Hypers)\nWeight Var: %.2f, Bias Var: %.2f" % + (model.covar_module.weight_var.item(), model.covar_module.bias_var.item()), fontsize=20) axs[0].set_ylim(-7, 8) axs[0].legend() plot_posterior(axs[1], model_optimize) -axs[1].set_title("I-BNN (Optimized Hypers)\nWeight Var: %.2f, Bias Var: %.2f" % +axs[1].set_title("I-BNN (Optimized Hypers)\nWeight Var: %.2f, Bias Var: %.2f" % (model_optimize.covar_module.weight_var.item(), model_optimize.covar_module.bias_var.item()), fontsize=20) axs[1].set_ylim(-7, 8) plot_posterior(axs[2], model_matern) -axs[2].set_title("GP (Matern Kernel)\nLength Scale: %.2f" % - model_matern.covar_module.lengthscale.item(), +axs[2].set_title("GP (Matern Kernel)\nLength Scale: %.2f" % + model_matern.covar_module.lengthscale.item(), fontsize=20) axs[2].set_ylim(-7, 8) @@ -161,7 +161,7 @@ def plot_posterior(ax, model, n_draws=5): # # The I-BNN has three hyperparameters: the number of hidden layers, the variance of the weights, and the variance of the bias terms. Here, we visualize how modifying these hyperparameters impacts the posterior. -# In[6]: +# In[18]: fig, axs = plt.subplots(1, 4, figsize=(20, 4)) @@ -179,7 +179,7 @@ def plot_posterior(ax, model, n_draws=5): ax.legend() -# In[7]: +# In[19]: fig, axs = plt.subplots(1, 4, figsize=(20, 4)) @@ -197,7 +197,7 @@ def plot_posterior(ax, model, n_draws=5): ax.legend() -# In[8]: +# In[20]: fig, axs = plt.subplots(1, 4, figsize=(20, 4)) @@ -222,7 +222,7 @@ def plot_posterior(ax, model, n_draws=5): # **Define High-dimensional Function and BO Setup**: We will optimize the output of a multilayer perceptron (MLP) with 2 hidden layers, 50 nodes per layer, and ReLU nonlinearities. # -# In[9]: +# In[21]: class MLP(nn.Module): @@ -263,8 +263,10 @@ def f(x): # **Define BO functions**: We use Sobol sampling to initialize the BO problem, and we use the Expected Improvement acquisition function. -# In[10]: +# In[22]: + +from botorch.acquisition.analytic import ExpectedImprovement, LogExpectedImprovement def generate_initial_data(f, bounds, n, input_dims): train_x = draw_sobol_samples(bounds=bounds, n=n, q=1).to(**tkwargs) @@ -273,12 +275,12 @@ def generate_initial_data(f, bounds, n, input_dims): return train_x, train_y -def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, optimize_hypers=False): +def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, acqf_class, optimize_hypers=False): train_x = init_x.clone() train_y = init_y.clone() for iteration in range(n_iterations): - + # fit model to data model = SingleTaskGP(train_x, train_y, outcome_transform=Standardize(m=1), covar_module=kernel) if optimize_hypers: @@ -288,7 +290,7 @@ def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, optimize_hypers= # optimize acquisition function candidate_x, acq_value = optimize_acqf( - acq_function=ExpectedImprovement(model, train_y.max()), + acq_function=acqf_class(model, train_y.max()), bounds=bounds, q=1, num_restarts=10, @@ -301,14 +303,14 @@ def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, optimize_hypers= train_y = torch.cat([train_y, f(candidate_x)]) return train_x, train_y - # **Compare I-BNN with GP with Matern kernel and RBF kernel**: On this high-dimensional problem, the I-BNN significantly outperforms the standard Matern and RBF kernels and is able to find better rewards. -# In[11]: +# In[23]: +from functools import partial # define kernels ibnn_kernel = InfiniteWidthBNNKernel(2, device=device) ibnn_kernel.weight_var = 10.0 @@ -322,23 +324,40 @@ def gp_bo_loop(f, bounds, init_x, init_y, kernel, n_iterations, optimize_hypers= train_x, train_y = generate_initial_data(f, bounds, n=N_INIT, input_dims=INPUT_DIMS) # run BO loop -ibnn_x, ibnn_y = gp_bo_loop(f, bounds, train_x, train_y, ibnn_kernel, n_iterations=N_ITERATIONS, optimize_hypers=False) -matern_x, matern_y = gp_bo_loop(f, bounds, train_x, train_y, matern_kernel, n_iterations=N_ITERATIONS, optimize_hypers=True) -rbf_x, rbf_y = gp_bo_loop(f, bounds, train_x, train_y, rbf_kernel, n_iterations=N_ITERATIONS, optimize_hypers=True) +acqf_classes = {"LogEI": LogExpectedImprovement} +results = {} +for acq_name, acqf_class in acqf_classes.items(): + run_bo_with_acqf = partial(gp_bo_loop, f=f, bounds=bounds, init_x=train_x, init_y=train_y, acqf_class=acqf_class, n_iterations=N_ITERATIONS) + ibnn_x, ibnn_y = run_bo_with_acqf(kernel=ibnn_kernel, optimize_hypers=False) + matern_x, matern_y = run_bo_with_acqf(kernel=matern_kernel, optimize_hypers=True) + rbf_x, rbf_y = run_bo_with_acqf(kernel=rbf_kernel, optimize_hypers=True) + results[acq_name] = { + "BNN": (ibnn_x, ibnn_y), + "Matern": (matern_x, matern_y), + "RBF": (rbf_x, rbf_y), + } -# In[12]: +# In[24]: -def plot_cum_max(y, label): +import matplotlib +def plot_cum_max(y, **kwargs): cum_max = (torch.cummax(y, dim=0)[0]).cpu() - plt.plot(range(len(cum_max)), cum_max, label=label) + plt.plot(range(len(cum_max)), cum_max, **kwargs) plt.figure(figsize=(8, 6)) -plot_cum_max(ibnn_y[N_INIT-1:], "I-BNN") -plot_cum_max(matern_y[N_INIT-1:], "Matern") -plot_cum_max(rbf_y[N_INIT-1:], "RBF") +colors = matplotlib.cm.get_cmap("tab10").colors +linestyles = {"LogEI": "-"} +for acq_name, res in results.items(): + ls = linestyles[acq_name] + ibnn_y = res["BNN"][-1] + matern_y = res["Matern"][-1] + rbf_y = res["RBF"][-1] + plot_cum_max(ibnn_y[N_INIT-1:], label=f"I-BNN ({acq_name})", color=colors[0], ls=ls) + plot_cum_max(matern_y[N_INIT-1:], label=f"Matern ({acq_name})", color=colors[1], ls=ls) + plot_cum_max(rbf_y[N_INIT-1:], label=f"RBF ({acq_name})", color=colors[2], ls=ls) plt.xlabel("BO Iterations") plt.ylabel("Max Value") @@ -346,9 +365,3 @@ def plot_cum_max(y, label): plt.legend() plt.show() - -# In[13]: - - - - diff --git a/v/latest/files/meta_learning_with_rgpe.ipynb b/v/latest/files/meta_learning_with_rgpe.ipynb index 56f366b6fa..b183ec883c 100644 --- a/v/latest/files/meta_learning_with_rgpe.ipynb +++ b/v/latest/files/meta_learning_with_rgpe.ipynb @@ -1,9 +1,10 @@ { "metadata": { "kernelspec": { + "name": "python3", "display_name": "python3", "language": "python", - "name": "python3" + "isCinder": true }, "language_info": { "codemirror_mode": { @@ -25,7 +26,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "62d6d3ed-36ff-4609-bc82-1451f8093bd9", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "## Meta-Learning with the Rank-Weighted GP Ensemble (RGPE)\n", @@ -38,12 +41,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "0ffb7b6f-bf4e-4217-8576-90ea5809758a", + "originalKey": "6fc093c3-2d62-49c1-a4cc-558193bcff8b", "collapsed": false, - "requestMsgId": "77454c63-2768-4035-900d-72df5be4f2e1", + "requestMsgId": "6fc093c3-2d62-49c1-a4cc-558193bcff8b", "customOutput": null, - "executionStartTime": 1668649821223, - "executionStopTime": 1668649821286 + "executionStartTime": 1724948296406, + "executionStopTime": 1724948298817, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 2277.4769549724 }, "source": [ "import os\n", @@ -57,13 +63,30 @@ "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")" ], "execution_count": 1, - "outputs": [] + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "I0829 091817.060 _utils_internal.py:292] NCCL_DEBUG env var is set to None\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "I0829 091817.061 _utils_internal.py:310] NCCL_DEBUG is forced to WARN from None\n" + ] + } + ] }, { "cell_type": "markdown", "metadata": { "originalKey": "2352cd6d-70b4-4426-9fdc-e0b8ac91aac5", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "### Toy Problem\n", @@ -82,7 +105,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "babbfc73-97d4-491c-87e0-be07d1acc2d7", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "#### Toy Problem Setup\n", @@ -93,12 +118,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "2e0a763a-7c6f-42bf-8bfa-131c11f3e05e", + "originalKey": "4c0c2b47-6313-4450-bdec-366db7c00643", "collapsed": false, - "requestMsgId": "b0b2495e-85d2-40eb-949e-745e2aead226", + "requestMsgId": "4c0c2b47-6313-4450-bdec-366db7c00643", "customOutput": null, - "executionStartTime": 1668649821519, - "executionStopTime": 1668649821525 + "executionStartTime": 1724948297830, + "executionStopTime": 1724948298839, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 1.4142158906907 }, "source": [ "NUM_BASE_TASKS = 5 if not SMOKE_TEST else 2\n", @@ -122,7 +150,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "d5650131-21e8-40d4-9003-d89b7431fb29", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "Then, let's define our function $f(x, s_i)$ and set bounds on $x$." @@ -131,12 +161,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "a5b45eac-b614-414a-ad97-503874721080", + "originalKey": "c1abc54e-b410-437d-beee-ce06d248706f", "collapsed": false, - "requestMsgId": "bfeb815c-d04e-4ddf-9835-c746843ccb82", + "requestMsgId": "c1abc54e-b410-437d-beee-ce06d248706f", "customOutput": null, - "executionStartTime": 1668649821835, - "executionStopTime": 1668649825651 + "executionStartTime": 1724948298726, + "executionStopTime": 1724948298909, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 15.071736183017 }, "source": [ "BOUNDS = torch.tensor([[-10.0], [10.0]], dtype=dtype, device=device)\n", @@ -156,7 +189,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "d9896be8-4cd9-487e-9832-768e533635c4", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "#### Sample training data for prior base tasks" @@ -166,7 +201,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "f27bbe94-c3cb-4d58-9d82-ebd60d4ebd59", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "We sample data from a Sobol sequence to help ensure numerical stability when using a small amount of 1-D data. Sobol sequences help prevent us from sampling a bunch of training points that are close together." @@ -175,12 +212,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "0631a7f8-a1bc-4adb-8824-fcb5cdd88f74", + "originalKey": "75962b70-ca73-4ab4-97fd-e28de1abdd81", "collapsed": false, - "requestMsgId": "13a2b624-8023-4eb8-be2b-60fea151f277", + "requestMsgId": "75962b70-ca73-4ab4-97fd-e28de1abdd81", "customOutput": null, - "executionStartTime": 1668649825899, - "executionStopTime": 1668649825991 + "executionStartTime": 1724948300185, + "executionStopTime": 1724948301395, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 1088.0531340372 }, "source": [ "from botorch.utils.sampling import draw_sobol_samples\n", @@ -219,7 +259,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "80336086-3253-4a0a-8875-e5db7440b362", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "#### Let's plot the base tasks and the target task function along with the observed points" @@ -228,12 +270,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "ccc19fcc-b6de-457c-ad4a-dd335feef34d", + "originalKey": "aeff41b6-623a-4b10-a583-d47563b29700", "collapsed": false, - "requestMsgId": "6a373fdc-ab7b-4b95-bce4-9c38596a7f80", + "requestMsgId": "aeff41b6-623a-4b10-a583-d47563b29700", "customOutput": null, - "executionStartTime": 1668649826317, - "executionStopTime": 1668649827119 + "executionStartTime": 1724948301524, + "executionStopTime": 1724948303012, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 1299.3806430604 }, "source": [ "from matplotlib import pyplot as plt\n", @@ -270,16 +315,20 @@ ], "execution_count": 5, "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "W0829 091822.520 font_manager.py:1403] findfont: Font family ['Liberation Sans', 'Noto Sans TC', 'Noto Sans SC', 'Noto Sans Thai', 'Noto Naskh Arabic UI', 'Noto Sans UI'] not found. Falling back to DejaVu Sans.\n" + ] + }, { "output_type": "display_data", "data": { - "text/plain": "
    ", - "image/png": 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\n" 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}, - "metadata": { - "bento_obj_id": "140065305836560", - "needs_background": "light" - } + "metadata": {} } ] }, @@ -287,7 +336,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "25d7a014-8982-40e0-8025-f12bba83dc45", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "### Fit base task models" @@ -297,25 +348,30 @@ "cell_type": "markdown", "metadata": { "originalKey": "cdf621a8-1057-4cf3-a129-668abb1f9453", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ - "First, let's define a helper function to fit a FixedNoiseGP with an fixed observed noise level." + "First, let's define a helper function to fit a SingleTaskGP with an fixed observed noise level." ] }, { "cell_type": "code", "metadata": { - "originalKey": "2622d12d-2e2b-4cd4-ae54-52031a0a4e2d", + "originalKey": "5474e712-cefa-4a7d-b673-af10fcf83239", "collapsed": false, - "requestMsgId": "fafb578c-2be7-4ffa-8124-699f2551711e", + "requestMsgId": "5474e712-cefa-4a7d-b673-af10fcf83239", "customOutput": null, - "executionStartTime": 1668649827363, - "executionStopTime": 1668649827449 + "executionStartTime": 1724948316352, + "executionStopTime": 1724948316501, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 2.9860010836273 }, "source": [ "from gpytorch.mlls import ExactMarginalLogLikelihood\n", - "from botorch.models import FixedNoiseGP\n", + "from botorch.models import SingleTaskGP\n", "from botorch.fit import fit_gpytorch_mll\n", "\n", "\n", @@ -324,11 +380,7 @@ " Get a single task GP. The model will be fit unless a state_dict with model\n", " hyperparameters is provided.\n", " \"\"\"\n", - " Y_mean = train_Y.mean(dim=-2, keepdim=True)\n", - " Y_std = train_Y.std(dim=-2, keepdim=True)\n", - " model = FixedNoiseGP(train_X, (train_Y - Y_mean) / Y_std, train_Yvar)\n", - " model.Y_mean = Y_mean\n", - " model.Y_std = Y_std\n", + " model = SingleTaskGP(train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar)\n", " if state_dict is None:\n", " mll = ExactMarginalLogLikelihood(model.likelihood, model).to(train_X)\n", " fit_gpytorch_mll(mll)\n", @@ -336,28 +388,33 @@ " model.load_state_dict(state_dict)\n", " return model" ], - "execution_count": 6, + "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "originalKey": "fe570963-41f1-47a5-8e53-5cf08e6390ba", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ - "#### Now let's fit a FixedNoiseGP for each base task" + "#### Now let's fit a SingleTaskGP for each base task" ] }, { "cell_type": "code", "metadata": { - "originalKey": "b4189524-69eb-4240-8a9e-d92059805acd", + "originalKey": "a7bd3664-5585-47e9-9743-2ee53d7a259f", "collapsed": false, - "requestMsgId": "72049870-a8d5-4ab6-8de5-3428ac74737b", + "requestMsgId": "a7bd3664-5585-47e9-9743-2ee53d7a259f", "customOutput": null, - "executionStartTime": 1668649827755, - "executionStopTime": 1668649829083 + "executionStartTime": 1724948317183, + "executionStopTime": 1724948318815, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 1460.1275878958 }, "source": [ "# Fit base model\n", @@ -371,7 +428,7 @@ " )\n", " base_model_list.append(model)" ], - "execution_count": 7, + "execution_count": 9, "outputs": [ { "output_type": "stream", @@ -384,14 +441,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "Fitting base model 1\nFitting base model 2\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Fitting base model 3\nFitting base model 4\n" + "Fitting base model 1\nFitting base model 2\nFitting base model 3\nFitting base model 4\n" ] } ] @@ -400,7 +450,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "6ea07cc4-db47-4d01-9840-220e9615b6a3", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "### Implement the RGPE\n", @@ -426,12 +478,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "b5ccfad8-7f6d-47ab-9090-ae2667e0434e", + "originalKey": "58ed8284-8181-459c-9025-42532793929f", "collapsed": false, - "requestMsgId": "c5fee371-e5fd-401a-852e-f6a56635e578", + "requestMsgId": "58ed8284-8181-459c-9025-42532793929f", "customOutput": null, - "executionStartTime": 1668649829305, - "executionStopTime": 1668649829311 + "executionStartTime": 1724948320494, + "executionStopTime": 1724948320631, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 2.3661230225116 }, "source": [ "def roll_col(X, shift):\n", @@ -440,18 +495,21 @@ " \"\"\"\n", " return torch.cat((X[..., -shift:], X[..., :-shift]), dim=-1)" ], - "execution_count": 8, + "execution_count": 10, "outputs": [] }, { "cell_type": "code", "metadata": { - "originalKey": "6d8b4a2f-9342-4fc2-a3b0-a04ac03cfd1b", + "originalKey": "bde867e5-67e9-47f0-bae3-20112bebd51d", "collapsed": false, - "requestMsgId": "98ccd70c-d5d1-494b-906e-157949f335f0", + "requestMsgId": "bde867e5-67e9-47f0-bae3-20112bebd51d", "customOutput": null, - "executionStartTime": 1668649829522, - "executionStopTime": 1668649829528 + "executionStartTime": 1724948325542, + "executionStopTime": 1724948325683, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 3.575277980417 }, "source": [ "def compute_ranking_loss(f_samps, target_y):\n", @@ -493,14 +551,16 @@ " ).sum(dim=-1)\n", " return rank_loss" ], - "execution_count": 9, + "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "metadata": { "originalKey": "158d11b4-020f-478c-9ec4-8655ae8c2aac", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "Define a function to:\n", @@ -511,12 +571,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "3cd50e99-32ef-4f7d-b6b1-78f24a292fe4", + "originalKey": "91127a10-93d6-4cc1-9eaa-3ac49e7be678", "collapsed": false, - "requestMsgId": "e28db90c-570a-48ea-bd4f-665ec01fb670", + "requestMsgId": "91127a10-93d6-4cc1-9eaa-3ac49e7be678", "customOutput": null, - "executionStartTime": 1668649829754, - "executionStopTime": 1668649829837 + "executionStartTime": 1724948361037, + "executionStopTime": 1724948361226, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 4.2148299980909 }, "source": [ "def get_target_model_loocv_sample_preds(\n", @@ -556,18 +619,21 @@ " sampler = SobolQMCNormalSampler(sample_shape=torch.Size([num_samples]))\n", " return sampler(posterior).squeeze(-1)" ], - "execution_count": 10, + "execution_count": 12, "outputs": [] }, { "cell_type": "code", "metadata": { - "originalKey": "52c68f20-7069-485c-b2be-d90c33d67ed0", + "originalKey": "298fa009-eae9-4199-a682-df730e10c20e", "collapsed": false, - "requestMsgId": "5bb7c537-d427-4e23-bbce-eed63f15b3f9", + "requestMsgId": "298fa009-eae9-4199-a682-df730e10c20e", "customOutput": null, - "executionStartTime": 1668649830063, - "executionStopTime": 1668649830147 + "executionStartTime": 1724948370606, + "executionStopTime": 1724948370882, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 3.5223178565502 }, "source": [ "def compute_rank_weights(train_x, train_y, base_models, target_model, num_samples):\n", @@ -616,18 +682,21 @@ " )\n", " return rank_weights" ], - "execution_count": 11, + "execution_count": 13, "outputs": [] }, { "cell_type": "code", "metadata": { - "originalKey": "6c780b66-5e11-47ed-90a3-bc2c30e844e3", + "originalKey": "5393f2d7-07c3-4e41-a68f-0cf92fb2aa8f", "collapsed": false, - "requestMsgId": "88e80458-0dfa-434b-a7f5-021eb3d30095", + "requestMsgId": "5393f2d7-07c3-4e41-a68f-0cf92fb2aa8f", "customOutput": null, - "executionStartTime": 1668649830380, - "executionStopTime": 1668649830385 + "executionStartTime": 1724948386869, + "executionStopTime": 1724948387021, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 5.0720849540085 }, "source": [ "from botorch.models.gpytorch import GPyTorchModel\n", @@ -673,8 +742,8 @@ " model = self.models[raw_idx]\n", " posterior = model.posterior(x)\n", " # unstandardize predictions\n", - " posterior_mean = posterior.mean.squeeze(-1) * model.Y_std + model.Y_mean\n", - " posterior_cov = posterior.mvn.lazy_covariance_matrix * model.Y_std.pow(2)\n", + " posterior_mean = posterior.mean.squeeze(-1)\n", + " posterior_cov = posterior.mvn.lazy_covariance_matrix\n", " # apply weight\n", " weight = non_zero_weights[non_zero_weight_idx]\n", " weighted_means.append(weight * posterior_mean)\n", @@ -685,14 +754,16 @@ " covar_x = PsdSumLazyTensor(*weighted_covars)\n", " return MultivariateNormal(mean_x, covar_x)" ], - "execution_count": 12, + "execution_count": 14, "outputs": [] }, { "cell_type": "markdown", "metadata": { "originalKey": "49b770aa-d0c1-4e37-8366-ca1debde2f40", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "### Optimize target function using RGPE + qNEI" @@ -702,21 +773,24 @@ "cell_type": "code", "metadata": { "scrolled": false, - "originalKey": "54de1c4d-3424-49ce-8d4c-a9ed70e1aced", + "originalKey": "4670c8c2-c171-4e3e-87f6-0ca3543140df", "collapsed": false, - "requestMsgId": "7ea92cbf-ab86-4be7-b107-d5a6dfa8eb4e", + "requestMsgId": "4670c8c2-c171-4e3e-87f6-0ca3543140df", "customOutput": null, - "executionStartTime": 1668649830711, - "executionStopTime": 1668649889473 + "executionStartTime": 1724948469002, + "executionStopTime": 1724948513622, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 44297.466597985 }, "source": [ - "from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement\n", - "from botorch.sampling.normal import SobolQMCNormalSampler\n", - "from botorch.optim.optimize import optimize_acqf\n", - "\n", "# suppress GPyTorch warnings about adding jitter\n", "import warnings\n", "\n", + "from botorch.acquisition.logei import qLogNoisyExpectedImprovement\n", + "from botorch.optim.optimize import optimize_acqf\n", + "from botorch.sampling.normal import SobolQMCNormalSampler\n", + "\n", "\n", "warnings.filterwarnings(\"ignore\", \"^.*jitter.*\", category=RuntimeWarning)\n", "\n", @@ -773,7 +847,7 @@ " # create model and acquisition function\n", " rgpe_model = RGPE(model_list, rank_weights)\n", " sampler_qnei = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))\n", - " qNEI = qNoisyExpectedImprovement(\n", + " qNEI = qLogNoisyExpectedImprovement(\n", " model=rgpe_model,\n", " X_baseline=train_x,\n", " sampler=sampler_qnei,\n", @@ -820,7 +894,7 @@ " vanilla_nei_sampler = SobolQMCNormalSampler(\n", " sample_shape=torch.Size([MC_SAMPLES])\n", " )\n", - " vanilla_qNEI = qNoisyExpectedImprovement(\n", + " vanilla_qNEI = qLogNoisyExpectedImprovement(\n", " model=vanilla_nei_model,\n", " X_baseline=vanilla_nei_train_x,\n", " sampler=vanilla_nei_sampler,\n", @@ -855,7 +929,7 @@ " best_random_all.append(best_random)\n", " best_vanilla_nei_all.append(best_vanilla_nei)" ], - "execution_count": 13, + "execution_count": 18, "outputs": [ { "output_type": "stream", @@ -892,6 +966,20 @@ "Trial 5 of 10\n" ] }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[W 240829 09:21:28 optimize:564] 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" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[W 240829 09:21:28 optimize:564] Optimization failed on the second try, after generating a new set of initial conditions.\n" + ] + }, { "output_type": "stream", "name": "stdout", @@ -920,6 +1008,20 @@ "Trial 9 of 10\n" ] }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[W 240829 09:21:46 optimize:564] 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" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[W 240829 09:21:46 optimize:564] Optimization failed on the second try, after generating a new set of initial conditions.\n" + ] + }, { "output_type": "stream", "name": "stdout", @@ -933,7 +1035,9 @@ "cell_type": "markdown", "metadata": { "originalKey": "3dbf06b6-28ec-4b73-99f3-a9327b074159", - "showInput": false + "showInput": false, + "outputsInitialized": false, + "language": "markdown" }, "source": [ "#### Plot best observed value vs iteration" @@ -942,12 +1046,15 @@ { "cell_type": "code", "metadata": { - "originalKey": "6a6999d9-8458-4daf-a124-91d27eed8bc7", + "originalKey": "77d08a71-caf0-444c-994c-94a0f63efc42", "collapsed": false, - "requestMsgId": "2ef11962-209a-41ed-9b19-0bbd675553b5", + "requestMsgId": "77d08a71-caf0-444c-994c-94a0f63efc42", "customOutput": null, - "executionStartTime": 1668649889797, - "executionStopTime": 1668649889993 + "executionStartTime": 1724948509190, + "executionStopTime": 1724948514271, + "outputsInitialized": true, + "language": "python", + "serverExecutionDuration": 412.50938293524 }, "source": [ "import numpy as np\n", @@ -960,22 +1067,22 @@ "x = range(RANDOM_INITIALIZATION_SIZE, RANDOM_INITIALIZATION_SIZE + N_BATCH + 1)\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", - "# Plot RGPE - NEI\n", + "# Plot RGPE - LogNEI\n", "ax.errorbar(\n", " x,\n", " best_rgpe_all.mean(axis=0),\n", " yerr=1.96 * best_rgpe_all.std(axis=0) / math.sqrt(N_TRIALS),\n", - " label=\"RGPE - NEI\",\n", + " label=\"RGPE - LogNEI\",\n", " linewidth=3,\n", " capsize=5,\n", " capthick=3,\n", ")\n", - "# Plot FixedNoiseGP - NEI\n", + "# Plot SingleTaskGP - LogNEI\n", "ax.errorbar(\n", " x,\n", " best_vanilla_nei_all.mean(axis=0),\n", " yerr=1.96 * best_vanilla_nei_all.std(axis=0) / math.sqrt(N_TRIALS),\n", - " label=\"FixedNoiseGP - NEI\",\n", + " label=\"SingleTaskGP - LogNEI\",\n", " linewidth=3,\n", " capsize=5,\n", " capthick=3,\n", @@ -997,20 +1104,31 @@ "ax.legend(loc=\"lower right\", fontsize=10)\n", "plt.tight_layout()" ], - "execution_count": 14, + "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { - "text/plain": "
    ", - "image/png": 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\n" 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" }, - "metadata": { - "bento_obj_id": "140065305910480", - "needs_background": "light" - } + "metadata": {} } ] + }, + { + "cell_type": "code", + "metadata": { + "originalKey": "bc7cf5ae-bdf2-465c-b918-a69c8f2e3e8f", + "showInput": true, + "customInput": null, + "language": "python" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] } ] } diff --git a/v/latest/files/meta_learning_with_rgpe.py b/v/latest/files/meta_learning_with_rgpe.py index 6d30777c80..8d643bf237 100644 --- a/v/latest/files/meta_learning_with_rgpe.py +++ b/v/latest/files/meta_learning_with_rgpe.py @@ -147,13 +147,13 @@ def f(X, shift=TARGET_SHIFT): # ### Fit base task models -# First, let's define a helper function to fit a FixedNoiseGP with an fixed observed noise level. +# First, let's define a helper function to fit a SingleTaskGP with an fixed observed noise level. -# In[6]: +# In[8]: from gpytorch.mlls import ExactMarginalLogLikelihood -from botorch.models import FixedNoiseGP +from botorch.models import SingleTaskGP from botorch.fit import fit_gpytorch_mll @@ -162,11 +162,7 @@ def get_fitted_model(train_X, train_Y, train_Yvar, state_dict=None): Get a single task GP. The model will be fit unless a state_dict with model hyperparameters is provided. """ - Y_mean = train_Y.mean(dim=-2, keepdim=True) - Y_std = train_Y.std(dim=-2, keepdim=True) - model = FixedNoiseGP(train_X, (train_Y - Y_mean) / Y_std, train_Yvar) - model.Y_mean = Y_mean - model.Y_std = Y_std + model = SingleTaskGP(train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar) if state_dict is None: mll = ExactMarginalLogLikelihood(model.likelihood, model).to(train_X) fit_gpytorch_mll(mll) @@ -175,9 +171,9 @@ def get_fitted_model(train_X, train_Y, train_Yvar, state_dict=None): return model -# #### Now let's fit a FixedNoiseGP for each base task +# #### Now let's fit a SingleTaskGP for each base task -# In[7]: +# In[9]: # Fit base model @@ -211,7 +207,7 @@ def get_fitted_model(train_X, train_Y, train_Yvar, state_dict=None): # The weights are then computed as: # $$w_i = \frac{1}{S}\sum_{s=1}^S\mathbb 1\big(i = \text{argmin}_{i'}l_{i', s}\big)$$ -# In[8]: +# In[10]: def roll_col(X, shift): @@ -221,7 +217,7 @@ def roll_col(X, shift): return torch.cat((X[..., -shift:], X[..., :-shift]), dim=-1) -# In[9]: +# In[11]: def compute_ranking_loss(f_samps, target_y): @@ -268,7 +264,7 @@ def compute_ranking_loss(f_samps, target_y): # 1. Create a batch mode-gp LOOCV GP using the hyperparameters from `target_model` # 2. Draw a joint sample across all points from the target task (in-sample and out-of-sample) -# In[10]: +# In[12]: def get_target_model_loocv_sample_preds( @@ -309,7 +305,7 @@ def get_target_model_loocv_sample_preds( return sampler(posterior).squeeze(-1) -# In[11]: +# In[13]: def compute_rank_weights(train_x, train_y, base_models, target_model, num_samples): @@ -359,7 +355,7 @@ def compute_rank_weights(train_x, train_y, base_models, target_model, num_sample return rank_weights -# In[12]: +# In[14]: from botorch.models.gpytorch import GPyTorchModel @@ -405,8 +401,8 @@ def forward(self, x): model = self.models[raw_idx] posterior = model.posterior(x) # unstandardize predictions - posterior_mean = posterior.mean.squeeze(-1) * model.Y_std + model.Y_mean - posterior_cov = posterior.mvn.lazy_covariance_matrix * model.Y_std.pow(2) + posterior_mean = posterior.mean.squeeze(-1) + posterior_cov = posterior.mvn.lazy_covariance_matrix # apply weight weight = non_zero_weights[non_zero_weight_idx] weighted_means.append(weight * posterior_mean) @@ -420,16 +416,16 @@ def forward(self, x): # ### Optimize target function using RGPE + qNEI -# In[13]: +# In[18]: -from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement -from botorch.sampling.normal import SobolQMCNormalSampler -from botorch.optim.optimize import optimize_acqf - # suppress GPyTorch warnings about adding jitter import warnings +from botorch.acquisition.logei import qLogNoisyExpectedImprovement +from botorch.optim.optimize import optimize_acqf +from botorch.sampling.normal import SobolQMCNormalSampler + warnings.filterwarnings("ignore", "^.*jitter.*", category=RuntimeWarning) @@ -486,7 +482,7 @@ def forward(self, x): # create model and acquisition function rgpe_model = RGPE(model_list, rank_weights) sampler_qnei = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES])) - qNEI = qNoisyExpectedImprovement( + qNEI = qLogNoisyExpectedImprovement( model=rgpe_model, X_baseline=train_x, sampler=sampler_qnei, @@ -533,7 +529,7 @@ def forward(self, x): vanilla_nei_sampler = SobolQMCNormalSampler( sample_shape=torch.Size([MC_SAMPLES]) ) - vanilla_qNEI = qNoisyExpectedImprovement( + vanilla_qNEI = qLogNoisyExpectedImprovement( model=vanilla_nei_model, X_baseline=vanilla_nei_train_x, sampler=vanilla_nei_sampler, @@ -571,7 +567,7 @@ def forward(self, x): # #### Plot best observed value vs iteration -# In[14]: +# In[19]: import numpy as np @@ -584,22 +580,22 @@ def forward(self, x): x = range(RANDOM_INITIALIZATION_SIZE, RANDOM_INITIALIZATION_SIZE + N_BATCH + 1) fig, ax = plt.subplots(1, 1, figsize=(10, 6)) -# Plot RGPE - NEI +# Plot RGPE - LogNEI ax.errorbar( x, best_rgpe_all.mean(axis=0), yerr=1.96 * best_rgpe_all.std(axis=0) / math.sqrt(N_TRIALS), - label="RGPE - NEI", + label="RGPE - LogNEI", linewidth=3, capsize=5, capthick=3, ) -# Plot FixedNoiseGP - NEI +# Plot SingleTaskGP - LogNEI ax.errorbar( x, best_vanilla_nei_all.mean(axis=0), yerr=1.96 * best_vanilla_nei_all.std(axis=0) / math.sqrt(N_TRIALS), - label="FixedNoiseGP - NEI", + label="SingleTaskGP - LogNEI", linewidth=3, capsize=5, capthick=3, @@ -621,3 +617,9 @@ def forward(self, x): ax.legend(loc="lower right", fontsize=10) plt.tight_layout() + +# In[ ]: + + + + diff --git a/v/latest/index.html b/v/latest/index.html index 2af85d4e16..a0ba2863bf 100644 --- a/v/latest/index.html +++ b/v/latest/index.html @@ -48,13 +48,13 @@

  • Construct an acquisition function:

    from botorch.acquisition import LogExpectedImprovement
 
-logNEI = LogExpectedImprovement(model=gp, best_f=Y.max())
+logEI = LogExpectedImprovement(model=gp, best_f=Y.max())

  • Optimize the acquisition function:

    from botorch.optim import optimize_acqf
 
 bounds = torch.stack([torch.zeros(2), torch.ones(2)]).to(torch.double)
 candidate, acq_value = optimize_acqf(
-    logNEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
+    logEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
 )
 candidate  # tensor([[0.2981, 0.2401]], dtype=torch.float64)
    diff --git a/v/latest/tutorials/closed_loop_botorch_only.html b/v/latest/tutorials/closed_loop_botorch_only.html index fbb729b86c..6254f3dd40 100644 --- a/v/latest/tutorials/closed_loop_botorch_only.html +++ b/v/latest/tutorials/closed_loop_botorch_only.html @@ -72,10 +72,10 @@
    -

    Closed-loop batch, constrained BO in BoTorch with qEI and qNEI

    In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.

    +

    Closed-loop batch, constrained BO in BoTorch with qLogEI and qLogNEI

    In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.

    In general, we recommend for a relatively simple setup (like this one) to use Ax, since this will simplify your setup (including the amount of code you need to write) considerably. See the Using BoTorch with Ax tutorial.

    However, you may want to do things that are not easily supported in Ax at this time (like running high-dimensional BO using a VAE+GP model that you jointly train on high-dimensional input data). If you find yourself in such a situation, you will need to write your own optimization loop, as we do in this tutorial.

    -

    We use the batch Expected Improvement (qEI) and batch Noisy Expected Improvement (qNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is

    +

    We use the batch Log Expected Improvement (qLogEI) and batch Noisy Expected Improvement (qLogNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is

    $$f(x) = -\sum_{i=1}^4 \alpha_i \exp \left( -\sum_{j=1}^6 A_{ij} (x_j - P_{ij})^2 \right)$$

    over $x \in [0,1]^6$ (parameter values can be found in botorch/test_functions/hartmann6.py).

    In real BO applications, the design $x$ can influence multiple metrics in unknown ways, and the decision-maker often wants to optimize one metric without sacrificing another. To illustrate this, we add a synthetic constraint of the form $\|x\|_1 - 3 \le 0$. Both the objective and the constraint are observed with noise.

    @@ -85,7 +85,7 @@

    Closed-l

    -
    In [1]:
    +
    In [14]:
    import os
@@ -100,24 +100,6 @@ 
    Closed-l
    -
    -
    -
    -
    -
    -
    I0214 132746.769 _utils_internal.py:247] NCCL_DEBUG env var is set to None
-
    -
    -
    -
    -
    -
    -
    I0214 132746.770 _utils_internal.py:265] NCCL_DEBUG is forced to WARN from None
-
    -
    -
    -
    -
    @@ -128,7 +110,7 @@

    Problem setup
    -
    In [2]:
    +
    In [15]:
    from botorch.test_functions import Hartmann
@@ -160,13 +142,14 @@ 
    Model initialization
 
    
-
    In [3]:
    
+
    In [16]:
    
 
    
 
    
-
    from botorch.models import FixedNoiseGP, ModelListGP
+
    from botorch.models.transforms.input import Normalize
+from botorch.models import FixedNoiseGP, ModelListGP
 from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
 
-NOISE_SE = 0.5
+NOISE_SE = 0.25
 train_yvar = torch.tensor(NOISE_SE**2, device=device, dtype=dtype)
 
 
@@ -183,12 +166,18 @@ 
    Model initializationdef initialize_model(train_x, train_obj, train_con, state_dict=None):
     # define models for objective and constraint
-    model_obj = FixedNoiseGP(train_x, train_obj, train_yvar.expand_as(train_obj)).to(
-        train_x
-    )
-    model_con = FixedNoiseGP(train_x, train_con, train_yvar.expand_as(train_con)).to(
-        train_x
-    )
+    model_obj = FixedNoiseGP(
+        train_x,
+        train_obj,
+        train_yvar.expand_as(train_obj),
+        input_transform=Normalize(d=train_x.shape[-1]),
+    ).to(train_x)
+    model_con = FixedNoiseGP(
+        train_x,
+        train_con,
+        train_yvar.expand_as(train_con),
+        input_transform=Normalize(d=train_x.shape[-1]),
+    ).to(train_x)
     # combine into a multi-output GP model
     model = ModelListGP(model_obj, model_con)
     mll = SumMarginalLogLikelihood(model.likelihood, model)
@@ -210,11 +199,10 @@ 
    
 
    
 
    
 
    
-
    In [4]:
    
+
    In [17]:
    
 
    
 
    
-
    from botorch.acquisition.objective import ConstrainedMCObjective
-
+
    from botorch.acquisition.objective import GenericMCObjective
 
 def obj_callable(Z: torch.Tensor, X: Optional[torch.Tensor] = None):
     return Z[..., 0]
@@ -224,11 +212,7 @@ 
    
     return Z[..., 1]
 
 
-# define a feasibility-weighted objective for optimization
-constrained_obj = ConstrainedMCObjective(
-    objective=obj_callable,
-    constraints=[constraint_callable],
-)
+objective = GenericMCObjective(objective=obj_callable)
 
    
 
    
 
    
@@ -243,7 +227,7 @@ 
    Define a h
 
    
 
    
 
    
-
    In [5]:
    
+
    In [18]:
    
 
    
 
    
 
    from botorch.optim import optimize_acqf
@@ -292,7 +276,7 @@ 
    Define a h
 
    
 
    
 
    
-
    Perform Bayesian Optimization loop with qNEI
    The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps:
    
+
    Perform Bayesian Optimization loop with qLogNEI
    The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps:
    
 
      
 
    1. given a surrogate model, choose a batch of points $\{x_1, x_2, \ldots x_q\}$
      2. 
 
    2. observe $f(x)$ for each $x$ in the batch
      3. 
@@ -305,16 +289,16 @@ 
      Perform Bayesian Optimizat
 
    
 
    
 
    
-
    In [6]:
    
+
    In [19]:
    
 
    
 
    
 
    import time
 import warnings
 
 from botorch import fit_gpytorch_mll
-from botorch.acquisition.monte_carlo import (
-    qExpectedImprovement,
-    qNoisyExpectedImprovement,
+from botorch.acquisition import (
+    qLogExpectedImprovement,
+    qLogNoisyExpectedImprovement,
 )
 from botorch.exceptions import BadInitialCandidatesWarning
 from botorch.sampling.normal import SobolQMCNormalSampler
@@ -332,7 +316,6 @@ 
    Perform Bayesian Optimizat
 
 best_observed_all_ei, best_observed_all_nei, best_random_all = [], [], []
 
-
 # average over multiple trials
 for trial in range(1, N_TRIALS + 1):
 
@@ -369,23 +352,25 @@ 
    Perform Bayesian Optimizat
         qmc_sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))
 
         # for best_f, we use the best observed noisy values as an approximation
-        qEI = qExpectedImprovement(
+        qLogEI = qLogExpectedImprovement(
             model=model_ei,
             best_f=(train_obj_ei * (train_con_ei <= 0).to(train_obj_ei)).max(),
             sampler=qmc_sampler,
-            objective=constrained_obj,
+            objective=objective,
+            constraints=[constraint_callable],
         )
 
-        qNEI = qNoisyExpectedImprovement(
+        qLogNEI = qLogNoisyExpectedImprovement(
             model=model_nei,
             X_baseline=train_x_nei,
             sampler=qmc_sampler,
-            objective=constrained_obj,
+            objective=objective,
+            constraints=[constraint_callable],
         )
 
         # optimize and get new observation
-        new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qEI)
-        new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qNEI)
+        new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qLogEI)
+        new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qLogNEI)
 
         # update training points
         train_x_ei = torch.cat([train_x_ei, new_x_ei])
@@ -443,988 +428,9 @@ 
    Perform Bayesian Optimizat
 
    
 
    
 
    -Trial  1 of 3 
    
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    [W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([0.2733], dtype=torch.float64), std = tensor([0.4715], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([0.2733], dtype=torch.float64), std = tensor([0.4715], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([-0.1566], dtype=torch.float64), std = tensor([0.8721], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([0.3780], dtype=torch.float64), std = tensor([0.5675], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([-0.1304], dtype=torch.float64), std = tensor([0.8767], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([-0.0395], dtype=torch.float64), std = tensor([0.8258], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([0.4358], dtype=torch.float64), std = tensor([0.5339], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([-0.0392], dtype=torch.float64), std = tensor([0.8183], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([-0.0300], dtype=torch.float64), std = tensor([0.7871], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([0.4556], dtype=torch.float64), std = tensor([0.5301], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([0.0638], dtype=torch.float64), std = tensor([0.8431], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([-0.0959], dtype=torch.float64), std = tensor([0.7738], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([0.5355], dtype=torch.float64), std = tensor([0.5411], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([0.0237], dtype=torch.float64), std = tensor([0.8017], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([-0.1337], dtype=torch.float64), std = tensor([0.7963], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([0.5354], dtype=torch.float64), std = tensor([0.5118], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([0.0950], dtype=torch.float64), std = tensor([0.8086], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:13 assorted:202] Input data is not standardized (mean = tensor([-0.1167], dtype=torch.float64), std = tensor([0.8570], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:13 assorted:202] Input data is not standardized (mean = tensor([0.6034], dtype=torch.float64), std = tensor([0.5379], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:29 assorted:202] Input data is not standardized (mean = tensor([-0.1478], dtype=torch.float64), std = tensor([0.8259], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:29 assorted:202] Input data is not standardized (mean = tensor([0.6057], dtype=torch.float64), std = tensor([0.5178], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:44 assorted:202] Input data is not standardized (mean = tensor([0.6277], dtype=torch.float64), std = tensor([0.4990], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:02 assorted:202] Input data is not standardized (mean = tensor([0.6372], dtype=torch.float64), std = tensor([0.4828], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:13 assorted:202] Input data is not standardized (mean = tensor([0.7375], dtype=torch.float64), std = tensor([0.6084], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([-0.2584], dtype=torch.float64), std = tensor([0.7726], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([0.7108], dtype=torch.float64), std = tensor([0.6077], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([0.0704], dtype=torch.float64), std = tensor([0.7945], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:33 assorted:202] Input data is not standardized (mean = tensor([-0.2652], dtype=torch.float64), std = tensor([0.7475], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:33 assorted:202] Input data is not standardized (mean = tensor([0.6736], dtype=torch.float64), std = tensor([0.6210], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([-0.2269], dtype=torch.float64), std = tensor([0.7911], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([0.7016], dtype=torch.float64), std = tensor([0.6427], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([0.0155], dtype=torch.float64), std = tensor([0.8040], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([-0.2558], dtype=torch.float64), std = tensor([0.7850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.7303], dtype=torch.float64), std = tensor([0.6473], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.0330], dtype=torch.float64), std = tensor([0.7920], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([-0.2332], dtype=torch.float64), std = tensor([0.7745], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.7330], dtype=torch.float64), std = tensor([0.6406], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.0029], dtype=torch.float64), std = tensor([0.8178], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.6627], dtype=torch.float64), std = tensor([0.8222], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([-0.2511], dtype=torch.float64), std = tensor([0.7655], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.7176], dtype=torch.float64), std = tensor([0.6282], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.0095], dtype=torch.float64), std = tensor([0.8024], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.6257], dtype=torch.float64), std = tensor([0.8225], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.2089], dtype=torch.float64), std = tensor([0.7925], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.7042], dtype=torch.float64), std = tensor([0.6213], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.0026], dtype=torch.float64), std = tensor([0.7856], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.5965], dtype=torch.float64), std = tensor([0.8144], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.1965], dtype=torch.float64), std = tensor([0.7931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.6865], dtype=torch.float64), std = tensor([0.6127], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.0005], dtype=torch.float64), std = tensor([0.7878], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.5893], dtype=torch.float64), std = tensor([0.8044], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([-0.2281], dtype=torch.float64), std = tensor([0.8216], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.6766], dtype=torch.float64), std = tensor([0.6235], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.0016], dtype=torch.float64), std = tensor([0.7704], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5766], dtype=torch.float64), std = tensor([0.7922], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([-0.1866], dtype=torch.float64), std = tensor([0.8565], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.6880], dtype=torch.float64), std = tensor([0.6248], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.0048], dtype=torch.float64), std = tensor([0.7548], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.1254], dtype=torch.float64), std = tensor([0.6022], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5027], dtype=torch.float64), std = tensor([1.2975], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-Trial  2 of 3 
    
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    [W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.0739], dtype=torch.float64), std = tensor([0.5614], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.3059], dtype=torch.float64), std = tensor([1.2141], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.2051], dtype=torch.float64), std = tensor([0.5907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.4776], dtype=torch.float64), std = tensor([1.1252], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2421], dtype=torch.float64), std = tensor([0.7397], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.3858], dtype=torch.float64), std = tensor([1.1179], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.1629], dtype=torch.float64), std = tensor([0.5363], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2150], dtype=torch.float64), std = tensor([1.2032], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.4086], dtype=torch.float64), std = tensor([0.7939], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1987], dtype=torch.float64), std = tensor([1.1134], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1204], dtype=torch.float64), std = tensor([0.5100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.2172], dtype=torch.float64), std = tensor([1.1218], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.5324], dtype=torch.float64), std = tensor([0.9596], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.1259], dtype=torch.float64), std = tensor([1.1775], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.2220], dtype=torch.float64), std = tensor([0.5402], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.0006], dtype=torch.float64), std = tensor([1.2874], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.6056], dtype=torch.float64), std = tensor([1.0648], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.0996], dtype=torch.float64), std = tensor([1.1398], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.2459], dtype=torch.float64), std = tensor([0.6041], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([-0.0298], dtype=torch.float64), std = tensor([1.2304], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.6541], dtype=torch.float64), std = tensor([1.0531], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0220], dtype=torch.float64), std = tensor([1.1516], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.3704], dtype=torch.float64), std = tensor([0.7145], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0785], dtype=torch.float64), std = tensor([1.1710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.6305], dtype=torch.float64), std = tensor([1.0355], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.0104], dtype=torch.float64), std = tensor([1.0959], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.5133], dtype=torch.float64), std = tensor([0.9123], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.1256], dtype=torch.float64), std = tensor([1.1236], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6377], dtype=torch.float64), std = tensor([1.0196], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.0550], dtype=torch.float64), std = tensor([1.0710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6463], dtype=torch.float64), std = tensor([0.9966], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.1539], dtype=torch.float64), std = tensor([1.0888], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.5757], dtype=torch.float64), std = tensor([1.0170], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.0256], dtype=torch.float64), std = tensor([1.0642], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.6964], dtype=torch.float64), std = tensor([1.0204], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.2024], dtype=torch.float64), std = tensor([1.0637], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.5354], dtype=torch.float64), std = tensor([0.9934], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.0055], dtype=torch.float64), std = tensor([1.0357], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.7830], dtype=torch.float64), std = tensor([1.0484], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([-0.2246], dtype=torch.float64), std = tensor([1.0465], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.5546], dtype=torch.float64), std = tensor([0.9647], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.0322], dtype=torch.float64), std = tensor([1.0190], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.8242], dtype=torch.float64), std = tensor([1.0807], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.2051], dtype=torch.float64), std = tensor([1.0388], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.5227], dtype=torch.float64), std = tensor([0.9521], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.0076], dtype=torch.float64), std = tensor([1.0119], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.7937], dtype=torch.float64), std = tensor([1.0924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.2510], dtype=torch.float64), std = tensor([1.0452], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.5390], dtype=torch.float64), std = tensor([0.9474], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.0277], dtype=torch.float64), std = tensor([0.9907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.8655], dtype=torch.float64), std = tensor([1.1495], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.2606], dtype=torch.float64), std = tensor([1.0160], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.5780], dtype=torch.float64), std = tensor([0.9779], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.0618], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.8900], dtype=torch.float64), std = tensor([1.1546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.2629], dtype=torch.float64), std = tensor([1.0080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.6193], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.1043], dtype=torch.float64), std = tensor([0.9695], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.9010], dtype=torch.float64), std = tensor([1.1830], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.2370], dtype=torch.float64), std = tensor([1.0052], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.5845], dtype=torch.float64), std = tensor([0.9868], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.0820], dtype=torch.float64), std = tensor([0.9546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.9435], dtype=torch.float64), std = tensor([1.1793], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.2761], dtype=torch.float64), std = tensor([1.0077], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.6265], dtype=torch.float64), std = tensor([0.9853], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.0924], dtype=torch.float64), std = tensor([0.9369], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.9787], dtype=torch.float64), std = tensor([1.1862], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.2420], dtype=torch.float64), std = tensor([0.9960], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([0.6123], dtype=torch.float64), std = tensor([0.9678], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.0746], dtype=torch.float64), std = tensor([0.9263], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([1.2043], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.2621], dtype=torch.float64), std = tensor([0.9816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([0.5825], dtype=torch.float64), std = tensor([0.9687], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.0862], dtype=torch.float64), std = tensor([0.9230], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([1.0427], dtype=torch.float64), std = tensor([1.2031], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.2598], dtype=torch.float64), std = tensor([0.9646], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.5553], dtype=torch.float64), std = tensor([0.9707], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.1290], dtype=torch.float64), std = tensor([0.9302], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([1.0351], dtype=torch.float64), std = tensor([1.2100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2435], dtype=torch.float64), std = tensor([0.9776], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.3545], dtype=torch.float64), std = tensor([0.3441], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2680], dtype=torch.float64), std = tensor([0.7962], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-Trial  3 of 3 
    
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    [W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.4261], dtype=torch.float64), std = tensor([0.5340], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.6275], dtype=torch.float64), std = tensor([0.9777], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.3743], dtype=torch.float64), std = tensor([0.3378], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.4575], dtype=torch.float64), std = tensor([0.8915], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.6648], dtype=torch.float64), std = tensor([0.7171], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.7409], dtype=torch.float64), std = tensor([0.9093], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.3962], dtype=torch.float64), std = tensor([0.3885], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.4020], dtype=torch.float64), std = tensor([0.9841], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.7743], dtype=torch.float64), std = tensor([0.7166], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.7568], dtype=torch.float64), std = tensor([0.8463], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.3175], dtype=torch.float64), std = tensor([0.4189], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.4621], dtype=torch.float64), std = tensor([0.9163], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.9228], dtype=torch.float64), std = tensor([0.7735], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.7598], dtype=torch.float64), std = tensor([0.8169], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.3610], dtype=torch.float64), std = tensor([0.5045], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.5577], dtype=torch.float64), std = tensor([0.8854], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([1.0540], dtype=torch.float64), std = tensor([0.8249], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.8083], dtype=torch.float64), std = tensor([0.7994], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([0.4168], dtype=torch.float64), std = tensor([0.5080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.6147], dtype=torch.float64), std = tensor([0.8524], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([0.8136], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.7390], dtype=torch.float64), std = tensor([0.9137], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([0.5018], dtype=torch.float64), std = tensor([0.5476], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.6296], dtype=torch.float64), std = tensor([0.8479], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([1.0463], dtype=torch.float64), std = tensor([0.8390], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.7581], dtype=torch.float64), std = tensor([0.8850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([0.5743], dtype=torch.float64), std = tensor([0.5818], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.6208], dtype=torch.float64), std = tensor([0.8064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([1.1631], dtype=torch.float64), std = tensor([0.8880], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.7808], dtype=torch.float64), std = tensor([0.8668], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([0.6677], dtype=torch.float64), std = tensor([0.7461], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.6186], dtype=torch.float64), std = tensor([0.8060], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([1.2158], dtype=torch.float64), std = tensor([0.9105], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.7430], dtype=torch.float64), std = tensor([0.8541], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([0.7965], dtype=torch.float64), std = tensor([0.8424], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.6100], dtype=torch.float64), std = tensor([0.7746], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([1.2303], dtype=torch.float64), std = tensor([0.8849], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.7314], dtype=torch.float64), std = tensor([0.8314], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([0.8024], dtype=torch.float64), std = tensor([0.8711], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.5337], dtype=torch.float64), std = tensor([0.7928], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([1.1456], dtype=torch.float64), std = tensor([0.9094], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.7164], dtype=torch.float64), std = tensor([0.8053], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([0.8021], dtype=torch.float64), std = tensor([0.8747], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.5722], dtype=torch.float64), std = tensor([0.7890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([1.1029], dtype=torch.float64), std = tensor([0.8993], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.7169], dtype=torch.float64), std = tensor([0.7839], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([0.8495], dtype=torch.float64), std = tensor([0.8904], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.5345], dtype=torch.float64), std = tensor([0.7764], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([1.1081], dtype=torch.float64), std = tensor([0.9595], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.6900], dtype=torch.float64), std = tensor([0.8444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([0.8781], dtype=torch.float64), std = tensor([0.9012], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.5189], dtype=torch.float64), std = tensor([0.7619], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([1.0653], dtype=torch.float64), std = tensor([0.9486], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.6231], dtype=torch.float64), std = tensor([0.8772], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([0.8641], dtype=torch.float64), std = tensor([0.8816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.5111], dtype=torch.float64), std = tensor([0.7444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([1.0327], dtype=torch.float64), std = tensor([0.9331], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5712], dtype=torch.float64), std = tensor([0.9219], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([0.9016], dtype=torch.float64), std = tensor([0.9030], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5222], dtype=torch.float64), std = tensor([0.7306], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([1.0067], dtype=torch.float64), std = tensor([0.9164], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.5624], dtype=torch.float64), std = tensor([0.9069], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([0.9085], dtype=torch.float64), std = tensor([0.8961], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.4901], dtype=torch.float64), std = tensor([0.7506], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.9833], dtype=torch.float64), std = tensor([0.9078], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.5367], dtype=torch.float64), std = tensor([0.9050], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.8983], dtype=torch.float64), std = tensor([0.8931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.4601], dtype=torch.float64), std = tensor([0.7749], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9557], dtype=torch.float64), std = tensor([0.8952], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.5011], dtype=torch.float64), std = tensor([0.9009], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9365], dtype=torch.float64), std = tensor([0.8924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.4550], dtype=torch.float64), std = tensor([0.7684], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9053], dtype=torch.float64), std = tensor([0.9064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.5207], dtype=torch.float64), std = tensor([0.8890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9151], dtype=torch.float64), std = tensor([0.9004], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.4307], dtype=torch.float64), std = tensor([0.7606], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.8586], dtype=torch.float64), std = tensor([0.9161], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.5021], dtype=torch.float64), std = tensor([0.8843], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.9372], dtype=torch.float64), std = tensor([0.9005], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.4370], dtype=torch.float64), std = tensor([0.7482], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-
    
+Trial  1 of 3 ....................
+Trial  2 of 3 ....................
+Trial  3 of 3 ....................
    
 
    
 
    
 
    
@@ -1439,7 +445,7 @@ 
    Plot the results
 
    
-
    In [7]:
    
+
    In [20]:
    
 
    
 
    
 
    import numpy as np
@@ -1462,13 +468,13 @@ 
    Plot the resultsfig, ax = plt.subplots(1, 1, figsize=(8, 6))
 ax.errorbar(iters, y_rnd.mean(axis=0), yerr=ci(y_rnd), label="random", linewidth=1.5)
-ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qEI", linewidth=1.5)
-ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qNEI", linewidth=1.5)
+ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qLogEI", linewidth=1.5)
+ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qLogNEI", linewidth=1.5)
 plt.plot(
     [0, N_BATCH * BATCH_SIZE],
     [GLOBAL_MAXIMUM] * 2,
     "k",
-    label="true best objective",
+    label="true best feasible objective",
     linewidth=2,
 )
 ax.set_ylim(bottom=0.5)
@@ -1484,15 +490,15 @@ 
    Plot the results
 
    
 
    
-
    Out[7]:
    
+
    Out[20]:
    
 
    
-
    <matplotlib.legend.Legend at 0x7fae17b14a00>
    
+
    <matplotlib.legend.Legend at 0x7fd6c7bbb910>
    
 
    
 
    
 
    
 
    
 
    
-No description has been provided for this image
+No description has been provided for this image
 
    
 
    
 
    
diff --git a/v/latest/tutorials/closed_loop_botorch_only/index.html b/v/latest/tutorials/closed_loop_botorch_only/index.html
index fbb729b86c..6254f3dd40 100644
--- a/v/latest/tutorials/closed_loop_botorch_only/index.html
+++ b/v/latest/tutorials/closed_loop_botorch_only/index.html
@@ -72,10 +72,10 @@
 
    
 
    
 
    
-
    Closed-loop batch, constrained BO in BoTorch with qEI and qNEI
    In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.
    
+
    Closed-loop batch, constrained BO in BoTorch with qLogEI and qLogNEI
    In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.
    
 
    In general, we recommend for a relatively simple setup (like this one) to use Ax, since this will simplify your setup (including the amount of code you need to write) considerably. See the Using BoTorch with Ax tutorial.
    
 
    However, you may want to do things that are not easily supported in Ax at this time (like running high-dimensional BO using a VAE+GP model that you jointly train on high-dimensional input data). If you find yourself in such a situation, you will need to write your own optimization loop, as we do in this tutorial.
    
-
    We use the batch Expected Improvement (qEI) and batch Noisy Expected Improvement (qNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is
    
+
    We use the batch Log Expected Improvement (qLogEI) and batch Noisy Expected Improvement (qLogNEI) acquisition functions to optimize a constrained version of the synthetic Hartmann6 test function. The standard problem is
    
 
    $$f(x) = -\sum_{i=1}^4 \alpha_i \exp \left( -\sum_{j=1}^6 A_{ij} (x_j - P_{ij})^2  \right)$$
    
 
    over $x \in [0,1]^6$ (parameter values can be found in botorch/test_functions/hartmann6.py).
    
 
    In real BO applications, the design $x$ can influence multiple metrics in unknown ways, and the decision-maker often wants to optimize one metric without sacrificing another. To illustrate this, we add a synthetic constraint of the form $\|x\|_1 - 3 \le 0$. Both the objective and the constraint are observed with noise.
    
@@ -85,7 +85,7 @@ 
    Closed-l
 
    
 
    
 
    
-
    In [1]:
    
+
    In [14]:
    
 
    
 
    
 
    import os
@@ -100,24 +100,6 @@ 
    Closed-l
 
    
 
    
 
    
-
    
-
    
-
    
-
    
-
    
-
    I0214 132746.769 _utils_internal.py:247] NCCL_DEBUG env var is set to None
-
    
-
    
-
    
-
    
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-
    
-
    I0214 132746.770 _utils_internal.py:265] NCCL_DEBUG is forced to WARN from None
-
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
@@ -128,7 +110,7 @@ 
    Problem setup
 
    
 
    
-
    In [2]:
    
+
    In [15]:
    
 
    
 
    
 
    from botorch.test_functions import Hartmann
@@ -160,13 +142,14 @@ 
    Model initialization
 
    
-
    In [3]:
    
+
    In [16]:
    
 
    
 
    
-
    from botorch.models import FixedNoiseGP, ModelListGP
+
    from botorch.models.transforms.input import Normalize
+from botorch.models import FixedNoiseGP, ModelListGP
 from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
 
-NOISE_SE = 0.5
+NOISE_SE = 0.25
 train_yvar = torch.tensor(NOISE_SE**2, device=device, dtype=dtype)
 
 
@@ -183,12 +166,18 @@ 
    Model initializationdef initialize_model(train_x, train_obj, train_con, state_dict=None):
     # define models for objective and constraint
-    model_obj = FixedNoiseGP(train_x, train_obj, train_yvar.expand_as(train_obj)).to(
-        train_x
-    )
-    model_con = FixedNoiseGP(train_x, train_con, train_yvar.expand_as(train_con)).to(
-        train_x
-    )
+    model_obj = FixedNoiseGP(
+        train_x,
+        train_obj,
+        train_yvar.expand_as(train_obj),
+        input_transform=Normalize(d=train_x.shape[-1]),
+    ).to(train_x)
+    model_con = FixedNoiseGP(
+        train_x,
+        train_con,
+        train_yvar.expand_as(train_con),
+        input_transform=Normalize(d=train_x.shape[-1]),
+    ).to(train_x)
     # combine into a multi-output GP model
     model = ModelListGP(model_obj, model_con)
     mll = SumMarginalLogLikelihood(model.likelihood, model)
@@ -210,11 +199,10 @@ 
    
 
    
 
    
 
    
-
    In [4]:
    
+
    In [17]:
    
 
    
 
    
-
    from botorch.acquisition.objective import ConstrainedMCObjective
-
+
    from botorch.acquisition.objective import GenericMCObjective
 
 def obj_callable(Z: torch.Tensor, X: Optional[torch.Tensor] = None):
     return Z[..., 0]
@@ -224,11 +212,7 @@ 
    
     return Z[..., 1]
 
 
-# define a feasibility-weighted objective for optimization
-constrained_obj = ConstrainedMCObjective(
-    objective=obj_callable,
-    constraints=[constraint_callable],
-)
+objective = GenericMCObjective(objective=obj_callable)
 
    
 
    
 
    
@@ -243,7 +227,7 @@ 
    Define a h
 
    
 
    
 
    
-
    In [5]:
    
+
    In [18]:
    
 
    
 
    
 
    from botorch.optim import optimize_acqf
@@ -292,7 +276,7 @@ 
    Define a h
 
    
 
    
 
    
-
    Perform Bayesian Optimization loop with qNEI
    The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps:
    
+
    Perform Bayesian Optimization loop with qLogNEI
    The Bayesian optimization "loop" for a batch size of $q$ simply iterates the following steps:
    
 
      
 
    1. given a surrogate model, choose a batch of points $\{x_1, x_2, \ldots x_q\}$
      2. 
 
    2. observe $f(x)$ for each $x$ in the batch
      3. 
@@ -305,16 +289,16 @@ 
      Perform Bayesian Optimizat
 
    
 
    
 
    
-
    In [6]:
    
+
    In [19]:
    
 
    
 
    
 
    import time
 import warnings
 
 from botorch import fit_gpytorch_mll
-from botorch.acquisition.monte_carlo import (
-    qExpectedImprovement,
-    qNoisyExpectedImprovement,
+from botorch.acquisition import (
+    qLogExpectedImprovement,
+    qLogNoisyExpectedImprovement,
 )
 from botorch.exceptions import BadInitialCandidatesWarning
 from botorch.sampling.normal import SobolQMCNormalSampler
@@ -332,7 +316,6 @@ 
    Perform Bayesian Optimizat
 
 best_observed_all_ei, best_observed_all_nei, best_random_all = [], [], []
 
-
 # average over multiple trials
 for trial in range(1, N_TRIALS + 1):
 
@@ -369,23 +352,25 @@ 
    Perform Bayesian Optimizat
         qmc_sampler = SobolQMCNormalSampler(sample_shape=torch.Size([MC_SAMPLES]))
 
         # for best_f, we use the best observed noisy values as an approximation
-        qEI = qExpectedImprovement(
+        qLogEI = qLogExpectedImprovement(
             model=model_ei,
             best_f=(train_obj_ei * (train_con_ei <= 0).to(train_obj_ei)).max(),
             sampler=qmc_sampler,
-            objective=constrained_obj,
+            objective=objective,
+            constraints=[constraint_callable],
         )
 
-        qNEI = qNoisyExpectedImprovement(
+        qLogNEI = qLogNoisyExpectedImprovement(
             model=model_nei,
             X_baseline=train_x_nei,
             sampler=qmc_sampler,
-            objective=constrained_obj,
+            objective=objective,
+            constraints=[constraint_callable],
         )
 
         # optimize and get new observation
-        new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qEI)
-        new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qNEI)
+        new_x_ei, new_obj_ei, new_con_ei = optimize_acqf_and_get_observation(qLogEI)
+        new_x_nei, new_obj_nei, new_con_nei = optimize_acqf_and_get_observation(qLogNEI)
 
         # update training points
         train_x_ei = torch.cat([train_x_ei, new_x_ei])
@@ -443,988 +428,9 @@ 
    Perform Bayesian Optimizat
 
    
 
    
 
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-[W 240214 13:27:48 assorted:202] Input data is not standardized (mean = tensor([0.2733], dtype=torch.float64), std = tensor([0.4715], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([-0.1566], dtype=torch.float64), std = tensor([0.8721], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([0.3780], dtype=torch.float64), std = tensor([0.5675], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:00 assorted:202] Input data is not standardized (mean = tensor([-0.1304], dtype=torch.float64), std = tensor([0.8767], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([-0.0395], dtype=torch.float64), std = tensor([0.8258], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([0.4358], dtype=torch.float64), std = tensor([0.5339], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:15 assorted:202] Input data is not standardized (mean = tensor([-0.0392], dtype=torch.float64), std = tensor([0.8183], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([-0.0300], dtype=torch.float64), std = tensor([0.7871], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([0.4556], dtype=torch.float64), std = tensor([0.5301], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:30 assorted:202] Input data is not standardized (mean = tensor([0.0638], dtype=torch.float64), std = tensor([0.8431], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([-0.0959], dtype=torch.float64), std = tensor([0.7738], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([0.5355], dtype=torch.float64), std = tensor([0.5411], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:28:44 assorted:202] Input data is not standardized (mean = tensor([0.0237], dtype=torch.float64), std = tensor([0.8017], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([-0.1337], dtype=torch.float64), std = tensor([0.7963], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([0.5354], dtype=torch.float64), std = tensor([0.5118], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:00 assorted:202] Input data is not standardized (mean = tensor([0.0950], dtype=torch.float64), std = tensor([0.8086], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:13 assorted:202] Input data is not standardized (mean = tensor([-0.1167], dtype=torch.float64), std = tensor([0.8570], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:13 assorted:202] Input data is not standardized (mean = tensor([0.6034], dtype=torch.float64), std = tensor([0.5379], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:13 assorted:202] Input data is not standardized (mean = tensor([0.1994], dtype=torch.float64), std = tensor([0.8243], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:29 assorted:202] Input data is not standardized (mean = tensor([-0.1478], dtype=torch.float64), std = tensor([0.8259], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:29:29 assorted:202] Input data is not standardized (mean = tensor([0.6057], dtype=torch.float64), std = tensor([0.5178], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:29:44 assorted:202] Input data is not standardized (mean = tensor([0.6277], dtype=torch.float64), std = tensor([0.4990], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:02 assorted:202] Input data is not standardized (mean = tensor([0.6372], dtype=torch.float64), std = tensor([0.4828], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:13 assorted:202] Input data is not standardized (mean = tensor([0.7375], dtype=torch.float64), std = tensor([0.6084], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:13 assorted:202] Input data is not standardized (mean = tensor([0.1418], dtype=torch.float64), std = tensor([0.7763], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([-0.2584], dtype=torch.float64), std = tensor([0.7726], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([0.7108], dtype=torch.float64), std = tensor([0.6077], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:25 assorted:202] Input data is not standardized (mean = tensor([0.0704], dtype=torch.float64), std = tensor([0.7945], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:33 assorted:202] Input data is not standardized (mean = tensor([-0.2652], dtype=torch.float64), std = tensor([0.7475], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:33 assorted:202] Input data is not standardized (mean = tensor([0.6736], dtype=torch.float64), std = tensor([0.6210], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([-0.2269], dtype=torch.float64), std = tensor([0.7911], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([0.7016], dtype=torch.float64), std = tensor([0.6427], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:46 assorted:202] Input data is not standardized (mean = tensor([0.0155], dtype=torch.float64), std = tensor([0.8040], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([-0.2558], dtype=torch.float64), std = tensor([0.7850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.7303], dtype=torch.float64), std = tensor([0.6473], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:30:58 assorted:202] Input data is not standardized (mean = tensor([0.0330], dtype=torch.float64), std = tensor([0.7920], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([-0.2332], dtype=torch.float64), std = tensor([0.7745], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.7330], dtype=torch.float64), std = tensor([0.6406], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:04 assorted:202] Input data is not standardized (mean = tensor([0.0029], dtype=torch.float64), std = tensor([0.8178], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.6627], dtype=torch.float64), std = tensor([0.8222], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([-0.2511], dtype=torch.float64), std = tensor([0.7655], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.7176], dtype=torch.float64), std = tensor([0.6282], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:16 assorted:202] Input data is not standardized (mean = tensor([0.0095], dtype=torch.float64), std = tensor([0.8024], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.6257], dtype=torch.float64), std = tensor([0.8225], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.2089], dtype=torch.float64), std = tensor([0.7925], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([0.7042], dtype=torch.float64), std = tensor([0.6213], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:26 assorted:202] Input data is not standardized (mean = tensor([-0.0026], dtype=torch.float64), std = tensor([0.7856], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.5965], dtype=torch.float64), std = tensor([0.8144], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.1965], dtype=torch.float64), std = tensor([0.7931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([0.6865], dtype=torch.float64), std = tensor([0.6127], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:36 assorted:202] Input data is not standardized (mean = tensor([-0.0005], dtype=torch.float64), std = tensor([0.7878], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.5893], dtype=torch.float64), std = tensor([0.8044], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([-0.2281], dtype=torch.float64), std = tensor([0.8216], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.6766], dtype=torch.float64), std = tensor([0.6235], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:46 assorted:202] Input data is not standardized (mean = tensor([0.0016], dtype=torch.float64), std = tensor([0.7704], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5766], dtype=torch.float64), std = tensor([0.7922], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([-0.1866], dtype=torch.float64), std = tensor([0.8565], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.6880], dtype=torch.float64), std = tensor([0.6248], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.0048], dtype=torch.float64), std = tensor([0.7548], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.1254], dtype=torch.float64), std = tensor([0.6022], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:31:57 assorted:202] Input data is not standardized (mean = tensor([0.5027], dtype=torch.float64), std = tensor([1.2975], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-Trial  2 of 3 
    
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    [W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.0739], dtype=torch.float64), std = tensor([0.5614], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.3059], dtype=torch.float64), std = tensor([1.2141], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.2051], dtype=torch.float64), std = tensor([0.5907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:08 assorted:202] Input data is not standardized (mean = tensor([0.4776], dtype=torch.float64), std = tensor([1.1252], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2421], dtype=torch.float64), std = tensor([0.7397], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.3858], dtype=torch.float64), std = tensor([1.1179], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.1629], dtype=torch.float64), std = tensor([0.5363], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:18 assorted:202] Input data is not standardized (mean = tensor([0.2150], dtype=torch.float64), std = tensor([1.2032], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.4086], dtype=torch.float64), std = tensor([0.7939], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1987], dtype=torch.float64), std = tensor([1.1134], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.1204], dtype=torch.float64), std = tensor([0.5100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:31 assorted:202] Input data is not standardized (mean = tensor([0.2172], dtype=torch.float64), std = tensor([1.1218], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.5324], dtype=torch.float64), std = tensor([0.9596], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.1259], dtype=torch.float64), std = tensor([1.1775], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.2220], dtype=torch.float64), std = tensor([0.5402], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:42 assorted:202] Input data is not standardized (mean = tensor([0.0006], dtype=torch.float64), std = tensor([1.2874], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.6056], dtype=torch.float64), std = tensor([1.0648], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.0996], dtype=torch.float64), std = tensor([1.1398], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([0.2459], dtype=torch.float64), std = tensor([0.6041], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:32:54 assorted:202] Input data is not standardized (mean = tensor([-0.0298], dtype=torch.float64), std = tensor([1.2304], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.6541], dtype=torch.float64), std = tensor([1.0531], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0220], dtype=torch.float64), std = tensor([1.1516], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([0.3704], dtype=torch.float64), std = tensor([0.7145], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:09 assorted:202] Input data is not standardized (mean = tensor([-0.0785], dtype=torch.float64), std = tensor([1.1710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.6305], dtype=torch.float64), std = tensor([1.0355], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.0104], dtype=torch.float64), std = tensor([1.0959], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([0.5133], dtype=torch.float64), std = tensor([0.9123], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:17 assorted:202] Input data is not standardized (mean = tensor([-0.1256], dtype=torch.float64), std = tensor([1.1236], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6377], dtype=torch.float64), std = tensor([1.0196], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.0550], dtype=torch.float64), std = tensor([1.0710], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([0.6463], dtype=torch.float64), std = tensor([0.9966], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:22 assorted:202] Input data is not standardized (mean = tensor([-0.1539], dtype=torch.float64), std = tensor([1.0888], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.5757], dtype=torch.float64), std = tensor([1.0170], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.0256], dtype=torch.float64), std = tensor([1.0642], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([0.6964], dtype=torch.float64), std = tensor([1.0204], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:27 assorted:202] Input data is not standardized (mean = tensor([-0.2024], dtype=torch.float64), std = tensor([1.0637], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.5354], dtype=torch.float64), std = tensor([0.9934], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.0055], dtype=torch.float64), std = tensor([1.0357], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([0.7830], dtype=torch.float64), std = tensor([1.0484], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:33 assorted:202] Input data is not standardized (mean = tensor([-0.2246], dtype=torch.float64), std = tensor([1.0465], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.5546], dtype=torch.float64), std = tensor([0.9647], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.0322], dtype=torch.float64), std = tensor([1.0190], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([0.8242], dtype=torch.float64), std = tensor([1.0807], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:37 assorted:202] Input data is not standardized (mean = tensor([-0.2051], dtype=torch.float64), std = tensor([1.0388], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.5227], dtype=torch.float64), std = tensor([0.9521], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.0076], dtype=torch.float64), std = tensor([1.0119], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([0.7937], dtype=torch.float64), std = tensor([1.0924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:40 assorted:202] Input data is not standardized (mean = tensor([-0.2510], dtype=torch.float64), std = tensor([1.0452], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.5390], dtype=torch.float64), std = tensor([0.9474], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.0277], dtype=torch.float64), std = tensor([0.9907], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([0.8655], dtype=torch.float64), std = tensor([1.1495], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:44 assorted:202] Input data is not standardized (mean = tensor([-0.2606], dtype=torch.float64), std = tensor([1.0160], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.5780], dtype=torch.float64), std = tensor([0.9779], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.0618], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([0.8900], dtype=torch.float64), std = tensor([1.1546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:49 assorted:202] Input data is not standardized (mean = tensor([-0.2629], dtype=torch.float64), std = tensor([1.0080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.6193], dtype=torch.float64), std = tensor([0.9771], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.1043], dtype=torch.float64), std = tensor([0.9695], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([0.9010], dtype=torch.float64), std = tensor([1.1830], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:33:57 assorted:202] Input data is not standardized (mean = tensor([-0.2370], dtype=torch.float64), std = tensor([1.0052], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.5845], dtype=torch.float64), std = tensor([0.9868], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.0820], dtype=torch.float64), std = tensor([0.9546], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([0.9435], dtype=torch.float64), std = tensor([1.1793], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:02 assorted:202] Input data is not standardized (mean = tensor([-0.2761], dtype=torch.float64), std = tensor([1.0077], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.6265], dtype=torch.float64), std = tensor([0.9853], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.0924], dtype=torch.float64), std = tensor([0.9369], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([0.9787], dtype=torch.float64), std = tensor([1.1862], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:10 assorted:202] Input data is not standardized (mean = tensor([-0.2420], dtype=torch.float64), std = tensor([0.9960], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([0.6123], dtype=torch.float64), std = tensor([0.9678], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.0746], dtype=torch.float64), std = tensor([0.9263], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([1.2043], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:13 assorted:202] Input data is not standardized (mean = tensor([-0.2621], dtype=torch.float64), std = tensor([0.9816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([0.5825], dtype=torch.float64), std = tensor([0.9687], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.0862], dtype=torch.float64), std = tensor([0.9230], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([1.0427], dtype=torch.float64), std = tensor([1.2031], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:19 assorted:202] Input data is not standardized (mean = tensor([-0.2598], dtype=torch.float64), std = tensor([0.9646], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.5553], dtype=torch.float64), std = tensor([0.9707], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.1290], dtype=torch.float64), std = tensor([0.9302], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([1.0351], dtype=torch.float64), std = tensor([1.2100], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2435], dtype=torch.float64), std = tensor([0.9776], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([0.3545], dtype=torch.float64), std = tensor([0.3441], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:22 assorted:202] Input data is not standardized (mean = tensor([-0.2680], dtype=torch.float64), std = tensor([0.7962], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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-Trial  3 of 3 
    
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    [W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.4261], dtype=torch.float64), std = tensor([0.5340], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.6275], dtype=torch.float64), std = tensor([0.9777], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([0.3743], dtype=torch.float64), std = tensor([0.3378], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:32 assorted:202] Input data is not standardized (mean = tensor([-0.4575], dtype=torch.float64), std = tensor([0.8915], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.6648], dtype=torch.float64), std = tensor([0.7171], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.7409], dtype=torch.float64), std = tensor([0.9093], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([0.3962], dtype=torch.float64), std = tensor([0.3885], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:43 assorted:202] Input data is not standardized (mean = tensor([-0.4020], dtype=torch.float64), std = tensor([0.9841], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.7743], dtype=torch.float64), std = tensor([0.7166], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.7568], dtype=torch.float64), std = tensor([0.8463], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([0.3175], dtype=torch.float64), std = tensor([0.4189], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:34:55 assorted:202] Input data is not standardized (mean = tensor([-0.4621], dtype=torch.float64), std = tensor([0.9163], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.9228], dtype=torch.float64), std = tensor([0.7735], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.7598], dtype=torch.float64), std = tensor([0.8169], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([0.3610], dtype=torch.float64), std = tensor([0.5045], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:04 assorted:202] Input data is not standardized (mean = tensor([-0.5577], dtype=torch.float64), std = tensor([0.8854], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([1.0540], dtype=torch.float64), std = tensor([0.8249], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.8083], dtype=torch.float64), std = tensor([0.7994], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([0.4168], dtype=torch.float64), std = tensor([0.5080], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:15 assorted:202] Input data is not standardized (mean = tensor([-0.6147], dtype=torch.float64), std = tensor([0.8524], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([1.0160], dtype=torch.float64), std = tensor([0.8136], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.7390], dtype=torch.float64), std = tensor([0.9137], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([0.5018], dtype=torch.float64), std = tensor([0.5476], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:25 assorted:202] Input data is not standardized (mean = tensor([-0.6296], dtype=torch.float64), std = tensor([0.8479], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([1.0463], dtype=torch.float64), std = tensor([0.8390], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.7581], dtype=torch.float64), std = tensor([0.8850], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([0.5743], dtype=torch.float64), std = tensor([0.5818], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:36 assorted:202] Input data is not standardized (mean = tensor([-0.6208], dtype=torch.float64), std = tensor([0.8064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([1.1631], dtype=torch.float64), std = tensor([0.8880], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.7808], dtype=torch.float64), std = tensor([0.8668], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([0.6677], dtype=torch.float64), std = tensor([0.7461], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:48 assorted:202] Input data is not standardized (mean = tensor([-0.6186], dtype=torch.float64), std = tensor([0.8060], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([1.2158], dtype=torch.float64), std = tensor([0.9105], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.7430], dtype=torch.float64), std = tensor([0.8541], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([0.7965], dtype=torch.float64), std = tensor([0.8424], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:35:59 assorted:202] Input data is not standardized (mean = tensor([-0.6100], dtype=torch.float64), std = tensor([0.7746], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([1.2303], dtype=torch.float64), std = tensor([0.8849], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.7314], dtype=torch.float64), std = tensor([0.8314], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([0.8024], dtype=torch.float64), std = tensor([0.8711], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:06 assorted:202] Input data is not standardized (mean = tensor([-0.5337], dtype=torch.float64), std = tensor([0.7928], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([1.1456], dtype=torch.float64), std = tensor([0.9094], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.7164], dtype=torch.float64), std = tensor([0.8053], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([0.8021], dtype=torch.float64), std = tensor([0.8747], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:12 assorted:202] Input data is not standardized (mean = tensor([-0.5722], dtype=torch.float64), std = tensor([0.7890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([1.1029], dtype=torch.float64), std = tensor([0.8993], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.7169], dtype=torch.float64), std = tensor([0.7839], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([0.8495], dtype=torch.float64), std = tensor([0.8904], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:18 assorted:202] Input data is not standardized (mean = tensor([-0.5345], dtype=torch.float64), std = tensor([0.7764], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([1.1081], dtype=torch.float64), std = tensor([0.9595], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.6900], dtype=torch.float64), std = tensor([0.8444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([0.8781], dtype=torch.float64), std = tensor([0.9012], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:26 assorted:202] Input data is not standardized (mean = tensor([-0.5189], dtype=torch.float64), std = tensor([0.7619], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([1.0653], dtype=torch.float64), std = tensor([0.9486], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.6231], dtype=torch.float64), std = tensor([0.8772], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([0.8641], dtype=torch.float64), std = tensor([0.8816], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:34 assorted:202] Input data is not standardized (mean = tensor([-0.5111], dtype=torch.float64), std = tensor([0.7444], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([1.0327], dtype=torch.float64), std = tensor([0.9331], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5712], dtype=torch.float64), std = tensor([0.9219], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([0.9016], dtype=torch.float64), std = tensor([0.9030], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:41 assorted:202] Input data is not standardized (mean = tensor([-0.5222], dtype=torch.float64), std = tensor([0.7306], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([1.0067], dtype=torch.float64), std = tensor([0.9164], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.5624], dtype=torch.float64), std = tensor([0.9069], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([0.9085], dtype=torch.float64), std = tensor([0.8961], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:48 assorted:202] Input data is not standardized (mean = tensor([-0.4901], dtype=torch.float64), std = tensor([0.7506], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.9833], dtype=torch.float64), std = tensor([0.9078], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.5367], dtype=torch.float64), std = tensor([0.9050], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([0.8983], dtype=torch.float64), std = tensor([0.8931], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:36:56 assorted:202] Input data is not standardized (mean = tensor([-0.4601], dtype=torch.float64), std = tensor([0.7749], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9557], dtype=torch.float64), std = tensor([0.8952], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.5011], dtype=torch.float64), std = tensor([0.9009], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([0.9365], dtype=torch.float64), std = tensor([0.8924], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:03 assorted:202] Input data is not standardized (mean = tensor([-0.4550], dtype=torch.float64), std = tensor([0.7684], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9053], dtype=torch.float64), std = tensor([0.9064], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.5207], dtype=torch.float64), std = tensor([0.8890], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([0.9151], dtype=torch.float64), std = tensor([0.9004], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:07 assorted:202] Input data is not standardized (mean = tensor([-0.4307], dtype=torch.float64), std = tensor([0.7606], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
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    [W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.8586], dtype=torch.float64), std = tensor([0.9161], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.5021], dtype=torch.float64), std = tensor([0.8843], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([0.9372], dtype=torch.float64), std = tensor([0.9005], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-[W 240214 13:37:13 assorted:202] Input data is not standardized (mean = tensor([-0.4370], dtype=torch.float64), std = tensor([0.7482], dtype=torch.float64)). Please consider scaling the input to zero mean and unit variance.
-
    
+Trial  1 of 3 ....................
+Trial  2 of 3 ....................
+Trial  3 of 3 ....................
    
 
    
 
    
 
    
@@ -1439,7 +445,7 @@ 
    Plot the results
 
    
-
    In [7]:
    
+
    In [20]:
    
 
    
 
    
 
    import numpy as np
@@ -1462,13 +468,13 @@ 
    Plot the resultsfig, ax = plt.subplots(1, 1, figsize=(8, 6))
 ax.errorbar(iters, y_rnd.mean(axis=0), yerr=ci(y_rnd), label="random", linewidth=1.5)
-ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qEI", linewidth=1.5)
-ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qNEI", linewidth=1.5)
+ax.errorbar(iters, y_ei.mean(axis=0), yerr=ci(y_ei), label="qLogEI", linewidth=1.5)
+ax.errorbar(iters, y_nei.mean(axis=0), yerr=ci(y_nei), label="qLogNEI", linewidth=1.5)
 plt.plot(
     [0, N_BATCH * BATCH_SIZE],
     [GLOBAL_MAXIMUM] * 2,
     "k",
-    label="true best objective",
+    label="true best feasible objective",
     linewidth=2,
 )
 ax.set_ylim(bottom=0.5)
@@ -1484,15 +490,15 @@ 
    Plot the results
 
    
 
    
-
    Out[7]:
    
+
    Out[20]:
    
 
    
-
    <matplotlib.legend.Legend at 0x7fae17b14a00>
    
+
    <matplotlib.legend.Legend at 0x7fd6c7bbb910>
    
 
    
 
    
 
    
 
    
 
    
-No description has been provided for this image
+No description has been provided for this image
 
    
 
    
 
    
diff --git a/v/latest/tutorials/compare_mc_analytic_acquisition.html b/v/latest/tutorials/compare_mc_analytic_acquisition.html
index 293492a53e..c51519fbcd 100644
--- a/v/latest/tutorials/compare_mc_analytic_acquisition.html
+++ b/v/latest/tutorials/compare_mc_analytic_acquisition.html
@@ -79,13 +79,13 @@ 
    Analytic an
 
    
 
    
 
    
-
    Comparison of analytic and MC-based EI
    
+
    Comparison of analytic and MC-based EI
    Note that we use the analytic and MC variants of the LogEI family of acquisition functions, which remedy numerical issues encountered in the naive implementations. See https://arxiv.org/pdf/2310.20708 for more details.
    
 
    
 
    
 
    
 
    
 
    
-
    In [16]:
    
+
    In [ ]:
    
 
    
 
    
 
    import torch
@@ -110,10 +110,12 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [17]:
    
+
    In [ ]:
    
 
    
 
    
-
    train_x = torch.rand(10, 6)
+
    torch.manual_seed(seed=12345)  # to keep the data conditions the same
+dtype = torch.float64
+train_x = torch.rand(10, 6, dtype=dtype)
 train_obj = neg_hartmann6(train_x).unsqueeze(-1)
 model = SingleTaskGP(train_X=train_x, train_Y=train_obj)
 mll = ExactMarginalLogLikelihood(model.likelihood, model)
@@ -132,13 +134,13 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [18]:
    
+
    In [ ]:
    
 
    
 
    
-
    from botorch.acquisition import ExpectedImprovement
+
    from botorch.acquisition.analytic import LogExpectedImprovement
 
 best_value = train_obj.max()
-EI = ExpectedImprovement(model=model, best_f=best_value)
+LogEI = LogExpectedImprovement(model=model, best_f=best_value)
 
    
 
    
 
    
@@ -153,13 +155,13 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [19]:
    
+
    In [ ]:
    
 
    
 
    
 
    from botorch.optim import optimize_acqf
 
 new_point_analytic, _ = optimize_acqf(
-    acq_function=EI,
+    acq_function=LogEI,
     bounds=torch.tensor([[0.0] * 6, [1.0] * 6]),
     q=1,
     num_restarts=20,
@@ -173,24 +175,15 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [20]:
    
+
    In [ ]:
    
 
    
 
    
-
    new_point_analytic
+
    # NOTE: The acquisition value here is the log of the expected improvement.
+LogEI(new_point_analytic), new_point_analytic
 
    
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[20]:
    
-
    
-
    tensor([[0.4730, 0.0836, 0.8247, 0.5628, 0.2964, 0.6131]])
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
@@ -201,18 +194,18 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [21]:
    
+
    In [ ]:
    
 
    
 
    
-
    from botorch.acquisition import qExpectedImprovement
+
    from botorch.acquisition.logei import qLogExpectedImprovement
 from botorch.sampling import SobolQMCNormalSampler
 
 
 sampler = SobolQMCNormalSampler(sample_shape=torch.Size([512]), seed=0)
-MC_EI = qExpectedImprovement(model, best_f=best_value, sampler=sampler)
+MC_LogEI = qLogExpectedImprovement(model, best_f=best_value, sampler=sampler, fat=False)
 torch.manual_seed(seed=0)  # to keep the restart conditions the same
 new_point_mc, _ = optimize_acqf(
-    acq_function=MC_EI,
+    acq_function=MC_LogEI,
     bounds=torch.tensor([[0.0] * 6, [1.0] * 6]),
     q=1,
     num_restarts=20,
@@ -226,24 +219,15 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [22]:
    
+
    In [ ]:
    
 
    
 
    
-
    new_point_mc
+
    # NOTE: The acquisition value here is the log of the expected improvement.
+MC_LogEI(new_point_mc), new_point_mc
 
    
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[22]:
    
-
    
-
    tensor([[0.4730, 0.0835, 0.8248, 0.5627, 0.2963, 0.6130]])
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
@@ -254,7 +238,7 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    In [23]:
    
+
    In [ ]:
    
 
    
 
    
 
    torch.linalg.norm(new_point_mc - new_point_analytic)
@@ -262,16 +246,6 @@ 
    Comparison of analytic and MC-ba
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[23]:
    
-
    
-
    tensor(0.0002)
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
@@ -283,7 +257,7 @@ 
    Using a to
 
    
 
    
 
    
-
    In [24]:
    
+
    In [ ]:
    
 
    
 
    
 
    from botorch.sampling.stochastic_samplers import StochasticSampler
@@ -291,11 +265,11 @@ 
    Using a to
 from botorch.optim import gen_batch_initial_conditions
 
 resampler = StochasticSampler(sample_shape=torch.Size([512]))
-MC_EI_resample = qExpectedImprovement(model, best_f=best_value, sampler=resampler)
+MC_LogEI_resample = qLogExpectedImprovement(model, best_f=best_value, sampler=resampler)
 bounds = torch.tensor([[0.0] * 6, [1.0] * 6])
 
 batch_initial_conditions = gen_batch_initial_conditions(
-    acq_function=MC_EI_resample,
+    acq_function=MC_LogEI_resample,
     bounds=bounds,
     q=1,
     num_restarts=20,
@@ -303,7 +277,7 @@ 
    Using a to
 )
 batch_candidates, batch_acq_values = gen_candidates_torch(
     initial_conditions=batch_initial_conditions,
-    acquisition_function=MC_EI_resample,
+    acquisition_function=MC_LogEI_resample,
     lower_bounds=bounds[0],
     upper_bounds=bounds[1],
     optimizer=torch.optim.Adam,
@@ -319,28 +293,19 @@ 
    Using a to
 
    
 
    
 
    
-
    In [25]:
    
+
    In [ ]:
    
 
    
 
    
-
    new_point_torch_Adam
+
    # NOTE: The acquisition value here is the log of the expected improvement.
+MC_LogEI_resample(new_point_torch_Adam), new_point_torch_Adam
 
    
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[25]:
    
-
    
-
    tensor([[0.4527, 0.1183, 0.8902, 0.5630, 0.3151, 0.5804]])
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
-
    In [26]:
    
+
    In [ ]:
    
 
    
 
    
 
    torch.linalg.norm(new_point_torch_Adam - new_point_analytic)
@@ -348,16 +313,6 @@ 
    Using a to
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[26]:
    
-
    
-
    tensor(0.0855)
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
@@ -368,12 +323,12 @@ 
    Using a to
 
    
 
    
 
    
-
    In [27]:
    
+
    In [ ]:
    
 
    
 
    
 
    batch_candidates, batch_acq_values = gen_candidates_torch(
     initial_conditions=batch_initial_conditions,
-    acquisition_function=MC_EI_resample,
+    acquisition_function=MC_LogEI_resample,
     lower_bounds=bounds[0],
     upper_bounds=bounds[1],
     optimizer=torch.optim.SGD,
@@ -389,28 +344,18 @@ 
    Using a to
 
    
 
    
 
    
-
    In [28]:
    
+
    In [ ]:
    
 
    
 
    
-
    new_point_torch_SGD
+
    MC_LogEI_resample(new_point_torch_SGD), new_point_torch_SGD
 
    
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[28]:
    
-
    
-
    tensor([[0.3566, 0.0410, 0.7926, 0.3118, 0.3758, 0.6110]])
    
-
    
-
    
-
    
-
    
 
    
 
    
 
    
-
    In [29]:
    
+
    In [ ]:
    
 
    
 
    
 
    torch.linalg.norm(new_point_torch_SGD - new_point_analytic)
@@ -418,16 +363,6 @@ 
    Using a to
 
    
 
    
 
    
-
    
-
    
-
    
-
    Out[29]:
    
-
    
-
    tensor(0.2928)
    
-
    
-
    
-
    
-
    
 
    
 
    Download Tutorial Jupyter Notebook
    Download Tutorial Source Code